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NVIDIA started with the wrong technology, learned the right one from three textbooks bought at Fry’s, and went on to invent most of the major breakthroughs in modern computing. At Startup School 2026, YC's Garry Tan sits down with NVIDIA Founder and CEO Jensen Huang to talk about confronting...

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We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

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I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team
1:13:20

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team

Grant

166,818 Aufrufe • vor 1 Monat

Fresh off the launch of Opus 5, Claude Code creator Boris Cherny (Boris Cherny) joins YC's Diana at Startup School 2026 to talk about what the newest models can do, how Claude Code came to be, and what it means to build products when the underlying capabilities keep accelerating. 00:07 — What Makes Opus 5 Different 02:06 — Solving Prompt Injection 03:21 — Why Claude Code Deleted 80% of Its System Prompt 06:37 — Press Delete on Your AI Product 07:20 — How to Rebuild Your System Prompt 10:30 — Product Overhang and “Unhobbling” AI 14:26 — Give Claude Harder Problems 19:32 — Prompt Engineering Is Changing 21:57 — The Two-Week Claude Code Prompt 24:42 — Running Thousands of AI Agents 30:15 — Coding Is (Almost) Solved 32:20 — What Every CS Student Should Still Learn Anthropic Claude
35:51

Fresh off the launch of Opus 5, Claude Code creator Boris Cherny (Boris Cherny) joins YC's Diana at Startup School 2026 to talk about what the newest models can do, how Claude Code came to be, and what it means to build products when the underlying capabilities keep accelerating. 00:07 — What Makes Opus 5 Different 02:06 — Solving Prompt Injection 03:21 — Why Claude Code Deleted 80% of Its System Prompt 06:37 — Press Delete on Your AI Product 07:20 — How to Rebuild Your System Prompt 10:30 — Product Overhang and “Unhobbling” AI 14:26 — Give Claude Harder Problems 19:32 — Prompt Engineering Is Changing 21:57 — The Two-Week Claude Code Prompt 24:42 — Running Thousands of AI Agents 30:15 — Coding Is (Almost) Solved 32:20 — What Every CS Student Should Still Learn Anthropic Claude

Y Combinator

72,357 Aufrufe • vor 3 Tagen

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included
1:27:33

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Aakash Gupta

42,853 Aufrufe • vor 1 Jahr

Full episode of opmpod with HF Reflections on... 01:12 Introducing Hedge Fund Reflections 04:04 The Untraditional vs. the Traditional Path 10:20 Publishing Investment Research 15:09 First 3 Hedge Fund Jobs 20:30 Fundamental Edge AI Academy 21:59 How to Pick the Right Hedge Fund to Work At 27:44 Early Days at First Successful Hedge Fund 32:00 The Path to a Multi-Billion Dollar Fund 38:54 Being the #2 at a Large Fund 44:32 Decision to Launch Your Own Firm 47:52 The Economics of Launching Your Own Firm 54:18 The “Two Guys and a Bloomberg” HF Model 58:06 Thoughtful Planning & Insights for a HF Launch 01:05:40 How to Create Allocator “Buzz” 01:12:10 Tell Your Whole Network What You Are Doing 01:15:08 All You Need is One Person to Say Yes 01:18:43 Strategies for Staying in Business 01:24:33 Service Providers and Day 1 Staff Needs This episode is brought to you by Brett Caughran Learn more about their upcoming AI Academy for buyside professionals: Podcast platforms👇👇👇 Apple: Spotify: YT:
1:32:45

Full episode of opmpod with HF Reflections on... 01:12 Introducing Hedge Fund Reflections 04:04 The Untraditional vs. the Traditional Path 10:20 Publishing Investment Research 15:09 First 3 Hedge Fund Jobs 20:30 Fundamental Edge AI Academy 21:59 How to Pick the Right Hedge Fund to Work At 27:44 Early Days at First Successful Hedge Fund 32:00 The Path to a Multi-Billion Dollar Fund 38:54 Being the #2 at a Large Fund 44:32 Decision to Launch Your Own Firm 47:52 The Economics of Launching Your Own Firm 54:18 The “Two Guys and a Bloomberg” HF Model 58:06 Thoughtful Planning & Insights for a HF Launch 01:05:40 How to Create Allocator “Buzz” 01:12:10 Tell Your Whole Network What You Are Doing 01:15:08 All You Need is One Person to Say Yes 01:18:43 Strategies for Staying in Business 01:24:33 Service Providers and Day 1 Staff Needs This episode is brought to you by Brett Caughran Learn more about their upcoming AI Academy for buyside professionals: Podcast platforms👇👇👇 Apple: Spotify: YT:

Max Wiethe

115,444 Aufrufe • vor 11 Monaten

Mark Pincus (mark pincus) is the founder of Zynga and the author of "Life at the Speed of Play: Launch Products People Love", where he distills the product philosophy and founder playbook he developed across five companies. In this episode of Main Function with Garry Tan, Mark reflects on building through three platform shifts: the web, social networking, and now AI. He shares his "Proven, Better, New" framework for product development, explains how founders can recognize real product-market fit, and discusses why the biggest consumer AI companies are still waiting to be built. 01:09 — Why Mark wrote the book 03:44 — Three eras of the Internet 04:22 — How Napster started social networking 06:19 — The Opus 4.5 Moment 09:46 — Proven, Better, New 12:59 — Why Investors are wrong about consumer 14:07 — The distribution problem 16:36 — Killing your ego to kill bad ideas 18:41 — When the fish are running 21:23 — Founder Mode is for everyone 28:13 — Tokenmaxxing 37:04 — Intelligence on tap 38:41 — The business plan of free
40:42

Mark Pincus (mark pincus) is the founder of Zynga and the author of "Life at the Speed of Play: Launch Products People Love", where he distills the product philosophy and founder playbook he developed across five companies. In this episode of Main Function with Garry Tan, Mark reflects on building through three platform shifts: the web, social networking, and now AI. He shares his "Proven, Better, New" framework for product development, explains how founders can recognize real product-market fit, and discusses why the biggest consumer AI companies are still waiting to be built. 01:09 — Why Mark wrote the book 03:44 — Three eras of the Internet 04:22 — How Napster started social networking 06:19 — The Opus 4.5 Moment 09:46 — Proven, Better, New 12:59 — Why Investors are wrong about consumer 14:07 — The distribution problem 16:36 — Killing your ego to kill bad ideas 18:41 — When the fish are running 21:23 — Founder Mode is for everyone 28:13 — Tokenmaxxing 37:04 — Intelligence on tap 38:41 — The business plan of free

Y Combinator

57,180 Aufrufe • vor 1 Monat

Varun Vummadi (Varun Vummadi) is the co-founder of Giga, which builds AI agents for customer support for some of the biggest companies in the world, including DoorDash, one of the largest crypto exchanges in the US, and a top-three global telecom provider. At Startup School India, Varun sat down with YC's Ankit Gupta to talk about why he turned down a high-paying quant job to start a company, the multiple pivots it took to find the right problem, and how their small team of eight beat a 400-person competitor to land a contract with DoorDash. 01:33 — Early Days & Origin Story 03:37 — The YC Interview Disaster 06:28 — Pivoting Away From EdTech 07:25 — Finding the Real Idea 08:39 — Beating a Well-Funded Competitor 10:00 — Winning DoorDash With 8 People 11:09 — What GigaML Looks Like Now 12:40 — Advice for College Students 15:51 — Why Charge Early? 17:33 — The Next Big Bet 18:43 — Running the Company on AI 20:19 — How They Hire Engineers 22:04 — Product Over Sales 23:37 — Burn the Boats
24:29

Varun Vummadi (Varun Vummadi) is the co-founder of Giga, which builds AI agents for customer support for some of the biggest companies in the world, including DoorDash, one of the largest crypto exchanges in the US, and a top-three global telecom provider. At Startup School India, Varun sat down with YC's Ankit Gupta to talk about why he turned down a high-paying quant job to start a company, the multiple pivots it took to find the right problem, and how their small team of eight beat a 400-person competitor to land a contract with DoorDash. 01:33 — Early Days & Origin Story 03:37 — The YC Interview Disaster 06:28 — Pivoting Away From EdTech 07:25 — Finding the Real Idea 08:39 — Beating a Well-Funded Competitor 10:00 — Winning DoorDash With 8 People 11:09 — What GigaML Looks Like Now 12:40 — Advice for College Students 15:51 — Why Charge Early? 17:33 — The Next Big Bet 18:43 — Running the Company on AI 20:19 — How They Hire Engineers 22:04 — Product Over Sales 23:37 — Burn the Boats

Y Combinator

148,580 Aufrufe • vor 2 Monaten

My first interview with Lulu Cheng Meservey, Founder of Rostra. 0:07 How to Destroy a Terrorist Group 3:01 What Makes a Great Cult Leader 4:42 Unleashing Palmer Luckey 7:34 Why Elon Is Unpredictable 10:41 Demanding a Hardcore Culture After the X Acquisition 13:41 How Napoleon Rallied Troops to Volunteer for a Suicide Mission 18:24 Choosing Who to Alienate 20:59 Picking Someone to Fight For 22:59 Deterrence and Shaping Incentives 25:19 Why Google Had an Activist Problem 29:12 Tyrant Mode: Stopping a Leaky Culture 32:11 Building Loyalty 35:58 Why Visuals Are So Powerful 37:40 Time to Train AlexNet: Jensen Huang and Inventing Metrics 39:36 Why People Root for You 42:15 Recruit Based on the Spirit Not the Letter 43:20 The Three Levels of Story 51:01 Secret Truths and Trusting Yourself 55:25 Cicero’s Impossible Trial 58:11 Offense vs Defense 1:03:44 The Roman Concept of Auctoritas
1:05:25

My first interview with Lulu Cheng Meservey, Founder of Rostra. 0:07 How to Destroy a Terrorist Group 3:01 What Makes a Great Cult Leader 4:42 Unleashing Palmer Luckey 7:34 Why Elon Is Unpredictable 10:41 Demanding a Hardcore Culture After the X Acquisition 13:41 How Napoleon Rallied Troops to Volunteer for a Suicide Mission 18:24 Choosing Who to Alienate 20:59 Picking Someone to Fight For 22:59 Deterrence and Shaping Incentives 25:19 Why Google Had an Activist Problem 29:12 Tyrant Mode: Stopping a Leaky Culture 32:11 Building Loyalty 35:58 Why Visuals Are So Powerful 37:40 Time to Train AlexNet: Jensen Huang and Inventing Metrics 39:36 Why People Root for You 42:15 Recruit Based on the Spirit Not the Letter 43:20 The Three Levels of Story 51:01 Secret Truths and Trusting Yourself 55:25 Cicero’s Impossible Trial 58:11 Offense vs Defense 1:03:44 The Roman Concept of Auctoritas

Ti Morse

314,725 Aufrufe • vor 2 Monaten

⚫️ UNCANNY VALLEY: THE AI CLASSROOM REVOLUTION: ARE TEACHERS READY? What if AI isn’t just disrupting education… but detonating it? Ethan Mollick, Professor at The Wharton School, joins Dr Danish for one of the most explosive Uncanny Valley episodes, lifting the lid on how classrooms are collapsing, colleges are scrambling, and apprenticeships are vanishing in real time. From AI tutors replacing professors to the rise of one-person unicorns, this isn’t just a change in learning…it’s a reset of work, meaning, and what it even takes to succeed. This episode doesn’t ask whether AI will change education. It shows you how it already has. Fridays at 4:20PM ET. Only on 𝕏. 00:18 – Is AI destroying school, or forcing us to teach better? 01:13 – “100% they’re cheating.” The honesty about academic dishonesty. 02:41 – Why good pedagogy still matters—even with AI tutors. 03:51 – Elon Musk says college is obsolete. Is he right — or just early? 05:01 – “AI gives you the answer—but you don’t learn.” The Turkey study. 06:31 – From calculators to GPT: How cheating evolves—and what to do. 08:24 – What the flipped AI-powered classroom of the future looks like. 09:23 – Inside Ethan’s Wharton classes: Simulations, games, and AI everywhere. 10:09 – “AI is an always-on tutor.” What humans still do better. 11:08 – Can AI actually launch a company? Where Ethan draws the line. 12:44 – “AI cofounder” is real, but jagged edges still slow it down. 14:07 – Why bad ideas fail faster when filtered through AI. 15:45 – Confidence vs. capability: the psychology of starting up. 16:51 – The average founder is 42. What that really means for AI. 18:04 – Will a flood of new entrepreneurs fix—or break—the market? 19:50 – AI as advisor: How a chatbot could help your catering business. 21:37 – Why most Americans are founders-in-waiting—and AI unlocks them. 22:30 – Prototyping is cracked. Scaling? Not yet. 24:33 – Youth unemployment and the collapse of on-the-job learning. 25:51 – “The apprenticeship model is broken.” And how to fix it. 27:06 – Losing the talent pipeline — and why companies must step up. 28:22 – Why the youth don’t want factory jobs—and shouldn’t. 29:26 – Is AGI inevitable—or just imagined? 31:07 – What should we teach our kids? The answer might scare you. 32:27 – Bundled jobs, fragmented futures: how humans stay relevant. 33:59 – The real singularity? When we can’t predict what happens next. 35:25 – The AI assumption no one wants to question. 36:55 – What are agents, really? Why no one agrees on the definition. 37:55 – Co-intelligence vs. substitution: what agents skip over. 38:50 – Plain-English goals, rogue pricing, and collusion-by-default. 40:02 – Nested agents are here, and Wharton’s building them. 40:58 – Management > Coding: What great prompters actually do. 41:12 – Product managers might be more vital than ever.
42:02

⚫️ UNCANNY VALLEY: THE AI CLASSROOM REVOLUTION: ARE TEACHERS READY? What if AI isn’t just disrupting education… but detonating it? Ethan Mollick, Professor at The Wharton School, joins Dr Danish for one of the most explosive Uncanny Valley episodes, lifting the lid on how classrooms are collapsing, colleges are scrambling, and apprenticeships are vanishing in real time. From AI tutors replacing professors to the rise of one-person unicorns, this isn’t just a change in learning…it’s a reset of work, meaning, and what it even takes to succeed. This episode doesn’t ask whether AI will change education. It shows you how it already has. Fridays at 4:20PM ET. Only on 𝕏. 00:18 – Is AI destroying school, or forcing us to teach better? 01:13 – “100% they’re cheating.” The honesty about academic dishonesty. 02:41 – Why good pedagogy still matters—even with AI tutors. 03:51 – Elon Musk says college is obsolete. Is he right — or just early? 05:01 – “AI gives you the answer—but you don’t learn.” The Turkey study. 06:31 – From calculators to GPT: How cheating evolves—and what to do. 08:24 – What the flipped AI-powered classroom of the future looks like. 09:23 – Inside Ethan’s Wharton classes: Simulations, games, and AI everywhere. 10:09 – “AI is an always-on tutor.” What humans still do better. 11:08 – Can AI actually launch a company? Where Ethan draws the line. 12:44 – “AI cofounder” is real, but jagged edges still slow it down. 14:07 – Why bad ideas fail faster when filtered through AI. 15:45 – Confidence vs. capability: the psychology of starting up. 16:51 – The average founder is 42. What that really means for AI. 18:04 – Will a flood of new entrepreneurs fix—or break—the market? 19:50 – AI as advisor: How a chatbot could help your catering business. 21:37 – Why most Americans are founders-in-waiting—and AI unlocks them. 22:30 – Prototyping is cracked. Scaling? Not yet. 24:33 – Youth unemployment and the collapse of on-the-job learning. 25:51 – “The apprenticeship model is broken.” And how to fix it. 27:06 – Losing the talent pipeline — and why companies must step up. 28:22 – Why the youth don’t want factory jobs—and shouldn’t. 29:26 – Is AGI inevitable—or just imagined? 31:07 – What should we teach our kids? The answer might scare you. 32:27 – Bundled jobs, fragmented futures: how humans stay relevant. 33:59 – The real singularity? When we can’t predict what happens next. 35:25 – The AI assumption no one wants to question. 36:55 – What are agents, really? Why no one agrees on the definition. 37:55 – Co-intelligence vs. substitution: what agents skip over. 38:50 – Plain-English goals, rogue pricing, and collusion-by-default. 40:02 – Nested agents are here, and Wharton’s building them. 40:58 – Management > Coding: What great prompters actually do. 41:12 – Product managers might be more vital than ever.

Mario Nawfal

1,613,286 Aufrufe • vor 1 Jahr

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives
40:56

Demis Hassabis (Demis Hassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads Google DeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's Garry Tan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives

Y Combinator

358,228 Aufrufe • vor 3 Monaten