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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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506,750 views • 2 months ago •via X (Twitter)

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Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research
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Here's my conversation with Demis Hassabis, CEO of Google DeepMind, all about the future of AI & AGI, simulating biology & physics, video games, programming, video generation, world models, Gemini 3, scaling laws, compute, P vs NP, complexity, energy (solar & fusion), and much more. This was a fun & fascinating super-technical conversation. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:21 - Introduction 2:06 - Learnable patterns in nature 5:48 - Computation and P vs NP 14:26 - Veo 3 and understanding reality 18:50 - Video games 30:52 - AlphaEvolve 36:53 - AI research 41:17 - Simulating a biological organism 46:00 - Origin of life 52:15 - Path to AGI 1:03:01 - Scaling laws 1:06:17 - Compute 1:09:04 - Future of energy 1:13:00 - Human nature 1:17:54 - Google and the race to AGI 1:35:53 - Competition and AI talent 1:42:27 - Future of programming 1:48:53 - John von Neumann 1:58:07 - p(doom) 2:02:50 - Humanity 2:05:56 - Consciousness and quantum computation 2:12:06 - David Foster Wallace 2:19:20 - Education and research

Lex Fridman

1,454,510 views • 11 months ago

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Dwarkesh Patel

260,187 views • 1 year ago

A masterclass on Google's TPU v8 Networking. Two TPU chips? Pssh. We already knew workload-specific silicon was here. But two scale-up networking topologies? That's the actual Google TPU news. Workload-specific interconnects. Think about that. New Semi Doped with Vikram Sekar and Austin Lyons. Copper? Yep. Optics? Yep. What we cover: - TPU splits in two: 8t training, 8i inference. - Virgo: 47 Pb/s scale-out fabric, 100% OCS. - Boardfly scale-up: copper PCB + AECs inside racks, OCS between groups. 16 hops → 7. - Training uses 3D torus (Rubik's Cube). - Inference doesn't. Workload-specific topologies now. - Dragonfly traces to a 2008 paper by Kim, Dally, Scott, Abts. Abts went on to build Groq's interconnect before Nvidia. Chapters: 0:00 Intro 0:21 Two TPUs for two workloads 2:31 HBM, SRAM, and Axion CPUs 7:22 Why networking is the new bottleneck 17:14 Virgo: rebuilding scale-out on optics 25:24 3D torus Rubik's Cube scale-up for training 34:50 Boardfly: scale-up for MoE inference 42:07 Workload-specific everything $GOOGL
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A masterclass on Google's TPU v8 Networking. Two TPU chips? Pssh. We already knew workload-specific silicon was here. But two scale-up networking topologies? That's the actual Google TPU news. Workload-specific interconnects. Think about that. New Semi Doped with Vikram Sekar and Austin Lyons. Copper? Yep. Optics? Yep. What we cover: - TPU splits in two: 8t training, 8i inference. - Virgo: 47 Pb/s scale-out fabric, 100% OCS. - Boardfly scale-up: copper PCB + AECs inside racks, OCS between groups. 16 hops → 7. - Training uses 3D torus (Rubik's Cube). - Inference doesn't. Workload-specific topologies now. - Dragonfly traces to a 2008 paper by Kim, Dally, Scott, Abts. Abts went on to build Groq's interconnect before Nvidia. Chapters: 0:00 Intro 0:21 Two TPUs for two workloads 2:31 HBM, SRAM, and Axion CPUs 7:22 Why networking is the new bottleneck 17:14 Virgo: rebuilding scale-out on optics 25:24 3D torus Rubik's Cube scale-up for training 34:50 Boardfly: scale-up for MoE inference 42:07 Workload-specific everything $GOOGL

Semi Doped

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Early on, Jonathan Ross foresaw the unprecedented demand for compute of the 2020s. After pioneering Google's TPUs, he founded Groq, a "Language" Processing Unit dedicated for inference. After joining NVIDIA, he is betting on the GPU+LPU combo to power the agentic chains of AI calling AI calling AI. This interview is part of my larger series on dedicated AI chips, so drop any suggestions/questions below. Thank you NVIDIA for hosting us at your HQ! 00:00 Intro 01:00 The Google TPU & Groq origin story 02:41 How is Groq different from a GPU? 04:43 Static scheduling makes Groq faster 05:47 Does Groq work with Mixture-of-Experts? 09:27 Are LPUs limited to text models? 11:03 Diffusion models 13:41 NVIDIA Vera Rubin: the GPU+LPU combo 15:26 Will Groq still be sold as a standalone chip? 16:49 How does agentic AI impact inference economics? 19:21 Will AI replace CUDA kernel engineers? 21:08 Will AI democratize hardware design? 26:11 Jevon's paradox: An endless demand for compute 29:04 What should kids learn in the AI age?
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Anjney Midha

79,195 views • 3 months ago

The Jeff Dean & Noam Shazeer episode. We talk about 25 years at Google, from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and soon to ASI. My favorite part was Jeff's vision for AGI as one giant MoE that is grown in bits and pieces over time like a forest, rather than trained all at once. Specialization, distillation, inference time scaling all emerge organically rather than by design. Noam bites every bullet: 100x world GDP soon; let’s get a million automated researchers running in the Google datacenter; living to see the year 3000. Links below. Enjoy! Timestamps 0:00:00 - Intro 0:03:29 - Joining Google in 1999 0:06:20 - Future of Moore's Law 0:11:04 - Future TPUs 0:13:56 - Jeff’s undergrad thesis: parallel backprop 0:15:54 - LLMs in 2007 0:25:09 - “Holy shit” moments 0:27:28 - AI fulfills Google’s original mission 0:32:00 - Doing Search in-context 0:36:12 - The internal coding model 0:37:29 - What will 2027 models do? 0:43:20 - A new architecture every day? 0:49:10 - Automated chips and intelligence explosion 0:53:07 - Future of inference scaling 1:02:38 - Already doing multi-datacenter runs 1:08:15 - Debugging at scale 1:12:41 - Fast takeoff and superalignment 1:20:51 - A million evil Jeff Deans 1:24:22 - Fun times at Google 1:27:51 - World compute demand in 2030 1:34:37 - Getting back to modularity 1:44:48 - Keeping a giga-MoE in-memory 1:49:35 - All of Google in one model 1:57:59 - What’s missing from distillation 2:03:10 - Open research, pros and cons 2:09:58 - Going the distance
2:15:30

The Jeff Dean & Noam Shazeer episode. We talk about 25 years at Google, from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and soon to ASI. My favorite part was Jeff's vision for AGI as one giant MoE that is grown in bits and pieces over time like a forest, rather than trained all at once. Specialization, distillation, inference time scaling all emerge organically rather than by design. Noam bites every bullet: 100x world GDP soon; let’s get a million automated researchers running in the Google datacenter; living to see the year 3000. Links below. Enjoy! Timestamps 0:00:00 - Intro 0:03:29 - Joining Google in 1999 0:06:20 - Future of Moore's Law 0:11:04 - Future TPUs 0:13:56 - Jeff’s undergrad thesis: parallel backprop 0:15:54 - LLMs in 2007 0:25:09 - “Holy shit” moments 0:27:28 - AI fulfills Google’s original mission 0:32:00 - Doing Search in-context 0:36:12 - The internal coding model 0:37:29 - What will 2027 models do? 0:43:20 - A new architecture every day? 0:49:10 - Automated chips and intelligence explosion 0:53:07 - Future of inference scaling 1:02:38 - Already doing multi-datacenter runs 1:08:15 - Debugging at scale 1:12:41 - Fast takeoff and superalignment 1:20:51 - A million evil Jeff Deans 1:24:22 - Fun times at Google 1:27:51 - World compute demand in 2030 1:34:37 - Getting back to modularity 1:44:48 - Keeping a giga-MoE in-memory 1:49:35 - All of Google in one model 1:57:59 - What’s missing from distillation 2:03:10 - Open research, pros and cons 2:09:58 - Going the distance

Dwarkesh Patel

544,788 views • 1 year ago

Failing to Understand the Exponential, Again? My conversation with Julian Schrittwieser - Julian Schrittwieser (Anthropic, AlphaGo Zero, MuZero) - on Move 37, Scaling RL, Nobel Prize for AI, and the AI frontier: 00:00 - Cold open: “We’re not seeing any slowdown.” 00:32 - Intro — Meet Julian 01:09 - The “exponential” from inside frontier labs 04:46 - 2026–2027: agents that work a full day; expert-level breadth 08:58 - Benchmarks vs reality: long-horizon work, GDP-Val, user value 10:26 - Move 37 — what actually happened and why it mattered 13:55 - Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel? 16:25 - Discontinuity vs smooth progress (and warning signs) 19:08 - Does pre-training + RL get us there? (AGI debates aside) 20:55 - Sutton’s “RL from scratch”? Julian’s take 23:03 - Julian’s path: Google → DeepMind → Anthropic 26:45 - AlphaGo (learn + search) in plain English 30:16 - AlphaGo Zero (no human data) 31:00 - AlphaZero (one algorithm: Go, chess, shogi) 31:46 - MuZero (planning with a learned world model) 33:23 -Lessons for today’s agents: search + learning at scale 34:57 - Do LLMs already have implicit world models? 39:02 - Why RL on LLMs took time (stability, feedback loops) 41:43 - Compute & scaling for RL — what we see so far 42:35 - Rewards frontier: human prefs, rubrics, RLVR, process rewards 44:36 - RL training data & the “flywheel” (and why quality matters) 48:02 - RL & Agents 101 — why RL unlocks robustness 50:51 - Should builders use RL-as-a-service? Or just tools + prompts? 52:18 - What’s missing for dependable agents (capability vs engineering) 53:51 - Evals & Goodhart — internal vs external benchmarks 57:35 - Mechanistic interpretability & “Golden Gate Claude” 1:00:03 - Safety & alignment at Anthropic — how it shows up in practice 1:03:48 - Jobs: human–AI complementarity (comparative advantage) 1:06:33 - Inequality, policy, and the case for 10× productivity → abundance 1:09:24 - Closing thoughts
1:09:56

Failing to Understand the Exponential, Again? My conversation with Julian Schrittwieser - Julian Schrittwieser (Anthropic, AlphaGo Zero, MuZero) - on Move 37, Scaling RL, Nobel Prize for AI, and the AI frontier: 00:00 - Cold open: “We’re not seeing any slowdown.” 00:32 - Intro — Meet Julian 01:09 - The “exponential” from inside frontier labs 04:46 - 2026–2027: agents that work a full day; expert-level breadth 08:58 - Benchmarks vs reality: long-horizon work, GDP-Val, user value 10:26 - Move 37 — what actually happened and why it mattered 13:55 - Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel? 16:25 - Discontinuity vs smooth progress (and warning signs) 19:08 - Does pre-training + RL get us there? (AGI debates aside) 20:55 - Sutton’s “RL from scratch”? Julian’s take 23:03 - Julian’s path: Google → DeepMind → Anthropic 26:45 - AlphaGo (learn + search) in plain English 30:16 - AlphaGo Zero (no human data) 31:00 - AlphaZero (one algorithm: Go, chess, shogi) 31:46 - MuZero (planning with a learned world model) 33:23 -Lessons for today’s agents: search + learning at scale 34:57 - Do LLMs already have implicit world models? 39:02 - Why RL on LLMs took time (stability, feedback loops) 41:43 - Compute & scaling for RL — what we see so far 42:35 - Rewards frontier: human prefs, rubrics, RLVR, process rewards 44:36 - RL training data & the “flywheel” (and why quality matters) 48:02 - RL & Agents 101 — why RL unlocks robustness 50:51 - Should builders use RL-as-a-service? Or just tools + prompts? 52:18 - What’s missing for dependable agents (capability vs engineering) 53:51 - Evals & Goodhart — internal vs external benchmarks 57:35 - Mechanistic interpretability & “Golden Gate Claude” 1:00:03 - Safety & alignment at Anthropic — how it shows up in practice 1:03:48 - Jobs: human–AI complementarity (comparative advantage) 1:06:33 - Inequality, policy, and the case for 10× productivity → abundance 1:09:24 - Closing thoughts

Matt Turck

235,526 views • 8 months ago

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
1:14:27

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

70,916 views • 26 days ago

Here's my conversation with Sundar Pichai, CEO of Google and Alphabet. I've gotten to know Sundar over the past 2 years. His life story and approach to leadership are truly inspiring. So this conversation was a pleasure and honor for me 🙏 We're living through the most exciting period of technological innovation in human history. And Google & Alphabet are at the center of it. This chat made me even more excited for the future! Oh, and they just now released a new model (gemini-2.5-pro-preview-06-05) that's the new state of the art at #1 on lmarena 🤯 Full conversation is here on X and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:08 - Introduction 2:18 - Growing up in India 8:27 - Advice for young people 10:09 - Styles of leadership 14:29 - Impact of AI in human history 26:39 - Veo 3 and future of video 34:24 - Scaling laws 38:09 - AGI and ASI 44:33 - P(doom) 51:24 - Toughest leadership decisions 1:02:32 - AI mode vs Google Search 1:15:22 - Google Chrome 1:30:52 - Programming 1:37:37 - Android 1:42:49 - Questions for AGI 1:48:05 - Future of humanity 1:51:26 - Demo: Google Beam 1:59:09 - Demo: Google XR Glasses 2:01:54 - Biggest invention in human history
2:12:04

Here's my conversation with Sundar Pichai, CEO of Google and Alphabet. I've gotten to know Sundar over the past 2 years. His life story and approach to leadership are truly inspiring. So this conversation was a pleasure and honor for me 🙏 We're living through the most exciting period of technological innovation in human history. And Google & Alphabet are at the center of it. This chat made me even more excited for the future! Oh, and they just now released a new model (gemini-2.5-pro-preview-06-05) that's the new state of the art at #1 on lmarena 🤯 Full conversation is here on X and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:08 - Introduction 2:18 - Growing up in India 8:27 - Advice for young people 10:09 - Styles of leadership 14:29 - Impact of AI in human history 26:39 - Veo 3 and future of video 34:24 - Scaling laws 38:09 - AGI and ASI 44:33 - P(doom) 51:24 - Toughest leadership decisions 1:02:32 - AI mode vs Google Search 1:15:22 - Google Chrome 1:30:52 - Programming 1:37:37 - Android 1:42:49 - Questions for AGI 1:48:05 - Future of humanity 1:51:26 - Demo: Google Beam 1:59:09 - Demo: Google XR Glasses 2:01:54 - Biggest invention in human history

Lex Fridman

938,431 views • 1 year ago

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion
1:10:03

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion

Matt Turck

93,678 views • 9 months ago

Latest episode of the AGI Podcast is out, featuring Wei Zhou, Head of AIUR at SemiAnalysis. Token Wei and I go deep on compute, capital markets, tokenomics, and and the future of AGI. Inside the conversation: 01:00 — Wei’s background and early journey 3:38 — The most cracked researchers in China 5:30 — My path after finishing a Math PhD at Stanford 7:52 — A based, unhinged take on Andrew Ng 10:29 — Choosing money vs “contributing to society” 19:32 — What Wei learned working with George Soros 24:00 — Intel vs Nvidia and the road to becoming the next compute central bank 27:15 — CUDA’s early days and how it became Tencent 2.0 for AI 35:22 — The true base unit of token cost: gigawatts vs dollar capex/opex 39:18 — “How do you price everything?” — a compute-first worldview 42:22 — How investment flows actually move in public markets 43:54 — Understanding depreciation distribution 52:00 — Is memory/storage scarcity real, and what drives it? 54:41 — Should storage cost be part of tokenomics models? 57:44 — Why inter-chip interconnect defines the economics of AI tokens 1:05:56 — Wei’s surprising love for Pop Mart 1:10:52 — How insiders really define AGI and ASI Watch the full podcast here 👇
1:11:41

Latest episode of the AGI Podcast is out, featuring Wei Zhou, Head of AIUR at SemiAnalysis. Token Wei and I go deep on compute, capital markets, tokenomics, and and the future of AGI. Inside the conversation: 01:00 — Wei’s background and early journey 3:38 — The most cracked researchers in China 5:30 — My path after finishing a Math PhD at Stanford 7:52 — A based, unhinged take on Andrew Ng 10:29 — Choosing money vs “contributing to society” 19:32 — What Wei learned working with George Soros 24:00 — Intel vs Nvidia and the road to becoming the next compute central bank 27:15 — CUDA’s early days and how it became Tencent 2.0 for AI 35:22 — The true base unit of token cost: gigawatts vs dollar capex/opex 39:18 — “How do you price everything?” — a compute-first worldview 42:22 — How investment flows actually move in public markets 43:54 — Understanding depreciation distribution 52:00 — Is memory/storage scarcity real, and what drives it? 54:41 — Should storage cost be part of tokenomics models? 57:44 — Why inter-chip interconnect defines the economics of AI tokens 1:05:56 — Wei’s surprising love for Pop Mart 1:10:52 — How insiders really define AGI and ASI Watch the full podcast here 👇

Bill Sun

33,068 views • 6 months ago

"Projects like the New Deal, the Apollo program pale in comparison to what we're doing right now." 🆕 Greg Brockman (Greg Brockman) joins us to talk GPT-5, GPT-OSS, and what's next on OpenAI's road to crystallizing all of human intelligence! “Energy turns into compute, turns into intelligence… crystallizing compute into potential energy you can release again and again.” 0:00:04 - Introductions 0:01:04 - The Evolution of Reasoning at OpenAI 0:04:01 - Online vs Offline Learning in Language Models 0:06:44 - Sample Efficiency and Human Curation in Reinforcement Learning 0:08:16 - Scaling Compute and Supercritical Learning 0:13:21 - Wall clock time limitations in RL and real-world interactions 0:16:34 - Experience with ARC Institute and DNA neural networks 0:19:33 - Defining the GPT-5 Era 0:22:46 - Evaluating Model Intelligence and Task Difficulty 0:25:06 - Practical Advice for Developers Using GPT-5 0:31:48 - Model Specs 0:37:21 - Challenges in RL Preferences (e.g., try/catch) 0:39:13 - Model Routing and Hybrid Architectures in GPT-5 0:43:58 - GPT-5 pricing and compute efficiency improvements 0:46:04 - Self-Improving Coding Agents and Tool Usage 0:49:11 - On-Device Models and Local vs Remote Agent Systems 0:51:34 - Engineering at OpenAI and Leveraging LLMs 0:54:16 - Structuring Codebases and Teams for AI Optimization 0:55:27 - The Value of Engineers in the Age of AGI 0:58:42 - Current state of AI research and lab diversity 1:01:11 - OpenAI’s Prioritization and Focus Areas 1:03:05 - Advice for Founders - It's Not Too Late 1:04:20 - Future outlook and closing thoughts 1:04:33 - Time Capsule to 2045 - Future of Compute and Abundance 1:07:07 - Time Capsule to 2005 - More Problems Will Emerge
1:08:36

"Projects like the New Deal, the Apollo program pale in comparison to what we're doing right now." 🆕 Greg Brockman (Greg Brockman) joins us to talk GPT-5, GPT-OSS, and what's next on OpenAI's road to crystallizing all of human intelligence! “Energy turns into compute, turns into intelligence… crystallizing compute into potential energy you can release again and again.” 0:00:04 - Introductions 0:01:04 - The Evolution of Reasoning at OpenAI 0:04:01 - Online vs Offline Learning in Language Models 0:06:44 - Sample Efficiency and Human Curation in Reinforcement Learning 0:08:16 - Scaling Compute and Supercritical Learning 0:13:21 - Wall clock time limitations in RL and real-world interactions 0:16:34 - Experience with ARC Institute and DNA neural networks 0:19:33 - Defining the GPT-5 Era 0:22:46 - Evaluating Model Intelligence and Task Difficulty 0:25:06 - Practical Advice for Developers Using GPT-5 0:31:48 - Model Specs 0:37:21 - Challenges in RL Preferences (e.g., try/catch) 0:39:13 - Model Routing and Hybrid Architectures in GPT-5 0:43:58 - GPT-5 pricing and compute efficiency improvements 0:46:04 - Self-Improving Coding Agents and Tool Usage 0:49:11 - On-Device Models and Local vs Remote Agent Systems 0:51:34 - Engineering at OpenAI and Leveraging LLMs 0:54:16 - Structuring Codebases and Teams for AI Optimization 0:55:27 - The Value of Engineers in the Age of AGI 0:58:42 - Current state of AI research and lab diversity 1:01:11 - OpenAI’s Prioritization and Focus Areas 1:03:05 - Advice for Founders - It's Not Too Late 1:04:20 - Future outlook and closing thoughts 1:04:33 - Time Capsule to 2045 - Future of Compute and Abundance 1:07:07 - Time Capsule to 2005 - More Problems Will Emerge

Latent.Space

305,090 views • 11 months ago

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 views • 7 months ago

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

49,373 views • 1 month ago

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

1,615,983 views • 6 months ago

People of AI is back with a new co-host Christina Warren who joins Ashley Oldacre on Season 5 to highlight the developers, startups, and founders shaping the future of AI, including their challenges and triumphs 💪 First up, Clément Farabet, VP of Research at Google DeepMind, shares his engineering journey, AI work, and the balance between research and real-world products, and how these advancements are paving the way for an agentic future. Key Moments: 0:00 Intro season 5 02:36 Neural Nets to Transformers 08:16 Were Transformers inevitable? 10:45 Time at NVIDIA 13:05 Why join Google DeepMind? 16:00 Research vs Production or both? 24:00 AI studio - product experimentation 27:32 The future of agents 30:53 Agent accountability 36:30 How does Clement use agents? 40:00 Clement’s vision for the future 47:20 Rapid Fire Questions
52:36

People of AI is back with a new co-host Christina Warren who joins Ashley Oldacre on Season 5 to highlight the developers, startups, and founders shaping the future of AI, including their challenges and triumphs 💪 First up, Clément Farabet, VP of Research at Google DeepMind, shares his engineering journey, AI work, and the balance between research and real-world products, and how these advancements are paving the way for an agentic future. Key Moments: 0:00 Intro season 5 02:36 Neural Nets to Transformers 08:16 Were Transformers inevitable? 10:45 Time at NVIDIA 13:05 Why join Google DeepMind? 16:00 Research vs Production or both? 24:00 AI studio - product experimentation 27:32 The future of agents 30:53 Agent accountability 36:30 How does Clement use agents? 40:00 Clement’s vision for the future 47:20 Rapid Fire Questions

Google for Developers

17,576 views • 11 months ago

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

64,952 views • 1 month ago

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

63,839 views • 5 months ago

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models
1:16:04

How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 00:00 - Intro 01:01 - What Reasoning Actually Means in AI 02:32 - Chain of Thought: Models Thinking in Words 05:25 - How Models Decide How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to OpenAI: Growing up in Poland, Dropping out of School, Trading 20:32 - Working on Robotics and Rubik's Cube Solving 23:02 - A Day in the Life: Talking to Researchers 24:06 - How Research Priorities Are Determined 26:53 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models

Matt Turck

451,229 views • 9 months ago