a16z Podcast - a16z Podcast: Software has eaten the world...and healthcare is next

Episode Date: August 15, 2019

Back in 2011, a16z cofounder Marc Andreessen first made the bold claim that software would eat the world. In this episode (originally recorded as part of an event at a16z), Andreesseen and a16z general partner on the bio fund Jorge Conde (@JorgeCondeBio) take a look back at that thesis, and think about where we are now, nearly a decade later—how software has delivered on that promise… and most of all, where it is yet to come. In the wide-ranging conversation, the two partners discuss everything from the translatable learnings of software’s transformation of the music and automotive industries, to how software will now eat healthcare (including what exactly changed in the fields of bio and computer science to make Marc eat his own words!). The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments for which the issuer has not provided permission for a16z to disclose publicly as well as unannounced investments in publicly traded digital assets) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

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. For more details, please see A16Z.com slash disclosures. Hi and welcome to the A16Z podcast. I'm Hannah. In this episode, A16Z co-founder Mark Andresen and general partner on the bio fund, Jorge Kande, take a look back at Mark's software will eat the world thesis and think about where we are now, nearly a decade later, how software has delivered on that promise and where it is yet to come. In the wide-ranging conversation, the two partners discuss everything from the learnings of software's transformation

of the music and automotive industries to how software will now eat health care, including what exactly changed in the fields of bio and computer science to make Mark eat his own words. This conversation was originally recorded as an event at A16Z, so you'll also hear me sharing the questions that were asked at the end so that you listeners can hear Mark's answers. So thrilled here to have our founder, our co-founder, and general partner, Mark Andresen. For those of you that are traveling home after this,

via an airport, you will probably see this smiling face on the cover of a magazine. Magazines, I'm told that this is a new technological device. It's content that comes pre-printed on a paper. Apparently, it's got excellent battery life, but it doesn't update very fast. You can swipe, but you could grip it. You can be very careful. You can try swiping. So what we thought we'd do here is spend some time talking about how technology can transform industries.

And I think there's no better person really anywhere to talk a bit about how technology does transform the world we live in. So I thought we'd start from the very, very beginning. Part of the reason why you're on the cover of a paper computer right now is because the firm is about 10 years old. And around the launch of the firm, you articulated your vision of what was happening in the world, the software is eating the world. I've seen you on stage with Clay Christensen, who is a Harvard Business School professor who coined the term disruptive innovation.

One of the things he spends a lot of his time on is describing what disruptive innovation is and what it is not. So I thought maybe one place to start is to have you describe what, in your mind, softwareity in the world means and what it doesn't mean. Sure. So it's a, yeah, so the term is from an essay that I wrote that the Wall Street Journal ran in, I think, 2011. So shortly after we started the firm. And so the basic observation was that the tech industry, the sort of modern tech industry, kind of as we understand it in the Silicon Valley, you know, that you're sitting in the middle, of right now. It was about a 70-year-old industry started right after World War II when there were a total of like five computers on the planet. And then over the course of the next 70 years,

basically figured out a way to pack, you know, leading edge, state-of-the-art supercomputer technology that used to cost $25 million into a $500 product that we all now have. There's like $4 billion smartphones on the planet now on the way to $7 billion. And so there's like the seven-year journey to basically get everybody on a computer and everybody on the internet that worked. And it was a long journey and lots of drama and lots of fits and starts. But it did fundamentally work. And then so it's kind of like, okay, like is the industry finished? Like are we done? Like congratulations, everybody has a computer mission accomplished. What's next? Everybody's on the internet, mission accomplished, what's next? You know, is there anything that follows? And especially back

then, you know, this is after the financial crisis, there was like a prevailing kind of mood of pessimism, you know, about the global economy and the American economy and the technology industry. And there were lots of press coverage at the time was like, you know, text just in another stupid bubble and there's nothing interesting happening. There's nothing left to do. You know, innovation's dead. This stuff is all, you know, from here on now. It's all just stupid little silly games and things that don't matter. And so I, in my view, it's sort of the exact opposite, right, which is not only we're not done, we're just beginning, right?

Which is, okay, now we have a computer in everybody's pocket. It's like an incredibly powerful computer with like a lot of capabilities, which we'll talk about related to health. And then everybody's on the internet. Everybody's connected to everybody else and to, you know, kind of an entire universe of services and information and communications and everything else. Like, to me, it's just like, okay, that's the beginning, right? It took 70 years to build a platform, get into a position.

It's like, okay, now what can we do on top of that? And so what I tried to do with the concept of software rates, the world is kind of say, okay, how does this unfold from here, kind of across industries? And the way I described it was in three layers, and I was sort of three claims, which I would say increase as you go in audacity or arrogance, depending on your point of view, or just flat out hubris, which is another possibility. So the base level claim is, the first claim is any product or service in any field that can become a software product will become a software product, right? And so if you're used to doing something on the phone, that'll go to software, if you're used to doing something in the phone, that'll go to software. if you're used to doing something in person, and then that can go to software, it'll go to software.

If you've had a physical product, you know, and think about things like, you know, remember telephone answering machines, right? Or tape players, boom boxes, like all these, you know, all the things Radio Shack used to sell. Like, they're all apps on the phone, right? Cameras, yeah, remember there used to be a physical product called a camera.

You know, that got vaporized. Right, by the way, you know, physical newspapers, physical magazines. If it can become bits, it becomes bits. And, right, why does it become bits? It's like, well, if it's better in, like, in a lot of ways. It's like, it's like, you know, so bits like are zero marginal cost.

So it's easy, they're easy to replicate at scale, become much more cost effective. A lot of, a lot of bits just drop to free. By the way, they're much more environmentally friendly, you know, which is an increasing thing for a lot of people. You know, you can change bits much more quickly. You can innovate much more quickly, add new features, add new capabilities. So there's just lots and lots of reasons why it's good to get things from physical form into software if you can. And so anything that can get into software will get into software. The next kind of, next kind of claim from there then is every company in the world that,

is in any of these markets in which this process is happening, therefore has to become a software company, right? And so companies that historically either did not have a technology component to what they did or maybe have the classic conception of technology and business, which is called, you know, sort of like IT, right? Sort of, you know, we've got these gnomes in the back office, right? And they've got their lab codes and they've got their mainframes and they kind of do their thing.

And they print out these reports. And for some reason, the reports are still in all caps. You know, like, there's that. But then there's like, okay, like modern, which you might call sort of modern software development. right? And especially like customer experiences, like what's the actual interface to the customer? Any company that, you know, any company deals with customers, especially consumers, is going to have to, I think, really radically up its game in terms of its ability to build the kinds of UIs and experiences that people expect these days. So every company becomes a software company. And then the most audacious claim is, as a consequence of one and two, in the long run in every market, the best software company will win.

And that doesn't mean necessarily that it would be a new company that starts as a software company that enters an existing market that wins. but it also doesn't necessarily mean that an incumbent that adapts to being a software company will win. And increasingly, and you see this, right, in many industries, including health care, including insurance, right? You'll see many cases now where you'll have, you know, kind of these new pure play software companies

entering these incumbent markets. And, you know, usually from a position of, like, youth naivete, and maybe they're wrong and maybe the idea is stupid, or maybe it's Uber and Lyft, right, entering the taxi market, and maybe they just have a fundamentally better software-driven approach. And then you've got incumbents, right,

scrambling to try to basically figure out how to become software companies, which is tricky, Because true software, the way we think about it, like, it's different. It's not the same. It's different. It's a different kind of product to develop than a lot of people are used to. The culture of a software company is different than the culture of most existing companies. And then the kinds of people you need to hire to build software, especially modern software, especially things like mobile software, AI software, cloud software. These are special people. These are, say, highly creative individuals. Is it just a random example, the defense contractors and intelligence agencies are having to revamp all their drug use. policies, like right now, like the whole P&A Cup thing before you get hired, like, doesn't work if you're trying to hire modern software development capabilities. So just, like, one random example, but, like, there are lots of instances where these cultures are different.

And then you can kind of say, okay, if that's the framework, then you can kind of go industry by industry and say, okay, for each industry, like which industries are more prone for that to happen in? And obviously, in some industries, it's like super clear. The media industry is an example where it's just, like, obvious how fast that's happening. There are other industries, like I would say cars is an example we might talk about quite a bit. So transportation, I would say it's kind of right in the middle.

which is like the incumbents in the auto industry have a really good claim on the idea that building cars is like incredibly hard, incredibly dangerous, very regulated. And the idea that a bunch of software founders out in the valley are going to start car companies is kind of absurd. But there's, you know, 500 self-driving car startups within 50 miles of where we sit. And what those founders would tell you is all the value, 90% of the value of the car in five or 10 years is going to be in software because the car is going to be an autonomous electric vehicle. It's going to be autonomous. It's going to be self-driving, which means it's going to have all this software that the legacy car companies don't know how to make. and then it's going to be electric, so it's not going to have all the internal combustion components of these car companies who spend 100 years optimizing. And then, by the way, the car might go from being a consumer product that people buy to just being a service that people access on demand, right?

And so ride-sharing networks in the self-driving world might just be, you don't own a car, you just press a button, and a self-driving car shows up and takes you where you need to go. And so, you know, so I would say there's a pitch battle kind of shaping up in the auto industry. And then there's a bunch of other industries in which I would say the incumbents are, you know, much more comfortable that they don't face this kind of disruptive challenge. and maybe they're right. Yeah. They're entrepreneurs now. There are software-driven entrepreneurs, Silicon Valley-style entrepreneurs, sort of trying to figure out, like,

trying to, by the way, including, like, really big, like, education. Education is becoming a very hot market, right? Education is not a market that you would characterize as having had a lot of innovation over the last thousand years. And there's, you know, there's a new generation of a founder out here that has this is a pretty compelling new offerings to education. I would say even real estate. There's a lot of surprising amount of innovation happening in real estate.

actually law as a field, which again, it's not like traditionally super innovative. There's a lot of new software entrants into the legal field. And so there's people are going to be trying in basically every industry. And so I want to make sure we get to the healthcare-shaped elephant at some point in the room. But to look back on the software, each of the world thesis, the three audacious claims, as you call them, any surprises that you've seen in the intervening years that you said, okay, if I were to rewrite that today, I would have taken a different view. Yeah, so I think the big one I mentioned already, but I think that what's happening

in the car industry, like, when we started the firm 10 years ago, I'd never imagine that we'd be investing in, like, literally new car companies like that. Just think about how crazy that. The auto industry was like an entrepreneurial industry in like 1890, right? And like, you know, and then in the 1920s, like Henry Ford, you know, it's kind of the Bill Gates of his era, kind of figured the whole thing out. And then there were literally no new American car companies. There was one major new American car company since the 1920s. So there were like hundreds of new car companies in like the 1910s and they shrunk to basically three. And then they stabilized. And then there was a huge new car. There was an attempt.

There was an entrepreneur named Preston Tucker in the 1950s that created a car company called Tucker Automotive. It was me the bold new thing. And it was such a catastrophe. They made a movie about what a catastrophe it was called Tucker. And so like if you were an entrepreneur attempted to start a car company, you just watch the movie Tucker. And it's like, okay, I'm not doing that. And so the idea that an industry that established would be opening up the way that it is has been very striking. That's been the most striking one. By the way, I use the term kind of software very broadly, just in the sense of code that runs on chips and networks. You've all I'm sure been reading about and seeing.

The rise of this sort of concept you hear under the terms machine learning, deep learning, artificial intelligence. Like in the valley, there are like two profound technological revolutions happening right now and that have the best engineers the most excited, and that's one of them. By the way, the other one is cryptocurrency blockchain, which is a whole other conversation, but sort of machine learning, deep learning AI is an incredibly fertile area of creativity right now and is advancing at an incredibly high rate of speed technologically. And so the other question that I think is increasingly coming up when we think about the kinds of companies and founders we back is kind of how AI-native or ML, sort of machine learning native,

the founders are and the companies are. And even in the valley, there's a big, there's a big spread, I think, between the software founders that have really figured out this new technology and how to use it and the founders that still haven't kind of tuned up on it. So it's like very much in flux. And if that stuff works the way that it looks like it might work, you know, that could really be transformative even beyond just the idea of software. Oh, I think that's right. So if we look at a couple of the industries that have been responsive and receptive, I mean, the auto industry, you're right, I think it's a big surprise that they would have adapted to the fact

that cars are becoming more sort of software-centric. What about industries that have been almost entirely transformed? So take, for example, the music industry. I think if you live outside of Silicon Valley, if you sort of looked at the first wave of the internet, one of the first industries that was fundamentally transformed was the music industry. Do you think that other industries will likely suffer that fate

that music has? Yeah, so it's a funny thing. So music was like, I think, is something like a triple whammy. So first of all, one of the interesting things about music was, it turns out people really love music. And I say that because, like, generally, when we fund startups, like, the question always is, like, will the dogs eat the dog food? Like, are people actually going to want this thing? And the thing with music is, like, what was the huge issue with music? It was, like, piracy.

Like, all of a sudden, you know, the music listeners went crazy and I'll start breaking the law and I'll start to listen to music online. And the record labels all freaked out. And they were like, well, what, you know, basically, like, our customers have turned evil. And it's like, well, you know, maybe. but, like, first of all, like, wow, isn't it great that they all love music so much? And for some reason, the music executives I never thought that was a very good point,

but I thought it was interesting. I'm like, look, they want the thing. Like, they're showing, normally in business, when the customers line about the door and they're like, I want to consume, you know, music digitally, you would normally want to say, okay, I want to find a way to service them. You know, the music label heads went,

no, you shouldn't be able to get music digitally. And so that was the first interesting thing. It was the reverse of the normal supply and demand problem you have. And it was literally overwhelming consumer demand for online music, streaming music, music and it was overwhelmingly suppliers refusing to accommodate it. So that was weird. So then it was like, okay, well, why is that happening? Well, then you get into the pricing, right? And as you guys all

know, the pricing had become, you know, there's 12 albums, you know, there's 12 songs in the album. The album costs 17 bucks and I want one of the songs, right? And so congratulations, I could just pay $17 for a song, you know, and then another 11 songs I don't want. And so then it's like, okay, well, that's weird. Like, you know, is that really, but the whole structure of the, of the record industry had gotten built up around that. And then there was the thing that it actually got down to, which took a while to kind of surface, but it ultimately did finally come out, which was it was a cartel. It was like a full-on, and a competitive, monopolistic cartel with price fixing. And we now know that because there were antitrust cases from this era that

finally appealed the whole thing. So this has all become since public record. But they were all colluding. And so there were, you know, four or five labels and they were all getting together and setting prices. And that's why they were, that's in retrospect why they were so dug in. And it was a magical business model, right? I mean, it's like, if you could, like, let's imagine you could collude. And then let's imagine as a consequence of that you could overcharge by like a factor of 10. Like, wouldn't that be great? And so that, that in retrospect was the thing that I think a lot of us out here missed because their behavior was just so illogical otherwise. You know, the problem was that lasted until it didn't last, right? And then, you know,

the way that didn't last is first the consumers. The consumers, I think, like, the consumers were breaking the law, but however, they had actually, they were breaking the law. They were doing the wrong thing, but for the right reasons, they had concluded that the industry that was servicing them was actually immoral, which was actually correct. It actually was immoral. It is immoral to price fix and collude and illegal. So, right, you had illegal customers, legal customer behavior and illegal supplier behavior, like super healthy market. And so, you know, so what's the moral of the story? You know, what's the moral of the story? Well, it's like, okay, that which can become software will become software, there was just overwhelming. And, you know, look, we all live

this today. Like, how do I want to listen to music? I pull up Spotify on my phone, listen to music. Like the idea being forced back to, you know, figuring out which box in the garage has the CDs. You know, it's just, you know, sounds like medieval torture. And so the thing that can become software will become software. And then, you know, prices are going to rationalize. And we could talk more about that. But there's like, there's a big, I think, rationalization of prices happening across the economy that's pretty interesting as a consequence of the increased transparency. And then, you know, the suppliers, like the cartels, you know, the cartels attached the old technology aren't going to survive.

Like that kind of transformation is going to be a really big deal. And it took time. Like, you know, it took 15 years. Maybe it's the other thing. Like, you know, it was 15 years of the record labels trying to hold out. And by the way, it was 15 years of tech startups that tried to solve this problem. And so you probably remember, if you're into music, it's like, I don't know, there was, I forget, you know, there was Napster, which got put out of business early on. It could have been the thing.

But then there was Kazah, and there was LimeWire, and there was BitTorn, and then there were all the early streaming services. And actually, it's interesting is they were all terrible venture investments. They were all catastrophes, right? Because they were too early, like, because they couldn't get the rights to the music because the labels wouldn't do the trade, they wouldn't do the deal. And so they could never get rights to the music. And so they could never actually offer a service the consumers actually wanted that was also legal. And so they were actually all bad investments. But then finally, after 15 years, the pressure built to the point where it actually was time for fundamental change.

And that's when Spotify kind of catalyzed. Actually, a lot of U.S. like us actually did not invest in Spotify at that time because there was this 15-year history that all the other attempts to do what Spotify was doing had failed. But the time had actually come. And now it's obvious what happens, which is like music is like $10 a month and it's all you listen to it. And Spotify has, I don't know how many. But Spotify is going to end up with like a half billion or a billion subs at like $10 a month. and then they're parceling out all the money to the artists. And everything that in music could become software, has become software.

And the one thing that still you have to do in person is the experiential part of going to see a musician perform. So that's where musicians today make a lot of their money, right, in terms of going and having the in-person piece. And I think if you look at the healthcare industry, I mean, I think there's probably some element to that. There's always going to be a human element, an in-person component to treating and managing disease and patients.

Well, actually, there's a related point there, which is actually there's this weird, Clay Christensen actually points this out. There's this weird thing where you often see in many industry structures, when one layer commoditizes, the next layer can become incredibly valuable. And so it's this deceptive thing because people are focused on the layer that's commoditizing and kind of the shrinkage kind of revenue and market cap that's happening. And they tend to think that means the whole industry is going down. But like, you know, look, the music contracted, right? The amount of money people spent on recorded music shrunk dramatically. It's finally started to grow again with streaming, but like it shrunk dramatically over the course of, you know, 15, 20 years. what actually happened is super interesting is the compliment expanded dramatically. So over that same time period, I think the U.S. Market for Live concerts over the last 15 years grew 4x in aggregate dollars, aggregate inflation-adjusted demand. And in a sense, it kind of makes sense, which is like, okay, congratulations, you know, Mr. Consumer, congratulations, you now have unlimited access to all the recorded music you want. It's now free. Everybody has it. You know, there's no status. You don't have the, like, record labels. You know, you don't have the, you know, the LPs lined up on your shelf. And if you have, you

you're, you know, courting a young man or young woman and they come over and you want to show up your music. You know, you don't get to do it. It's like, hey, look at my Spotify, right? It's not the same. So, you know, so there's no social effect to it. It's not really fun. You know, it's good. It's like, it's consumer nirvana, except it's like they've drained out all the fun. And so what's fun is like going to the concert, right? And by the way, I'm not spending as much money in recording music, and therefore I have more money available to actually buy concert tickets. And so the concert business, the sort of experience side of it has exploded in revenue. And you might, you could easily hypothesize the exact same thing happening in healthcare,

For example, if more of the actual products and services in health care could get commoditized, and over time you could break the cost curves and actually shrink, you know, maybe concierge medicine would just explode, right? Maybe what people actually, maybe a lot more people actually want the kind of concierge experience. Today, they can't afford it, but if you, if you crack the price curve and a lot of the other stuff, maybe you could open that up. And so, yeah, so it's basically the moral of that is just pay attention to compliments. It's not just a single factor. There are other implications for other areas of spending. Yeah, and so actually on that note,

You know, given in the health care industry, we're, of course, one-way shape or form. We're all customers of the health care industry over our lifetime we will be. You served on the board of the Stanford Hospital for five, six years. Could you talk a little bit about what you learned about the delivery of health care from that, from serving on the board of a hospital and really coming in as a layperson to the industry? I would say the best thing about it was, you know, the mission of the place was obviously just amazing. And I would say the mission both in terms of the actual health care, but also the mission of the translation of medical research, you know, the integration of the medical school, and all the research happening. It was a nonprofit with highly motivated people, which was exciting to see.

You know, and then, yeah, there was innovation happening all over the place. And in fact, it was actually exciting because we got, we had the chance to actually design and build a new hospital, which I'm delighted to say is opening finally this fall. So we greenlit the project, I believe, in 2005. We're opening it in 2019. These are all 15-year cycles. These are 15-year cycles. And then, you know, that got, you know, we spent a lot of time on the design of the new hospital,

which was super interesting. You know, the two things that were probably the biggest, I don't know, surprise, the things that just kind of really jumped out, it's like 25 board members, right? So our boards, like our well-functioning boards at our companies, it's like seven people. Beyond seven people, you can't have a good discussion.

And so, you know, 25 people is like a UN summit. And so I would not describe the board meetings. It's highly dynamic. And we didn't really get into a lot of the issues. And then the other kind of just really thing that blew me away, which I'm still kind of tracking and I'm fascinated by, was the issue of quality. So I happened to join the board right after we hired our first chief quality officer, which was a guy who'd come out of management consulting, Six Sigma, kind of manufacturing quality thing. And for those of you who kind of know the history of these things, the U.S. auto industry, like, was a huge ascend in the industry of the 50s and 60s, but it had this massive quality problem, which was like literally people were dying. Like, there were no seatbelts in the cars.

like the steering columns were like impaling people. Like there were all kinds of horrific problems. And then when the Japanese and the Germans came in with safer cars, like it catalyzed a huge crisis in the U.S. auto industry. And Ralph Nader made his name originally by crusading. The book was called Unsafe at Any Speed, which was a reference both to the car and to the industry. And then starting in the 70s, 80s, 90s,

the auto industry implemented this thing, the TQM total quality management, 6 Sigma, which is a process to kind of get all the bugs out, you know, kind of the idea of defect-free manufacturing. Which is why generally, if you buy a car today, like it's a far higher quality experience than a car 50 years ago. And usually it's actually a much better experience even than a car 10 or 20 years ago. Like they're quite good now.

You read these histories of like, I don't know, when they figured out collar and the water or stuff like this, they figured out like what germs are and what infection is. And it's like, you know, it was 18, whatever, 1880 or something. They figured out it's a good idea to wash your hands before you perform surgery. And so it's 2004 and there's still doctors walking into rooms and getting people sick. The compliance rates for the scrubbing into the rooms. It's like, I don't know, 34% or something. I was just like, oh, fuck.

Like, sorry. It's like, how can you... Anyway, so that was at the front end of that. You know, it's been fascinating to track that because on the one hand, it's very clear that they've made a lot of progress. On the other hand, there is innovation yet to be done. I mean, so...

And you guys, I think you guys must know... I'm sure you guys know all this, but, like, you know, medication compliance. Like, the data on medication compliance is absolutely horrifying, right? It's like, I don't know, something like two-thirds of all prescribed medications are not...

It's like a third of all prescribed medications are unfilled, right? Another third are not, people don't take them on schedule, right? And then you get the details on it. And like a lot of people, especially older people, you give them like eight or ten or twelve different medications. They're supposed to track it. They'll just dump all their medications into like the candy bowl and like mix it all

up. And every day they'll take a handful of pills, right? And actually, that's pretty good, right? That's better than just it's all on a shelf somewhere. They can't get the bottles open. And so, you know, medication compliance is a train wreck. It's actually the, I read this thing the other day. Medication compliance on medication compliance on the medication after

organ transplants, it's actually terrible. It's only like, kidney transplants, it's only like 60% compliance. It's incredible. It's incredible, right? And it's like, it's in the other 40% like you're going to die, right? And they still can't get compliance, right?

And so, and there's eight different reasons for that. And so there's that issue. Another is just like, yeah, literally tracking the doctors. Like an idea, just an idea that we should fund. Like, we're seeing all these new, actually, we're seeing all these new technologies now to do things like, for example, to watch an assembly line environments

to have like cameras that like watch everybody's, basically time and motion, like in the factory, and then you can use these machine learning technologies to kind of decode, like, are people doing the right thing? Are they tightening the screws, tightening the bolts? Are the machines running properly? You know, and maybe we should have, like, a camera outside, you know, every patient door and, like, are the doctors and nurses

actually, like, scrubbing their hands? Like, you know, Purell. Yeah, Purell. Like, you know, Purell track. So, like, fairly basic stuff is still, I mean, I think the reality is, I think there's a lot of basic stuff that's still not being done. And so there's, yeah, there's, yeah, and the problem with this kind of thing is,

it's like, okay, like, you know, what is it, what's the medical errors are the what most common in the hospital? I think they're the third. Yeah. And then, of this whole issue of infection, you know, hospital-borne infections, like it's, I think, an open question how much of that is fixable and errors, you know, compliance issues. And it's definitely not getting better. Yeah, right, exactly.

Yeah. And so. And while you were on the board of the hospital, you know, a lot of, when folks think about software and healthcare, people just automatically assume, you know, EMR is sort of the example that a lot of people gravitate towards. Did you go through the experience of incorporating and implemented an EMR at Stanford while you were on the board? Tell us a little bit about that process. Oh, yeah, yeah, yeah, yeah.

We put that out to bed. I think we got back one viable bid, I think, for the complexity of Stanford Hospital. It was epic. It was a $400 million project, probably, I don't remember the fact, probably 100 to Epic or something like that. And then after 300, we went out for integration bids, and this is where I almost started crying. It was Perot Systems. Ross Perot Systems, which was Perot. Systems was the follow-up to EDS, Ross Perrault's company, which is now owned by Dell.

And so, yeah, so it's a 400 million to help project pro systems. I remember this was 2005, I think, where we started the Epic implementation, and they were very excited. They were very excited in the demo, and I was very excited because I was like, wow, this is like a new hospital information. It's probably going to be mobile. This is when smartphones are starting to take off, and it's going to be mobile, and it's going to be sensors and, like, all this stuff.

It's going to be great. And it was like they were super excited because they had just moved to the Windows 95 UI, right, in 2005. There's like the big upgrade from Windows 3.1. And I was like, oh, my God. And so, but, you know, as you know, like, it's 20, it's 2019, and it's still, right, it's obviously, it's still, it's still the same thing. Right. Yeah.

And the other incredibly entertaining thing about Epic is that they are so, you know, out here, it's like, out here there's a big focus on software interoperability. And so it's like, can one piece of software work with another? You know, there's whole concept of what are called, there's entire companies now that are called API companies that build, basically software building blocks that you plug together. There's open source. And so out here, it's just this constant process of everybody building and everybody else's creativity and kind of the whole thing rises, except for Epic, which. It has an absolute prohibition on third-party integration that does not tolerate. It will sue you if you attempt to integrate with it. So when you launched the firm, you famously said, or in the early days of the firm,

you famously said that you won't see A16Z investing in bio and healthcare, and that's obviously changed. Can you talk a little bit about the evolution of that thought process? Modern venture capital is like roughly 50 years old. It kind of started in the 1970s and kind of the modern form. And there are basically two fields within venture that actually worked. There's sort of what you might call, you know, digital technologies, computer-based technologies, IT broadly defined, and then there's biotech. And biotech kind of broke down traditionally into new pharma, new therapies,

and then new treatments, and then new medical devices. And actually, a lot of the best venture capital firms actually had dual practices, right? And so there are many examples, this Kleiner Perkins being a very prominent one for a long time. They had dual practices, and so they have the digital team, then they have the health care team. And, you know, once upon a time, they kind of collaborated, they all work together. And then what happened, right, is the economics of those two sectors just, like, fundamentally diverged. And the fundamental reason for this is, right, in the sort of digital technologies and digital venture,

you're fundamentally writing this curve called Moore's Law, right, which is sort of the, basically the price of the underlying components for software and hardware basically falls in half every 18 months. So you get this amazing kind of downward cost curve, and that's why you keep coming up with new applications for computers because everything keeps getting cheaper. And really quickly, right? And then in new pharma and in new medical devices, you had the reverse of more. law literally, which is called E-R-R-O-O-M, which is more backwards. And E-R-R-O-M-Law is the cost to bring a new drug or a new medical device to market doubles every N-year. Right. So the cost goes

the wrong direction, right? And 20 years ago or 15 years ago, what happened was the VCs that were in both basically decided that they didn't work anymore. The economic cycles were too different. So you could fund, you know, Facebook, you know, with whatever, $20 million, or you could fund a new pharma effort with a billion dollars, right, and still probably have to raise another $3 billion by the time you're done, right, or end up selling out to Big Pharma at some point. And so they just became kind of two fundamentally different domains. And then by the way, they were two fundamentally different sciences, right? Because they were sort of computer science on the one side, biological science on the other side. And they didn't really intersect.

You didn't really use computers that much doing new drug discovery or new medical devices. And so that was the situation we saw in 2009. It was kind of, they were actually separating out. And actually, the leading kind of biotech VCs now are not names that anybody in Silicon Valley would even necessarily know because it's just in such different worlds. What we started to see, starting about six years ago, starting around 2012, probably 2013, we started to see a new kind of founders show up. We started seeing founders showing up with PhDs and biology, you know, often MDs, and then also either degrees in computer science or the equivalent of degrees in computer science. Sometimes actually like dual PhDs, but also sometimes it was just,

you know, I'm a PhD in bio, but I've actually been programming computers since I was 10. I've got, you know, 25 years, you know, I've got 20 years or whatever, sort of the equivalent of, like, you know, educational experience of computer science. And so, and then these founders are showing up with these kinds of hybrid, right, technologies that were kind of half bio-health, half computer science. And then honestly, they would come in and pitch us, right? And then I, this is why I was called the dogs watching TV, you know, phenomenon, you know, because they'd be up there and they'd be talking about the genome and this and, you know, the exome and ribly nucleic, this and that, the other thing. And, you know, we're all just kind of like, you know,

sort of vague, you know, I've heard these words before. I don't quite know what they mean. And then they would say, you know, algorithm, and we'd all go, woof, right? Like, you know, we get that. And then, you know, and then we didn't know quite what to make of these, right? And so then we'd ask the founders, just be like, well, what happened? When you go pitch the bio-VCs, the healthcare VCs, like, what are they doing? And it's like, you know, it's so weird. It's like dogs watching TV, you know, except, you know, we go on and on and on about machine learning, and they just look at us puzzled, and then we say, you know, ribble to clay, and they get all excited. And so what happened was, we basically said,

there's this missing middle, right, which is basically the convergence of these. Actually, actually, it's interest is the convergence of the scientific domains, right? And then as a consequence, it's the conversion of the technological domains, and then that means the convergence of the industries. And so we just started to see this repeating pattern of these new kinds of founders. And we said, well, look, we said it's unlikely that these bioVCs have gotten so detached from computer science that they're unlikely to figure this out. A bunch of the computer science VCs just got done shutting down their healthcare practices. They're not probably going to leap back into it. Maybe there's this new thing in the middle. And then there we got VJ, or a partner

VJ, who was a professor of Stanford, where he was literally right in the middle of this convergence in his time at Stanford, and he came over, and he kind of spun us up on this whole domain, and then, and then Jorge joined us subsequently. And I think we've, like, I think we've discovered, like, there's a real, there's a real vein here, right? And it's interesting, because, like, we are seeing more of the CS-focused VCs starting to edge in now and adapt. We're also seeing more of the life sciences VCs starting to edge in, but it's still there's this, there's this thing in the middle. And then, as you were indicating, like, with the, with the epic question, like, you know, there's, for sure, it's like, I mean, there's, like, the

pure, like, convergence, which is like the concept of digital therapeutics, right? So, you know, and things like, for example, diabetes and so forth. And then there's all these potentially new kinds of diagnostic, new kinds of sensors, you know, use the sensors in the phone to do diagnostics, things like that. And then there's a lot of work happening in, like, bioinformatics and, you know, the research side, you know, sort of cloud biology is a big thing that we have. And then there's actually applying information technology to the operations of the actual healthcare industry, which gets into things like medical records and hospital management services and stuff like that. And so we've basically decided, we're taking a very broad,

We're taking a very broad brush at this, and we're working in all those areas. And I think we're finding it to be a very dynamic and very fertile area. Absolutely. What advice, someone who's been investing in, has been an entrepreneur, has been investing in entrepreneurs now for above a decade, what advice would you give industry leaders in terms of how to engage with innovators with entrepreneurs? And conversely, what advice should we be given to entrepreneurs to engage with folks that are leading the industry?

Yeah, so the big thing to think about, the big difference between kind of how the Valley works and kind of the rest of the business world works is as follows, which is, like, in most of the business world, like, you've got some existing position in something and you're trying to figure out, like, what to do with it, and you're trying to figure out how to defend it, you know, defend a market, or you're trying to figure out how to advance, innovate within the market, but like you're kind of dealing with a big existing, you know, big existing companies, big existing businesses. You know, out here, we don't generally have that. You know, we're generally starting from scratch. And I think the way to think about it is, Silicon Valley startups, they're, They're experiments, first and foremost. And they're experiments often in technology. We're trying to take actually much scientific experimental risk, but there are technological experiments. Can you build the product? And they're business experiments, which is like, you know, is anybody going to want this thing?

Am I going to be able to make a business on it? Am I going to be able to turn a profit on it? They're experiments. And, like, you might say, well, that's dumb. Like, why would you, like, risk all this money and effort launching experiment for a product that you don't know whether you can build and you don't know anybody would want? And most of the world doesn't run those experiments. And so maybe there should be one place that does.

Right? and this is that place. And so the ethos of the valley is these are experiments. And that's actually what leads to this interesting phenomenon in the valley, which is you'll have founders that have a company that's like just like almost, in some cases, like a famous train wreck, like it just didn't work at all. And like, you know, five years later, they'll go start the next company and they'll easily raise money for it. And again, like a lot of the rest of the world would be like, well, why are you getting behind somebody who already failed? And the valley, it's like, well, if they learned along the way, right, and they're now better at running the

experiments the second time, let's find him to run the second experiment. And in fact, a lot of the best companies in the valley are founded by people who had one or two significant failures before they, you know, before they founded the winner. And so, so I viewed as like it's, it's an incredibly fertile landscape of experiments, and there's, you know, there's thousands of experiments being run. And, you know, these are pretty big, you know, can we build the self-driving car? It's like a pretty big experiment. So then it's when kind of, you know, people, especially from established industries come here, it's kind of, it's kind of like the temptation is to evaluate, like, each experiment one by one. So it's like, you know, you look at a

given startup, and it's like, well, I don't know, like, you know, this thing might work, the technology might work, the business might work, you know, this, whatever, this might be the right founder. Don't quite know there's all this idiosyncratic risk with this experiment. And I feel like I should make the decision whether or not to talk to this startup or work with this startup based on the characteristic of this, you know, of this particular instance. That's one way to do it. The other way to do it is more like what we do, which is also what I think the big companies that are good at doing this also do, which is you can say, well, look, it never makes sense to just run one experiment, but it might make sense to run 10 experiments. And so it might be that the partnership model that makes sense is let's put together a portfolio. Like, let's figure out 10 areas that we think are potentially interesting.

Let's find the 10 most interesting startups in those areas. And then let's try 10 partnerships. And let's think about it very explicitly as a portfolio of, you know, portfolio of investments, a portfolio of partnerships, a portfolio of new supply relationships, whatever it is. And then let's evaluate the result of those 10 experiments as a basket. Right. And just the nature of probabilities being what it is. Some of them are going to work. Some of them are not.

Right. But the ones they're going to work might work really, really well. Right. And what I just described you is literally what we do. It's like, it's the venture capital mentality. But it's also, I think, the best construct for thinking through how to engage with startups as a big company. By the way, the other side of it is the temptation for thinking about like, you know, a new joint venture or a new investment or something, a new product is, you know, will it succeed or not? And so it's like, and the traditional way you report this to the board is it's like, you know, green light, yellow light, red light, like is a famous management consulting chart with like the bulbs, right?

And you want all the lights to be green. If any of the lights are yellow, people have very stern looks in the face. And if any lights are red, like, is a disaster. somebody gets fired. And so, you know, it's past fail, right? I actually think that the way that you want to think about this is, you know, it's not a question of like, does it work. It's a question of like, if it works, like, how big can it get? Like, if it works, how big of impact could it have? So a new technology you might be looking at in your business that might be a new route to market or a new way to cut costs, like if it works, you know, if it cut costs good. Does it cut a million

dollars worth of cost or a billion dollars worth of cost, right? That might be the actual relevant question, right? So as opposed to just success failure. And just the nature of these things is like these experiments often don't work, but when they do work, they can actually work really, really well. They can get really, really, really big and have a really, really big impact. And so, yeah, so that's the general model. It's a portfolio approach, and then an understanding and appreciation of the asymmetric nature of the winds relative to the odds that there will be some set of losses. Great. Silicon Valley has been pretty visible in movies and television, et cetera. You may have had a hand in some of that yourself in terms of advising

some of these shows? No comment. What do you... Only the good ones. What do you wish that people that didn't live here in Silicon Valley knew about Silicon Valley? It's funny, I've been listening to the Elon Musk audiobook. You just may remember, like, before the Model

S shipped, like everybody thought he was just completely full of it, right? And he was going to make this car, and there's just like no way, it's impossible, can't be done. And then this freaking car comes out, right? It's like the Model S comes out, and it literally wins, like, hard of the year awards everywhere. It has, like, the best safety rating of any car ever made. And there were all these people who were like, oh, yeah, he's a fraud. They're literally

mouse hanging open, like cannot believe it, right? And so that actually is kind of the more common story. And so the scientific and technical substance of what happens out here does tend to be quite real. But then the other side of it is what I said. These are experiments. Like if this stuff was a slam dunk, like if it was, if it's obvious how to apply a scientific result into a technological product and then build a business around it, like big companies are going to do all that. Like there are lots and lots of big companies in the world, you know, in health care, outside of health care in the tech industry that are good at doing the obvious stuff. And so by the nature of the valley, we're doing the non-obvious stuff. We're doing the stuff that's not yet proven. We're doing

the stuff that's controversial. We're doing the stuff that really might fail. And so there is risk with each and everything that we do, whether or not it will work. But God willing, when it does work, it could get really big. Wonderful. Thank you. So let me see if, are there any questions in the audience that we could field? So we're here today in a place that's known as a center of innovation, but many of us have to go be agents for innovation and change in industries that aren't necessarily as open to it. What's your advice for that? How do you think about doing something innovative that you believe in, that you think will work when others might say, oh, we're more traditional.

This is the way things are done. So there's actually a term of art in the industry in the valley for what it's called the evangelistic sale. And it's actually really interesting. It's like our companies come to market with a new product, a new widget that does something. They'll go hire sales reps out of companies that sell normal products. And those sales reps will come in and they'll just completely whiff. because they'll get back this reaction for every customer being like, yeah, I'm used to buying whatever Oracle databases from you, but I don't know what to do with this new thing.

And then those sales reps actually don't know how to sell that thing, and those marketing people don't know how to market that thing. It's a different kind of thing. And so there's a specific kind of seller, sales rep and marketing person out here. It's sort of the evangelistic seller, the evangelistic marketer. And, you know, it honestly, I don't know, there's magic in it. It has to do, it has to do with painting a vision, right? It has to do with painting a vision of the future.

There's the marketing. You know, it's sort of the old Steve Jobs used to say is like that, you know, The problem with consumer research is nobody knew they wanted a Macintosh, right? Like, until the Macintosh, or nobody knew they wanted an iPhone, like until the thing showed up, people can't visualize new products on their own. And so you have to paint a picture. And the picture has, you know, that has to be vivid, right? And this is where some of these guys, like Elon get criticized for kind of overselling.

But, like, they have to paint a vivid picture. As an example. So Elon comes out with a Model S. Here's a great example. Elon comes out with the Model S. Congratulations, it's a car that you plug into the specialized charging ports. Well, how many specialized charging ports are out there that I can plug this car into? Zero.

Okay. Now I'm going to buy a model. It's like buying the first fax machine. It's like, congratulations. I now have the first fax machine. Who can I fax? You know, I now have a very expensive doorstop. Like, you know, good job.

And so like, so what Elon did when he launched a Model S is he painted a picture. You know, he painted a picture. He went up and gave a big presentation. He said, look, we're going to put these supercharger stations, right? And all these different locations along all these freeways. And he mapped the whole thing out. And he's like, here you're going to be able to drive cross country. You know, and you're going to get, you're in charge for free the entire way.

And by the way, none of those charging stations existed at that point. But he did, he did lay that vision out. And then he said, look, here's the thing you'll put in your garage, you know, and it'll hook up and here's how much it'll cost. And then, you know, within a year, he had the, you know, people were putting these things in their garages. And he was putting the charging stations. The superchargers were up. And, like, it worked. And then he sold enough of the cars into that vision that he was actually able to afford to build all those charging stations.

And so it's painting the picture in a way that people can believe. It's painting the picture, by the way, also consistent with reality. Like, I believe, you know, there does have to be a substantive claim of the whole thing in work. And then I think, honestly, I think the other thing is it has to attach, maybe obvious, it has to attach to humans. psychology. And this is the other thing the evangelistic sellers are really good at, which is, so in sales speak, the evangelistic sellers are really good at qualifying, which is there are certain customers where they're just not going to do new things, like where they're just focused on downside risk. They want, you know, it's like they go to work every day, they go home with their

family. Like, I don't want to do anything that might cause me to look bad and get fired. And that's a completely legitimate, you know, way to operate. A lot of people are like that. And so the evangelistic seller, one of the things they do is they just qualify those people out. I'm not going to spend any time with those people. But what they find are the minority of people who are like, okay, like, I don't want to spend my career just protecting downside. I would like to become known as within my own company as somebody who's innovative, right, and in the future. And I would like to basically stake a career bet myself, right, on a new technology. And the nature of that career bet, if it doesn't work, I'm going to look bad.

But if it does work, wow, I'm going to look like a hero, and I'm going to get promoted, and I'm going to be the next CEO of the company. And so it's kind of like the evangelistic seller meets the early adopter buyer who's got the right psychological mindset. And then what's super interesting actually is those people, actually become very close. You know, the salesperson then becomes what we call a consultative seller. They become actually very tightly integrated into the lives of the sponsoring executives on the other side of the table.

And they're basically fundamentally trying to make each other heroes in their respective organizations. And they often end up extremely personally close because they're on a shared mission to do something new. And so anyway, this is kind of what we advise our companies to do. But like that's the process. And then it's the process of like, okay, then it's the gut check, which is like, okay, are those early adopters actually out there?

right? Do they actually exist, right? Do they have the authority to actually make those kinds of decisions, right? Or at some point, by the way, if they don't exist, that itself is an interesting market signal. Like, if the early doctors don't exist, it may just be time to start a new company in that market. So that's the other thing that happens is the founders are like, oh, it's like imagine being Travis Kalinick and trying to start Uber, and your first idea is I'm going to build taxi dispatch software and I'm going to try to sell it to taxi cab companies. And then you spend two years trying to get taxi cab companies to buy this taxi dispatch software and they all say no, you might have That is literally what happened, but you could very easily imagine that happening. And so that is the other thing that emerges out of this is people just decide to start the company. What do you believe are some of the biggest challenges to getting new technologies and solutions adopted, particularly in the healthcare space? The general thing that happens, which is really relevant to all these healthcare markets, is the product works, and I can't get paid. Like, one form of that problem is, I just can't get paid. Like, literally, like, the customer is not going to pay for this product because it's going to be reimbursed. It's a third-party model, and it doesn't matter how many patients want X if the insurance company.

he's not going to pay for it. And so that's a particularly stark example of can't get paid. There's another example I can't get paid, which is I just can't get paid enough. We have a lot of companies that have a problem that I call too hungry to eat, which is basically, like, imagine a starving person 10 feet away from a plate of filet mignon, but like I'm starving, and I don't have the energy to pull myself to the plate. The Silicon Valley version of that is, I have a great product. My customers really want it, but I'm charging very little money for it. Usually these are naive product founders who don't quite understand business, and so they think if they charge less, they'll sell more. But they actually charge less, they end up selling less.

And the reason is because they don't charge enough for the product. They're not getting enough revenue back into the company. They're not getting literally enough calories back into the company, dollars into the company. And then they can't afford to hire the kinds of sales and marketing people to do the kind of sale that we're talking about. And then they just get stuck. Right? They get stuck. It's like the product works in theory.

The customers want it in theory, but they don't have, the company doesn't have the internal funding. They're not making enough money on each sale to be able to justify the cost of sale. And this is actually a very funny conversation to have with the founder because it's literally like, the conversation is so weird. It's like, okay, this, you know, tell me about your product. oh, it's the best product ever, and machine learning and this and that. It revolutionizes, it's going to save our companies $10 million each in saved expenses. And it's like, okay, what are you charging for it? $50,000.

It's like, well, you're going to save them $10 million. Why are you only charging $50,000? Well, it's going to be easy to sell. Like, it's just going to, we're going to sell it and go to the next customer. It's like, well, what do you have to do to convince the customer to buy the thing that's going to save them $10 million? And it's like, oh, we've got to send in, you know, eight people for, you know, six months. Well, what does that cost? Well, it costs.

Well, it costs a million and a half bucks. Okay. So congratulations, right? You're now down, you know, $1.45 million of negative cash burn on every sale you make. That's your strategy, right? And then you're going to make it up in volume, right? That's just like, and literally what I'm describing is like literally what we see happens. And by the way, a lot of the time it's actually the founder themselves who's actually on site with the customer. And right, it's like what's their time worth, right? Because their time's getting sucked down. It's like the entire future of the company, right, is being basically bled out. And so like a big rule we have, a big thing we always have here. Like the principle is like you have to get paid. like the customer has to pay for the thing. If it's an indirect thing, you have to, and this is the thing with, right, all the healthcare startups. Like, they have to be able to decode the system, right? Which is why it's so great to have you all here.

And then on top of that, like, you really do, in a lot of cases, like the right answer actually is raise prices, which is weird because it's like, well, technology is supposed to drive down prices. What are you doing, raising prices? It's like, well, you have to make enough money per sale. The internal economics of your business have to be such, you make enough money per sale that you can afford to actually build the company. Right?

And then you can build the company. You can build the sales and marketing engine. you can get the thing known and get the thing adopted, you can get references. And then once you're at scale, then you can start to drive the price down. Yeah, and I would just add in the healthcare realm, and this is an overgeneralization, but what we often see is, you know, the business model failures are often a lack of recognition that the person who will benefit from your solution is often not the buyer. So you have sort of this mismatch that often happens in the health care system,

that you're targeting your customer, but your customer there's a different buyer. So that's a very difficult thing that can usually be addressed with business model, but it has to be recognized. The second one is point solution versus complete solution. It's very hard for a startup to big bang an A to Z solution, but oftentimes that's oftentimes that's sort of buyer needs because they don't need another point solution. So it's figuring out what the insertion point is going to be for any particular innovation. And then the third one that we always see with a lot of our startups that they have to be

very thoughtful as to how they approach it is recognizing how you can introduce a new technology without disrupting existing workflows because the job that happens in health care delivery is incredibly complex. So even if you have a better mousetrap, if that better mousetrap requires you to change the way you work, it's very hard to implement. So those are, I think, the three big challenges

that all of our entrepreneurs see from business model standpoint. So if you can overcome those, I think you have a much better chance of having an innovation get adopted. Thank you for having us. Thanks very much. Thank you, Mark.

Thank you.