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PostHog (YC W20) started as an open source product analytics tool. Today, its AI systems are already generating a portion of the company's own pull requests, and its vision is much bigger: self-driving software that identifies product problems and automatically ships fixes. At Startup School Paris, PostHog CEO James...

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Coding agents are no longer a distant idea—they're already starting to reshape how we work. YC's Tom Blomfield (Tom Blomfield) and David Lieb (David Lieb) discuss how AI coding tools are transforming software development, why small, high-agency teams will be able to do what once took armies of engineers, and why there's never been a better time to start something new. They explore the bigger picture too: a future where there's more abundance, knowledge work becomes more accessible, and founders have more leverage than ever before. If you're thinking about building, there's no better moment than right now. 0:00 - Intro 0:46 - The combine harvester argument for AI 02:23 - Building with no-code tools 5:37 - The arguments against AI coding 7:01 - Jevons Paradox 9:26 - What happens to the software jobs of today? 12:16 - How will these tools affect knowledge work? 15:08 - An abundant future 16:21 - Advice for future founders
19:11

Coding agents are no longer a distant idea—they're already starting to reshape how we work. YC's Tom Blomfield (Tom Blomfield) and David Lieb (David Lieb) discuss how AI coding tools are transforming software development, why small, high-agency teams will be able to do what once took armies of engineers, and why there's never been a better time to start something new. They explore the bigger picture too: a future where there's more abundance, knowledge work becomes more accessible, and founders have more leverage than ever before. If you're thinking about building, there's no better moment than right now. 0:00 - Intro 0:46 - The combine harvester argument for AI 02:23 - Building with no-code tools 5:37 - The arguments against AI coding 7:01 - Jevons Paradox 9:26 - What happens to the software jobs of today? 12:16 - How will these tools affect knowledge work? 15:08 - An abundant future 16:21 - Advice for future founders

Y Combinator

212,424 Aufrufe • vor 1 Jahr

Today on MCG: Orynth w/Giannis Grigoriadis | $ORYNTH Giannis built a launchpad where every listing is tied to a live product and verified through authorized X and GitHub links, so founders can't fake who they are or what they shipped. He breaks down the pivot from a failed AI website builder to one of the hottest launchpads on Solana, and why they are building on Meteora Here's the breakdown: 00:04 - Intro 01:28 - Meet Giannis 05:26 - Why Product Hunt 😸 was the inspiration 06:14 - Curated vs democratized launches 10:07 - How verification works 10:13 - Mitigating rugs and keeping quality high 15:36 - The $ORYNTH token question and buybacks 19:12 - The social contract between founders and traders 22:40 - The Virtuals Protocol 60-day PMF mechanism 24:33 - Traction 25:29 - Founder pipeline and GTM 31:57 - The launch that went viral 33:48 - Why Solana, why Meteora
35:34

Today on MCG: Orynth w/Giannis Grigoriadis | $ORYNTH Giannis built a launchpad where every listing is tied to a live product and verified through authorized X and GitHub links, so founders can't fake who they are or what they shipped. He breaks down the pivot from a failed AI website builder to one of the hottest launchpads on Solana, and why they are building on Meteora Here's the breakdown: 00:04 - Intro 01:28 - Meet Giannis 05:26 - Why Product Hunt 😸 was the inspiration 06:14 - Curated vs democratized launches 10:07 - How verification works 10:13 - Mitigating rugs and keeping quality high 15:36 - The $ORYNTH token question and buybacks 19:12 - The social contract between founders and traders 22:40 - The Virtuals Protocol 60-day PMF mechanism 24:33 - Traction 25:29 - Founder pipeline and GTM 31:57 - The launch that went viral 33:48 - Why Solana, why Meteora

MCG

11,137 Aufrufe • vor 2 Monaten

Science is building a retinal implant that restores vision to people who have gone blind. One patient has already used it to read a 300-page novel. Building a company like that requires a lot more than getting the technology right. At Startup School 2026, Science Corporation CEO Max Hodak explains how the company buys things and hires people, and why those systems determine how fast it can move. He also gets into why founders can’t delegate their judgment, and why there’s no set of five bullet points that makes a startup work. 01:02 - Science’s Retinal Implant 02:41 - The Hidden Infrastructure of a Startup 03:25 - How Your 17th Employee Buys Things 06:15 - How Infrastructure Creates Speed 07:03 - What Does an Experiment Actually Cost? 09:27 - How the Best Startups Hire 10:18 - Building a Rigorous Hiring Process 13:26 - Judgment, Horsepower, and Agency 16:17 - Rethinking Performance Reviews 18:53 - Rate of Iteration Separates Success From Failure 20:43 - You Can’t Delegate Your Judgment 22:47 - Action Produces Information 24:31 - The Operating System of a Company 25:10 - Q&A
57:02

Science is building a retinal implant that restores vision to people who have gone blind. One patient has already used it to read a 300-page novel. Building a company like that requires a lot more than getting the technology right. At Startup School 2026, Science Corporation CEO Max Hodak explains how the company buys things and hires people, and why those systems determine how fast it can move. He also gets into why founders can’t delegate their judgment, and why there’s no set of five bullet points that makes a startup work. 01:02 - Science’s Retinal Implant 02:41 - The Hidden Infrastructure of a Startup 03:25 - How Your 17th Employee Buys Things 06:15 - How Infrastructure Creates Speed 07:03 - What Does an Experiment Actually Cost? 09:27 - How the Best Startups Hire 10:18 - Building a Rigorous Hiring Process 13:26 - Judgment, Horsepower, and Agency 16:17 - Rethinking Performance Reviews 18:53 - Rate of Iteration Separates Success From Failure 20:43 - You Can’t Delegate Your Judgment 22:47 - Action Produces Information 24:31 - The Operating System of a Company 25:10 - Q&A

Y Combinator

58,751 Aufrufe • vor 23 Tagen

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg
53:58

Today, a16z is announcing the Machine Age Fund, a new $1.1 billion fund for founders rebuilding what intelligence runs on: chips, memory, networking, systems software, power, and the machines that bring AI into the physical world. Ben Horowitz, Martin Casado, and Raghu Raghuram see a new economic law. A thousand engineers cannot erase a two-year software lead, but a massive GPU cluster can turn capital directly into capability. Models are improving faster than the memory, interconnect, power, and cooling beneath them. The founder map is changing with it. Some of the strongest teams are moving from pure software into complex hardware because every constraint in the stack is now a company-building opportunity. In this conversation with Erik Torenberg, they explain what founders can build, why the opportunity reaches all the way down to the physical stack, and why a16z created a fund for it. 00:00 Intro 01:08 Why AI needs an entirely new infrastructure 02:02 The bottleneck is no longer the model 02:40 Founders saw it before investors did 06:07 Sold out through 2028 07:14 Why this time is different from 1999 08:57 The company whose idle hardware gained value 10:51 "Why didn't this fund exist five years ago?" 13:51 "Nobody likes to use AI more than AI" 17:06 The startup law that capital just broke 21:05 What Grok Bot got right 26:43 The case for a custom chip per model 28:23 Why AC power isn't good enough 32:07 Lots of new electricians 34:14 What one gigawatt can power 35:03 Why utilities can't just build faster 37:22 The data centers that give power back 38:01 Why we should never have called it AI 39:43 Why Nvidia will willingly leave money on the table 48:08 Hardware founders are older 52:36 Why America has to lead YouTube: benahorowitz.eth Raghu Raghuram martin_casado Erik Torenberg

a16z

593,551 Aufrufe • vor 3 Tagen

.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
32:09

.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

Y Combinator

70,457 Aufrufe • vor 1 Monat

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice
40:36

Figma co-founder and CEO Dylan Field (Dylan Field) on how AI will transform design and why craft and attention to detail will be the ultimate differentiators. At AI Startup School in San Francisco. 01:38 – How Dylan and Evan Started Figma 02:58 – WebGL: From Games to Tools 04:10 – Buying Time to Build 05:48 – Staying Motivated Through Early Exploration 06:10 – Cold Emails, Early Feedback & First Users 08:24 – Launch Timing and Lessons on Shipping Early 10:08 – Culture of Constraints & Creative Problem-Solving 10:50 – Recognizing Product-Market Pull 13:32 – Why Design is the Differentiator in the AI Era 16:34 – Figma’s AI Product Launches & Expanding Beyond Design 18:56 – The Blurring of Design, Development, and Product 21:00 – The “MS-DOS Era” of AI Interfaces & Future Surfaces 23:26 – The Role of Design in AI Research & the Future of Designers 27:36 – Audience Q&A: AI Tools, Open Source, Principles, and Advice

Y Combinator

178,646 Aufrufe • vor 1 Jahr