Welcome to The Knowledge Project, I'm your host Shane Parrish.
I'm the author of the Furnham Street blog, a website with over 70 ,000 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 our thinking.
On this episode, I have Chris Dixon.
Chris is a partner at perhaps the most famous venture capital firm in the world, Andriesen Horowitz, or commonly known as A16Z.
We talk about the history of venture capital, why companies fail, the future of artificial intelligence, and the idea maze.
I hope you liked this interview as much as I did.
I'd love to hear your feedback.
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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 VC. 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 with 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.
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 Paul Graham blog post.
It's called Maker Schedule and Manager Schedule, I think, where he can address kind of the So, like a computer programmer, or a carpenter, or what have you, versus sort of a manager.
And VC is very much a manager.
So, it's sort of one hour scheduled meetings, as opposed to kind of eight hours of getting into a flow state and thinking about a topic.
So the bad part is it's sort of a lot of state -swishing, and kind of jumping around.
The good thing is, 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, you know, if you're kind of like, 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, and 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 know, you name it.
So, it's a fun job.
Sounds amazing. Can we just back up for one second, just so I don't make any assumptions here?
So you work for like a venture capital firm, A16Z.
I'm an outsider. Can you explain, like, what does that mean?
Yeah, yeah. So, yeah, I work for Andreessen Horowitz, which is the shorthand is A16Z.
It was a firm founded in 2009 by Mark Andreessen and Ben Horowitz.
Mark and Ben, both prior to that had, Mark had 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, yeah, maybe if you want, I'd kind of back up and tell you what venture capital is more generally and a little about the history of it.
Would that be helpful?
Yeah, that'd be awesome.
Great. So, basically venture capital, I mean, as an industry started in the, I don't know what, the 40s and 50s 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, 200 years ago, a lot of them after they made money would then invest in new, you know, another entrepreneurs who didn't, who maybe had a good idea and some good new technology, but not enough money to run it.
So actually, I just read a great book recently about is it was how the world was won.
It was about the laying of the first transatlantic cables in the 1840s.
They actually laid the tables onto the ground.
And, you know, if you read about all those things, they were always funded by, you know, some cases governments, but a lot of times some crazy, you know, entrepreneur who'd made money and then was investing in some other new thing.
So, there was always this practice going way back.
But then what happened is in like the really, I guess, really the 1960s, it became kind of formalized as an industry called venture capital.
And there were firms kind of around, it all happened in Silicon Valley around, you know, the rise of firms like Intel, Apple, you know, those kinds of things, 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 and the way we're in became 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, you know, a lot of our money comes from places like universities.
But you're not investing your own money?
No, no. So, most VCs, I mean we do invest our own money in the...
So, 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 a lot of it doesn't, a lot of it comes from like, for example, large universities.
A lot of it's pioneered by places like Yale, for example, very famously started doing this in the 60s where basically they have their endowment, you might read about it, Yale has a large endowment.
And they basically put some portion of that in bonds and some portion in stocks and they want to put some portion in to other asset classes, as they call them, that have long -term horizons.
So basically, what's nice about those pools of capital is that they kind of plan things out in a 10 -year or even 30 -year horizon, which matches kind of our time horizon.
So basically, that's what this industry is.
It gets a lot of attention in the press venture capital, but it's actually a very small industry.
So, there are 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, for Apple and Google, for example.
So as much as it gets a lot of press, 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 if you ask.
The cynics would say it is clubby and kind of insiderish or something.
I think my, I would argue, my feeling is it's actually, it's small and people know each other, but there's an ethos of kind of inclusion.
Anyone who's worked in the tech industry for more than a few years has seen people rise, media, like, you know, incredibly quickly.
So, you know, I knew, you know, tech people like Mark Zuckerberg or all these kinds of entrepreneurs like this, like anyone who's worked in the industry for 10 years has met these people, has met, you know, 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 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 LA 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 spreading?
I think part of it is people see the success of Silicon Valley and want to emulate it.
I think part of it is, you know, I see a lot of people who move to California to join the tech industry and then decide they, you know, it's too much of an industry town and then they want to move to New York, for example, to have more diversity.
And, you know, to have, you know, what's great about places like New York and LA as an example, I spent time in both places is, you know, 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 industry.
And as it spreads out, you have it kind of propagating to more places.
Yeah, I mean, you know, China and there's other specific things like China is its own story probably where they're really, you know, it's a country that sort of decided that tech is strategic and has invested heavily in it.
So there's it's a multifaceted kind of story there, I think.
So the firm that you're a partner at, A16Z 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 a little different than I think than the traditional philosophy in the industry.
So 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 10 or so firms and pitch them your idea.
And, you know, I think these firms, they kind of thought of themselves like the little bit of the way that maybe hedge fund things of itself as their job is to come up with theories about where the, you know, the future is going and, you know, pick the best entrepreneurs and then once they invest, they
kind of hang back and kind of monitor their investment in the same way that a hedge fund or someone might.
With our firm, we've kind of, we think we've kind of flipped the model where we think of ourselves primarily as a service firm.
So we think of ourselves a way of maybe a law firm or a talent agency or someone would where our first job is to serve, is to provide services for the entrepreneur.
And so we, you know, in our secondary job is to sort of pick the right company.
So the service that we provide for entrepreneurs, we basically have, we're staffed very differently and structured very differently than most VC firms.
We have over a hundred employees who are not investors at our firm whose sole job it is to help companies do things like recruit employees, you know, build their, you know, their customers base.
So now that people are seeing success with that, are your competitors copying that model or?
I think to some extent it's also, it's a very different financial structure.
So it's hard to copy because we basically, the traditional structure is basically that VC funds charge fees 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 to copy us, they'd have to kind of dramatically change their own compensation and pay structure.
Which isn't likely, right?
Well, I mean, I think it, what it ends 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'll 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.
Yeah. It's better for the system just to have kind of, it's just more alignment between the investors and the entrepreneurs and investors acting themselves more like entrepreneurs who are taking risks.
And then our 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 successful, not because we collect fees.
So yeah. And you know, 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 that 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 50 billion or something, what do you see as the implications and second order effects of this?
This seems like an unprecedented kind of scale.
That's a great question.
So I mean, you know, part of the answer is why are they doing it?
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 VCs are investing in companies like Uber at later stages.
Actually, I mean, we don't do those kinds of investments for the most part.
It's actually what is happening is firms that historically have been public investors.
So for example, Fidelity, T.
Rowe Price, like a whole 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 there, 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 complicated story.
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, you know, look at if you just go read whatever the Barons or the Wall Street Journal and things, there's an extreme focus on kind of what happens next quarter.
Do they make their numbers that quarter as opposed to are they investing for the next five to 10 years?
I just push back a little on that.
Aren't Fidelity and like the T.
Rowe prices of the world who 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?
Yeah, that's a good point.
And I think that would be a good counter argument.
And I'm not saying this is a subtle question.
I think this is sort of two sides to the debate.
So one side of the debate would say that public market investors are short -sighted.
The other side would say what you said exactly, which is, you know, there are these very long -term investors.
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, you know, Facebook and Google, most prominently they have dual -class stock, which means that they basically, before they went public, the founders, you know, 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.
I'm over -poned to that.
You know, it's just look at what's happening.
I just don't think you can plan technology investments on anything shorter, like just the way technology products and life cycles work.
I think it works on a minimum of, let's say, three to five -year cycle.
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 that they have superpowers 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 don't know, I just look at what Facebook and Google are doing right now.
I mean, you know, I also have specific experience with some of these companies where it's just, you know, I don't want to name the specific companies, but some of these public companies which were 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 planned in a longer term horizon.
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, you know, short -term capital gains to disincentivize 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, you know, world.
I'm a proponent of long -term thinking.
And I think dual -class stock is 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, like, 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 like, maybe you can walk me through some of the thinking.
You're saying from the investor, from the venture capitalist perspective?
From your perspective, yeah.
I mean, I don't even have a thought.
What are the stages and why would you invest at a particular stage?
And what are you looking for just briefly?
Yeah. Yeah. So, basically, there's, I mean, there's just some quick nomenclature.
There's generally like what's called seed investing, which is, you know, one or two entrepreneurs, you know, maybe a couple, and an idea.
And that's also called angel investing sometimes.
Right. And usually it's people writing, you know, individuals writing checks or small firms.
And maybe they'll raise something like a million or $2 million.
And that kind of gives them enough money to build a small software team that can build a first product.
Okay. And that's something I used to do at, you know, at A16Z.
We don't do much of that anymore.
We do some of that, but not as much because we have a bigger fund.
And we tend to focus on what's called Series A and Series B.
And so, Series A is after a company has, usually after a company has built like an initial version of a product and is now ready to, you know, 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, you know, 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, $10 million might be an average investment size and the Series B $20 million 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 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.
Okay. So basically it's like a, yeah, it just lets us kind of keep investing some, not a whole, not the whole round.
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 usually...
The industry's changed a lot like in the past, maybe 10 plus years ago, VCs would actually take control of a company and in many different respects, including the board of directors that happens very...
Like a hands -on kind of...
Yeah. And well, it actually could like, there's all these horror stories of like them firing the founders and things.
Oh, wow. That that's not something that we do.
We really just don't even take control for the most part.
Most of us do now. 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, right?
You'd have very short career basically.
So not that we want to, but even if we did, 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 $10 million and an exchange, let's say we buy 15 to 20 % of the company, which means if the company sells for whatever and million dollars, we get 15 to 20 % of that.
For the most part, that's kind of what it is.
There's a little bit more structure.
There's things called preferences, which basically means that we get paid disproportionately more on the down, on certain downside cases and things like this.
But it's relatively simple.
To what extent in this, you say you do more series A than angel investing.
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 A are you investing in people versus investing in the idea?
Great question. I think it's series A, certainly people is 90 % of it and the ideas are 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.
I remember when Dropbox, I'll just take an example, raise their series A, I think it was like 2008.
At the time, it was really pre -mobile.
I mean, the iPhone had come out, but it was much less widespread than it is today.
So if you look back at the original pitch deck for that company or let's say 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 just wasn't, it was still feature phones.
It was still like those little Motorola phones where you type the, you have the little keyboards and stuff.
So the world, the computing world dramatically changed in the last seven years.
So all of those companies, 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 were investing in a mobile apps company that eventually would buy a messenger and buy Instagram and all things.
So I would say 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 what you really are kind of looking for, it's kind of like these kind of black swan anti -fragile ideas of you're really looking for what some people call optionality, meaning things which you can't predict the future, but you can see that there are certain scenarios where this people.
You're disproportionately rewarded.
Exactly. I mean, so that's the thing you have to understand.
It's hard to understand about the model of VCs.
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.
And then some small portion will be huge hits and some other portion will be modest hits or something.
I wonder if most people even get that in the stock market.
Yeah, no, 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, cdixon .org called The Babe Ruth Effect.
And it actually, we have data.
I remember reading that, yeah.
Yeah, there's a lot of data in the VC industry that actually, interestingly enough, the best firms actually have a higher loss rate, meaning they lose money more frequently than the less.
But when they win, the magnitude is so much greater.
Exactly. So it's like, I don't actually, I'm not a big sports guy, but in baseball, it's what you call slugging percentage, which is how 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?
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 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 you're rooting for them.
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 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, it gets bundled into Google releases the same product and gives it away for free or something.
Whatever it might be that just sort of like things happen that are beyond your control that just make it, or regulators just decide it's, you create a new kind of drug and the FDA decides it's not to approve it or something.
There's certain things are just external factors and that's probably some, you know, it's like, I don't know, I'm just making up a number 25 % of the time.
There's some external factor that is completely beyond your control.
And then I think some portion of the time, you're just sort of the hypothesis is wrong about the product and the market and that's a pretty high percentage of the time.
I think then the question becomes, I think with really good entrepreneurs, they're able to kind of adapt then.
And as some people call it a pivot or something where you change what you're doing.
And so, you know, that's always an interesting kind of scenario.
But I would say that my overall learning having done this for, I don't know, eight or nine years now, as a, I've only been a VC for two and a half years, but I was investing personally before that for whatever six and a half years.
Successfully too, am I done?
It was pretty good.
I would say my biggest learning is it's probably more people than I ever, like I probably thought originally it was 70 % people and now I think it's 98 % people.
Or something like that.
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 a lot of it is not necessarily that they're good or bad, but it's how, it's what we have a concept we call founder market fit.
So, the kind of fit between the founder and the market meaning kind of are they uniquely suited 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 and computer science, probably frankly, 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, it's very typical stories.
I was at Berkeley and I worked on in their big data lab and I invented this new open source data analysis tool and now I want to go make a business out of it.
And that's literally a company we funded called Databricks which is a technology called Spark.
That's probably a third to half of our company.
So, someone with very, very deep expertise.
And then they have to learn, they're 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 we kind of make the assumption that that's easier to learn than the opposite.
It's easier to teach the computer scientists, the 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, a lot of it is that, it's like technical expertise.
Sometimes it's domain expertise.
So, someone will come out of, a person comes out of the media industry or the fashion industry or you name it, whatever industry it might be and says, I've been working in this industry and I realized it's done.
There's a whole bunch of things that are done in backwards ways and I have ideas on how to improve them.
And it comes from years of experience and deep expertise in that field.
That's another common one.
Another one will be 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 just sort of maybe it was a generational thing, or I don't know what it was but people, they had been sort of sleeping on friends'
couches and things, seeing that behavior emerge and sort of built out, kind of rode that cultural wave.
So, that's typically very important, this sort of founder market fit.
I think also a lot of it is just tenacity.
Almost all companies we're involved with run to extreme adversity.
I've almost never been involved with a company that didn't have moments of almost failure and so it's how resilient are the entrepreneurs.
How do you go about determining that?
I mean, how do you go about testing their grit, tenacity, or not testing?
Yeah, that's a good 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 their personal backgrounds.
It's one reason why you'll see a lot of VCs will invest in repeat entrepreneurs as an example.
So, if you look at Travis who founded Uber, he had been involved and I think he started two companies before and had a long track record and he had varied levels of success.
But people who knew him spoke very highly of him as a tenacious and resourceful founder.
There's lots of examples of that, people with some kind of track record.
If they don't have a track record, it's hard and 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 pop culture trendiness or something.
There's a lot of news articles about startups and venture capitalists.
It's become very sexy, right?
Yeah, in the social networking movie.
So, I think what we're having now is a bunch of people entering the industry who maybe are coming in for the wrong reasons, who come to try to make sort of quick money or something and don't and it's just really just not.
They don't appreciate how hard it is and how do you pick somebody like that out?
Like when they come and they present to you, how do you determine that, you know, oh, I think they're in it for the money versus I think they're in it because they're passionate about the idea or some other, you know, narrative that we want to wrap around that?
Yeah, a lot of it's just depth of experience.
How long have they been working on the problem?
I'll just give you an example.
Like I was an early investor in Kickstarter and they didn't have Perry and Yancey 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, let's see, I'd say seven years at the time and had tried everything to kind of get funded and you talk to them and it was really motivated.
The original idea for 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 serve as like Kickstarter for himself because I think he had tried to organize a thing where like a band that he wanted to come play would come
play and he had a bunch of fans who wanted to see them and he just didn't have a way to kind of coordinate the two things to have the fans put up the money.
He 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, the going back to the Renaissance Italy and things and how the internet could kind of let you reimagine that model and, you know, when you talk to
him, so I think I invested I don't know when it was like 2008 or 9 when he was first starting, you know, it was clear this was a person who was, you know, this was his kind of white whale he'd been pursuing for forever.
Yeah. And you ask you could tell just you ask him questions and this was, you know, the depth of thinking he had thought of everything, you know, he had gone through.
We have this concept we call the DMAs and the idea is sort of the idea that the sort of ideas aren't really just kind of a static thing.
It's kind of like, you know, you see the TV or the movies and they have the way they kind of, you know, have these, you know, someone has this epiphany and I imagine that'll be like a, you know, intermittent windshield wiper or something.
You know, in reality is 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 of thinking that they have, you know, 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, like 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 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. But the answer, 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 been very hard to predict these things.
So I don't know. I was hoping you had like this secret recipe for us.
I wish I did. And I've certainly tried.
Many people have tried.
A lot of it's like any kind of creative endeavor.
You know, how do you pick a musician early on?
How do you pick a, you know, writer early on?
There's certainly like having spent years practicing in the field is very helpful, but ultimately a lot of it comes down to kind of an art, I guess.
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?
Good question. I don't know about no one because I think.
Or very few people then.
I mean, maybe I could change the question.
Sure. 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 at a company called Coinbase, which is the leading Bitcoin company.
So I'm very excited.
Bitcoin is an example, which I think is somewhat controversial.
Digital currencies.
Digital currencies.
I was an investor in Oculus, which is a virtual reality company, the Facebook acquired.
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, I'm at the extreme end of excitement here, but there'll be the PC, the Macintosh or something like this.
Then the internet, because you have the next key moment, and then the mobile phone, like the iPhone.
And then I think virtual reality will be the next wave.
What about artificial intelligence?
That's another interesting one.
That's sort of separate, but related.
I'm happy to talk about that too.
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.
But I think in the next few years after that, it will, it will be used much more broadly.
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 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, communications, social things.
Do you think that'll bring the world closer together?
I do, I think it will.
I think it will. 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 and 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 in that case, there was a guy who had dreamed his whole life about seeing this and he was crying and he was I would never be able to see this in any other way.
And I think what it does is it lets people, I think for example, a big application will be virtual tourism.
So just going and simply visiting the Great Wall of China and all sorts of other kinds of things and things which right now are very expensive.
And one of my favorite demos, I say demo now because it's all very early and it's not like they're not full products, but it's a thing called Ocean Rift, which lets you just like scuba dive around the ocean and observe different aquatic sharks and underwater.
Having the experience but not going, right?
Yeah, exactly. I think it'll be all sorts of things like that.
I think actually the gaming side is probably in some ways overblown.
But we underestimate everything else you think.
I think so. So that's VR.
So I'm very excited about that.
AI, when we talk about AI or?
Yeah, I'd love to hear more about that.
The singularity, let's talk about this whole like...
So, okay. That's a lot to say about AI.
So I think that, well, I think there's two things.
When people talk about AI, they often are really kind of talking about multiple things, right?
So there's AI like the sort of like the how singularity, like when we have a talking computer and then there's automation and like our, you know, will computers take jobs away and things like this.
So I think a lot of people have what I would call kind of a world's fair view of technology.
So you remember like the world's fair and I don't know if you see like kind of 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 so a lot of people, you know, sort of a simplified way to think of technology is like the, you know, 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 less obvious.
So I'll give you an example.
Like you just take 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 of a company called Zenefits.
So Zenefits is a company that lets you...
is a, you know, a web product that lets you, if you're a small business, go and, you know, sign up new employees for healthcare 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 company because you don't know, you now no longer have to hire, you know, somebody to do that job, right?
So what I would argue is things like Zenefits, like a lot of automation doesn't really look like automation.
It looks like just regular software.
That's, a lot of ways what AI 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 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 of all the software interacting together.
I think a lot of the real AI, the kind of the real automation ends up sneaking up on you.
Now there's this other kind of AI, which is the kind of more spectacular stuff that you read about, which is, you know, it's a headline.
Yeah. You know, so Siri is an example, like, you know, which is, which is speech recognition.
And then, you know, Google's doing a lot of interesting stuff with image recognition.
I do think this, this, this stuff is at a, a lot of people in Silicon Valley believe that this kind of AI is at a inflection point now, specifically around a technology called deep learning, which is basically a, I don't know if you remember neural networks or neural networks of the trendy, you know,
there are a lot of books written about them and things in like the nineties, which are basically computer systems that were kind of designed to replicate the way that the, 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 kind of 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, 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, you know, 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, that's what, you know, a couple of years ago, Google did a very famous experiment where they basically took, I think it was in the order of tens of thousands of computers, had them study YouTube videos.
And at the end of it, those computers were able to correctly identify cats, like whether it contained a cat with a very high degree of accuracy.
That was one of the kind of results they released that really kind of shocked people as to how accurate it was.
Because basically a lot of this stuff in AI, and we've used Siri as an example, a lot of it is relatively easy to get to like 80, 90 % accuracy.
It turns out if you just like a regular programmer downloads a bunch of open source software and spends a weekend, you can make like a decent replica of Siri in like a weekend.
Oh, wow. Okay. But to get higher accuracy on that is like exponential.
80%, really quickly.
And then it turns out all of the work is in the last 20%.
So like self driving cars, another good example where if somebody tells you, Oh, I saw 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 a programming ability can build.
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, two jugs, dogs jump out, you know, 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 kind of to this point of like 99 % or something like that, which no one had ever gotten to before, it might have been 97, 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 the scene.
And the results are getting very, very good there.
So, you know, you'll take an image and the computer will say this is, you know, three children eating pizza, and it's right, you know, and like things like that.
And so, so there's been a lot of really promising results.
And it's still very early.
I mean, you know, look at your phone and and the auto correct and every day you'll see it makes like ridiculous mistakes.
Right. I mean, like, so, you know, and we can't we can't make an auto correct today that that seems to work, you know, even most of the time.
So you get the swear words, right?
Like, you know, so I still think we have a long way to go.
I think the sort of the I would call the laboratory results are very promising.
Those laboratory results require like 10 ,000 computers.
Yeah, it's unfeasible right now to have that.
Yeah, that's right.
That's right. So the 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 you know, there's Moore's law as there's all sorts of questions around Moore's law.
So we will 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, you know, is this whole new kind of theoretical area of computer basically how to build computers that use quantum effects.
So things from quantum mechanics, I would say the optimistic people, and, you know, including some very well respected, you know, computer science professors at Stanford, for example, believe that in five to 10 years, we'll have quantum computing in the mainstream.
If that happens, you could see a dramatic AI will take off.
It could it could lead to a dramatic acceleration in the performance of AI.
Yeah, there's a bunch of things.
One of the reasons it's very hard to predict these technology things is you often have these things that have kind of feedback loops, which means, like, if we get quantum computing, and if, you know, and if we get, you know, that will let us compute things faster, which will let us store more, you know,
and then we'll build a store more data and, And that'll have all sorts of second and third order effects.
Yes, exactly. It's a very 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, you know, there are certain scenarios people can conceive of.
Yeah, see that it's much sooner.
So 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 signal from noise when you're, you know, surfing the internet?
That's a great question.
I'm a huge fan of, you know, Twitter, for example, I use Twitter constantly.
And for me, it's, it's probably one of my most important work tools in that I've, have a carefully curated list of people I follow who, essentially, I have whatever it is, 2000 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, you know, 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, you know, 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.
You know, a lot of what we try and do is just kind of be in the flow of a lot of different, you know, interesting groups of people working on new things.
So what do you think people are focused on?
It'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.
So I'm an investor in a company called Soylent, which you may have heard of, which is kind of.
Okay, so so I think with Soylent, I mean, the idea with the Soylent is that we were trying to create kind of what we would consider scientifically perfect food.
Kind of the idea is you go and the guys, you know, have a team of scientists who went and 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.
Soylent is 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, you know, if you look at the US right now, the diabetes and obesity are really at the epidemic level.
And a lot of that's caused by, you know, 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, you know, everything is filled with junk sugar 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, you know, we we we like to say sugar is a new smoking.
So we 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 you know, what I think is interesting are the people that are trying to think more broadly about how to reform the industry, not just for people that can afford organics.
A lot of my friends would call that, you know, the luxury of the rich, right?
Yeah, exactly. So like, just what can we do more broadly?
So that's interesting one.
I know, I'm very interested also in healthcare, generally, we're doing a lot more, spending a lot more time making investments in sort of areas that intersect between healthcare and computer science.
And, and just think there's a lot of things there that, you know, if you just look at the statistics of, you know, what wire healthcare costs going up so dramatically, a lot of it has to do with the inefficiencies in the system, you know, everything from, you know, medical records are still kept on paper,
you know, the insurance system is very complex and, and, and, and in many ways backwards, the you know, it's incentives seem all over the place, incentives are all over the place, it costs more and more now to this, people debate the exact reason, but basically, it costs more and more to create new
drugs. Right. So there's all there's all sorts of interesting things there, I think they can be improved.
Listen, I'm totally conscious of your time here where I'm 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?
Man, I think, you know, I think when I was in high school, I read a girdle Escher Bach, you know, Douglas Hofstadter's book.
I don't know if you know that book.
Yeah, I'm gonna look it up now though.
So I was interested in computers since I was a kid.
And this book was sort of a tie, it tied together computers and philosophy and music and it, for me, it was really important because it really broadened my horizons and ended up majoring.
I went to when I'm in college, I majored in philosophy.
And that I think that book kind of got me to do that.
That would be that would be a huge one for me.
Also anything by Daniel Dennett, you know, you read Daniel Dennett, yeah, yeah, like consciousness explain all this stuff.
Yeah, that kind of, there's this whole kind of thread of, I think, you know, Oliver Sacks who sadly just passed away and ruled I used to just read all of those kind of popular.
No wonder you're 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 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?
I just read what's it called, The Three Body Problem.
I just finished it.
We've read this book.
It's this Chinese author who just read it.
It's a science fiction book.
It just won the Hugo award or something.
It's really interesting book.
What else did I read?
I read, I'm reading, I just bought this, was it called The Martian?
Which is, I guess, this popular book that's not made into a movie.
And you know, books are one thing I like to, I'm not digital on books, I buy on me.
Oh, you're still physical.
Why is that? I just really like physical books.
I like having them on my shelf.
I like the feeling of reading a book.
I don't know, I just, it's something where...
I'm in the same way.
I live in this like 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, right?
Like it's... That's true.
That's true. That's a problem.
I just like the feeling of them.
And it's also, I feel like I look at the screen too much as it is.
Yeah. It's distracting too.
To have like the thing I read on the, on the iPhone and then I can like check to whatever it's like it's too distracting.
Oh, I just read the Elon Musk book, which I thought was good.
Sort of the biography of Elon Musk.
And oh, I read a really good book called Sapiens.
Have you heard of this?
Oh, I heard of that book.
Yeah. Somebody else recommended that to me.
It's sort of like, it's kind of like Gungerms 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 Homo Sapiens.
Yeah. I heard that was amazing.
Yeah. 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?
Out of anybody or? Yeah.
Out of anybody in the world.
Can you narrow it down a little or?
No. Like who would you like to hear me interview, I guess?
Yes. That's a good question.
I'm going to call them and tell them you recommend that they come on.
Okay. It would be good.
I know who'd be great is Ben Cat from Ribbon Farm?
I do. Yes. Yes. Yes.
He's one of my favorite writers.
He's already agreed to come on.
Oh, wow. Okay. Well, there you go.
I have too much overlap.
That's awesome. No, that's a good choice.
You know who's like, who's incredibly brilliant is Ben Thompson.
He has this thing. It's called Stratecory, you know.
Yeah, 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, my pleasure and thanks for having me.
Hey, guys. This is Shane again.
Just a few more things before we wrap up.
You can find show notes at fernamstreetblog .com slash podcast.
That's F -A -R -N -A -M -S -T -R -E -E -T -B -L -O -G dot com slash podcast.
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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.
