Mark Zuckerberg & Priscilla Chan: How AI Will Help Cure Disease

Summary

Podcast Summary: The a16z Show

Episode: Mark Zuckerberg & Priscilla Chan: How AI Will Help Cure Disease

Date: July 9, 2026
Host: Andreessen Horowitz
Guests: Mark Zuckerberg & Dr. Priscilla Chan (Chan Zuckerberg Initiative)

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Overview

In this in-depth conversation, Mark Zuckerberg and Dr. Priscilla Chan join the a16z Show to discuss the ambitious vision, strategy, and tangible progress of the Chan Zuckerberg Initiative (CZI) in leveraging AI and technology to accelerate fundamental science, with the ultimate mission of helping cure, prevent, or manage all diseases by the end of this century. They explore the creation of biohubs, the development of open-source biological data tools like Cell by Gene, the promise and challenges of virtual cell modeling, and the importance of bridging AI and biology for the advancement of medicine. The episode blends personal motivation, technical details, industry insight, and reflections on organization and collaboration in science.

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Major Themes & Key Discussion Points

1. Why Cure Disease? The Motivation and Strategy

• Personal motivations: Dr. Priscilla Chan, a pediatrician, cites witnessing unsolved medical mysteries in children as pivotal to her focus on basic science ([02:15]-[03:18]).

• Tool-building as leverage: Both guests emphasize that scientific breakthroughs are often predicated on new tools (e.g., microscopes, telescopes). CZI’s strategy is to build foundational infrastructure, datasets, and software to enable scientific progress rather than fund incremental therapeutic work ([03:25]-[05:02], [04:11]):

  • “Most major breakthroughs are basically preceded by the invention of a new tool to observe phenomena in a new way...” — Mark Zuckerberg [03:25]

• Focus on ecosystem acceleration: Rather than curing diseases directly, the aim is to empower scientists everywhere with shared resources, infrastructure, and collaborative projects ([05:29]-[06:30]):

  • “We're super focused on actually making every scientist better. ... We can't do this alone.” — Dr. Priscilla Chan [05:29]

2. The Challenges of Ambition, Credit, and Time Horizons

• Skepticism and ambition:

  • Initial disbelief from scientific community; “most scientists couldn't look at us with a straight face” — Dr. Chan [00:23]/[05:29]
  • “The biology folks looked at it as if it were crazy ambitious. The AI folks are like, well, that's kind of boring. That's just automatically going to happen.” — Mark Zuckerberg [00:45]/[06:42]

• Long-term philanthropic vision:

  • Grand challenges set on 10- to 15-year horizons — mirrors venture timeframes and enables undertaking high-risk, high-reward projects ([09:25]-[10:23])
  • “When you look at it, you're like, I see a path. ... Have some risk appetite. ... If we do it, like, the returns could be higher than even expected.” — Dr. Priscilla Chan [09:25]

3. The Biohub Model: Infrastructure, Focus Areas, and Collaboration

• Structure:

  • Three biohubs: San Francisco (deep imaging/transcriptomics), Chicago (tissue/cell communication), New York (cell engineering) ([09:47]-[11:04])
  • Choice of locations based on partner universities and their strengths.

• Collaboration and interdisciplinary work:

  • Biohubs as “operating philanthropies” — bring scientists and engineers together, unconstrained by traditional academic silos ([09:47]-[11:04]).
  • “…really the main focus of our philanthropy at this point.” — Mark Zuckerberg [07:23]

4. Impact and Vision for Precision & Personalized Medicine

• From rare to common diseases:

  • Focus on rare/idiopathic diseases, but the approach is applicable to all diseases due to underlying biological diversity ([12:01]-[13:52]).
  • “Most diseases should be thought of as rare diseases because each one of our biology is different and right now we just get lumped.” — Dr. Priscilla Chan [13:45]

• Data sets and enabling therapeutics:

  • Open-source cell atlases and tools already powering innovation in academia and startups ([14:11]-[15:19]).

5. The Cell Atlas and Cell by Gene: Building Biology’s “Periodic Table”

• Genesis and impact:
  • Developed out of necessity to annotate and standardize single-cell data quickly ([16:13]-[17:52]).
  • “Cell by Gene is like almost an accident, though.” — Dr. Priscilla Chan [15:19]
  • The result: a global, standardized platform, 75% community-driven, enabling new science at scale ([16:13]-[17:52], [18:01]).

6. Virtual Cell Models: The Fusion of AI and Biology

• Ambition:

  • Developing virtual models to simulate cellular and molecular behavior — a “new model organism” ([18:25]-[24:10])
  • “Of all the things that we've worked on ... science research has had by far the biggest return. So we've just doubled down on it over and over…” — Mark Zuckerberg [07:23]

• Key advances and models:

  • VariantFormer: predicts impact of specific cell edits ([24:10])
  • Synthetic cell generation via diffusion models for simulating rare biological configurations
  • First biological reasoning models (“first reasoning model over biology”) ([25:23])
  • Hierarchical model-building (from proteins to cellular systems to virtual immune systems) ([26:15]–[27:38])

• Utility and accuracy:

  • Models don’t need to be perfect to drive discovery—must lower the risk and cost of exploratory research ([23:10]):
    • “You just want to be able to de risk the idea on the front end a little bit.” — Dr. Priscilla Chan [23:10]

7. Organizational News and Philosophical Reflections

• Structural shift:

  • CZI consolidates its science and technology efforts into a unified Biohub entity under Alex Reeves ([28:05]).
  • The aim is to complete the “flywheel” from data generation to AI model building and back to experimental science ([30:09]-[31:23]).
  • Importance of feedback loops, physical and digital collaboration, and intentional interface and community design ([31:23]-[34:45]).

• Network model vs. central growth:

  • Expansion of both more biohubs and a centralized AI team ([35:01]).
  • Recognizes the enduring value of decentralization, but highlights the power of formal collaboration structures ([35:11]-[36:43]).
  • “It's interesting how many organizational problems you can fix just by having two teams sit together.” — Mark Zuckerberg [36:51]

• Unique role and founder-market fit:

  • CZI/Biohub fills a gap between academia, biotech, philanthropy, and industry — nobody else is building these global biology tools at this scale ([43:33]–[43:43]):
    • “If we didn’t exist, would it be a problem? Yes... one of us is an engineer, the other one scientist.” — Mark Zuckerberg [43:47]/[43:53]

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Notable Quotes & Memorable Moments

• On ambitious goals and skepticism:

  • “When we first set out the goal to cure and prevent disease by the end of the century, people ... couldn't look at us with a straight face.” — Dr. Priscilla Chan [00:23]
  • “The biology folks looked at it as if it were crazy ambitious. The AI folks are like, well, that's just automatically going to happen.” — Mark Zuckerberg [00:45]

• On building tools for science:

  • “Without those kind of tools, it’s like you're coding without being able to step through the code and debug things.” — Mark Zuckerberg [03:25]
  • “So that's kind of the theory, right?” — Mark Zuckerberg [04:11]

• On open-source impact:

  • “There's a startup ... using your Cell by Gene atlases to look at millions of single cells… try to pinpoint the fibroblasts... try to use that to inform... a new drug target…” — Host [14:11]

• On virtual cell models:

  • “This is going to be one of the most important sets of tools ... building up the kind of hierarchy... from proteins ... to a virtual immune system.” — Mark Zuckerberg [18:34]

• On risk, experimentation, and lowering the barrier:

  • “If you had a virtual cell model ... you could actually then start testing at and tinkering on the computational side and ask riskier questions.” — Dr. Priscilla Chan [21:35]

• On organizational innovation:

  • “It's interesting how many organizational problems you can fix just by having two teams sit together.” — Mark Zuckerberg [36:51]
  • “We are not expanding like a lot of square footage per se, but we're expanding our computer.” — Dr. Priscilla Chan [38:43]

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Timestamps for Important Segments

| Time | Segment Summary | |-----------|-------------------------------------------------------------------------------------------------| | 00:00-01:48 | The leverage of AI in biology; the goal to cure/prevent disease; intro to tools and approach | | 03:25-05:02 | “Breakthroughs follow tool invention”; why tools > ad hoc funding; strategy for impact | | 09:25-11:24 | 10-15 year “grand challenge” model; Biohubs’ structure and priorities | | 12:01-13:52 | Precision medicine vision; connecting genomics/AI to individual diseases | | 14:11-17:52 | Cell by Gene, data openness, how community and standardization evolved | | 18:25-24:10 | Virtual cell models: promise, technical approaches, and exploratory value | | 24:10-27:38 | On new AI architectures (VariantFormer, synthetic modeling, reasoning engines) | | 28:05-31:23 | Announcement: transition to unified Biohub under new leadership & operating model | | 35:01-38:55 | Philanthropy organization: growth, organizational learnings, “sitting together” | | 39:08-40:36 | Compute as the new scientific “lab space”; resource sharing with wider community | | 42:12-44:00 | On feedback, patience, measuring impact through tool adoption | | 43:33-43:53 | On unique value and “founder-market fit” |

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Insights, Takeaways, and Future Perspectives

• Paradigm shift: CZI is betting that enabling basic science with open, AI-ready tools offers more societal leverage than traditional philanthropic or governmental grants.
• Open infrastructure as community good: The “Cell by Gene” project is a successful case of network effects in science, analogous to foundational internet protocols.
• AI as accelerator, not magic: AI in biology is both over- and underestimated. Its power relies on the quality and specificity of data, models, and domain knowledge.
• Collaboration as a force multiplier: Interdisciplinary teams, streamlined data standards, and feedback loops between experimental and computational science are key.
• Patience + iteration: Bringing ambitious scientific infrastructure to life requires long horizons, resilience in the face of ambiguity, and a willingness to be both patient and impatient.

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Final Thoughts

CZI’s bold, tool-centric approach is uniquely positioned at the intersection of philanthropy, science, and software. By building foundational infrastructure, promoting open science, and integrating AI at core levels, they are not only propelling biological research forward—but also inspiring a new organizational and incentive model for science in the decades to come.

“There's just gonna be a huge amount of leverage with AI ... I'm very excited about that.”
— Mark Zuckerberg [44:08]

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Transcript

[Mark Zuckerberg]

This is a space that there's just gonna be a huge amount of leverage with AI. It still seems like there could be a lot more effort in this space around building tools. And 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. We think that this is like probably one of the most important sets of tools that you need to build.

[Dr. Priscilla Chan]

When we first set out that the goal to cure and prevent disease by the end of the century, people like, honestly, most scientists couldn't look at us with a straight face.

[Podcast Host (Interviewer)]

They're like, they're crazy.

[Dr. Priscilla Chan]

Yes. And it was true. Because if you just decided to spend the money funding the next best grant for every single lab in the country, there was no pathway to that being true.

[Mark Zuckerberg]

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 going to happen. I know. It's like, okay, there's something in between there that needs to be bridged.

[Podcast Narrator]

For our summer Replay series, we're bringing back one of our favorite conversations from the past year. As AI continues to reshape nearly every industry, one of its most important applications may be in science itself. In this conversation, Mark Zuckerberg and Dr. Priscilla Chan discuss the Chan Zuckerberg's initiative's long term effort to accelerate scientific discovery by building new tools for biology. Rather than focusing on individual therapies, they're investing in the infrastructure, data sets and AI systems they believe researchers will need to make faster progress against disease. We explore cell atlas, virtual cell models, biohub, and the idea that the future of medicine may depend as much on better tools as on better treatments.

[Podcast Host (Interviewer)]

Mark, Priscilla, welcome to the Asic Z podcast.

[Dr. Priscilla Chan]

Thanks for having us.

[Mark Zuckerberg]

Yeah, great to be here. Excited.

[Podcast Host (Interviewer)]

All right. Excited to have you. You're doing exciting stuff.

Yeah. To that end, almost a decade ago, you gu Chan Zuckerberg Initiative with the mission and intent to cure, prevent, manage all disease by the end of this century. There's a lot of missions that you guys could have poured your time and resources into. Take us behind the conversations of why you guys picked this one. Maybe. Priscilla, why don't we start with you and hear your side of the story?

[Dr. Priscilla Chan]

It always surprises people when I talk about how we work in basic science research. I trained as a pediatrician and people always think, oh, it must be about medicine. And for me, I went into medicine because I wanted to improve people's lives. I wanted to make a difference. I wanted to be able to help others. And I think 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. And they might have a specific gene that they could name if they were lucky, or they could be grouped into a bunch of other diseases and there'd be a general sort of PDF they'd print out, like, this is what we know. And then it was my job as an intern or resident to try to translate like a few lines of information to how we were supposed to take care of the patient. And for me, 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.

[Podcast Host (Interviewer)]

Yeah. And why did you think you could cure all disease? Because that's like a very like aggressive goal.

[Dr. Priscilla Chan]

Do you want to answer that one?

[Mark Zuckerberg]

Yeah. Well, I mean, we're not going to cure all diseases, to be clear. I mean, 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. And the theory that we had was if you look at the history of science, most major breakthroughs are basically preceded by the invention of a new tool to observe phenomena in a new way, right? So think about things like the microscope, right? Being able to observe bacteria or other fields, the telescope or you. But it's just to use an engineering example, without those kind of tools, it's kind of like you're coding without being able to step through the code and debug things, Right?

[Podcast Host (Interviewer)]

That's like the old days.

[Mark Zuckerberg]

So our whole approach on this is basically let's help build tools that will accelerate the pace of the whole field. And I think that there's a niche that I think fits that because if you look at how funding works in science, the vast majority of funding comes from the government and NIH grants. It's parceled out into these relatively small grants that allow individual investigators to investigate usually pretty near term things. And 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. So that's kind of the theory, Right?

[Podcast Host (Interviewer)]

And it seems like there's also something that is you don't really get credit for the tools in a lot of ways. I mean, we have companies that use your tools and they're very happy about it. But I didn't even know that that was the case. And so that's why it's philanthropy. Yeah, well, it is, but most people do philanthropy to get credit, too. I mean, that's kind of a part of it. So I guess. Did you think about that or were you just like, no, like, this is going to work. And if it works, that's all we need.

[Dr. Priscilla Chan]

We're super focused on, like, actually making every scientist better and beyond science, like startups, startup founders. Because the point is, we can't do this alone. And when we first set out the goal to cure and prevent disease by the end of the century, people like, honestly, most scientists couldn't look at us with a straight face.

[Mark Zuckerberg]

That's crazy.

[Dr. Priscilla Chan]

Yes. And it was true. Because if you just decided to spend the money funding the next best grant for every single lab in the country, 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, right? They were like, well, like, there's no shared tools or we're not working on big projects and building the right data sets. And we're like, okay, well, then we can start doing something about that. And so that's where the idea of building shared tools, because no one right now in the science.

[Podcast Host (Interviewer)]

That's so interesting. So basically you're like, we're going to cure all disease. And they're like, can't. Yeah, can't be done. Why can't it be done? Well, because we don't have the tools. Okay, that's pretty. That's a pretty cool sequence.

[Mark Zuckerberg]

Yeah. I mean, there's also this funny thing where 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 going to happen. I mean, it's like there's something in between there that needs to be bridged. And if you can, like, kind of use the kind of modern AI tools in order to build the types of tools that biologists need. So that's a big part of how we think about our work, is AI

[Podcast Host (Interviewer)]

has got to be the most overestimated and underestimated technology ever, like simultaneously?

[Mark Zuckerberg]

I mean, yeah, well, probably like the Internet early on, but we kind of think about ourselves and the work that we're doing at the BioHubst as frontier biology paired with frontier AI.

[Podcast Host (Interviewer)]

Right.

[Mark Zuckerberg]

So there are labs that do frontier AI that basically, you know, are building the most advanced models. And then there are lots of biological research organizations that effectively do very leading edge research to build to either discover new data sets or are looking to certain challenges. But so far there hasn't been anyone who's tried to do both of those at once. And when you look at, I mean, 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. Right. 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. So I think that that's like a pretty interesting zone to be in. And of all the things that we've worked on, you know, actually when we started czi, we kind of actually focused on a number of areas and what we found is just that the science research has had by far the biggest return. So we've just doubled down on it over and over and over until now. We're at the point that we're 10 years in and Biohub is really the main focus of our philanthropy at this point. But yeah, I mean that's kind of, that's basically the focus.

[Podcast Host (Interviewer)]

Well, it's good.

Maybe you're not giving yourselves enough credit because you're sort of saying, well, there's bite sized science, we didn't want to do that. There's centuries scale science. And that seemed like a long time horizon, but achievable, ambitious, but you've actually identified which I think is really fantastic, grand scientific challenges that are right in between. They're 10 to 15 year horizons, at least per kind of the way you communicate about them and the way you energize the scientific community about them. 10 to 15 is kind of an interesting time horizon, sort of like similar to the time horizon of a venture backed company. Similar to the time horizon on which a team can work together for that period of time. How did you get to that number? And then how are you thinking about the challenges that you take on in each 10 to 15 year wave? Because that's concrete, achievable, you build a lot of credibility around it. The way that you've announced those Challenges.

[Dr. Priscilla Chan]

Well, I'm curious how you guys think about it. But for us, when we looked at the grand challenges from the 10 to 15 year time horizon, it needs to be like, when you look at it, you're like, I see a path. 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.

[Podcast Host (Interviewer)]

Ambitious enough.

[Dr. Priscilla Chan]

Yeah. Have some risk appetite.

[Podcast Host (Interviewer)]

Right.

[Dr. Priscilla Chan]

So we want things where we're like, there's a credible pathway, someone who is at the helm who can do this. And there's enough ambiguity where we feel like we could take on that risk. And if we do it, like, the returns could be higher than even expected. And the way we modeled that in the biohubs is 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. Can we engineer cells to go in and detect, read it out, or to take certain actions? 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 and that work. The locations are not by accident. We also look at the partner universities because we have folks who come to the biohubs to do this work, collaborative, interdisciplinary, and sort of unconstrained by the traditional lab. But we also build off of the labs at these academic institutes that support the work. And so that's how we sort of choose the grand challenge and the locations. And then the sort of layering and the large language models and AI coming into the picture has been so interesting because we were already building tools to measure interesting data, building the data sets, but we didn't really know what to do with them yet. And large language models coming onto the scene were like, wow, we can make sense of all of this now.

[Podcast Host (Interviewer)]

I'm curious what you view success as in the therapeutic realm. So, you know, we think a lot about understanding biology and sometimes we bet on startups that want to unlock completely new biological areas, diseases where we don't know what's going wrong. And then there's another group of folks who kind of say, hey, okay, now that we understand what's going wrong, let's fix it. Let's come in with a drug, let's come in with a new type of chemistry, a new type of antibody. How do you, what do you think Success for the CZ Bio Hub looks like 10, 20, 50 years from now in terms of the new medicines that you've enabled?

[Dr. Priscilla Chan]

We want there to be like an explosion of a community who are building these. Just the new wave of what it means to be deploying precision medicine, I think for rare diseases and common diseases alike. You're really talking about individual biology that we sort of lump together and we often don't know how it happens.

[Podcast Host (Interviewer)]

Right.

[Dr. Priscilla Chan]

We know that you have this mutation or the worst nightmare is you have a variant of unknown significance. What does that even mean?

[Podcast Host (Interviewer)]

The horrible vus?

[Dr. Priscilla Chan]

Yes, horrible. And you're like, you tell someone, you kind of know something, but we don't know what it means. But if you look at the way we've been able to look at variants and look at single cell transcriptomics, we're starting to be able to say, okay, this variant actually impacts this set of downstream cells. And then we start looking at the proteins that get expressed and how it looks similar or different to what a healthy cell would look like. Then you can start targeting. Okay, like let's look at that as a target. And you both know the specificity of the target you want to build based on the ability to connect mutation to protein expression as well as to be able to predict off target effects. What are the side effects, because you also know where else that drug will be able to interact with the body. And so those are rare, like, and, and, But 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. But truly each one of our biology is different. And say like, if you look at hypertension or depression, like we kind of just go by trial and error and saying like, let's just try that drug and see what happens. But what should really happen is being able to precisely and accurately and quickly treat people by looking at individuals biology. We want to enable the basic science and we would be thrilled if people picked up the models that we build to be able to build the diagnostics, the therapeutics that need to come.

[Podcast Host (Interviewer)]

You've built amazing data sets, I have to say. Like, I mean, you may not hear the feedback from the startup community and the pharma community and the R and D community, but it's there because you've committed to open source. And so people may not be, they may not all be writing papers, but they are using those tools. There's a startup in our portfolio working on idiopathic pulmonary fibrosis. The name tells you how vexing the disease is. It's idiopathic we don't know why it happens. The IPF is named that way. And so, you know, he was telling me that he used your Cell by Gene atlases to look at millions of single cells in patients with disease without disease, try to pinpoint the fibrobl. Double click on the fibroblasts and their gene expression.

[Dr. Priscilla Chan]

It's incredible.

[Podcast Host (Interviewer)]

And try to use that to inform, hey, where could I go after a new drug target in this disease that's fundamentally a strange clump of idiopathic origin? So I think there's a huge group of innovators who love the tools, the visualizations, the query systems, and really the software approach that you've built to making that data incredibly accessible.

[Dr. Priscilla Chan]

So cell by gene is like almost an accident, though.

[Podcast Host (Interviewer)]

Tell us more.

[Dr. Priscilla Chan]

So do you want to share a little bit about cell by Gene or do you want me to start?

[Mark Zuckerberg]

Well, I mean, I don't know which part you want to get into, but I mean, but the cell atlas work overall, I mean, it's kind of this crazy thing that we're, you know, here in, you know, 2025 and there's not the kind of periodic table of elements equivalent for biology.

[Podcast Host (Interviewer)]

Right.

[Mark Zuckerberg]

So that was sort of a lot of the inspiration of it was, all right, 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? And when we were starting off, we didn't even necessarily have in mind that we were going to use that to build virtual cell models. I think that that's sort of just come into focus as the AI work has advanced. But that's a very exciting thing. We should definitely spend a bunch of time on the virtual cell models. But I'm not sure what you wanted to get into on the cell atlas.

[Dr. Priscilla Chan]

Well, the single cell work is, was one of our first RFAs 10 years ago. We started and we were like, okay, we think this is possible. We actually funded the methodology for it to, to standardize how it was going to be done. So that was 10 years ago. And we then we seeded a few labs to start building out that data set. But we were like, there are like millions or billions of different cell types and different permutations, like, how are we going to do this? And especially with like a burgeoning technique. And so we ended up seeding a few groups and they started doing work. And then they told us they had a problem. There was a, there was a bottleneck in their workflow because they couldn't annotate the data fast enough. And so we built 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 cel 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. 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 for the entire scientific community. We only funded about 75% of it. Sorry, that's wrong. We've only funded 25% of it. 75% came from the broader community saying this is useful and there's an easy way for us to standardize and build these together.

[Podcast Host (Interviewer)]

Have the same metadata.

[Dr. Priscilla Chan]

Yeah, that's right.

[Mark Zuckerberg]

That's like an interesting, what you'd call a network effect.

[Podcast Host (Interviewer)]

Yeah, I was going to say it sounds like the Internet.

Come for the annotations, stay for the virtual cell model.

[Mark Zuckerberg]

Well, it was very important when we were getting started with the work to have everyone who was doing it have a consistent format so that way it could be used and portable. And then once that kind of took off as the way that it would get done, then other people just found it valuable.

[Podcast Host (Interviewer)]

Yeah. And even relative to prior databases like GEO and whatnot, they're just simply not as standardized or qc.

Let's get into virtual cells. One of the great challenges, the grand challenges you would focus on maybe talk about what is the promise or the hope and maybe some of the challenges or where we're at with it.

[Mark Zuckerberg]

Yeah, I mean, we think that one of this is going to be one of the most important tools at this point, is basically building up the kind of hierarchy from proteins to, to just different structures within the cell to whole to like whole like a virtual immune system or different levels of hierarchy. And we know that this is going to end up being like a very important set of tools for people to effectively generate hypotheses for, for different science work. You know, even before you get to the point where you're really running full experiments in it, you can come up with some estimate of how that might run. It will be useful for some of the precision medicine type examples that Priscilla was talking about a few minutes ago. But we think that this is like probably one of the Most important sets of tools that you need to build. And it's not a single thing. Right. So there's different angles to come at this from the cell atlas data is helpful for understanding things on a cellular level. One of the kind of most important things that we're doing right now, there's this great company, Evolutionary Scale, actually had a bunch of researchers who'd formerly worked at Meta on protein folding models, is joining a biohub. And Alex Reeves, 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. I think just kind of speaks a little bit to where we think the relative weight of these things is. But I mean, we basically view, you know, like Priscilla was saying, with the different biohubs, New York doing cellular engineering will basically make it so that you can have cells that can record different things that are going on around the body and share that data. And then you can build that into models. The Chicago biohub being able to record inflammation and basically study that in order to kind of help understand, like, that's a different data set. We have the Imaging Institute, which is. We just trained our first set of models around that, which are the first, like spatial models around understanding the way that kind of cells look in different states. And eventually, just like you have this analogy on the kind of the industry side around language models where you have different capabilities and then over time you train them into models and it gets more and more general. That's kind of the idea here. So we'll build the biohubs around grand biological challenges. The biohubs will build tools that will generate novel data sets. We will build models based on those and then eventually combine the models into an increasingly general view of a virtual cell that will be useful both for scientists and hopefully startups and companies that are working on finding drugs, which is not our part of the whole thing, but I think is obviously a really important part of what needs to happen.

[Dr. Priscilla Chan]

Yeah, and, you know, you guys think about risk all the time in terms of when you make investments. Like, I think the promise of being able to do virtual biology using a virtual cell model is you can actually take on riskier ideas right now. Like, grant funding can be hard to come by, and the wet lab work is expensive and slow. And it's not just, you know, Money, it's also time. And so you have to choose something that you think is going to have some likelihood of success to keep your lab career going. And so it naturally lends people to take on like some risk, but not a lot of risk because they need to make sure that they are hitting like a certain percentage of the time to make tenure or publish or whatever they need to do. But if you had a virtual cell model where you could simulate really high quality biology, you could actually then start testing at 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 and actually see if there is promise doing the experiments in silico before you make the time and money investment in the wet lab.

[Podcast Host (Interviewer)]

Do you think of it kind of like a model organism?

Yeah.

Like it's the new fruit fly.

Yeah. I was going to ask, given the complexity of a cell, like how close, like how accurate do you think you'll get the model to? I mean, just assuming, I mean, maybe you get it to like a perfectly accurate representation of a cell, but like how accurate to be useful would the virtual cell have to be?

[Dr. Priscilla Chan]

I think it will obviously iterate and get better and better because right now we like right now we're still just talking about transcriptomics. We're expanding into different ways of looking at the cell, but you get more and more accuracy. But I don't think it needs to be 100% accurate to be useful because you just want to be able to de risk the idea on the front end a little bit. And the more and more you de risk it, the more efficient it gets, obviously. But it will be useful if you even get signal. And yes, I do. We do think about it like as a model organism, but in a way that's like has fidelity to the human body. Like, you know, like I don't want to.

[Podcast Host (Interviewer)]

All models are wrong. Some are useful.

[Mark Zuckerberg]

Yeah, yeah.

[Podcast Host (Interviewer)]

Has utility on certain axes. Exactly.

[Mark Zuckerberg]

And just like the language models you build in specific capabilities. So it's not so. For example, you know, one of the models that we're where publishing is variant former.

[Podcast Narrator]

Right.

[Mark Zuckerberg]

It basically, you know, makes it so that it's trained on a bunch of effectively pairs of. You have a cell, you apply CRISPR to it in a place, you see what comes out at the other side. So it basically is able to make that kind of a prediction. Like, okay, if you have this edit that you're doing to a cell, what is likely going to happen? Another one of the models is, it's this diffusion model. Basically you can describe a type of cell that you would like it to simulate. Then it will just produce a kind of synthetic model of the cell. Again, I mean, it's kind of interesting because to Priscilla's point before about how everyone is different and different cells have kind of. You want to be able to simulate these kind of rare configurations. Having at least a synthetic version of what that could look like is interesting. And then you can test against that. The cryo model I think is interesting because it's spatial. So it kind of gives you a sense of there are all these different models that you can have that allow you to basically look at different kinds of things and then you just train them in to be increasingly general over time.

[Podcast Host (Interviewer)]

Very interesting. And is the modeling technology basically LLMs or is there a reasoning model? Is it like just.

[Mark Zuckerberg]

Oh, that's actually. Yeah, no, that's a fascinating one too because one of the new models, I think this one is very early, but it's basically the first reasoning model over biology. So the idea is that, that yeah, 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 in terms of what it's found, but actually be able to kind of reason through how things would evolve and why things would happen. I think that one's quite early, but it is interesting conceptually as what I think is clearly going to be an important direction in terms of how these models evolve.

[Podcast Host (Interviewer)]

Yeah, because that's what I was thinking, you know, that if it doesn't work, the next question you have is why.

[Mark Zuckerberg]

Yeah, you know, like. But I think what you find in reasoning the analogy, you're married to your hypothesis. Well, sure, sure, yeah. I mean the. Yeah, I thought you were saying if the reasoning model doesn't work, why. I mean, I think the perspective, I mean the language model analogy for that would be you need better kind of world models or better pre trained models in order to get the reasoning to be good. But yeah, you just, you build more, you build more capabilities into it. And I think that there's probably an order too. So the work that Alex and the evolutionary scale folks worked on is a lot of it is protein, which is interesting because that's at a kind of smaller resolution obviously than the cellular data and the cell atlas. But, but part of the hypothesis is that you can look at all these different cells and you can kind of simulate how they might behave. But you're going to have a somewhat shallow understanding unless you actually have this hierarchical understanding of how the sub components of the cells are going to interact. So our view is that you basically want to build up a state of the art protein model and then have that be a part of the state of the art cellular model. And then once you have that, you build things like the virtual immune system, which allows you to much more complicated systems. But it's sort of this like hierarchical approach to building up these, these virtual

[Podcast Host (Interviewer)]

models that makes a lot of sense because also as you get into personalization, you've got like common proteins combining into a unique cell. So that makes it like from a systems standpoint, that makes it like much more manageable. That, that makes a lot of sense. Interesting.

[Mark Zuckerberg]

Yeah, yeah, no, it's very fascinating stuff.

[Podcast Host (Interviewer)]

Yeah.

So you guys are announcing some big news this week. Do you want to give us a sneak preview?

[Dr. Priscilla Chan]

Well, the big news is thinking about how we are going to be coming together as one team. And in the past we've run biohubs and we've built software, we've done some AI research, But all of it has been really thinking about, has been a little bit decentralized. But now under Alex's leadership, we are going to be come together as the biohub, an operating philanthropy where we are doing the science in service of a singular goal together. And how do we actually advance the state of biology and research at the intersection of AI and biology?

[Podcast Host (Interviewer)]

Amazing. Alex is amazing.

[Mark Zuckerberg]

Yeah, no, he's great. And then, and then the other thing is the piece that I mentioned earlier, which is just. Yeah, I mean CZI has focused on a number of different things. We've really just found over time that we feel like we've been able to make the biggest difference in science. So we've just kept on doubling down on it and we're going to continue doing work in education, we're going to continue supporting local communities and in those different pieces. But going forward, the Bio Hub is really going to be the main thrust of our philanthropy and we're very excited about that because I think that this is. There has been when we started the mission to see if we could help the scientific community cure and prevent diseases by the end of the century. I do think with the advances in AI that should be possible to do significantly sooner. And that is a very worthy and important and very exciting goal that we think we kind of have a unique place in the ecosystem that we can help empower others to make fast progress on that.

[Podcast Host (Interviewer)]

So there's Obviously plenty of advantages to decentralization from a management communication overhead and so forth. And so what are you trying to add by adding this kind of new layer unification on top? Like what, what are the outputs and then I guess what are the complexities to that? Because that's. I'm sorry to ask a CEO question.

[Mark Zuckerberg]

No, no. I mean I'm like assessed for a frame.

[Podcast Host (Interviewer)]

We think about this.

You want to go for it and

[Mark Zuckerberg]

then I can jump in.

[Dr. Priscilla Chan]

Yeah. So they're obviously amazing groups doing frontier AI and a lot of groups doing great frontier biology. And where we think we can do uniquely is actually tie these two together. And we are, we've funded data sets, we've built data sets, we're like building the instrumentation now to be able to look at the cell, whether it's you know, for, at the tissue cell cell communication, our cryo em where we can look at the cell at nearly atomic level. So we have the ability to not only build the data sets but actually shape and form them the way we want based on what we see as necessary to complement the existing body of knowledge. And so we have amazing that work. And we're building these AI models and so what the reason to do it together is then we can actually complete the flywheel. Like, you know, 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.

[Podcast Host (Interviewer)]

Yeah.

[Dr. Priscilla Chan]

And so if we can close that loop, which is our goal and bringing everyone together, I think it's gonna be incredibly powerful. And it's, it's more than it, it's more than just like you know, writing down a spec and saying like please deliver this. Like these people need to be sort of working shoulder to shoulder and shaping each other's work for this to actually be the more and more accurate model of how the human cell works.

[Podcast Host (Interviewer)]

Well, you know, that's so interesting cause that is exact. Like that's has been the biggest surprise in the industry for us in AI world. Like forget biology for one second is that the domain specific models have been like super interesting. The original thesis will like there's just some AIs are 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 because you can actually get to a way better result if you put the two together. Like, yeah, we're seeing that over and over again in a way that is, I would say, very counterintuitive to the whole narrative kind of going into it.

And in biology, it used to be the, or at least one assumption was all the data sets aren't on the Internet. So part of the reason you need a domain specific model is that the data sets are not public. You guys are kind of bucking that trend too by creating a lot of open source access to the data. And then even then, it sounds like you're betting, you know, on the trend that we're seeing in other industries. But still there will be nuance in how you annotate that data, curate that data.

Well, on how you talk to a scientist. Right? Like so, because you have to not only know the data and the model and so forth, but like the conversation is what we keep finding out ends up being very, very important.

[Dr. Priscilla Chan]

Right? So rich and so important in how

[Podcast Host (Interviewer)]

you actually, a scientist isn't going to talk to it. Like, you know, I talked to ChatGPT or whatever.

Well, this is the fruit fly you can talk to.

Yeah, that's super exciting.

[Dr. Priscilla Chan]

And the user interface is actually really important. You talked about, you guys have a founder who's using Cell by Gene. That user interface was intentionally designed to not need to have a computational or really a very deep biological background to be able to use because you want people coming from different fields to look at the problem, look here, help us solve problems here. And so building that user interface in a way where it's not a very high barrier to ent, to be able to poke around and learn something and bring knowledge back to your work, that's intentional. And we're really hoping when we build these virtual models that we get to a place where we can allow a lower and lower barrier entry for people to say, like, you know, like, I have some knowledge about this, maybe I can contribute. A very pertinent example is, turns out I think immunology has a ton to do with neurodegeneration. Right?

[Podcast Host (Interviewer)]

But it seems like immunology's behind all everything, so might be part of your sensory vision.

[Dr. Priscilla Chan]

So you need to be able to allow the immunologist to come in and understand neurodegeneration and understand how their world fits in. And so the more you lower the barrier to entry allows people to actually think in a sort of truly collaborative and interdisciplinary way.

[Podcast Host (Interviewer)]

So Will the Bio Hub grow as a team? Like will you employ more people at the Bio Hub proper, or are you moving towards more of a network model with more sites, more labs, more community driven data sets? Like which, which is the thrust? Or maybe it's both.

[Mark Zuckerberg]

Probably a little of both. And we've added new biohubs over time and then we're also building up more of this like central AI team.

[Podcast Host (Interviewer)]

Cool.

[Mark Zuckerberg]

So, but I don't know, I think that these organizational questions of how do you set this up? Are fascinating and a lot of our approach is sort of informed by what the rest of the field is doing. Because I. You kind of think about science as it's this portfolio, right? Society has a portfolio of stuff that it's trying to do. And as in terms of philanthropy, you want to be the most additive that you can be by trying to figure out what else is underrepresented. So science by default is very decentralized.

[Podcast Host (Interviewer)]

Right.

[Mark Zuckerberg]

It's like kind of the, the way that granting has worked the way that I think scientists by default want to work. So I think a lot of what we've found is that figuring out ways to encourage collaboration in ways that otherwise seem very simple but weren't happening before can unlock a lot of value. So the very first biohub, what we did, there were two kind of interesting things. One was it was this collaboration between ucsf, Stanford and Berkeley. And there are all these really smart people at all these different places who previously, I guess in theory they could have figured out a way to work together, but there was not really a formal construct for them to do that. And this just allowed a lot more collaboration. The other one is cross discipline, basically having biologists sit next to engineers and this view that like these two disciplines are things that need to. And I, I don't know, I mean, I'm, I'm sure, you know, you've seen this in a lot of, in a lot of the companies. But like it's, there's so many interesting.

[Podcast Host (Interviewer)]

The companies, they always set them apart.

[Mark Zuckerberg]

Well, it's interesting. No, it's interesting how many organizational questions or problems you can fix just by having two teams sit together.

[Podcast Host (Interviewer)]

Right.

[Mark Zuckerberg]

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 and until you get this thing to work. And that's something I really believe in.

[Podcast Host (Interviewer)]

So you have time?

Yeah.

You have 10 to 15 years.

Well, no, it's all like communication is such an underrated problem in General. Yeah. In. In all kinds of. And building anything or solving anything. So that's pretty neat.

[Mark Zuckerberg]

Yeah. And it's just really kind of simple stuff, but I think it's sort of novel as a model. And one of the things that's. So we've now copied this from the first biohub to the biohub network and expanded it to other models. But it's also just been neat to see other folks who are working in the field also adopt similar models because it's a pretty intuitive thing. But at some point you'll reach the point where actually it's really good to have decentralized work too.

[Podcast Host (Interviewer)]

Right.

[Mark Zuckerberg]

So it shouldn't be that we're not saying that this is like the way that all science should work. We're just saying that there's a space for this, it can unlock a lot of value because it, for whatever reason hasn't been the default to.

[Dr. Priscilla Chan]

And we still rely on like, yeah,

[Podcast Host (Interviewer)]

there's famous like stories in the MIT lab about that that's how they invented lasers and so forth. Is it put a bunch of people from different departments in the same space. The medium.

[Dr. Priscilla Chan]

Yeah, well, actually physics is where we got a lot of the inspiration. Like physics has just historically been like, labs have just rallied around big projects and big, big shared resources. And we will, you know, we are relatively centralized, but we still depend on a lot of labs who are doing sort of exact frontier work or complementary work to come together to support this. There's that. But one more thought on your expansion question is like, and maybe this is like the modern AI lab, We are not expanding like a lot of square footage per se, but we're expanding our computer.

[Mark Zuckerberg]

The researchers, they don't want employees working for them. They don't want space, they just want GPUs.

[Dr. Priscilla Chan]

It's just like in a sense that's new lab space. It's much more expensive than wet lab space.

[Podcast Host (Interviewer)]

And you guys have always been creative on that. Even in the last few years you've created ways to share access to compute. You've enabled academic labs to. I forgot the name of your program.

[Dr. Priscilla Chan]

Kind of like Scientists in Residence or

[Podcast Host (Interviewer)]

something like that rental kind of program.

[Dr. Priscilla Chan]

The core of it is, you know, if you look at individual labs, they'll have like, like a large lab would have tens of GPUs. And we were the first to really build a large scale compute cluster. A thousand. Now we have plans to move to the 10,000 range. And that one requires a different type of project. Obviously you're able to ask different types of questions. And, and it's a resource that we use. But also we've invited scientists to apply and say like, what question do you have that could use this amount of resource and be able to stem sort of seed collaborations that way.

[Podcast Host (Interviewer)]

And so if a scientist is out there listening, like who's not employed by the biohub or working at the biohub but wants to collaborate with the biohub.

[Dr. Priscilla Chan]

Yeah.

[Podcast Host (Interviewer)]

That you're gonna create, come talk to us. Interesting doors to utilize the resources. That's awesome.

[Mark Zuckerberg]

Yeah. I mean the GPUs are somewhat zero sum.

[Dr. Priscilla Chan]

Right.

[Mark Zuckerberg]

So the data isn't. So.

[Podcast Host (Interviewer)]

Yeah, yeah, fair enough.

Yeah.

So you're about to celebrate 10 years doing this. As you look out in the years to come, what else can you tell us about either things that you're thinking about for the future or maybe even principles or a North Star that's going to guide how you guys grow and evolve going forward?

[Dr. Priscilla Chan]

You know, it's been really interesting in the past 10 years because I actually spent the first few years completely envious of people working for for profit companies because there's so much clarity. Like the market will tell you whether or not it's private or public will tell you if you're doing a good

[Mark Zuckerberg]

job, if they think you're doing a

[Dr. Priscilla Chan]

good job, if they think you're doing.

[Mark Zuckerberg]

They're not always right. Yeah, they're not always right.

[Dr. Priscilla Chan]

But I was still envious because that was, I was like, I craved that feedback, like, am I doing a good job? And 10 years in, the reason why we're doubling down on biology is not only did we achieve what we said we were going to do and when we set out to set out on these projects, it actually delivered more than we thought we were going to. And I was like, okay, that's a signal I can latch onto and that's a signal we can really continue doubling down and doing more of that. And so I think it's continuing to tolerate the early ambiguity when you're like, okay, I'm going to do more of this and being patient, but 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 to get lucky. Ready. Having built data, data sets to take advantage of AI and large language models. That's because of all the work that we have been doing. And so being able to continue moving forward in this ambiguity and sometimes lack of signal on a Big goal. I think we sort of set the DNA for that.

[Podcast Host (Interviewer)]

Amazing.

[Dr. Priscilla Chan]

Oh, no pun intended.

[Podcast Host (Interviewer)]

Yeah.

[Mark Zuckerberg]

But we get to see how many people use the tools and the feedback. Yeah, yeah.

[Podcast Host (Interviewer)]

You have customers, which is pretty cool.

[Mark Zuckerberg]

Yeah, yeah.

[Podcast Host (Interviewer)]

For philanthropy. Like, that's awesome.

[Mark Zuckerberg]

Yeah, no, it's. It's one of the fun things about building tools is, like, you kind of get to see how valuable do people find the tools. Do people use the tools in order to publish important work?

[Podcast Host (Interviewer)]

Right, right, right, right. Yeah. Well, I mean, our feedback is they're awesome and completely unique, by the way. So, like, the. The other thing is, like, what would you use if you didn't have this? It's like, there's nothing.

[Mark Zuckerberg]

No, yeah, It's a real kind of void. I mean, there's this whole pipeline that needs to exist from accelerating basic science to funding a lot of people to use it to. Then you can get into the biotechs that basically can start to work on basically coming up with novel therapies, and then you get the pharma companies that do them at scale. And then there's a space for philanthropy on the other side of public health, of basically taking the therapies and kind of bringing them out to everyone in the world. But this is a space that. That there's just gonna be a huge amount of leverage with AI. And it is. Yeah, it's. It still seems like there could be a lot more effort in this space around building tools and just accelerate the whole thing a lot better.

[Podcast Host (Interviewer)]

Yeah. And I do think it is the place where you are completely unique. Right. The other things, there are other people who can do that, but there's nobody doing what you're doing.

[Mark Zuckerberg]

That's got good, good founder market. Yes, founder market.

[Podcast Host (Interviewer)]

If we didn't exist, would it be a problem? Yes. Like, those questions really land.

[Mark Zuckerberg]

As a vc, one of us is an engineer, the other one scientist. Doctor.

[Podcast Host (Interviewer)]

Yeah. Very happy in this direction.

[Dr. Priscilla Chan]

Yeah.

[Podcast Host (Interviewer)]

We thank you very much, not only for our companies, but for us as humans for working on this work. It's amazing work. Really cool.

[Mark Zuckerberg]

Thank you.

[Podcast Host (Interviewer)]

Thank you, guys.

Thank you so much.

[Podcast Narrator]

Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to, like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our [e-mail] thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.