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

this is a a a space that I mean that

there's just going to be a huge amount

of leverage with AI. It still seems like

there could be a lot more effort in this

space around building tools and it's

kind of this crazy thing that we're you

know here in you know 2025 and there's

not the kind of periodic table of

elements equivalent for biology. We

think that this is like probably one of

the most important sets of tools that

you need to build. When we first set out

that the goal to cure and prevent

disease by the end of the century,

people like honestly most scientists

couldn't look at us with a straight

face.

>> And that's crazy.

>> Yes. And it was true because if you just

decided to spend the money funding the

next best grant for every single lab in

the country, like you there was no

pathway to that being true. The biology

folks, I think, looked at it as if it

were crazy ambitious. And then the AI

folks are like, well, that's kind of

boring. That's just automatically going

to happen. I know. It's like, okay,

there's something in between there that

needs to be bridged.

>> Mark Priscilla, welcome to the Asz

podcast.

>> Thanks for having us.

>> Yeah, great to be here. Excited.

>> All right. Excited to have you. You're

doing exciting stuff.

>> Yeah. Well, to that end, almost a decade

ago, you guys started the Chan

Zuckerberg initiative with the mission

and intent to cure, prevent, manage all

disease by the end of this century.

There's a lot of missions that you guys

could have poured your time and

resources into. Why don't we talk about

take us behind the conversations of why

you guys picked this one? Maybe

Priscilla, why don't we start with with

you and you hear your side of the story.

>> It always surprises people when I talk

about how we work in basic science

research. Um, I trained as a

pediatrician and people always think

like, oh, it must be about medicine. And

for me, it w, you know, I went into

medicine because I wanted to improve

people's lives. I wanted to make a

difference. I wanted to be able to help

others. And I think training as a

pediatrician at UCSF, I met a lot of

patients and frankly like little kids

and families for which like we just had

no idea what the problem was. and they

might have like a specific gene that

they could name if they were lucky. Um,

or they could be grouped into a bunch of

other diseases and there'd be a general

sort of PDF they'd print out like this

is what we know. And then it was my job

as an intern or resident to try to

translate like like a few lines of

information to how we were supposed to

take care of the patient. And for me,

that's when I really like realized the

power of basic science and how we need

to work on basic science to advance the

forefront of what's possible and without

that there's sort of I think of it as

the pipeline of hope.

>> Yeah. And why did you think um

you could cure all disease? Because

that's like a very like aggressive goal.

>> Um do you want to do you want to answer

that one?

>> Yeah. Well, well, I mean we're not going

to cure all diseases to be clear. I mean

the the strategy is to help scientists

and the scientific community cure all

diseases. So the strategy is really one

of accelerating the pace of basic

science and the theory that we had was

if you look at the history of science

most major breakthroughs are basically

preceded by the invention of a new tool

to observe phenomena in a new way.

Right? So think about things like the

microscope, right? Being able to observe

bacteria or you know other fields, the

telescope or

>> um you know, but it's

>> just to use an engineering example, you

know, it's without those kind of tools,

it's kind of like you're coding without

being able to step through the code and

debug things, right? So it's um that's

like the old days

>> when you

so so our our whole approach on this is

basically let's help build tools that

will accelerate the pace of the whole

field and I think that that there's a

niche that I think fits that because if

you look at how funding works in science

you know the vast majority of funding

comes from the government and NIH

grants. it's parcled out into these

relatively small grants that allow

individual investigators to investigate

usually pretty near-term things. Um, and

the development of these kind of new

types of tools, whether it's imaging or

building now a lot of AI things like

virtual cell models, um, are longer

term, often times more expensive to

develop. So think about like on the

order of a hundred you know maybe you

know hundred million to a billion

dollars over a um over a 10 to 15 year

period and then you you try to unlock

those tools and give them to the

scientific community to accelerate the

pace. So that's that's kind of the the

theory

>> right and and there it seems like

there's also something that that's is

you don't really get credit for the

tools in a lot of ways. I mean, we've

been noted, well, we have companies that

use your tools and they're very happy

about it. But, um, you know, I didn't

even know that that was the case. And

so,

>> that's why it's philanthropy.

>> Yeah. Well, it is, but most people do

philanthropy to get credit, too. I mean,

you know, like that's a you know, that

that's kind of a part of it. So,

how did you I guess did you think about

that or were you just like, no, like

this is going to work and if it works,

that's all we need. We're super focused

on like actually making every scientist

better and and beyond science like

startups, startup founders because I the

point is we can't do this alone. And

when we first set out that the goal to

cure and prevent disease by the end of

the century, people like honestly most

scientists couldn't look at us with a

straight face

>> and crazy.

>> Yes. And it was true because if you just

decided to spend the money funding the

next best grant for every single lab in

the country like you there's no pathway

to that being true. But if you forced

people to really think about this and

like okay what is the most credible

pathway to doing this and what are the

barriers to that credible pathway then

we sort of got somewhere right? They

were like, well, like there's no shared

tools or like we don't have we're not

working on big projects and building the

right data sets. And we're like, okay,

well then we can start doing something

about that. Um, and so that's where the

idea of building shared tools cuz no one

right now in the science.

>> Well, that's so interesting. So

basically, you're like, we're going to

cure all disease and they're like,

>> yeah, can't be done. Why can't it be

done? Well, because we don't have the

tools. Okay, that's pretty that's a

pretty cool sequence.

>> Yeah. Yeah. I mean, there's also this

funny thing where the the biology folks,

I think, looked at it as if it were

crazy ambitious. And then the AI folks

are like, well, that's kind of boring.

That's just automatically going to

happen. I know it's like, okay, there's

something in between there that needs to

be bridged. And if you can like kind of

use the the kind of modern AI tools in

order to build the types of tools that

biologists need. So that's a big part of

how we think about our work is um

>> AI has got to be the most overestimated

and underestimated technology ever like

simultaneously. So weird. I mean, yeah,

we'll probably like the internet early

on, but but we kind of think about

ourselves and the work that we're doing

at the Biohub as frontier biology paired

with frontier AI, right? So, there's

there are labs that do frontier AI that

uh basically, you know, are building the

most advanced models. Um and then there

are lots of biological research

organizations that that effectively do

very leading edge

>> research to build um you know to either

discover new data sets or or or looking

to certain challenges.

>> But so far there hasn't been anyone

who's tried to do both of those at once.

And when you look at I mean even

something like AlphaFold which is

amazing right it's it was built off of

this data set that was a public data set

that had been produced decades ago right

and um

>> what what I think you have the

opportunity to do if you do both of

those together is produce specific data

sets for the purpose of training AI

models to build virtual cells that can

do specific things

>> right

>> so I think that that's like a a pretty

interesting zone to be in

>> and of all the things that that we've uh

that we've worked on. You know, actually

when when we started CZI, we we kind of

actually focused on a number of areas

and what we found is just that the

science research has had by far the

biggest return. So, we've just doubled

down on it over and over and over until

now we're at the point that, you know,

we're 10 years in and Biohub is really

the like main focus of of our of our

philanthropy at this point.

>> Um, but yeah, I mean, that's kind of

that's basically the focus. Maybe you're

not giving yourselves enough credit

because you're sort of saying, "Well,

there's bite-size science. We didn't

want to do that. There's century scale

science and that seemed like a long time

horizon, but achievable, ambitious." But

you've actually identified, you know,

which I think is really fantastic, grand

scientific challenges

>> that are right in between. They're 10 to

15 year horizons, at least per kind of

the way you communicate about them and

the way you energize

>> the scientific community about them. 10

to 15 is kind of an interesting time

horizon sort of like similar to the time

horizon of a venturebacked company

similar to the time horizon on which a

team can work together for that period

of time I think it's how did you get to

that number and then how are you

thinking about the challenges that you

take on in each 10 to 15 year wave

because that's concrete achievable

you know you build a lot of credibility

around it the way that you've announced

those challenges

>> well I'm curious how you guys think

about it but for us when we looked at

the grand challenges for on the 10 to 15

year time horizon it needs to be like

when you look at it you're like I see a

path

>> right

>> not everything needs to be solved for us

to take it on in fact if everything's

solved then that feels like that should

just go

>> ambitious enough

>> yeah like you like we we have we have

some risk appetite right so we want

things where we're like there's a

credible pathway someone who is at the

helm who can do this And there's enough

ambiguity where we feel like we could

take on that risk and if we do it like

the the returns could be higher than

even expected and the way we modeled

that from you know in the biohubs is we

we have three biohubs. We have one in

San Francisco, one in Chicago, one in

New York. The one in New York works on

cell engineering. You know can we

engineer cells to go in and detect

signals, read it out or to take certain

actions. In Chicago, we're building

tissues and looking at uh tissue cell

communications within tissues. And then

in San Francisco, we're looking at deep

im imaging and uh transcrytoics.

And that work the locations are not by

accident. We also look at the partner

universities because we have folks who

come to the biohubs to do this work

collaborative interdisciplinary

um and sort of unconstrained by the

traditional lab. But we also build off

of the labs at these academic institutes

that support the work. And so uh that's

how we sort of choose the grand

challenge and um and the locations. And

then the sort of layering and the uh

large language models and AI coming into

the picture has been so interesting

because we were already building tools

to measure interesting data building the

data sets but we didn't really know what

to do with them yet. Um and large

language models coming onto the scene

we're like wow we can make sense of all

of this now. I'm curious what you view

success as in the therapeutic realm. So,

you know, we think a lot about

understanding biology and sometimes we

bet on startups that want to unlock

completely new biological areas,

diseases where we don't know what's

going wrong. And then there's another

group of folks who kind of say, hey,

okay, now that we understand what's

going wrong, let's fix it. Um, let's

come in with a drug. Let's come in with

a new type of chemistry, a new type of

antibbody. How do you what do you think

success for the CZ Biohub looks like 10,

20, 50 years from now in terms of the

new medicines that you've enabled?

>> We want there to be like an explosion of

a community who are building these um

just the new wave of what it means to be

deploying precision medicine. like we

like I think for rare diseases and

common diseases alike, you're really

talking about individual biology that we

sort of lump together. Um and uh they

and we often don't know how it happens,

right? We know that you have this

mutation or the worst nightmare is you

have a variant of unknown significance.

What does that even mean?

>> The horrible us.

>> Yes. Horrible. And you're like you tell

someone you kind of know something but

we don't know what it means. But if you

look at the way we've been able to look

at variants and look at single cell

transcrytoics, we're starting to be able

to say, okay, this variant actually

impacts this set of downstream cells and

then we start looking at the proteins

that get expressed and how it looks

similar or different to what a healthy

cell would look like. Then you can start

targeting. Okay, like let's look at that

as a target. And you both know the

specificity of the target you want to

build based on the ability the ability

to connect mutation to protein

expression as well as to be able to

predict off target effects. What are the

side effects? Because you also know

where else that drug will be able to

interact with the body. And and so those

are rare like and and but I really think

most diseases should be thought of as

rare diseases because each one of our

biology is different and right now we

just get lumped right we get lumped

based on age demographics ancestry if

we're lucky uh to have that level of

understanding but truly each one of our

biology is different and say like if you

look at hypertension or depression like

we kind of just go by trial and error

and saying like let's just try that drug

and see what happens. happens. But what

should really happen is being able to

precisely and accurately and quickly

treat people by looking at individuals

biology. We want to enable the basic

science and we would be thrilled if

people picked up the models that we

build to be able to build the

diagnostics, the therapeutics that need

to come.

>> You've built amazing data sets. I have

to say like I mean you may not hear the

feedback from the startup community and

the pharma community and the R&D

community but it's there because you've

committed to open source and so people

may not be they may not all be writing

papers but they are using those tools.

Um there's a startup in our portfolio

working on idiopathic pulmonary

fibrosis. The name tells you how vexing

the disease is. It's idiopathic. We

don't know why it happens. The IPF is

named that way. And so, you know, he was

telling me that he used your cell by

gene atlases to look at millions of

single cells in patients with disease,

without disease, try to pinpoint the

fibroblasts, double click on the

fibroblasts and their gene expression.

It's try to, you know, use that to

inform, hey, where could I go after a

new drug target in this disease that's

fundamentally a strange clump of

idiopath, you know, idiopathic um

origin. So um I think there's a huge

there's a huge group of innovators who

are who love the tools, the

visualizations, the query systems and

really the software approach that you

built to making that data incredibly

accessible. So

>> cell by gene is like almost an accident

though.

>> Tell us more.

>> So do you want to share a little bit

about cellene or do you want me to

start? Well, I mean, I don't know which

part you want to get into, but I mean,

but the cell atlas work overall, I mean,

it's kind of this crazy thing that

we're, you know, here in, you know, 2025

and there's not the kind of periodic

table of elements equivalent for

biology, right? So, that was sort of a

lot of the inspiration of it was all

right, how do we both through work that

we're going to do in the Biohub and

through other grants um be able to pull

together and standardize a format where

you can have all this data. And when we

were starting off, we didn't even

necessarily have in mind that we were

going to use that to build virtual cell

models. I think that's sort of just come

into focus as the AI work has advanced,

but that's a very exciting thing. We

should definitely spend a bunch of time

on the virtual cell models, but I'm not

sure what you wanted to get into on the

cell atlas.

>> Well, the single cell work is was one of

our first RFAS 10 years ago we started

and we were like, okay, we think this is

possible. We actually funded the

methodology for it to to standardize how

it was going to be done. So that was 10

years ago. And we then were we seated a

few labs to start building out that data

set. But we were like there are like

millions or billions of different cell

types and different permutations. Like

how are we going to do this? And um

especially with like a burgeoning

technique. And so we ended up um seating

a few groups and they started doing work

and then they told us they had a

problem. There was a uh there was a

bottleneck in their workflow because

they couldn't annotate the data fast

enough. Um and so we built cell by gene

was an annotation tool. That's the

original source of this. So we built the

annotation tool to make it easy for

people to who are doing single cell

science to be able to annotate the data.

And then we put we put the data that we

collected publicly so people could

share. But because everyone started

using the same annotation tool, everyone

was standardized then on the same data

formats

>> and then there started being a a

community around the tool and they

wanted to share back and build the

atlas. So now after 10 years there are

millions of cells that have been built

into this uh shared resource for the

entire scientific community. We only

funded about 75% of it. Sorry that's

wrong. We've only funded 25% of it. 75%

came from the broader community saying

this is useful and there's an easy way

for us to standardize and build the same

metadata.

>> That's right.

>> It's like an interesting what you'd call

a network effect, right?

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

the internet. Yeah.

>> Come for the annotation, stay for the

stay for the virtual cell model.

>> Well, it was very important when we were

getting started with the work to have

everyone who was doing it have a

consistent format. So that way it could

be used and portable. And then once that

kind of took off as as the way that it

would get done, then other people just

found it valuable.

>> Yeah. And even relative to prior data

bases like GIO and and whatnot, they're

just simply not as standardized or QC.

>> Yeah. Control.

>> Yeah.

>> Let's get into virtual cells. One of the

the great challenges that the grandchild

you would focus on. Um maybe talk about

what is the promise or the hope and

maybe some of the challenges or where

we're at with it.

>> Yeah. I mean, we think that one of this

is going to be one of the most important

tools at this point is basically

building up the kind of hierarchy from

proteins to um to

just different structures within the

cell to whole to like whole like a

virtual immune system or different

levels of hierarchy. And we think that

this is going to end up being like a

very important set of tools for people

to effectively generate hypotheses for

for different science work. um you know

even before you get to the point where

you're really running full experiments

in it you can come up with some um

estimate of how that might run um it

will be useful for some of the precision

medicine type um examples that Priscilla

was talking about a few minutes ago but

we think that this is like probably one

of the most important sets of tools that

you need to build um and it's not a

single thing right so there's different

angles to to come at this from the cell

atlas data is helpful for understanding

things on a cellular level. Um, one of

the the kind of most important things

that we're doing right now, the the um

there's this this great company,

Evolutionary Scale, who actually had a

bunch of researchers who'd formerly

worked at Meta on protein folding

models, um, is joining a Biohub and and

Alex Reeves, the the, uh, leader of it,

is actually going to be the the kind of

head of the whole science program, which

is actually kind of interesting.

>> Yeah. when you think about it where it's

like you have AI and biology coming

together and really it's like an AI

person who understands biology is

running it rather than a biologist who

has some understanding of AI. I think

just kind of speaks a little bit to

where we think the the relative um

weight of these things is. But I mean we

basically view, you know, like Priscilla

was saying with the different biohubs.

Then New York doing cellular engineering

will basically make it so that you can

have cells that can record different

things that are going on around the body

and and share that data and then you can

build that into models. The Chicago

Biohub being able to record inflammation

um and and basically study that in order

to kind of help understand um like that.

That's a that's a different data set. We

have the imaging institute which is we

just trained our our first set of models

around that which are the first like

spatial models around understanding like

the way that that kind of cells look in

different states and eventually just

like you have this analogy on the um

kind of the industry side around

language models where you have different

capabilities and then over time you

train them into models and it gets more

and more general.

>> That's kind of the idea here. So we'll

we'll we'll build the biohubs around

grand biological challenges. The biohubs

will build tools that will generate

novel data sets. We will build models

based on those and then eventually

combine the models into an increasingly

general view of a virtual cell that will

be useful um both for scientists and

hopefully startups and companies that

are working on finding drugs which is

not our part of the whole thing but but

I think is obviously a really important

part of what needs to happen.

>> Yeah. And you know, you guys think about

risk all the time in terms of when you

make investments like I think the

promise of being able to do virtual

biology using a virtual cell model is

you can actually take on riskier ideas.

right now like grant funding can be hard

to come by and the wet lab work is

expensive and slow and it's not just you

know money it's also time and so you

have to choose something that you think

is going to have some likelihood of

success to keep your lab career going

and so it naturally lends people to take

on like some risk but not a lot of risk

because they need to make sure that they

are hitting like a certain percentage of

the time to make tenure or publish or

whatever they need to do. But if you had

a virtual cell model where you could

simulate really highquality biology, you

could actually then start testing and

tinkering on the computational side and

like ask riskier questions, things that

would have been expensive and t costly

in terms of time and resources to do in

the lab and actually see if there is

promise doing the experiments in

silicone before you make the time and

money investment in the wet lab.

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

organism?

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

>> Yeah.

I was going to ask given the complexity

of a cell, like how close um like how

accurate do you think you'll get the

model too? I mean just assuming I mean

maybe you get it to like a perfectly

accurate representation of a cell, but

like how accurate to be useful with the

virtual cell have to be?

>> I think it will obviously iterate and

get better and better because right now

we we like right now we're still just

talking about uh transcrytoics. are

expanding into different ways of looking

at the cell, but you get more and more

accuracy and but I don't think it needs

to be 100% accurate to be useful because

you just want to be able to derisk the

idea on the front end a little bit. Um,

and the more and more you derisk it, the

the more efficient it gets obviously,

but it will be useful if you even get

directional signal. And yes, I do. We do

think about it like as a a model

organism, but in a way that's like has

fidelity to the human body, like you

know, like I don't want to

>> All models are wrong. Some are useful.

>> Yeah.

>> This is hopefully has has utility on

certain access.

>> Exactly. And just like the language

models, you build in specific

capabilities. So it's not so for

example, you know, one of the models

that uh we're we're publishing is is

variant former, right? basically you

know makes it so that um it's trained on

a bunch of effectively pairs of you you

have a cell you apply crisper to it in a

place you see what comes out at the

other side so it's it basically is able

to make that kind of a prediction like

okay if you have this edit that you're

doing to to a cell what is likely going

to happen um another one of the models

is it's this diffusion model basically

you can describe a type of cell that you

would like it to simulate and it will

just produce a kind of synthetic model

of of of the cell Um, again, I mean,

it's kind of interesting because to

Priscilla's point before about how

everyone is different and and like and

different cells have have kind of um,

you know, you want to be able to

simulate these kind of rare

configurations. Um, having at least a

synthetic version of what that could

look like is interesting and then you

can test against that. The cryo model I

think is interesting because it's

spatial. So it kind of gives you a sense

of there are all these different models

that you can have that allow you to um

basically look at different kinds of

things and then you just train them in

to be increasingly general over time.

>> Wow. Very interesting. And is the is the

modeling technology basically LLMs or

like like is there is there a reasoning

model? Is it like a just

>> Oh, that's actually Yeah. I know that's

a fascinating one too and because one of

the new models um I think this one is

very early but it's um it's it's

basically the first reasoning model over

biology. So the the idea is that um

yeah, you you you effectively have these

models that that kind of simulate world

models in different ways and then you

want it to be able to not just um be

able to spit out correlations, right, in

terms of like what it's found, but

actually be able to kind of reason

through how things would would evolve

and why things would happen. Um I think

that one's quite early but it's uh but

it is interesting conceptually as what I

think is clearly going to be an

important direction

>> um in terms of how these models evolve.

>> Yeah. No it because that's what I was

thinking you know that if it doesn't

work the next question you have is why?

>> Yeah.

>> You know like

>> but I think what you find in reasoning

the the analogy

>> you're married to your hypothesis. Well,

yeah. Sure. Sure. Yeah. I mean, the the

uh

>> Yeah. I thought I thought you're saying

if if the reasoning model doesn't work,

why? I mean, I think the

kind of way in No, it's I mean, the

language model analogy for that would be

you need better kind of world models or

or better pre-trained models in order to

get the reasoning to be good. But, but

it's yeah, you just you build more

>> you build more capabilities into it. And

I think that there's probably an order,

too. So the work that Alex and the

evolutionary scale folks worked on is a

lot of it is protein um which is

interesting because that's at a kind of

smaller resolution obviously than the

cellular data the cell atlas but

>> part of the hypothesis is that you can

look at all these different cells and

you can kind of simulate how they might

behave but you're going to have a

somewhat shallow understanding unless

you actually have this hierarchical

understanding of what um how the sub

components of the cells are going to

interact. So

>> our view is that you basically want to

build up a state-of-the-art protein

model and then have that be a part of

the state-of-the-art cellular model and

then once you have that you build things

like the virtual immune system which

allows you to simulate um much more

complicated systems. But it's sort of

this like hierarchical approach to

building up these these uh virtual

models. That makes a lot of sense

because also as you get into

personalization, you've got like common

proteins combining into a unique cell.

So that

makes it like from a systems standpoint

that makes it like much more manageable.

That that makes a lot of sense.

Interesting.

>> Yeah.

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

fascinating stuff.

>> Yeah.

>> So you guys are announcing some big news

this week. Do you want to give us a

sneak preview? Well, I the big news is

uh thinking about how we are going to be

coming together as one team. Um and you

know in the past we have done we've run

biohubs and we've done built software

we've done some AI research but all of

it has been really thinking about has

been a little bit decentralized but now

under Alex's leadership we are going to

come together as the biohub a uh an

operating philanthropy where we are

doing the science um in service of a

singular goal together and how do we

actually advance the state of biology

and research um at the intersection of

AI and biology.

>> Amazing. Alex is amazing. So,

>> yeah. No, he's great. And then and then

the other thing is that the piece that

that I mentioned earlier, which is just

Yeah. I mean, CCI has focused on a

number of different things. We've really

just found over time that we we feel

like we've been able to make the biggest

difference in science. So, we've just

kept on doubling down on it and we're

going to continue doing work in

education. We're going to continue

supporting local communities and and in

those different pieces. But going

forward, the biohub is really going to

be the main thrust of our philanthropy

and we're very excited about that

because I think that this is there there

has been you know when we started

>> the mission to see if we could help the

scientific community cure and prevent

diseases by the end of the century. I do

think with the advances in AI that

should be possible to do significantly

sooner and that is a very worthy and

important and very exciting goal that we

think we kind of have a unique place in

the ecosystem that we can help empower

others to make fast progress on that. So

there there's obviously like plenty of

advantages to decentralization from a

management communication overhead and so

forth and so like what are you trying to

add by adding this kind of new

layer/unification

on top like what what are the outputs

and then I guess what are the

complexities to that because that's um

I'm sorry to ask a CEO question.

>> No no I I mean I'm like super

you want to go for it then I can jump

in.

>> Yeah. So there are obviously amazing

groups doing frontier AI and a lot of

groups doing uh great frontier biology

and where we think we can do uniquely is

actually tie these two together and we

are we've funded data sets we've built

data sets we're like building the

instrumentation now to be able to look

at the cell whether it's you know for at

the tissue cell communication our cryoEM

where we can look at the cell at nearly

atomic level. So we have the ability to

not only build the data sets but

actually shape and form them the way we

want based on what we see as necessary

to complement the existing body of

knowledge. And so we have amazing teams

doing that work and we're building these

AI models. And so what the reason to do

it together is then we can actually

complete the flywheel like you know the

model is looking like it has some gaps

and blind spots in this area. Okay, who

do we talk to? How do we build um the

next data set? And you know we're seeing

this in the lab like the metadata is

going to be so rich that we can feed

back into the way that we do this

modeling. Yeah.

I think it's going to be incredibly

powerful. And it's it's more than it's

more than just like, you know, writing

down a spec and saying like please

deliver this. Like these people need to

be sort of working shouldertoshoulder

and shaping uh each other's work for

this to actually um be the more and more

accurate model of how the human cell

works.

>> Well, yeah. It's so interesting because

that is exact like that's has been the

biggest surprise in the industry for us

in AI world like forget biology for one

second is that the domain

specific models have been like super

interesting like the original thesis

were like there's just some AIs are get

so smart they're going to be smarter

than everybody at everything but um

>> like on video models like every video

model is best at something but not

everything And so knowing what problem

you're solving actually turns out to be

sort of ironically very important in AI

um because you can actually get to a way

better result. Yes.

>> If you put the two together like yeah

we're we're seeing that over and over

over again uh in a way that that is

>> I would say very counterintuitive to the

whole narrative kind of going into it.

>> And in biology it used to be the or at

least you know one assumption was well

the data sets aren't on the internet. So

part of the reason you need a domain

specific model is that the data sets are

not public. you guys are kind of bucking

that trend too by creating a lot of

open- source access to the data and then

even then it sounds like you're betting

you know on the trend that we're seeing

in other industries but still there will

be nuance in how you annotate that data

curate that data

>> well and how you talk to a scientist

right like so because you have to not

only know the the data and the model and

so forth but like the conversation is

what we keep finding out ends up being

very very important right

>> so rich and so important how you

actually

>> a scientist isn't going to talk to it

like you know I talked to chat at GPT or

whatever. So, this is the fly you can

talk to.

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

that's super exciting.

>> And the user interface is actually

really important. Um you talked about uh

you guys have a founder who's using Cell

by Gene. That user interface was

intentionally designed to not need to

have a computational or really a very

deep biological background to be able to

use because you want people coming from

different fields to look at the problem.

It's like look here, help us solve

problems here. And so building that user

interface in a way where it's not a very

high barrier to entry to be able to poke

around and learn something and bring

knowledge back to your work, that's

intentional. And we're really hoping

when we build these virtual models um

that we get to a place where we can

allow a lower and lower barrier entry

for people to say like you know like I

have some knowledge about this maybe I

can contribute. Um a very pertinent

example is turns out I think immunology

has a ton to do with neuro degeneration

right but

>> seems like immunology is behind all this

so might be part of your century vision.

>> Uh so you need to be able to allow the

immunologists to come in and understand

neuro degeneration and understand how

their world fits in. And so the more you

lower the barrier to entry allows people

to actually think in a sort of truly

collaborative and interdisciplinary way.

So will the Biohub grow as a team? Like

will you employ more people at the

Biohub proper or are you moving towards

more of a network model with more sites,

more labs, more communitydriven data

sets? Like which which is the thrust? Or

maybe it's both.

>> Probably a little of both. And we've

added new biohubs over time. Um and then

we're also building up more of this like

central AI team.

>> Cool. So um but I don't I think that

these organizational questions of how do

you set this up are fascinating and a

lot of our approach is sort of informed

by

what the rest of the field is doing

because I you kind of think about

science as it's this portfolio right

society has a portfolio of stuff that

it's trying to do and as in terms of

philanthropy you want to

>> be the most additive that you can be by

trying to figure out what else is

underrepresented. So science by default

is very decentralized, right? It's like

kind of the the way that granting has

worked, the way that I think scientists

by default want to work.

>> Um

>> so I think a lot of what we've found is

that figuring out ways to encourage

collaboration in um ways that otherwise

seem very simple but weren't happening

before can unlock a lot of value. So the

very first Biohub what we did there were

two kind of interesting things. One was

it was this collaboration between UCSF,

Stanford and Berkeley and there are all

these really smart people at all these

different places who previously I guess

in theory they could have figured out a

way to work together but there was not

really a formal construct for them to do

that and this just allowed a lot more

collaboration. Mhm.

>> The other one is cross- discipline.

Basically having biologists sit next to

engineers and this view that like these

two disciplines are things that need to

um and I I don't know. I mean I'm sure

you know you've seen this in a lot of in

a lot of the companies but like

>> it's there's so many interesting

>> in the companies they always like set

them apart.

>> Well, it's interesting. No, it's

interesting how many organizational

questions or problems you can fix just

by having two teams sit together, right?

It's like it doesn't matter what the or

chart is or like whatever. It's like you

guys need to sit next to each other and

until you get this thing to work and

>> um

>> that's something I really believe in.

So,

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

>> Well, no, it's all like communication is

such an underrated problem in general.

Yeah. Uh in in all kinds of in building

anything or solving anything. So,

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

>> Yeah. Yeah. And it's it's just really

kind of simple stuff, but but I think

it's um

>> it's sort of novel as a model. And one

of the things that's so we've now copied

this

>> from the first Biohub to the Biohub

network and expanded it to other models,

but it's also just been neat to see um

other folks who are working in the field

also adopt similar models because it's a

pretty intuitive thing.

>> But you know, at some point you'll reach

the point where you know, actually it's

really good to have decentralized work

too, right? So it shouldn't be that like

we're not saying that this is like the

way that all science should work. We're

just saying that there's a space for

this. It can unlock a lot of value

because it for whatever reason hasn't

been the default.

>> Yeah. And we still rely on like

>> Yeah. There's famous like stories in the

MIT lab about that. That's how they

invented lasers and so forth is they put

a bunch of people from different

departments in the same

>> the lab. Yeah. Well, actually physics is

where we got a lot of the inspiration.

like physics has just historically been

like labs have just rallied around big

projects and big shared resources. Um

and we will you know we are relatively

centralized but we still depend on a lot

of labs who are doing sort of exact

frontier work or complimentary work to

come together to support this. There's

that. But one more thought on your

expansion question is like and maybe

this is like the uh modern AI lab. We

are not expanding like a lot of square

footage per se, but we're expanding our

compute. Um

>> yeah,

>> the research they don't want employees

working for them. They don't want space.

Yeah. They just want GPUs,

>> agents. So it's just like in a sense

that's new lab space. Um it's much more

expensive than wet lab space.

>> And you guys have always been creative

on that. Even in the last few years,

you've created ways to share access to

compute. You've enabled academic labs to

you know um I forgot the name of your

program kind of like

>> scientists and residents or something

like that

rental kind of hoteling.

>> The core of it is clusters. um you know

if you look at individual labs they'll

have like

>> like a large lab would have tens of GPUs

>> um and we were the first to really build

a large scale compute cluster um a

thousand now we're we have plans to move

to the 10,000 range and that one

requires a different type of project

obviously you're are able to ask

different types of questions

>> um and uh it's a resource that we use

but also we've invited scientists to

apply and say like what question do you

have that uh could use this amount of

resource and be able to uh stem uh sort

of seed collaborations that way

>> and so if a scientist is out there

listening like who's not employed by the

biohub or working at the biohub but

wants to collaborate with the biohub

>> that you're going to create interesting

>> interesting doors

>> to utilize the resources that's awesome

>> yeah I mean the GPUs are somewhat zero

sum Right. So that the data isn't. So

yeah.

>> Yeah. Fair enough.

>> Yeah. So you're about to celebrate 10

years um doing this. As as you look out

in the years to come, what else can you

tell us about either things that you're

thinking about for the future or maybe

even principles or a northstar that's

going to guide how you guys grow and

evolve going forward?

You know, it's been really interesting

in the past 10 years because I actually

spent the first few years completely

envious of people working for for-profit

companies because there's so much

clarity. Like the market will tell you

whether or not it's private or public

will tell you if you're doing a good

job.

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

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

right.

>> They're not always different. But I was

still envious cuz that was I was like I

craved that feedback like am I doing a

good job? And you know 10 years in you

the reason why we're doubling down on

biology is like not only did we achieve

what we said we were going to do and

when we set out to set out on these

projects it actually delivered more than

we thought we were going to. And I was

like okay that's a signal I can latch on

to and like that's a signal I we can

really continue doubling down and doing

more of that. And so I think it's uh

continuing to tolerate the early

ambiguity when you're like, "Okay, I'm

gonna do more of this." Um and uh and

being patient, but uh uh being willing

to have a long time horizon, but be

impatient at the same time. because it's

all those iterations along the way that

have sort of allowed us to get to this

place where you know to get lucky ready

having built data data sets to take

advantage of AI and large language

models that's because of all the work

that we have been doing and so being

able to continue moving forward in this

ambiguity and sometimes lack of signal

on a big goal like I think we've sort of

set the DNA for that.

>> Amazing.

>> Oh, no pun intended.

>> Yeah. But we get to see how many people

use the tools and the feedback. Yeah.

Yeah.

>> Yeah. You have customers which is pretty

cool.

>> Yeah.

>> For philanthropy. Like that's awesome.

>> Yeah. No, it's it's one of the fun

things about building tools is like you

kind of get to see

>> Yeah.

>> How valuable do people find the tools?

Do people use the tools in order to

publish important work?

>> Right. Right. Right. Right. Yeah. And

well, I mean our feedback is they're

awesome.

>> Feedback

and and completely unique by the way. So

like

>> the the other thing is like what would

you use if you didn't have this? It's

like there's nothing.

>> No. Yeah. It's a real it's a real kind

of void. I mean there's this whole

pipeline that that needs to exist from

accelerating basic science to funding a

lot of people to use it to then you can

get into the biotechs that basically can

start to work on on on basically coming

up with novel therapies and then you get

the pharma companies that do them at

scale. And then there's a space for

philanthropy on the other side of public

health of basically taking the the

therapies and and kind of bring them out

to everyone in the world. But this is a

a space that and that there's just going

to be a huge amount of leverage with AI

and it is um yeah it's it still seems

like there could be a lot more effort in

the space around building tools and just

accelerate the whole thing a lot better.

>> Yeah. And I do think it is the place

where you are completely unique. Right.

The other things there are other people

who can do that but there's nobody doing

what

>> that's got good good founder market.

>> Yes. Founder market fit. I mean if we

didn't exist would it be a problem? Yes.

Like those questions uh really land you

know as a VC

>> like one of us as an engineer the other

one scientist doctor.

>> Yeah very happy this direction.

>> Yeah

>> we thank you very much not only for our

companies but for us as humans um for

working on this work. It's amazing work.

Thank you.

>> Thank you guys.

>> Thank you so much.