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Narration: the data efficiency black hole. 00:00:00 – What is really driving AI progress? 00:03:11 – Comparing human vs AI sample efficiency 00:08:46 – Does sample efficiency matter? Also on pod and YouTube feed.

Dwarkesh Patel

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203,325 views • 1 month ago •via X (Twitter)

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Ashlee Vance

38,839 views • 7 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

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 • 8 months ago

Hard Fork ep 139: The Trump-Musk Fallout + A DOGE Coder Speaks + ChefGPT 00:00:51 - It's Kevin's birthday! 00:02:39 - This week on the show 00:03:05 - The Musk/Trump breakup 00:19:24 - Interview with Sahil Lavingia, former DOGE staffer 00:46:58 - NYT's Pete Wells on AI for chefs

Hard Fork ep 139: The Trump-Musk Fallout + A DOGE Coder Speaks + ChefGPT 00:00:51 - It's Kevin's birthday! 00:02:39 - This week on the show 00:03:05 - The Musk/Trump breakup 00:19:24 - Interview with Sahil Lavingia, former DOGE staffer 00:46:58 - NYT's Pete Wells on AI for chefs

Kevin Roose

12,096 views • 1 year ago

François Chollet (François Chollet) on the ARC Prize and how we get to AGI. At AI Startup School in San Francisco. 00:00 - The Falling Cost of Compute 00:57 - Deep-Learning’s Scaling Era & Benchmarks 01:59 - The ARC Benchmark 03:02 - The 2024 Shift to Test-Time Adaptation 05:01 - What Is Intelligence? 07:12 - Why Benchmarks Matter (and Mislead) 08:57 - ARC 1 Exposes Scaling Limits 10:58 - ARC 2: Compositional Reasoning Arrives 12:55 - Humans vs. Models on ARC2 14:58 - Previewing ARC3 & Interactive Agency 17:00 - Kaleidoscopic Hypothesis and Abstractions 22:00 - Type 1 vs. Type 2 Abstractions 26:00 - Discrete Program Search & Inventive AI 29:00 - Fusing Intuition with Symbolic Reasoning 32:00 - Building AGI Through Meta-Learning Systems

François Chollet (François Chollet) on the ARC Prize and how we get to AGI. At AI Startup School in San Francisco. 00:00 - The Falling Cost of Compute 00:57 - Deep-Learning’s Scaling Era & Benchmarks 01:59 - The ARC Benchmark 03:02 - The 2024 Shift to Test-Time Adaptation 05:01 - What Is Intelligence? 07:12 - Why Benchmarks Matter (and Mislead) 08:57 - ARC 1 Exposes Scaling Limits 10:58 - ARC 2: Compositional Reasoning Arrives 12:55 - Humans vs. Models on ARC2 14:58 - Previewing ARC3 & Interactive Agency 17:00 - Kaleidoscopic Hypothesis and Abstractions 22:00 - Type 1 vs. Type 2 Abstractions 26:00 - Discrete Program Search & Inventive AI 29:00 - Fusing Intuition with Symbolic Reasoning 32:00 - Building AGI Through Meta-Learning Systems

Y Combinator

231,796 views • 1 year ago

When mathematicians make breakthroughs, they hallucinate too. They reach beyond established results. But unlike AI, they’ve learned to tell a promising hallucination from a dead end. Number theorist Ken Ono on AI, creativity, and mathematical discovery. -- Timestamps -- 00:00:00 – Why predicting problem difficulty is so hard 00:00:27 – How mathematicians and AI both “hallucinate” 00:02:15 – AI as a copilot for mathematical discovery 00:04:00 – How AI helped reveal new formulas for primes 00:09:02 – The promise and peril of AI 00:15:22 – What makes a great mathematical question 00:17:54 – Teaching by making deliberate mistakes 00:21:22 – Will AI reshape the world like the Industrial Revolution?

When mathematicians make breakthroughs, they hallucinate too. They reach beyond established results. But unlike AI, they’ve learned to tell a promising hallucination from a dead end. Number theorist Ken Ono on AI, creativity, and mathematical discovery. -- Timestamps -- 00:00:00 – Why predicting problem difficulty is so hard 00:00:27 – How mathematicians and AI both “hallucinate” 00:02:15 – AI as a copilot for mathematical discovery 00:04:00 – How AI helped reveal new formulas for primes 00:09:02 – The promise and peril of AI 00:15:22 – What makes a great mathematical question 00:17:54 – Teaching by making deliberate mistakes 00:21:22 – Will AI reshape the world like the Industrial Revolution?

Epoch AI

18,095 views • 9 months ago

BG2. AI Bubble or Buildout? $3T CapEx, Stablecoin Boom, and Gurley’s Next Chapter 👊💥 Brad Gerstner Bill Gurley Bg2 Pod (00:00) Intro (02:00) Two Years of BG2 (03:45) Bill Stepping Back to Write His Book (07:00) The AI CapEx Bubble? (13:00) Nvidia’s Investments: Healthy or Hype? (19:15) $3 Trillion CapEx: Are We Overbuilding AI? (25:30) OpenAI’s Race for Escape Velocity (28:30) AI Regulation: State Patchwork Madness (30:30) Why Federal Preemption Is Critical (35:00) Stablecoin Surge: $18T in Settlements (36:45) Gurley’s Crypto Conversion (38:30) From PIX to FedNow: The Future of Digital Money (41:00) Coinbase, Circle, and the 4% Yield Revolution (46:00) Brad on Invest America (Trump Accounts) (49:00) Implementation Timeline & Treasury Update (50:30) Bill’s Book: Running Down a Dream (53:15) The Great Career Reset: Passion vs. Grind (55:00) Bezos’ Regret Minimization Framework (57:00) Who the Book Is For (59:00) Gurley’s Next Mission

BG2. AI Bubble or Buildout? $3T CapEx, Stablecoin Boom, and Gurley’s Next Chapter 👊💥 Brad Gerstner Bill Gurley Bg2 Pod (00:00) Intro (02:00) Two Years of BG2 (03:45) Bill Stepping Back to Write His Book (07:00) The AI CapEx Bubble? (13:00) Nvidia’s Investments: Healthy or Hype? (19:15) $3 Trillion CapEx: Are We Overbuilding AI? (25:30) OpenAI’s Race for Escape Velocity (28:30) AI Regulation: State Patchwork Madness (30:30) Why Federal Preemption Is Critical (35:00) Stablecoin Surge: $18T in Settlements (36:45) Gurley’s Crypto Conversion (38:30) From PIX to FedNow: The Future of Digital Money (41:00) Coinbase, Circle, and the 4% Yield Revolution (46:00) Brad on Invest America (Trump Accounts) (49:00) Implementation Timeline & Treasury Update (50:30) Bill’s Book: Running Down a Dream (53:15) The Great Career Reset: Passion vs. Grind (55:00) Bezos’ Regret Minimization Framework (57:00) Who the Book Is For (59:00) Gurley’s Next Mission

Bg2 Pod

559,858 views • 9 months ago

Human generated data has fueled incredible AI progress, but what comes next? 📈 On the latest episode of our podcast, Hannah Fry and David Silver, VP of Reinforcement Learning, talk about how we could move from the era of relying on human data to one where AI could learn for itself. Watch now → 00:00 Introduction 01:50 Era of experience 03:45 AlphaZero 10:19 Move 37 15:20 Reinforcement learning and human feedback 24:30 AlphaProof 29:50 Math Olympiads 35:00 Experience based methods 42:56 Hannah's reflections 44:00 Fan Hui joins

Human generated data has fueled incredible AI progress, but what comes next? 📈 On the latest episode of our podcast, Hannah Fry and David Silver, VP of Reinforcement Learning, talk about how we could move from the era of relying on human data to one where AI could learn for itself. Watch now → 00:00 Introduction 01:50 Era of experience 03:45 AlphaZero 10:19 Move 37 15:20 Reinforcement learning and human feedback 24:30 AlphaProof 29:50 Math Olympiads 35:00 Experience based methods 42:56 Hannah's reflections 44:00 Fan Hui joins

Google DeepMind

226,921 views • 1 year ago

Jordan Fisher (jordan ezra) is the co-founder & CEO of Standard AI and now leads an AI alignment research team at Anthropic. In his talk at AI Startup School, he frames the future of startups through questions rather than answers—asking how founders should navigate a world where AGI may be just a few years away. 0:00 – Why He's More Confused Than Ever 2:00 – Should You Even Start a Startup? 5:00 – Planning Beyond the Next Model 8:00 – Will Software Be Commoditized? 11:00 – Building AI-Native Teams 14:00 – Trust, Security, and Agents 18:00 – Auditing & Guardrails 21:00 – Alignment & Economic Pressure 24:00 – Data, Defensibility, and Hard Problems 28:00 – Neutrality and Control 32:00 – Changing the World vs. Just Making Money 36:00 – Build Something People Want (and Need) 39:00 – Audience Q&A

Jordan Fisher (jordan ezra) is the co-founder & CEO of Standard AI and now leads an AI alignment research team at Anthropic. In his talk at AI Startup School, he frames the future of startups through questions rather than answers—asking how founders should navigate a world where AGI may be just a few years away. 0:00 – Why He's More Confused Than Ever 2:00 – Should You Even Start a Startup? 5:00 – Planning Beyond the Next Model 8:00 – Will Software Be Commoditized? 11:00 – Building AI-Native Teams 14:00 – Trust, Security, and Agents 18:00 – Auditing & Guardrails 21:00 – Alignment & Economic Pressure 24:00 – Data, Defensibility, and Hard Problems 28:00 – Neutrality and Control 32:00 – Changing the World vs. Just Making Money 36:00 – Build Something People Want (and Need) 39:00 – Audience Q&A

Y Combinator

112,173 views • 9 months ago