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

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Speaker 1: Radio this week on the podcast. I know you guys

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Speaker 1: bust my chops for saying I have an extra special guest,

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Speaker 1: But I have an extra special guest. Michael Lewis sat

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Speaker 1: with me for nine hours. We talked about everything he's

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Speaker 1: ever written since he was nine years old, and it

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Speaker 1: is absolutely spectacular. So, with no further build up or

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Speaker 1: ado my conversation with Michael Lewis, I have an extra

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Speaker 1: special guest today. His name is Michael Lewis. What can

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Speaker 1: I say You know about him? From Liars Poker, The

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Speaker 1: New new Thing money Ball, The Blindside, The Big Short, Uh,

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Speaker 1: The Undoing Project, The Fifth Risk. He has a new project,

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Speaker 1: Against the Rules, a podcast about referees in life and

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Speaker 1: whatever Happened to Fairness, which I have to tell you

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Speaker 1: I found quite fascinating. Michael Lewis, Welcome to Bloomberger. Thanks

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Speaker 1: for having me, my pleasure. We I really enjoyed speaking

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Speaker 1: with you in Florida at the That Giant conference, and

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Speaker 1: I felt we did not have enough time until fully

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Speaker 1: fleshed the reason you felt that way because we did

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Speaker 1: not have enough time. So tonight uh, you'll be free

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Speaker 1: around midnight. You'll get to go downtown and then we'll

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Speaker 1: we'll set you loose. Let's set the table and start.

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Speaker 1: In the nineteen eighties, you're working at one of the

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Speaker 1: hottest bond firms in the world, but you don't care.

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Speaker 1: You had started a diary and had delusions of becoming

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Speaker 1: a writer. Explain what you were thinking about before I

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Speaker 1: went to work for Solomon Brothers. I had started publishing

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Speaker 1: magazine pieces and the decided that's what I wanted to do,

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Speaker 1: to be a writer, but had no sense that you

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Speaker 1: could make a living doing this. You thought it was

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Speaker 1: a hobby. I thought that I didn't know. I just

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Speaker 1: I didn't know it because I was being paid by

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Speaker 1: the Economist magazine ninety dollars for what amounted to three

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Speaker 1: weeks work. I think didn't see how this was going

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Speaker 1: to work out as a living. So I thought I

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Speaker 1: thought I was gonna write. But this job lands in

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Speaker 1: my lap. I go take the job, and it's about

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Speaker 1: six months before I realized that I'm going to write

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Speaker 1: about the job. That it was such an extraory. I

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Speaker 1: was saying, you know, if you think back to where

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Speaker 1: it was, Solomon Brothers is making so much more money

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Speaker 1: than every other Wall Street back. It looks like it's

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Speaker 1: in a different business. Um, I'm i I land as

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Speaker 1: the sales in the sales arm of John Merryweather's derivative

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Speaker 1: his his proprietary trading book. So I'm selling derivatives. Um,

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Speaker 1: that's the stair way to start, sitting sort of sitting

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Speaker 1: right in the middle of the firm and right in

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Speaker 1: the middle of it's new h And you know, it

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Speaker 1: was just it became pretty clear that you know, I

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Speaker 1: say this glibly, but actually pretty clear. I had stories

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Speaker 1: to tell that just just from my experience. I published

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Speaker 1: a few of them while I was there. I had

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Speaker 1: to write under a pseudonym because they caught me writing

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Speaker 1: out of my real name and they let's talk about that.

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Speaker 1: You under your actual name while working at Solomon Brothers,

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Speaker 1: you published a column in the Wall Street Journal essentially

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Speaker 1: calling bankers overpaid babies. And you describe what actually happened,

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Speaker 1: what who called you into the office, and what was

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Speaker 1: So I was in London, uh, and I got to

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Speaker 1: my desk in the morning, and waiting for me at

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Speaker 1: my desk was the head of Solomon Brothers International to

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Speaker 1: say that he had had it just a horrible night

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Speaker 1: because of this piece that I had written in the

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Speaker 1: Wall Street Journal. And I was so pleased I had

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Speaker 1: a piece in the Wall Street Journal. I thought that

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Speaker 1: would be proud of me. But at the bottom of

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Speaker 1: it said Michael Lewis is an associated Solomon Brothers in London.

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Speaker 1: And he said, you don't understand. Michaele said, the board

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Speaker 1: of directors has been meeting on the phone about this piece.

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Speaker 1: And for complicated reasons, well maybe not that complicated reasons,

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Speaker 1: they couldn't fire They didn't work gonna fire me. They

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Speaker 1: were gonna fire me because I was generating a lot

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Speaker 1: of money for the firm I had. I had lucked

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Speaker 1: into a really big customer, so they didn't want to

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Speaker 1: fire me. Uh. They wanted to reach some agreement that

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Speaker 1: would prevent me from doing this again. And he basically

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Speaker 1: said you shouldn't write. And I said, but you don't understand.

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Speaker 1: I'm gonna write. And he was simple, you know Solomon,

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Speaker 1: but was filled with interesting people. He was sympathetic to that.

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Speaker 1: He knew, he understood was like to like to do something. Uh.

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Speaker 1: He said, he said, could you write under a pseudonym?

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Speaker 1: And I said, did that work. I don't care. Nobody

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Speaker 1: knows who I am. Uh. And we decided the pseudonym

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Speaker 1: I should write under it was my mother's maiden name,

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Speaker 1: Diana Bleaker. Uh he and he said, that's great because

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Speaker 1: no one will ever guess that a woman's a man.

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Speaker 1: So you were using You weren't doing Michael Bleaker. You

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Speaker 1: were doing Diana Bleaker. And I published a few pieces,

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Speaker 1: not many under the Diana the name Diana Bleaker, but

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Speaker 1: they got a lot of attention because I because I

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Speaker 1: felt liberated. I could write about what was happened right

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Speaker 1: next to me. Now no one would ever track it

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Speaker 1: back to me. And one evening, uh, this was there.

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Speaker 1: Two years into my Solomon brother's tenure, I got a

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Speaker 1: call from Chevy Chase's father, Ned Chase, who was so

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Speaker 1: Chevy Chase from Saturday U Live and the movies. His

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Speaker 1: dad was a big shot in New York editor, a

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Speaker 1: book editor at Simon and Schuster. And he said, I figured,

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Speaker 1: I found out that Diana Blaker is you. And I

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Speaker 1: read these pieces, and you really have a book in

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Speaker 1: you when you write a book. And I didn't know

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Speaker 1: what they were gonna pay. I didn't have at that

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Speaker 1: moment I realized, I can I can I eke out

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Speaker 1: a living doing this? So at that moment I was

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Speaker 1: out out the door in my head. I had to

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Speaker 1: wait a few months from my bonus to hit the account.

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Speaker 1: But once my bonus, my bonus arrived, I was out

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Speaker 1: the door and I and I and working. But you know,

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Speaker 1: I say that I was going to just write a

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Speaker 1: book about my experience. But the book I sold Liars Poker.

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Speaker 1: I recently saw the proposal. It really was just like

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Speaker 1: a history of Wall Street. I wasn't even in the

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Speaker 1: book proposal that I sold. And then when I started

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Speaker 1: writing it, it was so clear to me that my

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Speaker 1: variance was richer material than all this other stuff I

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Speaker 1: was going to write about. Um, they just took off

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Speaker 1: in that direction, and uh, that's the beginning of my career.

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Speaker 1: How did ned Chase figure out that you were Diana Bleaker? Like?

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Speaker 1: What was his clue? It wasn't. In the end, it

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Speaker 1: wasn't that hard because the piece that had caught his

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Speaker 1: attention was in The New Republic then edited by Mike Kinsley,

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Speaker 1: and Mike Kinsley refused to let me use a pseudonym

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Speaker 1: without saying on the bottom Diana Bleaker is a pseudonym.

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Speaker 1: And uh and so I think he called Kinsley and

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Speaker 1: Kinsley Gate told him he gave me up that. So,

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Speaker 1: so here's the really interesting thing. You're making so much

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Speaker 1: money at Solomon Brothers. Friends and family say he wants

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Speaker 1: to become a writer. We must stage an intervention. What

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Speaker 1: what on earth was that evening? So it wasn't it

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Speaker 1: wasn't even friends and family. It was it was friends

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Speaker 1: and family. But my boss is a Solomon Brothers. When

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Speaker 1: I told him what I was gonna do, hauled me

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Speaker 1: into a room, and they actually they could have cared less.

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Speaker 1: I was gonna go write a book about Wall Street,

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Speaker 1: which sounds naive, right, But they're just like, sure, go

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Speaker 1: write your book about Wall Street. They were worried about

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Speaker 1: my sanity, they thought, they said, look, you made to

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Speaker 1: fifty last year, You're probably gonna make twice that next year,

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Speaker 1: and then from there you're gonna make lots of money. Here, Um,

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Speaker 1: you're crazy to go do this. Don't go do this,

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Speaker 1: do this? Went do this later and my father, who

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Speaker 1: my father said to me the same kind of thing.

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Speaker 1: He said, because my dad's sitting in New Orleans Louisiana

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Speaker 1: had a very nice career as a lawyer, but all

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Speaker 1: of a sudden, his son, at age twenty four is

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Speaker 1: probably making roughly as much money as he is, and he's,

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Speaker 1: what are you doing leaving that job? And he said,

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Speaker 1: and he's never been one to give me advice, but

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Speaker 1: he said, look, just wait, wait ten years. Did did

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Speaker 1: your twenty four was twenty six at that point, Wait

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Speaker 1: ten years, and when you're in your mid thirties, you

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Speaker 1: can go take your million of dollars and go write

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Speaker 1: your novel. And the problem was that, you know, when

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Speaker 1: you're twenty six, thirty six teams ancient. And I looked

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Speaker 1: up to the people who were thirty six at Solivon Brothers,

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Speaker 1: and I could not imagine any of them leaving that

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Speaker 1: you you got so needful of the money and the

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Speaker 1: position in the status. I was afraid I would become

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Speaker 1: that and I wouldn't I lose this desire to do

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Speaker 1: this other thing. So it was I didn't have any trouble.

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Speaker 1: None of these, none of this advice had any effect

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Speaker 1: on me whatsoever. Uh the golden handcuffs with that obvious

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Speaker 1: people had levered up their mortgages and they were they

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Speaker 1: were living a life that I didn't particularly want to live,

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Speaker 1: but that they were all living that life. They were

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Speaker 1: all living a life that required millions of dollars um

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Speaker 1: and they were in in addition the other, I mean,

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Speaker 1: my other dirty little secret was that it was the

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Speaker 1: job was really interesting for a year year and a

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Speaker 1: half because I was learning so much. But after a

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Speaker 1: year year and a half, you know you're not learning

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Speaker 1: that much. You're doing the same thing over and over

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Speaker 1: and once I sort of got a sense of what

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Speaker 1: this all all was. The idea of coming and doing

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Speaker 1: it again day after day after day was just deadening.

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Speaker 1: I just I didn't actually believe deep down that if

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Speaker 1: I had stay ten years, i'd have gotten rich, because

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Speaker 1: I thought my disinterest in it would eventually have revealed itself.

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Speaker 1: It makes a lot of sense. I have to ask

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Speaker 1: one more question about Liars Poker before we move on

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Speaker 1: to some of the other books. You've said very specifically

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Speaker 1: that you thought Lars Poker would be an admonition, a

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Speaker 1: warning to people about the evils of Wall Street. It

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Speaker 1: turned out it didn't really happen, So I didn't actually

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Speaker 1: say that you said you said, so what I had

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Speaker 1: a different at it I had. I had a different

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Speaker 1: view of the whole thing. By the way, you and

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Speaker 1: I have had this conversation coming around to your view

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Speaker 1: that what you write may not be what other people read.

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Speaker 1: So I didn't think Wall Street was evil. That wasn't

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Speaker 1: the point. I thought it was preposterous, Okay. I thought

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Speaker 1: that the idea that I was being paid that money

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Speaker 1: to give financial advice was insane. So I thought it

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Speaker 1: was absurd, and the only thing that really disturbed me

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Speaker 1: about it at the time that got me upset. I mean,

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Speaker 1: there were times when I felt a little uncomfortable about

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Speaker 1: what I was expected to do to my customers. But

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Speaker 1: but um, but the thing that made me really queasy

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Speaker 1: was seeing people who cared about other things being drawn

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Speaker 1: into Wall Street just because that's where the money was.

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Speaker 1: I had friends in college who really just like they were.

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Speaker 1: I had a good friend who was like born to

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Speaker 1: be an oceanographer, and oceanography just looked like something he

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Speaker 1: couldn't afford to do when Goldman Sachs is offering him

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Speaker 1: this job coming out out of Princeton, So that I

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Speaker 1: thought what the purpose To the extent I had a

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Speaker 1: social purpose with the book. I thought young people will

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Speaker 1: read it. They'll see what this is. They'll understand it's preposterous.

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Speaker 1: They if they have not got nothing better to do

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Speaker 1: with their lives, they'll go to Wall Street. Or if

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Speaker 1: they have a passion for finance, they'll go to Wall Street.

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Speaker 1: But if they've got some other plan, they won't be

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Speaker 1: distracted by the money. That's not That's not how people

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Speaker 1: read it. What happened instead was um two months after

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Speaker 1: the book could come out, I had a stack of

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Speaker 1: letters as high as my nique on the floor from

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Speaker 1: kids at colleges, uh say, and they all read the

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Speaker 1: kind of the same way. Dear Mr Lewis, I loved

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Speaker 1: your book about how to get Ahead on Wall Street.

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Speaker 1: I think I've got all the principles, but I really

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Speaker 1: want to know some things that other people don't know

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Speaker 1: so that I can get there quicker. Is there anything

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Speaker 1: you left out that will help me, like make it

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Speaker 1: on Wall Street? So it became a how to manual.

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Speaker 1: So you and I have had this discussion before. You

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Speaker 1: have made the claim that very often the book you

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Speaker 1: write and set out into the world is very different

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Speaker 1: than the book that people actually read. They bring their

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Speaker 1: own biases and filters and they see things differently than

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Speaker 1: perhaps you expect. I'm really to make even stronger case,

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Speaker 1: and it would be that if a book is any good,

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Speaker 1: it has the capacity to be misunderstood and and and

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Speaker 1: it's and it's for this reason that if I had

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Speaker 1: been all aflame about the evils of Wall Street, um,

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Speaker 1: instead of just kind of funny about it, nobody would

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Speaker 1: have read the book as a how to manual. No

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Speaker 1: one read the book anyway, but they wouldn't have read

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Speaker 1: the book it had been tedious, like who cares what

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Speaker 1: you think? Tell me the story. And if you tell

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Speaker 1: a story and you're telling it kind of honestly, you're

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Speaker 1: leaving room for people to think different things about the story,

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Speaker 1: just as people naturally would about any story. You tell

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Speaker 1: ten people the same story, and they hear ten different stories,

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Speaker 1: they emphasize different things in their minds. You leave, You

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Speaker 1: try to leave holes for the reader to have their

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Speaker 1: own point of view. Now I didn't, I didn't do

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Speaker 1: that intentionally with the first book. But I think the

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Speaker 1: fact that I was feeling kind of detached about Wall Street,

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Speaker 1: you know, I didn't. I wasn't. It wasn't a moralistic

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Speaker 1: diatribe about Wall Street. Um, and the book is very funny,

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Speaker 1: like what I enjoyed with the people in the humor, right,

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Speaker 1: it was that that the characters in the situation was

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Speaker 1: was It was rich and funny and interesting, And I

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Speaker 1: was at some level always grateful that Solomon Brothers gave

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Speaker 1: me a job and at some level always kind of

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Speaker 1: love the place because of how curious it was. It

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Speaker 1: let it, it, let me in, It let all kinds

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Speaker 1: of characters in who wouldn't naturally fit into a more ordinary,

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Speaker 1: corporate kind of environment. How much did that shape what

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Speaker 1: you looked for in future books? Because all of your

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Speaker 1: writing contains interesting, odd bowl quirky characters. Every book has

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Speaker 1: that person as a driver. They all have teachers as drivers.

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Speaker 1: I mean some of them, You're right, some of them

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Speaker 1: are odd, but they all all the all the main

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Speaker 1: characters have the capacity to teach the reader something. I'd say,

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Speaker 1: that's what they have in common. I would say, like

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Speaker 1: Brad Kottsi Yama and Flashboys. As a wildly quirky character. Um,

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Speaker 1: you on the podcast which will get too later, you

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Speaker 1: describe him as this unique bird in the it's he's

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Speaker 1: a completely ordinary Canadian. You put him on Wall Street.

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Speaker 1: He's a freak, right, because they're no completely ordinary Canadians

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Speaker 1: on Wall Street. Uh So that that he was working

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Speaker 1: at Royal Bank of Canada, which is headquartered what in Toronto.

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Speaker 1: I mean, he's kind of in the midst of it.

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Speaker 1: It was he was there, well, he was down here

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Speaker 1: in New York. He was there their U S stock

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Speaker 1: Trader had of US stock trading. But but uh so

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Speaker 1: what Liars Poker. Uh the only one, very specific effect

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Speaker 1: on what I wrote. So subsequently it made it possible

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Speaker 1: for me to write the big short that a lot

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Speaker 1: of the people who were in the middle of the

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Speaker 1: financial crisis, people who made a lot of the bad

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Speaker 1: bets at the banks, I got into the business after

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Speaker 1: reading Liars Poker, so they felt in a connection to

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Speaker 1: me that led them to open up and talk to me,

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Speaker 1: so that it was really important it It really did

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Speaker 1: lead to that book. But other than that, most of

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Speaker 1: Liars Poker's effect on the rest of my writing life

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Speaker 1: has been negati if I just don't want in that

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Speaker 1: and that I don't want to write the same book twice.

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Speaker 1: So I'm not looking for another Liars Poker, just the opposite,

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Speaker 1: looking for something completely different. And and if we just

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Speaker 1: take the course of um, the course of your bibliography, well, well, clearly,

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Speaker 1: um moneyball is something completely different. The new new thing

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Speaker 1: is something totally different. I mean, it's not too hard

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Speaker 1: to see how you go from subject to subject. There's

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Speaker 1: a thread through all of these things that are fairly recognizable,

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Speaker 1: but they're clearly different topics and different approaches and in

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Speaker 1: virtually all the others, with the exception of my little

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Speaker 1: Parenthood book. In the book I did about my high

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Speaker 1: school baseball coach, um, someone else is the main character

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Speaker 1: and not me. Uh, it's fun to have me is

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Speaker 1: the main character. Makes it easy in some ways. But

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Speaker 1: that's what I had in a Liars Poker. I could

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Speaker 1: I myself could stand in for Billy Being or Bradcott's

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Speaker 1: Yama or Jim Clarker. I mean that I could carry

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Speaker 1: the book because I had had that experience. Makes perfect sense.

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Speaker 1: I want to ask you about the fifth risk. What

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Speaker 1: what motivated you too suddenly take on the transition of

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Speaker 1: the newly elected president Rick Perry. In short, when Trump

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Speaker 1: appointed Rick Perry to be Secretary of Energy, I just

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Speaker 1: something clicked in my brain and it was it was well,

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Speaker 1: first the memory of Rick Perry calling for the elimination

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Speaker 1: of the Department of Energy when he was a presidential

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Speaker 1: candidate but not being able to remember its name on

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Speaker 1: stage when he was calling for its elimination, and he

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Speaker 1: did so cavalier, I'm just gonna get rid if I

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Speaker 1: was president, that's I get rid of that. I thought, well,

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Speaker 1: that's curious to pick a guy who says he wants

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Speaker 1: to get rid of the thing to run the thing.

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Speaker 1: He clearly, and then he came Then he came right

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Speaker 1: full circle and said, I just didn't know what the

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Speaker 1: thing did. I'm sorry. I said that. Now I'm glad

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Speaker 1: to be running it. He thought it actually was involved

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Speaker 1: in energy as opposed to nuclear weapons and cleaning up.

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Speaker 1: I didn't know. So I really wise at that point, WHOA,

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Speaker 1: I don't know what the Department of Energy does? What

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Speaker 1: does it do? Like? What do we just hand this

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Speaker 1: guy who doesn't know what it does? And it really

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Speaker 1: should be called the Department of Nuclear Weapons. Uh, it's

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Speaker 1: it's a vast science project that that, among other things,

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Speaker 1: manages the nuclear arsenal. So I so then I thought,

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Speaker 1: what happens in a society, in a government where you're

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Speaker 1: putting people so ill equipped to run the thing into

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Speaker 1: the position of running the thing. Uh. And it led,

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Speaker 1: you know, once I started digging, it led to the

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Speaker 1: story of the transition, which was an incredible story that

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Speaker 1: there essentially wasn't one that that this meant to be, uh,

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Speaker 1: a pretty elaborate hand over the government from one administration

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Speaker 1: to the next, and that the Obama administration had prepared

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Speaker 1: essentially this great course on how the government works. Uh,

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Speaker 1: like a thousand people in the Obama administration and spent

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Speaker 1: six months preparing these books, preparing these talks so that

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Speaker 1: whoever won would come in and they could say, look,

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Speaker 1: this is this is how we dealt with the a

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Speaker 1: bowla virus. This is how you assemble a nuclear weapon,

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Speaker 1: this is these are the loose nukes that were worried

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Speaker 1: about in Eastern Europe. You know, there's lots of practical information. Uh.

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Speaker 1: And that the Trump administration Trump had fired his entire

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Speaker 1: transition team moments after the election. The Trump adminstration had

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Speaker 1: not shown up to get that knowledge. I thought, well, this,

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Speaker 1: it's given me first a device I'm gonna go get.

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Speaker 1: I'm gonna go get these briefings. I'm gonna go hear

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Speaker 1: these things that they didn't bother hearing, and in fact,

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Speaker 1: you're the only person who actually heard some of these

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Speaker 1: many I think, I think the majority of the briefings

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Speaker 1: I heard, I'm the only one who heard them. No,

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Speaker 1: it's more than astonishing. It's like it's and they were boring.

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Speaker 1: You know, you would think government when if you said

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Speaker 1: if we did like a word association game two years ago,

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Speaker 1: and you said government, I'd say boring and actually riveting, riveting,

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Speaker 1: especially riveting when it's in the hands of people who

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Speaker 1: have no idea what it does. But but the the

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Speaker 1: degree what the U. S. Government does in so many

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Speaker 1: different sectors is so incredibly important, including and including places

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Speaker 1: where um, you don't much think the government has a place.

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Speaker 1: Basic science research largely corporate America has a banned in

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Speaker 1: long term scientific research, and so it's moved into the government.

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Speaker 1: And there are mission critical science projects scattered around the

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Speaker 1: government that if we don't do them, you know, we

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Speaker 1: don't have a food supply in thirty years kind of thing.

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Speaker 1: And and it's a data collection. There's data in the census,

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Speaker 1: the weather data, so and so forth, unbelievably valuable. If

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Speaker 1: if we don't have um prescription drug data that the

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Speaker 1: government collects. We probably still don't know there's an opioid epidemic.

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Speaker 1: You've got people, You've got thousands of people dying every year,

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Speaker 1: tens of thousands of people dying every overdose. And the

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Speaker 1: only reason it was found out was the federal government

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Speaker 1: made available all the prescription drug data and geeks started

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Speaker 1: hacking in and said, well, this is a lit law

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Speaker 1: of these pattern the pattern of distribution of of opioid

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Speaker 1: prescriptions doesn't make any sense, like more pills being dispensed

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Speaker 1: in West Virginia than there are West Virginians, and uh

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Speaker 1: so it's the government is like the source of that.

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Speaker 1: Important things are going on there, and so it's that's

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Speaker 1: surprised me. The other thing that surprised me. If you's

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Speaker 1: asked me play that word association game government worker, I

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Speaker 1: just said sleepy, maybe a little ambitious beta. I don't

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Speaker 1: know what. I wouldn't have been flattering terms. The people

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Speaker 1: I met and there was like permanent civil servants were

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Speaker 1: the best people on the planet. They were like if

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Speaker 1: they were mission driven, passionate people go to the weather,

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Speaker 1: the weather if you spent two days and like the

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Speaker 1: storm center at the University of Oklahoma, where they tracked

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Speaker 1: tornadoes with the National Weather Service people there, you will

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Speaker 1: find yourself in a room with like the really smart

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Speaker 1: kid from the fifth grade, who's who's who's Who's Like

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Speaker 1: dog was killed when a tree blew over in a

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Speaker 1: storm that no one warned them about, and became obsessed

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Speaker 1: with predicting weather events, so no one so no one

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Speaker 1: got hurt. And they're they're like, they can make more

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Speaker 1: money twice as much money doing something else, but it's

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Speaker 1: in the National Weather surface where they're where their passion

404
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Speaker 1: has the biggest consequence in In the book, you write

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Speaker 1: a number of really fascinating factoids, and I'm gonna come

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Speaker 1: to some of them, But on weather prediction, you described

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Speaker 1: this as one of the great unsung achievements of the

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Speaker 1: twentieth century is how far our ability to forecast dangerous

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Speaker 1: storms has has advanced, not just dangerous storms, like whether

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Speaker 1: it's gonna be sunny or rainy five days from now.

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Speaker 1: You're your fifth day forecast now is as good as

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Speaker 1: your one day was like twenty five years ago. It's

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Speaker 1: gotten that much better. And I don't know, I grew

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Speaker 1: up in New Orleans, so you know, hurricanes used to

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Speaker 1: when I was a kid used to like come out

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Speaker 1: of nowhere and you didn't know where they were going

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Speaker 1: to hit, and you just kind of go out with

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Speaker 1: a frisbee and wait until I got two windy that

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Speaker 1: you that was your warning system. But it's the the

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Speaker 1: precision with which these storms now can be tracked. It's

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Speaker 1: a great it's a great intellectual achievement. And it's an

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Speaker 1: achievement maybe more to the point that it's not just

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Speaker 1: the government, but without the government it doesn't happen. The

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Speaker 1: government collects all the data. So so let's talk about

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Speaker 1: that because in the book you describe a congressman who

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Speaker 1: wants to get the government out of the weather forecasting

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Speaker 1: business and turn it all over to one of the

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Speaker 1: private which which uses all of the government data. Yeah,

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00:22:51,200 --> 00:22:54,240
Speaker 1: it's like which is like a three billion dollar year.

430
00:22:54,840 --> 00:22:57,080
Speaker 1: The congressman said to the head of NOAH and the

431
00:22:57,200 --> 00:23:01,679
Speaker 1: National Oceanic and Atmospheric Administration and here ring UH and

432
00:23:01,960 --> 00:23:04,800
Speaker 1: the weather services inside of NOAH, said why do I

433
00:23:04,840 --> 00:23:06,880
Speaker 1: need you. I can get my weather from ACU weather.

434
00:23:07,359 --> 00:23:09,240
Speaker 1: And she said, you know where do you think ACU

435
00:23:09,320 --> 00:23:12,399
Speaker 1: weather gets its weather? It gets it from us. You

436
00:23:12,440 --> 00:23:15,720
Speaker 1: know that without us ACU Weather can't make a forecast.

437
00:23:15,800 --> 00:23:18,360
Speaker 1: They don't have the booty data. They'll have the satellite data,

438
00:23:18,359 --> 00:23:21,320
Speaker 1: they don't have the radar data. Plus the weather models

439
00:23:21,480 --> 00:23:24,680
Speaker 1: all originate out of the National Weather Service. Now that's

440
00:23:24,720 --> 00:23:27,199
Speaker 1: true that each of these private weather companies now has

441
00:23:27,200 --> 00:23:30,040
Speaker 1: his own little wrinkle on weather prediction, But the bulk

442
00:23:30,080 --> 00:23:32,920
Speaker 1: of the both both the information you need to make

443
00:23:32,920 --> 00:23:35,280
Speaker 1: the prediction and the intellectual work about how to handle

444
00:23:35,280 --> 00:23:38,040
Speaker 1: that information came out of the government. So it's it's

445
00:23:38,119 --> 00:23:40,800
Speaker 1: insane to say you give it over to the private sector.

446
00:23:41,560 --> 00:23:44,640
Speaker 1: The private sector wouldn't in the first place collect the data.

447
00:23:45,119 --> 00:23:48,879
Speaker 1: Now having said that, um, it's also true that the

448
00:23:48,880 --> 00:23:52,360
Speaker 1: private sectors has made contributions. It's important, and the government

449
00:23:52,760 --> 00:23:55,240
Speaker 1: encourages this partnership. It opens it gives all this data

450
00:23:55,240 --> 00:23:57,000
Speaker 1: everywhere for free. Hack away at it. If you can

451
00:23:57,000 --> 00:23:59,360
Speaker 1: find a better way to predict, like when the tornado

452
00:23:59,400 --> 00:24:03,359
Speaker 1: is gonna happen, we're all ears, um. But it's a

453
00:24:03,440 --> 00:24:05,720
Speaker 1: kind of like an open source thing that everybody has

454
00:24:05,760 --> 00:24:09,439
Speaker 1: to have access to this and into the position of

455
00:24:09,520 --> 00:24:13,200
Speaker 1: running this operation, Trump is appointed the see the former

456
00:24:13,280 --> 00:24:17,439
Speaker 1: CEO of VACU Weather, who has campaigned for twenty years

457
00:24:17,800 --> 00:24:21,720
Speaker 1: to essentially prevent the National Weather Service from communicating with

458
00:24:21,720 --> 00:24:25,520
Speaker 1: the American people on the grounds that if you can

459
00:24:25,560 --> 00:24:28,119
Speaker 1: make money with this in the private sector, the government

460
00:24:28,119 --> 00:24:33,520
Speaker 1: shouldn't be competing with you. Uh, which is I mean nuts.

461
00:24:34,119 --> 00:24:36,560
Speaker 1: The American taxpayers paid for all of his data. How

462
00:24:36,640 --> 00:24:40,640
Speaker 1: much have we paid for all that data? Uh? It's

463
00:24:41,000 --> 00:24:44,439
Speaker 1: I mean several billion dollars a year. It's a running cost,

464
00:24:44,640 --> 00:24:48,200
Speaker 1: it's uh, it's it's close to half the budget of

465
00:24:48,240 --> 00:24:52,920
Speaker 1: the Department of Commerce. This weather collect data collection. And

466
00:24:53,640 --> 00:24:56,239
Speaker 1: the idea that the Weather Service, like he prevented them

467
00:24:56,240 --> 00:24:59,280
Speaker 1: from having an appu, but that they should They shouldn't

468
00:24:59,280 --> 00:25:02,000
Speaker 1: have a website, they shouldn't have it. It It is insane. Um.

469
00:25:02,240 --> 00:25:06,400
Speaker 1: And if you restrict the access to the data uh

470
00:25:06,440 --> 00:25:08,159
Speaker 1: to a handful of companies that happen to have an

471
00:25:08,160 --> 00:25:10,960
Speaker 1: inside track right now, the Weather Channel or Acue, whether

472
00:25:10,960 --> 00:25:14,199
Speaker 1: whoever it is, you prevent the next smart geek in

473
00:25:14,240 --> 00:25:17,439
Speaker 1: his basement from finding new patterns in the data that

474
00:25:17,640 --> 00:25:21,359
Speaker 1: they help us predict the weather. Um. So it's a

475
00:25:21,760 --> 00:25:24,359
Speaker 1: it is a there's a public enterprise side to this,

476
00:25:24,760 --> 00:25:27,479
Speaker 1: just like there's a a public good side to this.

477
00:25:27,520 --> 00:25:29,280
Speaker 1: And that's like there's a public good side to long

478
00:25:29,400 --> 00:25:34,240
Speaker 1: term scientific research, and it isn't that offers a natural

479
00:25:34,320 --> 00:25:37,639
Speaker 1: role for the government, And that attracts some really interesting people.

480
00:25:37,760 --> 00:25:40,800
Speaker 1: And that's which surprised me, you know, And I think

481
00:25:40,840 --> 00:25:43,440
Speaker 1: I I had the felt I had the luxury of

482
00:25:43,520 --> 00:25:45,119
Speaker 1: not paying much attention to it because it seemed to

483
00:25:45,160 --> 00:25:48,399
Speaker 1: like kind of run itself badly or well, and whatever

484
00:25:48,520 --> 00:25:50,200
Speaker 1: I thought or said about the government wasn't gonna have

485
00:25:50,240 --> 00:25:52,119
Speaker 1: any effect. But now all of a sudden, it feels

486
00:25:52,160 --> 00:25:54,600
Speaker 1: like it is at risk of being seriously disabled. So

487
00:25:54,640 --> 00:25:56,119
Speaker 1: all of a sudden, it's gotten to be kind of

488
00:25:56,160 --> 00:26:00,000
Speaker 1: interesting material. So some of the some of the specific

489
00:26:00,160 --> 00:26:03,480
Speaker 1: factoids you bring up in the book, I just pulled

490
00:26:03,520 --> 00:26:07,119
Speaker 1: five or six aside because I were just fascinated by these.

491
00:26:07,680 --> 00:26:12,639
Speaker 1: There are two million federal employees, seventy of whom work

492
00:26:12,680 --> 00:26:16,480
Speaker 1: in national security. That's a mind blowing stat I assumed

493
00:26:16,880 --> 00:26:20,280
Speaker 1: there were all these bureaucrats and pencil pushers. Most of

494
00:26:20,320 --> 00:26:24,439
Speaker 1: the government spends their time working and personnel working to

495
00:26:24,520 --> 00:26:28,560
Speaker 1: keep everybody else. Say, that's the basic function of the government. Uh.

496
00:26:28,600 --> 00:26:32,600
Speaker 1: And and it's also it's also you know, another kind

497
00:26:32,640 --> 00:26:36,600
Speaker 1: of misunderstanding is that like the federal workforce is just

498
00:26:36,720 --> 00:26:41,000
Speaker 1: out of control. There fewer federal workers per capita than

499
00:26:41,040 --> 00:26:44,560
Speaker 1: they were thirty years ago. The thing that's really alarming

500
00:26:44,600 --> 00:26:48,119
Speaker 1: about the federal workforce is how it's aged, uh that

501
00:26:48,720 --> 00:26:50,919
Speaker 1: young people don't want to go work there anymore. And

502
00:26:50,960 --> 00:26:55,640
Speaker 1: you've got so you've got um in information technology alone

503
00:26:55,760 --> 00:26:58,320
Speaker 1: in the federal in the in the federal government, five

504
00:26:58,400 --> 00:27:01,080
Speaker 1: times more people uh over the age of sixty than

505
00:27:01,160 --> 00:27:04,240
Speaker 1: under the age of thirty. So you know, no tech

506
00:27:04,320 --> 00:27:07,240
Speaker 1: company could function that way. And so this is when

507
00:27:07,240 --> 00:27:09,600
Speaker 1: you look at what's what's happening is we're kind of

508
00:27:09,640 --> 00:27:13,280
Speaker 1: living on borrowed time with the federal government running on fumes.

509
00:27:13,400 --> 00:27:17,360
Speaker 1: And it's all a consequence of this enterprise being basically

510
00:27:17,400 --> 00:27:20,640
Speaker 1: demonized and a lot of decades now and a lot

511
00:27:20,720 --> 00:27:25,119
Speaker 1: and and and allowed, partly by Congress UH to atrophy.

512
00:27:25,359 --> 00:27:27,439
Speaker 1: I mean, it's it's just too hard to hire and

513
00:27:27,480 --> 00:27:30,359
Speaker 1: fire people. It's too who wants to go work for

514
00:27:30,400 --> 00:27:33,399
Speaker 1: a place where the only thing, the only time you

515
00:27:33,480 --> 00:27:35,760
Speaker 1: ever recognized is if you do screw up, and if

516
00:27:35,800 --> 00:27:36,960
Speaker 1: you screw up, you're in the front page of the

517
00:27:37,000 --> 00:27:40,320
Speaker 1: Washington Post. But but but you do something great like

518
00:27:40,680 --> 00:27:44,240
Speaker 1: who's nobody pays any attention. Uh, you're actually gonna get rich.

519
00:27:44,840 --> 00:27:49,479
Speaker 1: Uh So it's it's why why can't we find out more?

520
00:27:49,600 --> 00:27:52,920
Speaker 1: Short of the next Michael Lewis book, why aren't more?

521
00:27:52,960 --> 00:27:56,360
Speaker 1: Of a perfect example, I didn't know until I read

522
00:27:56,400 --> 00:28:00,320
Speaker 1: the book that fracking, the technology for fracking which has

523
00:28:00,320 --> 00:28:04,120
Speaker 1: brought out so much gas and natural gas and and

524
00:28:04,320 --> 00:28:07,879
Speaker 1: crude oil and allowed the United States to effectively become

525
00:28:08,160 --> 00:28:11,320
Speaker 1: energy independent, that was the brainchild of the Department of

526
00:28:11,400 --> 00:28:15,080
Speaker 1: Energy Research. Yes, how is that not known by people

527
00:28:15,400 --> 00:28:20,480
Speaker 1: or people or that, or that the entire solar power

528
00:28:20,520 --> 00:28:23,960
Speaker 1: industry begins with funding from the Department of Energy, or

529
00:28:24,000 --> 00:28:27,760
Speaker 1: that the Tesla factory that makes cars in Fremont, California

530
00:28:28,320 --> 00:28:30,920
Speaker 1: starts with a loan from the Department of Energy. That

531
00:28:31,000 --> 00:28:35,840
Speaker 1: they're they're both big loan guarantee programs and essentially of

532
00:28:36,440 --> 00:28:40,200
Speaker 1: essentially a venture capital fund run by really smart people

533
00:28:40,480 --> 00:28:43,280
Speaker 1: who come in and out from industry, from universities to

534
00:28:43,440 --> 00:28:48,280
Speaker 1: fund kind of very long term ideas that aren't going

535
00:28:48,320 --> 00:28:50,040
Speaker 1: to pay off for the wrong term. I mean, that's

536
00:28:50,080 --> 00:28:53,200
Speaker 1: the gap in the market. Right if if if there's

537
00:28:53,240 --> 00:28:55,200
Speaker 1: research that will pay in two or three years, the

538
00:28:55,200 --> 00:28:58,080
Speaker 1: corporations will fund it, but if there's research that that

539
00:28:58,440 --> 00:29:02,000
Speaker 1: may not pay off for the tow years. Uh, there's

540
00:29:02,000 --> 00:29:04,160
Speaker 1: no there's you know, there used to be more of

541
00:29:04,200 --> 00:29:07,440
Speaker 1: that in corporate America, Bell Labs or Xerox park or,

542
00:29:07,640 --> 00:29:11,880
Speaker 1: but the markets have encouraged are discouraged long term that

543
00:29:11,920 --> 00:29:13,640
Speaker 1: kind of long term investment would be. The government is

544
00:29:13,680 --> 00:29:17,640
Speaker 1: even more important. Um, China isn't hesitating to make No,

545
00:29:17,760 --> 00:29:21,960
Speaker 1: that's exactly right, This is exactly right. I think. So

546
00:29:22,040 --> 00:29:23,920
Speaker 1: here's what I think. I think we're an inflection point.

547
00:29:24,000 --> 00:29:26,640
Speaker 1: I think that the society, if it's going to succeed,

548
00:29:27,280 --> 00:29:30,240
Speaker 1: can't be hostile to its own government. We're bizarre in

549
00:29:30,280 --> 00:29:32,680
Speaker 1: this way. And there isn't another democracy where people think

550
00:29:32,680 --> 00:29:37,880
Speaker 1: the government is evil or or the problem. Uh. Now,

551
00:29:37,920 --> 00:29:40,280
Speaker 1: people in Europe might say, the French might say, or

552
00:29:40,320 --> 00:29:43,960
Speaker 1: the British might say, the European government's a problem. But

553
00:29:43,960 --> 00:29:47,360
Speaker 1: but but that of kind of turning your ire are

554
00:29:48,600 --> 00:29:52,280
Speaker 1: on your own government, it's it's like self defeating. I

555
00:29:52,320 --> 00:29:56,080
Speaker 1: think someone is going to emerge who can sell the government.

556
00:29:56,120 --> 00:29:58,480
Speaker 1: I think politicians going to emerge, because it's not that

557
00:29:58,520 --> 00:30:00,920
Speaker 1: hard to sell once you start, once start explaining people

558
00:30:00,960 --> 00:30:03,320
Speaker 1: what it does, and we once you trot out some

559
00:30:03,360 --> 00:30:05,840
Speaker 1: of the people who are doing what they're doing this,

560
00:30:06,280 --> 00:30:09,160
Speaker 1: it's a it's an inspiring place in many ways. So

561
00:30:09,240 --> 00:30:12,920
Speaker 1: like a Mayor Pete or I think I think Mayor

562
00:30:13,000 --> 00:30:17,080
Speaker 1: Pete might be the ideal person to do this. So yes,

563
00:30:17,320 --> 00:30:20,680
Speaker 1: for me, I'm just an opportunist. I'm looking for stories.

564
00:30:20,720 --> 00:30:23,760
Speaker 1: I'm I'm looking for stories to tell that's surprise and

565
00:30:23,800 --> 00:30:27,160
Speaker 1: interests me that might surprise an interest to reader. And

566
00:30:27,360 --> 00:30:32,760
Speaker 1: Trump was a gift because his willingness to entirely neglect

567
00:30:34,080 --> 00:30:38,240
Speaker 1: this enterprise, two million person enterprise he's tasked to run,

568
00:30:39,040 --> 00:30:42,440
Speaker 1: left an opening, uh to go learn about it. Huh.

569
00:30:42,680 --> 00:30:45,320
Speaker 1: So last three bullet points from the fifth risk that

570
00:30:45,520 --> 00:30:49,040
Speaker 1: I'm fascinated by either Tech or A and M. So

571
00:30:49,080 --> 00:30:52,120
Speaker 1: anytime you see Texas A and M or Virginia Tech.

572
00:30:52,520 --> 00:30:55,520
Speaker 1: All these colleges were formed on the basis of US

573
00:30:55,640 --> 00:30:59,360
Speaker 1: land grants. Yes, back in the mid nineteenth century, the

574
00:30:59,440 --> 00:31:03,040
Speaker 1: same legist body of legislation that created the part of

575
00:31:03,040 --> 00:31:09,000
Speaker 1: agriculture creates these these schools. And it's it's Lincoln's observation.

576
00:31:09,080 --> 00:31:14,160
Speaker 1: I think that we need to make uh, we need

577
00:31:14,200 --> 00:31:19,000
Speaker 1: to bring science to agriculture and let's talk about that

578
00:31:19,080 --> 00:31:22,960
Speaker 1: because again from your book, in eighteen seventy two, the

579
00:31:23,040 --> 00:31:27,480
Speaker 1: average farmer fed four people, and due to the AGG

580
00:31:27,600 --> 00:31:31,000
Speaker 1: colleges and the A and M colleges, the average farmer

581
00:31:31,040 --> 00:31:33,960
Speaker 1: now feeds a hundred and fifty five people. Yes, so

582
00:31:34,040 --> 00:31:36,000
Speaker 1: think about that. What that does is it enables a

583
00:31:36,120 --> 00:31:38,360
Speaker 1: modern life that you free people from the farms and

584
00:31:38,400 --> 00:31:40,520
Speaker 1: the burden of having to grow stuff and they can

585
00:31:40,560 --> 00:31:44,160
Speaker 1: do other things. So of the labor pool used to

586
00:31:44,200 --> 00:31:48,000
Speaker 1: be farm based and now it's single low, single digits tiny.

587
00:31:48,640 --> 00:31:50,760
Speaker 1: It's amazing. And then the last one I just have

588
00:31:50,840 --> 00:31:56,960
Speaker 1: to ask you about disposable diapers invented by NASA, is Yeah,

589
00:31:57,000 --> 00:31:59,760
Speaker 1: that was a throwaway line, that's right. One of the

590
00:31:59,760 --> 00:32:02,160
Speaker 1: care archers in the book is Kathy Sullivan, who ran

591
00:32:02,240 --> 00:32:05,520
Speaker 1: who was the head of the National Oceanic and Atmospheric

592
00:32:05,600 --> 00:32:08,040
Speaker 1: Administration and so ran the National Weather Service under it,

593
00:32:08,240 --> 00:32:12,440
Speaker 1: and she was before that, uh, the first she was

594
00:32:12,440 --> 00:32:14,800
Speaker 1: an astronaut, and she was the first American woman to

595
00:32:14,840 --> 00:32:19,520
Speaker 1: walk in space. Uh. Extraordinary characters. But this is in

596
00:32:19,520 --> 00:32:22,400
Speaker 1: an illustration of my general point that you'd be amazed

597
00:32:22,480 --> 00:32:24,480
Speaker 1: who you find in government when you start banging on

598
00:32:24,480 --> 00:32:29,959
Speaker 1: the doors quite fascinating. His latest project, for reasons unfathomable

599
00:32:30,080 --> 00:32:34,400
Speaker 1: to me, a podcast by the name of Against the Rules.

600
00:32:35,480 --> 00:32:38,880
Speaker 1: You're you're a writer, You're you're an author. Why dabbling

601
00:32:39,160 --> 00:32:42,440
Speaker 1: in this podcasting? You're not feeling threatened, are you? Yeah,

602
00:32:42,440 --> 00:32:46,720
Speaker 1: that's what I'm This is a competitive concern. By the way,

603
00:32:46,880 --> 00:32:50,280
Speaker 1: I've listened to your whole first season, except for episode six,

604
00:32:50,280 --> 00:32:54,400
Speaker 1: which wasn't ready for me. It's great. I really love it.

605
00:32:54,400 --> 00:32:57,840
Speaker 1: It's totally your voice. I mean, I don't mean you speaking,

606
00:32:57,840 --> 00:33:01,120
Speaker 1: which is also takes place, but it's so Michael Lewisy.

607
00:33:01,240 --> 00:33:05,760
Speaker 1: It's just infused throughout that each one of these segments,

608
00:33:05,800 --> 00:33:08,640
Speaker 1: each one of these podcasts are like a chapter in

609
00:33:08,680 --> 00:33:11,480
Speaker 1: a Michael Lewis book, which kind of raises the question,

610
00:33:11,680 --> 00:33:14,760
Speaker 1: why not a book. It's a really good question. And

611
00:33:15,520 --> 00:33:18,360
Speaker 1: the answer is that, so there's some kind of material

612
00:33:18,360 --> 00:33:20,800
Speaker 1: that seemed to be very naturally a book. Narrative material

613
00:33:20,840 --> 00:33:23,000
Speaker 1: is what I really prefer, and that's where you have

614
00:33:23,040 --> 00:33:25,400
Speaker 1: a character that you can follow from beginning to end.

615
00:33:25,440 --> 00:33:27,280
Speaker 1: And that's most of the real books I've done that

616
00:33:27,600 --> 00:33:31,080
Speaker 1: there's a it's the structure is not all that different

617
00:33:31,120 --> 00:33:34,360
Speaker 1: from a novel. This was an idea, and the idea

618
00:33:34,480 --> 00:33:38,000
Speaker 1: was referees in American life. It's it's people whose job

619
00:33:38,120 --> 00:33:43,400
Speaker 1: is to maximize fairness, enforce the rules. Uh, not just

620
00:33:43,440 --> 00:33:46,680
Speaker 1: sports referees, but judges and government regulators. And I mean

621
00:33:46,720 --> 00:33:51,520
Speaker 1: we kind of wander with the idea. But each this

622
00:33:51,600 --> 00:33:56,520
Speaker 1: was essay material. This wasn't narrative material. Um each chapter

623
00:33:56,960 --> 00:33:59,120
Speaker 1: and they have seven episodes, so there's seven chapters in

624
00:33:59,120 --> 00:34:02,600
Speaker 1: a book. They all go together, but you wouldn't feel

625
00:34:02,600 --> 00:34:05,280
Speaker 1: like you were reading exactly the same story. There's a

626
00:34:05,360 --> 00:34:09,200
Speaker 1: theme that connects everything. Every they're all distincts. They're all distincts.

627
00:34:09,200 --> 00:34:11,920
Speaker 1: It's like a bunch of short stories around set in

628
00:34:11,960 --> 00:34:15,600
Speaker 1: the same place. Um, well not always. Some of its

629
00:34:15,600 --> 00:34:18,399
Speaker 1: New Jersey, some of it's Berkeley. But but it's right.

630
00:34:18,480 --> 00:34:23,680
Speaker 1: But but I thought that this material. I've wondered about

631
00:34:23,840 --> 00:34:26,960
Speaker 1: your medium. I've wondered about you know where that could

632
00:34:26,960 --> 00:34:29,360
Speaker 1: pull off a podcast. It's a very lazy medium to

633
00:34:29,480 --> 00:34:31,600
Speaker 1: do what I do, which is conn people to come

634
00:34:31,640 --> 00:34:33,960
Speaker 1: in here and have a conversation with me. So I

635
00:34:34,080 --> 00:34:37,200
Speaker 1: have it easy. Yours is a very well, it's narrative.

636
00:34:37,400 --> 00:34:39,880
Speaker 1: It's narrative, but it's it's it's like it's a series

637
00:34:39,880 --> 00:34:42,720
Speaker 1: of short stories that are essays that are strung together.

638
00:34:43,480 --> 00:34:45,360
Speaker 1: And I had the feeling that if we did it

639
00:34:45,400 --> 00:34:48,840
Speaker 1: this way, it would feel more like one story because

640
00:34:48,920 --> 00:34:52,040
Speaker 1: you have that person's voice there all the time, and

641
00:34:52,040 --> 00:34:53,600
Speaker 1: it was just it was material. I never would have

642
00:34:53,640 --> 00:34:55,359
Speaker 1: written a book about it. I never would have written

643
00:34:55,360 --> 00:34:57,680
Speaker 1: a book about So let's start with the first episode

644
00:34:57,680 --> 00:35:01,799
Speaker 1: about referees, which is kind of interesting. And referees in

645
00:35:01,800 --> 00:35:05,120
Speaker 1: the nba UM I have found it amusing you bringing

646
00:35:05,120 --> 00:35:08,640
Speaker 1: your son in, who who seems to not be thrilled

647
00:35:08,719 --> 00:35:11,759
Speaker 1: with with the officiating in in his uh place. For

648
00:35:11,960 --> 00:35:14,640
Speaker 1: he played, he's moved on. He played for a Japanese

649
00:35:14,680 --> 00:35:17,920
Speaker 1: Buddhist temple in the Japanese Buddhist Temple League, and he

650
00:35:17,920 --> 00:35:20,000
Speaker 1: was getting thrown out of games. I mean, you got

651
00:35:20,000 --> 00:35:22,200
Speaker 1: to get booted from fouling up. But he come out

652
00:35:22,200 --> 00:35:24,000
Speaker 1: and he beat steam. ConA is here to be screaming

653
00:35:24,000 --> 00:35:27,120
Speaker 1: at the referee ten years old or eleven years old,

654
00:35:27,440 --> 00:35:29,279
Speaker 1: but he picked it up from the Warriors. That's where

655
00:35:29,280 --> 00:35:31,800
Speaker 1: he got. That's where he watch. He's watching, He's watching

656
00:35:31,840 --> 00:35:34,719
Speaker 1: Steph and Clay and Durant, and they're screaming at the

657
00:35:34,719 --> 00:35:38,680
Speaker 1: refs too, and uh, and so you know, that was

658
00:35:39,680 --> 00:35:42,520
Speaker 1: one of the things that got me interested because he's otherwise.

659
00:35:42,560 --> 00:35:45,120
Speaker 1: My son is actually very mild mannered, kind of sweet

660
00:35:45,120 --> 00:35:48,279
Speaker 1: and detached, and but reffs drive him nuts and he's

661
00:35:48,320 --> 00:35:51,160
Speaker 1: always sure he's a victim, like he's they're they're all

662
00:35:51,320 --> 00:35:54,680
Speaker 1: it's always against him, which which is preposterous. The guy

663
00:35:54,680 --> 00:35:58,160
Speaker 1: on the court's like a Buddhist, He's not against anybody,

664
00:35:58,800 --> 00:36:03,479
Speaker 1: and it makes no sense. So so um, you tell

665
00:36:03,520 --> 00:36:06,959
Speaker 1: the story about the replay center in New Jersey, which

666
00:36:07,000 --> 00:36:09,240
Speaker 1: was mind blowing. So there's a bigger point. The bigger

667
00:36:09,280 --> 00:36:14,560
Speaker 1: point is there's more friction now between especially the star players,

668
00:36:14,560 --> 00:36:16,560
Speaker 1: but the players on the court and the coaches and

669
00:36:16,600 --> 00:36:20,480
Speaker 1: the NBA reffs than there's ever been that it's regard

670
00:36:20,520 --> 00:36:22,040
Speaker 1: the NBA is starting to regard this as a kind

671
00:36:22,040 --> 00:36:25,799
Speaker 1: of crisis, like this problem, that this conflict, And of

672
00:36:25,840 --> 00:36:28,400
Speaker 1: course this bleeds into the way the fans treat the reffs,

673
00:36:28,640 --> 00:36:30,120
Speaker 1: the way the weffs are treated on the street. They

674
00:36:30,160 --> 00:36:32,560
Speaker 1: need bodyguards to go back and forth to their hotels.

675
00:36:32,640 --> 00:36:35,359
Speaker 1: It's insane. But you also point out that the officiating

676
00:36:35,400 --> 00:36:38,239
Speaker 1: has never been better. There's no question I mean, this

677
00:36:38,800 --> 00:36:42,760
Speaker 1: is no question if you back away from it. Um.

678
00:36:42,800 --> 00:36:45,280
Speaker 1: It used to be a handful of kind of dumpy

679
00:36:45,280 --> 00:36:47,120
Speaker 1: white guys, a lot of whom went to the same

680
00:36:47,200 --> 00:36:49,359
Speaker 1: high school, who were not who were there because they

681
00:36:49,360 --> 00:36:52,880
Speaker 1: were part of a club, who didn't get any particular training.

682
00:36:53,640 --> 00:36:57,280
Speaker 1: Who who who? Uh? Those are the refs, and they

683
00:36:57,320 --> 00:36:59,840
Speaker 1: were not checked, really checked in any way. We have

684
00:37:00,080 --> 00:37:04,280
Speaker 1: now a highly professional, highly trained workforce that has video

685
00:37:04,320 --> 00:37:06,759
Speaker 1: replay to go to to fix its some of its

686
00:37:06,760 --> 00:37:10,480
Speaker 1: mistakes when it makes mistakes, acknowledges it makes mistakes. Uh,

687
00:37:10,520 --> 00:37:14,319
Speaker 1: so we'll fix them. Has to review any era it

688
00:37:14,400 --> 00:37:17,359
Speaker 1: makes after the games, so they're you know, they're told

689
00:37:17,400 --> 00:37:20,759
Speaker 1: where you made mistakes. In addition, they've been studied every

690
00:37:20,760 --> 00:37:23,880
Speaker 1: which way for racial bias, for home court bias, for

691
00:37:23,920 --> 00:37:26,720
Speaker 1: all these and they've been informed about their various biases.

692
00:37:26,760 --> 00:37:30,120
Speaker 1: There's no way that referee is not more accurate than

693
00:37:30,160 --> 00:37:32,760
Speaker 1: the referee of twenty thirty years. The home court bias

694
00:37:32,840 --> 00:37:34,920
Speaker 1: was a real thing, much less so today than it

695
00:37:35,000 --> 00:37:37,920
Speaker 1: once was. That's a really good example that it's a

696
00:37:38,120 --> 00:37:41,520
Speaker 1: it's a good example of of why you want good

697
00:37:41,560 --> 00:37:45,120
Speaker 1: independent refs. It's a good example of how independence gets

698
00:37:45,200 --> 00:37:49,200
Speaker 1: undermined home court. A large part of home court advantage

699
00:37:49,360 --> 00:37:52,600
Speaker 1: was simply the ref's favoring the home team. And they

700
00:37:52,640 --> 00:37:57,799
Speaker 1: do that because if you don't, you get screamed at, right,

701
00:37:58,360 --> 00:38:03,160
Speaker 1: That's it's human behavior. They now do it much less.

702
00:38:03,680 --> 00:38:05,880
Speaker 1: There is less of a home court advantage in the NBA.

703
00:38:06,120 --> 00:38:08,160
Speaker 1: The home team is not as likely to win as

704
00:38:08,200 --> 00:38:12,000
Speaker 1: a as it used to be, and the rests are

705
00:38:12,080 --> 00:38:14,879
Speaker 1: mercilessly treated by the by the crowds. So, so let's

706
00:38:14,920 --> 00:38:17,120
Speaker 1: talk about another segment because I could talk about that.

707
00:38:17,280 --> 00:38:20,080
Speaker 1: We'll come back to basketball later. So you talk to

708
00:38:20,120 --> 00:38:22,919
Speaker 1: a woman who has an issue with her student loan.

709
00:38:23,360 --> 00:38:26,319
Speaker 1: She's a teacher, and she believes she's qualified to get

710
00:38:26,360 --> 00:38:30,160
Speaker 1: a waiver after ten years of the band. There's a program,

711
00:38:30,280 --> 00:38:33,200
Speaker 1: so Congress creative program in two thousand seven to relieve

712
00:38:33,400 --> 00:38:38,920
Speaker 1: the student debt of public servants, so public teachers, firemen, policeman, soldiers,

713
00:38:39,560 --> 00:38:42,480
Speaker 1: and UH and set up you know, the criteria for

714
00:38:42,560 --> 00:38:46,120
Speaker 1: qualifying for this program. And this is a woman who

715
00:38:46,200 --> 00:38:50,960
Speaker 1: did in fact qualify for the program. UH. Whose servicer

716
00:38:51,320 --> 00:38:57,080
Speaker 1: navigant Um, a publicly traded, publicly traded company whose interest

717
00:38:57,280 --> 00:38:59,640
Speaker 1: is keeping her in her student loan they get paid

718
00:38:59,680 --> 00:39:04,360
Speaker 1: for thing here there. Um made it verstly impossible for

719
00:39:04,360 --> 00:39:06,800
Speaker 1: her to get into the program. And and this program

720
00:39:06,840 --> 00:39:10,640
Speaker 1: that was created, people have applied to it and should

721
00:39:10,719 --> 00:39:14,440
Speaker 1: that they're probably another there's probably another another thirty thousand

722
00:39:14,480 --> 00:39:17,520
Speaker 1: who are eligible who never even heard of it. Nineties

723
00:39:17,520 --> 00:39:22,640
Speaker 1: six people have qualified for the program. And the broader

724
00:39:22,680 --> 00:39:25,160
Speaker 1: story was, this is a story about the Consumer Financial

725
00:39:25,160 --> 00:39:29,800
Speaker 1: Protection Bureau. It's it's like, who should referee this space

726
00:39:30,040 --> 00:39:34,640
Speaker 1: between big finance and ordinary Americans who are dealing with

727
00:39:34,680 --> 00:39:39,320
Speaker 1: big finance and this it seems to me very obvious

728
00:39:39,680 --> 00:39:43,400
Speaker 1: that this space needs a referee. In every other aspect

729
00:39:43,400 --> 00:39:46,200
Speaker 1: of our consumer lives as a referee, there's a consumer

730
00:39:46,800 --> 00:39:50,920
Speaker 1: UH Product Safety Commission. Your toaster doesn't blow up because

731
00:39:50,960 --> 00:39:53,080
Speaker 1: because because you've got people who are there who will

732
00:39:53,080 --> 00:39:56,920
Speaker 1: pull it off the market if it does. Um And

733
00:39:56,920 --> 00:39:59,960
Speaker 1: and the Consumer Financial Protection Bureau was created on the

734
00:40:00,000 --> 00:40:02,880
Speaker 1: back into the financial crisis to essentially be a referee.

735
00:40:03,239 --> 00:40:05,240
Speaker 1: And the story, I think the story of the Consumer

736
00:40:05,280 --> 00:40:07,520
Speaker 1: Financial Production Bureau is how hard it is to create

737
00:40:07,520 --> 00:40:10,279
Speaker 1: a referee in this world, even when it's obvious that

738
00:40:10,320 --> 00:40:12,520
Speaker 1: you need one. Because what's been happening the last two

739
00:40:12,600 --> 00:40:15,319
Speaker 1: years this Trump has been gutting it. Uh. And they

740
00:40:15,360 --> 00:40:18,480
Speaker 1: even tried to rename rename it the Bureau of something,

741
00:40:19,440 --> 00:40:24,600
Speaker 1: just like a multimillion dollar project to waste money doing that,

742
00:40:24,640 --> 00:40:28,399
Speaker 1: to make it less attractive to the consumer out there. Right,

743
00:40:28,560 --> 00:40:31,000
Speaker 1: you talk about that, it's it's yeah. And by the way,

744
00:40:31,040 --> 00:40:34,040
Speaker 1: the theme in all of this, which kind of is

745
00:40:34,080 --> 00:40:37,279
Speaker 1: a theme in in In the Big Short, it's a

746
00:40:37,320 --> 00:40:40,480
Speaker 1: theme in money Ball, It's certainly a theme and Flashboys,

747
00:40:41,160 --> 00:40:45,719
Speaker 1: is how unfair so much of this life is. And

748
00:40:45,800 --> 00:40:48,200
Speaker 1: here's a person who just wants to make it right.

749
00:40:48,520 --> 00:40:53,759
Speaker 1: That that is absolutely consistent throughout all of against the

750
00:40:53,840 --> 00:40:57,640
Speaker 1: Rules and so many of your books. Or do you disagree? No?

751
00:40:57,800 --> 00:41:01,560
Speaker 1: I agree? I agree in the curiously about the podcast

752
00:41:01,760 --> 00:41:04,799
Speaker 1: was You've got? It's about that. It's the story of

753
00:41:04,840 --> 00:41:08,960
Speaker 1: this character, the referee, who's under attack at a time

754
00:41:09,000 --> 00:41:13,320
Speaker 1: when when issues of fairness seemed to be more heated

755
00:41:13,320 --> 00:41:15,560
Speaker 1: than they've ever been in American life. I mean, maybe

756
00:41:15,560 --> 00:41:19,719
Speaker 1: not since since like a century ago, that the national politics,

757
00:41:19,719 --> 00:41:24,120
Speaker 1: the political campaigns that gained traction, the Sanders Bernie Sanders campaign,

758
00:41:24,160 --> 00:41:27,240
Speaker 1: of the Trump campaign. We're all all got their energy

759
00:41:27,280 --> 00:41:32,120
Speaker 1: from anger about unfairness. Uh. And that in this environment,

760
00:41:32,520 --> 00:41:36,200
Speaker 1: we're making it hard for referees. It's perplexing. I think

761
00:41:36,200 --> 00:41:40,279
Speaker 1: there's an answer, there's a reason, But what's thesis. My

762
00:41:40,320 --> 00:41:43,839
Speaker 1: pet thesis is referees have a harder time the more

763
00:41:43,920 --> 00:41:47,000
Speaker 1: unequal the environment there. Refereing is so if you have

764
00:41:47,280 --> 00:41:50,360
Speaker 1: two people who are just who are basically the same power,

765
00:41:50,480 --> 00:41:54,400
Speaker 1: money and so on, it's easy to ref that situation

766
00:41:54,440 --> 00:41:58,839
Speaker 1: compared to having Lebron James versus bench Warmer and and

767
00:41:59,000 --> 00:42:01,440
Speaker 1: I think it's a the pressure on One of the

768
00:42:01,719 --> 00:42:07,280
Speaker 1: sources of pressure on referees is is inequality in the society.

769
00:42:07,560 --> 00:42:11,359
Speaker 1: Another source of pressure is technology. I mean that that

770
00:42:11,440 --> 00:42:14,640
Speaker 1: when they screw up now it's all over the it's

771
00:42:14,680 --> 00:42:17,760
Speaker 1: all over the internet, and and people who are upset

772
00:42:17,800 --> 00:42:20,480
Speaker 1: by whatever the mistake was can gather together to cause

773
00:42:20,560 --> 00:42:23,320
Speaker 1: more trouble for the resure. Look what happened pre Supable

774
00:42:23,480 --> 00:42:27,440
Speaker 1: with the blown call. It's a really good example, right

775
00:42:27,640 --> 00:42:31,000
Speaker 1: that those kind of calls have probably happened all the

776
00:42:31,040 --> 00:42:35,279
Speaker 1: time in the NFL. But we've gotten the forces that

777
00:42:35,320 --> 00:42:38,680
Speaker 1: are there to assault the referee have gotten stronger and

778
00:42:38,719 --> 00:42:42,720
Speaker 1: stronger and stronger, and there's no there's no counterbalancing force.

779
00:42:42,800 --> 00:42:45,440
Speaker 1: To protect the referee. So you have the city of

780
00:42:45,440 --> 00:42:48,959
Speaker 1: New Orleans out on the streets during the Super Bowl

781
00:42:48,960 --> 00:42:51,719
Speaker 1: instead of watching the boycotting. That just wouldn't you know

782
00:42:51,880 --> 00:42:54,080
Speaker 1: if that if a similar sort of injustice had taken

783
00:42:54,120 --> 00:42:56,400
Speaker 1: place twenty years ago, it would have been people have

784
00:42:56,440 --> 00:42:58,399
Speaker 1: been angry for a moment and forgot about it. So

785
00:42:58,440 --> 00:43:01,840
Speaker 1: what was your experience like doing the podcast? Did you

786
00:43:01,960 --> 00:43:04,440
Speaker 1: enjoy it? Was it fun? Was it more work than

787
00:43:04,520 --> 00:43:08,040
Speaker 1: you expected? Shall we expect another seven episodes next year?

788
00:43:08,320 --> 00:43:12,160
Speaker 1: To tell us about that? That experience you had creating this?

789
00:43:12,680 --> 00:43:16,120
Speaker 1: It was? It was both more work and more fun

790
00:43:16,160 --> 00:43:18,399
Speaker 1: than I thought it was gonna big. I got into

791
00:43:18,440 --> 00:43:21,800
Speaker 1: it because my two friends Jacob Weisberg and Malcolm Gladwell,

792
00:43:21,840 --> 00:43:27,120
Speaker 1: who've created podcasting. Basically, it will be a lot of

793
00:43:27,120 --> 00:43:29,359
Speaker 1: fun and it's not that big. It's not that hard.

794
00:43:29,880 --> 00:43:31,759
Speaker 1: In fact, you know this, this crap that you and

795
00:43:31,800 --> 00:43:33,880
Speaker 1: I are doing, this is easy. This is I. I

796
00:43:34,000 --> 00:43:38,560
Speaker 1: live a a gentry, gentrified life. What you did that

797
00:43:38,840 --> 00:43:43,320
Speaker 1: serious production writing? Like there's you could tell a boatload

798
00:43:43,320 --> 00:43:45,600
Speaker 1: of work one a lot of work. We didn't do it. Um,

799
00:43:45,760 --> 00:43:48,880
Speaker 1: So I don't know if I'm going to do another season.

800
00:43:49,719 --> 00:43:52,719
Speaker 1: It will be. That decision will be driven entirely by

801
00:43:52,719 --> 00:43:55,440
Speaker 1: the reception to this one. If people were if I

802
00:43:55,480 --> 00:43:59,440
Speaker 1: can get to people in this medium, I'll try again.

803
00:44:00,560 --> 00:44:03,120
Speaker 1: But if I can't, pointless to try again, because it

804
00:44:03,160 --> 00:44:04,960
Speaker 1: really was a lot of work. So I could tell

805
00:44:05,000 --> 00:44:07,080
Speaker 1: you so far you have an audience of one like

806
00:44:07,760 --> 00:44:12,000
Speaker 1: I really enjoyed it. But to be fair, I listened

807
00:44:12,000 --> 00:44:15,520
Speaker 1: to it a few weeks after our conversation in Miami

808
00:44:15,640 --> 00:44:18,000
Speaker 1: in a I'm sorry, a few weeks after our conversation

809
00:44:18,440 --> 00:44:22,520
Speaker 1: in Hollywood, Florida, and in anticipation of this, so I

810
00:44:22,600 --> 00:44:26,120
Speaker 1: kind of crash course it. But I I really enjoyed it.

811
00:44:26,239 --> 00:44:30,640
Speaker 1: Your your pal Malcolm Gladwell is doing a revisionist history.

812
00:44:31,600 --> 00:44:34,680
Speaker 1: I've heard that he could not tell you it wasn't

813
00:44:34,719 --> 00:44:37,000
Speaker 1: a lot of work, because that sounds like a lot

814
00:44:37,040 --> 00:44:39,680
Speaker 1: of work. Also, you know, he said afterwards that maybe

815
00:44:39,680 --> 00:44:44,080
Speaker 1: he'd underestimated the amount of work involved. Right, I'm serious.

816
00:44:44,440 --> 00:44:46,239
Speaker 1: This is fun for me. This is easy I put

817
00:44:46,280 --> 00:44:49,320
Speaker 1: to I read all your books, I put some questions together.

818
00:44:49,520 --> 00:44:51,480
Speaker 1: You do all the heavy lifting. My job is to

819
00:44:51,560 --> 00:44:53,800
Speaker 1: just get out of your way creatively. It's an interesting

820
00:44:53,840 --> 00:44:57,520
Speaker 1: experience though that it's different from writing a book in

821
00:44:57,560 --> 00:45:00,000
Speaker 1: a bunch of ways, but and bunch of little ways,

822
00:45:00,000 --> 00:45:03,480
Speaker 1: and Biggs. One of the big ways is what when

823
00:45:03,520 --> 00:45:06,480
Speaker 1: you can hear the voices of the characters. When I

824
00:45:06,520 --> 00:45:08,759
Speaker 1: write the voice of the character, you know it's just

825
00:45:09,000 --> 00:45:12,600
Speaker 1: quoting them. You don't really hear the voice. You don't

826
00:45:12,640 --> 00:45:15,080
Speaker 1: hear that how what it sounds like. When you hear

827
00:45:15,080 --> 00:45:18,719
Speaker 1: what someone sounds like, you learn a lot. You know,

828
00:45:18,760 --> 00:45:22,200
Speaker 1: you hear the woman whose student loans have crushed her life.

829
00:45:22,600 --> 00:45:24,640
Speaker 1: Her teeth are falling out of falling her heads. You

830
00:45:24,640 --> 00:45:26,480
Speaker 1: have three little kids, and she's a public school teacher,

831
00:45:26,960 --> 00:45:29,160
Speaker 1: and she's grinding her teeth and they're falling out of

832
00:45:29,160 --> 00:45:34,400
Speaker 1: her head. And you don't think that person's a fraud.

833
00:45:35,200 --> 00:45:37,600
Speaker 1: You don't think that person is lazy. You don't think

834
00:45:37,640 --> 00:45:41,400
Speaker 1: that person is anything but in a sad state that

835
00:45:42,400 --> 00:45:45,279
Speaker 1: no one should be in. It's an unfair situation. But

836
00:45:45,520 --> 00:45:48,520
Speaker 1: in a way I could never get across in print

837
00:45:49,200 --> 00:45:52,680
Speaker 1: because you just wouldn't believe me, But you believe her.

838
00:45:53,200 --> 00:45:58,080
Speaker 1: So there's something about those voices that enable you to

839
00:45:58,160 --> 00:46:03,279
Speaker 1: cut through prejudice are preconceived ideas that it's harder to

840
00:46:03,320 --> 00:46:05,239
Speaker 1: do as an author. A lot of what I do

841
00:46:05,280 --> 00:46:07,600
Speaker 1: as an author in a funny way. Is is preached

842
00:46:07,600 --> 00:46:10,120
Speaker 1: to converted. I don't know how many minds I change

843
00:46:10,200 --> 00:46:15,480
Speaker 1: when I am writing. Um, I think I think this

844
00:46:15,520 --> 00:46:18,400
Speaker 1: form can change minds. Well, I have to tell you

845
00:46:18,440 --> 00:46:20,719
Speaker 1: I really enjoyed it. I hope you bring it back

846
00:46:20,760 --> 00:46:23,600
Speaker 1: for a second season. I am looking forward to hearing

847
00:46:23,640 --> 00:46:27,120
Speaker 1: the finished product. What I heard was like three quarters done. Um,

848
00:46:27,239 --> 00:46:30,200
Speaker 1: I can easily I can strongly recommend it. It It was

849
00:46:30,280 --> 00:46:32,279
Speaker 1: really interesting. Can you stick around a little bit. I

850
00:46:32,280 --> 00:46:35,160
Speaker 1: have some more questions for you. We have been speaking

851
00:46:35,160 --> 00:46:38,440
Speaker 1: to Michael Lewis. He is the author of you know

852
00:46:38,719 --> 00:46:42,760
Speaker 1: his books and his new upcoming podcast is Against the Rules.

853
00:46:43,200 --> 00:46:45,440
Speaker 1: If you enjoy this conversation, we'll be sure and come

854
00:46:45,480 --> 00:46:47,640
Speaker 1: back for the podcast extras, where we keep the tape

855
00:46:47,719 --> 00:46:52,600
Speaker 1: rolling and continue discussing all things Michael Lewis related. You

856
00:46:52,600 --> 00:46:58,080
Speaker 1: can find that at iTunes, Overcast, SoundCloud, Bloomberg, uh, Stitcher,

857
00:46:58,200 --> 00:47:02,560
Speaker 1: wherever your finer podcast sold. We love your comments, feedback

858
00:47:02,560 --> 00:47:06,080
Speaker 1: and suggestions right to us at m IB podcast at

859
00:47:06,080 --> 00:47:09,480
Speaker 1: Bloomberg dot net. Check out my daily column at Bloomberg

860
00:47:09,600 --> 00:47:13,640
Speaker 1: dot com slash Opinion. You can follow me on Twitter

861
00:47:13,800 --> 00:47:16,719
Speaker 1: at Rit Halts chant on my daily column at Bloomberg

862
00:47:16,760 --> 00:47:20,840
Speaker 1: dot com slash Opinion. Follow me on Twitter at Rit Halts.

863
00:47:21,360 --> 00:47:24,640
Speaker 1: I'm Barry Riholts. You're listening to Master's in Business. I'm

864
00:47:24,680 --> 00:47:41,360
Speaker 1: Bloomberg Radio. Welcome to the podcast, Michael, Thank you so

865
00:47:41,440 --> 00:47:43,800
Speaker 1: much for doing this. I have to share a funny

866
00:47:43,840 --> 00:47:51,040
Speaker 1: story with you, UM about the referee segment in UH

867
00:47:51,040 --> 00:47:54,520
Speaker 1: in against the Rules, UH the NBA referee segment, and

868
00:47:54,560 --> 00:47:57,360
Speaker 1: you're the perfect person to tell this. Here's here's a

869
00:47:57,360 --> 00:48:02,919
Speaker 1: little bit of NBA fish creating and behavioral economics all

870
00:48:02,920 --> 00:48:05,719
Speaker 1: wrapped up in one. So I'm a long suffering Nick fan.

871
00:48:05,920 --> 00:48:10,520
Speaker 1: During the Ewing Patrick Ewing, Charles Oakley, John Stark's Anthony

872
00:48:10,560 --> 00:48:12,920
Speaker 1: Mason Era, I lived a few blocks from the garden,

873
00:48:13,040 --> 00:48:14,799
Speaker 1: I had season tickets. I would go all the time.

874
00:48:15,200 --> 00:48:20,160
Speaker 1: The officiating made me crazy. You mentioned in the podcast

875
00:48:20,200 --> 00:48:23,640
Speaker 1: you mentioned Larry Bird. Really, Michael Jordan was the guy

876
00:48:23,719 --> 00:48:27,359
Speaker 1: who had his own set of rules unofficially, and and

877
00:48:27,400 --> 00:48:31,240
Speaker 1: he got away with everything. He was constantly tormenting the Knicks.

878
00:48:31,360 --> 00:48:36,520
Speaker 1: And then a few years later upcomes this monstrosity Shaquille O'Neal,

879
00:48:36,719 --> 00:48:40,560
Speaker 1: and he's Patrick Ewing is a big guy Shaquille O'Neill

880
00:48:40,680 --> 00:48:44,720
Speaker 1: is constantly bodying him, hitting him, fouling him, no calls,

881
00:48:44,880 --> 00:48:49,280
Speaker 1: and I just couldn't understand how completely unfair and terrible

882
00:48:49,280 --> 00:48:52,320
Speaker 1: the officiating is. Put a pin in that. A second,

883
00:48:52,760 --> 00:48:55,319
Speaker 1: A buddy of mine invites me to an n i

884
00:48:55,440 --> 00:48:59,319
Speaker 1: T Invitational game at the Garden. This next hear of

885
00:48:59,680 --> 00:49:05,279
Speaker 1: col Ledge players below the march madness. Uh, you know,

886
00:49:05,400 --> 00:49:08,359
Speaker 1: I'm not a crazy college basketball fan, but they're on

887
00:49:08,400 --> 00:49:12,880
Speaker 1: the floor. I'll go big. Joe Bessaker of Emerald Asset Management.

888
00:49:13,080 --> 00:49:16,520
Speaker 1: He's six five, he's three hundred pounds, and his team

889
00:49:16,640 --> 00:49:20,080
Speaker 1: is in in this this game. So we're sitting there courtside.

890
00:49:20,200 --> 00:49:22,359
Speaker 1: You can't miss him. He's a mountain of a man.

891
00:49:23,200 --> 00:49:26,680
Speaker 1: And I'm watching the game and I have no vested

892
00:49:26,760 --> 00:49:29,759
Speaker 1: interest in the outcome. I could not care less who

893
00:49:29,760 --> 00:49:33,840
Speaker 1: wins losers. But these are fantastic seats. The players are great.

894
00:49:33,880 --> 00:49:36,120
Speaker 1: I have a front row. Wow, this is awesome. I've

895
00:49:36,160 --> 00:49:38,279
Speaker 1: never seen a game. I was up in sections two

896
00:49:38,280 --> 00:49:42,680
Speaker 1: oh five normally courtside. Awesome, except for the fact that

897
00:49:42,840 --> 00:49:47,560
Speaker 1: Joe Besker screaming at the ref cons relf. You're walking, Joe.

898
00:49:47,600 --> 00:49:49,719
Speaker 1: That's one and a half stuff, that's a foul. What

899
00:49:49,719 --> 00:49:51,600
Speaker 1: do you do it? No, Joe, the ball is part

900
00:49:51,600 --> 00:49:53,880
Speaker 1: of the hand. It's that and every and he's pulling

901
00:49:53,880 --> 00:49:55,600
Speaker 1: his hair and and said, oh my god, this is

902
00:49:55,640 --> 00:49:57,759
Speaker 1: the worst. And all of a sudden, I just had this,

903
00:49:57,880 --> 00:50:00,600
Speaker 1: like fifteen years ago. I have a realization. You were

904
00:50:00,640 --> 00:50:05,600
Speaker 1: that guy. I was that guy. That's subjective lack of

905
00:50:05,640 --> 00:50:09,319
Speaker 1: ability to pull out my rooting for my team for

906
00:50:09,400 --> 00:50:13,680
Speaker 1: any objective ability to evaluate the game. So take your

907
00:50:13,680 --> 00:50:15,759
Speaker 1: son to a game with two other teams that he

908
00:50:15,800 --> 00:50:18,760
Speaker 1: doesn't care about and see what he says about the officiator.

909
00:50:18,880 --> 00:50:20,840
Speaker 1: That's exactly right. No one ever has a problem if

910
00:50:20,880 --> 00:50:22,759
Speaker 1: they don't have a root. If it's not your team,

911
00:50:22,800 --> 00:50:25,400
Speaker 1: you you know, why is it that the officials suck

912
00:50:25,520 --> 00:50:27,560
Speaker 1: with your team? But with the rest of the league

913
00:50:27,560 --> 00:50:29,520
Speaker 1: where your teams that involved? And and why isn't it

914
00:50:29,640 --> 00:50:32,920
Speaker 1: Both sides think the ref suck. Both sides think the

915
00:50:32,920 --> 00:50:35,400
Speaker 1: refs have screwed them in some way. That's not possible.

916
00:50:35,719 --> 00:50:38,160
Speaker 1: If somebody got screwed, the other person is the beneficiary

917
00:50:38,200 --> 00:50:41,640
Speaker 1: of it. Uh, it's zero sum one. What's think right,

918
00:50:41,719 --> 00:50:44,160
Speaker 1: It's it's amazing. So we think for everybody who's upset

919
00:50:44,160 --> 00:50:45,600
Speaker 1: with the ref. There'd be someone on the other side

920
00:50:45,600 --> 00:50:47,440
Speaker 1: who's happy with the ref. But that's not the way

921
00:50:47,440 --> 00:50:50,040
Speaker 1: it works. So as I listened to the first episode

922
00:50:50,200 --> 00:50:53,160
Speaker 1: of Against the Rules, all I can think of was

923
00:50:53,160 --> 00:50:56,480
Speaker 1: Big Joe Bessica screaming and me saying, you know, there's

924
00:50:56,520 --> 00:51:00,719
Speaker 1: a subjectivity and a lack of of ability to separate

925
00:51:00,760 --> 00:51:04,040
Speaker 1: yourself from your desires and goals. And so then throw

926
00:51:04,120 --> 00:51:08,319
Speaker 1: into that in addition to that, um the idea that

927
00:51:08,320 --> 00:51:10,520
Speaker 1: you're making the rest are actually getting better, that they're

928
00:51:10,520 --> 00:51:14,040
Speaker 1: actually making more accurate calls. Who's going to be furious

929
00:51:14,040 --> 00:51:16,000
Speaker 1: when that happens. People who are used to getting the

930
00:51:16,040 --> 00:51:18,920
Speaker 1: Bennett who used to getting calls. So the problem now

931
00:51:18,960 --> 00:51:22,040
Speaker 1: in the Superstars are furious because they're not getting as

932
00:51:22,040 --> 00:51:24,399
Speaker 1: many calls as they used to get and they expect them.

933
00:51:24,880 --> 00:51:27,839
Speaker 1: Uh So you describe in and you we we kinda

934
00:51:28,360 --> 00:51:31,720
Speaker 1: passed by this too quickly before you describe in the

935
00:51:31,760 --> 00:51:36,479
Speaker 1: podcast the Replay Center that every time there's a basketball game,

936
00:51:37,080 --> 00:51:39,680
Speaker 1: not too far from where the high frequency co Located

937
00:51:39,960 --> 00:51:44,360
Speaker 1: Traders servers are is a giant warehouse filled with hundreds

938
00:51:44,400 --> 00:51:47,319
Speaker 1: and hundreds of screens, hundred and ten television screens and

939
00:51:47,440 --> 00:51:50,520
Speaker 1: nothing on them but direct feeds fib optic. There's fiber

940
00:51:50,520 --> 00:51:53,360
Speaker 1: optic cable from that replay center in secaucas to the

941
00:51:53,360 --> 00:51:57,600
Speaker 1: twenty nine arenas in the NBA where they play, and

942
00:51:57,600 --> 00:52:01,279
Speaker 1: and the pictures on the screen are nothing but the

943
00:52:01,320 --> 00:52:03,960
Speaker 1: cameras the angles of the court. So you don't get

944
00:52:04,000 --> 00:52:05,640
Speaker 1: you don't get the scores, you don't you don't even

945
00:52:05,680 --> 00:52:09,960
Speaker 1: you don't even know who's no commercials, it's whatever is

946
00:52:09,960 --> 00:52:12,000
Speaker 1: happening in that arena. So if you went and dance

947
00:52:12,080 --> 00:52:14,400
Speaker 1: naked in the middle of the floor in the you

948
00:52:14,440 --> 00:52:19,600
Speaker 1: know on someday when they see Jersey, Uh, no matter

949
00:52:19,640 --> 00:52:22,440
Speaker 1: when you did. It's just constant. And so that center

950
00:52:22,560 --> 00:52:28,360
Speaker 1: was built to defend referees from this growing chorus of outrage.

951
00:52:28,880 --> 00:52:33,040
Speaker 1: Uh the reason, I mean fifteen million bucks it costs

952
00:52:33,080 --> 00:52:34,960
Speaker 1: to build that center. And they only changed two calls

953
00:52:34,960 --> 00:52:39,680
Speaker 1: a game, uh two calls. But it's to create it's

954
00:52:39,719 --> 00:52:43,440
Speaker 1: just a weapon that to try to defend the refs

955
00:52:43,480 --> 00:52:46,400
Speaker 1: from these growing attacks. That's that's what you need it

956
00:52:46,520 --> 00:52:50,200
Speaker 1: because if you don't have it, the refs. The refs

957
00:52:50,200 --> 00:52:52,879
Speaker 1: are doing a pretty good job anyway, and they only

958
00:52:53,120 --> 00:52:57,160
Speaker 1: turned changing two calls a game here. Um, but it's

959
00:52:57,200 --> 00:53:00,239
Speaker 1: it's it's like it's needed in order to assure you're

960
00:53:00,280 --> 00:53:04,200
Speaker 1: everybody that there that they know stuff you don't, that

961
00:53:04,360 --> 00:53:07,040
Speaker 1: someone objectively is looking at this over and over and

962
00:53:07,080 --> 00:53:09,400
Speaker 1: giving you the right answer. Because they don't. Nobody trusts

963
00:53:09,400 --> 00:53:12,000
Speaker 1: the rests anymore. So, So let's stay with basketball, and

964
00:53:12,080 --> 00:53:15,080
Speaker 1: the shift gears on you away from the podcast and

965
00:53:15,280 --> 00:53:18,799
Speaker 1: back towards the Undoing Project. You tell the story in

966
00:53:18,800 --> 00:53:23,120
Speaker 1: the beginning about essentially the Billy being of the NBA,

967
00:53:23,800 --> 00:53:27,440
Speaker 1: UM Daryl Daryl Morey Millam of the Houston Rockets. So

968
00:53:27,440 --> 00:53:31,440
Speaker 1: so again there's a theme that seems to run consistently

969
00:53:31,560 --> 00:53:34,160
Speaker 1: through your work, which is he was a really interesting

970
00:53:34,200 --> 00:53:37,840
Speaker 1: guy in a challenging job who wants to approach it

971
00:53:38,040 --> 00:53:41,000
Speaker 1: very very differently. Tell us a little bit about how

972
00:53:41,000 --> 00:53:44,120
Speaker 1: you discovered him and how that got worked into the

973
00:53:44,200 --> 00:53:48,920
Speaker 1: Undoing Project. So he got in touch with me after Moneyball,

974
00:53:48,960 --> 00:53:52,720
Speaker 1: came out to thank me because he thought the book

975
00:53:52,760 --> 00:53:55,920
Speaker 1: was responsible for him getting hired to run a basketball team. Really,

976
00:53:57,040 --> 00:53:59,440
Speaker 1: because the owner of the Rockets had read the book

977
00:53:59,520 --> 00:54:02,880
Speaker 1: and said, and nobody is using data in the NBA

978
00:54:03,160 --> 00:54:07,080
Speaker 1: as well as it could be used. Uh, and there

979
00:54:07,080 --> 00:54:10,560
Speaker 1: will be inefficiencies that we can exploit to And Daryl

980
00:54:10,640 --> 00:54:13,640
Speaker 1: was part of the business side of the Boston Celtics,

981
00:54:13,680 --> 00:54:16,200
Speaker 1: of the of the ownership group that had bought the

982
00:54:17,040 --> 00:54:20,879
Speaker 1: Boston Celtics, and I guess he the owner heard about

983
00:54:20,960 --> 00:54:23,879
Speaker 1: him and said, you know about these analytics, come here,

984
00:54:24,160 --> 00:54:27,160
Speaker 1: and he gave him the job, so Daryl, and so

985
00:54:27,360 --> 00:54:29,400
Speaker 1: from that moment, I started paying more attention to what

986
00:54:29,480 --> 00:54:32,200
Speaker 1: Daryl was doing in the Houston Rockets. And it's been

987
00:54:32,320 --> 00:54:36,000
Speaker 1: riven ng because it's for him, it's like one big

988
00:54:36,080 --> 00:54:38,920
Speaker 1: lab experiment. I mean, he's trying to win, but he's

989
00:54:38,920 --> 00:54:40,600
Speaker 1: also trying to learn. He's trying to just kind of

990
00:54:40,600 --> 00:54:44,160
Speaker 1: create new basketball knowledge. And where he came into the

991
00:54:44,280 --> 00:54:49,040
Speaker 1: Undoing Project was here was a person who was sort

992
00:54:49,080 --> 00:54:54,959
Speaker 1: of state of the art in in both both analytics

993
00:54:55,480 --> 00:54:59,960
Speaker 1: and in sort of being aware of the mistakes people

994
00:55:00,120 --> 00:55:04,720
Speaker 1: make when they're making judgments about basketball or basketball players. UM.

995
00:55:04,800 --> 00:55:07,400
Speaker 1: And then, by the way, the parallel back and forth

996
00:55:07,480 --> 00:55:12,799
Speaker 1: between UM money Ball and the Undoing Project, what some

997
00:55:12,840 --> 00:55:16,120
Speaker 1: of the scouts said in in money Ball is he

998
00:55:16,160 --> 00:55:19,520
Speaker 1: doesn't have a player's body. He's got an ugly girlfriend

999
00:55:19,760 --> 00:55:24,799
Speaker 1: like the cliches, and and prejudices were absurd. But there

1000
00:55:24,840 --> 00:55:27,799
Speaker 1: are different set of cliches with the basketball players, right

1001
00:55:27,880 --> 00:55:30,000
Speaker 1: and so, but it interested in me. Was here as

1002
00:55:30,000 --> 00:55:31,840
Speaker 1: a person. He'd been running by the time I walked

1003
00:55:31,840 --> 00:55:34,440
Speaker 1: into his life to write that chapter in the Undoing Project.

1004
00:55:34,840 --> 00:55:36,680
Speaker 1: I mean, he'd been on the job for seven or

1005
00:55:36,680 --> 00:55:41,080
Speaker 1: eight years, and he had purged the operation of the

1006
00:55:41,160 --> 00:55:44,719
Speaker 1: kind of dumb scouting approach. To give us some some

1007
00:55:44,800 --> 00:55:48,200
Speaker 1: example and well, I mean he nicknames, yeah, we no

1008
00:55:48,320 --> 00:55:51,160
Speaker 1: so so. But but even in his scouting, even with

1009
00:55:51,200 --> 00:55:54,279
Speaker 1: all smart people he had around the table, they had

1010
00:55:54,320 --> 00:56:00,760
Speaker 1: an incredible difficulty keeping the mistakes that the human makes

1011
00:56:01,080 --> 00:56:04,200
Speaker 1: when it's making judgments out of the room, and he

1012
00:56:04,239 --> 00:56:07,160
Speaker 1: had to be They built they would they built in

1013
00:56:07,280 --> 00:56:10,720
Speaker 1: kind of rules to try to guard against errors and judgments.

1014
00:56:10,760 --> 00:56:13,120
Speaker 1: So for example, they made when they saw when they

1015
00:56:13,120 --> 00:56:15,600
Speaker 1: made a mistake, he would analyze the mistake. And when

1016
00:56:15,680 --> 00:56:18,920
Speaker 1: they made a mistake, they had opportunity to draft uh

1017
00:56:19,080 --> 00:56:22,160
Speaker 1: Mark Gasol, Paul Gasol's little brother of future NBA All

1018
00:56:22,239 --> 00:56:26,680
Speaker 1: Star um and one of the scouts pulled up a

1019
00:56:26,680 --> 00:56:29,720
Speaker 1: picture of Gasol on the Internet and he was without

1020
00:56:29,719 --> 00:56:34,200
Speaker 1: a shirt on and he had man boobs fleshy flabby boobs,

1021
00:56:34,680 --> 00:56:37,319
Speaker 1: and the scout gave whenever he was referred to. From

1022
00:56:37,360 --> 00:56:39,840
Speaker 1: then on, he was referred to as man Boobs, and

1023
00:56:39,880 --> 00:56:42,880
Speaker 1: they didn't draft him. And you think that's why he thinks,

1024
00:56:43,080 --> 00:56:45,000
Speaker 1: there's no question in the fact that they gave him

1025
00:56:45,000 --> 00:56:47,880
Speaker 1: a nickname distorted everybody's judgment. From the moment he was

1026
00:56:47,920 --> 00:56:49,440
Speaker 1: called man boobs, there was no way they were going

1027
00:56:49,480 --> 00:56:53,239
Speaker 1: to draft. So he banned nicknames, said, no nicknames, it

1028
00:56:53,400 --> 00:56:57,480
Speaker 1: distorts our judgment of the person. Another example, he um

1029
00:56:57,560 --> 00:57:01,640
Speaker 1: he noticed that when UH scouts were arguing for a player,

1030
00:57:02,600 --> 00:57:06,320
Speaker 1: they draw analogies. They'd say, he looks like Michael Jordan's

1031
00:57:06,440 --> 00:57:09,000
Speaker 1: he reminds me of magic, or you know, he looks

1032
00:57:09,040 --> 00:57:13,880
Speaker 1: like Shaquille O'Neal, or whoever it is. Invariably, he noticed,

1033
00:57:15,120 --> 00:57:20,200
Speaker 1: Um the analogies were within the same race. Black guys

1034
00:57:20,200 --> 00:57:22,360
Speaker 1: always reminded people of black guys, and white guys arts

1035
00:57:22,400 --> 00:57:27,080
Speaker 1: mander people of white guys, and UH nobody thought, oh,

1036
00:57:27,120 --> 00:57:29,480
Speaker 1: a black eye reminded him of Larry Bird, or a

1037
00:57:29,480 --> 00:57:33,480
Speaker 1: white guy reminded of the Magic, and he thought that,

1038
00:57:34,160 --> 00:57:39,120
Speaker 1: he said, he banned right analogy analogies within race, so

1039
00:57:39,280 --> 00:57:41,440
Speaker 1: you can compare a player to another player and say, oh,

1040
00:57:41,480 --> 00:57:43,600
Speaker 1: he plays a lot like X if the guy is

1041
00:57:43,640 --> 00:57:46,320
Speaker 1: a different race. And he said, at that moment, it

1042
00:57:46,480 --> 00:57:50,440
Speaker 1: just stopped. It just stopped. So I actually love my

1043
00:57:50,520 --> 00:57:54,600
Speaker 1: favorite part of the genesis story of the book came

1044
00:57:54,640 --> 00:58:00,440
Speaker 1: about because you you write the you right? Um? Was

1045
00:58:00,480 --> 00:58:03,760
Speaker 1: it money Ball or the Big Short? That Taylor and

1046
00:58:03,840 --> 00:58:06,400
Speaker 1: Sunstein right and say, hey, you guys love there's Moneyball.

1047
00:58:06,640 --> 00:58:09,240
Speaker 1: So they they that I had written Moneyball, and you know,

1048
00:58:09,280 --> 00:58:11,880
Speaker 1: I knew. I thought I knew what that book was about.

1049
00:58:11,960 --> 00:58:15,040
Speaker 1: And it was about this this front office. It found

1050
00:58:16,520 --> 00:58:19,200
Speaker 1: using data better ways to value baseball players and baseball

1051
00:58:19,200 --> 00:58:23,040
Speaker 1: strategies and reveal that markets make huge misjudgments when they

1052
00:58:23,080 --> 00:58:27,640
Speaker 1: value people. When the book came out, UH, Richard Thaylor

1053
00:58:27,680 --> 00:58:30,600
Speaker 1: and Cass Sunstein Um wrote a review in The New

1054
00:58:30,640 --> 00:58:32,920
Speaker 1: Republic basically saying that I didn't know what my own

1055
00:58:32,920 --> 00:58:35,560
Speaker 1: book was about, and that what it really was was

1056
00:58:35,600 --> 00:58:39,160
Speaker 1: a case study and of Conomen and diverse Key's work.

1057
00:58:39,320 --> 00:58:42,200
Speaker 1: And I went who, I didn't know who they were? Uh,

1058
00:58:42,240 --> 00:58:45,080
Speaker 1: And this was Danny Kneman and Amos Diverseky and Kneman

1059
00:58:45,120 --> 00:58:47,280
Speaker 1: had won the Nobel Prize in Economics the year I

1060
00:58:47,320 --> 00:58:50,400
Speaker 1: wrote Moneyball. Um, you have to be alive to win

1061
00:58:50,400 --> 00:58:52,360
Speaker 1: the prize to Versky had passed away or that's right,

1062
00:58:53,360 --> 00:58:56,480
Speaker 1: and uh and you, by the way, so I'm an

1063
00:58:56,480 --> 00:59:00,840
Speaker 1: interject here. You were uniquely situated to write this book

1064
00:59:00,960 --> 00:59:05,440
Speaker 1: for a number of reasons. And you you you've pushed

1065
00:59:05,480 --> 00:59:07,880
Speaker 1: back about this, but you're wrong. And I want to

1066
00:59:07,880 --> 00:59:13,200
Speaker 1: get you to project because first of all, you had

1067
00:59:13,200 --> 00:59:17,000
Speaker 1: written money Ball, which was effectively case study in their works,

1068
00:59:17,280 --> 00:59:20,000
Speaker 1: sort of like because what they had done is classify

1069
00:59:20,160 --> 00:59:23,560
Speaker 1: all the mistakes that the Oakland A's were exploiting. But

1070
00:59:23,560 --> 00:59:28,840
Speaker 1: but more importantly, you had a relationship with Kneman or

1071
00:59:28,880 --> 00:59:32,439
Speaker 1: at least his family that you were barely even so

1072
00:59:32,880 --> 00:59:37,120
Speaker 1: in some ways, before I write a book, I do

1073
00:59:37,240 --> 00:59:39,000
Speaker 1: like to tell myself a story about why I'm the

1074
00:59:39,040 --> 00:59:42,200
Speaker 1: one who should be writing this book. And usually the

1075
00:59:42,360 --> 00:59:45,600
Speaker 1: story holds up pretty well in my mind. In this case,

1076
00:59:45,720 --> 00:59:48,600
Speaker 1: the story I told before the undoing project was Yes,

1077
00:59:49,400 --> 00:59:52,840
Speaker 1: it's got a connection to Moneyball. Yes, I know the

1078
00:59:52,880 --> 00:59:56,800
Speaker 1: Tversky family. Yes, Danny Knoman lives up the hill from

1079
00:59:56,800 --> 00:59:59,960
Speaker 1: me in Berkeley. But where I hit up, where I

1080
01:00:00,240 --> 01:00:03,200
Speaker 1: where I hit a roadblock, was No, I don't know

1081
01:00:03,240 --> 01:00:08,520
Speaker 1: a thing about the field. Of psychology and um and no,

1082
01:00:08,840 --> 01:00:11,440
Speaker 1: I don't know a thing about the origins of the

1083
01:00:11,480 --> 01:00:15,160
Speaker 1: state of Israel and and the Hebrew University department in

1084
01:00:15,200 --> 01:00:18,720
Speaker 1: which all of this stuff got cooked up. And now

1085
01:00:18,960 --> 01:00:22,120
Speaker 1: with most things I read, most books, I mean, part

1086
01:00:22,120 --> 01:00:23,720
Speaker 1: of the joy writing the book is having to learn

1087
01:00:23,760 --> 01:00:27,200
Speaker 1: new things. So that's not you know, of itself, that's

1088
01:00:27,240 --> 01:00:29,600
Speaker 1: not the problem. The big problem was I did have

1089
01:00:29,640 --> 01:00:32,800
Speaker 1: a sense, with these two characters condom in Diversity that

1090
01:00:32,920 --> 01:00:34,720
Speaker 1: I was I was writing about people who were so

1091
01:00:34,800 --> 01:00:37,240
Speaker 1: much smarter than I was, that I was never going

1092
01:00:37,280 --> 01:00:39,200
Speaker 1: to be able to get my mind around them. That

1093
01:00:39,200 --> 01:00:41,360
Speaker 1: that that was that it was gonna always feel like

1094
01:00:41,440 --> 01:00:45,080
Speaker 1: the B student writing about the A student. And that

1095
01:00:45,760 --> 01:00:49,760
Speaker 1: caused me to hesitate for years writing the book. Had

1096
01:00:50,160 --> 01:00:53,080
Speaker 1: I spent I mean normally from the moment I just

1097
01:00:53,160 --> 01:00:56,280
Speaker 1: get a get like a Jones for a subject, to

1098
01:00:56,320 --> 01:00:58,920
Speaker 1: the moment I got a book out, it's like three years,

1099
01:00:59,280 --> 01:01:03,680
Speaker 1: and in this case it was nine, and I shoved

1100
01:01:03,680 --> 01:01:05,160
Speaker 1: it aside and said I wasn't going to do it

1101
01:01:05,200 --> 01:01:07,200
Speaker 1: four or five times. So so what made you finally

1102
01:01:07,240 --> 01:01:11,000
Speaker 1: pull the trigger and say? People started to die? The

1103
01:01:11,040 --> 01:01:13,000
Speaker 1: people who were helping, the people who were helping me

1104
01:01:13,080 --> 01:01:15,440
Speaker 1: with the book. I mean, their colleagues there. The story

1105
01:01:15,560 --> 01:01:19,800
Speaker 1: was dying, and I looked around and I asked myself, well,

1106
01:01:20,040 --> 01:01:22,760
Speaker 1: who else is going to write this? And it became

1107
01:01:22,920 --> 01:01:24,520
Speaker 1: quite clear no one else is gonna write it. And

1108
01:01:24,560 --> 01:01:26,840
Speaker 1: it's effectively a love story, isn't it is? It's a

1109
01:01:26,880 --> 01:01:32,320
Speaker 1: love story between the It's a platonic love story. There

1110
01:01:32,360 --> 01:01:35,439
Speaker 1: were these two guys, each of who each of them

1111
01:01:35,520 --> 01:01:38,600
Speaker 1: is a great literary character, and together it's this combustible,

1112
01:01:38,600 --> 01:01:43,080
Speaker 1: wonderful force, and it's it's about that relationship and how

1113
01:01:43,120 --> 01:01:45,680
Speaker 1: that relationship, you know. I framed the book in my

1114
01:01:45,720 --> 01:01:48,120
Speaker 1: mind lots of different ways and lots of different moments.

1115
01:01:48,120 --> 01:01:49,400
Speaker 1: But one of the ways I framed it for a

1116
01:01:49,400 --> 01:01:52,360
Speaker 1: while was it's just about the power of collaboration, to

1117
01:01:52,560 --> 01:01:54,840
Speaker 1: which two minds can do together when they really fit together.

1118
01:01:54,920 --> 01:01:57,480
Speaker 1: It's just so different from what mind one mind will

1119
01:01:57,520 --> 01:01:59,920
Speaker 1: do on its own. One plus one is three. Was

1120
01:02:00,120 --> 01:02:02,760
Speaker 1: that feeling, you know, And and you really bring it

1121
01:02:02,840 --> 01:02:05,840
Speaker 1: so so richly to life, the story of them being

1122
01:02:05,840 --> 01:02:09,760
Speaker 1: behind closed doors and just peals of laughter hours and

1123
01:02:09,800 --> 01:02:11,800
Speaker 1: hours at a time, and everybody wondering what the hell

1124
01:02:11,880 --> 01:02:16,360
Speaker 1: is going on? And also but it's a Israel makes

1125
01:02:16,400 --> 01:02:20,000
Speaker 1: academics much more interesting than most other places because the

1126
01:02:20,040 --> 01:02:23,200
Speaker 1: academics are forced to interact with the society. Everybody's gonna

1127
01:02:23,200 --> 01:02:25,480
Speaker 1: be useful, everybody's got to be in the army. So

1128
01:02:25,520 --> 01:02:31,240
Speaker 1: they were drawing there their insights from rich interactions with

1129
01:02:31,320 --> 01:02:35,000
Speaker 1: the world around them, and that that helped bring I mean,

1130
01:02:35,040 --> 01:02:37,200
Speaker 1: what was would otherwise be a kind of a dusty

1131
01:02:37,240 --> 01:02:41,400
Speaker 1: academic story to life. Well, the whole thing with Tversky

1132
01:02:41,440 --> 01:02:44,800
Speaker 1: and the fighter pilots and the reversion to the mean

1133
01:02:45,360 --> 01:02:48,160
Speaker 1: like too, there's there's still a giant leap that gets

1134
01:02:48,240 --> 01:02:53,400
Speaker 1: made from when you're yelling at a um enlisted or

1135
01:02:53,480 --> 01:02:56,800
Speaker 1: pilot or what have you. Well, if they're underperforming, they

1136
01:02:56,800 --> 01:03:00,600
Speaker 1: will eventually mean revert, and if they're overperforming, they'll mean reverts.

1137
01:03:00,600 --> 01:03:03,800
Speaker 1: So praise doesn't help, and yelling at them seems to work. Yes,

1138
01:03:04,400 --> 01:03:08,240
Speaker 1: that that this is conomans insight, that that life has

1139
01:03:08,320 --> 01:03:11,880
Speaker 1: this horrible feedback loop. This lesson. It teaches us that

1140
01:03:12,680 --> 01:03:15,640
Speaker 1: we get response to our punishment but not to our praise,

1141
01:03:16,800 --> 01:03:19,840
Speaker 1: because because you're praising someone when they're naturally performing better

1142
01:03:19,840 --> 01:03:23,760
Speaker 1: than usual, and you're punishing them or criticized them when

1143
01:03:23,760 --> 01:03:26,840
Speaker 1: they're naturally performing worse than usual, and mean reversion will

1144
01:03:26,880 --> 01:03:30,120
Speaker 1: make will tend to make them better after you've criticized them,

1145
01:03:30,400 --> 01:03:32,400
Speaker 1: and tend to make them worse after you've praised them.

1146
01:03:32,440 --> 01:03:35,360
Speaker 1: So so I took, I actually took. When I learned

1147
01:03:35,360 --> 01:03:38,400
Speaker 1: that from him, I changed the way I coached little kids.

1148
01:03:39,160 --> 01:03:43,280
Speaker 1: I basically stopped. I stopped with a criticism except except

1149
01:03:43,400 --> 01:03:47,200
Speaker 1: when it was really lathered in and when it almost

1150
01:03:47,280 --> 01:03:49,520
Speaker 1: sounded like praise. And what was the result when you

1151
01:03:49,640 --> 01:03:52,040
Speaker 1: as much of the happier environment? Sure anything I said

1152
01:03:52,080 --> 01:03:55,160
Speaker 1: ever had any effect, but everybody was happier. That that

1153
01:03:55,200 --> 01:03:57,040
Speaker 1: makes That makes a whole lot of sense. Let last

1154
01:03:57,120 --> 01:04:03,120
Speaker 1: question on Knomen and Tversky. Um, they describe the role

1155
01:04:03,200 --> 01:04:06,440
Speaker 1: of luck in life, and they really emphasize it a lot.

1156
01:04:07,120 --> 01:04:12,440
Speaker 1: Tell us a little bit about about that part of um,

1157
01:04:12,480 --> 01:04:16,120
Speaker 1: their beliefs, their writings. How important is luck to who

1158
01:04:16,200 --> 01:04:20,320
Speaker 1: we are and how successful we become? You know? They

1159
01:04:20,360 --> 01:04:23,800
Speaker 1: they I think the way they would put it as

1160
01:04:23,800 --> 01:04:28,600
Speaker 1: that people people have extraordinary pattern recognition abilities, and they

1161
01:04:28,600 --> 01:04:33,240
Speaker 1: see patterns even in randomness. Uh So they don't They're

1162
01:04:33,240 --> 01:04:39,120
Speaker 1: not good at seeing randomness and understanding what randomness means. Um.

1163
01:04:39,160 --> 01:04:42,440
Speaker 1: I mean you saw this with the that was that

1164
01:04:42,520 --> 01:04:46,000
Speaker 1: was sort of one of the themes that ran through

1165
01:04:46,040 --> 01:04:48,760
Speaker 1: the Moneyball story that one of the things that Oakland

1166
01:04:48,760 --> 01:04:51,000
Speaker 1: A's front office was able to do was exploit other

1167
01:04:51,000 --> 01:04:56,240
Speaker 1: people's misunderstanding of randomness. That if a picture pitched a

1168
01:04:56,240 --> 01:04:59,480
Speaker 1: few innings and pitched them really well, uh, they start

1169
01:04:59,520 --> 01:05:03,240
Speaker 1: to get over valued a very small sample with lots

1170
01:05:03,240 --> 01:05:06,680
Speaker 1: of randomness baked into it, um, and they could sell

1171
01:05:06,720 --> 01:05:09,680
Speaker 1: them from more than he was worth. Conversely, someone who

1172
01:05:09,720 --> 01:05:15,240
Speaker 1: had a short bad streak would start to get undervalued. UM.

1173
01:05:15,880 --> 01:05:21,200
Speaker 1: But the so so that the general idea that that

1174
01:05:22,080 --> 01:05:25,160
Speaker 1: Diversky econom and do play with a bit is is

1175
01:05:25,200 --> 01:05:30,760
Speaker 1: it people are mistaking a random event for a meaningful

1176
01:05:30,800 --> 01:05:35,320
Speaker 1: event and vice versa. Um. The broader thing that interests

1177
01:05:35,360 --> 01:05:37,360
Speaker 1: me is the way people dismiss it in their lives.

1178
01:05:38,120 --> 01:05:41,640
Speaker 1: I mean, there's so much luck in life, and people

1179
01:05:42,120 --> 01:05:45,680
Speaker 1: after the fact are really good at telling a story

1180
01:05:45,720 --> 01:05:50,160
Speaker 1: about how it was inevitably so um. Kind of. Traversky

1181
01:05:50,200 --> 01:05:54,640
Speaker 1: had this wonderful line that, um, that life isn't a point,

1182
01:05:54,720 --> 01:05:58,120
Speaker 1: it's a cloud of possibilities, and that that at any

1183
01:05:58,160 --> 01:06:02,040
Speaker 1: given time, there's anything would have happened and just because

1184
01:06:02,080 --> 01:06:04,000
Speaker 1: it did happen didn't mean it had to have happened.

1185
01:06:04,080 --> 01:06:07,240
Speaker 1: So we we we we we're sort of like deterministic

1186
01:06:07,280 --> 01:06:11,200
Speaker 1: machines in a probabilistic world. There's there are certainly elements

1187
01:06:11,280 --> 01:06:13,200
Speaker 1: of that, but there's also a whole lot of ego

1188
01:06:13,520 --> 01:06:16,360
Speaker 1: that if it's luck, then it wasn't my genius and

1189
01:06:16,440 --> 01:06:18,600
Speaker 1: my hard work and I made this happen. If you're

1190
01:06:18,600 --> 01:06:22,240
Speaker 1: gonna say it's lucky, you're taking my accomplishment away from me.

1191
01:06:22,520 --> 01:06:26,680
Speaker 1: Or so some people seem to behave absolutely I mean

1192
01:06:26,720 --> 01:06:31,600
Speaker 1: it's a really flattering successful people are people who feel successful,

1193
01:06:31,680 --> 01:06:34,440
Speaker 1: don't are I think, probably less likely to acknowledge the

1194
01:06:34,560 --> 01:06:38,000
Speaker 1: role of chance in their lives. Right. Uh, I don't

1195
01:06:38,240 --> 01:06:41,760
Speaker 1: completely get this because it's I'm not sure it actually

1196
01:06:41,880 --> 01:06:45,360
Speaker 1: leads to happiness to ignore the role of chance in

1197
01:06:45,400 --> 01:06:48,160
Speaker 1: your life. I think that I think that a great

1198
01:06:48,280 --> 01:06:51,439
Speaker 1: source of happiness is gratitude, and if you just think

1199
01:06:51,440 --> 01:06:53,560
Speaker 1: you did it all yourself, you have nothing to be

1200
01:06:53,560 --> 01:06:58,720
Speaker 1: grateful for. Uh, it's um And I think like seeing

1201
01:06:58,760 --> 01:07:01,840
Speaker 1: just how many different ways the ball could have bounced

1202
01:07:02,360 --> 01:07:06,400
Speaker 1: does leave you more grateful when it bounces your way,

1203
01:07:06,600 --> 01:07:08,720
Speaker 1: and that leads to better happiness. I can tell you

1204
01:07:08,800 --> 01:07:12,040
Speaker 1: from experience sitting in this chair, I'm shocked at the

1205
01:07:12,160 --> 01:07:16,720
Speaker 1: number of really successful people have brought up the concept of,

1206
01:07:16,800 --> 01:07:19,720
Speaker 1: you know, you gotta get lucky. Sometimes you get you

1207
01:07:19,720 --> 01:07:22,880
Speaker 1: get a lucky bounce or a good call, and it's

1208
01:07:22,920 --> 01:07:27,120
Speaker 1: life changing. And it's nice to see certain people recognize that.

1209
01:07:27,960 --> 01:07:31,560
Speaker 1: And the converse is also true. Unluckiness can have you know,

1210
01:07:31,760 --> 01:07:35,680
Speaker 1: oh died in fact for sure. So let me ask

1211
01:07:35,720 --> 01:07:38,320
Speaker 1: you about a book you did not write. Right. You

1212
01:07:38,400 --> 01:07:42,800
Speaker 1: did this giant article on long term capital management and

1213
01:07:42,880 --> 01:07:46,200
Speaker 1: with all due respect to Roger Lowenstein When Genius Failed,

1214
01:07:46,240 --> 01:07:50,320
Speaker 1: which is one of my favorite books. You that article

1215
01:07:50,400 --> 01:07:52,160
Speaker 1: you did for I don't remember who was the New

1216
01:07:52,200 --> 01:07:55,600
Speaker 1: York Times magazine it well, okay, that was a monster piece.

1217
01:07:55,720 --> 01:08:00,000
Speaker 1: I I very specifically remember that that could have easily

1218
01:08:00,040 --> 01:08:03,360
Speaker 1: you become that sort of book. What Why did you

1219
01:08:03,400 --> 01:08:09,320
Speaker 1: not go down that rabbit ale? Um? I think I

1220
01:08:09,360 --> 01:08:12,760
Speaker 1: had an aversion of going back to Wall Street from

1221
01:08:12,840 --> 01:08:15,200
Speaker 1: for yet another book at that time. Was it too soon?

1222
01:08:15,400 --> 01:08:19,320
Speaker 1: It was too soon? And um I also felt like

1223
01:08:19,400 --> 01:08:22,200
Speaker 1: a magazine article. In a magazine article, I had said

1224
01:08:22,200 --> 01:08:25,720
Speaker 1: everything I really wanted to say about it. Uh, And

1225
01:08:25,760 --> 01:08:29,760
Speaker 1: didn't have that much more interest. You know, often I

1226
01:08:29,800 --> 01:08:33,280
Speaker 1: start out, almost always the book start out as magazine pieces.

1227
01:08:33,400 --> 01:08:34,800
Speaker 1: You don't think, oh, I'm going to write a book

1228
01:08:34,800 --> 01:08:36,920
Speaker 1: about that. You you think, oh, that's interesting, or write

1229
01:08:36,920 --> 01:08:38,280
Speaker 1: a little something about it, and then it gets even

1230
01:08:38,280 --> 01:08:41,320
Speaker 1: more interesting. So it's three words mushrooms, and all all

1231
01:08:41,320 --> 01:08:43,120
Speaker 1: of a sudden you realize I've written ten thousand words,

1232
01:08:43,160 --> 01:08:45,680
Speaker 1: but really I wish I could write eighty. And that

1233
01:08:45,720 --> 01:08:48,640
Speaker 1: didn't happen with that subject. I had already written a

1234
01:08:48,640 --> 01:08:52,840
Speaker 1: bunch of it in Liars Poker, the stuff that might

1235
01:08:52,880 --> 01:08:55,559
Speaker 1: have fleshed it out into a book, And it was

1236
01:08:55,600 --> 01:08:58,559
Speaker 1: about this very narrow, peculiar thing that happened to those

1237
01:08:58,600 --> 01:09:01,600
Speaker 1: guys at Long Term Capital, and I just thought it

1238
01:09:01,640 --> 01:09:03,360
Speaker 1: would be useful to try to explain it to a

1239
01:09:03,439 --> 01:09:05,960
Speaker 1: lay audience in a way that it wasn't being explained.

1240
01:09:06,000 --> 01:09:08,840
Speaker 1: And a lot of those players from Solomon, right, they

1241
01:09:08,880 --> 01:09:13,360
Speaker 1: were my colleagues. I knew them quite well. Um, I

1242
01:09:13,400 --> 01:09:15,400
Speaker 1: mean really, there were people I talked to you every day.

1243
01:09:15,840 --> 01:09:19,720
Speaker 1: So yes, uh, if I had one of the alternative

1244
01:09:19,720 --> 01:09:21,799
Speaker 1: paths in my life, if I had stayed at Solomon

1245
01:09:21,840 --> 01:09:25,120
Speaker 1: Brothers and managed to remain interested and actually continue to

1246
01:09:25,160 --> 01:09:28,240
Speaker 1: do well. I'd have been there. I'd have been there.

1247
01:09:28,240 --> 01:09:29,840
Speaker 1: I wouldn't have been that one would ever let me

1248
01:09:29,880 --> 01:09:32,120
Speaker 1: trade anything, but I would have been I would have

1249
01:09:32,120 --> 01:09:35,640
Speaker 1: been their sales arm uh, because my that's where my

1250
01:09:35,680 --> 01:09:38,880
Speaker 1: boss was. Um, that's amazing. That's what you could have

1251
01:09:38,920 --> 01:09:43,760
Speaker 1: been ltcms uh institutional sales right. Yes, that's that's a

1252
01:09:43,800 --> 01:09:46,760
Speaker 1: different career path, different career path, and it would have been.

1253
01:09:46,880 --> 01:09:49,679
Speaker 1: It would have been, you know, as interesting a life

1254
01:09:49,680 --> 01:09:53,000
Speaker 1: in the financial sector as you could have had, very

1255
01:09:53,080 --> 01:09:54,800
Speaker 1: lucrative until it all went to zoo and then all

1256
01:09:54,840 --> 01:09:58,040
Speaker 1: of a sudden. Yes, they didn't make the turn at

1257
01:09:58,040 --> 01:10:00,200
Speaker 1: the end. They sent the record on the straighta way,

1258
01:10:00,200 --> 01:10:02,559
Speaker 1: but they So I have a question for you. My

1259
01:10:02,720 --> 01:10:05,080
Speaker 1: question for you is, I mean, so I'm now and

1260
01:10:05,160 --> 01:10:07,599
Speaker 1: I'm just an interloper in on Wall Street. I when

1261
01:10:07,600 --> 01:10:10,880
Speaker 1: I I dip back in every now and then when

1262
01:10:10,920 --> 01:10:12,720
Speaker 1: it seems to be a story to write, and then

1263
01:10:12,840 --> 01:10:15,360
Speaker 1: got to educate myself all over again about what's going

1264
01:10:15,400 --> 01:10:18,520
Speaker 1: on because they don't follow it that closely day to day.

1265
01:10:18,840 --> 01:10:21,760
Speaker 1: If you were to dropped me anywhere now into the

1266
01:10:21,760 --> 01:10:25,599
Speaker 1: financial sector to write stories, where would you drop me? Wow,

1267
01:10:25,640 --> 01:10:29,920
Speaker 1: that's a really interesting question, Um, stories that you're interested in,

1268
01:10:29,960 --> 01:10:33,040
Speaker 1: Our stories that you're interested in. Well, first, you know,

1269
01:10:33,160 --> 01:10:36,679
Speaker 1: I'm a giant behavioral finance junkie, so you've already covered

1270
01:10:37,240 --> 01:10:40,760
Speaker 1: that space and in two or three books. UM. So

1271
01:10:40,840 --> 01:10:43,400
Speaker 1: there's three or four things that are are kind of intriguing.

1272
01:10:43,520 --> 01:10:46,720
Speaker 1: Let me let me take them off. I'm fascinated by

1273
01:10:46,800 --> 01:10:50,800
Speaker 1: people who have a fundamental flaw in their model of

1274
01:10:50,840 --> 01:10:55,839
Speaker 1: the universe, and for whatever reason, they become gold blow bugs,

1275
01:10:55,880 --> 01:11:00,559
Speaker 1: they become bitcoin fanatics, they become short sellers. Not that

1276
01:11:00,680 --> 01:11:05,160
Speaker 1: each of those groups are delusional, um as a as

1277
01:11:05,400 --> 01:11:08,240
Speaker 1: any individual can be rational and like gold or rational

1278
01:11:08,280 --> 01:11:13,320
Speaker 1: and by bitcoin or whatever. But but these like little society,

1279
01:11:13,320 --> 01:11:19,639
Speaker 1: these little communities grow up around these concepts that like,

1280
01:11:19,680 --> 01:11:22,519
Speaker 1: if you think about bitcoin as no government and we're

1281
01:11:22,560 --> 01:11:25,040
Speaker 1: gonna well, when the government really goes to hell and

1282
01:11:25,040 --> 01:11:28,120
Speaker 1: there's no electricity and running water, good luck getting that

1283
01:11:28,160 --> 01:11:30,880
Speaker 1: little thumb drive to buy you you know the the

1284
01:11:31,640 --> 01:11:35,560
Speaker 1: you know what I mean, the there's no the rationality

1285
01:11:35,640 --> 01:11:37,880
Speaker 1: sort of fades at a certain point. So that's one

1286
01:11:37,960 --> 01:11:40,960
Speaker 1: area I'm fascinated about. It's um And there was a

1287
01:11:40,960 --> 01:11:44,760
Speaker 1: book called The Heretics written by an Australian and it

1288
01:11:44,840 --> 01:11:46,519
Speaker 1: had a different name in the US. I'm drawing a

1289
01:11:46,560 --> 01:11:50,360
Speaker 1: blank on it. But all these people flat earthers and

1290
01:11:50,360 --> 01:11:55,640
Speaker 1: and Holocaust deniers and like just you know, really crazy.

1291
01:11:56,000 --> 01:11:58,640
Speaker 1: Turns out they're not crazy. There's just something wrong with

1292
01:11:58,720 --> 01:12:01,720
Speaker 1: their basic model of the universe, and everything built on

1293
01:12:01,760 --> 01:12:05,280
Speaker 1: top of it is jana that's gonna collapse. So that's

1294
01:12:05,320 --> 01:12:10,000
Speaker 1: one area. Um. The other area that's really fascinating is

1295
01:12:12,120 --> 01:12:15,200
Speaker 1: so there's parts of Wall Street that's incredibly successful, the

1296
01:12:15,280 --> 01:12:19,639
Speaker 1: rise of indexing, the rise of ETFs UM, the move

1297
01:12:19,680 --> 01:12:24,760
Speaker 1: towards lower cost, and everybody in those spaces are petrified

1298
01:12:24,840 --> 01:12:28,240
Speaker 1: that they're going to be made obsolete, even though they're

1299
01:12:28,280 --> 01:12:33,920
Speaker 1: the drivers of this. So we're replaced by robots. Yes

1300
01:12:33,960 --> 01:12:36,720
Speaker 1: and no maybe, I mean there's this I'll give you

1301
01:12:36,720 --> 01:12:39,120
Speaker 1: another thing, and you you I don't understand that. Why

1302
01:12:39,120 --> 01:12:42,920
Speaker 1: if it's if indexing is obviously a huge success, etf

1303
01:12:43,080 --> 01:12:46,160
Speaker 1: a huge stess, why what are they worried about? So

1304
01:12:46,160 --> 01:12:49,000
Speaker 1: so if we just wiped out or or if we

1305
01:12:49,200 --> 01:12:55,000
Speaker 1: fill in the blank indexers, passive e t f UM manufacturers,

1306
01:12:55,520 --> 01:12:59,759
Speaker 1: low cost advocates, if we just completely disrupted the old guard,

1307
01:13:00,600 --> 01:13:03,080
Speaker 1: then clearly the acyclical, who's going to come along and

1308
01:13:03,080 --> 01:13:05,960
Speaker 1: disrupt us? And then the third area that I would

1309
01:13:06,040 --> 01:13:11,760
Speaker 1: drop you into the issue of income inequality and wealth

1310
01:13:11,800 --> 01:13:17,080
Speaker 1: inequality that hearkens back to the pre nine crash days,

1311
01:13:18,240 --> 01:13:21,040
Speaker 1: is very much tied into what you you're writing about

1312
01:13:21,040 --> 01:13:25,200
Speaker 1: fairness and what you're seeing in about government. And we're

1313
01:13:25,200 --> 01:13:28,799
Speaker 1: in an alternative timeline. We've kind of jumped our timeline.

1314
01:13:28,800 --> 01:13:32,800
Speaker 1: We're on a different track. And like you, if you

1315
01:13:32,840 --> 01:13:34,920
Speaker 1: were a Star Trek fan, you remember the bearded Spock

1316
01:13:35,320 --> 01:13:38,840
Speaker 1: in the Alternative University. So I kind of feel that

1317
01:13:39,040 --> 01:13:41,400
Speaker 1: we've jumped the timeline and this is in our universe.

1318
01:13:41,479 --> 01:13:44,479
Speaker 1: We we need to get back to the timeline where

1319
01:13:45,080 --> 01:13:47,960
Speaker 1: the United States is the country that defeated the Nazis,

1320
01:13:48,160 --> 01:13:52,360
Speaker 1: and that we haven't demonized government, and that my goddamn

1321
01:13:52,600 --> 01:13:56,560
Speaker 1: street can be isn't filled with potholes because Grover Norquist

1322
01:13:56,640 --> 01:14:01,000
Speaker 1: hasn't demonized the idea of how dare government pave the roads?

1323
01:14:01,040 --> 01:14:04,760
Speaker 1: What are you doing? It's confiscation wanting to have an infrastructure,

1324
01:14:04,760 --> 01:14:08,360
Speaker 1: how dare you? Like I think everybody should take blown

1325
01:14:08,400 --> 01:14:11,840
Speaker 1: out tires and broken axles and dump them on Grover

1326
01:14:11,920 --> 01:14:14,680
Speaker 1: Norquist's front lawn, so it looks like a junkyard a

1327
01:14:14,720 --> 01:14:18,880
Speaker 1: thousand feet high of broken car parts. And but hold

1328
01:14:19,000 --> 01:14:22,439
Speaker 1: hold that pet peeve aside the the income inequality, the

1329
01:14:22,479 --> 01:14:26,760
Speaker 1: wealthy inequality. There's this fantastic chart I'll email it to

1330
01:14:26,800 --> 01:14:31,600
Speaker 1: you that you ask people how badly they think the

1331
01:14:31,680 --> 01:14:35,880
Speaker 1: wealth distribution is and how much does the top of

1332
01:14:35,960 --> 01:14:38,160
Speaker 1: the top ten percent, of the top one percent, top

1333
01:14:38,360 --> 01:14:41,720
Speaker 1: tenth of one percent owned relative And they lay out

1334
01:14:41,800 --> 01:14:45,559
Speaker 1: this this picture of this really unequal society, and they're

1335
01:14:45,600 --> 01:14:48,360
Speaker 1: off by like a factor of a hundred. It's much

1336
01:14:48,400 --> 01:14:52,040
Speaker 1: worse than people really believe. It's astonishing if people knew

1337
01:14:52,040 --> 01:14:55,400
Speaker 1: how bad it was, right, And I would drop you

1338
01:14:55,479 --> 01:14:57,840
Speaker 1: if if you're gonna say, tell me what next book

1339
01:14:57,840 --> 01:14:59,880
Speaker 1: to write. But I don't know if that's so much

1340
01:15:00,080 --> 01:15:03,759
Speaker 1: Michael Lewis book as something that would color a Michael

1341
01:15:03,840 --> 01:15:08,040
Speaker 1: Lewis book. That's right, that's right. No, I think I'm

1342
01:15:07,680 --> 01:15:10,920
Speaker 1: the whole. I think inequality is going to be the

1343
01:15:10,920 --> 01:15:13,080
Speaker 1: backdrop of the next thing I do. I haven't figure

1344
01:15:13,120 --> 01:15:15,559
Speaker 1: out what it is, but it does seem the subject

1345
01:15:15,560 --> 01:15:18,160
Speaker 1: of our time. It does seem, it does seem a

1346
01:15:18,200 --> 01:15:22,759
Speaker 1: subject that infects everything else. You name the topic climate

1347
01:15:22,840 --> 01:15:27,080
Speaker 1: change the books at the moment, uh that our ability

1348
01:15:27,080 --> 01:15:31,479
Speaker 1: to grapple with it is hamstrung by inequality. Uh now,

1349
01:15:31,560 --> 01:15:35,439
Speaker 1: now drove the Venn diagram of inequality and then drove

1350
01:15:35,479 --> 01:15:39,760
Speaker 1: the Venn diagram of all the cognitive eras and behavioral

1351
01:15:39,800 --> 01:15:44,320
Speaker 1: issues that condamin In Tavarsky you have written about. And

1352
01:15:44,600 --> 01:15:47,240
Speaker 1: maybe you can explain to me why people don't understand

1353
01:15:47,240 --> 01:15:52,080
Speaker 1: what's in their own best interest, why they consistently are

1354
01:15:52,120 --> 01:15:55,160
Speaker 1: bamboozled by politicians. And I'm not picking one side of

1355
01:15:55,240 --> 01:15:57,960
Speaker 1: the other, but it's pretty clear that most of America,

1356
01:15:58,200 --> 01:16:02,719
Speaker 1: or at least tansent hand, some millions of Americans every

1357
01:16:02,720 --> 01:16:05,800
Speaker 1: election vote against their own interests. There an economic their

1358
01:16:05,800 --> 01:16:09,400
Speaker 1: own economic interests, right and and so what what matters more?

1359
01:16:09,479 --> 01:16:13,439
Speaker 1: Some gen d up you know, social divisional issue that

1360
01:16:13,960 --> 01:16:18,599
Speaker 1: so if so, my issue tribal identity matters a lot

1361
01:16:19,280 --> 01:16:23,000
Speaker 1: I gotta do is go to SEC football game, for sure.

1362
01:16:23,640 --> 01:16:26,000
Speaker 1: But but there's a difference between going with a football

1363
01:16:26,000 --> 01:16:30,719
Speaker 1: game and saying, look at West Virginia and the cold people,

1364
01:16:30,920 --> 01:16:34,040
Speaker 1: we're gonna bring back Cole No, you're not that ship

1365
01:16:34,080 --> 01:16:36,719
Speaker 1: as sale. Wouldn't you be better off if we brought

1366
01:16:36,720 --> 01:16:40,960
Speaker 1: you solar jobs or some other retraining or is everybody

1367
01:16:41,160 --> 01:16:45,280
Speaker 1: is p t bottom right? Is everybody a soccer? Enough

1368
01:16:45,920 --> 01:16:49,519
Speaker 1: enough far enough far that that's quite shocking. Now we're

1369
01:16:49,520 --> 01:16:54,240
Speaker 1: getting into depressing. So so, but there is a giant

1370
01:16:54,280 --> 01:17:00,040
Speaker 1: overlap between your interest and behavior stuff and unfairness and

1371
01:17:00,080 --> 01:17:02,880
Speaker 1: how it manifests itself in society. So what can be

1372
01:17:02,920 --> 01:17:05,080
Speaker 1: done to fix that? I'm that that's the next book.

1373
01:17:05,520 --> 01:17:08,120
Speaker 1: Go Mike, go fix it? Right, go fix it? Can

1374
01:17:08,160 --> 01:17:11,080
Speaker 1: you fix us? We need some help? Don alright? So

1375
01:17:11,120 --> 01:17:15,639
Speaker 1: that'll be uh in So all right, now that we've

1376
01:17:15,640 --> 01:17:17,960
Speaker 1: depressed the hell out of everybody, let's get to our

1377
01:17:18,000 --> 01:17:22,160
Speaker 1: speed round, our favorite um that you refuse to I

1378
01:17:22,160 --> 01:17:23,880
Speaker 1: didn't want to see the question. You wanted to be

1379
01:17:23,960 --> 01:17:26,760
Speaker 1: kept objective, objective. I used to want to have to

1380
01:17:26,760 --> 01:17:30,519
Speaker 1: steal surprise. All right? What was the first car you have?

1381
01:17:30,560 --> 01:17:35,640
Speaker 1: Our own? Make earin model? It was a nineteen seventy

1382
01:17:35,680 --> 01:17:42,120
Speaker 1: five c J five jeep right yellow. Interesting? Um, So,

1383
01:17:42,880 --> 01:17:44,479
Speaker 1: I don't know if you can answer this question. What's

1384
01:17:44,520 --> 01:17:46,600
Speaker 1: the most important thing we don't know about you? I

1385
01:17:46,680 --> 01:17:49,040
Speaker 1: kind of feel like you're an open book. What don't

1386
01:17:49,040 --> 01:17:54,120
Speaker 1: we know about you? I am? I am an insane

1387
01:17:54,160 --> 01:17:58,400
Speaker 1: sports dad. Yeah, you kind of wrote a book about that. No, no, no,

1388
01:17:58,439 --> 01:18:01,080
Speaker 1: I didn't really write about that. No, no, you kept

1389
01:18:01,120 --> 01:18:04,040
Speaker 1: a lot out of that. My peak insanity is when

1390
01:18:04,040 --> 01:18:07,519
Speaker 1: I'm watching my kids play sports. That is peak insanity.

1391
01:18:07,600 --> 01:18:09,640
Speaker 1: And it's the refs make you crazy. No, it's not

1392
01:18:09,680 --> 01:18:11,960
Speaker 1: the ref not no, no, no, it's not the refs.

1393
01:18:11,520 --> 01:18:15,160
Speaker 1: It's my kids. You want them to do better. Yes,

1394
01:18:16,680 --> 01:18:19,400
Speaker 1: I'm just too invested, so so I try to hide it,

1395
01:18:19,479 --> 01:18:22,440
Speaker 1: try to hide it. I'm not I'm not doing anything embarrassing,

1396
01:18:22,920 --> 01:18:25,400
Speaker 1: but I am pacing. I'll walk, I have my fitbit.

1397
01:18:25,439 --> 01:18:27,439
Speaker 1: I'll do twenty thousand steps in the outfield during a

1398
01:18:27,479 --> 01:18:31,719
Speaker 1: football game because I can't be near anybody. It's peak insanity. Really,

1399
01:18:32,120 --> 01:18:36,400
Speaker 1: that's quite that's quite fascinating. Um, your mentors who mentored

1400
01:18:36,479 --> 01:18:42,080
Speaker 1: the way you thought about finance writing. So the people

1401
01:18:42,080 --> 01:18:44,280
Speaker 1: who were most influential were when I was a little kid.

1402
01:18:44,840 --> 01:18:47,320
Speaker 1: My dad easily is the most and then after that

1403
01:18:48,360 --> 01:18:51,519
Speaker 1: the baseball coach I wrote about, Billy Fitzgerald just kind

1404
01:18:51,520 --> 01:18:54,519
Speaker 1: of like spirit how you approach life. When when I

1405
01:18:54,560 --> 01:18:57,439
Speaker 1: finally got to putting words on a page for a living,

1406
01:18:57,960 --> 01:19:00,120
Speaker 1: the first editor who really got inside my head was

1407
01:19:00,160 --> 01:19:04,000
Speaker 1: Michael Kinsley, the editor of the New Really yes, uh,

1408
01:19:04,040 --> 01:19:06,559
Speaker 1: and it was. And the message he delivered was a

1409
01:19:06,640 --> 01:19:09,160
Speaker 1: really simple message, is like, what are you trying to say?

1410
01:19:09,360 --> 01:19:11,800
Speaker 1: Like kind of get rid of the fancy stuff? Does

1411
01:19:11,840 --> 01:19:15,240
Speaker 1: tell me what is it? Does that make any sense? Uh?

1412
01:19:15,280 --> 01:19:18,599
Speaker 1: And just he taught me a certain ruthlessness towards my

1413
01:19:18,640 --> 01:19:22,400
Speaker 1: own pros. That's interesting. Tell us how you invest your

1414
01:19:22,439 --> 01:19:26,800
Speaker 1: own money, very very simply. I mean, I do think

1415
01:19:26,800 --> 01:19:30,680
Speaker 1: that I do like to spend as little time as

1416
01:19:30,720 --> 01:19:34,120
Speaker 1: possible thinking about money. So so it's so it's it's

1417
01:19:34,120 --> 01:19:39,519
Speaker 1: making some broad decision about asset allocation, meaning the proportion

1418
01:19:39,560 --> 01:19:43,960
Speaker 1: of it's sort of like I've been I have in

1419
01:19:43,960 --> 01:19:48,919
Speaker 1: the stock market, so and then probably ten in California

1420
01:19:49,000 --> 01:19:53,000
Speaker 1: mun Muni bonds and the rest and kind of cash

1421
01:19:53,000 --> 01:19:57,280
Speaker 1: at any given time bonds just cash. So uh. And

1422
01:19:57,360 --> 01:20:01,320
Speaker 1: the stock market stuff is either index funds or Berkshire Hathaway.

1423
01:20:01,479 --> 01:20:03,720
Speaker 1: Now that's kind of interesting because you used to kind

1424
01:20:03,720 --> 01:20:06,679
Speaker 1: of dunk on Warren Buffett. I dunked once on Warren Buffett,

1425
01:20:06,680 --> 01:20:08,320
Speaker 1: but it made me famous because no one else would

1426
01:20:08,360 --> 01:20:12,200
Speaker 1: do it when I did it. Um, I'm invested in

1427
01:20:12,280 --> 01:20:15,439
Speaker 1: him for the same reason I dunked on him, and

1428
01:20:15,520 --> 01:20:18,160
Speaker 1: that was that is he's got he's got special deals.

1429
01:20:18,200 --> 01:20:21,720
Speaker 1: His capital gets a special treatment in the market, So

1430
01:20:22,160 --> 01:20:25,040
Speaker 1: you want to be on that side for free. Uh

1431
01:20:25,200 --> 01:20:27,680
Speaker 1: So you know, And and I quite admire him and

1432
01:20:27,800 --> 01:20:31,200
Speaker 1: like him and uh and like the idea of him,

1433
01:20:31,240 --> 01:20:33,280
Speaker 1: and I think his model of the business is going

1434
01:20:33,320 --> 01:20:35,800
Speaker 1: to survive him. I think, oh, I think they've kind

1435
01:20:35,800 --> 01:20:37,880
Speaker 1: of got it right. Yeah, They've put together a great

1436
01:20:37,880 --> 01:20:41,879
Speaker 1: team there, and their capital will continue to be highly valued.

1437
01:20:42,040 --> 01:20:44,840
Speaker 1: So so anyway, he's my when that's when I'm feeling

1438
01:20:44,880 --> 01:20:47,479
Speaker 1: sexy and I don't want to buy just an index fund.

1439
01:20:47,479 --> 01:20:51,200
Speaker 1: I'd buy some Berkshire Hathaway. So tell us about your

1440
01:20:51,240 --> 01:20:55,840
Speaker 1: favorite books. This is everybody's favorite question. Non Michael Lewis books.

1441
01:20:57,280 --> 01:21:00,679
Speaker 1: So these aren't the best books, they're favorite the books

1442
01:21:00,680 --> 01:21:02,080
Speaker 1: that have hit me at a time in my life

1443
01:21:02,080 --> 01:21:04,800
Speaker 1: where they just stuck with me. Huckleberry Finn when I

1444
01:21:04,840 --> 01:21:08,439
Speaker 1: was seventeen years old, Confederacy of Dunces when I was

1445
01:21:09,880 --> 01:21:13,080
Speaker 1: eighteen or nineteen, The Movie Goer by Walker Percy when

1446
01:21:13,120 --> 01:21:17,240
Speaker 1: I was seventeen or eighteen, George Orwell's Collected Essays when

1447
01:21:17,280 --> 01:21:22,920
Speaker 1: I was maybe one Tom Wolfe's Radical Chic and Mao Mauting,

1448
01:21:22,920 --> 01:21:25,240
Speaker 1: the Flatcatchers, and then the Right Stuff when they came

1449
01:21:25,240 --> 01:21:28,439
Speaker 1: out basically when when I was a teenager, All all

1450
01:21:28,439 --> 01:21:30,639
Speaker 1: of it, almost all of it is stuff that hit

1451
01:21:30,720 --> 01:21:35,960
Speaker 1: me between the ages of fourteen, fifteen and four since

1452
01:21:36,160 --> 01:21:39,919
Speaker 1: since I became a writer. Um, I just read more critically.

1453
01:21:40,200 --> 01:21:42,400
Speaker 1: It's hard for starter for stuff to get in quite

1454
01:21:42,439 --> 01:21:44,479
Speaker 1: as deeply the stuff I love when I read it.

1455
01:21:44,520 --> 01:21:48,360
Speaker 1: But it just doesn't. It didn't infect doesn't infect me

1456
01:21:48,400 --> 01:21:51,960
Speaker 1: in the same way. Quite quite interesting? Tell us about

1457
01:21:52,080 --> 01:21:54,439
Speaker 1: you say quite interesting? Does that mean it's not interesting? No,

1458
01:21:54,560 --> 01:21:57,000
Speaker 1: it's just a verbal tick to move on to the

1459
01:21:57,040 --> 01:22:00,240
Speaker 1: next question, as opposed to saying nothing and move on,

1460
01:22:00,400 --> 01:22:03,360
Speaker 1: which is what someone who is professional would do. But

1461
01:22:03,600 --> 01:22:06,080
Speaker 1: after two hundred and fifty of these, I have now

1462
01:22:06,080 --> 01:22:08,639
Speaker 1: worked my way up to mediocre. I'm at that point

1463
01:22:08,920 --> 01:22:13,479
Speaker 1: in the Dunning Krueger curve. Okay, Um, tell us about

1464
01:22:13,479 --> 01:22:16,080
Speaker 1: a time you failed and what you learned from the experience.

1465
01:22:16,320 --> 01:22:18,000
Speaker 1: By the way, I just get by on witt and

1466
01:22:18,120 --> 01:22:22,680
Speaker 1: charm and actual skills are nowhere to be found. A

1467
01:22:22,880 --> 01:22:28,160
Speaker 1: time I failed and what I learned from the experience? Um,

1468
01:22:28,200 --> 01:22:33,880
Speaker 1: I mean there, there's lots to choose from. Pick one, um,

1469
01:22:33,920 --> 01:22:36,120
Speaker 1: all right, I'll pick one. In the first grade. In

1470
01:22:36,160 --> 01:22:39,920
Speaker 1: the first grade, we walked over to the library every

1471
01:22:39,920 --> 01:22:42,800
Speaker 1: every day for a library period. In the library had

1472
01:22:42,880 --> 01:22:44,880
Speaker 1: these chairs that kind of holes in the back, you know,

1473
01:22:44,960 --> 01:22:47,040
Speaker 1: cut out back where you're back on a lower back.

1474
01:22:47,600 --> 01:22:51,080
Speaker 1: And I spent the whole library period wondering if I

1475
01:22:51,080 --> 01:22:53,519
Speaker 1: could climb out of my chair through the hole rather

1476
01:22:53,600 --> 01:22:56,479
Speaker 1: than just normally getting up out of it. And at

1477
01:22:56,479 --> 01:22:59,040
Speaker 1: the end of library I dove through the hole and

1478
01:22:59,120 --> 01:23:02,439
Speaker 1: got so that not only the whole class have to

1479
01:23:02,439 --> 01:23:05,400
Speaker 1: file out past me as I'm wearing this chair, but

1480
01:23:05,439 --> 01:23:08,479
Speaker 1: the janitor to come with like a hack stack and

1481
01:23:08,600 --> 01:23:10,720
Speaker 1: cut the chair off me because they couldn't pull it

1482
01:23:10,720 --> 01:23:14,320
Speaker 1: off me. And I'd say, what I learned from that is,

1483
01:23:14,360 --> 01:23:16,880
Speaker 1: if you're gonna do something really radical and different, do

1484
01:23:17,000 --> 01:23:19,680
Speaker 1: it alone in your home, and don't do it in

1485
01:23:19,720 --> 01:23:21,840
Speaker 1: front of a big crowd. But that's the first time,

1486
01:23:22,040 --> 01:23:24,000
Speaker 1: that's right, that's a sample set of one. So if

1487
01:23:24,000 --> 01:23:26,479
Speaker 1: you were successful, you would have been the hero and

1488
01:23:26,520 --> 01:23:28,719
Speaker 1: every would have been talking about. I think the upside

1489
01:23:28,720 --> 01:23:31,960
Speaker 1: of success in that case, it's probably pretty trivial compared

1490
01:23:32,040 --> 01:23:34,479
Speaker 1: to the downside of failure. It was just mortifying. It

1491
01:23:34,560 --> 01:23:37,960
Speaker 1: was mortifying. So I know what you do for fun,

1492
01:23:38,160 --> 01:23:41,479
Speaker 1: which is basically coach your kids and and do some travel.

1493
01:23:41,640 --> 01:23:45,120
Speaker 1: But what what else do you do for fun outside

1494
01:23:45,120 --> 01:23:54,280
Speaker 1: of that? Um? I hike. I do. It's endless, endless

1495
01:23:54,360 --> 01:24:00,960
Speaker 1: kind of activity. Play tennis. Um, I write, I ride

1496
01:24:00,960 --> 01:24:03,240
Speaker 1: bikes with friends. I don't know. I live in just

1497
01:24:03,360 --> 01:24:04,920
Speaker 1: like the best place to do this kind of stuff,

1498
01:24:04,920 --> 01:24:09,200
Speaker 1: to Bay Area. Uh so, my my work life is

1499
01:24:09,240 --> 01:24:13,519
Speaker 1: so sedentary, you know, it's sitting there writing. I can't

1500
01:24:13,680 --> 01:24:17,479
Speaker 1: run and right, so, uh, everything else tends to be

1501
01:24:17,600 --> 01:24:20,679
Speaker 1: just like active. I go off for a week every

1502
01:24:20,720 --> 01:24:24,200
Speaker 1: year with five guy friends into the woods. Uh. We

1503
01:24:24,280 --> 01:24:26,599
Speaker 1: have this annual trip that we've been doing for six

1504
01:24:26,680 --> 01:24:29,280
Speaker 1: or seven years, like the best week of the year,

1505
01:24:30,000 --> 01:24:34,040
Speaker 1: just hiking and biking in the wilderness. Um, that sort

1506
01:24:34,080 --> 01:24:38,040
Speaker 1: of thing. Simple. I'm not I don't have hobbies. I've

1507
01:24:38,040 --> 01:24:41,439
Speaker 1: never had. Hobbies. Is a hobby that kind of I

1508
01:24:41,560 --> 01:24:43,160
Speaker 1: like to do this stuff I like to do, but

1509
01:24:43,160 --> 01:24:45,400
Speaker 1: I don't really have I only have hobbies. I don't

1510
01:24:45,400 --> 01:24:48,800
Speaker 1: collect anything. I don't collect stamps. I don't I don't don't.

1511
01:24:49,479 --> 01:24:51,800
Speaker 1: I don't know. You know, I don't collect books. I don't.

1512
01:24:51,960 --> 01:24:54,880
Speaker 1: I read a lot, but I don't know. I'm not

1513
01:24:54,920 --> 01:24:58,680
Speaker 1: interested in cars, stuff like stuff doesn't interest me. All

1514
01:24:58,720 --> 01:25:02,960
Speaker 1: that experiences over. That's exactly right, right, That's Thomas Gilovich

1515
01:25:03,120 --> 01:25:06,400
Speaker 1: and Alan Krueger both said, if you want happiness, spend

1516
01:25:06,439 --> 01:25:09,040
Speaker 1: your money on experiences. Don't you spend your money on junk?

1517
01:25:09,560 --> 01:25:14,240
Speaker 1: And I think that life experience bears that out. It

1518
01:25:14,320 --> 01:25:17,800
Speaker 1: makes me happier. Stuff doesn't make me happy. So a

1519
01:25:17,840 --> 01:25:20,080
Speaker 1: millennial comes to you and says, I'm thinking about a

1520
01:25:20,120 --> 01:25:23,479
Speaker 1: career in either finance or writing. What sort of advice

1521
01:25:23,520 --> 01:25:29,599
Speaker 1: would you give them? Um? If someone put it that way,

1522
01:25:29,640 --> 01:25:32,080
Speaker 1: I'm thinking about a career in writing when they're young,

1523
01:25:32,240 --> 01:25:35,040
Speaker 1: I'd worry about them as opposed to I really love

1524
01:25:35,120 --> 01:25:40,000
Speaker 1: to write, I really love to write. What do I do? Um?

1525
01:25:40,920 --> 01:25:43,439
Speaker 1: And I tell you why? Because so many young people

1526
01:25:44,120 --> 01:25:46,680
Speaker 1: who have come to me saying, more or less I

1527
01:25:46,720 --> 01:25:49,519
Speaker 1: want a career as a writer. More or less they've said,

1528
01:25:49,520 --> 01:25:52,720
Speaker 1: how do I get to have your life? Uh? I

1529
01:25:52,760 --> 01:25:55,439
Speaker 1: asked them, do you do you actually want to write?

1530
01:25:55,560 --> 01:25:57,760
Speaker 1: Or do you want to be a writer? And most

1531
01:25:57,760 --> 01:25:59,880
Speaker 1: of them want to be a writer. They don't act,

1532
01:26:00,080 --> 01:26:02,839
Speaker 1: have some romantic size notion about what it is all about,

1533
01:26:02,920 --> 01:26:04,519
Speaker 1: and you get to go on TV and talk about

1534
01:26:04,520 --> 01:26:07,840
Speaker 1: your books and you know all that stuff writers do.

1535
01:26:08,000 --> 01:26:13,040
Speaker 1: But so if you actually love to write, you're just

1536
01:26:13,040 --> 01:26:17,800
Speaker 1: gonna write. And uh, just then I tell that person, Uh,

1537
01:26:17,960 --> 01:26:20,439
Speaker 1: go do something to interest you and write about it.

1538
01:26:20,520 --> 01:26:24,479
Speaker 1: Let's start there. Uh. The in finance, I don't know

1539
01:26:24,560 --> 01:26:27,800
Speaker 1: what to tell people because it's so moved on from

1540
01:26:27,800 --> 01:26:30,240
Speaker 1: where I was when I was coming in. It doesn't

1541
01:26:31,439 --> 01:26:36,200
Speaker 1: it's not an obviously joyful industry. There was quite a

1542
01:26:36,200 --> 01:26:37,760
Speaker 1: bit of joy in it when I rolled. I was

1543
01:26:37,760 --> 01:26:40,880
Speaker 1: gonna say, there are periods where it's more joyful and periods,

1544
01:26:40,520 --> 01:26:42,920
Speaker 1: and that's on where you are in it. I mean

1545
01:26:43,040 --> 01:26:46,080
Speaker 1: venture capital world, some of the hedge funal world, some

1546
01:26:46,120 --> 01:26:49,520
Speaker 1: of the private equity world that seems kind of fun um,

1547
01:26:49,560 --> 01:26:52,879
Speaker 1: working for a big Wall Street bank that seems freaking miserable.

1548
01:26:53,080 --> 01:26:56,080
Speaker 1: Well it was fun back in the day. But but

1549
01:26:56,360 --> 01:26:58,639
Speaker 1: you know, people wrote books like mine and they shut

1550
01:26:58,680 --> 01:27:02,320
Speaker 1: down the fund. Uh because it's you. It's partly my fault.

1551
01:27:03,160 --> 01:27:07,360
Speaker 1: It's just like just generally working in corporate publicly traded

1552
01:27:07,400 --> 01:27:11,800
Speaker 1: corporate America does not my idea of fun except for

1553
01:27:12,320 --> 01:27:15,200
Speaker 1: those stock options. People can make real money with that,

1554
01:27:15,320 --> 01:27:18,400
Speaker 1: but that's different from fun for sure. So so I

1555
01:27:18,760 --> 01:27:21,760
Speaker 1: wouldn't I guess if I were if like my son

1556
01:27:21,920 --> 01:27:25,280
Speaker 1: said to me, Daddy, I really want to be a

1557
01:27:25,320 --> 01:27:28,400
Speaker 1: Wall Street person, I would say, I try to ship

1558
01:27:28,479 --> 01:27:31,040
Speaker 1: him down to Silicon Valley into a venture capital firm

1559
01:27:31,080 --> 01:27:32,800
Speaker 1: and see if he could be useful there. I think

1560
01:27:32,880 --> 01:27:35,280
Speaker 1: something like that, What do you know about the world

1561
01:27:35,479 --> 01:27:39,680
Speaker 1: of finance, trading, investing, and writing today you wish you

1562
01:27:39,760 --> 01:27:45,400
Speaker 1: knew thirty years ago when you were first starting out. Um,

1563
01:27:45,600 --> 01:27:47,840
Speaker 1: ask the question again. Let me think about it while

1564
01:27:47,880 --> 01:27:50,559
Speaker 1: you asked that question again, What what do I wish

1565
01:27:50,600 --> 01:27:53,479
Speaker 1: I knew? So this isn't I wish I knew to

1566
01:27:53,520 --> 01:27:57,519
Speaker 1: buy Amazon at a dollar? It's what knowledge do you

1567
01:27:57,560 --> 01:28:00,360
Speaker 1: wish you had when you began your career year in

1568
01:28:00,520 --> 01:28:05,840
Speaker 1: finance that would have been helpful along the thirty five

1569
01:28:05,920 --> 01:28:09,439
Speaker 1: years that perhaps you learned much later down the path.

1570
01:28:11,320 --> 01:28:13,840
Speaker 1: You know that even the smart people on Wall Street

1571
01:28:13,880 --> 01:28:17,040
Speaker 1: didn't really know what they were doing. I took the

1572
01:28:17,520 --> 01:28:20,719
Speaker 1: biggest mistake I had in my head when I left

1573
01:28:21,320 --> 01:28:24,200
Speaker 1: Solomon Brothers was that the people at the top of

1574
01:28:24,240 --> 01:28:28,000
Speaker 1: Solomon Brothers, the people that the people in the middle

1575
01:28:28,000 --> 01:28:34,000
Speaker 1: of the trading had some mystical knowledge that I lacked.

1576
01:28:34,720 --> 01:28:38,720
Speaker 1: And I mean, the thing that shocked me the most

1577
01:28:38,720 --> 01:28:41,160
Speaker 1: about the financial crisis with it was that these firms

1578
01:28:41,360 --> 01:28:44,920
Speaker 1: made these stupid, stupid bets that in these you know,

1579
01:28:45,360 --> 01:28:48,559
Speaker 1: these zero sum bets, that they were all long one

1580
01:28:48,560 --> 01:28:52,639
Speaker 1: way or another the subprime mortgage market. Um, it would

1581
01:28:52,640 --> 01:28:55,880
Speaker 1: have been there had been some interesting writing to do

1582
01:28:56,640 --> 01:29:00,400
Speaker 1: about those firms between the time I left the time

1583
01:29:00,439 --> 01:29:02,519
Speaker 1: I came back for the financial crisis, if I had

1584
01:29:02,520 --> 01:29:09,040
Speaker 1: appreciated just how moronic the the moronically they were managed. Uh.

1585
01:29:09,360 --> 01:29:12,360
Speaker 1: What's what's interesting is though many of those same people

1586
01:29:12,439 --> 01:29:14,920
Speaker 1: came to you and tried to convince you to stay

1587
01:29:14,960 --> 01:29:19,439
Speaker 1: in finance, and you had enough confidence in your own

1588
01:29:20,400 --> 01:29:23,320
Speaker 1: understanding of the world to say, thanks, but I got

1589
01:29:23,360 --> 01:29:25,639
Speaker 1: other stuff to do, you guys, But I always had

1590
01:29:25,640 --> 01:29:27,599
Speaker 1: other stuff to do. That was my so for me.

1591
01:29:28,040 --> 01:29:32,639
Speaker 1: For me, it was a really lucky detour uh into

1592
01:29:32,720 --> 01:29:35,800
Speaker 1: Wall Street because it gave me material, But it was

1593
01:29:35,880 --> 01:29:38,160
Speaker 1: always a detour. I mean, I could just as easily

1594
01:29:38,200 --> 01:29:41,640
Speaker 1: have gotten I don't know, uh, job selling insurance. I

1595
01:29:41,640 --> 01:29:44,080
Speaker 1: mean I was going to need a job doing something

1596
01:29:44,560 --> 01:29:46,840
Speaker 1: and it just was really lucky I ended up in

1597
01:29:46,880 --> 01:29:51,000
Speaker 1: Wall Street. I didn't have any particular passion for Wall Street. Um,

1598
01:29:51,120 --> 01:29:53,240
Speaker 1: it just worked out that this worked out that way,

1599
01:29:53,280 --> 01:29:58,160
Speaker 1: and so so, but there's no like, is there any

1600
01:29:58,240 --> 01:30:04,479
Speaker 1: great I feel like, Um, my ignorance has been a

1601
01:30:04,600 --> 01:30:09,479
Speaker 1: huge advantage to me. So knowing more starting out would

1602
01:30:09,479 --> 01:30:11,840
Speaker 1: have been a disadvantage because it forced you to go

1603
01:30:11,880 --> 01:30:15,600
Speaker 1: do the homework, and that homework each each of that

1604
01:30:15,640 --> 01:30:21,040
Speaker 1: homework segments became a book. Yes, that's right. So, UM,

1605
01:30:21,080 --> 01:30:25,120
Speaker 1: I don't I don't actually honestly wish for some prior

1606
01:30:25,200 --> 01:30:30,919
Speaker 1: knowledge of any of the stuff that, especially in finance.

1607
01:30:31,120 --> 01:30:35,320
Speaker 1: So last question about Solomon Brothers. It was a gentleman

1608
01:30:35,479 --> 01:30:39,559
Speaker 1: in the training program with you, named Hans huff Schmidt.

1609
01:30:40,080 --> 01:30:42,880
Speaker 1: You both left the same training program in New York,

1610
01:30:43,240 --> 01:30:46,320
Speaker 1: you went to the same London training floor. You're both

1611
01:30:46,360 --> 01:30:51,840
Speaker 1: supervised by John Merryweather. And you had written that over

1612
01:30:51,880 --> 01:30:56,240
Speaker 1: the years you tracked your progress by how well you

1613
01:30:56,320 --> 01:31:00,240
Speaker 1: were doing relative to what Hans huff Schmid was doing,

1614
01:31:00,240 --> 01:31:02,800
Speaker 1: and you frequently felt, God, if I would have stayed there,

1615
01:31:02,880 --> 01:31:04,960
Speaker 1: Look how much money he's doing. So he's doing so well.

1616
01:31:05,360 --> 01:31:08,599
Speaker 1: And then one day, long term capital management blows up.

1617
01:31:08,920 --> 01:31:11,280
Speaker 1: He ended up there where you said you would have gone,

1618
01:31:11,880 --> 01:31:17,400
Speaker 1: And now his salary and and net worth plummets to zero,

1619
01:31:17,640 --> 01:31:21,400
Speaker 1: and you wrote, I was once again satisfied to be

1620
01:31:21,520 --> 01:31:25,600
Speaker 1: paid by the word. Do you remember writing that? I

1621
01:31:25,640 --> 01:31:28,839
Speaker 1: do remember writing that, and I don't want your audience

1622
01:31:28,880 --> 01:31:31,639
Speaker 1: to think that. I was actually like stalking Hans Hofschman,

1623
01:31:31,720 --> 01:31:33,800
Speaker 1: wondering how much money he made each year. I would

1624
01:31:33,840 --> 01:31:35,880
Speaker 1: just every now and then here that did you hear

1625
01:31:35,920 --> 01:31:38,439
Speaker 1: that Hans Hoshman is now worth fifty million dollars? And

1626
01:31:38,479 --> 01:31:42,200
Speaker 1: I think, what the hell you want? That was because

1627
01:31:42,200 --> 01:31:45,479
Speaker 1: that was my alternate, that was my thro the path

1628
01:31:45,600 --> 01:31:50,280
Speaker 1: I would have maybe had. And I never actually thought

1629
01:31:50,400 --> 01:31:52,400
Speaker 1: I wished I had done that, because I loved what

1630
01:31:52,479 --> 01:31:54,880
Speaker 1: I was doing. It wasn't quite that, but it was like,

1631
01:31:54,960 --> 01:31:57,760
Speaker 1: that's the opportunity cost. The opportunity cost of being a

1632
01:31:57,760 --> 01:32:00,400
Speaker 1: writer is that I don't have Hans Hashman's fifty million dollars,

1633
01:32:00,560 --> 01:32:02,680
Speaker 1: and when that went to zero, it made being a

1634
01:32:02,680 --> 01:32:06,000
Speaker 1: writer feel even better. Now I think the truth is

1635
01:32:06,080 --> 01:32:09,000
Speaker 1: since then hams Har Harshman has gone and remade his fortune,

1636
01:32:09,240 --> 01:32:12,639
Speaker 1: so I probably am still. I'm still finishing second here

1637
01:32:13,040 --> 01:32:14,880
Speaker 1: right I gotta I gotta get some cash over to

1638
01:32:14,960 --> 01:32:16,639
Speaker 1: Hans because he seems to know what the hell he's

1639
01:32:16,680 --> 01:32:20,600
Speaker 1: doing with capital. Michael Lewis, thank you for being so

1640
01:32:20,640 --> 01:32:23,879
Speaker 1: generous with your time. We have been speaking with author

1641
01:32:24,400 --> 01:32:29,560
Speaker 1: and podcaster extraordinaire Michael Lewis. If you enjoy this conversation,

1642
01:32:29,840 --> 01:32:31,400
Speaker 1: We'll be sure to look up an inch or down

1643
01:32:31,439 --> 01:32:35,240
Speaker 1: an inch on Apple, iTunes, Stitcher, Overcast, Bloomberg dot com

1644
01:32:35,280 --> 01:32:38,240
Speaker 1: and you could see any of the other two hundred

1645
01:32:38,280 --> 01:32:42,240
Speaker 1: and fifty or so of these conversations that we have had.

1646
01:32:42,800 --> 01:32:46,479
Speaker 1: Um we love your comments, feedback and suggestions right to

1647
01:32:46,600 --> 01:32:50,200
Speaker 1: us at m IB podcast at Bloomberg dot net. I

1648
01:32:50,200 --> 01:32:52,559
Speaker 1: would be remiss if I did not thank the crack

1649
01:32:52,680 --> 01:32:57,000
Speaker 1: staff that helps put together these podcasts each week. Modina

1650
01:32:57,080 --> 01:33:01,559
Speaker 1: Parwana is my producer slash audio engineer. Our bookers are

1651
01:33:01,560 --> 01:33:05,240
Speaker 1: Taylor Riggs and Michael Boyle uh A. Tica val Brun

1652
01:33:05,360 --> 01:33:08,240
Speaker 1: is our project manager. Michael bat Nick is our head

1653
01:33:08,280 --> 01:33:12,439
Speaker 1: of research. I'm Barry Ritolts. You've been listening to Masters

1654
01:33:12,439 --> 01:33:14,439
Speaker 1: in Business on Bloomberg Radio.