Biohub: The Future of Biology is Open-Source with Mark Zuckerberg, Priscilla Chan, and Alex Rives

We'll have a bigger impact by getting this in more scientist hands quicker by doing it as open source projects instead. It's not just like there's some factory somewhere that you can pay to produce the data. You actually need to invent new novel scientific approaches. The theory isn't that we're going to cure the diseases. We're not. It's that we want to help accelerate the pace of progress for the whole scientific field.

Today on No Priors, we're joined by Mark Zuckerberg, Priscilla Chan, and Alex Reeves. We'll be talking about Biohub and all their various efforts to now start applying AI at scale to do world models of cells and different levels of interactions across biology. Mark, Priscilla, thank you for doing this.

Yeah, thanks for having us. This was fun.

Thank you.

You guys made BioHub your primary philanthropic effort and then committed $500 million to this virtual biology initiative. Can you tell us a little bit about, you know, why do that and how did you go from we should fund this to this is like who we are?

No. No. And to be clear, we don't think that we're going to be the ones curing the diseases. Our goal was always to build tools that could accelerate the whole scientific field. That way, the scientific field collectively could cure all the diseases. But still, I thought that by the end of the century was a stretch. Now, I think it's like too conservative. And so we kept being like, 'Okay, well, we had these series of funny, awkward, educational conversations where we're like, okay, but like why? Like why do you think it's impossible?' And like, you know, just being the person in the room is just like, 'Well, I don't know why. You tell me.' Finally, we got people to like they're like, 'Fine, if you really must know.' And we're like, 'No, we do. It seems important.' They were like, well we work in silos and when you publish information doesn't get shared it gets locked up for long periods of time and we don't have tooling. You know, they gave the example of like we build a great tool by one postdoc in a lab and it lives on their computer and when they graduate the tool is gone. And what we heard was very hard to build shared tools to move science faster, build the shared knowledge base to quickly move science faster. And that's sort of where we began in thinking about okay like if those are the problems like what can we contribute.

Yeah, I mean so the original biohub model was basically focus on long-term tool development by bringing together engineers and scientists across multiple universities to focus on long-term tool development and it basically worked. And you know we started off with CZI doing a number of different things and I think over time we just felt like okay the science piece is really working and we just kept on investing more and more and more in it until now it is basically the primary and main thing that we're doing. And we've expanded the original San Francisco Biohub to a handful now at this point. There's New York, there's Chicago. The real focus and the unifying theme at this point is the virtual biology initiative around taking the unique data sets that are able to be generated in order to model effectively starting with the smallest pieces of proteins but then eventually cells and whole biological systems. But that's kind of how we've evolved is, you know, this idea that we talk about around that some of this is an AI problem and you want to build a frontier AI lab, but you need to couple that with a frontier biology effort that can do the work of basically being able to understand and get the data that you need to actually be able to build these models. Because unlike language models, well, there's just like a lot of data out there on the internet. That's not really the case with biology. I mean, there are obviously a bunch of different data sets that exist that academia and scientists have generated over the decades, but a lot of the stuff that I think we want to put into this, it doesn't exist, right? It's like you want to be able to visualize things that people haven't been able to see before, which is why we're doing the imaging work. You want to be able to record things that are going on inside the body, which is why we're doing the kind of cellular engineering work, right? You want to be able to measure things like inflammation in ways that haven't been possible which is why the Chicago Biohub is focused on building those kind of devices and being able to do that and that will fundamentally create new types of data sets that will allow new types of models and I think is just a very exciting thing that going back to what you're saying if the scientific field primarily needs kind of tool development that now is going to empower scientists across the field to be able to do their work faster, that's what we think we can provide through this kind of long-term focus on tool development.

But I think there's a fun throughline on where we started and you know bringing us to our work with that Alex is driving now is that our very first request for application RFA here was around single cell sequencing and we wanted to look at sort of like the RNA that is transcribed in individual cells and that was possible but it was still pretty early on in understanding how different cells were expressing their DNA to the point where at the beginning we were just funding methods like getting people to describe how to do it so that others could share that methodology and then that became us funding the human cell atlas which is now one of the largest databases of single cell transcriptomes. It was getting hard for scientists to annotate the data. So we built Cell by Gene which was like a very simple annotation tool that scientists could use to make use of that data. Then a community came around Cell by Gene built around Cell by Gene and started contributing more and more data that we had nothing to do with sort of creating or funding or making happen in the world. And now Cell by Gene is a corpus of knowledge that a lot of the transcriptomic based models are based off of and is used regularly by the scientific community. But still there are always critiques like this is just stamp collecting like you're just gathering bits of knowledge well sorry bits of data and we're not going to be able to pull scientific knowledge and wisdom and insights out of. And we're like well we didn't have an answer for a while and then imagine our delight when large language models became a huge topic of conversation that could make sense of large amounts of data. And I just for me is what if we could actually understand how biology worked? Move it from a discovery based science to an engineering based science where we could systematically understand how living beings, living cells worked and be able to understand why things go wrong. And so when we saw that moment, we're like, this is it. Something really big could happen here.

Alex, you started at Evolutionary Scale but you were on the path to you know you had assembled a team and you raised venture and you were making progress in your models. What was the pitch from Mark and Priscilla who said like that's actually the right way to go after the mission?

Well, I think for me it was really kind of the moment when I understood that they really saw this as an integration of frontier AI and frontier biology. And I think I had developed conviction that this is really a new era of science that's just beginning kind of what's going to be possible with artificial intelligence and you know we're in the age of information theory at scale and we have these systems that can basically kind of predict the next token and they can learn world models from that they can learn biology from the data. And so I think that it just it was really clear that to build kind of that next institution for the next era, you would really need to have frontier artificial intelligence. You would have to have frontier biology. You would need to start to put those things in feedback and really have models that are learning from the biology. And I think you know just and you need the right scale and the right people and so this just really felt like the way to do that.

There's a variety of different models that you all have been working on and I think it's kind of interesting because some of the earliest breakthroughs in biology were things like AlphaFold where you know it was a Google model that showed that you could do protein folding at scale in a really interesting way that people didn't realize was very tractable and this was pre sort of the really big transformer waves that came later and then you're working on a variety of different things at different scale right you're doing incremental molecular modeling and protein folding you're doing cell-based stuff you're thinking about interrogating larger scale systems in biology how well do you think that extends from sort of the micro to the macro. You mentioned almost starting with building blocks and building up, but modeling cellular behavior is very different from modeling protein folding. The data is very different. The modeling is different. I'm just curious like do you think it's all similar in terms of it's just data and you train stuff or do you think it's actually there's some differences in terms of how you actually have to deal with these systems?

I mean, there are probably some differences. I mean, you can probably talk more to the specifics around this, but like I mean, I think each layer is going to end up being somewhat qualitatively different, right? I mean but you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to cells in a way without understanding the protein modeling and then if you're trying to understand something like the immune system works or a bunch of cells interact together then it's tough to do that without first understanding cells. I mean you might be able to at like a very high level of abstraction simulate a system but if you really want to like understand how it's going to work you kind of want to build the simulations at each level hierarchically. So that's basically the approach that we're going through starting with the building blocks and the protein. But yeah, I mean I think that there's going to be different types of data that you want to collect for each the modeling techniques. I think we'll see. I mean that'll all keep on advancing across the board. But I do think that like a big part of the strategy is this view that you need to build it up hierarchically. And you know, one of the things that's unique about us in the space is we were very intentional that the AI efforts and the wet lab efforts were a single effort and we've done a lot of work to bring them together. And the really neat thing that we can do is really try to pull and gather data that helps us connect across sort of the hierarchy. You know, you can look at transcriptomics with space within a cell and look at where it's localizing. We can look at translucent zebrafish and look at the development across different cells and when the brain develops. We have sensors that allow us to look at cell cell communication and different molecules. And so we can be strategic about the types of experiments and data we want to collect that helps us bridge across these that makes it so that there's some connective tissue that helps drive the modeling that you know the modeling magic that happens.

Yeah. The reason I asked the question by the way is I used to be a biologist. I have a PhD in biology and I worked in wet labs for almost a decade and everything else.

Are you looking for a job?

Um I'll we can talk about that later. At this point in my career, you know, I love my aggressive recording. I'm like I'm like Danny Glover, you know, in the Lethal Weapon. I'm almost at retirement. Um but I think you know, one of the things that was always lacking was this integrative nature across the different layers of biology. And the developmental biologists would work on their own, the molecular biologists would be doing different experiments. And so that's what I was curious about. Yeah.

Yeah. And if I could add something there, you know, it's I think that we're in the age of this kind of information theory in biology. And so, you know, there are levels of complexity and hierarchy in biology and kind of each level is made up of and constituted by the lower levels. And so, as you want to have that kind of more complete description and you want to have systems that can really generalize and begin to actually answer experimental questions digitally that you could ask in the lab, you know, you need to have kind of the right basis for modeling at every level. And so I think what's really unique about what we can do is to as Priscilla and Mark were saying, really build information at each of these different layers, collect them, collect kind of those connection points, but then also really kind of do it at the scale that will reveal that underlying information architecture. And that's going to be really critical to actually be able to build digital representations that can answer new experimental questions.

One of the things that inspires me most about this effort is really what Priscilla said, which is like, well, there's so much we actually don't understand about biology and what if we could, which I think is actually very different from lots of other incredibly interesting and useful AI problems we attack. We're like trying to replicate human behavior and I'm like, a lot of that data is, you know, on the internet or captured and without pretending to understand all human behavior, you can predict a lot of it. I thought one of the most interesting things in your release was actually the mechanistic interpretability stuff you alluded to which is can we actually extract new knowledge from what the model believes is happening right? Can you talk a little bit about that?

Yeah, I'm really excited about that. So I think you know in mechanistic interpretability kind of traditionally it's been applied to large language models with the goal of understanding kind of what is the representation space of a large language model how does it compute things and does that really connect to what we understand about our intuitive understanding of the world. And so there's I think this really rich toolkit that has been developed to start to be able to ask those questions. So kind of what does that mean for biology? One of the classes of models that we train are these protein language models so they're really trained on the codes of proteins and so anything they learn about biology is kind of emergent and we've seen that they can learn things like biological structure and biological function and that's just kind of emergent from this token prediction training task. So you know as we think about like mechanistic interpretability in those models we're really seeing the unknown because the models have been trained on billions of protein sequences. They've been trained on both known and unknown biology. And yet they're developing these representations that start to kind of capture things that we can really see correspond to that reductive picture of biology that's been built up over the centuries. So kind of you can start to connect the dots between proteins where we kind of really don't know anything about them with proteins where we do know something because there's that kind of underlying structure grammar that's linking them in the representation space of the model.

And at the extreme it could be you know we're going to understand systems in the body that we didn't before or the mechanism of action for a new treatment because we can ask the model right interrogate that representation.

That's right. The hope is that you kind of really learn the underlying basis for how it's making the predictions and so you open up the black box and you can actually understand kind of the biology that the model is representing.

So asking for a friend, you know you guys all believe in venture-backed companies as a way to have impact on the world. What was it like collecting data on zebrafish or the span of the data or the wet lab work or just the scale? Like what makes this a better fit for this big nonprofit, you know, ecosystem effort versus a venture-backed company?

Um, well, I think we just want to give tools to the whole scientific community. And I mean like so I think in order to have the biggest impact I mean part of it is just we're I mean it's not actually clear that we couldn't run it as a business if we wanted to. I just think that we'll have a bigger impact by getting this in more scientist hands quicker by doing it as open source projects instead. So yeah I mean I think that's kind of the approach but I don't know it's an interesting question. I'm not sure that I mean obviously you were doing it as a nonprofit company a bunch of the modeling before then you run into certain issues. I mean you have to raise a large amount of money in order to build compute clusters. You know I mean it's I think in a lot of ways the data is actually even more of a constraint and because if you look at like the scale of these models compared to language models they're smaller but they're smaller because the amount of data is less. In order to get the data it's not just like there's some factory somewhere that you can pay to produce the data like you actually need to invent new novel scientific approaches to be able to do the for example the type of cellular engineering we're doing in New York or the types of devices in Chicago which is why when we're talking about this concept of frontier biology and frontier AI the frontier biology is you need to do real science to advance different biological methods in order to be able to observe the things that create the data that go to the model. So it's not just like an off-the-shelf thing that you can create. Now, that's a pretty big effort. I don't know that there are like that many things like that that are done as biotechs. I think it's just the scale of the ambition of what we're doing, the horizon over which we're committed to doing it. I think part of the theory is like if you're building tools that are this complicated, you kind of want to have a 10 to 15 year time horizon on building out these efforts. And then the scale of capital required. I mean, I guess there's no rule that said that you couldn't do it as like an incredibly well-funded startup, but I think that this just made more sense. And then it also is simplifying strategically to not have to think about how you're going to make money with the different things. I mean, we just we want to get the models in people's hands. We release them as open source. I think that that's like a very valuable thing to do. And again, I mean, the theory isn't that we're going to cure the diseases. We're not. It's that we want to help accelerate the pace of progress for the whole scientific field.

Do you have any thoughts or predictions in terms of what disease areas this work will impact first? I know it's very hard to be predictive about these things, but just given the nature of the work and the nature of the models, are there areas you're most optimistic about in the short to medium term?

Yeah. That's a very exciting vision because you're basically saying you can bring generalizable tools to provide very personalized things for each individual person.

And that's the power of the approach is you have these big models that you build that can then apply anywhere. I know that you mentioned earlier that you were going to try and cure prevent all diseases within a hundred years and you mentioned hey it could actually be sooner now given all the advances in AI. Do you have some thought of when we think we'll be closer to that goal or some?

I mean, I'm optimistic it'll be sooner. I mean, I think that the thing that's complicated is that it's a dynamic system, right? So, if you fix something, there will obviously be future things that you need to work on. So, I don't think that the current set of things that we're aware of are going to be the only things that need to get worked out. But I don't know. I think that the progress with AI is obviously very exciting on this. The other thing that I'd say just adding to what you were saying a second ago is we really look at more kind of systems than specific diseases. So for example, one area that seems really important to understand is inflammation. We talked about this a bunch. This is a big focus of the Chicago Biohub. There's a lot of data on that that's very it seems quite clear that it's connected to a bunch of different diseases but we don't rather than studying the specific diseases we think that by trying to understand inflammation more broadly that will make it so that other companies that can then use these tools can work on specific therapies. Another example is and I think that the immune system I think is a very good case to study for some of the work that we're doing in cellular engineering and when we're kind of lad up from proteins to cells to like whole dynamic systems within the body. I think that that one makes sense. I mean it's sort of privileged. The cells can travel around through the body all that you know. So obviously that has a big part in addressing different diseases. How do you make the immune system function better? But exactly how do you connect that last mile I think is going to be more something that biotech or other academics individually studying things will be better suited to do. So this is like kind of how we think about building out the tool set that just helps accelerate all these other folks.

Whether the timeline is 10 years hopefully you know less than 100 now I think it's useful for maybe your average doctor or patient human being everybody's a patient to think about like what's externally visible in the progress here. You worked with patients for a long time at UCSF like what should doctors look out for what should people look out for if you're actually accelerating progress?

Mhm. Maybe I could say something about our most recent launch because I think it also

Please, we should explicitly

So, just a week ago, we announced the new ESMFold. This is basically an open system for scientific discovery in protein biology. It's a world model of protein biology that's been trained as a language model on billions of protein sequences. It learns emergent representations of protein biology, and we can use it to make predictions of atomic resolution protein structure. It's blazing fast, illustrating the Pareto optimal frontier of speed and accuracy in structure prediction. This allows us to characterize vast stretches of the protein universe. We folded over 1.1 billion proteins, predicted their structures, and identified features connecting them through mechanistic interpretability. The most exciting thing about this model is that it's a really general model of protein biology. You can use it as a world model to search the space and design new proteins. It's hitting state-of-the-art across pretty much every structure prediction benchmark, especially on protein-protein interactions and protein-antibody interactions, which is critical for therapeutic design. We found you can use the model to design proteins and single-chain antibodies. You can do all of this digitally, then in a small number of experimental trials—like a 96-well plate—select from hundreds of thousands of digital trajectories, synthesize 96 proteins, and test them in the lab in a short experimental cycle. We found nanomolar binders there, which is the level for therapeutic activity. This shows you can have general-purpose models where protein design emerges as a property. We didn't design a model for antibodies; we designed a model that could understand proteins. It also illustrates the power of open science and open source. We released this as an open discovery engine, so anyone can build on it. It takes intensive laboratory experiments where you screen through hundreds of thousands or millions of antibodies and lets you spin up compute to generate them.

That's right. It's really critical to actually go and characterize these molecules in the lab. We have a structural biology center here with incredibly powerful cryo-EM microscopes, so we can look at these proteins biophysically and functionally. We designed proteins for several therapeutically relevant targets and were able to confirm their function.

Correct. I know a lot of your work is focused on basic research and building out the fundamentals. If I look at actual translation into drugs, often a clinical trial will be 15 years and cost $1.5 billion. About $50 million of that is the molecule and pre-clinical work, and the other $1.45 billion and decade-plus is the drug development side. A lot of that is gated on regulatory issues, recruitment, and the failure of drugs in trials around absorption or toxicity. Have you considered tackling that other chain of molecular design, or is the primary focus more on the basic biology and initial molecules?

Maybe something just to add to that. You described the conventional drug development process, and these tools have the potential to have a lot of impact on that process. But what's interesting is to really start to think about the new paradigms that can open up. What does it mean if the barrier to develop a drug, design a molecule, and get through all those stages is so much lower? You have programmable biology and can really start to create a medicine for every individual patient. That has enormous implications for how we do drug development and what the future of medicine looks like.

Yeah. It'll be an exciting day when the FDA accepts a virtual clinical trial for phase one or something based on some personal view of that person.

Yeah. The recruitment could also change. We have this program, Rare, and the basic idea is that a lot of people focus on the most common diseases, but there's this long tail and the economics don't quite work out for companies to focus on those diseases. But if you can make it so that groups of patients can come together and organize and say, 'Hey, we would take an experimental drug on this,' then because of the cost you're talking about and how that's a huge amount of the overall cost, if you can flip that, then it actually makes the economics make a lot more sense to generate something more easily and pair it with a group of people. One of the interesting things from science and engineering is that often you can hit your head against the wall on common problems and diseases, but a lot of times you learn a lot more about a system from finding some rare or weird side thing that's happening in an edge case. I think that's always been an interesting part of this that connects pretty well to this because now you're going to be able to enable a long tail of new ideas to get tried and enable them to potentially get tested more easily.

But I think to some degree you're going to need something like this because there are going to be many more new things that can get created. But that doesn't mean that for the general population you're not going to want the same level of vetting that we've had historically. But making it so that people who want to be on more of the frontier have the ability to do that is also going to be pretty helpful.

All three of you have mentioned at different points the power of open ecosystems in such a large space. I think some of that logic around open source and the breadth or diversity of data collection you were describing should also apply in the language model world and the multimodal AI world. Do you think that's right? Does any of the work you're doing here change how you think about AI and Meta?

I mean, I think it's sort of a similar philosophy overall. Priscilla was talking about this—a lot of our focus is building tools that empower individuals to do things, and that's a common theme across a lot of the things I work on: putting technology in individuals' hands. We don't believe in this very centralized future where there should be a small number of institutions advancing all this stuff. Our vision is not that there's going to be some central super intelligence that solves all of science. I think people are really important and will be more important in the future. Giving people more tools to be more productive is going to be a critical part of any positive future. That's how progress has always been made historically—it's not through centralization. It's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought the good ideas had already been done. That's very central to the whole ethos of why you create something like social media—to give people a voice. A lot of the stuff I care about in terms of empowering people with individual AI—open source is one instantiation of it. It's not the only way to do it, but it's one way where you're saying we're going to take this technology and put it in everyone's hands. In terms of science, I think it really makes sense, and we're deeply committed to open source. There are obviously interesting considerations around biosafety and things like that that we're going to need to balance and think through how to handle. But overall, this is very deep in the ethos of the work we're doing both at Biohub and probably a theme for a lot of the stuff I do: we believe that a positive future is one where you build a technology as a tool, put it in individuals' hands, and that's how society makes progress.

You have this incredibly ambitious mission at Biohub, and yet the AI scientists that work here could also go work in commercial enterprises. How do you think about the talent and how to bring people to Biohub?

Where do you want to start? It's a very hot market for AI researchers, but part of what that means is there's a lot of demand, and they're very in demand and can work on the things they want to work on.

And I think this gets back to this point again about frontier AI and frontier biology. The AI researchers who work here could go work on language models or things at any of the main labs, but those labs don't have the frontier biology part attached to it. There's also a very large mission component: there's an ability to do this unique work here that you just can't really do at other places. If that's your focus, I don't actually think there's any other organization in the world that's doing both the frontier biology and the frontier AI.

I mean, I think it's really simple. Our mission is to take care of and prevent disease, and I think it's just such a...

It's very serious now. There's no more...

Yeah, I mean, it's just—scientists are very motivated by that. It's something people are deeply motivated by, and I think we're at this moment in time where that actually seems like something that can be achieved. We're building a really unique place where we're tackling that problem, and we have the resources and the right things to actually really go after that and do that.

Yeah. That resonates with me as somebody who talks to and hires a lot of research scientists. They want to know if you have the data, the tools, the compute, the talent, and then what the mission is. And so I actually think that's super competitive.

The other thing is that you don't need a very large team. It's an interesting thing about the world: people care about different missions, and that's good. Part of why building these tools and giving people the ability to explore what they care about—whether across science or across everything—is such a powerful way to make progress in society. People care about different things, and in order to make progress in AI, you don't need many hundreds or thousands of AI researchers. I think you can really make progress with a very strong group of a dozen or a couple dozen people. Finding people who care about this mission is not a particularly hard thing. This is a super important thing in the world. It's just kind of a cool thing about the world that people are drawn to different missions.

So, I think the simplest mental models that folks have, even if they're paying attention to the space, are essentially: okay, structure prediction models for proteins and protein-protein interaction models. There's this one piece which is fundamental understanding, and then there's this theory that someday we're just going to be able to zero-shot things into the clinic with a much better hit rate. What needs to happen for us to go from ESMFold 2 to this other piece? Is that feasible?

I think that's a great question. I'm really optimistic on that. These are problems that historically people could spend an entire career working on—how do you effectively optimize a drug, get it through pre-clinical, do the early safety? When you have a new scientific paradigm, questions that were once hard become simplified through the new paradigm. I'm very optimistic that many of these core problems will be solved in an emergent way through these models. One great example is toxicity. If you can really digitally simulate everything and predict where a drug is going to distribute and bind across the human body, you have the beginning of a solution to that kind of problem. Once you have these accurate representations at the molecular level, we're going to start to see really rapid progress on a lot of these core problems.

What is the most exciting use or experimentation with the models you've seen in the last week since release?

It's just been great to see it get integrated in all kinds of things. One of the really interesting things we've been seeing is people connecting it with agentic systems to just do automated design and automate that whole process. It's another example of bringing together agentic and frontier AI with the ability to have a world model for biology and actually reason about biology and really start to automate the entire design process.

How do you decide what the next step in the research agenda is? It's like world model for biology, and then I could scale it up, add more data. Adding data is a non-trivial thing in terms of new methods and domains. Do you take input from the larger ecosystem about how people are using it? What would make it more useful, or is it really like we understand the next step of structures or coverage that we're looking for?

I think there's two things. We have a view on the next big challenge, which is the virtual cell and really being able to ladder up the hierarchy of biological complexity to the cell.

Sorry, very basic question: this virtual cell model—what is the input and output I should expect?

I think there are different views on that, but what you ultimately want is a system that can really model each of the levels of complexity: the proteomic layer, the genetic layer, the transcriptomic layer, and connect that to the phenotype. You need enough generality so that you can ask the model questions about a new intervention in a context that it hasn't been trained on and get an answer from it. The gap we need to close as a field is being able to really make those predictions that can generalize. That's going to require an enormous effort to generate data.

And then in terms of what you decide to do next, I think this is a pretty normal process of constraint management. Every lab in every field across the world probably feels compute constrained. I think that's probably true here too. There are always questions: should we double down more on advancing the protein piece? Should we do more of the cellular stuff? Those are ongoing debates in terms of how you sequence that. Within that, there's being at the Pareto frontier about how much you want to train the different models, and the size of the models is also dependent on the scale of the data you have. Some of that is just where you want to be on the curves and then normal constraints. I think this is probably the same process that any research organization goes through: you want to go in all these different directions and you're just trying to constraint-optimize and make enough progress to do world-class work at one thing at a time while planting some seeds that can blossom over the next couple years as well.

Yeah, this has been the most dynamic period of technology, at least I've seen over my career. It's so exciting in terms of everything that's happening with AI, and every week there's something new that's changed.

I'm both.

Yes, it's a combination of invigorated and exhausted.

Well, I think we have a pretty clear view of this hierarchical set of world models that we want to build around biology. The other part of that is that we want to do the highest quality work in the world. I think we're basically set up to do that between having a world-class AI research team and this collection of Biohubs, which are world-class life sciences research organizations. That's fundamentally a setup that no other organization in the world has. But you can have a lot of great ingredients and that doesn't guarantee that you succeed. To me, 5 years from now, looking back, I'm sure other labs or efforts will try to produce things that approximate what we're trying to do. I just think that we should be able to do something that is meaningfully better and a unique intellectual contribution to the world. That's kind of what whenever you do any kind of research, that's what you're trying to do. So if we do that, I think we'll all feel very good. I would also expect that at some point we'll just start seeing a lot more idea generation from the people using the models. But I have enough faith that that part will materialize that for me it's more just about making sure that we do world-class work, and I think if we do, the rest almost will take care of itself.

Very last question for you. Snapshot of it's mid-2026. What's the biggest update in your own thinking about Biohub or the domain from the last year?

Well, from the last year—I mean, you joined in the last year. I think the biggest thing is that we basically rotated and, in the last year, we basically formalized that Biohub is the main focus of our philanthropy. So I think this has been a very big shift. But Alex and the team coming in has been interesting not only because it's a world-class group—you guys have worked together for a while. I also—you talked about how stuff is changing so much in the field. I think one thing that's underrated is this is an extremely talented group of people who also know each other and work well together and are stable and good. I think that's also underestimated in terms of the compounding benefit of people being able to work well in a stable environment over time. So I think that's a really important piece. But part of what we wanted to do was—prior to Alex leading the effort, the previous leaders of the Biohub were basically primarily biologists who were interested in technology. Now I think this is the point where we really flipped that. Obviously, you have a background in biology as well, but you are primarily an AI researcher who has a background in AI and biology. I think that's a deep reflection on the way that we expect this is going to drive more value in the future. So those are probably the biggest updates in the last year in terms of the work that we're doing. It's a new leader, not just a leader, but a team. I think that's really good. And then on the rest of the industry, it's on track. I think it's kind of this crazy thing because when you have an exponentially growing curve, the way that an exponential curve feels is it's growing so quickly that the emotional feeling is it can't possibly keep going. But the nature of an exponential curve is it doesn't just keep going—it keeps accelerating. Exponential growth is accelerating. I think that has all these emotions and psychology attached to it, but fundamentally when you look at the curve in the industry, the fundamental thing is it is on track. It has remained on that curve, which I think has all these very profound implications for all of these domains. But certainly it validates and makes one feel very good about making a very big investment in the things that will play out if you stay on that track. And it seems like we are. So that I think is very good news.

I think the most important aspect of what you're doing there is you're actually closing the loop with the actual biology. Because with code and research, it's closed-loop systems and so they're very fast to iterate. This is an open-loop system, so you're closing a loop, and that's really crucial to progress.

Amazing. Well, to teams being on the curve, thank you guys for doing this.

Thank you.

Thank you.

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