The Exchanges

Q For some of the audience who aren't familiar with you and your work at Arc and Beyond, how do you describe what's your moonshot? What is what you're trying to do?

A I want to make science faster, right? You know, we can frame this in high level philosophical goals, like accelerating scientific progress, Maybe that's not so tangible for people. I think the most important thing is science happens in the real world. If it's not AI research, which moves as quickly as you can iterate on GPUs, right? You have to actually move things around atoms, clear liquids from tube to tube to actually make life-changing medicines. And these are things that take place in real time. You have to actually grow cells, tissues, and animals. And I think the promise of what we're doing today with Machine learning in biology is that we could actually accelerate and massively, uh, massively parallelize this, and so our moonshot is really to make virtual cells at Arc and simulate human biology with foundation models, and, you know, we'd like to figure out something that feels useful for experimentalists, people who are skeptical about technology, you know, they just want to see the data and see the results, that it's actually the default tool that they go to use when they want to do something with cell biology.

AI assessment note: “our moonshot is really to make virtual cells at Arc and simulate human biology”

Q In Dario Amadei's essay, Machines of Love and Grace, he predicts, among other things, the prevention of, of, of many infectious diseases and the doubling of lifespans, perhaps in as soon as the next decade. What, what's your reaction to his, his essay, his bullishness and some of his predictions?

A I think the core intuition that Daru had was the idea that sign like important scientific discoveries are independent, right? Or they're largely independent. And if they are, you know, statistically independent, then it would stand to reason that we could multi parallelize. And so we had models that were sufficiently predictive and useful. You could have not just a hundred of them, but Millions, billions of these discovery agents or processes running at a time which should compress the timeline to new discoveries, um, and turn it into a computation problem, right? I think that is a very futuristic framing for something that is actually very tangible today, right? Um, and if we can have virtual cell models at work, for example, that can start to Do these kinds of things that we've been talking about. Help us, you know, we can have, you know, molecular design models. We can have docking models. We can then have, you know, when you bind to this thing in this cell versus all the other off-target proteins, will a cell kind of be corrected in the right way, right? These kind of layers of abstraction and complexity start to get to things that feel very tangible through drug discovery. If you could actually traverse these steps Reliably and in sequence, you can start to see how you can get the compression, right? And, and so I think in the long run of time, this should be possible.

AI assessment note: “so I think in the long run of time, this should be possible.”

Q In Dario Amadei's essay, Machines of Love and Grace, he predicts, among other things, the prevention of, of, of many infectious diseases and the doubling of lifespans, perhaps in as soon as the next decade. What, what's your reaction to his, his essay, his bullishness and some of his predictions?

A I think the core intuition that Daru had was the idea that sign like important scientific discoveries are independent, right? Or they're largely independent. And if they are, you know, statistically independent, then it would stand to reason that we could multi parallelize. And so we had models that were sufficiently predictive and useful. You could have not just a hundred of them, but Millions, billions of these discovery agents or processes running at a time which should compress the timeline to new discoveries, um, and turn it into a computation problem, right? I think that is a very futuristic framing for something that is actually very tangible today, right? Um, and if we can have virtual cell models at work, for example, that can start to Do these kinds of things that we've been talking about. Help us, you know, we can have, you know, molecular design models. We can have docking models. We can then have, you know, when you bind to this thing in this cell versus all the other off-target proteins, will a cell kind of be corrected in the right way, right? These kind of layers of abstraction and complexity start to get to things that feel very tangible through drug discovery. If you could actually traverse these steps Reliably and in sequence, you can start to see how you can get the compression, right? And, and so I think in the long run of time, this should be possible.

AI assessment note: “I think the core intuition that Daru had was the idea that”

Q Can we flesh out the virtual cell concept? Why that's the ambition we we've landed on and what it's going to take to get there or what are the bottlenecks?

A I would say the most kind of famous success of ML in biology is AlphaFold, right? And this solved the protein folding problem of, you know, when you take a sequence of any amino acid, what is the protein look like, right? And, you know, it's pretty good. It's not perfect. It certainly doesn't simulate the biophysics and the molecular dynamics, but it gives you a sense of what the end state is with 90% plus accuracy, right? And that's the alpha fold moment that people talk about, right? Where any time you want to, you know, work with a protein, if you don't have an experimentally self-structure, you're just going to fold it with this, uh, with this algorithm. And we kind of want to get to that point with virtual cells as well. And the way that at Arc we're operationalizing this is to do a perturbation prediction, right? Where the idea is you have some manifold of cell types and cell states, right? Um, that can be a heart cell, a blood cell, a lung cell, and so on, and you know that you can kind of move cells across this manifold, right? Sometimes they become inflamed, sometimes they become apoptotic, sometimes they become cell cycle rested, they become stressed, they're metabolically starved, they're hungry in some way, and so if you have this, uh, sort of this representation of universal sort of cell space, right, can you figure out What are the perturbations that you need to m…

AI assessment note: “operationalizing this is to do a perturbation prediction”

Q I think it's a great place to wrap. Gearing towards, towards closing, anything, uh, upcoming for ARK that you'd like us to know about? Anything you want to tease? Anything, for people who want to learn more, what should they know about?

A So AlphaFold was, uh, in any ways came out of a Protein folding competition called CASP, right? Critical assessment of the structure of proteins. And, um, you know, we created our own virtual cell challenge at virtualcellchallenge.org where we have, you know, a 100,000 dollar prizes sponsored by NVIDIA and Tenex Genomics and Ultima and others. And it's an open competition that anyone can enter where you can train perturbation prediction models and we can Openly and transparently assess these model capabilities both today and in subsequent years, follow them to get to that ChatGPT moment, right? And so I'm extremely excited about this. Um, you know, um, we, we like more people to, you know, train models and apply both bio ML experts and engineers in any other domain. And, you know, I'm, you know, I, I, I just, I want this thing to exist in the world. You know, hopefully we're important parts of making that happen, but I just be happy that someone does it.

AI assessment note: “we created our own virtual cell challenge at virtualcellchallenge.org”

Q Can we flesh out the virtual cell concept? Why that's the ambition we we've landed on and what it's going to take to get there or what are the bottlenecks?

A I would say the most kind of famous success of ML in biology is AlphaFold, right? And this solved the protein folding problem of, you know, when you take a sequence of any amino acid, what is the protein look like, right? And, you know, it's pretty good. It's not perfect. It certainly doesn't simulate the biophysics and the molecular dynamics, but it gives you a sense of what the end state is with 90% plus accuracy, right? And that's the alpha fold moment that people talk about, right? Where any time you want to, you know, work with a protein, if you don't have an experimentally self-structure, you're just going to fold it with this, uh, with this algorithm. And we kind of want to get to that point with virtual cells as well. And the way that at Arc we're operationalizing this is to do a perturbation prediction, right? Where the idea is you have some manifold of cell types and cell states, right? Um, that can be a heart cell, a blood cell, a lung cell, and so on, and you know that you can kind of move cells across this manifold, right? Sometimes they become inflamed, sometimes they become apoptotic, sometimes they become cell cycle rested, they become stressed, they're metabolically starved, they're hungry in some way, and so if you have this, uh, sort of this representation of universal sort of cell space, right, can you figure out What are the perturbations that you need to m…

AI assessment note: “we kind of want to get to that point with virtual cells as well.”

Q Shifting gears a little bit, we've been talking about science and biotech, but you're, in addition, you're an elite AI investor more broadly. So, so I want to talk about, um, how you're, I want to talk about where your investment focus is right now, just as it relates to AI more broadly. Where are you excited? Where are you spending time? Where are you, you know, looking forward to?

A Oh yeah. My, my goal is to really try to figure out ways that we can improve the human experience in our lifetime. I kind of think of, like, if I think about the future that we're going to leave to our children, right, there are a few things that if we get them right in our lifetime will fundamentally change the world, right, and, you know, how we live in it. I think synthetic biology is obviously one, right, you know, think, you know, GLP ones, right, things that improve sleep, right, things that can, you know, improve longevity, right, these are, these are all things that are kind of, you know, Easy to get excited about. I do, I think, um, Uh, brain-computer interfaces, um, is another area where, um, we're gonna see really important breakthroughs over the decades to come, and then I think the third is in, uh, is in robotics, both industrial and consumer robotics, right, um, that allow us to basically, like, scale physical, like, labor, right, in, in, in interesting ways, and, you know, you can kind of see how each of these three things, even in the sort of medium cases of success, Really kind of change the world, and so I'm very interested in helping make these kinds of things possible, right? And so there's sort of, you know, in the kind of techno-optimist sort of vision of the world, right, there's a few different types of scarcity, right? There's, you know, it's very easy …

AI assessment note: “synthetic biology is obviously one... brain-computer interfaces... the third is in robotics”

Q It seems that biotech and pharma has, has been a shrinking, um, in the rate of growth. What's it going to take for these, um, innovations in, in, in the science to reflect themselves in, in business models and in, in growth for the industry?

A A lot of these biotech startups would try to initially sell software to pharma companies, and then they would kind of realize, oh, wow, we're like competing for SaaS budgets, um, which aren't very large. And then, you know, now they're realizing, oh, we have to compete for R&D budgets, right? And I think, you know, there is this narrative from the current generation of these companies that, oh, our biological agents will compete for R&D budgets and replace headcount or something like that, right? Just like we're seeing in, you know, Agents across different verticals, right? Whether or not that will, I think, pan out, I think depends on just whether or not these things meaningfully allow us to, you know, build drugs more effectively in the pharma context, right? And I think that's just sort of the most important thing in, in, in this industry. And so I think we believe in virtual cells, not just because we think it will be a fountain of fundamental mechanistic insights for discovery, But also because if in the case of success, it could be industrially really useful, right? But, you know, we'll, we'll, we'll, we'll have to see over time, right? If we have 90% of drugs failing clinical trials, right? That kind of means two things, and you're not sure what percent of which, right? One is we're targeting the wrong target in the first place. The second is the composition, the drug ma…

AI assessment note: “depends on just whether or not these things meaningfully allow us to, you know, build drugs more effectively”

Q Shifting gears a little bit, we've been talking about science and biotech, but you're, in addition, you're an elite AI investor more broadly. So, so I want to talk about, um, how you're, I want to talk about where your investment focus is right now, just as it relates to AI more broadly. Where are you excited? Where are you spending time? Where are you, you know, looking forward to?

A Oh yeah. My, my goal is to really try to figure out ways that we can improve the human experience in our lifetime. I kind of think of, like, if I think about the future that we're going to leave to our children, right, there are a few things that if we get them right in our lifetime will fundamentally change the world, right, and, you know, how we live in it. I think synthetic biology is obviously one, right, you know, think, you know, GLP ones, right, things that improve sleep, right, things that can, you know, improve longevity, right, these are, these are all things that are kind of, you know, Easy to get excited about. I do, I think, um, Uh, brain-computer interfaces, um, is another area where, um, we're gonna see really important breakthroughs over the decades to come, and then I think the third is in, uh, is in robotics, both industrial and consumer robotics, right, um, that allow us to basically, like, scale physical, like, labor, right, in, in, in interesting ways, and, you know, you can kind of see how each of these three things, even in the sort of medium cases of success, Really kind of change the world, and so I'm very interested in helping make these kinds of things possible, right? And so there's sort of, you know, in the kind of techno-optimist sort of vision of the world, right, there's a few different types of scarcity, right? There's, you know, it's very easy …

AI assessment note: “synthetic biology is obviously one... brain-computer interfaces... third is in robotics”

Q Why don't you talk about the difference between the simulation of biology and the actual understanding, and what would it take to actually be able to model the extremely complex human body?

A You know, some people don't like the phrase virtual cells because it sounds too media friendly. It's not rigorous enough, right? But I, I've always, um, found it funny that, you know, but, you know, many people are okay with like digital twins. And digital avatars, which, you know, talks about modeling biology at a way higher level of abstraction. You know, I think virtual cells, if anything, is actually way more scoped and rigorous than modeling a digital twin or avatar. But, you know, I think these are useful words because they describe the goal and the ambition, right? That no, in the long run, we don't care about Predicting the, you know, kind of perturbation responses of an individual cell at all, actually, right? Obviously, we want to be able to predict drug toxicity. We want to be able to predict aging. We want to be able to predict why a liver cell becomes cirrhotic when you repeatedly challenge it with ethanol molecules or whatever, right? And, you know, the, these sort of chemical or environmental perturbations should be predictable. I think you just, Kind of have to layer on the complexity, right? Like, why are we so worried about modeling entire bodies over time when we can't do it for an individual cell, right? Where we sort of, you know, accept or broadly believe that this is a kind of, you know, fundamental unit of biological, you know, computation, if you will, …

AI assessment note: “I think you just, Kind of have to layer on the complexity”

Q It seems that biotech and pharma has, has been a shrinking, um, in the rate of growth. What's it going to take for these, um, innovations in, in, in the science to reflect themselves in, in business models and in, in growth for the industry?

A A lot of these biotech startups would try to initially sell software to pharma companies, and then they would kind of realize, oh, wow, we're like competing for SaaS budgets, um, which aren't very large. And then, you know, now they're realizing, oh, we have to compete for R&D budgets, right? And I think, you know, there is this narrative from the current generation of these companies that, oh, our biological agents will compete for R&D budgets and replace headcount or something like that, right? Just like we're seeing in, you know, Agents across different verticals, right? Whether or not that will, I think, pan out, I think depends on just whether or not these things meaningfully allow us to, you know, build drugs more effectively in the pharma context, right? And I think that's just sort of the most important thing in, in, in this industry. And so I think we believe in virtual cells, not just because we think it will be a fountain of fundamental mechanistic insights for discovery, But also because if in the case of success, it could be industrially really useful, right? But, you know, we'll, we'll, we'll, we'll have to see over time, right? If we have 90% of drugs failing clinical trials, right? That kind of means two things, and you're not sure what percent of which, right? One is we're targeting the wrong target in the first place. The second is the composition, the drug ma…

AI assessment note: “allow us to, you know, build drugs more effectively in the pharma context”

Q If not too difficult, I want to ask, uh, Jorge a question adopted to these, uh, additional spaces, um, robotics, um, sort of BCI and, and longevity if appropriate in terms of, and the three questions I believe were, what's overhyped? What's, uh, where do you see, uh, uh, opportunity or path and what's got heft already?

A I think the cool thing about agents generally is that They do real work, right? Compared to like SaaS companies that came before, agents replace real productivity, right? And I think, you know, they have a lot of errors today, and I would say the computer use agents will probably trail the coding agents by maybe a year, right? But, but it's coming, and we'll follow the trajectory as these go from doing, you know, Minutes of work without error to hours to days, right? And I think, you know, you're going to get a completely different product shape as we march through that across legal, BPO, you know, medicine, healthcare, whatever, right? And we'll kind of follow that as an industry, and that's, that's going to be really exciting. And I think that's where we're going to see real heft is because most of the economy is services spent. It's not software spent. And, you know, the reason why we're all excited about this stuff is that it can attack You know, the, the, the, the services economy. And I would say, like, you know, where, where is their hype? There's tremendous amount, right? That's, that's, that's no doubt. The hype is in the model capabilities, right? And, you know, it's, we, we, we're working with an architecture that, you know, dates back to 2017, right? And if you look at the history of deep learning, it's like kind of every eight years, there's something Really differ…

AI assessment note: “I think the cool thing about agents generally is that They do real work”

Q Why don't you talk about the difference between the simulation of biology and the actual understanding, and what would it take to actually be able to model the extremely complex human body?

A You know, some people don't like the phrase virtual cells because it sounds too media friendly. It's not rigorous enough, right? But I, I've always, um, found it funny that, you know, but, you know, many people are okay with like digital twins. And digital avatars, which, you know, talks about modeling biology at a way higher level of abstraction. You know, I think virtual cells, if anything, is actually way more scoped and rigorous than modeling a digital twin or avatar. But, you know, I think these are useful words because they describe the goal and the ambition, right? That no, in the long run, we don't care about Predicting the, you know, kind of perturbation responses of an individual cell at all, actually, right? Obviously, we want to be able to predict drug toxicity. We want to be able to predict aging. We want to be able to predict why a liver cell becomes cirrhotic when you repeatedly challenge it with ethanol molecules or whatever, right? And, you know, the, these sort of chemical or environmental perturbations should be predictable. I think you just, Kind of have to layer on the complexity, right? Like, why are we so worried about modeling entire bodies over time when we can't do it for an individual cell, right? Where we sort of, you know, accept or broadly believe that this is a kind of, you know, fundamental unit of biological, you know, computation, if you will, …

AI assessment note: “why are we so worried about modeling entire bodies over time when we can't”