Mark Zuckerberg & Priscilla Chan on AI-Driven Biohub and the Future of Biology

Source material

Transcript

Hello, and welcome back to the cognitive revolution.

Today, I'm excited to share a special crossover episode from the latent space podcast.

Laten space, surveys say, is the number one podcast for AI engineers.

And I find hosts, swix and LSEO, 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 bio-hub, with the goals of leading a new era of AI-powered biology, and ultimately equipping 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 ESM3, and the appointment of CEO Alex Rivas to lead the combined program.

Their 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 and of one treatment designed based on each individual's unique biology.

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 space, including Jeff Dean at Google, Dario and Chris Ola at Anthropic, and various researchers at Open AI 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 budgets and generally acting against medical advice, society stands to benefit tremendously when self-made tech billionaires turn their formidable talents and immense resources through 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 U.S. government.

And in my view, they remain essential for societal dynamism and resilience today.

With that, I'm grateful to Swix for allowing me to cross post this conversation.

I, of course, recommend subscribing to the latent 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 Weight and Space Podcast.

Hey everyone, welcome to the Latins-based podcast.

This is Alasio, from the Colonel Labs.

I'm joined by Swix, editor-over-bladespace.

Hello, we're so delighted to be in the imaging Institute of CCI with literally C and Z.

Welcome, Mark and Priscilla.

Thanks for having us.

Thanks for getting nerdy.

Yeah, we're excited to do this.

We do, we still 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 is 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 deciding to pursue a basic science instead of translational work.

Well, I mean, I think one of the core things for us with CCI was just getting started earlier.

Or 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 it's 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 yeah, I mean, like you mentioned, I mean, this is we're coming up on November the 10-year anniversary if when we started CCI.

And there's a lot of work that we're really proud of that we've been a part of including, you know, work in education and supporting communities.

But, 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 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.

You know, for the next period, we really want to make science the main focus of what we're doing and specifically the bio hub 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.

Yeah, when we started 10 years ago, we had this idea like, okay, I bring experience as a physician marks 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 try to 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 kind of 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 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 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 us at the bio hub 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 we can see the first decade and I will have to get your tickets 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 apply modeling side at one point each of the side that just doing the tooling was matter versus, you know, you could have cure malaria in Africa or some other disease, you know what yeah, I mean, take a step back and this is kind of related to your first question too.

I mean, there's like, first of all, saying the space is huge, you know, there are lots of other philanthropy, 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 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 microscopy 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 time frame 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 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 often hundreds of millions 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 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 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 yeah yeah the mission is cure prevent all diseases so by the way a lot of people outside of the cci worlds are still kind of find this concept very alien but talking to the cci people they really truly believe it and it's impressive how you pick the right mission to motivate everyone to work towards this enormous task well it's kind of a funny thing I mean it's I kind of we like to talk about the mission is like helping scientists do it because we're not actually curing the diseases we're just trying to build the tools to balancing the data models yeah like basically accelerating scientists work towards that but you know a funny thing about it is we had this initial time frame of by the end of the century and you know if 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 when you ask AI people it's like should be really easy like why are you so unambitious that you're you know shooting for just the end of the 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 you know it's your point about the first decade was you know sort of about doing work like the cell Atlas to to be able to help understand basically all of the the kind of specifics and date 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 you know 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 but the thing that's interesting to integrate is like okay so from a I totally agree that in our intersection of AI and biology the AI folks are like yep the biologist really and I think it's actually that confidence of conversations that lead both the biologists to be like okay I'm really uncomfortable about this idea in timeline but if I'm really pinned down to think about it what are like you really force people to think through like okay what are actually the barriers what would you need to do and you're forcing that conversation from the biologist side and from the AI side really getting a sense of okay what like data is not just data you guys know this 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 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.

The leaving is the first step.

There's a Joe pattern of software eating a world and I think AI eating a world is kind of like the next version of this I was talking to Garrett outside who says it's biologists but you know I think he's using models like Sam from from Meta.

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

What is a biologist?

I have been 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.

Yeah totally yeah I think one of those things that you know referencing the wet lab is all of the the key approaches that you're pursuing is turning things in pursuing the virtual cell.

Turning things from mostly wet lab into something and so to go.

How far along are we?

I mean it's pretty early.

Right I mean I think the the first step which I think is easy to overlook is basically what Priscilla was talking about 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'm in both it meta and and here it's um it's like you have you have teams that like are not working 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 what we're doing it's not just bringing together the biologists and the engineers which is 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 in biohub 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 it's just such a clear win just the kind of a 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 and we're having people sit together.

So then you get that and then you get these people who are like half biologists, half AI engineer because they they kind of have some experience doing it and I don't know I mean we can we can talk to the specific models and there's a lot of exciting stuff there but I'd say it's um it's an early glimpse of where this is all going.

I think like you want to kind of build up these models hierarchically 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 in your 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 like which parts of the genome are kind of being expressed in different ways 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 basically like how how these cells work and and you kind of you sort of be able to look at stuff from different perspectives and then put them together and you build like a richer and richer model of kind of how these cells work but we are definitely at the beginning of this journey.

But it's like slow and fast, slow and fast right so when we build the human cell out less we started 10 years ago.

It was one of our first first RFAs and we actually the first RFA was to fund the methodologies of how you would get a single cell transcriptone 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 transcriptones 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 we actually were responsible for maybe 25% of the data and the rest of the ecosystem contributed 75% of that.

That's an incredible asset and has been very important in modeling work.

Similarly if you look at alpha fold they build up 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 in its slow and expensive but with cryo 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 all happening just faster and faster.

Hey we'll continue our interview in a moment after we're at some of our sponsors.

Want to accelerate software development by five hundred percent?

Meet Blitzie the only autonomous code generation platform with infinite code context purpose built for large complex enterprise scale code bases.

While other AI coding tools provide snippets of code and struggle with context Blitzie ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line level dependency.

With a complete contextual understanding of your code base Blitzie is ready to be deployed at the beginning of every sprint creating a bespoke agent plan and then autonomously generating enterprise grade premium quality code grounded in a deep understanding of your existing code base services and standards.

Blitzie's orchestration layer of cooperative agents thinks for hours to days autonomously planning building improving and validating code it executes spec and test driven development done at the speed of compute.

The platform completes more than 80% of the work autonomously typically weeks to months of work while providing a clear action plan for the remaining human development.

Used for both large scale feature additions and modernization work Blitzie is the secret weapon for Fortune 500 companies globally.

Unlocking 5x engineering velocity and delivering months of engineering work in a matter of days.

You can hear directly about Blitzie from other Fortune 500 CTOs on the modern CTO or CIO classified podcasts or meet directly with the Blitzie team by visiting Blitzie.com.

That's BLI TZY.com.

Schedule meeting with their AI solutions consultants to discuss enabling an AI native SDLC in your organization today.

AI agents may be revolutionizing software development, but most product teams are still nowhere near clearing their backlogs.

Until that changes, if it ever does, designers and marketers need a way to move at the pace of the market without waiting for engineers.

That's where Framer comes in.

Framer is an enterprise-grade website builder that works like your team's favorite design tool, giving business teams full ownership of your.com.

With Framer's AI wireframer and AI workshop features, anyone can create page scaffolding and custom components without code in seconds.

And with real-time collaboration, a robust CMS with everything you need for SEO, built in analytics and AB testing, 99.99% uptime guarantees, and the ability to publish changes with a single click.

It's no wonder that speed, design, and data obsessed companies like Proplexity, Miro, and mixed panel run their websites on Framer.

Learn how you can get more from your.com from a Framer specialist or get started building for free today at Framer.com slash cognitive and get 30% off a Framer Pro annual plan.

That's Framer.com slash cognitive for 30% off.

Framer.com slash cognitive rules and restrictions may apply.

How do you think about the layers?

So you have compute and we'll talk about that later.

On the data side, you build this amazing microscopes.

I learned that they're all built for you.

By spec, they're not of the shelf things.

Anybody?

Yeah.

How much of a bottleneck is that still?

Like, can we convert the world of atoms into bits now at the right example or do we need?

Do we need more work on the microscope themselves, too?

And you're never done.

Right.

Yeah.

We'll 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 play 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 a, I don't know, the exact number.

There are maybe tens of these microscopes in the world.

So that's one bottleneck.

And I think really is like, when I have saying it's slow and then fast, there's so many other dimensions that we don't have yet.

The cool thing here is with the transcriptome work, we're looking 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, too.

But that's still like two dimensions connected.

Time is another dimension.

We need to get dynamic imaging in place.

That's so much resolution, yeah.

But like really cool biological innovation, but we need innovation in the way we can look at things like stain free, die free.

So we can look at things without sort of human intervention with time as a dimension is another because 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 knowledge.

And obviously 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 alternative.

It's like, okay, it's a 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 it approaches?

Well, the better it gets.

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 long and understand and 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 and 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.

But I mean, in 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.

Yes.

And then it told there's, you know, so one of the models that, again, I mean, this is, it's really early work.

But the, the RBO 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 a vessel 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 different things that you can image.

You guys like to see cool stuff.

It's not here, but at our San Francisco sky.

We do image see through fish called zebrafish.

It's another good example.

Like, so another good model.

Like, all right.

It's like how what's a good way to imaging a living thing.

It's like, take a see through things.

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'm very interested.

For zebrafish.

Yeah.

Mark Mark's person for fish.

I'm okay on zebrafish.

But you you need to you 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.

On the data side, can you just give a 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%.

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

Yeah.

I mean, he kind of a wild thing.

And this was a big part of the cell out.

Yeah.

Project is like, there wasn't even, it's kind of like imagine the periodic table in chemistry, but you, you know, just like it doesn't end.

Well, you know, we know it's billions.

You 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 researchers in humans, right?

So species, ancestors.

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.

Oh, ask a little bit of a, 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?

Yeah, I mean, I think that that's sort of the point of doing all the measurement and being able to have all this real.

It's like, so you have the diffusion model for generating cells that we put out.

And it's like one of the recent models.

And it's like, it's cool because you can basically, if 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 wanted 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 institution 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 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 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, you know, it's the 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, whatever the problem space is they'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 Alpha Fold, 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 Biohab 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 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, it's like a much more integrated approach that allows you know, designing the things that you need that that should eventually get to it's 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.

What's the hilt climbing in this scenario?

So like we'll language models, we have benchmarks, you look at the benchmark, you'll just make that with this things you have to bring it back to the real world.

So as you build these models, like how do you bring the two teams together to get feedback?

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 biologist be part of continuously improving the models.

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

And then like you can have three goals.

Yeah, and we have to build a lot of them out.

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 can not take them any or?

Um, I don't know the answer to that yet.

I think the throughput on sort of established metrics in the wet lab is actually getting quite fast.

You can run paralyzed a lot of experimentation.

Um, so, uh, but it's not at the time, easily at the tens of thousands of verifications, but it will have to, well, we actually have to see if we'll probably need to be smart about how we do it.

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, which I think is basically the way that every AI model is deployed into even in court and in other places.

Totally.

Yeah.

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, you 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.

And I think makes the scientists, and those ideas both, you know, can be sourced with AI's a tool, but really it's really about making the scientists less hesitant to explore big ideas.

Yeah.

Obviously, that's a lot of the success of the model of CGI, which is serving this part of research that is underserved because there was basically no benefactor or 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 biohub model.

I think it's very analogous to the foundation model in a frontier lab approach, right?

Where you bring together people, different disciplines, you have much longer time horizons than anyone else.

Are there any other key elements to the strategy of the biohub that you're taking?

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

Joining us.

Let's talk about the announcement.

Yeah.

I mean, this is like probably the most talented team working on AI and biology, right?

And like at the intersection of doing, I was like basically good biology background and also, you know, to have just been working on ESM3.

Yeah, some of the top protein models for a long period of time.

Yeah.

I mean, I think if you want to build an organization that is doing frontier biology in 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, um, join an Alex is basically going to be running the program.

So I think it's sort of an interesting decision.

I think to have the AI person basically be running the overall program partnering with these 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, um, for biological research.

I think now, um, there's some others are doing a two, but we're also building on that and you know, we really, we plan to release French or models.

Hey, we'll continue our interview in a moment after I wear it with my sponsors.

Your IT team wastes half their day on repetitive tickets.

Password resets, access requests, onboarding, all pulling them away from meaningful work.

With survival, you can cut helpdesk tickets by more than 50%.

While legacy players are bolting AI onto decades old systems, survival allows your IT team to describe what they need in plain English and then writes automations in seconds.

As someone who does AI consulting for a number of different companies, I've seen firsthand how painful and costly manual provisioning can be.

It often takes a week or more before I can start actual work.

If only the companies I work with were using survival, I'd be productive from day one.

Several powers the fastest growing companies in the world, like Proplexity, Verkata, Mercor, and Clay, and survival guarantees 50% helpdesk automation by week four of your free pilot.

So get your team out of the helpdesk and back to the work they enjoy.

Book your free pilot at servil.com slash cognitive.

That's S-E-R-V-A-L dot com slash cognitive.

Everyone listening to this show knows that AI can answer questions, but there's a massive gap between here's how you could do it, and here I did it.

Casclet closes that gap.

Casclet is a general purpose AI agent that connects to your tools and actually does the work.

Describe what you want in plain English, triage support emails and file tickets in linear, research 50 companies, and draft personalized outreach, build a live interactive dashboard, pulling from Salesforce, and strive on the fly.

Whatever it is, Casclet does it.

It connects to over 3,000 apps, any API or MCP server, and can even spin up its own computer in the cloud for anything that doesn't have an API.

Set up triggers and it runs autonomously, watching your inbox, monitoring feeds, firing on a schedule, all 24-7, even while you sleep.

Want to see it in action?

We set something up just for cognitive revolution listeners.

Click the link in the show notes, and Casclet will build you a personalized RSS monitor for their show.

It will first ask about your interests, and then notify you when relevant episodes drop.

However, you prefer.

Email?

Text?

You choose.

It takes just two minutes, and then it runs in the background.

Of course, that's just a small taste of what an always-on AI agent can do, but I think that once you try it, you'll start imagining a lot more.

Listen to my full interview with Casclet founder and CEO Andrew Lee, try Tasclet for free at tasclet.ai, and use code, co-grab, for 50% off your first month.

The activation link is in the show notes, so give it a try at tasclet.ai.

Do you see that as the 10-year output?

Like in the next 10 years, we look back at the videos yesterday.

They say it's faster than that.

But AI people are...

There are.

There are.

I was in a hurry.

We have AI in two years, so would that be a satisfactory result for you guys?

You fast forward 10 years.

You have 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?

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 in scientists are using them, but you really want to make sure that it's like accelerating clinical impact.

That's the goal, right?

Like 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?

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.

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 variants 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 the genetics.

Most likely they'll come back and be like, bear these three things that are not usual, but we also don't know why.

And you're like, okay, 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 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 the predisposes you are 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 and 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.

I'll just say depression.

Right now, it's empirical.

Right?

We just say, like, you're depressed.

Like, here, let's try this anti-depressant.

And it's like, usually the one that the doctor is more familiar with or maybe one that you've heard of, but like, and then you've to try it for months before it's like, did it work?

Do it not work?

Months?

Yes.

That's the cycle.

I don't have that.

That is very difficult.

And in 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 and as individuals and use the biology and science very directly to keep them well.

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 CCI app, I guess.

Well, it won't be, first of all, that's not what we're building.

We're building the basics.

We're understanding like cells and molecules.

So I'm painting 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.

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 end of one, we're understanding you and designing therapeutics for you.

Yeah.

I like the mission of rare as one as well.

That's, that's a great framing.

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

Well, I think we'll see how far we can get.

But 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, what format does this take technologically?

I would imagine you're taking these different types of virtual cell models and eventually merging them into the equivalent of like a biological omnimodel, kind of like how on the language model side you had people that did language and then you know, people who did divisions of media models and perception and all that and then eventually you just kind of merge that and then, you know, aim to get positive transfer by merging it so that way, it's not just combining capabilities but getting everything else to be stronger.

So yeah, I mean, technologically, I think that's basically what it looks like is, you know, over whatever it is, a five or ten year period, we're building up a series of biohub models that like increasingly get all these different dimensions 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.

I would say five years ago without sort of the large language model supporting us, I don't think it would have been possible to really, biology's 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 a 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 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 of biology.

What is the role of the doctor in that future, right?

If you're going to 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?

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 the doctor's 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, 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 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.

Well, so bids eye manner, ultimately.

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 that really early.

Right?

Similarly, if someone gets a mutation that it looks like it might become cancerous, then you can just treat it a lot better if you know that early rather than like showing up to a doctor when it's already metastasized and you have a bunch of of issues on that.

So I don't know, I think there are going to be a lot of opportunities to fundamentally improve the health care system overall, but I think 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.

I think we discover more diseases the longer we live.

Is it possible to not die?

You know, obviously that's a meme that's coming to fruition.

If you theoretically cure all diseases, maybe death is a disease.

Mark just said we had extreme alignment, which I love.

Thank you, honey.

This is one that we don't necessarily do.

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.

There's other things to do.

There's so much to do in terms of like, 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 and biased.

And I haven't thought as much on the other end of the spectrum.

But I don't know.

I'm 40 me may I should, but I feel like I can still focus on the little ones.

I think the strategy is the same.

I mean, it's like 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 there are a lot of people who are going to focus on each of these individual things.

So, I don't know.

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.

I'll put to you that because of because of aging and environments, our mutations are so diverse.

You actually have a high concentration of grouping in the early years and it should have more diversity in terms of the cell types and the problems that you face 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 pitching it any particular direction.

I mean, I think it's clearly if you look at the trend over the last, I don't know what it is, a hundred 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.

It's like we're going to run tests and have controlled experiments.

And since that happened, the average life expectancy is basically increased by, I think it's about a quarter of a year, every year, over the last hundred years.

Now, a lot of that, like Priscilla said, is basically making it so that a lot of people don't die young.

Yeah.

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, I think there's other things that you want to focus on here, too.

It's not just like how long you live.

It's like how slowly you have the life for your, 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 think like there's lots of different ways to improve.

And it's there's like, you know, all these different analogies that you can throw at this.

But I think there's just a lot of a lot of room to improve here.

Yeah.

And then the other element, I want it to come back to on the engineering side, which is when you presented a high-dimensional LED problem, you want to reduce things into little boxes that you can manipulate at a higher fraction.

And that's something I try 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 is the boxes I can move around and not worry about it?

My phase of physics analogy is in the regular world, you don't have to worry about quantum physics.

But here we kind of do.

I think you want to build it up a little bit higher, archically.

And like when you're trying to understand proteins, understanding molecules makes a big difference.

But it's some level you can kind of just look at correlations and cells.

But if you want to 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 kind of you have some amount of abstraction.

But I think you want the models just like language models, or I think our brains work to basically build up different levels of abstraction and pattern matching.

And I think that that's here too.

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

It's weird.

The number of levels, which you have to tell us, go up and down.

It's mind boggling.

And I think like that's when people say dimensions, they typically mean orthogonal dimensions.

But here is sort of like nested.

Yeah.

And yeah, 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 scale are like never spoken.

Yes.

People have the next scale.

Yeah.

Yeah.

Physics is there.

Chemistry is here.

Yeah.

Yeah.

Bios 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.

And they are actually working together.

Yeah.

And then there's this goal of the virtual immune system that you're working to worlds.

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 the technologies like the people, should maybe ramp throughput for like, how do you think about the future?

The virtual immune system, I think is is, you know, obviously, I think of a subset of sort of the generalized model eventually will 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, D 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.

And it also sometimes goes awry and causes disease with autoimmune disease, 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 Cartysel's, where we reprogramt cells to go in and fight cancer.

And our New York bio hub, we're doing cellular engineering to say like, hey, can you go in to this person's heart?

Check if they have plaques that are causing problems, read it into your DNA, cell flights, and then we can read out the signal, a cell free DNA, and give us a binary answer.

Yes, her no.

Then we can put in other engineers 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 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, those are the ones examples of ones we know.

I think there are other things that are autoimmune that we don't understand.

Like dementia can have an auto immunity, you can play a large role in that.

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 human body is maintained.

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.

Amazing.

It's only one system.

I think it's like so.

So it's a subset.

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 unique and a lot of the ways that you said.

But there's lots of other parts of the body to understand too.

I think we're running out of time.

So we have two questions to close.

One, again, 100 years maybe still long, right?

What would it take to do it in 50 and 25?

And to make those happen, like what should auto people build to support your work?

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 just this constant ongoing debate around what are the time frames for getting to very strong AI.

And I think if you get that, then I think it's pretty optimistic that with the right investments and 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 time frame will probably come down to the AI time frame.

There's obviously a ton of stuff to do in biology.

But it's not, I mean, like I think that what what other people do, I mean, other people doing more frontier biology and helping to collect this type of data in selfies.

Problems are super helpful to that, too.

It doesn't automatically happen.

But I guess if we're 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.

I was going to agree with you.

I think it's a lot needs to, I think we're in a path to get a lot of important biological data through advances in laboratory technique, but it's not a given.

And there are different groups that are expert at this all across the nation in the across the world.

And so we need to be continuing to push the research and the methodologies.

And I want to say that the cell Atlas was not glamorous work.

People were not going to get their tenure track paper by sort of analyzing the 120 millionth cell.

That is just not it, right?

And so rethinking the way that this work gets done in a collaborative, like doing big things to other in science.

Like that's what is going to need to happen to sort of get the knowledge we need to build models that give us this type of insight.

I guess one thought 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.

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 in a lot of investment there.

And at some level you could probably have the smartest day I model in the world.

But if it doesn't actually have data to understand the stuff it's like okay you can't just like reason from first principles 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 there.

You know they believe a lot of AI.

They believe in the technological progress.

They've generated some significant wealth for building their companies and now they're investing in science research.

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 going to make it better every difference.

Cool.

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

I mean check out the models.

Check out the tooling.

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

Yeah I would say let's do this together.

I like we need lots of people coming together to do this work.

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

Trying to help others do it.

All right thank you.

Thank you.

If you're finding value in the show we'd appreciate it if you take a moment to share with friends post online right review on Apple Podcasts or Spotify or just leave us a comment on YouTube.

Of course we always welcome your feedback guests and topics suggestions and sponsorship inquiries either via our website cognitiverevolution.ai or by DMing me on your favorite social network.

The cognitive revolution is part of the Turpentine network a network of podcasts which is now part of a 16Z where experts talk technology, business, economics, geopolitics, culture and more.

We're produced by AI podcasting.

If you're looking for podcast production help for everything from the moment you stop recording to the moment your audience starts listening check them out and see my endorsement at AI podcast.aing.

And thank you to everyone who listens for being part of the cognitive revolution.