Dr. Priscilla Chan

1. Key Themes

Biology Lacks Its "Periodic Table" — and That's the Core Problem to Solve

The foundational insight driving CZI's entire strategy is that biology has no shared, standardized reference framework equivalent to the periodic table of elements. This gap means researchers can't build on each other's work efficiently, and it's what CZI set out to fix before writing a single grant check.

"It's kind of this crazy thing that we're, you know, here in 2025 and there's not the kind of periodic table of elements equivalent for biology. That was sort of a lot of the inspiration of it — how do we both through work that we're going to do in the Biohub and through other grants be able to pull together and standardize a format where you can have all this data?" 00:00:00

Tools Over Therapies: The Highest-Leverage Philanthropic Bet in Science

Rather than funding individual labs or therapies, CZI chose to fund shared infrastructure — the tools, data sets, and platforms that every researcher in the world can use. This compounds across the entire field rather than producing isolated results. They also identified that the traditional NIH grant structure structurally under-funds tools because grants are small, short-term, and discovery-oriented — not infrastructure-oriented.

"The development of these kind of new types of tools, whether it's imaging or building now a lot of AI things like virtual cell models, are longer-term, oftentimes more expensive to develop. So, think about like on the order of maybe $100 million to a billion dollars over a 10- to 15-year period. And then you try to unlock those tools and give them to the scientific community to accelerate the pace." 00:04:37

The 10-to-15 Year Grand Challenge as the Right Unit of Ambitious Science

CZI identified a strategic "Goldilocks zone" for scientific ambition — not annual grant cycles, and not century-scale visions, but 10-to-15 year grand challenges where a credible path exists, meaningful risk remains, and a team can sustain effort together. This framing mirrors the venture-backed company lifecycle and makes ambition tractable.

"When you look at the grand challenges on the 10 to 15 year time horizon, it needs to be like, when you look at it, you're like, I see a path. Right. Not everything needs to be solved for us to take it on. In fact, if everything's solved, then that feels like that should just go... we have some risk appetite." 00:09:25

Virtual Cell Models as the Next Great Scientific Infrastructure

CZI is building hierarchical AI models of biology — from proteins to cells to tissues to virtual immune systems — that will let scientists run experiments in silico before committing expensive wet lab resources. This is framed explicitly as the "new model organism," analogous to the fruit fly but with human fidelity.

"The promise of being able to do virtual biology using a virtual cell model is you can actually take on riskier ideas... if you had a virtual cell model where you could simulate really high quality biology, you could actually then start testing and tinkering on the computational side and like ask riskier questions, things that would have been expensive and costly in terms of time and resources to do in the lab." 00:21:35

The Flywheel: Biology Data → AI Models → Better Biology Data

The strategic unification of CZI's biohubs under one operating structure is designed to close a loop: biological experiments generate novel data, AI models identify gaps, and the biology team designs the next experiment to fill those gaps. This flywheel doesn't work if the teams are separate.

"The model is looking like it has some gaps and blind spots in this area. Okay, who do we talk to? How do we build the next data set? And, you know, we're seeing this in the lab. Like the metadata is going to be so rich that we can feed back into the way that we do this modeling. And so if we can close that loop... it's going to be incredibly powerful." 00:30:53

Cell by Gene: An Accidental Platform Built From an Annotation Bottleneck

Cell by Gene — now the world's leading single-cell data platform — was not planned as a platform. It was built to solve a narrow workflow bottleneck (annotation speed) but became a standard by accident of adoption. 75% of the data in it was contributed by the broader community, not CZI.

"Cell by Gene was an annotation tool. That's the original source of this. So we built the annotation tool to make it easy for people who are doing single cell science to be able to annotate the data... because everyone started using the same annotation tool, everyone was standardized then on the same data formats. And then there started being a community around the tool and they wanted to share back and build the atlas." 00:16:40

AI + Biology Together, Not Separately — With AI in the Lead Role

CZI's model deliberately pairs frontier biology with frontier AI under the same roof, and notably, the new science program leader (Alex Rives of Evolutionary Scale) is an AI person who understands biology — not a biologist who dabbles in AI. This inversion signals where CZI thinks the leverage is.

"It's like an AI person who understands biology is running it rather than a biologist who has some understanding of AI. I think just kind of speaks a little bit to where we think the relative weight of these things is." 00:19:57

Domain-Specific AI Models Beat General Models in Complex Scientific Fields

A16Z partner Vineeta Agarwala validated from the investment side what CZI is doing on the science side: domain-specific AI models consistently outperform generalist models for specialized tasks. The original thesis that one AI would be smarter than everyone at everything has been repeatedly falsified in practice.

"The domain specific models have been like super interesting. Like the original thesis was like there's just some AIs are going to get so smart. They're going to be smarter than everybody at everything. But like on video models, like every video model is best at something but not everything. And so knowing what problem you're solving actually turns out to be sort of ironically very important in AI." 00:31:49

Proximity as Organizational Technology: Sit Teams Together Until It Works

A recurring organizational theme: the most powerful collaboration tool is physical proximity between disciplines. CZI's biohubs were designed to force biologists and engineers to sit next to each other. The lesson extends broadly to any organization trying to integrate two different knowledge domains.

"There's so many interesting... you can fix just by having two teams sit together, right? It's like, it doesn't matter what the org chart is or like whatever. It's like, you guys need to sit next to each other until you get this thing to work. And that's something I really believe in." 00:36:24

2. Contrarian Perspectives

Most Diseases Should Be Treated as Rare Diseases

The standard medical model lumps patients into broad categories (hypertension, depression) and applies trial-and-error drug protocols. Priscilla Chan argues this is fundamentally wrong — individual biology is so distinct that essentially every patient has a "rare" version of their disease, and precision medicine should reflect that.

"I really think most diseases should be thought of as rare diseases. Because each one of our biology is different. And right now we just get lumped, right? We get lumped based on age, demographics, ancestry, if we're lucky to have that level of understanding... if you look at hypertension or depression, like, we kind of just go by trial and error." 00:13:01

Curing All Disease by End of Century Is Both Too Crazy and Too Conservative Simultaneously

Biologists thought CZI's mission was insane; AI researchers thought it was boring and inevitable. The correct answer is neither — and the gap between those two reactions is precisely where the real work needs to happen. With AI, the timeline may compress dramatically from a century.

"The biology folks, I think, looked at it as if it were crazy ambitious. And then the AI folks are like, well, that's kind of boring. That's just automatically gonna happen... There's something in between there that needs to be bridged." 00:00:45 "I do think with the advances in AI, that should be possible to do significantly sooner." 00:29:15

Funding More Grants Is Not the Path to Curing Disease

The intuitive philanthropic move — give money to as many labs as possible — is explicitly not the strategy. CZI concluded it produces no systematic progress. The counter-intuitive answer is to fund shared infrastructure that no individual lab would fund, not to expand the grant portfolio.

"If you just decided to spend the money funding the next best grant for every single lab in the country, like there's no pathway to that being true. But if you forced people to really think about this and like, okay, what is the most credible pathway to doing this? And what are the barriers to that credible pathway? Then we sort of got somewhere." 00:00:33

Compute Is the New Lab Space — and It's More Expensive

CZI's growth strategy is not expanding physical square footage but expanding GPU clusters. They already have a 1,000-GPU cluster and are planning to scale to 10,000. This reframes what "building a research institution" means in the AI era.

"We are not expanding like a lot of square footage per se, but we're expanding our compute... In a sense, that's new lab space. It's much more expensive than wet lab space." 00:38:36

AlphaFold Was Built on Decades-Old Public Data — Purpose-Built Data Sets Will Unlock a Much Bigger Leap

AlphaFold, the most celebrated AI-biology breakthrough, was trained on a public dataset assembled decades earlier. CZI's thesis is that deliberately generating novel, purpose-built data sets for specific AI training objectives will produce breakthroughs far beyond what was possible with legacy data.

"Even something like AlphaFold, which is amazing, right? It was built off of this data set that was a public data set that had been produced decades ago. And what I think you have the opportunity to do if you do both of those together is produce specific data sets for the purpose of training AI models to build virtual cells that can do specific things." 00:07:28

3. Companies Identified

Evolutionary Scale

An AI-biology company focused on protein language models and foundational biological AI. Founded by researchers who previously worked on protein folding models at Meta. Alex Rives, the company's leader, has been named head of the entire CZI science program — a significant organizational signal about the primacy of AI expertise in frontier biology.

"There's this great company Evolutionary Scale. We actually had a bunch of researchers who'd formerly worked at Meta on protein folding models... and Alex Rives, the leader of it, is actually going to be the kind of head of the whole science program." 00:19:30

CZ Biohub (San Francisco, Chicago, New York)

CZI's operating philanthropy running three geographically distributed research hubs. San Francisco focuses on deep imaging and transcriptomics; Chicago on tissue biology and cell communication/inflammation; New York on cellular engineering (engineering cells to detect and record biological signals). Each hub is co-located near partner universities (UCSF/Stanford/Berkeley, University of Chicago system, NYC institutions).

"We have three biohubs. We have one in San Francisco, one in Chicago, one in New York. The one in New York works on cell engineering... In Chicago, we're building tissues and looking at cell communications within tissues. And then in San Francisco, we're looking at deep imaging and transcriptomics." 00:09:44

Cell by Gene (CZI Tool)

The single-cell data annotation and exploration platform built by CZI. Now the dominant standard for single-cell RNA sequencing data sharing, hosting millions of cells contributed 75% by the broader scientific community. Used by biotech startups, pharma, and academics globally for drug target discovery.

"We built the annotation tool to make it easy for people who are doing single cell science to be able to annotate the data. And then we put the data that we collected publicly so people could share. But because everyone started using the same annotation tool, everyone was standardized then on the same data formats." 00:17:04

4. People Identified

Alex Rives

Leader of Evolutionary Scale and newly named head of CZI's entire science program. Background in AI with deep expertise in protein language models; formerly led protein folding AI research at Meta. His appointment to run CZI's biology program — as an AI expert overseeing biologists rather than vice versa — is the clearest signal of CZI's strategic bet on AI primacy in biological research.

"Alex Rives, the leader of it, is actually going to be the kind of head of the whole science program. Which is actually kind of interesting when you think about it, where it's like you have AI and biology coming together. And really it's like an AI person who understands biology is running it rather than a biologist who has some understanding of AI." 00:19:30

Co-founder of CZI, pediatrician trained at UCSF, and architect of CZI's scientific strategy. Her clinical experience — seeing families with diseases medicine couldn't explain or treat — directly drove the focus on basic science infrastructure rather than incremental therapies. She articulated the "pipeline of hope" framework.

"Training as a pediatrician at UCSF, I met a lot of patients and, frankly, like little kids and families for which we just had no idea what the problem was... That's when I really realized the power of basic science and how we need to work on basic science to advance the forefront of what's possible. I think of it as the pipeline of hope." 00:02:15

Mark Zuckerberg

Co-founder of CZI and Meta CEO. Provided the engineering and systems-thinking framing for CZI's tool-building strategy, including the compute-as-lab-space insight, the network effect dynamics of Cell by Gene, and the strategic decision to keep doubling down on science as CZI's primary philanthropic focus after seeing returns exceed expectations.

"We're not going to cure all diseases, to be clear. The strategy is to help scientists and the scientific community cure all diseases. So, the strategy is really one of accelerating the pace of basic science... most major breakthroughs are basically preceded by the invention of a new tool to observe phenomenon in a new way." 00:03:25

5. Operating Insights

The "Annotation Bottleneck" Startup Pattern: Solve a Workflow Problem, Accidentally Build a Platform

Cell by Gene became a dominant scientific platform not by being designed as one, but by solving a specific, painful workflow bottleneck (annotation speed) for a community that desperately needed it. The standardization effect kicked in organically. For operators building in any data-intensive domain: find the annotation/workflow bottleneck, solve it with great UX, make the output format open and portable, and the network effect does the rest.

"Cell by Gene was an annotation tool. That's the original source of this... because everyone started using the same annotation tool, everyone was standardized then on the same data formats. And then there started being a community around the tool and they wanted to share back and build the atlas. So now after 10 years, there are millions of cells that have been built into this shared resource... We've only funded 25% of it. 75% came from the broader community." 00:16:40

Lower the Barrier to Entry for Your Tools to Attract Cross-Disciplinary Insight

CZI intentionally designed Cell by Gene to be usable without deep computational or biological training. The strategic logic: breakthroughs in one discipline often come from someone in an adjacent field who sees the problem fresh. Immunology turning out to be central to neurodegeneration is the example they give. Operators building research or data tools should obsess over making them accessible to non-experts.

"Building that user interface in a way where it's not a very high barrier to entry, to be able to poke around and learn something and bring knowledge back to your work. That's intentional... A very pertinent example is, turns out, I think immunology has a ton to do with neurodegeneration... so you need to be able to allow the immunologists to come in and understand neurodegeneration." 00:33:50

Be Impatient on Iterations, Patient on the Goal

Priscilla Chan's operating philosophy for managing long-horizon projects without losing urgency: tolerate the ambiguity of a large goal, but be relentlessly impatient about the individual feedback loops and iterations along the way. The decade of work building data sets was not passive — it was iterating constantly, which is what positioned CZI to capture the AI wave.

"Being willing to have a long-time horizon but be impatient at the same time. Because it's all those iterations along the way that have sort of allowed us to get to this place where, you know, to get lucky, ready, having built data sets to take advantage of AI and large language models." 00:41:29

6. Overlooked Insights

The First Biological Reasoning Model Is Already Being Built — and It's the Most Important AI-Biology Development Nobody Is Talking About

In passing, Mark Zuckerberg mentions that CZI is publishing what he calls "the first reasoning model over biology" — a model that doesn't just output correlations from training data but actually reasons through causal chains of biological events. This is mentioned almost as a throwaway detail amid discussion of other models, but it is categorically more important than all the other models described. Every prior biological AI (including AlphaFold and cell atlas models) is fundamentally a pattern-matcher. A reasoning model that can explain why a biological process unfolds the way it does — and predict novel sequences causally — would be the foundational tool for drug discovery, not just hypothesis generation. The other participants moved past this point quickly, but it deserves enormous attention.

"It's basically the first reasoning model over biology. So the idea is that you effectively have these models that kind of simulate world models in different ways. And then you want it to be able to not just be able to spit out correlations, right? In terms of like what it's found, but actually be able to kind of reason through how things would evolve and why things would happen." 00:25:23

Immunology May Be the Unifying Key to Neurodegeneration — and CZI Is Quietly Positioned at That Intersection

In a single sentence, Priscilla Chan drops the observation that immunology appears to underlie neurodegeneration — and the sentence is left almost completely undiscussed. This is not a new scientific hypothesis, but CZI's specific combination of single-cell transcriptomics infrastructure, the Chicago Biohub's inflammation research program, and the cellular engineering capabilities in New York means they are quietly assembling all three tools needed to actually crack this connection at scale. Any investor or founder working in neurodegeneration should be tracking CZI's inflammation data sets and the Chicago Biohub output closely — they may be building the target discovery engine for the next generation of Alzheimer's and Parkinson's drugs without explicitly framing it that way.

"A very pertinent example is, turns out, I think immunology has a ton to do with neurodegeneration. Right. Seems like immunology is behind all this. Everything." 00:34:16