The AI Product Going Viral With Doctors: OpenEvidence, with CEO Daniel Nadler

28:09They certainly don't have endowments, like universities don't endowments, like some hospitals do. Then they can't afford to go pay $10 ,000 for an enterprise SaaS subscription to some software product. So that, all of that is what opened men. We got a letter from a doctor in Albany, Georgia, who said he's the director of a cancer center in Georgia. And he's in community practice, he's a community oncology practice. And that open evidence has become a lifeline to his daily practice in his cancer center, and has been a game changer for patients in treating his cancer patients. And like most people, I didn't know much about Albany, Georgia.

28:51So I looked up Albany, Georgia. And very quickly on Wikipedia, I discovered that Albany, Georgia is in Southwestern, Georgia. It's 75 % African American. It has a median household income of $43 ,000 a year. And I started to piece together the situation. You know, there's doctors probably the only oncologist in a 50 mile radius, maybe there's a second one. And they're serving an enormous geography of fairly poor people. And there's no way that this doctor has the resources to pay 10 or $20 ,000, SAS, subscription, software rates for anything. And that to me is what Open means. It means the doctor in Fairbanks, Alaska, who wrote us a letter saying that she practices in, again, a community setting in Fairbanks, Alaska, very limited access to subspecialists and open evidence has been a game changer for her in allowing her to sort of access a sub -specialty level medical knowledge without direct access to human sub -specialists in Fairbanks, Alaska.

30:04Again, if you look up that situation or just even think through that situation, she's not paying, you know, she's in a community practice. She doesn't work for a hospital that can afford this kind of stuff. So that's what open means. It's every doctor in the country, we're really proud that we're used, not just at Mayo Clinic and Cleveland. We love the Mayo Clinic. We were partly incubated at the Mayo Clinic. We love these sort of elite hospital settings. We have a lot of users at these elite hospital settings that also use it for free. But we're not just use of the Mayo Clinic. We're not just use of the Cleveland Clinic.

30:38We're used across the country. We're used in the middle of the country. We're used at Walter Reed where our nations' warriors and veterans are treated without the government having to go through a three -year procurement process to decide whether they want to use open evidence. You know, that's another example. So one of the biggest health systems is the VA. To me, that's one of the most important health systems because it treats our warriors and our veterans. For the VA to decide to do anything is probably a three -year exercise and we're open evidence, not open to answer your question. We would probably still be in year 1 .5 of three to four years of a government procurement process to decide whether doctors in the VA could treat their patients who are warriors and veterans with the benefit of open evidence.

31:24And thankfully, we didn't go that route and we're getting letters from doctors in the VA talking about using open evidence to make a treatment decision at the point of care for a wounded warrior. You know, that's, it just energizes me waking up every day. So that's what open means. Okay, so you guys, you built something of a killer app from medicine and it's working. It's working really well. And we have a lot of people typically who listen to the show who are themselves trying to build killer apps of some sort for AI. So I'm curious using AI. So I'm curious, how did you build it? Like what, you know, is this a wrapper on top of GPT3 or GPT4?

32:07Like what is the, what's going on under the hood? How did you build it? So I think what I'm going to, I'm going to sort of have two parts to what I say. what is applicable for a lot of people listening to us. Now, I'm guessing a lot of people listening to us are building applications that don't necessarily have the same requirements that medicine has. So I do wanna address that. In the case of medicine, the way we attack the problem is by bringing together a team of PhD level scientists who are working in the field. We, you know, my co -founder Zach Rizegler who's a brilliant computer scientist is from Harvard, studied with Alexander Rush in his natural language processing lab at Harvard.

32:55That was one of the leading labs before ChatGPTV and came out. Evan Hernandez comes from Jacob Andres's lab at MIT. So I could go on on Eric Lehman from MIT as well. I mean, we've put together a team of elite scientists who are working at the frontier of language models at the top, you know, two of the top three maybe labs, if not two of the top two labs, at the time in the country, in the world, actually. And we needed to do that because we're trying to solve medicine. And we're trying to solve the application of language models in medicine. And that was a very high standard and a very high bar.

33:35And it hadn't been solved. And what the very large consumer companies were putting out was creating all sorts of embarrassing moments in medicine for those companies at the time, sort of recent and out that we probably all remember this. And so we needed sort of that level of almost the intersection of academia and engineering to go after this problem. And we needed to, in our case, produce original research and original knowledge. So we attacked the problem in a different way. Everyone was trying to, at the time, focus on us, you know, scaling these language models, larger and larger and larger and larger.

34:15And we were very, very early. You know, now this is sort of consensus with deep seek and all this stuff, but rewind, this is 2022. We were very early to the insight that smaller, highly specialized models over trained on in -domain data without performing much larger models on those in -domain tasks. They were very rigid. They wouldn't write you a poem. They'd fall over very quickly. The second you go outside the domain, but in the domain they were beautiful. They outperformed. And we published our work. We approached this very academically. My background is, you know, academic as well. I did my PhD at Harvard.

34:49And we were all academics by background. And we published our work in this paper. Do we still need clinical language models? Which was awarded of the best paper and machine learning in 2023 at the leading conference and machine learning and healthcare and attracted a lot of attention. It was really the first paper in the field altogether that showed that in medicine, the best way to attack the problem was these smaller, more specialized models. Again, that's become consensus today, but you have to sort of pretend you're not listening to this today, pretend you're listening to this in 2022, pre -chat GBT.

35:29It wasn't obvious because what was coming out at the time was like the chinchilla paper from DeepMind, and everything was about larger, larger, larger scale, scale, scale. And we just took this very different approach. And in a way, like, is very, with the benefit of hindsight, everything's obvious. With the benefit of hindsight, it's kind of obvious, right? If you go and listen to Jensen's interview of Ilya, Ilya uses the metaphor of JPEG compression, basically. It's like these language models basically like a JPEG compression of the world. And that's okay. Well, if they're a JPEG compression of the world, what's the world?

36:07What's the world that you're compressing? And it goes back to what we talked about public internet. If you're doing a JPEG compression of the public internet, then what, which is what these large language models that we're focusing on scale, where we're basically they're token limited. They're like, give me as many tokens as possible for me to train on. And so, well, Where do you find all the tokens in the world you find them on the public internet? Well to go back to Ilya's point. Well, what are you compressing then? What are you JPEG compressing? You're compressing the public internet and then you get all these sort of embarrassing outputs that that were sort of of that late 2022 or early 2023 vintage in our case we sort of said Let's make a JPEG compression of medicine and so let's let's over train on Again peer -reviewed medical science stuff that comes out of the FDA the CDC We had the advantage, this is way before the New England Journal of Medicine partnership, but we had the advantage of that under copyright law anything created by the US government is public domain.

37:02That's how Wikipedia does a lot of what it does. So in the early days, we started with sort of like creative commons, public domain stuff that was available. And we were very lucky that in medicine, this wouldn't work in every other field because in other fields like law or counting or tax, there's a lot of stuff that's behind walls. But in medicine, it turned out that a lot of the, the really great stuff was created by the US government in the form of the FDA and the CDC and what they had put out. So we over trained on that stuff and saw the copyright issue that way early on, which then allowed us to bootstrap something awesome enough that people could download it, which then one over users at places where there were copyright considerations like the need of general medicine, which then started the flywheel of them reaching out to us and then now it's having the benefit of the stuff that is under copyright the need of general medicine.

37:59But that was our approach. It was very technical and it was very academic and was very scientific because accuracy mattered that much given our domain in medicine. Yeah, I was going to ask you about that. So, you know, you have doctors using open evidence to make clinical decisions at the point of care. Yeah. So in most applications, a hallucination is annoying. With open evidence, a hallucination could be literally life -threatening, given how it's being used. So how do you do with hallucinations in particular? Well, in just a reiterate, the first thing you said, you know, and maybe this is advice for entrepreneurs or engineers who are listening to this, There are scenarios where hallucinations are not even annoying.

38:42There are scenarios where hallucinations is a feature. One of my favorite applications is mid -journey. Yeah. Illucinations of feature. Yeah, yeah. Of mid -journey. Maybe one takeaway is find applications where the biggest hesitation actually gets judo moved into a feature as opposed to a limitation. And just to total aside, an example, and someone should go start this company. Again, my first company was in finance, and I feel like I kind of grew up on Wall Street, and I think that way. And if I wasn't running open evidence, I wouldn't run another company because I'm going to work in medicine, I think, for the rest of my career because of the impact.

39:28But if I were pressed to sort of like, what would, what would I do in finance now that large language models exist, I would actually begin from thinking about hallucination as a feature as opposed to a limitation. So where is hallucination a feature in finance? Well, it's certainly not in like doing retrieval on the PE ratio. You need that to be right. Well, where else could it be used? What about risk management? A lot of finance is about figuring out what the Black swans are. A lot of finances, what could go wrong? And then at the extreme tail, there's a lot of money at stake. What is very unlikely, but that could go really, really wrong.

40:11And the first two questions most people can reason through without the aid of computers. What can go right and what can go wrong? You can kind of reason through that with your web brain. But in terms of like what could go really, really, really wrong at the level of the 2008 financial crisis. This, that's harder for most on -aided brains to imagine that. But these language models would be pretty good, and I've tested some of this in my own portfolio management at hallucinating those things. I've actually, as an experiment done this in my own portfolio management, just use these language models in a way that the hallucination is a feature where I give it certain details of my portfolio.

40:55I talk about the company, I give it in the context window, enough information so that the company does and so on. And it comes up with all sorts of scenarios that are kind of on the long tail of what could go wrong. And I'm like, huh, I never thought that I love Nvidia, but I never thought about that happening to Nvidia. That's interesting. So at a high level, I think there's enormous opportunity. I think we're like 1 % of the market captured today in 2025 in terms of applications built that even begin to think about hallucination or riffing as an advantage as opposed to as a limitation. So that's for all the entrepreneurs listening There's 99 % is still up for grabs.

41:45Yes, in my specific domain medicine, none of that is fair game And so the way, you know, we had to deal with that is by not connecting open evidence and models that we train to the public internet is by only training on peer -reviewed medical knowledge and go back to Ilias Point. The JPEG compression that we made in our models, in our smaller specialized retrieval models and ranking models, you know, we don't use one model without getting into our sort of trade secrets, like it's an ensemble architecture. There's multiple models that do different things. There's half a dozen models. They hand off tasks to each other.

42:29You can't get the accuracy level of open evidence by training one large language model. It's under the hood gonna be this sort of cooperative ensemble architecture. And those are made up largely of smaller models that do very specialized things like retrieval and ranking and other things. And for those models, the JPEG compression is exclusively of peer -reviewed medical literature. So it's never going to be at risk of regurgitating or surfacing something that is not in the peer -reviewed medical literature, which is, as they say in GI Joe, more than half the battle, right? And then the other half the battle is allowing transparency and interrogation of the answer.

43:11And we were very early to that. I've seen now that Chattach and PT and others start to do that, but we're probably the first application. I would go on a limit and say, we're like the first application that grounded our answers and references that you could drill down and drill through to see the underlying sources of we did that in early 2023 long before Chat and others started to come out with similar features and that's how we won over users in the early days because doctors saw that not only is this not saying anything egregiously wrong in terms of regurgitating something on the public internet, but it's also even within the domain of the answers that it gives based on peer -reviewed medical literature, allowing me as the physician to go interrogate where it's getting that thing that it's saying, where it's getting that source of information from and drill down all the way to the reference and then go and read the reference, which by the way also created a beautifully symbiotic relationship with the publishers, because instead of just compressing all their knowledge and then giving it away as some folks did.

44:17As a result of just trying to make this as accurate as possible, inadvertently stumbled upon a model of cooperation with publishers that ended up being very good for them, too, because we send an enormous amount of traffic to medical journals. We send millions and tens of millions, tens and tens of millions of visits from doctors to medical journal pages hosted by those medical journals, including traffic they might not have otherwise have gone because the doctor is going to that journal because of some detail deep in the methodology section that the doctor would have never known to go to that journal for.

44:58Really this virtuous circle kind of all around. And then we had medical societies that write guidelines reaching out to us saying, hey, we notice that you index this other society's guidelines. Can you go index our guidelines? Because we want the traffic. It's beautiful, right? So you get accuracy. You get a symbiotic and a mutually beneficial relationship with medical journal owners. But critically, you get the right information back to the doctor using open evidence who then will make the better decision as a result of having better information for their patient at the point of care. And it's been a decade or so since Kinsho got going.

45:39And obviously there's been a lot of progress in the field of AI machine learning since then. If we were to inspect the underlying architectures of Kinsho and open evidence, how much is the same and how much is different? And I guess part of the question behind the question is, how much of what goes into making and AI application that actually works is recent breakthroughs. And how much of it is more sort of classical engineering machine learning principles? Kenchia was pre -large language models, pre -language models, pre -small language models, pre -burt, pre -anything, almost pre -fire. So it's hard to compare, right?

46:24I mean, Kenchow was very early in LP. Not when I sold the company by the time I sold the company was much more sophisticated, but I'm talking about when I founded Kenchow in 2013. So it's very different today. What they have in common is there's an enormous infrastructure component to building this stuff. So, you know, we train our own models, I talked about that over the last few minutes. But even if you're not training your own models, even if you're just like using an API to one of the usual suspects, you know, that's gonna fall over at some point if you're successful. And you want to be successful and you want to get to the point where it falls over and it will fall over.

47:06And at that point, you need to have all the traditional things that you have in traditional software engineering like infrastructure and really good infrastructure. And that is very similar to Ken Cho, because both were critical systems, you know, in the case of finance, There's enormous amounts of money being moved around on the basis of this information. You can't have it just stop or fall over in the middle of a trade. And I think that's a good thing. I think that one of the things that everybody was concerned about post the chat, GPT moment was that all the rules of the game had changed. And I'm here to sort of tell you that they haven't.

47:44Yeah, the technology is better, but that's a continuum. The technology has always been better. The technology was better from 1982 or 1983 to 1987, right? And from 93 to 97, right? The technology's always gone better. Yes, there's a step function now. Yes, there's non -linearity. Yes, there's an exponential rate of increase. Yes, everything Ray Kurzweil said is correct. Turned out to be correct. But it's a continuum. Even Ray Kurzweil thinks about the stuff as a continuum. And when you think about something as a continuum, It's very, it's a relief in a way because a continuum is something where the laws of physics aren't changing along the continuum, even if you think about the metaphor of travel toward the speed of light.

48:29Yes, the technology that would get us from one tenth of the speed of light to one half, the speed of light, in a spacecraft is highly non -linear at its sophistication. But the laws of physics in that acceleration are not changing. It's just the technologies on a non -linear continuum, but it's still a continuum. And that same continuum, not linear, but continuum exists in engineering and entrepreneurship more broadly, but specifically in AI, where everything that mattered at Kenchow continues to matter today. And the intelligence level of the people that you're bringing to bear on the task matters, you know, Ken show in Open Evidence are identical in that we were able to be successful because we brought people with really high IQs to bear on the task.

49:18Let's talk about that. Yeah, you mentioned Zach in Evan and Eric and Micah. How do you attract people like that? Why do they choose, for all the options they have, why do they choose to work on Open Evidence? It's impossible for me to answer that without just repeating what Steve Jobs has said, which itself has been repeated so many times, but I don't have a better way of phrasing it. A -players want to work with A -players. It's that simple. Elite people want to work with elite people. A lot of people who sign up to Buds, which is the sort of screen process for Navy SEALs, do so because they just want to see if they can keep up with the other people that are doing it.

50:06They want to test themselves. They want to see what their limits are. That's as old as Achilles. That's not new. It doesn't matter whether it's warfare or engineering or sports or any other domain finance. The very best people in the world want to see just how good they are. they want to see what they're made of and the only way to do that, the only way to learn that, is to put yourself around other elite people and see how you stack up against those people. So, that's the common denominator to what I did. A catcher that worked very well to what I'm doing here that's working out very well, which is, you know, and it's kind of controversial or at least was controversial for a minute.

50:50You know, you couldn't talk about IQ. You couldn't say out loud for a while like I just want people with really high IQs. I don't care about anything else. I don't care who you are, what your background is, what you look, I just want someone with a really, really high IQ. But that's the honest truth. I just don't know how to sugar code it. I don't know how to say it differently. I don't know how to talk around that fact. And so, you know, if you think about the people on, you know, the first four or five people, Zachary Ziegler, Jonas Wolf, Evan Hernandez, Eric Lehman, Micah Smith that came together sort of senior people on my team initially.

51:35Yeah, every one of them, if I have to sort of classify this way, you know, came from a PhD program at Harvard or MIT. But that's not because I'm like, I'm only going to recruit from Harvard and MIT. It was because I had the country experience, and I learned from that experience that if you bring very high IQ people with very high velocity of learning to bear on a very difficult problem, they make more progress far more quickly than a team 100 times that size. That's a more normal team. And I think the really reassuring thing for everybody listening in this moment is the rules of the game haven't changed.

52:18The physics haven't changed. All the things that used to matter, still matter. You know, an elite team, high IQ people, high velocity people, hungry people, very motivated people, people with very high neuroplasticity. And by the way, when I say high IQ, what I mean is high neuroplasticity. I mean, something very neurologically specific. I don't mean speed at solving a Rubik's Cube which actually doesn't necessarily correlate very highly tight Q. So the Francois Chellet definition of the ability to efficiently acquire new skills? Absolutely. It's the ability at which you can learn completely new information and assimilate that new information.

52:58That's what I mean by very high IQ. And guess what? That mattered a thousand years ago. That had three thousand years ago. The domain was different. It showed up in warfare and tactics and sunsue and other sorts of things. but whatever humans were doing at any moment in history, what mattered was neuroplasticity. You know, I spent a lot of time reading in my personal life of on -cloudswits and Machiavelli and Sunsu and the history of warfare. It's a subject I'm very interested in. And the history of course Napoleon and Alexander the Great and these folks. And it's all just neuroplasticity. You know, But if you had to sort of say in a few words what differentiated these people, I mean none of these people were the physically largest people in their armies, not even close.

53:52What they all had in common is the facts on the ground could change very rapidly as tends to happen in war. And they're not just their decisions, but their entire frameworks for thinking would just like adapt. That's the quality that like an Napoleon or an Alexander the Great had, which is, you know, yeah, they overprepared for the battles that they went into and thought through every single thing that the adversary could do. But then none of that preparation would exactly match to what happened in the battle and what differentiated down from even very good generals or very good military leaders is they would just completely adapt the way of thinking about the battle to the facts on the ground that we're developing in real time in the battle.

54:43And that's, you know, the standard way of describing that as neuroplasticity, or at least in some branches of cognitive science, which is very high in neuroplasticity individuals. So what humans have been doing over the last, let's say, 3 ,000 years has kind of changed a lot, right? I mean, unfortunately, there are elements of what humans were doing 3 ,000 years ago that persist to this day and the war does persist. But not everybody today in 2025 is engaged in warfare in a way that might have been the case during the time of the Greek city -states. People, thankfully, today that are engaged in things other than city -state warfare.

55:20But what hasn't changed are sort of the the neuro processes and cognitive qualities that are required for outlier success. Awesome. Let's jump into a lightning round. Sure. Okay. Question number one. I know this number is impossible to measure because it requires a counterfactual. But we suspect that the way open evidence is being used, it's saving lives. Like it's helping doctors make better clinical decisions. On what date will we be able to say that open evidence has saved a million lives? A million lives. Well, in a way this feels like a McKinsey interview because you got a sort of reason through like, well, if you have 150 ,000 or 500 ,000 doctors using it in those doctors each see a certain number of patients and what percentage of those patients are in life threatening your situations.

56:19I'm doing this sort of kind of thing. Yeah, yeah. You told me you're a math. My math is, you know, it depends on where you look, but kind of 300 ,000 to 800 ,000 lives per year lost due to just straight up medical mistakes. Not all of those are going to be attributed to doctors making decisions at the point of care. There could be other things that happen. But let's take the low end of that 300 ,000. Let's cut it in half. That's 150 ,000. That says it's about six and a half years. You know until you get there and that's a fully ramped open evidence So we'll give you a couple years to keep growing I don't know maybe eight or nine years from now So we'll call it like 20 we'll call it November 4th 20 34 I'm gonna use this as an interview question The only thing I would add is is you know, maybe in the 20 30s You have a million lives saved through the use of open evidence, but what that doesn't count is the patient today, who's amassed and get worse because the dermatologist used the wrong biologic and that's happening today, right?

57:19And for every black and white life saved, you know, there are those examples. There was a doctor who uses open evidence in Rhode Island who wrote us that he saved his patients life by using open evidence to reason through whether the patient's presentation of symptoms was consistent with appulmonary embolism and literally used up an evidence as like a curbside consult to sort of reason through that patient's presentation of symptoms, realizing that actually it was the patient's presentation of symptoms was consistent with appulmonary embolism. They rushed the patient back to the emergency room and saved that patient's life.

57:54So, lives have already been saved through the use of open evidence and we know that because doctors tell us that. But for every one of those things that happens, it's just, you know, the MS not getting worse or it's some comorbidity not getting worse. That's in the order of millions today. Yeah. Yeah. Yeah. Broad domain general purpose foundation models. They are commoditizing. Yes or no? I think you're getting better and better. I think the costs are coming down. I think everything raker as well says is typically always right. So, you know, the frontier of the frontier doesn't get commoditized.

58:29It's always the frontier, but, you know, the, yeah, the costs are going to, The costs of the wow factor that the first chat GPT produced are going to converge to zero, which is why I think all the interesting stuff is going to increase in business perspective. There's still phenomenal interesting work being done at the Foundation Model Layer intellectually, academically, scientifically. From a business perspective, I think so much of the interesting work, the great companies to be blunt, are going to be at the application line. Yeah, we'll put, we'll put.

59:02AGI, on what date, did we or will we reach AGI? I think we've already reached, we keep moving the goalposts. We've reached AGI, we've passed the Turing test. And even, so we just keep moving the goalposts and what AGI is, what people really mean when they talk about AGI is consciousness and they don't know how to say that. So they're, what they really, because like they try to sort of say, Well, it's this thing or it's this other thing and then the AI does that thing and like what's the ability to have High school level expertise in multiple different fields. Okay, it reaches that fine fine It's not that a GI is college level expertise and then it reaches that and now it's like you know PhD level expertise in everything from coding to medicine That would be a GI that's never gonna and then it reaches that what they really mean is consciousness That's what people I think I think underneath it all mean, when do you get where you get in the movies when an AI becomes aware and conscious?

1:00:01I'm not sure that ever happens because I don't know that consciousness is an emergent property of sufficient density of an neural network. That's a philosophical question. Yeah. Yep. For AI founders, AI builders, AI fans, other than this podcast, what one piece of content should they consume?

1:00:25Ted Chang's novella understand. Okay, tell us why. I want you to have the joy of experiencing it. I won't spoil the story. It's a Ted Chang's one of the great science fiction authors of all time. He wrote a rival which became a major major motion picture and the novella understand was written in the early 90s and it is the best encapsulation of what without any spoilers. It is the best encapsulation of what non -linear acceleration and intelligence looks like. It's my touchstone for everything that I do. Most people expect a non -negative fiction answer. There's great nonfiction, there's tons of nonfiction reading, including just like go read the chinchilla paper, gray paper.

1:01:24If you want really just a touchdown for what it is that's happening in our civilization right now, it's this Ted Chang story, understand, because that just captures narratively really the feeling of nonlinear acceleration. Yes, awesome. Love it. All right, last question. What is the most optimistic or positive thing that you can imagine AI bringing to the world in the next couple of decades? How are all of our lives be better thanks to AI? I have to go with a kind of an extrapolation of the field that I'm kind of obsessed with, but which is not truly possible today, which is personalized medicine by which I mean.

1:02:12So personalized medicine has been just over the horizon. It's kind of like quantum computing. It's kind of like fusion. You know, it will happen on some civilization scale. It's been just over the horizon for a very long time. What it means changes in a way we've been talking about at this whole time because using open evidence to say that, hey, if you have psoriasis and a mass means you should use this biologic versus this other biologic, that is the beginning of personalized medicine. That's personalized to your comorbidity versus any other person with psoriasis. But that's just scratching the surface of what personalized medicine can be.

1:02:46I think in 10 years from now, whether it's open evidence or whether it's a constellation of these types of AIs, the exact specific fact pattern of your specific medical case is going to be matched to everything that is known in the entirety of medical knowledge about everything that is relevant to your case and a plan of care is going to be formulated that is hyper tailored to everything that is specific to you and that is specific about your case. And I mean to me that's just enormously exhilarating and I think that is just over the horizon but that is feasible and that will change. That's how you really start to push the ceiling on life expectancy.

1:03:33That's how you start to get into maybe 120, one 30 is no longer the ceiling anymore. And you get into these sort of ancient Greek metaphors and paradoxes of a thesis's ship and replacing every plank on the ship to the point where there's no plank in the ship anymore that was the original plank, but you're walking around and you still have of your memory of your wife and your child and your relationship to them in the destroy life to watch your own child turn 100 and those things. And I'm an optimist in that regard. I have an atomistic view of human biology. And I think the feceship approach to human biology is just over the horizon and a lot of it turns on the sort of personalized medicine stuff.

1:04:18Awesome. Daniel, thanks for joining us. Thanks, Pat. thoughts.