No Priors: Artificial Intelligence | Technology | Startups - A New Operating System for Physicians with OpenEvidence Founder Daniel Nadler

Episode Date: September 4, 2025

How does a new technology get adopted by 40% of American doctors in just 18 months? In an era where the golden age of biotechnology has also created a dark age of physician burnout, OpenEvidence found the answer by fundamentally changing how doctors access critical information. OpenEvidence founder Daniel Nadler sits down with Sarah Guo and Elad Gil to discuss how his company solved the semantic search problem in medicine. He talks about the strategy of treating doctors as consumers, striking the balance of keeping patients in the loop in medical conversations, and how technology will reshape both medicine and medical education. Plus, Daniel gives his thoughts on the roots of motivation, as well as his philosophy for recruitment.  Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @EvidenceOpen  Chapters: 00:00 – Daniel Nadler Introduction 00:08 – OpenEvidence’s Success  01:54 – How OpenEvidence Works 06:35 – Dealing with Ambiguity 11:53 – Treating Knowledge Workers as Consumers 18:03 – Balancing Keeping Patients in the Loop 21:32 – How Technology May Shape the Future of Medicine 24:16 – How Technology Will Change Medical Education 32:44 – Examining Consumer Adoption of Preventative Health Measures 38:06 – Lessons for Other Fields 39:32 – Rationalism vs. Will 43:17 – Daniel’s Thoughts on Motivation 44:48 – Daniel’s Recruiting Philosophy 46:30 – Conclusion

Daniel, thanks for doing this. Happy to be here. So give us a sense of this incredibly viral sensation that has been open evidence in terms of what type of coverage it has of American doctors today. As much as we would like to think that it's going especially well for us, I would sort of say as a qualifying point that in all of the sub-industries of AI, you're seeing an acceleration and compression, right? So the adoption cycles, even outside of open evidence, before we get to open evidence, in other fields of knowledge work and coding and so on are hyper-compressed, right? It used to take, you know, half a decade or a decade for something to become standard,

and now it seems to happen in two years or a year. So the same thing's happened with open evidence. In about 18 months, it's become the operating system for clinical knowledge in the United States. is used something like 20 times more than the next most used platform of any kind in our specific segment, which is high-stakes clinical decision support for doctors. So high-stakes clinical decision report for doctors is a specific category of medicine. It's distinct from, say, paperwork or it's distinct from scribing. Those things are, you know, part of the workflow of being a doctor, but the stakes and the consequences are different. If you get it wrong, you can go back and do it

again, that's not the case with a patient. You have to get it right. You have one shot to get it right. And so clinical decision making of which clinical decision support is in service of is unquestionably the highest stakes area of medicine. We're probably the only company working at the tip of that sphere. Most people have self-selected themselves out of the problem of high stakes. Clinical decision making, certainly through an AI lens, because they view it as ambitious. And could you explain it or to our value in this because I think kind of it's about picking information and then translating that in the specific either recommendations or diagnosis for a patient.

Can you tell us more about how that works? Yeah. One way to sort of simplify it down is at its foundation, it's a search problem, but it's a very semantic search problem. So most search traditionally works with keywords, right? So like, you know, flights to Barcelona or hotels in Barcelona, most of the, you know, most of the keywords there can be captured in like a couple of words and certainly in a sentence. And that's sort of traditional Google search. Even if you were to think about clinical decision support as a search problem, simply describing your search query,

if you want to think about it that way, usually takes many sentences. So an example I like to give is you have a 44-year-old female patient. She has moderate to severe psoriasis, that's the red stuff on your skin. You know, you're a dermatologist. That's so far so simple. You would just prescribe one of the many creams

you see commercials for on television, except she has MS. So now it gets interesting because you want to treat her psoriasis, but you don't want to make the MS worse. And you are not a neurologist, you're a dermatologist, so neurology is not your specialty. But you don't want to go refer to a neurologist because you want to treat her psoriasis, and if you just keep referring people in circles, medicine never happens. From the ether, you might have heard as a dermatologist that the new classes of psoriasis

treatments, which are biologics, there are IL-17 inhibitors and IL-23 inhibitors, might have some interactivity with the neurological dimension of a patient's condition. That's about all you know. You didn't learn this in medical school because IL-23s were FDA approved in 2019, right? And so one of the great themes of open evidence is that the sort of golden age of biotechnology is sort of the dark ages of physician burnout because it's just impossible to keep up with all the new drugs and all the new mechanisms of action and so on. So it was approved in 2019, you might have graduated medical school in 2005, right?

So you didn't cover the medical school and that's it. that's kind of, that's what you know. So your question then is, you know, for a 44-year-old female patient with margariousyriasis, is an IL-17 inhibitor and an IL-23 inhibitor more appropriate and more safely tolerated with respect to not aggravating the MS. Now, that's not a academic question. That's a very consequential question. IL-17 inhibitors will actually make the MS worse. IL-23 inhibitors are safe and well-tolerated case of MS. That's an example of where medicine can go wrong because even five or 10 years ago, either you're referring that person to a neurologist, in which case you're just getting referrals and circles and medicine is not happening,

or unfortunately, what would more likely happen is they would just 50-50 and that MS might be aggravated. And it's well known and it's been often repeated that medical error is a third leading cause of death in the United States after heart disease and cancer. But even that kind of, that statistic kind of understates it because that's just looking at death, right? In the case of my, in my example, this patient is not going to die as a result of taking an IL-17 inhibitor. She's going to have a relapse of MS. And so it's not just that medical error historically was a leading cause of death. It's that as many people died from medical error, probably a factor of 10 to 100 as many people had a comorbidity or condition that became aggravated and got

worse and so on. So coming back to your question, that whole string is the search query. And so you can't just do search in a traditional way where you sort of say, you know, Isle 17, because that's not really what the question's about, nor does the physician have the time to go read book chapters on this stuff. What you need is a semantic understanding of the query in the way that another human physician would semantically understand that query. And then it's actually quite deterministic and simple after that. Once you semantically understand the query, you can, from the world of published biomedical literature, you could find the exact snippets in a phase three RCT, a randomized control

trial in the England Journal of Medicine that tested each of these things and found that one aggravated MS and the other didn't, right? So once you have a semantic understanding of the query, the rest is fairly deterministic and it's almost a search problem. But all of the juice is in, you know, connecting the very complex semantic meaning of a medical scenario to the answer where the answer might be in a phase 3 RCT in the New England Journal of Medicine and in a snippet in not even in the in the abstract, but in the methodology section or in the population.

I didn't care about that ambiguity actually because I feel like in the context of medical formation, there's things that are in pre-bay clinical guidelines, you know. Yeah. Certain types of conditions, we're going to do X, Y, Z, and that's where the recommended path. There's stuff that's kind of recently published, there's evidence in a certain direction, or maybe it's by the label or something else. And there's a budget stuff that's a bit more TBD in terms of it as clinical trials that they kind of picked each other a little bit, or maybe other information that may be a bit more sporadic. How do you deal with that third

bucket of ambiguity and how do you think and tell about capturing that broader knowledge role is over time? So the first way to deal with that third bucket of ambiguity is ensure that your users are physicians and not patients. And we've made that strategic decision. And we keep thinking we're going to change that decision. And we've been talking about changing that decision since the inception of the company and so far have not changed that decision. For all the reasons implicit in your question, there's an enormous luxury that we have as builders in having doctors as users because the MD is attached to their name. So they need to protect that MD. And they're going to use us as a tool in the same way as a Wall Street trader might use a Bloomberg terminal.

If a Bloomberg terminal, for example, produced, you know, an inaccurate quote on a bond that was very obviously inaccurate, you know, was off by an order of magnitude. And the trader, you know, in a hedge fund, just sort of, well, I mean, that's odd. Do you indicate in the user on our pace that pay there's some ambiguity around this or is complete evidence? And here's the... Absolutely. So there are areas of medicine where there's a lot of conflicting evidence and that's indicated. And it's not presenting answers. You know, we're used by 40% of doctors in the United States daily on average. It's about 20 times as much usage as the next thing that could be described as a clinical decision support platform. It's become the default operating system

of clinical knowledge. And a lot of the value proposition early on is that we made references and citations of first-class citizen before that was in ChatGBT. So we were actually providing references in citations six months or in nine months before Chat-T started doing that. That was a big reason we had adoption because people could interrogate and audit the source. Right. So right there, difference because then it's not an answer engine. It was never presented as an answer engine. It was always presented as a search engine. We did two things that are very smart. We did a number of things are very smart. You go to open evidence. Since the beginning of open evidence, the words AI never appeared. And the words answers were not used in the framing of what we provided.

The way we did frame it was as part of the long continuum of search and Google. We're a Google portfolio company, and I've always framed this as part of the very long continuum of search engines, as opposed to something net new, because I do view technology as a progression of continuum. And that created a certain social contract with the users who, in addition to being physicians and have that MD that they need to defend, on top of it, viewed this as a router to the Phase 3 RCT in the New England Journal of Medicine, and maybe the conflicting Phase 3 RCT in JAMA, right? And we'd route them to both. Very useful for attention.

So the users do look at source materials some of the time. All the time. I would say it's almost the default behavior of a user to start with some complex query that you could not put into Google for the reasons I mentioned because it's a paragraph long. And then have it produce within, you know, from a search space or a surface area of 35 million biomedical publications, the exact three to five, you know, canonical. landmark, phase three RCTs, or guidelines, or other sources of information that are responsive, not answers, that are responsive to their question. And then it was almost the default behaviors,

then they go out. You know, I think we're one of the largest sources of referral traffic to the England Journal of Medicine after Google. I don't know if a number two or three or four, but we're one of the largest sources of referral traffic to our partner to the England Journal of Medicine. That's a testament to the way people use it. Historically, it was very hard to do two things. describe a complex patient scenario or a case into a search engine and have it come out with anything useful. And it was hard to find from the tens of billions of tokens, if you want to think of it as an engineer, that constitute the world of peer-reviewed public medical literature. It's very difficult to find, you know, the seven snippets that are directly responsive to

a question and to the semantic meaning of the question, as opposed to a few keywords. So we just We just did those two things, just did those two things extremely, extremely well. We framed the right social contract. We picked our audience extremely well. And all of those things start to stack into something that looks more like a, you know, a Bloomberg terminal for doctors, where it's just a pro tool. They're using this because it has, you know, the right data that goes in because AI is golden, gold in, garbage out, garbage out.

So they know this is not training on tweets. They know this is trained on England Journal of Medicine and JAMA and the rest. They know that we have these partnerships, these strategic partnerships with the gold standards of medical knowledge. They know that they're not going to get an answer from open evidence. They're going to get a routing to a source that answers the question. And so I think all these things sort of stack into something that feels just like a pro tool.

I want to rewind for a minute. You were already a successful entrepreneur before you started open evidence. You wanted to build an impact-driven company, like you wanted to work in health. What was the moment of decision to serve physicians versus consumers? Because you also think a lot like a consumer entrepreneur in terms of growth. Well, I served both. So this was a hack. I wanted to build a consumer internet company for knowledge workers.

And I don't think that had ever been done before. So I didn't want to build a health care company at all. I love Sequoia's quote that Open Evidence is a consumer internet company masquerading as a health care company. I had zero interest in building a health care company. Open evidence is not a health care company. I wanted to build a consumer internet company, but I wanted to do something that no one had ever done before, which is treat knowledge workers like consumers. So my whole career had been, you know, prior to this dealing with knowledge workers, right? And people have a reductive view of consumers.

They think of, you know, they think of like 14-year-olds on TikTok, and that tends to be like their archetype. of what a consumer is. And it's one type of consumer. But, you know, traders on Wall Street are consumers and people. Lawyers are consumers and people. And doctors are consumers and people. And what I realized is no one had ever treated doctors that way before. Doctors were just kind of treated as these appendages of health systems and these health systems where the decision makers and the gatekeepers. And you had, you know, chief people with titles like chief medical information officer making decisions about what doctors would get to use, despite the fact that in many of those cases, those CMIOs have no medical degree whatsoever. And it was interesting. I was like,

it's an interesting way to organize the medical system and the health system. And, you know, you start to investigate and pull the threat a little bit, and you start to understand why, you know, there are very few things that people can agree about in America. They can agree Congress is dysfunctional, and they agree that American health care is dysfunctional. It's like bipartisan universal consensus. But you start to really investigate and, you know, you come across two or three things and you're like, maybe that begins to explain the dysfunctionality, right? And to me in particular, the idea that doctors who were the fighter pilots, who were the

knowledge workers, who were the people who have that MD on the line and have to make that high-stick decision, weren't even their own gatekeepers as far as the technology they used. That was a pretty profound realization. And so we did something that had never been done before ever, which is we treated them as consumers and as people that could go onto the app store and download a free app and start using it. And it sounds so stupidly simple, but it was really profound and was really effective because no one ever done that before.

It's kind of almost analogous to in relationships, whether friendships or romantic relationships, People can get caught at these sort of cul-de-sacs where there's a rigidity to their dynamic and to their relationship. And then there's a breakthrough where one person says something that they've just never said it before or they've just never said it in that way before. And then there's like a breakthrough. It hits different, right? And in psychiatry or psychology and therapy, a lot of that field is encouraging this behavior in others, is to just sort of of break free of cul-de-sacs of dialectics of relationship dynamics and just say something in a way

that's never been said before or do something that, you know, that hits different. And long story short, you know, we did that with doctors and it was, it wasn't the complexity of the idea. It was just no one who had ever addressed them as consumers before. And, you know, we had this realization, which is pretty obvious that while this wouldn't have been possible 20 years ago, today, virtually every doctor in America is walking around with a computer in their pocket that they own, called an iPhone or an Android phone usually. And they own that computer, right? The CMIO or all these other people that would purport to be gatekeepers to that doctor. I feel like there been a number of apps that I've seen before that have gotten some position adoption. And then to your

point action, the CMI or somebody actually blocks it eventually. So I have seen one or two as as we've started adopting things. And then they try to use it a bit more in the funnable practice and they try to bring it into a health system, and the health system kind of shuts it down. In your case, it's really spread and kept going. They stopped trying to do that now. Maybe six or nine months ago, there were a few cases where they tried to do that. There was one place, of course, I won't name which one, but the CMIO there, you know, didn't fancy being circumvented as a gatekeeper.

It's quite technically difficult to control what applications people use on their iPhones. On phones. Right? Because they lock up the desktops. They're locking down desktops. This is, you know, NSA can do it. But if the IT departments of these hospital systems were good enough to do what you're describing, healthcare would be in a better state in America.

So to begin with, they can't really do that. But they can threaten to do that and they can make a lot of noise. In one particular health system, they threatened to do that and made a lot of noise. The only problem for that health system was, Within six months of launch, 64% of the physicians in the health system were already using open evidence daily on average. It's amazing, yes. I would say it hasn't happened before to this scale. Now, it's really cool, yeah.

I mean, the velocity of it and usefulness and values reflective in that velocity. The scale and the speed of it. And more common cases are cases in which the leadership of the hospital system are very avid users. So the entire, you know, this whole senior leadership of UCSF, of MGH, of Mayo Clinic. of Cleveland Clinic, New York Presbyterian, Mount Sinai, Cedar Sinai, you know, right up to the chief medical officers, the chief physicians, and the CEOs in many cases, are personally avid users. The reality, too, is that people are basically using Google for some of these use cases

or are they using company tools with this word carrier? I'm a sort of slightly separate question, which is maybe back to the consumer versus medical or physician side of this, because, you know, I started a digital health company maybe a decade or 15 years ago. And one of the things, and we were basically initially providing really key genetic information. We had a position in the loop at all times. But one of the things we ran into is what I characterized is almost a journalistic viewpoint in the medical community towards what information your patients should and should not get.

And I think part of that was a real concern about what the patients have to do in terms of its acting on information. But I think a lot of it was just wanting to be a gatekeeper or part of it was just not going to deal with the questions of the patient. How do you think about that philosophically in terms of what type of information should patients have access to versus not, how much do patients be able to advocate for themselves? So I've experienced both sides of this. So I've been on the patient side and I'm very sympathetic to that because the reality is medicine is not perfect. If it were, you know, everyone would be living

to 80 or 90 years old. So clearly medicine is not perfect and in a world where it's not perfect patients to definitely have some role in agency in that. What we have done is encourage physicians to use open evidence to generate patient handouts. And that's actually a very widely used secondary. It's mainly clinical report, but we have all these secondary use cases like prior authorization letters and insurance appeal letters. And one of the most common of those sort of secondary use cases is generating these patient handouts. The other side of this that I can appreciate is it took me personally taking my first graduate level statistics course at Harvard to really understand these clinical trials, right? And so I'm sympathetic to the idea that a patient simply

finding some clinical trial published in the New England Journal of Medicine because it was mentioned on CNN or Fox News. And then going and trying to read it, especially through the lens of fear or hope, is not necessarily going to result in the most sort of constructive decision-making process. I mean, there's no good answer. The reality is very tough, right? You want to enable patients with all the answers that are clear in consensus, and certainly you want to give them the tools to make sure that their physician is not missing anything. At the same time, you don't want, you can imagine all the failed cases where that could go wrong, where they're coming and saying, well, why aren't you putting my mother on this drug

with their own handouts. And the answer might be a very technical answer, right? The answer might be that because your mother also has this other comorbidity, and if you look at the P value, the P value of the efficacy of this drug is not statistically robust in the presence of this other comorbidity. And the patient is like, what's a P value? But they're not going to just stop at what's a P value. They're going to get really upset.

It says, in this case, that this other treatment. treatment is effective, and then you're just in this endless circle where the physician, who has, by definition, taken at least one graduate level statistics course, is trying to explain to a civilian what a P value is. And I think that's probably not a constructive outcome. So it's a balance. We encourage physicians to use open evidence to use patient handouts, especially where guideline-based medicine is concerned. So I think you mentioned something really interesting earlier, which is the velocity at which your product got adopted was incredibly fast, and I think part of that was just incredibly valuable, and I should have a lot of these new and have different tools.

And I think that's one of the almost underappreciated aspects of this way of AI is not only is there a fundamental technology shift that's enabling all sorts of new products, but also there's this massive shift in terms of the openness of adoption and the people and the organization's new technologies. And that's in terms of what you've been doing with evidence. It's to your point of the medical subscribing thing. It's companies like a bridge or commure or others. If you think I had 10 or 20 years, and this may be impossible to extrapolate, how do you think the change of medicine or the state of medicine changes in general? Like, are you still going to the doctor's office for visits?

Are you interacting with some online tool and it's backed up by a doctor? I'm just sort of wondering at a high level how you think about the whole industry of all-man or changing, given both some of the markets are open in ways that they weren't before, but also there's new technology ways that are going to impinge on the markets. It's getting difficult. The definition of a singular event horizon is you cannot even project, you know, into the near future, let alone the far future. And I think we're, you know, we're probably in the midst of something like that. With respect to doctors in the loop, planes have been able to land themselves for a very long time. It's a peek into, in a way, a future by analogy, because that's a, that's a domain or an industry where there's no debate, really, as to whether the technology is there. And yet you don't see this sort of mass. movement of airline passengers to get the pilots out of cockpits. There just isn't. I'm not aware of one mass movement to get pilots out of cockpits. Then the question was why. And of course, that is a attribute of human psychology that we are anthropologically tribal. And we don't

abstract trust well. We personify trust and we trust things that we personify. and anthropomorphize. And there's a whole history. So already doing a lot of the chatbots, right? In other words, there are people who effectively give themselves as being in relationships where... Yeah, they don't have bodies yet. I mean, you can start to reason by analogy.

Would there be any more of a mass public movement to have computers land planes? If you still had a cockpit, if you just remove the two seats, no one wants that. Okay? What if you keep the two seats, but they're empty? I still think no one wants that. What if you keep the two seats and there are... mannequins, essentially, that act as visual surrogates for the computer system and what it's doing. I think if you were to poll people, that'd be the first time you see this little

uptick in willingness. I think it would still be the minority. Can I ask the question if we're talking about the near future? You've mentioned before, like, we are in an era of, you know, in an amazingly optimistic way, like an explosion of biomedical knowledge. And it should accelerate. mentioned before that the half-life of the knowledge you learn in med school as a physician is decreasing rapidly. Do you think that's going to change, like, how you were educated as a doctor? I think medical education is going to radically change. I think doctors are going to be in the loop for a very long time. They have been a loop since, you know, the ancient Greeks, if not, you know, the ancient Egyptians. I think you're going to be in a loop for very, very, very long time and for the rest of our

lifetimes, if not longer. Medical education is going to change radically because it's just, you know, the statistic I cite, and all of this is in peer-review public-publicly available medical literature, the rate of doubling of medical knowledge as measured by citations in 1950 was every 50 years. So every 50 years, the number of total citations of peer-review medical literature doubled. Today, it's every 73 days by an estimate in the British Medical Journal. and one in nature. I think that methodology was a little bit aggressive because they were looking at the totality of all publications. Not all publications are equal. But we came up internally with a more conservative one because we didn't want to drink the Kool-Aid. So we said, okay, let's just

look at the top quartile of peer-reviewed medical literature. And let's pretend that physicians never need to read the bottom three-quarters of medical literature, which is not really true. But let's just let's do this with one hand tie behind her back. And if you do it that way, it's every five years. So if you use the more conservative methodology, it's not every 73 days, but every five years, the total sum of the top quartile of peer-revemedical literature by citations doubles. Now, you could say, well, look, luckily for humans, medicine has become specialized. So your dermatologist doesn't, you know, need to read everything in neurology. That was my initial example.

And now they have open evidence, so they can bridge some of this stuff. So why don't we go even more conservative still and say, if a physician just needed to read the top 10% of peer-reviewed medical literature in their own specialty. So now this is very conservative. There's no cross-functional interdisciplinary medicine at all. Everybody's hyper-specialized. It's not a great outcome. But let's just pretend that's the case.

what would that mean? Well, now you're in the realm of doable. Obviously, every 73 days and every five years is not doable. But now you're in the realm of doable, but that physician would need to spend on average nine hours a day, just reading the top 10% of peer-reviewed medical literature just in their own discipline. Of course, the university patients, you spend time with their family and so on. Now, you can sort of keep going more and more conservative with these methodologies. And realistically, not everything even within pediatric cardiology is relevant to every pediatric and so maybe it's not nine hours, maybe it's four hours, maybe it's three hours a day. But there's some point at which it's going to be like you'd want them to know all this stuff, even narrowed down all the way, and it still is kind of impractical. At minimum, I think that this framework of medical school being a very defined period in time and then having continuing medical education, which has kind of historically been this sort of like, uh-huh, okay, sort of, you know, wink-wink kind of thing, that is going to more or less invert, where the continuing medical education is going to be the majority of medical

education. And that's already happening. That's not a future projection, right? If you speak to really phenomenal, you know, world-class physicians, they will tell you very openly that 90-95% of what they practice, they learned post-graduating medical school. And in most cases, their fellowships, bed fellowships, and residencies for their residencies. And some of the greatest physicians that I've ever met and spoken with tell me, you know, extreme things like the majority of what they practice today, they learned in the last two years. And I've had, I've had a 70-year-old physician tell me that. Now, these are world-class people.

But what that shows for everybody is that you're going to need to invert the construct of Does that change the nature of a residency? or the way that physicians are trained is very structured today in a very specific sequence of steps that was based in some part on how you should train somebody 50 years ago. Yeah. No, it's going to change. It is, it is changing. There are these very avant-garde approaches to residency at some of the top places like Mayo, Cleveland, UCSF, which are trying to deconstruct the 50-year-old model. What if they do differently? They encourage evidence-based medicine, not just guideline-based medicine. they encourage the curbside consult.

They basically try to solve the problem of information overload through, you know, distributed hive mind. So what don't the curbside consult me? So a curbside consult sounds fancy, but it just means, you know, go ask some other physicians who might know something about this. I mean, all of these things sound obvious. Who wouldn't want evidence-based medicine? Who wouldn't want physicians asking a panel of other physicians who might also know something about it, you know, about the thing? The demands on a knowledge worker are highly correlated to the number and complexity of the tools available, right? Like in 1917, you know, at the end of World War I, your tools were basically nothing.

You know, you had gauze and some scissors, right? So this is all very, very new that getting into my early example, like IL-17 inhibers for Zile, 23 inhibers, and biologics and the treatment of psoriasis where someone has a neurological comorbidity. Like, that's all the last like five seconds from a historical perspective. So, of course, the profession has to change. And it's going to change evidence-based medicine, curbside consults, distributed decision-making. You know, that's a big part of it, like a lot of what's so incredible about all these famous places that are rightly famous, Mayo, Cleveland, UCSF, MGH, others, is they really are sort of at the vanguard of thinking about distributed decision-making.

Like, if there's a patient with a complex fact pattern, let's bring in sort of interdisciplinary. Let's bring a group of doctors, you know, across disciplines and look at this in an interdisciplinary way. Let's have a cardiologist and a neurologist and an oncologist. Look, the issue is that's very expensive. As I'm describing this, I'm just thinking real time, like, it's really expensive to do. So then there's this equity issue where it's pretty clear what the right way to practice medicine is in 2025. in light of this explosion of treatments in the golden age of biotechnology, it's not clear how to pay for that because now it's not just one extremely expensive specialist.

Now it's three or four. We don't have that many specialists. We're not making more oncologists at any faster rate than we really. I'll just kind of be sort of AI driven tooling or things like that that help augment that. The hope, and this is kind of where we're in the midst of this is that in under-resourced areas as an example, you know, we have, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, we, We have physicians using open evidence in every state electoral county and zip code in the United States, including rural Alaska and southwestern Georgia.

And, you know, we get letters from doctors because when you make something awesome that's free, when you make something awesome that has a subscription, I think people like it, but they don't send you fan mail. When you make something awesome that's free, they send you fan mail. So we get fan mail from, you know, southwestern rural Georgia from an oncologist who's like, I'm one of two oncologists in a 50-mile radius serving a 75% African-American population with a median household income of $43,000 a year, and I use open evidence as my curbside consult, by which he means, you know, as my panel of other. So that starts to bridge it. And I think

increasingly, certainly rural areas and health care deserts at the fringes and edges of health care in the United States, that's absolutely how certainly open evidence is being used and how I think broadly is going to be used at least to sort of bridge that gap. And I think that's a real clear silver lining or positive side of AI right. now? What do you think consumers might do productively in the future in terms of preventative health? You're treating doctors and knowledge workers as consumers. There's not enough of them. Hopefully you will multiply their productivity dramatically. Do you imagine consumers will be responsible for some piece of their own health differently? This is not going to be a popular

answer or a politic answer, but if you go spend five seconds in Japan, I'm obsessed with Japan, I named my first company Kencho. I was in Japan. Two months ago, I've been in Japan a dozen times. I'm obsessed with Japanese culture. The difference in why there's so many differences, some of which are genetic, but a big difference in why they're so healthy in Japan is they just do all the things that everyone know. And I'm not generalizing to all Japanese, and there's now Western food in Western culinary traditions, that I've entered Japan, and it's all complex, we live a globalized world. But disclaimer, disclaimer, disclaimer. But there isn't some net new list, right? So I was in Japan a couple months ago, and it is striking. It is shocking, the extent to which, especially if you go

outside the big cities and go to places like Kyoto or smaller cities like Hacone or so on, they're all walking. They're all, just the average Japanese, and at all ages, you have 70, 80-year-olds we're walking 10,000, 15,000 steps today. It's a walking culture. And it's not just my sort of romanticized illusion as a white Western looking at it. Like, I've gone pretty deep on this. I've been there, again, like a dozen times.

I've had long conversations with people that are there and not just academics and scholars, but just ordinary people on the street, taxi cab drivers and so on. You know, they like walking. And also, the older they get, the more they like walking. The younger kids actually are, you know, the ones that are 65 and 70,

They'll just go walk four miles to work. They don't retire. They don't fetishize retirement. They have concepts in their culture of, you know, what Plato called, you know, a good life. But in Japanese culture, a good life isn't extricable from a life with purpose. You know, an idle life cannot in Japanese culture be a good life. Like those are incompatible notions, you know, idleness and fulfillment. So there's no concept of fetishizing, like, I'm just going to work really hard and make

a lot of money at 65, you know, I'm going to hang out on the beach. That's just not a concept really in at least the traditional culture absent the Western recent Western influences. So people work past 65 into their 70s, into their 80s. You know, that's when it really matters, right? Like that's when that's when risk of mortality starts to go to go up a lot. And then of course, famously, diet sort of, it's not just, you know, sort of a pescatarian's good diet, but it's also the fact that, you know, you can almost eat anything if it's in the

right portions. You know, they don't, they don't gouge themselves on food. They eat until 70, 80% full, all these things that are famously now. And I think at least we're having a conversation about it now in the United States. For the longest time, you had things that every doctor believed. No one would, there's no, I've never met a doctor who disagrees that, you know, as you get past a certain point in body weight, your risk of all sorts of things goes up. But 10, 15 years ago, no one wanted, no doctor would have wanted to say that aloud because it sounded like, well, how do we break that culturally? Because I think ultimately, to your point, you know, physicians are viewed as people who have extra knowledge. Yeah.

Who are supposed to be helping patients and obviously they're very focused on that. And sister's a doctor, you know, like, yeah, it's that, you know, for many people I know it's really core of why they became a physician. Yeah. But at the same time, political culture took over it prevented them from speaking their minds on things that were really clear on evidence. That has a huge impact for the patient population, yet nobody would stand up and say, actually, it's really bad that we're glorified in the fact that, you know, being dramatically overweight is healthy. I think the pendulum swings back and forth. I think all these issues are deeply entwined. I think that we're now for the first time in a long time having a more open conversation

that is not just reduced through the lens of identity politics around health, life choices. And it's not just obesity versus, or it's not just overweight versus not overweight. Let's use something that has nothing to do with weight. Neurigenerative. Now, there's a strong genetic component to neurodegenerative, and there are definitely people who have never used their brain in their entire life and never get Alzheimer's. That's obviously true. But no serious neurologist will dispute the fact that a mitigant to neurogenital disease is to continue to use your brain over the course of your life.

It just feels like, you know, now at least you can have this sort of more open conversation around, like, you know, if you want to at least mitigate the risk of neurogenital disease, you know, continue to do all the things Sanjay Gupta do. You know, if you're left-handed right with your right-hand once in a while, if you're right-handed right with your left-hand once in a while, like just sit. really things like that that will form, you know, new neural pathways. This is a different type of AI application. And you are getting adoption with a type of knowledge worker where people are surprised by the pace, generally considered conservative industry, has fakekeepers, everything that you described earlier. What do you believe about what would happen, what can happen in other fields?

Or like, are there lessons for lots of entrepreneurs that listen to this podcast? While medicine is obviously very specific, the human psychology is not. And everything that was true in that we've seen through the sort of hyperpace consumer internet growth curve adoption by the most traditionally skeptical knowledge workers shows that in any industry or subfields that tech might want to touch, the same basic rules of the game psychologically apply, which is if you address people, as people and as consumers and if you speak to them in a way they've ever spoken to before and it sort of hit them different, you know, in a way that no one's ever kind of come at them in that way before. That at minimum will be very refreshing and different and will lead to them considering the thing with an open mind and in all likelihood will break the mold that has typically been the rate limit of the adoption curve of whatever had defined that industry.

I think for a long time, like if you build it, they will come, has been just like laughed at as an idea amongst much of the tech community. Why do you think there's such skepticism when like there are the cases of, you know, consumer internet companies or things like with an evidence? I don't think if you build it, they will come as true. And nor would I say that, you know, Apple or Steve Jobs is a story. that if you build it, they will come. To me, you know, Apple or Steve Jobs is a story that if you have extraordinary will to power and you see reality as malleable and you believe, as Nietzsche says, that, you know, ideas and rational thought are our second order, you know, after projections of the will,

you know, then you'll succeed. But that's not a, you know, that's not a fairy tale that, that's not a fairy tale that, you can, you know, tell to Y Combinator kids or to MBAs, right? And there's this tension, and this has been discussed by many people at length, but there's this tension in the history of Western thought between, you know, rationalism and will, right? Reason and will or the intellect and will. And the enlightenment was this sort of Cambrian moment and the explosion of rationalism

and ideas and this sort of faith, and it really is a faith, Because the irony of the enlightenment is that the notion that reason is supreme was not arrived at through reason but through faith. And there was this faith that reason would ultimately govern and that humans are in their first order rational and co-gito ergosome and Descartes. And so much of everything that waterfalls down today to like what MBAs or why accommodate her kids believe, which is just like, You know, so tell me, Daniel, when you had the idea for open evidence where you're in a coffee shop, what kind of coffee shop? What coffee were you drinking? Like what was the circumstantial thing that gave rise to the idea, right? And all of that is actually just, you know, a derivative idea of Cartesian thought. And I think a more useful question for people than, you know,

what coffee shop, what was the person drinking when they had the idea for something they admire is where can I find a level of motivation that is almost compulsive, right? And that's different for different people. There's no one answer, right? There are a lot of people that find that from proving somebody wrong, somebody said to something to them when they're a kid that really just hit them in the right way when they were really psychologically vulnerable. And they've spent the rest of their life trying to prove that person wrong with that person

as a parent or a friend or a teacher. I mean, how many famous examples are there of people trying to prove a teacher wrong that is literally dead, you know, and that this person's, I've met these people. They're 75 years old and they're trying to prove a teacher wrong that's been dead for 30 years. But it turns out that those things work and those ingredients work. And it doesn't need to be proving someone wrong. It could be people that are born with an enormous amount of aggression and found a constructive way to channel that aggression. You know, in my case, I was born with just an unbelievable amount of aggression. And through a combination of trading my intellect and just luck, I found a more useful channel for that aggression. But you need to find this sort of perfect storm of things. And it has very little to do with ideas. You know, the idea for open evidence is the most obvious idea in the world.

It's the same as it's no more creative than let's go to the moon. Let's do something really hard. What are the hard things? Do you actively seek to find more motivation for yourself? No. And actually the opposite. One of the things I think is unhelpful about the contemporary cult of psychoanalysis and psychology and psychiatry that sort of traces its origins

early 20th century in Freud and these guys is it doesn't appreciate that in the analysis and description of something you kill it. So I've actually resisted. I resisted exploring trauma. I've resisted going back to the origins of my motivation. I've resisted going back to the origins of my aggression. I have kind of like a partially developed map from childhood and other experiences. But the second I feel myself going close to analyzing it, I resist the urge to analyze it.

Because in the analysis of something is the deletion of it in a way. And you already have the well. And the well is deep. so it doesn't you don't need it i i don't need more of it and quite the opposite um i i resist trying to discover uh what the propulsion system is you know most propulsion systems originate from from trauma this is the uh the the the the the what's now become the sort of famous like sequoia methodology of you know doug and these guys of like talking about your early childhood and all this stuff i mean i i think there's a lot of truth to except um you don't want to go

too close to that stuff because you'll actually kill the propulsion system in analyzing it. What of this lens of motivation do you take to recruiting for your own team? I quickly learned in my first company even that there is a, there's only a moderate correlate, there's like a .65 correlation between Freakishly Smart and output. I think you have to find people that are obviously exceptionally intelligent, but to all the things I've been saying, have some propulsion system. They don't need to know where it comes from. But we've all met people that are extremely aggressive, are extremely driven. They might have very little understanding of why they are. That's better, not worse, better. And those are the people that end up, you know,

that I try to recruit and that I seek out in recruiting. Because then all the other stuff that is, you know, I actually don't like management. And I don't want to practice the art of management and so much of management needs to come into play in the absence of those things, right? Like a lot of this stuff, you know, I'm not an MBA by background. I've never had, never gone to business school. I've never had, I never gone to one business school class, you know, but I have friends that have and I've, there are people I respect that, that have done those things and, you know, a lot of that world is like how to motivate people, how to inspire people, like how to give people constructive feedback and constructive criticism and all of this stuff. And I think

there's definitely a body of knowledge there. Like, you can definitely do better or worse at doing those things. But what I seek out in recruiting are the people for whom all of that is just entirely redundant because there's just no, like, they're just, they're driven on their own warpath, and the best you can do is sort of get out of their way. Awesome. Thanks for doing this, Daniel. Thank you. Happy. Find us on Twitter at No Pryor's Pod. Subscribe to our YouTube channel. If you want to see our faces, follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no dash priors.com.