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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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Y Combinator

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89,365 просмотров • 4 дней назад •via X (Twitter)

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Today, NVIDIA is widely recognized as the most consequential technology company in the world. But when Founder and CEO Jensen Huang hatched the idea more than three decades ago, it was viewed as little more than an incredibly risky bet on a new approach to computing. How did Huang, an immigrant from working class roots in Taiwan, build this company into what it is today? In the first installment of the Hoover Institution's Only in America series, Condoleezza Rice sits down with Huang to explore why his rise—and that of NVIDIA—couldn't have happened anywhere else. 04:42 Jensen Huang's journey to the US 07:54 Jensen's experience at boarding school in Kentucky 14:23 How Jensen came to Silicon Valley 17:23 Founding NVIDIA 23:40 The evolution and growth of NVIDIA 27:08 Sustaining motivation amid challenges 29:01 America's exceptional tech sector 32:28 Why Huang is a "cautious optimist" on AI 35:10 Jensen Huang's Only in America story
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Today, NVIDIA is widely recognized as the most consequential technology company in the world. But when Founder and CEO Jensen Huang hatched the idea more than three decades ago, it was viewed as little more than an incredibly risky bet on a new approach to computing. How did Huang, an immigrant from working class roots in Taiwan, build this company into what it is today? In the first installment of the Hoover Institution's Only in America series, Condoleezza Rice sits down with Huang to explore why his rise—and that of NVIDIA—couldn't have happened anywhere else. 04:42 Jensen Huang's journey to the US 07:54 Jensen's experience at boarding school in Kentucky 14:23 How Jensen came to Silicon Valley 17:23 Founding NVIDIA 23:40 The evolution and growth of NVIDIA 27:08 Sustaining motivation amid challenges 29:01 America's exceptional tech sector 32:28 Why Huang is a "cautious optimist" on AI 35:10 Jensen Huang's Only in America story

Hoover Institution

121,953 просмотров • 1 месяц назад

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
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Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

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.Alexandr Wang's advice to his 18-year-old self: develop your own internal compass for how the future will unfold, and hold conviction in it against the noise. At Startup School 2026, the Scale AI (YC S16) founder — now leading Meta’s Superintelligence Labs — talks with Garry Tan about rebuilding a frontier lab from scratch, why talent density compounds, and how to spot the exponential worth betting your twenties on. 00:07 — How Alexandr Wang Started Scale AI 03:25 — Pivoting to the Right Idea 06:23 — Conviction Before Consensus 09:06 — Why This Is the Best Time to Start a Company 11:27 — What Personal Superintelligence Looks Like 13:10 — Building a Frontier AI Lab 16:36 — Why AI Models Need to Be Cheap 20:01 — Vision Will Matter More Than Intelligence 24:06 — Systems Thinking in the AI Era 26:51 — The Biggest Opportunity in AI Today 29:25 — Advice to My 18-Year-Old Self
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.Alexandr Wang's advice to his 18-year-old self: develop your own internal compass for how the future will unfold, and hold conviction in it against the noise. At Startup School 2026, the Scale AI (YC S16) founder — now leading Meta’s Superintelligence Labs — talks with Garry Tan about rebuilding a frontier lab from scratch, why talent density compounds, and how to spot the exponential worth betting your twenties on. 00:07 — How Alexandr Wang Started Scale AI 03:25 — Pivoting to the Right Idea 06:23 — Conviction Before Consensus 09:06 — Why This Is the Best Time to Start a Company 11:27 — What Personal Superintelligence Looks Like 13:10 — Building a Frontier AI Lab 16:36 — Why AI Models Need to Be Cheap 20:01 — Vision Will Matter More Than Intelligence 24:06 — Systems Thinking in the AI Era 26:51 — The Biggest Opportunity in AI Today 29:25 — Advice to My 18-Year-Old Self

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50,739 просмотров • 1 день назад

François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI
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151,442 просмотров • 4 месяцев назад

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Michael Truell (Michael Truell), co-founder and CEO of Anysphere, the company behind Cursor, joined Garry Tan to talk about building one of the fastest-growing startups of all time—and why he's betting everything on a world beyond code. He walks through the early insights that led him and his co-founders to leave a promising AI-powered CAD project and instead chase a bigger dream: reinventing how software is written. From the hard years of false starts and rewrites to Cursor's breakthrough moment, Michael explains what it really takes to build a tool that could eventually replace programming as we know it. He also reflects on their first 10 hires, why taste still matters, and how the decade ahead will unlock a new kind of creativity for builders everywhere. 01:00 - A new way to build software 04:50 - Two ways to view LLMs 05:50 - Bottlenecks to superhuman agents 08:30 - New approaches to coding UI 09:40 - Why taste still matters 12:15 - Niche software opportunities 13:30 - Cursor origin story 16:00 - The first problem they tried solving 17:20 - Why they abandoned the CAD idea 21:00 - Pivoting to Cursor 23:00 - Following the scaling laws 27:00 - Getting to PMF 30:00 - “Dogfooding” 31:00 - First 10 hires 32:50 - How to evaluate great engineers 33:45 - Maintaining the hacker mindset 35:00 - What are the moats for AI coding tools? 37:00 - Looking ahead

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University of Austin (UATX)

27,770 просмотров • 6 месяцев назад

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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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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FULL INTERVIEW: Airbnb CEO Brian Chesky joins TBPN to discuss recent earnings, founder mode, the future of consumer AI, marketing, and more. 01:04 How Airbnb got its startup energy back 03:40 Founder mode and fixing one corner of the business at a time 06:14 How Chesky would disrupt Airbnb today 07:18 Why chatbots are wrong for travel 09:37 The SaaSpocalypse and AI interfaces 12:25 Why everyone is building enterprise AI 17:05 Why consumer AI hasn’t broken out yet 18:15 Why Silicon Valley keeps chasing trends 23:08 Advice for designers in the AI era 31:43 Why different marketing works best

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how to build, launch and grow your startup: complete basics that every founder learns by accident 00:00 Intro to building 00:14 The build-measure-learn loop 04:12 What makes a good one-liner 05:40 Rewriting bad one-liners 08:43 Billboards: the Monaco trick 10:12 Set a specific 5-week goal 13:43 Goals that never end will break you 15:42 Intro to Content 18:15 Why luck is the real product 25:03 Fix your bio right now 28:24 Cheat sheet for viral launches 30:49 Step 1: Find 10 strangers (not founders) 33:07 Step 2: The "oh, that makes sense" moment 34:26 Step 3: A visually clear use case 42:29 The $100 launch video that hit 4M 46:04 Context in the first 2 seconds 48:15 Writing with an eraser 51:11 Framework 1: Comparison 54:29 Framework 2: Antagonist 56:01 Framework 3: The Gap 59:37 Be the world's leading expert 01:06:42 Steal your customer's FYP 01:11:47 The 25-video spreadsheet 01:19:36 Intro to Growth 01:21:32 The only metric that matters 01:23:20 Why 1M LP hits is bad growth 01:26:53 The #1 rule on user calls 01:27:15 The AI realtor story 01:34:19 Why Reddit beats Twitter 01:40:15 The landing page that turns pink
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Founders Inc

20,720 просмотров • 3 месяцев назад

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"
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In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

Y Combinator

155,571 просмотров • 1 месяц назад

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not
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Harry Stebbings

106,162 просмотров • 10 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 3 дней назад

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 просмотров • 1 год назад

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 просмотров • 11 месяцев назад

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 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

⚫️ 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 просмотров • 1 год назад

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 просмотров • 3 месяцев назад