Episoder
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Frank Basso, VP of Infrastructure at Lightning AI, joins Jon Krohn for a rare ground-level tour of the one layer of the AI stack the show had never covered in over a thousand episodes: the physical data center. Frank explains how Lightning AI provisions its 35,000-plus GPUs through hyperscale co-location, why everything new is liquid-to-chip cooled, how GPUs talk to each other over ultra-fast east-west networks, and what it’s actually like to stand inside a 110-decibel AI data hall. He also debunks the most persistent myths about data-center water and electricity use, and makes the case for fuel cells, nuclear power, and 800-volt DC distribution as the path forward.
Additional materials: https://www.superdatascience.com/1003
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(02:47) What actually makes an AI data center different from a traditional one
(06:04) How Lightning AI provisions its 35,000+ GPUs through hyperscale co-location
(24:01) Why liquid cooling doesn’t waste water, debunking the biggest data-center myth
(29:46) East-west vs. north-south networks, explained
(43:47) “Screaming banshees”: why AI data halls run at 105–110 decibels
(51:52) Why data centers don’t actually drive up your power bill -
Anthropic’s Claude Fable 5 was the most capable AI model ever released to the public and it lasted just three days before the US government forced it offline. Jon Krohn unpacks both halves of the story: what makes Fable 5 special, and why it was pulled. Fable 5 and its locked-down sibling Mythos 5 are the same model separated only by safeguards, in a new “Mythos-class” tier above Opus. Jon covers its state-of-the-art benchmarks, premium $10/$50-per-million-token pricing, conservative safety classifiers, and the federal export-control directive, reportedly sparked by an Amazon-flagged “jailbreak” that took it down.
Additional materials: www.superdatascience.com/1002
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
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For this episode #1001 special, the tables are turned: SuperDataScience founder Kirill Eremenko takes the host’s chair and Jon Krohn is the guest. They trace Jon Krohn’s path from an Oxford neuroscience PhD to a New York hedge fund to founding the AI consulting firm Y Carrot, why he regrets leaving academia and how tools like Claude Code erased his hard-won technical moat and why that makes skilled engineers more valuable than ever. Along the way: whether AI is a bubble, Jevons paradox and the data-center boom, the RICE framework for choosing AI projects, the single biggest reason AI projects fail and how a well-built AI agent could give anyone “Christopher Nolan–like” focus.
Additional materials: https://www.superdatascience.com/1001
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(03:42) From an Oxford neuroscience PhD to AI consulting
(17:25) Defining AGI and why consciousness isn’t required
(30:39) Are we in an AI bubble? Why we benefit either way
(46:32) Jevons paradox: why cheaper AI means more data centers
(01:08:31) The RICE framework for prioritizing AI projects
(01:15:08) The number-one reason AI projects fail in production
(01:31:50) AI, attention, and protecting your wellbeing -
For this landmark 1,000th episode and the show’s 10-year anniversary, host Jon Krohn is joined by SuperDataScience founder Kirill Eremenko, who hosted the podcast for its first 400-plus episodes before handing over the reins. In a first for the show, the episode was recorded live with the audience invited to join on air, alongside surprise appearances from the team, longtime guests, and even Jon’s family. Together, Jon Krohn and Kirill look back on a decade of the podcast and field listener questions on AI’s biggest opportunities, the build-versus-buy dilemma, how to break into the field today, and how to stay grounded amid the relentless pace of AI.
Additional materials: www.superdatascience.com/1000
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
Chip Huyen joins host Jon Krohn for this milestone episode 999 to talk about her record-breaking book "AI Engineering" the most-read title on the O'Reilly platform last year and how the AI landscape has shifted since her last appearance. Chip breaks down what separates AI engineering from machine learning engineering, makes the case for a "start simple" workflow, gets candid about the real costs of running LLMs in production, and shares why she's now fascinated by physical AI, robotics, and world models and why the durable problems worth solving are increasingly human ones. Jon Krohn guides the conversation from the practical content of the book through to where the field is heading next.
Additional materials: https://www.superdatascience.com/999
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(06:48) What separates AI engineering from machine learning engineering
(14:44) The “start simple” approach: prompting, then RAG, then fine-tuning
(18:19) Why web search is so painfully expensive in production
(35:11) Is the “ChatGPT moment” for physical AI really here?
(52:21) Why the durable problems left to solve are people problems -
In this month’s episode of ICYMI, Jon Krohn explores how AI agents are simultaneously creating new risks and unlocking powerful new ways of working with data. Hear from Anneka Gupta, Cal Al-Dhubaib, Trevor Manz, Jazmia Henry, Jeremy Mumford, and Jacob Miller, discussing why the old cybersecurity playbook breaks down in the age of Claude Mythos, how the notebook became an AI agent’s working memory, what it really takes to build a foundation model from scratch, and why failing slowly is the most expensive mistake an AI team can make.
Additional materials: www.superdatascience.com/998
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(00:40) Why Claude Mythos Changes Everything About Cybersecurity
(08:11) Why Your Notebook Should Be Your Agent’s Working Memory
(13:19) What It Actually Takes to Build a Foundation Model From Scratch
(20:46) Failing Slowly Is the Most Expensive AI Mistake -
Dr. Andrey Kurenkov returns to the show to talk about Astrocade's astronomical growth from pre-alpha to over 20 million engaged users, what it actually takes to build a vibe-coding platform that scales, and how the broader AI landscape has shifted since his last appearance. Andrey shares behind-the-scenes lessons from building B2C user-generated content products, why the real moat is community rather than tech, and his current thinking on humanoid robotics, AGI, and the AI risks people actually overlook.
Additional materials: https://www.superdatascience.com/997
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(02:11) The Astrocade elevator pitch and how it grew to 20M users
(16:19) Why there's no secret sauce behind the platform
(24:56) UGC as the real moat, not the AI
(46:57) Why household humanoid robots are now 2–3 years away
(58:33) What AGI actually means, and why Andrey is an ASI skeptic -
TrueFoundry co-founder and CEO Nikunj Bajaj speaks to Jon Krohn about how enterprises like Nvidia and Siemens are realizing returns of over $100 million from single agent deployments, the AI gateway architecture that makes it possible to connect, observe, and govern agents at scale, and why the familiar advice to “start small” is the wrong way to roll out AI agents inside a large organization.
Additional materials: www.superdatascience.com/996
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(01:21) What TrueFoundry does and why agents in production need a control plane
(06:32) Breaking down the AI gateway: the model, MCP, and agent gateways
(16:47) Taming tool sprawl with scoped, read-only MCP access
(19:10) Why the agent gateway is the hard part and the kill switch most teams lack
(22:24) The five-workflow framework behind $100M agent deployments -
Jazmia Henry joins Jon Krohn to break down what it actually takes to build end-to-end foundation models for the energy industry. From wrangling decades of handwritten oil-and-gas documents into usable training data, to bespoke tokenizers, reinforcement learning, and inference at scale, Jazmia walks through every stage of the stack. Along the way she explains why reinforcement learning models are "bursty," what reward hacking is and how her Grounded Continuous Evaluation framework fixes it, and revisits the 2023 NeurIPS paper that argued, to widespread skepticism at the time, that scaling bad data degrades model performance.
Additional materials: https://www.superdatascience.com/995
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(10:06) The User Agnosticism Tenet
(20:02) The Zillow Offers parable
(23:25) Why workflows should come before agents
(29:57) Why data engineering is the bedrock of AI
(52:41) Why velocity is the only durable moat -
Unemployment for recent computer-science graduates now rivals rates for fine-arts and anthropology majors, and undergraduate CS enrollment fell 11% in 2025. In this Five-Minute Friday, Jon Krohn walks through the data on both sides of the debate, from Stanford research showing a 13% employment drop for young workers in AI-exposed jobs, to Federal Reserve studies finding no statistically detectable link between AI adoption and reduced hiring. Jon shares his own view on where the truth lies and offers five concrete pieces of advice for graduates and senior professionals alike on how to get hired in 2026.
Additional materials: www.superdatascience.com/993
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
For years, AI content has come in the form of “use this library, use this tool” tutorials that age out within months. Jacob Miller and Jeremy Mumford, co-authors of the brand new Wiley book Architected Intelligence, wanted to write something different, a guide to the higher-level principles of building AI products and AI-first organizations that will still be relevant in five or ten years. In this episode, the two Pattern engineers walk Jon Krohn through the core ideas of their book: why you should design products and processes so they can be executed by a human, an AI agent, or any hybrid combination; why most companies are still treating hallucinations as a model problem when they’re actually a data curation problem; why the natural progression of AI development goes skills, workflows, agents, not straight to agents; and why velocity, not models or data, is the only durable competitive advantage left.
Additional materials: https://www.superdatascience.com/993
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(10:06) The User Agnosticism Tenet
(20:02) The Zillow Offers parable
(23:25) Why workflows should come before agents
(29:57) Why data engineering is the bedrock of AI
(52:41) Why velocity is the only durable moat -
While “tokenmaxxing”, the social media trend of maximizing AI token consumption as a vanity metric, takes off online, the physical infrastructure behind AI is slamming into serious bottlenecks. In this Five-Minute Friday, Jon Krohn maps out the four overlapping supply-chain constraints choking AI compute: GPUs (with NVIDIA Blackwell sold out through mid-2026), high-bandwidth memory (quintupled demand since 2023, only three manufacturers worldwide), CPUs (agentic AI requires 12x more CPUs per GPU than chatbots), and electricity (Gartner projects power shortages will restrict 40% of AI data centres by 2027). Find out why the five biggest hyperscalers are on track to spend $725 billion on AI infrastructure in 2026, where the reasons for optimism lie, and why Jon says you should definitely not tokenmaxx.
Additional materials: www.superdatascience.com/992
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
Dr. Trevor Manz of Marimo talks to Jon Krohn about Marimo Pair, an open-source agent skill that teaches coding agents like Claude Code how to drive a reactive Python notebook, reading cell state, running Python in the kernel, taking screenshots of cells, and iterating on data tasks the way agents iterate on traditional software. Trevor also unpacks recursive language models, his AnyWidget project that bridges Python and the web, and his journey from a Wisconsin small town and Harvard bioinformatics research to founding-engineer life at Marimo. Listen to the episode to hear why no matter where AI takes us, curiosity and going deep on a topic will always be valuable.
Additional materials: www.superdatascience.com/991
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(07:04) What Marimo Pair is and how it teaches agents to use notebooks as a tool
(13:03) How agent skills work as folders of markdown files
(24:15) Trevor's day-to-day workflow combining Claude Code and Marimo Pair
(31:51) Recursive language models and why they could be the future of agentic reasoning
(57:33) Career advice on curiosity, going deep, and becoming a domain expert -
Anthropic has built a frontier AI model so capable at finding software vulnerabilities that it has decided not to release it to the general public. In this Five-Minute Friday, Jon Krohn breaks down Claude Mythos Preview, a general-purpose model whose hacking abilities emerged as a side effect of broad improvements in code understanding and reasoning. Find out how Mythos achieved a nearly 100x improvement over Opus 4.6 on Firefox exploit generation, why Mozilla patched 271 vulnerabilities in a single release using an early version of the model, and what Project Glasswing Anthropic’s gated industry consortium means for the future of cybersecurity. Jon also shares practical tips for securing the code you’re generating with AI tools.
Additional materials: www.superdatascience.com/990
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
Rubrik’s Anneka Gupta and Cal Al-Dhubaib speak to Jon Krohn about cybersecurity measures, the risks AI in business might pose for malicious attacks, and why AI should be kept “boring.” Find out how Rubrik safeguards client data, what zero trust is in the context of cybersecurity, and why cyber-resilience needs to be a top priority for companies looking to adopt AI.
Additional materials: www.superdatascience.com/989
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(02:25) All about Rubrik
(08:51) The announcement of Claude Mythos
(26:26) Utilizing zero trust
(40:36) About the Rubrik agent cloud -
In this month’s episode of In Case You Missed It, Jon Krohn talks to guests about memory and education, and how artificial intelligence is continuing to help lower the barriers to access. Hear from Matt Glickman, Traci Walker-Griffith, Richmond Alake, and Linda Haviv, discussing the foundations of AI agent memory, how engineers can develop at scale, and why they believe AI could be your child’s perfect tutor in the classroom.
Additional materials: www.superdatascience.com/988
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. -
Linda Haviv talks to Jon Krohn about staying current on AI matters, why open-source technology is narrowing the gap in its race with proprietary models, and how being a content creator in tech is key to career growth and longevity. She emphasizes that non-linear pathways to a career in tech can give applicants an edge, and stresses the importance of continuous upskilling to “stay relevant.” In her view, systems thinking is becoming more important than coding skills. Hear why in this episode.
Additional materials: www.superdatascience.com/987
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(03:43) Linda Haviv on AI education
(13:16) The future of coding
(27:00) Having a side hustle in today’s economy
(31:01) On becoming a content creator for tech
(1:00:14) How open source could disrupt the AI landscape -
CTO of Propel Software Kishore Subramanian talks to Jon Krohn about how product lifecycle management (PLM) software and quality management systems (QMS) help ensure compliance, record management, and quality assurance. Listen to the episode to hear Kishore Subramanian talk about best practices for getting started with Agentforce 360, his top tips for deploying AI projects, and why yoga and meditation could make you better at building AI products!
Additional materials: www.superdatascience.com/984
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(05:21) How Propel Software meets its customers’ demands
(07:57) About Propel One AI
(13:31) A case study for Salesforce’s Agentforce 360 Platform
(17:08) How to build an enterprise-ready agent with Agentforce 360
(19:21) How to get your AI tool into production -
Oracle’s Director of AI Developer Experience Richmond Alake returns to the show to talk to Jon Krohn about agent memory; the network of systems, models, databases and LLMs that enable AI agents to learn and adapt over time. Listen to the episode to hear about Richmond’s “100 Days of Agent Memory” initiative, retrieval-augmented generation’s (RAG) limitations with AI agents, the layers of the AI agent stack, and what makes the Oracle AI database so useful to developers.
Additional materials: www.superdatascience.com/985
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(03:15) What agent memory is and why it’s important
(28:28) RAG’s limitations for AI agents
(35:19) What matters in the AI agent stack beyond memory
(41:34) Why memory was undervalued in the AI agent stack -
Raju Malhotra, Chief Product and Technology Officer at Certinia, talks to Jon Krohn about the so-called SaaSpocalypse and how agentic AI is proving the doomsayers wrong. Listen to the episode to hear more about Certinia’s work with Salesforce and building with Agentforce 360, the three elements required for enterprise-grade agents, how AI agents have benefitted Certinia’s customers, and how to keep your work portfolio fresh and interesting to recruiters.
Additional materials: www.superdatascience.com/984
Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
In this episode you will learn:
(01:24) What Certinia does for professional services companies
(08:45) Why the "SaaSpocalypse" is wrong
(13:19) Agentforce 360 and how Certinia builds on it
(15:06) The three elements required for enterprise-grade agents
(18:02) How AI agents have impacted Certinia's customers - Se mer
