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Every panel on AI and geopolitics seems to default to the same cliché: “the US–China race.” In this episode of Differential Understanding, I wanted to sit with someone who has actually lived inside DC, Silicon Valley, and the US–China tech corridor, and ask whether that framing still makes sense.
My guest is Kevin Xu, founder of Interconnected Capital – a global hedge fund focused on the picks and shovels of AI – and author of the Interconnected newsletter, which sits at the intersection of tech, business, and geopolitics. Kevin’s path runs from Obama campaign staffer and White House / Commerce Department comms to GitHub’s international expansion lead, and now to full-time investor–writer with a very explicit geopolitical lens.
We start with why he insists on “thinking in public” as an investor, and why he believes ideas soulocking in a vault. From there, we dive into his critique of the “race” narrative and his alternative concept of US–China co-opetition – a messy mix of competition, cooperation, and outright co-opting of each other’s models and research. That leads naturally into China’s open-source AI ecosystem, the Manus–Meta deal, and what he would need to see before feeling comfortable owning the upcoming MiniMax and Zhipu IPOs in Hong Kong.
In the second half, we zoom out to sovereign AI: why South Korea might be one of the few countries outside the US and China with a shot at true full-stack AI sovereignty; how to read OpenAI’s Stargate initiative as an explicit American export play; and why the Gulf – particularly the UAE – is emerging as an AI “swing vote”, combining abundant energy, sovereign wealth, and a 1.5 million-strong construction workforce into a potential global compute hub. We close with Kevin’s differentiated view on China: AI diffusion is far more visible there, but the economic impact is not necessarily greater, and Beijing may end up being the first government forced to confront AI’s social implications.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Timestamps (chapters):
* 00:57 – From DC to GitHub to Interconnected Capital
* 02:39 – Why Kevin “thinks in public” and writes Interconnected
* 04:44 – US–China AI is not a “race”: co-opetition explained
* 09:27 – Open-source / open-weight AI as last bastion of global cooperation
* 12:14 – Capital flows, decoupling and why “capital finds a way”
* 15:36 – Manus x Meta: product quality, viral growth, rationality in AI
* 19:40 – MiniMax & Zhipu IPOs: revenue reality vs AI lab hype
* 24:39 – Can Chinese labs win the Global South with cheaper AI?
* 27:09 – Sovereign AI 101 and why South Korea looks uniquely powerful
* 32:03 – Stargate as de-facto US sovereign AI and export strategy
* 34:44 – Kevin’s trip to UAE: Gulf AI strategies and the “swing vote” thesis
* 40:06 – Sovereign funds, MGX, and attracting talent from Hong Kong & beyond
* 44:18 – Non-consensus bet: UAE Stargate as a global compute hub
* 46:57 – Differentiated views on China AI diffusion and economic impact
* 51:29 – Embodied AI, “aunties pressing elevator buttons” and social risk
* 55:01 – Robotaxis, delivery drivers, and why China may go slower than expected
AI-generated transcript
Grace Shao (00:00)Hey everyone, welcome back to another episode of Differential Understanding. This is your host, Grace Shao And joining me today is Kevin Xu
Kevin Xu is the founder of Interconnected Capital, a global hedge fund focused on the picks and shovels of AI. He writes the Interconnected newsletter on SubSac, which covers tech, business, and geopolitics.
His insights have been frequently cited by the New York Times, Bloomberg, Economist, CNBC Information, Financial Times, Wall Street Journal, among many other media outlets. He previously worked as a senior executive at GitHub, the world’s largest developer platform, and served in the White House and Commerce Department during the Obama administration. He studied international relations at Brown University and law and computer science at Stanford.
Grace Shao (00:40) Hey Kevin, thank you so much for joining us today. I already introduced you, but for listeners who may not know you that well, can you introduce yourself, the different hats you wear today, running Interconnected Capital, writing Interconnected Newsletter, and operating at the intersection of US, Asia, tech, geopolitics, and investing.
Kevin Xu (00:57) Yeah, first of all, thank you for having me. So as you mentioned, what I do currently during the day is I write the interconnected newsletter on the intersections of geopolitics, technology, and business. I also run my own long only fund called Interconnected Capital, focused on the picks and shovels of AI, both hardware and software. Prior to that, I actually work as an operator inside multiple Silicon Valley tech startups. The most recent one is GitHub, which is the Microsoft-owned developer
platform. I was their lead for international expansion strategy. That was my most recent real job, if you will. I also spent a bunch of time at different startups of varying sizes. And before that, actually started my career in politics. So I joined the first Obama administration’s campaign back in 2008. I was a campaign staffer. That was my first job out of college and then moved with the campaign team to DC, ⁓ worked in a few different roles.
in the Commerce Department as well as the White House doing mostly press and communications work. So that is ⁓ my sort of all over the place background that led me to what I’m doing today, which is investing and writing ⁓ in technology, but with a very heavy geopolitical lens to the process.
Grace Shao (02:13) I think that’s really interesting and explains to why you have a geopolitical lens, given that you actually have a DC background, right? But you run a fund and you actually keep most of your thinking, public, So instead of just keeping it mostly all private, which is what most investors do, why do you publish a very opinionated, very insightful sub stack and, share it very, I think generously with the public?
What kind of conversation gap are you trying to fill when you start the interconnected newsletter?
Kevin Xu (02:39) I think there are two elements to that. First is that this is more of my Silicon Valley ethos, which is that no idea is that worth keeping in secret. It’s all about the execution. Like I’m not, I didn’t come from a Wall Street finance background, right? Where a proprietary trading algorithm or some secret information you got from a meeting is this big trade secret that you want to lock into a vault inside Goldman Sachs or whatever. And that’s going to make you billions of dollars. That is not my approach to investing.
I think thinking in public, sharing in public, and really getting the feedback that I get from writing is much more valuable than keeping all these thoughts in my head as if they’re the next best thing since sliced bread. When you actually write it down, when you put it out into the internet, half of them are good, half of them are actually crap. And I use writing and writing in public specifically basically as a canvas for me
to think better, to hold my thoughts more clearly. I think knowing how to think is the most important skill for any investor to be able to succeed for the long term. And if any idea that I shared out there benefits somebody else, and you made some money off of it for free, so be it. Good for you that you actually understood some of the value from the writing, even perhaps more than I did as the writer. But for me, that’s not something that I keep very possessively as a trade secret.
Grace Shao (04:00) I really relate to that and I think exactly to your point you’re like writing everything down is the way of thinking through your thoughts sometimes it’s all jumbled up in there and then also people ask me why do you keep AI Pro all free? I was like well if it really benefits you you know I don’t really mind I’m not trying to make money off of like selling you know just my content but really the content is my thinking and to your point sometimes I put in so much work and then the result and feedback is so bad and then some things I just kind of like throw out there
And then it actually really sticks with people you never know. It’s really good to get the feedback from the public as well. Well, I think now I want to ask you about your journey into investing and really covering China and US. So you are based in the US, but you are Chinese.
birth, right? ⁓ So does that play into why you cover China-U.S. related work right now? And I do want to talk about your recent article, which you said you think calling the China-U.S. relationship in tech and AI a race is quite lazy. You said instead of seeing it as pure competition, you think it’s more of a competition. So cooperation plus competition. Do walk us through that and how you kind of came out with this frame.
Kevin Xu (04:44) Correct.
So just to put a finer print on it, as far as the personal history is concerned, I was born in China. I moved to Canada when I was little, similar to you, think, Grace. And I moved to US later on. So obviously, I work in the US government. So I’m a US citizen. So I’m actually a card-carrying Canadian as well as an American right now. And I think having had a very global citizen-ish
Grace Shao (05:22) Mm-hmm.
Kevin Xu (05:30) upbringing and life experience was the lens that wanted to bring to my newsletter when I first started writing it roughly five and a half, six years ago. Some of it has to do with the US China. Some of it actually just has to do with a specific industry trend in the software, in hardware, in the nerdy techie stuff, in open source that I like to talk about. And I think only the US China stuff got picked up for one reason or another. People started to pay more attention to
Grace Shao (05:31) Mm-hmm.
Kevin Xu (05:58) to
the stuff they’re writing when there is a China-US lens. And maybe it’s just because there’s a dearth of content out there that actually brings a level of nuance to the conversation. And that brings to what you asked me about, which is this notion of a US-China AI co-opetition, is how I like to call it, as opposed to calling it a race, which is the kind of intellectually lazy approach that I have fallen into multiple times.
Throughout my own writing just calling the race calling it a race But not really thinking what that implies which is that one a race implies that there is an end point There is a finish line to this race But for AI there really isn’t
Even the most fervent believer of what an AGI is does not believe that is a static endpoint in which once you reach it, you’re done. And of course, there is the implication that this whole thing is a very zero sum dynamic if you call it a race. But in reality, if you look at everything that’s actually happening on the ground between the US and China on AI, it is a manifestation of co-op petition, which is that there’s a lot of competition between different firms.
between different labs, both within China and between the US and China, lots of startups. There is also a lot of cooperation. The cooperation stuff gets probably shoved over to the side or doesn’t get mentioned as much because of the geopolitical toxicity of the conversation. But there are lots of papers, academic institutions, adjoined productions in terms of research and collaboration that is still happening both in academia and frankly in a lot of startups where the approach to all this is much more pragmatic
and
less geopolitical. And then the last element I actually want to introduce to this fake word is co-opting. There’s a lot of co-opting between leading AI labs from both sides. When the initial chat GBT moment happened three years ago, every single Chinese lab more or less used Lama as their basic building block.
to advance their ⁓ model building. Every single hyperseal in China used Lama as one of their leading cloud services to get things going, right? That is a co-opting of an American, I guess, production, if you will, of a model, just to use model as an example. And then as Chinese open source became much more well-known, much more prevalent, much more popular from B-seq to Quinn to whatever, now we have Airbnb being one of the biggest users of Quinn.
We have a UiPath being one of the biggest users of Quinn and a bunch of startups that they don’t want to talk about using Chinese models to really bring down their own costs so they can run a profitable startup, co-opting each other’s work. So I think co-op petition is the most accurate way to talk about it, but I also understand it’s probably not the easiest way to say the word. And so I’m not...
counting on the word catching on at all, but at least for my own intellectual honesty sake, that is the word or the way that I plan to talk about this dynamic going forward because I think it’s the most accurate way to reflect reality on the
Grace Shao (08:56) I think definitely your writing is one of the more nuanced kind of work that I’ve come across on the internet where it does touch on China, US, where it talks about the cooperation as well as competition and give the audience a geopolitical background ⁓ but still focus on the business, the society and offer that more neutral un biased, I think, analysis of the businesses,
But from where you sit, where do you think the founders, engineers, investors actually feel like they are really collaborating? Give me some more concrete examples.
Kevin Xu (09:27) I think open source AI, open weight AI, the rise of that is probably the best and most concrete example of collaboration and cooperation happening despite all the resistance, the challenges to cooperating, right? There is a lot of resistance to cooperating on anything. And the natural way is to kind of go towards the path of least resistance. But something that is happening that is, I think, probably the biggest
in 2025 is the rise of China’s open AI ecosystem becoming all of a sudden leading the world. Not just pretty good, not just, oh, it’s also happening, but is flooding the zone as far as models are concerned. And the nature of open source is open collaboration. There is no deep-seek open model.
without the lineage of all the innovation that came out of GPT-2 that was actually open source back in the days, or Lama, or whatever the open things that the US lab...
was feeling comfortable doing until it no longer felt comfortable doing. And then you need a lot of Chinese labs to give back to the whole ecosystem as well, entirely without charge. That is the other thing about open source is that you can do whatever you want with open source product for the most part. And DeepSeek and Quinn really led the way from not just opening it, but also having the legal license to permit
just proliferation everywhere. You can do anything with a Quinn model. You don’t have to tell Alibaba you’re doing something. You don’t have to really pay Alibaba a cent. You don’t have to even give credit to the Alibaba team. Just kind of go forth and prosper, right? Now it’s very hard to track.
⁓ what that diffusion really looks like. Having worked at GitHub, for example, which is the home of all open source code for the entire internet pretty much prior to AI, I know how hard it is to track. We’ve tried to do that internally with our data. We have some rough sense of which country is contributing on GitHub more than other country, which company, but we don’t ever get too deep into the people behind that for privacy reasons and whatnot.
But you know from a institution perspective and an intuitive sense that it’s gonna proliferate everywhere, right? And the only surprising thing is that this came out of China, which shocked a lot of people. I don’t know why it should shock a lot of people, but it did. But.
But that’s kind of where the big story comes from. So I think cooperation is happening regardless. And open source is probably sort of this last bastion of global collaboration as the world splinters into its own camp as geopolitics and AI kind of co-mingle together to make everything feel more cagey. This is still the last kind of remaining source of cooperation.
Grace Shao (12:14) I think you talk about the technology being much more cooperative than people expect or want to admit. But what about capital? Over the years, we’ve seen that. first for context, think people need to understand in the 90s and early 2000s, US capital were the predominant capital that were actually behind a lot of the Chinese big tech we see today. But today, now we know there is a decoupling in terms of US investment into China, especially in the sensitive areas such as AI, robotics, and
semiconductors, right? So do you think this is something structural or cyclical? Like, are we going to see more opening up from the US government to allow these US funds to invest in China again? Because a lot of them are obviously still interested in doing so.
Kevin Xu (12:57) I think the rumor is it is loosening up. I think there’s a lot of chatter that Chinese VCs who for a period of time just could not raise any USD fund
for probably like five to six years or so is starting to do so again and I think that spigot is slowly but surely going to open up and it probably won’t be like as wide open as it used to be before but my personal feeling is that capital finds a way it’s just like water it’s going to flow towards whatever the final destination it needs to go to even if it has to go around mountains it has to go through a bunch of rocks it’s going to grind that rock to a smooth edge
that being geopolitics sooner or later, but it will probably take more time than most people have the patience for. And you know, to come back to what I talked about open source real quick, if we can double click on that, I think the contrast between capital flow and source code flow in terms of open source is that ⁓ engineers, doesn’t matter which country you come from, want to work on
⁓ the most open piece of software or code that is open source and you can collaborate with the rest of world, right? Like that’s why GitHub became so popular because engineers, whether you’re from China or the US or Germany, you identify with the code that you build. You don’t necessarily identify as much with the nationality that you were born into. That isn’t really a big part of your work at all.
Right? Even calling something a Chinese open model is a bit of an anathema because like what is it? What part does it really is Chinese versus when it’s out in the open, it’s just like this piece of common good in the internet now. Right? Like no one can really control it. So what’s the point of calling a Chinese or American or whatever? And that’s how engineers like to operate.
So that’s why there’s this tug and pull between the geopolitics force and really the engineering and the builder force that is by definition very global.
Grace Shao (14:51) Yeah, I think the engineers and scientists you speak to definitely are not geopolitically driven or as ideology driven as I think sometimes the business people because they need the support of their government for certain policies. So I think the business people who seem to sound geopolitically driven are not actually geopolitically driven. They just need to do so before for their business survival. And that’s just the reality of how the businesses work. Right. So I want to put you on the spot. We touch on this quickly before we start recording.
Manus, speaking of the most famous US injection into a Chinese AI company is Manus. And I just woke up to the news, ⁓ day of recording is December 30th, that, you know, Manus was just bought out by Meta
I I used to manage this really good product. How do you view this whole thing?
Kevin Xu (15:36) I also use Manus. I think I got an early access code actually before it even launched. I was going to say back in the days, but that was only like a few months ago. It was actually like less than a year ago, right? And this company ⁓ went from zero to a hundred million dollar ARR in about eight months, which is just astronomical.
Grace Shao (15:40) Yeah.
It’s so crazy. Yeah.
Kevin Xu (15:57) ⁓ growth on the back of essentially its product quality. And I think that is one of the most interesting takeaway for me as an investor, as a technologist, which is that we talk all about like geopolitics and, you know, this and that none of this is actually about the product or the tool, right? About AI, but Manas, this little bitty startup, basically proved all of us wrong, which is that ⁓ product quality still matters.
if you have good
Quality product people will share you people will talk about you people will you know? Do word-of-mouth to tell other people to use your product I think one of their more famous element is their ability to kind of crack this black magic of viral marketing without spending any money Right back in the days when they first shared their first version, you know, Jack Dorsey tweeted about it all these like Silicon Valley Luminary started sharing about it and it’s because their product actually spoke
for itself. And it continued to evolve very, very quickly to capture not just attention, but actually revenue, which is very, very hard in this current climate of AI kind of bubble-licious noisiness that we are living through. And on the outcome itself, first of all, congratulations to the entire team. I think it’s very impressive, this outcome to be bought out by Meta. At this moment, we don’t know how much it actually paid for. Maybe by the time this app was released, we actually know how much Meta
paid for, but the last round they raised that was $500 million valuation, right? Which in AI land is actually really, really cheap because we have, you know, 10 to $20 billion startups being funded in the U.S. right now that has zero product, zero revenue, and more or less a bet on a very impressive team, which could still come out okay, but we will see what happens. But I think this Manus deal
to me is a very I want to say it’s evidence that rationality still matters. It’s evidence that like economic kind of pragmatism still has its moment in the day and doesn’t have to be whiplashed by geopolitical consideration. So I find that the deal very heartwarming as an investor who really just hopes for more economic rationality for everybody who’s involved.
Grace Shao (18:18) Yeah, I think ⁓ to your point, like it’s definitely like a positive signal because it means that people are evaluating the products how good they are instead of just the narrative of the geopolitical kind of cloud above it. And I think it’s really interesting. Like when I was speaking to people like about this deal this morning, they’re saying actually, you know, people overestimate the PR they done back in the day when they first released it. It wasn’t because, you know, they did some black magic PR.
It was simply because they didn’t even have the compute capability to actually serve too many people. So they sent it to people to try first. And I think I have a lot of startups that come to me being like, how do I achieve madness PR? I was like, it’s not just the PR. The best PR you can possibly do is to have a really, really strong product and have the product speak for itself. Right. So yeah, it’s, think it’s a very interesting time and it’s, and it’s interesting to see probably one of the first Chinese homegrown.
company in AI being completely separated from the Chinese market now and operating in the West per se and now being bought up by American US company. Okay, talking about startups, I want to ask your opinion on MiniMax and Zhipu They both submitted their prospectus now. We are expecting them to go public in Hong Kong.
What would you need to see before you feel comfortable owning one of these IPOs and how do you evaluate these companies as they go public?
Kevin Xu (19:40) So first of all, I am actually a public market investor. So I don’t do any VC at this moment. So I’m very, very interested in how the Zhipu and Minimax listing happen. Even though as a rule, I don’t invest in IPOs because they’re quite frothy and confusing. I’m happy to wait it out. I think there are a couple of signals. And this is actually interesting as a comparison to Manus. If you look at the revenue numbers that Zhipu and Minimax have shared, they’re both in the
Grace Shao (19:45) Okay.
Kevin Xu (20:08) double digit USD million range, right, which is very modest. And it’s even more modest compared to their losses, which is all in the hundreds of millions of USD as far as how much money they’re losing right now as companies. And you compare that to Manus, which probably is like reasonably profitable at this point as like a hundred million dollar ARR company, not revenue ARR, but still they’re small, they’re growing and they’re probably managing their costs.
because they’re not model trainers, right? Like I think Manus was very clear that we don’t build models. We don’t really have expertise in that, but we are very good at wrapping a model into a very good, trusting user experience. But Drupal and Minimax both became or started out as the model makers, which is a very expensive endeavor. So as far as what I look for as an investor is concerned,
It’s very, hard. I think a path to profitability, and specifically, think EBIT profitability, so earning before interest in taxes, is going to be key for me to see how does a business ⁓ like this, which has a...
I don’t know. I feel like they’re limited to the China market, which is big, but not huge, I would say. And I think MiniMax does have some consumer product similar to ChatGPT, which is going to be how they can maybe justify their higher evaluation, even though most of the revenue comes from serving up their model as a form of APIs, which is a B2B play. How do they balance those two, which are two very different go-to-market motion? It’s going to be interesting.
pretty clear path or lane at this point, which is I make my models and I’m very good at serving large, older legacy enterprises and governments that is a very specific type of customer with a very specific taste, if you will. And you have to really orient your whole company to cater to that kind of customer. And Drupal kind of has cornered that market for now, at least. So that could work really well from a profitability perspective over time, even though those are very tough customers.
customers to track. But the big takeaway, I think, is that the revenue is still very modest and certainly very modest compared to the large labs that we just sort of talk about willy-nilly in the US, like OpenAI, which is going to have $20 billion.
in ARR by the end of this year, probably, right? Like Anthropic is projected to have five, $6 billion in ARR. These are two orders of magnitude larger than Whatchupu and Minimax has shared to the public. But the enthusiasm for investing in AI pure play is still very high in the public market. And I know Hong Kong’s IPO market has been doing very well this year and probably will continue. So that energy can be kept
hopefully by these two companies because they actually need the money, right? That goes to what you were mentioning before, which is that I think if we had done this, Chad, in 2018, there will probably be multiple rounds of VC in China with USD backing that are readily available to fund the Gipu and Minimax for maybe two, three more rounds. So they don’t have to go public.
Grace Shao (22:53) They need the money.
Kevin Xu (23:14) They can still operate as a private company, raise more money, mag around, just like what we have been doing here in the US. But that option has basically run out of this course.
right now for any Chinese VC-backed company. So they kind of have to touch the public market earlier in their life cycle for fundraising, which may not be a bad thing for organizational perspective, because you do become a more disciplined, well-run company for the most part, I think, when you become public. But it does expose you also to the public market. But they need the funding clearly, so that’s why they’re doing it.
Grace Shao (23:47) Yeah, I actually just interviewed one of the leaders at Zhipu recently for the podcast and he was saying candidly, for them, it’s really about survival at this point because they’ve just run out of money. And if they don’t want to be swallowed by someone else and if there even is a desire to swallow them, because given that, you know, all the BATs we see have very, very strong labs themselves, they don’t really need to acquire a talent, new talent pool. So then there’s no way to keep going unless they go public because they need that money.
But on this point on them going public and you know, actually it’s, it coincide with them trying to go global, right? A lot of them, like you said, they’re currently serving China as a market, but they are selling their model as a service to the global South, maybe for a much cheaper price than a lot of the US labs and the peers out there. How do you view that? Do you think that’s something that could potentially work out for them just by selling cheaper services compared to maybe the open AIs of the world?
Kevin Xu (24:39) I think it could.
Yeah, I think it could. mean, I think USAI, American AI is very expensive. Like the quality may or may not justify the premium, but it’s very expensive, right? Like we have like thousands of dollars.
Grace Shao (24:44) Mm.
Kevin Xu (24:51) I think the max chat GBD plan is like 200 bucks. People want like $1,000, know, no rate limit, chat GBD plans. And we’re spending a lot of money. And that’s partly goes into these revenue numbers, right? The billions that we’re talking about. Like you can think that is like an inflation almost of AI product costs here in the US for the most part. But we know that Chinese entrepreneurs are very good at reducing costs, right? They’re already released their models because the models are commodities. They’re open source. There’s not a whole lot of value capture really that happens at the
Grace Shao (25:14) Hmm.
Kevin Xu (25:20) auto
layer. And if you can wrap that around with really good services for just throughout random examples of like a city government in Malaysia, right, or a hospital in Thailand, for example, right, these are all the kind of unsexy industries in very unsexy countries when it comes to AI adoption that we don’t ever really think about. But if they have a strategy to go after them, and I do think listing Hong Kong as opposed to on the
you know, Shanghai market, which I think could have done as well. But choosing Hong Kong is very smart because it increases their name recognition, their exposure ⁓ in that part of the world. As much as you and I talk about these companies like everybody should have heard about them eons ago, most people don’t know what these companies are. They don’t know what the differences are. They have no idea. They probably have heard of Chachi PT, but that’s about it. They probably don’t even know what Ethlopiq really is. Right. But if you can really
tap into that capital market and use the public listing as a way to raise your profile for these second tier market and second tier countries, then I think there’s a decent business to be made there.
How much will it fetch a premium in the public market? I will never know. But I think that’s been a playbook for a lot of Chinese companies that were shut out from what’s called the premium markets globally, which is the US, Canada, and Western Europe. And they have to go to the so-called global south to make a living. And they’ve been able to make it work. And there’s no reason to just assume that these companies can’t make it work either.
Grace Shao (26:40) Yeah.
Yeah, for sure. Okay. I want to talk about sovereign AI. You’ve written a lot about sovereign AI and you’ve used South Korea as an example.
Why is South Korea a champion basically in the region as for sovereign AI?
Kevin Xu (27:09) I think to back up a little bit, sovereign AI is one of these things that ⁓ I’ve been really fascinated with for a better part of this year, ⁓ which is, it’s the first time I’ve seen where a major technological transformational period has been
aggressively embraced by national governments everywhere, right? Part of that has to do with Jason Huang of Nvidia just being the incredibly charismatic salesman that he is, right? Like sovereign AI, he did not come up with the term. I think it came from the EU in 2019 or something, but he really embraced it as the next wave of AI adoption. So more countries can have their own AI, which initially I thought, oh, this is just like a clever sales pitch, you know, to kind of sell more chips. But if you really think of
about it, ⁓ all these AI models do have a way of encoding culture. Encoding not just your mainstream culture, but also your minority culture, your different languages and whatnot. And the countries have learned, I think, their lesson by being really hands-off during the first wave of the internet and especially social media, but not caring about how does technology impact their domestic
situation, if you will. You can talk about in terms in the context of Arab Springs or, you know, violence in Myanmar or just generally speaking data privacy, social media, all this sort of stuff that countries used to have just by definition a lot of control over by having sovereignty and they’re actually losing sovereignty.
to the wave of technology. So with this AI coming together, this wave, more and more countries are actually exerting that notion of sovereignty without really knowing what it means, but they’re exerting it right now more aggressively than ever before. Now I picked on South Korea because sovereignty is kind of this big fuzzy word that means different things to different people. But if you use sovereignty as a proxy to talk about control, South Korea actually has probably one of the better
set of tools to exert more control over their own AI future more than other countries. Because if you really think about full stack AI from top to bottom, from land power chip models and then applications, only the US and China really have a grasp of every single layer of that stack.
Grace Shao (29:24) component, yeah.
⁓
Kevin Xu (29:25) in diff to
different degree, obviously, but you know that they have control over every single step, right? Every other country for the most part is a customer of one of those stacks coming from the US or coming from China, except I think for a handful of countries, South Korea being one of them because it has a very strong memory.
⁓ ecosystem for chip fabrication, not for logical chip, but for memory. And high bandwidth memory is basically an exclusive South Korean national export at this point coming out of SK Hynix and Samsung. Yeah, we have some Micron over here in the US too, but the two thirds of the market is dominated by two Korean players.
Grace Shao (29:46) Yeah.
Mm-hmm.
Kevin Xu (30:03) And then they have their pretty cool little internet ecosystem as well with Naver, with KakaoTalk. They’re all very Korea-centric. They don’t do so well outside of Korea, but inside Korea, just like how we go to China, we have to install WeChat. If you go to Korea, you have to install KakaoTalk. You have to install Naver for your map. Otherwise, you just can’t get anywhere, right? So you kind of have...
Grace Shao (30:23) There is Google Map.
Yeah.
Kevin Xu (30:24) Exactly.
So they have that set up cone coming into it. So they actually have a bunch of different good dominant controls nationally throughout all that layer. So when Jensen visited South Korea recently to sign a bunch of deals and allocated a bunch of black wall chips to different major players among these Chibos, I just thought this is like an actual manifestation of a South Korean sovereign AI at play. Now they’re still using American chips, but part of that American chip is fused with South Korea made memory.
And that gives them a lot more say, at least, to sovereignty of the AI application that they’re hoping to adopt. And South Korea is just very digitally forward, I think, in general. It’s one of the most digitally connected society, period, of any country in the world. And so ⁓ I think they have a good shot at actually making sovereignty real in the AI era.
Grace Shao (31:03) Yeah.
It’s interesting because I just went to Korea I think earlier in the year and I was talking to investors on the ground and they were saying that South Korean startups are actually a lot more, again going back to our point, non-geopolitically driven or minded and a lot more agnostic about which kind of, what countries models they use. However, for the country itself right now, the government, they’re still pushing US models forward. And I think it’s really interesting to see to your point like
They actually have such a small but closed ecosystem in the digital infrastructure. Like everything is with, they don’t use American apps like for social media. They don’t use Chinese apps for social media. They’re actually completely independent. So it would be an interesting kind of case studies to follow through with, I think. When we look at sovereign AI and I look at, know, projects like Stargate, is that something like a de facto US sovereign AI project? Like, how do we understand that?
Kevin Xu (32:03) That’s how I understand it. I think Stargate is, first of all, for people who don’t follow this stuff as closely, is this brainchild from OpenAI to build these massive multi-gigawatt data center, not just in the United States anymore, but actually throughout the world, to support its global multi-trillion dollar ambition. And it’s in countries that are willing to be on Team America. So in a way, it’s a sovereign extension
Grace Shao (32:05) Mm-hmm.
Kevin Xu (32:31) of American AI in a way is also a reduction of sovereignty in whichever country is willing to receive American AI and be a proxy of American AI, right? And we have a few different sites already announced. We have ones in Argentina, in the UAE, in Norway. I think these are the ones outside the US Stargate projects, maybe perhaps India as well. And that is the most aggressive.
expression of American sovereign AI and the most explicit one as well. And the US government is very honest about this as well. Like they want to promote and literally sell the American stack to countries around the world that want to buy American projects. It’s basically like a big
know, White House driven go to market strategy, right? Where the content of the product is actually open AI, Anthropic, Nvidia chips, Oracle, construction, and all the American kind of major companies that come together into literally a package, right? And then we want to sell that abroad to different countries around the world, including the global South as well. And I think that’s one of the things that a little bit of a shift geopolitically is that the US is no longer
giving up the global south as this also ran that it is no longer paying attention to in the way that China has been paying very close attention to for two decades at this point. It’s no longer willing to surrender that part of the world commercially. And Stargate and AI export program is actually a way to express that re-interest in those regions, if we will. And Stargate is just kind of the tip of the iceberg there.
Grace Shao (33:56) Mm-hmm.
I think to talk about sovereign AI, we have to talk about Middle East and it’s something I really know nothing about. I was really fascinated by your recent article and your series in sovereign AI. So first for listeners, can you tell us about your trip? You just got back from Abu Dhabi, I think a week or two ago, you wrote a really insightful long piece on just how the Middle East is building out their AI strategy. And you talked about it as a region, also kind of breaking it down the whole, looking at the Gulf separately, the UAE, the Saudi Arabia, Qatar, Bahrain, each of the...
AI strategies, right? Can you kind of walk us through, first of all, why did you go? What was the event for? And then just some of the high level takeaways from that trip.
Kevin Xu (34:44) So I went to that trip from the exact same position that you are now, which is that I’ve never been to the region. I’ve heard a lot of things about the region. Just in the AI conversation alone, we’ve had major announcements and deals being signed by Saudi Arabia, by the UAE, with the United States. We know Chinese tech have been in that region for a very long time as well. There a lot of robot taxi Chinese companies that are deploying their self-driving vehicles on the ground as we speak. So it’s a region that I’ve been really wanting
to go if I get the chance to go for a long time. And just by happenstance, I was invited to be part of a delegation among other Washington, D.C. think tankers to go to visit the UAE for a week. So we are an American delegation, right? So that’s important context for you to know. If you were to read the post that I wrote and understand what I was trying to convey and how I learned things, we spent a whole week in both Abu Dhabi and in Dubai meeting with pretty much everybody
that has a hand in its AI future, from government officials to investors to all the funds that you have heard of. And the major takeaway for me was, first of all, just to see stuff on the ground, which is that
They are very, ⁓ from an AI perspective in particular, just take away the other stuff for now, from an AI perspective, they’re very much in Team America’s camp. They really want to be building UAE Stargate. That’s one of the very few Stargate projects outside the United States that has actually broken ground. Like there are actual buildings that have been built in the desert.
⁓ ready to receive NVIDIA BlackWall GPUs if expert control were to be permitted from the US side to let them buy as many as they would like to buy. So that’s number one. I think number two, they are in this very interesting geopolitical position as a very tiny country of 10 million people where they don’t want to actually be Switzerland. They’re not neutral. That was the message that I received from a lot of people that they have a point of view on where they want to be in this global
a big game of AI, of geopolitical influence, which is that they can vote for one country or one side, but they also have their opinion to build a society of their own. That’s a very modern Arabic.
you know, society, I think there’s a lot of stereotypes to, know, how does it, what is it like to be in a be a woman in the Middle East? What is it like to operate in the Middle East? Lots of cultural stereotypes that they want to debunk. It’s Dubai is one of the most modern cities I’ve ever ever been to. Right. And that is kind of the cultural takeaway that they want us to have. And then lastly, when it comes to this US China conversation, frankly, they’re a little bit tired that they always get mentioned in that context. Right. Every time the US official goes to the UAE is about
What are you doing with China? then, know, presumably when the Chinese official visits, they’re also like, what are you all doing with the Americans? But they want to be seen on their own term. And they certainly have the wealth to do so.
as well. So it’s a fascinating region and I think they’re playing both sides very well. I call them the swing vote of the global AI competition. They can swing one way, can swing the other, but they have a lot of leverage in this conversation because they need to have the best of both worlds to feel an economy that is in the desert that literally grows nothing.
So they have to export import, sorry, they have to import basically everything from talent, from food, from vegetables, from, you know, the only thing they have is energy coming out of the ground, oil, but they’re trying to diversify away from that, which is the only point where they’re investing all this technology stuff in the first place. And that has been happening for 20 years at this point. So it’s not like a Chad GBT moment thing per se. So a lot of takeaway there, but happy to answer more questions because it’s a trip that I’m still processing, to be honest, because that was first time in the region, had a lot of
coming at me and I’m trying to still come to terms with ⁓ what I understand now but also what I still don’t understand even though I just went there.
Grace Shao (38:40) Yeah, I think that that’s super interesting. And I’ve been really fascinated by the region as well. Actually, we were just talking about this offline. A lot of people in Hong Kong are now being recruited over on the point of talent. And I think, you know, as a lot of these countries have huge sovereign funds, they’re looking for top tier investor talent to go to whether it’s UAE or Saudi or Qatar to really deploy that capital, whether it’s an AI or not. And it’s really interesting kind of to see how
You mentioned they have, what, UAE has 10 million people, but 90 % of that is actually foreign workers, including laborers, as well as knowledge workers. And people kind of forget that actually these are extremely wealthy countries per GB per capita. So I think it’s interesting to hear that they don’t really want to be put in the middle as a China camp or a US camp country now. Similarly to Singapore, where we also talked about, know, like Singapore is
Tiny small, you know peninsula has actually really made it work from themselves and Pretty much have to import everything from groceries and labor is sometimes from Malaysia and even energy to talent from around the world and now mostly China It’s kind of like in that sense not a Switzerland like Singapore right like you said in your article. I want to understand better actually How do we understand the sovereign funds behind?
these investment funds are investing in AI because it actually is so different from private capital in the US and even how Chinese capital is structured.
Kevin Xu (40:06) The way I-
the sovereign fund in the UAE in particular, know, just that part of the Middle East have not been to Saudi Arabia or anything. Obviously Saudi Arabia is a major, major player as well. So is Qatar, which actually announced their own AI initiative while we were in the UAE as part of the Doha Forum. So there’s a lot of, let’s also call it co-op petition as well, among the Middle Eastern countries as well. Like they’re presented as this sort of monolith sometimes, but there’s actually a lot
Grace Shao (40:23) Exactly,
Kevin Xu (40:37) of rivalry or friendly competition between, in particular, these three Middle Eastern golf countries, Saudi Arabia, UAE, and Qatar. Now, the UAE sovereign wealth fund in particular, again, we met with everybody there. so their strategic purpose is, of course, to diversify away from oil wealth.
Right, but that’s easier said than done. What do you do when you have all this money? From selling oil that you know, it’s gonna run out at some point or you don’t want to be overly dependent on this one source of wealth, right? So Mubarak is their kind of marquee sovereign wealth fund that plays very actively in the world of technology investing and they’ve been Investing for 20 plus years around the world. They’ve had offices in China in South Korea in Brazil
obviously in the United States, in Europe for many, many, years. They’ve been placing bets and serving mostly as LPs to local VC firms for
a long time. They’ve also bought a global foundry, which is the chip manufacturing plant, similar to TSMC. But you know, that was kind of a spin out out of AMD, I believe, back in the days. So they’ve kind of placed their bet in the chip ecosystem, again, long before AI was a thing. Now, that doesn’t mean they’re all successful, because the diversification justification is very different from ABC, who is motivated to generate the largest
financial outcome right per fund per fund and they actually did understand more recently why that’s not such a good model which is directly related to your point about Hong Kong professionals finance professionals being recruited
to go to the UAE because they started this new fund called MGX, which is basically more of classic VC fund that has all the incentive structures of a Silicon Valley VC firm like a Sequoia or a 16Z. Mubadala is one of the anchor GPs, but they’re raising money from around the world just like a normal VC would because they need to attract the best talent, which they actually could not if you just run a
sovereign wealth fund because sovereign wealth fund is kind of like a quasi government institution, right? They’re still kind of government employees at the end of the day. They don’t get a huge carry or a payout because one of their funds hit it out of the park and got less than the NASDAQ. They’re just kind of collecting their paycheck, right? They’re more like a pension fund manager. And that doesn’t get you the best, most hungry, I don’t know, money.
making talent from London or Hong Kong or wherever. So they’re just very recently started to restructure that because it’s an evolution of sovereign wealth fund being managed, one, to diversify and then to get into the best technology and then to actually generate a good return and get the best talent, which is really a long-term play because if they can lock down the best talent from Hong Kong for a decade or two to live in Dubai, to live in Abu Dhabi, then that is a long game that they can, again, supplant this 10 million people that needs to be constantly replenished.
with better talent and more diversified talent. So the sovereign wealth fund, the game is very complex, I think, and they probably played it better than most people that I’ve seen coming out of any sovereign wealth fund. Singapore sovereign wealth fund is very sophisticated as well, but that took a long time to evolve GIC and Tomasic.
Grace Shao (43:49) Yeah.
Yes, yes. That’s really interesting context. I think I have one last question for you on Middle East, just given the time constraint, but I would love to talk more about this offline. If you had one non-consensus bet on the Middle East and how it may shape AI globally in the next few years, what would it be? Like, how do we understand the Middle East role going forward, especially amongst this US-China co-petition?
Kevin Xu (44:18) I think I was skeptical going into the trip that it’s going to be a region that actually would matter because there so many data centers being built everywhere. But coming out of it, ⁓ I think there is a good chance that the UAE Stargate will house a significant amount of compute for not just that region, but for the entire world.
First, because its energy is abundant. Second, its construction force, which is something that we did not talk about explicitly. They have 1.5 million construction workers. So 15 % of the population in the UAE is constructing something. They wake up, they’re building something. It could be a hotel, it could be a resort, or it could be a data center. That is something that we’re actually very...
Grace Shao (44:59) these are mostly workers from abroad, right? From India, Pakistan, Philippines. Yeah.
Kevin Xu (45:02) These are almost, these are entirely workers from abroad, right? These are Pakistanis,
a lot of South Asians who are there on workers visa. So they’re not, you know, living some glamorous life. They’re just a construction worker life, right? And there are a lot of kind of like issues with that approach, if you think about it. But as far as the capacity is concerned, they’re able to really build stuff faster than just about any country that I’ve seen. And as the United States hits its challenges, I think, when it comes to labor,
when it comes to energy capacity and I think will also become a domestic political issue very very soon especially this upcoming year with the midterm election that could grind a lot of the pace to a bit of a halt and the UAE is ready to risk kind of
receive all that chips that are being made in Taiwan. And I think that will really be something that people haven’t really thought about as far as where their computer will actually physically live, which really will bring again the sovereign AI story of the UAE to
to life because it’s not just another talking point anymore. They actually have a significant amount of compute that could be used for training models and it can also service a bunch of the region over there because the telecommunication cable between the UAE and say India, for example, or Pakistani, the speed there is like 30 or sub 30 milliseconds, which is super, super fast. So you can actually serve a bunch of users from the UAE to India if you’re okay with that kind of, you know, data center set up.
So that’s something that I think people are probably still sleeping on. We may see that becoming a more real just in another 12 months or so.
Grace Shao (46:39) Interesting. Kevin, you’re so knowledgeable and everything. I love reading your work and I just really enjoy this conversation. I have one last question for you, which is a question I ask every single guest that comes on the podcast. What is one differentiated view you hold? Non-consensus, something maybe even controversial that you truly believe in that maybe your peers don’t?
Kevin Xu (46:57) I think, I’ll share two, but they’re interconnected. Obviously they’re really one, but in two parts. One is that I think there’s a consensus that China AI, AI in China is diffusing better than the US. I think from an economic perspective, from an economic impact perspective, that is actually not true. If you just compare the revenue number,
between Gipu and Minimax to any lab that we have here in the US. It’s peanuts, right? Now you can say we have a bit of a token price inflation over here in the US, as I’ve admittedly mentioned during our conversation, but it’s not 100x premium as far as like that delta is concerned. So there’s actually a lot of economic ⁓ value being captured here in the US just by the diffusion pace that we’ve been able to push out here alone.
So that’s sort of a non-consensus thing, the one. And the other thing that is related is that because the pace of diffusion in China is a bit more up and down the stack, you know, not just in knowledge worker, but in factories, in on the road with self-driving and in robotics and whatnot, let’s just assume all these will just kind of continue at pace faster than any other country in the world. Then China will also be the one country
That has to deal with all the social ramifications of AI before any other country in the world So this is a very interesting moment where the Chinese government and regulators will have to lead the world Into this kind of dark space as we’re all filling out what the hell this AI is gonna do Before anybody else and I’m really interested to wait to see how much they’re willing to share their learning
their failures, their successes from a rulemaking perspective? And also, how humble will the European regulators and the American regulators be willing to learn from the Chinese failures so we don’t screw up too much in our own backyard?
That will be something that I think will happen for sure, but it could really determine the direction of where all this is going. And we kind of saw a little bit of that with the most recent regulation coming out of China when it comes to regulating the chatbots. It’s much more prescriptive than the usual list of harms when it comes to data privacy and whatnot. It touched very specific use cases, like if a chatbot is going to talk a lot about, you know,
Grace Shao (49:02) What’s you say?
Kevin Xu (49:16) the giving mental health advice or all these much more personal use cases that could lead to self-harm the regulators in China is having a very particular point of view on how this should be Diffused in its society whether that lesson good or bad gets learned here in the US and elsewhere in the world is Gonna be interesting but China will have to lead on this front ⁓ Which is a position that I don’t think the Chinese regulators are used to
Grace Shao (49:42) Even expected,
Kevin Xu (49:42) ⁓ up to this point.
Yeah, they’re used to learning from outside. They’re very good at absorbing the best rules from Europe and the US to bulk up their own regulatory capacity and knowledge. But this could be the one thing where they will be the first to step into the abyss and they have to help us get out of it.
Grace Shao (49:59) I think a really, really interesting point. And I actually been thinking about this as well. To your point, I diffusion in China is so much more obvious to the human naked eye because it’s seen through consumer usage, through just the rampant digital infrastructure buildup that we’ve seen in China. So everyone, like you said, from random auntie to knowledge workers will be using AI. But the actual capital gain, the real money has not been proven to be greater than...
than the US and already we can see that from just IPOs like MiniMax and Zhipu And I think the regulation that you were talking about actually came out interestingly right after MiniMax and Jhipu actually released their prospectus to the public. So it’s like, I think regulators are really keeping a keen eye and a hand on it and trying to see what could potentially happen. We speak to people in China practicing AI, like the actual builders and the scientists, they say, there’s less of a discussion about this like.
Doomerism kind of view people are taking more pragmatic view, you know, people are really focused on technological advancements less about societal implications Yes, I kind of believe that being probably the case given that you know in China last 20 years people really saw technology as a Path to economic prosperity, but however, I think what your point is is really interesting is that actually this time they can’t see what happens how the US regulates by tech
they have to do and start themselves, right? So that’s a really interesting point. I actually will think about that a bit more as well. ⁓ Thank you, Kevin.
Kevin Xu (51:29) Yeah, there’s a 100
% chance that China will have to be the first country to lay off a bunch of delivery drivers and, know, ride sharing drivers if robot taxi becomes a thing, right? What will Wuhan do? I think everybody else in the world will want to know when that happens. Yeah.
Grace Shao (51:46) Yeah, yeah, especially when
embodied AI becomes more of a but okay on this point I wonder your thoughts on this because When we go to China, it’s really interesting you have these random jobs that are like placed for sure not for like actual practical reason like you know those aunties who sit in elevators and press the button for you or Like a uncle who sits there like an older kind of larger man who sits outside the parking lot who just pressed the toll button for you like
Kevin Xu (52:03) Mm-hmm.
That’s Right. That’s right.
Grace Shao (52:13) These
jobs frankly are not needed, but they’re implemented I think for societal harmony purposes because you need employment. You need to give these frankly not very skilled laborers a job. So if you’re gonna push for embodied AI in China and these physical, whether it’s robots or whatnot, are gonna replace a lot of these lower skilled jobs, what’s gonna happen to society? Do you think they’ll actually?
implemented at mass or do you think they would actually take a more cautious decision?
Kevin Xu (52:43) My read on that is they will be very, very cautious, which again goes to the non-consensus view that I just shared on the diffusion narrative about China and AI. Right now, the consensus is that, oh, China’s diffusion is so much faster. They’re going to push all this AI. We’re screwed here in the US. But really, there is a very good human reason to not do that.
⁓ You know, this is not exactly public knowledge, so I won’t cite it. But if you look at the pace of deployment of the self-driving companies operating in China alone, right? know, you Baidu, you have Pony, you have Rewrite, you have some of smaller players. It is, they’re all born there. They have very good regulatory environment to experiment and develop their technology. But they’re actually throttled by local permit capacity.
on a city by city basis as far as how many of these cars can they actually deploy on the road? Because it’s not a free for all at all. Every city is looking at the numbers and be like, okay, if we actually do this tomorrow, like let the floodgate open because the technology is actually really, really good already. And we already know the Chinese, yeah, well the Chinese OEMs can pump them out really quickly, right? I think that the safety concerns actually getting really, really good. But what would the delivery drivers do?
Grace Shao (53:49) It’s not a safety concern.
Kevin Xu (53:59) What would the DD drivers do? So there is this toggling already between how much are the government willing to let this technology loose versus taking care of the aunties who pressing the buttons and the dachu who’s letting you into the parking lot because there has to be a pathway there. It’s just not obviously a subsidy program, but it’s clearly a government funded economically irrational employment program.
Right? Like the only corollary we have in the US is the greeters at Walmart stores. I’ve never been to a Walmart super center. There’s like this person who just says hi to you and you walk in and you get your Walmart stuff. Like does that person need to exist? It’s of Walmart’s premium user experience for shopping there. But we don’t have as much of that here in the US, but we certainly have a little bit of that too. Right? So again, China is going to hit that at scale.
Grace Shao (54:23) Yeah.
It’s part of user experience, Kevin. They want you to feel welcome.
Kevin Xu (54:45) before any other country. And they’re trying to figure out the right balance right now as we speak, but we don’t really have a good sense, at least from the outside, of what are the rationales, can we learn from that, can they share more of the thinking, so we can all kind of benefit from that, from a rulemaking perspective.
Grace Shao (55:01) Yeah. And you saw that with the urbanization demand, like what, 10 years ago, we saw a huge rise of young men from rural areas moved to urban cities to become delivery workers, whether food delivery or package delivery courier, that created a lot of economic gain for the country. And then when COVID hit, it was crazy. A lot of people got laid off from their white collar jobs. And then you saw a huge increase of essentially Chinese Uber ride drivers.
Kevin Xu (55:27) That’s right.
Grace Shao (55:29) So all of a sudden people all became drivers and there’s a huge oversupply of riders and now you can call a DD and any car would shut up within like a minute. It will be interesting where would these people go if you’re gonna introduce all these self-driving cars, self-driving delivery man, whatnot. It will be interesting because that makes up a huge part of the urban economy right now. Yeah.
Kevin Xu (55:43) Yeah.
That’s right. That’s right.
And you know, one approach is just that you don’t, right? You just say, okay, we know we have the tech, you can export to the UAE all you want, which though they’re doing really well in the UAE, the Chinese are all with taxi companies. But at home, you’re going to pace yourself because we have a lot of people who are going to get really, really upset if this thing gets unleashed tomorrow, which it can. And that kind of goes against the whole China that just defuses everything because China loves AI sort of narrative.
Grace Shao (56:16) Yeah, interesting. Thank you again, Kevin. Really appreciate your time and your insights.
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In this episode, I sit down with Alan Zhang (Principal & Portfolio Manager at Ox Capital Management) to map China’s tech landscape through an investor’s lens. We break down how Alibaba, Tencent, and ByteDance are approaching AI, and why the “AI OS” is the real endgame. Finally, we analyze what’s changing in China’s consumer internet, EV ecosystem, and embodied AI pipeline. We also unpack China’s delivery wars (Alibaba vs Meituan vs JD), why quick commerce is structurally different from traditional e-commerce, and how markets price geopolitical risk into China tech valuations.
Alan Zhang is a Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia; previously, he spent years as an investment analyst on the Asia team at Platinum Asset Management.
He studied Actuarial Science and Commerce at the University of New South Wales, and he’s even taught advanced econometrics. So if you like the intersection of fundamentals, market structure, and Asia platform businesses, well then, this one’s for you.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
01:34 Alan’s background: quant → Asia equities
03:11 US vs China AI: frontier vs “two-legged” approach
05:25 “Uninvestable” China and what changed
07:31 Beyond BAT: Xiaomi, Meituan, Mindray, MicroPort
09:24 BAT AI strategies and the AI OS thesis
13:45 Tencent: tools, data, distribution, and model strategy
16:33 AI-native phones: ByteDance × ZTE and what’s next
26:51 China EV landscape: BYD, Huawei, Xiaomi, Zeekr
31:28 Why phone OEMs can compete in EVs
34:16 Embodied AI: robotics parts, redundancy, and Unitree
39:38 Valuation + geopolitics: why Asia tech trades discounted
41:53 China delivery wars: subsidies, quick commerce, Meituan’s edge
50:27 12–18 month predictions + what investors miss (healthcare)
AI-Generated Transcript
Grace Shao (00:00)In today’s world, there’s no shortage of information. Knowledge is abundant. Perspectives are everywhere. But true insight doesn’t come from access alone. It comes from differentiated understanding — the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend — someone who can help us see things differently.
So today, joining me is Alan Zhang. And I’m Grace Shao. Alan, really excited to have you. I’m excited about today’s conversation because we’re going to get into the investor’s perspective on Asia tech and emerging markets — with a proper markets-and-math backbone.
Alan Zhang is Principal and Portfolio Manager at Ox Capital Management, a boutique investment firm focused on emerging market equities that he co-founded in 2021. At OxCap, Alan leads investments across Asia. Before that, he spent years as an investment analyst on the Asia team at Platinum Asset Management. He studied actuarial science and commerce at the University of New South Wales, and he’s even taught advanced econometrics.
So if you like the intersection of fundamentals, market structure, and Asia platform businesses, this episode is for you. Alan, welcome.
Alan Zhang (01:31)Thank you, Grace. Pleasure to be here.
Grace Shao (01:34)Alan, to start, why don’t you tell us about yourself — your background — and what it is that you cover now?
Alan Zhang (01:40)I grew up partly in Hong Kong, mainland China — Shenzhen particularly — and in Australia. I spent close to a decade in Australia doing my schooling and education, and worked for a firm called Platinum Asset Management, then co-founded Ox Capital with Joseph Lai.
I studied actuarial science, so I’ve had a lot of experience manipulating numbers, cleaning up data — and that helped me tremendously in public equities. Nowadays there’s no shortage of financial data, and the ability to understand them — and the intent behind them — is crucial to investing.
Grace Shao (02:34)Yeah, yeah.
Alan Zhang (02:46)At Ox Capital, we also built a tool called the Mode Model, which distills more than a million financial data points from various sources to help us understand our coverage region a lot more. In terms of my coverage, I build quant models, I look at equities, and I also help with portfolio positioning based on macroeconomics in Asia.
Grace Shao (03:11)That’s interesting because you started off in quant, but now you’re looking at equities — the fundamentals, right? You’re covering a lot of ADRs, and a lot of China’s big tech.
Let’s talk about that. What is the China big tech internet ecosystem looking like right now? How does it compare to the US?
Alan Zhang (03:20)In the US, they are focusing more on frontier models, while Chinese companies are taking more of a two-legged approach — tackling AI with different approaches. The US has invested a lot of resources into advancing frontier models. On one hand, we see successful cases like Gemini, Anthropic, and OpenAI, while we also see a lot of AI subscriptions cutting their prices by more than 90% in the last few years.
If you remember in 2023 and 2024, many subscriptions were priced at a few hundred — some over $1,000 a month — based on investment assumptions. Now they’re cutting prices to sub-$100 a month. Some may never make their money back based on those assumptions, but it’s not being discussed today because the benefit of AI far outweighs that blip, and large-cap companies are investing enough to offset the impact.
If we look at China, they haven’t gone through this episode — and I don’t think they will. Anyone who looks at Asia understands Asian users will never assume people will pay over $1,000 a month for subscriptions. China is working on frontier models, applications, and infrastructure at the same time.
In summary, China is still the runner-up, but they’re developing AI in a more balanced manner. And it’s also good to see the US pivoting — in the recent 12 months, we’re seeing more US companies investing in software and applications rather than just frontier models.
Grace Shao (05:25)China was deemed uninvestable, especially for Western investors. Your fund is based in Australia and Hong Kong, and your LPs are non-Chinese. For public investors who want exposure to China’s AI upside — what are they looking at? What are they thinking?
Alan Zhang (05:46)Usually the big tech. China went through the property adjustment and the antitrust campaign in the internet space. It was painful — people called it uninvestable because they couldn’t see new growth drivers. And if they could, they were too insignificant compared to the two most important industries at the time: internet tech and property, which were both recalibrating.
But things are different now because investors can see new growth drivers scaling up. In hindsight, these adjustments also helped innovation: talent that dreamed of landing a job at Meituan, Tencent, Alibaba went to smaller firms or startups; capital that made easy money in real estate went to new areas.
Economic transformation is still a work in progress, and investing in China becomes more attractive if we see AI, consumption, and advanced manufacturing play a bigger role. We’re still in that phase. But we’re glad to see some companies bottoming out and making progress under the current setup.
Grace Shao (07:19)In a pragmatic way, does that mean we’re looking at BAT? What companies should we be looking at for exposure to Chinese AI and economic transformation?
Alan Zhang (07:31)Besides Alibaba and Tencent, people should look at relatively smaller cap — but still large-cap — companies like Xiaomi and Meituan. And also industries outside the internet. For example, Mindray in healthcare, or MicroPort in surgical robotics — they can implement AI into their products and make their portfolio more attractive.
Grace Shao (07:41)When we chatted offline, you said a lot of companies are overlooked. Beyond BAT — what are some “1.5 tier” or “second-tier” companies that are huge by market cap but not well known in the West?
Alan Zhang (08:09)People will naturally see them more over time. Tencent and Alibaba were making active efforts overseas; now as the market matures, more companies are going global. If I’m on a roadshow, people ask about Keeta, which is a subsidiary of Meituan. Xiaomi is opening more stores in Europe — even Africa and South America. People will naturally see them more.
If you come to China and compare what’s here to where you live, you’ll see a clear difference.
Grace Shao (09:24)Let’s double click on BAT — Alibaba, Tencent, and ByteDance. At a high level, how do you compare their AI strategies? Are they playing the same game, or different playbooks?
Alan Zhang (09:52)Same, but different. They’re all investing heavily in frontier models and infrastructure. Ultimately, they all want to build the AI OS people will use. The DoorDash–OpenAI collaboration was a good example of what AI and a commerce company can do. Whether it’s an app within an app or an app within a phone — that’s still an open question.
Alibaba is e-commerce and cloud. They have to build a competitive model or their cloud becomes commoditized. Tencent is a platform — they build tools. In LLMs or AGI, late movers can have an advantage because users may be indifferent as long as security and usability are similar. ByteDance, as a private company with strong feed algorithms, has been AI-native for a long time — even back in 2018 they were investing heavily in AI and user intent.
So they’re all trying to build an AI OS for users, just from different starting points.
Grace Shao (12:29)I love that framing — I’ve been writing that 2026 is about the AI OS. Tencent has signaled they’ll double down on LLMs. It’ll be interesting to see whether late-mover advantage shows up — and whether they need to spend less on pure infra.
How should we think about Tencent’s positioning? They’re late on LLMs, but AI is already integrated across touch points — WeChat, gaming, fintech, mini programs. Should they continue using open-source models like DeepSeek, or focus on proprietary models like Alibaba integrating Qwen?
Alan Zhang (13:45)They’ll do both. With Yao Shunyu reporting to Martin Lau, they’ll try to build their own model like every tech giant. At the same time, Tencent’s bread and butter is building tools — AI tools to help merchants and users and improve the experience.
Whether it’s an app within an app or an app on a physical phone — like the Doubao phone we saw — Tencent has the ingredients: ecosystem, quality data, and distribution.
Grace Shao (14:37)When you say “building tools,” how is that different from Alibaba building tools for businesses? And how is that different from ByteDance’s “app factory” approach?
Alan Zhang (15:10)One example: in WeChat’s input bar, if you long press, you can translate. People type in their own language and WeChat translates to the recipient.
I also visited their AI showroom recently. They showed mapping genetic pools and building a genetic bank for seeds and animals — they have quality data. They can also build full simulators for flights and cockpits — one of only a few companies that can do that. They’re investing in spatial intelligence and data banks, and building tools inside WeChat.
I think it’s only a matter of time before they move more properly into e-commerce and release something like what DoorDash and OpenAI shipped.
Grace Shao (16:33)On hardware — can we talk about ByteDance and ZTE’s partnership? ByteDance worked with ZTE and launched an AI-native operating system on a ZTE phone. Instead of building their own phone, they partnered with OEMs. What do you make of that?
Alan Zhang (17:11)As a user, I looked forward to it. A product like this may take longer to be widely available because it disrupts a lot of vested interests. But the trend is inevitable — AI OS will be valuable in ways we can’t even measure.
This is what I envision for Xiaomi and Tencent too. Companies like these — and Apple — are planning for that day, but they’ll move when stakeholders are ready. OEMs have the protocols to make it happen. Tencent also has content and intent — ads revenue — plus distribution. Tencent and Xiaomi will try to tackle this new market.
Grace Shao (18:13)Is ByteDance moving faster because it’s private? Xiaomi and Tencent are public companies — does that slow them down?
Alan Zhang (18:29)Absolutely. ByteDance can try something new; if it fails, it doesn’t impact the core. If Tencent or Xiaomi do this, they can agitate business partners and users.
Grace Shao (19:10)For an American audience, is there an apples-to-apples comparison to US peers?
Alan Zhang (19:32)It’s difficult. These companies are mature and make decisions based on their own opportunity sets. In many spaces, Chinese companies are leading, while the US is still exploring new frontiers. Tencent has been relatively quiet until recently, and they work quietly with industries to understand how their AI stack helps.
In 2015, Tencent founded a learning program called Tencent X — “X” stands for another Tencent. They work with business schools, bring entrepreneurs and business leaders to site visits and exchanges, and use the process to understand how to develop their stack to empower Chinese industries. A successful example was Pinduoduo — through this program, they found Colin Huang and supported the company through traffic. Tencent can find more companies like this in their own way.
Grace Shao (20:46)[Connection drop]
Grace Shao (21:04)Could you restart that sentence?
Alan Zhang (21:08)[Repeats Tencent X explanation]
Grace Shao (22:07)Looking at 2026 — what consumer AI applications might look different? Any sprouts inside super apps that people aren’t noticing yet?
Alan Zhang (23:07)2026 will likely be an interpolation of 2025. I don’t expect a completely new form factor. Most Chinese companies are already super apps, boundaries are ambiguous, and they’re fighting for the same consumer pockets.
But ads revenue will shift. Previously, ecosystems charged a lot for ads because of captive customers. With AI, people are reconsidering how they use apps — budgets will relocate to new apps.
Grace Shao (24:20)On infrastructure: it feels like everyone is shipping models — not just BAT and the “four tigers,” but also Kuaishou, Meituan, Xiaomi, even EV players. Why?
Alan Zhang (24:58)They have enough users, and AI improves experience and broadens reach. For example, older users didn’t use search much, but with AI they can adopt faster. AI makes products more interactive and easier to use.
EV companies want more engaging products. Cars are becoming commoditized, so they invest in infotainment and ecosystems. That’s why every sizable Chinese company will try to build a model. And we’re still in the investment phase — nobody knows who wins, so everyone tries. It’s not as expensive as it sounds.
Grace Shao (26:25)Isn’t it costly for EV companies?
Alan Zhang (26:32)It’s costly, but a lot of money is spent on chips research and manufacturing. The LLM itself isn’t as expensive as people imagine.
Grace Shao (26:51)Let’s double click on EVs. Who are the biggest players in China beyond BYD and Zeekr?
Alan Zhang (26:55)BYD and Huawei. Emerging ones: Xiaomi and Zeekr.
Grace Shao (27:15)How do you position them?
Alan Zhang (27:21)Xiaomi’s selling point is ecosystem. You can call “Xiao Ai Tong Xue” — the voice assistant — to operate devices through the ecosystem, especially with HyperOS 3.
BYD’s advantage is manufacturing — they can build similar-quality cars cheaper through supply chain management.
Huawei has HarmonyOS and strong brand equity — customers pay up, so they can stack a more luxurious experience into the car.
Grace Shao (28:15)How does that compare to “luxury EVs” like Nio — are they still relevant?
Alan Zhang (28:24)They’re still relevant. Li Auto is more family-oriented than luxury. Nio targets younger consumers who want the driving experience. Huawei’s models skew more toward corporate executives and founders — generally 40 and above.
Grace Shao (29:08)So there’s a shift — five years ago it was BYD, Nio, Xpeng, Li Auto; now Xiaomi and Huawei are making strides because of AI operating systems. Is that right?
Alan Zhang (29:28)Yes. China’s auto market has many brands and licenses, no shortage of production capacity — and there’s overcapacity. The “anti-involution” campaign has targeted autos. The industry is commoditized, so companies need differentiated advantage. Xiaomi and Huawei have ecosystems; BYD differentiates through cost and can scale domestically and overseas.
Grace Shao (30:41)Why are Xiaomi and Huawei able to lead? Does that mean EV-first companies become less competitive?
Alan Zhang (31:28)EVs have fewer parts than ICE cars. Historically you needed over 10,000 parts; now EVs might have a few hundred to just over a thousand. You can break it into powertrain, battery, chassis, and battery management — and the rest is non-core. Many parts are commoditized except the battery and system.
Xiaomi and Huawei can repurpose capabilities from phones: chips, screens, packaging. Xiaomi can repackage Qualcomm chips and repurpose them to be auto-grade; Huawei can do similar. Cars also have bigger screens than phones — manufacturing capability transfers.
EV-first companies like Nio, Xpeng, and Li Auto spend on manufacturing and also on chips, because their bigger vision is robotics. They’ve said chips for EVs alone wouldn’t pay back — the bigger scheme is robotics.
Grace Shao (34:16)So in embodied AI: you have Unitree, “Galabots,” UBTECH; you have EVs; you have Xiaomi/Huawei tech stacks. Who wins? Is it just cost and price?
Alan Zhang (34:54)Cost, price, and redundancy for physical movement. Even traditional automation companies like Inovance are building robots. A robot shares parts with EVs — optics, gears, batteries — but also has new parts like PLC controllers where you need redundancy. On these fronts, many are on a level playing field.
Grace Shao (36:12)Do Chinese EV firms have an edge in spatial intelligence, or is it mainly cost?
Alan Zhang (36:21)China is still runner-up in spatial intelligence and will spend time to catch up. But China has a short feedback loop: optical components and supply chain are local; ideas can turn into products quickly and iterate fast. Not an advantage yet, but not far behind.
On who wins: too early to say. Unitree is the one that can make a more agile robot and do more stunts than other players.
Grace Shao (37:42)Where does AI show up in embodied systems — is it just visible “smart” functions, or more invisible?
Alan Zhang (38:19)Besides user experience, AI processes many parameters in the background. With enough computing, embodied AI can make simultaneous decisions — what to move and what not to move. Humans blink, walk, and raise hands at once; without AI it’s harder for robots to act like that. With AI, robots can handle more parameters and make simultaneous moves.
Grace Shao (39:38)How do you price geopolitical risk into valuation positioning? Export controls, trade wars, domestic regulation — how should investors look at China?
Alan Zhang (40:17)The market is already pricing a discount. Asia tech trades at a discount to US peers — Samsung and SK Hynix versus Micron; BAT versus the Magnificent Seven. Tools may be less available, which can slow advancement, but it’s also encouraging to see alternate solutions like DeepSeek. Over time companies can become more technologically independent.
For large caps, investors may feel safer sizing up. For small caps, we start small and see how it plays out. Entrepreneurs are agile and prepare for change.
Grace Shao (41:53)A reader question: China’s delivery wars. Alibaba vs Meituan — subsidies, vouchers — why is this happening now?
Alan Zhang (43:43)Meituan has led quick commerce — 30-minute delivery — and it surprised me Baba took so long to react, because quick commerce will take share from traditional e-commerce. A few years ago Meituan delivered iPhones at launches — a wake-up call for JD. The new delivery war kicked off with JD’s initiative around April; JD spent heavily to buy consumers, and Baba joined a month or two later.
Money could be better spent elsewhere, but I understand Baba — if they lose relevance in e-commerce, other businesses stop making sense. E-commerce is the core.
Despite growing daily volume from 30–40 million to 80 million — sometimes 90 — it’s discouraging Baba hasn’t improved delivery efficiency much. Meituan was already profitable at around 40 million drops a day by carrying multiple deliveries per trip and improving dispatching. It’s sad for investors that many platforms are still loss-making due to subsidies, but Meituan’s underlying efficiency advantage remains. As a consumer, the subsidies are great.
Grace Shao (46:29)Why does Meituan have such an advantage in dispatching and logistics compared to Alibaba, which has massive logistics and warehouse footprint?
Alan Zhang (47:39)It comes down to the core. Meituan built it through local business development — ditui — integrating merchants into inventory and payment systems. Inventory is kept locally, so Meituan focuses on dispatching and rider movement. Their algorithm can even predict demand and move riders toward hotspots ahead of time.
JD invested heavily in centralized logistics hubs and infrastructure — that makes them slower to pivot. Baba used an asset-light model early, working with ZTO, and is more centralized — mostly Hangzhou. Meituan is more decentralized and localized. In quick commerce, doing well in one city doesn’t guarantee another — but once dominant, you can use profit from one pocket to subsidize another. Traditional e-commerce is more centralized.
Grace Shao (50:03)That’s a fascinating lens — culture and management style mapping to business model outcomes.
Alan Zhang (50:04)And risk. In food delivery, you can’t hold inventory. Meituan works on the assumption you don’t hold inventory. Baba and JD have more of a culture of holding inventory and keeping products in storage longer.
Grace Shao (50:27)Closing: biggest prediction for China tech in the next 12–18 months?
Alan Zhang (50:51)One for EVs, one for internet. In EVs, OEMs with a pure domestic focus and without an ecosystem will lose relevance in 12–18 months — consumers are making up their minds. In internet, with Tencent hiring Chief AI Scientist Yao Shunyu, we’ll see more AI functionality built into Tencent’s ecosystem.
Grace Shao (51:54)What’s one company or subsector global investors are sleeping on?
Alan Zhang (51:56)Healthcare. It can be resilient regardless of overall spending. The market is focused on frontier-model spending and ROI, but healthcare companies aren’t budgeting for “latest and greatest” models — they’re looking at applications that improve products and ecosystems. Even if we stopped advancing frontier models for four months, there’s tremendous value to extract from current models.
I see a mindset shift among healthcare executives to build AI into products and sell superiority — historically, tech adoption was cost-driven; now it’s revenue-generative. Mindray in Shenzhen, or MicroPort in the Yangtze Delta — great companies. Surgical robots and medical devices are not far behind other systems.
Grace Shao (54:15)Final question: what’s one differentiated view you have that’s non-consensus?
Alan Zhang (54:37)Instead of focusing only on AGI timelines or capex or cloud consumption, we should think about daily businesses and smaller-scale businesses extracting real value from AI — even financial companies. I’m excited to see new form factors and more AI functions in consumer products.
Grace Shao (55:24)So focus on practicality and real use cases — not just headline spending.
Alan Zhang (55:33)Absolutely. Look beyond the top three, top five — and don’t go too far down the risk spectrum.
Grace Shao (55:39)All right. Thank you so much, Alan. Thanks for your time today.
Alan Zhang (55:43)Thank you, Grace. Pleasure to be here.
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Manglende episoder?
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In this episode, I sit down with Zixuan Li, who leads the chat API and global partnerships at Z.ai, one of China’s leading LLM labs (one of the four tigers) and now one of the first to head toward an IPO.
Z.ai started as THUDM, a Tsinghua data-mining lab best known in open-source circles for GLM and CogVideo, and has since grown into a model-as-a-service platform powering millions of devices and thousands of enterprises in China and beyond.
We talk about what it actually means to be an “independent” lab in a market dominated by platform giants like Alibaba, ByteDance, and Tencent, why Z.ai pivoted from SOE-heavy infrastructure projects to a product-led GLM stack, and how they landed on a different business model, and the creation of the GLM Coding Plan, instead of charging by tokens. Zixuan is very candid about pricing (“If Anthropic charges $200, we charge 200 yuan”), the realities of on-prem-first China vs cloud-first West, and what it’s like to race against Minimax and Moonshot with fewer GPUs and less cash.
We also zoom out and look at China’s AI talent pipeline (and the meme that the AI race is “Chinese in China vs Chinese in the US”), how he thinks about AGI as self-learning agents that live on your phone, why he’s comfortable being a white-label backbone in the Global South, and where he sees China’s AI landscape in the next 6–12 months. If you want a ground-level view of how a Tsinghua spinout is trying to survive, and maybe win, in the LLM wars, this one’s for you.
Newly launched (Dec. 22) GLM 4.7:
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
01:20 – From THUDM to Z.ai: rebrand, Tsinghua roots, and model-as-a-service
03:30 – Quiet period & IPO: pride, pressure, and the business challenge of LLMs
06:33 – Pivoting from SOEs: infra projects, agentic models, and why strategy followed capability
07:25 – Competing with Minimax, Moonshot & DeepSeek: focus, compute, and capital constraints
08:34 – Chasing benchmarks vs real-world IQ: math, humanities, and alignment trade-offs
11:05 – On-prem vs cloud: why Chinese SOEs still won’t touch APIs
13:43 – Zero-retention and trust: can China’s culture around data ever shift?
14:07 – Inventing the GLM Coding Plan: subscriptions, stickiness, and “pay by value, not tokens”
16:00 – “If Anthropic charges $200, we charge 200 yuan”: pricing strategy and margins and GLM’s open-source flywheel
19:41 – Who really pays: sticky indie devs, big tech customers, and bargaining power
23:32 – GLM Coding Plan vs Cursor/Qwen/Claude: plans, agents, and avoiding lock-in
25:57 – Z.ai’s AGI ladder: AutoGLM, self-learning, and personalized weights
27:03 – Independent labs vs platforms in China: speed, resources, and “dirty work”
29:34 – Moonshot vs Z.ai: chasing the moon vs being “down to earth”
30:53 – Will China’s LLM market consolidate?: 5–10 players, Doubao, and video-generation winners
31:44 – Doubao phone & Honor partnership: bargaining power with OEMs
34:11 – Beyond China–US: Global South strategy and being a white-label backbone
35:29 – Being comfortable as infrastructure: letting others own the brand
38:05 – Who joins Z.ai and AI talent: thriving with scarce resources
40:07 – Culture, 007 hours, and survival: what it takes to be infrastructure
42:33 – Social welfare, AI safety, and cheap tools in India & Indonesia
44:38 – How China actually talks about AI safety (or doesn’t)
47:29 – Differentiated view: why Zixuan believes you should “enjoy lacking resources”
AI-Generated Transcript
Grace Shao:Hey Zixuan, thank you so much for joining us today. Really excited to have you on. Walk us through your journey and what led you to Z.ai to start off with.
Zixuan Li:Yeah, so currently I’m the head of Zhipu AI’s chat API services and also head of global partnerships. I collaborate with LMSys Chatbot Arena, OpenRouter, Vercel, these large companies, and ship our products through their platforms.
The reason why I joined Zhipu is it’s one of the leading AI labs in China and I can do overseas businesses, because I have a background at MIT’s Schwarzman College of Computing. So that brings my knowledge into real-world practice.
Grace:I see. Was there any incentive for you to move back to China versus stay in the US?
Zixuan:I think it’s more personal, because my wife’s based in China and she’s used to her work, so there’s no way she can move to the US.
Grace:Fair enough.
So let’s talk about the company’s mission and origins, because I think it does seem a bit mysterious, especially to people outside of China. From the outside, people know Zhipu, Z.ai as one of the leading Chinese LLMs. But that doesn’t really capture everything you guys do, right?
In your recent prospectus, you describe yourself as a MaaS — model-as-a-service — company first. So tell us about that.
Zixuan:Okay, so before Zhipu AI, we were called Zhipu or THUDM, because we named ourselves by the AI lab’s name. We originated from Tsinghua University’s data mining group — THUDM. But I think it’s hard to pronounce, and also “Zhipu” is also very hard to pronounce. So this year we bought the Z.ai domain and finally changed our name to Z.ai.
When we were called THUDM, we were very famous inside the open-source community because we had a lot of repos, a lot of models under the THUDM name. And we open-sourced not only text models, also CogVideo, CogView, these models. I think they were sold at that time.
But with the launch of VEO, Hailuo, and also a lot of current top models, we began to be more focused — basically more focused on text models, visual understanding, and so on. So I think that’s the origination of the lab.
But as you said, there’s this terminology called model-as-a-service. From our side, when we compete with large companies like Alibaba and ByteDance, we need to be more focused. They have their inference level, they have their cloud services, but we don’t. So we try to let the model itself provide the service — like the API, or technologies like visual understanding — and try to use the model itself to be the selling point.
Grace:I definitely want to double-click on how you position yourself compared to peers — a few of them you just mentioned, whether it’s Minimax and Moonshot, and then you also mentioned the BATs.
But to start off with, you’re currently in your quiet period as your prospectus just hit the public. And if successful, you will become one of the first major LLM startups globally to be listed on a stock exchange. How does that feel?
Zixuan:I think we are proud of it, but things are very challenging, because it’s really hard to do LLM inference. Both OpenAI and Anthropic have very high revenue, but a lot of loss on their income statements. So we have to figure out how to make money from large language models and also provide cheaper service to the customer.
So I think it’s only a starting point for us.
Grace:Definitely. I think right now only the big tech companies in many ways are essentially seeing ROI, and the model companies and the model labs themselves are really finding it hard to make a profit.
I want to ask you about the branding. You did say you guys changed your company’s name to Z.ai this year, partially because Zhipu is just hard to pronounce. But was that also related to the fact that you guys seem to have made a pivot into really focusing on going global? Z.ai seems to be a lot more non-Chinese-native-speaker friendly, right? So is that the push right now?
Zixuan:I think that played an important role, because we have observed the success of DeepSeek, Qwen — they got famous globally and Chinese people will think that they are the “SOTA” in the domain and their models are the best. They are recognized by NVIDIA and other large company CEOs. So I think that’s one factor.
But the other factor is when we changed the name to Z.ai, the dot also plays an important role. We want people to enter that URL into their browser and try to visit our website. Yeah, two factors.
Grace:And tell me about your origin story, actually. You mentioned earlier you started off from the Tsinghua data mining group. Maybe provide some context to people outside of China. What does Tsinghua represent? I mean, it’s an institution, it’s a university, but why are so many of these LLM companies or even deep tech companies coming out of Tsinghua right now?
Zixuan:I think it’s kind of a combination of Stanford and MIT. So talents are everywhere and there’s a lot of funding from internally and also externally. And also people are chasing the highest IQ there. So it will be very natural to pursue AI in Tsinghua University.
Grace:So I have a question on that, because a lot of tech companies, even the previous generation internet companies that came out of Tsinghua, had some kind of connection with Beijing city. And my understanding is Zhipu’s original business model was also very focused on SOEs and local government work, both in China and even across Southeast Asia.
Before the more recent pivot leaning into tools and APIs, what were the reasons for the pivot from the heavy AI infrastructure focus and SOE projects to a much more product-led tools and API strategy?
Zixuan:I think it depends on the capabilities of the model, because nowadays the model can perform agentic tasks, use tools, use coding to perform tasks. But before that, we could only do customer service, data processing — these “dirty work.” I think it’s better for SOEs or other scenarios.
But with the change of Cloud Code, GLM 4.5, these agentic stuff, people can really use the model in other areas like Manna, Gainsburg, Lobe. So I think it’s not only our strategy, but also the capabilities of the model have changed a lot.
Grace:Yeah, and I think to put you on the spot, where do you see yourself compared to your peers — like the DeepSeeks and the Minimaxes and the Moonshots of the world?
Zixuan:I think compared to Minimax and Moonshot, we are close competitors. We are startups, but DeepSeek is like another kind of enterprise because they have Qwen. So I think they’re very unique, and also ByteDance, Alibaba, they’re sitting at the same table. So they’re from large enterprises.
We are all chasing somehow the same direction, but we lack compute, we lack money compared to these giant enterprises. So we need to stay very focused.
Like Moonshot, they focus on the Kimi K2 series. They only release Kimi K2, another K2 and K2 Thinking this year. And also Minimax — they’ve become more focused and they kind of shift away from multimodal to text models. I think it will be very fierce. The competition will be very fierce in the coming months.
Grace:And for yourself, when you say you’re chasing the same direction, what does that direction look like in layman’s language?
Zixuan:In layman’s language, I think… more practical. Because the reason why we do coding and agentic is that we see people using it. We see people using Codex, Manna, Claude Code. So that represents high token usage.
And also we are chasing AGI, or the IQ. So we want the model to solve very hard math problems, to memorize a lot of hard stuff. As you can see from a lot of benchmarks like ARC-AGI, HLE, we’re also chasing in that way.
So we balance the two: figure out how to balance the performance on benchmarks and in real-world development.
Grace:I actually have a question on that that’s a bit off-track from the business strategy side of things, but I wonder how you view this.
So you’re saying you’re chasing benchmarks on math problems, IQ, advanced physics, etc. But what about the humanity side of things? I think people are still questioning whether AI can be used to replace humans in a more humanities-focused industry or sector.
Zixuan:So that’s a very big issue. But for now, I think it’s still not there yet, because we see hallucinations happen inside the model and instruction following is not very good.
So we test the status of that harm and try to assess what we can do with this model and try to synthesize a lot of data to make it more aligned to human judgment or other things. I studied alignment at MIT. I know a lot of stuff, but when I came back to China, I thought we were not there yet.
So capabilities, I think, are still more important than alignment at this stage. But we need to focus on the future and try to prevent something really bad happening. I’ve learned a lot of news like suicide or emotional feelings, depression. But somehow I think it’s still not that harmful yet.
We try to incorporate as much human judgment or human alignment into the model as we can. But as I said, it’s kind of a balance between different aspects.
Grace:Yeah, it’s always a balance between setting up the guardrails and actually still allowing the technology and innovation to continue, right?
I want to reshift the focus back on business model, pricing, deployment. Reading the prospectus, what stood out to me was how much you support both on-prem and cloud.
What are the main product lines today of Z.ai or Zhipu, and how do you map those onto on-prem versus cloud deployments in terms of how customers actually adopt GLM? Because I do believe I read that in China there’s a very different preference. In China, it seems like more people prefer on-prem, right? Whereas in the US it’s more cloud — or did I understand that incorrectly? Please explain.
Zixuan:Yeah, I think you have a very good understanding of the current status, because large SOEs, large enterprises in China, prefer on-premise or more private deployment. So it’s hard to do API services with them.
But currently a lot of tech companies accept API services. So we collaborate with nine out of ten of the largest Chinese tech firms or social media firms with our API services. So it depends on their needs.
We try to sell API, but actually some people have privacy concerns. They have policies not accepting API services. They don’t want any data to go away from their servers. So basically it depends on the users’ needs.
Grace:This is actually kind of a reflection of what happened during the SaaS era too, right? Chinese SOEs and big companies would rather build their own app — maybe not even be as good — but they just don’t want to give their data out to anyone and have that potential security risk, right?
So do you think that will change in terms of company culture as we see AI continue to develop, or do you think that will continue to be the trend in China — that this would be the differentiating point between the Chinese market and maybe the Western markets?
Zixuan:I think it will continue to be the trend. As you said, we had that pattern in the era of SaaS. And when we go to the AI era, nothing changed.
But somehow, we can figure out a way to balance, because there is more “private host on cloud” service. And we’re trying to store user data in a more secure way, with a zero-data-retention policy. That will mitigate the risk and the issues and try to let them feel more comfortable with it.
Grace:I see.
A lot of Western developer tool companies now go pure usage-based, but you guys also have a GLM Coding Plan — basically for developers with very low entry points. Why did you choose a subscription approach versus going with other pricing models? I guess this part, I just want to understand how you guys are making money right now, especially as you’ve just had your prospectus go public.
Zixuan:Yes, I think we are the company that invented this coding-plan business model, because we found out that API users are not sticky. One day they use Claude, they can switch to Gemini or GPT another day. It’s the same with Chinese models.
So we remembered: why do we pay for subscriptions — Spotify or YouTube service? Maybe we just listen once during the whole month, but we don’t regret it, right? So we don’t want our users to pay by tokens. We want them to pay by value or by the product itself.
So if they just use it once or twice within a month, I think it’s totally fine. If they want to subscribe the other month, we’ll try to provide better service. We have GLM 4.5, GLM 4.6, GLM 4.7, trying to ship better models. But if they decide to quit, I think it’s still good for us because they paid for one month, not just several tokens.
So the users may be very sticky here, and we have our branding — not only the model, not only GLM, but also the subscription, GLM Coding Plan. So when we use Cursor…
Grace:But if they were to use it a lot, would it be loss-making for you guys then?
Zixuan:I think it’s still an issue for Claude Code and also Codex. You have to balance the rate limit and also the service level. So for us, we are very generous, but we’re trying to operate globally, because that will make our traffic more stable — not receiving very high demand at one time and no demand during the nighttime.
Grace:I see.
I know that you’ve been quite active in a lot of podcasts recently. You were on ChinaTalk, you were on Steven Hsu’s. And one of the lines you said, I think it was on ChinaTalk, you said, “If Anthropic charges $200, we charge you 100 yuan.” I thought it was quite funny. It was very memorable.
So how did you make that kind of decision, and how does it work in practice? Does that mean you have a long-term structural advantage, or does that mean you charge less and therefore have smaller margins?
Zixuan:I think we serve different customer needs. For example, someone sells Rolls-Royce to people, but we sell Benz to people. Both are good cars, but Rolls-Royce charges way more than Benz. The performance, I think, is very close.
But like I said, Anthropic deserves that premium. But by selling Benz, we can still earn a lot of money. Maybe the profit margin is very thin currently, but we can lower the inference cost. We can change our infrastructure to make it more profitable. So it’s a long-term strategy, not focused on the current cost structure.
We’re trying to make people more sticky to the brand, more sticky to the service. I think it’s essential at this time.
Grace:Essentially, you’re saying the utility purpose of having a car — getting from point A to point B — is the same, but maybe you’re selling a Toyota then or a Honda, right? Not even Mercedes, which still charges a pretty premium margin.
I remember in the same interview, you were kind of challenged, saying: look, you only really take up about 5–6% market share in China for general-purpose models. But you said, “Wait, 5% is enough.” What exactly are you thinking when you say 5% is enough?
You serve — I think from public disclosures — 123 large enterprise clients on-premise deployments, plus around 5,500 customers using cloud services. How does that actually stack up to your peers? Because it doesn’t look like huge numbers, to be honest. And that already is 5–6% of China’s market share.
Zixuan:I believe that the 5% refers to the percentage of all the GLM services.
Grace:Yes, sorry, GLM.
Zixuan:GLM services, because we open-source our models. And it’s hard to get revenue when you open-source your model because you have to compete on speed and stability.
But I think our model is good enough. Maybe it’s not like Toyota — it’s kind of a Benz. And we let more people adopt GLM, like what Qwen did in the past. They open-sourced their reflection models and more people tried out Qwen. They got famous, so people believed they got better service from Alibaba.
It’s the same underlying methodology from our side. So if GLM gets really famous, even 5% is enough for us. But if it’s not famous, 5% is totally not okay. We’re trying to make our model more influential, like DeepSeek, like Qwen.
Grace:I see. I do want to go into GLM and your tools later as well. But one last question on the business side of things. We kind of touched on this: you said a lot of your customers are the big tech companies, but in the beginning, they were the SOEs, right?
So right now, is there any pattern you’re seeing in terms of who becomes the most valuable users and who becomes the most sticky users and who are actually willing to pay the big bucks for your product or for your service?
Zixuan:So from my department, I think two types of customers. One is individual developers, because we have the GLM Coding Plan. Someone bought a yearly max plan. A lot of users bought yearly plans. They are very sticky.
And the other type is large tech companies, because we are still leading the open-source models. So we have bargaining power. Maybe they want to shift away from our model and choose other models, but we keep evolving from 4.5 to 4.6 and 4.7. Every time they try to change the model, they find that we can ship better models.
So these customers are very sticky. And they care more about performance because we are leading in performance. They care less about cost or relationship.
Grace:Nice.
Let’s actually double-click on GLM. You mentioned GLM 4.5 and 4.6. They’ve been positioned as highly competitive on coding and reasoning, and you’ve often been the highest-ranked Chinese model on public leaderboards.
When you compare the GLM series to US and Chinese peers, what dimensions matter most to you beyond the leaderboard scores right now? And where do you think GLM actually genuinely stands out compared to other peers, whether it’s American ones or Chinese peers?
Zixuan:I think real-world development, real-world practices, and general chat — these real practices — are more important than benchmarks. And in terms of real-world experience, we are tier two, because I believe Anthropic, DeepMind, and OpenAI have better user experience compared to us.
But I think we are enough compared to other open-source models, because we understand user needs. We have better quality in data — pre-training data and post-training data — and we’ve figured out ways to synthesize agentic tool-use trajectories and very hard problems. That makes us stand out in solving these really tough problems.
Because when you look at the benchmarks, they are not for real-world practices. Some are very tough, but it doesn’t mean they stand for human practices. Because we have a lot of customers… yeah.
Grace:Yeah, I’m going to challenge you on that actually. What about the Alibabas of the world? Because when I speak to Alibaba or Tencent, they also say their biggest differentiating point is real use-case data. And frankly, they have all the existing touch points with their users, whether it’s getting data through helping with businesses, enabling businesses, or consumer use. They probably have the best data, right? So how do you compete with that?
Zixuan:I think that’s their advantage in 2024, but not 2025. Because in 2025, most of the high-quality data we need, we have never met in real-world use cases.
When you want to create a slide, you first do search and then come back and do another round of thinking, and then choose a design tool or something like that. Nobody interacts with Alibaba’s product like that. So you have to fully understand Cursor, Claude Code, Manna — how these tools interact with people.
So ByteDance and Alibaba’s customer data cannot play a role in today’s agentic era. We have understanding of maybe Claude Code or Codex — we try to understand how a top-performing agent manipulates tools and how our model can be integrated in that system.
Grace:I was actually going to ask you about the GLM Coding Plan. So for context for listeners, it’s essentially their tool, like a Cursor tool.
So how does the GLM Coding Plan actually compare with Cursor or Alibaba’s coder, as you mentioned, or Claude, in terms of coding experience? For a developer who already knows these tools, how would you explain the distinction — or, you can be frank, is it mainly a pricing advantage here?
Zixuan:Okay, so I want to compare Cursor with GLM Coding Plan, not the model. Within Cursor, you have one coding agent and you can switch between different models. But with GLM Coding Plan, you first select the model and then you can switch between different tools.
You can integrate GLM into Claude Code, Kimi Code. You can even use GLM in Cursor with GLM Coding Plan. That made our product or model widely accepted or widely integrated into these systems — not just for Claude Code, but also it can be integrated into Cursor or Kimi Code.
We understand different coding agents and try to synthesize data that best fits these coding agents’ needs. And there’s no lock-in for our users.
Grace:So your GLM Coding Plan is not only your proprietary model, right? You actually are open to multimodal?
Zixuan:Yes, it’s a model. GLM Coding Plan is called a plan, not an agent, not something like Claude Code. You subscribe to an API, you’re not subscribing to a product. You use that API maybe in Claude Code, maybe in Kimi Code. So you can choose the mode.
Grace:Yeah. Okay.
Okay, thanks for explaining that to me. That’s helpful, I was getting a bit confused there.
Now I wanted to ask: in your prospectus, you laid out five stages of progression into AGI. We talked about your vision of AGI earlier. You said it’s about real-life implications, real-life practicality, usage of AI.
When you look at where you guys are at right now, what does crossing the next stage look like in terms of concrete capabilities or products or tools? Or maybe a more straightforward way of asking this is: what should we be expecting from you guys in 2026 to help you progress on your so-called AGI pursuit?
Zixuan:Maybe self-learning. Because currently when we do reinforcement learning, we synthesize all the data, we prepare the data beforehand, but the weights of the model won’t change during the interaction.
For example, we have this AutoGLM. It’s a model that can be deployed on your phone and can manipulate different apps for you. It can order food or order an Uber for you, but it’s the same model for everyone.
To chase AGI, we might have AutoGLM for everyone. When you interact with the model, the weights of the model may change. Currently, we have a memory engineering package that’s more on the engineering side — handling this memory stuff.
But for AGI, it needs to be very personalized. Every model needs to be personalized. The model learns from the environment, from the interaction. We also call it on-policy reinforcement learning.
Grace:I see.
Let’s take a step back and look at China’s overall LLM landscape and competition. You kind of alluded to this earlier — you guys are in the same pool as the Minimaxes and Moonshots of the world. Then there are the big techs like Alibaba, ByteDance, Baidu, Tencent, even Huawei these days, right? There’s so many. Everyone’s producing their own LLMs now.
From inside the ecosystem, what do you see as the structural differences between independent labs versus the big tech platforms — in terms of commercialization of their models as well as their incentives and objectives in the coming year or two?
Zixuan:Strategy and objectives. Because we lack resources, we need to be very focused. And when we are very focused, we need to move very fast.
For talents, our team is very small. I lead a team…
Grace:It’s not that small — a couple hundred, right? You guys have like 800 people now?
Zixuan:But for every team, there are just a bunch of people. We have sales, we have product solution, but for the product team, product solution, or training team, sometimes you need to be very lean. You don’t have to hire a lot of people, because they chase different directions.
Sometimes you have to hire people that can do “dirty work.” Maybe one person is enough to do all the training on this side, and you have a bunch of people preparing data or understanding customer needs for you.
Like I said, you have to understand Claude Code, you have to understand these coding agents. So there will be people studying all the products, looking inside these products to see why they are performing so well.
But for large enterprises, they can hire a lot of researchers. They have enough resources to do a lot of experiments. They have compute, so they worry less. Maybe they can find some scientific breakthrough from those experiments.
But in terms of model performance, I think our competitive advantage is we are closer to users and customers, because we move faster together with our users.
Grace:So that’s how you position the startups versus incumbents. But what about just within the startups yourselves? How do you differentiate yourselves between one another?
Zixuan:I think compared to Moonshot — because we both originated from Tsinghua University, we know each other pretty well — I think we are more down to earth. We are the ones that care more about real-world usage or practices.
Moonshot is kind of… they have this “AGI plan,” chasing the moon or landing on the moon, and they have more imagination on the surface. We’re also chasing AGI, but when we train the model, we care more about real-world practice and usage.
Grace:You’re taking a more pragmatic approach. And they’re definitely, I think, a very eccentric bunch, right? Even the name — how it came about — was quite interesting.
So do you think eventually in the Chinese LLM space it’s going to be winner-takes-most? Maybe not winner-takes-all, but winner-takes-most? Or is it going to be able to support multiple strong players?
Because there’s been rumors about consolidation for a while. There are quite a few players for how big the market is, and like you said, it’s extremely capital intensive. Not everyone has this much money to keep burning through it. So where do you see the direction of this fragmented landscape right now?
Zixuan:I think the market is enough to include 5 to 10 players. I think it’s enough. And like I said, the large enterprises only accept on-premise deployments, so there’s no way a winner can take it all, because there are thousands of large enterprises. You don’t have the team to deploy models for every single enterprise.
But in terms of applications, maybe Doubao will take more than half of the consumer side. And also for video generation, there will be a winner. But I think the market is still very large to have all these players, and they will compete for a long time. I can guarantee that they will compete for a long time.
Grace:What do you think of the Doubao phone situation? This is completely random. This is not relevant to our LLM conversation, but I’m quite curious to hear your thoughts on it, because I think it’s making a lot of noise outside of China. People are quite curious to see where that will lead to.
Zixuan:So we are the first company to launch this phone use agent. But I think the issue is bargaining power. We also collaborate with a phone company, and instead of using something like a “GLM phone,” we finally used their name. Their phone, powered by our model.
Grace:Which phone is this?
Zixuan:Rongyao.
Grace:Okay — Honor. I think it’s called Honor, yes.
Interesting. You know what? I really haven’t heard about it, but I should look into it. Is it actually already available to the mass market or no?
Zixuan:I think the phone was launched last year, not this year.
Grace:Okay, super interesting. I’ll look into it.
Zixuan:Yeah, so a lot of phones at that time were powered by AutoGLM’s capabilities. But we don’t have the same bargaining power as ByteDance, so we cannot name the phone by our name. We just power their scenarios.
So it’s about bargaining power, I think. Because like there’s the ByteDance vs Tencent issue, also with WeChat — it really depends on how you split the revenue, the value, how you make sure that you won’t influence other people’s business.
So finally, you have a line: maybe this app will collaborate with you, and that app rejects your endpoint.
Grace:Yeah. For context for you listeners, WeChat rejected Doubao phone’s direct access, and there was a huge headline war on this like two weeks ago.
Okay, I want to pivot a little bit. Right now there’s a lot of focus on the China–US lens. And you yourself spent time in China and the US as well.
But I did notice in the beginning days of Zhipu you guys were actually really focused on the so-called Global South — for lack of better words — Southeast Asia, Latin America, maybe even Africa. Is that still a strategy you guys are pursuing? Looking to sell or actually embrace markets that go beyond just China and the US?
Zixuan:Yes, definitely. Because I think in GLM 2, GLM 3, we only had Chinese and English capabilities, but now we have more than 100 languages. So that can support us going beyond English-speaking countries. Maybe in Brazil, maybe in Malaysia, we have opportunities to showcase our model or showcase our product solutions to people and finally compete with those large enterprises.
But I think things are really different in those countries, because they also want their data as private as possible. They accept on-premise, and maybe they want white label — they fine-tune the model and they want to ship it to their citizens under their name, not GLM or Zhipu’s name. So we have to meet their needs and see what we can offer.
Doing business in the US, I think it’s much simpler because you have this API, you have products, you can do a coding agent, you can earn money. But when you do business in other countries, you have to go really deep, twist a lot of things, and try to make it happen.
Grace:It’s also interesting — I think you touched on something. You’re quite comfortable being that white-label provider, versus I think a lot of other companies, whether it’s ego or belief, are not as comfortable. They definitely want their name on it.
So it seems like you guys are actually the backbone supporting a lot of technology or clients without really having your name attached to it.
I want to ask you about talent. This is a question we touched on in the beginning — you said yourself you came back to China for personal reasons, because your wife is in China. But I assume that’s not the case for everyone.
There’s this interesting and funny joke going around saying right now in the AI war or AI race, it’s really between the Chinese in China and the Chinese in the US. It’s just funny — there does seem to be a high percentage of ethnic Chinese or Chinese nationals or Chinese-naturalized Americans or ABCs. If we’re being non-PC, people who look Chinese in the field.
Why is that? I don’t understand. Did Chinese people just get a tip-off saying AI is gonna be really big early on and they went into this field earlier, or what happened?
Zixuan:I think I cannot explain it, because doing math problems is simple for us. I’m not sure why other people won’t pursue this business.
Because when I did internships and research at MIT, I saw a lot of talented people beyond Chinese — they’re still talented. They finally went to Anthropic, OpenAI. But somehow people only care about Chinese because they are co-launching products with Sam Altman or Elon Musk.
I think people overrated the influence of Chinese people in the large language model area, because still there are a lot of enterprises not relying on Chinese.
Grace:It’s quite funny — it’s kind of like the last generation, where every Chinese student in the US is either studying to be a lawyer or a banker, and now everyone switched over.
Actually on a more serious note, how does the talent competition play out then? Do you see yourself at Zhipu having to really convince people to join you compared to a US peer?
Or do you think there’s certain tendencies for certain researchers that would prefer to work for a Chinese lab or return to China? How do you see that play out?
Zixuan:I think we finally choose the people that best match our environment. Like I said, we lack resources, but some people really enjoy the lack of resources — like me. Because I think it’s good to have a small team competing with a very large team, and you have better enjoyment when you conquer a puzzle or problem, or you finally win at the end.
So people who enjoy this feeling, we try to hire them. And like I said, we want to move really fast. We want people — both the product team and the training team — to understand the user scenarios, to understand the data itself, not just theory or the algorithm.
So we try to find those people, and they will finally choose us because they don’t care about compute or resources, or they find it too toxic competing with other teams doing the same experiments and the same thing. Because that happens a lot in large enterprises — a lot of teams doing the same thing.
Grace:Yeah, for sure. I think even when I speak to the BATs in China, there’s so much internal competition that drives people crazy. It’s internal politics that drives people crazy. But that also becomes an incentive for people to really push.
On that note, you guys are about what, 800 to 1,000 people altogether, roughly around 100 to 200 in R&D — something around that rough figure. It’s essentially not really a startup company anymore — it’s just small compared to how big the big tech incumbents are.
So at this size, and as you guys head into becoming a publicly listed company, do you see the culture changing? And what are the ways you keep your researchers, scientists, and engineers motivated? Are we seeing crazy salary numbers as well, like the ones coming out of Meta? How do you keep people motivated?
Zixuan:I think we are more lean, more entrepreneurial. Especially in our team, because I only slept 50 minutes for the past 24 hours. So we want to move really fast, faster than everyone else. Yeah… beyond that.
Grace:You’re going beyond 996. This is not 996, this is 007.
Zixuan:Because the competition is really fierce. Moonshot, Minimax — they’re doing an excellent job. And we also have DeepSeek, Qwen — not to mention the frontier AI labs in the United States. So we have to keep pushing. I think there’s no other choice.
Because when we try to do AI, we want to survive. Frankly speaking, survival is a very high standard for the tech industry. When we look at operating systems: Windows, macOS, Linux — I think that’s enough. And when we look at phones — only Android and iOS.
So the competition must be fierce when you want to be the infrastructure for the industry.
Grace:Yeah, I agree on that. Okay, well, I hope you get some rest soon after this call. I really appreciate you jumping on the call after 50 minutes of sleep today.
Looking at your long-term vision and where you guys are headed now, especially with an imminent IPO: in your public materials, you talk about AGI integration with the physical world and social welfare as a long-term vision. I think this is something not many AI companies frankly are really thinking about.
Even within our conversation, you’ve talked about the balance between tech acceleration and actually putting up safety guardrails, essentially to prevent more sad, tragic happenings caused by AI psychosis, etc.
When we look at this, how do you personally reconcile the social-welfare North Star with the commercial realities and the pressure you just talked about? Where are the areas where you guys are frankly more okay to let go a little bit for business gains? What areas are definitely your red lines that you cannot cross, where you really want to hold people accountable and ensure there are no AI-caused tragedies?
Zixuan:Yeah, I want to answer this by giving an example. We have this GLM Coding Plan — it’s very cheap, three dollars a month. A lot of people in India, Indonesia, or even in the United States use GLM Coding Plan to do their side projects or even their startup.
I just talked to a person today. He’s doing a startup that uses GLM Coding Plan to write a program that can collect recyclable bottles. They scan the bottle and recognize it, try to differentiate trash from recyclable products, and make it a real business. So we truly use AI to empower these businesses. You can see there is a lot of social welfare behind this.
People just use the coding, but you can use coding to do a lot of stuff. We provide the service, but we let people decide whether they try to contribute more or only care for themselves. So I think it’s a starting point for us.
With more powerful products — maybe next year — we can empower larger scenarios. Maybe we can empower robots.
Grace:And in terms of this topic, I think in the US there’s a very dominant voice and discussion about the potential risk of AI or the negative impact it might have on society. AI Proem and Differentiated Understanding, frankly, very much focus on the business strategy of technology. So a lot of my guests and myself, we focus on capital deployment and feasible business models.
But I do want to ask: you’re plugged in, you are in China, in the LLM space, in the AI space. Is there a discussion about AI safety, or are people really just quite focused on acceleration and pragmatic deployment and diffusion?
Zixuan:I think compared to the United States, not that much. It’s more pragmatic. But that’s still on people’s minds, because AI safety is still an issue for us.
We can see the ceiling, the threshold of all the current capabilities, and understand what’s the top priority for our model or our scenario, and try to fix those before going to the next step. But we always keep the security and safety issue in our head. And when that day finally comes, we can be fully prepared.
I’m engaged in a lot of these conversations in the US and I’m also part of Concordia AI. It’s an organization focused on AI safety. I’m part of it in Beijing. But when I left that company, I saw them — and for anyone talking about this — it’s not because people don’t care. We can train a model with better capabilities and also a safer system.
So there’s no trade-off at the current stage because we don’t have to balance performance with safety concerns. We can improve them at the same time.
Grace:I see, it’s more like taking a mindful approach.
I want to end on two quick questions. One is: usually people ask, “Where do you see yourself in the next five years?” Right. But I think for AI we can’t really ask that right now — no one will know what five years looks like.
But for our listeners: where do you think China’s AI space will look like in, let’s say, six to twelve months? Where do you think the focus will be, or the potential breakthrough?
Zixuan:Potential breakthrough may be integration with the physical world. When we see a lot of robotics companies and we see a lot of smart glasses, people are shifting focus from AI companies to these, we call it broader intelligence companies. So that might be a shift.
And also DeepMind — I think they’re doing the same path. When you look at Gemini, it’s not just a large language model but also it can perform world knowledge or integrate with real-world use cases.
When we look at Gemini 3 Pro use cases, someone is controlling the camera or trying to integrate with the computer. So there are a lot of things we can do with large language models.
Grace:Okay, I think the last question I have for you is a question I ask every single guest, which is: what is one differentiated view you have — a non-consensus view? It could be about anything: about the industry, about how you see the world.
Zixuan:I think for me, I’ll just share my thought: you should enjoy lacking resources — lacking people, lacking everything. In the AI world, that pushes you to the boundary. That pushes DeepSeek to change their architecture, to really do something innovative.
For me, I don’t train models, but I build products and do marketing. I had this GLM Coding Plan thought because we don’t have very loyal customers. When they use API, they try to shift from GLM to someone else and then come back one day. So I noticed these difficulties. That’s what we aim for: to try to solve these really tough difficulties.
Grace:Yeah, I think to your point — when you lack resources, it also means you have the agility and the flexibility to change things, because there’s no bureaucracies, there’s no chain of command, and it’s much faster.
I appreciate that in itself too. I was talking to a friend about that recently as well. Since I left big tech and left traditional media to do this myself, you have so much more flexibility, and sometimes you’re upset that you don’t have the access or the resources you used to have. But it does help you build faster and connect with your community faster.
Thank you so much anyway, Zixuan. I really, really appreciate your time. Please get some sleep after this.
Zixuan:Thank you too. Yeah, I truly agree that you have your competitive advantage, because those large media companies — their journalists won’t reach out to me. So it’s my honor being here, but also a good opportunity for you to understand the Chinese market.
Grace:Yeah, for sure. And I really appreciate you giving me your insights during this time. And for all the other Chinese AI labs out there, if you’re listening to this, please reach out. I would love to have a conversation. Thanks again.
Zixuan:Yeah, thanks.
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In this episode, I sit down with Kyle Chan (Brookings Institution) to unpack the thinking behind his provocative New York Times op-ed, “In the Future, China Will Be Dominant, the U.S. Will Be Irrelevant.” We start with the DeepSeek moment and why it surprised the West, why it didn’t surprise many China-watchers, and why Kyle sees it as only “the tip of the iceberg.”
From there, we zoom out into the bigger story: China’s rise isn’t just one breakthrough model or one champion company. It’s a system of interlocking capabilities: EVs, batteries, renewables, industrial automation, robotics, and AI, advancing in parallel and reinforcing each other through spillovers, supply chains, and fast-moving “Swiss Army Knife companies” like Xiaomi and Huawei.
We also dig into what people often get wrong about China’s state role: not pure top-down command, but a mix of industrial policy + private-sector experimentation, including practical mechanisms like compute vouchers and local-government support. Finally, we cover India’s trajectory, geopolitical constraints, and Kyle’s “hedges”—scenarios in which today’s narratives (in both China and the U.S.) could still break in unexpected directions.
Relevant links: https://www.brookings.edu/people/kyle-chan/
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
00:00 — The NYT op-ed + the DeepSeek catalyst: why Kyle wrote the piece, what he wanted to correct, and why DeepSeek was a wake-up call (“tip of the iceberg”).
06:53 — Kyle’s origin story: infrastructure obsession (high-speed rail) → the path into tech & industrial policy.
12:31 — China’s “electric tech stack” + spillovers: EVs, batteries, renewables, robotics, AI moving in parallel—and why “Swiss Army Knife” firms (Xiaomi/Huawei) can leap across categories.
19:12 — Why autonomy pairs with EVs: the technical and architectural reasons autonomous systems “almost always” sit on EV platforms.
24:01 — China AI ecosystem in practice: startups + hyperscalers + policy “tailwinds” (compute vouchers, industrial parks, local government support) and how that differs from the U.S. model.
29:46 — China’s development playbook vs others + the India comparison: proactive bottleneck-solving (“ground game”), plus India’s tailwinds and constraints over the next decade.
41:00 — The hedges + the wrap: what could derail or reshape the trajectory (trade backlash, geopolitics, bubble risk, robotics paths), and Kyle’s non-consensus take on policy intervention.
AI-generated transcript
Grace Shao (00:00)Hi, Kyle. Thank you so much for joining us today. Kyle, I want to start with your recent New York Times op-ed, which had a pretty provocative headline. It’s called, In the Future, China Will Be Dominant, the U.S. Will Be Irrelevant. When I saw that, I was like, whoa, this guy, someone’s going to go get blood now. How did that piece actually come about? What was the main, I guess, objective or goal out of that piece?
Kyle Chan (00:16)Yeah, yeah. Thanks for asking about that piece. Yeah, that piece, it got quite a reaction. I was surprised. And there’s been a number of pieces I feel like now—sort of, it’s almost become a genre of like all the things that China’s doing, all the things that the US is doing, the sort of divergent trajectories of the two countries, especially on technology. And for me, one thing I really wanted to focus on was—
So we had the big DeepSeek moment earlier in the year, and that really got people to wake up and take notice of what’s happening in China and China’s tech development in a way that really, I mean, I can’t remember the last time that something like that happened. And so that was quite a big wake up call. But as someone following a number of different sectors in China for a while, I was like, this is just the tip of the iceberg.
I mean, first off, within AI itself, DeepSeek—I think it was kind of funny—was surprising for a lot of people who follow actually China’s AI industry quite closely, because I think we might have been expecting some of the bigger tech companies to have made a bigger splash, but DeepSeek seemed a little bit out of left field. But within AI in China in general, there is so much talent, so much engineering talent, a vibrant developer community, top-notch researchers.
So like when you look at, say, who is accepted, whose papers are accepted for NeurIPS, one of the top AI conferences, right? It’s many, many, many names from Peking University or Tsinghua University or Zhejiang University. And so if you follow that space for a while, DeepSeek was not so surprising. Maybe it was surprising that it was DeepSeek itself, but that China could produce a world-class AI model on par or nearly on par with some of the best in the US—that was maybe not so surprising.
And then it’s not just AI. The other thing is like when you follow EVs or when you follow batteries, or if you follow anything related to clean tech—solar, wind, hydrogen fuel cells. If you follow robotics, anything related to industrial automation, industrial robotics, also self-driving cars, smart driving systems. I mean, the list goes on and on for all these different areas.
And then it gets even down to sort of like the basics, right? So like some of these traditional industries where you just see this like classic China chart. Call it like the classic China chart where it’s like the share of global manufacturing for shipbuilding, say, or steel—is like, at first you see like China is growing and then soon it’s like eclipsing the rest of world combined.
So to me, it was this bigger story that I really want to highlight: not just DeepSeek and not just AI China, but more broadly speaking, what is this bigger trend and why should we care? How is this going to shape not just Chinese society, but the rest of world?
Grace Shao (03:11)I think to your point, DeepSeek was very secretive, yet it wasn’t like it’s within the AI industry in China—people were already noticing it and people were talking about, I think maybe six months before even they came out with their first R1 and then V1. But I think to your point, yeah, it was a shot to the West because it was like, wow, we always knew that China had strong industrial capacities, right? Like you said, like we had the manufacturing capabilities, the factories and whatnot, the hardware capabilities.
They didn’t expect something like a software to come out of China that was almost on par with what they could produce in the West. I guess my question for you next is then to highlight that—what was your goal really? What was your real message that brought you public? Why did you publish an op-ed on the New York Times?
Kyle Chan (03:55)Yeah, so part of it was to kind of point to the underlying drivers for what was happening because I also wanted to kind of correct this image of China, not only in terms of like China’s tech development, but also what really was responsible for some of that.
So like the image I want to correct was basically this very old notion of China making, you know, low value added commodities like household goods, basic consumer electronics maybe—stuff that maybe is good for economic growth, but isn’t so impressive technologically and doesn’t really challenge, say, the US or Europe or other industry incumbents in these areas.
I want to first point out that, yeah, this is different China now. And this process has been unfolding for a long time, actually. So I was trying to highlight some of the efforts that the government was trying to do to help accelerate not just industrialization, but innovation itself.
This idea that it’s still so deeply controversial in the US—the idea that the government might have a positive role to play in supporting private sector development, supporting cutting edge technology—I think that that is still something that’s debated very hotly in the United States. And I wanted to point out how China has been able to use—not successfully every time, and there’s definitely issues along the way, but overall, quite effectively—it has been able to use industrial policy to really move the needle and support its industries and its private sector.
And so this combination too of like, it’s not just one or the other. It wasn’t just sort of all top-down state driven and it wasn’t just all sort of bottom-up private markets. It was this interesting combination that has produced, I think, these sort of like world beating industries.
And I think the lesson—a big part of the piece was about the US side of it and what lessons we might take away and how the US might need to step up its game. I don’t know if this competitive framing is the right one, but in general, a realization that, okay, there’s a lot happening. This assumption that China would always be the center of low-cost manufacturing and the United States would be the center of high-tech R&D, innovation, Silicon Valley—that the picture was much, much blurrier than that. So that was sort of like my overarching goal.
Grace Shao (06:27)I definitely want to double click on the part where you talk about how the state and the private sector actually work together. And we can talk about that later. But I want to get a sense on what kind of feedback or pushback or even maybe criticism did you get from that piece?
Then furthermore, I want to get understanding: how did you get involved in all of this? You’re in the US, right? You’re in New York, right? How did you get into studying China’s industrial policy? Tell us about your background.
Kyle Chan (06:53)Yeah, yeah. Yeah, I mean, I just recently joined Brookings based in DC, which is a DC-based think tank that has a really great China center that does outstanding research on policy issues related to China, US-China relations. And my focus now is on China’s tech and industrial policy.
But getting to this point has been like an interesting journey. So originally actually—I mean to go all the way back, I don’t know how far you want to go back—but like my family is actually from Hong Kong originally. And so I was born in California.
And my parents—it was sort of that generation where my parents really wanted me to learn Mandarin. They’re like, that’s gonna be the useful language. And it turned out to be very useful.
But also growing in California, I took cars everywhere. It was like a very, very much like a private transportation kind of city. And it was like a revelation to me—I mean, this sounds so ridiculous to anyone who’s grown up in a city with good public transportation—but it was like a revelation to me to later live in places like Chicago or even San Francisco and then later on Beijing and Delhi and Berlin, to be in places with like functioning subway systems and functioning public transportation.
So I got really interested in infrastructure, actually, not necessarily industrial policy. That kind of came later, but infrastructure. And here there is really sort of like a very strong role for the government to play in coordinating, if not actually building and maintaining infrastructure, whether you’re talking about roads, highways, bridges, railways, subways, electricity grids.
And I just found it really interesting then later on traveling to China—how this seemed to be like completely different there. I mean, and I remember at the time really being amazed by the high speed rail system there.
And China didn’t have a bullet train system for most of its, most of the existence of its railway system until basically starting in the 2000s. They started to take seriously this idea of like, okay, maybe we can like really roll out a nationwide bullet train system. And they did a lot of R&D.
And I became really fascinated by how they did this, how they built what ended up being, you know, within a decade, the world’s largest high-speed rail system, how they acquire the technology and how they built on top of that to create this sort of like truly like made in China kind of transportation system.
And then how they did that repeatedly, not just for high-speed rail, but like for regular highways, expressways, for, you know, any kind of infrastructure.
Okay, so that was sort of my foray into infrastructure and that was actually the focus of my dissertation. So I did field work actually for a number of years in China and also in India. I was based in Beijing and Delhi.
And really it was like an enormous privilege to be able to travel around often by train across those two countries trying to understand their systems. And the railways were really useful for like understanding not just the railways and transportation, but understanding deeper political economy questions, like the structure of the governments, how their bureaucracy works, what are the main issues with building a mega project of that scale.
And yeah, and so that for a long time was my focus. And then ironically, for an American audience, it was tougher to convince people that railways was interesting. I think most people were like, well, China can build high-speed rail because it’s the top down society and they just decide where to build and they build. I was like, no, no, it’s much more complicated than that. But it was hard to get traction.
But then I realized like some of the same tools and the same patterns, some of the same institutions in China were also involved in boosting and accelerating development in key industries. So I mentioned clean tech, electric vehicles and batteries and solar, but also traditional sectors.
And so I was really fascinated by this pattern that was, again, it kind of goes back to the New York Times piece. It wasn’t just one industry. It wasn’t just one company. It wasn’t just one state-owned enterprise, but this whole, like across the board effort to, you know, accelerate development overall.
So that to me was so interesting—this process of industrial upgrading, which many, many countries are interested in doing. And that actually got more traction in terms of like, you know, I joke that the U.S.—every country in a sense is a developing country, right? There are areas where we’re trying to improve and areas where we’re trying to catch up.
And I think now, you know, the question is like, what role can the right policies play in helping, say, the United States in a similar process? So that’s a long way of saying that it was—it was a long journey. But yeah, it’s an exciting time to study these topics because so much is changing. Every day it feels like.
Grace Shao (11:30)That’s really good context and I think, you know, for me how I found your work was exactly because you had such a high level kind of view, a bird’s eye view of everything that was happening and how you were piecing it together.
So I believe I came across one of your pieces on High Capacity, which is your newsletter on Substack for audiences who don’t know. You had this Venn diagram where you’re like, this is what China’s good at here, here, here, here, here. And this is what’s happening right now. And this is how it like actually relates to the current EV build out, the renewables, the AI, the robotics. It’s a very big ecosystem.
And in some ways you argue that, you know, all these different sectors operate in parallel, whether is, you know, a top down direction or a directive, or it was organic, you know, growth, but they did grow in parallel. So therefore they’re now able to kind of find synergy and leverage each other’s strength, right? With LiDAR sensors, drones, robotics, all coming together.
Could you tell us a bit about what that diagram really means? I’ll try to pull it up as well in the video. I think just help us explain that in a high level.
Kyle Chan (12:31)Yeah, yeah. So what I try to capture with that diagram was this idea that it wasn’t just one sector or wasn’t just one area, one technology that China had been able to grow and foster maybe through industrial policy or some kind of state support, but it was this combination of these interlocking technologies.
Now there’s a new term that’s coming into vogue, like the electric tech stack, or the tech industrial stack, or the electric industrial stack. And it’s really interesting because I think that really captures sort of this new paradigm that we’re entering.
Those technologies—you mentioned a number of them—electric vehicles, autonomous vehicles, which for various reasons we can get into are built on electric vehicles. There’s strong reasons why those technologies go together. Lithium batteries, which also feed into drones, autonomous delivery systems.
We can think about just regular consumer electronics, smartphones, but also more sophisticated robotics. I mentioned industrial automation as well. There’s a lot of overlap there.
And then the big circle overlapping all of these is AI—different models, software platforms that might intersect with say autonomous driving and, I don’t know, even sort of the humanoid robotics world.
So I just find it so interesting that China was making progress in a number of these different sectors at the same time, and progress in one sector would support greater development in another sector. So EVs and EV batteries grew up together in China.
So the development of, say, lithium iron phosphate batteries that were increasingly inexpensive, that were increasingly energy dense on a, say, kilowatt hour per kilogram basis, that were safer—those developments within the battery world made Chinese EVs more competitive and more attractive overall.
And then developments in China’s EV sector fed back into the battery world and also fed into other related sectors.
And then the other big thing is that I really wanted to highlight companies that lay at the intersection of these different areas. So I think probably right now, one of the hottest companies is Xiaomi, right? When I was younger, Xiaomi to me was inexpensive smartphones and relatively like affordable, like household products, like air purifiers and things like that.
And I think they had built up a brand and a very strong sort of customer base around this general idea. And what was so fascinating was seeing Xiaomi jump into electric vehicles and having such success with the SU7 and now the YU7 SUV, which both of which were like sold out for a long time and are very much in demand.
And they have like incredible features, they have incredible performance, and they have smart driving capabilities. And so it just sort of like showed like, wow, this company that was originally kind of like a smartphone company could make this shift over into the EV world.
And I would argue that it wasn’t just Xiaomi and Ledron’s entrepreneurship. I mean, a lot of credit goes to them for sure, but it was also because of this broader foundation that existed in China that allowed for this common supply chain ecosystem that would feed into these different worlds that would allow a company like Xiaomi to make that pivot.
And you see it again and again. I mean, now you see—and this is where I came up with this term like the sort of Swiss Army Knife companies—now you see these companies like XPeng get into like a whole range of industries, right? So not just EVs, but also humanoid robots, drones, flying cars even.
Huawei is probably the ultimate example of the Swiss Army Knife company, originally starting in telecom equipment, but then branching out into everything from sort of every aspect of consumer electronics—smartphones, tablets—into now AI chips. They’re a major player in semiconductors. For a while undersea cables. I mean, the list kind of goes on and on—EVs as well and smart driving, autonomous driving.
There was just this like burst of companies coming out of China that could do all these different things and branch out into new areas very, very rapidly. And it was like shocking to me how big some of these bets were, like Xiaomi making a multi-billion bet on a new already highly competitive industry, the EV industry.
But again, I think it all comes back to, in part, this foundation that was there in China—this like what I call these overlapping tech industrial ecosystems.
Grace Shao (17:17)I have so many thoughts I want to throw out. One is, I think to your point on Chinese tech companies going into EV, it’s really fascinating because to your point, there’s obviously this price war and this crazy competition in China.
But what I’ve heard from a lot of people who actually do purchase the Xiaomi cars and the Huawei cars and the Xpengs—they say that the technology itself is incredible. You have all the lights, the voice control, you have amazing AI-empowered functions. But that said, they’re not actually as good of a drive, like they’re not as smooth.
So like if you pick a traditional OEM like a Mercedes or BMW, their voice control usually apps the crap, to be honest. Like they go off—like you see the reviews online because we were looking at family cars and it was like the reviews were horrible. Like they just get triggered by really random sounds, they can’t pick up like accents, you know, they’re not really good with other languages.
And on the Xiaomi/Huawei/Xpeng/Zeekr side, they’re really, really good at this. But they’re not as good for driving yet. So it’s interesting that, like you said, what they’re good at in terms of day as the Chinese companies are the kind of technology that is quite recent and quite modern, but they’ve not really actually honed in on the craftsmanship or the, I guess, capability of building a really, really smooth driving car as Germans have—as they’ve honed the skill for over the last like four or five decades, right?
So it is interesting where they’re good at. And then in terms of EV, I had a question you mentioned earlier. Most autonomous vehicles are now—sorry—most EVs are now being tried for autonomous driving. Why is that? What’s the synergy there? Why can’t old OEMs actually have strong autonomous driving functionalities?
Grace Shao (19:00)Kyle, one thing we were talking about is the synergy between EVs and autonomous driving. Why is it that it’s better to actually build in autonomous driving functionality within EVs compared to like maybe traditional OEMs?
Kyle Chan (19:12)Yeah, so there it’s basically because you get a lot more control and precision. And you can have things like steer by wire where rather than sort of mechanical steering, you can have basically a signal be sent directly to the transmission or directly to the engine or directly to the brakes. So it’s sort of all.
And then on top of that, it’s helpful to have large battery capacity to handle sort of all of the different computing demands that would—including all the sensors that might be feeding data into the whole system.
So yeah, you basically—the two almost always go together: having autonomous robotaxis built on top of EVs.
Grace Shao (19:51)That makes a lot of sense actually. Okay, I never thought of it that way. You described China as building systems of capabilities and you kind of touched on this earlier with your Venn diagram. You talk about how China is not really just picking one winner or one winning sector, right?
So how does actually EVs, batteries, renewables end up supporting each other? And then how does that actually extend out and spill over into the AI era with robotics, physical AI, or even the consumer AI products we’re seeing out there today?
Kyle Chan (20:18)Yeah. So at one level, there’s an underlying driver here that’s almost not even specific to China per se. It’s something more about these technologies themselves and this broader convergence across them.
So, I mean, I pointed out Swiss Army knife tech companies in China, but to be fair, right in the U.S., you have companies like Tesla that are going into many different areas, or even Google with probably one of the world’s best autonomous driving companies.
So I think there’s something deeper happening here where there is this convergence of what we might have thought of sort of like lower end, like smartphone technologies or consumer electronics. Again, the lowly battery, right? Something so simple. This innovation in lithium batteries, making them cheaper, more reliable, and being able to scale up production—like that alone unlocked so much in terms of basically any kind of electronic device.
And so I think that’s why we are kind of seeing this emergence of this like whole new technology cluster.
And then for China, I think there is an awareness of the sort of synergies across different industries. And you can even go back further to China’s earlier industrial policy efforts, right? Take Made in China 2025, which came out in 2015.
And some of the target industries there were chosen not just because they might in and of themselves be useful or important, but also because they had broader spillover effects. You think about things like telecom equipment, IT infrastructure, anything related to energy, or anything related to communication in general.
Also CNC machines, right? So these are sort of like—they may not be like general purpose technologies in the way that electricity itself or computers are, but they might be sort of like multi-purpose technologies with broad applications in a range of different areas.
And so by making that bet on those types of technologies, you know, whether or not you succeed in becoming a global leader in that area, it will help feed into everything else that builds on that kind of tech stack.
So that’s what I see happen again and again, where for China’s approach to technology, where it’s not just about a single bet on a single technology, but trying to find these parts of the value chain that have large spillover effects and trying to support those—even if they in and of themselves might not be totally economically viable or the best businesses to invest in from a pure return on investment perspective, but they have these broader economic and technological spillovers.
Grace Shao (22:57)Yeah, and I think to your point on like a lot of the planning from top down, it’s not like they were just more strategic in like picking the right track.
But when I spoke to David Fishman a couple of weeks ago, he was saying China’s strategic planning on building electricity capacity is actually not because they foresaw like AI, the AI boom and data centers. You know, electricity is just simply urbanization actually was driving increase of energy demand as well.
They knew that in the future, a lot of things had to kind of move from traditional coal to renewable to kind of actually even have the capability to power what is needed of the future. So it was a grander vision versus just like, I know AI is gonna come in 10 years. I’m gonna build up renewable energy and the renewable energy is gonna help power data centers. So it’s not like there are profits or anything. So that’s interesting to hear.
I think I wanna understand how has that shift in ambition with like—
Kyle Chan (23:42)Totally.
Grace Shao (23:49)really China’s desire to move away from low wage, low margin, that trap into like higher value services and really like how has that shaped and driven the AI innovation that we’re seeing right now coming out of China.
Kyle Chan (24:01)Yeah. So I think what’s really interesting is, on the one hand, you have like a lot happening within the AI sector itself. You have obviously this like very vibrant ecosystem of startups, of big tech companies, even down to servers and data center construction firms, and even the major state-owned telecom operators involved in data center construction.
You have all these applications and developers trying to build on top of this whole set of foundation models, for example.
And that’s sort of just within the AI industry, right? And then on top of that, you have the fact that in all these other sectors, whether you’re talking about manufacturing, you’re talking about biotech, healthcare, there’s a lot of investment and progress in trying to move up the ladder in each one of those industries.
And so then you see people finding ways—either from those industries or from the AI side—trying to find ways to incorporate, to integrate AI.
And actually there’s something that I really love about your work where you’re highlighting those areas where it’s not just about like the latest benchmarks on the latest models and this sort of like endless race, but about like, how is AI like actually being deployed? How’s it being integrated into existing services and platforms in a way that would really boost, say, drug discovery or would really, I don’t know, improve tutoring and education services for students.
So I think that’s what’s so interesting here. And yeah, some of it might be supported by policy efforts. I think of some things like compute vouchers, for example, where startups—AI startups—might have trouble getting access to compute.
So we’re not talking about like the Alibabas and Tencents of the world. We’re talking about the little guys who may not be able to afford to build a giant data center dedicated just for them. And then local governments might offer compute vouchers—subsidized compute—basically access to public infrastructure, essentially, to help them sort of get off the ground and have that little bit to deploy on and develop with.
And so that’s an example of an area where you do have some government intervention stepping in and not trying to do it in a heavy handed way, but just trying to offer kind of like a tailwind of support.
And ultimately, and this is one thing that I think is sort of special about software and the AI industry in general is, I mean, a lot of this is sort of like, you know, this creative explosion of different ideas from the private sector—from all these entrepreneurs—like trying to look in areas that are related maybe to their own areas of expertise or just kind of like scouring: where can we plug in AI? Where can we make improvements, even very small ones into existing industries?
Grace Shao (26:45)That’s really interesting on compute bit, where I just met someone at Google in Singapore a couple of weeks ago, and he was saying that in some ways, the big tech in the West are actually operating in that capacity. Instead of incubating them and just taking another part of equity, and instead of just giving them capital, they’re giving them compute, essentially vouchers for these startups.
So I guess my question is, how do you see the relationship between startups and big tech in China versus startups and big tech in the West? And in what way, I guess, in what way do the state actually play a positive role or negative role in all of this in the whole ecosystem?
Kyle Chan (27:17)Yeah, that’s a question. I mean, in some ways it is a similar story, right? You have China’s own hyperscalers providing AI cloud computing services to a whole range of different players, you know, be they AI startups or, you know, large corporations or other, you know, maybe hospitals or other state-owned enterprises, for example. So that part might not be so different.
But I think what might be a little bit different is the sort of like extra on the margin support that the government in China—or especially local governments in particular—might offer.
Yeah, compute vouchers is one. Also these industrial parks where—and this is going back to like an almost an older model applied to like the age of AI—where, you know, AI startups, they still need office space. They still need help setting up a business. They might need help figuring out how to network with new customers.
And those are other areas too, where local governments in China might play a more active role in troubleshooting, trying to bring startups up to speed, trying to connect entrepreneurs.
And that’s something that I think is quite different than in the US system, where yes, you might have the large hyperscalers like Google or Microsoft providing that underlying infrastructure, that service for access to compute, but you won’t really see that kind of intervention or stepping in from the local government side, at least not so proactively by any measure.
Grace Shao (28:46) Yeah, I think definitely the West what you hear more about is like applying for fellowships or acceleration programs within whether it’s VC funds or like you said the big hyperscalers. Whereas even in Hong Kong here, like the Hong Kong government offers startups like staff support, back office admin sharing, teams to share, then like even offices in like Cyberport out like, you know, in Pok Fu Lam.
So like definitely the state plays a more active role and I think it’s kind of sometimes misunderstood by the West what that role means. It’s really many times it’s just like an incubator, a parent to someone, or even a mentor.
So we talked about you living in many, many different cities across the world and you study industrial policies across different developed economies. You mentioned that you lived in India, then you studied obviously China. So from that perspective, that international global perspective, what do you think China has actually done differently compared to maybe other developing nations that started in similar circumstances maybe say three, four decades ago?
Kyle Chan (29:46)Yeah, that’s a great question. So the thing that China has done that really has stood out to me, that really makes it so different than I would argue most other developing countries, is a very deliberate effort to basically build up industrial capacity and to move up the value chain.
It wasn’t like, you know, if we invest in education, if we invest in sort of these general factors that go into development, that over time, you know, you would eventually sort of get there. It was like: how can we bring in foreign companies to form joint ventures with our own domestic firms and share that kind of knowledge? How can we build up world-class research programs and build interesting scientific collaborations with the Europeans or the Japanese or the Americans even?
It was like: how can we try to like find those bottlenecks in the process?
And I think this kind of goes back to your point where it wasn’t like, this is the one direction we’re going to make this huge bet and that’s what turned out to be correct. It was more of these sort of— you know, to use like a sports analogy—like kind of like the ground game, right? It was like oftentimes kind of more tactical things trying to backfill areas.
Like let’s say for batteries, right? You need to have access to lithium. And so building a global network of lithium processing facilities and supply chains was really crucial to feed into EV batteries and then the EV industry itself.
And yeah, so I think that’s something that I see other countries do like a little bit, but really for China, it’s sort of at a very deep level: this effort to not just sort of hope that you got most of the pieces right and then let the story unfold, but to proactively find ways to support industrial development and technological development.
Grace Shao (31:44)What was actually, more personal, what was the most memorable thing living in Beijing and then commuting in New Delhi and then moving around the world so much over the last few years?
Kyle Chan (31:54)Yeah, I mean, there’s so many things. Yeah, I mean, I can tell you from a research standpoint, I interviewed government officials in both countries. And I can tell you that it’s much easier to get access to government officials, at least at the central government level, in India.
And so some of my sharpest memories are of long conversations over chai with railway officials in India, where I almost was kind of like a therapist for them in some ways, because they would have these complaints about the bureaucracy, about, frankly, their colleagues, about many thoughts about their country, about China, that they were just like very generous in sharing with me.
And it was like really fascinating to kind of like see the world from their perspective. I think in particular in India, there is a bureaucratic elite, there is a civil service elite who are highly educated. They often have to go through extremely competitive national exams to sort of like get to their positions.
And I think in some ways, I found this group of people to be both very proud, but also oftentimes very frustrated by some of the bureaucratic hurdles that they faced.
On the Chinese side, yeah, like when I did get access to government officials, sometimes even getting access, right, like it might take a while to get to like a genuine conversation where people can kind of open up more.
And it was so interesting because like I have my own theories about why say China was able to build high-speed rail so quickly, but it was always interesting to hear people’s own perspectives. To see like, what did they think was like really, you know, the thing that moved the needle.
And I heard like sort of everything from, you know, that China is just a much more sort of like coordinated and aligned system across the board to cultural explanations to, I mean, you name it. But it was always fascinating to hear like from people within the system.
So like to the extent that I was able to get that, and then I got to visit—I got to actually visit like the construction sites for like ongoing railway projects. And that was really cool.
So yeah, I mean, there, there’s so many. Like the two countries are so fascinating and you could spend a lifetime in either one and feel like it’s not enough, in terms of exploring and getting to see different parts.
Also, I did get to travel a lot. And like India, I don’t know if many people know this, like India has like the whole Northeast region, which is sort of like very distinctive culturally in terms of geography. It feels very different from the rest of the country.
And, you know, like you could explore that whole area, or you could travel to the South and there’s like a big sort of North-South cultural divide that anyone from the North or South will tell you about.
So yeah, it’s just like—I mean, these are sort of like continental-size countries. And I think they have that kind of continental-size complexity to them.
Grace Shao (34:51)
Definitely, I would love to visit India one day.
Do you think what we’ll see India kind of become the next China, if you must put it that way? Because for many years people said, oh, Vietnam’s going to be the next China, right? And obviously all the talent, all the capital, all the interest in that right now moving to India, as well as even a lot of the supply chain, right? For a lot of the big companies in Europe as well as the US, what should we expect of India in the next maybe decade or so?
Kyle Chan (35:14)Yeah, that is like the big question. And I will highlight some factors that are very much in India’s favor, and then I can point out a few challenges.
So in general, the India story is like really fascinating because if it weren’t for China’s high growth story—like if you kind of remove that from the equation—India would have been the envy of the world.
I think in many ways they have already proven to be able to have high rates of sustained growth over multiple decades now. So like in that sense, they’re already getting there in some ways.
The other things that are really working in their favor are: there is a big focus on manufacturing, on trying to build up industrial capacity in a way that is very reminiscent of China. There’s a huge push in industrial policy targeting areas like consumer electronics, the automotive slash EV industry. There are big ambitions for the semiconductor industry in India.
And the third thing I would point out is that there, up until recently, was a bit of a geopolitical moment for India with different companies trying to move away or diversify from China to some extent, diversify their supply chains.
And this was really quite an opening for India where you saw, like for example, Apple was able to shift a fairly large share—I think up to like a quarter—of its iPhone assembly was shifted to India. And with that also began a process where some of the suppliers would start to follow. And so I think that was actually honestly, to me, surprising how quickly that happened.
So those are some factors in their favor. But on the other hand, there are some very deep structural constraints. And one is that the bureaucracy, especially around anything related to labor, is very, very tricky to navigate.
And I think for companies who want stability, who want sort of like a certain stable business environment, it’s harder to operate, and there’s a lot of regional variations. So some of the southern states like Tamil Nadu, Karnataka tend to be seen as more business friendly, open to foreign investment, open investment of many different kinds. And that is where you see the electronics industry really growing quickly.
But overall, there are some bureaucratic issues. And then at a more fundamental level, something that China has been able to do is often reform its own internal organizational structure—sometimes pretty quickly, sometimes shockingly fast.
And sometimes it takes a while, like the railways did take a while, the railway ministry. But for India, I think there are some problems that everyone knows exist in a policy sense, but nobody knows how to address and break, say, like a political deadlock. That happens a lot. It probably also happens—I mean, I know it does happen in China as well. But I do see it more clearly and especially in the areas that India is trying to target for growth.
Grace Shao (38:10)And do you see actually a lot of the Chinese companies we talked about earlier, like the Xiaomis, BYDs and Huaweis of the world—are they exporting to India or even exporting their know-how and their supply chain and manufacturing? Because I know a lot of these Chinese companies have been doing that, like moving their talent, their know-how and also selling to consumers in Southeast Asia. But what is kind of the South Asia market looking like for these Chinese companies?
Kyle Chan (38:35)Yeah, I think from the Chinese companies perspective, they would be eager to reach one of the largest markets in the world, right? And then South Asia more generally.
And I think you see other areas like Pakistan and Nepal—Chinese, say, EVs or other smartphones or other consumer goods—like really taking off.
India and China specifically, though, have a bit of rocky geopolitical relationship. And so that definitely cast a chill over cross-border investment and business flows for a while.
And more recently, it seems like things are warming up, maybe gradually, and I think this is taking years. But I think there’s a recognition, certainly from the Indian side, that it helps to bring in that expertise and know-how—to have Chinese engineers, technicians, and managers participate in India’s own industrial development process.
I think, I don’t know if we’ll see those days again when you had, say, Alibaba invest like, you know, hundreds of millions in PayTM, which was India’s sort of equivalent of Alipay. I don’t know if we’ll see those days again, but maybe there is a sort of warming up of relations between the two countries and we might see greater sort of business flows following that.
Grace Shao (39:48)Yeah, because it seems like India has their own ecosystem of like FinTech as well as their own ecosystem of social media. So it’s not like they really need China’s software export.
But I think on the hardware side, it was really interesting reading Patrick McGee’s book and he was saying how back then it was the Taiwanese and the Americans that actually had to—well, the Americans first trained the Taiwanese and the Taiwanese came to train the Chinese.
And now it seems like if the supply chain is moving over to India, I’m sure the business will be business and, you know, it would make sense for the business to actually have the Chinese now train the Indian factory workers to really take over that new labor demand, right? Yeah, but geopolitics aside, that is.
Let’s talk about AI quickly. You know, in your conversation with Heath Yap—also I love his show and a good friend of mine—really grateful for Keith connecting us actually.
In your conversation with Keith, you said that you’re always careful to hedge. I kind of chuckled when I heard that. You’re talking about China’s rise and China’s rise in AI. You’ve obviously been quite vocal talking about how China has done some things right. But yet, obviously, as an American, you are saying it from a perspective of what we can learn as Americans.
And what are the scenarios where your own projections—China’s technological rise—don’t pan out or that could derail this story, right?
Kyle Chan (41:00)Yeah, that’s a really good question. So there are some ways that this story can turn out differently.
I do think that China has been able to benefit for a long time from especially international partnerships. And I think the extent to which there might be skepticism or even suspicion about those sort of international partnerships—that could affect, say, research collaborations or business partnerships across border.
At the same time, though, I guess at this point, I can point to areas outside of AI where there is a lot of interest, say, from German or European automakers in partnering with Chinese ones, trying to learn some of that know-how and technology. But that’s a big wild card.
And then another big one is also the extent to which there is a bit of a trade backlash, right, to Chinese exports and to what extent other countries might push back and maybe put up more tariffs or maybe even just demand greater investment and localize manufacturing in their own countries. So that could change things, not necessarily in a worse way, perhaps, but it could change things in some ways.
And then I do think that the relationship with the US is really crucial. And I think for China’s development, the instability in the relationship—I mean, it doesn’t help anyone, I think. So that’s sort of another wild card in all this.
And then from the US side, I mean, it’s sort of interesting to think about American AI development right now, because I literally like every day my feeds are like talking about bubbles and talking about like, you know, circular investment deals and things like that.
And at the same time, like there’s still so much enthusiasm and the data center build out is like seems to be still going full blast. So here’s like where I will offer like a hedge perspective where it could go in many different directions.
It could be a bubble and we might see a pullback and it might turn out that these investment deals just don’t pencil out. The economics just don’t work out.
But it could also be that we’re not—that this talk about AGI, I don’t know if like AGI per se will be achieved—but that broad-based artificial intelligence like computers that can do a huge range of tasks, not just coding, which is already impressive, and not just writing and things like that, but really sort of a broad range of tasks—that could take off.
And the United States huge investment in compute capacity could give it an edge in the long run.
So those are different ways things could pan out. And then the other wild card is like embodied AI and robotics and how that will pan out.
Because I think there’s like different approaches being taken by different companies, by different countries. I think some companies are looking to a version of AGI for robotics, kind of a universal foundation model for robotics that can operate across any different sort of hardware platform.
And other companies—I think about like Unitree—are focused on just deploying fast, like building tons and tons of like quadruped and humanoid robots now that have become kind of like the new hardware platform for many robotics developers, including in the United States.
So there’s many different ways things can go.
And then like more recently, there’s like, I have this tweet about like highlighting like street cleaning robots and just like very practical, like immediate real world use, right? So that could be another area where, you know, maybe we won’t get the AGI robots, but maybe we will have many, many different types of robots doing more specialized tasks.
And that could be transformative for either country. These are all areas that I’ve followed closely. Yeah, at this point, if I had a crystal ball, maybe I would be in a different job.
Grace Shao (44:57)
I think he definitely touched on something I feel like it resonates with me. Like is when I speak to people in the US—not really just the West, but really just in Silicon Valley—I feel like exactly to your point, there’s such a polarized kind of take on where AI is going.
It’s either it’s such a bubble or it’s going to burst. It means nothing. This is complete fad, right? Or it’s—or even, you know, people are like, this is going to ruin our lives. We must stop it right now.
Or it’s a very polarized view, which is like, OK, this is like the best thing that’s ever happened to us, like, you know, AGI or nothing.
And in China, when people ask me about what’s happening on the ground, it does feel like—I’m not sure pragmatic is actually the right word anymore. I don’t like using that word anymore because I feel like it’s now overdone. But I do feel like it’s a bit more, I would say centered.
As in everyone kind of feels like it’s just another wave of technological advancement. Whether how it might pan out—to your point—it might not be AGI, it might not be this crazy intelligent being that will take over our thinking capabilities, but it might be something that will transform how we humans interact with each other, how we work, how we actually increase productivity.
And I think it might have some—it might lean on the fact that frankly China really did see this kind of technological advancement driving productivity gains only just recently, within the last generation. Whereas in the US, you haven’t really seen this kind of crazy gain for the mass in a while, right? Since the Industrial Revolution. So people don’t feel it as much and there’s more fear around it.
Now I have another question on this. Just to kind of wrap everything up: you look at a lot of the industrial policies and you look at how the state works with the private side in the AI sector right now in China. How much are we seeing that is actual genuine market pull versus state-driven push?
I think you kind of alluded to the fact that like Chinese government does work with helping talent and raising capital. But right now with the big names—like the Unitrees, the UP techs, the deep CXC, Galabots and the whatnots, right?—that we’re seeing, are the state behind this or like the West often seems to think that way. How do we understand that?
Kyle Chan (46:56)Yeah, so I think it’s a complex relationship. So I don’t think it’s sort of like a top-down, the government is sort of like dictating what direction these companies need to take.
And I think there are probably national priorities where I think Beijing or local governments would want to see companies focus on more.
And ironically, like there’s a very strong convergence right now on wanting to make progress on AI more generally from the public and the private sector.
And then I do think that some of it comes down to problem solving on the ground. So this is something we talked about earlier: trying to figure out what issues the private sector is running into, what issues certain firms are running into, and then trying to troubleshoot and support them in some of those areas.
And then, yeah, overall, sort of like offering this broader roadmap or this broader set of like this broader package of sort of like a policy support, whether it’s sort of like a robotics-specific national strategy plan.
And part of it is just about even signaling that there is state support for these sectors, that these are areas where if you venture in as a private business, like you will not have all your problems solved, but you could at least have ways to have some of your sort of like more day-to-day issues addressed.
Grace Shao (48:17)Kyle, if you look at the AI sector today in China, how much do you think we’re seeing genuine market pull versus state-driven push? And especially now we’re also seeing the AI plus policy being pushed out. Is that diffusion driven by the state or is it driven by the actual entrepreneurs, especially amongst the Unitrees, the UP techs, the DeepSeeks and the MiniMax, Moonshots of the world?
Kyle Chan (48:39)Yeah, so that’s a great question. And I think what’s interesting about this moment is there’s a strong alignment between the public sector and the private sector, where clearly Beijing at a national level, and then many of the local governments want to see a booming AI industry in China.
They want to see China as a whole make progress in applying AI to all different parts of life—support economic growth and improve social services and improve education and all these good things.
And at the same time, you have so many different private sector firms from the big tech companies to the smaller startups really jumping in and also eager to innovate.
And I think one thing that’s really sort of exciting about all this is you can really like drill down into like subsectors or different aspects of China’s AI industry and just see how vibrant the startup ecosystem is now in terms of all the different, say, new coding tools that are emerging or different efforts to try to expand overseas or interesting ways to integrate AI into e-commerce or social media or EVs or advanced manufacturing—you name it.
And so right now, I just think that there’s just like this huge sort of creative explosion of different ideas and an eagerness, especially in this sort of post DeepSeek moment, to try new things and take that risk.
Grace Shao (50:04)Super interesting. Kyle, I really appreciate your time today. And for listeners, we actually had a few technical glitches today. That’s why we’re going to have to chop up the conversation a little bit. It might sound a little jumpy than my usual podcast.
My last question for you, which is a question I ask everyone: what is one differentiated view or non-consensus view you hold? I guess your New York Times piece was a pretty strong opinion and you really publicly put it out there. But is there anything else you think that, you know, you think differently or you have a different kind of view on that compared to your peers maybe?
Kyle Chan (50:35)That’s a question. I do think in general that my take—maybe it’s becoming more mainstream—that there is a role for very creative and very thoughtful policy intervention in supporting strategic sectors, in boosting technological innovation.
I think that’s something maybe perhaps widely accepted in other countries, but at least in United States, still something that a lot of Americans are not quite comfortable with.
And there are certainly risks along the way, but I think also to keep in mind, there are risks of not doing anything and of not trying to support R&D, scientific development and all these different things and to leave it purely to the market. That’s sort of my slightly left field take.
Grace Shao (51:21)Thank you so much, really appreciate your time and your insights again. Thank you, Kyle.
Kyle Chan (51:24) My pleasure.
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Most AI policy conversations still orbit around Washington and Brussels, but Asia-Pacific is already writing a very different rulebook. In this episode, I talk with George Chen, Digital Partner at The Asia Group and former Meta policy executive, about how AI is actually being governed, built, and deployed across APAC, China, and the global south.
George traces his own path from journalism to big tech to advisory work, and uses that vantage point to explain why APAC is not “one market”—and why the EU analogy breaks down almost immediately. Countries like Japan, Korea, Singapore, and China are leaning into AI as a tool for economic recovery and industrial upgrading, often taking a much more pro-innovation, pro-growth stance than the EU’s more precautionary approach. At the same time, Southeast Asia is becoming the physical backbone of the AI build-out: Singapore as HQ and regulatory hub, with Malaysia, Indonesia, Thailand, and the Philippines hosting the data centers, power, and connectivity—along with all the local tensions that come with that.
We also get into what “responsible AI” actually looks like inside a company. Beyond the buzzwords, George breaks it down to three pillars—security, safety, and privacy—and talks through how mature players like Microsoft or Meta build these into product design from day one, versus the reality for startups trying to ship fast with one lawyer and a single policy person supporting multiple markets. He also makes the case that fragmented regulation and the lack of international standards are becoming a real tax on innovation, especially outside the US and EU.
Another big thread is the emerging US–China competition over AI governance itself. It’s no longer just about who has the best models or chips; it’s also about who exports their rules, norms, and defaults to the rest of the world. The US is pushing an “America-first” innovation and safety model to allies, while China is pitching AI as a kind of public good to the global south—combined with a more cost-efficient, top-down deployment model and very strict cyber and real-name rules at home. George argues this divergence is already shaping how content, deepfakes, and AI-generated media are treated in different jurisdictions.
We talk about the local edge of Chinese models—why in places like Beijing, models such as DeepSeek can be more useful than ChatGPT or Gemini for everyday queries because they’re trained on more localized, timely data. From there, we zoom out into the new AI talent map: countries like Indonesia, Vietnam, Kazakhstan, and Uzbekistan trying to position themselves as low-cost AI talent hubs and “back offices” for global AI companies as coding gives way to prompting and applied ML.
We close on a more philosophical note: should AI be built as a subordinate assistant or a true partner? George shares his uncertainty here, and we talk about what happens when we give AI more agency, emotional intelligence, and continuous workloads. At some point, the conversation shifts from safety checklists to ethics, culture, and even “digital colonialism”: whose values, whose norms, and whose worldview are encoded into the systems that end up mediating how we see the world.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
AI-generated transcript.
Grace Shao (00:00)
Hey George, thank you so much for joining us today. I’ve been really excited and waiting for this chat. You know, you are a very busy man. You’re constantly traveling. I can barely reach you in Hong Kong. So really appreciate your time today. Sit down with me and share your insights with my followers and some of our listeners. To start with, you’ve worn many, many hats. A journalist, tech executive, policy advisor, and now a partner at the Asia Group where you advise a lot of force, you’re probably helping companies on, I believe, geopolitical positioning, right?
George Chen (00:29)
Thank you. First of all, thanks for the invite. It’s quite an honor to join a growing cohort of guests for your program. Really happy to have a discussion about tech and policy issues because I think you’re right. My first 10 years in media, similar to your background, and most recent decade, I work very much on the intersection between technology and policy.
My biggest takeaway from my last job at Meta, one of the platform operators in the world, is sometimes we very much focus on technology development, like the breakthrough, while the resources for policy support are actually quite limited, especially in the Asia-Pacific region compared with the US. think for all the...
Big tech in the US, given the politics domestically, they have to do a lot on political and policy part. But for Asia Pacific, the policy work, compared with other investments, like in data center, technology, hiring of engineers, it’s still very, very, very understaffed, under-resourced, and sometimes under-appreciated. This is why we need to...
address some concerns about policy issues as we advance the technological part. Because I always tell my students, tell my friends, tell my partners that the key challenge, even you have CharGBT 5.0 or 6.0, the key challenge is how to get the government to understand new technologies and also get the users to have more trust in those new technologies. Otherwise, nobody use it, nobody trust those things. And that makes them.
Grace Shao (02:15)
I think that’s super helpful. A lot of times when we think about policy or safety issues, we think about it as like a siloed part of the ecosystem. But really like exactly to your point, like, you know, we need the developers to understand the concerns of the users. We need the users to understand the safety risks of the products. We need the regulators to understand what it means to implement these like technology throughout our economy, right? So there’s it’s like, it’s actually all interrelated.
I think today to start off with, let’s like go into big tech, just give in your background with Metta, working with a lot of these big tech companies. You’re based in Hong Kong for the listeners, but actually work predominantly for American big tech companies. What is like the, I guess, the fundamental feel right now as we see the evolution be from a social media company for AI to AI of focused company as this is now the forefront of their strategy.
George Chen (03:11)
Right, so for the Asia-Pacific region, it’s big. I always try to explain to my clients and friends, when people talk about Asia-Pacific, the first gross perception, perhaps from Western perspective, is, okay, treat Asia-Pacific like the EU, right? But EU is a single market. They have very much shared the language, English, also one currency and they have the European Parliament to pass legislation for EU member countries. Asia-Pacific is far diverse, far different, and much bigger. So it’s hard to just copy whatever works in EU and then let’s also do it in APAC. Using AI regulations as a clear and classic example, you know, you is the first You know government, you know to have the world’s first AI act, right? But the so-called the Brussels effect didn’t really happen this time in Asia Pacific countries You didn’t see like all the countries, you know, like Singapore or you know Japan to quickly follow up on You know to have a similar like a risk-based approach or penalty focused approach to AI, right? Instead, you know if you look at Japan. They are very much welcoming. Japan declared to be, they want to be the most friendly open country for AI developments. The first data exception for AI testing was actually in Japan. And then Singapore followed, and Hong Kong’s also not considering, right? So APAC took a very different regulatory approach to AI versus EU. I think this is something all the American tech companies have to realize. It’s not like America leads technology and then EU matters because of the special relationship between US and EU. So as I mentioned at the beginning, the resources for public policy work are very limited in AIPAC, but EU still enjoy a lot of resources, this English-speaking market that has lot of political connections. And then Asia-Pacific, when it comes to policy enforcement, like policy support it feels more like a third country, overall speaking Asia-Pacific as a whole. So there’s still a lot of educational process, the learning curve for big tech, largely from the US to understand what are the challenges, what are the opportunities in the Asia-Pacific market. However, I also need to highlight for many big platforms, Asia-Pacific is actually not just the largest market by internet users for American tech companies, for almost for all of them, right? You know, in terms of user base. It is also a very important revenue source, know, the source of revenue for those American companies. So now you see the imbalance, right? You you make a lot of money from Asia-Pacific, but the support you give to Asia-Pacific is quite limited, know, compared to in the US ⁓ and EU. So the learning curve is there.
American tech companies want to have a more sustainable development and want to have a more constructive relationship, sort of a more constructive partnership with Asian governments. I think there’s still a lot of work to do.
Grace Shao (06:31)
I think that’s really helpful to help listeners understand because sometimes people also approach me, they’re like, what’s APAC? I’m like, APAC is gazillion different markets and it’s actually so fragmented, right? And I think people sometimes misunderstand it kind of similar to like what you said. They think it’s like a EU. It’s not like actually there’s no consistency in currency. There’s no consistency language or no consistency actually even income or anything. So it’s quite scattered. that sense, I actually want to ask you, you mentioned something just now.
George Chen (06:39)
That’s right.
Grace Shao (06:58)
Japan and Korea this time is taking a more proactive actually approach as the countries themselves are taking more proactive approaches to really embracing AI and you know actually compared to EU’s more wait and see or more protective measures right which is not very yeah not not not what they usually would do what do think the trade-offs are actually in that sense do you actually think that means we are seeing more innovation or more technological breakthroughs or even economic diffusion of the technology right now in Japan and Korea.
George Chen (07:30)
Yeah, yeah, let me put it this way. So AI technology, you know, we believe, you know, still in the very early stage, right? Even you talking about, you know, trying to redefine Polisero, you know, but, you know, if you put that in the overall development for AGI, you know, we are still very much under the, in the early stage of the curve. So for Asia Pacific region, yes, it’s diverse, you know, but we can still see some sort of patterns, similarities in terms of different AI strategies. At the Asia group, my firm, we did a research paper on the different regulatory approaches to AI governance in the vast Asia-Pacific region, from Australia to even in Mongolian. Long story short, you are right. Some countries in Asia-Pacific take ⁓ a more economic benefit focused approach, right? Take a more innovation focused approach. Countries like Japan, Korea, Singapore, they want to see how AI can help them to drive economic impact, right? It doesn’t mean like they don’t care about the safety, the security issues, but they want to have certain flexibility, to encourage more startups to succeed, right?
in to a certain degree, actually maybe too many surprising because China is very well known as one of the strictest internet market in the world. Basically, none of the American, very few, I will say, like very few American tech companies can really succeed in China. The only two exception in my mind are like Tesla and Apple. But they are more like consumer related if you touch on content.
We talk about Google and Meta, that’s a completely different story. But even so, China at this time is also taking a more pro-innovation, pro-economy approach to AI development because this is a very top-down approach because President Xi saw the success of DeepSeek and he basically wanted more success stories like DeepSeek. Japan and Korea are in more or less the same category, like pro-innovation, pro-economic recovery. For Japan,
I talked with my friends and colleagues in Japan. The sentiment in Japan is like, we’ve lost 30 years, guess, three decades in terms of economic recovery. This is like our last chart. And Japan has been quite strong in robotics, those fundamental technology development. So that’s the sentiment in Japan. We have to grab the AI opportunity. In EU, have to say, part of the reason why EU is so keen to develop regulations, legislation in recent like five to 10 years. In my view, some may argue and disagree. I think the EU does come with a sense of protectionism, right? Because if you look at all the market leaders, you name it, OpenAI, Google, Microsoft, AWS, all of them are big tech from America, right?
I remember there was a chart to list the top 10 most advanced AI models. There’s only one model from EU, actually from France. The rest are from the US and China. So that tells a lot. If you are the EU regulators, look at from a competition perspective, you will more or less have a sense of anxiety. And then you will look at all those big tags like, no, we need to do something, like a country that pays in the name of safety and security. I’m not blaming EU regulators for doing it. But in the meantime, we also hear more and more concerns, even from the state heads, like French President Macron. He’s concerned that tough regulation in EU on AI will harm innovation in the EU rather than help European startups.
Grace Shao (11:14)
I think we can double click on China later. It’s going to have its own special segment for sure. China is just such a big story. But for some context for lot of listeners, Meta and Google, the likes of these companies actually do exist in mainland, but they mostly only have their ad services there. So basically they help enterprises with their ad sales to the West. But to Georgia’s point, they’re not really operating at the full capacity that you would see them elsewhere in the world.
George Chen (11:33)
That’s right.
Grace Shao (11:39)
Now I do want to kind of finish up on the APAC kind of narrative and then the APAC focus right now, which is for ASEAN right now. Let’s set apart like South Korea and Japan and China, just the Northeast Asian countries are frankly economically much more, you know, like developed as well as more economically focused, right? For ASEAN right now, especially since I just went to Singapore last week, it’s really interesting. Like we basically have the players, like you said, OpenAI, Google, Meta, all of these. Well, APAC headquarters based in Singapore, even the 10 cents and the bite dance of the world, right? However, Singapore is tiny, like just in terms of size and its resources. So what we’re seeing is they’re extracting essentially all the compute energy data centers, connectivity, any of the infrastructure you need to think of actually to Malaysia, in Malaysia, in Indonesia, in Thailand, even they’re building them out over there. How do we actually understand this right now? Is this a net benefit for these economies? Or is it actually really hurting the local economies and, you know, in some ways exploiting them and really just only serving the companies based out in Singapore? How do we understand that?
George Chen (12:45)
That’s right. So let’s talk about Southeast Asia. It’s complicated. When we’re talking about APEC, actually the most complicated part, I think it’s like Southeast Asia. Because when we talk about Korea, Japan, China, even China is a socialist country, but in terms of economic models, there’s a lot of elements related to capitalism. So those are the most economic economics in the Northeast Asia. Southeast Asia is very diverse, very different from each other.
Singapore is like the exception, the most advanced economy in South East Asia. But they come in terms of population, the user base is pretty small, like 4 million, 5 million population, even smaller than Hong Kong. You’re right, a lot of the tech companies, even before AI become a trend, they talk about like Meta, Google, Apple, they all had their headquarters in Singapore. It has really become the hub for big tech over the past 10 decades. Unfortunately, Hong Kong, thank God that we still have big banks like JP Morgan, Goldman Sachs in Hong Kong, we remain as a financial center. But in the aspect of tech innovation, you have to give some respect to Singapore. They did very well to attract those tech headquarters. So this also became, you are right, sort of a point of
I don’t know how to describe it. Some of the neighboring countries are jealous, certainly jealous of the success in Singapore, right? And then countries like Indonesia or Malaysia also wondering like how to get the benefits from the fact that all the big tech have their headquarters, regional headquarters in Singapore, right? But if they only care about the relationship with Singapore or in government, because they have headquarters in Singapore and their neighboring countries will not get any benefits, Malaysia actually founded their own ways in the regional AI race. And their offer is data center because of the stable supplies of electricity, relatively much cheaper labor costs and land costs and overall cost for data center operations. So this is why Malaysia got a lot of attention from Big Tech too, like AWS, Microsoft, they all made huge investments in Malaysia. Not AI, R &D, maybe yet, but first our data center. In the AI industry, we have a popular saying that AI is like electricity. Sam Altman said that. Basically, this is like the new kind of utilities for everyone’s life, right? But to develop AI you also need electricity. You need a lot of investments in infrastructure. This is why Malaysian already stand out and Philippines too in a way, as sort of the cheap, reliable alternative to data center investments in addition to Singapore. Everybody complains about Singapore in terms of living costs, even like how difficult it is to get work committed in Singapore these days. Even you have a call like qualify the job, but it doesn’t mean like you will get work permit immediately. Actually, in comparison, Hong Kong is doing quite well to attract the talents more easily these days in the tech and financial space. Back to the AI governance issue, yes, Southeast Asia also took a very different approach in comparison with Korea and Japan. think Singapore is an exception. Otherwise, if you look at the countries like Indonesia, if you look at countries like Vietnam, they still take a very more security-focused ⁓ approach, especially Vietnam, given their political system, right? So, Meta used to, and I think Meta still have a lot of problems in Vietnam. One of the key issues is about content and moderation, right? There’s a lot of human rights and similar struggles, Thailand too. So those countries, I feel like the sort of the older problems from the social media era was not really solved yet.
And those problems will be brought into the AGI era. And when the government look at the AI, their first question is, okay, so how can I prevent people from using AI to cause any unnecessary trouble, which means like a social instability, right? So that will be the same older problems facing big tech companies. And that tells you a lot when those countries look at AI, they still come from very much a security focused on mindset.
Grace Shao (17:09)
That’s really fascinating because actually when we were just talking about the infrastructure build out on my end, really just I’ve done some research and writing on, you know, the Johor build out and the over capacity with data centers right now. And the unfortunate cause that’s just like the local infrastructure is not able to actually support the the rampant build out. It’s actually affecting the livelihood of people. Right. But your point is really interesting. I didn’t really think about it that way. It’s actually for the from the perspective of these big tech actually.
It’s to prevent bad actors using their technology to actually propel even further, like, you know, bad, intentional, harmful content, right? And then essentially, like you said, cause social unrest that would really be very troublesome for the local government. So I guess from the policy perspective, from the social media era. But what would be something different? What would be something that, you know, big tech will have to start thinking about that they didn’t even have to worry about before.
George Chen (18:03)
Right. Well, you know, as I said, know, lot of the older problems from social media era will remain in the AI era, such as misinformation, know, skin, political speech, you know, especially for countries like Vietnam and Thailand, the real content. It’s always, you know, when I worked at the Meta, you know, those South Eastern countries are always considered as like a high risk countries, you know, when it comes to content policy risks, right?
On the other side, Vietnam, Thailand, Indonesia are much bigger. They are also smart. They also consider AI as opportunity. So they are thinking, how can I use AI to train the next generation of talents, digital talents? Those countries also have relatively younger demographics. So there’s a lot of smart kids who can get on AI and then to learn. So I think that also posed the opportunity for partnership for those American tech companies. Can we do some training program for the purpose to grow the next generation of AI talents in those countries? I think those governments will be very much welcome those initiatives. And this is not just happening in Southeast Asia. You may know somehow I also have my exchange of career experience in Central Asia. I can tell you even countries like Kazakhstan and Uzbekistan trying to focus on talented developments. Because they believe, if you think about learning how to code 10 years ago, this is actually quite an expensive experiment. You need to get professional tutors. You need to get long hours to learn one language. When I grew up, I learned like, I don’t know if you know, we started with Microsoft, the DOS system, and then C12, no one talking about it.
So it took like a year to just get a basic sense of those languages, right? But like AI, you don’t need to learn ⁓ the code. It’s more important for you to understand how to write a proper talk. So those countries like Uzbekistan and Kazakhstan also catch up trying to be the back office for big tech to train, to grow the basic, like the junior engineers. So hopefully they can get some basic work done in those countries for cheap labor cost reasons rather than you need to hire all those engineers in Silicon Valley. And I think that posed the same sort of opportunities for Indonesia, Vietnam, Thailand and other Asian countries.
Grace Shao (20:30)
That’s really interesting. So there’s like a reshuffling of talent and then also like just the talent strategy is actually changing from the social media era or just like the big tech era. I want to kind of look at responsible AI. So we hear the phrase a lot, right? Responsible AI, AI safety from your experience right now.
What does responsible AI actually look like inside of a company and what changes in org charts, KPIs, or decision making when we’re talking about responsible AI? What are the metrics we must track?
George Chen (20:59)
That’s right. Okay, so you mentioned that I wear a lot of hats. You I don’t want to speak like a professor, but I do teach a course at the University of Hong Kong and the Tsinghua University. My course is about digital society and governance. One of the lectures is actually about AI governance for corporates. So responsible AI is a term, you know, very popular, not just in the tech industry, but you now hear more and more just in business in general, right?
It’s, in my view, responsible AI is something like the privacy statement, right? You know, for different companies, you know, when you go to a website now, like the privacy statement already become like the very normal thing, right? You know, when you use a service, you know, have to get, you know, they have to get the user consent first, and they need to tell you, you know, what kind of data they’re collecting for what purpose. That’s the privacy statement. Every website, you will find, you know, a privacy statement. Responsible AI is similar.
So the government is doing their job ⁓ from regulatory perspective, from self regulatory perspective, the government work with NGOs and associations to have an industry code. But for corporates, responsible AI is kind of like the business led principles. I want to use Microsoft as a perfect example. I think Microsoft is leading the way how business can take a more responsible, sustainable approach to AI. Microsoft is responsible for AI. They call it the trustworthy AI, but it’s just the name change, more or less the same. Microsoft very much focused on three pillars, and I believe many other AI type companies focus more or less the same. First is security. You have to have a very secure AI system. That’s the basis. That’s also where the user tries to come from. Second is about safety you talk about online safety, particularly for those more vulnerable groups like children and women, how to address those issues. Again, the same social media problems like harassment, online safety, even suicide prevention, exists, if not get worse. The last one, at least, is privacy. That’s easy to understand. So, safety and security privacy. The three pillars are the key foundations for responsible or trustworthiness or other names. When we talk about the process for big tech or just traditional business like Starbucks, when they want to implement AI in their business, we have a massive means called the privacy by design in the social media era, which means privacy should be the first thing to consider when you develop a product. This is like a rule. ABC like a 101 for any product manager, right? You know, when I worked at the Meta, we always got a reminder like, you know, the engineers, right? It’s not like you have a great product idea and you talk to everyone and finally you think about, okay, I should talk to my privacy lawyer. All right, you should do it the other way around. The first of all, you should talk to is the privacy legal, the privacy team, right? Responsible AI poses a very similar approach. The first thing, when you develop an upgrade or a new service backed by AI, you should think about whether you can tick the three boxes, security, safety, and privacy for the AI services and product you’re going to launch. Microsoft has set a very good example when they’re developing the co-pilot. That’s their AI platform. for you users. So I hope that can give you a very rough sense of what responsible AI is about.
Grace Shao (24:39)
I think what you mentioned just now that stood out to me is that a lot of these big tech companies like Microsoft or Sell, they have very mature, legal, and safety teams in place, right? So it’s much easier for the developers to actually tap into their know-how and their knowledge. And obviously, like you said, an extension of how they use their regular content as well as not just content moderation, but also just product safety. But for startups, I don’t know if you work with them at all or not, but like,
I just the proliferation of AI tools right now, right? It’s like, it’s very, it’s very crazy right now. Basically, like you also kind of hinted at this where like, you know, developing a new product is so much easier than it was say 30 years ago. It’s not only that, like, you know, the language of coding has made it easier, but now we have a jented coding tools, right? So you can have vibe coding, whatnot. How do we actually understand product safety and like responsible AI when we start talking about new products within these startups. And also my question is on a broader like picture, how do we understand responsible AI in a big market like China where a lot of products are consumer facing AI versus maybe the US where it’s a lot more enterprise facing. Can you kind of give us some color on that?
George Chen (25:55)
Right. So first of all, startup, yes, you we do have some startup clients. I’m very glad that the startup clients we work with in the tech sector are very much, you know, either backed by some leading figures in the Central Valley or by global VCs. So I think that they do have, you know, like a stronger internal compliance control. Right. And over the years, I think all the big tech, you from know, met up to Microsoft, you know, to other companies. I think all the classic incidents, know, the lessons, remember, you know, when Mark, when Mark Zuckerberg had to apologize, you know, you know, the Cambridge Analytical incident, right? It feels like a not too far away, you for people who had short memory. I think that those incidents that did, you know, ⁓ serve as very good lessons, you know, for those, you know, I will say like a more US-funded back than startups. I think their goal is clear. If you want to get listed on Nasdaq someday, you’d better do things very right from the beginning. There are some naughty boys, cases from China. You probably noticed there are some AI-cub startups from China.
Like grab the content from Disney, know, Parliament, know, Sony, right, you know, to make those funny, like the AI, you know, effects. But in fact, it was like a serious violation of, you know, IP prototype content, you know, but those start up like, I don’t care. I just like to have fun. Like, let’s see how it goes. then suddenly, you know, they got like a 1 million to 2 million, you know, and then to 10 million users within a week. So, but they’re not going to go far away, right? There’ll be like a long series of this and that. So this is not the right approach. I do think that startups need to be very clear about the boundaries. It’s not like, okay, you are a startup, so you can lower your compliance requirement to do whatever you want. And the end of the day, you need to be responsible, not just responsible AI for the users, you also need to be responsible for your investors, right? So that’s on the on the startup part. In terms of compliance, think the startups in general do pay a lot of attention to compliance with different, especially now AI, as we mentioned, right? If you look at the APAC, there’s no unified approach to AI governance, right? It’s not like EU has AI efforts. So the compliance cost is indeed very high startups. This is the luxury Big Tech have. We just discussed Microsoft as a case study. So Microsoft has pretty decent size of legal team, security team, enforcement team, to support those three pillars, like safety, security, and privacy. But for a startup company, you can imagine they probably only have one legal, one policy manager for everything.
That is it. That is a challenge. And then this is also why a lot of companies complain about very tight regulatory environment in EU because as a startup you don’t want to spend all your money, not even like half of the money as your compliance cost, right? So I always joke with my friends like if you hire more lawyers than engineers for a tech company, I don’t think that’s right. So this is a constant challenge for startup, how to comply, but in the meantime, also keep innovating.
Grace Shao (29:28)
I think that’s really interesting because essentially, like you said, whether it’s a startup or enterprise, in many ways, it’s faced, they’re facing the same issue. But my issue right now with kind of the AI space is actually there’s lack of international standardization, right? So for example, like globally, wherever you go, you can’t really go stab someone. The rules around drugs or even other issues like driving and other safety issues may vary, but there is like a standardized base normalization or what we believe as humans that should not be done, which is essentially don’t kill people. Homicide is illegal anywhere you go, right?
So now with AI, regulation, is that like right now we’re not seeing countries come together and say, this is a sanitized belief that we should just not have. Maybe like you mentioned, child pornography and child safety is something very high on the radar, but even that can be quite subjective from culture to culture. So how do we make of that when we’re going to have AI proliferation across the economy and different touch points in our daily lives?
George Chen (30:33)
That’s a very important, interesting question and point you make. You you reminded me, you know, when I teach my students in the classroom, one of the examples I give them is, you know, I travel a lot, right? So different countries, to different countries, the first question I ask myself, like, which socket do they use for plug, right? And then even in EU, you know, like, well, in the UK, it’s no longer taught on EU.
But even you cross the border sometimes, know, from country to country, you need to, that’s why we always bring a travel adapter, right? So when it comes to AI governance, it’s actually the same problem. You absolutely right in the very spot, EU has the EU AI Act. If you are a startup, you want to expand into EU, no argument, no negotiation, you have to comply with EU AI Act. Plus, several other regulations like the Digital Market Act, the Digital Service Act, then plus GDPR. So to expand into EU is not easy. The compliance cost will be very high. But same thing in APAC. You go to different markets. Indonesia is going to have their own AI regulation versus Singapore, versus other markets. Ideally, the UN should take up a bigger role, a more powerful role to sort of you know, have, you know, control or supervision over like how AI should be used. Right. You know, think about the same question about telephone, you know, when telephone was invented, right? Why, you know, Hong Kong’s, you know, country code is A52, right? Why China is like A6, why US is 001? Because someone made the standard and for telephone code,
That was ITU, the International Telecommunications Union. So some people say we also need someone like ITU. Maybe the UN has an AI panel, but I don’t know how powerful the UN AI panel is. I mean, not to mention that the US government is not really a big fan of UN these days. So I agree, we should have international standards on AI, especially on AI safety as a key part of AI governance. We should have some principles.
So I think this is something all the countries are looking to. We will very soon have the new annual AI summit in India in February in 2026. I think that India also wants to use the AI summit as opportunity to discuss those standard issues. And also to a point, I don’t know how many already realize, actually US and China are not just competing.
In the aspect of AI technologies, like official recognition, deepfake, and other issues, but also compete with each other on AI governance. Basically, the and China are competing, like who’s going to write the rules for AI usage for the next generations. So this is also another flashpoint between the US and China when it comes to technology innovation, not to mention the two countries who are continuing to compete in the aspect of AI technologies, you who’s going to have more faster motors, whether it’s Gemini or DeepSeek in winning the battle. So that would be also a story we watch very closely in terms of competition and struggles.
Grace Shao (33:57)
Yeah, I think it’s also just because the technology is moving so fast right now. It’s really hard for regulators to keep up even domestically in each country at this point. So yeah, I do agree. I think we need some kind of international standardization. I met with Quaishou’s representative a week ago and it was very interesting to hear. They’re very ⁓ focused on the text to image and text to video kind of space. And basically they said in China,
To your point cyber security laws are one of the strictest in the world actually in terms of AI Content AI GC is also one of the strictest in the world She said that actually if you remove the watermark that is actually a criminal offense or like literally you will be like, you know Yeah, it’s quite interesting. And I mean on one hand you think it’s very extreme on the other hand I think it’s very needed right like to make sure that deep fake or the mouth practice or you know, Fabricated content does not spread
George Chen (34:37)
That’s right, yeah.
Grace Shao (34:52)
And kind of lead to the social arrest you mentioned earlier or company disturbance, etc. Or even human to a Harm. Anyway on that note, I want to talk about China. You are interviewed a lot by the media on China US Whether you want to frame as tensions or competition or you know or the race? you know, whatever we want to frame it there is going to be right now two camps essentially, right? ⁓ How do we actually understand the two ecosystems at a high level? Where are the real fault lines? Are they chips, cloud, data, or like you mentioned, regulatory rules? Help us understand the two ecosystems.
George Chen (35:21)
Right. So let’s talk about China. So US and China don’t just compete in technology. US and China also get more more clashes on AI governance in how the way AI should be regulated. US published the AI action plan under the Trump administration. The AI action plan published by the Trump administration is actually already a shift from the AI policy approach taken by President Biden when he was in office. When Biden was in office, it was more like about protection. Biden focused very much on the online safety and this and that. They even set up the US AI Safety Institute. When ⁓ Trump took over, things changed quite differently. Now, Trump is taking American first approach for know, like America’s version of AI innovation, right? Which means like how we can keep American competitive in the aspect of AI technologies. In the meantime, think the Trump administration also want to export the US governance model on AI to the rest of the world, to many of its allies, especially in Asia, you have like Japan, Taiwan, Korea. While China is also trying to influence perhaps mostly global South countries and Bayer Road countries to be more aligned with China’s AI governance ⁓ model. So the two countries are not just competing in technology, but also in the way how AI should be governed.
Grace Shao (37:00)
Think on that note, if you’re a developing country right now, whether you’re in Asia, Africa, Middle East, and you’re listening to pitches from both Washington and Beijing, like you said, essentially they want to capture the rest of the world, what questions should you be asking to avoid being locked into one ecosystem?
George Chen (37:17)
Right, I’m always asked by my friends from global service countries, which side should I take? And my answer is no, you shouldn’t take any side. You should take whatever that fits you to have a sort of combination of the best that you can take from both the US model and also the China model. In some ways, China was quite innovative to solve some unique challenges caused by like a... know, defake and this and that. But in the meantime, the people will say, oh, wow, you know, but you have to sacrifice a lot of, you know, all your privacy, right? You know, even the internet, you you to get on the internet in China, you you must be a real person. know, China has the real ID, you know, policy, right? In Hong Kong, not the same, you you can’t just have, you know, like a, a, a, like a, don’t need to, you know, you have, you know, even for the mobile phone, need to register a number with your real ID. But in the US, this is quite unthinkable. But the real ID approach in Hong Kong and China can certainly strengthen a lot of people’s policy concerns. Versus in the US, everybody can join the party, basically. That will also waste a lot of time. think in China, you will see very much led by companies ⁓ like the traditional BAT and DeepSeek and Huawei to enhance their AI governance through a more company-led plus state-supervised model on AI governance.
Grace Shao (38:48)
I think that’s really interesting. You just mentioned something that struck a chord with me because I was just in Singapore and I was reflecting on how I basically hated my experience there six years ago, seven years ago when I was single without kids because it feels very like, not control, but everything feels very watched and very sterile and you know, your point, everything is very top down. But this time going as a mother of very young children.
George Chen (39:04)
Right.
Grace Shao (39:10)
I loved it. was like, wow, it’s so clean. It’s so safe. I rather give them all my data so they can protect me. They know where to track like ad actors. And I think to your point is very interesting. The idea or the value of Liberty per se may be very different in different cultures and also might change as you go through different phases in your life. So it will be interesting to see how companies or countries choose which ecosystem to join, right? Based on their own.
George Chen (39:17)
Ha ha ha
Grace Shao (39:38)
belief system or value system. I want to ask you, how are the extra controls right now actually affecting companies operating between China and US? Because I know a lot of your clients probably are operating between these two large economies. You sit in Hong Kong and most of your clients, would assume, are actually like MNCs and have some kind of a... You use Hong Kong as some kind of a gateway to intern exit, mainland China, right?
George Chen (39:48)
Mm-hmm.
That’s right. think that the China model so far, the China model is basically a more multi-barad... the more parallelism, right? So like, you know, to work with different countries, more stakeholders approach. This is also what, know, Premier Li Chang, know, when he was in Shanghai, I think in July for the World AI Conference, you he also called AI as a public goods. You know, I find that that concept was quite interesting. Basically, said this is not just something you and I should exclusively hold, right? This is public goods. This is for, you know, like, almost like, you know, this is like for the fate of the whole mankind in the future. So we need to share the success, share in the growth, versus like the American approach is very clear. You know, this is America first, we need to take the lead. And America has always taken the in AI and technology ⁓ innovation. And again, you know, don’t get me wrong. I think both Li Qiang ⁓ as Premier for China and President Trump have their own very good reasons to manage AI in their own ways. One is to keep raising the American flag high and to make yourself a role model, right? The other is to have a more open model everybody can come and share. I think so far the Chinese model perhaps is more appealing to a lot of developing countries, given the-
It’s more like a cost efficiency and a top-down approach also boosts highly efficiency rather than more like a button-up, democratic approach. You need to talk to 10 companies that get alignment, this and that.
Grace Shao (41:39)
Yeah, actually, you just touched on something I was going to ask you. China has pushed up the AI Plus initiative and they were, like you mentioned, Li Qiang and them are just kind of embracing this idea of exporting AI to global south. But beyond the branding, I was going to ask you, do you think it’s actually successful? But it sounds like they are, right? It sounds like the global south is adopting China’s AI ecosystem because it’s more cost efficient, deployable, scalable given that’s open source, open weight, right? I think I want to ask you one last question on this section, is, you comment on China’s AI ecosystem law in the media. What is something that we’re missing here? What are people kind of missing, maybe even in mainstream media that you think is very important for people to know?
George Chen (42:06)
Right. That’s an interesting question. think that the international cooperation part is something I’m quite concerned about. China has a lot of good engineers. Actually, we also saw a lot of engineers coming back to China from the US. However, both sides, the US and China, should talk to each other more to maximize the research capacity for the overall interest of the whole mind can. So far it’s not happening. And then the result is, I also think AI, the reason why AI is so special is I believe AI also touches on ideology, the way how people think about things. So the US right now is very ⁓ US-centric, just focused on their AI. And then China is very much oriental focused, trying to focus on their version. So when the two AI is a basically, you know, a developer like in the parallel approach, you know, you don’t talk to each other and that will result in a more divided world when it comes to content moderation, when it comes to understanding, you know, certain issues, you know, which policy approach you take, you know, to explain a historical event, you know, for example, this, if the two countries, US and China, they don’t talk to each other, it’s not going to be helpful.
For the overall development in R &D. So again, when I teach my course about the YouTube governance, I use the world’s most popular apps as example. Can you believe, it may not be a surprise for you, actually seven out of 10 most popular apps are in English, originally from the US, very much from California. The other three apps are either from China or Singapore, it tells you something. I think when social media companies began to expand into the Asian region, a lot of countries were fearful of the impact that American social media could bring to their markets. They are also talking about the so-called digital colonialism. Which AI you use that will influence your thinking.
So I think in a way, people also need to be mindful whether you are too much into the US model AI, and then that also begin to change the way how you think about things. I actually tested the Chinese AI, and I tried some new features of the AI models. Sorry, I’m trying to think about which model I use. But my point is, the Chinese models are very local, very efficient. For example, when I’m in...
Beijing, right? I’m not going to use Chai Chi Bti, not because of BVPN, but I just find like the DeepSeek, you know, the database they have is more practical and efficient and timely than like say Gemini and Chai Chi Bti 4, right? So if I look for the best noodle restaurant, you know, the DeepSeek in Beijing actually, the answer from DeepSeek, you know, could be much better, more accurate, you know, than Gemini and OpenAI.
Grace Shao (45:17)
That’s really interesting because I think it reminded me of one of the Chinese LLM startups and they said that they’re actually working with local governments in the global south and exactly to your point is that they localize information, localize the culture or language. It goes beyond just the surface language, right? I think that’s really interesting. I wanted to ask you one last question, which is...
What is one differentiated view or non-consensus view you hold? This could be about the AI sphere or it could be about something in life.
George Chen (45:56)
I’m still trying to understand how we should position AI. There is a debate in the industry whether we should position AI as your assistant or more as your partner. I don’t have a clear answer on that. In some cases, I want AI just to be my assistant, which means I tell AI to do what and then you do exactly what I want you to do, right? But...
I also understand if you just position AI as your assistant, that that will also limit the potential of the development capacity of AI. But when you treat AI more as your partner, I’m thinking about one of my favorite movies. I don’t know if you remember, there was a movie called Her. There was an engineer talking to the computer. Scarlett Johansson played the sound part.
Grace Shao (46:41)
Scarlett Johansson, Yeah.
George Chen (46:50)
for the AIs and that was quite a romantic movie, but the ending was not very, it was not a happy ending. So I’m trying to think like if you produce AI as a partner, you can empower AI to do more things, but then, know, whether eventually we will also enter some dangerous territory, you know, to have AI to have, then we need to talk more like ethical issues, right? You treat the AI more as a partner. There were discussions, know, yes, know, AI doesn’t have feeling.
But should we also ask the AI to work for like a nonstop? If you think about human rights, should, if human will work for like 10 hours, 12 hours, you should have to get a break. Why we shouldn’t just keep asking AI all these questions, keep them running the models to get the result. And even AI doesn’t have the touch feeling, does AI have emotional intelligence? I believe at some point that AI will have emotional intelligence. That is to say, if you use AI as sort of a slave.
they will also be unhappy. So back to my point, I don’t have a clear answer, but I’m still wondering which sort of status, like a category, we should put AI into, more as AI assistant or as ⁓ a human car parking.
Grace Shao (47:58)
I think that conversation can like, warrants another conversation on its own, that topic, because I think to your point on the technology aspect, we are seeing a shift from AI just to consumer and just as a chatbot to agent AI, right? So to your point, know, ⁓ AI can actually start completing tasks for you. They can be more proactive, remind you to do things. They are more like a thought partner versus an assistant. But again, to like, you know, to even the conversation we had earlier, it’s like who...
George Chen (48:01)
Ha ha!
Grace Shao (48:26)
Who can play God? Who is to say, where is the line, right? And your 10 to 12 hour work ethic thing is very Chinese and American. Definitely in Europe, people are not working 12 hours a day. That is like a, that is a normal work day for Americans and Chinese. But again, yeah.
George Chen (48:40)
That’s right. You already see the cultural difference here, even for the real world and for the AI in different parts of the world.
Grace Shao (48:46)
Right? So who gets to set the standards? And I think it will become harder and harder. It’s because then it becomes more philosophical and ethical than just, you know, ⁓ practical, which is right now what we’re talking about AI safety is just like, okay, child pornography is fundamentally wrong. Like, homicide videos is not allowed. Don’t create fake videos of people doing fake things. That is very black and white. We can almost just all universally agree.
But when it becomes a cultural, evidently because they’re culture norms, even language norms, societal norms, et cetera, right? Or even each person’s emotional capacity is even different. Then who gets to decide when AI needs to stop, right? That’s definitely like a very interesting topic. And I’ve been having this conversation with friends as well. It’s like, has technology hit a point of actually further development does not progress society as a whole anymore? Or are we still actually benefiting from technological advancements. So anyway, I really, really appreciate that. And I can go on forever. This is an interesting topic. Thank you so much for your time, George. I really, really appreciate your insights and all the expertise and experience you bring to us.
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In this episode of Differentiated Understanding, I talk with James Wang, general partner at deep-tech fund Creative Ventures, author of What You Need to Know About AI: A Primer on Being Human in an Artificially Intelligent World, and writer of the newsletter Weighty Thoughts. James has sat on nearly every side of the table — Bridgewater investor, startup founder/CTO in healthcare, engineer at Google — and now backs “real-world AI” from semiconductors and interconnects to diagnostics and industrial systems.
We start with how the AI investing landscape has evolved since 2016: why “AI” used to be a dirty word in pitch decks, how the post–ChatGPT boom funneled capital into a small set of model companies, and why so many AI startups shot up to tens of millions in ARR only to fall back as incumbents absorbed their features. James explains where he still sees real opportunity — especially in vertical AI built on hard-to-replicate proprietary data — and why moats in healthcare and industrial AI look very different from the “GPT wrapper” era.
From there, we zoom out. We compare China vs. the US on AI pragmatism, industrial policy, and consumer vs. enterprise strengths; unpack the open-source vs. closed-source model debate; and talk about how agentic AI is already furthest along in developer tools. James also breaks down the energy reality of AI: why GPUs turn power into intelligence, how much additional load AI really adds to the grid, and what the Inflation Reduction Act and its partial rollback actually changed (and didn’t) for data centers and renewables.
We close with James’s differentiated view: that over time, AI’s gains will be largely socialized — diffused into everyday life via cheap, ubiquitous models (often running at the edge) rather than captured as persistent monopoly profits by a tiny set of firms.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Topics we covered:
* What “real-world AI” means: interconnects, power, semis, diagnostics, industrial systems
* How AI investing has changed from “don’t say AI” to “everyone is an AI startup”
* Why many high-flying AI startups lacked moats and saw revenue fall back to earth
* The case for vertical AI built on scarce, proprietary data (e.g., medical imaging, acoustics)
* China’s strength in industrial AI and consumer apps vs. the US edge in enterprise SaaS
* Open-source vs. closed-source models, and what really matters for enterprise buyers
* What “agentic AI” actually is, and why dev tools are still the most advanced real use case
* AI’s power appetite, data centers going “behind the meter,” and the limits of US grid politics
* Why James believes most of AI’s value will show up in broad productivity gains, not just in a few mega-caps
AI-generated transcipt
Grace Shao (00:01)
Hey everyone, welcome back to another episode of Differential Understanding. Today, joining me is James Wong. James is a general partner at Creative Ventures, spearheading investments in AI across the stack. He was previously the co-founder and CTO of Lioness Health, and before that he was on the core investment team at Bridgewater Associates. He founded a nonprofit consulting firm specializing in microfinance and had a short stint at Google. I’m very excited to actually have you on today, James.
James Wang (00:33)
Super excited to be here too. Thanks so much, Grace.
Grace Shao (00:36)
James, it’s really great to actually finally meet you, I guess, in person. We were just kind of laughing about this. We’ve talked on and off on Substack on WhatsApp, on email for quite a while now. I’ve been a really big fan of your writing and you are actually one of the first paid, I think, subscribers to my own newsletter, AI Proem as well. So for listeners, his newsletter is called Weighty Thoughts. He writes everything about the startup space, VC, AI, know, FinTech, I think, and know, bigger pictures as well, right? But ⁓ start with James, why don’t you tell us about your day job? What is it that you do when you say you invest in AI? What are you investing in? And what kind of businesses are you looking at these days?
James Wang (01:19)
Yeah, sounds great. yeah, glad to be one of the early subscribers to AI Proem because yeah, as a VC, you have to catch the good thing early. it’s a part of the job there. Yeah. So Creative Ventures is an early stage deep tech fund. So deep tech has gone through quite a few evolutions in terms of what it is or isn’t to people.
For us, it is things with harder science and IP barriers. So that includes things like battery manufacturing platforms that cost a billion dollars a piece, AI diagnostics that are completely end to end with no clinicians in the loop, things like materials discovery platforms using AI. So a lot of these different areas that have gotten pretty exciting with AI as well. think one of the interesting things is deep tech, especially within say like some of the materials discovery spaces, the bio space, like a lot of these areas have accelerated quite a bit with AI’s ⁓ involvement at this point. And there’s a lot of exciting things coming up in those areas.
Grace Shao (02:27)
I think you know beyond a business background which a lot of investors have you actually have a technical background as well ⁓ What do you think that like does that make you? More understanding of the deep tech that you’re looking into or do you have any unique perspective on technology companies when you look at investing in them?
James Wang (02:47)
Yeah, totally. So for us, for our team, actually, I’m one of the few folks without a PhD. So a of the team does actually have that kind of background, which is needed within deep tech ⁓ in large part because it’s you do need to understand how the technology works in order to understand the market that it goes into. That being said, like like most technology startups, the ultimate challenge is finding the right market and scaling.
But if you don’t understand the technology on a base level in terms of what it does, it’s really, really hard to actually figure out how to scale the thing. So a lot of that technical background, especially within these areas is quite critical. And I guess just my opinion as well, like a lot of different asset management areas undergo evolution. VC historically has been one that has allowed a lot of generalism within it just because of the nature of how a lot of the software boom went came up and went through and everything. But our opinion is actually a lot of the investors in this particular area will get more and more niche, especially with AI, which I think we might jump into as well. AI does actually involve and help enable a lot of vertically integrated industries ⁓ in interesting ways, which means that, you end up with investors who get more and more specialized in their areas.
Grace Shao (04:00)
Mm-hmm. How big are these ticket sizes that we’re talking about when you’re investing in and how early are you looking at?
James Wang (04:13)
Yeah, typically speaking for us, we are often the first institutional investor in that being said, some of our companies have five, 10, even like 20 million dollars in non dilutive government grant funding or research funding before we actually invest. So it’s kind of hard to say when you’re trying to pin that down. That being said, yeah, we’re among the first investors in. Usually we invest around a million in terms of initial check size and sort of ramp from there.
Grace Shao (04:28)
I see.
James Wang (04:42)
⁓ And then our companies obviously like as they get larger and larger later on They can have quite a bit of range in terms of like where they end up
Grace Shao (04:52)
I see. I definitely want to double click on the vertical AI space later. But to start with, another personal question I want to touch on is your book. You’ve just launched a new book. It came out in October, I believe, right? It’s called What You Need to Know About AI, A Primer on Being Human in an Artificially Intelligent World. Can you tell us a bit about the book, a preview of like, I mean, the gist of your book or why we should go read your book?
James Wang (05:19)
Yeah, totally. Well, think the most recent thing I can remember was someone told me the other day, think yesterday actually, that this is a great stocking stuffer for all the boomers in your life. I believe that was a compliment. So ultimate, I think so. So the book is actually a end to end. Here’s what you need to know to kind of get up to speed on AI.
Grace Shao (05:33)
It sounds like a compliment.
James Wang (05:44)
⁓ It goes through the history of it. It goes through some of the technical background. ⁓ Not too deep, but then again, like also doesn’t really pull too many punches in terms of like actually getting into the structure of it. And then finally it goes into how it’s being used today and some implications of it coming up. So it’s meant to take you through end to end. ⁓ You know, we have a lot of interesting endorsements of it. Reid Hoffman actually had read through it and gave a great endorsement of it as well and I’ve had both people within the AI business sector. So basically people trying to market AI and push it out into market as well as engineers, both tell me that they’ve learned something from it. So I think actually a lot of people can get something out of it. It’s just different people will find different parts of the book difficult or not. but it does attempt to like step you through it. so that was the aim that I had writing the book and, ⁓ hopefully I achieved it so far. sounds like it, if it’s a boomer, baby boomer stocking stuffer.
Grace Shao (06:44)
I think I need to get a book for myself. Does it overlap with what you write about on Weedy Thoughts or is it a bit different?
James Wang (06:52)
It does, but it ⁓ gets into more depth and basically takes you through A to Z a little bit more. Since I’m sure you know as well, it’s like your experience as well there. It’s like for a substack post, inevitably you leave a lot of things out. You kind of hit the high level, you hit the points, but yeah, ⁓ you can’t make the article 10,000 words long or something like that. On the other hand with a book, you do need to actually bring it from beginning to end.
Grace Shao (07:21)
What inspired you to write the book? Because I’m sure you have a busy day job already.
James Wang (07:24)
Yeah, I mean, the first part of it, ⁓ which is the real part of the the marketing story that I give is that which is actually true as well. But the marketing story I give is I sort of looked at the landscape and generally speaking, there’s a lot of great technical resources. There are actually a lot of great sub stack newsletters on AI. A lot of other good places to dive into to get a sense of what’s going on. The problem is in general with the book landscape, a lot of the stuff has been like productivity, get rich quick, et cetera, sort of things within AI, or somewhat more alarmist or very thesis specific driven. It’s like, ⁓ AI is going to do this thing. It’s going to kill us all. It’s going to take all our jobs. It’s going to revolutionize this. Like they’re pushing an agenda. I didn’t really see anything out in the landscape that takes you from A to Z for people who didn’t actually know what was going on and wanted to get up to speed. So I got kind of tired not being able to recommend a book for all the friends who are like, hey, you know what’s going on with the say hi stuff. I don’t know what’s going on with this. I stuff. Where can I go read a single book to find that out? So that was like a big impetus on why I decided to write the book. And in terms of like how I came to it, the publisher actually found me through my sub stack and kept bugging me to write a book. And eventually when I decided to go on this direction, they were like- Are you sure you don’t want to write another thing about how it’s going to kill us all or something? We think that might come off and fly off the shelves a little bit more or like draw people in a little bit more than a light textbook. But nonetheless, it has actually got number one in lot of categories on Amazon for a couple of weeks now. So I think it’s working so far, which I’m happy about.
Grace Shao (09:13)
I think that’s a really pragmatic way of approaching it. like, your point, I think I’ve also noticed in just the AI sphere in general, there’s like kind of the, you know, the investors are talking about money. What’s the return? What’s the return? It’s only about monetary return, right? The tech people, when you speak to them, sometimes frankly, they’re a bit ignorant to the societal implications or they’re not, most of them, let’s just say, people are not evil intrinsically. They just think, okay, tech acceleration is a fundamental goal for them.
And I think sometimes to put the blinders on and they forget about the potential implications of society and the environment on, you know, shifts in like, you know, even dynamics between people and countries. And then like you said, unfortunately, I think the people who really try to bring the awareness and be mindful sometimes can go too extreme. And then what happens is like, let’s reject it. But the reality kind of sits somewhere in between where like you can’t really reject a technology once it’s out of the Pandora box, right?
And then you can’t really only look at the value creation in terms of monetary terms. And you can’t really say, OK, let’s only focus on technology, but not think about all the other consequences. So I think to start our interview, let’s really talk about your philosophical view on this. I think your book really ⁓ resonates with me. What I write about also is trying to help people understand, OK, these are the business implications. This is where the return will be. This is what will be.
This is how the technology change your interactions with each other. But it’s not like you can’t approach it mindfully, right? So I’ll throw it back to you. How do we understand the relationships between if you have to simplify the three kind of caps that we see right now?
James Wang (10:54)
Yeah, I mean, in terms of camps, mean, there’s definitely the I mean, it’s been interesting, right? Because for a while you had did have a group that basically said AI is just a fad. It’s not going to actually do anything. Ultimately, it doesn’t matter. Blah, blah. Not a big deal. You have another group that’s basically like, AI is the AI is going to either kill us or AI is going to take off in terms of singularity. And we’ll have a post. ⁓
We’ll have post-scarcity utopia where everyone will have UBI and AI will do everything for us kind of thing. ⁓ So between those two extremes, I mean, you do also have people who are like, yeah, this is a great business opportunity. We’ll go after it. It has some aspect of all of these things. And like you’re saying, from a societal perspective, I think a lot of the people who boost AI quite a bit, ⁓
Don’t take into account. Yes, there’s going to be disruption. mean, even if you look at the Internet boom, which I think at this point, people have ⁓ misremembered certain aspects of it because it came and went and a lot of our economy has been restructured around it. AI is the same way. It’s going to create disruptions. It’s going to create winners and losers, but it’s going to also help accelerate productivity and do a lot of good in the world, too, like most technologies have ⁓ in the entirety of history.
So it’s a big part of just needing to understand what it can do, what it can’t do, and really where we will see the benefits come out. Because I think otherwise, if you’re just very much on the utopian or doomer perspective, you lose the reality of what ⁓ AI actually is, which is a tool, and what the capabilities of that tool is.
Actually, in terms of this, I think China currently in terms of Chinese AI, which we’ll probably get into and Chinese AI companies have generally had a more pragmatic view of this. ⁓ I’ve had a lot of conversations in Silicon Valley, including with different folks at the model companies, where some of the goal and some of the ultimate aim was, hey, we’re going to get to AGI and then we win. And then it’s all it’s all done and that like, we don’t have to worry about anything else.
I think a lot of that particular mindset viewpoint folly has sort of gone away in the past year or two. But even so, like it tells you something about like the way that a lot of people are looking at this, which is almost pseudo religious.
Grace Shao (13:27)
Yeah, I think definitely to your point, the the caps kind of become bit of like cults themselves as well. It’s even when you cover this space, it’s interesting to meet people who are like all or nothing, very much all or nothing, right? They’re either like, let’s go all in and ignore all the noise and all the issues or go like, let’s reject this. This is just ⁓ inherently evil, which to your point, like all technology disrupts what we know, but
Like you can’t reject it, right? Okay. I think moving on from that, I want to talk about investing in AI. You’ve been investing AI since the early 2020s. I wouldn’t say it’s earliest, but you’ve been in space for longer than most people where, you know, they really jumped in after the chat GPT moment in 2022. So how has that relationship between AI companies and capital change? Like, we’re now hearing a lot of buzzwords. Is this a bubble? Is this like, it’s going to flop? Like, where are you seeing the market kind of like
Where is it at right now? And has that relationship between the founders and the investors changed over the last few years?
James Wang (14:35)
Yeah, it’s an interesting question because yeah, when we had started out, and that was 2016, AI was actually kind of a dirty word just because whenever someone tried to throw AI at something, it was like, yeah, this is kind of scammy. It probably isn’t going to actually work. So you literally had different startup pitches pull AI out of their pitch and basically say, no, no, it’s just statistics. Maybe it’s machine learning, but it’s really just statistics and stuff like that.
So, mean, the way and the evolution of it like changed quite a bit. I think around 2020 was when it started to get a little bit more accepted. And we started to see like certain pitches where it’s like, hey, look, we’re going to do AI for movie studios. And really what we want is to do motion capture for this or something like this. Actually, I think I saw two or three startups around that period trying to do this. And what you really want to do is like, you really want to fund us. We’re going to gather a ton of data.
And then we’re going to train a huge model and then we’re going to win the market. So that was actually kind of a popular thing at that particular point in time, which also ended up becoming unpopular because it ended up not working. but it was post yeah, like some getting some towards the chat, GPT moment that suddenly like everyone knew about AI. took off. A lot of people started putting money into the LLM company, model companies, lot of the companies adjacent to it. And at this point now, I mean, it’s.
Interesting, right? Because I think if you look at headlines, would think anything that has AI in its name instantly gets a ton of funding. But I can tell you just being on the ground, that’s actually not hugely the case. Private markets and VC have still been somewhat more sluggish ⁓ since 2022, since interest rates rose, and since there haven’t been super significant exits across the board, which means a lot of that capital market has been frozen.
If you look at the stats, actually for all of the startup funding and AI funding, a huge proportion of it is actually just the giant model companies having mega round after mega round, or some of the second tier model companies who’ve also had a bunch of mega rounds. We haven’t actually seen like tons of AI company like Dragon, a ton of startup financing. ⁓ Even so, like
AI currently still is the hot thing. So if you’re trying to raise money, especially in Silicon Valley, you generally will probably try to put some sort of AI pitch into your thing, whether or not it actually makes sense or not.
Grace Shao (17:07)
Yeah, I was just gonna say it’s really funny when you said like it used to be a dirty word, whereas now you meet any company, like they could be selling chocolate bars and they’d be like, we’re AI, we’re AI company. They’re really trying to use AI as like the kind of buzzword to hook people in. And it’s interesting to hear from your perspective that actually AI startups are not getting a lot of funding, right? Because in China at least, what I cover in this part of the world,
There’s already the jokes about the last round of AI startups dying out already, like phasing out recently. Yeah, so I don’t know. Is that happening in SF as well?
James Wang (17:45)
Yeah, so it’s interesting. maybe I’ll make the amendment that it’s not AI startups are not getting a lot of funding. AI startups are getting more funding than other types of startups. So one of our companies actually just recently was told that, hey, you’re actually an AI startup, not a health care startup. They’re definitely both. But that was their greatest compliment because that meant that the investor was actually interested in putting money into them. So it’s like it tells you.
Grace Shao (18:12)
That’s so funny.
James Wang (18:14)
Yeah, it tells you something about the landscape.
Grace Shao (18:14)
Yeah.
James Wang (18:15)
⁓ yeah, AI companies get more funding. But yeah, it’s definitely not as much of a bonanza ⁓ as you might think from the outside just by just the numbers thrown at the screen because a lot of the big companies are absorbing. But it is definitely the case that I’ve talked with a couple of other investors who’ve told me about some of the revenue numbers for some of their companies. As much as the revenue numbers shot up, ⁓
Grace Shao (18:27)
Yeah, yeah.
Mm-hmm.
James Wang (18:43)
Let’s see, I’ll obfuscate what this specific company is, but there’s one company that I know of that basically was like shot up to 20, 30, 50 million in ARR in a very short period of time. And the investor who I was talking to was saying, yeah, they’re definitely going to get to 100, 120, whatever it is. The last time I checked in, I think they had dropped back down to 20 and maybe stabilizing down towards 10. So in terms of AI startups dying, the interesting thing is like
A lot of these startups don’t actually have moats. ⁓ Whether or not it’s like some random model company that likely isn’t ever going to get off the ground in terms of having enough compute resources or whatever, or a GPT wrapper, which has become a dirty word or had become a dirty, like derogatory, like phrase to say to some of these companies that just wrap their product around like a chat, GPT API. A lot of these companies don’t have any barriers to entry.
So we’re already seeing them shoot very high up because their products are actually useful. And we can get into that. A lot of these are like programming developer tools, agentic ish tools within developer realm. They go up very quickly. They’re actually quite useful, but then everyone else can utilize, everyone else can build the similar kind of thing very quickly or
Say as Codex came out from ChatGBT or as Cloud Code got better and better and added more capabilities, you have a lot of the big model companies themselves end up just incorporating the functionality that these developer tool AI companies tried to put in, but now they’re obsolete.
Grace Shao (20:24)
Yeah, I think that’s something like I’ve been writing about for the Chinese tech space as well, right? The incumbents end of day have a distribution and what they call the flywheel effect, right? I used to hate the word because I think it sounds really silly, but now I think it really makes sense, right? Like in this case of AI, it’s like if it’s not like we have a new device that we’re interacting with it, so whoever already had existing reach on these operating systems can easily basically just swallow another business, like a newcomer.
and just create a function. And to your point on the coding, the agentic side, I know like Alibaba just came out with ⁓ Codar, who I interviewed a while back. ⁓ ByteDance has their similar tool. Obviously Cursor is still the leader globally right now in terms of capability. ultimately, if one day they all reach a similar, I guess, efficiency or user experience, then if you’re already using Alibaba Cloud and you’re already using their like blah, blah, blah service,
you’re already buying your API there, then why wouldn’t you just use your tool, right? I’m sure it’s the same in the US, like the big players just kind of capture all at this point. ⁓ I want to talk about creative ventures specifically. You guys say you invest in real world AI, right? ⁓ That’s really much like you kind of even touched on healthcare, robotics, manufacturing. It’s maybe less about like the consumer side of things, right? What are exciting businesses you’re seeing and you think are being overlooked right now?
James Wang (21:52)
Yeah, I mean, think two areas. One is there are still a lot of interesting things within. For us, real world AI does actually include things like interconnect companies. It includes power management, storage. It includes like different like semiconductor based companies or semiconductor tool companies. So there’s actually a lot of interesting things going on in that realm. It has been a, for example, with like optical interconnects, optical switches, these other things. That’s been a
place where the semiconductor industry has been interested in going for years. And there’s been roadmaps and industry like things talking about like how we need to go that direction. But ultimately, no one actually ever moved because while we have an existing business, it costs time, it costs money to actually move into that. But the interesting thing with the AI boom has been, OK, all of a sudden there is an impetus to actually start adopting a lot of these technologies with some of the optical interconnects and whatnot.
And there’s been actually some large exits within the space even recently. So that’s an interesting area to us still. And it’s an area that most investors have still shied away from because there’s still been a historical wariness, especially within VC towards hardware, which is ironic since that’s actually where Silicon Valley started in terms of VC. But on the other side, too, there’s a lot of stuff within health care that has been something that we’ve been pretty excited about, at least in the US. Medtech and health care has gone through
Quite a few years now actually of kind of a funding winter where a lot of well-known health care companies, digital health companies just didn’t do very well. A lot of them also tanked on the public market. So it’s just been a super unpopular area for investors. But some of the most interesting and exciting things that I’ve seen within AI have been within the healthcare sector. There’s some that are basically like healthcare productivity optimization.
One of our companies, not to talk up our book too much, but one of our companies is currently the only ⁓ and first and only currently end to end AI diagnostic for them. They’re starting in lung disease, but essentially they’re a diagnostic tool that now you press a button. It sends off the scans. It comes back and gives a diagnostic and gets paid reimbursed by Medicare and all the private insurers. Like there’s no clinician, no technician, nothing in the loop, which actually doesn’t just
like increase margins to software like levels is actually insane in the healthcare sector because there’s just so much red tape. There’s so much red, so many regulatory barriers. There’s so many like pieces that can go wrong and thus you need to check and thus you need to have all these other layers that if a piece of software can actually take all that away and be FDA approved, that’s actually a massive productivity improvement in the space. Again, ours is currently the first and only, but I don’t expect it’s going to stay the only one there’s going to be a lot of really interesting things happening, especially within healthcare and biotech.
Grace Shao (24:51)
It’s interesting because I was just listening to a podcast with a 16 C’s podcast. They’re saying that actually a lot of biotech and med tech companies are routing their trials actually in China, just given that there’s less red tape around a lot of these issues. And I met with a company in Singapore just last week. They are one of the leading AI companies actually using AI to do clinical research and trial a clinical trials. And it’s really interesting. Like you said, AI is advanced enough in some ways that they can actually guarantee there are no issues in these kind of like more tedious work or knowledge work that it doesn’t really need that much human judgment and then the efficiency gain is crazy. So that’s an interesting space. think you’re right. And I think it’s going to blow up not just in the US, but also maybe in China space as well. ⁓ I want to ask you on China, on China AI, open source, closed source, that’s the big topic, right? It’s a whole China’s embracing open source.
The US may be still leading on the closed source models. How do these choices really actually affect the products and the margins ⁓ when you’re looking at these companies? there’s a lot of conversation about their performance, about deployment, but what about when it comes to actual nitty-gritty implementation for the companies? What does it really mean?
James Wang (26:11)
Yeah, I mean, the way that the Chinese ecosystem and the US ecosystem, it’s partially just path dependent. They’ve evolved in very different ways. In the US, you still have a lot of API or subscription usage, like specifically, you know, open AI and anthropic and Google sticking Gemini in essentially every single productivity tool that they own. ⁓ So the way that that market works is you have a lot of paid usage go out there like they wrap, they do GPT wrappers and whatnot. And because China ⁓ was later to some of the, to some of this in terms of like big breakout, essentially some of the open weight, open source stuff helped ⁓ spur adoption. So for some of the people who do not want to simply pay for chat, GBT or Anthropics API and just wrap their stuff around it and want to control some of their own destiny.
It’s great to have something like Quen that you can basically fine tune. can locally host. You can locally host DeepSeq. You might not choose to. Ultimately, you might go to a number of different providers who all allow you to have it available there. But nonetheless, you basically have an easier way of saying that you won’t have lock-in. You’ll be able to use this. You’ll be able to go out there and...
Uh, go out there and integrate it. So it is a, again, a little bit of path dependency there. It’s a little bit catching up in, from that perspective as a whole, ultimately in the longterm, I do think like some of the open weight stuff probably does make sense for the same reason. Open source made sense within a lot of the software ecosystem. It does actually spur a lot of enterprise uptake. can pay for support and other things around it. And the bigger thing will ultimately be, can you sell it faster? One of the things about SaaS, so software as a service, has always been, there’s no real barrier to entry for you switching to someone else, except for the fact that I made, especially for enterprise SaaS, an enterprise decision, and I don’t want to switch away from your product now there’s nothing really at the end of the day that makes Salesforce versus some other CRM versus some other productivity tool that different from one another. And at the end of the day, for a lot of the LLMs, especially as we start hitting plateaus in noticeable performance improvement for people. They might become quite interchangeable in which case it becomes similar to a SaaS decision. Do I want to choose the closed source one where
I will have to pay for it forever and also potentially have it go down and only have a single source vendor. Or am I going to take the open weight, open source version where maybe I’ll still pay for it to be hosted, but I can always be comforted that I can always like take it, roll it, and put it in my own infrastructure as well.
Grace Shao (29:15)
Yeah, I think right now where we’re at as the models, their own performance are getting closer and closer and like the gap is not as wide anymore. I see your point. It becomes like an infrastructure. And then I guess for enterprises, biggest issue or the hurdle is really the switching cost of like the bureaucratic switching costs. It’s like going through the legal work internally, making sure all your departments are upgraded the same way that that becomes a switching cost. So then
I guess incumbents still have the advantage once they’re like chosen as the default provider, they get to kind of own that space, right? ⁓ I want to talk about agents. You kind of mentioned earlier, like enterprises like Google are essentially plugging in Gemini into every single productivity tool. And right now we’re hearing a lot about agentic AI, how that’s going to even increase the capabilities of these productivity tools even further. How do we understand
James Wang (29:53)
Yeah.
Grace Shao (30:13)
what even is a gendered AI right now. I think a lot of people still think of AI as just chat GPT chatbots.
James Wang (30:20)
Yeah, I mean, agentic AI became a big buzzword. personally, my personal take, I’ll define it a second, but my personal take is eventually agentic as a term will go away. And we’re just going to say AI because all the big model companies are also going that direction in being agentic. Now, agentic, what does it mean? Well, definitions vary, but at the end of the day, it’s, it doesn’t just chat with you.
It goes and does something. So instead of I plug into chat, GPT say, Hey, where would be a good place to go on vacation? I would tell the agent, okay, I want to go on vacation. helps me research, but then it also helps me book the tickets, the hotel and like give me like the roadmap and plan and stuff like that and do the things for me. So it’s that layer of being able to interact with the world, whether it’s like true real world or whether it’s like digital world in terms of booking stuff, it can actually do things for you. Now, why, why I say it’s eventually just all going to be agentic, in which case we’re stopped going to stop saying agentic. It’s because the direction of where the model companies have gone is this direction. Uh, as some of the performance differences have disappeared, they’ve implemented more and more agentic tools. would say the most advanced agentic area, even though we don’t usually term it that way, is a lot of the developer tools. Ultimately, at the end of the day, for all the developer tools, they will take your input and they will make changes on your machine, on your code, and commit it. So at the end of the day, it’s doing things.
James Wang (32:19)
So at the end of the day, in terms of these agentic tools, where developer tools have been the most advanced because they actually go out there and make changes on your machine, your code, commit it, ⁓ all the different model companies have been rolling out more and more tools that specifically are helping you do things in the real world. As you stop having as much difference in how well they chat with you, there’s going to need to be other differentiation. And the place where they’re going, where there’s lower hanging fruit, is being able to actually implement and do things for you. that’s why I think agentic is interesting. Agentic is actually going to be big, but it’s just not that interesting of a term because ultimately most all the AI companies will likely go into it and implement it with their models.
Grace Shao (33:07)
You’re right, because I think even just this week Deep announced a new like v3 too and they’re saying oh our capabilities are really focused on a genetic AI and every single model is kind of coming out say the same thing and then this is a bit random but it reminded me of this like funny thing was growing up my friend who’s German descent one day asked me she said grace in your household do you guys say eating Chinese food Chinese food do you call Chinese food Chinese food I said no we just say we’re gonna eat food tonight you know and because he normalized and that’s default thing then you wouldn’t really like actually add these prefaced ⁓ adjectives, right? So I get your point about that. ⁓ What kind of agentic products are actually good right now, actually on this point? While models are all becoming more agentic, what actually is it being used in right now? And what tools are actually proving themselves to be really capable and productivity and enforcing?
James Wang (34:03)
Yeah, I mean, I’ve seen various attempts to do things like ⁓ shopping aids, ⁓ different things with, yes, travel booking, concierge services, ⁓ email responses, bot, chat bot, like customer service ticket, chat bot kind of things. I would still say like among these different things, the most advanced is actually still the developer tools ⁓ ultimately.
Grace Shao (34:07)
Mm.
James Wang (34:30)
⁓ it’s close to the companies. It’s close to the people creating it. It’s right now the most advanced area that I see. ⁓ but there’s been a lot of experiments in many of these different areas and they are starting to get better and better and work better and better. So I do actually expect for a lot of these different things where, especially where I guess the framework that I would put on it is if the agentic application is low enough stakes from the perspective of there is a human in the loop that will check it at some point, like, hey, I’m going to book your vacation. Here’s all the pieces of booking your vacation. And you look at it and go, yes, you are correct. I am going to Athens, Greece, not Athens, Georgia, and something like that. If there’s a human in the loop and something where there’s a check, ⁓ these are actually applications that the AI can do quite well and is actually something that’s very implementable currently. So that’s kind of the layer that I put on it. But yeah, like some of these areas are getting quite advanced.
Grace Shao (35:32)
Yeah, like you said, you need to have that human verification. It reminded me of that new show. It was like a silly rom-com. It’s like they bring these women to Paris, but it’s actually like Paris, Texas, and everyone get off the flight and they’re like, my God, this is not the Paris I imagined. Okay, I wanna kinda go into China a little bit. Like I said, you were one of the first paid subscribers to my newsletter and I very much focus on the China space, although I do cover a bit of APAC and different companies as well. What piqued your interest in China AI? Because it’s not like you actually directly invest in China AI, right? So tell us a bit about that.
James Wang (36:11)
Yeah, we’re not able to for various ⁓ investment restrictions, some of our investors and whatnot. But at the end of the day, ⁓ it’s a global market. China is a huge market and China also has a lot of talent within it. It’s kind of funny because it’s like, why was I interested, for example, with your newsletter? ⁓ One, you write well, so there’s that. But also it’s just like having the perspective within the market is super important.
So I actually still have a lot of conversations with Chinese companies. They know I can’t invest, but they’re always kind of interested in also learning about what’s happening in Silicon Valley, et cetera. So I keep a pulse on the Chinese market that way. And that helps inform my decisions, but also my understanding of like, what does the landscape look like in the U S it’s both compare and contrast, but it’s also thinking longer term. What’s going to happen as all of these companies go more and more global.
whether or not they’re competing directly in China or the US, you’re going to encounter each other in the rest of the world, right? So there’s a question in terms of that. As for why, there’s also not very many good sources on China. I think you covered this in some of your articles about some of your own personal history, Grace, but it’s just like, even recently when I was trying to prepare a presentation for talking to some folks about some of the things happening in China where AI is being pushed.
especially by the state into a lot of areas like insurance and whatnot. I was trying to Google like what’s going on in China with that, like with just like very simple terms. And really what came up for me was like New York Times articles about Chinese surveillance state is China like doing these things to the Uyghurs is like what it’s like all of these different things that were obviously had its own like bias, let’s say. ⁓ And at the end of the day, it’s like China.
Grace Shao (37:36)
Hmm.
James Wang (38:02)
especially for the West has been a little bit of a cipher. It’s either can’t innovate at all and only copies things, or it’s like the crazy, huge country that suddenly like will be able to overtake everyone and like whatever it is. It’s the country that who’s the state dictates everything and thus has like total control and everything. At the same time, it’s like, it’s like a super like, like, you know, lot of the private sector stuff does things, but also like the states can’t seem to like do anything right. And then there’s corruption and ⁓ rockets, rocket fuel being used in hot pot or whatever. And the reality is it’s like, it’s all of these things together. Right. But a lot of the way that the media portrays it is fairly biased in terms of that. So it’s always very useful. And I’d say critical for investors in any part of the world to have a pulse on definitely the two biggest economies in the world. Right.
Grace Shao (38:58)
Yeah, I think I can go on forever about the media coverage. I wouldn’t even say it’s biased. I would just say the media business model itself actually awards, you know, clicks and attention and in this time and age and what gets attention, the joke amongst a lot of like I expect journalists in the APAC region, it would say it’s big China, bad China, weird China. So I was like, my God, it’s so many people. It’s so big, weird kind of, my god, they eat dogs, which like honestly, majority of people don’t, but sure, I’m sure some certain small segments of people do have weird diets, right? And then it’s like, like bad, bad, bad, like it’s so bad. So I think that gets the clicks, but I do get your point, you know, not to kind of bring it back to myself too much, but it really is why I started writing about it. was like, there should be a nuance understanding what’s happening, especially in the business space when it’s sometimes not really relevant to what we just talked about, the big, bad and weird. It’s just innovation and business. So I want to bring it back to that. lot of people are talking about China being very, very strong and in industrial planning, right? I think this is something that’s all of a sudden for some reason, making headlines all over the U S and last like three months, whereas like industrial planning didn’t come out three months ago. They they’ve been around for the last 30 years, really.
From your investor lens, where do you see China moving the fastest? it like only sectors within industrial policy support, like the EVs and the hardware and the robotics? Or do you think China actually has its own mayor and certain sectors are being overlooked? And in kind of comparing that to where you’re seeing the US in terms of the companies you’re investing in, what are things you can actually learn from? I think we can talk about that a bit.
James Wang (40:44)
Yeah, I mean, in terms of China, there’s definitely a strong advantage in some of those physical industrial areas, ⁓ including say industrial AI, because it doesn’t exist in the US. The US doesn’t really build that many things anymore. It’s really hard to actually get any sort of manufacturing up. I can tell stories in terms of lioness. I can tell stories in terms of some of the med tech companies have helped try to like figure out where to do manufacturing. Essentially, you can’t do it in the US.
So all of those sectors and areas ultimately do end up being a very strong advantage for China because it exists there and it doesn’t exist here. In terms of like other things that are overlooked, mean, China and actually Asia in general has had an interesting brilliance within consumer, like the consumer sort of trends there, the apps, the like different ways that, yeah, sure. Like WeChat and other things like.
Overall, like China has a much more interesting grasp of like some of the consumer landscape than the US has. I think the, the, I wouldn’t say it’s the, it is, it’s like the stereotype is essentially US companies are the ones that do enterprise SaaS. And the other side of it, which isn’t really spoken at least around here is actually China and actually a lot of Asia is really good at consumer.
A larger consumer market, maybe you can argue it’s like some aspects of that, but maybe you can argue it’s some aspect of taste as well. That may be changing ⁓ over time, especially as like the two markets are more and more divorced from each other. ⁓ Ultimately, the U.S. will probably have its own like consumer ecosystem because it’s divorced away from some of the Asian companies in China in particular. China will get separated from like the enterprise sass in the U.S., in which case there has to be its own stack.
So maybe that will change over time, but there definitely is like sort of a strong, I wouldn’t say internal cult. I wouldn’t say like cultural from a cultural perspective, but more like cultural from there have been entrepreneurs, successful tech companies and sort of playbooks on like, how do you do this? That are much more mature, say in like the Chinese ecosystem than in the U S ecosystem. Well, you know, the last big consumer app was Snapchat and before that, you know, Instagram and Facebook rewinding to the dinosaurs.
Grace Shao (43:06)
Yeah, I think that’s interesting. And I think I’ve been hearing more from founders in AIPAC, not just China, but they’re saying with AI, they actually think there’ll be more opportunities in the enterprise space. And the reasoning is because a lot of the reasons why China or South Korea, a lot of these countries at that time, I would say in the 90s, did not want to adopt a lot of SaaS from the US was actually because they frankly didn’t even have the infrastructure in place as in you know internet was not that like you know, you took What’s the word for it ubiquitous and then it was like they don’t have the ability to actually even Serve the you know the need and then on top of that It’s not just China that has a lot of SOEs actually a lot of Asian countries have a lot of state majority companies and I think people again in the West might not realize that
And because of that, there’s actually a lot of concern on data privacy issues. And they’d rather have maybe even a shittier quality product than actually jeopardize their data being ⁓ taken by someone else, a third party. So that also goes back to why a lot of companies now in Asia actually want to adopt open source AI versus sending their data across the world. Not quite literally, but giving that re-access to like say a chat jpg or a google gemini so i think there’s a lot of reasons why people might find more use cases of sas ai in asia now ⁓ and they might actually you might see more entrepreneurs in this space popping up ⁓ i think on that i want to ask a question on vertical ai i think we kind of touched on healthcare right now we’re talking about there’s a potential growth in enterprise softwares are ai empowered
How do we understand vertical AI? Will they still basically have to rely on the incumbents ⁓ infrastructure to build their tools? should we actually kind of see them grow out? should they be compared to the Googles and the Microsofts of the world? Or should they have their own kind of racetrack themselves?
James Wang (45:19)
I think it’s more their own racetrack because it’s kind of a very different ecosystem based on how AI is evolving. So I think the first layer to talk about is just do any of the LLM model companies have a strong, durable advantage outside of, know, OpenAI and chat GBT has a big brand. Google has a lot of distribution. Alibaba has a lot of distribution. It’s like, is their durable advantage? All of their compute tends to be the same.
You know, they’re using the same GPUs. They’re mostly still on CUDA. Maybe it’s moving a little bit, but they’re mostly still on Nvidia GPUs. The models are basically the same. They’re all transformer based models, essentially. And their data at the end of the day, scraped from the internet, is largely the same. Yeah, maybe it varies a little bit, but it’s largely the same. They’re ultimately going to be very, very similar. That just doesn’t give you a lot of differentiation across that. that goes towards like, well, ultimately it might be capped in terms of how different these things are.
That goes into the difference between that and the internet, right? The internet was very much an aggregated landscape where it’s like everything’s on the internet. You can go find, like go through to Google, whatever, find whatever you’re looking for. You can go to centralized marketplaces, you know, how about like Amazon, whatever you can find your things. And ultimately like it very much like put people into the same place.
What AI is doing, interestingly, is if you think about where there’s actual durable advantage, it’s probably still the computers largely going to be the same. The models in terms of deep learning models, whether they’re transformers or not, are probably also going to be fairly similar. The difference is going to be in the proprietary data. And once you actually get down to the proprietary data level, we’re not just talking about, within your individual corporation, you have your OK.
Internal documents or whatever fine. That’s like one thing Maybe you can still use like chat GBT or like one or whatever like some sort of open source LLM But what if you have raw acoustic data from ultrasounds to be able to detect liver disease? You’re not gonna be able to put that into chat GBT at the same time like that data Right now with the current models and compute you don’t actually need that sophisticated of a model
or even that much compute to make it actually do something really interesting with where AI has advanced to at this point. The same has happened with a lot of the drug discovery area, material science area, industrial AI, which again, China has an advantage in terms of this with their actual like running operations and data gathering exercises there. But that’s where the vertical AI comes into play. If you essentially have the ability to have this proprietary data that’s very expensive and difficult to generate,
Grace Shao (47:40)
Yeah.
James Wang (48:09)
that you have a great proprietary source for, you can build a durable advantage with your specific models there. So that’s where we’re seeing a lot of split in this area. And the way that this will work is less like aggregation, like the horizontal aggregation of search engines or marketplaces. Instead, what you have is a lot of vertical productivity, where you can build.
Like say for drug discovery, can very easily imagine where if you can make a huge amount of productivity increase in drug discovery, that is a massive market, even if you stay just there forever.
Grace Shao (48:46)
Yeah, and it’s not like these big tech incumbents are going to move into that space. Frankly, it’s not it’s not just like your point. It’s not even just having the money to buy these data. It’s also having the know-how and the like decades of, you know, data gathering that you had to collect. And you can’t really get that immediately right now, right? From the market, from just like the public market or anything. It’s very different from the data you’re scraping from the Internet. Yeah.
James Wang (49:09)
They also do have their own flywheels that prevent followership. So the flywheel is specifically this. You get the data when it’s cheap because no one thinks it’s valuable. We have one company that literally did this in terms of raw acoustic data. No one usually keeps raw ultrasound data. You usually just have the images, if that, that is kept. No one keeps the raw ultrasound. They bought a lot of it.
Afterwards, in terms of other AI companies that come, they go, ⁓ actually, this stuff is super useful. We didn’t realize that they also approach hospitals and say, let’s buy this. The hospitals wise up, right? They’re like, ⁓ this is actually useful. We’re not going to sell you it for next to nothing. We’re going to sell it to you for a lot of money. In the meantime, the other AI company has been using this data to generate revenue, to do these things, to get to a certain level of quality, such that now it’s like, the gap is getting bigger and bigger. The data is getting more and more expensive because it’s becoming like clear that it’s valuable. At the same time, the level of productivity, the level of quality of your AI is likely going to lag the incumbent that already gathered a bunch of data and is generating data at the same time and continuing to improve the model. So they have their own flywheel effects with these as well.
Grace Shao (50:24)
I see, see. And I think, okay, I don’t know if this is too small of a use case, but I can already imagine, like I just had a baby, right? And then, you know, we went through the private clinic kind of system in Hong Kong where you basically get, they know you have corporate insurance, so they just like make you pay it a ton. And you go in every month, which is absolutely ridiculous. But at same time, when you are a mother, you want to check in and see if there’s anything that needs to be, you know, there’s any flaring issues. Whereas I know in the public sector, if you go to the public system,
You don’t get like ultrasounds until you’re what, like three months into your pregnancy, 12 weeks in. You don’t get checked very regularly. It’s usually every trimester maybe once or twice max. ⁓ So yeah, like for something that’s not simple, but as predictable as like a healthy pregnancy, I can imagine if you have an AI telling the mom and you just pay like 20 bucks a month, like everything looks normal, everything looks fine.
I can totally imagine that being quite useful for the general mass. if they flare up anything like glaring, that’s something that needs more attention, you can then go into a medical staff’s office for proper checkup.
James Wang (51:27)
Totally.
Right. Well, for one, congratulations. But for two, there’s all there’s so think about it. So one of our companies actually has a product like this, not for the consumer. But ⁓ I’m sure after the baby was born, the doctor did a child hip dysplasia check. So basically checking whether or not the child has this specific condition where if you catch it early, all you need to do is it’s
Grace Shao (51:41)
Thank you.
Mm-hmm.
James Wang (52:07)
not all you need to do. It’s kind of annoying. You do have to put braces on the child and everything, but it fixes through a few months the problem for the child’s entire life. So it’s very worth it versus having a condition the child’s entire life. One of our companies, ultrasound company has an AI. Basically their end to end product is to do a quick sweep with the ultrasound where it tells you hip dysplasia. Yes, no. So in terms of that,
Grace Shao (52:29)
Mm-hmm.
Yeah, actually, my baby, we had to go through for that and you have to go to like you have to wait like two weeks for the specialist to check you and the specialist you have to wait hours in a clinic like it’s a long tedious process and it’s expensive. So I can imagine this just being lot more affordable and you can actually deploy it to the mass like across like mass market a lot faster.
James Wang (52:46)
Yes. Well, even with that, it’s just like, again, taking it from like a, I don’t know, evil commercial VC hat, but not really. It’s just like, think about it from a hospital’s perspective. That check is really easy to miss. Like you have checklists, you have all of these things, but you can forget quite easily. That is a huge impact if you forget and it isn’t caught, right? At the same time, it’s like, it’s a doctor that needs to go do the thing. It’s a very valuable resource that needs to stop get sectioned out to go do the thing. If you are a hospital and you are able to basically just have a nurse do a quick sweep and scan and tells you yes, no, and you’ve checked it off the box, you’re probably willing to actually pay a lot for that at the end of the day. And it actually is more beneficial for the consumer too because it is actually doing the thing, super important. It gets done. It’s very accurate.
A lot of the vertical AI areas are this way. like, it’s not just productivity increases from the, it’s not just like cost decreases maybe is how I see it. It’s not just the cost goes down. It’s just that the quality of it, the productivity of it, the like accessibility that goes up. And if anything, a lot of cases, the hospitals that whatever the vertical AI case is perfectly happy to actually pay up to their cost, previous cost of the thing.
because it’s just so much more reliable, easier, and takes away other workflow concerns. So that’s why a lot of this vertical AI stuff is interesting. Its individual price tends to be actually higher, which is not what you typically think with AI, but it’s just more accurate, easier, smoother, and better for the workflow.
Grace Shao (54:25)
Yeah. I can see that. Yeah, that’s really interesting. I want to talk a little bit about the big picture policies between China and the US right now. I know you invest across the stack, including some of the infra stuff. When we talked about earlier, you said, look at data centers as well, So help us understand this. Data centers aren’t new.
Like, you know, AI needs a lot of energy. AI needs a lot of data centers. How do we understand the relationship between these moving pieces?
James Wang (55:11)
Yeah, mean, the big thing is AI data centers tend to take a lot more power ⁓ in general for lot of the internet services and other things. ⁓ Because if you’re using GPUs inference, these tend to be much more power hungry components. For internet services, part of the reason why SAS could basically make money with queries that are fractions, fractions of ascent.
is because essentially it’s almost free. Like you can use a lot of the way that the internet worked is much more around uptime. So if you actually have, and this may get a little bit technical, if you say have like AWS cloud provider share resources, you’re able to surge up and down your capacity and share it across in terms of virtual instances. actual cost of service, a lot of websites, even massive ones is actually not super high. It’s only high from the perspective of like it may be millions of dollars.
But then again, you’re making billions of dollars off of your service that you’re servicing it from. It’s actually not very high. For AI in general, ⁓ its inference costs have been dropping a lot. But even so, with larger models, with needing a ton of memory, with needing a ton of these different things, with GPUs that themselves are both power hungry, but also heat, generate a lot of heat, you basically need to spend the currency of AI is essentially power.
Grace Shao (56:21)
Mm-hmm.
James Wang (56:38)
You need to spend power to literally power the GPUs or whatever XPUs, like TPUs, whatever thing you’re doing to run the AI. And you also need to cool it, which also is generally active, which means it’s also power. So all of it boils down to, okay, we need to spend power to be able to do this thing. It’s the closest thing to it is actually like cryptocurrencies in terms of you actually think of the one-to-one translation between power and actually the thing, ⁓ like what the thing does. So.
Because of that, the sheer density of power requirements means that usually some of these data centers that are trying to serve AI might exceed the power able to be provided from a local grid that was otherwise serving, just like city, resident, and like normal kind of activity. And you are seeing a lot of these data centers for that reason basically doing their own power purchase agreements.
having their own power plants. So they’re not actually on the grid, but they’re basically connected to their own power plants or connected to some of these power systems that are not within like say residential grids or something like that. So that’s been a big part of like why AI has needed that.
Grace Shao (57:38)
Mm-hmm.
As like a average user of AI, should that mean that we should just be more mindful and not use so much AI? Or does that mean that the future of energy consumption will drop as technology advances? Like, how do we understand that? Because like, you know, when we use the internet, it’s not like we think about, my God, how much power consuming, right?
James Wang (58:11)
Yeah. And the thing that I said before was if you take various stats, it’s somewhere between like 70 to 90 % decrease in inference cost each, like each year. So why haven’t like, you know, inference costs falling through the floor while we’re getting more advanced models, we get reasoning models, which actually use way more tokens or words in order to spit out like the same number of tokens that you see.
Grace Shao (58:36)
Yeah.
James Wang (58:38)
So we’re using more and more and more. And that’s why, even though the cost has been dropping so rapidly, we’ve basically kept pace or exceeded it in terms of power. That being said, there’s a question. Where will some of that power requirement ultimately go? How much will be needed? And yeah, will it be the case that we end up just needing exponentially more power? So there’s actually a piece on my sub stack that a
a hedge fund buddy of mine, hedge fund friend of mine from Bridgewater wrote, he does a commodity hedge fund now. His point is actually, even if you take very aggressive estimates as for how much power needs will grow for AI, it’s around like a 3.5 % incremental. That 3.5 % is basically the growth rate that we had during the 1950s in terms of the US power grid growing.
That can pretty easily be hit by renewables, which have intermittency problems. So you basically need battery storage, which is why we also invest in stationary batteries in that area. Or it can be hit by natural gas, or it can even be hit by just retiring coal plants slower. So actually, a lot of the power needs are not as insurmountable as you might think. And I personally suspect it will ultimately be the case that as we plateau in terms of, hey, this thing like
We don’t need it to like give us like, has much reasoning anymore. We just needed to book us vacation tickets or something like that. That’ll ultimately level off while the requirements in terms of compute costs, in terms of power costs will keep falling too.
Grace Shao (1:00:14)
I see, I see. And I think it’s also interesting, so just spoke to David Fishman recently. He’s an energy expert on the China space. And he was saying that, like he kind of mentioned in passing the US side, which is like essentially the US energy kind of, I guess conundrum is more exacerbated because
the center of living has not increased drastically. So people’s consumption of energy have not actually increased drastically. Whereas in China, over the last two decades, energy consumption has been increasing anyway because of urbanization, because of modernization of maybe your home, the economy as a whole. So there’s been more energy planning in China to actually support that kind of energy increasing demand.
And when that AI is now part of the picture, it doesn’t feel like a sudden gap that needs to be filled because you have the renewables, you have small nuclear plants being built out, et cetera. So it’s interesting to hear your perspective that actually the increase in demand, the increasing energy demand is actually not that significant. I think, again, headlines of news articles often really highlight that and really showcase a different picture where sometimes it’s more about like, OK.
People are experiencing higher utility bills. The grid cannot actually support local economies or local people’s livelihood anymore. It seems like it’s causing a big issue for the average citizen. ⁓ But yeah, thank you for putting that into perspective.
James Wang (1:01:42)
It’s totally, well, it’s a self-inflicted issue on the US side. Again, like the US has expanded faster than that at periods in its history. There’s a lot of different energy sources that you can actually use to go after that. It’s just the problem is political in part, like the US has a lot of bureaucracy red tape that’s hard to cut through, in which case it’s hard to build anything economically in the US, which is part of the problem. There’s no nuclear being built.
Grace Shao (1:01:47)
Mm-hmm.
James Wang (1:02:09)
So like you’re saying, China is actually building nuclear at a pretty rapid clip. The US is at best unretiring or maybe retiring slowly its existing nuclear capacity. It’s actively retiring its coal capacity, whereas China is what building a new coal. I think it’s one or two new coal plants every week or something like that in terms of the pace. like it’s just a very different kind of environment.
But it’s also not because yeah, the U S has no technological ability to go after that. It’s yes. Like you’re saying it’s like we have plateaued and a lot of our energy use. There’s also been a big push towards green renewable energy sources, which especially with the U S grid, low power storage, ⁓ it has its own challenges and can’t actually do the base load for AI. So if we wanted to, the U S could actually pretty quickly solve its problem. The question is, is there the political will and is there the willingness to stomach some of the trade offs for sake?
James Wang (1:03:09)
higher carbon cost.
Grace Shao (1:03:11)
Yeah, and I think that’s something David talked about as well in that episode ⁓ where it’s like the trade-off in China is more like, okay, we need more energy so we build more coal, but it doesn’t mean that we stop our renewable. But just because we have renewable doesn’t mean that we stop our coal. The trade-off obviously can be criticized, know, environmental issues, pollution, et cetera. But again, it’s just state level, I guess, mandate or state level priorities a bit different. ⁓
So we’re not a political show. We’re going to move on from that. I want to ask you about the Inflation Reduction Act. So this relates to what you just talked about, a lot of the push on renewable energy. then Trump kind of taking it 180 degree on this. So the IRA was introduced in 2022. It tried to make solar and wind more affordable on the grid. How did that actually work out?
What does it mean now with the Trump’s one big beautiful bill? Give us a high level explanation what’s happening there.
James Wang (1:04:09)
⁓ let’s see, data center developers keep getting whiplash in terms of renewables being good and then bad and then maybe not so bad, but not good either. Something like that. I think that’s sort of the quick high level. I mean, so, ⁓ a lot of the incentives, ⁓ were definitely something, things that a lot of data centers, lot of other folks, like hyperscalers tried to take advantage of, ⁓ when the inflation reduction act was more the law of the land before, you know,
Some of that got thrown out, big, beautiful bill, et cetera. ⁓ But I mean, the big challenge for the US, though, even just stepping back from that, is regardless of how much legislation you throw at it, it’s just like the CHIPS Act, right? You can throw as much legislation at the CHIPS Act to say, we’re suddenly going to build all our chips in the US now, or something like that. And it’s like, well, ⁓ you’re not spending enough money to do that.
And also legislation doesn’t like magically change things unless it specifically hit some of the core problems, which is yeah, the US doesn’t have for chips is like the US doesn’t have enough like labor for this like expertise moved over for sure. It’s ever for the power side. The problem actually goes back to the same thing we just talked about. Transmission interconnects lines old, hard to do, lots of red tape, lots of bureaucracy. It’s hard to build much anywhere.
unless you’re building in places that might not actually be super optimal for say like data centers. So, you know, some of the South in terms of Texas or Southwest has been more amenable to some of the data center and like power build out. It’s also hot there. It would really be nice to put it in a colder place. So you have less power needs to cool the thing too. ⁓ The bigger story, I think with all of this, there’s been a lot of legislation that the US keeps throwing out.
Grace Shao (1:05:54)
Yeah.
James Wang (1:06:00)
Maybe the bigger story I’d say is just the legislation has done some things around the margins. It has not made like a huge 80 20 change, at least from what I’ve seen. It’s like the same problems, the same ultimate macro problems that plague the U.S. and building stuff. And also it’s aging power grids and interconnect problems between different grids are still the same ones, like regardless of the legislative regime that we’re in.
Grace Shao (1:06:24)
Yeah.
An agent issue with the grid is actually like also just a reflection of like, frankly, the US developed and modernized so much earlier than China. And the grid just by nature is older and therefore the capacity and capability is like weaker because technology advance. Right. I think sometimes people forget about that. Just the reality that China didn’t become China that we know of today until like this decade. And the US has been basically the US that we know of today. The last four decades. Right. ⁓
Grace Shao (1:07:33)
Then I have one last question for you. I have one last question for you and it’s a question I ask every single guest that comes on the show, which is what is one differentiated view you have? Our show is called Differentiated Understanding. It’s about how you piece together the information you have and how you form a differentiated view, right? So what is something that you think is a bit non-consensus or against what the majority might think?
James Wang (1:07:35)
Sounds good. Yeah, I mean, I probably would have said it was my view about the vertical AI thing before, because I was talking about that a lot earlier than a lot of other folks, when there was still the talk about foundational models, which still is somewhat talked about. People are really pushing that a little bit less, that foundational models will cover every single use case in existence. And I think there’s been a lot more consensus moved towards that. So maybe
That was a very non-consensus view I had. The consensus has moved more towards. Let’s see, is there any other big non-consensus view right now? ⁓ I think I have one, actually. So another one. So my personal take, because of the way that LLMs have developed and everything, and a lot of the different AI areas have developed, I actually think a lot of the value
of AI from a GDP economy, et cetera, perspective will ultimately be socialized. I don’t mean that as in the government will. Yeah, I don’t mean the government will take it and redistribute it. I don’t mean like something will happen from that perspective or socialism will suddenly take over the US or something like that. What I mean is in terms of economic theory and whatnot, you can either have excess profits be captured by specific corporations and companies.
Grace Shao (1:09:03)
What does that mean?
James Wang (1:09:25)
which is frankly as a VC what I’m trying to invest in and basically have essentially monopolistic power, whatever, and base essentially have a lot of rents from society gathered towards the corporation or the company, or you can have a go to labor or you can actually have that value be socialized. Meaning because of competition, because of diffusion of the technology, because it can’t be controlled as much, it just improves society’s lives.
and isn’t actually excess captured by any single company. Even though like we have these huge model companies, they’re absorbing a lot of money, all these different things are happening. My personal take is like, they don’t actually have such strong barriers. Do I think OpenAI will go to zero? No, I think they have a pretty strong consumer brand. Do I think Google will go to zero? No, they have a lot of things to like distribute out. There’s a lot of uses for it. The companies will still survive, but they won’t become like essentially like world like consuming companies in the way that some people have talked about AI or talked about AI as in it’s a sector where a couple of large companies will suddenly take over everything. I actually think AI will diffuse within the economy quite a bit where we’ll use it in our everyday lives, but we won’t necessarily need to pay a company a huge amount to do it. For example, in the future, you might have edge models that just run on a very like a fairly powerful inference chip on your smartphone.
And you don’t need to pay ChatGPT or anyone else for that. It’s just something that makes your life easier, better. And it’s just there. So that’s one of my takes. I actually think the majority of the value hard to measure as that is will probably be socialized.
Grace Shao (1:11:07)
That’s really interesting. think that reminds me of something I wrote about recently and I think we engaged online about this as well, which is ⁓
the diffusion of AI will in some way look like the diffusion of internet, where it’s not like we just think of four companies as internet companies anymore, but even the tangible real world. Like, you you think about food delivery, you would have never imagined a food delivery company is an internet company. However, it is an internet company these days, whether it’s Food Panda or, you know, like Maytwan or, you know, Seamless in the US, that’s actually like...
not a physical world business only, right? And like when you think of a ride hailing, when you think about even like, I don’t know, apartment hunting, whatnot, it’s not limited to just the physical world. Internet companies actually encompasses all these things that we do. It’s just become the infrastructure. So you’re saying AI essentially will just be part of everything we do and it’ll be empowering everything we do. And it won’t just be limited to like the five companies that we think about nowadays. Yeah. Cool.
Thank you so much, James. Really, really, really helpful, really insightful conversation. And I really enjoyed talking to you.
James Wang (1:12:16)
Enjoy talking with you too, this was great, thanks so much, Grace.
Grace Shao (1:12:19)
Thank you.
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In this episode, I speak with Bei Zhang, VP of Growth at Tanka, about the company’s mission to empower AI-native founders. The conversation covers why persistent, organization-wide memory is the missing ingredient for truly proactive agents, how Tanka stitches together chat, email, calendars, and documents into a single “remembering” teammate, and what agentic work could look like over the next 12 to 18 months. We also take a closer look at the future of founding teams and how agent tools can enable a super-individual way of working without losing control, auditability, or taste.
Tanka sits inside a three-layer stack incubated by Shanda Group. EverMind is the AI infrastructure arm that builds a long-form memory orchestration platform. MiroMind is the research lab, built on Qwen models, focused on long-term memory and reasoning. Tanka is the consumer-facing agentic workspace that applies those capabilities to help startup founders run their day-to-day.
All three were incubated by the family office of Tianqiao Chen, the Chinese internet entrepreneur and investor behind Shanda.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Topics we covered:
* Tanka’s Mission: to empower future AI-native founders to transform their ideas into successful businesses swiftly and efficiently.
* The Problem Tanka Aims to Solve: Founders often struggle with information overload, with critical insights scattered across various platforms such as Slack, Google Drive, and numerous AI tools.
* How Tanka Works: Tanka’s unique AI memory framework.
* The Team: Tanka’s diverse team is rooted in the heart of Silicon Valley, comprising individuals with rich backgrounds in big tech and startups.
* Competition is not with general agents—focused and niche market.
* Connecting Founders with Investors: It actively seeks to connect founders to investors, creating a community and offering consulting services as well.
* Risks of Using AI agents: Human quality control remains essential; a hybrid model is a sustainable, long-term work model.
AI-generated transcript
Grace Shao (00:00)
Hi, Bei thank you so much for joining us today. I understand you lead Tanka’s growth right now. It’s a very, very exciting startup. I’ve heard a lot about it. Why don’t we start with your role and just tell us about the company, Tanka, the founding mission, what problem you guys are trying to solve, and just a bit about the team.
Bei (00:16)
Sounds good. Sounds good. Hi, Grace. Thank you for having me. Hi, everyone. My name is Bei. ⁓ I lead the product and growth in Tanka. Before I joined Tanka, I had been in various roles in different AI and SaaS companies, mostly in the GTM function. So what Tanka is about? Tanka is on a mission to empower the future AI native founders to go from ideas to founded very fast, very efficiently. And the core technology we’re putting behind the Tanka is the long-term memory behind the agents. End of the day, we’re trying to create a proactive companion, or we say that AI co-founder, because compared to typical AI chatbots, we are putting more power behind our AI agents that can remember all the conversation, remember all the relationship, and eventually can be the proactive.
AI partner to propel the founder to move as fast as possible. So that’s essentially our mission. And we’re hoping we believe the future is the world of super individuals and the lean teams. We’re trying to make the Tanka to be the powerful operating system for the future startups.
Grace Shao (01:25)
So I think there’s some fun and irony in that, right? what does it mean really when you say it’s an AI co-founder? Like for someone like myself, I’m a independent or I would say like a founder of a startup, I have a small team. What is Tanka really helping me do in a very practical sense?
Bei (01:43)
Yeah, yeah, great question. We are essentially the kind of startup, so we help ourselves, right? We’re trying to leverage the resources to help others too. what it would mean, maybe we’ll take a step back to get down to the problems we’re trying to solve. Essentially, we being the center of the Silicon Valley, we’ve been hanging out with a lot of founders, or a lot of individual, a lot of lean teams, a lot of them are just like you, Grace. ⁓ You are a super individual. We also have friends being just a three to five person teams. And the common problem we’re seeing they’re facing is the highly scattered information, overload of information, a bloat of different AI tools, and a very spread of key knowledge across different platforms. So even though we’re saying we’re putting the many of the platforms are wonderful. You got all the nice conversations on the Slack. You got all your documents in Notion and the Google Drive. And there are some offline chats. I’m sure there are valuable informations embedded into various GPT tools or AI chatbots. So the core challenge is not having the right tool. The core challenge is when founders are all of a sudden going from a single threat, trying to take on the world, trying to build a business, the tendency is that there is overloading of the information from all kinds of directions. Because for example, we’ve had a very good friend being a very technical researcher in Stanford. But the moment when he or she step into the founder role, he or she will have to handle not only the product, but also engineer the sales, the marketing, the product dev, legal and tax and BD, right? All kinds of stuff going on. So essentially having all those information scattered in different places create a few effects. Number one, it create a huge overload on the human brain, right? Nobody can process the information so effectively. Especially, we even come across multiple founders doing multitasking because they are trying different ideas, right? Which they will just multiply the pins. And separately, when the brain is overloaded, it instantly distracts the founder from the core duty, which is building the product. So that is causing many problems to be happening. It is causing key information getting lost. It is causing one part of the valuable information not necessarily getting fit into the other nice tools or very powerful AI agents, so the outcome isn’t as optimal. It’s far from it, right? The outcome is far from optimal when they’re trying to make a progress on the project. So that’s the mission. That’s what we’re trying to solve in Tanka. So in Tanka, here are a few things we’re trying to tackle the problem. Number one is the AI memory. Without putting the fancy word out here, just thinking...
As of you have, let’s say, today, whichever, most of the AI tools are not really memorizing your conversations. Because when you open a window, it has a conversation with you. But the moment you close the session, it doesn’t really record anything. So the next conversation is new. So with the 10Cut AI memory framework, all the conversations and all the documents you put in the tool are automatically compressed, stored properly, and also stored with a high fidelity so that when you have a conversation once, the future conversation will always remember what you had before. So it put a piece of mind to founder’s head so that you know there is a trusted partner that never forgets anything. So every company is about moving forward, not to remember what happened in the past. So on top of that, we’re adding the connectors, making sure Tanka can digest information not only happening within Tanka, but also connected from other sources as a deep memory and context. And with the memory, we’re able to put in the right AI agents, whether to produce the business plan, whether to just do the deep thinking and a deep conversation, or whether to produce an investor-ready pitch deck.
They are all based on the actual information in greater details, without you having to chase across all different things. So that’s what we say. That’s the actual specifics we’re putting in behind the tanker, because we’re not calling that just, we want to go beyond the typical AI assistant, because when we say AI assistant, meaning there is some, it’s a reactive, right? There is a AI sitting there and waiting for me to ask the questions or waiting for me to give the proper prompt. So we almost have to treat the typical, even for the very powerful AI chat bot, we have to carefully curate. We have to carefully protect the conversation, making sure it doesn’t generate anything wrong because garbage in, garbage out principle. But with Tanka, because the more you work with Tanka, the more Tanka knows about you, we almost can forget about prompting. It is an actually intelligent person sitting right next to you as a founder. So whenever the conversation happens, we just keep marching forward. And we’re even building more of a proactive AI functions because now that Tanka knows everything, what do we have happening in theory? You should know what I need to do next. even before, in theory, even before I ask,
Tanka to do anything, there should be more proactive actions. For example, hey, I need to follow up with certain investors. I need to update the pitch deck, for instance. Some of them are already realized, and many are definitely on the road as we speak. But that’s what we mean by AI co-founder, because we want to essentially have an AI that can essentially propel you to go forward instead of just waiting there for you to comment the way I do things for you.
Grace Shao (07:43)
That’s super interesting. think to me, when I heard that, I was like, that’s going to be so helpful for me. Cause like you said, there’s so many to do things on the to do list every morning. And then if someone’s actually proactively reminding me or getting things done, that would be really helpful. I first want to talk about the team.
First before we get into the product. Just like I understand you guys have a pretty diverse team. A lot of you guys, ⁓ including your founder, came from even ex big tech. How did your team come together? What’s the background? And I guess what is your edge right now making an agentic tool like this, especially with a lot of even the big AI labs are pushing out agentic tools. Like what is your niche and edge?
Bei (08:05)
Yeah, yeah, great question. So you’re right, we’re a very diverse team. We’re headquartered in Redwood City, California. We do have a global team across different parts of the world. But the core leadership and the product team are right here located in the center of the Silicon Valley because we are a company building for the founders. We want to be where our customers are to shine light on a few other things you covered. When Tanka was born, essentially it was born within a family office that has been actively curating multiple companies and also has been actively investing in hundreds of early stage startups. All the memory problems and all the context switching, all the information overload are very much experienced firsthand, both for the funding members within the family office and also being well observed by the company, right? The family office has been investing and curating in. So it’s a common problem that hasn’t found a solution yet. So that’s where I would say one of the edge is our deep understanding.
We’re not an enterprise tool and we’re not so much to a pure consumer tool. We’re living in a breathing in the startup world because the people has been working in the company or surrounding the company has either been advisors, investors, ex-founders of this kind of startups. So we know the problem from a different angles. So that’s number one. And number two is you’re absolutely right, the CEO, Kisson.
She came from a Meta, from TikTok. So definitely had a good discipline and a very structured approach from well-formed companies. She also co-founded another company that has a similar form of Tanka. So she brought in tremendous discipline in both the AI agent and from 0 to 1 and from 1 to 100 scale.
And I personally come from Grammarly. I happened to have an experienced growing company at a scale and also helped establish the B2B function from the beginning. And other than that, we do have ⁓ members coming from various startups. So we have all been experiencing the problem, first hand, left hand, right? So that gives us a deep understanding on what we want to solve for ourselves.
Grace Shao (10:53)
But $29 a month is quite steep, let’s be honest, especially if founders are cost-conscious. I want to understand what was the thinking behind that. And again, how does it compare with peers, even more general AI tools like Manus coming out of Singapore right now, obviously, as well as the incumbents that have been integrating AI into their apps like Slack, Salesforce, Microsoft Teams, even Zoom AI companion, right? Like in some capacity, they’re all trying to become a more proactive, I guess, whether you can call it a co-founder or a colleague per se, they’re all trying to be there to be more present to help you actually get things done, right? How do you compete with such an array of competitors, essentially?
Bei (11:36)
Yeah, yeah, good question. So to your first question about pricing, we put out a pricing more to create ⁓ a sense of familiarity to begin with. So purely on the number, I think it’s a mid-tier. It’s not that high. It’s not that low either. But it’s something people can, our users can correlate to.
And if you look at our free tier, we actually have a pretty generous free tier. We have daily bonuses. I think for lot of users to get a feeling, the free tier actually can get a lot done to truly feel the memory behind the agents. And also, separately, we’re paying much less attention on the pricing versus our attention on the value.
Because at end of the day, what our users weigh in is how much benefits, how much value they are getting out of the tool. So we’re just putting the pricing as a stake in the ground. We’ve been doubling down on understanding what our users need. They need a collaboration, so we built the AI agents in the chat to empower the team.
They need a generative function to turn the conversation into the actual shareable documents. So we did that. We made a very smooth process to go from the chats and the team conversation into the outcomes without you having to reprompt. The users are also looking for more help in the fundraisin, related features just so when they are ready for investor conversation, they can get it funded faster. So we have a whole pipeline of efforts to empower the founders to realize the benefits. in that, our goal is to make everyone feel like the price is a huge bargain. So that’s something we’ve been actively validating. And also separately, to your point, there are it’s an agentic world, right? Everyone, every company, whether the big ones or whether the startups are making various kind of AI agents. We do keep an eye on a lot of the big names, like you mentioned. I do have a lot of admirations to the great tools. But at this point, our belief is that in this age, the AI tool will come out in different formats and different forms.
So I like to think of them as inspirations and role models, right? More so than the competitions. If they are doing something similar, right? We would say, how can we fill our own gaps, right? How can we do better than them? But often than not, actually have way more gaps. We think even this big names are not even addressing between on the path, right? Between the ideas to startups getting funded. So we’re hyper focusing on filling the gaps more so than worry about the competition. Because we believe the world is big. The world is big. In the future, everyone will be a builder. Everyone will be a founder. If a user don’t use us, it will not be because of a competition. It will be because we’re not delivering our promise and not creating the value for the users. So that’s where our minds are, mainly.
Grace Shao (14:46)
So instead of trying to compete on distribution reach right now, you’re really focused on serving a very niche kind of audience, right? And then really just delivering exactly what they need instead of a general mass audience.
Bei (14:56)
That’s correct. We’re not trying to build a tool for everyone. That’s the job for the big tech. That’s the job for Tech GPT and Cloud. We are in the center of the Silicon Valley. We are hanging out with all the founders who are using all the tools you’re mentioning, but are still struggling in pushing the ideas into tangible business plan. And even for serious entrepreneurs, they are very struggling in getting connecting to the right investors and getting funded very efficiently. So we’re just hyper-focusing on this persona. Because again, we deeply emphasize wisdom because we are them. So if we get this part of the job done, we’ll be very proud of this. We’ll be very proud of our efforts.
Grace Shao (15:40)
Actually, one thing you just mentioned, how do you connect these founders with investors? What’s the strategy there? Because that’s not a product strategy. Is that just your connection, your network?
Bei (15:50)
More so than that. So there are multiple approaches. ⁓ number, think about this in a few different approaches. So number one, this is actually interesting challenge because our founder friends are, most of our founder friends are struggling looking for investors and most of our investor friends are still struggling and looking for quality startups, even though they might be in the same room. So that’s still a ⁓ friction. we tackle this in a few different layers. So many of the founders are not effectively connecting to the investors because they’re not ready. They’re not ready. first, we want to make sure Tanka has the capability for them to chat with the team, for them to carry through all the conversations, and making sure all the minute details are reflected in the business plan and the pitch deck so they appear. They are more buttoned up.
So that’s where we do the effort in preparing them to be investor ready, because investors are ready in the other end of the room. So that’s low-hanging fruit. And then separately, we are very active in the Bay Area funder communities. ⁓ So if anything, we have no lack of is there is an abundant funder communities here in the valley.
And we’ve been actively in the community facilitating the conversation. We’re inviting investors to give advice on how we can build a tool to better empower the founders. We’re doing this in different directions. So in a way, by having a presence in such communities, we’re already acting as a connector between the two parties. And furthermore, what do we do have on the product roadmap. our features like investor database and the investor matching, because that’s a low-hanging fruit. We do want to provide the founders more value by making it very easy for them to see that based on their business plan and the sector, who might be the right person they should be talking to. we are also evaluating the options such as the data room analyzer or the even warm intros because we’re even discussing with the actual human expert fundraising agencies as a potential layer because we do believe this is, AI is not ready to take over the world yet, As awesome as AI can ever be, humans do bring tremendous amount of value. So on a needed basis, there needs to be a human layer on top of the AI workflow.
And even if the human layer just evolved for 10 % of the time, we believe that’s where potentially the 90 % of the value may come from. So this is where end of the day we foresee we likely will build ourself into a hybrid solution where 90 % are conducted by the AI or focusing on this path addressing the problems many of the tools are really not addressing specifically.
And we’re connecting the human brain, the different part of the party much, much closer in solving this problem. So yeah, does that make sense?
Grace Shao (18:58)
And you know where else founders should be talking? They should be talking on my podcast because that’s where investors are listening as well and media is listening. And that’s how you get your story out there as well.
Bei (19:07)
They should. Investors should be listening, too.
Grace Shao (19:14)
Investors are listening. Actually, my main audience are investors in the US and Europe. I think, you know, interesting founders should be DMing me now. But on a more serious note, I think you just talked about like, agents can do what 90 % of work, you still got to have 10 % of human quality control, right? So end of the day, what are things at least at this point, or the next, say, 12 months, we can delegate agents, what are things that we still really need that human touch or humanity to kind of guardrail, the kind of progression of technology or our workflow or the usage of AI.
Bei (19:49)
Yeah, we’ve been thinking this day in and day out. So definitely when it’s related to the information gathering, information collecting, the document generation, document refinement, and web scraping. So without saying the features, that basically meaning how you turn from your conversations and inputs, documents, team chats into the pitch deck, into their data room documents, and how to scrape online, how to go to the linking. Those delegatable missions, those missions that tend to be competitive but yet time consuming. If it’s a delegatable, if you can put into a ⁓ SOP or standard operating procedure, we should try our best to let AI to do this as much as possible.
However, we do acknowledge that sometimes it takes a lot of judgment in this process because when the funders are so early, would the investors invest into the project or are they investing into the persons? Most likely, earlier they are, the earlier the investors are putting their weight on the persons. But many of the persons’ attributes and experience are not quantifiable.
So there are certain things that I cannot build into the AI agents to automate everything. So that’s where we do need a human to better probably connecting with the macro, better putting in the latest reflections, and better just to step in, making sure we’re not misjudging certain startups in either of the directions. And also separately, I would say, we also, Even with all the AI tools out there, we also had very, very top-notch founders who are deeply in the research world. So they just don’t have time. They are very busy. They do want to focus on building their product. Can they learn how to do the whole fundraising business plan or so? They surely can. But it’s more valuable for them to focus on what they do best.
That’s where I think sometimes often it just makes sense for the human layer to just step in and take it over. And it could also be entirely 100 % human touch, which could be well suited for the situation. But just wanting to make it possible whether the human touch is 0 % or 10 % or 100%, it is how this startup works. And we should build our product to be seamlessly connected and adapted to the reality here.
Grace Shao (22:22)
And I think it’s important to kind of note, like, you know, as you mentioned, as we’re all hyping up the AI agents right now, there is some mindfulness to be said to have to about the potential risks, right? So when people are using AI agents, I think this is as an AI agent question as a whole, not just Tanka but who audits the process ensures there are no mistakes, right? When the machines are starting to complete tasks, how do we actually ensure or how do we human ensure that we minimize the mistakes and the risks that they may come with.
Bei (22:55)
Yeah, it’s increasingly a more critical question as the adoption rate for the AI are increasing. So I don’t have a perfect answer. I don’t think anyone has really found the answer yet. I would say it’s the process. Process meaning when we’re building the product, because we’re building Tanka to be very deep thinking, deep researching, and working on very, very serious projects.
We try to use our best model, most expensive one that does the deep thinking and the reasoning to the best extent. So we don’t try to save money using the cheaper model for faster speed, ⁓ which might be introducing more errors. We’re carefully balancing that. We would rather deliver higher quality at a higher cost, but for higher quality. So that’s number one.
And number two is because that’s really actually where the memory comes in. Whenever we build a 10-cut AI to help brainstorm with the founders on next steps, we make sure it all ties back to the prior memory or it ties back to the traceable sources. for all the conversation and the generations, there is a link back to where you can point out to.
But that being said, it’s not 100%. It’s not like we can disregard any human efforts not to look closely. We still are constantly calibrating, and sometimes errors happen. And that’s even because the LLM, sometimes because the core server, it has variations. Maybe a question from the same LLM vendor may generate different answers.
One is more correct than the other one. So I would say it takes both efforts, even though that’s why we do want to emphasize the value of the human, because here’s AI. And we as a human, we still need to be very carefully guarding our own outcome. And then we introduce the human expert to further enhance the quality. I mentioned a lot of fundraising, and we actually have a lot of friends and mentors and advisors from other areas, such as sales and go to market, tax and legal, who are actually ready to engage and looking to find ways to help out the founders. So we’re not building us as a marketplace yet, but essentially we do want to, our vision is we do want to make a Tanka to be the center console where the founders work with Tanka, but also using other tools where it applies. We’re not here to replace anyone.
And we would definitely encourage or we may build a bridge between the Tanka with the human experts so that the human and the AI and human harmonically work together to further minimize the hallucination and the errors.
Grace Shao (25:40)
That’s really interesting. didn’t realize it’s kind of like building up an in-house incubator or like a consultancy, right? Like you have Tanka as your main touch point, and then you expand into your human expertise. Actually on the technicalities, I want to ask what models are you using and how is that decided by the agent? What I put in a prom when I’m using your agent, how does the backend look?
Bei (26:00)
Yeah, so I’ll say, maybe without disclosing a specific model, we do use a combination of the top tier models. maybe that’s the best way to say it. Using the AI memory, I was too aspect. The AI memory layer is built a little differently. It is called EverMind. It’s actually went open source a few days back. So we built our own prior proprietary and memory layer using a set of the algorithm. And that’s one. And when we build our Tanka AI agents, we do have a router option. We do build a AI. We do have a few preset prompt. Whereas depending on the type of the questions and depending on how the different steps of the agents that can execute, you will automatically pick the best model for the task. So it’s not just one, one deal, right? The kind of large language model will vary. It depends on whether you’re asking to generate a rough idea or whether you’re generating a very buttoned up business plan. So it’s different. And separately, I do want to say because of the memory, that’s where things are a little different, right? So because we do have the AI memory,
The large language model is capable of working with ever evolving context and the memory. So even the same question would absolutely yell the different answer the more you engage, the more you evolve with the AI. So I would say the LLM is a commodity. They’re very powerful. They are the necessity. But that’s where at the end of the day, we do think it’s probably going to be safe, whether you’re using Google or OpenAI or Cloud. At some point, it’s going to be indifferentiable, So that’s where, how to make sure it works for you, right? Not for a general purpose. It’s more critical.
Grace Shao (27:49)
That’s interesting. think that’s what a lot of the AI agents companies been saying as well. Like eventually, you know, the user experience will not, the users will not be able to actually differentiate which model they’re using, but it’s really just on how the interface interacts with the user and if it’s for a specific task. So I kind of want to go in on the product itself. Walk us through the product surface. Like, what is the experience like when I’m a user, I’m a founder, when I go on Tanka what should I expect?
Bei (28:16)
Yeah, we put in so much sense into the product, but if we, let’s say, we simplify, as a founder, you go into the Tanka, first of all, there is a place you can work with Tanka agent one by one basis. So on this cases, it’s essentially not too crazy different compared to the other AI agents out there, right? You still interact with the agent, you still ask all the questions, right?
Further develop your initial idea into a very buttoned up plan and further refining and fine tuning on that. Again, the main differentiation is ⁓ our window never closes. Our window stays always on and never worry about missing any information. So that’s the one. And then let’s say you as a founder, you get an idea from ⁓ a raw impression into something more tangible, you need to work with your team, right? And if today, whether the team is your co-founder, or whether it is your friend or your son-in-law, right, advisor, there needs to be a joint effort because often the wisdom come up in the conversations, right? So that’s why we have a second portion of the tanker to be a chat, right? Whereas we, whoever you invite into the tank to discuss the ideas, to hear the feedbacks, whether positive ones or constructive ones, and whether you both share or you all share any external references. All those conversations are precisely memorized and processed to be the high definition by the AI agent. then when
That’s essentially where ideally your business plan will evolve from your own work. And with the other AI agents, you would have to reprocess the information. You will have to bring all this conversation into a prompting and making sure, let’s say, that GBT understands what you have talked about. But it was tank up because the AI is sitting there. The AI is sitting there with you in the conversation. After you finish the conversation, after you are aligned,
You and your partner or your mentor are aligned on certain solution. Well, you can simply tell the tech to say, go make the next version. In that case, there is no transfer of information. And then there is no loss of communication in between. So that’s the next step, because we see the collaboration being a very core part of the founder. Very few people can pull off the one person team, even for one person, assume, right? You as a super individual, you probably collaborated with many, right? To develop your own business, right? And the last but not least is we are building Tankard to be a very open platform, right? Because this is where we fully acknowledge that everyone will probably use some other tools, whether it’s Slack, right? Whether it is Minos, right? My favorite tool. Again, we’re not trying to compete, right? We’re trying to say, if those critical contacts happen in other platforms, we want to make sure there is a way to bring those contacts into the Tanka so Tanka agent can sync with more deeper memory in mind and thus generate more high quality contents, right? And then the other direction is also true because we actually keep the memory well organized. If at some point the organization or the startup outgrow the Tanka capability, and we are building the MCP to make sure all the memories are exportable to the next tools you’re trying to use. So we are here for the specific purpose. And then there is a beginning point and there is an end point. We’re not trying to do everything. Again, we try to do the best in the part of the problem we’re trying to solve.
Grace Shao (31:59)
That’s super interesting. I was just going to ask you, where do you think founders can outgrow Tanka? Because you’ve been really focused on saying, helping them out in the very early stages. it’s interesting that you’re quite mindful that eventually, if a company grows to certain size, there is potential that the company or the founder himself might outgrow your app and they will move on to the next agent, next tool. I guess on that note, I kind of want to end on a big picture question, which is,
Bei (32:05)
So yeah.
Grace Shao (32:24)
What do you think is the future of work for knowledge workers, especially startup founders, what you’re witnessing in Silicon Valley? I think you alluded to this a little bit, that there are more and more of these called super power or super one-person bands, whatever. But what should we expect? Are we still going to see the kind of startups of couple of people with different technical skills kind of coming together, founding a company, to scaling it?
Bei (32:38)
Yeah.
Grace Shao (32:50)
and then becoming a big corporation or are we going to see complete that mode, complete transition revolve.
Bei (32:56)
Yeah, it’s a loaded question. again, we’ve been very actively thinking along the lines of that too. So here are a few things we believe the future will evolve to. Well, there definitely will be big organizations. That’s just the case. Some businesses are better to be at a bigger scale. Let’s say if you build a robot company, you better be. You need a scale. However, we do see that with all the tools empowering people to go from ideas to the apps very quickly, we definitely see there will be exponentially more super individuals. And when we say individuals, it means either one person or either three to five person. Because eventually, everyone, we do see the traditional roles being very blurred, right? There will no longer be like a PM or front end or back end or marketer, right? Essentially one person likely that’s gonna pick up multiple roles, right? I assume, Grace, you probably were many, many roles at the same time as the owner yourself. I think that’s incredible. And then there will definitely, many of the businesses don’t have to be that big. We do see many companies will probably stay it’s pretty small, right, 5 % or 10%. For instance, Gamma achieved a $2 billion valuation at 50%. That’s incredible. And I think there will be more and more companies like that. So that’s what we are inspired to solve for them. And also, adding one more thing is we do think the future collaboration will be multi to multi, right? That meaning is no longer going to be one person being employed by one company for a long time. Because hey, when everyone can do so many things, if that person has a capacity, why couldn’t he work on multiple projects with multiple teams? That’s also where we are creating the tank to be not constrained by an entity. You don’t have to be the same entity because we fully expect anyone can work with anyone. And we want to embrace that and empower that too. And last but not least, again, there are many good thoughts. I think it’s probably a book worthy if we had more time. So I do think this is where, for the first time, in AI can, in the past, in order to value whether the workforce or organization, whether it’s effective, you kind of have to wait until the quarter end or year end to see the outcome, to see that. Because many of the information are not really recorded. But now, because everyone used so many tools, and also AI has a memory, and AI can understand how things work, I think the efficiency will be exploding. Because the AI is able to catch where the inefficiency, where the blocker is happening. That’s also, again, that’s why we built AI to be not just one-on-one, but to be in the team, just so AI can discover, right? It can observe what’s working, what’s not working, and making sure that the team always work. Whether your own team or whether the cross-functional team is always in optimal status before too late to essentially the performance review happening every second. So that’s also back to the super individuals, right? The super individuals can measure their own success in real time and furthermore be more successful.
Grace Shao (36:21)
All right, Bei, we’ve had a wonderful conversation. I have one last question for you, which is a question I ask every single guest that comes on my show. What is one differentiated view you have or something unique you believe in about the industry, about the future of tech and AI, or even something just in general in life?
Bei (36:37)
Let’s see, I have a couple, but I’ll pick one. even as an agent, we’re probably, I think it ties back to our conversation today, right? We are building, I am actively building the AI product, right? So we want to build a co-founder or even a super powered AI solutions. But I do want to acknowledge that the penetration and adoption of the AI in the real world is so, so low. And chasing after a technical advantage, going after, I think ⁓ sometimes it’s just almost a wrong direction for builders to say, let’s make this PowerPoint generation even more smoother or nicer. And while ignoring that, there are massive amount of human workforce are not even closely in leveraging even basic AI to do things. They are struggling with the basic data scraping. They are suffering with the basic information gathering and to be truly embedded in their workflow. This is actually tied back to our chat in the whole fundraising journey. I we’re talking about the most capable, the smartest, the bravest, the most ambitious founder who can build everything. But I mean, why are they still struggling in knowing where to find all the investors? Who is the right investor to work with? Am I ready for the investor conversation? What else do I need to prepare? How good is good enough? And what to anticipate?
Why there are so many basic questions that are not solved. Sometimes I think it’s a, I don’t know whether it’s a differentiator. I just want to, we are doing practicing ourselves. Sometimes we try not to be ⁓ bad at in how we can build this tool to be better than the other competitors. But we go back to the basis on what problem are we solving? How is the problem, how people are tackling the problem today and how we can leverage the technology to best solve the problem. Because I definitely observe when we go chase after the technology advancement, we’re going after like 0.1 % improvements. But when we go back to the basic problem resolution, when we look at how the real world is being operated, we’re looking at like 90 % of the problem are not even remotely empowered. So that’s where I’d like to put out here.
There is still a long way to go. And there are so many things to be built. So I’m very excited about the journey and all the possibility and all the value we can bring to the community.
Grace Shao (39:11)
Thank you, Bei. That’s really thoughtful. And I think that you do highlight a point where I think when we’re all so embedded in the tech and AI scene, we assume people are all adapting and adopting it. But to your point, actually, the general mass is really not up to speed with it. And there’s so much work that needs to be done in terms of educating them and actually working together and actually a lot of issues are not solvable by technology, but it still requires that human expertise. So really appreciate that. Thank you so much for your time today. Is there anything else you would like to share with us before we hop off?
Bei (39:44)
Well, first of all, thank you for the time. I love all the very thoughtful questions. It’s been a pleasure chatting with you and I’m grateful for the opportunity to organize the mind and the share with you and your audience as well. The last thing will be just any recommendation, any suggestions is welcome from you. I I hope this is a...
This is the start of the conversation, more so than the end of the conversation. And again, you’ve been a super individual. I want this product to be helpful for you. And also, I would love this product to be helpful for your audience, whether they are investors or they are founders. So I’m just putting, I’m definitely very, very open. We’re a sponge. We’re a sponge. We’re here to take on any suggestions or feedbacks and that’s the only way we can get better and really focus on the right problem to solve and we need everyone’s help. So thank you, thank you, Grace and thank everyone in advance for all the nice thoughts.
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David Fishman is a Principal at The Lantau Group who advises on energy development, infrastructure, and electricity markets across East Asia, with a focus on China. His expertise spans power-sector policy and economics, grid development, project bankability, and transaction support, backed by regulatory and economic intelligence across China’s solar, wind, coal, nuclear, hydro, transmission, and power markets. He has led work on policy forecasting and tariffs, renewable-asset due diligence, China business matchmaking, and green-power procurement for multinationals.
In our conversation, David unpacks how China’s decades-long planning underpins its energy transition and how renewables, storage, and grid build-out are looking to be able to meet AI-era compute demand. We also touch on China’s East Data West Compute and how it leveraged strong geographical planning, as well as discuss the cultural and commercial reasons behind the global retail adoption of solar energy.
For me, the most interesting point he brought up is that electricity used to be bound to scarce resources, but as the saying goes, the sun shines, wind blows, and water flows everywhere. Access to reliable power will become more evenly distributed, which can raise living standards in places left out of prior industrial revolutions - and Chinese technology is driving that change.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
Introduction to China’s Energy Landscape
The Evolution of China’s Energy Demand
Nuclear Energy: Pros and Cons
Data Centers and Electricity Consumption
Main Drivers of China’s Growing Electricity Demand
Challenges of Renewable Energy for Data Centers
Geographical Dynamics of Energy Supply in China
Infrastructure Challenges in Southeast Asia
Commercial Reasons for Renewable Energy Adoption
China’s 2030 Renewable Energy Goals and Beyond
The Transition to an Electricity Civilization
Transcript generated by AI
Grace Shao (00:00)
David, welcome to Differentiated Understanding. Thank you so much for joining us today. I have been following a lot of your work on X and LinkedIn and you’re such a prolific writer yourself. And thank you so much for dissecting the industry, but really also breaking down the jargon on energy-related industry policies. So today I think we’re going to cover quite a broad range of topics, but really starting off from the high-level China energy planning, how it came about, and why their leaders are right now.
How that plays into right now, obviously, the energy competition within the AI boom, and then the companies that are backing these developments and growth, whether it’s like PV or solar. But yes.
Thank you so much. So to start off with, why don’t we start with your work and what you do at Lantau Group?
David Fishman (00:44)
Yeah, so I’m, I’m a principal, I’m a principal consultant at the Lantau group. We’re an energy economics consultancy. We’re focused on the commercial and economic aspects of the business of electricity or energy broadly around the region. And in China, I’m focused entirely on the business of electricity. That could mean either working with generators, right, producers of electricity, or those who invest in generation projects.
It could mean working with the markets, the grid, either the physical infrastructure of the grid or the commercial or virtual infrastructure of power markets that help connect power generators to power buyers. And then the ultimate user of the electricity, the end user, which could be a large producer of physical goods or IT infrastructure like data centers. Anyone who has a lot of exposure to electricity as a buyer or seller would be somebody that we work with in our business.
Grace Shao (01:40)
Perfect, so you are the perfect person to go to help us understand China’s energy buildup then. We always hear about China being the biggest emitter, But then at the same time, they’re now the leaders in renewable energy generation. How did that really come about? What’s the kind of dynamic there now?
David Fishman (01:56)
it’s all driven by the need for electricity consumption demand, which has been rising incredibly rapidly at the same pace as the Chinese economy, right, electricity or energy consumption really closely correlated with GDP growth. So as long as your, your economy is doing more things, it starts needing more energy as well. And we went from a period where, you know, the the pace of the growth was even outpacing the ability of energy sector players to meet.
that need. They were not able to build enough electricity infrastructure. They were not able to find enough primary energy sources to meet the growing demand. We’re talking about the 90s, the 2000s. And then in the last 20 years or so, we swung around more towards being in a position of relative abundance on the energy side, where it’s possible to have more energy than the economy is currently calling for. And that’s where we got to where we are now. We built up a huge energy
electricity generation base that was primarily powered by what at the time was the best option, which was coal. Coal-fired power became the backbone of the entire Chinese electricity grid. And because China is huge, whatever is the leading share of something in China becomes just massive in the world. It becomes massive overall. The renewables didn’t really come on the scene until about 2011, 2012 is when the first really strong installation capacity subsidy programs were put into place to really encourage generators to build wind and solar farms and started ramping up the scale of the industry overall. But at that point you already had tons and tons of coal fired generation that was, you know, the backbone of the entire fleet. And that wasn’t going to go away so quickly. So over the last 10 years, we can add tons and tons of wind and solar, but it doesn’t, you know, it only stems the growth of coal. It hasn’t really even started taking away from the total generation of coal fired power. So that’s how you end up having, you know, the largest coal generation sector in the world, the most emissions in the world, and the largest renewable sector in the world all at the same time.
Grace Shao (04:01)
But how did China become so dominant in solar and all the renewable manufacturing? was like scale, was it cheaper capital, cheaper labor, policy support? How do we understand this? Because right now we’re seeing that the US is talking about transitioning to a more, you know, green energy, but like economy.
David Fishman (04:18)
Yeah, well, mean, every to have an industry that is somewhat speculative, relies on new emergent or unproven technology. And in 2011, China didn’t necessarily set out to say, we’re going to go all in on wind and solar, and we’re going to count on this becoming world beating in the next 10 to 15 years. At the time, it was just, you know, we’ve built up a lot of production capacity or a decent amount of production capacity for for solar panels and wind turbines, and we’ve been exporting them.
And now we would like to start installing some of them domestically. But it’s not going to be so competitive or so profitable if we do it right now. So let’s make sure there are very comfortable, good incentives in place so that anybody who builds a wind or a solar farm will be guaranteed a good rate of return on their expenditures. So you start out by saying state support. We need to offer state support to incentivize certain types of things to happen that the market wouldn’t want to build on its own.
⁓ And then we need to be able to, you know, apply pressure throughout the value chain, wherever there would be somebody who’s unhappy about not getting an acceptable rate of return on their activities, generating electricity or producing solar panels or lending money to people who want to do this. Everybody needs to be incentivized to participate in this game. We’re trying to create electricity from wind and solar and it’s not very economically competitive right now. So how do we help them, right? That’s where you get Yes, your subsidies, your state support, your low interest loans, your affordable land access, things like that, all of those things, those help thing scale up, right? Those give you scale. And once you start getting that scale, you start to enjoy the economies of scale, you start to enjoy the effects of competitors on the production side entering into price wars to try to maintain market share. You get these jumps forward in innovation. I’m going to squeeze an extra 1 % out of my solar panels. going to I’m to beat the other guys, right? The scale turns into a bit of a snowball where all these other effective, enjoyable benefits, your economies of scale, you’re increasingly lower costs, you’re increasingly more attractive technology are all piling up. And all along the way, you’ve still got the state presidents hanging out in the background saying, will help, will make sure that your rate of return is acceptable. If things look like they’re getting out of hand, we’ll tweak things so that you still make enough money to keep yourself solvent. And then finally, one really important factor in all of this is the state owned enterprises themselves. The state owned enterprises are mostly involved in the capital intensive section of the industry. So that means building and operating the solar farms and the wind farms. That’s the really risky part of the entire value chain.
And so they’re willing to take on a lot more risk than a private developer might be able to. They’re willing to accept lower profit margins than private capital might be willing to. And that acts as a great big lubricant for the whole system so that you can continue keeping capacity numbers high even if rates of return sometimes are a little questionable.
Grace Shao (07:20)
So, how should we understand this, actually? Most of the energy players, whether throughout the supply chain, are SOEs or do we have major private players as well?
David Fishman (07:30)
Well, so the equipment makers are almost all private companies, right? Your solar panel manufacturers, your wind turbines, your batteries, those are all private companies. They get support from the state. They have subsidies, they have land grants and things like that, but they’re all private companies. And then on the generation investor project development side, you have mostly SOEs. Private companies are certainly happy to participate when they find good opportunities with acceptable rates of return. But for everyone else where maybe the rate of return is only a 6 % return on capital, right? That’s not attractive to private equity. That’s not attractive to a fund usually, but state-owned enterprises are happy to do that kind of work because that’s their mandate. So it really is a partnership between the private sector doing certain things and the state sector doing other things.
Grace Shao (08:21)
That’s quite commonly actually seen in China, Like across a lot of industries. How do we understand the grid mix right now in China?
David Fishman (08:28)
Yeah, so right for for 2024, which is the last year we have full full data sets for I think was something like 58 % coal fired power and that’s been declining as a percentage even as it increases in total volume, right? Because other sources have been growing faster than coal has been growing but 58 % coal and then we had wind water solar
Wind, solar together came out to, I think it was about 32%, something like that, with wind and solar being in the 18 % range and hydro in the 14 % range. Hydro has a lot of annual variability, depending on whether it’s been a good year for rainfall or not. And then the balance, that last 10 % is made up of nuclear, which about five, five, six percent. You’ve got gas to power, which may be two or three percent, and then other, includes things like biomass, waste to power, experimental technologies like that, but still very fossil fuels dominant.
Grace Shao (09:28)
How does that compare to other major economies? We might look at Germany or even the US.
David Fishman (09:33)
Yeah, it varies a lot depending on their natural resource endowment. So the United States has been shutting down a lot of its coal capacity and instead leaning heavily into gas to power. And gas to power is now one of I think it should be the largest generation source of the United States. There’s a lot of gas in the United States. Then you take a large country like ⁓ France, right? France has nuclear, built nuclear decades ago. Nuclear is still the largest contributor to France’s electricity mix. You switch over to somewhere like Brazil. Brazil’s got lots and lots of hydropower and hydropower is the main driver of Brazil’s economy, of their electricity economy. you know, natural resource endowment really matters. Major countries that are still using coal a lot, like China, you’ve got places like China, India, ⁓ Indonesia, those are the ones that come to mind, especially in East Asia.
And then throughout Europe, course, Poland is well known as being a huge, huge coal consumer. But if you’ve got access to gas, you use gas to power instead. South Korea uses a lot of gas. Currently, Japan uses a lot of gas. Gas is very common throughout Southeast Asia, usually imported gas or LNG. So it’ll be quite, quite expensive if you’re using that for electricity, which can sometimes contribute to high power costs, lot of gas in Europe as well.
Grace Shao (10:51)
That was really good context. I actually want to double click on the nuclear topic. It seems like there’s quite a bit of controversy with a lot of the SMRs being like the small ⁓ nuclear plants that are being reactivated right now. How do we understand, I guess, the pros and cons of nuclear? Is it really still quite dangerous for local communities or is it like potentially a big possible solution for us as we’re seeing electricity shortage?
David Fishman (11:16)
That’s like three different, very different questions. So I’ll try to answer them in order there. So SMRs are next generation kind of experimental ideas for nuclear that they could be maybe more cost effective or more flexible in a smaller format, something like 100 or 200 megawatts instead of a thousand megawatts. Right now there are only a couple of SMRs operating around the world for commercial civil power use.
⁓ China’s got one, for example. and mostly in the United States, the conversation has revolved around restarting some previously retired or mothballed but not shut down or decommissioned, nuclear power plants. That now that there’s this energy crunch, an electricity crunch that some of that retired generation that maybe wasn’t competitive in the market landscape of whatever year it was, decommissioned in or retired in, mothballed in,
Currently the market climate has changed that there’s such demand for electricity now in such a scarcity that the buyer of the electricity is willing to finance the refurbishment of the plant and willing to finance the long-term operations of the plant by becoming a buyer of electricity. So when you look at your major tech companies that have just signed an agreement to restart a nuclear power plant, that’s because the operator, the owner of the nuclear power plant has secured a very lucrative contract to sell electricity from that power plant to that data center for a long period of time. On the safety aspect, yeah, look, we’ve had a couple of notable kind of headline grabbing, world attention grabbing accidents over several decades of the nuclear industry’s operations. I always consider it to be kind of like one of those airplanes versus cars thing, right? A lot of people are afraid of flying, although it’s incredibly safe relative to driving on a statistical basis, right? But you know, an airplane crash grabs the headlines in a way that a car crash never will. And that’s, you know, a similar situation with the safety of different energy types, right? A nuclear power incident once every several decades grabs headlines, but the long term damage to human health and livelihood caused by combusting fossil fuels has been immensely larger, incredibly, incredibly larger. So everything has its own kind of trade-offs and how you evaluate it is up to you, but I do invite everyone to think of it as an airplane crash versus a car accident risk scenario.
Grace Shao (13:40)
That’s a very interesting way of framing it. I think like you said, a lot of times the headlines really focus on what’s big or what’s more exotic right? And that’s kind of in the case that nuclear is just not as commonly heard about, therefore everyone actually pays attention to it more. I really want to double click on what you just talked about on the big headlines of big tech buying up, or reviving plants to power data centers. Because when we were talking before this interview, you joked you said,
Hey, look, I’ve been an energy guy forever. No one really wanted to talk to me that much before. For now, all of a sudden, everyone wants to talk about energy, right? We are in the midst of an AI boom. And the bottleneck, especially for the US, or the choke point right now, for a lot of these big tech companies, is securing enough energy to power data centers that are required to power their training or influence whatnot, right? So help me understand this. The AI boom is capital intensive.
So is energy. What, how do we understand the relationship actually between energy and data centers and AI right now? Why don’t we start with that big question?
David Fishman (14:42)
Yeah, well, mean, there are a few other productive activities in the world that rely so much. On electricity as a primary input as the operation of chips in a data center. I mean, maybe the only thing that’s similar is Bitcoin, right? Mining Bitcoin because the operations you’re doing are just constant calculations that require consumption of electricity to be performed. data center operations are similar. In the physical world, the only thing I think comparable is something like ⁓ non-ferrous metals smelting, like aluminum or copper smelting is also just, we call it solidified electricity.
That’s what aluminum is. So data centers are constantly performing actions and tasks that use electricity. It’s a direct relationship to do what it needs to do to be productively useful at all. needs to consume electricity. so from an economics perspective, you just say this is a demand driver of electricity, almost a direct demand driver of electricity. It’s not an aluminum smelter, it’s a data center, but we need more electricity to serve its needs so that it may serve its function, its customers, which are asking for computing power. And so from the electricity sector perspective, I just see that as a number that used to be 1 % year on year demand growth, and now it becomes 3 % year on year demand growth, or 5 % year on year demand growth.
And that’s something where, if we build one or two generation assets, we will meet the anticipated demand for the future. Now it’s, need to build eight generation assets or 10 generation assets, or I can’t allow that large electricity user to actually connect to the grid and start demanding power. Cause I don’t have enough. I don’t have enough electricity to serve them as a grid operator. Maybe that’s my perspective, right? You’re not allowed to connect to my grid, you need too much electricity. And when you’re in that circumstance as a data center operator, you’re saying, well, I got to bring my own power, right? Bring my own electricity. And so that’s kind of the way they’re thinking about it now. If I want to set up my compute in an area that, you know, for whatever reason, it’s beneficial for me to be sited here, but this local grid doesn’t have enough electricity for me, I got to come up with my own solution. I could build my own assets. I could have a captive power plant, perhaps I could support ⁓ local generators to build something and sign a long-term contract with them so that they’ll build the asset, I'll build all the power. Or maybe I’m looking at something like, you know, something more creative where I say, Hey, there’s already an asset. It’s in the grid system. It’s nearby. It’s this nuclear power plant that’s been mothballed for 15 years. Let’s get that going again. I’ll buy all the electricity. Whatever it is, it’s it’s, you know, because of this very strong direct relationship.
A data center operator is a wonderful customer for an owner or operator of a power plant, right? I’m in the business of making electricity, you’re in the business of using electricity, like, surely we can work something out. So that’s where you how you end up with this kind of like, we call it a PPA, a power purchase agreement. That’s the direct relationship that’s established between the generator and the consumer.
Grace Shao (17:48)
Who are you seeing as biggest consumers of power right now amid this AI boom that you’re seeing in China specifically.
David Fishman (17:54)
Well, yeah, it’s going to be your big Chinese tech companies, right? Your Tencent, your Alibaba, and ByteDance, companies like that that just have massive need for tech, you know, and then your emerging AI companies. And of course, the big tech companies have their own AI plays always. And then they’re going to be independent or third party AI companies that are looking to train or whatever it is. And they’ll
They’ll need a lot of that too. So in the IT space, that’s who you’d expect. The big tech companies and your frontier model creators.
Grace Shao (18:27)
And what do you think, if you had to put a number on it, how big is this AI driven load as a percentage in terms of the incremental electricity demand we’ve seen, I guess, in the last three, four years and comparing it to next three to five years of production?
David Fishman (18:40)
So it’s interesting, it grabs all the headlines, but remember, Chinese power consumption is already growing at a stunning rate for every other reason already. So this is just one additional thing. So in the last five years, think it’s been ⁓ a modest amount of the growth can be attributed to ⁓ data center needs.
⁓ Looking at something less than 15 percent I’d say maybe 10 10 15 percent of the growth can be directly attributed to data center needs And then over the next five years I’ve seen forecasts from like state-grade energy research Institute where they’re saying you know of all the different sources that are driving power consumption growth Maybe maybe 20 % will be attributed to to AI so or to data center so not not like nothing, but also not as much as you guess or expect, certainly not compared to other countries where it really is maybe 50 % of the load growth or more can be attributed in that way.
Grace Shao (19:36)
I see. What are actually the top drivers for China’s demand, growing electricity demand, other factors?
David Fishman (19:42)
Yeah, so among those, the same report that I saw that said, you 20 % can be AI. In that same report, it was another 10 to 15 % or so. it was 10 % is charging for EVs as a driver of growth. And then another 10 % was electrolysis of hydrogen. So you can produce hydrogen by running a current through water, essentially electrolysis of hydrogen, which replaces our other ways of producing.
Grace Shao (19:57)
Okay.
David Fishman (20:09)
hydrogen, which are usually quite dirty and fossil fuels intensive. And hydrogen is very useful as a as a input for for production of ammonia or methanol, many other very useful chemicals. So that’s about 40 % of the growth was attributed to those three things, these three emergent sectors. And so the other 60 % of the growth will be traditional sectors. So that’s your heavy industry, and uses tons of electricity all throughout it. That’s your services or your tertiary industry. So you’re looking at growth of AC use in shopping malls and hotels and things like that. And then the smallest portion of that is residential power use. All three of those traditional sectors are all rising in China as well. And in addition to these new three sectors that are emerging as part of the clean tech revolution.
Grace Shao (20:58)
That’s really interesting. I wish we hadn’t time to go into the EV talk today, but see if we do later, but let’s focus on AI first. I actually want to understand why can’t we go full renewable with these data centers right now? What’s the issue with, you know, using renewable solutions instead of traditional solutions? Cause there’s been a huge debate around that, right?
David Fishman (21:16)
Yeah, well, mean, and so when you have a power load, doesn’t matter what it is. If you have a power load that draws on power pretty continuously, the easiest way to meet that kind of load will be something that generates power pretty continuously. It’s not impossible to meet it otherwise. It’s just the easiest, the most straightforward way to meet that load. If it has certain types of flexibility in the way it operates, then you can also start to address it with more flexible generation sources. But overall, a ⁓ data center load is a more stable large load. And large stable loads are really good matches for our conventional generation sources. Our hydropower short, but you know, coal, gas, nuclear, great matches for the way a data center needs electricity. Now we can meet those needs with a combination of solar and wind and storage and flexible generation sources like that. But operating those in that way generally incurs a larger cost, either a direct cost in terms of operating cost of the assets or a systemic cost.
And in order to have all those variable assets in there, we also needed to keep some gas generators on standby. We needed to pay some capacity payments to a battery station, something like that. Those are systemic costs that also usually end up being assessed to someone, ideally the one who caused the need. But sometimes it’s assessed to an end user who didn’t cause the need for that variable generation, but they still paid for it. So when we look at can we meet the ⁓data center needs with renewable, certainly we can. It’s just more complex and it takes more planning and maybe it incurs more system costs and sometimes the system costs aren’t really well tracked. So China does intend to start meeting a lot of its data center growth with renewable sources. We talk about where the new data centers are going in, the parts of the country that they’re going in and there’s this program called the National Hub Nodes.
Which is part of the East Demand West Compute program. And so under this scenario, any new data centers added into those national hub zones need to be consuming at least, I think it’s 80 % of their electricity needs to be renewable. It’s a pretty high percentage. So if it weren’t possible, there wouldn’t be such a requirement, but it is trickier, it’s more complex. And so that’s how China is proposing to drive most of its data center build out now is with these renewable energy sources. It’s a way to avoid this growth of a new load sector resulting in increased fossil fuels consumption.
Grace Shao (23:57)
I see. I’ve heard about like, you know, the challenges with battery solutions right now for the intermittent kind of nature of renewables. Do we have actually strong enough battery solutions right now to solve that issue?
David Fishman (24:10)
I mean, batteries, the question is, can you you scale it cost effectively? Right? Can you can you get your battery storage to be able to cover what two hours, four hours, eight hours? How long of a backup solution do you need there? How what type of gaps are you expecting to need to meet in the context of all the other power that you’ve contracted for or that you have available to you? If if you’re in a region where there’s tons and tons of solar power, it’s sunny all the time. The sun is up most days and it’s producing well most days. Okay, well, you’ve got a solution for part of the day. And then it’s like windy in the evening. So you’ve got a solution for the evening. But how do we cover that evening peak period when it’s not particularly sunny or windy? Okay, batteries, sure. How long are the batteries good for? Can we be sure that the batteries will be charged and ready with no loss of load incident every day? Or at least at a very, very slow failure rate, something like that. When you start doing some type of probabilistic assessment, how many batteries are enough so that we can ensure a failure happens once every 10,000 operating years or something like that, whatever your threshold is, you start to realize, woo, it’s a lot. I need a lot of backup. I need a lot of storage. And so maybe that’s where you run into some of the problems where the project economics might not work. They might not make sense anymore. Ideally, I’m describing an extreme scenario, but ideally you can find a solution that involves not using massive, massive amounts of batteries that you barely use most of the time.
Grace Shao (25:38)
I see. It just sounds like there’s a lot more operational risk, right? I want to understand East data, West compute. From your perspective, like how should we understand that, I guess, from the energy framework? And can you help us understand the geographical importance of having the energy suppliers on Western regions of China?
David Fishman (25:57)
Yeah, it’s absolutely driven by geography. maybe you’ve seen the famous line before, you can draw a line through the center of China and everything on the west side of the line is 7 % of the population and everything on the east side of the line is 93 % of the population. It’s roughly half and half of the country. That’s just where all the Chinese people, all the load, all the industry is, is along the coasts in the east and the south of the country.
And so your need for electricity, your need for, your demand for any energy use of any kind is mostly going to be out there. But the good energy resources in China are in the West. They’re in the North, the Northeast, and the Northwest, and the West. So you’ve got to find a way to get them to each other. You want, you know, the wind blows all day, the sun shines all day, and the mountains are just heaped with coal in Western China.
So we got to connect them to Eastern China. In the case of data centers, you’ve got two options, right? Either we’re going to bring power lines from the West to the East, or we’re going to bring fiber optics from the East to the West, right? So either I bring the electricity to where the load is, or I send the compute to where the electricity is. And they are doing a mixture of both. So this is where you end up with a bifurcation of the types of computing needs that you have.
The short term, very rapid response compute needs to stay in the east, close to where the demand is. And so for those, we bring the electricity to the compute. And then for like a longer lead time, you know, maybe we’re training a model or something like this, we can send it out to the west, send it closer to where the cheap electricity is. So this is a consideration that makes sense for China, because China is huge and it’s got very different ⁓ dispersion of its resources. A smaller country could do it on a smaller scale, of course. I know in the UK they joke about London data computed in Scotland or something like that because they’ve got the offshore wind up there in Scotland. So it’s a similar concept, just in China’s case it has to happen on a continental scale.
Grace Shao (27:56)
I’m also interested in actually how the relationship between the provinces are. How do they work together? Do they have different mandates for each of the provinces or is it just a very top-down kind of mandate from the federal level?
David Fishman (28:10)
Yeah, so in China, is a tiered, a hierarchical relationship between kind of national government, provincial governments, and municipal governments. Generally, you’d expect planning at the more macro level to come out of Beijing for the whole country, and execution is going to be left to provinces and municipalities. So when they say you need to enable cross-regional power transmission, okay.
Like that’s that’s something that comes out of Beijing, but now it’s up for the individual provinces to work out how to do that. Eventually, they’ll execute their trades through the state grid power exchange, which is up in Beijing. But all the negotiations and all the interactions have to happen at the provincial level or even lower at municipal levels. That’s that’s broadly true for for almost all Chinese policy, not just the power sector and not just data centers, but in general, broad strokes laid out at the top and then executional happening at provincial or low.
Grace Shao (29:05)
That’s interesting to hear. I kind of want to shift gear and kind of double click on the private sector right now. You you just talked about along the supply chain, lot of the coin makers are actually private companies. We’ve got the PV makers, the battery makers. think, you know, people in the West will probably know of CATL, Longi, Trina. There were some also even international ambitions of these companies kind of set up manufacturing hubs in the US or even across Southeast Asia. Can you kind of help us understand their global ambitions and where they’re at now.
David Fishman (29:34)
Yeah, mean, so the initially the Chinese market was very initially, they were built in China to be sold internationally. And then the Chinese market got so much larger than everywhere else that many of these equipment makers had plenty to do just selling to the Chinese market. But because of the very, very stiff domestic competition, there’s always that interest of like, well, could we could we diversify a little bit out of this incredibly competitive environment by selling?
broad as well. And so initially they could sell abroad from China again, as some of those trade barriers started going up that really incentivized the expansion into Southeast Asia, into Vietnam, for example, that you could have Trina solar panels produced in Vietnam and exported to markets that were maybe closed, or tariffed for for Chinese exports. And then we saw, I mean, with the United States and its recent tariff policies, that tariffs were placed on the Southeast Asian countries as well. And then specifically if there was evidence of trans shipment of Chinese panels heading to Vietnam and then going to the United States that they would be taxed or tariffed even more. So a lot of that has, you know, eventually ended up coming down to just one place, right? If you’re a Chinese solar producer, and you want to sell to the United States, you’d better be ready to produce in the United States. That seems like the way I’ve talked to a couple of them, and that’s the way they’re thinking about it. If they had production already, they say, okay, we’re gonna keep producing for the American market from our production facilities there. If they’re thinking about starting a new, opening a new factory, well, it’s been a scary environment for that kind of thinking recently, right? Of course, there was the recent⁓ raid on a South Korean under construction battery facility in the United States, right? There’s a lot of concern about, you know, that kind of capex heavy investment for a market that just maybe isn’t geopolitically that friendly, and it would come back to bite you in the end. There was another case, I think, where a Chinese solar panel producer built a facility in the United States, and then it wasn’t going to be eligible for the IRA tax credits and they were forced to sell it to an American company, which is now producing solar panels from that facility. So if you’re a Chinese solar panel manufacturer or battery manufacturer and you’re looking at this kind of geopolitical climate, certainly you’re willing to consider building an overseas factory to get around tariffs, whether it’s in Southeast Asia or even in your target market. But some markets seem like they’re a little bit more geopolitically favorable than others right now.
Grace Shao (32:08)
It’s obviously quite challenging for a lot of these companies trying to sell to the US right now. Just like you said, given the kind of backdrop. Okay, I want to ask about Southeast Asia. We know that a lot of these companies are building out, you know, manufacturing hubs, even data centers in Southeast Asia. A lot of the big tech companies, Chinese big tech companies are now said to be the biggest hyperscalers across Southeast Asia as well. Like they’re buying up all the data centers.
But there are some concerns around infrastructure challenges in Southeast Asia, right? So there have been complaints about, you know, the utility prices rising. There’s issues in Johor, Malaysia, where frankly, local infrastructure such as water, land, roads, it’s not really there yet. How can you, how do you help us make sense of all of this? Like, who are the buyers? Who are the investors? Who are the users? Is this really helpful right now? Are they actually helping the local economy? And are they actually, are these companies getting what they need in those regions?
David Fishman (33:00)
Yeah, so anywhere in the world, including China or even very, very transparent power markets ⁓ like parts of the US or in Europe, it has proven to be remarkably difficult.
to attribute changes in the price of electricity to any one thing. Data centers have become a ready and available target. And I think it’s surely true in some cases that it is the thing that’s driving higher power prices for that region or for that node. But in other cases, I think it would be more difficult to make that assessment. And it’s something that is speculated in the media and then kind of becomes true by default without really having a good investigation into it. I haven’t looked at Malaysia or Vietnam enough to comment whether I could attribute the data center, no matter who owns it, the data center demand to rising power costs. It does make sense usually in an economic sense, right? Your demand load has increased, your supply hasn’t changed, and so we end up at a higher point on this cost curve for more periods of, you know, throughout the day.
So that’s, mean, it’s something that is theoretically a problem. If it gets to the point where the data center load starts to really affect the competitiveness of electricity for other parts of the economy, either the industrial, other industrial segments are saying our energy inputs are too expensive now, or you’ve got residential power costs skyrocketing, you’ve got a developing economy, and you’ve got, you know, residential people can’t necessarily afford the costs of higher energy costs, then you’d expect the national policymakers, regulators to step in and say, if you’re going to be allowed to operate in this way, we need you to bring your own electricity, for example, ⁓ or you need to be willing to cover the increased costs of other sectors of the economy through some type of ⁓ payback scheme or kickback scheme, right? That kind of thing is a reasonable thing to ask of a data center operator when they’re coming in and creating those types of problems for a generation overall. But I mean, remember, they are also creating their own economic benefit. They’re not just leeches on the system, which I think many would argue something like cryptocurrency mining is that you’re just sucking up tons and tons of electricity, and you’re only creating value for a smaller group of people who believe in the value of the cryptocurrency. Data centers, on the other hand, are performing tasks that are considered to be productive and valuable tasks for their clients, for their customers.
Maybe you could argue you think that using AI video generation is not a productive and useful task, but hey, like somebody thinks it’s valuable and useful. Enough people think it’s valuable and useful. So that’s what you weigh the two against each other, right? You’ve created a burden on the grid system, a burden that is having knock-on effects on other parts of the economy. You need to account for yourself. You need to take responsibility for the burden that you’ve created on the system.
But also, you you’re not a leech. You’re not a parasite on the system. You are in fact doing a useful thing and it’s the responsibility of energy planners and economic planners to try to make energy available to you. So it’s a delicate balance. And it’s, I wouldn’t, it’s not fair, I think, to attribute blame to anyone unless they’re intentionally trying to get around policy or screw over other people.
Grace Shao (36:28)
Yeah, I have a question that’s a bit more anecdotal, but when I drive through Europe or China, right, you see like solar panels on private citizens houses across the countries, right? Like, you know, in Spain, Italy and Germany, you really see across China, like Hebei in Songsu, you really see people embracing it as a society. Can I do you think there’s a cultural difference in terms of kind of embracing renewable in the US versus China or Europe or what kind of incentivizes them.
David Fishman (37:02)
There’s a cultural difference, but in the case you cited there, there’s a very strong commercial difference as well. In Europe, residential solar is primarily motivated by the decision making of the people that own the house, that own the rooftop where the residential solar goes in. In China, rooftop solar is driven by developers who don’t see the they see the rooftops as real estate.
So developers go in, they knock on doors and say, I see you have some nice unoccupied rooftop space up there. I will pay you to lease your rooftop. I would like to put solar panels on it and sell electricity back to the grid. So the Chinese business model is very unique in that case. I would say the United States and Europe are more similar in the ways that the mechanism for motivating, motivating residential installations. And then China is off doing its own thing because it has this incredibly effective, but unique scenario where developers are the ones promoting solar panel development to residential users who weren’t very aware of, you know, solar energy at all. And then also, they don’t even have much electricity consumption, frankly. So they’re not super motivated to go install solar panels. ⁓ residential electricity costs in China are very cheap. So it’s not like they’re trying to offset a high power bill.
The whole motivation for the residential sector in China is ⁓ collecting a rent on their rooftop space so that some developer can generate electricity and then sell it to the grid.
Grace Shao (38:31)
I see. I totally thought it had something to do with just kind of this overall societal embrace of new technology and this 2030 renewable energy goal. Can you actually, I guess the last question I have for you, can you help us understand what is this grand 2030 goal of China to really convert, you know, transition majority of the energy consumption to renewable? How do the EV sector, the battery sector, and various sectors related to energy really play a role in this?
David Fishman (38:58)
So the 2030 goal is the peaking goal, the emissions goal. So they want to peak carbon emissions by 2030. And then they say carbon neutrality neutralize the emissions by 2060. So we got to reach a peak, then we got to draw down and neutralize. So by 2030, I mean, maybe we’ll be down to a 50 to 55 % share of coal in the power sector. I don’t know if it will slip below 50%. By that point, it’ll be getting close to it.
And then you’ve got all your other non power sources of emissions, right? Transportation, building, heating, and industry. That’s the major one, industrial inputs. So by 2030, the goal was to just peak the emissions across all emissions generating sectors. Now it looks cautiously like we might have reached an early peak this year. I say cautiously and I hedge my phrasing here because, well, there’s lots of different segments that create emissions.
Power is maybe gonna stay flat, maybe gonna be, you know, peaked by 2025, 2026, but we’ll see what happens to new capacity for wind and solar next year. If wind and solar and hydro and nuclear can’t keep up with the pace of consumption growth, then the only way to meet the additional consumption growth is by using more coal, right? So that’s the power sector. Fingers crossed that they managed to figure it out. Transportation fuels maybe is going to peak in this year or next year. We’re talking about the EVs coming in such massive numbers that they’re replacing petroleum ICE vehicles.
Grace Shao (40:28)
What’s the percentage actually? What’s the percentage of EV cars on the roads right now, do think?
David Fishman (40:33)
It’s you know, it varies widely by province in Shanghai. It’s over 50 % now for sure. Major cities is 50%. But you know, I was recently in, like Shanxi, a northern, you know, Chinese province, and it was probably less than 10%. ⁓ So it’s I don’t know what the number is nationally. It’s it varies so incredibly by city, I think it’s or by province, I think it’s best to look at that on a provincial level. But the idea being that new
Grace Shao (40:38)
Wow.
David Fishman (40:57)
New EVs should have already started outpacing the sales of ICs and that should continue so that we would be able to, know, more vehicles are going on the road but the EVs are being added more rapidly and that petroleum consumption for passenger transportation should be peaking, you know, within the next two years or so. And you’ve still got aviation and maritime fuels and long-distance trucking and all the other things. Excuse me.
And then finally, you’ve got the industrial sector and there’s different aspects. Some are growing, some are rising, know, steel is maybe flat, cement is dropping for the next 15 or 20 years, but hey, petrochemicals and coal chemicals are rising for the next 15 or 20 years. So you’ve got all these different, you know, sources of emissions to consider. That’s what’s all going into that 2030 goal. So if they manage to peak by 2030, like wonderful, lovely, enough of those sources have peaked and started drawing down that we can make up for the ones that have not peaked and that are not drawing down. And then we’ve got another 25, 30 years beyond that to figure out how to get to neutrality after peaking in 2030. If they happen to incidentally peak this year or next year, I don’t think they’re gonna claim it. I don’t think they’re gonna crow about it. I think gonna stick to the 2030 emissions peak, give a little bit of time just in case a little bit of buffer.
Grace Shao (42:08)
Really?
David Fishman (42:13)
But it seems very likely that we’re flirting with the peak a little bit early versus their own targets.
Grace Shao (42:20)
What’s the practical impact on society and the economy then?
David Fishman (42:24)
Well, the ideal goal is to have as little impact as possible, right? That business as usual growth as usual continues, economic abundance of energy is compatible with energy transition. That should be the goal that the only outcome in fact is that the business of the energy transition is good business, that it contributes in fact to GDP.
⁓ installing solar farms and building wind turbines and upgrading industrial equipment is all like good economically productive activity. And so that should be the only impact. That it’s a good impact and that otherwise everybody just goes on driving cars, they just happen to be EVs now. They go on using electricity, it just happens to be increasingly clean electricity. They go on using steel, it just happens to be green steel. That’s the best case scenario.
Grace Shao (43:16)
That’s a good world to be looking forward to. Okay, David, thank you so much for your time today. What is one differentiative you you may have? It could be about your industry or it could be about something else broader in life.
David Fishman (43:28)
One differentiating view that I have.
Yeah, well, I mean, I’ll talk about my other great passion, which is kind of economic development in general. I got into electricity because I cared about development. And I stay in electricity because I continue to see it as important for development. And I am pretty agnostic when it comes to the different isms of the world, the different political science factions that say it’s this ism or that ism.
jWhat works is the best ism, whatever it is, I don’t care what label it has. And my observation right now about where we are as a civilization is that we are right on the precipice of moving from the fossil fuels phase of humanity to the electricity phase of humanity that electricity will be an interim period, because eventually we’ll probably figure out fusion power and harnessing plasma, and that will take us to the stars. But in the interim phase, we’ll be in the electricity age. We went from biomass to mechanical energy to fossil fuels to electricity generated from the natural elements. And that this electricity phase should be the phase where energy stops being additive.
And starts being substitutive instead. All the previous energy phases that we went to, we had to use more energy to upgrade. But to upgrade to electricity should allow us to start using less primary energy. That using wind and solar and hydro and nuclear are less intensive for primary fuels, which means we’ve freed up space for developing nations in the world to start to enjoy a fraction of the energy abundance, the civilizational abundance that so much of the rest of the world is already enjoying. They’re already there, right? As long as we stayed locked in the fossil fuels era, they’re not allowed to enjoy that abundance. They’re locked out of it because we have no more carbon budget to spend. We need to work on this, you know, civilizational existential challenge of transitioning our energy. And for the moment, there’s no more carbon budget for the developing countries of the world.
Getting into the electricity era, the electricity civilization era, frees up that carbon budget, enables them to start pursuing the same type of abundance that everyone else is already enjoying in developed nations.
That’s the great thing that motivates me throughout all of this, that the electricity civilization era is one where regardless of what ISM introduced it or ushered it into the world, is one that is going to be incredibly beneficial ⁓ for humanity. And at this moment, I think I’m working in the country that is most likely to usher in that era. And here’s the controversial portion, right? I think China is the one to bring the world right now into the electricity civilization era. And that will be such an incredible boon for humanity in general.
Grace Shao (46:27)
That’s a really, really interesting way of framing it. I really appreciate that. Thank you so much for your thoughtful answers and just your time and your insights today. I really appreciate your time and just your generosity in sharing with us.
David Fishman (46:38)
Thank you for having me. It was a great pleasure.
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“So our philosophy here is to integrate the globally optimal models and give users the best results.” — Hang Yu, Head of Product at Qoder, Alibaba
This is the first episode in a series of founder and builder dispatches, featuring interviews with the people creating the future. If you are a founder, builder, or investor in this space and would like to share your story, please reach out.
Today, I am joined by two guests from the Qoder team at Alibaba: Hang Yu, Head of Product, and Christian Hu, Head of Global Marketing and Operations. The Qoder team launched just over two months ago, joining the likes of Cursor, Warp, and Copilot to make coding more agentic, so today we get to learn from them directly about their unique positioning being part of the Alibaba ecosystem.
Hang discusses the thinking behind designing Qoder, how it differentiates itself from peers currently available on the market, the future of agentic work, his fears and excitement about the pursuit of AGI, and finally, challenges the notion that the future of AI may not be based on Transformers.
Christian walks us through Qoder’s business positioning, global ambitions, how it fits into the Alibaba ecosystem, and the reasons for routing between models, beyond just Qwen.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Topics we covered:
Product
* Introduction to Qoder and AI Coding Agents
* The Transition from Copilot to Agentic AI
* The Future of Developer Productivity with AI
* Addressing Developer Bottlenecks
* Multi-Model Strategy and (Qwen) Integration
* Differentiated Views on AGI and AI’s Future
Business
* Understanding Qoder’s Positioning in the Market
* The Competitive Landscape of Coding Tools
* Qoder’s Role in Alibaba’s AI Strategy
* International Ambitions and Challenges
Transcript (AI-generated)
A. Hang Yu, Head of Product at Qoder
Grace Shao
And again, I just want to say thank you so much for joining me today, Hang and Christian. So today, the first half of our conversation will really focus on the product design of Qoder and the transition that we’re seeing from Copilot to Agentic. And then we’ll move into the second half of the conversation, which will really focus on the business strategy of Qoder, international expansion goals, objectives, and then how it really fits into the bigger Alibaba AI plan and the bigger AI playbook. So with that, I just want to bring in Hang. Hang, it’s lovely to meet you and thank you so much for joining us today. Let’s start with the very, very basics. What is Qoder in plain language and what does it actually do for developers day to day?
Hang
Hey, great. Thanks for having me. So yeah, so in one sentence, Qoder is an AI coding assistant that helps developers maintain and improve existing software system, not just build some new stuff from scratch. And I think that distinction is actually really important. So when we look at what developers actually do every day, we found that 95 % of professional developers spend their time maintaining what we call real software.
So commercially valuable, long-lived systems that are not building new projects every day from zero. So that’s why we design code around that reality. We are optimizing for the messy, important work of understanding existing code, making targeted improvements, and maintaining production systems.
Grace Shao
That’s really interesting. you know, there’s for me again, not a technical person. The hype we hear about is all this vibe coding. And then there’s even these marketing phrases calling it like ⁓ AI coding, build your app from a single prompt. That’s not what’s really happening, right? How does it actually really work?
Hang
Yeah, exactly. actually, that’s the flashy use case everyone talks about, like building one app from a single prompt. But if you’re working at a real company maintaining a five-year-old code base with 500,000 lines of code and 20 different developers who have touched it over time, you need help understanding what’s actually here. So you need help making careful modifications without breaking things.
That’s where code delivers value. So in terms of how we think about the AI developer relationship, we see it evolving through three stages. So the first is assistive programming. In this stage, AI helps the developer while human leads. So like code completion, fixing syntax errors. And the second stage is collaborative programming, like co-pilot. Like the human and AI works together like pair programming.
And the final stage is autonomous programming. In this stage, AI takes on complete tasks independently. So the developer can delegate work, and the AI runs in the background and comes back with results. That’s what our Qoder Quest mode does.
Grace Shao
So the really unique bit of Qoder Quest is the autonomous piece, right? And I really want to dig into that a bit more, a bit later. So right now I’m curious. So when you say the user experience is intuitive even for non-technical users, what design choices really led to that? Because honestly, a lot of developer tools are pretty intimidating, especially for people like myself who’ve never done any coding. But what I’m hearing on the street is people are going out of their way, even as non-technical people, building their own apps now with the help of AI. How are they able to do that?
Hang
Yeah, great question. So we have this philosophy. So don’t make users think about things they shouldn’t have to think about. So for example, if you look at some products, they have like 40 different AI models in a dropdown menu. Honestly, that creates a lot of cognitive load. So developers end up becoming model select or instead of focusing on building their own product. So our philosophy here is integrate the globally optimal models and give users the best results. So we will auto select the right model based on the task. So we believe model selection will be better than human selection. And the same thing with context management. The users shouldn’t have to manually figure out, OK, which files to include, what tokens to optimize. Our context engineering handles that automatically. So the goal here is to remove the cognitive overhead. And let developers focus on what they are trying to build, not on configuring the AI tool.
Grace Shao
I have a really dumb question, but is there a latency then between me prompting like, can you help me build this versus the machine telling me which model is optimized? Is there like a latency? It’s automatic.
Hang
No, no, there would be no latency. Yeah, it’s all automatically. The user will not feel it.
Grace Shao
that’s amazing. Okay, let’s talk about kind of the hype right now, the copilot versus the agentic transition right now in the coding space. Many tools are being called assistants or some are called copilots, You know, the cursors of the world. And cursors essentially been leading this. So where does coders autonomous capability really truly defer or is unique or different? And what’s really the big breakthrough we’ve seen here?
Hang
Yeah, this is the defining question, So Cursor has ⁓ perfected the co-pilot approach, real-time help while you’re coding. Their tab completion, tab, tab, tab, is industry-leading after two years of their custom model training. But I’ll say this. So Cursor’s tab completion compatibility is catching up fast. We have made significant progress in recent months and rapidly closing the gap.
But here’s where we see the real future, the autonomous programming. So you delegate a complete task, implement this feature, fixing this bug, et cetera, whatever. And then the AI works on it in the background. You don’t have to watch every line of code being written. You just come back and review the result. now sophisticated autonomous coding and production skill is still involved.
Hang
We think it’s about two to three years out, maybe 2027 or 2028, before it’s really mature. But that creates an opportunity window.
Grace Shao
That’s actually quite soon. So what does that really mean practically?
Hang
Yeah,so it means With autonomous coding you can actually Delicate your work and make and the the AI agentic will can work it background in the cloud so Like you can close your laptop and the AI keeps working
go to a meeting, go home, do whatever you want, the coder is still running. So in other words, you can spin up 10 parallel sessions working on different tasks, and it doesn’t slow down your own machine. As one colleague said, he said, I’m managing 10 agents. My productivity went up 10 times, and it didn’t mess up my work-life balance. So yeah.
Hang
So this cloud execution model is pretty similar to what cursor recently launched with their cloud agents feature. So both approaches let you handle your tasks to agent running remotely. The key advantage here is you are not tied to your machine. You can dedicate the work and then close your laptop and then come back to complete the result. And then when the
Hang
Yeah, and then when the agent finishes, we just need to review the results.
Grace Shao
So that’s actually my question. Like when you talk about reviewing the results, is it very obvious to kind of find the issues only in the result or do you have to go back to the process? Like how do you audit the whole process actually?
Hang
Yeah, so the agents will present to you summary of, this is what I’ve done. This is the result of the question, or this is the feature. You can see it through the browser, or the agents will submit a PR to your GitHub to your routing workflow. And you can easily check, OK, is it done good, or it needs to be modified again.
Grace Shao
So does that mean that junior developers in a way will be replaced then? Because essentially you only really need people who understand a higher level of the code and the execution can be actually outsourced so easily.
Hang
Hmm. Good question. So simple, agentic task works well today. You can already dedicate things like write a unit task for this function or add log into this module and come back get some good results.
So sophisticated, agentic coding at production scale, where AI can take a high level of business requirements and then autonomously design, implement, test, and deploy a complex feature across multiple systems, that’s still two to three years far away. So what this means for software development is a fundamental shift in how developers spend their time. ⁓
Hang
So right now, developers spend maybe, I think, 30 % of the time on creative problem solving and 30 % of the time on mechanical work, like writing border plates, fixing syntax errors, ⁓ updating documentation, writing tests, et cetera. But as agentic tools mature, that flips. So AI handles the mechanical work. Developers ⁓ can focus on what AI can do yet, like understanding what actually needs to be built.
Hang
making architectural decisions that require domain knowledge and validating that the code actually does what it should be.
Grace Shao
Mm-hmm. So in the end, it’s still like, there are still aspects of humanity that can’t be replaced by machines yet. Let’s get a bit more practical. I want to understand how precisely Qoder can actually turn a prompt or product spec into working code? What types of tasks or repos does it handle best today, right now?
Hang
So Qoder handles best what we call a bounded, well-defined task today. For example, implement authentication for our user login system or add relimits to our API endpoints or generate a dashboard for monitoring system health metrics. So these are tasks where requirements can be clearly specified, the scope is contained, and the success criteria are measurable.
Grace Shao
Mm-hmm.
Hang
But what’s harder today are tasks that require deep domain knowledge or ambiguous requirements like improved user engagement, that’s too vague, or like a required entire authentication system, that’s too large, that’s too large and risky for autonomous execution.
Grace Shao
So it seems like a lot of the tasks it can do is still pretty much something that’s very easily verifiable. It’s a bit more like a black and white kind of answer kind of task, but not so much things with nuance, right? I want to understand better. So, thinking about the developer workflow, and correct me if I’m wrong, there’s the planning stage, the code writing stage, the running tests, the debugging, version control, and deployment, right? Where does Qoder natively sit the strongest, like in the most helpful, and where do you hand off to other tools?
Hang
Great question. Let me break it down. So the first is plan and design stage, So Qoder is strong here through the spec generation. We can help the developer translate business requirements into technical specs. And the second stage is writing code. This is our core strings. The Qoder can write code across multiple files, handle complex logic, and generate boilerplate. And for the run and test stage,
Qoder can generate unit tests, integration tests, and run them either locally or in cloud sandbox. That’s building feature. And for debugging stage, Qoder can diagnose its errors from test results and fix them autonomously. But for production debugging with live user data, you still need the traditional tools. That needs to be taken care of. And as for version control or call out, we integrate with Git, GitHub, and GitLab. So a coder can create branches, commit changes, and create pull requests. But the actual code review and collaboration discussion happens in your existing tools, like GitHub, GitLab, whatever you use. But yeah, in the final stage, the deployment, we hand off here. So deployment involves your CI-CD pipelines, like infrastructure, monitoring system. So Qoder creates the code and the tests, but you own your deployment process.
Grace Shao
I see. I kind of want to take a step back and understand another big kind of hovering question a lot of people have right now, which is if we’re going to have agentic tools really implemented the work process, how do we understand developer productivity for the future? Will developer productivity still be measured the same way that it’s currently being measured?
Hang
Yeah, I think the fundamental shift here is this. So AI changes what developers spend their time on, not just how fast they work. So again, right now, developers spend maybe 30 % of the time on mechanical work, like writing boilerplate, debugging syntax errors, searching documentation, setting up environments. And only 30 % of the time goes to creative high-value stuff, like understanding what needs to be built. Like understanding what needs to be built, making architectural decisions, and validating that solution actually solves the problem. But with AI, agent AI flips that ratio. So AI handles the mechanical work. Developers focus on the part that actually requires human judgment. So when we measure productivity, we are not counting lines of code or tickets closed.
So we are looking at, can developers spend more time on higher value work? Can they ⁓ ship features faster without burning out? So what we are seeing is promising. 99 % of our paid users actively use agent model. Nearly 99 % of our paid users actively use agent mode. So it’s become core to their workflow.
80% renewal rate tells us that people see the real value here. And enterprise reports two to three times improvements in the deployment frequency. So the real metric here is simpler. Developers tell us that they are less frustrated. They are not stuck debugging environments or writing repetitive code. They are solving interesting problems. And that’s the productivity gain that matters.
Grace Shao
I see, I see. That’s really interesting because I think as a writer, when people really initially rejected AI, the idea was also that there was a lot of mistakes, AI slop, hallucination, whatnot. But then what people started realizing is that you can actually use it as a productivity tool. And like to your point, it doesn’t change how you think as a writer building out the framework, using your critical thinking and really still rely on your own creativity. But you know, you what you’re outsourcing is actually just like the execution that was a lot of the grunt work frankly. Right. Interesting, interesting.
Hang
Yeah, yeah, exactly. Yeah, you free up your hands and you, yeah, yeah, you free up your hands and focus on your head.
Grace Shao
Okay, so I wanted to ask another question on productivity and bottlenecks. So what is, I guess, one of the top bottlenecks or what are a few that you see currently in this space for developer right now? And I guess can we actually separate the separately answer this question, the first half being what are professional developers bottlenecks and what are kind of the new age, like casual developers bottlenecks and how are you helping these two differently?
Hang
Good one. Let me hit the main ones. So usually the bottlenecks, like the first big bottleneck is about the environment setup, So this is a huge time thing. The coders, and then the coder can help you set up the environment automatically, whether you are running it locally or in the cloud, pre-configured environments, automatic dependency resolution.
So you don’t have to spend two hours debugging your Python version complex or just trying to build your dev environments. And then for the flaky tests, coder can also detect flaky tests by running them multiple times and identifying inconsistency. So you can also suggest fix based on the failure patterns and the test outcomes. And then for...
For another bottleneck here is the legacy code. This is where the Ripple Wiki comes in. So remember how I said the documentation is always out of date for developers? Ripple Wiki uses... So Ripple Wiki delivers value here. So Ripple Wiki features here can use AI to generate up-to-date documentation from the code itself, plug the Git history. So it’s not just...
Here’s what the function does, documentation. But here’s a business logic and why it was architecture this way and what changed and why documentation. So the documentation itself stays fresh. When code changes, the documentation regenerates automatically. So we have measured about five times speed improvements, so from 60 minutes down to 12 minutes for team documentation.
And the last but not least bottleneck here is the context limit. So this is a technical challenge. Models have tokens limits, So we have our context engineering figures out what’s actually relevant to the task. So we don’t just dump the entire code base, which will crush your token limits. We intelligently select what the task needs and what the AI needs. and we ⁓ gain a better solution from the ⁓ context we select.
Grace Shao
That’s really interesting. So I think I want to talk about the models and orchestration here. So Qoder is a multi-model platform, right? You guys use Qwen, your in-house built models, but you also use other models like you mentioned earlier, use whatever is like kind of state of art model for the right task, right? How do you route between the models, thinking about latency, cost, evaluations, languages? Do you usually like have a preference for Qwen or the third party models when you’re assigning these models to tasks?
Hang
OK, so what we are trying to do here is integrate the globally optimal models. ⁓ So what we are trying to do here is to integrate the globally optimal models and give users the best results. We are not limited to just Alibaba’s model. So if one frontier model is better for a specific task, we use that. So like if GPT Excel somewher, we use GPT. If Qwen is the right fit, we use Qwen. So now, why does this matter? So first, it best has time. I’ll be direct. So Qwen model isn’t the best model in the world for every coding task yet, but it’s improving fast. So by using the best global models today, can serve users well while our own models can catch up. And if Qwen becomes the best model globally in your year, is our goal, then naturally we’ll use it more. And secondly, yes, sir. Yeah.
Grace Shao
But actually, just want to jump in on that. But actually, Curser is even using Qwen isn’t that fascinating that we just found out recently.
Hang
Yeah, so the cursor’s composter is... They didn’t officially admit it, it’s... So the community thinks they’re fine-tuned and post-trained based on Qwen or some Chinese open source model. Yeah. So first is the best time. And second, the cost optimization without compromising quality is our second goal. So not every task needs the most expensive frontier model. So simple completion is a smaller, faster model. But for a complex reasoning, we use the frontier model. And last but not least is about the reliability. So you don’t want your entire product to stop working because one API provider has an outage. Here we saw OpenAI has multi-day outage in 2023. we want to our reliability to our customer. Yeah, that’s why we use multi-modal strategy. Yeah.
Grace Shao
I see, I see. I was actually going to ask you on that. What’s the thinking behind designing the product using a multi-model design versus only proprietary Qwen? I guess you already answered that partially. ⁓ I also wanted to kind of double click on the fact that Alibaba is throwing 53 billion US dollars into AI infrastructure right now. know, isn’t it, is there not some pressure coming from up top to really hone in on Qwen or, know, like, I guess the question’s more about - Is Qoder really developing based on what’s best for the user and use whichever model that’s best for them? Or is it more focused on being part of an ecosystem, another tool out of the Alibaba set of tools that they provide? Does that make sense?
Hang
Yeah, great question. So it comes down to serving users best today while building towards to the future. look, Qwen models costs us like 1 fifth or 1 sixth of what Frontier model API costs. So that’s an 80 % cost reduction, right? That’s a structural advantage. But Qwen isn’t as good as the top Frontier models on context reasoning tasks.
That’s why we use a multi-modal setup at the balance cost, capability, and reliability. But the key in size is that this isn’t a permanent state. Qwen improves, as coding capability, sorry, as Qwen.
So the key inside is that this isn’t a permanent state. As Qwen improves, as its coding capabilities get stronger, the balance will shift. So we are not philosophically opposed to vertical integration. And we are actually pragmatically choosing what serves users best today while building towards our ownership tomorrow. So for enterprise customers in China, components actually require a domestic model anyway. But for international customers, they often trust well-known frontier models or GPT models. So multi-models board let us serve both.
Grace Shao
I think that’s a really interesting business strategy because end of the day, to your point, it’s a very pragmatic approach to actually serve your customers best and to serve your customerbest, you actually get more business. That’s just how it actually the virtuous cycle works, right? Instead of building up these guardrails and the paywalls. I want to kind of pivot to strategy soon, but before we do that, I really want to ask you a few other questions just on more, a more broad general question. One is, you’ve been working in this space for a long time. There’s a lot of hype around Agentic tools, not just a Agentic tools in coding, right? How do you view a Agentic AI in the next 12 to 18 months? And how do you think it will actually affect what even the general mass think of AI use AI?
Hang
I think agents will swallow the whole market. ⁓ People will use more more agents in their daily workflow. So when you’re trying to use a chatbot, you need to copy paste a lot of data, your contacts, your own data, your domain knowledge.
To chatbot and then generates some specific task results. But by using agent, agentic AI, you don’t need to do that. The agentic AI will live in your workflow. It knows and it contains your domain knowledge. And it knows your context by nature. So this means agentic AI knows ⁓ what you’re trying to do, what you did.
What you are trying to do and what you want. That’s actually a big difference between the big difference between the LLM and the agent AI. So I think, yeah, yeah.
Grace Shao
I see. I think that that leads me to the next question, is like, so for teams who are customizing tools or agents, like you just said, they would have domain knowledge. They already know your work processing really well. And how does it actually work? What’s the plug-in API story with Qoder in this sense again?
Hang
Yeah, so this is about the domain knowledge question and the accessibility work. Let me break down how the domain knowledge and the accessibility works. So first, we have MCP supports. We support the modal context protocol, which is becoming the industrial standard for connecting AI agents to external tools and data source. So this means the coder can ⁓
integrated with thousands of tools in the ecosystem, database, API, version control, and project management through a standardized interface. And then we also support subagents and skills. So teams can create their own specialized subagents. Think of this as task-specific AI workers with their own contacts and capabilities. So ⁓ you want a code review subagents turned into a
So you want a code review, sub-agents turn to your team standard. You can define it. You need a security scanning sub-agents for your compilers requirements, build it by your own. So these are the version controlled and shareable across the whole team, and can also run in parallel. And as for the domain knowledge, you can inject your own documentation, design system guidelines, and your coding standards directly into coders context.
And when Qoder generates code, it will follow your rules automatically. So now, ⁓ here’s the key part. How you actually use all this. So you can think of Qoder like a Stripe for payments or Tailor for communications. You just embed it into your existing development workflow. You don’t need to replace your IDE. You integrate Qoder into VS Code, into JetBrains, into Temrano, into your CI-CD pipelines, whatever and wherever you already work. And there’s actually a precedent here. So Alibaba’s Model Studio platform has enabled over 800,000 custom agents across different domains. And we are also trying to bring the same extensibility here to coding workflows.
Grace Shao
That’s really really interesting. Thank you so much for sharing all that and I do apologize if any of the bit that I didn’t like sound that smart because it’s so technical but I really appreciate you breaking it down for me and really explaining it to me in very simple language. I have one last question for you which is something I always ask all my guests. ⁓ What is one differentiated view you hold? And this doesn’t have to be about work. It could be. It could be about your space could be about agenda coding. It could also be about anything else in the world. It could be about your experience in Silicon Valley versus China. Just a differentiated opinion review of something that you think is not that mainstream or it be a bit against consensus.
Hang
So we talk about something related to agentic coding. actually, I don’t think the transformer-based LLMs in the future. Because I actually do agree with the point that LLMs is just a compressor of the tags. They don’t really understand what they are talking about.
But I think as the develops, as the technology goes, one day we will have the truly AGI. But it’s not based on Transformer.
Grace Shao
What does AGI mean then?
Hang
AI means the AI can really understand what it’s talking, what it’s doing, and really have emotions. Yeah.
Grace Shao
Fully human-like, emotionally, intellectually functioning being.
Hang
Yeah, like human are…From this perspective, they have emotions, have logic, they understand the work, the 3D work, and they have logic of, okay, why I’m doing this, and what I’m going to do in the next.
Grace Shao
Is that scary to think? Because in many ways, they would be more powerful than us, right? They can speak every single language on Earth. They will know more knowledge than us as an individual. But can we though? What if, you know, what if there’s charge and they can charge themselves these days, these humanoid robotics?
Hang
we can cut off the power. You mean to take off and charge by themselves?
Grace Shao
Yeah, like, do you ever worry about this AGI achievement going rogue?
Hang
To be honest, I’m both a bit excited about this and a bit scary.
Grace Shao
Yeah, I think especially for the next generations, right? Like what does it mean for them? Yeah. Thank you so much for your time. I really appreciate it.
B. Christian Hu, Head of Global Marketing and Operations at Qoder
Grace Shao
Christian, thank you so much for joining us. I just spoke to your colleague, Yu Hang. he was super helpful in explaining to me the technicalities of Qoder and the design of the product. My interest right now is really shifting towards the strategy and business side of Qoder. I’m really glad that you can join us today. So why don’t we start with the beginning? Why build Qoder inside of Alibaba? What was the thinking behind that? And what unique advantages does that really give you?
Given that you have someone who just threw $53 billion into AI infrastructure that’s backing you, right? In terms of distribution, your infrastructure, your data, your model access, how does that really advantage you in many ways?
Christian
Okay. Thank you. Thank you, Grace. Thanks for having me here. You know, Alibaba has a very big plan and has very big ambition for AI ⁓ and his position in the future in the AI industry. And as you know, Alibaba has a full stack. We call the full step strategy from the cloud to model and to application. So that’s the full stack strategy. And for, ⁓ you know, a home computer, you know, we don’t build Qoder in a vacuum. Yeah, we build Qoder from the real context because we can find, we have got so many, maybe thousands of engineers from inside Alibaba. They are facing very real software problems in real software development. They have many issues in interface and how to fix the issues. But for the existing AI coding tools, they are maybe reactive. cannot...resolve some existing problems, maybe some complex and sophisticated problems in the real software development process. So they are trying to refer to a new product. we build Qoder just from inside. We have so many inside, the mind from the inside engineers. also, as you know, be part of Alibaba give us, mean, have so many advantage because first of all, we own models.
Because you know, you know, we have early access to Qwen and that’s how I’d propose large-scale models We don’t just use Qwen we also reinvent and feed the Qwen with our real data from our AI coding Qoder base. So we just really reinvent the Qwen and we are coordinated with the Qwen team to improve the performance of Qwen models and
Secondly, we own the cloud. I mean, the bottom layer of Alibaba’s AI strategy. that’s the cloud. Every digital tools and maybe even every AI coding tools should be based on large cloud infrastructures, consume very large computing powers. So cloud is also a very important factor in AI coding platforms.
The third, think we, I think the most, last but not least, we also have the advantage of Alibaba has so many enterprise customers and so many business, you know, we got some business lines maybe from the consumer to the from the SMB to large enterprises, from the consumer to industries. So we got so many data across, as real data for us to, how to do involve the, the area AI to, you know, to ⁓ upgrade our coding platforms from the real data. that’s the, so, ⁓ so back to the, the origins of the Qoder So we just not, you know, we’re just not create another AI coding platform just for the tool, which we want to build a Qoder as a new platform for a Agentic platform for the real software. We want to build the Qoder for real software, not just for fun, not just for the fun making, but for the real software.
Grace Shao
Thank you, that’s really helpful for everyone to understand. I think saying we have a model is the most humble thing anyone can refer to Qwen as, because you guys have one of the best leading open source, open weight models in the world right now. Actually, so on that right now, you know, we’ve been hearing a lot of news about people adopting Quinn globally, and it’s really being used not just in China right now. ⁓ Who are the people using Qoder actually?
Are they mostly Chinese developers or are you guys actually expanding globally? That’s, I guess, the first half of that. And the other half is, are these mostly professionals or are they like students or are they enterprises? Like, how do we understand the demographic here?
Christian
Yeah, good question. We are building Qoder for global developers from day one. Yeah, so that’s not just for Chinese developers, but for global ones. And for now, ⁓ actually for now, our users are mainly come from the, we call them individual developers, not just the enterprise users because know we the enterprise editions on it our way we are just we want to start with the individual you just at the first and and the funding you know geographically from China to the overseas market so we want to some difference between you know preference different preference for different you’re just in different regions yeah from in China they would prefer to the more, more integrated coding system existing with the existing customers. It may be fully integrated with existing systems. And in the Western countries, maybe in some in US or maybe some in ⁓ Malaysia, the users will prefer to more flexible workways. So they’d have a different preference for different coding languages. And for now, we are trying to solve the real software development problems. the most of the users come from the professional users. Because they have a real problem to resolve in the real software development process. Because they doing their work. Because they doing their work and they want to increase their productivity levels to solve more problems. So the main users come from the professional users.
But we also find the increasing adoption of the individuals and some new learners, maybe some product designers, maybe some UI, maybe some UX designers, they want to deploy the AI coding tools to to expand some ⁓ new interface or maybe some new mini apps to increase their productivity is, yeah, so that’s the trend. we believe, uh, Qoder is created to solve the real problem real software development problems. But we also find that the, our agentic, uh, models and the questing models can allow, uh, more users, just like the new learners and new indies, new learners to, uh, new individuals to use our software to create something new, something more powerful. Yeah.
Grace Shao
So my understanding is that Qoder itself came out of the desire to help professional developers. But Qoder Quest, that mode you can go into, can actually help people with less technical backgrounds to be able to play around with it and potentially still build their own thing.
Christian
Yes, Quest Mode is maybe the key to unlock more space for web coding. What is web coding? Web coding is for non-professionals to use the Qoder tools to create something new. I think the Quest Mode may be the new way to unlock more web coding. But our Quest Mode is different from the existing agent model of other competitors or maybe other coding platforms because our Quest Mode is always is really is also a Agentic is a Agentic about also ⁓ we called a delegated and you can control the workflow and control the result on the question model. So that’s that’s for real software. Yeah, so the Quest Mode of coding is also is for real software.
Grace Shao
I see. I think I have a question for you. I asked Yu Hang earlier as well. But basically, my question was that, you know, there are a lot of tools out there already, like the co pilots Curser co whisper Warp you know, there’s there’s a whole array of them. How do I understand where Qoder sits so Yu Hang explained it to me in the technical sense of where it sets between co pilot and a agentic. Can you explain to me where Qoder sits in terms of like the business positioning.
Christian
Okay, we have a slogan for a Qoder We Qoder a agentic platform, a agentic coding platform for real software. So we got two keywords, Agentic and the real software. I just explained what is the software. I can explain more about the software because many developers, mean, no matter professional developers or non-professional developers, they found something critical in their developing process because they need to know the existing Qoder base. They need to understand what the existing Qoder means, what the existing documents mean for the codes. So they need to understand so many documents, so many files to understand what the coding process will be like.
So we created a Ripple Wiki and a non-context memory function to understand the codes, understand the documents, understand the behaviors of the developers. So for the new entrants, for the new developers, they know how to get the real software down. So that’s the issue in real software development context.
Yeah, that maybe seems different from what we call the rubber coating. So that’s for real software. And for Agentic, for most coding platforms, we call it your prompt and the Agent, the React. for Qoder we call it your delegate, then Qoder delivers. So then Qoder that deliver the real software and real results and the real apps for your delegations. So that’s the difference from the existing, I mean, maybe some most of the competitors to agentic platforms. So we are just not want to respond because of the delegation that can, you know, to free you from the desk. mean, for agent, for most agents, you also need to speak to the agent.
You need to communicate with the agent while you are sitting alongside the desk. But the dedication can free you. You just don’t need to communicate with the agent. don’t need to interact with it for line by line, word by word. You just need to monitor the per science and maybe some need to check with the workflow so that the Qoder can deliver the result. That’s the difference between way from the other competitors.
Grace Shao
Actually, let’s take a step back. I’ve noticed that when I was doing some research on you guys that, you’re, it’s not just Alibaba that is creating these coding tools. You know, we have even just in China, we have ByteDance creating something similar. Obviously, Microsoft has things like has Copilot. So then we are looking at obviously then the startups and the Frontier Labs all kind of swarming into coding tools. Why is that? What is really the reason for focusing on coding right now as the next kind of use case?
Christian
Yes, actually, coding, as we just talked, coding may be acceptable for every engineer, maybe a non-engineer, maybe you’re just a digital learner, maybe just an analyst for the AI. So coding is the most certain way for token consuming, for the ⁓ infrastructure consuming, the tool to the cloud infrastructure. Maybe the coding is the most important way. And for different players, they are playing different games. For giants, you just mentioned the Bydance, maybe Microsoft, maybe AWS, they have very large cloud infrastructure. They just to integrate the new AI application to the infrastructure. And for SRAPAC, maybe some other front-end labs, are... ⁓
They are trying to find the new path to the developers, maybe some to the application levels. for Cursor, they are just a new service. They are deploying the large-scale models to make it accessible to the new developers. Cursor has...
I think for new startups, have their weakness, maybe they have their cost structure weakness. mean, they are the users of the large-scale algorithm. They can’t dominate the large-scale algorithm model and maybe they will be at risk to disengage with the large-scale algorithm model owners. So that’s the cost structure that is at risk for them.
And for Qoder know, as I just mentioned, the Qoder is born within Alibaba. Alibaba has full stack from ⁓ cloud infrastructure to model to application. So I think that’s a great advantage. As you know, ⁓ as you just mentioned, Qwen is very popular around the world. some, you know, I just kind of found the news that Airbnb, Airbnb,
Grace Shao
Yeah, Brian Chesky
Christian
Yeah, yes, I said adaptedQwen open source model to the real invent their own infrastructure That’s very popular and for it for cursor. Yeah, it’s just anything for a cursor itself They just came up with a new composer composer model because it’s a small small model for coding but We have a speculation that the this small model comes from the Qwen or maybe some cheap. Both of them are from China. So we will find that the full integration and full connection with the model and the client structure will benefit a for Qoder in the future.
Grace Shao
Actually, on the point of how Qoder sits in Alibaba, can we kind of zoom in on that? Can you help us understand the big picture? Where does Qoder sit in the Alibaba Grand AI strategy across the stack?
Christian
Yeah, I think Alibaba AI strategy is a very big picture and also very long roadmap for what we call the super artificial intelligence. We call it super artificial AI. Yes, wait, wait, wait, ASI, that’s the roadmap. so from the map, so when we look at the map, I think it will form the bottom to the top level.
I just mentioned is the cloud infrastructure. Yeah. The middle level, mean, the Qwen, maybe some other large Niagara models in the middle layer. And the Qoder, I think it sits on the application level alongside with some other applications. Maybe, you know, Dink Talk, maybe Quark, maybe some other applications on the application levels. think that’s the full, we call, so we call the full stack strategy for Alibaba in the near future. Yeah.
Grace Shao
I understand you lead Qoder, international operations and marketing, right? And that’s a pretty big title you got here. What’s your GTM focus right now? Where are you kind of focusing on essentially selling your product to? Like which markets, through what channels?
Christian
Yes.
Grace Shao
How is this kind of working out for you?
Christian
Yeah, so I think it’s a tough task. I know because we are new, we’re just a baby. We just launched our products about two months, no longer than three months. We’re just a new one in the market. our ambition is here. We want to ⁓ become the top global coding platform for global users.
So for me, think the go-to-market strategy for ⁓ us to go global, think we need more partnerships, we need more integration with the local communities. So for me, that’s why I’ve followed to different regions to meet up with local community developers, maybe the leaders of local communities. We want to talk about it.
What kind of preference for them, what kind of products they want to prefer in their context, in their context, maybe within the enterprise, maybe within the individual developing context. And also we are aiming to the global products, we are not going to separate.
China with the global markets. We want to offer two different products. We just offering one product, one platform, one user interface for all the users around the global. So that’s the challenge. But we believe we can do that because we are trying to interact directly or maybe talk directly with ⁓ the developers from the different corners of the world.
Grace Shao
Yeah, that’s definitely quite different from how Chinese companies used to sell their products abroad, right? Because it used to always be a one app location or one interface, domestic one interface for the globe, for the rest of the world. So actually on that, think my final question for you really is just how are you navigating the current climate? Obviously it’s not.
Probably not easy given just current situation with geopolitics, with the competition, with everything, right? How are you navigating differences between China and the global markets in terms of adoption, compliance, data residency requirements, and even developer culture? Because I know you were just in Singapore, like you mentioned, you’re flying around a lot, you’re meeting people from different parts of the world. How are you making it all work?
Christian
Mm-hmm. Mm-hmm. Yes.
Quite different. I think it’s quite different. I have seen huge difference in the developers in China from the developers in Southeast Asia. Because I just flew to Singapore and met up with some local developers. The difference comes from different angles.
User interface and the language and maybe some other features of the software. So the difference, is huge difference between the different rappers geographically and demographically. Yeah, so all the differences are very, very huge. And we also find some, because we are trying to offer our enterprise edition in the near future. So we also started a very
We have made a study for the enterprise levels in different markets. We find quite different stories because in China, the developers in enterprise, maybe in the small companies, in large small companies, they would prefer the company to purchase the software. They prefer to use the existing software. The truth is using software can be embedded into the existing OA system or existing software ecosystem. So they prefer the existing, they prefer the integrations. But in other countries, maybe in other markets, they prefer more flexible and more athletic applications. They don’t care about the integration, they don’t care about the...
I mean the sophisticated integrals, maybe sophisticated workflow and controlled by the company owners. So that’s the different culture. So you can find it’s very common for individual users to buy our software, buy our products in other countries, mean outside China. Yeah, so that’s the huge, that’s the huge difference.
Grace Shao
That’s really interesting. I would have thought that integration part is something that everyone kind of wanted because it makes your workflow so much more seamless, right? And that was the selling point for all of Microsoft’s tools essentially, right? In the software era. Interesting. Yeah.
Christian
Yes. But it doesn’t mean that we don’t need to make an integration or some ecosystem with the... You just mentioned Microsoft, mean, the OA or maybe some Workday, some other software platforms. We also want to explore more connection with existing software.
Grace Shao
Thank you so much, Christian. I really appreciate your time. Thank you.
Christian
Thank you. Thank you so much.
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“Retail is simple. Retail is just how do you sell something, and make someone’s eye light up. AI or any technology you add to it, is just another way to do that,”
— Sharon Gai, retail tech and AI expert, former Alibaba executive.
Joining me today is Sharon Gai, an expert in AI and innovation, with a focus on retail. She was an executive at Alibaba, where she advised brands and heads of state in crafting their digital strategy with programmatic marketing and AI.
In this conversation, Sharon shares her journey from working at Alibaba to becoming a consultant in AI technology for global companies. She discusses her experiences in e-commerce, particularly the evolution of live streaming and innovative marketing strategies in China. Sharon emphasizes the importance of AI integration in retail operations and the future of shopping with AI avatars. The conversation concludes with insights on simplifying retail to focus on core selling principles.
Sharon was selected as a RETHINK Retail’s Top Retail Expert and a LinkedIn Community Top Voice in 2024. She has two books, E-commerce Reimagined and How to Do More with Less Using AI. For more of her work, go to sharongai.com.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
02:27 Experiences at Alibaba: The Global Leadership Program
05:11 E-Commerce Evolution: Insights from Tmall and Live Streaming
07:54 Innovative Marketing Strategies in Chinese E-Commerce
10:23 The Rise of Live Streaming in E-Commerce
31:33 The Evolution of AI Avatars in Retail
34:09 The Impact of AI on Shopping Experiences
41:00 Challenges in Retail During the AI Revolution
43:22 Integrating AI into Retail Operations
48:43 The Simplicity of Retail: A Unique Perspective
Transcript [AI generated]
Grace Shao (00:00)
Hey Sharon, thank you so much for joining us today. It’s great to reconnect with you. We met like, I think four or five years ago back in Shanghai and you were still with Alibaba, right? So why don’t you start with telling us about your journey? Like you grew up in Canada, you worked in Hangzhou, Alibaba. I know now you live in New York. How does it like, how did you kind of bring together all those expertise to what you do today, which is help?
Sharon Gai (00:27)
Sure. So when we met, I was working at Alibaba still. For me, as somebody who was born in China and raised in North America, and then I chose specifically to go back to China to work for a bit, the reason why I did what I did is I just knew that going forward 10, 20 years out, the two major superpowers would be the US and China. And I already had a pretty good understanding of things that were going on in North America.
⁓ Going back to where I was born and getting the chance to work there at one of the tech companies there really opened my eyes to how both countries work. I think in the future it’s going to be a ping ponging back and forth of I’m sure different ⁓ globalized companies and projects between the two places.
⁓ And so that had blended pretty well to what I do now, which is a lot of writing and keynote speaking and consulting for global companies that both have a footing in China and the U.S. So it all ties together pretty well now, but definitely as I was going through my through line or life trajectory, it seemed very confusing in the beginning phases.
Grace Shao (01:35)
Yeah, tell us a bit more about your time at Alibaba, because I know you were part of quite a special cohort. was a of a test and trial group of international cadets, per se.
Sharon Gai (01:47)
I’ve never been in cadets before. But I guess with anyone joining Alibaba, it feels like going into entering some, a ⁓ corporate army of sorts. But the program was originally set up by, or it was a brainchild of Jack Ma’s. He had always, I think he had similar thoughts, which was, you know, eventually you’ll hit the ⁓ bottleneck of about a billion or so. ⁓
Sharon Gai (02:09)
internet users in China, where do you then grow the company beyond the billion users? You have to find it outside of China. And so the first place of external search was Southeast Asia and then into the Middle East, Africa, Europe, and then eventually the US. And so his long-term vision was
to recruit people who came from those places, those corners of the earth, to get them to come to China to be in green with Alibaba’s culture, a way of working, and to bring them back out again, and then ping pong back and forth, just as I thought. So I think his vision and my own personal vision aligned pretty well. So that’s what got me to join the program. And yes, it was definitely an experiment. There were many, what I would call seasons of us or cohorts.
every single year there were new people that came in, from different cultures based on the strategy of the company at the time. think in certain years, they really wanted more, a certain language to be spoken. So they, really hired for that specific language. and it, definitely changed at different versions. but the idea was to bring in.
people who are bicultural, multicultural to eventually lead some of the business units that was trying to expand outside of...
Grace Shao (03:22)
And actually on that point, what were you doing at Alibaba? I believe you were involved with Tmall, right? So the international business, flagship business of Alibaba’s e-commerce sides. Could you tell us a bit more about that?
Sharon Gai (03:34)
So the first sort of business unit I was in was called Tmall Global. Our larger BU was called Tmall Import Export. of course, as you would know, Chinese tech companies always like to change names and just change things. Embracing change is one of the values.
So at first it was called Tmall, import and export. And I was first on the import side. And then I went to the export side, which is what we call Tabout Tmall world, where there’s about 50 million Chinese diaspora around the world. And they also will use Tabout as a shopping app. At the time was also the growing footprint of the Shiians and Tmus of the world, where these local Chinese e-commerce apps were trying to leave China.
And so Taobao Tmall World was also part of that exercise. And so those were my main two. the first was, or sorry, one of them was Taobao Tmall Import-Export. And then I moved to Tmall Classic, which is the domestic side of Tmall.
where the brands, most brands were either Western brands selling into China or Chinese brands selling to local Chinese consumers.
Grace Shao (04:47)
So you’ve really got a good look into how the retail e-commerce digital evolution happened in China. You’re super plugged in. And you were there for like six, seven years, right? So when you left, you published a book, you published E-Commerce Reimagined. And I believe at that time it was COVID, pre-COVID, and just everything kind of changed again. And we saw the rise of e-commerce really.
⁓ being part of the daily lives of North American consumers as well. Tell us a bit about like, I guess, first your book and then tell us about what you witnessed over the 10 decades, sorry, about six or seven years while you were Alibaba.
Sharon Gai (05:25)
Yeah, so in 2017 when I joined, was the height of live streaming starting in China. When I joined T-Mall Classic, I was actually one of the first teams to set up a live stream and take it to the US. So funny today, I’m back in, funny today I’m in Kuala Lumpur, Malaysia where I’m joining you for this podcast, where Jackson Wang had a concert here yesterday.
And he was one of the celebrities that we collaborated with first to do one of those live streams. At the time when we were trying out and testing out live stream rooms, we didn’t know about the flow, how to direct things, what questions to ask him, how do you showcase the products in a very natural, organic way. All of those were tests and experiments that we figured out throughout the process.
But during that time, I mean, in a lot of, so I do a lot of keynote speaking today and in a lot of the keynotes that I do, I always start off with comparing just the size of the consumer economy of China, where it is the largest one in the world. It has the most number of internet users. It also just has a very voracious consumption habit. It also has the highest,
internet penetration in the world, the number of mobile users in the world, and people, and out of those users, people who are buying things online. There’s a high amount of trust online because historically from Tmall, from JD, there’s been very, very high standards by merchants. So when merchants enter a marketplace, there’s usually very demanding.
terms for them to host returns, be able to accept returns, to deliver things on time. And that standardization eventually increases trust in the marketplace that even if there is a new seller, new tab out seller that emerges, the consumer will most likely trust them versus if you had that same transaction happen in the US, there’s a lot less trust in the marketplace.
So 2017, 2018, we’re laying out all of these foundations. And I think what I took away is the immense competitiveness of that space. And so out of competition, naturally there’s more innovation because as a merchant, you’re fighting for the same eyeball that your competitor is. So either you’re going to lower your price or
better your brand or better your quality. There’s some sort of lever that you have to tweak to be better than your competitor. I’m sure you’ve heard the term involution. In China, it’s an involutionary environment in schools, in academia, and definitely in business. And so from a brand’s perspective, there’s just a lot of...
⁓ playful and competitive things that they can do and that’s definitely
Grace Shao (08:20)
Can you give us some examples?
Like what kind of playful gimmicks or tricks would people have to use to kind of draw more eyeball over?
Sharon Gai (08:28)
So back then I was part of ⁓ the Tmall food team and I was in category management at the time. And this is a traditional sort of key account structure in all sorts of countries or categories where your top accounts will produce roughly 70 to 80 % of your total GMB.
And then the next tier, mid-tier type of brands will produce about 10%. And then the long tail produce another roughly 10 % or so. And so in...
Grace Shao (09:01)
So is that just in China for Taobao and Alibaba? Is that like a reflection of the whole industry similar kind of to how the marketplace play out in the US?
Sharon Gai (09:10)
It’s similar in the US for sure. If you look at the large FMCG companies, Procter & Gamble, Unilever and Nestle, the large three will take up about 70 to 80 % of the market share. And then depending on the subcategory or the category that they’re in, they will pretty much dominate the market or they’ll dictate a lot of how the market shakes out.
Grace Shao (09:12)
Okay.
Sharon Gai (09:36)
So during that time, or let me preface by saying that’s what you see in a traditional offline heavy space. So in a supermarket, for instance, where things are a lot more traditional. So in 2017, 2018, around that time, it was when online, there was that huge push into online. And in the...
food space, there was a lot of DTC brands that sprung out of that. At the time, China didn’t have that green light, red light policy. So the consumption sector was really robust. lot of consumption startups, FMCG related startups raise a lot of VC dollars and wanted to IPO. So at the time with their VC dollars in their hands, they were able to create all sorts of new brands.
So the Chagees, the Nice Nose, these sort of tea brands and bakery brands that you see often in Southeast Asia or in China, those were all sort of created around that time, 2017, 2018 time. And I remember...
going back to your earlier question, so what sort of creative things did I see? There was this milk brand that experimented with adopting cows. So in China, depending on where your listeners are, the dairy industry is very traditional. And in China, there’s Meng Niu and Yili are sort of the two very, very archaic, hundred-some-year-old companies.
been in China just for so many years and everyone knows them. And around that time, would not really do much in the e-commerce space because they knew they had that foothold in the market. And so out came this new brand called, in Chinese it’s called Zhinyang Yitou Niu, which translated is Adopt a Cow. Funny name, and I think part of their success was also
the cleverness in developing that name. I wrote about this case study in my book too. Their goal or the goal of their founder was traceability. So a couple of years earlier than that, there was this huge milk powder incident where several babies, I think infants died because of this milk powder was poorly made. And so his...
Focus was could we have every family in China quote-unquote own a cow that they can milk from afar? And that when they get their milk delivery, it’s from that cow that they’ve raised or that quote-unquote they adopted So that was his ultimate vision That’s how that’s how the name came about
⁓ and the things that we did was, selling milk cards. we, so, so traditionally on an e-commerce, have skews where you’re buying the actual product. This was the first time where we’re buying, a, almost a gift card, but this card would just be for this one brand and it would be sold at a much steeper discount. so.
That’s why on the consumer side, you would want to buy it. On the merchant side, you would want to sell it. And we would roll this product out to a lot of other categories for a lot of other products where people knew they would be spending that amount of GMV. So anything like toilet paper, rice, flour, milk, health supplements, any sort of category where you knew you were gonna spend that much.
in a year’s time for you yourself and your family because it’s just one of those products that you frequently shop for. And so that was something that eventually the entire platform rolled out to the entire team all rolled out, but it started with that one company. And the reason why that company did that was because there were two very large incumbents.
And the only way that he could compete is if he was innovative enough to think of something else. And throughout my time, I worked very closely with that team to see them from day one where they had two followers on their store to now if you go and look at that brand, it’s the millions of followers they have. think by now they’ve also IPO’d because they were able to IPO before the red light, green light policy.
and they set a new standard because, out from out of that, there was a new type of, DTC, all sort of mindset that came out of T-Mall where, you know, if you were a no-name brand, if nobody knew who you were, as long as you were able to think of attractive enough and innovative enough things to keep attention and to keep your consumers.
to for you to for first of all your customers to discover you and then for them to come back again and again you you were able to Survive even if you were a newcomer and even if there were very large established incumbents in your category and so that was a slice of what I learned and witnessed and there was many many more examples, but
It’s a that one’s a pretty notable and memorable one for me.
Grace Shao (14:47)
That’s like so interesting. have so many questions, but I don’t know if we’re gonna be able to double click on everything. Number one is, do you think it had anything to, this is like, we don’t have to go fully into this, but I do feel like 2017, 2018, like you said, it was a peak of like also private equity in China. And like, did you see consumer brands were just like going nuts? Like whether it’s like makeup, cosmetics, like FMB, definitely like a lot of brands peaked.
Sharon Gai (14:49)
No.
Grace Shao (15:12)
I’m surprised that this one’s still living, which is great, but it’s just like a lot of the makeup brands, like, you know, Perfect Diary also kind of did this, like they kind of just fizzled out, right? There was like these coffee shops all across the country that were coming up that were selling like specialized filtered coffee, whatnot, right? So it’s interesting to see that this company was able to utilize innovation in their marketing and branding, but actually sustain this business.
Number two, comment is how can they actually track which cow? It seems pretty crazy. Like surely they weren’t actually getting the milk from that specific cow, right?
Sharon Gai (15:49)
Which one should I answer? On the note of the cow, that was his vision. No, today, think, well, I also stopped following the brand after I left the company. To my knowledge, it did not go to that extent. However, the extent that it did go to was the founder did do a lot more beyond just the cart.
Grace Shao (15:53)
Yeah, so it’s not actually like played out, right?
Sharon Gai (16:14)
He also wanted to and I’m sure you also probably covered a lot of it 2017-2018 was a big push in agriculture from a Chinese government standpoint. He also wanted to create these he wanted to turn his factory into like a like a like a touristic activity where people could go visit the cat like your cow or you could go and
Grace Shao (16:35)
Okay.
Sharon Gai (16:36)
and experience how milk was made, how it was pasteurized, the entire process. It’s actually a very, very complicated process before it gets, know, we sometimes take for granted like packaged or milk from our fridge is taken and drink it, but it goes through so many steps. he, so within that sort of touristic factor, you could see,
certain cows were penned off and there was a video camera on each of them and each cow was numbered. So you could technically see it, like see your adopted cow. But to date, I don’t think it eventually, or that model stayed, but there were many, experiments that he did. And I’m sure there were...
Grace Shao (17:11)
Okay. Okay.
⁓
Okay, we’re going way off track.
session with these cows. I’m just like bring it back and I’m going to look up these cows afterwards. But it reminds me also this Alibaba thing where people try to gather points and they can own their own trees and they can plant a tree on Alipay. Right? Okay. So anyway, let’s bring it back. Okay. Let’s rewind and go back to retail and AI. and technology and innovation and retail. So live stream, you mentioned it, 2016, 2017.
Sharon Gai (17:37)
Mm-hmm. That’s happening.
me.
Grace Shao (17:51)
You’re one of the first, you know, part of the teams that were kind of, you know, really, I guess, leading the frontier of live streaming technology or even the strategy. It’s still not that mainstream in the West. So to start with, could you tell us what even is lives from e-commerce and how has live stream shopping really change in e-commerce in China? Is that something that you’re seeing? And like, I guess, ⁓
adoption in the US right now with TikTok or anything else.
Sharon Gai (18:22)
Mm-hmm. So live streams, e-commerce is when a seller and you can be a brand or you can literally be a person who owns something that you want to get rid of on the internet. You turn on your camera and you showcase this product live to an audience. And this audience could be some could be your actual followers or just be strangers. And then this sale is made technically the old in the old days on the in the western side.
The first adoption of live stream shopping was on Facebook live where Facebook live used to be a big thing. People would go live all the time. And then you host a room and then you make a bunch of sales and you’re actually recording a lot of the addresses and whoever bought you bought something by hand. It was very, very manual. And then on tab outside, they started with live streaming in app.
And in the US, think, you know, the Walmarts, Amazons tried their shot at live streaming. They invested a lot of dollars in creating these very posh and professional looking streaming rooms, setting up streaming studios. And all of that didn’t really go anywhere. So it had its spurt of interest. I think I think at the end of the day, it’s a it’s
One, it’s a timing thing and two, it’s sort of the way that it’s done. Yeah, and then I think the third, the host has something to do with it. So first timing in China, when I played with it, it was in 2017. That was still newish in China. China didn’t really take off with it. It didn’t really become mainstream until 2019-ish. That’s when we really understood. That’s when every single.
brand, merchant, you that, and if I’m outsourcing my operations to a TP, my TP better have a streaming room in their company. A ⁓ TP is a team all partner. if you don’t want to run your own e-commerce operations, you can outsource this to a company that knows this space very well. And really,
Grace Shao (20:12)
Sorry, TP is like a, a TP is. T-Mall partner, yes. And to give the audience, sorry to interrupt, but to give the audience some context, like how big are we talking about live stream e-commerce? Like give people like the headline numbers, like a leading influencer during Double Eleven or their annual GMV. What are we talking about?
Sharon Gai (20:40)
I think in total, I don’t know the exact numbers, but it’s north of a couple billion per year for sure for some streamers. Which is crazy.
Grace Shao (20:55)
which is crazy. This is like a single salesperson
if you think about it.
Sharon Gai (21:00)
Well, on camera they’re one person, but behind them is hundreds of people. And I’ve been through their product selection process. I’ve worked with them throughout the night. They do not sleep. Their streams start at 8 p.m. That’s not actually when they wake up. A streamer’s day is at 2 p.m.ish in the afternoon. They’ll wake up.
Grace Shao (21:03)
Yes.
Sharon Gai (21:21)
and they will start getting prepared. Their director will usually tell them today, is the, you know, we’re going through the final list. This is the final price that we’re gonna sell all these products at. At 8 p.m., usually 8 p.m., maybe sometimes six, sometimes nine, the show starts. You’re streaming for about four to five hours, so you usually finish at midnight-ish. And then midnight.
Starting midnight to around 5 a.m. You start to review what you just did because you have literally just talked for 45 four to five hours You sold a bunch of product products during double 11 It might be tons of millions of dollars that you’ve just sold per show And then tomorrow it’s gonna start again. So
You’re going to review everything that’s worked, that’s not worked, what you said was right or not right because some of these products repeat again the next day. So they’re always fixing their script and they’re always fixing how to say it better.
Grace Shao (22:15)
they buy the inventory or they take a cut from the brands? How does a business work?
Sharon Gai (22:19)
They don’t buy the
inventory. Streamers will not buy the inventory. The only fee that’s given to them from a per brand basis is a, is a, a, is is a, like a slot fee. So if I’m a brand and you’re the streamer, I’ll pay you $5,000 for you to show my product. But beyond that, you’ll also take a 10 to 15 % commission per product that you’re selling.
Grace Shao (22:48)
So this is very different
from how Instagram influence is kind of where my point is. Like for Instagram influence, it’s like, okay, you place this product here, that’s one off fee versus a live streamer in China is actually making money continuously as long as like people are buying through their link, right? And people sometimes don’t realize that.
Sharon Gai (23:07)
They don’t. Yeah. So this very traditional financial model for streamers in China is not the same in the US. In the US, it depends. Some streamers are just hired to talk for two, three hours at a time, and they’re almost paid on a per hour basis. Some streamers are paid.
They’re paid a set fee for the entire stream. It’s not even commission based. Their whole job is to, if they’re selling clothes, they just put it on, turn in front of the camera, comments will fly in and say, can you try this on another color? They take it off, put it on the other color. And so it’s almost very robotic and you sort of follow what the comments are saying. It’s not commission based. So they think less. They have to think less. They’re a lot less strategic also with placement of products. Which one goes first? Which one goes second?
because that also changes, that also impact GMB. So the two financial models are pretty different. But going back to your question earlier, is this happening in the US? It is absolutely happening in the US. It’s actually, the last numbers I checked was about $60 billion in the US, and that was last year’s number. So it should be a lot more now. The biggest US player is this app called Whatnot. I think that’s the most successful app.
They’re about as there. think they’re a series D startup now. They started with collectibles and I know some of their early collectible sellers. They sell trading cards. That’s the start of live streaming in the US is trading cards. Like there’s a huge fan base for these trade. There’s this Italian brand that makes this specific card and there’s a bunch of also intricacies with how to detect if a card is fake or if a card is real.
⁓ but, that’s the starter product is very niche.
Grace Shao (24:52)
So still quite niche. It’s not mainstream
and prolific in China right now, like how everyone knows what live stream e-commerce is, like any auntie on the street will know about it. So it’s just a very different kind of model. I wanna bring it back and talk about what that means. Does it mean it’s like...
shopping felt more personalized or did it mean like you said it was more rapid? Did it mean more interaction? Like I just want to see does that kind of foreshadow the future of technology and shopping experience?
Sharon Gai (25:23)
in the US? ⁓ I think it does. does. The way that I look at it is at the end of the day, you just want to be more, you want to reduce your barrier between the merchant and the buyer. The reason why live streaming works so well is because it’s live.
Sharon Gai (25:42)
Um, it’s so the, in some of the keynotes that I do, one slide that I have is the evolution of e-commerce. So in the nineties, you Craig list type listings, where it was just a product title and a description. There were not even photos because you didn’t have to put any photos. So some sellers were lazy and they just wrote down that I’m selling this leather jacket for $200. Um, and then Amazon came along and enforced the a plus content or, um, their, their product detail pages have to be.
put together in a certain way. So it became photos and then there were, was mandatory to have product videos. Then it was mandatory to, well, that part wasn’t mandatory, but then e-commerce became different influencers showcasing it. And then TikTok started Instagram, all of that. And so it’s pretty natural that things will just evolutionize. I think this goes back to competition.
which is China is just more competitive. So they jumped into live streaming a lot faster because we noticed that the merchants that live streamed did better than the ones that didn’t. It was as simple as that. And so the ones that didn’t would start doing it and they would have to, they were forced to learn it even if they didn’t really want to because that’s just what worked. And so in the U.S. it’s
it’s worked for these collectibles, toys companies. And I think other brands see that. now have, now what not has become cross category. Now it’s, they started with collectibles, but now a lot of fashion brands have jumped in, food brands have jumped in. Supplements also. And then in the TikTok shop world, it’s pretty, it’s more and more common for D to C brands to start streaming also.
⁓ to outsource their streaming to. Now there’s a new term called TTPs, which are TikTok Shop Partners, to do this for them.
Grace Shao (27:32)
I have a question. seems like in some ways that the China side, you said, the influencers, like not influencers, the live streamers themselves actually don’t need to be influencers. In many ways, it’s not that people go to them. It’s not like they build any, they bring any extra credibility. They’re just the avatar or, you know, the mannequin in many cases. Whereas in the U.S. it’s e-commerce is so heavily reliant on like the actual influencers, like, know, which celebrity endorses what.
you know, makes a big deal. Do you think we’ll continue to see that kind of divergent path? Or you think, you know, people will actually care less and less about who the influencer is?
Sharon Gai (28:12)
It’s actually kind of the reverse if we’re just talking about live streaming in that in the US, the people who are streaming are sort of no name people. And then in China, it’s the well known streamers, not the celebrities. I mean, on both sides of celebrities, let’s take out celebrities because they, ⁓ you know.
Sharon Gai (28:35)
They’re their own world. Also, some of them do
it sometimes. Yeah, there’s definitely pro, there’s definitely very well known live streamers like the Austin Lees of the world. They’re now a semi celebrity themselves because they’re so famous now. Like that type of profile is non-existent in the US where they’ve just gotten so good at the streaming side of things where they’ve in the branding side of things.
Grace Shao (28:53)
Mm-hmm.
Sharon Gai (28:59)
So will it diverge? think for the US at least, you’ll have more full-time B2C, more traditional Instagram influencers jump in and try it out. And it’s definitely gonna be a word of mouth type of thing.
⁓ like what I see on tech talk today is, there are certain influencers who, who are traditional influencers and now they’ve gotten in, they’ve jumped into the live streaming side. so I think in the U S it’s still a testing the waters type of thing. as for China, I think the big concern is the AI piece, where now if you go to any branded room,
If it’s an off hour, if it’s an off peak time, it’s some sort of AI avatar that’s streaming. To date, I’m sure you’ve heard of the Loyong Hall live stream room in 4.6.18. I think it was this year where he did the AI live stream and he sold X million of dollars. And it was more than he would do when he streamed himself.
in person as a human live. And that in the media space, there was a big wave of, are we going to be replaced by these AI streamers now? I think that was definitely a big, was definitely more of a PR push. Like it’s very easy to tweak those numbers so that you do make it seem like your AI version can sell a lot more. ⁓
Grace Shao (30:24)
Mm-hmm.
Sharon Gai (30:25)
You can play with pricing, the product that it sells, a lot of things. Yeah. So, but so in China, I think the future is going to be, are those big name streamers going to want to employ an AI version of themselves to conduct live streams? I think that is something that a lot of them are thinking about. Like once we let it happen, are we going, is it, how is that going to change the industry?
Grace Shao (30:50)
Actually, that’s where I really want to kind of take our conversation to next, which is like, we’ve talked about live streaming and that was, that’s kind of like the next technological, I guess, breakthrough in e-commerce for the US given that like, you know, Asia, East Asia is already like really, have, e-commerce already really, really, sorry, I want to say is like this live streaming model is already very prevalent across East Asia. ⁓
And like you said, the next stage of technological breakthrough for e-commerce in China or even including East Asia is really in avatars or AI avatars. And we already kind of saw Alibaba with their kind of like fake avatar, like these like cartoon avatars for a while now. Can you tell us a bit more about that? Like actually give us the context of like
how the AI avatar technology within Alibaba has evolved over the years and what we’re going to potentially see in the future. Because what you just said with the loyal health thing is essentially this frees up his actual free time, his time, and his avatar can just sell for him. Then do we still need him? Or, you know, is he going to become the brain behind it? And what’s the future of AI within this, this whole conversation?
Sharon Gai (31:57)
There’s I think there’s a difference to note between a real person creating an avatar of themselves and Being like the the Wizard of Oz the Oz and like puppeteering their avatar Versus I think what you were talking about is the Aya ease of the world where it’s like very futuristic looking She is a completely fictitious person person
Grace Shao (32:02)
Mm-hmm.
Yes.
Sharon Gai (32:23)
Her face is made up, the things that she wears is all made up. That type of, maybe for a better term, that type of sort of metaverse looking, futuristic looking thing is not so much an avatar. But it does happen. There’s also, Lil Mikaela is similar in Brazil.
Grace Shao (32:40)
Mm-mm.
Sharon Gai (32:47)
She’s also like, yeah, she’s like forever 19 years old. She’s worked with all sorts of major luxury brands. We’ll work with her now. So that model is proliferating around the world. I think that way of selling something or the evidence of that in the market was pretty big in...
the 2020-21 time of the world, it’s definitely died down a bit. To date, I have not seen new versions of Ayayi because technically if Ayayi worked really well, there should have been many, many versions of her. from a brand standpoint, we should also see, like each brand would have sort of their own version of Ayayi. think this brand called Florisys.
Grace Shao (33:22)
Right.
Sharon Gai (33:39)
which is this makeup brand. created one too, but she’s also kind of disappeared. So I think that that... Right. It’s like another look. Yeah. Yeah.
Grace Shao (33:46)
It’s almost like mascots, the mascots, right? He had to see like, right? Like, and they kind of just died and yeah, but people actually still want
a human looking thing, whatever, like a human being to try on the product. So to your point, like the Loi Yonghao example, it’s like people still like the fact that it looked like him, like a human, but it’s not him, right?
Sharon Gai (34:09)
Well, I think the Lui Yonghao specific one was just the novelty of it. yesterday, I was trying to buy something directly in Chachi BT because of their partnership with Etsy. I just wanted to see if it’ll get delivered, when it’ll get delivered versus if I just bought from a traditional dot com website. Like there’s a novel and I just came out of Capgemini.
panel a couple of days ago before that where every single panelist was like, we’re all shopping and try GPT right now. like, how, I don’t know how long this actually gonna last.
Grace Shao (34:42)
How good is
experience good? Because I can’t do it in Hong Kong. Is experience good? you... Is the whole...
Sharon Gai (34:47)
Yeah, you can definitely buy things within the app for sure. Not every single product.
Grace Shao (34:55)
But is it a good experience? But is it a good experience? how does it differ from going to a Alibaba.com versus like a Taobao.com versus like Amazon.com?
Sharon Gai (35:04)
I think it’s very dependent on if you’re very sure of this product because the weakness of ChadGBT and just shopping via LLMs is it’s way worse display. Because usually product photos are very beautiful and they hired models and background sets to picture this product very nicely. But in ChadGBT, a lot of these pictures are compressed.
Sharon Gai (35:29)
If they were originally long, they’re like short now. Like a lot of these photos are, there’s no standardization. And then also it cannot show videos. It can show YouTube videos inside a chain of chat, but there’s no, like if there was a product video, it can’t show the video. So if it’s something that you already know that you already buy all the time, then I think that’s, it’s.
Grace Shao (35:29)
Yeah.
Sharon Gai (35:51)
It’s easier to buy through an LLM, but if it’s something that you kind of want to browse and look at and maybe like flip through a couple of pages of reviews or photos, it’s not the best shopping experience. I would still go back to our traditional dot com website.
Grace Shao (36:04)
So basically like the agent will just go from your intent all the way to purchase all like in one go. You don’t have to click through a bunch of buttons. You just say, buy me this using this credit card go. Kind of thing. Okay. Actually tell us about more about that. Like are you seeing more brands that you’re working with right now adopt like AI within their purchasing process?
Sharon Gai (36:28)
⁓ From the perspective of a brand, I think all brands right now, the big to do for all brands right now is AEO, if you’ve heard of that term. So it’s a differentiation from the old SEO world where if I wanted to buy like a winter pea coat, I put it in Google, a bunch of blue links show up. I click through the Macy’s link, the Bloomingdale’s link, maybe another one.
Ralph Lauren or something. And I look at it and I browse through it, maybe at Dakar, maybe at Leave, I go to another site. That type of experience is being changed because now a lot of people are searching for their products through a ChachiBT, a Claude, a Perplexity. And so now as a brand, how do you not show up as the blue link, but how do you show up in the answer? So if I’m putting in the same query,
So I want to buy a winter p-coat and this is my budget and this is my type of style. I want it for this warmth because New York is usually zero to eight degrees in the winter. What are my choices? And then there will be choices that show up and some brands will show up and some brands won’t. So to get your link or to get that skew to show up, that’s called AEL or stands for answer engine or optimization.
Grace Shao (37:49)
And how do you get on that list? How do you make sure you’re on that list?
Sharon Gai (37:50)
There’s a lot of things you have to do. And it’s also something that all brands and also startups are trying to figure out right now. So it’s like an empty space for startups. in, give it another two quarters, you’ll see a lot of seed stage startups that do exactly this, which is an AO. ⁓
AEO product, like use me and I will make sure you show up in an answer engine. It’s not a sort of, a, or fortunately, it’s not a very straight cut thing. Like if you do X, then you will show up. There’s a lot of things you to do behind the scenes from just reorienting your product detail page to make it more crawlable.
Some websites want to wear off bots. Whenever you see the Cloudflare pop up where it says, check if you are a human. Most websites don’t want bots to crawl all over their site by default. And so you have to remove that. You have to also make sure that your product detail pages, or any web page rather, is very, very intricate. So much so that if anybody searched for a certain ⁓ question,
that whatever your product detail page says will pull up. So for instance, even with the example that I just said, a peacoat in the winter for New York, and it’s perfect for zero to eight degrees. That was part of my question. It’s very specific because the way that we search has just changed. We went from typing a couple of keywords to Google to now, even for yourself maybe when you look into China GPT or when you’re looking for an answer, your prompts are getting longer and longer.
Grace Shao (39:15)
It’s so specific.
Mm-hmm.
Sharon Gai (39:32)
just what you are looking for is more and more, because it’s easier and easier to find information that directly hits what we want to find. So in the zero to eight degree example, if one coat seller had that listed, would surface in the answer engine versus the coat seller that just said, this is a beautiful coat. And like providing no additional
Grace Shao (39:49)
Right.
Sharon Gai (39:57)
⁓ intricate detail for an answer engine to pull off of. But beyond that, there’s so much more. There’s more on reviews, how well your brand is guarded, general sort of reviews, Google reviews of your brand. And then back in the day,
Grace Shao (40:12)
the credibility.
Sharon Gai (40:14)
Back in the day, these LLMs pulled a lot from Reddit. This month or these months, they’re removing that Reddit portion more and more because people say random things on Reddit. Also, Reddit is 50 % bots anyway. So they’re cutting down the amount of information they’re going to pull from Reddit. They’re now going to pull more from very more credible sources. But to get your...
Grace Shao (40:18)
Mm-hmm.
Yeah.
Sharon Gai (40:38)
a coat to show up, a lot of brands used to do Reddit campaigns. So they would hire an agency and create a subreddit for their brand and make sure that these skews from their new collection was very well reviewed. a lot of them were fake. But that at least it was talked about in the Reddit community because Reddit content was very highly regarded by LLMs. And it’s sort of a moving target.
Grace Shao (40:57)
Mm-hmm.
Yeah. Would you say like,
yeah, would you say then like getting on the AEO is probably like these retailers biggest pain point or choke point right now?
Sharon Gai (41:11)
It’s definitely on the forefront for a lot of them, for sure. ⁓
Grace Shao (41:15)
What other issues are they kind
of facing during this like kind of this AI evolution or revolution?
Sharon Gai (41:21)
⁓
In regards to, well, right now for retail, it’s just a very tough quarter. Like this year, we had no growth in retail in the US ⁓ and the Q4, we might go backwards a bit. So we might go negative one percentage point, but that’s mainly caused by just people want to cut back on spending. People are buying more private labels instead of original, like being loyal to brands. They just want something more.
Sharon Gai (41:48)
bang for the buck, more quality over or same quality for same dollars that they’re spending. And then with all of the inflation, all the tariffs, it also makes goods. There’s just a lot of instability in the market. So everyone is trying to hold on and just maintain and survive. There’s also so many retail chains that’s closed and
Grace Shao (41:49)
Mm-hmm. more expensive. Yeah.
Yeah.
Sharon Gai (42:14)
just gone
bankrupt in the past year. So 2025 has been just a major shakeup for retail. I think a lot of brands want to, their CFOs are telling them to AI-ify their teams and to start employing agents instead of more people and increasing headcount in their teams. But I think for a lot of retailers,
Grace Shao (42:35)
And what kind of agents?
What kind of agents are we talking about?
Sharon Gai (42:37)
Agents for all sorts of every segment of the value chain from product creation, the merchandising piece to buying. So how can you AI-ify your procurement team? Maybe not completely remove the person, but at least stop them from increasing the team and to start outsourcing a lot of the work to AI. To the redesign of websites, writing, copy, creating.
product detail pages, creating marketing content, how do you sell, how do you market this product better to your consumers. All of that can definitely be, your productivity can definitely increase from ⁓ using AI and that’s what a lot of brands are thinking about doing and implementing this year and probably the next.
Grace Shao (43:08)
And that’s where a lot of the work focused on that you do. You help them integrate a lot of these AI tools and the AI agents into their work processes.
Sharon Gai (43:34)
Mm-hmm.
I think a big, actually, missing link from the retail side is there’s no one... When people learn about AI, it’s through today, I read this headline. Tomorrow, I read that headline. The next day, I took an executive education course with MIT and I got a certificate. It’s all... It’s very...
It’s very sporadic and it’s very disparate and everyone has different information points. There’s, I think there’s an absence in learning the foundations and the fundamentals and the applicability, the application part for your business. think those three core things, a lot of retailers are not formally doing because from the tech company side, now you have AI skills as...
Grace Shao (44:20)
Mm-hmm.
Sharon Gai (44:25)
mandatory, a mandatory piece of your job. But retail is so retail is a very traditional business, actually. So they’re there, unless you’re a D to C type of company, so your your team is naturally younger, everybody’s in their 20s, they’re already playing with Sora on their phone every day. You won’t really be part of that sphere.
Grace Shao (44:39)
Mm.
Sharon Gai (44:48)
So what’s needed is a level set of just learning about AI. Yeah.
Grace Shao (44:53)
Yeah.
So if you are, I like this question and I always, I thought it was quirky, but if you were, if you were given a hundred million dollars to really help revamp a traditional retailer, like let’s say Macy’s right. Or Bloomingdale’s or whatnot. how would you actually,
use and spend that money in terms of adopting AI and technology? What areas would you actually spend the money on if you want to make your company future proof in the next five to 10 years with this whole AI revolution?
Sharon Gai (45:22)
I would, instead of looking at where to spend money first, I think I would look back on our business metrics and what number do we want to shoot up more or tweak? Do we want to build our awareness? I think for Macy’s probably not because everyone’s already very aware of this retailer. Do we want our revenues, revenue numbers to go higher? Do we want to increase the number of locations?
Do we want to increase profit? I think I would zone in into an actual business metric. And then from there, cut into what is our bottleneck? So what part of the way that our current team is oriented, are they spending the most time on? Per role, what part of your job should be? Per role, if you were to do a task organization exercise where
There are, an e-commerce manager for instance, in your day-to-day, there’s definitely things that you do repeatedly because every time you upload a new SKU or you’re onboarding a new SKU, there’s definitely repeated things there. And there’s also things that are never repetitive where, know, if for instance, your SKU was just magically picked up by...
Grace Shao (46:27)
Mm-hmm.
Sharon Gai (46:37)
a Sabrina carpenter and now it’s, you know, floating all over the internet. That’s like a one of a kind thing that AI is not really good with in handling and capitalizing on. And so if we figure out the bottleneck and the repetitive pieces of your job to then find the tools that will take away that repetition.
Grace Shao (46:57)
Mm-hmm.
Sharon Gai (47:02)
to free you, to give that time back to you, and to free you in whatever else you can do to make the business better. I think that’s where, that’s sort of the stepwise sequence of things that I would do. And I think that in terms of a dollar figure standpoint, honestly, a lot of these AI companies don’t really know how to best price their products.
Sharon Gai (47:24)
The whole industry is just in a huge moment of experimentation right now, where a lot of people don’t know whether to price it by the more traditional SaaS models, or should we do this in a... A lot of companies are going for a consultancy, like a consulting services play, because Palantir popularized that, ⁓ or like a per time, per query.
Sharon Gai (47:51)
way a usage number and I get a lot of emails from partner vendors from their partner teams and every quarter or so they’re like, we we changed numbers again. This from now on, our pricing is gonna be this for this many queries that come in. So a lot of things are in flux.
But I think the important things that shouldn’t be in flux or that would stay is that those foundational three things.
Grace Shao (48:20)
That’s a very actually thoughtful answer. I was just looking for a headline. But no, I think that that’s really, really fair assessment. There’s just such an early stage in the whole industry right now that these tools don’t even know how to price themselves. But eventually they will figure out their own business models. And then I think for the users, whether these retail companies that you advise or not, they will figure out how to price that into their business.
Okay, mindful of time, I do want to ask, what do think is the most overhyped idea in retail tech right now? This could be AI related or not, but just in general, where do you think people are putting a lot of attention in and whether it’s overhyped or not? What do you think is an interesting retail tech that people might not know about? Actually, that’s a better framing of the question.
Sharon Gai (49:02)
something they might not know about is, well, I have mixed feelings about this, but in 2017, when I, before I joined Alibaba, well, it was right around the year. They actually set up this traveling road show called, Gateway. If, if you’ve heard of that, they did it first in Michigan. So I’ve ever re used to really want to bounce.
Grace Shao (49:19)
No, I’ve never heard of it.
Sharon Gai (49:24)
bond themselves with the Republican Party and go to all these Republican states and say China is a huge consumption power. We’re going to import so much meat and a lot of chicken and a lot of soy and grain. And so they had this traveling conference called Gateway and it would go through different states. And I remember there, there were these Alibaba developers that would make everyone
do this demo of putting on a VR headset and looking at Taobao, like three dimensional. So you can browse through Taobao and through the headset. I think that is something that people might not know about. It’s not so much hyped, I guess. It’s definitely something in the future, but it’s definitely not something here in the next three to five years. I think what we have.
Grace Shao (50:13)
You think like wearables, like wearables for shopping.
Sharon Gai (50:16)
Yeah, wearables for shopping or anything where it’s like a digital 3d store. Like all of the things that we talked about in the metaverse, like those are very, very far away from us. Yeah.
Grace Shao (50:31)
Yeah, I feel like those like VR
sets just like didn’t really take off. They had like a year of hype and then the technology wasn’t good enough. But I do see your point. Like even for myself, like if I could just wear something, I’m sound so lazy, but I can try out outfits and then like, you know, click order. That would be really helpful because right now it’s like I shop on Net-A-Porter and it comes to me that I have to like send it back, right? If it doesn’t fit, but that technology would really change how to shop.
Sharon Gai (50:57)
I yeah. Amazon has that, where you can change your outfit. And then you can also.
Grace Shao (51:02)
Mm-hmm, but it’s not feel like
as real yet, right? Like it doesn’t fit my body, my changing body and you know, like just like, it’s just like a fixed avatar, right?
Sharon Gai (51:13)
Yeah, it’s two dimensional, you, but you, it’s hard to do that too with a VR headset.
Grace Shao (51:15)
Mm-hmm.
I see what you Okay.
Sharon Gai (51:20)
Yeah, you would need some sort of mirror to do like that sort of, that was, that was a tech that was big too and then also died, which is like in-store interactive displays where guests implemented it, where you don’t have time to try on all those outfits. So they uploaded every single.
Grace Shao (51:36)
Yeah.
Sharon Gai (51:43)
item into this mirror and you can just plop plop the mirror and you’re standing in front of the mirror so you can see, so you can check out your outfit without actually physically putting it on. ⁓
Grace Shao (51:55)
Yeah, Lululemon had the fitness
ones, right? The fitness mirrors. then they kind of, it was the same technology behind it, but it also kind of died after COVID. Although I think a few Chinese companies are trying to make it happen again. They’re saying it’s AI empowered. But right now it doesn’t feel like much more than a big screen where you get to see reflection kind of just following the structures and instructions and that’s it, right? It’s not huge breakthrough yet. Yeah. I have one last question for you. Cause we were kind of going off on our
Grace Shao (52:22)
tangent again, the two of us, they first started with cows and now it’s like the mirrors. What is one unique belief or differentiated view you may hold about industry that you think is quite different from others in the industry?
Sharon Gai (52:35)
on the note of retail, I think.
Grace Shao (52:37)
on the note of retail and or retail and technology.
Sharon Gai (52:40)
I think retail is pretty simple. I think people complicate it too much with all these bells and whistles. At the end of the day, retail is how do you sell something and make the other person interested? That’s all. How do you take any product that you have, you can put any sort of shell over it, and someone’s eyes light up.
That’s what retail is. I think all the AI that we add, the VR things, the personalization and this or the other, those are all ways to do it. But I think it’ll just change. So I think...
I think not enough retailers go to the heart of what they’re selling or what they’re doing. And they often, it’s also understandable because, know, it’s most like in any Pyramidic industry, you always look at what the top key account or top brand is doing and they’re usually producing the most GNV. So you say, ⁓ they’re selling a lot. I’m going to do what they do.
And then everyone kind of swarms in that direction. And it’s not that, I think that’s for any industry, that’s just how I guess the world works. But I think the right way to approach it isn’t to look at that model. I think it’s more just to answer the question of what are you selling and how are you gonna sell in a way that attracts attention.
Grace Shao (54:06)
I think that’s like pretty applicable to like every industry. Like when you’re saying that, like really I was just thinking about like the business of media content or anything, right? Like fundamentally go back to like what, what is the problem you’re trying to solve? What answer are you trying to provide? What are you solving? Right? Like, and then, you know, go find your core versus all the kind of frivolous things outside of it.
Those are just tools. anyway, really, really appreciate your time. And I actually really love that you just walked us through the evolution of technology and retail, the intersection of technology and retail from, you know, what we know of e-commerce marketplace to live streaming to, you know, AI adoption. And we kind of even touched on our VR.
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Joining me today is Chim Lee, Senior Analyst at the Economist Intelligence Unit. He works in EIU’s China and Asia teams, and is based in the company’s Beijing and Hong Kong offices.
He leads EIU’s research on China’s advanced technologies, Climate change, Energy, Semiconductors, and Artificial intelligence, and also covers how China’s industrial policies link up with the broader diplomatic and macroeconomic dynamics.
Our conversation starts with China’s newly announced 15th Five-Year Plan proposal, which reveals the country's next priority and how it may impact its economy, society, and trade relations with the rest of the world.
We then dove into the current involution 内卷 issue, particularly zooming in on the solar and EV sectors. Then we look at the data center build-out driven by the AI boom and how local and regional governments are making sure involution does not hamper this sector.
Finally, Chim reflects on his work and his analysis of China’s economic planning and innovation direction.
--
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
They delve into the strategic priorities outlined in the plan, including self-reliance in technology, maintaining manufacturing dominance, and the role of private and public sectors in driving economic growth. The conversation also touches on the challenges of overcapacity and the evolving landscape of China’s international cooperation.
--
* China’s Five-Year Plan signals strategic priorities.
* Focus on self-reliance in technology and innovation.
* Maintaining manufacturing dominance is crucial.
* Private sector plays a key role in economic growth.
* Overcapacity remains a challenge in various sectors.
* International cooperation is evolving in China’s strategy.
* AI and new energy are critical emerging industries.
* China’s economic planning involves both public and private sectors.
* The plan addresses geopolitical tensions and trade flows.
* China’s approach to technology is both strategic and adaptive.
Get full access to AI Proem at aiproem.substack.com/subscribe -
In this conversation, I spoke with Tom Nunlist from policy consultancy Trivium, about China’s AI Plus plan and its implications for the economy and society. We discussed the role of digital infrastructure in AI adoption, the transformation of production relations, demographic challenges, and the government’s role in connecting academia and industry.
The conversation also covers the complexities of navigating China’s regulatory landscape, municipal and provincial implementations of AI policies, and the measurement of AI’s economic impact.
Tom shares insights on how MNCs can better align corporate strategies with government objectives during the AI growth era, and talks about the emerging AI pilot zones and how China balances between innovation and regulation.
Tom Nunlist is the Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores the intersection of politics and technology, with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100 clients covers policy analysis, risk assessment, government relations, and communications.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
04:27 Understanding China’s AI Plus Plan
10:55 Transforming Production and Society with AI
15:54 Government’s Role in AI Development
24:59 Measuring AI’s Economic Impact
27:12 Local Adaptation in Policy Implementation
28:01 Understanding Chinese Policymaking for MNCs
28:59 Aligning Corporate Goals with Government Objectives
31:19 AI Pilot Zones and Innovation Hubs
33:26 Promising Use Cases for AI Adoption
35:56 Balancing Innovation and Regulation in AI
42:52 Shifts in Government Priorities for Technology
45:56 Tracking Real AI Diffusion in the Economy
48:57 The Skills Gap Created by AI
AI Generated Transcript
Grace Shao (00:00)
Joining me today is Tom Nunlist, Associate Director of Tech and Data Policy at Trivium, a leading China policy research consultancy. Tom’s work explores intersection of politics and technology with a focus on data and artificial intelligence. His hands-on consulting work with Fortune 100, clients, covers, policy analysis, risk assessment, government relations, and communications. Tom, it’s so great to have you here and it was lovely meeting you online actually a couple weeks ago at one of the panels we were on together.
So today, will unpack China’s AI Plus plan, what it means for the real economy and how AI governance is viewed by China, sorry, viewed in China and compare that to what’s happening really in the US. But to start, tell us about Trivium and what’s your own professional journey. How did you kind of end up in Shanghai?
Tom Nunlist (00:45)
Cool, thanks, Grace. That was a nice introduction and likewise, good to meet you and good to be here on your podcast. Definitely flattering. I’ve seen your upcoming guest list and lots of exciting personalities coming up to be on the podcast. So yeah, a little bit about Trivium. We were founded ⁓ in, I think, 2017. So we’ve been around about eight years now. We are a China-focused, or right now China-only policy consultancy.
⁓ And so we really our kind of like value ⁓ is that we really know how the sausage is made here in terms of policy and politics in China and we help our clients mostly multinationals and investor clients understand that. So, you know, for example, a new policy like AI plus, you know, comes out, you know, we can come in very quickly and, you know, help inform our clients, you know, what this is, where it comes from, its overall context.
and then forming scenarios for how it’s gonna play out and kind of what they might wanna do. As you mentioned, I’m on our tech team, but we cover a lot more ⁓ than tech, really kind of the whole nine yards of ⁓ policy making, be it from economics to labor to kind of everything in between.
Tom Nunlist (02:01)
Yeah, as for for myself, I think I have a pretty typical China story, you know, insofar as, you know, long time expats. I came here in 2008, you know, more or less on a lark as a study abroad students, you know, to figure it all out and, know,
then life happened, got interested in it. I moved here permanently in 2013. My undergrad background is in journalism. So I studied here for a bit. Then I worked at a business review magazine and then eventually kind of made my way into the consulting space. Not too much of a very strict career plan, but again, one thing sort of leading to the next and here we are.
Grace Shao (02:43)
Awesome. So I think we’re going to go straight into it. What everyone’s interested right now is in the AI Plus plan. So that rolled out in August this year, believe, late August. It’s quite new. think people are still trying to understand what it really means. So the Chinese State Council published a high level paper that was basically pushing all sectors to really embrace AI.
It’s said to be the most comprehensive blueprint for AI development domestically, and even touches on China’s international ambitions or diplomacy as well. So to start off with, can you just tell us, high level, what is this really about? How do we understand this policy?
Tom Nunlist (03:24)
So this is the second high level AI policy to come out of China. The first was some years ago already back in 2017. That was about, you know, it was about the new generation of AI. AI Plus, the concept has been around for two years already now. It was originally announced at the two sessions. Hopefully our listeners know what that is. an annual meeting like that sets policy. It was announced two years ago, talked about again this year, but you know, not many
details were revealed about it. There was some assumptions which they were correct that it would be a bit like a former policy from 2015 called Internet Plus, was kind of following in that same vein. And just to sort of like set the stage of like what a document like this is, so it’s called a State Council opinion, you know we’re referring to it as a plan, it’s called Plan in the name, but it’s really a sort of like high level
like political document that is setting the priorities for what the nation wants to do, right? Like here’s the direction we’re gonna move in. Here are some like broad, you know, over the horizon kind of KPIs. This is where we all wanna go. ⁓ And then, you know, the plan will cascade kind of down from there. We’ll get more details over time. In terms of the name, AI Plus, that’s AI Plus or added to everything.
So everything in the economy, in all sectors, in society, we wanna see, the government wants to see AI make its way into there to get the most use out of it, to really ultimately transform the way that the economy and society works. It’s big, it’s a big vision is the answer.
Grace Shao (05:04)
think it’s really interesting you mentioned Internet Plus because I remember when that came out. So you said roughly 10 years ago, It was really embraced by every part of the economy and society. So have you seen any attitude changes or shifts or how people view AI Plus just being on the ground in China right now?
Tom Nunlist (05:22)
Well, in terms of views on the ground, like in terms of people talking about it publicly, not a whole bunch. I think what’s really happened here is these both internet plus and AI plus are responding to things that are already happening. So the internet certainly did not arrive in China in 2015. was ⁓ like.
Grace Shao (05:42)
Yeah.
Tom Nunlist (05:43)
very, very much going strong and was already one of the world’s leading digital economies at that time. And so what it was really seeking to do is kind of take the momentum for things that were already happening and push that further. So obviously, in 2015,
you know, the consumer internet, know, Alibaba, Taobao, like those type of, certainly WeChat Pay was introduced in 2013. You know, these were making waves, making big changes in the way that society just kind of basically works. And then Internet Plus was like, yeah, let’s take this momentum and apply it to everything else. Let’s have Internet Plus healthcare. How can we use the Internet there? You how can we use it in government services? And again, AI Plus is sort of doing the same thing. You know, the, you know, this is an introducing the AI wave to China, the AI wave is here, it’s happening, everyone’s using it, everyone’s excited. And so this is getting behind that momentum that is naturally already here and attempt to build a policy framework around it and like, yeah, really, where are we going with all this momentum, right? What are we aiming to achieve?
Grace Shao (06:52)
Yeah, I think actually on that, I am curious, has China’s highly digitalized society and the infrastructure made AI implementation or diffusion any easier in your eyes? Or how has that kind digital infrastructure played a part in just the mass consumer adoption of AI we’re seeing right now in China?
Tom Nunlist (07:05)
Mm-hmm.
Yeah, it’s a huge part. mean, from just the consumer side, China is like the US or Europe, just an extremely connected society. Everyone, even in the most remote places in the country, has their smartphone, has probably even has like 5G.
WeChat is something of a national infrastructure at this point. It’s a messaging app that everyone uses for work and life. It really is absolutely indispensable. And so having that infrastructure already there, or having everybody with a phone in their pocket automatically makes these tools accessible. I think...
any age, any person you might come across, you know, do they have DeepSeek on their phone? Chances are, yes, they probably do. On the back end, which I think is, you know, just as ⁓ important. So the past few years, or as we all know now, right, the...
know, the biggest part of the biggest spend of the AI boom is building out, you know, massive, massive data centers, right? And making that kind of infrastructure work. It’s a huge race right now in the United States. And so there was already a national plan to have ⁓ a nationwide network of data centers, you know, put in place as kind of before this big AI wave.
It’s a bit to do with some broader reasons of internet and energy and having some of this infrastructure in place. ⁓ Actually, in terms of energy, that’s one of the ways that I think a big leg up that China has in terms of the US is the amount of energy infrastructure it has built out compared to other parts of the world. So they’re ready to sort of do this, where I think other places maybe a little bit less so.
Grace Shao (08:57)
Yeah, and I think we can kind of talk a little bit more about the East data, West compute and all these different government initiatives that’s really boosted the data center built out. like, to your point, priority even the AI boom. But actually, let’s take a step back and kind of look at how the policies really affect a society, right? I think in September, one of your colleagues Kendra, her shape for a road, a blog post saying,
The state council’s AI Plus directive is to reshape the paradigm of human production and life. When I read that, was like, what does this mean? It seems kind of crazy. Like, are we going to make AI babies now? What does it mean to promote a revolutionary leap in productivity and profound changes in production relations and accelerate the formation of a new intelligent economy? So how do we kind of like break this down? What does it mean? And, you know, when we were chatting prior to this podcast recording, you said this is a grandiose term.
It’s three-shaping human production, but what does that actually mean? Like, is this quite literally reproduction? Like, how do we understand that?
Tom Nunlist (09:54)
Well, I don’t know, hopefully reproduction will stay traditional. in terms of, know, these types of policies sometimes have these like really big grandiose framing. know, again, back to what I said earlier, the point of this, it’s a political document at the end of the day. It’s establishing a vision, right?
and the promise of AI, which is not... ⁓
news to a Western audience is that it will be transformative of society that will kind of like change sort of how things work. This specific language that they’re using there, like talking about transforming production, you know, that’s a bit in the sort of like communist Marxist language, you know, of China. And then the context that it’s kind of living in now as well, ⁓ there’s this like really big deep sense of urgency in China of like kind of like
the need to move from ⁓ an economic model that is waning, that sort of reliance on labor intensive ⁓ economy and land sales and things like that into a ⁓ new area where they’re get new types of growth, new and better growth, the switch from quantity or quantity.
⁓ to quality. These goals were kind of already there. There’s another, you know, wonky Chinese policy term called new quality factors, new quality production factors, and what are all of these, you know, types of new things, you know, ⁓ AI, self-driving cars, and so on. And...
it wants to leverage these into making new growth opportunities happen, basically.
Grace Shao (11:33)
Yeah, and I think you touched on one thing, which is like, you know, the traditional economy is very like labor heavy and it really relied on just the mass population of the mass workforce, right? But what we’re seeing right now in China, but not only China, a lot of like actually very developed economies across Asia, including Japan and South Korea right now.
is that there are just not enough people. Like the population is declining, people are not willing to have children, right? And kind of given that backdrop of an aging population, a shrinking population, what is kind of, I guess, the goal from the government when we look at the labor force? And how will AI and technology play a role in that? Are we really just going to see like robots implemented or is it more automation? Or how do we understand this? Yeah.
Tom Nunlist (12:22)
think in some cases, we will see robots replacing physical workers. ⁓ But I think that’s the smaller part of the story. The bigger part of the story is this broader question of actually just avoiding the middle income trap. And so in order for China to take care of its aging population, to sort of weather this big demographic shift that is happening now,
no matter what, even if birth rates double or triple next year, it’s gonna happen. And the way to do that, that the government sees is to raise people’s incomes per capita.
and do that very quickly over the next 10 years, More profitable companies have more prosperous people, have a bigger tax base. And so that the country is just able to deal with this challenge as it emerges. Again, it’s inevitable. And so back to this new quality production factors or this transformative effect that AI is gonna have, the transformative effect is that it will be a productivity multiplier, right?
enabled everyone and companies from big to small to be vastly more productive and vastly more valuable and really help China earn enough and become wealthy enough, maybe right before it ages too much. So I think a bit indirect there, but it’s about the whole economic story together.
Grace Shao (13:51)
Yeah, and I think the whole approach to AI development and progress has been extremely pragmatic and economic driven for China, which is a bit different from what I get the sense in DC and even for sure Silicon Valley. Actually, on the topic of what you just mentioned, which is the government’s role in promoting companies and companies’ profitability, I have heard of this thing where the government is playing a role becoming a networker between the academics.
Tom Nunlist (14:00)
Yeah.
Grace Shao (14:19)
and the researchers and the companies. And I think for the audience, a lot of people sitting in West, we know about Alibaba Tense and Huawei, these mega big tech companies having talent schemes, quite similar to how basically there’s campus recruitment for like Meta and Google, whatnot, right? But how is the government now playing a role for SMEs or even smaller companies in terms of how are they connecting talent and...⁓ policy people and kind of the resources in the public space and the private space.
Tom Nunlist (14:51)
This is a great question, think, and a really important thing that’s emerged just over the last couple years. It’s not just AI, it’s sort of like all areas of science, technology, and engineering. But what it seeks to do is to bridge the corporate world and the academic and research world in a better way, right? So you can have like...
⁓ needs and talents and coms flowing both ways. So for example, this might be setting up round tables or some kind of like platform or any kind of mechanism that brings these parties together. So going in one direction from corporates, setting up links with universities so they can go into departments and say, hey, we’re doing biosciences, we’re doing AI, we’re doing you know, some type of metallurgy, you know, these are the types of talents we need. And can you focus on that? Can you help get us, you know, train that talent that we need?
or going the other way, having researchers see what’s going on in the corporate world and having a solution for that, or green fielding their own research, right? They’ve been doing this for a state institution or a university, and now they wanna take it out of there and find the right entrepreneurial partners to do that with.
Right. You know, as you mentioned, know, like large companies have done this sort of thing for for a very long time and have prospered, you know, because of those links. mean, indeed, I mean, a lot of the American tech giants, you know, came. That’s a famous story. It came out of a university or dropped out of a university, you know, and now, you know, maintain those links. It’s same in China, but, know, that’s a lot harder to do if you’re an SME. I mean, everything’s harder to do if you’re an SME because you don’t have the resources. Right. So providing that meeting place,
facilitating that is I think a really important program and one that I’m pretty confident will see solid results in the next couple of years.
Grace Shao (16:49)
So what agency or what government entity is actually helping facilitate that kind of meeting right now? And this question is to lead to the next question, which I’m going to actually ask now, which is, for me, I’m not a policy person. I’m getting confused when I read these papers, right? Because there’s the NDRC, which is in charge of the economic planning.
Then MIIT, which is in charge of the information technology, the ministry, the CAC, which is a cybersecurity regulator, right? Then there’s the MOST, and then there’s a party central science technology commission. There’s just so many of these government agencies that seems to be all involved in pushing the progress of AI technology at this point, as well as being a regulator for safety and policy work, right?
Could you kind of just break that down? Who’s in charge of what?
Tom Nunlist (17:39)
All right, okay, let’s just address that one, because that’s like a pretty big question. So for this, for the AI Plus plan in particular, the main administrative body for this is the NDRC. So for those of you don’t know, that is the macroeconomic planner. They’re in charge of kind of like setting the big direction.
of the ⁓ economy, right? So NDRC is in the coordinating role of this plan, right? So from there, it’ll go to the other ministries of the state council, some of whom you’ve just mentioned, right? So the industrial ministry, that’s MIT, science and technology ministry, most. ⁓
⁓ CAC, the cyberspace administration, all across the board. And those ministries will be in charge of taking the big idea and making it specialized or setting specific goals for their various sectors. And we’ve already seen that happen, actually. just a couple of weeks ago, the National Energy Administration, the NEA, came out with the very first ministerial AI Plus plan, which is AI Plus Energy.
And we’ll skip most of the details there, but suffice it to say, it is gonna use AI to help make the energy transition happen, which is very cool. From there, it will cascade down further into localities. And localities is really, that’s where the rubber meets the road and where all of the action happens. So we’ll see cities, they’re already AI plus plans.
There’s one in Beijing and Suzhou, those are explicit. And then like Shanghai has one basically, although it’s not called AI Plus, but they have one as well. Interestingly enough, those also actually predate the national plan, which is something that kind of happens in China at various points. And so a lot of the like actual like funding decisions and a lot of where the funding comes from will be at the local level.
And then there’ll be like, you know, a national pool of money as well that will like help support those, right? So, you know, it’s a top, you know, people say, China’s a top-down system. That’s of course true. And what I just described is how that top-down system works, right? So from the central planner down to ministry needs, down to local level, which has all of those ministries at the local level and, kind of being funded ⁓ from there. And then of course there’s like special national projects here and there.
Grace Shao (19:59)
So I’m just trying to understand this. In the RSC, the economic planner basically makes a big grand plan and they push out the AI Plus that we’ve been talking about that was pushed out in August. But a lot of the execution that’s done is actually trickled down into localities like the local governments, the provincial governments, city governments, whatnot. And so something like, just taking this as an example, something like the facilitation of maybe a researcher at Tsinghua meeting private company for potential, let’s say commercialization plan, that could be actually led by say the Beijing Education Department or how does that, I just wanna understand how to execute that works. Okay.
Tom Nunlist (20:36)
Yeah, yes, yes, yes.
Those might exist at different levels, I’m not sure. But yeah, the local level would certainly be implementing stuff like that. Or in another more sort of ⁓ direct way, Shanghai has money now where it can like say, companies that are in AI space are eligible for X amount of money.
funding for their first year, right? And like that funding decision, that’s made at the local level at Shanghai.
Grace Shao (21:03)
And that will be decided, I guess, by what the city might mean. So each city, each province, given their strong, they have their own economic factors, right? Like for example, like I think I was researching Harbin, like, you know, people think it’s just like a really cold place for the ice festival, but actually it’s an industrial city with a lot of legacy in robotics, traditional robotics, mechanics, industrial machinery. So their money might be put into developing physical AI.
Tom Nunlist (21:12)
Yes.
Grace Shao (21:29)
like embodied AI, right? And then maybe in Shanghai, we’re thinking about like maybe consumer driven products, right? Like just, just kind of high level thinking, but that that that’s kind of what happens. ⁓ So I want to understand how does the KPI work then, like in terms of like, how do we understand, I guess, how these, because what I’ve heard also is like these cities to cities, compete with each other, they compete for talent, they compete for, like, bringing in different businesses, how does that work? And then in terms of like, how do they actually
Grace Shao (21:59)
a measure, right? Like the technology or AI’s contribution. Because we talk a lot about, like people talk a lot about like how companies are trying to measure AI’s like actually, ⁓ you know, contributions to the company right now, the profitability. How do we actually understand AI’s contributions to the economy? I guess it’s two separate questions, but yeah, help me understand that.
Tom Nunlist (22:19)
Yeah, this is a really interesting question. And I think frankly, it’s one that the government is just trying to figure out itself. For years, of course, it was just GDP. So you win if you bring your area GDP, which is great for encouraging growth until it encourages the wrong kind of growth or encourages the wrong kinds of projects. And so I’m a little bit less familiar off the top of my head, but it’s something my colleagues have looked into. ⁓
as well is how these KPIs might be changing. And again, from this shift from quantity to quality, I think at the end of the day, probably something like GDP is simply the easiest thing to sort of see. But certainly, and that’s like if you’re like a mayor, I guess. But for people that maybe work within different ministries or in like...
specialist areas, whether or not they do a cool project along these lines, whether or not they brought, they fostered the emergence of a new giant in their district. That’ll be looked on favorably. So in terms of who actually sets these KPIs, I think that would actually go down to the personnel department ⁓ and how they interact, how the personnel department decides
things to include on there, some of which will be from NDRC’s AI plan and some of which will be from like totally other different things. I can’t tell you what their score rubric looks like. But again, the message here of going this like broad top-down kind of thing, what officials will be doing is, you
looking at the communication of these targets, right? Looking at the messaging and interpreting them for their district, right? So what do I need to do to make that happen here? And that’s the way forward for my career, right? And also to connect this with what you were just talking about in terms of local specialization, right?
what’s going on in Harbin, the local conditions there are different from in Shanghai or in Hangzhou. And so I think in the ideal way, and the government uses this phrasing a lot, is to have things definitely specially adapted for your local conditions, right? Don’t just do exactly what we’re saying, like make it work for you.
Right. And so in the ideal world, you would have like different things going on everywhere and they would all be complimentary. I think what happens, what tends to happen is that you have duplicative efforts, you know, which of course we see, you know, everyone’s talking about now in the auto industry. my gosh, there’s a hundred auto companies and they’re all, you know, in a giant battle Royale that is destroying value, you know, rather than. Yeah.
Grace Shao (25:06)
Yeah, the price war right now. Yeah.
Actually, how do we understand this? think because for the sake of, know, understanding Chinese policymaking for say, Western investors or Western companies, like say, MNCs operating in China, and in the day is to help them
better their operations, right? So then how do we understand this from that perspective? Say your client’s M &C and they’re saying, seeing, okay, AI plus is being rolled out on a central level. Then they are like, how do they decide? I don’t know where to put their plan, to build out their operations. How do they kind of make that judgment comparing provinces to provinces? And I think to your point, you kind of have...
answer this in the sense of like maybe if you’re industrial machinery you go to Harbin right but if you’re consumer goods you’re Shanghai but are there any other things that companies need to be aware of or investors investing in companies are coming out of these different problems should be aware of?
Tom Nunlist (26:00)
Yeah, great question. So I think probably the first choice, the first thing to look at is just, you know, where are the hubs for what you’re doing, right? If you’re an automotive company and you’re looking to make any of these, well, might go to, you might go to Enhui, right? Hefei, sorry, I forgot it for second. You might go to Hefei because that’s where a lot of the new energy vehicles are.
Right, and then from there, and this I think is a bit more unique ⁓ to China, is if you’re a corporate and you’re trying to be successful here, one of the first things you need to do is align with whatever the government is trying to do. You know, that doesn’t mean do exactly what the government asks you, right? But you know, figure out what officials there want, what their KPIs are, what their existing programs are, and how do you align your corporate goals with that?
⁓ And that’s how you get support. That’s how you get buy-in. That’s how you’re ultimately successful, right? You know, as in sure it’s no secret to anyone, you know, the Chinese government just has a much bigger voice in the direction that the economy is going, right? And the things that are happening in the economy and, you know, companies and investors absolutely, you know, have to listen to what that voice is saying.
I think for investors as well. So where are these companies collected? Where are the big hubs for the industry that we’re investing in? And also, what is the government itself saying that it wants? And which companies do we think can...
Obviously, of course, first deliver on the market promise, like do what they’re saying you’re trying to do, but are there opportunities here? Will they get this kind of support from the government that is a factor that is larger here than it is in other places? Probably maybe any other place.
Grace Shao (27:46)
Yeah, I think it’s also like the point you’re saying, it’s not really like you have to do what the government says, but it’s like you might as well lean into, like, I guess, lean into it, right? Like there are going to be favorable policies for your industry, certain areas, municipal areas, you might as well lean into it to optimize or to like maximize your success rate or your success possibility, right? So on that
Grace Shao (28:10)
point actually, I’ve heard that there are quite a few AI pilot zones. Like, you know, right now, I think for the West, people only know about Shenzhen, Hangzhou being kind of the tech innovation centers, obviously Beijing, Shanghai playing a big role for corporate headquarters and obviously where investors sit, policymakers sit. What are some other major cities that are actually quite relevant to this like AI growth right now or are considered AI pilot zones?
Tom Nunlist (28:35)
think those would honestly be the main ones. know, Shanghai, Beijing, you said, Shenzhen, Guangzhou, Hangzhou, like these are the places where, you know, a lot like the most action is happening, right? Especially in an area where we’re talking about, I mean, it depends on what we mean, right? So like if we’re talking about just raw AI development, making new LLMs and stuff like that, you know, one of the big, you know, stories is that there’s only so much talent out there that can do that.
⁓ and this talent will gravitate towards some center. And there’s only a few of those, only, not everyone can have those people. Not everyone, those people won’t go everywhere.
Right, AI, but back to what AI Plus is about, right? AI Plus, all of these other things, right? And having that in various sectors, I think where other cities will excel or have the opportunity to excel is where those hubs are, right? So if we’re trying to add AI into auto manufacturing, that’s gonna happen in an auto manufacturing hub.
Right. And I think that actually speaks to the important thing that folks need to be looking out for. You know, at this point, know, we’re, you of course, at the high level, you know, we’re talking about sectors. OK, we AI in the research sector or want AI in the health care sector. But I think what’s most important is going to be looking out for not which sectors it revolutionizes, but which specific use cases, right, are going to be.
Grace Shao (29:59)
Mm-mm, I see.
Tom Nunlist (30:06)
most obvious to implement.
Grace Shao (30:08)
And actually on that point, which use cases, let’s put it that way instead of sectors, do you think are kind of showing the most promising mass consumer adoption of AI, gen AI as we know it? So I’m not talking about like the buildup of LLMs and everything. I’m saying, know, when DPC came out, there was a media frenzy of stories about how China’s like home appliances are even adopting AI, EVs are trying to adopt AI, you know.
Tom Nunlist (30:13)
Yeah.
Yeah.
Grace Shao (30:34)
I mean, obviously that kind of hype has gone, like, moved past us, but like, in terms of whether you want to use sectors or use cases, where do you see actually China right now really leading in adoption? And where do you think we’re seeing the trend going towards maybe in the next three to five years?
Tom Nunlist (30:50)
Yeah, think ⁓ it will continue to penetrate more ⁓ on the consumer side, just on of like AI services that are available to everyone. mean, that’s sort of the biggest thing right now. Whether or not we can get consumers to pay in China, I think is a little bit different of a question. But in terms of specific areas, I think it’ll be where we’ve already seen AI ⁓ have quite a bit of traction. So in like logistics and transportation where, you know,
with like self-driving is kind of almost here and you know we have the the nev is this it’s a software-defined vehicle and we’re going to be like a ready integration for ai into the features of the vehicle that’ll definitely be one you know another one thinking about ⁓ that comes to mind is is agriculture which i you know ⁓ i can’t name a specific company or or a project but ⁓
you know, drones are becoming ⁓ large and, you know, helping to manage big farms, like do things like crop spraying, you know, or inspecting or like, also not just in agriculture, in inspecting power lines, drones are not used to do that. It’s actually physically hard to get up there, right? And so there’s AI use cases for that, right? It can go into like visually inspecting, right? Or visually help, you know, irrigate your crops and so on and so forth.
So it’ll be things like that, right? Where we’ve already started to see new things happen, AI being used a bit. And now these new tools and the growing power of these tools will enable it to really actually happen.
Grace Shao (32:28)
Yeah, definitely. think like, when I first saw and tried out a few of the EV cars, this is even like during COVID, this is like three, four years ago, I was shocked by I wouldn’t say they’re like genuine power, but how tech savvy they already are. had voice control, each of them already had a built in robot, you can control your like windows, you control your heat, like the heat of your seats by voice recognition, voice control. And I think like you said,
Tom Nunlist (32:42)
yeah.
Grace Shao (32:52)
implementing GEN.AI into it just means that it can actually embolden it more, right? Do more things or right. So that’s really interesting. I think I want to double click on one question that a lot of people are kind of debating. know, China’s approach innovation often is said to be, you know, innovate and then regulation comes later. Europe obviously takes another extreme case of like hyper or not hyper, but like a lot more.
Tom Nunlist (32:57)
Yes.
Grace Shao (33:16)
cautious and safety, you know, safety cautious and like, you know, regulation comes first. And some people are complaining about how it’s hindering innovation or innovation going into production. Right. So I guess my question right now is you’ve been in the AI safety and policy space for a long time now in China. Do you think that actually you must give up safety for innovation or are there other ways that you’re seeing people actually being able to have safety and
innovation co-exist and co-develop and maybe taking data privacy as an example or how did Deepsea come through if there was so or let’s just talk about that space.
Tom Nunlist (33:50)
you
Yeah, this is an excellent question. And frankly, I think one of the most underappreciated or even like misunderstood aspects of the AI story as it stands right now in China. I mean, there was a point not too long ago before the EU AI Act, which you mentioned where, you know, China had, you know, the strictest AI regulations on the books in the world. And yet, you know, DeepSeq was still clearly able to emerge here and,
you know, become what it is, right? And I think the story here is that, you know, China is, I think, as most people will understand, a very security conscious, you know, country, but it is also highly flexible, right? And the interesting thing, the sort of interesting story, like when ChatTPT first came out, there was this mad scramble.
among regulators to get a handle on it, right? Because it was gonna flood the internet, you know, with these tools and man, what are the impacts gonna be, you know, like just a real sense of urgency to try to like write something immediately. And so there was a period of about a year and a half where you had regulation after regulation and, you know, they...
you know, if you looked at them in line, you’d think they were different, but they were actually kind of rewriting one another, and it was like all very ⁓ messy and a very confusing space. But then, you know, China was able to kind of like find what its bottom line is.
and then be flexible and adapt from there, right? So it was, you know, hurry up, let’s do something. Let’s kind of see what’s gonna work, where it might be too far, and then dynamically kind of like dial back.
So one of the interesting, I think probably the most interesting single event of this story was there was a registration system that was created where if you want to publicly release an AI tool like a chat TPT, it has to be registered with the state and blah, blah. And then some requirements started to be built on top of that. And there was a draft that said at one point,
all of your data that you need to train your LLM with needs to be verified as true. And the AI research community came back and said that this is impossible. Like if this is implemented, will, know, progress will grind to a halt. There is no way we can do this, right? There was no official response to that, but the final version of the rule did not contain that.
Right, was that was walked back. was an idea that was tried out, that was an explored, you know, and eventually, you know, was abandoned because it didn’t work. And so I think, you know, one of the sort of like, again, underappreciated or even unknown strengths of China’s regulatory system is that it can be flexible in that way.
Grace Shao (36:27)
Right.
Tom Nunlist (36:46)
in an ungenerous interpretation of this, which you hear from a lot of foreign companies and rightly so because there are drawbacks of this, is that regulation can kind of seem all over the place and arbitrary and you never know what things are gonna change next.
And certainly in emerging areas, that is true and very challenging, you know, if you’re in a corporate compliance type situation. But the plus of that is it can be, you know, quite flexible and adapt to, you know, what the perceptions of the needs are kind of as they’re coming up, which in, you know, an environment as fast developing as this one, where again, new problems might emerge tomorrow. I think that’s a really important strength or really useful strength to have.
Grace Shao (37:29)
We could be quite reactive in the sense that they would actually react to what the industry and the actual practitioners at the leading frontier, technology development, want or need, right? To really help and regulate the technology, yet also not hinder any progress. I think that’s really interesting and it’s a very fresh take on it. I haven’t really heard that before, but I think it’s
it makes sense. And it also kind of explains what you said about some people’s kind of complained or misunderstanding of this whole like murkiness. so you said that the AI Plus initiative really it’s been around, like not been around, but like the AI policy or the plan has been around or the idea has been around. And then there was a 2017 National General AI Plan as well,
There’s also the made in China 2025 plan, all these big grandiose plans that have been really pushing forward AI or robotics and just technology development in general. as you said, policymakers and regulators can actually be quite reactive. So over the last, I guess, 10 years as these three mega plans been rolled out.
How have you seen these things change or how has the policy makers really a change in terms of their sentiment or the attitude towards this technology?
Tom Nunlist (38:40)
Let’s say the biggest thing, so taking a bit of a longer view, so science, tech, and manufacturing development has been a priority of the governments for a very long time, since the late 90s. It’s been kind of on this top priority list. And so one shift I’ve seen in the past few years is side tech development moving from one of the list of important things into the top thing.
like the most important thing. It’s like that is ⁓ kind of an organizational principle, right? Or like a driving organizer of the whole party, right? And again, that’s because of the perception of what the state’s needs are at this point. In the past, in the sort of like last formulation, right? Of like what the country needed, right? It just needed growth.
It’s like, it’s the late 70s, we’re into the 90s and 2000s. We need to just grow. We need more people and jobs, we need production. That’s what we need. Now that’s not what they need. We need quality growth, we need to move up the value chain, we need to avoid the middle income trap if we can, expand people’s incomes, become a more efficient and a more technologically driven society. And so the sort of prioritization,
and some of the character of these plans have changed sort of in line with that. Some other things I think have stayed the same or strengthened rather, right? So with Made in China 2025, which this not really talked about explicitly anymore because of the political sensitivities it creates in the US, right? But the sort of view, right, was that China
you know, doesn’t want to be vulnerable, basically to, you know, always reliant on outside technology and wants, you know, these things for its own, right? Wants them to be secure and controllable. It wants to have, have its own thing, right? That of course, I think, you know,
in light of the subsequent ⁓ US effort to strangle the semiconductor sector in China is even more of a priority. So it’s not just move the value chain and get incomes up, it’s also create these fundamental technologies which we absolutely cannot have as a vulnerability.
Grace Shao (41:03)
Essentially kind of push more honed in on the self-reliant focus than they previously didn’t really have to, right? It was also kind of a reaction as well. Okay, I think I want to go in some quick questions. ⁓ You did answer a bit of it, but one overhyped and one underhyped province or city that you think people are not noticing enough outside of China.
Tom Nunlist (41:28)
Yeah, again, would say,
Yeah, they’re not really as specific over under Hype City that I can think of. But yeah, I would say go back, double down on the point of like, you know, look at where different specific hubs are, right? So right now, you know, especially the US talking about AI development sort of in general, right? Like the rush to AGI, you know, so on and so forth, right? The AI plus plan is about doing things in the real world, right? So I think where a lot of like really fascinating stuff is going to happen is where those real world
things are in China, right? So like where we have many filtering use cases actually emerge and that’s going to be sort of all over the country.
Grace Shao (42:02)
Right.
Right, like CN maybe for renewables, but like Hefei for you say auto, and then like even like Baoding and Hebei for like auto. You get at least like second, third year cities that are just like actually relevant, but only if you’re in the sector, you would know, right? And that’s a really interesting take. So what is one metric that a policy analyst like yourself should be really tracking or focusing more on?
instead of just, you know, maybe what we’re seeing on the headline is like, you know, this crazy chase for like benchmark frontier technology, frontier of LLM benchmarking. How do we actually track or judge real AI diffusion in the economy?
Tom Nunlist (42:48)
would say it’s probably more along the lines of traditional measures, So penetration, productivity, profitability, wage and efficiency growth. Again, the emergence of those scenarios, Are people actually out there using it in the real world? So I think it’s look for those traditional.
tangible things, right? Again, I mentioned that Chinese consumers tend to not want to pay for consumer-grade AI tools. If that’s something that changes, right? If they’re good enough where people are willing to pay for it, wow, maybe that would be an enormous indicator.
Grace Shao (43:16)
Yeah.
I don’t think anyone’s gonna want to pay for like, you know, consumer app. The culture, right? Like no one wants to pay. I don’t know, I switched my brain on and off when I use like Western apps versus Chinese apps. And when I’m on a Chinese app, they pop up, they’re like, pay for premium. I don’t want that filter anymore. I don’t need this sticker anymore. I’m like, I’m not paying. You just have a different mentality, right? Because you do get too many goodies for free already. It’s very...
Tom Nunlist (43:27)
I think he wants to go to pay for a I know.
Yeah.
Yeah.
Grace Shao (43:51)
It’s very hard, think. The barrier is very high. The threshold. I have one last question for you. And it’s a question I ask everyone that comes on to differentiate understanding, which is what is an unconventional view you hold? And this could be about work or something in life, you know? But what is something that you think about and you’re like, oh, maybe I don’t say this out loud, or maybe this is quite different from what my peers think?
Tom Nunlist (44:13)
What was an unconventional view I hold?
I’ll go one with topic specific here. That’s because it’s come up recently in ⁓ fights I get into, Twitter fights I get into with people. There is this interesting and I think not totally off the mark concern with AI that it’s gonna basically make us all dumber. Students are gonna outsource learning to AI. There was a case study that did the rounds about doctors using AI tools to help them spot
certain types of cancer got worse at it, know, like after relying on the tool. And that’s a real concern. I think it’s something that, you know, there’s some red flags that seem to say that that might actually be happening. But I think the real problem might be a bit more nuanced than that. think it might, my hypothesis is that it will create something like a ⁓ skills or performance gap.
between different parts of the population and exaggerate it. So, whereas some groups of people might become reliant on it and become de-skilled in their job, definitely. And then in some cases, that might be what we wanna happen. I we don’t want everybody, I mean, that’s sort of the promise, not have to do certain boring things. But I think for a smaller portion of the population, it is gonna be a massive learning and development.
accelerator, right, to really help you to get good and improve. And so, you know, I mean, beyond, you know, whether or not I’m right, I don’t know I’m right, it’s just a bit of a guess. You know, I’m wondering where that gap kind of might be and how large it’ll be, right? So is it going to be 90 % of people get dumber and 10 % of people become super learners? You know, or is it, you know, somewhere in between?
That’s my unconventional view. It’s gonna create a skills disparity.
Grace Shao (46:04)
Yeah, I actually kind of agree with I think it would make people who are relying on it for skill set like vocational skill almost like just the art of, know, not art, but the skill or ability to write a press release or draft a basic news piece or you know, build a DCF model or you know, do some quick basic research that might become dumber in the sense that you don’t know how to
do it in a traditional way. But I think the arguments also like say 34 years ago, people are like, you have the internet now. You don’t even know how to use a library anymore, which I think our generation honestly, I don’t really know how to use the library very well. Like I go on my loss. I don’t know how, you know, how to find books essentially from alphabetical order and, you know, like finding the topics, but we do learn how to find more information in some sense, right?
But I think to your point of like, ⁓ it will help people learn a lot faster, but it will require a new kind of skillset, is like, you can access all this information, can you decipher it? Can you dissect it? Can you actually pick out what is correct? What is actually relevant? Because there’s so much noise and clutter, which is kind of similar again to our generation where we had to use the internet to find information, Versus like our parents generation had to like walk into the library and just like.
Grace Shao (47:19)
go through like 10 books, right? ⁓ But that’s super interesting. Thank you, Tom. Really, really appreciate your time. This was super insightful. It was really helpful for me to even learn about how to understand how policy was made in China, how it might affect businesses and investors. And yeah, this was just super insightful and a lovely conversation.
Tom Nunlist (47:21)
Yeah. Yeah.
Yeah, thank you, really. It was really lovely to be on the pod.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to AI Proem at aiproem.substack.com/subscribe -
Joining me today is Natalia Cote-Munoz, a policy strategist, writer, and International Strategy Forum (ISF) Fellow— a program by Schmidt Futures that supports rising leaders at the intersection of geopolitics, technology, and public service. Natalia has served in the U.S. State Department, leading foreign policy think tanks and crisis diplomacy roles. She is a graduate of the Harvard Kennedy School and speaks English, Spanish, and Mandarin, among other languages.
In this conversation, we discuss Natalia’s unique upbringing as a third-culture kid, her experiences in tech diplomacy, and the evolution of US-China relations in the tech sector. Natalia reflects on her recent return to Beijing after a decade, sharing insights on the rapid technological advancements in China, particularly in AI and digital payments.
We also discuss her observations of how diplomats are trained as an international relations teacher at China’ Foreign Affairs University, how AI cannot be replacing humans in diplomacy, her embrace of AI in productivity and creativity work while she was experiencing a concussion, and lastly her unconventional belief about the societal views on Labubus, highlighting the cultural differences in perceptions of childishness and professionalism.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
01:30 A Third Culture Kid’s Journey
04:21 Evolution of US Tech Diplomacy
11:32 Reflections on Beijing After a Decade
17:54 Exploring the Red App and AI Conversations - Doomism vs. Optimism
28:35 Education and Talent Development in AI
38:20 Exploring Student Aspirations in International Affairs
40:33 The Role of International Faculty in Education
41:56 STEM vs. Liberal Arts: Educational Mindsets
47:41 AI as a Productivity Partner: A Personal Journey
56:24 AI in Diplomacy: The Human Element
01:01:26 Legitimacy in AI: Who Builds It Matters?
01:02:00 Cultural Perspectives on Professionalism
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to AI Proem at aiproem.substack.com/subscribe -
Kevin is an investor at a family office, where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. In his spare time, Kevin writes East Wind, which is focused on technology investing.
In this conversation, Kevin Zhang shares his insights on the evolving landscape of AI investments, the implications of hyper-scaler capital expenditures, and the future of AI model training. He discusses the cultural differences between investment ecosystems in the US and China, the valuation of private market companies, and the role of neoclouds in the AI sector. Kevin emphasizes the importance of capital and distribution in determining the success of AI companies and reflects on the future of work in the context of AI adoption.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
00:00 Kevin Zhang's Journey From Software Engineering to VC to Equity Investment
02:04 The Hyper-Scaler Capex Debate
04:31 The Capital-Intensive Nature of AI Models
07:49 Future of AI Capex and Market Dynamics
11:43 Understanding Private Market Valuations
14:49 Consensus Capital and Investment Strategies
17:10 Cultural Differences in Investment Ecosystems
21:50 The Future of Chinese AI Companies
23:50 Capital and Distribution in AI
27:47 Open Source vs. Closed Source Models
32:22 The Role of Neoclouds in AI
40:40 Investment Opportunities in AI and Beyond
Transcript generated by AI
Grace Shao (00:01)
Hi everyone, this is Grace Shao. Joining me today is Kevin Zhang. Kevin is an investor at a family office where he leads AI investments across asset classes. His career has spanned roles as a venture capitalist, startup founder, and software engineer, with experience in both Silicon Valley and New York, before moving to Asia. So he’s now based in Asia. He brings deep technical and product expertise across domains from machine learning to enterprise software. And in his spare time, Kevin writes a blog on Substack called East Wind. Go check it out.
It’s focused on technology investing. So Kevin, thanks so much for joining us today.
Kevin Zhang (00:33)
Hi, great pleasure to be here.
Grace Shao (00:36)
Yeah, tell us about yourself. You’ve had quite a journey, you know, from Silicon Valley and now into your base in greater China. You’ve worked as an engineer and now an investor. You know, that’s quite unique. Tell us about your professional journey.
Kevin Zhang (00:50)
Great. So I guess going all the way back to my college days, ⁓ studied computer engineering, both in Canada and then states for grad school. And then spent most of my career in the States ⁓ in Silicon Valley and New York. So started as an engineer at a company called Salesforce. So they make CRM software before ⁓ transitioning to a couple of venture funds, ⁓ one in the Bay Area, one in New York. So primarily the focus has been on
early stage software, AI investing. And in these days, I look primarily at public equities, specifically focusing on the companies that have exposure to AI or ⁓ businesses who ⁓ will see a re-acceleration of growth because of AI.
Grace Shao (01:39)
Awesome, so we’re gonna go straight into this. I think we have a lot to cover. ⁓ First, let’s get into the hyper scalar capex kind of story. We’ve been seeing jaw dropping capex commitments, alphabet raising, AI capex too, 85 billion, meta committing hundreds of billions, ba-ba 10 cent I think over the next couple years, also committing like 300 billion upwards. It’s just these crazy numbers.
Do you see this as a necessary investment or do you think they’re absolutely overspending right now and they’re creating a bubble?
Kevin Zhang (02:11)
I think if we look at past ⁓ technology cycles, there will always be, or there has always been ⁓ some overspend across the industry. ⁓ So I think ⁓ bring it down to on a per company level, I think things get more nuanced, right? So looking at, for example, alphabet, right? How much of that is internal workloads for search, right? For their Gemini answers, ⁓ looking at open AI where how, ⁓
whatever their, can we maybe start this again? Let’s do a rewind. Okay, great. ⁓ So I think across the industry, if we look at past ⁓ technology waves, ⁓ most famously in the ⁓ initial tech kind of a dot-com bubble, the industry has overspent, right? But as we zoom out, ⁓
Grace Shao (02:49)
Just go ahead, just restart your answer.
Kevin Zhang (03:11)
the spend becomes more normalized and then the demand ⁓ ends up catching up. And so I think the real question is on a per company basis, right? Whether it’s Alphabet, Meta, Azure, with Microsoft, AWS, how much of that is overspending? How much demand can they generate, right? With their ⁓ whale customers? And then if they end up overspending, how many of these players can survive, right? So for some of the smaller players,
who are comparatively more leveraged, who don’t have the cash flows to support some of the CapEx, right? They’re maybe in a little bit more of a dangerous territory than one of the Meg-7, where they’re generating a significant cash flow to ⁓ fund their operations.
Grace Shao (04:01)
And I think this kind of goes into also one of your writings recently. You were saying that, like, look, it’s all these big tech that are able to afford ⁓ spending on, like, LLM training and inference, as well as whatever infrastructure that’s needed to really build out, like, sophisticated LLMs. And you said it’s basically foundation models are a rich company’s game. Why is it the model layer is so capital intensive? And do you think that means we’re going to see the startups just kind of
in this field kind of just die out one by one or acquired or what’s trajectory going forward?
Kevin Zhang (04:38)
I think there are two paths that the industry can take. So the default path is if we look at ⁓ the progression of ⁓ costs for model training, whether it’s pre-training or post-training, ⁓ each generation has been significantly more expensive. So ⁓ many years ago, it might have been ⁓ several hundred thousand dollars. Then it went to the millions, tens of millions, hundreds of millions to train a model.
So assuming that trend holds, ⁓ we’ll see kind of billion dollar training runs, right? So taking a billion dollars to train, let’s say GPT-6 or GPT-7. And so if that is kind of where the world goes, then these companies will need to raise more and more capital to fund their training. They will raise more and more capital to fund their inference, right? So once you train a model, how do you serve it to...
⁓ and users, right? That’s also very, very expensive. ⁓ However, if you’re of the view that there will be, or transformers, which is kind of the models that are used in ⁓ LLM or ⁓ used in things like ChatGPT these days, ⁓ if you believe that there will be other model architecture paradigms ⁓ that are going to be significantly cheaper, then maybe there will be another kind of startup that comes and disrupts.
the entire business model of an OpenAI or Anthropic or any of these labs.
Grace Shao (06:07)
and was DeepSeat one of those that kind of disrupted the whole model.
Kevin Zhang (06:11)
⁓ I think not necessarily. think ⁓ going a little bit into the weeds, the, I think, five, six million dollar ⁓ final training run touted in their paper, ⁓ that was only for the final training room, which is not inclusive of the GPUs that they’ve acquired, ⁓ their human capital.
all the prior training runs and experiments that they’ve run. And then also within kind of AI training, if you basically train last year’s model today, right, it’s significantly cheaper than if you want to train a frontier model. ⁓ And I think Anthropic had a paper where, or had a blog post where basically ⁓ for if they want to train a similarly similar model to the DeepSeq R1 a year ago, it would have been 10x more.
Right, so essentially the costs track. And so it’s less so disruptive ⁓ than I think some of the folks in media might have thought. And certainly they have ⁓ made certain architectural improvements as well as inference improvements at DeepSeek.
Grace Shao (07:32)
So like if that’s the thinking, like looking ahead next three to five years or even a longer run, is this like the hyperscaler arms, I guess, if you put it that way, is it gonna just keep on climbing up that capex or will it eventually plateau or is this question we will never know.
Kevin Zhang (07:49)
⁓ it depends on like, like how long the labs, right? The, the open AI anthropics, even Google can sustain, ⁓ this pace of model improvements, right? So we we’ve seen a little bit of plateauing in, in, in the past year or so, ⁓ as, as we’ve kind of reached the limits of pre-training, right? So, ⁓ right now, a lot of the emphasis is on like these like thinking models, right? So, ⁓ when you actually type a prompt,
into ChatGP, you know, think for a while and then that tends to ⁓ generate better answers for you. ⁓ And so it feels like the like assuming compute requirements continue to wait, what can we start the question again? What was the specific question? Like what is the arm? ⁓ Okay.
Grace Shao (08:43)
Yeah, I’ll just redo it.
So if we use this logic, right? Like, does that mean that in the next three to five years, we will in the longer run that the capex numbers will just continue to climb? Or eventually we will see this hyperscaler arms race kind of plateau out a little bit because I don’t know the day, like these are already like crazy numbers, right? Like we’re looking at a couple hundred billion dollars put into training in the next three to five years. That’s the plan. But how does one keep up with this kind of money?
Kevin Zhang (09:13)
Okay, so there’s two things, right? There’s training and inference. ⁓ And so on the training set of things, the assumption is ⁓ labs will continue to require more and more compute to train more and more expensive models, right? So let’s say the next model takes 10 billion to train, right? And the model after that takes 50 billion to train. Then theoretically on the training side, that tracks. And then I think where a lot of this capex is going, especially if you look at
⁓ the Meg 7 where they’re putting 70 to $100 billion ⁓ per year per company. I think a lot of that is the expectation of inference demand. So as you put these models to production, whether it’s large language models, whether it’s recommender systems, image models, video models, that demand will catch up. So as it stands, there’s a mismatch between ⁓ the capital outlay
into these data centers versus the revenues that Gen.ai companies are generating. So if we look at OpenAI, Anthropic, they are the primary beneficiaries in terms of how fast their revenues have grown and the absolute scale or the relative scale of their revenues relative to even companies like Cursor, who grew very, very quickly to 500 million in ARR.
And so ⁓ in terms of like software revenues, we’re kind of in the tens of billions range, whereas ⁓ for ⁓ hardware CapEx or data center CapEx, we’re in the hundreds of billions, right? So assuming that ⁓ software revenues, two to three X year over year, then eventually it will catch up to CapEx if end users.
⁓ enterprise customers find that they’re not getting ROI from these Gen.ai ⁓ apps, then I think that’s where the house of cards ⁓ collapses.
Grace Shao (11:14)
Yeah, actually, let’s just like talk about the private market valuation quickly. Like right now, OpenAI is valued at like over 500 billion, something like 180 billion, right? Like startups like Hercer, Lovable, Chasing Billions, or what they’re calling trillion dollar ambitions right now. ⁓ I think the Lovable CEO said they want to become the first trillion dollar business in Europe, right? How should we make sense of these numbers? Like, I’m not a quant person. They just sound like humongous numbers.
Can you explain this to us, like how to make sense of this? these are just, ⁓ does it make sense for these companies to be valued at this high in the private market right now?
Kevin Zhang (11:52)
Yeah, so I think that’s a really good question. ⁓ So the ambition for a frontier lab like OpenAI Anthropic is to be one of the big boys at some point in the future. And so taking ⁓ OpenAI as an example, right? So if an investor is of the belief that they will eventually build their own cloud, they will get into robotics, ⁓ their ⁓ core lines of business, right? Chatchi PD as well as their APIs become
really large businesses, right? So let’s say TriGPT is embedded in various enterprise ⁓ kind of customers. And if they’re actually able to charge, let’s say two, 300 bucks a month, right? As ⁓ more and more white collar workers are reliant on open-air technology, that tracks to a market, even in the enterprise side of things, that’s several hundred billion dollars, right? And then if you’re also of the belief that
Google search will be disrupted and Gemini somehow fails to catch up to OpenAI, then they could also run ads on the consumer side of things. So once you add all these kinds of lines of businesses together, an ⁓ optimistic person might see kind of a line of sight for OpenAI to be this like three to five trillion dollar company that some of the Megs have been at already.
are an investor at the $500 billion ⁓ mark, then I think that’s the return profile that you’re looking at ⁓ before kind of taking into account all the dilution from subsequent funding rounds, stock options, et cetera. And then moving down to the application layer, I think these companies are making is if we’re able to replace broad swaths of labor,
⁓ and you are able to command pricing that’s at some proportion of the ROI that you deliver relative to just replacing like a human headcount, then the exit values for these become enormous, right? So then cognition at 10 billion might sound really reasonable. I think as the issue right now is the exits will be very spiky, meaning we’ll see a lot of zeros.
⁓ and you’re going to see a lot of companies really become those $10,000,000,000 companies. And then for a VC fund, you have limited shots on goal. And so as the entry valuations ⁓ increase, you have less shots on goal. And so on a per investment basis, ⁓ your risk ⁓ increases quite a bit.
Grace Shao (14:40)
So in that sense, you don’t think we’re nearing the ceiling of model layer evaluations or anything. We haven’t hit the peak of the bubble or anything yet.
Kevin Zhang (14:49)
⁓ The markets are definitely frothy, but the winner will be much bigger than we, ⁓ I think, originally estimated. And so if you are one of those investors that are in these assets, I think you’re going to be fine. If you’re not, then I think, which is probably the majority of these funds, ⁓ I think they’re going to be hurt.
Grace Shao (15:10)
So in your writing on Substack, you’ve argued that, you know, consensus capital is crowding into foundation models and fra robotics, I think. But, you know, are there areas that you think are under invested and still in the private market? Like, where do you see, like, overlooked opportunities right now?
Kevin Zhang (15:26)
I think it’s less so ⁓ maybe overlooked opportunities in AI, right? So like a generalist VC is able to allocate capital across different things, right? So that could be psychedelics, that could be robotics, that could be biotech. so, or consumer as we’ve talked about before. ⁓ so figuring out kind of what the market dynamics are for those industries where
you’re just non consensus enough to be that first check in, but you’re consensus enough that at the next round, ⁓ whatever you’ve invested becomes consensus. And this was like the subject of some Twitter debate ⁓ with Martin at Andreessen where ⁓ he was arguing it’s not bad to invest in consensus deals because like in the end, like some of these deals ⁓
end up generating huge returns. And we know that even with an AI, like if you’re in a consensus bet that ⁓ pans out, assuming OpenAI is that company, then you’re still seeing like a 10x gross return, right? Assuming one of these companies becomes like five trillion.
Grace Shao (16:43)
Yeah, because consensus, guess, it’s for a reason, right? I was speaking to a few VC investors in the Bay Area a couple of weeks ago, and they were saying, like, some of them are kind of complaining that their bosses are just chasing logos rather than the differentiated bets. But I think in some ways, like you mentioned, if it’s an open AI and it’s still going to be the market leader, market winner, you’re still going to come out on top, I guess. ⁓ Yeah. I want to hear, OK, taking a step back from...
Kevin Zhang (17:05)
Yep.
Grace Shao (17:10)
these questions, think from a cultural perspective, you you worked in Silicon Valley, New York, and now like, you you moved around in greater Asia, greater China. ⁓ What do you think, like differentiates the two ecosystems the most in terms of like the investment space and then maybe even just like some kind of high level work, cultural tech tech space observations?
Kevin Zhang (17:33)
Yeah, I mean, there’s a couple of things, right? So one is the abundance versus the scarcity of capital. ⁓ And so ⁓ in the US, there’s still a relative abundance of capital where ⁓ as an entrepreneur, it’s comparatively easy, easier to be funded versus ⁓ a similar entrepreneur in Europe or Asia. And so and the other thing is,
you have capital at every stage, right, from seed through growth. ⁓ And so the market as a whole has more shots on goal, more opportunity to experiment versus China, right, where ⁓ there is a comparatively ⁓ or significantly less capital, especially US dollar funds in the past couple of years. And so on the investor side of things, ⁓ they are
also more risk off, right? Because for some of these ⁓ Chinese VCs, they might not be able to raise another fund, right? So each shot on goal ⁓ is a very heavy bet, right? Versus entries, and if you deploy like a fund very quickly, could probably just raise another one very quickly as well. ⁓ And so ⁓ if, you know, entrepreneurs can be more risk on in the US, investors can be more risk on than
one of these bets will pan out and then that becomes the next big company versus in China where you could raise less capital at lower valuations, less capital at growth. ⁓ And I think where the domestic VCs might be ⁓ extremely careful and maybe not having the same kind of venture parallel mindset. I think that’s...
that reflects on the products that you can build and the scope of ambition for entrepreneurs. And I think broadly this is why more more companies are trying to do the true high model, right? Where they might raise their first round of funding ⁓ in China, but then quickly pivot to a Singapore or Canada, the United States, right? And then raising capital in the US.
Grace Shao (19:54)
Do think it’s like really affected the dynamics between investors and founders as well? Or do you think that relationship actually is still quite similar?
Kevin Zhang (20:02)
⁓ For the US, I think it’s still an entrepreneur’s market for the best entrepreneurs. ⁓ Like given the abundance of capital, that has not really translated to kind of a linear increase in top founders. think capital is still chasing founders every year or
taking a step back, there’s a limited number of great founders per year that can build these generational companies. And so when AI investing becomes consensus and these founders ⁓ tend to be in the US, then you have quite a bit of capital chasing these select founders. ⁓ And then within China, like there’s probably higher pricing ⁓ or
there’s more power on the buy side, right? Where if you’re one of the 10, 20 funds that still have dry powder ⁓ at the early stages or one of the five to 10 funds at the growth stages, then comparatively it’s less competitive ⁓ for the investor versus the US.
Grace Shao (21:18)
How do you think that’s affected, I guess, this generation of AI entrepreneurs coming out of China? you know, like actually, if you look at the six dragons or four tigers, whatever you want to call them these days, ⁓ you know, like they’re definitely nowhere as like valued as high as the American counterparts. ⁓ There are a lot of them actually finding funding from the BATs instead of, you VCs. So like has this affected their
future trajectory or the entrepreneur’s mindset or their business model.
Kevin Zhang (21:50)
Yeah, I mean, I think it’s ⁓ all TBD based on or TBD, like given the pace of innovation, right? So to our earlier conversation, like can one of these labs build ⁓ something that’s post transformer, right? Or ⁓ can one of these win in other modalities, right? Whether it’s video or image or something else. ⁓ But from a capital perspective,
it’s really hard to raise like another 2-3 billion for any of these companies. And so, like my hypothesis is ⁓ some of these companies going public is a way for them to get another turn of the card, right? So it’s like, hey, if we’re able to raise 500 million a billion ⁓ on the kind of Hong Kong stock exchange, that gives us another two to three years, right, to figure things out versus OpenEI or Anthropic where every round is
heavily oversubscribed and they’re able to ⁓ basically chase everything, right? Chase capex, chase kind of their core products, ⁓ as well as some of the moon shots, With opening at going into robotics, ⁓ going into ⁓ video image, et cetera, et cetera. ⁓ Obviously, maybe not to the same level of success as some of the other kind of image players or video players.
Grace Shao (23:12)
When we last spoke, you kind of made this like big statement thinking that eventually these Chinese ⁓ companies will potentially be irrelevant or they’ll stay within their ecosystem, right? It’s a big statement. But I was kind of curious if you could like elaborate on that a bit more. Like, I know you’re very bullish on the American leaders right now, market leaders right now, but why can’t a deep seek or a moonshot, you know, maybe
do well globally as well, or would Alibaba’s open source eventually kind of take on a leadership role?
Kevin Zhang (23:50)
Yeah, so it’s two things, right? I think it’s capital and it’s distribution. On the capital side, like we’ve talked about, that ⁓ model training inference follow the current trajectory, then it’s a capital game. And so the companies that are able to generate the most revenues the fastest and is able to also raise the most capital will win, right? Under this kind of paradigm. And then secondly,
Even for companies like OpenAI, think they realize that there’s a limit to, for example, how much you could charge for APIs. And so they’re definitely moving into applications, whether it’s ChatGP or something else. And that’s where you ⁓ can grab higher kind of margins than purely API revenues. And then as you go into workflows, as you go into the replacement of human labor, as you...
Take advantage of your initial tech advantage to embed yourself into kind of the fortune 500 clients They’re not gonna kind of rip you out versus and then swap swap potential your Chinese competitor in right so I think the speed of distribution and how fast you’re able to embed yourself into ⁓ Customer workflows where those customers have a very high likelihood to pay or a high willingness to pay I think that that’s kind of the name of the game
So capital and then distribution.
Grace Shao (25:18)
Because you’re basically saying monetization still has to be on the enterprise end. What about China’s AI application on the consumer end? Do you see that being one of their advantages or something that they’ve done really well in terms of adoption rate? I deep sea hit like, not deep sea, dobao has hit more than 450 million in the MAU. Deep sea even higher than that. It’s kind of crazy in terms of scale. think about just like compared, it’s almost comparable to the leading American.
⁓ applications right now.
Kevin Zhang (25:49)
Yeah, I think from a usage perspective, for sure. ⁓ The more interesting thing is how do these companies and these products monetize? If we look at Alibaba, Tencent, guess like Baidu, ByteDance, and then even like some of the newer players like Xiaohongshu, how do they monetize? It’s through ads, it’s through e-commerce. And so I think for these,
new Chinese companies, it’s figuring out how to monetize this chat interface ⁓ catering towards a Chinese audience. And I think the company that’s able to figure it out, and it could be one of the giants, existing giants, they’re going to capture that slice of revenue. But as far as the willingness to pay for a Chinese consumer, I think that’s significantly less than ⁓
an equivalent US consumer where the number of people in China who are willing to pay 20 bucks a month or 200 bucks a month for kind of the higher tier of opening, that’s gonna be limited. So you have to win on ⁓ scale, right, scale of users and you have to win on these other potentially less obvious sources of monetization.
My guess is it’s gonna be advertising, it’s gonna be commerce.
Grace Shao (27:17)
Yeah, it’ll be like value added services or like invisible ways of making money. It’s not going to be like a subscription model or anything. I agree with that. Yeah. Let’s just actually double click on China. ⁓ know, Bill Gurley, I think has been one of the more I would say pro China, but more outspoken investors in Silicon Valley. That’s not anti China, at least. I think he recently just talked about going to China as well, doing a big trip with his daughter. ⁓ You wrote about Bill Gurley being wrong.
Kevin Zhang (27:20)
Yeah.
Mm-hmm.
Grace Shao (27:47)
about China’s open source models. Can you explain to the audience what was your whole thesis and why do you think he’s not analyzing the space correctly?
Kevin Zhang (27:58)
Yeah, I first off, like I think where he is right are a couple points, right? So the entrepreneurs, investors, ⁓ folks in technology and finance definitely pay way more attention to the US ecosystem and learn from the US ecosystem way more than vice versa, right? And so that creates a huge blind spot for US entrepreneurs and investors. And then secondly, I think there’s
Quite a vibrant and talent dense kind of ecosystem ⁓ in China here as well ⁓ I think where he is maybe not ⁓ Where he might have like missed the mark one is this kind of like grasses greener on the other side syndrome, right where a US investor might feel like like China’s like the land of opportunity because of potentially lower valuations because of
⁓ very competitive entrepreneurs, ⁓ etc. Whereas, and they might have this feeling that ⁓ China is like kind of the buyer’s market, right, or the investor’s market. ⁓
Grace Shao (29:12)
But wait, that was
what happened with Internet error, right? So it’s not like he has no basis with this mentality or thinking.
Kevin Zhang (29:20)
I think that’s partially correct. I guess the question is how does an American VC ⁓ monetize ⁓ this ⁓ insight if they are potentially unable to invest in these ⁓ underpriced but potentially competitive assets given some of the limitations for American investors? And then secondly,
⁓ I think the open source closed source debate is more interesting. So I think ⁓ Bill Gurley is a proponent of open source. And so the framework is like, if you are a front to your lab and you are training models that cost ⁓ tens or hundreds of millions of dollars per training run, you have to monetize it in some way.
And so if you are a lab that gives it away for free, that truly limits your ability to monetize. ⁓ And that is like very different from Facebook open sourcing their models because they have a very, different business model than some of the labs here in China. So it goes back to even opening in Anthropic where initially they have the tech edge, they have the best models.
eventually folks are going to catch up. So they’re really in this race to maintain being one of the top players, right, top two to three players, and then ⁓ winning distribution, right? So if you are at this Google scale as an opening eye where you have multiple software assets that people are using daily and your model is, let’s say, top one or two or three, that becomes very, very hard to displace compared to a lab that only has a model but no product.
And so I think that confluence of how do you build product on top of your model layer while you have the lead is really the name of the game versus, hey, I’m going to train a pretty good model and then I’m going to monetize via API and then just give it away for everyone else. I think that’s probably not the winning game in this era.
Grace Shao (31:38)
Then what’s the game that Deep Seeker moonshot or any of these, ⁓ the kit moonshot that’s behind Kimmy, what, what, what should be the game for them, I guess, for them to be able to monetize eventually.
Kevin Zhang (31:51)
That’s a great question. I don’t know.
Grace Shao (31:53)
Yeah, I think that’s why like for Baba, it made sense, right? It’s kind of like the meta llama business model because end of the day, they own distribution and they own the infrastructure. but but like it’s interesting to see because even the deep seat narrative like, he’s a billionaire, he funds himself. But then that’s actually not that much money to fund. like we just talked about capex is like this is hundreds of billions. This guy’s got a billion. Like, there’s still a pretty big gap in between that he’s not going to bankrupt himself to fund this. Right. So
Kevin Zhang (31:54)
Yeah.
Yeah.
Grace Shao (32:22)
It’ll be interesting to see how he monetizes or make this into something bigger.
Kevin Zhang (32:22)
Yeah.
Yeah,
I mean, the other thing is like the market is very dynamic and for every one of these labs, you’re a one hit product away from monetizing, right? And I think it’s never a wise thing to count a player out, especially if like a founder is very good and very thoughtful. ⁓ And so if some of these like companies
end up going public and raising, let’s say, their few hundred million or like a billion, they’ll have another two, two, three years to figure out the monetization piece, right? While they try to catch up in some model modality, right? So they might, they might figure out, or mini-macs might figure out, our video models are really good and truly world-class, and we’re going to build a bunch of these workflows and we’re going to be adopted by the world, right? That could be kind of interesting as well.
Grace Shao (33:22)
It could be a business model that we haven’t even seen before. could be something completely innovative, right? Something completely new.
Kevin Zhang (33:28)
Yeah, potentially.
Grace Shao (33:30)
Yeah. Okay, I think I want to zoom out a little bit. When we talked, you said you are looking at Neo clouds, and this is a space where I frankly know very little about. So I want to hear from you and please do explain things in layman terms and dumb it down for me. But alongside the hyperscalers, we’re seeing Neo cloud players like Coreweave, Nebius emerge, right? Like they’re making headlines. ⁓
How does the dynamic really work between the Neo clouds and our old traditional cloud players?
Kevin Zhang (34:03)
Yeah, mean, for some of these, so defining Neo clouds like the way I think about it is it’s GPU as a service, right? So whereas a traditional cloud provider like AWS or Azure or GCP, they might provide a bunch of services, right? So they might offer databases, they might offer compute, they might offer a bunch of these other pieces of software storage. ⁓ Neo clouds kind of simplify what they offer and
The core of what they do is they help companies with training models, doing inference on the models that they train. And so the dynamics so far has a couple of dimensions. So if we look at ⁓ the upstream, NVIDIA, so they basically supply GPUs to both the hyperscalers and the Neo clouds. Their incentive is to have or to not have the hyperscalers be that big because they know that
hyperskillers like Google, like ⁓ AWS, ⁓ or Azure, they’re building their own hardware. so, NVIDIA is very incentivized to build credible competitors to hyperskillers where they reduce their customer concentration. And then for the Neo Clouds themselves, like a few of them were kind of in the crypto mining space before pivoting to kind of this like AI workflow, like AI compute.
And I think the game they’re trying to win is, we have this wedge where Nvidia will give us some allocation of their ⁓ latest GPUs because of those dynamics that we talked about a couple minutes ago. And we’re going to use this as a wedge to eventually become a really big player ⁓ in the AI space and maybe ⁓ for the players with even grander ambitions.
to become kind of the next hyperscaler. And so you could look at like Oracle a few years ago where their cloud business was a very, very distant forth, right? In the U.S. But because of these recent contracts ⁓ with ⁓ these larger customers like OpenAI, that they’re able to ⁓ see significant appreciation in their ⁓ market cap ⁓ and kind of this like...
like exponential increase in their RPR remaining performance obligations because open eyes saying, hey, we’re, we’re, we’re willing to spend a couple hundred billion dollars on, on compute with you, right. Versus, ⁓ within Azure. So, so I think the dynamic is hyperscalers, ⁓ trying to maintain their, their lead, ⁓ while trying to build more, more and more of their hard, hardware in house. And then on the Neo cloud side, it’s, Hey, we have a wedge right with.
⁓ a ⁓ demand for AI, both on the training and inference side. And we’re going to use that to grow our revenues very, very quickly, potentially take on a lot of debt, right? And then become kind of one of these large dominant players tomorrow.
Grace Shao (37:12)
So you said that there are essentially service providers. Could you actually elaborate a bit more on that in the sense that, ⁓ again, this is really new to me, this whole sector, but people have said, NeoClouds are a real estate business. Others saying it’s a software layer value ad service. How do we actually see this? Can you explain that to me?
Kevin Zhang (37:35)
Yeah, so at its core, like, Neo Clouds are just clouds with like GPUs, right? And so how do they monetize? And so there are basically three ways to monetize that we’ve been kind of diving a little bit deep into, right? So one is, hey, I am an OPENAI and I’m just gonna rent your GPUs, right? I’m gonna rent your infrastructure and I’m gonna do everything ⁓ myself, right? And so for the Neo Clouds,
That’s kind of the lowest margin type business because you’re basically taking some profit ⁓ or taking some margin on top of your cost ⁓ to provide that compute, right? So part of that is your initial cost to acquire that hardware. Part of it is your kind of ⁓ ongoing electricity costs or costs to run the data center. And so that’s kind of bucket number one of
monetization for Neo clouds and then bucket number two is kind of the managed services, right? So if you want to do, if you want to provide software for training, if you want to provide software for experiment tracking, for AB testing, things of that nature. So that gets you much closer to software margins, right? Traditional SaaS margins. And so I see more and more Neo clouds going there, right? And that BS included, uh, uh,
CoreWeave as well with their acquisition of weights and biases. ⁓ And then the third part is what if we provide, excuse me, APIs as a service, right? What if we give you inference, we’ll host the models ourselves and then you just pay based on the tokens generated. And so that’s like kind of the third category. And so ⁓ how this space ends up playing out is what’s the purpose
portion of revenues that these Neo clouds will be able to generate from each bucket of services and products, where if they’re able to generate more and more revenues from kind of the bucket two and three, that makes Neo clouds a much higher margin business than comparatively under differentiated, you know,
GPs as a service infrastructure, although running these data centers isn’t that easy. And there’s a lot of nuance between ⁓ even some of these more leading edge cloud players.
Grace Shao (40:09)
I see. ⁓ I think I’m going to actually ⁓ ask you one last question on investment, is I think energy is something that people have been talking about being affected by AI, obviously. And then like you said, software businesses, you know, we’re obviously already spoke about LLMs, cetera. So what do you think is a sector that is going to be affected by AI and you’re looking at it as an investor? ⁓
but are not as obvious to the public or not as obvious right now.
Kevin Zhang (40:40)
Yeah, I think that’s hard. ⁓ So just given ⁓ most of our efforts are on the ⁓ kind of public side of things, we are kind of looking at every layer of the sack, right? So from ⁓ semiconductors, kind of ⁓ the companies that build the underlying kind of infrastructure, right? So your transformer companies, your... ⁓
power companies ⁓ to your application companies. And I think it’s more ⁓ what’s undervalued relative to kind of market consensus and, ⁓ you know, rewinding the clock back one or two months, Nebius was one of those players, right? And Oracle was one of these players. So it’s more which player within which kind of layer of the sandwich is undervalued and then how do we think about
our risk adjusted returns, which is a little bit of a cop out answer. But our objective is to build a basket of these securities across the stack where we believe that these businesses will offer outsize returns. then going maybe a little bit deeper is for a lot of these businesses, they don’t have kind of a pure exposure to AI. Meaning even if you invest in a company like
a Microsoft, right, or an Amazon. They have ⁓ their traditional minds of businesses that you have to price. And then those tend to have, especially if they’re more mature, those tend to have a slower growth rates, right? So even if your AI revenues are exploding, they might be dragged down by some of these at scale, ⁓ mature ⁓ business units ⁓ or products.
And so how do we think about the blended returns for, let’s say, a ⁓ much, much more or much kind of like traditional player in the kind of transformer space?
Grace Shao (42:47)
All right, Kevin, ⁓ anything else you think you want to share with the audience? ⁓ Mindful time or wrapping up our episode? Is there anything you want to talk about that I did not touch on?
Kevin Zhang (42:59)
Sure. I think a very interesting thought experiment is how fast AI will displace work. So I think my perspective is going to be much slower than folks might think in Silicon Valley and much faster than the rest of the world thinks. Meaning next year, 90 % of the code is not going to be written by
or at least like code and production is not going to be written by AI, right? Or I don’t see like broad swaths of ⁓ workers gets automated. ⁓ But I think is AI adoption going to be a 20 year kind of journey? think, no. I think ⁓ for a lot of these professions, it’s going to be this like five to 10 year disruption.
And then we’re already seeing some of this ⁓ for new grad hires, right? Where a combination of AI giving more experienced workers a higher leverage, ⁓ as well as some of the broader macro headwinds ⁓ in the US ⁓ affecting kind of new grad job placements. And so I think my intuition is it’s gonna spill over to kind of the mid-level folks.
as well, right, comparatively soon.
Grace Shao (44:26)
Yeah, I think actually I have an episode coming out literally today, which is with ⁓ Diana David. She’s the director of features at ServiceNow. And she was saying kind of like, instead of thinking about how it’s going to displace jobs, it’s going to change where the workforce will go towards. It’s just that we need that time to kind of figure out where it’s going. But like you said, right now, unfortunately, it’s hitting the young junior staff the most because their work is usually, you know, more
you know, the hands-on kind of like the grunt work that is really easily done right now by AI. Anyway, thank you so much for your time today. I have one last question for you, which is a question I ask everyone. What is a view you hold that is unconventional or you think it’s against consensus? It could be something related to investing or anything, you know, in life.
Kevin Zhang (45:49)
Let me try to think something that’s unconventional.
Okay, ⁓ so I think something that’s unconventional, especially folks ⁓ within finance, think folks in finance tend to be very sensitive to kind of market fluctuations as well some of the macro headwinds or tailwinds. And so, especially right now, where I think we’re in potentially a more challenging part of the cycle.
with employment, with kind of the degradation of ⁓ kind of global connection. think in the long run, ⁓ collaboration will probably win out, at least that’s my hope. ⁓ And that bodes well for entrepreneurs, right, who want to play in multiple ecosystems. That bodes well for investors who want to play at ⁓
these multiple ecosystems right across across Asia Europe and in North America and I think ⁓ the game is Right now is especially for for some of these venture firms in China is like who can survive right because I think those that can survive and Those who can continue to raise they’re gonna be fine and they’re gonna they’re gonna see some very very good vintages ⁓ and I think the same goes for ⁓
when the US also sees their down cycle, the folks that have a lot of dry powder who are able to deploy through those tougher periods, think that’s where you’ll see good ventures as well. so the question of like, given some of these macro headwinds, is that the death of the buy side in Asia, I think that’s probably a little bit overblown.
And then like I five years from now, 10 years from now, like the ecosystem will be mature and healthy.
Grace Shao (47:53)
You think that USD denominated funds will see a revitalization or do you think that we’ll just see a complete different kind of ecosystem from five, 10 years ago?
Kevin Zhang (47:59)
Maybe?
I think it either might be a completely different ecosystem or ⁓ there’s other global capital that’s interested in the broader Asia ecosystem. And I think they’ll come. It may not be at the same scale as ⁓ the era of like Hulianwang, right? But like as long as the ecosystem continues to be vibrant, as long as ⁓ entrepreneurs are able to build
good products and generate or build large businesses, then there will be some capital somewhere in the world that’s willing to back these entrepreneurs.
Grace Shao (48:46)
There’s a lot of Middle Eastern money coming into China, actually, and I think Southeast Asia, family office money, and even, I think, European money. But it’s definitely not the same scale as what we saw during the Indian era with institutional US investors, right? But that’s encouraging. That’s positive note to end on.
Kevin Zhang (48:52)
Thank you.
Yeah.
Yeah, yeah. I think like for some of these LPs, right, there’s definitely a learning curve, right, in terms of LP sophistication and being very long-term oriented. Whereas ⁓ some of these ⁓ OG US LPs, they’ve had decades of experience investing in the VC asset class, right? And so when that learning curve catches up, then... ⁓
At least on the LP to GP side, ⁓ we’ll see some revival.
Grace Shao (49:39)
Great, thank you so much, Kevin. Kevin Jang, thank you.
Kevin Zhang (49:42)
Great, thank you.
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In this conversation, Jing Yang, Asia Bureau Chief of The Information, a former WSJ reporter, discusses the evolution of China's tech landscape over the past decade.
We explore the corporate strategy and positioning differences between established tech giants like Baidu, Alibaba, and Tencent and newer entrants like Pinduoduo and Shein. Jing also talks about her reporting on Shein and Temu and their attempts to be publicly listed in the West.
The conversation delves into the regulatory challenges faced by these companies both domestically and internationally, and how that has led to a belief shared amongst Western investors that “China is uninvestible.”
She dives into the implications of the AI arms race between China and the US, and the shifting dynamics in the venture capital landscape in China. She explains the differences between RMB-denominated funds and US-dollar-denominated funds, as well as how the VC ecosystem has evolved over the last few years.
—
Jing Yang is the Asia Bureau Chief at The Information. Based in Hong Kong, Jing leads a team of reporters covering the region's vibrant tech and venture capital scene and has written and overseen agenda-setting stories on topics ranging from AI to semiconductors to marquee companies like Nvidia and ByteDance.
Prior to joining The Information, Jing was a Senior Correspondent at The Wall Street Journal where she covered a broad range of topics, including Wall Street’ foray into China, Beijing’s crackdown on internet platforms, and the 2022 Beijing Winter Olympics. Jing also has reporting stints at Bloomberg News and the South China Morning Post.
She has won three Society of Publishers in Asia Awards and three Best in Business Awards at the Society for Advancing Business Editing and Writing in the US. She is an honorary lecturer at her alma mater, the University of Hong Kong’s Journalism and Media Studies Centre, and a board member of the Foreign Correspondents’ Club in Hong Kong.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
00:00 Introduction to Jing Yang and Her Journey
02:35 The Evolution of Chinese Tech Mindset
08:08 Comparing Old and New Chinese Tech Giants
11:16 The Unique Business Models of Pinduoduo and Shein
17:41 Regulatory Challenges for Chinese Tech Companies
22:04 The Impact of AI on Chinese Tech Giants
32:30 US-China AI Arms Race: Context and Implications
34:55 Bridging the Gap: US and China Perspectives
39:10 China's AI Strategy: Open Source vs Closed Source
44:32 Emerging Players in China's AI Landscape
51:41 The Impact of Decoupling on VC Landscape: Sequioa to HongShan
01:05:44 Future Trends in China's AI Tech Space
Auto generated transcript for reference, typos might occur
Grace Shao (00:00)
Hi, this is your host Grace Shao and joining me today is Jing Yang, Asia Bureau Chief of Information. Previously, she was a senior correspondent at the Wall Street Journal in Hong Kong where she covered a range of topics including Wall Street’s foray into China, the globalization of China’s most prominent tech companies, and the country’s domestic tech regulatory crackdown. Jing, it’s so great to have you on and ⁓ it was lovely running into you actually recently at Karen’s book launch in Hong Kong. We’ve known each other for a few years now, you know, over like our paths crossing.
whether in journalism or when I was working in Alibaba, you’ve covered Chinese business and tech for over a decade across from SCMP, Bloomberg, Wall Street Journal, and now obviously leading the information out here in Asia. So looking back right now, like over the last decade, what’s really changed the most in terms of talent, capital, and company building China, and just your own kind of observations of the whole industry?
Jing Yang (00:52)
Sure. Thank you for having me. Yeah, it was indeed good to run into you at Karen (Hao)’s book talk. So I think the one thing that sort of really struck me is that when I started covering the, started ⁓ working as a reporter, 10 plus years ago, at that time, if you recall, then China was rapidly still integrating into sort of the West led global order.
A lot of the Chinese companies and executives and entrepreneurs, think their mindset was still like there’s so much we can learn from the West, from the US, from Europe, from what companies have achieved there. And then now that has changed for various reasons we can get into later into sort of, know, this is the way actually, you know, we do things in China and it has in so many cases proved to be actually working better or
or there’s more this awareness of things are different in China, whether it’s cultural or economical or social. And I think then you also have seen sort of equally some examples from Silicon Valley or elsewhere sort of looking eastward and looking at what are the things that China or Chinese companies have done that we may actually
there are some lessons or experiences we can draw from. I think that is a very interesting sort of shift. It’s not just one way anymore, it’s more two-way.
Grace Shao (02:17)
For sure. But I think in that sense still, you know, we have people like Bill Gurley going on his podcast saying like Chinese entrepreneurs or business leaders are just so much more attuned to what’s happening in the U.S. compared to vice versa. But there’s definitely more more interest coming from the West to China. Right. I think on that, like one interesting, you know, Chinese tech culture that’s really being adopted and, you know, seeing it being embraced by Silicon Valley right now is the 996. You know, like
That’s an interesting phenomenon. What do you think of that? Do think people are realizing that you got a 996 to really push ahead?
Jing Yang (02:50)
Yeah, that is a very good question. And I think I’d like to actually unpack what we are talking about when we’re talking about 996. When it first started in China, and by the way, that wasn’t just something adopted by tech companies, but also in general, a lot of big companies in the private sector in China were doing that. And I think what in that sense, from the mid to late 2010s,
When companies were adopting 996, it was mostly codified. Remember, that was a pre-COVID world. You are expected, when you’re working, you’re expected to show up in the office, at your booth or whatever. So that means you have to clock in at 9 a.m., clock out at 9 p.m., and then for six days a week. But why you actually do in those 12 hours and six days a week is another question.
And I think over time, some companies realize there’s actually some kind of waste and inefficiency that this codified system has created. For example, a lot of companies, ⁓ employers, in order to entice employees to work longer hours, they provide dinner and then they provide, say, you can expense your cabaret home if you work past 9 p.m., that kind of thing. And over time, then, I think that actually did lead to some sort of inefficiency in the sense that
Maybe some people just want to stay back. If I’m going to go home and have a take on myself, then why don’t I do it at the office when it’s paid for? I’m not saying everybody does that, right? But certainly there are people who did that. And I think while I’m not discounting at all, Codefine 996 has led to burnout and even much worse things. But on the flip side.
is that when you codify something like that, that is the problem. So then if we look at in the US, for example, if you are a Wall Street banker or lawyer or a management consultant, I think you are expected to work long hours when you are servicing a client who has urgent needs or you are just on some really tight project deadline or say if there is a market meltdown.
And then nobody would say anything about, that is 996 or not. And then so I think it’s easy to throw the labels around and then not to actually look at what is happening. It’s not like in the US or anywhere in this world that people just don’t work extra hours. That’s certainly not true. And in terms of, I read the reports. I think there was a wide article that wrote in depth about this.
But the one thing that I think is quite different is that we haven’t seen this being adopted as a policy across-broad, especially by big companies in the US or anywhere else. And in China, when they loosened the 996, what was loosened was just like you are not expected to clock in and out at 9 a.m. and 9 p.m. But you do still expect to work when needed.
And then, and I think in a way, the post COVID world where most workplaces is set up to have employees work, you know, virtually from anywhere has actually made it worse for people everywhere, right? You know, as long as you still have your laptop or even your phone with you, you’re expected to say if your boss messages you at, you know, 10 PM with some questions.
I think it’ll be unthinkable that I just ignore that, right? And so I think that is actually the real issue than when we say, you know, 9-8-6.
Grace Shao (06:26)
Yeah, think it like I totally agree with you in many ways that like this whole working from home thing has actually made everyone feel like they’re more like glued to their devices and more clocked in. But I think it also just showcases that this whole adoption of so-called 996 in the US right now is really driven by, you know, just the excitement and I guess competition now we’re seeing in AI because, you know, for big tech for many years, we’re used to this kind of story of like, you know, ⁓
not a very like, it’s a very cushy job, not that competitive internally once you get in, you get all the perks of like the gyms and the free food and everything and people can just kind of go for walks in the middle of afternoon, right? and that culture’s completely changed. And I do want to really double click on that on the China US AI arms race per se later on. But before we get into that, I really want to kind of look backwards first, try to start of your career, right?
You’ve been covering China’s tech and business space for more than 10 years, essentially. Comparing the earlier BATs, whether it’s the Baidu, Baidans, Alibaba, Tencent ones, to the new companies like the Pinduoduo, Shianti, Moos, what are the kind of differences in their corporate structures or management styles or even their so-called Chu Hai strategy, like their going global strategy? What are some observations on that front?
Jing Yang (07:44)
Yeah, I think overall, I would say, you know, since you mentioned BAT, I think what the B-stands for Baidu are Bytedance, right? But if you look at just the A&T, you know, the Jack Ma and the Pony Ma, I think they represent sort of the last generation of Chinese entrepreneurship. And then moving on to like Jiang Yimin, who’s younger, and then obviously, Kuanlin Huang as well. And then to like the...
the founders of the hottest startups nowadays in China. think one big shift is that the entrepreneurs, the builders who grew up, who were born post 1985, post 1990, they grew up in an era when China was rapidly integrating with the world.
And they likely grew up watching a lot of American TV, really engrossing a lot of American culture. then that sort of... And it was also during that time when China was really just thinking like, we are the students learning from the superpowers from the West, that kind of era, right? So they sort of grew up to have a more global view.
of the last generation of founders and entrepreneurs. then so now this is why the companies that they are trying to build right from Bydowns, which is already a giant today, to the smaller startups that we’re seeing, they want to compete at a global level from day one. Whereas in the case of Tencent, Alibaba, or Baidu, what they did was we
took something that has worked on, for example, Amazon or Paypal in the US, and then we replicate that model, adapting it to the Chinese soil. And then we make it work first in China. Then if that has worked, then let’s see if we can export that success somewhere else. I think that that’s probably the biggest difference.
Grace Shao (09:41)
And I know that beyond the BAT, you know, covered actually, Temu and SHEIN quite extensively. I feel like these two companies are a bit more mystical to the world because, know, they feel like in some ways they were the first companies that really succeeded at not being seen as Chinese, even when they went global. Yet they were caught in the geopolitical rows when the first round of trade wars happened. And, you know, now they’re not really making the front of the headlines anymore. So what do you really make of these two companies?
I guess their positioning, how they’re doing now and their journey to potentially trying to get listed in the West and just frankly failing at it at this point or not being able to.
Jing Yang (10:21)
Yeah, I mean, maybe we’ll just separate PDD slash Temu and SHEIN a little bit because as you know, Temu is a subsidiary of PDD, right? So I think Xin is a very interesting company in the sense that it’s kind of, I mean, of course, you know, people see them as an e-commerce company. But however, if you look back at the roots of the company and the background of their founders, it’s not really a tech company in the sense that, you know, they are
very good at doing international trade, foreign trade in China. And then e-commerce is just internet, it’s just a way through which they made their, it was like a marketing and a sales channel for them. But the company didn’t start as like say with tech or internet in its genes. It was...
For example, in the early years of SHEIN, they really basically went to like all these markets in Guangzhou or somewhere else and then see what we can sell to overseas markets. And then ⁓ they’ve sold a bunch of random things from teapots to wedding gowns and then, know, basically whatever.
sold, they will sell it. ⁓
Grace Shao (11:33)
It was like a glorified
drop shipping business essentially, right? Like a bigger scale drop shipping business essentially in the beginning.
Jing Yang (11:39)
Yeah, exactly.
So that’s how the company got started. But obviously, they then fine-tuned their business model over the years and then did make some true innovations in terms of how to adopt technology and making fast fashion more, not only more efficient, but also more trendy and also through the process, it really drove down the cost.
But all of that was happening against the backdrop of China really has achieved that kind of manufacturing base that enabled this kind of opportunity to arise. And I think that is the part of the Shenzhen’s business model that was most misunderstood and why there was all these allegations of slavery for slavery. Because it’s just the...
the cost efficiency was just mind blowing to a lot of people. And then obviously what Temu has done is to expand, know, Shin’s business model, replicate that from not just apparel, but to many other sectors. But essentially these two companies and or as e-commerce platforms per se, they’re actually quite
you know, different in a sense, because what SHEIN has been selling, apparel, is like a pretty non-standardized kind of merchandise, right? Every piece of clothing is different. And then they change so rapidly in terms of what is the market trend. Whereas when you are selling, say, toys or home appliances, these are standard goods that don’t really change that much over time. And also,
⁓ the marketing and the shipping and everything is also a lot, is quite different. But in terms of like sort of how they were misunderstood, I think the rise of SHEIN team was really to a large extent misunderstood in the West because what I said earlier about, know, because those kind of business models was enabled because of the decades of, you know, China being the worst factory really
accumulated the manufacturing capabilities. when I say manufacturing capabilities, doesn’t just mean factory, the efficiency of the factory floors, but also everything that goes behind it, you know, the infrastructure, the logistics, etc. And then in the process of that rise, I think in the typical Chinese mindset is like, I don’t want to talk about my success or how great I am. I just want to
You know, this very, maybe very pure mindset. I just want to make money. I just want to build a successful business. Right. I only need to satisfy my customers. Like my customers are the only stakeholder I need to think about. Right. They don’t think about, there is also, you know, investors or potential investors, regulators, politicians.
I used to joke back when Shane started to face all this backlash in the US. I used to joke that the people who came out against criticizing Shane in the US are like the pirates of their customers. That shows you sort of where the mismatch is.
Grace Shao (14:52)
Yeah, I think it’s so interesting that you brought that stakeholder engagement part up and it’s really funny, I think. I remember SHEIN when they were like, they made it to the front page of Bloomberg saying, like you said, being accused for labor malpractice or whatnot. They were just trying to hire so like crazy, like frantically hire people who can manage their PR. But it was kind of one of those things where frankly it was a bit too late. Like they really didn’t have the sense to.
actually put out their messaging and put out that what like explain what they’re doing beforehand. But I actually do want to kind of bring it back to the regulatory side of things. So basically, where are we at right now with the two companies? ⁓ Like, well, I guess, and more on the team side, as far as our she inside, are they still trying to pursue a IPO in the West? Or, you know, what where we are right now? And what’s really the hurdle? Is it like an international?
kind of a geopolitical hurdle, or do you think it’s actually a domestic regulatory?
Jing Yang (15:50)
Right. I think it’s sort of a both. I mean, I’m just like repeating what has been reported out there that SHEIN now as a confidentially submitted application to listen in Hong Kong. By the way, that is sort of an exception made by the Hong Kong Stock Exchange because usually, like typically the only times that the Hong Kong listing regulatory regime does not usually allow confidential filing unlike in the US and in the times that they would
usually ground that exemption is for companies that already listed elsewhere. You remember all that homecoming listing wave that happened a few years ago, Alibaba and Baidu, cetera, that when they were applying to list in Hong Kong, they were all exempted to file confidentially. And the Hsing, in that sense, being a company that is not listed elsewhere, that is a of a waiver that the Hong Kong Stock Exchange gave them. And then in terms of hurdles, mean,
All of this happened after the Didi debacle, right? And after which CSRC tightened the regulatory framework for companies, for Chinese companies taking the list overseas. And then SHEIN sort of was caught in, know, SHEIN and many other companies IPO have suffered significant delays because of that.
And in SHEIN’s case, because the company is really quite big in terms of the size and also all the attention it has attracted. But if you look at from, like, say, a pure domestic Chinese angle, what is what SHEIN is as a company? It really, as I said earlier, in the China domestic angle, SHEIN is an employer. SHEIN is a company that that is a big customer to a lot of factories in China.
That’s essentially what it is, because it doesn’t sell in China. So then in that sense, SHEIN is a very big contributor in terms of the whole economy that it has given rise to, as well as the tax dollar, the number of employment that either directly and indirectly has contributed. And then that sort of makes it like a case.
Grace Shao (17:34)
Exactly.
Jing Yang (17:57)
in the regulatory context, that case that cannot be, that has to be really, say, deliberated on. But then does China want a company like this to live, in the US or in London? I think that’s why there is the interplay between the domestic consideration and the geopolitical tensions and also the backlash to that.
that came with it.
Grace Shao (18:20)
Yeah, I think that explains it really well. think for a lot of people outside of China, they don’t realize SHEIN actually is not like a household name at all. The people, the consumers actually are, it’s not a consumer facing company in that sense in China at all. It’s actually to be business per se in China, right? I think it’s perfect. Exactly. I think it’s great you brought up the Didi (Chuxing) situation and I think that’s where I want to kind of bring it back to last question on the big tech space in China, which is like, you know,
Jing Yang (18:32)
Exactly.
is like a B2B wholesale company, essentially.
Grace Shao (18:47)
We all know there was a domestic, domestic regulatory kind of tightening over, you know, between 2020, 2023 per se. know, Didi being kind of at the very top of the epitome of what was happening. And then Baba, Tencent being faced with the, situation as well, right? Like the two choose one between the two kind of camp situation, the regulatory problems. So, but after that.
Basically international investors pulled out of China. People were kind of scared of the China regulatory crackdown. think people outside of China don’t really fully understand why the domestic regulators were cracking down on these companies. Could you give some context on that? And then I think what I really want to also ask you, the second part of the question is, are we seeing a revival of these companies now with
AI being supercharged into their strategies? know, recent BABA and tens of earnings have done really well, all driven by AI, right? Their new AI strategy. Or are they kind of just, you know, the last generation staying there back in 2023, they just kind of stalled and stopped there? Are they becoming relevant again? So I think it’s two parts of the question.
Jing Yang (19:52)
Yeah, so yeah, let me tackle the first part. I try my best even back when I started, you know, this whole thing happened when I was still at the Wall Street Journal. And back then, I think in the beginning, it’s been called the China tech crackdown a lot. And I actually made a point when I was writing about it at the journal, after a while, it has become very clear it’s not a tech crackdown. It’s not a
crackdown on tech companies. Because if you look at sort of the hard tech companies, either it’s a Huawei or any other that’s in that space, they were all fine, right? Essentially, the crackdown was targeted at internet platforms, right? Which sort of is equivalent of big tech in Silicon Valley or in the US when we talk about it. But in China, there is actually a differentiation between
Internet platforms and tech companies. So that’s the first thing. And secondly, I think in terms of that regulatory assault, there are, you know, there are, you know, legitimate sort of economical and regulatory reasons to do so. Right. As you mentioned, the Arsheng, the truth one from two, that was that was indeed a violation of China’s antitrust regulation where
e-commerce merchants were forced to only choose to sell their wares between Alibaba and JD, for example. when this whole thing happened, was indeed sort of the regulatory incentive to do that. was indeed raining years of flouting.
anti-trust regulation and other types of regulation. But then obviously, the problem that came with it is that the way that the Chinese regulatory framework and the Chinese government in general works is that it doesn’t really care about doing a very good job at telegraphing its regulatory intention.
That’s not just on the tech space, like overall, So that’s why if you remember the online tutoring crackdown, Just essentially what happened was just, that was very scarring, by the way, for a lot of investors, right? Because they targeted a sector where the biggest companies are listed in the US.
So then when you can’t just do this, issuing a piece of document that just evaporated the entire sector, that decimated the entire sector overnight. That was very scary. ⁓
Grace Shao (22:31)
Yeah, it was so sudden. That’s what it was. There was no hints or clues. I feel like with the anti-monopoly, was actually, you know, there was like momentum building up to it.
Jing Yang (22:41)
Yeah, so that really, I think in my recollection, that really crystallized the saying that China is uninvestable. When people say China is uninvestable, they mean Chinese stocks are uninvestable because that really crystallized how precarious, so to speak, that Chinese policymaking can be. And then so...
Similar thing with the Didi debacle. think the biggest problem with that is, one can argue on Didi’s behalf that the company didn’t do anything wrong because what happened at the time was there really was just no regulation governing Chinese companies listing overseas. Things were just not formulated. And obviously, Chinese regulators realized that was the problem. that’s why they...
came up with this new framework that has been in effect since early 2023. However, the absence of a formulated framework did not stop them from punishing companies in the first place for flouting rules that are not explicit. Then that is another piece that shows, OK, so this is unpredictable.
this whole virtual environment.
Grace Shao (23:58)
And the second part of the question is, you think we’re seeing a comeback? Because essentially, now you can’t really get into China’s private AI sector, right? If you’re a foreign investor and the way they can get some kind of exposure, I guess, is through the US listed companies, tech companies. In this case, it would be the Alibaba’s of the world, right? So are we seeing kind of a shift in interest again, or a revival of these tech giants?
Jing Yang (24:23)
Yeah, I like to answer that question by going a little bit further back first. So you remember the Shanghai Stock Exchange when they first came out with the NASDAQ-style starboard, right? There was a lot of discussions on what are qualified as innovation so that a company can qualify as being listed on starboard.
Grace Shao (24:36)
Mm-mm.
Jing Yang (24:47)
And you remember Xiaomi actually was going to become the first company and then it didn’t happen. And I remember having conversations because I was covering IPO and capital markets of Wall Street Journal at the time. I remember having conversations with some lawyers who consulted with who advised the Shanghai Stock Exchange on designing the rules for Starboard. And there were still a lot of undecided issues such as, OK, for example, did the other time remain unlisted?
Then the conversation was like, Didi as a company, even though it wasn’t an innovation in terms of the technology of like, ride-hailing, for example, but it was innovation in the business model. Then does innovation in business model qualify as true innovation, therefore qualified being listed on Starboard? There was all these questions at the time. And obviously, we now know the answer with what happened with Didi later on.
And I’m bringing this up because shortly after the crackdown was on the Internet platforms, followed ⁓ the zero COVID and the whole Chinese economy and many other things related to that just were in a really depressing, know, slipped into a really depressing state. Then
Around that time, toward the end of zero COVID, had the arrival of Chagabitie that sort of shocked the core of a lot of tech companies and researchers and investors in China, which we can get into later. But I think what the crackdown on internet platforms made people believe that actually China’s paramount leader, Xi Jinping, is probably not a fan of internet platforms.
He probably does not think the type of innovation in business model that I just talked about qualifies as true innovation. Instead, the things that achieve their companies at Huawei are true innovation, are the real technological advancement that can help bring China forward. And so I think that coupled with the arrival of ChatGPT,
sort of, you know, really served as a wake up call, I think, to both the tech incumbents and the startups in China that we really need to, you know, we are actually behind. We have been, you know, because you remember like in the mid 2010s, you know, China had this four AI dragons in the computer vision age. And then you have a lot of people proclaiming that China is ahead of the US now in terms of AI, right? And obviously, that was all.
That sort of dream or awareness was just shattered. All things came together between late 2022 and early 2023. And then that’s where we are now. So it’s hard to tell whether when we see the BATs nowadays...
really doing some of them doing really good work and innovation in AI is driven by the regulatory reason or else. But I would say just the way that things have played out seem to point out to the direction that this really is the era that they have to go through.
Grace Shao (28:01)
Yeah, like to your point, I think there was like an awakening where the focus or the wanted focus was on the so-called hard hard power competition, right? And like you said, that the kind of slippery slope downward trajectory for the industry was really not just caused by one thing, but it was just the macro situation, the regulatory situation, the companies also not innovating in some people’s eyes, were not doing tech for good for the community somewhat.
You remember the common prosperity rolled down, right? All these things are kind of just adding up together. And then there was the whole COVID zero thing that just really made everything even worse. think that that definitely fed into the fear for international investors and international, I guess, China watchers per se, if you have to put it that way. But I think I want to really now go into the next section of our conversation, which is what you’ve kind of touched on already, which is China and AI, right? And China.
Jing Yang (28:31)
yeah.
Grace Shao (28:57)
China’s AI space and how China is positioning itself right now. I think from the West, especially Western media, it’s very, very, very much focused on this idea of arms race between China and the US. You know, there’s a lot of ⁓ comments about how deep the deep sea moment woke Silicon Valley up, made people realize that China was catching up, you know, there’s some fear mongering, frankly, I think, but there’s some
I think some charged by actual fear or confusion or even surprise. How do you kind of make up that? Like, I guess just a high level context first before we go into details.
Jing Yang (29:34)
Yeah, mean, truly, when we talk about US-China AI race, we cannot talk about without talking about US-China competition, whether it’s, I think people now generally call them this strategic rivalry. That is all happening against this broad backdrop. And then the one thing I would sort of
⁓ note though is that, you know, it’s not like there’s a lot of companies or builders or funders in China from day one thinking about, I want to like outcompete ChatGPT or, you know, Anthropic with what I’m doing right now. I don’t think that’s... Yeah.
Grace Shao (30:13)
Yeah, that’s the point. Yeah, like, like, there’s not that strong narrative in China domestically, right? And I think
the information you guys because you guys write for such a frankly, sophisticated audience and people who are kind of more focused on really the business. I feel like your coverage is not as geopolitically charged. It’s actually just talking about what the innovation is, how the capital market is moving. So I can think from your perspective, like, what how do we make a business? this all noise? Is it just like
because of American domestic political reasons that there’s this kind of geopolitical narrative? Or do you think there’s some sense that in the tech space in the US, they genuinely see China as a rivalry that they have to hold down versus like we said, like the China tech space actually just, they don’t talk about that as much. If anything, I think the China tech space actually admires American tech space quite a lot, like as in they really worship a lot of business leaders. They study their business models, you know, like there’s less of a rivalry sense, right?
Jing Yang (31:08)
Yeah, so I think in the US there is a bit of a boast of what you just talked about. I’m not an expert on America, but what I have observed, at least the China relevant people that I talked to from Silicon Valley to Washington DC, I do sort of see like increasing bridging. I think in the past, you know,
Silicon Valley, obviously, we know is more left leaning and then, know, this is different. then with, you know, Trump’s presidency, you see all of that, you know, that that gap is bridging. And that actually ended. And when I say the gap bridging, the gap is also bridging when it comes to the China discourse. So on one hand, there is definitely fear mongering. And that fear mongering, think previously probably mostly concentrated in the D.C. and now is like spreading to Silicon Valley. That’s what I.
observed, right? And also, when I was talking about in China, how people were shattered with the release of ChatGPT in late 2022. Equally, think in the US, what do we see about 12 months later is that with the advent of a DeepSeek R1, a lot of people in the US are like, know, America is far ahead, is the indisputable global leader in AI.
from the China-GDP moment to then the deep sea moment is that China is faster catching up or in some cases they even say China is already ahead. And also that in particular is very true, the fear mongering in the semiconductor space as well, because we can’t talk about it without talking about semi. So I’ll just be very quickly talking about here.
from Jensen Huang to like many other people. I think the last year or so they have been talking up of Huawei and other homegrown semiconductor companies in China and the capabilities of their chips. But the reality, I’m not saying Huawei is not progressing, but the reality as our reporting has shown.
the information. The reality is that actually the BATs themselves actually do not want to adopt a Huawei’s ascent chips for various reasons. First is just the tech is just still not there. Nvidia is still the gold standard. And the second is also because all these companies compete with Huawei in the cloud computing business as well. Like why do you want to enable a big competitor? But you don’t see this being talked about when in the US.
From Silicon Valley to DC, when people talk about how Huawei is threatening Nvidia, how US semi control policy has enabled, has forced China to compete and innovate faster. So I hope that makes sense, by the way.
Grace Shao (33:52)
Yeah, yeah, definitely. think that’s something I wrote about as well. just like, it’s actually people are not realizing from a very selfish business perspective. At the of the day, these companies are for profit. they’re public listed companies. Their goal is to make money. They’re not like, you know, following government orders to like, know, create something on a national level for the sake of that. So like, to your point, like they don’t want to give money and give business to Huawei because essentially one of biggest competitors, right?
Jing Yang (34:15)
Yeah.
Grace Shao (34:20)
because like Baba and Tencent, they all have their own cloud business. I think I want to double click on one thing. said, know, there was, when Deepseek came out, R1 came out, well, V3 and then R1 back to back, know, Silicon Valley said, maybe China is ahead in some ways, right? At that point, it was talking about the engineering and efficiency, You know, from your reporting, where is China actually ahead?
behind or really differentiated in terms of their whole AI strategy? And this is a broad question. So it could be about the companies particularly, or do you think the whole ecosystem is operating a different way? How do you see that?
Jing Yang (34:55)
I think obviously DeepSeek sort of made it cool, made it the open source and open weight school, sort of school of thinking. Cool, right? I think if you remember before DeepSeek became phenomenal, from Baidu to Alibaba to Bydance, everybody was just doing their own closed-source models.
If you do open source, only release smaller or more inferior models. You only open source those. And then now that what happened with DeepSeek, just really made in the LLM space, made people realize actually China can be ahead. China can have a real edge if it pushes ahead with open source. And then
And then that and also when you open source your model models, it also just naturally encourages like a broader and wider adoption of your models. It sort of can travel beyond the national borders on its own. Right. And if China were to let’s say if, you know, by the way, as we established, right, it’s not like the Chinese companies that are building large language models are thinking about what the policy.
or what the government officials are telling us. They really truly are just thinking about, like, I want to be better, right? How do I get... If you build a model, obviously you want your model to be used. You want developers to build applications on top of your models, right? You want the cloud providers to include your models as part of their offering. So I think that’s what’s driven that. And I think the open source thing really sort of shown that, you DeepSeek made it...
a lot people realize that a lot of people in China realize actually open source is the way for China to pull ahead. And that has happened obviously with Alibaba’s Qwen and also I think most recently, Zhipu as well as Moonshot came up with their own latest models that also have impressed a lot of AI watchers in the US as well. So I think that’s where,
That is where China is different. And I just want to add one more point as well. In the US, there’s a lot of money to be made in providing enterprise software solutions. then naturally, that’s when AI applications are being built. You want to build for survival. You want to build to be able to become profitable.
naturally the enterprise software solutions, right? You build a 2B business. But in China, it’s completely the opposite. Chinese companies in general just are very stingy in terms of paying for software, right? And then so that sort of is the way the ecosystem works. And it has always been like that, right?
enterprise customers just naturally go to the next cheaper solution. then, you know, entrepreneurs and also venture capitalists that see who see these companies know that this is the environment, then then they also know that, you know, if you go down that route, you’re just basically, you know, waiting to to go out of business. And then so that’s also what makes the open source led, you know,
an open source led business model, you know, like have a better chances of working in China. Right.
Grace Shao (38:04)
Yeah, definitely. I think the open source versus closed source discussion has been even, you know, rooted out in the software era. like, to your point in China, it’s really like a market share like business. you it’s like you commoditize open source models, you try to capture all the market share, right? Like it’s it’s very consumer facing right now. A lot of the LLM they’re all rolling on consumer applications versus the US to your point. You know, it’s it’s you actually make you try to make higher margins on a lot of these enterprise products, but you just can’t in China.
like the willingness to pay is still so low and not just trying to buy things across Asia in general, like the willingness to pay or I don’t know if it’s a cultural thing or it’s just like people just don’t want to pay. you know, I was even shocked I think when I was working for one of the big companies and like a Chinese company for a while where you would even have like pirated software in their company computers. like, ⁓ you definitely don’t need to save from that money. But it’s just a mentality and then there’s a lack of like.
I must pay for privacy, whatever mentality around it. So culturally, that definitely has shaped the markets quite differently. So I think we touched on quite a few of the startups. So like you mentioned, was Drupal, Bytron, Moonshot, Minimax. Do you think there are any companies that are kind of going unnoticed still in the West? Like we named the four that are still called, there’s still a...
call it what was the four dragons at one point? Wait, four tigers. I get the mixed up with just tigers, dragons.
Jing Yang (39:30)
Six little dragons. Yeah. Yeah.
Grace Shao (39:33)
Yeah, they keep on updating them. It’s
like new versions of Dragons and Tigers. are there any online companies right now that you think are going kind of under a notice? Because DeepSeek in many ways actually was not being, they don’t have strong PR. Liang Wenfeng clearly is a very low profile guy. And I don’t think people really knew about him outside of China and China’s AI ecosystem until V3 came out. So like, are there any kind of...
Jing Yang (39:38)
Yay.
Grace Shao (39:59)
companies that you’re eyeing or covering that you think could be the next one, or you think the LLM space is already pretty saturated and incumbents are going to kind of stay as leaders at this point, or we might use the consolidation.
Jing Yang (40:10)
Yeah, so back when we talking about the six little dragons or tigers or whatever, DeepSeek was part of it, of the six, but it was probably the least talked about because it’s just very different from all the other five, right? All the other five had half venture capital funding, have outside investors. DeepSeek remains fully funded by High Flyer Capital Management till this day. But if we’re limiting the...
the scope to just the LLM developers and I would say that I don’t see the possibility of having anyone that we haven’t seen out there. As a matter of fact, I’m actually surprised that the consolidation hasn’t happened at a bigger scale with the exception of of Alibaba essentially acquiring 0.1.AI. We haven’t simply seen other
consolidation happened yet. And I’m actually surprised. If you ask.
Grace Shao (41:05)
For context,
sorry, readers, sorry, listeners, that one’s the one that Lee Kaifu founded. outside of Tsinghua campus, yes.
Jing Yang (41:09)
Yes.
Yes. And then now we see that Jhipu has filed to or is sort of looking at to go IPO in both, you know, Chinese, Asia market and Hong Kong. You know, I think whenever they release their prospectus, people will be reading it with a lot of interest. Other than that, you know, I’m just surprised that the so-called dachang, the tech incumbents in China have not consolidated.
I think there was a time in my, you know, I recall in my reporting, like say in my conversations with sources about 12 months ago, there was a time when some tech companies were seriously thinking of acquiring some of the companies which is mentioned. And for one reason or the other that didn’t happen. And all the tech companies in China actually decided to build their own as well, right? So this is quite different from Silicon Valley if you look at it, right?
we’ve seen from Google to the other companies, are crying pretty sizable startups, or not really are crying, are quite higher. So I do think that the Chinese LLM space doesn’t need some consolidation. It’s just not sustainable. need the level of compute that is needed and coupled with the chip
shortage China finds itself in. It’s just not sustainable.
Grace Shao (42:32)
It’s such a high capex game here, so how do these startups continue to fund themselves? I want to go into the VC space later, but mean, end of the day, it’s only the big check comes to have the money to keep on even chucking into that machine.
Jing Yang (42:39)
Yes.
Yeah,
that’s exactly what I was trying to get to, just not sustainable to have, say, 10, 12 LLM developers.
Grace Shao (42:55)
Exactly. I think it’s
interesting, a lot of them are financially backed by them, but not at a very big scale at this point. It’s like, you know, like a hundred million dollars here and there, but nothing bigger than that rate.
Jing Yang (43:08)
Yeah, exactly. if I were to, I Alibaba has probably backed it the most, right? And obviously, it did it also for the sake of promoting, know, helping the cloud business, you know, expand its market share as well. But if I were to take a guess, I would say that some of these companies will just have to really run out of money.
And then to the point that existing investors are willing to sell, say, don’t know, five cents on the dollar or whatever. So that it becomes financially attractive for any of the tech companies to actually acquire them. Otherwise, you know, it’s just not going to, you know, I don’t know the tech companies. They’re all very, you know, the people sitting on top of this company are all very like sophisticated. They’re not just going to be, you know, spending like, I don’t know.
20 billion dollars or even not 20 billion, like 10 billion dollars or five billion dollars by a technology that they think maybe they already have on their own, or they can do better on their own. So the only way that it can work is that, you know, things have to just drag along a little bit longer for the pricing and expectation to match. Right now, I think there’s a pretty big gap. And also, lot of the entrepreneurs of the
Grace Shao (44:21)
Yeah.
Jing Yang (44:25)
among the six little dragon camp. But they also don’t want to call it quits yet, I think.
Grace Shao (44:30)
Yeah, I think that people are holding on to their dream. And I think I like people ask me about what I think of the AI startup founders of this generation as well. Like to your point earlier, they kind of grew, they grew up differently from the last generation entrepreneurs. And there’s less of a, want to make a quick buck kind of mentality. They are really much more mission driven when you, you know, like hear, hear about them or, know, their media appearances or what they say out loud, you know, like where you’ve been the moonshot guy, Yang Zhilin really, really just talking about how much of a
know, philosophical pursuit it is for him to chase AGI. Actually, you know, I want to kind of pivot into the VC space. We touched on the fact that China’s VC space, frankly, is not as vibrant right now. You know, obviously the majority of the money right now, even to back these startups are from the VATs or the situation like any of the bigger internet companies from the last generation. So how has the regulatory reset
since 2021 really reshaped the local domestic VC landscape because we saw that Sequoia was probably one of first that started the decoupling effort, splitting out their Chinese business called Hongshan, which is like a literal translation to Sequoia, the tree. Are we continuing to see this play out or do you think we’ve kind of finished it, wrap that up already? It’s been a couple of years and the VC space is getting a bit of an energy back or...
recapitalization.
Jing Yang (45:53)
Yeah, I’ll talk about decoupling first. I I think the US China venture capital decoupling is already over, right? Like it’s already decoupled. And it’s decoupled mostly for driven by factors out of the US, out of American politics, right? When the Treasury Department in Biden’s final month, in the Biden administration’s final month,
came up with this long expected rule that essentially restricted any American investors, be it institutional individual to invest in tech and AI related sector in the US. That basically just shows this is over. That piece of regulation is not retroactive. So the money that American LPs have committed to Chinese GPs still remains like, okay, to invest.
But other than that, once the dry powder runs out, the dry powder that’s raised before earlier this year, when the regulation came into effect, then it’s pretty much over. And a lot of American LPs realize that. And then that’s what makes it difficult for Chinese GPs, for Chinese venture capital firms, because
If you are the CIO of a Endowment Fund, you know that, even though you remain committed to about the opportunities in China, but to be honest, DeepSeek only proved that the most potentially lucrative opportunities coming out of China is in the AI and the AI land, which is the various sector that you cannot have any exposure, then what do you do?
So then because of this conundrum, that’s what makes it so difficult for Chinese venture capitalists, the GPs, to raise funds from American LPs. just so you know that American LPs traditionally, think before the decoupling that started from around 2021, American LPs actually made up for about half. I mean, there’s no like sort of
consensus, statistics on this. I’m giving you a number that was given to me by a lot of people in the industry. Before that, before the deep coupling, American LPs make up for like about at least a half in terms of capital raised by Chinese VC firms. And then so that is a big chunk of money that is sort of people know is going to be gone.
is gradually dying. So then, you know, to make up for that, you know, we’ve seen Chinese VC firms going to other parts of Asia, Europe, Middle East, to try to, you know, fundraise. And that really hasn’t gone that well for various reasons, right? It takes time to build relationships, to understand the culture.
and the thinking behind all these ⁓ LPs and all these big institutions that you are trying to basically ask money for. And then that is sort of on the fundraising part. And on the exit part, for Chinese VCs, the biggest issue right now, the biggest bottleneck, the single, if you ask any Chinese venture capitalists, what is the biggest problem, the difficulties that you are facing?
tell you it is because it’s a difficulty to exit. We talked a little bit about the new framework guiding overseas IPOs by Chinese companies. And that has created a significant bottleneck on VC exits. And then so when you are managing a fund, if you can exit and then return capital to investors and then show it in your DPI,
then obviously it’s very hard for you to show that this is what I’ve done so that you can raise a new fund. And on top of that, we still have like Ant. Let’s not forget about Ant. I was recently in a conversation with an LP investor in the US who started investing in the China VC space from
like the 2000s, so pretty early on, right? He was telling me that, you know, almost every big American, you know, endowment or pension fund is still remains like locked in Ant. And without that being unlocked, without some kind of exit in Ant, it also really affects the appetite for this, for them to continue to invest.
in the China VC space. I think we just need to see, I know that in terms of the IPO pipeline that we’ve seen, for example, last year, there was a trail of autonomous driving companies being approved to list in the US or Hong Kong, but that’s just not enough, right? We still haven’t seen like say a billion dollar sized IPO.
from the tech space. We just haven’t seen that yet. And I think with the absence of clear sign of the IPO pipeline being cleared up without clear sign of a sizable exit, a sizable IPO, sizable meaning at least a billion dollar, it’s just going to be very difficult for Chinese GPs.
Grace Shao (51:09)
Yeah, I know you actually cover quite extensively when you’re still with the journal. It’s probably like your main story for the last year where you had the journal read before you moved over to information. What are some potential scenarios actually on that point? Like, you know, like you said, this year in Hong Kong, it’s supposedly Hong Kong Exchange has hit like the most filings since like pre-COVID, right? But majority are relatively small.
AI or tech companies, they’re not making international headlines. The last one that was really like a major international like candy, eye candy was really Ant and where are we kind of at with that? I mean, this is a bit off topic, but I just find it so fascinating they brought it up.
Jing Yang (51:49)
Yeah, I mean, just a quick thought on the record amount of filings we see in Hong Kong. It’s basically a lot of smaller companies as well as a lot of companies are already listing in a share market and doing a share listing. With the H-share listings of say ATL and SF, it’s just something that for the moment that Chinese regulators are more amenable to.
But in terms of your end question, I think what I have come to believe, and I’m willing to be proven wrong because I’m really only just a journalist, but I think if the NIP were to come back one day, will most unlikely come back in its original form, if that makes sense.
It was supposed to be the world’s largest IPO ever. They were on the cusp of raising $35 billion. I just don’t think when Ant finally was going to IPO again, it will come back in that exact shape or form.
Grace Shao (52:46)
Fair enough. think the company has restructured quite a bit as well, right? Since then. So I wanted to kind of get a sense. I’m not familiar with the space, but are R &B denominated VC funds versus what we were just talking about predominantly being like USC denominated VC funds in China? Are the incentives different for their funds? And do they kind of invest in different kinds of companies? Or are they actually fighting for the same deals?
Jing Yang (53:12)
Yeah, so first, the incentives are quite different. It’s really like two quite different worlds, right? Different worlds. So in the RMB world, one of the biggest pockets of money comes from government guidance funds and SOE-related funds. And then these types of LP, they are driven by local GDP growth targets kind of incentives. And then so then
invariably when they write a check to you, they would require you to bring back some of the investment back to their local city or province. Say I write you a 10 million yuan check, then that means that usually there’s a very detailed percentage requirement written saying that you need to bring back, say for example, 1 % of your portfolio investments or you need to invest this much in our province or city.
And then that is obviously some can argue not very market driven. That is the reality. And I will probably also just play a bit of a devil’s advocate if you are asking for money from a local government related fund, then obviously that is sort of the bottom line. That is the profitability, so to speak, that they are thinking about.
Other than that part of the difference, the other difference is that, it’s funny though, like say, know, for a while you don’t see the R &B world and the UST world sort of overlap. For quite a long time, it would just like coexist and then they know that they go into, they all have their own strengths and then they do quite different types of deals or do different types of...
going to different types of sectors. However, since this USD funding drought that we have just talked about, what do you see is that some of the Chinese VC firms that were only raising in USD also have started exploring raising our yuan denominated funds. And then there are mostly two reasons behind that phenomenon. First is obviously driven by
you know, the USD funding. Second reason though is also because of the geopolitics made it as we talked about impossible for a lot of American dollar investor to invest in social sectors. So that some of the venture capitalists in China realize there are sectors, for example, semiconductor, right? That is a sector that has just become like not possible.
⁓ for ⁓ USD funding to get any exposure in, then if they want to get exposure, if they want to invest in that sector, the only way to do that is through raising our UN-denominated fund and invest. So that’s why you see the two walls that previously just co-exist in parallel now sort of start to overlap.
Grace Shao (55:57)
Thanks, Zach, lay out.
Jing Yang (56:06)
love it.
Grace Shao (56:07)
Thank you for that explanation. Actually, that’s really helpful for even me to understand. think I was also noticing this trend. I was like, why are all these former USD denominated funds, like primarily now actually raising RMB funds, but it’s up to your point to make sense. You know, the Manus AI agent company was probably the most high profile Chinese AI company that received USD VC money. If anything, the only one, right?
this past year, what do make of that? And you know, that they moved their operations from Shanghai to Singapore, you know, where do you see this? Is this like the start of a new opening or like a new way for Chinese companies to go find US investment? Or do you think this is like a one-off thing because they did actually receive scrutiny from the US, not so much actually attention from the Chinese that it doesn’t seem like, at least not publicly. So what do you make of that?
Jing Yang (56:56)
Well, ⁓ Manus is just the latest example, right? But if we look at, if you remember a company, Heijin, that also gained some traction about two years ago, think what we’ve seen that what all these companies, all these startups represent is this new generation of Chinese ⁓ founders and builders, right? They were born and raised in China, some of them educated in China, some of them a bit of China and elsewhere.
But they all grew up with that global vision, global view that they want to compete at a global level. However, it’s just so unfortunate that it’s not possible anymore for a company to even try to succeed in both China and global ex-China if you are building a consumer-facing product. That’s just the reality these days. So then realizing that, I think a lot of them now just, you know, if I have to choose China or global ex-China, then...
Some choose the former and some choose the latter. And then for those that have chosen the latter, we’ve seen example exemplified by metas, by hygiene and many others.
Grace Shao (57:59)
I think just one last question, which is if you look ahead three to five years, we can’t predict a future, but what do you think will be the next big story in the China AI tech space?
Jing Yang (58:07)
Yeah, that is a very difficult question. if I were to try to answer that, would say just follow. I was in the conversation with someone else the other day, know, like, what is a Chinese startup these days? Like, how do you define that? The definition has to change and it has already started to change, right? A company like Manus, right? A company like HeyGen, a company like, you know, GenSpark.
Genspar is slightly different than Manus. They actually incorporated as a company in California, but they are funded by two Chinese entrepreneurs who used to work in China. So I think what is really worth watching is these builders and these entrepreneurs. Chinese entrepreneurs, they are definitely going to make waves in the
⁓ AI and tech space globally, not just in China, right?
Grace Shao (59:02)
Yeah, I think we’ve even seen that shift. Remember when Zoom went IPO and everyone was freaking out? Oh my god, it’s a Chinese company, but he’s like an American Chinese. Well, I think he goes naturalized. And just that kind of mentality completely shifted. now, especially in the AI era, more than 50 % of AI researchers are essentially of Chinese descent. So how do you then define it, right? That’s a really interesting take. I really, really appreciate your time. I know you said you had a hard cut off today.
Thank you so much, Jing.
Jing Yang (59:30)
Yeah, thank you so much for having me.
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In this episode, Wency delves into the details of her journey, from working at a leading Chinese-language tech publication to joining an international VC firm, and now writing about China’s AI and tech ecosystem for a global audience. Wency provides a holistic view of the different players from the LLM startups to the leaders in consumer applications and to the rising domestic Nvidia challengers. She is also someone who’s really on the ground and plugged in with the startup community in China, so she sheds light on the real hustle culture and dichotomy of “lying flat” vs. “involution”.
Highlighting the differences between tech events in China and the US. She dissects the discourse in China’s youth today, where two seemingly contrasting sentiments coexist. China’s generational gap feels like it happens every five years because of the vast difference of the speed of technology development. Comparing the differences between tech events in China and the US, the invisible hand of the local governments encourages innovation and commerce.
She then also addresses misconceptions about Chinese startups and emphasizes the importance of understanding the unique dynamics of the Chinese tech landscape and how Chinese founders still see the US as the benchmark. And ending the conversation with a personal reflection of how her father had never dreamed of the economic growth and personal gains he would experience in a lifetime, a look into the average Chinese man’s sentiment towards the country’s technological development, and showcasing her collection of Labubus.
Spotify link here.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
00:00 Wency’s Journey from VR to China Journalism to VC and to International Journalism
06:48 The Evolution of China Tech from 2020-2025: Trends and Change
11:51 Deep Dive into AI: Observations and Major Players
23:19 China’s Youth: “Lying Flat,” Involution and the Hustle Culture
30:15 Generational Perspectives on Success
32:14 The Mission-Driven Entrepreneurial Spirit
34:58 Contrasting Global Tech Events
38:12 Misconceptions About Chinese Startups
41:13 Trade Policies Affecting The Average Business and Average Person
43:51 Perceptions of Tech Competition with the West
47:58 The Future of Technology and Society
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to AI Proem at aiproem.substack.com/subscribe -
For long-term China tech investors and journalists, she needs no introduction. Rui Ma is a leading voice in the space, a trusted China internet analyst. She is the founder of TechBuzz China. She began her career in traditional finance and then early-stage investing. In recent years, she has advised AI companies and established herself as the founder of an AI school to prepare the next generation for the AI era.
In this conversation, we discuss the evolution of the Chinese tech ecosystem and the current trends in AI investment - her bullish view on EVs and robotics. Rui shares insights on the major players in the industry and common misconceptions about Chinese tech.
Based in the Bay Area, Rui travels to China regularly, bringing a nuanced understanding of the two worlds and bridging that information gap. The conversation also touches on cultural attitudes towards technology and the future outlook for AI in China. Finally, she discusses her unconventional approach to introducing screens, technology, and now AI to her children.
Spotify link here.
00:00 Journey from Finance to Tech and AI
02:24 Evolution of the Chinese Podcasting Landscape - going on Acquired
05:43 Investment Trends in EVs, AI and Robotics
08:10 Results > Subsidies
10:50 AI bubble?
13:34 WAIC: Willingness to pay for LLMs isn't there
27:48 Robotics at the front and center
31:35 Energy Innovations and Geopolitical Implications
35:07 Global Interest in Chinese Technology - Esp. from India
40:01 Understanding Cost and Value in Technology Adoption
44:42 Unconventional attitude towards technology and education
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to AI Proem at aiproem.substack.com/subscribe -
Joining me today is Diana Wu David, Director of Futures at ServiceNow. Diana is ranked number #2 futurist in the world by Global Gurus. She ran her own business for nearly a decade called Future Proof Lab where she worked with C-suite executives and boards to help them create future-focused, resilient organizations. She is an expert in guiding professionals and leaders in this ever-evolving business landscape, leveraging strategic foresight to turn uncertainty into a competitive advantage.
In this conversation, Grace and Diana explore the transformative impact of AI on the future of work, discussing how organizations can adapt to this change. They delve into the challenges and opportunities presented by AI, the importance of measuring its effectiveness, and the cultural differences in technology adoption between the US and Asia. Diana emphasizes the need for a shift in organizational structures and the importance of preparing future generations for a rapidly evolving workforce. The discussion also touches on the necessity of fostering critical thinking and creativity in education to equip individuals for the future.
Spotify link here.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
00:00 Navigating the Information Overload
04:04 The Future of Work and AI Integration
08:54 AI's Impact on Productivity and Organizational Structure
14:48 Measuring AI Effectiveness in Enterprises
19:41 The Evolution of Work and Education
29:40 Cultural Perspectives on Technology Adoption
39:24 Unconventional Views on the Future of Work
Get full access to AI Proem at aiproem.substack.com/subscribe -
Dr. Brian Wong is a HKU-100 Assistant Professor in Philosophy at the University of Hong Kong. His research examines the ethics and dynamics of authoritarian regimes and their foreign policies, historical and colonial injustices, and the intersection of geopolitics, political and moral philosophy, and technology. At HKU, he serves as a Fellow at the Centre on Contemporary China and the World, sits on the Steering Committee for the Hong Kong Ethics Lab, and advises the Interdisciplinary Dynamics: Ethics, AI, and Society at the Institute of Data Science. A Rhodes Scholar and Kwok Scholar, Brian holds a DPhil in Politics from the University of Oxford.
In this conversation, Dr. Brian Wong discusses his research at the intersection of moral philosophy, technology, and geopolitics. He emphasizes the importance of understanding AI's impact on employment and the need for academia to adapt to teach AI effectively. Dr. Wong also explores China's tech diplomacy, highlighting its focus on self-sufficiency and global partnerships. He argues that Hong Kong's unique status is crucial for its role in global tech and governance, and he concludes with a reflection on the future of technology and humanity, stressing the importance of human values in the tech race.
Spotify link here.
In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
For more information on the podcast series, see here.
Chapters
00:00 Introduction to Dr. Brian Wong's Research
02:47 Exploring the Intersection of Philosophy, Technology, and Geopolitics
10:38 Chinese Tech Diplomacy and AI Development
21:39 China's Global Tech Goals and Misconceptions
36:16 Innovation in the Chinese Ecosystem
41:20 The Impact of AI on Workforce Dynamics
52:16 Navigating AI in Education
57:21 Hong Kong's Unique Role in the Global Landscape
01:06:43 The Future of Technology and Humanity
AI Proem is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
Get full access to AI Proem at aiproem.substack.com/subscribe -
Today we speak to Cameron Johnson, who delves into the complexities of global supply chains, particularly focusing on the dynamics between the US and China. We explore the implications of trade policies, the evolving role of Asian countries in manufacturing, and the intersection of AI and technology with supply chain management. Cameron shares insights from his extensive experience in the field, highlighting the multifaceted nature of supply chains and the importance of understanding the broader ecosystem that supports them. Furthermore, we discuss the intricate relationship between government regulations, robotics, and supply chains in China. And finally, Cameron shares his conviction that well-managed supply chains can foster peace among nations.
Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.
Get full access to AI Proem at aiproem.substack.com/subscribe - Vis mere
