Gradient_Dissent_Conversations_on_AI_He_Raised_$70M_to_Cure_Every

0:00If we want to go and cure all diseases, understand how the brain works and solve aging. And AI seemed like the right way to do that. When it comes to the world as we experience it, most things are pretty well understood except for biology. And to this day, we don't have the wiring diagram for the human brain. We kind of have the wiring diagram for the fly.

0:16and that's like the best that we have. And so, we've got started in 2022. Remember, we didn't have any notion of like reasoning models or whatever, right? It was like the first multi-agent system that we showed that was capable of like doing the full loop of scientific discovery. And it came up with a new hypothesis about a way to treat a form of blindness called age-related macro-generations. It actually just got published in Nature two days ago. Since we launched Cosmos, people have probably used Cosmos to make like 20 or 30,000 novel scientific findings, which is wild. The pharmacomics of the future will be much more lean.

0:44You're going to be able to pursue many more drug programs in parallel than you can today with the same number of people. There's a long history of AI over promising and drug discovery. Oh, yeah. Is this time different? I promise you this time, it is different. You're listening to Gradient Descent, a show about making machine learning work in the real world, and I'm your host, Lucas B. Wald. All right, I'm here with Sam Rodriguez, the founder and CEO of Edison Scientific and Future House, he's right at the forefront of using agents for scientific innovation. And he's got a lot to say on how he's agents well and science itself. I really hope you enjoyed this podcast. All right, Sam, thanks for doing this. I've really been looking forward to it. Mike Woax. All right, so let's get started. So you had this incredibly promising, successful career in neuroscience and bioengineering. I think you studied theoretical physics originally.

1:41And then you moved into AI. What were you thinking? Why did you do it, Sam? Why would I give up such a promising career? Yeah, great question. So I started doing theoretical physics, the problem with physics is there are actually no unsolved problems left in physics. So a slight exaggeration, we need to build quantum computers and there are some Interesting important material science problems and we still like don't know how like the universe works, right? But like actually, you know, I think one of the key insights is If you look around it like any phenomenon that you can see in the room today like in this room that we're sitting in I Could probably explain to you how that phenomenon works down to the level of like some atomic particles, right except for like you

2:32And like, or like, you know, the maps and like, you know, why does the mouse do what it wants to do? Why do you like, you know, uh, wake up at the time you wake up and why, how do you get sick and so on? Right? And so like, actually, like when it comes to the world as we, as we experience it, um, everything is, is kind of most things are pretty well understood except for biology, right? Um, and so that was originally what got me into biology. Um, and then, and so I did my PhD at MIT. I was, um, uh, I'm an inventor at heart. I invented a bunch of different technologies. But the kind of key thing that I learned at MIT is that doing biology is that really, there are three things that you need to do to do science. You need capital, you need money, you need logistics, which is like, do you have the things that you need to run the experiments you need to run in the place to run them when you need them? And you need talent.

3:30And capital scales and logistic scales, right? And talent just like does not scale and fundamentally in biology we're limited by talent. And so the thing that I got to think about is like how do we remove talent as a bottleneck in science, right? Like if we want to go and cure all diseases and understand how the brain works and solve aging and so on, we just like need to figure out how to scale talent.

4:00And AI seemed like the right way to do that. So yeah, I mean, it was kind of crazy. I just like basically built up this career and I kind of just like threw it, you know, jumped off, threw it away and jumped off a cliff. And that was pretty wild, but it's been working out well so far. And originally the company that you started now, which you better explain what that does, I think it started as a new kind of not-for-profit, right? Yeah, yeah, yeah.

4:27So we basically, so this was 2022, and I had kind of figured out that the most important thing, it seemed like that was going to happen in science in the next 10 years, was going to be like, you know, figure out how bone AS scientists, precisely because it was going to unblock talent, right? And, but at that time, I mean, in biology, you're used to things going really, really slowly, right?

4:53And so we were at this like basic, we were at this point where, you know, GPT-3 was out and it was like, it could kind of save things. It was very impressive. You can kind of see where things were going, but it seemed like it was going to take a really long time to get there. And I didn't know how to commercialize it. I didn't know what it was. And so we were like, well, we should do this as a non-profit, just go and do the basic research, which was great.

5:21And so I teamed up with Andrew White, my co-founder, who is really a pioneer in the space of AI agents for science. He was working with OpenAI on GPT-4 at the time and was a professor at the University of Rochester. And kind of like we had the same vision for building AI scientists. I kind of like knew the biology side and Andrew actually knew how to do it.

5:48Technically, right? And so we teamed up but it was just it felt like the right thing to do was to be a non-profit just because it was gonna take you know We thought I was gonna take five or ten years which was complete nonsense. I mean that was like totally not right. I mean as we all know now Within we launched future as we announced it in In November of 2023 And like within two years we have like extremely powerful AI scientists already right so the timing thing was not something that ended up being very important I will say like I think at the nonprofit the fact that we started that was not the only reason we started future as a nonprofit We also like really wanted and continue to want Those technologies to like benefit the entire scientific community and so as future as we open source a lot of stuff which I think is also super super important But yeah, and then what just ended up happening was

6:44by kind of spring of 2025, you know, one day, like Andre Carpathy tweeted or something, and then all of a sudden the entire world knew what AI agents were. And we started getting phone call. We were like, literally went from being like, we would have to explain to people in our decks, like, what is an agent and how is it different from a language model? We went from that to being like, to having like senior executives at pharma companies call us.

7:13to say, oh my god, how do we use your agents? How do we use your thing? And so it became evident pretty quickly. We were going to have to have a for-profit spin out in order to satisfy that demand, right? Do you feel like there's a role for nonprofits in technology then? I mean, we now have two examples of nonprofits developing interesting technology and mainly turning it to for-profits to get that technology out into the world. Is there something broken about the nonprofit model?

7:42No. First of all, is there a role for non-profit and technology development? Yes, absolutely. Is something broken about non-profits like, no, definitely not. This just feels like, this is like, non-profits just, or it feels like basically what this is is for profits, doing the thing that for profits do well, which is scaling.

8:12First of all, let's back up. When I was in my PhD, I was working on a kind of Tomax project. So this is a project that basically kind of Tomax is figuring out how map all the connections between neurons and brain.

8:28And, you know, just like, can we, a core piece of understanding how the brain works is figuring out how to like get the wiring diagram. And to this day, we don't have the wiring diagram for the human brain. We don't have the wiring diagram for the mouse brain. We like, kind of have the wiring diagram for the fly. And that's like the best that we have. And it's really, really difficult to understand how the brain works without the wiring diagram. Okay, so we're gonna go and we wanna go and map the wiring diagram in the brain.

8:57It's really, really challenging to do that, basically because it involves tracing these tiny, tiny fibers, tiny meaning like, you know, one-one hundredth of the width of like a human hair, right? Through like the...

9:17this gigantic tangle of the brain and you have to make no errors because if you make errors then you are like connecting neurons that are not actually connected to each other right it's like you know if you try to imagine mapping like like all of the roots in a field of grass right like it's like that but way way harder and so we And so that was the problem I was interested in, and I tried to do it in an academic lab, and I was like, wow, there's like no way actually that I'm going to be able to gather engineering resources, like the capital, the talent that I'm going to need in order to do this. But also, you can't do that for profit, right? You actually can't go in a for profit setting.

10:03like get money to map the brain because what are you gonna do with the map of the brain, right? You're gonna go like develop drugs and that will take like 20 years or something and it's just like, that's not an attractive proposition for investors. And so I was like, okay, I can't do this academia, I can't do it for profits. So basically there's actually no way for me to do this right now.

10:23And so I propose these things to have focused research organizations, which is this idea that for some problems like mapping the brain that are too big for academia, but that can't be done for profit, we need a third way. We need some alternative kind of structure. And the idea of a focused research organization is that it is a nonprofit that looks a lot, or that operates like a company.

10:49Not like you're slow-moving foundation that you think of when you think of normal nonprofits. It's like fast-moving, hard-charging, research organization that has a specific goal, pursue that goal, and then when it's done with that goal, it's not. You can actually spin down that nonprofit. Actually, since then, we've gotten philanthropists to fund a bunch of these.

11:11a bunch of these focused research organizations, and they're often, they're doing projects that you just like could not imagine happening in a for-profit setting. And so, and sometimes when they're, you know, if they really work out, if they hit, right, and they go, they go really well, then indeed, what is the right step afterwards? It might be just been our for-profit. And then it's kind of always been the idea. And so, you know, with Future House, We started it, goal of building an AI scientist, we didn't know how to commercialize it, non-profit made sense. Plus we wanted to be able to share the basic research with the world. Then it went really well, we made a bunch of progress. What is the next most sensible step? Spent out a for-profit, allow you to scale. This was actually not the plan, we didn't think at the beginning. We thought maybe we'll spend on for-profit in five years or 10 years.

12:06We certainly didn't think it would take us two years or three years, but it was kind of like, you know, I think that this is just like, it's a consequence of success, right? As it was also in opening eyes case. So what did you see that made you feel like the technology was even more promising than you thought? Oh, yeah. I mean, look, when we were, when we got started in 222, remember, you know, Well, we had RLHF and we had like instructor PT. And so you could like, you know, the language models of them like knew how to respond to you, right? But we were very much in a world of like these models they just can't. We didn't have any notion of like reasoning models or whatever, right? They just seemed, it was very rudimentary. And we, it was like really, it was like 18 months or 24 months.

13:00until they started to make discoveries. And the first discovery that they made was actually this paper, we described it in this paper about a system called Robin, which is a multi-agent system. It was like the first multi-agent system that we showed that was capable of doing the full loop of scientific discoveries. So hypothesis generation, experiment planning, and then we would go and run the experiments, and then it would analyze the data, come up with new experiments. And it came up with a new hypothesis, about a way to treat a form of blindness called age-related macular degeneration, specifically dry age-related macular degeneration, which affects like 5% or 10% of people over the age of 50. And this was in May of 2025. Our agent came up with a new way of treating it, proposed a new way of treating it, that we were able to go and validate in some experiments in the wet lab.

13:57And subsequently, we've been able to validate them in animals. It actually just got published in Nature two days ago. And that was the thing where we looked at it, we're like, oh shit, the future is here. That makes sense. Yeah. And so where's it gone since then? It's just like, I mean, that was like discovering number one back in May. We since released an updated version of our agent called Cosmos.

14:23This is like a much more powerful version of Robin. That is, Robin was kind of on rails. So Robin could do, you know, it was like an orchestra. We like manually orchestrated a bunch of different agents and to get it to do one specific thing, which was identified in new ways of treating diseases, particularly around kind of drug repurposing. And with cosmos, what we did was we built in an orchestrator so that it orchestrated itself.

14:52and then we also built in this notion of world models, which is really basically a very sophisticated context management tool, but basically allows Cosmos to build up a kind of integrated notion of the knowledge in a field over the course of dozens, hundreds of sub-agent runs, which then allows it to is a core to the way that it makes discoveries. But since we launch Cosmos, we've probably, you know, People have probably used Cosmos to make like 20 or 30,000 novel scientific findings, which is wild. So I think anyone that's used AI for any technical application, maybe even non-technical applications, notices that the intelligence is really spiky in surprising ways. There's some things that does so much better than a human and some things just shockingly worse.

15:44Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it. Tell me about it.

16:03like kind of refuse to acknowledge any weakness in their algorithms and it gets really boring and weird because you know these models are gonna be struggling with some stuff but yeah like I mean but actually in the practical fields like where do you feel like it's really strong and and where do your customers get surprised that it can't be strong in two areas it's strong on things that are verifiable and strong on things where throughput matters a lot okay so verifiable means you know that you can tell whether or not an answer is correct This has always historically been where AI has been strongest because it allows you to get feedback very quickly and then you can go and you can use reinforcement learning. AI is strongest where there's a lot of data and where it's verifiable and where the problems are verifiable.

16:49So coding is verifiable, verifiable, math is verifiable, science is very much non-verifiable. It's verifiable in principle because you can go and run experiments. Right, but there's a loop is expensive. The loop is expensive and it takes forever. I mean, literally people are like, oh, why don't you just do closed-loop RL to teach it how to find new drugs, and I'm like, because that loop is like, I need to go and prove that the drug is safe, and then go and dose some humans, and then wait for six months, and the loop is going to be three years long.

17:19The other thing I hear a lot is why don't you go and build a gigantic science warehouse where you just have pipe-heading robots doing experiments over and over again, which is a better idea. And there are a lot of things for which that is probably great and a very useful thing to do. But in general, that requires the experiments that you're doing in the lab to be reflective.

17:45of what you want, right? In our case, like we want medicines for humans, I can't like test whether a drug works in a human using a petting robot in a lab, I have to test the human, right? And so, your model's only as good as what you train it on. But yeah, so basically there are two, the AI is good at two things, it's good at test, they're verifiable, and it's good tasks that require high throughput. On the high throughput side, This is, I think, in science where it has really shined so far because it's able to go and consider so much more evidence than any human is able to consider. It's able to test so many more hypotheses than a human is able to test. You have to control for p-hacking and multiple hypothesis testing and so on. But there's just, in my order, we have a problem of throughput.

18:44And this is sort of on statistical like synthesizing data. I'll give you an example of reading papers or what are we talking about? I'll give you an example. One of our future as postdoctoral fellows is interested in figuring out how to cure autoimmune diseases. Okay. All of them. I mean, I do. Yeah. I mean, you know, we'll start with like we'll start with we'll start with one but but actually we don't need to be picky. In order to treat autoimmune diseases, what you need is ways to manipulate the immune system. Okay.

19:12What in nature if we go to nature for inspiration where has nature figure out how to manipulate the human autoimmune the human immune system Okay parasites like you know before modern hygiene humans just lived with parasites and the way that that worked with that the parasites had figured out how to manipulate the immune system to turn down immune reactions. Wow, you know, if we could go and we could figure out how they do it, maybe we can do it on ourselves in order to cure autoimmune diseases, right? This is the idea. But, you know, there are many, many, many parasites and each parasite has a genome with thousands or tens of thousands of proteins in it.

19:50What is the mechanism? How are you going to go and figure out what mechanism the parasites use to regulate the immune system? We're actually now using our agents every single protein in any parasite genome. We are running our agents to look at that protein, look at its structure, look at its sequence, look at the context in which it appears in the genome, look at the biology of that organism, and figure out Is this could this be a candidate for how this parasite modulates the immune system? From that, we've come up with a short list of proteins that we need to test, and we're now going and testing them in the lab, right? That is something that just previously, there was no way to do that, it would have been completely impossible. Right, but presumably the hard part of that is building tools to, when you say look at a genome. Yeah.

20:42It's obviously not feeding the genome into the context window. It's finding tools to look at what's going on. But the biology, but I call this high throughput reasoning. Yes, you can go and you can just run some homology. You can come up with an algorithm to look at, you can use a model of protein structure, and you can just look at the structure of all the proteins that doesn't tell you what role.

21:11That protein might play right in the biology of the organism which is fundamentally what we're interested in another good example is bacteria There are many proteins that just have no known function and in bacteria in particular, and if we knew what their functions are, we might be able to figure out like create new bioengineering tools, right? They create fine new enzymes that do functions that we can't do today, right? And one of the ways that you can do this is that bacteria organize their genomes into these units called operons that are all functionally linked. And so if you have one protein that's known inside of an operon and then also in that operon you have a protein

21:58that has a known function, you can reason about what the function of that ladder protein, based on the former protein that has the known function, and that's something where you really just need intelligence in order to go in and think about what is the biochemical role of this operon.

22:21What does it roll? Does it play in the bacterial life cycle? Based on that, can we come up with hypotheses for what this protein of unknown function might do? But to test that hypothesis, presumably you have to do something.

22:36physical, right? Absolutely. And so in the case of the autoimmune, the parasite proteins, we've now gotten a bunch of them synthesized on a DNA chip. And we're going and we're screening them to see what effect they have on T cells, right? And so absolutely, you can't just reason your way to solving science. You have to do experiments, but coming up with hypotheses, right, is something that is limiting.

23:05is one of the steps that is limiting, and that is something where the models are able to help us. Got it. Presumably, your customers are using your models mostly for drug discovery, is that right? Yeah. When the customers use it in two ways. I just talked about the hypothesis generation, the other place where you can imagine that these models will have a major impact is in the kind of operational work of science, right? So, you know, how do we, how do we like, you know, actually get all the materials together that we need?

23:48for to run an experiment. Once you figure out what experiment you want to do, you need to literally go and order them and organize them and figure out what the protocol is. When you get into testing on humans, you need to coordinate all the resources that you need in order to do the clinical trials. You need to figure out what the clinical trial sites are. You prepare your regulatory documents. All of that is part of the process of doing science.

24:12And so we get used in both areas. So we get used both for the kind of early stage hypothesis generation. And then also for the actual operational tasks of development, how do we turn this drug into how do we get this drug through the pipeline and to patients as quickly as possible? That's really interesting because weights and biases customers do.

24:32both also. So we at first mostly drug discovery applications and then post LLMs, we started to see all these operational applications and I started to think maybe the operational applications are more important. Like certainly I think more dollars goes into that and it's more of the bottleneck. Where do you think it goes? Do you have like a passion for the drug discovery side and kind of want to focus on that or do you think the bigger business here is maybe on the operational side? I mean, there's this very funny situation in science where progress comes from discovery, but the commercial values in development, okay? And the reason is that discoveries just pan out so infrequently, right? And you can't tell whether or not they're valuable until 10 years or something after you came up with the

25:19your hypothesis. And so, really what matters is being able to run experiments faster. What matters commercially and frankly what matters practically is being able to run experiments faster. So you can come up with as many hypotheses as you want, but the experiments that matter are human clinical trials, right? That's what tells us whether or not the hypothesis is actually work in practice. If you want to accelerate the process of kind of medicine, you do make those experiments faster.

25:47That's what the commercial value is. That's where a lot of the scientific value is. This is not to say, which is not to say that the OECD discovery is not important. I mean, it's also critically important. So we've had a whole slew of guests on this podcast doing different parts of the drug discovery pipeline. We've had CEOs and researchers. We've had notorious farmer, bro Martin Schrely kind of giving his take. He was actually kind of down on the drug discovery with AI. That's my... I'm curious how you...

26:17think about the entire drug discovery market and how it's changing, like what's kind of working, what's not, like what's changing, what's static. Yeah, great question. So, I mean, I think obviously, there are like two big areas where AI is having a major impact.

26:36The first one is what I talked about, which is all of the reasoning, and that includes both the hypothesis generation which we do and the operational aspects, the development. How do you get these molecules through two patients faster, which we also do? The second major important part is coming up with the molecule.

26:57you know, have this hypothesis that agonizing the GRP1 receptor is going to cause people to lose weight, right? In order to test that, I need to have a molecule that inside of a human is going to go and agonize that receptor, bind to the receptor, activate it, and so on, right? And that is way harder than it sounds, because the odds are your molecule, you know, let's imagine that you want to do this orally, right? So with a pill that you just swallowed, odds are it's not going to go into your bloodstream.

27:25Odds are that if it goes into your bloodstream, it's just gonna get filtered out immediately by the liver. Odds are, even if it doesn't get filtered out by the liver, it's not gonna stay in your blood long enough to have the effect. Odds are that even if it activates the receptor, maybe it activates the receptor in the wrong way.

27:40odds are, even if it activates the receptor in the right way, maybe it activates a hundred other receptors, that you don't need to want to activate the lead to bad side effects, blah, blah, blah, blah, blah, blah. And then there's the hypothesis that your original hypothesis might not work, right? And so coming up with a molecule that satisfies all of those criteria, right, that it has the right bioavailability, i.e. that it gets into the body, that it has the right distribution of metabolism and pharmacokinetic characteristics, that has the right toxicity characteristics. That's all another extremely critical portion of the process of discovering developing drugs. And that's where there have been really exciting and revolutionary companies like Chai Discovery, Isomorphic, which is spun out of Google, Bolts, Londolabs, Profilo and Bio, like a lot of great companies there that are working on that problem of coming up with the actual molecule.

28:37And so the upshot of this is just that I think that you're going to, the biotech, the pharma companies of the future will be much more lean, right? Which is to say you're going to be able to pursue many more drug programs in parallel than you can today with the same number of people. And I think it is like time for us to start thinking, like given that that is the case. This is what I mean when I say we're gonna like remove the talent bottleneck, right? Given that that is the case, we're going to be able to just like pursue cures for so many more diseases. And what we should really be thinking about is something like, you know, what would the human genome project look like for medicine? But I feel like earlier you said the bottleneck was the human clinical trials. And what you just said makes it sound more like it's kind of coming up with the candidates for the clinical trials and it's really the bottleneck. Which is it? No, no, so the...

29:31So, for example, they just say that there are many bottlenecks that all need to be overcome. But I think that fundamentally at the end of the day, there's an operational bottleneck, which is that you need humans in order to run the clinical trials. If we had twice as much money and twice as many kind of operators in drug discovery, Then in most areas it's not always true because sometimes you're limited by patience and so on But in most areas you would just be able to pursue twice as many ideas, right? So we're actually limited and like I said before capital scales, right if we had more good ideas that were investible then There would be more money that would be available for them. Do you think that the new upstarts like that you mentioned like isomorphic and try and others Have a structural advantage over the incumbents in this process?

30:31I mean, yes, in that. Well, so mostly what those companies are doing is that they are coming up with molecules that they then partner with the farming companies on developing. So they are mostly not, at least to my knowledge, mostly not today developing their own drugs, right? And are instead trying to get this technology everywhere. And I think that the farming companies don't have the expertise in house to to do it themselves, and so that's why they're going out in their partner. I mean, they do have teams that supposedly work on drug discovery, right? Oh, they, I mean, they, the farmer companies have strong AI, have good AI teams, right? Actually, we have been very impressed with the quality of the AI people who are inside of the, who are inside of the farmer, who we find inside of many farmer companies, but,

31:27you know, when it comes to figuring out, when it comes to training the best model in the world for generating, you know, a new antibody, that's just like not the farm company's game, right? In the way that it is CHI's game and it is a latent labs game and so on. Okay, so Sam, tell me about your peptide stack.

31:52I had to think about what you meant for a minute because we're actually, with one of our partners, we're working on training a model that is better at reasoning about peptides. And so I was like, wait, how does know about that? Um, uh, my peptide stack. Okay, so I'm gonna admit, I'm like not a biohacker. Whoa, no peptides? No, no peptides. Wow. Yeah, I know. And the reason is I know too much about biology. And I think that you'll find this, but like the really, um...

32:21Okay, I won't put this person on the record. So I was, you know, I was hanging out the other day with the head of research and development at one at a very large, a top 20 from a company. And, you know, he was talking about the kind of like peptide fat. And he was just like, he was just like, yeah, these people, they like don't understand what can go wrong. Oh, I see. Which, like, and I think that that's true. I mean, that We when you when you are like study when you have studied biology and drug development You just get an appreciation for everything that can possibly go wrong that including things. They're like very very hard to identify if you're not looking carefully interesting And you also gain a deep appreciation for things like like placebo like the strength of placebo's

33:16which just, those two things together just make you very intrinsically skeptical. So are you nervous even about something like a Zempik that tons of people take? I mean, a Zempik has been used to treat diabetics for a long time, right? Or at least, a GOP1 icon has had been. And so I'm not that concerned about it. And even, so it's been through, the GOP1 icon has not been through many, many robust, well-controlled.

33:43Trials and so I don't worry about that a lot of these peptides where people just go in and they find some hypothesis and they're like great like let's just take this peptide and inject it into ourselves as I said before like you have no idea About if that peptide is even like staying in your body from long enough to do anything Right, let alone if it is like actually doing what you think it's doing if it's having the effect these are like separate concerns, right? Like does it not do anything or does it hurt me? Yeah, those are different concerns, right? But like The issue, I mean, I'm also just a scientist, right? And I think the issue is that when you think about, like, trying peptides on yourself, not never been studied in a robust trial setting, you know, you don't know, you actually have no way to know whether they're working, really. And people, they can make people feel better. That's great. I'm, you know,

34:43Placebo's also make you feel better and so that doesn't teach you doesn't oh you anything that makes you feel better Right even if you say oh look at everyone who takes it feels better. Yeah, that's the point. Yeah, I know That's right and if like this group of people were systematically, you know, even having like a very large the group of people taking some of these peptides even systematically had like, you know a 10% incidence of like cardiac arrest or something We wouldn't know. No one is looking, no one is tracking those adverse events when people just go and dose themselves or pet that, right? And so I think that now I don't want to sound too much like a, you know, so my recommendation would always be like, probably don't, you know, I feel like I have a professional and ethical or more obligation to say to people like, generally doing this is ill-advised, right?

35:42But this is totally off the record, so you can say anything you want on this podcast. Yeah, I'm sorry, completely off the record. But then again, I will say, I do admire people who have a lot of admiration for the biohacker movement. I have a lot of admiration for people who go out and want to just try things, because I'm a fan of just trying things. We just need to make sure that you just need to be clearied about what the risks are.

36:11because there are no guarantees with a lot of these things. And they can do damage, I mean, to the field. Would you change anything about the clinical trial process that we have in the United States if you give... So the flip side of what I just said about peptides, right, is that part of the reason why people feel...

36:32Compiled to go and test things on themselves is because like the the process for testing them in humans in a robust and rigorous and more controlled manner is so onerous and is so Take so long right like you know if it were possible to go and run like really high quality well controlled studies in a way that is like you know well-regured and so on quickly then maybe We would you know, people would be doing that instead, right? Which would probably be better. And so there are a lot of things, there are a huge number of things that we should be doing. The first one, which is just the most obvious bone-headed thing, like absolutely we should be doing, it's crazy that we're not doing this, is that in Australia and in China, for early stage studies, the process of getting a study approved, a clinical trial approved, is decentralized, okay?

37:28relative to the way it is in the US. So in the US, you need to, even just to do like a small scale initial trial, you need to get central approval from the FDA. And in China and in Australia, you are individual centers that run trials that are capable of approving trials, which then means that those centers can compete for how you know, how easy they can make it to do trials while maintaining within the regulatory, while saying within the regulatory guidelines, right? And so that is a drive for efficiency. And that is great. And as a result, like US biotechs are going to Australia and are going to China to do their clinical trials. This is obvious, right? We should be fixing it.

38:16And actually, if there's any administration that is going to fix it, you would think that the born or China shop kind of approach that this administration takes would be a great candidate to do it. And so they need to be doing that. And then I think that there are other, so that's just obvious. I feel like most people would probably agree on that.

38:40There are other things that we should definitely be looking at. One of the more obvious ones is loosening the requirements for efficacy. So the FDA requires you to prove two things in order to go to, in order to proceed to get your drug approved. They require you to prove that your drug is safe and they require you to prove that your drug is effective.

39:10for treating a specific condition. And actually, drugs usually fail the second. They usually fail on efficacy. And the thing about this that's a little bit perverse is that often even drugs will work in a specific subpopulation.

39:26Like you'll work in one population of patients, but it does not work in, you know, the entire population of trial. Therefore, the trial fails. Therefore, it can't get approved. Even though it works on subsets of patients, that feels like a failure of the system. The alternative is to only require that drugs be proven to be safe, right? And then determine that they are effective in the course of using them on patients in the field.

39:53Right, now you can imagine that sometime that this would like not be ethical and this would not be something that you would want to sign up for in all cases. Like if there is a standard of care that is known to be effective and then there's a drug where it's like we know it's safe, but it's not necessarily effective. We don't know if it's effective. You might choose that you want the effective one on the flip side if the effective one doesn't work for you. Right, and you have no other options. You might choose to go with this other one and that I think we'll probably reduce the barrier to doing clinical trials in many diseases, right? Not always, but... It's hard to know what's effective, right? I feel like I have lots of smart friends that take lots of medicine or things like that that they think is effective. And in my mind, I'm thinking probably not because the science doesn't show that it's effective. I'm actually not even sure who's more likely to be right. But what you can do, absolutely, but...

40:44So the way that we deal with this is with randomized control trials in When you're in the standard process, right? So you have Patients and half of them get the actual medicine half of them get Either don't get the medicine or more often just get the standard of care because it's usually you would consider unethical to deny a patient a medication depending again depends on the condition depends on the disease but But the other way that you could do it, if we were to relax the FXZ requirement so that you only have to show a drug is safe, then you could look at the extent to which a single patient improves, like you could look at within patient improvement. So if you take drug A, if you have depression, and obviously a famously anti-depressants,

41:36are very variable in whether they work in and give an individual and so maybe you take anti-depressant number one and it doesn't work for you when you're something's processed and then take anti-depressant number two and suddenly you get much better that's like you know that's pretty good as far as evidence goes because like the only variable there that has been a change is which medicine you're taking right that avoids the placebo problem to some extent, right, because the issue with the placebo is you go from not taking medicine to taking medicine. And simply taking a medicine is effective often regardless of whether medicine works. The other way that you can think about doing it is like patients who are on drug A, which is known to be effective versus patients who are on drug B, which is not known to be effective. If drug B is more effective, then like if the patients on drug B have a better outcome than the patients on drug A, that's very strong evidence in favor, right? Although I'd imagine this all sounds good in theory, but then

42:34I think it'd be very vulnerable to pee hacking. If you, you know, if you're just sort of doing it, these natural experiments in the wild and tracking everybody, yeah, they're looking for effects like this. P hacking is not, um, especially I think it's usually something you control after the fact pick your sub population. Right. So that's a problem. So picking the sub populations post hoc can be a problem. Although, again, this is, it's, it's really It just becomes a question of power. So p-hacking is just always a question of power. Except that you don't know all the possibilities that were considered, right? Well, you need to kind of control for that. A pre-register what you're going to look at. Let's just take this example of...

43:20Let's take the example of vaccine because vaccines, real-world evidence for vaccines is very unambiguous because you either get the disease or you don't get the disease, okay? And so if I go out and I have a vaccine, I don't know if it's effective and I just give it to a bunch of people. And then afterwards, and let's say at a population level, it's not effective, right? Which is to say people without the vaccine get the disease at a rate that's indistinguishable for people who get the vaccine.

43:48Then I'm going to go in and I'm going to look at, like, oh, but what about, like, you know, just men, women, like, or, you know, men under the, above the age of 18, but below the age of 35, or, right? Like, I'm going to go and look at all the different sub-populations. And inevitably, I will find one where it's like no one in that population got the disease, and therefore, I'm going to say, it's 100% effective, and you're going to be like, no, it's just p-hacking, and you'd be correct. You know, if I have like a thousand patients, maybe. Let's imagine that I have 10 million patients.

44:17and across 10 million patients, I still find that somehow men between the age of 18 and 35 who get my vaccine never get the disease, whereas all other populations get the disease at the same rate. No, that's not P-hacking anymore, that's obviously not P-hacking. Something is going on, we don't know what it is, but that's obviously not P-hacking. That's why I just mean it's a question of power.

44:44that if you get enough real-world data, you can robustly go back and do and kind of identify these sub-populations in which a drug is effective without worrying about p-hacking. Now, you may still want to go and do more experiments because you may have no idea why, right? And you may want to go and do a confirmatory experiment, right? But, you know, you can imagine that It's better to be able to go back in and these find those hypotheses Then it is just like have the trial fail and I mean how often do we have real-world data at that scale where there's not sort of weird selection bias? Yeah, so vaccines are are often given at population scale But you know, you could imagine actually antidepressants are a great example. You could imagine getting enough Real-world data on antidepressants are able to be able to do this. You know the more common cancers

45:40I mean, cancer is a specific thing because the treatment is obviously life or death. And so the treatment is, you know, you need to be more careful you never want to deny cancer patient, the standard of care, right? But, you know, the common cancer is breast cancer, colon cancer, and so on. You'll have, you know, hundreds of thousands, millions of patients. And so I think that, yeah, obviously, for rare disease, you know, you won't get a huge amount of real-world data, but also for rare disease, you don't have that many subpopulations usually. Right. Do you think that a model like yours could go through the existing real-world data and find new patterns you haven't seen before? Absolutely, and we do that. We have several clients that we're working with that end up those kind of work. The challenge there is always the quality of the data.

46:37which is to say that real world data is gathered in the real world and real world people as probably many people watching this understand do not always go to their follow-up appointments do not always take the medication on the schedule that they're supposed to take it on do not always you know doctors do not always take high-quality notes they, you know, patients move between healthcare systems and then you lose the records end up disconnected. And so for all these reasons, like the real world evidence is never, existing real world evidence is like never as good as you want to be. And there are a number of great companies that are trying to fix this. One of them is a company called Empower Medicine, which is focused on gathering just extremely high quality kind of electronic healthcare record.

47:32data from patients so that they, you know, you can kind of design synthetic clinical trials. There are other companies like Tempest, Komodo, and so on that do this as well. But- I mean, I guess in the single treatment case, it makes sense there will be a lot of public research and someone who would want to decide once and for all is this effective or not. I've found in my life, when I have medical issues, they feel high stakes.

48:00And when I go look at the research and the data, it never seems clear. Often people, there's conflicting research and every single data set seems biased in different ways. And I find myself more and more using LLMS to try to synthesize the research into making like a sensible decision. For example, fertility was a big issue for me and my family.

48:21And it's expensive, and there's upsides and downsides. And I really wanted a model like yours. And I think I asked you to use your model for a recent thing that I was looking at. Do you expect that people might use your model in that way to look at their individual specific situation and try to go out and dispassionately synthesize the research into a decision? I mean, my mother does.

48:46No way, tell me about that, that's cool. Well, I don't know that my mother wants me to air her show. For medical history on a project. But this is totally off the record, man. I know I forgot. Yeah, yeah, yeah, exactly. No, the look, I have a bunch of friends who, you know, who use it.

49:08I have one friend who had a breast cancer scare, for example, who wanted to figure out if they delayed treatment, if she delayed treatment. She had a reason why she wanted to delay treatment for a month or two, and if she delayed treatment, what would that do to the recovery or survival odds? Luckily, she turned out to be fine, which is good.

49:37One of the things that our platform is very good at is going out and dispassionately surveying the evidence and giving you an answer that is really directly grounded in hard evidence from papers, from the literature as opposed to vibes. All right, actually your recent father, have you used these models or your own model for your child yet? Yeah, so we have been lucky enough so far that we've not needed to use them for medical reasons.

50:07Um, but, um, you know, 100% I ask it things like, you know, when does when will my baby start to walk and crawl and so on? Yeah, yeah, yeah. I'm sure you will. I actually I will tell you I had a recent interesting experience with my life where we both.

50:22put in the same symptoms and incident with our daughter, actually she hit her head. We both described it identically, and the model told me it's fine and told my wife to take the child to the doctor, which I think might have been a different kind of incentive in the model of sort of, I do feel like they kind of want to tell you what you want to hear. Maybe sort of some RLHF kind of gives it that, so I wonder if it sort of implicitly knew.

50:46or it has the history, part of each of you. We know who we are, it definitely is. Like I said before, what we focus on is making sure that our answers are grounded and fact and scientific. But it does mean it can be frustrating. So you will go, you can ask, chat you be here, you can ask, Claude.

51:04you know, about if you should take your child to the doctor and they will say no child's fine. If you ask Cosmos, our agent, if you should take your child to the doctor, Cosmos will probably come back and say, you know, children who like hit their heads have a, you know, X percentage chance of developing like, you know, this serious problem and Y percentage chance of doing this and then it will give you something like, you know, on average, like, there's like a, whatever, 97% chance that if you don't take your child to the doctor, they're gonna be fine. And that can be more frustrating sometimes. Honestly, this is fantastic, I love it. I love it. Yeah, but.

51:44It's a question of actually like how much information you want, and it's very interesting to think about. We're entering this era where information and intelligence are just like so much more abundant than they were before, but it's not always good that we're gonna want that, right? Like more information is not always better for making decisions, right? Totally true. Although I don't like to admit that. I know, but often it's like you need the right information.

52:14Yeah, but not the most right? So open AI and entropic and deep behind obviously are working on similar things to you at least in the sense of like deep research making the models more advanced making agents that think more Do you feel like you need to keep some kind of structural advantage over what they're doing? I mean Yes. I mean, if you're building a company in general, you want to try to have a structural advantage of what other people are doing. Okay, so what is your structural advantage? Right, great question. So there are a couple of ways to think about this.

52:58Um, the, the, the, the, the, the, we were you yawning through my question. Yeah. It was a bad question. Yeah, it's great. Sorry, man. I just, and my whole life gets my whole life. I get asked always like, you know, open air. I have brought me to do this. I was like, oh, I'm giving it a shuffle, man. Let's go. No. So, um, so now I'm worried. Is this an illusion move on? No, no, no, no, no, no. No, no, no. It's a great question. All right. I've done just teasing you. It's a great question. Um, the, So the way that, you know, fundamentally at the end of the day, opening eye anthropic deep mind are focused on building, you know, AGI, or artificial superintelligence or whatever, right? Now, I think the key thing to understand is that having a specialized model for a particular task

53:57is you should expect is always going to be better than having a generous model for that task. Even when you have super intelligence or whatever, and the reason is just that the generous model has to do everything in the weights, and the specials model only has to do something. Wait, do you have a specials model?

54:23Absolutely, we train reasoning models on specific scientific tasks. I see. And what we see is that with a very small amount of data, you can get enormous gains for those specific tasks over the frontier models. And so that's the first thing. The first thing is just having specialized agents, specialized models, which also, by the way, comes with specialized user experiences. Like I'm mentioning, we put way more.

54:49test time compute, interminimizing hoods and nations, then the others would also use the bigger models, right? Yeah, yeah, yeah, absolutely. We use the bigger models in some areas, right? Like, we don't want to be better than Anthropic at coding, for example, right? But when it comes to like very niche biological reasoning, we have our own models internally that are superior. Doesn't the coding thing actually show an example of a specialized, I mean, these coding models are not really specialized in coding, right?

55:18So, they definitely, like, if your question is like, is it also true that if you had a specialized coding model, could the specialized coding model beat, like, you know, GB 5.5, or beat? It seems unlikely. You think it seems unlikely? Well, I think a lot of people are trying to do it and they haven't. I think they haven't yet. I kind of, I mean, Coding is very special because I think all the labs have realized at this point that coding is the thing that they need to be really, really good at. And so maybe it's not true in coding for that reason. But I can kind of guarantee you that if you want a model that is good, if you want to train a specialized model for reasoning about synthetic chemistry, synthetic pathways.

56:13I think that the specialized model is going to be better than the generalist model. Now, the place where this may fall down is when you get to intelligence saturation. The thing I tell people about saturation is that, if you're thinking about tic-tac-toe, any super intelligence will be exactly as good as humans at playing tic-tac-toe.

56:33No amount of intelligence above humans will improve performance at TikTok. Because humans are optimal at TikTok. You can always... Some humans. Some humans, not all humans. But there exists humans who are optimal at TikTok. Similarly, you should imagine that at some point, the intelligence will get strong enough that we will have saturated. That more intelligence will not improve your ability to reason about chemistry.

57:03I don't know where that point is, but it's feasible that we would get there. And then at that point, my argument might fall down, right? Except, you know, in so far, yeah, at that point, the argument might fall down. But so that's like the first thing, the second thing, but I think we're very far away from that point, right? And this is why the second thing that you think about is that all of the pharma companies have their own internal data sets. They need to have proprietary advantages over each other. They need models that are trained on their data because if everyone has the same intelligence, then no one has any advantage in R&D. Those are the areas where we really...

57:56where we really shine and the reason right now why all pharma companies or it feels like all pharma companies are fighting to do a deal with us and we're just overwhelmed with demand is because we have the best models for science. We do the last mile integration to get those models deployed internally and actually really accelerate the pipeline and we train on their data which provides them with their sustainable advantage, which is something that at least today, Open-Aid and Throbic don't want to do because they don't want to have a different model for every customer, right?

58:26It's kind of interesting. I feel like when I talked to you six months or a year ago, you talked more about the sort of agent framework and the evals that you were doing. Has everything changed or is that less exciting to talk about? No, I think that we have matured or we are in the process of like our kind of the way that we think about what we do and about the value proposition is matured, right?

58:56It remains the case that, you know, it remains the case that our agents are, I think, the best at Tions and are substantially further ahead of the offerings that, you know, Anthropic has and that opening of has and that GDM has and so on. And I think that we'll be able to maintain that edge for a while, right? But I think that, like, when you think about the macro dynamics of, like, what will the market look like in...

59:24you know, three years or four years or five years. There are a couple of key things happening. The first one is that these companies need models that are trained on their data to maintain their advantage. The second one is that they don't want to be locked into a single model provider, so they don't want to be locked into just Anthropic or just OpenAI or whatever, because last year OpenAI was way ahead and today Anthropic feels like their way ahead and whatever, right? And so those dynamics are kind of pushing push customers to work with us.

59:54Do you think when you look at the major model providers, they're different enough that using them for specialized use cases is a valuable thing to do or are they essentially interchangeable? This is a great question. I think that at the moment they are mostly... They're spiky, right? So I think at the moment actually... But are the spikes overlapping or are they...? I think there's a lot of non-overlapping spiky. Oh, interesting. So...

1:00:21Give me one example. Yeah, great. For example, we have a benchmark that we're building that we will release shortly that involves reproducing analyses that have been done in scientific papers previously. And at least on our evaluations, as of when we're recording this, it may change. But at least as of the last day that I saw, Anthropic is particularly good at that.

1:00:48more significantly more so than OpenAI or significantly more so than DeepMind. Another good example, which is actually maybe even makes the point to a greater extent when there was a time early in the development of Cosmos where there was a specific task that we needed the models to do in the process of updating Cosmos' world model. And at the time, the only model that we could find that was able to do that task is Gemini 2.5 and like none of the other models we could not figure out how to get any of the other models to do that task well which was wild. And so there's a lot of that spikiness right they have different say different personalities obviously so it's also interesting but yeah we've been surprised at the extent to which you can get real you can actually get much better results on tasks by

1:01:47by combining models across different providers. Oh see. When I fire up like Cosmos today, how long does it run for, like how many calls are making? Yeah, great question. So we released a version, we released like the original version of Cosmos back in November. And that would run for like six to 12 hours. Would write 45,000 lines of code.

1:02:13would read 1500 papers in a single run, and it was just way more powerful than any agent that anyone had seen. And he's still today, I think, one of the most powerful, most intensive agents out there.

1:02:26The issue with it was a UX issue, which is that like actually if you are a scientist and you're doing some research, you like don't really want to just like ask a question and walk away and come back six to 12 hours later. And so we have just recently announced a significant update to Cosmos that now puts a kind of interactive front end on it so that it can still go and do those extremely long running tasks. But is more But you can give it feedback throughout. You can steer it. It's more interactive, right? Let's go to rapid-fire random questions here. Awesome. Yeah, let's do it. All right, you said I saw that you said scientific progress has slowed down a lot. That was different than how I think about scientific progress. What did you mean by that?

1:03:10Well, that depends on which time I said this, but you're talking about. Do you still stand by that point that I pulled out of context? Okay, it is empirically the case that progress in medicine has slowed down. What does that mean empirically? This means that we have in medicine was called e-rooms law, which is more spelled backwards, which is the observation that the amount of money in real terms that it costs to develop a new drug has doubled.

1:03:38Um, uh, over the past, whatever, 40 years. And I think I, I forget what the devil would have is it might be as doubled every nine years or something like that, right? So like, you know, prices on some of the connectors have come, have had this exponential drop, prices on drugs, but this exponential increase. Um, and there are various reasons why people have argued that this might be, it actually broke that trend broke in around 2011 or 2012. People think it's largely because of the human genome project. Actually, the human genome project made it easier to find drugs. But now it's about flat, and certainly productivity is not yet increasing. Is that because we found all the best drugs? One of the reasons maybe a lack of low-hanging fruit. One of the reasons maybe what is called the better than the beetles.

1:04:28problem, which is that if you have a drug that treats a condition X, if you have a drug that treats depression, maybe, for example, whatever, you have a drug that treats colon cancer. You need to come out with a drug that is better than that drug in order to...

1:04:47be in order to gain market share, right, in order for it to get approved, even just in order for it to get approved, and in order for it to be used. For it to be useful. Exactly. Why are you doing it better than the Beatles? Just because there's this observation that the argument is, the Beatles are still extremely popular. I see. The Beatles have not been displaced as a band.

1:05:08right they were like first any band that comes afterwards like if the bill said I existed maybe there would be some other band and like they're still good bands but they like don't get as much you know, they're kind of like early rock bands, like most of them don't get as much share as the Beatles because they kind of like took that space in the contestant. I don't know if that's the case. So basically they're like all disease of and cured. No, but this is the thing they haven't been cured. Right. It's just that we can't find better treatments for them. It's just like no one is going to go out and argue that depression has been cured or that colon cancer is cured, right? But you know, if you want your drug to get approved, you have to be better than the the the best in class.

1:05:48But you guys think that's some kind of, I mean, that's a, that's a problem. You, of course, need to be better than the best in class, right? But that's just getting harder, right? It's getting harder to come up with. And because you don't, you don't build on, in semiconductors, right? You were able to build on the previous innovations, right? In a way that is less the case in medicine, right? You don't, why not?

1:06:14Well, because if you have a drug that uses a particular mechanism in order to cure cancer, or to fight cancer, you need to come up with a different mechanism. Like there's only so much that you can get from juicing that specific mechanism, right? You can optimize the drug, you can make it marginally better, and so on, and there's a lot of gains that you get out of that. But then like, you know, it's often the first in class drugs that are, that lead to the dramatic improvements. I see. Right. Interesting. Where it's like, there's some new mechanism that we're using.

1:06:44Yeah, and so I think that's a science is moving more slowly for that for that reason I also just think as in the case of physics, right like as you discover more things It just becomes harder to discover more things, right? Interesting. All right is a PhD or formal science theory still worth pursuing? Right. Wow. Yeah, good question The I don't know If I were starting my boy answer you can yawn your turn to yawn I think that the answer is yes because I don't think that like human researchers are going anywhere anytime soon and fundamentally like the point of a PhD is to learn how to do research right and if you never learn how to do research you definitely like are not going to be effective at doing research using the but aren't you a lot of many research

1:07:39we are, or like, certainly accelerating it. The question is, the more interesting question is, like, are we gonna need human scientists? Yeah, it's a related question, for sure. What do you think? I think the answer is yes, for a substantial amount of time. And the reason is that science is nonverifiable. And so, fundamentally, at the end of the day, we need human scientists for their taste. Or there's like, I'm not sure that it's gonna be, like, maybe in 20 years, on a five, 10 year time scale, I'm not sure that we're gonna get to a point.

1:08:07It's not clear to me yet whether we will get to a point where the models will just be like obviously better taste than the humans, right? Well, it's interesting because here it says you said language models will eventually be better than humans coming up with ideas. Yeah, but but like you think it that's far out there. I mean, eventually and and better exists along many different axes, right? So better can mean you know, better can be mean more likely to work. I mean, I think that's definitely the case, right? The language wants you way better than humans are coming up with ideas that are better, that are more likely to work, right? But when it comes to like specific like which of these problems is the most interesting to pursue, it's just harder because it's not verifiable.

1:08:53Like, this is just uncertainty, right? Like, you know, like I said, I'm a scientist. I admit when I don't know things, I don't know whether or not we're going to get to a point within five years where we're just like, oh, you know, there's no point in asking, you know, Francis Arnold, Nobel laureate, right? What she thinks about evolving proteins is because we can just ask the model. I mean, you know, I think it's a pretty tall bar. And when I look today at like, I'm going to go and ask Cloud 3.7, what it thinks we should be doing in using direct evolution to improve chemistry, or to open up new avenues in chemistry versus asking Francis. There's no question that Francis is going to have better ideas. Interesting. Today. Today. In two years, we're going to be sitting here again, you're going to be like, well, you have said that. In this off-the-record podcast.

1:09:51All right, so, um, we're just gonna work going forward to tell guests it's off the record. Um, all right, shop them Alex rules. Yeah, yeah, the mouse rules. I was literally just in an event where I was on a panel and they're like, it's off the record, you know, and I was like, looking at I was like, what about the cameras that are pointing to me? Oh my god, nothing's off the record. All right. So, um, you, you've got quite a lot of money. Yeah. I think I have 70 million in my notes. That even accurate. Yeah. That's right. 70 million. And but I was just the for profit, the nonprofit raised more.

1:10:21And, you know, I think, unlike most of the labs, the Neo Labs and the kind of companies that I come across, most of your investors are actually folks that I don't know. They're kind of coming from more the formal world. I think, why do you think that is? Well, we, for us, success is getting inside of the pharma companies and the pharma investors are the ones who have that capability, right?

1:10:50We get very high value out of all of our investors. So Yasmin Razafi is an investor at Spark who co-led around. And she is absolutely incredible. She's on the board of Anthropics. She led their first VC round and has extraordinary perspective, extraordinary insight and has really helped us with talent and so on. But when I think about the concrete Like business traction the value that you know the investors we have who have contributed the most value Jeff Huber who was the CEO of Grail and somehow seems to know literally every single person in pharma and then the We have a unknown as they like to call themselves an unknown or

1:11:43What do they say? An unnamed, institutional biotech investor, very large, unnamed institutional biotech investor, which would be displeased if I said in this forum, in this off-the-record forum, who they are. But I think most people who are in biotech will know what that means because they're very well-known. And they have been extremely, they also co-led around. They have been extremely valuable at in terms of setting us up with high level connections into companies and getting us deployed. It's interesting, so the people actually know you feel better are more bullish on you than the maniacs doing AI investment in Silicon Valley.

1:12:24Yeah, I mean, I think the maniacs, I mean the maniacs are very excited about many things. Right. Are very excited about the concept of, you know, we're gonna automate science. I mean, everyone's very excited about, let's go automate science, let's go automate drug discovery, right? But the insiders know the problems. And so the insiders really viscerally know where the value is, right?

1:12:50Let me put this way. I don't get any of the insiders asking me how we're differentiated versus Anthropic and OpenAI and Google. I see. I get asked that every single tech investor will ask me why won't Anthropic and OpenAI and Google do what we're doing. But anyone who has operated inside a pharma company who has large positions in pharma companies and biotechs, we never get that question from them.

1:13:16Okay, this is a long history of AI over promising and drug discovery. Oh, yeah, is this time different? Yes, I promise. I promise you this time it is different. Yeah, man, back to like 2012, people have been saying that pharma, AI is going to revolutionize pharma and it's been one flop after another with the notable exception of alpha fold, right? Alpha fold really changed the way that a lot of things are done in now.

1:13:43It changed the way a lot of things are done in one area of drug discovery development, which is kind of the actual process of coming up with the molecule because you have the structure of the proteins. But I think there's a ton of skepticism. That said, it is kind of crazy to think that this time will not be different. We have so much evidence already that this time it's really going to be different. But I think the thing I do want to emphasize is the proof is in the pudding and the pudding is approved drugs.

1:14:13So you're gonna hear a lot of stuff in the next couple of years of like my model came up with this this drug and my model, you know Like you know discovered this fundamental aspect of biology and so on and anyone who has been in Pharma is not gonna care until they see the outcome of a pivotal phase three clinical trial or an approval letter from the FDA Right, so it seems like replaced and yeah, thanks then cool Thanks so much for listening to this episode of Great In Descent. Please stay tuned for future episodes.