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Outset (YC W23) is building an AI customer research platform used by companies like Google, Microsoft, and Nestlé. Its AI interviewers have now conducted millions of conversations, giving companies the scale and speed of a survey with the depth of a one-on-one interview. In this founder fireside, @OutsetAI co-founder and CEO @AaronLCannon sits down with YC's @harjtaggar to talk about building a product before the market was ready for it, how they helped create the category of AI-moderated research, and what comes next. Aaron shares how they're expanding beyond interviews with Outset's new Simulations Lab and Digital Twins, which let companies simulate their customers and get feedback on everything from messaging and pricing to new products. 00:24 — What Outset Does 01:18 — Building a Category Before the Market Was Ready 06:31 — Landing the First Enterprise Customer 08:14 — How Better AI Models Changed the Product 12:57 — When the Market Finally Caught Up 14:45 — From AI Interviews to Customer Simulations 19:40 — Aaron’s Path to Becoming a Founder
Tune in: youtu.be/x3XOeDckUX0
Congrats to @billythalheimer, @mikeklinker1, and @regentcraft (W21) on their $240M Series B! They build seagliders, electric craft that skim just above the water and combine the speed of an aircraft with the convenience of a boat. Their Viceroy Seaglider prototype is designed to carry 12 passengers at up to 180 mph, and its first human flight is imminent. Regent’s commercial order book spans six continents, and production is starting at their newly completed 255,000-square-foot factory in Rhode Island.
Today, more than 3% of the world's lawyers use Legora, and the company has grown from $1 million to $100 million in ARR since launching in October 2024. At Startup School 2026, @WeAreLegora co-founder and CEO @MaxJunestrand shares how they built one of the fastest-growing enterprise software companies in the world, from cold emailing lawyers and moving into a customer's office to freezing sales for six months to rebuild the product. He explains why building a company is ultimately about people, how to create a culture that wants to win, and why founders have to learn to love the hustle. 00:07 — From 3 Engineers to $100M ARR 03:15 — How Legora Got Started 05:09 — Learning the Legal Industry From Scratch 07:22 — Getting Rejected by YC 09:36 — Moving Into a Customer's Office 11:11 — Going From Zero to $1M ARR During YC 13:26 — Why We Froze Sales for Six Months 15:18 — Rebuilding the Product From Scratch 17:26 — Building a Company Is About People 21:51 — Creating a Culture That Wants to Win 25:26 — You Have to Love the Hustle 27:55 — Do Founders Need Domain Expertise? 29:14 — Betting on Models Getting Better 36:29 — When Should You Start a Company? 45:04 — How to Become More Ambitious 48:18 — The Skills Founders Need Today
Tune in: youtu.be/o0ORPbSEgd8 Transcript: ycrootaccess.com/p/max-junestra…
Big labs don’t have a monopoly on AI research. More than 20 YC startups have published research recently at top conferences like NeurIPS, ICLR, and ICML. empirical.health/blog/yc-startu…
Congrats to @hellomundoai on their $20M Series A! Mundo partners with frontier AI labs and companies to build the datasets, evaluations, and research that help AI better perceive and understand the real world across audio, video, and emerging modalities. mundoai.world/research/perce…
What does the stack of the most 'cracked' builders look like these days? @steipete’s latest progression: cli → app → web (multiplayer agent sessions) Watch the full conversation with @raphaelschaad linked below.
Raphael Schaad@raphaelschaad·Caught up backstage w ClawFather @steipete; this is a packed one: * Loops, Graphs, and Florps — what's latest in agentic engineering? * Outie vs. Innie — avoid his personal vs. work agent fighting? * Startup ideas in 2026 — what to build? Lots of incredible zingers in here:
This week's Paper Club is focused on data. As models have scaled across tasks and languages, old assumptions about training and evaluation are starting to break. So we gathered three domain experts to break down the challenges and frontiers of training data, benchmarks, and multilingual pre-training. Thanks to the following presenters: 00:00 - @FrancoisChauba1: Why a whole night about data? 9:51 - @vincentsunnchen: The Art & Science of Benchmarking Agents 28:44 - @volokuleshov: Inception: Diffusion Language Models for Production 40:26 - @ShayneRedford: ATLAS: Practical Scaling Laws for Multilingual Models
Tune in: youtu.be/IfoPg2QefF8
In 2013, @cjoneslevy created the SAFE at YC. It's since been used by YC founders to raise $15B+ and has become the standard way startups raise money. So we're launching Send a SAFE: a free tool to generate, sign, & send one in ~2 minutes, built to be used by founders (and their AI agents).
@cjoneslevy Learn more: ycombinator.com/safe
Michael Kratsios (@mkratsios47) has seen the AI boom from both sides: as COO of Scale AI and inside the White House. Today, as the director of the White House Office of Science and Technology Policy, he helps shape America’s national strategy on AI, science, and emerging technology. At Startup School 2026, he sat down with YC's @lutherlowe, to talk about how Washington makes technology policy, why the White House supports open source AI, and why little tech needs a seat at the table. 02:38 — From Tech to the White House 04:39 — How Technology Policy Actually Gets Made 05:51 — The White House on Open Source AI 08:28 — How Washington Sees AI Differently 09:53 — Regulating a Technology That Changes Every Six Months 11:37 — Which AI Risks Are Overblown? 13:03 — Giving Little Tech a Seat at the Table 17:31 — Regulation Without Creating Incumbent Moats 18:01 — Born Free vs. Born in Captivity Technologies 20:46 — What Working in the White House Is Actually Like 25:56 — A New Golden Age of American Science 31:32 — Quantum, Congress, IP, and What Comes Next 37:54 — Why Technologists Should Consider Public Service
Tune in: youtu.be/zLUZclThLhU Transcript: ycrootaccess.com/p/michael-krat…
Before joining Apple as the Macintosh artist, @SusanKare was an art history PhD who barely knew anything about computers. She went on to design many of the icons, typefaces, and symbols that helped make the original Mac feel understandable and human, defining a visual language for personal computing that still influences software today. At Startup School 2026, Susan shares the stories behind the Happy Mac, Command key, Chicago font, and more, along with the design lessons she learned from Steve Jobs, Paul Rand, and the original Mac team: make things meaningful and memorable, embrace constraints, iterate constantly, and use just enough detail to make an idea instantly clear. 00:00 — Making Things Meaningful and Memorable 01:51 — How Susan Joined Apple 05:01 — Designing the Original Macintosh 06:35 — A Computer Anyone Could Use 08:26 — Creating Chicago and the Mac Typefaces 11:47 — MacPaint and the First Icons 14:45 — The Story Behind the Happy Mac 17:08 — Why Simple Icons Work Better 18:25 — The Icons That Didn’t Work 21:23 — Designing Symbols That Last 22:44 — How the Command Key Got Its Symbol 25:20 — What Ancient Symbols Can Teach Designers 30:18 — Designing After Apple 36:32 — Lessons From Steve Jobs, Paul Rand, and More
Tune in: youtu.be/YEvLKzsEwMw Transcript: ycrootaccess.com/p/susan-kare-o…
Congrats to @AndersForslund1 and @heartaerospace (W19) on the first flight of X1! It's the largest battery-electric aircraft ever flown, with a 106-foot wingspan and a takeoff weight over 25,000 pounds. The entire 27-minute flight ran on about $5 worth of electricity. Next comes the ES-30, their 30-seat hybrid-electric airliner targeting service in 2031.
Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, @physical_int cofounder @chelseabfinn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A
Tune in: youtu.be/cRZNwgvcWUg Transcript: ycrootaccess.com/p/chelsea-finn…
Congrats to @adijayaprakash, @adityamaru27, @aayush_shah15, and @useblacksmith (W24) on their $45M Series B at a $550M valuation! AI tools mean developers are writing more code than ever, but all of it still has to be checked before it reaches production. Blacksmith builds the infrastructure that tests and validates code before it ships. In under a year, customers grew from just over 700 to more than 5,000, including Supabase, Clerk, and Mercury. They hit their first $10M in run rate with only 10 employees, and revenue has since grown to tens of millions. t.co/6Fr8sduVE5
In this episode of Full Stack, @circlebackai co-founder Ali Haghani (@iAligator) gives us a look at how he runs the company with AI. From coding agents and automated ops to using Circleback as a company brain, he shares the tools, workflows, and systems that have changed how he works as a founder. 00:44 - Ali's hardware setup + gadgets 02:51 - Unexpected ways of using Circleback 05:21 - OpenClaw/Telegram vs coding tools 05:48 - Different agents made in Telegram 07:42 - Time spent in terminal vs everything else 09:36 - Testing and writing prompts 10:48 - Tokenmaxxing? 12:24 - Things Ali will not let agents do 12:59 - Why should companies start recording more meetings? 14:04 - Where is software engineering headed?
Tune in: youtu.be/4YWO4sSRrTE
Last November, @steipete was annoyed that there was no good way to talk to his coding agents from his phone, so he built one himself. A few months later, OpenClaw exploded into one of the biggest open source AI projects in the world, with nearly 3,000 contributors and a peak of 4.7 million weekly downloads. At Startup School 2026, Peter tells the story of what happened when OpenClaw took off, what he got wrong as it grew, and how he eventually stopped using the product he had built for himself. He shares why the best products often start with something that annoys you, why focus matters more as building gets easier, and why, in his words, “fun is velocity.” 01:16 — How OpenClaw Started 05:10 — Finding Product-Market Fit 07:01 — The Night OpenClaw Went Viral 09:50 — When the Project Exploded 11:05 — When the Attention Almost Broke Him 12:00 — Did You Sell Out? 14:17 — Your Name Can’t Be Forked 15:03 — What OpenClaw Got Wrong 20:40 — Hype Is Like the Weather 21:37 — When It Stopped Being Fun 25:30 — Fun Is Velocity 26:40 — What’s Next for OpenClaw 28:57 — Three Lessons From Building OpenClaw 30:08 — Q&A
Tune in: youtu.be/whcfSGN6CAU Transcript: ycrootaccess.com/p/peter-steinb…
This week’s Paper Club is all about robotics. Every year for the last decade, someone has promised that the era of robotics is just around the corner. But we’re still waiting. So we gathered a bunch of the top researchers working in AI and robotics to present the latest findings on where we are and what comes next. Thanks to the following presenters: 0:00 – @FrancoisChauba1: Ten years of “next year, robotics is solved” 7:59 – @marceltornev: MEM - Multi-Scale Embodied Memory for Vision Language Action Models (t.co/czkSDRpaGc) 20:21 – Milan Ganai: Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning (t.co/YovZkHCoB7) 33:42 – @tylerlum23: SimToolReal - An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation (t.co/a44yRaeu14) 51:21 – @NikolausWest: Why the next great robotics companies will start with teleoperation 1:08:30 – @BillJiao930 & @Guanming717: World action models and what comes after VLAs
Tune in: youtu.be/myDCd0hNqQU Transcript: ycrootaccess.com/p/memory-simto…
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, @ScienceCorp_ CEO @maxhodak_ 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
Tune in: youtu.be/Xc4klGbq8v8 Full transcript: open.substack.com/pub/ycrootacce…
AI isn't just changing the tools designers use. It's changing how they build, ship, and stand out. In this episode of Design Review, @stephenhaney, founder of AI-native design tool @paper, joins YC's @aaron_epstein to demo the agent-first workflow that's making Paper one of the fastest-growing design tools since Figma. Using live redesigns of user-submitted websites as examples, they break down the most common AI design tells, show how to fix them in seconds, and explain why the biggest risk for founders isn't moving too slow, it's shipping something that looks like everyone else. 01:04 - What is Paper and why build it? 03:17 - What makes Paper agent-native 05:11 - Demo: Shaders, image generation, and brand design 11:32 - Design-to-code and the new agent stack 16:48 - Design Review: Legion Health 24:11 - How to avoid AI design slop 28:26 - Design Review: Sytex 32:42 - The biggest tells of AI-generated design 37:39 - Design Review: Moreta 41:11 - Can AI learn taste? 43:56 - How Paper uses agents internally 46:52 - Building a community around Paper 49:15 - Lessons from Steven’s first startup 53:30 - What’s next for design and Paper
Tune in: youtu.be/P06RgnUKX_I
Congrats to @GrantLaFontaine, @loganhead13, and @whatnot (W20) on their $545M Series G at a $20B valuation! They run the biggest live commerce platform in North America, the UK, and Europe. Six months into 2026, they’ve already passed their entire 2025 GMV of more than $8 billion. Buyers have more than doubled over the past year, and more than 650,000 people are joining the platform every week. t.co/IvWEfaNBrp
The next generation of startups will be built by smaller teams than ever before. At Startup School 2026, YC's @garrytan explains why we're we're entering the era of personal AGI: AI agents that run on your own infrastructure, compound your knowledge over time, and dramatically increase your ability to build. He shares the tools and workflows he uses every day, why every founder should own their intelligence instead of renting it, and what it means to build under your own power. 00:07 — What Founders Can Learn From Spinoza 04:46 — Personal AGI Is Already Here 07:57 — Why AI Makes One Person More Powerful Than Ever 12:29 — Your Life Is a Library 15:15 — Inside My Personal AI System 17:06 — Markdown Is Code 18:20 — Latent Space vs. Deterministic Code 21:13 — Building a Company of One 24:16 — How to Build Your Own Personal AGI 27:58 — Why Most People Will Quit Too Soon 29:09 — Own Your Skills Before Someone Else Does 32:24 — Personal AGI Means Owning Your Intelligence 35:21 — Why I Open Sourced Everything 38:30 — A Personal AGI for One Small Boy 40:18 — It's All Made Up. You Get to Make It Up.
Tune in: youtu.be/eRrc1pUY5oU Full transcript: ycrootaccess.com/p/garry-tan-ow…
there is no better time to start a startup x.com/JeffDean/statu…
Philip Johnston (@PhilipJohnston) is the co-founder and CEO of @Starcloud_, the company building data centers in space. In November 2025, Starcloud launched an Nvidia H100 GPU into orbit and trained the first large language model in space. They've since raised $200 million, hit a billion-dollar valuation just 17 months after YC demo day and filed with the FCC to deploy 88,000 more satellites. In this episode of @LightconePod, Philip walks us through their wild origin story, the engineering challenges behind the Starcloud-1, why they booked a SpaceX launch before they even knew what they were building and how data centers in space make sense both economically and politically. 00:39 — Why Build Data Centers in Space? 01:40 — The Insight That Started Starcloud 04:43 — Launching Starcloud-1 09:45 — The Engineering Behind Data Centers in Orbit 13:49 — Why 100 VCs Said No 16:12 — Book the Launch Before You Build the Product 18:32 — The Roadmap to Commercial Space Compute 21:10 — Building NVIDIA GPUs for Space 23:41 — Raising $170M for a Hard Tech Startup 26:37 — Hiring the World's Best Space Engineers 31:25 — Why AI Compute Is Moving to Space 34:49 — Advice for Hard Tech Founders
Tune in: youtu.be/A9JDkiYEhfY Full transcript: ycrootaccess.com/p/starcloud-so…
Congrats to @pablorpalafox, @PaarupLuis, @javipalafox, and @HappyRobot (S23) on their $150M Series C at a $1.2B valuation! They build AI agents that run the phone calls, emails, and scheduling behind enterprise operations. They proved the platform in logistics and are now expanding into insurance, energy, telecom, and airlines. Revenue has grown more than 5x since their Series B less than a year ago, and they now work with more than 150 enterprises including DHL, Uber, and Repsol. t.co/neSdWfLsyD
Waymo’s first autonomous demo took eighteen months. The product took fifteen years. Today the @Waymo Driver runs 500,000 trips a week — 4 million fully autonomous miles across 15 cities, with 17x fewer serious-injury crashes than human drivers. At Startup School 2026, Waymo co-CEO @dmitri_dolgov shares the seven lessons behind that journey, from bridging the gap between a demo and a real product to building systems that can safely operate in the physical world. 00:07 — 7 Lessons From Building Waymo 02:21 — Why Physical AI Is Different 06:52 — The Gap Between a Demo and a Product 11:34 — Why Reliability Lives on an Exponential Curve 14:17 — Pick the Right Technology Curve 16:05 — Why Waymo Uses Cameras, LiDAR, and Radar 21:07 — Ride Every Technology Wave 24:43 — Inside the Waymo Foundation Model 30:09 — The Bitter Lesson Still Wins 36:41 — Why Every Physical AI Company Needs a Simulator 41:36 — Build an AI Flywheel 43:22 — Evals Are Your Competitive Advantage 46:09 — How Waymo Became 17x Safer Than Human Drivers 47:59 — The Next Decade of AI Will Be Physical
Tune in: youtu.be/Gp4zrV3-6N8 Full transcript: ycrootaccess.com/p/dmitri-dolgo…
Waymo’s first fully autonomous demo took eighteen months. The product took fifteen years. Today the @Waymo Driver runs 500,000 trips a week — 4 million fully autonomous miles across 15 cities, with 17x fewer serious-injury crashes than human drivers. At Startup School 2026, Waymo co-CEO @dmitri_dolgov shares the seven lessons behind that journey, from bridging the gap between a demo and a real product to building systems that can safely operate in the physical world. 00:07 — 7 Lessons From Building Waymo 02:21 — Why Physical AI Is Different 06:52 — The Gap Between a Demo and a Product 11:34 — Why Reliability Lives on an Exponential Curve 14:17 — Pick the Right Technology Curve 16:05 — Why Waymo Uses Cameras, LiDAR, and Radar 21:07 — Ride Every Technology Wave 24:43 — Inside the Waymo Foundation Model 30:09 — The Bitter Lesson Still Wins 36:41 — Why Every Physical AI Company Needs a Simulator 41:36 — Build an AI Flywheel 43:22 — Evals Are Your Competitive Advantage 46:09 — How Waymo Became 17x Safer Than Human Drivers 47:59 — The Next Decade of AI Will Be Physical
Tune in: youtu.be/Gp4zrV3-6N8 Full transcript: ycrootaccess.com/p/dmitri-dolgo…
The best way to learn AI is to work at an AI startup. On August 15, we're inviting ambitious students to YC HQ to meet founders and engineers from 50+ YC companies. Roam the expo hall to meet founders (and collect swag), startups pitch you, interview onsite, and land your Summer 2027 internship – co-ops and more.
Apply to join: events.ycombinator.com/2027-summer-in…
In 2009, @patrickc and @collision went to Startup School in Berkeley, got sushi in Potrero Hill afterward, and decided on the walk home to start @Stripe. The reasoning, as Patrick remembers it, was that “we might as well because it probably won’t be that hard.” It took them two years to launch. Seventeen years later, at Startup School 2026, he talks with YC's @harjtaggar about dropping out of MIT twice, why founders should ask what happens if they succeed, and what Stripe’s own data says about the best time to start a company. 00:07 — What Should You Still Learn in the Age of AI? 02:01 — Knowledge Still Matters 05:12 — Should You Drop Out of College? 09:58 — Why Stripe Worked 12:10 — Building Stripe Before Launching 17:20 — Is the Lean Startup Still the Right Playbook? 19:07 — The Hidden Reward of Building Stripe 22:36 — Will AI Kill Your Startup? 25:17 — Why It's Never Been a Better Time to Start a Company 29:23 — What Stripe's Data Says About the AI Economy 30:45 — Build Something People Truly Need
Tune in: youtu.be/5d6y3poKwK4 Full transcript: ycrootaccess.com/p/patrick-coll…
We’ve decided to open-source a multi-agent harness we use internally at YC. We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!). The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.
Some features: • Triggers (crons, webhooks), memory, shared files • Connectors for a company brain • Agent browser support • Shareable web app artifacts • Multi-player projects Like many things we do at YC, it’s an experiment. It’s early and has bugs. But it’s been surprisingly useful for us so far, and we’re excited to share it and get feedback from the community. If you want to host it yourself, try telling your coding agent of choice to “deploy t.co/yKmDDWRIcX”
In 2001, @JeffDean and Sanjay Ghemawat did the math and realized Google’s entire search index would fit in RAM — then shipped it in a few days, and search got fast. In 2013, another napkin calculation showed that three minutes of daily speech recognition per user would require doubling Google’s server fleet. That one became the TPU. At Startup School 2026, Google’s Chief Scientist talks with YC’s @sdianahu through the thought experiments behind both, why inference hardware is the next specialization, and where two or three people in a room can still win. 00:07 — Are AI Models Already Junior Engineers? 01:44 — AI Systems That Improve Themselves 02:40 — The Google Search Breakthrough That Changed Everything 04:38 — AI Agents Will Run for Weeks 05:58 — The Napkin Math That Led to TPUs 09:20 — How to Find Breakthrough Ideas 10:25 — The AI Engineer's New Mental Model 12:33 — Why AI Is Really an Energy Problem 16:11 — Context Engineering Is the Next Frontier 19:46 — The Skill That Made AI Better at Optimization 22:13 — Why Long-Running Agents Fail 25:21 — Where Startups Can Still Beat Google 31:19 — How to Become an AI-Native Founder 36:36 — Question Your Biggest Assumptions 42:08 — AI That Builds Better AI 50:02 — Build Something That Truly Matters
Tune in: youtu.be/CxXgV54KzpQ Full transcript: ycrootaccess.com/p/jeff-dean-th…
At our latest YC Paper Club, researchers and builders presented on multi-GPU kernels, intelligence per watt, heterogeneous inference, and more. Thank you to our presenters: 0:00 – @FrancoisChauba1: The case for chip and kernel specialization 7:16 – @stuart_sul: Parallel Kittens - Systematic and Practical Simplification of Multi-GPU Al Kernels (t.co/4J8ejfQBX6) 21:29 – @JonSaadFalcon: Intelligence per Watt - Measuring the Intelligence Efficiency of Local and Cloud AI (t.co/ULvvcvlxL7) 31:05 – @MarkSaroufim: When Al Starts Writing Systems Code 47:04 – Misha Smelyanskiy: Why AI Inference Needs Heterogeneous Hardware 1:04:33 – @shacklettbp: Building a High-Throughput Game Engine that Runs ENTIRELY on the GPU (t.co/jslIWjJRtQ)
Tune in: youtu.be/n8dz2FX0_uY Full transcript: ycrootaccess.com/p/multi-gpu-ke…
.@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 @garrytan 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
Tune in: youtu.be/sJ4VJWycX9M Full Transcript: ycrootaccess.com/p/alexandr-wan…
Congrats to @tellidotcom on their $15M seed! They're building AI agents for B2C customer operations, handling the calls, lead qualification, appointment booking, and follow-ups that companies currently run through a maze of CRMs, call centers, and manual work. Their agents already handle millions of conversations for customers including Sky, Enpal, and Vaillant. t.co/5BVzdDCWS9
Congrats to @ArrayLabs on their $21M raise! They're building clusters of small, mass-manufacturable radar satellites that fly in formation and work together as one distributed sensor, delivering a continuously updated picture of everything moving on or above the Earth. Traditional satellites take static snapshots. Array's gives you real-time tracking of ships, aircraft, and missiles from orbit. They've already won contracts with the Air Force, Space Force, Navy, Army, SOCOM, and DARPA. t.co/guujT1NnlT
accept no substitutes
accept no substitutes
In 1969, we landed on the moon and flew Concorde. Half a century later, we could do neither. Blake Scholl (@bscholl) founded @boomsupersonic (YC W16), the startup building America’s first supersonic airliner, to change that. At Startup School 2026, he shares how a cardboard mockup with Office Depot seats became XB-1, the first independently developed jet to break the sound barrier, and why founders have to build for both the worst day and the best day. 00:08 — Why the Future Stopped Moving Faster 03:14 — Why I Started Boom Supersonic 05:57 — Building a Supersonic Jet From Scratch 08:33 — The Worst Day and the Best Day 10:14 — How We Changed US Law 11:36 — How 50 People Built a Supersonic Jet 15:05 — Designing Hardware Like Software 17:54 — Financing a Multi-Billion-Dollar Startup 19:30 — Great Ideas Are Hiding in Plain Sight 24:05 — How AI Is Changing Hardware 27:16 — Working With Regulators 30:55 — Build Something You Love 36:27 — How to Build Confidence 39:35 — Learning Hard Things From First Principles 44:17 — When Should You Start a Company?
Tune in: youtu.be/byAj35QlGbs Transcript: ycrootaccess.com/p/blake-scholl…
In 2005, @sama was a Stanford sophomore in YC’s first batch, building a startup in a little Cambridge office while @paulg cooked the founders dinner. Twenty years later, as co-founder & CEO of OpenAI, Sam closed Startup School 2026 in conversation with @garrytan — on agents, ambition, and why the ceiling for what a startup can take on has never been higher. 00:07 — From YC’s First Batch to Today 03:04 — What PG Taught Sam 04:17 — Why Startups Matter More Than Ever 06:57 — The Coming Golden Age of Ambitious Startups 09:30 — Why Startups Win During Technology Shifts 11:46 — Building OpenAI When Nobody Believed in AGI 14:45 — Finding People Who Share Your Conviction 18:05 — Help People Before You Know Why 19:43 — Earnestness, Ambition, and Ignoring the Haters 24:04 — The AI Safety Incident That Changed the Stakes 26:58 — Preventing AI From Concentrating Power 30:41 — How Fast AI Models Will Improve 36:06 — The Best Version of an AI Future 37:47 — “It’s All Going to Work Out”
Tune in: youtu.be/ZIaOBAjvc38 Full Transcript: ycrootaccess.com/p/sam-altman-n…
The deadline to apply for YC F26 is tonight at 8pm PT! If you're an earnest builder working on something people want, we'd love to hear from you: ycombinator.com/apply
Fresh off the launch of Opus 5, Claude Code creator Boris Cherny (@bcherny) joins YC's @sdianahu 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 @AnthropicAI @claudeai
Tune in: youtu.be/qyPCVqFUyDo Full transcript: ycrootaccess.com/p/boris-cherny…
The deadline to apply for the YC Fall 2026 batch is tomorrow, July 27th, at 8pm PT! Apply at ycombinator.com/apply.
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 @garrytan sits down with @nvidia Founder and CEO @JensenHuang to talk about confronting reality, learning your way into new domains, and why resilience — getting through one day at a time — matters more than anything else. 01:07 — NVIDIA's wrong algorithm 03:14 — Buying textbooks to save the company 05:29 — The real big idea 07:06 — The Sega story 09:37 — The $300M IPO 10:33 — Seeing AlexNet differently 12:44 — Reinventing the full stack 13:30 — How to build a first-principles org 17:01 — Build the car to fit your driving style 19:12 — Frontier algorithms 20:50 — Systems thinking is the new coding 23:26 — Should you own your own AI? 25:43 — How NVIDIA sses agents internally 30:51 — AI and job creation 34:21 — The ChatGPT moment for robots 37:24 — Where physical AI shows up first 39:18 — Why Jensen just joined X 41:01 — What to learn that still matters 44:32 — The mindset you should have - "How hard can it be?"
Tune in: youtu.be/I4B37S1dyQQ Full transcript: ycrootaccess.com/p/jensen-huang…
Live in the future, then build what's missing. Excited to kick off day 1 of Startup School 🚀
gm startup school
Since the start of the year, @OpenCode (YC W21) — an open source alternative to Claude Code and Codex that works with any model — exploded to 4.6 million weekly active users, 13 million monthly actives, and roughly $40M in annualized revenue. In this episode of The Lightcone, @harjtaggar, @snowmaker, and @sdianahu talk with Jay V (@jayair), OpenCode’s CEO, about what’s driving this wild growth, the Anthropic clampdown that inadvertently fueled it, and the almost 20 year founder journey that led him here. 00:44 — OpenCode’s Explosive Growth 01:16 — 20x Growth, 13M Users, and 7 Trillion Tokens 03:39 — The Anthropic Controversy That Changed Everything 05:43 — Bringing AI Coding Agents to the World 06:39 — When Open Source Models Became Good Enough 08:56 — What Millions of Developers Are Actually Using 13:31 — Why OpenCode Is Huge Outside the US 15:27 — Why Fortune 500 Companies Choose OpenCode 16:36 — The Economics of AI Tokens 20:02 — How Enterprises Are Using Coding Agents 22:58 — AI’s New Unit Economics 24:56 — Why Model Choice Matters 29:55 — The Product Decisions Behind OpenCode 34:21 — A 16-Year Overnight Success 41:16 — Why Jay Never Gave Up
Tune in: youtu.be/_O6x4ktK6JA Full Transcript: ycrootaccess.substack.com/p/how-opencode…
Dust (t.co/DrluimC3FZ) helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (@spolu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's @collinmathilde sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem
Tune in: youtu.be/DbBnd9PYob4
If you’re a scientist or researcher, you may be more prepared to start a company than you think. Before becoming founders, Arvind Veluvali, @sir_aymansaleh, and @yanovskyd all worked at NASA, where they learned to tackle hard problems, run experiments, and keep going when the outcome was uncertain.
You don’t need a business background. You just need the ambition to build. If you're making something people want, we'd love to hear from you. Apply to the Fall 2026 batch by July 27th: ycombinator.com/apply
Supabase (YC S20) is one of the fastest-growing developer platforms in the world, with more than 10 million developers using it. As AI coding agents like Claude Code, Lovable, Bolt, and Codex have taken off, they've become the backend powering millions of AI-generated apps. At Startup School Paris, @Supabase co-founder and CEO Paul Copplestone (@kiwicopple) sat down with YC's @dessaigne to discuss why AI coding agents have changed the way developers build software, how open source helped Supabase compete with the cloud giants, and why the next frontier is self-driving databases. 00:56 — Paul's Origin Story 02:13 — Two Failed Startups First 04:12 — Why Postgres? Why Open Source? 07:11 — The Pivot That Actually Worked 08:03 — Targeting YC Startups from Day One 09:45 — Fully Open Source — The Risky Choice 12:04 — Winning Developer Trust 13:33 — Time to Value: 8 Minutes → 5 Seconds 15:45 — The AI Second Life Begins 17:48 — Supabase for Platforms 19:09 — Everything Going Up Into the Right 20:44 — When Agents Became the Users 22:26 — Advice for Founders Building Dev Tools Today 23:47 — Why Open Source Won the LLM Era 25:32 — $500M Round, $10B Valuation 26:01 — What Keeps a Founder Grounded 28:31 — 360 People, 60 Countries, Zero Offices
Tune in: youtu.be/sG5aB79TE44
We're excited to announce that Michael Kratsios (@mkratsios47) is speaking at Startup School! He's the Director of the White House Office of Science and Technology Policy and the President's science advisor — the person shaping how the U.S. government thinks about and competes in AI. He previously served as the 4th CTO of the United States. t.co/2BomPbqPo2
Congrats to @meticulousgabe and t.co/SNKDkx4ql3 on their $15M Series A! AI agents are writing code faster than developers can review and test it, creating longer review cycles and more bugs. Meticulous fixes that by analyzing a codebase and simulating user flows before and after every code change, so developers can see the full impact in minutes rather than hours. In the past year they've grown ARR 5x, with customers including Notion, ElevenLabs, Dropbox, and Wiz. t.co/wMRgiknyJV
Congrats to @Splash_Robotics on their $4.2M raise! They're building fully autonomous drone boats for contested logistics and maritime surveillance. Similarly-capable vessels run $300-600k, while their Typhoon starts at $30k and takes just 8 hours to assemble. At RIMPAC this year, they successfully completed unmanned resupply missions to the USS Essex and USS Theodore Roosevelt. t.co/gmj58mnRNN
AI is moving into the physical world. We're excited about a new wave of startups rebuilding the systems that power the real world, from education and healthcare to defense, finance, infrastructure, and work itself. ycombinator.com/rfs
The Primer @amiklas The best education has always come from one-on-one tutoring, but that privilege has historically been reserved for very few people. AI could give every child a patient, adaptive tutor that grows with them over the years, beginning with reading, writing, and arithmetic, and eventually helping them learn to think and reason. For the first time, Neal Stephenson’s Primer is starting to feel possible.
AI for the Aging Population @maxkolysh By 2030, one in five Americans will be over 65, and there are nowhere near enough caregivers to support them. AI could help older adults live safely and independently through better voice interfaces, monitoring, robotics, and tools for family caregivers. It is one of the world’s largest and most underserved markets, and it is growing every day.
New Operating Systems for the Physical World @charliewarren Most of the global workforce does not sit at a desk, but the software used to manage physical work has barely changed in decades. The next operating systems will coordinate humans, robots, and AI agents together. The companies that build them could manage far more than software workflows. They could manage the labor itself.
