Sarah Guo – PJFP.com

Mark Zuckerberg, Priscilla Chan, and AI researcher Alex Rives sat down with the No Priors podcast to explain why CZI Biohub became the primary focus of their philanthropy, why they committed $500 million to a virtual biology initiative, and why they are giving the resulting AI models away as open source instead of building a company. The conversation moves from a goal that Nobel laureates once laughed at, curing, preventing, and managing all disease by the end of the century, to a concrete technical strategy: build world models of biology layer by layer, from proteins to cells to whole systems, and put them in every scientist’s hands.

TLDW

This is the clearest public articulation yet of how the Chan Zuckerberg Initiative thinks about AI and biology. The throughline starts a decade ago when Zuckerberg and Chan asked scientists how to cure all disease and learned the real bottleneck was tooling, siloed labs, and unshared knowledge, not a lack of ambition. That insight produced the Human Cell Atlas, the CELLxGENE annotation tool, and a corpus of single-cell transcriptomics that large language models could finally make sense of. Now Biohub couples a frontier AI lab with frontier wet-lab biology under one roof across San Francisco, New York, and Chicago, organized around the virtual biology initiative and the long-term goal of a virtual cell. Alex Rives, the AI researcher behind the ESM protein language models, walks through their newly released ESM-based world model of protein biology: trained on billions of protein sequences, it predicts atomic-resolution structures blazingly fast, folded over 1.1 billion proteins, designs novel proteins and single-chain antibodies as an emergent property, and found nanomolar binders in a single 96-well plate. The discussion covers mechanistic interpretability as a way to extract genuinely new biological knowledge, personalized medicine driven by understanding the chain from gene variant to protein to disease, predicting off-target toxicity before human trials, rare-disease patient organizing, the baby KJ CRISPR case, biosafety tradeoffs of open source, talent and why frontier biology plus frontier AI is a recruiting moat, and what success looks like five years out.

Thoughts

The most important claim in this conversation is also the easiest to miss because it is delivered casually: protein design is an emergent property of a model that was never asked to design proteins. Rives is explicit that they did not build a model for antibodies and did not build a model to bind a particular target. They built a model that understands proteins, trained on raw sequence with a next-token objective, and protein design, structure prediction, and antibody generation fell out of it. That is the language-model bet transplanted into biology, and the fact that it produced nanomolar binders, the threshold for actual therapeutic activity, in a single 96-well plate rather than a high-throughput screen of millions is the kind of result that quietly resets what a small team can attempt. If that generalizes, the binding curve for “design a molecule” bends the same way the cost curve for “write working code” did.

What makes the strategy coherent, rather than just a well-funded AI lab, is the insistence that the wet lab and the AI lab are a single effort. Most of biology’s useful data does not exist on the internet the way human language does. You cannot pay a factory to produce it. Someone has to invent the cellular engineering in New York, the inflammation-sensing devices in Chicago, the translucent-zebrafish imaging, and that is the actual product of frontier biology: new instruments that generate data nobody has ever seen, which in turn make new classes of models possible. This is the part venture-backed competitors will struggle to replicate, because it requires patience measured in 10 to 15 year horizons and a willingness to spend on data generation that has no business model attached. Zuckerberg is almost dismissive about it, noting they could probably run it as a business but that not having to think about monetization is strategically simplifying. The nonprofit structure is not charity window-dressing here. It is what lets them release the models as an open discovery engine and harness the entire academic and biotech field rather than competing with it.

The mechanistic interpretability thread deserves more attention than it will get. Interpretability has mostly been a safety and alignment story for language models, a way to peer inside the black box and check that the representations match our understanding of the world. Rives flips it: the protein models have been trained on both known and unknown biology, billions of sequences including proteins we understand nothing about, and they are building representations that connect the unknown proteins to the known ones through an underlying structural grammar. The promise is that interpretability becomes a discovery tool, not just an audit tool. You open the box and find biology the field has not characterized yet, the mechanism of action for a treatment, a system in the body nobody mapped. That is a fundamentally more optimistic use of the same toolkit, and it is the part of the launch Sarah Guo and Elad Gil both flag as the most interesting.

Chan’s framing of personalized medicine is worth sitting with because it reframes the entire goal away from “cure disease X.” She wants to treat the individual as an individual: understand this person’s genetics, their risk profile, the mechanistic chain from a specific gene variant through a protein to a disease process, and then design a drug bespoke to them. The current reality she describes, sitting in PubMed reading a paper’s supplement asking “am I represented in this cohort,” guessing whether a drug that kind of impacts a pathway that is probably implicated might do something, is a brutal and accurate picture of how non-standard cases are actually handled today. The vision is generalizable tools delivering personalized answers, which is the same put-the-tool-in-the-individual’s-hands philosophy Zuckerberg applies to open-source AI and, by his own analogy, to social media. Whether you find that analogy reassuring or not, the consistency of the worldview is real: they genuinely do not believe in a central super-intelligence solving science, and the whole architecture follows from that.

The honest gap they name is the clinic. Chan is candid that the science will start moving fast but that translating to patients requires changing how clinical research itself works, and that part is still shaping up. The most interesting near-term lever is not a virtual FDA trial but the recruitment and economics flip for rare disease: patient groups self-organizing registries, biobanks, and natural-history studies, compressing timelines from decades to a handful of years, paired with models that lower the cost of generating a candidate. The baby KJ case, a custom CRISPR therapeutic to edit a single mutation, delivered to liver cells specifically because that target was deliverable, is the proof of concept for why disease selection and delivery creativity matter as much as the molecule. The molecule is becoming the cheap part. The rest of the chain is where the next decade of work actually sits.

Key Takeaways

CZI Biohub is now the primary philanthropic focus of the Chan Zuckerberg Initiative, a shift the team formalized in the past year.
They committed $500 million to the virtual biology initiative, the unifying theme across the Biohubs.
The original goal, set roughly 10 years ago, was to cure, prevent, and manage all disease by the end of the century. Zuckerberg now thinks “end of the century” is too conservative.
Nobel Prize winning scientists initially laughed at the all-disease ambition. When pressed for why it was impossible, the real answers were silos, locked-up unpublished information, and the inability to build shared tools.
The recurring example: a postdoc builds a great tool, it lives on their computer, they graduate, and the tool is gone. Shared, durable tooling was the missing layer.
CZI is explicit that they are not the ones who will cure diseases. Their role is building tools that accelerate the entire scientific field so the field collectively cures them.
The first request for application was single-cell sequencing, funding methods so scientists could share how to do it.
That work led to funding the Human Cell Atlas, now one of the largest databases of single-cell transcriptomics.
They built CELLxGENE, a simple annotation tool, around which a community formed and contributed data CZI had nothing to do with creating. It is now a corpus underpinning many transcriptomic models.
Critics called the data gathering “stamp collecting.” The arrival of large language models, which can make sense of large amounts of data, answered that critique.
The ambition is to move biology from a discovery-based science to an engineering-based science, systematically understanding how living cells work and why things go wrong.
Biohub couples a frontier AI lab with a frontier biology effort. Unlike language models, biology lacks abundant internet-scale data, so new science is required to generate the data the models need.
The Biohubs are specialized: New York focuses on cellular engineering, Chicago builds devices to measure things like inflammation, plus imaging work and translucent-zebrafish development studies.
Alex Rives, who built the ESM protein language models and founded EvolutionaryScale after working at Meta FAIR, now leads the AI effort. The team raised venture capital before joining CZI’s nonprofit structure.
The strategy is hierarchical: model proteins first, then cells, then whole systems, because you cannot understand cells without understanding protein interactions.
They collect data strategically to bridge across the hierarchy, for example spatial transcriptomics showing where RNA localizes within a cell, and sensors that observe cell-to-cell communication.
The newly released ESM-based model is a world model of protein biology, trained on billions of protein sequences, predicting atomic-resolution structure extremely fast at a Pareto-optimal frontier of speed and accuracy.
They folded over 1.1 billion proteins and predicted their structures, identifying connecting features through mechanistic interpretability.
The model hits state of the art on structure prediction benchmarks, especially protein-protein and protein-antibody interactions, which are critical for therapeutic design.
Protein and antibody design are emergent properties. They designed a model to understand proteins, not to bind any specific target, and design capability fell out of it.
In one experiment, they selected from hundreds of thousands of digital trajectories, synthesized 96 proteins in a single well plate, and found nanomolar binders, the threshold for therapeutic activity.
Results were validated with the Biohub’s cryo-EM microscopes and structural biology center, confirming function and atomic-resolution binding interfaces.
Mechanistic interpretability is reframed as a discovery tool: open the black box to find biology nobody has characterized, not just to audit the model.
Chan’s vision of personalized medicine: understand a person’s genetics, the mechanistic chain from gene variant to protein to disease, then design a bespoke drug and intervene.
A comprehensive model of how cells work could predict off-target effects, like a receptor on kidney cells causing renal toxicity, before human trials.
They study systems rather than individual diseases. Inflammation is a major Chicago focus because it connects to many diseases.
A typical drug trial runs about 15 years and $1.5 billion. Only roughly $50 million is the molecule and preclinical work. The other $1.45 billion is drug development, much of it gated on regulation, recruitment, and failures from toxicity or absorption.
The baby KJ case at CHOP delivered a custom CRISPR therapeutic to edit a single mutation, chosen carefully because his liver cells were a deliverable target.
CZI’s “Rare As One” program supports rare-disease patient groups self-organizing registries, biobanks, and even their own clinical trials, compressing gene-therapy timelines from decades to 3 to 5 years.
Letting people opt in to frontier trials, while preserving historical vetting for the general population, is named as a key shift that could accelerate biology.
The open-source philosophy mirrors Zuckerberg’s broader ethos: empower individuals with tools rather than centralizing power in a few institutions or a single super-intelligence.
Biosafety is acknowledged as a real consideration that open-source biology will need to balance and handle carefully.
On talent: AI researchers could join any frontier lab, but no other organization pairs frontier biology with frontier AI, which is the recruiting moat.
You do not need a huge team. Zuckerberg argues real AI progress can come from a strong group of a dozen or a couple dozen people.
Researchers have been connecting the released model to agentic systems to automate the entire protein design process.
The next big challenge is the virtual cell: a system that models the proteomic, genetic, and transcriptomic layers and connects them to phenotype, generalizing to interventions it was never trained on.
Like every lab, Biohub is compute and data constrained, constantly deciding whether to double down on proteins or push further into cellular work.
Five-year success: a hierarchical set of world models of biology and doing the highest-quality, uniquely contributive work in the world, a setup the team believes no other organization has.
The biggest update of the past year: formalizing Biohub as the philanthropy’s core, and flipping leadership from biologists interested in technology to an AI researcher with a biology background.
Zuckerberg’s read on the broader industry: the exponential curve is on track and still accelerating, which validates making a very big long-term investment.

Detailed Summary

From “cure all disease” to a tooling problem

The origin story is a decade old. Zuckerberg and Chan wanted to build an organization that could cure, prevent, and manage all disease by the end of the century, and a series of meetings with famous, Nobel Prize winning scientists produced laughter rather than encouragement. Instead of retreating, they kept asking why it was impossible. The answers, once scientists relented, were not about biology being too hard. They were about how science is organized: researchers work in silos, published information gets locked up for long periods, and there is no good way to build and share durable tools. The image that stuck was a postdoc building an excellent tool that lives on a single computer and vanishes when that person graduates. The bottleneck was infrastructure and shared knowledge, and that is where CZI decided it could contribute.

The path from single-cell sequencing to a world model

The original Biohub model brought engineers and scientists together across universities for long-term tool development, and it worked. CZI’s first request for application targeted single-cell sequencing, funding the methods so scientists could share how to read the RNA transcribed in individual cells. That seeded the Human Cell Atlas, now one of the largest single-cell transcriptomics databases. When annotation became a bottleneck, CZI built CELLxGENE, a simple annotation tool, and a community formed around it and contributed data CZI never funded. Critics dismissed it as stamp collecting, gathering bits of data without extracting wisdom. Then large language models arrived and demonstrated they could make sense of exactly that kind of large-scale data, and Chan describes the delight of realizing the missing engine had appeared.

Frontier AI married to frontier biology

The unifying theme is the virtual biology initiative, and the structural insight is that the AI effort and the wet-lab effort are a single integrated organization, not two collaborating ones. Biology lacks the internet-scale data that language models enjoy. You cannot buy the data from a factory. So Biohub invents the science that generates it: cellular engineering in New York to record what happens inside the body, devices in Chicago to measure inflammation, imaging to visualize the previously invisible, and translucent zebrafish to watch development unfold across cells as the brain forms. Each new instrument creates a new dataset, which enables a new class of model. Rives, who built the ESM models and founded EvolutionaryScale before joining, frames this as the start of a new era of science, where systems that predict the next token can learn world models of biology from the data, provided you build at the right scale with the right people.

Building biology hierarchically

The team is deliberate that each layer of biology is qualitatively different and must be built up in order. You cannot jump to cells without understanding protein interactions, and you cannot model the immune system without first understanding cells. So the approach starts with the building blocks, the proteins, and ladders upward. The advantage of a single integrated effort is the ability to gather data that connects the hierarchy: spatial transcriptomics that show where RNA localizes inside a cell, sensors that capture cell-to-cell communication, developmental imaging in zebrafish. That connective tissue is what lets the modeling generalize across levels. The interviewer, a former wet-lab biologist with a PhD, notes that the reductionist and systems camps of biology historically never worked together deeply, and that bridging them is one of the genuinely novel things about the effort.

The ESM-based protein world model

The launch at the center of the conversation, roughly a week old at recording, is an open system for scientific discovery in protein biology: a language-model-based world model trained on billions of protein sequences. It learns emergent representations of protein biology and predicts atomic-resolution structure at blazing speed, sitting on a Pareto-optimal frontier of speed and accuracy. They folded over 1.1 billion proteins and used mechanistic interpretability to identify features connecting them. It reaches state of the art across structure-prediction benchmarks, with particular strength on protein-protein and protein-antibody interactions that matter for therapeutics. The headline result: they used the model to design proteins and single-chain antibodies digitally, selected from hundreds of thousands of trajectories, synthesized just 96 in a single well plate, and found nanomolar binders, replacing high-throughput screens of millions of antibodies. Validation came from the Biohub’s cryo-EM structural biology center, confirming both function and the atomic-resolution binding interfaces.

Interpretability as discovery, and personalized medicine

Rives reframes mechanistic interpretability, usually aimed at language models, as a way to extract new biological knowledge. The protein models are trained on both known and unknown biology and develop representations that connect uncharacterized proteins to understood ones through an underlying structural grammar. Opening that black box could reveal systems in the body or mechanisms of action for treatments that the field has never mapped. Chan connects this to a personalized-medicine vision: understand an individual’s genetics and the mechanistic chain from gene variant to protein to disease, then design a bespoke intervention. She contrasts it with today’s reality of reading PubMed supplements and guessing whether you are represented in a study cohort. For some diseases, simply knowing which gene variants cause disease is already empowering. For others, the chain is understood and the missing piece is the ability to change a protein’s function, which is where designed proteins could actually cure.

Drug development, off-target effects, and rare disease

The interviewers press on translation, noting a typical trial runs 15 years and $1.5 billion, with only about $50 million in the molecule and preclinical work and the rest in development gated on regulation, recruitment, toxicity, and absorption failures. Chan’s hope is that comprehensive cell models predict off-target effects, like an unanticipated receptor on kidney cells causing renal toxicity, before human trials. They study systems such as inflammation and the immune system rather than chasing individual diseases. The baby KJ case at CHOP, a custom CRISPR therapeutic editing a single mutation delivered to liver cells, illustrates how careful disease and delivery selection unlocks first applications. The “Rare As One” program shows rare-disease patient groups self-organizing registries, biobanks, and trials, compressing timelines from decades to a few years, and the molecule becoming cheap flips the economics of the long tail of niche diseases.

Open source, talent, and the five-year view

Zuckerberg ties the open-source posture to a consistent worldview: empower individuals with tools rather than centralizing intelligence in a few institutions. He does not believe in a single super-intelligence solving all of science, and sees decentralization, the same instinct behind giving people a voice, as how progress is historically made, with biosafety as a real tradeoff to manage. On talent, the pitch is that frontier biology attached to frontier AI is work you cannot do anywhere else, and that meaningful progress needs only a dozen or two dozen strong people, not thousands. Researchers are already wiring the model into agentic systems to automate design. The next frontier is the virtual cell, modeling proteomic, genetic, and transcriptomic layers and connecting them to phenotype with enough generality to answer untrained questions. Five years out, success is a hierarchical set of world models and doing uniquely high-quality work, with Chan adding that the teams are now “arms linked,” directed and interlocked rather than merely moving in the same direction.

Notable Quotes

“We didn’t design a model for antibodies. We didn’t design a model to be able to bind one particular target. We just designed a model that could understand proteins.”
Alex Rives, on protein design emerging from a general model

“The theory isn’t that we’re going to cure the diseases. We’re not. It’s that we want to help accelerate the pace of progress for the whole scientific field.”
Mark Zuckerberg, on why CZI builds tools rather than cures

“My goal is to be able to treat the individual as an individual, understand the mechanisms and be able to intervene.”
Priscilla Chan, on the vision for personalized medicine

“It’s not just like there’s some factory somewhere that you can pay to produce the data. You actually need to invent new novel scientific approaches.”
Mark Zuckerberg, on why frontier biology has to generate its own data

“If we could design a protein to actually change the physiology, then we can actually cure someone.”
Priscilla Chan, on the payoff of protein design

“You open up the black box and you can actually understand the biology that the model is representing.”
Alex Rives, on mechanistic interpretability as a discovery tool

“We don’t believe in this like very centralized future where there should be a small number of institutions that basically are advancing all this stuff.”
Mark Zuckerberg, on the open-source ethos behind Biohub

“Before we had amazing teams moving generally in the same direction. But now we are arms linked moving together.”
Priscilla Chan, on how the Biohub teams now operate under Alex Rives

Watch the full conversation with Mark Zuckerberg, Priscilla Chan, and Alex Rives on the No Priors podcast here.

Tag: Sarah Guo

Mark Zuckerberg, Priscilla Chan, and Alex Rives on CZI Biohub, Open-Source AI, and Building World Models of Biology to Cure All Disease