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Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters),...

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Lex Fridman

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914,670 Aufrufe • vor 7 Monaten •via X (Twitter)

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Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks

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1,456,032 Aufrufe • vor 1 Jahr

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Here's my conversation with the founding team of Cursor, a popular code editor (based on VSCode) that specializes in AI-assisted programming. This is a super technical conversation that is bigger than just about one code editor. It's about the future of programming and, in general, the future of human-AI collaboration. It's here on X in full, and is up on everywhere else. Timestamps: 0:00 - Introduction 0:59 - Code editor basics 3:09 - GitHub Copilot 10:27 - Cursor 16:54 - Cursor Tab 23:08 - Code diff 31:20 - ML details 36:54 - GPT vs Claude 43:28 - Prompt engineering 50:54 - AI agents 1:04:51 - Running code in background 1:09:31 - Debugging 1:14:58 - Dangerous code 1:26:09 - Branching file systems 1:29:20 - Scaling challenges 1:43:32 - Context 1:48:39 - OpenAI o1 2:00:01 - Synthetic data 2:03:48 - RLHF vs RLAIF 2:05:34 - Fields Medal for AI 2:08:17 - Scaling laws 2:17:06 - The future of programming

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1,262,904 Aufrufe • vor 1 Jahr

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life
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Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life

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30,193,549 Aufrufe • vor 2 Jahren

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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

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Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,317 Aufrufe • vor 8 Monaten

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Here's my conversation with Peter Steinberger (Peter Steinberger 🦞), creator of OpenClaw, an open-source AI agent that has taken the Internet by storm, with now over 180,000 stars on GitHub. This was a truly mind-blowing, inspiring, and fun conversation! It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:30 - Introduction 5:36 - OpenClaw origin story 8:55 - Mind-blowing moment 18:22 - Why OpenClaw went viral 22:19 - Self-modifying AI agent 27:04 - Name-change drama 44:15 - Moltbook saga 52:34 - OpenClaw security concerns 1:01:14 - How to code with AI agents 1:32:09 - Programming setup 1:38:52 - GPT Codex 5.3 vs Claude Opus 4.6 1:47:59 - Best AI agent for programming 2:09:59 - Life story and career advice 2:13:56 - Money and happiness 2:17:49 - Acquisition offers from OpenAI and Meta 2:34:58 - How OpenClaw works 2:46:17 - AI slop 2:52:20 - AI agents will replace 80% of apps 3:00:57 - Will AI replace programmers? 3:12:57 - Future of OpenClaw community

Lex Fridman

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My conversation with Dylan Patel on everything you ever wanted to know about the incredible buildout happening in AI infrastructure This is a whirlwind tour through the companies, deals, models, chips, power sources, partnerships, and people powering AI Dylan is some sort of genius. I wish a version of him existed for every subject. I hope you enjoy this as much as I did. Timestamps: 0:00 Introduction 0:39 The Infinite Money Glitch 4:42 Oracle's Bet on OpenAI 6:06 Nvidia's Strategic Investment and Deal Mechanics 9:17 Why Bigger Models Aren't Always Better 15:07 The Adoption Curve Problem 19:01 The Tokenomics of AI 23:59 Over-Parameterization and Model Learning 28:10 Building Environments for AI Training 32:16 AI in Everyday Life 34:46 The Future of Reasoning and Compute Scaling 44:34 The Spectrum of AI Optimism 46:29 Timeline to AGI 49:25 Talent Wars 58:37 Power Dynamics in the AI Ecosystem 1:18:42 AI for Material Science and Hard Tech 1:22:21 Building Infrastructure 1:29:55 US vs China: Who Really Needs AI to Win? 1:37:42 Favorite AI Bears 1:48:08 Speed Round: Company Impressions 1:55:11 The Death of Traditional SaaS Business Models 2:00:24 The Kindest Thing

Patrick OShaughnessy

548,858 Aufrufe • vor 11 Monaten

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
1:13:56

Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI

Matt Turck

101,314 Aufrufe • vor 3 Monaten

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

939,144 Aufrufe • vor 1 Jahr

Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution. This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:33 - Extreme co-design and rack-scale engineering 3:18 - How Jensen runs NVIDIA 22:40 - AI scaling laws 37:40 - Biggest blockers to AI scaling laws 39:23 - Supply chain 41:18 - Memory 47:24 - Power 52:43 - Elon and Colossus 56:11 - Jensen's approach to engineering and leadership 1:01:37 - China 1:09:50 - TSMC and Taiwan 1:15:04 - NVIDIA's moat 1:20:41 - AI data centers in space 1:24:30 - Will NVIDIA be worth $10 trillion? 1:34:39 - Leadership under pressure 1:48:25 - Video games 1:55:16 - AGI timeline 1:57:29 - Future of programming 2:11:01 - Consciousness 2:17:22 - Mortality
2:25:58

Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution. This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:33 - Extreme co-design and rack-scale engineering 3:18 - How Jensen runs NVIDIA 22:40 - AI scaling laws 37:40 - Biggest blockers to AI scaling laws 39:23 - Supply chain 41:18 - Memory 47:24 - Power 52:43 - Elon and Colossus 56:11 - Jensen's approach to engineering and leadership 1:01:37 - China 1:09:50 - TSMC and Taiwan 1:15:04 - NVIDIA's moat 1:20:41 - AI data centers in space 1:24:30 - Will NVIDIA be worth $10 trillion? 1:34:39 - Leadership under pressure 1:48:25 - Video games 1:55:16 - AGI timeline 1:57:29 - Future of programming 2:11:01 - Consciousness 2:17:22 - Mortality

Lex Fridman

2,535,643 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 10 Monaten

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts
1:28:10

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts

Matt Turck

37,531 Aufrufe • vor 9 Monaten

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing
41:16

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 Aufrufe • vor 2 Monaten