Faster Science, Better Drugs

26:38Like those are the two fail modes and that happens all too often. And so I think a lot of the stuff that Patrick's describing is going to basically improve our hit rate or our batting average on figuring out what to go after and then making the right thing to go after, set thing. The challenge we have, I think in the industry, is that the bottlenecks still are the bottlenecks and the biggest bottleneck we have, which is a necessary one is we have to prove that whatever we make, that we have the right thing to go after the right thing, so to speak, and that when we have it, that it's going to be as de -risked as possible before you put it into humans.

27:13And we have to be good at making them in the first two. And we have to make them too. Yeah, exactly. And so that bottleneck is a necessarily important one. That bottleneck should exist. I'm not suggesting we better remove it, but are there ways to reduce the cost and time associated with getting through the bottleneck of human clinical trials. And it's interesting because we talk about all of the very stakeholders when you're making a drug. There are the companies, there's of course the science that supported the company that's trying to commercialize a product and they're the regulatory agencies.

27:47Everyone is trying to ensure again that what's first and foremost is the ability to to discover and commercialize drugs that are safe and effective for humans, that middle part of actually getting through that bottleneck is hard to speed up in a very obvious way. Like you can increase the rate, the way you enroll clinical trials. You can use better technology to change the way we design these clinical trials. So maybe they can be faster or shorter, et cetera. But some of them just have a natural timeline. They have to go through. Like if you wanna demonstrate that a cancer drug promotes survival, guess what?

28:19you're going to have it's going to take some time to demonstrate a survival benefit or if you know you want to do a longevity drug That by definition is a lifetime You know of a trial in terms of length. So there's a lot of these bottlenecks are really hard to get through So what helps the industry? I think there are a couple of things that help the industry one is capital intensity Well, hopefully at some point go down over time as technology gets better Capital intensity is something that our industry faces in some ways it looks a little bit like AI now, right? In terms of the cost of training these models, but the capital intensity is very, very high.

28:53That has not come down. So we got to get to success rates up to impact capital intensity to get it down. The second thing is, where can we compress time? So good models can help us compress early discovery time. We still haven't seen, and I think it's coming, but it hasn't happened yet. We haven't seen artificial intelligence or other technologies massively compress the amount of time it takes us to do the clinical development, the clinical trials, the enrollment of patients all those things. We're seeing some interesting things coming. We haven't seen sort of the payoff there yet. And the third thing is if we can make better drugs, going after better things, the effect size should be higher.

29:31So therefore the answer should be obvious sooner. If we can get those three things right, reduce capital intensity, compress timelines, and effectively increase effect size in some very tough sort of intractable diseases, that is what I stage at the early stage in terms of being early stage investors, the reason why that helps us is if the capital intensity goes down and the value creation goes up, it becomes easier to invest in these companies in the early days because you get rewarded for coming in early. The problem we have right now is that most companies aren't, you're not seeing rewards happening when there's value inflection.

30:11So you come in early, you bear the brunt of the capital intensity, and even if a company successful, that success isn't reflected in the valuation. So we're not seeing the stepups that you see in other parts of the industry. And that's just really, really hard from an investing standpoint. So I think we need to see those various factors addressed for this space to really get fixed to use your word. Yeah, that was great. I have a lot to add on to this. Please, add away. Just a few simple observations. The first is the The amount of market cap added to Lily and Novo based on the development of GOP1s, it's like over $3 trillion is more, I mean, no stock has decreased a lot.

30:53So $3 trillion, let's say, is more than the market cap of all bow tech companies combined over the last 40 years have been started. And I think one of the kind of interesting kind of core layers of this is that when we have a 10 % clinical trial success rate for preclinical drug matter, you tend to circle the wagons a bit and try to manage your risk. The way that you try to go after really well -established disease mechanisms, where if I developed new drugs that go after well -understood biology, it should work the way that I hope it will in the trial, which is really expensive and cost a lot more in many ways than the preclinical research.

31:38The problem with this is you go after very well validated disease mechanisms, but with really small patient populations. Then the expected value of this actually is relatively low. One of the things that we've seen with GOP1s is just the value that you can create when you go after really large patient populations. And I think that has culturally ruling net increase the ambition of the industry both from the investor and from the drug developer side. And I think that's something that we should keep our foot on the gas floor. Yeah, and look, I think the trend on that is positive. I would argue the trend on that is positive.

32:21It's perhaps the right, like the demonstration of the value that has been created with the use of increasing use of GLP ones. and the value transfer that's gone to companies like Lily and Nova, I would argue, is very merited because they've cracked an endemic social problem in terms of managing diabetes and eventually helping manage obesity. And so I think that's remarkable. And there's a lot of value that goes to that because they tackled, they cracked a very, very challenging problem for society, beyond just science. So that's great. And I agree with you, like, the prize, the juice needs to be worth the squeeze, right?

32:59You're right, a lot of biotech has been around, like go after the low -hanging fruit because it's low risk and we gotta eat today. So you go get it, you know, and you just have to push off the big ambitious indication, the large population, or the really tough to crack disease. But, you know, I do think we're seeing more and more of that. And by the way, like we can get into some of these genetic medicines, but some of these genetic medicines are going after some of the hardest problems. So things that you quite literally couldn't address, but for editing, you know, DNA. And, you know, I think that's incredibly, remarkable and lottable and frankly inspiring.

33:31But the fundamental element of the industry have to work, so the capital formation is there to support those kinds of things. And right now it's hard, right? Because of the issues we talked about before. Fifteen years from now, we're back in this room. We've barely escaped being part of the permanent underclass. And we're reflecting on the, on sort of the GPT -3 moment or maybe Maybe the legacy of GOP wants sort of beyond where they are now. What do you think could be or where I'm curious to do your take on what do you think is going to be the technological breakthrough that we're going to point back to and say, oh, this is really what set it all that already think it's going to be sort of, you know, multifactored combination.

34:09Yeah, look, I think it's going to go back to sort of where we started this conversation, conversation, excuse me, GOP wants as a drug or, you know, what are four decades in the making or something like that, you know, these are not overnight successes. But I do think what we are going to see more of in our hope is that when you combine the fact that we're getting better at understanding what to target, getting better at designing medicines to hit those targets. By the way, in a whole array of new creative ways. So we have small molecules, the natural products that we got from boiling leaves, as you said earlier, like those have gotten, we're getting really good at designing smarter and better, smaller, small molecules that do new things, that function in ways that they didn't before.

34:56We've gotten quite good at designing biologics or proteins with a lot of help from things like alpha -fold that helps understand how proteins fold. We're going to get a lot better at designing some of the more complex modalities like the gene therapies of the world or the gene editors of the world. And when you can do that and combine that with our ability to hopefully use things like virtual cell models to really understand what to go after, like we're going to have drugs, I would hope and I would expect that the industry will continue to bring forward drugs that have very large effect size for very difficult diseases that hopefully affect a lot of patients.

35:28If that's true, then we'll start to see some of these really, really difficult diseases that affect all of society get tackled. Hopefully, you know, one by one by one by one. And so we have obesity. We have mental bog disorder. We're dealing with cardiovascular mental bog disease. We're starting to see interesting promising things happening in like neurodegenerative diseases, you know, if we can, you know, tackle cancer or at least, you know, several cancers that now have begun to be treated more like a chronic condition than a death sentence that they were in the past. The more we see of that, like, I think that value to society will accrete over time.

36:03And I think this should be an industry that is extraordinarily valued by society and candidly by the markets. We have to deliver. If we play this out, and let's say these AI models work, and you can make a trillion binders in silico that will, you know, exquisite drug matter, right? We still need to make these things physically and test them in animals and hopefully predictive models and then actually in people, right? And I think, you know, that will increasingly be the bottleneck in many ways, right? And, And my friend Dan Wang recently released a book called Breakneck, which talks about the US and China and the difference between the two countries and their philosophy the way they approach markets.

36:53Where the country of lawyers or country of engineers? Exactly. China is an engineering state. It's kind of polyp bros, folks who have engineering degrees, you need to build bridges and roads and buildings. And these are the ways that we solve our problems. Whereas I think from, you know, the first 13 American presidents, 10 of them practiced law from 1980 to 2020, all democratic presidential candidates, both VP and president went to law school. Right. And so you kind of see the echoes of that in the FDA and the regulatory regime and, And all the bottlenecks that people talk about developing drug stateside.

37:39And increasingly, you see folks thinking about how we can run phase one's overseas, build data packages that we can bring back domestically for phase two efficacy trials. I think that's interesting, directionally, but it's not enough. And I think we need to figure out these two bottoms, the making and the testing. Even if we can solve the designing part. Oh, I agree. Yeah, that's the bottleneck. Yeah. You know, we joke about it. And you have to do is you have to get a molecule that can go, you know, first in mice and then in muts and then in monkeys and then in man. Like, there's, you know, that takes a long time.

38:16And it's so hard to compress that. And so when you do, you should make the journey worth, you know, make the journey worth it, right? So when you fail on the other end of that, like, that's obviously horrible. And so finding ways to make sure that when you walk that path, that it'll be a successful journey as often as possible is what this industry desperately needs. Alpha fold solved protein folding problem, but what in itself, judge discovery, or more broadly, what would it take to get AI drugs? What is sort of the bottleneck on the on the sex side at least? On the sex side? Maybe another way to ask the question is that because I always ask the founders a version of this question like the AI ones that are like, oh, we're going to do AI for a life for drug discovery.

39:01So my question that I was like to ask founders is give me examples where you think AI is hyped. Yeah, potentially overly hyped. Um, where there's real hope like the sort of what do we expect? What's next? And where we already see real heft. Yeah. So like if I asked you like an AI, we know where is their hype? Where is their hope? And where are we seeing heft today? I would say there's hype and toxicity prediction models. Okay. So that's the idea that we will say, I'm going to show you a molecule and you're going to tell me the model is going to tell me it's going to be toxic or not. That's right.

39:38Right. Right. There's heft in anything to do with proteins. Right. Obviously protein binding, but increasingly in protein design. Right. And I think there's real heft there. And then, you know, where there's hype is in multi -modal biological models, whatever that means. Right. And I think, you know, pick your favorite layers. It could be, you know, molecular layers. It could be spatial layers. It could be, you know, I mean, actually, I would say there's also heft in the pathology AI prediction models, you know, like, you know, automating the work of pathologists and radiologists. That's that's that's, yeah, powerful use.

40:17Yeah. Yeah. And there's a lot of stuff where you don't have to train, you know, weird biology foundation models and you can write, you know, regulatory filings and reports and things like that. That's impactful and important. So now I go back to Eric's question. Why hasn't AI turned out drugs yet? I think that was your question, right? You know, AI for drugs is one of these weird things where everyone who works in industries trying to claim that their drugs like the first day of design molecule, right? I feel like in, you know, I mean increasingly in just a few years, this will just be a native part of the stack.

40:57Just like we use the internet and we use phones, we're going to have AI and all parts of the stack, right? And so it's just going to become a native part of everything that we do. And so why hasn't it worked yet? Is this long multifactorial process that we've been talking about today? There's designing, there's the making, there's the testing, there's the approvals side of it. I think I do think safety and efficacy as the two pillars in the industry are the two things that we need to get right. We need to be able to figure out faster ways that we can predict whether or not molecule will work. If it's going to be safer not, I mean, there are ways that AI can operationalize this.

41:44If you designed a small molecule, you could now computationally docket to every protein in the proteome and see if it's likely to bind to off -target molecules. You can use this to tune binding selectivity and affinity that might be ways to predict, you know, safety and efficacy, right? And, you know, how will that work? Well, that's a feedback loop that we'll have to actually test in the lab. And that's part of what's slow is the testing, you know, takes real hours, days, months, right years. And you know, that's really why we've picked at our virtual cell models as our initial ledge because we think it can integrate a lot of these different pieces.

42:26In Dario Amade's essay, Machines of Love and Grace, he predicts among other things the prevention of many infectious diseases and the doubling of lifespan perhaps in as soon as the next decade. What's your reaction to his essay? His bullishness in some of his predictions? I think the core intuition that Dario had was the idea that sign like important scientific discoveries are independent, right? Or they're largely independent. And if they are, you know, statistically independent, then it would stand to reason that we could multi -paralyze. And so we had models that were sufficiently predictive and useful.

43:04You could have not just a hundred of them, but millions, billions of these discovery agents or processes running at a time, which should compress the timeline to new discoveries and turn it into a computation problem. I think that is a very futuristic framing for something that is actually very tangible today. If we can have original cell models at work, for example, that can start to do these kinds of things that we've been talking about help us, we can have molecular design models, we can have docking models, we can then have when you bind to this thing in this cell versus all the other off -target proteins will a cell be corrected in the right way.

43:53These layers of abstraction and complexity start to get to things that feel very tangible to drug discovery. If you could actually traverse these steps reliably and in sequence, you could start to see how you can get the compression, right? And so I think in the long run of time, this should be possible. One of the course depositions in building a good virtual cell model is that we are feeding it all the relevant data. The right data, yeah. The right data. And so we'll work to, you know, it's gene expression data or it's DNA data or, you know, any number of factors, protein and protein interactions, all the things you describe.

44:36What if we're missing a core element? Like what if we just haven't discovered the core or whatever? Like we just don't know what we don't know. And therefore, what we're feeding the model is fundamentally or importantly incomplete. I think that's almost certainly true, right? Like, it seems almost obvious that we're not measuring many of the most important things in biology, right? And you can of course find many important exceptions for any of these measurement technologies. Like in biology, we ultimately have two ways to study it in high -throughputs, imaging and sequencing, right? But there are so many other types of things that you would care about that those things aren't necessarily going to do a scale, right?

45:18And that's really why I think the stuff that we're talking about of the RNA layer as a mirror for other layers of biology is one that we've spent a lot of time thinking about. And there's a difference between a mechanistic model and a meteorological simulation type of model. So for example, if you want to predict the weather, right, you can build AI models that will predict whether or not it will rain next Tuesday, it won't explain physically or geologically or whatever, why and how that happens. But as long as it knows if it's going to rain next Tuesday, you're probably happy, right? And I would say similarly with a virtual cell model, it may not tell me literally why.

46:02Just like a alpha -fold doesn't tell me literally why did the protein fold this way and how. But it just told me the end state and it was reasonably accurate. I think that would already be very important. And shifting gears a little bit, we've been talking about science and biotech, but you're in addition, you're an elite AI investor more broadly. So I want to talk about how you're, I want to talk about where your investment focus is right now, just as it relates to AM more broadly. Where are you excited? Where are you spending time? Where are you looking forward to? Oh, yeah. My goal is to really try to figure out ways that we can improve the human experience in our lifetime.

46:38I kind of think of, like if I think about the future that we're going to leave to our children, right? There are a few things that if we get them right in our lifetime, we'll fundamentally change the world, right? And, you know, how we live in it. I think synthetic biology is obviously one, right? You know, think, you know, GOP ones, right? Things that improve sleep, right? Things that can, you know, improve longevity, right? These are all things that are kind of easy to get excited about. I think brain computer interfaces is another area where we're going to see really important breakthroughs over the decades to come.

47:20And then I think the third is in robotics, both industrial and consumer robotics, right? That allows to basically scale, physical labor in interesting ways. And you can see how each of these three things, even in the sort of medium cases of success really kind of changed world. And so I'm very interested in helping make these kinds of things possible. Right. And so there's sort of, you know, in the kind of techno optimist sort of vision of the world, right? There's a few different types of scarcity. Right. There's, you know, it's very easy when you do research to come up with important ideas. The hard thing is to tackle them in the right time frame.

48:05Right? It's like, you know, writing futuristic sci -fi things is not that hard. Being able to actually execute on it in the next five years or eight years, much, much harder. Right? And I would say, you know, academic discovery is littered with plenty of ideas that are interesting and important, but, you know, kind of long before their time. And in many ways, the story of technology development is, you know, trying to use new technologies to solve old tricks. Right? Like most of our tools are, you know, for productivity, right? In many ways, whether that's the industrial revolution or the computer revolution or the current AI revolution, we're trying to kind of do the same stuff.

48:43And, you know, and so there, you know, I think there's a relatively small set of very powerful ideas. New technologies give us new opportunities to attack them. And there's a set of people and teams that are going to be positioned to be able to do that. they need to have technical innovation and then an intuition about product and business in a way that, you know, you know, you kind of in the RPG dice roll of the skills that you get in these three domains, people start at different base levels, right? And, you know, you might have an incredibly technical founder who doesn't know how to think commercially, or someone who's just natively a very commercial thinker who, you know, it doesn't have very strong product sense, right?

49:24Even though they could sell the crap out of it, right? And And so I think these sort of, this sort of three broad categories of capabilities, you need kind of bring together in a way that you can allocate capital to in the right times in order to make these ideas possible in a really differentiated way. Like, this thing literally wouldn't happen if we didn't get these people together and fund it at the right time in the right way, right? And that's really what motivates me. And these are the kinds of things that I've been excited about, you know, backing, you know, longevity companies like Neuliman, right?

49:59BCI companies like Nudge, right? Robotics companies like the bot company, right? You know, these are some of the examples of kind of, you know, things that I think must happen in the world and man, therefore it should happen and you know, how do we actually find the right people and the right time to actually kind of Go on the fellowship of the ring hunt. Yeah, yeah If not too difficult, I want to ask Jorge's question adopted to these additional spaces, robotics, sort of BCIs and longevity of appropriate terms and the three questions I believe were, what's overhyped, where do you see an opportunity or path and what's got half to all right?

50:41I think the cool thing about agents generally is that they do real work, right? It compared to like SaaS companies that came before, agents replace real productivity. And I think they have a lot of errors today. And I would say the computer use agents will probably trail the coding agents by maybe a year. But it's coming and we'll follow the trajectory as these go from doing minutes of work without error to hours to days. And I think you're gonna get a completely different and product shape as we march through that across legal, BPO, medicine, healthcare, whatever, right? And we'll kind of follow that as an industry and that's gonna be really exciting.

51:30And I think that's where we're gonna see real heft is because mostly the economy service is spent. It's not software spent. And the reason why we're all excited about this stuff is that it can attack the services economy. And I would say, where is there hype? there's tremendous amount. That's that's that's no doubt. The hype is in the model capabilities. Right. And you know, we were working with an architecture that dates back to 2017. And if you look at the history of deep learning, it's like, kind of every eight years, there's something really different. And we feel like in 2025, we're really overdue for some net new architecture.

52:14And I think there are lots of really interesting research ideas that are bubbling up that could do that thing. And in many ways, there's a set of really interesting academic ideas, especially in the golden age of machine learning research from, I don't know, like 2009 to 2015, right? There's so many interesting ideas and little archive papers that have like 30 citations or less. And as the marginal cost of compute goes down year on year, I think you're going to be able to take all of these ideas and actually scale them up, right? Where you don't see the scaling laws when you're training them at 100 million or 650 million parameters like back then.

52:53But if you can scale them up to 1B, 7B, 35B, 70B, right? You start to see, whether or not these ideas will pop, right? And I think that's very exciting because because there's just going to be a lot of opportunity for new super intelligence labs to do things beyond what the established foundation model companies are doing today. In addition to these research teams, these are in many ways becoming applied AI companies. They need to build product shape and all kinds of different enterprises and do RL for businesses and make money, right? And I think or or or build coding agents and make API revenue and that's important.

53:37And I think, you know, a timely race to survive today. But I'm just, you know, a very blush on the research of say like a Sikana AI, right? Which was founded by one of the authors of Attention is All You Need, right? Ian Jones and they're doing incredibly interesting stuff on model merging and how you can have a evolutionary selection of different models in MOE. And I think they are opportunities here in the long run to move beyond just RL gyms, for example, also to figure out new ways to learn and find reward signals is going to be really exciting. It's a great place to wrap. Gearing towards closing anything upcoming for ARC that you'd like us to know about anything you want to tease, for people who want to learn more, which they know about.

54:34So AlphaFull, in many ways, came out of a protein folding competition called CASP, a critical assessment of the structure of proteins. And we created our own virtual cell challenge, at VisualSaleChallenge .org where we have, you know, $100 ,000 prizes sponsored by Nvidia and 10X genomics and Ultima and others. And it's an open competition that anyone can enter where you can train perturbation prediction models and we can openly and transparently assess these model capabilities both today and in subsequent years, follow them to get to that that chatch -ypt moment, right? And so I'm extremely excited about this.

55:15You know, we like more people to, you know, train models and apply both bio -ML experts and engineers in any other domain. And, you know, I'm, you know, I just, I want this thing to exist in the world. You know, hopefully we're important parts of making that happen, but I just be happy that someone does it. Yeah. That's inspiring note to wrap on. Patrick, well, hey, thanks so much for the conversation. Thanks so much, guys. Yes, we should wrap it.

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