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Speaker 1: Pushkin. On the tenth of November twenty twenty two, Sam

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Speaker 1: Bankwin Freed tweeted that he had eft UP. As we

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Speaker 1: explored in our last episode, that was something of an understatement. FTX,

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Speaker 1: the digital currency exchange platform where individuals could trade cryptocurrencies

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Speaker 1: and store their funds for safekeeping, imploded when it emerged

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Speaker 1: that Sam bankmin Freed had been diverting funds into his

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Speaker 1: crypto hedge fund Alamedia Research fraud. Yes, but as an

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Speaker 1: effective altruist, he was doing it for a good cause,

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Speaker 1: wasn't he. In his own misguided way, he thought he

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Speaker 1: was just following the teachings of Will mccaskell, the founder

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Speaker 1: of the Effective Altruism movement, and that can't be bad,

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Speaker 1: can it. If you've not listened to that episode yet,

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Speaker 1: please do before listening to this special Cautionary conversation with

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Speaker 1: Michael Lewis, the man who had a ringside seat for

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Speaker 1: the spectacular fall of the cryptocurrency vunderkind. Michael, Welcome to

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Speaker 1: Cautionary Tales.

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Speaker 2: Tim. Good to see you again.

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Speaker 1: It's great to see you. I think the last time

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Speaker 1: we saw each other, I was trying to teach you

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Speaker 1: how to play an obscure German board game.

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Speaker 2: Then that's exactly right. I can't even remember what book

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Speaker 2: led you to get interested. Maybe the Big Short. I

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Speaker 2: think it was The Big Short. But the FtM were like,

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Speaker 2: oh no, you can't just talk to Michael Lewis about

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Speaker 2: his book. You have to talk to Michael Lewis about

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Speaker 2: his book while you teach him a weird German board game.

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Speaker 2: I thought it was actually ingenius because I try to

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Speaker 2: do this with subjects, and especially if you only have

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Speaker 2: a short time with them. You're so much better off

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Speaker 2: doing something with someone than just talking to them, if

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Speaker 2: you're trying to kind of get some insight into how

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Speaker 2: they tick. The best job interview I ever had it

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Speaker 2: was to lead teenage girls through Europe when I was

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Speaker 2: twenty two years old. I went to the tour agency

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Speaker 2: and the guy who ran the tour agency said, God,

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Speaker 2: I forgot you had an interview. This day. We're supposed

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Speaker 2: to move the furniture from this office down the hall

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Speaker 2: of the other office. Could you just help me do that,

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Speaker 2: and then if we have time left over, we'll talk.

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Speaker 2: And I spent an hour moving furniture with this guy

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Speaker 2: and I left. He said, I'll call you about the

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Speaker 2: interview later and I left bewildered. Yeah, like a week

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Speaker 2: later he said, you have the job. And two months

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Speaker 2: later I'm in a hotel room with my fellow leader

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Speaker 2: in like Bruges, and I said, you know, this was odd.

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Speaker 2: I was never interviewed, and I explained what happened. He said,

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Speaker 2: I moved that furniture too back the other way, and

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Speaker 2: it was a really smart way to kind of figure

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Speaker 2: out whether someone was collaborative, whether they could pick up

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Speaker 2: stuff quickly, how they interacted with people. So I took

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Speaker 2: it as a sign you were a clever journalist when

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Speaker 2: you did this.

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Speaker 1: Yeah, well, or I have a clever editor. But yeah, Michael,

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Speaker 1: I'm already feeling bad that I haven't brought a board

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Speaker 1: game this time. But I'm sure we'll be fine. I'm

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Speaker 1: sure we'll be fine. We are going to talk about

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Speaker 1: the time you spent with Sam Bankmanfried, what you made

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Speaker 1: of him, what you made of the influence of the

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Speaker 1: defective altruism had on him. And we're also going to

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Speaker 1: be answering listener questions, and we'll be talking about your

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Speaker 1: new book Who Is Government. Before all of that, here

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Speaker 1: is the caution details thing. The most remarkable thing about

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Speaker 1: Going Infinite is that when you started working on it,

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Speaker 1: you had no idea that you were going to be

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Speaker 1: covering the fraud of the century. You were just intrigued

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Speaker 1: by Sam bankman Fried And this book turned into a

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Speaker 1: very different book during the course of writing it. And

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Speaker 1: it feels like this is not the first time you've

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Speaker 1: been in the right place at the right time. I'm

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Speaker 1: curious how how always hey that you find your subjects.

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Speaker 2: You know, I stumbled into this. I had no idea

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Speaker 2: that there was like fraud going on. So I was

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Speaker 2: asked to evaluate him by an investor who was doing

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Speaker 2: a deal with him, and it was a friend, and

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Speaker 2: I said sure, I had no idea who he was.

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Speaker 2: He turns up in my doorstep in Berkeley, and we

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Speaker 2: spent a couple of hours together, and in a couple

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Speaker 2: of hours I realized I had this character who was

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Speaker 2: one he thought about the world in a very unusual way,

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Speaker 2: in a very persuasive way. But two he would possibly

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Speaker 2: give me access to several places I wanted access. One

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Speaker 2: was Jane Street in the world of high frequency trading,

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Speaker 2: because he'd spent three years at Jane Street and he'd

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Speaker 2: hired a bunch of people out of there, and they're

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Speaker 2: notoriously opaque and won't talk to journalists. So I thought, ah,

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Speaker 2: maybe I get to Jane Street throw him money in politics,

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Speaker 2: because he was handing out money left right and centered

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Speaker 2: to American politicians and the crypto world, which he was

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Speaker 2: very skeptical of, and so he wasn't a religionist, so

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Speaker 2: he wasn't defensive about it. So I remember just saying

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Speaker 2: to him, I don't know where this is going to

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Speaker 2: end up. Could I just hang out watch? You know?

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Speaker 2: He was the world's richest person under the age of

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Speaker 2: thirty according to Forest Magazine. It wasn't the big brain

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Speaker 2: wave on my part. It was kind of surprising he

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Speaker 2: let me do it, especially in view of what was

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Speaker 2: going to happen. So I just follow my nose. Yeah,

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Speaker 2: And my nose isn't like, oh, what's going to be

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Speaker 2: the next big story. My nose is more what's not dull,

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Speaker 2: and sometimes that works out, you know, sometimes that ends

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Speaker 2: up being what you should be paying attention to. So

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Speaker 2: as you.

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Speaker 1: Hung out with Sam Magmunfred Moore, you must have become

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Speaker 1: aware of this whole effective altruism thing seemed to play

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Speaker 1: a huge role in his life and how he thought.

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Speaker 2: You're exactly right. It kind of blew my mind. I'd

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Speaker 2: never heard of this, the idea that he had gone

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Speaker 2: to Wall Street with absolutely no interest in money, and

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Speaker 2: his parents would both professor law professors at Stanford. They're

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Speaker 2: like non materialist people. They don't care about stuff, they

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Speaker 2: don't care about money. You know, in another generation, he'd

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Speaker 2: have been like a high school physics teacher or maybe

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Speaker 2: a college professor. He was not the social type who

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Speaker 2: goes to Wall Street. And he'd forced himself into it

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Speaker 2: because he had discovered, before he'd discovered Wall Street, this

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Speaker 2: movement called effective altruism that just resonated with him.

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Speaker 1: Yeah, it was seduced into it by the philosophers.

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Speaker 2: And they proselytize and they actually will, mcgaskill told me.

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Speaker 2: He says, you know, it turned out that the ideal

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Speaker 2: market for these ideas were nerdy math science people at

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Speaker 2: American universities, people who were attracted to this kind of

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Speaker 2: quantitative solution of how to lead your life maximize the

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Speaker 2: number of lives you save.

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Speaker 1: I mean, just as an asside I've been writing a

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Speaker 1: column about the Trump administration's decision to cut foreign aid.

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Speaker 1: And it's slightly controversial exactly how much foreign aid they've cut,

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Speaker 1: exactly what they're cutting, and the conflicting statements, but plausibly

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Speaker 1: sensible independent analysts reckon that decision is going to kill

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Speaker 1: a million people a year yep. And it's mostly people

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Speaker 1: with HIV, although were there a few other things that

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Speaker 1: have some vaccinations, as tuberculosis. But you have HIV, you

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Speaker 1: have very effective medication. As long as you take the medication,

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Speaker 1: you'll find the moment you stop taking the medication, the

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Speaker 1: virus starts to come back, and pretty soon you're going

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Speaker 1: to die. And I'm writing about this, and somehow I

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Speaker 1: can't quite get as outraged about those million people as

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Speaker 1: if they were being rounded up and executed for being

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Speaker 1: a legal immigrants, or if they'd started some war that

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Speaker 1: was likely to kill a million people. It just feels

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Speaker 1: different somehow. It is dead right.

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Speaker 2: Part of the reason it feels different is it's a number, right,

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Speaker 2: especially the bigger than number. It just becomes a number.

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Speaker 2: You would feel much more raged if you saw just

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Speaker 2: a single child die.

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Speaker 1: Yeah.

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Speaker 2: We don't respond to the data. We respond to anecdote.

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Speaker 1: Yeah, unless you're Sam Bgmund Freed and unless you're Sam

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Speaker 1: Bakman Freed, which is one to the data. You know,

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Speaker 1: there's a jumble of my previous books.

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Speaker 2: That are sort of popping into my head when he

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Speaker 2: starts talking this way. One is Moneyball Oakland A has

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Speaker 2: succeeded because they ignored the anecdote and look to the data.

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Speaker 2: And two was the Ndoing Project. Yeah, Economy and Taversky

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Speaker 2: were all about the way our minds mislead us when

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Speaker 2: we trust our intuitive sense of things rather than the data.

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Speaker 2: It was interesting that this was wholly persuasive to this

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Speaker 2: kind of person and not just Sam Bagmfree, but there

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Speaker 2: was a whole crowd. I mean it was cult like.

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Speaker 2: As McCaskill said. They tend to be on kind of

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Speaker 2: on the spectrum. They tended to be male, they tend

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Speaker 2: to be math or physics. They tend to be socially awkward.

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Speaker 2: There were people who for whom emotion was a weak

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Speaker 2: guide to action. Whatever you or I feel that leads

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Speaker 2: us to do things, they didn't feel so much, but

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Speaker 2: they could talk themselves into using logic and numbers as

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Speaker 2: a guide to action in the way that emotion is

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Speaker 2: a guide to action for us.

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Speaker 1: Are the numbers a better guide to action than emotions.

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Speaker 2: Depends on what you're doing. Yeah, I mean, let's go

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Speaker 2: back to the moneyball case. Most of the time in baseball,

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Speaker 2: the statistics information that you are way better off than

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Speaker 2: guided by than your nose. Every now and then that's

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Speaker 2: not true. But most of the time in life you

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Speaker 2: don't really have the numbers, like how you're going to

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Speaker 2: decide who you're gonna marry, or where you're gonna live,

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Speaker 2: or even where you're gonna go to college, or even

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Speaker 2: where you're gonna have dinner tonight. How do you reduce

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Speaker 2: that to a statistical problem. We are kind of forced

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Speaker 2: by the dearth of statistical information to constantly rely on

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Speaker 2: our intuitive judgment, and so that it leaves us kind

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Speaker 2: of sleepy to the possibility that there is some statistical

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Speaker 2: information that could inform our judgment. Often when there is,

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Speaker 2: though yes, I think it is better, Connomin and Traversky

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Speaker 2: showed just it's amazing how even when you're looking at

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Speaker 2: people we would all think of that as experts doctors

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Speaker 2: that algorithms outperform the experts when rendering diagnoses. That kind

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Speaker 2: of thing.

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Speaker 1: One of the things that struck me exploring The story

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Speaker 1: for Cause Details was when effective altruism took this what

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Speaker 1: seems to be a little bit of a weird turn.

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Speaker 1: So on the one hand, you you've got the people

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Speaker 1: who are like, Okay, I've got one thousand dollars and

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Speaker 1: i want to save the maximum number of lives. They

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Speaker 1: crunch the numbers and they go, yeah, it's probably bed

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Speaker 1: nets impregnated with anti mosquit typesticides. If you give it,

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Speaker 1: you give out a lot of bed nets, you could

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Speaker 1: save a life for whatever one thousand dollars, three thousand dollars.

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Speaker 1: So it's cheap. It's cheap, and that's better than maybe

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Speaker 1: spending the money on vaccinations, even though vaccinations are good

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Speaker 1: and vaccinations are better than spending the money on work

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Speaker 1: to improve governance. And improve governance is better than giving

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Speaker 1: it to a donkey sanctuary because all of the donkeys

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Speaker 1: are living in luxury. They're all fine. So that all

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Speaker 1: makes sense up to that point. You go the effective

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Speaker 1: altruisms at on it. I'm with this is great. I'm

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Speaker 1: with them too, so I feel the same way. I'm

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Speaker 1: with them too. I've makes it.

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Speaker 2: They just make me at that point, they just make

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Speaker 2: me feel guilty for how I'm living my life.

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Speaker 1: And then they start having these conversations like, well, you know,

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Speaker 1: there could be a lot of people in the world

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Speaker 1: in the future. The future could go on for a

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Speaker 1: very long time. We could be talking about trillions of people.

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Speaker 1: We could be talking about intergalactic ciations or human civilization

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Speaker 1: lasting a million years. But maybe AI ruins all that.

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Speaker 1: So maybe if we have a workshop about AI safety,

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Speaker 1: maybe there's only a one in a billion chance that

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Speaker 1: it will do anything. But if it does something, it

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Speaker 1: could save a trillion lives. So a one and a

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Speaker 1: billion chance of saving a trillion lives, that's a thousand lives. Right,

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Speaker 1: that's really good, and so it's totally worth spending fifty

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Speaker 1: thousand dollars on our AI workshop. At which point you

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Speaker 1: start like, where did it go wrong? Or are they right?

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Speaker 2: Am I wrong? So I exactly say a feeling it

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Speaker 2: jumped the shark Around twenty and twelve and Toby Ord

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Speaker 2: writes this book about existential risk where he's putting numbers

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Speaker 2: on the likelihood of various events wiping out humanity, and

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Speaker 2: it's AI and pandemics and meteor strikes and nuclear war

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Speaker 2: and so on and so forth. Very hard to put

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Speaker 2: numbers on that, but of course you can't do the

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Speaker 2: calculations about the numbers, so they just accept roughly good numbers,

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Speaker 2: and it's just not at all clear to me the

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Speaker 2: numbers are okay at all. The math become much shakier.

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Speaker 2: That's one thing that happens. The number has become much bigger.

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Speaker 2: You're not just talking about saving lives in Africa right now,

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Speaker 2: You're talking about all future humanity and infinite people. And

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Speaker 2: so the sums of money required to address these problems

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Speaker 2: also becomes much much bigger. One thousand dollars will be

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Speaker 2: useful in Africa right now. One thousand dollars right now

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Speaker 2: to prevent AI from eating us all one day is nothing.

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Speaker 2: Sam Begun Freed sits down and does a back of

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Speaker 2: the envelope calculation. He thinks he needs at least, you know,

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Speaker 2: one hundred billion dollars to make a dent in this

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Speaker 2: or that problem. So all of a sudden, it gets

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Speaker 2: very grandiose, and it's completely even if they were never

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Speaker 2: entirely interested in the emotional residence of saving the life

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Speaker 2: of a small child in Africa today, there was at

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Speaker 2: least some emotional content there, and all of a sudden

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Speaker 2: it vanishes. It's purely an abstract math problem. And then,

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Speaker 2: of course, as you just said, what can't you justify

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Speaker 2: when the numbers are that big, the number of people

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Speaker 2: you're going to save that big, what can't you justify

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Speaker 2: doing to attack the problem? It seems perfectly rational to

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Speaker 2: do whatever you need to do to make one hundred

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Speaker 2: billion dollars to eliminate or at least defray this existential risk.

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Speaker 1: Yeah, this is the end of the world. We're talking

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Speaker 1: about the end of human civilization. You just be able

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Speaker 1: to cut some coolness at that point.

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Speaker 2: So what you're doing, also, if you think about it,

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Speaker 2: is you're puffing yourself up. You're a superhero. Now you're

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Speaker 2: now going to save humanity. You may only be increasing

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Speaker 2: the odds that humanity is saved by a few percentile,

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Speaker 2: but nevertheless you're in the realm of saving humanity. So

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Speaker 2: it creates a grandiosity to the whole thing.

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Speaker 1: And well, as I understand it, the sound bragmue of

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Speaker 1: people have said it, if I'm giving myself a five

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Speaker 1: percent chance of saving the world, that I'm five percent

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Speaker 1: of Superman.

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Speaker 2: Right there we go. So this is how he was

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Speaker 2: thinking when I first met him, and I thought it

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Speaker 2: was bonkers, but I thought it was interesting. It's not wrong,

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Speaker 2: you know, it's not obviously wrong. It's probably obviously right

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Speaker 2: that there are these extinction level events that could happen.

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Speaker 2: The question is, like, is it barkers think you could

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Speaker 2: do much about them? There's something that goes on when

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Speaker 2: you're doing things that have such a remote probability of

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Speaker 2: having any effect. It's very different from buying a bed

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Speaker 2: net and saving a kid. The degree of uncertainly gets

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Speaker 2: so high. And so when you look at how what

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Speaker 2: happen to the EA movement around Sam. They had their

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Speaker 2: arguments about this, but the people who move with Sam

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Speaker 2: all bought into you know, we're saving humanity, and they

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Speaker 2: left behind a collection of original EA's who sort of

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Speaker 2: disapproved and who continued to, you know, buy bed nets

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Speaker 2: for kids in Africa.

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Speaker 1: Do you think it is fair to blame effective altruism

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Speaker 1: for what Sam Maguinfree did and you know, the fraud

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Speaker 1: and him going to prison and all of that. To

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Speaker 1: what extent can you track it all back to.

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Speaker 2: Effective Can we assign fractional blame?

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Speaker 1: I think that's totally in the spirit.

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Speaker 2: Of the heart. Let's in the spirit of Sam Bankman Freed,

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Speaker 2: let's do a little shares of blame. So I would say,

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Speaker 2: the little sliver of blame that effective algorism gets is

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Speaker 2: in how it was sold to the Sam Bankman Freeds

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Speaker 2: of the world. You are this nerd with a high

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Speaker 2: sense of your own self importance, but it hasn't been

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Speaker 2: appreciated by the world who is coming of age in

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Speaker 2: a world where very weirdly, you can get rich very

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Speaker 2: quickly because your particular gifts are all of a sudden

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Speaker 2: in demand at Jane Street and Hudson River Trading and

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Speaker 2: Citadel and all these quantitative trading shops and also tech firms.

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Speaker 2: So all of a sudden, you're the most monetizable twenty

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Speaker 2: two year old on the planet, maybe a professional athlete.

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Speaker 2: And if you unleash your money making skills for the

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Speaker 2: good of this movement, you're a superhero. They're given a

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Speaker 2: kind of license to behave in unorthodox ways.

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Speaker 1: Maybe it never could to will that somebody would, Oh, well,

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Speaker 1: I guess I could commit a massive fraud and as

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Speaker 1: long as I saved the planet, that's fine. I mean,

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Speaker 1: possibly never oc could to the philosophers that anybody would

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Speaker 1: think to do that.

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Speaker 2: It's possible. That's a funny thing you say that. I

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Speaker 2: think that's possible. So I think if Sam Beginfree is

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Speaker 2: a creature of anything. Effect of altruism is really important,

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Speaker 2: but so is Jane Street. The high frequency trading world,

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Speaker 2: they live to game systems. You're in there to figure

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Speaker 2: out how to game markets, and people who are really

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Speaker 2: good at playing the game are the people who win.

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Speaker 2: And Sam was really good at playing the game. Obviously

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Speaker 2: you're not supposed to break the law, but it puts

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Speaker 2: you in a certain frame of mind when all you're

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Speaker 2: doing is gaming markets day after day after day, you're

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Speaker 2: looking for little edges, and if I were assign another

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Speaker 2: slipper of blame, kind of the grown up world of finance.

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Speaker 2: It was obvious to anybody who walked into the FTX

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Speaker 2: that there was a basic conflict of interest. He had

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Speaker 2: this private trading firm that he'd started first out of

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Speaker 2: which grew this public crypto exchange, and that his private

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Speaker 2: trading from traded on the crypto exchange and was in

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Speaker 2: a little hut next door to it in the Bahamas.

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Speaker 2: So some blame there, but of course also blame to Sam.

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Speaker 2: What isn't in Sam that leads him to do this.

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Speaker 2: You know, he danced his way out of lots of

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Speaker 2: problems before. I think he thought he was smart, so

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Speaker 2: smart that he could kind of figure this out. And

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Speaker 2: in fairness to him, I think he was overwhelmed. If

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Speaker 2: you'd seen their business, your first reaction would be this

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Speaker 2: is pure chaos. There's no organization chart. No one knows

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Speaker 2: who the job actually is of The corporate psychiatrist is

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Speaker 2: the person who knows the most about the business because

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Speaker 2: he's the only one everybody talks to. You know, it's

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Speaker 2: like one thing like that after another. What it didn't

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Speaker 2: feel like and still doesn't feel like to me, And

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Speaker 2: I get in trouble when I say things like this

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Speaker 2: is malice. What he wasn't and isn't is like this

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Speaker 2: natural crook. He doesn't have cruelty in him. Oddly, he

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Speaker 2: doesn't have a lot of dishonesty in him. He has some,

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Speaker 2: but up to the moment that it's discovered that he

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Speaker 2: has the money in the wrong place, that he's taking

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Speaker 2: used as customers' money to do all kinds of things

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Speaker 2: he didn't have permission to do. There's not really a

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Speaker 2: trace in Sam Bankman's Freed's life of criminal behavior or

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Speaker 2: corrupt behavior. Didn't cheat on tests, didn't cheat a golf,

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Speaker 2: never played golf. You know, you go back to his childhood,

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Speaker 2: there's no sign of anything like this, and so it's

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Speaker 2: not like this is who he is. It makes it

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Speaker 2: even a weirder event.

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Speaker 1: Do you think he was a kind personal? Is a

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Speaker 1: kind person?

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Speaker 2: Yes, I mean, you know, qualify it. But he paved

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Speaker 2: very well to the little people around him who helped him.

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Speaker 2: He would get worked up and scream every now and then.

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Speaker 2: But I think because he was on the receiving end

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Speaker 2: of it when he was a kid a lot. I

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Speaker 2: think he was highly sensitive to suffering. I think suffering

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Speaker 2: bothered him. And he was also highly averse to conflict,

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Speaker 2: Like he was really uncomfortable getting in a conflict. He'd

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Speaker 2: going high rather than fight. Every now and then he

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Speaker 2: get upset and everybody around him get scared. But it

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Speaker 2: was very private. It was like watching a little volcano

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Speaker 2: go off. So yeah, I think he's basically clan person.

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Speaker 2: And to this day, I'm still in touch with him.

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Speaker 2: He writes a prison diary and he sends it to me.

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Speaker 2: He's pretty careful about his interactions with other people. He's

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Speaker 2: in no conflict. People kind of like him because he

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Speaker 2: kind of watches out for them a bit. I mean,

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Speaker 2: we did it was really wrong, but when you know

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Speaker 2: this personality, it's even stranger.

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Speaker 1: He did it interesting, very interesting. Well, Michael, stay with us.

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Speaker 1: I'm going to give you questions from our loyal listeners,

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Speaker 1: particularly on the subjects of kindness and altruism, and we

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Speaker 1: shall hear them after the break. We're back. I'm talking

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Speaker 1: to Michael Lewis, the author of Going Infinite, The Rise

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Speaker 1: and Fall of a New Tycoon, which tells the story

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Speaker 1: of Sam Bankmin Freed, the world's most infamous effective altruists.

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Speaker 1: And Michael, we've asked our listeners to send in their

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Speaker 1: questions about altruism. Are you going for answering a few?

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Speaker 2: Sure?

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Speaker 1: So here's one from I think from Nocum Apologies if

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Speaker 1: I've mispronounced your name, that they write as an admirer

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Speaker 1: of mister Lewis's books since Liar's Poker. When I heard

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Speaker 1: he was answering questions about altruism, I had to ask this.

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Speaker 1: According to the latest data I could find, the most

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Speaker 1: important cause for effective altruists, after poverty and health, is

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Speaker 1: AI risk. There are real challenges to the adoption of AI,

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Speaker 1: but to put it so high on the list of

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Speaker 1: causes to donate to seems misguided at best. Can you

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Speaker 1: explain how AI overtook such obvious harms as global warming

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Speaker 1: or the public benefits of the spread of free markets,

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Speaker 1: free speech, or democracy as the greatest good and effective

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Speaker 1: altruists can do all the best, No, com This is.

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Speaker 2: One of those really great questions I probably can't answer satisfactorily,

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Speaker 2: but it is a great question. I think in the

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Speaker 2: minds of Sam bagman Freedom and the people around him,

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Speaker 2: they thought of these existential risks in two ways. How

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Speaker 2: salient are they? How likely are they they cause problems

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Speaker 2: to actually happen? And how tractable are they? What can

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Speaker 2: we actually do about this? And in Sam bankman Fried's

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Speaker 2: mind when he did that calculation, it wasn't AI that

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Speaker 2: bubbled to the surface. It was pandemics. He threw much

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Speaker 2: more money into pandemic prevention than AI. Even though he

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Speaker 2: thought AI was the greater risk. He couldn't figure out

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Speaker 2: what to do about it.

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Speaker 1: Would this had been pre COVID When he was throwing

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Speaker 1: money into pandemic prevention, it.

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Speaker 2: Was pre COVID. When he was thinking about it and

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Speaker 2: when he started to have real money we were in

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Speaker 2: the thick of COVID. It was like, if a really

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Speaker 2: even more deadly one comes along, how can we be

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Speaker 2: better defending ourselves against it or detecting it and preventing

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Speaker 2: in the first place. That kind of thing the one

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Speaker 2: move he made in AI prevention other than funding people

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Speaker 2: with small sums of money who had interesting ideas, but

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Speaker 2: he bought a big chunk of anthropic which was the

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Speaker 2: kind of Salonda refuse for open AI. People who thought

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Speaker 2: that open AI wasn't paying enough attention to the risks

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Speaker 2: and that fine enough end up being worth many, many

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Speaker 2: billions of dollars to his creditors. So why were they

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Speaker 2: so alive to AI as an existential risk? Because your

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Speaker 2: question is right, it's not obvious. It's not obvious to me.

432
00:22:31,050 --> 00:22:33,530
Speaker 1: Let me flip it around and say, why are they

433
00:22:33,570 --> 00:22:36,930
Speaker 1: not worried about climate change as an existential risk? For example?

434
00:22:37,010 --> 00:22:38,930
Speaker 2: The answer to that is they think that climate change,

435
00:22:38,930 --> 00:22:40,530
Speaker 2: no matter how bad it gets, they'll still be human

436
00:22:40,570 --> 00:22:41,130
Speaker 2: beings alive.

437
00:22:41,490 --> 00:22:43,490
Speaker 1: Yeah, and they're probably right, Yeah, I mean, it's not

438
00:22:43,530 --> 00:22:45,450
Speaker 1: going to turn the earth into venus right, it could

439
00:22:45,450 --> 00:22:48,610
Speaker 1: make things really uncomfortable. We could really regret we didn't

440
00:22:48,650 --> 00:22:52,170
Speaker 1: do more earlier. But the idea that it ends the

441
00:22:52,250 --> 00:22:53,970
Speaker 1: human race seems unlikely.

442
00:22:54,130 --> 00:22:54,250
Speaker 2: Yea.

443
00:22:54,570 --> 00:22:57,130
Speaker 1: So they're looking for things that will actually, like end it,

444
00:22:57,210 --> 00:23:00,730
Speaker 1: absolutely end human civilization, and there, I guess you're looking

445
00:23:00,730 --> 00:23:03,410
Speaker 1: at nuclear weapons and AI are more plausible.

446
00:23:03,410 --> 00:23:06,570
Speaker 2: Maybe that's right. AI had the capacity to do the

447
00:23:06,650 --> 00:23:10,330
Speaker 2: job completely in a way even pandemic wouldn't. I don't

448
00:23:10,330 --> 00:23:13,290
Speaker 2: even think nuclear weapons are that plausible. So they could

449
00:23:13,330 --> 00:23:15,890
Speaker 2: play out a scenario where they were no longer people

450
00:23:15,890 --> 00:23:16,570
Speaker 2: because of AI.

451
00:23:16,690 --> 00:23:19,250
Speaker 1: It throws a light on this the difference between a

452
00:23:19,250 --> 00:23:21,330
Speaker 1: long term, effective altruist and the rest of us. The

453
00:23:21,330 --> 00:23:22,810
Speaker 1: rest of us would go, hey, if there was a

454
00:23:22,890 --> 00:23:25,890
Speaker 1: nuclear war and ninety nine percent of the human race

455
00:23:26,210 --> 00:23:29,570
Speaker 1: was killed and the rest of us were reduced to

456
00:23:29,610 --> 00:23:31,770
Speaker 1: the Stone Age and had to build from scratch, that

457
00:23:31,930 --> 00:23:35,890
Speaker 1: sounds incredibly bad. Whereas for an effective altruist, they're like, well,

458
00:23:35,930 --> 00:23:37,650
Speaker 1: if you think really long term, yeah, and on a

459
00:23:37,730 --> 00:23:39,810
Speaker 1: two million year time horizon, that's right. It's not the

460
00:23:39,810 --> 00:23:40,250
Speaker 1: worst thing.

461
00:23:40,450 --> 00:23:42,930
Speaker 2: It just shows you where you can go when you're

462
00:23:43,050 --> 00:23:47,730
Speaker 2: untethered from normal human feeling and convention. That's the real

463
00:23:47,770 --> 00:23:50,450
Speaker 2: thing about the effect of altrus is that at bottom,

464
00:23:50,570 --> 00:23:53,490
Speaker 2: it's a status movement. It's a group of people who

465
00:23:53,530 --> 00:23:58,410
Speaker 2: feel underappreciated, slightly ostracized, who know in some ways they're

466
00:23:58,450 --> 00:24:01,970
Speaker 2: smarter than their peers. Essentially, the starting point is everybody

467
00:24:01,970 --> 00:24:04,050
Speaker 2: else is stupid, they don't know, and it takes them

468
00:24:04,050 --> 00:24:04,930
Speaker 2: to weird places.

469
00:24:05,250 --> 00:24:08,330
Speaker 1: Let me throw in a thought from listener, Matt. I'm

470
00:24:08,330 --> 00:24:10,570
Speaker 1: going to paraphrase, but it's relevant, I think to what

471
00:24:10,650 --> 00:24:13,650
Speaker 1: you're just saying, Michael. So, Matt conjures up the idea

472
00:24:13,970 --> 00:24:18,610
Speaker 1: that people who eat meat are in a way very

473
00:24:18,650 --> 00:24:23,690
Speaker 1: good for pigs and chickens and cows, in.

474
00:24:23,610 --> 00:24:25,450
Speaker 2: The same way that people who want ducks are very

475
00:24:25,450 --> 00:24:29,050
Speaker 2: good for ducks because you created the need for these

476
00:24:29,090 --> 00:24:30,650
Speaker 2: animals to exist. Yeah.

477
00:24:30,690 --> 00:24:32,770
Speaker 1: You know, if a Martian came down and looked and said,

478
00:24:32,810 --> 00:24:36,690
Speaker 1: you know, what are the dominant species on Earth, then

479
00:24:36,730 --> 00:24:38,690
Speaker 1: there's a good case that some of the dominant species

480
00:24:38,770 --> 00:24:41,410
Speaker 1: are the species that humans eat. And the reason they're

481
00:24:41,450 --> 00:24:44,210
Speaker 1: dominant there are so many of them have taken over

482
00:24:44,250 --> 00:24:48,690
Speaker 1: all these different ecosystems is because that's what the humans want. Anyway,

483
00:24:48,730 --> 00:24:53,690
Speaker 1: he draws a parallel between that perspective and effective altruism.

484
00:24:54,330 --> 00:24:56,530
Speaker 1: Is it too harsh to think of effective altruists as

485
00:24:56,570 --> 00:25:01,570
Speaker 1: benign butchers ready to sacrifice real people today for the

486
00:25:01,610 --> 00:25:04,930
Speaker 1: sake of a notional gain to humankind over eons. So

487
00:25:04,970 --> 00:25:07,570
Speaker 1: there's no end to the suffering you can tolerate today

488
00:25:07,650 --> 00:25:10,650
Speaker 1: as long as your maximized using the quantity of humanity

489
00:25:10,690 --> 00:25:11,370
Speaker 1: in the long run.

490
00:25:11,570 --> 00:25:13,730
Speaker 2: I'd be willing to play this game with him, Except

491
00:25:13,770 --> 00:25:16,410
Speaker 2: the effect of altruismselves do not acknowledge they were willing

492
00:25:16,410 --> 00:25:19,770
Speaker 2: to inflict suffering on people today. They just thought they

493
00:25:19,810 --> 00:25:22,250
Speaker 2: were shifting their attention from people to day to people

494
00:25:22,250 --> 00:25:24,690
Speaker 2: in the future. They were shifting their neglect, if you

495
00:25:24,730 --> 00:25:27,170
Speaker 2: want to think about it the other way. So they

496
00:25:27,210 --> 00:25:32,890
Speaker 2: weren't actively thinking of themselves as inflicting suffering on present humans,

497
00:25:33,250 --> 00:25:36,450
Speaker 2: although Sam did. Although Sam did, although the logic of

498
00:25:36,450 --> 00:25:40,010
Speaker 2: their argument would lead them to do it if necessary.

499
00:25:40,170 --> 00:25:44,650
Speaker 1: Yes, interesting question for Victor. Victor asks why is no

500
00:25:44,730 --> 00:25:48,130
Speaker 1: one talking about why we need altruism in the first place?

501
00:25:48,170 --> 00:25:50,770
Speaker 1: What are we doing wrong as societies that some people

502
00:25:50,850 --> 00:25:54,930
Speaker 1: are left behind and there's a need for altruism. I

503
00:25:54,970 --> 00:25:57,010
Speaker 1: suppose this gets to the idea of if you had

504
00:25:57,010 --> 00:25:59,450
Speaker 1: a benign government, or if you had the right rules

505
00:25:59,570 --> 00:26:03,170
Speaker 1: or the invisible hand of Adam Smith. But if we

506
00:26:03,210 --> 00:26:06,170
Speaker 1: somehow organize society better, no one would have to be

507
00:26:06,250 --> 00:26:09,010
Speaker 1: kind or altruistic, because all these problems would be.

508
00:26:08,970 --> 00:26:14,210
Speaker 2: So this fair question too. Without human sympathy, let's not

509
00:26:14,250 --> 00:26:15,890
Speaker 2: call it altruism, Let's just call it kind of a

510
00:26:15,890 --> 00:26:20,010
Speaker 2: basic sympathy for our fellow creatures. Without that, why would

511
00:26:20,050 --> 00:26:24,130
Speaker 2: you bother to organize society in a kinder way? Like

512
00:26:24,330 --> 00:26:27,050
Speaker 2: that is what's at the bottom of attempts to organize

513
00:26:27,050 --> 00:26:29,690
Speaker 2: society so that you don't need it. It's a funny

514
00:26:29,690 --> 00:26:32,930
Speaker 2: situation in a way, because when I think of altruism,

515
00:26:33,370 --> 00:26:37,290
Speaker 2: they kind of selflessness. Every time you scratch the surface,

516
00:26:37,370 --> 00:26:39,490
Speaker 2: if you find some human motive for what they're doing,

517
00:26:39,930 --> 00:26:42,690
Speaker 2: selfish might be too strong, but it's not exactly selfless.

518
00:26:43,450 --> 00:26:46,730
Speaker 2: So when you ask this question, my mind goes in

519
00:26:46,770 --> 00:26:48,810
Speaker 2: a completely different direction, and my mind goes in the

520
00:26:48,810 --> 00:26:51,650
Speaker 2: direction of do we actually even have altruism? Is there

521
00:26:51,730 --> 00:26:55,370
Speaker 2: such a thing? I think it's more complicated than that.

522
00:26:56,050 --> 00:26:58,370
Speaker 2: The minute you were sitting in a room with Sam

523
00:26:58,410 --> 00:27:02,170
Speaker 2: bankman Fried and is effective altruists at FTX, you realize

524
00:27:02,210 --> 00:27:04,330
Speaker 2: they were engaged in a kind of competition with other

525
00:27:04,370 --> 00:27:07,290
Speaker 2: effective altruists. Who's going to be the biggest deal in

526
00:27:07,330 --> 00:27:10,170
Speaker 2: the effective altruism community. It's like who's going to save

527
00:27:10,210 --> 00:27:13,890
Speaker 2: the most future lives. They never put it quite so crudely,

528
00:27:13,930 --> 00:27:15,810
Speaker 2: but it was in the air. Maybe that's good jolly.

529
00:27:15,850 --> 00:27:18,810
Speaker 2: You've got these kind of killer competitive instincts. And it

530
00:27:18,890 --> 00:27:20,970
Speaker 2: could be people kidding each other with machetes, or it

531
00:27:20,970 --> 00:27:23,010
Speaker 2: could be Wall Street traders trying to make the most money.

532
00:27:23,090 --> 00:27:26,650
Speaker 2: The beauty of early sam Bekman Freed and his crowd

533
00:27:27,410 --> 00:27:31,570
Speaker 2: was so we have this machine called Wall Street. It

534
00:27:31,650 --> 00:27:34,930
Speaker 2: is gotten better and better at extracting rents. The people

535
00:27:34,970 --> 00:27:38,250
Speaker 2: who are the peak predators on Wall Street now are

536
00:27:38,250 --> 00:27:41,530
Speaker 2: the high frequency traders, the Citadel's, the Jane Streets, the

537
00:27:41,570 --> 00:27:45,530
Speaker 2: jump trading, the Hudson River trading virtue, all these places

538
00:27:46,330 --> 00:27:50,130
Speaker 2: generating more money for individuals than have ever been generated

539
00:27:50,170 --> 00:27:53,610
Speaker 2: on Wall Street before. And all of a sudden, the

540
00:27:53,730 --> 00:27:56,650
Speaker 2: kind of person who's good at that has this religion

541
00:27:56,810 --> 00:27:59,690
Speaker 2: about giving the money away. I thought, for a brief

542
00:27:59,770 --> 00:28:02,210
Speaker 2: moment there was this kind of robin Hood thing that

543
00:28:02,290 --> 00:28:05,330
Speaker 2: might go on. And in fact, Jane Street, which is

544
00:28:05,410 --> 00:28:09,450
Speaker 2: the leader of the pack. Jane Street started to worry

545
00:28:09,970 --> 00:28:12,450
Speaker 2: that too many of the people that they were recruiting

546
00:28:13,050 --> 00:28:15,890
Speaker 2: were effective altress. And the problem with the effective altress

547
00:28:15,890 --> 00:28:17,770
Speaker 2: is they couldn't control them in the way you control

548
00:28:17,810 --> 00:28:19,690
Speaker 2: a normal person who just wants a fourth house and

549
00:28:19,730 --> 00:28:23,010
Speaker 2: a third yacht. They didn't have the same materialist needs.

550
00:28:23,050 --> 00:28:26,450
Speaker 2: They were doing it for this kind of quasi religious reason,

551
00:28:26,930 --> 00:28:29,810
Speaker 2: and it made them much harder to manage. But I thought,

552
00:28:29,810 --> 00:28:33,170
Speaker 2: what a great problem. Wouldn't it be cool if instead

553
00:28:33,170 --> 00:28:35,570
Speaker 2: of like reforming Wall Street because we'll never do it,

554
00:28:36,010 --> 00:28:38,770
Speaker 2: that it's sort of weirdly reformed itself. Because the kind

555
00:28:38,770 --> 00:28:41,450
Speaker 2: of person who got into the position of making the

556
00:28:41,450 --> 00:28:44,330
Speaker 2: most money felt like it was his religion to give

557
00:28:44,370 --> 00:28:47,330
Speaker 2: it away. That would have been cool.

558
00:28:47,650 --> 00:28:50,050
Speaker 1: Yeah, No, it would have been would have been so

559
00:28:50,210 --> 00:28:52,690
Speaker 1: a quick question from our listener Richard, which I think

560
00:28:52,810 --> 00:28:55,770
Speaker 1: is tied to that point. It's an exequat question really.

561
00:28:55,930 --> 00:28:58,290
Speaker 1: He says, if you give money to charity, should you

562
00:28:58,330 --> 00:28:59,450
Speaker 1: tell everybody about it?

563
00:28:59,530 --> 00:28:59,650
Speaker 2: Well?

564
00:28:59,650 --> 00:29:02,010
Speaker 1: Should you just do it? Should you just do it quietly? Well,

565
00:29:02,050 --> 00:29:04,370
Speaker 1: one hand, you should do it quietly because it's undignified

566
00:29:04,410 --> 00:29:06,250
Speaker 1: and it's not about you, It's about the charity. But

567
00:29:06,290 --> 00:29:08,650
Speaker 1: on the other hand, if you tell your friends you're

568
00:29:08,650 --> 00:29:10,930
Speaker 1: giving money to charity, then maybe that will encourage them

569
00:29:10,970 --> 00:29:12,210
Speaker 1: to give as well.

570
00:29:12,210 --> 00:29:14,090
Speaker 2: That's an interesting way to put it. I don't think

571
00:29:14,130 --> 00:29:16,930
Speaker 2: people who put their names on buildings are doing it

572
00:29:17,130 --> 00:29:19,130
Speaker 2: because they want to encourage other people to put their

573
00:29:19,210 --> 00:29:22,850
Speaker 2: names on buildings. It's self advertisement. Yeah. I was raised

574
00:29:22,890 --> 00:29:24,890
Speaker 2: to be very quiet about this sort of thing, that

575
00:29:25,410 --> 00:29:28,490
Speaker 2: you give money if you always listed as anonymous. I've

576
00:29:28,530 --> 00:29:31,530
Speaker 2: had organizations ask we put our name on it, and

577
00:29:31,570 --> 00:29:33,650
Speaker 2: I said, and when they want that, I let them

578
00:29:33,690 --> 00:29:37,370
Speaker 2: do it. My answer to the etiquee question is let

579
00:29:37,530 --> 00:29:41,010
Speaker 2: the recipient decide whether they want your name on it

580
00:29:41,090 --> 00:29:43,610
Speaker 2: or not, what's better for the recipient, and do whatever

581
00:29:43,730 --> 00:29:44,930
Speaker 2: is better for them.

582
00:29:45,250 --> 00:29:48,250
Speaker 1: Thank you, Michael, brilliant answers. Hold on with us, and

583
00:29:48,330 --> 00:29:51,530
Speaker 1: let's talk about your new book, Who Is Government. After

584
00:29:51,530 --> 00:30:02,210
Speaker 1: the break, we're back. I'm here with my fellow Pushkin podcaster,

585
00:30:02,330 --> 00:30:08,410
Speaker 1: the brilliant writer Michael Lewis. Michael, you very kindly agreed

586
00:30:08,570 --> 00:30:11,850
Speaker 1: to come on the podcast and talk about Sam Bankmnfreed

587
00:30:11,930 --> 00:30:14,090
Speaker 1: and talk about going infinite. And then I got a

588
00:30:14,130 --> 00:30:16,330
Speaker 1: message saying, oh, by the way, Michael has a new

589
00:30:16,370 --> 00:30:18,690
Speaker 1: book out. You should probably ask him some questions about that.

590
00:30:19,210 --> 00:30:21,170
Speaker 1: I have to admit my heart sank a little bit.

591
00:30:21,170 --> 00:30:24,930
Speaker 1: I thought, I don't really have time to read another

592
00:30:24,970 --> 00:30:29,490
Speaker 1: book in preparation for this podcast. And of course, the

593
00:30:29,570 --> 00:30:32,690
Speaker 1: moment I opened the first page, I was immediately drawn

594
00:30:32,730 --> 00:30:35,850
Speaker 1: in and I loved the book. So congratulations, But tell

595
00:30:35,930 --> 00:30:37,050
Speaker 1: us about who is government?

596
00:30:37,250 --> 00:30:39,170
Speaker 2: So I wrote a book about a set in the

597
00:30:39,210 --> 00:30:42,010
Speaker 2: first Trump administration called The Fifth Risk, where I wandered

598
00:30:42,010 --> 00:30:46,570
Speaker 2: around the administration, the executive branch, and got essentially an

599
00:30:46,690 --> 00:30:50,250
Speaker 2: education from the various departments that Trump himself refused to get.

600
00:30:50,370 --> 00:30:53,450
Speaker 2: So how the Agriculture Department worked, how the Energy Department worked,

601
00:30:53,450 --> 00:30:56,410
Speaker 2: what went on inside these places. And the longer I

602
00:30:56,410 --> 00:30:58,930
Speaker 2: spent there, the more taken I was with the actual

603
00:30:59,010 --> 00:31:03,090
Speaker 2: characters in government. Whatever the stereotype of the bureaucrat is

604
00:31:03,690 --> 00:31:06,850
Speaker 2: in the American mind, they violated it. There were these

605
00:31:07,170 --> 00:31:11,450
Speaker 2: breathtakingly devoted public servants who were experts and all kinds

606
00:31:11,490 --> 00:31:16,650
Speaker 2: of arcane and in some cases spintanely frightening fields who

607
00:31:17,130 --> 00:31:21,130
Speaker 2: were doing the work that kept the society together. And

608
00:31:21,250 --> 00:31:24,090
Speaker 2: I thought, you know, there really was a project coming

609
00:31:24,170 --> 00:31:27,090
Speaker 2: back and just doing profiles of these people. And it

610
00:31:27,130 --> 00:31:30,490
Speaker 2: wasn't so much because I saw Doge coming. I'm surprised

611
00:31:30,530 --> 00:31:34,250
Speaker 2: Doge came. It was more that I thought just generally

612
00:31:34,290 --> 00:31:39,130
Speaker 2: the conversation around American government had gotten so dumb because

613
00:31:39,130 --> 00:31:41,290
Speaker 2: people didn't really appreciate what the government did and who

614
00:31:41,330 --> 00:31:43,850
Speaker 2: the people were who did it. So I recruited six writers,

615
00:31:44,410 --> 00:31:48,770
Speaker 2: Dave Eggers, John Lanchester, Geraldine Brooks, Casey Sepp, Camal Bell,

616
00:31:48,890 --> 00:31:53,130
Speaker 2: and Sarah Val none of them really conventional journalists Comal

617
00:31:53,210 --> 00:31:57,090
Speaker 2: Bell's a stand up comedian, Dave and Geraldine mostly novelists,

618
00:31:57,090 --> 00:31:59,730
Speaker 2: same as John, and just dropped them into the government,

619
00:31:59,770 --> 00:32:02,250
Speaker 2: said look, find a story, and I did two of them.

620
00:32:03,090 --> 00:32:06,570
Speaker 2: And the idea was just like inoculate the American public

621
00:32:06,610 --> 00:32:09,770
Speaker 2: against these really stupid critiques of their government. I mean,

622
00:32:09,810 --> 00:32:13,530
Speaker 2: these things ran one by one each week in the

623
00:32:13,730 --> 00:32:16,410
Speaker 2: eight weeks running up to the election, and then we

624
00:32:16,450 --> 00:32:18,290
Speaker 2: put them together the book because they got so much attention.

625
00:32:18,890 --> 00:32:21,290
Speaker 2: The thing that really got me was actually the quality

626
00:32:21,330 --> 00:32:24,090
Speaker 2: of the material. Like the first story is about a

627
00:32:24,090 --> 00:32:27,890
Speaker 2: guy named Chris Mark who figured out how to prevent

628
00:32:27,930 --> 00:32:30,090
Speaker 2: the roofs of coal mines falling in on the heads

629
00:32:30,090 --> 00:32:33,370
Speaker 2: of coal miners, and you think, well, that's an arcane problem,

630
00:32:33,450 --> 00:32:36,370
Speaker 2: and how big a deal could that be? Fifty thousand

631
00:32:36,410 --> 00:32:39,930
Speaker 2: American coal miners killed by roof falls in the last century,

632
00:32:40,210 --> 00:32:42,810
Speaker 2: and who knows how many more around the world, And

633
00:32:43,130 --> 00:32:45,650
Speaker 2: there was just an imperfect science and how to keep

634
00:32:45,690 --> 00:32:48,570
Speaker 2: the roof of a coal miner up, and how this

635
00:32:48,650 --> 00:32:52,730
Speaker 2: person comes to his expertise, why he does it, how

636
00:32:52,770 --> 00:32:55,010
Speaker 2: he does it is literate. It was just like this

637
00:32:55,090 --> 00:32:58,130
Speaker 2: stuff of novels. You find that over and over again

638
00:32:58,170 --> 00:32:58,690
Speaker 2: in the Government.

639
00:32:58,730 --> 00:33:01,370
Speaker 1: The story about Christopher Market. We don't want too many spoilers.

640
00:33:01,370 --> 00:33:04,050
Speaker 1: But his father is kind of, among other things, an

641
00:33:04,090 --> 00:33:06,490
Speaker 1: expert in why Gothic cathedrals don't fool.

642
00:33:06,290 --> 00:33:08,210
Speaker 2: Down, and he rebels against the father.

643
00:33:08,410 --> 00:33:10,170
Speaker 1: Yeah, they don't get on a tour, they don't get

644
00:33:10,210 --> 00:33:11,850
Speaker 1: on He leaves home and.

645
00:33:11,770 --> 00:33:14,890
Speaker 2: Refuses to get a college education. He goes and joins

646
00:33:14,970 --> 00:33:19,690
Speaker 2: the working class and then essentially reprises his father's career underground.

647
00:33:20,130 --> 00:33:22,370
Speaker 2: And when I tell him that, he gets angry at me. No,

648
00:33:22,490 --> 00:33:24,170
Speaker 2: it has nothing to do with my father. Yes, my

649
00:33:24,210 --> 00:33:26,490
Speaker 2: father figured out how the roofs of Gothic cathedrals don't

650
00:33:26,490 --> 00:33:28,290
Speaker 2: fall down, but that has nothing to do with how

651
00:33:28,330 --> 00:33:30,050
Speaker 2: the roofs of coal mines don't fall down.

652
00:33:30,290 --> 00:33:32,370
Speaker 1: Yeah, And of course it's it's the same problem.

653
00:33:32,530 --> 00:33:36,130
Speaker 2: It's the same basically, some little differences, but from any

654
00:33:36,210 --> 00:33:39,210
Speaker 2: kind of perspective outside of his own, it's the same problem.

655
00:33:39,250 --> 00:33:42,450
Speaker 2: And so that there were these psychological portraits to draw

656
00:33:42,490 --> 00:33:44,890
Speaker 2: that were just fun. I don't know how you think

657
00:33:44,890 --> 00:33:47,090
Speaker 2: about what you do. I think about what I do

658
00:33:47,210 --> 00:33:50,730
Speaker 2: is like goal mining. I'm a prospector. I wander around,

659
00:33:50,970 --> 00:33:54,810
Speaker 2: you know, the mountains of California, kicking up dirt under

660
00:33:54,810 --> 00:33:56,890
Speaker 2: my feet, looking for a place where I can sink

661
00:33:56,970 --> 00:34:00,570
Speaker 2: my pick and maybe find some gold. And it's kind

662
00:34:00,570 --> 00:34:02,850
Speaker 2: of random when I find it. There's not a great

663
00:34:02,930 --> 00:34:05,370
Speaker 2: science to the finding of the gold. You get good

664
00:34:05,410 --> 00:34:08,810
Speaker 2: at sort of analyzing the landscape. You got a sense

665
00:34:08,810 --> 00:34:10,690
Speaker 2: of this might be and where it might not be.

666
00:34:11,170 --> 00:34:14,330
Speaker 2: But it's still a lot of luck involved. I felt

667
00:34:14,370 --> 00:34:18,090
Speaker 2: that with the fifth risk, I found this unbelievably rich

668
00:34:18,730 --> 00:34:22,650
Speaker 2: vein of ore. I can only scoop out a fraction

669
00:34:22,810 --> 00:34:24,450
Speaker 2: of it because there was so much of it. And

670
00:34:24,490 --> 00:34:25,810
Speaker 2: I was working with my hands, and I had a

671
00:34:25,810 --> 00:34:28,530
Speaker 2: bucket on my back, and there was all this stuff

672
00:34:28,650 --> 00:34:31,010
Speaker 2: left behind, and I thought it would all be gone

673
00:34:31,050 --> 00:34:33,610
Speaker 2: by the time I came back to it. And in fact,

674
00:34:33,610 --> 00:34:35,690
Speaker 2: it's like noem would bother to go find the mind.

675
00:34:35,970 --> 00:34:36,730
Speaker 2: No one's interested.

676
00:34:37,090 --> 00:34:40,170
Speaker 1: There's this line by Geraldine Brooks, one of the writers

677
00:34:40,170 --> 00:34:43,690
Speaker 1: in Who Is Government. She's profiling a guy who is

678
00:34:43,730 --> 00:34:51,010
Speaker 1: a jiu jitsu instructor, tennis coach, fights terrorists and pedophiles,

679
00:34:51,210 --> 00:34:53,250
Speaker 1: but it is a qualified accountant and works for the

680
00:34:53,290 --> 00:34:56,010
Speaker 1: in non revenue service. And she says, you know, if

681
00:34:56,010 --> 00:34:58,810
Speaker 1: this was a novel, this would be malpractice, right, you

682
00:34:58,850 --> 00:35:01,690
Speaker 1: can't just make this sort of stuff up. But because

683
00:35:01,730 --> 00:35:03,290
Speaker 1: it happens to be true and this guy is a

684
00:35:03,290 --> 00:35:05,250
Speaker 1: real person, I'm actually allowed to have him as a

685
00:35:05,330 --> 00:35:06,290
Speaker 1: character in this story.

686
00:35:06,330 --> 00:35:08,970
Speaker 2: And it tells you something. This person who is like

687
00:35:09,450 --> 00:35:11,850
Speaker 2: the profit center of the United States government because he's

688
00:35:11,850 --> 00:35:15,850
Speaker 2: busting up cyber crime rings and raking in their bitcoin

689
00:35:15,890 --> 00:35:18,850
Speaker 2: and sticking it in the treasury. He's generated billions with

690
00:35:18,890 --> 00:35:22,410
Speaker 2: his team, billions of dollars of free money for the government.

691
00:35:22,970 --> 00:35:26,650
Speaker 2: And the Trump administration has disabled him. They've fired half

692
00:35:26,690 --> 00:35:29,410
Speaker 2: his unit and he can't do what he did as before.

693
00:35:29,690 --> 00:35:33,610
Speaker 2: Then our population isn't just completely outraged by this is

694
00:35:33,650 --> 00:35:36,290
Speaker 2: incredible to me, but they wouldn't know to be outraged

695
00:35:36,610 --> 00:35:38,770
Speaker 2: unless you knew the story. And you only know the

696
00:35:38,770 --> 00:35:41,210
Speaker 2: story if you read Geraldine's piece because it's not in

697
00:35:41,250 --> 00:35:41,610
Speaker 2: the news.

698
00:35:41,650 --> 00:35:45,010
Speaker 1: Otherwise it's astonishing and he busted Peter followings, there's one

699
00:35:45,050 --> 00:35:47,410
Speaker 1: point where somebody from Hamas's tweets and says, oh, what

700
00:35:47,490 --> 00:35:50,330
Speaker 1: you can donate to the revolutionary cause. He's send your

701
00:35:50,330 --> 00:35:54,610
Speaker 1: bitcoin to this address, and he redirects it basically hacks

702
00:35:54,650 --> 00:35:56,770
Speaker 1: their bank account. I have to say the details of

703
00:35:56,770 --> 00:35:58,650
Speaker 1: exactly how this happened to lost on me, but anybody

704
00:35:58,650 --> 00:36:01,690
Speaker 1: who donated bitcoin it ended up going to victims of

705
00:36:01,690 --> 00:36:04,290
Speaker 1: state sponsored terrorism, right and anybody who clicked on the

706
00:36:04,290 --> 00:36:07,730
Speaker 1: Hamas logo got directed to Rick Castley singing never going

707
00:36:07,770 --> 00:36:09,770
Speaker 1: to give you up. So who says the Buroucuts don't

708
00:36:09,770 --> 00:36:11,890
Speaker 1: have a sense of humor, Oh no, they do. That

709
00:36:12,090 --> 00:36:14,810
Speaker 1: they don't have, by and large is a sense of

710
00:36:14,850 --> 00:36:17,770
Speaker 1: themselves as characters. This was the thing that all.

711
00:36:17,690 --> 00:36:21,090
Speaker 2: The writers came away thinking, they like, I'd find this

712
00:36:21,170 --> 00:36:23,130
Speaker 2: person who had done this unbelievable thing, and I'd say

713
00:36:23,170 --> 00:36:24,450
Speaker 2: I want to talk to you about it, and he goes, well,

714
00:36:24,450 --> 00:36:26,730
Speaker 2: it wasn't really me. It was the team that you

715
00:36:26,770 --> 00:36:29,530
Speaker 2: really have to talk to my bosses. It was very

716
00:36:29,570 --> 00:36:32,770
Speaker 2: little ego. I guess what it is. These jobs self

717
00:36:32,850 --> 00:36:36,170
Speaker 2: select for people who really like doing big, important things

718
00:36:36,210 --> 00:36:38,970
Speaker 2: but don't care much about credit or money. It's hard

719
00:36:39,010 --> 00:36:41,570
Speaker 2: to believe that such people still exist in American life.

720
00:36:41,810 --> 00:36:44,290
Speaker 2: Everybody else seems to be looking for fame and fortune.

721
00:36:44,530 --> 00:36:47,410
Speaker 2: These are still like the opposite of reality TV stars.

722
00:36:47,610 --> 00:36:50,410
Speaker 2: They got interested in a problem. They've worried the problem

723
00:36:50,450 --> 00:36:53,090
Speaker 2: to death for thirty years, has had enormous consequences, and

724
00:36:53,090 --> 00:36:54,610
Speaker 2: they don't expect anybody to pay attention.

725
00:36:54,850 --> 00:36:56,890
Speaker 1: I need. You get nervous when people do, or at

726
00:36:56,970 --> 00:36:59,130
Speaker 1: least the bureaucracy around them gets nervous.

727
00:36:59,170 --> 00:37:01,970
Speaker 2: Well, that's right. That's really actually an important point because

728
00:37:02,010 --> 00:37:04,210
Speaker 2: that was the other thing all the writers noticed. And

729
00:37:04,250 --> 00:37:07,250
Speaker 2: this is maybe by way a way to explain why

730
00:37:07,290 --> 00:37:11,490
Speaker 2: there's this inefficiency and information about this, is that the

731
00:37:11,530 --> 00:37:15,450
Speaker 2: bureaucracy has gotten so used to all attention being bad attention.

732
00:37:15,570 --> 00:37:17,930
Speaker 1: So journalist shows up and says, I'd just like to

733
00:37:18,010 --> 00:37:20,370
Speaker 1: know what you're doing and why you're doing.

734
00:37:20,290 --> 00:37:22,570
Speaker 2: It, and they assume this is going to end with

735
00:37:22,650 --> 00:37:26,250
Speaker 2: this like a congressional hearing and me getting fired and

736
00:37:26,330 --> 00:37:28,410
Speaker 2: humiliated and prevented from doing anything for.

737
00:37:28,330 --> 00:37:30,690
Speaker 1: The rest of my life, whereas in fact, it's like, no, no,

738
00:37:30,810 --> 00:37:32,290
Speaker 1: I just want to know what you're doing, because it's

739
00:37:32,290 --> 00:37:33,930
Speaker 1: amazing what you're doing, and I want to tell everyone

740
00:37:33,930 --> 00:37:35,490
Speaker 1: what you're doing that's right in a good way.

741
00:37:35,610 --> 00:37:38,570
Speaker 2: They themselves are that not as wary because they aren't

742
00:37:38,570 --> 00:37:42,010
Speaker 2: doing anything bad. It's the political people above them are

743
00:37:42,090 --> 00:37:44,370
Speaker 2: worried that this story will somehow make the White House

744
00:37:44,370 --> 00:37:47,170
Speaker 2: look bad, and they can't imagine how it would make

745
00:37:47,210 --> 00:37:48,170
Speaker 2: the White House look good.

746
00:37:48,370 --> 00:37:51,970
Speaker 1: Yeah, and there is a story to be told. I

747
00:37:51,970 --> 00:37:56,370
Speaker 1: think also about how the poor reputation of government bureaucracy

748
00:37:56,890 --> 00:38:00,290
Speaker 1: just makes their life harder, irrespective of the politics itself.

749
00:38:00,410 --> 00:38:03,770
Speaker 1: I think you profile the woman who studies rare diseases.

750
00:38:04,050 --> 00:38:07,250
Speaker 2: Yes, so she doesn't really study rare diseases. She's had

751
00:38:07,290 --> 00:38:11,010
Speaker 2: a bunch of them stone And let me just tell

752
00:38:11,010 --> 00:38:12,890
Speaker 2: you how I found her, because that sort of explains

753
00:38:12,890 --> 00:38:15,250
Speaker 2: how what a role is in the world. So back

754
00:38:15,290 --> 00:38:17,490
Speaker 2: when I was working on my COVID book The Premonition,

755
00:38:17,970 --> 00:38:20,970
Speaker 2: one of the characters was a researcher named Joe Derriesi

756
00:38:21,050 --> 00:38:25,370
Speaker 2: who's like he was a superhero in BioResearch and Joe

757
00:38:25,650 --> 00:38:27,530
Speaker 2: he was working on COVID, but at the same time

758
00:38:28,050 --> 00:38:31,290
Speaker 2: he had discovered what he thought might be a treatment

759
00:38:31,410 --> 00:38:35,970
Speaker 2: for a rare brain eating amiba called balamuthia. I'd never

760
00:38:35,970 --> 00:38:37,970
Speaker 2: heard of this thing. Nobody had ever heard of this thing.

761
00:38:37,970 --> 00:38:40,130
Speaker 2: It did, wasn't It was discovered in like the nineteen

762
00:38:40,210 --> 00:38:41,650
Speaker 2: nineties in the San Diego Zoo.

763
00:38:41,730 --> 00:38:44,810
Speaker 1: But subsequently now I'm now having nine minutes about it.

764
00:38:44,930 --> 00:38:46,250
Speaker 1: So thank you for telling me it's.

765
00:38:46,210 --> 00:38:49,970
Speaker 2: Time should be you should yeah, because there's some tens

766
00:38:50,010 --> 00:38:52,610
Speaker 2: of thousands of cases each year in the United States

767
00:38:52,930 --> 00:38:57,410
Speaker 2: of people dying of unidentified encephalitis. It's like something's going

768
00:38:57,410 --> 00:38:59,450
Speaker 2: on in their brain and they never figure it out.

769
00:38:59,690 --> 00:39:04,010
Speaker 2: They die it's just encephalitis. There was a woman whose

770
00:39:04,050 --> 00:39:06,170
Speaker 2: brain was being eaten by something they couldn't figure out

771
00:39:06,210 --> 00:39:10,650
Speaker 2: what rolls into the UCSF emergency room. Joe gets involved.

772
00:39:10,770 --> 00:39:13,250
Speaker 2: Eventually she dies. He gets involved too late, but he's

773
00:39:13,290 --> 00:39:16,850
Speaker 2: able to identify the bug that's in her brain, and

774
00:39:16,890 --> 00:39:20,410
Speaker 2: it's this Ballamuthia Mandrillis. It's called it's a brain eating

775
00:39:20,450 --> 00:39:23,970
Speaker 2: a meba. He then, very cleverly he says, well, like,

776
00:39:24,890 --> 00:39:27,330
Speaker 2: what could you do to treat this in his lab,

777
00:39:27,410 --> 00:39:31,730
Speaker 2: he has as his graduate students bombard balamuthia with every

778
00:39:31,810 --> 00:39:34,290
Speaker 2: drug that's been approved either in Europe or the United States,

779
00:39:35,250 --> 00:39:38,690
Speaker 2: and find that one drug, a UTI drug used in

780
00:39:38,690 --> 00:39:43,490
Speaker 2: Europe called nitroxylene, kills the Baalamuthia clearly doesn't kill people.

781
00:39:43,570 --> 00:39:46,410
Speaker 2: People are taking it for UTIs, so like, why not

782
00:39:46,490 --> 00:39:49,890
Speaker 2: try this and lo and behold even though it's rare.

783
00:39:50,090 --> 00:39:54,130
Speaker 2: Pretty shortly thereafter, someone else rolls into the emergency room

784
00:39:54,370 --> 00:39:57,130
Speaker 2: has the same symptoms. They find he has ballamuthi in

785
00:39:57,130 --> 00:40:00,210
Speaker 2: his brain. They give him the nitroxylene and he survives.

786
00:40:00,810 --> 00:40:01,010
Speaker 1: Yeah.

787
00:40:01,370 --> 00:40:03,210
Speaker 2: I say to him, I said, man, that's great. Now

788
00:40:03,250 --> 00:40:05,410
Speaker 2: everybody will know there's at least something you could treat

789
00:40:05,410 --> 00:40:07,610
Speaker 2: it with. And he says, Nope, doesn't work that way,

790
00:40:07,930 --> 00:40:11,170
Speaker 2: he said, may we'll be able to publish a scientific paper.

791
00:40:11,650 --> 00:40:15,930
Speaker 2: Maybe doctors will notice it. But there's every possibility that

792
00:40:15,970 --> 00:40:18,290
Speaker 2: if you roll into a hospital with Ballymouthie anywhere else

793
00:40:18,290 --> 00:40:20,930
Speaker 2: in America, that they won't even know we did this work.

794
00:40:21,050 --> 00:40:23,050
Speaker 1: There's two problems, right, So one problem is you didn't

795
00:40:23,090 --> 00:40:25,330
Speaker 1: run a round the most trial, so you can't be sure.

796
00:40:25,370 --> 00:40:26,050
Speaker 1: Maybe it was a flu.

797
00:40:26,210 --> 00:40:26,570
Speaker 2: Correct.

798
00:40:26,650 --> 00:40:29,730
Speaker 1: It's still interesting that you had a theory, and you

799
00:40:29,770 --> 00:40:33,730
Speaker 1: gave him the drug and he got better. So maybe,

800
00:40:33,770 --> 00:40:35,970
Speaker 1: But then your second problem is, you know, how do

801
00:40:36,010 --> 00:40:38,370
Speaker 1: you tell people that maybe yes.

802
00:40:38,410 --> 00:40:40,970
Speaker 2: But with these rare diseases, you're never going to have

803
00:40:41,490 --> 00:40:45,250
Speaker 2: a randomized trial. And when they're fatal, if you roll

804
00:40:45,290 --> 00:40:49,090
Speaker 2: in Tim with Ballymouthie in your brain, would you rather

805
00:40:49,170 --> 00:40:53,610
Speaker 2: them throw some nitroxyline in you or not? If they don't,

806
00:40:53,730 --> 00:40:55,890
Speaker 2: we know what's going to happen if they don't, So

807
00:40:56,010 --> 00:40:59,170
Speaker 2: your brain is going to get eaten. That's bad, so

808
00:41:00,570 --> 00:41:03,210
Speaker 2: it why not? And the other side of this is

809
00:41:03,250 --> 00:41:06,370
Speaker 2: that because it's a rare disease, the pharmaceutical industry has

810
00:41:06,410 --> 00:41:08,450
Speaker 2: no interest in it. It's like there's no money in it. No,

811
00:41:08,650 --> 00:41:10,410
Speaker 2: none of people going to have this happen to them.

812
00:41:11,010 --> 00:41:12,970
Speaker 2: By the way, the way you apparently get is by

813
00:41:13,130 --> 00:41:16,210
Speaker 2: ingesting dirt, So be careful with dirt when your garden.

814
00:41:16,490 --> 00:41:20,370
Speaker 2: Don't put your hands to your mouth anyway. So Joe

815
00:41:20,370 --> 00:41:22,890
Speaker 2: says to me, this is a natural place for government

816
00:41:23,090 --> 00:41:25,810
Speaker 2: to intervene, to at least collate, because people like me

817
00:41:25,850 --> 00:41:28,090
Speaker 2: are doing this stuff all the time. And there's a

818
00:41:28,130 --> 00:41:31,770
Speaker 2: woman in the Food and Drug Administration named Heatherstone who

819
00:41:31,810 --> 00:41:35,290
Speaker 2: is kind of all by herself, basically decided to tackle

820
00:41:35,290 --> 00:41:38,410
Speaker 2: this problem. She's created an app that try to gather

821
00:41:39,210 --> 00:41:43,170
Speaker 2: every instance of a rare disease being treated around the world,

822
00:41:43,170 --> 00:41:45,370
Speaker 2: how it was treated in what the outcome was. There's

823
00:41:45,450 --> 00:41:49,210
Speaker 2: hope for people who have rare diseases. But the government

824
00:41:49,250 --> 00:41:53,130
Speaker 2: does not have the energy anymore to get behind Heatherstone's creation.

825
00:41:53,170 --> 00:41:55,930
Speaker 2: They're unwilling to really promote it. She's a woman on

826
00:41:55,970 --> 00:41:59,050
Speaker 2: her own to go into medical conferences trying to persuade

827
00:41:59,090 --> 00:42:03,930
Speaker 2: doctors to plug in their rare disease treatment their cases

828
00:42:04,050 --> 00:42:08,290
Speaker 2: into her app. It's going nowhere. What happens? The story

829
00:42:08,330 --> 00:42:10,490
Speaker 2: in the end is about girl and six year old

830
00:42:10,530 --> 00:42:12,970
Speaker 2: girl in Arkansas who rolls in and takes them forever

831
00:42:13,010 --> 00:42:15,250
Speaker 2: to find that she's got balimuthia. But she has ballymuthia

832
00:42:16,450 --> 00:42:20,210
Speaker 2: the completely screwed up way in which her life is saved.

833
00:42:21,290 --> 00:42:26,890
Speaker 2: The little girl in Arkansas parents google around and find

834
00:42:26,970 --> 00:42:30,050
Speaker 2: that Heatherstone has been thanked by JODORESI for helping me

835
00:42:30,170 --> 00:42:33,610
Speaker 2: get his hands on nitroxylene, and they get personally in

836
00:42:33,650 --> 00:42:36,250
Speaker 2: touch with her, and she makes sure that they get

837
00:42:36,290 --> 00:42:39,050
Speaker 2: the drug and her life is saved. But at the

838
00:42:39,210 --> 00:42:43,490
Speaker 2: very same time, just like forty miles from Jodoresi's office

839
00:42:43,490 --> 00:42:47,410
Speaker 2: in California, another little girl got Ballymuthia, never heard of

840
00:42:47,410 --> 00:42:49,890
Speaker 2: the cure and died. And it's sort of like a

841
00:42:49,970 --> 00:42:52,850
Speaker 2: parable of good and bad government, Like, do you what

842
00:42:53,010 --> 00:42:54,810
Speaker 2: kind of government do you want to have? A government

843
00:42:54,850 --> 00:42:58,610
Speaker 2: that has emboldened and strengthened to actually follow through on

844
00:42:58,650 --> 00:43:01,010
Speaker 2: this really good idea of how to deal with an

845
00:43:01,010 --> 00:43:04,050
Speaker 2: intractable problem, or a government that actually has kind of

846
00:43:04,050 --> 00:43:06,970
Speaker 2: lost its spirit and its energy because she created the

847
00:43:07,010 --> 00:43:10,970
Speaker 2: app cure idea. It's called it should be useful, it

848
00:43:11,050 --> 00:43:13,530
Speaker 2: should be something people pay attention. It should be something

849
00:43:13,570 --> 00:43:17,250
Speaker 2: that the government throws its credibility behind. But then you know,

850
00:43:17,250 --> 00:43:20,210
Speaker 2: the government has less credibility. And it's a story of

851
00:43:21,250 --> 00:43:27,130
Speaker 2: the frustrating of the impulses of people who previously might

852
00:43:27,170 --> 00:43:29,890
Speaker 2: have done really great things in the government. So it's

853
00:43:29,930 --> 00:43:32,570
Speaker 2: a sad story. It's a sad story masquerading as a

854
00:43:32,570 --> 00:43:36,170
Speaker 2: happy story, but a kind of amazing story.

855
00:43:36,250 --> 00:43:37,410
Speaker 1: It's absolutely amazing.

856
00:43:37,570 --> 00:43:41,170
Speaker 2: There's this thing that kind of springs from between the

857
00:43:41,210 --> 00:43:45,010
Speaker 2: lines of the whole book. These people feel like people

858
00:43:46,210 --> 00:43:49,930
Speaker 2: who have figured out the way to lead a meaningful life,

859
00:43:50,050 --> 00:43:52,570
Speaker 2: and this brings us back to effective altruism. One of

860
00:43:52,650 --> 00:43:55,210
Speaker 2: the keys to a meaningful life is to find ways

861
00:43:55,250 --> 00:43:58,770
Speaker 2: outside of yourself, find ways to live for things other

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00:43:58,850 --> 00:44:02,850
Speaker 2: than yourself. You know, Sam Begman Freed groped towards this

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00:44:02,890 --> 00:44:05,850
Speaker 2: in his own weird way. But these people put their

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00:44:05,850 --> 00:44:08,610
Speaker 2: finger on it right away. There are problems I can

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00:44:08,730 --> 00:44:11,050
Speaker 2: solve and it will help others. And never mind how

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00:44:11,130 --> 00:44:13,890
Speaker 2: much I'm paid or whether I'm acknowledged for it. And

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00:44:14,330 --> 00:44:16,930
Speaker 2: I'm not wired to do that. I need more attention

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00:44:17,370 --> 00:44:20,210
Speaker 2: than I should. But they are and we should be

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00:44:20,250 --> 00:44:23,530
Speaker 2: disgrateful for them instead of heaping scorn and derision upon them.

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Speaker 1: I've been talking to Michael Lewis. Michael is the author

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00:44:27,130 --> 00:44:31,290
Speaker 1: of Going Infinite and a new book with co authors,

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00:44:31,570 --> 00:44:34,330
Speaker 1: Who Is Government. Michael has been great to talk to you.

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00:44:34,410 --> 00:44:37,210
Speaker 2: Thank you. I always find Tim and.

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00:44:37,170 --> 00:44:40,010
Speaker 1: You can listen to Michael's podcast Against the Rules wherever

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00:44:40,050 --> 00:44:42,810
Speaker 1: you get your podcasts. As for me, I will be

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00:44:42,890 --> 00:44:49,890
Speaker 1: back next week with another Cautionary Tale. For a full

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00:44:49,930 --> 00:44:58,930
Speaker 1: list of our sources, see the show notes Timharford dot com.

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00:44:58,970 --> 00:45:02,210
Speaker 1: Cautionary Tales has written by me Tim Harford with Andrew

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00:45:02,210 --> 00:45:06,210
Speaker 1: Wright Alice Fines and Ryan Dinney. It's produced by Georgia

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00:45:06,290 --> 00:45:10,330
Speaker 1: Mills and Marilyn Rust. The sounders are and original music

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00:45:10,490 --> 00:45:13,970
Speaker 1: are the work of Pascal Wise. Additional sound design is

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00:45:14,010 --> 00:45:18,490
Speaker 1: by Carlos San Juan at Brain Audio. Bend A Dafhaffrey

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00:45:18,690 --> 00:45:22,290
Speaker 1: edited the scripts. The show features the voice talents of

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00:45:22,330 --> 00:45:27,610
Speaker 1: Melanie Guttridge, Stella Harford, Oliver Hembrough, Sarah Jupp, messaam Monroe,

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00:45:27,850 --> 00:45:31,850
Speaker 1: Jamal Westman and Rufus Wright. The show also wouldn't have

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00:45:31,850 --> 00:45:35,130
Speaker 1: been possible without the work of Jacob Weisberg, Greta Cohne,

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00:45:35,570 --> 00:45:40,370
Speaker 1: Sarah Nix, Eric Sandler, Carrie Brody, Christina Sullivan, Kira Posey,

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00:45:40,570 --> 00:45:45,810
Speaker 1: and Owen Miller. Cautionary Tales is a production of Pushkin Industries.

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00:45:45,930 --> 00:45:50,170
Speaker 1: It's recorded at Wardore Studios in London by Tom Berry.

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00:45:50,690 --> 00:45:53,490
Speaker 1: If you like the show, please remember to share, rate

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00:45:53,730 --> 00:45:55,890
Speaker 1: and review. It really makes a difference to us and

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00:45:55,930 --> 00:45:58,570
Speaker 1: if you want to hear the show, add free sign

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00:45:58,650 --> 00:46:01,250
Speaker 1: up to Pushkin Plus on the show page on Apple

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00:46:01,290 --> 00:46:04,970
Speaker 1: Podcasts or at pushkin dot Fm, slash plus