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

Mark Zuckerberg 0:00 This is a, a, a space that, I mean, 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, you know, 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.

Priscilla Chan 0:23 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.

Mark Zuckerberg 0:31 [laughs]

Priscilla Chan 0:32 And because-

Mark Zuckerberg 0:33 They're like, "You're crazy."

Priscilla Chan 0:33 Yes.

Mark Zuckerberg 0:34 [laughs]

Priscilla Chan 0:34 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, you-- there's no pathway to that being true.

Mark Zuckerberg 0:45 The biology folks, I think, looked at it as if it were crazy ambitious, and then the AI folks are like, "Well, that's kind of boring. That's just automatically gonna happen."

Erik Torenberg 0:55 [laughs]

Mark Zuckerberg 0:55 So, I mean, it's like, okay, there's something in between there that needs to be bridged.

Erik Torenberg 0:59 [upbeat music] Mark, Priscilla, welcome to the a16z Podcast.

Priscilla Chan 1:05 Thanks for having us.

Mark Zuckerberg 1:06 Yeah. Great to be here. Excited.

Erik Torenberg 1:07 All right. Excited to have you. You're doing exciting stuff. Yeah. Well, to, to that end, almost a decade ago, you guys started the 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. Why don't we talk about-- Take us behind the conversations of why you guys picked this one. Maybe Priscilla, why don't we start with, with you and you-your, your side of the story?

Priscilla Chan 1:31 It always surprises people when I talk about how we work in basic science research. Um, I trained as a pediatrician, and people always think like, "Oh, it must be about medicine." And for me, it wa-- Uh, you know, 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, like, we just had no idea what, what the problem was. And they might have, like, a specific gene that they could name if they were lucky, um, 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, uh, like, a few lines of information to how we were g- supposed to take care of the patient. And for me, that's when I really, like, realized the power of basic science and how we need to work on basic science to advance the forefront of what's possible. And without that, there's sort of-- It-- I think of it as the pipeline of hope.

Erik Torenberg 2:41 Yeah.

Priscilla Chan 2:42 Hmm.

Erik Torenberg 2:42 And why did you think, um, you could cure all disease? 'Cause that's, like, a very, like, aggressive goal.

Priscilla Chan 2:49 Um, do you wanna, do you wanna answer that one?

Mark Zuckerberg 2:51 Yeah. Well, well, I mean, we're not gonna cure all diseases-

Erik Torenberg 2:54 Yeah

Mark Zuckerberg 2:54 ... to be clear. I mean, the, the strategy is to help scientists and the scientific community cure all diseases.

Erik Torenberg 3:01 Yeah.

Mark Zuckerberg 3:01 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?

Erik Torenberg 3:18 Right.

Mark Zuckerberg 3:18 So think about things like the mi-microscope, right?

Erik Torenberg 3:20 Right. Right.

Mark Zuckerberg 3:20 Being able to, you know, observe bacteria or-

Erik Torenberg 3:22 Sure

Mark Zuckerberg 3:22 ... you know, other fields, the telescope or-

Erik Torenberg 3:24 Yeah

Mark Zuckerberg 3:25 ... um, you know. But it's, just to use an engineering example, you know, it's-- 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?

Erik Torenberg 3:36 [laughs] Yeah, yeah. Debugging.

Mark Zuckerberg 3:36 So it's, um-

Erik Torenberg 3:38 That's like the old days, when you-

Mark Zuckerberg 3:39 Yeah, yeah, yeah. [laughs]

Priscilla Chan 3:41 [laughs]

Mark Zuckerberg 3:41 So,

Mark Zuckerberg 3:42 so our, 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 that-- There's a niche that I think fits that, because if you look at how funding works in science, you know, 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.

Erik Torenberg 4:09 Right.

Mark Zuckerberg 4:09 Um, 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, um, are longer term, oftentimes more expensive to develop. So think about, like-

Erik Torenberg 4:23 Right

Mark Zuckerberg 4:24 ... on the order of a hundred, you know, m-maybe, you know, a hundred million to a billion dollars over a, um, over a ten to fifteen year period, and then you, you try to unlock those tools and give them to the scientific community to accelerate the pace. So that's, that's kind of the, the theory.

Erik Torenberg 4:39 Right. And, and there-- It seems like there's also something that, that, that-- is you don't really get credit for the tools in a lot of ways. I mean, we've been noted, we have companies that, uh, use your tools and very happy about it. But, um, you know, I didn't even know that that was the case, and so-

Mark Zuckerberg 4:55 That's why it's philanthropy.

Erik Torenberg 4:56 Yeah, well, it is.

Mark Zuckerberg 4:58 [laughs]

Erik Torenberg 4:58 But most people do philanthropy to get credit too. I mean, you know, like, that's a, you know, that's kind of a part of it. So how did you-- I, I guess, did you think about that, or were you just like, "No, like, this is gonna work, and if it works, that's all we need"?

Priscilla Chan 5:13 We're s-super focused on-

Erik Torenberg 5:15 Yeah

Priscilla Chan 5:15 ... like, actually making every scientist better, and, and beyond science, like startups, startup founders, because I-- the point is, we can't do this alone.

Priscilla Chan 5:26 And 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.

Mark Zuckerberg 5:34 [laughs]

Priscilla Chan 5:35 And because-

Mark Zuckerberg 5:36 They're like, "You're crazy."

Priscilla Chan 5:37 Yes.

Mark Zuckerberg 5:38 [laughs]

Priscilla Chan 5:38 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, you-- 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 like, "We don't have-- We're not working on big projects and building the right datasets." And we're like-Okay, well then we can start-

Vineeta Agarwala 6:11 Let's do that

Priscilla Chan 6:11 ... doing something about that. Um, and so that's where the idea of building shared tools, 'cause no one right now in the scientific-

Vineeta Agarwala 6:17 Oh, that's so interesting. So basically you're like, "We're gonna cure all disease," and they're like-

Priscilla Chan 6:22 Can't

Vineeta Agarwala 6:22 ... "Yeah, c-can't be done." Why can't it be done? Well, 'cause we don't have the tools. Okay. [laughs] That's pretty, that's a pretty cool sequence.

Mark Zuckerberg 6:29 Yeah, I mean, there's also this funny thing where the, the biology folks I think looked at it as if it were crazy ambitious, and then the AI folks are like, "Well, that's kind of boring. That's just automatically gonna happen."

Vineeta Agarwala 6:41 [laughs] Yeah.

Mark Zuckerberg 6:41 So I know, that's like, okay, there's something in between there that needs to be bridged.

Priscilla Chan 6:44 Yeah.

Mark Zuckerberg 6:45 And if you can like kind of use the, 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, um-

Vineeta Agarwala 6:55 AI's got to be the most overestimated and underestimated technology [laughs] ever.

Priscilla Chan 6:59 [laughs]

Vineeta Agarwala 6:59 Like simultaneously. It's so weird.

Mark Zuckerberg 7:00 I, I mean, yeah, well probably like the internet early on.

Vineeta Agarwala 7:03 Yeah. Yeah, yeah.

Mark Zuckerberg 7:03 But, but we kind of think about ourselves and the work that we're doing at the Biohub as frontier biology paired with frontier AI.

Vineeta Agarwala 7:10 Yeah.

Mark Zuckerberg 7:10 Right? So there's-

Vineeta Agarwala 7:11 Oh, right

Mark Zuckerberg 7:11 ... there are labs that do frontier AI that, uh, basically, you know, are building the most advanced models. Um, and then there are lots of biological research organizations that, that effectively do very leading edge-

Vineeta Agarwala 7:26 Mm-hmm

Mark Zuckerberg 7:26 ... research to build, um, you know, to either discover new data sets or, or, or-

Vineeta Agarwala 7:32 Right

Mark Zuckerberg 7:32 ... 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-

Vineeta Agarwala 7:41 Mm-hmm

Mark Zuckerberg 7:42 ... right? It's, it was built off of this data set-

Vineeta Agarwala 7:44 Right

Mark Zuckerberg 7:44 ... that was a public data set that had been produced d- decades ago, right? And, um, what, 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.

Vineeta Agarwala 8:01 Yeah. Right. Right. Right.

Mark Zuckerberg 8:01 So I think that that's like a, a pretty interesting zone to be in.

Vineeta Agarwala 8:05 Yeah.

Mark Zuckerberg 8:06 And of all the things that, that we've, uh, that we've worked on, you know, actually, when, when we started CZI, we, 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 returns, so we've just doubled down on it over and over and over until now we're at the point that, you know, we're 10 years in, and Biohub is really the, like, main focus of-

Vineeta Agarwala 8:27 Mm

Mark Zuckerberg 8:27 ... of our, of our philanthropy at this point. Um, but yeah, I mean, that's kind of, that's basically the focus.

Vineeta Agarwala 8:33 Well, I think if you-- maybe you're not giving yourselves enough credit 'cause you're sort of saying, "Well, there's bite-sized science. We don't wanna do that. There's century s-scale science [laughs] , and that seemed like a long time horizon, but achievable, ambitious." But you've actually identified, you know, which I think is really fantastic, grand scientific challenges that are right in between. They're 10 to 15-year horizons, at least per-

Mark Zuckerberg 8:54 Mm-hmm

Vineeta Agarwala 8:54 ... kind of the way you communicate about them-

Priscilla Chan 8:56 Yep, that's right

Vineeta Agarwala 8:56 ... and the way you energize-

Mark Zuckerberg 8:57 Yeah

Vineeta Agarwala 8:57 ... the scientific

Vineeta Agarwala 8:58 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, on which a team can work together for that period of time. I think that's-- 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 know, you build a lot of credibility around it the way that you've announced those challenges.

Priscilla Chan 9:26 Well, I'm curious how you guys think about it, but for us, when we looked at the grand challenges for, on the 10 to 15 ti- year time horizon, it needs to be like, when you look at it, y- you're like, "I see a path."

Vineeta Agarwala 9:39 Right.

Mark Zuckerberg 9:39 Mm-hmm.

Priscilla Chan 9:39 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.

Vineeta Agarwala 9:46 Then it wasn't ambitious enough.

Priscilla Chan 9:46 Yeah.

Vineeta Agarwala 9:47 [laughs]

Priscilla Chan 9:47 Like you, like we, we have, we have some risk appetite, right?

Vineeta Agarwala 9:50 Yeah.

Priscilla Chan 9:51 So we want things where we're like, there's a credible pathway, someone, uh, 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, the returns could be higher than even expected. And the way we modeled that from, you know, in the Biohubs is we, we have three Biohubs. We have o- one in San Francisco, one in Chicago, one in New York. The one in New York works on cell engineering. You know, can we engineer cells to go in and detect signals, read it out, or to take certain actions? In Chicago, we're building tissues and looking at, uh, tissue c- ce- cell-cell communications within tissues. And then in San Francisco, we're looking at deep in- imaging and, uh, 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, um, 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, uh, that's how we sort of choose the grand challenge and, um, and the locations. And then the sort of layering or sh- and the, uh, l- large language models and AI coming into the picture has been so interesting

Priscilla Chan 11:17 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. Um, and large language models coming onto the scene, we're like, "Wow, we can make sense of all of this now."

Vineeta Agarwala 11:33 I'm curious what you view success as f- in the therapeutic realm. So, you know, we think a lot about understanding biology, and sometimes we bet on startups that wanna 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."

Priscilla Chan 11:53 Mm-hmm.

Vineeta Agarwala 11:53 Um, 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 Biohub looks like 10, 20, 50 years from now?In terms of the new medicines that you've enabled

Priscilla Chan 12:09 We want there to be like an explosion of a community who are building these, um, just the new wave of what it means to be deploying precision medicine. Like w-- like I think for rare diseases and common diseases alike, you're really talking about individual biology that we sort of lump together. Um, and, uh, they-- and we often don't know how it happens, right? We know that you have this mutation or the worst nightmare is you have a variant of unknown significance. What does that even mean?

Vineeta Agarwala 12:40 The horrible VUS.

Priscilla Chan 12:41 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 w- 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 bil-- the ability to connect mutation to protein expression, as well as to be able to predict off, uh, target effects. What are the side effects? Because you also know where else that drug will s- be able to interact with the body. And, and so those are rare,

Priscilla Chan 13:32 like and, and but I really think most diseases should be thought of as rare diseases because each one of our biology is different.

Vineeta Agarwala 13:41 Yeah.

Priscilla Chan 13:41 And right now we just get lumped, right? We get lumped based on age, demographics, ancestry, if we're lucky, uh, 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.

Vineeta Agarwala 14:20 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&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. Um, there's a startup in our portfolio working on idiopathic pulmonary fibrosis. The name tells you how vexing the disease is. It's idiopathic.

Priscilla Chan 14:46 Mm-hmm.

Vineeta Agarwala 14:46 We don't know [laughs] why it happens, but IPF is named that way. And so, you know, he was telling me that he used your Cell by Gene atlases-

Priscilla Chan 14:54 Mm-hmm

Vineeta Agarwala 14:54 ... 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-

Priscilla Chan 15:04 It's incredible

Vineeta Agarwala 15:05 ... and try to, you know-

Mark Zuckerberg 15:05 Yeah

Vineeta Agarwala 15:05 ... 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, you know, idiopathic, um, origin. So, um, I think there's a huge, there's a huge group of innovators who are, who love the tools, the visualizations, the query systems, and really the software approach-

Priscilla Chan 15:25 Mm-hmm

Vineeta Agarwala 15:25 ... that you've built to making that data incredibly accessible. So thank you.

Priscilla Chan 15:28 Cell by Gene is like almost an accident, though.

Vineeta Agarwala 15:30 [laughs]

Mark Zuckerberg 15:31 [laughs]

Priscilla Chan 15:31 Um-

Vineeta Agarwala 15:32 Tell us more.

Mark Zuckerberg 15:33 That's it, yeah.

Priscilla Chan 15:33 So do you want to share a little bit about Cell by Gene, or do you want me to start?

Mark Zuckerberg 15:36 Well, I mean, I don't know which part you want to get into, but I mean, but the

Mark Zuckerberg 15:39 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, right? So that was sort of a lot of the inspiration of it was, all right, how do we both, through work that we're gonna do in the Biohub and through other grants, um, 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.

Priscilla Chan 16:09 Yeah.

Mark Zuckerberg 16:09 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.

Priscilla Chan 16:21 Well, the single cells work is, was one of our first RFAs 10 years ago we started.

Mark Zuckerberg 16:26 Mm-hmm.

Priscilla Chan 16:26 And we were like, "Okay, we think this is possible." We actually funded the methodology for it to, to standardize how it was gonna be done. So that was 10 years ago. And we then were-- 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, um, especially with, like, a burgeoning technique. And so we ended up, um, seeding a few groups, and they started doing work, and then they told us they had a problem. There was a, uh, there was a, a bottleneck in their workflow because they couldn't annotate the data fast enough. Um, and so we built, Cell by Gene was an annotation tool. That's the original source of this.

Mark Zuckerberg 17:13 Huh.

Priscilla Chan 17:13 So we built the annotation tool to make it easy for people to, who are doing single cell science to be able to annotate the data. And then we put, 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.

Vineeta Agarwala 17:31 Hmm.

Priscilla Chan 17:31 And then there started being a f- 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, uh, shared resource for the entire scientific community. We only funded about seventy-five percent of it. Sorry, that's wrong. We've only funded twenty-five percent of it. Seventy-five percent came from the broader community saying, "This is useful, and there's an easy way for us to standardize and build these together."

Vineeta Agarwala 18:00 The default, you have the same metadata.

Mark Zuckerberg 18:02 Yeah.

Priscilla Chan 18:02 That's right.

Vineeta Agarwala 18:03 Formatting format.

Mark Zuckerberg 18:03 It's like an interesting, it's like what you'd call a network effect, right? [laughs]

Vineeta Agarwala 18:06 Yeah.

Mark Zuckerberg 18:07 Exactly. [laughs]

Vineeta Agarwala 18:07 Yeah, I was gonna say, it sounds like the internet. Yeah, yeah. Like come for the annotations, stay for the-

Mark Zuckerberg 18:11 Yeah.

Vineeta Agarwala 18:12 Stay for the virtual cell models.

Priscilla Chan 18:13 Mm-hmm.

Mark Zuckerberg 18:13 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.

Vineeta Agarwala 18:21 Yeah.

Mark Zuckerberg 18:21 And then once that kind of took off as, as the way that it would get done, then other people just found it valuable too.

Vineeta Agarwala 18:26 Yeah, and even relative to prior data bases like Geo and, and whatnot, they're just simply not-

Mark Zuckerberg 18:30 Yeah

Vineeta Agarwala 18:30 ... as standardized or QC.

Priscilla Chan 18:32 Yeah.

Mark Zuckerberg 18:33 Yeah.

Vineeta Agarwala 18:33 Yeah.

Mark Zuckerberg 18:34 Le-let's get into virtual cells. One, one of the-

Priscilla Chan 18:35 Sure

Mark Zuckerberg 18:36 ... the great challenges, the grand challenges you would focus on. Um, maybe talk about what is the promise or the hope and maybe some of the challenges or where we're at with it.Yeah, I mean, w-we think that one of-- this is gonna be one of the most important tools at this point, is basically building up the kind of hierarchy from proteins to, um, to just different structures within the cell to whole str- to like whole, like a virtual immune system or different levels of hierarchy. And we think that this is gonna end up being like a very important set of tools for people to, uh, effectively generate hypotheses for, for different science work. Um, you know, even before you get to the point where you're really running full experiments in it, you can come up with some, um, estimate of how that might run. Um, it will be useful for some of the precision medicine type, um, e-e-examples that Priscilla was talking about a, a few minutes ago.

Mark Zuckerberg 19:27 But we think that this is like probably one of the most important sets of tools that you need to build. Um, and it's not a single thing, right? So there's different angles to, to come at this from. The Cell Atlas data is helpful for understanding things on a cellular level. Um, one of the, the kind of most important things that we're doing right now, the, the, um-- There's this, this great company, Evolutionary Scale, who actually had a bunch of researchers who'd formerly worked at Meta on protein folding models, um, is joining a, a Biohub, and, and Alex Reeves, the, the, uh, leader of it, is actually gonna be the, the kind of head of the whole science program, which is actually kind of interesting-

Erik Torenberg 20:07 Yeah.

Mark Zuckerberg 20:07 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-

Erik Torenberg 20:15 Right. Yeah

Mark Zuckerberg 20:15 ...who has some understanding of AI, I think just kind of speaks a little bit to where we think the, the relative, um, weight of these things is. But I mean, we basically view, you know, like Priscilla was saying with the different Biohubs, and 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, and share that data, and then you can build that into models. The Chicago Biohub being able to record inflammation, um, and, and basically study that in order to kind of help understand, um, like that-that's a, that's a different data set. We have the Imaging Institute, which is w-we just trained our, our first set of models around that, which are the first like spatial models around understanding like the way that, that kind of cells look in different states. And eventually, just like you have this analogy on the, um, 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.

Erik Torenberg 21:15 Right.

Mark Zuckerberg 21:15 That's kind of the idea here. So we'll-

Erik Torenberg 21:17 Huh.

Mark Zuckerberg 21:17 We'll, 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-

Erik Torenberg 21:31 Mm-hmm

Mark Zuckerberg 21:31 ...of a virtual cell that will be useful, um, both for scientists and hopefully startups and companies that are working on finding drugs, which is not our part of the whole thing-

Erik Torenberg 21:41 Mm-hmm

Mark Zuckerberg 21:41 ...but, but I think is obviously a really important part of what needs to happen.

Priscilla Chan 21:44 Yeah. And you know, you guys think about risk all the time in terms of when you make investments. Like, I think

Priscilla Chan 21:51 the promise of being able to do virtual biology using a virtual cell model is you can actually take on riskier ideas. Right now-

Mark Zuckerberg 21:59 Yeah

Priscilla Chan 22:00 ...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 gonna 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 'cause 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 and tinkering on the computational side and like ask riskier questions, things that would've been expensive and ti- 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.

Erik Torenberg 22:55 Do you think of it kind of like a model organism?

Mark Zuckerberg 22:58 Yeah.

Erik Torenberg 22:58 Like it's the new fruit fly?

Mark Zuckerberg 23:00 Yeah. [laughs]

Priscilla Chan 23:00 [laughs]

Erik Torenberg 23:01 I was gonna ask, given the complexity of a cell, like how close, um, 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?

Priscilla Chan 23:18 I think it will obviously iterate-

Erik Torenberg 23:20 Yeah

Priscilla Chan 23:20 ...and get better and better because right now we, we-- like right now we're still just talking about, uh, transcriptomics. We're expanding into different ways of looking at the cell. But y-you get more and more accuracy and-- but I don't think it needs to be a hundred percent accurate to be useful.

Erik Torenberg 23:36 Mm-hmm. Yeah.

Priscilla Chan 23:36 Because you just want to be able to de-risk the idea on the front end a little bit. Um, and the more and more you de-risk it, the, the more efficient it gets obviously.

Erik Torenberg 23:46 Right.

Priscilla Chan 23:46 But it'll be useful to, if you even get directional signal. And yes, I do, we do think about it like as a, a model organism, but in a way that's like has fidelity to the human body. Like, you know, like I don't want us-

Erik Torenberg 24:00 All models are wrong, some are useful.

Priscilla Chan 24:02 Yeah. [laughs]

Mark Zuckerberg 24:02 Yeah.

Priscilla Chan 24:03 Yes.

Erik Torenberg 24:03 This hopefully-

Mark Zuckerberg 24:04 Yeah

Erik Torenberg 24:04 ...has, has utility on certain axes.

Mark Zuckerberg 24:06 Exactly. And just like the language models, you build in specific capabilities.

Erik Torenberg 24:10 Yeah.

Mark Zuckerberg 24:10 So it's not-- So for example, you know, one of the models that, uh, we're, we're publishing is, is variant former, right? It basically, you know, makes it so that, um, it's trained on a bunch of effectively pairs. If you, you have a cell, you apply CRISPR to it in a place, you see what comes out at the other side. So it's, it basically is able to make that kind of a prediction.

Erik Torenberg 24:31 Mm.

Mark Zuckerberg 24:31 Like, okay, if you have this-

Erik Torenberg 24:33 Edit

Mark Zuckerberg 24:33 ...edit that you're doing to, to a cell, what is likely going to happen? Um, 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, and it will just produce a kind of synthetic model of, of, of the cell. Um-A-again, I mean, it's kind of interesting because to Priscilla's point before about how everyone is different and, and like, and different cells have, have kind of, um, you know, so you, you wanna be able to simulate these kind of rare configurations, um, 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 'cause it's spatial.

Erik Torenberg 25:11 Mm-hmm.

Mark Zuckerberg 25:12 So it kind of gives you a sense of there are all these different models that you can have that allow you to, um, basically look at different kinds of things, and then you just train them in to be increasingly general over time.

Erik Torenberg 25:23 Wow. Yeah, no, very interesting. And is the, is the modeling technology basically LLMs or like, like is there, is there a reasoning model? Is it like a just a-

Mark Zuckerberg 25:32 Oh, that's actually a-- That, yeah, no, that's a fascinating one too.

Erik Torenberg 25:34 Yeah.

Mark Zuckerberg 25:34 Um, 'cause one of the new models, um, I think this one is very early.

Erik Torenberg 25:38 Yeah.

Mark Zuckerberg 25:38 But it's, um, it's, it's basically the first reasoning model over biology. So the, the idea is that, um, yeah, you, you, you effectively have these models that, that kind of simulate world models in different ways, and then you want it to be able to not just, um, 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, would evolve and why things would happen.

Erik Torenberg 26:04 Right.

Mark Zuckerberg 26:04 Um, I know it's quite early, but it's, uh, but it is interesting conceptually as what I think is clearly going to be an important direction-

Erik Torenberg 26:14 Yeah

Mark Zuckerberg 26:14 ...um, in terms of how these models evolve.

Erik Torenberg 26:17 Yeah, no, it, it-- B-because that's what I was thinking, y-you know, that if it doesn't work, the next question you have is why. [laughs]

Mark Zuckerberg 26:24 Yeah.

Erik Torenberg 26:24 You know, like-

Mark Zuckerberg 26:25 But I think what you find in reasoning, the, the, the analogy to-

Erik Torenberg 26:28 [laughs] 'Cause sometimes you're married to your hypothesis. [laughs]

Mark Zuckerberg 26:30 Well, yeah, sure. Sure. Yeah, I mean, the, the, uh, yeah. I, I thought, I thought you were saying if, if, if the reasoning model doesn't work, why? I mean, I think the-

Erik Torenberg 26:37 Yeah, well, yeah, yeah.

Mark Zuckerberg 26:38 That's kind of-

Erik Torenberg 26:39 You're, you're way in the details. Yeah

Mark Zuckerberg 26:39 ...the making of your... No, that's-- I mean, the language model analogy for that would be you need better kind of world models or s- or, or better pre-trained models in order to get the reasoning to be good. But it's, 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, um, which is interesting because that's at a kind of smaller resolution obviously than the cellular data, the cell, Cell Atlas.

Erik Torenberg 27:09 Right. Yeah.

Mark Zuckerberg 27:09 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 gonna have a somewhat shallow understanding unless you actually have this hierarchical understanding of what, um, how the subcomponents of the cells-

Erik Torenberg 27:25 Mm

Mark Zuckerberg 27:25 ...are going to interact. So our view is that you basically wanna build up a state-of-the-art protein model-

Erik Torenberg 27:32 Mm

Mark Zuckerberg 27:32 ...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 simulate, um, much more complicated systems. But it's sort of this like hierarchical approach to building up these, these, uh, virtual models.

Erik Torenberg 27:48 Uh, y- that makes a lot of sense because a-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 [laughs] standpoint, that makes it like much more manageable. That, that makes a lot of sense. Interesting.

Mark Zuckerberg 28:06 Yeah.

Erik Torenberg 28:06 Wow.

Mark Zuckerberg 28:07 Yeah, no, it's, it's, it's very fascinating stuff.

Erik Torenberg 28:09 Yeah.

Vineeta Agarwala 28:09 So you guys are announcing some big news this week. Do you wanna give us a sneak preview?

Priscilla Chan 28:14 Well, I,

Priscilla Chan 28:15 uh, the big news is, uh, thinking about how we are going to be coming together as one team. Um, and, you know, in the past, we have done, we've run BioHubs, and we've done 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 come together as the Biohub, a, uh, an, an operating philanthropy where we are doing the science, um, in service of a singular goal together, and how do we actually advance the state of biology and research, um, at the intersection of AI and biology.

Vineeta Agarwala 28:56 Amazing.

Priscilla Chan 28:56 Yeah.

Vineeta Agarwala 28:56 Alex is amazing, so [inaudible] .

Mark Zuckerberg 28:58 Yeah, no, he's great. And then, and then the other thing is the, the piece that, 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, we feel like we've been able to make the biggest difference in science, so we've just kept on doubling down on it.

Erik Torenberg 29:11 Mm.

Mark Zuckerberg 29:11 And we're gonna continue doing work in education. We're gonna continue supporting local communities and, and in those different pieces. But going forward, the Biohub is really going to be the main thrust of our philanthropy.

Erik Torenberg 29:23 Mm.

Mark Zuckerberg 29:24 And we're very excited about that 'cause I think that this is-- There, 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. I do think with the advances in AI, that should be possible to do significantly sooner.

Erik Torenberg 29:39 Yeah.

Mark Zuckerberg 29:39 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-

Erik Torenberg 29:48 Yeah

Mark Zuckerberg 29:48 ...make fast progress on that.

Erik Torenberg 29:50 So there, there, there's obviously like plenty of, uh, advantages to decentralization from a management communication overhead and so forth. And so, like, 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? 'Cause that's, um... I'm sorry to ask a CEO question. [laughs]

Mark Zuckerberg 30:12 No, no, I, I mean, I'm, I'm like obsessed with this stuff.

Vineeta Agarwala 30:14 Ask for a friend.

Erik Torenberg 30:15 Yeah. I'm obsessed.

Vineeta Agarwala 30:16 We, we think about this-

Erik Torenberg 30:16 You, you wanna go for it, and then I can jump in.

Priscilla Chan 30:18 Yeah, so there are obviously amazing groups doing frontier AI and a lot of groups doing, uh, great frontier biology. And where we think we can do uniquely is actually tie these two together. And we

Priscilla Chan 30:32 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 teams doing that work, and we're building these AI models. And so what w- the reason to do it together is then we can actuallyComplete the flywheel. Like, you know, the m- model is looking like it has some gaps and blind spots in this area. Okay, who do we talk to? How do we build-

Erik Torenberg 31:21 Yeah

Priscilla Chan 31:21 ... um, the next data set? And, you know, we're seeing this in the lab, like the metadata is gonna be so rich that we can feed back into the way that we do this modeling.

Erik Torenberg 31:31 Yeah.

Priscilla Chan 31:31 And so if we can close that loop, which is our goal in bringing everyone together, um, it's, uh, 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, uh, each other's work for this to actually, um, be the, a more and more accurate model of how the human cell works.

Erik Torenberg 31:58 Well, you know, that's so interesting 'cause that is exact- Like, that's just 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.

Mark Zuckerberg 32:14 Mm-hmm.

Erik Torenberg 32:14 Like the, the, the original thesis was like there's just some AIs are gonna get so smart-

Mark Zuckerberg 32:18 Mm-hmm

Erik Torenberg 32:18 ... they're gonna be smarter than everybody at everything. But, um, like on video models, like every video model is best at something but not everything. [chuckles]

Mark Zuckerberg 32:27 Mm-hmm. Mm-hmm.

Erik Torenberg 32:27 And so knowing what problem you're solving actually turns out to be sort of ironically very important in AI, um, because you can actually get to a way better result-

Priscilla Chan 32:39 Yes

Erik Torenberg 32:39 ... if you put the two together. Like, yeah, we're, we're seeing that over and over, over again, uh, in a way that, that is, I would say-

Mark Zuckerberg 32:47 Yeah

Erik Torenberg 32:47 ... very counterintuitive to the whole narrative kind of going into it.

Priscilla Chan 32:51 In biology, it used to be the, or at least, you know, one assumption was all the data sets aren't on the internet.

Erik Torenberg 32:56 Yeah.

Priscilla Chan 32:56 And so part of the reason you need a domain specific model is that the data sets are not public.

Erik Torenberg 33:00 Yeah.

Priscilla Chan 33:01 You guys are kind of bucking that trend too by creating a lot of open source-

Erik Torenberg 33:04 Mm-hmm

Priscilla Chan 33:04 ... 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.

Erik Torenberg 33:11 Well-

Priscilla Chan 33:11 But still there will be nuance in how you annotate that data, curate that data-

Erik Torenberg 33:14 Well, and how you talk to a scientist, right? Like, so-

Priscilla Chan 33:17 ... and refine the model

Erik Torenberg 33:17 ... 'cause you have to not only know the-

Priscilla Chan 33:19 Yeah

Erik Torenberg 33:19 ... the data and the model and so forth, but like the conversation is what we keep-

Priscilla Chan 33:23 Yeah

Erik Torenberg 33:23 ... finding out ends up being very, very important, right? So-

Priscilla Chan 33:25 So rich-

Erik Torenberg 33:25 Yeah

Priscilla Chan 33:25 ... and so important in how you actually-

Erik Torenberg 33:27 And a scientist isn't gonna talk to it like, you know, I talk to-

Mark Zuckerberg 33:30 Yeah

Erik Torenberg 33:30 ... ChatGPT or whatever, so having this together.

Priscilla Chan 33:32 Well, this is the first fly you can talk to. [chuckles]

Erik Torenberg 33:33 Yeah. Yeah, yeah. No, that-

Mark Zuckerberg 33:34 Yeah

Erik Torenberg 33:34 ... that's really, that's super

Erik Torenberg 33:36 exciting.

Priscilla Chan 33:37 And the user interface is actually really important. Um, y- you talked about, uh, 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-

Erik Torenberg 33:55 Right

Priscilla Chan 33:55 ... to look at the problem.

Erik Torenberg 33:56 Asking questions.

Priscilla Chan 33:56 It's like, "Look here. Help us solve problems here." And so building the user interface in a way where it's not a very high barrier to entry to be able to poke around and learn something, and bring knowledge back to your work, that's intentional, and we're really hoping when we build these virtual models, um, that, uh, 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." Um, a very pertinent example is turns out, I think immunology has a ton to do with neurodegeneration, right? But-

Erik Torenberg 34:32 Seems like immunology is behind all diseases-

Priscilla Chan 34:33 Everything

Erik Torenberg 34:34 ... so might be part of your century vision.

Priscilla Chan 34:36 Uh, so you need to be able to allow the immunologists to come in-

Erik Torenberg 34:41 Right

Priscilla Chan 34:41 ... and understand neurodegeneration and understand how their world fits in.

Erik Torenberg 34:45 Right.

Priscilla Chan 34:45 And so the more you lower the barrier to entry allows people to actually think in a sort of truly collaborative and interdisciplinary way.

Erik Torenberg 34:53 So will the Biohub grow as a team? Like, will you employ more people at the Biohub 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 35:10 Probably a little of both, and we've added new Biohubs over time. Um, and then we're also building up more of this like central AI team.

Erik Torenberg 35:20 Cool.

Mark Zuckerberg 35:20 So, um, but I don't-- I think that these organizational questions of how do you set this up are fascinating.

Erik Torenberg 35:27 [laughs]

Mark Zuckerberg 35:27 And a lot of our approach is sort of informed by what the rest of the field is doing, because

Mark Zuckerberg 35:34 I-

Erik Torenberg 35:34 Mm.

Mark Zuckerberg 35:34 You kind of think about science as it's this portfolio, right? Society has a portfolio of stuff that it's trying to do.

Erik Torenberg 35:40 Mm.

Mark Zuckerberg 35:40 And as in terms of philanthropy, you wanna be the most additive that you can be by trying to figure out what else is underrepresented. So science by default is very decentralized, right? It's like kind of the-

Erik Torenberg 35:52 Super, yeah

Mark Zuckerberg 35:52 ... the way that granting has worked, the way that I think scientists by default want to work.

Erik Torenberg 35:57 Mm-hmm.

Mark Zuckerberg 35:57 Um, so I think a lot of what we've found is that figuring out ways to encourage collaboration in, um, ways that otherwise seem very simple, but weren't happening before, can unlock a lot of value.

Erik Torenberg 36:11 Right.

Mark Zuckerberg 36:11 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.

Erik Torenberg 36:30 Yeah.

Mark Zuckerberg 36:30 And this just allowed a lot more collaboration.

Priscilla Chan 36:32 Mm-hmm.

Mark Zuckerberg 36:33 The other one is cross-discipline, basically having biologists sit next to engineers, and this view that like these two disciplines are-

Erik Torenberg 36:42 Yeah

Mark Zuckerberg 36:42 ... things that need to, um... And I, I don't know. I mean, I'm, I'm sure, you know, you've seen this in a lot of, in, in a lot of the companies, but like it's, uh, there's so many interesting-

Erik Torenberg 36:51 [chuckles] And the companies, they always set them apart.

Mark Zuckerberg 36:53 Well, it's interesting. [laughing] No, it's interesting how many organizational questions or problems you can fix just by having two teams sit together.

Erik Torenberg 37:00 Oh, yeah.

Mark Zuckerberg 37:00 Right? It's like it doesn't matter what the org chart is or like whatever.

Erik Torenberg 37:02 Yeah.

Mark Zuckerberg 37:02 It's like you guys need to sit next to each other and-

Erik Torenberg 37:05 Yeah

Mark Zuckerberg 37:05 ... until you get this thing to work. And, um-That's something I really believe in, so-

Priscilla Chan 37:10 Then you have time.

Erik Torenberg 37:10 Yeah.

Priscilla Chan 37:11 You have 10 to 15 years.

Erik Torenberg 37:12 Well, no, it's all like communication-

Priscilla Chan 37:14 Figure out how it works, yeah

Erik Torenberg 37:14 ... is such an underrated problem in general.

Mark Zuckerberg 37:16 Yeah.

Erik Torenberg 37:17 Uh, in, in all kinds of-- in building anything-

Mark Zuckerberg 37:20 Mm-hmm

Erik Torenberg 37:20 ... or solving anything. So, uh, that's, uh, that's pretty neat-

Mark Zuckerberg 37:24 Yeah, and it's, it's just really kind of simple stuff, but, but I think it's, um, 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 b- just been neat to see, um, other folks who are working in the field also adopt similar models because it's a pretty intuitive thing. But, you know, at some point you'll reach the point where, you know, actually it's really good to have decentralized work too, right? So it shouldn't be that like, uh, 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 that can unlock a lot of value because it, for whatever reason, hasn't been the default to date.

Priscilla Chan 38:02 Yeah. And we still rely on like-

Erik Torenberg 38:04 Yeah, there's famous like stories in the MIT lab about that. That's how they inve-invented lasers and so forth, is they [chuckles] put a bunch of people from different departments in the same space.

Priscilla Chan 38:14 The Media Lab.

Erik Torenberg 38:14 Yeah.

Priscilla Chan 38:15 Yeah.

Mark Zuckerberg 38:15 Yeah.

Priscilla Chan 38:15 Well, actually, physics is where we got a lot of the inspiration. Like, physics has just historically been r- like labs have just rallied around big projects and big shared resources. Um, and we will st-- 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, uh, one more thought on your expansion question is like, and maybe this is like the, uh, modern AI lab, we are not expanding like a lot of square footage per se, but we're expanding our compute. Um- [laughs]

Mark Zuckerberg 38:50 [laughs] Yeah.

Priscilla Chan 38:51 So-

Mark Zuckerberg 38:51 The researchers, they don't want employees working for them. They don't want space.

Erik Torenberg 38:54 Yeah.

Mark Zuckerberg 38:54 They just want GPUs.

Priscilla Chan 38:55 Yeah.

Erik Torenberg 38:56 Yeah.

Priscilla Chan 38:57 So it's just like, in a sense, that's new lab space.

Erik Torenberg 39:00 Mm-hmm.

Priscilla Chan 39:00 Um-

Mark Zuckerberg 39:01 Yeah

Priscilla Chan 39:01 ... it's much more expensive than wet lab space.

Erik Torenberg 39:03 [laughs]

Priscilla Chan 39:03 [laughs]

Vineeta Agarwala 39:03 Mm-hmm. 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, you know, um, I forgot the name of your program.

Priscilla Chan 39:15 Yeah. Um-

Vineeta Agarwala 39:16 But it was, it had kind of like a-

Priscilla Chan 39:17 Scientists in residence or something like that.

Vineeta Agarwala 39:19 Yeah.

Mark Zuckerberg 39:19 Yeah.

Vineeta Agarwala 39:19 But your-

Mark Zuckerberg 39:19 Yeah. No, I think we, we-

Vineeta Agarwala 39:20 ... compute rental kind of pro- hotelling of-

Priscilla Chan 39:22 The core of it is-

Vineeta Agarwala 39:23 ... you know, clusters

Priscilla Chan 39:23 ... um, you know, if you look at individual labs, they'll have like, like a large lab would have tens of GPUs.

Vineeta Agarwala 39:31 Right. Right.

Priscilla Chan 39:32 Um, and we were the first to really build a large scale compute cluster. Um, a thousand hour, we have plans to move to the ten thousand range, and that one requires a different type of project, obviously.

Vineeta Agarwala 39:47 Mm-hmm.

Priscilla Chan 39:47 You're, you're able to ask different types of questions. Um, and, uh, it's a resource that we use, but also we've invited scientists to apply and say like, "What question do you have that, uh, could use this amount of resource?" And be able to, uh, stem, uh, sort of seed collaborations that way.

Vineeta Agarwala 40:06 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-

Priscilla Chan 40:13 Yeah

Vineeta Agarwala 40:14 ... that you're gonna co-create-

Priscilla Chan 40:15 Come talk to us

Vineeta Agarwala 40:16 ... really interesting, interesting doors-

Priscilla Chan 40:18 Mm-hmm

Vineeta Agarwala 40:19 ... to utilize the resources. That's awesome.

Mark Zuckerberg 40:21 Yeah. I mean, the GPUs are somewhat zero sum, right? [laughs] So that, that-- the data isn't, so yeah.

Vineeta Agarwala 40:26 Yeah. Fair enough. Fair enough.

Mark Zuckerberg 40:27 Yeah.

Erik Torenberg 40:28 So you're about to celebrate t-ten years, um, doing this. As, as you look out in the years to come, w-what else can you tell us about either things that you're thinking

Erik Torenberg 40:38 about for the future or maybe even principles or a North Star that's gonna guide how you guys g-grow and evolve g-going forward?

Priscilla Chan 40:44 You know, it's been really interesting in the past ten years because I actually spent the first few years completely envious of people working for for-profit companies.

Erik Torenberg 40:54 Mm-hmm.

Priscilla Chan 40:55 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 job.

Mark Zuckerberg 41:03 If they think you're doing a good job.

Priscilla Chan 41:04 If they think you're doing [laughs] a good job.

Erik Torenberg 41:05 [laughs] They're not always right.

Mark Zuckerberg 41:07 Yeah. [laughs]

Priscilla Chan 41:07 They're not always right.

Erik Torenberg 41:07 I know, it's a bit different. [laughs]

Mark Zuckerberg 41:09 [laughs]

Priscilla Chan 41:09 But I was still envious 'cause that was-- I was like, I craved that feedback like, "Am I doing a good job?" And, you know, ten years in, you know, the reason why we're doubling down on biology is like, not only did we achieve what we said we were going to do a-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 like that's a signal I-- we can really continue doubling down and doing more of that. And so I think it's, uh, continuing to tolerate the early ambiguity when you're like, "Okay, I'm gonna do more of this." Um, and, uh, and being patient, but, uh, uh, being willing to have a long time horizon, but be impatient at the same time.

Vineeta Agarwala 41:55 Mm-hmm.

Priscilla Chan 41:56 'Cause it's all those iterations along the way that have sort of allowed us to get to this place where, you know, to get lucky, ready, having built data, 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, like I think we sort of set the DNA for that.

Vineeta Agarwala 42:21 Amazing.

Priscilla Chan 42:21 Oh, no pun intended.

Erik Torenberg 42:22 [laughs]

Mark Zuckerberg 42:23 Yeah. But we get to see how many people use the tools-

Priscilla Chan 42:25 Blueprint

Mark Zuckerberg 42:25 ... and, and the feedback. Yeah. Yeah. Yeah.

Erik Torenberg 42:27 Yeah. You have customers, which is pretty cool.

Mark Zuckerberg 42:29 Yeah.

Priscilla Chan 42:30 Yeah.

Erik Torenberg 42:30 For philanthropy, like that's awesome.

Mark Zuckerberg 42:32 Yeah. No, it's, it's one of the-

Erik Torenberg 42:33 Yeah

Mark Zuckerberg 42:33 ... fun things about building tools-

Erik Torenberg 42:35 Yeah

Mark Zuckerberg 42:35 ... is like you kinda get to see-

Erik Torenberg 42:37 Yeah

Mark Zuckerberg 42:38 ... how valuable do people find the tools. Do people use the tools in order to publish important work?

Erik Torenberg 42:43 Right. Right, right, right. Yeah. No, well, I mean, our feedback is they're awesome.

Priscilla Chan 42:48 Our feedback's great. We have textbooks. [laughs]

Erik Torenberg 42:49 And, and, and completely unique, by the way.

Priscilla Chan 42:51 Yeah.

Erik Torenberg 42:51 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 42:58 No. Yeah.

Erik Torenberg 42:58 It's really incredible.

Mark Zuckerberg 42:59 It's, it's a real-

Erik Torenberg 42:59 Yeah

Mark Zuckerberg 42:59 ... it's a real kind of void. I mean, there's this whole pipeline that, 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-

Erik Torenberg 43:12 Yeah

Mark Zuckerberg 43:12 ... on, 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, the therapies and, and kind of bringing them out to everyone in the world. But-

Erik Torenberg 43:26 Yeah

Mark Zuckerberg 43:26 ... this is a, a, a space that, I mean, that there's just gonna be a huge amount of leverage with AI, and it is, um, yeah, it, 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.

Erik Torenberg 43:42 Yeah. And I do think it, 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 43:51 It's got good, good founder market.

Erik Torenberg 43:53 Yes.

Mark Zuckerberg 43:53 Yeah.

Erik Torenberg 43:53 Founder market is unique.

Mark Zuckerberg 43:55 [laughs]

Vineeta Agarwala 43:55 [laughs]

Erik Torenberg 43:56 If, if we didn't exist-

Mark Zuckerberg 43:57 Yeah

Erik Torenberg 43:57 ... would it be a problem? Yes. [laughs] Like those questions, uh, really land.

Mark Zuckerberg 44:01 Yeah.

Erik Torenberg 44:02 As a VC-

Mark Zuckerberg 44:02 It's like one of us is an engineer, the other one-

Erik Torenberg 44:04 Yeah. [laughs]

Mark Zuckerberg 44:04 ... a scientist, doctor.

Erik Torenberg 44:06 Yeah.

Mark Zuckerberg 44:06 So yeah.

Erik Torenberg 44:06 Very happy in this direction.

Mark Zuckerberg 44:07 Yeah.

Erik Torenberg 44:08 We thank you very much-

Priscilla Chan 44:10 Yeah

Erik Torenberg 44:10 ... not only for our companies, but for us as humans-

Mark Zuckerberg 44:13 Yeah

Erik Torenberg 44:13 ... um, for working on this work. It's amazing work.

Mark Zuckerberg 44:16 Oh, thank you.

Erik Torenberg 44:16 Really amazing.

Priscilla Chan 44:16 Thank you.

Mark Zuckerberg 44:17 Thank you. Thank you, guys.

Vineeta Agarwala 44:18 Thank you guys.

Priscilla Chan 44:18 Thank you so much. [outro jingle]