biohub

Zuckerberg’s Biohub Wants to Cure All Disease. Here’s What’s Missing Can AI help cure all disease? Alex Rives (@AlexRives), Head of Science at @Biohub, says the biggest holdup is likely not the AI but rather the data: we have barely measured how cells behave. In this conversation, he explains the $1.8 billion effort to generate the data needed to build a "virtual cell." We talk about how a language model trained on proteins started revealing biology on its own, what a virtual cell actually is, and why so much of this money goes to measurement. We also get into zero-shot protein design, what could shorten the 10-year path to a new drug, what custom medicines for a single patient would require, why Alex says biorisk today comes down to the lab rather than the AI, and the idea of a universal immune system. Biohub is a nonprofit founded by Mark Zuckerberg (@finkd) and Priscilla Chan, with a mission to cure or prevent all disease. It releases its main protein models openly, including the ESM family that Alex started at @Meta's fundamental AI research lab. CHAPTERS 0:00 Preview 0:42 Introducing Alex Rives and Biohub 1:48 The $1.8 billion announcement 2:52 Alex on the effort to map the cell 3:37 What is a virtual cell? 4:58 The data gap: why proteins are ahead of cells 7:34 ESM: the first large language model for proteins 9:43 Proteins explained: a 20-letter alphabet 10:30 ESM vs. AlphaFold 13:14 How ESM learns biology by filling in the blanks 14:39 From proteins to cells 15:23 What data do we need to generate? 16:55 Transcriptomes and spatial transcriptomics explained 17:44 Why this needs wet labs 18:56 Will AI replace researchers? 21:01 Why drugs take 10 years, and how to shorten that 23:01 The future of custom medicines 23:40 Zero-shot protein design 25:20 A custom therapy for every patient 26:57 From discovery science to engineering science 29:05 What's missing for custom medicines? 30:22 N-of-one therapies and baby KJ 31:46 Do we need more wet labs? 33:42 Why wasn't anyone generating this data? 38:07 How serious is AI biorisk? 39:51 Gene synthesis screening explained 40:51 A universal immune system 43:37 A call to action KEY TAKEAWAYS - Alex calls the core problem a "fundamental data gap." AI for proteins was built on about half a century of data, and nothing comparable exists yet for cells. - A virtual cell is a model that predicts what a cell will do if you change something. The test is whether its predictions match what happens in a real experiment. - ESM learned by guessing hidden amino acids. Nobody told it about structure or function, but that information emerged in what it learned. - Zero-shot design narrows hundreds of thousands of computer-generated candidates to about 100 to test in the lab. Alex describes it as 24 hours of computation and seven days of lab work. - Custom medicines aren't here yet. Therapies still have to be safe and developable, and the infrastructure and regulatory rules weren't built for a drug only one person will take. - On biorisk, Alex says "today it's not a risk" and that the key barrier is the experimental laboratory, not AI. He says we need gene synthesis screening, and that defensive applications need to stay ahead. - Most of this isn't built yet. Alex expects a first dataset in about a year and useful models within five, and those are expectations rather than promises.