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Benedict Evans Talks About the Tech Bubble · Masters in Business

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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,
• 0:50
  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
• 2:09
  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
• 2:16
  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
• 3:56
  Speaker 1: three years. So were you always the tech guy or
• 3:59
  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
• 4:08
  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
• 4:26
  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
• 4:55
  Speaker 1: worked for Orange, So I went to Paris and I
• 4:57
  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
• 5:14
  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.
• 5:41
  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
• 5:45
  Speaker 1: got a phone and that was it. And then they
• 5:46
  Speaker 1: went from being amazing. They went from being Google to
• 5:49
  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
• 5:57
  Speaker 1: in concept videos and presentations from the mob All operated
• 6:00
  Speaker 1: is in you're going to do all of this stuff,
• 6:03
  Speaker 1: and they were going to do it all, and of
• 6:05
  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
• 6:12
  Speaker 1: there's a lot of echoes of that. For example, if
• 6:14
  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
• 6:18
  Speaker 1: will be and how everything will change, and you have
• 6:20
  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
• 6:30
  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
• 6:34
  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
• 6:40
  Speaker 1: or forgotten like Apple um and no one had ever
• 6:42
  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
• 7:11
  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
• 8:35
  Speaker 1: the future will be like, and I think the punchline
• 8:39
  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
• 8:44
  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
• 8:47
  Speaker 1: to Talcos now is it's as though a municipal water
• 8:51
  Speaker 1: company looked at the mineral water business and they said,
• 8:53
  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
• 9:01
  Speaker 1: McKinsey and wolf Fallens and they build the whole thing,
• 9:04
  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
• 9:08
  Speaker 1: they look at it next to yether of two D
• 9:09
  Speaker 1: brands and they think, hang on a second, something not
• 9:12
  Speaker 1: quite right here. Um, So, how did you go from
• 9:17
  Speaker 1: working in London or Paris as a telecom man? Was
• 9:20
  Speaker 1: too I know, I'm going to move six thousand miles
• 9:24
  Speaker 1: away and become a imagineer. And I just asked them
• 9:27
  Speaker 1: for a job. I don't know why people say it's
• 9:28
  Speaker 1: hard to get a job in venture. You know, you
• 9:30
  Speaker 1: go ask them for a job, they say, yes, okay,
• 9:32
  Speaker 1: that's it. Yeah, it was easy. How did you how
• 9:34
  Speaker 1: did you? Well? First hear of instance, so well, so
• 9:38
  Speaker 1: there's a process here. And as I mentioned, I actually
• 9:40
  Speaker 1: did my degree in history, which is analysis, and so
• 9:42
  Speaker 1: the question was sort of picking up and looking for
• 9:44
  Speaker 1: things where you can apply that to analysis, and so
• 9:46
  Speaker 1: I left university and became a south Side analyst, and
• 9:49
  Speaker 1: is sort of many people will know that, for many
• 9:52
  Speaker 1: various reasons, stop being a fun thing to do, or
• 9:54
  Speaker 1: even a thing that anyone could do. So I think
• 9:56
  Speaker 1: when I was in telecoms, merl Lynch, you have had
• 9:58
  Speaker 1: something like fifty um up in telecoms analysts, and the
• 10:01
  Speaker 1: last time I looked, I think they had less than
• 10:03
  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,
• 10:10
  Speaker 1: first at Orange and then at um NBC Universal and
• 10:13
  Speaker 1: Channel four again both in London. So from sort of
• 10:16
  Speaker 1: sitting on the outside in sitting on the inside out
• 10:18
  Speaker 1: trying to work out what we do and how do
• 10:20
  Speaker 1: we how do we understand this? And it's kind of
• 10:22
  Speaker 1: interesting because the kind of the questions changed and suddenly
• 10:24
  Speaker 1: you've got a spreadsheet that lists all the stuff you've
• 10:25
  Speaker 1: been trying to work out from the from the public financials.
• 10:28
  Speaker 1: But then you're just trying to work out different stuff.
• 10:30
  Speaker 1: But the process is the thought process at the state.
• 10:32
  Speaker 1: Because I did that for a while. Um. I left
• 10:34
  Speaker 1: NBC Universal the week that General Electric share price fell fifty,
• 10:37
  Speaker 1: which is obviously a called direct causal relationship. Sometimes you know,
• 10:41
  Speaker 1: things happened and you could just draw a straight line
• 10:44
  Speaker 1: from A to B exactly. I recall seeing the Wall
• 10:46
  Speaker 1: Street General Peace, Ben lick Evans departs NBC Universal gu
• 10:50
  Speaker 1: started out in their exactly we went up up by half,
• 10:53
  Speaker 1: and that the same amount the next week. Of course,
• 10:55
  Speaker 1: um so I left when the financial that is in
• 10:58
  Speaker 1: the financial crisis. It was so yeah, sound about that,
• 11:02
  Speaker 1: an old and boring story of what happened there. Um
• 11:04
  Speaker 1: and then I went as I worked as a consultant
• 11:07
  Speaker 1: in London, so I was doing a lot of sort
• 11:09
  Speaker 1: of strategy consultancy and sort of producing research reports forum
• 11:14
  Speaker 1: around the European media and telecom space, and so I
• 11:16
  Speaker 1: was writing both about kind of what's happening in fashion
• 11:19
  Speaker 1: magazines and you know record retail and DVD retail. Would
• 11:22
  Speaker 1: also water Google and Facebook doing what's happening with smartphones,
• 11:26
  Speaker 1: And so I started a blog, and more or less
• 11:29
  Speaker 1: the same time, I went on Twitter at that time,
• 11:30
  Speaker 1: which he was kind of new, and so you could
• 11:32
  Speaker 1: were quite easy to get noticed if you're doing stuff,
• 11:34
  Speaker 1: and I was kind of writing stuff. The way I
• 11:36
  Speaker 1: describe it is basically I would write stuff that a
• 11:39
  Speaker 1: cell side analyst or a senior strategy person or somewhat
• 11:41
  Speaker 1: at Apple could write, except that either they couldn't public it,
• 11:45
  Speaker 1: publish it, or they would be writing for a very
• 11:48
  Speaker 1: different audience. They'd be writing for, you know, buy cell
• 11:50
  Speaker 1: whole audience or um or they wouldn't have the analytic
• 11:55
  Speaker 1: background to write it. So like a sell side analyst
• 11:57
  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,
• 12:01
  Speaker 1: but they are not used to writing stuff and they
• 12:03
  Speaker 1: can't publish it anyway. Somebody and McKinsey knows all this
• 12:05
  Speaker 1: stuff that they're not allowed to say that's not going
• 12:07
  Speaker 1: to work. Um. So I was kind of an interesting,
• 12:10
  Speaker 1: kind of kind of niche in the vendor kind of
• 12:12
  Speaker 1: little segment on your vend diagram of somebody who had
• 12:15
  Speaker 1: an analytic and strategy background was used to writing it
• 12:17
  Speaker 1: that about this stuff and explaining it, could say it
• 12:19
  Speaker 1: in public at a time when very few other people
• 12:22
  Speaker 1: were doing it. And so for those reasons, UM, I
• 12:25
  Speaker 1: sort of got noticed. And you know, I spent like
• 12:27
  Speaker 1: two or three years writing a bog posts and getting
• 12:29
  Speaker 1: like a hundred page vs a months, and then I
• 12:31
  Speaker 1: went through a period where I was getting a couple
• 12:33
  Speaker 1: of thousand page vis a day. Um. And that sort
• 12:35
  Speaker 1: of happened in the course of two thousand thirteen. And
• 12:38
  Speaker 1: then and I kind of picked up my my bag
• 12:40
  Speaker 1: and thought, well, what else do I want to do?
• 12:41
  Speaker 1: What do I want to do next? Where could I
• 12:43
  Speaker 1: deploy this? And you know, you you have those conversations
• 12:45
  Speaker 1: at various stages in your life. And I thought, well,
• 12:47
  Speaker 1: I should go and do you do this in venture capital?
• 12:50
  Speaker 1: And where's the place to go and do venture capital?
• 12:51
  Speaker 1: And you the global cluster with San Francisco, and what
• 12:55
  Speaker 1: was the kind of firm where it felt like this
• 12:58
  Speaker 1: kind of innovative approach to its explaining things and adding
• 13:01
  Speaker 1: value in public would work, and A sixteen Z was
• 13:03
  Speaker 1: thought of at the top of that list, and you
• 13:06
  Speaker 1: reached out to them as opposed to visus. So I
• 13:08
  Speaker 1: got a couple of introductions. I went in and I said, well,
• 13:11
  Speaker 1: this is sort of what I do. Is there a
• 13:13
  Speaker 1: place for this in this firm? Does this fit within
• 13:15
  Speaker 1: the firm? Is it's useful? And they kind of said yeah,
• 13:18
  Speaker 1: just like that or less. So um, lots of other
• 13:23
  Speaker 1: people blogers should be inundating a sixteen Z with their
• 13:26
  Speaker 1: resumes and saying, look, I've been doing this also. You know,
• 13:29
  Speaker 1: I don't think you need a resume. It's it's kind
• 13:32
  Speaker 1: of like, you know, if you want a sales job,
• 13:33
  Speaker 1: you should be able to get the meeting. If you
• 13:35
  Speaker 1: can't get the meeting, you shouldn't have a sales job.
• 13:36
  Speaker 1: And it's kind of the same. You prove that you
• 13:38
  Speaker 1: can do the job by doing it. That makes that
• 13:40
  Speaker 1: makes a lot of sense. So the focus that you
• 13:44
  Speaker 1: have today is no longer telecom the way it was,
• 13:49
  Speaker 1: but certainly the concept of mobility as a source of
• 13:54
  Speaker 1: um possible changes in technology is a key factor. What
• 13:59
  Speaker 1: do you focus on these things? Well, I think talking
• 14:01
  Speaker 1: about mobile now is a bit like talking about PCs
• 14:04
  Speaker 1: ten years ago. Right, Yes, this is the center of everything,
• 14:06
  Speaker 1: but it's happened, and so we're not arguing about iOS
• 14:10
  Speaker 1: versus Android, or whether everyone's going to have one of
• 14:12
  Speaker 1: these things, or apps versus the web, all of those
• 14:14
  Speaker 1: kind of things, though, they're not interesting conversations anymore. It's
• 14:16
  Speaker 1: like arguing, you know, is everybody get onto going to
• 14:18
  Speaker 1: get onto the web? Well? Yes, Now what next question?
• 14:21
  Speaker 1: So you look? So there's one answer is well, what
• 14:24
  Speaker 1: are the next questions? What are the next kind of
• 14:27
  Speaker 1: mega trends that are happening. The second is there's a
• 14:30
  Speaker 1: lot of conversation around what happens with that stuff? What
• 14:33
  Speaker 1: happens with the stuff that we've already had so ten
• 14:35
  Speaker 1: years ago? What do we do with broadband and browsers?
• 14:38
  Speaker 1: So well, let's talk about search, let's talk about social,
• 14:40
  Speaker 1: Let's talk about what happens once everyone has a PC.
• 14:43
  Speaker 1: Now what happens once everyone has a smartphone and everyone
• 14:46
  Speaker 1: is on Facebook? So we have those kinds of conversations
• 14:48
  Speaker 1: about the world, where the world that we've just built,
• 14:50
  Speaker 1: what happens with that, and then we think, well, what
• 14:51
  Speaker 1: are the next things? So machine learning, autonomous cars, mixed reality, cryptocurrency,
• 14:57
  Speaker 1: What are the next fundamental trends that will shape the
• 14:59
  Speaker 1: tech industry the way first the PC and then mobile
• 15:01
  Speaker 1: shape the tech industry in the last twenty or thirty years.
• 15:03
  Speaker 1: I think then within that there's a kind of you know,
• 15:06
  Speaker 1: for what I try and do as a question of
• 15:09
  Speaker 1: looking for the arguments or looking for the questions. So
• 15:12
  Speaker 1: what are the places where there's an on the one hand,
• 15:15
  Speaker 1: On the other hand, is it going to look like
• 15:17
  Speaker 1: this or is it going to look more like that?
• 15:18
  Speaker 1: Are they going to be winner not? Will there be
• 15:20
  Speaker 1: autonomous cars? Yes not? Will it be in fifteen or
• 15:24
  Speaker 1: twenty years? Who knows, don't know. It's more like are
• 15:27
  Speaker 1: they going to be winner? Takes all effects in this?
• 15:28
  Speaker 1: Is it going to look like Android? Or is it
• 15:30
  Speaker 1: going to look like abs where there's like a widget
• 15:32
  Speaker 1: that everyone buys and it doesn't matter. Is what's going
• 15:35
  Speaker 1: to happen in mixed reality? Is this going to be
• 15:37
  Speaker 1: something that's going to be every hardware manufacturer? Is it
• 15:40
  Speaker 1: going to be super concentrated? How do we think about
• 15:42
  Speaker 1: machine learning? How do we try and understand what that
• 15:44
  Speaker 1: might change inside big companies, and so kind of there's
• 15:48
  Speaker 1: like what are the topics and then how do we
• 15:49
  Speaker 1: work out what the kind of the useful questions to
• 15:52
  Speaker 1: talk about might be within that? So you're less extra
• 15:55
  Speaker 1: bolending current trends out into some future date and instead
• 15:58
  Speaker 1: thinking about, well, here's what we are several steps beyond that.
• 16:03
  Speaker 1: What are going to be the subsequent developments that this
• 16:06
  Speaker 1: might lead to. You're really several steps ahead, that's right.
• 16:10
  Speaker 1: I mean that's partly a consequence of the state of
• 16:12
  Speaker 1: the industry. So like five years ago, okay, what's it
• 16:15
  Speaker 1: was a horse race who's gonna win? But is Apple
• 16:17
  Speaker 1: and Google? What's going to happen? Is Apple going to
• 16:19
  Speaker 1: survive even though Android has got this open story and
• 16:22
  Speaker 1: this open source story and so on? So is there
• 16:24
  Speaker 1: going to be one for Apple? Is there going to
• 16:25
  Speaker 1: be win for a third entry? And what will happen
• 16:27
  Speaker 1: to BlackBerry? Will black Bey killing on? And in niche?
• 16:28
  Speaker 1: Will Windows going to be able to break in? So
• 16:30
  Speaker 1: all of that was it was the motorating I'm sorry Windows, Yeah, yah, yeah,
• 16:33
  Speaker 1: I don't I don't recall that. Yeah, yeah, that's it's
• 16:36
  Speaker 1: a painful subject for some people. So there's it was
• 16:39
  Speaker 1: a horse race Steve, Steve Bomber in particular. Yeah, yeah,
• 16:42
  Speaker 1: he's three chairs. But that was you are you're calling
• 16:46
  Speaker 1: the place. That's that the American phrase, you're you're going
• 16:48
  Speaker 1: to work out what was going on? Now you're not.
• 16:50
  Speaker 1: That's we know what happened. And we don't have those
• 16:52
  Speaker 1: kind of day by day tactical questions around autonomer's cars
• 16:55
  Speaker 1: because like there aren't in your automo's cars. We don't
• 16:57
  Speaker 1: have those kind of day by day questions around mixed
• 17:00
  Speaker 1: reality because you can't buy a mixed reality headset yet,
• 17:03
  Speaker 1: mixed reality glasses yet. So the character of the question
• 17:05
  Speaker 1: changes because we're at a different point in the S curve. Basically,
• 17:08
  Speaker 1: when the S curve is going near vertical, then you're
• 17:10
  Speaker 1: trying to work out, oh my god, what's going on?
• 17:13
  Speaker 1: Is the rocket ship going to blow up? When is
• 17:14
  Speaker 1: it going to start flattening out. Where we are now
• 17:17
  Speaker 1: is we've got the oldest curve has flattened out right
• 17:19
  Speaker 1: at the top of the of the scale, and we're
• 17:21
  Speaker 1: talking about what you can on the top of it,
• 17:22
  Speaker 1: and the new S curves are kind of under the
• 17:24
  Speaker 1: radar if I can mix my metaphors. One of the
• 17:26
  Speaker 1: ways I described this, it's kind of a good Manhattan metaphor.
• 17:28
  Speaker 1: It's like you walk past your construction site every day
• 17:30
  Speaker 1: for six months and there's a bunch of construction worker
• 17:32
  Speaker 1: is kind of scout standing around, scratching their backside, is
• 17:34
  Speaker 1: not doing anything. You think all these guys are lazy.
• 17:37
  Speaker 1: Then you walk past on Monday morning and they put
• 17:38
  Speaker 1: fifteen stories of steel frame up me You think, wow,
• 17:40
  Speaker 1: they're really busy over the weekend. You're missing the slow
• 17:46
  Speaker 1: And then there's a period at the end where they're
• 17:47
  Speaker 1: like putting the facade on and doing all the fit out,
• 17:49
  Speaker 1: and again that looks so boring and so mobile is
• 17:51
  Speaker 1: at the stage where you know you're putting the facade
• 17:53
  Speaker 1: down and you're doing the fit out and the other
• 17:56
  Speaker 1: staff are still kind of hulling the ground and you
• 17:57
  Speaker 1: can't really see what's going on. Um And so that
• 18:01
  Speaker 1: means the character the questions change. In the early stages
• 18:04
  Speaker 1: of investments. There there is no data, there's no discounted
• 18:07
  Speaker 1: cash forlor model. You're really dealing with two founders and
• 18:12
  Speaker 1: a PowerPoint presentation and some numbers which are more or
• 18:15
  Speaker 1: less best guesses. Yeah, so the way, it's an interesting
• 18:21
  Speaker 1: shift in being an equity analyst because you're right at
• 18:23
  Speaker 1: one end of the risk profile. You know, if you
• 18:25
  Speaker 1: have tourn of buying tea bills is at the other end.
• 18:28
  Speaker 1: You're at the stage where half of the deals you
• 18:30
  Speaker 1: do will return less than invested capital. And that's the plan,
• 18:35
  Speaker 1: and five pent will produce more than a ten x
• 18:39
  Speaker 1: return and that gets you a kind of a three
• 18:41
  Speaker 1: x return over ten years. And so everything you do
• 18:45
  Speaker 1: has to be capable of being amazing, and if it's
• 18:48
  Speaker 1: capable of going from two people at a power point
• 18:50
  Speaker 1: to being an amazing thing worth hundreds of millions of
• 18:52
  Speaker 1: billions of dollars, it kind of has to be implausible
• 18:55
  Speaker 1: and crazy, and they have to be a bunch of
• 18:56
  Speaker 1: reasons why it might not work. And therefore what you
• 19:00
  Speaker 1: have to do is sort of suspend disbelief and not think,
• 19:02
  Speaker 1: here are all the reasons why this might not work,
• 19:04
  Speaker 1: But what if this did work, what would it be
• 19:07
  Speaker 1: and are these the people who can make that happen,
• 19:10
  Speaker 1: Which is a very very different approach than thinking about,
• 19:14
  Speaker 1: all right, will this new widget or this new management
• 19:17
  Speaker 1: team or whatever sell enough to move the earnings pre
• 19:20
  Speaker 1: share calculus of this publicly trading exactly. I think the
• 19:25
  Speaker 1: kind of the key way to think about Silicon Valley
• 19:27
  Speaker 1: is it's a machine for running experiments, and most of
• 19:30
  Speaker 1: the experiments won't work, and that's the plan. And yes,
• 19:33
  Speaker 1: you know you might. You know, you could do some
• 19:36
  Speaker 1: an experiment that's clearly a terrible idea and it was
• 19:38
  Speaker 1: never going to work, and you could kind of mess
• 19:39
  Speaker 1: it up and blow up your lab, and you know,
• 19:40
  Speaker 1: people will look down on you for that, but no
• 19:42
  Speaker 1: one will look down on you for the fact that
• 19:44
  Speaker 1: you ran an experiment in a produced negative result that
• 19:46
  Speaker 1: was just okay, well we tried, and do you do
• 19:49
  Speaker 1: the do you run the experiment, right, that's a different question.
• 19:51
  Speaker 1: But you ran the experiment, it didn't work, Okay, that's fine.
• 19:54
  Speaker 1: And the ones that do work justify the whole exercise
• 19:57
  Speaker 1: and pay for the whole exercise and produce mobile phone,
• 20:01
  Speaker 1: saw produced Apple and Google and so on. So let's
• 20:03
  Speaker 1: let's let's zoom in on that a little bit. When
• 20:06
  Speaker 1: you're at this side of the funnel, when you're looking
• 20:08
  Speaker 1: at these really early stage companies, if half of them
• 20:13
  Speaker 1: are effectively money losers for you, for the farm as
• 20:16
  Speaker 1: an investment. How do you conceptualize what you're looking at?
• 20:22
  Speaker 1: Is it is it the founders themselves? Is it the idea?
• 20:26
  Speaker 1: Is it something that, hey, this is just so crazy
• 20:29
  Speaker 1: it might work. What what is the thought process like when,
• 20:33
  Speaker 1: especially where you sit, where there's an endless stream of
• 20:37
  Speaker 1: people coming to Silicon Valley to pitch ideas to vcs. Well,
• 20:41
  Speaker 1: so I think most venture capitalists look at this stuff
• 20:44
  Speaker 1: mostly in the same way. You can argue a little
• 20:46
  Speaker 1: bit about emphasis, um, but there is the question of
• 20:50
  Speaker 1: what is the market opportunity here? And um? Are these
• 20:55
  Speaker 1: the people who are going to be able to work
• 20:57
  Speaker 1: out find discover that market opportunity because they never to
• 21:00
  Speaker 1: PE's not going to be exactly the original idea. It's
• 21:02
  Speaker 1: going to be something sort of adjacent to that. You'll
• 21:04
  Speaker 1: kind of twist around and find it pivot shall we?
• 21:07
  Speaker 1: Not so much that I'm more iterate. I would say
• 21:09
  Speaker 1: pivoty is more like, Okay, we built a whole company
• 21:11
  Speaker 1: on that promise and that didn't work at all, so
• 21:13
  Speaker 1: now let's try something else. Um, that's not really the
• 21:15
  Speaker 1: same thing. It's more kind of well, we were kind
• 21:18
  Speaker 1: of that and we sort of moved around, we got,
• 21:20
  Speaker 1: we made, we found it worked. There's this whole sort
• 21:23
  Speaker 1: of concept in silicon value product market fit, and so
• 21:26
  Speaker 1: you have sort of a product and you're trying to
• 21:27
  Speaker 1: work out, well, what the market being, what would the
• 21:29
  Speaker 1: product be around this space until we can find something
• 21:32
  Speaker 1: that meshes and takes off. And so the earlier you are,
• 21:36
  Speaker 1: the more in a sense you're betting on the ability
• 21:39
  Speaker 1: of the founders to find that, But you're also betting
• 21:42
  Speaker 1: on is this a great market and is there some
• 21:45
  Speaker 1: angle or some way that they're going to find in
• 21:47
  Speaker 1: order to make in order to turn this into a thing.
• 21:49
  Speaker 1: So what I'm hearing from you as future growth prospects
• 21:53
  Speaker 1: or and valuation is well, well so, um, it depends.
• 22:01
  Speaker 1: But the more progress you make and the more numbers
• 22:04
  Speaker 1: you have, the more that you start looking at metrics
• 22:07
  Speaker 1: and the less that you start thinking about potential. Um.
• 22:10
  Speaker 1: So it's sort of the super super early stage. Then
• 22:13
  Speaker 1: the valuations all tend to look the same as you
• 22:16
  Speaker 1: go further in. Then you start getting much more specific
• 22:19
  Speaker 1: about well, how well are they doing and what do
• 22:21
  Speaker 1: we think this looks like? Um. Then it gets sort
• 22:23
  Speaker 1: of gets kind of case by case, and you know,
• 22:25
  Speaker 1: you get up to a company that's got billions of
• 22:27
  Speaker 1: dollars a revenue, and then you're doing dcs, and you're
• 22:29
  Speaker 1: doing multiples like anybody else, but at the super early stage.
• 22:32
  Speaker 1: In a sense, doing a DCF on two people with
• 22:34
  Speaker 1: a power point is just an exercise in self deception.
• 22:37
  Speaker 1: Sure you could do it. You could. You could sort
• 22:39
  Speaker 1: of say, well, if they managed to sell this thing
• 22:42
  Speaker 1: to two billion people, well there will be some value
• 22:44
  Speaker 1: that trying to do a dcattle that. Well, like, what
• 22:47
  Speaker 1: does that get me? It doesn't tell me anything. You're
• 22:48
  Speaker 1: just making Yeah, and the things that really work, um,
• 22:53
  Speaker 1: in a sense, it doesn't kind of matter if this
• 22:55
  Speaker 1: is you know, if you've got you know, take give
• 22:57
  Speaker 1: me a kind of hypothetic hypothetical example. You've got to
• 23:00
  Speaker 1: fifty million dollar seed fund and you make a significant
• 23:03
  Speaker 1: investment in something that turns out to be worth fifty
• 23:05
  Speaker 1: billion dollars. Does it really matter if it's worth forty
• 23:08
  Speaker 1: billion or sixty billion. The amount of your assist in
• 23:11
  Speaker 1: the start, the amount of the amount that it's returned
• 23:14
  Speaker 1: your fund is sign is sufficient that those numbers don't
• 23:16
  Speaker 1: really make any difference. So so that that's really fascinating
• 23:21
  Speaker 1: and end Mark and reason when when we said then
• 23:24
  Speaker 1: had a conversation said something similar. But looking back at
• 23:28
  Speaker 1: a perspective of twenty years of doing this, and if
• 23:31
  Speaker 1: you go back and if you would have paid double
• 23:33
  Speaker 1: for everything, it wouldn't mean any difference whatsoever. Yeah, I
• 23:36
  Speaker 1: think there's there's a famous investor whose name I think,
• 23:39
  Speaker 1: I think I should think it was somebody in Hollywood,
• 23:41
  Speaker 1: of famous producer. He said something, if I had said
• 23:43
  Speaker 1: yes to all the ones I said no to, and
• 23:44
  Speaker 1: no to all the ones I've said yes to, you,
• 23:46
  Speaker 1: I would have come out exactly the same place. So
• 23:48
  Speaker 1: there's always, you know, this is kind of the index
• 23:50
  Speaker 1: indexing story. There's always you can always kind of push
• 23:52
  Speaker 1: these arguments and say, well then we should just nobody
• 23:55
  Speaker 1: knows anything, And there's a little bit more to it
• 23:57
  Speaker 1: than that. Let's talk a little bit of out technology.
• 24:01
  Speaker 1: You you have a quote I really like, you have
• 24:04
  Speaker 1: several quotes I really like. Um, And let's start with
• 24:08
  Speaker 1: one or two of thesegncy where they go. All social
• 24:11
  Speaker 1: apps grow until you need a news feed or news
• 24:14
  Speaker 1: news feeds grow until you need an algorithm. All algorithmic
• 24:18
  Speaker 1: feeds grow until you get fed up seeing the wrong stuff.
• 24:20
  Speaker 1: And leave for a new app with less information overload.
• 24:25
  Speaker 1: So is the nature of all tech Rinse, lather, repeat
• 24:29
  Speaker 1: and and something becomes an incumbent becomes successful and the
• 24:34
  Speaker 1: new guys are just going to come up and eat
• 24:35
  Speaker 1: their launch. So I have notes through a blog post
• 24:38
  Speaker 1: in my phone which is called something like um technology determinism.
• 24:42
  Speaker 1: And so there are as it might be half a
• 24:45
  Speaker 1: dozen things which are just steady processes, and now half
• 24:48
  Speaker 1: a dozen things that of cycles. So steady process would
• 24:51
  Speaker 1: most obvious one would be More's law. Uh, there was
• 24:53
  Speaker 1: a process of technology news from research labs to start
• 24:57
  Speaker 1: ups to pick companies. Um. You know, you can imagine
• 24:59
  Speaker 1: like kind of half a dozen of those. Um. Then
• 25:01
  Speaker 1: there are cycles. And so there are cycles where you
• 25:04
  Speaker 1: go from bundling to unbundling, You go from the client
• 25:07
  Speaker 1: to the server and back again. You go from the
• 25:10
  Speaker 1: public markets. We had conglomerates and then deconglomerization and then
• 25:14
  Speaker 1: re conglomerates. Exactly exactly. Colleague, you at Steven Sinofsky who
• 25:19
  Speaker 1: used to run office. He says, all products expand until
• 25:22
  Speaker 1: they can edit photographs. That's very funny, like word can
• 25:26
  Speaker 1: edit photographs, can edit photographs? All products expand until they
• 25:28
  Speaker 1: can edit it. That's the endpoint of software. And so
• 25:31
  Speaker 1: there were kind of there and it's a comment about,
• 25:33
  Speaker 1: you know, feature creep or whatever it is you want
• 25:34
  Speaker 1: to say. And so there were these kind of inevitable
• 25:36
  Speaker 1: processes or inevitable pieces of logic that kind of flow through.
• 25:39
  Speaker 1: Now the one that you were sort of quoted, particularly
• 25:41
  Speaker 1: in sort of an observation about I'm actually I wrote
• 25:44
  Speaker 1: a blog post about this last week. It's sort of
• 25:45
  Speaker 1: a combination of dune Bar's number and zarka Berg's law.
• 25:48
  Speaker 1: Dunebar's number is like, you know, like a hundred and
• 25:50
  Speaker 1: fifty or two hundred people, well enough that you would
• 25:53
  Speaker 1: trend them on Facebook at the way least. And you've
• 25:56
  Speaker 1: got these social apps which make it easy to post
• 25:58
  Speaker 1: stuff and share stuff. And because it's one too many,
• 26:01
  Speaker 1: you're not emailing it to someone or texting it to someone.
• 26:04
  Speaker 1: You can close quite a lot because you don't feel
• 26:05
  Speaker 1: like you're kind of imposing on people to do that.
• 26:07
  Speaker 1: But you've got two hundred friends and they post five
• 26:11
  Speaker 1: things a day, Okay, Now you've got a thousand things
• 26:13
  Speaker 1: a day in your news feed, and you can't read notes,
• 26:17
  Speaker 1: and so this is the logic that gets Facebook to
• 26:19
  Speaker 1: producing what is you know, what's called an algorithmic feed,
• 26:22
  Speaker 1: which is just like engineers speak for, let's try and
• 26:25
  Speaker 1: work out which of your friends we care about, and
• 26:27
  Speaker 1: maybe we should put those at the top. And let's
• 26:28
  Speaker 1: work out that you like these kind of things. You
• 26:30
  Speaker 1: don't really like new stories from the Guardian. You prefer
• 26:32
  Speaker 1: to look at pictures of baby So let's put the
• 26:34
  Speaker 1: babies in front of the new stories from the Guardian.
• 26:36
  Speaker 1: And you kind of you, you kind of you come
• 26:38
  Speaker 1: up and you create that. And so now you've got,
• 26:41
  Speaker 1: instead of, so to speak, a random sample, which is
• 26:43
  Speaker 1: I open the app, what have people posted in the
• 26:45
  Speaker 1: last hour, because I'm not going to scroll past the
• 26:47
  Speaker 1: last hour's worth of post So actually it's random, the
• 26:50
  Speaker 1: random point fact of being what time did open the app.
• 26:52
  Speaker 1: So that's the linear feed. The chronological feed is random.
• 26:56
  Speaker 1: So then you said, well, maybe we should put the
• 26:57
  Speaker 1: stuff that's important at the beginning. But then you think, okay,
• 27:00
  Speaker 1: but it's now you are arguing, well, why isn't the
• 27:03
  Speaker 1: Guardian all the New York Times at the top. That
• 27:05
  Speaker 1: should be up at the top, because that's important as
• 27:06
  Speaker 1: a public benefit to that and you got this wrong.
• 27:09
  Speaker 1: My friend posted this thing I wanted to see, and
• 27:11
  Speaker 1: I didn't see it, and so you get that sense of, well,
• 27:14
  Speaker 1: maybe this isn't actually working, and you have Russians trying
• 27:17
  Speaker 1: to game it, and you have all sorts of problems
• 27:18
  Speaker 1: with trying to make that feed work, and so then
• 27:21
  Speaker 1: you then you can say, well, actually, what I want
• 27:23
  Speaker 1: to do is if I really care about this stuff
• 27:24
  Speaker 1: and I want people to see it, I'll send them
• 27:26
  Speaker 1: a text message, I'll do it and WhatsApp, I'll do
• 27:27
  Speaker 1: it on Facebook Messenger. But then I've got fifteen parallel
• 27:31
  Speaker 1: conversations or twenty parallel conversations with people. And then we
• 27:34
  Speaker 1: start creating WhatsApp groups where twenty people from school or
• 27:37
  Speaker 1: can work and all talk to each other. And then
• 27:39
  Speaker 1: everyone is like posting more and more stuff, and you think,
• 27:41
  Speaker 1: I really like to have a screen in this app
• 27:43
  Speaker 1: that just showed me like the important stuff from all
• 27:44
  Speaker 1: of these chats that I would see it, and maybe
• 27:47
  Speaker 1: that should be sorted by which are the ones that
• 27:49
  Speaker 1: I want to see, And so like you kind of
• 27:51
  Speaker 1: create the same process over and over again. Now this
• 27:54
  Speaker 1: is an opinion. This may be entirely be wrong. This
• 27:56
  Speaker 1: may not be how it works, but you can certainly
• 27:58
  Speaker 1: sort of see that problem that if you create tools
• 28:01
  Speaker 1: that let everyone you've ever met share anything they've they've
• 28:04
  Speaker 1: ever been interested in, then you're not going to be
• 28:06
  Speaker 1: able to read it all. Well, this leads me to
• 28:09
  Speaker 1: the very related quote of Facebook's engineering effort goes to
• 28:13
  Speaker 1: stuffing more noise into your news feed and the other
• 28:16
  Speaker 1: fift is working on ways to filter it out, or
• 28:19
  Speaker 1: it's another expression at the same point. This is this
• 28:21
  Speaker 1: is classic joke and I read in a book by
• 28:23
  Speaker 1: Casting Glany, which is that somebody's dug a hole to
• 28:26
  Speaker 1: build a to create the foundation to build put up
• 28:28
  Speaker 1: a building, and they asked their friend what should I
• 28:31
  Speaker 1: do with all this earth that's come out of the whole,
• 28:32
  Speaker 1: and their friends says, We'll just dig another hole and
• 28:34
  Speaker 1: put it in that, And well, it's just not gonna
• 28:38
  Speaker 1: work like that. You can't do it like that. And
• 28:40
  Speaker 1: if you've created a system that lets anybody you've ever
• 28:42
  Speaker 1: met send you anything that they feel like sending, then
• 28:44
  Speaker 1: you're not going to be able to fiddle. And there's
• 28:45
  Speaker 1: not like some magic algorithm that's going to make that work.
• 28:48
  Speaker 1: You're and you're ever gonna have a sample. So I
• 28:50
  Speaker 1: want to talk to you about some of your favorite
• 28:52
  Speaker 1: technologies and future projects. Do we want to discuss it
• 28:56
  Speaker 1: all what's been going on with Facebook this year and
• 29:00
  Speaker 1: everything from Russian bots to the um just changes with
• 29:06
  Speaker 1: Cambridge Analytics and scraping. First of all, I want to
• 29:10
  Speaker 1: say that UM Oxford Analytics is actually not nearly as
• 29:12
  Speaker 1: good as Candy Channelized. Okay, for sure, a little little
• 29:17
  Speaker 1: almamount of joke, but how much. I'm kind of surprised
• 29:21
  Speaker 1: at how shocked people are about this full disclosure. I
• 29:27
  Speaker 1: have not been a Facebook heavy user ever. I chafed
• 29:32
  Speaker 1: at everything they've ever asked me to give them. They
• 29:34
  Speaker 1: don't have my real birthday, they don't have my real phone,
• 29:36
  Speaker 1: they don't have my real email address, they don't have
• 29:39
  Speaker 1: my real anything. So anyone who wants to scrape other
• 29:42
  Speaker 1: than where I went to college, where I went to
• 29:44
  Speaker 1: grad school, and and a couple of jobs I've worked
• 29:48
  Speaker 1: at that are public, they could scrape what they want
• 29:51
  Speaker 1: from me. They're they're getting nothing. But I think this
• 29:54
  Speaker 1: whole headache was readily foreseeable by anyone who who forget
• 30:00
  Speaker 1: reading Facebook's terms of use. No, you're not entitled to
• 30:04
  Speaker 1: any of that private information. I'm not sharing that with you.
• 30:07
  Speaker 1: I'll find what I what I'm interested in my own
• 30:09
  Speaker 1: or am I just outside of their demographic I'm an
• 30:12
  Speaker 1: old fogy And so I think there's a bunch of
• 30:17
  Speaker 1: kind of unresolved feeling about Facebook. Is it private or public?
• 30:23
  Speaker 1: If I post stuff in the news feed, will my
• 30:25
  Speaker 1: friends see it or not? What does that mean? Do
• 30:28
  Speaker 1: I want to share? Do I want to share this
• 30:29
  Speaker 1: stuff or not? And like, we sort of understand that
• 30:33
  Speaker 1: if you search on Google for something, it'll show you
• 30:35
  Speaker 1: or try to show you what you asked for, even
• 30:37
  Speaker 1: if it's something you shouldn't have searched for. We don't
• 30:40
  Speaker 1: really feel like if I my racist uncle posts that
• 30:43
  Speaker 1: story on Facebook, should I see it or not? How
• 30:46
  Speaker 1: do we think about that? Well, if you like it
• 30:48
  Speaker 1: or reposted. But we also think we also sort of
• 30:51
  Speaker 1: think or maybe Facebook shouldn't be showing that at the
• 30:53
  Speaker 1: top of the list. Well, well it is my he
• 30:55
  Speaker 1: is my uncle, and he did post it. So we
• 30:58
  Speaker 1: have a bunch of sort of of I don't think
• 31:01
  Speaker 1: we have a clear sentiment about put its other extreme,
• 31:05
  Speaker 1: we are confident that are back. We are comfortable with
• 31:07
  Speaker 1: the fact that our banks know how much money we have.
• 31:09
  Speaker 1: For sure, we're comfortable that our mobile operators and know
• 31:11
  Speaker 1: where our phone is and that there's a kind of
• 31:13
  Speaker 1: a legal apparatus around that if the police need to know,
• 31:16
  Speaker 1: then they can find out. But it's just not available
• 31:18
  Speaker 1: to anybody. I think we sort of have that feeling
• 31:20
  Speaker 1: around Google mostly. I don't think we have like a
• 31:24
  Speaker 1: resolved feeling around Facebook. Um. Now, this particular story is
• 31:28
  Speaker 1: sort of fascinating because of how many pieces of there
• 31:30
  Speaker 1: there are to that. So Facebook creates this developer platform
• 31:34
  Speaker 1: that allows you to install an app that can access
• 31:37
  Speaker 1: your information and also access information about your friends. And
• 31:40
  Speaker 1: there's a sort of a bunch of reasons why that
• 31:42
  Speaker 1: would be useful, like I want to create in a
• 31:44
  Speaker 1: calendar entry, or I want to you know, I want
• 31:47
  Speaker 1: to see who else is using this app. Um. And
• 31:50
  Speaker 1: a large part of like the advocacy around the tech
• 31:53
  Speaker 1: industry with wall gardens are bad, Facebook is bad because
• 31:55
  Speaker 1: it's closed. People being need to be able to get
• 31:57
  Speaker 1: their information out. You need to be able other people
• 31:59
  Speaker 1: need to be able to use this to innovate. So
• 32:02
  Speaker 1: there was this whole sort of ideological argument um that
• 32:04
  Speaker 1: Facebook would be evil for not doing this, and Facebook
• 32:06
  Speaker 1: needed to do this, So you create this platform. Um,
• 32:10
  Speaker 1: it turns out that people are able to exploit that
• 32:13
  Speaker 1: and do stuff with it that was not really anticipated.
• 32:16
  Speaker 1: You think it's not anticipated because from my perspective, I
• 32:19
  Speaker 1: look at it as by design that Okay, so yes
• 32:23
  Speaker 1: and no, So let's get let's go to an analogy.
• 32:25
  Speaker 1: So do you remember word macro viruses? Um? Sure about?
• 32:30
  Speaker 1: So office is supposed to be an open development environment.
• 32:35
  Speaker 1: The people suing them before antitrust is suing them from
• 32:37
  Speaker 1: making it closed in half for third parties to work with.
• 32:40
  Speaker 1: So they create all these API s and they create
• 32:42
  Speaker 1: this whole macro language. One of the things on page
• 32:44
  Speaker 1: fifteen of the textbook is you can make a macro run.
• 32:47
  Speaker 1: When you open the document page forty eight of the textbook.
• 32:50
  Speaker 1: You can get it to look at your email addresses.
• 32:52
  Speaker 1: Page seventy two. You can get it to send an email. Okay,
• 32:56
  Speaker 1: so I get an email word document, I open it,
• 32:58
  Speaker 1: it emails a copy of itself to everyone in might
• 33:00
  Speaker 1: s book at. Okay, that's not what we expected, but
• 33:05
  Speaker 1: it's really that is not its intended but it's not
• 33:07
  Speaker 1: its intended purpose exactly. But all of the individual A
• 33:11
  Speaker 1: p I s were there, and so you've got this
• 33:13
  Speaker 1: period from Microsoft where they're thinking, okay, so are we
• 33:15
  Speaker 1: supposed to not have macros that we're supposed to be closed? Now,
• 33:18
  Speaker 1: how do we think about this? And they had to
• 33:20
  Speaker 1: go through this like hundred and eighty degree turn as
• 33:23
  Speaker 1: they went from thinking we should make it as easy
• 33:25
  Speaker 1: as possible for anybody to do this to what would
• 33:28
  Speaker 1: happen if it's going to moderate my language? What would
• 33:32
  Speaker 1: happen if a bad person, um what my seven year
• 33:37
  Speaker 1: old would call a dingly head, decided to read the
• 33:40
  Speaker 1: textbook Because it's not like you found bugs in it.
• 33:42
  Speaker 1: They're doing stuff it was in the textbook, that was
• 33:44
  Speaker 1: in the manual, but you weren't expecting them to use
• 33:46
  Speaker 1: them in those ways. And I think there's a very
• 33:48
  Speaker 1: strong parallel there with Whatness Cambridgewn Litigo was doing, which
• 33:52
  Speaker 1: was you were able to install an app. The app
• 33:53
  Speaker 1: can get your information, The app can ask for your
• 33:55
  Speaker 1: friends information. If you say yes, it will get your
• 33:57
  Speaker 1: friends information. Yeah, but we didn't expect that people would
• 34:00
  Speaker 1: use that to exportrate eighty million people's profiles, just as
• 34:04
  Speaker 1: we didn't expect someone would make a word document that
• 34:06
  Speaker 1: could email a copy of itself to me. So now,
• 34:08
  Speaker 1: so now let me I'm gonna go toe to toe
• 34:10
  Speaker 1: on technology with you, which is clearly a mistake on
• 34:13
  Speaker 1: my pod. But Microsoft notorious for having all these weak
• 34:18
  Speaker 1: security setups and easily exploitable that is such a foreseeable issue.
• 34:24
  Speaker 1: Granted this little bit of hindsight bioso in hindsight, right,
• 34:28
  Speaker 1: but you know Microsoft is home of the exploitable security era.
• 34:36
  Speaker 1: My relationship with Amazon and with Apple is that I
• 34:39
  Speaker 1: pay them for stuff and I expect a different level
• 34:43
  Speaker 1: of trust and a different level of Hey, I'm already
• 34:45
  Speaker 1: giving you money, don't exploit my personal data for other reasons. Well,
• 34:48
  Speaker 1: let me let me give you a hypothetical. Um, you
• 34:50
  Speaker 1: can install an app on your smartphone. It can pop
• 34:52
  Speaker 1: up a box and ask for your friends, and there's
• 34:55
  Speaker 1: an awful lot of sensible reasons, like you're playing a game,
• 34:57
  Speaker 1: who else? Which of your friends are playing? The game?
• 34:59
  Speaker 1: Installed the Instagram? Who are your friends? So you can
• 35:01
  Speaker 1: follow them? Like we were asked if you want to
• 35:03
  Speaker 1: split a fair with somebody, any basic logical reasons why
• 35:08
  Speaker 1: you wanted to do that? Okay, So that app has
• 35:10
  Speaker 1: just downloaded six hundred people's home addresses. Is that a breach? Well,
• 35:15
  Speaker 1: it has a downloaded their email address or address or
• 35:19
  Speaker 1: have they looked at the home address? And then people
• 35:23
  Speaker 1: second order question it needs their email address or their
• 35:25
  Speaker 1: phone number to work. That's the idea. There's there is
• 35:27
  Speaker 1: no other identify, so it's got six d people's email addresses. Okay,
• 35:30
  Speaker 1: is that a breach of privacy? Is that Apple's faults?
• 35:33
  Speaker 1: Well maybe a little bit. I would say it is not.
• 35:38
  Speaker 1: But Apple popped up They said, here is this capability.
• 35:41
  Speaker 1: Apple pops up the thing. Do you want this app
• 35:42
  Speaker 1: to to have your address or not? And the sort
• 35:45
  Speaker 1: of a point where it's not kind of black and white.
• 35:48
  Speaker 1: It's not like somebody hacked into Google and gave them email.
• 35:51
  Speaker 1: And so I think Facebook has been sort of they
• 35:54
  Speaker 1: they have this record of pushing this thing to the
• 35:57
  Speaker 1: kind of the outer envelope um for the last fifteen years.
• 36:00
  Speaker 1: And I think what's happened here slightly ironically, because they
• 36:03
  Speaker 1: actually closed off all of these APIs like a couple
• 36:05
  Speaker 1: of years ago. So sort of what happened is like
• 36:07
  Speaker 1: the stable door was open if you were a developer,
• 36:10
  Speaker 1: if you were in tech, if you went to the
• 36:12
  Speaker 1: Developer event. They stood up on stage and said, hey,
• 36:14
  Speaker 1: look we leave our stables store always doors open so
• 36:16
  Speaker 1: you can do all this stuff. Isn't this great? And
• 36:18
  Speaker 1: their doors stable doors are opened for like five years
• 36:21
  Speaker 1: and then they maybe this isn't a good idea. We're
• 36:24
  Speaker 1: going to close the door now. And a bunch of
• 36:25
  Speaker 1: people said, oh, evil, Facebook, you shouldn't close the doors.
• 36:27
  Speaker 1: You should allow free open access. And now here we
• 36:30
  Speaker 1: are in two thousand and eighteen and people go, wow,
• 36:31
  Speaker 1: a lot of people went into the stable and stole
• 36:33
  Speaker 1: all the horses. What a shark? And Facebook, Yeah, but
• 36:40
  Speaker 1: like we near, the doors were open. So there's a
• 36:43
  Speaker 1: bunch of you can kind of see this as they
• 36:44
  Speaker 1: try and work out how we talk about this, how
• 36:46
  Speaker 1: do we think about this stuff. We have been speaking
• 36:48
  Speaker 1: with Benlic Devans of Andres and Horowitz. If you enjoy
• 36:52
  Speaker 1: this conversation, be sure and check out our podcast extras,
• 36:55
  Speaker 1: where we keep the tape rolling and continue discussing all
• 36:58
  Speaker 1: things technology. We love your comments, feedback and suggestions right
• 37:04
  Speaker 1: to us at m IB podcast at Bloomberg dot net.
• 37:08
  Speaker 1: Check out my daily column on Bloomberg Vie dot com.
• 37:10
  Speaker 1: You could follow me on Twitter at Ri Halts, I'm
• 37:14
  Speaker 1: Barry Hults. You're listening to Masters in Business on Bloomberg Radio.
• 37:31
  Speaker 1: Welcome to the podcast, Bennetick, Thank you so much for
• 37:34
  Speaker 1: doing this. You're you're talking about stuff that I really
• 37:37
  Speaker 1: find endlessly fascinating, including, UM, the responsibility of Facebook to
• 37:45
  Speaker 1: either have an open platform and what that risk entails,
• 37:49
  Speaker 1: or to control their own APIs and control who access
• 37:53
  Speaker 1: what from your UM feed, what nobody is talking about.
• 37:58
  Speaker 1: And by the time this airs, he will have Mark
• 38:01
  Speaker 1: Zuckerberg will have already um done his congressional testimony. But UM,
• 38:08
  Speaker 1: there's still a tremendous amount of responsibility on the individual
• 38:11
  Speaker 1: user who willingly said, hey, here's a ton of private
• 38:15
  Speaker 1: personal information about my me, try not to mess it up.
• 38:20
  Speaker 1: I was always too skeptical. I was always too why
• 38:23
  Speaker 1: do you want this information? Can kind of noted it
• 38:26
  Speaker 1: from the other end, which was I think people have felt.
• 38:28
  Speaker 1: I think for years and years and years people have
• 38:31
  Speaker 1: just sort of presumed that nothing on Facebook with public
• 38:33
  Speaker 1: with private, right, that's correct, and therefore just sort of
• 38:36
  Speaker 1: treated it as a public forum. If you were smart,
• 38:38
  Speaker 1: that's what you presumed, or if you were I should
• 38:40
  Speaker 1: say knowledgeable. That should have been your work, even think
• 38:43
  Speaker 1: I didn't even think beyond that. So, like I've heard
• 38:45
  Speaker 1: people say things like I don't use Facebook. I wouldn't
• 38:47
  Speaker 1: say that on Facebook Messenger because I don't want it
• 38:49
  Speaker 1: to be public, and of course favorit. Messager isn't public.
• 38:51
  Speaker 1: I mean technically Facebook can, but anybody can screenshot and
• 38:55
  Speaker 1: anybody can. Yeah. Well that's like people thought it was
• 38:58
  Speaker 1: his public and stuff on their on their public profile,
• 39:00
  Speaker 1: and I went, I joined Facebook, and I forget when
• 39:02
  Speaker 1: it launched in the UK, but everyone's profile was public.
• 39:05
  Speaker 1: I mean it was public if you were to if
• 39:06
  Speaker 1: to like if you're in the London network, so anyone
• 39:09
  Speaker 1: could join the London network, so it was public. And
• 39:11
  Speaker 1: then so there was a period of like a year when,
• 39:13
  Speaker 1: like everybody I've been at school, at university, we've had
• 39:16
  Speaker 1: a profile on Facebook and it was public and I
• 39:17
  Speaker 1: could go and look, and then everyone kind of turned
• 39:19
  Speaker 1: the privacy things on. But I just and even then
• 39:22
  Speaker 1: it's not for I don't feel like you would say
• 39:25
  Speaker 1: stuff on Facebook that you expect to be secret. That's
• 39:29
  Speaker 1: what I'm getting at, And I don't think many people
• 39:30
  Speaker 1: ever actually thought that, which comes again to my point
• 39:33
  Speaker 1: about sort of unresolved feelings, you can kind of you
• 39:35
  Speaker 1: can go to the extreme and say, um, everything on
• 39:39
  Speaker 1: Facebook should have been completely private and everyone should have
• 39:42
  Speaker 1: understood that and everyone you know, that's not realistic. But
• 39:44
  Speaker 1: I don't think that's actually very realistic. I think it
• 39:46
  Speaker 1: was much more kind of nuanced and fuzzy than that.
• 39:48
  Speaker 1: And I think you could argue everything in a WhatsApp
• 39:50
  Speaker 1: group should be private, but I think that's a very
• 39:52
  Speaker 1: different kind of form to posting on your news feed.
• 39:55
  Speaker 1: But when I ask people, um, you know, sometimes you
• 39:59
  Speaker 1: have to come out the discussion from an oblique angle.
• 40:02
  Speaker 1: And I like to use radio as an example. I
• 40:04
  Speaker 1: ask people what is radio cell and invariably they say advertising,
• 40:08
  Speaker 1: And that's the wrong answer. The advertisers of the buyers.
• 40:12
  Speaker 1: Radio sells an audience, and Facebook more or less has
• 40:16
  Speaker 1: the same business model. So I find this interesting. There's
• 40:19
  Speaker 1: a sort of there's a common meme even kind of
• 40:20
  Speaker 1: in the tech industry where people say Facebook sales for information.
• 40:24
  Speaker 1: Now they sell and you as a part of an audience. Well,
• 40:26
  Speaker 1: it's interesting because in in a literal sense, if you're
• 40:28
  Speaker 1: an advertising on Facebook, you don't get given a zip
• 40:31
  Speaker 1: file of the profile of everybody who saw that. So
• 40:34
  Speaker 1: in a literal sense, it's give not true. In a
• 40:36
  Speaker 1: kind of a metaphorical sense, well, they're sort of they're
• 40:39
  Speaker 1: selling the fact that they know like even like kind
• 40:41
  Speaker 1: of a metaphorical sense. That kind of struggles me. I
• 40:43
  Speaker 1: struggle with that as a statement, But I think the
• 40:46
  Speaker 1: fact that people have those conversations reflects against sort of
• 40:50
  Speaker 1: unresolved feelings about how we should think about this stuff.
• 40:52
  Speaker 1: It's the same with this idea that's going around now
• 40:54
  Speaker 1: that all the Facebook's problems can be blamed on the
• 40:56
  Speaker 1: ad model, and you think, well, if they had a
• 40:58
  Speaker 1: subscription model, would they not be having to how to
• 41:01
  Speaker 1: develop a platform? Would you not have been posting your
• 41:03
  Speaker 1: personal stuff to your news feed? Like why would that
• 41:06
  Speaker 1: have made any difference? And you would you would still
• 41:08
  Speaker 1: have the same opportunity for apps to come in and
• 41:11
  Speaker 1: scrape that data before they closed the barn door. Yeah,
• 41:15
  Speaker 1: And I mean I was talked about this because somebody,
• 41:17
  Speaker 1: I think you say on Twitter's somebody who's had a journalism,
• 41:19
  Speaker 1: have had a journalism in school, and they posted like
• 41:21
  Speaker 1: an explanation of what came don We've been doing it.
• 41:23
  Speaker 1: This is three weeks ago, and it's just it was
• 41:28
  Speaker 1: interesting simply that they were. Three weeks on, we're still
• 41:30
  Speaker 1: having people are still writing explanations of what happened because
• 41:33
  Speaker 1: it's all sort of fuzzy and a pay can no
• 41:35
  Speaker 1: one quite understandable. It's complex, and people like simple narratives
• 41:38
  Speaker 1: with defineable good guys and bad guys. That makes it
• 41:41
  Speaker 1: easier to to do. Nuance sort of gets lost on
• 41:45
  Speaker 1: on cable television to the least. All right, let's talk
• 41:50
  Speaker 1: about some of your favorite technologies. Autonomy of filling the blank,
• 41:55
  Speaker 1: autonomous cars, autonomous uh, whatever, how do we how do
• 42:00
  Speaker 1: you see the development of AI and autonomous everything? Have
• 42:04
  Speaker 1: you got another hour? I do? I don't know if
• 42:07
  Speaker 1: we are are, so I think what can we say
• 42:11
  Speaker 1: about this? So, first of all, the reason that we're
• 42:13
  Speaker 1: talking about autonomous cars now is because of what we
• 42:16
  Speaker 1: call AI, which really means this new technology called machine learning,
• 42:19
  Speaker 1: or technology that just started working called machine learning, which
• 42:22
  Speaker 1: offers the prospect that a bunch of problems around autonomous
• 42:24
  Speaker 1: cars might be solvable in a way that they really
• 42:26
  Speaker 1: didn't seem to be easily solvable before. So, in that sense,
• 42:29
  Speaker 1: you could say autonomy is a spinoff of machine learning
• 42:31
  Speaker 1: all the break the advanced. The fact that we're interested
• 42:33
  Speaker 1: in autonomy is a spinoff in machine learning, but there's
• 42:34
  Speaker 1: altos of other stuff that machine learning does as well
• 42:37
  Speaker 1: as we go to autonomy. UM. The sort of the
• 42:40
  Speaker 1: way I kind of talk about this is I have
• 42:42
  Speaker 1: a slide with a picture of a horseless carriage from
• 42:45
  Speaker 1: It's a carriage with no horse, but otherwise it's nothing's changed.
• 42:49
  Speaker 1: And I think that's what I hear when people say
• 42:51
  Speaker 1: driverless car, that you've taken the steering wheel out maybe,
• 42:55
  Speaker 1: but like nothing else has changed and it will still
• 42:57
  Speaker 1: drive around like like autonomous cars mean us that drive
• 43:00
  Speaker 1: like people, but without making mistakes or making the speed limit.
• 43:03
  Speaker 1: And that's that's just a really shortsighted way of thinking
• 43:05
  Speaker 1: about this. I think there's kind of two kind of
• 43:07
  Speaker 1: building blocks to think about. The first is you can
• 43:09
  Speaker 1: if you have when we have a fully autonomous world
• 43:11
  Speaker 1: in end decades forty years, depending on what you think
• 43:15
  Speaker 1: the s cards will look like. UM. And of coursely
• 43:18
  Speaker 1: you have periods where some places will be much go
• 43:20
  Speaker 1: much quicker, So like you might you might say Manhattan
• 43:22
  Speaker 1: is autonomous only in the week in twenty thirty or something. Um.
• 43:26
  Speaker 1: But at that point there's no accidents, well, certainly much
• 43:30
  Speaker 1: less well basically no accidents because all the accidents are
• 43:33
  Speaker 1: caused by human error, and you have much less congestion
• 43:36
  Speaker 1: because you don't have traffic waves, you don't have accidents
• 43:38
  Speaker 1: which cause a third of congestion or something. UM. You
• 43:40
  Speaker 1: can have vehicles on freeways driving a hundred miles and
• 43:42
  Speaker 1: our two apart from each other. So you radically change
• 43:45
  Speaker 1: what congestion looks like. You change radically change all the
• 43:48
  Speaker 1: vehicles look like. So you could have a vehicle that
• 43:50
  Speaker 1: will never you know, if you're got calling an on
• 43:52
  Speaker 1: demand vehicle in Manhattan, it will not go over twenty miles.
• 43:55
  Speaker 1: An how it could be a golf cart. Um. If
• 43:58
  Speaker 1: you're going to go to JFK, they and you would
• 44:00
  Speaker 1: send a different vehicle. And so you can radically just
• 44:03
  Speaker 1: redesign the vehicle in the same way that when we
• 44:04
  Speaker 1: got rid of the horse, you radically redesigned the vehicle.
• 44:07
  Speaker 1: And you can also radically redesign the city or change
• 44:09
  Speaker 1: your assumptions about the city in the same way that
• 44:11
  Speaker 1: we did whom we got rid of the horse, um,
• 44:13
  Speaker 1: except that you're no longer trying to design a city
• 44:17
  Speaker 1: that will constrain the nineteen year old guy on a
• 44:20
  Speaker 1: souped up Chevy Tomorrow. Um, you're designing a city based
• 44:24
  Speaker 1: on You can create rules. You can tell the cars
• 44:26
  Speaker 1: where to go and what they can do or not do.
• 44:27
  Speaker 1: You can have dynamic real time road right saying that
• 44:29
  Speaker 1: you can say, do you want to pay a lot
• 44:31
  Speaker 1: to get there in fifteen minutes or you're willing to
• 44:33
  Speaker 1: pay less to get there in thirty minutes. You can
• 44:35
  Speaker 1: tell the cars at any given instant which road to
• 44:38
  Speaker 1: say should be taking and how fast they should be going.
• 44:40
  Speaker 1: And so what you have is like a change in
• 44:42
  Speaker 1: the structure of a city that has more a lot
• 44:45
  Speaker 1: in common with like the way the city changes a
• 44:47
  Speaker 1: result of the car. UM kind of the example I
• 44:50
  Speaker 1: often give, which works well since we're in Manhattan is
• 44:53
  Speaker 1: imagine if you live in Brooklyn and it's November and
• 44:57
  Speaker 1: you want to go to that cool new restaurant in Manhattan.
• 45:00
  Speaker 1: How are you going to get there? Where you could
• 45:01
  Speaker 1: walk ten minutes in the rain to the subway station,
• 45:05
  Speaker 1: you could get a cab UK well, presuming you can
• 45:07
  Speaker 1: get a cab. Presume you can get a cab O care.
• 45:09
  Speaker 1: It's going to cost you what dollars each way you
• 45:12
  Speaker 1: could drive, then one of you can't drink on the
• 45:14
  Speaker 1: way back, and you're going to have to park, and
• 45:16
  Speaker 1: you have to pay for parking in little twenty minutes
• 45:18
  Speaker 1: to find a place to park there. Let's not go
• 45:20
  Speaker 1: out of that place, okay, Now go to a fully
• 45:23
  Speaker 1: autonomous world. You raise your watch, you say, hey, alex
• 45:27
  Speaker 1: or I need a car. The nip hod that's around
• 45:29
  Speaker 1: the block stops outside your door within thirty seconds, and
• 45:33
  Speaker 1: there is no congestion. The pod just takes you there.
• 45:35
  Speaker 1: It drops you off outside the door um if it's
• 45:38
  Speaker 1: your pod, um, and then it goes and waits for
• 45:40
  Speaker 1: you somewhere where parking is cheap, or it waits for
• 45:42
  Speaker 1: you somewhere else. If it's an on demand pod, it
• 45:44
  Speaker 1: immediately starts driving other people around, so there's no parking, remember,
• 45:48
  Speaker 1: and then you can go home when you can drink
• 45:50
  Speaker 1: as much as you like. I mean, remember all those
• 45:52
  Speaker 1: those photographs of European and East Coast cities from before cars,
• 45:56
  Speaker 1: and you think where the streets are all twice as
• 45:58
  Speaker 1: wide because you don't have cars hop down both sides
• 46:00
  Speaker 1: of the street. What we could go back to, and
• 46:03
  Speaker 1: so you have like kind of radical change in how
• 46:06
  Speaker 1: we think about what the city is. Therefore, in what
• 46:09
  Speaker 1: does a gas station mean? Very obviously, but also what
• 46:11
  Speaker 1: does big box we tell mean? Where are you willing
• 46:13
  Speaker 1: to shop? What does your commute look like? You know? Today?
• 46:17
  Speaker 1: So I live in San Francisco, which calls itself a city,
• 46:19
  Speaker 1: and I work in Menlo Park, which is an office park.
• 46:22
  Speaker 1: Forty five minutes drive south if there's not much traffic, Um,
• 46:25
  Speaker 1: I should say an hour, but I'll admit to forty
• 46:27
  Speaker 1: five minutes south. Now, supposing there really is no traffic, well,
• 46:32
  Speaker 1: then I can get there in half an hour. But
• 46:35
  Speaker 1: supposing I'm able to read instead of drive, I might
• 46:38
  Speaker 1: be able to live further away and have it spend
• 46:40
  Speaker 1: longer in the car because I don't need to just
• 46:42
  Speaker 1: sit there staring at the road all day. So where
• 46:44
  Speaker 1: do you live, where do you commute? Where does the
• 46:46
  Speaker 1: retail go? All those sorts of changes that happened, like
• 46:50
  Speaker 1: the stuff that happened as a result of cars, which
• 46:51
  Speaker 1: basically what I'm talking about. Like, there's this great saying
• 46:53
  Speaker 1: that it was easy to predict mass ownership of cars,
• 46:55
  Speaker 1: but hard to put itt Walmart. It's hard to put
• 46:57
  Speaker 1: it drive through a t M s like that kind
• 47:01
  Speaker 1: of stuff. Those second and third auto consequences will flow
• 47:04
  Speaker 1: out of this stuff. Let's talk about smart fill in
• 47:07
  Speaker 1: the blanks, smart homes, smartphone, smart cars, smart speakers. What
• 47:12
  Speaker 1: is it about the the preface smart that suddenly everybody
• 47:17
  Speaker 1: wants to move in that direction? So so software and okay,
• 47:23
  Speaker 1: so three or four blocks to talk about here. The
• 47:24
  Speaker 1: first block is smartphone supply chain. One and a half
• 47:27
  Speaker 1: billion smartphones sold last year. All of those chips are
• 47:30
  Speaker 1: available as like a fire hose of stuff to make
• 47:32
  Speaker 1: stuff with. So before smartphones, if you wanted to put
• 47:34
  Speaker 1: computing into something, you had to use PC components, So
• 47:37
  Speaker 1: an a t M is a PC. But those are big,
• 47:40
  Speaker 1: and they're heavy, and they need mains power, they need
• 47:42
  Speaker 1: power and so on. Smartphone components much cheaper, much smaller,
• 47:45
  Speaker 1: much lighter. So suddenly you can make a connected door lock,
• 47:47
  Speaker 1: and like you can get the parts really easily. UM
• 47:50
  Speaker 1: plus um UBER called as internet plus machine learning, means
• 47:53
  Speaker 1: that like a camera can actually be able to tell
• 47:56
  Speaker 1: if there's something moving or not. So you've got all
• 47:58
  Speaker 1: this stuff. Suddenly the stuff was not working. I think
• 48:01
  Speaker 1: The best analogy for this is like our grandparents could
• 48:04
  Speaker 1: have told you how many electric motors they owned. There
• 48:06
  Speaker 1: was one in the car, they had a vacuum cleaner,
• 48:09
  Speaker 1: there was one in the fridge. They owned maybe five
• 48:10
  Speaker 1: electric motors in total, Like there was only one electric
• 48:13
  Speaker 1: motor in the car. Let's start a major that was it. Today,
• 48:15
  Speaker 1: who has a clue have any electric motors are in
• 48:17
  Speaker 1: your car? There's like twenty And if you tell your
• 48:19
  Speaker 1: grandfather that you can press a button to adjust your
• 48:21
  Speaker 1: wing moor, he'd hit you on the back of the head.
• 48:23
  Speaker 1: But that's just how it worked, because that's just how
• 48:25
  Speaker 1: the technology got deployed. The same thing in your home.
• 48:28
  Speaker 1: There was a period when everything was going to have
• 48:29
  Speaker 1: a DC motor. Now everybody has you like you have
• 48:32
  Speaker 1: an you have a microwave, You maybe have a toaster,
• 48:34
  Speaker 1: you may have a kettle, you have a blender. Nobody
• 48:37
  Speaker 1: has all of those things. Everyone in East Agea has
• 48:38
  Speaker 1: a rice cooker. Everyone in the UK has a kettle.
• 48:41
  Speaker 1: In America, you maybe don't have a kettle, but you
• 48:42
  Speaker 1: have a coffee machine. But nobody has an electric carving knife.
• 48:47
  Speaker 1: And so what happens is like there are those underlying
• 48:49
  Speaker 1: components as cheap commodities were in this period of trying
• 48:51
  Speaker 1: to work out what you should do with them and
• 48:53
  Speaker 1: how they should all get plugged together, and should they
• 48:55
  Speaker 1: all be the same system or not sure they all
• 48:56
  Speaker 1: talked to Alexa or not everything, And then you've got
• 48:59
  Speaker 1: like the austrial logic. So like somebody the Samson board
• 49:02
  Speaker 1: sat down and said, everything we sell must have the
• 49:04
  Speaker 1: Samsing Voice Assistant, because then people will be more likely
• 49:07
  Speaker 1: to buy the Samson fridge that sorts to the Samsing
• 49:09
  Speaker 1: dish watcher when it talks to the samthing this, um,
• 49:12
  Speaker 1: how's it working out? Well? Then you so it's great
• 49:14
  Speaker 1: you see this at CS because you can see like
• 49:16
  Speaker 1: the fridge people thought this is a fantastic I mean
• 49:18
  Speaker 1: metaphorically speaking, the fridge people thought, this is a fantastic
• 49:20
  Speaker 1: idea to dish watch. Your people are like, don't damn it. Okay,
• 49:23
  Speaker 1: we'll put the voice assistant in the dish wash but
• 49:27
  Speaker 1: a that and be they want to sell it to
• 49:30
  Speaker 1: people who also in an LG fridge and a sub
• 49:32
  Speaker 1: zero fridge. So they've got the Samson voice is this,
• 49:35
  Speaker 1: and they've got home kit and they've got Alexa in
• 49:37
  Speaker 1: it because they only need to sell dish watches. They
• 49:39
  Speaker 1: don't care about the Greek strategy. It's like when Everythingy
• 49:42
  Speaker 1: product had a memory stick, and there was some and
• 49:44
  Speaker 1: like the Sony group had said everything's gonna have have
• 49:46
  Speaker 1: a memory stick, and some bits of Sony thought this
• 49:48
  Speaker 1: is great, and some bits were like, God, damn it, um,
• 49:51
  Speaker 1: what are we gonna do? What are we going to
• 49:52
  Speaker 1: do with this? And as with an electric carving knife
• 49:54
  Speaker 1: versus the blender, like some of this stuff will make them,
• 49:57
  Speaker 1: so some of it won't. It would be quite nice
• 49:59
  Speaker 1: to be able to say to my oven, um, okay,
• 50:02
  Speaker 1: pre heat the oven to three degrees, as opposed to
• 50:05
  Speaker 1: having to learner the interview and walk over and touch it.
• 50:08
  Speaker 1: I have my apartment, I have an infrared sensor in
• 50:10
  Speaker 1: the bathroom. I walk into the bathroom, the light comes on.
• 50:13
  Speaker 1: It's fantastic. My parents hated because they get up at
• 50:16
  Speaker 1: four o'clock in the morning, then the lights come on.
• 50:18
  Speaker 1: I think you program that because it's not anything. It's
• 50:21
  Speaker 1: just literally it is just a dumile sensor from forty
• 50:23
  Speaker 1: years again. So so here's where the smart world starts
• 50:26
  Speaker 1: to get more interesting. So I have a long, twisty
• 50:29
  Speaker 1: driveway because we're Our house is said off the main road,
• 50:33
  Speaker 1: and we have lights along it. And the first phase
• 50:37
  Speaker 1: was having a switch that put the lights on and
• 50:39
  Speaker 1: put the lights off. And then the next phase was
• 50:41
  Speaker 1: having a timer that has the lights going on at
• 50:45
  Speaker 1: seven a pm and off at eleven pm. But the
• 50:48
  Speaker 1: problem with that is seven pm is light sometimes of
• 50:51
  Speaker 1: the year and dark. I thought, no, no, nothing like
• 50:56
  Speaker 1: that's a contemporary house. But the new switch that is
• 51:00
  Speaker 1: literally going in this weekend is built into the lights
• 51:06
  Speaker 1: which as you can set your latitude and that lets
• 51:10
  Speaker 1: whatever you said as your like going on thirty minutes
• 51:14
  Speaker 1: after sunset will change throughout the year regardless and you
• 51:18
  Speaker 1: don't have to deal with it. So that's our sort
• 51:21
  Speaker 1: of smart application of software. Are like, we don't have
• 51:24
  Speaker 1: any you know, your your phone changes its clock when
• 51:27
  Speaker 1: the time don't changed automatically things the satellites. So I
• 51:30
  Speaker 1: think there's a there's a kind of question to where
• 51:32
  Speaker 1: will the complexity set And this is like the electric
• 51:33
  Speaker 1: carving knife. There will be stuff that just doesn't make
• 51:35
  Speaker 1: sense the other you do electric carving knife have been
• 51:38
  Speaker 1: around for decades and they never would the other the
• 51:40
  Speaker 1: other really so well. Some people have a like I
• 51:42
  Speaker 1: can show you where they took a chunk out of
• 51:44
  Speaker 1: my finger. The other extreme is like the other. The
• 51:49
  Speaker 1: other way to think about this is it's old saying
• 51:51
  Speaker 1: that basically a computer should never ask you a question
• 51:53
  Speaker 1: that it ought to be able to work out on itself.
• 51:56
  Speaker 1: And that used to mean, like you plug the printer in,
• 51:59
  Speaker 1: the computer should know what the printer is. It shouldn't
• 52:00
  Speaker 1: ask you what printer it is. Um, then it means
• 52:03
  Speaker 1: your phone doesn't ask where you are when you call
• 52:05
  Speaker 1: a car because it's got GPS and it knows. Then
• 52:07
  Speaker 1: it means like the light should know what the times
• 52:09
  Speaker 1: it is, so the light should just go on right,
• 52:11
  Speaker 1: that's right. And the chair that the tension point and
• 52:14
  Speaker 1: all of those is, is it actually more hassle to
• 52:16
  Speaker 1: configure the thing to do that? Sometimes it is, sometimes
• 52:19
  Speaker 1: it is, sometimes it isn't. So my beef about Siri,
• 52:22
  Speaker 1: which was the leading voice app to begin with before
• 52:27
  Speaker 1: Alexa began to eat its lunch, is asking you questions
• 52:31
  Speaker 1: that it should, from the context be able to figure out.
• 52:34
  Speaker 1: This is just paying a database, paying a location, um
• 52:39
  Speaker 1: software ping something it has access to all that stuff.
• 52:42
  Speaker 1: This so this is a different sense of the word smart.
• 52:46
  Speaker 1: So let's talk about voice for a minute. Okay, So
• 52:50
  Speaker 1: we have this thing called machine so okay, the right
• 52:52
  Speaker 1: way putting this. So when you talk to a computer,
• 52:55
  Speaker 1: the three things that are going on. Step one is
• 52:58
  Speaker 1: it is taking the audio wave form and turning that
• 53:01
  Speaker 1: into text. So it's just transcribing it and turning it
• 53:04
  Speaker 1: into words, which I think is basic math and and
• 53:07
  Speaker 1: well no it was. And this is something where it
• 53:09
  Speaker 1: used to work okay, like three quarters of the time,
• 53:12
  Speaker 1: but you remember using dictation apps like National actually speaking,
• 53:17
  Speaker 1: it's sort of work three quarters. There are more and
• 53:20
  Speaker 1: it would get better like half a point every year.
• 53:22
  Speaker 1: A machine learning comes along and it goes from working
• 53:25
  Speaker 1: three quarters at the time to working at the tent
• 53:29
  Speaker 1: at the time. And so machine learning gives you this
• 53:30
  Speaker 1: radical change in that it also then there's a second piece,
• 53:34
  Speaker 1: which is you needed to go from the text to
• 53:36
  Speaker 1: a structured query. You need to actually work out what
• 53:39
  Speaker 1: the verb and the nan is and what is his asking,
• 53:41
  Speaker 1: which is a completely different computation. Um. And so when
• 53:45
  Speaker 1: you talk to your computer, that's it's two very almost
• 53:47
  Speaker 1: unrelated things going on. Machine learning. Also, this is called
• 53:50
  Speaker 1: natural language processing. UM. Machine learning also made that work way, way,
• 53:55
  Speaker 1: way way better. UM. Then you have the third problem
• 53:59
  Speaker 1: we're is, Okay, I've created this structured query, do I
• 54:02
  Speaker 1: have anything to give it to? And you should have. Well,
• 54:07
  Speaker 1: so this is the problem, which is what you've actually
• 54:11
  Speaker 1: got here is an IVYR. You've got a voice tree,
• 54:13
  Speaker 1: Press one for this, press to for that. Machine learning
• 54:16
  Speaker 1: means you can now and we've we've all kind of
• 54:18
  Speaker 1: experienced this, saying you phone the airline and they say,
• 54:20
  Speaker 1: tell us what you want to ask about, and there's
• 54:22
  Speaker 1: only fifteen things you could ask so they can recognize
• 54:25
  Speaker 1: it the text, and then they can work out what
• 54:27
  Speaker 1: you're asking about and they can read you to the
• 54:28
  Speaker 1: right number inside that they're pretty awful also, But what
• 54:32
  Speaker 1: we don't have. Machine learning gives us a way of
• 54:34
  Speaker 1: automating the transcription and getting the natural language processing to work.
• 54:39
  Speaker 1: But you might ask it any one of three or
• 54:41
  Speaker 1: four million things, and so effectively, what I mean when
• 54:45
  Speaker 1: I say a structured query is you can fill in
• 54:48
  Speaker 1: a dialog box by talking to the computer, and the
• 54:50
  Speaker 1: computer can work out which dialog box you were asking for.
• 54:54
  Speaker 1: But somebody has to have made the dialog box by hand. Okay,
• 54:58
  Speaker 1: so you know if you're if you're a phone company,
• 55:01
  Speaker 1: so if you're a phone manufacturer and you have a
• 55:04
  Speaker 1: few million or maybe even a few billion queries, but
• 55:07
  Speaker 1: how uses your frame of reference? But so you can
• 55:10
  Speaker 1: make the top fifty categories, all the top thousand categories. Well,
• 55:15
  Speaker 1: and if you're gonna ask something, really is the curve
• 55:18
  Speaker 1: gets really really steep really quickly, and you don't know
• 55:21
  Speaker 1: what you can ask. So you cannot ask. You can
• 55:26
  Speaker 1: get the top five things, and you can say you
• 55:28
  Speaker 1: can ask for weather, you can ask for timer, you
• 55:30
  Speaker 1: can ask for a unit conversion, you can ask for
• 55:32
  Speaker 1: the time in a different city. You can ask me
• 55:35
  Speaker 1: to play music and you'll get the music right roughly
• 55:37
  Speaker 1: half of the time. This is you know, you ask
• 55:38
  Speaker 1: it to play an album and it plays you like
• 55:41
  Speaker 1: a weird cover of it because it doesn't understand um.
• 55:44
  Speaker 1: But then I was like, what are the thousand things
• 55:48
  Speaker 1: you might ask your ear to do? And how many
• 55:51
  Speaker 1: of those get really complex really quickly? Like can you
• 55:55
  Speaker 1: rebook my meeting this afternoon? Okay, that's not a simple query,
• 55:59
  Speaker 1: that's like five hundred things that you need to know
• 56:02
  Speaker 1: that And so the problem is as I said. What
• 56:05
  Speaker 1: you have with a voice assistant is you have an
• 56:07
  Speaker 1: IVR that will always understand what you asked. Machine learning
• 56:09
  Speaker 1: means it will always correctly root you to the right number.
• 56:12
  Speaker 1: What it doesn't have is a way to have somebody
• 56:14
  Speaker 1: at that number automated automate what that person at the
• 56:17
  Speaker 1: number will do. And so you have to create those
• 56:20
  Speaker 1: dialog boxes one by one. So give you an example.
• 56:22
  Speaker 1: Sirie learns how to do cricket. You know it didn't
• 56:25
  Speaker 1: somebody at Apple sat down and wrote the cricket module. Okay,
• 56:28
  Speaker 1: now Sirie can do hurling. Okay, Well, somebody at Sirie
• 56:33
  Speaker 1: at Apple had to sit down and write that, and
• 56:35
  Speaker 1: then Siri can do call me a uber, right, Well,
• 56:38
  Speaker 1: someone at Uber had to write that, and then it
• 56:40
  Speaker 1: can do um tell me whether my flight is delayed? Right.
• 56:44
  Speaker 1: Someone had to write that, And every single one of those,
• 56:47
  Speaker 1: some human being has to sit down and spend an
• 56:49
  Speaker 1: afternoon writing those. And the problem with this is it
• 56:52
  Speaker 1: doesn't scale. And in a sense, if you could do that,
• 56:55
  Speaker 1: you'd have made how nine thousand and how nine thousand
• 56:58
  Speaker 1: is not the aggregate of like some of you sitting
• 57:00
  Speaker 1: and writing those one at a time. That's that other
• 57:02
  Speaker 1: thing that's machine learning, a big data and a bunch
• 57:05
  Speaker 1: of things. So that's the gap. You know, we have
• 57:07
  Speaker 1: a way to get the transcription and the NLP to
• 57:09
  Speaker 1: work completely accurately, Well, we don't have. It is a
• 57:11
  Speaker 1: way to get to automatically make a system that could
• 57:14
  Speaker 1: answer any question you could possibly answer human being to
• 57:18
  Speaker 1: think of what all of those would be. So that
• 57:20
  Speaker 1: is a description of So let me answer answer this
• 57:24
  Speaker 1: question another way. So imagine in the nineteenth century someone
• 57:30
  Speaker 1: tries to make a mechanical horse. There's no I mean,
• 57:33
  Speaker 1: you've seen the pattern drawing things. There's no law of
• 57:35
  Speaker 1: physics that says you can't make a mechanical horse. It's
• 57:38
  Speaker 1: just that the degree of complexity required to get that
• 57:41
  Speaker 1: to work was impossible in the nineteenth century, and arguably
• 57:43
  Speaker 1: it's impossible now. I mean, Boston Dynamics are now trying
• 57:45
  Speaker 1: to do this pretty close. But that's like a hundred
• 57:47
  Speaker 1: and fifty years later, um and so and so. What
• 57:49
  Speaker 1: you can actually do is you could make a bicycle
• 57:51
  Speaker 1: and the steam engine, and a bicycle is a lot
• 57:52
  Speaker 1: simpler than a horse, and it can't do everything that
• 57:54
  Speaker 1: a horse can do. But it turns out that was
• 57:55
  Speaker 1: kind of useful anyway. The same thing with with AI.
• 57:58
  Speaker 1: What people tried to do with AI until a couple
• 58:00
  Speaker 1: of years ago was they would try and write rules.
• 58:03
  Speaker 1: So if you wanted to recognize a cat, what you
• 58:05
  Speaker 1: do is you do edge detection, and you do text
• 58:08
  Speaker 1: your analysis, and you try and make something that looks
• 58:10
  Speaker 1: for two eyes in roughly the right place relative to
• 58:12
  Speaker 1: two ears, and you try and make something that tries
• 58:14
  Speaker 1: to look for legs and hopefully, and you try and
• 58:17
  Speaker 1: work out how the hell are we going to tell
• 58:18
  Speaker 1: the difference between a cat and a dog? And it
• 58:20
  Speaker 1: would sort of work, like three quarters at the time,
• 58:23
  Speaker 1: it would never really work for the same reason that
• 58:25
  Speaker 1: the mechanical horse would sort of work but never actually work.
• 58:29
  Speaker 1: And then what machine learning does is say no, no, no no,
• 58:31
  Speaker 1: no no. You've given a million pictures labeled cat and
• 58:33
  Speaker 1: a million pictures of labeled dog, and you let the
• 58:35
  Speaker 1: machine ripe the rules. That's what machine learning means. The
• 58:38
  Speaker 1: machine generates a millionaire statements that will allow it to
• 58:41
  Speaker 1: calculate this difference between a cat picture and a dog
• 58:44
  Speaker 1: picture with ninety two point seven percent accuracy, and that
• 58:50
  Speaker 1: gives us a breakthrough of a whole class of problem.
• 58:52
  Speaker 1: It solves image recognition itselves, speech itselves, language, itselves, a
• 58:56
  Speaker 1: whole kind of level of pattern recognition. When it doesn't
• 59:00
  Speaker 1: do is give you a human ten year old. It
• 59:02
  Speaker 1: doesn't give you general intelligence. Basically, what you've built is
• 59:06
  Speaker 1: you've built an enormous spreadsheet with a million IF statements
• 59:11
  Speaker 1: meshed together and linked together. And you can give this
• 59:14
  Speaker 1: spreadsheet a picture of a cat and a picture, and
• 59:16
  Speaker 1: it will tell you whether the cat in it or not,
• 59:18
  Speaker 1: and that's all it will do. So another way of
• 59:21
  Speaker 1: thinking about this is like, we have this fantasy of
• 59:23
  Speaker 1: domestic robots that will walk around your home and do
• 59:25
  Speaker 1: the housework. We have domestic robots. It's called a washing machine.
• 59:29
  Speaker 1: That's what machine learning gives you. It gives your washing machine.
• 59:32
  Speaker 1: It gives your washing machine that can recognize family pictures.
• 59:35
  Speaker 1: It gives your washing machine that can tell me is
• 59:37
  Speaker 1: there strange behavior happening on this network. It gives your
• 59:39
  Speaker 1: washing machine that can recognize handwriting. It gives your washing
• 59:42
  Speaker 1: machine that can do X. But it can only do
• 59:44
  Speaker 1: that one thing. It can't do anything else, and it
• 59:46
  Speaker 1: can't clean. You can't wash your dishes, And so that's
• 59:49
  Speaker 1: what machine only gives it gives you this step change
• 59:52
  Speaker 1: in capability. So one of the way I'm kind of
• 59:54
  Speaker 1: I'm sort of giving you a stack of metaphors here.
• 59:56
  Speaker 1: I think a really good way to think about what
• 59:57
  Speaker 1: machine learning gives us is it's like thinking about relations databases.
• 1:00:01
  Speaker 1: It's a relational databases gives you this amazing step change
• 1:00:04
  Speaker 1: in what you can do with computers. Suddenly you can say,
• 1:00:06
  Speaker 1: show me X by Y Bloomberg. Going would not exist
• 1:00:10
  Speaker 1: without this, SAP would not exist without this, just in times,
• 1:00:13
  Speaker 1: supply chains would not exist without this totally transformed our world.
• 1:00:17
  Speaker 1: But everything you use now but it's not AI. It is.
• 1:00:22
  Speaker 1: But it's like if it's AI, then everything is AI.
• 1:00:25
  Speaker 1: It doesn't get you how nine thousand. Let me jump
• 1:00:27
  Speaker 1: into some of my favorite questions. Tell us the most
• 1:00:31
  Speaker 1: important thing that people who work with you don't know
• 1:00:34
  Speaker 1: about you? I hate personal questions? Is that true? I
• 1:00:39
  Speaker 1: don't know. I find I kind of look at this,
• 1:00:40
  Speaker 1: this email, this, I didn't puzzled by these kind of questions.
• 1:00:42
  Speaker 1: I don't know, what is it the people who work
• 1:00:44
  Speaker 1: with that, we have no idea. They probably don't know
• 1:00:46
  Speaker 1: have a dog. There you go, Well, because sometimes it's
• 1:00:49
  Speaker 1: revealing some people have told us deep dark secrets from
• 1:00:53
  Speaker 1: decades ago, and case there is a joke that the
• 1:00:57
  Speaker 1: most secure form of encryption is anything you say in
• 1:01:00
  Speaker 1: the second hour of a podcast. Here we are, I
• 1:01:02
  Speaker 1: can say it now, and it's you, me, and a
• 1:01:06
  Speaker 1: handful of friends that are hearing that nobody else is
• 1:01:08
  Speaker 1: gonna hear. Um, tell us about your early mentors who
• 1:01:10
  Speaker 1: affected the way you looked at technology and venture inversiting.
• 1:01:15
  Speaker 1: I would say it's not really about how I look
• 1:01:18
  Speaker 1: at technology. It's more sort of mental processes and ways
• 1:01:21
  Speaker 1: of thinking about things and ways to try and answer questions.
• 1:01:25
  Speaker 1: And so the stuff I did at university, you know,
• 1:01:27
  Speaker 1: people talking to me about medieval history, and it's not
• 1:01:30
  Speaker 1: this is no, it's not that didn't happen. This didn't happen.
• 1:01:33
  Speaker 1: It's more, okay, how do you actually understand what we're
• 1:01:36
  Speaker 1: trying to think about here? So, like I remember like
• 1:01:38
  Speaker 1: my first or second, like you know, the first. The
• 1:01:41
  Speaker 1: way it came Bridge history works is you do an
• 1:01:42
  Speaker 1: You get given an essay title and a reading list,
• 1:01:44
  Speaker 1: and you come back in a week with your essay
• 1:01:46
  Speaker 1: and that's it. You don't have to get lectures. And
• 1:01:49
  Speaker 1: so I think. My first essay was the one that,
• 1:01:51
  Speaker 1: of course everyone screws up, and it was like was
• 1:01:53
  Speaker 1: the King of England more or less powerful after ten
• 1:01:56
  Speaker 1: exty six? And you know I gave the long answer
• 1:01:59
  Speaker 1: and my professor said, look, you have to think about
• 1:02:01
  Speaker 1: this is what does it actually mean to be powerful?
• 1:02:03
  Speaker 1: And power is the ability to get people to do
• 1:02:04
  Speaker 1: something they don't want to do. And so you can
• 1:02:06
  Speaker 1: give all this other stuff about all the sophistication of
• 1:02:09
  Speaker 1: the Anglo Saxon monarchy and they had their own much
• 1:02:11
  Speaker 1: more sophisticated coinage and blah blah blah. No that was
• 1:02:15
  Speaker 1: two years later, But like, who is more Is he
• 1:02:17
  Speaker 1: actually more powerful? William the conqueror can tell people to
• 1:02:20
  Speaker 1: do stuff and they have to do it. And the
• 1:02:21
  Speaker 1: King of the sax the king before the conquest, King
• 1:02:23
  Speaker 1: of England, like no one really paid any attention to
• 1:02:26
  Speaker 1: what he said except for the coin like the complicated coinage.
• 1:02:28
  Speaker 1: So my point is like it's not so much that
• 1:02:31
  Speaker 1: someone told me how this is how to think about technology.
• 1:02:34
  Speaker 1: It's more how to sort of think systematically and try
• 1:02:38
  Speaker 1: and break up problems and try and look for peace
• 1:02:40
  Speaker 1: of components and try and look kind of look at
• 1:02:43
  Speaker 1: things in those kinds of ways quite personally. Tell us
• 1:02:46
  Speaker 1: about some of your favorite books, be they fiction, nonfiction, technology,
• 1:02:50
  Speaker 1: or what have you. Um, I don't know. I'm sort
• 1:02:53
  Speaker 1: of I'm always reading and I'm always buying books, and
• 1:02:56
  Speaker 1: those two process as are completely independent. I read almost
• 1:02:59
  Speaker 1: no business books or technology books, partly because I feel
• 1:03:02
  Speaker 1: that all business books are basically a short magazine article
• 1:03:04
  Speaker 1: padded out to There are some authors in particular I
• 1:03:08
  Speaker 1: can name, well, that's their entire over. Well, if you
• 1:03:11
  Speaker 1: feel like if you've written fifteen business books on the
• 1:03:14
  Speaker 1: same theme, then like you're either a very bad writer
• 1:03:16
  Speaker 1: or very good writer, depending on your very good marketer,
• 1:03:18
  Speaker 1: Well that too. Perhaps, I know. I think I read
• 1:03:22
  Speaker 1: things that intrigued me and interest me. The last thing
• 1:03:24
  Speaker 1: I read with the Iliad, which I've never read before. Wait,
• 1:03:27
  Speaker 1: the history major never read the Iliad. Well, so there's
• 1:03:31
  Speaker 1: this great line from Italia Calvino where he says the
• 1:03:33
  Speaker 1: number of something like the number of essential works is
• 1:03:35
  Speaker 1: so great that nobody, no matter how much they're reading,
• 1:03:37
  Speaker 1: has read more than a fraction. Um, you know, literature
• 1:03:40
  Speaker 1: as a sample, you can't read all the books. There
• 1:03:42
  Speaker 1: was a period when you could have seen all the movies.
• 1:03:44
  Speaker 1: Now you can't see all the movies, you can't listen
• 1:03:45
  Speaker 1: to all the music. You just have to take samples
• 1:03:48
  Speaker 1: and take do things that stimulate you or make you
• 1:03:50
  Speaker 1: think in interesting ways, particularly things that aren't about what
• 1:03:53
  Speaker 1: you work on. So give us another example from that's
• 1:03:57
  Speaker 1: that's newer than two thousand years old. Um, I don't know.
• 1:04:01
  Speaker 1: By the way, this is everybody's favorite question, the one
• 1:04:04
  Speaker 1: I get. I read books and then I can't remember
• 1:04:07
  Speaker 1: what it is? What? What? What? The last book I
• 1:04:09
  Speaker 1: read was, Um, what stands out to you? What has
• 1:04:12
  Speaker 1: really read? Really? The Iliad really stood out for me.
• 1:04:14
  Speaker 1: That was a kind of a couple of weeks ago,
• 1:04:15
  Speaker 1: I read this fascinating book about medieval manuscripts called Encounters
• 1:04:20
  Speaker 1: with Fascinating Manuscripts or something like that by college librarian
• 1:04:23
  Speaker 1: in Cambridge, and it's one of these sort of new
• 1:04:26
  Speaker 1: kind of genre of books which kind of looked like
• 1:04:27
  Speaker 1: a big hardback nonfiction book. But they're all in color
• 1:04:30
  Speaker 1: all the way through, which is funny to do with
• 1:04:31
  Speaker 1: the development of printing technology, which is there's there's loads
• 1:04:34
  Speaker 1: of books that have color illustrations all the way through,
• 1:04:36
  Speaker 1: which is but aren't art books. It's kind of interesting
• 1:04:38
  Speaker 1: and it's just sort of about how books evolved. So
• 1:04:40
  Speaker 1: it's kind of fascinating observation that books or codeses used
• 1:04:44
  Speaker 1: to be rectangular because you made them out of papyrus.
• 1:04:46
  Speaker 1: So you would lay your sheets, you know, you lay
• 1:04:48
  Speaker 1: the strips of plant kind of cross weights like plywood,
• 1:04:52
  Speaker 1: and you'd make them square shaped because you had the
• 1:04:54
  Speaker 1: strips of all the same length, So that produced to
• 1:04:56
  Speaker 1: square shaped piece of papyrus. And then you moved to um,
• 1:05:00
  Speaker 1: what's it called animal skin? What's the word? I've forgotten
• 1:05:03
  Speaker 1: the word? My mind is gone blank now too, um Um.
• 1:05:06
  Speaker 1: You moved to parchment from animal skins. And there's this
• 1:05:10
  Speaker 1: great line. Animals tend to be oblong, so you get
• 1:05:13
  Speaker 1: oblong pieces and then you fold them up and that
• 1:05:16
  Speaker 1: produces an oblong book. And so when you go from
• 1:05:19
  Speaker 1: papyrus department, you go from square books to oblong books.
• 1:05:22
  Speaker 1: And here we are years later and the books are
• 1:05:24
  Speaker 1: still oblong, and that's just the residual of the earlier
• 1:05:27
  Speaker 1: technology of parchment. Yeah, exactly. I think those sorts of
• 1:05:31
  Speaker 1: I mean, coming back to you know, why is this interesting?
• 1:05:33
  Speaker 1: I do think those sorts of how did stuff happen,
• 1:05:36
  Speaker 1: and why did the what's the thread of causation and
• 1:05:39
  Speaker 1: how things can be completely random is sort of interesting
• 1:05:42
  Speaker 1: and how do these does this stuff change how we
• 1:05:44
  Speaker 1: think about it? So on the flight here, I've read
• 1:05:46
  Speaker 1: a book about kind of lighting tech, the evolution of
• 1:05:48
  Speaker 1: lighting technology in the eighteenth and nineteent century, as we
• 1:05:51
  Speaker 1: go from candles to light to kind of lamps that
• 1:05:53
  Speaker 1: hold the candle and produce five times more light, how
• 1:05:55
  Speaker 1: we go to now or the gas lights to to
• 1:05:58
  Speaker 1: um earlier electric light which used weren't very bright, and
• 1:06:01
  Speaker 1: then use tungsten filament and again, so there's the evelation
• 1:06:03
  Speaker 1: of light. This observation that stuck in my mind and
• 1:06:06
  Speaker 1: I and now need to go and check which is
• 1:06:08
  Speaker 1: an assertion that Dutch seventy century Dutch pictures have windows
• 1:06:12
  Speaker 1: but no cuts, because why would you keep the light out.
• 1:06:16
  Speaker 1: Everybody wants to light and you want the light in.
• 1:06:18
  Speaker 1: And of course, as we all sort of no, light
• 1:06:19
  Speaker 1: was really expensive and used to be a luxury and
• 1:06:22
  Speaker 1: now it's not. But I just thought that was a
• 1:06:24
  Speaker 1: really revealing way of thinking about how your whole sense
• 1:06:27
  Speaker 1: of the world changes when lighting is cheap. Why would
• 1:06:30
  Speaker 1: you have curtns that that's quite fastening. The book How
• 1:06:33
  Speaker 1: We Got to Now has there's six inventions, one of
• 1:06:36
  Speaker 1: which is lighting, and the process of watching the price
• 1:06:40
  Speaker 1: plummet over over the centuries is really is really fastening,
• 1:06:45
  Speaker 1: all right. The interesting thing that comes out of that
• 1:06:47
  Speaker 1: is that when light was expensive, state of symbol was
• 1:06:50
  Speaker 1: to stay up all night. So rich people would get
• 1:06:53
  Speaker 1: up at lunchtime and go to bed at l five
• 1:06:55
  Speaker 1: in the morning because they could afford to, because they
• 1:06:57
  Speaker 1: could afford candles. Everybody else gets up with the croaker, think, um,
• 1:07:01
  Speaker 1: what do you do for fun? What do you do
• 1:07:02
  Speaker 1: to relax outside of the office in San Francisco? Um?
• 1:07:06
  Speaker 1: So I have a seven year old and a dog,
• 1:07:10
  Speaker 1: so my time is not my own understood completely? What
• 1:07:15
  Speaker 1: sort of advice would you give to a millennial or
• 1:07:17
  Speaker 1: someone who just graduated college that was interested either in
• 1:07:21
  Speaker 1: a career in technology or a venture capital So I
• 1:07:27
  Speaker 1: sort of struggle to give an advice party because my
• 1:07:29
  Speaker 1: career has been a series of random lurches and kind
• 1:07:32
  Speaker 1: of companies going out of business. I won't apply for
• 1:07:36
  Speaker 1: a job at cling Back. Don't worry, I think there's
• 1:07:38
  Speaker 1: a The only thing I've sort of observed is there is? Um,
• 1:07:42
  Speaker 1: there is the kind of things that you are good
• 1:07:44
  Speaker 1: at doing and the kind of mental processes that you
• 1:07:47
  Speaker 1: are good at. Are you good at people or not?
• 1:07:50
  Speaker 1: Are you good at process and project management and making
• 1:07:53
  Speaker 1: sure everything gets done right? Or is that not what
• 1:07:55
  Speaker 1: it interests you? Are you good at trying to kind
• 1:07:58
  Speaker 1: of explain an empathy sizing with people? Are you good
• 1:08:01
  Speaker 1: at trying to explain and discuss and argue with people
• 1:08:04
  Speaker 1: and persuade people? Um? What are the things that you're
• 1:08:08
  Speaker 1: good at doing? And those are not necessarily what was
• 1:08:10
  Speaker 1: your degree in, and they aren't necessarily specific to two jobs.
• 1:08:14
  Speaker 1: So like I have contemporaries who went off and become
• 1:08:17
  Speaker 1: what England we call a barrister, which is basically a
• 1:08:19
  Speaker 1: lawyer who only goes to court is in England and
• 1:08:21
  Speaker 1: law is split solicitors to be the paper what barristers
• 1:08:23
  Speaker 1: get called as well. Exactly that's all, but that's all
• 1:08:25
  Speaker 1: the effectively and it's a completely different profession. Um, And
• 1:08:28
  Speaker 1: I have contemporaries who are barristers, and you know I
• 1:08:31
  Speaker 1: could have done that. I could have gone and argued
• 1:08:33
  Speaker 1: for a living, and you know you kind of you
• 1:08:34
  Speaker 1: go and do you look at the problem and you
• 1:08:35
  Speaker 1: pull it apart and you work out what story to
• 1:08:37
  Speaker 1: tell and how to place added to the law and
• 1:08:39
  Speaker 1: try and persuade the judge and the jury. Um So,
• 1:08:42
  Speaker 1: in a sense, I have those skill sets. On the
• 1:08:44
  Speaker 1: other hand, I look at these contemporaries and it says,
• 1:08:46
  Speaker 1: you know, um, Charlie is probably the leading up and
• 1:08:51
  Speaker 1: coming advocate in complex of your maritime insurance disputes. And
• 1:08:54
  Speaker 1: I think, well, I hope he's paid a lot to that,
• 1:08:59
  Speaker 1: but his interests did in the argument and the explaining
• 1:09:02
  Speaker 1: and the discussion. The fact that it's insurance is kind
• 1:09:05
  Speaker 1: of neither here nor there really, um So, I think
• 1:09:08
  Speaker 1: you have to just kind of work out, well, what
• 1:09:10
  Speaker 1: is it What are the things that fascinate you, and
• 1:09:12
  Speaker 1: what are the kind of mental processes that fascinate you,
• 1:09:16
  Speaker 1: And and our final question, what is it that you
• 1:09:18
  Speaker 1: know about the worlds of technology that you wish you
• 1:09:21
  Speaker 1: knew twenty years ago? Other than like buy Apple or something, No, no,
• 1:09:26
  Speaker 1: nothing like that. That that's you know, anyone could look
• 1:09:29
  Speaker 1: at prices twenty years ago and said hey, by Amazon,
• 1:09:32
  Speaker 1: by Apple. But generally in terms of thinking in terms
• 1:09:36
  Speaker 1: of broad processes, what would have helped you early in
• 1:09:39
  Speaker 1: your career had you figured it out sooner. So that
• 1:09:41
  Speaker 1: actually might be a good way of me framing my
• 1:09:46
  Speaker 1: technology to determinism peace because it's a sort of I mean,
• 1:09:50
  Speaker 1: the other thing people talk about about a lot in
• 1:09:52
  Speaker 1: venture capitalist patent recognition that this sort of looks like
• 1:09:55
  Speaker 1: things that will work. This looks like things that that
• 1:09:58
  Speaker 1: never work and why and the thing that never work
• 1:10:01
  Speaker 1: eventually they do work, and that's where there's a hundred
• 1:10:03
  Speaker 1: billion dollar outcome. So that's also an interesting kind of
• 1:10:05
  Speaker 1: fruitful copy conversation. But it's sort of understanding these patents
• 1:10:09
  Speaker 1: of Okay, you have to think, okay, how is this
• 1:10:11
  Speaker 1: going to fit within this ecosystem? This is what is
• 1:10:15
  Speaker 1: the timing for this? It's those sorts of mechanics of
• 1:10:18
  Speaker 1: the process um that are interesting. I think that's quite fascinating.
• 1:10:23
  Speaker 1: We have been speaking with Ben Nick Evans of Andres
• 1:10:26
  Speaker 1: and Horowitz. If you enjoy this conversation, be sure and
• 1:10:29
  Speaker 1: look up an Inch or down an inch on Apple,
• 1:10:31
  Speaker 1: iTunes or SoundCloud, overcast, Bloomberg dot com wherever your final
• 1:10:37
  Speaker 1: podcasts are sold, and you could see any of the
• 1:10:39
  Speaker 1: other two hundred such conversations we've had over the past
• 1:10:43
  Speaker 1: four years. We love your comments, feedback and suggestions right
• 1:10:47
  Speaker 1: to us at m IB podcast at Bloomberg dot net.
• 1:10:50
  Speaker 1: I would be remiss if I did not thank the
• 1:10:52
  Speaker 1: crack staff that helps us put this podcast together each week.
• 1:10:56
  Speaker 1: Taylor Riggs is our book or slash producer. Medina Parwana
• 1:11:00
  Speaker 1: is our audio engineer slash producer. Mike Batnik is our
• 1:11:03
  Speaker 1: head of research. I'm Barry Retolts. You're listening to Master's
• 1:11:07
  Speaker 1: in Business on Bloomberg Radio.