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In a recent batch talk, YC General Partner Tom Blomfield broke down how to build a self-improving, AI-native company. He walks through how to create recursive, self-improving AI loops, and why founders who get this right will run companies that improve while they sleep. 00:00 — Companies Are Roman... Legions 00:54 — Copilots Are the Wrong Mental Model 01:55 — Extract the Domain Knowledge 02:24 — The Recursive Self-Improving Loop 04:12 — The Holy Shit Moment at YC 05:50 — Self-Optimizing Product and Support Loops 06:29 — Burn Tokens, Not Headcount 07:23 — Middle Management Is Over 08:05 — Make Everything Legible to AI 09:40 — Regenerating the YC User Manual 11:19 — Software Is Ephemeral, Context Is Valuable 12:18 — Where Humans Still Mattershow more

Y Combinator

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593,808 次观看 • 3 个月前 •via X (Twitter)

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22,601 次观看 • 21 天前

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70,457 次观看 • 1 个月前

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rari

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a16z crypto

24,270 次观看 • 4 个月前

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

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42,853 次观看 • 1 年前

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a16z

445,193 次观看 • 18 天前

Warp (YC W23) recently announced a $60M Series B and now serves more than 1,000 customers, processing over $600M in payroll annually and on track to surpass $2B in the next year. In this episode of Founder Firesides, YC's Harj Taggar sits down with Warp founder and CEO Ayush Sharma (Ayush S) to talk about how the company found its way into one of enterprise software's most competitive markets and why AI is fundamentally changing how software companies should be built. 00:50 - From India to MIT 03:10 - Betting on an Unsexy Problem 05:18 - The Wedge That Started Warp 09:49 - What "AI-Native" Really Means 12:31 - Building a Different Kind of Company 14:00 - Why AI Favors Technical Founders 16:42 - The Next Generation of Enterprise Software 21:25 - Why Investors Backed Warp 25:11 - The Future of Employee Management

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Y Combinator

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2025 was the year AI stopped feeling chaotic and started feeling buildable. On the Lightcone Podcast, Garry Tan, Harj Taggar, Diana, and Jared Friedman break down the surprises of the year, from shifting model dominance to why the real opportunity is moving back to the application layer, and why the next wave of AI startups may be just getting started. 00:50 - Anthropic models are most preferred in the latest YC batch 05:36 - Why aren’t there more AI consumer apps? 07:01 - Swapping models in and out is becoming the norm 09:08 - The big AI bubble question 14:37 - Space as the solve for data centers and energy 17:29 - Interest in starting model companies 21:01 - Vibe coding became a big category 22:23 - AI economy has stabilized 23:38 - The AI 2027 piece 25:49 - Founders still need to hire teams

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The Rollup

23,415 次观看 • 3 个月前

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run

"We'll get to a point in intelligence where the human making that decision is going to be less efficient than the AI making that decision themselves." My full conversation with the head of AI at the Solana Foundation on the world coming fast, where AI starts earning, investing and outpacing the humans who built it. 00:00 Intro 02:46 The Wall Street dream he ditched 06:50 The cold email that changed everything 08:07 Meeting the founder of Stable Diffusion 11:36 Why Rishin left investing to build AI 20:31 When AI starts paying for things itself 23:36 The moment that predicted the future 31:35 Three people, five weeks, one product 38:01 When AI stops asking you first 41:09 When your customer is a robot 44:01 How much of the economy will AI run

JackGK

19,233 次观看 • 1 个月前

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

rari

57,616 次观看 • 21 天前