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Speaker 1: Pushkin. Hello everyone, Tim Harfford here it is our listener

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Speaker 1: Q and a episode our very first Listener Q and

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Speaker 1: a episode of caution the Tales. You have been sending

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Speaker 1: in your questions to Tales at Pushkin dot Fm. Thank

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Speaker 1: you very much. I have been thinking about how to

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Speaker 1: answer them, but I'm not going to just read out

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Speaker 1: the emails. No, I need one of the maestros of podcasting.

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Speaker 1: I need Jacob Goldstein to help me. Jacob, welcome to

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Speaker 1: Cause Need Sales. Hi, Tim, thanks for having me on.

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Speaker 2: I'm here to read emails, and I also have a

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Speaker 2: few of my own questions for you that I'm gonna

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Speaker 2: sprinkle into the mat.

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Speaker 1: Oh. I'm looking forward to that. So, Jacob, do you

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Speaker 1: want to just tell people who you are for the

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Speaker 1: nighted souls who don't already know of your work?

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Speaker 2: Yes, So, like you, I host a podcast for Pushkin,

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Speaker 2: the show that I hope it's called What's Your Problem.

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Speaker 2: It's it's great, not exactly the opposite of Cautionary Tales,

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Speaker 2: but it's an interesting compliment.

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Speaker 1: Right.

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Speaker 2: Your show is basically about things going wrong. What's Your

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Speaker 2: Problem is basically about people figuring out how to solve

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Speaker 2: technological problems. I talk to the people who are right

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Speaker 2: now trying to solve big, interesting technological problems, to solve

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Speaker 2: things like you know, getting away from carbon. I used

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Speaker 2: to hose planet money. I wrote a book called Money,

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Speaker 2: the True Story of made Up Thing. And I've been

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Speaker 2: interviewing you for more than ten years, so I'm really

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Speaker 2: delighted to be interviewing you again. Let me just give

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Speaker 2: you a question. Yeah, I'm gonna let you say anything else.

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Speaker 2: Let's just let's just get to it. This one comes

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Speaker 2: from Peter Massey. He sent a bunch of questions. We're

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Speaker 2: going to start with this one. Tim, if you stood

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Speaker 2: for Parliament, you'd be voted in like a shot. Par

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Speaker 2: It's more of a comment than a question, but it's nice.

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Speaker 1: I mean, I could object to the premise, but go on.

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Speaker 2: Well, he does say you'd be voted in like a shot,

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Speaker 2: but I expect you'd have more sense. However, here's the question,

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Speaker 2: what is your sage advice for a party leader of

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Speaker 2: any persuasion or country who believes that the truth is

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Speaker 2: important and that evidence and data based on argument is valuable.

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Speaker 1: It's a really difficult question, and I'm completely unqualified to

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Speaker 1: offer any political advice, and maybe I could offer some

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Speaker 1: slightly nerdy policy advice. Politicians are directly or indirectly in

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Speaker 1: charge of the statistical infrastructure of countries. We only think

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Speaker 1: of statistics or data as being infrastructure in the way

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Speaker 1: that you know our roads are, or the electricity grid is,

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Speaker 1: or the water, but they really are. You want to

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Speaker 1: know what's going on in the world, you need good statistics,

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Speaker 1: and we kind of, I think even those of us

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Speaker 1: who are a bit nerdy, kind of have this mental

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Speaker 1: model that statistics are just out there, you kind of

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Speaker 1: download them from a spreadsheet somewhere. And the problem comes

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Speaker 1: because people lie with statistics, or they misrepresent statistics, or

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Speaker 1: they don't listen to statistic Of course those things are problems,

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Speaker 1: but the deeper issue is that the statistics don't make themselves.

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Speaker 1: They've got to be gathered, they've got to be assembled,

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Speaker 1: and we don't pay nearly enough attention to that. And

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Speaker 1: I would like to see politicians really supporting and valuing

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Speaker 1: the process of deciding what gets counted, what gets measured,

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Speaker 1: because without that you really have nothing.

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Speaker 2: Those numbers don't just exist in the world like people

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Speaker 2: work to get them.

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Speaker 1: Yeah, and they can be gathered in smart ways or

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Speaker 1: less smart ways. So one really striking example from about

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Speaker 1: fifteen twenty years ago in the UK is we used

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Speaker 1: to measure immigration by having people stand at airports and

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Speaker 1: just politely stop people as they pass through the airports

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Speaker 1: and say, hey, would you mind answering a few questions?

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Speaker 1: And the questions are mostly about tourism, like how much

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Speaker 1: did you pay for your ticket, but a few of

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Speaker 1: them are relevant to migration, like how long are you

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Speaker 1: planning to stay in the country. And that was how

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Speaker 1: we measured immigration, from just randomly sampling people coming through

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Speaker 1: Heathrow Airport. We had this huge problem in about two

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Speaker 1: thousand and five two thousand and six. We hadn't know

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Speaker 1: it at the time, but just at the moment that

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Speaker 1: more and more people were coming from Eastern Europe because

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Speaker 1: they joined the European Union, they had the right to

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Speaker 1: come to the UK. Just at that moment, some Hungarian

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Speaker 1: entrepreneurs set up a cheap airline called wiz Air, which

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Speaker 1: flew people no frills, not to Heathrow Airport or any

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Speaker 1: of the major airports, but they threw them to all

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Speaker 1: these tiny, little regional airports like luten We just weren't

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Speaker 1: counting the people coming in from Luten. Oh, we had

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Speaker 1: this way of measuring migration. It kind of worked, and

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Speaker 1: then the world changed a bit, and then our migration

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Speaker 1: statistics were completely off. And when it was finally discovered

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Speaker 1: that the statistics were off, that it was these huge

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Speaker 1: political ramifications, the shock of suddenly realizing, oh, there's like

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Speaker 1: hundreds of thousands of people in the country and we

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Speaker 1: never even knew they in the country because we weren't

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Speaker 1: counting them properly. So it's just one example, but this

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Speaker 1: stuff matters. There's a cautionary tale in this somewhere. I

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Speaker 1: need to have a think about this and start working

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Speaker 1: on it.

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Speaker 2: Funny, you should mention that there is another question from

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Speaker 2: one Jonathan Hiller, and he writes, in part, my main

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Speaker 2: question is about how you sift through historical incidents to

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Speaker 2: evaluate which ones fit your criteria for cautionary tales. What

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Speaker 2: vectors inform you as you sift through humanity's foibles? Also,

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Speaker 2: do the failings of individual protagonists make for better tales

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Speaker 2: than those of institutions? And there were a lot of

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Speaker 2: questions like this. There were a lot of the how

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Speaker 2: do you make the sausage type questions.

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Speaker 1: Well, thanks everyone for sending in the questions. Jonathan has

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Speaker 1: made it sound way more scientific and systematic than it is.

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Speaker 1: Actually the process that you just heard of me kind

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Speaker 1: of telling a story and going hang on a minute,

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Speaker 1: that'd be a good cautionary tale. That's actually much more

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Speaker 1: true to the life of this podcast. You know, I

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Speaker 1: read stuff. I'm a journalist, I write stuff, I listen

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Speaker 1: to other podcasts I come across ideas. Obviously, I have

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Speaker 1: my eyes open for things going wrong. I mean I

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Speaker 1: have collections of books about things going wrong, Like people

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Speaker 1: write books specifically, like here are one hundred terrible things

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Speaker 1: that happened and you've kind of read these. Here are

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Speaker 1: one hundred military mistakes. Here's a book about frauds. Here's

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Speaker 1: a book about business failures. So I have all these

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Speaker 1: different sources, and the main thing I'm looking for is variety. Actually,

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Speaker 1: it's very easy to get stuck on economic disasters, booms

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Speaker 1: and busts and crashes and business failures, or only stories

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Speaker 1: involving men, because I'm a guy and history has been

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Speaker 1: written by the white guys. So can we have some

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Speaker 1: more diverse stories about different parts of the world. That's

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Speaker 1: really what I'm looking for. Because I am a nerd,

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Speaker 1: I keep finding myself writing scripts about something more systemic,

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Speaker 1: some more abstract point. It trains as an economist, you know,

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Speaker 1: what can you do? And then I find myself trying

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Speaker 1: to look around for some protagonists to put into the

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Speaker 1: story to make the story more relatable and easier to follow.

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Speaker 1: But individual failures, individual errors make for better stories every time.

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Speaker 2: I mean, the basic model of the show is there's

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Speaker 2: a story and there's a lesson, right, and it seems

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Speaker 2: like the individual helps you with the story. But often

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Speaker 2: the lesson, to me, the most interesting lessons in the

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Speaker 2: show are often the ones about institutions, about systems, because

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Speaker 2: you know, people are sort of irredeemably flawed, but we

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Speaker 2: hope that we can create systems and institutions to sort

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Speaker 2: of put a floor under that. Right. That seems like

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Speaker 2: one of the recurring themes on the show.

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Speaker 1: Absolutely it is. Some of these stories have basically three lessons,

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Speaker 1: four lessons. They're really tightly woven. There's a lot of

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Speaker 1: different stuff going on, and sometimes there isn't really a lesson.

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Speaker 1: Sometimes it's just hey, this thing happened. It's really sad,

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Speaker 1: but it's a really compelling story. Let me tell you

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Speaker 1: the story. So I think it's okay to sometimes not

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Speaker 1: how lesson than to sometimes have several, But you're right, Yeah,

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Speaker 1: Usually I'm trying to draw out the lesson and systemic

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Speaker 1: failures have the more easily analyzable lessons, I think than

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Speaker 1: a person did a bad thing or a person did

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Speaker 1: a stupid thing.

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Speaker 2: So that reminds me of one of the episodes of

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Speaker 2: your show that I wanted to ask you about. This

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Speaker 2: is a question from me. There was a show you

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Speaker 2: did about the guy who invented amazingly both chlorofluorocarbons that

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Speaker 2: put a hole in the ozone layer of the whole

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Speaker 2: planet and leaded gas. Yeah, incredible. Remind me just his

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Speaker 2: name was.

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Speaker 1: Midgley, Thomas Midgley. Yeah, and he also rather tragically invented

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Speaker 1: he got polio and he was partially paralyzed, and he

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Speaker 1: invented this apparatus to get him out of bed and

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Speaker 1: it ended up strangling him. At least that's the official story.

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Speaker 1: So if you believe that, then he invented CFC's that

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Speaker 1: caused the ozon whole, and he invented lead and petrol,

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Speaker 1: and he invented something that accidentally killed him.

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Speaker 2: So unextraordinary narrative about an individual. And also you bring

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Speaker 2: in this big idea, which is you talk first about

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Speaker 2: unanticipated consequences from a twentieth century sociologist Merton, and then

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Speaker 2: you talk about unintended consequences. And you know, having covered

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Speaker 2: economics unintended consequences, economists talk about all the time unanticipated consequences.

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Speaker 2: I hadn't heard before. Do you want to just briefly

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Speaker 2: draw that distinction. I'm going somewhere with this.

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Speaker 1: Yeah, there was an interesting slip in the language. So

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Speaker 1: Robert Merton, who's amazing thinker, he originally wrote about unanticipated consequences,

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Speaker 1: and then over time he and other people used to

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Speaker 1: say unintended consequences instead. But there is a distinction. It's

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Speaker 1: an important distinction. So unanticipated consequences is like, you couldn't

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Speaker 1: have seen this coming with the hole in the ozone layer.

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Speaker 1: Who could have predicted? They te did this CFCs for toxicity,

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Speaker 1: They tested them in all kinds of ways. Who could

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Speaker 1: have foreseen despite all the safety testing, that CFCs would

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Speaker 1: have this chemical reaction in that would open this hole

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Speaker 1: in the ozone layer. So that's unanticipated. But unintended is different.

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Speaker 1: Unintended is like, well, we didn't mean to do it,

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Speaker 1: but I mean maybe you could have foreseen that it

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Speaker 1: would have happened. So leaded petrol is not unanticipated. The

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Speaker 1: idea that if you put lead, which is a known

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Speaker 1: toxin in fuel and it's coming out of the exhaust,

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Speaker 1: that that might be a problem. I mean that was known.

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Speaker 1: Midlely didn't intend to cause a problem. He argued that

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Speaker 1: there probably wouldn't be a problem. But it's not true

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Speaker 1: to say that it was unanticipated. Now that it was

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Speaker 1: anticipated is on the record. People warned him and he

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Speaker 1: brushed those warnings away, and I think that slipped from

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Speaker 1: unanticipated to unintended, which is like, well I didn't mean

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Speaker 1: to do it. That's just a very different moral standard.

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Speaker 1: I think.

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Speaker 2: Where I'm going with this, Here's why I wanted to

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Speaker 2: talk about it. I want to talk about AI right

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Speaker 2: now because you know, we're in this extraordinary moment of

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Speaker 2: AI development, and it seems like the most anticipated bad

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Speaker 2: potential consequences I know of in the history of technology,

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Speaker 2: Like the people at the very vanguard, you know, Open AI,

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Speaker 2: the makers of chat GPT, the model that is knocking

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Speaker 2: everybody's socks off. They started their company in part because

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Speaker 2: they were so scared of what AI could do in

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Speaker 2: the wrong hands. It is the opposite of unanticipated. They

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Speaker 2: are anticipating the bad consequences. And the more people know

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Speaker 2: about AI, the people working on AI are the ones

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Speaker 2: most worried about it, which seems so different than the

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Speaker 2: Midgley story than most of the history of technology. And

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Speaker 2: I don't quite know where to go with that. You know,

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Speaker 2: that's largely a comment what it just made. But what

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Speaker 2: I feel like you're very good at is like taking

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Speaker 2: something like that and then landing it somewhere like, Yeah,

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Speaker 2: what do we make of this?

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Speaker 1: Yeah, there's a few different angles that you could take.

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Speaker 1: And there's another parallel I would make, very different parallel,

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Speaker 1: and that's with Cambridge Analytica. So we still don't really

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Speaker 1: know what Cambridge Analytica exactly did, but they were helping

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Speaker 1: various political campaigns, including the Trump campaign in twenty sixteen,

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Speaker 1: to target different kinds of ads at people depending on

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Speaker 1: their personality types and if you believe them, and if

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Speaker 1: you believe what Facebook was saying at the time, this

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Speaker 1: can be incredibly effective. Cambridge Analytica were basically saying it's

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Speaker 1: like mind control. We can just get people to do

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Speaker 1: whatever we want because we really understand their personalities. And

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Speaker 1: then when the whole thing blew up into a scandal,

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Speaker 1: then the question is, well, maybe actually it wasn't in fact

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Speaker 1: that good. Maybe it didn't really make much difference. Maybe

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Speaker 1: they were just snake oil salesmen. Still don't know. But

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Speaker 1: the reason I draw a parallel with AI, obviously AI

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Speaker 1: is in the long run, I think a lot more

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Speaker 1: consequential is when these people who are designing these AIS

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Speaker 1: are saying, we're kind of worried that this is going

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Speaker 1: to take over the world. Are they worried that it's

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Speaker 1: going to take over the world or is this just

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Speaker 1: a backhanded way of saying, we're working on this incredibly

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Speaker 1: awesome technology, you should give us more money. And I

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Speaker 1: really don't know what to make of it, but there

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Speaker 1: is this strategy of kind of using disaster scenarios to

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Speaker 1: overhype the importance of your work, to get more funding

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Speaker 1: for your work, and to distract from what might actually

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Speaker 1: be the real problem.

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Speaker 2: There's a fundamental question inherent in what you're suggesting, which

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Speaker 2: is do the people who are working on AI who

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Speaker 2: say they're worried about it, you know, destroying humanity. Do

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Speaker 2: they really mean it or are they just doing marketing? Right?

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Speaker 2: I tend to believe they really mean it, but I

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Speaker 2: could be wrong.

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Speaker 1: Listening to US Special Q and a episode of Portionary Tales.

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Speaker 1: I'll be back with Jacob Goldstein in just a moment.

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Speaker 2: I'm gonna ask another listener question. Now, this one comes

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Speaker 2: from Peter Lancashire. Sounds like a very British name. The

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Speaker 2: question comes from his PS and it's a question that

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Speaker 2: many listeners asked. He writes, PS, I notice that in

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Speaker 2: the Pushkin podcasts you adapt your language units and currency

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Speaker 2: for a US audience. Do they really need this? Shouldn't

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Speaker 2: they get out more?

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Speaker 1: Well, let's put it this way. About half our listeners

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Speaker 1: are in the United States. It is the most important

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Speaker 1: country in terms of volume of listeners, and so it

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Speaker 1: kind of makes sense. And Pushkin is an American company also,

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Speaker 1: so it does make sense to use the units that

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Speaker 1: they are most likely to recognize. And I'm sure Americans

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Speaker 1: can cope, Well can you, Jacob? Can you cope with

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Speaker 1: degrees centigrade and kilometers and so on? Is that just bewildering?

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Speaker 2: I mean, you know, there's upsides and downsides to be

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Speaker 2: in an American. One of the upsides is people accommodate you.

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Speaker 2: I'll take it, but.

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Speaker 1: I mean the question is who should be accommodated. So

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Speaker 1: there's three possible answers. One is the Americans who are

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Speaker 1: the main audience. The second answer is most of the

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Speaker 1: rest of the world, who are used to dealing with

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Speaker 1: Americans and who are used to having to cope with

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Speaker 1: American language. And the third answer implicitly is I should

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Speaker 1: be the one being accommodated. I'm British, so I should

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Speaker 1: be using the units that I find personally most convenient.

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Speaker 1: But that doesn't seem to be the right answer. I

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Speaker 1: think people wouldn't ask this question of you, Jacob because

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Speaker 1: you're American, wouldn't ask it of Michael Lewis or Jill Lapaul.

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Speaker 1: They ask it of me because I'm British. But I

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Speaker 1: feel that I should be bending to fit the audience

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Speaker 1: in this respect. Doesn't that make sense?

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Speaker 2: Yeah? The one the one that's hard for me still, Like,

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Speaker 2: you know, I know what a pound is worth more

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Speaker 2: or less, I know how far a kilometer is. It's

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Speaker 2: really hard for me to go from Celsius to fahrenheit, Like,

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Speaker 2: I definitely think in fahrenheit, and I don't have an

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Speaker 2: intuition for celsius, partly because it's weird, right.

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Speaker 1: That translation is difficult. I used to live in Washington,

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Speaker 1: d C. For a couple of years, and in Washington,

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Speaker 1: d C. The temperature range across the year is much

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Speaker 1: more than the temperature range in England. It gets colder,

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Speaker 1: it gets hotter, and so every morning you listen to

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Speaker 1: the radio, they tell you what the temperature was in fahrenheit,

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Speaker 1: and so pretty soon you got a sense of what

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Speaker 1: this means. But you do have to actually live under

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Speaker 1: the other system to get that intuition. I think usually

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Speaker 1: if I'm doing temperatures, I usually give both fahrenheit and celsius.

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Speaker 2: That seems very accommodating.

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Speaker 1: Well, I just want everyone to understand. I mean, there's

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Speaker 1: obviously a cost to explaining everything, to saying everything two

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Speaker 1: or three times, but you know, for temperatures, yeah, I

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Speaker 1: want people to understand what's going on, and that usually

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Speaker 1: means giving both units.

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Speaker 2: This next question comes from Tom Quincy, and he writes,

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Speaker 2: I'm curious about what inventions didn't quite make the cut

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Speaker 2: for your series Fifty Things that made the modern economy,

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Speaker 2: or if in hindsight there's anything you regret not including Now,

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Speaker 2: let me just before you answer, is it right that

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Speaker 2: you actually did another fifty you did you in fact

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Speaker 2: ended up doing one hundred or one hundred and one

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Speaker 2: things that made the modern economy.

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Speaker 1: I think it may have been one hundred and two.

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Speaker 1: In the end, it was like fifty, then another fifty,

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Speaker 1: then like a listener special, I forget, I forget exactly.

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Speaker 2: So good news for Tom Quincy. Tom. If you want

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Speaker 2: more things that made the modern economy, Tim's got a

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Speaker 2: lot for you. So give us the whatever one hundred

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Speaker 2: and third you got one just on deck.

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Speaker 1: Well, there's one I'm working on right now for Cautionary Tales,

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Speaker 1: which could have been of fifty things that made the

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Speaker 1: modern economy. And as with many of them, it's not

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Speaker 1: because it's an incredibly important invention. It's because there's a

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Speaker 1: surprise there. There's a broader principle, and that's the laser disc.

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Speaker 1: So I'm doing a cautioning Tales about the laser disc.

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Speaker 1: I don't want to introduce too many spoilers, but the

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Speaker 1: gist of it is the BBC in the nineteen eighties,

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Speaker 1: decided that they were going to launch this epic project

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Speaker 1: to go out and interview lots of people, take photographs,

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Speaker 1: measure the country. It was like this informal survey census thing,

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Speaker 1: and school kids from all over the country were involved,

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Speaker 1: and the whole thing was put on this amazing, super

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Speaker 1: modern laser disc system that schools could buy this laser

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Speaker 1: disc and computer, and so it was kind of like

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Speaker 1: Wikipedia in nineteen eighty six. And the cautionary tale is

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Speaker 1: that within fifteen years became a genuine problem to find

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Speaker 1: any system that was capable of actually reading the laser discs.

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Speaker 1: So there was supposed to be this generational effort, this

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Speaker 1: time capsule that would last hundreds of years using this

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Speaker 1: super modern technology, and almost immediately the thing was obsolete

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Speaker 1: and they couldn't read it. And so I'm just drawing

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Speaker 1: out like what the lessons are and these kind of

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Speaker 1: heroic nerd efforts to get the data back, and what

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Speaker 1: it took to get the data back and what happened.

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Speaker 1: So the laser disc would have been one if I

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Speaker 1: if I was writing it today.

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Speaker 2: Okay, here's a question from Fay Edwards, who writes, I'm

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Speaker 2: a huge fan of your work, and especially the Cautionary

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Speaker 2: Tales podcast in its original, rich storytelling format. My question

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Speaker 2: is you seem to have changed the format recently to

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Speaker 2: include more interview style episodes and conversational pieces. Actually like

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Speaker 2: this show that we're doing right now, aside slowing down

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Speaker 2: the pace of delivery of the episodes that I just

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Speaker 2: can't get enough, is there a reason for the change?

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Speaker 1: So, and a few people have asked questions like this,

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Speaker 1: So we haven't actually changed the frequency of the show

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Speaker 1: for a while. So it used to be we'd have

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Speaker 1: weekly shows and a series would be like, I don't know,

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Speaker 1: eight episodes or ten episodes, and then we thought, well,

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Speaker 1: hang on, if we do it every two weeks, we

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Speaker 1: could just keep going forever. So we could do we think,

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Speaker 1: twenty six episodes a year. So that's what we did,

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Speaker 1: and we made that decision in early twenty twenty two,

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Speaker 1: so we've been doing that for a year and interspersing

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Speaker 1: the occasional conversation. So usually we have a classic, fully

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Speaker 1: worked caution Retales episode every two weeks. Sometimes we will

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Speaker 1: skip a fortnite and we'll put a Cautionary conversation in instead.

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Speaker 1: So the first answer is I don't see it like that.

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Speaker 1: The Cautionary Conversations are supposed to be like a bonus.

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Speaker 1: I think they're really fun. But if you don't like it,

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Speaker 1: then you know, just skip and we'll be back next

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Speaker 1: week with the full thing. But I'll be interested in

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Speaker 1: people's thoughts, whether people think, no, they're brilliant. Absolutely love them,

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Speaker 1: They're just as good as any other episode of Cautionary Tales.

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Speaker 1: Don't touch them, or maybe people think, you know, they're

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Speaker 1: fine as a bonus. I don't love them, but you know,

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Speaker 1: I enjoy them from time to time, or maybe people

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Speaker 1: are actively like this is very annoying to have any

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Speaker 1: conversations in the feed at all. I ever want to

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Speaker 1: hear it. I'd like to get a sense of how

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Speaker 1: people think about that, So, you know, let us know

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Speaker 1: tales at pushkin dot fm, let us know what you think.

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Speaker 2: Here's another question. Hi, Tim, I am Amelia. I am

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Speaker 2: eleven years old, and my favorite color is pink. I

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Speaker 2: love listening to the show every time I'm in the car.

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Speaker 2: I have two questions to ask you for the Q

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Speaker 2: and A episode. The first one is which is your

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Speaker 2: favorite episode of Cautionary Tales, and then the second one

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Speaker 2: is how does cautionary tales compare to editing the newspaper?

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Speaker 2: Thank you for reading this, Amelia, eleven years old, England, Cheshire.

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Speaker 1: Well, thank you, Amelia. My son is eleven years old

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Speaker 1: and he also likes pink and he also likes listening

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Speaker 1: to cause news sales, although I worry that some of

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Speaker 1: the course news tales are not really appropriate for eleven

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Speaker 1: year old one.

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Speaker 2: What's your favorite episode of the show.

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Speaker 1: Since Amelia asked so nicely, I am actually going to

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Speaker 1: tell her, but only nobody else listened, just Amelia, it's

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Speaker 1: the one about the airships. It's the Deadly Airship Race,

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Speaker 1: which is it's probably not the best episode. It's not

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Speaker 1: the most elegant, it's not the most important. It's the

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Speaker 1: one that I had in my head when I first

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Speaker 1: said to Pushkin, we should do a series of podcasts

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Speaker 1: about stuff going wrong, and it's the first one that

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Speaker 1: I wrote. So that's my favorite, the Deadly Airship Race.

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Speaker 1: But that's just between the two of us.

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Speaker 2: Her other question is how quessionary tales compared to editing

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Speaker 2: the newspaper. I believe she means writing a column for

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Speaker 2: the Financial Times. If I know your work correctly, I.

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Speaker 1: Think the main difference is the teamwork. So with Cautionary Tales,

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Speaker 1: when I've written it, I will send it to my

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Speaker 1: co writer Andrew Wright. He will always find loads of comments,

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Speaker 1: loads of improvements. He'll send them back, or sometimes Andrew

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Speaker 1: writes them, and Andrew sends them to me. Is he

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Speaker 1: usually better? So I usually have less stuff to say

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Speaker 1: about them. But we're sending each other scripts and we're

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Speaker 1: working on each other scripts, and then there's an editorial

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Speaker 1: process where we do table reads and we get comments

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Speaker 1: from other people, and people say that this was confusing,

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Speaker 1: you didn't start it in the right direction. You need

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Speaker 1: to change various stuff. So it's a very collaborative process.

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Speaker 1: And then afterwards it's out of my hands and Pascal Wise,

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Speaker 1: our composer and sound designer, does this amazing music. We

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Speaker 1: sometimes have brilliant actress, some people like Jeffrey Wright, Helen

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Speaker 1: and the Bottom Carter. I didn't get to meet them,

429
00:24:09,250 --> 00:24:11,130
Speaker 1: just get to listen to the results of Helen and

430
00:24:11,130 --> 00:24:15,330
Speaker 1: Bottom Carter playing Florence Nightingale or Jeffrey Wright playing Martin

431
00:24:15,410 --> 00:24:17,970
Speaker 1: Luther King. So there's this real sense of this whole

432
00:24:18,010 --> 00:24:21,410
Speaker 1: thing being bigger than just me and being a team effort.

433
00:24:21,970 --> 00:24:24,290
Speaker 1: For the newspaper, it's different. There is, of course a

434
00:24:24,330 --> 00:24:27,010
Speaker 1: team effort involved in the newspaper. There is an editorial team,

435
00:24:27,450 --> 00:24:31,730
Speaker 1: but it's much more linear, it's quicker. So caution Tales

436
00:24:31,810 --> 00:24:34,770
Speaker 1: episodes can take months to finally see the light of day,

437
00:24:34,770 --> 00:24:37,810
Speaker 1: whereas the newspaper column it's a matter of days. I

438
00:24:37,810 --> 00:24:40,690
Speaker 1: will write it, I'll send it. They might have a

439
00:24:40,690 --> 00:24:42,530
Speaker 1: couple of questions, they might make a couple of tweaks,

440
00:24:43,410 --> 00:24:45,210
Speaker 1: and then it just it goes on the page and

441
00:24:45,210 --> 00:24:47,130
Speaker 1: there's a cartoonist and they'll send it to me and

442
00:24:47,130 --> 00:24:49,170
Speaker 1: I'll just check that I'm happy with any edits. There's

443
00:24:49,250 --> 00:24:51,250
Speaker 1: much less back and forth, and that's fine. I like

444
00:24:51,410 --> 00:24:53,490
Speaker 1: both of them. I'm very proud to write for the

445
00:24:53,530 --> 00:24:56,570
Speaker 1: FT and I really love writing caution news sales. But

446
00:24:56,650 --> 00:24:58,090
Speaker 1: that different process is the main thing.

447
00:25:00,130 --> 00:25:02,850
Speaker 2: Here's a question. I'm gonna go big on this one.

448
00:25:04,010 --> 00:25:07,330
Speaker 2: So you've made fifty or so episodes of cautionary tales

449
00:25:07,370 --> 00:25:13,250
Speaker 2: by now, if you step back, is there some transcendent

450
00:25:15,170 --> 00:25:17,450
Speaker 2: lesson that comes through a lot of them? Is there

451
00:25:17,530 --> 00:25:20,490
Speaker 2: like a cautionary tale that emerges from all of the

452
00:25:20,530 --> 00:25:21,490
Speaker 2: cautionary tales?

453
00:25:21,850 --> 00:25:24,010
Speaker 1: Probably not, a single one. I mean, as I say,

454
00:25:24,050 --> 00:25:27,410
Speaker 1: I am always looking for variety, but there are a

455
00:25:27,490 --> 00:25:32,130
Speaker 1: few that come up again and again. One is just

456
00:25:33,290 --> 00:25:36,330
Speaker 1: that we tend to blame the individuals when in fact

457
00:25:36,410 --> 00:25:40,570
Speaker 1: it's the system. That's a very common thing. The other

458
00:25:40,770 --> 00:25:44,250
Speaker 1: is that a lot of disasters are just very unlucky,

459
00:25:45,050 --> 00:25:47,410
Speaker 1: like a lot of things needed to go wrong in

460
00:25:47,530 --> 00:25:51,330
Speaker 1: order for the disaster to happen. But you know, the

461
00:25:51,370 --> 00:25:55,090
Speaker 1: world's a big place. There are lots of moments where

462
00:25:55,250 --> 00:25:57,730
Speaker 1: things can start to go wrong, and so in the end,

463
00:25:57,770 --> 00:26:00,170
Speaker 1: someone is going to be really unlucky. And that's going

464
00:26:00,210 --> 00:26:04,090
Speaker 1: to happen often enough that I don't anticipate running out

465
00:26:04,090 --> 00:26:10,170
Speaker 1: of caution de Tales anytime soon. Listening to a special

466
00:26:10,290 --> 00:26:13,330
Speaker 1: Q and a episode of Cautionary Tales. We'll be back

467
00:26:13,530 --> 00:26:14,650
Speaker 1: in just a moment.

468
00:26:25,450 --> 00:26:28,330
Speaker 2: So Tim. On the show that I host podcast called

469
00:26:28,330 --> 00:26:30,570
Speaker 2: What's Your Problem? It's available wherever you get your podcasts.

470
00:26:30,650 --> 00:26:31,970
Speaker 1: Great show. People should listen to it.

471
00:26:32,210 --> 00:26:34,890
Speaker 2: We close with the Lightning Round, and I want to

472
00:26:34,930 --> 00:26:37,050
Speaker 2: close this show with the Lightning Round, So.

473
00:26:37,170 --> 00:26:39,250
Speaker 1: I want the Lightning Round. I love the Lightning rounds.

474
00:26:39,690 --> 00:26:41,530
Speaker 1: I hope I'm worthy go for it.

475
00:26:42,890 --> 00:26:44,930
Speaker 2: What's one tip for someone who wants to become a

476
00:26:44,970 --> 00:26:46,650
Speaker 2: better storyteller.

477
00:26:47,770 --> 00:26:53,450
Speaker 1: Read good stories and think about how they work. I'm

478
00:26:53,450 --> 00:26:55,330
Speaker 1: going to give you a second tip, which is think

479
00:26:55,370 --> 00:26:57,770
Speaker 1: about how the story is going to end. If you

480
00:26:57,810 --> 00:27:00,850
Speaker 1: know how it's going to end, that really helps you throughout,

481
00:27:00,890 --> 00:27:02,410
Speaker 1: and particularly when you're right in the beginning.

482
00:27:03,570 --> 00:27:06,170
Speaker 2: So one of the sort of signature features of cautionary

483
00:27:06,210 --> 00:27:08,970
Speaker 2: tales is the dramatic reenactment. If you've had some fame

484
00:27:09,450 --> 00:27:13,570
Speaker 2: actors do those, which is fun, and so I'm curious

485
00:27:14,210 --> 00:27:17,690
Speaker 2: if there was a dramatic reenactment about part of your life,

486
00:27:19,130 --> 00:27:21,650
Speaker 2: who would play you? Who would play me?

487
00:27:22,410 --> 00:27:27,410
Speaker 1: Oh gosh. The person who springs to mind is Alan Cumming,

488
00:27:27,490 --> 00:27:29,050
Speaker 1: and he springs to mind because he's one of the

489
00:27:29,050 --> 00:27:31,450
Speaker 1: great actors who's been on caution details. He has this

490
00:27:32,210 --> 00:27:34,210
Speaker 1: role in a Bond movie where he just plays this

491
00:27:34,290 --> 00:27:37,890
Speaker 1: sort of magnificent nerd who thinks he's brilliant but is

492
00:27:38,010 --> 00:27:42,090
Speaker 1: kind of an idiot. And I think I'm brilliant and

493
00:27:42,130 --> 00:27:45,850
Speaker 1: I'm probably an idiot. So so, Alan Cumming, is there

494
00:27:45,890 --> 00:27:52,930
Speaker 1: some book or essay that you think everybody should read?

495
00:27:53,250 --> 00:27:59,050
Speaker 1: So on the nerdy side, I am fond of the

496
00:27:59,170 --> 00:28:03,250
Speaker 1: good productivity stuff. So David Allan's Getting Things Done, for example,

497
00:28:03,290 --> 00:28:06,090
Speaker 1: I think is really good. And on the less nerdy side,

498
00:28:06,130 --> 00:28:08,890
Speaker 1: the more philosophical side. I have a really soft spot

499
00:28:09,530 --> 00:28:13,170
Speaker 1: for The Tower of Pooh by Benjamin Hoff, which tries

500
00:28:13,210 --> 00:28:18,930
Speaker 1: to explain Taoism through the medium of reflecting on win

501
00:28:19,010 --> 00:28:23,250
Speaker 1: of the Pooh stories. And I read that at college

502
00:28:23,890 --> 00:28:27,130
Speaker 1: and it was important to me. And I've been doing

503
00:28:27,170 --> 00:28:32,570
Speaker 1: taichi for thirty years now, so yeah, that's a book

504
00:28:32,610 --> 00:28:33,090
Speaker 1: worth reading.

505
00:28:33,970 --> 00:28:36,370
Speaker 2: How many episodes of TV have you watched in the

506
00:28:36,450 --> 00:28:38,770
Speaker 2: last twenty years. Not many.

507
00:28:38,850 --> 00:28:43,130
Speaker 1: I've never owned a TV, but obviously with Netflix, with computers,

508
00:28:43,170 --> 00:28:44,290
Speaker 1: it all starts to merge.

509
00:28:44,330 --> 00:28:45,010
Speaker 2: I mean I've.

510
00:28:46,570 --> 00:28:50,050
Speaker 1: There were probably whole years where it was like one

511
00:28:50,570 --> 00:28:53,770
Speaker 1: in the entire year, but I think more recently, since

512
00:28:53,810 --> 00:28:58,610
Speaker 1: the pandemic and since Netflix, I probably watch like two

513
00:28:58,610 --> 00:28:59,050
Speaker 1: a month.

514
00:28:59,330 --> 00:29:06,690
Speaker 2: Maybe when you're kickboxing, what hurts the most the pushups?

515
00:29:07,970 --> 00:29:09,010
Speaker 2: We don't see that coming.

516
00:29:09,130 --> 00:29:12,450
Speaker 1: It's like it's the exercise. It's the fitness exercises that

517
00:29:12,490 --> 00:29:14,650
Speaker 1: really hurt. I'm sure when I have my black belt,

518
00:29:14,650 --> 00:29:16,850
Speaker 1: people will start hitting me hard, but at the moment,

519
00:29:16,930 --> 00:29:18,770
Speaker 1: the idea is not that people hit you hard. So

520
00:29:18,810 --> 00:29:21,450
Speaker 1: if someone hits you hard then that they made a mistake.

521
00:29:22,690 --> 00:29:26,970
Speaker 2: What do you think people who are not economists most

522
00:29:27,090 --> 00:29:29,490
Speaker 2: often get wrong about economics.

523
00:29:30,690 --> 00:29:33,130
Speaker 1: I think they miss the fact that a lot of

524
00:29:33,130 --> 00:29:36,610
Speaker 1: economics isn't zero some. So we naturally think in terms

525
00:29:36,650 --> 00:29:40,250
Speaker 1: of zero some, like anything that I gain, you have

526
00:29:40,330 --> 00:29:42,730
Speaker 1: to lose. That's just a natural way of thinking about

527
00:29:42,770 --> 00:29:45,890
Speaker 1: the world. But economics is all about opportunities to create

528
00:29:46,010 --> 00:29:49,210
Speaker 1: gains from trade. When when opportunities or just stuff when

529
00:29:49,930 --> 00:29:53,090
Speaker 1: where things could be better, better in total, better for everybody,

530
00:29:53,290 --> 00:29:57,610
Speaker 1: and and we miss that. And conversely, economists can be

531
00:29:57,650 --> 00:30:01,570
Speaker 1: blind to conflict. Sometimes It's like sometimes things are zero

532
00:30:01,690 --> 00:30:03,170
Speaker 1: some and we're kind of a bit naive about the

533
00:30:03,170 --> 00:30:04,850
Speaker 1: politics of that.

534
00:30:04,850 --> 00:30:08,090
Speaker 2: That was true for me, you know, I never studied

535
00:30:08,090 --> 00:30:10,210
Speaker 2: economics before I went worked at Planet Money. I sort

536
00:30:10,250 --> 00:30:13,530
Speaker 2: of learned it there. The fact that the world can

537
00:30:13,570 --> 00:30:16,530
Speaker 2: be positive some in so many places, the pie can

538
00:30:16,530 --> 00:30:19,050
Speaker 2: get bigger, and that, in fact, the sort of history

539
00:30:19,130 --> 00:30:22,210
Speaker 2: of the kind of material experience of humanity for the

540
00:30:22,250 --> 00:30:26,530
Speaker 2: last two hundred years has been of people overall getting

541
00:30:26,530 --> 00:30:29,650
Speaker 2: better off was a revelation to me, like that is

542
00:30:29,730 --> 00:30:32,530
Speaker 2: the great lesson of economics as far as I'm concerned.

543
00:30:32,730 --> 00:30:34,250
Speaker 2: I feel like it is not intuitive.

544
00:30:34,530 --> 00:30:38,330
Speaker 1: It's also not the branding of economics, So economics is

545
00:30:38,330 --> 00:30:41,810
Speaker 1: famously known as the dismal science. Economics is associated with

546
00:30:42,450 --> 00:30:45,170
Speaker 1: like why we can't have nice things, scarcity, all of

547
00:30:45,170 --> 00:30:47,930
Speaker 1: this kind of stuff. It feels like quite a grim

548
00:30:48,970 --> 00:30:51,570
Speaker 1: topic from the outside that somehow we've managed to paint

549
00:30:51,610 --> 00:30:55,290
Speaker 1: ourselves in that light. Yet from the inside, it's just

550
00:30:55,410 --> 00:30:57,810
Speaker 1: full of like, hey, there's a way to do this better. Oh,

551
00:30:57,850 --> 00:30:59,650
Speaker 1: this is getting better all the time. That's getting better

552
00:30:59,690 --> 00:31:01,250
Speaker 1: all the time. You know, we can improve this, we

553
00:31:01,290 --> 00:31:05,170
Speaker 1: can solve that problem. It's a much more optimistic discipline

554
00:31:05,170 --> 00:31:06,250
Speaker 1: from the inside, I think.

555
00:31:06,770 --> 00:31:09,650
Speaker 2: So, I know you played Dungeons and Dragons, So I

556
00:31:09,690 --> 00:31:11,130
Speaker 2: asked some friends of mine who played D and D

557
00:31:11,250 --> 00:31:12,490
Speaker 2: what I should ask you. So I'm just going to

558
00:31:12,570 --> 00:31:14,690
Speaker 2: read some of the questions from them. What's the most

559
00:31:14,730 --> 00:31:16,730
Speaker 2: surprising emotion you've felt at the table.

560
00:31:18,850 --> 00:31:24,090
Speaker 1: I once played a visually impaired character like I completely

561
00:31:24,450 --> 00:31:29,250
Speaker 1: she couldn't see at all, and I took to wearing

562
00:31:29,290 --> 00:31:32,250
Speaker 1: a blindfold around the house just to try to understand

563
00:31:32,330 --> 00:31:34,450
Speaker 1: what it was like. I'm not sure this is an emotion,

564
00:31:34,530 --> 00:31:37,170
Speaker 1: but that's kind of like the biggest lesson that was

565
00:31:37,210 --> 00:31:42,010
Speaker 1: just transformative to to realize how difficult it was, at

566
00:31:42,090 --> 00:31:44,010
Speaker 1: least if you hadn't had any practice to just not

567
00:31:44,050 --> 00:31:44,770
Speaker 1: be able to see.

568
00:31:45,330 --> 00:31:47,610
Speaker 2: There's a thing I think in D and D where

569
00:31:47,610 --> 00:31:49,730
Speaker 2: it's like a two by two matrix where you can

570
00:31:49,770 --> 00:31:53,850
Speaker 2: be like lawful evil or chaotic good or whatever.

571
00:31:54,090 --> 00:31:56,850
Speaker 1: Yeah, it's a three by three matrix. But that's fine,

572
00:31:57,130 --> 00:31:57,690
Speaker 1: we'll allow it.

573
00:31:57,810 --> 00:32:00,490
Speaker 2: How about this. What's the most fun to play on

574
00:32:00,570 --> 00:32:01,250
Speaker 2: that matrix?

575
00:32:01,490 --> 00:32:04,610
Speaker 1: So it's probably chaotic good because you get to think

576
00:32:04,610 --> 00:32:06,250
Speaker 1: of yourself as the good guy, and you get to

577
00:32:06,290 --> 00:32:07,970
Speaker 1: be the good guy, but you also get to kind

578
00:32:07,970 --> 00:32:10,410
Speaker 1: of rock and roll and improv and do whatever you

579
00:32:10,490 --> 00:32:11,970
Speaker 1: want and you don't have to follow any rules as

580
00:32:12,010 --> 00:32:13,690
Speaker 1: long as you're doing good stuff.

581
00:32:13,930 --> 00:32:16,010
Speaker 2: That is the creative dream. I would love to be

582
00:32:16,090 --> 00:32:18,290
Speaker 2: chaotic good. Maybe I used to be chaotic good and

583
00:32:18,290 --> 00:32:19,210
Speaker 2: now I'm lawful good.

584
00:32:19,530 --> 00:32:22,730
Speaker 1: I'd like to and to answer the question you didn't ask,

585
00:32:22,770 --> 00:32:25,450
Speaker 1: but you sort of was implicit, I like to think

586
00:32:25,490 --> 00:32:27,610
Speaker 1: of myself as chaotic good, but I'm actually probably just

587
00:32:27,690 --> 00:32:30,410
Speaker 1: lawful neutral. I'm just a rules follower deep down who

588
00:32:30,450 --> 00:32:32,330
Speaker 1: thinks he's kind of rock and roll and jazz.

589
00:32:33,810 --> 00:32:35,450
Speaker 2: What's your favorite role playing game. Right now.

590
00:32:36,450 --> 00:32:38,490
Speaker 1: It is in front of me on the desk. It's

591
00:32:38,490 --> 00:32:42,690
Speaker 1: called Scum and Villainy. It's very improvisational. I've not played

592
00:32:42,730 --> 00:32:45,210
Speaker 1: it yet. I'm going to run my first game on Monday,

593
00:32:45,730 --> 00:32:49,370
Speaker 1: and it's kind of designed to enable you to run

594
00:32:49,530 --> 00:32:53,290
Speaker 1: kind of Star Wars he heists with Han Soloy or

595
00:32:53,370 --> 00:32:58,090
Speaker 1: Cassian and or Y kind of characters, so smugglers and rebels,

596
00:32:58,930 --> 00:33:01,410
Speaker 1: and I think it's it's going to be really fun,

597
00:33:01,490 --> 00:33:03,770
Speaker 1: but it might be a disaster. We'll see. Name one

598
00:33:03,770 --> 00:33:07,690
Speaker 1: thing Cambridge does better than Oxford what economics. I mean,

599
00:33:07,690 --> 00:33:10,850
Speaker 1: they have an economics undergraduate course and Oxford doesn't. So

600
00:33:11,090 --> 00:33:13,650
Speaker 1: economics at Cambridge it is amazing.

601
00:33:14,570 --> 00:33:16,730
Speaker 2: Is there any story from your life that would make

602
00:33:16,730 --> 00:33:17,690
Speaker 2: a good cautionary tale.

603
00:33:18,370 --> 00:33:21,090
Speaker 1: I think there were cautionary anecdotes in my life, the

604
00:33:21,130 --> 00:33:23,810
Speaker 1: same as anybody else's. And just to give you on

605
00:33:24,010 --> 00:33:27,410
Speaker 1: very quickly, my first job when I finished my master's

606
00:33:27,410 --> 00:33:30,290
Speaker 1: degree in economics was as a management consultant. And I

607
00:33:30,330 --> 00:33:33,170
Speaker 1: was a really bad management consultant. I was allergic to

608
00:33:33,210 --> 00:33:35,970
Speaker 1: my suit I would cry in the office. I just

609
00:33:36,050 --> 00:33:39,050
Speaker 1: hated the job, and I stuck with it for a

610
00:33:39,090 --> 00:33:41,930
Speaker 1: while because all my friends were saying, hey, it's a

611
00:33:41,970 --> 00:33:43,770
Speaker 1: good job, it's well paid. You need a couple of

612
00:33:43,850 --> 00:33:46,490
Speaker 1: years on your resume. You can't be seen as just

613
00:33:46,570 --> 00:33:49,930
Speaker 1: like quitting a job after a few months. And it

614
00:33:50,010 --> 00:33:51,810
Speaker 1: was a friend of mine, a gaming friend, actually a

615
00:33:51,850 --> 00:33:54,610
Speaker 1: D and D friend, if you like, who told me.

616
00:33:54,650 --> 00:33:57,650
Speaker 1: He actually literally said, if you're taking actual hit points damage,

617
00:33:58,210 --> 00:34:00,890
Speaker 1: you should quit immediately. He actually said it like that.

618
00:34:01,250 --> 00:34:03,610
Speaker 1: But more importantly, he was older, he was from a

619
00:34:03,610 --> 00:34:07,130
Speaker 1: different industry, He had a different perspective, and he was like,

620
00:34:07,290 --> 00:34:10,730
Speaker 1: why would you build your reputation and your skills and

621
00:34:10,730 --> 00:34:15,570
Speaker 1: your contacts in this industry that you hate. Why don't

622
00:34:15,570 --> 00:34:18,250
Speaker 1: you quit as quickly as possible and go and do

623
00:34:18,290 --> 00:34:20,530
Speaker 1: something else? And I did, and you never look back,

624
00:34:20,570 --> 00:34:23,090
Speaker 1: And it was you know, that was the right piece

625
00:34:23,130 --> 00:34:26,010
Speaker 1: of advice. But I think the two cautional elements about

626
00:34:26,010 --> 00:34:29,010
Speaker 1: that are one the group think like all the people

627
00:34:29,130 --> 00:34:32,410
Speaker 1: my age in my position saw it the same way

628
00:34:32,450 --> 00:34:35,010
Speaker 1: I did, which is, I guess I'm stuck. I guess

629
00:34:35,010 --> 00:34:37,570
Speaker 1: I just have to tough this out. And also that

630
00:34:37,690 --> 00:34:40,810
Speaker 1: I felt so stuck even though in fact I wasn't

631
00:34:40,810 --> 00:34:42,570
Speaker 1: stuck at all. I had loads of options. The economy

632
00:34:42,610 --> 00:34:43,810
Speaker 1: was very strong. I could just go and do it

633
00:34:43,850 --> 00:34:46,570
Speaker 1: whatever I liked, but it didn't seem that way from

634
00:34:46,610 --> 00:34:47,290
Speaker 1: the inside.

635
00:34:48,370 --> 00:34:52,770
Speaker 2: Great. Thanks for letting me come and ask your questions.

636
00:34:52,810 --> 00:34:54,770
Speaker 1: Tim, Can we do it again. I hope we get

637
00:34:54,850 --> 00:34:57,890
Speaker 1: more questions, and you know, i'd love it if you'd

638
00:34:57,930 --> 00:34:58,970
Speaker 1: come back, would you.

639
00:34:59,210 --> 00:35:01,050
Speaker 2: Yes, you know, I'll tell you. A lot of the

640
00:35:01,130 --> 00:35:03,570
Speaker 2: questions this time were sort of about the show and

641
00:35:03,610 --> 00:35:05,210
Speaker 2: the making of the show. And the thing I would

642
00:35:05,250 --> 00:35:08,890
Speaker 2: like to say in terms of request for questions is

643
00:35:09,890 --> 00:35:12,290
Speaker 2: you are a very smart person and you know a lot,

644
00:35:12,370 --> 00:35:15,570
Speaker 2: and you're very good at answering questions, and so I

645
00:35:15,610 --> 00:35:18,770
Speaker 2: would love more questions for you, not about the show

646
00:35:18,810 --> 00:35:22,090
Speaker 2: per se, but that are about the world. Basically, Yeah,

647
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Speaker 2: you're a smart guy. Answer my question about the world.

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Speaker 1: Thank you so much, Jacob, Thanks Tim. I hope you

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Speaker 1: enjoyed this special Q and a episode of Cautionary Tales.

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00:35:34,250 --> 00:35:36,690
Speaker 1: We will be back with more shows like this one.

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Speaker 1: So if you didn't hear your question answered today, then

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00:35:39,930 --> 00:35:44,210
Speaker 1: fear not. There will be another opportunity. Email any queries

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00:35:44,250 --> 00:35:47,690
Speaker 1: you might have, however big or small, to Tales at

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00:35:47,810 --> 00:35:51,690
Speaker 1: Pushkin dot FM. That's t a l E. S At

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00:35:51,770 --> 00:35:55,650
Speaker 1: Pushkin dot Fm, and that email address is also in

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00:35:55,690 --> 00:36:03,330
Speaker 1: the show notes. Cautionary Tales is written by me Tim

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00:36:03,410 --> 00:36:08,090
Speaker 1: Harford with Andrew Wright. It's produced by Alice Fines with

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00:36:08,210 --> 00:36:12,610
Speaker 1: support from Edith Ruslo. The sound design and original music

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00:36:12,810 --> 00:36:16,570
Speaker 1: is the work of Pascal Wise. The show wouldn't have

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00:36:16,610 --> 00:36:20,090
Speaker 1: been possible without the work of Jacob Weisberg, Ryan Dilley,

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00:36:20,490 --> 00:36:25,610
Speaker 1: Julia Barton, Greta Cohne, Little Millard, John Schnaz, Carli Migliori,

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00:36:26,130 --> 00:36:32,210
Speaker 1: Eric Sandler, Maggie Taylor, Nicolmrano and Morgan Ratno. Cautionary Tales

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00:36:32,330 --> 00:36:35,810
Speaker 1: is a production of Pushkin Industries. If you like the show,

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00:36:36,210 --> 00:36:39,490
Speaker 1: please remember to share, rate and review. It helps us

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00:36:39,490 --> 00:36:42,850
Speaker 1: for mysterious reasons, and if you want to hear the show,

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Speaker 1: add free sign up for Pushkin Plus on the show

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00:36:46,530 --> 00:36:51,130
Speaker 1: page and Apple Podcasts, or at Pushkin dot Fm, slash

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Speaker 1: plus