No PriorsBiohub: The Future of Biology is Open-Source with Mark Zuckerberg, Priscilla Chan, and Alex Rives
EVERY SPOKEN WORD
55 min read · 11,149 words- 0:00 – 1:02
Cold Open
- MZMark Zuckerberg
We just wanna give tools to the whole scientific community.
- SPSpeaker
We wanna understand how biology works. I wanna understand the genetics of this person. I wanna understand the risks they have to different illnesses. My goal is to be able to treat the individual as an individual, understand the mechanisms, and be able to intervene.
- MZMark Zuckerberg
We'll have a bigger impact by getting this in more scientists' hands quicker by doing it as open-source projects instead. It's not just like there's some factory somewhere that you can pay to produce the data. You actually need to invent new, novel scientific approaches. The theory isn't that we're gonna cure the diseases. We're not. It's that we wanna help accelerate the pace of progress for the whole scientific field.
- ARAlex Rives
We folded over 1.1 billion proteins and predicted their structures, and we didn't design a model for antibodies. We didn't design a model to be able to bind one particular target. We just designed a model that could understand proteins.
- SPSpeaker
If we could design a protein to actually change the physiology, then we can actually cure someone.
- SGSarah Guo
[upbeat music]
- 1:02 – 1:26
Mark Zuckerberg, Priscilla Chan, and Alex Rives Introduction
- SGSarah Guo
Today on No Priors, we're joined by Mark Zuckerberg, Priscilla Chan, and Alex Rives. We'll be talking about Biohub and all their various efforts to now start applying AI at scale to do world models of cells and different levels of interactions across biology.
- EGElad Gil
Mark, Priscilla, thank you for doing this.
- MZMark Zuckerberg
Yeah, thanks for having us.
- SPSpeaker
Great to be here.
- MZMark Zuckerberg
This is fun.
- EGElad Gil
Alex, congratulations on new missions.
- 1:26 – 8:27
Why Biohub and Their Mission
- ARAlex Rives
Thank you.
- EGElad Gil
You guys made Biohub your primary philanthropic effort, and then committed $500 million to this virtual biology initiative. Can you tell us a little bit about, you know, why do that, and how did you go from, "We should fund this," to, "This is, like, who we are"?
- SPSpeaker
So Biohub, in its current form, we're super excited about. We feel like it's a really good fit for who we are and what we bring to the table and what we can achieve together. But this work started ten years ago when we were thinking about how can we give back. And Mark had-- Mark wanted to build an organization that could cure, prevent, and manage all disease by the end of the century. And we had a series of hilarious meetings with scientists that, like, famous Nobel Prize-winning scientists were just laughing at us.
- EGElad Gil
Is that-- Was that your starting line? "We're just gonna cure all disease"? [laughs]
- MZMark Zuckerberg
No, no. And to be clear, we don't think that we're gonna be the ones curing the diseases. Our, our goal was always to build tools that could accelerate the whole scientific fields, that way the scientific field collectively could cure all the diseases. But still-
- SPSpeaker
But still, people laughed at us
- MZMark Zuckerberg
... I mean, people thought that by the end of the century was a stretch. Now I think it's, like, uh, too conservative.
- SPSpeaker
And so we kept being like, "Okay, well," we had these series of funny, awkward educational conversations where we were like, "Okay, but, like, why?"
- EGElad Gil
[laughs]
- SPSpeaker
Like, why do you think it's impossible? And, like, you know, just being, uh, the, the person in the room who's just like, "Well, I don't know why. You tell me." Finally, we got people to, like, they're like, "Fine, if you really must know." And we're like, "You know, we do. It seems important." [laughs]
- EGElad Gil
[laughs]
- SPSpeaker
Um, it's, you know, they were like, "Well, we work in silos, and, um, when you publish, information doesn't get shared. It gets locked up for long periods of time, and we don't have tooling." You know, they gave the example of, like, we build a great tool by one post-doc in a lab, and it lives on their computer, and when they graduate, the tool is gone. And they just-- It was-- What we heard was that very hard to build shared tools to move science faster, build a shared knowledge base to quickly move science faster, and that's sort of where we began in thinking about, okay, like, if those are the problems, like, what can we contribute?
- MZMark Zuckerberg
Mm-hmm. Yeah, I mean, so the original Biohub model was basically focus on long-term tool development by bringing together engineers and scientists, um, across multiple universities to focus on long-term tool development. And it basically, it, it, like, worked. And, you know, we started off with, um, with CZI doing a number of different things, and I think over time, we just felt like, okay, the science piece is really working, and we just kept on investing more and more and more in it until now it is basically the primary and m-main thing that we're doing. And we've expanded the original San Francisco Biohub, um, to a handful now at this point. There's New York. There's Chicago. Um, the real focus and the unifying theme at this point is, um, is the virtual biology initiative around taking the unique, um, data sets that are able to be generated, um, in order to model, um, e-e-effectively starting with the smallest pieces of, of proteins, but then eventually cells and whole biological systems. But that's kind of h-how we've evolved is, you know, this, this idea that, um, that we talk about ar-around that some of this is an AI problem, and you wanna build a frontier AI lab, but you need to couple that with a frontier biology effort that can do the work of, um, of, of basically being able to, uh, understand and get the data that you need to actually be able to build these models. Because unlike language models where there's just, like, a lot of data out there on the internet, that's not really the case with biology. I mean, there are obviously a bunch of different data sets that exist that academia and scientists have generated over the, the decades, but a lot of the stuff that I think we wanna put into this, it doesn't exist, right? It's like you wanna be able to visualize things that people haven't been able to see before, which is why we're doing the, the imaging work. You wanna be able to record things, uh, that are going on inside the body, which is why we're doing the kind of cellular engineering work. Or you wanna be able to measure things like inflammation, um, in ways that haven't been possible, which is why the, you know, Chicago Biohub is focused on building those kind of devices and being able to do that. And that will fundamentally create, um, new types of data sets that will allow new types of models, and I think it's just a very exciting thing that, um, going back to what you were saying, if the, if the scientific field, it primarily needs, um, kind of tool development that now is going to empower scientists across the, the field to be able to do their work faster, that's what we think we can provide through this kind of long-term focus on tool development.
- SPSpeaker
But the-- I, I think there's a f-fun through line on where we started and, you know, bringing us to our work to, uh, with-- that Alex is driving now, is that our very first request for application, RFA here, was around single-cell sequencing. And, um, and we wanted to look at sort of like the RNA that is transcribed in individual cells.
- EGElad Gil
Mm-hmm.
- SPSpeaker
And that bo- that was possible, but it was still pretty early on in understanding how different cells were expressing their DNA, to the point where at the beginning, we were just funding methods, like getting people to describe how to do it so that others could share that methodology. And then that became, um, us funding the Human Cell Atlas, which is now one of the largest, um, databases of, uh, single-cell transcriptomes. It was getting hard for scientists to annotate the data. So we built Cell by Gene, which was like a very simple annotation tool that scientists could use to make use of that data. Then a community came around Cell by Gene, built around Cell by Gene, and started contributing more and more data that we had nothing to do with sort of creating or funding or, uh, making happen in the world. And now Cell by Gene is a corpus of knowledge that a lot of, uh, the, um, transcriptomic-based models are based off of and is used regularly by the scientific community. But still, there are always critiques, like, this is just stamp collecting. Like, you're just gathering bits of knowledge, well, sorry, bits of data, um, and we're not gonna be able to pull scientific knowledge and wisdom and insights out of. And, and we're like, "Well," we didn't have an answer for a while. And then imagine our delight when large language models, uh, became a huge topic of conversation that could make sense of large amounts of data, and I just-- for me, it was like, what if we could actually understand how biology worked? Um, move it from a discovery-based science to an engineering-based science, where we could systematically understand how living beings, living cells worked, and, um, be able to understand why things go wrong. And so when we saw that moment, we were like, "This is it. Something really big could happen here."
- 8:27 – 9:45
Integrating Frontier AI and Frontier Biology
- EGElad Gil
Alex, you were, uh, you started at Metafair, um, but you were on the path to, you know, you'd assembled a team at Evolutionary Scale, and you'd raised venture, and you were making progress in your models. What was the pitch from Mark and Priscilla where you said, like, "That's actually the right way to go after the mission?"
- ARAlex Rives
Well, I think for me, it was really kind of the moment when I understood that, um, you know, they, they really saw this as, as an integration of frontier AI and frontier biology. And I think, um, I had developed conviction that, you know, this is really a, a new era of science that's, that's just beginning kind of what's gonna be possible with artificial intelligence and, you know, we're, we're in the age of information theory at scale, and we have these systems that can basically kind of predict the next token, and they can, you know, learn world models from that.
- EGElad Gil
Mm-hmm.
- ARAlex Rives
They can learn biology from the data. And so, you know, I, I think that it just, it was really clear that, you know, to build kind of that next, that next kind of institution for the next era, you would really need to have frontier artificial intelligence. You would have to have frontier biology. You would need to start to put those things in feedback and really have models that are learning from the biology. And I think, you know, it just-- and you need the right scale and the right people. And so this, this just really felt, I think, like the way
- 9:45 – 14:22
Micro to Macro Biological Modeling
- ARAlex Rives
to do that.
- SGSarah Guo
There, there's a variety of different models that you all have been working on, and I think it's kinda interesting because some of the earliest breakthroughs in biology were things like AlphaFold, where, you know, it was a Google model that showed that you could do protein folding at scale in a really interesting way that people didn't realize was very tractable, and this was pre sort of the really big transformer waves that came later. And then you're, you're working on a variety of different things at different scale, right? You're doing incremental molecular modeling and protein folding. You're doing cell-based stuff. You're thinking about interrogating larger scale systems in biology. How well do you think that extends from sort of the micro to the macro? You mentioned almost starting with building blocks and building up, but modeling cellular behavior is very different from modeling protein folding. The data is very different. The modeling is different. I'm just curious, like, do you think it's all, uh, similar in terms of it's just data and you train stuff, or do you think it's actually, uh, there's some differences in terms of how you actually have to deal with these systems?
- MZMark Zuckerberg
I mean, there are probably some differences. I mean, you can probably talk more to the specifics around this, but, like, I mean, I think each layer is gonna end up being somewhat qualitatively different, right? I mean, the, the-- but you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to cells in a way without understanding the protein modeling.
- SGSarah Guo
Mm-hmm.
- MZMark Zuckerberg
And then if you're trying to understand something like the, you know, the way the immune system works or a bunch of cells interact together, um, then, um, you know, it's tough to do that without first understanding cells. I mean, you might be able to, at, like, a very high level of abstraction, simulate a system, but if you really wanna, like, understand how it's gonna work, you kind of wanna build the simulations at each level hierarchically. So that's basically the approach that we're going through, starting with the, um, the building blocks and the, and the protein. But yeah, I mean, I think that there's gonna be different types of data that you wanna collect for each. Um, the modeling techniques, I think we'll see. I mean, that'll all keep on advancing across the board. But I do think that, like, a big part of the strategy is this view that you need to build it up hierarchically.
- SPSpeaker
And, you know, one of the things that's unique about us in this space is we were very intentional that the, the AI efforts and the wet lab efforts were a single effort. And we've done a lot of work to bring them together. And the really neat thing that we can do is really try to pull and gather data that helps us connect, um, across sort of the hierarchy. You know, you can look at-Transcriptomics with space within a cell and look at where it's localizing. We can look at, um, translucent zebrafish and look at the development across, uh, different cells and when the brain develops. We have sensors that allow us to look at cell-cell communication in different molecules. And so we can be strategic about the types of, uh, experiments and data we wanna collect that helps us bridge across these, that makes it so that there's some connective tissue that helps drive the modeling that, you know, the modeling magic that happens.
- ARAlex Rives
Yeah, the reason I asked the question by the way is I used to be a biologist. I have a PhD in biology and I worked in-
- SPSpeaker
Oh
- ARAlex Rives
... wet labs for almost a decade and everything else.
- SPSpeaker
Are you looking for a job?
- ARAlex Rives
[laughing] Um, I'll, I'll-- We can talk about that later. [laughing]
- SPSpeaker
[laughing]
- MZMark Zuckerberg
It's not a no.
- ARAlex Rives
At this point in my career, you know.
- MZMark Zuckerberg
It's, yeah.
- SPSpeaker
I'm hearing-
- EGElad Gil
I love my aggressive recruiter.
- ARAlex Rives
I'm like, I'm like Danny Glover, you know, in, uh, Lethal Weapon. I'm almost at retirement. Um, but I think, um, you know, one of the things that was always lacking was this integrative nature across the different layers of biology, and the developmental biologists would work on their own, the molecular biologists would be doing-
- SPSpeaker
Totally
- ARAlex Rives
... different experiments, and so that's why I was curious about-
- MZMark Zuckerberg
Yeah
- ARAlex Rives
... typically there's a reductionist view of biology, and there's a systems view, and those people didn't really work together deeply. And so one of the exciting things about what you're doing actually is how you're bridging that, and so that's, that was kind of the basis for the question as well. Yeah, and if, if I could add something there. You know, it's, I think that, you know, we're in the age of this kind of information theory in biology and so, you know, there, there are levels of, of complexity and hierarchy in biology and kind of each level is, is made up of and, you know, constituted by the lower levels. And so as you want to have that kind of more complete description, you want to have systems that can really generalize and begin to actually answer, you know, experimental questions digitally that you could ask in the lab, you know, you need to have kind of the right basis for modeling at every level. And so I th- I think what's really unique about what we can do is to, as, as, as, as Priscilla and Mark were saying, you know, really build information at each of these different layers, collect them, collect kind of those connection points, but then also really kind of do it at the scale that will reveal that underlying information architecture, and that's gonna be really critical to actually be able to build digital representations that can answer new experimental questions.
- 14:22 – 16:58
Mechanistic Interpretiability
- EGElad Gil
One of the things that inspires me most about this effort is really what Priscilla said, which is like, well, uh, there's so much we actually don't understand about biology and what if we could? Which I think is actually very different from lots of other incredibly interesting and useful AI problems we attack where we're, like, trying to replicate human behavior, and like, a lot of that data's, you know, on the internet or captured and without pretending to understand all human behavior, you can predict a lot of it. I, I thought one of the most interesting things in your release was actually, you know, the, like, mechanistic interpretability stuff you alluded to, which is can we actually extract new knowledge from, um, you know, what the model believes is happening, right? Uh, can you talk a little bit about that?
- ARAlex Rives
Yeah. I'm really excited about that. So I, I think, you know, in, in mechanistic interpretability, kind of traditionally it's been applied to large language models with the goal of understanding, you know, kind of what is the representation space of a large language model. How does it, um, compute things, and does that really connect to, you know, what we understand about, um, our intuitive understanding of, of the world. And so there's I think this really rich toolkit that has been developed to, um, to start to be able to ask those questions. So kind of what does that mean for, for biology? One of the classes of models that we train are these, uh, protein language models, so they're really, you know, it's trained on the codes of proteins. And so anything they learn about biology is, is kind of emergent, and we've seen that they can learn things like biological structure and biological function, and that's just kind of emergent from this, you know, token prediction training task. So, you know, as, as we think about, like, mechanistic interpretability in those models, you know, they're-- we're, we're really seeing the unknown because the models have been trained on billions of protein sequences. They've been trained on, you know, both known and unknown biology, and yet they're developing these representations that start to kind of capture things that we can really see correspond to that reductive picture of biology that's been built up over the centuries. So kind of you can c- you can start to connect the dots between proteins where we kind of really don't know anything about them, um, with, with proteins where we, we do know something because there's that kind of underlying structure grammar that's, that's linking them in the representation space of the model.
- EGElad Gil
Uh, and at the extreme it could be, you know, we're gonna understand systems in the body that we didn't before, or the mechanism of action for a new treatment bec- and because we can ask the model, right? Interrogate that representation.
- ARAlex Rives
That's right.
- EGElad Gil
Yeah.
- ARAlex Rives
The hope is that you kind of really learn the underlying basis for how it's making the predictions, and so you open up the black box and you can actually understand kind of the biology that the model is representing.
- 16:58 – 21:41
Why Biohub is a Non-Profit
- EGElad Gil
So a- asking for a friend, um, uh, you know, you, you guys all believe in, uh, venture-backed companies as a way to have impact on the world. Um, what it-- was it, like, collecting data on zebrafish or the span of the data or the wet-lab work or just the scale? Like, what makes this a better fit for this big nonprofit, you know, ecosystem effort versus a venture-backed company?
- MZMark Zuckerberg
Um, well, I think we just wanna give tools to the whole scientific community. And I mean, like, so I, I think in order to have the biggest impact, I mean, part of it is just we're-- I mean, it's not actually clear that we couldn't run it as a business if we wanted to. I just think that we'll have a bigger impact by getting this in more scientists' hands quicker-
- EGElad Gil
Mm-hmm
- MZMark Zuckerberg
... um, by doing it as open source projects instead. So, um, yeah, I mean, I think that that's, uh, that's, that's kind of the approach. But I, I don't know. It's an interesting question. I, I'm not sure that-- I mean, obviously you were doing it as a, as a nonprofit-- uh, sorry, as a for-profit company, um, a bunch of the modeling before. Then you run into certain issues. I mean, you have to raise a large amount of money in order to build the compute clusters. Um, you know what I mean? It, it's-- I, I think in a lot of ways the data is actually even more of a constraint and, um-Because if you look at, like, the scale of these models compared to language models, they're smaller, but they're smaller because the amount of data is less. In order to get the data, it's not just like there's some factory somewhere that you can pay to produce the, the data.
- SPSpeaker
Mm-hmm.
- MZMark Zuckerberg
Like, you actually need to invent new novel scientific approaches to be able to do the, you know, for example, the type of cellular engineering we're doing in New York or the types of devices in Chicago, which is why, you know, when we're talking about this concept of frontier biology and frontier AI, the frontier biology is you need to do real science to advance different biological methods in order to be able to observe the things that create the data that go into the model.
- SPSpeaker
Mm-hmm.
- MZMark Zuckerberg
Um, so it's not just, like, an off-the-shelf thing that you can create. Now, that's a pretty big effort. I don't know that there are, like, that many things like that that are done as, um, as biotechs. I think it's just the scale of the ambition of what we're doing, the ti- horizon over which we're committed to doing it. I think part of the theory is, like, if you're building tools that are this complicated, you kinda wanna have a ten to fifteen-year time horizon on, on building out these efforts. And then the scale of capital required, I mean, I guess there's no rule that said that you couldn't do it as, like, an incredibly well-funded startup, but I think that this just made more sense. And then it also is, is simplifying strategically to not have to think about how you're gonna make money with the different things. I mean, we just-- We wanna get the models in people's hands. We release them as open source. I think that that's, like, a very valuable thing to do. And again, I mean, the, the, the theory isn't that we're gonna cure the diseases. We're not. Um, it's that we wanna help accelerate the pace of progress for the whole scientific field.
- SPSpeaker
As the person least experienced with making money here, I would say that there, uh, you-- The sort of neutral nonprofit nature of our work actually helps harness more people to enter this, uh, effort. Um, and to actually achieve the mission of, like, understanding the wh-- totality of human biology and to cure, prevent, manage all disease, you actually do need the entire academic biotech industry to come together and to work on this in a sort of unified way, um, in part because there's a lot of talent out there, and it's, uh, it's not helpful to leave any talent, uh, exclude any talent from the effort. And there's a super long tail of diseases. Um, there are the common ones, and even the common ones, I think if you unbundle heart disease, cancer, neurodegenerative diseases, even if you unbundle, like, dementia or, uh, depression, there are many, many, many subcategories that become more and more niche, and that's not even looking at the long, long tail of rare diseases. Those often get orphaned and don't get brought along when we're sort of looking at what the most efficient way to impact the lives of many. But if you sort of decentralize the effort and put the tools in many people's hands, you start getting people who are like, "You know what? I am super interested in spinal mas- muscular atrophy, and that's something I care deeply about." And if you put the tools in that person's hands, they're gonna be able to make progress. In a way, if you had to focus your efforts and make big bets, you probably wouldn't because it's just a s-- niche individual d-- uh, small group disease that actually will in turn, if we can understand that disease process, helps us unlock knowledge about a lot more about the human bo-- how the human body
- 21:41 – 24:23
Understanding How Biology Works
- SPSpeaker
works.
- MZMark Zuckerberg
Mm-hmm.
- SPSpeaker
Do you have any thoughts or predictions in terms of what disease areas this work will impact first? I know it's very hard to be predictive about these things. But just given the nature of the work and the nature of the models, are there areas you're most optimistic about in the, uh, short to medium term? I-- That's actually not how I think about it, at least.
- MZMark Zuckerberg
Mm-hmm, mm-hmm.
- SPSpeaker
The way I think about it is, like, we wanna understand how biology works. The ideal world is you would say, "I understand, I understand the genetics of this person."
- MZMark Zuckerberg
Mm-hmm.
- SPSpeaker
So I want, I wanna think about people at the individual level. I wanna understand the genetics of this person. I wanna understand the risks they have to different illnesses. I, I wanna understand the mechanistic connection between, say, uh, a diff-- uh, a gene variant, a protein, and a disease process. Because if you understand that through chain, then you can design a protein, design a drug bespoke to them, and actually make an intervention. And right now, and, and I'm sure we've all had experiences being sick, and if you have something that's even remotely, um, non-standard, you go, you go into PubMed, you look up a paper, you look up the supplement, and then you start going through the methods, and you're like, "Am I represented in this paper?" And we're just making guesses. We really have no mechanistic understanding. We're saying like, "Okay, you're kinda like these people that we studied, and this drug kind of impacts the pathway that we think is implicated. Let's try and see if anything happens." And time passes, and sometimes it works, and sometimes it doesn't. So my goal is to be able to treat the individual as an individual, understand the mechanisms, and be able to intervene. And there are different diseases that are at different stages of filling out that whole through line. And so for some diseases, you just wanna understand which genet-- gene variants actually cause disease and which don't. And that is, that in itself can be super empowering to patients. Um, and if beyond that, it, there are some diseases where we understand the chain. We just can't intervene and change a specific protein function. That's super exciting too. Like, if we could design a protein to actually change the physiology, then we can actually cure someone. But to me, like, that is just as exciting as understanding, contributing to our understanding of, like, how someone gets sick in the first place.
- MZMark Zuckerberg
Yeah, no, so it's a very exciting vision because you're basically saying you can bring generalizable tools to provide very personalized things for each individual person.
- SPSpeaker
Yes.
- MZMark Zuckerberg
And that's the power of the approach is you have these big models that you build that can then apply
- 24:23 – 26:25
Timeline for Curing All Diseases
- MZMark Zuckerberg
anywhere. Uh, I know that you mentioned earlier that you were gonna try and cure, prevent all diseases, um, within a hundred years, and you mentioned, hey, it could actually be sooner now given all the advances in AI. Do you have some thought of when we think we'll be closer to that goal or some-
- SPSpeaker
I, I mean, I'm optimistic it'll be sooner. I mean, I think that the thing that's complicated is that it's a dynamic system, right? So if you fix something, there will obviously be future things that you need to work on. So I don't think that the current set of things that we're aware of are gonna be the only things that need to get worked out, but I don't know. I think that the progress with AI is, is really, um, is, is obviously
- MZMark Zuckerberg
You know, very exciting on this. Um, the other thing that, that I'd say just, I mean, adding to, to what you were saying, um, a second ago, is we really look at more kind of systems than, um, than specific diseases. So for example, one area that seems really important to understand is inflammation. We talked about this a bunch. This is a big focus of the Chicago Biohub. There's a lot of data on that. And that's very-- it's, um, it, it seems quite clear that it's connected to a bunch of different diseases, but we don't... Rather than studying the specific diseases, we think that by trying to understand inflammation more broadly, that will make it so that other companies that can then use these tools can work on specific therapies. Um, another example is, and I think that the, um, the immune system, I think, is a very good, um, case to study for some of the work that we're doing in cellular engineering and when we kind of ladder up from proteins to cells to, like, whole dynamic systems within the body. I think that that one makes sense. I mean, it's sort of privileged. It can, you know, the cells can travel around through the body, all that. You know, so obviously, that has a big part in addressing different diseases is how do you make the immune system function better? But exactly how do you connect that last mile, I think, is gonna be more something that biotech or other, um, academics individually studying things will be better suited to do. So this is, like, kind of how we think about building out the tool set that just helps accelerate all these other
- 26:25 – 28:04
Translating Research to Patient Impact
- MZMark Zuckerberg
folks.
- EGElad Gil
Whether the timeline is, uh, ten years, a hund- hopefully, you know, less than a hundred now, um, I, I think it's useful for maybe your, uh, average doctor or patient, human being, everybody's a patient, um, to, to think about, like, what's externally visible in the progress here. You worked with patients for a long time at UCSF. Like, what should doctors look out for? What should people look out for if you're actually accelerating progress?
- SPSpeaker
This is the part-- I, you know, I'm super excited about the progress, especially with this launch, um, that, uh, Alex and his team have put forward. And I think it's very clear that science is gonna start moving pretty quickly.
- EGElad Gil
Mm-hmm.
- SPSpeaker
Um, and I think the thing that's less clear to me is, um, exactly how we translate to the clinic and what that looks like. And I think what has to change is actually the way we do clinical research. Um, and, um, my hope is that we're really shortening the distance between bench research and patient impact. Um, but there's a lot of steps there that we need, um, people who actually take care of patients to think creatively and, um, think about how to deploy safely. And that's, uh, that's a gap that we have some work in. We partner with Jennifer Doudna on our CRISPR Cures, um, uh, program at UCSF. So we ha-- we're dipping our toe in understanding how the deployment of research needs to change given how quickly, um, uh, research will be progressing. But that one is still, I think, is still shaping up.
- 28:04 – 32:13
Launch of ESMFold2
- MZMark Zuckerberg
Mm-hmm.
- ARAlex Rives
M-maybe I could say something about our most recent launch-
- SPSpeaker
Yeah
- ARAlex Rives
... 'cause I think it also kind of-
- EGElad Gil
Oh, yeah, please
- ARAlex Rives
... you know, illustrates-
- EGElad Gil
We should ask you explicitly about it.
- MZMark Zuckerberg
[laughs]
- SPSpeaker
Yeah. Yeah.
- ARAlex Rives
Yeah. So, you know, 'cause I guess it was just a week ago a-
- EGElad Gil
Yep
- ARAlex Rives
... about now. So we, um, announced, uh, the new ESMFold. And so this is basically, um, uh, an, an open system for scientific discovery in protein biology. It's a world model of protein biology that's been trained. Um, it's, it's, it's, it's a language model base, so it's been trained on billions of protein sequences, kind of learns these emergent representations of protein biology, and then we can use it to make predictions of atomic resolution protein structure, and we can use it to, um... And it's, it's, it's, it's really fast. So it's blazing fast. So it's kind of, um, y-you know, illustrating this Pareto optimal frontier of kind of speed and accuracy in structure prediction. And so this allows us to kind of characterize, you know, r-really vast kind of stretches of the protein universe. So we folded over one point one billion proteins and, and predicted their structures and, and identified kind of features connecting, um, all of them through mechanistic interpretability. But I think the thing that, that I thought was most exciting about this model is it's, it's this really general model of, of kind of protein biology. And so you can, you can use it as a world model. You can actually really start to search the space of the world model to design new proteins. And, um, it's, it's really, you know, hitting state-of-the-art across, um, pretty much every structure prediction benchmark, and especially on protein-protein interactions and protein antibody interactions, which is really critical for therapeutic design. And so what we found is you can actually now use the model to design proteins and design actually single chain antibodies. Um, and so you can do all of this digitally and then, you know, really in a small number of experimental trials, basically like a ninety-six well plate, um, you know, select, select from hundreds of thousands of trajectories digitally, actually synthesize, you know, ninety-six, um, proteins, test them in the lab in a really kind of short, easy experimental cycle, and we found nanomolar binders there. And so, you know, that's really the level, um, for, for therapeutic activity. So it's, it's, it's really, I think, showing that you can have these kind of general purpose models, um, that, that can-- You know, we didn't design a model for antibodies. We didn't design a model to, you know, to be able to bind s- one particular target. You know, we just designed a model that could understand proteins, and you kind of get protein design as an emergent property. And then I also think it illustrates, um, this, this kind of the power of open science and open source because, you know, we, we release this as basically an open discovery engine, and so really anyone can build on it. And so it takes what are these really, um, intensive laboratory experiments where, you know, you have to screen through hundreds of thousands or millions of antibodies and high-throughput screens in the lab, and, you know, you can really just kind of spin up an instance and compute and now, you know, be able to generate antibodies.
- SPSpeaker
You should say more about sort of, like, we took that data, uh, when we looked at an antibody screen, and then we validated it. We looked at PDL in cells, and then we looked at it under the, uh, cryo-EM and sort of how all that complemented-- validated what you were seeing in the models.
- ARAlex Rives
That's right. Yeah. So I mean, I think it's really critical, you know, to, to actually go and, and characterize these molecules in the lab. And it's, you know, we have a, um, a, a structural biology center here. We have, um, incredibly powerful, uh, cryo, uh, EM microscopes and, and so we're really able to kind of look at these proteins biophysically and functionally. And so, you know, we design proteins for, um, several, uh, therapeutically relevant targets, and we're able to confirm their, their function in cellular assays.
- SPSpeaker
It's delightful when it works the way it's supposed to.
- MZMark Zuckerberg
Mm-hmm. Yeah, it's very amazing.
- ARAlex Rives
We're able to look at the structure also.
- SPSpeaker
Yeah.
- ARAlex Rives
So you can see atomic resolution,
- 32:13 – 38:39
Tackling Off-Target Effects and Edge Cases
- ARAlex Rives
kind of at the binding interfaces.
- MZMark Zuckerberg
Mm-hmm. Correct. I know a lot of your work is really focused on basic research and kind of building out the fundamentals. If I look at actual translation into drugs or drug development, often a clinical trial will be fifteen years, it'll cost one point five billion dollars. About fifty million of that often is the molecule and preclinical work, and it's a few years of work. And then the other one point four five billion and decade-plus is actually the drug development side of it. A lot of that seems to be gated on some regulatory issues, some of it's recruitment, it's a variety of things, but a lot of it also has to do with the failure of drugs in trials around things like absorption or toxicity or things like that. Have you considered at all tackling that other chain of sort of molecular design and thinking, or is the primary focus more on the basic biology and sort of the initial sort of molecules?
- SPSpeaker
I mean, at least my hope in, uh, building this like comprehensive model of how, you know, cells work is actually also being able to predict off-target effects.
- MZMark Zuckerberg
Mm.
- SPSpeaker
I think you can do some of that actually with, um, biological models. Uh, because right now, some of the off-target effects are we just didn't know, you know, your kidney cell also expressed this receptor. And then when we test it in human, like we see it happening, and we see k-- uh, renal toxicity. And so being-- And if you have a single cell atlas that looks at all the different cell types, um, some of which actually were not predicted before we modeled them, you can start looking at which cells actually do have receptors for the target you thought you were exclusively targeting and be able to predict some of these downstream effects before we get into the human trials. And I think that that's, that's actually one of the more exciting applications of, uh, the, uh, like a transcriptomic, uh, model to, to understand actually how the different cells will react when you intervene and do something. Um, and you know, as I-- but I think when you think about delivery mechanisms and, um, patient care, you start-- that's where you start having to be creative about, um, when you asked like what disease do you want to cure first. There are certain diseases that will be easier to, uh, like deliver a therapeutic to or, uh, the risk/reward is, uh, makes more sense. And you know, I think we were all inspired by Baby KJ, uh, I think last year now, when, um, the team at CHOP was able to deliver a CRISPR therapeutic to edit a mutation that he had would have-- that would have inevitably led him to, uh, significant, uh, neurodegener-- neurotoxicity and, um, s-- altered his life. But we were able to, uh, that disease was very s-- carefully chosen because we needed to target his liver cells and if we could easily deliver, um, uh, a product that would work in his liver. And I think that's when the creativity, the, um, the wherewithal to choose the right applications can help us unlock the first applications.
- MZMark Zuckerberg
Mm-hmm.
- ARAlex Rives
Maybe something just to add to that also, you know, because I mean, kind of you described the conventional, you know, drug development process, right? And I, I think, you know, these kind of tools have the potential to have a lot of impact on that process. But you know, what's, what's interesting is to really start to think about kind of the new paradigms that can open up. And you know, what does it mean if, if you can-- you know, the barrier to develop a drug, to design a molecule, you know, to kind of get through all of those stages is so much lower. And so you have programmable biology, and you can, you know, really start to, you know, create a, a medicine for every individual patient. I think that has enormous implications for how we, you know, how we do drug development, um, and what the future of medicine looks like.
- MZMark Zuckerberg
Mm-hmm. It'll be an exciting day when the FDA accepts like a virtual clinical trial for the phase one or something, or-
- ARAlex Rives
Mm-hmm
- MZMark Zuckerberg
... you know, that's based on some person's view of that person.
- ARAlex Rives
Yeah.
- SPSpeaker
Yeah.
- MZMark Zuckerberg
Right.
- EGElad Gil
But even short of that, like thinking about the specific like mechanisms where you see this acceleration, like I imagine if people feel like they can predict impact in kidney cells, um, or have a stronger perspective on tox because they have this broader understanding, they'll be willing to try many more, um, programs, right?
- ARAlex Rives
Yeah.
- MZMark Zuckerberg
Yeah. The recruitment could also change, and we, we have this program, Rare, as one, and the basic idea is that a lot of people focus on the, the most common diseases, but there's this long tail. And the economics don't quite work out for companies to focus on those diseases, but if you can make it so that the groups of patients can kind of come together and organize and say, "Hey, we would take a, an experimental drug on this," then it actually, because of the cost that you're talking about and how that's a huge amount of the, the overall cost, if you can flip that, then it actually makes it, um, so the economics make a lot more sense to then if you can generate something more easily, and you can pair it with, um, a group of, of people. I think one of the interesting things from, you know, science and engineering is that often, you know, you can hit your head against the wall on the common problems and, and in this case, diseases. But a lot of times you like learn a lot more about a system from finding some kind of, you know, rare or like weird side thing that's happening.
- EGElad Gil
Edge case. Yeah.
- MZMark Zuckerberg
Edge case.
- ARAlex Rives
Yeah.
- MZMark Zuckerberg
Um, so I don't know. I, I think that that's like always been kind of an interesting part of this that actually connects pretty well to this because now you're gonna be able to enable a long tail of new kind of-... ideas to get tried and enable them to potentially get tested more easily.
- SPSpeaker
Yeah. That's a really good point on rare. Um, on, in our rare disease cohorts, first of all, they're incredibly inspiring and powerful, but patient groups are self-organizing patient registries, natural history registries, um, biobanks. Um, they're organizing their own clinical trials. There's gene therapy that, uh, o- one disease group has moved forward over the course of like, I wanna say like three to five years rather than decades, and the speed is so fast because, um, the patients themselves have organized the resources that a, a scientist or a clinician might need to... And it's, it's, it's, it's incredible.
- MZMark Zuckerberg
But I think to some degree you're gonna need something like this, because there are gonna be many more new things that can get created. But that doesn't mean that for like the general population, that you're not gonna want the same level of vetting that we've had historically.
- SPSpeaker
Yeah.
- MZMark Zuckerberg
But making it so that people who wanna be on more of the frontier have the ability to do that is, is I think also gonna be pretty helpful.
- ARAlex Rives
Mm-hmm. Yeah, letting people opt in to be part of trials, I think, is one of the big shifts that is starting to happen, but
- 38:39 – 41:06
Putting the Tech in Individual Hands
- ARAlex Rives
could really help accelerate biology in general.
- EGElad Gil
All three of you have mentioned, um, at different points, like the power of open ecosystems in such a large space. Like, I think some of that logic around open source and the breadth or diversity of data collection that you guys were describing, um, it should also apply in the, like, language model world and the multimodal AI world. Like, do you think that's right? Does any of the work you're doing here change how you think about AI and Meta?
- MZMark Zuckerberg
I mean, I think it's sort of a similar philosophy overall, and you know, Priscilla was talking about this, that, you know, a lot of our, our focus is building tools that empower individuals to do things. And that's a sort of a common theme across a lot of the things that, that I work on, is just kind of m- putting the technology in individuals' hands. We don't believe in this like very centralized future where there should be a small number of institutions that, uh, that basically are, are advancing all the stuff. Our vision is not that there's gonna be like some central super intelligence that solves all of science. I think, like, people are really important and I think will be more important in the future, and giving people more tools to be more productive is gonna be like a critical part of any kind of positive future. That both-- And that's how progress has always been made historically, right? It's not, um, through centralization, it's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought they were good ideas that already have been done. Um, so I, I think that that's, that's very central to the whole ethos of, um... I mean, to some degree it's like why you create something like social media, right? To give people a voice. It's, you know, I think a lot of the, the stuff that we, that I care about in terms of empowering people with individual AI. Open source is one instantiation of it. It's not the only way to do it. Um, it certainly is one way that you basically are saying, "We're gonna take this technology and put it in everyone's hands." In terms of science, I think it really makes sense, and we're deeply committed to open source. Um, there are obviously interesting considerations on this that are important too, because there's a lot of considerations around biosafety and things like that, that we're gonna need to balance and think through how to, how to handle. Um, but I think overall this is like very deep in the ethos of the work that we're doing, both at Biohub and like probably a theme for a lot of the stuff that I do, is just like we, we believe that a positive future is one where you build a technology as a tool, you put it in individuals' hands, and that's kind of how
- 41:06 – 44:25
Talent at Biohub
- MZMark Zuckerberg
society makes progress.
- EGElad Gil
Y- you have, um, uh, this, like, I think, uh, incredibly ambitious mission at Biohub, and yet, you know, um, the AI scientists that work here could also go work in commercial enterprises. How do you think about the talent and to, like, how to bring people to Biohub?
- MZMark Zuckerberg
Um, I mean, where do I start?
- EGElad Gil
[laughs]
- MZMark Zuckerberg
I, I think, you know, um, yeah, I mean, it's, it's a very, um, hot market for AI researchers, but I think that part of the-- part of what that means is that, um, there's a lot of, uh, demand and you, like, they're very in demand and can work on the things that they wanna work on.
- EGElad Gil
Mm.
- MZMark Zuckerberg
Um, and it-- I think this gets back to this point again about frontier AI and frontier biology, right? So if, um, so yeah, I mean, I think like the AI researchers who work here could go work on, on language models or things at any of the, the main labs. Um, but those labs don't have the frontier biology part attached to it. So I think that there's like also a just very large mission component of this, which is like there's an, an ability to do this unique work here that you just can't really do at the other places. Um, uh, if, so if you're, if that's what your focus is, then this, um, then, you know, I, I don't actually think that there's any other organization in the world that's bo- doing both the frontier biology and the frontier AI.
- SPSpeaker
Yeah. Why are you here, Alex? [laughs]
- ARAlex Rives
I mean, I think it's, it's really simple. Yeah.
- EGElad Gil
[laughs]
- ARAlex Rives
Our, our mission is take care of pre- prevent disease, and, and I think, you know, there's, it's, it's just such a powerful-
- EGElad Gil
And you say it with a straight face and a less than hundred-year timeline.
- SPSpeaker
Oh, it's very serious now.
- EGElad Gil
Yeah. Yeah.
- SPSpeaker
There's no more...
- MZMark Zuckerberg
That's... Yeah. Yeah.
- ARAlex Rives
It's, it's a really powerful mission, and I-
- EGElad Gil
Yeah
- ARAlex Rives
... I think, you know, you, um, yeah, I mean, it's, it's just, you know, scientists, I think are very motivated by that.
- EGElad Gil
Mic drop. That simple. Yes.
- ARAlex Rives
It's, uh, yeah.
- EGElad Gil
Yes.
- ARAlex Rives
It's, it's, it's something people are deeply motivated by, and I think, you know, we're at this moment in time where that actually seems like something that can be achieved, and I think, you know, we're building a really unique place, um, where, where we're, we're tackling that problem, and, you know, we have the resources and, I think, kind of the, the right, the, the right things to actually really, really go after that and do that.
- EGElad Gil
Yeah. I, I mean, that resonates with me as somebody who, you know, talks to and hires a lot of research scientists. They wanna, they wanna know if you have the data, if you have the tools, if you have the compute, if you have the talent, and then what the mission is. And so I actually think, uh, uh, I think that's super competitive.
- MZMark Zuckerberg
The other thing is that you don't need a very large teamRight. So I, I think it's like, it's like an interesting thing about the world is that people care about different missions, and that's good. I think that's like part of the whole and part of why giving-- building these tools and giving people the ability to explore what they care about, whether it's like across science or just across everything, is like such a powerful way to make progress in society, is that people care about different things. And in order to make progress in AI, you don't need like many, many hundreds of AI researchers, um, or thousands or anything like that. I think you can really make progress with, um, you know, a very strong group of a dozen or a couple dozen people. And yeah, I mean, finding people who, like, care about this mission is not a particularly hard thing. I mean, this is like a super important thing in the world. So I think that that's-- Yeah, it's, it's just kind of a cool thing about the world is that people obviously
- 44:25 – 46:10
What’s Next After ESMFold2
- MZMark Zuckerberg
are, are drawn to different, different missions.
- EGElad Gil
So I, I think the, like, simplest mental models that, uh, folks have, even if they're paying attention to the space, are essentially like, okay, you know, um, structure prediction models for, um, for proteins and protein-protein interaction models. Uh, and then so there's this one piece which is fundamental understanding, and then there's this, like, theory of someday we're just gonna be able to, like, zero-shot things into either the clinic or the clinic with much, uh, much better hit rate. Um, what needs to happen for us to go from ESMFold2 to this other piece?
- ARAlex Rives
Yeah, so I-
- EGElad Gil
Is, is that feasible?
- ARAlex Rives
I think that's a great question. I mean, I, I would say that I'm really optimistic on that. So I, I think, you know, on the one hand, you know, these are problems that historically, you know, people could spend kind of an entire career working on, like how do you, how do you figure out how to effectively optimize a drug? How do you get it, you know, get it through preclinical? How do you do the early safety? I think that, you know, when you have a new scientific paradigm, kind of, you know, questions that were once hard, um, kind of become simplified through the new paradigm. And so I'm very optimistic that kind of many of these core problems will be solved kind of in an emergent way, uh, through these models.
- EGElad Gil
Mm-hmm.
- ARAlex Rives
And I think one great example of that is, is toxicity, whereas if, if you can kind of really digitally, um, di-digitally kind of simulate everything and be able to predict, you know, where a drug is going to distribute and bind across the human body. You know, like you, you kind of have, um, the, the beginning of a solution to that kind of problem. So I, I, I think that, I think that once you have these kind of accurate representations at the molecular level, you know, you, you-- we're gonna start to see really rapid
- 46:10 – 46:51
Connecting ESMFold2 to Agentic Systems
- ARAlex Rives
progress on a lot of these core problems.
- EGElad Gil
What is the most, uh, exciting use or experimentation, uh, with the models you've seen in the last week since release?
- ARAlex Rives
Yeah, I mean, it's, it's just been great to kind of see it get integrated in all kinds of things. I think one of the really interesting things that we've been seeing is people kind of connecting it with agentic systems to just kind of do automated design, um, and, and kind of just automate that, that whole process. So it's, it's really, I think, another example of how you can kind of see bringing together, um, agentic and frontier AI with, you know, the ability to have a world model for biology and actually reason about biology and, you know, really kind of start to automate, um,
- 46:51 – 49:33
The Virtual Cell
- ARAlex Rives
the, the entire design process.
- EGElad Gil
Are you taking, um-- You know, how do you decide what the next step in the research agenda is? Um, it's like world model for biology, and then I could, I'm just gonna be very coarse here, like I could scale it up. I could add more data. I could add sh-- like adding data is a non-trivial thing in terms of new methods and domains. Like what is-- Do you take input from the, um, the larger ecosystem about, you know, how people are using it and what would make it more useful? Or is it really like we, we understand like the next step of structures or coverage that we're looking for?
- ARAlex Rives
I mean, I think there's two things. So, like we have a view on kind of the next big challenge, which I think is, you know, the, the virtual cell.
- EGElad Gil
Mm-hmm.
- ARAlex Rives
And, you know, really being able to kind of ladder up the hierarchy of biological complexity to the cell. And-
- EGElad Gil
Sorry, very basic question.
- ARAlex Rives
Yeah.
- EGElad Gil
This virtual cell model, like what is the input and output I should expect?
- ARAlex Rives
Yeah.
- EGElad Gil
Yeah.
- ARAlex Rives
I mean, I think there's different views on that.
- EGElad Gil
Yeah.
- ARAlex Rives
But I think kind of what you ultimately want is, uh, a system that can really model each of the levels of complexity. So, you know, the, the, the proteomic layer, the genetic layer, the transcriptomic layer, and connect that to the phenotype. And you need enough generality so that you can, um, ask the model questions about, uh, a new intervention in a context that it hasn't been trained on and, and kind of get, uh, an answer from it. And, you know, the gap that we s- we need to close as a field is being able to, um, uh, really make those predictions that can generalize. So that's gonna require, you know, enormous effort to generate data.
- MZMark Zuckerberg
Yeah. And then, I mean, in terms of what you decide to do next, I think this is like, you know, a pretty normal process of constraint management, [chuckles] right? I mean, it's like, like I think every lab in every field across the world probably feels compute constrained. I think that that's probably true here too, right? It's like, um, so I mean, I know like, you know, there's always questions like, "Okay, should we double down more on advancing the protein piece? Should we do more of the cellular stuff?" I think that those are kind of ongoing debates in terms of how you sequence th-that. Um, and then, yeah, within that, there's kind of being at the Pareto frontier about how much you want to train the different models in order to-- Like, and, and the size of the models is also dependent on the scale of the data that you have-
- EGElad Gil
Mm-hmm
- MZMark Zuckerberg
... because, you know, yeah, for, for obvious reasons. So yeah, I mean, I think it's-- There's some of that is just wh-where you want to be on the curves and the normal constraints, but I think that this is like probably the same process that like any research organization goes through of like you want to go in all these different directions, and you're just trying to constraint, optimize, and make enough progress to do world-class work at one thing at a time while planting some seeds that can, um, blossom over the,
- 49:33 – 51:52
Defining Success for Biohub
- MZMark Zuckerberg
the next, uh, couple years as well.
- ARAlex Rives
Yeah. Th-this has been the most dynamic, uh, period of technology at least I've seen over my career.
- MZMark Zuckerberg
Uh-huh.
- ARAlex Rives
I mean, it's so exciting in terms of everything that's happening with AI, and ev-every week there's something new that's changed.
- EGElad Gil
Are you tired or invigorated?
- ARAlex Rives
I'm, I'm, I'm both. [laughs]
- MZMark Zuckerberg
Yeah.
- EGElad Gil
Wired. Wired.
- MZMark Zuckerberg
I feel like that, I feel like that's how everyone feels.
- EGElad Gil
Yeah.
- ARAlex Rives
I feel like everybody's in this-
- EGElad Gil
Yeah
- ARAlex Rives
... manic phase.
- MZMark Zuckerberg
Yes.
- EGElad Gil
Mm-hmm.
- MZMark Zuckerberg
It's a combination of invigorated and exhausted.
- ARAlex Rives
Yeah, it's wonderful. And so, [chuckles] um, I guess, uh, you know, things are very unpredictable right now. It's really hard to know what's coming. We have this, um, almost like early signs of exponentiation on the model side with agentic, uh, flows that we're starting to see in really interesting ways, models starting to help more and more with models, but that's still very, very early days for that. If you're thinking back five years from now and you were to define what success was relative to your efforts-
- SGSarah Guo
And I know things have, uh, are very dynamic, things changed a lot, but you have this common thread of tooling for the Biohub, you have a common thread of empowering scientists at scale. You're looking back five years from now, is there a specific thing that you really wanna make sure that you've accomplished or achieved or a primary goal?
- MZMark Zuckerberg
Well, I mean, I think we have a pretty clear view of this, like, hierarchical set of world models that we wanna build around biology, and the other part of that is that we wanna do the highest quality work in the world, right? I mean, I, and I think we're basically set up to do that between having a world-class AI research team and this collection of, of Biohub's such a world-class life sciences research organizations. I think that that's, like, fundamentally a setup that no other organization in the world has. Um, but you know, you can have a lot of great ingredients, and that doesn't guarantee that you succeed. So I mean, to me, like five years from now looking back, I, I think, you know, it's, it-- other-- I'm sure other labs or efforts will try to produce, like, things that approximate what we're trying to do, and I just think that we should be able to do something that is meaningfully better and a unique intellectual contribution to the world, right? I think that that's kind of what you-- whenever you do any kind of research, that's what you're trying to do, right? So, um, yeah. So if we do that, I think we'll all feel very good. I would also expect that at some point we'll just start seeing a lot more idea generation from the people using the models, but I have enough faith that that part will materialize that for me it's more just about, like, making sure that we do world-class work, and I think if
- 51:52 – 56:20
Biohub Strategy Update
- MZMark Zuckerberg
we do, like, the rest almost will take care of itself.
- SPSpeaker
Very last question for you. Snapshot of its mid twenty twenty-six, what's the biggest update in your own thinking about Biohub or the domain from the last year?
- MZMark Zuckerberg
Well, from the last year, I mean, you joined in the last year.
- SPSpeaker
[laughs]
- MZMark Zuckerberg
I mean, I think the, the biggest thing that, that we basically rotated and, and, and I think in the last year we basically kind of formalized that Biohub is the main focus of our philanthropy. So I think this is like, uh, a very, very big shift. Um, but Alex and the team coming in, I think has been interesting, not only because it's, it's a world-class group, right? I mean, you guys have worked together for a while. I think also when you talked about how stuff is changing so much in the field, I think one thing that's underrated is, like, this is like a extremely talented group of people who also are like know each other and work well together and, like, are stable and good and like, I think that that also is underestimated in terms of the compounding benefit of, like, people being able to, like, work well in a stable environment over time. Um, so I think that that's a really important piece. Um, but part of what we wanted to do was prior to Alex leading the effort, the previous leaders of the Biohub were basically primarily biologists-
- SPSpeaker
Mm-hmm
- MZMark Zuckerberg
... who were interested in technology, right? And now I think we-- this is the point where we really flip that, right? Where, I mean, o-obviously, you have a background in biology as well, but like, you are primarily an AI researcher who has a background in, in, in AI and in, in biology. I think that that's like a deep reflection on, on kind of the way that I-- that we expect that this is going to, um, kind of drive more value in the future. So th-those are probably the biggest updates in the last year in, in terms of the work that, that we're doing. I mean, it's a new leader, not just the leader, but a team, um, that I think has been is, is, is like a really good. And then, yeah, I mean, I think on the rest of the industry, it's like it's on track. I mean, I think like every-- It's, it's kind of this crazy thing because like when you have an exponentially growing curve, I think the way that an exponential curve feels is it's growing so quickly that it-- the, the kind of emotional feeling is it can't possibly keep going.
- SPSpeaker
Mm-hmm. [laughs]
- MZMark Zuckerberg
Right? Because like it's-- uh, 'cause it's just like... But, but I mean, the nature of an exponential curve is it, it doesn't just keep going, it keeps accelerating, right? Exponential growth is accelerating. Um, so I think that that has all these like emotions and psychology attached to it, but I think fundamentally, when you look at the curve in the industry, um, the kind of fundamental thing is it is on track. It, it has remained on that curve, um, which I think has all these very profound implications for all of these domains, but certainly it validates and makes one feel very good about making a very big investment in, in the things that, that will play out if that-- if you stay on that track, and it seems like we are. So that I think is very good news.
- SGSarah Guo
I think the most important aspect of what you're doing there is you're actually closing the loop with the actual biology-
- MZMark Zuckerberg
Mm-hmm
- SGSarah Guo
... because with code and research, it's closed loop systems, and so they're very fast to iterate. This is an open loop system, so you're closing a loop, and that's, that's really crucial to progress.
- SPSpeaker
Yeah.
- MZMark Zuckerberg
Yeah.
- SPSpeaker
For me, one of the biggest changes, uh, with, uh, the strategy we're driving now and Alex at the helm is, you know, before we had amazing teams moving generally in the same direction and understanding, uh, like the potential, uh, collaborations and interconnectedness of our work. But, uh, now we are arms linked, moving together-
- SGSarah Guo
It feels very directed.
- MZMark Zuckerberg
Yeah
- SPSpeaker
... with a singular goal. It's very directed and, um, it's very exciting. It's a little bit scary, uh, but it's like truly a team, um, playing off each other and trying to make progress towards this goal. And, um, that has, uh, taken a, a lot of work, but also the maturity, our teams being able to have their work at a level of maturation where it actually does make sense to interlock.
- SGSarah Guo
Amazing. Well, to teams being on the curve, thank you guys for doing this.
- MZMark Zuckerberg
Yeah.
- SPSpeaker
Thank you.
- SGSarah Guo
Thank you.
- MZMark Zuckerberg
Thank you.
- SPSpeaker
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Episode duration: 56:20
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