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

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0:00:10 tax or investment advice, or be used to evaluate any investment or security, and is not directed
0:00:13 at any investors or potential investors in any A16Z fund.
0:00:16 For more details, please see a16z.com/disclosures.
0:00:20 Hi, and welcome to the A16Z podcast.
0:00:21 I’m Hannah.
0:00:26 In this episode, A16Z co-founder Mark Andreessen and general partner on the bio fund Jorge
0:00:30 Andre, take a look back at Mark’s software will eat the world thesis and think about where
0:00:33 we are now, nearly a decade later.
0:00:36 How software has delivered on that promise and where it is yet to come.
0:00:40 In the wide-ranging conversation, the two partners discuss everything from the learnings
0:00:44 of software’s transformation of the music and automotive industries to how software
0:00:49 will now eat healthcare, including what exactly changed in the fields of bio and computer
0:00:52 science to make Mark eat his own words.
0:00:57 This conversation was originally recorded as an event at A16Z, so you’ll also hear me
0:01:02 sharing the questions that were asked at the end so that you listeners can hear Mark’s
0:01:03 answers.
0:01:08 So thrilled here to have our founder, our co-founder and general partner, Mark Andreessen.
0:01:14 For those of you that are traveling home after this via an airport, you will probably see
0:01:17 this smiling face on the cover of a magazine.
0:01:23 I’m told that this is a new technological device, it’s content that comes pre-printed
0:01:24 on a paper.
0:01:31 Apparently, it’s got excellent battery life, but it doesn’t update very fast, so.
0:01:36 You can swipe, you can rip it, you can try swiping.
0:01:41 So what we thought we’d do here is spend some time talking about how technology can transform
0:01:47 industries and I think there’s no better person really anywhere to talk a bit about
0:01:50 how technology does transform the world we live in.
0:01:53 So, I thought we’d start from the very, very beginning.
0:02:00 Part of the reason why you’re on the cover of a paper computer right now is because the
0:02:06 firm is about 10 years old and around the launch of the firm, you articulated your vision
0:02:11 of what was happening in the world as software is eating the world.
0:02:15 I’ve seen you on stage with Clay Christensen, who is a Harvard Business School professor
0:02:18 who coined the term disruptive innovation.
0:02:23 One of the things he spends a lot of his time on is describing what disruptive innovation
0:02:25 is and what it is not.
0:02:28 So I thought maybe one place to start is to have you describe what in your mind, software
0:02:30 eating the world means and what it doesn’t mean.
0:02:31 Sure.
0:02:36 So, the term is from an essay that I wrote that’s the Wall Street Journal random in I
0:02:40 think 2011, so shortly after we started the firm.
0:02:45 And so the basic observation was that the tech industry, the sort of modern tech industry
0:02:48 kind of as we understand it in the Silicon Valley, that you’re sitting in the middle
0:02:49 of right now.
0:02:53 It was about a 70-year-old industry, started right after World War II when there were like
0:02:57 a total of like five computers on the planet.
0:03:01 And then over the course of the next 70 years, basically figured out a way to pack leading
0:03:05 edged state-of-the-art supercomputer technology that used to cost $25 or $50 million into a
0:03:07 $500 product that we all now have.
0:03:11 There’s like four billion smartphones on the planet now on the way to seven billion.
0:03:14 So there’s like the seven-year journey to basically get everybody on a computer and everybody
0:03:18 on the internet that worked and it was a long journey and lots of drama and lots of fits
0:03:19 and starts.
0:03:20 But it did fundamentally work.
0:03:22 And then so it’s kind of like, okay, is the industry finished?
0:03:23 Like are we done?
0:03:26 Like congratulations, everybody has a computer, mission accomplished.
0:03:27 What’s next?
0:03:28 Everybody’s on the internet, mission accomplished.
0:03:29 What’s next?
0:03:30 Is there anything that follows?
0:03:34 And especially back then, this is after the financial crisis, there was like a prevailing
0:03:37 kind of mood of like pessimism about the global economy and the American economy and the
0:03:38 technology industry.
0:03:41 And there were lots of press coverage at the time was like, “Text just in another stupid
0:03:43 bubble and there’s nothing interesting happening.
0:03:44 There’s nothing left to do.
0:03:45 Innovation is dead.
0:03:48 This stuff is all, from here on out, it’s all just stupid little silly games and things
0:03:50 that don’t matter.”
0:03:52 And so my view is sort of the exact opposite, right?
0:03:54 Which is not only we’re not done, we’re just beginning, right?
0:03:59 Which is okay, now we have a computer in everybody’s pocket with like incredibly powerful computer
0:04:02 with like a lot of capabilities, which we’ll talk about related to health.
0:04:06 And then everybody’s on the internet, everybody’s connected to everybody else and to kind of
0:04:10 an entire universe of services and information and communications and everything else.
0:04:12 Like to me, it’s just like, okay, that’s the beginning, right?
0:04:15 It took 70 years to build the platform, get in the position, it’s like, “Okay, now what
0:04:17 can we do on top of that?”
0:04:19 And so what I tried to do with the concept of software is the world was kind of say,
0:04:22 “Okay, how does this unfold from here kind of across industries?”
0:04:27 And the way I described it was in three layers and I was sort of three claims, which I would
0:04:32 say increase as you go in audacity or arrogance, depending on your point of view, or just flat
0:04:33 out hubris, which is another possibility.
0:04:38 So the base level claim is, the first claim is any product or service in any field that
0:04:41 can become a software product will become a software product, right?
0:04:44 And so if you’re used to doing something on the phone, that’ll go to software.
0:04:46 If you’re used to doing something on paper, that’ll go to software.
0:04:48 If you’re used to doing something in person, and then that can go to software, it’ll go
0:04:50 to software.
0:04:53 If you’ve had a physical product and think about things like, remember telephone answering
0:04:55 machines, right?
0:04:59 Or tape players, boomboxes, all the things Radio Shack used to sell, they’re all apps
0:05:00 on the phone, right?
0:05:01 Cameras, yeah.
0:05:05 Remember, there used to be a physical product called a camera.
0:05:06 That got paperized, right?
0:05:10 By the way, physical newspapers, physical magazines, if it can become bits, it becomes bits, right?
0:05:11 Why does it become bits?
0:05:14 It’s like, well, if it’s bits, it’s better in a lot of ways.
0:05:20 So bits like our zero marginal cost, so they’re easier to replicate at scale, become much
0:05:21 more cost effective.
0:05:22 A lot of bits just drop to free.
0:05:26 By the way, they’re much more environmentally friendly, which is an increasing thing for
0:05:27 a lot of people.
0:05:29 You can change bits much more quickly.
0:05:32 You can innovate much more quickly, add new features, add new capabilities.
0:05:35 So there’s just lots and lots of reasons why it’s good to get things from physical form
0:05:37 into software if you can.
0:05:40 And so anything that can get into software will get into software.
0:05:45 The next claim from there then is every company in the world that is in any of these markets
0:05:49 in which this process is happening therefore has to become a software company.
0:05:53 So companies that historically either did not have a technology component to what they
0:05:57 did, or maybe have the classic conception of technology and business, which is called
0:05:58 IT.
0:06:03 We’ve got these gnomes in the back office, and they’ve got their lab coats, and they’ve
0:06:06 got their mainframes, and they do their thing, and they print out these reports, and for
0:06:10 some reason the reports are still in all caps.
0:06:11 There’s that.
0:06:14 But then there’s like, okay, like modern, which you might call sort of modern software
0:06:15 development, right?
0:06:19 And especially like customer experiences, like what’s the actual interface to the customer.
0:06:22 Any company that deals with customers, especially consumers, is going to have to, I think, really
0:06:26 radically up its game in terms of its ability to build the kinds of UIs and experiences
0:06:28 that people expect these days.
0:06:32 So every company becomes a software company, and then the most audacious claim is as a consequence
0:06:37 of one and two, in the long run in every market, the best software company will win.
0:06:41 And that doesn’t mean necessarily that it would be a new company that starts as a software
0:06:44 company that enters an existing market that wins, but it also doesn’t necessarily mean
0:06:47 that an incumbent that adapts to being a software company will win.
0:06:51 And increasingly, and you’ll see this in many industries, including healthcare, including
0:06:52 insurance, right?
0:06:56 You’ll see many cases now where you’ll have kind of these new pure play software companies
0:07:00 entering these incumbent markets, and usually from a position of like youth and naivete,
0:07:03 and maybe they’re wrong, and maybe the idea is stupid, or maybe it’s Uber and Lyft entering
0:07:07 the taxi market, and maybe they just have a fundamentally better software driven approach,
0:07:10 and then you’ve got incumbents, right, scrambling to try to basically figure out how to become
0:07:14 software companies, which is tricky, because software, the way we think about it, like
0:07:15 it’s different.
0:07:16 It’s not the same.
0:07:17 It’s different.
0:07:19 It’s a different kind of product to develop than a lot of people are used to.
0:07:22 The culture of a software company is different than the culture of most existing companies,
0:07:25 and then the kinds of people you need to hire to build software, especially modern software,
0:07:29 especially things like mobile software, AI software, cloud software, like these are special
0:07:35 people, and I say special, you know, multiple definitions of special, like these are, these
0:07:38 are, let’s say, highly creative individuals.
0:07:41 Just a random example, the defense contractors and intelligence agencies are having to revamp
0:07:45 all their drug use policies, like right now, like the whole P and a cup thing before you
0:07:49 get hired, like, doesn’t work if you’re trying to hire modern software development capabilities.
0:07:53 It’s just like one random example, but like there are lots of instances where these cultures
0:07:54 are different.
0:07:57 And then you can kind of say, okay, if that’s the framework, then you can kind of go industry
0:08:00 by industry, and say, okay, for each industry, like which industries are more prone for that
0:08:03 to happen, and obviously in some industries, it’s like super clear.
0:08:06 The media industry is an example where it’s just like obvious how fast that’s happening.
0:08:09 There are other industries, like I would say cars is an example we might talk about quite
0:08:12 a bit, so transportation, I would say it’s kind of right in the middle, which is like
0:08:16 the incumbents in the auto industry have a really good claim on the idea that building
0:08:20 cars is like incredibly hard, incredibly dangerous, very regulated, and the idea that a bunch
0:08:24 of software founders out in the valley are going to start car companies is kind of absurd.
0:08:28 But there’s, you know, 500 self-driving car startups within 50 miles of where we sit,
0:08:31 and what those founders would tell you is all the value, 90% of the value of the car
0:08:35 in five or 10 years is going to be in software, because the car is going to be an autonomous
0:08:36 electric vehicle, right?
0:08:38 So it’s going to be autonomous, it’s going to be self-driving, which means it’s going
0:08:41 to have all this software that the car, the legacy car companies don’t know how to make,
0:08:44 and then it’s going to be electric, so it’s not going to have all the internal combustion
0:08:47 components of these car companies that’s been 100 years optimizing.
0:08:50 And then by the way, the car might go from being a consumer product that people buy to
0:08:52 just being a service that people access on demand, right?
0:08:56 And so ride sharing networks in the self-driving world might just be you don’t own a car, you
0:08:59 just press a button, and a self-driving car shows up and takes you where you need to go.
0:09:03 And so, you know, so I would say there’s like a pitch battle kind of shaping up in the auto
0:09:06 industry, and then there’s a bunch of other industries in which I would say the incumbents
0:09:10 are much more comfortable that they don’t face this kind of disruptive challenge, and
0:09:11 maybe they’re right.
0:09:12 Yeah.
0:09:16 They’re entrepreneurs now, they’re software-driven entrepreneurs, Silicon Valley-style entrepreneurs,
0:09:21 sort of trying to figure out, like, by the way, including like really big, like education.
0:09:23 Education is becoming a very hot market, right?
0:09:27 Education is not a market that you would characterize as having had a lot of innovation over the
0:09:29 last thousand years.
0:09:33 And there’s, you know, there’s a new generation of founder out here that has this pretty compelling
0:09:38 new offerings to education, I would say, even real estate, there’s a lot of surprising
0:09:42 motivation happening in real estate, actually law as a field, which again, it’s not like
0:09:45 traditionally super innovative, there’s a lot of new software interest into the legal
0:09:46 field.
0:09:51 And so there’s, people are going to be trying in basically every industry.
0:09:52 Yeah.
0:09:56 And so I want to make sure we get to the healthcare-shaped elephant at some point in the room.
0:10:02 But to look back on the software, each of the world thesis, the three audacious claims,
0:10:06 as you called them, any surprises that you’ve seen in the intervening years that you’ve
0:10:11 said, okay, you know, if I were to rewrite that today, I would have taken a different
0:10:12 view.
0:10:13 Yeah.
0:10:15 So I think the big one I mentioned already, but I think that what’s happening in the car
0:10:18 industry, like when we started the firm 10 years ago, I would never imagine that we’d
0:10:20 be investing in like literally new car companies like that.
0:10:24 Just think about how crazy that, the auto industry was, the auto industry was like an
0:10:27 entrepreneurial industry in like 1890, right?
0:10:32 And then in the 1920s, like Henry Ford, it’s kind of the Bill Gates of his era, kind of
0:10:33 figured the whole thing out.
0:10:36 And then there were literally no new American car companies.
0:10:40 There was one major new American car company since the 1920s, so there were like hundreds
0:10:43 of new car companies in like the 1910s, and they shrunk to basically three.
0:10:44 And then they stabilized.
0:10:47 And then there was a huge new car, there was an attempt, there was an entrepreneur named
0:10:51 Preston Tucker in the 1950s that created a car company called Tucker Automotive.
0:10:55 It was the bold new thing, and it was such a catastrophe, they made a movie about what
0:10:57 a catastrophe it was called Tucker.
0:11:00 And so like if you were an entrepreneur tempted to start a car company, just watch the movie
0:11:02 Tucker and it’s like, okay, I’m not doing that.
0:11:07 And so the idea that that, you know, an industry that established would be opening up the way
0:11:10 that it is has been very striking.
0:11:11 That’s been the most striking one.
0:11:15 By the way, I use the term kind of software very broadly just in the sense of like code
0:11:18 that runs on chips and networks, while I’ve sure been reading about and seeing the rise
0:11:22 of this sort of concept you hear under the terms machine learning, deep learning, artificial
0:11:23 intelligence.
0:11:27 Like in the valley, that’s in the valley, there are like two profound technological revolutions
0:11:33 happening right now and that have the best engineers the most excited, and that’s one
0:11:34 of them.
0:11:38 By the way, the other one is cryptocurrency blockchain, which is a whole other conversation,
0:11:43 but the sort of machine learning, deep learning AI is an incredibly fertile area of creativity
0:11:48 right now and is advancing at an incredibly high rate of speed technologically.
0:11:52 And so the other question that I think is increasingly coming up when we think about
0:11:56 the kinds of companies and founders we back is kind of how AI native or ML is sort of
0:12:00 machine learning native, the founders are and the companies are, and even in the valley
0:12:03 there’s a big, there’s a big spread, I think, between the software founders that have really
0:12:06 figured out this new technology and how to use it and the founders that still haven’t
0:12:07 kind of tuned up on it.
0:12:11 And so it’s like very much in flux and if that stuff works the way that it looks like
0:12:14 it might work, you know, that could really be transformative even beyond just the idea
0:12:15 of software.
0:12:17 Oh, I think that’s right.
0:12:21 So if we look at a couple of the industries that have been responsive and receptive, I
0:12:25 mean, the auto industry, right, I think it’s a big surprise that they would have adapted
0:12:29 to the fact that cars are becoming more sort of software centric.
0:12:32 What about industries that have been almost entirely transformed?
0:12:33 So take, for example, the music industry.
0:12:39 I think if you live outside of Silicon Valley, if you sort of looked at the first wave of
0:12:43 the internet, one of the first industries that was fundamentally transformed was the
0:12:45 music industry.
0:12:51 Do you think, you know, that other industries have will likely suffer that fate that music
0:12:52 has?
0:12:53 Yes.
0:12:54 It’s a funny thing.
0:12:55 So music was like, I think it’s something like a triple whammy.
0:12:58 So, so first of all, one of the interesting things about music was it turns out people
0:13:00 really love music.
0:13:03 And I say that because like generally when we fund startups, like the question always
0:13:05 is like, well, the dogs eat the dog food, like are people actually going to want this
0:13:06 thing?
0:13:08 And the thing with music is like, what was the huge issue with music?
0:13:09 It was like piracy.
0:13:13 Like all of a sudden, you know, the music listeners went crazy and I’ll start to break
0:13:16 in the law and I’ll start to listen to music online and the record labels are freaked out
0:13:19 and they were like, well, what, what, you know, basically like our customers have turned
0:13:22 evil and it’s like, well, you know, maybe.
0:13:25 But like, first of all, like, wow, isn’t it great that they all love music so much?
0:13:28 And for some reason, the music executives, I knew never thought that was a very good
0:13:29 point.
0:13:30 But I thought, I thought it was interesting.
0:13:34 I’m like, look, they want, they want the thing like they’re showing normally in business
0:13:37 when the customers line about the door and they’re like, I want to consume, you know,
0:13:38 music digitally.
0:13:40 You would normally want to say, okay, I want to find a way to service them.
0:13:43 You know, the music label heads went, no, you shouldn’t be able to get music digitally.
0:13:45 And so that, that, that was the first interesting thing.
0:13:48 It was, it was the reverse of the normal supply and demand problem you have.
0:13:51 And it was literally overwhelming consumer demand for online music, streaming music,
0:13:52 digital music.
0:13:55 And it was overwhelmingly suppliers refusing to accommodate it.
0:13:56 So that was weird.
0:13:58 So then it was like, okay, well, why is that happening?
0:14:00 Well, then you get into the, into the pricing, right?
0:14:04 And then as you guys all know, like the pricing had become, you know, there’s 12 album, you
0:14:07 know, there’s 12 songs in the album, the album costs 17 bucks and I want one of the songs,
0:14:08 right?
0:14:12 You know, I can just pay $17 for a song with, you know, and then another 11 songs I don’t
0:14:13 want.
0:14:17 And so then it’s like, okay, well, that’s weird, like, you know, is that really, but
0:14:20 the whole structure of the record industry had gotten built up around that.
0:14:23 And then there was the thing that it actually got down to, which took a while to kind of
0:14:27 surface, but it ultimately did finally come out, which was, it was, it was a cartel.
0:14:31 It was like a full on and a competitive, monopolistic cartel with price fixing.
0:14:34 And we now know that because there were antitrust cases from this era that finally impealed the
0:14:35 whole thing.
0:14:38 So this, this has all become since public record, but they, they were all colluding.
0:14:41 And so they were, you know, four or five labels and they were all getting together and setting
0:14:42 prices.
0:14:45 And that’s why they were, that’s in retrospect why they were so dug in.
0:14:47 Because it was, and it was a magical business model, right?
0:14:50 I mean, it’s like, if you could, like, let’s imagine you could collude and then let’s imagine
0:14:53 as a consequence of that, you could overcharge by like a factor of 10, like, wouldn’t that
0:14:55 be great?
0:14:59 And so that, that in retrospect was the thing that I think a lot of us out here missed because
0:15:04 their behavior was just so illogical otherwise, you know, the problem was that lasted until
0:15:05 it didn’t last, right?
0:15:09 And then, you know, the way that didn’t last is first the consumers, the consumers, I think,
0:15:14 like, the consumers were breaking the law, but however, they had, they had actually,
0:15:15 they were breaking the law.
0:15:17 They were doing the wrong thing, but for the right reasons, they had concluded that the
0:15:21 industry that was servicing them was actually immoral, which was actually correct.
0:15:22 It actually was immoral.
0:15:24 It is immoral to price fix and collude and illegal.
0:15:28 So, right, you had illegal customers, illegal customer behavior and illegal supplier behavior,
0:15:32 like super healthy market.
0:15:34 And so, you know, so what’s the moral of the story?
0:15:35 You know, what’s the moral of the story?
0:15:38 Well, it’s like, okay, that which can become software will become software.
0:15:41 There was just overwhelming, you know, look, we all live this today, like, how do I want
0:15:42 to listen to music?
0:15:46 I pull up Spotify on my phone and listen to music, like the idea of being forced back
0:15:49 to, you know, figuring out which box in the garage has the CDs, you know, it’s just, you
0:15:52 know, sounds like medieval torture.
0:15:55 And so, the thing that can become software will become software.
0:15:58 And then, you know, prices are going to rationalize, and we could talk more about that, but there’s
0:16:01 like, there’s a big, I think, rationalization of prices happening across the economy that’s
0:16:04 pretty interesting as a consequence of the increased transparency.
0:16:07 And then, you know, the suppliers, like the cartels, you know, the cartels, the cartels
0:16:09 attach the old technology aren’t going to survive.
0:16:13 Like that kind of transformation is going to be a really big deal.
0:16:16 And it took time, like it, you know, it took 15 years, maybe is the other thing, like it,
0:16:18 you know, it was 15 years of the record labels trying to hold out.
0:16:21 And by the way, it was 15 years of tech startups that tried to solve this problem.
0:16:24 And so, there, you probably remember, if you’re into music, it’s like, I don’t know, there
0:16:27 was, I forget, you know, there was Napster, which got put, you know, put out a business
0:16:30 early on that could have been the thing, but then there was Kaza, there was LimeWire, and
0:16:33 there was BitTorrent, and then there were all the early streaming services.
0:16:36 And actually, what’s interesting is they were all terrible venture investments.
0:16:39 They were all catastrophes, right, because they were too early, like, because they couldn’t
0:16:42 get the rights to the music, because the labels wouldn’t do the trade, they wouldn’t do the
0:16:43 deal.
0:16:45 And so, they could never get the rights to the music, and so, they could never actually
0:16:48 offer a service the consumers actually wanted that was also legal.
0:16:50 And so, they were actually all bad investments.
0:16:53 But then finally, after 15 years, the pressure built to the point where it actually was time
0:16:55 for fundamental change, and that’s when Spotify kind of catalyzed.
0:16:59 Actually, a lot of, a lot of VCs like us actually did not invest in Spotify at that time, because
0:17:03 there was this 15-year history that all the other attempts to do what Spotify was doing
0:17:04 had failed.
0:17:07 But the time had actually come, right, and now it’s obvious what happens, which is like
0:17:11 music is like 10 bucks a month and it’s all you want, you listen to it, and Spotify has,
0:17:14 I don’t know how many, but Spotify is going to end up with like a half billion or a billion
0:17:18 subs at like 10 bucks a month, and then they’ll, they’re parceling out all the money to the
0:17:19 artists.
0:17:22 And everything that in music could become software has become software.
0:17:23 It has become software.
0:17:27 The one thing that still you have to do in person is the experiential part of going to
0:17:29 see a musician perform.
0:17:34 So that’s where musicians today make a lot of their money, right, in terms of going and
0:17:35 having the in-person piece.
0:17:38 And I think if you look at the healthcare industry, I mean, I think there’s probably
0:17:39 some element to that.
0:17:42 There’s still, there’s always going to be a human element, an in-person component to
0:17:45 treating and managing disease and patients.
0:17:48 Well, actually, there’s a related point there, which is actually, there’s this weird, Clay
0:17:49 Christiansen actually points this out.
0:17:53 There’s this weird thing where, you often see in many industry structures, when one layer
0:17:56 commoditizes, the next layer can become incredibly valuable.
0:17:59 And so it’s this deceptive thing, because people are focused on the layer that’s commoditizing
0:18:02 and kind of the shrinkage, kind of, you know, revenue in the market cap that’s happening.
0:18:04 And they tend to think that means the whole industry is going down.
0:18:07 But like, you know, look, the music industry contracted, right, the amount of money people
0:18:09 spent on recorded music shrunk dramatically.
0:18:12 It’s finally started to grow again with streaming, but like it shrunk dramatically over the course
0:18:14 of, you know, 15, 20 years.
0:18:17 What actually happened is super interesting is the complement expanded dramatically.
0:18:21 So over that same time period, I think the U.S. market for live concerts over the last
0:18:23 15 years grew 4X.
0:18:24 Wow.
0:18:28 And that’s in aggregate dollars, aggregate inflation-adjusted demand.
0:18:32 And it kind of makes sense, which is like, okay, congratulations, you know, Mr. Consumer,
0:18:33 congratulations.
0:18:35 You now have unlimited access to all the recorded music you want.
0:18:36 It’s now free.
0:18:37 Everybody has it.
0:18:38 You know, there’s no status.
0:18:41 Like, there’s, you know, there’s no, you don’t have the, like, record labels.
0:18:44 You know, you don’t have the, you know, the LPs lined up on your shelf, and if you’re,
0:18:46 you know, courting a young man or young woman, and they come over and you want to show off
0:18:49 your music, you don’t get to do, you know, it’s like, hey, look at my Spotify, right?
0:18:50 It’s not the same.
0:18:52 So, you know, so there’s no social effect to it.
0:18:53 It’s not really funny.
0:18:55 It’s good.
0:18:58 It’s like, it’s consumer nirvana, except it’s like they’ve drained out all the fun.
0:19:01 And so what’s fun is like going to the concert, right?
0:19:03 And by the way, I’m not spending as much money in recording music, and therefore I have more
0:19:05 money available to actually buy concert tickets.
0:19:09 And so the concert business, the sort of experience side of it has exploded in revenue, and you
0:19:14 might, you could easily hypothesize the exact same thing happening in healthcare, right?
0:19:18 For example, if more of the actual products and services in healthcare could get commoditized,
0:19:21 and over time you could break the cost curves and actually shrink it, you know, maybe concierge
0:19:24 medicine would just explode, right?
0:19:27 Maybe what people actually, maybe a lot more people actually want the kind of concierge
0:19:28 experience today.
0:19:29 They can’t afford it.
0:19:30 But if you, if you, if you correct the price curve and a lot of the other stuff, maybe you
0:19:31 could open that up.
0:19:32 And so, yeah.
0:19:35 So it’s basically the moral of that is just pay attention to the compliments.
0:19:36 It’s not, it’s not just a thing.
0:19:37 It’s never a single factor.
0:19:40 There are other implications for other errors in spending.
0:19:41 Yeah.
0:19:44 And so actually on that note, you know, given in the healthcare industry, we’re of course
0:19:49 one way, shape, or form, we’re all customers of the healthcare industry over our lifetime
0:19:50 we will be.
0:19:55 You served on the board of the Stanford hospital for five, six years.
0:20:00 Could you talk a little bit about what you learned about the delivery of healthcare from
0:20:06 that, from serving on the board of a hospital and really coming in as, as a layperson to
0:20:07 the industry?
0:20:09 I would say the best thing about it was, you know, the mission of the place was obviously
0:20:10 just amazing.
0:20:13 And I’d say that the mission, both in terms of the actual healthcare, but also the mission
0:20:18 of the translation of, of, of medical research, you know, the, the integration with the medical
0:20:19 school, you know, the research happening.
0:20:23 It was an on profit with highly motivated people, people, which was exciting to see.
0:20:26 You know, then, yeah, there was innovation happening all over the place.
0:20:28 And in fact, it was actually exciting because we got, we had the chance to actually design
0:20:34 and build a new hospital, which I’m delighted to say is opening finally this fall.
0:20:39 So we, we agree, we agree and let the project, I believe in 2005 and we’re opening it in
0:20:40 2019.
0:20:41 These are all 15 year cycles.
0:20:43 These are 15, 15, 15 year, 15 year cycles.
0:20:45 And then, you know, that, that got, you know, that we spent a lot of time on the design
0:20:47 of the new hospital, which was super interesting.
0:20:50 You know, the two things that were probably the biggest, I don’t know, the surprises, the
0:20:54 things that just kind of really jumped out, it’s like 25 board members, right?
0:20:57 So our boards, like our well functioning boards at our companies, it’s like seven people,
0:21:00 beyond seven people, you can’t have a good discussion.
0:21:04 And so, you know, 25 people is like a UN summit.
0:21:07 And so I would not describe the board meetings as highly dynamic and we didn’t really get
0:21:09 into a lot of the issues.
0:21:12 And then the other kind of just really thing that blew me away, which I’m still kind of
0:21:15 tracking and I’m fascinated by was the issue of quality.
0:21:19 So I happened to join the board right after we hired our first chief quality officer,
0:21:23 which was a guy who had come out of management consulting at Six Sigma, kind of, you know,
0:21:24 manufacturing quality thing.
0:21:27 You know, for those of you who kind of know the, kind of the history of these things,
0:21:31 the U.S. auto industry, like, was a huge ascended industry in the 50s and 60s, but had this
0:21:35 massive quality problem, which was like literally people were dying, like there were no seatbelts
0:21:38 in the cars, like the steering columns were like impaling people, like there were all
0:21:40 kinds of horrific problems.
0:21:43 And then when the Japanese and the Germans came in with safer cars, like it catalyzed
0:21:48 a huge crisis in the U.S. auto industry, and Ralph Nader made his name originally by crusading.
0:21:51 The book was called “Unsafe at Any Speed,” which was a reference both to the car and
0:21:52 to the industry.
0:21:56 And then starting in the ’70s, ’80s, ’90s, the auto industry implemented this thing,
0:22:00 the TQM Total Quality Management, Six Sigma, which is a process to kind of get all the
0:22:04 bugs out, you know, kind of the idea of defect-free manufacturing, which is why generally if you
0:22:08 buy a car today, like it’s a far higher quality experience than a car 50 years ago, and usually
0:22:12 it’s actually a much better experience even than a car 10 or 20 years ago, like they’re
0:22:13 quite good now.
0:22:16 You read these histories of, like, I don’t know, when they figured out collar on the
0:22:17 water or stuff like this.
0:22:19 They figured out, like, what germs are and what infection is.
0:22:22 And it’s like, you know, it was 18, whatever, 1880 or something, they figured out it’s
0:22:25 a good idea to wash your hands before you perform surgery.
0:22:30 And so it’s 2004, and there’s still doctors walking into rooms and getting people sick.
0:22:34 The compliance rates for the scrubbing into the rooms is, like, I don’t know, 34 percent
0:22:35 or something.
0:22:36 And I’m just like, “Oh, fuck.”
0:22:37 Like, sorry.
0:22:41 It’s like, how can you—anyway, so that was at the front end of that.
0:22:44 You know, it’s been fascinating to track that because, on the one hand, it’s very clear
0:22:45 that they’ve made a lot of progress.
0:22:48 On the other hand, there is innovation yet to be done.
0:22:52 I mean, so—and you guys, I think you guys must know—yeah, I’m sure you guys know all
0:22:55 this, but, like, you know, medication compliance, like, the data on medication compliance is
0:22:56 absolutely horrifying, right?
0:22:59 It’s like, I don’t know, something like two-thirds of all prescribed medications are
0:23:03 not—it’s like a third of all prescribed medications are unfilled, right?
0:23:06 Another third are not—people don’t take them on schedule, right?
0:23:09 And then you get the details on it, and, like, a lot of people, especially older people,
0:23:12 you give them, like, eight or ten or twelve different medications, they’re supposed to
0:23:13 track it.
0:23:16 They’ll just dump all their medications into, like, the candy bowl and, like, mix it all
0:23:18 up, and every day they’ll take a handful of pills, right?
0:23:22 Like, and actually, that’s pretty good, right?
0:23:25 That’s better than just it’s all on a shelf somewhere or they can’t get the bottles open.
0:23:27 And so, you know, medication compliance is a train wreck.
0:23:31 It’s actually the—I read this thing the other day—medication compliance on—medication
0:23:35 compliance on the medication after organ transplants is actually terrible.
0:23:38 It’s only like—you know, like, kidney transplants, it’s only, like, 60% compliance.
0:23:39 That’s incredible.
0:23:40 It’s incredible, right?
0:23:43 And, like, it’s in the other 40%, like, you’re going to die, right, and they still can’t
0:23:44 get compliance, right?
0:23:47 And so, and there, you know, there’s eight different reasons for that.
0:23:48 And so, there’s that issue.
0:23:51 Another is just, like, yeah, literally tracking the doctors.
0:23:56 Like an idea—just an idea that we should fund, like, we’re seeing all these new—actually,
0:23:59 we’re seeing all these new technologies now to do things like, for example, to watch in
0:24:03 assembly line environments, to have, like, cameras that, like, watch everybody’s, you
0:24:06 know, basically time and motion, like, in the factory, and then you can use these machine
0:24:09 learning technologies to kind of decode, like, are people doing the right thing?
0:24:10 Are they tightening the screws?
0:24:11 Tightening the bolts?
0:24:12 Are the machines running properly?
0:24:14 You know, and maybe we should have, like, a camera outside, you know, every patient
0:24:17 door and, like, is the—are the doctors and nurses actually, like, scrubbing their hands?
0:24:18 Like, you know, Pure-El.
0:24:20 Yeah, Pure-El, like, you know, Pure-El track.
0:24:24 So, like, fairly basic stuff is still—I mean, I think the reality is I think there’s a lot
0:24:26 of basic stuff that’s still not being done.
0:24:29 And so, there’s—yeah, there’s—yeah, and the problem with this kind of thing is it’s
0:24:35 like, okay, like, you know, what is it—what’s the—medical errors are the what most common
0:24:36 in the hospital?
0:24:37 Or the third?
0:24:38 Yeah.
0:24:39 And then, of course, and then this whole issue of infection, you know, hospital-borne infections,
0:24:45 like, it’s, I think, an open question, how much of that is fixable or, you know, compliance
0:24:46 issues.
0:24:47 And it’s definitely not getting better.
0:24:48 Yeah, right, exactly.
0:24:49 Yeah, and so—
0:24:52 And while you were on the board of the hospital, you know, a lot of—when folks think about
0:24:57 software and healthcare, people just automatically assume, you know, EMR is sort of the example
0:25:00 that a lot of people gravitate towards.
0:25:05 Did you go through the experience of incorporating and implementing an EMR at Stanford while you
0:25:06 were on the board?
0:25:07 Yep.
0:25:08 Tell us a little bit about that process.
0:25:09 Oh, yeah, yeah, yeah, yeah, we put that out to bed.
0:25:12 I think we got back one viable bed, I think, for the complexity of Stanford Hospital.
0:25:13 It was EPIC.
0:25:18 It was a $400 million project, probably—well, I don’t know, probably a hundred to EPIC or
0:25:19 something like that.
0:25:23 And then after 300, we went out for integration bids, and this is where I almost started crying.
0:25:25 It was Perot Systems.
0:25:26 Ross Perot.
0:25:31 Ross Perot Systems, which—Perot Systems was the follow-up to EDS, Ross Perot’s company,
0:25:33 which is now owned by Dell.
0:25:36 And so, yes, it’s a $400 million project, Perot Systems.
0:25:41 This was 2005, I think, when we started the EPIC implementation, and they were very excited.
0:25:42 They were very excited.
0:25:44 I was very excited because I was like, “Wow, this is like a new hospital, it’s probably
0:25:45 going to be mobile.”
0:25:47 This is when smartphones are starting to take off, and it’s going to be mobile, and it’s
0:25:49 going to be sensors, and all this stuff.
0:25:50 It’s going to be great.
0:25:54 And it was like they were super excited because they had just moved to the Windows 95 UI in
0:25:55 2005.
0:25:58 It was like the big upgrade from Windows 3.1.
0:26:01 And I was like, “Oh, my God.”
0:26:06 And it’s 2019, and it’s still—right, it’s obviously still the same thing.
0:26:07 Right.
0:26:08 Yeah.
0:26:10 The other incredibly entertaining thing about EPIC is that they are so—out here, it’s
0:26:13 like—out here, there’s a big focus on software interoperability.
0:26:16 And so, it’s like in one piece of software, work with another, there’s this whole concept
0:26:19 of what I call—there’s entire companies now that are called API companies that basically
0:26:21 software building blocks that you plug together, there’s open source.
0:26:24 And so, out here, it’s just this constant process of everybody building and everybody
0:26:29 else’s creativity, and the whole thing rises, except for EPIC, which has an absolute prohibition
0:26:32 on third-party integration that does not tolerate it.
0:26:35 We’ll sue you if you attempt to integrate with it, so—
0:26:39 So when you launched the firm, you famously said—or in the early days of the firm, you
0:26:45 famously said that you won’t see A16Z investing in bio and healthcare, and that’s obviously
0:26:46 changed.
0:26:47 Yeah, that’s changed.
0:26:50 Can you talk a little bit about the evolution of that thought process?
0:26:51 Modern venture capital is like roughly 50 years old.
0:26:54 It kind of started in the 1970s in kind of the modern form, and there are basically two
0:26:57 fields within venture that actually worked.
0:27:01 There’s sort of what you might call digital technologies, computer-based technologies,
0:27:06 IT broadly defined, and then there’s biotech, and biotech kind of broke down traditionally
0:27:10 into new pharma, new therapies, and then new treatments, and then new medical devices.
0:27:14 And actually, a lot of the best venture capital firms actually had dual practices, right?
0:27:15 And so, there are many examples of this.
0:27:17 Kleiner Perkins being a very prominent one for a long time.
0:27:20 They had dual practices, and so they have the digital—they call it the digital team,
0:27:22 and then they have the healthcare team.
0:27:25 And once upon a time, they kind of collaborated, they all worked together.
0:27:29 And then what happened, right, is the economics of those two sectors just like fundamentally
0:27:30 diverged.
0:27:34 And the fundamental reason for this is, right, in the sort of digital technologies and digital
0:27:38 venture, you’re fundamentally writing this curve called Moore’s Law, right, which is
0:27:41 sort of the—basically the price of the underlying components for software and hardware basically
0:27:43 falls in half every 18 months.
0:27:46 So, you get this amazing kind of downward cost curve, and that’s why you keep coming
0:27:50 up with new applications for computers because everything keeps getting cheaper.
0:27:51 And really quickly, right?
0:27:54 And then in new pharma and in new medical devices, you had the reverse of Moore’s Law
0:27:59 literally, which is called Erum’s Law, E-R-O-O-M, which is more backwards.
0:28:06 And Erum’s—and Erum’s Law is the cost to bring a new drug or a new medical device
0:28:09 to market doubles every end years, right?
0:28:13 So the cost goes—it goes the wrong direction, right, up to—and 20 years ago or 15 years
0:28:17 ago, what happened was the VCs that were in both basically decided that they didn’t work
0:28:19 anymore, that the economic cycles were too different.
0:28:23 So you could fund, you know, Facebook, you know, with whatever, $20 million, or you could
0:28:27 fund a new pharma effort with a billion dollars, right, and still probably have to raise another
0:28:31 $3 million by the time you’re done, right, or end up selling out to big pharma at some
0:28:32 point.
0:28:35 And so they just became kind of two fundamentally different domains, and then by the way, they
0:28:38 were two fundamentally different sciences, right, because they were sort of computer
0:28:41 science on the one side, biological science on the other side, and they didn’t really
0:28:42 intersect.
0:28:45 You don’t really use computers that much doing new drug discovery or new medical devices.
0:28:49 And so that was—that was the situation we saw in 2009, was kind of—they were actually
0:28:53 separating out, and actually the leading kind of biotech VCs now are not names that anybody
0:28:57 in Silicon Valley would even necessarily know, because it’s just such different worlds.
0:29:03 What we started to see starting about six years ago, starting around 2012, probably 2013,
0:29:06 we started to see a new kind of founder show up, and we started seeing founders showing
0:29:11 up with PhDs in biology, you know, often MDs, and then also either degrees in computer science
0:29:15 or the equivalent of degrees in computer science, sometimes actually like dual PhDs, but also
0:29:19 sometimes it was just, you know, I’m a PhD in bio, but I’ve actually been programming
0:29:20 computer since I was 10.
0:29:23 Like, I’ve got, you know, 25 years, you know, I’ve got 20 years or whatever, sort
0:29:27 of the equivalent of like, you know, educational experience in computer science.
0:29:31 And then these founders are showing up with these kinds of hybrid, right, technologies
0:29:34 that were kind of half bio, half computer science.
0:29:37 And then, honestly, they would come in and pitch us, right, and then this is why I always
0:29:40 call the dogs watching TV, you know, phenomenon, you know, because they’d be up there and
0:29:44 they’d be talking about the genome and this and, you know, the exome and riblinocleic this
0:29:48 and that and the other thing, and you know, we’re all just kind of like, you know, sort
0:29:51 of vey, you know, I’ve heard these words before, I don’t quite know what they mean.
0:29:55 And then they would say, you know, algorithm, and we’d all go woof, right, like, you know,
0:29:56 we get that.
0:29:59 And then, you know, and then we didn’t know quite what to make of these, right, and so
0:30:02 then we’d ask the founders, just feel like, well, what happened when you go pitch the
0:30:04 bio VCs, the healthcare VCs, like, what are they doing?
0:30:08 It’s like, you know, it’s so weird, it’s like dogs watching TV, you know, except, you
0:30:10 know, we go on and on and on about machine learning and they just look at us puzzled
0:30:13 and then we say, you know, riblinocleic and they get all excited.
0:30:17 And so what happened was we basically said there’s this missing middle, right, which
0:30:20 is basically the convergence of these, actually, it’s interesting, it’s the convergence of
0:30:24 the scientific domains, right, and then as a consequence, it’s the converging of the
0:30:27 technological domains and then that means the convergence of the industries.
0:30:30 And so we just started to see this repeating pattern of these new kinds of founders and
0:30:35 we said, well, look, we said it’s unlikely that these bio VCs, these bio VCs have gotten
0:30:38 so detached from computer science that they’re unlikely to figure this out.
0:30:42 A bunch of the computer science VCs just got done shutting down their healthcare practices,
0:30:44 they’re not probably going to leap back into it.
0:30:47 Maybe there’s this new thing in the middle and then there we got VJ, our partner VJ,
0:30:50 who was a professor at Stanford where he was literally right in the middle of this convergence
0:30:54 in his time at Stanford and he came over and he kind of spun us up on this whole domain
0:30:57 and then Jorge joined us subsequently.
0:31:00 And I think we’ve, like, I think we’ve discovered, like, there’s a real, there’s a real vein
0:31:01 here, right.
0:31:05 And it’s interesting because like we are seeing more of the CS focused VCs starting to edge
0:31:09 in now and adapt, we’re also seeing more of the life sciences VCs starting to edge in
0:31:11 but it’s still, there’s this thing in the middle.
0:31:15 And then as you were indicating, like, with the epic question, like, you know, there’s
0:31:18 for sure, it’s like, I mean, there’s like the pure like convergence, which is like the
0:31:19 concept of digital therapeutics, right.
0:31:22 So, you know, and things like, for example, diabetes and so forth.
0:31:26 And then there’s all these potentially new kinds of diagnostic, new kinds of sensors,
0:31:28 you know, use the sensors in the phone to do diagnostics, things like that.
0:31:31 And then there’s a lot of work happening in like bioinformatics and, you know, the research
0:31:35 side, you know, sort of cloud, cloud biology is a big thing that we have.
0:31:38 And then there’s actually applying information technology to the operations of the actual
0:31:43 healthcare industry, which gets into things like medical records and hospital management
0:31:44 services and stuff like that.
0:31:47 And so we basically decided we’re taking a very broad, we’re taking a very broad brush
0:31:50 at this and we’ll basically, we’re working in all those areas.
0:31:54 And I think we’re finding it to be a very dynamic and very fertile area.
0:31:55 Absolutely.
0:31:59 What advice that someone who’s been investing in has been an entrepreneur has been investing
0:32:02 in entrepreneurs now for above a decade.
0:32:08 What advice would you give industry leaders in terms of how to engage with innovators
0:32:10 with entrepreneurs?
0:32:13 And conversely, what advice should we be given to entrepreneurs to engage with folks that
0:32:14 are leading the industry?
0:32:15 Yeah.
0:32:19 So the big thing to think about, the big difference between kind of how the valley works and kind
0:32:23 of the rest of the business world works is as follows, which is like in most of the business
0:32:26 world, like you’ve got some existing position in something and you’re trying to figure out
0:32:27 like what to do with it.
0:32:29 And you’re trying to figure out how to defend it, you know, defend a market or you’re trying
0:32:31 to figure out how to advance, innovate within the market.
0:32:34 But like you’re kind of dealing with a big existing, you know, big existing companies,
0:32:36 big existing businesses.
0:32:40 You know, out here, we don’t generally have that, you know, we’re generally starting from
0:32:41 scratch.
0:32:45 And I think the way to think about it is selling of all these startups, they’re experiments
0:32:46 first and foremost.
0:32:50 And they’re experiments often in technology, we’re trying to take actually much scientific
0:32:54 experimental risk, but they are technological experiments, can we build the product?
0:32:57 And they’re business experiments, which is like, you know, is anybody going to want
0:32:58 this thing?
0:32:59 Am I going to be able to make a business on it?
0:33:01 Am I ever going to be able to turn a profit on it?
0:33:02 They’re experiments.
0:33:05 And like you might say, well, that’s dumb, like why would you like to, you know, risk all
0:33:08 this money and effort and launching experiment for a product that you don’t know whether
0:33:10 you can build and you don’t know what anybody would want.
0:33:12 And most of the world doesn’t run those experiments.
0:33:14 And so maybe there should be one place that does, right?
0:33:15 And this is that place.
0:33:18 And so the ethos of the valley is these are experiments.
0:33:21 And that’s actually what leads to this interesting phenomenon in the valley, which is you’ll have
0:33:25 founders that have a company that’s like just like almost in some cases like a famous train
0:33:26 wreck.
0:33:27 Like it just didn’t work at all.
0:33:29 And like, you know, five years later, they’ll go start the next company and they’ll easily
0:33:30 raise money for it.
0:33:31 Right.
0:33:32 And again, like a lot of the rest of the world would be like, well, why are you getting behind
0:33:33 somebody who already failed?
0:33:37 And the valley is like, well, if they learned along the way, right, and they’re now better
0:33:40 at running the experiments the second time, let’s find them to run the second experiment.
0:33:43 And in fact, a lot of the best companies in the valley are founded by people who had one
0:33:48 or two significant failures before they, you know, before they founded the winner.
0:33:52 And so, so I’d view it as like it’s an incredibly fertile landscape of experiments in and there’s,
0:33:55 you know, there’s thousands of experiments being run and, you know, these are pretty big,
0:33:59 you know, can we build the self-driving cars like a pretty big experiment.
0:34:01 So then it’s when kind of, you know, people, especially from established industries come
0:34:06 here, it’s kind of like the temptation is to evaluate like each experiment one by one.
0:34:09 So it’s like, you know, you look at a given startup and it’s like, well, I don’t know,
0:34:11 like, you know, this thing might work, the technology might work, the business might
0:34:13 work, you know, this, whatever, this might be the right founder.
0:34:14 Don’t quite know.
0:34:17 There’s all this idiosyncratic risk with this experiment.
0:34:20 And I feel like I should make the decision whether or not to talk to the startup or work
0:34:25 with the startup based on the characteristic of this, you know, of this particular instance.
0:34:26 That’s one way to do it.
0:34:29 The other way to do it is more like what we do, which is also what I think the big
0:34:32 companies that are good at doing this also do, which is you can say, well, look, it never
0:34:35 makes sense to just run one experiment, but it might make sense to run 10 experiments.
0:34:40 And so it might be that the partnership model that makes sense is let’s put together a portfolio.
0:34:43 Like let’s figure out 10 areas that we think are potentially interesting.
0:34:45 Let’s find the 10 most interesting startups in those areas.
0:34:49 And then let’s run, let’s, let’s try 10 partnerships and let’s, and let’s think about it very explicitly
0:34:53 as a portfolio of part, you know, portfolio investments, a portfolio partnerships, a portfolio
0:34:55 of new supply relationships, whatever it is.
0:35:00 And then let’s evaluate the result of those 10 experiments as a basket, right?
0:35:02 And just the nature of probabilities being what it is.
0:35:03 Some of them are going to work.
0:35:04 Some of them are not, right?
0:35:07 But the ones that are going to work might work really, really well, right?
0:35:11 And what I just described you as literally what we do, it’s like, it’s the venture capital
0:35:15 mentality, but it’s also, I think the best construct for thinking through how to engage
0:35:17 with startups as a, as a big company.
0:35:21 By the way, the other side of it is the temptation for thinking about like, you know, a new joint
0:35:25 venture or a new investment or something, a new product is, you know, will it succeed
0:35:26 or not?
0:35:29 And so it’s like, and the traditional way you report this to the board is it’s like,
0:35:31 you know, green light, yellow light, red light, like a famous management consulting
0:35:32 shirt with like the bulbs, right?
0:35:35 And you want all the lights to be green, and if any of the lights are yellow, people have
0:35:36 very stern looks in their face.
0:35:39 And if any of the lights are red, like as a disaster and somebody gets fired.
0:35:40 And so, you know, it’s past fail, right?
0:35:44 I actually think that the way that you want to think about this is, you know, it’s not
0:35:45 a question of like, does it work?
0:35:48 It’s a question of like, if it works, like how big can it get?
0:35:50 Like if it works, how big of an impact could it have?
0:35:53 So a new technology you might be looking at in your business that might be a new route
0:35:56 to market or a new way to cut costs, a new way to cut costs.
0:36:00 Like if it works, you know, if it costs good, does it cut a million dollars worth of cost
0:36:02 or a billion dollars worth of cost, right?
0:36:04 That might be the actual relevant question, right?
0:36:08 So as opposed to just success, failure, and just the nature of these things is like they
0:36:11 often, these experiments often don’t work, but when they do work, they can actually work
0:36:12 really, really well.
0:36:14 They can get really, really big and have a really, really big impact.
0:36:19 And so, yeah, so that’s the general model is a portfolio approach and then an understanding
0:36:23 and appreciation of the asymmetric nature of the winds relative to the odds that there
0:36:24 will be some set of losses.
0:36:25 Great.
0:36:31 Silicon Valley has been pretty visible in movies and television, et cetera.
0:36:36 You may have had a hand in some of that yourself in terms of advising some of these shows.
0:36:37 No comment.
0:36:40 Only the good ones.
0:36:45 What do you wish that people that didn’t live here in Silicon Valley knew about Silicon
0:36:46 Valley?
0:36:47 It’s funny.
0:36:48 I’ve been listening to the Elon Musk audiobook.
0:36:51 He gives me a remember like before the Model S shipped, like everybody thought he was just
0:36:52 completely full of it, right?
0:36:54 And like he was going to make this car and just, there’s just like no way it’s impossible,
0:36:55 can’t be done.
0:36:57 And then this freaking car comes out, right?
0:37:00 It’s like the Model S comes out and it literally wins like hard of the year awards everywhere.
0:37:02 It has like the best safety rating of any car ever made.
0:37:04 And there were all these people who were like, oh yeah, he’s a fraud.
0:37:07 They’re literally mouse hanging open, like cannot believe it, right?
0:37:10 And so that actually is kind of the more common story.
0:37:14 And so the scientific and technical substance of what happens out here does tend to be quite
0:37:15 real.
0:37:17 But then the other side of it is what I said.
0:37:18 These are experiments.
0:37:24 Like if this stuff was a slam dunk, like if it was, if it’s obvious how to apply a scientific
0:37:28 result into a technological product and then build a business around it, like big companies
0:37:29 are going to do all that.
0:37:32 Like there are lots and lots of big companies in the world, you know, in healthcare, outside
0:37:35 of healthcare in the tech industry that are good at doing the obvious stuff.
0:37:38 And so by the nature of the Valley, we’re doing the non-obvious stuff.
0:37:40 We’re doing the stuff that’s not yet proven.
0:37:41 We’re doing the stuff that’s controversial.
0:37:43 We’re doing the stuff that really might fail.
0:37:47 And so there is risk with each and everything that we do, whether or not it will work.
0:37:50 But, God willing, when it does work, it could get really big.
0:37:51 Wonderful.
0:37:52 Thank you.
0:37:55 So let me see if there are any questions in the audience that we could field.
0:37:59 So we’re here today in a place that’s known as a center of innovation, but many of us
0:38:04 have to go be agents for innovation and change in industries that aren’t necessarily as open
0:38:05 to it.
0:38:06 What’s your advice for that?
0:38:09 How do you think about doing something innovative that you believe in, that you think will work
0:38:11 when others might say, “Oh, we’re more traditional.
0:38:13 This is the way things are done.”
0:38:17 So there’s actually a term of art in the industry in the Valley for what’s called the
0:38:18 evangelistic sale.
0:38:19 And it’s actually really interesting.
0:38:23 It’s like our companies come to market with a new product, a new widget that does something.
0:38:26 They’ll go hire sales reps out of companies that sell normal products, and those sales
0:38:29 reps will come in and they’ll just completely whiff, because they’ll get back this reaction
0:38:32 from every customer, being like, “Yeah, I’m used to buying whatever Oracle database is
0:38:34 from you, but I don’t know what to do with this new thing.”
0:38:37 And then those sales reps actually don’t know how to sell that thing, and those marketing
0:38:39 people don’t know how to market that thing.
0:38:40 It’s a different kind of thing.
0:38:44 And so there’s a specific kind of seller sales rep and marketing person out here, sort
0:38:49 of the evangelistic seller, the evangelistic marketer, and honestly, I don’t know if there’s
0:38:50 magic in it.
0:38:52 It has to do with painting a vision, right?
0:38:54 It has to do with painting a vision of the future.
0:38:55 There’s the marketing.
0:39:00 It’s sort of the Steve Jobs used to say, “The problem with consumer research is nobody knew
0:39:04 they wanted a Macintosh, until the Macintosh, or until nobody knew they wanted an iPhone,
0:39:07 like until the thing showed up, people can’t visualize new products on their own.”
0:39:11 And so you have to paint a picture, and that picture has to be vivid, right?
0:39:14 And this is where some of these guys like Elon get criticized for kind of overselling,
0:39:15 but like they have to paint a vivid picture.
0:39:16 That’s an example.
0:39:18 So Elon comes out with a Model S, or he was a great example.
0:39:22 Elon comes out with a Model S, “Congratulations, it’s a car that you plug into, specialized
0:39:23 charging ports.”
0:39:26 Well, how many specialized charging ports are out there that I can plug this car into?
0:39:27 Zero.
0:39:28 Okay, so now I’m going to buy a model.
0:39:29 It’s like buying the first fax machine.
0:39:31 It’s like, “Congratulations, I now have the first fax machine.
0:39:32 Who can I fax?”
0:39:35 You know, I now have a very expensive doorstop, like, you know, good job.
0:39:38 And so like, so what Elon did when he launched the Model S is he painted a picture.
0:39:39 You know, he painted a picture.
0:39:40 He went up and gave a big presentation.
0:39:44 He said, “Look, we’re going to put these supercharger stations, right, in all these different locations
0:39:45 along all these freeways.”
0:39:46 And he mapped the whole thing out.
0:39:49 And he’s like, “Here, you’re going to be able to drive cross country, you know, and
0:39:50 you’re going to get, you’re going to charge for free the entire way.”
0:39:53 And by the way, none of those charging stations existed at that point, but he did, he did
0:39:54 lay that vision out.
0:39:57 And then he said, “Look, here’s the thing you’ll put in your garage, you know, and it’ll hook
0:39:58 up and here’s how much it’ll cost.”
0:40:00 And then, you know, within a year, he had the, you know, people were putting these things
0:40:04 in their garages and he was putting the charging stations, the superchargers were up and like
0:40:05 it worked.
0:40:08 And then he sold enough of the cars into that vision that he was actually able to afford
0:40:10 to build all those charging stations.
0:40:12 And so it’s painting the picture in a way that people can believe.
0:40:16 It’s painting the picture, by the way, also consistent with reality, like I believe, you
0:40:20 know, there does have to be a substantive claim that the whole thing can work.
0:40:23 And then I think, honestly, I think the other thing is it has to attach, maybe obvious, it
0:40:26 has to attach to human psychology and this is the other thing that evangelistic sellers
0:40:30 are really good at, which is, so in sales speak, the evangelistic sellers are really
0:40:34 good at qualifying, which is there are certain customers where they’re just not going to
0:40:35 do new things.
0:40:38 Like where they’re just focused on downside risk, they want, you know, it’s like they
0:40:42 go to work every day, they go home with their family, like I don’t want to do anything that
0:40:43 might cause me to look bad and get fired.
0:40:46 And that’s a completely legitimate, you know, way to operate and a lot of people are like
0:40:47 that.
0:40:50 And so the evangelistic seller, one of the things they do is they just qualify those
0:40:51 people out.
0:40:52 I’m not going to spend any time with those people.
0:40:55 But what they find are the minority of people who are like, okay, like I don’t want to spend
0:40:59 my career just protecting downside, I would like to become known as within my own company
0:41:03 as somebody who’s innovative, right, and in the future.
0:41:07 And I would like to basically stake a career bet myself right on a new technology and the
0:41:08 nature of that career bet.
0:41:09 If it doesn’t work, I’m going to look bad.
0:41:12 But if it does work, wow, I’m going to look like a hero and I’m going to get promoted
0:41:14 and I’m going to be the next CEO of the company.
0:41:19 And so it’s kind of like the evangelistic seller meets the early adopter buyer who’s
0:41:21 got the right psychological mindset.
0:41:26 And then what’s super interesting actually is those people actually become very close.
0:41:29 You know, the salesperson then becomes what we call a consultative seller, they become
0:41:33 actually very tightly integrated into the lives of the sponsoring executives on the other
0:41:34 side of the table.
0:41:37 And they’re basically fundamentally trying to make each other heroes in their respective
0:41:38 organizations.
0:41:41 And they often end up extremely personally close because they’re on a shared mission
0:41:42 to do something new.
0:41:46 And so anyway, this is kind of what we advise our companies to do, but like that’s the
0:41:47 process.
0:41:51 And then it’s the process of like, okay, then it’s the gut check, which is like, okay,
0:41:53 are those early adopters actually out there, right?
0:41:55 Do they actually exist, right?
0:42:00 Do they have the authority to actually make those kinds of decisions, right?
0:42:03 Or at some point, by the way, if they don’t exist, that itself is an interesting market
0:42:04 signal.
0:42:06 Like if the early adopters don’t exist, it may just be time to start a new company in
0:42:07 that market.
0:42:11 So that’s the other thing that happens is the founders are like, oh, it’s like imagine
0:42:14 being Travis Callan, I can try to start Uber and your first idea is I’m going to build
0:42:17 taxi dispatch software and I’m going to try to sell it to taxi cab companies.
0:42:20 And then you spend two years trying to get taxi cab companies to buy this taxi dispatch
0:42:23 software and they all say, no, you might as, no, that isn’t literally what happened.
0:42:25 But you could very easily imagine that happening.
0:42:28 And so that is the other thing that emerges out of this is people just decide to start
0:42:29 the company.
0:42:32 What do you believe are some of the biggest challenges to getting new technologies and
0:42:35 solutions adopted, particularly in the healthcare space?
0:42:39 The general thing that happens, which is really relevant to all these healthcare markets,
0:42:42 is the product works and I can’t get paid.
0:42:46 One form of that problem is I just can’t get paid, literally the customer is not going
0:42:48 to pay for this product because it’s going to be reimbursed.
0:42:52 It’s a third-party model and it doesn’t matter how many patients want X if the insurance
0:42:53 company is not going to pay for it.
0:42:55 And so that’s a particularly stark example of can’t get paid.
0:42:59 There’s another example of can’t get paid, which is I just can’t get paid enough.
0:43:02 We have a lot of companies that have a problem that I call too hungry to eat, which is basically
0:43:06 like, imagine a starving person 10 feet away from a plate of filet mignon, but like I’m
0:43:09 starving and I don’t have the energy to pull myself to the plate.
0:43:12 The Silicon Valley version of that is I have a great product.
0:43:15 My customers really want it, but I’m charging very little money for it.
0:43:19 Usually these are naive product founders who don’t quite understand business and so they
0:43:22 think if they charge less, they’ll sell more, but they actually charge less, they end up
0:43:23 selling less.
0:43:24 And the reason is because they don’t charge enough for the product, they’re not getting
0:43:26 enough revenue back into the company.
0:43:30 They’re not getting literally enough calories back into the company, dollars into the company.
0:43:32 And then they can’t afford to hire the kinds of sales and marketing people to do the kind
0:43:34 of sale that we’re talking about.
0:43:36 And then they just get stuck, right, they get stuck.
0:43:40 It’s like the product works in theory, the customers want it in theory, but the company
0:43:43 doesn’t have the internal funding, they’re not making enough money on each sale to be
0:43:45 able to justify the cost of sale.
0:43:48 And this is actually a very funny conversation I have with the founder, because it’s literally
0:43:52 like the conversation is so weird, it’s like, okay, tell me about your product.
0:43:54 Oh, it’s the best product ever, and machine learning and this and that, it revolutionizes,
0:43:58 it’s going to save our companies $10 million each in saved expenses, and it’s okay, what
0:44:00 are you charging for it?
0:44:01 $50,000.
0:44:04 It’s like, well, you’re going to save them $10 million, why are you only charging $50,000?
0:44:06 Well, it’s going to be because it’s going to be easy to sell, like it’s, you know, it’s
0:44:09 just going to, we’re going to sell it to the next customer, it’s like, well, what do you
0:44:12 have to do to convince the customer to buy the thing that’s going to save them $10 million?
0:44:15 And it’s like, oh, we got to send in, you know, eight people for, you know, six months.
0:44:16 Well, what does that cost?
0:44:18 Well, it costs a million and a half bucks.
0:44:19 Right.
0:44:20 Okay, so congratulations, right?
0:44:25 You’re now down, you know, $1.45 million, a negative cash burn on every sale you make.
0:44:26 That’s your strategy, right?
0:44:28 And you’re going to make it up in volume, right?
0:44:32 That’s just like, and literally what I’m describing is like literally what we see happens.
0:44:35 And by the way, a lot of the time it’s actually the founder themselves who’s actually on site
0:44:36 with the customer.
0:44:38 And right, it’s like, what’s their time worth?
0:44:39 Right.
0:44:40 Because their time’s getting sucked down.
0:44:43 It’s like the entire future of the company, right, is being basically bled out.
0:44:47 And so like a big rule we have, a big thing we always have here, like the principle is
0:44:52 like, you have to get paid, like the customer has to pay for the thing.
0:44:54 If it’s an indirect thing, you have to, and this is the thing with, right, all the healthcare
0:44:57 startups, like they have to be able to decode the system, right, which is why it’s so great
0:44:59 to have you all here.
0:45:02 And then on top of that, like you really do in a lot of cases, like the right answer
0:45:06 actually is raise prices, which is weird because it’s like, well, technology is supposed to
0:45:07 drive down prices.
0:45:08 What are you doing?
0:45:09 Raising prices.
0:45:10 It’s like, well, you have to make enough money per sale.
0:45:13 The internal economics of your business have to be such that you make enough money per
0:45:16 sale that you can afford to actually build the company, right?
0:45:17 And then you can build the company.
0:45:18 You can build the sales and marketing engine.
0:45:20 You can get the thing known and get the thing adopted.
0:45:21 You can get references.
0:45:23 And then once you’re at scale, then you can start to drive the price down.
0:45:24 Yeah.
0:45:28 And I would just add in the healthcare realm, and this is an overgeneralization, but what
0:45:35 we often see is the business model failures are often a lack of recognition that the person
0:45:39 who will benefit from your solution is often not the buyer.
0:45:42 So you have sort of this mismatch that often happens in the healthcare system that you’re
0:45:45 targeting your customer, but your customer, there’s a different buyer.
0:45:49 So that’s a very difficult thing that can usually be addressed with business model,
0:45:50 but it has to be recognized.
0:45:53 The second one is point solution versus complete solution.
0:46:00 It’s very hard for a startup to big bang an A to Z solution, but often times it’s what
0:46:02 a buyer needs because they don’t need another point solution.
0:46:07 So it’s figuring out what the insertion point is going to be for any particular innovation.
0:46:11 And then the third one that we always see with a lot of our startups that they have to be
0:46:16 very thoughtful as to how they approach it is recognizing how you can introduce a new
0:46:20 technology without disrupting existing workflows.
0:46:24 Because the job that happens in healthcare delivery is incredibly complex.
0:46:27 So even if you have a better mousetrap, if that better mousetrap requires you to change
0:46:30 the way you work, it’s very hard to implement.
0:46:34 So those are, I think, the three big challenges that all of our entrepreneurs see from business
0:46:35 model standpoint.
0:46:38 So if you can overcome those, I think you have a much better chance of having an innovation
0:46:39 get adopted.
0:46:40 Thank you for having us.
0:46:41 Thanks very much.
0:46:42 Thank you, Mark.
0:46:43 Thank you.
0:46:43 [applause]
0:46:44 [end of transcript]
0:46:54 [BLANK_AUDIO]