To date, finding a drug has been a process of guess & check… screening millions of molecules hoping one binds. @chaidiscovery is changing the paradigm… describe the molecule you want, and the model designs it. @joshim5 and @Mattmcpartlon1 took antibody hit rates from roughly 1 in 1,000 to 1 in 7. That's the jump from search to engineering. On this episode of Training Data, they get into where the field actually is, why 2024 was the moment to start, what drug discovery looks like when the computer does the design, and more. 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

Most startups celebrate their first couple million of revenue. @FactoryAI gave it back. They didn’t have to. They chose to. They decided that the product just wasn’t good enough yet, and they wanted to know that they’d really earned it. They wanted their customers to be obsessed. Two years later they shipped Droid CLI, and they’ve been ripping ever since. The product is fantastic and the team is even better - particularly having been hardened by their “two years in the desert”. @matanSF was one of our very first guests on Training Data, and we are delighted to welcome him back. 00:00 Introduction 01:37 Enterprise Lock In Fears 04:26 No Lock In Promise 05:24 Two Years Early 07:24 Refunding Revenue 13:49 Droid CLI Breakthrough 16:44 Harness Frontier Tactics 26:42 Natural Language Routing 27:32 Open Models Catch Up 29:26 Token Share Forecasts 31:02 Automating Low Leverage Work 32:36 Pricing Beyond Tokens 35:56 Software Factory Vision 43:43 AI Transformation Playbook 46:13 Async Agents And Optimism