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Welcome to the Knowledge Project, I'm your host, Shane Persch, I'm the author of the Farnam Street blog, a website with over seventy thousand readers that's dedicated to helping us learn by mastering the best of what other people have already figured out in the Knowledge Project. I interview amazing people from around the world so that we can all learn from them, expand our minds and challenge the thinking. On this episode, I have Chris Dixon. Chris is a partner at perhaps the most famous venture capital firm in the world, Andreessen Horowitz, or commonly known as a 16 Z.


We talk about the history of venture capital, why companies fail the future of artificial intelligence. And the idea is, I hope you like this interview as much as I did. I'd love to hear your feedback on that Farnam Street on Twitter. That's at R and S t r e t on Twitter. Before I get started, here's a quick word from our sponsor, Greenhaven Road Capital is a small hedge fund inspired by the early Warren Buffett partnerships. We have a fair fee structure and our portfolio manager is the largest investor in the fund.


Our minimum investment is one hundred thousand dollars. Accredited investors can learn more at Greenhaven Road Dotcom.


Chris, thank you so much for coming on. Thanks for having me.


I'm wondering, what's a typical day look like for you for this week?


Yeah, so it's a good question. It's an interesting job. I think I guess I would divide my calendar somewhat between, I guess, two broad categories of things. One would be meeting with entrepreneurs who are starting companies and raising money and coming to us to talk about potentially investing and then the other half of the time working with existing investments to try to help them. You know, everything from maybe they're raising more money or they're trying to recruit somebody or close a sale or some other kind of thing like that.


So roughly, I would say kind of half and half kind of looking for new things. And what that means is basically a lot of meetings. So it's a big change for some people. Like I had a background in computer programming. There's a great program blog post. It's called like make your schedule and manage your schedule, I think, where you can address kind of the schedule of somebody who builds things so like a computer programmer or a carpenter or what have you versus sort of a manager.


NBC's very much a manager. So it's sort of on our schedule meetings as opposed to kind of, you know, eight hours of getting into a flow state and thinking about a topic. So the good the bad part is it's sort of a lot of state switching and kind of jumping around. The good thing is it's very it's you know, it's a fascinating job. You get to meet with incredibly smart and passionate people who are doing interesting things and they tell you all about what they're doing.


So if you're kind of intellectually curious, it's a pretty amazing job because it's sort of, you know, one minute someone's telling you about some breakthrough in biotech and the next minute you're talking about data center infrastructure. In the next minute you're talking about computer security. The next minute you're talking about, you know, I don't know what the transportation industry, you name it. So it's a fun job. It sounds amazing.


Can we just back up for one second just so I don't make any assumptions here? You see, you work for like a venture capital firm, A16. I'm an outsider. Can you explain what does that mean? Yeah.


So so. So, yeah, I work for is interesting Horowitz, which is the shorthand is it was a firm founded in 2009 by Marc Andreessen about Horowitz, Marc and Ben, both prior to that. And Marc co-founded Netscape, which you know was the first kind of commercial popular Internet browser, and then they went on to do a bunch of other interesting startups.


So and maybe if you want to, I'm going to back up and tell you what venture capital is more generally and a little bit the history of it. It would be helpful.


Yeah, it'd be awesome. Great. So basically, America, I mean, as an industry started in the I don't know what the forties and fifties or something. I mean, the kind of the practice goes back further. If you go back and look at kind of wealthy entrepreneurs, even in the, you know, whatever, two hundred years ago, a lot of them, after they made money, would then invest in new and other entrepreneurs who didn't who maybe had a good idea and some good new technology, but not enough money to to run it.


So actually, I just read a great book recently about it was How the World Was one. It was about the laying of the first transatlantic cables in eighteen forties, you know, which they actually laid the cables onto the ground. And if you read about all the things they were always funded by, you know, some cases governments, but a lot of times some crazy entrepreneur who made money and then was investing in some other new thing. So.


So there was always this practice going way back. But then what happened is in like the really I guess really the nineteen sixties, it became kind of formalized as an industry called venture capital, and there were firms kind of around. It was it all happened in Silicon Valley around, you know, the rise of firms like Intel, Apple, you know, those kinds of things at Microsoft. And there were firms like Sequoia and Kleiner Perkins and other firms basically started off as people investing their own money in new technology companies and then at some point kind of became formalized in the way we're in the game, the way it works today, which is today, we don't really invest our own money.


We invest other people's money specifically. We invest, know a lot of our money comes from places like universities.


But you're not investing your own money. No, no. We know. So most VCs I mean, we do invest our own money in the way it works is we have a fund and we raise money for a fund. Some of that money does actually come from us personally. But but a lot of it doesn't. A lot of it comes from like, for example or. Large universities, a lot of it was pioneered by places like Yale, for example, very famously started doing this in the 60s, where basically they have you know, they have their endowment, you might read about it, a large endowment.


And they basically, you know, they put some portion of that in bonds and some portion stocks and they want to put some portion in to other asset classes, as they call them, that have a long term horizon. So basically, what's nice about those about those those pools of capital is that they you know, they kind of plan things out in a 10 year or even 30 year horizon, which which matches kind of our time horizon. So so basically, that's what this industry is.


Actually, it gets a lot of attention in the press venture capital, but it's actually a very small industry. So, you know, they're probably there are a few dozen kind of firms that most people that constitute the majority of the industry, maybe a few thousand people work in the industry at the most. The amount of money invested is on the order of like 10 to 20 billion per year, which sounds like a lot, but it's actually smaller than the R&D budget for a lot of, you know, for Apple and Google, for example.


So, you know, so as much as it gets a lot of press, it's actually a very it's actually what most people consider kind of a cottage industry, the tech world.


I mean, it seems pretty clubby from the outside. To what extent is that true?


Yeah, I guess it depends who you ask. The the cynics would say it is clubby and kind of insider ish or something. I think my you know, I would argue my feeling is it's actually it's small and people know each other. But but but it's there's a there's an ethos of kind of inclusion. Anyone who works in the tech industry for more than a few years has seen people rise media incredibly quickly. So I knew tech people like Mark Zuckerberg or all these kinds of entrepreneurs like this, like anyone who's worked the industry for 10 years, has met these people, has met people like that who are now incredibly prominent back when they weren't and is very used to kind of new, very successful people coming out of nowhere.


And as a result, the industry is very I think it's very sort of inclusive and people just sort of expect, you know, new things to pop up. And people are very responsive, I think, to, you know, I don't know, new people kind of coming. It's a everyone there in Silicon Valley. And by the way, Silicon Valley people say Silicon Valley. But I think it's also that kind of spirit is now happening in places like New York and L.A. and Canada and Europe and Asia.


Do you think that's a byproduct or do you think that's something conscious, like people are trying to develop the same culture, or do you think it's just happening naturally?


Why is that? Why is it spreading? Yeah, I think part of it is people people see the success of Silicon Valley and want emulated. I think part of it is, you know, I see a lot of people who who moved to California to join the tech industry and then decide that it's too much of an industry town. And then they want to move to New York, for example. They have more diversity. And yet have you know what's great about places like New York and L.A.?


As an example, I spent time in both places is you you're surrounded with people that are in the arts and media and all sorts of other kinds of industries, and that kind of creates a different creative dynamic. And so I think it's just the natural kind of maturation of an industry. And as it spreads out, you have kind of propagating to more places. Yeah, I mean, China is other specific things like China is its own story, probably where there really know a country that sort of decided that tech is strategic and is invested heavily in it.


So it's a multifaceted kind of story there. I think.


So the firm that you're a partner at age 16 has a stellar reputation. I mean, how did that come about? What do you guys do differently?


Yeah, well, so, you know, the kind of philosophy of the firm is is is a little different than I think than than the traditional philosophy in the industry that traditionally the industry there were basically there were very few venture capitalists. And so what happened was if you were an entrepreneur, you had to go and you had to, you know, basically go to one of these ten or so firms and and pitch them your idea. And I think these firms, they kind of thought of themselves like the little bit of the way of maybe a hedge fund thinks of itself as their job is to come up with theories about where the the future's going and, you know, pick the best entrepreneurs.


And then once the investor going to hang back and kind of monitor their investment in the same rate that a hedge fund or someone like with our firm, we kind of we think we've kind of flipped the model where where we think of ourselves primarily as a as a service firm. So we think of ourselves a way of maybe a law firm or a talent agency or someone would wear. Our first job is to serve to provide services for the entrepreneur. And so we in our secondary job is to is to sort of pick the right company.


So the service we provide for entrepreneurs, we basically have we're staffed very differently and structured very differently than most U.S. firms. We have over one hundred employees who are not investors that our firm, whose sole job it is to help companies do things like recruit employees.


Build their you know, their customers base, so now that people are seeing success with that or your competitors copying that model or I think to some extent is also it's a very different financial structure.


So it's hard to copy because we basically the traditional structure is basically that the funds charge fees. And then and then most of those fees go to the partner salaries. We don't. Instead, we put our fees towards these operating teams. So for our competitors, the capacity have to kind of dramatically change their own compensation and pay structure, which isn't likely.


Well, I mean, I think it would up I mean, I think we are seeing probably I think you will see more and more of our kind of style. I think it will probably come from newer firms.


And, you know, look, I mean, my broader view would be I think it would be great if more firms did what we did. I mean, yes, it would be competition on the one hand.


On the other hand, I think it would be good for entrepreneurs and good for the better for the system and it better for the system just to have kind of just more alignment between the investors and the entrepreneurs and investors themselves, more like entrepreneurs who are taking risk right now and are sort of compensation is aligned with the entrepreneurs. Like we basically, you know, most of the money, if we make money on this, will be because our company is successful, not because we collect fees.


So, yeah, so we've worked very hard to help entrepreneurs, which we think is primarily where hopefully, you know, our positive reputation comes from.


It seems like from an outsider's perspective, the companies are staying private longer than longer in the funding cycle than they ever have before.


And the valuations for some of these companies, I mean, before they beat the rumored valuations, before they become public, like Uber at fifty billion or something, what do you see as the implications and second order effects of this is this seems like an unprecedented kind of scale.


It's a great question. So, I mean, part of the answer is why are they doing it? Because they're able to do it because basically what you're basically seeing is that if you read the press, they kind of confuse this issue a lot. They say that because you're investing in companies like Uber at later stages, actually. I mean, we don't like we don't do those kinds of investments. For the most part. It's actually what you what is happening is firms that are that historically have been public investors.


So, for example, Fidelity, T. Rowe Price, like Wellington, all of these kind of well-known public market investors have now moved to invest in private companies. And so there are a lot they're sort of the firms that are leading a lot of these late stage investments and basically for a variety of reasons. I mean, so it's a it's a complicated story. And one reason that companies are staying private longer is the perception among the technology community that the public markets are somewhat short term focused.


So if you look at if you just go read whatever the Barron's or The Wall Street Journal and things, there's an extreme focus on kind of what happens next quarter. Do they make their their numbers that quarter as opposed to are they investing for the next five to 10 years?


You push back a little on that, like Fidelity and the T. Rowe prices of the world. You control hundreds of millions, billions of dollars in shares in a company.


Aren't they the ones that could be setting that in the public market to drive the expectations to be longer term?


Yes, you know, that's a good point. And I think that that would be the that would be a good counter argument. And this is again, I'm not saying this is a settled question. I think this is the sort of two sides of the debate. So one side of the debate would say the public market investors are short sighted the other side. But say what you said exactly, which is, you know, there are these very long term investors.


And like and to your point, look at Amazon as an example where it seems as though the investors have accepted the idea that they'll be investing for the long term and forgo profits for a very long time. So, you know, but there is on the flip side, Facebook and Google, most prominently, they have dual class stock, which is which means that they basically went before they went public. The founders structured it so that they could never, basically never get fired by Wall Street.


What do you think of that? I think it's great. It's if you just look at what's happening, I just don't think I don't think you can plan technology investment on a anything shorter, like just the way technology products, life cycles work. I think it works on a minimum of, let's say, three to five year cycle, OK? And I just think it's very, very hard to have that kind of managed by a committee. So I'm not saying that those founders, you know, it's less it's less that they have sort of super powers and more just simply that you really need sort of a small group of people or one person who's managing for a very long term horizon.


And it seems to be I mean, if you just look at.


I know I. Just look at what Facebook and Google are doing right now.


I mean, I also have specific experience with some of these companies where it's just I don't want to name the specific companies, but of course. But some of these public companies which where the where I think the CEOs felt like they were, you know, completely handcuffed or something, just couldn't just simply couldn't make the kinds of investments they want to make. So I don't know. I generally think things need to be in technology. Things need to be.


And a longer term horizon. There are there are a variety of different ways you could accomplish that. One of them is dual class stock. I mean, there are other proposals out there, for example, to increase short term capital gains to to to incentivize short term trading. I think that's another good idea. You know, there's a variety of kind of proposals out there. I do think, though, that in general that, you know, long term planning, long term thinking is very good for us as a industry, country, world.


But I'm a proponent of long term thinking.


So and I think the classic is one of the one mechanism to get there. I don't know if it's the best mechanism, but I think it's one of them.


So I have a question, but maybe you can explain funding right before that.


But my question is, to what extent is the first round of funding really about preserving optionality for the future so you can double and triple down on success? Or is it more about funding the idea fully?


Or maybe you can walk me through some of the you from the investor, from the venture capital, from your perspective?


Yeah, I mean, I don't even have a what are the stages and why would you invest in a particular stage and what are you looking for? Sure. Briefly. Yeah. Yeah.


So basically there's I mean just to some quick nomenclature, that's just generally like what's called seed investing, which is one or two entrepreneurs, maybe a couple and an idea. And that's also called angel investing sometimes. And it's people writing individuals writing checks or small firms. And maybe they'll raise something like a million or two million dollars. And that kind of gives them enough money to build a small software team that can build a first product. And that's that's something I used to do.


And, you know, it it's we do we don't do much of that anymore. We do some of that, but not not as much because we have a bigger fund and we tend to focus on what's called series A and Series B and C series A is after a company is usually after a company is built like an initial version of a product and is now ready to build, build and build, kind of build out the product more and start selling it or taking it to market.


Series B is usually a little later when they've got some initial results and now we're trying to accelerate those results. And those will typically be in series A, let's say, you know, ten million dollars might be an average investment size and twenty million dollars or something like this.


And so and your series, a deal usually includes the right first right of refusal, I would guess, on further funding or.


Yeah, yeah. It it usually lets us have what we call pro rata rights, which means we're allowed to invest a certain portion in the next round of funding. And mathematically it's like enough that we can preserve our kind of ownership and equity. Yeah. So basically it's like a yeah. It just lets us kind of keep investing some not not a whole lot, the whole ground.


It's kind of technical, but basically the idea is just that we have the right to kind of keep investing some amount. Yeah. So that's, that's usually the industry's change a lot like in the past, maybe 10 plus years ago, VCs would actually take control of a company in many different respects, including the board of directors that it's on and what it actually could like.


There's all these horror stories of like them firing the founders and things like that. That's not something that we do. We really just don't even take control for the most part right now. We don't we couldn't fire the founders if we wanted to. Not that we do want to, but we couldn't.


And if you get a reputation for that, you'd probably stop seeing deals. You'd have a very short career, basically. So not that we want to, but we did it just it's just not the norm to have those kinds of provisions. So basically, for the most part, it's a very simple transaction. It's actually in all the areas of finance, it's relative. It's probably the simplest, which is we give somebody, let's say, ten million dollars in an exchange.


Let's say we buy 15 to 20 percent of the company, which means that the company sells for, you know, whatever, ten million dollars we get 15 to 20 percent of that. It's for the most part, that's kind of what it is. There's a there's a little bit more structure.


There's things called preferences, which basically means that we get paid disproportionately more on the down uncertain downside cases and things like this.


But it's relatively simple to what extent in this you say you do more seriously than angel investing. And if I understood what you were saying correctly, you're more investing on people in the angel stage. I mean, to what extent in the series are you investing in people versus investing in the idea?


Great question. I think it's I think it's a. It's definitely certainly people is 90 percent of it, and the idea is also important with the proviso that the idea will at that point, we know it will change. So it's kind of more like you're investing in the general direction of the idea because just the world changes. I'll just give you something like, you know, I remember when Dropbox for I'll just take an example, raise their series. I think it was like two thousand eight.


And at the time it was it was there was really pre mobile. I mean, the iPhone had come out, but it was it was much less widespread than it is today. Right. And so if you look back at the original pitch deck for that company or let's say for Facebook, for that matter, or LinkedIn or all these companies today, Pinterest, none of them really had mobile as a big part of their business plan because Mobile didn't just wasn't you know, it was still feature films.


Right? It was still like this little Motorola phones where you type the you have little keyboards and stuff. And so the world, the computing world dramatically changed in the last seven years. And so all of those companies. So, you know, so if you invested in those companies early on like a Facebook, you knew you were investing in a social network, you didn't know you're investing in a mobile apps company that eventually would buy a messenger and buy Instagram and things.


Right. So so you see, I would say you kind of directionally you're investing in an idea and you're investing in people. But you also know the world will change dramatically in unexpected ways. And so, you know what you really are kind of looking for? It's kind of like, you know, these kind of black swan antifragile ideas of you're really looking for, you know, what some people call, you know, optionality, meaning things which, you know, you don't you can't predict the future, but you can see that there are certain scenarios where this these are disproportionately rewarded.


Exactly. I mean, that's the thing that's and it's hard to understand about the model of this is it's very hard to internalize, I should say, which is that the best VC funds lose money at least half the time, which means half of our if we're doing a good job, half of our investments will fail. Right. And then some small portion will be huge hits and some and some other portion will be modest.


Is there something I wonder if most people even get that in the stock market?


Yeah, no, it's very it's very it's very skewed in that way. And in fact, it's interesting, I wrote a blog post about it. If anyone's interested, it's on my website, Seligsohn dot org called the Babe Ruth Effect. And it actually we have data reading that. Yeah, yeah.


There's a lot of data in the industry that actually, interestingly enough, the best firms actually have a higher loss rate, meaning they lose money more frequently than the then the less when they win.


The magnitude is so much greater than so it's like I don't actually I'm not a big sports guy, but in baseball, you know, it's what you call slugging percentage, which I'm runs. You hit even if you have more strikeouts, the two tend to be correlated. So it takes it's sort of an unnatural way to think in some ways because when you meet entrepreneurs, you're not you're sort of thinking somewhat like, will they succeed? But you're also thinking probably more about if they succeed, how big could it get?


Right. And so you have to kind of train yourself to think that way and frankly, train yourself to be accepting that a lot of what you do will will fail. And it's a little bit it's just it's one thing to realize that in the abstract and to write a blog post about it like I did, it's very different to actually experience it because these entrepreneurs are your friends and. Yeah, you know, and you're rooting for them and to and and the reality of this job is you spend a lot of time kind of helping people in tough situations.


So if you had to group the failures into kind of three buckets between leadership execution and idea, how would you what percentiles would you kind of put on those?


I think that's a good question. I guess it depends on the stage. It's it's very different at different stages.


But there's some reasonable percentage of the time where the entrepreneur kind of does everything right and just the market, you know, whatever some you know, it gets, you know, bundled into Google, releases the same product and gives it away for free or something or whatever it might be that that just sort of like things happen that are beyond your control, that that just make it or regulators just decide it's you know, you you create a new kind of drug and the FDA decides it's, you know, not to not to approve it or something like this.


There's certain things are just external factors. And that's probably some you know, it's it's OK to make up a number. Twenty five percent of the time there's some external factor that that is completely beyond your control, you know? And then I think some portion of the time it's the sort of the hypothesis is wrong about about the product in the market. And that's a pretty high percentage of the time. I think. Then the question becomes, you know, I think what's really good entrepreneurs, they're able to kind of adapt then and, you know, as some people call it, a pivot or something where you change what you do.


And so that's always an interesting kind of scenario. But I would say the.


My overall learning, having done this for, I don't know, eight or nine years now as a I'm not a believer in A, B, C for two and a half years, but I was investing personally before that for whatever, six and a half years successfully to my dad was pretty good.


The I would say my biggest learning is it's it's probably more people than I ever like. I probably thought originally it was 70 percent people and now I think it's ninety eight percent people like it's a lot of people that kind of begs the question, like, what's the difference then between a bad founder and a good founder, so to speak?


Not to categorize them.


But yeah, I think I think a lot of it is not necessarily that they're good or bad, but it's how it's we we have a concept called product I'm sorry, founder market fit. So that kind of fit between the founder of the market, meaning, you know, kind of are they uniquely suited to do to do something in that market. And so a lot of times in our business, that means they have a strong technical background. So maybe they have a PhD from Stanford and MIT Computer Sciences, probably, frankly, I don't know, a third, if not half of our investments are like that, or people just with very, very strong technical backgrounds who worked.


I worked in a lab is very typical stories. I worked in a I was at Berkeley and I worked on in their big data lab.


And I invented this new, you know, open source data analysis tool. And now I want to go make a business out of it. And that's that's literally a company we funded called Data Bricks, which is a technology called Spark. Like, you know, that's probably a third to half of our company. So someone with very, very deep expertise and then they have to learn their obviously their background is in, let's say, computer science or some other technical field.


They have to then go learn kind of how to run a business and how to hire people and how to get customers. But, you know, we kind of make the assumption that that's easier to learn than than the opposite. It's easier to teach computer sciences, business and vice versa. You're never going to teach a business person computer science on the job. You have to go to school for that generally or have some kind of work like long work experience.


So so a lot of it is that is a technical expertise. Sometimes it's domain expertise. So, you know, someone will come out of a person, comes out of the media industry or the fashion industry or you name it. Right. Whatever industry might be and says, you know, I've been working in this industry and I realized it's done. And there's a whole bunch of things that are done in backwards ways. And I have ideas on how to improve them.


And when it comes from years of experience and expertise in that field, that's another common one.


Another one will be kind of like, you know, maybe like Airbnb, where it's just for whatever reason, it seems like those founders kind of were part of a certain cultural movement that was around, you know, just sort of maybe was a generational thing or I don't know what it was. But people, you know, they had been sort of sleeping on friends couches and things like that behavior emerge and and and sort of, you know, built out kind of road that kind of know that cultural wave.


So that's typically very important, you know, the sort of founder market fit. I think also a lot of it is just tenacity. Almost all companies were involved with run into extreme adversity. Every I've almost never been involved with a company that didn't have moments of almost failure. And so it's how how resilient are the entrepreneurs and how do you how do you go about determining that?


Like I mean, how do you go about testing their grit, their tenacity or not?


I mean, is there a question? It's a good question. It's very hard to do. I mean, we do spend a lot of time with the entrepreneurs and try to get to know them. I think a lot of it will come in through there, through their personal backgrounds.


You know, it's one reason why you'll you'll see a lot of VCs will invest in repeat entrepreneurs as an example. And so, like, if you look at like Travis who founded Uber, he had been involved. And I think he started two companies before and had a long track record. And people he had varied levels of success, but people who knew him spoke very highly of him as a tenacious and and resourceful founder. There's lots of examples of that sort of people with some kind of track record.


If they don't have a track record, it's it's hard and it's and it's it's something you really don't know until the moment the adversity comes.


What are the obvious things you're trying to avoid in founders?


Well, I think at the moment, startups are having a moment of kind of pop culture trendiness. There's something there's a lot of news articles about startups and venture capitalists become very sexy, right?


Yeah. You know, in the social networking movie thing. So I think what we're having now is a bunch of people entering the industry who maybe are coming into the for the wrong reasons, who come to try to make sort of quick money or something and and don't.


And it just really just not they don't appreciate how hard it is. And how do you pick somebody like that, like. When they come and they present to you, how do you determine that? Oh, I think they're in it for the money versus I think they're they're in it because they're passionate about the idea or some other narrative that we want to wrap around that.


Yeah, a lot of it's just depth of experience. How how long have they been working on the problem? I'll just give you an example. Like, I was a I was an early investor in Kickstarter and, you know, they didn't have an answer. And the founders of Kickstarter, they didn't have kind of the classic computer science background I described. They had basically been working on the idea for, I would say, seven years at the time and and had tried everything to kind of get funded and, you know, and had and, you know, you talk to them and and it was really motivated.


The original idea of Kickstarter, Perry, was he was living in New Orleans and he was involved in the kind of music and art scene and had wanted to actually a service like Kickstarter for himself because he I think I think he had he had tried to organize a thing where, like a band that he wanted to play would come play. And he had a bunch of fans who wanted to see them. And he didn't have a way to kind of coordinate the two things, to have the fans put up.


The money didn't have Kickstarter. Right.


And he kept thinking about that and he kept thinking about, you know, the kind of going back in the history of the arts and the patronage model, you know, going back to Renaissance Italy and things and and how the Internet could kind of let you reimagine that model and, you know, when when you talk to him. So I think I invested in I don't know, when it was a two thousand eight or nine when he was first starting, it was clear this was a person who was this was his kind of white whale he'd been pursuing for, you know, forever.


And I mean and you ask and you could tell just you you ask him questions. And this was, you know, hit the depths of thinking. He had thought of everything he had gone through. We have this concept. We call the DMAs. And the idea is sort of the idea that that this sort of ideas aren't really just kind of a static thing. It's kind of like, you know, you see the the TV or the movies.


And they have the way they kind of have these you know, someone has this epiphany. And I imagine that it'll be like a, you know, intermittent windshield wiper or something. And, you know, in reality, it's much more of kind of a maze, meaning like, you know, you sort of imagine how the product might work, but then you imagine if the world responds in a certain way or the technology changes in a certain way, here's how I'll adapt.


And you sort of imagine yourself traversing through a maze and at various points in the maze, there might be a dead end or there might be a trap or there might be a prize or something like this. And you don't really know how the maze is going to turn out when you first start. But really obsessed founders will have thought through all the possibilities. And so a lot of what I like to do, at least in my when I meet with entrepreneurs, is kind of try to traverse that maze with them and understand the depth thinking that they have kind of gone through to get there.


And so in a case like Kickstarter, you know, I mean, it was just it was I mean, it was I'm not saying it was easy. It was an obvious investment or that it was, you know, obviously going to work. But I will say that, like, it was obvious that they had thought it through very, very deep. It was a mission for them. It was not you know, it was sort of a fun, you know, whatever a new career choice or something or something done for some kind of more mercenary reason.


So I don't know in the end. But the answer to look is this is not there's no great science to this.


People have tried many, many times to use data science and other things to try to quantify these kind of questions you're asking. And the results have been pretty, pretty poor. It's very it's been very hard to predict these things. I don't have always have you had, like, this secret recipe for us? I wish I did. And I've certainly tried.


Many people tried a lot of it's like any kind of creative endeavor. How do you pick a musician early on? How do you pick a writer early on? There's there's certainly like having spent years practicing in the field is is very helpful. But ultimately, a lot of it comes down to kind of an art, I guess. So so many things do.


So switching gears just a little bit here, what's one thing that you think the future holds that no one is talking about in your question?


I don't know about no one, because I think we're very few people. Then, I mean, maybe maybe I could change the question. Just say that some of the things I'm excited about I mean, I think some of the investments I've made have been things that are somewhat unpopular. So, for example, I'm not unpopular, but I would say, I don't know, controversial or I don't know. So I'm an investor in a company called Coinbase, which is the leading Bitcoin company.


So Bitcoin is an example which which I think is some digital currencies. So currencies I was an investor in Oculus, which Facebook, which is a real company, Facebook acquired. I'm very I'm very, very excited about virtual reality.


How do you think that's going to change our lives? Virtual reality. So I think it's the next day, I think it will be like when we look back on the history of computing, it will be the key milestones will be I mean, this is I'm at the extreme end of excitement here, but they'll be the PC and the Macintosh or something like this. Then the the Internet is the next key moment and then the mobile phone, like the iPhone.


And then I think virtual reality will be the next the next wave.


What about artificial intelligence? That's another interesting one. And as I said, that's sort of separate. But related to, I think, virtual reality, though, Will. I mean, I don't know how long it will take. Oculus is going to release their it's announced they're going to release their consumer product at the beginning of next year. And I think initially for the first year or two, it'll be primarily used by people playing video games. Right.


But I think in the next few years after that, it will it will be used much more broadly. You I think it will be the predominant way that people at some point interact with computers and other people at long distances. I think in 10 years we'd be probably having this conversation in virtual reality and we'd be looking at each other across the room and it would feel like we're in the same room. And and, you know, I think it's going to have it'll be the implications are far beyond gaming and it'll be all kinds of, I think, movies.


There's lots of interesting health related applications, communication, social things.


Do you think that bring the world closer together? I do. I think I think it will. I think it will. You know, there's some great videos on YouTube. I encourage people to go on there and check them out. If you just search YouTube for Oculus or virtual reality, you'll see a lot of them where there's one the other day where it was a guy who was using virtual reality, a demo to experience. I think it was like the Apollo moon landing or something.


And you watch these videos, people are literally crying at the end of the day. I mean, I've never seen a computing medium that has such a strong emotional impact because, you know, in that case, it was a guy who had dreamed his whole life about seeing this and he was crying and he was like, I would never be able to see this in any other way. And, you know, I think I think it was I think what it does is it lets people, you know, I think like I think, for example, a big application will be virtual tourism.


So just going and simply visiting the Great Wall of China. And it's also the kinds of things and things which right now are very expensive. And and one of my favorite demos, it's a demo now because it's all very early and it's not like they're not full products, but it is a thing called ocean rift, which is just lets you just like scuba dive around the ocean and and observe different aquatic. The sharks and the animals are having the experience, but none going right.


Yeah. Yeah, exactly. So, you know, I think it'll be all sorts of things like that. I think actually the gaming side is probably in some ways overblown because we underestimate everything else.


You think? I think so. I think so. So this VR, I'm very sad about that. I always like about. They are. Yeah.


I'd love to hear more about the singularity. Let's talk about this whole like so let's let's it. So I think that I think there's two things people talk about A.I. they often are really kind of talking about multiple things. Right. So there's I like there's sort of like the how singularity, like when we have a talking computer and then there's automation and like our computers take jobs away and things like this.


So I think a lot of people have what I would call kind of a a world's fair view of technology. So you remember the World's Fair. And I don't know if you see, like the Captain America movie or a bunch of other movies where they show, you know, it was like Howard Hughes type guys and they're showing, you know, the Tesla coils and the Android robots and the flying cars. And a lot of people, you know, sort of a simplified way to think of technology is is like the the robots are coming and people are going to build robots that take away our jobs.


If you actually look at how automation works, it's actually, I think, a lot more nuanced and and less obvious. So I'll give you an example. Like, you just kind of take any almost any technology company that's on our website that we've invested in. I'll just pick an arbitrary example, a company called Benefits. So benefits as a company is let's use is a Web product that lets you, if you're a small business, go and sign up new employees for health care and other benefits.


Right. So it sounds like just like, you know, it's whatever it's a benefit software. Right. Actually, what it ends up doing is it ends up letting you hire fewer people at your companies. You don't, you know, no longer have to hire somebody to do that job. Right. So what I would argue is things like like a lot of automation doesn't really look like automation. It looks like just regular software. And that's that's a lot of ways.


What I really is, is sort of taking what smart people do and embedding it in software and giving that software out to lots of people. And so every new piece of software that you see in some ways is a sort of a piecemeal form of AI. And then when you sort of. When you combine it all together, what you get is kind of this broader kind of functioning super system, all the software interacting together, I think a lot of the really the kind of the real automation ends up sneaking up on you.


Now, this is other kind of A.I., which is the kind of more spectacular stuff that you read about, which is gets the headlines. Yeah. So serious an example like, you know, which is which is speech recognition. And then Google's doing a lot of interesting stuff with image recognition.


I do think this this this stuff is that a lot of people in Silicon Valley believe that this kind of A.I. is at a inflection point now and specifically around a technological deep learning, which is basically a I don't if you remember neural networks, a neural networks of the Trinity, there are a lot of books written about them and things like the 90s, which are basically computer systems that were kind of designed to replicate the way that the human brain does.


And it was sort of held out as a promise. And then it was and then it was sort of there was a letdown afterwards because it didn't deliver the results people wanted. But basically what we've now discovered is it turns out that if you do neural networks and you use a lot more computing power, which we now have available because of Moore's Law, which is, you know, the idea that basically all computing gets faster and cheaper very quickly over time.


Basically, if you take neural networks and you make them a lot, lot, a lot more computing power, a lot more storage, a lot more memory, a lot more networking, a lot more computing resources, it works really well. That's and that's what a couple of years ago, Google did a very famous experiment where they basically took I think it was on the order of tens of thousands of computers, had to study YouTube videos.


And at the end of it, those computers were able to correctly identify cats like a cat with a very high degree of accuracy. That was one of the kind of results they released that really just kind of shocked people as to how accurate it was, because basically a lot of this stuff and I mean, if you use Syria as an example, a lot of it is it's a relatively easy to get to like 80, 90 percent accuracy.


But it turns out if you just like a regular program or downloads a bunch of open source software and spends a weekend, you can make like a decent replica of Siri in like a weekend, but you get higher accuracy is like exponential really quickly.


And then it turns out all of the work is in the last 20 percent. So like self-driving cars. Another good example where if somebody tells you a self-driving car and it was able to drive on the highway during daylight, that's actually not very impressive. And that's actually something that almost anyone can build. I mean, anyone with the ability can build what's what's hard is all of the millions of edge cases. So by edge cases, I mean, it's dark, it's raining.


A dog jumps out to jog dogs, jump out a shadow, looks like a dog, jumps out like, you know, whatever, like this, you name it. There's a million little special cases where to learn all those different special cases takes lots and lots of additional effort. And so the kind of the big breakthrough with the with the cat video and Google was that they'd gotten to this point of like ninety nine percent or something like that, which no one had ever gotten to before.


I might have been ninety seven. I forgot the exact number, but it was very high number. And they've since gone on to do more experiments where they've done things that do like what they call image classification, which is basically take an image and describe what's in that scene. And the results are getting very, very good there. So you'll take an image and then the computer will say this is three children eating pizza and it's right, you know, and like things like that.


And so so they've been a lot of really promising results. And it's still very early. I mean, you look at your phone and and the auto. Correct. And every day you'll see it makes look correct. Right. I mean, like so, you know, and if we can't, we can't make an auto correct today that that that seems to work even most of the time. Just get the swearwords right.


You know, so I still think we have a long way to go. I think the the sort of I would call the laboratory results are very promising in those laboratory results require like ten thousand computers. Yeah.


It's unfeasible right now to have that. Yeah, that's right. That's right. So one of the questions will be just kind of how long does it take for that kind of computing power to for the price to drop and become more ubiquitous? And there's Moore's Law. There's all sorts of questions around Moore's Law. Some people think Moore's Law is slowing down. I think one of the big potential interesting things here is what's called quantum computing, which is is this whole new kind of theoretical area of computer of basically how to build computers that use quantum effects.


So things from quantum mechanics, I would say the optimistic people and including some very well respected computer science professors at Stanford, for example, believe that in five to ten years we'll have quantum computing in the mainstream. If that happens, you could see a dramatic I'll take off.


It could it could lead to a dramatic acceleration in the. Performance of A.I., Yeah, there's a bunch of things, one of the reasons it's very hard to predict these technology things is that you often have these things that have kind of feedback loops, which means like if we get quantum computing and if and if we get, you know, that will let us compute things faster, which will let us store more, and then we'll build a store more data and and that'll have all sorts of second and third order effects.


Yes, exactly. Everything this complex kind of feedback loop systems. And so if I had to bet, I think we're still pretty far away from kind of a singularity. But but there are certain scenarios people can conceive of is that it's much to do with all these different companies, like how do you filter information and how do you do that personally?


Like how do you know what's important and what's not important? Like how do you determine a signal from noise when you're surfing the Internet?


That's a great question. I'm a huge fan of Twitter, for example. I use Twitter constantly and for me it's probably one of my most important work tools and that I have a carefully curated list of people I follow. Essentially, I have whatever it is, two thousand of the smartest people in the world finding information for me and telling me what to read.


That's how I view Twitter. So that's obviously very important. You know, at the firm we have a whole bunch of different things we do, including lots of people that we interact with and that we talk to regularly.


We try to do things like, for example, we have a big academic conference coming up in a few weeks where we invite 50 plus of the top computer scientists in the world to come and kind of do like a mini almost like TED talks or something at our firm. We we do lots of outreach with academia and things. We try to get involved also in like the open source communities, you know, go to lots of events, do lots of press outreach.


A lot of what we try and do is just kind of be in the flow of a lot of different interesting groups of people working on new things. Right. So what do you think people are focused on?


That's a waste of time. Like what do you think? Misplaced attention. Where would that lie?


Good question. So in the tech world specifically or.


Yeah, or in general, I mean, like other than Donald Trump, I think that I'll give you. Well, I guess I'll give you one example of the food industry. I'm an investor in a company called Soylent, which you may have heard of, which is kind of tried their stuff. OK, so so so I think Soyland I mean, the idea was Soylent is that we were trying to create kind of what we would consider a scientifically perfect food.


Kind of. The idea is you go in and the guys have a team of scientists who when read every scientific paper about nutrition and then design and build this perfect food. I think when you look at the food industry, there's an interesting movement. So that's one example. There's a bunch of other Silicon Valley startups that are trying to do new things around food. I think that's an industry which is it's just a very backwards industry today. It's if you look at the US right now, the the diabetes and obesity are are are really at the epidemic level.


And a lot of that's caused by excessive sugar and other kinds of ingredients like that. And if you just go, you know, just down, just down just now, this morning, trying to find something healthy to eat at the at the local store. And everything is filled with junk, sugar, soda and all sorts of other things. And it's really just an industry built around advertising and marketing and distribution. And almost no money is put into actually researching healthier and better foods.


So that's that's something I'm very passionate about. I think we we like to say sugar is anti smoking.


So we think when you look back 20 years from now, people will just be stunned by the kind of foods that we ate today. I think the whole organic food movement is a great thing. I think I think that's mostly only accessible to wealthier people. So I think a lot of what I think is interesting to the people that are trying to think more broadly about how to reform the industry, not just for people that can afford organic.


A lot of my friends would call that the, you know, the luxury of the rich, right?


Yeah, exactly. So, like, just what can we do more broadly? So that's interesting. Know, I'm very interested also in health care generally. We're doing a lot more spending a lot more time making investments in sort of areas that intersect between health care and computer science. And and just think there's a lot of things there that, you know, if you if you just look at the statistics of why our health care costs going up so dramatically, a lot of it has to do with the inefficiencies in the system.


Everything from, you know, medical records are still kept on paper. The insurance system is is very complex. And and and in many ways, backwords the you know, it's an incentive system all over the place to.


Vendors are all over the place. It costs more and more now to people debate this exact reason, but basically it costs more and more to create new drugs. Right. So there's all this all sorts of interesting things there. I think that could be improved.


Listen, I'm totally conscious of your time here. We're nearing the end. I have three questions that I always ask everybody.


So what's the one book you've read that had the greatest influence on your life?


Oh, man. I think you know, I think when I was in high school, I read a Westerbork, you know, Douglas Hofstadter's book. And if you know that book, it's.


No, I'm going to look it up now, though. So I was in some computers since I was a kid. And this book was sort of tied, tied together computers and philosophy and music. And it for me it was really important because it really broadened my horizons at any major I went to when I was in college. I majored in philosophy and in that I think that book kind of got me to do that. That that would be that would be a huge one for me.


And also anything by Daniel Dennett reading it like consciousness. Explain the stuff you said. This I have kind of there's this whole kind of thread of I think Oliver Sacks is sadly just passed away. And I used to just read all of those kind of popular.


Why are you so smart? I don't know. Those were great. Those are great teachers. So when I was a kid, you know, whatever high school, college, I read all those books and I and those were all just sort of hugely influential on me.


So what's on your nightstand right now? What are you reading right now that you're really into it?


Just read what's called the three body problem finished. Read this book. It's, uh, it's this it's this Chinese author who just write it's a science fiction book. It just won the award or something. It's really is really amazing book. What else I read. I read. I'm reading. I just bought this was it called The Martian, which is I guess is a popular book that's not made into a movie. And my books are one thing I like to I I'm, I'm not digital on books I buy.


Oh, you're still physical. Well, it's really like physical books. I like having them on myself. I like the feeling of reading a book. I don't know. I just it's it's it's something we're on the same way.


I live in this weird world where, you know, I read physical books and then go out and buy a Kindle copy just because I can't keep every physical book or I like it.


That's true. That's true. That's a problem. I just like the feeling of it. And it's also I feel like I look at the screen too much as it is distracting to to have, like, the thing I read on the on my iPhone and then I can, like, check Twitter, like it's distracting. Oh, I just read the Elon Musk book, which I thought was good, sort of the biography of Austin Brown. And oh I read that a really good book called Sapiens.


You heard of this. Oh, I heard that book. Yeah, somebody else recommended that. To me, it's sort of like it's kind of like guns, germs and steel, like one of these. I think they're calling the genre big history where it's kind of the panoramic view of history and it's just the history of homosapiens. That was amazing. Yeah, it's it's it was really awesome. I highly recommend it.


And so, you know, what I'm trying to do with the knowledge project, who would you like to see on the show instead of anybody or anybody in the world?


Can you can you narrow it down a little or. No, like who would you like to hear me interview?


I guess that's a good question.


Um, I'm going to call them until you tell them you recommend that it would be good.


You know, it'd be great. As you know. Venkat from Reben Farm. I do. Yes, yes, yes. He's one of my favorite favorite pieces. He's already agreed to. Come on. Oh, wow. OK, well, there you go.


OK, we have too much overlap. That's awesome. No, it's a good show. He's like he's incredibly brilliant as Ben Thompson. He has this thing called Street.


Definitely. I just started following him on Twitter. Yeah, he's awesome. Well, thanks so much, Chris.


This has been great fun. I really appreciate you taking the time.


Yeah, well, it's my pleasure. Thanks for having me. Hey, guys, this is Shane again, just a few more things before we wrap up. You can find Schnur's at Farnam Street blog, dotcom slash podcast. That's fair. And s-t r e t blog, dot com slash podcast. You can also find information there on how to get a transcript. And if you'd like to receive a weekly email from MIT filled with all sorts of brain food, go to Farm Street blog, dot com slash newsletter.


This is all the good stuff I found on the Web that week that I've read and shared with close friends, books I'm reading and so much more.


Thank you for listening.