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Speaker 1: M This is Mesters in Business with Very Renaults on

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Speaker 1: Bluebird Radio. This week on the podcast, I have an

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Speaker 1: extra special guest. Previously, I spoke with Sebastian Mallaby when

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Speaker 1: he released his book The Man Who Knew All about

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Speaker 1: Alan Greenspan. I would argue that Greenspan wasn't the Man

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Speaker 1: Who Knew. We avoided talking about anything having to do

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Speaker 1: with the Maestro or the Federal Reserve, or interest rates

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Speaker 1: or inflation, and instead spent the full conversation discussing Mallaby's

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Speaker 1: new book, The Power Law, Venture Capital in the Making

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Speaker 1: of the New Future. I was so so fan of

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Speaker 1: the green Span book because I'm not a fan of

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Speaker 1: green Span. I loved Mallaby's prior book, More Money than God,

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Speaker 1: all about hedge funds, and and this book is I

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Speaker 1: think his best yet. The History of Silicon Valley told

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Speaker 1: from the perspective of historian. He he really brings a

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Speaker 1: very different lens and filter uh to looking at how

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Speaker 1: Silicon Valley developed. All the things that are so different

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Speaker 1: relative to um traditional investing, East Coast investing versus West

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Speaker 1: Coast invents that investing, how they embrace risk, what a

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Speaker 1: power law is, while you're why you're not looking for diversification,

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Speaker 1: why you expect most of your investments to fail. And

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Speaker 1: it's just a handful of companies that are responsible, uh

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Speaker 1: for for the vast majority of your returns, and hence

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Speaker 1: the tendency to spread a lot around money around on

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Speaker 1: a lot of companies and a lot of entrepreneurs and

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Speaker 1: a lot of startups looking for that unicorn that's going

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Speaker 1: to really be the driver of your funds returns. I

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Speaker 1: really really like the book, and I don't just say that.

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Speaker 1: I I thought it was tremendous. I plowed through it

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Speaker 1: over a couple of weekends in the dead of winter.

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Speaker 1: I think you'll not only like the book, but you'll

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Speaker 1: enjoy the conversation. So, with no further ado, my interview

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Speaker 1: with Sebastian Mallaby. This is Masters in Business with Very

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Speaker 1: Results on Bluebird Radio. HI extra special guest this week

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Speaker 1: is Sebastian Mallaby. He is the Paul Volker Senior Fellow

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Speaker 1: for International Economics at the Council on Foreign Relations. He

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Speaker 1: is also a two time finalist for the Pulitzer Prize

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Speaker 1: and editorial writing. He has been a columnist at The

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Speaker 1: Washington Post, The Financial Times, The Economist, The Atlantic. He

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Speaker 1: is the author of multiple books, including The Man Who Knew,

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Speaker 1: the Life and times of Alan Greenspan, More Money Than God,

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Speaker 1: all about hedge funds, and the making of a New Elite.

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Speaker 1: His latest book is out February one, The Power Law,

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Speaker 1: Venture Capital and the Making of a New Future. Sebastian Mallaby,

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Speaker 1: Welcome to Bloomberg. Great to be witty, Barry, Great to

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Speaker 1: have you again. Last we spoke was about five years ago,

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Speaker 1: after the Greenspan book came out, and I have to

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Speaker 1: tell you I've really enjoyed your book on venture capital.

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Speaker 1: But before we get to that, I want to just

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Speaker 1: for people who may not be familiar with your career, Uh,

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Speaker 1: just do a little background. How did how did you

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Speaker 1: get started in journalism and what were you covering early

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Speaker 1: in your career. Well, I joined The Economist magazine right

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Speaker 1: out of college, and I had stints as the Africa correspondent,

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Speaker 1: the Toke Care correspondent ben A bit later, I was

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Speaker 1: Washington Bureau chief, had some time in London when I

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Speaker 1: was covering sort of fund management and finance UM and

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Speaker 1: I was in South Africa actually when Nelson Mandela walked

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Speaker 1: out of Jailand. I always say, my career has been

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Speaker 1: downhill ever since. Really intrigued. So so you were covering apartheide,

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Speaker 1: you also wrote a book on that. How did you

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Speaker 1: pivot towards markets and technology and the economy when it

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Speaker 1: was one of those sort of unplanned, step by step journeys, um,

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Speaker 1: as I was saying, you know, I was, I was

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Speaker 1: covering South Africa. Mandela came out of jail. It was

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Speaker 1: incredibly exciting, and that was the springboard for my first

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Speaker 1: book after apartheid, which was about what would happen next

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Speaker 1: in South Africa? And I wrote that, um, you know,

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Speaker 1: as a sort of young man in a hurry in

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Speaker 1: my late twenties and didn't write another book for for

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Speaker 1: you know, maybe a dozen years or so. And then

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Speaker 1: I wrote a book about the World Bank and development economics,

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Speaker 1: and so there was an overlap with the previous book.

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Speaker 1: It was, you know, had a bit of Africa in it,

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Speaker 1: and it was about lifting countries out of poverty and development,

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Speaker 1: and but there was also this other side to it,

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Speaker 1: which was the economics, um and that was kind of

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Speaker 1: the segue to then writing about finance. I don't know

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Speaker 1: how the financial journalism, but I hadn't written books about finance,

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Speaker 1: but I I I took on this challenge of writing

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Speaker 1: my hedge funds, and I spent a good long time

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Speaker 1: on that and all my books. The reason I get

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Speaker 1: to come on your show um at these five year

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Speaker 1: intervals is I need to speed up in the tabolism

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Speaker 1: so I get to talk to you more often. Well,

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Speaker 1: I will tell you. I think the reason is you

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Speaker 1: put so much time and effort and research into the

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Speaker 1: book that it's not the sort of thing that I'm

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Speaker 1: always impressed with the people who can crank out a

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Speaker 1: book every twelve to eighteen months. It's pretty clear that

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Speaker 1: you put a ton of heavy lifting and deep, deep background.

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Speaker 1: And I'm looking at a advanced copy, so I see

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Speaker 1: all the footnotes of which and end notes of which

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Speaker 1: there are thousands, but I don't see the index. I

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Speaker 1: have to imagine, Um, you put a ton of work,

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Speaker 1: research work into this book. Yes, I mean my view

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Speaker 1: is better the right a book that's really worth the

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Speaker 1: reader's time, um, and to take my time over it

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Speaker 1: and really get it right. I mean I'm a perfectionist

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Speaker 1: by nature, and I indulge that side of my character

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Speaker 1: when I'm when I'm doing these books, so, um, more

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Speaker 1: money than God. The hand Fund book took me, you know,

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Speaker 1: four or five years. The next book was about Alan Greenspan,

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Speaker 1: so that was another slice of financial history instead of

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Speaker 1: public markets in the central banking m And now I've

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Speaker 1: taken another five years or so to to do a

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Speaker 1: deep dive into technology investing in venture capital. Um so um.

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Speaker 1: You know, one thing leads to the next. So let's

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Speaker 1: start talking about the power law and we'll get to

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Speaker 1: exactly what that is in a bit. I want to

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Speaker 1: start just so so listeners have an idea of how

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Speaker 1: far back your research goes to nineteen fifty seven and

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Speaker 1: your discussion of what you call liberation capital or defection capital,

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Speaker 1: which is really a group of folks working for a

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Speaker 1: particular company in California and they decide they've had enough

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Speaker 1: and they want to go out on their own. Tell

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Speaker 1: us a little bit about the genesis of that adventure. Sure,

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Speaker 1: I mean liberation capital is a term I used to

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Speaker 1: capture the absolutely key thing about venture capital and what

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Speaker 1: they were doing right at the beginning of the history

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Speaker 1: of venture capital. So back in the nineteen fifties, it

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Speaker 1: was the time of big business, big labor, big government,

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Speaker 1: and so on, and and people who worked in these

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Speaker 1: big bureaucratic institutions were famously profiled in the book of

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Speaker 1: the Time Organization Man, and you know, the title kind

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Speaker 1: of tos you what you need to there, all about

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Speaker 1: loyalty to the organization. And then in along comes this, uh,

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Speaker 1: this financier Arthur Rock, who was really the pioneer of

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Speaker 1: West Coast Center Capital, and he shows up in the

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Speaker 1: valley and he liberates eight scientists who were working at

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Speaker 1: one tech company they didn't like and they want to

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Speaker 1: leave that company, and he raised his capital for them

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Speaker 1: so that they can set up their own company. And

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Speaker 1: that's called Fetchild Semiconductor. And really that liberation of those

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Speaker 1: those eight scientists, and it was such a radical thing

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Speaker 1: to do at the time. They were known as the

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Speaker 1: eight traitors, like leaving your former employee is a treachery um.

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Speaker 1: And and from the time that they got that money

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Speaker 1: from Arthur Rock and they were able to be liberated

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Speaker 1: and to found their own company, you know, from that

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Speaker 1: moment on the old corporate ideas about hierarchy and loyalty

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Speaker 1: and lifetime employment and retiring with a gold watch, all

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Speaker 1: that stuff. It was forced into the defenses, and talent

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Speaker 1: had been liberated and the revolution had begun. So I

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Speaker 1: want to get into some of the details of of

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Speaker 1: exactly how this talent was liberated, but to paint the

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Speaker 1: broader picture, there's a data point in the book that's

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Speaker 1: really quite astonishing. So fair Child dates back to ninety seven.

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Speaker 1: By well over half a century later, seven zero of

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Speaker 1: the of the publicly traded companies in Silicon Valley trace

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Speaker 1: their lineage back to fair Child. That's really an astonishing

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Speaker 1: data point. And what happened to explain that data point

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Speaker 1: is that once off the Rock that further adventure Capital

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Speaker 1: had sort of liberated the eighth Scientists to set up

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Speaker 1: fair Child. He then turned around and liberated some of

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Speaker 1: the members of that group of eight another time. You know,

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Speaker 1: he would spin them out, raised capital, move them to

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Speaker 1: some other company that he had invested in. And at

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Speaker 1: the end of the story in Um he liberated even

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Speaker 1: the two leaders of fair Child and they set up

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Speaker 1: Intel with capital raised by Arthur Rock. And one of

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Speaker 1: the eight Scientists was Eugene Kleiner. And I don't want

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Speaker 1: to jump ahead too much in the story, but an

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Speaker 1: Incliner set up kind of Perkins, which in turn invested

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Speaker 1: in all these other value companies. So the point is that,

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Speaker 1: you know, one liberation led to others, and it set

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Speaker 1: off a kind of Cambrian explosion of all these startups

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Speaker 1: in Silicon Valley. And I think it really illustrates the

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Speaker 1: point that if Arthur Rock had not come along and

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Speaker 1: financed fair Child, Sunly Conductor, the valley as we know

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Speaker 1: it today might never have developed. Huh. That that's really intriguing.

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Speaker 1: One of one of the really fascinating observations you make

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Speaker 1: in the book is the difference between the East Coast

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Speaker 1: form of I don't even know if I could call

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Speaker 1: it venture capital. It's really more private equity or asset management.

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Speaker 1: It's very risk averse, it's very diversified, it's a little

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Speaker 1: slow and and maybe I can even use the word timid,

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Speaker 1: whereas the West Coast is much more aggressive. To what

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Speaker 1: do you ascribe those really radical differences in risk tolerance? Well,

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Speaker 1: I think the East Coast, um, I mean, as you're

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Speaker 1: as you're indicating, had a whole financial tradition, um. And

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Speaker 1: if we're thinking about the late fifties, we need to

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Speaker 1: remember that that financial tradition were still shaped by the

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Speaker 1: memory of the nineteen twenty nine crash from the depression

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Speaker 1: in the you know, hadn't really quite recovered um into

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Speaker 1: the nineteen fifties. I mean, people were you know, the

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Speaker 1: companies were called fidelity, they were called prudential. The very

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Speaker 1: names signaled sort of responsibility and risk aversion. And so

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Speaker 1: although there was some venture capital around Boston, uh and

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Speaker 1: indeed in New York, it was less risk hungry than

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Speaker 1: than the West Coast kind. I remember speaking to one

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Speaker 1: of the Boston one of the early Boston venture capitalists,

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Speaker 1: and he told me kind of proudly, um that he

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Speaker 1: had made I don't know, forty bets or something in

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Speaker 1: his career on different forty forty different startups, and only

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Speaker 1: one of them had lost money. And he presented this

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Speaker 1: as a great achievement. Of course, if you said that

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Speaker 1: to a West Coast venture capitalist, the response would be, well,

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Speaker 1: you're a loser. I mean you you're not taking enough

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Speaker 1: risk because only one of them fails. You're being way

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Speaker 1: too timid. You could never really make a you know,

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Speaker 1: ten X class return if you're not taking not sticking

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Speaker 1: your neck out more than that. So there is a

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Speaker 1: different financial culture. I think it began with Arthur Rock,

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Speaker 1: as I've been saying, I think he just he just

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Speaker 1: had a willingness to back outsiders, and he was very

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Speaker 1: quick and very early to understand you know, the key

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Speaker 1: point that I think the East Coast didn't didn't get

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Speaker 1: and the West Coast did get. And that was precisely

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Speaker 1: the parallel the idea that the way to win in

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Speaker 1: venture capital is not to avoid losses, because startups are

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Speaker 1: intrinsically risky and you will lose money on lots of them.

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Speaker 1: The way to make money is to make sure that

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Speaker 1: when you win, you win. We're really big. This is

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Speaker 1: a home run business. This is not a business where

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Speaker 1: you try to make you a five percent gain, a

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Speaker 1: tempercent gain here and there. This is about swinging for

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Speaker 1: the fences and the best kind of defenses offense. And

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Speaker 1: Arthur Rock would would say this. I mean, I went

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Speaker 1: back and read his speeches that he gave him the

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Speaker 1: early sixties, um and he was pretty clear about saying,

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Speaker 1: you know, it's not about whether I lose on some

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Speaker 1: of my bets. I mean, you can only lose one

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Speaker 1: times your money. What matters if the bets where you make,

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Speaker 1: you know, ten times fifteen times, twenty times what you

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Speaker 1: what you pretend that's the home game? Huh. And the

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Speaker 1: other factor that I thought was really fascinating that I

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Speaker 1: was aware of but didn't realize how important it was.

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Speaker 1: But you do a nice job of explaining this in

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Speaker 1: the book. California does not allow non compete agreements for

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Speaker 1: corporations relative to their employees. If you want to quit

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Speaker 1: McDonald's and walk across the street to Burger King, the

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Speaker 1: lord doesn't prevent you from doing that. That was a

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Speaker 1: very different setup then a lot of other states, especially

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Speaker 1: back east, had to tell us what the lack or

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Speaker 1: the illegality of non compete did to the culture in

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Speaker 1: Silicon Valley. When I think a key insight about how

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Speaker 1: innovation happens and why some innovation clusters are more productive

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Speaker 1: and creative than others is that you've got to You've

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Speaker 1: got to circulate people inside the cluster. It's all about

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Speaker 1: you know, you've got You've got a certain amount of

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Speaker 1: human talent, engineers, marketing executives, people who know it to

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Speaker 1: make startups work. And these people are conducting experiments. Each

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Speaker 1: each startup is an experiment, and each is a long

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Speaker 1: shot experiment because the majority you are going to sail.

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Speaker 1: And so the whole game here is that that network,

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Speaker 1: that ecosystem needs to circulate talent rapidly UM in order

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Speaker 1: to move the people into the right places where they

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Speaker 1: can be that a talent can be best put to use. UM.

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Speaker 1: And if you've got a start up and it's raised

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Speaker 1: some capital normally, um, you know the capital is enough

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Speaker 1: runway to last say six months, nine months, and then

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Speaker 1: you identify the talent you want to hire with that money.

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Speaker 1: If you had to wait for six months because of

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Speaker 1: some non compete agreement before that person joins your startup,

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Speaker 1: well then you run out of runway before they even

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Speaker 1: get that. And so the ability to hire people and

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Speaker 1: have them moving quickly is key. And UM, that's what

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Speaker 1: California law makes easier because you cannot enforce none competes

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Speaker 1: very easy easily in California court. And that's different to

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Speaker 1: no states in the US. And to put this into

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Speaker 1: context about how easy it was to set up a

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Speaker 1: company and move forward, Bud Coyle, when when the traitorous

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Speaker 1: eight were ready to leave and set up Fair Child,

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Speaker 1: he pulled out ten CRISP one dollar bills and proposed

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Speaker 1: that all eight men should sign each of them and

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Speaker 1: that will was their contract during the early days of

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Speaker 1: liberation capital. Was it really that simple, here, all of us,

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Speaker 1: let's sign a dollar bill and that will loosely be

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Speaker 1: our our agreement. I mean the bud Cone after Rock's

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Speaker 1: partner on the fair Child financing and when you're right,

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Speaker 1: when he and Rock reached the agreement with the eight

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Speaker 1: fair Child scientists, they all signed dollar bill, and of

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Speaker 1: course it was symbolic, right, this is not a real contract,

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Speaker 1: But I thought it was a pretty vivid signal, right,

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Speaker 1: because it's partly about the informality of venture contracts that

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Speaker 1: although I think they had another contract, a real contract

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Speaker 1: which was drawn up a bit later, in terms of

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Speaker 1: kind of the blood bond between them all, you know,

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Speaker 1: signing that dollar bill was was the sign that they

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Speaker 1: were all in. And so it's partly the informality and

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Speaker 1: partly the way that fundamentally all of the invention and

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Speaker 1: entrepreneurship in the valley is founded on the financing that

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Speaker 1: underwrites the risk. So the fact that you know, what

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Speaker 1: they signed was money struck me as quite a vivid

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Speaker 1: symbol of how Cilicensanni got going. Huh, quite quite fascinating.

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Speaker 1: So let's talk a little bit exactly what power laws are.

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Speaker 1: Most of us are familiar with the Bell curve or

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Speaker 1: more traditional Gaussian distribution that are kind of evenly spread out.

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Speaker 1: It's a nice smooth distribution. Power laws are not like that.

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Speaker 1: Could you explain to us what exactly our power laws

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Speaker 1: relative to what we're usually used to, Right, so, whether

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Speaker 1: Bell curve or normal distribution, nearly all the observations are

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Speaker 1: close to the average. So and a good example is,

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Speaker 1: you know, the average American man is five ft turn

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Speaker 1: inches tool and two thirds of American men are within

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Speaker 1: three inches of that. So you know there are some

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Speaker 1: basketball players there are way more or whatever, but it's

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Speaker 1: it's rare. And stock market returns are another example of

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Speaker 1: something which isn't perfectly normal, but it's kind of approximately close,

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Speaker 1: and really wild market swings happen, and that's why we

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Speaker 1: have crashes, but they're actually statistically pretty unusual. You know,

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Speaker 1: most of the time the market is just oscillating a

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Speaker 1: little bit from day to day. But some things in

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Speaker 1: life absolutely do not follow anything like that. Normal distributions.

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Speaker 1: For example, whereas the height of people is a normal distribution,

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Speaker 1: the wealth of people is a power law distribution, meaning, um,

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Speaker 1: you know, some people will be just massively richer than

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Speaker 1: the average, and we'll pull the average up um or

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Speaker 1: take academic citations, some small fraction of academic papers capture

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Speaker 1: the lion's share of all the sites. And these skewed

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Speaker 1: distributions are called power law distributions. And that's what you

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Speaker 1: get with with venture capital and startups. Most startups fail

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Speaker 1: and the investors return is zero. They lose all their money.

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Speaker 1: UM A few like maybe depending exctly on on you know,

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Speaker 1: which period of time you're looking at, how strong the

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Speaker 1: tech market is and what have you. But if you

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Speaker 1: are gonna just take off into the stratosphere and have

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Speaker 1: this exponential rights and so that that minority, it's a

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Speaker 1: bit like if you think about the cinema, the the

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Speaker 1: analogy of the cinema, and you know, the tallest guy

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Speaker 1: walks out, it's not going to change the average height

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Speaker 1: in the cinema very much. But if you're talking about

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Speaker 1: the wealth of the people in the cinema and Jeff

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Speaker 1: Bevels is in the cinema and he walks out, it's

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Speaker 1: going to radically change the average. And and that's what

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Speaker 1: you've got you're you're looking at with. With venture capital,

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Speaker 1: there's a few absolutely start come benese um which dominate

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Speaker 1: the returns that venture capitalists are going to earn. And

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Speaker 1: once you understand that, it means as a venture capitalist

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Speaker 1: you can't just invest by going for a modest return

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Speaker 1: while protecting your downside. The whole game is to get

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Speaker 1: a piece of the exponential winners. Venture capital is a

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Speaker 1: game of grand slams um and I think that power

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Speaker 1: lew is so central to the way that venture capitalists

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Speaker 1: have to think that that's why I took it as

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Speaker 1: my title. It's a parallel. So so to put some

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Speaker 1: numbers on this venture capital firm, Horseley Bridge ran an

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Speaker 1: analysis over the investments they made over the course of

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Speaker 1: it looks like thirty years into seven thousand startups that

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Speaker 1: they backed, and it turned out that only five of

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Speaker 1: those those startups generated of the returns over the total funds,

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Speaker 1: and some other people have said it's even more lopsided.

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Speaker 1: Peter Tiel pointed out the biggest secret and venture capital

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Speaker 1: is that the best investment in a successful fund usually

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Speaker 1: equals or outperforms the entire rest of the fund. So

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Speaker 1: so that sounds like that is really very skewed compared

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Speaker 1: to what we typically think of, at least in a

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Speaker 1: diversified portfolio. Yeah, I mean the whole idea of diversification

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Speaker 1: that something that basically got thrown out of the window

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Speaker 1: when venture capital was invented. UM. You know, if you

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Speaker 1: think about the normal idea, you make a lot of bets,

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Speaker 1: you try to diversify, UM, you're thinking about your risk

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Speaker 1: return balance. UM. That kind of public market mentality is

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Speaker 1: totally alien to venture capital investing, where you're making concentrated,

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Speaker 1: all liquid bets in actual companies that you can't exit UM,

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Speaker 1: and they're either gonna do incredibly well and take off

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Speaker 1: or they're gonna you know, run into the ground, UM

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Speaker 1: and so. And you know, they're all in tech. It's

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Speaker 1: not avert side. And in fact, a lot of venture

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Speaker 1: capitalists specialized personally in some subsection of tech. You know,

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Speaker 1: they're they're fast dcs or they are um, you know,

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Speaker 1: med tech, medical technology vcs or whatever it is. So

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Speaker 1: they are completely the opposite of diversified UM. And it's

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Speaker 1: kind of like, you know, it's all in boots, boots

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Speaker 1: on the ground, no, no no hedging at all. UM.

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Speaker 1: And in a way. You know, That's what's partly what

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Speaker 1: sort of attracted me to to writing about venture capital

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Speaker 1: is just so different to public market investing UM in

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Speaker 1: in many ways, but that's one of them. So I

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Speaker 1: really like the way the various ages of venture capital

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Speaker 1: are elucided in the book. You started with Liberation Capital.

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Speaker 1: Let's talk a little bit about the next phase of

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Speaker 1: of venture investing, hands on activism and stage by stage finance.

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Speaker 1: Let's let's discuss each of these, right, So, after Arthur

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Speaker 1: Rock established the idea of Liberation Capital, the next phase

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Speaker 1: is the seventies, and this was marked by the founding

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Speaker 1: of two famous partnerships, both in Seka Capital and China Perkins.

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Speaker 1: And as you say, the first innovation that these guys

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Speaker 1: brought was really to be hands on, to be to

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Speaker 1: roll your sleeves up and get involved in the shaping

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Speaker 1: of the company. Um And one of the Coya's first

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Speaker 1: investments was in the pioneering video game maker Atari. They

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Speaker 1: had a game called Pong. It was pretty simple. You

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Speaker 1: pappled the you moved paddle up and down and and

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Speaker 1: you tried to kind of you know, hit the hit

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Speaker 1: that little dot on the screen that was coming towards

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Speaker 1: the paddle. Uh. And I think the instructions were basically

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Speaker 1: one line, avoid missing ball for high school. So you

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Speaker 1: could put this game in a bar and didn't matter

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Speaker 1: how drunk you were, you could still play. And and

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Speaker 1: so Dog Valentine, the founder of the Choir, backed Attari

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Speaker 1: because the games were popular and they were selling. But

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Speaker 1: at the same time, Attari as a company was an

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Speaker 1: absolute managerial disaster. I mean there were no financial controls.

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Speaker 1: The board meetings were held in a hot tub, and

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Speaker 1: it was you know, people would get paid travel expensive

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Speaker 1: before they traveled, and they would just make off with

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Speaker 1: the money and never show up again. Um. You know,

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Speaker 1: on Friday afternoons, people would race to the car park

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Speaker 1: to jump in their cars to get to the bank

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Speaker 1: and cash their paycheck because whoever didn't move fast enough

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Speaker 1: to find there was no money to collect any money

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Speaker 1: and no money left in the bank account. Um. So

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Speaker 1: you know, most investors would have looked at this mess.

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Speaker 1: They would have visited the factory and inhaled the marijuana

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Speaker 1: smith of heavy in the air and they would have said, hey,

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Speaker 1: and I can't do this. But Don Valentine, the founder

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Speaker 1: of Sequia, was not intimidated when they said the board

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Speaker 1: meeting will now take place in the hot tub, he

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Speaker 1: just took his clothes off and got right into that

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Speaker 1: hot tub. By the way, he was a former Navy

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Speaker 1: water polo player, so this business of showing off his

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Speaker 1: chest actually probably worked in his favor. And because of

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Speaker 1: his physical and intellectual force of character, he basically beat

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Speaker 1: the Atari guys over the head until they had a

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Speaker 1: company that actually did function, and it got to the

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Speaker 1: point where it was functional enough for a serious company,

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Speaker 1: Warner Brothers to buy it, and Sakia got out with

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Speaker 1: a great profit. So the point here is this is,

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Speaker 1: you know, this is not for the faint of heart.

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00:27:00,160 --> 00:27:04,760
Speaker 1: This is you know, you see the glimmer of genius

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Speaker 1: in a creative startup that has got a good team

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Speaker 1: of engineers who are building, um, you know, pioneering video games.

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Speaker 1: You say, I can make something of that, even though

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Speaker 1: the rest of the company is a totally chaotic mess UM.

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Speaker 1: And so that was that, that was their hands on

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Speaker 1: And then the second thing in the seventies UM, which

429
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Speaker 1: is you know, equally important, is the idea of investing

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Speaker 1: stage by stage, you know, putting some money in watching

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Speaker 1: the progress and then if there is progress, you put

432
00:27:39,840 --> 00:27:42,720
Speaker 1: some more money in. And the best example here was

433
00:27:42,760 --> 00:27:47,320
Speaker 1: probably the company Genentech, the first biotech company which created

434
00:27:47,400 --> 00:27:53,280
Speaker 1: artificial incidents. And when the Genetech founders tried to raise money,

435
00:27:53,320 --> 00:27:57,120
Speaker 1: they went to Tom Perkins, the co founder of Kline Perkins,

436
00:27:57,680 --> 00:28:01,280
Speaker 1: and they asked for half a million dollars to hire scientists,

437
00:28:01,280 --> 00:28:03,919
Speaker 1: you know, set up a lab and get close to

438
00:28:03,960 --> 00:28:07,080
Speaker 1: a first product. And Tom Perkins looked at this and

439
00:28:07,119 --> 00:28:10,720
Speaker 1: he thought, well, look, you know, making the first ever

440
00:28:11,560 --> 00:28:16,120
Speaker 1: artificial incident, that is a serious technical challenge, and it's

441
00:28:16,240 --> 00:28:19,240
Speaker 1: just too much for me to risk half a million

442
00:28:19,240 --> 00:28:24,000
Speaker 1: dollars on something which is serious frontier technology. So instead

443
00:28:24,000 --> 00:28:26,720
Speaker 1: of betting you half a million, which would have been

444
00:28:26,880 --> 00:28:31,000
Speaker 1: painful to lose, he instead invested a hundred thousand bucks

445
00:28:31,119 --> 00:28:34,639
Speaker 1: and told Gnantech to use it to eliminate what he

446
00:28:34,720 --> 00:28:38,520
Speaker 1: called the white hot risks, so, in other words, the

447
00:28:38,560 --> 00:28:41,000
Speaker 1: most obvious things that could just kill the whole idea

448
00:28:41,040 --> 00:28:44,600
Speaker 1: of debt. And if they could get past the white

449
00:28:44,640 --> 00:28:48,480
Speaker 1: hot risks with just a hundred thousands, then he would

450
00:28:48,480 --> 00:28:51,800
Speaker 1: give them some more money and they could go to

451
00:28:51,840 --> 00:28:55,000
Speaker 1: the next set of risks. And that way, if gnantech

452
00:28:55,200 --> 00:28:59,280
Speaker 1: was to sail at least it would fail cheaply, and

453
00:28:59,360 --> 00:29:03,200
Speaker 1: that idea stage by stage financing turned a company that

454
00:29:03,240 --> 00:29:06,000
Speaker 1: would have just been too risky and expensive to bet

455
00:29:06,040 --> 00:29:09,840
Speaker 1: money on into something that actually became a very attractive investment.

456
00:29:10,360 --> 00:29:14,200
Speaker 1: And today we would think of that really is angel

457
00:29:14,320 --> 00:29:16,920
Speaker 1: and then seed and then a round, B, round C

458
00:29:17,160 --> 00:29:21,480
Speaker 1: round that they were inventing the playbook as they went.

459
00:29:22,040 --> 00:29:25,800
Speaker 1: It didn't exist the way it does today. Let's stay

460
00:29:25,840 --> 00:29:29,720
Speaker 1: with the concept of these new developments and talk a

461
00:29:29,760 --> 00:29:34,320
Speaker 1: little bit about the network effect what took place in

462
00:29:34,400 --> 00:29:39,640
Speaker 1: Silicon Valley as they progressed to create a network that

463
00:29:39,760 --> 00:29:43,440
Speaker 1: impacted the entire region. Right, So if we if we

464
00:29:43,480 --> 00:29:46,440
Speaker 1: think about that, the ark of the history um you know,

465
00:29:46,520 --> 00:29:49,120
Speaker 1: the the late fifties and sixties is about the idea

466
00:29:49,160 --> 00:29:52,600
Speaker 1: of liberation capital as we discussed the kind of first

467
00:29:52,600 --> 00:29:56,600
Speaker 1: half of the seventies is about proving these ideas of

468
00:29:57,000 --> 00:30:01,200
Speaker 1: UM hands on investing and stay ah by stage financing.

469
00:30:01,640 --> 00:30:04,560
Speaker 1: And then the next thing that happens is you've got

470
00:30:04,560 --> 00:30:07,600
Speaker 1: the basic tool of the basic venture capital tool kit

471
00:30:08,440 --> 00:30:12,720
Speaker 1: and you layer on top of that an explosion in

472
00:30:12,760 --> 00:30:16,320
Speaker 1: the number of centure capitalists who are out there using

473
00:30:16,360 --> 00:30:20,240
Speaker 1: these tools. Um and what happened is that, you know,

474
00:30:20,320 --> 00:30:25,640
Speaker 1: there were a couple of tax changes and regulatory changes

475
00:30:25,680 --> 00:30:29,480
Speaker 1: about which kinds of institution could put money into into

476
00:30:29,560 --> 00:30:36,600
Speaker 1: venture capital, and suddenly fundraising by these these went up massively.

477
00:30:36,720 --> 00:30:39,800
Speaker 1: So you know, the average in the mid seventies of

478
00:30:39,840 --> 00:30:43,120
Speaker 1: like forty two million dollars a year. Between seventy eight

479
00:30:43,120 --> 00:30:46,360
Speaker 1: and eight three, it was nine hundred and forty million

480
00:30:46,560 --> 00:30:50,400
Speaker 1: a year, so an enormous increase in the amount of money.

481
00:30:50,880 --> 00:30:52,880
Speaker 1: And that meant that one of a sudden, there are

482
00:30:52,960 --> 00:30:56,280
Speaker 1: enough centure capitalists running around Silicon Valley that they fundamentally

483
00:30:56,520 --> 00:31:01,400
Speaker 1: changed the business culture. Everything speeds up. Startups are getting

484
00:31:01,480 --> 00:31:05,320
Speaker 1: formed faster, there are more of them, more new technologies

485
00:31:05,360 --> 00:31:08,960
Speaker 1: are getting built, human talent is circulating from one startup

486
00:31:09,000 --> 00:31:11,720
Speaker 1: to another one at a higher rate, and all of

487
00:31:11,760 --> 00:31:15,600
Speaker 1: that creates this flywheel where Cilicon Family becomes just the

488
00:31:15,640 --> 00:31:20,560
Speaker 1: most productive and creative and inventive innovation cluster in the

489
00:31:20,560 --> 00:31:24,360
Speaker 1: world thanks to you. Just it's great to have a

490
00:31:24,360 --> 00:31:28,480
Speaker 1: few small venture capitalists using the basic tools, but when

491
00:31:28,520 --> 00:31:31,360
Speaker 1: you have a lot of them all running around at

492
00:31:31,360 --> 00:31:35,400
Speaker 1: the same time, it's more than just a few deals.

493
00:31:35,440 --> 00:31:40,080
Speaker 1: It's a whole culture of taking risk, having the guts

494
00:31:40,120 --> 00:31:42,560
Speaker 1: to start a new company. All of that becomes enabled

495
00:31:43,400 --> 00:31:49,400
Speaker 1: by venture capital. So there's a fascinating tale about how

496
00:31:49,560 --> 00:31:54,520
Speaker 1: some companies that seem to have a hard time getting

497
00:31:54,640 --> 00:32:00,760
Speaker 1: funded instead get passed from venture capitalist events a capitalist.

498
00:32:01,400 --> 00:32:05,800
Speaker 1: Rather than just say no, it seems there's this tendency

499
00:32:05,840 --> 00:32:09,240
Speaker 1: to say, I know somebody who you might be better

500
00:32:09,240 --> 00:32:12,160
Speaker 1: suited to speak to than me. Tell us a little

501
00:32:12,160 --> 00:32:16,240
Speaker 1: bit about that network effect and why it makes Silicon

502
00:32:16,320 --> 00:32:21,000
Speaker 1: valleys such an economic powerhouse. Sure, well, I think it

503
00:32:21,120 --> 00:32:23,840
Speaker 1: comes back to this idea. I just hinted that a

504
00:32:23,880 --> 00:32:28,200
Speaker 1: bit earlier as we were talking about how the key

505
00:32:28,280 --> 00:32:32,320
Speaker 1: to innovative experiments is to have the right people there

506
00:32:32,360 --> 00:32:35,960
Speaker 1: to to conduct them, and so moving people around a

507
00:32:36,120 --> 00:32:39,320
Speaker 1: cluster is super important. This is I mean just a

508
00:32:39,320 --> 00:32:42,479
Speaker 1: little digression here, but Um, one of the things I

509
00:32:42,560 --> 00:32:44,760
Speaker 1: was puzzling over as I was working on this book

510
00:32:46,240 --> 00:32:49,720
Speaker 1: is that you know, in the economics literature, which I

511
00:32:49,800 --> 00:32:53,920
Speaker 1: was familiar with, UM, when you when you wrote about

512
00:32:53,920 --> 00:32:57,400
Speaker 1: a clusters, I mean, when economists talked about clusters, they

513
00:32:57,400 --> 00:33:01,480
Speaker 1: are talking about you know, if you put everybody in

514
00:33:01,480 --> 00:33:04,320
Speaker 1: the same place who does movies in Hollywood or finance

515
00:33:04,400 --> 00:33:06,920
Speaker 1: in New York or what have you. This is good

516
00:33:07,000 --> 00:33:10,600
Speaker 1: because you know, if you want a particular special effects

517
00:33:10,920 --> 00:33:15,440
Speaker 1: um actor, you can find them in Hollywood, because it's

518
00:33:15,440 --> 00:33:18,360
Speaker 1: just like exactly the kind of person who jumps out

519
00:33:18,360 --> 00:33:21,320
Speaker 1: of a four story window and does a certain kind

520
00:33:21,360 --> 00:33:23,760
Speaker 1: of you know, somesault on the way down or whatever.

521
00:33:24,280 --> 00:33:27,720
Speaker 1: Whatever specialty you need in a in a deep labor

522
00:33:27,760 --> 00:33:31,920
Speaker 1: market which will be provided by a cluster, you can

523
00:33:31,920 --> 00:33:35,040
Speaker 1: find it. And so it's this kind of optimal matching

524
00:33:35,240 --> 00:33:40,280
Speaker 1: of skills to the needs, which is why clusters work.

525
00:33:41,040 --> 00:33:43,560
Speaker 1: And that's all very well and quite persuasive, but it

526
00:33:43,560 --> 00:33:47,520
Speaker 1: doesn't tell you why if you have got two clusters

527
00:33:47,520 --> 00:33:50,200
Speaker 1: that have the same number of people in each, why

528
00:33:50,240 --> 00:33:53,680
Speaker 1: would one cluster do better than the other cluster. And

529
00:33:53,720 --> 00:33:59,360
Speaker 1: that's pretty much what was going on around when you

530
00:33:59,440 --> 00:34:05,200
Speaker 1: compared Silicon Valley to the Boston Tech cluster. There was

531
00:34:05,240 --> 00:34:07,680
Speaker 1: this route one thing. It grew out of the military

532
00:34:07,680 --> 00:34:11,120
Speaker 1: industrial complex. There had been these companies like Raycion and

533
00:34:11,239 --> 00:34:14,600
Speaker 1: Deck and and Wang and and so on, and so

534
00:34:14,760 --> 00:34:18,759
Speaker 1: there were these two rival tech centers in the US,

535
00:34:18,840 --> 00:34:24,720
Speaker 1: and Silicon Valley during the pulled ahead and absolutely crushed Boston.

536
00:34:25,480 --> 00:34:28,960
Speaker 1: Why was that? And the best explanation I could find

537
00:34:29,800 --> 00:34:35,239
Speaker 1: was from a sociologist, not an economist, at Berkeley called

538
00:34:35,280 --> 00:34:37,920
Speaker 1: analy Saxony and who wrote a book called Regional Advantage,

539
00:34:38,600 --> 00:34:41,680
Speaker 1: where had a story which I find completely persuasive, is

540
00:34:41,680 --> 00:34:47,840
Speaker 1: basically that you know, they were vertically integrated, hierarchical, secretive

541
00:34:47,920 --> 00:34:52,760
Speaker 1: companies around Boston, and if somebody in a Boston company

542
00:34:52,880 --> 00:34:56,920
Speaker 1: like Deck or Wang or whatever had a brilliant new

543
00:34:56,960 --> 00:35:01,080
Speaker 1: idea and the boss didn't like it, the idea was dead.

544
00:35:01,120 --> 00:35:03,799
Speaker 1: The engineer was not allowed to pursue that idea, and

545
00:35:03,840 --> 00:35:06,560
Speaker 1: the idea would not be leaked to a rival company

546
00:35:06,600 --> 00:35:09,680
Speaker 1: because everybody was secretive and there was no cross pollination

547
00:35:09,719 --> 00:35:14,920
Speaker 1: between these companies, whereas in Silicon Valley there was this

548
00:35:15,080 --> 00:35:18,960
Speaker 1: bubbling cauldron of startups and you know, people would go

549
00:35:19,000 --> 00:35:22,160
Speaker 1: to the there was this you know, dina kind of

550
00:35:22,239 --> 00:35:25,920
Speaker 1: bar players called Walker's Wagon Wheel, and all the engineers

551
00:35:25,920 --> 00:35:28,680
Speaker 1: would meet there after work and they would trade ideas

552
00:35:28,719 --> 00:35:31,080
Speaker 1: about stuff they were working on. Nobody cared about trade

553
00:35:31,080 --> 00:35:35,759
Speaker 1: secrets um and that meant that you had this circulation

554
00:35:35,800 --> 00:35:38,920
Speaker 1: of ideas going on. And as we've discussed, there were

555
00:35:38,960 --> 00:35:42,240
Speaker 1: no noncomplete so you could also move from one company

556
00:35:42,239 --> 00:35:46,960
Speaker 1: to another. And so the point is, whereas ideas were

557
00:35:46,960 --> 00:35:49,719
Speaker 1: sort of bottled up in these secrets hierarchies. In one

558
00:35:49,760 --> 00:35:55,080
Speaker 1: cluster Boston, ideas were circulating, and so we're people circulating

559
00:35:55,800 --> 00:35:59,880
Speaker 1: in the other cluster, Cilicon Valley. That's why Cilicon Valley one.

560
00:36:00,640 --> 00:36:04,480
Speaker 1: And what I'm trying to add with my book um

561
00:36:05,040 --> 00:36:08,359
Speaker 1: is to put on top of that good work by

562
00:36:08,400 --> 00:36:12,759
Speaker 1: Unily Saxonian an additional idea, which is to say, okay,

563
00:36:12,800 --> 00:36:17,200
Speaker 1: So it was the circulation within the cluster, the fast

564
00:36:17,880 --> 00:36:21,480
Speaker 1: moving of ideas, people and money until they reached their

565
00:36:21,560 --> 00:36:24,880
Speaker 1: optimal use. That's what made Silicon Valley work. That's what

566
00:36:24,960 --> 00:36:30,200
Speaker 1: made innovation turbo charged. But where did that fast circulation

567
00:36:30,280 --> 00:36:34,480
Speaker 1: come from? And my argument is it comes from centric

568
00:36:34,520 --> 00:36:38,799
Speaker 1: capitalists venture capitalist of the people who are financially incentivized

569
00:36:38,840 --> 00:36:40,600
Speaker 1: to get up in the morning, have breakfast with one

570
00:36:40,640 --> 00:36:43,759
Speaker 1: person who is an entrepreneur that they might fund, and

571
00:36:43,800 --> 00:36:46,000
Speaker 1: then have fourteen cups of coffee before they get to

572
00:36:46,040 --> 00:36:49,000
Speaker 1: bed with different people because either it's another deal they're

573
00:36:49,040 --> 00:36:51,960
Speaker 1: trying to do, or it is a meeting with somebody

574
00:36:52,000 --> 00:36:54,160
Speaker 1: that they funded last year and now they need from

575
00:36:54,200 --> 00:36:57,719
Speaker 1: advice or it's a company that you know, needs to

576
00:36:57,800 --> 00:36:59,920
Speaker 1: have five more engineers, and so they're going to interview

577
00:36:59,920 --> 00:37:02,600
Speaker 1: the is the VC is going to interview the engineers.

578
00:37:02,760 --> 00:37:05,880
Speaker 1: Dcs are like the flowers flying around the garden pollinating

579
00:37:05,880 --> 00:37:08,960
Speaker 1: the flowers moving or the bees, I guess, moving the

580
00:37:09,480 --> 00:37:12,719
Speaker 1: problems from one flower or another. And and that's what

581
00:37:12,880 --> 00:37:16,960
Speaker 1: connects up the cluster, the the network. And that's sort

582
00:37:17,000 --> 00:37:21,000
Speaker 1: of just super important for getting all the limited resources

583
00:37:21,040 --> 00:37:24,160
Speaker 1: of people and ideas and money into the right mixtures

584
00:37:24,760 --> 00:37:29,399
Speaker 1: to create really fertile experiments that make the value work.

585
00:37:29,440 --> 00:37:33,040
Speaker 1: And so I think, you know, you know, I'm not

586
00:37:33,040 --> 00:37:34,759
Speaker 1: sure I've given you quite the answer you wanted, but

587
00:37:34,920 --> 00:37:39,400
Speaker 1: in a general way, the key thing about venture capital

588
00:37:39,440 --> 00:37:43,600
Speaker 1: networks is that they connect up networks and they transform

589
00:37:43,680 --> 00:37:48,040
Speaker 1: their productivity really interesting. Let's let's talk about two other

590
00:37:48,160 --> 00:37:54,480
Speaker 1: developments in the venture world, speed and size. And let's

591
00:37:54,480 --> 00:37:59,479
Speaker 1: start with size talking about soft Bank, when when they

592
00:37:59,600 --> 00:38:03,480
Speaker 1: came to California from Japan, their approach was, we have

593
00:38:03,640 --> 00:38:06,520
Speaker 1: very deep pockets, and we want to give you not

594
00:38:06,719 --> 00:38:09,440
Speaker 1: just a few hundred thousand dollars or a few million dollars,

595
00:38:09,800 --> 00:38:12,080
Speaker 1: but here's a hundred million dollars and if you don't

596
00:38:12,120 --> 00:38:14,719
Speaker 1: take our money, we're going to go to your competitor

597
00:38:14,800 --> 00:38:17,600
Speaker 1: and offer them a hundred million dollars. There's only room

598
00:38:17,640 --> 00:38:20,000
Speaker 1: in the space for one of you. And whoever takes

599
00:38:20,000 --> 00:38:23,440
Speaker 1: our money wins. Tell us a little bit about the

600
00:38:23,480 --> 00:38:28,000
Speaker 1: impact and advantage of size, right, So that's a story

601
00:38:28,040 --> 00:38:32,040
Speaker 1: you're you're you're alluding to of the financing of Yahoo

602
00:38:33,080 --> 00:38:36,680
Speaker 1: when mass Son came and made exactly that proposal. Basically,

603
00:38:37,320 --> 00:38:41,399
Speaker 1: you know, you said to Jerry Yango Yahoo, Um, I'll

604
00:38:41,400 --> 00:38:43,080
Speaker 1: write you a check for a hundred million. And when

605
00:38:43,160 --> 00:38:45,040
Speaker 1: Jerry Yangford I don't want it, I don't need it.

606
00:38:45,520 --> 00:38:48,279
Speaker 1: He said, Jerry, everybody needs a hundred million and if

607
00:38:48,280 --> 00:38:51,799
Speaker 1: you don't take it, our financial competitor um. And what

608
00:38:52,000 --> 00:38:54,560
Speaker 1: was sort of you know, the significance of that moment

609
00:38:54,719 --> 00:38:59,799
Speaker 1: was partly that the VC who had funded Yahoo in

610
00:38:59,840 --> 00:39:04,280
Speaker 1: the Series A round was Michael Morrett's of Sequoia Capital,

611
00:39:04,320 --> 00:39:08,200
Speaker 1: who was just done emerging as sort of the leader

612
00:39:08,840 --> 00:39:14,320
Speaker 1: of Sequoia along with Doug Leone, his partner, and Morett

613
00:39:14,480 --> 00:39:19,120
Speaker 1: took away from that experience an absolutely firm determination that

614
00:39:20,400 --> 00:39:24,440
Speaker 1: he wouldn't be muscled again. He wouldn't allow somebody to

615
00:39:24,480 --> 00:39:26,759
Speaker 1: come in and say, you know, this is a take

616
00:39:26,800 --> 00:39:28,920
Speaker 1: it or leaving offer. It's an offer you can't refuse,

617
00:39:29,480 --> 00:39:32,560
Speaker 1: you know, Don corleone style. You know he was going

618
00:39:32,600 --> 00:39:36,560
Speaker 1: to avoid that. And that is why Sequoia in the

619
00:39:36,680 --> 00:39:41,320
Speaker 1: late nineties started to try to get its own big

620
00:39:41,400 --> 00:39:45,600
Speaker 1: check writing capability off the ground. In other words, a

621
00:39:45,680 --> 00:39:48,680
Speaker 1: growth fund which wouldn't just be doing as you say,

622
00:39:48,800 --> 00:39:52,440
Speaker 1: five minute ten million checks to Series A, Series B,

623
00:39:53,280 --> 00:39:57,040
Speaker 1: but would be writing much bigger checks series C, series

624
00:39:57,120 --> 00:40:03,200
Speaker 1: D two companies and allowing them to carry on growing

625
00:40:03,239 --> 00:40:07,400
Speaker 1: before going public. Now you can see the logic right that,

626
00:40:07,480 --> 00:40:11,120
Speaker 1: if if one player like Massi's son from soft Bank,

627
00:40:11,480 --> 00:40:15,440
Speaker 1: has that godfather likability, you know, take it or leave

628
00:40:15,480 --> 00:40:19,319
Speaker 1: it um, others are going to want to muscle up

629
00:40:19,360 --> 00:40:22,880
Speaker 1: and get that capability as well. Whether it's good for

630
00:40:22,920 --> 00:40:25,359
Speaker 1: the venture couple system is a different question. I'm not

631
00:40:25,400 --> 00:40:28,200
Speaker 1: sure it is, because I think that at a certain point,

632
00:40:28,239 --> 00:40:35,160
Speaker 1: going public brings transparency to tech companies, and that can

633
00:40:35,200 --> 00:40:38,319
Speaker 1: be healthy. I didn't think that staying private for too

634
00:40:38,360 --> 00:40:43,040
Speaker 1: long is necessarily the best way to govern tech companies.

635
00:40:44,000 --> 00:40:47,880
Speaker 1: All right, So that's the size discussion. Let's talk about speed,

636
00:40:48,680 --> 00:40:52,440
Speaker 1: uh and and in particular Tiger Global, who seems to

637
00:40:52,480 --> 00:40:57,160
Speaker 1: be investing at a record pace and and forcing the

638
00:40:57,320 --> 00:41:01,840
Speaker 1: rest of the VC industry to to keep up. Is

639
00:41:01,880 --> 00:41:04,719
Speaker 1: this a smart way to make investments and what are

640
00:41:04,719 --> 00:41:11,080
Speaker 1: the ramifications of this emphasis on speed? Yeah, great question.

641
00:41:11,120 --> 00:41:14,320
Speaker 1: I mean I I spent some time with Tiger Global

642
00:41:14,400 --> 00:41:17,799
Speaker 1: when I was doing the research, and I and I

643
00:41:17,840 --> 00:41:20,960
Speaker 1: talked to the two leaders chose Coleman and scotch Life

644
00:41:20,960 --> 00:41:24,279
Speaker 1: for quite quite a bit, and I was spect him

645
00:41:24,280 --> 00:41:26,560
Speaker 1: a lot, and I you know, they're very smart investors

646
00:41:26,560 --> 00:41:29,239
Speaker 1: and they've built an amazing company. And I think the

647
00:41:29,400 --> 00:41:32,080
Speaker 1: critics outside to say, you know, this is just purely

648
00:41:32,080 --> 00:41:35,759
Speaker 1: training money at the wall are exaggerating because I think,

649
00:41:36,200 --> 00:41:39,960
Speaker 1: you know, these guys are smarter than that. But um,

650
00:41:40,000 --> 00:41:43,160
Speaker 1: I actually don't think that what they're doing is particularly

651
00:41:43,200 --> 00:41:49,040
Speaker 1: healthy for the technology ecosystem. I think, you know, it's

652
00:41:49,120 --> 00:41:53,600
Speaker 1: better when capital, you know, is a bit tougher to raise.

653
00:41:53,960 --> 00:41:57,400
Speaker 1: Investors cannot be taken for granted, and if you want money,

654
00:41:58,080 --> 00:42:03,400
Speaker 1: you need to be you know, transparent, responsible and have

655
00:42:03,480 --> 00:42:07,280
Speaker 1: a convincing plan about how you're going to use the money.

656
00:42:07,480 --> 00:42:11,160
Speaker 1: And I think Tiger probably does a much better job

657
00:42:11,160 --> 00:42:16,480
Speaker 1: than most at being able to combine some sense of

658
00:42:16,560 --> 00:42:20,160
Speaker 1: what they're investing in speed right, because they've got a

659
00:42:20,160 --> 00:42:25,440
Speaker 1: whole machine which has figured out which kind of which

660
00:42:25,440 --> 00:42:28,560
Speaker 1: which sort of segments of the tech space they believe

661
00:42:28,560 --> 00:42:31,359
Speaker 1: they're going to do well. Who are the market leaders

662
00:42:31,400 --> 00:42:35,279
Speaker 1: in those spaces? I mean they do almost by a matrix, right,

663
00:42:35,320 --> 00:42:37,560
Speaker 1: they have this, you know, here are the here are

664
00:42:37,560 --> 00:42:41,719
Speaker 1: the turn technologies we think are going to thrive. Here

665
00:42:41,760 --> 00:42:44,240
Speaker 1: are the number one and number two players in each space.

666
00:42:44,719 --> 00:42:46,960
Speaker 1: We're going to back the two leaders because we think

667
00:42:47,000 --> 00:42:48,920
Speaker 1: that this is generally a winner takes Also, one of

668
00:42:48,920 --> 00:42:52,200
Speaker 1: the top two is going to win. UM. And if

669
00:42:52,239 --> 00:42:55,600
Speaker 1: you take those boxes, then we don't really need to

670
00:42:55,640 --> 00:42:57,680
Speaker 1: ask any more questions. We know we want to invest

671
00:42:57,719 --> 00:43:00,920
Speaker 1: in you, and we will move incredibly fast, beat the competition,

672
00:43:01,480 --> 00:43:04,920
Speaker 1: and we will not weigh you down. If you're the CEO,

673
00:43:05,080 --> 00:43:08,000
Speaker 1: we don't. We understand you don't want your investor cheering

674
00:43:08,040 --> 00:43:11,239
Speaker 1: up your time because you've got other stuff to do. UM.

675
00:43:11,280 --> 00:43:14,480
Speaker 1: So that's their playbook. It works for them, it's a

676
00:43:14,480 --> 00:43:17,880
Speaker 1: good competitive tool. It probably works for their investors. I

677
00:43:17,880 --> 00:43:20,680
Speaker 1: don't think it's healthy for the for the tech world

678
00:43:20,680 --> 00:43:23,320
Speaker 1: as a whole, because I think you end up forcing

679
00:43:23,360 --> 00:43:26,360
Speaker 1: others to be fast, which means they don't do due diligence,

680
00:43:26,400 --> 00:43:29,560
Speaker 1: which means there's just a kind of a race to

681
00:43:29,600 --> 00:43:33,080
Speaker 1: write checks. And that's not thoughtful, it's not you know,

682
00:43:33,160 --> 00:43:36,759
Speaker 1: discriminating as between good companies and bad companies. And I

683
00:43:36,800 --> 00:43:39,400
Speaker 1: think in the end that just inflates bubbles. And we

684
00:43:39,440 --> 00:43:43,480
Speaker 1: may be feeling that right now. So we already discussed

685
00:43:43,600 --> 00:43:49,080
Speaker 1: power laws, which are the non typical bulk of distribution,

686
00:43:49,200 --> 00:43:54,399
Speaker 1: where it's a tiny percentage of the sample set are

687
00:43:54,440 --> 00:43:59,680
Speaker 1: responsible for the vast majority of the performance. Let's talk

688
00:43:59,719 --> 00:44:03,719
Speaker 1: about some other laws that come up, starting with Moore's law.

689
00:44:03,800 --> 00:44:07,360
Speaker 1: Tell us a little bit about Moore's law. Well, Gordon

690
00:44:07,440 --> 00:44:12,319
Speaker 1: Moore was the one of the founders of fetch Our Semiconductor,

691
00:44:12,400 --> 00:44:15,399
Speaker 1: that company we started by discussing, and then he went

692
00:44:15,440 --> 00:44:17,920
Speaker 1: on to be a co founder of Intel, and he

693
00:44:18,000 --> 00:44:20,480
Speaker 1: made this observation which wasn't really a law, it was

694
00:44:20,520 --> 00:44:24,120
Speaker 1: just an empirical observation about this is how things were working,

695
00:44:24,960 --> 00:44:28,360
Speaker 1: is that you know, semi conductors would double empower every

696
00:44:28,360 --> 00:44:34,360
Speaker 1: two years. And that's sort of one example of something

697
00:44:34,400 --> 00:44:38,759
Speaker 1: which some venture capitalists referred to as tech beta. In

698
00:44:38,760 --> 00:44:43,040
Speaker 1: other words, if you can invest in a company that

699
00:44:43,239 --> 00:44:46,400
Speaker 1: is making something using semiconductors and you know that the

700
00:44:46,400 --> 00:44:50,319
Speaker 1: semi inductor is going to become twice as powerful two

701
00:44:50,440 --> 00:44:55,279
Speaker 1: years from now, you know that whatever you're making is

702
00:44:55,320 --> 00:45:00,560
Speaker 1: going to improve in performance and quality and stability to

703
00:45:00,640 --> 00:45:05,640
Speaker 1: delight consumers just because of Moore's law is is is

704
00:45:05,800 --> 00:45:08,160
Speaker 1: kind of like the wind that you're back, so you

705
00:45:08,200 --> 00:45:10,919
Speaker 1: can invest in things and if you're skating to where

706
00:45:10,920 --> 00:45:14,600
Speaker 1: the part will be um you know, you know that

707
00:45:15,000 --> 00:45:17,200
Speaker 1: you may be not making much of a margin on

708
00:45:17,239 --> 00:45:21,640
Speaker 1: the product today, but in two years time, the component

709
00:45:21,719 --> 00:45:26,640
Speaker 1: in your gadget will be twice as powerful and you

710
00:45:26,640 --> 00:45:28,440
Speaker 1: will be able to either charge more for it or

711
00:45:28,480 --> 00:45:32,799
Speaker 1: maybe you'll you know, use fewer of the semiconductors in

712
00:45:32,800 --> 00:45:35,680
Speaker 1: the gadget because each one is twice as powerful, but

713
00:45:36,040 --> 00:45:40,560
Speaker 1: you'll have that technological change in your favor. And it's

714
00:45:40,600 --> 00:45:43,720
Speaker 1: just you know, that's one of the reasons why venture

715
00:45:43,760 --> 00:45:48,680
Speaker 1: investing can generate these incredible returns of thirty x you know,

716
00:45:48,719 --> 00:45:55,440
Speaker 1: your money, because there is this technological progress driving the

717
00:45:55,719 --> 00:45:59,279
Speaker 1: exponential takeoff of your returns. So if Moore's law is

718
00:45:59,320 --> 00:46:04,040
Speaker 1: the beta is just the background increase in capability let's

719
00:46:04,080 --> 00:46:07,480
Speaker 1: talk about Metcalf's law and the value of networks. Tell

720
00:46:07,520 --> 00:46:12,799
Speaker 1: us about that. So, Bob Metcalfe was an engineer who

721
00:46:12,880 --> 00:46:19,480
Speaker 1: invented the ethernet cable to link up computers to devices,

722
00:46:19,600 --> 00:46:22,200
Speaker 1: or link up computers to each other. And this was

723
00:46:22,239 --> 00:46:25,800
Speaker 1: the start of local area networks, which came before the Internet.

724
00:46:26,440 --> 00:46:28,680
Speaker 1: And he in fact started a company called three com

725
00:46:28,800 --> 00:46:32,640
Speaker 1: to to market his ethernet invention. And that's one of

726
00:46:32,680 --> 00:46:35,680
Speaker 1: the story I had in my book that illustrates very

727
00:46:35,760 --> 00:46:38,640
Speaker 1: nicely the way that you know he he bust his

728
00:46:39,520 --> 00:46:44,360
Speaker 1: proverbial trying to raise money from East Coast venture capitalists

729
00:46:44,360 --> 00:46:46,239
Speaker 1: because he came from Boston and he didn't like the

730
00:46:46,280 --> 00:46:49,000
Speaker 1: West Coast gang. And he ended up coming back with

731
00:46:49,080 --> 00:46:51,680
Speaker 1: his turtle between his legs and raising West Coast venture

732
00:46:51,719 --> 00:46:55,200
Speaker 1: capital because they were the guys who really understood risk

733
00:46:55,280 --> 00:46:59,160
Speaker 1: and you're willing to back him. Um, But he Bob

734
00:46:59,200 --> 00:47:03,600
Speaker 1: Metcalfe had the observation as he was building um ethernet

735
00:47:03,600 --> 00:47:08,120
Speaker 1: cables that created networks of computers, that the value of

736
00:47:08,239 --> 00:47:12,960
Speaker 1: the network would rise as the square of the number

737
00:47:13,000 --> 00:47:18,920
Speaker 1: of users. So um, if you think about um, you know,

738
00:47:19,120 --> 00:47:22,719
Speaker 1: I I've got a computer and I'm linked up to

739
00:47:22,760 --> 00:47:26,480
Speaker 1: one other computer like coworkers computer. Now there are two

740
00:47:26,480 --> 00:47:28,960
Speaker 1: of us on the network. Let's say my value as

741
00:47:28,960 --> 00:47:31,520
Speaker 1: a square of two, it's four. Now, if you put

742
00:47:32,000 --> 00:47:34,439
Speaker 1: two more people into our network, that we've got four

743
00:47:34,440 --> 00:47:38,040
Speaker 1: people we didn't. That's doubling the number of computers on

744
00:47:38,080 --> 00:47:41,960
Speaker 1: the network. But actually the value to me um now

745
00:47:41,960 --> 00:47:44,480
Speaker 1: that I can talk to three other computers and they

746
00:47:44,480 --> 00:47:47,760
Speaker 1: can talk to each other is actually sixteen. It's gone,

747
00:47:47,800 --> 00:47:51,640
Speaker 1: it's squared, It hasn't doubled. And that's a story that

748
00:47:52,000 --> 00:47:55,839
Speaker 1: you know, applies to any kind of network. So when

749
00:47:55,840 --> 00:47:59,239
Speaker 1: you get to the Internet and you're building any kind

750
00:47:59,280 --> 00:48:03,200
Speaker 1: of social media a net company, or or a platform

751
00:48:03,239 --> 00:48:07,240
Speaker 1: like eBay to do auctions or anything that you're building

752
00:48:07,280 --> 00:48:10,360
Speaker 1: on top of the Internet, where you're recruiting more and

753
00:48:10,400 --> 00:48:13,600
Speaker 1: more users, you get these network effects where the more

754
00:48:13,640 --> 00:48:17,000
Speaker 1: people sign up, the more valuable it is to everybody

755
00:48:17,000 --> 00:48:21,000
Speaker 1: else on the network. And it's just an enormous tail wind.

756
00:48:21,000 --> 00:48:24,120
Speaker 1: I mean, it's like More's law, but even more dramatic.

757
00:48:24,160 --> 00:48:26,840
Speaker 1: And of course, the key thing here is that it

758
00:48:26,960 --> 00:48:31,520
Speaker 1: wasn't an either or for venture capitalists who are backing

759
00:48:32,320 --> 00:48:35,960
Speaker 1: companies like eBay. This was both, and you know, you

760
00:48:36,040 --> 00:48:39,720
Speaker 1: had the advantages More's law, which meant that the hardware

761
00:48:39,800 --> 00:48:42,959
Speaker 1: that you were using was becoming twice as powerful every

762
00:48:42,960 --> 00:48:45,440
Speaker 1: a couple of years. And you had the power of

763
00:48:45,520 --> 00:48:47,920
Speaker 1: metcast law, which said that as you grew the network,

764
00:48:48,880 --> 00:48:52,200
Speaker 1: the value of the network was rising as the square

765
00:48:52,200 --> 00:48:54,880
Speaker 1: of the number of people you recruited. And so these,

766
00:48:55,080 --> 00:48:58,200
Speaker 1: you know, I called this sort of turbo power law. Companies,

767
00:48:58,640 --> 00:49:02,200
Speaker 1: companies like eBay that just did extraordinarily well in the

768
00:49:02,280 --> 00:49:07,080
Speaker 1: nineties and made enormous amounts of money for Benchmark, which

769
00:49:07,120 --> 00:49:10,640
Speaker 1: was the VC partnership that backed eBay. And one of

770
00:49:10,680 --> 00:49:14,839
Speaker 1: the laws we didn't talk about is Perkins law. Tell

771
00:49:14,920 --> 00:49:18,120
Speaker 1: us about I believe that's the Perkin of Kleiner Perkins.

772
00:49:18,320 --> 00:49:21,880
Speaker 1: What is Perkins law? Yes, so the co founder of

773
00:49:21,960 --> 00:49:27,120
Speaker 1: Klina Perkins, Tom Perkins, who was a wonderfully flamboyant figure,

774
00:49:27,360 --> 00:49:31,239
Speaker 1: you know, who would be criticized occasionally for his unbelievable

775
00:49:31,239 --> 00:49:35,520
Speaker 1: extravagance um and he would say things like, you know, hey,

776
00:49:35,520 --> 00:49:37,560
Speaker 1: I'm the king of Silicon Valley, why can't I have

777
00:49:37,640 --> 00:49:42,439
Speaker 1: the biggest fantas in San Francisco or equivalent comments like that,

778
00:49:42,920 --> 00:49:45,680
Speaker 1: And he was he was unashamed about, you know, roaring

779
00:49:45,760 --> 00:49:49,000
Speaker 1: up in his ferrari outside some cremins startup. He just foundered,

780
00:49:49,320 --> 00:49:51,680
Speaker 1: and yeah he'd screamed into every dollar on the deal,

781
00:49:51,840 --> 00:49:55,319
Speaker 1: but there he wasn't a Ferrari. And anyway, Perkins's law

782
00:49:55,440 --> 00:49:59,239
Speaker 1: stated a very simple idea that it's quite profound, which

783
00:49:59,320 --> 00:50:05,359
Speaker 1: is that technical risk is inversely proportional to business risk,

784
00:50:06,239 --> 00:50:10,399
Speaker 1: because if you solve a really hard technical problem, you're

785
00:50:10,400 --> 00:50:14,560
Speaker 1: not going to face much competition from business competitors because

786
00:50:15,040 --> 00:50:17,680
Speaker 1: they don't know how to solve your problem. So if

787
00:50:17,680 --> 00:50:20,000
Speaker 1: you've got a company where you know, let's say it's

788
00:50:20,000 --> 00:50:22,520
Speaker 1: genet Tech, and they're gonna they're saying, where the first

789
00:50:22,520 --> 00:50:27,239
Speaker 1: biotech company we're going to solve for this challenge of

790
00:50:27,239 --> 00:50:31,280
Speaker 1: building artificial insulin. No one's ever done anything like this before.

791
00:50:31,600 --> 00:50:34,799
Speaker 1: It's super difficult. So that's a huge technical challenge, so

792
00:50:34,840 --> 00:50:38,440
Speaker 1: it's very risky to fund it. But if you manage

793
00:50:38,480 --> 00:50:41,200
Speaker 1: to make the artificial insulin, you're going to have a

794
00:50:41,239 --> 00:50:43,879
Speaker 1: big competitive mode. People will not be able to come

795
00:50:43,920 --> 00:50:47,479
Speaker 1: after you and compete because you know you've done something

796
00:50:47,520 --> 00:50:51,240
Speaker 1: technically hard and therefore you can charge a big margin

797
00:50:51,320 --> 00:50:53,799
Speaker 1: on that product. On the other hand, if you've got

798
00:50:53,840 --> 00:50:58,720
Speaker 1: something which is simple to build, it's just an app um.

799
00:50:58,840 --> 00:51:01,879
Speaker 1: Then the busines this risk is going to be much

800
00:51:01,920 --> 00:51:05,240
Speaker 1: more intense. The competition from people coming into your space,

801
00:51:05,800 --> 00:51:08,040
Speaker 1: it's going to be much higher. And to do a

802
00:51:08,040 --> 00:51:14,080
Speaker 1: little compare and contrast. Obviously, any sort of DNA manipulation

803
00:51:14,239 --> 00:51:19,440
Speaker 1: when Genentech first began was unprecedented. On the other hand,

804
00:51:20,239 --> 00:51:23,960
Speaker 1: what did Yahoo own. They essentially were just a little

805
00:51:23,960 --> 00:51:29,160
Speaker 1: early to to manually telling people what they might want

806
00:51:29,160 --> 00:51:32,279
Speaker 1: to look at on the Internet. But there was no

807
00:51:32,520 --> 00:51:36,200
Speaker 1: technological mote there, that's right. I mean it was two

808
00:51:36,280 --> 00:51:39,960
Speaker 1: PhD students who actually, we're not doing something particularly technical.

809
00:51:40,480 --> 00:51:45,520
Speaker 1: They were just compiling lists of wacky websites that they

810
00:51:45,520 --> 00:51:48,960
Speaker 1: found amusing and growing and growing that list and doing it,

811
00:51:49,040 --> 00:51:52,560
Speaker 1: as you say, mostly by hand. So there was, you know,

812
00:51:52,680 --> 00:51:56,200
Speaker 1: to to a climb Perkins's law to that there was

813
00:51:56,239 --> 00:52:00,760
Speaker 1: not much technical risk. Who obviously manual company the website

814
00:52:00,800 --> 00:52:03,799
Speaker 1: list is easy, but there was a huge amount of

815
00:52:04,239 --> 00:52:08,799
Speaker 1: business and commercial risk because other people could compete. So

816
00:52:08,880 --> 00:52:12,800
Speaker 1: let's talk about some other people who compete with the Yahoo.

817
00:52:13,120 --> 00:52:18,520
Speaker 1: I love the story of angel investors, typically described as

818
00:52:18,680 --> 00:52:24,000
Speaker 1: successful executives or entrepreneurs who have already had their exit

819
00:52:24,520 --> 00:52:28,960
Speaker 1: from their their first company or second company and their board,

820
00:52:29,000 --> 00:52:31,399
Speaker 1: and they have a big check books, and they want

821
00:52:31,440 --> 00:52:33,920
Speaker 1: to keep their fingers in the pie. They want to

822
00:52:33,920 --> 00:52:38,080
Speaker 1: stay involved in technology, and so they'll write checks to

823
00:52:38,400 --> 00:52:42,839
Speaker 1: startups to really be give them their very beginning. Who

824
00:52:42,840 --> 00:52:46,560
Speaker 1: wrote the hundred thousand dollar check to Google where the

825
00:52:46,600 --> 00:52:50,279
Speaker 1: Google founders said, Hey, this check is men out to

826
00:52:50,360 --> 00:52:53,279
Speaker 1: Google link. We're not even incorporated, we don't have a

827
00:52:53,280 --> 00:52:57,960
Speaker 1: bank account yet. Yeah, that was the funny story. So

828
00:52:58,239 --> 00:53:03,359
Speaker 1: Andy Bestel Time, the legendary Valley engineer who was one

829
00:53:03,400 --> 00:53:07,200
Speaker 1: of the co founders of Sun Microsystems back in the

830
00:53:07,360 --> 00:53:10,719
Speaker 1: in the nineties and had done you know, pretty well,

831
00:53:10,800 --> 00:53:14,279
Speaker 1: he'd done Son, he'd done another company after that. He

832
00:53:14,320 --> 00:53:16,479
Speaker 1: had plenty of money. He wasn't bored by the way

833
00:53:16,520 --> 00:53:19,520
Speaker 1: because he he was still running a company, but he

834
00:53:19,560 --> 00:53:25,320
Speaker 1: was fascinated by up and coming technologies and young entrepreneurs

835
00:53:25,320 --> 00:53:29,000
Speaker 1: who kind of reminded himself, reminded him of himself when

836
00:53:29,000 --> 00:53:34,200
Speaker 1: he had been starting fun And so he heard about uh,

837
00:53:34,239 --> 00:53:39,200
Speaker 1: you know, Harry and Sergey, the two Google founders, and

838
00:53:39,280 --> 00:53:41,920
Speaker 1: he came over to meet them one day, and you know,

839
00:53:42,200 --> 00:53:45,560
Speaker 1: this as this was described to me. You know, he

840
00:53:45,680 --> 00:53:48,640
Speaker 1: rolls up in his silver Porsche, you know, jumps out,

841
00:53:49,160 --> 00:53:54,120
Speaker 1: watches a demo of how Google can search for results

842
00:53:54,239 --> 00:53:57,000
Speaker 1: much better than any other product on the market at

843
00:53:57,000 --> 00:54:00,480
Speaker 1: the time, and he says, wow, that's cool, right. You know,

844
00:54:00,560 --> 00:54:02,160
Speaker 1: he has a hundred thousand dollar check and he just

845
00:54:02,200 --> 00:54:05,520
Speaker 1: writes it right there, just runs to his porsche, you know,

846
00:54:05,600 --> 00:54:08,959
Speaker 1: gets the gets the checkbook out, rushes back, says Google link,

847
00:54:09,120 --> 00:54:12,239
Speaker 1: hundred thousand bucks. There you go. And you know, as

848
00:54:12,280 --> 00:54:14,960
Speaker 1: you say, Larry and Serge, the founders of them, we

849
00:54:14,960 --> 00:54:16,880
Speaker 1: don't have a bank account. He just fine, you know,

850
00:54:17,080 --> 00:54:18,520
Speaker 1: stick the check in there when you do have a

851
00:54:18,560 --> 00:54:21,480
Speaker 1: bank account. Whatever, it doesn't matter. And then he leaves.

852
00:54:21,480 --> 00:54:24,560
Speaker 1: So he hasn't asked, you know what, how many shares

853
00:54:24,640 --> 00:54:27,680
Speaker 1: he just bought in the company, what the terms of

854
00:54:27,719 --> 00:54:30,759
Speaker 1: the deal were. Nothing. He just writes the check and

855
00:54:30,880 --> 00:54:33,840
Speaker 1: he drives off, and you know, a hundred thousand dollars

856
00:54:33,840 --> 00:54:36,480
Speaker 1: to any better side. You know, you've done two successful companies.

857
00:54:36,920 --> 00:54:39,200
Speaker 1: That wasn't a big bite out of his bank balance.

858
00:54:39,920 --> 00:54:43,960
Speaker 1: But you know you just spread the money and um,

859
00:54:44,239 --> 00:54:47,200
Speaker 1: that didn't happen. Of course, in the early period of

860
00:54:47,239 --> 00:54:50,839
Speaker 1: Civicken Valley because there wasn't enough entrepreneurs who had made

861
00:54:50,840 --> 00:54:53,200
Speaker 1: the cash to be able to do that. But as

862
00:54:53,239 --> 00:54:55,920
Speaker 1: you get into the nineties and even more later on

863
00:54:56,719 --> 00:54:59,680
Speaker 1: UM there were people who could write those checks and

864
00:54:59,680 --> 00:55:02,919
Speaker 1: then enjoy doing it. And you know, typically what would

865
00:55:02,960 --> 00:55:05,360
Speaker 1: happen is nobody would have a clue, you know what

866
00:55:05,480 --> 00:55:09,160
Speaker 1: shared the company Andy Battels time at board. But when

867
00:55:09,200 --> 00:55:13,160
Speaker 1: the more serious, more deliberative next investment ran took place,

868
00:55:13,719 --> 00:55:15,440
Speaker 1: somebody would sit down and say, well, what do we

869
00:55:15,520 --> 00:55:18,080
Speaker 1: think that that's worse and they would kind of awards

870
00:55:18,160 --> 00:55:20,920
Speaker 1: some number of shares to the Battles Tim and he,

871
00:55:21,000 --> 00:55:23,560
Speaker 1: you know, he wasn't really counting, but no doubt he

872
00:55:23,680 --> 00:55:28,480
Speaker 1: made you know, more money on whatever number of shares

873
00:55:28,480 --> 00:55:30,840
Speaker 1: he'd got in Google and let's probably end up being

874
00:55:30,840 --> 00:55:33,799
Speaker 1: worth more to him than some microsystems had been. Yeah,

875
00:55:33,960 --> 00:55:36,359
Speaker 1: and he did, Okay, it turned out. So a lot

876
00:55:36,440 --> 00:55:39,720
Speaker 1: of the famed venture capitalists who who really put together

877
00:55:39,760 --> 00:55:43,440
Speaker 1: a string of astounding performance in the nineties, eighties and nineties,

878
00:55:43,920 --> 00:55:47,080
Speaker 1: they haven't done as well since. What are your thoughts

879
00:55:47,080 --> 00:55:51,239
Speaker 1: as to why the the star funds from from the

880
00:55:51,280 --> 00:55:54,520
Speaker 1: early days of VC have been lagging over the past

881
00:55:54,560 --> 00:55:58,319
Speaker 1: decade or two. Not only of them are lacking, but

882
00:55:58,360 --> 00:56:01,279
Speaker 1: you're right that some do. And think there's a couple

883
00:56:01,320 --> 00:56:03,320
Speaker 1: of problems that come up. You know, one of the

884
00:56:03,440 --> 00:56:09,520
Speaker 1: succession problem, where um, there isn't a good mechanism for

885
00:56:09,719 --> 00:56:16,120
Speaker 1: handing control from the senior partners who maybe you know,

886
00:56:16,520 --> 00:56:18,640
Speaker 1: getting to a point where they might think about retiring,

887
00:56:18,640 --> 00:56:21,320
Speaker 1: but they don't really want to retire yet. The younger

888
00:56:21,360 --> 00:56:25,359
Speaker 1: people that maybe plugged into the new technology, the young entrepreneurs,

889
00:56:25,760 --> 00:56:28,520
Speaker 1: and they really ought to be taking over of control,

890
00:56:29,360 --> 00:56:32,760
Speaker 1: but the senior people don't want to see that control,

891
00:56:32,840 --> 00:56:34,920
Speaker 1: and then there's a fight about who gets what and

892
00:56:35,600 --> 00:56:39,000
Speaker 1: and that that can wind up causing a partnership to

893
00:56:39,040 --> 00:56:44,160
Speaker 1: break up. Another kind of issue, there is actually a

894
00:56:44,160 --> 00:56:49,279
Speaker 1: problem of success. Where a partnership does really well, all

895
00:56:49,400 --> 00:56:53,040
Speaker 1: the general partners who have a share of the carry

896
00:56:53,280 --> 00:56:57,080
Speaker 1: are suddenly wealthy enough to go off and start their

897
00:56:57,120 --> 00:57:01,000
Speaker 1: own venture partnerships by themselves as they want to, and

898
00:57:01,080 --> 00:57:03,879
Speaker 1: they're kind of sick of putting up with each other,

899
00:57:04,560 --> 00:57:08,080
Speaker 1: and they split up. And you know, a good illustration

900
00:57:08,120 --> 00:57:13,120
Speaker 1: of this um is kind of Perkins, which was absolutely

901
00:57:13,160 --> 00:57:17,920
Speaker 1: the top venture partnership. You know, Circuit two thousands. You know,

902
00:57:18,000 --> 00:57:21,640
Speaker 1: the UM top money maker at kind Of Perkins in

903
00:57:21,640 --> 00:57:25,040
Speaker 1: two thousand one was you know, Coostler, who was the

904
00:57:25,120 --> 00:57:28,680
Speaker 1: number one on the Fobs Mindus list, and then there

905
00:57:28,760 --> 00:57:31,800
Speaker 1: was John Door, who was the number three on the

906
00:57:31,840 --> 00:57:35,560
Speaker 1: Forbes Midus list if I called correctly UM that year.

907
00:57:36,200 --> 00:57:38,000
Speaker 1: So you have the number one and the number three

908
00:57:38,120 --> 00:57:40,960
Speaker 1: VC in the whole world, and they're at the same partnership.

909
00:57:41,000 --> 00:57:42,840
Speaker 1: They are an absolute dream team. And then there's a

910
00:57:42,840 --> 00:57:45,560
Speaker 1: bunch of people around them who are also good and

911
00:57:45,600 --> 00:57:48,280
Speaker 1: who have been them with them, you know, for for

912
00:57:48,320 --> 00:57:52,280
Speaker 1: a decade or so, and they know each other well

913
00:57:52,400 --> 00:57:55,400
Speaker 1: enough that they can kind of challenge each other and

914
00:57:55,560 --> 00:57:58,480
Speaker 1: beat the check and the balance. If somebody is getting

915
00:57:58,480 --> 00:58:01,520
Speaker 1: too enthusiastic and over there he is about a potential investment,

916
00:58:02,400 --> 00:58:05,160
Speaker 1: the other people in the partnership have the standing and

917
00:58:05,160 --> 00:58:07,760
Speaker 1: the stature to say, wait a second, you know, just

918
00:58:07,760 --> 00:58:09,880
Speaker 1: just take a deep breath here and and think hard

919
00:58:09,920 --> 00:58:11,760
Speaker 1: before you do that, because I'm not sure I agree

920
00:58:11,800 --> 00:58:15,040
Speaker 1: with you. And just kind of Perkins got to a

921
00:58:15,080 --> 00:58:19,760
Speaker 1: point where around two thousand three, two thousand four, so

922
00:58:19,920 --> 00:58:23,240
Speaker 1: just a couple of years after their peak, UM people

923
00:58:23,320 --> 00:58:26,360
Speaker 1: started to leave and they made so much money that

924
00:58:26,480 --> 00:58:29,640
Speaker 1: they could go off and do their own fund and

925
00:58:29,920 --> 00:58:33,720
Speaker 1: the note Coastal left and started um Coastal Adventures his

926
00:58:33,800 --> 00:58:36,720
Speaker 1: own company, and a few other people left and started

927
00:58:36,720 --> 00:58:40,160
Speaker 1: their own company, and John Doe was sort of left

928
00:58:40,200 --> 00:58:42,600
Speaker 1: standing and there was nobody around him with quite the

929
00:58:42,680 --> 00:58:47,000
Speaker 1: stature to challenge him. And at that point he fastened

930
00:58:47,000 --> 00:58:50,640
Speaker 1: onto the idea of clean tech, the investing in clean technologies.

931
00:58:51,480 --> 00:58:53,760
Speaker 1: And I think if he had had the right culture

932
00:58:53,800 --> 00:58:57,280
Speaker 1: around him, of a proper partnership where people could challenge him,

933
00:58:57,280 --> 00:58:59,200
Speaker 1: he might have been a bit more cautious about the

934
00:58:59,200 --> 00:59:02,520
Speaker 1: way he went into at but he didn't. At that point.

935
00:59:02,560 --> 00:59:07,280
Speaker 1: He was head and shoulders the most prestigious and successful

936
00:59:07,320 --> 00:59:10,680
Speaker 1: investor in the partnership, and he just ran with it

937
00:59:10,880 --> 00:59:14,280
Speaker 1: too far, too fast. Um. And he did the same thing,

938
00:59:14,320 --> 00:59:16,240
Speaker 1: by the way, in another good cause. I mean, clean

939
00:59:16,280 --> 00:59:19,440
Speaker 1: tech is a good cause in terms of saving the planet.

940
00:59:20,000 --> 00:59:23,600
Speaker 1: He also wanted to advance women, and he perted women

941
00:59:24,240 --> 00:59:26,080
Speaker 1: and that was a good thing, and in fact, some

942
00:59:26,120 --> 00:59:29,240
Speaker 1: of the women you know went on to be extraordinarily

943
00:59:29,640 --> 00:59:34,080
Speaker 1: good investors. You know. Aileen Lee comes to mind. She's

944
00:59:34,080 --> 00:59:38,400
Speaker 1: the one who invented the term unicorn. Um, but they

945
00:59:38,400 --> 00:59:43,120
Speaker 1: didn't become successful investors very much internally within kind of Perkins,

946
00:59:43,200 --> 00:59:46,920
Speaker 1: because although John Doe was good at promoting women, he

947
00:59:47,000 --> 00:59:50,040
Speaker 1: was not good at creating a culture amongst the rest

948
00:59:50,040 --> 00:59:52,880
Speaker 1: of his partners that would really make it possible for

949
00:59:52,920 --> 00:59:56,200
Speaker 1: those women to thrive. So kind Of Perkins wound up

950
00:59:56,200 --> 01:00:00,680
Speaker 1: with a sexual harassment suit, wound up with you know,

951
01:00:01,240 --> 01:00:05,640
Speaker 1: I mean I should say that, um, they they I

952
01:00:05,640 --> 01:00:08,080
Speaker 1: think they got the upper hand in the verdict on

953
01:00:08,120 --> 01:00:10,560
Speaker 1: that trial, though it was a bit of a messy one.

954
01:00:10,640 --> 01:00:15,000
Speaker 1: But um so, you know, stipulating that in their favor.

955
01:00:15,520 --> 01:00:18,280
Speaker 1: But they but it turned out to be difficult to

956
01:00:18,320 --> 01:00:21,560
Speaker 1: build the culture in a new way that allowed women

957
01:00:21,600 --> 01:00:24,000
Speaker 1: to thrive. And it also turned out to be hard

958
01:00:24,000 --> 01:00:26,400
Speaker 1: to make money off clean tech in the first iteration

959
01:00:26,400 --> 01:00:29,320
Speaker 1: of clean tech, and so kind Of Perkins went from

960
01:00:29,360 --> 01:00:33,720
Speaker 1: being you know, consistently ranked number one to being not

961
01:00:33,800 --> 01:00:36,600
Speaker 1: even in the top ten. It was really quite a

962
01:00:36,600 --> 01:00:40,320
Speaker 1: precipitous decline. And I think that that has to do with,

963
01:00:40,440 --> 01:00:42,440
Speaker 1: you know, you need to pay attention to the to

964
01:00:42,600 --> 01:00:45,520
Speaker 1: the glue within the partnership. You can't just be out

965
01:00:45,640 --> 01:00:47,800
Speaker 1: investing in other companies and making sure that they have

966
01:00:47,880 --> 01:00:51,120
Speaker 1: good governments. You need to look at your own company

967
01:00:51,200 --> 01:00:55,360
Speaker 1: and your own internal governments. Really really interesting. Let's let's

968
01:00:55,400 --> 01:01:00,320
Speaker 1: talk about a venture funds that has probably, since the

969
01:01:00,000 --> 01:01:04,480
Speaker 1: the decline of Klana Perkins, become the hardest VC in

970
01:01:04,560 --> 01:01:08,120
Speaker 1: Silicon Valley, and that would be A sixteen Z and

971
01:01:08,120 --> 01:01:10,960
Speaker 1: Andrews and Horowitz. Tell us a little bit about your

972
01:01:10,960 --> 01:01:13,880
Speaker 1: thoughts on them the past few years. They seem to

973
01:01:13,960 --> 01:01:17,200
Speaker 1: be very focused on crypto and blockchain. What are your

974
01:01:17,200 --> 01:01:20,960
Speaker 1: thoughts on on Mark and Reason and Ben Horowitz and

975
01:01:21,040 --> 01:01:23,880
Speaker 1: what they've built. Yeah, it's funny when you were saying,

976
01:01:23,920 --> 01:01:27,240
Speaker 1: you know, the hottest partnership in Silicon Valley, I thought

977
01:01:27,240 --> 01:01:32,440
Speaker 1: you were about to introduce Sikoria Capital. I think they

978
01:01:32,440 --> 01:01:36,440
Speaker 1: are probably got the best returns and they've also scaled globally.

979
01:01:37,440 --> 01:01:44,840
Speaker 1: They've got Skoya goes all the way back to the eighties. Right, Yes,

980
01:01:44,920 --> 01:01:46,880
Speaker 1: so they're not that's right. So if you're talking about

981
01:01:46,880 --> 01:01:49,960
Speaker 1: the hottest new entrance, then I agree with you. Anyway,

982
01:01:50,000 --> 01:01:53,600
Speaker 1: let's talk about A sixteen Z and Reason herrowitz Um.

983
01:01:54,120 --> 01:01:57,440
Speaker 1: I just wanted to give a mention, Uh, the ka

984
01:01:58,120 --> 01:02:03,480
Speaker 1: um and Us horrods Um I think you know started

985
01:02:03,480 --> 01:02:06,400
Speaker 1: out in two thousand nine, they had a bunch of

986
01:02:06,440 --> 01:02:09,440
Speaker 1: public relations around what was going to make them distinctive.

987
01:02:09,560 --> 01:02:12,880
Speaker 1: I'm not sure that that anything they said there was

988
01:02:12,920 --> 01:02:15,720
Speaker 1: really the key to why they did well. I think

989
01:02:15,720 --> 01:02:19,600
Speaker 1: they did well because both Mark Andrewson, who of course

990
01:02:19,760 --> 01:02:22,880
Speaker 1: was the one of the key engineers or maybe the

991
01:02:23,000 --> 01:02:29,040
Speaker 1: key engineer behind Netscape uh and and the first graphical

992
01:02:29,960 --> 01:02:36,120
Speaker 1: um web browser. Ah. So he was a towering computer scientist.

993
01:02:36,240 --> 01:02:41,320
Speaker 1: And then Ben Horowitz, who himself was a terrific computer scientist,

994
01:02:41,400 --> 01:02:45,360
Speaker 1: had also founded a company and despite the two thousand

995
01:02:45,440 --> 01:02:48,680
Speaker 1: tech rash, has solded through that and made it, you know,

996
01:02:48,800 --> 01:02:52,800
Speaker 1: into a successful exit. So you had to really really

997
01:02:52,840 --> 01:02:57,560
Speaker 1: strong founding partners in Andrews and Horrods, and they were

998
01:02:57,600 --> 01:03:01,400
Speaker 1: both computer scientists, and they found it in two thousand nine,

999
01:03:01,600 --> 01:03:04,600
Speaker 1: right about the time when you know, the iPhone had

1000
01:03:04,640 --> 01:03:11,040
Speaker 1: come on stream, cloud computing was taking off, and software,

1001
01:03:11,640 --> 01:03:15,200
Speaker 1: to quote and recent famous phrase, was about to eat

1002
01:03:15,240 --> 01:03:17,840
Speaker 1: the world. In other words, software was just going to

1003
01:03:17,960 --> 01:03:23,520
Speaker 1: displace all kinds of other technologies as the way to

1004
01:03:23,600 --> 01:03:28,120
Speaker 1: build value UM. And so you have these two founders.

1005
01:03:28,120 --> 01:03:31,880
Speaker 1: They really understand dn They know which coders are the best.

1006
01:03:31,960 --> 01:03:35,240
Speaker 1: The coders respect them and so they're happy to take

1007
01:03:35,240 --> 01:03:39,280
Speaker 1: their capital and that I think explains how they got

1008
01:03:39,280 --> 01:03:45,160
Speaker 1: into companies like Nissira, UM, Octa, UM, some of the

1009
01:03:45,320 --> 01:03:48,560
Speaker 1: you know, they did a great turnaround deal with Skype,

1010
01:03:49,120 --> 01:03:54,120
Speaker 1: UM the the voice over IP telephony company, UM, so

1011
01:03:54,720 --> 01:03:56,720
Speaker 1: they I think I think it was about having these

1012
01:03:56,760 --> 01:03:59,960
Speaker 1: these two strong individuals who were who were really strong

1013
01:04:00,000 --> 01:04:04,480
Speaker 1: on the hottest technology of all, namely coding. They're now

1014
01:04:04,640 --> 01:04:08,480
Speaker 1: moving and innovating, and I think that's impressive. They're moving.

1015
01:04:08,880 --> 01:04:12,960
Speaker 1: They moved strongly into crypto and and blockchain and and

1016
01:04:13,000 --> 01:04:16,680
Speaker 1: Web three, and I think what's fascinating to watch there

1017
01:04:16,840 --> 01:04:19,760
Speaker 1: is that, you know, Web three is kind of at

1018
01:04:19,800 --> 01:04:25,360
Speaker 1: its its moment in terms of Internet time, where you know,

1019
01:04:26,480 --> 01:04:29,200
Speaker 1: the Internet was something that um, you know a few

1020
01:04:29,200 --> 01:04:33,760
Speaker 1: early adopters were really passionate about and excited by, but

1021
01:04:33,880 --> 01:04:39,640
Speaker 1: you haven't got to the killer app, namely Netscape, the

1022
01:04:40,040 --> 01:04:44,920
Speaker 1: graphical browser, which turned the Internet into something that mainstream

1023
01:04:45,320 --> 01:04:49,240
Speaker 1: consumers would actually want. And now with Web three, you're

1024
01:04:49,280 --> 01:04:51,040
Speaker 1: at the same point. I would say, you know, you've

1025
01:04:51,040 --> 01:04:55,200
Speaker 1: got some gaming stuff that's that's that's breaking out, but

1026
01:04:55,240 --> 01:04:58,360
Speaker 1: it's still in the world where it hasn't quite gone

1027
01:04:58,400 --> 01:05:02,240
Speaker 1: mainstream and despite all the US and I think we're

1028
01:05:02,280 --> 01:05:05,120
Speaker 1: looking for the Kinder app that really establishes this as

1029
01:05:05,880 --> 01:05:10,720
Speaker 1: as you know, a totally mainstream product. And what Andrews

1030
01:05:10,760 --> 01:05:13,400
Speaker 1: and Herowits are doing is that they've put enough capital

1031
01:05:14,200 --> 01:05:20,880
Speaker 1: into a crypto blockchain Web three focused part of money

1032
01:05:20,960 --> 01:05:25,560
Speaker 1: that they can really experiment with, backing lots of you know,

1033
01:05:25,680 --> 01:05:28,680
Speaker 1: lots of ventures, one of which would probably be the

1034
01:05:28,840 --> 01:05:32,840
Speaker 1: Netscape as it were for web three. I didn't think

1035
01:05:32,880 --> 01:05:34,440
Speaker 1: they found it, or we don't know if they have

1036
01:05:34,560 --> 01:05:37,320
Speaker 1: found it. Sometimes you can only see the things in retrospect.

1037
01:05:37,360 --> 01:05:40,120
Speaker 1: But it's one of the most interesting stories going on

1038
01:05:40,200 --> 01:05:45,240
Speaker 1: right now in sort of company, really really interesting. Um So,

1039
01:05:45,320 --> 01:05:49,280
Speaker 1: we we talk a lot about the VCS successes, and

1040
01:05:49,320 --> 01:05:52,520
Speaker 1: we talked about the power law distribution. But one of

1041
01:05:52,560 --> 01:05:56,520
Speaker 1: the things we haven't really discussed weren't just the companies

1042
01:05:56,560 --> 01:05:59,320
Speaker 1: that didn't make it. You know, we could look at

1043
01:05:59,360 --> 01:06:02,520
Speaker 1: a movie Pass or Quimby or pets dot Com or whatever,

1044
01:06:03,120 --> 01:06:07,240
Speaker 1: but the ones that just blow up spectacularly. And and

1045
01:06:07,360 --> 01:06:10,600
Speaker 1: I'm not so much looking at uber or we work

1046
01:06:10,720 --> 01:06:17,040
Speaker 1: as I am Farinos, which really appears to be a

1047
01:06:17,040 --> 01:06:21,960
Speaker 1: a fraud. How do you draw the distinction between an

1048
01:06:22,040 --> 01:06:25,240
Speaker 1: idea that just doesn't catch fire the way it was

1049
01:06:25,320 --> 01:06:30,360
Speaker 1: hoped with outright you know, deception and hey, you know

1050
01:06:30,440 --> 01:06:33,800
Speaker 1: Elizabeth Holmes was you know, just convicted on four accounts

1051
01:06:33,840 --> 01:06:40,040
Speaker 1: of of the frauding investors. How does one make that distinction? Yeah,

1052
01:06:40,080 --> 01:06:42,440
Speaker 1: I think freud is pretty clearly different from just not

1053
01:06:42,560 --> 01:06:47,680
Speaker 1: making money, right. I mean, when you actually misrepresent your product,

1054
01:06:47,800 --> 01:06:50,240
Speaker 1: you claim that your blood tests done with your machine,

1055
01:06:50,240 --> 01:06:53,400
Speaker 1: but actually you're using the modern machine you brought from

1056
01:06:53,400 --> 01:06:59,200
Speaker 1: another company the results of Ferny. I mean, that is

1057
01:06:59,280 --> 01:07:03,440
Speaker 1: just cross thing a line. Um. I mean, people sometimes

1058
01:07:03,480 --> 01:07:06,760
Speaker 1: when they're criticizing Citic and Valley and trying to use

1059
01:07:06,800 --> 01:07:10,440
Speaker 1: their enough there's a way of um saying it's, you know,

1060
01:07:10,680 --> 01:07:13,800
Speaker 1: this is just a sign of how corrupt Cicnvoudy is.

1061
01:07:14,280 --> 01:07:18,160
Speaker 1: They kind of blur that distinction between outright fraud and

1062
01:07:18,280 --> 01:07:22,120
Speaker 1: simply business failure. But I think it's a you know,

1063
01:07:22,280 --> 01:07:25,960
Speaker 1: actually it's a pretty clear difference between On the one hand,

1064
01:07:26,680 --> 01:07:28,400
Speaker 1: you know, you set out to make a product and

1065
01:07:28,440 --> 01:07:32,040
Speaker 1: the product either can't be built because it's too technically difficult,

1066
01:07:32,160 --> 01:07:34,080
Speaker 1: or you build up and theybody wants it so you

1067
01:07:34,120 --> 01:07:37,320
Speaker 1: don't get any revenue. Those are just business failures. But

1068
01:07:37,320 --> 01:07:41,520
Speaker 1: but if you lie, you're crossing a line. And that

1069
01:07:41,680 --> 01:07:45,040
Speaker 1: was just not more than just an occasional lie, that

1070
01:07:45,200 --> 01:07:51,160
Speaker 1: was a consistent pattern of fraud and misrepresentation. And I

1071
01:07:51,520 --> 01:07:55,280
Speaker 1: completely agree with you. You can't lump the two together,

1072
01:07:55,800 --> 01:08:00,200
Speaker 1: regular business failure and fraud. So so before we get

1073
01:08:00,240 --> 01:08:02,320
Speaker 1: to our favorite questions, I have I have one last

1074
01:08:02,360 --> 01:08:07,240
Speaker 1: curveball I have to throw at you, which is something

1075
01:08:07,240 --> 01:08:09,800
Speaker 1: pretty fascinating. I learned about you when I was doing

1076
01:08:09,840 --> 01:08:13,200
Speaker 1: a little homework when you were in your twenties. Your

1077
01:08:13,280 --> 01:08:17,880
Speaker 1: your father was the UK ambassador to Germany and uh

1078
01:08:17,920 --> 01:08:21,519
Speaker 1: for for five years, and then his next gig was

1079
01:08:22,320 --> 01:08:27,240
Speaker 1: UK an ambassador to France. Tell us about that experience.

1080
01:08:27,800 --> 01:08:30,760
Speaker 1: How did that shape your view of history? I know

1081
01:08:30,840 --> 01:08:35,280
Speaker 1: you studied history at Oxford. What was being the son

1082
01:08:35,320 --> 01:08:39,439
Speaker 1: of an ambassador like for someone who is you know,

1083
01:08:39,560 --> 01:08:44,800
Speaker 1: delving into that space. Well, by the time my dad

1084
01:08:44,840 --> 01:08:48,200
Speaker 1: became an ambassador, you know, I was in my twenties

1085
01:08:48,240 --> 01:08:52,200
Speaker 1: and I was being a fern curvednded in Africa. And

1086
01:08:52,240 --> 01:08:55,360
Speaker 1: in fact, that the funny story, because you know, I

1087
01:08:55,520 --> 01:08:57,840
Speaker 1: actually litten in Babwe. I made that my base and

1088
01:08:57,880 --> 01:09:01,880
Speaker 1: I roamed around different African countries and um in November

1089
01:09:04,439 --> 01:09:09,719
Speaker 1: there was the election in Namibia to elect the first

1090
01:09:10,760 --> 01:09:14,719
Speaker 1: majority rule government. So that was the end of white

1091
01:09:14,800 --> 01:09:20,240
Speaker 1: minority rule and the start of majority rule. And this

1092
01:09:20,320 --> 01:09:23,040
Speaker 1: election was being oversee by the United Nations. That was

1093
01:09:23,760 --> 01:09:28,559
Speaker 1: absolutely massive, you know, foreign presence, the historic occasion, the

1094
01:09:28,720 --> 01:09:32,720
Speaker 1: end of colonial the colonial political set up, and all

1095
01:09:32,760 --> 01:09:36,880
Speaker 1: of the press pack that was covering this election Namibia,

1096
01:09:37,600 --> 01:09:41,200
Speaker 1: me included thought great, you know where Africa correspondents normally

1097
01:09:41,240 --> 01:09:44,040
Speaker 1: we get onto page fifteen of the newspaper for a

1098
01:09:44,160 --> 01:09:46,880
Speaker 1: lucky but now finally we're going to be on the

1099
01:09:46,960 --> 01:09:51,360
Speaker 1: front page. And on the day that the Namibian election

1100
01:09:51,439 --> 01:09:57,080
Speaker 1: result was announced, in war came down and you know,

1101
01:09:57,160 --> 01:10:00,200
Speaker 1: all of these Africa correspondents were on page fifteen, game

1102
01:10:00,640 --> 01:10:03,680
Speaker 1: if they were lucky. And so it was funny for

1103
01:10:03,720 --> 01:10:06,400
Speaker 1: me because there I was, you know, my story had

1104
01:10:06,439 --> 01:10:09,240
Speaker 1: been killed. But whatever my dad's story, you know, he

1105
01:10:09,360 --> 01:10:11,800
Speaker 1: was the UK and BATHTD in Germany. That was the

1106
01:10:11,840 --> 01:10:15,880
Speaker 1: story that the whole wide world was talking about. And

1107
01:10:16,479 --> 01:10:19,439
Speaker 1: you know, he told me afterwards that he flew straight

1108
01:10:19,479 --> 01:10:22,760
Speaker 1: into Berlin where the war was coming down, and she

1109
01:10:22,880 --> 01:10:25,080
Speaker 1: realized that you know that was the end of the

1110
01:10:25,120 --> 01:10:28,280
Speaker 1: Cold War, and and and that was, you know, super

1111
01:10:28,280 --> 01:10:30,880
Speaker 1: exciting moment for him. So I didn't know if it

1112
01:10:31,000 --> 01:10:35,280
Speaker 1: shaped my view of history directly, but it did make

1113
01:10:35,320 --> 01:10:38,040
Speaker 1: for a funny family story, yeah, to say, to say

1114
01:10:38,040 --> 01:10:40,840
Speaker 1: the very least. So let's jump in our last few

1115
01:10:40,840 --> 01:10:44,200
Speaker 1: minutes to our favorite questions that we asked all of

1116
01:10:44,200 --> 01:10:48,360
Speaker 1: our guests, Uh, starting with what have you been streaming

1117
01:10:48,400 --> 01:10:53,080
Speaker 1: these days? What has kept you entertained during lockdown when

1118
01:10:53,120 --> 01:10:58,320
Speaker 1: you weren't researching or writing the book? So I think,

1119
01:10:58,400 --> 01:11:00,920
Speaker 1: like probably a lot of people who listened to your

1120
01:11:00,920 --> 01:11:04,960
Speaker 1: show where I love Succession UM the kind of you know,

1121
01:11:05,280 --> 01:11:11,759
Speaker 1: Quazi Murdoch family drama. And I also have quite enjoyed

1122
01:11:11,760 --> 01:11:16,200
Speaker 1: a couple of French UM series My My my mother

1123
01:11:16,320 --> 01:11:20,120
Speaker 1: was French and maybe that's why. But there's cool My Agent,

1124
01:11:20,439 --> 01:11:27,600
Speaker 1: which about Yeah, that's funny, it's about UM movie Agency.

1125
01:11:28,760 --> 01:11:32,520
Speaker 1: By the way, I always have to tell my American

1126
01:11:32,640 --> 01:11:36,360
Speaker 1: friends that I recommend that too, that the people who

1127
01:11:36,400 --> 01:11:41,559
Speaker 1: play the actors on that show are actually very famous

1128
01:11:41,600 --> 01:11:45,599
Speaker 1: French actors, but to an American they just look like

1129
01:11:45,680 --> 01:11:52,960
Speaker 1: another French person in the show. Right. I mean, if

1130
01:11:53,000 --> 01:11:55,840
Speaker 1: you don't know that you know, for us, if we

1131
01:11:55,880 --> 01:11:58,920
Speaker 1: would have a Brad Pitt or a Matt Damon show

1132
01:11:59,000 --> 01:12:03,160
Speaker 1: up on a show about you know, talent agents, everyone

1133
01:12:03,200 --> 01:12:05,599
Speaker 1: in America would know who they are when when you

1134
01:12:05,640 --> 01:12:08,639
Speaker 1: watch and I think the French version is called ten percent,

1135
01:12:09,080 --> 01:12:12,599
Speaker 1: but when you watch that show, um, and it's how

1136
01:12:12,640 --> 01:12:18,280
Speaker 1: my wife and I keep our French uh passable. Um.

1137
01:12:18,320 --> 01:12:21,519
Speaker 1: It's always interesting to see the actual actors who show up.

1138
01:12:21,840 --> 01:12:24,000
Speaker 1: But I interrupted you who what what else have you

1139
01:12:24,040 --> 01:12:27,719
Speaker 1: been streaming besides Succession? Did the other? The other French

1140
01:12:27,720 --> 01:12:30,559
Speaker 1: when I enjoyed for a while is a kind of

1141
01:12:31,800 --> 01:12:34,920
Speaker 1: It's called Le Bux and it's about the French secret

1142
01:12:35,760 --> 01:12:38,280
Speaker 1: uh service like the CIA and the French d I A.

1143
01:12:38,400 --> 01:12:41,479
Speaker 1: And they're fighting all kinds of wars all over the

1144
01:12:41,560 --> 01:12:46,640
Speaker 1: Middle Eastern sort of exciting. My My wife likes it

1145
01:12:46,680 --> 01:12:50,799
Speaker 1: because the French secret agents, you know, devastatingly good looking.

1146
01:12:51,520 --> 01:12:55,519
Speaker 1: And I tolerate that because the female leads are quite

1147
01:12:55,520 --> 01:12:58,160
Speaker 1: good too. But you know, it has a lot of

1148
01:12:58,200 --> 01:13:02,919
Speaker 1: good French suspense and we've enjoyed that as well. Very interesting.

1149
01:13:03,240 --> 01:13:06,080
Speaker 1: Tell us about your early mentors who helped to shape

1150
01:13:06,320 --> 01:13:10,720
Speaker 1: your career well. I joined the Economist as I was

1151
01:13:10,760 --> 01:13:13,120
Speaker 1: saying at the beginning, right out of college, and there

1152
01:13:13,160 --> 01:13:17,040
Speaker 1: were just a terrific group of talented people there who

1153
01:13:18,120 --> 01:13:22,200
Speaker 1: helped me. And I remember, you know, there was Neil Harmon,

1154
01:13:22,400 --> 01:13:28,880
Speaker 1: who was one of the older journalists who was incredibly

1155
01:13:28,880 --> 01:13:32,559
Speaker 1: good mentor. And I would fire copy and he would say,

1156
01:13:32,960 --> 01:13:36,160
Speaker 1: you know, he would tip his his his halfmoon spectacles

1157
01:13:36,200 --> 01:13:39,280
Speaker 1: down his nose, look look over them at me and

1158
01:13:39,360 --> 01:13:42,280
Speaker 1: say just just just come sit here for a minute.

1159
01:13:42,800 --> 01:13:46,880
Speaker 1: And he I would watch him edit my words on

1160
01:13:46,920 --> 01:13:50,120
Speaker 1: the screen and just add top spin more and more

1161
01:13:50,160 --> 01:13:53,000
Speaker 1: top spin. You know, he just had this knack for

1162
01:13:53,000 --> 01:13:57,800
Speaker 1: for turning a reasonable phrase into a good phrase, and

1163
01:13:57,880 --> 01:14:01,280
Speaker 1: that that gave me a kind of special appreciation for

1164
01:14:01,320 --> 01:14:05,200
Speaker 1: the magic of of really you know, the craftsmanship of

1165
01:14:05,240 --> 01:14:09,080
Speaker 1: writing um. And but you know, in other ways too,

1166
01:14:09,120 --> 01:14:13,160
Speaker 1: there were there were colleagues who just thought globally um.

1167
01:14:13,800 --> 01:14:21,320
Speaker 1: They thought across finance and politics and economics. They could

1168
01:14:21,360 --> 01:14:25,719
Speaker 1: handle the ideas without getting um sort of bogged down

1169
01:14:25,760 --> 01:14:29,880
Speaker 1: in detail. But they were also serious about being accurate it.

1170
01:14:30,320 --> 01:14:32,760
Speaker 1: I would say that the whole experience of spending twelve

1171
01:14:32,880 --> 01:14:36,120
Speaker 1: years or thirteen years on the staff of the Economists

1172
01:14:36,240 --> 01:14:40,959
Speaker 1: was was my formative experience. Let's talk about books besides

1173
01:14:41,000 --> 01:14:44,320
Speaker 1: your own. What are some of your favorites and what

1174
01:14:44,360 --> 01:14:47,920
Speaker 1: are you reading right now? Well, the no favorite which

1175
01:14:47,960 --> 01:14:51,760
Speaker 1: I often mentioned is um is The Money Game. Have

1176
01:14:51,840 --> 01:14:55,200
Speaker 1: you read that, Adam Smith? Sure, yeah, of course I

1177
01:14:55,240 --> 01:14:58,080
Speaker 1: thought you would have done. And I mean it's just

1178
01:14:58,160 --> 01:15:01,479
Speaker 1: you know, full of laugh out loud caricatures with these

1179
01:15:01,720 --> 01:15:06,639
Speaker 1: people in the nineties sixties go go Bullmarket, when side

1180
01:15:06,720 --> 01:15:11,240
Speaker 1: burned gun slingers were ramping stocks, and and and it's

1181
01:15:11,280 --> 01:15:15,439
Speaker 1: it's just a it's kind of you know, financial writing

1182
01:15:15,439 --> 01:15:19,760
Speaker 1: of comedy, and I always enjoyed that. Um. More recently,

1183
01:15:20,320 --> 01:15:23,760
Speaker 1: I've been really a novel called A Little Life by

1184
01:15:23,880 --> 01:15:29,120
Speaker 1: Hanya Yanagi Hara san noting that right, Um, you that

1185
01:15:29,280 --> 01:15:33,800
Speaker 1: you know that? So it's it's I'm not a great

1186
01:15:33,840 --> 01:15:37,600
Speaker 1: novel reader, but this one is so well done. It's captivating.

1187
01:15:37,640 --> 01:15:43,200
Speaker 1: It's a long saga of um for New Yorkers who

1188
01:15:43,240 --> 01:15:45,880
Speaker 1: graduate at college together, they come to the city and

1189
01:15:45,920 --> 01:15:49,840
Speaker 1: they they make their lives in different professions, and there's

1190
01:15:49,880 --> 01:15:53,759
Speaker 1: a sort of a bit of as a really engrossing

1191
01:15:53,840 --> 01:15:56,920
Speaker 1: tragedy at the center of the life of the main character.

1192
01:15:57,880 --> 01:16:01,720
Speaker 1: That's a novel, But in terms of non Si shan Um.

1193
01:16:01,760 --> 01:16:07,360
Speaker 1: A bit late I read Um Sheila called hot Cars

1194
01:16:07,439 --> 01:16:12,040
Speaker 1: Black Edge about s AC. I thought that was incredibly

1195
01:16:12,120 --> 01:16:16,439
Speaker 1: well done, sort of suspense story about a hedge fund

1196
01:16:16,479 --> 01:16:25,720
Speaker 1: that goes wrong. Um. And I enjoyed Black Gold. I

1197
01:16:25,720 --> 01:16:31,200
Speaker 1: think it's I'm getting right digital gold. Maybe it's cooled. Sorry. Um.

1198
01:16:31,200 --> 01:16:39,599
Speaker 1: And that's Nathaniel Popper's book about bitcoin, and was already right, yeah,

1199
01:16:39,720 --> 01:16:41,280
Speaker 1: that's right. Again. I was a bit late to that,

1200
01:16:41,400 --> 01:16:44,080
Speaker 1: but I it basically, it tells the story of how

1201
01:16:44,160 --> 01:16:50,960
Speaker 1: bitcoin got traction because different ways of enthusiasts got on board.

1202
01:16:51,040 --> 01:16:52,960
Speaker 1: So there were the coders who loved the code because

1203
01:16:52,960 --> 01:16:55,880
Speaker 1: it was elegant. There were the libertarians who liked it

1204
01:16:55,880 --> 01:16:59,439
Speaker 1: for political reasons. There were the people who wanted to

1205
01:16:59,439 --> 01:17:04,559
Speaker 1: do to an ordeals in drugs and um and guns

1206
01:17:04,600 --> 01:17:06,599
Speaker 1: and so forth. That was the sort of road thing.

1207
01:17:07,200 --> 01:17:09,960
Speaker 1: There were Latin Americans who wanted to remit money back

1208
01:17:09,960 --> 01:17:13,960
Speaker 1: to Argentina. Um. Then there were the entrepreneurs who showed

1209
01:17:14,040 --> 01:17:15,840
Speaker 1: up and said, hey, we could do a wallet or

1210
01:17:15,880 --> 01:17:19,280
Speaker 1: build some some business on top of all this. And

1211
01:17:19,320 --> 01:17:22,160
Speaker 1: I don't think, frankly my own perspective, I don't think

1212
01:17:22,200 --> 01:17:26,280
Speaker 1: any of these individual groups had a killer argument as

1213
01:17:26,320 --> 01:17:31,439
Speaker 1: to why the world really needed bitcoin, but cumulatively they

1214
01:17:31,560 --> 01:17:35,160
Speaker 1: created enough momentum that it's stuck, and I think it's

1215
01:17:35,200 --> 01:17:39,439
Speaker 1: now here to stay. Huh. Really really intriguing. And in

1216
01:17:39,560 --> 01:17:43,160
Speaker 1: our final two questions, what sort of advice would you

1217
01:17:43,200 --> 01:17:46,040
Speaker 1: give to a recent college graduate who was interested in

1218
01:17:46,080 --> 01:17:53,000
Speaker 1: a career in either finance, investment or journalism and book writing. Yeah,

1219
01:17:54,160 --> 01:17:57,519
Speaker 1: so on the journalism and book writing. I have a

1220
01:17:57,560 --> 01:18:01,320
Speaker 1: standard line which which I role out because people ask me.

1221
01:18:01,360 --> 01:18:05,320
Speaker 1: There's quite a bit, and essentially I try to dissuade

1222
01:18:05,360 --> 01:18:09,760
Speaker 1: people because I think you've got to really really want

1223
01:18:09,800 --> 01:18:12,000
Speaker 1: to do it, um if you're going to go in

1224
01:18:12,040 --> 01:18:14,680
Speaker 1: that direction. And you know, if people listen to what

1225
01:18:14,800 --> 01:18:16,799
Speaker 1: I say and then they do it anyway, I delighted.

1226
01:18:17,920 --> 01:18:20,640
Speaker 1: But I think there's kind of a thing where, you know,

1227
01:18:20,720 --> 01:18:24,920
Speaker 1: people go to college, they enjoy their work in college,

1228
01:18:24,960 --> 01:18:27,680
Speaker 1: they write papers in college, and I think, how can

1229
01:18:27,720 --> 01:18:29,800
Speaker 1: I extend this and just do more of the same,

1230
01:18:30,920 --> 01:18:33,439
Speaker 1: And they don't necessarily look left and right and think

1231
01:18:33,479 --> 01:18:35,639
Speaker 1: about other things they could be doing with their talent.

1232
01:18:36,520 --> 01:18:39,880
Speaker 1: And I think it's good to experiment and and and

1233
01:18:39,880 --> 01:18:42,680
Speaker 1: and do other stuff. And then if you decide that

1234
01:18:42,720 --> 01:18:44,720
Speaker 1: you actually really do want to write because you like

1235
01:18:44,880 --> 01:18:48,320
Speaker 1: the process of writing, even though it's solitary, even though

1236
01:18:48,640 --> 01:18:52,120
Speaker 1: it's you know, a huge amount of time to produce

1237
01:18:52,160 --> 01:18:54,320
Speaker 1: something of value. I mean, my books do take me

1238
01:18:54,439 --> 01:18:57,200
Speaker 1: five years, and it's it's a lot of rejection when

1239
01:18:57,240 --> 01:18:59,920
Speaker 1: you're beginning a new project and people think, why would

1240
01:18:59,920 --> 01:19:03,720
Speaker 1: I talk to some book writer who you know, who

1241
01:19:03,720 --> 01:19:05,320
Speaker 1: knows if this book will even come out? And I

1242
01:19:05,360 --> 01:19:07,240
Speaker 1: have to try to let work my way in and

1243
01:19:07,600 --> 01:19:10,000
Speaker 1: you know, by the end of course, the same flips,

1244
01:19:10,040 --> 01:19:13,160
Speaker 1: and you know you've got enough momentum that people that

1245
01:19:13,240 --> 01:19:15,920
Speaker 1: you didn't call are now calling you because they want

1246
01:19:15,920 --> 01:19:18,559
Speaker 1: to talk to you, because they understand your book is

1247
01:19:18,560 --> 01:19:21,920
Speaker 1: going to be serious and make an impact. But you

1248
01:19:21,920 --> 01:19:25,120
Speaker 1: know that's it's it's it's not all plane sailing, um

1249
01:19:25,880 --> 01:19:28,960
Speaker 1: and um. So I try and dissuade people, but then

1250
01:19:29,000 --> 01:19:31,840
Speaker 1: I'm happy, I say if they do it. Anyway, if

1251
01:19:32,080 --> 01:19:36,240
Speaker 1: someone would ask you about a a job on Wall Street,

1252
01:19:36,280 --> 01:19:38,720
Speaker 1: what would you say to them? I think Wall Street CHOI.

1253
01:19:38,840 --> 01:19:42,400
Speaker 1: Wall Street is a bit you know, is regulated, is

1254
01:19:42,439 --> 01:19:45,160
Speaker 1: the main feature of it. So if you're a lawyer,

1255
01:19:45,240 --> 01:19:49,320
Speaker 1: that's great. If you're an investor, or an entrepreneur, it's

1256
01:19:49,360 --> 01:19:51,840
Speaker 1: not great, UM, you might want to go to a

1257
01:19:51,880 --> 01:19:57,240
Speaker 1: fintech instead, or go to a hedge fund which is

1258
01:19:57,280 --> 01:20:00,759
Speaker 1: still a bit less regulated, and where know you can

1259
01:20:01,000 --> 01:20:06,639
Speaker 1: you can really try to apply original thinking to markets UM. Yeah,

1260
01:20:06,920 --> 01:20:09,720
Speaker 1: and our final question, what do you know about the

1261
01:20:09,760 --> 01:20:15,880
Speaker 1: world of finance, journalism, markets investing today that you wish

1262
01:20:15,920 --> 01:20:18,040
Speaker 1: you knew thirty or forty years ago when you were

1263
01:20:18,080 --> 01:20:23,320
Speaker 1: first starting out. I think what I've learned is that

1264
01:20:24,160 --> 01:20:29,280
Speaker 1: the way investors think UM is actually quite useful for life.

1265
01:20:29,760 --> 01:20:31,360
Speaker 1: And you know, when I was writing my book about

1266
01:20:31,360 --> 01:20:39,040
Speaker 1: hedge funds, the central I guess if epistemological the serfect

1267
01:20:39,040 --> 01:20:42,960
Speaker 1: discovery was this idea of asymmetric pairs that you know,

1268
01:20:43,080 --> 01:20:45,679
Speaker 1: sometimes you don't know if you're right, or you don't

1269
01:20:45,680 --> 01:20:49,160
Speaker 1: know if you're wrong about it's that col But what

1270
01:20:49,200 --> 01:20:52,519
Speaker 1: you should look at is if you were to be right,

1271
01:20:53,600 --> 01:20:57,200
Speaker 1: would the pay out be bigger than the loss would

1272
01:20:57,200 --> 01:20:59,120
Speaker 1: be if you were wrong. So there are things where

1273
01:21:00,160 --> 01:21:02,720
Speaker 1: you know, you don't know if this is the right

1274
01:21:03,640 --> 01:21:06,360
Speaker 1: direction to go in, but but you should give it

1275
01:21:06,400 --> 01:21:09,400
Speaker 1: a shot because because if it works, it's going to

1276
01:21:09,479 --> 01:21:14,320
Speaker 1: be big. And that's you know, a basic thing about

1277
01:21:14,360 --> 01:21:16,479
Speaker 1: a lot of macro investing in hedge funds is the

1278
01:21:16,479 --> 01:21:19,519
Speaker 1: best basically about you know, you bet against the currency peg.

1279
01:21:19,560 --> 01:21:22,080
Speaker 1: If you're wrong, the peg isn't going to move because

1280
01:21:22,120 --> 01:21:24,479
Speaker 1: the peg won't break, so you won't lose much from

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01:21:24,479 --> 01:21:27,280
Speaker 1: your position. But if you're right, the peg collapses, is

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01:21:27,360 --> 01:21:29,439
Speaker 1: going to move twenty percent, You're going to make a

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01:21:29,479 --> 01:21:31,880
Speaker 1: huge killing. So this is a This is a basic

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01:21:32,320 --> 01:21:36,559
Speaker 1: macro investing hedge fund strategy, but it's also a useful

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01:21:36,600 --> 01:21:40,240
Speaker 1: thing for life, about life decisions. And in the same

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01:21:40,280 --> 01:21:42,759
Speaker 1: way you know with venture capital, I think the power

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01:21:42,840 --> 01:21:49,200
Speaker 1: lower idea that you know, sometimes low probability but high

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01:21:49,320 --> 01:21:54,960
Speaker 1: consequence bets are worth trying. That you know, rather than

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01:21:55,080 --> 01:21:59,400
Speaker 1: following the pack, you should try and do something different.

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Speaker 1: Maybe I like this argument because when I go off

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Speaker 1: and bury myself in some specialized corner of finance for

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Speaker 1: five years, you know, I feel a bit like I'm

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01:22:09,360 --> 01:22:12,680
Speaker 1: taking myself away from mainstream debate to really get specialized

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01:22:12,720 --> 01:22:16,760
Speaker 1: in deep on one niche um. But I think I

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01:22:16,800 --> 01:22:19,720
Speaker 1: think it is healthy too to have those ideas in

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Speaker 1: mind and think about how to differentiate yourself, how to

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Speaker 1: do something risky but that might have a really good

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Speaker 1: outcome if you get it right, really intriguing. Sebastian, thank

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Speaker 1: you for being so generous with your time. We have

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01:22:34,560 --> 01:22:39,080
Speaker 1: been speaking with Sebastian Malby, author of The Power Law

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01:22:39,120 --> 01:22:42,679
Speaker 1: of Venture Capital and the Making of the New Future.

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Speaker 1: If you enjoy this conversation, well, be sure and check

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01:22:46,360 --> 01:22:49,240
Speaker 1: out any of our previous I keep saying four hundred,

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01:22:49,520 --> 01:22:52,559
Speaker 1: We probably crossed that already. Four hundred or so prior

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01:22:52,640 --> 01:22:58,040
Speaker 1: interviews where UH we discuss all things finance related. You

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01:22:58,120 --> 01:23:02,200
Speaker 1: can find those at un Spotify, Bloomberg, wherever you get

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01:23:02,240 --> 01:23:06,400
Speaker 1: your UH podcast from. We love your comments, feedback, end

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01:23:06,439 --> 01:23:10,240
Speaker 1: suggestions right to us at m IB podcast at Bloomberg

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01:23:10,360 --> 01:23:14,280
Speaker 1: dot net. You can sign up for my daily reading

1310
01:23:14,360 --> 01:23:18,720
Speaker 1: list at Ridholtz dot com. Follow me on Twitter at Ridolts.

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Speaker 1: I would be remiss if I did not thank the

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01:23:21,680 --> 01:23:24,880
Speaker 1: crack staff that helps put these conversations together each week.

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01:23:25,479 --> 01:23:30,439
Speaker 1: Marx and Escalchie is my audio engineer. Paris Walt is

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01:23:30,479 --> 01:23:36,360
Speaker 1: my producer. Sean Russo is my research assistant. Attica val

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01:23:36,400 --> 01:23:40,400
Speaker 1: Bron is our project manager. I'm Barrier Rehults. You've been

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Speaker 1: listening to Master's Business on Bloomberg Radio.