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Every AI lab is starving for compute. Except Google. I spoke with Thomas Kurian, Google Cloud's CEO, to understand why Google doesn't just hoard compute before AGI, their relationship with Anthropic, and that viral tweet about Google's engineering culture. Watch now: 0:00 – Intro 0:42 – Google's Insane Compute...

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Matthew Berman

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512,779 Aufrufe • vor 4 Monaten •via X (Twitter)

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State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI

Matt Turck

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Lex Fridman

939,144 Aufrufe • vor 1 Jahr

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93,678 Aufrufe • vor 11 Monaten

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Bill Sun

33,144 Aufrufe • vor 8 Monaten

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models
1:38:17

Just after Gemini 3 was released, OpenAI's research chief Mark Chen stopped by to chat. Here's our full Core Memory pod with Mark. We start with the Great Recruitment Wars and then get into Mark's career and how OpenAI's research arm works and the tricks the company thinks it has up its sleeve. Think you'll learn more about Mark's personality than ever before. This pod is brought to you by Brex. We run our company on Brex, and you should too. It's also made possible by the investing gurus E1 Ventures 🇺🇸. Timestamps 00:00:00 Intro 00:05:02 Allocating Compute and Priorities 00:08:36 Finding the Next AI Paradigm 00:14:14 Research vs. Engineering Dichotomy 00:16:25 Gemini 3 and Math Puzzles 00:20:07 AI’s "Move 37" Moment 00:28:42 ChatGPT Replacing Technical Interviews 00:35:58 From Wall Street to AI 00:44:26 Joining Early OpenAI in 2018 00:48:08 Inside the OpenAI Coup 00:53:46 Impact of Losing Top Researchers 01:00:10 Is AI Scaling Dead? 01:09:29 Automating Nobel Prize Discoveries 01:13:43 Automating Research 01:15:35 The Insatiable Demand for Compute 01:18:23 Jony Ive and AI Hardware 01:27:50 Reaction to China’s DeepSeek 01:34:03 Preventing Scheming AI Models

Ashlee Vance

80,718 Aufrufe • vor 9 Monaten

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up
1:12:54

State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up

Matt Turck

49,647 Aufrufe • vor 3 Monaten

My first interview with Sam Altman, Co-Founder of OpenAI. 0:04 How to start a startup 3:30 Trusting exponentials 4:57 Operating in chaotic environments 6:12 Learning to enjoy painful experiences 8:03 Creating abundant intelligence 11:15 Keeping core suppliers on OpenAI’s timelines 12:10 Invention of the joint-stock company 15:30 The best CEOs aren’t sociopaths 16:46 We are in the singularity 18:09 AI authoritarianism vs liberty 19:24 Texting 300-400 people a day 20:32 Having a small number of deep beliefs about the future 21:41 Critical path 22:25 Thinking about what’s next 23:38 Getting on planes in marginal situations 28:16 Buying lots of compute 30:51 Ambition 34:00 Google shouldn’t have let OpenAI survive 37:54 Having his life shot through a cannon after the launch of ChatGPT 41:20 The growth of Codex 42:02 The Death Star tweet 44:46 Status games and desire to be useful 47:57 Not being ambitious enough on compute investments 50:26 Execution 51:45 Ask for what you want 54:11 First few weeks of OpenAI 55:15 Shutting down Sora to focus on Codex 57:44 Designing beautiful products 59:01 Getting addicted to TikTok 1:01:03 Inventing a new device 1:02:53 Try to get better at your strengths 1:05:57 Masa is an n of 1 1:07:11 Real trends vs fake trends
1:09:38

My first interview with Sam Altman, Co-Founder of OpenAI. 0:04 How to start a startup 3:30 Trusting exponentials 4:57 Operating in chaotic environments 6:12 Learning to enjoy painful experiences 8:03 Creating abundant intelligence 11:15 Keeping core suppliers on OpenAI’s timelines 12:10 Invention of the joint-stock company 15:30 The best CEOs aren’t sociopaths 16:46 We are in the singularity 18:09 AI authoritarianism vs liberty 19:24 Texting 300-400 people a day 20:32 Having a small number of deep beliefs about the future 21:41 Critical path 22:25 Thinking about what’s next 23:38 Getting on planes in marginal situations 28:16 Buying lots of compute 30:51 Ambition 34:00 Google shouldn’t have let OpenAI survive 37:54 Having his life shot through a cannon after the launch of ChatGPT 41:20 The growth of Codex 42:02 The Death Star tweet 44:46 Status games and desire to be useful 47:57 Not being ambitious enough on compute investments 50:26 Execution 51:45 Ask for what you want 54:11 First few weeks of OpenAI 55:15 Shutting down Sora to focus on Codex 57:44 Designing beautiful products 59:01 Getting addicted to TikTok 1:01:03 Inventing a new device 1:02:53 Try to get better at your strengths 1:05:57 Masa is an n of 1 1:07:11 Real trends vs fake trends

Ti Morse

965,417 Aufrufe • vor 1 Monat

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.
1:10

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.

Ricardo

1,616,942 Aufrufe • vor 8 Monaten

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
49:06

Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science

Matt Turck

66,936 Aufrufe • vor 2 Monaten

While Silicon Valley obsesses over AGI, Mistral AI is betting that big enterprises and sovereign nations will want to own, not rent, their intelligence My conversation with co-founder & CTO Timothee Lacroix (Timothee Lacroix), for his first US podcast ever Was reminded during this conversation that Mistral is barely 2.5 years old - remarkable 00:00 — Intro 01:27 — Mistral vs. The World: From Research Lab to Sovereign Power 03:48 — Inside Mistral Compute: Building an 18,000 GPU Cluster 08:42 — The Trillion-Dollar Question: Competing Without a Big Tech Parent 10:37 — The Reality of Enterprise AI: Escaping "POC Purgatory" 15:06 — Why Mistral Hires Forward Deployed Engineers (FDEs) 16:57 — The Contrarian Take: Why "Agents" are just "Workflows" 19:35 — Trust & Autonomy: The Truth About Agent Reliability 21:26 — The Missing Stack: Governance and Versioning for AI 26:24 — When Will AI Actually Work? (The 2026 Timeline) 30:33 — Beyond Chat: The "Banger" Sovereign Use Cases 35:46 — Mistral 3 Architecture: Mixture of Experts vs. Dense 43:12 — Synthetic Data & The Post-Training Bottleneck 45:12 — Reasoning Models: Why "Thinking" is Just Tool Use 46:22 — Launching DevStral 2 and the Vibe CLI 50:49 — Engineering Lessons: How to Build Frontier AI Efficiently 56:08 — Are Enterprises Ready for AGI? & The Future of Intelligence
58:20

While Silicon Valley obsesses over AGI, Mistral AI is betting that big enterprises and sovereign nations will want to own, not rent, their intelligence My conversation with co-founder & CTO Timothee Lacroix (Timothee Lacroix), for his first US podcast ever Was reminded during this conversation that Mistral is barely 2.5 years old - remarkable 00:00 — Intro 01:27 — Mistral vs. The World: From Research Lab to Sovereign Power 03:48 — Inside Mistral Compute: Building an 18,000 GPU Cluster 08:42 — The Trillion-Dollar Question: Competing Without a Big Tech Parent 10:37 — The Reality of Enterprise AI: Escaping "POC Purgatory" 15:06 — Why Mistral Hires Forward Deployed Engineers (FDEs) 16:57 — The Contrarian Take: Why "Agents" are just "Workflows" 19:35 — Trust & Autonomy: The Truth About Agent Reliability 21:26 — The Missing Stack: Governance and Versioning for AI 26:24 — When Will AI Actually Work? (The 2026 Timeline) 30:33 — Beyond Chat: The "Banger" Sovereign Use Cases 35:46 — Mistral 3 Architecture: Mixture of Experts vs. Dense 43:12 — Synthetic Data & The Post-Training Bottleneck 45:12 — Reasoning Models: Why "Thinking" is Just Tool Use 46:22 — Launching DevStral 2 and the Vibe CLI 50:49 — Engineering Lessons: How to Build Frontier AI Efficiently 56:08 — Are Enterprises Ready for AGI? & The Future of Intelligence

Matt Turck

64,519 Aufrufe • vor 6 Monaten