Will AI Make Scientists Obsolete? | Arc Institute’s Patrick Hsu

0:00 When I was 13, I got expelled from college. >> Expelled from college. You're already in college. >> Well, not anymore. Research is 99% failure. All your very best ideas kind of suck except the one or two that you're remembered for. Research is an even playing field by definition. No matter how great or accomplished you are, we all have the same shot. I would say research, taste, and intuition is only increasing in relative importance. Like I would buy call options on it if I could.

0:24 >> Getting to the point where a model that makes a cell biologist feel emotion. Can you say more about what that means? >> Right. You can take your own fibroblasts and turn them into induced plur potent stem cells in a dish. It is one of the most kind of profound and magical and semi-religious feelings I've ever had. How do we have those holy moments in biology? >> Do you have many of those currently? >> Well, we've created them and sort of the first time that AI has been used to create entire viable organisms. I think we will create evolutionarily distinct forms of life. 100 years ago, infectious disease was maybe the number one killer.

0:58 Right? today that's heart disease. We may see both of those go away, right? One of the kind of big ironies of writing papers is I don't really know if I'm writing them for fellow humans anymore. >> Do you fear becoming obsolete? >> If you simply do nothing, the default is to become obsolete. So, it's not even a fear. It's actually just reality. Being able to be bold, to be fearless, to be risk-taking. They're kind of survival traits. I think science is really hard.

1:23 It's such a long journey and it's mostly failure. So, if you don't have a way of bringing the energy and the vibe and the vitality, it's going to be pretty miserable. You need to figure out a way to make the discovery process fun because you're mostly eating Welcome to Dialectic episode 60 with Patrick Sue. Patrick is a scientist, a founder, and investor. He co-founded the ARK Institute and is a core investigator there. ARK is a new type of research organization in collaboration with a number of universities that is doing groundbreaking work at the intersection of technology and science. Patrick is also an assistant professor of pathology at Stanford University and a venture partner at Thrive Capital. Patrick works at the rare intersection of biology, engineering, and artificial intelligence and has worked across a number of different innovations over the course of his research career, including crisper very early on and most recently on bio foundation models, including EVO1 and EVO 2, as well as cell models. He is a true polymath and it was so fun to sit down with him and talk about ARC, all of the work they are doing to use AI to push the frontiers of biotech, what might be possible, what makes these institutions work, and more. Patrick has also been incredibly prodigious going to college as a young teenager and has continued to find new ways to grow, reinvent himself, and continue to push what is possible in the domains that interest him. And frankly, those domains keep increasing. I hope you are inspired to raise your bar, continue to reinvent yourself, and to have fun in the process. If you enjoy the conversation, please share it with a friend. I'd also like to thank Notion, Dialectics presenting partner. Patrick is actually on the board of Notion, and Notion is a collaborative workspace for your life's work. It is a tool you can use alone or with your collaborators to create documents and databases, collaborate across systems, and get leverage from the increasingly powerful AI and agent tools that so many people are so excited about. The great thing about Notion is that it plays a Switzerland role. It is friendly with every AI in that it integrates with whatever model, whatever tool, whatever agent you're using. Not only that, but models are available typically in notion the day they release. And you can tag any agent from within notion right in the document or tag Notion from within whatever other AI tool you might be using. You can even connect Notion to agents like Muse or Instinct and tell them to fill all the context, memory, or important information from your conversations into notion so that you have that context wherever you go. Even as all of the tools around you might change as capabilities are changing, you can trust notion as the single place that doesn't change and that will work with whatever tool you use next. You can learn more about Notion or check it out if you haven't in a while at notion.com/dialectic.

4:26 With that, here is my conversation with Patrick Sue. Patrick Sue, thank you for having me. Thank you for joining me on the podcast. >> It's a joy. Thanks, Jackson. Should be fun. We are in the botanical garden that is is also home. People if people could see the amount of plants that are in my vision right now, it would be amazing. You could >> I water them myself. >> You love life. What can we say? >> I want people to know this >> impressive work. You have been prolific and you have also been prodigious. I think you went to college at 15, PhD was finished in your early 20s. Most of us are undercalibrated on risk and on boldness. and I think particularly now as technology just makes like everything less capacity gated and more like will gated or even imagination gated.

5:12 >> you have a fervor and boldness that is really broadly impressive and especially suited for this time. I'm curious where you think that comes from. >> You know, I I I grew up the dumb son and so I always had to try really hard, right? And I think the the funny thing is I think absolutely I you know went to school young had accelerated education started working in a lab very early you know started running one very early and got very lucky to be part of important discoveries quite young but this all came after an entire childhood of feeling like I had to really work >> you know for everything I've got and things didn't come nearly as naturally to me and I think that kind that work ethic combined with this sense that you have to really push for what you want really shaped who I am today and that's the kind of thing that I try to teach my graduate students or postocs when we're trying to we train superstar scientists right that's our goal here and as long as we're aligned on the big picture vision how we get from A to B is going to be weird and murky research is 99% failure and so it's a very weird profession because there are so many incredibly talented people like if you took my my graduate class of you know kind of 15 biology PhD students none of them would have said that I'm the smartest I'm certainly not right but I think I was one of the hardest working right >> there are people who are incredibly hardworking who are maybe to oversimplify it sort of trapped in bounded games >> or they're climbing to the top of the corporate ladder or whatever. there there are plenty of ladders to climb and on some level of course you are climbing ladders or you have climbed ladders but there's also like an outside of the box boldness here whether it be on the explicit kind of specific research side or even something like ARC. and so clearly, yes, obviously you're very hardworking and very driven, but there's an element of, again, maybe that I don't know what the right word is, maybe boldness, maybe creativity. I'm curious where that part and and maybe when that part showed up.

7:32 >> I think I learned about the importance of infinite games pretty early on, right? I think school, like the reason I went and did a PhD was because I wanted to do something really hard. I had no other good reason. I just felt I wasn't done with learning. I wasn't done with research. I wanted to go from cosplaying being a scientist and working on parts of a research problem and learning how to actually take an idea end to end to you know at the time a paper and I think now you know a drug and your ambition increases and your vision increases over time but back then I just wanted to learn how to do science right and I I think things like alpha school are showing this really well where you know for just two hours a day you can get done all of your academics and if you give students the right cognitive scaffolding which is something that you know like AI assisted math training like math academy does really well right you can just speedrun a lot of things that normally take people many many years into something that's 10% of the time or less and there's no real magic trick about it this is something that anybody can do and so I think I was able to quite early on speedrun a lot of things that have a lot of incumbency and a lot of this is the way you're supposed to do things or you're slowed down by the kind of large batch size by the way that we do public education and it's something that was extremely personalized to me kind of being homeschooled until college and I think things like piano things like science you know are examples of kind of infinite games where no matter how good you are there's always another level, right? And you know the this is sort of the weird thing about research is all your very best ideas kind of suck except the one or two that you're remembered for, right? by definition almost everything you try will fail spectacularly. And so this sort of breeds kind of by necessity a humility and a criticality to take something, think about it super rigorously and figure how to poke holes at every angle of your idea so you can succeed and you know kind of get out of this kind of you know web of kind of no direction and actually find a way that you can actually ski downhill, right? yet at the same time that humility and wonder and kind of doggedness needs to be paired with this supreme confidence that somehow you can do it otherwise it just wouldn't be psychologically tenable to do this type of work in the first place and so I don't know this is like you know kind of you know deep insecurity plus sheer megalomania kind of have to melt together in weird complicated ways you see this in ML research you see this in biomedical research I So somehow we we try to kind of bring these two together at ARC. Yeah. So it's a it's a fun experiment at the institutional scale.

10:40 >> You do that you see that amongst entrepreneurs of course as well and so I I I don't think it's that too much of a surprise that you've blended a bunch of those worlds. >> You said you're remembered for kind of like one idea. >> I don't know. It's funny to imagine you've got a long career ahead of you, but it's funny to if you were to try to predict what you would be remembered for. I don't know that it would be so obvious. There's probably a more interesting way of asking it beyond just like how are you both prolific and really deep like how how do you have breadth and depth at the same time? But like >> I'm curious and especially given the sort of like scope of like I don't know at some altitude you're thinking about >> broad decade level change and arc and change on the institutional level and then you're also like we're actually going to do cell models in this very very specific way.

11:24 >> Is that just you're comfortable in both of those zones? Is that like a deliberate thing you've cultivated? How does that happen? >> So, in in some sense, I work three jobs. >> Yeah. >> Right. And you know, maybe part of the thing that I'm worried about is maybe I'm just going to be bad at all three. >> Sure. Yeah. Yeah. Yeah. >> But the way that I justify it to myself both kind of intellectually and emotionally is I think these are all different facets of trying to solve the same fundamental problem, right? Like ultimately if I try to zoom out and think what do I care about doing? It's really like deep scientific innovation combined with capital and a sense of product in order to make something in the real world, right? And my own career and life has been a really kind of, you know, put my hand on the elephant attempt to try to discover what that process is.

12:18 >> Yeah. >> How it happens and how to make it more repeatable in our lifetime. Right. And so in a way, we're very lucky to live through some sort of really unprecedented technological and hopefully productivity revolution. And so when when a rising tide lifts all boats, often times you just need to be in the right zip code, right? And I think I have been able to be in the right place at the right time at multiple points through, you know, the, you know, the gene editing revolution, right? the the AI revolution in many ways there's a a a sort of a capital revolution that's happening as well in how you actually power a lot of this type of innovation and so >> you're saying specifically in regard to science >> yeah absolutely right so let's say in the 1940s right the vast majority of R&D spend was from industry the you know NIH and a lot of our kind of post-war government funding apparatus became the dominant way by which we kind of funded incentivized kind of biomedical innovation in this country.

13:27 And then I think now today we seem to be in a place where that kind of 50-year sort of default is now historically weak and the sort of market share right of kind of corporate agenda and ideas and leadership of of innovation is now ascendant right and that seems to be a trend that's likely to only continue right Eli Liy for example is you know the largest bout company in the world in the It's valued about a trillion dollars. The, you know, GLP1 revenue is going to be something like $90 billion this year. And there's going to scale their internal R&D budget linearly or along with their GLP1 revenue, which they project over time will be half of the entire pharma industry. So half will be GLP1, the other half will be everything else.

14:23 >> Wow. Now this is interesting because Eli spends more money on R&D than the entire country of Germany. Not just the government of Germany but the aggregate national output of all German companies. >> Wow. >> Right. in in in in in health and bioarma. Right. And so in the by the early to mid 2030s they'll surpass the United States if GLP1 revenue continues to ramp the way that it has. sort of the power law we saw in software and technology happening in in pharma on some basic level or is that maybe that's an oversimplification >> the question is how repeatable this will be right GLP1 is really special because it's so horizontal right other medicines while you know really important generally work for a very specific disease right but targeting obesity seems to have target so many other coorbidities and co-pathologies everyone's trying to figure out what's the GLP1 on for for sleep, right? For vitality, for health span, you know, that's I think ultimately why everyone's so intrigued and interested and skeptical about peptides. Like, why are peptides such a thing? Why does everyone ask me about them at dinner, right? It's sort of this idea that, you know, modern medicine has failed us. The drug industry has failed us. It's given all these really expensive, really targeted drugs that work only for a very specific thing with crazy side effects. And for the first time, we found that there's something that feels magical, right?

15:56 Just the product feels magical. And we can see it, we can feel it. It's very tangible and visceral. It's not, you know, these weird pharma ads with happy people now able to stand up again and walk around, but you you see all of your own friends, your entire friend group or mine is kind of often is being shaped by this, right? And the idea that this could extend to, you know, other parts of, you know, you know, things that plague humans or could enhance humans, I think is in incredibly tantalizing. So this sort of proof of existence of biomedical product that can be for everybody combined with this revolution in you know all of the AI models for biology I think kind of is creating today a massive wave of excitement for what geniuses in a data center can do when they break outside of the data center. Right. Is the implication there that you think there could be more GLP1s? Not in the sense of more peptides for X, but more that category goes from one to N in terms of these sort of like interventions that are broadly useful across a whole suite of categories.

17:10 >> One of the funny things about innovation is it's very obvious post talk, right? And for you, I I was recently thinking about this, you know, like, you know, with a 4-minute mile, right? As soon as, you know, Roger Bannister ran faster than four minutes, so many people, like 10 people broke it within some very short period of time, right? As soon as, you know, we knew that, you know, you are able to make a nuclear bomb, right?

17:36 You know, other people are able to replicate it. And so, both in scientific discovery and in quantrading, right? oftentimes just knowing that there's alpha and that someone is making progress in this general area. You don't need to know a lot of specific details. That's sufficient, right? And this has come up recently in the discussion between the Frontier AI labs about who's actually solving the Millennium Price problems and you know kind of how do they learn about specific mathematical approaches you know kind of I'm not wellversed in the merits of you know that kind of academic credit discussion but I think the you know the fact that multiple people and multiple models are able to kind of converge on the same problem at the same time is something very kind deep in how discovery happens.

18:25 >> And I I think that's that's you know that that's something that's really interesting that just knowing that this is possible >> creates the existence proof that gives people and markets somehow the courage and changes something about the plumbing of the universe to make it possible. Right? And so I just think it is going to happen, >> right? because now people think it's possible. the prize is so large that it incentivizes a completely different way of thinking about how you create new types of medicines. how you design the experiments in the first place, how you run the clinical trials, right? Like a lot of companies are explicitly trying to attack this playbook of GLP-1 being tested first in diabetes, then obesity, then everybody. So now there's this blueprint of small disease where you can have very clear mechanism bigger disease than everybody. So you could go after narcolepsy, right? then you can go after sleep, then you can go after wakefulness and attention and neurotropics and enhancements, right?

19:38 And you know, how do you kind of chart that path across multiple other things? Yeah. >> Yeah. It's funny. There's whether you there's the there's a view that it's sort of like there's a time for an idea and then it shows up or it's as you say maybe the right word is just courage or belief but it's just it's amaz one of my favorite it's not exactly the same idea one of my favorite versions of this is >> I think it's like the fastest 100 meter dash >> it's like obviously Usain Bolt has the record and then like the next two guys I think are we're racing against him.

20:08 >> Yeah. Yeah. >> There's something something. So, I mean, that's that's pretty inspiring in terms of what could happen over the next 25 years. >> Absolutely. >> Yeah. >> What what a very open-ended question, but what makes a good biologist? >> I think I have a huge gripe with the way that biology is taught. >> it's incredibly boring. >> You're saying taught in high school. >> Yeah, taught in high school and college. the pedagogy of our field is terrible, right? And this is it's just not compelling information. It's you're constant you're memorizing all kinds of weird esoteric stuff. And a lot of it just doesn't feel tangible the way that you take it in class, right? And I got very lucky because I started working in a research lab in high school at a university and I learned by doing.

21:07 Right. >> So back that wasn't totally luck. How did that how did that happen? >> I emailed so when I was 13 I got expelled from college. >> Expelled from college. >> we moved down to Palo Alto. >> So you were already in college. >> well not anymore. >> Wait. I thought you started college at 15. >> that was the that was round two. That was that was round two. Yeah. Yeah. >> Of course. >> I was going to say cuz I I think I read somewhere your brother graduated at 12 and so you were >> >> maybe that's incorrect.

21:39 >> he he graduated 15 and then started he started his PhD at Stanford when he was like but point being you actually were on a similar track and then you got expelled. I I I was homeschooled on a mountain with internet, you know. I was just I didn't have a calibrated sense of what normal people do. Yeah. >> Why did you get expelled? >> I was hacking my professor's email. >> Okay. Okay. >> Yeah. >> Okay. So, you got expelled and then you were looking for >> I thought the world was ending at the time. Damn. I'm I'm kind of proud.

22:11 >> Different all kinds of 13-year-olds have different problems, I suppose. >> Totally. Totally. >> Okay. cuz you got expelled and you're like, I need something else to do. I'm going to email a bio like maybe to ask it a different way. >> if we are bad at teaching biology, what clicked for you? What picked up for you that was like biology is actually interesting. I do want to go work in a lab. >> My So I don't have any doctors or professors in my family and I didn't, you know, know that much about biology.

22:36 But when I was 11, my grandfather got Alzheimer's and he was living with us at the time and that was deeply moving for me and >> it created you know like it the idea of decline and death became extremely visible and tangible for me in a way that you know normally you live hopefully a happy and healthy life surrounded by other happy and healthy people. There's sort of this saying, you know, and an an a healthy man has 99 problems and an unhealthy one only has one, right? Sort of kind of becomes like that.

23:08 >> And that that's sort of what made the problem feel relatable to me as an 11, 12, 13year-old. and kind of, you know, >> got me into into the lab. Yeah. And I've never stopped. I've never stopped since. Yeah. >> Okay. So, we we aren't great at teaching biology. you get into a lab and you get a relationship to it that makes it way more >> exciting to Yeah. It's project based learning where you're actually solving a real problem. That's really what was intoxicating for me. It wasn't just following some syllabus of book learning, but it was real research, right? When so I I did high school science fair until ISF. Now Regeneron sponsors it.

23:53 Regeneron ISF. and I got selected at Intel ISF for this experience where you got to meet Nobel laureates. Cool, >> right? And it was like you know Dudley Hersbach who won the Nobel in chemistry and you know kind of Richard Roberts and you know a whole bunch of other >> epic legends, right? And so I I was like wow I'm going to meet these science gods, right? And I remember telling them about my project very kind of enthusiastically and went behind the ears and telling him, you know, about one of the research problems I was trying to solve and trying to get his advice. And he kind of looked at me and he's like asked me a question. He's like, "What do you think?" And that was crazy. That was kind of deeply moving for me because kind of made me realize intuitively in that moment he had no idea, right, that like this like science god knew as much or well probably in retrospect much less >> yes >> than I did about this problem. And I was like research is an even playing field kind of by definition. No matter how great or accomplished you are, we all have the same shot. And that that made me feel so motivated and yeah >> there's sort of open territory on the map.

25:18 >> Exactly. And actually most things in the world are like this like the reason why you're driven to found something right. It's fundamentally an act of desperation of why has the universe you know not created this thing that is so obvious to me that this should happen. But that's just actually how cominatorics work, right? Is most things in the world haven't happened. And so the kind of act of research creation or the act of founding something tap into this this this the same kind of energy this sort of will of existence.

25:51 >> Yes. It's very easy for us to underrate them when they're in unbounded space. Yeah. >> We can't really imagine. >> I guess that speaks to like the interest in biology and and the opportunity. What I'll ask again though like what makes a good or what has made you a good biologist? >> So sorry I I'll actually answer the question. I think there are a few primitives. The first is you need analytical depth, >> right? that I think that's maybe the most important one is you need to be able to think very clearly and logically and objectively and harness your mind and your attention to detail and your sharpness around pattern matching and knowing when something looks off pattern, right?

26:40 I think that it becomes something that you train to the point it becomes deeply intuitive right so like doing biology research is very different from you know math or physics. I was going to say is not knowing much and certainly not being a scientist that sort of sounds like something that would be useful across science. You think this is very distinct or not only >> the way that you deploy it in biology is different because it's fundamentally a nonquantifiable or nonquant not wellquantified type of sort of sport, right?

27:11 There are very like biotechnologies have been invented ground up to try to make sense of these weird miniature kind of atomic scale structures and dynamics and interactions that create life and you know back to you know kind of Robert Hook pulling glass right or you know folks trying to figure out how to image these microscopic microorganisms how to extract genetic information from them and amplify them exponentially.

27:46 you know, to, you know, create these programmable molecules that can go inside of complex cell environments and turn it into a digital code so we can manipulate them in living cells. Like biotechnology is weird and crazy. And so we kind of try to compress deep complexity and dynamics into something we can measure. And that's very that's like very lossy, very lowdimensional characterization of something that's very complex. And I think one thing that I'm super bullish about is just the increasing scale of what we're able to measure and kind of have really kind of high content understanding and multiple kind of multiparameter measurements of what biology is to start to look at it more holistically rather than compressing it into kind of individual kind of vectors so to speak. which is what we've been doing for most of the last 50 years of biology and you know so I think you need that taste that comes from being able to have this intuitive model of what the data is telling you about a system.

28:55 >> Yes. >> And have the analytical clarity to be able to navigate in and around and out of this maze. And you need to be extremely resilient. you have to be really psychologically it's like irrational to in the face of just constant failure still think you can make it work. You need persistence, you need analytical depth, you need conceptual creativity and I think some sense of style. Yeah.

29:26 >> Yeah. >> Yeah. >> A few a bunch of questions actually on that. >> one is in this world where sort of one of the meta skills here is identifying like breaks in the pattern you know in the world we're going in biology in a lot of large part thanks to work you have been a part of we're getting much more holistic models much more we just way more data way broader tooling do you think that skill becomes less important in that world >> which skill >> the this sort of like intuition anti-pattern >> I really struggle between the modes of being long human and long agi >> >> And so I feel kind of insecure. Whatever I say now is going to become wildly dated, you know, a decade.

30:12 >> Maybe maybe a better way of asking would just be like, what have you seen the last two years? Like is is that skill persisting? Is it moving to a different part of part of the maze? Like is it becoming more important? Is it becoming like wow actually a lot of this a lot of the things that maybe previously would have required a certain research t or intuition are more evident in the model now. I would say research taste and intuition is only is only increasing in importance in relative importance like I would buy call options on it if I could right in terms of how important it's going to become >> it's just biology is so highdimensional and the models today need so much inductive bias and so we just we need a lot more kind of creative architecture and data I I so if you take something like math right where we're able to solve millennium prize problems today right we have this verifiable problem and we can so such to the point where we can unleash like 10,000 agents to simultaneously try to solve a problem from every possible angle. This is not possible in biology today. First of all we we can't token max in this way because the agents fundamentally don't have enough context. They don't have the right eval. They don't know enough. So like I think biology over the next decade is going to be a discipline of context maxing.

31:37 >> Is that just a scale problem or you think that doesn't go away if you had 10x the scale? >> I think scaling naively will be really inefficient but it will work. >> Okay. >> Which is I guess broadly been true in language models too. >> Yeah definitely. Yeah. I mean there's so much work on data efficient learning and you know continual learning and all of that that may pay dividends over time but we just have such crazy unprecedented scale of data in in text and image modeling that we just don't have in biology and so there's a huge amount of interest in trying to figure out what is the demand for biological data what data type should we be creating what is the scale or merore surge for biodata actually look like you know and you know wearing my investor hat we think a lot about the demand and versus supply side of that equation. But, you know, fundamentally, I think it's obvious that we all just need more and that we kind of need to build complexity up, right? So, like there's a lot of interest in talking about digital twins, right? And predicting drug toxicity, right? But for me, in many ways, I I worry, you know, that starts to only work when you have something that's a whole complete world model of biology, >> whatever that means. Yeah. Right. And if you can't even model what happens in a single cell, how can you model multisellular systems and then kind of organismal kind of kind of you know level things like whether or not this drug is going to be toxic, right? You need to be able to model every organ and how they respond and interact across them. And so it will I'm I'm very AGI pill on one hand but I think we just we need to figure out how to create the right eval right context for these models to be able to kind of work on these kind of you know semi-verifiable problems. It sounds like you're saying there's while there's certainly a data problem like if we had whatever 10 100 thousandx the current data sets which I know you guys are also working on >> it would be a lot better and maybe there's something else here that is true in biology that doesn't perfectly analogize from language or math around evals. I almost wonder if in some level this is this is probably a poor analogy but like >> I I keep talking with friends about like it's just amazing like I don't know Astra can do Blender and all these things and yet it still can't write well. Part of that might just be like there's not a huge economic incentive to write well, but maybe there's something else about the verifiability of grid writing. And maybe there's something else here. Or put a different way like do you think if you just had a thousandx the data sets like we would make the same progress we've seen in AI broadly over the last few years?

34:19 >> I would say yes and no depending on the type of modeling objective that you have. So for example, if you ask a machine learning person, what type of biological data do you like desperately want? Right? They'll generally one of the kind of major categories would be time series data. >> Okay. >> Right. And biology does not tend to have great time series data. >> Right. >> Right. We'll have time points. >> Right. We can take a time point at 24 hours, at 48 hours, or if it's a patient, right? You can kind of look before and after. But you know, you're not able to wearables are one example of being able to track really low content things with really high resolution like heart rate or skin conductance or glucose or whatever. But it's sort of obvious in some future we want really multipplexed multi-analyte continuous measurement hormones dopamine serotonin cortisol you know you know testosterone progesterone all of those things you would want to continuously monitor. similarly in genomics you want to be able to measure gene expression profiles or protein expression profiles at the single cell resolution across time. Our current technologies don't do that. We're looking at cell sets and averages.

35:38 >> There's so much lower fidelity. >> Exactly. Exactly. And so there's like a fundamental need for biotechnology invention >> that creates that shape of data to train that type of model. >> Right. Right. Right. And is that a hardware problem? Is that a like there like biological understanding? Like what is the sort of shape of that type of technology? >> I'm not sure I'm sure it's a super super wide set but >> so we have a number of lines in the water at ARC to try to invent those capabilities right other there are a lot of other brilliant labs and investigators who are trying to do some similar some distinct approaches technologically. and I think you know so for example one of our brilliant faculty Felix Horns at ARC who's also a faculty at Stanford has invented these these nanoparticles that can be buted out of cells right and you can engineer them to basically take a a a hand if you will and grab RNAs from inside that cell and then bud them out of the cell.

36:44 >> These are >> artificial and these are biological. >> They're they're they're engineered proteins. Wow. >> Right. That create a nano cage. >> Okay. >> And sort of export them from the cell. So it's sort of like sending out a FedEx package, you know, every few hours, right? >> just sorry, a random aside. I I suspect you and biologist broadly have to get really good at analogies. >> Yeah. No, teaching teaching definitely involves trying to make things visceral and relatable.

37:13 >> Yeah. And so yeah, we're like you have to create this FedEx sort of center where you know the house becomes you know the place where you kind of want the things that you want to measure. You ship them out and >> current technologies involve destroying the cell >> harvesting its contents and then that cell's dead afterwards. So you don't know what happens next because it's dead. You can only know what happens next to other cells that were similar.

37:38 >> Right? >> Got it. But if you want to know what happened in that exact cell, right, you need something that's noninvasive, right? And doesn't kill it. And so this type of exporter technology can allow you to take non-invasive multiple time points with deterministic single cell resolution, right? And so that's really why we felt something like ARC needed to exist. No individual research lab or company really has both the ability and the time scale to work across the invention bucket, the engineering and then the scaling bucket. So on one hand we're scaling an internet scale data set of perturbed single cells and I've are building the largest sort of repository of this in the entire industry. Right?

38:23 Yet on the same on the other hand, we also need to invent the technologies to create a fundamentally new way of measuring this because we understand the fundamental limitations of what you can do with scaling perturb today. >> On top of all that, we want to train the models that allow us to capitalize on that data. So you just need something deeply multi-disiplinary across, you know, biology, machine learning, the engineering that you need to bring all these things together. That's that's quite rare. It's sort of funny because I think today it feels like an obvious model and you know >> the the arc model.

39:02 >> Yeah. Yeah. But you know when we started ARC there is so much deep skepticism that it could possibly work. Yeah. Do you think how much of that last statement was on some level being right around the timing or fortunate with the timing of AI broadly and ML broadly? You know, part of our initial thesis of ARC was that we needed much more computation and much more scale in biology.

39:32 And we also had a sort of an institutional bet that we needed to have our own institute that was still deeply partnered with universities because they have sort of like the lifeblood of innovation in biomedical research. But these organizations are large sprawling bureaucracies that and the current grant funding system doesn't really unlock our best investigators to do their best work. We put those things together and you know I think but like find figuring out the right cultural ingredients and talking about it and writing blog posts about it is very different from actually operating right in real life to actually do the thing right and we're still very day one at ARC but I think it's you know we always said that the long-term success of ARC is that there are many more things like it and I think we kind of are are seeing that across both our kind of earlier fast grants work on rapid science funding the ARC model there's a lot of interest in propagating this model forward which I think is kind of the ultimate compliment yeah >> and the simplest articulation of that being and by that I mean like more arcs being this sort of multi-disiplinary angle on it the combination of scale and computation engineering like what is the what is the argininess that specifically if you were I don't know and maybe there have been over the last 5 years like new things that are cropping up and you're like okay we have evidence like what is the sort of atomic unit of that >> I think this idea that you need something that's kind of full stack in being multi-disiplinary I see >> yeah and you see other efforts that have very similar ingredients that you know like periodic labs for example is trying to really close the loop between I I mean machine learning and material science kind of in in sort of material design and innovation making things like room temperature semiconductors or you know new semiaterials and you know I think that's an example of something that I think we have the right ingredients to make many more periodics or arcs today and I'm I'm I'm so like deeply excited for you know seeing more of those. Yeah, >> it doesn't seem super obvious. I mean there's I can think of a bunch of reasons why academia it would struggle on various dimensions of this but the multi-disiplinary part of it doesn't seem so obvious to me like in some sense it seems like >> I don't know Stanford or whatever it would be like a great place for multi-disiplinary things to happen what what is happening structurally that is causing these things to be so siloed >> top down Stanford in principle >> like has all of these departments right they have a computer science department they have a biochemistry department etc etc they have all the capital equipment all the human talent, right? But it's first of all spread across an entire campus and multiple parts of a campus and the sort of individual atomic unit of research which are individual research groups are incentivized to generally do their own thing right and of course there are interdisiplinary labs but they'll usually be able to work across one domain boundary right but if you have to do three or four >> that's you know kind of exponentially declines so I'll give you an example right we spent the 5 years at ARC building out this really scalable platform for making brain organoids where you can take human embriionic stem cells and turn them into the three fundamental cell types of the brain. Neurons, ashites and microglea.

43:09 We can grow them in 2D and in 3D to make these mini brain models. There are entire labs dedicated just to making these mini brain models which is sort of like cell biology and neuroscience and developmental biology. Right? And so that's sort of like three different fields that come together to make this possible. Now we kind of add on top of this the sort of ability to do functional genomics, right? Where we kind of have state-of-the-art methods for doing genetic perturbations in these gene editing them, being able to put in different genetic risk mutations from Alzheimer's because we're trying to model Alzheimer's and then do this at unprecedented scale where people generally make a few organoids at a time. we're able to make thousands, >> right? And but then, you know, just doing that alone is very slow. And so we're now bringing in kind of like frontier lab automation, right, where we take humanoid arms, right, that are able to orchestrate the workflows. and so there are sort of two different types of lab robots today. They're like box robots, similar to, you know, clea like CO diagnostics, right? They're really brittle and very scalable for one specific workflow. But most biology research, you have to chain together like 20 different primitives in order to, you know, do an endto-end complicated project. And the lab automation has sucked for decades.

44:35 People have wanted this to create these cloud labs or whatever, but they just don't work. This is starting to work now with you know kind of you know aentic physical intelligence and >> it's really like you're actually you have that in the lab now. >> We're we're we're we're deploying these in the lab to automate >> all aspects of this work the cell culture the genomics you know the the sort of the kind of genetic perturbations the profiling the sequencing really kind of making these really kind of endto-end data pipelines.

45:08 And even if you're a top lab that does the functional genomics and the cell biology, you won't have the machine learning part. And if you have the machine learning research part, you won't have the engineering and infrastructure. you know, you know, ability to do distributed node pre-training, right? The ability to do RL, you know, all of those things have to come together, right? >> It sounds like you're describing some of the ways that ARC works as being more like a company. and again, maybe that's just like you're more flexible, more dynamic, whatever. I'm curious if that's broadly true. and then second, I guess, what is why are you partnered with I think it's UCSF, Berkeley, and Stanford. What is the reason for the academic kind of collaboration and how does that how do those two things kind of work together?

46:01 one of the really interesting thing things about companies and biotech in my experience anyway is they tend to get frozen in time relative to the time of their founding. Right? So they'll often have the right to be founded because they come on the back of some technological innovation or inflection point where you've invented some new way of making a molecular glue or whatever and you start the company and then five, six, seven years later passes and that's your platform. But other people have invented a whole bunch of other things that can be more faster, more scalable about all companies.

46:41 >> This is true for all companies. >> Some reinvent themselves. Right. Right. >> Yeah. But yeah, and I think you in in at least in biology, right, the way that the system is set up is the kind of basic science labs have to are sort of at the frontier of pushing for innovation, trying really crazy new stuff that are in the invention bucket. They're not in the engineering or scaling bucket, which is what industry really needs to specialize at, right?

47:13 And so we're in many ways sort of in the first third of the equation of trying to really be at the fountain of discovery and innovation and then bring them from the invention to the scaling bucket. Right? >> So it's sort of that if if academia only has the first thing you've sort of like clipped off a little bit of the the the next phase and integrated it at a kind of like foundational level that makes it easier to go into. Is that a >> that's our hope. That's our hope. Right.

47:39 Yeah. And then our hope and belief is that this will be an Edison lab for many companies over time. I see. Right. On the basis of technology or insights that are much more well validated and ready to spin out at the right time. Yeah. And then the integration with the universities is it just sort of like you you still want to be adjacent to either the pedigree or the talent or the inventiveness or >> in many ways it's talent capture.

48:11 >> Okay. >> Right. I think the in our in our industry there is this it's sort of this annually renewing resource for some of the best global talent and it has insane pull >> into this like like you sort of zoom out and say what makes the Bay Area great right like why is it the birthplace of the AI revolution >> right it's because all the AI PhDs are here >> yes >> right and they they they trained here and you can combine the AI with the engineering to actually make it click right and I think for bio medicine it's also very similar and we're combining both of those so then that's why we can also only be here and so it's the the the innovation and the kind of interdisciplinary reach across all of these domains that the universities have had historically and then putting all of those ingredients into this sort of you new organization that's able to, you know, really kind of operationally unlock all these people to work on integrative problems together. Like we ultimately have two flagship problems.

49:23 We're not trying to solve 50 problems, right? We're trying to find drug targets for Alzheimer's. We're trying to make these virtual cell models. >> Has that been the case since the very beginning? >> We I mean, we started ARC the labs came online in July 22, right? you know, we sort of were working on it for a year and a half, two years before that. So, this is pre-C ChachiBT certainly. >> we certainly believe computation would happen, but we didn't predict that, you know, I mean, I don't think anyone really predicted the AI revolution in quite this way.

49:53 >> And, you know, so I think we were again at the right place at the right time with the right intuitions and the right ingredients, but then you have to try to meet the moment. >> Yeah. I'd like to take a brief break from the conversation with Patrick and tell you a bit about Gemini 4 Argon, which recently launched. This episode is also supported by Google, and I got early access to Argon to take a spin on things. With Gemini 4 Argon, Google focused on long horizon work. Teams inside of Google are daily driving it for massive code migrations and quantum algorithmic optimization. I used Argon inside of anti-gravity, Google's agentic coding platform, and fed it all 60 dialectic transcripts every single episode, over a million words, and asked it to think about what we might do together. We landed on what Argon called a dialectic compendium, essentially a book that is interactive that you can go through and highlights various themes that run across a bunch of the episodes.

50:50 And it also includes a dossier section on every individual episode highlighting kind of a core model and some core ideas from each person. There's also a 3D interactive book on the web that highlights some of Argon's 3D abilities and allows you to turn page by page through this compendium. You can skim through and ultimately find an episode that you're excited to go deeper on and to listen to. Instead of summarizing episodes one by one, Argon synthesized across all 60 of them, tracing how ideas from early episodes connect to ideas and later ones. Taste is an overdone subject sometimes, but one idea that came up across guests was how we can borrow taste from those around us. I talked about this with Tamar Winter, Charles Bascowski, Tyler Cowan, Niel Keshi. I also had Argon go through over 300,000 of my words and create a mirror for me that highlights the ways that my questions have evolved and changed over the course of 60 episodes. I will link to the compendium in the description if you want to check it out. I hope you enjoy sifting through these ideas and more importantly, I hope you give Gemini for Argon a try for yourself. It is available in the Gemini app, anti-gravity, AI studio, and the API.

51:54 Please send me what you make with it. Thank you again to Google for supporting Dialectic. Now, back to the conversation. You mentioned talent. There's a couple of excerpts from I think it's the very first ARC blog post. First you say nearly 80% of fast grants which is what you guys had been working on in COVID to kind of bootstrap some some quick not solutions but quick progress and and be a little less bureaucratic let's say nearly 80% of fast grants recipients said they would change their research program quote a lot if all of their existing funding could be spent in an unconstrained fashioned and then later the funding for these labs which we envision will grow to 10 to 20 people each is completely flexible because we are betting on people rather than specific project ideas. By preunding our scientists, we hope to avoid the Gordon KN of incentives that currently entangle grant funding success single author credit high impact publications.

52:47 Obviously, this is a really interesting frame. What makes for a good sort of like entrepreneurial scientist? It seems to me that that is like one of the most for for the sort of people certainly leading arc labs but maybe everyone there there's like an element of this entrepreneurialism that might not be obviously there in the broader academia and I'm curious how you guys have sort of selected for that especially if you're going to trust somebody for an 8-year term to work on whatever they want. I suppose >> I think there's an aspect of your question which is how do you select a good founder?

53:21 >> Yes. >> Right. And in a way you expect these top scientists to be founders of what hopefully becomes a field right or you know fundamentally new way of thinking right and so they you need multiple qualities beyond like everyone is very technical right and you know spectacularly technical but you need so many other qualities beyond that right you need to be able to convince and coax and you know give good talks you know, like recruit people. you know, you need to be not just the CEO or the chief innovation officer, but you also be the chief scientific officer, the chief marketing officer. You know, you need, this is also a business in a way. You need to be able to, you know, against your topline revenue decide how you're investing your resources into different >> You have solved some of that what you're saying. You're going to have we're going to we're going to solve the fundraising for you for a while, but even so, you still have to allocate. you still have to do BD. You still have to figure out how to bring, you know, you know, your research program together with others to make something that's a much bigger collaboration than what you can do alone. I think you kind of try to distill the those very similar primitives into investigator selection.

54:38 Yeah. >> Right. I think end of the day, you also just like there needs to be a sense of style in the work, right? that the world with the innovations that these people create will I think just like fundamentally enrich science in a different way. Yeah. And I don't know if we've done a perfect job at this but that's at least been our our hope. Yeah. You mentioned the word style just now and also earlier. there was a comment you made somewhere on some podcast about grad school being one of the few places where you still have this kind of traditional master and apprentice model >> as maybe the rest of education has gotten very scaled.

55:20 I'm curious how specifically you benefited from that under Fong or otherwise and how you guys try to implement that at ARC. Maybe specifically in this direction of like style or research taste or creativity or whatever you want to call it. >> I think one of the most important things I learned from my PhD adviser was how to simplify. Biology is really complicated and most projects and teams get mired in endless complexity, right? There's just too many variables. It's hard to decide how to prioritize. You can talk yourself out of any idea, right? It's very easy to be too smart, right? And just he had such a specific way. This is fun or this is >> fun has such a like such a unique talent in distilling right and I I got to learn and see and do right over that period of time and I think that that was something that I feel really grateful to have studied under him and and learned and try to implement in our own group and at the at the arcwide level as well right like the concept of finding drug targets for Alzheimer's right is sounds in a way enormously complicated, right? And maybe it is, right? But we're we have like a very specific scientific strategy by which we're trying to causally map all of the human genetic risk mutations and map all of those mutations to pathways to try to mechanistically understand the initiating steps of the disease to then find those drug targets, right? as opposed to the traditional way to do it which has been record what's going wrong in these old patient brains. They have high amaloid, they have high tao. Let's drug those things. We're trying to go back to the mechanism. I think mechanistic understanding of disease is really going to be our most fundamental way out of this crazy maze. Yeah, >> it's a little more maybe like top down like coming over the top with a sort of a hypothesis or a theory of things rather than just being purely observational or is that am I am I misunderstanding?

57:37 >> Yeah, we're we're big believers in perturbation, right? Most biotechnologies are observational. You image something, you sequence something, you're just trying to figure out what's happening, right? >> it's you're just kind of taking pictures. >> Yeah. Right. So, there's a very interesting company called General Intuition that's building world models, right? And it's trained on a supreme amount of data. >> This pim's company. Okay. Funnily enough, they spun out of metal. This gaming >> Exactly. Exactly. It was gaming videos where they would do screen recording of you know like kind of pro gamers.

58:11 and they would they would have a key logger where they could record every sort of key press. So they would be able to map the action space into the frames, right? So they can go frames in, actions out, actions in, frames out, right? And the really amazing thing is they can take real videos, just normal videos like in real life >> and then they can pass it through this model that's frames in, actions out, and then create an action space that would have created this video.

58:40 >> Oh my gosh. if you had been playing it as a video game even though it's a real life video, right? And you can use this to control a drone, a robot, you know, something dextrous. I think it's actually starts to get really trippy when you have that ability to have those labels, >> which you wouldn't if it like it would be much more inefficient to train such a model if you didn't have the labeled actions, right? And so in many ways perturbational data in biology is having the labeled actions for what happens before and after as opposed to the majority of observational data that we have today. That's been many ways our fundamental bet across our institute initiatives.

59:19 >> I see. I see. how does that frame connect to sort of I mean the two at least as I understand it a lot of the work is either models or interventions. Can we get way better data and thus build way better models and then what can what can we do on the intervention side? On the model side, the two big things that at least you guys have talked a lot publicly about are the EVO models and the cell models.

59:43 >> >> is that perturbation perturbation sort of frame inside of both of those? Is that I I I think I've heard you specifically talk about it on the cell model side, but is that the sort of like working frame for how you do all of this work? >> In a way, yes. I mean, obviously EVO's trained or the current publicly released EVO models are trained on, you know, evolutionary distributions of DNA. >> And maybe it's worth saying EVO's the EVO 2 is the biggest biomod basically ever created >> for now.

60:17 >> For now, >> they're only getting bigger. >> Bio foundation model maybe is the right frame, right? >> Yes. And we're training bigger ones as are others. And you know the goal is not to just have DNA language models alone, right? They need to be combined with these cell models because you know you learn in high school a genotype and environment create phenotype, right? And so models can't be of cells alone or cell states alone without understanding their genotype. For example, we know different cancer driver mutations will create cancers in different organs, right? the mutations that create pancreatic cancer are different from the ones that create breast cancer even though both cell types have the same genome, right? And so cell state is mediated in part by inputs from the environment but then also inputs from the genotype. And so the kind of the these model paradigms need to be brought together in order to create you know people talk a lot about like you know these use these AI analogies in biology.

61:15 There's lots of talk about world models of biology, right? What the hell does that mean? >> I think for me, end of the day, if you think about it from a molecular or genomics point of view, you need something that goes from sequence to function to state, right? >> in order to predict, you know, how cells will respond. >> Sequence mean genomic >> genomic sequence, right? to how the sequence gets interpreted epigenetically to create RNA and protein dynamics, right? Which then creates some sort of functional consequence, right? Like ultimately you're not just trying to predict a genomic profile of a cell, but also if this cell is going to be more effective at killing this tumor, right?

62:00 If it's going to become you need a testing environment. >> Exactly. Exactly. So the lab you know is the place where you can do active learning right where the model will make predictions zero shot you can test those in the lab feed them back into the model and then improve the model's predictions in multi- round loops right and so >> sorry just to forgive my ignorance here this is the evo style genome models or cell models >> so so I'm talking about this sort of general paradigm of lab in the loop right that will work both for genome models and for cell models and then for the jointly modeled space y >> right and we need to do both right and you know simil this this this >> this extends all the way to clinical perturbational information right like people are training data on electronic health records right where you have you know like you know someone took a drug and then you have their EHR before and after right and then people try to use this to create like patient embeddings and patient clinical trajectories and try to use this to predict you know like what you should be doing for certain kind of patient classes. this is like very very coarse right and I think gathering the right type of clinical perturbations with kind of genomic resolution information is going to be one of the most important outcomes for really like a new type of medicine or health maxing.

63:28 Is there a threshold you sort of have in mind for cell models that is sort of that either has been crossed or that is in the future that is sort of like allows them to be a good enough proxy for some level of like very material progress. >> Yeah. Yeah. So in 2009 I think Shini Yamanaka won the Nobel Prize for discovering the reprogramming factors which have been very popular lately for epigenetic reprogramming in longevity right and he basically discovered when you turn on four transcription factors you can take any cell type in the body and turn it into a stem cell right and that stem cell is universal and can be turned into any other cell type in the body right and that's you know traversing this sort of Wington landscape was so powerful because you can do like epigenetic reset or a cell identity reset, right? And folks have been trying to figure out how you can use this in aged cells to make them, you know, kind of transcriptionally or functionally younger, right? And you know that was a that would be like an eval a Nobel Prize winning eval from you know 15 years ago, right? Can a virtual cell model rediscover Yamanaka's reprogramming factors? Right.

64:48 >> I see >> that would be one really tangible example that's >> eval on ground truth. >> Yep. >> Right. And >> the question is if you can find this such important discovery from you know a decade and a half ago then what are the prospective ones that you can make moving forward? You need to be able to recapitulate like the most important discoveries of human labeled biology in order to trust the machine ones. >> Yeah.

65:14 >> Yeah. It's not I mean again probably oversimplifying but that that sounds actually a lot like a lot of the eval stuff in AI broadly. What is the what is the current state of the sort of cell models now and and maybe what is the rate limiter on progress there? Is it is that also largely a data problem? Is that just this kind of broad complexity of scale and compute and a million other things? Is it I mean I know you guys have this I think it's like a billion or at least on on track to a billion perturbed cells >> but like what is the what is the right way to think about progress there?

65:43 >> It's a good question and I think the short answer is it's all of the above. Okay. >> Right. It's data, it's model architecture, it's and and it's eval, right? And all three need to kind of co-evolve together to bring us to really pragmatic benchmarks like being able to simulate in a computer all perturbed seek data, right? That's sort of our explicit goal for the models that ARC is building at least in this current phase of our our kind of virtual cell modeling work, right? and we've created this virtual cell challenge that's sort of like a CASP or Kaggle like competition where I think this this is the second year we've done it over a thousand teams have submitted their own virtual cell models right and this is a way bigger community than just this very small and narrow biio community right there are all these blogs that have tried to translate the bio stuff into something that engineers can understand so they're just sort engineers at tech companies all over the world who are trying to you know train and test their own virtual cell models against kind of internal eval data that we've generated in-house in our labs so there's like no hacking of it for example >> and sorry what how do you how do you win that competition what is it what is it it's it's just about the overall quality of the model >> yeah so we have we have sort of a variety of different evals that try to score the model's ability to make predictions for how cell will respond after perturbation, right? In quantitative metrics and you know it's a public leaderboard. It's at virtual cellchallenge.org. But our goal was, you know, to ask the question of how well do virtual cell models work, you know, where how quickly are they progressing, not just in our labs, but this is an industry-wide problem and kind of a holy grail in the field that we should have this public open and kind of transparent evaluation of all of the models that everyone's trying. So it's not just overfitted on the two or three specific evals that they're testing in their paper, but it's, you know, across a variety of metrics and it's all like, you know, out in the open.

67:57 >> What's the right way to think about how you guys use and benefit from sort of state-of-the-art general AI models in in this process and like what the lines are? A super unsophisticated view might say like the learning from the bitter lesson and a bunch of the scaling stuff is actually just like whatever. GPT9 is just going to be the best at everything including bio. Like from everything I can sort of loosely understand that seems like decidedly not the case. But like what is the role beyond just speeding up workflow and everything else?

68:26 >> So periodic you know recently released their own kind of open- source mid-train models on their own internally generated lab data for material science stuff. Okay. >> Right. And then they compared it to Astra, right? And they were basically able to show that, you know, their their their own custom model is state-of-the-art and has half the inference cost of Astra, but Astra sort of kind of max thinking is just below >> their, you know, kind of, you know, custom data specific model, right? And I think that's that's that's itself very interesting, right? And so the short answer is I don't know is are all kind of domain specific or data specific models going to just become tool calls for these sort of you know kind of super agents or do you have to take that data and add it into training these big models across a variety of different domains that's playing out in in real time right but I think we're >> kind of agnostic to that future since the end of the day I care a lot about the the capability existing so that we can >> make real progress.

69:42 >> Yeah. Make real progress on science but then also on the products that will become unlocked in the tech tree of creating that scientific kind of product or or or discovery. Yeah. you you have this comment somewhere where you said I think it was maybe with with the Sequoia folks on our podcast getting to the point where a model that makes a cell biologist feel emotion. >> Can you say more about what that means? >> Yeah, I think take these Yamanaka factors for example.

70:14 You can physically and and like I have done these experiments myself with my own hands, right? You can take cells from an animal or from yourself, right? You can take your own like fibroblasts and turn them into induced plur potent stem cells in a dish. It is one of the most kind of profound and magical and semi-religious feelings I've ever had, right? The idea that a model could like rediscover this and then find things beyond it, right? I think are is sort of when you start to, you know, in this in the same reason where like when when you're watching progressions of image or videogen models from, you know, from Runway or Genie or you know, World Labs or whatever, they're just making these every generation these the outputs of these things gets crazier and crazier. You have all these holy moments.

71:04 >> How do we have those holy moments in biology? >> Do you have many of those currently? >> Do we have many of those currently? well, we've we've created them. We've created some of them. I think we recently have had an had a a paper from one of our kind of ARC and Stanford colleagues designing entire organisms, right? And sort of the first time that AI has been used to create entire viable organisms, right? And I think the idea of being able to do this not just for really small genomes but for you know things like mamleon or ukareotic genomes over time is this like a really fundamentally new paradigm for designing biology rather than just observing it. Yeah. one of the sort of tenets at least of your lab at ARC and obviously so much of the work you did very early in your career is on what you would call programmable biology in that vein. I remember first hearing about crisper whatever it was about a decade ago. There's been a lot of progress in the broader programmable biology side of things. And then there's also maybe part of what you were I think maybe just gesturing at is also like synthetic human biology. Mhm.

72:20 >> I love just like an update on kind of your thinking on programmable biology in terms of where we sit now in late 2026. and then >> how the sort of like recent progress on the technology side has ramped that to whether it's informed by the the the model stuff or otherwise like >> Yeah. Yeah. Yeah, >> cuz I think that when people think about sci-fi and biology, like this feels like the the kind of the big thing and and obviously there's been a tremendous amount of progress in the last decade on on the crisper side and RNA and otherwise, but like >> it doesn't necessarily at the very least doesn't seem to be coming to me as a lay person in this like very vivid way.

73:06 Yeah, I think first of all taking these machines and putting them in a human body has been really hard. This is sort of the the delivery problem. Got it. >> In gene and cell therapy. but there's also been a design problem fundamentally which is these tools or the kind of first generation of these kind of the kind of crisper revolution has been around fixing individual broken genes right that lead to a monogenic disease like cickle cell anemia cystic fibrosis musculardrophe and so on and so so that that's sort of like one problem where you've had to create a crisper product for every different genetic mutation.

73:52 >> I see. >> Right. And so you're making very small precise targeted changes, but end of the day like you couldn't program things. >> Doesn't feel like GLP ones maybe an hour, >> right? You want to you want to make much larger scale changes. >> Yeah. And so a lot of the work that we've been doing in my lab lately has been developing large scale genome editors beyond crisper known as bridge RNAs and bridge recominases that allow us to change up to a million bases in the genome at a time. The problem is once you start to be able to >> what is a million bases is what kind of scale >> megabase right so >> but like as a percent I don't know like >> well the human genome is extremely large but you know a bacterial genome can be you know three megabases right so be a third of an entire bacterial genome for example or you know kind of like you know 2 3 4% of an entire human chromosome >> wow >> and you know the problem is once you're making changes of this scale, you quickly run out of ideas about what to do because we only know how to change things at the individual gene level. When you have 100 genes, >> oh, I see.

74:59 >> How do I design how a 100 genes should be interacting in order to create an entire biological program or higher order logic? >> Got it. >> And so I started my research career in how you make programmable tools for manipulating the biology and the living molecules inside of cells. Our more recent work has really been around how do you model right so it's like writing and then thinking right how do you think about all of these different genetic components how they interact how they create higher order logic that allows us to do top down design >> I see >> right and then by combining >> we're currently rate limited by our understanding of how these things will combine >> exactly we we barely speak the foreign language of the genome or the cell >> and by understanding and predicting that better >> we use these programmable kind of manipulation tools to make those changes much in a much kind of higher success and more targeted way. Right.

75:56 >> Got it. what is how would you think about roughly like the the sort of near-term progress in this domain like let's let's say on a I don't know three-year time horizon like do you think we're going to make material largely due to the model side or otherwise we make material progress on the understanding side like what would progress actually look like if some of this goes right like what is the implication on a drug or a pharma or otherwise level in in the next few years I'm I'm not saying like I maybe it's worth talking about where things could go in 20 years too. But like I'm I'm curious on the sort of like on deck side of things.

76:30 >> Yeah. So so take RNA interference. It was it was sort of the original programmable genetic manipulation technology before crisper, right? Kind of came out in the early 2000s before my time, right? And over the last 20 25 years, it has really matured in terms of it's sort of the chemical modifications you need to make on these RNAs so they can last a really long time in the body and also how you deliver them really efficiently to the liver and to other organs in the body, right? And so folks have been using this to create medicines for like you know for high LDL, >> right? And so, you know, today you take a statin, small molecule, right? Works okay. but then now folks are targeting, you know, like PCSK9, right?

77:19 They're also targeting high triglycerides, right? And they're creating arrowhead creating multipplexed RNAI that can target two things at the same time. So, both high LDL and high triglycerides, right? And the I think the, you know, heart disease is still the number one killer in the world, right? And if we can actually we could significantly reduce the incidence of coronary heart disease by just targeting these two well-established biomarkers in a programmable multipplex fashion directly in the human patient liver. And this is going from these daily pills that people forget to take to a quarterly injection, right? Because we've dialed in the chemistry so much.

78:00 You just shoot your you shoot yourself four times a year. Wow. >> Right. And then you can just wipe this out, right? I think that is so tremendous to the degree that 100 years ago infectious disease was maybe the number one killer, right? Today that's heart disease. We may see both of those go away, right? in terms of their relative disease share and you know well that sort of leaves us with cancer with Alzheimer's disease and then you know there's going to be this really long tale of chronic autoimmune disorders right >> yeah then whatever else we find once we don't no longer deal with heart disease you had a comment about ML for bio is not just drug design >> obviously on some level part of that is all the progress on the modeling side but I'm curious more broadly What what's kind of inside that statement?

78:53 >> I think we need to be able to test things in people. There are different types of tokens, if you will, for the models. There's like a protein structure token. There's maybe a DNA sequence token. There's maybe a cell perturbation token. We also need human tokens, right? Like the most valuable thing in the industry is data on how a human body will respond. Yes. >> Right. >> and so I think that's going to be really important. is going to be innovations around our clinical trial sort of regulations and enrollment and recruitment and endpoints in order to be able to test things faster in people, right? That's going to be an incredibly important trend. I'm actually also just personally very excited about agricultural innovation as well. So the the bridge recominases that we've been developing have been getting deployed in plant gene insertion which has been historically a huge problem.

79:48 >> Yeah. and being able to kind of you know u genetically endow them with you know the ability to be more nutritious to resist cold snaps to you know be larger and so on. And I think many of the University of California's most valuable patents are for engineered strawberries, right? I think plant genome editing has been something that people have talked about for a very long time, but just hasn't worked really well. There's also a big real world deployment gap. I think seeing more of that is going to be really exciting.

80:22 >> Broadly, when it comes to >> I think a lot of people's response to genetic engineering in general, whether it be in plants or certainly in humans, is like or or frankly even stuff like peptides. I think a lot of people's response is like, we don't have longitudinal data. >> How do we know these things are good? >> I think some of that could be ignorance. Some of that could actually be we we we don't have these answers. I'm curious how you think about maybe how so much of the stuff we've talked about in in maybe particularly the modeling side might allow us to have >> more trust data whatever it is to allow us to say hey yeah wait if drug discovery or any of these implementations stop being like we we can do them much faster the bottleneck is going to go somewhere else and it might be in testing it might actually just be in people's belief that they should be eating whatever your super corn I I think the idea that you can scrape Reddit data for example there are all kinds of subreddits for these niche patient communities who are trying all kinds of things for their chronic underserved multi-system disease long co is exhibit A for this right where they're >> I mean I think something like 10 20 million Americans have had long co right I think something like four to 5 million you know still have you know deficit and these are like these very chronic multi-system disorders that we don't do really good job of diagnosing and treating like medicine is really structured around treating single injuries like acute care or single organ diseases right that's how like the residency system sort of set up right if you have this autoimmune disorder you have to see five different specialists right? for your liver, for your GI, you know, etc., etc. And I think the idea that you can kind of train models on this kind of anecdotal observational data could be a much more effective way to design like very clear like interventional smaller end trials. Yes.

82:33 >> Right. Maybe there's a bit of a tongue-in-cheek example, right? But there's this huge wave of bro science, right? from the 1980s to the early 2000s totally I would say where a lot of the things that the bros tried right for gym maxing is standard >> today right and we sort of at this sort of in this kind of community population scale figured out a bunch of interventions that you know work >> right >> and we didn't do RCTs for all of these things in the traditional kind of clinical setting and so I think there are like with the right oversight, regulation, informed consent, safety, there there should be ways to create, you know, the the right like and additional mechanisms for for human testing, right? For example, there's so many people trying peptides today that if we had the ability to record what these people are doing, actually track and understand what's happening, >> we could deliver, I think, tremendous value. you know in just in terms of like knowledge and whether or not the these things actually work.

83:43 >> Do you think that's mainly a crowd sourcing like a coordination problem or like a actually collecting the data data technology problem? >> Both. Okay. >> Both. Yeah. I mean people are just yoloing it today, right? >> Well, sorry. I mean in terms of capturing like if you could if I could give put you on a text thread with every single person triing peptides in the Bay Area >> Yeah. and they were like down to like whatever fill out 10-minute form once a month, would that solve the problem or like are there a whole bunch of other complexities around this in terms of getting the right data from them and >> well I think it depends on the peptide and its puditive mechanisms of action but if you could do you know deep kind of blood profiling >> of you know all of those patients before and after over time I think that would be tremendously interesting data set.

84:26 >> Yeah. >> Yeah. Yeah. I guess it comes back to longitudinal data sets again and again and again. A few more things. Obviously like in some so many ways you are playing across nonprofit and more traditional academia science world open source stuff and then like proprietary commercial business stuff. and it seems that you've been really thoughtful about like the ways to use all of those shapes in tactical ways to get outcomes. you said somewhere researchers don't think in products, they think in papers.

85:03 I guess I'm curious what your encouragement might be to maybe the academic side of the house around thinking about the commercial side of things in a way that isn't like let's just commercialize everything, but in a way that is like thoughtful and nuanced and and very ultimately focused on sort of like outcomes. I think in academia you're in semin right and a lot of the kind of non-academic side of my life has been an attempt to understand those variables right you know so when I when I kind of look at build or invest in companies I think a lot about business genetics right and it's actually very funny because you know Different types of companies have different genetic substrates and profiles and signatures, right? and they interact in weird and nonlinear ways to create emerging phenotypes much like in cell biology, right? You sort of are doing comparative genomics. When I look at a deal in defense and in robots and in healthcare and in I don't know like RL environments and in cyber security, they all have different markets. they have different competitors, they have different players, they have all these kind of different variables that you have to learn and it's you know kind of similar and or analogous to all the variables of kind of the different kind of genes and gene programs and how they interact. And so I think I I kind of have this comparative genomics view of how I look across business genetics, right? And so I think the kind of analytical quality that you have to solve these biomedical problems are in a way very transferable into the for-profit sector it's just with a very different set of kind of like variables and players but the kind of core way that you you kind of frame and look at the problems are similar. Yeah.

87:08 What about when it comes to solving a specific problem like I don't know one end of the spectrum you have FRO's or nonprofit structures or whatever on the other end of the spectrum you have whatever Thrive spinning out isomeorphic from Google >> with a crazy amount of capital I imagine there's a broad middle ground there too in in some sense ARC is like embodying this which is like as I at least as I understand it a huge part of the goal here is eventually to start spinning things out that are commercialized but like what is the right model to think about sort of like the nonprofit side of this spectrum into the in some sense commercial and obviously practical side of the spectrum.

87:49 >> I think it's all downstream of capability breakthroughs, right? >> Okay. >> like OpenAI was and is a nonprofit, right? The Open Foundation is the largest foundation in the world. It owns 25 26% of the company of the PBC, right? I think with ARC, our genuine hope is that we'll be able to bring to bear different flavors and sources of capital and expertise on our, you know, basic science breakthroughs over time in order to actually turn them into real products. We have a number of programs that are very translational that have come out of ARC labs lately, right? So Issha Jane for example at ARC and at UCSF has invented small molecules that can tune how oxygen binds to red blood cells, right? So you can do like altitude therapy in a pill.

88:41 >> Oh yeah, I read about this. >> That's like, you know, insane. And she's using it to treat neuro degenerative diseases, to treat cancer. >> And that's already been commercialized. >> they're they've tested it in animals and they're testing in larger animal models. They're creating drugs out of this that have like real drug-like properties, right? there's a bunch of machine learning models that ARC investigators have been developing like like Hani Gdari at ARC has been training all kinds of models for designing RNA medicines for example and there's a number of other companies that are being incubated at ARC that will you know that will you know kind of become public to the world soon that kind of take the principles that we we use to try to recruit everybody and there there's a lot of net new generative kind of projects that we couldn't have predicted when we founded ARC but have happened through the unique mix of people and ideas that we brought together. So this is like real time this is happening right it's not like theoretical stuff so yeah it's it's quite fun.

89:47 >> What do you think would cause the biggest inflection to arc in in the positive direction before 2030 if if it could if you could figure it out if you could get it right? if you could change something. >> More GPUs. >> Okay. A few questions about maybe just some futuristic stuff, I guess. one, you talked about interception a couple of times in my research. which is I loosely understand. It's kind of this more eastern philosophy around how the body and the brain are connected and when you're you feel something in your stomach and then it turns out to be true or whatever. any updates on this broadly? I don't know. I like it it seemed both really interesting and like not obviously in this wheelhouse and so that's why I was curious about it.

90:33 >> Yeah. Yeah. this is actually one of our craziest discoveries so far. >> Okay. >> that hasn't come out yet but it's a a profound interosception modulator right which sort of the the biology of the gut body brain axis and how different organs communicate with your brain and nervous system. So they're like deep biological mechanisms for how you know like cognition creates stress right and there's actually a mechanistic circuit for how they create you know sort of the nervous system innervates the brain or the gut and can create the release of pro-inflammatory cytoines that leads to astroite inflammation that creates ulcers for example and so it's you know like we've always sort of known if you're hyper stressed you can get ulcers but like arpis have discovered exactly how this works, right? And you know, vice versa, right? Kind of inflammatory signals from the gut can lead to brain inflammation. We've been >> figuring out what are modulators of these pathways and you know, I think these will be >> why has there been so little progress on this historic? I mean, maybe there maybe I'm ignorant, but it seems like that this is not something that's been a huge area of discovery >> because it's multi-system. I see.

91:44 >> Right. People are used to studying individual organs and this these are really complicated multi-system models that are built on top of decades of infrastructure that we've created around different types of knockout animals being able to you like you need to use very specific models to cut nodes of connectivity between the gut and the entic nervous system, the entic nervous system and the central nervous system, the brain and then back right and sort of We kind of needed the trans the right transgenic drivers to map each of these nodes which we kind of now have combined with the high throughput genomic and proteomic and spatial transcrytoic technologies to view what's happening and then the ability to synthesize and test those perturbations or modulators right so we're like doing a lot of work to discover new peptides at ARC for example and you know I think they will be much more sciencebacked certainly than the things that people are injecting in their bodies every day.

92:47 >> Maybe speaking of peptides, what do you think is not obvious that sort of will be consensus in let's call it five years around peptides? I think there are a tremendous number of bioactive peptides, like really tremendous. Most of them don't have drug-like properties. So, they're released spatially in very specific places locally and they signal sort of, you know, to things that are nearby and they degrade really rapidly in circulation, right? And so a lot of the sort of work to create peptides you can take as drugs involves advancements in peptide chemistry which is like this multi-deade long running story where we don't have deterministic simple ways to just make peptides last in the bloodstream really potently or to control their bio distribution or what receptors they bind to. Yeah.

93:52 >> Right. And I think as we start to kind of tame these nodes, we'll be able to create them as an entirely new class of drugs or like a large class of drugs beyond the kind of individual kind of success stories that we have today, right? Like outside of GLP1s, there aren't that many super successful peptide drugs, right? But I think that, you know, market share will really increase. Yeah. >> If we solve those chemistry problems.

94:22 But you think those are near-term solvable at least on some level? >> We have figured out key nodes that seem to be repeatable that work well for some peptides and not others. >> Can you give an example of something that would be maybe not obvious that could be like if you could solve some of these problems would be like a really miraculous peptide drug? >> Okay, I I'll give you some like I'll kind of go down one level. So attaching a fatty molecule to a peptide can really increase its half-life. A lot of peptides also have proteases that bind and cleave them and cut them up.

94:58 And so figuring out what those protease sites are and then blocking them, right? So these are sort of two of our major nodes for increasing peptide like phicodnamics, right? also predicting what receptors peptides bind to. which is very imperfect today, but like with really good protein structure prediction, we should be able to solve this problem with AI will help us take advantage of all the single cell data that we have to map what the likely biological mechanism of that peptide is based on what receptor it's targeting.

95:31 And if we had working virtual cell models, we would probably we could figure out what that receptor is sort of signaling network is intracellularly. So you have like a functional fingerprint for every new peptide that you discover computationally. This is sort of how like this has been traditionally like wet lab experiments done over the course of many many years. And the more of this reasoning we can push into computation is how this geniuses in a data center idea can run across many different peptide sequences in parallel.

96:02 >> Yep. Yep. so that that's sort of like how we make like I think like unprecedented progress in this area across many different sequences. >> to to go back up a level, can you give an example of like a type of peptide that people if if we pull this off could be >> I don't know. >> Yeah. Sleep peptides is a is a great >> just straight up like you inject and you just >> can sleep less. Yeah, I think for for modulating wakefulness for example for you know creating better and higher quality deep sleep or REM sleep would be another >> an unbounded like things we want to modify in the human body we might be able to use peptides for like a really really broad >> I think the I think there are like ways that we can make sleep modulators that could be peptide drugs or they could be small molecules they could be antibodies I'm much less predicated on the drug class and much more on the product. That makes sense, right? Yeah. And by understanding the mechanism by which we can modulate sleep biology, right? Like there are these ereexenagnists that are small molecules. they're also reexagonists that are peptides, but like whichever one dukes it out better in the market kind of doesn't matter. The end patient wants something that can safely and in a long-lasting fashion modulate wakefulness where you feel like a vitality of attention and wakefulness as opposed to this sort of drug induced like speedy wakefulness, right? We don't seem to have many drugs for muscularkeeletal injuries >> and it seems like at least when we talk about it longevity is actually like one of the things that like most significantly impacts people's feeling of feeling young or feeling healthy.

97:47 >> There's a lot of work on muscle modulation >> really. >> Yeah. Yeah. With targeting actin and mouse pathways. So there are these sort of if you just Google mouse straten cow. >> Okay. >> Right. you'll you they're these like insanely jacked Arnold Schwarzenegger cows, right? because you they sort of are mutated in this sort of regulator of muscle synthesis. So when you lose it, you know, they get insanely jacked. >> And so there's work now to try to pair these with GLP1s, right, in order to preserve lean muscle mass as you're losing weight. The really interesting at least early clinical data so far is that it's not so simple where muscle is both a metabolic tissue but it's also a functional tissue. Whereas fat is mostly metabolic tissue in the sense that even if you have larger muscles they if you're not training them right then it may not be functional. Right. And so it's not it's not quite as simple. Like you can preserve more lean muscle mass, but to actually be stronger, you also have to work it out in the right way.

98:52 >> Or to solve your back pain, you have to >> actually not just sit all day. >> I actually think pain is another extremely exciting area. Chronic pain, nonopioid, kind of pain modulators. >> lots of really interesting work here. >> Yeah. >> we haven't even gotten into like brain computer interfaces and how you can use these to modulate pain pathways non-invasively with ultrasound or whatever. >> Maybe it's brain computer interfaces, but I'm curious if there are any sort of like out there moonshots that come to mind as being maybe you are disproport disproportionately excited or optimistic about that we could see in the next, let's call it decade around human biology.

99:35 in the next decade. >> Something like that seems like the right relative. >> I don't know the difference between five and 10 years, frankly, on some of this stuff. >> Yeah, >> especially given the rate of progress. >> I'm I'm personally super excited about BCI. >> Cool. >> Yeah. I'm also really interested in in sleep and how actually the the inner the union of BCI and sleep, right? For example, we spend, you know, eight hours a day totally unconscious. What are like more functional ways that we can use that time? like can you much more like deterministically write memory consolidation >> right during a sleep period.

100:18 >> You think sleep is inefficient. >> I don't think we know nearly enough about sleep to have an opinion one way or another. >> Yeah. Yeah. So it might actually be very loadbearing but it would be nice to know. >> Yeah. Yeah. I mean I think there's been a lot of work from Megan Natergard and others showing that when you sleep you significantly increase the rate of cerebral spinal fluid flow in your brain which seems to be sort of flushing out accelerated by neurons as waste. Sort of like rcology or like waste it's like flushing the toilet for your brain when you're sleeping. It's a way of flushing out the toxins, right? That's been like one molecular mechanism that has been worked out over the last decade. There there are probably many other complimentary mechanisms that we haven't really worked out yet. but you know obviously in addition to toxicity modulation, it's also clearly involved in learning in memory and you know understanding that better could allow us to write those memories much more effectively. Yeah.

101:20 any >> I love biofuturism by the way. Like we're only just getting started. This could be a whole other hour. Yeah. >> Yeah. Yeah. We'll do this on part two. Any maybe given the the crisper past any use of gene insertions that you think are sort of like maybe similarly placed like not real but maybe on the edge of viable that could be really I don't know >> amazing. I think a lot about you know a lot of synthetic there's an entire community of synthetic biology that's working on like the minimal genome >> okay >> right sort of how much fat is there in our genomes can we make them as small as possible to figure out the most essential minimal components I guess I find this actually I think a very important field but fundamentally less interesting than what are like the net new synthetic functions that we can add And for example, it's there there are these studies I've shown like a single enhancers can be toggled to create a tail or not have a tail, right? So there's there are just like all these insane biological programs, some of which are vestigial that are just sort of encoding our body.

102:33 >> and like maybe having a tail is sort of like a silly sort of lurid example, but else >> can we can we do something much much more functional, right? I think the combination of models that can think, these genetic manipulation tools that can synthesize long information and recombine and rearrange them in the right ways is going to allow us to move beyond, you know, 10,000 base pair genomes to 100,000 base per genomes to a million base pair genomes to 10 million over the, you know, n years to come, right? And as these models get higher fidelity, I think I think we will create like evolutionarily distinct forms of life at least by sequence, right? That will be as distinct from existing known species as you know species that we have sequenced today, right?

103:30 >> I think that will happen soon. >> Yeah, it's already happening in a way, right? And the question is how different can those functions be and you know like like how complex can those organisms become? >> Yeah, >> you have said a few different places. >> maybe one of the first kind of instances of it I found is you talked about being in that wet lab at a university at like age 14 >> and knowing you loved it after like two weeks. M >> and you've talked about in a bunch of places this sort of like through line of like making sure to have fun. Some somewhere else you said be you referenced the Scottish proverb be happy when you are alive for you are a long time dead. I don't know that you operate in fields that are always full of people who are you would describe as having a ton of fun or or being radically self-expressive or whatever.

104:25 Where does that come from and how do you sort of like maintain that and keep keep that sort of at the center of things? I think science is really hard, right? It's such a long journey and it's mostly failure. So, if you don't have a way of bringing the energy and the vibe and the vitality, it's going to be it's going to be pretty miserable. I also think the profession incentivizes and rewards a rigor of criticality that is not great in other domains of your life like relationships for example, >> right? And so learning when and where to use these traits or skills, like everyone has superpowers that make them great at what they do and who they are, but you know, there's, you know, a dark side to all of those superpowers, you know, that are also your foibless, right? And so I think being really kind of self-aware of when and where to use those things is really important because absolutely my scientific training has made me much more picky and much more critical. But you know you have to know when and where to use that. And similarly I think you need to figure out a way to make the discovery process fun because you're mostly eating >> That's a good answer.

105:53 Anthropic just announced that they are giving cloud access to a wet lab, I believe, as I understand things. any thoughts? >> yeah. Yeah, they've been working on this for a while. I'm I'm a fan of a bunch of the people working on that team. A lot several of them trained with me. I'm excited about the vision of cloudmaxing science, right? I think the default way that we we were we're going to use these agents for everything, right? It's going to be a default interface by which we interface with scientific knowledge, right? One of the kind of big ironies of writing papers is I don't really know if I'm writing them for like fellow humans anymore. Right. If you think about like the long arc of >> history talks about this with the books too and all the blog posts. Yeah.

106:44 >> Yeah. like they they're just ingesting all of this. So like the way that I would write >> for my colleagues and the way I was trained to do that I don't think is going to last the test of time because the I don't know the if you think about like the flops of intelligence that will be used to process any thought I've ever had will be primarily from these agents, right? And like that's really trippy. It pressurizes the scientific literature and scientific publications and journal enterprise like never before. Like what does it mean to record and disseminate human knowledge? Who's going to be the one who's thinking about these things? yeah, I don't I I struggle with that a lot.

107:28 >> Yeah. >> And and that if anything >> emphasizes the importance of having fun, >> right? That only matters more. >> Yeah. >> Do you fear becoming obsolete? the profession is one of self invention and reinvention. Yeah. Like if you simply do nothing, the default is to become obsolete, right? And you see this across like like lives are long and careers are long. So many generations of scientists became prominent for some discovery or some innovation in an area that >> had its moment and then another trend came >> and then >> you are obsolete. So it's not even a fear. It's actually just reality, right?

108:20 And so being able to be bold, to be fearless, to be risktaking are actually like they're they're kind of survival traits, right? you I I realized I never actually asked for clarification. You said at the top you have three jobs. >> Is that ARC kind of running ARC, Arc, your lab and Thrive? Is Thrive not counted in that? >> What is >> I'm also a professor at Stanford. >> Right. Right. Right. >> So the ARC, Stanford, Thrive.

108:49 >> Okay. Okay. how does the Stamford I would assume mostly teaching work and the investing which is obviously very broad and you do a a wide range of things. How does that inform or inspire the what I would presume is the main job? >> The trainees are so good, right? I mean I I just took a PhD student from Stanford, one of the Stanford graduate programs this cycle. We'll have more over the years. And these are just such brilliant, ambitious young scientists who want to do ambitious things. The really interesting thing is I think in the age of AI, the folks who decide to do a PhD are weirdly more ambitious, right? You you'd sort of think, oh, like maybe the best talent in the world is getting starved and they're all just going to AI labs or something. But in my experience, that's not really true.

109:47 because the big labs are able to do a very specific type of research, right? And we in moments of great change tend to, you know, overrate the the way that I mean like people are concentrating into these areas very sensibly, but there is going to be a huge amount of capability that's unlocked downstream of the models that folks who are focused on long the long tale of research are going to be the most well positioned to capitalize on. Yeah. It's more of a longtermist view in some sense.

110:17 >> Yeah. >> Yeah. >> If you could spend one year sbatical doing anything else, but you can't do something you're doing now, what would you do? >> I would do something in the real world that's really like esoteric and physical in nature. >> Okay. >> maybe I would I don't know what that is. I think I would learn a completely different domain of science. >> Cool. >> Yeah. >> Cool. Okay. Maybe someday. Maybe when the when the robots make this part obsolete someday.

110:45 >> Exactly. >> You have I think it would be fair to say gone very fast in your life. Where do you think it is a virtue to go slow? actually knowing what knowing thyself well enough to identify your gas and breaks is really important to live a fulfilling life and to live a fast life in the first place, right? Because if you want to be high performing or whatever, if that's you know like not everyone should be or needs to be or wants to be but you have to be able to intuit it and understand your energies because I can't go ham the way I did in my teens, right? I have to understand those energies to avoid burning out. If anything, I kind of specialize in how to, you know, not burn out, >> right? Yeah. Because you can Yeah.

111:43 you're just ripping at full throttle all the time. It's very easy to break something, >> right? >> So, knowing important. >> Yeah. You have to know when to go slow a lot of the time. Like, you know, there's sort of that adage like slow is smooth and smooth is fast. It's very, very true. >> Yeah. >> Yeah. >> Yeah. >> I think it was in the conversation with the founder of Benchling, you said he asked about your superpower and you said you have a deep internal sense, a compass that is like a right direction to point. What where are you pointed?

112:14 I think the there's a lot of discussion especially you know these days with cyber security and millennium prize etc about the inflecting capability of these models and how scaling laws don't seem to be anywhere in sight or the end of scaling laws doesn't seem to be in sight and I feel like the interface of this model super intelligence with the physical world right is the kind of thing that the market hasn't really priced in but is really inevitable. I'll give you an example. I was recently watching a video of a model of of a robotics model that was controlling a robot hand to try to do a complex dexterity task.

112:57 And the model was hooked up to a 3D printer that could print new appendages and limbs. >> Oh wow. in order to change the way it could physically interact in real like dexterity sort of sort of engagement in >> it can sort of RL with the ability to change the sort of physical substrate >> right >> so I found this really trippy obviously >> and that sort of core concept you can lens across many different physical tasks if you could do directed evolution in the physical world across many domains, you know, things will just things are going to be really interesting, right, and really crazy and trippy and hard to predict. And so I spend a lot of my time trying to think about that 2030 and beyond future. a lot of my kind of kind of work today involves thinking through like what are the bottlenecks and what is essentially the supply and demand side of creating super intelligence right my ARB is really how do we apply this in the domain of biio medicine right and be at the frontier of doing that right and then you know how how does that kind of change the rest of the physical world and so I spend a lot of my time pondering that >> directed evolution is a good answer from a biologist Apologies. Patrick, that's all I got. Thank you.

114:25 >> Thanks so much. That was a joy. >> Thanks again for listening to the conversation with Patrick. If you want to learn more, you can visit dialectic.fm/patrick-sue. And I have the transcript and all the linked references and everything like that. Also, if you enjoyed the conversation, please share it with a friend. And finally, I'd like to thank Notion one more time for continuing to support and present Dialectic. Notion is a company that from the top down with founder Ivan Jiao is deeply thoughtful about technology and the tools we use and how we work. The ethos related to that that I respond most to is Ivan's idea of thinking together as the capability of our tools about AI and agents gets even more extreme.

115:09 Having a place and a tool that is committed to being the best place where you and your collaborators alongside the incredible leverage of these new agents and AI can do your best work and to think together is I think a deeply aspirational and deeply human orientation. If that where the tools and products and the idea of a single place that holds all of the context across all of the AI tools you use is interesting to you, you should consider working with the team at Notion. they're hiring across roles including product engineering, design, storytelling, and more. And if you think that's you, you can reach out to them directly or to me at hello@ dialectic.fm and I will put you in touch with the right people. It has been a privilege to watch Notion's continued reinvention over the time we've worked together and their commitment to being the place that you and your collaborators can do amazing work. Thank you again to them and you can learn more at notion.com/dialectic.

116:09 I will see you next time.