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Sequoia CapitalChai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem
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Episode Details
EPISODE INFO
- Released
- August 4, 2026
- Duration
- 47m
- Channel
- Sequoia Capital
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter lesson: scale data, models, and compute, and the model can learn what a hand-built pipeline simply couldn't capture. The results are concrete: Chai-2 pushed de novo antibody design from a sub 0.1% hit rate to 16%, turning a needle-in-a-haystack search into something more like designing a key to fit a lock. Josh argues, counterintuitively, that biology is more verifiable than code, and explains why the goal should be more lab experiments, not fewer. Their bet: a design suite that collapses drug discovery from nine months to nine days, and arms the pharma industry rather than competing with it. Hosted by Pat Grady and Sonali Singh, Sequoia Capital 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
SPEAKERS
Pat Grady
hostPartner at Sequoia Capital and podcast host/interviewer.
Sonali Singh
hostPartner at Sequoia Capital and podcast host/interviewer.
EPISODE SUMMARY
In this episode of Sequoia Capital, featuring Pat Grady and Sonali Singh, Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem explores Chai Discovery argues AI drug design improves by scaling and simplicity Chai Discovery describes its mission as making drug development look more like engineering by moving from “discovery” via massive screening toward “design” where desired properties and constraints can be specified upfront and generated by models.
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