Y Combinator

With Theranos and Elizabeth Holmes back in the conversation, we’re revisiting one of the most remarkable firsthand accounts of what actually happened inside the company. Tyler Shultz joined Theranos fresh out of Stanford as a true believer in its mission. His grandfather, former Secretary of State George Shultz, sat on the company’s board, and Elizabeth Holmes had become a mentor and even a presence at family gatherings. But once Tyler began working directly with Theranos’s technology, he discovered that the reality inside the lab looked very different from what the company was telling the world. On The Social Radars, Tyler sat down with Jessica Livingston and Carolynn Levy to tell the full story of how he went from employee to whistleblower: the failed blood tests and red flags he uncovered, the culture of fear that kept employees silent, his attempts to raise concerns internally and with regulators, and the extraordinary legal pressure he faced after speaking to The Wall Street Journal. This episode was originally recorded on December 4, 2024. 00:00 — Tyler Shultz’s Theranos Story 01:53 — Meeting Elizabeth Holmes 04:24 — The First Red Flags 08:03 — Theranos Launch Day 11:20 — Inside the Theranos Lab 16:33 — How Theranos Built a Culture of Fear 24:04 — What the Technology Couldn’t Do 27:43 — The Syphilis Test That Changed Everything 31:56 — Raising Concerns With Elizabeth 34:52 — Resigning From Theranos 51:53 — How Elizabeth Holmes Sold the Story 01:18:29 — Erika Cheung Speaks Up 01:21:17 — Trying to Convince George Shultz 01:36:43 — The Wall Street Journal Comes Calling 01:44:34 — Becoming a Source 01:46:41 — Theranos Goes Into Attack Mode 01:52:10 — The Ambush at His Grandfather’s House 02:16:05 — $500,000 in Legal Fees and Private Investigators 02:22:46 — Hitting the Lowest Point 02:30:41 — The Wall Street Journal Story Finally Breaks 02:37:01 — Going on the Record 02:45:20 — Life After Theranos

This week’s Paper Club is all about AI for biology. Sequencing costs have collapsed, biology is generating more data than researchers can make sense of, and the models are beginning to catch up. The opportunity now is to move from reading biological data to modeling and designing living systems. We brought together researchers and builders working across cancer genomics, virtual cells, genome language models, and biological design to show what that shift looks like in practice. We start with a single patient, asking how AI can build a research program around their cancer, then zoom out to whole cells and genomes. Along the way, we look inside a language model trained on DNA and explore how in-context learning emerged even though the model was never explicitly trained to do it. 00:00 - Is the Turing test solved? A live experiment 03:22 - Why now is the best time for AI Bio 08:26 - Alfredo Gonzalez: cancer as a data problem 29:50 - Yusuf Roohani: virtual cells and autonomous research 51:47 - Garyk Brixi: what a model learns from DNA 1:09:57 - Divya Nori: few-shot prompting for biological design

Replit was building toward the AI coding era years before it had a name. In 2024, the company shipped an agent that could take a request in plain English, work through errors, provision its own infrastructure, and keep going until the software actually worked. The model companies even reached out to ask how Replit had pulled it off. Co-founder and CEO Amjad Masad had been thinking about making programming radically more accessible since 2008, and Replit’s original pitch deck predicted AI-generated code before the Transformer paper was published. Today, that same technology is moving into banks, manufacturers, and other large companies, where Amjad says teams are replacing software projects once scoped at $10 million with $250,000 of Replit. On How to Build the Future, @paulg sits down with @amasad to talk about why @Replit was so well positioned when coding agents arrived, what comes after vibe coding, and his bigger vision for the “self-driving company,” where AI agents increasingly build and operate the software a company needs. 00:00 - "You guys basically invented vibe coding" 02:43 - Watching users copy-paste from ChatGPT 05:27 - The origins of Replit 08:00 - People underestimate their ability to predict the future 10:27 - Grieving the craft 12:52 - A culture of obsessing over users 15:13 - Why enterprises pick Replit 17:28 - The agent was just another user 20:17 - It barely worked, and users loved it 23:13 - $10 million projects now for $250,000 25:45 - Sandboxing for humans 28:14 - "The self-driving company" 30:34 - The way AI flows into companies 33:28 - What Steve Jobs knew before the iPad 35:59 - Killing the sacred cow 38:25 - The rest of the world is two years behind 41:15 - The compute brought on AI 43:46 - The person who assumes dramatic change

When Rolls-Royce pulled out as @boomsupersonic's engine partner, it looked like a potentially fatal setback. Instead, it pushed Boom to build its own engine, a decision that ultimately helped unlock boomless supersonic flight and opened up an entirely new business: gas turbines for the massive power demands of AI data centers. Boom founder and CEO @bscholl returns to The Social Radars with @jesslivingston and @cjoneslevy to tell the story of how that crisis became one of the company’s biggest opportunities. They talk about why building the engine in-house changed the airplane itself, how Boom realized the same technology could generate power for data centers, and why the new turbine business could help fund the development of its supersonic airliner. 00:00 — Rolls-Royce Walks Away 02:39 — The Breakup Goes Public 07:39 — Deciding to Build the Engine In-House 09:01 — The Brian Chesky Advice That Changed Everything 10:25 — Why Building the Engine Was the Right Call 11:06 — Unlocking Boomless Supersonic Flight 13:35 — The Problem of Funding a Supersonic Airliner 14:37 — The AI Power Shortage Creates an Opportunity 16:23 — Turning a Jet Engine Into a Gas Turbine 18:26 — Why Hard Tech Is So Difficult to Fund 21:20 — From Paycheck to Paycheck to a New Business 22:26 — Why America Stopped Building Power 25:56 — The Aerospace Industry Stopped Innovating 27:21 — Designing the Engine and Airplane Together 32:55 — Rethinking the Passenger Experience 39:03 — Rebuilding America’s Industrial Base

Tractian (W21) is building physical AI for asset-heavy industries, helping factories and industrial operators monitor critical equipment, predict failures, and prevent costly downtime. Their platform combines proprietary sensors, software, and AI to monitor hundreds of thousands of assets in real time. They’ve now crossed $100 million in ARR, growing 90% year over year, and recently raised a $130M Series D at a $1.4B valuation. In this fireside, co-founder and CEO @igmarinelli joins YC’s @harjtaggar to share how Tractian grew from a problem he first saw through his father’s work as a plant manager into a platform used by some of the world’s largest industrial companies. They dig into how @Tractian won its first enterprise customers, why AI is changing the economics of industrial maintenance, and how the company is moving from monitoring machines toward a future where factories can increasingly operate themselves. 00:05 — What Tractian Does 02:20 — How Tractian Monitors Machines 05:38 — Why the MVP Had to Be “Awesome” 09:13 — The Problem Igor Grew Up With 11:07 — 88 Factory Visits in Year One 15:25 — Winning the First Customer 18:15 — Selling Without Social Proof 20:24 — How to Structure a Paid Pilot 22:14 — How AI Changed Tractian 25:55 — Why Smaller Models Can Win 31:09 — AI and the Industrial Workforce 35:04 — Robotics in the Factory 38:18 — From Monitoring to Autopilot 41:32 — Fundraising at $1.4B 46:58 — The Future of Industrial Work

Weave (W25) helps engineering teams understand whether their AI coding tools are actually making them more productive. Its models measure the work humans and agents produce and compare that output with AI spending. The company recently raised a $13.5M Series A led by Standard Capital and launched Weave Router, which selects models for different coding tasks. In this fireside, @WorkWeave co-founders @adambcohen93 and @AndrewChurchiII join YC's @snowmaker to share how the company evolved alongside the rapid rise of AI coding, from measuring the impact of tools like Cursor to helping teams build software factories that can work autonomously. They explain how the best engineering teams are learning to measure the real output of their agents, spread the habits of their strongest AI users, and optimize for a future where much more software is written without a human at the keyboard. 00:00 — Optimizing AI Coding With Weave 04:02 — Getting More From Your AI Budget 08:39 — Measuring Work Beyond Lines of Code 12:30 — From AI Adoption to Optimization 16:47 — Measuring AI ROI and Coaching Engineers 20:16 — Building Software Factories 25:38 — The Future of Model Routing

Google, Anthropic, and Databricks were all started by founders with PhDs. But they’re exceptions to the rule. Many of the instincts that help researchers succeed in academia can often work against them when building a startup. YC Visiting Partner @FrancoisChauba1 knows both worlds. He’s currently a PhD student, previously did research in Fei-Fei Li’s lab, and founded and ran Focal Systems (W16). In this episode of Startup School, Francois breaks down 10 mindset shifts researchers need to make to become formidable founders, from learning to thrive without grades or deadlines to distinguishing interesting problems from important ones, moving faster with imperfect information, and starting with what customers actually want. 00:53 - Vanity metrics vs. what people want 01:45 - Interesting vs. important 02:27 - Novelty vs. plagiarism 03:54 - There is no finish line 04:32 - Get on a plane and talk to people 05:53 - Hyperbole is okay 06:28 - The 70% rule 07:11 - You can't delegate the hard stuff 07:52 - Become a capitalist 08:16 - Nobody cares how smart you are 09:04 - Get out of your office

Blacksmith (W24) is building a faster cloud for continuous integration (CI), helping developers test and ship code as AI coding tools generate more of it. As software production accelerates, testing and validation are becoming the new bottleneck. That shift has helped @useblacksmith grow 20x over the last year, and the company recently raised a $66 million Series C at a $1 billion valuation. The founders, @adijayaprakash, @aayush_shah15, and @adityamaru27, started as outsiders to DevOps and built their first infrastructure using the same high-performance CPUs found in gaming PCs, renting servers originally designed for Minecraft and Call of Duty. Today, they run a much larger fleet with a team of just 30 people, helping companies cut CI waits from minutes to seconds. They share how they found the idea, won their first 200 customers, and why faster testing will become even more important as agents write more of the world’s code. 00:00 — Making CI Faster With Gaming CPUs 03:46 — Winning Over Investors and Developers 06:46 — Scaling CI for the AI Coding Era

A lot of what makes a great engineer also makes a great founder. Before starting @layersbio and @BootLoopAI, Shivani Patel and Chris Markus worked at SpaceX, where they learned to take ideas from zero to one, solve hard problems, and own the outcome.

Your first enterprise customer is the hardest one to get. At YC, they come to you. Last week we launched Early Access Network: 30 companies, 100+ enterprise leaders, one room, months before Demo Day. Now part of every YC batch. Apply for W27 by Nov 2: ycombinator.com/apply

Supabase has become one of the default backends for the AI coding era. The open source Postgres platform now has 13 million users, spins up more than a million databases every week, and roughly 70% of its new activity is coming from agents. As coding shifts from developers writing software by hand to agents building on their behalf, Supabase has been rebuilding itself around that new user. Supabase co-founder and CEO Paul Copplestone joins Garry, Harj, and Jared on the Lightcone to talk about what happens when everyone can become a builder. They dive into why personal software may become as common as personal documents, how to design products that agents actually want to use, and why knowing what to build may matter more than knowing how to build it. 01:18 - Growth and 4x Revenue 02:35 - Three Eras of Supabase 04:34 - “Builders” vs. Traditional Devs 08:42 - Personalized Apps 12:12 - Personal Databases & Data Ownership 15:56 - Scaling to Billions 17:58 - AI Adoption At Supabase 19:58 - Lessons Learned Along The Way 27:29 - Building For Personal Agents, Dogfooding 30:01 - Agent-First Design and Evals 38:42 - The Future Of Coding 42:36 - Opening Up More Entrepreneurship

In collaboration with @olam_labs, we ran a series of experiments letting agent swarms compete in Minecraft and Mindustry. We wanted to see what kinds of agent dynamics would emerge when they shared a world over long periods of time. We found civil bureaucracy, thousands of telegraphic A2A messages, and OpenAI models retrieving the game's source code to beat Claude. More on our findings below:

Broccoli (W22) is building an AI front office for home services businesses, answering calls, following up with leads, and booking appointments across voice and text. Today, they serve more than 500 customers, are booking jobs at a run rate of over $1 billion a year, and expect to end the year at more than $60 million in ARR, up from zero just two years ago, after recently raising a $30 million Series B. In this fireside, @call_broccoli's founders @abhishekjain25 and @adityaketkar join YC's @gustaf to share the long path to finding that business, from an earlier EV startup that never found product-market fit to visiting 140 contractors across the Bay Area to understand their biggest problems firsthand. They explain how embedding inside a plumbing company led to the first version of Broccoli, why answering missed calls became the wedge, and how AI could eventually reshape the work of technicians themselves. 00:00 — What Broccoli Does 01:30 — The Long Road to YC 04:22 — Building Telematica in the Wrong Market 07:11 — Why the First Idea Didn’t Work 09:27 — Finding the Right Customer 11:13 — Going Door to Door 14:49 — Discovering the Real Problem 17:53 — Getting the First Customers 19:11 — The First Version of Broccoli 20:50 — What Product-Market Fit Felt Like 23:09 — Scaling to $60M ARR 26:19 — How AI Will Change the Trades