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Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: His name is ben Nick Evans, and he is a

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Speaker 1: partner at the famed technology venture capital firm in Silicon

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Speaker 1: Valley Andrews and Horowitz, perhaps known to the aficionado as

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Speaker 1: a sixteen z Um. I have actually some swag from

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Speaker 1: Andreas and Harrowitz. I've blue a sixteen z hat which

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Speaker 1: matches my car and I just leave that in the car,

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Speaker 1: Um when I'm driving around trying to look cool to

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Speaker 1: a very select group of tech geeks. Um, if you

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Speaker 1: are at all interested in the development of technology, of ecosystems,

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Speaker 1: of of autonomous everything, of smartness, of really where the

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Speaker 1: puck is going to be the future of technology, you

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Speaker 1: will find this to be a fascinating conversation. He absolutely

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Speaker 1: has an encyclopedic knowledge of what's taken place and why

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Speaker 1: and has tremendous insight into the likely direction that uh,

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Speaker 1: this space is going. So, with no further ado, my

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Speaker 1: conversation with Andres and Horowitz is Benedict Evans. I'm Barry

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Speaker 1: rid Hilts. You're listening to Master's in Business on Bloomberg Radio.

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Speaker 1: My special guest today is Benedict Evans. He is a

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Speaker 1: partner at the legendary venture capital firm Andrees and Horowitz,

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Speaker 1: where he works as an analyst and does a weekly

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Speaker 1: consumer newsletter blog What have You, covering everything from mobile

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Speaker 1: platforms to AI to autonomous everything. He is also a

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Speaker 1: recommended follow. He began his career as an equity analyst

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Speaker 1: in investment banking, moving on to rategy and business development

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Speaker 1: at such August firms as Orange and NBC Universal. Benlict Evans,

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Speaker 1: Welcome to Bloomberg. Thank you for having me. Do do

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Speaker 1: you prefer Benlicht or Ben or Benedictio? Is better? S? A?

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Speaker 1: Okay for sure? Um, there is an advantage to having

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Speaker 1: an unusual first or last name for SEO purposes. I'm

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Speaker 1: convinced if my name was Barry Smith, no one would

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Speaker 1: know who I am. But we can talk about s

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Speaker 1: c O a little later. So you graduate Cambridge in

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Speaker 1: right in the midst of the dot com and technology boom,

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Speaker 1: not too long before the bust. What was it like

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Speaker 1: coming out into the banking world in the midst of

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Speaker 1: that era. Well, so I actually joined an equity capital

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Speaker 1: markets team. So we were doing europ and tech I

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Speaker 1: P A S and Safe for about nine months. I thought,

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Speaker 1: this is going to be a fun career, right, this

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Speaker 1: is easy. You just throw it out there and everybody

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Speaker 1: buys it. He's some random thing, and a sovereign wealth

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Speaker 1: fund puts in an order for three extra floats. And

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Speaker 1: then in about well early spring of two thousand, everything

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Speaker 1: started going down and we sort of forget now that

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Speaker 1: we talked about the dot com bubble. But you know,

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Speaker 1: particularly in Europe, there was also a mobile bubble. But

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Speaker 1: there was also a mobile bubble, and there was also

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Speaker 1: the fixed line broadband fiber bubble. So you kind of

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Speaker 1: had three bubbles that kind of had happened more or

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Speaker 1: less independently for different reasons, and kind of merged and

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Speaker 1: combined into one enormous thing. I would then when then

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Speaker 1: the market went down for three years, I would argue

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Speaker 1: you had a semi conductor bubble, you had a software bubble,

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Speaker 1: you had a hardware bubble, you had a storage bubble.

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Speaker 1: It all started a band with bubble. In the US,

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Speaker 1: we had fiber uptick and telecom bubble, where you had

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Speaker 1: you had a tech bubble, but you also had a

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Speaker 1: mobile bubble. Which is very only for very distant reasons,

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Speaker 1: was happening at the same time. It was happening at

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Speaker 1: the same time, and then there was a fiber bubble,

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Speaker 1: and yet so they all combined and we got this

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Speaker 1: enormous kind of explosion and then an enormous pot and

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Speaker 1: the market went down without stopping more or less for

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Speaker 1: three years. So were you always the tech guy or

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Speaker 1: or did you was your background more finance or something

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Speaker 1: well above? So I did my degree in history, which

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Speaker 1: is really an analytic subject. So here is a bunch

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Speaker 1: of information, try and work out something interesting or meaningful

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Speaker 1: or insightful to say about it, or something original and

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Speaker 1: interesting to say about it. And so I went from

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Speaker 1: that into sort of fairly conventional routing into invest in

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Speaker 1: banking UM, and sort of quickly decided that e c

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Speaker 1: M was kind of project management and whereas research was

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Speaker 1: probably something I was likely to be better at. And

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Speaker 1: so I went into at research and I did did

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Speaker 1: did UM. So I went and did my European mybile

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Speaker 1: stocks for a couple of years, and that was in

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Speaker 1: London or New York and London. And did you did

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Speaker 1: you spend any time in New York or did you

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Speaker 1: go straight London to well, I was on Valley. Uh No, So,

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Speaker 1: I mean I came to New York for work and

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Speaker 1: I worked for NBC Universal, so I used to come

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Speaker 1: over here. Um, but now the first time I actually

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Speaker 1: worked properly in the States was to go to work

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Speaker 1: for a sixty and oh so London with really just

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Speaker 1: brief sojourns in New York City. Yeah, I mean I

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Speaker 1: worked for Orange, So I went to Paris and I

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Speaker 1: worked for NBC Universal. So I came to New York,

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Speaker 1: but that my job job spot was in London. And

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Speaker 1: so you're covering telecom starts, recovering Mobile, You're covering a

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Speaker 1: lot of the technology related space as an analyst. What

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Speaker 1: was that like in the midst of wild overvaluation and

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Speaker 1: collapsing share price? So it's it's kind of interesting. I mean,

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Speaker 1: there's there's there's a couple of different things that one

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Speaker 1: of them is it's interesting in hindsight to to look

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Speaker 1: at them in hindsight in that they were the go

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Speaker 1: go growth, exciting, dynamics, sexy, disruptive companies. Me and Mobile

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Speaker 1: had gone from nothing in oh my god, everyone on

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Speaker 1: Earth is going to have one of these things, and

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Speaker 1: they were really kind of like they thought of themselves

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Speaker 1: as poets and they connected everybody and that was it.

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Speaker 1: And you know, there's obviously there's still a lot of

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Speaker 1: stuff going on in emerging markets, but like everyone Knework

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Speaker 1: got a phone and that was it. And then they

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Speaker 1: went from being amazing. They went from being Google to

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Speaker 1: being water companies in like a year two years effect.

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Speaker 1: But so you look at the all the stuff we

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Speaker 1: do on our smartphones, now, this was all kind of

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Speaker 1: in concept videos and presentations from the mob All operated

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Speaker 1: is in you're going to do all of this stuff,

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Speaker 1: and they were going to do it all, and of

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Speaker 1: course it all happened, but it wasn't done by them,

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Speaker 1: And so that perspective of how kind of I think

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Speaker 1: there's a lot of echoes of that. For example, if

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Speaker 1: you look at cars now, you have this all this

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Speaker 1: future ology of what's going to happen and what cars

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Speaker 1: will be and how everything will change, and you have

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Speaker 1: kind of the renderings of the glass cars, which is

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Speaker 1: like the renderings of the glass phones in two thousands

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Speaker 1: when there were no phones with color screens, and you think, well,

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Speaker 1: you might get like three quarters of this, right, But

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Speaker 1: like the last quarter of it is going to be

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Speaker 1: where all the money is. And that's the difference between

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Speaker 1: it being knock you and Microsoft in it being companies

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Speaker 1: you've never heard of like Apple and Samsung and Google

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Speaker 1: or forgotten like Apple um and no one had ever

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Speaker 1: heard of Google. And so it's interesting to look back

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Speaker 1: and think, how did we think about what was going

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Speaker 1: to happen then, and how much of it was right

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Speaker 1: and wrong? And what would you have had to have

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Speaker 1: said then in order to get it right. So you're

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Speaker 1: describing a form of historical futurism because when we've what

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Speaker 1: we've seen over the years is expectations of the future

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Speaker 1: turn out to be wildly either over optimistic or pessimistic,

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Speaker 1: but rarely right on the nose. Is that a fair

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Speaker 1: And I think that's right. I mean, as I said,

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Speaker 1: I think there's sort of several bits. There's sort of

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Speaker 1: several lessons you can learn from, like not so much

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Speaker 1: to drop the bubble per se, but the kind of

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Speaker 1: the way people thought about it. And so there was

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Speaker 1: this whole idea in the early nineties of the thing,

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Speaker 1: this thing called the Information super Highway, which the whole

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Speaker 1: just the name conveys the fact that it was going

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Speaker 1: to be kind of centrally controlled, and it would be

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Speaker 1: the cable company and News Corporation and twenty Century Fox

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Speaker 1: and the New York Times, and they would kind of

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Speaker 1: get together every six months and decide what you were

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Speaker 1: going to have and how. And of course what we

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Speaker 1: had instead was permission in this innovation and the open Internet,

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Speaker 1: and there was no central authority, and anyone could do

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Speaker 1: what they wanted in just to some extent. And so

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Speaker 1: that is interesting to compare that now with the way

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Speaker 1: we for example, people talk about cars all the way

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Speaker 1: people talk about what's happening to TV now. Um, I

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Speaker 1: think the kind of the but as I just said,

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Speaker 1: the fact that you could have got you could have

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Speaker 1: guessed like eighty or nine of it and it's still

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Speaker 1: have missed the important parts is also fascinating. Like you

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Speaker 1: could have said in nineteen in two thousand, okay, everyone

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Speaker 1: will have one of these things. Everyone will have Internet

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Speaker 1: on the phone. It will be a real operating system

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Speaker 1: and not a future phone. It will be open Internet. Um,

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Speaker 1: therefore it will not be the carriers that would have

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Speaker 1: can describe today's well perfectly, But you would have still

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Speaker 1: have said, well then it'll be Microsoft K Nokia that

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Speaker 1: do that. What's amazing is there was this a T

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Speaker 1: and T commercial. I very vividly remember. I think Tom

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Speaker 1: Selleck did the voice over and he talks about what

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Speaker 1: the future will be like, and I think the punchline

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Speaker 1: on the beach you will. And it turned out to

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Speaker 1: be dead right, except for the fact that a T

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Speaker 1: has or more nothing to do with it exactly. I mean.

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Speaker 1: One of the ways I described this when I talked

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Speaker 1: to Talcos now is it's as though a municipal water

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Speaker 1: company looked at the mineral water business and they said,

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Speaker 1: you know, come on, we've got brand, we've got water,

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Speaker 1: we've got trusted. You wouldn't buy water from a company

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Speaker 1: you don't trust. We should be doing And they hire

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Speaker 1: McKinsey and wolf Fallens and they build the whole thing,

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Speaker 1: and like two years later, that first pallet of water

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Speaker 1: balls into Walmart and it goes onto the shelves and

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Speaker 1: they look at it next to yether of two D

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Speaker 1: brands and they think, hang on a second, something not

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Speaker 1: quite right here. Um, So, how did you go from

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Speaker 1: working in London or Paris as a telecom man? Was

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Speaker 1: too I know, I'm going to move six thousand miles

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Speaker 1: away and become a imagineer. And I just asked them

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Speaker 1: for a job. I don't know why people say it's

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Speaker 1: hard to get a job in venture. You know, you

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Speaker 1: go ask them for a job, they say, yes, okay,

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Speaker 1: that's it. Yeah, it was easy. How did you how

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Speaker 1: did you? Well? First hear of instance, so well, so

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Speaker 1: there's a process here. And as I mentioned, I actually

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Speaker 1: did my degree in history, which is analysis, and so

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Speaker 1: the question was sort of picking up and looking for

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Speaker 1: things where you can apply that to analysis, and so

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Speaker 1: I left university and became a south Side analyst, and

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Speaker 1: is sort of many people will know that, for many

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Speaker 1: various reasons, stop being a fun thing to do, or

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Speaker 1: even a thing that anyone could do. So I think

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Speaker 1: when I was in telecoms, merl Lynch, you have had

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Speaker 1: something like fifty um up in telecoms analysts, and the

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Speaker 1: last time I looked, I think they had less than

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Speaker 1: half a dozen. So that industry ceased to be fun.

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Speaker 1: And so I left and went and worked in strategy,

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Speaker 1: first at Orange and then at um NBC Universal and

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Speaker 1: Channel four again both in London. So from sort of

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Speaker 1: sitting on the outside in sitting on the inside out

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Speaker 1: trying to work out what we do and how do

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Speaker 1: we how do we understand this? And it's kind of

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Speaker 1: interesting because the kind of the questions changed and suddenly

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Speaker 1: you've got a spreadsheet that lists all the stuff you've

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Speaker 1: been trying to work out from the from the public financials.

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Speaker 1: But then you're just trying to work out different stuff.

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Speaker 1: But the process is the thought process at the state.

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Speaker 1: Because I did that for a while. Um. I left

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Speaker 1: NBC Universal the week that General Electric share price fell fifty,

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Speaker 1: which is obviously a called direct causal relationship. Sometimes you know,

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Speaker 1: started out in their exactly we went up up by half,

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Speaker 1: and that the same amount the next week. Of course,

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Speaker 1: um so I left when the financial that is in

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Speaker 1: the financial crisis. It was so yeah, sound about that,

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Speaker 1: an old and boring story of what happened there. Um

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Speaker 1: was writing both about kind of what's happening in fashion

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Speaker 1: and I was kind of writing stuff. The way I

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Speaker 1: at Apple could write, except that either they couldn't public it,

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Speaker 1: publish it, or they would be writing for a very

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Speaker 1: different audience. They'd be writing for, you know, buy cell

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Speaker 1: whole audience or um or they wouldn't have the analytic

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Speaker 1: background to write it. So like a sell side analyst

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Speaker 1: could write this stuff, but they're writing for a different audience.

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Speaker 1: A senior person at a company knows all of this stuff,

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Speaker 1: but they are not used to writing stuff and they

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Speaker 1: can't publish it anyway. Somebody and McKinsey knows all this

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Speaker 1: stuff that they're not allowed to say that's not going

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Speaker 1: to work. Um. So I was kind of an interesting,

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Speaker 1: an analytic and strategy background was used to writing it

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Speaker 1: that about this stuff and explaining it, could say it

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Speaker 1: in public at a time when very few other people

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Speaker 1: two or three years writing a bog posts and getting

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Speaker 1: like a hundred page vs a months, and then I

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Speaker 1: went through a period where I was getting a couple

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Speaker 1: of thousand page vis a day. Um. And that sort

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Speaker 1: then and I kind of picked up my my bag

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Speaker 1: and thought, well, what else do I want to do?

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Speaker 1: What do I want to do next? Where could I

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Speaker 1: deploy this? And you know, you you have those conversations

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Speaker 1: at various stages in your life. And I thought, well,

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Speaker 1: I should go and do you do this in venture capital?

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Speaker 1: And where's the place to go and do venture capital?

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Speaker 1: And you the global cluster with San Francisco, and what

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Speaker 1: kind of innovative approach to its explaining things and adding

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Speaker 1: thought of at the top of that list, and you

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Speaker 1: got a couple of introductions. I went in and I said, well,

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Speaker 1: this is sort of what I do. Is there a

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Speaker 1: place for this in this firm? Does this fit within

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Speaker 1: the firm? Is it's useful? And they kind of said yeah,

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Speaker 1: just like that or less. So um, lots of other

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Speaker 1: people blogers should be inundating a sixteen Z with their

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Speaker 1: resumes and saying, look, I've been doing this also. You know,

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Speaker 1: I don't think you need a resume. It's it's kind

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Speaker 1: of like, you know, if you want a sales job,

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Speaker 1: you should be able to get the meeting. If you

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Speaker 1: can't get the meeting, you shouldn't have a sales job.

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Speaker 1: And it's kind of the same. You prove that you

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Speaker 1: can do the job by doing it. That makes that

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Speaker 1: makes a lot of sense. So the focus that you

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Speaker 1: have today is no longer telecom the way it was,

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Speaker 1: but certainly the concept of mobility as a source of

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Speaker 1: um possible changes in technology is a key factor. What

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Speaker 1: do you focus on these things? Well, I think talking

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Speaker 1: about mobile now is a bit like talking about PCs

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Speaker 1: ten years ago. Right, Yes, this is the center of everything,

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Speaker 1: but it's happened, and so we're not arguing about iOS

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Speaker 1: versus Android, or whether everyone's going to have one of

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Speaker 1: these things, or apps versus the web, all of those

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Speaker 1: kind of things, though, they're not interesting conversations anymore. It's

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Speaker 1: like arguing, you know, is everybody get onto going to

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Speaker 1: get onto the web? Well? Yes, Now what next question?

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Speaker 1: So you look? So there's one answer is well, what

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Speaker 1: are the next questions? What are the next kind of

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Speaker 1: mega trends that are happening. The second is there's a

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Speaker 1: lot of conversation around what happens with that stuff? What

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Speaker 1: happens with the stuff that we've already had so ten

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Speaker 1: years ago? What do we do with broadband and browsers?

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Speaker 1: So well, let's talk about search, let's talk about social,

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Speaker 1: Let's talk about what happens once everyone has a PC.

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Speaker 1: Now what happens once everyone has a smartphone and everyone

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Speaker 1: is on Facebook? So we have those kinds of conversations

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Speaker 1: about the world, where the world that we've just built,

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Speaker 1: what happens with that, and then we think, well, what

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Speaker 1: are the next things? So machine learning, autonomous cars, mixed reality, cryptocurrency,

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Speaker 1: What are the next fundamental trends that will shape the

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Speaker 1: tech industry the way first the PC and then mobile

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Speaker 1: shape the tech industry in the last twenty or thirty years.

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Speaker 1: I think then within that there's a kind of you know,

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Speaker 1: for what I try and do as a question of

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Speaker 1: looking for the arguments or looking for the questions. So

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Speaker 1: what are the places where there's an on the one hand,

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Speaker 1: On the other hand, is it going to look like

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Speaker 1: this or is it going to look more like that?

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Speaker 1: Are they going to be winner not? Will there be

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Speaker 1: autonomous cars? Yes not? Will it be in fifteen or

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Speaker 1: twenty years? Who knows, don't know. It's more like are

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Speaker 1: they going to be winner? Takes all effects in this?

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Speaker 1: Is it going to look like Android? Or is it

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Speaker 1: going to look like abs where there's like a widget

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Speaker 1: that everyone buys and it doesn't matter. Is what's going

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Speaker 1: to happen in mixed reality? Is this going to be

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Speaker 1: something that's going to be every hardware manufacturer? Is it

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Speaker 1: going to be super concentrated? How do we think about

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Speaker 1: machine learning? How do we try and understand what that

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Speaker 1: might change inside big companies, and so kind of there's

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Speaker 1: like what are the topics and then how do we

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Speaker 1: work out what the kind of the useful questions to

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Speaker 1: talk about might be within that? So you're less extra

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Speaker 1: bolending current trends out into some future date and instead

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Speaker 1: thinking about, well, here's what we are several steps beyond that.

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Speaker 1: What are going to be the subsequent developments that this

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Speaker 1: might lead to. You're really several steps ahead, that's right.

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Speaker 1: I mean that's partly a consequence of the state of

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Speaker 1: the industry. So like five years ago, okay, what's it

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Speaker 1: was a horse race who's gonna win? But is Apple

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Speaker 1: and Google? What's going to happen? Is Apple going to

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Speaker 1: survive even though Android has got this open story and

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Speaker 1: this open source story and so on? So is there

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Speaker 1: going to be one for Apple? Is there going to

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Speaker 1: be win for a third entry? And what will happen

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Speaker 1: to BlackBerry? Will black Bey killing on? And in niche?

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Speaker 1: Will Windows going to be able to break in? So

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Speaker 1: all of that was it was the motorating I'm sorry Windows, Yeah, yah, yeah,

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Speaker 1: I don't I don't recall that. Yeah, yeah, that's it's

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Speaker 1: a painful subject for some people. So there's it was

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Speaker 1: a horse race Steve, Steve Bomber in particular. Yeah, yeah,

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Speaker 1: he's three chairs. But that was you are you're calling

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Speaker 1: the place. That's that the American phrase, you're you're going

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Speaker 1: to work out what was going on? Now you're not.

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Speaker 1: That's we know what happened. And we don't have those

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Speaker 1: kind of day by day tactical questions around autonomer's cars

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Speaker 1: because like there aren't in your automo's cars. We don't

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Speaker 1: have those kind of day by day questions around mixed

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Speaker 1: reality because you can't buy a mixed reality headset yet,

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Speaker 1: mixed reality glasses yet. So the character of the question

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Speaker 1: changes because we're at a different point in the S curve. Basically,

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Speaker 1: when the S curve is going near vertical, then you're

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Speaker 1: trying to work out, oh my god, what's going on?

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Speaker 1: Is the rocket ship going to blow up? When is

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Speaker 1: it going to start flattening out. Where we are now

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Speaker 1: is we've got the oldest curve has flattened out right

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Speaker 1: at the top of the of the scale, and we're

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Speaker 1: talking about what you can on the top of it,

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Speaker 1: and the new S curves are kind of under the

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Speaker 1: radar if I can mix my metaphors. One of the

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Speaker 1: ways I described this, it's kind of a good Manhattan metaphor.

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Speaker 1: It's like you walk past your construction site every day

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Speaker 1: for six months and there's a bunch of construction worker

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Speaker 1: is kind of scout standing around, scratching their backside, is

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Speaker 1: not doing anything. You think all these guys are lazy.

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Speaker 1: Then you walk past on Monday morning and they put

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Speaker 1: fifteen stories of steel frame up me You think, wow,

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Speaker 1: they're really busy over the weekend. You're missing the slow

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Speaker 1: And then there's a period at the end where they're

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Speaker 1: like putting the facade on and doing all the fit out,

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Speaker 1: and again that looks so boring and so mobile is

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Speaker 1: at the stage where you know you're putting the facade

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Speaker 1: down and you're doing the fit out and the other

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Speaker 1: staff are still kind of hulling the ground and you

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Speaker 1: can't really see what's going on. Um And so that

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Speaker 1: means the character the questions change. In the early stages

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Speaker 1: of investments. There there is no data, there's no discounted

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Speaker 1: cash forlor model. You're really dealing with two founders and

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Speaker 1: a PowerPoint presentation and some numbers which are more or

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Speaker 1: less best guesses. Yeah, so the way, it's an interesting

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Speaker 1: shift in being an equity analyst because you're right at

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Speaker 1: one end of the risk profile. You know, if you

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Speaker 1: have tourn of buying tea bills is at the other end.

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Speaker 1: You're at the stage where half of the deals you

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Speaker 1: do will return less than invested capital. And that's the plan,

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Speaker 1: and five pent will produce more than a ten x

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Speaker 1: return and that gets you a kind of a three

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Speaker 1: x return over ten years. And so everything you do

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Speaker 1: has to be capable of being amazing, and if it's

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Speaker 1: capable of going from two people at a power point

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Speaker 1: to being an amazing thing worth hundreds of millions of

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Speaker 1: billions of dollars, it kind of has to be implausible

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Speaker 1: and crazy, and they have to be a bunch of

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Speaker 1: reasons why it might not work. And therefore what you

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Speaker 1: have to do is sort of suspend disbelief and not think,

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Speaker 1: here are all the reasons why this might not work,

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Speaker 1: But what if this did work, what would it be

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Speaker 1: and are these the people who can make that happen,

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Speaker 1: Which is a very very different approach than thinking about,

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Speaker 1: all right, will this new widget or this new management

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Speaker 1: team or whatever sell enough to move the earnings pre

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Speaker 1: share calculus of this publicly trading exactly. I think the

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Speaker 1: kind of the key way to think about Silicon Valley

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Speaker 1: is it's a machine for running experiments, and most of

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Speaker 1: the experiments won't work, and that's the plan. And yes,

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Speaker 1: you know you might. You know, you could do some

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Speaker 1: an experiment that's clearly a terrible idea and it was

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Speaker 1: never going to work, and you could kind of mess

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Speaker 1: it up and blow up your lab, and you know,

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Speaker 1: people will look down on you for that, but no

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Speaker 1: one will look down on you for the fact that

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Speaker 1: you ran an experiment in a produced negative result that

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Speaker 1: was just okay, well we tried, and do you do

432
00:19:49,200 --> 00:19:51,480
Speaker 1: the do you run the experiment, right, that's a different question.

433
00:19:51,680 --> 00:19:54,080
Speaker 1: But you ran the experiment, it didn't work, Okay, that's fine.

434
00:19:54,640 --> 00:19:57,920
Speaker 1: And the ones that do work justify the whole exercise

435
00:19:57,960 --> 00:20:00,960
Speaker 1: and pay for the whole exercise and produce mobile phone,

436
00:20:01,040 --> 00:20:03,880
Speaker 1: saw produced Apple and Google and so on. So let's

437
00:20:03,920 --> 00:20:06,680
Speaker 1: let's let's zoom in on that a little bit. When

438
00:20:06,680 --> 00:20:08,840
Speaker 1: you're at this side of the funnel, when you're looking

439
00:20:08,880 --> 00:20:13,080
Speaker 1: at these really early stage companies, if half of them

440
00:20:13,240 --> 00:20:16,679
Speaker 1: are effectively money losers for you, for the farm as

441
00:20:16,720 --> 00:20:22,120
Speaker 1: an investment. How do you conceptualize what you're looking at?

442
00:20:22,359 --> 00:20:25,720
Speaker 1: Is it is it the founders themselves? Is it the idea?

443
00:20:26,359 --> 00:20:29,280
Speaker 1: Is it something that, hey, this is just so crazy

444
00:20:29,280 --> 00:20:32,560
Speaker 1: it might work. What what is the thought process like when,

445
00:20:33,200 --> 00:20:37,199
Speaker 1: especially where you sit, where there's an endless stream of

446
00:20:37,240 --> 00:20:41,440
Speaker 1: people coming to Silicon Valley to pitch ideas to vcs. Well,

447
00:20:41,520 --> 00:20:44,480
Speaker 1: so I think most venture capitalists look at this stuff

448
00:20:44,600 --> 00:20:46,760
Speaker 1: mostly in the same way. You can argue a little

449
00:20:46,760 --> 00:20:50,479
Speaker 1: bit about emphasis, um, but there is the question of

450
00:20:50,520 --> 00:20:54,960
Speaker 1: what is the market opportunity here? And um? Are these

451
00:20:55,040 --> 00:20:57,119
Speaker 1: the people who are going to be able to work

452
00:20:57,119 --> 00:21:00,040
Speaker 1: out find discover that market opportunity because they never to

453
00:21:00,119 --> 00:21:02,520
Speaker 1: PE's not going to be exactly the original idea. It's

454
00:21:02,520 --> 00:21:04,360
Speaker 1: going to be something sort of adjacent to that. You'll

455
00:21:04,440 --> 00:21:06,879
Speaker 1: kind of twist around and find it pivot shall we?

456
00:21:07,440 --> 00:21:09,160
Speaker 1: Not so much that I'm more iterate. I would say

457
00:21:09,200 --> 00:21:11,439
Speaker 1: pivoty is more like, Okay, we built a whole company

458
00:21:11,440 --> 00:21:13,159
Speaker 1: on that promise and that didn't work at all, so

459
00:21:13,280 --> 00:21:15,840
Speaker 1: now let's try something else. Um, that's not really the

460
00:21:15,880 --> 00:21:18,600
Speaker 1: same thing. It's more kind of well, we were kind

461
00:21:18,600 --> 00:21:20,720
Speaker 1: of that and we sort of moved around, we got,

462
00:21:20,760 --> 00:21:23,280
Speaker 1: we made, we found it worked. There's this whole sort

463
00:21:23,280 --> 00:21:26,040
Speaker 1: of concept in silicon value product market fit, and so

464
00:21:26,160 --> 00:21:27,720
Speaker 1: you have sort of a product and you're trying to

465
00:21:27,800 --> 00:21:29,520
Speaker 1: work out, well, what the market being, what would the

466
00:21:29,520 --> 00:21:32,240
Speaker 1: product be around this space until we can find something

467
00:21:32,280 --> 00:21:35,960
Speaker 1: that meshes and takes off. And so the earlier you are,

468
00:21:36,400 --> 00:21:39,560
Speaker 1: the more in a sense you're betting on the ability

469
00:21:39,600 --> 00:21:42,800
Speaker 1: of the founders to find that, But you're also betting

470
00:21:42,800 --> 00:21:45,360
Speaker 1: on is this a great market and is there some

471
00:21:45,480 --> 00:21:47,439
Speaker 1: angle or some way that they're going to find in

472
00:21:47,560 --> 00:21:49,560
Speaker 1: order to make in order to turn this into a thing.

473
00:21:49,800 --> 00:21:52,920
Speaker 1: So what I'm hearing from you as future growth prospects

474
00:21:53,040 --> 00:22:00,120
Speaker 1: or and valuation is well, well so, um, it depends.

475
00:22:01,040 --> 00:22:04,840
Speaker 1: But the more progress you make and the more numbers

476
00:22:04,880 --> 00:22:07,600
Speaker 1: you have, the more that you start looking at metrics

477
00:22:07,760 --> 00:22:10,800
Speaker 1: and the less that you start thinking about potential. Um.

478
00:22:10,960 --> 00:22:13,520
Speaker 1: So it's sort of the super super early stage. Then

479
00:22:13,560 --> 00:22:16,480
Speaker 1: the valuations all tend to look the same as you

480
00:22:16,520 --> 00:22:19,520
Speaker 1: go further in. Then you start getting much more specific

481
00:22:19,600 --> 00:22:21,399
Speaker 1: about well, how well are they doing and what do

482
00:22:21,440 --> 00:22:23,640
Speaker 1: we think this looks like? Um. Then it gets sort

483
00:22:23,640 --> 00:22:25,040
Speaker 1: of gets kind of case by case, and you know,

484
00:22:25,080 --> 00:22:27,159
Speaker 1: you get up to a company that's got billions of

485
00:22:27,200 --> 00:22:29,160
Speaker 1: dollars a revenue, and then you're doing dcs, and you're

486
00:22:29,200 --> 00:22:32,800
Speaker 1: doing multiples like anybody else, but at the super early stage.

487
00:22:32,800 --> 00:22:34,600
Speaker 1: In a sense, doing a DCF on two people with

488
00:22:34,640 --> 00:22:36,840
Speaker 1: a power point is just an exercise in self deception.

489
00:22:37,040 --> 00:22:39,080
Speaker 1: Sure you could do it. You could. You could sort

490
00:22:39,080 --> 00:22:42,480
Speaker 1: of say, well, if they managed to sell this thing

491
00:22:42,520 --> 00:22:44,680
Speaker 1: to two billion people, well there will be some value

492
00:22:44,720 --> 00:22:47,040
Speaker 1: that trying to do a dcattle that. Well, like, what

493
00:22:47,080 --> 00:22:48,600
Speaker 1: does that get me? It doesn't tell me anything. You're

494
00:22:48,640 --> 00:22:53,240
Speaker 1: just making Yeah, and the things that really work, um,

495
00:22:53,400 --> 00:22:55,280
Speaker 1: in a sense, it doesn't kind of matter if this

496
00:22:55,400 --> 00:22:57,160
Speaker 1: is you know, if you've got you know, take give

497
00:22:57,160 --> 00:22:59,960
Speaker 1: me a kind of hypothetic hypothetical example. You've got to

498
00:23:00,000 --> 00:23:03,040
Speaker 1: fifty million dollar seed fund and you make a significant

499
00:23:03,040 --> 00:23:05,639
Speaker 1: investment in something that turns out to be worth fifty

500
00:23:05,640 --> 00:23:08,560
Speaker 1: billion dollars. Does it really matter if it's worth forty

501
00:23:08,560 --> 00:23:11,879
Speaker 1: billion or sixty billion. The amount of your assist in

502
00:23:11,920 --> 00:23:13,960
Speaker 1: the start, the amount of the amount that it's returned

503
00:23:14,000 --> 00:23:16,600
Speaker 1: your fund is sign is sufficient that those numbers don't

504
00:23:16,600 --> 00:23:21,280
Speaker 1: really make any difference. So so that that's really fascinating

505
00:23:21,280 --> 00:23:23,959
Speaker 1: and end Mark and reason when when we said then

506
00:23:24,040 --> 00:23:28,440
Speaker 1: had a conversation said something similar. But looking back at

507
00:23:28,440 --> 00:23:31,760
Speaker 1: a perspective of twenty years of doing this, and if

508
00:23:31,760 --> 00:23:33,359
Speaker 1: you go back and if you would have paid double

509
00:23:33,440 --> 00:23:36,600
Speaker 1: for everything, it wouldn't mean any difference whatsoever. Yeah, I

510
00:23:36,600 --> 00:23:39,040
Speaker 1: think there's there's a famous investor whose name I think,

511
00:23:39,359 --> 00:23:41,240
Speaker 1: I think I should think it was somebody in Hollywood,

512
00:23:41,240 --> 00:23:43,160
Speaker 1: of famous producer. He said something, if I had said

513
00:23:43,200 --> 00:23:44,800
Speaker 1: yes to all the ones I said no to, and

514
00:23:44,840 --> 00:23:46,200
Speaker 1: no to all the ones I've said yes to, you,

515
00:23:46,200 --> 00:23:48,359
Speaker 1: I would have come out exactly the same place. So

516
00:23:48,400 --> 00:23:49,880
Speaker 1: there's always, you know, this is kind of the index

517
00:23:50,040 --> 00:23:52,200
Speaker 1: indexing story. There's always you can always kind of push

518
00:23:52,240 --> 00:23:55,200
Speaker 1: these arguments and say, well then we should just nobody

519
00:23:55,240 --> 00:23:57,240
Speaker 1: knows anything, And there's a little bit more to it

520
00:23:57,280 --> 00:24:01,200
Speaker 1: than that. Let's talk a little bit of out technology.

521
00:24:01,760 --> 00:24:04,320
Speaker 1: You you have a quote I really like, you have

522
00:24:04,400 --> 00:24:07,760
Speaker 1: several quotes I really like. Um, And let's start with

523
00:24:08,080 --> 00:24:11,120
Speaker 1: one or two of thesegncy where they go. All social

524
00:24:11,200 --> 00:24:14,080
Speaker 1: apps grow until you need a news feed or news

525
00:24:14,119 --> 00:24:17,960
Speaker 1: news feeds grow until you need an algorithm. All algorithmic

526
00:24:18,000 --> 00:24:20,560
Speaker 1: feeds grow until you get fed up seeing the wrong stuff.

527
00:24:20,920 --> 00:24:25,000
Speaker 1: And leave for a new app with less information overload.

528
00:24:25,280 --> 00:24:28,960
Speaker 1: So is the nature of all tech Rinse, lather, repeat

529
00:24:29,080 --> 00:24:34,000
Speaker 1: and and something becomes an incumbent becomes successful and the

530
00:24:34,040 --> 00:24:35,960
Speaker 1: new guys are just going to come up and eat

531
00:24:35,960 --> 00:24:38,440
Speaker 1: their launch. So I have notes through a blog post

532
00:24:38,600 --> 00:24:42,920
Speaker 1: in my phone which is called something like um technology determinism.

533
00:24:42,960 --> 00:24:45,119
Speaker 1: And so there are as it might be half a

534
00:24:45,160 --> 00:24:48,520
Speaker 1: dozen things which are just steady processes, and now half

535
00:24:48,560 --> 00:24:51,120
Speaker 1: a dozen things that of cycles. So steady process would

536
00:24:51,119 --> 00:24:53,720
Speaker 1: most obvious one would be More's law. Uh, there was

537
00:24:53,760 --> 00:24:56,920
Speaker 1: a process of technology news from research labs to start

538
00:24:57,000 --> 00:24:59,600
Speaker 1: ups to pick companies. Um. You know, you can imagine

539
00:24:59,600 --> 00:25:01,960
Speaker 1: like kind of half a dozen of those. Um. Then

540
00:25:01,960 --> 00:25:04,439
Speaker 1: there are cycles. And so there are cycles where you

541
00:25:04,480 --> 00:25:07,159
Speaker 1: go from bundling to unbundling, You go from the client

542
00:25:07,280 --> 00:25:10,639
Speaker 1: to the server and back again. You go from the

543
00:25:10,720 --> 00:25:14,760
Speaker 1: public markets. We had conglomerates and then deconglomerization and then

544
00:25:14,800 --> 00:25:19,480
Speaker 1: re conglomerates. Exactly exactly. Colleague, you at Steven Sinofsky who

545
00:25:19,680 --> 00:25:22,400
Speaker 1: used to run office. He says, all products expand until

546
00:25:22,400 --> 00:25:25,919
Speaker 1: they can edit photographs. That's very funny, like word can

547
00:25:26,080 --> 00:25:28,840
Speaker 1: edit photographs, can edit photographs? All products expand until they

548
00:25:28,840 --> 00:25:31,159
Speaker 1: can edit it. That's the endpoint of software. And so

549
00:25:31,200 --> 00:25:33,200
Speaker 1: there were kind of there and it's a comment about,

550
00:25:33,200 --> 00:25:34,800
Speaker 1: you know, feature creep or whatever it is you want

551
00:25:34,800 --> 00:25:36,359
Speaker 1: to say. And so there were these kind of inevitable

552
00:25:36,400 --> 00:25:39,080
Speaker 1: processes or inevitable pieces of logic that kind of flow through.

553
00:25:39,280 --> 00:25:41,560
Speaker 1: Now the one that you were sort of quoted, particularly

554
00:25:41,800 --> 00:25:44,040
Speaker 1: in sort of an observation about I'm actually I wrote

555
00:25:44,160 --> 00:25:45,720
Speaker 1: a blog post about this last week. It's sort of

556
00:25:45,720 --> 00:25:48,440
Speaker 1: a combination of dune Bar's number and zarka Berg's law.

557
00:25:48,840 --> 00:25:50,919
Speaker 1: Dunebar's number is like, you know, like a hundred and

558
00:25:50,920 --> 00:25:53,120
Speaker 1: fifty or two hundred people, well enough that you would

559
00:25:53,160 --> 00:25:56,200
Speaker 1: trend them on Facebook at the way least. And you've

560
00:25:56,240 --> 00:25:58,800
Speaker 1: got these social apps which make it easy to post

561
00:25:58,880 --> 00:26:01,920
Speaker 1: stuff and share stuff. And because it's one too many,

562
00:26:01,960 --> 00:26:03,960
Speaker 1: you're not emailing it to someone or texting it to someone.

563
00:26:04,000 --> 00:26:05,680
Speaker 1: You can close quite a lot because you don't feel

564
00:26:05,720 --> 00:26:07,400
Speaker 1: like you're kind of imposing on people to do that.

565
00:26:07,960 --> 00:26:11,040
Speaker 1: But you've got two hundred friends and they post five

566
00:26:11,040 --> 00:26:13,680
Speaker 1: things a day, Okay, Now you've got a thousand things

567
00:26:13,680 --> 00:26:16,480
Speaker 1: a day in your news feed, and you can't read notes,

568
00:26:17,240 --> 00:26:19,359
Speaker 1: and so this is the logic that gets Facebook to

569
00:26:19,600 --> 00:26:22,880
Speaker 1: producing what is you know, what's called an algorithmic feed,

570
00:26:22,880 --> 00:26:25,240
Speaker 1: which is just like engineers speak for, let's try and

571
00:26:25,280 --> 00:26:27,000
Speaker 1: work out which of your friends we care about, and

572
00:26:27,040 --> 00:26:28,840
Speaker 1: maybe we should put those at the top. And let's

573
00:26:28,840 --> 00:26:30,600
Speaker 1: work out that you like these kind of things. You

574
00:26:30,640 --> 00:26:32,879
Speaker 1: don't really like new stories from the Guardian. You prefer

575
00:26:32,960 --> 00:26:34,560
Speaker 1: to look at pictures of baby So let's put the

576
00:26:34,560 --> 00:26:36,240
Speaker 1: babies in front of the new stories from the Guardian.

577
00:26:36,520 --> 00:26:37,959
Speaker 1: And you kind of you, you kind of you come

578
00:26:38,000 --> 00:26:41,160
Speaker 1: up and you create that. And so now you've got,

579
00:26:41,200 --> 00:26:43,600
Speaker 1: instead of, so to speak, a random sample, which is

580
00:26:43,640 --> 00:26:45,879
Speaker 1: I open the app, what have people posted in the

581
00:26:45,960 --> 00:26:47,919
Speaker 1: last hour, because I'm not going to scroll past the

582
00:26:47,960 --> 00:26:50,399
Speaker 1: last hour's worth of post So actually it's random, the

583
00:26:50,480 --> 00:26:52,520
Speaker 1: random point fact of being what time did open the app.

584
00:26:52,960 --> 00:26:56,280
Speaker 1: So that's the linear feed. The chronological feed is random.

585
00:26:56,320 --> 00:26:57,639
Speaker 1: So then you said, well, maybe we should put the

586
00:26:57,640 --> 00:27:00,480
Speaker 1: stuff that's important at the beginning. But then you think, okay,

587
00:27:00,480 --> 00:27:03,000
Speaker 1: but it's now you are arguing, well, why isn't the

588
00:27:03,000 --> 00:27:05,000
Speaker 1: Guardian all the New York Times at the top. That

589
00:27:05,000 --> 00:27:06,719
Speaker 1: should be up at the top, because that's important as

590
00:27:06,760 --> 00:27:09,240
Speaker 1: a public benefit to that and you got this wrong.

591
00:27:09,480 --> 00:27:11,480
Speaker 1: My friend posted this thing I wanted to see, and

592
00:27:11,480 --> 00:27:14,280
Speaker 1: I didn't see it, and so you get that sense of, well,

593
00:27:14,320 --> 00:27:17,000
Speaker 1: maybe this isn't actually working, and you have Russians trying

594
00:27:17,040 --> 00:27:18,719
Speaker 1: to game it, and you have all sorts of problems

595
00:27:18,720 --> 00:27:21,240
Speaker 1: with trying to make that feed work, and so then

596
00:27:21,280 --> 00:27:23,000
Speaker 1: you then you can say, well, actually, what I want

597
00:27:23,040 --> 00:27:24,840
Speaker 1: to do is if I really care about this stuff

598
00:27:24,840 --> 00:27:26,199
Speaker 1: and I want people to see it, I'll send them

599
00:27:26,200 --> 00:27:27,840
Speaker 1: a text message, I'll do it and WhatsApp, I'll do

600
00:27:27,880 --> 00:27:31,880
Speaker 1: it on Facebook Messenger. But then I've got fifteen parallel

601
00:27:31,880 --> 00:27:34,560
Speaker 1: conversations or twenty parallel conversations with people. And then we

602
00:27:34,560 --> 00:27:37,480
Speaker 1: start creating WhatsApp groups where twenty people from school or

603
00:27:37,560 --> 00:27:39,000
Speaker 1: can work and all talk to each other. And then

604
00:27:39,040 --> 00:27:41,000
Speaker 1: everyone is like posting more and more stuff, and you think,

605
00:27:41,320 --> 00:27:43,000
Speaker 1: I really like to have a screen in this app

606
00:27:43,040 --> 00:27:44,959
Speaker 1: that just showed me like the important stuff from all

607
00:27:44,960 --> 00:27:46,920
Speaker 1: of these chats that I would see it, and maybe

608
00:27:47,000 --> 00:27:49,800
Speaker 1: that should be sorted by which are the ones that

609
00:27:49,840 --> 00:27:51,399
Speaker 1: I want to see, And so like you kind of

610
00:27:51,400 --> 00:27:54,679
Speaker 1: create the same process over and over again. Now this

611
00:27:54,760 --> 00:27:56,679
Speaker 1: is an opinion. This may be entirely be wrong. This

612
00:27:56,800 --> 00:27:58,679
Speaker 1: may not be how it works, but you can certainly

613
00:27:58,680 --> 00:28:01,959
Speaker 1: sort of see that problem that if you create tools

614
00:28:01,960 --> 00:28:04,400
Speaker 1: that let everyone you've ever met share anything they've they've

615
00:28:04,400 --> 00:28:06,320
Speaker 1: ever been interested in, then you're not going to be

616
00:28:06,359 --> 00:28:08,560
Speaker 1: able to read it all. Well, this leads me to

617
00:28:09,119 --> 00:28:13,240
Speaker 1: the very related quote of Facebook's engineering effort goes to

618
00:28:13,280 --> 00:28:16,440
Speaker 1: stuffing more noise into your news feed and the other

619
00:28:16,440 --> 00:28:19,280
Speaker 1: fift is working on ways to filter it out, or

620
00:28:19,280 --> 00:28:21,320
Speaker 1: it's another expression at the same point. This is this

621
00:28:21,480 --> 00:28:23,479
Speaker 1: is classic joke and I read in a book by

622
00:28:23,480 --> 00:28:26,240
Speaker 1: Casting Glany, which is that somebody's dug a hole to

623
00:28:26,359 --> 00:28:28,959
Speaker 1: build a to create the foundation to build put up

624
00:28:28,960 --> 00:28:31,000
Speaker 1: a building, and they asked their friend what should I

625
00:28:31,000 --> 00:28:32,680
Speaker 1: do with all this earth that's come out of the whole,

626
00:28:32,760 --> 00:28:34,399
Speaker 1: and their friends says, We'll just dig another hole and

627
00:28:34,440 --> 00:28:38,280
Speaker 1: put it in that, And well, it's just not gonna

628
00:28:38,280 --> 00:28:40,200
Speaker 1: work like that. You can't do it like that. And

629
00:28:40,280 --> 00:28:42,400
Speaker 1: if you've created a system that lets anybody you've ever

630
00:28:42,440 --> 00:28:44,360
Speaker 1: met send you anything that they feel like sending, then

631
00:28:44,360 --> 00:28:45,840
Speaker 1: you're not going to be able to fiddle. And there's

632
00:28:45,840 --> 00:28:48,080
Speaker 1: not like some magic algorithm that's going to make that work.

633
00:28:48,080 --> 00:28:50,440
Speaker 1: You're and you're ever gonna have a sample. So I

634
00:28:50,480 --> 00:28:52,120
Speaker 1: want to talk to you about some of your favorite

635
00:28:52,120 --> 00:28:56,280
Speaker 1: technologies and future projects. Do we want to discuss it

636
00:28:56,400 --> 00:28:59,640
Speaker 1: all what's been going on with Facebook this year and

637
00:29:00,320 --> 00:29:06,040
Speaker 1: everything from Russian bots to the um just changes with

638
00:29:06,120 --> 00:29:10,000
Speaker 1: Cambridge Analytics and scraping. First of all, I want to

639
00:29:10,040 --> 00:29:12,560
Speaker 1: say that UM Oxford Analytics is actually not nearly as

640
00:29:12,600 --> 00:29:17,400
Speaker 1: good as Candy Channelized. Okay, for sure, a little little

641
00:29:17,440 --> 00:29:21,840
Speaker 1: almamount of joke, but how much. I'm kind of surprised

642
00:29:21,880 --> 00:29:27,560
Speaker 1: at how shocked people are about this full disclosure. I

643
00:29:27,600 --> 00:29:32,280
Speaker 1: have not been a Facebook heavy user ever. I chafed

644
00:29:32,320 --> 00:29:34,760
Speaker 1: at everything they've ever asked me to give them. They

645
00:29:34,760 --> 00:29:36,760
Speaker 1: don't have my real birthday, they don't have my real phone,

646
00:29:36,760 --> 00:29:39,239
Speaker 1: they don't have my real email address, they don't have

647
00:29:39,320 --> 00:29:42,200
Speaker 1: my real anything. So anyone who wants to scrape other

648
00:29:42,280 --> 00:29:44,600
Speaker 1: than where I went to college, where I went to

649
00:29:44,640 --> 00:29:48,320
Speaker 1: grad school, and and a couple of jobs I've worked

650
00:29:48,320 --> 00:29:50,959
Speaker 1: at that are public, they could scrape what they want

651
00:29:51,040 --> 00:29:54,200
Speaker 1: from me. They're they're getting nothing. But I think this

652
00:29:54,280 --> 00:30:00,360
Speaker 1: whole headache was readily foreseeable by anyone who who forget

653
00:30:00,440 --> 00:30:04,120
Speaker 1: reading Facebook's terms of use. No, you're not entitled to

654
00:30:04,160 --> 00:30:06,680
Speaker 1: any of that private information. I'm not sharing that with you.

655
00:30:07,040 --> 00:30:09,400
Speaker 1: I'll find what I what I'm interested in my own

656
00:30:09,880 --> 00:30:12,240
Speaker 1: or am I just outside of their demographic I'm an

657
00:30:12,240 --> 00:30:17,400
Speaker 1: old fogy And so I think there's a bunch of

658
00:30:17,720 --> 00:30:22,760
Speaker 1: kind of unresolved feeling about Facebook. Is it private or public?

659
00:30:23,280 --> 00:30:25,280
Speaker 1: If I post stuff in the news feed, will my

660
00:30:25,320 --> 00:30:28,120
Speaker 1: friends see it or not? What does that mean? Do

661
00:30:28,160 --> 00:30:29,840
Speaker 1: I want to share? Do I want to share this

662
00:30:29,920 --> 00:30:33,200
Speaker 1: stuff or not? And like, we sort of understand that

663
00:30:33,280 --> 00:30:35,520
Speaker 1: if you search on Google for something, it'll show you

664
00:30:35,680 --> 00:30:37,240
Speaker 1: or try to show you what you asked for, even

665
00:30:37,280 --> 00:30:39,920
Speaker 1: if it's something you shouldn't have searched for. We don't

666
00:30:40,080 --> 00:30:43,320
Speaker 1: really feel like if I my racist uncle posts that

667
00:30:43,400 --> 00:30:46,239
Speaker 1: story on Facebook, should I see it or not? How

668
00:30:46,280 --> 00:30:48,640
Speaker 1: do we think about that? Well, if you like it

669
00:30:48,840 --> 00:30:51,560
Speaker 1: or reposted. But we also think we also sort of

670
00:30:51,560 --> 00:30:53,640
Speaker 1: think or maybe Facebook shouldn't be showing that at the

671
00:30:53,680 --> 00:30:55,520
Speaker 1: top of the list. Well, well it is my he

672
00:30:55,600 --> 00:30:58,600
Speaker 1: is my uncle, and he did post it. So we

673
00:30:58,760 --> 00:31:01,920
Speaker 1: have a bunch of sort of of I don't think

674
00:31:01,920 --> 00:31:04,960
Speaker 1: we have a clear sentiment about put its other extreme,

675
00:31:05,160 --> 00:31:07,000
Speaker 1: we are confident that are back. We are comfortable with

676
00:31:07,040 --> 00:31:08,680
Speaker 1: the fact that our banks know how much money we have.

677
00:31:09,440 --> 00:31:11,640
Speaker 1: For sure, we're comfortable that our mobile operators and know

678
00:31:11,640 --> 00:31:13,400
Speaker 1: where our phone is and that there's a kind of

679
00:31:13,400 --> 00:31:16,240
Speaker 1: a legal apparatus around that if the police need to know,

680
00:31:16,320 --> 00:31:18,080
Speaker 1: then they can find out. But it's just not available

681
00:31:18,120 --> 00:31:20,360
Speaker 1: to anybody. I think we sort of have that feeling

682
00:31:20,360 --> 00:31:24,240
Speaker 1: around Google mostly. I don't think we have like a

683
00:31:24,280 --> 00:31:28,440
Speaker 1: resolved feeling around Facebook. Um. Now, this particular story is

684
00:31:28,440 --> 00:31:30,600
Speaker 1: sort of fascinating because of how many pieces of there

685
00:31:30,720 --> 00:31:34,640
Speaker 1: there are to that. So Facebook creates this developer platform

686
00:31:34,840 --> 00:31:37,360
Speaker 1: that allows you to install an app that can access

687
00:31:37,360 --> 00:31:40,560
Speaker 1: your information and also access information about your friends. And

688
00:31:40,600 --> 00:31:42,400
Speaker 1: there's a sort of a bunch of reasons why that

689
00:31:42,440 --> 00:31:44,920
Speaker 1: would be useful, like I want to create in a

690
00:31:44,960 --> 00:31:47,120
Speaker 1: calendar entry, or I want to you know, I want

691
00:31:47,160 --> 00:31:49,840
Speaker 1: to see who else is using this app. Um. And

692
00:31:50,000 --> 00:31:53,040
Speaker 1: a large part of like the advocacy around the tech

693
00:31:53,080 --> 00:31:55,840
Speaker 1: industry with wall gardens are bad, Facebook is bad because

694
00:31:55,880 --> 00:31:57,680
Speaker 1: it's closed. People being need to be able to get

695
00:31:57,680 --> 00:31:59,480
Speaker 1: their information out. You need to be able other people

696
00:31:59,520 --> 00:32:01,920
Speaker 1: need to be able to use this to innovate. So

697
00:32:02,080 --> 00:32:04,680
Speaker 1: there was this whole sort of ideological argument um that

698
00:32:04,720 --> 00:32:06,840
Speaker 1: Facebook would be evil for not doing this, and Facebook

699
00:32:06,880 --> 00:32:10,360
Speaker 1: needed to do this, So you create this platform. Um,

700
00:32:10,400 --> 00:32:13,080
Speaker 1: it turns out that people are able to exploit that

701
00:32:13,280 --> 00:32:16,080
Speaker 1: and do stuff with it that was not really anticipated.

702
00:32:16,400 --> 00:32:19,719
Speaker 1: You think it's not anticipated because from my perspective, I

703
00:32:19,760 --> 00:32:23,400
Speaker 1: look at it as by design that Okay, so yes

704
00:32:23,440 --> 00:32:25,520
Speaker 1: and no, So let's get let's go to an analogy.

705
00:32:25,600 --> 00:32:30,520
Speaker 1: So do you remember word macro viruses? Um? Sure about?

706
00:32:30,920 --> 00:32:34,840
Speaker 1: So office is supposed to be an open development environment.

707
00:32:35,040 --> 00:32:37,480
Speaker 1: The people suing them before antitrust is suing them from

708
00:32:37,520 --> 00:32:39,760
Speaker 1: making it closed in half for third parties to work with.

709
00:32:40,200 --> 00:32:42,120
Speaker 1: So they create all these API s and they create

710
00:32:42,160 --> 00:32:44,560
Speaker 1: this whole macro language. One of the things on page

711
00:32:44,560 --> 00:32:47,160
Speaker 1: fifteen of the textbook is you can make a macro run.

712
00:32:47,160 --> 00:32:50,160
Speaker 1: When you open the document page forty eight of the textbook.

713
00:32:50,520 --> 00:32:52,280
Speaker 1: You can get it to look at your email addresses.

714
00:32:52,400 --> 00:32:56,160
Speaker 1: Page seventy two. You can get it to send an email. Okay,

715
00:32:56,200 --> 00:32:58,239
Speaker 1: so I get an email word document, I open it,

716
00:32:58,240 --> 00:32:59,920
Speaker 1: it emails a copy of itself to everyone in might

717
00:33:00,080 --> 00:33:04,959
Speaker 1: s book at. Okay, that's not what we expected, but

718
00:33:05,080 --> 00:33:07,360
Speaker 1: it's really that is not its intended but it's not

719
00:33:07,400 --> 00:33:10,920
Speaker 1: its intended purpose exactly. But all of the individual A

720
00:33:11,000 --> 00:33:13,239
Speaker 1: p I s were there, and so you've got this

721
00:33:13,320 --> 00:33:15,840
Speaker 1: period from Microsoft where they're thinking, okay, so are we

722
00:33:15,880 --> 00:33:18,640
Speaker 1: supposed to not have macros that we're supposed to be closed? Now,

723
00:33:18,840 --> 00:33:20,440
Speaker 1: how do we think about this? And they had to

724
00:33:20,440 --> 00:33:22,959
Speaker 1: go through this like hundred and eighty degree turn as

725
00:33:23,000 --> 00:33:25,120
Speaker 1: they went from thinking we should make it as easy

726
00:33:25,160 --> 00:33:28,040
Speaker 1: as possible for anybody to do this to what would

727
00:33:28,040 --> 00:33:32,800
Speaker 1: happen if it's going to moderate my language? What would

728
00:33:32,800 --> 00:33:37,080
Speaker 1: happen if a bad person, um what my seven year

729
00:33:37,080 --> 00:33:40,080
Speaker 1: old would call a dingly head, decided to read the

730
00:33:40,120 --> 00:33:42,800
Speaker 1: textbook Because it's not like you found bugs in it.

731
00:33:42,920 --> 00:33:44,680
Speaker 1: They're doing stuff it was in the textbook, that was

732
00:33:44,720 --> 00:33:46,520
Speaker 1: in the manual, but you weren't expecting them to use

733
00:33:46,560 --> 00:33:48,640
Speaker 1: them in those ways. And I think there's a very

734
00:33:48,680 --> 00:33:52,040
Speaker 1: strong parallel there with Whatness Cambridgewn Litigo was doing, which

735
00:33:52,120 --> 00:33:53,760
Speaker 1: was you were able to install an app. The app

736
00:33:53,800 --> 00:33:55,840
Speaker 1: can get your information, The app can ask for your

737
00:33:55,840 --> 00:33:57,959
Speaker 1: friends information. If you say yes, it will get your

738
00:33:57,960 --> 00:34:00,600
Speaker 1: friends information. Yeah, but we didn't expect that people would

739
00:34:00,640 --> 00:34:04,560
Speaker 1: use that to exportrate eighty million people's profiles, just as

740
00:34:04,600 --> 00:34:06,400
Speaker 1: we didn't expect someone would make a word document that

741
00:34:06,400 --> 00:34:08,359
Speaker 1: could email a copy of itself to me. So now,

742
00:34:08,440 --> 00:34:10,479
Speaker 1: so now let me I'm gonna go toe to toe

743
00:34:10,480 --> 00:34:12,960
Speaker 1: on technology with you, which is clearly a mistake on

744
00:34:13,040 --> 00:34:18,160
Speaker 1: my pod. But Microsoft notorious for having all these weak

745
00:34:18,239 --> 00:34:23,840
Speaker 1: security setups and easily exploitable that is such a foreseeable issue.

746
00:34:24,480 --> 00:34:28,200
Speaker 1: Granted this little bit of hindsight bioso in hindsight, right,

747
00:34:28,400 --> 00:34:35,239
Speaker 1: but you know Microsoft is home of the exploitable security era.

748
00:34:36,040 --> 00:34:39,239
Speaker 1: My relationship with Amazon and with Apple is that I

749
00:34:39,360 --> 00:34:42,560
Speaker 1: pay them for stuff and I expect a different level

750
00:34:43,160 --> 00:34:45,640
Speaker 1: of trust and a different level of Hey, I'm already

751
00:34:45,640 --> 00:34:48,520
Speaker 1: giving you money, don't exploit my personal data for other reasons. Well,

752
00:34:48,600 --> 00:34:50,480
Speaker 1: let me let me give you a hypothetical. Um, you

753
00:34:50,480 --> 00:34:52,839
Speaker 1: can install an app on your smartphone. It can pop

754
00:34:52,880 --> 00:34:55,120
Speaker 1: up a box and ask for your friends, and there's

755
00:34:55,120 --> 00:34:57,200
Speaker 1: an awful lot of sensible reasons, like you're playing a game,

756
00:34:57,200 --> 00:34:58,839
Speaker 1: who else? Which of your friends are playing? The game?

757
00:34:59,200 --> 00:35:01,880
Speaker 1: Installed the Instagram? Who are your friends? So you can

758
00:35:01,920 --> 00:35:03,759
Speaker 1: follow them? Like we were asked if you want to

759
00:35:03,800 --> 00:35:08,520
Speaker 1: split a fair with somebody, any basic logical reasons why

760
00:35:08,560 --> 00:35:10,399
Speaker 1: you wanted to do that? Okay, So that app has

761
00:35:10,440 --> 00:35:15,879
Speaker 1: just downloaded six hundred people's home addresses. Is that a breach? Well,

762
00:35:15,960 --> 00:35:19,759
Speaker 1: it has a downloaded their email address or address or

763
00:35:19,760 --> 00:35:22,520
Speaker 1: have they looked at the home address? And then people

764
00:35:23,320 --> 00:35:25,279
Speaker 1: second order question it needs their email address or their

765
00:35:25,280 --> 00:35:27,120
Speaker 1: phone number to work. That's the idea. There's there is

766
00:35:27,200 --> 00:35:30,319
Speaker 1: no other identify, so it's got six d people's email addresses. Okay,

767
00:35:30,360 --> 00:35:33,080
Speaker 1: is that a breach of privacy? Is that Apple's faults?

768
00:35:33,920 --> 00:35:38,560
Speaker 1: Well maybe a little bit. I would say it is not.

769
00:35:38,680 --> 00:35:41,200
Speaker 1: But Apple popped up They said, here is this capability.

770
00:35:41,239 --> 00:35:42,879
Speaker 1: Apple pops up the thing. Do you want this app

771
00:35:42,920 --> 00:35:45,760
Speaker 1: to to have your address or not? And the sort

772
00:35:45,840 --> 00:35:48,040
Speaker 1: of a point where it's not kind of black and white.

773
00:35:48,040 --> 00:35:51,160
Speaker 1: It's not like somebody hacked into Google and gave them email.

774
00:35:51,719 --> 00:35:54,000
Speaker 1: And so I think Facebook has been sort of they

775
00:35:54,640 --> 00:35:56,920
Speaker 1: they have this record of pushing this thing to the

776
00:35:57,000 --> 00:36:00,680
Speaker 1: kind of the outer envelope um for the last fifteen years.

777
00:36:00,760 --> 00:36:03,560
Speaker 1: And I think what's happened here slightly ironically, because they

778
00:36:03,560 --> 00:36:05,640
Speaker 1: actually closed off all of these APIs like a couple

779
00:36:05,640 --> 00:36:07,600
Speaker 1: of years ago. So sort of what happened is like

780
00:36:07,640 --> 00:36:10,400
Speaker 1: the stable door was open if you were a developer,

781
00:36:10,440 --> 00:36:12,040
Speaker 1: if you were in tech, if you went to the

782
00:36:12,120 --> 00:36:14,200
Speaker 1: Developer event. They stood up on stage and said, hey,

783
00:36:14,200 --> 00:36:16,120
Speaker 1: look we leave our stables store always doors open so

784
00:36:16,200 --> 00:36:18,759
Speaker 1: you can do all this stuff. Isn't this great? And

785
00:36:18,880 --> 00:36:21,040
Speaker 1: their doors stable doors are opened for like five years

786
00:36:21,600 --> 00:36:24,040
Speaker 1: and then they maybe this isn't a good idea. We're

787
00:36:24,080 --> 00:36:25,239
Speaker 1: going to close the door now. And a bunch of

788
00:36:25,280 --> 00:36:27,480
Speaker 1: people said, oh, evil, Facebook, you shouldn't close the doors.

789
00:36:27,520 --> 00:36:29,960
Speaker 1: You should allow free open access. And now here we

790
00:36:30,000 --> 00:36:31,640
Speaker 1: are in two thousand and eighteen and people go, wow,

791
00:36:31,680 --> 00:36:33,279
Speaker 1: a lot of people went into the stable and stole

792
00:36:33,320 --> 00:36:39,120
Speaker 1: all the horses. What a shark? And Facebook, Yeah, but

793
00:36:40,239 --> 00:36:43,080
Speaker 1: like we near, the doors were open. So there's a

794
00:36:43,120 --> 00:36:44,680
Speaker 1: bunch of you can kind of see this as they

795
00:36:44,719 --> 00:36:46,359
Speaker 1: try and work out how we talk about this, how

796
00:36:46,400 --> 00:36:48,640
Speaker 1: do we think about this stuff. We have been speaking

797
00:36:48,640 --> 00:36:51,919
Speaker 1: with Benlic Devans of Andres and Horowitz. If you enjoy

798
00:36:52,000 --> 00:36:55,080
Speaker 1: this conversation, be sure and check out our podcast extras,

799
00:36:55,280 --> 00:36:58,560
Speaker 1: where we keep the tape rolling and continue discussing all

800
00:36:58,680 --> 00:37:04,080
Speaker 1: things technology. We love your comments, feedback and suggestions right

801
00:37:04,200 --> 00:37:07,480
Speaker 1: to us at m IB podcast at Bloomberg dot net.

802
00:37:08,120 --> 00:37:10,759
Speaker 1: Check out my daily column on Bloomberg Vie dot com.

803
00:37:10,960 --> 00:37:14,160
Speaker 1: You could follow me on Twitter at Ri Halts, I'm

804
00:37:14,200 --> 00:37:18,520
Speaker 1: Barry Hults. You're listening to Masters in Business on Bloomberg Radio.

805
00:37:31,560 --> 00:37:34,239
Speaker 1: Welcome to the podcast, Bennetick, Thank you so much for

806
00:37:34,280 --> 00:37:37,800
Speaker 1: doing this. You're you're talking about stuff that I really

807
00:37:37,840 --> 00:37:45,000
Speaker 1: find endlessly fascinating, including, UM, the responsibility of Facebook to

808
00:37:45,840 --> 00:37:49,040
Speaker 1: either have an open platform and what that risk entails,

809
00:37:49,640 --> 00:37:53,360
Speaker 1: or to control their own APIs and control who access

810
00:37:53,520 --> 00:37:58,280
Speaker 1: what from your UM feed, what nobody is talking about.

811
00:37:58,440 --> 00:38:01,520
Speaker 1: And by the time this airs, he will have Mark

812
00:38:01,640 --> 00:38:07,480
Speaker 1: Zuckerberg will have already um done his congressional testimony. But UM,

813
00:38:08,160 --> 00:38:11,680
Speaker 1: there's still a tremendous amount of responsibility on the individual

814
00:38:11,800 --> 00:38:15,920
Speaker 1: user who willingly said, hey, here's a ton of private

815
00:38:15,960 --> 00:38:19,480
Speaker 1: personal information about my me, try not to mess it up.

816
00:38:20,000 --> 00:38:23,160
Speaker 1: I was always too skeptical. I was always too why

817
00:38:23,200 --> 00:38:26,239
Speaker 1: do you want this information? Can kind of noted it

818
00:38:26,239 --> 00:38:28,839
Speaker 1: from the other end, which was I think people have felt.

819
00:38:28,920 --> 00:38:31,120
Speaker 1: I think for years and years and years people have

820
00:38:31,160 --> 00:38:33,560
Speaker 1: just sort of presumed that nothing on Facebook with public

821
00:38:33,600 --> 00:38:36,680
Speaker 1: with private, right, that's correct, and therefore just sort of

822
00:38:36,680 --> 00:38:38,720
Speaker 1: treated it as a public forum. If you were smart,

823
00:38:38,800 --> 00:38:40,680
Speaker 1: that's what you presumed, or if you were I should

824
00:38:40,719 --> 00:38:43,279
Speaker 1: say knowledgeable. That should have been your work, even think

825
00:38:43,520 --> 00:38:45,399
Speaker 1: I didn't even think beyond that. So, like I've heard

826
00:38:45,400 --> 00:38:47,840
Speaker 1: people say things like I don't use Facebook. I wouldn't

827
00:38:47,840 --> 00:38:49,560
Speaker 1: say that on Facebook Messenger because I don't want it

828
00:38:49,560 --> 00:38:51,880
Speaker 1: to be public, and of course favorit. Messager isn't public.

829
00:38:51,880 --> 00:38:55,319
Speaker 1: I mean technically Facebook can, but anybody can screenshot and

830
00:38:55,320 --> 00:38:58,000
Speaker 1: anybody can. Yeah. Well that's like people thought it was

831
00:38:58,000 --> 00:38:59,920
Speaker 1: his public and stuff on their on their public profile,

832
00:39:00,080 --> 00:39:02,799
Speaker 1: and I went, I joined Facebook, and I forget when

833
00:39:02,800 --> 00:39:05,399
Speaker 1: it launched in the UK, but everyone's profile was public.

834
00:39:05,440 --> 00:39:06,759
Speaker 1: I mean it was public if you were to if

835
00:39:06,760 --> 00:39:09,440
Speaker 1: to like if you're in the London network, so anyone

836
00:39:09,480 --> 00:39:11,759
Speaker 1: could join the London network, so it was public. And

837
00:39:11,800 --> 00:39:13,719
Speaker 1: then so there was a period of like a year when,

838
00:39:13,880 --> 00:39:16,160
Speaker 1: like everybody I've been at school, at university, we've had

839
00:39:16,160 --> 00:39:17,920
Speaker 1: a profile on Facebook and it was public and I

840
00:39:17,920 --> 00:39:19,279
Speaker 1: could go and look, and then everyone kind of turned

841
00:39:19,280 --> 00:39:22,480
Speaker 1: the privacy things on. But I just and even then

842
00:39:22,560 --> 00:39:25,439
Speaker 1: it's not for I don't feel like you would say

843
00:39:25,480 --> 00:39:29,000
Speaker 1: stuff on Facebook that you expect to be secret. That's

844
00:39:29,040 --> 00:39:30,800
Speaker 1: what I'm getting at, And I don't think many people

845
00:39:30,880 --> 00:39:33,279
Speaker 1: ever actually thought that, which comes again to my point

846
00:39:33,320 --> 00:39:35,759
Speaker 1: about sort of unresolved feelings, you can kind of you

847
00:39:35,800 --> 00:39:39,279
Speaker 1: can go to the extreme and say, um, everything on

848
00:39:39,320 --> 00:39:42,200
Speaker 1: Facebook should have been completely private and everyone should have

849
00:39:42,280 --> 00:39:44,759
Speaker 1: understood that and everyone you know, that's not realistic. But

850
00:39:44,840 --> 00:39:46,840
Speaker 1: I don't think that's actually very realistic. I think it

851
00:39:46,920 --> 00:39:48,719
Speaker 1: was much more kind of nuanced and fuzzy than that.

852
00:39:48,840 --> 00:39:50,560
Speaker 1: And I think you could argue everything in a WhatsApp

853
00:39:50,600 --> 00:39:52,600
Speaker 1: group should be private, but I think that's a very

854
00:39:52,640 --> 00:39:54,919
Speaker 1: different kind of form to posting on your news feed.

855
00:39:55,040 --> 00:39:59,480
Speaker 1: But when I ask people, um, you know, sometimes you

856
00:39:59,480 --> 00:40:02,120
Speaker 1: have to come out the discussion from an oblique angle.

857
00:40:02,719 --> 00:40:04,840
Speaker 1: And I like to use radio as an example. I

858
00:40:04,880 --> 00:40:08,560
Speaker 1: ask people what is radio cell and invariably they say advertising,

859
00:40:08,920 --> 00:40:12,239
Speaker 1: And that's the wrong answer. The advertisers of the buyers.

860
00:40:12,960 --> 00:40:16,560
Speaker 1: Radio sells an audience, and Facebook more or less has

861
00:40:16,600 --> 00:40:19,200
Speaker 1: the same business model. So I find this interesting. There's

862
00:40:19,200 --> 00:40:20,920
Speaker 1: a sort of there's a common meme even kind of

863
00:40:20,920 --> 00:40:23,680
Speaker 1: in the tech industry where people say Facebook sales for information.

864
00:40:24,200 --> 00:40:26,319
Speaker 1: Now they sell and you as a part of an audience. Well,

865
00:40:26,320 --> 00:40:28,919
Speaker 1: it's interesting because in in a literal sense, if you're

866
00:40:28,920 --> 00:40:31,120
Speaker 1: an advertising on Facebook, you don't get given a zip

867
00:40:31,120 --> 00:40:34,440
Speaker 1: file of the profile of everybody who saw that. So

868
00:40:34,560 --> 00:40:36,799
Speaker 1: in a literal sense, it's give not true. In a

869
00:40:36,880 --> 00:40:39,080
Speaker 1: kind of a metaphorical sense, well, they're sort of they're

870
00:40:39,080 --> 00:40:41,680
Speaker 1: selling the fact that they know like even like kind

871
00:40:41,680 --> 00:40:43,799
Speaker 1: of a metaphorical sense. That kind of struggles me. I

872
00:40:43,840 --> 00:40:46,759
Speaker 1: struggle with that as a statement, But I think the

873
00:40:46,760 --> 00:40:49,799
Speaker 1: fact that people have those conversations reflects against sort of

874
00:40:50,080 --> 00:40:52,920
Speaker 1: unresolved feelings about how we should think about this stuff.

875
00:40:52,920 --> 00:40:54,799
Speaker 1: It's the same with this idea that's going around now

876
00:40:54,960 --> 00:40:56,800
Speaker 1: that all the Facebook's problems can be blamed on the

877
00:40:56,800 --> 00:40:58,800
Speaker 1: ad model, and you think, well, if they had a

878
00:40:58,840 --> 00:41:01,719
Speaker 1: subscription model, would they not be having to how to

879
00:41:01,760 --> 00:41:03,960
Speaker 1: develop a platform? Would you not have been posting your

880
00:41:03,960 --> 00:41:06,360
Speaker 1: personal stuff to your news feed? Like why would that

881
00:41:06,400 --> 00:41:08,879
Speaker 1: have made any difference? And you would you would still

882
00:41:08,920 --> 00:41:11,480
Speaker 1: have the same opportunity for apps to come in and

883
00:41:11,520 --> 00:41:15,560
Speaker 1: scrape that data before they closed the barn door. Yeah,

884
00:41:15,600 --> 00:41:17,279
Speaker 1: And I mean I was talked about this because somebody,

885
00:41:17,320 --> 00:41:19,640
Speaker 1: I think you say on Twitter's somebody who's had a journalism,

886
00:41:19,800 --> 00:41:21,520
Speaker 1: have had a journalism in school, and they posted like

887
00:41:21,560 --> 00:41:23,600
Speaker 1: an explanation of what came don We've been doing it.

888
00:41:23,840 --> 00:41:27,960
Speaker 1: This is three weeks ago, and it's just it was

889
00:41:28,040 --> 00:41:30,120
Speaker 1: interesting simply that they were. Three weeks on, we're still

890
00:41:30,120 --> 00:41:33,080
Speaker 1: having people are still writing explanations of what happened because

891
00:41:33,120 --> 00:41:35,120
Speaker 1: it's all sort of fuzzy and a pay can no

892
00:41:35,120 --> 00:41:38,719
Speaker 1: one quite understandable. It's complex, and people like simple narratives

893
00:41:38,719 --> 00:41:41,839
Speaker 1: with defineable good guys and bad guys. That makes it

894
00:41:41,880 --> 00:41:45,960
Speaker 1: easier to to do. Nuance sort of gets lost on

895
00:41:45,960 --> 00:41:50,160
Speaker 1: on cable television to the least. All right, let's talk

896
00:41:50,160 --> 00:41:55,200
Speaker 1: about some of your favorite technologies. Autonomy of filling the blank,

897
00:41:55,239 --> 00:42:00,120
Speaker 1: autonomous cars, autonomous uh, whatever, how do we how do

898
00:42:00,160 --> 00:42:04,640
Speaker 1: you see the development of AI and autonomous everything? Have

899
00:42:04,760 --> 00:42:07,520
Speaker 1: you got another hour? I do? I don't know if

900
00:42:07,560 --> 00:42:11,520
Speaker 1: we are are, so I think what can we say

901
00:42:11,520 --> 00:42:13,319
Speaker 1: about this? So, first of all, the reason that we're

902
00:42:13,320 --> 00:42:16,480
Speaker 1: talking about autonomous cars now is because of what we

903
00:42:16,560 --> 00:42:19,520
Speaker 1: call AI, which really means this new technology called machine learning,

904
00:42:19,600 --> 00:42:22,359
Speaker 1: or technology that just started working called machine learning, which

905
00:42:22,400 --> 00:42:24,880
Speaker 1: offers the prospect that a bunch of problems around autonomous

906
00:42:24,920 --> 00:42:26,680
Speaker 1: cars might be solvable in a way that they really

907
00:42:26,680 --> 00:42:29,399
Speaker 1: didn't seem to be easily solvable before. So, in that sense,

908
00:42:29,440 --> 00:42:31,600
Speaker 1: you could say autonomy is a spinoff of machine learning

909
00:42:31,600 --> 00:42:33,120
Speaker 1: all the break the advanced. The fact that we're interested

910
00:42:33,120 --> 00:42:34,960
Speaker 1: in autonomy is a spinoff in machine learning, but there's

911
00:42:34,960 --> 00:42:36,960
Speaker 1: altos of other stuff that machine learning does as well

912
00:42:37,400 --> 00:42:40,759
Speaker 1: as we go to autonomy. UM. The sort of the

913
00:42:40,800 --> 00:42:42,719
Speaker 1: way I kind of talk about this is I have

914
00:42:42,719 --> 00:42:44,920
Speaker 1: a slide with a picture of a horseless carriage from

915
00:42:45,840 --> 00:42:48,880
Speaker 1: It's a carriage with no horse, but otherwise it's nothing's changed.

916
00:42:49,200 --> 00:42:51,560
Speaker 1: And I think that's what I hear when people say

917
00:42:51,640 --> 00:42:55,400
Speaker 1: driverless car, that you've taken the steering wheel out maybe,

918
00:42:55,440 --> 00:42:57,319
Speaker 1: but like nothing else has changed and it will still

919
00:42:57,400 --> 00:43:00,560
Speaker 1: drive around like like autonomous cars mean us that drive

920
00:43:00,600 --> 00:43:03,239
Speaker 1: like people, but without making mistakes or making the speed limit.

921
00:43:03,280 --> 00:43:05,520
Speaker 1: And that's that's just a really shortsighted way of thinking

922
00:43:05,520 --> 00:43:07,520
Speaker 1: about this. I think there's kind of two kind of

923
00:43:07,520 --> 00:43:09,560
Speaker 1: building blocks to think about. The first is you can

924
00:43:09,680 --> 00:43:11,799
Speaker 1: if you have when we have a fully autonomous world

925
00:43:11,880 --> 00:43:15,239
Speaker 1: in end decades forty years, depending on what you think

926
00:43:15,239 --> 00:43:18,200
Speaker 1: the s cards will look like. UM. And of coursely

927
00:43:18,280 --> 00:43:20,400
Speaker 1: you have periods where some places will be much go

928
00:43:20,560 --> 00:43:22,320
Speaker 1: much quicker, So like you might you might say Manhattan

929
00:43:22,360 --> 00:43:25,759
Speaker 1: is autonomous only in the week in twenty thirty or something. Um.

930
00:43:26,320 --> 00:43:30,040
Speaker 1: But at that point there's no accidents, well, certainly much

931
00:43:30,120 --> 00:43:33,279
Speaker 1: less well basically no accidents because all the accidents are

932
00:43:33,280 --> 00:43:36,080
Speaker 1: caused by human error, and you have much less congestion

933
00:43:36,160 --> 00:43:38,280
Speaker 1: because you don't have traffic waves, you don't have accidents

934
00:43:38,320 --> 00:43:40,600
Speaker 1: which cause a third of congestion or something. UM. You

935
00:43:40,640 --> 00:43:42,920
Speaker 1: can have vehicles on freeways driving a hundred miles and

936
00:43:42,960 --> 00:43:45,839
Speaker 1: our two apart from each other. So you radically change

937
00:43:45,840 --> 00:43:48,120
Speaker 1: what congestion looks like. You change radically change all the

938
00:43:48,200 --> 00:43:50,640
Speaker 1: vehicles look like. So you could have a vehicle that

939
00:43:50,840 --> 00:43:52,840
Speaker 1: will never you know, if you're got calling an on

940
00:43:52,880 --> 00:43:55,799
Speaker 1: demand vehicle in Manhattan, it will not go over twenty miles.

941
00:43:55,800 --> 00:43:58,480
Speaker 1: An how it could be a golf cart. Um. If

942
00:43:58,520 --> 00:44:00,319
Speaker 1: you're going to go to JFK, they and you would

943
00:44:00,320 --> 00:44:03,000
Speaker 1: send a different vehicle. And so you can radically just

944
00:44:03,080 --> 00:44:04,759
Speaker 1: redesign the vehicle in the same way that when we

945
00:44:04,760 --> 00:44:06,840
Speaker 1: got rid of the horse, you radically redesigned the vehicle.

946
00:44:07,280 --> 00:44:09,520
Speaker 1: And you can also radically redesign the city or change

947
00:44:09,520 --> 00:44:11,360
Speaker 1: your assumptions about the city in the same way that

948
00:44:11,400 --> 00:44:13,239
Speaker 1: we did whom we got rid of the horse, um,

949
00:44:13,360 --> 00:44:17,040
Speaker 1: except that you're no longer trying to design a city

950
00:44:17,480 --> 00:44:20,840
Speaker 1: that will constrain the nineteen year old guy on a

951
00:44:20,840 --> 00:44:24,280
Speaker 1: souped up Chevy Tomorrow. Um, you're designing a city based

952
00:44:24,320 --> 00:44:26,279
Speaker 1: on You can create rules. You can tell the cars

953
00:44:26,280 --> 00:44:27,719
Speaker 1: where to go and what they can do or not do.

954
00:44:27,960 --> 00:44:29,880
Speaker 1: You can have dynamic real time road right saying that

955
00:44:29,920 --> 00:44:31,600
Speaker 1: you can say, do you want to pay a lot

956
00:44:31,640 --> 00:44:33,440
Speaker 1: to get there in fifteen minutes or you're willing to

957
00:44:33,440 --> 00:44:35,839
Speaker 1: pay less to get there in thirty minutes. You can

958
00:44:35,880 --> 00:44:38,080
Speaker 1: tell the cars at any given instant which road to

959
00:44:38,120 --> 00:44:40,080
Speaker 1: say should be taking and how fast they should be going.

960
00:44:40,560 --> 00:44:42,680
Speaker 1: And so what you have is like a change in

961
00:44:42,680 --> 00:44:45,359
Speaker 1: the structure of a city that has more a lot

962
00:44:45,400 --> 00:44:47,560
Speaker 1: in common with like the way the city changes a

963
00:44:47,600 --> 00:44:50,440
Speaker 1: result of the car. UM kind of the example I

964
00:44:50,480 --> 00:44:53,560
Speaker 1: often give, which works well since we're in Manhattan is

965
00:44:53,600 --> 00:44:57,200
Speaker 1: imagine if you live in Brooklyn and it's November and

966
00:44:57,239 --> 00:44:59,560
Speaker 1: you want to go to that cool new restaurant in Manhattan.

967
00:45:00,000 --> 00:45:01,439
Speaker 1: How are you going to get there? Where you could

968
00:45:01,440 --> 00:45:04,040
Speaker 1: walk ten minutes in the rain to the subway station,

969
00:45:05,360 --> 00:45:07,759
Speaker 1: you could get a cab UK well, presuming you can

970
00:45:07,800 --> 00:45:09,359
Speaker 1: get a cab. Presume you can get a cab O care.

971
00:45:09,400 --> 00:45:12,319
Speaker 1: It's going to cost you what dollars each way you

972
00:45:12,320 --> 00:45:14,640
Speaker 1: could drive, then one of you can't drink on the

973
00:45:14,640 --> 00:45:16,040
Speaker 1: way back, and you're going to have to park, and

974
00:45:16,080 --> 00:45:18,239
Speaker 1: you have to pay for parking in little twenty minutes

975
00:45:18,280 --> 00:45:20,839
Speaker 1: to find a place to park there. Let's not go

976
00:45:20,880 --> 00:45:23,680
Speaker 1: out of that place, okay, Now go to a fully

977
00:45:23,719 --> 00:45:27,440
Speaker 1: autonomous world. You raise your watch, you say, hey, alex

978
00:45:27,520 --> 00:45:29,640
Speaker 1: or I need a car. The nip hod that's around

979
00:45:29,640 --> 00:45:32,880
Speaker 1: the block stops outside your door within thirty seconds, and

980
00:45:33,000 --> 00:45:35,600
Speaker 1: there is no congestion. The pod just takes you there.

981
00:45:35,719 --> 00:45:38,160
Speaker 1: It drops you off outside the door um if it's

982
00:45:38,200 --> 00:45:40,719
Speaker 1: your pod, um, and then it goes and waits for

983
00:45:40,760 --> 00:45:42,680
Speaker 1: you somewhere where parking is cheap, or it waits for

984
00:45:42,680 --> 00:45:44,520
Speaker 1: you somewhere else. If it's an on demand pod, it

985
00:45:44,560 --> 00:45:48,040
Speaker 1: immediately starts driving other people around, so there's no parking, remember,

986
00:45:48,320 --> 00:45:50,319
Speaker 1: and then you can go home when you can drink

987
00:45:50,320 --> 00:45:52,120
Speaker 1: as much as you like. I mean, remember all those

988
00:45:52,160 --> 00:45:56,480
Speaker 1: those photographs of European and East Coast cities from before cars,

989
00:45:56,560 --> 00:45:58,200
Speaker 1: and you think where the streets are all twice as

990
00:45:58,239 --> 00:46:00,759
Speaker 1: wide because you don't have cars hop down both sides

991
00:46:00,800 --> 00:46:03,719
Speaker 1: of the street. What we could go back to, and

992
00:46:03,800 --> 00:46:06,239
Speaker 1: so you have like kind of radical change in how

993
00:46:06,280 --> 00:46:09,520
Speaker 1: we think about what the city is. Therefore, in what

994
00:46:09,600 --> 00:46:11,759
Speaker 1: does a gas station mean? Very obviously, but also what

995
00:46:11,800 --> 00:46:13,560
Speaker 1: does big box we tell mean? Where are you willing

996
00:46:13,560 --> 00:46:17,040
Speaker 1: to shop? What does your commute look like? You know? Today?

997
00:46:17,120 --> 00:46:19,800
Speaker 1: So I live in San Francisco, which calls itself a city,

998
00:46:19,840 --> 00:46:21,959
Speaker 1: and I work in Menlo Park, which is an office park.

999
00:46:22,120 --> 00:46:25,600
Speaker 1: Forty five minutes drive south if there's not much traffic, Um,

1000
00:46:25,640 --> 00:46:27,439
Speaker 1: I should say an hour, but I'll admit to forty

1001
00:46:27,480 --> 00:46:32,719
Speaker 1: five minutes south. Now, supposing there really is no traffic, well,

1002
00:46:32,760 --> 00:46:35,120
Speaker 1: then I can get there in half an hour. But

1003
00:46:35,360 --> 00:46:38,399
Speaker 1: supposing I'm able to read instead of drive, I might

1004
00:46:38,400 --> 00:46:40,720
Speaker 1: be able to live further away and have it spend

1005
00:46:40,760 --> 00:46:42,400
Speaker 1: longer in the car because I don't need to just

1006
00:46:42,440 --> 00:46:44,719
Speaker 1: sit there staring at the road all day. So where

1007
00:46:44,719 --> 00:46:46,400
Speaker 1: do you live, where do you commute? Where does the

1008
00:46:46,480 --> 00:46:49,759
Speaker 1: retail go? All those sorts of changes that happened, like

1009
00:46:50,000 --> 00:46:51,799
Speaker 1: the stuff that happened as a result of cars, which

1010
00:46:51,800 --> 00:46:53,600
Speaker 1: basically what I'm talking about. Like, there's this great saying

1011
00:46:53,600 --> 00:46:55,720
Speaker 1: that it was easy to predict mass ownership of cars,

1012
00:46:55,880 --> 00:46:57,799
Speaker 1: but hard to put itt Walmart. It's hard to put

1013
00:46:57,800 --> 00:47:01,000
Speaker 1: it drive through a t M s like that kind

1014
00:47:01,040 --> 00:47:04,200
Speaker 1: of stuff. Those second and third auto consequences will flow

1015
00:47:04,200 --> 00:47:07,799
Speaker 1: out of this stuff. Let's talk about smart fill in

1016
00:47:07,840 --> 00:47:12,239
Speaker 1: the blanks, smart homes, smartphone, smart cars, smart speakers. What

1017
00:47:12,440 --> 00:47:17,520
Speaker 1: is it about the the preface smart that suddenly everybody

1018
00:47:17,560 --> 00:47:22,960
Speaker 1: wants to move in that direction? So so software and okay,

1019
00:47:23,000 --> 00:47:24,719
Speaker 1: so three or four blocks to talk about here. The

1020
00:47:24,760 --> 00:47:27,520
Speaker 1: first block is smartphone supply chain. One and a half

1021
00:47:27,520 --> 00:47:30,520
Speaker 1: billion smartphones sold last year. All of those chips are

1022
00:47:30,520 --> 00:47:32,480
Speaker 1: available as like a fire hose of stuff to make

1023
00:47:32,520 --> 00:47:34,680
Speaker 1: stuff with. So before smartphones, if you wanted to put

1024
00:47:34,680 --> 00:47:37,279
Speaker 1: computing into something, you had to use PC components, So

1025
00:47:37,320 --> 00:47:39,959
Speaker 1: an a t M is a PC. But those are big,

1026
00:47:40,000 --> 00:47:42,279
Speaker 1: and they're heavy, and they need mains power, they need

1027
00:47:42,320 --> 00:47:45,080
Speaker 1: power and so on. Smartphone components much cheaper, much smaller,

1028
00:47:45,160 --> 00:47:47,120
Speaker 1: much lighter. So suddenly you can make a connected door lock,

1029
00:47:47,160 --> 00:47:50,759
Speaker 1: and like you can get the parts really easily. UM

1030
00:47:50,840 --> 00:47:53,839
Speaker 1: plus um UBER called as internet plus machine learning, means

1031
00:47:53,880 --> 00:47:56,319
Speaker 1: that like a camera can actually be able to tell

1032
00:47:56,320 --> 00:47:58,400
Speaker 1: if there's something moving or not. So you've got all

1033
00:47:58,480 --> 00:48:01,040
Speaker 1: this stuff. Suddenly the stuff was not working. I think

1034
00:48:01,080 --> 00:48:04,520
Speaker 1: The best analogy for this is like our grandparents could

1035
00:48:04,560 --> 00:48:06,879
Speaker 1: have told you how many electric motors they owned. There

1036
00:48:06,920 --> 00:48:09,000
Speaker 1: was one in the car, they had a vacuum cleaner,

1037
00:48:09,040 --> 00:48:10,879
Speaker 1: there was one in the fridge. They owned maybe five

1038
00:48:10,920 --> 00:48:13,480
Speaker 1: electric motors in total, Like there was only one electric

1039
00:48:13,480 --> 00:48:15,800
Speaker 1: motor in the car. Let's start a major that was it. Today,

1040
00:48:15,880 --> 00:48:17,480
Speaker 1: who has a clue have any electric motors are in

1041
00:48:17,480 --> 00:48:19,480
Speaker 1: your car? There's like twenty And if you tell your

1042
00:48:19,520 --> 00:48:21,640
Speaker 1: grandfather that you can press a button to adjust your

1043
00:48:21,640 --> 00:48:23,319
Speaker 1: wing moor, he'd hit you on the back of the head.

1044
00:48:23,440 --> 00:48:25,560
Speaker 1: But that's just how it worked, because that's just how

1045
00:48:25,600 --> 00:48:27,879
Speaker 1: the technology got deployed. The same thing in your home.

1046
00:48:28,120 --> 00:48:29,560
Speaker 1: There was a period when everything was going to have

1047
00:48:29,600 --> 00:48:32,520
Speaker 1: a DC motor. Now everybody has you like you have

1048
00:48:32,600 --> 00:48:34,840
Speaker 1: an you have a microwave, You maybe have a toaster,

1049
00:48:34,920 --> 00:48:37,160
Speaker 1: you may have a kettle, you have a blender. Nobody

1050
00:48:37,200 --> 00:48:38,880
Speaker 1: has all of those things. Everyone in East Agea has

1051
00:48:38,880 --> 00:48:41,040
Speaker 1: a rice cooker. Everyone in the UK has a kettle.

1052
00:48:41,080 --> 00:48:42,920
Speaker 1: In America, you maybe don't have a kettle, but you

1053
00:48:42,960 --> 00:48:46,200
Speaker 1: have a coffee machine. But nobody has an electric carving knife.

1054
00:48:47,080 --> 00:48:49,000
Speaker 1: And so what happens is like there are those underlying

1055
00:48:49,000 --> 00:48:51,480
Speaker 1: components as cheap commodities were in this period of trying

1056
00:48:51,480 --> 00:48:52,960
Speaker 1: to work out what you should do with them and

1057
00:48:53,000 --> 00:48:55,359
Speaker 1: how they should all get plugged together, and should they

1058
00:48:55,400 --> 00:48:56,960
Speaker 1: all be the same system or not sure they all

1059
00:48:56,960 --> 00:48:59,680
Speaker 1: talked to Alexa or not everything, And then you've got

1060
00:48:59,680 --> 00:49:02,640
Speaker 1: like the austrial logic. So like somebody the Samson board

1061
00:49:02,680 --> 00:49:04,800
Speaker 1: sat down and said, everything we sell must have the

1062
00:49:04,840 --> 00:49:07,759
Speaker 1: Samsing Voice Assistant, because then people will be more likely

1063
00:49:07,800 --> 00:49:09,680
Speaker 1: to buy the Samson fridge that sorts to the Samsing

1064
00:49:09,719 --> 00:49:12,280
Speaker 1: dish watcher when it talks to the samthing this, um,

1065
00:49:12,280 --> 00:49:14,399
Speaker 1: how's it working out? Well? Then you so it's great

1066
00:49:14,440 --> 00:49:16,160
Speaker 1: you see this at CS because you can see like

1067
00:49:16,200 --> 00:49:17,960
Speaker 1: the fridge people thought this is a fantastic I mean

1068
00:49:18,000 --> 00:49:20,160
Speaker 1: metaphorically speaking, the fridge people thought, this is a fantastic

1069
00:49:20,239 --> 00:49:23,760
Speaker 1: idea to dish watch. Your people are like, don't damn it. Okay,

1070
00:49:23,960 --> 00:49:25,920
Speaker 1: we'll put the voice assistant in the dish wash but

1071
00:49:27,760 --> 00:49:30,120
Speaker 1: a that and be they want to sell it to

1072
00:49:30,160 --> 00:49:32,640
Speaker 1: people who also in an LG fridge and a sub

1073
00:49:32,719 --> 00:49:35,160
Speaker 1: zero fridge. So they've got the Samson voice is this,

1074
00:49:35,320 --> 00:49:37,880
Speaker 1: and they've got home kit and they've got Alexa in

1075
00:49:37,920 --> 00:49:39,600
Speaker 1: it because they only need to sell dish watches. They

1076
00:49:39,640 --> 00:49:42,200
Speaker 1: don't care about the Greek strategy. It's like when Everythingy

1077
00:49:42,200 --> 00:49:44,319
Speaker 1: product had a memory stick, and there was some and

1078
00:49:44,400 --> 00:49:46,560
Speaker 1: like the Sony group had said everything's gonna have have

1079
00:49:46,600 --> 00:49:48,400
Speaker 1: a memory stick, and some bits of Sony thought this

1080
00:49:48,440 --> 00:49:51,000
Speaker 1: is great, and some bits were like, God, damn it, um,

1081
00:49:51,040 --> 00:49:52,160
Speaker 1: what are we gonna do? What are we going to

1082
00:49:52,239 --> 00:49:54,880
Speaker 1: do with this? And as with an electric carving knife

1083
00:49:54,960 --> 00:49:57,400
Speaker 1: versus the blender, like some of this stuff will make them,

1084
00:49:57,440 --> 00:49:59,520
Speaker 1: so some of it won't. It would be quite nice

1085
00:49:59,560 --> 00:50:01,960
Speaker 1: to be able to say to my oven, um, okay,

1086
00:50:02,000 --> 00:50:05,279
Speaker 1: pre heat the oven to three degrees, as opposed to

1087
00:50:05,360 --> 00:50:08,080
Speaker 1: having to learner the interview and walk over and touch it.

1088
00:50:08,560 --> 00:50:10,759
Speaker 1: I have my apartment, I have an infrared sensor in

1089
00:50:10,760 --> 00:50:12,640
Speaker 1: the bathroom. I walk into the bathroom, the light comes on.

1090
00:50:13,360 --> 00:50:16,200
Speaker 1: It's fantastic. My parents hated because they get up at

1091
00:50:16,200 --> 00:50:18,440
Speaker 1: four o'clock in the morning, then the lights come on.

1092
00:50:18,480 --> 00:50:21,239
Speaker 1: I think you program that because it's not anything. It's

1093
00:50:21,280 --> 00:50:23,839
Speaker 1: just literally it is just a dumile sensor from forty

1094
00:50:23,880 --> 00:50:26,480
Speaker 1: years again. So so here's where the smart world starts

1095
00:50:26,520 --> 00:50:29,600
Speaker 1: to get more interesting. So I have a long, twisty

1096
00:50:29,719 --> 00:50:32,719
Speaker 1: driveway because we're Our house is said off the main road,

1097
00:50:33,280 --> 00:50:37,120
Speaker 1: and we have lights along it. And the first phase

1098
00:50:37,320 --> 00:50:39,560
Speaker 1: was having a switch that put the lights on and

1099
00:50:39,600 --> 00:50:41,920
Speaker 1: put the lights off. And then the next phase was

1100
00:50:41,960 --> 00:50:45,040
Speaker 1: having a timer that has the lights going on at

1101
00:50:45,040 --> 00:50:48,680
Speaker 1: seven a pm and off at eleven pm. But the

1102
00:50:48,719 --> 00:50:51,880
Speaker 1: problem with that is seven pm is light sometimes of

1103
00:50:51,880 --> 00:50:56,160
Speaker 1: the year and dark. I thought, no, no, nothing like

1104
00:50:56,200 --> 00:51:00,839
Speaker 1: that's a contemporary house. But the new switch that is

1105
00:51:00,880 --> 00:51:06,360
Speaker 1: literally going in this weekend is built into the lights

1106
00:51:06,360 --> 00:51:10,520
Speaker 1: which as you can set your latitude and that lets

1107
00:51:10,840 --> 00:51:14,320
Speaker 1: whatever you said as your like going on thirty minutes

1108
00:51:14,360 --> 00:51:17,960
Speaker 1: after sunset will change throughout the year regardless and you

1109
00:51:18,000 --> 00:51:21,120
Speaker 1: don't have to deal with it. So that's our sort

1110
00:51:21,160 --> 00:51:24,120
Speaker 1: of smart application of software. Are like, we don't have

1111
00:51:24,160 --> 00:51:26,920
Speaker 1: any you know, your your phone changes its clock when

1112
00:51:27,000 --> 00:51:30,319
Speaker 1: the time don't changed automatically things the satellites. So I

1113
00:51:30,320 --> 00:51:32,000
Speaker 1: think there's a there's a kind of question to where

1114
00:51:32,000 --> 00:51:33,880
Speaker 1: will the complexity set And this is like the electric

1115
00:51:33,840 --> 00:51:35,600
Speaker 1: carving knife. There will be stuff that just doesn't make

1116
00:51:35,640 --> 00:51:38,440
Speaker 1: sense the other you do electric carving knife have been

1117
00:51:38,480 --> 00:51:40,520
Speaker 1: around for decades and they never would the other the

1118
00:51:40,560 --> 00:51:42,759
Speaker 1: other really so well. Some people have a like I

1119
00:51:42,800 --> 00:51:44,360
Speaker 1: can show you where they took a chunk out of

1120
00:51:44,360 --> 00:51:49,239
Speaker 1: my finger. The other extreme is like the other. The

1121
00:51:49,239 --> 00:51:51,200
Speaker 1: other way to think about this is it's old saying

1122
00:51:51,239 --> 00:51:53,680
Speaker 1: that basically a computer should never ask you a question

1123
00:51:53,680 --> 00:51:55,640
Speaker 1: that it ought to be able to work out on itself.

1124
00:51:56,640 --> 00:51:59,080
Speaker 1: And that used to mean, like you plug the printer in,

1125
00:51:59,120 --> 00:52:00,840
Speaker 1: the computer should know what the printer is. It shouldn't

1126
00:52:00,880 --> 00:52:03,279
Speaker 1: ask you what printer it is. Um, then it means

1127
00:52:03,360 --> 00:52:05,000
Speaker 1: your phone doesn't ask where you are when you call

1128
00:52:05,040 --> 00:52:07,640
Speaker 1: a car because it's got GPS and it knows. Then

1129
00:52:07,680 --> 00:52:09,479
Speaker 1: it means like the light should know what the times

1130
00:52:09,520 --> 00:52:11,239
Speaker 1: it is, so the light should just go on right,

1131
00:52:11,400 --> 00:52:14,200
Speaker 1: that's right. And the chair that the tension point and

1132
00:52:14,320 --> 00:52:16,840
Speaker 1: all of those is, is it actually more hassle to

1133
00:52:16,880 --> 00:52:19,680
Speaker 1: configure the thing to do that? Sometimes it is, sometimes

1134
00:52:19,760 --> 00:52:22,279
Speaker 1: it is, sometimes it isn't. So my beef about Siri,

1135
00:52:22,480 --> 00:52:26,760
Speaker 1: which was the leading voice app to begin with before

1136
00:52:27,560 --> 00:52:31,200
Speaker 1: Alexa began to eat its lunch, is asking you questions

1137
00:52:31,200 --> 00:52:34,439
Speaker 1: that it should, from the context be able to figure out.

1138
00:52:34,760 --> 00:52:38,600
Speaker 1: This is just paying a database, paying a location, um

1139
00:52:39,160 --> 00:52:42,520
Speaker 1: software ping something it has access to all that stuff.

1140
00:52:42,719 --> 00:52:45,760
Speaker 1: This so this is a different sense of the word smart.

1141
00:52:46,280 --> 00:52:48,920
Speaker 1: So let's talk about voice for a minute. Okay, So

1142
00:52:50,000 --> 00:52:52,359
Speaker 1: we have this thing called machine so okay, the right

1143
00:52:52,400 --> 00:52:54,840
Speaker 1: way putting this. So when you talk to a computer,

1144
00:52:55,360 --> 00:52:58,239
Speaker 1: the three things that are going on. Step one is

1145
00:52:58,320 --> 00:53:01,040
Speaker 1: it is taking the audio wave form and turning that

1146
00:53:01,120 --> 00:53:04,120
Speaker 1: into text. So it's just transcribing it and turning it

1147
00:53:04,160 --> 00:53:06,719
Speaker 1: into words, which I think is basic math and and

1148
00:53:07,000 --> 00:53:09,480
Speaker 1: well no it was. And this is something where it

1149
00:53:09,640 --> 00:53:11,879
Speaker 1: used to work okay, like three quarters of the time,

1150
00:53:12,400 --> 00:53:16,440
Speaker 1: but you remember using dictation apps like National actually speaking,

1151
00:53:17,120 --> 00:53:20,200
Speaker 1: it's sort of work three quarters. There are more and

1152
00:53:20,200 --> 00:53:22,359
Speaker 1: it would get better like half a point every year.

1153
00:53:22,800 --> 00:53:25,280
Speaker 1: A machine learning comes along and it goes from working

1154
00:53:25,280 --> 00:53:29,120
Speaker 1: three quarters at the time to working at the tent

1155
00:53:29,160 --> 00:53:30,600
Speaker 1: at the time. And so machine learning gives you this

1156
00:53:30,680 --> 00:53:34,000
Speaker 1: radical change in that it also then there's a second piece,

1157
00:53:34,040 --> 00:53:36,239
Speaker 1: which is you needed to go from the text to

1158
00:53:36,640 --> 00:53:39,360
Speaker 1: a structured query. You need to actually work out what

1159
00:53:39,440 --> 00:53:41,360
Speaker 1: the verb and the nan is and what is his asking,

1160
00:53:41,400 --> 00:53:45,160
Speaker 1: which is a completely different computation. Um. And so when

1161
00:53:45,200 --> 00:53:47,400
Speaker 1: you talk to your computer, that's it's two very almost

1162
00:53:47,480 --> 00:53:50,520
Speaker 1: unrelated things going on. Machine learning. Also, this is called

1163
00:53:50,600 --> 00:53:55,759
Speaker 1: natural language processing. UM. Machine learning also made that work way, way,

1164
00:53:55,800 --> 00:53:59,640
Speaker 1: way way better. UM. Then you have the third problem

1165
00:53:59,760 --> 00:54:02,440
Speaker 1: we're is, Okay, I've created this structured query, do I

1166
00:54:02,480 --> 00:54:06,759
Speaker 1: have anything to give it to? And you should have. Well,

1167
00:54:07,960 --> 00:54:11,000
Speaker 1: so this is the problem, which is what you've actually

1168
00:54:11,040 --> 00:54:13,799
Speaker 1: got here is an IVYR. You've got a voice tree,

1169
00:54:13,960 --> 00:54:16,880
Speaker 1: Press one for this, press to for that. Machine learning

1170
00:54:16,920 --> 00:54:18,759
Speaker 1: means you can now and we've we've all kind of

1171
00:54:18,800 --> 00:54:20,799
Speaker 1: experienced this, saying you phone the airline and they say,

1172
00:54:20,880 --> 00:54:22,319
Speaker 1: tell us what you want to ask about, and there's

1173
00:54:22,320 --> 00:54:25,200
Speaker 1: only fifteen things you could ask so they can recognize

1174
00:54:25,200 --> 00:54:27,080
Speaker 1: it the text, and then they can work out what

1175
00:54:27,120 --> 00:54:28,759
Speaker 1: you're asking about and they can read you to the

1176
00:54:28,840 --> 00:54:32,440
Speaker 1: right number inside that they're pretty awful also, But what

1177
00:54:32,560 --> 00:54:34,560
Speaker 1: we don't have. Machine learning gives us a way of

1178
00:54:34,600 --> 00:54:38,719
Speaker 1: automating the transcription and getting the natural language processing to work.

1179
00:54:39,239 --> 00:54:41,600
Speaker 1: But you might ask it any one of three or

1180
00:54:41,600 --> 00:54:45,600
Speaker 1: four million things, and so effectively, what I mean when

1181
00:54:45,640 --> 00:54:48,040
Speaker 1: I say a structured query is you can fill in

1182
00:54:48,040 --> 00:54:50,719
Speaker 1: a dialog box by talking to the computer, and the

1183
00:54:50,719 --> 00:54:53,920
Speaker 1: computer can work out which dialog box you were asking for.

1184
00:54:54,840 --> 00:54:58,440
Speaker 1: But somebody has to have made the dialog box by hand. Okay,

1185
00:54:58,520 --> 00:55:00,960
Speaker 1: so you know if you're if you're a phone company,

1186
00:55:01,080 --> 00:55:04,160
Speaker 1: so if you're a phone manufacturer and you have a

1187
00:55:04,200 --> 00:55:07,680
Speaker 1: few million or maybe even a few billion queries, but

1188
00:55:07,800 --> 00:55:10,839
Speaker 1: how uses your frame of reference? But so you can

1189
00:55:10,920 --> 00:55:15,160
Speaker 1: make the top fifty categories, all the top thousand categories. Well,

1190
00:55:15,400 --> 00:55:17,920
Speaker 1: and if you're gonna ask something, really is the curve

1191
00:55:18,000 --> 00:55:21,799
Speaker 1: gets really really steep really quickly, and you don't know

1192
00:55:21,840 --> 00:55:26,120
Speaker 1: what you can ask. So you cannot ask. You can

1193
00:55:26,160 --> 00:55:28,239
Speaker 1: get the top five things, and you can say you

1194
00:55:28,239 --> 00:55:30,640
Speaker 1: can ask for weather, you can ask for timer, you

1195
00:55:30,680 --> 00:55:32,960
Speaker 1: can ask for a unit conversion, you can ask for

1196
00:55:32,960 --> 00:55:35,239
Speaker 1: the time in a different city. You can ask me

1197
00:55:35,280 --> 00:55:37,160
Speaker 1: to play music and you'll get the music right roughly

1198
00:55:37,200 --> 00:55:38,919
Speaker 1: half of the time. This is you know, you ask

1199
00:55:38,960 --> 00:55:40,920
Speaker 1: it to play an album and it plays you like

1200
00:55:41,000 --> 00:55:44,520
Speaker 1: a weird cover of it because it doesn't understand um.

1201
00:55:44,560 --> 00:55:47,920
Speaker 1: But then I was like, what are the thousand things

1202
00:55:48,000 --> 00:55:51,399
Speaker 1: you might ask your ear to do? And how many

1203
00:55:51,400 --> 00:55:55,040
Speaker 1: of those get really complex really quickly? Like can you

1204
00:55:55,080 --> 00:55:59,000
Speaker 1: rebook my meeting this afternoon? Okay, that's not a simple query,

1205
00:55:59,280 --> 00:56:01,560
Speaker 1: that's like five hundred things that you need to know

1206
00:56:02,719 --> 00:56:05,239
Speaker 1: that And so the problem is as I said. What

1207
00:56:05,280 --> 00:56:07,120
Speaker 1: you have with a voice assistant is you have an

1208
00:56:07,120 --> 00:56:09,759
Speaker 1: IVR that will always understand what you asked. Machine learning

1209
00:56:09,800 --> 00:56:11,960
Speaker 1: means it will always correctly root you to the right number.

1210
00:56:12,480 --> 00:56:14,400
Speaker 1: What it doesn't have is a way to have somebody

1211
00:56:14,440 --> 00:56:17,600
Speaker 1: at that number automated automate what that person at the

1212
00:56:17,680 --> 00:56:20,200
Speaker 1: number will do. And so you have to create those

1213
00:56:20,200 --> 00:56:22,360
Speaker 1: dialog boxes one by one. So give you an example.

1214
00:56:22,880 --> 00:56:25,600
Speaker 1: Sirie learns how to do cricket. You know it didn't

1215
00:56:25,760 --> 00:56:28,840
Speaker 1: somebody at Apple sat down and wrote the cricket module. Okay,

1216
00:56:28,880 --> 00:56:32,960
Speaker 1: now Sirie can do hurling. Okay, Well, somebody at Sirie

1217
00:56:33,040 --> 00:56:35,279
Speaker 1: at Apple had to sit down and write that, and

1218
00:56:35,320 --> 00:56:38,799
Speaker 1: then Siri can do call me a uber, right, Well,

1219
00:56:38,840 --> 00:56:40,840
Speaker 1: someone at Uber had to write that, and then it

1220
00:56:40,880 --> 00:56:44,560
Speaker 1: can do um tell me whether my flight is delayed? Right.

1221
00:56:44,640 --> 00:56:47,440
Speaker 1: Someone had to write that, And every single one of those,

1222
00:56:47,719 --> 00:56:49,920
Speaker 1: some human being has to sit down and spend an

1223
00:56:49,960 --> 00:56:52,400
Speaker 1: afternoon writing those. And the problem with this is it

1224
00:56:52,440 --> 00:56:55,440
Speaker 1: doesn't scale. And in a sense, if you could do that,

1225
00:56:55,480 --> 00:56:58,000
Speaker 1: you'd have made how nine thousand and how nine thousand

1226
00:56:58,120 --> 00:57:00,359
Speaker 1: is not the aggregate of like some of you sitting

1227
00:57:00,400 --> 00:57:02,520
Speaker 1: and writing those one at a time. That's that other

1228
00:57:02,600 --> 00:57:05,160
Speaker 1: thing that's machine learning, a big data and a bunch

1229
00:57:05,160 --> 00:57:07,200
Speaker 1: of things. So that's the gap. You know, we have

1230
00:57:07,239 --> 00:57:09,440
Speaker 1: a way to get the transcription and the NLP to

1231
00:57:09,560 --> 00:57:11,480
Speaker 1: work completely accurately, Well, we don't have. It is a

1232
00:57:11,520 --> 00:57:14,479
Speaker 1: way to get to automatically make a system that could

1233
00:57:14,480 --> 00:57:18,280
Speaker 1: answer any question you could possibly answer human being to

1234
00:57:18,320 --> 00:57:20,480
Speaker 1: think of what all of those would be. So that

1235
00:57:20,680 --> 00:57:24,040
Speaker 1: is a description of So let me answer answer this

1236
00:57:24,480 --> 00:57:30,040
Speaker 1: question another way. So imagine in the nineteenth century someone

1237
00:57:30,040 --> 00:57:33,320
Speaker 1: tries to make a mechanical horse. There's no I mean,

1238
00:57:33,320 --> 00:57:35,840
Speaker 1: you've seen the pattern drawing things. There's no law of

1239
00:57:35,840 --> 00:57:38,640
Speaker 1: physics that says you can't make a mechanical horse. It's

1240
00:57:38,680 --> 00:57:41,200
Speaker 1: just that the degree of complexity required to get that

1241
00:57:41,240 --> 00:57:43,520
Speaker 1: to work was impossible in the nineteenth century, and arguably

1242
00:57:43,520 --> 00:57:45,480
Speaker 1: it's impossible now. I mean, Boston Dynamics are now trying

1243
00:57:45,520 --> 00:57:47,000
Speaker 1: to do this pretty close. But that's like a hundred

1244
00:57:47,000 --> 00:57:49,760
Speaker 1: and fifty years later, um and so and so. What

1245
00:57:49,800 --> 00:57:51,360
Speaker 1: you can actually do is you could make a bicycle

1246
00:57:51,400 --> 00:57:52,960
Speaker 1: and the steam engine, and a bicycle is a lot

1247
00:57:52,960 --> 00:57:54,720
Speaker 1: simpler than a horse, and it can't do everything that

1248
00:57:54,720 --> 00:57:55,920
Speaker 1: a horse can do. But it turns out that was

1249
00:57:55,960 --> 00:57:58,520
Speaker 1: kind of useful anyway. The same thing with with AI.

1250
00:57:58,640 --> 00:58:00,880
Speaker 1: What people tried to do with AI until a couple

1251
00:58:00,880 --> 00:58:02,760
Speaker 1: of years ago was they would try and write rules.

1252
00:58:03,520 --> 00:58:05,800
Speaker 1: So if you wanted to recognize a cat, what you

1253
00:58:05,880 --> 00:58:08,200
Speaker 1: do is you do edge detection, and you do text

1254
00:58:08,240 --> 00:58:10,479
Speaker 1: your analysis, and you try and make something that looks

1255
00:58:10,480 --> 00:58:12,600
Speaker 1: for two eyes in roughly the right place relative to

1256
00:58:12,640 --> 00:58:14,600
Speaker 1: two ears, and you try and make something that tries

1257
00:58:14,640 --> 00:58:16,960
Speaker 1: to look for legs and hopefully, and you try and

1258
00:58:17,000 --> 00:58:18,160
Speaker 1: work out how the hell are we going to tell

1259
00:58:18,160 --> 00:58:20,160
Speaker 1: the difference between a cat and a dog? And it

1260
00:58:20,200 --> 00:58:23,240
Speaker 1: would sort of work, like three quarters at the time,

1261
00:58:23,520 --> 00:58:25,400
Speaker 1: it would never really work for the same reason that

1262
00:58:25,440 --> 00:58:28,840
Speaker 1: the mechanical horse would sort of work but never actually work.

1263
00:58:29,320 --> 00:58:31,120
Speaker 1: And then what machine learning does is say no, no, no no,

1264
00:58:31,120 --> 00:58:33,840
Speaker 1: no no. You've given a million pictures labeled cat and

1265
00:58:33,840 --> 00:58:35,680
Speaker 1: a million pictures of labeled dog, and you let the

1266
00:58:35,720 --> 00:58:38,520
Speaker 1: machine ripe the rules. That's what machine learning means. The

1267
00:58:38,600 --> 00:58:41,760
Speaker 1: machine generates a millionaire statements that will allow it to

1268
00:58:41,800 --> 00:58:44,120
Speaker 1: calculate this difference between a cat picture and a dog

1269
00:58:44,160 --> 00:58:50,000
Speaker 1: picture with ninety two point seven percent accuracy, and that

1270
00:58:50,320 --> 00:58:52,840
Speaker 1: gives us a breakthrough of a whole class of problem.

1271
00:58:52,960 --> 00:58:56,880
Speaker 1: It solves image recognition itselves, speech itselves, language, itselves, a

1272
00:58:56,920 --> 00:58:59,920
Speaker 1: whole kind of level of pattern recognition. When it doesn't

1273
00:59:00,040 --> 00:59:02,920
Speaker 1: do is give you a human ten year old. It

1274
00:59:02,960 --> 00:59:06,200
Speaker 1: doesn't give you general intelligence. Basically, what you've built is

1275
00:59:06,240 --> 00:59:10,920
Speaker 1: you've built an enormous spreadsheet with a million IF statements

1276
00:59:11,080 --> 00:59:13,920
Speaker 1: meshed together and linked together. And you can give this

1277
00:59:14,000 --> 00:59:16,800
Speaker 1: spreadsheet a picture of a cat and a picture, and

1278
00:59:16,800 --> 00:59:18,280
Speaker 1: it will tell you whether the cat in it or not,

1279
00:59:18,600 --> 00:59:21,840
Speaker 1: and that's all it will do. So another way of

1280
00:59:21,880 --> 00:59:23,760
Speaker 1: thinking about this is like, we have this fantasy of

1281
00:59:23,800 --> 00:59:25,720
Speaker 1: domestic robots that will walk around your home and do

1282
00:59:25,760 --> 00:59:28,400
Speaker 1: the housework. We have domestic robots. It's called a washing machine.

1283
00:59:29,160 --> 00:59:31,400
Speaker 1: That's what machine learning gives you. It gives your washing machine.

1284
00:59:32,040 --> 00:59:35,120
Speaker 1: It gives your washing machine that can recognize family pictures.

1285
00:59:35,160 --> 00:59:37,280
Speaker 1: It gives your washing machine that can tell me is

1286
00:59:37,280 --> 00:59:39,600
Speaker 1: there strange behavior happening on this network. It gives your

1287
00:59:39,600 --> 00:59:42,240
Speaker 1: washing machine that can recognize handwriting. It gives your washing

1288
00:59:42,240 --> 00:59:44,520
Speaker 1: machine that can do X. But it can only do

1289
00:59:44,600 --> 00:59:46,800
Speaker 1: that one thing. It can't do anything else, and it

1290
00:59:46,840 --> 00:59:49,960
Speaker 1: can't clean. You can't wash your dishes, And so that's

1291
00:59:49,960 --> 00:59:52,200
Speaker 1: what machine only gives it gives you this step change

1292
00:59:52,200 --> 00:59:54,560
Speaker 1: in capability. So one of the way I'm kind of

1293
00:59:54,600 --> 00:59:56,280
Speaker 1: I'm sort of giving you a stack of metaphors here.

1294
00:59:56,280 --> 00:59:57,680
Speaker 1: I think a really good way to think about what

1295
00:59:57,720 --> 01:00:00,680
Speaker 1: machine learning gives us is it's like thinking about relations databases.

1296
01:00:01,400 --> 01:00:04,280
Speaker 1: It's a relational databases gives you this amazing step change

1297
01:00:04,280 --> 01:00:06,919
Speaker 1: in what you can do with computers. Suddenly you can say,

1298
01:00:06,960 --> 01:00:10,560
Speaker 1: show me X by Y Bloomberg. Going would not exist

1299
01:00:10,600 --> 01:00:13,720
Speaker 1: without this, SAP would not exist without this, just in times,

1300
01:00:13,720 --> 01:00:16,600
Speaker 1: supply chains would not exist without this totally transformed our world.

1301
01:00:17,200 --> 01:00:22,000
Speaker 1: But everything you use now but it's not AI. It is.

1302
01:00:22,040 --> 01:00:24,400
Speaker 1: But it's like if it's AI, then everything is AI.

1303
01:00:25,360 --> 01:00:27,560
Speaker 1: It doesn't get you how nine thousand. Let me jump

1304
01:00:27,600 --> 01:00:31,120
Speaker 1: into some of my favorite questions. Tell us the most

1305
01:00:31,200 --> 01:00:34,800
Speaker 1: important thing that people who work with you don't know

1306
01:00:34,880 --> 01:00:39,120
Speaker 1: about you? I hate personal questions? Is that true? I

1307
01:00:39,160 --> 01:00:40,720
Speaker 1: don't know. I find I kind of look at this,

1308
01:00:40,720 --> 01:00:42,680
Speaker 1: this email, this, I didn't puzzled by these kind of questions.

1309
01:00:42,680 --> 01:00:44,360
Speaker 1: I don't know, what is it the people who work

1310
01:00:44,400 --> 01:00:46,000
Speaker 1: with that, we have no idea. They probably don't know

1311
01:00:46,080 --> 01:00:49,160
Speaker 1: have a dog. There you go, Well, because sometimes it's

1312
01:00:49,200 --> 01:00:53,120
Speaker 1: revealing some people have told us deep dark secrets from

1313
01:00:53,320 --> 01:00:57,480
Speaker 1: decades ago, and case there is a joke that the

1314
01:00:57,480 --> 01:01:00,040
Speaker 1: most secure form of encryption is anything you say in

1315
01:01:00,080 --> 01:01:02,440
Speaker 1: the second hour of a podcast. Here we are, I

1316
01:01:02,480 --> 01:01:06,040
Speaker 1: can say it now, and it's you, me, and a

1317
01:01:06,080 --> 01:01:08,120
Speaker 1: handful of friends that are hearing that nobody else is

1318
01:01:08,160 --> 01:01:10,840
Speaker 1: gonna hear. Um, tell us about your early mentors who

1319
01:01:10,880 --> 01:01:14,960
Speaker 1: affected the way you looked at technology and venture inversiting.

1320
01:01:15,840 --> 01:01:18,560
Speaker 1: I would say it's not really about how I look

1321
01:01:18,600 --> 01:01:21,919
Speaker 1: at technology. It's more sort of mental processes and ways

1322
01:01:21,960 --> 01:01:24,840
Speaker 1: of thinking about things and ways to try and answer questions.

1323
01:01:25,400 --> 01:01:27,760
Speaker 1: And so the stuff I did at university, you know,

1324
01:01:27,800 --> 01:01:30,400
Speaker 1: people talking to me about medieval history, and it's not

1325
01:01:30,640 --> 01:01:32,919
Speaker 1: this is no, it's not that didn't happen. This didn't happen.

1326
01:01:33,360 --> 01:01:36,120
Speaker 1: It's more, okay, how do you actually understand what we're

1327
01:01:36,120 --> 01:01:38,400
Speaker 1: trying to think about here? So, like I remember like

1328
01:01:38,440 --> 01:01:41,160
Speaker 1: my first or second, like you know, the first. The

1329
01:01:41,200 --> 01:01:42,680
Speaker 1: way it came Bridge history works is you do an

1330
01:01:42,960 --> 01:01:44,480
Speaker 1: You get given an essay title and a reading list,

1331
01:01:44,520 --> 01:01:45,960
Speaker 1: and you come back in a week with your essay

1332
01:01:46,120 --> 01:01:49,600
Speaker 1: and that's it. You don't have to get lectures. And

1333
01:01:49,760 --> 01:01:51,440
Speaker 1: so I think. My first essay was the one that,

1334
01:01:51,480 --> 01:01:53,480
Speaker 1: of course everyone screws up, and it was like was

1335
01:01:53,560 --> 01:01:56,280
Speaker 1: the King of England more or less powerful after ten

1336
01:01:56,440 --> 01:01:59,560
Speaker 1: exty six? And you know I gave the long answer

1337
01:01:59,560 --> 01:02:01,360
Speaker 1: and my professor said, look, you have to think about

1338
01:02:01,400 --> 01:02:03,080
Speaker 1: this is what does it actually mean to be powerful?

1339
01:02:03,120 --> 01:02:04,760
Speaker 1: And power is the ability to get people to do

1340
01:02:04,840 --> 01:02:06,880
Speaker 1: something they don't want to do. And so you can

1341
01:02:06,880 --> 01:02:09,400
Speaker 1: give all this other stuff about all the sophistication of

1342
01:02:09,480 --> 01:02:11,800
Speaker 1: the Anglo Saxon monarchy and they had their own much

1343
01:02:11,800 --> 01:02:15,560
Speaker 1: more sophisticated coinage and blah blah blah. No that was

1344
01:02:15,600 --> 01:02:17,640
Speaker 1: two years later, But like, who is more Is he

1345
01:02:17,680 --> 01:02:20,120
Speaker 1: actually more powerful? William the conqueror can tell people to

1346
01:02:20,160 --> 01:02:21,320
Speaker 1: do stuff and they have to do it. And the

1347
01:02:21,400 --> 01:02:23,800
Speaker 1: King of the sax the king before the conquest, King

1348
01:02:23,840 --> 01:02:26,200
Speaker 1: of England, like no one really paid any attention to

1349
01:02:26,240 --> 01:02:28,440
Speaker 1: what he said except for the coin like the complicated coinage.

1350
01:02:28,440 --> 01:02:31,320
Speaker 1: So my point is like it's not so much that

1351
01:02:31,440 --> 01:02:34,360
Speaker 1: someone told me how this is how to think about technology.

1352
01:02:34,920 --> 01:02:38,120
Speaker 1: It's more how to sort of think systematically and try

1353
01:02:38,160 --> 01:02:40,600
Speaker 1: and break up problems and try and look for peace

1354
01:02:40,640 --> 01:02:43,200
Speaker 1: of components and try and look kind of look at

1355
01:02:43,240 --> 01:02:46,760
Speaker 1: things in those kinds of ways quite personally. Tell us

1356
01:02:46,760 --> 01:02:50,720
Speaker 1: about some of your favorite books, be they fiction, nonfiction, technology,

1357
01:02:50,840 --> 01:02:53,800
Speaker 1: or what have you. Um, I don't know. I'm sort

1358
01:02:53,840 --> 01:02:56,600
Speaker 1: of I'm always reading and I'm always buying books, and

1359
01:02:56,600 --> 01:02:59,240
Speaker 1: those two process as are completely independent. I read almost

1360
01:02:59,240 --> 01:03:02,479
Speaker 1: no business books or technology books, partly because I feel

1361
01:03:02,480 --> 01:03:04,800
Speaker 1: that all business books are basically a short magazine article

1362
01:03:04,840 --> 01:03:08,880
Speaker 1: padded out to There are some authors in particular I

1363
01:03:08,880 --> 01:03:11,880
Speaker 1: can name, well, that's their entire over. Well, if you

1364
01:03:11,960 --> 01:03:14,000
Speaker 1: feel like if you've written fifteen business books on the

1365
01:03:14,040 --> 01:03:16,160
Speaker 1: same theme, then like you're either a very bad writer

1366
01:03:16,280 --> 01:03:18,600
Speaker 1: or very good writer, depending on your very good marketer,

1367
01:03:18,920 --> 01:03:22,000
Speaker 1: Well that too. Perhaps, I know. I think I read

1368
01:03:22,040 --> 01:03:24,360
Speaker 1: things that intrigued me and interest me. The last thing

1369
01:03:24,400 --> 01:03:27,880
Speaker 1: I read with the Iliad, which I've never read before. Wait,

1370
01:03:27,920 --> 01:03:31,120
Speaker 1: the history major never read the Iliad. Well, so there's

1371
01:03:31,120 --> 01:03:33,200
Speaker 1: this great line from Italia Calvino where he says the

1372
01:03:33,280 --> 01:03:35,280
Speaker 1: number of something like the number of essential works is

1373
01:03:35,320 --> 01:03:37,240
Speaker 1: so great that nobody, no matter how much they're reading,

1374
01:03:37,280 --> 01:03:40,280
Speaker 1: has read more than a fraction. Um, you know, literature

1375
01:03:40,280 --> 01:03:42,680
Speaker 1: as a sample, you can't read all the books. There

1376
01:03:42,720 --> 01:03:44,160
Speaker 1: was a period when you could have seen all the movies.

1377
01:03:44,160 --> 01:03:45,560
Speaker 1: Now you can't see all the movies, you can't listen

1378
01:03:45,600 --> 01:03:47,960
Speaker 1: to all the music. You just have to take samples

1379
01:03:48,040 --> 01:03:50,280
Speaker 1: and take do things that stimulate you or make you

1380
01:03:50,320 --> 01:03:53,200
Speaker 1: think in interesting ways, particularly things that aren't about what

1381
01:03:53,240 --> 01:03:57,000
Speaker 1: you work on. So give us another example from that's

1382
01:03:57,160 --> 01:04:01,000
Speaker 1: that's newer than two thousand years old. Um, I don't know.

1383
01:04:01,800 --> 01:04:04,320
Speaker 1: By the way, this is everybody's favorite question, the one

1384
01:04:04,600 --> 01:04:07,840
Speaker 1: I get. I read books and then I can't remember

1385
01:04:07,880 --> 01:04:09,400
Speaker 1: what it is? What? What? What? The last book I

1386
01:04:09,480 --> 01:04:11,920
Speaker 1: read was, Um, what stands out to you? What has

1387
01:04:12,000 --> 01:04:14,520
Speaker 1: really read? Really? The Iliad really stood out for me.

1388
01:04:14,600 --> 01:04:15,920
Speaker 1: That was a kind of a couple of weeks ago,

1389
01:04:15,960 --> 01:04:20,280
Speaker 1: I read this fascinating book about medieval manuscripts called Encounters

1390
01:04:20,360 --> 01:04:23,600
Speaker 1: with Fascinating Manuscripts or something like that by college librarian

1391
01:04:23,600 --> 01:04:25,880
Speaker 1: in Cambridge, and it's one of these sort of new

1392
01:04:26,040 --> 01:04:27,920
Speaker 1: kind of genre of books which kind of looked like

1393
01:04:27,920 --> 01:04:30,240
Speaker 1: a big hardback nonfiction book. But they're all in color

1394
01:04:30,280 --> 01:04:31,720
Speaker 1: all the way through, which is funny to do with

1395
01:04:31,760 --> 01:04:34,240
Speaker 1: the development of printing technology, which is there's there's loads

1396
01:04:34,240 --> 01:04:36,080
Speaker 1: of books that have color illustrations all the way through,

1397
01:04:36,080 --> 01:04:37,960
Speaker 1: which is but aren't art books. It's kind of interesting

1398
01:04:38,120 --> 01:04:40,000
Speaker 1: and it's just sort of about how books evolved. So

1399
01:04:40,000 --> 01:04:44,360
Speaker 1: it's kind of fascinating observation that books or codeses used

1400
01:04:44,400 --> 01:04:46,800
Speaker 1: to be rectangular because you made them out of papyrus.

1401
01:04:46,800 --> 01:04:48,800
Speaker 1: So you would lay your sheets, you know, you lay

1402
01:04:48,840 --> 01:04:52,160
Speaker 1: the strips of plant kind of cross weights like plywood,

1403
01:04:52,200 --> 01:04:53,920
Speaker 1: and you'd make them square shaped because you had the

1404
01:04:54,520 --> 01:04:56,080
Speaker 1: strips of all the same length, So that produced to

1405
01:04:56,120 --> 01:05:00,720
Speaker 1: square shaped piece of papyrus. And then you moved to um,

1406
01:05:00,720 --> 01:05:03,640
Speaker 1: what's it called animal skin? What's the word? I've forgotten

1407
01:05:03,640 --> 01:05:06,439
Speaker 1: the word? My mind is gone blank now too, um Um.

1408
01:05:06,480 --> 01:05:10,240
Speaker 1: You moved to parchment from animal skins. And there's this

1409
01:05:10,280 --> 01:05:13,840
Speaker 1: great line. Animals tend to be oblong, so you get

1410
01:05:13,880 --> 01:05:16,560
Speaker 1: oblong pieces and then you fold them up and that

1411
01:05:16,600 --> 01:05:19,080
Speaker 1: produces an oblong book. And so when you go from

1412
01:05:19,120 --> 01:05:22,040
Speaker 1: papyrus department, you go from square books to oblong books.

1413
01:05:22,400 --> 01:05:24,400
Speaker 1: And here we are years later and the books are

1414
01:05:24,400 --> 01:05:27,800
Speaker 1: still oblong, and that's just the residual of the earlier

1415
01:05:27,840 --> 01:05:31,440
Speaker 1: technology of parchment. Yeah, exactly. I think those sorts of

1416
01:05:31,520 --> 01:05:33,600
Speaker 1: I mean, coming back to you know, why is this interesting?

1417
01:05:33,600 --> 01:05:36,720
Speaker 1: I do think those sorts of how did stuff happen,

1418
01:05:36,840 --> 01:05:39,160
Speaker 1: and why did the what's the thread of causation and

1419
01:05:39,200 --> 01:05:42,280
Speaker 1: how things can be completely random is sort of interesting

1420
01:05:42,280 --> 01:05:44,680
Speaker 1: and how do these does this stuff change how we

1421
01:05:44,720 --> 01:05:46,600
Speaker 1: think about it? So on the flight here, I've read

1422
01:05:46,640 --> 01:05:48,920
Speaker 1: a book about kind of lighting tech, the evolution of

1423
01:05:48,960 --> 01:05:51,240
Speaker 1: lighting technology in the eighteenth and nineteent century, as we

1424
01:05:51,280 --> 01:05:53,360
Speaker 1: go from candles to light to kind of lamps that

1425
01:05:53,440 --> 01:05:55,360
Speaker 1: hold the candle and produce five times more light, how

1426
01:05:55,440 --> 01:05:58,160
Speaker 1: we go to now or the gas lights to to

1427
01:05:58,560 --> 01:06:01,520
Speaker 1: um earlier electric light which used weren't very bright, and

1428
01:06:01,520 --> 01:06:03,920
Speaker 1: then use tungsten filament and again, so there's the evelation

1429
01:06:03,960 --> 01:06:06,400
Speaker 1: of light. This observation that stuck in my mind and

1430
01:06:06,400 --> 01:06:08,200
Speaker 1: I and now need to go and check which is

1431
01:06:08,240 --> 01:06:12,640
Speaker 1: an assertion that Dutch seventy century Dutch pictures have windows

1432
01:06:12,680 --> 01:06:16,240
Speaker 1: but no cuts, because why would you keep the light out.

1433
01:06:16,400 --> 01:06:18,040
Speaker 1: Everybody wants to light and you want the light in.

1434
01:06:18,160 --> 01:06:19,600
Speaker 1: And of course, as we all sort of no, light

1435
01:06:19,680 --> 01:06:22,320
Speaker 1: was really expensive and used to be a luxury and

1436
01:06:22,320 --> 01:06:24,240
Speaker 1: now it's not. But I just thought that was a

1437
01:06:24,280 --> 01:06:27,440
Speaker 1: really revealing way of thinking about how your whole sense

1438
01:06:27,480 --> 01:06:30,200
Speaker 1: of the world changes when lighting is cheap. Why would

1439
01:06:30,240 --> 01:06:33,560
Speaker 1: you have curtns that that's quite fastening. The book How

1440
01:06:33,600 --> 01:06:36,720
Speaker 1: We Got to Now has there's six inventions, one of

1441
01:06:36,760 --> 01:06:40,240
Speaker 1: which is lighting, and the process of watching the price

1442
01:06:40,400 --> 01:06:45,320
Speaker 1: plummet over over the centuries is really is really fastening,

1443
01:06:45,640 --> 01:06:47,600
Speaker 1: all right. The interesting thing that comes out of that

1444
01:06:47,720 --> 01:06:50,600
Speaker 1: is that when light was expensive, state of symbol was

1445
01:06:50,680 --> 01:06:53,320
Speaker 1: to stay up all night. So rich people would get

1446
01:06:53,360 --> 01:06:55,280
Speaker 1: up at lunchtime and go to bed at l five

1447
01:06:55,280 --> 01:06:57,200
Speaker 1: in the morning because they could afford to, because they

1448
01:06:57,200 --> 01:07:01,080
Speaker 1: could afford candles. Everybody else gets up with the croaker, think, um,

1449
01:07:01,120 --> 01:07:02,280
Speaker 1: what do you do for fun? What do you do

1450
01:07:02,360 --> 01:07:06,760
Speaker 1: to relax outside of the office in San Francisco? Um?

1451
01:07:06,880 --> 01:07:10,240
Speaker 1: So I have a seven year old and a dog,

1452
01:07:10,760 --> 01:07:15,000
Speaker 1: so my time is not my own understood completely? What

1453
01:07:15,120 --> 01:07:17,560
Speaker 1: sort of advice would you give to a millennial or

1454
01:07:17,640 --> 01:07:21,920
Speaker 1: someone who just graduated college that was interested either in

1455
01:07:21,920 --> 01:07:26,640
Speaker 1: a career in technology or a venture capital So I

1456
01:07:27,240 --> 01:07:29,200
Speaker 1: sort of struggle to give an advice party because my

1457
01:07:29,280 --> 01:07:32,400
Speaker 1: career has been a series of random lurches and kind

1458
01:07:32,400 --> 01:07:36,120
Speaker 1: of companies going out of business. I won't apply for

1459
01:07:36,120 --> 01:07:38,880
Speaker 1: a job at cling Back. Don't worry, I think there's

1460
01:07:38,920 --> 01:07:42,680
Speaker 1: a The only thing I've sort of observed is there is? Um,

1461
01:07:42,720 --> 01:07:44,919
Speaker 1: there is the kind of things that you are good

1462
01:07:44,960 --> 01:07:47,320
Speaker 1: at doing and the kind of mental processes that you

1463
01:07:47,360 --> 01:07:49,880
Speaker 1: are good at. Are you good at people or not?

1464
01:07:50,000 --> 01:07:53,040
Speaker 1: Are you good at process and project management and making

1465
01:07:53,040 --> 01:07:55,520
Speaker 1: sure everything gets done right? Or is that not what

1466
01:07:55,600 --> 01:07:58,320
Speaker 1: it interests you? Are you good at trying to kind

1467
01:07:58,360 --> 01:08:01,240
Speaker 1: of explain an empathy sizing with people? Are you good

1468
01:08:01,240 --> 01:08:04,480
Speaker 1: at trying to explain and discuss and argue with people

1469
01:08:04,600 --> 01:08:08,160
Speaker 1: and persuade people? Um? What are the things that you're

1470
01:08:08,200 --> 01:08:10,400
Speaker 1: good at doing? And those are not necessarily what was

1471
01:08:10,440 --> 01:08:14,080
Speaker 1: your degree in, and they aren't necessarily specific to two jobs.

1472
01:08:14,200 --> 01:08:17,000
Speaker 1: So like I have contemporaries who went off and become

1473
01:08:17,200 --> 01:08:19,519
Speaker 1: what England we call a barrister, which is basically a

1474
01:08:19,600 --> 01:08:21,760
Speaker 1: lawyer who only goes to court is in England and

1475
01:08:21,800 --> 01:08:23,639
Speaker 1: law is split solicitors to be the paper what barristers

1476
01:08:23,680 --> 01:08:25,960
Speaker 1: get called as well. Exactly that's all, but that's all

1477
01:08:25,960 --> 01:08:28,880
Speaker 1: the effectively and it's a completely different profession. Um, And

1478
01:08:28,920 --> 01:08:31,360
Speaker 1: I have contemporaries who are barristers, and you know I

1479
01:08:31,360 --> 01:08:33,360
Speaker 1: could have done that. I could have gone and argued

1480
01:08:33,360 --> 01:08:34,559
Speaker 1: for a living, and you know you kind of you

1481
01:08:34,600 --> 01:08:35,720
Speaker 1: go and do you look at the problem and you

1482
01:08:35,760 --> 01:08:37,240
Speaker 1: pull it apart and you work out what story to

1483
01:08:37,280 --> 01:08:39,160
Speaker 1: tell and how to place added to the law and

1484
01:08:39,160 --> 01:08:41,920
Speaker 1: try and persuade the judge and the jury. Um So,

1485
01:08:42,080 --> 01:08:44,240
Speaker 1: in a sense, I have those skill sets. On the

1486
01:08:44,280 --> 01:08:46,840
Speaker 1: other hand, I look at these contemporaries and it says,

1487
01:08:46,960 --> 01:08:51,080
Speaker 1: you know, um, Charlie is probably the leading up and

1488
01:08:51,120 --> 01:08:54,479
Speaker 1: coming advocate in complex of your maritime insurance disputes. And

1489
01:08:54,520 --> 01:08:57,840
Speaker 1: I think, well, I hope he's paid a lot to that,

1490
01:08:59,000 --> 01:09:02,040
Speaker 1: but his interests did in the argument and the explaining

1491
01:09:02,040 --> 01:09:05,720
Speaker 1: and the discussion. The fact that it's insurance is kind

1492
01:09:05,720 --> 01:09:08,439
Speaker 1: of neither here nor there really, um So, I think

1493
01:09:08,439 --> 01:09:10,080
Speaker 1: you have to just kind of work out, well, what

1494
01:09:10,280 --> 01:09:12,720
Speaker 1: is it What are the things that fascinate you, and

1495
01:09:12,800 --> 01:09:15,720
Speaker 1: what are the kind of mental processes that fascinate you,

1496
01:09:16,160 --> 01:09:18,479
Speaker 1: And and our final question, what is it that you

1497
01:09:18,520 --> 01:09:21,240
Speaker 1: know about the worlds of technology that you wish you

1498
01:09:21,320 --> 01:09:26,320
Speaker 1: knew twenty years ago? Other than like buy Apple or something, No, no,

1499
01:09:26,439 --> 01:09:29,360
Speaker 1: nothing like that. That that's you know, anyone could look

1500
01:09:29,400 --> 01:09:32,360
Speaker 1: at prices twenty years ago and said hey, by Amazon,

1501
01:09:32,560 --> 01:09:36,639
Speaker 1: by Apple. But generally in terms of thinking in terms

1502
01:09:36,680 --> 01:09:39,560
Speaker 1: of broad processes, what would have helped you early in

1503
01:09:39,640 --> 01:09:41,760
Speaker 1: your career had you figured it out sooner. So that

1504
01:09:41,920 --> 01:09:46,160
Speaker 1: actually might be a good way of me framing my

1505
01:09:46,400 --> 01:09:50,280
Speaker 1: technology to determinism peace because it's a sort of I mean,

1506
01:09:50,479 --> 01:09:52,519
Speaker 1: the other thing people talk about about a lot in

1507
01:09:52,600 --> 01:09:55,800
Speaker 1: venture capitalist patent recognition that this sort of looks like

1508
01:09:55,880 --> 01:09:58,280
Speaker 1: things that will work. This looks like things that that

1509
01:09:58,360 --> 01:10:01,000
Speaker 1: never work and why and the thing that never work

1510
01:10:01,439 --> 01:10:03,320
Speaker 1: eventually they do work, and that's where there's a hundred

1511
01:10:03,320 --> 01:10:05,519
Speaker 1: billion dollar outcome. So that's also an interesting kind of

1512
01:10:05,520 --> 01:10:09,320
Speaker 1: fruitful copy conversation. But it's sort of understanding these patents

1513
01:10:09,479 --> 01:10:11,680
Speaker 1: of Okay, you have to think, okay, how is this

1514
01:10:11,720 --> 01:10:15,040
Speaker 1: going to fit within this ecosystem? This is what is

1515
01:10:15,040 --> 01:10:18,000
Speaker 1: the timing for this? It's those sorts of mechanics of

1516
01:10:18,080 --> 01:10:23,200
Speaker 1: the process um that are interesting. I think that's quite fascinating.

1517
01:10:23,840 --> 01:10:26,760
Speaker 1: We have been speaking with Ben Nick Evans of Andres

1518
01:10:26,760 --> 01:10:29,800
Speaker 1: and Horowitz. If you enjoy this conversation, be sure and

1519
01:10:29,840 --> 01:10:31,559
Speaker 1: look up an Inch or down an inch on Apple,

1520
01:10:31,640 --> 01:10:37,000
Speaker 1: iTunes or SoundCloud, overcast, Bloomberg dot com wherever your final

1521
01:10:37,080 --> 01:10:39,400
Speaker 1: podcasts are sold, and you could see any of the

1522
01:10:39,439 --> 01:10:42,479
Speaker 1: other two hundred such conversations we've had over the past

1523
01:10:43,040 --> 01:10:46,920
Speaker 1: four years. We love your comments, feedback and suggestions right

1524
01:10:47,000 --> 01:10:49,920
Speaker 1: to us at m IB podcast at Bloomberg dot net.

1525
01:10:50,439 --> 01:10:52,320
Speaker 1: I would be remiss if I did not thank the

1526
01:10:52,360 --> 01:10:56,240
Speaker 1: crack staff that helps us put this podcast together each week.

1527
01:10:56,560 --> 01:10:59,599
Speaker 1: Taylor Riggs is our book or slash producer. Medina Parwana

1528
01:11:00,080 --> 01:11:03,640
Speaker 1: is our audio engineer slash producer. Mike Batnik is our

1529
01:11:03,680 --> 01:11:07,599
Speaker 1: head of research. I'm Barry Retolts. You're listening to Master's

1530
01:11:07,640 --> 01:11:09,679
Speaker 1: in Business on Bloomberg Radio.