Benedict Evans Talks About the Tech Bubble

a very select group of tech geeks. Um, if you are at all interested in the development of technology, of ecosystems, of of autonomous everything, of smartness, of really where the puck is going to be the future of technology, you will find this to be a fascinating conversation. He absolutely has an encyclopedic knowledge of what's taken place and why and has tremendous insight into the likely direction that uh, this space is going. So, with no further ado, my

conversation with Andres and Horowitz is Benedict Evans. I'm Barry rid Hilts. You're listening to Master's in Business on Bloomberg Radio. My special guest today is Benedict Evans. He is a partner at the legendary venture capital firm Andrees and Horowitz, where he works as an analyst and does a weekly consumer newsletter blog What have You, covering everything from mobile platforms to AI to autonomous everything. He is also a

recommended follow. He began his career as an equity analyst in investment banking, moving on to rategy and business development at such August firms as Orange and NBC Universal. Benlict Evans, Welcome to Bloomberg. Thank you for having me. Do do you prefer Benlicht or Ben or Benedictio? Is better? S? A? Okay for sure? Um, there is an advantage to having an unusual first or last name for SEO purposes. I'm convinced if my name was Barry Smith, no one would know who I am. But we can talk about s

c O a little later. So you graduate Cambridge in right in the midst of the dot com and technology boom, not too long before the bust. What was it like coming out into the banking world in the midst of that era. Well, so I actually joined an equity capital markets team. So we were doing europ and tech I P A S and Safe for about nine months. I thought, this is going to be a fun career, right, this is easy. You just throw it out there and everybody

buys it. He's some random thing, and a sovereign wealth fund puts in an order for three extra floats. And then in about well early spring of two thousand, everything started going down and we sort of forget now that we talked about the dot com bubble. But you know, particularly in Europe, there was also a mobile bubble. But there was also a mobile bubble, and there was also

the fixed line broadband fiber bubble. So you kind of had three bubbles that kind of had happened more or less independently for different reasons, and kind of merged and combined into one enormous thing. I would then when then the market went down for three years, I would argue you had a semi conductor bubble, you had a software bubble, you had a hardware bubble, you had a storage bubble.

It all started a band with bubble. In the US, we had fiber uptick and telecom bubble, where you had you had a tech bubble, but you also had a mobile bubble. Which is very only for very distant reasons, was happening at the same time. It was happening at the same time, and then there was a fiber bubble, and yet so they all combined and we got this enormous kind of explosion and then an enormous pot and the market went down without stopping more or less for

three years. So were you always the tech guy or or did you was your background more finance or something well above? So I did my degree in history, which is really an analytic subject. So here is a bunch of information, try and work out something interesting or meaningful or insightful to say about it, or something original and

interesting to say about it. And so I went from that into sort of fairly conventional routing into invest in banking UM, and sort of quickly decided that e c M was kind of project management and whereas research was probably something I was likely to be better at. And so I went into at research and I did did did UM. So I went and did my European mybile stocks for a couple of years, and that was in London or New York and London. And did you did you spend any time in New York or did you

go straight London to well, I was on Valley. Uh No, So, I mean I came to New York for work and I worked for NBC Universal, so I used to come over here. Um, but now the first time I actually worked properly in the States was to go to work for a sixty and oh so London with really just brief sojourns in New York City. Yeah, I mean I worked for Orange, So I went to Paris and I worked for NBC Universal. So I came to New York,

but that my job job spot was in London. And so you're covering telecom starts, recovering Mobile, You're covering a lot of the technology related space as an analyst. What was that like in the midst of wild overvaluation and collapsing share price? So it's it's kind of interesting. I mean, there's there's there's a couple of different things that one of them is it's interesting in hindsight to to look at them in hindsight in that they were the go

go growth, exciting, dynamics, sexy, disruptive companies. Me and Mobile had gone from nothing in oh my god, everyone on Earth is going to have one of these things, and they were really kind of like they thought of themselves as poets and they connected everybody and that was it. And you know, there's obviously there's still a lot of stuff going on in emerging markets, but like everyone Knework got a phone and that was it. And then they

went from being amazing. They went from being Google to being water companies in like a year two years effect. But so you look at the all the stuff we do on our smartphones, now, this was all kind of in concept videos and presentations from the mob All operated is in you're going to do all of this stuff, and they were going to do it all, and of course it all happened, but it wasn't done by them, And so that perspective of how kind of I think

there's a lot of echoes of that. For example, if you look at cars now, you have this all this future ology of what's going to happen and what cars will be and how everything will change, and you have kind of the renderings of the glass cars, which is like the renderings of the glass phones in two thousands when there were no phones with color screens, and you think, well, you might get like three quarters of this, right, But like the last quarter of it is going to be

where all the money is. And that's the difference between it being knock you and Microsoft in it being companies you've never heard of like Apple and Samsung and Google or forgotten like Apple um and no one had ever heard of Google. And so it's interesting to look back and think, how did we think about what was going to happen then, and how much of it was right and wrong? And what would you have had to have

said then in order to get it right. So you're describing a form of historical futurism because when we've what we've seen over the years is expectations of the future turn out to be wildly either over optimistic or pessimistic, but rarely right on the nose. Is that a fair And I think that's right. I mean, as I said, I think there's sort of several bits. There's sort of several lessons you can learn from, like not so much to drop the bubble per se, but the kind of

the way people thought about it. And so there was this whole idea in the early nineties of the thing, this thing called the Information super Highway, which the whole just the name conveys the fact that it was going to be kind of centrally controlled, and it would be the cable company and News Corporation and twenty Century Fox and the New York Times, and they would kind of get together every six months and decide what you were

going to have and how. And of course what we had instead was permission in this innovation and the open Internet, and there was no central authority, and anyone could do what they wanted in just to some extent. And so that is interesting to compare that now with the way we for example, people talk about cars all the way

people talk about what's happening to TV now. Um, I think the kind of the but as I just said, the fact that you could have got you could have guessed like eighty or nine of it and it's still have missed the important parts is also fascinating. Like you could have said in nineteen in two thousand, okay, everyone will have one of these things. Everyone will have Internet on the phone. It will be a real operating system

and not a future phone. It will be open Internet. Um, therefore it will not be the carriers that would have can describe today's well perfectly, But you would have still have said, well then it'll be Microsoft K Nokia that do that. What's amazing is there was this a T and T commercial. I very vividly remember. I think Tom Selleck did the voice over and he talks about what the future will be like, and I think the punchline

on the beach you will. And it turned out to be dead right, except for the fact that a T has or more nothing to do with it exactly. I mean. One of the ways I described this when I talked to Talcos now is it's as though a municipal water company looked at the mineral water business and they said, you know, come on, we've got brand, we've got water, we've got trusted. You wouldn't buy water from a company

you don't trust. We should be doing And they hire McKinsey and wolf Fallens and they build the whole thing, and like two years later, that first pallet of water balls into Walmart and it goes onto the shelves and they look at it next to yether of two D brands and they think, hang on a second, something not quite right here. Um, So, how did you go from working in London or Paris as a telecom man? Was too I know, I'm going to move six thousand miles

away and become a imagineer. And I just asked them for a job. I don't know why people say it's hard to get a job in venture. You know, you go ask them for a job, they say, yes, okay, that's it. Yeah, it was easy. How did you how did you? Well? First hear of instance, so well, so

there's a process here. And as I mentioned, I actually did my degree in history, which is analysis, and so the question was sort of picking up and looking for things where you can apply that to analysis, and so I left university and became a south Side analyst, and is sort of many people will know that, for many various reasons, stop being a fun thing to do, or

even a thing that anyone could do. So I think when I was in telecoms, merl Lynch, you have had something like fifty um up in telecoms analysts, and the last time I looked, I think they had less than half a dozen. So that industry ceased to be fun. And so I left and went and worked in strategy, first at Orange and then at um NBC Universal and Channel four again both in London. So from sort of sitting on the outside in sitting on the inside out trying to work out what we do and how do

we how do we understand this? And it's kind of interesting because the kind of the questions changed and suddenly you've got a spreadsheet that lists all the stuff you've been trying to work out from the from the public financials. But then you're just trying to work out different stuff. But the process is the thought process at the state. Because I did that for a while. Um. I left NBC Universal the week that General Electric share price fell fifty,

which is obviously a called direct causal relationship. Sometimes you know, things happened and you could just draw a straight line from A to B exactly. I recall seeing the Wall Street General Peace, Ben lick Evans departs NBC Universal gu started out in their exactly we went up up by half, and that the same amount the next week. Of course, um so I left when the financial that is in the financial crisis. It was so yeah, sound about that,

an old and boring story of what happened there. Um and then I went as I worked as a consultant in London, so I was doing a lot of sort of strategy consultancy and sort of producing research reports forum around the European media and telecom space, and so I was writing both about kind of what's happening in fashion

magazines and you know record retail and DVD retail. Would also water Google and Facebook doing what's happening with smartphones, And so I started a blog, and more or less the same time, I went on Twitter at that time, which he was kind of new, and so you could were quite easy to get noticed if you're doing stuff,

and I was kind of writing stuff. The way I describe it is basically I would write stuff that a cell side analyst or a senior strategy person or somewhat at Apple could write, except that either they couldn't public it, publish it, or they would be writing for a very different audience. They'd be writing for, you know, buy cell whole audience or um or they wouldn't have the analytic

background to write it. So like a sell side analyst could write this stuff, but they're writing for a different audience. A senior person at a company knows all of this stuff, but they are not used to writing stuff and they can't publish it anyway. Somebody and McKinsey knows all this stuff that they're not allowed to say that's not going

to work. Um. So I was kind of an interesting, kind of kind of niche in the vendor kind of little segment on your vend diagram of somebody who had an analytic and strategy background was used to writing it that about this stuff and explaining it, could say it in public at a time when very few other people were doing it. And so for those reasons, UM, I

sort of got noticed. And you know, I spent like two or three years writing a bog posts and getting like a hundred page vs a months, and then I went through a period where I was getting a couple of thousand page vis a day. Um. And that sort of happened in the course of two thousand thirteen. And then and I kind of picked up my my bag and thought, well, what else do I want to do? What do I want to do next? Where could I deploy this? And you know, you you have those conversations

at various stages in your life. And I thought, well, I should go and do you do this in venture capital? And where's the place to go and do venture capital? And you the global cluster with San Francisco, and what was the kind of firm where it felt like this kind of innovative approach to its explaining things and adding value in public would work, and A sixteen Z was thought of at the top of that list, and you reached out to them as opposed to visus. So I

got a couple of introductions. I went in and I said, well, this is sort of what I do. Is there a place for this in this firm? Does this fit within the firm? Is it's useful? And they kind of said yeah, just like that or less. So um, lots of other people blogers should be inundating a sixteen Z with their resumes and saying, look, I've been doing this also. You know, I don't think you need a resume. It's it's kind of like, you know, if you want a sales job,

you should be able to get the meeting. If you can't get the meeting, you shouldn't have a sales job. And it's kind of the same. You prove that you can do the job by doing it. That makes that makes a lot of sense. So the focus that you have today is no longer telecom the way it was, but certainly the concept of mobility as a source of um possible changes in technology is a key factor. What do you focus on these things? Well, I think talking about mobile now is a bit like talking about PCs

ten years ago. Right, Yes, this is the center of everything, but it's happened, and so we're not arguing about iOS versus Android, or whether everyone's going to have one of these things, or apps versus the web, all of those kind of things, though, they're not interesting conversations anymore. It's like arguing, you know, is everybody get onto going to get onto the web? Well? Yes, Now what next question? So you look? So there's one answer is well, what

are the next questions? What are the next kind of mega trends that are happening. The second is there's a lot of conversation around what happens with that stuff? What happens with the stuff that we've already had so ten years ago? What do we do with broadband and browsers? So well, let's talk about search, let's talk about social, Let's talk about what happens once everyone has a PC. Now what happens once everyone has a smartphone and everyone

is on Facebook? So we have those kinds of conversations about the world, where the world that we've just built, what happens with that, and then we think, well, what are the next things? So machine learning, autonomous cars, mixed reality, cryptocurrency, What are the next fundamental trends that will shape the tech industry the way first the PC and then mobile shape the tech industry in the last twenty or thirty years.

I think then within that there's a kind of you know, for what I try and do as a question of looking for the arguments or looking for the questions. So what are the places where there's an on the one hand, On the other hand, is it going to look like this or is it going to look more like that? Are they going to be winner not? Will there be autonomous cars? Yes not? Will it be in fifteen or twenty years? Who knows, don't know. It's more like are they going to be winner? Takes all effects in this?

Is it going to look like Android? Or is it going to look like abs where there's like a widget that everyone buys and it doesn't matter. Is what's going to happen in mixed reality? Is this going to be something that's going to be every hardware manufacturer? Is it going to be super concentrated? How do we think about

machine learning? How do we try and understand what that might change inside big companies, and so kind of there's like what are the topics and then how do we work out what the kind of the useful questions to talk about might be within that? So you're less extra bolending current trends out into some future date and instead thinking about, well, here's what we are several steps beyond that. What are going to be the subsequent developments that this

might lead to. You're really several steps ahead, that's right. I mean that's partly a consequence of the state of the industry. So like five years ago, okay, what's it was a horse race who's gonna win? But is Apple and Google? What's going to happen? Is Apple going to survive even though Android has got this open story and this open source story and so on? So is there going to be one for Apple? Is there going to be win for a third entry? And what will happen

to BlackBerry? Will black Bey killing on? And in niche? Will Windows going to be able to break in? So all of that was it was the motorating I'm sorry Windows, Yeah, yah, yeah, I don't I don't recall that. Yeah, yeah, that's it's a painful subject for some people. So there's it was a horse race Steve, Steve Bomber in particular. Yeah, yeah, he's three chairs. But that was you are you're calling the place. That's that the American phrase, you're you're going

to work out what was going on? Now you're not. That's we know what happened. And we don't have those kind of day by day tactical questions around autonomer's cars because like there aren't in your automo's cars. We don't have those kind of day by day questions around mixed reality because you can't buy a mixed reality headset yet, mixed reality glasses yet. So the character of the question

changes because we're at a different point in the S curve. Basically, when the S curve is going near vertical, then you're trying to work out, oh my god, what's going on? Is the rocket ship going to blow up? When is it going to start flattening out. Where we are now is we've got the oldest curve has flattened out right at the top of the of the scale, and we're talking about what you can on the top of it, and the new S curves are kind of under the

radar if I can mix my metaphors. One of the ways I described this, it's kind of a good Manhattan metaphor. It's like you walk past your construction site every day for six months and there's a bunch of construction worker is kind of scout standing around, scratching their backside, is not doing anything. You think all these guys are lazy. Then you walk past on Monday morning and they put fifteen stories of steel frame up me You think, wow,

they're really busy over the weekend. You're missing the slow And then there's a period at the end where they're like putting the facade on and doing all the fit out, and again that looks so boring and so mobile is at the stage where you know you're putting the facade down and you're doing the fit out and the other staff are still kind of hulling the ground and you can't really see what's going on. Um And so that means the character the questions change. In the early stages

of investments. There there is no data, there's no discounted cash forlor model. You're really dealing with two founders and a PowerPoint presentation and some numbers which are more or less best guesses. Yeah, so the way, it's an interesting shift in being an equity analyst because you're right at one end of the risk profile. You know, if you have tourn of buying tea bills is at the other end. You're at the stage where half of the deals you

do will return less than invested capital. And that's the plan, and five pent will produce more than a ten x return and that gets you a kind of a three x return over ten years. And so everything you do has to be capable of being amazing, and if it's capable of going from two people at a power point to being an amazing thing worth hundreds of millions of billions of dollars, it kind of has to be implausible and crazy, and they have to be a bunch of

reasons why it might not work. And therefore what you have to do is sort of suspend disbelief and not think, here are all the reasons why this might not work, But what if this did work, what would it be and are these the people who can make that happen, Which is a very very different approach than thinking about, all right, will this new widget or this new management team or whatever sell enough to move the earnings pre

share calculus of this publicly trading exactly. I think the kind of the key way to think about Silicon Valley is it's a machine for running experiments, and most of the experiments won't work, and that's the plan. And yes,

you know you might. You know, you could do some an experiment that's clearly a terrible idea and it was never going to work, and you could kind of mess it up and blow up your lab, and you know, people will look down on you for that, but no one will look down on you for the fact that you ran an experiment in a produced negative result that was just okay, well we tried, and do you do the do you run the experiment, right, that's a different question.

But you ran the experiment, it didn't work, Okay, that's fine. And the ones that do work justify the whole exercise and pay for the whole exercise and produce mobile phone, saw produced Apple and Google and so on. So let's let's let's zoom in on that a little bit. When you're at this side of the funnel, when you're looking at these really early stage companies, if half of them are effectively money losers for you, for the farm as an investment. How do you conceptualize what you're looking at?

Is it is it the founders themselves? Is it the idea? Is it something that, hey, this is just so crazy it might work. What what is the thought process like when, especially where you sit, where there's an endless stream of people coming to Silicon Valley to pitch ideas to vcs. Well, so I think most venture capitalists look at this stuff mostly in the same way. You can argue a little bit about emphasis, um, but there is the question of

what is the market opportunity here? And um? Are these the people who are going to be able to work out find discover that market opportunity because they never to PE's not going to be exactly the original idea. It's going to be something sort of adjacent to that. You'll kind of twist around and find it pivot shall we? Not so much that I'm more iterate. I would say pivoty is more like, Okay, we built a whole company on that promise and that didn't work at all, so

now let's try something else. Um, that's not really the same thing. It's more kind of well, we were kind of that and we sort of moved around, we got, we made, we found it worked. There's this whole sort of concept in silicon value product market fit, and so you have sort of a product and you're trying to work out, well, what the market being, what would the product be around this space until we can find something

that meshes and takes off. And so the earlier you are, the more in a sense you're betting on the ability of the founders to find that, But you're also betting on is this a great market and is there some angle or some way that they're going to find in order to make in order to turn this into a thing. So what I'm hearing from you as future growth prospects or and valuation is well, well so, um, it depends.

But the more progress you make and the more numbers you have, the more that you start looking at metrics and the less that you start thinking about potential. Um. So it's sort of the super super early stage. Then the valuations all tend to look the same as you go further in. Then you start getting much more specific about well, how well are they doing and what do

we think this looks like? Um. Then it gets sort of gets kind of case by case, and you know, you get up to a company that's got billions of dollars a revenue, and then you're doing dcs, and you're doing multiples like anybody else, but at the super early stage. In a sense, doing a DCF on two people with a power point is just an exercise in self deception.

Sure you could do it. You could. You could sort of say, well, if they managed to sell this thing to two billion people, well there will be some value that trying to do a dcattle that. Well, like, what does that get me? It doesn't tell me anything. You're just making Yeah, and the things that really work, um, in a sense, it doesn't kind of matter if this is you know, if you've got you know, take give

me a kind of hypothetic hypothetical example. You've got to fifty million dollar seed fund and you make a significant investment in something that turns out to be worth fifty billion dollars. Does it really matter if it's worth forty billion or sixty billion. The amount of your assist in the start, the amount of the amount that it's returned your fund is sign is sufficient that those numbers don't

really make any difference. So so that that's really fascinating and end Mark and reason when when we said then had a conversation said something similar. But looking back at a perspective of twenty years of doing this, and if you go back and if you would have paid double for everything, it wouldn't mean any difference whatsoever. Yeah, I think there's there's a famous investor whose name I think, I think I should think it was somebody in Hollywood,

of famous producer. He said something, if I had said yes to all the ones I said no to, and no to all the ones I've said yes to, you, I would have come out exactly the same place. So there's always, you know, this is kind of the index indexing story. There's always you can always kind of push these arguments and say, well then we should just nobody knows anything, And there's a little bit more to it than that. Let's talk a little bit of out technology. You you have a quote I really like, you have

several quotes I really like. Um, And let's start with one or two of thesegncy where they go. All social apps grow until you need a news feed or news news feeds grow until you need an algorithm. All algorithmic feeds grow until you get fed up seeing the wrong stuff. And leave for a new app with less information overload. So is the nature of all tech Rinse, lather, repeat and and something becomes an incumbent becomes successful and the new guys are just going to come up and eat

their launch. So I have notes through a blog post in my phone which is called something like um technology determinism. And so there are as it might be half a dozen things which are just steady processes, and now half a dozen things that of cycles. So steady process would most obvious one would be More's law. Uh, there was a process of technology news from research labs to start ups to pick companies. Um. You know, you can imagine like kind of half a dozen of those. Um. Then

there are cycles. And so there are cycles where you go from bundling to unbundling, You go from the client to the server and back again. You go from the public markets. We had conglomerates and then deconglomerization and then re conglomerates. Exactly exactly. Colleague, you at Steven Sinofsky who used to run office. He says, all products expand until they can edit photographs. That's very funny, like word can edit photographs, can edit photographs? All products expand until they

can edit it. That's the endpoint of software. And so there were kind of there and it's a comment about, you know, feature creep or whatever it is you want to say. And so there were these kind of inevitable processes or inevitable pieces of logic that kind of flow through. Now the one that you were sort of quoted, particularly in sort of an observation about I'm actually I wrote a blog post about this last week. It's sort of a combination of dune Bar's number and zarka Berg's law.

Dunebar's number is like, you know, like a hundred and fifty or two hundred people, well enough that you would trend them on Facebook at the way least. And you've got these social apps which make it easy to post stuff and share stuff. And because it's one too many, you're not emailing it to someone or texting it to someone. You can close quite a lot because you don't feel like you're kind of imposing on people to do that.

But you've got two hundred friends and they post five things a day, Okay, Now you've got a thousand things a day in your news feed, and you can't read notes, and so this is the logic that gets Facebook to producing what is you know, what's called an algorithmic feed, which is just like engineers speak for, let's try and work out which of your friends we care about, and maybe we should put those at the top. And let's work out that you like these kind of things. You

don't really like new stories from the Guardian. You prefer to look at pictures of baby So let's put the babies in front of the new stories from the Guardian. And you kind of you, you kind of you come

up and you create that. And so now you've got, instead of, so to speak, a random sample, which is I open the app, what have people posted in the last hour, because I'm not going to scroll past the last hour's worth of post So actually it's random, the random point fact of being what time did open the app. So that's the linear feed. The chronological feed is random. So then you said, well, maybe we should put the

stuff that's important at the beginning. But then you think, okay, but it's now you are arguing, well, why isn't the Guardian all the New York Times at the top. That should be up at the top, because that's important as a public benefit to that and you got this wrong.

My friend posted this thing I wanted to see, and I didn't see it, and so you get that sense of, well, maybe this isn't actually working, and you have Russians trying to game it, and you have all sorts of problems with trying to make that feed work, and so then you then you can say, well, actually, what I want to do is if I really care about this stuff and I want people to see it, I'll send them a text message, I'll do it and WhatsApp, I'll do

it on Facebook Messenger. But then I've got fifteen parallel conversations or twenty parallel conversations with people. And then we start creating WhatsApp groups where twenty people from school or

can work and all talk to each other. And then everyone is like posting more and more stuff, and you think, I really like to have a screen in this app that just showed me like the important stuff from all of these chats that I would see it, and maybe that should be sorted by which are the ones that I want to see, And so like you kind of create the same process over and over again. Now this

is an opinion. This may be entirely be wrong. This may not be how it works, but you can certainly sort of see that problem that if you create tools that let everyone you've ever met share anything they've they've ever been interested in, then you're not going to be able to read it all. Well, this leads me to the very related quote of Facebook's engineering effort goes to stuffing more noise into your news feed and the other fift is working on ways to filter it out, or

it's another expression at the same point. This is this is classic joke and I read in a book by Casting Glany, which is that somebody's dug a hole to build a to create the foundation to build put up a building, and they asked their friend what should I do with all this earth that's come out of the whole, and their friends says, We'll just dig another hole and put it in that, And well, it's just not gonna

work like that. You can't do it like that. And if you've created a system that lets anybody you've ever met send you anything that they feel like sending, then you're not going to be able to fiddle. And there's not like some magic algorithm that's going to make that work. You're and you're ever gonna have a sample. So I want to talk to you about some of your favorite

technologies and future projects. Do we want to discuss it all what's been going on with Facebook this year and everything from Russian bots to the um just changes with Cambridge Analytics and scraping. First of all, I want to say that UM Oxford Analytics is actually not nearly as good as Candy Channelized. Okay, for sure, a little little almamount of joke, but how much. I'm kind of surprised at how shocked people are about this full disclosure. I

have not been a Facebook heavy user ever. I chafed at everything they've ever asked me to give them. They don't have my real birthday, they don't have my real phone, they don't have my real email address, they don't have my real anything. So anyone who wants to scrape other than where I went to college, where I went to grad school, and and a couple of jobs I've worked at that are public, they could scrape what they want

from me. They're they're getting nothing. But I think this whole headache was readily foreseeable by anyone who who forget reading Facebook's terms of use. No, you're not entitled to any of that private information. I'm not sharing that with you. I'll find what I what I'm interested in my own or am I just outside of their demographic I'm an old fogy And so I think there's a bunch of kind of unresolved feeling about Facebook. Is it private or public? If I post stuff in the news feed, will my

friends see it or not? What does that mean? Do I want to share? Do I want to share this stuff or not? And like, we sort of understand that if you search on Google for something, it'll show you or try to show you what you asked for, even if it's something you shouldn't have searched for. We don't really feel like if I my racist uncle posts that story on Facebook, should I see it or not? How do we think about that? Well, if you like it

or reposted. But we also think we also sort of think or maybe Facebook shouldn't be showing that at the top of the list. Well, well it is my he is my uncle, and he did post it. So we have a bunch of sort of of I don't think we have a clear sentiment about put its other extreme, we are confident that are back. We are comfortable with the fact that our banks know how much money we have.

For sure, we're comfortable that our mobile operators and know where our phone is and that there's a kind of a legal apparatus around that if the police need to know, then they can find out. But it's just not available to anybody. I think we sort of have that feeling around Google mostly. I don't think we have like a resolved feeling around Facebook. Um. Now, this particular story is sort of fascinating because of how many pieces of there

there are to that. So Facebook creates this developer platform that allows you to install an app that can access your information and also access information about your friends. And there's a sort of a bunch of reasons why that would be useful, like I want to create in a calendar entry, or I want to you know, I want to see who else is using this app. Um. And a large part of like the advocacy around the tech industry with wall gardens are bad, Facebook is bad because

it's closed. People being need to be able to get their information out. You need to be able other people need to be able to use this to innovate. So there was this whole sort of ideological argument um that Facebook would be evil for not doing this, and Facebook needed to do this, So you create this platform. Um, it turns out that people are able to exploit that and do stuff with it that was not really anticipated.

You think it's not anticipated because from my perspective, I look at it as by design that Okay, so yes and no, So let's get let's go to an analogy. So do you remember word macro viruses? Um? Sure about? So office is supposed to be an open development environment. The people suing them before antitrust is suing them from making it closed in half for third parties to work with. So they create all these API s and they create

this whole macro language. One of the things on page fifteen of the textbook is you can make a macro run. When you open the document page forty eight of the textbook. You can get it to look at your email addresses. Page seventy two. You can get it to send an email. Okay, so I get an email word document, I open it, it emails a copy of itself to everyone in might s book at. Okay, that's not what we expected, but it's really that is not its intended but it's not

its intended purpose exactly. But all of the individual A p I s were there, and so you've got this period from Microsoft where they're thinking, okay, so are we supposed to not have macros that we're supposed to be closed? Now, how do we think about this? And they had to go through this like hundred and eighty degree turn as they went from thinking we should make it as easy as possible for anybody to do this to what would

happen if it's going to moderate my language? What would happen if a bad person, um what my seven year old would call a dingly head, decided to read the textbook Because it's not like you found bugs in it. They're doing stuff it was in the textbook, that was in the manual, but you weren't expecting them to use them in those ways. And I think there's a very strong parallel there with Whatness Cambridgewn Litigo was doing, which

was you were able to install an app. The app can get your information, The app can ask for your friends information. If you say yes, it will get your friends information. Yeah, but we didn't expect that people would use that to exportrate eighty million people's profiles, just as we didn't expect someone would make a word document that could email a copy of itself to me. So now, so now let me I'm gonna go toe to toe on technology with you, which is clearly a mistake on

my pod. But Microsoft notorious for having all these weak security setups and easily exploitable that is such a foreseeable issue. Granted this little bit of hindsight bioso in hindsight, right, but you know Microsoft is home of the exploitable security era. My relationship with Amazon and with Apple is that I pay them for stuff and I expect a different level of trust and a different level of Hey, I'm already giving you money, don't exploit my personal data for other reasons. Well,

let me let me give you a hypothetical. Um, you can install an app on your smartphone. It can pop up a box and ask for your friends, and there's an awful lot of sensible reasons, like you're playing a game, who else? Which of your friends are playing? The game? Installed the Instagram? Who are your friends? So you can follow them? Like we were asked if you want to split a fair with somebody, any basic logical reasons why you wanted to do that? Okay, So that app has

just downloaded six hundred people's home addresses. Is that a breach? Well, it has a downloaded their email address or address or have they looked at the home address? And then people second order question it needs their email address or their phone number to work. That's the idea. There's there is no other identify, so it's got six d people's email addresses. Okay, is that a breach of privacy? Is that Apple's faults? Well maybe a little bit. I would say it is not.

But Apple popped up They said, here is this capability. Apple pops up the thing. Do you want this app to to have your address or not? And the sort of a point where it's not kind of black and white. It's not like somebody hacked into Google and gave them email. And so I think Facebook has been sort of they they have this record of pushing this thing to the kind of the outer envelope um for the last fifteen years.

And I think what's happened here slightly ironically, because they actually closed off all of these APIs like a couple of years ago. So sort of what happened is like the stable door was open if you were a developer, if you were in tech, if you went to the Developer event. They stood up on stage and said, hey, look we leave our stables store always doors open so you can do all this stuff. Isn't this great? And their doors stable doors are opened for like five years

and then they maybe this isn't a good idea. We're going to close the door now. And a bunch of people said, oh, evil, Facebook, you shouldn't close the doors. You should allow free open access. And now here we are in two thousand and eighteen and people go, wow, a lot of people went into the stable and stole all the horses. What a shark? And Facebook, Yeah, but like we near, the doors were open. So there's a bunch of you can kind of see this as they try and work out how we talk about this, how

do we think about this stuff. We have been speaking with Benlic Devans of Andres and Horowitz. If you enjoy this conversation, be sure and check out our podcast extras, where we keep the tape rolling and continue discussing all things technology. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. Check out my daily column on Bloomberg Vie dot com. You could follow me on Twitter at Ri Halts, I'm

Barry Hults. You're listening to Masters in Business on Bloomberg Radio. Welcome to the podcast, Bennetick, Thank you so much for doing this. You're you're talking about stuff that I really find endlessly fascinating, including, UM, the responsibility of Facebook to either have an open platform and what that risk entails, or to control their own APIs and control who access what from your UM feed, what nobody is talking about. And by the time this airs, he will have Mark

Zuckerberg will have already um done his congressional testimony. But UM, there's still a tremendous amount of responsibility on the individual user who willingly said, hey, here's a ton of private personal information about my me, try not to mess it up. I was always too skeptical. I was always too why do you want this information? Can kind of noted it from the other end, which was I think people have felt.

I think for years and years and years people have just sort of presumed that nothing on Facebook with public with private, right, that's correct, and therefore just sort of treated it as a public forum. If you were smart, that's what you presumed, or if you were I should say knowledgeable. That should have been your work, even think I didn't even think beyond that. So, like I've heard people say things like I don't use Facebook. I wouldn't say that on Facebook Messenger because I don't want it

to be public, and of course favorit. Messager isn't public. I mean technically Facebook can, but anybody can screenshot and anybody can. Yeah. Well that's like people thought it was his public and stuff on their on their public profile, and I went, I joined Facebook, and I forget when it launched in the UK, but everyone's profile was public. I mean it was public if you were to if to like if you're in the London network, so anyone

could join the London network, so it was public. And then so there was a period of like a year when, like everybody I've been at school, at university, we've had a profile on Facebook and it was public and I could go and look, and then everyone kind of turned the privacy things on. But I just and even then it's not for I don't feel like you would say

stuff on Facebook that you expect to be secret. That's what I'm getting at, And I don't think many people ever actually thought that, which comes again to my point about sort of unresolved feelings, you can kind of you can go to the extreme and say, um, everything on Facebook should have been completely private and everyone should have understood that and everyone you know, that's not realistic. But

I don't think that's actually very realistic. I think it was much more kind of nuanced and fuzzy than that. And I think you could argue everything in a WhatsApp group should be private, but I think that's a very different kind of form to posting on your news feed. But when I ask people, um, you know, sometimes you have to come out the discussion from an oblique angle. And I like to use radio as an example. I ask people what is radio cell and invariably they say advertising,

And that's the wrong answer. The advertisers of the buyers. Radio sells an audience, and Facebook more or less has the same business model. So I find this interesting. There's a sort of there's a common meme even kind of in the tech industry where people say Facebook sales for information. Now they sell and you as a part of an audience. Well, it's interesting because in in a literal sense, if you're an advertising on Facebook, you don't get given a zip

file of the profile of everybody who saw that. So in a literal sense, it's give not true. In a kind of a metaphorical sense, well, they're sort of they're selling the fact that they know like even like kind of a metaphorical sense. That kind of struggles me. I struggle with that as a statement, But I think the fact that people have those conversations reflects against sort of unresolved feelings about how we should think about this stuff.

It's the same with this idea that's going around now that all the Facebook's problems can be blamed on the ad model, and you think, well, if they had a subscription model, would they not be having to how to develop a platform? Would you not have been posting your personal stuff to your news feed? Like why would that have made any difference? And you would you would still have the same opportunity for apps to come in and

scrape that data before they closed the barn door. Yeah, And I mean I was talked about this because somebody, I think you say on Twitter's somebody who's had a journalism, have had a journalism in school, and they posted like an explanation of what came don We've been doing it. This is three weeks ago, and it's just it was interesting simply that they were. Three weeks on, we're still having people are still writing explanations of what happened because it's all sort of fuzzy and a pay can no

one quite understandable. It's complex, and people like simple narratives with defineable good guys and bad guys. That makes it easier to to do. Nuance sort of gets lost on on cable television to the least. All right, let's talk about some of your favorite technologies. Autonomy of filling the blank, autonomous cars, autonomous uh, whatever, how do we how do you see the development of AI and autonomous everything? Have

you got another hour? I do? I don't know if we are are, so I think what can we say about this? So, first of all, the reason that we're talking about autonomous cars now is because of what we call AI, which really means this new technology called machine learning, or technology that just started working called machine learning, which offers the prospect that a bunch of problems around autonomous cars might be solvable in a way that they really

didn't seem to be easily solvable before. So, in that sense, you could say autonomy is a spinoff of machine learning all the break the advanced. The fact that we're interested in autonomy is a spinoff in machine learning, but there's altos of other stuff that machine learning does as well as we go to autonomy. UM. The sort of the way I kind of talk about this is I have a slide with a picture of a horseless carriage from It's a carriage with no horse, but otherwise it's nothing's changed.

And I think that's what I hear when people say driverless car, that you've taken the steering wheel out maybe, but like nothing else has changed and it will still drive around like like autonomous cars mean us that drive like people, but without making mistakes or making the speed limit. And that's that's just a really shortsighted way of thinking about this. I think there's kind of two kind of

building blocks to think about. The first is you can if you have when we have a fully autonomous world in end decades forty years, depending on what you think the s cards will look like. UM. And of coursely you have periods where some places will be much go much quicker, So like you might you might say Manhattan

is autonomous only in the week in twenty thirty or something. Um. But at that point there's no accidents, well, certainly much less well basically no accidents because all the accidents are caused by human error, and you have much less congestion because you don't have traffic waves, you don't have accidents which cause a third of congestion or something. UM. You can have vehicles on freeways driving a hundred miles and our two apart from each other. So you radically change

what congestion looks like. You change radically change all the vehicles look like. So you could have a vehicle that will never you know, if you're got calling an on demand vehicle in Manhattan, it will not go over twenty miles. An how it could be a golf cart. Um. If you're going to go to JFK, they and you would send a different vehicle. And so you can radically just redesign the vehicle in the same way that when we got rid of the horse, you radically redesigned the vehicle.

And you can also radically redesign the city or change your assumptions about the city in the same way that we did whom we got rid of the horse, um, except that you're no longer trying to design a city that will constrain the nineteen year old guy on a souped up Chevy Tomorrow. Um, you're designing a city based on You can create rules. You can tell the cars where to go and what they can do or not do.

You can have dynamic real time road right saying that you can say, do you want to pay a lot to get there in fifteen minutes or you're willing to pay less to get there in thirty minutes. You can tell the cars at any given instant which road to say should be taking and how fast they should be going. And so what you have is like a change in the structure of a city that has more a lot in common with like the way the city changes a

result of the car. UM kind of the example I often give, which works well since we're in Manhattan is imagine if you live in Brooklyn and it's November and you want to go to that cool new restaurant in Manhattan. How are you going to get there? Where you could walk ten minutes in the rain to the subway station, you could get a cab UK well, presuming you can get a cab. Presume you can get a cab O care.

It's going to cost you what dollars each way you could drive, then one of you can't drink on the way back, and you're going to have to park, and you have to pay for parking in little twenty minutes to find a place to park there. Let's not go out of that place, okay, Now go to a fully autonomous world. You raise your watch, you say, hey, alex or I need a car. The nip hod that's around the block stops outside your door within thirty seconds, and

there is no congestion. The pod just takes you there. It drops you off outside the door um if it's your pod, um, and then it goes and waits for you somewhere where parking is cheap, or it waits for you somewhere else. If it's an on demand pod, it immediately starts driving other people around, so there's no parking, remember, and then you can go home when you can drink

as much as you like. I mean, remember all those those photographs of European and East Coast cities from before cars, and you think where the streets are all twice as wide because you don't have cars hop down both sides of the street. What we could go back to, and so you have like kind of radical change in how we think about what the city is. Therefore, in what does a gas station mean? Very obviously, but also what does big box we tell mean? Where are you willing

to shop? What does your commute look like? You know? Today? So I live in San Francisco, which calls itself a city, and I work in Menlo Park, which is an office park. Forty five minutes drive south if there's not much traffic, Um, I should say an hour, but I'll admit to forty five minutes south. Now, supposing there really is no traffic, well,

then I can get there in half an hour. But supposing I'm able to read instead of drive, I might be able to live further away and have it spend longer in the car because I don't need to just sit there staring at the road all day. So where do you live, where do you commute? Where does the retail go? All those sorts of changes that happened, like the stuff that happened as a result of cars, which

basically what I'm talking about. Like, there's this great saying that it was easy to predict mass ownership of cars, but hard to put itt Walmart. It's hard to put it drive through a t M s like that kind of stuff. Those second and third auto consequences will flow out of this stuff. Let's talk about smart fill in the blanks, smart homes, smartphone, smart cars, smart speakers. What is it about the the preface smart that suddenly everybody wants to move in that direction? So so software and okay,

so three or four blocks to talk about here. The first block is smartphone supply chain. One and a half billion smartphones sold last year. All of those chips are available as like a fire hose of stuff to make stuff with. So before smartphones, if you wanted to put computing into something, you had to use PC components, So an a t M is a PC. But those are big, and they're heavy, and they need mains power, they need power and so on. Smartphone components much cheaper, much smaller,

much lighter. So suddenly you can make a connected door lock, and like you can get the parts really easily. UM plus um UBER called as internet plus machine learning, means that like a camera can actually be able to tell if there's something moving or not. So you've got all this stuff. Suddenly the stuff was not working. I think The best analogy for this is like our grandparents could have told you how many electric motors they owned. There was one in the car, they had a vacuum cleaner,

there was one in the fridge. They owned maybe five electric motors in total, Like there was only one electric motor in the car. Let's start a major that was it. Today, who has a clue have any electric motors are in your car? There's like twenty And if you tell your grandfather that you can press a button to adjust your wing moor, he'd hit you on the back of the head. But that's just how it worked, because that's just how the technology got deployed. The same thing in your home.

There was a period when everything was going to have a DC motor. Now everybody has you like you have an you have a microwave, You maybe have a toaster, you may have a kettle, you have a blender. Nobody has all of those things. Everyone in East Agea has a rice cooker. Everyone in the UK has a kettle. In America, you maybe don't have a kettle, but you have a coffee machine. But nobody has an electric carving knife.

And so what happens is like there are those underlying components as cheap commodities were in this period of trying to work out what you should do with them and how they should all get plugged together, and should they all be the same system or not sure they all talked to Alexa or not everything, And then you've got

like the austrial logic. So like somebody the Samson board sat down and said, everything we sell must have the Samsing Voice Assistant, because then people will be more likely to buy the Samson fridge that sorts to the Samsing dish watcher when it talks to the samthing this, um, how's it working out? Well? Then you so it's great you see this at CS because you can see like the fridge people thought this is a fantastic I mean metaphorically speaking, the fridge people thought, this is a fantastic

idea to dish watch. Your people are like, don't damn it. Okay, we'll put the voice assistant in the dish wash but a that and be they want to sell it to people who also in an LG fridge and a sub zero fridge. So they've got the Samson voice is this, and they've got home kit and they've got Alexa in it because they only need to sell dish watches. They

don't care about the Greek strategy. It's like when Everythingy product had a memory stick, and there was some and like the Sony group had said everything's gonna have have a memory stick, and some bits of Sony thought this is great, and some bits were like, God, damn it, um, what are we gonna do? What are we going to do with this? And as with an electric carving knife versus the blender, like some of this stuff will make them,

so some of it won't. It would be quite nice to be able to say to my oven, um, okay, pre heat the oven to three degrees, as opposed to having to learner the interview and walk over and touch it. I have my apartment, I have an infrared sensor in the bathroom. I walk into the bathroom, the light comes on. It's fantastic. My parents hated because they get up at four o'clock in the morning, then the lights come on.

I think you program that because it's not anything. It's just literally it is just a dumile sensor from forty years again. So so here's where the smart world starts to get more interesting. So I have a long, twisty driveway because we're Our house is said off the main road, and we have lights along it. And the first phase was having a switch that put the lights on and put the lights off. And then the next phase was having a timer that has the lights going on at

seven a pm and off at eleven pm. But the problem with that is seven pm is light sometimes of the year and dark. I thought, no, no, nothing like that's a contemporary house. But the new switch that is literally going in this weekend is built into the lights which as you can set your latitude and that lets whatever you said as your like going on thirty minutes after sunset will change throughout the year regardless and you don't have to deal with it. So that's our sort

of smart application of software. Are like, we don't have any you know, your your phone changes its clock when the time don't changed automatically things the satellites. So I think there's a there's a kind of question to where will the complexity set And this is like the electric carving knife. There will be stuff that just doesn't make sense the other you do electric carving knife have been around for decades and they never would the other the

other really so well. Some people have a like I can show you where they took a chunk out of my finger. The other extreme is like the other. The other way to think about this is it's old saying that basically a computer should never ask you a question that it ought to be able to work out on itself. And that used to mean, like you plug the printer in, the computer should know what the printer is. It shouldn't

ask you what printer it is. Um, then it means your phone doesn't ask where you are when you call a car because it's got GPS and it knows. Then it means like the light should know what the times it is, so the light should just go on right, that's right. And the chair that the tension point and all of those is, is it actually more hassle to configure the thing to do that? Sometimes it is, sometimes

it is, sometimes it isn't. So my beef about Siri, which was the leading voice app to begin with before Alexa began to eat its lunch, is asking you questions that it should, from the context be able to figure out. This is just paying a database, paying a location, um software ping something it has access to all that stuff. This so this is a different sense of the word smart. So let's talk about voice for a minute. Okay, So we have this thing called machine so okay, the right

way putting this. So when you talk to a computer, the three things that are going on. Step one is it is taking the audio wave form and turning that into text. So it's just transcribing it and turning it into words, which I think is basic math and and well no it was. And this is something where it used to work okay, like three quarters of the time, but you remember using dictation apps like National actually speaking,

it's sort of work three quarters. There are more and it would get better like half a point every year. A machine learning comes along and it goes from working three quarters at the time to working at the tent at the time. And so machine learning gives you this radical change in that it also then there's a second piece, which is you needed to go from the text to a structured query. You need to actually work out what the verb and the nan is and what is his asking,

which is a completely different computation. Um. And so when you talk to your computer, that's it's two very almost unrelated things going on. Machine learning. Also, this is called natural language processing. UM. Machine learning also made that work way, way, way way better. UM. Then you have the third problem we're is, Okay, I've created this structured query, do I have anything to give it to? And you should have. Well, so this is the problem, which is what you've actually

got here is an IVYR. You've got a voice tree, Press one for this, press to for that. Machine learning means you can now and we've we've all kind of experienced this, saying you phone the airline and they say, tell us what you want to ask about, and there's only fifteen things you could ask so they can recognize it the text, and then they can work out what you're asking about and they can read you to the right number inside that they're pretty awful also, But what

we don't have. Machine learning gives us a way of automating the transcription and getting the natural language processing to work. But you might ask it any one of three or four million things, and so effectively, what I mean when I say a structured query is you can fill in a dialog box by talking to the computer, and the computer can work out which dialog box you were asking for.

But somebody has to have made the dialog box by hand. Okay, so you know if you're if you're a phone company, so if you're a phone manufacturer and you have a few million or maybe even a few billion queries, but how uses your frame of reference? But so you can make the top fifty categories, all the top thousand categories. Well, and if you're gonna ask something, really is the curve gets really really steep really quickly, and you don't know

what you can ask. So you cannot ask. You can get the top five things, and you can say you can ask for weather, you can ask for timer, you can ask for a unit conversion, you can ask for the time in a different city. You can ask me to play music and you'll get the music right roughly half of the time. This is you know, you ask it to play an album and it plays you like a weird cover of it because it doesn't understand um. But then I was like, what are the thousand things

you might ask your ear to do? And how many of those get really complex really quickly? Like can you rebook my meeting this afternoon? Okay, that's not a simple query, that's like five hundred things that you need to know that And so the problem is as I said. What you have with a voice assistant is you have an IVR that will always understand what you asked. Machine learning means it will always correctly root you to the right number.

What it doesn't have is a way to have somebody at that number automated automate what that person at the number will do. And so you have to create those dialog boxes one by one. So give you an example. Sirie learns how to do cricket. You know it didn't somebody at Apple sat down and wrote the cricket module. Okay,

now Sirie can do hurling. Okay, Well, somebody at Sirie at Apple had to sit down and write that, and then Siri can do call me a uber, right, Well, someone at Uber had to write that, and then it can do um tell me whether my flight is delayed? Right. Someone had to write that, And every single one of those, some human being has to sit down and spend an afternoon writing those. And the problem with this is it

doesn't scale. And in a sense, if you could do that, you'd have made how nine thousand and how nine thousand is not the aggregate of like some of you sitting and writing those one at a time. That's that other thing that's machine learning, a big data and a bunch of things. So that's the gap. You know, we have a way to get the transcription and the NLP to

work completely accurately, Well, we don't have. It is a way to get to automatically make a system that could answer any question you could possibly answer human being to think of what all of those would be. So that is a description of So let me answer answer this question another way. So imagine in the nineteenth century someone tries to make a mechanical horse. There's no I mean, you've seen the pattern drawing things. There's no law of

physics that says you can't make a mechanical horse. It's just that the degree of complexity required to get that to work was impossible in the nineteenth century, and arguably it's impossible now. I mean, Boston Dynamics are now trying to do this pretty close. But that's like a hundred and fifty years later, um and so and so. What you can actually do is you could make a bicycle and the steam engine, and a bicycle is a lot simpler than a horse, and it can't do everything that

a horse can do. But it turns out that was kind of useful anyway. The same thing with with AI. What people tried to do with AI until a couple of years ago was they would try and write rules.

So if you wanted to recognize a cat, what you do is you do edge detection, and you do text your analysis, and you try and make something that looks for two eyes in roughly the right place relative to two ears, and you try and make something that tries to look for legs and hopefully, and you try and work out how the hell are we going to tell

the difference between a cat and a dog? And it would sort of work, like three quarters at the time, it would never really work for the same reason that the mechanical horse would sort of work but never actually work. And then what machine learning does is say no, no, no no, no no. You've given a million pictures labeled cat and a million pictures of labeled dog, and you let the

machine ripe the rules. That's what machine learning means. The machine generates a millionaire statements that will allow it to calculate this difference between a cat picture and a dog picture with ninety two point seven percent accuracy, and that gives us a breakthrough of a whole class of problem. It solves image recognition itselves, speech itselves, language, itselves, a whole kind of level of pattern recognition. When it doesn't do is give you a human ten year old. It

doesn't give you general intelligence. Basically, what you've built is you've built an enormous spreadsheet with a million IF statements meshed together and linked together. And you can give this spreadsheet a picture of a cat and a picture, and it will tell you whether the cat in it or not, and that's all it will do. So another way of thinking about this is like, we have this fantasy of domestic robots that will walk around your home and do

the housework. We have domestic robots. It's called a washing machine. That's what machine learning gives you. It gives your washing machine. It gives your washing machine that can recognize family pictures. It gives your washing machine that can tell me is there strange behavior happening on this network. It gives your washing machine that can recognize handwriting. It gives your washing machine that can do X. But it can only do that one thing. It can't do anything else, and it

can't clean. You can't wash your dishes, And so that's what machine only gives it gives you this step change in capability. So one of the way I'm kind of I'm sort of giving you a stack of metaphors here. I think a really good way to think about what machine learning gives us is it's like thinking about relations databases. It's a relational databases gives you this amazing step change in what you can do with computers. Suddenly you can say,

show me X by Y Bloomberg. Going would not exist without this, SAP would not exist without this, just in times, supply chains would not exist without this totally transformed our world. But everything you use now but it's not AI. It is. But it's like if it's AI, then everything is AI. It doesn't get you how nine thousand. Let me jump into some of my favorite questions. Tell us the most important thing that people who work with you don't know about you? I hate personal questions? Is that true? I

don't know. I find I kind of look at this, this email, this, I didn't puzzled by these kind of questions. I don't know, what is it the people who work with that, we have no idea. They probably don't know have a dog. There you go, Well, because sometimes it's revealing some people have told us deep dark secrets from decades ago, and case there is a joke that the most secure form of encryption is anything you say in

the second hour of a podcast. Here we are, I can say it now, and it's you, me, and a handful of friends that are hearing that nobody else is gonna hear. Um, tell us about your early mentors who affected the way you looked at technology and venture inversiting. I would say it's not really about how I look at technology. It's more sort of mental processes and ways of thinking about things and ways to try and answer questions.

And so the stuff I did at university, you know, people talking to me about medieval history, and it's not this is no, it's not that didn't happen. This didn't happen. It's more, okay, how do you actually understand what we're trying to think about here? So, like I remember like my first or second, like you know, the first. The way it came Bridge history works is you do an You get given an essay title and a reading list, and you come back in a week with your essay

and that's it. You don't have to get lectures. And so I think. My first essay was the one that, of course everyone screws up, and it was like was the King of England more or less powerful after ten exty six? And you know I gave the long answer and my professor said, look, you have to think about this is what does it actually mean to be powerful? And power is the ability to get people to do

something they don't want to do. And so you can give all this other stuff about all the sophistication of the Anglo Saxon monarchy and they had their own much more sophisticated coinage and blah blah blah. No that was two years later, But like, who is more Is he actually more powerful? William the conqueror can tell people to

do stuff and they have to do it. And the King of the sax the king before the conquest, King of England, like no one really paid any attention to what he said except for the coin like the complicated coinage. So my point is like it's not so much that someone told me how this is how to think about technology. It's more how to sort of think systematically and try and break up problems and try and look for peace of components and try and look kind of look at

things in those kinds of ways quite personally. Tell us about some of your favorite books, be they fiction, nonfiction, technology, or what have you. Um, I don't know. I'm sort of I'm always reading and I'm always buying books, and those two process as are completely independent. I read almost no business books or technology books, partly because I feel that all business books are basically a short magazine article padded out to There are some authors in particular I

can name, well, that's their entire over. Well, if you feel like if you've written fifteen business books on the same theme, then like you're either a very bad writer or very good writer, depending on your very good marketer, Well that too. Perhaps, I know. I think I read things that intrigued me and interest me. The last thing I read with the Iliad, which I've never read before. Wait,

the history major never read the Iliad. Well, so there's this great line from Italia Calvino where he says the number of something like the number of essential works is so great that nobody, no matter how much they're reading, has read more than a fraction. Um, you know, literature as a sample, you can't read all the books. There was a period when you could have seen all the movies. Now you can't see all the movies, you can't listen

to all the music. You just have to take samples and take do things that stimulate you or make you think in interesting ways, particularly things that aren't about what you work on. So give us another example from that's that's newer than two thousand years old. Um, I don't know. By the way, this is everybody's favorite question, the one I get. I read books and then I can't remember what it is? What? What? What? The last book I read was, Um, what stands out to you? What has

really read? Really? The Iliad really stood out for me. That was a kind of a couple of weeks ago, I read this fascinating book about medieval manuscripts called Encounters with Fascinating Manuscripts or something like that by college librarian in Cambridge, and it's one of these sort of new kind of genre of books which kind of looked like

a big hardback nonfiction book. But they're all in color all the way through, which is funny to do with the development of printing technology, which is there's there's loads of books that have color illustrations all the way through, which is but aren't art books. It's kind of interesting and it's just sort of about how books evolved. So it's kind of fascinating observation that books or codeses used to be rectangular because you made them out of papyrus.

So you would lay your sheets, you know, you lay the strips of plant kind of cross weights like plywood, and you'd make them square shaped because you had the strips of all the same length, So that produced to square shaped piece of papyrus. And then you moved to um, what's it called animal skin? What's the word? I've forgotten the word? My mind is gone blank now too, um Um. You moved to parchment from animal skins. And there's this

great line. Animals tend to be oblong, so you get oblong pieces and then you fold them up and that produces an oblong book. And so when you go from papyrus department, you go from square books to oblong books. And here we are years later and the books are still oblong, and that's just the residual of the earlier technology of parchment. Yeah, exactly. I think those sorts of I mean, coming back to you know, why is this interesting?

I do think those sorts of how did stuff happen, and why did the what's the thread of causation and how things can be completely random is sort of interesting and how do these does this stuff change how we

think about it? So on the flight here, I've read a book about kind of lighting tech, the evolution of lighting technology in the eighteenth and nineteent century, as we go from candles to light to kind of lamps that hold the candle and produce five times more light, how we go to now or the gas lights to to um earlier electric light which used weren't very bright, and then use tungsten filament and again, so there's the evelation

of light. This observation that stuck in my mind and I and now need to go and check which is an assertion that Dutch seventy century Dutch pictures have windows but no cuts, because why would you keep the light out. Everybody wants to light and you want the light in. And of course, as we all sort of no, light was really expensive and used to be a luxury and now it's not. But I just thought that was a really revealing way of thinking about how your whole sense

of the world changes when lighting is cheap. Why would you have curtns that that's quite fastening. The book How We Got to Now has there's six inventions, one of which is lighting, and the process of watching the price plummet over over the centuries is really is really fastening, all right. The interesting thing that comes out of that is that when light was expensive, state of symbol was

to stay up all night. So rich people would get up at lunchtime and go to bed at l five in the morning because they could afford to, because they could afford candles. Everybody else gets up with the croaker, think, um, what do you do for fun? What do you do to relax outside of the office in San Francisco? Um? So I have a seven year old and a dog,

so my time is not my own understood completely? What sort of advice would you give to a millennial or someone who just graduated college that was interested either in a career in technology or a venture capital So I sort of struggle to give an advice party because my career has been a series of random lurches and kind of companies going out of business. I won't apply for a job at cling Back. Don't worry, I think there's

a The only thing I've sort of observed is there is? Um, there is the kind of things that you are good at doing and the kind of mental processes that you are good at. Are you good at people or not? Are you good at process and project management and making sure everything gets done right? Or is that not what it interests you? Are you good at trying to kind of explain an empathy sizing with people? Are you good at trying to explain and discuss and argue with people

and persuade people? Um? What are the things that you're good at doing? And those are not necessarily what was your degree in, and they aren't necessarily specific to two jobs. So like I have contemporaries who went off and become what England we call a barrister, which is basically a lawyer who only goes to court is in England and law is split solicitors to be the paper what barristers get called as well. Exactly that's all, but that's all

the effectively and it's a completely different profession. Um, And I have contemporaries who are barristers, and you know I could have done that. I could have gone and argued for a living, and you know you kind of you go and do you look at the problem and you pull it apart and you work out what story to tell and how to place added to the law and try and persuade the judge and the jury. Um So,

in a sense, I have those skill sets. On the other hand, I look at these contemporaries and it says, you know, um, Charlie is probably the leading up and coming advocate in complex of your maritime insurance disputes. And I think, well, I hope he's paid a lot to that, but his interests did in the argument and the explaining

and the discussion. The fact that it's insurance is kind of neither here nor there really, um So, I think you have to just kind of work out, well, what is it What are the things that fascinate you, and what are the kind of mental processes that fascinate you, And and our final question, what is it that you know about the worlds of technology that you wish you knew twenty years ago? Other than like buy Apple or something, No, no,

nothing like that. That that's you know, anyone could look at prices twenty years ago and said hey, by Amazon, by Apple. But generally in terms of thinking in terms of broad processes, what would have helped you early in your career had you figured it out sooner. So that actually might be a good way of me framing my technology to determinism peace because it's a sort of I mean, the other thing people talk about about a lot in venture capitalist patent recognition that this sort of looks like

things that will work. This looks like things that that never work and why and the thing that never work eventually they do work, and that's where there's a hundred billion dollar outcome. So that's also an interesting kind of fruitful copy conversation. But it's sort of understanding these patents of Okay, you have to think, okay, how is this going to fit within this ecosystem? This is what is the timing for this? It's those sorts of mechanics of

the process um that are interesting. I think that's quite fascinating. We have been speaking with Ben Nick Evans of Andres and Horowitz. If you enjoy this conversation, be sure and look up an Inch or down an inch on Apple, iTunes or SoundCloud, overcast, Bloomberg dot com wherever your final podcasts are sold, and you could see any of the other two hundred such conversations we've had over the past

four years. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. I would be remiss if I did not thank the crack staff that helps us put this podcast together each week. Taylor Riggs is our book or slash producer. Medina Parwana is our audio engineer slash producer. Mike Batnik is our head of research. I'm Barry Retolts. You're listening to Master's in Business on Bloomberg Radio.