AI transcript
0:00:07 Health care is like a fifth of the American economy in growing, but if this process continues it will eventually be half the economy and then the entire economy.
0:00:15 We have some of the best doctors in the world. We have the best technology in the world. We pay the most and we have the worst outcomes. How is that possible?
0:00:20 Ultimately, is it a policy regulation issue or is it a technology issue?
0:00:23 It may be the cure and cancer as a lot easier to pack in the suitcase.
0:00:28 I think what we’re really providing patients is agency and I think that’s what was kind of missing.
0:00:42 The health care industry has captured the attention of America and it’s no wonder the confusing and complex glosses makes up about 20% of GDP and growing, clocking in at an over $4 trillion industry.
0:00:53 People seem to agree on problems, but rarely the solutions. But as we kick off 2025, it’s hard not to wonder whether the latest platform shift may offer some answers.
0:01:08 I suspect that this is the dynamic that’s going to play out in health care. The more abstract, intellectual, knowledge-driven, data-driven the task, the easier it is to get the AI to do it, the more applied, the physical, messy, unpredictable sort of all the human elements are probably the hardest thing.
0:01:19 So who will finally crack this code? And what’s really driving up prices? Could competition flip that dynamic on its head? And what will finally catalyze change? Plus, is the technology even there yet?
0:01:24 What do you each perceive as still being too hard to apply AI to?
0:01:36 Joining us to discuss and answer all those questions today are A16Z General Partners Mark Andreessen, Vijay Pande and Julie Yu. Let’s get started.
0:01:50 As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.
0:01:56 Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.
0:02:02 For more details, including a link to our investments, please see a16z.com/disposures.
0:02:17 Let’s actually start with our quick takes on whether we believe it will be a healthcare-native company that finally cracks the nut on how technology will transform our industry, especially AI,
0:02:25 or do we think it will be a foreign body, an outsider, a non-healthcare company that will actually end up making the biggest play in our space?
0:02:34 I think it’s going to be a startup. I think it’s going to be really hard for an incumbent outside of healthcare to come into healthcare. It’s going to be really hard for healthcare to move into AI.
0:02:42 So a startup is a natural thing, and especially how that company is going to be built is that they will have to be AI-native and healthcare-native from the beginning.
0:02:48 And that’s something that’s really hard for incumbents. It’s just a straight down the middle typical disruption story.
0:02:57 Yeah, so I think the counter argument is to start by saying I’m usually a VJ’s side on these topics, and obviously we’re a venture capital firm, and so our day job is to try to fund exactly the kind of company that VJ is talking about.
0:03:07 So the easy thing to do is to just agree. But just for the purpose of conversation, here would be the argument on the other side, which is new technology entering healthcare up until now has been tools, right?
0:03:15 You go into an existing organization, an existing healthcare company of whatever kind, and you have a new kind of tool, and you basically try to get the existing organization to adopt the tool.
0:03:21 And of course, sometimes that happens, and hospitals run on hospital management systems today, and they didn’t used to and so forth, but that’s a big lift.
0:03:24 And that’s the, I think, inertia of VJ that you’re talking about, right?
0:03:27 It’s the mapping of the new technology into an existing organization. There’s the bottleneck.
0:03:35 The winning AI products might not come to market in that way. They might not be tools. They might be more almost like the equivalent of hiring workers, full stack.
0:03:42 And so I guess the question would be if the products are being delivered in a way where they almost slide into the organization as almost like the equivalent of a new employee,
0:03:47 or maybe even something bigger than a single employee, maybe as a new business unit, right, as a new organization, as a totality.
0:03:52 Does that change the dynamics of adoption such that big companies have a chance to think about this differently?
0:04:00 Yeah, or even as standalone businesses, by the way, that are powered by AI, that from the outside look like a regular provider group or a medical group that can contract with insurance companies
0:04:05 the same way that any other traditional medical group can contract with them and therefore get paid for what they’re doing.
0:04:13 But their internal economics and efficiency and their ability to scale nationwide is 10x what any traditional provider group might be.
0:04:22 So I think I would go all the way to say there might be standalone companies that are wearing sheep’s clothing in terms of the way that they’re packaged and the way they’re distributed in the market,
0:04:26 but internally look completely different in terms of the DNA of how they operate.
0:04:34 Let’s take an extreme argument where open AI creates AGI. Let’s just say it’s all the way to AGI. You’ve got the genius human being.
0:04:41 What does that know about healthcare? And I think a lot of what we have to do is we have to give the AI the equivalent of that work experience.
0:04:46 Is that something that an existing AI company will have? That’s a whole bunch of data that’s really hard to get.
0:04:55 It’s possible that an existing healthcare company will have that, but I think if you have to be really careful how you train that and whether they’ll have the AI experience.
0:05:00 So I think even still, not surprisingly, my bet’s on the startup, but I can imagine that is the way it’s going to happen.
0:05:06 But you’re making the point also that the startup has to be both healthcare native and AI native, not one or the other.
0:05:16 The other interesting thing about the way that Mark articulated this is this notion that one of the reasons I would argue that technology adoption in healthcare has been so slow in the past
0:05:28 is that the allocation of revenue within healthcare enterprises that goes towards technology, or IT as they would call it, has been in order of magnitude smaller in healthcare than other peer industries.
0:05:36 And what’s super interesting right now about these sort of like labor units that are how AI is packaged is that I’ve actually heard organizations now talk about,
0:05:47 oh, can we actually just tap into our labor budget? We have literally like a thousand open racks for nurses that we cannot fill through the existing human labor pool that is out there today.
0:05:56 Why not just allocate that one FTE’s worth of budget towards hiring the equivalent of like a hundred nurses using these AI tools that can do subclinical things,
0:06:03 but still things that are immensely important and critical to the workflows of these organizations based on what you guys are both observing across broader problem spaces,
0:06:08 whether it be creativity, education, law, software development, all the things that you mentioned earlier.
0:06:15 What do you each perceive as still being too hard to apply AI to within the healthcare domain?
0:06:19 I think we’re going to see the specialties probably be the last thing.
0:06:23 I think maybe the first thing would be the subclinical nursing and primary care.
0:06:28 Then inching into clinical primary care, I think it would not be that crazy to imagine.
0:06:34 Essentially, AI could be a great router, and especially think about like what a primary care physician has to do.
0:06:38 They have to ingest all this data and make a diagnosis and then send to a specialist.
0:06:41 That’s increasingly literally becoming a data science problem.
0:06:43 So I think that’s where we’re next.
0:06:49 The hardest thing is going to be like you take the extreme case of let’s say brain surgery is the canonical hard thing to do.
0:06:55 That’s something that sounds like it could be really hard for AI, but actually as surgeons use more robotic devices,
0:07:00 as other specialists use more digital things, you can imagine a way for it to creep in,
0:07:02 but that’s probably the furthest out.
0:07:07 Mark, a twist on that question for you is as you think about any industry where there’s like a frontier,
0:07:11 some boundary of what technology can do and not do, especially when it comes to AI,
0:07:18 do you think it’s mainly a question of data or are there other factors that you think we need to consider when it comes to how this might work in healthcare?
0:07:22 Yeah, so there’s this famous phenomenon in AI research called Moravex Paradox,
0:07:27 and it’s named after this AI researcher, Hans Moravec, and Moravec wrote in 1988,
0:07:32 “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers,
0:07:37 and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”
0:07:39 The modern version of that is very striking, which is sitting here today.
0:07:43 We have chat GPT and its analogs, and they will paint art for you.
0:07:45 They will compose music for you.
0:07:47 They will debate abstract philosophy for you.
0:07:51 They will explain quantum physics to you, but they can’t pack your suitcase.
0:07:54 They can’t clean your toilet. They can’t cook you lunch.
0:07:57 They can’t do anything that a normal person operating in the world can do.
0:08:00 There’s actually a real evolutionary theory underneath Moravex Paradox,
0:08:05 which is the parts of us that are physically embodied, the parts of us that basically involve functioning in the world,
0:08:08 and then the sort of sensory skills and motor skills required to navigate in the world,
0:08:10 and then the ability to process unusual situations.
0:08:14 It’s like the classic, you see the demos of the robots, humanoid robots making coffee,
0:08:17 and it’s like it works right up to the point, the coffee pod, you know, the pod gets jammed in the coffee maker,
0:08:19 and the robot doesn’t know what to do.
0:08:23 All of the sort of motor sensory skills, all that stuff took many millions of years to evolve,
0:08:27 billions of years to evolve, and then at the peak of human cognition, the cortex,
0:08:30 the sort of system two reasoning was a relatively recent evolution,
0:08:33 and maybe let’s even say it’s 500,000 years versus 4 billion years,
0:08:39 and this is the exact reverse of what is intuitively you would feel like packing the suitcase is a lot easier than curing cancer.
0:08:43 It may be the curing cancer is a lot easier than packing the suitcase, like for this reason, right?
0:08:46 I suspect that this is the dynamic that’s going to play out in healthcare.
0:08:50 It’s going to be the more abstract, intellectual, knowledge driven, data driven, the task,
0:08:56 the easier it is to get the AI to do it, the more applied, the physical, messy, unpredictable,
0:08:58 the sort of all the human elements are probably the hardest thing.
0:09:01 I think a tweak on that is that AI loves the digital world.
0:09:05 It does really well, and if AI can’t be in that world, it has nothing to learn from.
0:09:12 And so to the degree there’s telemedicine and things that are virtual, whether it be a phone call or zoom,
0:09:15 that’s something that actually is a world that AI could play in right now.
0:09:19 And I think it’s not a shock that we’re seeing that as a natural first application.
0:09:24 What is interesting is as medicine itself becomes more digitized through robotics,
0:09:28 in other words, driven by people, then AI could be in that world too.
0:09:34 So it might be that there’s a parallel path of building up the digital side of medicine that AI could be in.
0:09:39 But yeah, I mean, I think it can do quantum mechanics, but it can’t unpack my dishwasher.
0:09:43 It makes sense because it’s trained all those quantum gangs has never seen a dishwasher, right?
0:09:46 The Tesla self-driving car might be the reason for optimism here, right?
0:09:49 And Tesla learned this the hard way because they tried different approaches to AI,
0:09:52 and it turned out the thing that worked was basically put a neural network in the wild,
0:09:55 embodying the form of a car, and in fact embodying the form of a million cars,
0:09:58 and then let those guys drive around and collect the data and learn about reality.
0:10:01 The more you take the rules out of the system and the more you just expose it to the real world
0:10:04 and let it gather the data and let it build up the neural network, the better it gets at self-driving.
0:10:09 That’s another form of leapfrog opportunity in the sense that the primary reason why hospitals exist
0:10:14 are because the level of complexity of the equipment and the workflows
0:10:17 and all the operations that need to exist have to be centralized.
0:10:20 It’s nearly impossible today to unbundle the entirety of that
0:10:23 and make it available to people in their home environments or in their communities.
0:10:28 But you can imagine perhaps that’s another lever here where if we are able to atomize
0:10:31 the physical components of the hospital-based experience
0:10:34 that you could actually centralize all of care delivery
0:10:38 and make it available in different sites that are much cheaper with a lower cost structure.
0:10:41 Yeah, so there’s two related concepts that are very important.
0:10:43 So one is economic growth, as you said,
0:10:46 and you generally measure economic growth through things like GDP, right?
0:10:48 Or, you know, in a company, it’s amount of revenue.
0:10:50 But there’s this other very important concept, right, which is productivity growth.
0:10:55 And productivity growth is basically the economic question of basically, can you do more with less?
0:10:58 And so if you have an economy that has high productivity growth,
0:11:03 basically what you see over time is that economy is able to produce more output with lower inputs.
0:11:06 And by the way, the sort of story of the growth of Western civilization overall
0:11:10 and economic growth globally over the last 500 years has been a story primarily of productivity growth.
0:11:13 Basically, the Industrial Revolution, figuring out ways to apply machines
0:11:17 and then new management methods around the machines to be able to do more with less.
0:11:21 And therefore, the world we live in, which in the old days, 99% of people worked in agriculture
0:11:24 and yet people were still starving because there wasn’t enough food.
0:11:26 Whereas today, 3% of people work in agriculture
0:11:29 and we’re producing so much food that everybody’s getting obese, right?
0:11:31 And that was the result of productivity growth, right?
0:11:35 And so then you go into kind of the microeconomics of the healthcare industry
0:11:38 and basically what you see is either flat or negative productivity growth.
0:11:42 By the way, the industries that are like this are the healthcare industry, education, housing,
0:11:44 and then let’s say law and government.
0:11:47 Those four basically, if you measure them, they have flat or negative productivity growth.
0:11:51 And so basically they’re getting less efficient over time, not more efficient over time.
0:11:53 And you see that directly, as you said, you see it in the graphs
0:11:56 for all four of those industries where you’re spending more and more money over time
0:11:58 and you’re getting less and less output, less and less results,
0:12:01 which is precisely the opposite of what you want.
0:12:03 By the way, the economic growth that follows from that,
0:12:05 the fact that you still see revenue growth in these industries
0:12:09 is in part a consequence of another principle of economics called Bommel’s Cost Disease.
0:12:13 And Bommel’s Cost Disease has to do with the fact that in the healthcare industry
0:12:16 you are trying to hire people who have options to work in other industry sectors
0:12:18 that are high productivity growth.
0:12:22 And so those workers have alternatives that are able to be higher paid
0:12:25 working in more productive industries and so they charge you the same labor rate
0:12:27 even in the lower productivity industry.
0:12:29 And so you see this like massive cost inflation,
0:12:31 even though you’re not getting better results.
0:12:34 And so that’s been the state of affairs for a long time in the healthcare industry.
0:12:37 Like I said, there are these other major sectors of the economy that have that characteristic.
0:12:40 At the end of the day, there are really only two things that can happen.
0:12:42 One is that process can continue indefinitely.
0:12:46 And if that process continues indefinitely, then at the limit, you eat the economy.
0:12:48 And by the way, that’s what’s happening with healthcare sitting here today.
0:12:51 Healthcare is now like a fifth of the American economy in growing.
0:12:54 If this process continues, it will eventually be half the economy
0:12:56 and then the entire economy without necessarily better results.
0:12:58 And that may be what happens.
0:13:01 Or you can break those cost curves by introducing technology
0:13:03 and then using that technology to drive productivity growth,
0:13:05 which then lets you basically do more with less,
0:13:08 which then lets you get better results and spending less.
0:13:12 In addition to technology, I think the big question is how we’re using medicine
0:13:13 and how medicine is applied.
0:13:17 If you think about it, it sounds weird that we have some of the best doctors in the world.
0:13:19 We have the best technology in the world.
0:13:22 We pay the most and we have the worst outcomes.
0:13:23 How’s that possible?
0:13:27 I think part of it is to really recognize that there’s two aspects to healthcare broadly.
0:13:30 There’s acute care, which our system is amazing.
0:13:33 If you’re in a car accident or you have stage 4 cancer,
0:13:34 I think you want to be in America.
0:13:40 But for chronic care, that’s something that I think we, for the most part, treat as if it were acute.
0:13:43 We wait for things to get bad and then we get into it.
0:13:48 So imagine a world where your house doesn’t have smoke detectors, doesn’t have extinguishers,
0:13:50 doesn’t even have circuit breakers.
0:13:52 So I said, any little thing will cause a problem.
0:13:55 And then you wait for the house to be on fire and then you call the fire department.
0:13:59 I guess what, it’s going to be more expensive and you’re going to have worse outcomes.
0:14:03 Ultimately, is it a policy regulation issue or is it a technology issue?
0:14:11 Because absent changes in our payment regulation and payment policy about how we align incentives for paying for these therapies and whatnot.
0:14:12 Is there any hope?
0:14:16 What is our source of optimism given the fact that all the industries that you mentioned are so highly regulated?
0:14:20 That’s what’s really in the way of us making big changes.
0:14:23 Yeah, so it is that they’re regulated, but it’s not just that they’re regulated.
0:14:24 And here’s what I mean by that.
0:14:26 Healthcare, education, housing and government.
0:14:31 They’re regulated in a very specific way, which is they have constrained supply and then subsidized demand.
0:14:33 Let’s take housing as an obvious example.
0:14:36 There’s a massive shortage of housing in particular where there’s lots of economic opportunity.
0:14:41 This is an example, analysts generally think that the California Bay Area is missing like two million houses.
0:14:43 This is a giant gap.
0:14:47 In other words, if two million people could move to Northern California, they could all become economically more productive.
0:14:48 The whole region would benefit and grow.
0:14:49 The country would benefit and grow.
0:14:55 But all of the constraints around housing development and zoning laws and environmental regulations and everything prevent those houses from getting built
0:14:56 and so they don’t get built.
0:14:57 And so you have constrained supply.
0:15:02 What happens in a market where you have constrained supply and increasing demand is of course prices rise.
0:15:03 But it’s not just that.
0:15:05 You also have subsidized demand, right?
0:15:10 Because what happens is you’re in a market with constrained supply, prices are rising, and then there’s political outrage.
0:15:13 Because in this, take housing, people can’t afford it by houses.
0:15:18 And so politicians respond to that, not by deregulating on the supply side, but by subsidizing the demand side.
0:15:22 In theory, that helps people buying a house in the moment because they have more money to spend on a house.
0:15:26 But because you have constrained supply of housing, what that will just do is drive up prices more.
0:15:34 And so demand subsidies have the paradoxical result of driving up prices, which then makes those demand subsidies inadequate, which then causes politicians to subsidize demand more.
0:15:36 Same thing has happened with universities.
0:15:40 Tuition and colleges universities has been rising far faster than inflation for now for several decades.
0:15:43 The price of a four-year private college degree is up to $400,000.
0:15:45 It’s on its way to a million dollars.
0:15:48 Why is that is because you have constrained supply.
0:15:53 There’s only so many universities to get access to federal student loans, which you need to do because they’re so expensive.
0:15:56 If you want to start a new university, you need to get accredited.
0:16:01 Who accredits new universities are the existing universities, which of course they run the accreditation bodies.
0:16:04 And they have no intention of causing there to be more universities, which would be more competition.
0:16:05 So that doesn’t happen.
0:16:08 And so you just have a fixed number of seats in good colleges universities.
0:16:10 And then you have the federal student loan program.
0:16:15 And as the constrained supply drives up prices, you have more and more subsidies in the form of federal student loan subsidies.
0:16:17 And then that drives up prices.
0:16:19 It turns out health care works exactly the same way.
0:16:22 There’s just a certain number of hospitals you have to get accredited as a hospital.
0:16:24 There’s only a certain number of doctors.
0:16:28 And by the way, legitimately so you have to go to medical school and get licenses to doctor.
0:16:29 There’s only so many nurses.
0:16:34 There’s always this kind of tension around the nurse practitioners because there are arguments that maybe you should liberalize that a bit.
0:16:37 And I think those arguments are actually pretty good, but there’s only a certain number of nurses.
0:16:44 And so you just have fixed supply and then you have increased population growth and you have increased percentage of people in the economy who are old and sick.
0:16:47 And then you have subsidies and you have all of the programs.
0:16:53 And look, I wouldn’t want to live in the country without some set of these programs, but you’ve got all the Medicare, Medicaid, Obamacare, all these programs.
0:16:56 So again, restrict supply subsidized demand prices go to the moon.
0:17:00 And so left on check economically inevitably what’s going to happen.
0:17:03 The political system seems like completely unable to deal with this.
0:17:07 And as a consequence, everything in these four fields will get to be infinitely expensive.
0:17:11 This where I come out as a radical on this is the only way to break those cost curves is through technology.
0:17:16 Like the only way that you can get in front of this phenomenon, I think political system is completely unable to deal with it.
0:17:20 It’s an answer that has to come from the private sector and it has to come from the introduction of disruptive technologies.
0:17:25 You think about the cost of compute, the cost of TVs, cost of anything electronic, the cost of tech.
0:17:30 That feels like it’s the reverse exponential that has to hit into this inflation.
0:17:32 And it’s one exponential against another.
0:17:36 I mean, I’m curious, Mark, how do you know which exponential is going to win?
0:17:39 You know, it’s human choice, right?
0:17:41 It’s choice. It’s the things that we choose to do.
0:17:43 We know for a fact we have technologies as a tool.
0:17:46 By the way, technology has done what I’m describing in many other industries.
0:17:49 What television sets are like a case study of the opposite direction.
0:17:52 The price of television sets has crashed as the quality has exploded.
0:17:57 The line that I use is you’re going to have a flat panel TV and 32K resolution that’s going to cover your whole wall.
0:18:01 It’s going to cost a hundred bucks, but yet it’s going to cost you a million dollars to send your kid to college, right?
0:18:06 That’s a result of tremendous technology innovation and productivity growth in consumer electronics,
0:18:10 which is, by the way, consumer electronics unregulated, no restrictions on supply.
0:18:14 Anybody can build a TV factory, at least in theory, and then no subsidized demand.
0:18:17 You don’t get money from the government or anybody else to help you buy a TV.
0:18:20 And so as a consequence, the economics drive to low prices and high quality.
0:18:26 It’s a societal level choice and then a company by company and individual by individual choice as to whether we want that to happen in healthcare.
0:18:30 Along the lines of what we’re talking about here with these bastardized supply and demand curves,
0:18:37 one of the big trends that we’re thinking a lot about and we’ve written about as well is this notion of consumers as the new class of payer,
0:18:44 where there are a number of movements in the healthcare space to empower individuals with not only the ability to shop for their own services,
0:18:47 but actually lift shift budget from these monolithic health plan products
0:18:53 that uniformly treat everyone the same to individual budgets that they can go use on their own accord to shop with agency.
0:18:59 Can you guys talk about that and is that a way for us to blow up at least a portion of kind of the way our insurance system works today?
0:19:07 It’s interesting if you think of the world in maybe a bit of a simplistic way, but as this chronic care and this acute care system.
0:19:11 The current insurance is really thinking about the acute side. It’s covered well.
0:19:19 But if you’re generally healthy, you could imagine having a high deductible plan to handle that and that might save some money for something else.
0:19:26 In a world where there’s choice and things like ICRA where consumers could put together a plan that they want,
0:19:31 you can imagine them choosing other types of providers that are thinking about the chronic care side
0:19:36 and that they could put together a plan that includes both an acute care side and a chronic care side.
0:19:42 And this way it’s still being paid by their employer, but that they’re not having to pay it out of pocket.
0:19:49 Or I think up to a certain point, even people will pay out of pocket, not for brain surgery, but for chronic care.
0:19:53 500 a year, a thousand dollars a year, I think is imaginable.
0:19:57 And the reason why is that in the end, the consumer is the ultimate payer, right?
0:20:00 Not just in dollars, but the ultimate price.
0:20:04 And so we are a long-term payer, incentive for care.
0:20:09 We will care about chronic in a way that insurance companies just can’t based on the nature of incentives.
0:20:13 Yeah, and I’d say some of these other industries that we’re talking about that have the same kind of cost curve problem.
0:20:15 There’s some, I would say, green shoots.
0:20:19 We’re looking for basically signs that there’s basically breakpoints happening or to VJ’s point.
0:20:22 There’s elements of consumer choice that are coming in that didn’t used to exist.
0:20:24 And so I’ll just give a couple of examples.
0:20:25 One is education.
0:20:29 So education in the U.S. historically has been a K through 12 monopoly, right?
0:20:30 So the government runs K through 12.
0:20:33 And there’s always been a small homeschooling contingent, but it’s not been a mainstream activity.
0:20:38 And generally in the past, it was often specialized religious communities that are separated out from the broader society.
0:20:43 And then higher ed has always been a cartel through the accreditation process of colleges, universities.
0:20:49 And really in the last basically five years, homeschooling is rising as a percentage of students and pretty significant numbers.
0:20:50 It’s far from a majority.
0:20:53 It’s still single-digit percentages, but like homeschooling is way up.
0:20:57 And homeschooling is way up in particular, not just among religious communities, but in general.
0:21:01 And in fact, among higher income levels and higher class levels, the numbers are particularly striking.
0:21:06 And then look, there’s an industry around it’s forming up and there are companies and startups and there are people running microschools
0:21:09 and there are companies doing matching and teacher training and recruitment and all kinds of things.
0:21:11 You know, activities to be able to enable this.
0:21:19 And so that’s an example where you’re starting to see consumer choice entering a market in which previously everybody just assumed you’re just doing what you’re told top down.
0:21:21 And then housing, housing is still all screwed up.
0:21:24 But you know, the interesting thing on housing is remote work, right?
0:21:29 Up until COVID, the rule of geography and work was very simple, which is you had to move to where the work was, right?
0:21:31 Because employers didn’t hire remote.
0:21:37 And in fact, historically, like in tech, for example, this was one of the main reasons for people to move to Silicon Valley and, you know, take this as an extreme case.
0:21:43 The line in Silicon Valley used to be if you live in Palo Alto, you can get 20 other job offers anytime you want without changing your parking spot.
0:21:46 It’s because there are just so many tech companies around that will hire you.
0:21:52 Well, if you’re a tech worker now and you are remote, not only can you get 20 other job offers, you can get 10,000 other job offers, right?
0:21:54 The asymmetry goes in the other direction.
0:22:06 Even if most employers are not hiring tech workers remote, there are so many who are that as a tech worker, evaluating job offers from employers that will hire remote, there are thousands or tens of thousands or hundreds of thousands of those.
0:22:12 And so, paradoxically, if you’re a remote worker, you now have more employment options than if you’re geographically co-located.
0:22:14 And again, most people in tech have not moved.
0:22:21 Most of them are still in places like Silicon Valley, but the ones who move are doing great and have job offers coming out of their ears and they’re doing fantastically well.
0:22:25 And this is relevant to housing because that means now that they can move to places in which housing is a lot cheaper.
0:22:28 They can move to places where there aren’t a lot of companies.
0:22:30 They can move to a place that actually might be a better place to live.
0:22:36 Maybe they can have a big house on a lake for less money than it would cost to get a one-bedroom apartment in Palo Alto.
0:22:41 So again, it’s this green shoot crack in the matrix thing where it’s okay, you can see how the world can work a different way.
0:22:50 And so, at least, VJ, I think basically that’s a lot of what’s going to happen in healthcare from here on out, which is basically the existing system left its own devices is going to degrade further and further.
0:22:53 It’s going to give worse and worse results at higher and higher cost over time.
0:22:56 As a consequence, some percentage of people are going to try to break out of that.
0:22:59 One of our companies, we have many examples of this, but Levels is a great example.
0:23:09 If you want to take control of your own behavioral health, your own obesity risk or diabetes risk, you can sign up to Levels and they have a whole program and software and everything that will help you do that.
0:23:12 And I don’t even know if there is an insurance reimbursement option, but it’s tech.
0:23:13 It’s not expensive.
0:23:15 You happily pay out of pocket and off in a way you go.
0:23:17 And I tend to think that there’s going to be a lot more of that.
0:23:22 Yeah, it’s also the juxtaposition of the amount of money that you’re paying for something like a Levels experience or function health.
0:23:35 If you actually look at the amount of your wages that is being taken out of your salary to pay for health insurance and then certainly in a high deductible plan, which is increasingly more common in the employer-responsive world, your out-of-pocket spend actually tends to be even higher than that.
0:23:46 And many of these companies, what they’re doing is actually doing direct contract deals where they can take 40 different services like what function is doing that individually in aggregate would have cost you tens of thousands of dollars
0:23:52 under an insurance product, but they’re negotiating a direct contract as a bundle that says for hundreds of dollars, you can basically get the same thing.
0:23:59 And it’s just totally dislocating the price curve for what otherwise beneath the traditional insurance system is just completely unaffordable.
0:24:01 So I think we’re seeing a lot of that.
0:24:04 I think what we’re really providing patients is agency.
0:24:06 And I think that’s what was missing.
0:24:09 Like why can’t you just lose weight or eat better?
0:24:18 I think there needs some help there. And I think this sense of agency is starting to really grow where people have a ability to monitor the health and then do something about it and then repeat.
0:24:21 And that cycle actually I think works for a lot of people.
0:24:25 And being in control of your health feels very different than being at the mercy of a system.
0:24:29 Let’s zoom out a little bit to some of the bigger picture ideas here.
0:24:31 The whole notion of health tech in general, right?
0:24:43 This is a fairly new space. I think much of what has been possible with health tech only came because of the pandemic where there was a relaxation of things like virtual care laws and certifications that were required to practice medicine across state lines.
0:24:51 A lot of this was entrenched in the sense that you were not even allowed to treat patients across state lines prior to some of those emergency laws being put into place.
0:24:54 And so this space is very new and it’s also speculative.
0:25:02 Like a lot of people still have a lot of skepticism about whether this is a durable trend, whether this could actually result in a completely new and big industry over time.
0:25:09 But obviously us and especially you, Mark, having seen so many waves of industry transition and transformation happen in the course of time.
0:25:12 We know that these things sometimes take decades to actually work.
0:25:30 So Mark, specifically, what advice or parables can you share with folks who are watching the digital health space play out maybe from the early days of the internet even to inform how we interpret where we are in this industry from a maturity curve perspective and just providing again some perspective on how long sometimes these things take to play out?
0:25:33 We liked Lenin Mark’s installing quotes every now and then just to make an impression.
0:25:44 So there’s an old Lenin quote, V.I. Lenin, Vladimir Lenin, not John Lenin, to be clear. And he was talking about political change, but he said there are decades in which nothing happens and then there are weeks in which decades happen.
0:25:51 And then in tech, there’s a similar kind of thing called Amara’s Law that is basically changes in technology take a lot longer to happen than you think they will.
0:25:55 But when they happen, they have much more consequence than you think. They’re much more dramatic than you think.
0:26:03 We see this all the time where it can take years and years and years and years if it seems like absolutely nothing happened. And then there’s some sort of catalytic thing that happens and all of a sudden things take off.
0:26:14 And I think there’s a bunch of things that go into this. Part is sometimes it just takes a while to get the technology dialed in. I always like to point out the first smartphone hit the market in 1987 and you didn’t get the iPhone until 2007.
0:26:20 And so it was a full 20 years. And by the way, the first iPhone actually was 2G. It didn’t make phone calls properly. It had all kinds of issues.
0:26:28 You didn’t get the App Store until I think another four years after that. So it was 25 years from the inception of the smartphone industry to actually getting the modern iPhone on a real data network or the real App Store.
0:26:38 It’s just like all the componentry that went into the iPhone, it all had to get mature. It all had to develop like the screens and the batteries and the radios and everything had to get like really good before the whole thing packaged together.
0:26:45 So part of this is on the responsibility of the tech companies themselves, our companies to be able to actually get the right product to market and sometimes that takes time.
0:26:53 The other side of it is the catalytic effect. You know, in the old days to get something new adopted, you always had to do mass marketing, mass media campaigns and hopefully people responded.
0:26:59 A lot of it is now peer to peer. It’s people telling each other and we always reference a lot in our business. It’s okay.
0:27:05 Like when we’re looking at a given thing that might be a big deal as well, is there a subreddit for it? Is there a forum on Reddit where people are talking about it?
0:27:14 And if there isn’t, it almost certainly means that nobody has any of it exists. And then if there is a forum on Reddit where people are talking about it, you can get a sense of what the early adopters are saying and how close it might be to a vertical takeoff.
0:27:21 And then of course these days, the new version of that is like TikTok or Instagram reels or tweets. Is it on the big social networks? Is it taking off?
0:27:26 My guess more and more is it’s going to be peer to peer. When these things take off, it’s going to be because people learn about them online.
0:27:29 They learn about them from their friends. They learn about them from watching videos online.
0:27:34 This, by the way, is the natural frustration. This is the Dr. Google kind of effect extrapolated up, right?
0:27:43 Which is if doctors were frustrated by patients coming in with Google printouts, which I think has been the case for the last 20 years, I think doctors are going to get increasingly frustrated because patients are going to come in and they’re going to be like,
0:27:52 well, I saw this thing on TikTok and I think I should try it. And look, there are going to be downside versions of that, things that are not valid, but there are also going to be things that actually work that actually spread.
0:28:00 Well, I mean, look, just like even fitness, fitness now is almost entirely an online phenomenon. The way people learn how to work out now is almost entirely online.
0:28:06 By the way, also the way people learn how to cook, the way people learn how to eat healthy, like that’s almost entirely happening on social networks and on YouTube.
0:28:09 So I tend to think that phenomenon will happen for more and more areas of health.
0:28:19 And I think a corollary of that is that there will be grassroots movements, even political movements towards health and that this becomes a key part of our lives that people care about.
0:28:28 And so it’s not going to be a top down, wonky, inside Washington movement, but I think you’ll see grassroots political movements and we’re already seeing the beginnings of that as well.
0:28:29 Yeah, that’s right.
0:28:35 By the way, there’s also Dr. TikTok. So physicians are sharing notes with each other also about how to subvert the systems that shackle them.
0:28:38 So I think it’s happening on both the demand side and the supply side of our industry.
0:28:41 Let’s end with a fun one. So I think all three of us have young kids.
0:28:48 I certainly have a son who’s growing up very A.I. native and I’ve observed how he uses tools like even ones that we’ve invested in like character and curio.
0:28:50 I think Mark, your son has one of these two.
0:28:59 And he’s like really developed this comfort around just talking openly about his feelings and his curiosities and his dreams that I think will actually be very healthy for him in his mental health in the future.
0:29:15 So similarly, are there things that you guys observe your kids doing or the younger generations doing in general with A.I. that you think specifically will inform what health care behaviors will emerge over time that could eventually become mainstream and crack some of the nuts that we’ve been talking about today?
0:29:19 Douglas Adams, the great science fiction author who wrote Hitchhiker’s Guide to the Galaxy, had this famous.
0:29:24 He said there’s a three part generational kind of model of technology change, health society that’s to do technology.
0:29:25 He said it’s true for every new technology.
0:29:28 So if you’re under the age of 15, it’s just natural that this is how the world works.
0:29:34 If you’re between the ages of 15 to 35 when a new technology comes out, it’s like super cool and you might be able to get a job working on it.
0:29:39 And if you’re over the age of 35, it’s unholy and against the natural order of things and we’ll destroy all of society.
0:29:41 We, of course, are the exceptions.
0:29:43 We do not fall prey to these ancient patterns.
0:29:50 My version of this was a nine-year-old that when ChatGPT first got going probably what, two years ago, less than two years ago, when it really hit critical mass.
0:29:53 Yeah, so probably in the spring of 23, so he’s like probably eight.
0:29:57 I set up his little laptop for his classwork and so I set up ChatGPT on it and got him his own account.
0:29:59 I was like so proud of myself as a father.
0:30:03 I felt like I had brought down fire from the mountain for my son, right?
0:30:04 And I was going to give him like, “Here’s ChatGPT.
0:30:05 It’ll answer every question you have.
0:30:06 It’ll teach you anything.
0:30:08 It’s going to be with you for your entire life.
0:30:11 Like this is the thing that’s going to make you like a much more advanced version of you.”
0:30:15 And it’s just like the most powerful, amazing thing I can give you as a father.
0:30:19 And I sit him down and I teach him and I’m like, “Look, you type in any question it answers the question.”
0:30:21 And he shrugs and I’m like, “What’s the shrug?”
0:30:22 And he’s like, “It’s a computer.”
0:30:25 Obviously it answers questions like, “What else would you use a computer for?”
0:30:27 I’m like basically that’s it, right?
0:30:33 And so that’s the other part of this is I think a big part of all of everything we’ve been discussing also just as like a generational change.
0:30:36 It’s new cohorts of people coming up and just people are not going to tolerate.
0:30:38 Actually, I think we saw this in the meta.
0:30:42 You tell me, I think we saw this in the health field probably 20 years ago where before the internet,
0:30:44 how would you ever look up whether a doctor was good?
0:30:48 And then once the internet emerges, like all of a sudden is there’s a different size at like rate doctors.
0:30:51 And then by the way, how would you ever as a patient with some condition,
0:30:53 how would you ever read up on it yourself?
0:30:56 Because you can’t obviously it’s not going to you’re going to go read medical journals at the university library,
0:30:58 but then you go on Google.
0:31:04 And so I think we’ve already seen probably at this point several waves of that kind of adaptation as people with the same disease, everything like that.
0:31:05 Yeah, exactly.
0:31:07 They go from being something that’s inconceivable to something that’s common.
0:31:10 And a lot of that is on a cohort age basis.
0:31:14 You get these debates like in the press about is a new technology good or bad and should it be adopted or not?
0:31:18 Those are all beside the point because the fact is like young people are just going to use what’s helpful and useful,
0:31:20 and they’re not going to have the emotional reaction.
0:31:22 And I think that that will apply to many of the things that we’ve been talking about.
0:31:26 If you’re like an executive in their fifties and you’re exhausted,
0:31:30 you don’t have time, you don’t have time to play with this thing and to learn the skills.
0:31:33 The kids will have the playful attitude, will play around with it,
0:31:37 will learn and learn things that maybe even other people didn’t think they could do.
0:31:42 I think they’re at a huge advantage to come up to speed quickly because I think you have to learn how these tools work
0:31:45 and how to train them and how to get them to do what you want.
0:31:47 And I think people are still figuring that out.
0:31:50 But our children will definitely figure out faster than we will.
0:31:51 I think they already have.
0:31:54 Well, with that, thank you both for an amazing conversation about the future of health.
0:31:55 Thank you guys.
0:31:56 Thank you.
0:32:00 All right, that is all for today.
0:32:03 If you did make it this far, first of all, thank you.
0:32:07 We put a lot of thought into each of these episodes, whether it’s guests, the calendar touchers,
0:32:11 the cycles with our amazing editor Tommy until the music is just right.
0:32:17 So if you’d like what we’ve put together, consider dropping us a line at ratethespodcast.com/a16c.
0:32:20 And let us know what your favorite episode is.
0:32:23 It’ll make my day, and I’m sure Tommy’s too.
0:32:25 We’ll catch you on the flip side.
0:32:28 [MUSIC PLAYING]
0:32:31 [MUSIC PLAYING]
0:32:33 (upbeat music)
0:00:15 We have some of the best doctors in the world. We have the best technology in the world. We pay the most and we have the worst outcomes. How is that possible?
0:00:20 Ultimately, is it a policy regulation issue or is it a technology issue?
0:00:23 It may be the cure and cancer as a lot easier to pack in the suitcase.
0:00:28 I think what we’re really providing patients is agency and I think that’s what was kind of missing.
0:00:42 The health care industry has captured the attention of America and it’s no wonder the confusing and complex glosses makes up about 20% of GDP and growing, clocking in at an over $4 trillion industry.
0:00:53 People seem to agree on problems, but rarely the solutions. But as we kick off 2025, it’s hard not to wonder whether the latest platform shift may offer some answers.
0:01:08 I suspect that this is the dynamic that’s going to play out in health care. The more abstract, intellectual, knowledge-driven, data-driven the task, the easier it is to get the AI to do it, the more applied, the physical, messy, unpredictable sort of all the human elements are probably the hardest thing.
0:01:19 So who will finally crack this code? And what’s really driving up prices? Could competition flip that dynamic on its head? And what will finally catalyze change? Plus, is the technology even there yet?
0:01:24 What do you each perceive as still being too hard to apply AI to?
0:01:36 Joining us to discuss and answer all those questions today are A16Z General Partners Mark Andreessen, Vijay Pande and Julie Yu. Let’s get started.
0:01:50 As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund.
0:01:56 Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.
0:02:02 For more details, including a link to our investments, please see a16z.com/disposures.
0:02:17 Let’s actually start with our quick takes on whether we believe it will be a healthcare-native company that finally cracks the nut on how technology will transform our industry, especially AI,
0:02:25 or do we think it will be a foreign body, an outsider, a non-healthcare company that will actually end up making the biggest play in our space?
0:02:34 I think it’s going to be a startup. I think it’s going to be really hard for an incumbent outside of healthcare to come into healthcare. It’s going to be really hard for healthcare to move into AI.
0:02:42 So a startup is a natural thing, and especially how that company is going to be built is that they will have to be AI-native and healthcare-native from the beginning.
0:02:48 And that’s something that’s really hard for incumbents. It’s just a straight down the middle typical disruption story.
0:02:57 Yeah, so I think the counter argument is to start by saying I’m usually a VJ’s side on these topics, and obviously we’re a venture capital firm, and so our day job is to try to fund exactly the kind of company that VJ is talking about.
0:03:07 So the easy thing to do is to just agree. But just for the purpose of conversation, here would be the argument on the other side, which is new technology entering healthcare up until now has been tools, right?
0:03:15 You go into an existing organization, an existing healthcare company of whatever kind, and you have a new kind of tool, and you basically try to get the existing organization to adopt the tool.
0:03:21 And of course, sometimes that happens, and hospitals run on hospital management systems today, and they didn’t used to and so forth, but that’s a big lift.
0:03:24 And that’s the, I think, inertia of VJ that you’re talking about, right?
0:03:27 It’s the mapping of the new technology into an existing organization. There’s the bottleneck.
0:03:35 The winning AI products might not come to market in that way. They might not be tools. They might be more almost like the equivalent of hiring workers, full stack.
0:03:42 And so I guess the question would be if the products are being delivered in a way where they almost slide into the organization as almost like the equivalent of a new employee,
0:03:47 or maybe even something bigger than a single employee, maybe as a new business unit, right, as a new organization, as a totality.
0:03:52 Does that change the dynamics of adoption such that big companies have a chance to think about this differently?
0:04:00 Yeah, or even as standalone businesses, by the way, that are powered by AI, that from the outside look like a regular provider group or a medical group that can contract with insurance companies
0:04:05 the same way that any other traditional medical group can contract with them and therefore get paid for what they’re doing.
0:04:13 But their internal economics and efficiency and their ability to scale nationwide is 10x what any traditional provider group might be.
0:04:22 So I think I would go all the way to say there might be standalone companies that are wearing sheep’s clothing in terms of the way that they’re packaged and the way they’re distributed in the market,
0:04:26 but internally look completely different in terms of the DNA of how they operate.
0:04:34 Let’s take an extreme argument where open AI creates AGI. Let’s just say it’s all the way to AGI. You’ve got the genius human being.
0:04:41 What does that know about healthcare? And I think a lot of what we have to do is we have to give the AI the equivalent of that work experience.
0:04:46 Is that something that an existing AI company will have? That’s a whole bunch of data that’s really hard to get.
0:04:55 It’s possible that an existing healthcare company will have that, but I think if you have to be really careful how you train that and whether they’ll have the AI experience.
0:05:00 So I think even still, not surprisingly, my bet’s on the startup, but I can imagine that is the way it’s going to happen.
0:05:06 But you’re making the point also that the startup has to be both healthcare native and AI native, not one or the other.
0:05:16 The other interesting thing about the way that Mark articulated this is this notion that one of the reasons I would argue that technology adoption in healthcare has been so slow in the past
0:05:28 is that the allocation of revenue within healthcare enterprises that goes towards technology, or IT as they would call it, has been in order of magnitude smaller in healthcare than other peer industries.
0:05:36 And what’s super interesting right now about these sort of like labor units that are how AI is packaged is that I’ve actually heard organizations now talk about,
0:05:47 oh, can we actually just tap into our labor budget? We have literally like a thousand open racks for nurses that we cannot fill through the existing human labor pool that is out there today.
0:05:56 Why not just allocate that one FTE’s worth of budget towards hiring the equivalent of like a hundred nurses using these AI tools that can do subclinical things,
0:06:03 but still things that are immensely important and critical to the workflows of these organizations based on what you guys are both observing across broader problem spaces,
0:06:08 whether it be creativity, education, law, software development, all the things that you mentioned earlier.
0:06:15 What do you each perceive as still being too hard to apply AI to within the healthcare domain?
0:06:19 I think we’re going to see the specialties probably be the last thing.
0:06:23 I think maybe the first thing would be the subclinical nursing and primary care.
0:06:28 Then inching into clinical primary care, I think it would not be that crazy to imagine.
0:06:34 Essentially, AI could be a great router, and especially think about like what a primary care physician has to do.
0:06:38 They have to ingest all this data and make a diagnosis and then send to a specialist.
0:06:41 That’s increasingly literally becoming a data science problem.
0:06:43 So I think that’s where we’re next.
0:06:49 The hardest thing is going to be like you take the extreme case of let’s say brain surgery is the canonical hard thing to do.
0:06:55 That’s something that sounds like it could be really hard for AI, but actually as surgeons use more robotic devices,
0:07:00 as other specialists use more digital things, you can imagine a way for it to creep in,
0:07:02 but that’s probably the furthest out.
0:07:07 Mark, a twist on that question for you is as you think about any industry where there’s like a frontier,
0:07:11 some boundary of what technology can do and not do, especially when it comes to AI,
0:07:18 do you think it’s mainly a question of data or are there other factors that you think we need to consider when it comes to how this might work in healthcare?
0:07:22 Yeah, so there’s this famous phenomenon in AI research called Moravex Paradox,
0:07:27 and it’s named after this AI researcher, Hans Moravec, and Moravec wrote in 1988,
0:07:32 “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers,
0:07:37 and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”
0:07:39 The modern version of that is very striking, which is sitting here today.
0:07:43 We have chat GPT and its analogs, and they will paint art for you.
0:07:45 They will compose music for you.
0:07:47 They will debate abstract philosophy for you.
0:07:51 They will explain quantum physics to you, but they can’t pack your suitcase.
0:07:54 They can’t clean your toilet. They can’t cook you lunch.
0:07:57 They can’t do anything that a normal person operating in the world can do.
0:08:00 There’s actually a real evolutionary theory underneath Moravex Paradox,
0:08:05 which is the parts of us that are physically embodied, the parts of us that basically involve functioning in the world,
0:08:08 and then the sort of sensory skills and motor skills required to navigate in the world,
0:08:10 and then the ability to process unusual situations.
0:08:14 It’s like the classic, you see the demos of the robots, humanoid robots making coffee,
0:08:17 and it’s like it works right up to the point, the coffee pod, you know, the pod gets jammed in the coffee maker,
0:08:19 and the robot doesn’t know what to do.
0:08:23 All of the sort of motor sensory skills, all that stuff took many millions of years to evolve,
0:08:27 billions of years to evolve, and then at the peak of human cognition, the cortex,
0:08:30 the sort of system two reasoning was a relatively recent evolution,
0:08:33 and maybe let’s even say it’s 500,000 years versus 4 billion years,
0:08:39 and this is the exact reverse of what is intuitively you would feel like packing the suitcase is a lot easier than curing cancer.
0:08:43 It may be the curing cancer is a lot easier than packing the suitcase, like for this reason, right?
0:08:46 I suspect that this is the dynamic that’s going to play out in healthcare.
0:08:50 It’s going to be the more abstract, intellectual, knowledge driven, data driven, the task,
0:08:56 the easier it is to get the AI to do it, the more applied, the physical, messy, unpredictable,
0:08:58 the sort of all the human elements are probably the hardest thing.
0:09:01 I think a tweak on that is that AI loves the digital world.
0:09:05 It does really well, and if AI can’t be in that world, it has nothing to learn from.
0:09:12 And so to the degree there’s telemedicine and things that are virtual, whether it be a phone call or zoom,
0:09:15 that’s something that actually is a world that AI could play in right now.
0:09:19 And I think it’s not a shock that we’re seeing that as a natural first application.
0:09:24 What is interesting is as medicine itself becomes more digitized through robotics,
0:09:28 in other words, driven by people, then AI could be in that world too.
0:09:34 So it might be that there’s a parallel path of building up the digital side of medicine that AI could be in.
0:09:39 But yeah, I mean, I think it can do quantum mechanics, but it can’t unpack my dishwasher.
0:09:43 It makes sense because it’s trained all those quantum gangs has never seen a dishwasher, right?
0:09:46 The Tesla self-driving car might be the reason for optimism here, right?
0:09:49 And Tesla learned this the hard way because they tried different approaches to AI,
0:09:52 and it turned out the thing that worked was basically put a neural network in the wild,
0:09:55 embodying the form of a car, and in fact embodying the form of a million cars,
0:09:58 and then let those guys drive around and collect the data and learn about reality.
0:10:01 The more you take the rules out of the system and the more you just expose it to the real world
0:10:04 and let it gather the data and let it build up the neural network, the better it gets at self-driving.
0:10:09 That’s another form of leapfrog opportunity in the sense that the primary reason why hospitals exist
0:10:14 are because the level of complexity of the equipment and the workflows
0:10:17 and all the operations that need to exist have to be centralized.
0:10:20 It’s nearly impossible today to unbundle the entirety of that
0:10:23 and make it available to people in their home environments or in their communities.
0:10:28 But you can imagine perhaps that’s another lever here where if we are able to atomize
0:10:31 the physical components of the hospital-based experience
0:10:34 that you could actually centralize all of care delivery
0:10:38 and make it available in different sites that are much cheaper with a lower cost structure.
0:10:41 Yeah, so there’s two related concepts that are very important.
0:10:43 So one is economic growth, as you said,
0:10:46 and you generally measure economic growth through things like GDP, right?
0:10:48 Or, you know, in a company, it’s amount of revenue.
0:10:50 But there’s this other very important concept, right, which is productivity growth.
0:10:55 And productivity growth is basically the economic question of basically, can you do more with less?
0:10:58 And so if you have an economy that has high productivity growth,
0:11:03 basically what you see over time is that economy is able to produce more output with lower inputs.
0:11:06 And by the way, the sort of story of the growth of Western civilization overall
0:11:10 and economic growth globally over the last 500 years has been a story primarily of productivity growth.
0:11:13 Basically, the Industrial Revolution, figuring out ways to apply machines
0:11:17 and then new management methods around the machines to be able to do more with less.
0:11:21 And therefore, the world we live in, which in the old days, 99% of people worked in agriculture
0:11:24 and yet people were still starving because there wasn’t enough food.
0:11:26 Whereas today, 3% of people work in agriculture
0:11:29 and we’re producing so much food that everybody’s getting obese, right?
0:11:31 And that was the result of productivity growth, right?
0:11:35 And so then you go into kind of the microeconomics of the healthcare industry
0:11:38 and basically what you see is either flat or negative productivity growth.
0:11:42 By the way, the industries that are like this are the healthcare industry, education, housing,
0:11:44 and then let’s say law and government.
0:11:47 Those four basically, if you measure them, they have flat or negative productivity growth.
0:11:51 And so basically they’re getting less efficient over time, not more efficient over time.
0:11:53 And you see that directly, as you said, you see it in the graphs
0:11:56 for all four of those industries where you’re spending more and more money over time
0:11:58 and you’re getting less and less output, less and less results,
0:12:01 which is precisely the opposite of what you want.
0:12:03 By the way, the economic growth that follows from that,
0:12:05 the fact that you still see revenue growth in these industries
0:12:09 is in part a consequence of another principle of economics called Bommel’s Cost Disease.
0:12:13 And Bommel’s Cost Disease has to do with the fact that in the healthcare industry
0:12:16 you are trying to hire people who have options to work in other industry sectors
0:12:18 that are high productivity growth.
0:12:22 And so those workers have alternatives that are able to be higher paid
0:12:25 working in more productive industries and so they charge you the same labor rate
0:12:27 even in the lower productivity industry.
0:12:29 And so you see this like massive cost inflation,
0:12:31 even though you’re not getting better results.
0:12:34 And so that’s been the state of affairs for a long time in the healthcare industry.
0:12:37 Like I said, there are these other major sectors of the economy that have that characteristic.
0:12:40 At the end of the day, there are really only two things that can happen.
0:12:42 One is that process can continue indefinitely.
0:12:46 And if that process continues indefinitely, then at the limit, you eat the economy.
0:12:48 And by the way, that’s what’s happening with healthcare sitting here today.
0:12:51 Healthcare is now like a fifth of the American economy in growing.
0:12:54 If this process continues, it will eventually be half the economy
0:12:56 and then the entire economy without necessarily better results.
0:12:58 And that may be what happens.
0:13:01 Or you can break those cost curves by introducing technology
0:13:03 and then using that technology to drive productivity growth,
0:13:05 which then lets you basically do more with less,
0:13:08 which then lets you get better results and spending less.
0:13:12 In addition to technology, I think the big question is how we’re using medicine
0:13:13 and how medicine is applied.
0:13:17 If you think about it, it sounds weird that we have some of the best doctors in the world.
0:13:19 We have the best technology in the world.
0:13:22 We pay the most and we have the worst outcomes.
0:13:23 How’s that possible?
0:13:27 I think part of it is to really recognize that there’s two aspects to healthcare broadly.
0:13:30 There’s acute care, which our system is amazing.
0:13:33 If you’re in a car accident or you have stage 4 cancer,
0:13:34 I think you want to be in America.
0:13:40 But for chronic care, that’s something that I think we, for the most part, treat as if it were acute.
0:13:43 We wait for things to get bad and then we get into it.
0:13:48 So imagine a world where your house doesn’t have smoke detectors, doesn’t have extinguishers,
0:13:50 doesn’t even have circuit breakers.
0:13:52 So I said, any little thing will cause a problem.
0:13:55 And then you wait for the house to be on fire and then you call the fire department.
0:13:59 I guess what, it’s going to be more expensive and you’re going to have worse outcomes.
0:14:03 Ultimately, is it a policy regulation issue or is it a technology issue?
0:14:11 Because absent changes in our payment regulation and payment policy about how we align incentives for paying for these therapies and whatnot.
0:14:12 Is there any hope?
0:14:16 What is our source of optimism given the fact that all the industries that you mentioned are so highly regulated?
0:14:20 That’s what’s really in the way of us making big changes.
0:14:23 Yeah, so it is that they’re regulated, but it’s not just that they’re regulated.
0:14:24 And here’s what I mean by that.
0:14:26 Healthcare, education, housing and government.
0:14:31 They’re regulated in a very specific way, which is they have constrained supply and then subsidized demand.
0:14:33 Let’s take housing as an obvious example.
0:14:36 There’s a massive shortage of housing in particular where there’s lots of economic opportunity.
0:14:41 This is an example, analysts generally think that the California Bay Area is missing like two million houses.
0:14:43 This is a giant gap.
0:14:47 In other words, if two million people could move to Northern California, they could all become economically more productive.
0:14:48 The whole region would benefit and grow.
0:14:49 The country would benefit and grow.
0:14:55 But all of the constraints around housing development and zoning laws and environmental regulations and everything prevent those houses from getting built
0:14:56 and so they don’t get built.
0:14:57 And so you have constrained supply.
0:15:02 What happens in a market where you have constrained supply and increasing demand is of course prices rise.
0:15:03 But it’s not just that.
0:15:05 You also have subsidized demand, right?
0:15:10 Because what happens is you’re in a market with constrained supply, prices are rising, and then there’s political outrage.
0:15:13 Because in this, take housing, people can’t afford it by houses.
0:15:18 And so politicians respond to that, not by deregulating on the supply side, but by subsidizing the demand side.
0:15:22 In theory, that helps people buying a house in the moment because they have more money to spend on a house.
0:15:26 But because you have constrained supply of housing, what that will just do is drive up prices more.
0:15:34 And so demand subsidies have the paradoxical result of driving up prices, which then makes those demand subsidies inadequate, which then causes politicians to subsidize demand more.
0:15:36 Same thing has happened with universities.
0:15:40 Tuition and colleges universities has been rising far faster than inflation for now for several decades.
0:15:43 The price of a four-year private college degree is up to $400,000.
0:15:45 It’s on its way to a million dollars.
0:15:48 Why is that is because you have constrained supply.
0:15:53 There’s only so many universities to get access to federal student loans, which you need to do because they’re so expensive.
0:15:56 If you want to start a new university, you need to get accredited.
0:16:01 Who accredits new universities are the existing universities, which of course they run the accreditation bodies.
0:16:04 And they have no intention of causing there to be more universities, which would be more competition.
0:16:05 So that doesn’t happen.
0:16:08 And so you just have a fixed number of seats in good colleges universities.
0:16:10 And then you have the federal student loan program.
0:16:15 And as the constrained supply drives up prices, you have more and more subsidies in the form of federal student loan subsidies.
0:16:17 And then that drives up prices.
0:16:19 It turns out health care works exactly the same way.
0:16:22 There’s just a certain number of hospitals you have to get accredited as a hospital.
0:16:24 There’s only a certain number of doctors.
0:16:28 And by the way, legitimately so you have to go to medical school and get licenses to doctor.
0:16:29 There’s only so many nurses.
0:16:34 There’s always this kind of tension around the nurse practitioners because there are arguments that maybe you should liberalize that a bit.
0:16:37 And I think those arguments are actually pretty good, but there’s only a certain number of nurses.
0:16:44 And so you just have fixed supply and then you have increased population growth and you have increased percentage of people in the economy who are old and sick.
0:16:47 And then you have subsidies and you have all of the programs.
0:16:53 And look, I wouldn’t want to live in the country without some set of these programs, but you’ve got all the Medicare, Medicaid, Obamacare, all these programs.
0:16:56 So again, restrict supply subsidized demand prices go to the moon.
0:17:00 And so left on check economically inevitably what’s going to happen.
0:17:03 The political system seems like completely unable to deal with this.
0:17:07 And as a consequence, everything in these four fields will get to be infinitely expensive.
0:17:11 This where I come out as a radical on this is the only way to break those cost curves is through technology.
0:17:16 Like the only way that you can get in front of this phenomenon, I think political system is completely unable to deal with it.
0:17:20 It’s an answer that has to come from the private sector and it has to come from the introduction of disruptive technologies.
0:17:25 You think about the cost of compute, the cost of TVs, cost of anything electronic, the cost of tech.
0:17:30 That feels like it’s the reverse exponential that has to hit into this inflation.
0:17:32 And it’s one exponential against another.
0:17:36 I mean, I’m curious, Mark, how do you know which exponential is going to win?
0:17:39 You know, it’s human choice, right?
0:17:41 It’s choice. It’s the things that we choose to do.
0:17:43 We know for a fact we have technologies as a tool.
0:17:46 By the way, technology has done what I’m describing in many other industries.
0:17:49 What television sets are like a case study of the opposite direction.
0:17:52 The price of television sets has crashed as the quality has exploded.
0:17:57 The line that I use is you’re going to have a flat panel TV and 32K resolution that’s going to cover your whole wall.
0:18:01 It’s going to cost a hundred bucks, but yet it’s going to cost you a million dollars to send your kid to college, right?
0:18:06 That’s a result of tremendous technology innovation and productivity growth in consumer electronics,
0:18:10 which is, by the way, consumer electronics unregulated, no restrictions on supply.
0:18:14 Anybody can build a TV factory, at least in theory, and then no subsidized demand.
0:18:17 You don’t get money from the government or anybody else to help you buy a TV.
0:18:20 And so as a consequence, the economics drive to low prices and high quality.
0:18:26 It’s a societal level choice and then a company by company and individual by individual choice as to whether we want that to happen in healthcare.
0:18:30 Along the lines of what we’re talking about here with these bastardized supply and demand curves,
0:18:37 one of the big trends that we’re thinking a lot about and we’ve written about as well is this notion of consumers as the new class of payer,
0:18:44 where there are a number of movements in the healthcare space to empower individuals with not only the ability to shop for their own services,
0:18:47 but actually lift shift budget from these monolithic health plan products
0:18:53 that uniformly treat everyone the same to individual budgets that they can go use on their own accord to shop with agency.
0:18:59 Can you guys talk about that and is that a way for us to blow up at least a portion of kind of the way our insurance system works today?
0:19:07 It’s interesting if you think of the world in maybe a bit of a simplistic way, but as this chronic care and this acute care system.
0:19:11 The current insurance is really thinking about the acute side. It’s covered well.
0:19:19 But if you’re generally healthy, you could imagine having a high deductible plan to handle that and that might save some money for something else.
0:19:26 In a world where there’s choice and things like ICRA where consumers could put together a plan that they want,
0:19:31 you can imagine them choosing other types of providers that are thinking about the chronic care side
0:19:36 and that they could put together a plan that includes both an acute care side and a chronic care side.
0:19:42 And this way it’s still being paid by their employer, but that they’re not having to pay it out of pocket.
0:19:49 Or I think up to a certain point, even people will pay out of pocket, not for brain surgery, but for chronic care.
0:19:53 500 a year, a thousand dollars a year, I think is imaginable.
0:19:57 And the reason why is that in the end, the consumer is the ultimate payer, right?
0:20:00 Not just in dollars, but the ultimate price.
0:20:04 And so we are a long-term payer, incentive for care.
0:20:09 We will care about chronic in a way that insurance companies just can’t based on the nature of incentives.
0:20:13 Yeah, and I’d say some of these other industries that we’re talking about that have the same kind of cost curve problem.
0:20:15 There’s some, I would say, green shoots.
0:20:19 We’re looking for basically signs that there’s basically breakpoints happening or to VJ’s point.
0:20:22 There’s elements of consumer choice that are coming in that didn’t used to exist.
0:20:24 And so I’ll just give a couple of examples.
0:20:25 One is education.
0:20:29 So education in the U.S. historically has been a K through 12 monopoly, right?
0:20:30 So the government runs K through 12.
0:20:33 And there’s always been a small homeschooling contingent, but it’s not been a mainstream activity.
0:20:38 And generally in the past, it was often specialized religious communities that are separated out from the broader society.
0:20:43 And then higher ed has always been a cartel through the accreditation process of colleges, universities.
0:20:49 And really in the last basically five years, homeschooling is rising as a percentage of students and pretty significant numbers.
0:20:50 It’s far from a majority.
0:20:53 It’s still single-digit percentages, but like homeschooling is way up.
0:20:57 And homeschooling is way up in particular, not just among religious communities, but in general.
0:21:01 And in fact, among higher income levels and higher class levels, the numbers are particularly striking.
0:21:06 And then look, there’s an industry around it’s forming up and there are companies and startups and there are people running microschools
0:21:09 and there are companies doing matching and teacher training and recruitment and all kinds of things.
0:21:11 You know, activities to be able to enable this.
0:21:19 And so that’s an example where you’re starting to see consumer choice entering a market in which previously everybody just assumed you’re just doing what you’re told top down.
0:21:21 And then housing, housing is still all screwed up.
0:21:24 But you know, the interesting thing on housing is remote work, right?
0:21:29 Up until COVID, the rule of geography and work was very simple, which is you had to move to where the work was, right?
0:21:31 Because employers didn’t hire remote.
0:21:37 And in fact, historically, like in tech, for example, this was one of the main reasons for people to move to Silicon Valley and, you know, take this as an extreme case.
0:21:43 The line in Silicon Valley used to be if you live in Palo Alto, you can get 20 other job offers anytime you want without changing your parking spot.
0:21:46 It’s because there are just so many tech companies around that will hire you.
0:21:52 Well, if you’re a tech worker now and you are remote, not only can you get 20 other job offers, you can get 10,000 other job offers, right?
0:21:54 The asymmetry goes in the other direction.
0:22:06 Even if most employers are not hiring tech workers remote, there are so many who are that as a tech worker, evaluating job offers from employers that will hire remote, there are thousands or tens of thousands or hundreds of thousands of those.
0:22:12 And so, paradoxically, if you’re a remote worker, you now have more employment options than if you’re geographically co-located.
0:22:14 And again, most people in tech have not moved.
0:22:21 Most of them are still in places like Silicon Valley, but the ones who move are doing great and have job offers coming out of their ears and they’re doing fantastically well.
0:22:25 And this is relevant to housing because that means now that they can move to places in which housing is a lot cheaper.
0:22:28 They can move to places where there aren’t a lot of companies.
0:22:30 They can move to a place that actually might be a better place to live.
0:22:36 Maybe they can have a big house on a lake for less money than it would cost to get a one-bedroom apartment in Palo Alto.
0:22:41 So again, it’s this green shoot crack in the matrix thing where it’s okay, you can see how the world can work a different way.
0:22:50 And so, at least, VJ, I think basically that’s a lot of what’s going to happen in healthcare from here on out, which is basically the existing system left its own devices is going to degrade further and further.
0:22:53 It’s going to give worse and worse results at higher and higher cost over time.
0:22:56 As a consequence, some percentage of people are going to try to break out of that.
0:22:59 One of our companies, we have many examples of this, but Levels is a great example.
0:23:09 If you want to take control of your own behavioral health, your own obesity risk or diabetes risk, you can sign up to Levels and they have a whole program and software and everything that will help you do that.
0:23:12 And I don’t even know if there is an insurance reimbursement option, but it’s tech.
0:23:13 It’s not expensive.
0:23:15 You happily pay out of pocket and off in a way you go.
0:23:17 And I tend to think that there’s going to be a lot more of that.
0:23:22 Yeah, it’s also the juxtaposition of the amount of money that you’re paying for something like a Levels experience or function health.
0:23:35 If you actually look at the amount of your wages that is being taken out of your salary to pay for health insurance and then certainly in a high deductible plan, which is increasingly more common in the employer-responsive world, your out-of-pocket spend actually tends to be even higher than that.
0:23:46 And many of these companies, what they’re doing is actually doing direct contract deals where they can take 40 different services like what function is doing that individually in aggregate would have cost you tens of thousands of dollars
0:23:52 under an insurance product, but they’re negotiating a direct contract as a bundle that says for hundreds of dollars, you can basically get the same thing.
0:23:59 And it’s just totally dislocating the price curve for what otherwise beneath the traditional insurance system is just completely unaffordable.
0:24:01 So I think we’re seeing a lot of that.
0:24:04 I think what we’re really providing patients is agency.
0:24:06 And I think that’s what was missing.
0:24:09 Like why can’t you just lose weight or eat better?
0:24:18 I think there needs some help there. And I think this sense of agency is starting to really grow where people have a ability to monitor the health and then do something about it and then repeat.
0:24:21 And that cycle actually I think works for a lot of people.
0:24:25 And being in control of your health feels very different than being at the mercy of a system.
0:24:29 Let’s zoom out a little bit to some of the bigger picture ideas here.
0:24:31 The whole notion of health tech in general, right?
0:24:43 This is a fairly new space. I think much of what has been possible with health tech only came because of the pandemic where there was a relaxation of things like virtual care laws and certifications that were required to practice medicine across state lines.
0:24:51 A lot of this was entrenched in the sense that you were not even allowed to treat patients across state lines prior to some of those emergency laws being put into place.
0:24:54 And so this space is very new and it’s also speculative.
0:25:02 Like a lot of people still have a lot of skepticism about whether this is a durable trend, whether this could actually result in a completely new and big industry over time.
0:25:09 But obviously us and especially you, Mark, having seen so many waves of industry transition and transformation happen in the course of time.
0:25:12 We know that these things sometimes take decades to actually work.
0:25:30 So Mark, specifically, what advice or parables can you share with folks who are watching the digital health space play out maybe from the early days of the internet even to inform how we interpret where we are in this industry from a maturity curve perspective and just providing again some perspective on how long sometimes these things take to play out?
0:25:33 We liked Lenin Mark’s installing quotes every now and then just to make an impression.
0:25:44 So there’s an old Lenin quote, V.I. Lenin, Vladimir Lenin, not John Lenin, to be clear. And he was talking about political change, but he said there are decades in which nothing happens and then there are weeks in which decades happen.
0:25:51 And then in tech, there’s a similar kind of thing called Amara’s Law that is basically changes in technology take a lot longer to happen than you think they will.
0:25:55 But when they happen, they have much more consequence than you think. They’re much more dramatic than you think.
0:26:03 We see this all the time where it can take years and years and years and years if it seems like absolutely nothing happened. And then there’s some sort of catalytic thing that happens and all of a sudden things take off.
0:26:14 And I think there’s a bunch of things that go into this. Part is sometimes it just takes a while to get the technology dialed in. I always like to point out the first smartphone hit the market in 1987 and you didn’t get the iPhone until 2007.
0:26:20 And so it was a full 20 years. And by the way, the first iPhone actually was 2G. It didn’t make phone calls properly. It had all kinds of issues.
0:26:28 You didn’t get the App Store until I think another four years after that. So it was 25 years from the inception of the smartphone industry to actually getting the modern iPhone on a real data network or the real App Store.
0:26:38 It’s just like all the componentry that went into the iPhone, it all had to get mature. It all had to develop like the screens and the batteries and the radios and everything had to get like really good before the whole thing packaged together.
0:26:45 So part of this is on the responsibility of the tech companies themselves, our companies to be able to actually get the right product to market and sometimes that takes time.
0:26:53 The other side of it is the catalytic effect. You know, in the old days to get something new adopted, you always had to do mass marketing, mass media campaigns and hopefully people responded.
0:26:59 A lot of it is now peer to peer. It’s people telling each other and we always reference a lot in our business. It’s okay.
0:27:05 Like when we’re looking at a given thing that might be a big deal as well, is there a subreddit for it? Is there a forum on Reddit where people are talking about it?
0:27:14 And if there isn’t, it almost certainly means that nobody has any of it exists. And then if there is a forum on Reddit where people are talking about it, you can get a sense of what the early adopters are saying and how close it might be to a vertical takeoff.
0:27:21 And then of course these days, the new version of that is like TikTok or Instagram reels or tweets. Is it on the big social networks? Is it taking off?
0:27:26 My guess more and more is it’s going to be peer to peer. When these things take off, it’s going to be because people learn about them online.
0:27:29 They learn about them from their friends. They learn about them from watching videos online.
0:27:34 This, by the way, is the natural frustration. This is the Dr. Google kind of effect extrapolated up, right?
0:27:43 Which is if doctors were frustrated by patients coming in with Google printouts, which I think has been the case for the last 20 years, I think doctors are going to get increasingly frustrated because patients are going to come in and they’re going to be like,
0:27:52 well, I saw this thing on TikTok and I think I should try it. And look, there are going to be downside versions of that, things that are not valid, but there are also going to be things that actually work that actually spread.
0:28:00 Well, I mean, look, just like even fitness, fitness now is almost entirely an online phenomenon. The way people learn how to work out now is almost entirely online.
0:28:06 By the way, also the way people learn how to cook, the way people learn how to eat healthy, like that’s almost entirely happening on social networks and on YouTube.
0:28:09 So I tend to think that phenomenon will happen for more and more areas of health.
0:28:19 And I think a corollary of that is that there will be grassroots movements, even political movements towards health and that this becomes a key part of our lives that people care about.
0:28:28 And so it’s not going to be a top down, wonky, inside Washington movement, but I think you’ll see grassroots political movements and we’re already seeing the beginnings of that as well.
0:28:29 Yeah, that’s right.
0:28:35 By the way, there’s also Dr. TikTok. So physicians are sharing notes with each other also about how to subvert the systems that shackle them.
0:28:38 So I think it’s happening on both the demand side and the supply side of our industry.
0:28:41 Let’s end with a fun one. So I think all three of us have young kids.
0:28:48 I certainly have a son who’s growing up very A.I. native and I’ve observed how he uses tools like even ones that we’ve invested in like character and curio.
0:28:50 I think Mark, your son has one of these two.
0:28:59 And he’s like really developed this comfort around just talking openly about his feelings and his curiosities and his dreams that I think will actually be very healthy for him in his mental health in the future.
0:29:15 So similarly, are there things that you guys observe your kids doing or the younger generations doing in general with A.I. that you think specifically will inform what health care behaviors will emerge over time that could eventually become mainstream and crack some of the nuts that we’ve been talking about today?
0:29:19 Douglas Adams, the great science fiction author who wrote Hitchhiker’s Guide to the Galaxy, had this famous.
0:29:24 He said there’s a three part generational kind of model of technology change, health society that’s to do technology.
0:29:25 He said it’s true for every new technology.
0:29:28 So if you’re under the age of 15, it’s just natural that this is how the world works.
0:29:34 If you’re between the ages of 15 to 35 when a new technology comes out, it’s like super cool and you might be able to get a job working on it.
0:29:39 And if you’re over the age of 35, it’s unholy and against the natural order of things and we’ll destroy all of society.
0:29:41 We, of course, are the exceptions.
0:29:43 We do not fall prey to these ancient patterns.
0:29:50 My version of this was a nine-year-old that when ChatGPT first got going probably what, two years ago, less than two years ago, when it really hit critical mass.
0:29:53 Yeah, so probably in the spring of 23, so he’s like probably eight.
0:29:57 I set up his little laptop for his classwork and so I set up ChatGPT on it and got him his own account.
0:29:59 I was like so proud of myself as a father.
0:30:03 I felt like I had brought down fire from the mountain for my son, right?
0:30:04 And I was going to give him like, “Here’s ChatGPT.
0:30:05 It’ll answer every question you have.
0:30:06 It’ll teach you anything.
0:30:08 It’s going to be with you for your entire life.
0:30:11 Like this is the thing that’s going to make you like a much more advanced version of you.”
0:30:15 And it’s just like the most powerful, amazing thing I can give you as a father.
0:30:19 And I sit him down and I teach him and I’m like, “Look, you type in any question it answers the question.”
0:30:21 And he shrugs and I’m like, “What’s the shrug?”
0:30:22 And he’s like, “It’s a computer.”
0:30:25 Obviously it answers questions like, “What else would you use a computer for?”
0:30:27 I’m like basically that’s it, right?
0:30:33 And so that’s the other part of this is I think a big part of all of everything we’ve been discussing also just as like a generational change.
0:30:36 It’s new cohorts of people coming up and just people are not going to tolerate.
0:30:38 Actually, I think we saw this in the meta.
0:30:42 You tell me, I think we saw this in the health field probably 20 years ago where before the internet,
0:30:44 how would you ever look up whether a doctor was good?
0:30:48 And then once the internet emerges, like all of a sudden is there’s a different size at like rate doctors.
0:30:51 And then by the way, how would you ever as a patient with some condition,
0:30:53 how would you ever read up on it yourself?
0:30:56 Because you can’t obviously it’s not going to you’re going to go read medical journals at the university library,
0:30:58 but then you go on Google.
0:31:04 And so I think we’ve already seen probably at this point several waves of that kind of adaptation as people with the same disease, everything like that.
0:31:05 Yeah, exactly.
0:31:07 They go from being something that’s inconceivable to something that’s common.
0:31:10 And a lot of that is on a cohort age basis.
0:31:14 You get these debates like in the press about is a new technology good or bad and should it be adopted or not?
0:31:18 Those are all beside the point because the fact is like young people are just going to use what’s helpful and useful,
0:31:20 and they’re not going to have the emotional reaction.
0:31:22 And I think that that will apply to many of the things that we’ve been talking about.
0:31:26 If you’re like an executive in their fifties and you’re exhausted,
0:31:30 you don’t have time, you don’t have time to play with this thing and to learn the skills.
0:31:33 The kids will have the playful attitude, will play around with it,
0:31:37 will learn and learn things that maybe even other people didn’t think they could do.
0:31:42 I think they’re at a huge advantage to come up to speed quickly because I think you have to learn how these tools work
0:31:45 and how to train them and how to get them to do what you want.
0:31:47 And I think people are still figuring that out.
0:31:50 But our children will definitely figure out faster than we will.
0:31:51 I think they already have.
0:31:54 Well, with that, thank you both for an amazing conversation about the future of health.
0:31:55 Thank you guys.
0:31:56 Thank you.
0:32:00 All right, that is all for today.
0:32:03 If you did make it this far, first of all, thank you.
0:32:07 We put a lot of thought into each of these episodes, whether it’s guests, the calendar touchers,
0:32:11 the cycles with our amazing editor Tommy until the music is just right.
0:32:17 So if you’d like what we’ve put together, consider dropping us a line at ratethespodcast.com/a16c.
0:32:20 And let us know what your favorite episode is.
0:32:23 It’ll make my day, and I’m sure Tommy’s too.
0:32:25 We’ll catch you on the flip side.
0:32:28 [MUSIC PLAYING]
0:32:31 [MUSIC PLAYING]
0:32:33 (upbeat music)
Healthcare is a $4 trillion industry, making up nearly a fifth of the U.S. economy—but despite having some of the best doctors and advanced technology, the system often delivers poor outcomes at skyrocketing costs. Why is this the case, and what will it take to fix it?
In this episode, a16z cofounder Marc Andreessen and General Partners Vijay Pande and Julie Yoo tackle some of the biggest questions shaping the future of healthcare:
- Is the solution to our healthcare crisis a policy, technology, or competition problem?
- Will AI and technology transform the industry, or are regulatory and structural barriers too entrenched?
- Who will crack the code—healthcare incumbents, tech giants, or AI-native startups?
From chronic care to cost curves, from disruptive technologies to shifting patient agency, this conversation offers an unfiltered look at what’s broken in the healthcare system and how it might finally change.
Resources:Â
Find Marc on X: https://x.com/pmarca
Find Vijay on X: https://x.com/vijaypande
Find Julie on X: https://x.com/julesyoo
The Biggest Company in the World
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Grand Challenges in Healthcare AI with Vijay Pande and Julie Yoo
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