Health Care’s AI Disruption, Ready or Not 

Show Notes

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• Incumbents and Insurgents Podcast
• The GenAI Juggernaut: US Healthcare is not Prepared

Transcript

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AI Usage Disclosure: This transcript was created with assistance from AI tools. It was reviewed and edited by KFF Staff.

Eric Larsen: I think U.S. health care has the greatest susceptibility to disruption from this technology. More than any other industrial vertical. It’s 18.3% of the U.S. GDP. It’s the single most labor-intensive sector in the U.S. economy. And when I think about Gen-AI in its first deployment, it’s about one thing. It’s about brute force, productivity augmentation and the systematic substitution of technology for labor.

Chip Kahn: Last week in our opening episode, Drew Altman and I talked about why KFF is launching the business of health and why we believe the policy world needs a clearer window into how health care delivery and financing actually work. This week we begin our first series on artificial intelligence in health care. Over the next many months, we’re going to take what may be an unprecedented deep dive into AI and health care. AI is not simply another technology being added to the toolkit. It is what economists call a general-purpose technology, a class of breakthrough on the level of the printing press, the steam engine and the Internet. Health care, as the largest industry at roughly $5 trillion and 18% of the GDP, is uniquely exposed to this disruption and I believe may be uniquely unprepared for it. Over the coming months, this series will examine what AI is doing to clinical practice, hospital operations, clinical performance and patient safety measurement, patient experience, payment reform, regulatory structure and delivery models here and internationally. Our guests are the people deploying this technology, managing its consequences and designing policy around it. But first we need the roadmap. We need to understand the wider landscape, the scale of what AI is changing, why health care is at once a fertile field for it and yet vulnerable to its unintended consequences. Who will make the critical decisions, and which philosophy will govern how this technology enters the most consequential industry in our economy.

That is why our first guest is Eric Larsen. Eric is president of TowerBrook Advisors and a member of a health care leadership team at TowerBrook Capital Partners. He spent 25 years at the advisory board, the last five as president advising health systems and payer CEOs on strategy, then co-led strategic health system partnerships at UnitedHealthcare Group after Optum’s acquisition of the advisory board. Over the past several years, Eric has focused intensely on AI’s impact on health care, producing a 127-page monograph through the Tower Brook Healthcare Institute: The Gen-AI Juggernaut. U.S. Health care is not prepared that laid out the case for why health care has the greatest surface area exposure to generative AI disruption of any industry in the economy. And he is now at work on a second volume that deepens the analysis. A year later, he combines deep institutional knowledge of how the largest health systems and payers operate with the investment perspective of someone evaluating which technologies create real value. There is no better person to open this series.

Chip Kahn: Erik Larson, welcome to KFF’s Business of Health. This is the first time we’re rolling.

Eric Larsen: I love it. We’re in a real studio and everything. This is awesome.

Chip Kahn: So, let’s get going on the big issue of the day.

Eric Larsen: Yes.

Chip Kahn: Everybody’s talking about AI.   Many are talking about AI in health care. What is it? What is generative AI and how is it different from, I mean, artificial intelligence has been around.

Eric Larsen: Artificial intelligence as a coined term existed since like 1956. There’s this famous Dartmouth retreat where some of the real preeminent intellects of the age got together for two months at Dartmouth College for the summer and they sort of conceptualized this idea of artificial intelligence. And then over that 70-plus year period, it’s just gone through so many different iterations. I mean, there have been two or three really kind of punctuated AI winters where the academic research sort of decelerated, where the funding dried up. But in answer to your question directly, Chip, I mean generative AI is categorically different and unique. And it’s basically in the name. It’s generative. It’s not a classification system or where you kind of ontologize data and you can pull insights from it. It’s really about generating new speech or imagery or video or genetic or molecular predictive models. So the key to understanding it, it’s basically a, for the first time where you can take all of these synthesized ingested inputs and create something that’s combinatorial, something that is potentially new. And we’ll get into the nuances of that, you know, because there are some people still kind of deprecating of it and they’re like, hey, this thing is, you know, a stochastic parrot. It’s an autoregressive model that predicts the next word. I think this is actually a speciation event. I think we’ve created a new non biological intelligence. I think this is the most consequential technology humanity’s ever developed. But when you get to core definitions, it’s a real, it’s a distinction between sort of a classification system and a generative system, something new.

Chip Kahn: So, I guess it’s been called a general purpose technology. That puts it at the level of the printing press and fire and other things. What does that mean?

Eric Larsen: Yeah, I mean look, this is a little bit of a Rorschach test, right? Like some people look at it and they’re like, hey, this is incremental. I look at this and I would put it in the pantheon of those kinds of technologies that you’re talking about. GPT is general purpose technologies. And you know, academics have different definitions of what a GPT is, but essentially it’s one of these like society and civilization and shaping technologies. And I think there have been probably 20 to 25 of these over the last 4,500 years of human civilization. And you can, it’s sort of an arbitrary definition of when does human civilization start? For our purposes, I’ll say it started with the advent of writing, which is about 4,500 years ago. And since then you can see this progression of technologies that really changed how humans live. And it’s, you know, agriculture and animal husbandry and the wheel. And you mentioned the printing press and then you sort of had them punctuate every couple centuries. But then you saw at the eve of the 18th century this absolute sort of Cambrian explosion of advancement. Starting with the Industrial revolution in the 1760s, with the steam engine. Three industrial revolutions. First was mechanization, second was electrification, and the third was computerization. And we’ll get into this, but I think we’re entering the fourth and most significant which is agentification. But these technologies, everything from the steam engine to Arkwright’s mill to railroads, to chemical development to electricity to the internal combustion engine, to the transistor, to the microprocessor to the mainframe computer, to the PC, like there’s just been a real telescoping. And you know, I am a techno optimist, right? I love technology, I revere technology. I think technology has marked the upward flourishing of humanity, certainly in a material sense. I’m a little agnostic on a metaphysical or a spiritual sense. But you know, think about at the beginning of the 18th century, the average lifespan was 25 years. Ninety percent of humanity lived in abject poverty. There were 800 million humans on the earth. Right? Fast forward 250 years and three industrial revolutions later, lifespan is now 76 to 80 years. We’ve seen the flourishing of democracy and sanitation and literacy and safety and mobility. And you’ve seen just a real upward surge of material well being. And so, I’m a techno optimist now. There are obviously negative externalities with technology. One of my favorite quotes is, with the invention of the ship came the invention of the shipwreck, right? And technology is intrinsically neutral. And if you believe as I do, that AI in its current incarnation and its emerging capabilities, is the most civilizationally consequential technology we’ve ever seen. With that kind of power, you know, you can engineer a cancer cure, you can engineer a pathogen of incredible lethality, right? So, you asked about these GPTs, these general purpose technologies, and it’s sort of curious in like a cosmic significance way that this AI is also called a GPT generative pre-trained transformer. And you know, I just think there’s sort of a cosmic, cosmically interesting sort of like coincidence there. but I’ll stop the monologue by, Chip, gen AI is fundamentally a multiplication of intelligence. And if you think about what’s defined the supremacy of our species, what allowed us to for better, for worse, to subjugate nature? What’s the hierarchy of humans? It’s based on two things. It’s based on our intelligence and our sociability, right? So, what’s defined the supremacy of our species is this sort of collective intelligence. We are fundamentally tool makers. We create tools. Now some primates, you know, will use a stick to, you know, pull out an ant from the ant hill, but those are so primitive and you know, and we’ve had, we’ve had tools since the Paleolithic age, right? But this tool is so consequential because we’ve created a synthetic intelligence that in many quantifiable and measurable ways supersedes our biological intelligence. Right, there’s, and we can talk about this if it’s interesting, but I think there are some really fascinating characteristics that are emergent in this intelligence that have massive implications for society, economy, our culture, et cetera. But that’s kind of my unasked for 4,500 years of civilization in five minutes.

Chip Kahn: Well, that’s a lot to take in and I want to get to health care. But before we get to health care, this transformation you’re describing, the one thing you didn’t say when you gave your list a second ago, was the individual. How is this going to affect, and I’ll be parochial, the individual American and change their lives?

Eric Larsen: I think it’s going to change all of our lives in super profound and frankly unimaginable ways. If you are creating a synthetic intelligence, intelligence is responsible for every civilizational advance we’ve had. And if there’s more of it. If it’s a multiplication of it, well, in every way that we define progress, there should be an acceleration. And given the potency of the raw tool, there’s also a deep risk that folks could use it for malicious purposes. There’s also a risk that I don’t know if we’re going to get into. Geoffrey Hinton, who won the Nobel prize for physics last year and is one of the godfathers of AI, sort of soberly observed. There’s not a single example in evolutionary or biological history of a less intelligent creature controlling a more intelligent creature. So, there’s the merely catastrophic problems of disinformation and misinformation and deepfakes and election manipulation and bioengineering pathogens. You know, those are pretty consequential. But then there’s the really catastrophic issues of if we really do lose control of this tool. And you know, we’re already seeing the tools manifest some, some pretty disconcerting personality traits, scheming, deception. As we talk about this, it’s important to be cognizant that this is unmapped territory. But for the average American, it is really hard to predict. You know, there are those that believe this is going to be very augmentative, right? It’s going to support our productivity, it’s going to make us more valuable in our work. There are those that believe it’s going to be substitutive, that you’re going to see massive technological unemployment. I tend to be in the latter category on that. I think we’re going to see massive disruption, especially in health care labor, which I presume we’ll get into. But, you know, you ask a really important question. If this is as consequential as I think it is, then the appropriate question is, what is it not going to touch? You know, I think it’s going to expand longevity. I think it’s going to change, our nature of mobility with a lot of, you know, anything that moves will be autonomous. Cars are going to be autonomous, you, planes are going to be autonomous. I think it’s going to revolutionize how we approach biology and designing molecules in biologics. I think it’s going to revolutionize the very definition of a GDP. Elon just recently came out and said we’re going to 10x the global GDP in the next decade. We’re going to go from 127 trillion to 1.2 quadrillion in a decade, which is kind of an abstraction. Right? But we’re already starting to see a pretty material uptick in GDP growth. I mean, there are those who believe that we’re going to have a five handle or a six handle on GDP growth this year. And economists like Larry Summers have been predicting this great decoupling between productivity growth and labor. So, we’re already starting to see a little bit of a decoupling where you’re getting 3.5% Q3 GDP growth and basically stagnant employment growth. And so the implications are so far reaching we almost need to sort of decompose the topic set. I mean, we’ll talk about merely health care and the 20% of the GDP…

Chip Kahn: well, there is an issue about data. You’ve said that you thought at least in terms of public data, it’s sort of becoming commoditized. And then there’s all of the private data, whether it’s health care data or commercial data, that’s what’s going to make the difference because that’s what makes the world go round right now. How is that all going to be worked out with these new mechanisms? Or will it just crash through whatever was private?

Eric Larsen: It’s a great question. I mean, how did we get to this moment? What was the alchemy that created this sort of vertical growth in intelligence? And it’s really three things. It’s hardware, software and data. You know, the hardware is, is, are, the GPUs. And you know, Jensen Huang has the most valuable company in the world at $5 trillion. He’s got over $1 trillion in orders for Blackwell and Vera Rubin chips, right? So, the hardware has gone stratospheric and the software, the algorithms, right, this, the famous transformer paper in 2017 that sort of inaugurated this whole, whole thing. But there have been so many sonic booms of advancements in algorithmic capability. You know, we’ve gone from, you know, just sort of generating responses and notorious hallucinations where it was just making stuff up and to reasoning models which are very calibrated, to memory, to tool control, to now agentic capabilities and these algorithmic advancements…it is that sort of intelligence valence, it’s going up and up. But the third is the data. We took all of the data on the Internet, which is about 100 trillion tokens, right? And you know, about a third of that is duplication and SEO, you know, debris. And you know, so the models were trained on all of the data that was available on Common Crawl, right? Which is, which is all of the publicly available data. And it just so happened that, you know, when you put a lot of data and a lot of compute and pretty sophisticated software. You got this alchemy and Anthropic, which is company I very much admire, one of the founders there says, we’re not so much building these machines as we’re growing them because we really don’t understand the neurology of how they work. There’s a certain, like, discontinuity that happens when you put all these things together. And then it began simulating something that looked like human reasoning and then surpassed human reasoning. So, the data question’s really interesting because for a long time we thought we were going to hit sort of peak oil, we were going to hit peak data. And then, you know, there was going to be this sort of degradation in the models. And some of the engineers were calling it catastrophic forgetting or perplexity. And, you know, basically when models that were trained on a certain quantum of data started training their successors, you’d get almost this sort of incestuous relationship and the models would, would, would just decay. We’re not seeing that. And part of that is because the models started reasoning, right? And that, that famously happened with ChatGPT01. And this was in about September of ‘24, I think. And then toward the end of that year, you had 03, which is a real advancement. And it just so happened that when the models paused and thought, right, the fidelity of their answers went way up. I tell my kids this all the time, stop and pause. And it just so happens you get more intelligence from that. And then, not to get super esoteric, but arguably the most important words in AI in 2025 are functional verifiability. And what that means, Chip is if there’s math or coding involved, if there’s a right or wrong answer, if there’s an answer that is provable, that is automatable. I mean, software used to be if you could specify the function, you could automate it or program it. Now it’s if you can verify the function, you can automate or program it. So, one of the reasons that we’ve seen all these step function breakthroughs in coding, and we’re seeing a lot of autonomous coding and programmers becoming 10x or 100x more productive. And some of the models are saying, look, 75 or 80% or 90% of the AI coding is done by AI itself. And you get to this moment where the AIs are recursively improving themselves. And I don’t think we’re running out of data. We’re actually creating really high-quality synthetic data because the reasoning models, again where there’s math or coding and there’s verifiability, that’s actually pretty constructive, usable data to recursively improve the models. So, there are 180 zettabytes of data in the world And a third of that data is in health care by the way. And we still haven’t accessed a lot of these proprietary company-owned or enterprise owned data sets. And I think it’s going to be very valuable in customizing the models and allowing the models to be more effective. I think the data is valuable but not monetizable. I don’t think people are going to be able to sell their data. Although OpenAI and Anthropic have bought some data sources. But I think going forward, you’re going to see less monetization and more just customization around what people need their models to do.

Chip Kahn: So let’s get into health care. In a sense you’ve talked about a ball rolling down the road that’s unstoppable. and health care you’ve pointed out, is exposed, but it’s always been sort of resistant to technology driven change. What’s going to be different this time?

Eric Larsen: Well, I’ll start by saying that I think U.S. health care has the greatest susceptibility to disruption from this technology more than any other industrial vertical. And I say that for four reasons. I mean, you know, first, like let me just give you a sentence on how my mental model for health care. Health care is a juggernaut, right? It’s $5.3 trillion, it’s 18.3% of the U.S. GDP. It’s the single most labor-intensive sector in the U.S. economy. And when I think about Gen AI, you know, when you strip away the dazzling generative elements, the text to photorealistic image or the text to cinematic quality video, or the two Nobel Prizes that it got awarded last December, one in chemistry, one in physics. In its first deployment. It’s about one thing, it’s about brute force, productivity augmentation and the systematic substitution of technology for labor. And so health care has the greatest exposure to this for four reasons, starting with the labor intensity. I mean U.S. health care employs 23.8 million Americans, right? One out of every six working age working adults is employed in health care. It’s the only industrial vertical to see negative productivity growth. I mean if you look at the employment data over the last year, U.S. health care added 750,000 jobs to the U.S. labor force. You take out health care, the entire economy lost 200,000 jobs. U.S. health care is atlasing the whole labor force and therefore the economy. And so when you think about the labor intensity, and if you believe as I do, that this is largely going to be augmentative of some things in the labor force, but substitutive for a lot of other things, I think it’s going to be predominantly substitutive in health care and we can decompose that a little bit. But the first observation is that U.S. health care’s labor addiction makes it very vulnerable to this tech. The second is its past impenetrability to every tech phase shift. You know, every other industry, vertical, manufacturing, industrials, hospitality, banking, has been enjoying the productivity augmentation, and the deflationary impact of technology. You know, every tech phase shift of the past generation, Internet, mobile, social cloud, big data and analytics, enterprise SaaS, blockchain, sort of ripped through every other market and you’ve seen corresponding productivity jumps, right? Not in health care. Health care has been largely impervious to a lot of these for good reasons and for bad. I mean part of it is just the sensitivity. Like we go too fast, it can endanger people’s lives. And so I say that with a loving but critical eye. I don’t think we’ve been a bastion of technological forward leaning adoption and therefore we’ve got a lot of accumulated tech debt and a lot of accumulated productivity debt. So if the technology is going to be augmentative of productivity in other sectors, I think it’ll be substituted in health care because there’s a lot to be automated. You know, the number of doctors in the country from 1970 to today increased by about 150%. The number of health care administrators increased by about 4300%. And I’m trying to say that with no value judgment, it’s a fact. But if there are certain tasks that are amenable to automation, I think you’re going to see them in health care, even in a more pronounced way from other industries.

The third reason that I think health care has a lot of vulnerability is the data. You know, there’s about 50 zettabytes of data in health care in the world. Much of this is unstructured. And prior to this technological phase shift with natural language processing and vector enablement and companies like Palantir that can,do their ontologies around the data. And when you ontologize the data, you really create a usable framework. Well, suddenly all that data that was just sitting dormant is at least theoretically useful. And a lot of it’s still unaccessed. So that’s actually an area that I’m very excited about. The last reason is the most philosophical and that is that, you know, biology is the highest dimensional space in our world. Right. I think we’re sort of hitting an asymptote, we’re sort of stagnating in biomedical advances and scientific advances. The reason I say that is, you know, in the 1950s it took 50 years for the sum total of human medical knowledge to double. In 2019 it was 73 days. And I, working with Claude, came up with the number that it’s about every 10 to 11 days that the raw quantity of data is doubling. And you know, we could quibble on the methodology there, but let’s just suffice to say that the quantity and complexity of the data have exceeded humans comprehension.And, what is our reaction to complexity in medicine and biology? To draw smaller and smaller circles around our specializations. So, we kind of look at biology, we look at the body through a straw, we almost draw circles around body parts or organs and hyper specialize and sub specialize again. And as a result, you know, one of the negative externalities of that is this hyper balkanized U.S. health care system, right, where medicine is divided and subdivided endlessly. And you’ve got, you know, medical care here, pharmacologic care there, behavioral care all over here, SDOH over there. And as a result we just have this hyper fragmentation. And I mentioned that this is a multiplication of intelligence. Well, one of the positive externalities of that is that we are building an intelligence that can now assimilate all of these unstructured, semi structured and structured data and see patterns, see causation and correlation, have this sort of encyclopedic knowledge of the 3.3 million annual peer reviewed biomedical and scientific studies that are published every year and begin to resynthesize health care. And in fact we can talk about what I think the implications are for doctors going forward. But you know, I think after a certain point we’re not going to have primary care doctors and medical oncologists and cardiologists and gastroenterologists and nephrologists. I think we’re just going to have a universal doctor. And you know, eventually as the intelligence keeps increasing, we’re going to go from just extracting insights from data and causation and correlation. I think we’re going to go toward hypothesizing new molecules, new biologics, new care treatments and protocols. Dario Amodei, who I have great respect for, he and I did a podcast talking about this, we talk about this essay he wrote called “Machines of Love and Grace.” And in it he sort of prognosticates that we’re going to see 10 to 20 years added to human longevity in the next couple decades. And we’re finally going to eradicate some of these diseases, these complex diseases. We’ve largely eradicated infectious diseases, but the complex diseases we’ve made precious little progress on, you know, especially in neurodegenerative diseases. And you know, we’ve made a lot of incremental progress in cancer. But even still, a lot of these six figure immunooncology drugs add 57 days to life. So, I think the returns to intelligence are incredibly high, in fact asymmetrically high in biology.And, so that’s a fourth reason why I think health care is going to  have a lot of impact.

Chip Kahn: Well, if you think about health care, and obviously from what you just described, in a sense health care is a data machine. And you’ve pointed out that the average hospital generates about 50 petabytes of data annually. Let’s just look at the hospital. I mean that’s on unused data right now. What difference is that going to make? I mean, how are those four walls going to be different not too long from now with the impact of this sort of unstoppable ball going down the road?

Eric Larsen: So, the way I would categorize the impact in health care, and this will apply to the hospitals and separately to the payers, and separately to the med tech providers and to the big biopharmaceutical companies. But I would categorize it in four domains. First is administrative simplification? So that’s the trillion dollars that we spend annually on this sort of friction filled adversarial system. and I think there’s going to be monumental impact on that very soon. We’re already starting to see it. Second is in

care augmentation. Which is this notion of a medical superintelligence. It’s this sort of synthetic intelligence that is superhuman from a differential diagnostic point of view, from a care treatment and protocol point of view. I think we’re already there. I think, you know, the regulation and the policy isn’t there, but the technology is really starting to push out on the frontier. Third is in computational and synthetic biology, which is using AI to engineer biomolecules and biologics and really shrink. You know, the 10 years and the $2.6 billion for a successful molecule to go from bench to preclinical, to clinical trial files, to FDA to commercialization. And do it in one-tenth the time at one third the cost. And then there’s the consumer empowerment. I mean, ChatGPT has 930 million weekly active users. There are about 40 million users a day for health care. And so, you know, and some of that is fantastic and some of that is very troubling.Now let’s talk about hospitals for a second.Hospitals are the biggest single industrial vertical in the U.S. economy, $1.6 trillion in revenue. And it’s one of the most consolidated sectors. The top 10 health systems in the country represent $350 billion in revenue. The top 100 health systems represent $935 billion in revenue. It’s not an exaggeration to say that the U.S. health system sector is controlled by 84 men and 16 women. Those are the CEOs of the top 100 health systems. And if you think about the employment intensity for hospitals, hospitals employ 7.2 million Americans. And arguably it’s not just that intensity of employment, but there’s the multiplier effect. For every health care job, for every hospital job, the economy produces another 2.79 jobs. It’s almost a 3x step up because of all of the services around the hospital and the environmental services and the food services and the medical equipment, et cetera. And then because these are really well-paying middle-class jobs, you know, that actually empowers consumers to pay their mortgage and go to the restaurants and go to the movies. So, the implications of that are quite huge. But the average not for profit health system spends 57 cents of every dollar on salary, wages and benefits. Hospitals are the single biggest employer of doctors in the country. You know, there are 950,000 doctors in the country. 54% are employed by hospitals. So, when we talk about the 50 petabytes of data for every individual hospital, you know, there’s not a one-to-one correlation between what the data is going to do and what the impact is going to be. I think it’s a more nuanced sort of algorithm. You got a nuanced equation to think through. But, I think this is going to revolutionize hospitals, right? I think you’re going to see a lot of deinstitutionalization in the sense that this technology is permitting a lot of migration from inpatient to outpatient, outpatient to, retail, from retail to home, from home to virtual. I think you’re going to see a real resurgence of care in the home. I think you’re going to see massive advances in procedural capabilities, in diagnostic capabilities, in things that are done outside the hospital. Going back to the general purpose technology conversation we had, I think this is going to have massive ramifications for hospitals, but it’s going to begin with labor. I’ve been telling a lot of health system CEOs, embrace attrition. You will need fewer staff going forward. The average hospital attrits about 20% of its workforce every year. Most of that is voluntary. And what I’ve been telling health system CEOs is moratorium on all new hires, close out all open recs, embrace attrition, and begin to diffuse the models. We should talk about, you know, what to do.

Chip Kahn: That’s what I want to turn to next is there’s this great disruption that you’re describing that’s coming or has begun to seep in. But then there’s leadership. And you’ve talked about it being top down because the labor force there, can’t exercise this. Somebody’s got to implement it from above. What’s that all about? And I’m going to let you mention the specific number; you started into the number of people running hospitals. Who are the people that are going make the difference? And really, are they ready?

Eric Larsen: My mental model for health care and Chip, you’ve heard me say this and I’ve said it publicly. I love U.S. health care. I mean, for me, this is a consecration, right? What a gift. To be able to contribute in some small way to improving health care and people’s lives and vibrancy and health. You know, but I also think health care is unconscionably expensive. And I think we’re going to see with the diffusion of AI, I personally believe we’re going to see 5 to 700 basis points of the U.S. GDP allocated to health care decline. I think we’re going to go from 18, 19% down to 12 to 13% in the coming years. And I think predominantly it’s going to come through labor contraction. You know, it’s a $5.3 trillion sector, but $2.9 trillion of it is in labor. And one of the through lines in our discussion is I believe this is largely going to be substitutive for human labor. And by the way, you know, here we are in Washington D.C., our beloved hometown, and there’s this like Orwellian conspiracy of silence, like thou shalt not talk about job dislocation and AI and people I otherwise respect, like David Sacks, who’s the AI czar, who I have enormous regard for, you know, is sort of propagating this narrative that no, no, no, this is not going to be job displacing, you know, Jensen Huang, who I also have enormous respect for saying no, that’s actually like fear mongering or doomerism. I don’t think so. I think if you sort of decompose the tasks that are done in knowledge work, you know, OpenAI did a very, I thought, very methodologically rigorous study called GDP eval. And you know, they took the onetime, which is the database of the Bureau of Labor Statistics which you know, looks at occupations and decomposes them into tasks and workflows. This was last year; we talked about the exponentiality in the models. They just keep getting incredibly faster and better and smarter. You know, across nine professional verticals, nine industrial verticals, 44 occupations, 1,320 tasks. The number of knowledge worker workflows that could be done to equivalency or superiority by the models as judged by domain experts in each of those professions with an average of 14 years experience was about half, half. Now what I’m trying to do in working with one of the frontier labs is build a GDP eval for health care to actually decompose to the 23.8 million jobs in the BLS data and begin to do a GDP eval for health care to really understand how much is automatable, augmentable or eliminable, with the current and emerging capabilities in the tech. But just to land the plane, I think the industry is very oligopolistic. There are there are 150 CEOs in U.S. health care that guide this industry and they are incredibly altruistic. A lot of them are heart-centered kind of servant leaders. But this is a very personality dominated industry. And these are the top 100 health systems I mentioned. These are the seven publicly traded managed care company CEOs. These are the 33 Blue Cross Blue Shield CEOs that collectively underwrite 119 million Americans. It’s the 10 BioPharmace that have a 5 trillion market cap. Maybe I’ll include Judy Faulkner in there, Dr. Oz, Chris Klump, Abe Sutton, really and the folks at CMS and CMMI and HHS I have enormous regard for. I think they’re a real bunch of stars from this administration. And what I would say is that this is not going to be democratized because of the labor, the potential labor dislocation. I think this is going to have to come from the top down. You know, in a sense this isn’t just about job elimination. I want to be really clear on that. Because there’s something called the Jevons paradox, which is as a commodity becomes cheaper, you actually use more of it. Health care is quintessentially that. As health care becomes deflation, as we see a deflationary impact on health care, we’re gonna use more of it, right? It takes me 60 days to get into my primary care doctor. If I could talk to my primary care doctor once every month, I would be delighted to do so. And so, I think the challenge, Chip, is that we have this sort of institution-based health care system that is built for permanence, that is built for durability. I mean, you see all the churn in big tech and manufacturing and other industries where there isn’t a lot of permanence or durability. There’s a constant sort of creative destruction and new companies emerge and entrepreneurs dislocate slow moving incumbents. Incumbents versus insurgents. Not in health care. You know, Cleveland clinic dates from 1922. You know, Mayo Clinic dates even longer. Our most prestigious organizations have been here for a century, right? And here’s a technology that is moving faster than any other in history, colliding into these sort of like, you and I were joking before, you know, Hippocrates versus Mark Zuckerberg, right? 2,400 years. Separate them first, do no harm versus move fast and break things. Two totally orthogonal cultures. And so, you know, this sort of bleeds into what to do about this, right? But first, I think it’s about if I’m talking to CEOs and I do spend a lot of time talking to the 150 and really a lot of what I’ve been trying to do is bring I call them the AI10 and the Healthcare150 into collision together. And so, you know, health care I think has really kind of abdicated its co-development opportunity and I might even say responsibility. With every tech phase shift that we talked about, this tech is too important to sort of deputize 20-something techno solutionists in San Francisco to design health care for the United States and therefore the world. And I live in San Francisco as well as Washington D.C. and so I kind of, I almost feel like I live in two different planets, you know, traveling between the two because in San Francisco, it’s all about this techno solutionism. Technology is going to solve everything. And technology is incredibly powerful, and especially this particular one. But there’s so much accumulated wisdom and, and, you know, experience and perspective that’s embodied in the 150. And so, first of all, it’s really about colliding the two together. Second, all, it’s about, do the CEOs understand the tech and the way to best understand it is to use it. Are they using the tech every day to skills max, as Sam Altman would call it? How do you have it teach you? Now that we’re moving from generative to agentic, meaning the tools can do things for you and actually do things autonomously, do things without supervision, which is kind of terrifying in a way.

I always ask the CEOs, how much are you using this and in what ways, and how vulnerable are you being with your teams about your use cases? I’ve been telling CEOs to find their inner autocrat. It’s an intentionally disagreeable word because, you know, we have sort of a revulsion to that, to that word. But it so happens that in the installation phase of a technology, autocracy is better than messy democracy, than pluralistic Western liberal democracy, that is let a thousand flowers bloom. When we’re talking about the diffusion of a technology that has positive and negative externalities like this one, this isn’t something that you’re just going to say, hey, you know, use it if you want, or don’t if you don’t want to. You’re starting to see this outside of health care, where CEOs are becoming increasingly autocratic about this. You know, Jack Dorsey, who’s the CEO and founder of Block, just announced he’s removing 40% of the workforce because they can now augment the productivity for the remaining 60%. The CEO of Accenture just mandated that promotional qualifications are going to go to how much are you using the tool? Jensen Huang this week at GTC, which is their big, Woodstock, said, if I’m hiring an engineer, and I’ve got 43,000 employees in Nvidia, again, the most valuable company in the history of capitalism at almost a 5 trillion market capitalization. I got 43,000 employees, 38,000 are engineers. And if I’m paying $500,000 for an engineer this year, and at the end of the year, he or she comes to me and says, hey, I used $5,000 of tokens this year, Jensen said, I’m going to be furious. Tokens is the output, right? It’s a proxy for how much they’re using the tech. He’s like, for every $500,000 I’m paying for an engineer, I expect them to use $250,000 of tokens every year.

Chip Kahn: But look at a hospital, or health care in general. But in a hospital more than two thirds of the cost are workforce. Our labor, yes. So, you’re telling me that’s going to implode if they do what they ought to do?

Eric Larsen: The honest answer is none of us knows how this is going to play out. I’ll tell you, my intuition is that a lot of the administrative rules will be automated. You can do much more with less. There’s a jagged frontier of how this is going to play out. We at Towerbrook own the largest, revenue cycle management company, R1. We did a large privatization last year. A $9 billion take. Private and revenue cycle management is one of the areas where you’re going to see the technology be massively augmentative. Why? Because it shares those characteristics of functional verifiability. I mean coding is quintessentially a functionally verifiable area. The claim is either correct or not. And so our CEO, Joe Flanagan is one of the most Progressive technology minded CEOs in the industry in really in health care. And we’re starting to see massive augmentation in productivity. And to predict when a claim is going to be perfect and how do you preemptively modify it, that’s an area where you’re going to see massive improvements. The next domain is going to be areas where you’ve got codifiability or rules based, so legal, HR, et cetera. I mean the next domain is going to be anything that is decontextualized. A lot of the outsourced services, a lot of things that we’re giving overseas, those are automatable. You know, for health care, if we didn’t have so much unmet demand,. There is a fundamental supply and demand imbalance. We have 1.8 million unfilled jobs. So, I’m not suggesting we’re going to see unemployment lines in health care. What I am suggesting is that for the first time we’re going to begin to see a better equilibrium between supply and demand. And I do think you’re going to see a real stratification in performance among hospitals, those that deploy the technology and lower their SWB percentage. Right. So the not-for-profit average is 57 cents of every dollar. What is HCA’s average on salary, wage? The benefits? About 41 cents. So even pre gen-AI there was almost that 17 point differential. Right. There are spans of control possibilities. There are more horizontal, flatline, you know, managerial structures where you’ll have a lot more individual contributors and fewer middle managers. Like the current organizational structure, the hierarchical structure for our economy, including health care, dates from 1855, which was the Pennsylvania Railroad. That’s where the boxes on the org chart originated. Suddenly you’ve got the most powerful technology that we’ve ever seen. Is it really realistic to expect that our conventional traditional org structures are going to survive this? And I think about it as incumbents versus insurgents. We’re seeing this in every single industry, including health care, where this new generation of startups that are tech native, that are agentic and AI native, I almost think about it Chip, as inverted vanities. There’s a little bit of an empire building ethos in health care. I can always tell the temperament of the CEO is if you go to the landing page. How big is the picture? What pronouns does he or she use? Is it I, is it we? And how many syllables does it take to get to? How many tens of billions are in your enterprise and how many tens or hundreds of thousands of employees are there? And that’s the empire psychology. There’s an inverted psychology, an inverted vanity emerging in Silicon Valley as tiny teams or revenue per employee. So for Microsoft, I mean, is it, I actually don’t remember offhand what it is, but is it several hundred thousand dollars per employee of revenue? Same for Meta, same for Google, same for Nvidia. You know, by my count, there are about 20 startups in Silicon Valley that have fewer than 50 employees, but more than 250 million in ARR. Annual recurring revenue. And so the idea is like, can an incumbent reshape its workforce in light of the new technology faster than an insurgent can provide material value or maybe even displace some of the incumbents?

Chip Kahn: But there you’re building from the ground up. And here we’re talking about institutions. But I want to go back a little bit to the Zuckerberg quote about we’ll worry about it later, we’ll break it now, and the ethos of the Hippocratic oath that in health care we do no harm, and at the end of the day we’re talking about a touching, caring industry that all of us depend on, for our health.

Eric Larsen: Yes.

Chip Kahn: What’s the tension there with this juggernaut you’re describing. I, mean, it’s one thing on the business side, and, and we’re the business of health, so it’s all business. But at the end of the day, there’s the caring side that I argue is tied to the business side. But how is that going to be impacted in terms of the touching that’s so important in health care?

Eric Larsen: I mean, look, I’ve got a lot of cognitive dissonance on this question because on the one hand, I have reverence for the caregivers and the industry that is U.S. health care. And again, I mentioned it’s a consecration, not a vocation. I feel incredibly privileged. But on the other hand, you have to look at it with a critical eye just to be a little bit provocative. U.S. health care is amazing if you’re rich, white and urban. I remember being in the office of the CEO of Johns Hopkins a few years ago and he pointed outside his office and he said, you see that block, Eric? There is almost a 20-year lifespan difference on each side of the street. And you know, I think you can look at this from a multitude of perspectives. You can look at it as this industry is working fabulously and let’s proceed very cautiously, or you can look at it as there is a continent of things that we can do better and we ought to be optimistic and see how we can deploy this responsibly. And I think it’s somewhere in the middle where, if you believe, as Dario does, and I subscribe to his view, that we’re going to add decades to human longevity, that we’re going to have this synthetic superintelligence that’s divining and designing drugs that are going to eradicate some of the previously non eradicable diseases. And I believe, like, you know, let’s talk about behavioral for a second and the laying hands on patients. I think that it is actually going to be perceived as malpractice. What is malpractice? Malpractice is a deviation from an accepted upon standard. I think the accepted upon standard is going to be radically revised upward going forward. I mean, right now, 30% of clinical care variation is sort of the presiding state of affairs. And I’m not talking from state to state or from a system to system.

Chip Kahn: No, you’re talking about within systems.

Eric Larsen: Right. Like we sort of enshrined individual physician judgment. And again, I have enormous reverence for clinicians, but they’ve bristled against cookbook medicine and being prescriptive. And part of that is because we’ve never had an intelligence that could agglomerate all of the data and begin to say definitively this is the best practice. Deviating from this is malpractice. And so I actually think the counterfactual is going to be outside of this country. We are going to see the deployment of clinical AI happen in the GCC in the Middle East and in China in the CCP. We’re already seeing this in clinical trials as an example. I think it is going to be irresponsible as the tech evolves at the velocity with which it’s evolving that we don’t deploy. And by the way, right now you’ve got this sort of regulatory enclosure around health care and incumbents have a sort of determinative influence on how this plays out. Eventually the technology is going to break out of the box and other countries are going to deploy it and we may be reduced to reverse importation.

Chip Kahn: So we’re moving, moving towards our conclusion here. You talk about incumbents. I mean whether it’s IBM or Google or Amazon, over time, they looked at health care and said it’s exposed, we can do something about this, we have technologies to bring to bear. And how many times did Google change their health care staff? IBM just fell flat on its face. You know, Amazon, maybe the jury’s still out. Who knows if we’re going to get outside the box, which is where you were headed, I think. What’s going to happen to the incumbents? And maybe I’ll combine this with my conclusion, which is what your advice is to leaders. How do you position yourself if you’re one of those 150? I mean you describe them as needing to be tech savvy and use tech so they sort of understand it. But if they’re sitting back in the C suite, they gotta make some big decisions. What’s the direction?

Eric Larsen: Yeah, and I would say, look, I have a lot of disillusionment around big tech and health care. There have been so many false starts and so much like hyperbole about what they can and will do. And I have a lot of respect for Neil Lindsey and the team at Amazon and I think they’ve done some interesting things. But, you know, health care goes through the 150 and unless you are directly relevant, if not indispensable to the 150, you are extraneous, you are an outsider looking in you. There’s not a single example in tech history of an insurgent becoming an incumbent in health care. With the maybe exception of Epic. And that’s a, you know, that’s a 50…

Chip Kahn: Well, government came in and said, we’re going to subsidize electronic health records to an incredible extent. So, they made the market.

Eric Larsen: They were tailwinded from that. But unlike other industries where you’ve got creative destruction, right? I mean, Google originated out of the dot com burst, right? Meta originated after the dot com burst. Microsoft is one of those rare companies that’s sort of been resilient and has survived multiple tech phase shifts. Nvidia is another. I think they’re one of the. Jensen is the longest, longest serving CEO on the Fortune 500, if I’m not mistaken here. But the point is I’m not predicting that big tech is going to revolutionize health care. I think Google is going to be a fascinating player in this domain because they’ve got such an array of capabilities and they’re so multifactorial in terms of what they can do. And I also think Demis Hassabis, who’s one of the greatest, I think people in the world right now, won the Nobel Prize for chemistry, for AlphaFold. You know, wearing my venture capital hat at Thrive, we led the round into Isomorphic Labs, which is one of the most exciting companies in the world. I think Google can aspire to be very relevant in health care, but I think the frontier labs like Anthropic and OpenAI are really vying for primacy. I think ChatGPT has done some very interesting things in the consumer side. I think Dario, who’s a computational neurobiologist, has a real authentic commitment to health care. And I think Anthropic is doing some really remarkable things in partnering with enterprises. And I think Dario recognizes that the path to relevancy in health care goes through the 150. So how do you enfranchise the 150 and show that you can amplify their staff productivity and really augment them? From a clinical superintelligence point of view, we’re going into this new generative epistemology world where the scientific method of sort of hypothesizing and testing and looking for falsifiability is irrelevant. We can now just simulate billions, if not theoretically trillions of scenarios to find the right molecule or the right biologic. What Demis did with AlphaFold, which is mapping the 200 million proteins that undergird human life and how the amino, acids fold to create these proteins, I mean, it takes a single PhD five to seven years to map a single protein. And Demis did it with Alphafold and created the equivalent of saving a billion PhD years. And then he open sourced it to the world. There have been 2.4 million scientists from 200 countries around the world that have accessed this. I think AlphaFold is the greatest scientific achievement of the last 50 years. I’m unconvinced that big tech is going to be the  revolutionary here. They may be and we could talk about that, but the most important thing that we could end on is what to do. I think for the 150 it really is about becoming facile with the tech. I think it’s about sensitizing the org to how do you use this technology to augment your productivity? Now you could accuse me of a contradiction here because why would the staff augment their productivity with the tools if it’s going to lead to their obsolescence? I’m not convinced that it will. But the way I think about it is a numerator and a denominator question. So, a CEO has a choice. The denominator is our market served and the population density that we serve and inpatient versus outpatient versus home care, et cetera. It’s the entire market served, the addressable market. The numerator is the cost to serve and it’s predominantly staffing and cost of goods, etc. You can either keep your numerator the same, keep your staff the same, keep your inputs the same, but augment your denominator, go into new markets, get greater concentration of market share, or you can keep your denominator the same, serve the same market, serve the same population, but with fewer people. I think you’re going to see a real stratification of performance and I’m predicting toward the end of 26, the biggest market share shift in a generation of non contiguous health system mergers. Ones that are proficient in AI and thereby lowering their operating costs and augmenting their operating margin and going into new markets. We haven’t even talked about payers, but I think payers are entering an existential threat phase and this technology is going to rip through that industry and you’re going to see a major stratification of performance. Among the blues, among the seven publicly traded managed care companies, among some of the provider-sponsored health plans, and those that automate a lot of the SG&As, those that automate a lot of the adversarial payer provider dynamics that right now cost our industry about $600 billion per year. I think you’re going to see a real shift there. I mean the seven publicly traded managed care companies have lost over a half trillion dollars in market over the last half year. Two out of three blues plans lost money last year and you’re starting to see a real stratification among the blues. There are a couple CEOs that are just fantastic. Like Brian Pieninck down at Guidewell is very progressive and really thinking about how do I serve my enrollment base in a totally differentiated way. Kim Keck, who leads the [Blue Cross Blue Shield] Association, is incredibly thoughtful about these things. So, I have a lot of admiration for individuals among the 150 that are being very thoughtful and compassionate about this. Again, I don’t think this is about automatically reducing staff, although I think the ones that are efficient are going to grow and the ones that are not embracing this are going to contract. And I do believe there will be staff contraction there. The net winner chip is going to be the consumer. Right. I think this is going to be deflationary. There’s about $250 billion in medical debt right now. The number one cause of bankruptcy in this country is medical debt. And I think you’re going to see a real democratization of this. But for the 150 that have some agency, that have some self-determination in this moment, get really smart in the tech, begin to prepare the organizations for this, begin to think laterally about, gosh, I’ve been leading an organization in a stewardship sense, but I really need to be leading this from almost a disruptive, founder sense. You know, there’s a phrase in Silicon Valley called founder mode. Zuckerberg gets to do things that, you know, he’s got super voting shares but, but he’s a founder, right? Jensen is a founder. Elon is the quintessential founder. And they get to do things that they’ve earned the right to do.

What is the founder mode equivalent among the 150? And then how do we begin to recognize that our patients are moving much faster than the doctors? 40 million people every day are accessing ChatGPT for health. You’re going to see a stratification among the 950,000 doctors. You know, if the typical primary care panel is 1 to 20, 200, I think you’re going to see 1 to 20,000 panels. I think you’re going to see agentic doctors that are an amplification of the human doctor. You know, we didn’t talk about behavioral, but the number one use case for ChatGPT 1/10 of humanity accesses ChatGPT every week. The number one-use case is therapy and companionship. People tell the truth to a chatbot, but they’ll lie to a human. They’ll lie to their doctor to avoid stigmatization or embarrassment or judgment. You know, think about the power of that statement, of that truth. If for every medical case with a psychiatric or a behavioral comorbidity, your costs go up by 2 to 6x. Right. If you had a chatbot that is personalized to me, that knows my dietary habits, knows my exercise habits, has a theory of mind on what I’m thinking because it interacts with me constantly, couldn’t it nudge me to be adherent to my drug regiment? Couldn’t it nudge me to make a better behavioral choice or, a better nutritional choice? The answer is absolutely. And I’m incredibly optimistic about these things. I’m doubtful that our regulatory regime has the sort of elasticity to move with the tech. I mean, Pritzker, the governor of Illinois, to me, indefensibly signed a law that prohibits the use of the chatbots for therapeutic purposes. That is preposterous. And when he signed the law, he said, yeah, this is also defending jobs. And you’re, you know, one out of every three people in an industrialized country is lonely.

Chip Kahn: And we know what defending jobs does to an economy. And it doesn’t, at the end of the day, help the people you’ve tried to defend.

Eric Larsen: Not at all.

Chip Kahn: This has really been a wonderful conversation, and it’s done a lot for our podcast because we’re trying here to set a foundation for the next 19 or so episodes where we’re going to drill down on, institutions and apps and applications. And this was just a great start. And I just want to thank you. And ending as we did on the personal, in terms of how this is going to affect individuals is so important, because at the end of the day, health is all about caring. And I would argue the business of health we’re going to be looking at is really about making sure there’s caring for all Americans.

Eric Larsen: Absolutely. And I’ll close Chip, first off, with gratitude to you. I mean, I just

have such regard for what you’ve done for the industry and now continuing to do. But I’m a constitutional optimist. Right. I’m so excited about this moment. I try to be sober and look at the downsides. I try to ask some of the uncomfortable questions of myself and of our industry, you know, and there’s no hubris here. It’s quite the opposite. There’s a ton of, I feel humility to try to understand this, but I also, in the final analysis feel incredible urgency. You know, this technology is going whether we like it or not. I think with each turn of the crank in the technology, from reasoning to memory, to tool control to agentic capabilities, like we lose a little self-determination, we lose a little agency, we lose a little of our jurisdiction. And my clarion call to the 150 is, you cannot wait, you don’t have time if you want to co-create, let alone co-evolve with this. I believe there is nothing more important in the world right now than understanding this technology and trying to help shape it. And we have a very time delimited opportunity to do that now. The companies we own, where we can be autocratic, right. We are diffusing this with great speed and intentionality. And we’re going from this age of discovery to an age of implementation. And we’re going to continue with the discovery unabated. But now it’s about diffusion. And I think at the end, it’ll be improving quality, it’ll be augmenting lifespan, it’ll be massively deflationary and it’ll be very democratizing. And I think in the final analysis, it will actually help the ones that have been traditionally marginalized or disadvantaged, the socioeconomic and the demographic and the ethnic communities that aren’t part of that privileged white, urban and male class. And I feel like that is our obligation to figure this out and see if we can diffuse that not just to the United States, but then eventually to the globe.

Chip Kahn: Thank you, Eric.

Eric Larsen: Thank you, Chip.