Editor’s note: This article is based on an episode of the a16z Podcast, which you can listen to here.
Hi, and welcome to the a16z Podcast. I’m Hanne. In this episode, a16z co-founder Marc Andreessen, and general partner on the Bio Fund Jorge Conde, take a look back at Marc’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 healthcare—including what exactly changed in the fields of bio and computer science to make Marc eat his own words. This conversation was originally recorded as part of an event at a16z.
Jorge: I’m so thrilled to have our founder, our co-founder and general partner, Marc Andreessen here. 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.
Marc: Magazines. I’m told that this is a new technological device and its content comes pre-printed on the paper. Apparently, it’s got excellent battery life.
Jorge: It does.
Marc: But it doesn’t update very fast.
Jorge: You can’t swipe, but you could rip it. So you have to be very careful.
Marc: You can try swiping.
Jorge: 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, you know, around the launch of the firm, you articulated your vision of what was happening in the world, that 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.
Marc: Right.
Jorge: So, I thought maybe one place to start is to have you describe what, in your mind, software eating the world means, and what it doesn’t mean.
Marc: Sure. 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 as we understand it in Silicon Valley, the one you’re sitting in the middle of right now, is about a 70 year-old industry. It started right after World War II, when there were like a total of like five computers on the planet.
And then over the course of the next 70 years, it figured out a way to pack leading-edge, state-of-the-art, supercomputer technology that used to cost $25 million and $50 million into a $500 product that we all now have. There’s like four billion smartphones on the planet now, on the way to seven billion.
So there’s a 70-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, lots of hits and starts. But it did fundamentally work.
So that’s like, okay, is the industry finished? Like, are we done? Congratulations, everybody has a computer, everybody’s on the internet, mission accomplished. What’s next? And especially back then, this was after the financial crisis, there was a prevailing mood of pessimism about the global economy, and the American economy, and the technology industry. Lots of press coverage at the time was like, you know, tech’s just in another stupid bubble, and there’s nothing interesting happening. There’s nothing left to do. Innovation is dead. From here on out, it’s all just stupid, little silly games and things that don’t matter.
And so my view is sort of the exact opposite. Which is not only that we’re not done, we’re just beginning. Now that we have a computer in everybody’s pocket, an incredibly powerful computer with a lot of capabilities—which we’ll talk about—related to health. And then, everybody’s on the internet. Everybody is connected to everybody else, and to an entire universe of services, and information, and communications, and everything else.
To me, it’s just like, that’s the beginning, right? It took 70 years to build the platform, get into position. What can we do on top of that? And so what I tried to do with the concept of “software is eating the world” is say, “Okay, how does this unfold from here, across industries?” And the way I described it was in three layers, and 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 first claim is that any product or service in any field that can become a software product, will become a software product. And so if you’re used to doing something on the phone, that will go to software. If you’re used to doing something on paper, that will go to software. If you’re used to doing something in person, that can go to software — it will go to software. If you’ve had a physical product, and think about things like, telephone answering machines, or tape players, boom boxes, like, all the things Radio Shack used to sell. They’re all apps on the phone, right? Cameras? Yeah, I remember there used to be a physical product called the camera. And, you know, that got vaporized. By the way, the physical newspaper, physical magazines. If it can become bytes, it becomes bytes. Why does it become bytes? Well, if it’s bytes, it’s better in a lot of ways. Bytes are zero marginal cost, so they’re easy to replicate at scale, and become much more cost effective.
Anything that can get into software, will get into software.
A lot of bytes is disruptive-free. By the way, they’re much more environmentally-friendly, which is an increasing thing for a lot of people. You know, you can change bytes 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 claim is that every company in the world that is in any of these markets in which this process is happening, has to become a software company, right? 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 IT, right. We got these gnomes in the back office, and they’ve got their lab coats, and they’ve got their mainframes, and they 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 modern software development. And especially customer experiences — what’s the actual interface to the customer? And any company deals with customers, especially consumers, is going to have to 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 necessarily mean that a new company which starts as a software company entering an existing market will win, but it also doesn’t necessarily mean that an incumbent that adapts to being a software company will win.
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 necessarily mean that a new company which starts as a software company entering an existing market will win, but it also doesn’t necessarily mean that an incumbent that adapts to being a software company will win.
In fact, you see this increasingly in many industries, including healthcare and insurance. You’ll see many cases now where you’ll have these new pure play software companies entering these incumbent markets. And maybe they’re coming from a position of youth naivety, and maybe they’re wrong and maybe their idea is stupid, or maybe it’s Uber or Lyft entering the taxi market, and maybe they just have a fundamentally better software-driven approach.
And then you’ve got incumbents scrambling to try to basically figure out how to become software companies. Which is tricky, because software—the way we think about it—is different. It’s not the same. It’s different. It’s a different 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, mobile software, AI software, cloud software—these are special people. And I say ‘special,’ you know, multiple definitions of special. These are highly creative individuals. Just a random example: the defense contractors and intelligence agencies are having to revamp all their drug use policies right now. The whole pee-in-a-cup thing before you get hired doesn’t work if you’re trying to hire modern software development. That’s just, like, one random example. But there are lots of instances where these cultures are different.
And then you can say, okay, if that’s the framework, then you can go industry-by-industry and say, which industries are more prone for that to happen in? Obviously, in some industries, it’s super clear. The media industry is an example where it’s just obvious how fast that’s happening.
There are other industries, like cars, which is an example we might talk about quite a bit. Transportation is right in the middle—the incumbents in the auto industry have a really good claim on the idea that building cars is incredibly hard, incredibly dangerous, fairly regulated.
And the idea that a bunch of software founders out in the valley are going to start car companies is absurd. But there’s, you know, 500 self-driving car startups within 50 miles of where we sit. What those founders would tell you is all the value, 90% of the value of the car, in 5 or 10 years is going to be in software, because the car is going to be an autonomous electric vehicle, right? Since it’s going to be autonomous, it’s going to be self-driving, which means it’s going to have all the 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 that these car companies have spent 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, I would say there’s a pitch battle 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. They don’t face a disruptive challenge. Maybe they’re right, maybe they’re not. Education is becoming a very hot market. Education is not a market that you would characterize as having had a lot of innovation over the last 1,000 years. There’s now a new generation of founders out here that have some pretty compelling new offers in education. I would say even real estate, there’s a surprising amount of innovation happening. Actually law is a field which is not traditionally super innovative. There’s a lot of new software entrance into the political field. And so, people are going to be trying in every industry.
Jorge: Yeah. I want to make sure we get to the healthcare-shaped elephant in the room at some point. But to look back on the software eats the world thesis, the three audacious claims, as you called them—any surprises that you’ve seen in the intervening years that you’ve said, “Okay, you know, if I were to rewrite that today, I would have taken a different view”?
Marc: I think the big one, as I mentioned already, is what’s happening in the car industry. When we started the firm 10 years ago, I never imagined that we’d be investing in literally new car companies.
The auto industry was like an entrepreneurial industry in, like, 1890, right? And then in the 1920s Henry Ford, the guy who was the Bill Gates of his era, figured the whole thing out. And then there were no new American car companies. There was one major new American car company since the 1920s, but there were hundreds of new car companies in the 1910s, and they shrunk to basically three, and then they stabilized.
There was an entrepreneur named Preston Tucker in the 1950s who created a car company called Tucker Automotive, as the bold new thing. It was such a catastrophe—they made a movie about what a catastrophe it was, called Tucker. If you were an entrepreneur tempted to start a car company, just watch the movie Tucker, and it’s like, “Okay, I’m not doing that.” The idea that an industry that established would be opening up the way that it is, is very striking. It’s been the most striking one.
By the way, I use the term ‘software’ very broadly in the sense of code that runs on shapes and networks. You’ve all, I’m sure, been reading about and seeing the rise of these concepts called ‘machine learning,’ ‘deep learning,’ ‘artificial intelligence.’ In the valley, there are two profound and logical revolutions happening right now 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 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.
The other question that I think is increasingly coming up when we think about the kinds of companies and founders we back is how AI native or machine learning native the founders are, as well as their companies. Even in the valley, 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 tuned up on it. So it’s like very much in flux.
If that stuff works the way that it looks like it might work, that could really be transformative, even beyond just the idea of software.
Jorge: I think that’s right. If we look at a couple of the industries that have been responsive and receptive, and the auto industry, I think it’s a big surprise, that they would have adapted to the fact that cars are becoming more 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.
Marc: Yeah.
Jorge: Do you think that other industries will likely suffer that fate that music has?
Marc: Yeah, so it’s a funny thing. Music, I think, is something like a triple whammy. First of all, one of the interesting things about music was that people really love music. And I say that because generally, when we fund startups, the question always is, “Will the dogs eat the dog food” Are people actually going to want this thing? So what was the huge issue with music? It was piracy. All of a sudden, the music listeners went crazy and started breaking the law and started to listen to music online. The record labels all freaked out, and they were like, our customers have turned evil. Well, maybe. But, isn’t it great that they all love music so much?
And for some reason, the music executives who I knew never thought that was a very good point. But I thought it was. I was like, look, they want the thing. Normally in business, when the customers are lined up out the door and they’re like, “I want to consume music digitally,” you would normally want to say, “Okay, I want to find a way to service them.” 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. It was, literally, overwhelming consumer demand for online music, streaming music, digital music. It was overwhelming with suppliers refusing to accommodate it. So that was weird.
So why is that happening? Well, then you get into the pricing, right? There’s 12 songs on the album, album costs $17 bucks, and I want one of the songs, right? And congratulations, I can just pay $17 for a song and then another 11 songs I don’t want. Okay, well, that’s weird. The whole structure of the record industry was built up around that.
And then there was the thing that it actually got down to, which took a while to surface, but it ultimately did finally come out, which was it was a cartel. It was like a full on anti-competitive monopolistic cartel of price fixing. We now know that because there were antitrust cases from this era that finally appealed the whole thing, so this has all become public record.
But they were all colluding. There were four or five labels, and they were all getting together and setting prices. And that’s, in retrospect, why they were so dug in. It was a magical business model, right? Let’s imagine you could collude, and then let’s imagine as a consequence of that, you could overcharge by a factor of 10. Like, wouldn’t that be great? And so that, in retrospect, was the thing that I think a lot of us out here missed, because their behavior was just so illogical.
The problem was that lasted until it didn’t last, right? The consumers 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 correct. It actually was immoral. It is immoral to price fix and collude, and illegal. You had illegal customer behavior and illegal supplier behavior. What a super healthy market.
So what’s the moral of the story? Well, that which can become software will become software. There was just overwhelming demand. We all live this today. How do I want to listen to music? I pull up Spotify on my phone and listen to music. The idea of being forced to figure out which box in the garage has the CDs, you know, it just sounds like medieval torture.
And so the thing that can become software will become software. Prices are going to rationalize, and we could talk more about that. But there’s a big, I think, rationalization of prices happening across economy that’s pretty interesting, as a consequence of the increased transparency. The suppliers, like, you know, the cartels attached to the old technology aren’t going to survive. That kind of transformation is going to be a really big deal.
And it took time. It took 15 years. 15 years of the record labels trying to hold out. By the way, it was 15 years of tech startups to try to solve this problem. You probably remember if you’re into music, there was Napster, which got put out of business early on. But then there was Kazaa, and there was LimeWire, and there was BitTorrent. And then there were all the early streaming services.
Actually, it’s interesting as they were all terrible venture investments. They were all catastrophes, right? Because they were too early. Because they couldn’t get the rights to the music, because the labels, they wouldn’t do the deal, so they could never get the rights to the music, and so they can never actually offer a service that 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 a fundamental change. And that’s when Spotify catalyzed. Actually, a lot of VCs, like us, 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, right? And that was obvious. Music is like $10 a month, and you listen to it. Spotify has, I don’t know how many, but Spotify is going to end up with like a billion subs. They’re like $10 bucks a month, and then they’re parceling out all the money to the artists.
Jorge: Everything that in music could become software, has become software. 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 think there’s probably some element to that. There is always going to be human element, an in-person component to treating and managing disease and patients.
Marc: Actually, there’s a related point there. Clay Christensen actually points this out. There’s this weird thing where when one industry layer commoditizes, the next layer can become incredibly valuable. And this is deceptive thing, because people are focused on the layer that’s commoditizing and the shrinkage of the market cap. They tend to think that the whole industry is going down.
There’s this weird thing where when one industry layer commoditizes, the next layer can become incredibly valuable.
But look, the music industry contracted, right? The amount of money people spent on recorded music shrunk dramatically. It’s finally starting to grow again with streaming, but it shrunk dramatically over the course of, you know, 15-20 years. What actually happened that’s 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.
And it makes sense, which is like, “Okay, congratulations Mr. Consumer. Congratulations, you now have unlimited access to all the recorded music you want. It’s now free. Everybody has it.” There’s no status. You don’t have record labels. You don’t have LPs lined up in your shelf. And if you’re courting a young man or a young woman, and they come over and you want to show off your music, you go, “Hey, look at my Spotify,” it’s not the same. There’s no social effect to it. It’s not really fun. It’s like consumer Nirvana, except it’s like they drained all of the fun.
What’s fun is going to the concert. And, by the way, I’m not spending as much money recording music, and therefore, I have more money available to actually buy concert tickets. And so the concert business, the experience side of it, has exploded in revenue. You could easily hypothesize the exact same thing happening in healthcare, right?
Jorge: Yeah.
Marc: For example, if more of the actual products and services in healthcare could get commoditized, and over time you could break the cost curves and actually shrink it, maybe concierge medicine would just explode, right? Maybe a lot more people will actually want the concierge experience today. They just can’t afford it. But if you crack the price curve and a lot of the other stuff, maybe you could open that up.
So basically, the moral of that is just pay attention to complements. It’s never a single factor. There are other implications for other areas of spending.
Basically, the moral of that is just pay attention to complements. It’s never a single factor. There are other implications for other areas of spending.
Jorge: Actually, on that note, given the healthcare industry, we are in one way shape or form all customers of the healthcare 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 healthcare, from serving on the board of a hospital, and really coming in as a lay person to the industry?
Marc: I would say the best thing about it was, you know, the mission of the place was obviously just amazing. And I say that the mission, both in terms of the actual healthcare, but also the mission of the translation of medical research, you know, the integration with the medical school and all the research happening. It was a nonprofit with highly motivated people, which was exciting to see. There was innovation happening all over the place. And in fact, it was actually exciting because 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 green-lit the project, I believe in 2005. We’re opening it in 2019.
Jorge: These are all 15 years cycles that you’re describing.
Marc: We spend 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 surprises, the thing that just really jumped out, is one, there are 25 board members, right? So our boards, our well-functioning boards to our companies, are like, seven people. Beyond seven people, you can’t have a good discussion. And so, 25 people, it’s like a UN summit. I would not describe the board meetings as highly dynamic. We didn’t really get into a lot of the issues.
The other thing that blew me away, which I’m still tracking and fascinated by, was the issue of quality. I happened to join the board right after we hired our first chief quality officer, which was a guy who had come out of management consulting, Six Sigma, a manufacturing, quality thing.
For those of you who know the history of these things, the U.S. auto industry was a huge assembly industry in the 50s and 60s, but it had this massive quality problem, which was like, literally, people were dying. There were no seatbelts in the cars. The steering columns were impaling people. There were all kinds of horrific problems.
Then when the Japanese and the Germans came in with safer cars, 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, TQM, Total Quality Management, Six Sigma, which was a process to get all the bugs out, the idea of defect-free manufacturing. If you buy a car today, it’s a far higher quality experience than a car 50 years ago. And usually, it’s actually a much better experience than a car 10 or 20 years ago. They’re quite good now.
You read these histories when they figured out cholera in the water, what germs are, what an infection is. It was 1880 or something when they figured out it’s a good idea to wash your hands before you perform surgery. And so it’s 2004, and there are still doctors walking into rooms and getting people sick. The compliance rates for the scrubbing of 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. 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’m sure you guys know all this, but the data on medication compliance is absolutely horrifying, right? It’s like a third of all prescribed medications are unfilled, right? Another third or not, people don’t take them on schedule, right?
Older people, you give them like 8, 10, or 12 different medications, and they’re supposed to track it. They’ll just dump all their medications into the candy bowl, and 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 it’s all on a shelf somewhere and they can’t get the bottles open.
And so, medication compliance is a train wreck. It’s actually, I read this thing the other day. Medication compliance on the medication after organ transplants is actually, terrible. It’s only, for kidney transplants, like 60% compliance.
Jorge: It’s incredible.
Marc: It’s incredible, right? And the other 40%, like, you’re going to die, right? And they still can’t get compliance. And there’s very different reasons for that. And so there’s that issue. Another is literally tracking the doctors. Like, just an idea that we should fund. Actually, we’re seeing all these new technologies that do things, like for example, watch an assembly line environment, to have cameras that watch everybody’s time in motion, like in the factory. You can use these machine learning technologies to decode if people doing the right thing. Are they tightening the screws, tightening the bolts? Are the machines running properly? Maybe we should have a camera outside every patient door, and, like, are the doctors and nurses actually scrubbing their hands?
Jorge: Purell.
Marc: Yeah, Purell. Like, doing Purell tracking. I think the reality is that there’s a lot of basic stuff that’s still not being done. And the problem with this kind of thing is that the medical errors are the most common in the hospital.
I think the reality is that there’s a lot of basic stuff that’s still not being done.
Marc: I think they’re the third. And then this whole issue of infection, you know, hospital-borne infections. I think it’s an open question: how much of that is fixable errors and not compliance issues?
Jorge: And it’s still not getting better.
Marc: Yeah, right. Exactly.
Jorge: While you were on the board of the hospital, when folks think about software in healthcare, EMR is the example that a lot of people gravitate towards. Did you go through the experience of incorporating and implementing an EMR at Stanford, while you were on the board?
Marc: Yeah.
Jorge: Tell us a little bit about that process.
Marc: Oh, yeah. We put that out to bid. I think we got back one viable bid, I think, for the complexity at Stanford hospital. It was Epic. It was a $400 million project. And then of the 300, we went out for integration bids. And this is where I almost started crying. It was Perot systems.
Jorge: Ross Perot systems?
Marc: Ross Perot systems. Perot systems was the follow up to EDS. Ross Perot’s company, which is now owned by Dell. And so, yes, it’s a $400 million project Perot systems. This was 2005, I think, when we started the Epic implementation. They were very excited. They were very excited in the demo. I was very excited, because I was like, “Wow, this is like a new hospital, and it’s probably going to be mobile and there’s going to be sensors and like all this stuff. It’s going to be great.”
They were super excited because they had just moved to Windows 95 UI, right? In 2005. It was like the big upgrade from Windows 3.1. And I was like, “Oh, my God.” But, as you know, it’s 2019 and it’s still the same thing.
Jorge: Right.
Marc: The other incredibly entertaining thing about Epic is that they are so, you know, 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, is this whole concept of work. There are 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 on everybody else’s creativity, and the whole thing rises. Except for Epic—which has an absolute prohibition on third party integration. It does not tolerate it. Will sue you if you attempt to integrate with it.
Jorge: So, in the early days of the firm, you famously said that you won’t see a16z investing in bio and in healthcare. And that’s obviously changed. Can you talk a little bit about the evolution of that thought process?
Marc: Modern venture capital is like, roughly 50 years old. It started in the 1970s in the modern form. There are, basically, two fields within venture that actually worked. There are 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 therapies, and new treatments, and new medical devices. And actually, a lot of the best venture capital firms actually had dual practices, right? So there are many examples, Kleiner Perkins being a very prominent one for a long time. They’re dual practices, and so they have what they call the digital team, then they have the healthcare team.
Once upon a time, they collaborated, they all worked together. And then what happened is that the economics of those two sectors just fundamentally diverged. The fundamental reason for this is in the digital technologies and digital venture. You’re fundamentally writing this code called Moore’s Law, right? Basically, the pricing of the underlying components for software and hardware, falls in half every 18 months. So you got this amazing downward cost curve, and that’s why you keep coming up with new applications for computers, because everything keeps getting cheaper, and really quickly.
In pharma, in new pharma and in new medical devices, you have the reverse of Moore’s Law, literally, which is called Erooms’ Law, E-R-O-O-M, which is Moore backwards. And Eroom’s Law is the cost to bring a new drug or a new medical device to market, doubles every n years. So the cost goes the wrong direction, right? Upwards.
And basically 20 years ago, 15 years ago, what happened was the VCs that were in both decided that that didn’t work anymore. The economic cycles were too different. You could fund Facebook with $20 million and go to the moon, or you could fund a new pharma effort with a billion dollars, and still probably have to raise another three billion by the time you’re done. Or end up selling out to big pharma at some point. They just became two fundamentally different domains.
By the way, they were two fundamentally different sciences, right, because they’re sort of computer science on the one side, and biological science on the other side. They didn’t really intersect. You don’t really use computers that much doing your drug discovery and your medical devices. The situation we saw in 2009 was they were actually separating out. The leading biotech VCs now are not names that anybody in Silicon Valley would even necessarily know because it’s such a different world.
What we started to see about six years ago around 2012, probably 2013, is a new kind of founder show up. We started seeing founders showing up with PhDs in biology, often MDs, and then also either degrees in computer science, or the equivalent of degrees in computer science. Sometimes with dual PhDs, but also sometimes they were like, “I’m a PhD in bio, but I’ve actually been programming computers since I was 10.” I’ve got 20 years or whatever, sort of the equivalent of educational experience in computer science.
These founders are showing up with these kind of hybrid technologies that were half bio, half computer science. And then honestly, they would come in and pitch us, right? This is what I always called “The dogs watching TV”. They’d be up there and they’d be talking about the genome and this and that, and you’re like, “Whoa, I’ve heard these words before, I don’t quite know what they mean.”
And then they would mention algorithm, and we’d all go, “Woof!” Like, you know, we get that. But we didn’t know quite what to make of these. We’d ask the founders this. We’d be like, “Well, what happened?” You go pitch the bio VCs, the healthcare VCs, like, what are they doing? It’s like, “You know, it’s so weird. It’s like dogs watching TV, except we go on and on and on about machine learning, and they just look at us puzzled. And then we’d say, you know, ribonucleic, and they get all excited.”
Actually, it’s interesting. It’s the convergence of the scientific domains. It’s the conversion of the technological domains. And then that means the convergence of the industries. We just started to see this repeating pattern of these new kinds of founders. We said these bio VCs have gotten so detached from computer science that they’re unlikely to figure this out. A bunch of computer science VCs just shut down their healthcare practices, they’re not probably going to leap back into it. Maybe there’s this new thing in the middle.
It’s the convergence of the scientific domains. It’s the conversion of the technological domains. And then that means the convergence of the industries. We just started to see this repeating pattern of these new kinds of founders.
And then we got our partner, Vijay, who was a professor at Stanford, where he was literally right in the middle of this convergence in his time at Stanford. And he came over and he spun us up on this whole domain. And then [you] Jorge joined us, subsequently.
I think we’ve discovered there’s a real vein here, right? It’s interesting, because 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 still, there’s this thing in the middle.
There’s for sure the pure convergence, which was like the concept of digital therapeutics, for example diabetes, and so forth. And then there’s all these potentially new kinds of diagnostic and sensors; there’s the use of sensors in the phone to do diagnostics, things like that.
There’s a lot of work happening in bioinformatics, and the research side. Cloud biology is a big thing that we have. And then there’s applying information technology to the operations of the actual healthcare industry, which gets into things like medical records, hospital management services, and stuff like that. We basically decided 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.
Jorge: Absolutely. What advice would you give to industry leaders in terms of how to engage with innovators with entrepreneurs? And conversely, what advice should we be giving to entrepreneurs to engage with folks that are leading the industry?
Marc: The big difference between how the Valley works and the rest of the business world works is as follows. In most of the business world, you’ve got some existing position and you’re trying to figure out what to do with it, and you’re trying to figure out how to defend it, you know, like defend a market. You’re trying to figure out how to advance and innovate within the market, but you’re dealing with big existing companies, big existing businesses.
Out here, we don’t generally have that. We’re generally starting from scratch. I think the way to think about it is that Silicon Valley startups are experiments, first and foremost. They’re experiments often in technology. We try to take a lot of scientific experimental risk, but they’re technological experiments. Can we build the product? And they are also 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 even going to be able to turn a profit on it?
They’re experiments. You might say, well, that’s dumb. Why would you risk all this money and effort launching experiment for a product that you don’t know you can build, or anybody would want? 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.
The ethos of the valley is that these are experiments. That’s actually what leads to this interesting phenomena in the valley, which is you’ll have founders that have a company that, in some cases, are like a famous train wreck. It just didn’t work at all. And then five years later, they’ll go start the next company, and they’ll easily raise money for it, right? Again, the rest of the world will be like, “Well, why are you getting behind somebody who already failed?” and the valley is like, “Well, if they learned along the way, and they’re now better at running the experiments the second time, let’s fund them to run the second experiment.”
In fact, a lot of the best companies in the valley are founded by people who have one or two significant failures before they founded the winner. And so I view it like it’s an incredibly fertile landscape of experiments. There’s thousands of experiments being run. These are pretty big like, can we build the self-driving cars? Like, a pretty big experiment.
When people, especially from established industries, come here, there’s a temptation to evaluate each experiment, one by one. You look at a given startup, and it’s like, “Well, I don’t know. This thing might work, the technology might work, the business might work. There’s all this idiosyncratic risk with this experiment. And I feel like I should make the decision whether or not to talk to the startup or work with the startup based on the characteristic 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 you can say, “Well, look, it never makes sense to just run one experiment, but it might make sense to run 10 experiments.” It might be that the partnership model that makes sense is, let’s put together a portfolio. 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.
Let’s think about it very explicitly as a portfolio of investments, a portfolio of partnerships, a portfolio of new supplier relationships, whatever it is. And then let’s evaluate the result of those 10 experiments as a basket, right? The nature of probabilities being what it is, some of them are going to work, some of them are not, right? But the ones that are going to work might work really, really well, right?
What I just described to you is literally what we do. 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 that there’s a temptation to measure if a new joint venture or a new investment will succeed or not. The traditional way you report this to the board is like, you know, green light, yellow light, red light, like this famous management consulting chart with the bulbs. You want all the lights to be green—if any lights are yellow, people will have very stern looks. And if any lights are red, like, it’s a disaster, and somebody gets fired. And so, you know, it’s pass/fail, right?
It’s not a question of like, does it work? It’s a question of, if it works, how big can it get? If it works, how big of an impact could it have?
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, if it works, how big can it get? If it works, how big of an 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, if it cut costs, good. Does it cut a million dollars’ worth of cost, or a billion dollars’ worth of cost? That might be the actual relevant question, as opposed to just success/failure.
The nature of these things is, these experiments often don’t work, but when they do work, they can actually work really, really well. They can get 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 asymmetric nature of the winds relative to the odds that there will be some set of losses.
Jorge: Okay. Great. Silicon Valley has been pretty visible in movies and television, etc. You may have had a hand in some of that yourself, in terms of advising some of the shows.
Marc: No comment. Only the good ones.
Jorge: What do you wish that people that didn’t live here in Silicon Valley knew about Silicon Valley?
Marc: It’s funny, I’ve been listening to the Elon Musk audio book. You just made me remember, before the Model S shipped, everybody thought he was just completely full of it, right? And like, he was going to make this car and there was just no way, it’s impossible, it can’t be done.
And then this freaking car comes out, right? The Model S comes out, and it literally wins car-of-the-year awards everywhere. It has the best safety rating of any car ever made. There were all these people who were like, “Oh, yeah, he’s a fraud.” They were, literally, mouth hanging open, like, I cannot believe it, right? And so that is the more common story. The scientific and technical substance of what happens out here does tend to be quite real.
But then the other side of it are experiments. If this stuff was a slam dunk, or sorry, if it’s obvious how to apply a scientific result into a technological product and then build a business around it, big companies are going to do all that.There are lots and lots of big companies in the world in healthcare, outside of healthcare, in the tech industry, that are good at doing the obvious stuff.
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. There is a 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.
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. There is a 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.
Jorge: Wonderful. Thank you. So let me see if there are any questions in the audience that we could field?
Question: 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 is 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?
Marc: So there’s actually a term of art in the industry, in the valley, for it. It’s called “The Evangelist Sale.” It’s actually really interesting. It’s like our companies come to market with a new product, a new widget that does something, and they’ll go hire sales reps out of companies that sell normal products, and those sales reps will come in and they’ll take the product, and they’ll just completely whiff, because they’ll get back this reaction from 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, sort of the evangelistic seller, or the evangelistic marketer.
Honestly, I don’t know, there’s magic, and it has to do with painting a vision, right? It has to do with painting a vision of the future. That’s the marketing. It’s sort of what Steve Jobs used to say, the problem with consumer research is nobody knew they wanted a Macintosh, right? 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 that picture has to be vivid, right? This is where some of these guys like Elon get criticized for overselling. But they have to paint a vivid picture. For example, Elon comes out with the Model S, right? Congratulations, this is a car that you plug into specialized charging ports. Well, how many specialized charging ports out there that I can plug this car into? Zero. Okay, 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, good job.
And so what Elon did when he launched the Model S is he painted a picture; he went up, give a big presentation and said, “Look, we’re going to put these supercharger stations in all these different locations along all these freeways.” He mapped the whole thing out, and he’s like, “Here, you’re going to be able to drive across the country, and you’re going to get your charge for free the entire way.”
By the way, none of those charging stations existed at that point, but he did lay that vision out. He also 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 will cost.” And then within a year, he had people who were putting these things in their garages, and he was putting the charging stations, the super chargers were up, and it worked. He sold enough of the cars into that vision that he was actually able to afford to build all those charging stations.
It’s painting the picture in a way that people can believe. It’s painting the picture that’s, by the way, consistent with reality. There has to be a substantive claim that the whole thing can work.
It’s painting the picture in a way that people can believe. It’s painting the picture that’s, by the way, consistent with reality. There has to be a substantive claim that the whole thing can work.
Honestly, I think the other thing is it has to attach to human psychology. This is the other thing that evangelistic sellers are really good at. So in sales-speak, evangelistic sellers are really good at qualifying. There are certain customers that are just not going to do new things and are focused on downside risk. They go to work every day, 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 way to operate. A lot of people are like that.
And so the evangelistic seller, one of 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, “I don’t want to spend my career just protecting downside. I would like to become known within my own company, as somebody who’s innovative, and in the future. And I would like to basically stake a career about myself on a new technology, and the nature of that career. But if it doesn’t work out, 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.”
The evangelist seller meets the early adopter buyer, who’s got the right psychological mindset. What’s super interesting is those people actually become very close. The salesperson then becomes what we call a concentrated seller. They become 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. They often end up extremely close because they’re on a shared mission to do something new.
This is what we advise our companies to do. But like, that’s the process. Then it’s the gut check, which is like, okay, are those early adopters actually out there? Do they actually exist? Do they have the authority to actually make those kinds of decisions?
Then it’s the gut check, which is like, okay, are those early adopters actually out there? Do they actually exist? Do they have the authority to actually make those kinds of decisions?
Or at some point, right, if they don’t exist, that itself is an interesting market signal. If the early adopters 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 Kalanick, and trying to start Uber, and your first idea is, “I’m going to build taxi dispatch software, I’m going to try to sell it to taxicab companies.” And then you spend two years trying to get taxicab companies to buy this taxi dispatch software, and they’ll say, no. That is, literally, what happened. But you could very easily imagine that happening.The other thing that emerges out of this is people just decide to start the company.
Question: What do you believe are some of the biggest challenges to getting new technologies and solutions adopted, particularly in the healthcare space?
Marc: The general thing that happens, which is really relevant to all these healthcare markets, is the product works, and I can’t get paid.
One form of the problem is I just can’t get paid. Like, literally, 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 access if the insurance company is not going to pay for it. So that’s a particularly stark example of “can’t get paid.”
There’s another example of “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, 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, and they end up selling less. The reason is because they don’t charge enough for the product. They’re not giving enough revenue back into the company. They’re not giving 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 kinds of sale that we’re talking about. And then they just get stuck.
It’s like the product works in theory, and the customers wanted in theory, but 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.
This is actually a very funny conversation to have with the founder, because the conversation is so weird. It’s like, “Okay, tell us tell me about your product,” “Oh, it’s the best product ever, and machine learning and this and that, it revolutionized. It’s going to save our companies $10 million each in saved expenses.” And it’s like, “Okay, what are you charging for it?” “Fifty thousand dollars.” It’s like, “Well, you’re going to save them $10 million, why are you only charging $50,000?” “Well, because it’s going to be easy to sell. We’re going to sell it and move to the next customers.” And 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?” “Oh, we’ve got to send in, you know, eight people for, you know, six months.” “What does that cost?” “Well, it costs a million and a half bucks.” Okay, so congratulations. You’re now down $1.45 million in 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?
What I’m describing is literally what we see happen. A lot of the time, it’s the founder themselves who’s actually on site with the customer. And it’s like, what’s their time worth? Because their time is getting sucked down. It’s like the entire future of the company is being, basically, bled out. And a big thing we always have here is, the principle is that you have to get paid. The customer has to pay for the thing. This is the thing with all the healthcare startups. You have to be able to decode the system.
The principle is that you have to get paid. The customer has to pay for the thing. This is the thing with all the healthcare startups. You have to be able to decode the system.
And then on top of that, what you really do in a lot of cases is raise prices. Which is weird, because technology is supposed to drive down prices, so what are you doing raising prices? Well, you have to make enough money per sale. The internal economics of your business have to be such that you make enough money per sale, so that you can afford to actually build the company, right? You can build the company, you can build the sales and marketing engine, you can get the thing known, you can get the thing adopted, you can get references. And then once you’re at scale, then you can start to drive the price down.
Jorge: Yeah, and I would just add, in the healthcare realm, and this is an overgeneralization, but business model failures are often a lack of recognition that the person who will benefit from your solution is often not the buyer. You have this mismatch that often happens in the healthcare system where you’re targeting your customer, but there’s a different buyer. So that’s a very difficult thing. That can usually be addressed with a 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 what a 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 see with a lot of our startups, which 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 healthcare 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 standpoints. 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.
Marc: Thanks, everybody.
