this is a a a space that I mean 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 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. 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.

>> And that's crazy.

>> Yes. And it was true because if you just decided to spend the money funding the next best grant for every single lab in the country, like you there was no pathway to that being true. The biology folks, I think, looked at it as if it were crazy ambitious. And then the AI folks are like, well, that's kind of boring. That's just automatically going to happen. I know. It's like, okay, there's something in between there that needs to be bridged.

>> Mark Priscilla, welcome to the Asz podcast.

>> Thanks for having us.

>> Yeah, great to be here. Excited.

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

>> Yeah. Well, 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 hear your side of the story.

>> 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 w, 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 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 like a few lines of information to how we were supposed to take care of the patient. And for me, that's when I really 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 I think of it as the pipeline of hope.

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

>> Um do you want to do you want to answer that one?

>> Yeah. Well, well, I mean we're not going to cure all diseases to be clear. I mean the the strategy is to help scientists and the scientific community cure all diseases. So the strategy is really one of accelerating the pace of basic science and the theory that we had was if you look at the history of science most major breakthroughs are basically preceded by the invention of a new tool to observe phenomena in a new way. Right? So think about things like the microscope, right? Being able to observe bacteria or you know other fields, the telescope or

>> 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? So it's um that's like the old days

>> when you

>> so 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 parcled out into these relatively small grants that allow individual investigators to investigate usually pretty near-term things. 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, often times more expensive to develop. So think about like on the order of a hundred you know maybe you know hundred million to a billion dollars over a um over a 10 to 15 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

>> right and and there it seems like there's also something that that's is you don't really get credit for the tools in a lot of ways. I mean, we've been noted, well, we have companies that use your tools and they're very happy about it. But, um, you know, I didn't even know that that was the case. And so,

>> that's why it's philanthropy.

>> Yeah. Well, it is, but most people do philanthropy to get credit, too. I mean, you know, like that's a you know, that that's kind of a part of it. So, how did you 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.

We're super focused on 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. 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

>> and crazy.

>> Yes. And it was true because if you just decided to spend the money funding the next best grant for every single lab in the country 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 data sets. And we're like, okay, well then we can start doing something about that. Um, and so that's where the idea of building shared tools cuz no one right now in the science.

>> Well, that's so interesting. So basically, you're like, we're going to cure all disease and they're like,

>> yeah, can't be done. Why can't it be done? Well, because we don't have the tools. Okay, that's pretty that's a pretty cool sequence.

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

>> AI has got to be the most overestimated and underestimated technology ever like simultaneously. So weird.

I mean, yeah, we'll probably like the internet early on, 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, right? So, there's 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

>> research to build um you know to either discover new data sets or or or looking to certain challenges.

>> But so far there hasn't been anyone who's tried to do both of those at once. And when you look at I mean even something like AlphaFold which is amazing right it's it was built off of this data set that was a public data set that had been produced 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

>> right

>> so I think that that's like a a pretty interesting zone to be in

>> 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 return. 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 of our of our philanthropy at this point.

>> Um, but yeah, I mean, that's kind of that's basically the focus. Maybe you're not giving yourselves enough credit because you're sort of saying, "Well, there's bite-size science. We didn't want to do that. There's century scale science 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 kind of the way you communicate about them and the way you energize

>> the scientific community about them. 10 to 15 is kind of an interesting time horizon sort of like similar to the time horizon of a venturebacked company similar to the time horizon on which a team can work together for that period of time I think it'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

>> 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 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 you like we we have 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 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 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 cell communications within tissues. And then in San Francisco, we're looking at deep im imaging and uh transcrytoics.

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 and the uh large language models and AI coming into the picture has been so interesting because we were already building tools to measure interesting data building the data sets but we didn't really know what to do with them yet. Um and large language models coming onto the scene we're like wow we can make sense of all of this now. I'm curious what you view success as in the therapeutic realm. So, you know, we think a lot about understanding biology and sometimes we bet on startups that want to unlock completely new biological areas, diseases where we don't know what's going wrong. And then there's another group of folks who kind of say, hey, okay, now that we understand what's going wrong, let's fix it. Um, let's come in with a drug. Let's come in with a new type of chemistry, a new type of antibbody. 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?

>> 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 we 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?

>> The horrible us.

>> 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 transcrytoics, we're starting to be able to say, okay, this variant actually impacts this set of downstream cells and then we start looking at the proteins that get expressed and how it looks similar or different to what a healthy cell would look like. Then you can start targeting. Okay, like let's look at that as a target. And you both know the specificity of the target you want to build based on the ability the ability to connect mutation to protein expression as well as to be able to predict off target effects. What are the side effects? Because you also know where else that drug will be able to interact with the body. And and so those are rare like and and but I really think most diseases should be thought of as rare diseases because each one of our biology is different and right now we just get lumped right we get lumped based on age demographics ancestry if we're lucky 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. 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.

>> 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. We don't know why it happens. The IPF is named that way. And so, you know, he was telling me that he used your cell by gene atlases to look at millions of single cells in patients with disease, without disease, try to pinpoint the fibroblasts, double click on the fibroblasts and their gene expression. It's 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 idiopath, 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 that you built to making that data incredibly accessible. So

>> cell by gene is like almost an accident though.

>> Tell us more.

>> So do you want to share a little bit about cellene or do you want me to start? Well, I mean, I don't know which part you want to get into, but I mean, but the cell atlas work overall, I mean, it's kind of this crazy thing that we're, you know, here in, you know, 2025 and there's not the kind of periodic table of elements equivalent for biology, 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 going to 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. I think 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.

>> Well, the single cell work is was one of our first RFAS 10 years ago we started and we were like, okay, we think this is possible. We actually funded the methodology for it to to standardize how it was going to be done. So that was 10 years ago. And we then were we seated 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 seating a few groups and they started doing work and then they told us they had a problem. There was a uh there was 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. 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

>> and then there started being a 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 75% of it. Sorry that's wrong. We've only funded 25% of it. 75% came from the broader community saying this is useful and there's an easy way for us to standardize and build the same metadata.

>> That's right.

>> It's like an interesting what you'd call a network effect, right?

>> Yeah. I was going to say it sounds like the internet. Yeah.

>> Come for the annotation, stay for the stay for the virtual cell model.

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

>> Yeah. And even relative to prior data bases like GIO and and whatnot, they're just simply not as standardized or QC.

>> Yeah. Control.

>> Yeah.

>> Let's get into virtual cells. One of the the great challenges that the grandchild 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, we think that one of this is going to be one of the most important tools at this point is basically building up the kind of hierarchy from proteins to um to just different structures within the cell to whole to like whole like a virtual immune system or different levels of hierarchy. And we think that this is going to end up being like a very important set of tools for people to effectively generate hypotheses for for different science work. 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 examples that Priscilla was talking about a few minutes ago but we think that this is like probably one of the most important sets of tools that you need to build 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 Biohub and and Alex Reeves, the the, uh, leader of it, is actually going to be the the kind of head of the whole science program, which is actually kind of interesting.

>> Yeah. when you think about it where it's like you have AI and biology coming together and really it's like an AI person who understands biology is running it rather than a biologist who has some understanding of AI. I think just kind of speaks a little bit to where we think the the relative um weight of these things is. 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 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 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.

>> That's kind of the idea here.

So we'll 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 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 but but I think is obviously a really important part of what needs to happen.

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

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

>> Yeah. like it's the new fruit of fly.

>> Yeah. I was going to ask given the complexity of a cell, like how close um like how accurate do you think you'll get the model too? 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 with the virtual cell have to be?

>> I think it will obviously iterate and get better and better because right now we we like right now we're still just talking about uh transcrytoics. are expanding into different ways of looking at the cell, but you get more and more accuracy and but I don't think it needs to be 100% accurate to be useful because you just want to be able to derisk the idea on the front end a little bit. Um, and the more and more you derisk it, the the more efficient it gets obviously, but it will be useful 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 to

>> All models are wrong. Some are useful.

>> Yeah.

>> This is hopefully has has utility on certain access.

>> Exactly. And just like the language models, you build in specific capabilities. So it's not so for example, you know, one of the models that uh we're we're publishing is is variant former, right? basically you know makes it so that um it's trained on a bunch of effectively pairs of you you have a cell you apply crisper 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 like okay if you have this 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, 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, you want to 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 because it's spatial. So it kind of gives you a sense of there are all these different models that you can have that allow you to um basically look at different kinds of things and then you just train them in to be increasingly general over time.

>> Wow. 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

>> Oh, that's actually Yeah. I know that's a fascinating one too and because one of the new models um I think this one is very early 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. Um I think that one's quite early but it's uh but it is interesting conceptually as what I think is clearly going to be an important direction

>> um in terms of how these models evolve.

>> Yeah. No it because that's what I was thinking you know that if it doesn't work the next question you have is why?

>> Yeah.

>> You know like

>> but I think what you find in reasoning the the analogy

>> you're married to your hypothesis. Well, yeah. Sure. Sure. Yeah. I mean, the the uh

>> Yeah. I thought I thought you're saying if if the reasoning model doesn't work, why? I mean, I think the kind of way in No, it's I mean, the language model analogy for that would be you need better kind of world models or or better pre-trained models in order to get the reasoning to be good. But, 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 atlas 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 what um how the sub components of the cells are going to interact.

So

>> our view is that you basically want to build up a state-of-the-art protein model and then have that be a part of the state-of-the-art cellular model and then once you have that you build things like the virtual immune system which allows you to simulate um much more complicated systems. But it's sort of this like hierarchical approach to building up these these uh virtual models. That makes a lot of sense because also as you get into personalization, you've got like common proteins combining into a unique cell. So that makes it like from a systems standpoint that makes it like much more manageable. That that makes a lot of sense. Interesting.

>> Yeah.

>> Yeah. Know it's it's it's very fascinating stuff.

>> Yeah.

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

Well, I 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 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.

>> Amazing. Alex is amazing. So,

>> yeah. No, he's great. And then and then the other thing is that the piece that that I mentioned earlier, which is just Yeah. I mean, CCI 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 and we're going to continue doing work in education. We're going to 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 and we're very excited about that because 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 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.

So there there's obviously like plenty of 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 because that's um I'm sorry to ask a CEO question.

>> No no I I mean I'm like super you want to go for it then I can jump in.

>> 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 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 communication our cryoEM 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 the reason to do it together is then we can actually complete the flywheel like you know the model is looking like it has some gaps and blind spots in this area. Okay, who do we talk to? How do we build um the next data set? And you know we're seeing this in the lab like the metadata is going to be so rich that we can feed back into the way that we do this modeling. Yeah.

I think it's going to be incredibly powerful. And it's it's more than 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 shouldertoshoulder and shaping uh each other's work for this to actually um be the more and more accurate model of how the human cell works.

>> Well, yeah. It's so interesting because that is exact like that's has been the biggest surprise in the industry for us in AI world like forget biology for one second is that the domain specific models have been like super interesting like the original thesis were like there's just some AIs are get so smart they're going to be smarter than everybody at everything but um

>> like on video models like every video model is best at something but not everything And so knowing what problem you're solving actually turns out to be sort of ironically very important in AI um because you can actually get to a way better result.

>> Yes.

>> 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 very counterintuitive to the whole narrative kind of going into it.

>> And in biology it used to be the or at least you know one assumption was well the data sets aren't on the internet. So part of the reason you need a domain specific model is that the data sets are not public.

you guys are kind of bucking that trend too by creating a lot of open- source access to the data and then even then it sounds like you're betting you know on the trend that we're seeing in other industries but still there will be nuance in how you annotate that data curate that data

>> well and how you talk to a scientist right like so because you have to not only know the the data and the model and so forth but like the conversation is what we keep finding out ends up being very very important right

>> so rich and so important how you actually

>> a scientist isn't going to talk to it like you know I talked to chat at GPT or whatever. So, this is the fly you can talk to.

>> Yeah. Yeah. Yeah. That that's really that's super exciting.

>> And the user interface is actually really important. Um 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 to look at the problem. It's like look here, help us solve problems here. And so building that user interface in a way where it's not a very high barrier to 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 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 neuro degeneration right but

>> seems like immunology is behind all this so might be part of your century vision.

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

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 communitydriven data sets? Like which which is the thrust? Or maybe it's both.

>> 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.

>> Cool. So um but I don't I think that these organizational questions of how do you set this up are fascinating and a lot of our approach is sort of informed by

what the rest of the field is doing because I you kind of think about science as it's this portfolio right society has a portfolio of stuff that it's trying to do and as in terms of philanthropy you want to

>> be the most additive that you can be by trying to figure out what else is underrepresented. So science by default is very decentralized, right? It's like kind of the the way that granting has worked, the way that I think scientists by default want to work.

>> 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. So the very first Biohub what we did there were two kind of interesting things. One was it was this collaboration between UCSF, Stanford and Berkeley and there are all these really smart people at all these different places who previously I guess in theory they could have figured out a way to work together but there was not really a formal construct for them to do that and this just allowed a lot more collaboration.

Mhm.

>> The other one is cross- discipline. Basically having biologists sit next to engineers and this view that like these two disciplines are things that need to um and I I don't know. I mean I'm sure you know you've seen this in a lot of in a lot of the companies but like

>> it's there's so many interesting

>> in the companies they always like set them apart.

>> Well, it's interesting. No, it's interesting how many organizational questions or problems you can fix just by having two teams sit together, right? It's like it doesn't matter what the or chart is or like whatever. It's like you guys need to sit next to each other and until you get this thing to work and

>> um

>> that's something I really believe in. So,

>> and you have 10 you have 10 to 15 years.

>> Well, no, it's all like communication is such an underrated problem in general. Yeah. Uh in in all kinds of in building anything or solving anything. So,

>> that's a that's pretty neat.

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

>> Yeah. And we still rely on like

>> Yeah. There's famous like stories in the MIT lab about that. That's how they invented lasers and so forth is they put a bunch of people from different departments in the same

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

>> Um

>> yeah,

>> the research they don't want employees working for them. They don't want space. Yeah. They just want GPUs,

>> agents. So it's just like in a sense that's new lab space. Um it's much more expensive than wet lab space.

>> 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 kind of like

>> scientists and residents or something like that

rental kind of hoteling.

>> The core of it is clusters. um you know if you look at individual labs they'll have like

>> like a large lab would have tens of GPUs

>> um and we were the first to really build a large scale compute cluster um a thousand now we're we have plans to move to the 10,000 range and that one requires a different type of project obviously you're are 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

>> 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

>> that you're going to create interesting

>> interesting doors

>> to utilize the resources that's awesome

>> yeah I mean the GPUs are somewhat zero sum Right. So that the data isn't. So

>> Yeah. Fair enough.

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

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

>> If they think you're doing a good job,

>> if they think you're they're not always right.

>> They're not always different. But I was still envious cuz that was I was like I craved that feedback like am I doing a good job?

And you know 10 years in you the reason why we're doubling down on biology is like not only did we achieve what we said we were going to do and when we set out to set out on these projects it actually delivered more than we thought we were going to. And I was like okay that's a signal I can latch on to 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. because it's all those iterations along the way that have sort of allowed us to get to this place where you know to get lucky ready having built data 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've sort of set the DNA for that.

>> Amazing.

>> Oh, no pun intended.

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

Yeah. Yeah.

>> Yeah. You have customers which is pretty cool.

>> Yeah.

>> For philanthropy. Like that's awesome.

>> Yeah. No, it's it's one of the fun things about building tools is like you kind of get to see

>> Yeah.

>> How valuable do people find the tools? Do people use the tools in order to publish important work?

>> Right. Right. Right. Right. Yeah. And well, I mean our feedback is they're awesome.

>> Feedback

and and completely unique by the way. So like

>> the the other thing is like what would you use if you didn't have this? It's like there's nothing.

>> No. Yeah. It's a real 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 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 bring them out to everyone in the world. But this is a a space that and that there's just going to be a huge amount of leverage with AI and it is um yeah it's it still seems like there could be a lot more effort in the space around building tools and just accelerate the whole thing a lot better.

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

>> that's got good good founder market.

>> Yes. Founder market fit. I mean if we didn't exist would it be a problem? Yes. Like those questions uh really land you know as a VC

>> like one of us as an engineer the other one scientist doctor.

>> Yeah very happy this direction.

>> Yeah

>> we thank you very much not only for our companies but for us as humans um for working on this work. It's amazing work. Thank you.

>> Thank you guys.

>> Thank you so much.