Episodios
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This week we sat down with Hardik Kabaria, co-founder and CEO of Vinci, to demo the AI platform making physics simulation accessible to any engineer building hardware. A chip tape-out used to cost millions of dollars and take months. Thermal analysis that should take seconds was waiting days for a specialist with a PhD. Vinci is compressing all of that into minutes — and making it accessible to anyone on the design team, not just the simulation expert.We also got into why AI image and video generation still has not produced a Netflix movie, what has to change before it does, and how AI getting into hardware design raises the stakes around IP and watermarking in ways nobody is talking about yet.
We cover:
What Vinci does — making physics simulation accessible to anyone building hardware
Why a chip tape-out costs millions and takes months and how AI is compressing that cycle
How Vinci lets engineers get thermal and physics answers in seconds instead of days
The existing workflow — designers, performance evaluators, and manufacturers rarely even work in the same team
How simulation software went from requiring PhDs to something any engineer can access
The digital twin concept — running physics experiments in software before touching physical prototypes
Why the semiconductor and data center cooling market is the starting point
News — AI image and video generation still has not crossed the quality schism
Why there are no AI-generated movies on Netflix yet even though the models exist
How AI getting into hardware design changes the stakes around IP and watermarking
The parallel between language models democratizing reasoning and physics models democratizing simulation
If you are building hardware, working in semiconductors, or want to understand where AI is going beyond software, this episode is for you.
Find Hardik:
LinkedIn: linkedin.com/in/hardikkabaria
Twitter: x.com/hardikk13
Find Vinci:
Website: getvinci.ai
LinkedIn: linkedin.com/company/vinciphysics
Company Twitter: x.com/VinciPhysics
New episodes every Friday at BuiltThisWeek.com
TIMESTAMPS
[00:00] Intro
[01:14] Meet Hardik — co-founder and CEO of Vinci
[02:06] What Vinci does — making physics accessible for hardware builders
[03:24] How simulation has worked in hardware for decades
[04:33] The digital and physical world of experiments
[05:47] Getting physics answers in seconds instead of days
[06:28] How Vinci fits into the existing hardware design workflow
[08:10] The tape-out problem — millions of dollars and months per iteration
[10:00] Live demo — thermal simulation for semiconductor design
[13:00] Who is using Vinci and what problems they are solving
[15:30] The language model analogy — democratizing physics reasoning
[17:00] News — AI image generation and the quality schism
[19:30] Why there are no AI movies on Netflix yet
[21:00] AI getting into hardware and the IP watermarking problem
[23:50] The regulations and guardrails that will follow
[26:05] Where to find Hardik and Vinci
[26:43] Wrap up
#AI #Physics #Semiconductors #HardwareDesign #Vinci #ChipDesign #BuildWithAI #BuiltThisWeek #AIHardware #Simulation
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This week we sit down with Zach Rattner, CTO and co-founder of Yembo, to demo the computer vision platform that turns a ten minute phone walkthrough into a full home inventory, 3D model, and floor plan. Deployed in 35 countries, running thousands of inspections every day for moving companies and property insurers — and the end user never even knows AI is involved. We also dig into Claude Fable 5 being pulled without coding access, the 25,000 Chinese accounts exploiting the model, and whether AI has finally entered its regulatory adolescence.
We cover:
What Yembo does — computer vision that deeply understands the interior of homesHow a ten minute phone video replaces a 60 to 90 minute in-home inspectionThe visual inventory — color coded, numbered, time stamped photos of every itemWhy the real AI opportunity is doing what was previously impossible, not just fasterHow Yembo embedded AI in a workflow where the end user never knows it existsThe insurance use case — 3D model and floor plan from a single video walkthroughWhy 30 percent of their patents are on guiding the camera capture without any trainingClaude Fable 5 pulled and reinstated without coding access — what it means for developersAI reaching regulatory adolescence and whether the framework arrives in time25,000 Chinese accounts exploiting the model and the KYC failure behind itIf you are building with AI, working in insurance or logistics, or want an honest take on what happens when regulators start touching frontier models, this episode is for you.
Find Zach and Yembo:
Website: yembo.ai
LinkedIn: search Zach Rattner
New episodes every Friday at BuiltThisWeek.com
TIMESTAMPS
[00:00] Intro
[01:15] Meet Zach — CTO and co-founder of Yembo
[02:05] What Yembo does — computer vision for home interiors
[03:30] How a ten minute phone video replaces 90 minutes of in-home inspection
[05:10] Live demo — the visual inventory report
[07:00] The real AI opportunity — doing what was previously impossible
[09:30] Embedding AI so the end user never knows it exists
[11:00] Insurance use case — 3D model and floor plan from video
[13:00] 30 percent of patents on guiding camera capture without a tutorial
[14:30] News — Claude Fable 5 pulled and reinstated without coding
[16:20] AI adolescence — the regulation debate
[18:30] 25,000 Chinese accounts and the KYC problem
[20:30] Where to find Zach and Yembo
[21:00] Wrap up
#AI #ComputerVision #Yembo #Insurance #MovingIndustry #ClaudeFable #BuildWithAI #BuiltThisWeek #PropTech #AIRegulation
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This week we sit down with Ryan Aytay, President and COO of Code Metal, to dig into one of the most underestimated problems in tech — the legacy code running mission critical infrastructure across aerospace, defense, automotive, and medical devices. Code Metal translates that code to any hardware with mathematical proof it will behave identically. Provably correct. We also dig into Meta glasses, Jony Ive at OpenAI, and where AI hardware interfaces are really heading.
We cover:
What Code Metal does and why provably correct code mattersThe legacy code problem running mission critical infrastructure right nowWhy testing is not enough and what formal methods actually proveHow AI finally made formal verification scalable after 50 yearsThe Tesla over-the-air update analogy for code-to-hardware deploymentWhich industries Code Metal is built for and whyWhy vibe coding works for apps but terrifies Ryan for aerospace and defenseWhat brought Ryan from running Tableau at Salesforce to a code verification startupMeta Ray-Ban glasses — price drop, Kylie Jenner, and the veteran use caseJony Ive at OpenAI and what the mystery hardware device might beWhy every AI interface is converging on the same form factorIf you build software, work in hardware, or want to understand the infrastructure layer AI is about to touch, this episode is for you.
Find Ryan and Code Metal:
Website: codemetal.ai
LinkedIn: linkedin.com/in/ryanaytay
Company LinkedIn: linkedin.com/company/code-metal
Twitter: x.com/Code_Metal_AI
TIMESTAMPS
[00:00] Intro
[01:04] Meet Ryan — President and COO of Code Metal
[02:00] What Code Metal does in one sentence
[03:30] The legacy code problem in mission critical systems
[05:32] What provably correct means and why testing is not enough
[07:10] Formal methods — mathematical proof not just testing
[08:45] How AI made formal verification scalable
[10:20] Tesla over-the-air updates analogy
[11:40] ICP — aerospace, defense, automotive, medical
[13:05] Why vibe coding terrifies Ryan for mission critical systems
[15:10] From Salesforce and Tableau to Code Metal — the origin story
[17:30] News — Meta Ray-Ban glasses price drop and Kylie Jenner
[19:45] Meta glasses given to veterans — the real use case
[20:50] Jony Ive at OpenAI and the mystery hardware device
[22:30] Why all AI interfaces are converging on glasses
[23:05] Where to find Ryan and Code Metal
[23:45] Wrap up
New episodes every Friday at BuiltThisWeek.com
#AI #CodeMetal #FormalMethods #LegacyCode #Engineering #Defense #Aerospace #BuildWithAI #BuiltThisWeek #AIHardware
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This week on Built This Week we sit down with Jackson, co-founder of Realm AI, to demo the platform replacing months of manual due diligence with AI agents that find, compile, and cite every piece of data on any commercial property in the US in minutes. We also go deep on the SpaceX IPO, what the Cursor acquisition means for the Elon portfolio, and whether the finance textbooks are just wrong.
We cover:
What Realm AI is and how it works for commercial real estate due diligenceA live demo of the self-serve platform — any property in the US in minutesHow dozens of AI agents communicate and delegate to find property dataWhy Realm deliberately refuses to make investment recommendationsThe before and after — printed spreadsheets and button calculators vs AI agentsHow small firms with three analysts are beating 100-analyst firms to dealsWho is actually using Realm AI and what surprised them about the ICPSpaceX IPO — is the valuation frothy or are the finance textbooks just wrong167 launches in 2025, one every 2.2 days, and no real competitionThe Cursor acquisition confirmed and what it means for the SpaceX portfolioHow Tesla, SpaceX, xAI, and Cursor all connect into one bigger visionWhat it actually feels like to watch a SpaceX launch in personIf you are in real estate, building with AI agents, or want a fresh take on the SpaceX IPO from people who actually use the product, this episode is for you.
New episodes every Friday at BuiltThisWeek.com
TIMESTAMPS
[00:00] Intro
[01:04] Meet Jackson — co-founder of Realm AI
[02:03] What Realm AI does and the problem it solves
[03:19] Live demo — researching any US property in minutes
[05:10] How the waterfall of data agents works
[07:20] Why Realm refuses to make property recommendations
[09:08] Before vs after — the printed spreadsheet era
[11:27] Who is getting the most value — small firms vs big funds
[14:43] ICP and asset type diversity
[15:40] News — SpaceX IPO valuation above Amazon
[16:41] Jackson's take — throw away the finance textbooks
[17:40] The SpaceX monopoly on launches — 167 trips in 2025
[19:55] Jordan's bull case for the full Elon portfolio
[21:12] Cursor acquisition confirmed
[22:11] Watching a SpaceX launch in person
[23:15] Where to find Jackson and Realm AI
[23:46] Wrap up
#AI #RealEstate #PropTech #AIAgents #SpaceX #Cursor #BuildWithAI #BuiltThisWeek #CommercialRealEstate
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This week on Built This Week we sit down with Emanuele Melis, who leads Field Engineering at AI One, to demo how enterprises are deploying AI agents right now without needing a massive data transformation first. We also go deep on Claude 4 Fable — first impressions, token spend chaos, the '100x the design' prompt, and what the Jevons paradox means for AI consumption.
We cover:
Emmanuel builds a physics puzzle game from memory using Claude in 10 minutesJordan's first 36 hours with Claude 4 Fable — slow, expensive, and world classWhat happens when you type '100x the design' into FableWhy nobody knows how many tokens they are burning and why that needs to changeWhat AI One and Context One actually do for enterpriseThe autonomous ladder — how to deploy agents safely without giving them full controlThe sweet spot for AI agents: high complexity, low judgment workHow a financial firm cut a week-long investigation down to hours using agentsWhy 150,000 brittle rules are being replaced by a single agentMaking custom coloring books for kids with AI every dayJevons paradox — cheaper tokens, more consumption, no ceilingA PM had Claude Code listen to a live engineering meeting and build in real timeSpaceX IPO — the bull case, the wait and see case, and who is rightIf you are building with AI, deploying agents in enterprise, or just want an honest take on Claude 4 Fable, this episode is for you.
Find Emanuele and AI One:
Website: ai.one
LinkedIn: linkedin.com/in/emanuele-melis
Company LinkedIn: linkedin.com/company/ai-one-1
TIMESTAMPS
[00:00] Intro
[01:33] Emmanuel builds a physics game with Claude in 10 minutes
[03:30] Jordan's first take on Claude 4 Fable
[04:35] Token spend confusion and the slider nobody understands
[05:17] '100x the design' — what happened
[06:37] Fable in five — Jordan's verdict
[07:32] What AI One actually does — the context control layer
[09:03] The autonomous ladder — starting agents at zero
[10:12] High complexity low judgment — where agents win
[11:40] Financial investigation use case — from one week to hours
[13:25] 150,000 rules replaced by one agent
[14:03] Coloring books for kids — the fun use case
[15:07] News — Claude 4 Fable launch and what it means
[15:55] Jevons paradox — cheaper tokens, more consumption
[16:42] How fast AI has evolved in 18 months
[17:17] Claude Code listening to live engineering meetings
[18:49] SpaceX IPO — bull case vs wait and see
[22:17] Where to find Emmanuel and AI One
[22:50] Wrap up
New episodes every Friday at BuiltThisWeek.com
#Claude #Fable #AI #BuildWithAI #Enterprise #AIAgents #SpaceX #BuiltThisWeek #AIOne #ContextOne
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This week on Built This Week we sit down with Anis Bennaceur, co-founder and CEO of Attention, to demo the AI-native Revenue Operating System that automates CRM updates, sales coaching, and pipeline forecasting after every call. Sam also demos a Monte Carlo forecast dashboard he built on top of Attention's open source coaching stack.
We cover:
What Attention is and how it automates sales operations end to endSam's live Monte Carlo forecast dashboard built on Attention's open source stackHow AI listens to every call and updates your CRM automaticallyWhy filling the CRM is the painkiller every sales team actually needsReal-time coaching scorecards and how reps get scored during the callPipeline forecasting and how AI catches deal risk before you doHow to manage LLM token costs at scale without killing developer productivityWhy Anis personally approves every token budget overrage at AttentionAnthropic filing for an IPO and what it means for the AI raceWhy Uber capping developer token spend is the wrong moveAgents vs deterministic workflows — the honest truth about what actually works in productionWhy the companies letting their teams run free with AI are pulling aheadIf you are in sales, building a revenue operations stack, or want to understand where AI is taking enterprise software, this episode is for you.
Find Anis and Attention:
Website: attention.com
LinkedIn: linkedin.com/in/anis-bennaceur
Twitter: x.com/anisbennaceur1
Company LinkedIn: linkedin.com/company/attentiontech
Company Twitter: x.com/tryattention
TIMESTAMPS
[00:00] Intro
[01:03] Meet Anis — CEO and co-founder of Attention
[01:50] Sam demos his Monte Carlo forecast built on Attention's open source stack
[04:51] What Attention actually does — the core product
[06:30] Painkiller vs vitamin — why CRM accuracy is the real problem
[07:45] Real-time coaching scorecards and call scoring
[09:20] Pipeline forecasting and deal risk detection
[11:00] Who is using Attention and why
[12:30] Automating work for reps, managers, leaders, and RevOps
[14:00] News — Anthropic filing for an IPO
[15:10] Uber CEO capping developer token spend
[17:00] How Attention tracks LLM cost per client, per feature
[19:05] Claude token caps and how Anis approves overrages personally
[20:30] The token budget chaos — tracing where the spend went
[22:30] Agents vs deterministic workflows — what actually works
[24:34] Why workflows beat agents in most production scenarios
[25:10] How to find Anis and Attention
[25:30] Wrap up
New episodes every Friday at BuiltThisWeek.com
#AI #Sales #CRM #BuildWithAI #Anthropic #AIAgents #RevOps #BuiltThisWeek #Attention
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This week on Built This Week we sit down with Wiley Jones, CEO and co-founder of Doss, to demo an AI-native operations platform built for mid-market companies managing the flow of goods, dollars, and data. He incorporated the company a month before ChatGPT existed.
We cover:
What Doss is and why he calls it an Adaptive Resource Platform not an ERPA live demo of the platform including inventory management, procurement, and order managementHow Doss lets companies build their own data model instead of forcing them into the shape of the softwareDOSBOT — an AI agent that can self-introspect the entire system and answer complex operational questionsWhy switching costs in enterprise software are only relevant if you are talking to the wrong customerHow to identify the 5 to 10 percent of the market that actually wants to moveThe ICP — physical product companies doing 20 million to a few hundred million in revenueNews — Robinhood launching AI agents to trade stocks and whether that is innovation or gamblingWhether AI trading bots can actually generate alpha or just raise everyone's tide equallyIf you are building in enterprise software, working in operations, or want to understand where AI is taking business systems, this episode is for you.
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This week on Built This Week we sit down with Adir, CEO and co-founder of Autonomy AI, to demo the platform helping enterprise teams build, update, and ship product changes directly on top of their existing codebase.
No slides. Just a live walkthrough of the real product.
We cover:
What Autonomy AI is building
How non-technical teams can work directly with existing codebases
Why the handoff between product, design, and engineering is still broken
Building new product views from a simple prompt
Connecting AI-generated work to real APIs and pull requests
Design mode, Figma-style editing, and mobile responsiveness
Why companies are using less Figma and Jira
Who is actually buying AI product-building tools
Karpathy joining Anthropic and what it means for the space
Google’s new AI agents and the future of searchIf you are building with AI, managing product teams, or trying to understand how software development workflows are changing, this episode is for you.
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This week on Built This Week we sit down with Karim, founder and CEO of Breeze (heybreez.ai), to demo the enterprise voice agent platform built for companies that actually need to run AI at scale. No slides. No prep. Just a live walkthrough of the real product.
We cover:
A live demo of the platform built from a blank screenWhy multi-agent architecture matters for enterprise complianceHow to prevent voice agents from being jailbrokenThe latency and model selection decisions that make or break productionThe operational layer of voice AI that everyone is ignoringPhone groups, smart routing, and running 10,000 calls a dayWho is actually buying enterprise voice AI and whyBreeze's partnership model and expansion into MENA and South AmericaAnthropic raising at a $900 billion valuation and what it meansWhether AI is replacing junior developers and what comes next
What Breeze is and how it differs from other voice AI platformsIf you are building with AI, scaling voice agents, or trying to understand where enterprise AI is heading next, this episode is for you.
Find Karim and Breeze:
Website: heybreez.ai
LinkedIn: linkedin.com/in/kmalhas
Twitter: x.com/kmalhas_
TIMESTAMPS
[00:00] Intro
[01:05] Meet Karim — founder and CEO of Breeze
[02:12] Live demo — building a voice agent from scratch
[07:34] Multi-agent architecture explained
[11:55] Latency vs quality tradeoff in voice AI
[12:56] OTP security and jailbreak prevention
[14:13] The operational layer nobody is building
[16:41] Version control, compliance, and phone groups
[18:43] Who is the ideal customer for Breeze
[21:49] Partnership model and global expansion
[22:55] News — Anthropic raising at $900B valuation
[24:28] Will AI replace junior developers
[26:05] Building from the Middle East — why it matters
[28:23] Where to find Karim and Breeze
[28:52] Wrap up
New episodes every Friday at BuiltThisWeek.com
#VoiceAI #AIAgents #Enterprise #Anthropic #BuildWithAI #BuiltThisWeek #AIStartup #heybreez
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This week on Built This Week we sit down with Ramses Alcaide, founder of Neural, to demo a noninvasive brain computer interface that tracks your focus in real time through a pair of headphones. No surgery. No implants. Just data.
We cover:
What a brain computer interface actually is and how it worksA live demo of focus tracking during the episodeA browser plugin that adjusts podcast speed based on your brain activityHow the technology detects brain fatigue before you feel itGaming, medical, and sports applicationsHow AI finally unlocked BCI for consumer devices after 40 years in labsThe science behind ice baths affecting men and women differentlyWhy kids are bypassing age verification AI with a fake mustacheWhy AI security cameras are still failing at basic common senseThe edge compute problem nobody in consumer hardware wants to talk aboutWhy your brain signature might be the future of identity verificationIf you are building with AI, interested in wearables, or want to understand what brain computer interfaces actually are today, this episode is for you.
TIMESTAMPS
[00:00] Intro
[00:44] Meet Ramses — what is a brain computer interface
[01:40] How the headphones track focus in real time
[02:28] Live focus tracking demo on the podcast
[03:00] Browser plugin that adjusts podcast speed to your brain
[04:35] How to use biofeedback to stay focused
[07:02] Brain health tracking — cognitive strain and brain age
[07:33] Gaming use case — overclocking your brain with HP
[08:16] ER doctors and high stakes focus applications
[09:08] How AI finally brought BCI out of the lab
[10:01] Origin story — PhD, family tragedy, US Army backing
[11:45] How to find Ramses and the product
[13:09] Ice bath experiment — men vs women brain data
[14:45] News — AI security cameras calling dogs bears
[15:13] Why edge AI for consumer hardware is brutally hard
[16:28] Brain data and camera AI share the same constraint
[17:08] Kids bypassing age verification with a fake mustache
[19:23] Brain signature as the future of identity verification
[20:03] Wrap up
New episodes every Friday at BuiltThisWeek.com
#BCI #AITools #Neuroscience #BuiltThisWeek #BrainComputerInterface #AI #Wearables #EdgeAI
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This week on Built This Week, we break down one of the most interesting new AI product launches in recent memory: Claude Design.
No demos. No fluff. Just what happens when AI starts replacing traditional design workflows.
We cover:
• What Claude Design is and how it works
• Creating ad campaigns, decks, and full product redesigns with simple prompts
• Why it could become a serious competitor to tools like Figma
• How teams are exporting AI designs directly into production code
• The rumored xAI / Cursor deal and what it means for the coding race
• ChatGPT Images 2.0 and whether it lives up to the hype
• Why Google might be quieter now—but still dangerous long term
If you're building with AI, working in design, or trying to understand where creative tools are heading next, this episode is for you.
⏱ TIMESTAMPS
[00:00] Intro
[00:45] Claude Design overview
[01:50] First impressions after using Claude Design
[03:00] How the interface works
[04:20] Building decks, ads, and redesigns with prompts
[06:10] Creating ad campaigns for Hip Train
[07:45] Exporting projects, sharing, and production handoff
[10:15] Full internal app redesign with AI
[12:45] Is Claude Design a Figma killer?
[13:00] xAI / Cursor acquisition rumors
[16:15] ChatGPT Images 2.0 reactions
[18:30] Why AI is still in the early innings
[21:40] Google’s new TPUs and staying in the race
[22:40] Wrap up & what’s next for Built This WeekLinks
BuiltThisWeek.com
New episodes every FridayJordan Metzner
https://x.com/mrjmetzSam Nadler
https://x.com/Gravino05 -
This week on Built This Week, we break down how AI helped a non-technical teammate build a real internal product that now helps teams move faster across the company.
No buzzwords. No fake use cases. Just real AI in production.
We cover:
• The AI tool that automates meeting follow-up work
• How transcripts become tickets, reports, and dashboards
• Why internal AI products are becoming a huge advantage
• How companies can train non-technical teams to build
• What happens when everyone can create software
• Why the next wave of AI is about empowerment
If you're serious about using AI to improve your business, this episode is for you.⏱ TIMESTAMPS
(00:00) Intro
(00:32) Welcome back
(00:40) Guest introduction
(01:28) Inside the Radar tool
(02:36) Solving workflow bottlenecks with AI
(03:23) Instant task generation from meetings
(04:45) Smarter project visibility with dashboards
(05:56) Real productivity gains
(07:00) From personal tool to company product
(08:08) Future roadmap
(09:24) AI-generated business reviews
(10:19) Building an AI-first culture
(11:30) Teaching non-technical teams
(12:49) Real examples across departments
(13:57) Why this changes work forever
(15:06) News segment
(17:44) Closing thoughts🎙 HOST INFO
Hosted by Jordan Metzner and Sam Nadler
Co-founders of Ryz Labs
We build AI-native companies and tools used by startups, enterprises, and investors.🔗 CTA + LINKS
Subscribe for weekly breakdowns of real AI builds and what actually matters
New episodes every FridayFollow along:
YouTube: Built This Week
Spotify: Built This Week
Apple: Built This Week -
This week on Built This Week, we break down a real AI tool we built that’s already saving days of work in production.
No demos. No fluff. Just how AI is actually being used inside a real business.
We cover:
• The internal tool that replaced complex spreadsheets and cut turnaround time in half
• How we generate client-ready presentations instantly with AI
• Why tool selection matters more than ever in the agentic era
• Google AI Studio and how we use it to prototype fast
• Anthropic’s unreleased model and what it means for AI safety
• Meta’s latest push into AI and why competition is heating upIf you're building with AI or thinking about how to apply it inside your company, this episode is for you.
⏱ TIMESTAMPS
00:00 Intro
00:40 What we’re covering this week
01:30 The problem with planning large offsites
02:28 How you lose money without perfect cost visibility
03:23 The AI tool we built (Offsite estimator)
04:29 Hidden costs AI catches that humans miss
05:36 From spreadsheets to automated workflows
06:04 Instant client presentations with AI
07:19 Cutting turnaround time from 10 days to 3
08:09 Tech stack behind the tool (Codex, Supabase, React, AWS)
08:58 Real customer impact and results
10:05 What we’re building next (automation + client portal)10:40 Google AI Studio deep dive
11:14 How we actually use it for prototyping
12:55 Image, music, and video generation tools
14:48 When to use which AI tool15:33 The real framework for choosing AI tools
16:45 Anthropic’s unreleased model
17:54 Why it might be a security risk
18:32 Who should control powerful AI19:45 Meta’s new AI push
21:10 Why competition is accelerating22:30 Wrap up
🎙 HOST INFO
Hosted by Jordan Metzner and Sam Nadler
Co-founders of Ryz LabsWe build AI-native companies and tools used by startups, enterprises, and investors.
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Training gets the headlines.
Inference is where the money is.
In Episode 37 of Built This Week, we sit down with Mitesh, CEO of Positron AI, to break down one of the biggest bottlenecks in AI today: inference infrastructure.
While the world focuses on trillion-parameter models and frontier labs, the real constraint isn’t intelligence — it’s memory, bandwidth, energy, and cost.
We cover:
• Why inference is where 90% of AI spend happens
• The memory wall problem in large models
• Why GPUs weren’t designed for text generation
• How Positron is building terabyte-plus memory chips
• The economics of 10 trillion parameter models
• Why memory bandwidth utilization matters
• Why CPUs are suddenly back in demand
• The difference between speed-optimized and cost-optimized AI systems
• The slider bar future of AI infrastructureWe also dive into:
• OpenAI’s $122B valuation
• Anthropic vs OpenAI secondary market dynamics
• Why Nvidia isn’t going anywhere
• Why commodity memory might beat premium stacks in certain use cases
• The rise of agentic workflows and what that means for computeIf you care about the future of AI, silicon, infrastructure, or trillion-dollar companies — this episode is for you.
New episodes every Friday.
⏱ TIMESTAMPS
(0:00) Why inference is the real AI bottleneck
(2:00) What Positron AI is building
(4:30) The memory problem in trillion-parameter models
(6:30) Why GPUs struggle with inference economics
(9:00) Energy, bandwidth, and supply chain constraints
(12:00) Memory capacity vs memory speed tradeoffs
(16:00) The “slider bar” model of AI infrastructure
(18:30) OpenAI’s $122B valuation discussion
(21:00) Anthropic vs OpenAI secondary markets
(23:30) CPUs making a comeback
(26:00) Agentic workflows and compute demand explosion
(28:00) Closing thoughts on AI infrastructure -
Our recruiters are not video editors.
But now they can cut highlight reels in minutes.
In Episode 36 of Built This Week, we break down a tool we built internally at Ryz Labs that lets our recruiting team generate polished candidate highlight videos without touching Premiere, Final Cut, or CapCut.
The problem:
When presenting candidates to clients, resumes are standard.
But seeing a candidate speak for 60 seconds changes everything.The issue was speed.
Editing sizzle reels required our video team, added delays, and was not scalable.So we built a highlight reel generator powered by:
• EntreVista AI interview transcripts
• Claude and Codex for clip selection
• Remotion for video rendering via code
• AWS S3 for instant share linksThe system automatically:
• Analyzes transcripts
• Identifies high signal clips
• Groups them by communication, role fit, and personality
• Allows light manual adjustments
• Renders a branded video in 5 to 10 minutesNo editing experience required.
Then we dive into Remotion and why “video as code” is one of the most underrated AI enabled workflows right now.
Finally, we discuss the growing cost of AI usage inside organizations:
• Token spend management
• Surprise AI bills
• Model access guardrails
• Productivity vs cost tradeoffsAI is democratizing building.
But it is also introducing a new management layer.
New episodes every Friday.
⏱ TIMESTAMPS
(0:00) The problem: recruiters are not video editors
(0:25) Welcome to Episode 36
(1:20) Why highlight reels improve candidate selection
(2:30) The scalability issue with manual video editing
(3:30) Demo: AI Highlight Reel Builder
(4:15) How transcripts power automatic clip selection
(5:00) Communication, role fit, personality grouping
(6:10) Manual adjustments for recruiters
(7:00) Rendering time and infrastructure challenges
(8:00) Final sizzle reel output demo
(9:00) How it was built with Codex
(10:00) What is Remotion
(11:30) Video editing as code explained
(12:30) Other Remotion use cases: product trailers, documentation videos
(13:45) Democratizing creative production
(14:30) AI token costs inside organizations
(15:15) Surprise AI bills and infrastructure lessons
(16:30) Managing model access across teams
(17:30) Productivity vs spend tradeoffs
(18:15) Closing thoughts🔗 LINKS
Built This Week
New episodes every Friday
https://builtthisweek.comJordan Metzner
https://x.com/mrjmetzSam Nadler
https://x.com/Gravino05 -
Marketing teams are about to change forever.
Instead of hiring designers, copywriters, analysts, SEO specialists, and performance marketers… companies are starting to run AI marketing agents that handle everything.
From planning campaigns to creating content, analyzing performance data, generating ads, and optimizing strategy automatically.
In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Iliya Valchanov, CEO of Juma, to explore how AI agents are transforming modern marketing workflows.
Juma is building an AI marketing super-agent that can autonomously plan, execute, and optimize marketing campaigns across channels like social media, ads, analytics, and SEO.
During the episode, Ilia demos how a single prompt can generate a complete social media strategy, content calendar, and visual assets in minutes.
Even more surprising — Ilia explains how their own company replaced a 7-person go-to-market team with just one person using AI agents.
We also dive into the future of AI agents, how developers are working with coding agents like Claude Code and Codex, and why the next wave of AI tools may turn websites into constantly evolving, self-optimizing systems.
In this episode we discuss:
• How AI agents automate entire marketing workflows
• Turning a single prompt into a full social media calendar
• Why marketing teams are early adopters of AI
• How AI agents connect to tools like Google Analytics, HubSpot, and Meta Ads
• Why saving time isn’t the real benefit of AI
• How AI increases marketing quality and experimentation
• How agencies measure ROI and billable hour savings from AI
• Why companies need a dedicated “AI transformation leader”
• Claude Code vs Codex vs Cursor for AI coding workflows
• Andrej Karpathy’s new auto-research AI experimentsThis episode is a glimpse at how AI agents may reshape marketing, coding, and digital products over the next decade.
⏱️ TIMESTAMPS
(0:00) Welcome to Built This Week
(0:31) Introducing Ilia from Juma
(0:46) What Juma is building
(1:26) Live demo: AI marketing agent
(2:10) Generating a social media calendar with one prompt
(3:02) Researching competitors automatically
(3:52) Building a full content strategy
(4:30) Creating Instagram carousels with AI
(5:21) Integrations with Google Analytics, HubSpot, and Ads
(6:03) Can AI learn which content performs best?
(6:49) Who is using Juma today
(7:40) Marketing teams vs marketing agencies
(8:05) Replacing a 7-person marketing team with AI
(8:58) Publishing blog posts in 3 minutes
(9:40) Why AI unlocks new marketing opportunities
(10:27) Measuring ROI and billable hours saved
(11:06) How AI removes the need for specialized marketing roles
(12:00) Why ad optimization is the biggest AI opportunity
(12:27) Biggest lessons from running AI agents in marketing
(13:04) Why companies need an AI transformation leader
(14:02) Claude Code vs Codex vs Cursor
(16:00) The future of AI coding agents
(18:56) Why developers are reading less code
(20:45) How programming may change in the AI era
(22:06) Andrej Karpathy’s new auto-research AI tools
(24:38) AI experiments and self-optimizing websites
(27:10) Final thoughts on the future of AI agents
(28:06) Where to find Juma🔗 LINKS
Juma
https://juma.aiBuilt This Week
New episodes every Friday🎙️ HOSTS
Jordan Metzner
https://linkedin.com/in/jordanmetzner
https://x.com/mrjmetzSam Nadler
https://linkedin.com/in/sam-nadler-1881b75
https://x.com/Gravino05 -
Clinical trials are one of the slowest and most expensive processes in modern medicine.
It can take 10–15 years and up to $3 billion to bring a new drug to market — and many trials fail simply because they can’t enroll enough patients.
In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Dr. Chadi Nabhan, Chief Medical Officer at RyghtAI, to explore how AI-powered digital twins of clinical trial sites can dramatically improve the speed and success of clinical trials.
RyghtAI has built a platform that creates digital twins of thousands of clinical trial sites worldwide, allowing pharmaceutical companies to instantly identify the best locations and investigators for any given trial.
Instead of relying on manual site selection or reputation-based decisions, AI analyzes historical trial performance, patient demographics, biomarker capabilities, and infrastructure to determine which sites are most likely to enroll patients successfully.
The result: faster trials, better patient representation, and potentially life-saving therapies reaching the market sooner.
In this episode we discuss:
• Why 80% of clinical trials fall behind schedule
• Why half of clinical trial sites enroll 0–1 patients
• How AI parses 200-page trial protocols in seconds
• The role of digital twins in predicting trial success
• How AI improves patient diversity in clinical trials
• Why biomarker data is becoming essential in modern medicine
• How AI agents infer site capabilities from historical trial data
• Why informed patients using AI tools may actually improve healthcare outcomesIf AI can dramatically improve the speed and efficiency of clinical trials, it could reshape how quickly new treatments reach patients worldwide.
⏱️ TIMESTAMPS
(0:00) Welcome to Built This Week
(0:37) Introducing Dr. Chadi Nabhan from Ryght AI
(1:12) What RyghtAI is building
(2:14) The problem with clinical trial site selection
(3:07) Digital twins for clinical trial sites
(4:01) Manual vs AI-driven trial strategy simulation
(5:15) Why clinical trials fail
(6:03) The massive cost and time of drug development
(6:51) How AI identifies the best trial sites
(8:00) Ranking clinical trial sites using AI scoring
(9:03) Diversity challenges in clinical trials
(10:02) Using census data to improve patient representation
(10:35) Biomarkers and genomic trial requirements
(11:48) Predicting future trial success from past data
(12:14) How AI accelerates trial matching
(13:04) AI agents reading clinical trial protocols
(14:20) Parsing 200-page protocols in seconds
(15:00) AI identifying investigators and site contacts
(15:57) Helping overlooked clinical sites get discovered
(17:47) AI’s expanding role in healthcare innovation
(18:00) Eight Sleep raises $50M at a $1.5B valuation
(21:09) Apple releases a $599 MacBook
(23:00) Dr. Nabhan’s upcoming book: AI and Cancer Care
(23:33) Will AI replace Google for patient research?
(25:30) The future of personalized AI healthcare
(26:10) Final thoughts and wrap-up🔗 LINKS
Ryght AI
https://ryght.aiDr. Chadi Nabhan
https://chadinabhan.comBuilt This Week
New episodes every Friday🎙️ HOSTS
Jordan Metzner
https://linkedin.com/in/jordanmetzner
https://x.com/mrjmetzSam Nadler
https://linkedin.com/in/sam-nadler-1881b75
https://x.com/Gravino05 -
Construction has lagged behind every major industry in technology adoption.
Manual data entry. Spreadsheets. Email-based procurement. Slow invoice approvals. Paper delivery tickets.
That’s finally changing.
In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Eldar (Field Materials AI) to break down how AI is automating procurement for commercial and civil contractors — from reading quotes and invoices to verifying pricing, matching delivery tickets, and integrating directly with ERPs.
Field Materials builds AI agents that eliminate manual data entry across the procure-to-pay cycle for electrical, mechanical, concrete, drywall, and other commercial subcontractors working on hospitals, data centers, and billion-dollar infrastructure projects.
We also explore:
• Why construction productivity has barely improved in decades
• How AI agents read and process supplier quotes automatically
• How foundational model improvements upgrade products overnight
• Why procurement automation directly impacts margin
• The data center boom forcing construction to modernize
• The difference between “adding AI” and building AI-first software
• Whether incumbents like SAP and Salesforce are at risk
• Why we may be entering a golden era for construction technologyThis isn’t theoretical AI.
This is production AI operating inside large-scale commercial construction projects today.
⏱️ TIMESTAMPS
(0:00) Entering the golden era of construction tech
(0:24) Welcome to Built This Week
(0:43) Introducing Field Materials AI
(1:12) What Field Materials actually does
(1:41) Scenario modeling demo (BOM shock analysis)
(3:51) Pricing intelligence and risk modeling
(4:53) How the company started
(6:13) Automating quotes, invoices, and delivery tickets
(7:23) Who uses Field Materials (commercial subs)
(8:49) How procurement actually works today (manual chaos)
(10:07) Cutting overhead and scaling without hiring
(11:29) Reducing material waste and pricing errors
(12:25) Accelerating invoice approval cycles
(13:04) AI agents for different document types
(14:01) How foundational model upgrades improve the product
(15:09) Why construction underinvested in tech
(15:52) The data center boom forcing modernization
(16:49) AI + robotics + prefabrication
(17:31) Anthropic partnerships and enterprise AI integration
(18:39) The next wave: AI with “hands” in enterprise systems
(19:49) Why incumbents risk building gimmicks
(21:07) Salesforce, SAP, and retention vs innovation
(24:12) COBOL, modernization, and disruption cycles
(26:39) Why building real AI tools is still hard
(27:03) Where to find Field Materials🔗 LINKS
Field Materials
https://fieldmaterials.aiBuilt This Week
New episodes every Friday🎙️ HOSTS
Jordan Metzner
https://linkedin.com/in/jordanmetzner
https://x.com/mrjmetzSam Nadler
https://linkedin.com/in/sam-nadler-1881b75
https://x.com/Gravino05 -
DNA is just another language.
In Episode 32 of Built This Week, we sit down with Dov Gertz, founder of Converge Bio, to explore how generative AI is transforming drug discovery.
Every human can be represented as 3.2 billion nucleotides built from four letters: A, C, G, and T. If computers run on zeros and ones, we run on biological code.
Converge Bio is training frontier foundation models on DNA, RNA, proteins, and small molecules — helping biotech and pharma companies design better drugs, faster and cheaper.
We also demo a retro-inspired “Cell Defense Arena” game built for Converge to use at conferences.
Then we pivot into AI infrastructure and agent workflows:
The GPU bottleneck and pharma’s growing demand for compute
Why molecular AI is 5 to 10 years behind text models
How AI could reduce drug timelines from 10 years to 6 to 8
Why cancer and autoimmune diseases may benefit first
The limits of FDA regulation in shortening approval cycles
OpenClaw, multi-agent systems, and infinite AI teams
Cloud versus on prem in the era of foundation modelsThe big takeaway:
Chatbots are impressive.
But AI applied to biology could extend human life.If you work in biotech, pharma, AI research, or frontier infrastructure — this episode is for you.
New episodes every Friday.
⏱ TIMESTAMPS
(0:00) DNA as code: 3.2 billion nucleotides
(0:32) Welcome to Episode 32
(1:00) Meet Dov Gertz and Converge Bio
(2:02) Demo: Cell Defense Arena game
(3:25) Converge Bio’s $33M raise and mission
(4:05) Foundation models for molecular data
(5:00) Turning DNA, RNA, and proteins into machine-readable text
(6:02) How transformers apply to biology
(7:03) 400x more DNA than text on the internet
(8:02) Who Converge’s customers are
(9:21) Faster, cheaper, better drug discovery
(10:39) The three bottlenecks: data, architecture, compute
(12:02) The future of personalized medicine
(13:02) Which diseases benefit first: cancer, diabetes, autoimmune
(14:00) Regulatory realities and clinical trial timelines
(16:30) Will AI shorten drug approval cycles?
(17:01) NVIDIA, GPUs, and scaling molecular AI
(18:30) Pharma as a new AI infrastructure consumer
(19:13) Hard pivot: OpenClaw and agentic AI
(21:26) Managing teams of AI agents
(22:20) Cloud versus on prem debate
(25:02) Why developers must adapt weekly
(29:26) Closing thoughts and where to find Converge Bio🔗 LINKS
Converge Bio
https://converge-bio.comBuilt This Week
New episodes every Friday
https://builtthisweek.comJordan Metzner
https://x.com/mrjmetzSam Nadler
https://x.com/Gravino05 -
Private equity due diligence used to take hundreds of hours. Now it takes seconds.
In Episode 31 of Built This Week, we sit down with August Kiles, Head of Product at Emblem, to break down how AI is transforming investment funds — from venture capital to growth equity to private equity.
Emblem is building what they call the “last platform investors will ever need” — a system that ingests entire data rooms, extracts financials, compares deals, generates reports in Word, Excel, and PowerPoint, and helps funds get to a “no” faster.
We also demo a portfolio scenario simulation tool inspired by Emblem — showing how macro events like regulatory pressure or liquidity surges could impact a 30-company portfolio.
Then we dive into the latest AI news:
Amazon engineers pushing for Claude Code over internal toolsWhy Opus 4.6 is a step-function improvement for codingHow AI is changing software development workflowsElon Musk’s XAI reorg and what it signals about model competitionThe big takeaway:
AI is not eliminating analysts.
It’s increasing deal throughput and freeing them to focus on alpha.If you work in VC, private equity, family offices, or growth equity — this episode is for you.
New episodes every Friday.
⏱ TIMESTAMPS
(0:00) Emblem’s mission: the last platform investors will ever need
(0:25) Welcome to Episode 31
(0:55) Meet August Kiles from Emblem
(1:28) Building a portfolio scenario simulation tool
(2:05) Modeling regulatory pressure across a 30-company fund
(3:00) Liquidity supernova scenario explained
(4:00) What Emblem actually does for investment funds
(5:00) AI-powered due diligence and data room indexing
(6:00) From 100 hours of analysis to seconds
(7:20) The old way vs the AI-powered way
(8:30) Will AI reduce analyst headcount?
(9:40) Getting to “no” faster in private equity
(10:30) Where Emblem shines: seed vs private equity
(12:00) Multi-agent model orchestration inside Emblem
(13:00) How new models improved financial modeling
(15:00) Amazon engineers pushing for Claude Code
(17:30) Step-function improvements in Opus 4.6
(19:00) Coding workflows transformed by new models
(21:30) Elon Musk’s XAI reorganization
(23:00) Why model quality now matters more than IDE
(25:00) Final thoughts and wrap-up🔗 LINKS
Emblem
https://emblem.peBuilt This Week
New episodes every Friday
https://builtthisweek.comJordan Metzner
https://x.com/mrjmetzSam Nadler
https://x.com/Gravino05 - Mostrar más
