Y Combinator

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6月17日

How To Pick A Startup Idea

Many founders get stuck trying to find the perfect startup idea before they commit. But the perfect idea doesn't exist in the abstract. The only way to find what works is to pick one, go deep, and get feedback from real customers. In this episode of Startup School, YC's Jon Xu breaks down how to choose what to build, "burn the other boats," and go deep enough to practically run your customer's business— and why that depth is what surfaces the better idea underneath.

6月17日

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12 分鐘
6月3日

How to Build an AI-Native Services Company

Some of the biggest companies of the next decade won't be software businesses, they'll be services companies like insurance carriers, law firms, and tax practices rebuilt from scratch with AI doing most of the work. In this episode of Startup School, YC Visiting Partner Charlie Warren walks through the playbook for building AI native services companies, covering how to pick a market with the right traits, why variance kills these businesses faster than anything else, and the P&L math that’ll transform your business model.Chapters:00:00 — Intro to AI Services Companies01:01 — Picking the Right Market02:55 — Markets YC Likes Right Now03:43 — The Sam Altman Test04:35 — The Right Founding Team05:28 — Building the Product06:19 — Variance Is the Existential Problem07:08 — The Early Demand Trap07:53 — How to Price AI Services08:41 — The P&L Walkthrough09:33 — AI Operating Leverage10:27 — Don't Buy Your Way InApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs

6月3日

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11 分鐘
5月13日

Paul Graham: Should you move to Silicon Valley?

Paul Graham is a co-founder of Y Combinator. He's funded and mentored companies like Dropbox, Airbnb, DoorDash, and thousands of others through YC, and is one of the most influential voices in the startup world. In this talk at our YC | Stockholm event last month, Paul walks through why ambitious founders should move to Silicon Valley at least briefly, what makes it uniquely valuable — from serendipitous meetings and faster investor decisions to a deeply embedded pay-it-forward culture — and why returning home afterward may be one of the most powerful things a founder can do to help their local ecosystem, using Stockholm as a case study for what it would take to become the Silicon Valley of Europe.

5月13日

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22 分鐘
4月29日

How to Build the Future: 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. Chapters:00:00 — Intro00:46 — Demis Hassabis: From Chess Prodigy to DeepMind01:48 — What’s Missing Before We Get To AGI?03:36 — Why Memory Is Still Unsolved06:14 — How AlphaGo Shaped Gemini08:06 — Why Smaller Models Are Getting So Powerful10:46 — The 1000x Engineer12:40 — Continual Learning and the Future of Agents13:32 — Why AI Still Fails at Basic Reasoning15:33 — Are Agents Overhyped or Just Getting Started?18:31 — Can AI Become Truly Creative?20:26 — Open Models, Gemma, and Local AI22:26 — Why Gemini Was Built Multimodal24:08 — What Happens When Inference Gets Cheap?25:24 — From AlphaFold to the Virtual Cells28:24 — AI as the Ultimate Tool for Science30:43 — Advice for Founders33:30 — The AlphaFold Breakthrough Pattern35:20 — Can AI Make Real Scientific Discoveries?37:59 — What to Build Before AGI ArrivesApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs

4月29日

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41 分鐘
5月27日

How To Build Superintelligence Inside Your Company

Building superintelligence inside a company isn't about adding AI as a feature. It's about making it the operating system the whole organization runs on. In this episode of the Lightcone, we sat down with YC's Pete Koomen to talk for the first time about how he led the effort to build YC's internal agent infrastructure from the ground up. We cover how giving agents unrestricted access to one database changed everything, the self-improving skill loops that get smarter overnight and why he thinks we've arrived at the personal computer moment for AI.Chapters:00:00 — Intro00:39 — YC's AI Stack02:15 — The Finance Team Problem That Started It All05:07 — SQL Access Changes Everything07:20 — One Database to Rule Them All09:14 — Jevons Paradox 10:07 — Denormalizing for Agents (G-Brain)12:15 — The Single-Player Era of Agents14:16 — 350 Tools and a Shared Registry16:24 — Skillify, DRY, and MECE Resolvers18:23 — The Self-Improving Dream Cycle20:26 — The Two-Sentence Pitch Skill23:06 — How Super Intelligence Compounds25:10 — Recording Everything as a Building Layer27:10 — The Shared Organizational Brain29:18 — Trust-Default Culture as a Requirement30:44 — Raising the Floor for New Employees32:35 — Horseless Carriages Essay Explained34:24 — Why Chat Is the Best Interface for Agents36:10 — Garry's List → G-Brain Rewrite38:50 — Just-in-Time Software40:49 — Centralizing vs. Decentralizing AI43:32 — The Personal AI RevolutionApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs

5月27日

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46 分鐘
30/05/2025

State-Of-The-Art Prompting For AI Agents

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. The prompt from Parahelp (S24) discussed in the episode: https://parahelp.com/blog/prompt-design

30/05/2025

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31 分鐘
3月27日

How François Chollet Is Building A New Path To AGI

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 Lightcone, 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 Learning01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs03:04 - Why Ndea Isn’t Competing With Coding Agents05:20 - Why Everyone Might Be Wrong About Scaling LLMs07:22 - Why Coding Agents Suddenly Work So Well08:50 - The Limits of LLMs in Non-Verifiable Domains10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong)13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story18:20 - ARC Benchmarks Explained: From V1 to V322:49 - The RL Loop Powering Coding Agents Today27:03 - ARC-AGI V3: Measuring “Agentic Intelligence”31:14 - Inside the ARC Game Studio35:31 - Could AGI Fit in 10,000 Lines of Code?44:01 - Building Ndea: From Idea to Compounding Research Stack46:46 - The Future of ARC: Benchmarks That Evolve With AI47:21 - Why There’s Still Huge Opportunity for New AI Paradigms53:37 - How to Build a Breakout Open Source Project - Lessons From Kera56:39 - Advice For How To Think About AIApply to Y Combinator: https://www.ycombinator.com/applyWork at a startup: https://www.ycombinator.com/jobs

3月27日

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57 分鐘
18/06/2025

Alexandr Wang: Building Scale AI, Transforming Work with Agents & Competing With China

Alexandr Wang started Scale AI to help machine learning teams label data faster.It started as a simple API for human labor, but behind the scenes, he was tackling a much bigger problem: how to turn messy, real-world data into something AI could learn from. Today, that early idea powers a multi-hundred-million-dollar engine behind America's AI infrastructure—fueling everything from Fortune 500 workflows to real-time military planning. Just last week, Meta agreed to invest over $14 billion in Scale AI, valuing the company at $29 billion.Alexandr joined us on the Lightcone to share how Scale AI evolved from a scrappy YC startup into the backbone of some of the world's most advanced AI systems, how he thinks about competition with Chinese AI labs, and what it takes to build infrastructure that shapes the frontier.

18/06/2025

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1 小時 1 分鐘
30/04/2024

Where Is The REAL Cerebral Valley?

In this episode of the Lightcone Podcast, YC Group Partners chart the evolution of San Francisco as the center of the startup world and how AI has brought everyone back to the city. They also talk about why YC chose to open our new HQ in the Dogpatch neighborhood of San Francisco and share their advice for new founders moving to the city for the first time.

30/04/2024

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33 分鐘
26/08/2025

How This 25-Year-Old Built A $675M Legal AI Startup (With No Legal Experience)

In this episode of Founder Firesides, YC General Partner Gustaf Alströmer is joined by Max Junestrand, co-founder and CEO of Legora, one of the fastest-growing legal AI startups in the world. In just 13 months, Max and his team scaled from 10 to 100 people, raised $80M, and cracked the challenge of selling to one of the most skeptical industries. Max shares insights on building a successful vertical AI company, selling to conservative markets, and sheds light on what the future of legal tech looks like.

26/08/2025

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45 分鐘

Make something people want.