Full Transcript for Thrive Capital's Bet on Neuralink, OpenAI & the Future of Medicine

Thrive Capital has emerged as one of the most prolific venture capital firms of the past decade. Thrive has been at the center of the biggest AI deals lately. The last 10 years, we've spent about $40 trillion on healthcare. We have not moved our life expectancy in age. The life expectancy is dropping. Americans are not living as long as they used to.

And if that is not an indictment of the failure of the healthcare system, I don't know what is. $40 trillion for no change. More so than maybe any other time in history. The frontier of science and the frontier of what we've accomplished is moving faster than ever. We're seeing this explosion of AI and drug discovery. And I think that is changing the way in which we're going to deliver and make drugs.

And it's also going to change the entire value chain. Two -thirds of physicians in America are using OpenAI. The company says about 40% of the doctors in the U .S. right now are already using the chatbot. It's like a chat GPT for doctors. OpenAI is now one of the largest healthcare providers in America. They have 300 million people asking chat GPT questions every week about healthcare specifically.

Healthcare is the key hero use case for most people. Neuralink is a more interesting company than SpaceX. As a nation, I think we need a 10x nurse a lot more than we need a 10x engineer.

Dr. Wara, welcome to Sorcery. Thanks for having me. We have so much to cover today. I think it's like a really awesome time to go deep into Thrive and Thrive's portfolio in healthcare. There's a lot going on in biotech. You're up to 14 healthcare incubation. And Oscar Health was incubated at the time of Thrive launching. So there's a long lore here to get into.

Our first investment was a healthcare investment in Oscar. So it's been part of our DNA from the start. I just got back from Priscilla and Mark Zuckerberg's event for Biohub where they came together with the Department of Energy and a handful of other big leaders. Isomorphic Labs was actually there on the stage too. And so it seems like we're in this complete inflection point of biology and drug discovery and AI and healthcare.

And there's just a lot going on there. So I'd love to start with that and just kind of get your pulse on why now. I would say more so than maybe any other time in history, the frontier of science and the frontier of what we've accomplished is moving faster than ever. We're seeing this explosion of AI and drug discovery. And I think that is changing the way in which we are going to deliver and make drugs.

And it's also going to change the entire value chain. The same way that with AI we saw that the labs, we've seen that trade flow through the entire supply chain. Needs around compute, needs around memory, needs around kind of energy. We've seen that flow through. We're going to see the same thing with biotech where it's not just drug discovery.

It's going to be manufacturing. It's going to be sort of development. And how you do your clinical trials is going to be commercialization. We're going to see that flow all the way through. We're seeing these new trends in N of 1 medicine. So the ability to create actual customized treatments for an individual that can treat conditions that we can never sort of do before.

We've seen that already occur and already have an effect. We're only going to see sort of more and more of that. I think the last -eyed flag would be this interesting intersection between hardware and biotech as well that's allowing us to kind of unlock treatments we couldn't unlock before. I think of two of our companies, Neuralink and Fortel.

When you look at the problems that they're solving and the capabilities they let us have, they're almost biblical in proportion, right? The lame can walk. The mute can speak, right? It's quite amazing when you actually see what folks have been able to unlock. It's incredible. And for those who don't know Thrive's portfolio in healthcare, I'll just name off a few.

Companies like Open Evidence, OpenAI, Cadence, Norrish, and a handful of others. But in terms of the trillion -dollar company of that one being more of a traditional healthcare company, there's obviously an explosion of companies that are now really grasping the proliferation of using and utilizing data and how much data can be shared between one another.

And whether it's with AlphaFold or drug discovery, all these sorts of things. How do you make sense of the evolution of that and what buckets it's flowing into? Yeah. So if we think of AI for drug discovery and where people often think of companies like isomorphic, companies like chai, the magic of it is saying, look, a lot of times you have a target.

So you have some molecule that's out there that when we hit this target, it changes something within the body. But oftentimes we both need to understand what are the right targets to hit, right? Folks will call that biological risk. So if we do something to this target, if we change it, what happens to the rest of the body? If we upregulate it, if we downregulate it, does it make you sleepier? Does it make you more awake? Does it make you lose weight? So we need to understand what happens with that target.

And we need to understand all the off -target effects. So what are the side effects that it happens? So we want to do something. We want to make you lose weight. But we also don't want to make you nauseous. We don't want to make your heart rate go up. We don't want to give you liver toxicity. Those are kind of the off -target effects. So there's that kind of biological risk is the thing we're trying to solve.

We want to do the thing we want to do. And we want to make sure we don't do the things that we don't want to do. And the flip side of that is that we then have to make a molecule to bind that target. And when you talk of all these models that are protein folding models, molecular design, kind of AI for drug discovery, people often think of that problem of how do we actually make this molecule? And some molecules are easier to make than others.

So you'll hear this term undruggable. Undruggle means that the pocket's small. Sometimes there are molecules where I have to bind for part of it because all these things are three -dimensional shapes. So if I bind part of it, the molecule itself may move some so something else can bind. So they'll talk about kind of hidden pockets and things that make this stuff hard to do because you have to be able to predict in real time what it is now and how the shape changes over time.

So those are kind of the things that you're often trying to predict. So with these drug discovery models, the idea is, okay, can we first create a model that will let us understand what is the best thing to make to bind this? And that's kind of step one. The next step is that, okay, I've made something that can bind here. Can I also get something that will predict what happens after I bind this thing? And then after that, you also want to be able to predict, okay, but can I also predict what are the unwanted effects that I can get? And that's kind of the progression of the problems that we're trying to solve with these models.

And I think the first step of what molecule binds well is the thing we've actually gotten better and better and better at doing. But what folks have realized is, okay, now that we're able to do that better, there's still sort of room to grow and room to improve. But the actual thing that we want to be able to solve next is being able to predict what those effects are.

And with coding, with math, those things are very verifiable. So I know whether my code compiles or not. For these things, I have to also verify, did my molecule work? So does it bind to the other? I can actually test how well does this thing bind. For does this have the effect that I want? That's a more complicated question to ask. So I then have to be able to put it into a cell and see, does this change the cell in the way that I want to? Or to figure out what side effects does this have in a person, I need to first put it into a mouse to figure out, does it do what I want it to do in a mouse? And there's a saying we always have in research where it's, you know, we've

cured cancer in mice 50 times. So everyone's like, look, you know, if you want to cure your cancer, the best thing to do is to be a mouse, right? Because we've cured that 50 million times. And we have not cured it in all cases in humans. So oftentimes we'll put it in an animal and say, okay, how does it work in the animal? Because that's a good, you know, sometimes that's a proxy for the human.

But the real best way to know how is it going to affect you as a person is to put it into another person. So those are kind of the levels of data that we need to figure out. How do we test it in animals and humans more rapidly? Other people are looking at how do we actually get data that will give us good proxies for that. And that's what we actually want the models to be able to predict.

But before the models can predict how well what effects it will have, we have to have enough data to train them to make those predictions. And that's a bit of what you're hearing folks say when they say we need to generate the data to let us do that. There's both data to let us verify the experiments we're doing. But there's now another vertical of what's the data that will get the models good enough to actually make those predictions.

On stage at the Biohub event, Max was on this panel and I think it was the question was like, well, what do you want to see in the next five years? Like what would be your crazy goal? And he said for like 88 to 90 percent success rates. For a process right now that has 90 plus percent failure rates, takes 10 to 15 years and costs billions of dollars.

So what would change for that to happen? One of the things I love about ISO, I think there are folks on Twitter who are like, well, you know, ISO, they're really good at sort of protein folding. But protein folding is not the only problem. I'm like, look, you know, ISO 1, ISO and team invented protein folding with AlphaFold. Right. That is why Demis got the Nobel Prize is because he created this thing and created this whole field.

It's hard to do. It's hard to do, which everyone is now kind of enjoying and kind of running at, which is amazing. We want more people solving these hard problems. But, you know, AlphaFold came out in 2018. So they've been doing this for a while. They have also realized a while ago and been talking at least internally for a while about as we solve this problem, what are the other steps and what are the other processes to work on? So they've been thinking about all of those kind of other steps that you need to accelerate.

So they've been thinking about, you know, what does it look like if we're going to get a 90% sort of success rate, if we're going to make this go from a 10 -year process to a four -year process. It both means that we need to get better at our molecular design. We have to get better at our in vitro testing, so testing in the cells. We have to get better at our in vivo testing, our testing in the animals.

And we have to get better at our sort of predictions of what works so that, you know, we may not be able to one -shot everything all the way through all the phases of clinical trials. Rather than having a 5% success rate, I can have a 60% success rate. That's amazing because that radically changes the things I'm willing to do all the way throughout the process.

But it also means we've got to think a lot about what happens in the clinical trial process as well around sort of phase one, phase two, and phase three. And that is not just companies. That is, you know, us as a society thinking about how do we kind of optimize around this process. And, you know, credit to ARPA -H, they've been doing a lot of work recently around thinking about what could it look like to be able to drive efficiency around that portion as well.

Because what we don't want to do is get very good at sort of preclinical and still have the clinical be the bottleneck. And I think we have governmental agencies who are thinking about that. We also have folks within these companies that are also thinking about what needs to be true so that the rest of the system is ready to kind of absorb these innovations going through.

And even after we do that, again, the bottleneck continues to shift. And we talk a lot about oftentimes you have to plan for the end at the beginning sometimes. So we will also need to make sure that we're getting better at how do you manufacture these solutions as they're coming through. If we're going to be in a world where we can have new drugs coming out, you know, every sort of three years rather than every 10 years, that also means our manufacturing processes have to get better and faster and have the ability to absorb that demand.

To focus on isomorphic labs a little bit more, this is a really interesting story of how you guys even got into it. Demis wasn't raising. I think Josh came over to him and was like, maybe let's work something out. I read this in the Colossal piece, which was great on Thrive and Josh. But can you walk through like how you guys got involved? We've always had this philosophy that we want companies that are bilingual.

So we want companies that are truly the best at both of what they do. They speak two languages fluently. And I think ISO is such a perfect example of that. The AI researchers at ISO are world class, right, which makes sense given that Demis also founded and ran DeepMind. So these are some of the best AI researchers in the world. It is, you know, DeepMind and ISO and Anthropic and OpenAI.

They all hold the same bar when it comes to their AI researchers. There's chemists and biologists who sit within ISO. I've met these folks. These folks are world class. They are as good as the folks you would find at, you know, a Lilly, a Pfizer, a Broad Institute, a Wiese Institute. These are world class kind of chemists. They are truly world class in both.

They're bilingual, amazing at the biology, the chemistry, and the science, and amazing at the technology and the AI. I think some of the magic of what they do is that in ISO, they talk a lot about the ISO way and the fact that, you know, they're actually bringing folks together to be able to do both who are world class. So they have these amazing chemists who are integrating these AI tools but using them to solve problems and bringing their kind of knowledge of chemistry in the same way.

So this intersection of both and having that culture of excellence in both worlds but also being able to get that crosstalk so you can overlap, I think that's a lot of what makes that company so special. And I think for Josh, you know, he spent so much time kind of working with Demis, you know, letting them know that we appreciate kind of what they're doing.

To us, healthcare, healthcare is a space that we know and we're deeply familiar with and also that we're as passionate about this space as they are. You know, I think they're firm believers that a lot of the benefit that we are going to get from AI is going to come in the space of healthcare. That's also something that we believe and sort of I believe deeply to my core.

I think it's amazing and it's kind of a common theme through all of these AI healthcare conversations I've been having, whether it was with Annie Lamont the other week. And I even did one way back when with Commure. It's like equalizing the playing field for people in the middle of the country to receive the same kind of access to care that people in big cities are really like, you know, expensive, hard to get into hospitals can get.

I want to go into this bilingual part a little bit more of this model of what these companies like kind of hold for healthcare. So a really great example of this is Neuralink. Can you explain the different types of talent within Neuralink? Because you have scientists, you have neuroscientists, you have like engineers, you have hard tech, you have like all of these different components coming together and it's like crazy, like crazy pockets of talent.

One of my biggest critiques of healthcare is that I think we often do a very good job of extending life, but we don't do a very good job of improving people's quality of life, right, of giving them that sort of life back. And, you know, for Neuralink, they actually now focus on a lot of populations. But one of the first populations they focused on were folks who have, you know, ALS or paralysis.

So these are people who, whether through a disease, whether through a car accident, a lot of sort of veterans, unfortunately, who have been, you know, serving our country but maybe were hit with sort of an IED, no longer have usage of their arms and legs. And oftentimes these are also kind of young folks. So folks who are, you know, 30, 40, who now no longer have the ability to, you know, work because you often need your, you know, hands and legs to work.

You know, they can't be outside kind of playing with their children.

And, you know, what Neuralink does is they say, okay, well, what can we do to kind of give these people, you know, access to interact with the world again? So they've created an implantable device that then lets these individuals interact and control a computer with their minds. Motorized arms so that folks can now control a sort of robotic arm to sort of pick things and kind of grab things.

They have another trial where they implanted the device in the vocal center, the vocal sort of cortex of the brain so that someone who wasn't able to speak could speak. So, you know, that is the core of what they do is kind of let's use this implant to give people the ability to kind of manipulate the outside world again. So, you know, we may not be able to replace the kind of arms and legs that you've lost, but we can give you that functionality back.

So that is, that's what Neuralink does. Now, when you meet the folks at Neuralink, and to your point on why they're bilingual, Neuralink has these amazing hardware builders, which, you know, makes sense, right? Their hardware folks are at the same level of, you know, SpaceX and sort of Tesla. These are true kind of hardware builders who are building things with heavy complexity.

And an example of that would be one of the things they have is within the implant, which is, you know, very, very small. They have these kind of microscopic threads, and their thread count gives you the ability to kind of read from the neurons. So you have these kind of very small, like, microscopic threads. And, you know, I think at this point there are over 1 ,000 of them.

So they've increased the thread count as they went. That's amazing hardware in and of itself. Because the threads are so small, right, they actually created a separate robot to do the implantation. So, you know, because it's such a difficult sort of procedure to do with something that microscopic, and they wanted it done with extreme precision, extreme reliability, they created their own robot to do that.

Now, it's not surprising that, you know, when you have access to folks who are, you know, SpaceX, Tesla, Optimus level, you can also create a sort of surgical robot to do this elite kind of neurosurgery. It's just wild to think about the fact that there are other companies for whom that would be their whole business is we have created a neurosurgical robot.

For these folks, this is one vertical of what they do and what they're doing. But when you go in, you also meet the neurosurgeons who are sort of training with them. And I know some of these neurosurgeons, you know, they went to Harvard Med with me. These were the best, like, students and, you know, neurosurgeons in their classes. And these folks now are here doing that.

These are sort of practicing neurosurgeons who are there sort of co -developing these tools with them. They have pathologists. I remember the first time I went to their pathology labs. I've never met a company before that insourced their own pathology lab. And most places, you know, you send it out. You get your slides. You send it out. And you go forth from there.

What they realize, though, is that pathology is so key to what they're doing because they want to understand whether that's, you know, humans and their animals. You want to understand carefully whether or not there are kind of any effects this is having on the brain. And they want to be able to get that data and understand it because safety is so critical to what they do.

So, therefore, they're like, we are going to insource the pathology lab here because it's sort of what we do and it's critical to what we do. So, they brought in all these kind of world -class pathologists. But this, you know, bringing home that thesis that I think you see in so many of these Elon companies of we're going to kind of vertically integrate through all parts of our supply chain that are important.

If it's important, it's mission critical. We need to have it in -house. And seeing them do that across things as diverse as not just the sort of hardware but something like pathology, which most people don't think of, I think that was that impressive level of detail. That's so interesting. And I have to ask, like, I have to go deeper into this.

Okay. So, Neuralink is exceptionally great at hiring the world's best talent from pathologists, robotics, neurosurgeons, radiology because they're scanning the brain. Battery experts. Battery experts. Because when it's that small and you're– it's another thing, you know, when you actually look at the people using it, they're using this all day, which makes sense.

How often do you use your hands? You're using your hands all day. Your feet, you're using your feet all day. Maybe we're not using them right now. But, right, the idea that, oh, yes, you need a battery that's going to last all day because this is a device that people are using all day. Talk about product market fit. So how do they convince all these people to work on such a crazy moonshot, by the way? Incredible results already and outcomes.

But these are relatively new. This was, like, happening in the last couple years that, you know, the first implant happened and we started to see some of the real results. Yeah. I mean, I think, one, DJ, who is the president and kind of leads a lot of that company, he's just– he's a very compelling leader. I think when you meet him, you can tell that his passion is there.

In a lot of ways, I think passion is contagious. I also think excellence and talent density is contagious. Right? People who are truly N of 1 want to work with other N of 1 people. I also think they're working on such an important problem. And they were category defining. I think when Neuralink was first doing this, you know, no one else was doing it.

So, therefore, you know, they didn't just step out into the lead. They created the category. So everyone who cared about this space, right, they brought them in. And now as they've gone forward, their lead has only compounded as they've gone forward. I think the other thing, and I think maybe this is one of my hot takes, I actually think of– I think Neuralink is a more interesting company than SpaceX.

The brain is vaster than space. So a $3 trillion company? What are we thinking? We don't make it to space. We don't invent silicon chips without our brain. That's the fundamental substrate that has unlocked all of this. We talk a lot about, you know, compute and intelligence. You know, the brain is still a more efficient engine of compute than, you know, every chip that NVIDIA has ever made.

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That's ZoneFrontier .com to learn more. So on the topic of hardware and robotics, let's talk about Fortel. So explain Fortel and why hearing aids are just another extension that we need to continue to innovate on. Yeah. So let's talk about hearing aids and hearing loss. So, you know, hearing loss affects 1 .5 billion people worldwide. I think, you know, we all go to concerts.

We all have headphones in our ears at all times. I would only expect that number to increase as we go forward. Unfortunately, you and I are probably both going to have hearing loss when we get older. And it's also an issue that it's the greatest modifiable risk factor we have for dementia. So, you know, folks who have hearing loss also have a one -third increase in their likelihood to have dementia.

And, you know, we think about that for a lot of reasons. But your ability to kind of hear things is kind of anchoring for the brain. That's a lot of the ways in which, you know, vision and hearing are the brain's main sensory inputs. So having that and the loss of that as well as the social isolation that comes from not being able to hear, a lot of people theorize that those are the reasons, those are some of the main contributors we have to dementia.

So if you want to keep your brain sharp, one of the most important things you can do is not lose your hearing. Now, most hearing aids, what they do is they just amplify all sound, right, which that's actually not what hearing is. Hearing is both the sound, but it's also your brain dampens things in and out. It's part of why when you're in a coffee shop and having a conversation, you can have the conversation because your brain knows focus on the person I'm talking to and tune everything else out.

Most hearing aids just increase everything, which is why it's so hard for folks with hearing aids to go out to dinner, you know, be on a crowded street. Anywhere where there's background noise, it's actually very hard for them to be, which is why those folks often will just stay home where it's quiet. You know, Matt DeJong, amazing founder of him and his team.

They were some amazing hardware builders at a company called Butterfly, so the handheld ultrasound. He'd had grandparents who had unfortunately suffered from hearing loss for a long time, and he'd seen the devastating effect it had for him. So he'd spent 15 years thinking of this project and a lot of time working on it as kind of a side project.

He was fairly obsessed with a space that most people aren't obsessed with. But around five years ago, he brought his team together to actually work on this full time. And a lot of it was kind of the changes that he saw into two verticals. One was the changes he saw in silicon and in chips. So they've got this story where, you know, they went out to TSMC and they taped out their own chips, which is so hard to do.

I think you've talked to a bunch of these kind of folks who work at the new kind of chip fab companies. It's so hard to tape out your first chip. It's unheard of to tape it out successfully the first time. But in a lot of ways, you know, because of the way that a hearing aid works, they need it. They do need a custom chip because hearing aids are very small, right? It's the aesthetic.

No one wants this giant thing on the side of their ear. I know people have gotten them stuck a couple times. So you want something small, but it has to last all day because you're using it all day. So you need the battery life to go well. You actually have to have very fast inference because you're translating kind of incoming speech, right? So that's difficult.

There's a lot of complexity that you have to have, which is why they had to actually design and tape out their own sort of custom chips. The other thing was that they were thinking a lot about, okay, how do we actually do the sound processing such that we know what we want to sort of amplify versus not? And they talk a lot about how he got this idea for the semantic noise processing actually during the pandemic from Zoom.

And one of the things that folks were doing with all of the sort of noise counseling we would develop is that they would say, look, we're not just going to– we're going to dampen up and down the sound by knowing what kinds of sound this is. So different kinds of sound have different tenors. So being able to tell what's the sound of a conversation that's in front of you versus, you know, a car going by.

So what the actual noise itself is, processing and understanding that and using that to kind of dampen up and down is one of the strategies that they use. So, you know, for them, you know, they took this hard technical project and problem and decided to sort of build a company around it. And that was the company of Fortell. And I think for them they've had, you know, this kind of amazing one uptick in consumer validation and kind of technical validation.

So, you know, they had their– when they had their beta, they had some– you know, they had a range of folks. And I think everyone in their beta loved it. I think it's– I'm always careful with my healthcare companies not to divulge information they haven't already divulged, but especially with their patients. But these ones are public.

So, you know, Steve Martin and Henry Kravis. These are both folks who live very active lives and are hard judges of what quality is. Anybody who's worked for them or with them would, I think, say that, you know, these are hard judges. And I think for them, you know, they were– they have loved the product. They have been both folks who have been users, but they've also passed along their friends and said how amazing this is and how much this has changed their lives.

They had one of their beta customers who after the beta ended, they said, okay, well, you know, the beta's done. They're just letting us have this. You know, can we have this back? And the person said, absolutely not over my dead body. Will I give this beta product back to you? So I think the other is there– so there's this group that kind of is the third party that rates all hearing aids.

So, you know, they're– they have this, like, rating scale that they use. 4 .5 was their highest rating that a hearing aid had ever gotten on their five -point scale. Fortell recently did it and got a six. What? They broke the scale? They literally broke the scale. And I think that just shows both from a technical perspective as well as a consumer perspective just how well it is.

And it's just– I think that's a perfect example of creating this hardware product to meet a need in which, you know, you understand this unique problem you're solving. And then you go forward and say, okay, what is the– what are the advances in hardware and AI that will let us solve this in a very different way? And I think we're seeing that, you know, the market is loving it.

It's impressive to see people applying these tools to problems that are really debilitating for people's quality of life. My mom has hearing loss. She underwent chemo and, like, lost her hearing from it. And this was, like– this was years ago. But– so what is the process for people who want a Fortell hearing aid? Like, is the experience different? Like, how do you make it more customized? Yeah, yeah.

Me and Josh joke that we are very popular with the 70 -plus crowds now because we have so many people who will call us and say, like, hey, you know, my mother, my father, you know, my uncle really needs this. Like, can you help us get forward on the wait list? So, you know, so right now they have– A wait list? I know, I know. Come on. I know, I know.

So, you know, but they're working and ramping up. So the wait list should be going away very soon. But they have an– right now they have an office in New York. They also have their website in which– because, you know, they fit folks. They want to make sure that folks get to sort of try it beforehand before they buy it. But they have an office in New York.

They'll be expanding kind of rapidly since then. So they've created this great product. Now they're doing everything they can to make sure they have enough of it for everyone to utilize. Incredible. I know we've gone so far. We haven't even gone into your background yet. Dr. Wara, please tell me your life story. I was born on a snowy day in Columbus, Ohio.

No. So I think for me I talk a lot of it in a lot of ways. I've spent a bunch of time making good things big. So I spent a lot of time as startups trying to scale very effective models. And I've also spent a lot of time trying to make kind of big things good. So bringing that kind of startup mentality of, you know, tackling hard problems rapidly to kind of large -scale problems.

So I trained as a physician. I'm trained in, you know, internal medicine. I originally did neurobiology and health policy in undergrad. So I like both the social science and the science. You know, I studied medicine and business in grad school. So I went to business school and med school. I did residency. So I'm a trained, you know, internist and physician.

And then after that, I joined the founding team of a company called Citiblock Health. It's actually a thrive portfolio company. And what Citiblock does is they do integrated care for high -cost, high -social -need populations. So full stack, you know, social care, behavioral health care, kind of medical care for the patients who need it the most.

You know, so joined, you know, joined that company, you know, in its very kind of early days and saw it kind of scale out to now a national care infrastructure. And then I spent a bunch of time at Amazon launching what I guess now is Amazon Health. So we turned Pillback into Amazon Pharmacy. I served as our first chief medical officer there.

And then we launched a business called Amazon Clinic. So our first article with Amazon Pharmacy was saying, what's the space in which Amazon will have an advantage in health care? Like Amazon has this amazing logistics network. You know, they're very good at getting things from point A to point B, right? And like that would be our advantage in pharmacy.

If we're going to have a delivery pharmacy, it would make sense that we should be able to get these meds from point A to point B, being from the pharmacy to people's homes. And there was a lot of work on pricing and, you know, selection that we did. But that was the core of it. And we said, okay, once that started working, we saw that's going well.

Next question was, do we think we can get people– people will trust you with their medications? Will they trust you with their care delivery? So that was– when I launched Amazon Clinic and ran that business, that was a virtual care business saying, let's handle the kind of low -acuity, kind of more transactional portions of care because we felt like that fit more with the sort of shopper model you had on .com.

And once we got adoption there, we said, okay, well, the next step should be if they'll trust us with their kind of low -acuity transactional, will they also trust us with their kind of low -acuity relational? And that's what drove us to then pull and buy one medical and integrate that in. And all those pieces together became Amazon Health.

So it was kind of bringing a large tech company into a new space being healthcare. And then about, you know, what, two and a half years ago, I joined Thrive. So I've known, you know, Josh and Kareem since college. And I was familiar with their work in healthcare. I was familiar with them as people. Thrive had always been a group of people that I respected, a group of investors that I respected.

But they've been making some pretty big bets around AI. I was a big believer in, you know, AI as an opportunity. So came over to focus on all things healthcare. Incredible. I mean, I'm so interested between all those lines and now we're getting to the age of trillion -dollar companies. What do you think is– I'm I'm sure you've incurred a lot of challenges along the way with the healthcare system.

But like, as we get into the age of trillion dollar healthcare companies, is it still going to be policy that's going to be a bottleneck? Or what are the bottlenecks going to be? Like, how do these technology companies reach that level? Yeah, so I think a lot of ways. One, if you're truly AGI -pilled, as they say, right, you should be actually long healthcare.

So I think we're only going to see more and more of these trillion dollar companies in healthcare for a few reasons. One is that as these capabilities progress, right, what are the things that we as humans are going to want to spend our personal money on, our societal resources on as we go forward? You know, if you take AGI to the extreme, we all are going to want more time, and we're going to want to be able to enjoy the time that we have.

So that means the things that we know will be increased buckets will be healthcare, because that is the way in which we increase the time and the quality of the time that we have on this earth. And then, you know, some version of kind of entertainment, right? Like we can be long those two things. So I think that that macro trend, if you believe in kind of AI and AGI, will continue to go forward.

I think there's also a point you could say that, you know, since time in memoria, we've also been doing that. I had a mentor once who told me, and this is a bit of an aside, but a mentor once who told me there are three kind of professions that have existed for all time. And it's, you know, he said it's pastors, doctors, and teachers, because as long as they are humans, you will need people to care for the body, you'll need people to care for the mind, you'll need people to care for the soul.

So I think, you know, I could say that going forward, we'll spend more on healthcare, and I'll also argue that going backwards, we also did the same. So I think that's one reason is that the macro kind of tailwinds are there. I think the second is that we talked about this frontier of science, we're seeing it expand in such unique ways. So Keytruda is one of Merck's drugs.

You know, Keytruda's 2025 revenue of just that one drug was larger than the revenue of all the major labs combined, right? And that's one drug. So biotech is big business already. What does Keytruda do? So Keytruda is what we call a checkpoint inhibitor. So think of it as an immune booster, it can kind of boost up your immune system. But the interesting thing is that, you know, companies are continuing to find new use cases for a lot of these medications.

So you, you probably heard about Merck and Moderna's new cancer vaccine, which I think is an example of the kind of amazing frontier science that we're getting. MRNA, exactly. Right? Like Moderna, the, you know, the COVID goats of MRNA. So, you know, the way that they do it is, you know, melanoma, right? You take, you take the tumor out, you have your tumor surgery.

What they do is they say, okay, we're going to take this tumor because each tumor is unique, right? Like, you know, your, your mother's cancer is likely different in a lot of ways than the cancer of someone else. So we, we talk about cancer as this broad thing, but not just cancer, melanoma, a specific type of cancer, skin cancer. Each one is different because they mutate and change in different ways.

So what they do is you take the cancer out and you sequence it and you say, okay, what are the parts of this tumor and this tumor's expression, its tumor's own genome that makes it unique? And we're going to create a vaccine, an MRNA vaccine to target that exact cancer. So what they say is, okay, we take the cancer out. We're then going to make this vaccine that we're going to inject into you.

And then we're also going to give you K -truda to kind of give you an immune boost at the same time to make it easier for your immune system to really recognize that cancer. So then any microscopic pieces of that cancer that are still kind of floating around in your body, your immune system is now boosted to just wipe all of those out. But it also, because your immune system has memory.

So now if that cancer happens to come back in three years, four years, your immune system will remember it and your immune system will be able to fight that cancer while the cancer is still small and sort of get rid of it. And that was the thesis of cancer vaccines and that's exactly how it played out. I think in their phase three clinical trial, they found that people who had that were 50% less likely to have a recurrence of that cancer in five years.

So I think that's an example of both the frontier and the types of new treatments we're able to create, but also the fact that a key portion of these new treatments is now K -truda, this immune checkpoint inhibitor. So I think we're going to see more growth because we're seeing– because the frontier is able to tackle kind of more and more problems.

I think the third piece I'd throw in really goes back to the Lilly example. And for that, maybe it's worth kind of zooming out a bit on why I think at least like Lilly won, right? There was, you know, Lilly– they weren't first. Nova was first. And yes, they had a drug that had better weight loss. But at the end of the day, really, if you remember right when GOP first came out, the drug was so popular that there were shortages.

Yeah. Right? Like, you know, no one could get them. So Lilly leaned into its manufacturing and manufacturing capabilities and kind of innovated across the value chain. We talked about that. We talked about ISO. Got to innovate across the value chain and, you know, project forward to where the bottlenecks will be. So they were able to have more supply and get supply on board faster a whole year before.

I think, you know, they took a year before Novo could unblock their supply. Lilly got there a year beforehand. So they had more supply out there. But I think the other thing is in commercial, we all know kind of Lilly Direct, they were able to say, sure, we're a pharma company. But let's think in an innovative way around how we partner and how we lean into kind of care delivery.

And they unblocked that commercial aspect. I think that's actually why they've been able to be so successful and be the trillion -dollar drug is because they're like, we're not just going to focus on the science. We're going to be great at the science. But we're also going to think about manufacturing. We're going to think about sort of commercialization.

I think that's actually the opportunity is that we're seeing more and more of these companies the same way that in, you know, software and hardware and AI, we're saying it's easier to build with AI. So therefore, we're seeing companies that out of the gate have more surface area, right? The companies are more ambitious. I think we're seeing the same thing now in biotech where we now have companies who say, huh, it's actually a lot easier to kind of build things in all of these spaces.

Therefore, we're going to have more surface area. We're going to do the science and the manufacturing. We're going to do the science and more of the care delivery, which then gives them a– one, it makes their businesses more defensible, but it also gives them a much bigger TAM. And I think that's a big reason as to why we're going to see more of these trillion -dollar companies is because folks' ambitions have gotten larger.

We often say that in a lot of ways what we're seeing now is that our problems haven't gotten smaller, right? The tools we have have gotten bigger. And you helped OpenAI start up their healthcare platform. How did that get involved? So, yeah, so our time with OpenAI was– gosh, I guess it was two and change years ago. I was talking with Brad and Sam about healthcare kind of writ large.

And this push that, look, they should spend more time with more of the leaders in healthcare because this is going to be a key space for them. So, therefore, they should spend time with the incumbents both to understand how do they work with them and to understand the space better. So, organized a series of meetings with different leaders across healthcare.

So, it was some of the top biotech CEOs, some of the top payer CEOs, and some of the top health system CEOs as separate kind of events with the OpenAI leadership to actually just sort of talk through and say, here's the state -of -the -art in AI. Here are these folks' problems, and let's talk through. So, I remember with the health system leaders at the time, the O1 wasn't out yet.

So, reasoning was a brand -new thing. It hadn't happened yet. But because we were close to them, I'd be able to play with the reasoning models myself. And I'd started putting the New England Journal of Medicine case study. So, that's– think of the house MD -level difficulty hard cases. I would put the first half of them in because the way they'd tell the story is, you know, here's their history, here's this, here's that, and then something happened.

And then they'll tell you about what the expert said and what it was. So, I'd cut it off right there at the end that it happened. So, I just put that portion in every week. And I was just blown away by the fact that, you know, every single week they would get it right. And these are cases that, you know, neither me nor my colleagues, which are some of the best doctors out there, maybe we would get it right, you know, two times out of ten.

And they were, you know, they were– you know, OpenAI and the O1 was batting a ten out of ten, which was amazing to me. So, I remember going to that meeting and saying, like, look, you know, I think these models are amazing. These models can, you know, diagnose. And everyone was treating me like I was a heretic. It was like this is blasphemy.

Like, what do you mean? Like, this is impossible.

And I remember we then, like, showed them and so did a couple of them live. And just seeing the eyes of some of these, like, healthcare CEOs, some of whom were also doctors, it was just amazing starting to see it click for them that, oh, wow, this is the capabilities of these tools. This is what's happening. This is what's, you know, coming down the pipeline.

Same with one of the biotech CEOs. We were like, okay, what do you think of these tools? Let's talk through them. And they're like, well, I think they can be really good for customer service. You know, I don't really think, though, these tools will ever be useful in drug discovery. And now I'm seeing two years later, you know, one of those same CEOs has announced, you know, billions of dollars of spend in this.

So it's been also very interesting seeing the progression of folks' belief in the tools in such a short amount of time. That's a really good point. So how has the tool stack changed for these? I don't know, like, whether it's physicians, doctors, scientists, anybody in the healthcare space, how have the tools changed? Yeah, it's really interesting because I think it's, you know, you see it differently across each of the verticals.

So if we take, you know, healthcare services, one, I think where people are applying the tools has been very different. And there's where the system is applying the tools versus the individual. Right. So we'll say, you know, the system writ large. I always talk about there is front office, there's back office, and there's in office is kind of the division I like to use for healthcare.

You know, for back office, we've seen tons of adoption and revenue cycle management, which is, you think of it as how doctors and health systems optimize the amount of money that's coming into their offices. So, you know, it's them, you know, coding and putting in the right code so they can get paid the right amounts. You know, we've seen an explosion of tools doing that, which makes sense, right? People will adopt the tools that will help them make more money very rapidly.

So we've seen an explosion of tools there. We've also seen more and more tools start to pick up in the front office. So think of all the folks who are on phones, calling people, kind of scheduling people, following up with you to say you're due for your vaccine. I think as the voice agents have improved, we see more and more folks adopting tools in that space.

And I think the interesting part has been we've started to see more and more tools getting adopted in the in -office space. So, you know, I think Scribes were one of the first ones to kind of really sort of take off vertically within the in -office space. I think the other is Open Evidence, one of our companies, which, you know, I first heard about Open Evidence from friends of mine from residency.

And I think to me it was fascinating because I've never heard doctors rave about a clinical tool. We normally call it health IT because, you know, these things like it's like it's like Brussels sprouts, right? Like people are forced to use these tools. Someone says you have to use this pager or you have to use this enterprise tool. And like they drag doctors kicking and screaming and they monitor you for how much you're doing X, Y, Z.

And they're hitting you over the head and flogging you for using these like terrible tools. So therefore the thesis was always, oh, you know, doctors just don't like technology. It's like, well, no, because all the doctors have iPhones. They all have Macs at home. They just don't like the bad technology that they're forced to use all the time.

But this is a use case in which I was like, huh, people are willfully telling me about something with joy in their eyes. Like I'm very confused. So I tried the product myself and was like, this is this is differentiated. This is amazing. And then I learned kind of more and more about what their attraction was. I remember, you know, Dan had actually gone to college with me.

So I like called up a buddy and was like, hey, you know, where does he live? So I shot him an email. I was like, hey, Dan, it's good to see you. It's been a while. Can we catch up? Like, why don't I just come by your house? So, you know, hopped in a car, you know, drove down to his house. You know, we had a meeting that day. My clinician hat is what let me actually see, oh, wow, like this is a differentiated product.

And it's one of those things where if you're a clinician and you see it, you see it right away as to why that's an N of one product. And I think for them, the numbers are amazing. It's, you know, two thirds of physicians in America are using open evidence. And, you know, to put it into context, Epic, which everyone always thinks of as, you know, it is it is it is the behemoth in health care.

Yeah. Epic took 40 years to get to the scale it's at today. And Epic has less than 50 percent of physicians using their product after 40 years. Open evidence after three years got to two thirds. And like that is that is the amazing kind of scale that we can get in this new age with a user population that is notoriously hard to gain adoption for.

Because we didn't talk about this yet. Could you just frame out the numbers behind the health care system at large? Like it's insane whenever like even that that kind of like parameter around open evidence is one thing. But like writ large, the United States health care problem in the system we have, like what are the numbers behind that? Oh, it's it's it's wild.

I mean, so one, you know, one, we spend five trillion a year now. Right. And that's projected actually go to nine trillion by the end of the decade. So nine trillion a year in spend right now, five trillion. It is the number one employer in America. So, you know, 20 percent of all jobs are health care jobs. And I think that, you know, that alone is crazy to think about.

You know, it used to be sort of manufacturing. Our economy is now driven by health care. We talk a lot about kind of AI and AI's effect on jobs and, you know, where's the job changes come. That alone is insane. But it's also the fact that as we project forward, we project that 30 percent of our jobs going forward, the new jobs we created will also be in health care.

So it's our largest job engine now. And it's only projected to become a larger and larger share. It's the largest line item in the federal budget, six times the size of defense. So we talk about kind of the defense surgeons and how it's time to build big business in defense. That's great. But again, health care is six times larger. You know, it is it's quite impactful, both in terms of, yes, the impact it has on folks, but also just the the size of the industry itself.

There is a trend in sorcery interviews, if you notice. Now we're on the biotech age. We were just in defense world. We're going into biotech. Biotech. I mean, it's also when you think about like impact. So that's the thing about us, you know, there is there's all the money that's in health care. There's all the sort of jobs that are in health care.

The impact it has on the nation's economy, it can't be understated. But I think when you actually look at the outcomes as well, that's also fairly interesting. So I look at I think a lot about like life expectancy because we always talk about how we spend all this on health care. I think a lot of ways we actually be fine spending on health care if we had better outcomes.

Right. I always say, look, I'm actually fine with the trade. And maybe this is just me. If someone told me, hey, we're going to spend a trillion more on health care this year, but everyone gets to live to 100. I'll take that trade any day of the week. Right. Like, you know, cut cut somewhere else. Doesn't matter. Like if you were getting given everybody an extra 20 years of life all day, all day.

I will choose that for the nation. I would choose that for me. I would choose that for you. I'll choose that for my parents and your parents. But that's not the story we have. Right. We're spending a ton for very bad outcomes, some of the worst outcomes in the developed world. And I was looking through the life expectancy trends because I think that's the outcome.

Right. You got to stay oriented on the outcome that matters. It's not just the spend. It's the outcome. You know, between. So right now our life expense is about 80 years. That's where we are as a nation. Between 1900 and 1950, we went from a life expectancy of 50 years, which is wild, to a life expectancy of 70 years. And a lot of that was, you know, it was a mix.

Right. Some of that was, you know, increases in sanitation, increases in water. But a lot of it was that, you know, we improved our infant and child mortality. So people weren't, you know, people weren't dying during childbirth. Children weren't dying at an early age. And antibiotics. We got a bunch of really good antibiotics. We got a bunch of really good vaccines that were created.

And people got them. So then all of a sudden, we as a nation lived 20 years longer. And then our next kind of big spur was between 1990 and, you know, 2010. We managed to go from living to about 70 to getting to about 80. And the big spur of that was cardiovascular disease. So half of that was reductions in smoking. But the other half was all good science.

So it was statins to lowers people's cholesterol. We were talking about your cholesterol beforehand. Yep. Which is good. It's good. It's good. Great cholesterol. And she authorized me to disclose her information, I hope. Post -hoc authorization. But, you know, but it was also, you know, improvements in blood pressure control. So we reduced the number of strokes.

So it was half medication, half just people smoking less. Right? And like that gave us the next kind of 10 years. And then for the last 10 years, we've spent about $40 trillion on healthcare. We have not moved our life expectancy an inch. And if that is not an indictment of the failure of the healthcare system, I don't know what is. $40 trillion for no change.

Oh, my God. And, you know, it's interesting because if you break the numbers down, right, you know, there are stories to be told within. You know, I think, you know, in Hawaii, it's a tale of two cities as often. So in Hawaii, they live to be 80. In West Virginia, the average is 72. You know, the richest man, the richest 1% versus the poorest 1% has a gap of about, if you're a man, about 15 years.

So it is a tale of two cities. The other thing I think about for this, like, past decade that we've had is that we've actually had huge decreases in cancer mortality. Right? There are so many things that were death sentences that aren't anymore. Breast cancer, right? That used to be an almost fatal diagnosis. And we've seen that across a bunch of our cancers where we've had this huge uptick.

And it's been amazing. Again, the frontier science has gotten there. It's just been balanced out by, you know, things like an increase in overdoses, et cetera. So we've had that kind of balance of two worlds. But I really think if you look at this next 10 years, you know, this should be the period where, given where we're at with science and treatments now, that if we're doing our jobs right, we should look back at, you know, this next 10 -year period is a period where we went from 80 to 100.

I think the thing that's really interesting when you think of science is this trend of N of 1 medicine. Right. So I think the canonical example people often point to is, you know, baby KJ, the rare kind of genetic disease where the baby couldn't process ammonia. And this is a thing where more than half of the babies then die when they have this, which is just, you know, devastating.

As a parent, I cannot imagine anything worse. And what they did was at Penn, you know, they realized this was happening. And your only option is really– your only treatment is really liver transplant, which is a very complex and sort of hard thing to get. Organ transplant we don't have in and the supply of organs. So what they did was they actually designed a custom treatment to gene edit that baby right after birth.

So when the baby was seven months old, they delivered the gene therapy, right? It was successful. The baby now is two years old, walking, talking. That's crazy. Which is quite, you know, amazing. But in six months to be able to say, okay, we're going to do a diagnosis, we're going to create it, and we're going to sort of manufacture this treatment for just this individual.

And it worked, right? And now, you know, that family got to take home their child and is getting to sort of raise that child, you know. But the fact that we now have these N of 1 diseases that can cure these rare genetic problems is amazing, right? The science has gotten there in so many of these things, but it's, you know, the bottleneck, again, it shifts.

So I think they say it took a million and a half dollars to actually make the treatment and deliver it. Wow. Right? Which, again, it's a– I think this is one of the things that you grapple with in medicine and healthcare. As we talk about healthcare spend. Okay, well, a million and a half dollars so that this child doesn't die and lives, is that worth it or not? Right? Like I would say yes.

You know, tell me– you know, increase my taxes, cut somewhere else, you know. Tell me what I need to do. I am all for it. But, again, that's the debate we have in healthcare because it's aggregate, right? Like how do we afford these things? And part of it is, you know, deciding what we pay for, what we don't pay for, where our kind of values lie in these important life or death decisions because that is the story of healthcare, right? Every cancer treatment is life or death.

Every transplant is life or death. Every gene therapy is life or death. And, like, what is the appropriate price tag? You know, it sounds weird to say, but, like, what is the price of a life, the life of a child? That is the question that you're asking as a healthcare system on a regular basis. But the other side of it is that we have to be able to innovate, again, across the supply chain, across the stack such that how do we make it so it's not a million and a half to sort of develop this, right? What is the– what infrastructure needs to exist to make that more scalable and more repeatable and cheaper? So I think those are– that is both the excitement of the frontier

is that, you know, the science is getting to the point where we can do things like this, things that people had dreamed about for so long that have such massive impact. We can do these things. But then at the same time, as we can do more and more of them and do them more rapidly, we also then have to think about the rest of the system and how do we go about– you know, how do we go about making sure the manufacturing is effective? How do we make sure that we know how to sort of pay for these things? We have to think across the entire supply chain.

I think that's what's so exciting. Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx .com. Public is a multi -asset investing platform where you can automate your investing with AI agents. Create your own custom agent with a simple prompt.

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Get visas handled fast. And get back to building. Visit deal .com slash sorcery. That's D -E -E -L dot com slash sorcery. I'm so curious from your perspective, seeing on the private side of all these companies develop new innovations through many different types of healthcare. What have been the biggest outcomes or the biggest impact that you've seen on outcomes within the portfolio? Yeah.

Well, so one of our companies, Cadence. So what Cadence does is they're a remote patient monitoring company. So they send patients home with hardware. So they'll mail hardware to a patient's home. So think of a blood pressure cuff, a scale. That way they're getting kind of continuous data from these patients who are at home. And these are fairly sort of sick patients.

Now, beyond the hardware, they also have a system behind them. So they have both AI that is monitoring these signals at all times. And then they have people that are also watching and kind of interacting as well that the AI can kind of escalate to. The other thing they've done is I think there are people who believe in building in different ways.

Some people believe you build outside the system. Some believe you build with the system. Cadence is built with the system. So Cadence works and partners with health systems. You know, their partner, they're in about 7 ,000 sites of care today, over 100 ,000 patients who they're actively monitoring at all times. And, you know, they integrate in the EHRs of these patients.

That way the doctors are also aware of sort of what's happening. So they're both monitoring you at home, but they're also bridging your care with your doctor. And they have kind of AI monitoring and overseeing all of it. So that is Cadence's model. Think of it as the fact that, you know, let's say you see your doctor four times a year, right? That is four hours, if you're lucky.

Let's be real. It's more like one hour because the most visits are 15 minutes. But let's be generous. Four hours because your doctor spends an hour with you every time of your time of the entirety that's spent there. Most of the other 8 ,000 plus hours of your year are spent outside of that doctor's office. So if we're actually going to change your care, we need to be monitoring you at all of those moments.

And that's Cadence's special sauce is being able to actually monitor you at all of those times outside. So if you measure your blood pressure and your blood pressure is kind of high, right? Or let's say you have heart failure. And for heart failure, we need to know your weight so that we can make sure that your fluids are right. As you step on that scale, we can see, huh, your weight's higher than it normally is.

Or your blood pressure is too low or it's too high. It can intervene right away. And with that, they've been able to see, and they've had this published in the New England Journal of Medicine, the Journal of Cardiology, the Mayo Proceedings. They found that, one, they've been able to reduce people's hospitalizations by 25%. Wow. They've been able to increase the amount of folks who actually have their blood pressure controlled, which prevents you from getting strokes, by over 80%.

They've been able to reduce people's A1Cs and sort of diabetes by almost a full point. And I think one of the other things that's been amazing is they're also saving folks money and the healthcare system money. They save Medicare $2 .5 million every week. Every week, $2 .5 million. So they are both reducing costs and kind of increasing outcomes.

And this isn't outcomes like, you know, you feel better, which matters. Patient experience matters. But these are hard outcomes in terms of you being in the hospital. Is your blood pressure controlled? What's your A1C? Are you having less strokes? These are hard, hard outcomes. And it's amazing the way in which they do it because people talk a lot about, you know, doctor, how do doctors interact with kind of AI and sort of what's the system? Is it, you know, which is better? Is AI better? Is this doctor better? Who's better at diagnosing? And what they've said is, no, no, no.

The real question is how do we integrate these two things so that everyone is doing what they're best for? So they have these new AI agents now who are sort of driving some of the med titrations. And what they found is that they actually, you know, what the AI does is it'll learn all of that doctor's kind of practicing patterns over time.

And what they have now is a 98% concordance with the prescriber and the doctor. So when the prescriber looks at what they would have done, 98% of the time the doctor agrees with the recommendation that AI is putting forward to do the prescription. So they're truly working kind of, you know, syntonically together. I think the other thing that's interesting is that they found that they have a, they've had a 70% increase in the amount of getting cold of folks with AI.

So what they found is that when you measure your blood pressure, because the agents are always monitoring it, if your blood pressure is high, but then I call you an hour later to say, hey, your blood pressure is high. Are you feeling dizzy? You know, because if you're feeling dizzy, this might be a more serious thing. Or maybe you need to take this other medication.

If I wait an hour, I might not get you because you might be playing bingo. You might be a Trader Joe's. You're playing with your grandkids. You're not going to answer the call then. So the key thing is I need to call you right when you're taking the blood pressure measurement because now you're in that mode. And with AI, they can do that.

They can intervene at the moment they get the signal. They can get the signal, process it, grow through your pass charts to say, are you someone whose blood pressure just runs high? You know, 160 for you would be a problem. 160 for someone who's always at 160, you know, that's something that matters, but we're not going to change your medication for that sort of right away.

So they have that kind of broader context. And I think that's the power of what they're building because they've been able to integrate your doctor, your EHR, your healthcare system with their agents, with the devices that are in the patient's home. You're actually able to bring kind of those two worlds together. That's incredible. It's like it's kind of, you know, if you were if you asked a 10 year old, what should health care be like? I feel like cadence is what they would draw out.

It's so funny because all this stuff just seems so intuitive. It makes sense. Like in just it should have already been like that. Yep. Yep. But it's but it's hard. It's hard to do. And we didn't we didn't always have the tooling or the incentives to make it happen. Putting OK, we're going to keep the doctor hat on, but we're going to put the investor hat on again.

And it's been on, but I want to really put it on now. So talking throughout the portfolio, the investment thesis, especially I had this conversation with Annie Lamont. You were in one of the first waves of these like huge exit environments for health care companies at Amazon. What are we going to see now? Are we going to see labs acquiring companies? Are these big providers or these epics? Are they still going to be in the mix of acquiring them? Where do we land with, you know, where legacy industry is and how much they're going to adopt versus the competitive players on the market? I think that is I think that is the magic of what we've seen with.

And we've you know, we've seen it in core tech where you have these amazing new companies who are kind of growing up and they're able to be truly kind of dominant standalone companies. But the AI wave has also pushed incumbents to compete in a way they haven't before. Right. You know, we've seen Stripe making acquisitions. We've seen Google making acquisitions.

We've seen all of the large entities across, you know, tech across sort of fintech. They've been moving fast because they've realized, you know, we need we need to tool up in this new time, in this new period. I think we're going to see a similar trend play across in health care where we're both going. We're both going to see that existing companies are going to have enough sort of surface area and move fast enough that they'll be able to IPO and become large standalone companies.

But we're also going to see that the incumbents are not going to stand kind of flat footed in there. They're not right. You know, Lily, Lily launched Tune Labs with NVIDIA. A lot of these large entities are moving on their own. I think health systems are doing the same. You're seeing the large incumbents, the oracles of the world move fast as well.

You know, incumbents aren't flat footed in the age of AI. And they're going to either try to build or buy the capabilities they need to compete in the new world because everyone sees the new world is changing. I think folks are also seeing that the attack vectors are different. You know, the same way that I talked a lot about how these biotech companies are saying that, huh, you know what? Like care delivery is a space that we can go into to, you know, expand our TAM that's a logical extension.

We're seeing the same across different portions of the value chain. So there are a lot of these like large distributors, right? The, you know, McKessons of the world. You know, McKesson has gone out and bought large oncology providers, right? McKesson is a distributor of sort of medications, right? They've gone out and bought large care delivery assets.

That is a new thing that we hadn't seen before. But people are rethinking the lane that they sit in because they're realizing, you know, it's one, when it's easier to build, right? You start to move faster into these relevant adjacencies. Talking about exit and the exit environment, there's one company. We'll come back to this conversation we had in the beginning.

That's very important to thrive. And that was Oscar Health. And Oscar incurred a crazy comeback story. At one point, it was down 95%. So what happened with Oscar? Yeah. I mean, what won Oscar? You know, the first company that Thrive ever invested in. Josh co -founded Oscar when he co -founded Thrive. And I remember I was a know -it -all med student at the time.

And I was talking with Josh and Mario about Oscar. And I told him, I was like, this is a bad idea. You know, you can't, you know, the last health insurer that was created was in 1977. That was United Healthcare, right? You know, pre -internet. I mean, you can't just make a new health insurer. It's not a thing that's done. You know, it's too hard.

It takes too much scale. It takes too much capital. I was like, you can't just build things. And I was very glad to be proven wrong. Yeah. And I think a lot of ways, their whole vision of can we, if we build this for the consumer, using the, you know, the ACA as the time to actually be able to build a consumer -centric health insurer was true.

And it was amazing. To your point, you know, at one point it was down 95% from its peak value in December 22. Now it's back up above its IPO price. I think it's increased 100% year over year, which is quite amazing because, you know, health insurers writ large, right, have had a pretty hard kind of last year and a half. And it's called Oscar.

You could argue. I was telling Mario, I was like, you guys should call it Alphabet because you guys are kind of defying the gravity of health insurance writ large. And part of that is just like the way in which they've built it. You know, they built it with a tech -first infrastructure. And this infrastructure that they built kind of a decade ago, they built their own kind of claim stack, their own kind of claims processing.

But this infrastructure they built a decade ago has really been the thing that's enabled them to move so fast with AI today because they had a modern kind of tech stack. And they had that kind of tech DNA and that builder DNA ingrained within them already. So I know we went through so many companies. So many. This is why I love talking to you.

And we're definitely going to do this again. We talked through so many companies. I want to double back down on OpenAI because I think it is a bit underrated, their foyer into healthcare and what's going on there. So tell me a little bit more about that. Yeah. I mean, I think it's interesting. You could argue in a lot of ways that OpenAI is now one of the largest healthcare providers in America, right? They have 300 million people asking chat GPT questions every week about healthcare specifically.

Yeah. Right. In a lot of ways, like that makes them the largest healthcare provider in the world. And I think when you think about It's pretty amazing. They've not only ushered in the AI age writ large, but I think when it comes to healthcare, the idea that all of us now have access to a world -class physician in our pockets at all times, right? 2 a .m., 7 a .m., 8 p .m., we all have access to that knowledge at all times.

That is probably one of the largest changes that has happened in healthcare in a long time. And for most people, I think we talked about, again, healthcare is the key hero use case for most people, right? For Silicon Valley, it's coding. For the rest of America, it is healthcare. Find me someone who does not have a story of themselves or for their loved ones where they have put all of the information of some complex medical journey into ChatCBT to get an answer.

They partnered actually with Boston Children's and the Harvard Hospitals to actually take about 300 pediatric cases that were kind of unsolved, and they were able to get to new diagnoses for about 20 of them, which is quite amazing when you think of some of the capabilities. The fact that all of us have access to that at all times, that is a paradigm shift for healthcare, and it's one that people are using.

We all know those use cases. I think that's the part that's so inspiring is as you think about, one, the impact that has now is huge, but there's also the impact of what that does going forward and how that changes the rest of the system, how that changes where the bottlenecks are. Because if we can now understand more ourselves, we can treat more ourselves, what does that mean for where the healthcare system itself can kind of focus its resources? Is it able to then deploy more folks to making sure that we have more accessibility for the procedures that we need? Can we make sure that your average surgeon, they spend half of their time on non -operative cases? And even

now it's wild because there's a three -month, four -month wait for an orthopedic surgeon in LA. But if that surgeon was now seeing only cases that that surgeon needed to see, if you're able to optimize that supply and demand, all of a sudden now maybe it means there's a one -month wait now rather than a three -month wait. And by the way, I'm happier because I don't have to wait.

The surgeon's happier because they get to make more money just operating, but they're also doing the thing that they're trained to do. And I think those are the types of ways in which you think about what are the downstream effects of that kind of explosion of knowledge and access. I often say we talk a lot about the 10X engineer in tech.

I'm like, how do these tools not just enable the consumer, but how do they enable the rest of the system? Because as a nation, I think we need a 10X nurse a lot more than we need a 10X engineer. Incredible place to end it. Dr. Wara, thank you so much. Thank you. And I can't wait to do this again. And I'm sure we'll have to do this again soon because now the inflection point has happened and we're like hitting the chat GPT moment of biotech.

Exactly. Exactly. Well, thank you so much. Thank you. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, sorcery .vc, where we deliver a once a week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen.

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