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0:00:23 Hi everyone, welcome to the A6NZ podcast, I’m Sonal.
0:00:28 So this week, to continue our 10-year anniversary series since the founding of A6NZ, we’re
0:00:33 actually resurfacing some of our previous episodes featuring founders Mark Andreessen
0:00:34 and Ben Horwitz.
0:00:38 If you haven’t heard our latest episode with Stuart Butterfield turning the tables as the
0:00:42 entrepreneur interviewing them, please do check that out and our other episodes in this
0:00:46 series on our website at a6nz.com/10.
0:00:52 But this episode was recorded at our Innovation Summit in 2018 and features economist Tyler
0:00:57 Cowan interviewing them about everything from their partnership and how it works to
0:01:01 talent, tech trends, and software eating culture.
0:01:02 Thank you all for coming.
0:01:06 I’d like to start with the two of you as a couple.
0:01:07 Oh yeah.
0:01:10 How was it you met at Netscape in 1995?
0:01:13 Well, Mark interviewed me way back then and he interviewed me.
0:01:18 I think I was interviewing for product management role and he was the founder of the company
0:01:24 and I had worked on a product called Lotus Notes and Mark had a fascination with Lotus
0:01:26 Notes for a couple of reasons.
0:01:31 One was it was kind of sort of the closest proprietary thing to the internet and then
0:01:35 they had email in it and Netscape was looking at doing email.
0:01:40 So he had all kinds of questions for me and I remember him just being absolutely shocked
0:01:46 and flabbergasted that like 50% of our code base and Lotus Notes was just making like
0:01:52 all the LAN protocols work together, IPX and Apple Talk and Net Buoy, Net Bios.
0:01:55 You guys don’t even know what any of that is anymore.
0:01:59 He was like literally just making the network talk to each other, which he thought was like
0:02:02 ridiculous given there was TCP/IP.
0:02:04 That was the very first conversation we had.
0:02:06 Mark, how did he do in the interview?
0:02:07 He did very good.
0:02:08 He did very good.
0:02:10 So he had a giant asset.
0:02:12 He was the first employee from Lotus.
0:02:15 So he looked great in comparison to all the ones we haven’t seen before.
0:02:18 No, he did great.
0:02:20 He was super knowledgeable and actually it was actually a really big deal at the time
0:02:25 because Lotus Notes was a big thing at the time and what Ben just alluded to in the architecture
0:02:28 like it just basically assumed that the internet was not going to work, which was the dominant
0:02:29 assumption at that time.
0:02:32 It’s kind of how the whole thing was built and then most of the people working on it
0:02:35 I think probably agreed with that.
0:02:37 There were many, many arguments that the internet couldn’t possibly do what a system like Lotus
0:02:38 Notes did.
0:02:41 So Ben was, I would say, young enough and smart enough and knowledgeable enough to figure
0:02:46 out very early among his cohort of kind of the professionals in the space that it actually
0:02:51 was going to happen a different way and so we were a little fly by night startup.
0:02:53 So we fast forward to 1999.
0:02:55 You two do loud cloud together.
0:02:56 Why?
0:02:57 How’d that happen?
0:03:01 Well, so the big thing basically that happened was so it turns out the internet worked, which
0:03:03 turned out to be a big deal, go figure.
0:03:06 And then basically what happened was basically people were unprepared for the internet to
0:03:10 work at some fundamental level and then we saw a very specific kind of version of people
0:03:14 being unprepared for that, which is we sold Netscape in 1998 to AOL and so Ben and I and
0:03:17 a bunch of us were working at AOL and then AOL had this thing at the time.
0:03:22 AOL was a little bit like the Google of the era or something where they had like a firehose
0:03:25 of traffic that they could basically steer wherever they wanted to steer it.
0:03:27 And so if you went on AOL and you typed in by clothes.
0:03:29 Half the traffic on the internet went through AOL.
0:03:30 I remember that.
0:03:31 It was a really big deal.
0:03:32 Not my half, by the way.
0:03:33 Yeah.
0:03:34 But that’s a lot, still.
0:03:35 Half.
0:03:36 That’s right.
0:03:37 And then by the way, almost all consumers, right?
0:03:38 Almost all consumers were on AOL at the time.
0:03:43 So you type in by clothes on AOL and then they would sell to like J Crew or Gap or whatever
0:03:44 that slot.
0:03:48 So basically what happened is that advertising business and then AOL would turn on that ad
0:03:52 and then basically the firehose of traffic would just blast the website to bits, right?
0:03:56 If it was J Crew, they just blasted the J Crew website to bits and then the J Crew people
0:03:58 would spend weeks trying to get the website to work to actually take advantage of all
0:03:59 this traffic.
0:04:03 And so it was sort of, you know, it just seemed kind of obvious that we, you know, we were
0:04:05 kind of in this business and so you go talk to the people running these things and they
0:04:09 just, they didn’t, they were just unprepared for this kind of sophistication.
0:04:13 And so we sort of incubated this idea that, boy, what if there was a cloud, right?
0:04:17 What if basically there was a system in which you could take your content and all your apps
0:04:19 and you could run them and it could handle the load and you could, you know, it could
0:04:23 load balance and it could spike up in response to demand and then all these companies, instead
0:04:25 of being in the business of running all your own stuff, like why can’t you just hand it
0:04:26 over to the pros?
0:04:29 And so, and then this is in the full flush of the dot com boom.
0:04:31 So it just seemed like an obvious opportunity.
0:04:36 And so 2009 comes along and why is it it seems to you both then that’s the right time for
0:04:38 a new venture capital firm?
0:04:39 What was the thought?
0:04:44 Well, you know, it was really one of those ideas that just came out of our experience.
0:04:49 You know, we were customers of venture capital and, you know, we had a couple of observations.
0:04:53 One was, you know, we had noticed over the years and Mark really made the observation
0:04:59 first that most of the really great companies, like forget about successful, but like great
0:05:02 companies, companies you admire and tech were all run by their founders for a very long
0:05:03 time.
0:05:10 Like Thomas Watson at IBM or Dave Packard, Bill Hewlett at Hewlett Packard or, you know,
0:05:16 Bill Gates or whoever it was, all the really giant successes were run by their founders.
0:05:20 And the conventional wisdom and venture capital was set up to replace the founder.
0:05:25 And then in our own experiences as technical founders, we knew why that was true because,
0:05:30 you know, well, if you just took, you know, loud like nobody would have ever been able
0:05:32 to fix loud cloud other than us.
0:05:36 Like there was no way you could bring in a professional to do that.
0:05:39 And so getting to that next product after you get the first product, getting to the
0:05:43 next product market fit required an innovator.
0:05:46 And so if you wanted subsequent product cycles, you needed the founder.
0:05:50 And so we just thought there ought to be a firm that is designed to do that.
0:05:53 And what is it that the two of you figured out?
0:05:56 What is it you two understood that other people didn’t?
0:05:57 And how would you articulate that?
0:06:01 You know, like we were just, there was a bunch of things.
0:06:02 One differentiation.
0:06:06 So like we came from a company, so we’re like, first we’re going to tell a very clear
0:06:09 sharp story about a real network that we’ve built out systematically.
0:06:13 We’ve got people who are going on your board who know exactly how to build companies.
0:06:17 So sometimes it feels to some people, there’s a lot of money running around, but talent
0:06:18 is quite scarce.
0:06:22 What is it that you two in the company as a whole have understood about talent search
0:06:23 that other people have not?
0:06:26 So we actually thought of it as a talent business.
0:06:30 And a lot of the reason for that is, you know, a friend of ours, Michael Ovitz, who founded
0:06:33 the talent agency, CAA, was a board member of ours at Opsware.
0:06:41 And you know, he always thought about talent in amazing detail and like, in incredibly
0:06:46 specific and one of his biggest concepts about it was, it was a network.
0:06:50 And you had to run the, and it seemed simple, oh, it’s a network, okay, great.
0:06:52 But like, how do you build that network?
0:06:53 How does it work?
0:06:56 How do you take a long view of the relationships with the talent?
0:07:03 So you know, we really thought out very early on, we’re going to invest heavily in building
0:07:09 this network and having the time, the luxury of time to build real relationships as opposed
0:07:13 to transactional relationships with every single person that we run into, be they an
0:07:18 engineer, an entrepreneur, a corporate partner, or whomever.
0:07:21 And that all kind of came out of this original CAA concept.
0:07:26 If you take a long view of relationships, you can build a network that’s so powerful
0:07:27 that nobody will ever be able to match it.
0:07:30 And that was kind of the original inspiration.
0:07:35 And we were fortunate enough to just hire a really astoundingly good team to help us
0:07:36 do that.
0:07:38 And Mark, how do you think about talent assessment?
0:07:41 So I think, you know, the other side of it is just the entrepreneur assessment, right?
0:07:44 And so it’s just one of those things where, you know, there is kind of this fundamental
0:07:47 question of kind of how extreme are you willing to get, right, with some of these people, right?
0:07:52 Because you know, the kinds of people who start these companies are not normal.
0:07:55 And we can say that speaking from experience, having done it ourselves.
0:07:58 And so, and I would actually go so far as to argue, like, it may be that the founders
0:08:02 are getting less normal as sort of society gets, let’s just say, more interesting in
0:08:03 recent years.
0:08:06 And so, you know, the ideas are getting bigger, the technologies are getting more disruptive,
0:08:09 the companies that win are getting much larger, technology is more central in everybody’s
0:08:10 lives.
0:08:11 By the way, there’s more competition than ever.
0:08:12 There are more tech startups than ever.
0:08:16 And so the kind of very special person who’s going to conceive of an original idea and
0:08:19 then be able to build a team and be able to prosecute the idea is going to be a very extreme
0:08:20 person.
0:08:24 And so a lot of it is sort of this, you know, the discovery and then partnering with these
0:08:25 really extreme people.
0:08:28 I’m going to throw out a few questions about particular technologies.
0:08:33 And either of you feel free to answer blockchain, what will be the breakthrough application
0:08:34 for blockchain?
0:08:35 I’ll take that.
0:08:38 So first of all, like asking what the killer app is, nobody ever gets it right.
0:08:42 Like I remember the internet killer app, it was never Facebook, that’s for sure.
0:08:48 But the way that we think about blockchain technology is that it’s a new computing platform.
0:08:54 And like other new computing platforms that preceded it, it’s worse in every way, but
0:08:56 really a very few ways.
0:09:00 So if you think about the smartphone, when it came out, it was much worse than the PC
0:09:02 at a tiny screen.
0:09:05 It was far less powerful, et cetera, et cetera.
0:09:08 People were like, how am I going to run my spreadsheet on that little ass screen?
0:09:11 Like there’s no way it’s going to work.
0:09:13 But I had a couple of properties that you didn’t have in the PC.
0:09:14 It had a GPS.
0:09:15 It had a camera.
0:09:17 And so you can now build things like Lyft.
0:09:21 You can now build things like Instagram that you could never build on a PC, and you still
0:09:22 can’t build on a PC.
0:09:26 And it created a whole new world of applications.
0:09:27 Blockchain is like that.
0:09:28 It’s slower.
0:09:29 It’s harder to use.
0:09:30 It’s harder to program.
0:09:34 But it has a new feature, and that feature is trust.
0:09:39 And trust is really, really an interesting feature because it means that you don’t have
0:09:45 to trust the government or a corporation or your lawyer.
0:09:50 You just have to trust the mathematical properties and the game theoretic properties of the system,
0:09:51 and then you can do things.
0:09:56 And it opens up applications such as you can program money.
0:09:59 You can program contracts.
0:10:03 You can create digital property, which is, you know, if you just think about the art
0:10:07 world, it’s an amazing kind of world, and it’s all virtual value.
0:10:13 You know, Bosque, it’s $150 million, but like the canvas is probably less than $5.
0:10:16 But because you know that it’s one of one, it’s got value, well, you can now do that
0:10:17 digitally with blockchain.
0:10:19 So there’s those kinds of things.
0:10:21 And then you don’t have to trust companies.
0:10:25 So if you’re a developer and you’re building an application, you know, you don’t have to
0:10:28 trust Facebook to not go, well, like, you know, we decided, like, we’re changing our privacy
0:10:29 policy.
0:10:30 You can’t run anymore.
0:10:33 Or if you’re a consumer, you don’t have to say, OK, like, I’ve got to trust you with
0:10:34 my data.
0:10:38 So that property of trust, we think, is very, very powerful, and there is a large set of
0:10:40 applications that will come off of that.
0:10:44 But you know, like, it takes a while for developers to get used to it, for the technology to
0:10:45 mature and so forth.
0:10:48 But it’s one of the things we’re most excited about.
0:10:52 Paint a picture for me 15 years from now.
0:10:53 Retail and wearables.
0:10:57 What will tech do for me in those areas that it’s not doing right now?
0:11:00 So I think retail will basically be gone by then.
0:11:01 I mean, so.
0:11:02 Gone?
0:11:03 I can’t go to the shopping mall anymore.
0:11:04 Who wants to go to the shopping mall?
0:11:05 I want to go to the shopping mall.
0:11:06 Oh, you can go.
0:11:07 OK.
0:11:09 They’ll have special preserved shopping malls.
0:11:10 OK.
0:11:11 For the people.
0:11:12 You’re in Washington, DC.
0:11:13 The Smithsonian.
0:11:14 You’re in DC.
0:11:16 The Smithsonian will have a shopping mall that you can visit.
0:11:22 In fact, you can drive there on a special road for your non-self-driving car.
0:11:27 So, and tie your horse up out back.
0:11:32 So, I mean, look, there will be, the term is experiential retail.
0:11:33 So like, experiences.
0:11:36 Like, if it’s, look, I mean, if it’s like a Gucci boutique, and it’s a whole experience
0:11:39 to go there, or it’s an Apple store, or it’s a, you know, it’s got something where it’s
0:11:42 like there’s like real magnetic appeal to the experience, then fair enough, right?
0:11:43 And there’s, there’s actually a bunch of companies we’re involved in that are doing
0:11:44 things like that.
0:11:48 But like, you know, the idea of we’re going to buy a bunch of stuff that other people
0:11:51 make, and we’re going to put it in a big box, and then we’re going to make everybody
0:11:52 drive to the big box.
0:11:53 Like, the problems with that are kind of twofold.
0:11:57 Number one is consumers don’t actually want, what you want is you want the Star Trek replicator,
0:11:58 right?
0:12:00 Like, what you want is like, you press the button and like, there’s my stuff, right?
0:12:01 Like, it worked great on Star Trek.
0:12:02 We don’t have that yet.
0:12:03 Like, we don’t quite have that.
0:12:06 We don’t actually have the materializer yet, but we do have the ability to press the button
0:12:07 and stuff gets dropped off.
0:12:11 So, and the logistics infrastructure to support delivery is getting built up, right?
0:12:12 Very rapidly now.
0:12:15 And so, I think the consumer behavior is cutting over quite quickly.
0:12:18 And then the other problem is just kind of traditional, let’s say, third-party retail
0:12:21 where you’re not selling your own product, you’re selling somebody else’s product is,
0:12:24 you just can’t, basically, you’re levered as a retailer, right?
0:12:27 You basically live on the basis of credit from the suppliers.
0:12:29 And the problem with that, so you’re kind of like an overlevered bank in a lot of ways.
0:12:33 And so, the problem with that is you loot, there’s basically no retailer of other people’s
0:12:36 products where they could lose 30% of their revenue and then they stay in business.
0:12:39 Which is why you see these retailers just going bankrupt like all the time, right?
0:12:43 It’s like, Toys R Us was the most recent big one, like, it detonated and then it detonated
0:12:45 so hard that it actually went into full liquidation, right?
0:12:48 Which everybody thought, obviously, it’s Toys R Us, like, people love toys.
0:12:53 Like, obviously, somebody was going to buy Toys R Us and there was just no way to make
0:12:54 the math work.
0:12:57 And so, I just think it’s an overlevered business and that part is going to go down.
0:13:00 Now, what it’s going to do is very interesting, it’s going to open up all the space, right?
0:13:04 And so, there’s a whole revitalization of physical environments that’s going to happen,
0:13:05 right?
0:13:07 Including, you know, you see that happening already in cities, but I think it’s going
0:13:10 to happen all over the place because a lot of this space is going to open up for, let’s
0:13:11 say, more interesting uses.
0:13:15 So instead of going shopping, I’m going to do something with my wearables.
0:13:16 And what will that be?
0:13:17 What’s the potential in wearables?
0:13:21 I think the really big one right now is I think audio, you know, audio is on the rise,
0:13:22 just generally.
0:13:24 And particularly Apple, you know, with the AirPods is just, I think, hit an absolute
0:13:25 home run.
0:13:28 It’s one of the most deceptive, you know, things is it’s just like this little product
0:13:31 and like how important could it be and I think it’s like tremendously important because
0:13:34 it’s basically just like a voice in your ear anytime you want.
0:13:36 And so, you have, well, I’ll just give you one random example.
0:13:39 There are now these YouTube, you know, there’s these kind of new kinds of YouTube celebrities
0:13:41 and everybody’s kind of wondering like, what, you know, where are people getting all this
0:13:44 spare time to like watch all these YouTube videos and listen to all these YouTube, you
0:13:47 know, people, you know, in the tens and tens of millions and the answer is like they’re
0:13:48 at work.
0:13:49 Right?
0:13:54 They’ve got like, they’ve got like a Bluetooth thing in their ear and they’ve got a hat,
0:13:55 right?
0:13:56 That’s 10 hours on the forklift, right?
0:13:57 10 hours of Joe Rogan, right?
0:13:59 And so, like, that’s a big deal.
0:14:03 It’s a voice in your ear all the time and then, of course, speech as a UI is rapidly
0:14:04 on the rise.
0:14:07 And so I think audio is going to be, you know, titanically important.
0:14:10 I would say the second thing I nominate for wearables is just generally the concept of
0:14:11 sensors on the body, right?
0:14:14 And so here the Apple Watch is clearly out in the lead with what they’re doing with
0:14:18 the heartbeat sensor, but I think we’ll have a full complement of medical grade sensors,
0:14:22 you know, on our bodies in a way that we have chosen to over the next five or 10 years.
0:14:24 And I think we’re, I think we’ll get to the point where we’re going to be able to do things
0:14:26 like predict heart attacks and strokes before they happen.
0:14:29 But I think it’s like, I mean, talk about, like, talk about a killer app.
0:14:33 Like, I’m going to have a, beep, I’m going to have a heart attack in four hours.
0:14:34 Maybe I should drive to the hospital.
0:14:37 The survival rate for heart attack in the hospital is like 99%.
0:14:39 The survival rate for heart attack at home is like 50%.
0:14:43 Like, there’s an opportunity for like a massive increase of quality of life with the sensor
0:14:45 platforms people are going to have.
0:14:47 And then I think, I think optics are coming, right?
0:14:49 And it’s going to be a long road, but I think AR and VR are both going to work.
0:14:53 And I think they’re, I think we’re going to have heads-up displays that are, that honestly
0:14:55 are going to remove the need to, you know, what we have now, which is kind of this little
0:14:58 pane of glass that we’re expected to kind of experience the whole world through, right?
0:15:00 The whole world’s going to open up around us.
0:15:03 What are we, what are we going to do with augmented reality and virtual reality?
0:15:05 So I’m big believers in both.
0:15:09 I think AR has, you know, tons of potential applications both working at home.
0:15:10 We can spend a lot of time on that.
0:15:13 I think VR is going to be like a thousand times bigger in the valley right now.
0:15:16 This is a very contrarian view because I’ll, the general kind of theme that you hear in
0:15:18 the valley is AR is going to be bigger than VR.
0:15:22 And it seems like obviously AR should be bigger than VR because obviously if you can do things
0:15:26 overlaid on the real world, that should be sort of inherently more interesting than having
0:15:28 to construct sort of a synthetic world.
0:15:31 I just think that that’s only true for people who live in a very interesting place in the
0:15:34 real world, which we all do.
0:15:38 But you know, only, you know, something between like point 1% and 1% of people on earth live
0:15:41 in a place where it’s like they wake up every morning and they’re like, wow, there are so
0:15:42 many interesting things to see, right?
0:15:45 Like most people don’t live in a place like that, right?
0:15:50 And so for everybody who doesn’t already live on a college campus or in Silicon Valley or
0:15:53 in a major city, the new environments we’re going to be able to create in VR are going
0:15:56 to be inherently much more interesting, right, than the physical environments.
0:15:58 And there’s going to be a lot more of them to choose from.
0:16:00 And so it’s going to be amazing.
0:16:03 Ben, there’s a tweet I’ve been dying to ask you about.
0:16:10 There’s two types of people in this world, fresh prince of Bel Air people and Martin people.
0:16:13 I’m a Martin person for what it’s worth.
0:16:14 Who’s Martin?
0:16:15 That’s funny.
0:16:17 I think Nate wrote that tweet and I replied to it.
0:16:23 So there were two kind of major African-American television shows on in the early nineties.
0:16:28 The Fresh Prince of Bel Air, which was based on a rapper known as the Fresh Prince and his
0:16:31 DJ, DJ Jazzy Jeff.
0:16:33 The Fresh Prince was actually Will Smith.
0:16:35 Him I know, but the Martin is what puzzled me.
0:16:42 Martin is Martin Lawrence, who is a comedian, a genius comedian who is also incredibly crazy.
0:16:44 So crazy that his special was called You So Crazy.
0:16:48 He’s just like a very, very crazy guy.
0:16:53 But Fresh Prince of Bel Air was kind of like the hood Beverly hillbillies.
0:16:57 It’s kind of like, you know, you get the black people from the inner city and you put them
0:17:02 in Beverly Hills and it’s kind of funny and it’s safe for, you know, everybody.
0:17:03 There’s nothing too itchy.
0:17:05 You know, they keep it kind of easy.
0:17:11 And whereas Martin was like just full out, like Martin was like actually the hood and
0:17:13 he was nuts and like the whole thing was great.
0:17:15 And so that was my show.
0:17:16 Now it makes sense.
0:17:22 Could you recommend a rapper for people who think they do not like rap music?
0:17:23 Will Smith.
0:17:24 Will Smith.
0:17:26 The Fresh Prince of Bel Air.
0:17:28 And does Mark like Will Smith?
0:17:29 I don’t know.
0:17:31 Fire of Spotify tonight.
0:17:38 Mark, if we think about television as presenting conceptual material to us and every now and
0:17:41 then you’ll watch TV shows.
0:17:45 What’s a TV show you’ve been watching lately that has a lesson in it about venture capital
0:17:47 and what’s that lesson?
0:17:48 Can I name three?
0:17:49 Three, yes.
0:17:50 I watched a lot of TV.
0:17:53 Halt and Catch Fire, which actually recently ended after four seasons.
0:17:56 Halt and Catch Fire, when it came out, it came out right after Mad Men and it came out
0:17:58 as kind of people were like, well, it’s kind of like Mad Men, but it’s like much more of
0:17:59 like a pot boiler.
0:18:02 It’s like super like dramatic and they’re like, it’s just all the emotionality of it.
0:18:06 Like, you know, it’s about this creation of basically it’s about the creation of a compact,
0:18:10 the PC company in the early 80s, it’s a thinly bailed kind of starts out kind of with that,
0:18:11 the birth of the PC.
0:18:14 And so all the critics were like, well, this is like too dramatic.
0:18:18 And like Ben and I watch it and we’re like, you know, no, that’s, it’s about right.
0:18:19 Exactly.
0:18:21 It was like literally going back in time.
0:18:22 It was like.
0:18:23 It’s exactly right.
0:18:25 And it really is like that dramatic and that stressful and that crazy.
0:18:28 And so especially the first season of that show, I think it’s the most accurate portrayal
0:18:31 of what a tech startup is actually like that’s ever been aired.
0:18:32 So that’s one.
0:18:36 Another one that I really like for founders to watch and they always think I’m crazy,
0:18:37 but I really, really believe this.
0:18:42 There was a, there was a great show on USA years ago called Burn Notice, which is a
0:18:43 very fun show.
0:18:47 It was about a spy who’d gotten burned, a CIA spy who’d gotten burned and had a whole
0:18:50 of Miami try to clear his name and he took on all these odd jobs.
0:18:51 So fairly normal setup.
0:18:54 The conceit of the show was though, he had every conceivable skill you could possibly
0:18:55 have.
0:18:56 Right.
0:18:57 And so he knew how to make like explosives out of bleach.
0:19:00 Like he knew how to like, you know, I don’t know, like disarm somebody with a mop handle.
0:19:04 Like whatever circumstance he was in, he had the, and then there was a voiceover or he
0:19:05 would explain to you.
0:19:06 And you haven’t hired him yet.
0:19:09 Well, we would love to hire him, but basically, I look at him and it’s like, that’s kind
0:19:10 of, that’s a good founder.
0:19:14 Like a good founder has to basically have every conceivable skill.
0:19:16 Like you basically have to be good at product development and at marketing and at sales
0:19:21 and at finance, legal and at HR and management and, you know, on and on and on and on.
0:19:24 And there really is no substitute for actually being good at all these things.
0:19:26 And so I like that one.
0:19:31 And then the third one is succession, which I just finished, which is one of the darker
0:19:33 and funniest things I’ve ever seen.
0:19:37 Let’s just say it’s a, it’s inspiring for founders because it, I think, pretty accurately
0:19:42 shows the dysfunction at, let’s say, certain kinds of larger companies.
0:19:46 It’s a show about a succession battle at a major media company and I can’t recommend
0:19:49 it highly enough if you’ve got the stomach for bad words.
0:19:54 So Ben, the company is starting something called a cultural leadership fund.
0:19:58 What are the strengths and weaknesses of applying the venture capital model to culture and
0:19:59 entertainment?
0:20:02 Well, I think we’re trying to apply culture to the venture capital model.
0:20:04 So it’s a little bit the opposite.
0:20:07 Like, you know, you know, back to your earlier question, you know, we really pride ourselves
0:20:13 on being able to understand talent and talent of all kinds.
0:20:18 And you know, one of the things we did very early is we built a lot of relationships with
0:20:23 geniuses at moving culture and we thought this was important because as tech was moving
0:20:29 into much more kind of consumer oriented field when we started the firm, that the people
0:20:35 who really knew how to change and create new consumer behaviors would be interesting.
0:20:40 So we, you know, had relationships with all these cultural geniuses like Quincy Smith and
0:20:46 Sean Puffy Combs and Nas and so forth, but we were doing it kind of fairly one-off and
0:20:51 we thought, you know, it would be really great, you know, and it was a great advantage for
0:20:52 us.
0:20:55 And Oprah had one of our entrepreneurs on her favorite things show, but we thought, you
0:20:59 know, it was a good thing to share with the rest of the industry.
0:21:04 And so we would have these cultural geniuses, but you know, geniuses, but who didn’t look
0:21:08 like the geniuses, our guys were used to like, you know, Mark Zuckerberg or Brian Chesky.
0:21:13 They kind of felt different, but our guys were interested in working with them.
0:21:14 So we put them together.
0:21:18 They get to know each other, which has got value on both sides.
0:21:24 And it also gives a lot of value to our CEOs because not only do they get to kind of learn
0:21:27 how to move culture, but they also get to learn about how a different kind of talent
0:21:33 looks like, which is very, very valuable when you’re kind of in the war for talent.
0:21:38 And then we invested back in kind of young African Americans who are wanting to come
0:21:39 into tech.
0:21:44 So we created talent pipeline with the fund and we have straight access to the pipeline.
0:21:49 So I would just say we get a lot of credit for being nice, but we’re really just winning.
0:21:50 So it’s gone great.
0:21:51 It works well.
0:21:56 And look, you know, the main thesis is, you know, if you’ve got like a very small group
0:22:01 of people that created every new musical art form in the last century from jazz to blues
0:22:05 to hip hop to rock and roll, you know, like that’s a real thing.
0:22:06 Like to be able to do that.
0:22:12 And that’s a real talent base that, you know, we need to figure out how to get to.
0:22:14 And we’re here in Los Angeles.
0:22:16 We’re very close to Hollywood.
0:22:21 What is it conceptually that Hollywood grasps about venture capital and talent identification
0:22:23 where Silicon Valley lags behind?
0:22:27 Well, I think that, you know, I just think of their different kinds of talent.
0:22:32 So and this is the thing that I think people make a mistake on when they think about, you
0:22:37 know, how diversity works or how inclusion works and so forth is there’s talent that
0:22:38 looks different.
0:22:41 And then if you don’t have that talent, you might not be able to see it.
0:22:46 And so, you know, in Hollywood, they see certain kinds of talent that they’re used to because
0:22:51 they know what that is, they know how it pops on screen, they know how like people emotionally
0:22:52 connect to it.
0:22:56 And then in Silicon Valley, we know another kind of talent, you know, a talent for like
0:23:00 systems thinking and engineering and this kind of thing.
0:23:04 But both are very valuable when you put them together in a company.
0:23:09 And so I think that, you know, in these endeavors, what we try to do is to see talent that we’re
0:23:10 not.
0:23:11 And it’s not an easy thing to do.
0:23:15 And there’s a story I tell that Margaret had in her profile.
0:23:18 One of the things she looked at in her employees was helpfulness.
0:23:23 And when I saw that, it shocked me because I had been managing for like nearly 30 years
0:23:27 at the time and I’d never interviewed anybody on that.
0:23:28 I couldn’t even see it.
0:23:33 Like, there’s a thing that’s an important talent, very important talent to a services
0:23:36 firm like ours that I couldn’t even see.
0:23:38 So how am I going to hire it if I can’t see it?
0:23:42 And so we spent a lot of time at the firm trying to see talent that’s not like us.
0:23:46 I’d also for a good, the LA effect we’re down here.
0:23:51 So I think it’s also very interesting time because, you know, for basically from when
0:23:55 I entered tech in the early 90s up through call it maybe 2012, 2013, it was just kind
0:23:59 of taken as a given that the Silicon Valley companies were never going to figure out culture
0:24:00 and never going to figure out content.
0:24:03 And it was also taken as a given that the media companies were never going to figure out tech.
0:24:05 And there were tons of attempts to kind of cross over, but they basically didn’t, none
0:24:06 of them worked.
0:24:09 And it really isn’t the last like three years it feels like.
0:24:13 It feels like a bunch of the Valley companies are really starting to decode culture, but
0:24:16 to the big, I mean, Netflix being Netflix sort of, you know, sort of setting a new model,
0:24:20 but now being, you know, replicated by other companies, Amazon being the most notable example,
0:24:24 you know, becoming big forces in the, in the formation of culture and entertainment and
0:24:25 media.
0:24:28 And then also by the way, the other is the flip side is that a bunch of the big media companies
0:24:30 now have gotten to the point where they now take tech incredibly seriously and have, you
0:24:34 know, really sharp people working for them, working on very interesting projects.
0:24:37 And then there’s a whole tech thing obviously now happening down here in LA that’s that’s
0:24:39 of a new level of magnitude than before.
0:24:43 And so it does feel like both of the kind of central hubs of California are developing
0:24:44 and crossing over, you know, quite nicely now.
0:24:50 A general question, 20 years from now, will location and being in the Bay area matter more
0:24:51 or less?
0:24:52 Yes.
0:24:53 Clearly both.
0:24:55 So on the one hand, it is absolutely true.
0:24:59 I mean, in 20 years, you know, basically like telepresence technologies, right?
0:25:01 So video conferencing and VR and all these things, like 20 years from now, there’s no
0:25:05 question it’s going to be like much easier to run large distributed organizations, large
0:25:07 distributed efforts than it is today, right?
0:25:10 We’re going to have such high fidelity video conferencing everywhere.
0:25:11 You can actually see this today.
0:25:15 If you see the super high end video conferencing systems, it really is like you are there.
0:25:17 And then we have these robots that we love in our office.
0:25:21 Some of you have seen the beams, which are a prototype of what I think, I think telepresence
0:25:24 robots are actually going to be a very big deal because they give you a sense of physical
0:25:26 presence of somebody in a room that’s even different than a screen.
0:25:30 And so like those technologies and then collaboration technologies like Slack and GitHub, right, are
0:25:31 becoming really good today.
0:25:35 And in 20 years, they’re going to be, you know, just spectacularly amazing.
0:25:38 And so it’s going to be easier to run all these companies and be able to run all these
0:25:41 efforts on a broad basis, and then it’s just going to make it much more straightforward
0:25:44 for people all over the world to participate.
0:25:46 So that’s on the one hand, but the other thing though is just like, okay, that’s going to
0:25:49 be a world that’s much more connected, right, much more networked, right?
0:25:53 It’s going to be, you know, past 5G, we’re going to be like 9 or 10 or 11G, right?
0:25:54 It’s going to be bandwidth everywhere.
0:25:56 It’s going to be, everybody’s going to be online all the time.
0:25:59 We’re going to have all these, you know, wearables, being online is going to be part
0:26:01 of everybody’s daily experience all the time.
0:26:04 You know, the economy is going to reform itself around software network effects.
0:26:07 And so the winning companies, you know, the winning entrepreneurial companies 20 years
0:26:10 from now that win are going to be staggeringly large.
0:26:13 Like they’re going to be like, I don’t know, 10 or 100 times the size of Google and Facebook.
0:26:18 And so the prize to have a startup that scales and wins is going to become so large that
0:26:22 you’re going to want to hyper-optimize every possible thing you could possibly do to have
0:26:25 that extra chance that you’re going to be the one that wins, right?
0:26:28 And that’s going to mean like people in the same room together, right?
0:26:32 And so I think the valley is actually going to become more central, not less central,
0:26:35 even though the technologies that we’re building are making it possible for the world to distribute.
0:26:37 Ben, I love your book on management.
0:26:40 It’s the only book on management I’ve ever given to my daughter.
0:26:41 No, I appreciate that.
0:26:42 I knew I had one chance.
0:26:44 I picked yours.
0:26:46 In your view, what is the best predictor?
0:26:47 Not of innovation.
0:26:50 We’ve talked about that, but of simple managerial intelligence.
0:26:53 How do you spot that and what does it consist of?
0:26:55 Well, you know, it’s interesting.
0:26:59 It’s two skills that don’t normally go together.
0:27:07 So it’s systems thinking, which is, you know, and I hadn’t even noticed Mark actually pointed
0:27:10 this out to me many years ago, which is most people are not systems thinkers, meaning they
0:27:16 cannot think about, OK, if I change this here, then it’s going to affect that over there.
0:27:21 And you know, you know, as an economist, people always make these dumb mistakes like, OK,
0:27:23 well, move the minimum wage and nothing else will happen.
0:27:25 It’s like, well, no, no, it’s a system.
0:27:26 You have to think of it in terms of the system.
0:27:30 And so that’s kind of part of it and a big part of it.
0:27:37 But the other part, which is, can you actually see the people in your organization?
0:27:42 Like, do you know who they are as opposed to you’re talking to them like they’re you?
0:27:46 And meaning, do you understand their motivation?
0:27:49 Do you understand what they would think about something if they were in the room and you’re
0:27:52 making a decision?
0:27:57 Can you interpret them well enough so that it’s as though they’re there?
0:28:01 And can you understand the implications through the eyes of the people who work for you?
0:28:05 And if you have those two things together, those are the people who are really great,
0:28:06 but it’s a rare thing.
0:28:09 And you can kind of see it because you’ll be talking to them and like you might not
0:28:12 be able to articulate something and they can articulate it for you the way you would have
0:28:14 done it better.
0:28:19 Like somebody who’s that perceptive on people plus a systems thinker is really the those
0:28:21 of the people who are gifted.
0:28:24 And Mark, did you really invent the TweetStorm?
0:28:26 And if so, what’s the general lesson about innovation?
0:28:27 We should learn from that.
0:28:31 What is a general lesson?
0:28:36 Inability to shut up, I think might have had a lot to do with it.
0:28:37 But you did invent it.
0:28:39 I think there might have been sequences of tweets, but literally I couldn’t shut up.
0:28:41 So like, I think it kind of catalyzed.
0:28:44 Well, look, I mean, the big lesson from it has been that the big lesson actually have
0:28:47 a lot of the Internet platforms, which is emergent behavior is incredibly important, right?
0:28:52 The really successful platforms let the user surface the behaviors, right, that the creators
0:28:54 of the platform could have never thought of and, you know, Twitter set all kinds of issues
0:28:57 over the years, but like it always has been amazing.
0:28:59 Most of the compelling ways in which people use Twitter have been invented by the users.
0:29:03 I mean, I think it’s true, retweets were invented by users, right, like very, very fundamental
0:29:04 features.
0:29:05 And that’s not just Twitter.
0:29:06 That’s also been true.
0:29:07 It was true of actually personal computers.
0:29:08 It was true of smartphones.
0:29:10 It’s been true of, you know, many of these platforms.
0:29:15 And so it’s a useful principle of product design, which is let the users innovate.
0:29:20 Ben, you’re famous actually for your barbecue cooking, viewed as a problem of management
0:29:22 and also innovation.
0:29:26 What makes for the difference between good and truly excellent barbecue?
0:29:27 Time.
0:29:28 Say more.
0:29:29 Time.
0:29:30 So, you know, it’s funny.
0:29:36 I had an interesting conversation years ago with my wife’s cousin, Atlee, in Kanye West,
0:29:41 which was just like a weird thing because Atlee’s from Baton Rouge, Louisiana.
0:29:45 And I have him to be in New Orleans and Kanye was there and we’re all out to dinner.
0:29:50 And Kanye asked Atlee, he says, “What’s the definition of luxury?”
0:29:53 Which is like, if you’re from Baton Rouge, you just don’t think of that word, like you
0:29:55 never hear the word luxury.
0:29:59 And so Atlee says, “I just like to cook.”
0:30:00 And Kanye says, “Well, how do you cook?”
0:30:03 He says, “Well, like I like to make red beans and rice.”
0:30:04 And he’s like, “Well, how do you do that?”
0:30:08 He’s like, “Well, I take my time, you know, I cut the onions very slowly, I boil it for
0:30:10 a long time, I make sure it simmers.”
0:30:15 And Kanye says, “Exactly, time is luxury.”
0:30:17 Like that’s why I make luxury records.
0:30:18 I take my time.
0:30:21 And I was like, yeah, that’s it.
0:30:22 So.
0:30:28 Mark, do you prefer to eat for-profit sushi or nonprofit sushi?
0:30:33 This is Tyler’s favorite question to suss out whether people are actually pro-government,
0:30:34 pro-increased government services.
0:30:40 The idea of nonprofit sushi makes me so nauseous that I think I want to throw up on stage.
0:30:47 General question, what’s the one thing that Wall Street does not understand about technology
0:30:49 that you would change if you could?
0:30:51 I think part of it is it’s a 3,000-mile-like thing.
0:30:55 I think a big part of its culture, there’s just a delay, and there always has been.
0:30:58 And so, you know, if I wanted to fix that, I would say, like, we need to spend a lot
0:30:59 more time.
0:31:02 And by the way, the tech industry does need to spend a lot more time trying to tell people
0:31:05 what we’re doing, but at the same time, people from outside the tech industry need to spend
0:31:08 more time in the valley and understand what’s happening here.
0:31:10 And a lot of that is happening as well.
0:31:13 That’s how I’m not sure I want to fix it.
0:31:15 The question assumes I want to fix it, like, I don’t think I want to fix it, because that’s
0:31:17 a big part of the opportunity.
0:31:21 What’s the one thing the U.S. government does not understand about tech that you would change
0:31:22 if you could?
0:31:25 So I think the first thing is something that Andy Grove said many years ago, which is it’s
0:31:27 inevitable.
0:31:31 And so, you know, somebody had asked him, you know, is the microprocessor good or bad?
0:31:34 He said, well, that’s like asking a steal, a good or bad, he’s like, we’ve got to deal
0:31:36 with it, like, it’s here.
0:31:41 And I think that, you know, the biggest mistakes the government makes are assuming it’s not.
0:31:46 So, you know, stem cells is a great one where the U.S. government went to really hold that
0:31:47 back.
0:31:49 They end hold back stem cell development or research at all.
0:31:53 They just made it very inconvenient for people in the United States, and a lot of people died
0:31:57 and, you know, missed out on cures and all kinds of things because of that.
0:32:00 And so I think that’s number one.
0:32:05 And then I think the other thing is that technology is always had and always will have good and
0:32:06 bad implications.
0:32:12 Going back to the cotton gin, the printing press, you know, certainly the internet has
0:32:13 had good and bad.
0:32:18 But if you look at it overall, it’s overwhelmingly positive.
0:32:26 And more than that, we have to go back to our population levels in like 1750 if you’re
0:32:30 going to take away technology and take away technological advancement because the way
0:32:35 the human population is growing, there’s no way we can, you know, live the way we want
0:32:37 to live and have the lives we want to live without technology.
0:32:42 So getting into these debates of whether we should hold it back is just, you know, if
0:32:45 there’s one thing I would change, it’s like, let’s not have that debate.
0:32:47 Let’s have the debate how to make it great.
0:32:52 Mark, what’s the last thing software will eat?
0:32:53 Other than sushi.
0:32:55 Other than sushi.
0:32:59 So I think it’s fundamentally, the term that you used actually called it, I think, project
0:33:00 selection.
0:33:05 And so basically it’s this question of like, okay, how do you organize a small number of
0:33:07 people to do something new, right?
0:33:09 And by the way, that could be a startup company.
0:33:11 That could be many other kinds of efforts where you need a small number of people to
0:33:12 do something new.
0:33:14 It could be a new political campaign, a new activist movement, whatever, but a small number
0:33:16 of people to do something new, right?
0:33:19 And then how do you pick, if you’re going to finance or donate or fund those things,
0:33:23 how do you pick ones to donate to, and then how are those things actually going to run,
0:33:24 right?
0:33:27 So the new part there is really important, like the little known fact, for example, about
0:33:31 venture capital is that there’s a term in venture capital and hedge funds called Cary,
0:33:34 which is basically called Cary’s interest, which is the sort of profit participation
0:33:36 that the VCs or hedge fund managers make.
0:33:42 The term Cary actually comes from whaling in the 1600s off like, you know, in the Atlantic
0:33:46 Ocean, like literally their term Cary was the people who would finance the captain and
0:33:49 the crew of the boat, but the boat actually would run as a startup.
0:33:52 There was actually like equity participation for all the people in the boat, and then they
0:33:56 would pick a captain, you’d raise money in town, you’d raise capital, and then the boat
0:33:59 would go off and try to take down a whale, right?
0:34:03 And about, you know, three quarters of the time the boat would come back, the other quarter
0:34:04 of the time.
0:34:05 The whale would win.
0:34:06 The whale would win.
0:34:08 Moby Dick was not a joke.
0:34:12 And so the boat comes back about 75% of the time, and then literally Cary was the portion
0:34:15 of the whale that the investors got, right?
0:34:18 And so if you think about it, like the process, if you’re like in, you know, I don’t know,
0:34:22 whatever, Boston or wherever, in like, you know, 1675, and you’re trying to say, okay,
0:34:28 this ship, this captain, this crew, this mission, into these waters, right, with these weather
0:34:31 patterns, like, and how are they going to behave under pressure, and what’s going to
0:34:33 happen when things go wrong, and it’s a crew going to mute me, and like, are we going to
0:34:34 make any money doing this?
0:34:39 Like, the whole thing is just such an intricate kind of puzzle, and it revolves around people.
0:34:45 And so, if you’re sitting out at the whaling expedition, you know, the Santa Maria is the
0:34:46 same kind of thing.
0:34:47 Tech startups are the same way.
0:34:50 By the way, you know, green lighting a movie or a TV show in Hollywood is the exact same
0:34:51 kind of process.
0:34:55 And it’s just, it’s so intangible, and it’s so much based on the interaction of a small
0:34:57 number of people who are going to be under extreme pressure.
0:35:01 Like, if we could figure out a way to automate that, like, we’d fund that company and then
0:35:03 retire, but at least we don’t know how to do that.
0:35:07 For each of you, what’s an interesting book you’ve read lately?
0:35:11 So one of the most interesting books I’ve read lately is a Genghis Khan and the Making
0:35:17 of the Modern World by Jack Weatherford, and it turns out to be very unexpectedly the most
0:35:22 interesting book on the topic of how you think about inclusion that I’ve ever read, and Genghis
0:35:27 Khan is not known for his thoughts on inclusion because he’s mostly known for, like, being
0:35:29 just ruthless.
0:35:35 But he really, you know, he was a guy who came from kind of the border of northern Mongolia
0:35:39 and Siberia, a very bad part of the world, he had a very, very hard life growing up.
0:35:45 He was from kind of the lower, they had white bones and black bone, kind of the higher and
0:35:50 lower caste, he was a lower caste person, and, you know, a lot of his experience growing
0:35:56 up led him to this idea that he should choose for kind of talent, not the caste system,
0:36:00 and not even the tribe that he was in, which was a huge breakthrough at the time, you know,
0:36:04 nobody had done that, and the way he thought about it and the techniques that he used for
0:36:11 doing it were breakthroughs today, you know, and so I just found it to be like a super
0:36:15 interesting book, definitely a great management book for anybody who’s interested in that
0:36:16 topic.
0:36:17 Mark.
0:36:20 So, the best book, the book’s had the biggest impact on me, it’s an incredibly well-written
0:36:24 book, it’s, of course, out of print, it’s by actually a guy, I think, Tyler, you know,
0:36:25 Timur Karan.
0:36:26 Sure.
0:36:29 You know quite well, who’s a economics and…
0:36:30 Economist at Duke.
0:36:31 At Duke.
0:36:36 So it’s a book about, 20 years ago, it’s called Private Truth’s Public Lives, and it basically
0:36:38 tells the story, the theory that he basically has, he calls preference falsification, and
0:36:43 it’s basically the idea, it’s a situation in which people believe something in their
0:36:46 own head, and then they feel for social reasons that they can’t say it out loud.
0:36:50 And so it starts kind of with that as kind of an idea, and then it kind of extrapolates
0:36:54 out kind of what happens as a society becomes the kind of society in which people feel like
0:36:58 they can’t speak the things that they believe, and it turns out to be quite an elaborate
0:37:01 process because basically, right, you can have these very interesting situations where
0:37:04 you can have a majority of people who believe something, but then they all believe they
0:37:08 can’t say it, but then as a consequence, they all come to believe that there are many fewer
0:37:12 people who believe it than there actually are, and so you can kind of suppress a point
0:37:16 of view artificially for quite a long time, but then he describes in the book how you
0:37:19 can then kind of just get the reverse process, kind of get the whole thing, kind of the spring
0:37:24 to expand, which is if a few brave people start to speak up, then a lot of other people who
0:37:27 have had that secret belief all of a sudden realize they’re not alone, and then you start
0:37:30 a cascade, right, in which a lot of people start to speak up, and he basically models
0:37:34 in the book like that’s where revolutions come from, and it’s basically like an explanation
0:37:39 for the fall of the Berlin Wall, it’s an explanation for political revolution.
0:37:42 It so happens to be, I think, highly relevant to what’s happening both on the left and the
0:37:44 right in the U.S., like right now.
0:37:47 Like, I think it’s, as you read the book, you’re just like, okay, that’s the cleanest
0:37:51 explanation of the Trump phenomenon I’ve ever seen, and furthermore, it’s also the cleanest
0:37:53 explanation of the Bernie phenomenon I’ve ever seen, like I think it actually describes
0:37:54 both quite accurately.
0:37:58 For the last question, I’d like to return to Mark and Ben as a couple.
0:38:03 Ben, what’s Mark’s biggest misconception about you, and Mark, what’s Ben’s biggest
0:38:05 misconception about you?
0:38:11 So this is the sad thing, is that he knows me so well that I wish he had misperceptions
0:38:18 about me, but like, he actually knows who I am, and so this is, and it manifests itself
0:38:22 the worst, like if something’s going wrong at the firm, it’s always some combination
0:38:27 of his and my fault, and he’ll know exactly the flaws that I have that have led us to
0:38:34 that situation, and like, it’s unbearable, and vice versa, but I’ll let him answer.
0:38:36 And Mark, you have the last word.
0:38:39 Ben’s biggest misconception to me is he thinks I’m gonna go to my room tonight and listen
0:38:43 to Will Smith.
0:38:45 I thank you both very much for this dialogue.
0:38:46 Thanks everybody.
0:38:50 (audience applauding)
0:39:00 [BLANK_AUDIO]

0:00:05 The content here is for informational purposes only, should not be taken as legal business
0:00:10 tax or investment advice, or be used to evaluate any investment or security, and is not directed
0:00:16 at any investors or potential investors in any A16Z fund. For more details, please see
0:00:17 a16z.com/disclosures.
0:00:23 Hi, everyone. Welcome to the A6NZ podcast. I’m Sonal. So this week, to continue our
0:00:28 10-year anniversary series since the founding of A6NZ, we’re actually resurfacing some
0:00:32 of our previous episodes featuring founders Mark Andresen and Ben Horwitz. If you haven’t
0:00:36 heard our latest episode with Stuart Butterfield turning the tables as the entrepreneur interviewing
0:00:43 them, please do check that out and other episodes in this series on our website at a6nz.com/10.
0:00:49 But this episode was recorded at our annual Innovation Summit in 2017 and features writer
0:00:53 Stephen B. Johnson interviewing them about everything from their relationship to creative
0:00:54 inspirations.
0:01:01 All right. I’m delighted and honored to be here with you. And we’ve got a lot to cover.
0:01:04 And what the kind of architecture for this conversation is, in a sense, we’re going to
0:01:09 kind of zoom out. We’re going to start on a more personal level and broaden out to think
0:01:13 a little bit about tech cultures inside a given organization, and then start thinking a little
0:01:18 bit more about broader social trends coming out of technology and looking into the future
0:01:21 a little bit. But I wanted to start with something actually just listening to your conversation
0:01:25 with JJ, who I don’t know at all, but I’m going to call JJ. He was talking about that
0:01:31 first kind of literally magical moment going and seeing Universal Studios and then getting
0:01:35 into magic and how that was so transformative as an eight-year-old. And it occurred to me,
0:01:39 do you guys have a memory of something like that with tech at any point where you really
0:01:44 saw something? For me, it was late. It was hypercard, sophomore year in college, where
0:01:49 I was just like, oh, there is this whole possibility that I hadn’t imagined could happen on a screen.
0:01:53 Do you have similar stories? It’s funny. This is an embarrassing question because I’m sitting
0:01:59 next to Mark, but one of the ones I remember most vividly was seeing Mosaic because for
0:02:04 years in tech, there were all these ideas about like if you were in computer science
0:02:10 about what was possible from all the things that you ought to be able to do, but you could
0:02:15 never actually quite get them to work. And hypercard was like that in that way, but Mosaic
0:02:19 was really it. It was all there on it. And when you downloaded it, you were like, oh,
0:02:25 my God, the whole world is like right there. I can reach the world. That’s the most craziest
0:02:29 thing ever. But I hate to say that with him sitting here because I go right to his head.
0:02:33 Well, it was a really striking point because up until, certainly for me, and I think for
0:02:38 a lot of people, there was discussion about hypertext that had been circulating through
0:02:43 different subcultures. But I would say probably 80% of the preceded received at that point
0:02:48 was strangely enough about hypertext fiction. It was people who were writing these nonlinear
0:02:54 stories. And when you saw Mosaic for the first time, you’re like, oh, this isn’t some obscure
0:02:58 avant-garde postmodern literary device. This is the future of media.
0:03:01 I have a much better answer than that. So I actually just mentioned on stage, but like
0:03:05 the early PCs really were the mystery box, the magic box and that really, that just,
0:03:08 you know, the flesh and cursor had me from go. So that sense of potential was a really
0:03:14 big deal. The other, I swear to God that this is true. Knight Rider, who remembers Knight
0:03:16 Rider, Knight Rider. There we go. Knight Rider outstanding.
0:03:17 You’re talking about kids?
0:03:22 Kids. Holy shit. So I was, I forget, I was, it was 82. So yeah, I was 10, right? And so
0:03:25 this shows on, and I don’t know, it’s this guy in the leather jacket. And I don’t know,
0:03:29 he seems cool, whatever. But they did this very clever thing, the mystery box thing.
0:03:31 And then there was no internet, no, nothing couldn’t find anything. You just saw a few
0:03:36 commercials. They did not tell you that the car was like that special. And if you go back
0:03:41 and watch the pilot, it’s like 45 minutes in. And like, it’s the whole thing has happened.
0:03:44 He’s been shot in the face. He’s had reconstructive surgery. He’s got the new name. He’s got
0:03:49 the mission. He’s got the car. He’s driving along 45 minutes in the car talks. And like,
0:03:52 I think I fell out of the couch. Like, I think I just like literally, I was like, the car
0:03:56 is talking. Right. And then I started to remember what that felt like. And then I have to remember
0:04:00 the screens, like the dash on that thing, right? It was like being in the space shuttle.
0:04:03 And to this day, when I get in a car, that, you know, the modern cars are like that, right?
0:04:06 They’ve got up to and excluding the fact that they talk to you now. But you know, they got
0:04:08 all the screens in the distance of that and the dash and the tuzzling, the whole thing.
0:04:14 It’s still, I always still feel like I’m getting behind the dash of kit. That is the best answer.
0:04:20 So there’s a great thing about your, the relationship that you guys have, it’s a long enduring one,
0:04:24 incredibly productive one. There’s a line in the hard thing about hard things in your book,
0:04:28 not to embarrass you, Mark, but I just wanted to quote it here. This is, you’re talking
0:04:34 about the relationship. And what you said is, even after 18 years, he upsets me almost
0:04:41 every day by finding something wrong in my thinking. And I do the same for him. It works.
0:04:48 So first off, is that true? But more than that, are you guys, is there something predictably
0:04:52 wrong? Are you guys wrong? And are you finding yourselves correcting each other in ways that
0:04:57 are kind of, are there patterns to the way in which you disagree? Do you tend to err
0:05:01 on the side, this side, where Mark errors on another side?
0:05:06 You know, I think it’s, you know, we’re close enough in personality, but different enough
0:05:11 kind of in skills that we often see things from different angles. And then a lot of it
0:05:16 is Mark himself, which is like, Mark always likes to take the other side of the argument,
0:05:21 whatever side, like he just enjoys taking the other side. That’s his thing. And so, you
0:05:26 know, it just kind of goes that way. I think that the real key to it is that we somehow
0:05:33 got to a level of trust where we can really go at it in a way that would, for most people,
0:05:38 you just go like, if you like, you can’t talk to me that way. Like how, you know, like so
0:05:42 disrespectful, like you’re stepping on me, you’re asking me these questions that hurt
0:05:47 my feelings. But you know, for us, you know, it has still like, you know, sometimes like,
0:05:51 get close to that, but not, not all the way.
0:05:54 I think the big thing is the thing I decided at a certain point, because we get asked a
0:05:58 version of this question by the founding teams that we work with, or if we bring a CEO into
0:06:01 a company, help a founder, bring in a CEO, and they’re going to have a partnership that
0:06:03 hopefully works something like this, you know, get kind of asked kind of, how do you make
0:06:07 it work? Because it is so easy for the conflict, for the emotion to, to drive people apart.
0:06:12 And so the way I think about it is, it’s more important to me that we have the successful
0:06:17 partnership than it is that I’m right on any particular issue. And I’m proud to say that
0:06:21 Ben, of course, is the exact opposite. It’s far more important for him to be right than
0:06:27 absolutely. And so it meshes perfectly right hand and glove. I’m joking. That was a joke.
0:06:29 And so we both will argue it all the way up, but each of us will defer to the other. At
0:06:32 the end of it, if it’s an argument, it’s over which one, which of us is going to defer to
0:06:37 the other one, with each of us volunteering to do it, say most of the time.
0:06:42 And that’s really like, sometimes the argument will not resolve, but we’ll kind of know
0:06:48 who knows more about that thing. And we’ll yield in that way. And that’s been super productive.
0:06:53 And there are ongoing disputes about where the technology world is heading. Are there
0:06:57 kind of senses like, oh, no, you think this thing is going to be huge, but this is the
0:06:59 old argument we’ve been having for five years. It’s never going to happen.
0:07:02 Well, we both believe a lot and disagree and commit, right? And so it’s important. Like
0:07:05 as an example, one version of the question you asked is like, what if we’re arguing about
0:07:08 some startup we funded? And whether it’s, you know, we’re going to have some argument
0:07:11 about like that was a mistake or not or whatever. Like we basically, I don’t think ever have
0:07:15 those arguments. And the reason is because we may argue whether, and this is true of
0:07:18 our partnership or broadly, we may argue about whether to make the investment, but once we
0:07:21 make it, we’re in. And then at that point, it’s important that it’s the dynamic sort
0:07:25 of implicit promise in the team and including between the two of us as we’re all in this,
0:07:29 we’ve all committed. And I think that’s really critically important because that’s how you
0:07:32 maintain, that’s how you don’t have, I told you so.
0:07:35 And backbiting and talking about people when they’re not in the room and that kind of thing.
0:07:36 That’s just bad.
0:07:40 Do you all have a, I’m actually in the middle of writing a book about long-term, complex
0:07:46 decision-making. So I have my own kind of bias in this question. But do you have, when
0:07:48 you’re confronting a decision to say, for instance, like should we fund this company
0:07:52 or should we follow in this round or other life decisions, do you find that you have
0:07:59 a process for that decision-making act that you go through and think about as a series
0:08:04 of stages? Or is it something that’s more fluid and conversational and intuitive?
0:08:09 Yeah. So it’s interesting. This business is different than our last. So running a company,
0:08:16 you try to be more structured in how you do this. In some ways, in that speed is really
0:08:22 important. So if you’re running a company, your output is decisions and you rate it
0:08:27 on quality and speed. And if you have to make the trade, which you always have to, you generally
0:08:32 go towards speed because you have a lot of decisions to make. And if you don’t make them
0:08:39 fast, then you freeze the entire organization. In our new business, basically quality is everything.
0:08:45 And so we’ll go around the horn 50,000 times if we have to to make sure that we’ve explored
0:08:51 every corner and every crevice of the discussion and we’ve not missed something. So I would
0:08:57 say in some ways, we have a lot of a framework in our minds about how we think of investments
0:09:03 and deals and so forth. But we’re willing to go in many loops where we would never do
0:09:04 that in a company.
0:09:08 One of the things that I love investigating and talking to people about is their kind
0:09:13 of creative workflow and where they find inspiration. There’s a lot of research out there that some
0:09:16 of which that I’ve done and other people have done about the importance of kind of diversity
0:09:22 of influences in your kind of worldview, leading to more creative thinking. So I’m just curious
0:09:27 about your kind of daily information diet in a sense, beyond the kind of the routine
0:09:32 of the meetings that you have with the founders and the pitch meetings and so on. Where do
0:09:36 you find that kind of outside influence in new ideas?
0:09:41 So we sort of cheat in a sense, which is we have, we see 2000 inbound startups a year.
0:09:44 These are by definition and 2000 are the smartest people in the world in all the domains that
0:09:48 they’re operating in. And so, I mean, honestly, after that, it’s just, it’s hard to pick up
0:09:52 like a magazine and open it with any level of enthusiasm because it’s like, you know,
0:09:54 you kind of have this, you know, you’re kind of seeing the stuff months or years before
0:09:58 it shows up in the press. And so that’s part of it. Personally, I’ve been running this
0:10:02 year a big experiment and I’ve always been a big reader and sort of information on the
0:10:05 board. And it just, you know, I’ve always tried to kind of balance short term, long term,
0:10:09 you know, different kinds of different time horizons of material, different kinds of material.
0:10:12 So I’ve been running a big experiment this year, which is I’ve been trying to do a bar
0:10:17 bell. I’ve been trying to polarize it. And so I’ve stopped completely reading newspapers,
0:10:22 magazines, basically anything that has a time horizon, basically greater than let’s say
0:10:26 five minutes to, you know, anything basically between five minutes and five years, which
0:10:30 is to say I basically only read social media on the one hand and then only books on the
0:10:34 other hand, right? And just polarize it and gap it way out. So what’s interesting about
0:10:38 that is of course, being on social media like that process, you know, necessarily you end
0:10:42 up consuming a lot of news and that a lot of what’s there notwithstanding the false
0:10:46 reports of the death of the web, a lot of what social media is, is links to things that
0:10:49 are interesting, right? People who you’re following are interested in. And so, you know, I do
0:10:52 end up reading basically everything. But one of the experiments was, does it matter? Like
0:10:55 if you don’t see the homepage of the newspaper, do you miss things? And it turns out if you
0:10:58 follow the right people, you really don’t, because they surface all the interesting
0:11:01 stuff anyway. And you get to see a lot of stuff that you wouldn’t necessarily see looking
0:11:04 at the homepage. But the other side, honestly, and you know, you’re accomplished book author,
0:11:08 the other side of it is just books, you know, books that probably become the great underestimated
0:11:12 source of information relevant to our daily lives that just gets, you know, as there is
0:11:17 just such a surplus of kind of near term information and consumption. And let’s just say, as the
0:11:21 real world is getting continuously more interesting in real time, you can spend all day long just
0:11:24 following the ins and outs of what’s happening in the political scene or what’s happening
0:11:26 in the sports scene or what’s happening in, you know, the business world or whatever.
0:11:29 And so you can really get, you know, let’s talk about myself, I can get really trapped
0:11:33 in the present. And so the ability to at least have some time to be able to go back and be
0:11:37 able to read things that were written five or 10 or 50 or 100 years ago, that have stood
0:11:40 the test of time in the form of books has been I think is very valuable.
0:11:43 It has been very interesting. I mean, the book business is actually quite healthy and
0:11:47 people are reading, you know, reading print books, there’s a kind of return to print books.
0:11:51 And it does feel as if I think one of the things you don’t realize until you write them,
0:11:54 particularly with nonfiction books, but it’s true fiction as well that when you meet someone
0:12:00 who’s read one of your books, they have been living inside your mind for 12 hours, 20 hours,
0:12:05 depending how long the book is. And so it is still an unrivaled way to get complicated
0:12:09 ideas into other people’s minds. And so it’s been, I think a sign of health in the culture
0:12:13 of that books are actually thriving in the midst of all this kind of minute by minute
0:12:14 social media.
0:12:16 And also, by the way, as you well know, like audio books, right, I think there’s a renaissance
0:12:21 in audio books, which is just having the smartphone and now the wireless, you know, ear pods makes
0:12:26 it so much more convenient for your content, long form audio content. And podcasts, obviously
0:12:29 are a big part of that. But audio books in the course is drive time and wait time and
0:12:32 this time and, you know, morning time and so forth completely fit into my life in a
0:12:34 way that books didn’t use to.
0:12:38 I also wanted to ask you, Ben, about music, can you talk a little bit about that in terms
0:12:40 of your own kind of creative view of the world?
0:12:44 Yes. Well, it’s interesting and it’s very specific to hip hop for me and hip hop is an
0:12:49 unusual music form in that it’s a very kind of capitalistic form of music, which is completely
0:12:55 kind of unheard of in popular music. And that the main theme of hip hop, if you go through
0:12:59 all the great rappers is like, how do you build something out of nothing? You know, how do
0:13:03 you compete these kinds of things as opposed to R&B, which was maybe love songs and like
0:13:09 rock and roll, which is more communist. But it’s perfect. It’s a perfect analog to entrepreneurship.
0:13:13 It’s kind of the exact kind of motivational soundtrack for entrepreneurs. And that’s really
0:13:19 how I started with it, because any theme I wanted to write about, like it was a great
0:13:26 way to find inspiration. But it led to, if you say I made a contribution to the management
0:13:33 literature, it actually came out of rap music in that the big thing that was different in
0:13:38 my book was that the logic of management is not very complicated. Then you can understand
0:13:44 all the management theory. It’s just not that hard. But the emotional, psychological complexity
0:13:49 of doing it is incredibly difficult. And you know, we see tremendous fallout from brilliant,
0:13:55 brilliant people who can never get over that. And so the big challenge for me was like,
0:14:01 how do you communicate the emotional part of the lesson? And hip hop is great for that
0:14:06 because it carries the emotion. And it’s all about kind of the capitalism. So I wrote
0:14:12 a post, how do you handle politics in a company? And I went through all the things that cause
0:14:16 politics and the subtle things, like how somebody asking for a raise can do it and how you deal
0:14:22 with that technique and so forth. But a lot of it is the attitude of the manager. And
0:14:27 so the rap quote that I used was Rick Ross, who do you think you’re fucking with? I’m
0:14:33 the fucking boss. And like, once you get that, then you know how to do it. That’s great.
0:14:39 Okay, so let’s zoom out a little bit now. You were asking JJ Abrams about LA as the
0:14:43 kind of epicenter of the movie business. So with all the changes that we’ve seen in the
0:14:50 tech sector and all the volatility, the one constant really for half a century has been
0:14:55 that the Bay Area and Silicon Valley have been the epicenter of the technology world
0:15:00 really without any near arrival, probably for 50 years, I think it’d probably be fair
0:15:03 to say, despite the fact that it has gone through all these different revolutions and
0:15:08 you had big computers and then personal computers and then the web and then social media. So
0:15:13 really two questions I think, why, why they are, like what was it about that particular
0:15:18 configuration that rooted tech in that world? And do you think we’re going to look back
0:15:21 in 30 or 40 years and it’s going to have the same concentration?
0:15:25 Yeah. So the why, so the why is I think it’s history, right? And so just the fact that
0:15:28 it’s been a network effect, right? It’s been a snowball rolling down the hill, picking
0:15:32 up momentum now for 56, actually turns out 50, 60, 70, 80 years. A lot of ways it goes
0:15:37 back to the 1920s, 1930s, the early defense contractors. Steve Blank has a whole series
0:15:41 of videos called the secret history of Silicon Valley. He traces it all the way back almost
0:15:42 a hundred years.
0:15:43 Fantastic.
0:15:46 Fantastic shares. And the point of it is, it’s just, it’s this kind of network effect
0:15:49 that’s just kept rolling, right? And so it’s been this place where it’s just like, it’s
0:15:53 the place where the next really smart engineer programmer or, you know, equivalently salesperson,
0:15:58 marketing person, west door contact, whoever they are, finance person on the margin, right,
0:16:01 is more tempted to move to the valley than many other places, which isn’t to say that
0:16:04 there aren’t many capable people all over the world. It’s just on the margin. Many of
0:16:07 the ones who are super ambitious end up at the valley. And of course, I’m an example
0:16:10 of that. And as a consequence, right, it’s a story of imports, right? And so another
0:16:14 thing just to read, I’m sure if people are interested, Tom Wolf, the great novelist,
0:16:18 journalist wrote a piece in the 80s in Esquire about literally Bob Noyce, who was the original
0:16:22 CEO of Intel, one of the fathers of Silicon Valley and literally grew up in Iowa, grew
0:16:26 up in the Midwest and was the Silicon Valley import. And actually Wolf ascribes a lot of
0:16:31 modern Valley culture to literally Bob Noyce importing, interestingly, Midwestern culture,
0:16:35 right, including, by the way, egalitarianism, right. So the whole open floor plan thing,
0:16:39 stock option ownership, everybody owns a share in the company. He traces that actually back
0:16:43 to Midwestern culture. And so it just got established and it developed this ethic and
0:16:46 it’s probably not an accident that it’s the frontier, right? It’s probably not an accident
0:16:49 that this sort of gold rush happened, right? It’s just kind of this frontier out the mentality
0:16:53 has continued. So that’s the good news, right? The bad news is, as I discussed with JJ, like
0:16:56 it’s just number one, we’re just bursting at the seams, like it’s just become a hard
0:16:58 place to do business. And the number two is there’s great people all over the world and
0:17:02 like why on earth? So the joke in the valley is, you know, help wanted, right? Software
0:17:06 company puts up Silicon Valley, software company puts up a help wanted out on the internet
0:17:09 or whatever and says, you know, help wanted, you know, software engineer to work on new
0:17:14 collaboration software tool, online collaboration software tool that will enable people to work
0:17:19 together independent of geography all over the world. So in real time, PS must relocate
0:17:22 to San Francisco to apply. And so it’s this weird incongruity, which is we’re building
0:17:26 the technologies that in theory should let this stuff spread. And yet for some reason
0:17:30 in the last 10, 20 years, it’s actually been concentrating more and more. And so I’ve come
0:17:33 to believe it’s a maybe this is obvious to some people, but I would come to believe it’s
0:17:38 a human dynamics question. It’s a psychology, sociology question, not a technology question
0:17:42 in a lot of ways, which is just like how do people best work together, right? And it just
0:17:46 so happens that at least for the form of traditional companies, which you just see over and over
0:17:50 again is just when you can get everybody in the same room physically in the same room,
0:17:53 right, with the level of, say, fidelity of communication interaction where we’re sitting,
0:17:57 you know, it’s why, by the way, it’s why we’re all physically here. And there are a few successful
0:18:00 distributed companies, but there really aren’t very many as a consequence of that. And so
0:18:05 my hope is that we’re going to get there in the next, you know, let’s say 10 or 20 years,
0:18:09 my hope is that we’re going to get telepresence, right, in the form of video conferencing and
0:18:14 telepresence robots and VR and AR and all these things to collaboration software and
0:18:17 work group software and Slack and GitHub and all these amazing technologies are building
0:18:20 for collaboration. My hope is we’re going to get it to the point where it’s just going
0:18:23 to be obvious that we don’t all have to be in the same place. If that happens, you could
0:18:27 say it’s quote bad for the Valley in the sense of like maybe Silicon Valley is not central
0:18:31 anymore, but it would be so good for the world for that to be the case and we would all benefit
0:18:34 so much from that. I think it’s a very worthwhile thing to pursue and something I’m very fired
0:18:35 up about.
0:18:39 How much do you think, just to go back to the point about noise in the early days of
0:18:43 Silicon Valley and the history of it, to have written about this a little bit as well, how
0:18:51 much do you think that the participatory option granting culture, which is very different,
0:18:54 there were very few kind of East Coast firms that were doing that. So you had much more
0:19:00 traditional kind of top down equity systems in those corporate entities. How much do you
0:19:03 think that is part of the success of Silicon Valley? This is something I think that would
0:19:06 be interesting to go back and look at just economically.
0:19:10 So I think it ends up being very important because of the nature of technology companies.
0:19:18 So if you look at, there are other kinds of companies where the people are much more interchangeable
0:19:23 and this kind of gets into why the network effect is so important and so forth. And in
0:19:29 like a tech company, there’s lots of people who are extremely valuable and that innovation
0:19:36 as a way to get them their kind of proper compensation for their contribution, the great
0:19:39 conversation with Mark and Charles Koch, where he talked about like, you have to be
0:19:43 rewarded for what you contribute to others. And that really is key to any business and
0:19:47 any incentive system. And particularly in technology, because there are so many people
0:19:53 in the company who are so valuable and so fundamentally critical to the company’s success,
0:19:58 it really is one of a very few kinds of compensation systems that would work. And certainly, you
0:20:02 know, a lot of the systems on the East Coast would never work for tech companies to be
0:20:03 kind of world-class competitive.
0:20:11 So it’s been six years since Mark, you wrote the software eats the world essay. I went
0:20:15 back and looked at it and reread it. It was a great piece. It reminded me of, I’m sure
0:20:18 a lot of people have seen this. There was a great thing that was circulating on social
0:20:24 media a couple of years ago. It was an old kind of single page flyer for Radio Shack from
0:20:29 like 1988 or something like that. It was a list of like 30 products that Radio Shack sold.
0:20:36 And the answer machine was, you know, a VCR, an alarm clock, like a TRS 80 kind of descendant,
0:20:39 you know, a game console, something like that. And literally without exception, every single
0:20:43 one of them is now an app on your phone, right? The whole thing had gotten swallowed up by
0:20:48 software, which is of course a measuring productivity problem because all those things
0:20:55 in aggregate cost $30,000 in 1988. And now they’re free on a phone that costs $600, which
0:20:57 is actually progress, but doesn’t sometimes look like it.
0:21:03 So obviously I think that that was a very prescient forecast to make. Has anything kind
0:21:08 of surprised you six years later looking back on it? I mean, in it, you say the next big
0:21:12 stages are health and education. And I’m wondering, you know, particularly on those fronts, has
0:21:14 it lived up to the kind of promise you saw back then?
0:21:16 Yeah, they’re sort of the overall concept of software eats the world. But then there
0:21:20 was a specific framework that I proposed in the piece, which is sort of a weak form of
0:21:23 semi strong form and a strong form of this hypothesis, right? And so the weak form was
0:21:28 every product that can write every physical product will become a software product, right?
0:21:31 And that’s that’s exactly your radar check example. Things go from being physical products
0:21:37 to being apps. The second sort of semi strong version of that was therefore any company
0:21:41 that makes a product that can be turned into software will itself therefore have to become
0:21:44 a software company. Right. And in fact, I was thinking you could you could see this thing
0:21:47 for example, playing out right now in the car industry, right, where all the car companies
0:21:50 are spinning up software efforts, they’re buying software companies are spinning up
0:21:53 software and as fast as they possibly can because they see what’s coming with autonomy
0:21:58 and all these other software advances. And then the strong and sort of audacious slash
0:22:03 ambitious slash arrogant hubristic version of the thesis is in any industry as a result
0:22:07 of this dynamic in the long run, the winning company in the industry will be the best software
0:22:12 company, right, which is a provocative statement, right? Because in a lot of these industries,
0:22:16 and again, cars are a great example. You have incumbents who are really good at making cars
0:22:18 trying to become great software companies. And then you have great software companies
0:22:22 that have no idea how to build a car, right, who are going to start who are going to start
0:22:25 making cars, right? And then you’re going to have basically, right, this giant collision
0:22:29 between companies coming from two totally different backgrounds. And so I think that
0:22:34 you’re seeing lots of that first stage that week stage, lots of products transitioning,
0:22:37 you’re seeing lots of companies becoming software companies. I think we’re just entering in a
0:22:41 lot of industries were entering that Thursday’s where there’s this very interesting structural
0:22:44 battle that’s forming up. The other thing I says, yeah, I think you exactly nailed it
0:22:48 with healthcare and education, right, which is there are these giant sectors of the economy
0:22:53 in which not only is there no productivity growth, like overall in both healthcare and
0:22:57 education, there is no measured growth, there is no measured results in the application of
0:23:01 technology in those fields. And in fact, probably it’s negative productivity growth, right?
0:23:04 Like the typical university has been going backwards in productivity, right? You just
0:23:07 look at the charts, the number of administrators that they hire, right, per student is just
0:23:13 skyrocketing and that is literally negative technological productivity. And so those industries
0:23:18 are extremely enticing to Silicon Valley, because they’re so big, they’re gigantic.
0:23:22 Healthcare, healthcare is a sixth of the American economy, right? And left unchecked, it will
0:23:26 become a fourth and then a third and then a half and then two thirds and then three quarters.
0:23:30 Like it’s just left unchecked, it’s just going to keep growing. And so it’s so much money.
0:23:35 It’s so big. It’s so important. It’s very enticing. And the incumbent structure of there’s many
0:23:38 smart companies in that industry, but the incumbent structure of how the industry works
0:23:42 is just, is wired to go the wrong direction. And so there’s this huge opportunity to insert
0:23:46 into it, which obviously we’re going after hard, but that’s still like super early.
0:23:47 Yeah.
0:23:51 And education, what, Ben, do you have thoughts on that front? I mean, there’s this interesting
0:23:57 point we’re at where there seems to be a growing backlash to the presence of screens, particularly
0:24:02 in younger kids’ school classrooms that it hasn’t lived up to the potential. And maybe
0:24:06 the kids already have too much software in their lives as it is.
0:24:13 So, you know, it’s funny, or it’s not funny. It’s sad that we’ve not applied technology
0:24:19 that well. And a lot of it has to do with the kind of structure of the kind of political
0:24:22 regulatory structure of schools. And we have a company, Udacity, that’s worked hard on
0:24:27 this. And their final conclusion was to kind of run outside of the school system, but it’s
0:24:32 very powerful. I’ll tell you a quick story about that. But, you know, obviously, very
0:24:37 obviously, if you could have like, any teacher or the best teacher in the world teaching a
0:24:44 math class, if students have to study and then be tested, like, when do you take a test outside
0:24:49 of school, like, ever in life? Like, what the hell skill is that does this create like tremendous
0:24:53 anxiety and like give people complexes. But you ought to, with technology, you ought to
0:24:57 be able to measure how people are learning every step of the way, give them harder problems,
0:25:02 if they’re going very fast, or get them help if they’re going slow. And there’s a lot of
0:25:07 things that ought to be able to be done. But then I think the more kind of pressing thing,
0:25:13 and the thing that Udacity really addresses is the four year education, general education,
0:25:18 doesn’t work that well in the modern economy because people are switching careers very,
0:25:23 very often every, you know, two, three years sometimes. And, you know, like four years,
0:25:26 and then you never go back to school for the rest of your life doesn’t make any sense at
0:25:31 all because people need to get retrained jobs get displaced. And so what Udacity has come
0:25:36 up with is this thing, the nano degree, which is two months, three months, you can learn
0:25:42 to program an Android phone or build a self driving car, or learn to do technical marketing.
0:25:47 And those degrees are connected right to the job market. So you can roll right in with
0:25:53 a skill and a certificate that says you understand the material and you’re ready to work. And
0:25:58 that is a great innovation and something that we’re really excited about. And just quick
0:26:03 story on that. So one of the huge problems we have in this country is prison and the
0:26:08 need for prison reform because we’ve got, you know, 75% recidivism rate where people
0:26:12 who go to jail and come out, go back to jail. And the reason they go back to jail, they
0:26:16 can’t get jobs. And the reason they can’t get jobs is because two things. One is we’ve
0:26:23 outlawed college in prison and then two, once they come out, their record follows them wherever
0:26:30 they go. So, you know, I’ve got a friend who came out of jail and I said, go to Udacity.
0:26:35 He goes to Udacity and he’s coming up on his technical marketing degree and he’s already
0:26:38 got job offers. And it’s like, that’s what we need.
0:26:43 Yeah. And I think it’s almost as if school, particularly high school, and I have two kids
0:26:47 in high school, so I think about this a lot, it’s kind of trapped in this middle zone that
0:26:50 doesn’t really work in a sense. It’s much more effective to have those kind of nano
0:26:55 skills, right, where you can actually kind of apply them or the skills should be broader,
0:26:59 right? I mean, when you read through, again, a book like the hard thing about hard things,
0:27:04 I just think about how there are so many skills in there that no one ever thought to teach
0:27:08 me in high school, right? I mean, the skills about decision making skills about kind of
0:27:11 emotional intelligence, dealing with, you know, difficult decisions. My kid actually
0:27:15 in his high school, to its credit, is doing a kind of design thinking class. And they’re
0:27:20 basically learning how to brainstorm ideas, interview a customer, think about different
0:27:24 possibilities, do mock-ups. And it was like, this should be the default. This should not
0:27:27 be an elective. This should be the thing you learn. And then if you want to go off and
0:27:33 do advanced chemistry or do advanced calculus, that’s fine. But those types of skills that
0:27:36 are just, everyone is going to have to know on some level, but it’s very rare to encounter
0:27:38 that. We’ve got a very dated curriculum. There’s
0:27:42 no question. I ran on the board of trustees at Columbia. And there are certainly people
0:27:47 who are going to go to like an elite school and become a scholar or a PhD. And I think
0:27:51 the system works reasonably well for them. But for, you know, the kind of bulk of the
0:27:57 population who goes to college to get into the workforce, it’s really difficult. It’s
0:27:59 exactly, as you say, it’s kind of neither here nor there.
0:28:04 Let’s talk a little bit then, kind of segueing a little bit to the job and automation question
0:28:09 anyway. In general, I think we all agree that there has been this growing and now kind of
0:28:16 reaching Crescendo backlash against big tech and the tech sector that the last year has
0:28:20 particularly brought to the fore. And I feel it very strongly going back because I live
0:28:24 part of the time in Bay Area and part of the time in New York, when I’m back in New York,
0:28:29 you know, nine out of 10 kind of opinion like pieces written in these media are negative
0:28:30 pieces.
0:28:31 It’s only nine out of 10.
0:28:34 I mean, so I want to get into some of the specifics about why that is happening, how
0:28:39 you guys feel about it. But how much in general do you and how much recently have you found,
0:28:42 do you find yourselves taking that seriously and how much do you feel that people just
0:28:44 don’t understand what’s going on here?
0:28:45 We might give two different answers.
0:28:50 Yeah. So I would first say there’s a huge difference between what gets written in opinion
0:28:56 pieces and the actual opinions of the public. So if you look at approval ratings of tech,
0:29:01 they’re incredibly high. Like they’re the highest of any industry. And like Amazon’s
0:29:05 approval raising, which is one of the biggest targets is like 80. Whereas Congress is like
0:29:10 20 and the press is like 20. And so like the guys at 20 are saying the guys at 80 need
0:29:14 to be stopped because everybody hates them. So there is that dynamic. And I think it’s
0:29:15 very real.
0:29:19 This is the concept of false consciousness, right? So literally the whole problem with
0:29:22 the communist revolution was the business weren’t signed up for it. And so the intellectual
0:29:24 leaders were like, well, but we got to take down the capitalist.
0:29:29 The other thing is, I think there’s something else going on that this is a side effect of.
0:29:34 And I think it’s the rise in the last several years. And in particular, after the 2008 crisis,
0:29:38 credit crisis crash, I actually think was the catalyst for a lot of this. It’s the rise
0:29:42 of zero sum thinking in both economics and in politics. Let’s say zero sum as opposed
0:29:46 to positive sum, right, which is this is sort of game theory, right? Zero sum game is I
0:29:50 win, you lose. And by the way, if I’m winning, it must mean that you’re losing because it’s
0:29:53 zero sum. It’s only a question of how we slice up the pie, right? Whereas positive sum is
0:29:57 we can all win together. It’s actually a great book called finite and infinite games that
0:30:01 actually goes through. If you go back historically, basically, economist philosophers and so forth
0:30:06 thought the politics and economics were zero sum. And there were huge battles over resources.
0:30:09 And this was colonization, all these other horrible things that happened over years were
0:30:13 fought through mercantilism, trade wars, right? All these things were fought based on zero
0:30:16 sum. And about, you know, 300 years ago, Adam Smith and a whole bunch of other really smart
0:30:20 thinkers figured out, no, you can actually gain from trade and you can actually interact
0:30:24 with more people and it’s good for everybody. And politics can be positive some just because
0:30:27 I’m doing well might mean that you’re also going to do well because again, we’re able
0:30:30 to culturally trade, we’re able to educate each other, we’re able to, you know, contribute
0:30:33 each other’s thoughts, and we’re all able to succeed. And so in the wake of the credit
0:30:36 crisis, I think zero sum thinking kind of came snapping back. And what’s interesting
0:30:40 is you see that on both the political left and on the right, right? For the anti attack,
0:30:42 the bloodite sometimes to come out of the left and Marx actually was shot through with
0:30:45 with leadism, like that’s one of the things he didn’t understand was the positive sum nature
0:30:49 of productivity growth. And anyway, so you get that on the left, you also get it on
0:30:53 the right, right? And you get it on the right, you get in the form of populism, right, which
0:30:59 in the form of opposition to trade and opposition to immigration, right? And so I just think
0:31:02 as a culture as an economy as a country right now, if you think that the formulation is
0:31:08 zero sum, you will then do things that will cause it to get worse. For example, on the
0:31:11 right, you’ll want trade barriers, right? And so you’ll want to cut trade under the
0:31:14 theory that that will make your people better. In reality, cutting international trade makes
0:31:19 your people worse. You’re dividing up a smaller pie. Yeah, you’re shrinking the economy for
0:31:21 everybody for no reason other than that you’re just mad at other people because you think
0:31:24 it’s their fault that you’re not doing well. And so it’s zero some thinking. And then on
0:31:28 the left right now, it’s this anti tech sentiment where like if those tech people are doing well,
0:31:31 then somebody else must be suffering, somebody else must be eating it. And it’s just it’s
0:31:35 the same sort of extremely reductionist thinking. And of course, the risk is as that sentiment
0:31:39 builds that at least a policies that actually impair the ability to be able to make progress,
0:31:43 make progress in the economy, make progress with productivity growth, make progress with
0:31:46 job creation, make progress with wage creation. And so there’s a pretty big risk that this
0:31:50 is all gonna go pretty seriously sideways for the wrong reason. Right. Let’s take the
0:31:55 tech backlash argument from a slightly more maybe sympathetic level, which is critiques
0:32:01 that have come from within the tech sector that the original vision of the web that inspired
0:32:07 so much of us, which was going to be this decentralized platform that was going to distribute
0:32:11 the kind of power of self publishing and voice to far more people. And it was going to kind
0:32:17 of topple this big, heavy, top heavy mass media model. That’s what inspired a lot of people
0:32:21 to get involved in it in the first place. At the end of that process, we’ve ended up
0:32:26 with, you know, four or five companies that in terms of their command over people’s attention
0:32:31 probably are the most powerful companies that have ever been on this planet and also some
0:32:37 of the greatest concentrations of wealth. So inside the tech sector, people say, re decentralized
0:32:41 the web and then we need to look at technologies that will enable us to have, you know, a more
0:32:46 even distribution in terms of the companies in terms of people’s attention and so on.
0:32:49 And blockchain is part of that. There’s some argument that people have been making along
0:32:54 those lines. How sympathetic are you to that side of the case, which does align with some
0:32:58 of the critiques that big tech is too big that are coming from people outside the tech
0:33:02 sector? Yeah. So there’s a technical argument for a decentralization. And then there’s
0:33:08 the kind of other thing that you’re getting at, which is should there be some like policy
0:33:14 answer to the big tech companies? And I think that, you know, you have to be very careful
0:33:19 there and look at specifically what’s going on. Well, are they kind of harming? Are they
0:33:26 suppressing innovation? So do people like us no longer want to fund anything because,
0:33:30 you know, Facebook or Amazon will wipe it out. And if you look at the numbers, there’s
0:33:35 probably more startups than there have ever been. And what we’re seeing and what we’re
0:33:41 funding is like super interesting. And, you know, for the most part, isn’t existentially
0:33:47 threatened all the time by those companies. Once you introduce policy, the potential side
0:33:54 effects are, you know, really scary, cronyism, corruption, the people who have the best relationship
0:33:58 get the best deal and these kinds of things. And that has knock on effects that are very
0:34:02 difficult. And, you know, if you compare it to the early nineties, when Microsoft was
0:34:07 super strong, that was really actually a far bigger suppression of innovation. There was
0:34:12 way less venture capital. There were far fewer companies being created. But like the technology
0:34:16 took care of it over time. And I think technology is changing at a faster rate now than it was
0:34:20 then. And there’s blockchain and there’s quantum computing. And there’s many technologies
0:34:25 on the horizon that could rejigger the playing field, you know, without a policy intervention.
0:34:30 Another question about the blockchain possibilities, you know, I’ve been really enjoying reading
0:34:36 Chris Dixon writing about this over the last year or two. And there is really an interesting
0:34:44 new way of incentivizing and compensating people both inside a technical organization associated
0:34:49 with an open protocol, early users of the service where all of those people are participating
0:34:54 in the value that’s created with it. And thinking back to the early stock option participation
0:34:58 of noise, you know, I wonder whether this, this suggests maybe that there’s a new model
0:35:03 here that might be as revolutionary as those kind of option plans were.
0:35:06 So the good news is the tech industry has had two models for making forward progress.
0:35:10 One has been what you might call pure capitalism, which is corporations, right, which is sort
0:35:14 of C corporations, employees, stock options, all the things we can take companies public
0:35:17 with that traditional structure. And then there’s been this other structure all the
0:35:21 way over on the ideological spectrum, right, which is open source, right, which is basically
0:35:24 the tribe, right, of developers that are interested in having something happen, coming together,
0:35:28 by the way, geographically distributed all over the world in a lot of cases, right, and
0:35:31 great examples, Linux and the web itself is an example of this and so forth. Actually,
0:35:36 the Internet, TCPIP was an example of this, right, or the new project MIT was an example
0:35:40 of this and people, technical people coming together and volunteering, right, literally
0:35:43 with metaphors like barn raising, right, it’s just like come together and make sort of breathe
0:35:47 life into these projects without a financial incentive and generally without, you know,
0:35:51 at least direct financial rewards. So sort of polar opposite of corporations you can
0:35:58 get. Blockchain is the first new third thing in, I don’t know, probably 40 years, right,
0:36:01 free software open source is like 40 years old. It’s the first new structure in 40 years
0:36:05 and it’s an interesting one because it’s a hybrid. It’s got the, it’s your point, it
0:36:08 has the decentralization of open source, right. These are protocols. These are things
0:36:12 that run Internet wide. These are things that are not necessarily developed by a team of,
0:36:15 you know, 100 people in a building in the Bay Area. They have that kind of open source
0:36:20 characteristic to them and they are decentralized. Like their protocols are inherently decentralized,
0:36:25 but they’ve got capitalism wired in. They’ve got money wired in, right, right, into the
0:36:29 protocol, right, in a way where there is a direct reward and incentive for the people
0:36:34 who actually create the thing. There’s a reward and incentive for the people who use the thing.
0:36:37 And then there was a reward and incentive for the so-called miners, the people who actually
0:36:40 run all the computers all over the Internet that make these things work. And it’s just
0:36:44 been so fascinating to watch because this is one of those kind of moments where people
0:36:47 walk up to this idea. And if they walk up to it from the right, they’re like, what on
0:36:52 earth is this decentralized hippy, like what on earth are you people doing? If they walk
0:36:55 up from the left, they’re like, Oh my God, it’s got money in it. It must be evil, right.
0:36:58 It’s sort of this weird, you got to kind of wrap your head around it. And so what we see
0:37:01 is like, it is fundamentally a third model for innovation. And I will also say this,
0:37:06 the thing that we see that I think maybe other people are missing, many of the smartest programmers
0:37:13 and mathematicians and economists and theorists and systems builders in the world and photographers
0:37:17 in the world are obsessed with this, like they’re just magnetically drawn to it, not
0:37:20 because of the money or this or that or the hype or whatever, because of the technical
0:37:23 innovations that are underneath this that are making this possible and what can come
0:37:26 out of this. And we just think like that’s the most positive sign you can possibly see.
0:37:30 We just have about five minutes left. So I want to just cover a couple of other giant
0:37:36 topics, artificial intelligence and the superintelligence debate. Can we solve that in about two minutes?
0:37:41 Can you give me, is this a legitimate concern? Is it appropriate to be worrying about the
0:37:47 threat from superintelligence now? Of the really scary things in technology. I would
0:37:51 have that one pretty low on my list. I mean, I think that one, like, I think it’s a little
0:37:56 bit of a miss, you know, intelligence is a funny word, right? Like, what is intelligence?
0:38:01 And it’s not one dimensional. And there are a lot of things that we have considered intelligence,
0:38:05 like doing hard math problems. Computers are already more intelligent, like playing chess.
0:38:09 Computers are already more intelligent. But there’s a lot of dimensions of intelligence
0:38:15 that computers are nowhere on. And AI, nobody is demonstrating anything in AI that says
0:38:20 like it’s going to get comprehensively more intelligent and certainly nothing along the
0:38:26 lines of free will yet. So yeah, maybe, maybe it’ll happen. But of all the things, it’s
0:38:30 a very theoretical. So I think it’s a little overblown. I do think also that there’s a
0:38:34 motivation of technologists to, it’s a very kind of, it makes you seem very intelligent
0:38:38 when you can talk about the robots taking over the world. So it’s a great thing to talk
0:38:42 about. The thing that drives you bananas is it’s the freaking physicists. And it’s like,
0:38:47 I’m a computer scientist. I don’t have like crazy conspiracy theories about black holes.
0:38:51 You know, I guess I could, you know, like in theory, a black hole could open up here in
0:38:55 this room and swallow us all. Like, I don’t have crazy theories about dark matter. Like
0:38:57 I’m not worried there’s dark matter in the glass. I’m not going to go around telling
0:39:00 everybody it’s going to, it’s just like, I don’t know why.
0:39:04 Yeah, it’s hard to find an AI expert who goes, Oh, yeah, this is a big problem.
0:39:07 Well, in fact, in the AI experts, of course, tend to be worried about the opposite, which
0:39:09 is they’re like, Oh, shit, expectations are getting set.
0:39:12 Like, we’re never going to build that. We’re never going to build the robot apocalypse.
0:39:16 I’m still trying to get the thing to play Mario Brothers, right? Like, oh.
0:39:21 Okay. So last question, I’d love to hear what you think, looking forward to the next
0:39:27 kind of 20 years, what’s the thing that you’re most curious to see how it turns out, right?
0:39:30 Where you think maybe it’s going this way, but you really are just dying to fast forward
0:39:33 20 years and be like, Ah, that’s what happened with that. Like, what’s the biggest kind of
0:39:36 question mark that you have over the next, say, two decades?
0:39:41 So the thing that makes my brain melt is this, now that we can program biology, so that kind
0:39:48 of, or we’re getting to the point where we can program biology, you know, the first step
0:39:53 is, you know, your one kind of dimension of that is, you know, solving disease, you know,
0:39:59 in a much, much better way, you know, another aspect of it is creating better humans. And
0:40:04 I’m very fascinated to see how that comes out and what it ends up meaning. And, you know,
0:40:09 whether it goes horribly wrong or incredibly right, and what does that even mean better
0:40:15 humans and how will, like, are humans even suited to, like, figure that out. So that,
0:40:18 from a curiosity standpoint, I would say that for me is probably it.
0:40:23 Yeah, yeah. The thing I think a lot about is, so through all of recorded history, and this
0:40:26 is why I just think that a lot of the tech credit assistants are just misguided, through
0:40:31 all of recorded history, most people have not been, I would say, most people have not been
0:40:35 plugged into what we would consider to be modern systems, right? So most people have
0:40:38 not been literate. Most people have not been healthy. Most people have not been fed well
0:40:42 enough to be able to reach full health maturity. Most people have not been educated and still
0:40:47 aren’t right to the level that we could consider modern. Most people don’t have access to economic
0:40:51 opportunity that we would consider to be, you know, modern jobs. Most people don’t have
0:40:55 access to what we consider to be high quality healthcare. Most people don’t have access to
0:40:58 high quality housing, transportation, you just go right down the list of all these things
0:41:01 that we’ve been lucky enough in this country to enjoy, you know, large percentage of the
0:41:05 population for a long time. Most people in the world have not had access to those things.
0:41:10 And I know that the existing systems, existing education system, the existing healthcare system,
0:41:15 the existing transportation system has had, you know, 50, 100, 200, 500 years to get to
0:41:18 the 7 billion people on the planet. It’s only gotten to every one of those systems has only
0:41:22 gotten a fraction of the people. And now we finally have the way to get right to everybody.
0:41:27 We get we’re at the point where 3 billion smartphones on its way to 6, 7 billion on the planet, we’re
0:41:30 going to be able to connect everybody. We’re going to be able to get over time, we’re going
0:41:32 to be able to get everybody all the things that I went through, right, starting by the
0:41:36 way with education, right, as sort of a foundational one. And so what is it going to mean for the
0:41:41 planet when everybody around the planet all of a sudden starts to, I would say, become
0:41:45 part of the systems that we know and understand. And we literally have 10, 20 times the number
0:41:48 of people around the planet who are contributing in all these different areas. And I just don’t
0:41:52 understand how people can be possibly pessimistic about the future knowing that that’s the potential.
0:41:54 And I think we’re going to see that. And I think our kids are going to see that. And
0:41:59 I think that’s very exciting. Yeah, that is. Okay, so we covered Knight Rider, Karl Marx
0:42:03 and universal education for the planet. I think we’ve done our job. Thank you guys. That
0:42:07 was great. Thank you. Thank you, everybody. Thank you. Thank you.
0:42:10 (audience applauding)

0:00:03 The content here is for informational purposes only,
0:00:05 should not be taken as legal business tax
0:00:06 or investment advice,
0:00:09 or be used to evaluate any investment or security
0:00:11 and is not directed at any investors
0:00:14 or potential investors in any A16Z fund.
0:00:18 For more details, please see a16z.com/disclosures.
0:00:21 – Hi everyone, welcome to the A6 and Z podcast.
0:00:22 I’m Sonal.
0:00:25 So this week to continue our 10-year anniversary series
0:00:27 since the founding of A6 and Z,
0:00:28 we’re actually resurfacing
0:00:30 some of our previous episodes
0:00:33 featuring founders, Mark Andreessen and Ben Horwitz.
0:00:34 If you haven’t heard the latest episode
0:00:36 with Stuart Butterfield turning the tables
0:00:38 as the entrepreneur interviewing them,
0:00:41 please do check that out and other episodes
0:00:45 in this series on our website at a6andz.com/10.
0:00:48 But this episode was recorded in 2016
0:00:50 on the five-year anniversary
0:00:52 of Mark’s Wall Street Journal op-ed
0:00:55 on why software is eating the world.
0:00:57 And it features me and Scott Cooper
0:01:00 asking Mark and Ben about what’s changed since
0:01:03 and how software is now programming the world.
0:01:05 And we discuss everything from simulations
0:01:09 to distributed systems to other key computing shifts.
0:01:10 Welcome guys.
0:01:11 – Hey, thank you.
0:01:13 – Okay, so let’s just kick things off.
0:01:15 One of the things that I want to understand
0:01:16 is that it’s been since fund one,
0:01:18 which is what, seven years ago?
0:01:20 Yeah, seven years ago.
0:01:21 A lot’s changed in seven years.
0:01:22 And I’ve actually heard you argue, Mark,
0:01:24 that things have accelerated in that time period,
0:01:27 more so than previous decades before.
0:01:29 So what do you think are the biggest shifts now
0:01:31 that are important to us in this newest fund
0:01:34 and what changed in that period, like the biggest things?
0:01:37 – So in fund one, when we started,
0:01:39 we thought that our timing was really good,
0:01:42 despite the fact that I think the world thought
0:01:43 our timing was really bad
0:01:45 and starting a new venture capital fund.
0:01:47 And the reason why we thought that was that
0:01:50 there were three gigantic new platforms
0:01:51 hitting all at the same time,
0:01:56 which was kind of unprecedented in the history of technology.
0:01:58 One was mobile, the second was social,
0:02:00 and the third was cloud.
0:02:02 And that really proved out
0:02:04 through the course of the early history
0:02:06 that the applications on top of those,
0:02:11 particularly mobile and cloud were just spectacular.
0:02:13 And I think we’re coming a little bit
0:02:17 to the end of the first phase of the,
0:02:19 some of the obvious applications
0:02:20 that could be built on those things
0:02:22 and we’re moving into some new areas.
0:02:25 – Yeah, so let me go to the foundations.
0:02:26 So there’s different ways of looking at it.
0:02:27 The foundation levels.
0:02:30 One is Moore’s law has really flipped.
0:02:31 And this actually has happened.
0:02:32 I think this actually has happened
0:02:33 over the last seven or eight years,
0:02:35 actually almost exactly over the life of the fund,
0:02:37 which is for many, many years,
0:02:40 Moore’s law was a process of the chip industry
0:02:42 bringing out a new chip every year and a half
0:02:45 that was twice as fast as the last one at the same price.
0:02:47 And that continued for 40, 50 years.
0:02:49 And that’s, by the way, what resulted in everything
0:02:51 from mainframes, mini computers, PCs,
0:02:55 and then smartphones, about seven, eight, nine,
0:02:57 10 years ago, that process actually started to come in
0:02:59 and the way that it had worked up until then.
0:03:00 So chips have kind of topped out
0:03:02 at a speed of about three gigahertz.
0:03:04 And a lot of people have said, therefore,
0:03:06 like progress in the tech industry is gonna stall out
0:03:07 because the chips aren’t getting faster.
0:03:09 I think what’s actually happened is Moore’s law
0:03:10 has now flipped and the dynamic now,
0:03:12 instead of increased performance has reduced cost.
0:03:15 You now have this dynamic where every year, year and a half
0:03:16 the chip companies come out with a chip
0:03:18 that’s just as fast, but half the price.
0:03:21 And so this is this sort of, just this massive deflationary
0:03:23 force, I think in the technology world.
0:03:27 And I actually also suspect in the economy more broadly,
0:03:28 we’re basically computing is just becoming free.
0:03:31 Basically what we do in this business is we just kind of
0:03:33 chart out the graphs and then just kind of assume
0:03:34 at some point you’re gonna get to the end state
0:03:36 and the end state is gonna be the chips are gonna be free,
0:03:38 which means chips will be embedded in everything.
0:03:40 You’ll be able to use chips for literally everything.
0:03:42 And we’ve never lived in a world before where you can do that.
0:03:44 So that’s the first one.
0:03:46 Second one is just the obvious implication from that,
0:03:48 which is all those chips will be on the network, right?
0:03:50 So all those chips will be connected to the internet.
0:03:52 They’ll all be on wifi or mobile carrier networks
0:03:53 or wired networks or whatever,
0:03:55 but they’ll all fundamentally be on the internet.
0:03:57 You know, that’s something that’s not happening
0:03:59 at a very rapid pace.
0:04:01 And then the third is the continuation of the piece
0:04:03 that I wrote actually five years ago,
0:04:04 which was called software eats the world,
0:04:06 which is basically just say if you’re gonna live in a world
0:04:08 in which there’s gonna be a chip in every physical object.
0:04:11 And if you live in a world in which every physical object
0:04:12 therefore is going to be networked,
0:04:14 it’s gonna be smart because it has a chip
0:04:15 and it’s gonna be connected to the network.
0:04:17 Then basically you can then program the world.
0:04:19 You can basically write software
0:04:20 that applies to the entire world.
0:04:22 So you can write software that all of a sudden applies
0:04:24 to all cars or you can write software that applies
0:04:27 to all, you know, everything flying in the sky.
0:04:29 You can write software that applies to all buildings.
0:04:31 So you can write software that applies to, you know,
0:04:35 all homes or all businesses or whatever, all factories.
0:04:36 And so all of a sudden you can kind of,
0:04:37 you can program the world.
0:04:39 That’s really just starting.
0:04:40 And I think a lot of the,
0:04:42 there’s a number of things that make the entrepreneurs
0:04:44 we’re seeing these days in many ways more interesting
0:04:45 and more aggressive than entrepreneurs
0:04:47 who’ve seen the past and part of it is they just assume,
0:04:49 if there’s something to be done in the world,
0:04:51 there must be a way to write software to be able to do it.
0:04:54 That’s at a new level of power sophistication.
0:04:57 It’s a new scope of what the tech industry can do.
0:04:59 The consequence of that for us as a fund
0:05:01 is that we find ourselves evaluating business plans
0:05:03 and funding companies that are in markets
0:05:04 where I think seven or eight years ago
0:05:06 we would have never anticipated operating.
0:05:09 – So Mark, does that mean that there’s no new innovation
0:05:11 in platforms themselves and everything?
0:05:12 All the innovation will be applications
0:05:14 that ride on that existing infrastructure?
0:05:16 Or do you think there’s also the opportunity
0:05:19 to build a new platform, even given some of those trends?
0:05:20 – I think there are new platforms
0:05:21 and I think there will be new platforms.
0:05:23 I just think there’ll be different kinds of platforms
0:05:24 than we’ve had in the past.
0:05:26 The idea of a platform in the tech industry, as you know,
0:05:29 up until five or 10 years ago was there is a new chip
0:05:31 that has new capabilities is faster
0:05:33 and then therefore you build a new operating system for it.
0:05:36 And that might be Windows or it might be iOS
0:05:37 or whatever it is.
0:05:39 The platforms that we’re seeing getting built these days
0:05:42 are distributed systems, so scale out systems.
0:05:44 Instead of being built on a chip necessarily
0:05:45 with new unique capabilities,
0:05:47 they are platforms that are going to build
0:05:48 across lots of chips.
0:05:49 And so they’re in computer science terms,
0:05:51 they’re distributed systems.
0:05:52 Cloud is one of the first examples, right?
0:05:55 So anybody who uses AWS can now go on
0:05:57 and can program an application on AWS
0:05:59 that will run across 20,000 computers
0:06:02 and they can run it for an hour and it’ll cost 50 bucks.
0:06:06 And that’s a kind of platform that did not exist before.
0:06:08 And by the way, there are many specific elements to that.
0:06:10 So for example, we’ve seen the rise of,
0:06:11 in that category, seen the rise of Doop
0:06:14 and otherwise the spark for distributed data processing.
0:06:16 We’ve seen in financial technology,
0:06:18 we’ve seen the rise of Bitcoin and cryptocurrency,
0:06:20 which is a literally distributed platform for currency
0:06:22 and for exchanging value.
0:06:25 And now we’re seeing the emergence of a major new platform,
0:06:28 which is AI, machine learning and deep learning,
0:06:30 which is inherently, the great thing about machine learning
0:06:33 and deep learning is they’re inherently parallelizable.
0:06:34 They can run across many chips
0:06:36 and they get very powerful as you do that.
0:06:40 And you can do things in AI today as a consequence
0:06:41 of being able to run across many chips
0:06:43 that you just couldn’t even envision
0:06:44 doing five or 10 years ago.
0:06:46 – So let’s talk about the rise of the GPU
0:06:47 as part of this next platform ship.
0:06:49 I mean, I think the biggest surprise people have had
0:06:51 is that this is the graphical processor unit,
0:06:54 which is something that was developed in the gaming industry
0:06:57 for really high resolution graphics processing
0:06:59 and is now finding, I guess, unexpected.
0:07:02 Is it a surprise to us that it’s finding uses
0:07:05 in these new platforms like VR, AR, deep learning?
0:07:06 – It’s actually interestingly,
0:07:08 it’s a new application of an old idea
0:07:09 back when I was getting started 30 years ago
0:07:11 working in physics labs.
0:07:13 If you wanted to run just a normal program,
0:07:16 you just buy a normal computer and run the program.
0:07:19 But if you wanted to do, run a program,
0:07:21 many physics simulations had this property
0:07:24 where you would want to run a very large number
0:07:25 of calculations in parallel, right?
0:07:27 And so you could basically divide up a problem
0:07:29 of simulating anything from a black hole
0:07:32 or to different kinds of biological simulations.
0:07:34 You could basically write these algorithms
0:07:35 in a way that they could run,
0:07:36 you could basically parcel the problem
0:07:38 into many different pieces
0:07:39 and then run them all in parallel.
0:07:41 There was actually in the old days,
0:07:42 there was actually a whole industry
0:07:43 of what we’re called vector processors,
0:07:46 which were literally these kind of sidecar computers
0:07:48 that you would buy and you would hook up to your main computer
0:07:49 and they would let you run these parallel problems
0:07:51 much faster.
0:07:53 And so literally 30 years later, the GPU is a,
0:07:54 it’s basically a vector processor.
0:07:56 It’s basically a sidecar processor that sits along the CPU
0:07:58 and runs these parallel problems much faster.
0:08:01 And graphics are a natural application of that,
0:08:03 but as it turns out, graphics aren’t the only application.
0:08:05 – Yeah, actually interestingly,
0:08:07 and I was at a company making one of these
0:08:11 called Silicon Graphics and the applications then,
0:08:13 whereas Mark was saying a lot of physics applications,
0:08:16 computational fluid dynamics and simulating,
0:08:18 flight simulation and all these kinds of things
0:08:20 that are hard physics to calculate.
0:08:21 When you go into the virtual world
0:08:23 and you’re simulating the physics of the real world,
0:08:24 guess what?
0:08:27 You need the exact same processor to do it.
0:08:31 So it’s a super logical conclusion to what’s been going on,
0:08:35 but I think we’re also in the world of big data,
0:08:39 seeing kind of more reasons to do just lots of math in parallel.
0:08:42 And so it’s an exciting application.
0:08:43 – Yeah, you talk about platforms.
0:08:45 One of the really interesting hardware platforms
0:08:47 is emerging right now is NVIDIA,
0:08:49 which is a very well-established public chip company,
0:08:50 but very successful to your point,
0:08:53 doing graphics chips for a very long time,
0:08:55 has become seemingly overnight.
0:08:57 It’s really, of course, the result of years of work,
0:08:59 but seemingly overnight has become the market leader
0:09:03 in both not just GPUs, but also in chips being used for AI.
0:09:05 And it’s basically extensions of the GPU technology.
0:09:08 And we see this overriding theme,
0:09:09 which is kind of an amazing thing,
0:09:12 which is basically every sharp AI software entrepreneur
0:09:15 that comes in here is now building on top of NVIDIA’s chips,
0:09:17 which is, of course, a very different outcome
0:09:18 than entrepreneurs of previous years
0:09:20 who would have built other kinds of programs
0:09:22 primarily on top of Intel chips.
0:09:24 – We’ve mentioned AI and machine learning a couple of times here.
0:09:25 And one of the interesting things,
0:09:28 at least I think we see in the industry is,
0:09:29 at the same time we’ve got startups doing it,
0:09:32 we also see some of the very large established players
0:09:34 investing significantly in AI and machine learning.
0:09:37 So certainly Facebook and Google, Apple and others
0:09:39 are obviously building big operations.
0:09:41 How do you think about the universe
0:09:42 from an investment perspective?
0:09:44 What are the kinds of things that actually led themselves well
0:09:47 to startup opportunities in the AI space
0:09:49 versus things that actually might make sense,
0:09:52 kind of living inside of one of the larger companies
0:09:53 like a Facebook or Google?
0:09:55 – Yeah, so AI is extremely broad.
0:09:58 And I think one of the challenges that people have with it
0:10:01 is they try to paint it as a narrower thing than it is,
0:10:04 but one can think of it as an entirely new way
0:10:07 to write a computer program.
0:10:12 And so then it’s applicable to the universe of problems.
0:10:15 So there are things that advantage a big company.
0:10:18 If you’re building AI to analyze consumer internet data,
0:10:20 like that’s hard to take Google on at that.
0:10:22 They do have an awful lot of data.
0:10:26 And Facebook with AI, computing power matters
0:10:28 and the data set matters.
0:10:30 Having said that, there are a lot of areas
0:10:32 where nobody has any data yet
0:10:37 in the areas of healthcare and the areas of autonomy.
0:10:40 So there’s lots and lots of opportunities.
0:10:44 And there’s also interesting ideas
0:10:46 about, well, is there a better user interface
0:10:49 than the smartphone using AI techniques
0:10:50 and then what is the form of that?
0:10:51 – What do you mean by that
0:10:53 when you say there’s a better user interface?
0:10:55 – Well, if you think about a smartphone,
0:10:58 it was kind of an advance over what we used to call
0:11:03 the WIMP interface, Windows icons.
0:11:05 What was it?
0:11:06 – Menus.
0:11:07 – Menus.
0:11:07 – What was it, P?
0:11:08 – Pointer.
0:11:09 – Pointer, right.
0:11:12 – Which was like a big advance
0:11:15 over the text-based interface of DOS.
0:11:18 And then the smartphone with a touch interface,
0:11:20 so it was more of a direct manipulation
0:11:21 was an advance over that.
0:11:23 And so you’d go, okay, well,
0:11:27 but that’s not actually what people do in life, right?
0:11:30 Like it’s anthropologically,
0:11:34 it’s a backward step in terms of the natural interface
0:11:36 so that we’ve become accustomed to,
0:11:38 like for example, natural language.
0:11:40 With AI, you get into a world
0:11:44 where things like natural language and natural gestures
0:11:46 and so forth become much more plausible.
0:11:48 So there’s potentially an opportunity
0:11:52 to build interfaces for things that you couldn’t before.
0:11:54 I mean, I think there’s one really interesting thing,
0:11:57 which I’m sure, and I know that Google and Apple
0:11:59 and all the giant companies are very focused on,
0:12:02 which is how do you replace the current set
0:12:03 of user interfaces with it?
0:12:05 But there’s another dimension,
0:12:08 which is what are all the applications
0:12:10 that you just couldn’t have before
0:12:13 because you couldn’t build a workable user interface for it.
0:12:17 And AI seems very promising in those areas.
0:12:18 – You didn’t mention Amazon,
0:12:19 which is sort of the stealth player here
0:12:21 with Echo and Alexa.
0:12:23 I mean, really, George and Horace have been home.
0:12:26 Well, in a way, they’ve got an interesting advantage
0:12:28 in that they’re not tight
0:12:31 to the last generation of user interfaces
0:12:34 so that they don’t have to pay the strategy tax
0:12:39 for shoehorning in their AI into, say, the iPhone.
0:12:40 And that’s something.
0:12:41 – Yeah, that’s worth pointing out.
0:12:43 There’s sort of two kind of classic rules of thumb
0:12:44 in this industry.
0:12:46 One is for major new advances,
0:12:47 especially in things like interfaces,
0:12:49 if you don’t own a platform, you can’t do them.
0:12:52 And so the assumption I think had been up until recently,
0:12:53 that it would have to be Google or Apple
0:12:54 that does these kind of natural language
0:12:57 or interface advances ’cause they own iOS and Android.
0:13:00 The other rule, of course, is the exact opposite rule,
0:13:01 which is the one that Ben mentioned,
0:13:03 which is the problem that big established companies
0:13:06 get into is this, what he referred to as the strategy tax,
0:13:09 which is basically big companies with existing agendas
0:13:11 have to sort of fit their next thing
0:13:12 into their existing agenda,
0:13:14 and they often compromise it in the process.
0:13:16 And so it’s sort of this ironic twist of fate
0:13:18 that Amazon has all of a sudden taken the lead
0:13:19 from Google and Apple.
0:13:22 Even though Amazon famously flopped with their phone,
0:13:23 which is sort of the obvious place
0:13:24 where you would have a voice interface,
0:13:26 it didn’t matter because they came out with this new product,
0:13:29 which is basically the smart speaker called Echo.
0:13:32 And the fact that all of a sudden Amazon didn’t have a phone,
0:13:33 all of a sudden became an advantage
0:13:35 ’cause they could just do the clean,
0:13:36 actual breakthrough product
0:13:38 without worrying about tying it into the existing strategy.
0:13:40 – Right, and those are all still big companies, though,
0:13:42 is I’m not really hearing where startups
0:13:44 can really play in this space,
0:13:46 especially when you’re describing this huge data network effect
0:13:48 that all these big companies have.
0:13:50 – A year ago, we would have probably been sitting here
0:13:52 and say that AI was gonna be likely,
0:13:53 would be a domain of big companies
0:13:55 because of this sort of thing of like,
0:13:57 okay, only big companies can afford
0:13:58 the very large number of engineers
0:13:59 that are required to do AI.
0:14:01 Only big companies can afford the amount of hardware
0:14:03 required to do AI.
0:14:03 And then only big companies
0:14:06 can get the giant data sets required to do AI.
0:14:07 In the last 12 months, what we’ve seen basically
0:14:10 is all three of those changing very fast
0:14:11 into the advantage of startups.
0:14:14 We’ve seen a lot of AI technologies actually,
0:14:16 actually now interestingly standardizing.
0:14:18 So going to open source and then the next step
0:14:20 is gonna be they’re gonna go to cloud.
0:14:22 And we think we’re right on the verge of that.
0:14:23 We think all the major cloud providers
0:14:25 are gonna be providing AI as a service
0:14:27 and they’re gonna really radically reduce
0:14:29 the amount of technical knowledge you need to apply AI.
0:14:30 And so that plays very well to the startups.
0:14:32 – So there will be like an AWS for AI.
0:14:33 – Yeah, exactly.
0:14:34 And that may be literally AWS
0:14:35 or it may be Google or Microsoft
0:14:38 or all three of them in some combination
0:14:40 or it may be other companies yet to emerge.
0:14:42 – An example of the open source like TensorFlow,
0:14:43 like Google releasing TensorFlow.
0:14:44 – Yeah, and this is a big deal.
0:14:45 And of course, yeah, it’s right.
0:14:46 So Google open source to pretty significant part
0:14:47 of how they do deep learning.
0:14:48 And that actually now is something
0:14:50 that other companies can pick up and use directly.
0:14:51 And we see actually a lot of,
0:14:52 not only a lot of companies,
0:14:53 but like a lot of university,
0:14:54 a lot of student projects now
0:14:56 just kind of can pick that up and run with it.
0:14:59 So this technology is kind of trickling down very fast.
0:15:01 – Just this past weekend, we had a hackathon.
0:15:03 And I think most of the teams
0:15:06 had some machine learning AI component into their hacks.
0:15:07 And these are college kids.
0:15:11 – Yeah, if you’re a 21 year old junior in college
0:15:12 and you’re doing some project,
0:15:16 it’s just kind of, it’s becoming rapidly becoming very obvious
0:15:17 that you would have AI be part of it,
0:15:19 which was very much not the case even full months ago.
0:15:20 And that’s a direct to your point.
0:15:22 That’s a direct consequence of the open sourcing
0:15:24 and kind of this knowledge spreading out.
0:15:25 The second thing was the hardware costs.
0:15:27 And there again, the cloud, AI and the cloud,
0:15:29 just the existence of the cloud
0:15:30 is bringing down hardware costs across the board,
0:15:32 but AI and the cloud is gonna bring that down even further.
0:15:34 And by the way, these trends all slam together.
0:15:37 So you get what I think in a year is gonna be very common.
0:15:39 These sort of AI supercomputing chips
0:15:41 with the AI algorithms in the cloud
0:15:43 available to anybody for a dollar, right?
0:15:44 And so there’s gonna be this massive deflation
0:15:47 of hardware cost on that side.
0:15:48 These big data sets are interesting.
0:15:50 Ben made the case that the startups
0:15:51 can assemble big data sets.
0:15:53 And I think that there are certainly examples of that.
0:15:55 We also see another thing happening,
0:15:57 which is the newest generation of experts in deep learning
0:15:59 or many of them are specializing in the idea
0:16:01 of deep learning applied against small data sets.
0:16:03 If you talk to those folks, what they’ll tell you is,
0:16:06 oh, basically, they’ll basically say is
0:16:08 primitive and crude deep learning required big data sets,
0:16:09 but the really good stuff doesn’t.
0:16:11 It is small data sets are fine.
0:16:13 And so that’s still very early,
0:16:14 but it’s extremely enticing.
0:16:16 It’s an extremely enticing idea
0:16:18 because it really brings a lot of these problems
0:16:21 to your point further into being tractable
0:16:22 for small companies.
0:16:24 But actually, one of the things you can do
0:16:25 with these, especially with these GPUs
0:16:27 is you can literally use the same tools
0:16:29 that are used to make video games.
0:16:32 And you can create simulated versions of the real world.
0:16:34 And then you can actually let the AI train
0:16:35 inside the simulation.
0:16:36 And so if you’re building a new self-driving car
0:16:38 or a drone or something like that,
0:16:39 you can actually create simulated worlds
0:16:42 in which there are everything from earthquakes,
0:16:46 to floods, to thunderstorms, hail storms.
0:16:48 You can create birds, swarms of birds.
0:16:51 You can literally simulate the real world environment.
0:16:53 And then you can let the AI actually train inside that world.
0:16:54 And actually, it’s funny,
0:16:56 the AI actually has no idea it’s training
0:16:57 in the virtual world.
0:16:58 It’s learning just the same
0:16:59 as if it were learning in the physical world.
0:17:02 And so again, for startups with access to cloud-based AI,
0:17:05 you could potentially run basically millions of hours
0:17:07 of simulated training at a very low cost
0:17:08 and all of a sudden catch up to big companies.
0:17:12 – Interestingly, the very famous AI project
0:17:14 that Google did with DeepMind,
0:17:17 that whole dataset came from the game “Playing Itself”.
0:17:19 So, there wasn’t some dataset
0:17:21 that Google had collected over 20 years.
0:17:24 It was the game “Playing Itself”.
0:17:26 – So you guys have both mentioned simulations a few times.
0:17:27 Why are they so important?
0:17:28 ‘Cause I feel like there was this period,
0:17:29 like maybe even a decade ago
0:17:31 where simulations were almost frowned upon
0:17:33 as this promised thing
0:17:35 that didn’t really actually deliver
0:17:37 in what you needed to be able
0:17:40 to navigate complex environments in real life.
0:17:41 – Yeah, well, it’s interesting.
0:17:45 So was AI, was frowned upon 10 years ago,
0:17:48 saying it was all, it didn’t work.
0:17:50 And particularly neural nets and deep learning
0:17:53 were the most frowned upon area.
0:17:55 And there’s been similar kind of breakthroughs
0:17:56 with simulation.
0:17:58 And first of all, so if you think about the field
0:18:02 of data science and what you do with data,
0:18:04 there’s you have a giant set of data,
0:18:07 which is always historical in nature,
0:18:08 and you can analyze that.
0:18:10 And maybe it’s predictive of the future,
0:18:12 but oftentimes it’s not.
0:18:15 And we see this in particular in things like,
0:18:17 really dynamic things where the past affects the future,
0:18:20 like say stockpicking or the weather
0:18:23 or other kinds of things where data analysis
0:18:24 doesn’t get you an accurate answer.
0:18:26 Simulation is a flip side of that
0:18:28 where you can say, okay, here are all the entities
0:18:32 in the world and let’s generate their behavior over time.
0:18:34 And then their actual behavior feeds back
0:18:37 into the simulation, which is critical,
0:18:39 a critical component.
0:18:42 Historically, that’s been difficult at scale,
0:18:45 but there have been some really important breakthroughs
0:18:47 lately, particularly from a company
0:18:49 that we’re invested in called Improbable,
0:18:52 which is able to do very large scale,
0:18:55 scale out simulation using cloud computing techniques
0:18:59 and some very important new technology
0:19:00 that they’ve developed.
0:19:03 And so you can get a really complete picture of the world.
0:19:06 And as Mark was saying, you can actually generate
0:19:09 your own dataset rather than collecting it
0:19:10 for certain kinds of situations.
0:19:12 Yeah, let me add the thing to that.
0:19:14 So one way to think about it is it’s expensive
0:19:15 to make things happen in the real world.
0:19:17 Like it’s expensive to change things in the real world
0:19:18 because the real world is physical
0:19:20 and causing physical changes to happen.
0:19:21 I mean, everything from building roads to flying planes,
0:19:23 all these things are very expensive.
0:19:24 And then things in the real world,
0:19:26 changes have serious consequences, right?
0:19:28 And so if you, depending on where you put the dam
0:19:29 or where you put the airport
0:19:31 or what your evacuation plan you have for the city
0:19:32 and if something bad happens,
0:19:35 like these decisions have huge consequences.
0:19:36 Which banks you bail out.
0:19:38 Which banks you bail out.
0:19:39 Which banks you don’t bail out.
0:19:41 And so you always have these consequences
0:19:43 and people who have to make these decisions
0:19:44 are often flying blind
0:19:45 ’cause they don’t have any real sense
0:19:46 of what’s gonna happen
0:19:47 as a consequence of their decisions.
0:19:49 In contrast, if you can simulate a world
0:19:50 and if you can run an experiment,
0:19:53 if you can simulate the real world or some portion of it
0:19:55 like the highway system or the banking system or whatever.
0:19:58 And then you can basically introduce change
0:19:59 into that simulation
0:20:00 and you can see what the consequences are.
0:20:02 It’s very cheap to do that because Moore’s Law
0:20:04 and the collapse of chips and the rise of cloud computing
0:20:06 and all these other things we’ve been talking about
0:20:08 all of a sudden make it very cheap to run the simulations.
0:20:10 It’s much cheaper to do it in the simulated world.
0:20:11 And then there are no consequences.
0:20:13 You run a simulation and everything goes wrong
0:20:15 and everybody dies
0:20:16 or the entire financial system collapses or whatever.
0:20:17 It doesn’t matter.
0:20:19 You just erase it and you run it again.
0:20:20 – Yeah, you have infinite testability.
0:20:22 – Well, I went and challenged that.
0:20:25 There is Elon Musk simulation in which case
0:20:27 the consequences are quite dire for us.
0:20:27 – There is, yes.
0:20:29 There is a scenario that we’re all living in a simulation.
0:20:30 – Right, if we’re living in one place.
0:20:32 – In which case I would argue it’s gone badly awry
0:20:34 as evidenced by the current political situation.
0:20:36 – There is no do over button in this simulation.
0:20:38 – Yes, and then you basically, again,
0:20:40 you look at the progress of Moore’s Law
0:20:41 and the rise of these new technologies
0:20:43 and you say, okay, how about instead of running
0:20:44 one simulation, let’s run a million simulations
0:20:46 or let’s run a billion simulations
0:20:47 and let’s try every conceivable thing
0:20:49 we can possibly think of and let’s imagine,
0:20:51 let’s literally model all potential future states
0:20:54 of the world and then let’s decide which one of those,
0:20:57 which path is the one that leads to the best consequences.
0:21:00 And so we can then make these very big real world decisions
0:21:04 with a lot more foreknowledge of what will unfold afterwards.
0:21:06 – Maybe just to get concrete on some opportunities,
0:21:08 what are the other areas and maybe it’s life sciences
0:21:10 or what are some of the other kind of more tangible areas
0:21:11 that you think in your term,
0:21:13 as you think about kind of deploying this fund
0:21:15 or beyond over the next, you know, five to 10 years
0:21:17 that might be interesting for, you know, people to think
0:21:19 about in the context of real world applications
0:21:20 of this technology.
0:21:21 – Yeah, so as Mark was saying,
0:21:24 we’re coming into this era of new platforms
0:21:27 and with the intersection of health and computer science,
0:21:30 what we’re saying is really exciting new platforms
0:21:34 around data and around basically you being able
0:21:36 to get much more information about someone’s health
0:21:40 from a variety of techniques that have been developed,
0:21:43 you know, based on the kind of historic breakthroughs
0:21:47 and sequencing the genome, but beyond that as well,
0:21:51 where we can get really, really powerful data about people
0:21:54 and understand them better.
0:21:55 And once you have that data about people
0:21:58 when you can be predictive of diseases
0:22:00 that they might get or things that are wrong
0:22:02 and you aggregate that into a platform,
0:22:05 then you can actually make new scientific discovery
0:22:06 off it as well.
0:22:08 So that’s one interesting area.
0:22:11 There’s, if you think about the AI platform itself,
0:22:14 one of the things about it is the hardware
0:22:17 that’s been built for it or that’s been built historically
0:22:20 is for a completely different kind of computer programming.
0:22:23 And we’ve seen Google already announced a chip
0:22:25 to power their deep learning cloud.
0:22:28 And, you know, similarly there’s new breakthroughs
0:22:31 and quantum computing, which at least on the surface
0:22:33 look like they may be very promising
0:22:37 for much more powerful deep learning systems and so forth.
0:22:39 So there’s a lot of things
0:22:41 that are coming out of these platforms.
0:22:43 And then, you know, as we get to a chip and everything,
0:22:47 the platforms to run and manage and understand those chips
0:22:50 are equally as exciting.
0:22:52 – So, you know, one of the themes that’s come up through here
0:22:55 is that tech is reaching into places it never did before.
0:22:57 I mean, every company is becoming a tech company
0:23:00 or they have tech inside, or as Benedict likes to say,
0:23:01 growing the tech industry,
0:23:04 the reality is it’s permeating everywhere.
0:23:06 And the question I have for us
0:23:08 is that we are founded on this thesis
0:23:10 that software is eating the world, that’s our premise.
0:23:12 And yet we’ve seemed to have been making
0:23:14 a lot of hard investments, you know,
0:23:18 if you count things like Soylent, Oculus, Nutribox.
0:23:21 So are we changing our thesis about hardware
0:23:23 as a result of this software eating the world?
0:23:24 – No, I don’t think so.
0:23:26 I mean, I think that what we see
0:23:29 with the companies that you’ve named are interesting.
0:23:31 So, Oculus, I think we would all agree
0:23:33 that the software component of Oculus
0:23:37 is both more complex, has many more people working on it
0:23:39 and is kind of the core of the investment.
0:23:42 Sometimes if you have a breakthrough technology,
0:23:45 then you require a new hardware to actually support it.
0:23:46 And that’s the case there.
0:23:49 And I think that Soylent and Nutribox,
0:23:51 both of them apply computer science techniques
0:23:54 and information technology to get people to optimal health.
0:23:56 And that’s what we’re doing there.
0:24:00 So I think we’re big, big believers that, you know,
0:24:03 in the last 100 years, the great breakthroughs
0:24:05 and knowledge have been the breakthroughs
0:24:08 of people like Alan Turing and Claude Shannon
0:24:10 who gave us a new model of the world
0:24:12 and how to understand it.
0:24:16 And companies that build on that fundamental knowledge
0:24:18 breakthrough are what we’re about
0:24:20 and will continue to be about that.
0:24:22 – Even if some of them may ship their products in a box.
0:24:25 – Yes, the package is not a technology.
0:24:27 Let’s talk a little bit about SaaS.
0:24:27 As you’ve probably seen,
0:24:29 there’s been actually a bunch of acquisitions
0:24:31 in this space recently, but what’s left to do there?
0:24:34 So is the new platform, the salesforce.com
0:24:36 and others of the world or are there actually
0:24:37 both kind of vertical applications
0:24:39 and or are there other platforms
0:24:41 that actually might exist over time in that market?
0:24:43 – So there’s SaaS as the metaphorical
0:24:45 in the cloud version of all the stuff
0:24:49 that we had built over the previous, you know, 30, 40 years.
0:24:54 So that’s like workday, salesforce,
0:24:58 success factors, you know, the kind of big categories.
0:25:00 The thing that we believe that’s changed
0:25:02 as you go from on premise to the cloud
0:25:05 is the technology is so much easier to adopt
0:25:08 that we’re now seeing software applications
0:25:10 for things that you just would never do
0:25:13 as a software application because the cost of,
0:25:15 as we used to say in the old days, screwing it in
0:25:19 and paying the army of Accenture consultants
0:25:21 to get it going just wasn’t worth it
0:25:24 for say expense reporting, which, you know,
0:25:28 Concur, of course, built a really powerful product in that.
0:25:31 But like there was no packaged software
0:25:34 for expense reporting in the same way that there is now.
0:25:37 And I think there’s a gigantic number of categories
0:25:39 in everything that you do in business
0:25:41 that can be automated in that way.
0:25:43 In addition to that, you can scale down
0:25:45 to very, very small companies.
0:25:47 Companies below thousands of employees
0:25:49 never bought Oracle financials.
0:25:51 It would have been insane to do so,
0:25:53 but they’re absolutely buying, you know,
0:25:55 NetSuite and things like that.
0:25:59 And then beyond that, you now it becomes economical
0:26:02 and very interesting to build vertical applications
0:26:03 for industries.
0:26:06 So to build an application that revolutionizes,
0:26:09 say the real estate industry or something like that,
0:26:13 or the construction industry is becoming extremely viable
0:26:15 and not just as a niche business,
0:26:18 but as a real venture capital based kind of activity.
0:26:20 – One of the consequences that will be interesting
0:26:22 to watch play out is that historically,
0:26:24 enterprise software has been described
0:26:27 as represented by companies like Oracle SAP IBM.
0:26:29 Like that stuff was really only accessible
0:26:31 to the largest companies,
0:26:33 the top 500 or 1000 companies in a country.
0:26:36 And then in particular, only in a handful of countries,
0:26:39 those businesses, their revenue and their customer base
0:26:40 have always been dominated by, you know,
0:26:42 two or 3000 companies globally that are these,
0:26:44 you know, these giant multinational companies
0:26:45 that we’ve all heard of.
0:26:48 So big companies had this sort of inherent advantage
0:26:50 versus a lot of mid-sized small companies.
0:26:51 And then companies in the US and Western Europe
0:26:53 had this big advantage versus companies
0:26:54 in other parts of the world
0:26:56 where the companies, the large companies
0:26:58 and the large companies in the US and Western Europe
0:27:00 could just afford to make technology investments
0:27:01 that small and mid-sized companies
0:27:02 all over the world couldn’t make.
0:27:05 The sort of changes in SaaS that Ben described,
0:27:07 they lead you to an interesting conclusion
0:27:08 which is it may actually be interesting
0:27:12 for a smaller company or a company not in the US
0:27:13 or Western Europe to be able to adopt
0:27:16 the next generation of SaaS and cloud technology.
0:27:18 It’s almost like the folks who’ve been able to skip
0:27:19 landline telephones or just go straight to mobile phones.
0:27:21 You can just leapfrog the old stuff
0:27:22 ’cause you never had it
0:27:24 and you can just start using the new stuff out of the box.
0:27:26 And then the big established companies
0:27:27 might have a harder time adapting
0:27:29 ’cause they’ve made these giant investments
0:27:30 in the old systems and it’s hard to just jump
0:27:31 to the new thing.
0:27:34 And so there may be a power shift happening
0:27:36 from on the one hand large companies
0:27:37 to small and medium companies
0:27:40 that can now more aggressively adopt technology faster.
0:27:42 And then from companies in the US and Western Europe
0:27:43 to companies all over the world
0:27:45 that can also do the exact same thing.
0:27:48 And so at the very least a leveling of the playing field
0:27:49 and possibly even a national shift in balance
0:27:51 where small and mid-sized companies all over the world
0:27:53 may all of a sudden get a lot more competitive.
0:27:55 – So you’ve got kind of democratization at one point
0:27:56 and then to your point,
0:27:58 there’s one version of internationalization
0:28:00 which is adoption across international communities.
0:28:02 So how do you think about then
0:28:03 the other aspect of internationalization
0:28:04 which is company formation?
0:28:07 Should we then expect to see more new company formation
0:28:10 outside the US partly as a result of some of these trends
0:28:14 and why won’t we see or will we see 50 Silicon Valley’s
0:28:15 over the next 20, 30, 40 years
0:28:17 and how do you all think about what the strategy
0:28:19 should be vis-a-vis those opportunities?
0:28:21 – That would be probably the most amazing thing
0:28:23 for the world that could happen
0:28:25 in the realm of business and economics.
0:28:28 So we’re hoping for it
0:28:31 and certainly building kind of help trying
0:28:33 to build technologies that would facilitate it.
0:28:37 And I think the world has never been kind of more ripe
0:28:39 for that kind of thing.
0:28:40 Having said that, look,
0:28:42 there are real network effects,
0:28:44 geographical network effects
0:28:46 and Silicon Valley obviously has the biggest one
0:28:50 in technology and you always have to keep in mind
0:28:52 and this is something that gets lost
0:28:55 is there are no local technology companies, right?
0:28:58 There’s nobody who sells, you know,
0:29:01 internet search to Wyoming.
0:29:02 That’s not like a viable thing.
0:29:07 So when you’re competing globally, it does matter.
0:29:08 You know, do you have the best people?
0:29:09 Do you have the best executives?
0:29:10 Do you have the best engineers?
0:29:12 Do you have access to money?
0:29:15 Like all these things become real competitive things.
0:29:17 So we still are believers in Silicon Valley
0:29:20 and we’re very hopeful that the rest of the world grows
0:29:23 and that we can participate in that as well,
0:29:25 but that’s TBD.
0:29:27 – This is an interesting macro kind of thing
0:29:28 that’s happening in a lot of the, you know,
0:29:30 one of the really kind of negative stories
0:29:32 is that there’s basically the world is starved
0:29:34 for innovation and growth.
0:29:36 One of the data points you point to on that
0:29:39 is there’s now $10 trillion of money
0:29:41 in being held in government bonds,
0:29:42 governments all over the world,
0:29:44 trading at what’s called negative yield.
0:29:46 This is literally like the equivalent of a savings account
0:29:48 where instead of the bank paying you interest,
0:29:51 you have to pay the bank interest to hold your money.
0:29:53 And so there’s literally $10 trillion of capital parked
0:29:56 around the world that is actually losing money
0:29:58 as it sits there, which means people cannot find
0:30:00 enough productive places to deploy capital.
0:30:02 The conventional view, if you just pick up the newspaper
0:30:03 and read the economic section,
0:30:04 the horrible this is and how it means
0:30:06 the world is start for growth.
0:30:10 The optimistic side of it is there’s $10 trillion of money
0:30:12 sitting on the sidelines waiting for something productive
0:30:13 to be done with it.
0:30:15 What could be productively done with it?
0:30:17 New kinds of healthcare, new kinds of education,
0:30:20 new kinds of consumer products, new kinds of media,
0:30:21 new kinds of art, new kinds of science,
0:30:24 new kinds of self-driving cars, new kinds of housing,
0:30:27 all of these things that need to be done all over the world.
0:30:29 And so the world has never been more ripe
0:30:32 for a very large wave of innovation
0:30:34 that would actually be quite easy to finance.
0:30:36 A lot of the times you just can’t get things done
0:30:37 ’cause you don’t have money.
0:30:38 That’s just kind of the constant state of the world
0:30:39 for a very long time.
0:30:41 And now ironically, we live in a world
0:30:42 where the opposite is true.
0:30:44 There’s actually, quote unquote, too much money.
0:30:46 And more money than ideas.
0:30:46 More money than ideas.
0:30:48 Which really can’t be true.
0:30:49 It can’t be true, right?
0:30:50 Unlock the ideas.
0:30:52 Human creativity is boundless.
0:30:54 And so if you can get more smart people
0:30:55 around the world educated
0:30:57 and with the skills required to do these things
0:30:58 and if you can get them in environments,
0:31:00 either create new environments to do that
0:31:01 or figure out how to get more of the people
0:31:03 from other places in environments
0:31:04 where they can do new things,
0:31:06 we could do all kinds of new things globally.
0:31:08 And it’s something that we hope to contribute to
0:31:10 but I think is a very big opportunity for the world.
0:31:11 So do you think we’re getting to the point
0:31:13 where it’s kind of geopolitical risk
0:31:16 and rule of law issues that limit adoption
0:31:17 or deployment of some of these new technologies
0:31:19 in other countries outside the US?
0:31:23 It sounds like it’s less so technological advancement.
0:31:25 Well, I would say there’s bad news and good news.
0:31:27 So the bad news is we frequently have delegations
0:31:29 of folks coming into the Valley from all over the US
0:31:31 and all over the world.
0:31:32 And they basically come in
0:31:33 and it’s economic delegations of different kinds
0:31:35 or politicians or whatever.
0:31:36 And they come in and they’re like, okay,
0:31:38 what can we do to have our own Silicon Valley?
0:31:39 And then you kind of sit down with them
0:31:42 and you kind of go through ABCDEF, all these things.
0:31:44 Well, you don’t want rule of law,
0:31:46 you want ease of migration, you want ease of trade,
0:31:49 you want deep investments in scientific research,
0:31:51 you want no non-competes,
0:31:52 you want fluid labor laws to let companies
0:31:54 very easily both hire and fire.
0:31:55 You want the ability for entrepreneurs
0:31:57 to be able to start companies very quickly.
0:31:59 You want bankruptcy laws that make it very easy
0:32:01 to move on and start another company.
0:32:04 And at some point, the visitors get this stricken look
0:32:05 on their face and they’re like, well,
0:32:07 at the end of it, they’re like, okay,
0:32:08 but what if we want Silicon Valley
0:32:10 but we can’t do any of those things.
0:32:12 And so that’s the bad news.
0:32:14 – Well, they can hire Donald Trump to run their country.
0:32:15 – It’s sad that’s ironic that we have this guy running
0:32:17 for president who would seriously move us backwards
0:32:19 on a number of those topics.
0:32:21 So even we struggle with these things, right?
0:32:23 Like I would argue the formula is fairly well known.
0:32:25 It’s just people do not want to apply it
0:32:27 for reasons that have a lot to do with politics
0:32:29 and have a lot to do with other issues.
0:32:30 The good news is it can be done.
0:32:32 And then the other good news is it is happening.
0:32:35 And there are very, very, very exciting things happening
0:32:36 throughout much of the world.
0:32:38 They’re very active now startup scenes
0:32:41 all through South America, Brazil, Argentina, Buenos Aires.
0:32:42 Amazing things are happening in India.
0:32:44 There’s all kinds of startup activity
0:32:45 throughout the Middle East.
0:32:47 There’s startup activity now throughout Africa.
0:32:50 There’s, obviously China’s been a gigantic success story.
0:32:52 Korea has all kinds of interesting things happening.
0:32:55 So there are lots and lots of extremely positive
0:32:57 early indications of what’s possible
0:32:59 in many places all over the world.
0:33:00 That said, there are very big political questions
0:33:02 about whether or not those founders
0:33:03 are gonna be able to operate an environment
0:33:05 that’s really gonna let them succeed to the level
0:33:06 that they should be capable of doing.
0:33:08 The big reason that we raise the fund
0:33:12 and are excited about the fund is it is a backing
0:33:14 of our core belief system here,
0:33:18 which is we believe in the creativity
0:33:21 and genius and intelligence of human beings
0:33:23 and the entrepreneurs that we see
0:33:26 and come to Silicon Valley and around the world.
0:33:29 And we believe that these people absolutely have
0:33:32 the ability to change things and are changing things.
0:33:36 And there’s plenty of room to improve the world.
0:33:38 And there’s plenty of ideas to do so.
0:33:42 And that’s really what we’re about with fund five.
0:33:44 So let’s talk a little bit about kind of company building
0:33:45 and founders in particular.
0:33:49 So, undoubtedly you had a very distinct view
0:33:50 of what types of founders you wanted to back
0:33:53 when you started the firm now seven years ago.
0:33:54 How has that evolved?
0:33:56 If at all over time, what has changed either
0:33:57 in terms of the types of founders you see
0:33:59 or the types of qualities you see
0:34:01 that actually make founders successful
0:34:02 that’s caused you to either augment
0:34:05 or rethink some of the initial foundations for the firm.
0:34:08 You know, I think a lot of the things,
0:34:10 we had this great advantage when we started the firm
0:34:13 that we ourselves were founders.
0:34:16 I think that we’ve probably gotten,
0:34:18 I would say more risk tolerant
0:34:20 in our view of founders over time,
0:34:21 even though sometimes the risk–
0:34:22 – What do you mean by that?
0:34:24 What do you mean by getting risk more risk tolerant?
0:34:25 – Well, we have this thing we say at the firm,
0:34:27 which is we’re much more interested
0:34:29 in the magnitude of the strength
0:34:31 than the number of the weaknesses.
0:34:33 We always believe that intellectually,
0:34:36 I think that some of the number of weaknesses
0:34:39 were fairly terrifying early on,
0:34:43 just ’cause you do have a lot of founders
0:34:46 with a very small amount of experience these days,
0:34:47 which is also part of their strength
0:34:50 in that it’s hard to rewrite the world
0:34:52 if you’re too steeped in the world.
0:34:56 And so I think over time, we’ve kind of doubled down on that.
0:34:59 And really, the founders who have figured out
0:35:02 something really important or who are true geniuses
0:35:07 or have will to power that we can’t even contain in the room,
0:35:10 when they bring those things to the table,
0:35:11 whatever is wrong with them,
0:35:14 we tend to overlook and work with them on that.
0:35:16 And if they’re strong enough in those areas,
0:35:18 the really interesting thing for us has been,
0:35:21 those weaknesses do go away pretty quickly.
0:35:23 And that’s probably the biggest learning is,
0:35:25 I’d say we went in thinking that,
0:35:28 but we’ve gotten even more extreme
0:35:31 in our commitment to that kind of philosophy.
0:35:33 So almost in financial terms,
0:35:35 you’re buying volatility to a certain extent.
0:35:38 – Well, I think buying volatility in the sense
0:35:40 that we’re buying people have world-class strengths
0:35:42 where we care about them,
0:35:45 and regardless of whatever else.
0:35:46 There’s volatility in that,
0:35:49 but you can have a different kind of volatility.
0:35:51 You can have people who have gigantic weaknesses
0:35:54 that are spectacular without having the strengths.
0:35:57 And we’re not trying to buy that kind of volatility.
0:35:58 – How do you know, though,
0:35:59 that they’re going to be the ones
0:36:01 to actually build the companies at scale?
0:36:03 Because there seems to be this inflection point
0:36:05 where the very thing that makes you a founder
0:36:07 that’s going to punch through this tough industry
0:36:10 is also the thing that’s pretty much going to hold you back
0:36:11 from really building your company
0:36:13 in a really meaningful way
0:36:15 if you think you can do everything your way.
0:36:17 And there seems to be an inherent contradiction in that.
0:36:19 – I think that that would be right
0:36:21 if founders did not evolve.
0:36:23 So I think-
0:36:23 – And some don’t.
0:36:24 – And some don’t.
0:36:26 Like some don’t and they get stuck
0:36:28 and they can’t get past that point.
0:36:32 But it’s a real common characteristic in great founders
0:36:34 that they want to know absolutely everything
0:36:35 about the company and how it works
0:36:38 and every knob and every button.
0:36:43 And they really would like, have a strong desire
0:36:45 to actually be able to do every job
0:36:47 in the company themselves if it came down to it.
0:36:51 But those kind of founders also have great ambition
0:36:54 and it’s very logical and easy to understand
0:36:58 that there’s never actually been a gigantic long,
0:36:59 really important long lasting company
0:37:02 that had like five employees that those just don’t exist.
0:37:05 And so if you’re gonna have to have a bigger company
0:37:10 than that, you have to think about the company,
0:37:12 not only from the scale perspective,
0:37:15 but from the perspective of the people working there.
0:37:18 And how are you gonna get great people to work with you
0:37:21 if you’re literally making a redecision in the company?
0:37:22 And I think that, look,
0:37:24 not every founder can let go of that.
0:37:26 And sometimes it’s a psychological flaw
0:37:29 rather than a desire for greatness.
0:37:32 And if it’s a psychological flaw that they can’t overcome,
0:37:36 then it’s just like any flaw that any of us have.
0:37:37 We can’t stop eating ice cream or whatever.
0:37:40 And there’s nothing we can do at that point,
0:37:42 like we can give them the logical explanation,
0:37:44 but they’ve got to fix themselves.
0:37:46 One of the things that we’ve seen even in the short time
0:37:49 that the firm has been in business is companies
0:37:51 staying private longer, taking longer times to IPO.
0:37:53 What are some of the implications of that
0:37:54 on the company building process?
0:37:57 How do you kind of balance that new reality?
0:37:59 If it is a new reality around how companies stay private
0:38:01 with how you think about building management teams
0:38:03 and other issues around the company.
0:38:05 – Yes, I think this gets back to probably
0:38:09 one of the more neglected parts of company building,
0:38:10 which is like, what is the company culture?
0:38:11 What does it believe?
0:38:13 What’s our way of doing things?
0:38:14 When we come to work every day,
0:38:16 what does quality mean?
0:38:18 How do we prosecute an opportunity?
0:38:23 And the kind of philosophy onboarding,
0:38:25 training into that culture and so forth.
0:38:27 And so you kind of have to develop a philosophy
0:38:29 like what kind of employees do you want?
0:38:31 How do you want them to behave when they get there?
0:38:32 How do people contribute?
0:38:34 – As we’re getting close to wrapping up here,
0:38:36 what would be one piece of advice that you might give
0:38:37 either from a management perspective,
0:38:39 from a go-to-market perspective?
0:38:40 What would be a takeaway for people
0:38:42 listening to this podcast?
0:38:44 – From a management perspective,
0:38:46 I think the most common mistake that founders make
0:38:50 is they make decisions based on management decisions
0:38:52 and organizational design decisions
0:38:56 based on very kind of proximate perspective.
0:38:57 So what’s my perspective?
0:39:00 What’s a person I’m talking to perspective?
0:39:03 What’s my HR person’s perspective?
0:39:05 Without like taking the time to go,
0:39:07 okay, like how does everybody in the entire company
0:39:08 see this decision?
0:39:10 And how will they see it once it’s made?
0:39:13 Is it motivating people in the way that I think it will?
0:39:16 And let’s look past the person I’m talking to
0:39:18 feeling good about what I’m saying
0:39:20 and really make this for the long-term health
0:39:22 of the organization.
0:39:24 – Single biggest strategic piece of advice
0:39:25 we just see across all of our companies
0:39:27 is literally people just need to raise prices.
0:39:29 People need to charge more for their products and services.
0:39:31 The good news is you have all these new founders
0:39:33 with many different backgrounds who have come in.
0:39:35 Many of them have never run companies before
0:39:36 or run sales forces before.
0:39:38 And so they have these extremely sophisticated views
0:39:40 on things like products and design and engineering.
0:39:42 And then I think in some cases,
0:39:45 relatively naive views on how to actually prosecute
0:39:47 a campaign to be able to get the world to use your product.
0:39:50 And so the temptation we see from many founders
0:39:53 is to have a one-dimensional view of what I call
0:39:54 a one-dimensional view of the relationship
0:39:56 between price and volume.
0:39:58 Which is if I price my product cheap,
0:39:59 then I sell more of it.
0:40:01 ‘Cause the assumption is just that people just make
0:40:03 purchase decisions based on cost.
0:40:05 And so you drive down prices, you drive up volume.
0:40:07 And by the way, a lot of the history of the tech industry,
0:40:08 like the chip industry is,
0:40:10 drive down prices, drive up volume.
0:40:14 But a lot of startups really suffer from having that view.
0:40:16 Instead, we encourage companies to adopt
0:40:17 what I call kind of the two-dimensional view,
0:40:19 which is the advantage of raising prices.
0:40:21 Actually, there’s a couple of advantages.
0:40:22 So one big advantage, if you raise prices,
0:40:25 you can afford a bigger sales and marketing effort.
0:40:28 A lot of companies have prices that are actually too low
0:40:30 to be able to mount the kind of sales and marketing campaign
0:40:32 required to get people to ever actually buy the product.
0:40:34 And I call this the too hungry to eat problem, right?
0:40:38 I’m not selling enough, but I’m not selling enough
0:40:39 ’cause I don’t have the sales and marketing coverage required
0:40:41 to actually get the product out there.
0:40:43 And I don’t have that ’cause I’m charging too little.
0:40:44 And as a consequence, I’m not selling any,
0:40:46 despite my low prices.
0:40:47 The other really interesting thing
0:40:49 is that for a very large number of products,
0:40:50 it turns out if you charge higher prices,
0:40:52 the customers take the product more seriously.
0:40:53 They impute more value into it
0:40:55 when they’re making their purchase decision.
0:40:56 And then once they’ve purchased,
0:40:57 they’ve made a bigger commitment to it.
0:41:00 And in particular, anybody selling anything to businesses,
0:41:02 businesses will take something that they had to pay
0:41:03 a lot of money for a lot more seriously
0:41:05 than something that they didn’t have to pay
0:41:06 very much money for.
0:41:08 So you can get a much higher level of engagement
0:41:10 and stickiness and actual use of your product
0:41:11 if you charge more.
0:41:12 – Going through this,
0:41:13 this definitely has felt like swimming upstream
0:41:14 for the last several years.
0:41:16 We see some glimmers that more folks
0:41:17 are starting to figure this out.
0:41:19 – Okay, well, that’s all we have time for.
0:41:20 I think this is the first time
0:41:22 I’ve actually had all you guys together on the podcast
0:41:24 since we did our fifth anniversary podcast
0:41:25 a couple of years ago.
0:41:26 Kind of amazing how much has changed
0:41:28 even in that short amount of time.
0:41:29 So thank you.
0:41:30 Thanks everyone.

0:00:03 The content here is for informational purposes only,
0:00:05 should not be taken as legal business tax
0:00:06 or investment advice,
0:00:09 or be used to evaluate any investment or security
0:00:11 and is not directed at any investors
0:00:14 or potential investors in any A16Z fund.
0:00:18 For more details, please see a16z.com/disclosures.
0:00:22 – Hi everyone, welcome to the A6NZ podcast, I’m Sonal.
0:00:25 So this week to continue our 10-year anniversary series
0:00:27 since the founding of A6NZ,
0:00:29 we’re actually resurfacing some of our previous episodes
0:00:32 featuring founders Mark Andreessen and Ben Horwitz.
0:00:34 If you haven’t heard our latest episode
0:00:35 with Stuart Butterfield turning the tables
0:00:37 as the entrepreneur interviewing them,
0:00:40 please do check that out and other episodes in this series
0:00:41 that we’ve been running all week
0:00:45 on our website at a6nz.com/10.
0:00:48 But this episode was actually recorded in 2014
0:00:50 on the five-year anniversary of the firm
0:00:52 and features Michael Copeland interviewing Ben and Mark
0:00:54 about disruption theory,
0:00:57 as well as key traits of entrepreneurs.
0:00:59 – Disruption theory has been in the news of late
0:01:01 as it relates to Clayton Christiansen,
0:01:02 you know, the master of this.
0:01:04 And I just wanna ask you guys not so much
0:01:07 about the criticism of him,
0:01:10 but from where you set that theory
0:01:13 and his thinking kind of galvanized itself into a book
0:01:17 in 1997, you know, do you build companies differently today?
0:01:22 Does, do those theories still hold water or what’s changed?
0:01:26 – Yeah, so I think his book was actually quite brilliant.
0:01:28 It’s funny that it’s coming under criticism now
0:01:30 after he’s been proving like completely right
0:01:32 and the general idea that he had.
0:01:35 It’s kind of, it actually reminds me of the creationist attacks
0:01:39 on evolution where like, yes, from a,
0:01:41 it’s like intellectualism at its worst, right?
0:01:43 It’s like, oh, here’s something wrong
0:01:45 with Darwin’s original theory.
0:01:48 And it’s like, okay, now we’ve based all of biology on it.
0:01:49 We’ve made tremendous progress.
0:01:50 Like how about that?
0:01:52 And this is kind of like, you know,
0:01:55 I don’t believe in electricity, you know?
0:01:59 And you know, this is kind of the kind of business version
0:02:03 of that where, you know, he developed the theory,
0:02:05 all of us in high tech.
0:02:08 And it was an amazing business book at the time
0:02:11 because it explained a phenomenon that, you know,
0:02:12 and now is kind of obvious,
0:02:16 but in 1997 was tricky, which is why does there,
0:02:18 really why do there need to be new companies?
0:02:19 – Right.
0:02:22 – And what’s happened when we just got through talking about
0:02:24 like there’s an explosion of new companies
0:02:26 and these companies aren’t trivial,
0:02:28 they’re becoming very, very important company,
0:02:33 you know, companies like Google and Facebook and so forth.
0:02:37 And so he’s kind of been proven right.
0:02:39 And then not only has he been proven right
0:02:40 on kind of the large level,
0:02:45 but the mechanics that prevent the kind of incumbents
0:02:49 from innovating at the same rate as the new company
0:02:51 are still completely in effect.
0:02:56 And we, you know, use his models all the time
0:02:58 in our thinking and our analysis.
0:03:03 And no doubt there are probably some minor problems
0:03:07 with examples he’s used or like the way he worded it
0:03:10 or what have you, but like basically he was right.
0:03:11 – Yeah, I would also say two things.
0:03:14 Let’s say one is we actually use this theory basically
0:03:15 to tell us what not to invest in.
0:03:16 – Yes, right.
0:03:17 – As well as what to invest in.
0:03:18 – So how so?
0:03:20 – Well, so for example, we have this basically
0:03:22 this theory that basically it’s very, very dangerous.
0:03:24 So one of the great things about our industry
0:03:26 about venture capital is you get to do these things
0:03:28 that basically disrupt sort of the big
0:03:30 establishing incumbent companies.
0:03:32 Conversely, a very dangerous thing to do is to attack
0:03:35 companies that we, our internal term is the new incumbents.
0:03:37 And so it’s one thing to like go attack, you know,
0:03:39 a tech company that’s been in business for 50 years
0:03:41 that’s on its six C or something like that.
0:03:42 It’s another thing to go attack Google
0:03:43 being run by Larry Page.
0:03:44 – Right.
0:03:46 – Because Google being, you know, Larry Page
0:03:48 is like fully aware of the theory of disruption
0:03:49 and in full command of his company.
0:03:51 And if you like, he sees a disruptive threat coming.
0:03:53 He is quite capable of doing the things to head it off
0:03:55 that a, you know, fourth generation professional CEO
0:03:56 might not be able to do.
0:03:59 So anyway, so that was one thing I want to say.
0:04:01 The other thing I want to say is disruption.
0:04:02 It’s a, I agree with Ben.
0:04:04 It’s funny that this is a topic now,
0:04:05 but since it is, it’s worth talking about
0:04:09 which is the term disruption by its very nature,
0:04:12 the term itself has negative connotations, right?
0:04:13 It’s disruption seems like
0:04:15 it’s one step away from destruction.
0:04:17 And so it gets, it’s got this kind of,
0:04:19 you see it in this kind of popular kind of conception
0:04:21 that there’s something bad about it.
0:04:23 The actual way that Christensen used the term
0:04:25 was actually in a very sort of applied way
0:04:28 in a very specific circumstance in business.
0:04:31 And, and actually in a very positive way,
0:04:33 which is basically he described as a way
0:04:34 that progress happens, right?
0:04:37 So progress doesn’t happen by basically old companies
0:04:38 like deciding to do new things.
0:04:39 Company, progress happens
0:04:41 because new companies decided to do new things.
0:04:43 And then disruption is the process
0:04:44 by which the new things are able to take over
0:04:45 from the old things.
0:04:47 If you decide you don’t like disruption,
0:04:49 what you’re basically saying is you don’t like new things,
0:04:49 right?
0:04:51 It’s basically to be against disruption
0:04:53 is to basically be pro the status quo.
0:04:54 And pro the status quo means the way,
0:04:55 however the world is today,
0:04:58 like that’s it, like that’s all we’re gonna have.
0:04:59 Like the way things work today,
0:05:01 this is as good as it’s ever gonna get.
0:05:02 The disruption argument is no, no, no, no, no, no,
0:05:04 things can become much better.
0:05:06 Products can become much better.
0:05:07 Businesses can become much better.
0:05:09 Opportunities for people can become much better.
0:05:11 And so it’s a, it’s a negatively connotated term
0:05:13 that has very positive implications.
0:05:15 And I think that that’s really at least in the last couple
0:05:17 of years that’s been lost in a lot of the commentary.
0:05:19 – You mentioned Google and one of the things
0:05:20 that we’ve seen, you know,
0:05:22 through the technology industry’s history
0:05:25 is that it’s very, very hard to disrupt yourself
0:05:27 and kind of make a transition from one thing to another.
0:05:31 IBM, maybe the only company that’s done it.
0:05:34 Google, you know, they’re trying everything.
0:05:36 You know, and Facebook is trying everything.
0:05:39 And do these companies somehow change the rules?
0:05:42 Or is it the same rules applying and, you know,
0:05:45 disruption theory catches up with them in 50 years maybe.
0:05:48 – So I think you’re, I think kind of have to break
0:05:51 that back apart and go back to what Mark said.
0:05:53 I think that people often think of big companies
0:05:55 can’t innovate, little companies can,
0:05:59 but the real truth is new companies can innovate
0:06:03 and companies that are so old that the original inventors
0:06:06 are gone, have a lot of trouble doing it.
0:06:10 And so if you go back to HP or IBM
0:06:13 or any of these companies, when the founder,
0:06:15 when Thomas Watson was running the company,
0:06:17 when Dave Packard was running the company,
0:06:20 they didn’t have any trouble doing new things.
0:06:22 And they did a phenomenal, I mean, HP in particular
0:06:24 did like a crazy number of new things,
0:06:29 just amazing and in retrospect, really phenomenal.
0:06:30 And if Mark Zuckerberg’s running the company
0:06:32 or Larry Page is running the company,
0:06:33 you know, that’s not an old company.
0:06:34 That’s a new company.
0:06:39 And as innovators, they, you know, we believe
0:06:43 and this gets back to why we don’t attack them
0:06:46 because they’ll attack right back and very effectively.
0:06:48 You know, they’re going to be able to do new things.
0:06:50 And like sometimes that will mean
0:06:52 bringing in new talent through acquisition
0:06:55 or new technologies through acquisition,
0:06:59 but they’re going to be able to think about the problem
0:07:02 through a lens that is not the business they’re in.
0:07:05 And that’s kind of, this is the amazing thing
0:07:08 that Clayton Christiansen laid out was that, you know,
0:07:10 if you’re like, if you’re an old company
0:07:12 run by professional managers,
0:07:13 you’re really good at studying
0:07:15 and optimizing the business you’re in.
0:07:17 And so if there’s a new business that comes along
0:07:22 that doesn’t, is inconsistent with that, you get stuck.
0:07:23 But if you’re Mark Zuckerberg
0:07:26 who created a business from nothing,
0:07:27 then you have a very different view of the world.
0:07:31 And it’s not like, okay, how do I optimize the business
0:07:32 that I’m in?
0:07:33 It’s like, well, how do I get another business
0:07:34 that’s like Facebook?
0:07:36 That’s more the way you think about it.
0:07:37 – The other thing is the fact that Christiansen
0:07:38 was able to articulate this in a theory
0:07:40 that’s so clear and put in the book is,
0:07:43 I think that like the best professional CEOs
0:07:45 in the tech industry today, like now understand this
0:07:46 in a way that maybe their predecessors
0:07:48 10 or 20 years ago didn’t understand it.
0:07:49 So I’ll just give you two examples
0:07:51 of people I work with, John Donahoe at eBay.
0:07:53 Like when mobile came along, you know,
0:07:55 sort of classical professional CEOs,
0:07:56 when mobile comes along, you know, would look at it
0:07:58 and say, well, I’ve got this great business on the web.
0:08:01 If I move to mobile, it may or may not work as well.
0:08:03 And so maybe I don’t want to try to make the move.
0:08:05 Maybe I want to stay on the web and reinforce the web
0:08:07 and like not take the risk of quote disrupting myself
0:08:09 by making the jump to mobile.
0:08:11 But since John understands disruption theory
0:08:13 and it’s been like articulated and explained
0:08:15 in a way that makes sense, you know,
0:08:17 he was able to be based on a phenomenally successful job.
0:08:19 He went full throttle into mobile
0:08:21 and they made the jump and they’ve done it very well.
0:08:23 Meg Whitby doing the same thing with this.
0:08:25 I just, one example is this project Moonshot
0:08:27 which is these cartridge based servers.
0:08:28 – At HP, right?
0:08:31 – At HP that are a direct attack
0:08:32 on the existing blade server business.
0:08:33 And the blade server business at HP
0:08:35 is a very, very big and profitable business.
0:08:37 And HP is basically self disrupting
0:08:39 with this new kind of cartridge based server.
0:08:41 And so again, and when you have the discussion,
0:08:43 you know, HP board meeting and you have the discussion,
0:08:45 you’re like, okay, why are we taking the risk
0:08:47 of damaging this big existing profitable business
0:08:48 by doing this new thing?
0:08:50 The answer is because it’s the right thing to do
0:08:51 according to disruption theory.
0:08:54 Like it is, like there is a logical framework.
0:08:56 And again, think about what’s happening
0:08:58 which is something new is happening.
0:08:59 Progress is happening, right?
0:09:01 This is now the reason and the motivation
0:09:02 and the explanation and the justification
0:09:03 to be able to make progress.
0:09:06 So it’s an incredibly powerful positive thing.
0:09:09 – Let’s get to entrepreneurs and entrepreneurship.
0:09:11 You guys founded the firm.
0:09:15 In part, I’ve been told because you wished
0:09:18 you’d been told or helped in certain ways.
0:09:21 What’s one thing both of you wish you knew
0:09:25 or someone had told you as entrepreneurs?
0:09:28 – Well, that presumes we would have listened.
0:09:34 You know, there’s just so much that we did not know
0:09:36 going through it the first time.
0:09:37 And one of the great things
0:09:39 about the entrepreneurial experience
0:09:42 is it’s just an amazing learning curve
0:09:46 about everything from markets to organizational structures
0:09:48 to compensation to everything.
0:09:54 But probably one of the most challenging things
0:09:55 to learn while you’re out there
0:10:02 is kind of how macroeconomics impact markets
0:10:06 and particularly how they,
0:10:11 how private funding can change very, very rapidly.
0:10:14 You know, when we were, you know, particularly,
0:10:17 and this wasn’t as a harsh lesson at Netscape,
0:10:20 but at Opsware and LoudCloud,
0:10:23 it was like incredibly difficult for us
0:10:24 to go from the funding environment
0:10:28 where basically had the highest multiples
0:10:30 in the history of anything
0:10:32 to there was no money available, period.
0:10:33 I mean, like that was,
0:10:36 it was the most dramatic fall imaginable
0:10:38 from the highest of highs to the lowest of lows.
0:10:42 And, you know, to have the NASDAQ fall over 80%
0:10:46 and that not being, you know, that’s NASDAQ,
0:10:48 that’s not tech, tech fell 95%.
0:10:50 It’s just like not something you could even imagine
0:10:51 or get your head around.
0:10:55 So I wish, you know, like I wish we would have known that.
0:10:58 I wish, I don’t know if we would have believed anybody
0:10:59 if they had told us that,
0:11:03 but that would have probably made it a little less painful
0:11:05 if we had any idea how bad it could be.
0:11:07 – It would have made a worse book.
0:11:09 I’ll tell you that, that you wrote, but still.
0:11:10 – Yeah, yeah.
0:11:13 – On the other side of the table,
0:11:17 what do you want more or less from entrepreneurs,
0:11:19 more of or less of from entrepreneurs?
0:11:23 – Yeah, well, you know, it’s very different
0:11:25 across different businesses,
0:11:27 but like the one thing
0:11:30 that would probably be nice if there was less of
0:11:33 that’s pretty consistent is it’d be nice
0:11:36 if it wasn’t so important to entrepreneurs
0:11:40 what their peers valuations were.
0:11:44 Like that, that is probably the most meaningless thing
0:11:48 to focus your mind on as an entrepreneur imaginable.
0:11:50 It’s just like irrelevant.
0:11:53 – You don’t have anything else to base your value on, do you?
0:11:56 – No, no, no, that’s, you go ahead.
0:11:59 – Yeah, so it’s not actually,
0:12:02 you know, your company is your company,
0:12:03 their company is their company.
0:12:06 You’re looking at the price they got,
0:12:09 not any of the business metrics that they have
0:12:11 or like how the company is going.
0:12:13 So you’re not actually basing your valuation
0:12:15 on anything in that sense.
0:12:19 And there’s better data to be gotten for sure.
0:12:21 Like, you know, we have better data.
0:12:24 We can talk to them about all the kind of valuations
0:12:26 based on actual revenue and so forth,
0:12:28 as opposed to the person they went to school with
0:12:31 or the person they worked at their last company with.
0:12:33 And, but people get very wrapped around the axle on that
0:12:37 because there’s, you know, it’s kind of the thing
0:12:38 that Peter Thiel talks about,
0:12:40 whereas competition is actually like really destructive.
0:12:42 And that’s like the worst kind of competition
0:12:44 ’cause it’s competition that’s irrelevant
0:12:47 to anything in life other than, you know,
0:12:50 you can go tell your friend what valuation you got.
0:12:55 And I think that it causes bad, you know,
0:12:57 errors in judgment and delays in decisions
0:13:00 that need to be made quickly and things like that.
0:13:02 So, you know, it’s just,
0:13:05 it’s one of those things where humanity gets a better view.
0:13:10 And I wouldn’t like, less of that would be good.
0:13:13 – Mark, any, anything you would offer on that?
0:13:14 – Well, the thing that the great entrepreneurs
0:13:16 all have in common, we talk about this a lot,
0:13:18 but you just see it every day is the great entrepreneurs
0:13:20 all have amazing courage.
0:13:21 And so I would say we’re blessed
0:13:23 in that the entrepreneurs we work,
0:13:24 and we select for it.
0:13:25 I mean, we try very hard to select for it,
0:13:27 but the entrepreneurs we work with that are amazing.
0:13:28 One of the things they all have in common
0:13:29 is they’re incredibly courageous,
0:13:30 but which I mean, they don’t give up.
0:13:31 They don’t, they don’t quit.
0:13:33 Like they don’t, they don’t quit.
0:13:33 They don’t flinch.
0:13:34 They don’t get demoralized.
0:13:36 They don’t get, I mean, well, actually,
0:13:37 they may get demoralized or depressed,
0:13:39 but they show up to work the next day
0:13:41 and they work their way out of whatever problem they’re in.
0:13:43 And they just keep pounding and pounding
0:13:44 and pounding and pounding.
0:13:45 And I think there’s a little bit too much
0:13:48 in the Valley right now of the pivot
0:13:50 and the lean start, you know, the lean startup
0:13:51 and the, you know, the everything’s an experiment
0:13:54 and minimum viable product and failure is good
0:13:57 and kind of all of these excuses to be able to give up
0:13:59 when things aren’t going well.
0:14:02 And I think that the great entrepreneurs through history
0:14:04 have always been the opposite kind of personality
0:14:05 and all that they’ve always been.
0:14:06 “I’m gonna make this thing work
0:14:07 hell or high water no matter what.
0:14:09 I am going to knock my way, you know, headfirst
0:14:12 through any, you know, barrier that I run into.
0:14:13 I don’t care what people say about me.
0:14:15 I don’t care what kinds of problems I have.
0:14:18 I’m gonna figure this out and I’m not gonna give up.”
0:14:20 And so I would just say we love working with people
0:14:22 who have that personality type
0:14:23 and you can never have enough of them.
0:14:25 – Elon Musk comes to mind.
0:14:26 I mean, cars and space.
0:14:28 – Yeah, so to start, think about this,
0:14:30 to start a new electric car company.
0:14:32 And by the way, think about the last car company
0:14:33 started in the United States.
0:14:34 They literally made a movie about the catastrophe
0:14:36 that resulted, which is this movie, Tucker.
0:14:39 And so if you want like a story of like a horrible business.
0:14:40 – Which went better than DeLorean.
0:14:42 – Yes, well, actually, yeah, DeLorean.
0:14:43 Well, he had the added,
0:14:44 he had the cocaine smuggling business on the side,
0:14:47 which helped cover the, to free the expenses.
0:14:49 But, you know, car companies,
0:14:52 like all the car companies in the US that are successful
0:14:53 are like, you know, from the 1910s and 1920s.
0:14:55 And so to start a new car company
0:14:56 in the electric car category,
0:14:58 when all the electric cars had failed,
0:15:01 simultaneously to start the first new private rocketry company
0:15:04 in the United States in probably 40 years
0:15:05 to go straight up against the big boys,
0:15:07 to do those at the same time.
0:15:09 And then to go through the 2008 crash.
0:15:12 And he has actually recently opened up on this of like,
0:15:13 he almost lost both companies in 2008.
0:15:15 Like they’ve almost both vaporized.
0:15:17 And then a gut through both of those
0:15:18 and have both come out the other side,
0:15:21 like just like an excreaming successes is just a,
0:15:23 it’s a spectacular performance.
0:15:26 And a huge part of it is he didn’t give up.
0:15:28 – Ben, let’s touch on your book a little bit.
0:15:29 The hard thing about hard things.
0:15:31 One thing it got great reception,
0:15:34 but you’re like, well, yeah, that sounds good for you, Ben,
0:15:36 but that was your story.
0:15:38 How can I embrace that and make that my story?
0:15:41 But, you know, was there anything in the response
0:15:45 that you wish people had pushed you harder on?
0:15:48 – Well, the things that people pushed me on
0:15:49 actually annoyed me.
0:15:52 So I, it’s hard to say that I wish about that.
0:15:55 I mean, I think that to your point though,
0:16:00 it was my story and the reason for that,
0:16:02 I mean, there was a really specific reason for that,
0:16:04 which is building these companies
0:16:07 tends to be very dynamic and very situational.
0:16:10 And so a very frustrating thing
0:16:12 about management and advice in general,
0:16:15 and particularly, you know, both in books
0:16:18 and then things that you often get from board members
0:16:22 or kind of pattern matchers as it were,
0:16:23 is that they’re giving you advice
0:16:25 and it’s based on something.
0:16:28 And that advice and what it’s based on
0:16:30 may or may not be relevant to you.
0:16:31 And if you don’t know what it is,
0:16:33 it’s very difficult to interpret it.
0:16:35 And I always found that, you know,
0:16:37 management books would give like guidance
0:16:39 and you’d be like, well, okay,
0:16:41 is that what I should be doing?
0:16:44 But I have no idea where it came from.
0:16:45 And so it’s hard to say.
0:16:48 So a lot of putting my story in was just to say,
0:16:50 look, this is why I’m telling you this.
0:16:54 And like, if your situation is completely different
0:16:56 than this, then that might be the part of the book
0:16:59 that you ignore or like, at least,
0:17:01 or maybe you can map it on to what you’re doing.
0:17:03 But I think that without knowing
0:17:05 why somebody is telling you something,
0:17:09 it’s pretty difficult to get value out of it.
0:17:10 – And on the topic of the entrepreneurial journey,
0:17:11 we have to go see a pitch.
0:17:13 – All right, that’s what you guys get paid to do.
0:17:15 So Ben and Mark, thanks so much.
0:17:16 We will do this.
0:17:18 Well, it won’t do it in five years.
0:17:19 We’ll do it much sooner than that.
0:17:20 Thank you very much.
0:17:21 – Okay, thanks, Michael. – Thanks, Michael.

0:00:03 – Hi, and welcome to the A16Z podcast.
0:00:05 I’m Amelia Salyers.
0:00:07 Today’s episode is a special one,
0:00:09 since it’s the 10th anniversary of Andreessen Horowitz,
0:00:12 which was founded in late June, 2009.
0:00:14 So we decided to turn the tables
0:00:17 by asking Stuart Butterfield, founder and CEO of Slack,
0:00:20 to interview our founders, Ben Horowitz and Mark Andreessen.
0:00:22 The three of them discussed the differences
0:00:24 between founders in 2009 and today,
0:00:28 the business model of VC in A16Z’s history,
0:00:31 and technology trends then, now, and into the future.
0:00:32 And they also throw in a few good
0:00:35 summer book and TV recommendations at the end.
0:00:36 Please note that the content here
0:00:38 is for informational purposes only,
0:00:40 should not be taken as legal, business,
0:00:41 tax, or investment advice,
0:00:44 or be used to evaluate any investment or security,
0:00:45 and is not directed at any investors
0:00:48 or potential investors in any A16Z fund.
0:00:52 Any investments or portfolio companies mentioned, referred to,
0:00:53 or described in this podcast
0:00:56 are not representative of all A16Z investments,
0:00:57 and there can be no assurance
0:00:59 that the investments will be profitable,
0:01:01 or that other investments made in the future
0:01:03 will have similar characteristics or results.
0:01:05 A list of investments made by A16Z
0:01:08 is available at a16z.com/investments.
0:01:13 For more details, please see a16z.com/disclosures.
0:01:15 – One question I wanna ask is,
0:01:18 has the nature of the entrepreneurs or the founders changed?
0:01:22 So 2009, you have raised some money, you have some LPs,
0:01:24 now you’re out there looking for ways to invest.
0:01:26 Who are you meeting, where are they coming from,
0:01:27 and what are they like?
0:01:31 – Yeah, well, you know, I really feel like in retrospect,
0:01:35 that class of 2009 entrepreneurs were some of the most
0:01:38 special ones that we’ve met in the entire history
0:01:40 of the company, and when we see that again,
0:01:42 we always say like yourself,
0:01:45 Todd McKinnon, Martine, Brian Chesky.
0:01:47 You know, the thing all of you had in common
0:01:51 is you had gone through something like just unbelievable
0:01:53 to get to the position you were to start the company.
0:01:55 You know, earned your stripes.
0:01:59 Like everybody in 2009 seemed like they pay their dues,
0:02:03 like in a pretty serious way to get into position.
0:02:05 And I think, you know, one of the great things
0:02:07 that’s happened over the last 10 years
0:02:09 is it’s just become easier to start a company.
0:02:13 But as a result, you don’t have, you know,
0:02:16 people who know exactly what it is and what that means
0:02:21 and what they’re about to face, and still wanna do it,
0:02:25 which is, you know, that’s a very special thing,
0:02:26 but that’s an unusual person.
0:02:28 And then I had two categories of entrepreneurs
0:02:30 who have really risen in the last decade.
0:02:32 So one is, you know, we, Alex, our partner,
0:02:34 Alex kind of coined this term, O2O, you know,
0:02:39 kind of B2B, B2C, now O2O, and O2O is online to offline.
0:02:40 So it’s this whole broad category.
0:02:43 It’s, you know, Airbnb and Lyft and Uber
0:02:44 and many of these postmates in DoorDash and all these,
0:02:46 it’s sort of these companies that you have an online
0:02:48 experience that culminates in something happening
0:02:49 in the real world.
0:02:53 And so those founders are much more,
0:02:55 I would say, operationally focused maybe
0:02:56 than the previous generation, right?
0:02:57 Which is, it’s not just software,
0:02:59 but even beyond that, just being software, you know,
0:03:01 those companies have a big real world, you know,
0:03:04 logistical infrastructure operations component.
0:03:06 And so that has turned out to be different kind of founder,
0:03:08 which is really interesting.
0:03:09 Almost actually even a little bit of a throwback model.
0:03:12 There, those people arguably are a little bit more like
0:03:14 the semiconductor founders from like 30 years ago.
0:03:16 They’re like, they’re harder core.
0:03:17 They’re grouchier.
0:03:18 They’re dirt under the fingernails.
0:03:21 Yeah, and they have to worry about more real world stuff.
0:03:23 Yeah, stuff can go wrong, you know, people can die.
0:03:24 Like, you know, all kinds of, you know,
0:03:25 all kinds of stuff can happen.
0:03:29 So that’s been a particularly interesting kind of rise
0:03:30 of a new kind of founder, which has been super interesting
0:03:31 to work with those people.
0:03:33 And then the other on the bio side is we’ve gotten more
0:03:35 involved in bio and in healthcare.
0:03:38 The other is the rise of the deep domain expert
0:03:40 in a science like biology.
0:03:41 So somebody might come, you know,
0:03:43 biology PhD or chemistry PhD or something
0:03:45 where, you know, 10 years ago,
0:03:48 if you met a newly minted biology PhD out of Stanford or MIT,
0:03:50 they really wouldn’t know that much about computers.
0:03:51 It was kind of an afterthought
0:03:53 and they wouldn’t really know, you know,
0:03:54 they know a little bit how to code, but not much.
0:03:57 And now you meet a newly minted biology PhD out of Stanford.
0:04:00 They basically have a dual PhD in computer science.
0:04:02 Like they basically, you know, it’s kind of the story.
0:04:04 They’ve been programming since they were little kids
0:04:06 in most cases and then they kept current.
0:04:08 And in fact, a lot of the research that they did
0:04:10 to get their bio PhD often had to do with computer science
0:04:13 and math and algorithms and machine learning, right?
0:04:14 A lot of that stuff’s happening now.
0:04:17 And so you’ve got these kind of dual discipline founders
0:04:18 for the deep science stuff.
0:04:22 So dual discipline, biology CS, mechanical engineering CS,
0:04:24 physics CS, chemistry CS.
0:04:27 And that’s a real, like that those,
0:04:28 those people are like super enticing.
0:04:30 ‘Cause those are, you know, it’s like two superpowers.
0:04:32 Like, you know, he can, you know, he or she can fly
0:04:33 and they’re vulnerable.
0:04:35 Like is a really good combination.
0:04:37 And so a lot of the companies we’re seeing on the bio side
0:04:39 and in the harder sciences is that kind of founder.
0:04:40 I think that’s new.
0:04:41 – Yeah.
0:04:44 – Or like Satoshi economics and computer science.
0:04:45 – Actually, it’s funny.
0:04:46 There’s actually a revolution happening
0:04:48 even apart from crypto in the field of economics.
0:04:51 There’s a wrote in the, in the actual academic field
0:04:52 of economics, there’s a revolution happening
0:04:54 towards what’s called empirical economics,
0:04:56 quantitative economics in which a lot of the new economists
0:04:58 who are kind of 40 and below are very focused on data
0:04:59 and machine learning.
0:05:01 And, you know, the economists that are in their fifties
0:05:02 and sixties were more inspired by physics
0:05:04 and they’re, they’re much more into formulas
0:05:05 and they’re, they’re much more abstract about what happens
0:05:06 in the real world.
0:05:08 And so there are actually, there are actually,
0:05:10 there are companies increasingly driven by, started by,
0:05:12 or, you know, new invent, new inventions being created
0:05:15 by economists with a very strong CS background.
0:05:16 – That’s true. That’s true.
0:05:18 So from the beginning, I’m not sure if this was intentional
0:05:21 or not, but certainly the positioning in the press was,
0:05:24 this was different, like you’re going to blaze a new trail,
0:05:25 kind of a different model.
0:05:28 And right out of the gate, there was the investment in Skype,
0:05:30 which was not a thing that VCs normally did.
0:05:32 How, how intentional is that?
0:05:33 – Yeah, a lot of it was intentional.
0:05:35 So there, there was a big kind of throwback element
0:05:37 to what we were doing, which was basically,
0:05:38 we wanted to work with the best founders
0:05:39 to build the most important companies.
0:05:41 And there’s, we could talk a lot about kind of the history
0:05:43 of venture and the art and science of the whole thing,
0:05:45 but there is a rich tradition there
0:05:47 that we definitely drew from.
0:05:48 And we actually, we spent a lot of,
0:05:50 we, we, to get Ben and I leading up to this,
0:05:52 spent a lot of time really digging into the history
0:05:54 of kind of where all this stuff comes from
0:05:55 and kind of what made it work across various areas.
0:05:57 And so, so we were, you know, very inspired
0:05:59 by a lot of the people who came before us.
0:06:01 And then there were a bunch of new ideas.
0:06:03 And so, you know, maybe list several of the new ideas.
0:06:05 And so one of the new ideas was just that we thought
0:06:07 that a lot of the venture firms had lost their way
0:06:09 in the sense that they had been started by founders.
0:06:10 They’d been started by founders and operators
0:06:11 who had built businesses.
0:06:13 And so you would, you know, raise money from, you know,
0:06:15 a firm and you would get, you know,
0:06:17 somebody who’d been a CEO or general manager
0:06:18 of an important business on your board
0:06:20 and they could really help you figure things out.
0:06:21 And then over the years, the, a lot of the venture firms
0:06:23 just quote unquote, professionalized
0:06:24 and they ended up with a lot of GPS
0:06:25 who didn’t have that experience.
0:06:27 And so it started to get,
0:06:28 the advice started to get more abstract
0:06:30 and maybe less helpful.
0:06:31 So that was one difference.
0:06:33 Another difference was, you know, we, we took seriously
0:06:35 the, the, the idea of building the institution.
0:06:37 And then in particular around that building a network
0:06:41 and an ecosystem and making a real long-term systematic
0:06:42 and actually very costly investment in building a,
0:06:44 you know, building a network and that had to do
0:06:46 with the fundamental staffing model of the firm.
0:06:47 And then that, you know,
0:06:49 that’s why we have all these operating functions.
0:06:51 We have all these, all these, all these professionals here.
0:06:53 And then that rippled over even into things like compensation,
0:06:56 like we get paid very differently than most, than most VCs.
0:06:57 And so that was a big difference.
0:06:58 The Skype deal you alluded to,
0:07:00 we did start at the very beginning
0:07:01 with the idea of being stage agnostic.
0:07:03 And that was probably a pretty new idea at the time.
0:07:05 And the idea there basically was
0:07:07 if the priority is to work with the best founders
0:07:09 to help build the most important companies,
0:07:11 it shouldn’t matter that much what stage the company’s at.
0:07:14 I mean, ideally you’d like to start working with people early,
0:07:15 but like, you know, you make mistakes.
0:07:17 And so you miss things.
0:07:18 And we were starting from scratch.
0:07:19 And so there were a bunch of companies
0:07:20 we thought were very impressive
0:07:21 that we wanted to get involved in,
0:07:23 you know, even though they already existed.
0:07:25 And so we kind of had this idea
0:07:26 that there should be multiple entry points
0:07:27 from an investment standpoint.
0:07:28 And then there would be things
0:07:30 that we could actually do to be able to help
0:07:32 and work with the entrepreneur.
0:07:33 And so we kind of put ourselves in business
0:07:36 from the beginning to operate across all the stages.
0:07:37 You know, now that took time to get,
0:07:39 I would say it took time for us to get good at all the stages
0:07:41 and maybe we’re still, we’re still working on it.
0:07:44 But that core fundamental idea was in there.
0:07:45 – You referenced looking back at history
0:07:50 for some context and thinking about the investment theses.
0:07:52 For us, 10 years doesn’t seem like that long ago,
0:07:55 but then we hire people who have like three or four years
0:07:56 of experience, which means that they were born
0:07:59 even in college, sometimes 10 years ago.
0:08:00 – Yeah, we experienced that a lot.
0:08:02 – Increasingly frequently, yeah.
0:08:04 – But that’s interesting ’cause that was a very
0:08:08 particular moment in history that I have vivid memories
0:08:13 of the 2008 crisis that then like almost immediately
0:08:15 after that, the idea that we were in a tech bubble
0:08:17 and kind of the whiplashing back and forth.
0:08:18 What was that like?
0:08:20 – Well, you know, it was interesting when we started
0:08:23 and we had, we of course had been through
0:08:25 the actual tech bubble.
0:08:26 – The 2000.
0:08:27 – 2001 tech bubble, yeah.
0:08:31 The, you know, the 2008 crisis was a banking crisis,
0:08:33 not a tech crisis, it was a debt crisis,
0:08:34 not an equity crisis.
0:08:37 So it was very different in nature for startups.
0:08:39 So a big advantage we had was,
0:08:43 it really did not bother us at all.
0:08:46 And so walking in, one, like everybody said,
0:08:49 you can’t possibly raise a fund in 2009,
0:08:50 you can’t raise a venture capital fund.
0:08:53 So the only two new funds were raised that year,
0:08:56 us and COSLA, and because we, you know,
0:08:57 we didn’t know any better.
0:08:59 We were like, come on, like this isn’t bad.
0:09:02 This is like not bad at all.
0:09:04 And then like the, the other thing that really helped us
0:09:07 was the just grouchiness of a lot of the other VCs.
0:09:10 I remember I’ll go unnamed venture capitalists
0:09:12 ’cause I remember it so well
0:09:14 ’cause I ran back to tell Mark, you know,
0:09:15 he asked me, he’s like, well, like,
0:09:16 what are you interested in?
0:09:17 And I was telling him about OCTA
0:09:19 and how like important it was gonna be
0:09:20 in a SaaS world and so forth.
0:09:22 And he gave me like a 30 minute lecture
0:09:24 that SaaS was a bunch of BS.
0:09:26 It was never gonna happen
0:09:29 that the only successful SaaS company was Salesforce.
0:09:32 And that was ever gonna be the only SaaS company
0:09:34 was gonna be Salesforce and all this stuff.
0:09:35 And so it was just like that kind of,
0:09:38 like everybody was just like pretty grouchy
0:09:41 and like crusty and we were, you know, new.
0:09:44 So we were excited to invest in all that stuff.
0:09:47 – So thinking back to that point,
0:09:48 I don’t remember the exact year that everyone started.
0:09:51 Obviously, my company started in 2009.
0:09:54 I think Airbnb was like maybe that same year or year later.
0:09:56 Something’s happened a couple of years earlier
0:09:58 like AWS came out in 2006.
0:10:01 iPhone and nominally in 2007 or really 2008.
0:10:03 But a lot of those things hadn’t actually picked up.
0:10:05 So certainly my recollection is
0:10:08 none of that was really visible at that time.
0:10:09 But we were at this, in retrospect,
0:10:11 was an incredible inflection point.
0:10:13 To what degree do you think that worked to your advantage?
0:10:14 ‘Cause you hear you are starting to fund,
0:10:16 people are skeptical.
0:10:18 But meanwhile there’s these massive secular trends
0:10:21 which were pretty much invisible to everyone at that time.
0:10:22 – I think the two things that are true.
0:10:24 So one is the big secular trends do drive
0:10:25 a lot of what happens in this industry.
0:10:27 And so like it is absolutely the case in the last decade
0:10:29 that mobile and cloud in particular
0:10:31 like drove just giant growth and social as well.
0:10:35 So they made a lot of people look like geniuses,
0:10:36 including a few actual geniuses.
0:10:37 So that’s also true.
0:10:39 But I think it’s also true what Ben mentioned
0:10:40 is like super important underline,
0:10:42 which is like people don’t think these things
0:10:43 are obvious in the beginning.
0:10:45 Like they’re only obvious after the fact.
0:10:46 They really don’t look that obvious in the beginning.
0:10:48 And so you like you mentioned the iPhone came out.
0:10:50 Like I remember the iPhone in 2009,
0:10:51 it was like a cool gadget,
0:10:53 but like it couldn’t hold a phone call.
0:10:55 Like that was the era in which like it wasn’t even,
0:10:57 was it on 3G at that point?
0:10:59 It was like a big- – No it wasn’t, it was pre-3G.
0:11:01 – The original iPhone was actually pre-3G, right?
0:11:02 It had like edge data connection
0:11:04 and then the 3G iPhone was a big upgrade,
0:11:05 but then you couldn’t hold a phone call on the thing.
0:11:07 And that was in the era when Steve was telling people
0:11:08 that you were holding the phone wrong.
0:11:10 – Yeah. – If it was dropping
0:11:11 phone calls, you need that.
0:11:12 – And they built that crazy room
0:11:14 to prove it to the journalists.
0:11:16 – Exactly, and then they shipped everybody the bumper, right?
0:11:19 And so it’s like, okay, to squint from that to like,
0:11:20 okay, now it’s the mobile boom of all time
0:11:22 and we’re gonna be sitting here 10 years later
0:11:23 and they’re gonna have, you know, whatever it is now,
0:11:25 a billion and a half of these things, you know,
0:11:26 in the field and it’s gonna be kind of the defining,
0:11:28 you know, device and interface for a generation.
0:11:30 Like that wasn’t super obvious.
0:11:32 And then cloud, you know, cloud, I just, you know,
0:11:36 there were lots and lots and lots of companies of that era
0:11:37 that were incredibly powerful,
0:11:40 that were in the server business or networking business
0:11:41 or storage business or software business
0:11:44 where this whole cloud thing like AWS, like it’s a toy,
0:11:46 it’s a gimmick, like it’s never gonna make any money.
0:11:47 It is really interesting.
0:11:49 There is a big leap that has to happen
0:11:51 even when they are the really big megatrends.
0:11:54 Like it’s not, I had this in the early, the internet,
0:11:56 like a lot of people in the early 90s,
0:11:57 a lot of people in the press,
0:11:58 a lot of people in the investment community,
0:11:59 a lot of entrepreneurs.
0:12:01 – Well, the entire, actually you should tell the story
0:12:02 about like when you went to raise money,
0:12:04 you and Jim went to raise money
0:12:06 from all the magazines and the newspapers.
0:12:08 – Yeah, so we started in Escaping early ’94
0:12:10 and we went out and pitched all the media companies
0:12:11 to become customers, partners, investors
0:12:13 and every single big media company.
0:12:14 And in fact, at that point, they said,
0:12:16 no, no, the future is AOL
0:12:18 because AOL pays us for our content.
0:12:20 And on the internet, we have to spend money
0:12:20 to put our per content.
0:12:21 So that’s never gonna work.
0:12:22 And of course they all knew
0:12:24 that normal people wouldn’t use the internet
0:12:25 if it didn’t have Time Magazine on it.
0:12:26 Right, ’cause Time Magazine
0:12:28 would obviously be the killer for the internet.
0:12:29 You know, and by the way, it’s like,
0:12:31 a friend of mine says that this thing is happening.
0:12:32 It’s going to fundamentally change the world
0:12:33 and people poo poo it.
0:12:35 Like that might actually be the logical response
0:12:37 because there are many new things to come along
0:12:39 where people claim it’s going to change the world.
0:12:41 And then most of those things don’t change the world.
0:12:43 And so maybe, you know, on average,
0:12:46 the correct response is no, this thing is stupid.
0:12:48 And then maybe our lot in life as founders and VCs
0:12:50 is to, you know, be the fringe element
0:12:52 that like, that bucks that.
0:12:53 – Who’s wrong 97% of the time.
0:12:54 – Right, right, exactly.
0:12:55 And by the way, you know,
0:12:56 we looked dumb a lot of the time except, you know,
0:12:59 during the times we looked, you know, really, really smart.
0:13:01 And you know, and the reality is we’re probably neither, right?
0:13:02 We’re probably neither super dumb or super smart.
0:13:04 We’re probably just willing to take the risk
0:13:05 at a time when other people aren’t.
0:13:07 – So let’s fast forward a couple of years,
0:13:10 kind of like the middle era, 2012, Facebook went public.
0:13:12 But I think that, I don’t remember what year that happened,
0:13:14 but we started talking about unicorns
0:13:16 and there was another round of this is a bubble.
0:13:17 What was that like?
0:13:20 And what do you remember about the investment?
0:13:21 Like basically the partner meetings
0:13:24 after I would leave the room
0:13:26 and the debate was happening.
0:13:29 How much did, is this too expensive factor
0:13:30 into the conversations?
0:13:33 – Yeah, you know, we have in the entire time.
0:13:35 And I can say we’re totally consistent on this.
0:13:37 And I think it’s because of our history,
0:13:41 never thought it was a bubble in our entire time
0:13:42 doing the job.
0:13:44 And I think a lot of it has to look,
0:13:48 prices of companies are always incorrect,
0:13:49 like always, always, always incorrect
0:13:52 because they’re valued on like future performance,
0:13:53 which nobody knows what that is.
0:13:55 So most people are optimistic,
0:13:56 the prices go a little higher.
0:13:58 Most people are pessimistic.
0:14:00 At that time, the prices go a little lower.
0:14:01 But to get to a bubble,
0:14:03 everybody’s got to be optimistic.
0:14:07 And that’s what happened kind of in the 99, 2000 era.
0:14:08 And like in our whole time,
0:14:10 we never signed anything close to that.
0:14:13 Like they never went anywhere,
0:14:15 anywhere like within an order of magnitude
0:14:18 to what it did in 99, 2000
0:14:20 for similar kinds of companies.
0:14:22 And so we were always, no, there’s no bubble.
0:14:23 Like, what are you talking about?
0:14:24 There’s no bubble.
0:14:27 But people want to believe there’s a bubble so badly.
0:14:30 I think 2011, I was in a debate
0:14:32 with Steve Blank and the economist,
0:14:34 and I argued it wasn’t a bubble.
0:14:38 And he argued it was in 2011 tech bubble, right?
0:14:41 And at the end of the debate, he called me
0:14:44 and he said, Ben, like, I voted for you.
0:14:45 You won.
0:14:49 The economist readers voted 78%, 22% for him.
0:14:51 ‘Cause like that’s how much people wanted to believe
0:14:52 we were in a bubble.
0:14:53 – That still do.
0:14:55 – Yeah, there’s a, I’m not sure if it’s quite
0:14:58 a cognitive bias, but I feel like there is a predisposition
0:15:00 that a lot of people have to take the cynical bet.
0:15:03 So how that seems smarter, ’cause either way,
0:15:04 there’s a payoff.
0:15:06 And the payoff, if you said that’s bullshit,
0:15:08 and then it turns out you are right,
0:15:11 seems greater to people than the opposite.
0:15:11 And also there’s a little bit
0:15:13 that you can’t prove a negative,
0:15:16 popper, the valid hypotheses and stuff like that.
0:15:19 – Plus you get the victory as the most smug person
0:15:20 in the room too.
0:15:23 – Yeah, so you’re generally betting against the cynicism
0:15:24 in your business.
0:15:26 Is that like something you can actually take advantage of?
0:15:27 Or is that something that works in your favor,
0:15:29 that cynicism?
0:15:30 – Oh, 100%.
0:15:33 In fact, like we have this thing that our friend came up
0:15:35 with, which is the East Coast, West Coast arbitrage,
0:15:38 which is anything that the people in the East Coast
0:15:41 think is ridiculous in a toy and people in the West Coast
0:15:44 think is the next big thing, that’s the thing to bet.
0:15:46 – And we said, you just keep flying back and forth.
0:15:48 – Yeah, and you find out what those things are,
0:15:50 and then you just invest all your money in that.
0:15:53 – That really suggests a question for today.
0:15:56 There are some big things happening today
0:15:57 that aren’t obvious.
0:16:00 What kind of energy do you put in to find that?
0:16:02 Are there like specialist researchers?
0:16:06 Is it just every partner’s kind of contribution?
0:16:07 How much time do you spend looking
0:16:09 for what isn’t obvious today,
0:16:11 but will be obvious in retrospect?
0:16:14 – Yeah, I think that ends up being like half the job
0:16:18 is trying to understand like,
0:16:21 what is the next big platform?
0:16:22 Where are things going?
0:16:25 What’s going to be the user interaction model
0:16:27 after the iPhone?
0:16:29 That’s a big open question right now.
0:16:31 Like what’s the next platform?
0:16:33 What is AI really going to mean?
0:16:37 Is this whole crypto thing real?
0:16:41 These are all like VR and AR, like at what point?
0:16:42 It’s hard to imagine.
0:16:44 20 years from now, it’s not working.
0:16:47 So like how many years from now will that take?
0:16:51 – Yeah, and these are the kind of fundamental questions
0:16:54 always in the venture capital business, I think.
0:16:56 – And you might add genomics, you might add CRISPR,
0:16:58 you might add synthetic biology.
0:16:59 – Absolutely.
0:17:00 – It’s three of the big new frontiers
0:17:01 on the biological front.
0:17:02 They all have that characteristic.
0:17:04 – CRISPR seems like one to me
0:17:06 that is going to have really dramatic impact.
0:17:09 Obviously there’s big moral debate to be had
0:17:11 and policy debate to be had.
0:17:13 How do you take something like that
0:17:15 and try to look for the opportunities?
0:17:17 – Yeah, so the big thing is we default into thinking,
0:17:18 okay, this is going to happen.
0:17:21 We don’t spend a lot of time on, okay, will this happen?
0:17:23 Like is this going to be a thing?
0:17:25 We try, in fact, I tried one of my things,
0:17:26 I tried it at the firm, it started very hard
0:17:28 to actually kind of prevent us from having the discussion
0:17:29 of like, okay, is this going to happen?
0:17:30 It’s more a question of like, okay,
0:17:32 let’s assume it does happen, right?
0:17:34 And so then there’s kind of two really critical questions
0:17:35 that follow from that, which is like, okay,
0:17:37 if it does happen, then where does it go?
0:17:39 And so the financial version of that question
0:17:41 is kind of how, we call how high is up,
0:17:43 which is like, okay, how big could it get, right?
0:17:45 Which is sort of a very interesting question
0:17:46 for venture capitalists, because it’s like,
0:17:47 if you make an investment in something,
0:17:49 even if it happens, it turns out to be small,
0:17:50 then it’s still not worthwhile.
0:17:51 And so you’re looking for the things
0:17:52 that could get really, really big.
0:17:53 And then you’re obviously looking for,
0:17:55 you know, you’re then looking for the founders
0:17:56 and looking for the specific ideas
0:17:57 and applications that you spend a lot of time on that.
0:17:59 The other thing you think a lot
0:18:00 about in this business is timing.
0:18:02 And so like my observation is basically,
0:18:04 basically everything happens,
0:18:05 like my entire history in this industry,
0:18:07 and at least for 25 years is basically everything
0:18:09 that people said was going to happen happened at some point,
0:18:11 up to an including online pet food delivery,
0:18:13 like it all actually happens.
0:18:15 – All the things they made jokes about
0:18:19 in all the early 2000s movies are all actually.
0:18:19 – They’re all actually happening,
0:18:21 but it’s like, okay, when is it going to happen?
0:18:23 And like, is it ready now?
0:18:24 You know, is it going to be on this cycle?
0:18:25 And then how do you bet this?
0:18:28 Like sometimes these things take three, four, five cycles,
0:18:30 right, for the founders to really figure things out
0:18:32 for the technology to kind of fall into place.
0:18:33 So it’s kind of like, what are the exploratory bets?
0:18:36 How are you kind of vetting whether the stuff is real?
0:18:37 And then there’s this kind of multi-dimensional question
0:18:39 of like, you know, kind of to your point,
0:18:40 there’s this multi-dimensional question of like,
0:18:42 okay, is the technology ready?
0:18:44 And then you got to kind of cross that with like,
0:18:45 do you think the market’s ready?
0:18:47 Like, do you think people are going to want this?
0:18:48 And then you might have to cross like,
0:18:49 in CRISPR, you might have to cross other issues
0:18:51 like regulatory issues,
0:18:52 like are the regulators going to buy into this?
0:18:55 And so, and that’s where I think you have to kind of explore
0:18:56 as you go, right?
0:18:58 Which is you have to kind of feel your way through it.
0:19:00 Like it’s often not the first company in a category
0:19:01 that ends up being the winner, right?
0:19:03 Well, this is a Peter Thielism that we quote a lot.
0:19:05 It’s not the first company that gets all the money.
0:19:06 It’s the last company in the market
0:19:07 that gets all the money, right?
0:19:08 In other words, it’s the company
0:19:09 that actually takes the market, right?
0:19:11 It ends up actually being the dominant company
0:19:13 and forecloses the opportunity for there
0:19:14 to be new startups behind it.
0:19:17 And so sometimes that’s a pioneer, sometimes it’s not.
0:19:19 And so you’re kind of having this constant discussion
0:19:21 about timing.
0:19:23 And then, at the end of all that,
0:19:25 we kind of try to park that to a certain extent
0:19:26 and just start talking to entrepreneurs
0:19:28 and figure out who is the person
0:19:30 who’s got this the most decoded,
0:19:32 how much time and effort has that person put into it?
0:19:34 How qualified are they to pursue this?
0:19:35 What’s their personality?
0:19:36 And can they build a company around it?
0:19:39 Yeah, that’s what I was gonna actually go right there
0:19:42 because knowing you, as I do, I can’t imagine it’s ever,
0:19:44 we have a thesis that this thing is going to work out,
0:19:46 now we’re going to look for the entrepreneurs
0:19:47 who are doing this thing.
0:19:50 It’s much more gonna be the confluence of who you’re meeting,
0:19:53 who you get to talk to, what people are up to,
0:19:56 and these background theses that give you the opportunities.
0:19:57 Yeah, that’s right.
0:19:58 Yeah, I think that’s right.
0:20:00 And actually, I mean, I think for a while,
0:20:04 it was always like started with the entrepreneur
0:20:06 in the early days of it, just ’cause they were allowing
0:20:08 the two of us and we couldn’t cover all the spaces
0:20:10 and enough depth to do it any other way.
0:20:14 But the other thing kind of related to that
0:20:18 is the platforms that kind of we think
0:20:19 are getting proximate from a timing standpoint
0:20:22 are the ones where like the smartest entrepreneurs
0:20:24 are all working.
0:20:27 So if we see 20 genius entrepreneurs all working on crypto,
0:20:30 that makes us pay attention, for example.
0:20:30 Right.
0:20:33 So Mark, you’ve talked about five-year cycles in tech.
0:20:36 Is that something that you think is a good way
0:20:40 to imagine what’s going on or to picture it in context?
0:20:42 So I would say there’s two big sites.
0:20:43 It’s hard to, you know, the stuff is just,
0:20:44 these are just general frameworks.
0:20:46 And so they vary a lot in practice,
0:20:47 but they’re two big general concepts.
0:20:51 And so like I would say, one is the big technology changes,
0:20:52 like the ones we’ve been talking about,
0:20:54 like they’re generational changes,
0:20:54 like they’re quite literally,
0:20:56 they’re human generational changes.
0:20:59 So it’s like the typical cycle in those is like 25 years.
0:21:02 And the reason literally is because a lot of the time,
0:21:04 you just, the people who are in positions of power
0:21:06 and decision and influence when the new thing comes out,
0:21:08 they just will not accept it.
0:21:10 They won’t accept it, they won’t adapt to it.
0:21:11 They won’t recalibrate to it.
0:21:15 And fundamentally they need to age out of the cohort
0:21:16 that has the power, right?
0:21:18 Purchasing authority and all the decision-making authority
0:21:19 and all these other things.
0:21:21 And so they, and then you need a new generation
0:21:22 that like takes the stuff seriously
0:21:23 ’cause they grew up with it, right?
0:21:25 And you need them to age into the cohort, right?
0:21:27 So it’s like, it’s literally a generational turnover.
0:21:29 And so you see these, and that’s why you get these things.
0:21:31 You’ll see these, you know, some of these new things
0:21:32 that’ll grow for 25 years.
0:21:33 And I’m convinced like a big part of it
0:21:35 is just simply that generational effect.
0:21:37 So that’s the good news is like when they work,
0:21:39 you can have literally decades of growth
0:21:43 off of enormous skepticism from day one.
0:21:46 You know, the bad news is, as you’re well aware as a founder,
0:21:48 no individual company gets 25 years, right?
0:21:49 To prove something, right?
0:21:50 (laughing)
0:21:53 Nope, in fact, something well short of that, let’s say.
0:21:57 And so like our basic mental model is a company on average
0:21:59 gets maybe five years to prove something,
0:22:00 to prove the hypothesis.
0:22:01 Like, and you can kind of like,
0:22:03 if you’re a top end founder and you’re super credible,
0:22:04 you could probably raise the seed around,
0:22:06 you know, series A, series B,
0:22:07 you can get yourself five years of runway,
0:22:09 you can get engineers to some product people
0:22:11 to sign up for that and you can prove it or not.
0:22:12 But after five years, if it’s not working,
0:22:14 like you start to have a problem and,
0:22:16 or I should say you have two problems.
0:22:18 One is you start to have a morale issue
0:22:19 where people start to lose faith
0:22:21 and they spin off and go to other things.
0:22:23 You can also end up with an architecture issue, right?
0:22:25 Which is like, even if you’re right,
0:22:26 even if it starts to happen,
0:22:28 you’re built on the prior architecture, right?
0:22:30 And so, you know, imagine being a mobile developer
0:22:33 that started in, you know, 2002, right?
0:22:36 And even if they were still around when the iPhone came out,
0:22:37 you know, they’d built their entire, you know,
0:22:38 system on brew and, you know, Java
0:22:40 and all these technologies that were now archaic.
0:22:42 And so, so you kind of have this,
0:22:45 this kind of aging in place thing that happens.
0:22:47 And so each company kind of has a five year shot.
0:22:48 So then what happens is-
0:22:49 – There are exceptions.
0:22:50 – Yes, there are particular founders
0:22:51 who can, who can, who can get through this,
0:22:53 but it does tend to be the exception.
0:22:55 And so, but, but then you think about it,
0:22:56 then there’s sort of the psychological thing
0:22:57 that happens as a consequence,
0:22:59 which is if the founder starts the company
0:23:00 in the first cycle, runs for five years
0:23:02 and it doesn’t prove the hypothesis,
0:23:03 that founder usually ends up,
0:23:06 so bitter about the whole experience
0:23:07 that they become cynical about that category
0:23:09 for the rest of their lives, right?
0:23:11 And then, and then somebody else in sort of, you know,
0:23:13 cycle to generation two, three, four does figure it out.
0:23:15 And like, you know, that, by the way,
0:23:17 I’m speaking out of also myself out of experience,
0:23:18 like talk about upset,
0:23:19 the fact that you couldn’t get it to work
0:23:22 in this other person did, it’s just like absolutely maddening.
0:23:23 And that’s just human nature.
0:23:25 The part of it that really bites the VCs
0:23:27 is the VCs do that too.
0:23:29 If you as a VC make a bet and go on a board
0:23:31 and you’re in the board meetings for five years
0:23:32 and it doesn’t work and the company shuts down
0:23:36 and then a new kid shows up, you know, three weeks later
0:23:38 and says, hey, I’ve got an idea.
0:23:40 Why don’t we do that, right?
0:23:41 And of course, what really makes you frustrated as a VC
0:23:43 is that kid half the time isn’t even aware
0:23:44 of the previous failed experiments
0:23:47 ’cause like they literally weren’t paying attention, right?
0:23:49 And so what happens is actually the VCs will free,
0:23:52 it’s actually, the VCs will actually freeze themselves out.
0:23:54 And so, and it’ll be VCs who are much more naive
0:23:56 and much less aware of the previous failures
0:23:57 that will actually make the bet.
0:23:59 And so that puts you in this very weird spot.
0:24:00 If you think about being a VC
0:24:01 or running a venture capital firm,
0:24:03 which is you would like to say that the person
0:24:04 who knows the most about the domain
0:24:06 is the person who should make the investment decision.
0:24:07 But it may also be the case,
0:24:09 the person who knows the most about the domain
0:24:10 has the most scar tissue
0:24:11 and has the most followed up psychology.
0:24:14 And so a big part, exactly to your comment,
0:24:16 like a big part of this job, just like being a founder,
0:24:18 is like you have to suppress your natural instincts
0:24:20 to get bitter and resentful and envious and upset.
0:24:22 And it goes to even a more fundamental question is,
0:24:24 like, can you learn lessons, right?
0:24:26 Like what do you learn, like in this business,
0:24:27 what do you learn from a failure?
0:24:29 And maybe the answer is you should learn a lot
0:24:31 from a failure ’cause like it’s those are all hard-won lessons
0:24:33 and maybe the answer is you should learn absolutely nothing.
0:24:35 Maybe all the lessons are wrong, so.
0:24:37 – Yes, it is a lesson to just, that didn’t work.
0:24:38 – Yeah.
0:24:41 – That’s something that I spent a lot of time trying to,
0:24:44 to convince people on the team of that,
0:24:46 it’s the right brothers or Thomas Edison.
0:24:49 It’s just every day, what’s the best idea we got?
0:24:50 That didn’t work.
0:24:51 All right, what’s the next best idea we got?
0:24:55 And the characterization of celebrating failure
0:24:58 sometimes misleads people to characterize that as a failure.
0:24:59 ‘Cause if this plane didn’t fly
0:25:00 or this light bulb didn’t light up
0:25:02 or it blew up or whatever, is that a failure?
0:25:04 Or is that just the process of getting to the light bulb
0:25:06 that works, that they are playing that flies?
0:25:09 – Yeah, Edison tried 3000 compounds, I think,
0:25:10 for the light bulb.
0:25:11 – Yeah.
0:25:11 – Before he figured out the filament.
0:25:15 – That is a level of persistence I would like to hire.
0:25:18 So Mark, you just mentioned a little bit of like control
0:25:20 over your own emotions or your reactions, the cynicism.
0:25:22 Ben, that’s something that you talked a lot about
0:25:23 in the hard thing about hard things,
0:25:24 just like the power to overcome.
0:25:26 To what, I mean, obviously you’ve seen a lot of this,
0:25:29 you’ve experienced it yourself, to what degree
0:25:32 do you think you’ve been helpful and let me just say,
0:25:34 you have been helpful to me personally
0:25:36 in helping entrepreneurs through some of that.
0:25:38 Whether it’s the overwhelming emotional reaction
0:25:40 to a bunch of good stuff happening
0:25:42 or a bunch of bad stuff happening.
0:25:44 – Yeah, so I think, I mean, that’s probably
0:25:47 that the number one kind of consistent thing
0:25:49 that I get back on that book is like,
0:25:52 you know, what I’m feeling is so intense
0:25:55 and there’s nobody to talk to about it.
0:25:58 And then the book kind of goes like,
0:26:01 this is what it feels like, this is what it looks like.
0:26:05 And I think that just knowing that you’re not
0:26:07 the stupidest entrepreneur of all times
0:26:10 is like really valuable.
0:26:11 And it’s something that I always wish that I had.
0:26:13 It’s a lot of the reason I wrote the book
0:26:17 ’cause I used to go around, you know, and I, you know,
0:26:21 and I was like in the kind of horrible period of 2001
0:26:24 and I would talk to other founders and I’d be like,
0:26:25 you know, how’s it going?
0:26:27 And they’d be like, it’s amazing.
0:26:30 I can’t, this is the greatest experience of my life.
0:26:32 And I’d just be like, wow, I am like
0:26:34 the stupidest motherfucker of all times.
0:26:37 Like, ’cause my business is in horrible trouble.
0:26:38 And it just seems so bad.
0:26:40 But then like, you know, as because I’ve lived long enough
0:26:43 to see like most of those guys went bankrupt.
0:26:44 So they were all going through it.
0:26:45 I was going through.
0:26:46 They just like nobody would tell each other
0:26:48 ’cause it’s so embarrassing.
0:26:50 So it was, you know, one of those, those kinds of things.
0:26:53 But it’s been, you know, it’s been great help to me
0:26:57 in the work because when I sit down with an entrepreneur,
0:27:00 they go, okay, yeah, I know, you know what I’m talking about.
0:27:03 So we can talk about the real like horrible shit
0:27:06 and not just, you know, the happy stuff.
0:27:08 – I feel like there’s an obligatory question here
0:27:10 to talk about the things that you guys tried
0:27:12 that didn’t work and the failures.
0:27:14 But before we get there, just on the way,
0:27:15 there’s a bunch of things that obviously did work.
0:27:20 So building up a bunch of capabilities in the firm
0:27:21 and in the partnership with something
0:27:23 that is now pretty widely emulated,
0:27:26 like having those services that the companies can call on,
0:27:28 being a little bit more stage agnostic
0:27:32 and even like industry technology vertical agnostic
0:27:35 has been something that’s worked out really well.
0:27:38 When you look back before we get to the failure question,
0:27:40 what do you think the best decisions you made are
0:27:42 in the way that you set up the firm?
0:27:44 – Yeah, so it’s a great question.
0:27:49 I think, you know, at the core, I think just this belief
0:27:54 that a venture capital firm has got to be able
0:27:57 to help the technical founder grow into a CEO.
0:28:00 It’s just so, you know, in retrospect,
0:28:02 that turned out to be profound
0:28:04 and just differentiating and important.
0:28:06 And it really is the work.
0:28:08 So when we think about what do we do
0:28:11 and why are we here, that’s it.
0:28:13 And then kind of backing that up,
0:28:15 the thing that helped us the most
0:28:19 is just taking what Michael Ovitz had done at CAA.
0:28:22 And that jump started us, I mean,
0:28:24 we probably saved five years by copying his model.
0:28:27 So that, and I can’t even believe how well it worked,
0:28:29 like every aspect of it worked.
0:28:32 So, you know, those are probably the two things.
0:28:34 – What was it from Michael Ovitz that you were copying?
0:28:35 – He has censor it in the book.
0:28:36 And so there’s a great book.
0:28:38 – Yeah, he finally revealed some of it.
0:28:40 – Some of it’s in the book, not all of it’s in the book,
0:28:42 but it’s the book is called “Who is Michael Ovitz?”
0:28:45 And it’s a highly entertaining book and we recommend it.
0:28:46 I mean, it’s actually really funny.
0:28:48 He kind of sat down and described the whole thing.
0:28:50 And it basically was this idea of, you know,
0:28:51 we’re not just going to be a collection of individuals.
0:28:53 We’re going to be an actual true team.
0:28:55 And then it’s not just going to be the principles.
0:28:57 It’s going to be an entire system, right?
0:28:58 It’s going to be an entire operating platform,
0:28:59 an entire infrastructure.
0:29:00 It’s going to be professionals
0:29:02 across all these different domains.
0:29:04 And, you know, we’re going to build this
0:29:06 enduring long run network that’s going to, you know,
0:29:08 it’s just going to constantly compound year after year
0:29:09 and build more and more value.
0:29:11 And then the next client comes longer,
0:29:12 the next, you know, founder comes along
0:29:14 and they can plug into this entire system, you know,
0:29:15 that’s been built, you know,
0:29:17 and we’ve been building the system now for a decade.
0:29:19 And so, you know, a new founder who works for this today,
0:29:21 like they’re walking into a, basically a system
0:29:22 that’s been built for a decade.
0:29:23 So then he said, basically what happens is
0:29:24 then it’s compounding advantage,
0:29:25 which is every year that goes by,
0:29:27 you just get more and more differentiation
0:29:28 off of the status quo.
0:29:30 He was competing at the time with William Morris,
0:29:31 which was this huge talent agency.
0:29:33 And it’s like, well, why wouldn’t they just copy you?
0:29:34 And he’s like, well, they’d have to vote themselves
0:29:36 big salary cuts, right?
0:29:38 Like they’re paying themselves all the money right now, right?
0:29:40 And so they had to go hire, you know,
0:29:41 100 people to go do the stuff that we’re doing.
0:29:42 They’d have to like,
0:29:43 they’d have to free up that money from something.
0:29:45 So they’d have to vote themselves giant salary cuts.
0:29:47 And he’s like, they don’t like each other.
0:29:48 Like they don’t get along to start with.
0:29:50 And so imagine getting into the room.
0:29:50 And they’ve all got like, you know,
0:29:52 they’re like very successful people.
0:29:53 They’ve all got very high personal burn rates, right?
0:29:54 They’ve got all kinds of hobbies, you know,
0:29:56 they’ve got vineyards and yachts and all this stuff.
0:29:59 And so, you know, they’re going to now decide
0:30:01 to give themselves an 80% pay cut, right?
0:30:03 To compete with the startup, like no chance.
0:30:05 And so anyway, that was his explanation.
0:30:07 – Yeah, that’s been a long lasting advantage.
0:30:08 I think so.
0:30:10 – Yeah, but yeah, that continues.
0:30:11 As it actually turns out,
0:30:13 that didn’t just happen in the talent agency business.
0:30:16 What I discovered doing more research after that was that,
0:30:18 it was also exactly what happened for law firms.
0:30:20 It’s exactly what happened management consulting firms.
0:30:22 It’s what happened to ad agencies, accounting firms.
0:30:23 And then also investment banks,
0:30:25 private equity firms, hedge funds.
0:30:26 All these other industries have gone
0:30:27 through this transformation.
0:30:30 They basically professionalized and upleveled.
0:30:31 And it just happens that the venture industry
0:30:33 is doing that now.
0:30:34 And in fact, I think at this stage,
0:30:36 like it’s beyond just us.
0:30:36 – There’s something else there
0:30:38 that I actually hadn’t really realized,
0:30:40 but maybe it was implicit the whole time,
0:30:43 is CA’s investment was to make the people
0:30:44 that are representing more successful.
0:30:47 Smart idea, given that you got points
0:30:49 on their success is a little bit of the same thing.
0:30:51 ‘Cause you can make an investment decision
0:30:54 that it’s just like I’m gonna buy some copper futures
0:30:56 or oil or something like that.
0:30:59 And I can’t do anything about to make oil more valuable
0:31:01 for people or copper more valuable.
0:31:02 I invest in the startup
0:31:03 and there’s a ton of stuff I can do.
0:31:05 I have my connections and I know that personally
0:31:08 I benefited from being able to call on both of you
0:31:11 from John O’Farrell who joined our board,
0:31:14 from Margaret, from Jeff Stump on recruiting,
0:31:16 from the whole team running the EBCs.
0:31:18 There’s more than I can mention here.
0:31:20 And I think that has,
0:31:24 I don’t know what the ROI for you is on that,
0:31:26 on top of the dollars that you put in,
0:31:29 but I would say it’s probably 70% of the value to us
0:31:31 and 70% of the value created came
0:31:33 from that additional support beyond just the money.
0:31:36 – Yeah, and there’s also this little knock-on effect
0:31:37 which you appreciate, I’m sure,
0:31:42 which is part of the trouble with an inventor becoming a CEO
0:31:46 is you just don’t feel like a CEO.
0:31:48 You don’t know the people who CEOs know,
0:31:50 you don’t know how to do the things CEOs know how to do.
0:31:54 And so a lot of what you get out of the firm is,
0:31:55 no, I’m a CEO.
0:31:57 Like if I need to know how to do it, I’ll call Margaret.
0:32:00 Like, you know, I can do that, I can step up.
0:32:03 And so like that’s a lot of what it conveys
0:32:06 at the end of the day is just like that confidence
0:32:08 which is often that little difference
0:32:10 between being able to stay in the job
0:32:13 and having to raise your hand and tap out.
0:32:15 – All right, so I promised that I was gonna ask
0:32:20 about any dumb decisions, mistakes, failures along the way.
0:32:21 What do you got?
0:32:22 – We haven’t made any.
0:32:24 I don’t really know why you would even ask that question.
0:32:26 There’s obviously nothing to talk about.
0:32:29 So we have a very specific philosophy on that
0:32:30 and the book I’d really recommend.
0:32:32 We were lucky enough to have her in the podcast a while ago.
0:32:34 So Annie Duke wrote a book called “Thinking in Bets”
0:32:36 where she talks about basically what is the nature
0:32:39 of a mistake in a probabilistic domain,
0:32:41 you know, with uncertainty of outcome.
0:32:44 And she uses the term in the book “resulting.”
0:32:46 It’s basically the process of looking at a bet
0:32:48 that was made in a probabilistic domain
0:32:49 that did not pan out.
0:32:51 And then concluding that that was a mistake
0:32:53 as compared to a bet that didn’t pan out.
0:32:54 And so basically what she says in the book,
0:32:57 she says the book is basically resulting
0:32:59 is the root of all evil if you’re in a probabilistic business
0:33:00 ’cause you will learn the wrong lessons
0:33:03 and you’ll torture yourself mentally to death by doing that.
0:33:06 And so she says the thing to do is basically
0:33:08 to very clearly separate in your own mind process
0:33:09 and outcome, right?
0:33:11 So you’re in a probabilistic domain,
0:33:11 you don’t know the outcome
0:33:13 of any particular bet ahead of time.
0:33:15 And so you need to design the best possible process
0:33:18 to generate the best possible set of outcomes over time.
0:33:21 And then basically when things go quote unquote wrong,
0:33:23 you don’t second guess the outcome.
0:33:24 You go back and you just make sure the process
0:33:26 was as good as it could have been.
0:33:28 And so, you know, from the outside,
0:33:30 the mistakes of venture firm makes are always like,
0:33:31 well, what’s the investment that you didn’t make
0:33:33 that worked or the investment that you made
0:33:34 that didn’t work inside the firm?
0:33:35 What we try to do is say, okay,
0:33:37 what have we done well in our process
0:33:39 and how can we improve our process?
0:33:41 You know, that’s a much more boring topic to talk about
0:33:42 ’cause it has to do with things like meeting structure
0:33:44 and memo documents and, you know, research
0:33:46 and due diligence and all these topics.
0:33:49 But that is the actual answer to the question, which is,
0:33:51 you know, when we started with a, as Ben said,
0:33:53 we started with a relatively lightweight process
0:33:54 on investments because it was just Ben and me
0:33:57 and then over the time we’ve evolved
0:34:00 to a much more rigorous process.
0:34:01 Today we do a lot more work on the investments
0:34:03 than we used to.
0:34:05 And then the other side of that is to try to keep all that
0:34:06 work from preventing us from making
0:34:07 the controversial investments.
0:34:08 – Getting people to that position.
0:34:11 And I wish I could remember who gave me this analogy,
0:34:14 but if you got super drunk and then you drove home
0:34:16 and you didn’t have a crash,
0:34:18 it’s not that that was a good decision.
0:34:18 The result was good.
0:34:21 And we really, it’s a tough thing to build structures
0:34:23 inside the company to celebrate.
0:34:26 That was a great idea to change the homepage
0:34:28 so that whatever, and it turned out that it was wrong,
0:34:30 but you still got a bonus.
0:34:32 You get a bonus, you get a promotion,
0:34:35 you get recognition, even though it didn’t have the result
0:34:37 ’cause people do get super result fixated.
0:34:40 – Yeah, Bisa says real good thing where he says,
0:34:42 “We rate people on the inputs, not the outputs.”
0:34:45 – One of the last questions I wanted to get to
0:34:47 is the nature of the entrepreneurs.
0:34:48 Now it’s been long enough.
0:34:51 You’ve seen some two-time, three-time entrepreneurs
0:34:53 and you’ve backed some of them.
0:34:55 What kind of difference do you see between that,
0:34:56 the first time and the second time?
0:34:59 – So for top-end venture, basically the rule is
0:35:00 if you’re a first-time fund,
0:35:01 to get funded by a top-end venture firm
0:35:03 as a first-time founder, you have to have something working.
0:35:04 You have to have a product.
0:35:06 You have to have some of a product market fit.
0:35:08 Google.com already existed.
0:35:09 Facebook already existed.
0:35:11 Airbnb already existed when they raised money.
0:35:13 So that’s the general pattern.
0:35:15 And then the question of the first-time founders is,
0:35:16 do they know what they’re doing, right?
0:35:19 Can they then do the job of being a founder CEO
0:35:22 of a scaling company and some can and some can’t?
0:35:24 The second-time founders are like a huge relief
0:35:25 to deal with on the one hand
0:35:27 because it’s like, okay, they’ve been through it before.
0:35:28 Now they know what they’re doing, right?
0:35:30 They’ve got some experience and some gray hair
0:35:32 and some operational experience.
0:35:34 The problem with the second-time founders,
0:35:37 they can raise money before they have something working.
0:35:40 And then there’s this question of like, okay, what’s the idea?
0:35:41 And then we talk a lot about like,
0:35:44 is it an organic idea or is it like a synthetic idea?
0:35:45 Was the process, I have a great idea there
0:35:46 for I’m going to start a second company
0:35:48 or was the process, I want to start a second company
0:35:50 and therefore I have to come up with an idea.
0:35:52 And what you often find is they want to start
0:35:55 a second company so badly that they come up
0:35:58 with basically a fragmentary idea, partial idea.
0:36:00 It’s a conceptually interesting idea,
0:36:02 but with nothing underneath it.
0:36:03 And we have this other concept
0:36:06 we use called the idea maze, which basically is the process
0:36:08 that a founder uses to figure out what the actual idea is,
0:36:09 which is like a hundred-step process
0:36:11 to work your way through all the different permutations
0:36:12 of the idea before you actually finally figure out
0:36:13 the real thing.
0:36:15 And the second-time founders often just haven’t gone
0:36:17 through the idea maze, but it’s really bizarre as a VC
0:36:19 because it’s like, here’s this founder who you love
0:36:21 and like they’ve showed incredible persistence
0:36:22 in the last company and like you so badly
0:36:24 want to work with them again and you can just tell like,
0:36:26 for the idea has almost become interchangeable.
0:36:28 Right, and it’s just like, that’s a super bad sign.
0:36:30 And so that’s what tortures you on those.
0:36:32 – Yeah, from the entrepreneur side, I can tell you,
0:36:33 having a whole bunch of money does take away
0:36:36 a very critical forcing function, which is like,
0:36:38 I’m about to find out of money, I better figure this out.
0:36:41 So we added overcorrect for that.
0:36:42 – What’s the job of the founder?
0:36:45 Is the job of the founder to figure out the product,
0:36:47 figure out the market and get the idea nailed?
0:36:49 Or is the job of the founder to staff
0:36:51 an executive team and an employee base, right?
0:36:54 And those are two like, they’re overlapping responsibilities,
0:36:55 but there’s a lot of-
0:36:57 – The first one turns out to be a lot more important.
0:36:58 – Yeah, it’s like the startups where it’s like,
0:37:01 they’re doing all the outward facing things
0:37:03 involved with being a startup, but like there’s nothing there.
0:37:05 – Well, you know, and you always kind of know it
0:37:09 because the founding team is all vice presidents
0:37:10 and no engineers or something like that.
0:37:13 It’s just like, okay, what are you doing?
0:37:16 You can’t execute your way through like no ideas.
0:37:18 – 10 people in the company, they’ll have a chairman,
0:37:20 they’ll have a CEO, they’ll have a CEO, they’ll have a president,
0:37:21 they’ll have a VP of sales, a VP of marketing
0:37:23 and a VP of engineering.
0:37:25 That’s not a good, yep.
0:37:27 – A little bit of a pet peeve for me too.
0:37:30 So the first time that I met Ben, it was with Mark
0:37:32 and it was at the Creamery in Palo Alto.
0:37:34 And I think you were about probably about six months away
0:37:36 from starting the fund.
0:37:40 I never would have predicted how things would have turned out.
0:37:42 Did you predict how things would have turned out?
0:37:47 – You know, no, I think we dreamed that we would kind of
0:37:48 get to where we got to,
0:37:51 but it was a much longer timeframe on the dream.
0:37:54 I mean, things worked out way, way better
0:37:58 than I think either of us set out and expected.
0:38:01 And, you know, we had a lot of good luck along the way.
0:38:03 And then a lot of, you know, great help
0:38:07 from a lot of people, you know, people like actually starting
0:38:08 with like people like Jim Breyer,
0:38:13 who kind of taught us what it meant to like create an LP base
0:38:16 and how to think about investors and those kinds of things.
0:38:19 And then, you know, we ended up getting like very lucky
0:38:21 on the hiring, I think our first hire was Scott Cooper,
0:38:25 who we probably kind of built the firm with that.
0:38:28 And then, you know, our first consultant was Margaret.
0:38:31 And, you know, there’s no way we would have like
0:38:32 pulled off the marketing thing without her.
0:38:35 So a lot of, a lot of really great luck
0:38:36 and a lot of really great help.
0:38:38 Oh, and Andy Rackliffe, yeah, he helped us understand
0:38:39 what venture capital was.
0:38:41 – It turns out to be.
0:38:43 – Neither of us had any experience.
0:38:44 – Then obviously all the partners who have joined us
0:38:47 and so about 150, 150 people now,
0:38:49 by definition numerically, they get almost all the credit.
0:38:52 – So one thing I definitely want to get to is the transition
0:38:57 from a VC firm to a financial advisor for whatever that means.
0:38:59 And you were a little bit iconoclastic and different
0:39:00 from the beginning.
0:39:04 This also seems, we’ll see looking back at iconoclastic,
0:39:05 but definitely different.
0:39:06 What was the idea there?
0:39:08 – Yeah, so, you know, kind of the thing that catalyzed it
0:39:10 was actually crypto.
0:39:14 There’s a rule that exempts VCs from having to do
0:39:17 a bunch of kind of regulatory compliance stuff.
0:39:21 And part of the thing that keeps you as a VC
0:39:26 is you can’t invest more than 20% of your funds
0:39:30 and things that aren’t like primary equity investments.
0:39:33 So crypto would fall into that category secondary
0:39:34 and so forth.
0:39:37 And look, we believe crypto is going to be important.
0:39:39 Now there’s a lot of VCs who do,
0:39:41 who won’t take the step that we did to become regulated
0:39:43 in the way that we have.
0:39:46 But, you know, like this is kind of another advantage
0:39:49 from our background is we’re not afraid of governance
0:39:50 or regulation or these kinds of things.
0:39:52 And that, you know, it’s something that we understand
0:39:54 pretty well from being a public company.
0:39:55 We’ve done it before.
0:39:57 And it opens up a lot of opportunities
0:39:59 that we can now think about
0:40:02 because, you know, we’re in another category.
0:40:05 – Yeah, so this changed the categorization
0:40:06 and then the regulatory environment,
0:40:07 but you’re still a V-serfer.
0:40:08 – Yes, yeah.
0:40:11 Now we still are in the exact same business we always were.
0:40:13 – Mark, I gotta also do TV shows that you recommend
0:40:17 ’cause you are a source of excellent viewing.
0:40:18 – There we go, good.
0:40:19 All right, well, I’ll struggle.
0:40:20 So I can’t help myself.
0:40:23 Deadwood is the best TV show of all time.
0:40:24 And it’s actually very relevant for founders.
0:40:27 It’s basically, it’s the story of the American frontier
0:40:29 through kind of a modern lens.
0:40:31 And it’s just astonishingly high quality.
0:40:32 And it’s basically the creation of a city.
0:40:33 It’s basically the creation of a city
0:40:34 and ultimately the creation of a state,
0:40:36 the state of North Dakota.
0:40:39 And it is, you know, in the face of just like, you know,
0:40:41 horrifying obstacles.
0:40:42 You know, and by the way, you know,
0:40:44 many ethical issues along the way and everything else.
0:40:45 If you think starting a tech company is hard,
0:40:47 you wanna watch a couple of seasons of Deadwood.
0:40:48 It’ll put you in the right frame of mind.
0:40:50 – And then the movie that it got canceled,
0:40:51 it gave us a decade ago
0:40:52 and it got canceled after three seasons
0:40:54 and it really should not have been.
0:40:56 And so they did a very rare thing.
0:40:56 They went back 10 years.
0:40:59 And all these other people in the movie became huge stars
0:41:00 afterwards in the show.
0:41:01 So they got them all back
0:41:03 and made the fourth season into a movie.
0:41:04 – Wow.
0:41:05 All right, can’t wait to see it.
0:41:06 – Yep.
0:41:07 You can give me two books if you want, two books.
0:41:09 – Yeah, give me two books, give me five books.
0:41:11 – Favorite two books of the year.
0:41:14 Book number one, “Why History is Always Wrong?”
0:41:18 is not written by a historian and it is basically,
0:41:19 if you’ve read Nassim Taleb,
0:41:21 he talks about something called the narrative fallacy,
0:41:24 which basically is, okay, why did something happen?
0:41:25 And then there’s some story as to why it happened.
0:41:27 And then it usually turns out,
0:41:28 like if you talk to the principles involved,
0:41:29 it wasn’t that story at all.
0:41:31 It was something much more complex.
0:41:34 And so this is like the next level of that theory
0:41:36 that basically says all of recorded history
0:41:37 is the narrative fallacy.
0:41:39 And so everything that we think we understand
0:41:41 about why the American revolution happened
0:41:44 or why Rome fell, right, or why Christianity emerged
0:41:45 or like any of these stories that we,
0:41:47 all the, any of these things that we teach you,
0:41:50 we’ll take 12 years of history class in school
0:41:51 and all this stuff, like it’s all wrong.
0:41:53 Like it’s worse than wrong
0:41:55 because it’s not like it could be corrected.
0:41:57 You couldn’t actually make a bunch of edits
0:41:59 to the book and make it correct.
0:42:00 You can’t do that at all.
0:42:02 And the reason why it’s worse than wrong
0:42:03 and you can’t ever get it right
0:42:06 is because reality is so complicated, right?
0:42:07 Reality is a complex adaptive system
0:42:09 when you’ve got human agents involved in everything.
0:42:12 And so you’ve got, and anything big that happens,
0:42:13 you’ve got thousands or millions of people
0:42:15 who are making all kinds of random decisions every day
0:42:16 for all kinds of random reasons
0:42:19 and it just happens that things result in a certain way.
0:42:21 – Is this wrong in the same way to think that
0:42:22 it didn’t rain because God was mad
0:42:24 or it did rain because we performed the ritual
0:42:27 in the right way, like puts some agency into a system
0:42:29 that there isn’t anyone making decisions.
0:42:30 It’s just the emergence.
0:42:31 – Yeah, exactly, in reality, the weather system.
0:42:33 I mean, you know, this is after 100 years
0:42:33 of meteorological science
0:42:35 and they still can’t predict it’s gonna rain tomorrow.
0:42:37 And it’s ’cause the atmospheric system
0:42:38 is a complex adaptive system
0:42:40 is too complicated to model.
0:42:42 And just like, and you can keep throwing supercomputers at it
0:42:44 and it’s still too complicated to model.
0:42:46 So, by the way, it’s the same thing, the human body.
0:42:47 Like we don’t, it’s actually,
0:42:48 we think about this a lot in the bio fund.
0:42:49 It’s like, we don’t understand.
0:42:50 Like we don’t even, there’s not even settled
0:42:51 in traditional science.
0:42:53 Like we still don’t know, like there’s still,
0:42:55 and there’s no, a whole new category revision of science
0:42:58 now questioning this whole, the whole protein fat,
0:42:59 you know, the whole protein fat thesis.
0:43:02 And so like it’s, the example he uses in the book
0:43:04 at the fall of Rome is like the, you know,
0:43:06 the single most studied kind of historical story
0:43:07 is like the rise and fall of Rome.
0:43:09 And it’s like, and basically what happens is like,
0:43:10 if like a science is working properly,
0:43:12 you converge on the correct answer.
0:43:13 Like you converge on Newton’s law.
0:43:15 So you converge on quantum mechanics
0:43:16 or something like that.
0:43:18 He’s like, the problem in history is that
0:43:19 the more time goes, Pat,
0:43:21 the more explanations they come up with,
0:43:23 the more new explanations they come up with.
0:43:24 And there’s like historians have documented,
0:43:26 there’s like 250 now different causes
0:43:28 for the fall of Rome, right?
0:43:30 And so like, it just, it leaves you with nothing.
0:43:33 And so it’s, it’s, it’s a, it’s a disconcerting theory
0:43:34 ’cause it basically says getting a handle
0:43:37 on cause and effect in the world is impossible.
0:43:38 It’s a very convenient theory
0:43:41 ’cause it means you can just ignore history.
0:43:42 That saves you a lot of time.
0:43:43 It saves you a lot of time.
0:43:45 And then it’s an inspiring story.
0:43:48 Our theory, I find, ’cause it’s like, okay,
0:43:49 things can change.
0:43:51 Like nothing is actually carved in stone,
0:43:53 like not even a little bit.
0:43:55 And who knows what’s going to be the next person
0:43:56 who’s going to make the decisions
0:43:56 that’s going to cause everything
0:43:57 to go one way or the other.
0:43:58 And that could be you.
0:43:59 And so I find that inspiring.
0:44:03 And then the other book I love to tell people about
0:44:06 is David Goggins, who’s a, the only guy in history,
0:44:08 he’s a triple qualified as an ABCL and army ranger
0:44:11 and what’s called an Air Force tactical air controller,
0:44:13 which is a special forces unit of the Air Force.
0:44:15 So triple qualified special forces.
0:44:18 He wrote a book called “Can’t Hurt Me.”
0:44:19 And it is one of the most amazing stories
0:44:20 that anybody has ever written.
0:44:23 His story is really amazing.
0:44:25 He’s one of the only African-American Navy SEALs
0:44:29 in history and just manages incredible accomplishments
0:44:30 both inside and outside the military.
0:44:32 And it’s the book.
0:44:34 Like if Ben’s book is about like the struggle in business,
0:44:36 like David’s book is about the struggle in life.
0:44:39 And so anytime anybody feels mopey.
0:44:43 About what’s happening in their startup or in their life.
0:44:44 This is the book to read,
0:44:46 to kind of reset all the expectations.
0:44:46 All right, great.
0:44:48 Well, thank you so much.
0:44:51 It was a pleasure and an honor to be able to do this with you.
0:44:52 All right, Stuart, thank you so much.
0:44:53 Thank you, Stuart.