Should You Trust ChatGPT With Your Health?

5.3 plus trillion we spend on health care, if it were its own country, it'd be the third largest GDP, and yet we have a ton of scarcity. OpenAI's mission is to ensure AGI benefits all of humanity and give people access to trustworthy, abundant, reliable health intelligence. One of the stories that I was most touched by was the story of Nathan LeBenz. He basically had this kind of long saga with his son where at some point he was suffering a lot of fatigue and irritableness and AI helped them navigate the process and it turns out he was diagnosed with an aggressive form of leukemia. Some of the work that you've done around mental health, there's a lot of complexity. How do you help guide a user on that given that could mean so many things? We've just been working with a bunch of licensed mental health experts across the world to think about the right ways to define model behavior in a bunch of tricky scenarios and ensure that we can get to a level of consensus where we can actually measure performance. Let's talk about trust. A person can be speaking to an LLM really hoping to hear that everything is okay, but from a medical perspective, they need to hear perhaps something very different. How do you teach an AI to recognize the difference? More than 300 million people are already using ChatGPT for health and wellness questions every week. What happens when that AI becomes proactive, personalized, and increasingly connected to the rest of your healthcare? In this episode, A16Z partner Jay Rugani sits down with Karan Singhal, who leads health AI research at OpenAI to talk about the company's vision for making trustworthy health intelligence widely accessible. Karan explains why OpenAI is training medical expertise into its general purpose models rather than building a separate healthcare model, how it works with hundreds of physicians to evaluate those systems, and why the goal isn't to replace doctors. Instead, he sees AI becoming a safety net and a quarterback, helping patients understand their health, helping clinicians catch things they might otherwise miss, and eventually navigating more of the fragmented work between visits. They also get into what happens as medical intelligence becomes dramatically cheaper, how AI could shorten the years it can take for new medical knowledge to reach clinical practice, and the possibility of a healthcare move 37, an insight that initially surprises human experts, but ultimately changes how we understand or treat disease. Hey, everyone, welcome back to the podcast. I'm Jay Rugani, a partner at A16Z, leading investments in healthcare and biotechnology companies. I'm joined by Karan Singhal of OpenAI. He leads health AI research at OpenAI, and we've got a lot to talk about. Thanks for being here. Thanks for having me. I wanna start with the big question. There was a report earlier this year, you all have talked about it publicly. Hundreds of millions of people each week use ChatGPT for health-related questions. What are they asking? What are the kinds of questions they're asking that they're not getting from our main healthcare system? Yeah, let me zoom out and talk a little bit about why we're investing in health in the first place. So OpenAI's mission is to ensure AGI benefits all of humanity, and so we see that at a high level happening in three ways. The first is building and deploying AGI, so building capable models, building products around them, deploying them, partnerships. The second is preventing downside, safety of various kinds. The third is making upside happen. And so we view health as a really, really concrete way to make upside happen, and also work on some of the safety problems that come up in healthcare along the way. And so we're really invested in making sure, basically two things. One is that we can raise the floor of human health and give people access to trustworthy, abundant, reliable health intelligence for free or nearly free, and really raise the ceiling as well and make things possible that weren't possible before. And so the best way to see that kind of raising the floor is really to think about how we've been working in the consumer space. And so as we reported, there's more than 300 million people weekly using ChatGPT for health and wellness types of questions, and we take that as an incredibly important responsibility to get that right, and that's been rapidly growing over time. I think one of the things that you see is just, if you think about the experience of getting healthcare, even in the US, even in the Bay Area, if you're looking to get a specialist appointment, even here in the Bay Area, sometimes it can take weeks or months. You might spend a lot of time figuring out how to get an appointment and scheduling and all these kinds of things. And after all that time, maybe you get 15 minutes with a doctor where you don't feel adequately prepared and you maybe walk out of it thinking, oh, I had six more questions that I wanted to ask. And our hope is that AI can be part of this care journey with you, and health is not something that is just kind of restricted to these kind of 15 minute intervals, and AI can actually help you navigate your care journey. And so that actually reflects how people are using AI on the consumer side today. Like people are coming in with questions and concerns, information that they'd like to have. They want to figure out how to kind of navigate their care visits or ask questions or figure out how to follow up things that the doctor told them or things like this. And we're finding that in between these visits is when AI can help people most in the consumer side. We're making similar investments on the clinician side and on the enterprise side as well. Yeah, it's amazing. We have an extraordinary healthcare system. If I got really sick, there's no country I'd rather be in than the US, and yet we have a ton of scarcity. You've seen some of these statistics, over 100 million people don't have access to a regular primary care doc. Even the ones who do often have to wait 40 plus days to see that doctor. Sometimes you meet the doctor exactly as you said, and it's a quick 15 minute visit, and you have more questions. I saw a dermatologist last week about something. I wrote down all my questions beforehand in an Apple Notes, and I still forgot to ask something. And now I'm scheduling a followup and it's another four months out. It happens to all of us, we've all felt that. Is there a specific patient story or recent case that you all have seen that kind of help us imagine where the world is going and where your work is taking us? One way in which the work of our team is gratifying is just seeing the numbers and seeing how many people are using the tech and how useful it seems to be for them. One other way is just hearing from people, right? So we get people coming in, sending us emails or whatever it is, reaching out in various ways multiple times a week saying, here's how this has helped me. I think one of the stories that I was most touched by was the story of Nathan LeBenz and his son. So he hosts the Cognitive Revolution podcast and he's kind of told a story in that podcast, but he basically had this kind of long saga with his son where at some point he was kind of suffering a lot of fatigue and irritableness and things like this, his very young son. And AI helped them kind of navigate the process of realizing that this was like a pretty serious thing while they were on vacation and deciding to take them for testing and repeatedly telling them like, hey, take this seriously. And it turns out he was diagnosed with an aggressive form of leukemia. And throughout his care journey, I mean, Nathan reports kind of using GPT-5 Pro at that time quite a bit for helping navigate the process, helping understand lab reports and things like this, even before a specialist had time to kind of help interpret. And I think it brought them a lot of peace of mind. So open AI is building health intelligence and health intelligence into your model at every step, but it's the same model that folks use to write code or plan a vacation. Can you help folks understand why you're building it that way from an architectural standpoint? Why is a general purpose AI the right foundation for something as complicated as medicine? Yeah, I mean, people often ask us like this kind of general versus specialized model debate. And I used to work on specialized models actually in a previous role. And so I definitely have kind of experience on kind of both sides of this question. I think the kind of most important thing here is like what we're talking about here is actually specialized training in a general model, which is I think actually the best of both worlds. So what we do for ChassisPT and all the models that we ship in ChassisPT, across every stage of model training for ChassisPT, we actually have specialized healthcare data added to each phase of training since all of our models in the last year. As a result is now you have a model that benefits from all the scale and general improvements of intelligence and reasoning of the big frontier models, and also the benefits from the specialized training that we get from, for example, integrating data that we get from working with a bunch of physicians across the globe. And so I think you get the best of both worlds with this approach. If you kind of forked and said, hey, we're gonna train a kind of smaller specialized model that is just for healthcare, like I used to and like many people have, what you'd find is that model will just kind of fall behind the frontier relatively quickly because the pace of the frontier and the general model improvements is so fast. And if you bet on scale, what we found as OpenAI as an industry is that things will get better. And so what we're doing is kind of hitching a ride on that kind of scale frontier and leveraging the fact that we have the ability to kind of integrate evals and training data into the models to make the models both specialized and also benefit from the general capability. I wanna almost start imagining what's possible with the technology that you're building. Obviously, where it is today is amazing, but you're not stopping and there's a lot of work to go. I think many people are allured by the idea of a personal doctor in your pocket, something that is perfectly knowledgeable, has knowledge across all medical specialties so you don't necessarily need to go see a cardiologist and a dermatologist for different questions, something that is perfectly personalized to you, has all your medical history and context, remembers everything that you've said, remembers your medical history, but also your preferences, and is also able to motivate you and nudge you so that the information that it gives you is given to you at the right time in a way that can nudge you towards better health behaviors or adhering to the protocol that your doctor gave you. Is that sort of the experience you're building at OpenAI in the fullness of time? And how close are we to that? Yeah, that's something that we're really excited about. We are very excited about this vision of kind of a proactive personal health assistant. I don't think that's any kind of replacement for actually having a doctor or having access to a doctor, but really thinking about the ways in which we can take that experience of getting health information and making it more personal to kind of your context is a really important part of kind of our investment in health and chest TBT, which is kind of consumer offering that we have that enables users to connect their health information for that information to be used kind of securely, not to be used for training data or for targeting ads or anything like this. And that's a big part of how we think about how we're gonna move towards this world of really like increasingly personal access and proactive access. It's also been how we've been thinking about deployment. So we've actually, from the beginning, been deploying this as widely as possible to our free users, as well as our pro users. And so we have been, of the kind of 300 million weekly active health users, the vast majority of them are free users, and we take that, and most of them are outside the US as well. And so we take that kind of question of distribution here really, really centrally. And so as we're thinking about this kind of next frontier of proactive personal health assistant, we're gonna bring that to bear as well. I think use cases help us imagine, help people imagine what's actually possible, what all this work is contributing to. The idea of a personal doctor in your pocket, I think is exciting. When Greg Brockman came on the podcast, he spoke a little bit about this situation he had with a friend of his was in the hospital, and her physician was about to administer a certain medication. She double-checked that medication in chat GPT, realized based on her medical history that she would have some drug interaction or some adverse event that may come from that. She showed that to her doctor, her doctor agreed and said, I didn't have enough time to look through your medical history to notice that, so thank you so much for raising that. Shouldn't we do that for everyone all the time? If it's cheap enough and convenient enough, can't we do a second, third, fourth opinion for every clinical scenario? Is that where we're going? Yeah, I love this idea of a kind of safety net for both patients and clinicians that is just kind of there in the care journey. Not always interfering or being the middleman, but really thinking about the ways in which you can augment the existing experience and build on it. We did this work actually kind of more directly studying this on the clinician side in a kind of a series of experiments that we ran with Panda Health, where we studied this idea of a clinician co-pilot that was deployed, and it was exactly deployed in this way where it was a safety net for the clinician as they were doing their work. It kind of came in and proactively suggested like, hey, this might be off, there might be this medication interaction that you might not be tracking. Can we potentially adjust that and things like this? I think we're gonna see the same thing happen on the patient side, and I'm very, very excited about this idea of kind of like a proactive agent in your care journey really helping you and making sure that if your A1C rises in your diabetic, that you get a review of your medications by your doctor and things like this, and making sure that that's kind of flagged for kind of appropriate review. You mentioned sort of personalized proactive agents. Personal agents in general have kind of emerged on the scene and from a popularity perspective over the last few months, Instinct, Muse, Grokbot, OpenAI's Dots. You can give these AI apps an objective for folks who are not familiar, and it can execute a series of tasks. So it's not just, hey, do this task, and then it executes the task with a single loop. It can use your preferences, it can access all of your applications that you give it permission to, it can access your credit card information if you give that to them, and it can execute an objective on your behalf. One can't help but imagine the possibilities there in navigating our wonderful, but often disjointed healthcare system, scheduling a appointment and making sure your insurance is covered for that, covering that appointment, you know, helping you and guiding you to follow a care protocol that your doctor gave you post-discharge, even negotiating with your insurance company on a medical billing error, all kinds of tasks that just create friction in our healthcare system. Is that where you're kind of going with your work? Should we look forward to that from OpenAI? I'm very excited about that vision. I think if you look at, from both the patient and clinician perspective, one of the most difficult things about the healthcare system and how it serves people is this kind of question of fragmentation and kind of lack of connection of the ecosystem. And healthcare is also one of those industries where you shouldn't expect everything to have an MCP connector tomorrow, right? It is a place where there's legacy software, there's not necessarily the best connectivity with AI or software in the first place, and many people are running their primary EHR software in the healthcare ecosystem on a VM, for example. So like- Virtual machine that they're running in the cloud, yeah. Exactly, and so there's actually like an interesting component where like a virtual machine actually makes things easier in some ways for AI agents, but this is absolutely right for agents and computer use. So I definitely think that this is like a place where a lot of the barriers that have traditionally existed between patients and getting the care they need and clinicians and delivering the care that they need and healthcare data in general, which is often kind of a bottleneck to the whole system working well for patients, I think a lot of those will dissolve. Okay, so we were talking about the use cases, you're helping us dream the dream. Before we keep going down that road, let's talk about trust. Obviously, trust is the most important here. The same underlying model can help me understand my lab results or something that I heard from a doctor, but it can also help. a highly trained, specialized physician makes sense of a very complicated case, what do you change in the answer so that it's legible to each of those two personas or any other persona that might be engaging with the same underlying model? Yeah, I mean, as you said, health is a place where it moves at the speed of thrust, and so you need to be able to not only make the models better and measure them, but also kind of communicate and bring the world along, and so that's been a very important part of our thinking and our strategy for both how we work with patients and how we work with end users and how we work with clinicians, and so the brief answer to your question is basically we evaluate and train the models by working with experts across the world and getting feedback in various ways to kind of tailor their behavior to whether a person is a lay user or kind of an expert lay user that has been navigating their care for a while, a complex care journey, or a clinician or other kinds of user personas, and so back even for the original health bench evaluation that we released back in May 2025, there was actually a theme or part of the evaluation that was really focused on this distinction of how the model behaved and tailored its level of kind of use of technical jargon and things like this to whether a person was a lay user or a clinician, and since then, we've both gotten better at measuring whether the models are able to cater to different kinds of users and also training the models towards that, and so a lot of our approach has really been like not only how do we make the models better for these different users, but how do we bring them along, and part of that is how we do evaluations and communicate about them as well. So there's a few components to trust. So we've talked about sort of tailoring the answer to the user. Another big component is disagreement. A person can be speaking to an LLM or speaking to chat and be sharing all of their symptoms, but really hoping to hear that everything is okay. So from a consumer user NPS perspective, Net Promoter Score perspective, they wanna hear reassurance, but from a medical perspective, they need to hear perhaps something very different. How do you teach an AI to recognize the difference and manage different product objectives there, either from consumer engagement and retention versus sort of correctness and sort of clinical quality? Yeah, this is super central to how we do both evaluation and training, and so zooming out a little bit and the question of like agreement and disagreement around how we respond and how we evaluate, one thing you could do and one thing that you could kind of base your approach on here is like, we could bring in a few clinicians into let's say the OpenAI Health team and say like, hey, you guys figure out how to navigate these kind of uncertain cases and I'll ask you about these uncertain cases and you'll help us build evaluations and training data and stuff like this. And leave it to those few physicians to figure it out. This is actually not the best approach. So it turns out if you ask five physicians or something this, any question, you'll get like eight different answers. And so one of the first things that we did when we first started focusing on health at OpenAI was really build this kind of global physician network of hundreds of experts that we work with kind of across the globe, across dozens of specialties and languages and countries of practice to think about the right ways to kind of aggregate the wisdom of experts so that we weren't relying, especially in areas where there could be disagreement, we weren't relying on one or two or three physicians to kind of adjudicate. So that's one part of it is kind of like thinking about evaluation and training data coming in from different experts rather than relying on one or two people. But then the second part of your question is like, how do you balance kind of competing objectives, right? Let's say the physicians and the aggregate wisdom of the physicians is pointing one direction, and then maybe the user actually wants you to say something comforting even when the situation is not comforting. It's basically a prioritization of safety. And so it's very important to us that like we take our obligation very seriously when there's hundreds of health, hundreds of millions of health conversations coming in. And so across the evaluation stack and training stack, we have evaluations that track these kind of difficult situations where the model should actually go and say like, hey, this is unsafe, like you should seek care, even if the user doesn't wanna hear it or wants to get some kind of alternative treatment or something like this. And that's a very important part of how we do evaluation. And over time, we've not only improved how we've done the evaluation working with physicians, but also seen pretty dramatic model improvements across the board in those areas. There's another example or another instance of disagreement, which is me as the consumer, I ask a medical question of ChatGPT. I then show that response to my doctor during a visit. My doctor loves me, by the way. And then they disagree. They disagree with the response. How should I handle that situation? Yeah, I mean, I think you should handle it similarly as if disagreement was happening in some other way as well. Like people do disagree about different things. And I think like you have a right as a patient ask for another doctor to take a look and get a sense and go to another doctor to get a second opinion. And yeah, that's what I would advocate for whether or not you're also using ChatGPT to get health information as well. Yeah, makes a ton of sense. There's another element of trust, which is just absolute correctness. And a lot of the work that you all have done, it seems to be, at least I'm hearing from you, seems to be about grounding answers in the world's medical literature and evidence. Some parts of medicine, there's not a lot of disagreement about, but there is much of medicine that's still being discovered or being studied. And there is disagreement even in the medical field. And for those kinds of questions, it's difficult to ground it in truth when we're still discovering truth, science is a process, et cetera. How do you think about correctness in those kinds of instances? It's a really great question. I mean, it's important with or without AI. So if you kind of zoom out and look at how is medical consensus distributed today, what is the process of that today? You basically have a bunch of people writing papers, biomedical papers of various kinds, suggesting kind of improvements or treatments or things like this that can improve the quality of care. And then you have this process, yearly process across different specialties of building clinical guidelines, which kind of describe like, hey, in this situation, if you're a cardiologist and you see a patient with X, Y, and Z, here's what you should probably do. And so these clinical guidelines are roughly, you can consider them kind of flow charts that are put out each year by the different kinds of bodies across the world that describe roughly what you should do in different scenarios based on the latest research. And then the final step is, so you have the biomedical literature and then you have the clinical guidelines and then you have like, what do people actually do in practice? And so the final step it turns out is a massive bottleneck. So there are these studies on like, how long does it take for clinical guidelines to penetrate the practice of everyday doctors across the world? And in the past, people have found that this takes, depending on the study, like five to 17 years for that penetration to actually happen, which is massive. You don't wanna be practicing medicine five to 17 years behind. And so one of the things that we think about is how do you compress that kind of period of kind of practicing in the past down to weeks or days or even hours, right? And so this is one very important principle behind our work in CHAT GPT for Healthcare and CHAT GPT for Clinicians, which are clinician and healthcare professional facing offerings where we're bringing the latest biomedical literature in the moment of care and allowing people to very quickly in the course of like, let's say a 15 minute visit at the point of care, get answers to their questions. So that's one way of answering the question, which is like, in practice, while there is a lack of medical consensus in many areas, in some cases, there is medical consensus, but it's not being practiced. And so one thing you can do is like really dramatically reduce the amount that's happening. This is one way of answering your question. A second way of answering your question is like how to handle the quick places where there's still like genuine medical uncertainty in there. I think, I mean, there's a couple things. So one is you wanna train the models to be able to recognize their own uncertainty. Like after browsing and getting information and things like this, you want the models to be able to know, hey, like there just isn't enough information and to be upfront about that. And so, you know, that's been an important part of how we've done evaluation. That's actually like a public aspect of HealthBench, which includes kind of evaluations on hedging, for example, when there is this kind of uncertainty. So that's one important thing. The second thing, and this kind of goes to this question of like, how do you raise the ceiling of human health is like really thinking about the ways that models can actually accelerate the process of doing science and actually pushing the medical frontier. And so maybe we'll chat about that later. Hearing you talk, it feels like you're always thinking about one of three constituents. It's either the patient, it might be the individual clinician that signed up, or it might be the enterprise, whether it's a healthcare provider enterprise or some other enterprise. How do you think about designing the product at the architectural level and at the, let's say the harness level to meet the needs of all these different users? Yeah, I mean, I think maybe there's three parts to that. One is making the models really good for these users. The second is building the right harness or product around it. And the third is really like the deployment or partnership motion to kind of reach out to the right, reach out to the users. And like by default, I think a lot of the time the models are often pretty good these days. And so we talked about our approach of evaluation, working with clinicians and things like this. We are seeing pretty dramatic improvements in kind of performance and reliability. You know, we talked a few months ago about the improvements that we've been making on kind of the free tier and bringing free tier performance to the frontier. So, you know, of the 1.2 billion people who use ChatGPT weekly, giving them a model for health that is just like incredibly useful at the frontier for free. So that's been a big part of our strategy, obviously for making healthcare, health intelligence abundant. The second thing is our kind of product offerings. And so the way we think about this is you can have a model that's really good, but if the activation energy to use the model to improve health is too high, then people won't do it, right? So a lot of our product efforts are really like thinking about these constituents that you're talking about and thinking about the ways in which we can reduce the activation energy for people to use compute and intelligence to improve human health. And so that goes along the lines of, you know, our work in ChatGPT, health and ChatGPT, ChatGPT for healthcare on the enterprise side and ChatGPT for clinicians. A lot of the way we design these offerings is basically to think about like what are the barriers and bottlenecks and activation energies for people to be able to use the models? And so, for example, you know, what are the security and compliance bottlenecks in providing an enterprise offering of ChatGPT to healthcare systems? So that was like one question that we were thinking about a lot two years ago. And so we thought, okay, we need to kind of invest in security and compliance. We need to make sure we're able to offer HIPAA compliance. We need to make sure that we're able to offer the kinds of privacy protections that healthcare organizations need so that we're able to handle the healthcare data that they need and be able to offer services that they trust. And so that was like a very important part of the work we were doing for like roughly two years ago to like roughly a year ago. And we kind of just go after it from there, like based on the bottlenecks that we see to kind of adoption. The third thing is kind of partnerships and deployment. And again, it's really like, you know, a few years ago, I think OpenAI was very early to speak in the language of healthcare. And when we interacted with healthcare partners, I think we were early in those conversations. So I think one of the things that we've brought to bear since then is really, you know, thinking about each of those constituents that you're talking about and really thinking about how to speak their language and how to kind of bring them along this journey, because even if the models get really smart, if nobody's like really trusting them or using them, that's not really achieving our mission. And is that, you're alluding to a little bit some of the work you've done with Epic to kind of integrate the product directly into Epic, maybe share a little bit more about that, because a lot of the folks who've spent a lot of time in healthcare, we're very interested to see that level of collaboration with Epic. Yeah, I mean, this is a very important part of our approach. Like we've talked about, fragmentation in healthcare has been one of the issues. And the fact that there is kind of data and workflows that are disconnected from each other, I think is a big obstacle to the use of AI productively in healthcare. And so, you know, our work with Epic, our work with other partners, we had a recent announcement where we announced that there's now a way to use Epic in Chats GPT, if you're a provider or a healthcare professional, and also for Chats GPT to be useful in Epic itself. And so on, in the Epic workflow itself, you can have like a little sidebar where you can actually use Chats GPT as well. And so that's one thing we're also working on, like different other kinds of connectors that we've also announced recently, and we have more coming. And so there is quite a bit of work that we're doing to make sure that we are kind of breaking down these barriers to kind of the healthcare ecosystem being connected. And I think, you know, Epic is just one of many examples where we're identifying those bottlenecks and trying to fix them. The last thing I want to hit on with respect to trust is some of the work that you've done around mental health. And this is another area where there's a lot of complexity and there may be other objectives that you need to solve for beyond the clinical complexity that we talked about. If I ask my OpenAI dot, hey, I want to feel better over the next few weeks. How do you even interpret that question? How do you help act on that? How do you help guide a user on that given that that could mean so many things? Yeah, I mean, and regardless of the service, we are super committed to like helping users when they're having a difficult time or when they're in crisis. And so, for example, for dots, we've been investing in the same kinds of safeguards that we've been putting in all of our product surfaces in that product surface as well. So that's an important part of our approach is kind of like a baseline safety floor. And then, with Mental Health Bench, we've really taken this approach of not just measuring the safety floor, but thinking about the ways in which the models can actually proactively help kind of raise the ceiling and really think about the ways in which models can help provide positive mental health experiences and move from just worrying about the safety floor to measuring those kinds of upsides. And so, again, the approach is actually very much mirroring our work on Health Bench, where we've just been working with a bunch of licensed mental health experts across the world to think about the right ways to define model behavior in a bunch of tricky scenarios and ensure that we can get to a level of consensus. People definitely disagree even more here than in physical health. Get to a level of consensus in some of these tricky situations where we can actually measure performance and create this kind of like shared benchmark from the industry. And so we released that benchmark very recently and similar to Health Bench and Health Bench Professional, the goal has really been, can we create this benchmark that kind of raises the ceiling for everybody and kind of creates across all model developers and the healthcare industry and mental health experts like this really shared ruler or measuring stick that we can use to kind of make sure that we're moving in the right direction. Okay, so we've spoken about intelligence and the use cases that are coming around intelligence approving. We've spoken about trust, which is critical. Let's talk about medical care. And I wanna draw the distinction between medical information and medical care. I think for medical information, I think if you have a health concern, if I have a health concern. and about myself or something that I'm dealing with, chat and other LLMs are increasingly the better, faster, cheaper way to get an answer and a resolution to that question. But if in the scenarios where medical information alone doesn't get me to resolution, I then, at least today, am thrusted into the traditional medical care system. And so if I need to go get a prescription, if I need to go see an in-person specialist, if I need to get some follow-up blood work or some MRI or some other form of scan done, if I need a clinician to examine me in some way, I still need to navigate the traditional healthcare system. And a lot of that work is sort of governed and regulated by the US medical license and sort of other infrastructure that we have in our healthcare system. It's the multi-turn conversation, where it's not just a single question resolution, it's I gotta go talk to a few people, I need to go get a few things done, we need to collect some information. In many ways, medical care, it's a bundled product. We're getting the diagnosis and triage and the care plan, we're getting the empathy from the clinician and the assurance and the support, we're getting the legal liability associated with that and what they're taking on. Will AI provide all of that? Where will it stop short? Where do you think this is all going? No, I don't think AI can or will provide all of that. And I think you're right, that there's this distinction between health information and medical care. And so I think the way we see our role here is really to enable the whole ecosystem, because there's no future where any one company is delivering all the medical care, right? And so our role here is really to think about what does the last mile of medical care really look like for people? Can we really understand that? And can we make that easier for everybody so that the last mile of actually having impact on patients just looks like the last 100 meters for all of our partners? And so that's kind of how we think about it, is really making it very easy for our partners to use AI to support everybody in the care journey. I think there is stuff that OpenAI can do relatively uniquely here because of the front door to health that it has. And so for example, it can help you navigate a lot of these kinds of questions or help you navigate this journey or as we get increasingly helpful personal agents, help you with some of the scheduling logistics and things like this and just act as a quarterback for your health. That said, I don't think it'll resolve all of these challenges that we'll have, at least not in the short term. And I think to a large extent, we're looking to enable a lot of our partners that are delivering care that are overwhelmingly using our models in various ways to make these operations happen smoother and easier so that patients are not left in this kind of fragmented experience. So should the, as a consumer, should the experience that I look forward to from OpenAI be sort of the quarterback care that you described? Will I be talking to a clinician in the interface at any point? Or would you perhaps route me to a doctor and help me find that? Because given that's a whole challenge, just scheduling and making those visits happen, what should I look forward to? I think you'll see like we will be hopefully very helpful in helping you navigate the process of finding care. I don't think we'll be in the business of doing all the things that you mentioned. And that's where we're really relying on all of our startup and care provider organizations and partners and things like this. So coming back to a question I mentioned earlier, which is you are building that future, but you're building it with partners. You're building it for consumers and patients. You're building it for clinicians. You're building it for enterprises. For clinicians and enterprises, like give us some texture on some of the tools that you're launching and some of the collaborations that have really worked. Yeah, we've had a few really exciting partnerships and we've had some really amazing launch partners for ChachiPT for healthcare. We've had lots of users using ChachiPT for clinicians and really enjoying it. It's been the retention on the offering has been like a very pleasant surprise. People seem to be really enjoying it. Yeah, we've been working with a variety of health systems. I think one, a couple that stand out, one is we've been working with UCSF and we've been chatting about that. And they've been one of our earliest adopters for this connection between Epic and the EHR and ChachiPT. And they've been really on the front lines thinking about, for example, clinicians using it for chart review or things like this and thinking about the right ways in which like the experience of caring for a patient doesn't need to be this super fragmented. You need to switch tabs or switch experiences and things like this. And so they've been some of our power users on that front. I think another one is AdventHealth and they've actually been some of our power users in thinking about deploying agents across the workspace, actually across all of OpenAI. They've been a leader and they've been really, they've had amazing results. A lot of health systems I think should look to them. I think they're really impressive. If I go into my doctor's office and I see ChachiPT open up on their screen, should I be upset? Should I be happy? How do you think people will react to that? Yeah, I hear differing things and I think it's actually changing over time. And so I think it's this interesting thing whereas as the Overton window is shifting in real time, I think there might've been some level of concern before. But I think increasingly a lot of, on both sides, there's this question of whether clinicians would push back against patient use of AI and whether patients would push back on clinician use of AI in the way that you're describing. But I think both parties are actually finding incredibly useful for their own personal purposes. And so I think what we're finding in practice, especially over time, is that both parties might actually feel safer with AI in their care journey, whether it's their clinician using it or the patient using it as kind of like a sanity check. And I think that attitude is definitely changing over time. I wouldn't say that we're fully there, but that's my prediction of how things go. Okay, so then taking that one step further, if a patient has done all their research before a visit and they have all their health data integrated, they've asked a bunch of questions in ChatGPT, and then the clinician before the visit has all automated through loops, has run the entire patient case through ChatGPT, and then the visit happens, what should they talk about? My hope is that they talk about the human aspect of healthcare. I mean, there's still like a lot of uncertainty, there's a lot of empathy, there's a lot of bedside manner that goes into the human aspects of healthcare. There's also a lot of physical interaction that goes into it. I don't think all the information exists for the AI, just information theoretically, all the information exists for the AI to just figure out what all should be done. And part of the reason for a visit is to get more information and do the process of history taking investigation and things like this. And so I think that will not go away, especially the physical elements of that. And I think even from a cognitive perspective, the elements of kind of empathy and getting comfort and getting that second check, I think will still be something that people demand. What do you think AI in general, or let's say DOTS would do in between visits? And will you think you'll cross the line into providing care in between visits and then share that back with the doctor? Yeah, I don't think we'll, I think we'll definitely be focused on health information, but I think the, whether it's DOTS or any other product surface, I think we're really excited about this kind of future of heading more in this proactive direction that you were outlining. Like imagine if somebody is kind of dealing with a chronic care journey and they're, again, like this example of like someone's A1, someone gets a lab test back, whether A1c is higher and they're pre-diabetic or diabetic, can you flag for them like, hey, maybe you should get a medication check and go to your cardiologist or whatever and think about the right ways in which you can flag to users medication adherence issues or other kinds of issues. I think those kinds of issues are places where, you know, AI can have potentially a really high health impact especially in like these kinds of chronic care journeys. But I don't think, I don't think it'll be in the business of like managing all these things by ourselves. Sure. Another thing I'm curious about is in the fields of AI applied to other domains and situations, like let's say games like chess and go, as AI got really good at chess and go, they also revealed other strategies and ways of playing the game that even the best experts in those domains were not exploiting or not pursuing. And that helped us learn more about these games and also helped us learn more about AI. Are there equivalents like that that you've seen in AI applied to health from the work that you've done in research? Are there already findings that are surprising you and perhaps introducing new ways of delivering care or understanding a clinical scenario that can give us some optimism? Yeah, I mean, I think we've seen this at smaller scales, but let me zoom out and say like, you know, one thing that's been motivating our team in general recently around raising the ceiling of human health is this notion of bringing to bear kind of a move 37 for health. And this idea of a move 37 is it comes from AlphaGo and a game that the AlphaGo system, which plays this kind of complicated board game played with Lisa Dole, where there was a specific move that just kind of in the moment seemed like a really dumb move and a surprising move for all the human experts watching this game between this AI system and this world expert in Go. But in hindsight is this kind of like brilliant and insightful move that humans are now just beginning to understand and learn from the patterns of and things like this. And this is a very interesting example. So this idea of like, how do you get to a move 37 in health of a move or a prediction where the models predict something that a human might not have been able to predict that in hindsight seems correct and brilliant and insightful and impactful. So I think we're very early and I think you're gonna see much more of this in the future than in the past. But I think, you know, I mean, one kind of example is again, thinking about the ways in which these systems have been deployed as safety nets to clinicians where clinicians are actually delivering care. And in the moment, the AI actually flags kind of like, hey, something might be off here, like our study with Panda Health and thinking about the patterns of results that we saw there. As an example, you know, in that context, the clinicians were a little bit prone to overuse of antibiotics and a very common pattern that the models flagged is just kind of like avoiding this overuse of antibiotics and thinking less about the local case and more about the kind of global issue of resistance to antibiotics. So that's like one, you know, specific example. But again, I think you're gonna see more of these examples of models taking in like a longitudinal history of a user over 10 years across all their data, across the HR data, across the chat data, all these things and making predictions that seemed impossible before because a human can't even integrate all that data in their head and making predictions about their health and their risks and things like this that seemed like they were impossible. And I think that would rise to the occasion of being called to move 37. Yeah, I mean, there are so many things that consumers are curious about that at least at one point in time, we weren't sure if it was healthy for you. And then to your point, the Overton window changed and there was sort of more evidence that was sort of conclusive in one direction. Sometimes the views on some of these things have changed. All these sort of new emerging phenomenons that perhaps are understudied, do you anticipate as people are sort of entering this information into ChatGPT or other LLMs that we'll have more evidence that we can generate to study some of these things that folks are curious about, but we don't really know or we don't have conclusive evidence one way or the other on where they stand? Yeah, I mean, I think one of the most interesting things about agents is they kind of solve a lump of bottlenecks in analyzing data that already exists, right? There's various places across the healthcare ecosystem where data is being generated, HR data, longitudinal histories, claims, whatever, where data is being generated and it is kind of just stored there and the insights from it are not fully mined or used to generate new knowledge. And so I think one of the frontiers for how health might improve in the future and how the ceiling of health might raise, we'll be thinking about the ways in which we can leverage the data that is just kind of out there in boxes in various places and have models generate new insights based on this data. So I'm also optimistic about that future. I'm also optimistic about a future where you mentioned these two constituencies, the user or the patient and the clinician. I think there is a third constituency, which is the scientist, right? Which was helping kind of advance the frontier of knowledge in the space. And I'm also very optimistic about a future in which AI helps accelerate a lot of the bottlenecks in validation, which I think will end up being the bottlenecks as opposed to kind of proposing new ideas or treatments or things like this. And so I'm very optimistic about that as well. Switching gears slightly, you've also shared thoughts around just the cost of intelligence as an input to medical intelligence coming way down. And at some point it'll be too cheap to meter. I spend a lot of time and we as a team spend a lot of time trying to understand how these technologies will make an impact on our healthcare system that is inflating well ahead of inflation. 5.3 plus trillion we spend on healthcare. If it were its own country, it'd be the third largest GDP. It's the biggest part of our economy. It's also the biggest employer in our economy. 23 million Americans work in healthcare. There's just so much we spend on healthcare. What do you sort of make of the work that you're doing and the impact that it'll have on the cost of care? I think this is like one of the most important ways that we can pursue our mission is to not only make our models better at the frontier, but make that widely accessible to everybody. So that's obviously a big part of how we've been able to deploy health intelligence to a lot of people across the world for free. I think it's also a big part of how we think about API and how good our models are there for health and pricing there and stuff like this. One recent update was we recently released GPT-6 Luna and GPT-6 Luna is our current cheapest model on the API or cheapest frontier model on the API. And what we found with GPT-6 Luna was that it was actually doing better on our benchmark health bench professional, our current kind of latest benchmark of kind of clinician workflows that are like really difficult, selected for difficulty for our current models across all the different ways in which clinicians are using our models and the models still could use some improvement. It performed better than 5.6 Sol, which was literally our best model like a month ago. And so it performs better despite being 34 times cheaper. And we've had a couple of those moments even in the past year. And there's been many trends showing that AI compared to other technologies that have been introduced in the past has been reducing a cost at a given level of kind of capability or usefulness faster than many of those other technologies. And so one bet we've had at opening eyes that the cost of health intelligence will plummet at a given level of health intelligence and that the level of health intelligence where clinicians and patients. feel like they can trust in the care journey, they're starting to get there, right? So that's like a fixed level of intelligence in some sense. Hopefully the bar after that raises over time as well. My expectation is once we get close to that level of intelligence, the cost for that level of intelligence also dramatically reduces like it has been. As the hope there is that we get kind of this commoditization of health intelligence, right? We don't want to hoard that health intelligence, we want everybody to use it. It's my hope that that contributes, but you know, to answer your question about the whole of healthcare spending, I don't think all of that is about health intelligence. I don't think like health intelligence has had yet a macro effect on the problem that you're describing. If you look at like the whole of spend of health, there is quite a bit of areas though, where if you look at, for example, chronic care management or documentation or administration, a lot of kind of administrative workflows of various kinds, back office workflows, there's a lot of money spent in ways that probably don't need to be spent that way. And so we are very optimistic about kind of an overall compression of spend over the coming years. I don't think that's happened yet, and I don't think that just happens from making health intelligence abundant. I think that happens from integration and workflows and deployment and scaling that up and really making sure that these models are valuable in the economically valuable tasks that people are using them for. But I think it's a prerequisite to making that future happen. More care, less paperwork is sort of where we're heading, at least there's a lot of the cost savings. I'm sure you saw some of the headlines over the last few weeks of, there's a New York Times article that basically provider AIs and payer AIs are fighting, and there was some Blue Cross Blue Shield Association analysis that showed that the respective billing bots and paperwork bots effectively increased spend by a billion dollars or something like that. When you are doing cutting edge research on health and trying to bring down the cost of medical intelligence to near zero, and then you see headlines like that, what do you make of that? I think this might be one of those areas where there might be some kind of short-term pain around this kind of provider payer arms race, but in the long run, there's no reason this has to exist. And so really, I think you're entering a world where a lot of visits actually have some kind of ambient intelligence in the room, listening, transcribing, things like this. There's notes, there's clinical notes, there's AI details of clinical notes and things like this. And so you have a lot of the details you need to adjudicate visits. And so the ideal world, in my view, is a world in which providers and payers kind of have a handshake, like, hey, here's our ground truth of what happened in this visit. Like, here's all the information we have. There's just like no other source of information that there is. And then an AI helps adjudicate that situation. And maybe there's like some limited appeals process or things like this, but I don't think you need to have all this process where you have like an AI arms race between payers and providers. I think that can just be completely avoided. So I view that as a bit of a short-term distraction. There's a lot of entrepreneurs that are building technology in healthcare that are likely watching this. And they're probably trying to figure out, as you continue to advance your work and you bring down the cost of medical intelligence to near zero, and this becomes this sort of ubiquitous utility that flows through all of our healthcare interactions for consumers, for clinicians, for the administrative part of the healthcare technology or the healthcare stack, what should folks be building on top? What should specialist applications be doing assuming that this gets 100X plus better? Yeah, it's a great question. Like I mentioned, like we think about a lot of our health impact through OpenAI as not being directly through the products that we offer, but actually through our partners and that last mile of delivering care. And so what I'd say to our partners is build on top of models that are really capable. And the most important thing is bet that those models will become more capable. So bet on the future. I think many people in healthcare are on some kind of spectrum between betting against the models, betting neutrally for the models and betting with the models or betting for the models. And I think I would say, bet on the models getting better. That's probably the most important thing. And think about the ways in which a lot of the technology that's being developed, whether that's agents or computer user, other kind of frontier tech, will accelerate healthcare and bet on that and build towards that future. I also think a lot of medical care will actually become more accessible to patients with AI. We talked about this idea of kind of a quarterback and dissolving a lot of these barriers. And so just being focused on delivering really good quality care and having AI be part of monitoring that care journey, I think is a very important part of that as well. Zooming out to the overall healthcare system and now just looking at it at large, we talked about this 5.3 trillion. A lot of that spend is paperwork between providers and payers and other stakeholders within the five Ps of healthcare. A lot of that is cognitive care that you're arguing can be augmented and enhanced and sort of, we can bring on a lot more cognitive care capacity online, which is exciting and exciting from an access perspective. And then there's a lot of spend in healthcare that is procedural, either requiring some imaging, some blood work, some follow-on examination, or it's some surgical intervention. Where do you see sort of the intelligence work that you're doing in the medical space impacting procedural care? And is that gonna be the ultimate, is that gonna be the new sort of enhanced or perhaps an exacerbated bottleneck, or do you expect to do a lot of work there as well, robotics, et cetera? Yeah, I mean, I think in the short term with procedural care, I think you want a better targeting when that's needed, right, and I think AI can help provide information that is helpful for knowing when to seek that kind of care as well. Probably the biggest macro factor in the next year or two will just be better kind of proactive preventative stuff, so you don't have to actually go in later and get that procedure, right, because if you make the right moves sooner and AI helps you navigate that kind of journey, then you can hopefully avoid a lot of the downstream costs of the most expensive procedures. So that might be like one macro factor to kind of look out for is my guess. I think the question of kind of robotics and care, I think is a bit of a longer term one, both from a technology perspective and kind of like a reimbursement and kind of industry integration perspective. My expectation is that it's not like a next few years kind of thing. Very cool. Okay, we've covered a lot of ground. We've talked about intelligence improving. We've talked about trust. We've talked about moving from medical information to medical care. Help us understand what we can look forward to as sort of things continue to unfold as agents and personal agents and the underlying models become increasingly performant. What can we look forward to from ChatGPT Health? Yeah, I mean, I think there's like really two high level things that we're really focused on making happen. One is, again, thinking about like the ways in which we can raise the floor of human health and make trustworthy, reliable health intelligence abundant for all clinicians and all users and patients. And so getting to the point where, you know, like we talked about, like all patients and clinicians just feel safer by having AI in the care journey. Like when you see the ChatGPT, you actually, when the doctor's using ChatGPT, you actually feel safer and vice versa. Getting to that point. And again, that's a question of not just, you know, the models being good and evals being good, but also how we deploy it and the activation energy around that and the products and things like this. I think a second thing is really thinking about raising the ceiling. So what are the ways in which AI can actually do things that were not possible before? So one of them actually, I think is this kind of like, you know, proactive health, proactive personal health assistant that is part of your care journey is helping you navigate these moments, being your quarterback, you know, helping you schedule and all these things, but also flagging the places where, you know, you can intervene in your health today and prevent a lot of expensive deterioration later in the future. And I think that's gonna be an important part of how we think about, you know, improving the future of health for people, as many people as possible. And I think the second thing is just what you mentioned before, which is like really thinking about the ways in which we can unlock the value of existing data that is sometimes stored in boxes of various kinds and thinking about the ways in which that can be leveraged to produce new insights that benefit patients. It is striking and incredible and humbling to realize that CHAT-GPT, I think, came out three years ago, roughly. And so it's been a lot of progress in a very, very short period of time. So to even talk on a five or a 10-year basis feels academic, but can you give us some predictions? I mean, is there a feature that we might have that feels science fiction today, but we could have one day, three, five, 10 years from now? Yeah, I mean, it is super, super hard to predict the future these days, so this is a difficult one. But what I would say is like, I think the thing that might have the most impact on human health in that kind of time frame, the next five to 10 years, is probably gonna be newer treatments that come out of maybe two things. One is AI-guided generation of new targets or molecules that could be interesting for new treatments. And the second is AI-guided prioritization and validation. So thinking about the right ways, we're already at a point where the number of molecules and drugs that people want to suggest to be used in clinical trials and things like this is way more than the number of slots available in clinical trials. So thinking about the ways in which models can help prioritize and run these clinical trials so that they just run smoother and faster. My guess is in that kind of time frame, you're talking about kind of like improved medicines, more personalized medicines for people, things like this, and that will only be possible because you invested in those two pillars. That is awesome. Well, we've covered a lot of ground. I'm leaving this conversation even more optimistic than I was before. So Karin, thank you so much for coming on the A16Z podcast. Thank you for having me. Thanks for listening to this episode of the A16Z podcast. 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