The AI-Powered Biohub: Why Mark Zuckerberg & Priscilla Chan are Investing in Data, from Latent.Space

This crossover episode from the Latent Space podcast features Mark Zuckerberg and Priscilla Chan on the 10-year anniversary of the Chan Zuckerberg Initiative and their expanded Biohub vision. They discuss how a “Frontier Biology Lab” working in sync with a “Frontier AI Lab” could enable breakthroughs like a Virtual Cell and true N-of-1 precision medicine. The conversation covers the acquisition of Evolutionary Scale and ESM3, new biological data collection at scale, and how AI-powered biology might transform drug discovery and disease prevention. Sponsors: Blitzy: Blitzy is the autonomous code generation platform that ingests millions of lines of code to accelerate enterprise software development by up to 5x with premium, spec-driven output. Schedule a strategy session with their AI solutions consultants at https://blitzy.com Framer: Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week four at https://serval.com/cognitive Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai Claude Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro’s full capabilities at ⁠⁠https://claude.ai/tcr⁠ CHAPTERS: (00:00) About the Episode (04:27) CZI origins and focus (08:29) Why tools over cures (14:43) Virtual cells and imaging (Part 1) (20:19) Sponsors: Blitzy | Framer (23:24) Virtual cells and imaging (Part 2) (25:22) Data diversity and grounding (32:30) Evaluating models and Biohub (Part 1) (37:53) Sponsors: Serval | Tasklet (40:42) Evaluating models and Biohub (Part 2) (41:06) Future healthcare and aging (53:39) Modeling scales and immunity (58:53) Timelines, data, and collaboration (01:04:01) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome back to The Cognitive Revolution. Today, I'm excited to share a special crossover episode from the Latent Space Podcast. Latent Space, surveys say, is the number one podcast for AI engineers. And I find hosts Swix and Alessio a consistently outstanding source of insight into the latest trends in AI-powered coding and AI application development. Today's episode, though, is a bit different. A conversation with Mark Zuckerberg and Priscilla Chan, who are celebrating the 10-year anniversary of the Chan-Zuckerberg Initiative, about why they are doubling down on the interdisciplinary biohub, with the goals of leading a new era of AI-powered biology and ultimately equipping

• 0:41Nathan Labenz?

  scientists to cure or prevent all disease in the coming decades. In this conversation, Mark and Priscilla described their perspective on the current state of biology, the role they see AI playing going forward, and the strategy underlying their investments, with highlights including why the traditional funding model fails to bring scientists, engineers, and AI experts together to tackle the most important problems in the way we might hope, their vision for a frontier biology lab that works in sync with a frontier AI lab, the acquisition of Evolutionary Scale, creators of leading protein model ESM-3, and the appointment of CEO Alex Rivas to lead the combined program, their

• 1:23Nathan Labenz?

  plan to develop new data collection techniques which will naturally give rise to massive data sets on which new AI models can be trained, the roadmap to a virtual cell capable of simulating biological responses in silico, potentially revolutionizing not just drug discovery but our understanding of biology in general, and their ultimate vision for precision medicine, moving from clinical trial and error to true N of one treatments designed based on each individual's unique biology.

• 1:54Nathan Labenz?

  While the conversation itself focuses on the intersection of AI and biology, for me, it also serves as an important reminder of the unique role that private capital often plays in scientific progress, and, considering the current moment, the importance of classical liberal values more broadly. Ironically, for all we hear that the US must win the AI race to ensure that the best AI models project American rather than Chinese values around the world, I see actors across the political spectrum pushing America toward a more Chinese model of state dominance. The civil rights violations and abuses of power we're seeing right now from the federal government are plainly un-American, and I've been glad to see prominent voices in the AI

• 2:38Nathan Labenz?

  space, including Jeff Dean at Google, Dario and Chris Ola at Anthropic, and various researchers at OpenAI, speaking up against them. If anything, I think the AI industry ought to consider doing more, starting by signaling that they would be willing to withhold their technology from a government that proves itself unworthy to wield such power. But at the same time, and certainly to a much lesser degree right now, I do also worry that recent proposals for confiscatory taxes, if enacted, would make moonshots like the Biohub much rarer. I do agree, of course, with Warren Buffett that it's absurd that he pays a lower tax rate than his secretary, but at a time when the federal government is cutting research

• 3:19Nathan Labenz?

  budgets and generally acting against medical advice, society stands to benefit tremendously when self-made tech billionaires turn their formidable talents and immense resources to solving global problems and providing public goods. And even more generally, as fears of concentration of AI power grow, it only seems prudent that society should maintain a diverse set of independent power centers, which can hopefully balance and exist in equilibrium with one another. Such checks and balances were, of course, central to the framers' vision for the US government, and in my view, they remain essential for societal dynamism and resilience today.

• 4:01Nathan Labenz?

  With that, I'm grateful to Swix for allowing me to crosspost this conversation. I of course recommend subscribing to the Late in Space feed where they've just brought on new hosts to cover AI for science on a dedicated basis. And I hope you enjoy this preview of the future of AI-driven biology and medicine with Mark Zuckerberg and Priscilla Chan from the Late in Space Podcast.

• 4:28Speaker 01

  Hey, everyone. Welcome to the Late in Space Podcast. This is Alasio, founder of Kernel Labs, and I'm joined by Swix, editor of Late in Space.

• 4:34Speaker 02

  Hello. We're so delighted to be in the Imaging Institute of CCI with literally C and Z. Welcome, Mark and Priscilla.

• 4:41Speaker 03

  Thanks for having us. Thanks for getting nerdy.

• 4:43Speaker 04

  Yeah, we're excited to do this.

• 4:45Speaker 02

  We so don't often get to see this side of you, and so thank you for taking some time out to talk about this. And it's like, you know, sort of the 10-year anniversary kind of of CCI. So I just wanted to introduce people if, you know, people have not been caught up. One of the interesting things that we found out just from talking to your teams is there's an interesting difference between how you guys started CCI and the Gates Foundation. And I heard that Bill Gates is a mentor of yours. So maybe you could tell that story of like deciding to start CCI and decided to pursue basic science sort of translational work.

• 5:19Speaker 04

  Well, I mean, I think one of the core things for us with CCI was just getting started earlier. We got some advice that basically philanthropy and doing science, just like any other discipline, requires practice and you're not going to be good at it overnight. So we should just kind of dig in and start doing a few different iterations on it and see what we enjoy and where we think we can have an impact and go from there. So I'm, yeah, I mean, like you mentioned, I mean, this is, we're coming up on in November, the 10-year anniversary of when we started CCI. And, you know, there's a lot of work that we were really proud of, that we've been a part of, including, you know, work in education and supporting communities. But

• 6:02Speaker 04

  You know, when we reflect on it, we feel like the work that we've done in science really has had the biggest impact and in a lot of ways is accelerating. And with, especially with all the advances in AI that are coming, I think the ability to have an even bigger impact over the coming decade is, it just seems really clear like this is coming into focus. So, you know, for the next period, we really want to make science the main focus of what we're doing and specifically the Biohub organization that we're really proud of this model that we've helped pioneer that we can go into detail on is really going to be like the main focus of our philanthropy. And it's just something that we're very excited about.

• 6:41Speaker 03

  Yeah, when we started 10 years ago, we had this idea like, okay, I bring experience as a physician, Mark's an engineer and he builds things. And we have an opportunity to give back resources to make an impact on this world. And we sort of just, we tried a bunch of things. And the thing that in running a philanthropy, I'm incredibly envious of people who run companies is that like you guys can have a dashboard and there's like financial results and people tell you if you're on the right track, on the wrong track, and there's clarity. But in philanthropy, there's so much you can do and it takes a long time for you to get a sense of like, what has momentum? What are we doing that

• 7:20Speaker 03

  is actually bringing all of our both skills and resources to maximal impact? So over the past 10 years, I would say we've been getting a sense of what is that thing that really allows us to have the impact and makes the most of what we bring to the table? And it's really been around AI and biology where we're like, oh my gosh, this is it. And, you know, the ecosystem is big. And we really think our ability to bring great scientists, great AI researchers together between the wet lab and the compute, the ability to bring physicians and patients into the picture, that's a unique niche for

• 8:01Speaker 03

  us at the Biohub. And that's, you know, we need others to take the work to translation. The Gates Foundation has a strong focus on translation and the field. And we have had a number of really awesome collaborations and continue to where we really look at sort of the basic fundamental research and being able to partner with someone who's thinking about the translation layer is incredible.

• 8:30Speaker 01

  We kind of see the first decade, and I would love to get your take, as a decade of creating data, creating a science ecosystem and then starting to work on some of the models. And the next decade maybe is more of the applied modeling side. At what point did you decide that just doing the tooling was matter versus, you know, you could have cured malaria in Africa too, or some other disease?

• 8:53Speaker 04

  Yeah, I mean, take a step back, and this is kind of related to your first question too. I mean, there's, like Priscilla was saying, the space is huge. You know, there are lots of other philanthropies, including Gates, who I think they would say that they're primarily focused on public health and sort of administering. Like, once you know what a cure is, just getting it out to the world is a huge thing too. And someone needs to do that, and that's a lot of work and a lot of resources, and it's good that they're doing that. Basic science is another completely different part of the kind of innovation funnel to enable that. And our view is that the federal government basically dwarfs everyone else in terms of how much they invest through NIH.

• 9:33Speaker 04

  But there's a certain pattern to how they invest, which is really enabling a lot of individual investigators to do work. And our kind of observation is that if you look at the history of science, a lot of major advances are basically preceded by new tools or new ways of observing things, right? So the initial telescope allowed a lot of advances in astronomy. The microscope, the invention of that, allowed a lot of understanding of biology. And similarly, I think we're at a point in history where a lot of new tools are being built. Computational tools, tools to instrument the body in different ways and understand things. And often, those tool development just takes a longer term timeframe

• 10:17Speaker 04

  and a, sometimes a larger commitment of capital. Including, the way to do it isn't necessarily just to make grants to a lot of different people. You need to really operate it yourself, which I think is one thing that's different about the way that we've operated than others is, you know, most times when you think about philanthropy, you think about kind of giving money away in terms of grants. And a lot of what we're doing is actually building up these institutes and kind of building labs to do that kind of research ourselves by bringing in leading scientists and engineers and all that. But that's kind of the strategy is. We feel like there's a lot of new tools to develop. There's sort of been a hole in the ecosystem where tool development and kind of the 10 to 15 year runway that you need to do that and often hundreds of millions

• 11:01Speaker 04

  of dollars to build things like the microscopes and that you're imaging that you're seeing in this institute here. I think that that's been sort of underfunded. And that's where we think that if we do that kind of work, it can just give all these other scientists way more tools to accelerate the pace of of of research, hopefully discover cures, and then you have folks who are focused on public health who bring that out to the world and kind of deploy it to everyone.

• 11:24Speaker 03

  Yeah, I mean, our mission is to cure, prevent all diseases and like, that's not going to happen just in our four walls. So the strategy has to be, how do we make every single scientist and everyone better and more effective? And, you know, the strategy Mark talked about is sort of where we landed on how to actually maximally move the field forward.

• 11:46Speaker 02

  Yeah. Yeah. The mission is cure, prevent all diseases. By the way, a lot of people outside of the CCI worlds are still could have find this concept very alien, but talking to the CCI people, they really truly believe it. And it's impressive how you picked the right mission to motivate everyone to work towards this enormous task.

• 12:03Speaker 04

  Well, it's kind of a funny thing. I mean, it's, I kind of, we like to talk about the mission as like helping scientists do it, right? Because we're not actually curing the diseases. We're just trying to build the tools.

• 12:12Speaker 02

  Tools, data, models.

• 12:14Speaker 04

  Yeah, like basically accelerating scientists' work towards that. But, you know, a funny thing about it is we had this initial timeframe of by the end of the century. And, you know, when you ask biologists, there's a lot of questions are, okay, that's really ambitious. So we're going to be able to do that. And then when you ask AI people, it's like, that should be really easy. Like, why are you so unambitious that you're, you know, shooting for just the end of the century? And I do think that at the pace that that AI is improving things, I mean, I think it might be possible significantly sooner than that. I mean, I don't think it's necessarily worth putting a number on it or a date, but I think that that's, it's your point about the first decade was, you know, sort of about

• 12:57Speaker 04

  doing work like the Cell Atlas to be able to help understand basically all of the kind of specifics and data about all the different configurations of every cell in the body. When we did that, we kind of had this vague notion that that would be useful to advance science. But I think that, like a lot of people in the tech industry, we we have even been impressed by how quickly AI has accelerated. But that ended up being a really valuable thing to have done over the last 10 years, especially for where AI is now and now the models that can get built with that.

• 13:28Speaker 03

  But the thing that's interesting, don't you agree, is like, OK, so from a, I totally agree that in our intersection of AI and biology, the AI folks are like, yep, the biologist really, hmm. And I think it's actually that confluence of conversations that lead both the biologists to be like, OK, I'm really uncomfortable about this idea and timeline. But if I'm really pinned down to think about it, what are, like, you really force people to think through, like, OK, what are actually the barriers? What would you need to do? And you're forcing that conversation from the biologist's side and from the AI side, really getting a sense of, OK, what, like, data is not just data. You guys know this.

• 14:13Speaker 03

  Like, you need to know sort of how the data was collected and from where. And being able to connect the AI researchers to the folks who are actually gathering the data on a daily basis makes their work better. And so it's it's that conversation that's happening here that I think makes people outside so excited about this because it's credible. And they sort of have worked, really dug in and thought through how to, how that would work. And and they're excited and they believe and believing is the first step.

• 14:43Speaker 02

  Believing is the first step. There's a general pattern of software eating the world. And I think AI eating the world is kind of like the next version of this. I was talking with Garrett outside, who says he's a biologist, but you know, I think he's using models like Sam from Meta.

• 14:55Speaker 03

  You're like, you don't look like only a biologist.

• 14:59Speaker 04

  What does a biologist look like?

• 15:01Speaker 03

  Like, he's working on models out there. And that's like, biologists are working, using models, right? They're not just like imagination, like just using in the wet lab.

• 15:09Speaker 02

  Yeah, totally. Yeah, I think one of those things that, you know, referencing the wet lab is one of the key approaches that you're pursuing is turning things in, pursuing a virtual cell, turning things from mostly wet lab into something in silico. How far along are we?

• 15:24Speaker 04

  I mean, it's pretty early, right? I think the first step, which I think is easy to overlook, is basically what Priscilla was talking about of just getting these folks together. It almost, it's worth taking a beat just to talk about this, just because I think most people assume that this is like, obviously you would go do that, but it's somewhat novel in science because of, I think, the way that a lot of funding has been done. That is, basically, you grant individual teams, like relatively small grants, and people do a lot of science independently. It is, I think, pretty amazing how much progress you can make if you just have people from different disciplines sit together, right? It's, I mean, this is like over my career, I mean, both at Meta and here, it's, it's like you have, you have teams that are not working

• 16:09Speaker 04

  together for some reason, or they disagree on something. It's like, okay, physically just have them next to each other. And it like, actually is super helpful. So here, what are we doing? It's not just bringing together the biologists and the engineers, which was a core part of the initial Biohub model, but it was also unlocking the ability for people to work together across institutions. So the first Biohub that we started out here between Stanford, UCSF, and Berkeley allowed a lot more collaboration between scientists and engineers at those universities than was in practice happening before. And it's like, you can look at this and be like, all right, that seems really obvious. But it actually was sort of an interesting and novel experiment, and one that I'm really happy to see others also implementing, because I think

• 16:54Speaker 04

  it's just such a clear win, just the kind of the human side of bringing people together and having them sit together. So anyway, that I would say is kind of step one or step zero, and is probably quite overlooked, but is sort of a fundamental part of the model, that I guess also goes back to this idea of like, we're not just kind of like granting funds to other people, we're building an institution, we're having people sit together. So then you get that, and then you get these people who are like, half biologist, half AI engineer, because they, they kind of have some experience doing it. And, I mean, I don't know, I mean, we can, we can talk through the specific models, and, and there's a lot of exciting stuff there, but I'd say it's, It's an early glimpse of where this is all going. I think like you want to kind of build up these models hierarchically.

• 17:38Speaker 04

  So you give them a lot of data about specific proteins and they can model specific proteins in the cells and then you can model different cell behavior. And then eventually you get, you kind of zoom out and you're modeling like a virtual immune system or something like that. And it's sort of hard to simulate the immune system without having a good understanding of how a cell might work. And it's kind of hard to understand or simulate how a cell might work if you don't really understand how the proteins interact. So you kind of need systems that understand data at all different levels of this and then you kind of pull them together. And then if you look at the different models, there's, you know, there are versions that are kind of focused on, all right, like which parts of the genome are kind of being expressed in different ways.

• 18:22Speaker 04

  And the cryo model that I think is very interesting that's built off of the data here, the only model that I'm aware of, that's like a spatial model of like, of basically like how, how these cells work. And, and you kind of, you just want to be able to look at stuff from different perspectives and then put them together and you build like a richer and richer model of, of kind of how these cells work. But we are definitely at the beginning of this journey.

• 18:43Speaker 03

  But it's like slow and fast, slow and fast, right? So when we built the Human Cell Atlas, we started 10 years ago. It was one of our first RFAs. And we actually, the first RFA was to fund the methodologies of how you would get a single cell transcriptome. And it took us about 10 years to get to a place where we had, we now have one of the largest corpus of RNA transcriptomes, 125 million cells, cost a lot of money. And the really cool thing we discovered through that process was if we could seed the effort and make it easy for people to contribute, it happened. That's cell by gene. We actually, we're responsible for maybe 25% of the data and the rest of the ecosystem

• 19:27Speaker 03

  contributed 75% of that. That's an incredible asset and has been very important in modeling work. Similarly, if you look at AlphaFold, they built off publicly available data that was collected for 30 years prior, right? So that takes a long time. But now we're doing the billion cell project, and that is taking months and at a fraction of the price. You know, really slow to fast, but it's a single dimension and cells are so complicated. And here we're looking, like Mark said, at the three dimensional imaging structures. That's it, and it's slow and expensive. But with cryo, with the cryo model, it will get fast again. And you just have to repeat it. And so I think we'll get growth spurts, but it's

• 20:12Speaker 03

  all happening just faster and faster.

• 20:14Speaker 05

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 20:19Speaker 01

  How do you think about the layers? So you have compute, and we'll talk about that later. On the data side, you build these amazing microscopes. I learned that they're all built for you by spec. They're not off the shelf things that anybody Design partners.

• 20:32Speaker 03

  Yeah.

• 20:33Speaker 01

  How, how much of a bottleneck is that still? Like, can we convert the world of atoms into bits now at the right precision? Or do we need, do we need more work on the microscopes themselves, too? And you're never done. Right. Yeah.

• 20:49Speaker 03

  Well, speed for here, speed has been a big question of how, just getting the process through. So here we've worked on sort of the speed at which we can look at tomograms and the sort of contrast and resolution. And that's where the laser phase plate comes in. So to be able to make the data better and faster to get the data. But it's a bottleneck in so much as there's only, I don't know the exact number. There are like maybe tens of these microscopes in the world. So that's one bottleneck. And I think really is, like when I was saying it's slow and then fast, there's so many other dimensions that we don't have yet of like, the cool thing here is with the transcriptome work, we're looking

• 21:30Speaker 03

  at cellular expression. And with the imaging work, you're being, you're able to localize it in space. And now you want to connect those two. But that's still like two dimensions connected. Time is another dimension. We need to get dynamic imaging. In place.

• 21:45Speaker 04

  Oh, God.

• 21:47Speaker 04

  That's so much.

• 21:48Speaker 03

  I know.

• 21:48Speaker 04

  Yeah.

• 21:48Speaker 03

  Right. But like really cool biological innovation. We need innovation in the way we can look at things like stain free, dye free. So we can look at things without sort of human intervention with time as a dimension is another. Cause like, we are not frozen slices. So I think it's just continuously looking at what the next dimension. We want to sort of be able to either understand deeply or connect to our existing corpus of data and knowledge.

• 22:18Speaker 04

  And obviously, the the ideal would be you want to increasingly be able to image things inside living cells, right? So I mean, you can kind of, you can simulate it a bit by, okay, you can take a cell out or some culture. It's all destructive. Yeah. It's like, okay, it's the living for a little bit or something. But I mean, you really want to be able to kind of as much as possible, actually understand what's going on in living organisms. Can that be done? Is there, what, what, what are the approaches? The better it gets.

• 22:43Speaker 03

  Well, there's this cool methodology. So there is a really high intensity X-ray methodology you can use. The, the organ has to be dead. So like you can just shoot X-rays, high intensity X-rays at like a lung and understand at like a sort of molecular level, how the lung is assembled. And then you can correlate that with living imagery, right? MRIs of the lungs, CTs of the lungs. And look at the associations between the living images in real patients with the sample that you put into the high intensity X-ray. So that's another example of like correlating data types so that we can get that sort of high level specificity with clinical data that impacts humans.

• 23:29Speaker 04

  But I mean, at some level, that's sort of the point about building these AI biological models is you can have a lot of data and you can interpolate that on that space and understand that.

• 23:40Speaker 03

  Yes.

• 23:40Speaker 04

  And then there's, you know, so one of the models. That, again, I mean, this is, it's really early work, but the R bio model, the idea of doing reasoning is that then you don't just get correlation, but you get some understanding of like logic over how these things get together, too. So, yeah, I mean, I think it's probably going to be a while and people don't have great hypotheses on how you'd actually do like molecular imaging like of a cell deep inside a living organism. But the goal is to be able to approximate that as much as possible with like this kind of surround view of, of, of, of different things that you can image.

• 24:18Speaker 03

  You guys like to see cool stuff. It's not here, but at our San Francisco Skype, we do image see-through fish called zebrafish.

• 24:26Speaker 01

  Zebrafish, yeah.

• 24:26Speaker 04

  That's another, it's another good example. Like it's another good hack. It's like, all right, it's like, how, what's a good way to

• 24:31Speaker 03

  Yeah.

• 24:32Speaker 04

  imaging a living thing? It's like, take a see-through thing.

• 24:34Speaker 03

  Take a see-through thing. And then use a model to say, how does this see-through thing actually relate to us? Right? Like, I'm like, not that interested in curing disease, cure, prevent, manage all disease for zebrafish. I am very interested.

• 24:47Speaker 01

  Speak for yourself. Poor zebrafish. Yeah.

• 24:50Speaker 03

  Mark, Mark's pro zebrafish. I'm OK on zebrafish. But you, you need to use another application of large language models is looking at how, what is conserved and what is actually relevant and important to the way human biology works in a fish model. And so being able to have that translation be more effective. So we don't waste our time on things that won't apply in a model organism is another really interesting way to elevate biology.

• 25:17Speaker 01

  On the data side, can you just give an overview of how far we are? Like, what percentage of all cells are we image? And do we have, what's the distribution of them? You know, like when you say 150 million to 1 billion cells, is that a lot? Is that 10%?

• 25:34Speaker 03

  The funny thing is, until recently, we didn't know how many cell types.

• 25:37Speaker 04

  Yeah, I mean, this is kind of a wild thing. And this was a big part of the Cell Atlas project is like, there wasn't even, it's kind of like, imagine the periodic table in chemistry, but you, you know.

• 25:45Speaker 02

  It doesn't end.

• 25:47Speaker 03

  Well, it's.

• 25:48Speaker 04

  You don't have the squares.

• 25:49Speaker 03

  We know it's billions. We know there are billions of cell types in a human. And we've only truly looked at a fraction of them. And we looked at it in largely healthy cells. And so like, just the number of permutations of like, age, well, species, because not all research is in humans, right? So species, ancestries, like, what is your sort of genetic background? Age, like, babies are different than old people. Gender, all of those things actually are permutations. Environmental exposures, all of those things are permutations on the cell that actually you, you want to be able to understand in healthy and disease states. I feel confident that we are at the beginning of this.

• 26:32Speaker 02

  I'll ask a little bit of an obvious question in terms of the intersection of AI and bio, which is, don't we want precision in biology? Don't we want some grounding in a world model, maybe, that we don't normally get in a language model?

• 26:48Speaker 04

  Yeah, I mean, I think that that's sort of the, the point of doing all the measurement and being able to have all this real, you know, and it's like, so you have the, the diffusion model for generating cells that we, that we put out. And it's like, one of the, one of the recent models. And it's like, it's cool because you can basically, you have a model now that you can describe is like the conditions and it'll basically give you a synthetic cell. But yeah, you, you want it to be increasingly grounded. And that's a lot of the point of the biology and the engineering that we're doing is to be able to have these different facets of that. So the imaging institute is one part that gets you the spatial data. That's, that's very helpful. And the work that we're doing in the other bio hubs on cellular engineering

• 27:32Speaker 04

  and instrumenting inflammation and things like that. It's basically, it's scientific work to build new types of tools that allow us to measure new types of things that generate data that allow us to ground the models in different ways. One framing that we have on this that. I think is is pretty interesting is that you know, there's this concept of a frontier AI lab that is like, okay, it's building AI models that are sort of at the frontier of what's possible. And I think you can think about biology in that way, too. And there's sort of a concept of a frontier biology lab. Like, what is the idea of a, you know, it's like labs that are kind of at the cutting edge of like, building the most advanced imaging, like measuring, you know, inflammation or doing cellular engineering in the most advanced ways.

• 28:15Speaker 04

  Whatever the problem space is that you're at. And then I think that there's this interesting problem space of what happens if you're at the intersection of those two areas. Right? So, I mean, you mentioned the work that DeepMind did on AlphaFold, which is great. That's an example of a frontier AI lab using a data set that was just generated by other scientists like over decades. Right? But I think part of what we're trying to unlock here with Biohub is the idea of what actually, what happens if you do frontier biology and frontier AI in sync together? And you're designing the tools on the frontier biology side in order to specifically collect and be able to learn

• 28:58Speaker 04

  types of data that you then want to feed into specific types of models that you want to build so that it can understand the cells and the body at different types of resolution. I think you can just kind of, I don't know, you, it's like a much more integrated approach that that allows, you know, designing the things that you need that should eventually get towards more grounding and not just allowing, you know, folks who are good at AI to do the best they can with whatever biological data happens to be available.

• 29:25Speaker 01

  What's the hill climbing in this scenario? So like with language models, you have benchmarks, you look at the benchmark, you just make that go better. With these things, you have to bring it back to the real world. So as you build these models, like, how do you bring the two themes together to get feedback?

• 29:40Speaker 03

  I think it's very similar to what Mark just said. You want to be able to validate on the accuracy question. We don't expect that these models, they will get increasingly accurate. But you want to be able to have feedback. And it's not as easy as being like, you know, this, yeah, this output doesn't make sense. You have to actually take it to the wet lab, run the experiment, find out if it actually happened as predicted and feed it back into the model. And that's the virtuous cycle we want to build to help the AI best serve the biologists and the biologists be part of continuously improving the models.

• 30:16Speaker 01

  From like a numbers perspective, in a language model, you can run tens of thousands of tests.

• 30:22Speaker 04

  They're very false. And we have to build a lot of them out.

• 30:25Speaker 01

  Yeah. Yeah. And then on going to the wet lab, what do you think that's going to be like the feedback cycle? Like, as you start to have more of these things to be tested in the wet lab, do you feel like that's going to be a bottleneck that like we cannot take that many?

• 30:38Speaker 03

  Um, I don't know the answer to that yet. I think that the throughput on sort of established metrics in the wet lab is actually getting quite fast. You can run paralyzed a lot of experimentation. So, but it's not at the, easily at the tens of thousands of verifications. But it's, we'll have to, well, we actually have to see. We'll probably need to be smart about how we do it.

• 31:04Speaker 04

  But I mean, there's, you know, a lot of people, I think, often take these things to the extreme and are like, okay, pretty soon, if you have these models, you're just going to be able to run experiments with the models without even having to go to a wet lab. And it's like, no, I mean, I think that's kind of like, I think that that's sort of the biological version of like, eventually AI is going to automate every single thing in society. It's like, look, maybe you get there, right? And I think that there's like some chance over time. But well before you do, you're going to be able to have models that can help generate hypotheses. And scientists can apply their taste on which ideas or kind of suggestions come from this are worth testing. And then you test them and then you feed it back into the model,

• 31:46Speaker 04

  which I think is basically the way that every AI model is deployed into

• 31:51Speaker 01

  Even in coding.

• 31:52Speaker 04

  Work and other places.

• 31:52Speaker 03

  Totally. Like you, right now, because the wet lab is so expensive and relatively slow compared to sort of computational experimentation, like people are choosing like, I need something to hit. So people are going for hypotheses or ideas that are like, you know, Uh, in to use a sports analogy, like singles or doubles. And but like they it's just too risky. They only have so much grant funding and they need something to help move their work along. But like if we have a model that can help de-risk some of the bigger riskier ideas, that's going to move science faster. Um, and I I think makes the science and and those ideas both, you know, can be sourced

• 32:35Speaker 03

  with AI as a tool, but really it's really about making the scientists less hesitant to explore big ideas.

• 32:42Speaker 02

  Yeah, obviously, that's a lot of the success of the model of C Z I, which is serving this part of research that is underserved because there was basically no benefactor or no fun, no funding mechanism by which to do this. One thing that we're announcing when we release this podcast is this unification of the sort of bio hub model.

• 33:00Speaker 04

  Yeah.

• 33:00Speaker 02

  Um, I think it's very analogous to the foundation model and frontier lab approach, right, where you bring together people, different disciplines, you have much longer time horizons than than anyone else. Uh, are there any other key elements to the strategy of the bio hub that you're ticking?

• 33:16Speaker 04

  Well, I mean, one thing that we haven't talked about is the evolutionary scale team and Alex Reeves and and his team joining.

• 33:21Speaker 02

  Well, let's talk about the announcement.

• 33:23Speaker 04

  Yeah. Yes, I mean, I mean, this is like probably the most talented team working on AI and biology, right? And like at the intersection of doing of like basically good biology background and also, you know, they've just been working on.

• 33:36Speaker 02

  E S N three.

• 33:37Speaker 04

  Yeah, some of the top protein models for a long period of time. Yeah, I mean, I think if you want to build a an organization that is doing frontier biology and frontier AI, you need to have like world leading AI researchers and we're doing that by basically combining the team that we have that's already put out all the models that we're talking about today, plus having the evolutionary scale team, which is just like very renowned.

• 34:03Speaker 04

  Join and Alex is basically going to be running the program. So I think it's it's sort of an interesting decision, I think, to have the AI person basically be running the the overall program partnering with these leading leading biologists. I think gives a sense of how optimistic we are about the AI work being very fundamental to this, but we're very serious about building out like a leading part, a leading lab on the AI side as well. That goes for both the talent and the compute. I think we were probably the first to build out a large scale compute cluster for for biological research. I think now there are some others who are doing it too, but we're also building on that and, you know, we really, we plan to release frontier models.

• 34:44Speaker 05

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 34:49Speaker 01

  Do you see that as the 10 year output, like in the next 10 years?

• 34:53Speaker 03

  I just talked to them yesterday. They say it's faster than that.

• 34:56Speaker 04

  But AI people are.

• 34:57Speaker 03

  They're always, they're always in a hurry.

• 35:00Speaker 01

  We have AGI in two years, so. Would that be a satisfactory result for you guys? You fast forward 10 years, you have like the best, you know, the three best models in biology, or is there like a further goal that you want to have as an output of the foundation?

• 35:14Speaker 03

  I have to bring it back to the patient. I think like the AI models are, I think, will be very excited, both if we have great models and scientists are using them, but you really want to make sure that it's like accelerating clinical impact. Like, that's the goal, right? Like, that the AI models is a very challenging milestone that we've worked, we are working very hard on and we will get there. But how do you actually take those models and apply them to actually change the way people live? And there's so, there's two variants that I think about in the application of these models. Why are they important? One is, like, each one of our genetics is incredibly diverse and different.

• 35:57Speaker 03

  First, like, first of all, we are just all, the four of us are unique people, but we also have things like that are sort of known indicators of disease and unknown indicators of disease. And I actually find the variance of unknown significance to be the most interesting and the most frustrating. Say you know, someone that you love, it's sort of a diagnostic mystery. They need to go in and look at the genetics. Most likely they'll come back and be like, there are these three things that are not usual, but we also don't know why. And you're like, okay, like, should I panic? Should I not panic? Like, what do I do now? And what you really want to do, and I think these models will be able to do, is look at those variants and actually model out what is the impact in the different cells, how it

• 36:40Speaker 03

  influences cellular behavior, and whether or not that is tied to a pathway to disease or not. Like, that's a big deal, and I think we should be doing that. That is actually the future of medicine, where we think about each one of your biology based on your genetics, your exposure, and how it predisposes you or not to disease. Like, that's huge, and we want to be able to see that clinical application, but we can't. It's too expensive, too hard to model each person, impossible to model each person in the lab. But if we can build models around this, it is possible. And then we can start thinking with extreme precision. And I'm not just talking about rare disease. Like, they're like common diseases.

• 37:23Speaker 03

  I'll just say depression. Right now, it's empirical, right? We just say, like, you're depressed. Like, here, let's try this antidepressant. And it's like usually the one that the doctor's more familiar with, or maybe one that you've heard of. But like, and then you have to try it for months before it's like, did it work? Did it not work?

• 37:42Speaker 02

  Months?

• 37:43Speaker 03

  Yes.

• 37:44Speaker 02

  That's the cycle? I don't have familiarity with this. That's horrible.

• 37:47Speaker 03

  And meanwhile, if it doesn't work, it means the person's suffering. And this applies to like almost every disease, right? There has to be some biological explanation as to why some medications work and don't. So can we actually then look at each patient and say, based on who you are, we think this medication is going to work best for you? That's the future I want to live in, where we can actually understand individuals as individuals and use the biology and science very directly to keep them well.

• 38:18Speaker 02

  Yeah. So like, if there's a name for this tool that has the clinical impact, that is on the scale of the electron, how do you envision it, I guess? Like, I feel like it's almost going to be the CZI app, I guess.

• 38:33Speaker 03

  Oh, well, it won't be, first of all, that's not what we're building right now. We're building the basics. We're understanding like cells and molecules. So I'm painting

• 38:42Speaker 02

  Someone else will do it.

• 38:43Speaker 03

  We're painting a picture, like, we need partnerships. This is, you asked about the ecosystem before. Like, there are experts along the way of this pathway. And so we sort of are at the fundamental research side.

• 38:54Speaker 02

  Yeah.

• 38:54Speaker 03

  And you need to be able to partner with folks to bring this all the way through impact. But the way I think about it, people call it different things. But essentially you want to get to medicine where we it's truly precision medicine. It's N of one. We're understanding you and designing therapeutics for you.

• 39:13Speaker 02

  Yeah, I like the mission of Rare as one as well. That's that's a great framing.

• 39:17Speaker 01

  Do you feel like that's possible, like almost treating the body as like a compiler? It's like, because I know exactly what it looks like. I know exactly what's gonna happen. Or is the body just like, there's too many outside inputs and like over time it kind of deviates from what you have?

• 39:32Speaker 04

  Well, I think we'll see how far we can get, but I I mean, I'm pretty optimistic that we'll be able to make a bunch of progress. And yeah, I mean, there's like you basically, I mean, what format does this take technologically? I I would imagine you're taking these different types of virtual cell models and eventually merging them into the equivalent of like a biological Omni model. Kind of like how on the language model side, you had people that did language and then, you know, people who did different kinds of media models and perception and all that. And then eventually you just kind of merge that. And then you aim to get positive transfer by merging it. So that way it's not just combining capabilities, but getting everything else to be stronger.

• 40:10Speaker 04

  So, yeah, I mean, technologically, I think that's basically what it looks like is, you know, over whatever it is, a five or 10 year period, we're building up a series of Biohub models that like increasingly get all these different dimensions of of data and capabilities that can be used to help run individual science experiments and potentially eventually help with finding individual therapies for patients, although we're going to be less on the clinical side. We're going to be more in the kind of scientific tool development side. And the kind of main tool, if you will, is this like these Biohub virtual cell models.

• 40:50Speaker 03

  I would say five years ago, without sort of the large language models supporting this, I don't think it would have been possible to really, because biology is incredibly complex. And what we're essentially trying to do is break it down from a discovery-based science where you kind of get lucky, you kind of get clever, and you sort of figure out a hack to learn something new, to really making it closer to an engineering problem of like, this is how the system works, and when this breaks, what happens to the rest of the system. But like you said, there's just, there's far too many dimensions for for us to hold in our brains. That's why we're so excited about this intersection at this moment, because it is possible to consider so many more dimensions matching the complexity

• 41:35Speaker 03

  of biology.

• 41:37Speaker 01

  What is the role of the doctor in that future, right? If you can like predict everything out, and then if you take personal superintelligence seriously, do you kind of distribute some of the diagnosis and all of that work? Or how do you envision that?

• 41:52Speaker 03

  I've been thinking about this a lot. And I think one is, the model's not going to take you all the way. You're still going to need to really look at individual clinical situations. And and the doctor is going to be a form of data input into the model, right? And so the doctor, there's some judgment that comes into place. But there's already a lot of models that make doctors really good at what they do. For instance, looking at your skin, like, AI is really, really good at detecting lesions in your skin that are concerning. It's excellent. Retinal issues, it is excellent. So the AI modeling and mapping is really, really good. So it's already happening. So I think about like, what should future

• 42:37Speaker 03

  doctors be trained to do? And I really think care and compassion and sort of walking patients through understanding, I think, understanding why leads to trust in both the science and in the clinical pathway. And really walking alongside patients on that journey is, you know, it was the original calling of physicians, to be healers and to be using great tools to heal patients.

• 43:07Speaker 02

  Well, so bedside manner ultimately.

• 43:10Speaker 04

  I mean, I also think you can zoom out, though, from like the role of a doctor to I think everyone wants the health system to be more proactive and less reactive, right? So today, it's like you show up when you're sick and then like you have someone treat you or understand what's going on. I think the goal with a lot of these systems is to be much more proactive about this. So when we say that the vision is to try to help scientists cure and prevent all diseases, it doesn't mean that there's going to be like no bacteria in the world and like no one ever starts to get an infection. It's just that, all right, ideally, you can kind of understand all of that really early, right? Similarly, if someone, you know, if someone gets a mutation that looks like it might

• 43:52Speaker 04

  become cancerous, then you can just treat it a lot better if you if you know that early rather than like showing up to a doctor when it's, you know, already metastasized and you have a bunch of of issues on that. So, so I don't know. I think that there are going to be a lot of opportunities to fundamentally improve the healthcare system overall. But I think, I mean, I agree with everything that you said on this. And I also just think that like, when we say that we think it's going to be possible to prevent and cure all diseases, it's not literally that no one ever gets the beginning of a sickness. It's just that it like, it kind of can be managed in a way where everything is sort of manageable.

• 44:31Speaker 02

  I think we discover more diseases the longer we live. Is it possible to not die? Is, you know, obviously, that's a meme that's coming to fruition. If you theoretically cure all diseases, maybe death is a disease.

• 44:45Speaker 03

  Mark just said we had extreme alignment, which I love. Thank you, honey. This is, this is

• 44:51Speaker 04

  This is one that we don't necessarily agree on.

• 44:53Speaker 03

  This is one that I'm not sure we have extreme alignment on. I, in fact, just haven't thought about this one very much. Because I think there is so much

• 45:00Speaker 02

  There's other things to do.

• 45:01Speaker 03

  There's so much to do in terms of like, you know, I'm a pediatrician. I think about babies and like very sad things happen to very small people. And like, I think a lot about that. And how do we like maximize life quality and the things that harm small people? I'm biased. And I haven't thought as much on the other end of the spectrum. But I don't know, I'm 40, maybe I should, but I feel like I can still focus on the little ones.

• 45:29Speaker 04

  I think the strategy is the same, right? I mean, it's like we're, we're basically choosing to not focus on any specific disease and like verticalize. Our strategy is one of trying to accelerate scientific progress overall. And I think that there are a lot of people who are going to focus on each of these individual things. So I don't know.

• 45:47Speaker 03

  But we don't have to because that's not our strategy. Our strategy is to make sure that we have tools that make people do the best science possible out there.

• 45:55Speaker 02

  I'll put to you that, you know, because of, because aging and environments are, and mutations are so diverse, like you actually, you have a tight concentration of grouping in, in the early years, and it should have more diversity in terms of the, the cell types and, and the problems that you face in, in the later years. And so that, I mean, there might be some imbalance in terms of where all these things happen. But I'm not, you know, I'm not, I'm not pitching in any particular direction.

• 46:21Speaker 04

  No, I mean, I mean, I think it's clearly, if you look at the trend over the last, I don't know what it is, 100 years, I mean, there was this flip. And if you like pay attention to the history of science where it changed to kind of hypothesis driven scientific method of like, we're going to run tests and have controlled experiments. And since that happened, the average life expectancy has basically increased by, I think it's about a quarter of a year every year over the last 100 years. Now, a lot of that, like Priscilla said, is basically making it so that a lot of people don't die young.

• 46:54Speaker 02

  Yeah.

• 46:55Speaker 04

  So far had somewhat less of an impact on extending the maximum human life expectancy. Although the oldest people today, I do think in general are older than the oldest people, you know, 20 or 30 or 40 years ago. But I think that there's been a little bit less of an increase there and more just kind of making it so that people don't suffer and die prematurely from things. But I mean, there's other things that you want to focus on here, too. It's not just like how long you live. It's like the quality of the life while you're, you know, so I think it's like, you can live a full life and have that be high quality, or you can get sick in different ways that kind of add up over time. And I, I think like, there's lots of different ways to improve. I mean, it's, um,

• 47:40Speaker 04

  There's like, you know, all these different analogies that you can throw at this, but I think there's, there's just a lot of, lot of room to improve here.

• 47:47Speaker 03

  Yeah.

• 47:47Speaker 02

  And then the other element I wanted to come back to on the engineering side, which is when you presented a high dimensionality problem, you want to reduce things into little boxes that you can sort of manipulate at a higher abstraction. And that's something I tried to do with the folks outside. And it's really, we really struggled because over here you're imaging on like the atomic level. And then you're also worrying about proteins and then you're also trying to build a cell model. Is every abstraction leaky? Like where's the boxes I can move around and not worry about it? My physics analogy is in the regular world, you don't have to worry about quantum physics. But here we kind of do.

• 48:22Speaker 04

  I think you want to build it up a little bit hierarchically. And like, and when you're trying to understand proteins, understanding molecules makes a big difference. But at some level, you can kind of just look at like correlations in cells. But if you want to like really have the most accurate model, and if you want to be able to reason about things, then you probably also want to understand proteins well. And then I think that kind of extends. But yeah, I mean, that's part of the interesting challenge of this is that it's not just like one resolution that you're looking at it. I think in order to do it well, it's, yeah, I mean, you're, you're kind of, you have some amount of abstraction. But I think you want the models, just like language models, or I think how our brains work, to basically build up different levels of abstraction and pattern matching. And I think that that's here too.

• 49:06Speaker 04

  And you basically just need to be kind of like have some basic excellence in understanding at each of these different levels.

• 49:11Speaker 02

  It's weird that the, the number of levels at which you have the telescope up and down, it's mind boggling. And I think like that's when, when people say dimensions, they typically mean orthogonal dimensions. But here it's sort of like nested.

• 49:23Speaker 04

  Yeah.

• 49:23Speaker 02

  And yeah.

• 49:25Speaker 04

  Just different scales that are like oddly different disciplines to understand each specific scale. And it's like in a way that like the people who are good at understanding one's scale are like.

• 49:35Speaker 03

  Have never spoken to people at the next scale.

• 49:38Speaker 04

  Yeah.

• 49:38Speaker 02

  Yeah. Yeah, physics is there, chemistry is here.

• 49:41Speaker 04

  Yeah.

• 49:41Speaker 02

  Yeah, bio is there. It's nice to hear about it, but when you see it and you meet the people, you're like, oh, this is real. Like, and they are, they are actually working together.

• 49:51Speaker 01

  And then there's this goal of the virtual immune system that you're working towards. I would love to for you to chat about that. And also, like, if that happens, what should other people build? So there's obviously, you know, CRISPR and some of that technologies like that people should maybe ramp throughput for. Like, how do you think about the future?

• 50:08Speaker 03

  The virtual immune system, I think, is, you know, obviously, I think of a subset of sort of the generalized model eventually we'll get to. But the virtual immune system is super interesting for a couple of reasons. One, it's individual cells interacting with each other. There's, you know, a number of cells that we don't even fully understand what they do. B cells, T cells, and K cells. And so we can use our current technologies to understand these cells at a more granular level. So that's cool from a biology standpoint. But the clinical impact is huge of understanding the immune system. Because biology, turns out, has already given us a way to keep the body healthy.

• 50:52Speaker 03

  And it also sometimes goes awry and causes disease with autoimmune disease.

• 50:57Speaker 01

  Yeah.

• 50:57Speaker 03

  Right? And so it's a very complex system that has to stay in balance. And if it goes out of balance in either direction, you get sick. It can also go into your body, and it's a privileged system that is mobile and can go into places like your brain, your pancreas, your heart, to sort of either do maintenance or to collect signal. That's it's built in. So if we can understand the system, we can use it to keep people healthy. We already kind of do. So there's CAR T cells, where we reprogram T cells to go in and fight cancer. And our New York Biohub, we're doing cellular engineering to say like, hey, can you go in to this person's heart?

• 51:40Speaker 03

  Check if they have plaques that are causing problems? Read it into your DNA, self lys, and then we can read out the signal of cell-free DNA and give us a binary answer, yes or no. Then we can put in other engineered cells and imagine where you go in and you clear out the plaques using engineered immune cells that are your own. That is incredible. That is a tool that like is is realistic, too. I know it sounds sci-fi. It is realistic. It is happening. And then on the other end of understanding the balance, like so many autoimmune diseases, MS, lupus, those are the ones, examples of ones we know. I, I think there are other things that are autoimmune that we don't understand, like dementia can have an autoimmunity can play a large role in that.

• 52:24Speaker 03

  And so if we can understand the fine balance that the system needs to be kept in, then we can actually impact a lot of the ways the, the human body is maintained. Um, so I think it's both interesting from a biology perspective and feasible to model, and probably one of the highest sort of now impact systems if we can learn how to manipulate.

• 52:45Speaker 04

  Amazing. But it's only one system, right? I mean, it's, I think it's like the

• 52:48Speaker 03

  So it's a subset.

• 52:49Speaker 04

  If you're focused on curing and preventing diseases, the immune system is a pretty important one. And I think it's also interesting for all the, and unique in a lot of the ways that you said, but there's like lots of other parts of the body to understand too.

• 53:01Speaker 01

  And I think we're running out of time, so we have two questions to close. One, again, a hundred years, maybe it's too long, right? What would it take to do it in 50 and 25? And to make those happen, like what should other people build to support your work?

• 53:16Speaker 04

  I mean, I think a lot of this is going to end up coming down to how far a lot of these AI methods get, right? I think that there's like, people have, there's just this constant ongoing debate around what are the timeframes for getting to very strong AI? And I think if you, if you get that, then I think it's pretty optimistic that with the right investments in frontier biology, you should be able to get these systems that can allow you to have virtual cells that allow you to do the kind of precision treatments and preventative care that can achieve this kind of mission significantly sooner. But at the end of the day, I think a lot of that timeframe will probably come down to the AI timeframe.

• 53:59Speaker 04

  There's obviously a ton of stuff to do in biology, but, but it's, so it's not, I mean, like, I think that what, what should other people do? I mean, other people doing more frontier biology and, and helping to collect that this type of data and solve these problems is super helpful to that too. It doesn't automatically happen, but I guess if we're, you know, predicting whether it's going to take 10 or 20 or 40 years, that is probably more a function of the pace of AI development than it is a pace of the pure biology side.

• 54:25Speaker 03

  Yeah, I was gonna agree with you. I think it's a lot needs to, it's, I think we're on a path to get a lot of important biological data through advances in laboratory technique. But it's not a given, like, and there are different groups that are expert at this all across the nation and across the world. And so we need to be continuing to push the research and the methodologies. And I want to say that, like, you know, the Cell Atlas was not glamorous work. People were not going to get their 10-year track paper by sort of analyzing the 120 millionth cell.

• 55:03Speaker 03

  That is just not it, right? And so rethinking the way that this work gets done in a collaborative, like, doing big things together in science. Like, that's what Bill is gonna need to happen to sort of get the knowledge we need to build models that give us this type of insight.

• 55:20Speaker 04

  I guess one thought on, on like the type of biology that I think should get done is there is a certain orientation around choosing problems that will help generate data that can help make the models a lot smarter. I think that there's a, you do that when you are very optimistic about the pace of progress and what AI is going to enable. Because the classic reason that scientists generated data sets is so that they could basically look through the data sets to make advances. So it is a little bit of an inversion in the thinking, which is like, I'm now going to do this so I can like help train this other thing to be better and create more advances.

• 56:01Speaker 04

  And I think in a world where you really believe that there's going to be very significant AI progress, I think more frontier biology should be done in that way. But these data sets aren't going to get created by themselves. There's a lot of work that needs to get done and a lot of investment there. And at some level, you could probably have the smartest AI model in the world, but if it doesn't actually have the data to understand this stuff, it's like, okay, you can't just like reason from first principles about, about all these things. I mean, a lot of human knowledge comes empirically, not from first principles reasoning. I think that more This is kind of the whole biohub network idea that we're building. And I've been really happy to see other folks, especially a lot of people in technology, I think, have this orientation too.

• 56:45Speaker 04

  They're, you know, they, they believe a lot in AI. They believe in the technological progress. They've generated some, some significant wealth. We're building their companies and now they're investing in, in science research. And I think that's great. And I think doing it in this way where you're like building up these networks to solve specific, to basically build specific tools that generate data that make the models better. It's one approach. It's not like, it's not that all science should go in that direction, but it's one of the things that I'm quite optimistic about, that I think is gonna make a very big difference.

• 57:16Speaker 02

  Cool. It's probably all the time we have, but I'll just leave it to you guys for any calls to action, anything that you want biologists or engineers to check out.

• 57:25Speaker 04

  I mean, check out the models.

• 57:26Speaker 02

  Check out the models, the tooling.

• 57:28Speaker 04

  Yeah, I mean, it's, they're, they're early, but I think it's, they're, they're, it's kind of an interesting sense of where things are going. And, you know, we'd love feedback on it and it'll kind of just help this feedback loop of, of like what we should build next.

• 57:42Speaker 03

  Yeah, I would say let's do this together. Like, we need lots of people coming together to do this work.

• 57:48Speaker 02

  Well, thank you for organizing it and solving and curing all diseases.

• 57:53Speaker 04

  Trying to help others do it. All right.

• 57:56Speaker 02

  Thank you.

• 57:56Speaker 03

  Thank you.

• 58:08Nathan Labenz?

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• 58:52Nathan Labenz?

  And thank you to everyone who listens for being part of the Cognitive Revolution.