--:-- Host (Ben Horowitz): Welcome to the a16z Podcast. I'm Ben Horowitz, here with Erik Torenberg and Vineeta Agarwala. Today, we're joined by Priscilla Chan and Mark Zuckerberg from the Chan Zuckerberg Initiative.
--:-- Priscilla Chan: Thanks for having us.
--:-- Mark Zuckerberg: Thanks for having us.
--:-- Ben Horowitz: So, let's dive right in. This is a space that there's just going to 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.
--:-- Mark Zuckerberg: We think that this is like probably one of the most important sets of tools that you need to build. When we first set out the goal to cure and prevent disease by the end of the century, Honestly, most scientists couldn't look at us with a straight face. They're like, you're crazy.
--:-- Ben Horowitz: Yeah. 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.
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: I know, it's like, okay, there's something in between there that needs to be bridged. The scientific community needs fundamentally new tools to cure disease, not just more funding.
--:-- Ben Horowitz: For decades, biological research has been constrained by the same limitations. Small grants that fund incremental progress, isolated labs working on narrow questions, and the lack of shared infrastructure to tackle the biggest challenges in medicine.
--:-- Priscilla Chan: But what if we could change that? Today, you'll hear from Priscilla Chan and Mark Zuckerberg on their 10-year journey building the infrastructure for modern biological research.
--:-- Mark Zuckerberg: We discuss how they accidentally created the standard for biology data with their Cell Atlas project, cataloging millions of cells in an open source format. We explore why they're betting on virtual cells that let scientists test high-risk hypotheses in silico before investing in extensive wet lab work. And we dive into Biohub, their play to accelerate discovery by pairing frontier biology with frontier AI.
--:-- Ben Horowitz: Hope you enjoy. Your side of the story. 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.
--:-- Priscilla Chan: 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.
--:-- Ben Horowitz: 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.
--:-- Priscilla Chan: 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.
--:-- Ben Horowitz: 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.
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: Yeah. And why did you think you could cure all disease? Because that's like a very like aggressive goal.
--:-- 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.
--:-- Ben Horowitz: So the strategy is really one of accelerating the pace of basic science.
--:-- Mark Zuckerberg: 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?
--:-- Priscilla Chan: So think about things like the microscope, right? Being able to observe bacteria or other fields, the telescope or, you know, but it's just to use an engineering example, without those kinds of tools, it's kind of like you're coding without being able to step through the code and debug things, right?
--:-- Mark Zuckerberg: That's like the old days.
--:-- Ben Horowitz: So our whole approach on this is basically, let's help build tools that will accelerate the pace of the whole field.
--:-- Mark Zuckerberg: 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.
--:-- Priscilla Chan: It's parceled out into these relatively small grants that allow individual investigators to investigate usually pretty near-term things.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: So think about like on the order of maybe $100 million to a billion dollars over a 10 to 15-year period.
--:-- Mark Zuckerberg: And then you try to unlock those tools and give them to the scientific community to accelerate the pace.
--:-- Priscilla Chan: So that's kind of the thing.
--:-- Mark Zuckerberg: And it seems like there's also something that is you don't really get credit for the tools in a lot of ways.
--:-- Ben Horowitz: 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.
--:-- Mark Zuckerberg: And so—
--:-- Priscilla Chan: That's why it's philanthropy.
--:-- Ben Horowitz: Yeah, well, it is, but most people do philanthropy to get credit, too.
--:-- Mark Zuckerberg: I mean, that's kind of a part of it.
--:-- Ben Horowitz: 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?
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: And when we first set up 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: You're crazy. Yes.
--:-- Ben Horowitz: 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, like, there's no pathway to that being true.
--:-- Mark Zuckerberg: 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?
--:-- Priscilla Chan: They were like, well, like, there's no shared tools or we're not working on big projects and building the right data sets.
--:-- Mark Zuckerberg: And we're like, OK, well, then we can start doing something about that.
--:-- Ben Horowitz: And so that's where the idea of building shared tools, because no one right now in the science.
--:-- Priscilla Chan: That's so interesting. So basically, you're like, we're going to cure all disease. And they're like, can't be done.
--:-- Mark Zuckerberg: Why can't it be done?
--:-- Priscilla Chan: Well, because we don't have the tools.
--:-- Ben Horowitz: OK, 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.
--:-- Priscilla Chan: And then the AI folks are like, well, that's kind of boring. That's just automatically going to happen.
--:-- Mark Zuckerberg: I know that's okay. There's something in between there that needs to be bridged.
--:-- Ben Horowitz: 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.
--:-- Mark Zuckerberg: So that's a big part of how we think about our work is... AI has got to be the most overestimated and underestimated technology ever.
--:-- Priscilla Chan: Like simultaneously.
--:-- Mark Zuckerberg: I mean, yeah, we'll probably like the internet early on.
--:-- Ben Horowitz: But we kind of think about ourselves and the work that we're doing at the Biohub as frontier biology paired with frontier AI.
--:-- Mark Zuckerberg: So there are labs that do frontier AI that basically are building the most advanced models.
--:-- Priscilla Chan: 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 looking to certain challenges.
--:-- Mark Zuckerberg: But so far, there hasn't been anyone who's tried to do both of those at once.
--:-- Ben Horowitz: And when you look at, I mean, even something like AlphaFold, which is amazing, right?
--:-- Mark Zuckerberg: It was built off of this data set that was a public data set that had been produced decades ago, right?
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: So I think that that's like a pretty interesting zone to be in.
--:-- Ben Horowitz: And of all the things that we've worked on, actually when we started CZI, we kind of actually focused on a number of areas.
--:-- Mark Zuckerberg: And what we found is just that the science research has had by far the biggest returns.
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: And Biohub is really the main focus of our philanthropy at this point.
--:-- Ben Horowitz: But yeah, I mean, that's kind of, that's basically the focus.
--:-- Priscilla Chan: Maybe you're not giving yourselves enough credit because you're sort of saying, well, there's bite-sized science, we don't want to do that.
--:-- Mark Zuckerberg: There's century-scale science, and that seemed like a long time horizon, but achievable, ambitious.
--:-- Ben Horowitz: But you've actually identified, which I think is really fantastic, grand scientific challenges that are right in between.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: How did you get to that number?
--:-- Mark Zuckerberg: And then how are you thinking about the challenges that you take on in each 10 to 15 year wave?
--:-- Priscilla Chan: Because that's concrete, achievable. You build a lot of credibility around it the way that you've announced those challenges.
--:-- Mark Zuckerberg: Well, I'm curious how you guys think about it.
--:-- Ben Horowitz: But for us, when we looked 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 ambitious enough yeah like we have some risk appetite right 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.
--:-- Mark Zuckerberg: And the way we modeled that in the biohubs is we have three biohubs.
--:-- Mark Zuckerberg: We have one in San Francisco, one in Chicago, one in New York.
--:-- Priscilla Chan: The one in New York works on cell engineering.
--:-- Mark Zuckerberg: Can we engineer cells to go in and detect signals, read it out, or to take certain actions?
--:-- Priscilla Chan: In Chicago, we're building tissues and looking at cell communications within tissues.
--:-- Mark Zuckerberg: And then in San Francisco, we're looking at deep imaging and transcriptomics.
--:-- Ben Horowitz: And that work, The locations are not by accident.
--:-- Mark Zuckerberg: 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.
--:-- Priscilla Chan: But we also build off of the labs at these academic institutes that support the work.
--:-- Mark Zuckerberg: And so that's how we sort of choose the grand challenge and the locations.
--:-- Ben Horowitz: And then the sort of layering in 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 really didn't know what to do with them yet.
--:-- Mark Zuckerberg: And large language models coming onto the scene we like wow we can make sense of all of this now.
--:-- Ben Horowitz: I'm curious what you view success as in the therapeutic realm.
--:-- Mark Zuckerberg: 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.
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: Let's come in with a drug. Let's come in with a new type of chemistry, a new type of antibody.
--:-- Ben Horowitz: How do you, what do you think success for the CZ Biohub looks like 10, 20, 50 years from now in terms of the new medicines that you've enabled?
--:-- Mark Zuckerberg: 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.
--:-- Priscilla Chan: Like, I think for rare diseases and common diseases alike, you're really talking about individual biology that we sort of lump together.
--:-- Mark Zuckerberg: And we often don't know how it happens, right?
--:-- Priscilla Chan: We know that you have this mutation or the worst nightmare is you have a variant of unknown significance.
--:-- Mark Zuckerberg: What does that even mean?
--:-- Priscilla Chan: The horrible of the U.S.
--:-- Mark Zuckerberg: Yes, horrible.
--:-- Ben Horowitz: And you're like, you tell someone you kind of know something, but we don't know what it means.
--:-- Mark Zuckerberg: 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.
--:-- Priscilla Chan: 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.
--:-- Mark Zuckerberg: Then you can start targeting, okay, like, let's look at that as a target.
--:-- Ben Horowitz: 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.
--:-- Mark Zuckerberg: What are the side effects?
--:-- Priscilla Chan: Because you also know where else that drug will be able to interact with the body.
--:-- Mark Zuckerberg: And so those are rare.
--:-- Ben Horowitz: Like, and, but I really think most diseases should be thought of as rare diseases because each one of our biology is different.
--:-- Mark Zuckerberg: And right now we just get lumped, right?
--:-- Priscilla Chan: We get lumped based on age, demographics, ancestry, if we're lucky to have that level of understanding.
--:-- Mark Zuckerberg: But truly, each one of our biology is different.
--:-- Ben Horowitz: 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.
--:-- Mark Zuckerberg: But what should really happen is being able to precisely and accurately and quickly treat people by looking at individuals' biology.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: You've built amazing data sets, I have to say.
--:-- Mark Zuckerberg: I mean, you may not hear the feedback from the startup community and the pharma community and the R&D community, but it's there because you've committed to open source.
--:-- Mark Zuckerberg: And so people may not be, they may not all be writing papers, but they are using those tools.
--:-- Ben Horowitz: There's a startup in our portfolio working on idiopathic pulmonary fibrosis.
--:-- Mark Zuckerberg: The name tells you how vexing the disease is.
--:-- Ben Horowitz: It's idiopathic. We don't know why it happens.
--:-- Mark Zuckerberg: The IPF is named that way.
--:-- Ben Horowitz: 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 fibroblasts, double-click on the fibroblasts and their gene expression.
--:-- Mark Zuckerberg: It's incredible.
--:-- Ben Horowitz: And try to, you know, 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?
--:-- Mark Zuckerberg: 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 built to making that data incredibly accessible.
--:-- Ben Horowitz: Cell by gene is like almost an accident, though.
--:-- Mark Zuckerberg: Tell us more.
--:-- Ben Horowitz: 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, 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, 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.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: I think that that's sort of just come into focus as the AI work has advanced.
--:-- Mark Zuckerberg: But that's a very exciting thing.
--:-- Ben Horowitz: We should definitely spend a bunch of time on the virtual cell models.
--:-- Mark Zuckerberg: But I'm not sure what you wanted to get into on the Cell Atlas.
--:-- Mark Zuckerberg: Well, the single cell work was one of our first RFAs 10 years ago we started.
--:-- Mark Zuckerberg: And we were like, okay, we think this is possible.
--:-- Mark Zuckerberg: We actually funded the methodology for it to standardize how it was going to be done.
--:-- Mark Zuckerberg: So that was 10 years ago.
--:-- Mark Zuckerberg: And then we seeded a few labs to start building out that data set.
--:-- Mark Zuckerberg: But we're like, there are like millions or billions of different cell types and different permutations.
--:-- Mark Zuckerberg: Like, how are we going to do this?
--:-- Mark Zuckerberg: And especially with like a burgeoning technique.
--:-- Mark Zuckerberg: And so we ended up seeding a few groups and they started doing work.
--:-- Mark Zuckerberg: And then they told us they had a problem.
--:-- Mark Zuckerberg: There was a bottleneck in their workflow because they couldn't annotate the data fast enough.
--:-- Mark Zuckerberg: And so we built, Cell by Gene was an annotation tool.
--:-- Mark Zuckerberg: That's the original source of this.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: And then we put the data that we collected publicly so people could share.
--:-- Mark Zuckerberg: But because everyone started using the same annotation tool, everyone was standardized then on the same data formats.
--:-- Mark Zuckerberg: And then there started being a community around the tool, and they wanted to share back and build the atlas.
--:-- Mark Zuckerberg: So now after 10 years, there are millions of cells that have been built into this shared resource for the entire scientific community.
--:-- Mark Zuckerberg: We only funded about 75% of it.
--:-- Ben Horowitz: Sorry, that's wrong.
--:-- Mark Zuckerberg: We've only funded 25% of it.
--:-- Mark Zuckerberg: 75% came from the broader community saying, this is useful, and there's an easy way for us to standardize and build this together.
--:-- Mark Zuckerberg: The same metadata.
--:-- Ben Horowitz: Yeah, that's right.
--:-- Mark Zuckerberg: It's like an interesting, what you'd call a network effect.
--:-- Mark Zuckerberg: Yeah, I was going to say, it sounds like the internet.
--:-- Ben Horowitz: Come for the annotation, 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.
--:-- Mark Zuckerberg: So that way it could be used and portable.
--:-- Mark Zuckerberg: And then once that kind of took off as the way that it would get done, then other people just found it valuable.
--:-- Ben Horowitz: Yeah, and even relative to prior data bases like Geo and whatnot, they're just simply not as standardized or QC.
--:-- Mark Zuckerberg: Yeah.
--:-- Ben Horowitz: Let's get into virtual cells. One of the great challenges, the grand challenges you would focus on.
--:-- Mark Zuckerberg: 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 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 just different structures within the cell to like a virtual immune system or different levels of hierarchy.
--:-- Mark Zuckerberg: And we think this is going to end up being like a very important set of tools for people to effectively generate hypotheses for different science work.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: It will be useful for some of the precision medicine type examples that Priscilla was talking about a few minutes ago.
--:-- Mark Zuckerberg: But we think that this is like probably one of the most important sets of tools that you need to build.
--:-- Mark Zuckerberg: And it's not a single thing, right?
--:-- Mark Zuckerberg: So there's different angles to come at this from.
--:-- Mark Zuckerberg: The cell Atlas data is helpful for understanding things on a cellular level.
--:-- Mark Zuckerberg: One of the kind of most important things that we're doing right now, there's this great company, Evolutionary Scale, we actually had a bunch of researchers who'd formerly worked at Meta on protein folding models, is joining a biohub.
--:-- Mark Zuckerberg: 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 as 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.
--:-- Mark Zuckerberg: But, I mean, we basically view, you know, like Priscilla was saying with the different biohubs, then 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.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: We have the Imaging Institute, which is what we just trained our first set of models around that, which are the first like spatial models around understanding like the way that kind of cells look in different states.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: That's kind of the idea here.
--:-- Mark Zuckerberg: So we'll build the biohubs around grand biological challenges.
--:-- Mark Zuckerberg: The biohubs will build tools that will generate novel data sets.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: Yeah, and, you know, you guys think about risk all the time in terms of when you make investments.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: Right now, like, grant funding can be hard to come by, and the wet lab work is expensive and slow.
--:-- Mark Zuckerberg: And it's not just, you know, money, it's also time.
--:-- Mark Zuckerberg: And so you have to choose something that you think is going to have some likelihood of success to keep your lab career going.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: But 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 and actually see if there is promise doing the experiments in silico before you make the time and money investment in the wet lab.
--:-- Ben Horowitz: Do you think of it kind of like a model organism?
--:-- Mark Zuckerberg: Yeah.
--:-- Ben Horowitz: Like it's the new fruit fly?
--:-- Mark Zuckerberg: Yeah.
--:-- Ben Horowitz: Given the complexity of a cell, like how close, like how accurate do you think you'll get the model to?
--:-- Mark Zuckerberg: 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?
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: We're expanding into different ways of looking at the cell, but you get more and more accuracy.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: And the more and more you de-risk it, the more efficient it gets, obviously.
--:-- Mark Zuckerberg: But it will be useful if you even get directional signal.
--:-- Mark Zuckerberg: And yes, we do think about it like as a model organism, but in a way that's like has fidelity to the human body.
--:-- Mark Zuckerberg: Like, you know, like I don't want to. All models are wrong. Some are useful.
--:-- Ben Horowitz: Yeah.
--:-- Mark Zuckerberg: Yeah.
--:-- Mark Zuckerberg: Hopefully it has utility on certain acts.
--:-- Mark Zuckerberg: Exactly.
--:-- Ben Horowitz: And just like the language models, you build in specific capabilities.
--:-- Mark Zuckerberg: So for example, one of the models that we're publishing is variant former, right?
--:-- Mark Zuckerberg: It basically makes it so that it's trained on a bunch of effectively pairs.
--:-- Mark Zuckerberg: If you have a cell, you apply CRISPR to it in a place, you see what comes out at the other side.
--:-- Mark Zuckerberg: So it basically is able to make that kind of a prediction.
--:-- Mark Zuckerberg: Like, okay, if you have this edit that you're doing to a cell, what is likely going to happen?
--:-- Mark Zuckerberg: Another one of the models is it's this diffusion model.
--:-- Mark Zuckerberg: Basically, you can describe a type of cell that you would like it to simulate, and it will just produce a kind of synthetic model of the cell.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: The cryo model, I think, is interesting because it's spatial.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: Wow, very interesting.
--:-- Mark Zuckerberg: And is the modeling technology basically LLMs, or is there a reasoning model?
--:-- Mark Zuckerberg: Is it like a just a—
--:-- Mark Zuckerberg: Oh, that's actually—
--:-- Mark Zuckerberg: Yeah, no, that's a fascinating one, too.
--:-- Mark Zuckerberg: Because one of the new models—I think this one is very early, but it's basically the first reasoning model over biology.
--:-- Mark Zuckerberg: So the idea is that, yeah, you effectively have these models that kind of simulate world models in different ways.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: I know it'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.
--:-- Ben Horowitz: Yeah, because that's what I was thinking. If it doesn't work, the next question you have is why.
--:-- Mark Zuckerberg: Yeah.
--:-- Mark Zuckerberg: But I think what you find in reasoning, the analogy to—
--:-- Mark Zuckerberg: Because you're married to your hypothesis.
--:-- Ben Horowitz: Sure, sure.
--:-- Mark Zuckerberg: Yeah, I mean, the, yeah, I thought you were saying if the reasoning model doesn't work, why?
--:-- Mark Zuckerberg: I mean, I think that's kind of your way in the details.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: But it's, yeah, you just, you build more, you build more capabilities into it.
--:-- Mark Zuckerberg: And I think that there's probably an order too.
--:-- Mark Zuckerberg: So the work that Alex and the evolutionary scale folks worked on is a lot of it is protein.
--:-- Mark Zuckerberg: Which is interesting because that's at a kind of smaller resolution, obviously, than the cellular data, the cell outlas.
--:-- Mark Zuckerberg: 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 subcomponents of the cells are going to interact.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: And then once you have that, you build things like the virtual immune system, which allows you to simulate much more complicated systems.
--:-- Mark Zuckerberg: But it's sort of this hierarchical approach to building up these virtual models.
--:-- Ben Horowitz: That makes a lot of sense.
--:-- Mark Zuckerberg: Because also as you get into personalization, you've got like common proteins combining into a unique cell.
--:-- Mark Zuckerberg: So that makes it like from a systems standpoint, that makes it like much more manageable.
--:-- Mark Zuckerberg: That makes a lot of sense.
--:-- Ben Horowitz: Interesting.
--:-- Mark Zuckerberg: Yeah.
--:-- Ben Horowitz: Yeah, no, it's very fascinating stuff.
--:-- Mark Zuckerberg: So you guys are announcing some big news this week.
--:-- Mark Zuckerberg: Do you want to give us a sneak preview?
--:-- Mark Zuckerberg: Well, the big news is thinking about how we are going to be coming together as one team.
--:-- Mark Zuckerberg: And, you know, in the past we have done, we've run biohubs and we've done built software.
--:-- Mark Zuckerberg: We've done some AI research. But all of it has been really thinking about has been a little bit decentralized.
--:-- Mark Zuckerberg: But now under Alex's leadership, we are going to 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.
--:-- Ben Horowitz: Amazing.
--:-- Mark Zuckerberg: Alex is amazing.
--:-- Ben Horowitz: Yeah, no, he's great.
--:-- Mark Zuckerberg: And then the other thing is the piece that I mentioned earlier, which is just, yeah, I mean, CCI has focused on a number of different things.
--:-- Mark Zuckerberg: We've really just found over time that we feel like we've been able to make the biggest difference in science.
--:-- Mark Zuckerberg: So we've just kept on doubling down on it.
--:-- Mark Zuckerberg: And we're going to continue doing work in education.
--:-- Mark Zuckerberg: We're going to continue supporting local communities and in those different pieces.
--:-- Mark Zuckerberg: But going forward, the biohub is really going to be the main thrust of our philanthropy.
--:-- Mark Zuckerberg: And we're very excited about that because I think that this is, there has been, you know, when we started the mission to see if we could help the scientific community cure and prevent diseases by the end of the century.
--:-- Mark Zuckerberg: I do think with the advances in AI, that should be possible to do significantly sooner.
--:-- Mark Zuckerberg: 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.
--:-- Ben Horowitz: So there's obviously plenty of advantages to decentralization from a management communication overhead and so forth.
--:-- Mark Zuckerberg: And so what are you trying to add by adding this kind of new layer slash unification on top, like what are the outputs?
--:-- Mark Zuckerberg: And then I guess what are the complexities to that?
--:-- Mark Zuckerberg: Because that's, I'm sorry to ask a CEO question.
--:-- Ben Horowitz: No, no, I mean, I'm like, I'm just for a friend.
--:-- Mark Zuckerberg: We think about this.
--:-- Mark Zuckerberg: You want to go for it?
--:-- Mark Zuckerberg: Yeah, so there are obviously amazing groups doing Frontier AI and a lot of groups doing great Frontier biology.
--:-- Mark Zuckerberg: And where we think we can do uniquely is actually tie these two together.
--:-- Mark Zuckerberg: 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, at the tissue cell-cell communication, our cryo-EM, where we can look at the cell at nearly atomic level.
--:-- Mark Zuckerberg: 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.
--:-- Mark Zuckerberg: And so we have amazing teams doing that work, and we're building these AI models.
--:-- Mark Zuckerberg: And so the reason to do it together is then we can actually complete the flywheel.
--:-- Mark Zuckerberg: Like, you know, the model is looking like it has some gaps and blind spots in this area.
--:-- Mark Zuckerberg: Okay, who do we talk to? How do we build the next data set?
--:-- Mark Zuckerberg: And, you know, we're seeing this in the lab.
--:-- Mark Zuckerberg: Like, the metadata is going to be so rich that we can feed back into the way that we do this modeling.
--:-- Mark Zuckerberg: And so if we can close that loop, which is our goal, in bringing everyone together, I think it's going to be incredibly powerful.
--:-- Ben Horowitz: Well, yeah, that's so interesting because that is exactly, like, that's been the biggest surprise in the industry for us in AI world.
--:-- Mark Zuckerberg: Like, forget biology for one second, is that the domain-specific models have been, like, super interesting.
--:-- Mark Zuckerberg: Like, the original thesis was, like, there's just some AIs that get so smart, they're going to be smarter than everybody at everything.
