Claim◆Audio · 7:33 — 8:25

The vision is to move biology from a discovery-based science to an engineering-based science by using AI to systematically understand how living cells work and why things go wrong.

Priscilla Chan explains that the arrival of large language models provided the missing piece: the ability to make sense of massive biological datasets. This opened the possibility of understanding biology systematically — moving from stamp-collecting data to engineering-based intervention. ✦ AI generated

Priscilla Chan · No Priors · 2026-06-10 · original ↗

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plays this moment only · 7:33 — 8:25

But still, there are always critiques. Like, this is just stamp collecting. Like, you're just gathering bits of knowledge, sorry, bits of data. And we're not going to be able to pull scientific knowledge and wisdom and insights out of. And we're like, well, we didn't have an answer for a while. And then imagine our delight when large language models became a huge topic of conversation that could make sense of large amounts of data. And I just, for me, it was like, what if we could actually understand how biology worked, move it from a discovery-based science to an engineering-based science, where we could systematically understand how living beings, living cells worked and be able to understand why things go wrong. And so when we saw that moment, we're like, this is it. Something really big could happen here.

verbatim transcript · starts at 7:33

◉ Priscilla Chan

Around this claim

Evidence · 1

The goal of curing, preventing, and managing all disease by the end of the century was initially met with ridicule from scientists, but now that timeline may be too conservative due to the convergence of AI and biological data.Priscilla Chan · No Priors · conf 85%

In practice · 2

Biohub's unique approach is integrating frontier AI and frontier biology as a single unified effort, building information hierarchically from proteins to cells to whole systems, with data collection designed to bridge across levels.Mark Zuckerberg · No Priors · conf 80%Mechanistic interpretability of protein language models can reveal unknown biology by connecting dots between known and unknown proteins through the model's learned representation space, potentially discovering new mechanisms of disease and treatment.Alex Rives · No Priors · conf 75%

This moment responds to

extends → The ultimate goal is to treat the individual as an individual — understand the full mechanistic chain from a person's genetics through proteins to disease, and design bespoke interventions, rather than relying on population-level statistical guesses.Priscilla Chan · No Priors explains mechanism → The core problem in science is that researchers work in silos, don't share information, and build tools that disappear when a postdoc graduates, preventing the creation of shared tools and knowledge bases to accelerate progress.Priscilla Chan · No Priors