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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg

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Speaker 1: Radio this weekend. On the podcast, I have an extra

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Speaker 1: special guest. His name is Bill Janeway, and he is

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Speaker 1: the author of the Innovation Economy. He was an early

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Speaker 1: guest where quite bluntly, I really didn't know what the

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Speaker 1: hell I was doing, And I think this conversation is

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Speaker 1: far more detailed than in depth if you are at

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Speaker 1: all interested in venture capital, technology investing UH and the

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Speaker 1: role of both the Defense and Intelligence agencies specifically and

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Speaker 1: the federal government generally in impacting venture investing technology long

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Speaker 1: form investing where there may not be an immediate payoff,

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Speaker 1: but ultimately you end up with a really significant set

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Speaker 1: of payoffs. Whether we're talking about UH, the Interstate highway system,

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Speaker 1: the cross continental railroads, the space um race for the Moon,

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Speaker 1: all of these things had no immediate expected payoff, but

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Speaker 1: long term they've delivered a tremendous amount of value. And

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Speaker 1: the intersection between technology, venture capitalism, and economics UH is

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Speaker 1: something that Professor Janeway specializes in. I think you'll find

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Speaker 1: this conversation absolutely fascinating. So, with no further ado my

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Speaker 1: conversation with Bill Janeway, I'm Barry rit Halts. You're listening

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Speaker 1: to Masters in Business on Bloomberg Radio. My special guest

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Speaker 1: today is Bill Janeway. He has a storied background in

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Speaker 1: both economics and venture capital. He helped to create b

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Speaker 1: e A Systems, which connects software apps to database. Had

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Speaker 1: you invested about fifty million dollars into b e A,

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Speaker 1: UH when Bill started putting money into it, it would

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Speaker 1: have become over six billion dollars in six years. He

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Speaker 1: is an affiliated lecturer of Economics at the University of Cambridge,

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Speaker 1: where he teaches a class venture Capital in the Innovation Economy.

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Speaker 1: He's a senior advisor at Warburg Pincus, where he helped

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Speaker 1: build technology investing platform there for over thirty years. He's

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Speaker 1: on the board of the US Social Science Research Council,

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Speaker 1: the governing board for the Institute of New Economics, the

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Speaker 1: Field Institute for Research in Mathematical Sciences. UH. No less

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Speaker 1: a character than Mark Andresen called him a key creator

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Speaker 1: of the modern venture capital world. He is also the

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Speaker 1: author of Doing Capitalism in the Innovation Economy, Markets, Speculation,

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Speaker 1: and the State. Bill Janeway, Welcome back to Bloomberg. It

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Speaker 1: is great to be back here, Barry, especially with you.

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Speaker 1: That is quite the curriculum. Vita and I. We left

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Speaker 1: a ton of it off. We'll talk a little bit

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Speaker 1: about B E A systems in a bit. I want

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Speaker 1: to start with your academic background in economics, your val

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Speaker 1: Victorian Princeton. You get your doctorate from Cambridge and economics.

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Speaker 1: How do you go from that to venture capital? Well,

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Speaker 1: actually it's a closer connection than I knew at the

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Speaker 1: time it would be. When I got to Cambridge, I

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Speaker 1: was studying under the students, the top students who had

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Speaker 1: been taught by John Maynard Kean's and I wrote my

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Speaker 1: dissertation for Richard Khan, who invented the multiplier as a

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Speaker 1: way of looking at the impact of government spending or

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Speaker 1: taxes on the macro economy. But all of the work

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Speaker 1: that I did there, everything I learned there was about

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Speaker 1: decision making under conditions of uncertainty, decision making by investors, workers, consumers, businessmen,

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Speaker 1: politicians who cannot know what the full consequences of their

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Speaker 1: actions are gonna be. Now, isn't that effectively every actor

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Speaker 1: in the economy? You got it. But by the time

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Speaker 1: I finished my doctorate and was thinking I would pursue

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Speaker 1: a course in economics and academic economics. It turned out

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Speaker 1: that economics was converting itself for a long generation into

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Speaker 1: a kind of mechanical process of cranking out the efficient

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Speaker 1: outcome on the assumption that everybody knew everything that's The

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Speaker 1: marketplace itself is already reflecting all the information that's available,

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Speaker 1: and therefore, hey, nobody can really beat the market consistently

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Speaker 1: over time, or or so Chicago, Pharma and French and

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Speaker 1: right across the whole economy. The rational expectations hypothesis said,

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Speaker 1: government can't have any lasting impact on the economy because

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Speaker 1: people in the economy will react an offset whatever it

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Speaker 1: tries to do. In any case, I decided that at

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Speaker 1: the ripe old age of seven, I could not take

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Speaker 1: this stuff and spend my life pumping it into the

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Speaker 1: brains of innocent undergraduate. So I went on. I went

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Speaker 1: on what I talk of as my my thirty five

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Speaker 1: years sabbatical, where in the trenches of venture capital that

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Speaker 1: I evolved in through joining an extraordinary firm whose core

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Speaker 1: competence was understanding the science based industries, from chemicals to pharmaceuticals,

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Speaker 1: to electronics to computing, I discovered that what I learned

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Speaker 1: at Cambridge as an academic economist, was directly relevant to

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Speaker 1: try and to frame the sword of decisions and the

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Speaker 1: sort of ways of protecting yourself, your investors, and the

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Speaker 1: entrepreneurs you are backing from the necessary ignorance of operating

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Speaker 1: at the frontier of technology. So that's pretty fascinating. I

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Speaker 1: want to push back on the concept that, hey, this

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Speaker 1: efficient market hypothesis really doesn't get it right. A perfect

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Speaker 1: example I read over the weekend while I was doing

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Speaker 1: a little research for for our conversation. Some people have

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Speaker 1: been wondering why the US tax cuts haven't had a

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Speaker 1: larger impact on either employment or wages or R and D.

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Speaker 1: And the most interesting explanation I came across was, well,

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Speaker 1: everybody has already operated on the basis that they're tax havens,

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Speaker 1: and this thirty corporate tax rate is no big deal anyway.

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Speaker 1: We're all paying uh, eighteen percent or less. Therefore, a

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Speaker 1: giant corporate tax cut has much less of an impact

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Speaker 1: than you would imagine, true or false? And what does

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Speaker 1: that say about your belief that, hey, maybe the market

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Speaker 1: is less efficient than we think. For first, I think

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Speaker 1: that's correct. I don't think the average of about eight,

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Speaker 1: not thirty, and of course, particularly the digital companies that

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Speaker 1: can move their cash flows and their assets around by

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Speaker 1: keys on a computer. They're not manufacturing steel and build locomotives,

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Speaker 1: it's just code. Very mobile, they're very mobile. Second, however,

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Speaker 1: the tax cut has had a significant substantial impact unavailable

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Speaker 1: accessible cash flow, not reported earnings. So that but what's

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Speaker 1: happened to that cash flow? And this is something again

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Speaker 1: about the nature of the stock market, That cash flow

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Speaker 1: has overwhelmingly been devoted Apple being the most extreme example.

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Speaker 1: Stock buybacks and dividends, that not not raising wages for workers,

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Speaker 1: not investing in the new stuff, but but putting more

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Speaker 1: money in the in the pockets of stockholders, which you

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Speaker 1: know is a rational response of management, particularly when their stockholders,

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Speaker 1: who are increasingly index funds, are necessarily very short term

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Speaker 1: oriented and the executives are also short term oriented. Hey,

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Speaker 1: we we eliminated the agency problem by tying their compensation

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Speaker 1: to the stock price. So so you go on this

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Speaker 1: thirty year walk about, and you spend some time in

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Speaker 1: um Wearburg Pankis, you spent some time at Eberstaaten company.

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Speaker 1: What prompted you to say, Hey, this venture capital thing,

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Speaker 1: it's gonna be big one day I want to stay

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Speaker 1: involved in. You know, it's funny. It was actually a

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Speaker 1: crossover from being an economist to being a venture capitalist

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Speaker 1: in the In the mid late seventies, when I was

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Speaker 1: a kid on Wall Street, I was really interested in

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Speaker 1: the kind of longer term strategic issues around the economy.

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Speaker 1: There had been a big fiasco. One of the first

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Speaker 1: attempts to use computers to model the economy, not just

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Speaker 1: modeled the they modeled the world came out of M

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Speaker 1: I T and was kind of a fiasco, was called

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Speaker 1: the Limits of Growth Study for the Club of Rome,

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Speaker 1: and this was referenced in your book. Absolutely absolutely. But

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Speaker 1: the the the young guys at M I T who

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Speaker 1: have been caught up in this, they learned a big

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Speaker 1: lesson and they set out using computers to build very detailed,

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Speaker 1: very granular, local models of economic behavior. And I stumbled

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Speaker 1: on these guys as I was trying to find out

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Speaker 1: how to think about and how to handle the consequences

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Speaker 1: of the first oil crisis, which blew up all the

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Speaker 1: econometric models. They were useless because all the important variables

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Speaker 1: of the economy, from interest rates to exchange rates to

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Speaker 1: inflation rates had just been blown out. Of the historical database.

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Speaker 1: I found these young guys and I got a message.

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Speaker 1: The message was, Hey, this is what computers are interesting about.

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Speaker 1: It's not just being a flexible typewriter or or being

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Speaker 1: a faster or more flexible adding machine. You can build

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Speaker 1: simulations and explore the behavior of the world. That got

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Speaker 1: me interested in computers. From there, the first wave of

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Speaker 1: artificial intelligence, the first wave of the hypeer artificial intelligence

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Speaker 1: that got me out to Xerox Park, the kind of

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Speaker 1: the haven for the most creative people in the computing world.

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Speaker 1: Thanks to a friendship with John Ceelee Brown, who was

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Speaker 1: on his way to becoming director of Xerox Park, I

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Speaker 1: got immersed ahead of the game, a kind of kind

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Speaker 1: of unfair advantage and seeing where computing was gonna go.

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Speaker 1: I want to circle back to what you said about

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Speaker 1: the Xerox Research Center. Um that gave us things like

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Speaker 1: the graphical user interface, the mouse. Essentially that was Steve

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Speaker 1: jobs is aha moment that led to the first Macintosh.

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Speaker 1: You said it gave you an unfair advantage. Expand on

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Speaker 1: that it gave me the unfair advantage of seeing that

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Speaker 1: can puting computers were not which were dominated by the

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Speaker 1: IBM main frames with a secondary center in the digital

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Speaker 1: equipment mini computers and the competitors with digital equipment. These

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Speaker 1: were very centralized systems. They had dumb terminals, green screens.

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Speaker 1: You couldn't do any local work. Everything went back to

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Speaker 1: the main frame or the mini computer to be processed.

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Speaker 1: It was a very rigid, inflexible and of course, because

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Speaker 1: each of these systems were proprietary, if you were a

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Speaker 1: digital equipment customer, let alone an IBM customer, you have

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Speaker 1: to buy everything from them, and believe me, they made

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Speaker 1: money on that. So it was a giant centralized system

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Speaker 1: as opposed to the modern between phones and laptops and iPads,

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Speaker 1: we have a completely decentralized system. Although theoretically, as we

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Speaker 1: moved to the cloud, we're kind of moving back a

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Speaker 1: little interesting, but but in a very different way. But

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Speaker 1: I'll come back. That's a good point. Let's hold that thought.

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Speaker 1: What you could see at Deox Park were computers being

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Speaker 1: networked together with special functions that they could be optimized for.

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Speaker 1: While the machine in front of you was designed to

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Speaker 1: be helpful, but you know, it was map to how

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Speaker 1: people work, and it was much too expensive and Xerox

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Speaker 1: management back in on the East Coast. UH couldn't get

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Speaker 1: their heads around the notion of making the kind of

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Speaker 1: high risk, long term investments, So companies like Adobe in

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Speaker 1: three comm were founded by people who left Xerox Park.

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Speaker 1: That kind of raises the question, why did they even

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Speaker 1: have a Xerox Park At least with a T and

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Speaker 1: T and Bell Labs later loosened, they were using their

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Speaker 1: own research to build out new products or were they

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Speaker 1: up to a point? There was a deal, a T

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Speaker 1: and T in nineteen fifty six kind of deal with

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Speaker 1: what was then an economically active government unlike where we

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Speaker 1: are today, right back when it was a legitimate legal

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Speaker 1: and the deal was it could keep its monopoly on

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Speaker 1: long distance telephones if any technology developed at Bell Labs

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Speaker 1: that was not directly used for communications would be licensed

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Speaker 1: on a fair and non discriminatory basis to the world.

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Speaker 1: And that's where Unix came from. That's where a whole

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Speaker 1: raft of innovative technologies that had general purpose use, not

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Speaker 1: just for communications, were given to the world, given to

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Speaker 1: academic researchers, given to companies that were learning how to

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Speaker 1: what to do with this stuff, and laying the basis

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Speaker 1: for the digital revolution. That began to emerge in the

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Speaker 1: early so that that government deal with A T and

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Speaker 1: T was very different than what xerox parks deal was.

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Speaker 1: They they just didn't know what to do with that. Well,

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Speaker 1: two things one um neither today, T and T. They

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Speaker 1: licensed it. They the big deal they cut with the

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Speaker 1: government in where they gave up the long distance monopoly

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Speaker 1: and they broke up the company in return for the

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Speaker 1: opportunity to use the technology and go out and compete

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Speaker 1: with IBM and Digital demonstrated that I, A, T and

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Speaker 1: T had no ability to compete in commercial markets. Xerox

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Speaker 1: is a different story. Xerox Is monopoly was based on patents,

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Speaker 1: patents for this phenomenal ability to make copies very low cost,

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Speaker 1: and it generated an enormous amount of cash. It was

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Speaker 1: a different kind of innovator's dilemma. However, the existing business

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Speaker 1: was so good and so certain that when one of

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Speaker 1: I got a great idea for a business plan. Just

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Speaker 1: give me twenty million bucks and in five years I'm

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Speaker 1: gonna have a three million dollar business and it will

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Speaker 1: be profitable and it'll be worth you know, a billion

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Speaker 1: dollars and they look at them and say, wait a second,

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Speaker 1: that is so high risk. We could take that same

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Speaker 1: twenty million bucks and we'll hire a bunch of additional

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Speaker 1: engineers and salesmen. We know exactly what the return is

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Speaker 1: going to be. Go back, go back to your cave.

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Speaker 1: And they had they finally worked out, but it took

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Speaker 1: a long time. In the late eighties, they worked out that.

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Speaker 1: young entrepreneurs and took twenty percent of the company in

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Speaker 1: return for giving them the intellectual property, the patents, and

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Speaker 1: then let him go out and find venture capital. Xerox

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Speaker 1: wasn't trying to build the business. It was a beneficiary

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Speaker 1: of the work that had been done on its nickel

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Speaker 1: out at Yaks Park. That actually worked in a couple

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Speaker 1: of really valuable businesses. What came out of that documentum

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Speaker 1: for example, Major Company, they missed a ton of stuff.

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Speaker 1: They took all Adobe going down the lift. It was

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Speaker 1: just the first two a's. What else came out of

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Speaker 1: that Apple they never had a chance to invest in.

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Speaker 1: That was That was Apple. That was Steve Jobs being

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Speaker 1: the brilliant opportunist he was. And but it took him

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Speaker 1: ten years. It took the corporate bureaucrats back in Stanford

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Speaker 1: ten years to work out that would better to own

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Speaker 1: something versus of nothing. So let's let's talk about another

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Speaker 1: institution that came up with a similar idea, and that

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Speaker 1: is the n S, A, C, I, A, D O D.

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Speaker 1: Going back to the age of DARPA and DARPA net.

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Speaker 1: Why are our national security agencies so interested in startups

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Speaker 1: and technology? How much of this stuff actually finds its

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Speaker 1: way into real world spy versus by usage? Well, there's

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Speaker 1: no question that from speech recognition to the geographical positioning

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Speaker 1: satellites and onto all of the machine learning technologies. Uh,

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Speaker 1: the U S intelligence agencies have been major funders of

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Speaker 1: upstream research. Um. That goes back a long way. Of course,

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Speaker 1: the page rank algorithm. A couple of graduate students at

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Speaker 1: Stanford got a grant from the NSF which was directly

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Speaker 1: related to the n S as interest in being able

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Speaker 1: to do efficient search of what people were doing and

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Speaker 1: saying on the Internet. Um. So there's there's a history

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Speaker 1: there that goes back to the fifties. That's that's ironic

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Speaker 1: given that Google just rejected I mean, essentially they own

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Speaker 1: their own existence to the intelligence agencies? Is that is that?

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Speaker 1: Am I overselling that a little bit? Well? I think

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Speaker 1: that that kind There was a link certainly to the NSF.

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Speaker 1: I mean the funding for the research work as graduate

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Speaker 1: students that produced the page rank algorithm that was the

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Speaker 1: basis for Google came from the National Science Foundation. The

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Speaker 1: complex history, however, of how from silicon to software and

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Speaker 1: then onto the Internet, all of the fundamental building blocks

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Speaker 1: of the digital revolution were initially the consequence of upstream

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Speaker 1: research financed by the government, and the government as the

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Speaker 1: first customer for this stuff when it was still immature,

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Speaker 1: when it was too expensive and too unreliable for commercial use.

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Speaker 1: That changed. That changed, Big changes took place between nineteen

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Speaker 1: eight three. On the one hand, in night the PC

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Speaker 1: revolution began to take off, and the commercial markets began

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Speaker 1: to be bigger, whether it was for whether it was

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Speaker 1: for microsop microprocessors or for software, bigger than the government market. Second,

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Speaker 1: a bill was passed through Congress which said that DARPA

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Speaker 1: had to justify every dollar it invested in terms of

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Speaker 1: its direct military significance. It hadn't been that way before,

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Speaker 1: but a much broader much. You know, there's a There's

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Speaker 1: a great line from the from the play that became

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Speaker 1: Hello Dolly, Dolly says, you know, money is like manure.

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Speaker 1: For it to do any good, you've got to spread

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Speaker 1: it around. And that's what DARPA did. DARPA was an

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Speaker 1: extraordinary institution, particularly in the years from the from Sputnik,

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Speaker 1: which was why it was found in nineteen fifties seven

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Speaker 1: eight through the nineteen seventies. Seems kind of shortsighted to uh,

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Speaker 1: to cancel it. Let's let's talk about Warburg Pinkis because

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Speaker 1: you've been affiliated with them for a long time. You're

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Speaker 1: a senior adviser there. You helped to build their technology,

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Speaker 1: research and investing platform. Uh, tell us what brought you

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Speaker 1: to Warburg back in what I've known the firm for

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Speaker 1: almost a decade. Warburg Pinkers was one of the original firms.

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Speaker 1: It was the largest founding member of the National Venture

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Speaker 1: Capital Association. Was founded by two extraordinary men, Lionel Pinkis

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Speaker 1: and John Vogelstein, who had an idea going back to

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Speaker 1: the sixties that when when when the investment banking and

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Speaker 1: brokerage firms in Wall Street, you know, they did deals

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Speaker 1: and they do a movie deal and they do uh

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Speaker 1: an oil deal and roll one off and everyone in

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Speaker 1: a while they do us at a black box any

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Speaker 1: gravity machine tech deal that was going to cure cancer,

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Speaker 1: but it was very amateurish. Lionel and John had very

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Speaker 1: simple idea, if you actually focus your attention, all of

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Speaker 1: your attention on those investments, understand the context, the business issues,

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Speaker 1: the business model, the competitive situation, you would likely to

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Speaker 1: do a little better than just throwing money against the wall.

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Speaker 1: So the firm had been when I joined, the firm

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Speaker 1: was already more than twenty years old. It had not

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Speaker 1: been an active investor in technology. It had followed the

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Speaker 1: founding great firms like Kleiner Perkins, Asset Management, the Silicon

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Speaker 1: Valley firms. But as they had raised the first billion

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Speaker 1: dollar fund that anybody had raised, they decided that maybe

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Speaker 1: they should try to invest in technology the way they

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Speaker 1: invested everywhere else. As the lead strategic partner with management,

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Speaker 1: I had spent the previous ten years building an investment

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Speaker 1: practice of raising money for private companies. Sounds a little

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Speaker 1: bit like unicorns, but a big difference private companies from

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Speaker 1: institutional investors. Based on our our firm, the Eberstat Firm

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Speaker 1: deep work in understanding the science based industries. I had

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Speaker 1: come to John Vogelstein at Warburg Pinks again and again

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Speaker 1: with really interesting companies, but that required passive investment, no control,

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Speaker 1: no board seat. And John would again and again tell me,

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Speaker 1: you know, this looks like a really interesting business, it's

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Speaker 1: not an investment for Warburg Pinkers. In the mid eight

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Speaker 1: we sold our firm to a British merchant bank as

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Speaker 1: brokerage commissions, and our business model came under stress. And

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Speaker 1: it became clear after a time that if I was

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Speaker 1: going to do what I've been learning to do, I

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Speaker 1: had to go someplace else. So John and I had lunch,

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Speaker 1: We talked through we we just finished each other's sentences,

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Speaker 1: and I landed at Warburg pink Is in AD eight

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Speaker 1: at a wonderful time. Sure you're you're looking at the

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Speaker 1: late eighties in the early nineties. That is the golden

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Speaker 1: era of go down the list, semiconductors, software, mobile, and

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Speaker 1: that's still early days of biotech and judgments and things

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Speaker 1: like that, exactly right. And the technologies that I'd been

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Speaker 1: exposed to a Xerox park and then I've done some

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Speaker 1: investing in from Eberstat secondarily sort of supporting our institutional

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Speaker 1: clients as they made the investments. Now, these were beginning

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Speaker 1: to mature. So for ten years, from late eighties eighty

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Speaker 1: nine right through into the heart of the Internet dot

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Speaker 1: com bubble, we invested. We searched out as many ways

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Speaker 1: as we could find from making a big strategic bet.

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Speaker 1: And the bet was IBM dominance of commercial computing was vulnerable, exposed,

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Speaker 1: and we can help them lose their monopoly control. And

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Speaker 1: so we invested at the level of the the underlying

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Speaker 1: the infrastructure software that's b e A, in Veritas Enterprise Applications,

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Speaker 1: three or four companies, and we I think got a

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Speaker 1: major shift in the most important industry that now exists.

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Speaker 1: We got it right at the right time. So did

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Speaker 1: I get the numbers about b e A correct, because

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Speaker 1: they're just astonishing. Fifty four million dollars invested into b

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Speaker 1: A six years later become six point five billion. Is

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Speaker 1: that about right? Well, it is right, But there's a

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Speaker 1: there's a PostScript. Because John Voglostein was a great student

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Speaker 1: of markets, and he studied He knew bubbles come, bubbles

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Speaker 1: go when's when it's too good to be true, it's

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Speaker 1: too good to be true. I had actually written my

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Speaker 1: doctoral dissertation on nine to thirty one, so in a sense,

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Speaker 1: i'd seen the movie before two. So beginning in about

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Speaker 1: we had this portfolio that represented something like two million invested,

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Speaker 1: and we just started liquidating everything we could into the bubble.

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Speaker 1: We owned so much of b A that we only

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Speaker 1: got five percent out all right before the bubble ended.

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Speaker 1: But there was still another five six million that came

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Speaker 1: out later, so it actually with more than seven billion

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Speaker 1: on the on the five but a little longer time.

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Speaker 1: That's just astonishing. My guest today is Bill Janeway. He

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Speaker 1: is the author of The Innovation Economy, which there is

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Speaker 1: a new edition of and and let's talk about the

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Speaker 1: differences between the original edition, which came out five six

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Speaker 1: years ago exactly right, and the new edition. Given what's

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Speaker 1: been going on in the modern world of social networks Facebook, Twitter, LinkedIn,

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Speaker 1: go down the whole list, how has the universe of

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Speaker 1: digital companies changed versus what you were looking at back

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Speaker 1: in the nineties or the well, the first edition of

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Speaker 1: the book was, frankly a kind of celebration of this

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Speaker 1: extraordinarily constructive partnership between the public sector and particularly the

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Speaker 1: Defense Department and with DARPA as this point of the

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Speaker 1: spear and the private sector including the entrepreneurs and venture

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Speaker 1: capitalists like me. But looking looking back from the last

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Speaker 1: six to twelve months, something really fundamental has changed. The

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Speaker 1: digital revolution, which began to reach maturity thanks to the

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Speaker 1: extraordinary speculative funding of the late nineties the end of

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Speaker 1: the twentieth century. By the way, Dan Gross has an

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Speaker 1: amusing book called pop White, bubbles are eventually good for

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Speaker 1: the economy. That's exactly what you're referring to. Is if

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Speaker 1: you're laying fiber optic at some ungodly amount per mile

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Speaker 1: and then that company goes bust, well, at least we

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Speaker 1: still have the cable laid and somebody buys a cheap

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Speaker 1: out of bankruptcy. Just like the railroads a hundred years before,

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Speaker 1: or computers or televisions or automobiles, every new industry seems

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Speaker 1: to go through that boom and bus phase. A lot,

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Speaker 1: you know, a lot of bubbles leave you with nothing

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Speaker 1: but ranch houses in the in the Nevada Desert. Financial

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Speaker 1: bubbles don't leave you with the same, not quite the

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Speaker 1: same as technology. When the banking system, the credit system,

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Speaker 1: when they when they pop. The consequences are horrible. When

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Speaker 1: it's just in the public market, in the stock market

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Speaker 1: and it pops, there's no leverage, so so the But

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Speaker 1: in any case, in any case, what's happened. What's clear

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Speaker 1: over the last five years, the digital revolution has taken

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Speaker 1: on a momentum of its own. It's running out of control,

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Speaker 1: no longer needs support and subsidy from the public sector.

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Speaker 1: On the contrary, it's attacking the authority of the state

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Speaker 1: at every level from the most Is it the companies

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Speaker 1: and the platforms themselves or is it the end users

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Speaker 1: who have found ways to manipulate these platforms, perhaps for

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Speaker 1: the occasional nefarious uh objective, As Johnny Cash famously said,

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Speaker 1: why do I have to choose? Of course, it's both.

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Speaker 1: It's both the disruption of micro markets like the New

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Speaker 1: York market for transportation or accommodation. But on the other hand,

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Speaker 1: it was this i T revolution, this digital revolution, that

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Speaker 1: enabled the second Great globalization, the integration of financial markets,

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Speaker 1: critical to the financial crisis of two thousand and eight,

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Speaker 1: just by the way as the telegraph and the steamship

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Speaker 1: produced the first Great globalization at the end of the

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Speaker 1: nineteenth century, But this is happening at a very special time.

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Speaker 1: It's happening when the hard work over a long generation

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Speaker 1: of some very smart, committed people who knew they were

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Speaker 1: doing the right thing by their light to render the

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Speaker 1: government illegitimate as an economic agent. But has in that so,

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Speaker 1: let me push back on you at least a little bit,

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Speaker 1: hasn't There always been a group of people who and

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Speaker 1: and perhaps we know them more intimately these days, because

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Speaker 1: nothing is a secret. But there were always survivalists, and

431
00:28:16,960 --> 00:28:20,080
Speaker 1: there were always radical anarchists, and there were always people,

432
00:28:20,640 --> 00:28:23,800
Speaker 1: you know, I grew up with the moon landing when

433
00:28:23,800 --> 00:28:27,199
Speaker 1: I was in grammar school, and it seemed almost like

434
00:28:27,400 --> 00:28:30,000
Speaker 1: immediately it was, oh, that wasn't real. That was fake.

435
00:28:30,240 --> 00:28:34,520
Speaker 1: There's always been, but they've been disparate and not organized

436
00:28:35,080 --> 00:28:38,840
Speaker 1: that in nineteen Another thing that happened in nineteen two,

437
00:28:40,040 --> 00:28:45,400
Speaker 1: Ronald Reagan said, government isn't the solution. Government is the problem.

438
00:28:45,720 --> 00:28:49,240
Speaker 1: That view sat down with the entrepreneurs of Silicon Valley.

439
00:28:50,400 --> 00:28:55,000
Speaker 1: Intel wouldn't have existed without the government. Was the solution

440
00:28:55,360 --> 00:28:58,800
Speaker 1: to how do you do a startup into a capital intensive,

441
00:28:59,600 --> 00:29:04,960
Speaker 1: massively strategic industry that nonetheless needs time and support from

442
00:29:04,960 --> 00:29:08,760
Speaker 1: a collaborative customer as well as the benefit of the

443
00:29:08,880 --> 00:29:13,280
Speaker 1: upstream research funding and silicon processing and all that stuff.

444
00:29:14,240 --> 00:29:17,960
Speaker 1: The flip side we had, you know, in two thousand

445
00:29:18,040 --> 00:29:20,040
Speaker 1: and eight, when the world came to an end, for

446
00:29:20,080 --> 00:29:25,160
Speaker 1: about three months, the government was there around the world,

447
00:29:25,200 --> 00:29:28,640
Speaker 1: from from London to Beijing to New York to Wall Street.

448
00:29:28,640 --> 00:29:30,720
Speaker 1: If you if you remember in October o eight when

449
00:29:30,760 --> 00:29:34,520
Speaker 1: the TARP was first proposed on a Monday in Congress

450
00:29:34,640 --> 00:29:39,480
Speaker 1: and some of the Libertarians shot it down. Markets collapse,

451
00:29:39,560 --> 00:29:44,280
Speaker 1: and by that Friday it's scared Senators, fed chiefs, congressmen

452
00:29:44,640 --> 00:29:49,200
Speaker 1: enough that by Friday that said, all right, dollars. But

453
00:29:49,640 --> 00:29:53,400
Speaker 1: as soon as there was a floor under the financial

454
00:29:53,440 --> 00:29:57,640
Speaker 1: crisis and its economic collapse, then we were back to

455
00:29:57,720 --> 00:30:01,520
Speaker 1: the world in which the govern meant exists only to

456
00:30:01,640 --> 00:30:04,600
Speaker 1: screw up markets that otherwise give you the efficient outcome.

457
00:30:05,000 --> 00:30:08,440
Speaker 1: And that misses the point, the whole point of the

458
00:30:08,480 --> 00:30:11,840
Speaker 1: economics of innovation. The whole point is that you need

459
00:30:12,280 --> 00:30:17,920
Speaker 1: sources of funding that are not focused on immediate economic value,

460
00:30:18,160 --> 00:30:22,320
Speaker 1: that have a long term strategic purpose. And what we've

461
00:30:22,360 --> 00:30:25,920
Speaker 1: lost now it's two things from the rendering the government

462
00:30:25,960 --> 00:30:30,880
Speaker 1: and legitimate one very limited if any ability to respond

463
00:30:31,000 --> 00:30:35,880
Speaker 1: to the forces of globalization in a way that which

464
00:30:35,960 --> 00:30:39,280
Speaker 1: is damaging the constituents. Many of them don't know that

465
00:30:39,320 --> 00:30:43,760
Speaker 1: there's no other source of support. That's looking backwards, but

466
00:30:43,840 --> 00:30:47,160
Speaker 1: it's also there's a problem looking forward if we're going

467
00:30:47,200 --> 00:30:52,280
Speaker 1: to organize globally any kind of coherent response to climate change,

468
00:30:52,440 --> 00:30:55,880
Speaker 1: which may I stipulate is real. It's not a Chinese hoax.

469
00:30:55,960 --> 00:30:59,360
Speaker 1: I saw a tweet that said, it's just a Chinese house.

470
00:31:00,120 --> 00:31:02,920
Speaker 1: If we're gonna for we need exactly the same kind

471
00:31:03,000 --> 00:31:09,120
Speaker 1: of upstream funding, upstream commitment to research, to technology development,

472
00:31:09,400 --> 00:31:12,440
Speaker 1: to support of the deployment that we got with the

473
00:31:12,440 --> 00:31:17,200
Speaker 1: digital revolution. We're not doing it. We've we've abdicated. The

474
00:31:17,280 --> 00:31:20,520
Speaker 1: Chinese are doing it. And that's why the biggest message

475
00:31:20,720 --> 00:31:24,920
Speaker 1: from where my book stands today is that we may,

476
00:31:25,040 --> 00:31:28,200
Speaker 1: looking forward over the next generation, have the opportunity to

477
00:31:28,240 --> 00:31:33,440
Speaker 1: see only the second, only the second passage of leadership

478
00:31:33,640 --> 00:31:38,120
Speaker 1: of the innovation economy from the incumbent dominant nation now

479
00:31:38,200 --> 00:31:41,040
Speaker 1: the United States a hundred years ago, hundred and twenty

480
00:31:41,080 --> 00:31:44,680
Speaker 1: years ago Britain, to a new leader. So let's talk.

481
00:31:44,760 --> 00:31:47,200
Speaker 1: Let's delve into that, because there you raised two really

482
00:31:47,240 --> 00:31:53,080
Speaker 1: interesting issues about China, climate change and technology. I would

483
00:31:53,080 --> 00:31:55,400
Speaker 1: be remiss if I did not address the first is

484
00:31:56,400 --> 00:31:59,880
Speaker 1: you talk about having a government entity that can think

485
00:32:00,080 --> 00:32:03,440
Speaker 1: long term and make these long investments. That is not

486
00:32:03,600 --> 00:32:06,360
Speaker 1: the description of the United States. That is the description

487
00:32:06,400 --> 00:32:09,680
Speaker 1: of China. Whether it's their science funding, whether it's their

488
00:32:09,720 --> 00:32:14,000
Speaker 1: infrastructure there, high speed rail, go down the list. It

489
00:32:14,080 --> 00:32:17,920
Speaker 1: looks like China has said, hey, I'll be happy to

490
00:32:18,000 --> 00:32:21,880
Speaker 1: jump into the void that the United States is leaving leaving,

491
00:32:22,320 --> 00:32:26,320
Speaker 1: whether it's technology, politics, you name it. They're stepping up.

492
00:32:26,360 --> 00:32:29,800
Speaker 1: And this is not something they've done previously other than

493
00:32:29,840 --> 00:32:33,560
Speaker 1: that little genghis content. But short of that, China has

494
00:32:33,600 --> 00:32:37,000
Speaker 1: been very happy to be isolationist. These roles seem to

495
00:32:37,040 --> 00:32:38,920
Speaker 1: be reversing. Is that Is that a fair assess? I

496
00:32:39,520 --> 00:32:43,120
Speaker 1: think it is. I I see it in the work

497
00:32:43,360 --> 00:32:48,240
Speaker 1: that is being done to understand the strategic investments China's making.

498
00:32:48,240 --> 00:32:51,840
Speaker 1: And not just in China. They're bringing yeah, exactly in Africa,

499
00:32:52,120 --> 00:32:57,080
Speaker 1: in in Southern Europe, they're making major investments in transforming

500
00:32:57,080 --> 00:33:00,560
Speaker 1: the energy infrastructure of those parts of the world. Um,

501
00:33:00,680 --> 00:33:03,160
Speaker 1: the you do get some people saying, you know, the

502
00:33:03,240 --> 00:33:05,400
Speaker 1: Chinese are almost embarrassed because they thought it was going

503
00:33:05,480 --> 00:33:08,000
Speaker 1: to take another twenty or thirty years before they could

504
00:33:08,080 --> 00:33:10,600
Speaker 1: challenge us. Who knew we were gonna just see that

505
00:33:10,720 --> 00:33:14,680
Speaker 1: leadership mantlet. The second issue I have to bring up is,

506
00:33:15,520 --> 00:33:20,240
Speaker 1: so let's stipulate climate change is real. It mankind causes it.

507
00:33:21,240 --> 00:33:23,520
Speaker 1: You look at the history of the industrial evolution forward,

508
00:33:23,520 --> 00:33:26,640
Speaker 1: we're spewing out all this carbon. And yet when we

509
00:33:26,680 --> 00:33:33,600
Speaker 1: look at technology, the ability to move to wind, solar, geothermal, title,

510
00:33:33,840 --> 00:33:37,520
Speaker 1: they're just the sources of energy seemed to be um

511
00:33:37,560 --> 00:33:41,920
Speaker 1: the cost differential versus cheap carbon based is going from.

512
00:33:41,960 --> 00:33:44,280
Speaker 1: It used to be hugely expensive and only a handful

513
00:33:44,320 --> 00:33:47,400
Speaker 1: of of zealots did it. Then it became a little

514
00:33:47,400 --> 00:33:50,520
Speaker 1: more competitive, and now why would you use coal when

515
00:33:51,040 --> 00:33:54,280
Speaker 1: alternatives are just are cheaper and cleaner. But we still

516
00:33:54,320 --> 00:33:59,240
Speaker 1: need we need some really innovative new technology, particularly energy

517
00:33:59,400 --> 00:34:03,800
Speaker 1: storage for sources of energy that are intermittent. Did you

518
00:34:04,440 --> 00:34:07,960
Speaker 1: did you read recently about the UM I think it

519
00:34:08,040 --> 00:34:10,600
Speaker 1: was the professor at M I T who came up

520
00:34:10,640 --> 00:34:14,359
Speaker 1: with a methodology of pulling carbon out of the atmosphere.

521
00:34:14,840 --> 00:34:18,360
Speaker 1: And actually it's more of a chemical than a technology.

522
00:34:18,400 --> 00:34:23,000
Speaker 1: Now if that proves, But let's let's let's roll things

523
00:34:23,080 --> 00:34:28,040
Speaker 1: back to Okay, the transistor has been invented the night.

524
00:34:28,239 --> 00:34:33,279
Speaker 1: The notion that you can use solid devices with particular properties,

525
00:34:33,520 --> 00:34:37,759
Speaker 1: not vacuum tubes, to douce to switch signals, is that

526
00:34:37,880 --> 00:34:41,480
Speaker 1: our idea is out there. Nobody knows how to actually

527
00:34:41,520 --> 00:34:45,760
Speaker 1: turn that into working machines that can function at scale.

528
00:34:46,280 --> 00:34:49,680
Speaker 1: IBM makes a bet. BET's on a material called germanium.

529
00:34:50,160 --> 00:34:55,120
Speaker 1: Physicists will tell you germanium from a purely theoretical perspective's right,

530
00:34:55,160 --> 00:34:58,799
Speaker 1: because the electrons moved through it better than silicon exactly.

531
00:34:58,840 --> 00:35:03,960
Speaker 1: But absolute bear to produce at scale, and silicon is

532
00:35:03,960 --> 00:35:08,200
Speaker 1: easy to and and and the guys in Washington put

533
00:35:08,200 --> 00:35:10,399
Speaker 1: out a whole bunch of experiments. And it turns out

534
00:35:10,440 --> 00:35:14,800
Speaker 1: that this little, this little oil field instrument company in Houston,

535
00:35:15,360 --> 00:35:21,440
Speaker 1: Texas Instruments, works out how to process silicon. It's not

536
00:35:21,600 --> 00:35:25,200
Speaker 1: quite as good theoretically, but practically it's much better. Scales

537
00:35:25,320 --> 00:35:29,480
Speaker 1: up tremendously, and it scales up in response to government

538
00:35:29,600 --> 00:35:33,200
Speaker 1: orders when the government doesn't care that it's really expensive

539
00:35:33,239 --> 00:35:36,280
Speaker 1: because it's putting, you know, it's building the memory systems

540
00:35:36,320 --> 00:35:39,359
Speaker 1: for the guidance systems for the Minuteman missile. But then

541
00:35:39,400 --> 00:35:42,120
Speaker 1: they do something else they actually broke or a deal

542
00:35:42,400 --> 00:35:45,880
Speaker 1: between IBM and t I. The technology gets transferred to

543
00:35:45,920 --> 00:35:49,160
Speaker 1: IBM and return for IBM, giving t I a big order.

544
00:35:49,600 --> 00:35:54,800
Speaker 1: It's the kind of active intervention based on intelligent experimentation

545
00:35:55,560 --> 00:35:58,240
Speaker 1: we should be doing. R p E, which has got

546
00:35:58,640 --> 00:36:01,400
Speaker 1: barely two hundred and fIF the million dollars of funding

547
00:36:01,760 --> 00:36:05,000
Speaker 1: Darpen today still has over three billions. If we were

548
00:36:05,040 --> 00:36:08,240
Speaker 1: doing this seriously, and we had somebody at the Department

549
00:36:08,239 --> 00:36:11,560
Speaker 1: of Energy with a mission not to protect the coal

550
00:36:11,600 --> 00:36:16,359
Speaker 1: industry with all of its fifty employees, UM, we would

551
00:36:16,400 --> 00:36:20,359
Speaker 1: be doing the same kind of experimentation every imaginable form

552
00:36:20,400 --> 00:36:26,279
Speaker 1: of battery technology, the science of energy chemical conversion. We

553
00:36:26,320 --> 00:36:28,640
Speaker 1: need a new Manhattan project for this sort of thing.

554
00:36:29,040 --> 00:36:31,840
Speaker 1: We have been speaking with Bill Janeway. He is the

555
00:36:31,920 --> 00:36:36,920
Speaker 1: author of Doing Venture Capital in the Innovation Economy. We

556
00:36:37,040 --> 00:36:40,640
Speaker 1: love your comments, feedback and suggestions right to us at

557
00:36:41,280 --> 00:36:44,920
Speaker 1: m IB podcast at Bloomberg dot net. Be sure and

558
00:36:45,000 --> 00:36:47,840
Speaker 1: check out my daily column on Bloomberg dot com. You

559
00:36:47,880 --> 00:36:51,200
Speaker 1: can follow me on Twitter at rit Holts. I'm Barry

560
00:36:51,280 --> 00:36:55,000
Speaker 1: rit Holts. You're listening to Master's in Business on Bloomberg Radio.

561
00:37:08,560 --> 00:37:11,040
Speaker 1: Welcome to the podcast. Bill, Thank you so much for

562
00:37:11,120 --> 00:37:15,040
Speaker 1: doing this, UM I have I'm so glad the run

563
00:37:15,040 --> 00:37:18,440
Speaker 1: of questions I had you and I didn't get to

564
00:37:18,520 --> 00:37:21,160
Speaker 1: any of them, which is always a good sign of

565
00:37:21,200 --> 00:37:24,719
Speaker 1: a good conversation because you say something and it sends

566
00:37:24,760 --> 00:37:28,239
Speaker 1: me off in a different direction. So I I appreciate

567
00:37:28,320 --> 00:37:32,120
Speaker 1: your ability to UM to help guide where where this

568
00:37:32,200 --> 00:37:37,440
Speaker 1: conversation UH is going. And you certainly have a illustrious

569
00:37:37,560 --> 00:37:40,239
Speaker 1: enough career. There's not a lot of stuff that you

570
00:37:40,320 --> 00:37:43,920
Speaker 1: haven't seen. I know you're not big into biotech, but

571
00:37:44,080 --> 00:37:51,240
Speaker 1: pretty much anything involving software, electronics, semiconductors. You were right

572
00:37:51,239 --> 00:37:54,000
Speaker 1: there in the middle of that throughout the whole UH,

573
00:37:54,120 --> 00:37:56,640
Speaker 1: the whole run, well it was. It was a great

574
00:37:56,680 --> 00:37:59,960
Speaker 1: time to get engaged in the as as. Wait a second,

575
00:38:00,040 --> 00:38:03,280
Speaker 1: I have to stop you. What is that watch? Oh?

576
00:38:03,400 --> 00:38:05,640
Speaker 1: That is a yellow time X. I think it costs

577
00:38:05,680 --> 00:38:10,279
Speaker 1: eleven dollars by When I first saw it on your wrist,

578
00:38:10,320 --> 00:38:12,520
Speaker 1: I assumed, oh, that's an Apple watch, and then you

579
00:38:12,600 --> 00:38:15,480
Speaker 1: flipped it over and I'm like, wait a second, an

580
00:38:15,480 --> 00:38:19,839
Speaker 1: Apple watch forty one years ago. Yeah, I traded an

581
00:38:19,880 --> 00:38:25,440
Speaker 1: addiction to nicotine for an obsessive compulsion for running. Okay,

582
00:38:25,920 --> 00:38:30,000
Speaker 1: and so my default is a running watch, and I

583
00:38:30,080 --> 00:38:32,440
Speaker 1: have a good watch. I have a couple of decent watches.

584
00:38:32,800 --> 00:38:34,279
Speaker 1: But this is sort of what I put on in

585
00:38:34,280 --> 00:38:36,600
Speaker 1: the morning and take off if I'm going to a

586
00:38:36,640 --> 00:38:39,399
Speaker 1: fancy dinner party in the evening. And you know what

587
00:38:39,840 --> 00:38:41,920
Speaker 1: if I if I drop it off the side of

588
00:38:41,960 --> 00:38:45,239
Speaker 1: a rowboat, can you still replace it? Are they still

589
00:38:45,239 --> 00:38:47,319
Speaker 1: making Yeah? They still. You go into any running store

590
00:38:47,320 --> 00:38:49,080
Speaker 1: and you'll find something like this. Just go for the

591
00:38:49,080 --> 00:38:51,080
Speaker 1: bottom end. I don't have the bus. Yeah. If I

592
00:38:51,120 --> 00:38:52,759
Speaker 1: want to know how my heart's doing, you know, I

593
00:38:52,840 --> 00:38:55,200
Speaker 1: just take my pulse. I don't need a two hundred

594
00:38:55,239 --> 00:39:00,560
Speaker 1: and fifty dollars five hundred dollar, thousand dollar. Let's see

595
00:39:00,560 --> 00:39:03,160
Speaker 1: what my pulse is. So my pulse, I'm all excited.

596
00:39:03,200 --> 00:39:05,480
Speaker 1: My pulse is eighty three centing. He are talking. It's

597
00:39:05,520 --> 00:39:08,680
Speaker 1: been an exciting conversation. If I concentrate, I could get

598
00:39:08,680 --> 00:39:10,719
Speaker 1: it down to the low sixties. But I really have

599
00:39:10,760 --> 00:39:16,000
Speaker 1: to bore the audience and just ll out. But here,

600
00:39:16,080 --> 00:39:18,960
Speaker 1: let's see what what that just got it down to? Um? No,

601
00:39:19,160 --> 00:39:23,960
Speaker 1: still there you go. So you take your pulse. You

602
00:39:24,000 --> 00:39:29,560
Speaker 1: literally just count Yeah, all right, but analog, you know, analog.

603
00:39:29,680 --> 00:39:33,320
Speaker 1: Sometimes it's adequate, but truly analog. That's the first digital

604
00:39:33,400 --> 00:39:39,000
Speaker 1: watch that's truly analysts holding your fingers on your on

605
00:39:39,080 --> 00:39:43,520
Speaker 1: your wrist, that's an analog readout. So so you you

606
00:39:43,640 --> 00:39:48,480
Speaker 1: started really full born too. The VC side in the

607
00:39:48,600 --> 00:39:53,560
Speaker 1: late eighties, was it in my imagination. It's hard today

608
00:39:53,719 --> 00:39:56,600
Speaker 1: and it was easy then throw a dart. You're gonna

609
00:39:56,600 --> 00:39:58,600
Speaker 1: find a company that's gonna make money. You know. I'll

610
00:39:58,640 --> 00:40:01,440
Speaker 1: tell you what's really interesting, because this is now I'm

611
00:40:01,440 --> 00:40:04,400
Speaker 1: gonna put on my academic hat. There there's been a

612
00:40:04,480 --> 00:40:07,480
Speaker 1: lot of academic research on venture capital, and I'm a

613
00:40:07,520 --> 00:40:09,440
Speaker 1: student of it, and I've contributed to it and i

614
00:40:09,480 --> 00:40:12,200
Speaker 1: teach it. So I'm really I really know this data

615
00:40:12,280 --> 00:40:14,799
Speaker 1: and I know the analysis of the data. There are

616
00:40:14,840 --> 00:40:22,120
Speaker 1: two overwhelming what they call statistical stylized facts about the

617
00:40:22,160 --> 00:40:25,520
Speaker 1: history of venture capital. It's about since nineteen eighty that's

618
00:40:25,520 --> 00:40:27,800
Speaker 1: when we can start to have real data. The first

619
00:40:27,880 --> 00:40:32,000
Speaker 1: is there's incredible skew in the returns, meaning that it's

620
00:40:32,000 --> 00:40:34,200
Speaker 1: a fat head and a long tail, a handful of

621
00:40:34,239 --> 00:40:37,600
Speaker 1: giant winners. Now are you talking about the companies You're

622
00:40:37,600 --> 00:40:40,400
Speaker 1: talking about the venture funds. First, I'm talking about the

623
00:40:40,520 --> 00:40:44,400
Speaker 1: venture funds. If you take all the venture funds that

624
00:40:44,520 --> 00:40:47,640
Speaker 1: have ever been raised and invested since nineteen eighty, a

625
00:40:47,800 --> 00:40:52,759
Speaker 1: ridiculously small number of those funds have delivered all of

626
00:40:52,800 --> 00:40:57,080
Speaker 1: the excess return versus the Natstack index. But here's the

627
00:40:57,120 --> 00:41:00,600
Speaker 1: second fact, and this is distinctive, if not unique, to

628
00:41:00,680 --> 00:41:04,520
Speaker 1: venture capital. There is what they call the academics called

629
00:41:04,560 --> 00:41:09,720
Speaker 1: persistence in the returns. In other words, firm X raises

630
00:41:09,840 --> 00:41:14,560
Speaker 1: for fund one, and the performance of fund one predicts

631
00:41:15,000 --> 00:41:17,960
Speaker 1: the performance of fund too. And let me flesh that

632
00:41:18,000 --> 00:41:21,520
Speaker 1: out a little bit. So fun one does well, so

633
00:41:21,680 --> 00:41:24,960
Speaker 1: a institutions throw money at them. They're not wasting a

634
00:41:24,960 --> 00:41:27,880
Speaker 1: lot of time, energy resources trying to raise money because

635
00:41:27,880 --> 00:41:32,200
Speaker 1: people want part of that. But more importantly, the deal

636
00:41:32,280 --> 00:41:34,959
Speaker 1: flow that they get to look at it. They get

637
00:41:35,000 --> 00:41:39,279
Speaker 1: first choice everywhere exactly. So that's Kleiner Perkins. Let's go

638
00:41:39,360 --> 00:41:42,640
Speaker 1: down the list. Well, it evolves over time. It evolves

639
00:41:42,640 --> 00:41:45,919
Speaker 1: over time, andres and Horowitz was not on that list,

640
00:41:46,000 --> 00:41:50,839
Speaker 1: didn't um and and firms go through changes. I will

641
00:41:50,880 --> 00:41:56,080
Speaker 1: give uh. You know in the in the nineties, Benchmark

642
00:41:56,320 --> 00:41:59,000
Speaker 1: was founded and they've generated the twenty year record now

643
00:41:59,400 --> 00:42:04,280
Speaker 1: pretty good, really consistent partners. One well, Sequoia goes way back.

644
00:42:05,320 --> 00:42:08,800
Speaker 1: They've now expanded there were you know, they've really increased

645
00:42:08,800 --> 00:42:11,080
Speaker 1: the scale of the firm and the fund, and they're

646
00:42:11,120 --> 00:42:14,239
Speaker 1: investing globally, so we'll have to seek and they maintain

647
00:42:14,760 --> 00:42:18,960
Speaker 1: with a somewhat different profile the kind of outstanding results

648
00:42:18,960 --> 00:42:22,520
Speaker 1: they've had for a generation. Firms also go through generational

649
00:42:22,600 --> 00:42:25,600
Speaker 1: shifts and leadership, but by and large, the point about

650
00:42:25,680 --> 00:42:29,279
Speaker 1: this is if you're if you're if you're trustee of

651
00:42:29,320 --> 00:42:32,319
Speaker 1: a pension fund, and the and the consultants come and

652
00:42:32,320 --> 00:42:34,800
Speaker 1: tell you, you know, you've got to play venture capital.

653
00:42:35,280 --> 00:42:39,440
Speaker 1: Over time, you're gonna add incremental return to the portfolio.

654
00:42:39,760 --> 00:42:43,160
Speaker 1: The answer is, if you can get into the venture

655
00:42:43,640 --> 00:42:50,000
Speaker 1: funds that don't need your money, do it the best funds,

656
00:42:50,040 --> 00:42:53,200
Speaker 1: but don't just have a you know, three percent allocation

657
00:42:53,320 --> 00:42:56,120
Speaker 1: for a twenty billion dollar fund that has to be

658
00:42:56,200 --> 00:42:59,560
Speaker 1: invested in venture capital because there's a you know, there's

659
00:42:59,600 --> 00:43:02,520
Speaker 1: some really good stuff. In economics, there's a concept called

660
00:43:02,560 --> 00:43:06,000
Speaker 1: adverse selection sure which means if they need your money,

661
00:43:06,480 --> 00:43:07,960
Speaker 1: you don't want to give it to them. You don't

662
00:43:07,960 --> 00:43:09,799
Speaker 1: want to be a member of any club that will

663
00:43:09,840 --> 00:43:13,560
Speaker 1: have you. So by the way that data set, both

664
00:43:13,600 --> 00:43:18,360
Speaker 1: the persistency side and the fat headlong tail side is

665
00:43:18,400 --> 00:43:20,239
Speaker 1: true for the hedge fund world, it's true for the

666
00:43:20,239 --> 00:43:24,960
Speaker 1: private equity world, and arguably it's true for the world

667
00:43:25,040 --> 00:43:30,040
Speaker 1: of public companies if you're not indexing but buying individual

668
00:43:30,120 --> 00:43:34,279
Speaker 1: stocks of We used to think a disproportionate amount of

669
00:43:34,320 --> 00:43:37,520
Speaker 1: returns came from a small number of companies. The most

670
00:43:37,560 --> 00:43:41,000
Speaker 1: recent data, and I wrote about this some time ago

671
00:43:41,800 --> 00:43:45,479
Speaker 1: based on somebody else's study, it's even narrower than we thought.

672
00:43:45,520 --> 00:43:48,879
Speaker 1: It's a tiny percentage of companies delivering more than half

673
00:43:48,880 --> 00:43:51,360
Speaker 1: of the returns. It's it's amazing. Well go that goes

674
00:43:51,440 --> 00:43:55,400
Speaker 1: with this, this institutional change in the market, the rise

675
00:43:55,520 --> 00:44:00,399
Speaker 1: of people managing other people's money, right where if don't

676
00:44:00,480 --> 00:44:02,880
Speaker 1: do at least as well as the index guess what

677
00:44:03,880 --> 00:44:07,920
Speaker 1: their clients their investors take their money away. And then,

678
00:44:07,920 --> 00:44:10,440
Speaker 1: of course, when more and more of that institutional money

679
00:44:10,640 --> 00:44:14,920
Speaker 1: is passive index funds, you're, by charter, by contract, you

680
00:44:14,960 --> 00:44:19,160
Speaker 1: can't be a contrarian. So what it means is, you know, cumulatively,

681
00:44:19,200 --> 00:44:23,239
Speaker 1: you get more momentum investing, more hurting behavior. At the

682
00:44:23,320 --> 00:44:26,920
Speaker 1: same time, we've had this extraordinary phenomenon since two thousand,

683
00:44:27,400 --> 00:44:29,719
Speaker 1: the number of public companies in the United States, as

684
00:44:29,760 --> 00:44:33,759
Speaker 1: you know, it's half of what it was thousand is about,

685
00:44:35,000 --> 00:44:37,880
Speaker 1: which is that's kind of kind of hilarious. There was

686
00:44:37,920 --> 00:44:40,680
Speaker 1: a wonderful book in the mid nineties by a I

687
00:44:40,719 --> 00:44:44,360
Speaker 1: want to say, Cornell professor Robert Frank. I'm sure I

688
00:44:44,360 --> 00:44:47,840
Speaker 1: can find it if I just if I just clicked.

689
00:44:47,880 --> 00:44:51,160
Speaker 1: But it was called The Winner Take All Society, and

690
00:44:51,200 --> 00:44:57,040
Speaker 1: it looks at things like UM athletes and movies and

691
00:44:57,760 --> 00:45:02,400
Speaker 1: just how you end up with this just unbelievable concentration

692
00:45:03,320 --> 00:45:08,319
Speaker 1: UM yep, Johnson School of Management at Cordell Robert H. Frank. Now,

693
00:45:09,080 --> 00:45:12,600
Speaker 1: remember the columnist Robert Frank is a different, different person.

694
00:45:13,000 --> 00:45:18,120
Speaker 1: But we see that across Overville, these alternative investment platforms.

695
00:45:18,160 --> 00:45:20,759
Speaker 1: If you're not in the top let's call a ten,

696
00:45:21,840 --> 00:45:25,279
Speaker 1: you're you're you're more or less spending a lot of

697
00:45:25,320 --> 00:45:28,719
Speaker 1: money for a very low probability of retire. But now

698
00:45:28,760 --> 00:45:34,720
Speaker 1: that feeds into one of the phenomena that actually got

699
00:45:34,800 --> 00:45:40,000
Speaker 1: me back into looking at this whole process, that intersection

700
00:45:40,040 --> 00:45:43,560
Speaker 1: of government and the stock market and the market economy,

701
00:45:43,600 --> 00:45:45,640
Speaker 1: which what I call the three player game and in

702
00:45:45,680 --> 00:45:48,279
Speaker 1: my in my books and my lectures. It got me

703
00:45:48,320 --> 00:45:52,520
Speaker 1: going back into it was this amazing phenomenon, the unicorns,

704
00:45:52,560 --> 00:45:56,640
Speaker 1: the unicorn bubble, the Airbnb, the Ubers, the parin nos,

705
00:45:56,719 --> 00:46:00,319
Speaker 1: go down the list, the the well what to those?

706
00:46:00,360 --> 00:46:04,720
Speaker 1: Just are Uber? We know is private? Airbnb they haven't

707
00:46:04,719 --> 00:46:08,840
Speaker 1: their private, They're still private. Who else is on that list? There? Actually?

708
00:46:08,880 --> 00:46:10,680
Speaker 1: If you, if you, if you actually were to go

709
00:46:10,719 --> 00:46:16,000
Speaker 1: to Google, huh and type in unicorns tech unicorns just

710
00:46:16,160 --> 00:46:19,279
Speaker 1: unicorns just just straight up well Airbnb, you know they

711
00:46:19,800 --> 00:46:22,239
Speaker 1: in the in the data. Airbnb is usually counted in

712
00:46:22,320 --> 00:46:26,120
Speaker 1: accommodation and not as a tech company. But it's something

713
00:46:26,160 --> 00:46:30,279
Speaker 1: like two hundred and fifty globally. So the first first

714
00:46:30,280 --> 00:46:35,120
Speaker 1: one that comes up is unicorn, Wikipedia about the um

715
00:46:35,880 --> 00:46:42,040
Speaker 1: the mythological unicorn. With the second one is Fortune list

716
00:46:42,320 --> 00:46:48,919
Speaker 1: of uh unicorns and it's Uber, Ziomi, Airbnb, Palantier, Well,

717
00:46:49,000 --> 00:46:55,680
Speaker 1: Snapchat is no longer private, China, Internet plus SpaceX Pinterest.

718
00:46:56,200 --> 00:46:58,600
Speaker 1: Let's see the full list. This is already a little dated.

719
00:46:58,840 --> 00:47:03,720
Speaker 1: I've seen one that we were i I. It's something

720
00:47:03,840 --> 00:47:09,239
Speaker 1: over two hundred globally private companies valued at more than

721
00:47:09,280 --> 00:47:13,239
Speaker 1: a billion dollars. There the tons so that raises a

722
00:47:13,320 --> 00:47:18,040
Speaker 1: really interesting question, Um, and it goes back to you

723
00:47:18,080 --> 00:47:22,800
Speaker 1: mentioned the DARPA VC funds is barely three billion dollars.

724
00:47:23,040 --> 00:47:25,960
Speaker 1: It's not a VC funds, but it's a research fund

725
00:47:26,120 --> 00:47:28,799
Speaker 1: that that sort of looks and it's now much more

726
00:47:28,840 --> 00:47:32,920
Speaker 1: heavily towards military technology, not the general purpose investing that

727
00:47:33,080 --> 00:47:35,799
Speaker 1: used to be. Exactly, But what do you make of

728
00:47:35,840 --> 00:47:40,400
Speaker 1: the funds? Like soft bank has a hundred billion dollar funds,

729
00:47:40,760 --> 00:47:44,280
Speaker 1: that seems like a lot of capital. There are two things.

730
00:47:44,320 --> 00:47:48,520
Speaker 1: What one is they used a substantial hunk of that

731
00:47:48,600 --> 00:47:51,600
Speaker 1: money to help buy ARM not exactly a venture investment,

732
00:47:52,040 --> 00:47:56,080
Speaker 1: a huge ARM holding as a semiconductor it's really it's

733
00:47:56,200 --> 00:48:00,400
Speaker 1: it's an intellectual property company that licenses architectures for the

734
00:48:00,440 --> 00:48:05,440
Speaker 1: devices that dominate the mobile phone business, along with qualcom Um.

735
00:48:05,480 --> 00:48:09,400
Speaker 1: But they also they put five hundred million dollars into

736
00:48:09,880 --> 00:48:15,239
Speaker 1: a no revenue start up that is a game proposals

737
00:48:15,280 --> 00:48:21,760
Speaker 1: to deliver a platform for computer games called Remarkably enough,

738
00:48:22,000 --> 00:48:26,520
Speaker 1: the company is called Improbable. I didn't make it up.

739
00:48:27,640 --> 00:48:32,360
Speaker 1: Five hundred million dollars half a billion dollars. Do you

740
00:48:32,360 --> 00:48:34,640
Speaker 1: have any idea what you can do the kind of

741
00:48:34,680 --> 00:48:36,840
Speaker 1: party you can have with half a billion dollars. Well,

742
00:48:36,960 --> 00:48:40,160
Speaker 1: if you're doing ten million dollar deals, where you're taking

743
00:48:40,320 --> 00:48:43,160
Speaker 1: depending whether you're early stage or late stage, whether you're

744
00:48:43,200 --> 00:48:47,120
Speaker 1: taking ten percent or you could. You know, I would

745
00:48:47,200 --> 00:48:50,560
Speaker 1: rather roll the dice with fifty companies than one. Exactly,

746
00:48:50,880 --> 00:48:54,880
Speaker 1: that's the point given given the again that fat headlong tail.

747
00:48:55,600 --> 00:48:58,680
Speaker 1: To get one of the winners, you have to, you know,

748
00:48:58,560 --> 00:49:01,640
Speaker 1: you have to. There's a pony in here somewhere, right,

749
00:49:01,680 --> 00:49:04,000
Speaker 1: but you gotta. That's why they call it spray and

750
00:49:04,040 --> 00:49:06,520
Speaker 1: pray to A Couple of different things have gone have

751
00:49:06,600 --> 00:49:10,280
Speaker 1: gone on pray and prey. So one is the cost

752
00:49:10,320 --> 00:49:14,160
Speaker 1: of launching a startup has dropped like a stone. Two guys,

753
00:49:14,200 --> 00:49:16,799
Speaker 1: a laptop and an internet connection is for someone else.

754
00:49:16,840 --> 00:49:20,680
Speaker 1: Destroy software, open source software, rent your computer cycles and

755
00:49:20,719 --> 00:49:23,880
Speaker 1: your storage from Amazon. You only pay as you use it.

756
00:49:24,360 --> 00:49:28,400
Speaker 1: So getting something out and onto the net cost nothing.

757
00:49:28,520 --> 00:49:30,479
Speaker 1: So that the old days of ten or twenty million

758
00:49:30,520 --> 00:49:33,600
Speaker 1: dollars just to set up a firm, that's right gone.

759
00:49:33,680 --> 00:49:37,279
Speaker 1: So lots and lots of startups. But the cost of

760
00:49:37,400 --> 00:49:41,200
Speaker 1: building a business, the cost of getting to scale, and

761
00:49:41,239 --> 00:49:44,640
Speaker 1: the way these companies are doing it by not by

762
00:49:44,800 --> 00:49:49,239
Speaker 1: generating cash, by selling services and products to customers, but

763
00:49:49,280 --> 00:49:53,640
Speaker 1: by issuing securities to investors while they give away free

764
00:49:53,800 --> 00:49:58,520
Speaker 1: in order to get users. So that's Spotify, and that's

765
00:49:58,680 --> 00:50:01,800
Speaker 1: exactly right. You know, it's Airbnb had a business model

766
00:50:01,840 --> 00:50:05,840
Speaker 1: pretty much from the beginning, and Uber lost a billion

767
00:50:05,840 --> 00:50:09,480
Speaker 1: dollars last quarter in cash now, but they're making it

768
00:50:09,560 --> 00:50:12,160
Speaker 1: up in volume, so it all wokes out. When I

769
00:50:12,200 --> 00:50:15,040
Speaker 1: was growing up in the business, my mentor was a

770
00:50:15,040 --> 00:50:17,640
Speaker 1: guy called Fred Adler. Nobody remembers Fred anymore. He was

771
00:50:18,160 --> 00:50:22,759
Speaker 1: first generation investment venture capitalist, a lawyer who knew how

772
00:50:22,760 --> 00:50:24,960
Speaker 1: to turn around companies, and he had a He had

773
00:50:24,960 --> 00:50:29,239
Speaker 1: a motto. It was corporate happiness is positive cash flow. Right.

774
00:50:29,520 --> 00:50:32,239
Speaker 1: What it means is if if, if, if, what you're

775
00:50:32,600 --> 00:50:36,400
Speaker 1: getting paid by your customers generates more cash than the

776
00:50:36,480 --> 00:50:38,959
Speaker 1: cost to deliver the goods or service. Two things happen.

777
00:50:39,320 --> 00:50:42,279
Speaker 1: One you have a validation that what you're doing is

778
00:50:42,280 --> 00:50:47,239
Speaker 1: economically worthwhile, and two you're liberated from dependence on the

779
00:50:47,280 --> 00:50:50,040
Speaker 1: capital markets, which are there when they're there, and when

780
00:50:50,040 --> 00:50:53,680
Speaker 1: they're not there you start. Now, what's weird about this

781
00:50:53,840 --> 00:50:58,839
Speaker 1: unicorn bubble is that it's institutional investors who are used

782
00:50:58,880 --> 00:51:03,160
Speaker 1: to buying liquidity in the market, who are on the

783
00:51:03,200 --> 00:51:07,640
Speaker 1: one hand, prepared to accept so much more risk to

784
00:51:07,719 --> 00:51:10,239
Speaker 1: get a return in the zero real risk free rate

785
00:51:10,320 --> 00:51:13,560
Speaker 1: environment would you've had for almost ten years, and on

786
00:51:13,600 --> 00:51:17,200
Speaker 1: the other hand, are paying premium values relative to the

787
00:51:17,239 --> 00:51:21,320
Speaker 1: existing public companies, you know, the Googles and the facebooks

788
00:51:21,360 --> 00:51:23,560
Speaker 1: to get access for What they say is you know,

789
00:51:23,600 --> 00:51:27,480
Speaker 1: fomo fear of missing out because as you this goes

790
00:51:27,520 --> 00:51:30,239
Speaker 1: all the way back to saying that the returns are

791
00:51:30,320 --> 00:51:33,879
Speaker 1: so concentrated. But instead of saying, well, I'm gonna put

792
00:51:34,239 --> 00:51:37,240
Speaker 1: you know, a million bucks into fifty startups and hope

793
00:51:37,280 --> 00:51:39,600
Speaker 1: that two or three or four of them blow out,

794
00:51:39,680 --> 00:51:42,680
Speaker 1: and I write a million, but the five million gets

795
00:51:42,680 --> 00:51:45,600
Speaker 1: me big, big returns. They're writing checks that are fifty

796
00:51:46,239 --> 00:51:50,920
Speaker 1: five million to play that game and and and a

797
00:51:51,080 --> 00:51:54,839
Speaker 1: rich valuations. Not they're not early stage in that tree ridge,

798
00:51:55,120 --> 00:51:58,520
Speaker 1: and so they're exposed to I mean, the the unicorn bubble,

799
00:51:58,600 --> 00:52:01,400
Speaker 1: like all bubbles, will end. That's the law of bubbles.

800
00:52:01,400 --> 00:52:05,000
Speaker 1: Bubbles end um. And you can see two different ways

801
00:52:05,120 --> 00:52:13,000
Speaker 1: it can end. One is that the FED indeed normalizes rates,

802
00:52:13,200 --> 00:52:17,760
Speaker 1: which they seem to be in the process of credit

803
00:52:17,800 --> 00:52:21,680
Speaker 1: spreads open up and it becomes possible to get, you know,

804
00:52:21,800 --> 00:52:24,799
Speaker 1: a real return of five, six seven percent as an

805
00:52:24,800 --> 00:52:30,279
Speaker 1: institution investing in you know, TRIPLEB bonds um which will

806
00:52:30,320 --> 00:52:34,160
Speaker 1: take a lot of money shift money within the financial system.

807
00:52:34,280 --> 00:52:37,600
Speaker 1: The other is that you have you know, a fraud

808
00:52:37,680 --> 00:52:43,360
Speaker 1: like thearas. You have the genius, the incredible genius and

809
00:52:43,600 --> 00:52:47,200
Speaker 1: energy of Elon Musk. Now, let's say, subject to some

810
00:52:48,320 --> 00:52:54,319
Speaker 1: closer scrutiny, SpaceX, Boring Company or Tesla or whatever he's

811
00:52:54,360 --> 00:52:59,840
Speaker 1: cranking them out. And if there's a cumulative drumbeat a

812
00:53:00,160 --> 00:53:07,480
Speaker 1: companies that promised unbelievable transformational technology to the world failing

813
00:53:07,840 --> 00:53:12,120
Speaker 1: to meet those expectations, that changes you know how you

814
00:53:12,200 --> 00:53:16,680
Speaker 1: know better than I how market psychology, how investors shift

815
00:53:17,080 --> 00:53:20,239
Speaker 1: the frame and the lens through which they're looking at investments.

816
00:53:20,239 --> 00:53:22,680
Speaker 1: So so let's let's explore that a little bit because

817
00:53:22,680 --> 00:53:26,360
Speaker 1: my pet thesis is there's just so much damn money

818
00:53:26,520 --> 00:53:30,000
Speaker 1: slashing around that it's always looking for a home. And

819
00:53:30,000 --> 00:53:33,080
Speaker 1: when you say to somebody, hey, tenure, I could get you,

820
00:53:33,080 --> 00:53:36,120
Speaker 1: you know, two point two percent or wherever it will

821
00:53:36,160 --> 00:53:39,800
Speaker 1: be by the time this broadcast. I don't want two percent.

822
00:53:39,840 --> 00:53:44,040
Speaker 1: I'm looking for a six, seven, ten whatever. So first

823
00:53:44,960 --> 00:53:48,960
Speaker 1: is all this capital the reason a fraud like Tharakhous

824
00:53:49,080 --> 00:53:52,399
Speaker 1: could get was it nine million dollars in funding? I'm

825
00:53:52,400 --> 00:53:54,080
Speaker 1: doing that off the top. I think it was a

826
00:53:54,120 --> 00:53:58,000
Speaker 1: bit like it was still huge and you know whatever,

827
00:53:58,000 --> 00:54:01,400
Speaker 1: it was reality distortion fee old. It certainly is the

828
00:54:01,480 --> 00:54:03,680
Speaker 1: fact that there is a huge amount of money around now.

829
00:54:03,760 --> 00:54:07,279
Speaker 1: The two things to say about that. One is the

830
00:54:07,400 --> 00:54:11,040
Speaker 1: thing about liquidity is the more you needed, the less

831
00:54:11,040 --> 00:54:15,200
Speaker 1: there is liquidity comes there. Remember with the liquidity in

832
00:54:15,280 --> 00:54:19,719
Speaker 1: the buyout market in two thousand and six, Remember that

833
00:54:19,760 --> 00:54:23,239
Speaker 1: buy out of the Texas Utility Company Electric Utility Company?

834
00:54:23,360 --> 00:54:26,359
Speaker 1: But was it forty billion dollars? Some ungodly number? And

835
00:54:27,640 --> 00:54:31,040
Speaker 1: in September two thousand and eight you couldn't scrape two

836
00:54:31,120 --> 00:54:37,040
Speaker 1: nickels together. So the perception of it is this sort

837
00:54:37,080 --> 00:54:42,040
Speaker 1: of self validating process. There's so much money around today

838
00:54:42,080 --> 00:54:45,560
Speaker 1: that we can exsume it will always be around until

839
00:54:45,840 --> 00:54:49,520
Speaker 1: guess what it's not. That's where I think the real

840
00:54:49,640 --> 00:54:55,680
Speaker 1: challenge for the entrepreneurs and the core investors in these unicorns,

841
00:54:55,719 --> 00:55:00,279
Speaker 1: because the process of learning how to generate to pay

842
00:55:00,320 --> 00:55:03,200
Speaker 1: your bills because your customers are giving you more money

843
00:55:03,400 --> 00:55:07,239
Speaker 1: than you're paying out to serve them. That is a

844
00:55:07,360 --> 00:55:10,520
Speaker 1: really hard discipline to learn. And if you're being able

845
00:55:10,600 --> 00:55:14,000
Speaker 1: to raise one billion, two billion, three billion a year,

846
00:55:14,920 --> 00:55:20,319
Speaker 1: giving up almost no control, giving up minimal amounts of ownership,

847
00:55:20,680 --> 00:55:24,840
Speaker 1: minimal dilution, boy, that's that's that's the best heroin you

848
00:55:24,880 --> 00:55:27,719
Speaker 1: could ever have. So when you look at let's hold

849
00:55:27,760 --> 00:55:30,719
Speaker 1: thereinos aside, because clearly it was a fraud, right, I mean,

850
00:55:30,719 --> 00:55:32,799
Speaker 1: I don't believe it started as a fraud. And if

851
00:55:32,800 --> 00:55:34,800
Speaker 1: you haven't read the book Bad Blood, it was delfe

852
00:55:34,880 --> 00:55:38,040
Speaker 1: I read the newspaper columns in real time. Um, but

853
00:55:38,160 --> 00:55:42,160
Speaker 1: the book reveals stuff about it that the columns did

854
00:55:42,200 --> 00:55:45,480
Speaker 1: not like. The board of directors was a did you

855
00:55:45,520 --> 00:55:48,759
Speaker 1: know they had no voting shares that she had, Elizabeth,

856
00:55:49,120 --> 00:55:52,080
Speaker 1: and that you know there were no people from a

857
00:55:52,160 --> 00:55:56,040
Speaker 1: medical device biotech healthcare background on the board, at least

858
00:55:56,040 --> 00:55:59,960
Speaker 1: at first. With all respect to too great public service

859
00:56:00,920 --> 00:56:04,200
Speaker 1: Henry Kissinger and George Sultz, if you're ever given an

860
00:56:04,239 --> 00:56:07,520
Speaker 1: investment opportunity, we have two members of the board who

861
00:56:07,520 --> 00:56:12,760
Speaker 1: are former secretaries of State in their nineties, politely say

862
00:56:12,880 --> 00:56:16,600
Speaker 1: thank you and pass on by. There were people have

863
00:56:16,680 --> 00:56:21,160
Speaker 1: accused me of um having hindsight bias in this, but

864
00:56:21,400 --> 00:56:25,279
Speaker 1: there were some fairly obvious red flags, that being one

865
00:56:25,320 --> 00:56:30,719
Speaker 1: of them. Nobody, every single VC that specialized in medtech, healthcare,

866
00:56:31,200 --> 00:56:35,800
Speaker 1: biosciences took a hard pass. That's a screaming red flag.

867
00:56:37,120 --> 00:56:39,839
Speaker 1: Talk to any humatologists in the world and they will

868
00:56:39,880 --> 00:56:41,839
Speaker 1: tell you. And I'm not a humatologist, and I don't

869
00:56:41,840 --> 00:56:43,839
Speaker 1: play one on television, but I do know some very

870
00:56:43,840 --> 00:56:49,200
Speaker 1: good academic ones. You cannot run the full panel of

871
00:56:49,280 --> 00:56:52,319
Speaker 1: bloods that they do when they put that needle in

872
00:56:52,360 --> 00:56:55,520
Speaker 1: your take it a full vial of that blood. You

873
00:56:55,520 --> 00:56:57,720
Speaker 1: can't get that out of a drop of capillary blood

874
00:56:57,719 --> 00:57:02,400
Speaker 1: from your face. The process of ricking your finger um

875
00:57:02,440 --> 00:57:08,239
Speaker 1: introduces interstitial tissue and liquids and contaminants, So even if

876
00:57:08,280 --> 00:57:12,759
Speaker 1: you had enough blood, it's contaminated blood. But let's the

877
00:57:13,280 --> 00:57:15,360
Speaker 1: what I'm more interested in, and what I think is

878
00:57:15,440 --> 00:57:21,680
Speaker 1: much more significant, is that these digital platforms and marketplaces,

879
00:57:21,720 --> 00:57:26,760
Speaker 1: they are disruptive. They are creating opportunities for commerce on

880
00:57:26,880 --> 00:57:30,560
Speaker 1: a radically different basis. So we're talking about Airbnb and

881
00:57:30,800 --> 00:57:34,720
Speaker 1: Uber and lift and anything that is using the digital,

882
00:57:35,120 --> 00:57:39,480
Speaker 1: especially mobile interface that's a giant computer in your pocket

883
00:57:39,520 --> 00:57:45,000
Speaker 1: to turn things into a different um economic value proposition.

884
00:57:45,360 --> 00:57:49,360
Speaker 1: That's a fascinating an It also brings up, however, another

885
00:57:49,520 --> 00:57:53,480
Speaker 1: aspect of where the digital revolution is. I said it

886
00:57:53,520 --> 00:57:55,440
Speaker 1: was attacking the authority of the statement. Of course, a

887
00:57:55,440 --> 00:57:57,680
Speaker 1: lot of that's happening at the local level. It's not

888
00:57:57,800 --> 00:58:01,240
Speaker 1: and I'm not now talking about grand strategy and or

889
00:58:01,400 --> 00:58:03,240
Speaker 1: or the you know, the future of the planet and

890
00:58:03,240 --> 00:58:07,320
Speaker 1: climate change and all that. The problem here, and this

891
00:58:07,360 --> 00:58:11,280
Speaker 1: is a problem for investors, is that too many of

892
00:58:11,360 --> 00:58:15,440
Speaker 1: the genius entrepreneurs who know that they are creating a

893
00:58:15,440 --> 00:58:21,360
Speaker 1: new world have zero interest in understanding how we got

894
00:58:21,400 --> 00:58:25,160
Speaker 1: to this world that they're disrupting. And what that means

895
00:58:25,480 --> 00:58:28,960
Speaker 1: is that again and again and again they pay no

896
00:58:29,040 --> 00:58:35,560
Speaker 1: attention or they dismiss the existing ecosystem, which includes regulatory

897
00:58:35,640 --> 00:58:40,720
Speaker 1: but not just political and state regulations, practices, culture. A

898
00:58:40,840 --> 00:58:45,400
Speaker 1: black cab driver in London is not the same thing

899
00:58:46,000 --> 00:58:49,480
Speaker 1: as a cab driver in San Francisco operating a totally

900
00:58:49,480 --> 00:58:53,600
Speaker 1: different environment, meaning that that's highly regulated. They have to

901
00:58:53,640 --> 00:58:57,760
Speaker 1: pass the tests to be a cab driver. They're incredibly knowledgeable,

902
00:58:57,800 --> 00:59:00,880
Speaker 1: and they have a standing in the culture that's just

903
00:59:00,960 --> 00:59:05,840
Speaker 1: different in kind. So what happens when these disruptive which

904
00:59:05,880 --> 00:59:08,880
Speaker 1: which are much more efficient economically, no question about that,

905
00:59:09,320 --> 00:59:13,560
Speaker 1: and much more efficient technologically, but they run into these frictions,

906
00:59:13,640 --> 00:59:18,120
Speaker 1: these political and regulatory frictions. It slows things down. Now,

907
00:59:18,440 --> 00:59:21,200
Speaker 1: if you're promising to change the world and you have

908
00:59:21,240 --> 00:59:26,240
Speaker 1: investors investing in you in pursuit of that rate of

909
00:59:26,280 --> 00:59:30,720
Speaker 1: return that comes from a successful revolution, and you get

910
00:59:30,760 --> 00:59:33,520
Speaker 1: delayed by a year or two year, but that it

911
00:59:33,600 --> 00:59:37,959
Speaker 1: just kills the present value of that hope for home

912
00:59:38,080 --> 00:59:44,000
Speaker 1: run future. So it feeds back into the investment proposition

913
00:59:44,400 --> 00:59:47,920
Speaker 1: that you're looking at today and you're trying to value today.

914
00:59:47,960 --> 00:59:49,960
Speaker 1: So let me let me get more specific, because I

915
00:59:49,960 --> 00:59:52,760
Speaker 1: want to make sure I understand that. So you have

916
00:59:53,200 --> 00:59:56,960
Speaker 1: a set of a regulatory environment and a set of

917
00:59:57,040 --> 01:00:01,760
Speaker 1: cultural background in a place like London, and that doesn't

918
01:00:01,800 --> 01:00:07,040
Speaker 1: necessarily make it all that vulnerable to an outfit like Uber,

919
01:00:07,800 --> 01:00:13,080
Speaker 1: because their cab drivers are valued members of society who

920
01:00:13,120 --> 01:00:18,360
Speaker 1: contribute more than mere transportation. There an active part of

921
01:00:18,400 --> 01:00:21,200
Speaker 1: the history and the lore of London. Compare and contrast

922
01:00:21,240 --> 01:00:25,440
Speaker 1: with New York City, where I've made the case and

923
01:00:25,480 --> 01:00:28,680
Speaker 1: I don't think it's a stretch that you had a

924
01:00:29,160 --> 01:00:33,560
Speaker 1: artificially created monopoly, the number of medallions kept low, the

925
01:00:33,640 --> 01:00:37,240
Speaker 1: serve quality service terrible. Good luck funding cab in the

926
01:00:37,320 --> 01:00:40,640
Speaker 1: rain at rush hour. The first moment anything happens, there

927
01:00:40,640 --> 01:00:44,000
Speaker 1: are no cabs. I I kind of have gotten the

928
01:00:44,080 --> 01:00:48,520
Speaker 1: sense that the New York City Council and the medallion

929
01:00:48,640 --> 01:00:54,160
Speaker 1: owners conspired to create to artificially constrained market forces, and

930
01:00:54,200 --> 01:00:57,640
Speaker 1: as soon as Uber came along, it exploded because at

931
01:00:57,640 --> 01:01:01,000
Speaker 1: a certain point the market will not be denied. Exactly right, Barry,

932
01:01:01,000 --> 01:01:03,840
Speaker 1: I think you're absolutely right. But the point is exactly

933
01:01:03,880 --> 01:01:07,680
Speaker 1: that you need and it took Uber has an Airbnb

934
01:01:07,720 --> 01:01:11,200
Speaker 1: has been doing a better job of understanding that these

935
01:01:11,520 --> 01:01:13,960
Speaker 1: each local market that they go into has to be

936
01:01:14,000 --> 01:01:17,720
Speaker 1: evaluated its own terms, with its own history, and adapt

937
01:01:17,800 --> 01:01:20,640
Speaker 1: there too. And not all the regulations A lot of

938
01:01:20,640 --> 01:01:24,640
Speaker 1: them are, but not all the regulations are monopolists. Rents seeking.

939
01:01:24,760 --> 01:01:27,360
Speaker 1: You know, there's a reason why if you're having people

940
01:01:27,440 --> 01:01:29,720
Speaker 1: stay in your apartment, you want to have a fire

941
01:01:29,800 --> 01:01:34,840
Speaker 1: extinguisher and something like if your neighbors are living there,

942
01:01:35,120 --> 01:01:37,800
Speaker 1: they board an apartment, they weren't buying into a hotel.

943
01:01:38,320 --> 01:01:41,880
Speaker 1: That's a completely legiti exactly right. So my my point

944
01:01:41,920 --> 01:01:46,240
Speaker 1: here is only that, in addition to all the stem

945
01:01:46,360 --> 01:01:50,760
Speaker 1: discipline science, technology, engineering, mathematics that we all focus on,

946
01:01:50,880 --> 01:01:54,360
Speaker 1: is the secret for the future. A little more teaching

947
01:01:54,400 --> 01:01:59,640
Speaker 1: in history, a little more even reading relevant historical novels,

948
01:02:00,800 --> 01:02:06,160
Speaker 1: a little learning about the political the evolution of regulations

949
01:02:06,200 --> 01:02:10,680
Speaker 1: in particular domains, particular regimes. It could be very helpful

950
01:02:11,080 --> 01:02:14,440
Speaker 1: for the entrepreneur, or at least their financial investor, who's

951
01:02:14,440 --> 01:02:17,400
Speaker 1: trying to give them a strategic sense of where to

952
01:02:17,480 --> 01:02:21,200
Speaker 1: bob and where do weave and how to maximize the

953
01:02:21,320 --> 01:02:26,840
Speaker 1: likelihood that their transformational technology will convert into long term

954
01:02:26,880 --> 01:02:30,400
Speaker 1: economic value. So when when you're looking at a possible

955
01:02:30,440 --> 01:02:33,560
Speaker 1: investment in a startup and you're looking at the entrepreneurs,

956
01:02:34,080 --> 01:02:36,240
Speaker 1: do you find that a lot of these folks are

957
01:02:36,280 --> 01:02:39,680
Speaker 1: one dimensional. They have their tech skills, they've come up

958
01:02:39,680 --> 01:02:43,360
Speaker 1: with something that seems interesting, but they're lacking a broader

959
01:02:43,440 --> 01:02:46,439
Speaker 1: world view. Well, you know, I'm I'm I'm at more

960
01:02:46,440 --> 01:02:49,880
Speaker 1: than one remove away from this. I'm not running the

961
01:02:49,920 --> 01:02:53,320
Speaker 1: portfolio and I have it for years, um, but I

962
01:02:53,360 --> 01:02:58,680
Speaker 1: do stay tuned in. In particular, you mentioned in introducing me, Uh,

963
01:02:58,720 --> 01:03:02,280
Speaker 1: you know, I'm Tim o'ry. He's outside director at O'Reilly media,

964
01:03:02,840 --> 01:03:06,560
Speaker 1: Tim is just which is you Oley Media has and

965
01:03:06,640 --> 01:03:11,640
Speaker 1: certainly the throw weight in terms of insight foresight that

966
01:03:11,760 --> 01:03:14,880
Speaker 1: Tim has demonstrated over a long generation. And by the way,

967
01:03:14,960 --> 01:03:18,200
Speaker 1: let me just fleck his book with the amusing title

968
01:03:18,440 --> 01:03:21,880
Speaker 1: w t F What's the Future? Uh? Is a great

969
01:03:21,920 --> 01:03:27,600
Speaker 1: guide to reading the digital revolution as it has unwound,

970
01:03:27,640 --> 01:03:30,000
Speaker 1: as it has evolved over the last couple of years,

971
01:03:30,080 --> 01:03:35,400
Speaker 1: last twenty years, um, Tim, Tim has the exactly what

972
01:03:35,480 --> 01:03:40,040
Speaker 1: I'm talking about, a deep interest in the history and

973
01:03:40,160 --> 01:03:43,760
Speaker 1: not just the technical history, but the cultural and political

974
01:03:43,800 --> 01:03:49,240
Speaker 1: history of that we are transforming. UH. And you know,

975
01:03:49,680 --> 01:03:52,320
Speaker 1: this is not the first time that there's been a

976
01:03:52,320 --> 01:03:57,600
Speaker 1: political backlash from new technology. William Jennings Bryan, the populist

977
01:03:57,600 --> 01:03:59,800
Speaker 1: movement at the end of the nineteenth century. Was there

978
01:04:00,040 --> 01:04:03,240
Speaker 1: sponsor the railroads? How about the Mathusians. Isn't that a

979
01:04:03,320 --> 01:04:06,919
Speaker 1: giant rush back against you know, we'll never be able

980
01:04:06,920 --> 01:04:11,840
Speaker 1: to feed ourselves, so the there is now a giant

981
01:04:11,880 --> 01:04:15,520
Speaker 1: political pushback. You know, big tech is a target. UH.

982
01:04:15,960 --> 01:04:19,320
Speaker 1: Some of it has been invited by a kind of

983
01:04:19,440 --> 01:04:22,080
Speaker 1: arrogance that goes with it. I say, it goes with

984
01:04:22,120 --> 01:04:26,240
Speaker 1: the territory of being the source, the leader, the manager,

985
01:04:26,440 --> 01:04:33,520
Speaker 1: the driver of transformational technology into the economy. But it

986
01:04:33,520 --> 01:04:36,920
Speaker 1: will be best for everyone if there's a more deeper

987
01:04:37,040 --> 01:04:41,640
Speaker 1: understanding of the world that is being disrupted, not just

988
01:04:42,240 --> 01:04:45,960
Speaker 1: understanding of the forces of disruption. And you know, frankly,

989
01:04:46,160 --> 01:04:48,920
Speaker 1: let me let me chew that over a sec Better

990
01:04:49,480 --> 01:04:53,600
Speaker 1: to have a more complete understanding of the world being

991
01:04:53,680 --> 01:04:59,280
Speaker 1: disrupted than just the disruptive forces themselves, right, exactly right,

992
01:04:59,600 --> 01:05:02,680
Speaker 1: that's and that's what this new edition of my book

993
01:05:02,840 --> 01:05:07,040
Speaker 1: is really trying to deliver, and to deliver to audiences

994
01:05:07,080 --> 01:05:10,960
Speaker 1: that include those disruptors. It will be better for our

995
01:05:11,000 --> 01:05:13,480
Speaker 1: whole system and will certainly be better for us to

996
01:05:13,560 --> 01:05:16,640
Speaker 1: have a you know, a better chance of responding to

997
01:05:16,680 --> 01:05:21,040
Speaker 1: the Chinese challenge, which ain't going away. Um, of course

998
01:05:21,080 --> 01:05:24,480
Speaker 1: this comes at a peculiar moment in American history and

999
01:05:24,560 --> 01:05:28,880
Speaker 1: American politics, and peculiar to say the least. I mean,

1000
01:05:28,920 --> 01:05:32,800
Speaker 1: that's and and you know how long this persists, how

1001
01:05:32,840 --> 01:05:36,640
Speaker 1: long we have I've got to I got to share

1002
01:05:36,640 --> 01:05:38,880
Speaker 1: with you because I know you know how to read

1003
01:05:39,600 --> 01:05:46,000
Speaker 1: the net one of the more maybe most frustrating things

1004
01:05:46,000 --> 01:05:49,840
Speaker 1: you can do. Right now, Let's go to Google type

1005
01:05:49,840 --> 01:05:52,680
Speaker 1: in the letters o s t P that stands for

1006
01:05:52,840 --> 01:05:58,320
Speaker 1: Office of Science and Technology Policy, and you'll see white

1007
01:05:58,320 --> 01:06:05,040
Speaker 1: House dot OSTP dot gov. Right, if you bring that up, well,

1008
01:06:05,120 --> 01:06:07,320
Speaker 1: I'll ask you to just let it sit there on

1009
01:06:07,360 --> 01:06:10,720
Speaker 1: the Google screen. White white House dot gov is the

1010
01:06:10,760 --> 01:06:13,160
Speaker 1: first thing that comes up. Right. Now, look down the

1011
01:06:13,240 --> 01:06:17,520
Speaker 1: list and you'll find something that says OSTP dot Obama archive.

1012
01:06:18,560 --> 01:06:23,280
Speaker 1: That's the third thing. So the second thing is Wikipedia. Okay,

1013
01:06:23,360 --> 01:06:28,240
Speaker 1: Now go and click on the OSTP dot Obama archive, right,

1014
01:06:28,400 --> 01:06:32,320
Speaker 1: John Holden, Director, and you'll see that that's how it

1015
01:06:32,360 --> 01:06:35,960
Speaker 1: what it looked like on January telling me this has

1016
01:06:36,000 --> 01:06:38,600
Speaker 1: not been updated. No, I'm telling you that this is

1017
01:06:38,640 --> 01:06:40,600
Speaker 1: frozen because that's the way it was and that's why

1018
01:06:40,600 --> 01:06:43,760
Speaker 1: it's in the Obama archive. But if you go through it,

1019
01:06:43,800 --> 01:06:47,360
Speaker 1: you'll see that there's program after program, people and initiatives

1020
01:06:47,360 --> 01:06:51,720
Speaker 1: and conferences and all that. Now, after looking at that,

1021
01:06:52,080 --> 01:06:57,000
Speaker 1: now go and look at here's a November one article

1022
01:06:57,800 --> 01:07:01,120
Speaker 1: Donald Trumps Science Office is a ghost town. So go

1023
01:07:01,200 --> 01:07:04,240
Speaker 1: and look at OSTP dout gub white House dot OSTP

1024
01:07:04,400 --> 01:07:06,959
Speaker 1: dot gov. Al Right, just click the original. Just click

1025
01:07:07,000 --> 01:07:12,480
Speaker 1: the original, uh engagement at it's a one pager. That's

1026
01:07:12,520 --> 01:07:15,160
Speaker 1: that's it, there's no there's no there's no links. There

1027
01:07:15,200 --> 01:07:18,200
Speaker 1: are no links. That's all there is. That's it. So

1028
01:07:18,440 --> 01:07:22,320
Speaker 1: another word science and technology not not exciting to this group,

1029
01:07:22,520 --> 01:07:26,160
Speaker 1: doesn't exist. You know, if you look at the list

1030
01:07:26,240 --> 01:07:30,320
Speaker 1: of unfilled positions. And I'm not sure if if my

1031
01:07:30,360 --> 01:07:33,360
Speaker 1: original viewpoint on this is correct. I used to think

1032
01:07:33,920 --> 01:07:36,920
Speaker 1: he was running just for a branding exercise. He made

1033
01:07:37,360 --> 01:07:41,960
Speaker 1: most of his money, read him Um O'Brien's book, Trump Nation.

1034
01:07:42,560 --> 01:07:47,000
Speaker 1: Most of his wealth came from the t and forward

1035
01:07:47,080 --> 01:07:51,880
Speaker 1: election where there was a ton of branding opportunities. What

1036
01:07:51,920 --> 01:07:55,600
Speaker 1: have you. I suspected that he was an interest in winning,

1037
01:07:55,600 --> 01:07:59,200
Speaker 1: that this was just a great gift from the media

1038
01:07:59,280 --> 01:08:03,480
Speaker 1: and pub listening machine. And because most people run have

1039
01:08:03,600 --> 01:08:05,760
Speaker 1: their list of here's everyone we're gonna put in. The

1040
01:08:05,800 --> 01:08:07,880
Speaker 1: fact it's a year later and they still have all

1041
01:08:07,920 --> 01:08:11,440
Speaker 1: these unfilled roles kind of makes me think that he

1042
01:08:11,640 --> 01:08:15,560
Speaker 1: wasn't expecting to win. That said, now that you're there,

1043
01:08:16,200 --> 01:08:18,559
Speaker 1: perhaps you may want to fill some of these slots. Well,

1044
01:08:18,600 --> 01:08:22,240
Speaker 1: you know what's going on at the E p A.

1045
01:08:23,040 --> 01:08:26,519
Speaker 1: And by the way, we're recording this in between the

1046
01:08:26,560 --> 01:08:29,920
Speaker 1: weekend of the G seven events and the right before

1047
01:08:30,120 --> 01:08:34,240
Speaker 1: the summit with North Korea. So if there has subsequently

1048
01:08:34,280 --> 01:08:37,840
Speaker 1: been a nuclear war, that by the time this broadcast,

1049
01:08:38,280 --> 01:08:42,320
Speaker 1: neither of us anticipated that correct or or or the

1050
01:08:42,320 --> 01:08:45,360
Speaker 1: other thing, a complete nu de nuclearization. I don't think

1051
01:08:45,360 --> 01:08:47,960
Speaker 1: either of us are expected that. I'll tell you I'm

1052
01:08:48,080 --> 01:08:51,040
Speaker 1: I'm I'm not. I'm not born to be. I go

1053
01:08:51,240 --> 01:08:55,320
Speaker 1: practice as a political prognosticator. You read all about that

1054
01:08:55,400 --> 01:08:57,960
Speaker 1: in so many different places. I have those I respect,

1055
01:08:57,960 --> 01:09:00,519
Speaker 1: those I don't respect, But I will this and this.

1056
01:09:00,640 --> 01:09:03,600
Speaker 1: I think I have some expertise. And if you're going

1057
01:09:03,640 --> 01:09:07,080
Speaker 1: to maintain if a nation is going to maintain leadership

1058
01:09:07,800 --> 01:09:14,040
Speaker 1: in taking science, deriving from it working technology, and converting

1059
01:09:14,080 --> 01:09:21,679
Speaker 1: that technology into job creating, profit generating real business, then

1060
01:09:22,479 --> 01:09:27,400
Speaker 1: one player in that game has to be a legitimate, honest,

1061
01:09:27,800 --> 01:09:34,360
Speaker 1: functioning government. Without there you go, you know, but that

1062
01:09:34,800 --> 01:09:38,280
Speaker 1: some people legitimately that's the idiotic pushback, I know, And

1063
01:09:38,320 --> 01:09:41,880
Speaker 1: that's why going back into that using winners and losers.

1064
01:09:42,160 --> 01:09:44,880
Speaker 1: But you know, that's what going back into that history

1065
01:09:44,880 --> 01:09:48,280
Speaker 1: of the Defense Department in DARPA and silicon and software

1066
01:09:48,320 --> 01:09:52,240
Speaker 1: and microprocessors and the Internet. That's why it's so important

1067
01:09:52,760 --> 01:09:55,720
Speaker 1: going back to the history of how we tied the

1068
01:09:55,800 --> 01:10:00,479
Speaker 1: nation together with Transcontinental Railroads by taking nine of the

1069
01:10:00,640 --> 01:10:04,200
Speaker 1: land area of the lower forty eight states and giving

1070
01:10:04,280 --> 01:10:07,720
Speaker 1: it as a gift to subsidize the building of the

1071
01:10:07,720 --> 01:10:13,040
Speaker 1: Transcontinental Railroads. We built the interstate highway system. We built

1072
01:10:13,040 --> 01:10:17,560
Speaker 1: it under a Republican president. By the way, the legislation

1073
01:10:17,640 --> 01:10:21,960
Speaker 1: was called the National Defense Transportation Act. It was legitimate

1074
01:10:22,080 --> 01:10:25,559
Speaker 1: because they designed it so that every bridge was high

1075
01:10:25,720 --> 01:10:31,400
Speaker 1: enough that a transporter carrying an Atlas first generation intercontinental

1076
01:10:31,400 --> 01:10:35,559
Speaker 1: ballistic missile could be driven along the interstate. And that

1077
01:10:35,720 --> 01:10:41,400
Speaker 1: made the investment in the public transportation network for the country,

1078
01:10:41,439 --> 01:10:44,560
Speaker 1: made it politically legitimate. And am I misremembering this or

1079
01:10:44,680 --> 01:10:47,640
Speaker 1: is this just the myth? Every fifth mile of the

1080
01:10:47,680 --> 01:10:50,439
Speaker 1: interstate highway system how to be a straight mile that

1081
01:10:50,560 --> 01:10:53,519
Speaker 1: could double as a runway in an emergency. You know,

1082
01:10:53,600 --> 01:10:56,320
Speaker 1: I don't know that. As a fact. I liked the concept.

1083
01:10:56,720 --> 01:11:01,360
Speaker 1: It certainly was. There was a sense there that public

1084
01:11:01,479 --> 01:11:06,640
Speaker 1: investment for the public good was legitimate, appropriate and what

1085
01:11:06,720 --> 01:11:08,840
Speaker 1: do you call it? You didn't have to call it socialism,

1086
01:11:08,960 --> 01:11:11,240
Speaker 1: was just it was for the public good. The multiplier

1087
01:11:11,240 --> 01:11:14,000
Speaker 1: effect of the interstate highway system is still being effected.

1088
01:11:14,080 --> 01:11:16,599
Speaker 1: This felt to this day. All right, so I only

1089
01:11:16,600 --> 01:11:18,680
Speaker 1: have you for a finite amount of time. Let me

1090
01:11:18,800 --> 01:11:22,200
Speaker 1: jump to some of my favorite questions that we ask

1091
01:11:22,280 --> 01:11:25,800
Speaker 1: all our guests. Tell us the most important thing we

1092
01:11:25,960 --> 01:11:34,800
Speaker 1: don't know about you. Well, let's see, I guess the

1093
01:11:34,880 --> 01:11:38,040
Speaker 1: most important thing that you probably don't know is that

1094
01:11:38,080 --> 01:11:41,559
Speaker 1: when I was forty five, I ran a one nineteen

1095
01:11:41,680 --> 01:11:45,639
Speaker 1: half marathon in the fifty nine minute ten miler. And

1096
01:11:45,680 --> 01:11:48,559
Speaker 1: then that was the summer fifty in a ten mile.

1097
01:11:48,720 --> 01:11:51,360
Speaker 1: That's pretty good. That's under six minutes. Not bad for

1098
01:11:51,439 --> 01:11:54,960
Speaker 1: at five year old. Oh that's damn good. And ex smoker.

1099
01:11:55,640 --> 01:12:01,280
Speaker 1: And and then I made this fundamental decision because on

1100
01:12:01,360 --> 01:12:04,160
Speaker 1: the one hand, I was joining Warburg pain Kiss after

1101
01:12:04,240 --> 01:12:07,160
Speaker 1: I've been serving out my contract after we sold the

1102
01:12:07,240 --> 01:12:09,880
Speaker 1: Upper stat firm, and I knew that was gonna be

1103
01:12:09,880 --> 01:12:13,599
Speaker 1: a full time job. And on the other hand, my

1104
01:12:13,640 --> 01:12:16,280
Speaker 1: wife was running a consulting business, and I had worked

1105
01:12:16,320 --> 01:12:17,920
Speaker 1: out that if we really wanted to have a kid,

1106
01:12:18,560 --> 01:12:21,600
Speaker 1: staying in the same city as her her doctor suggested

1107
01:12:21,720 --> 01:12:26,160
Speaker 1: was probably a useful enabling friend. And so we were

1108
01:12:26,160 --> 01:12:30,479
Speaker 1: gonna have a kid. And I decided that you could

1109
01:12:30,479 --> 01:12:32,760
Speaker 1: have a full time job, you could try to be

1110
01:12:32,840 --> 01:12:37,360
Speaker 1: a decent parent, and you could run competitively at the

1111
01:12:37,360 --> 01:12:41,080
Speaker 1: club level of New York two out of three. That

1112
01:12:42,520 --> 01:12:44,240
Speaker 1: at the end of the matter, that's very funny. You

1113
01:12:44,600 --> 01:12:48,720
Speaker 1: mentioned one of your mentors earlier. Tell us repeat their name,

1114
01:12:48,760 --> 01:12:53,559
Speaker 1: and what other folks influence your perspective? Critical influence on

1115
01:12:53,560 --> 01:12:59,080
Speaker 1: my perspective, as I say, was Fred Adler. Uh. Fred

1116
01:12:59,160 --> 01:13:02,800
Speaker 1: Fred came from backstreets of Brooklyn, got went went from

1117
01:13:02,840 --> 01:13:07,720
Speaker 1: Brooklyn College to Harvard Law School, joined actually one of

1118
01:13:07,720 --> 01:13:10,879
Speaker 1: the leading Catholic law firms in New York, and discovered

1119
01:13:10,920 --> 01:13:14,400
Speaker 1: he was a terrific turnaround artist and put together the

1120
01:13:14,439 --> 01:13:18,920
Speaker 1: financing for the second successful mini computer company, Data General,

1121
01:13:19,720 --> 01:13:22,519
Speaker 1: back you know now forty five years ago. He and

1122
01:13:22,560 --> 01:13:26,000
Speaker 1: I connected. We collaborated effectively a lot about that in

1123
01:13:26,040 --> 01:13:30,400
Speaker 1: my book. Uh. He was a tough guy, and uh,

1124
01:13:30,720 --> 01:13:32,160
Speaker 1: I used to He used to tell me that I

1125
01:13:32,200 --> 01:13:34,800
Speaker 1: was really good at admitting my mistakes. And I used

1126
01:13:34,800 --> 01:13:36,920
Speaker 1: to tell him that the only compliment he ever gave

1127
01:13:36,960 --> 01:13:40,320
Speaker 1: me was he never offered me a job. But then

1128
01:13:40,560 --> 01:13:43,400
Speaker 1: then in the mid eighties, when I was really engaged

1129
01:13:43,479 --> 01:13:48,360
Speaker 1: in this new swinging world of information technology through through

1130
01:13:48,400 --> 01:13:51,040
Speaker 1: my my cousin who was out in St. Louis. He'd

1131
01:13:51,040 --> 01:13:54,000
Speaker 1: been at Monsanto. I met one of the most remarkable

1132
01:13:54,560 --> 01:13:58,200
Speaker 1: figures in the second half of the twentieth century, Himan Minsky. Oh,

1133
01:13:58,240 --> 01:14:02,080
Speaker 1: of course, Hi Menski was a Maverick renegade. He'd gotten

1134
01:14:02,120 --> 01:14:06,439
Speaker 1: his his doctorated at Harvard under the great Joseph Shampeter.

1135
01:14:07,160 --> 01:14:10,680
Speaker 1: But he he didn't follow. He refused to follow the

1136
01:14:10,760 --> 01:14:17,200
Speaker 1: new line of efficient markets and stability right, and he

1137
01:14:17,240 --> 01:14:19,519
Speaker 1: never put it into math, which made it hard for

1138
01:14:19,600 --> 01:14:22,840
Speaker 1: him to be expected accepted. But in all of us

1139
01:14:22,960 --> 01:14:27,040
Speaker 1: by the mainstream economists, who all suffer from a horrible

1140
01:14:27,439 --> 01:14:31,479
Speaker 1: sense of physics penis envy, it's it's that I think

1141
01:14:31,479 --> 01:14:35,439
Speaker 1: they've over mathsized economics. But I got to know, I

1142
01:14:35,479 --> 01:14:39,320
Speaker 1: got to know and right right. And he came east.

1143
01:14:39,320 --> 01:14:42,960
Speaker 1: He wound up at Leon Levy's Institute at Bard College,

1144
01:14:42,960 --> 01:14:45,040
Speaker 1: and I used to go there in the late eighties

1145
01:14:45,320 --> 01:14:48,920
Speaker 1: and sort of play hookey from venture capital. But it

1146
01:14:48,960 --> 01:14:51,439
Speaker 1: made me, gave me the chance to think and to

1147
01:14:51,760 --> 01:14:57,120
Speaker 1: interact with somebody who was deeply engaged in understanding the

1148
01:14:57,439 --> 01:15:01,360
Speaker 1: dynamics of the financial system. And as you say, how

1149
01:15:01,640 --> 01:15:07,280
Speaker 1: stability breeds instability, how confidence becomes overconfidence becomes overlending and

1150
01:15:07,560 --> 01:15:10,120
Speaker 1: leads to a crisis, and that that is going to

1151
01:15:10,160 --> 01:15:12,280
Speaker 1: have an impact on the real economy. It's not just

1152
01:15:12,400 --> 01:15:15,799
Speaker 1: happening off there on the markets. And that was a huge,

1153
01:15:16,320 --> 01:15:20,280
Speaker 1: huge benefit of course, going into the world of the

1154
01:15:20,360 --> 01:15:23,639
Speaker 1: last fifteen years. It's unfortunate he didn't live long enough

1155
01:15:23,920 --> 01:15:28,720
Speaker 1: to see his his research and his writings all come true.

1156
01:15:28,960 --> 01:15:32,840
Speaker 1: Just just amazing. UM. Any venture capitalists influenced the way

1157
01:15:32,880 --> 01:15:37,559
Speaker 1: you look at the world of VC investing, I meaning

1158
01:15:37,640 --> 01:15:40,960
Speaker 1: back in the day when oh yeah, sure. In addition

1159
01:15:41,000 --> 01:15:44,759
Speaker 1: to fred Um, one of the most phenomenally successful venture

1160
01:15:44,800 --> 01:15:49,480
Speaker 1: capitalists ever was Arthur Rock, who was the early investor

1161
01:15:49,560 --> 01:15:53,200
Speaker 1: into Intel, if I'm remembering correctly. Intel, he put the

1162
01:15:53,280 --> 01:15:57,720
Speaker 1: financing together originally for fair Child Semikin, which was the predecessor,

1163
01:15:57,760 --> 01:16:02,679
Speaker 1: Which was the predecessor. He was investor in Scientific Data Systems,

1164
01:16:02,680 --> 01:16:06,280
Speaker 1: which was another one of the the first generation many

1165
01:16:06,280 --> 01:16:09,600
Speaker 1: computer companies, was brought by Xerox, the first It was

1166
01:16:09,640 --> 01:16:11,880
Speaker 1: the first billion dollar exit in the history of the

1167
01:16:11,960 --> 01:16:15,839
Speaker 1: venture capital industry. UM and of course he was along

1168
01:16:15,880 --> 01:16:18,680
Speaker 1: with the venture arm of the Rockefeller family, he was

1169
01:16:18,720 --> 01:16:22,960
Speaker 1: the original investors in Apple. UM. So I hope you

1170
01:16:23,040 --> 01:16:28,439
Speaker 1: still invested. Um that that that's a that's a fascinating run.

1171
01:16:29,200 --> 01:16:34,320
Speaker 1: Let's talk about books, because you mentioned a few. Um,

1172
01:16:34,320 --> 01:16:41,200
Speaker 1: this is everybody's favorite question. Tell us your favorite books VC, economics, fiction, nonfiction.

1173
01:16:41,240 --> 01:16:43,040
Speaker 1: I don't care. What do you read and what do

1174
01:16:43,080 --> 01:16:45,719
Speaker 1: you think other people should read? Well, I I mentioned

1175
01:16:45,760 --> 01:16:52,439
Speaker 1: Tim O'Reilly's WTF, WTF what's the future there? They know?

1176
01:16:52,479 --> 01:16:54,519
Speaker 1: I've been. I read a lot of history. I read

1177
01:16:54,560 --> 01:16:57,720
Speaker 1: a lot of history. I've been just stumbled on an

1178
01:16:57,720 --> 01:17:00,720
Speaker 1: extraordinary book that I should have and existed, and I

1179
01:17:00,760 --> 01:17:02,479
Speaker 1: wish I had because I sure would have used it

1180
01:17:02,520 --> 01:17:04,479
Speaker 1: in my book and I will be using it in

1181
01:17:04,520 --> 01:17:08,080
Speaker 1: my economics course at Cambridge. It's called Funding a Revolution.

1182
01:17:09,240 --> 01:17:13,679
Speaker 1: It was published in at the peak of the Internet

1183
01:17:13,720 --> 01:17:19,960
Speaker 1: bubble by the National Academies of Sciences. It's a detailed,

1184
01:17:20,120 --> 01:17:26,519
Speaker 1: granular report on each program through which the United States

1185
01:17:26,560 --> 01:17:30,400
Speaker 1: government created the digital revolution. It's you know, it's not

1186
01:17:30,439 --> 01:17:33,400
Speaker 1: for everybody. Perhaps I found it riveting because you had

1187
01:17:33,439 --> 01:17:36,800
Speaker 1: the people, the names, the programs, where they came from,

1188
01:17:36,800 --> 01:17:41,200
Speaker 1: where they went. Um. But then on the other hand, UM,

1189
01:17:41,240 --> 01:17:48,360
Speaker 1: I read the Harvard historian then Beckert wrote a book

1190
01:17:48,640 --> 01:17:54,599
Speaker 1: on the cotton industry that the cotton textile industry, which

1191
01:17:55,160 --> 01:17:58,080
Speaker 1: came out about five years ago or so. It is

1192
01:17:58,200 --> 01:18:04,559
Speaker 1: an amazing examination of the first global industry, again, an

1193
01:18:04,600 --> 01:18:10,840
Speaker 1: industry which critically depended on the power of states two

1194
01:18:12,200 --> 01:18:17,080
Speaker 1: create the sources, the the the raw cotton coming in,

1195
01:18:17,800 --> 01:18:23,400
Speaker 1: and the dynamism of entrepreneurs and speculative capital. Let me

1196
01:18:23,439 --> 01:18:25,519
Speaker 1: ask you about a couple of books, because you you

1197
01:18:25,600 --> 01:18:29,559
Speaker 1: made me think about a few. Not Andy Groves, only

1198
01:18:29,600 --> 01:18:32,920
Speaker 1: the paranoids survived, But there was a book on the

1199
01:18:33,120 --> 01:18:41,200
Speaker 1: history of Intel along with Rock and Fairchild Semi is

1200
01:18:41,240 --> 01:18:44,280
Speaker 1: it inside Intel? Could have been. I think that's the

1201
01:18:44,280 --> 01:18:48,480
Speaker 1: book that really tracks the history. I thought that was fascinating.

1202
01:18:48,479 --> 01:18:50,360
Speaker 1: I don't know if you you read that. I don't

1203
01:18:50,400 --> 01:18:52,880
Speaker 1: know that one. I do know the you know, the

1204
01:18:52,960 --> 01:18:57,120
Speaker 1: Fumbling the Future at Xerox, which tells the story. It's

1205
01:18:57,160 --> 01:19:01,280
Speaker 1: pretty um, it's pretty tough. And then what about have

1206
01:19:01,360 --> 01:19:06,360
Speaker 1: you read Scott Galloway's The Four Google, Apple, Facebook, and Amazon?

1207
01:19:06,880 --> 01:19:10,000
Speaker 1: You know? I read. I read a lot of academic

1208
01:19:10,080 --> 01:19:13,160
Speaker 1: papers and I and that's one of the reasons I

1209
01:19:13,280 --> 01:19:15,800
Speaker 1: uh teach the course in a way. It forces me

1210
01:19:15,920 --> 01:19:18,479
Speaker 1: to stay up with the literature. And you know something

1211
01:19:18,520 --> 01:19:20,600
Speaker 1: we haven't talked about, but this is a message I

1212
01:19:20,600 --> 01:19:25,720
Speaker 1: wanted to deliver. UM two thousand and eight was a crisis.

1213
01:19:25,800 --> 01:19:28,599
Speaker 1: It was shocking, and it almost brought the world economy down.

1214
01:19:28,800 --> 01:19:33,600
Speaker 1: But for economics as a discipline, for finance as a discipline,

1215
01:19:33,760 --> 01:19:36,320
Speaker 1: it was the gift that keeps on giving. It has

1216
01:19:36,400 --> 01:19:41,360
Speaker 1: shocked those disciplines out of a kind of complacent assumption

1217
01:19:41,680 --> 01:19:45,360
Speaker 1: that the market will always deliver the right answer, reliably

1218
01:19:45,800 --> 01:19:50,200
Speaker 1: and with resilience, and it is generated a body of

1219
01:19:50,280 --> 01:19:55,400
Speaker 1: growing body, a much more realistic and relevant academic work

1220
01:19:55,479 --> 01:19:59,240
Speaker 1: based on a shift of focus from pure theory towards

1221
01:19:59,439 --> 01:20:04,160
Speaker 1: impure goal analysis. Economics gets its own Minsky moment. Yeah,

1222
01:20:04,600 --> 01:20:07,720
Speaker 1: that's exactly right, quite quite fascinating. Tell us about a

1223
01:20:07,800 --> 01:20:10,200
Speaker 1: time you failed and what you learned from these ah,

1224
01:20:12,200 --> 01:20:16,280
Speaker 1: more than one learning by failing? Um, Well, i'll tell

1225
01:20:16,360 --> 01:20:19,799
Speaker 1: you the stories in the book. UM. When we were

1226
01:20:19,800 --> 01:20:23,920
Speaker 1: working how to win, one of the passages towards investing

1227
01:20:24,000 --> 01:20:28,320
Speaker 1: and helping IBM ceased to be a monopoly came through

1228
01:20:28,360 --> 01:20:34,040
Speaker 1: a company that we started. Right around this was when

1229
01:20:34,080 --> 01:20:38,280
Speaker 1: the European Community, the Single Market was created, and we

1230
01:20:38,280 --> 01:20:41,680
Speaker 1: we we hooked up with a couple of American entrepreneurs

1231
01:20:42,000 --> 01:20:46,040
Speaker 1: who knew the European computer commercial computing industry really well,

1232
01:20:46,600 --> 01:20:48,880
Speaker 1: and they have perceived it was a kind of a

1233
01:20:48,880 --> 01:20:51,320
Speaker 1: hole in the market. In the US, you had a

1234
01:20:51,360 --> 01:20:56,040
Speaker 1: host of new companies, software companies that were delivering tools

1235
01:20:56,280 --> 01:20:59,560
Speaker 1: for making it easier and more efficient and more productive

1236
01:21:00,080 --> 01:21:05,400
Speaker 1: to use a big IBM machine, and that their technology,

1237
01:21:05,439 --> 01:21:07,680
Speaker 1: their products didn't get to Europe because they were too

1238
01:21:07,680 --> 01:21:11,680
Speaker 1: small to build a European channel. So the idea was

1239
01:21:12,040 --> 01:21:15,080
Speaker 1: we would get together. We would buy a set of

1240
01:21:15,280 --> 01:21:20,320
Speaker 1: service companies that provided people to the IBM data centers

1241
01:21:20,640 --> 01:21:24,080
Speaker 1: and spread across all of industrial Europe, just the way

1242
01:21:24,080 --> 01:21:27,200
Speaker 1: it was in the US, and then we would bring

1243
01:21:27,240 --> 01:21:31,040
Speaker 1: into them products that we licensed from those American companies,

1244
01:21:31,960 --> 01:21:35,799
Speaker 1: and that way we would build a pen European business.

1245
01:21:36,080 --> 01:21:41,400
Speaker 1: It was called easy soft Um. There were three entirely separate,

1246
01:21:42,240 --> 01:21:47,680
Speaker 1: independent reasons why this was a really bad idea. The

1247
01:21:47,760 --> 01:21:53,360
Speaker 1: first was between doing a project as a service company

1248
01:21:53,360 --> 01:21:57,160
Speaker 1: and walking away and selling a product that you are

1249
01:21:57,240 --> 01:22:00,479
Speaker 1: responsible for and you have to support is talk and

1250
01:22:00,680 --> 01:22:05,559
Speaker 1: cheese and very different, totally different. Second, those companies in

1251
01:22:05,600 --> 01:22:07,679
Speaker 1: the US, there were three things that could happen to them.

1252
01:22:07,800 --> 01:22:11,240
Speaker 1: The people we were licensing product from one. They could

1253
01:22:11,240 --> 01:22:13,280
Speaker 1: grow up and succeed and get big and want to

1254
01:22:13,320 --> 01:22:18,640
Speaker 1: take their products back too. They could fail completely and

1255
01:22:18,720 --> 01:22:20,960
Speaker 1: we were well and but we were stuck supporting the

1256
01:22:20,960 --> 01:22:23,439
Speaker 1: product without the tech the techies who had built it,

1257
01:22:24,120 --> 01:22:27,639
Speaker 1: or three halfway in between. They could be bought by

1258
01:22:27,680 --> 01:22:31,040
Speaker 1: Computer Associates, a really big, ugly company at that time,

1259
01:22:31,400 --> 01:22:33,160
Speaker 1: and that was the worst of all. And one of each,

1260
01:22:33,200 --> 01:22:36,759
Speaker 1: at least one of each happened. And then the final

1261
01:22:36,800 --> 01:22:40,799
Speaker 1: reason was it turned out that the IBM data center

1262
01:22:41,439 --> 01:22:46,160
Speaker 1: was no longer a rich, vibrant, growing market. It was stagnating,

1263
01:22:46,200 --> 01:22:50,240
Speaker 1: it was declining. That lesson was worth all the money

1264
01:22:50,240 --> 01:22:53,839
Speaker 1: we wrote off in easy Soft because it said, now's

1265
01:22:53,880 --> 01:22:58,200
Speaker 1: the time, Now we can go at IBM. What sort

1266
01:22:58,240 --> 01:23:00,599
Speaker 1: of advice would you give to a millennial or someone

1267
01:23:00,680 --> 01:23:07,599
Speaker 1: just beginning their career who is thinking about technology, venture capital, etcetera. Well,

1268
01:23:07,640 --> 01:23:10,040
Speaker 1: you know, in a way I've already given it. Um.

1269
01:23:10,080 --> 01:23:14,879
Speaker 1: You know, half of all the male undergraduates at Stanford

1270
01:23:14,880 --> 01:23:17,600
Speaker 1: are taking computer science, I think more than that of

1271
01:23:17,640 --> 01:23:19,280
Speaker 1: the m I t. You don't have to tell him

1272
01:23:19,280 --> 01:23:23,040
Speaker 1: to take computer science. What I would say is, along

1273
01:23:23,080 --> 01:23:28,440
Speaker 1: with your computer science, along with your double a read history,

1274
01:23:28,760 --> 01:23:33,439
Speaker 1: read the history of economics, but also politics, broadened their

1275
01:23:33,439 --> 01:23:36,559
Speaker 1: outlook for in your outlook and look for you know,

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01:23:36,640 --> 01:23:39,439
Speaker 1: the great novels. I'll tell you if more people had

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01:23:39,439 --> 01:23:42,880
Speaker 1: read Trollops the Way we Live now, which is about

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01:23:42,880 --> 01:23:47,839
Speaker 1: a fraud ster operating in the context of technology financial

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01:23:47,880 --> 01:23:51,640
Speaker 1: speculation about technology a hundred and fifty years ago, you know,

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01:23:51,800 --> 01:23:54,679
Speaker 1: we might not have had Fewer people would have followed

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01:23:54,680 --> 01:24:00,360
Speaker 1: Bernie made off right right, And and our final question,

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01:24:00,760 --> 01:24:03,320
Speaker 1: what do you know about the world's of venture investing

1283
01:24:03,360 --> 01:24:06,200
Speaker 1: today that you wish you knew thirty or forty years

1284
01:24:06,240 --> 01:24:09,880
Speaker 1: ago when you first started exploring this space? Well, I

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01:24:09,920 --> 01:24:15,160
Speaker 1: guess when I first started, I didn't perhaps have enough

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01:24:15,640 --> 01:24:20,480
Speaker 1: appreciation that the terms of the relationship with the entrepreneur

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01:24:21,400 --> 01:24:24,600
Speaker 1: matters so so much. And one of the things I

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01:24:24,960 --> 01:24:28,760
Speaker 1: worry about now. You know, you can see that there's

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01:24:28,760 --> 01:24:30,759
Speaker 1: been this sort of shift in the terms of trade.

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01:24:30,920 --> 01:24:32,840
Speaker 1: They used to say the golden rule is he who

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01:24:32,840 --> 01:24:35,240
Speaker 1: has the gold rules makes the rule. Right, Yeah, but

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01:24:35,280 --> 01:24:37,960
Speaker 1: that's not the case when you're talking about the founders.

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01:24:38,120 --> 01:24:44,400
Speaker 1: You know, whether it's Elizabeth Holmes, the voting share, there's

1294
01:24:44,400 --> 01:24:46,519
Speaker 1: some there can be you know, you can see that

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01:24:46,600 --> 01:24:49,120
Speaker 1: a Google and a Facebook with all that's going on

1296
01:24:49,120 --> 01:24:55,760
Speaker 1: in Facebook, because they're exempt from real governance by stockholders

1297
01:24:55,800 --> 01:24:59,840
Speaker 1: because of the ownership stake of the controlling stock by

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01:25:00,680 --> 01:25:06,080
Speaker 1: Facebook or with Page and Bryan at Google, right they

1299
01:25:06,080 --> 01:25:09,320
Speaker 1: can they can't afford to make upstream investments in science

1300
01:25:09,920 --> 01:25:14,080
Speaker 1: that other companies are not able to. They're supposed to

1301
01:25:14,120 --> 01:25:16,559
Speaker 1: take their cash, any extra cash they have, and use

1302
01:25:16,640 --> 01:25:21,040
Speaker 1: it to buy backstock. So I think that there's a

1303
01:25:21,320 --> 01:25:27,519
Speaker 1: challenge here because by and large, I actually do think

1304
01:25:27,600 --> 01:25:29,800
Speaker 1: that Serge Brand and Larry Page have done a pretty

1305
01:25:29,840 --> 01:25:32,960
Speaker 1: damn good job, certainly of building a great company and

1306
01:25:33,040 --> 01:25:36,960
Speaker 1: of exploring where they can invest this cash flow for

1307
01:25:37,000 --> 01:25:41,840
Speaker 1: the future. They've been brilliant acquirers of relevant technology. Look

1308
01:25:41,840 --> 01:25:45,680
Speaker 1: at YouTube Spectacular or or for that matter, all of

1309
01:25:45,720 --> 01:25:49,840
Speaker 1: the all of the mobile technology Android another home run

1310
01:25:50,080 --> 01:25:54,599
Speaker 1: Maps was brought unbelievable. The person behind Google Maps actually

1311
01:25:54,640 --> 01:25:57,960
Speaker 1: just has a book coming out, Yeah, Never Lost Again

1312
01:25:58,040 --> 01:26:00,880
Speaker 1: or something along those Just Win. So, you know, but

1313
01:26:00,960 --> 01:26:03,320
Speaker 1: I think that that is something I had to learn

1314
01:26:03,920 --> 01:26:08,920
Speaker 1: by doing learn the hard way in the job. We

1315
01:26:09,040 --> 01:26:13,120
Speaker 1: have been speaking with Bill Janeway. The Innovation Economy New

1316
01:26:13,280 --> 01:26:18,040
Speaker 1: edition is out with a whole digital um. Addition, if

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01:26:18,080 --> 01:26:20,880
Speaker 1: you enjoy this conversation, we'll be sure and look up

1318
01:26:20,880 --> 01:26:24,640
Speaker 1: an inch or down an inch on Apple iTunes, Bloomberg

1319
01:26:24,680 --> 01:26:29,479
Speaker 1: dot com, Stitcher, overcast, wherever finer podcasts are sold, and

1320
01:26:29,520 --> 01:26:31,760
Speaker 1: you could see any of the other two hundred or

1321
01:26:31,800 --> 01:26:37,360
Speaker 1: so such conversations that we've had previously. We love your comments, feedback,

1322
01:26:37,439 --> 01:26:41,400
Speaker 1: end suggestions right to us at m IB podcast at

1323
01:26:41,400 --> 01:26:44,840
Speaker 1: Bloomberg dot net. Check out my daily column on Bloomberg

1324
01:26:44,960 --> 01:26:47,600
Speaker 1: dot com. Follow me on Twitter at rid Holtz. I

1325
01:26:47,600 --> 01:26:49,840
Speaker 1: would be remiss if I did not thank the crack

1326
01:26:49,960 --> 01:26:53,879
Speaker 1: staff that helps put together these podcasts each week. Taylor

1327
01:26:53,960 --> 01:26:57,960
Speaker 1: Riggs is our booker slash producer. Medina Parwana is my

1328
01:26:58,040 --> 01:27:01,720
Speaker 1: audio engineer. Michael ad Nick is our head of research.

1329
01:27:02,320 --> 01:27:05,839
Speaker 1: I'm Barry Retolts. You've been listening to Master's in Business

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01:27:06,280 --> 01:27:07,439
Speaker 1: on Bloomberg Radio.