No Priors Ep. 130 | With OpenEvidence Founder Daniel Nadler

Daniel, thanks for doing this.

Happy to be here.

So, give us a sense of this incredibly viral sensation that has been OpenEvidence, 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 say, as a qualifying point, that in all of the subindustries of AI, you see an acceleration and compression, right? The adoption cycles, even outside of OpenEvidence—in other fields of knowledge work and coding and so on—are hyper-compressed, right? It used to take half a decade or a decade for something to become standard, and now it seems to happen in 2 years or a year. So the same thing's happened with OpenEvidence.

In about 18 months, it's become the operating system for clinical knowledge in the United States. It 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. High-stakes clinical decision support for doctors is a specific category of medicine. It's distinct from, say, paperwork, or it's distinct from scribing. Those things are 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, 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 spear. 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? Because I think fundamentally it's about picking information and then translating that into specific recommendations or a diagnosis for a patient. Can you tell us more about how that works?

Yes. One way to simplify it is that, at its foundation, it's a search problem, but it's a very semantic search problem. Most traditional search works with keywords. Flights to Barcelona or hotels in Barcelona—most of the keywords there can be captured in a couple of words, and certainly in a sentence, and that's 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. 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're a dermatologist, 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 refer her to a neurologist, because you want to treat her psoriasis. 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, are IL-17 inhibitors and IL-23 inhibitors. They might have some interaction 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 one of the great themes of OpenEvidence is that the 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. It was approved in 2019. You might have graduated medical school in 2005, right? So you didn't cover it in medical school, and that's it. That's what you know.

So your question then is: For a 44-year-old female patient with moderate-to-severe psoriasis, is an IL-17 inhibitor or an IL-23 inhibitor more appropriate and more safely tolerated with respect to not aggravating the MS? Now, that's not an academic question. That's a very consequential question. An IL-17 inhibitor will actually make the MS worse. IL-23 inhibitors are safe and well tolerated in cases of MS.

That's an example of where medicine can go wrong, because even 5 or 10 years ago, either you're referring that person to a neurologist, in which case you're just getting referrals in circles and medicine is not happening, or, unfortunately, what would more likely happen is they would just 50/50 it, and that MS might be aggravated.

It's well known and often repeated that medical error is the third leading cause of death in the United States, after heart disease and cancer. But even that statistic understates it, because that's just looking at death, right? In the case of 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. So it's not just that medical error historically was a leading cause of death. It's that for as many people as 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.

Coming back to your question, that whole string is the search query. You can't just do search in a traditional way where you say “IL-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, from the world of published biomedical literature you could find the exact snippets in a phase 3 RCT—a randomized controlled trial—in the New England Journal of Medicine that tested each of these things and found that one aggravated MS and the other didn't. 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 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, in a snippet not even in the abstract but in the methodology section or in the population.

I don't deal with that ambiguity, actually, because I feel like, in the context of medical information, there are things that are in pre-baked clinical guidelines. Certain types of conditions: We're going to do XYZ, and that's sort of the recommended path.

There's stuff that's recently published. There's evidence in a certain direction, or maybe it's by the label or something else. And there's a bunch of stuff that's a bit more TBD in terms of those clinical trials that may be contradictory and sporadic. How do you deal with that third bucket of ambiguity, and how do you think in detail about capturing that broader knowledge growth over time?

So the first way to deal with that third bucket of ambiguity is to ensure that your users are physicians and not patients. We've made that strategic decision, and we keep thinking we're going to change that decision. 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, right? 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 an inaccurate quote on a bond that was very obviously inaccurate—off by an order of magnitude—the trader in a hedge fund would just say, “Well, I mean, that's odd.”

Do you indicate in the user interface that there's some ambiguity around this, or that there's complete evidence?

Absolutely. There are areas of medicine where there is a lot of conflicting evidence, and that's indicated. It's not presenting answers. 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.

A lot of the value proposition early on was that we made references and citations a first-class citizen before that was in ChatGPT. We were actually providing references and citations 6 or 9 months before ChatGPT started doing that. That was a big reason we had adoption, because people could interrogate and audit the source.

Right there, there's a 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. The way we framed 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 and continuum.

That created a certain social contract with the users who, in addition to being physicians and having that MD that they need to defend, 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.

Users do look at source material 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, from a search space or surface area of 35 million biomedical publications, the exact 3–5 canonical landmark phase 3 RCTs, guidelines, or other sources of information that are responsive to their question—not answers, but sources that are responsive to their question.

I would say the next default behavior is that they go out. I think we’re one of the largest sources of referral traffic to the New England Journal of Medicine after Google. I don’t know if we’re number 2, 3, or 4, but we’re one of the largest sources of referral traffic to our partner, the New England Journal of Medicine. That’s a testament to the way people use it.

Historically, it was very hard to do 2 things. It was hard to describe a complex patient scenario or 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 medical literature, the 7 snippets that are directly responsive to a question and to the semantic meaning of the question, as opposed to a few keywords.

We just did those 2 things extremely well. We framed the right social contract, and we picked our audience extremely well. All of those things start to stack into something that looks more like a Bloomberg terminal for doctors, where it’s just a pro tool.

They’re using this because it has the right data that goes in, because AI is gold in, gold out; garbage in, garbage out. They know this is not trained on tweets. They know this is trained on the New England Journal of Medicine and JAMA and the rest. They know that we have these strategic partnerships with the gold standards of medical knowledge.

They know that they’re not going to get an answer from OpenEvidence. They’re going to get a routing to a source that answers the question. So I think all these things 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 OpenEvidence. You wanted to build an impact-driven company—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. 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 healthcare company at all.

I love Sequoia’s quote that OpenEvidence is a consumer internet company masquerading as a healthcare company. I had zero interest in building a healthcare company. OpenEvidence is not a healthcare 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.

My whole career prior to this had been dealing with knowledge workers. People have a reductive view of consumers. They think of 14-year-olds on TikTok, and that tends to be their archetype of what a consumer is. That’s one type of consumer.

Traders on Wall Street are consumers and people. Lawyers are consumers and people, and doctors are consumers and people. What I realized is no one had ever treated doctors that way before. Doctors were just treated as these appendages of health systems.

I thought, “That’s an interesting way to organize the medical system and the health system.” You start to investigate and pull the thread a little bit, and you start to understand why there are very few things that people can agree about in America. They can agree Congress is dysfunctional, and they agree that American healthcare is dysfunctional. It’s like bipartisan, universal consensus.

But you start to really investigate, and you come across 2 or 3 things and you’re like, “Maybe that begins to explain the dysfunctionality.” To me, in particular, the idea that doctors—who were the fighter pilots, who were the knowledge workers, who were the people who had that MD on the line and had to make that high-stakes decision—weren’t even their own gatekeepers as far as the technology they used, that was a pretty profound realization.

We did something that had never been done before, which is we treated them as consumers and as people who could go onto the App Store, download a free app, and start using it. It sounds so stupidly simple, but it was really profound and really effective because no one had ever done that before.

It’s almost analogous to relationships, whether friendships or romantic relationships. People can get caught in these sorts of cul-de-sacs where there’s a rigidity to their dynamic and to their relationship. Then there’s a breakthrough where one person says something that they’ve just never said before, or they’ve just never said it in that way before, and then there’s a breakthrough. It hits different.

In psychiatry, psychology, and therapy, a lot of that field is encouraging this behavior in others: to break free of cul-de-sacs, of dialectics, of relationship dynamics, and just say something in a way that’s never been said before. Do something that hits different.

Long story short, we did that with doctors, and it wasn’t the complexity of the idea. It was just that no one had ever addressed them as consumers before. 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.

Now it’s really cool. Yeah, I mean, the velocity of it and usefulness and value are reflected in that velocity.

The scale and the speed of it are evident in more common cases, in which the leadership of the hospital system are very avid users. The entire senior leadership of UCSF, MGH, Mayo Clinic, Cleveland Clinic, NewYork-Presbyterian, Mount Sinai, Cedar Sinai—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 a new tool to do the work? I have a sort of slightly separate question, which is maybe back to the consumer versus medical or physician side of this.

I started a digital health company maybe a decade or 15 years ago, and one of the things—we were basically initially providing really key genetic information. We had a physician in the loop at all times. But one of the things we ran into was what I came to see as an almost journalistic viewpoint in the medical community toward what information their patients should and should not get.

I think part of that was real concern about what the patients could do in terms of acting on information, but I think a lot of it was just wanting to be a gatekeeper. Or part of it was just not wanting 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 should patients be able to advocate for themselves?

I’ve experienced both sides of this. 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, 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 should definitely have some role and agency in that.

What we have done is encourage physicians to use OpenEvidence to generate patient handouts. That’s actually a very widely used secondary use case. It’s mainly clinical reports, but we have all these secondary use cases, like prior authorization letters and insurance appeal letters. One of the most common of those secondary use cases is generating these patient handouts.

The other side of this that I can appreciate is that it took me personally taking my first graduate-level statistics course at Harvard to really understand these clinical trials. 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 constructive decision-making process.

I mean, there’s no good answer. The reality is very tough, right? You want to give patients all the answers that are clear and have 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 to—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 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 is effective, and then you’re just in this endless circle where the physician, who has by definition taken at least 1 graduate-level statistics course, is trying to explain to a civilian what a p-value is. I think that’s probably not a constructive outcome.

So it’s a balance. We encourage physicians to use OpenEvidence and to use patient handouts, especially where guideline-based medicine is concerned.

I think you mentioned something really interesting earlier, which is the velocity at which your product got adopted was incredibly fast. I think part of that was simply that it’s incredibly valuable, as you have a lot of these new and different tools. I think that’s one of the almost underappreciated aspects of this wave of AI: not only is there a fundamental technology shift that’s enabling all sorts of new products, but there’s also this massive shift in the openness of adoption by people and organizations to new technologies.

That’s in terms of what you’ve been doing with evidence. To your point about the medical scribing thing, there are companies like Abridge and others. If you think ahead 10 or 20 years—and this may be impossible to extrapolate—how do you think medicine, or the state of medicine, changes in general? Are we still going to the doctor’s office for visits? Are you interacting with some online tool that’s backed by a doctor? Are drugs developed differently? I’m just wondering, at a high level, how you think about the whole industry evolving or changing, given that suddenly markets are open in ways that they weren’t before, but there are also new technological ways that are going to impinge on markets.

It’s getting difficult. The definition of a singularity event horizon is that you cannot even project into the near future, let alone the far future. I think 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 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 is, why? Of course, that is an attribute of human psychology: We are anthropologically tribal, and we don’t abstract trust well. We personify trust, and we trust things that we personify and anthropomorphize. There’s a whole history—

People are already doing a lot with chatbots, right? In other words, there are people who—

Effectively view themselves as being in relationships with—

Yeah. They don’t have bodies yet. I mean, you could 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 2 seats, no one wants that. Okay, what if you keep the 2 seats, but they’re empty? I still think no one wants that. What if you keep the 2 seats and there are mannequins, essentially—

Mhm. 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’d see this little uptick in willingness. I think it would still be the minority.

Can I ask a question? If we’re talking about the near future, you’ve mentioned before that we are in an era of, in an amazingly optimistic way, an explosion of biomedical knowledge, and it should accelerate. You’ve mentioned before that the half-life of the knowledge you learn in medical school as a physician is decreasing rapidly.

Do you think that’s going to change how you were educated as a doctor?

I think medical education is going to radically change. Doctors are going to be in the loop for a very long time. They have been in the loop since the ancient Greeks, if not the ancient Egyptians. I think they’re going to be in the loop for a very, very, very long time, and for the rest of our lifetimes, if not longer.

Medical education is going to change radically because the statistic I cite—and all of this is in peer-reviewed, publicly available medical literature—is that 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-reviewed 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. We came up internally with a more conservative one because we didn’t want to drink the Kool-Aid. 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 do this with 1 hand tied behind our back.

If you do it that way, it’s every 5 years. So, if you use the more conservative methodology, it’s not every 73 days but every 5 years that the total sum of the top quartile of peer-reviewed medical literature, by citations, doubles.

Now, you could say, “Look, medicine has become specialized for humans, so your dermatologist doesn’t need to read everything in neurology.” That was my initial example. Now they have OpenEvidence, so they can bridge some of this stuff.

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? 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 5 years is not doable. But now you’re in the realm of doable. That physician would need to spend, on average, 9 hours a day just reading the top 10% of peer-reviewed medical literature just in their own discipline. Of course, they would never see patients or spend time with their family, and so on.

You can keep going more and more conservative with these methodologies. Realistically, not everything even within pediatric cardiology is relevant to every pediatric cardiologist. Maybe it’s not 9 hours; maybe it’s 4 hours; maybe it’s 3 hours a day. But there’s some point at which 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 this framework of medical school being a very defined period in time and then having continuing medical education—which has historically been this “uh-huh, okay” wink-wink kind of thing—is going to more or less invert, where continuing medical education is going to be the majority of your medical education.

That’s already happening. That’s not a future projection, right? If you speak to really phenomenal, world-class physicians, they will tell you very openly that 90 to 95% of what they practice they learned after graduating from medical school, and in most cases after their fellowships and residencies.

Some of the greatest physicians that I’ve ever met and spoken with tell me extreme things, such as, “The majority of what I practice today, I learned in the last 2 years.” I’ve had a 70-year-old physician tell me that. These are world-class people.

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? It’s 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 changing. There are these very avant-garde—

Approaches to residency at some of the top places, like Mayo, Cleveland, and UCSF, which are trying to deconstruct the 50-year-old model. And—

What do 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 a distributed hive mind.

What does a curbside consult mean?

A curbside consult sounds fancy, but it just means, “Go ask some other physicians who might know something about this.” 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 the thing?

The demands on a knowledge worker are highly correlated to the number and complexity of the tools available. In 1917, at the end of World War I, your tools were basically nothing. You had gauze and some scissors, right?

This is all very new. Getting back to my early example, IL-17 inhibitors, IL-23 inhibitors, and biologics in the treatment of psoriasis where someone has a neurological comorbidity—that’s all the last 5 seconds from a historical perspective.

So, of course, the profession has to change, and it's going to change evidence-based medicine, curbside consults, and distributed decision-making. That's a big part of it. A lot of what's so incredible about all these famous places that are rightly famous—Mayo Clinic, Cleveland Clinic, UCSF, MGH, and others—is that they really are at the vanguard of thinking about distributed decision-making.

If there's a patient with a complex fact pattern, let's bring in a group of doctors across disciplines and look at this in an interdisciplinary way. Let's have a cardiologist, a neurologist, and an oncologist look at it. Now, the issue is that that's very expensive. As I'm describing this, I'm thinking in real time: this is really expensive to do.

So, 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 1 extremely expensive specialist. Now it's 3 or 4—

We don't have that ability. We don't have that many specialists.

We're not making more oncologists at any faster rate than we—

It all just translates into AI-driven tooling or things like that that help augment that. The hope—and this is where we're in the midst of this—is that, in under-resourced areas, as an example, we have physicians using OpenEvidence in every state, every county, and ZIP code in the United States, including rural Alaska and southwestern Georgia.

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 southwestern rural Georgia—from an oncologist who says, “I'm 1 of 2 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 OpenEvidence as my curbside consult,” by which he means “as my panel of other doctors.”

That starts to bridge it, and I think increasingly, certainly in rural areas and healthcare deserts, at the fringes and edges of healthcare in the United States, that's absolutely how OpenEvidence is being used and how AI, I think, broadly is going to be used, at least to bridge that gap. 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. Yeah, 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 5 seconds in Japan, I'm obsessed with Japan. I named my first company Kencho. I was in Japan 2 months ago. I've been in Japan a dozen times. I'm obsessed with Japanese culture.

The difference—there are so many differences, some of which are genetic—but a big difference in why they're so healthy in Japan is that they just do all the things that everyone knows are good for you. I'm not generalizing to all Japanese people, and there's now Western food and Western culinary traditions that have entered Japan, and it's all complex. We live in a globalized world, but—

Disclaimer, disclaimer, disclaimer.

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 Hakone and so on, they're all walking.

They're all just the average Japanese person, at all ages. You have 70- and 80-year-olds walking 10,000 to 15,000 steps a day. It's a walking culture. It's not just my romanticized illusion as a white Westerner looking at it. I've gone pretty deep on this; I've been there again, a dozen times.

I've had long conversations with people who are there—not just academics and scholars, but ordinary people on the street, taxi drivers, and so on. They like walking, and the older they get, the more they like walking. The younger kids, actually—the ones that are 65 and 70—will just walk 4 miles to work. They don't retire. They don't fetishize retirement.

They have concepts in their culture of what Plato called a good life. In Japanese culture, a good life is inextricable from a life with purpose. An idle life cannot, in Japanese culture, be a good life; those are incompatible notions: idleness and fulfillment. So, there's no concept of fetishizing, “I'm just going to work really hard, make a lot of money, and at 65 I'm going to hang out on the beach.” That's just not a concept, at least in the traditional culture, absent the recent Western influences.

So, people work past 65, into their 70s, into their 80s. That's when it really matters, right? That's when risk of mortality starts to go up a lot. And then, of course, famously, the diet—it's not just a sort of a pescatarian, scoop diet[?], but it's also the fact that you can almost eat anything if it's in the right portions. They don't gouge themselves on food. They eat until 70% or 80% full.

All these things are famously known, and I think at least we're having a conversation about it now in the United States. For the longest time, there were things that every doctor believed. I've never met a doctor who disagrees that, as you get past a certain point in body weight, your risk of all sorts of things goes up. But 10 or 15 years ago, no doctor would have wanted to say that out loud because it sounded like—

Well, how do we break that culturally? Because I think ultimately, to your point, physicians are viewed as people who have extra knowledge.

Yeah.

They're supposed to be helping patients, and obviously they're very focused on that. My sister's a doctor. I think it's that—

For many people I know, it's really core to why they became a physician.

Yeah.

But at the same time, political culture took over and prevented them from speaking their minds on things that were really clear in the evidence and that had a huge impact on the patient population. Yet nobody would stand up and say, “Actually, it's really bad that we're glorifying the fact that 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 and life choices. And it's not just obesity versus—or it's not just overweight versus not overweight. You know, let's use something that has nothing to do with weight: neurogenerative. Now there's a strong genetic component to neurogenerative, 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 neurogenerative disease is to continue to use your brain over the course of your life. It just feels like now at least you can have this sort of more open conversation around if you want to at least mitigate the risk of neurogenerative disease, continue to do all the things Sanjay Gupta does—you know, if you're left-handed, write with your right hand once in a while; if you're right-handed, write with your left hand once in a while—like just silly things like that that will form 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. It's generally considered a conservative industry; it has gatekeepers, everything you described earlier. What do you believe would happen? What can happen in other fields? Are there lessons for lots of entrepreneurs that listen to this podcast? While medicine is obviously very specific, human psychology is not.

Everything that was true and that we've seen through the hyper-paced consumer internet growth curve—adoption by the most traditionally skeptical knowledge workers—shows that in any industry or subfield that tech might want to touch, the same basic rules of the game psychologically apply. If you address people as people and as consumers, and if you speak to them in a way they've never really been spoken to before, and if you hit them differently, in a way that no one's ever come at them in before, that at a minimum will be very refreshing and different and will lead to them considering the thing with an open mind. In all likelihood, it 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, “If you build it, they will come” has just been laughed at as an idea amongst much of the tech community. Why do you think there's such skepticism when there are cases of consumer internet companies or things like OpenEvidence?

I don't think “If you build it, they will come” is true.

Nor would I say that Apple or Steve Jobs is a story of “if you build it, they will come.” To me, Apple or Steve Jobs is a story that if you have extraordinary will to power, you see reality as malleable, and you believe, as Nietzsche says, that ideas and rational thought are second-order after projections of the will, then you’ll succeed. But that’s not a fairy tale that you can tell to Y Combinator kids or MBAs, right?

There’s this tension in the history of Western thought between rationalism and will, reason and will, or the intellect and will. The Enlightenment was this sort of Cambrian moment, an explosion of rationalism and ideas, and this sort of faith. 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.

There was this faith that reason would ultimately govern, that humans are, in the first order, rational—cogito ergo sum, Descartes—and so much of everything that waterfalls down today to what MBAs or Y Combinator kids believe, which is just like, “So tell me, Daniel, when you had the idea for OpenEvidence, were you in a coffee shop? What kind of coffee shop? What coffee were you drinking? What was the circumstantial thing that gave rise to the idea?” All of that is actually just a derivative idea of Cartesian thought.

I think a more useful question for people than “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? That’s different for different people. There’s no one answer.

There are a lot of people who find that motivation from proving somebody wrong. Somebody said something to them when they were a kid that really hit them in the right way when they were psychologically vulnerable, and they’ve spent the rest of their life trying to prove that person wrong. That person might be a parent, a friend, or a teacher. I mean, how many famous examples are there of people trying to prove a teacher wrong who is literally dead? I’ve met these people. They’re 75 years old, and they’re trying to prove a teacher wrong who’s been dead for 30 years.

But it turns out that those things work. Those ingredients work. It doesn’t need to be proving someone wrong. It could be people who are born with an enormous amount of aggression and find a constructive way to channel that aggression outward.

In my case, I was born with an unbelievable amount of aggression, and through a combination of training my intellect and 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. The idea for OpenEvidence is the most obvious idea in the world. 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, psychology, and psychiatry, which traces its origins to the early 20th century and Freud and these guys, is that it doesn’t appreciate that in the analysis and description of something, you kill it.

So I’ve resisted exploring trauma. I’ve resisted going back to the origins of my motivation, and I’ve resisted going back to the origins of my aggression. I have a partially developed map from childhood and other experiences. But the second I feel myself getting 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 will, and the will is deep, so you don’t need it.

I don’t need more of it. Quite the opposite. I resist trying to discover what the propulsion system is. Most propulsion systems originate from trauma.

This—what’s now become the sort of famous Sequoia methodology of Doug and these guys, of talking about your early childhood and all this stuff—I think there’s a lot of truth to it, except 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 that there’s only a moderate correlation. There’s like a 65 correlation between being smart and output. I think you have to find people who are obviously exceptionally intelligent but, to everything I’ve been saying, have some propulsion system. They don’t need to know where it comes from.

We’ve all met people who are extremely aggressive, extremely driven. They might have very little understanding of why they are. That’s better, not worse. Better. Those are the people that I try to recruit and that I seek out in recruiting, because then all the other stuff is redundant.

I actually don’t like management, and I don’t want to practice the art of management. So much of management needs to come into play in the absence of those things, right? A lot of this stuff—I’m not an MBA by background. I’ve never gone to business school. I’ve never gone to one business school class.

I have friends who have, and there are people I respect who have done those things. A lot of that world is how to motivate people, how to inspire people, how to give people constructive feedback and constructive criticism, and all of this stuff. I think there’s definitely a body of knowledge there. 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 entirely redundant, because they’re just 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.