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Speaker 1: Hello, and welcome to another episode of The Odd Lots podcast.

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Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, welcome back.

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Speaker 1: You've been on vacation. It's so exciting to be recording

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Speaker 1: an episode with you again. It wasn't I missed you

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Speaker 1: for real. I missed you for real. Oh, thank you.

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Speaker 1: I actually I think this might be the first time

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Speaker 1: in my life that I've been happy to come back

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Speaker 1: from vacation. Honestly, a lot happened while I was away.

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Speaker 1: It was beautiful, by the way. I went to the

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Speaker 1: Seychelles for two weeks. It's not I'm not a huge

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Speaker 1: beach vacation person, so it wasn't a place that I

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Speaker 1: would normally go to, but it was absolutely gorgeous. Highly

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Speaker 1: recommend it. I think it's ruined me for all other

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Speaker 1: beaches in the future. But I am happy to be

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Speaker 1: back and recording with you once again. And I missed

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Speaker 1: you too, Joy, thank you. It's great to have you

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Speaker 1: back and you sound great. You know what I did

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Speaker 1: this week? I can take a guest, but go ahead.

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Speaker 1: I did a deadlift. I did I've been I've been.

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Speaker 1: I've been lifting weights at the gym and I did.

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Speaker 1: I'm not gonna say what I was about to say,

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Speaker 1: Dare I asked, I'm not going I'm still very weak.

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Speaker 1: I'm not going to say what my pr is, my

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Speaker 1: personal record is. But it was very satisfying. I felt

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Speaker 1: good and now I'm like gonna keep doing I've been

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Speaker 1: doing weights for a little while. But Joe, is it?

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Speaker 1: Is it a real deadlift if you're not bragging about

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Speaker 1: it on Twitter? When I'm when I hit bragging level,

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Speaker 1: I will definitely brag. But uh, I bet everyone can

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Speaker 1: figure out where I am going with that, because we're

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Speaker 1: going to be speaking today with one of the foremost

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Speaker 1: When I think of our this guest, I think of

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Speaker 1: as many things, but I think of him as one

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Speaker 1: of the foremost deadlift advocates in public. I of course

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Speaker 1: know exactly who you're talking about. Um, this is somewhat

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Speaker 1: and you know, certainly a personality and a character, especially

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Speaker 1: online on Twitter. I think we've both at various times

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Speaker 1: been blocked by him. Have quite a few people. Yeah,

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Speaker 1: so I think I tweeted this while I was on vacation, actually,

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Speaker 1: But one of the craziest things to me about twenty

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Speaker 1: twenty three is that I find myself not only unblocked

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Speaker 1: by this guest, but also nodding my head yes, sperously

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Speaker 1: in agreement with him on a variety of topics. Twenty

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Speaker 1: twenty three is a very weird year for that very reason,

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Speaker 1: because it's like, oh yeah, totally right. Anyway, we just

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Speaker 1: gotta get right into it. So we are, of course

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Speaker 1: speaking with the one and only Nessim Nicholas Teleb. He's

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Speaker 1: a professor at NYU. He is an advisor at the

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Speaker 1: Terrorist Fund Universa. He is the author of several books,

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Speaker 1: including The Black Swan and Anti Fragile and Fooled by Randomness.

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Speaker 1: And he is a deadlift advocate. And he has also

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Speaker 1: gotten into cycling lately, which is interesting. And he is

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Speaker 1: a flaneur. I don't even know where to start. This

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Speaker 1: seems thank you so much for joining us. Every time

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Speaker 1: I see the word flaneur, I forget what it means.

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Speaker 1: What does it mean? Yeah? I have to remember exactly

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Speaker 1: what it means, because the original designation is for someone

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Speaker 1: who walks around aimlessly, and I tried to journalize it

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Speaker 1: to someone who loves things aimlessly just for the fun

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Speaker 1: of it, without the prescribed plan. And if you find

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Speaker 1: something interesting, then you go with it. So it's funny

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Speaker 1: because I started off by saying that you I could

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Speaker 1: think of you as a deadlift advocate. But this year

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Speaker 1: you're getting into road biking, which is interesting because you know,

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Speaker 1: this kind of very different type of exercise. It's not

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Speaker 1: the type of exercise I associate you with. It's a

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Speaker 1: little a cyclist. Why what's the deal with getting into cycling?

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Speaker 1: I was a cyclist. We met last which is about

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Speaker 1: fifteen years ago, and I had a near miss with

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Speaker 1: the truck. And then I switched to a combination a

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Speaker 1: lot of walking and some intense but short episodes of

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Speaker 1: weight listing by weight lifting, and then I, you know,

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Speaker 1: I had to follow the evidence, started reading the literature,

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Speaker 1: and I realized that weight listing is not good for

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Speaker 1: your heart. So it's actually it's not good at all

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Speaker 1: on its own, but it's it's needed, it's necessary, but

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Speaker 1: you know, let's see evidence. It causes out stiffness a

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Speaker 1: lot of things. You know, there's an adaptation. What you

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Speaker 1: want to lift very heavy objects. Your body adapts by

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Speaker 1: doing things that are not helpful full lock term survivals.

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Speaker 1: You need to compensate, and how do you compensate instead

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Speaker 1: of just walking something a little more intense than walking,

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Speaker 1: but not very intense. So here you have a vart veil,

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Speaker 1: a lot of a lot of Arabic exercise low grade,

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Speaker 1: and the occasional full body weight list. So just a

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Speaker 1: variation on what I was doing. But you've got to

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Speaker 1: follow the otherness. I mean, the literature is stark that

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Speaker 1: wait listening is not follow your heart, but that because

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Speaker 1: it causes some adaptation by your heart that I'm not

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Speaker 1: very good, causes lock term heart failure. And if you

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Speaker 1: that by doing overcompensate by doing Arabic exercise, which is

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Speaker 1: more naturalistic than you got both. I think flexibility is

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Speaker 1: also a sort of underrated component of that as well.

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Speaker 1: But so I'm trying to think how to take it

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Speaker 1: from here, and I'm very I'm going to try to

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Speaker 1: avoid doing a lot of media navel gazing in this interview.

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Speaker 1: But there is one question that I have to ask,

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Speaker 1: just because I think it feeds in to a wider

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Speaker 1: point about your online presence. But why did you block

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Speaker 1: me and Joe and why have we been unblocked? I

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Speaker 1: think a largely a lot of my blocking is not

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Speaker 1: done by me directly, by some automated boss. You had

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Speaker 1: to understand that I got besieved by finance people, and

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Speaker 1: you know that time don't get wrong with the general

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Speaker 1: finance ground and by the crypto people, particularly after I

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Speaker 1: took positions that are not very favorable to the crypto people.

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Speaker 1: So you do block and cleanse up my sheet. Just

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Speaker 1: block things. And I had someone who happened to be

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Speaker 1: in the Ukraine at the time helping me. You know,

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Speaker 1: do automatic blocking. I believe, believe it or not the

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Speaker 1: best thing to do with your Twitter feed. Just block

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Speaker 1: groups because then then things become clean. Oh, I believe

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Speaker 1: it for sure. So so unfortunately that you guys, But

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Speaker 1: then I blocked people when I realized went too far.

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Speaker 1: Thank you. So the first reaction is called gianni gativa.

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Speaker 1: It's like you close the door and then you let

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Speaker 1: in those you think that we're excluded or will not

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Speaker 1: degrade defeat. That's a great answer. Doesn't have anything to

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Speaker 1: do with disagreements. It has to do with the style, right.

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Speaker 1: But I think the people that that annoy the most

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Speaker 1: of those are lippicks because of diversity conversation and lippicklers

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Speaker 1: are I meantrolls. You can see the trollstickers people don't

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Speaker 1: notice the liptickage. Well, since you mentioned it already, let's

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Speaker 1: just start with the crypto thing, because what's interesting to

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Speaker 1: me about your disagreements with crypti people, bitcoin maximalists, etc.

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Speaker 1: Is many of them, i think, looked up to you

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Speaker 1: and they read antifragil and that right, and they read

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Speaker 1: anti fragile, and they read fulled by randomness, and they

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Speaker 1: read the black Swan, and that informed them that it's like, okay,

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Speaker 1: we need to adapt, get it, get into this currency

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Speaker 1: that's very hard, that is anti fragile, Bitcoin the ultimate

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Speaker 1: anti fragile currency. And so to their mind, they were

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Speaker 1: many of them looked read your work and this is

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Speaker 1: what they took away. And so what did they get wrong? Okay,

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Speaker 1: So the first thing is that my work is first

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Speaker 1: about avoiding tailor risk. Basically, if we want to do well,

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Speaker 1: we must first survive. And it's not like a separable condition.

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Speaker 1: So one is going to avoid fragilities. And it turns

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Speaker 1: out that as much as a federal reserve induces for

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Speaker 1: agility in a system, and as mush as I dislike Bernachy,

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Speaker 1: it turns out that bitcoin is a lot worse. It

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Speaker 1: is itself a very fragile commodity, and it got of

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Speaker 1: course cartaeiled, you know, Russia people, it's a very small

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Speaker 1: level of people start controlling it, and it's fragile in

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Speaker 1: a sense that if one day, if the you know,

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Speaker 1: all the miners go to the beach for one day,

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Speaker 1: for an hour, it's god. Whereas if you have gold,

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Speaker 1: I have a necklace here. The gold necklace, if I

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Speaker 1: leave it on the gaund for one hundred thousand years,

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Speaker 1: will still be gold. It may its financial value, but

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Speaker 1: its physical quality will will not be altered. Whereas bitcoin

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Speaker 1: it's just a book entry that needs to be maintained

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Speaker 1: and would collapse. But a lot of other things promised

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Speaker 1: about bitcoin that are not delivered, like it was meant

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Speaker 1: to be a transactional thing, don't out to be a

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Speaker 1: speculative item. So I realized quickly that I made a

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Speaker 1: mistake with bitcoint like I made a mistake by avoiding

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Speaker 1: a Roman exercise, and of course I was I was

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Speaker 1: at some point an owner of bitcoin. I publicly said

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Speaker 1: that I made a mistake, and I went short bitcoin later,

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Speaker 1: but it was not good for the system, and I

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Speaker 1: tlied that the paper that was published from quantity to finance,

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Speaker 1: where you look at hey, what's the currency, what's the

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Speaker 1: inflation hedge? What is a refuge investment? And Bitcoin satisfied

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Speaker 1: none of these. So people, of course they get angry

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Speaker 1: because because they had a feeling that they're going to

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Speaker 1: believe you for shading your mind. They don't realize that

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Speaker 1: I'm not selling that, you know, a recipe, I'm selling process.

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Speaker 1: Certo is the way of thinking, way approaching things, and

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Speaker 1: if you realize that something is fragile, immediately do something

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Speaker 1: about it. So and remarkably, it's the same fluster of

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Speaker 1: people who read anti fragile and thought that, hey, you know,

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Speaker 1: what doesn't kill you makes you stronger. That's that's get

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Speaker 1: affected with the vaccine was that was covid, and let's

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Speaker 1: ignore covid. We're going to make a stronger it's going

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Speaker 1: to kill a few people. So that kind of eugionism

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Speaker 1: and that kind of stuff I realized was an imficult

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Speaker 1: to me, profoundly indamicul to me. So such the same

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Speaker 1: crowd that was denying COVID saying, hey, you know, it's

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Speaker 1: just a virus, it's going to make you stronger, didn't

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Speaker 1: realize that they explained an antifragile jumping one foot will

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Speaker 1: make your bones stronger, but a thousand feet will not

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Speaker 1: help you too much. I mean it may help you know,

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Speaker 1: the caretaker and people who organize funeral, but not not

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Speaker 1: you that so, so I realized very quickly there's a

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Speaker 1: cluster of people who were most into Bitcoin is a

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Speaker 1: very point, very naive reasoning, extremely naive reasoning thinking, hey,

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Speaker 1: you know, it's an inflation hedge. As we saw it

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Speaker 1: was a reverse inflation hedge. But the good thing that

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Speaker 1: I figured out quickly to pull out in time, in

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Speaker 1: sense that it lost its value and we realized there

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Speaker 1: was inflation in the same group of people who were

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Speaker 1: into conspiracies, all generals conspiracies. And that's not the crowd

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Speaker 1: I want that. That's not the crowd I want to

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Speaker 1: be associated. You mentioned that bitcoin was bad for the system,

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Speaker 1: and I think that's sort of the connective tissue that

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Speaker 1: leads into some more recent events with the banking system.

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Speaker 1: But can you talk a little bit more about that,

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Speaker 1: like how do you see bitcoin? Actually, yeah, okay, let's

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Speaker 1: look at why why we have bitcoin and why we

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Speaker 1: are talking about the point. Effectively, it's the incompetence of

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Speaker 1: I want to call Bernancism, you know, because sometimes you

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Speaker 1: got to put name to a tendency. That the federal

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Speaker 1: reserved job is not to do structural things. That fellow

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Speaker 1: reserved job is to engage in monetary policy and typically

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Speaker 1: the short term monitoring policies to and their mission was

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Speaker 1: an asthment and will be the stability of the United

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Speaker 1: States first. So their job is too ease. When when

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Speaker 1: economic condition and you know, threat inflation, and when when

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Speaker 1: a hard economic condition, but that you cannot replace a

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Speaker 1: structural policy with a moneitoring polst. In other words, we

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Speaker 1: had a problem was debt, and you can't solve that

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Speaker 1: problem by putting interest rates at zero for a long time,

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Speaker 1: or if you put interface at zero, it should be

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Speaker 1: for short periods of time while looking for an alternative.

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Speaker 1: So when they did for fifteen years, they put interface

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Speaker 1: at zero, and that that does create tumors were the first.

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Speaker 1: So let's say the root of everything is interest rates

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Speaker 1: at zero, which ironically created the point and of course

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Speaker 1: created balls. Is h I would say posy like class

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Speaker 1: of investments, because there's no time value of money anymore.

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Speaker 1: Your discount you don't know what even you know what

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Speaker 1: discount rate is. And we created generation of people who

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Speaker 1: don't know the cost of funds, cost of money. So

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Speaker 1: and anyone with fifteen years of experience in finance, you know,

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Speaker 1: and no more doesn't know anything about interest rates. So

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Speaker 1: interest rates as zero create stumers. Read estate values go

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Speaker 1: up dramatically because of custom holding mansion is close to

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Speaker 1: nothing or was closed nothing. And created a class of

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Speaker 1: investment called you know, VC funds. And these were in

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Speaker 1: the old days were promising you cash flow, okay, cash flow.

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Speaker 1: Today they're promising you around the funding where you're going

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Speaker 1: to sell it to someone else. So we moved from

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Speaker 1: the classical cash flow model or even if you're negative

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Speaker 1: cash flow, the promise of future cash flow to the

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Speaker 1: promise of selling the company to someone else. And you

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Speaker 1: have millionaires in Silicon Valley who you know, got rich

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Speaker 1: from companies that never made the penny. So that's that's

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Speaker 1: the background. And of course you're going to have a

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Speaker 1: story like the going take off because it doesn't cost

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Speaker 1: that goal. Since you mentioned it, I'm curious COVID specifically

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Speaker 1: in your criticism of anti vactors, and I find that

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Speaker 1: one of the things that I think is interesting, is

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Speaker 1: it's not clear to me how you think about these

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Speaker 1: problems because they're okay, the vaccine is fairly new, it

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Speaker 1: seems to be relatively untested as a technology, and when

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Speaker 1: there are other sort of scientific advantage that you've really

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Speaker 1: recoiled against. For example, I think you're very critical about

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Speaker 1: GMO crops and you're worried about the tail risks posed

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Speaker 1: by those. So can you talk a little bit about

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Speaker 1: your framework and thinking about why is something like the

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Speaker 1: covid vaccine you're comfortable with, whereas something like GMO crops

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Speaker 1: to you, creates an uncomfortable level of tail risk. Okay,

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Speaker 1: so before we start, let's say that you're not you

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Speaker 1: cannot compare vaccines to GMOs because vaccines are tested an individuals,

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Speaker 1: so you can see the side effect individual GMOs would

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Speaker 1: be a systemic they spread to the environment. And then

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Speaker 1: also you're not thinking of the vaccine, you know, for

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Speaker 1: because because you think it's that they stood or you

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Speaker 1: know it's going to be pleasant. So we take it

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Speaker 1: the vaccine. We've got a plot the vaccine versus covid,

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Speaker 1: and covid was not something benign, so comparing it's a

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Speaker 1: risk management difference between two items and two things I'd

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Speaker 1: like to mention here. The first one is that very rapidly,

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Speaker 1: you know, I waited a little bit, and very rapidly

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Speaker 1: I saw that I had very large number of vaccinated

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Speaker 1: people and no side effects, and people say, well, we

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Speaker 1: needed more time. They didn't understand that you can replace

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Speaker 1: sample size. Time was sample size in the sense that

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Speaker 1: if it's something related to genetic states are going to

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Speaker 1: take place, or something of the genetic nature, like cancer program,

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Speaker 1: that it would be the equips that the large sample

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Speaker 1: size compensates for lack of time. I actually overcompass because

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Speaker 1: we have the illusion that after Hiroshima people got cancer

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Speaker 1: about twelve and a half years later. That's not true.

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Speaker 1: Some people got cancer within a few months. But the distribution,

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Speaker 1: there's a distribution because we need X number of mutations.

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Speaker 1: Like when you go to Las Vegas. For an individual

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Speaker 1: to win eight times in a row take decades years

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Speaker 1: of waiting. But if you have a billion people in

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Speaker 1: a casino, you're going to have that, you know, every hour.

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Speaker 1: So this is where this is where very rapidly I

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Speaker 1: realized that the vaccine that not really pose a threat

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Speaker 1: of that nature, and I wrote technical comments of that.

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Speaker 1: But to go back to the pandemic, what would you

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Speaker 1: know my thinking? You know, basically my specialty is fat

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Speaker 1: tailed events. See, I've don't spend all my life dealing

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Speaker 1: with that central problem. How do you do statistical pools

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Speaker 1: for fat tailed stuff like masonic pandemic happened. I started

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Speaker 1: publishing in that field because people didn't realize that you

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Speaker 1: have to think differently when it comes to fat tails.

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Speaker 1: You see, you cannot take averages, you shouldn't do ni

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Speaker 1: forecasting and get involved in a few polemical discussions. But

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Speaker 1: published like seven or eight papers in ronals on on that,

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Speaker 1: including masks. And I may have one on vaccine if

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Speaker 1: people keep you denying the French or the risk defferential

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Speaker 1: between vaccine and the disease. But there's a lot of

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Speaker 1: stuff people don't get about the COVID. The first one,

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Speaker 1: I would say that it is not something that affects

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Speaker 1: the old. It affects everybody in proportion for the mortality,

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Speaker 1: so it's not particularly And otherwise if you say, okay,

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Speaker 1: it's only in the old, then you should you should

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Speaker 1: say okay, let's stop dealing with cancer, because cancer affects

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Speaker 1: the elderly disproportionately, or let's stop carreology. It costs too

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Speaker 1: much money. You know, Jim Bros. Don't need it because

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Speaker 1: they're thirty eight years old. Um, it's the same. It's

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Speaker 1: the same iselm. It builds us in proportion to age.

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Speaker 1: So in other words, if you your your mortality risk

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Speaker 1: goes up by eight percent, regardless about the age of thirty.

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Speaker 1: Of course above some specihold and that's not well understood.

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Speaker 1: So it's not an old person problem. And as diseases

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Speaker 1: not covid by itself. So the bunch of things people

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Speaker 1: didn't get. But I you see, I was known initially

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Speaker 1: by you other people for the Blacks soon as the

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Speaker 1: author of full Barandimus, and that book was misunderstood. Initially

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Speaker 1: I was saying that there is I'm not saying there's

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Speaker 1: you know, no skills, that there are no skills. I'm

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Speaker 1: saying the world is more rather than you think. But

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Speaker 1: I'm not saying it's all rival Well, actually I wanted it. Yes,

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Speaker 1: let me finish one point. Vandom is connected to the idea.

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Speaker 1: So one of the messages of the full barandomness is

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Speaker 1: and we tend to be swayed by anecdotes, and I

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Speaker 1: noticed that over time things got worse. I mean, the

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Speaker 1: circle is sold I don't know, seven eight nine million

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Speaker 1: copies world by But at the same time, and a

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Speaker 1: lot of people who made the same mistake. People are

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Speaker 1: swayed by the anecdote. So so whether it's COVID vaccines,

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Speaker 1: whether it's a nice story with a midcoin, whether it's

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Speaker 1: stuff about elections, were swayed by the anecdote. So our

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Speaker 1: world is becoming more complex requires more statistical sophistication, while

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Speaker 1: social media is driving the best to the most primitive

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Speaker 1: way of thinking. Sorry to interrupted your trace. No, no, no,

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Speaker 1: this is actually exactly what I wanted to ask you,

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Speaker 1: something that I've actually always wanted to ask you for

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Speaker 1: a long time. Is is there at tension between you know,

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Speaker 1: you say in a lot of your works that we

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Speaker 1: shouldn't trust experts necessarily you should be wary of you know,

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Speaker 1: I think you call them either bullshitters or other other words.

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Speaker 1: But but wait, but on the other hand, you know

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Speaker 1: it's something like the vaccine. I doubt that the average

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Speaker 1: person has the scientific background to look at the literature

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Speaker 1: and say, oh, this makes sense or it doesn't. And

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Speaker 1: in that case, it seems like we should be trusting experts.

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Speaker 1: So how do you square those two things? Yeah, no,

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Speaker 1: I made in the black swan to answer your a

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Speaker 1: precise question. Had to explain which fields in which field

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Speaker 1: the expert as an expert, and it's which field the

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Speaker 1: expert is what I call it the s met word. Okay,

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Speaker 1: And the difference has to do with fat delay. If

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Speaker 1: the micro is much easier to microbs than microbs, so

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Speaker 1: that lewis, the dentist is going to be an expert

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Speaker 1: a dentist, the plumber is an expert at being a plumber,

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Speaker 1: but the micro economists, we're not sure is an expert

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Speaker 1: at microeconomics. And the same thing happens in medicine if

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Speaker 1: the bologists were not really experts of what's going on

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Speaker 1: because it's fatal, but doctor doctor and visual doctor is

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Speaker 1: going to be an expert. And we're dealing with tim

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Speaker 1: tailed processes. When you look at the time series for

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Speaker 1: vaccines and things like that, vaccine is it's a tailed,

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Speaker 1: it's not a fat tailed one. So the that's the

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Speaker 1: difference with GMO. So sim tailed was the sat tailed

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Speaker 1: And of course it would be too complex to explain here,

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Speaker 1: but I explained it in the Black Swan. It's a

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Speaker 1: difference between the income of a speculator and the income

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Speaker 1: of the dentist. One has winner, the winner they call effects.

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Speaker 1: The other one is more narrowly distributive. So this is

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Speaker 1: where where I saw mainly a difference between experts and

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Speaker 1: non actually later on and it's some more thinking and

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Speaker 1: scanning the game, saying how can you solve the problem

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Speaker 1: and the things The difference is scan in the game

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Speaker 1: that if you have skin in the game, then you

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Speaker 1: have survival, and then we know if you're an expert,

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Speaker 1: or if there's any way to bust your claims okay

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Speaker 1: and and make you exit the pool, then then of

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Speaker 1: course an expert will will have a filter eliminating pseudo

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Speaker 1: experts and particularly those who represent risks for others. A

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Speaker 1: surgeon who you know, does that surgery is going to

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Speaker 1: ex the pool. So there's a mechanism and surgery, there's

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Speaker 1: a mechanism economic life. A grocer who doesn't understand NAMA

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Speaker 1: sheets or doesn't stand cash flow will go out of business.

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Speaker 1: But there are places where the process is delayed, namely technology,

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Speaker 1: namely microeconomics. Economics. I guess the difference. I keep going

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Speaker 1: back to the difference. Say your plumber is an expert

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Speaker 1: at plumbing, but forecaster is not an expert that forecasting.

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Speaker 1: And there are fields where you have a lot of

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Speaker 1: b s, like for example, psychology, psychology or what I mean,

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Speaker 1: economics are called not clinical the one that deals with

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Speaker 1: biases and stuff like that, and there's all bs and

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Speaker 1: they can't be caught, whereas medicine is is on firm grounds,

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Speaker 1: of course, and it's not perfect. Medicine made a lot

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Speaker 1: of stakes, but it's fundamentally self correct. So I get

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Speaker 1: the distinction. But I guess my other question is you

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Speaker 1: yourself as a flaneur, and um, you know a thinker,

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Speaker 1: you sort of you. You go from topic to topic

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Speaker 1: to topic, and you know you say that you're my rule.

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Speaker 1: Let me tell you my rule, right. My rule is

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Speaker 1: I published in purely journal and the topics two professional.

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Speaker 1: And that's my problem with Peterson all the guys is

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Speaker 1: that I speak about men that I have seven papers,

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Speaker 1: and I mean more than the local doctor in medical

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Speaker 1: uh topics, Okay, whether it's published in medical journals or

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Speaker 1: published in other scientific fields. I talk about genetics, I

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Speaker 1: have two published and two coming and genetics. Actually one published,

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Speaker 1: one accepted, one and two coming in genetics. So I

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Speaker 1: basically I never talked about the subject unless I engaged

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Speaker 1: the expert. And that was my fight with a lot

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Speaker 1: of people. So so my idea is not necessary to

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Speaker 1: publish a perio view journal. If you are in a

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Speaker 1: practical profession, like if you're a truck drivers talk about trucks,

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Speaker 1: you don't need to publish. But if you're sitting and

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Speaker 1: then in every tower in somewhere, you need to engage

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Speaker 1: the professional, not you know, not be an expert on

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Speaker 1: just on Twitter. And that's my rule. And people don't

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Speaker 1: realize that I'm subjecting myself to that discipline. Well, so

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Speaker 1: I got I did any papers after Blacksman eighty papers?

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Speaker 1: Technical papers? Why not? Because you know, it's not for

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Speaker 1: the image. It's because I require from others some kind

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Speaker 1: of technical expertise before listening to them. Speaking of I

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Speaker 1: guess bullshitters, and speaking of Twitter, and speaking of people

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Speaker 1: who say a lot of things on topics that they

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Speaker 1: either are not experts on or have not published in

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Speaker 1: a rigorous manner. There's Twitter, that's true, but there is

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Speaker 1: a certain class of people that in the see, you

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Speaker 1: have been going back and forth with venture capitalists for

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Speaker 1: several years now. Many of the prominent ones have fashioned

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Speaker 1: themselves and these sort of like philosopher kings, waning on

427
00:26:23,960 --> 00:26:27,520
Speaker 1: everything from tech to politics, to what the FED should do,

428
00:26:28,160 --> 00:26:31,600
Speaker 1: to declining fertility rates in the West, and all these

429
00:26:31,640 --> 00:26:34,440
Speaker 1: things that they're up and armed about, and they've gotten

430
00:26:34,440 --> 00:26:36,960
Speaker 1: really loud about how banking works in the wake of

431
00:26:37,000 --> 00:26:40,600
Speaker 1: the failure of SVB. You seem to have a special

432
00:26:40,680 --> 00:26:44,479
Speaker 1: place in your heart of disdain for many of these people, because,

433
00:26:44,520 --> 00:26:47,720
Speaker 1: of course, your natural inclination is still believes that natural

434
00:26:47,760 --> 00:26:52,600
Speaker 1: capitalists do a great job, that they contribute to society,

435
00:26:52,800 --> 00:26:55,879
Speaker 1: that the strength of them that we have to whatever

436
00:26:56,359 --> 00:27:01,720
Speaker 1: computer program we're us about, you know, for this podcast,

437
00:27:02,560 --> 00:27:07,919
Speaker 1: that's the inclination. When you scratch, you realize that maybe

438
00:27:08,119 --> 00:27:10,640
Speaker 1: maybe maybe that was a game, but the game has

439
00:27:10,720 --> 00:27:14,080
Speaker 1: changed dramatically. And you have a bunch of people who

440
00:27:14,160 --> 00:27:17,520
Speaker 1: package companies. They're good at packaging companies, and of course

441
00:27:17,720 --> 00:27:21,560
Speaker 1: the thing has public characteristics. And not only that, but

442
00:27:21,640 --> 00:27:24,399
Speaker 1: they think that society old them something because we use

443
00:27:24,440 --> 00:27:28,600
Speaker 1: an iPhone, okay, so so they have they had a feeling,

444
00:27:28,800 --> 00:27:30,720
Speaker 1: you know what you're using an iPhone. Therefore, you owe

445
00:27:30,760 --> 00:27:33,560
Speaker 1: me some things that were coming and they me up. Okay.

446
00:27:33,600 --> 00:27:37,119
Speaker 1: Plus a lot of these are libertarians, but it so

447
00:27:37,320 --> 00:27:42,080
Speaker 1: happened that a lot of people are libertarians until they

448
00:27:42,520 --> 00:27:46,560
Speaker 1: have the first brought out. So so DC, I mean,

449
00:27:46,560 --> 00:27:51,679
Speaker 1: in principle, it's a very normal professional professionals, but you

450
00:27:51,680 --> 00:27:54,879
Speaker 1: want to avoid the rent seeking by that profession as

451
00:27:54,920 --> 00:27:58,680
Speaker 1: a whole. And this is why every profession, although you

452
00:27:58,760 --> 00:28:02,199
Speaker 1: have reviewed with unpro in economics, you have to just

453
00:28:02,400 --> 00:28:07,800
Speaker 1: make sure there's some accountability, external accountability or an adult

454
00:28:07,840 --> 00:28:12,080
Speaker 1: supervision from the outside. And it looks like dcs are

455
00:28:12,200 --> 00:28:15,560
Speaker 1: not doing what they claimed to do. I mean, think

456
00:28:15,600 --> 00:28:18,879
Speaker 1: of all these billionaires, and you realize that there are

457
00:28:18,800 --> 00:28:22,800
Speaker 1: a lot of these billionaires are billionaires from funding. If

458
00:28:22,880 --> 00:28:28,000
Speaker 1: Tracy and I started a company tomorrow, we put ten

459
00:28:28,040 --> 00:28:31,600
Speaker 1: thousand dollars each, is it okay? Crazy? Well, put ten

460
00:28:31,600 --> 00:28:39,280
Speaker 1: thousand dollars. I think maybe I can afford ten ok

461
00:28:39,720 --> 00:28:43,520
Speaker 1: So we put okay, so so so Tracy and I

462
00:28:43,640 --> 00:28:45,920
Speaker 1: put in ten thousand dollars, and I have companies verse

463
00:28:46,000 --> 00:28:49,400
Speaker 1: twenty thousand dollars, right, and then we decide to sell

464
00:28:49,480 --> 00:28:54,960
Speaker 1: to Joe one percent of a company four thousand dollars.

465
00:28:56,440 --> 00:29:00,440
Speaker 1: Guess what, Tracy, and now now are you know have

466
00:29:01,000 --> 00:29:04,200
Speaker 1: three nine point some percent of who you know, our

467
00:29:04,240 --> 00:29:07,880
Speaker 1: pointy thousands became almost fifty thousand dollars. Joe, you're good

468
00:29:07,880 --> 00:29:12,080
Speaker 1: for one k, right, I could sweat so exactly so,

469
00:29:12,280 --> 00:29:14,080
Speaker 1: and then you have a friend who's going to spend

470
00:29:14,120 --> 00:29:18,360
Speaker 1: another k to buy point one percent, and the company

471
00:29:18,400 --> 00:29:20,280
Speaker 1: and so on. This is a lot of a lot

472
00:29:20,280 --> 00:29:24,640
Speaker 1: of these party A lot of characteristics are president and

473
00:29:24,720 --> 00:29:27,320
Speaker 1: a lot of systems. It's just you know, of course

474
00:29:28,200 --> 00:29:32,400
Speaker 1: there's value somewhere. They produce good stuff. We had a

475
00:29:32,440 --> 00:29:34,800
Speaker 1: lot of recently a lot of the countries, but we

476
00:29:34,840 --> 00:29:37,440
Speaker 1: have to be aware that there's a lot of smoke.

477
00:29:37,560 --> 00:29:42,920
Speaker 1: And also also I realized that that game was fueled

478
00:29:43,920 --> 00:29:47,920
Speaker 1: by low interest rates. Right. Well, so that is the

479
00:29:48,120 --> 00:29:52,120
Speaker 1: general the sort of like the general theory, the general

480
00:29:52,160 --> 00:29:55,360
Speaker 1: case against listening to a lot of these individuals on

481
00:29:55,400 --> 00:29:57,800
Speaker 1: every topic they wish to a pine on. Can you

482
00:29:57,840 --> 00:30:01,760
Speaker 1: just talk a little bit about the specific particularly in

483
00:30:01,800 --> 00:30:05,000
Speaker 1: the collapse of SVB and you've criticized several of them

484
00:30:05,000 --> 00:30:07,200
Speaker 1: which we don't need to mention my name, but about

485
00:30:07,240 --> 00:30:11,240
Speaker 1: their level of understanding of the banking system and finance

486
00:30:11,520 --> 00:30:15,240
Speaker 1: and finance risks, etc. What do you you know, what

487
00:30:15,320 --> 00:30:18,080
Speaker 1: do you think is it just a sort of bias

488
00:30:18,200 --> 00:30:21,080
Speaker 1: sort of like you know the cliche there's no libertarians

489
00:30:21,080 --> 00:30:22,840
Speaker 1: and a bank round or something, or is there a

490
00:30:22,880 --> 00:30:25,920
Speaker 1: deeper misunderstanding that many of them have about how the

491
00:30:25,920 --> 00:30:29,160
Speaker 1: structure of the banking system works. Yeah, I mean, first

492
00:30:29,360 --> 00:30:32,480
Speaker 1: they couldn't. I mean many of them understand the difference

493
00:30:32,520 --> 00:30:37,640
Speaker 1: between losing money for credit invest for credit reasons or

494
00:30:37,760 --> 00:30:41,880
Speaker 1: versus losing money because of the term structures. You see

495
00:30:42,200 --> 00:30:45,320
Speaker 1: that banks web made a mistake of investing Lockdome first

496
00:30:45,320 --> 00:30:49,280
Speaker 1: of all, and that's pretty much the way we think

497
00:30:49,280 --> 00:30:53,240
Speaker 1: about it universal that if you look at convex versus

498
00:30:53,280 --> 00:30:58,320
Speaker 1: callcave investment, you really have to have deep misunderstanding of

499
00:30:58,440 --> 00:31:01,080
Speaker 1: finance to invest very long to and bonds that pay

500
00:31:01,120 --> 00:31:04,640
Speaker 1: you no interest because basically you have no upside and

501
00:31:04,760 --> 00:31:08,360
Speaker 1: all downside from there, you see. So so these banks

502
00:31:08,360 --> 00:31:11,920
Speaker 1: are very fragile and they invested, and it's a current

503
00:31:11,960 --> 00:31:14,600
Speaker 1: play that the US government has got to pay that

504
00:31:14,760 --> 00:31:18,280
Speaker 1: debt and don't use it to think the praises of

505
00:31:18,320 --> 00:31:22,520
Speaker 1: bitcoin because bitcoin suffered from it, and effectively bitcoin rarely

506
00:31:22,600 --> 00:31:26,400
Speaker 1: when they built out the banks. So I'm given one example.

507
00:31:26,880 --> 00:31:29,200
Speaker 1: But then again, let me tell you, when you become prominent,

508
00:31:29,280 --> 00:31:32,960
Speaker 1: irresponsible for your words because you may influence others, right,

509
00:31:33,560 --> 00:31:35,360
Speaker 1: and that's my role. I mean, you can whatever I

510
00:31:35,400 --> 00:31:40,240
Speaker 1: say in public about public in private about public matters. Okay,

511
00:31:40,320 --> 00:31:44,120
Speaker 1: it's public, and I should be held accountable for all

512
00:31:44,200 --> 00:31:46,120
Speaker 1: my mistakes. And I made a lot of mistakes, and

513
00:31:46,160 --> 00:31:49,440
Speaker 1: they're accountable for a lot of other stakes. Well, you

514
00:31:49,600 --> 00:31:52,440
Speaker 1: justus You've got to be self correctly. Just on the

515
00:31:52,600 --> 00:31:57,040
Speaker 1: venture capital model. I mean, I take the funding point

516
00:31:57,040 --> 00:31:59,160
Speaker 1: and I wholly agree with it. But it seems like

517
00:31:59,240 --> 00:32:03,000
Speaker 1: also one of the reasons that VC and tech investments

518
00:32:03,040 --> 00:32:05,800
Speaker 1: in general became so popular during an era of low

519
00:32:05,840 --> 00:32:08,720
Speaker 1: interest rates was that, you know, if you can't get

520
00:32:08,760 --> 00:32:13,120
Speaker 1: a decent return from investing in traditional financial assets, then

521
00:32:13,160 --> 00:32:18,320
Speaker 1: why not basically purchase a lottery ticket for the next

522
00:32:18,400 --> 00:32:21,560
Speaker 1: Google or the next Amazon, or you know, even Bitcoin

523
00:32:21,760 --> 00:32:25,040
Speaker 1: at times has been described as a lottery ticket. Is

524
00:32:25,080 --> 00:32:31,080
Speaker 1: there an overlap between trying to identify tail risks, which

525
00:32:31,200 --> 00:32:35,360
Speaker 1: you know, almost by definition are unknowable and that kind

526
00:32:35,360 --> 00:32:39,160
Speaker 1: of model of trying to purchase a lottery ticket for

527
00:32:39,200 --> 00:32:41,520
Speaker 1: the next big thing, because it seems like both those

528
00:32:41,520 --> 00:32:45,160
Speaker 1: two things you never really know what the next black

529
00:32:45,200 --> 00:32:47,240
Speaker 1: Swan event is going to be or the next big

530
00:32:47,280 --> 00:32:51,720
Speaker 1: technological innovation. This is a big question. It's because people

531
00:32:51,800 --> 00:32:55,320
Speaker 1: keep telling me you like to engage in the trades

532
00:32:55,440 --> 00:32:59,240
Speaker 1: that have high probability of a small loss, a small

533
00:32:59,280 --> 00:33:03,640
Speaker 1: probabilities loss. Okay, why don't you just buy a lostry ticket?

534
00:33:04,520 --> 00:33:07,920
Speaker 1: And you want to explain the condition. The number one

535
00:33:08,040 --> 00:33:11,800
Speaker 1: condition is still have positively expective return. You see that

536
00:33:12,880 --> 00:33:15,920
Speaker 1: your met must have you must not be a turkey.

537
00:33:15,960 --> 00:33:18,440
Speaker 1: In other words, by the lucky. It is completely irrational.

538
00:33:18,960 --> 00:33:22,840
Speaker 1: Long run, you're not gonna make any money. But if

539
00:33:22,880 --> 00:33:27,920
Speaker 1: you engage in taylor Is trade. We believe that in myself,

540
00:33:27,920 --> 00:33:31,800
Speaker 1: which is published a paper explaining option pricing fifty years

541
00:33:31,800 --> 00:33:34,160
Speaker 1: after black trolls, to explain that a lot of people

542
00:33:34,200 --> 00:33:37,800
Speaker 1: think these options are expensive, like expensive lockery ticket effects,

543
00:33:37,880 --> 00:33:40,320
Speaker 1: because they have the long proble we made we made

544
00:33:40,320 --> 00:33:44,520
Speaker 1: the paper public. But the problem of the central problem

545
00:33:44,720 --> 00:33:50,120
Speaker 1: that you should focus on expected value and a lot

546
00:33:50,200 --> 00:33:54,280
Speaker 1: of places and to repeat that appeared to be negative

547
00:33:54,280 --> 00:33:58,520
Speaker 1: expected value and finance effects positively effective value and vice versas.

548
00:33:58,560 --> 00:34:01,040
Speaker 1: It's very rational to go buy to receive. He thinks

549
00:34:01,080 --> 00:34:04,120
Speaker 1: that you the dollar resturnment's two thousand dollars, But I

550
00:34:04,160 --> 00:34:06,640
Speaker 1: thought it will happen. Now the story is oversold, and

551
00:34:06,680 --> 00:34:11,200
Speaker 1: that was a low interest rate game because now people

552
00:34:11,239 --> 00:34:15,160
Speaker 1: are going to focus on profitability and these companies may

553
00:34:15,160 --> 00:34:17,920
Speaker 1: not survive. And if you buy, by the way, if

554
00:34:17,920 --> 00:34:20,000
Speaker 1: you buy into a lot of it. DC has a

555
00:34:20,000 --> 00:34:21,759
Speaker 1: lot of companies, So if you buy into a lot

556
00:34:21,840 --> 00:34:27,040
Speaker 1: of companies, you lose that skewed attribute, so you no

557
00:34:27,080 --> 00:34:31,920
Speaker 1: longer have a symmetry with small losses big gains. If

558
00:34:31,920 --> 00:34:35,440
Speaker 1: I invest on a million companies with small lost big games, okay,

559
00:34:35,719 --> 00:34:38,960
Speaker 1: I would have studied resturns if I have positive effective retirement.

560
00:34:40,080 --> 00:34:45,880
Speaker 1: What did Sam Bankman Freed misunderstand about positive EV Because

561
00:34:45,960 --> 00:34:49,480
Speaker 1: my understanding is that him and his whole crew thought, well,

562
00:34:49,640 --> 00:34:53,680
Speaker 1: huge risks were worth it if there's even a slight edge,

563
00:34:53,760 --> 00:34:56,480
Speaker 1: if there's a slight positive EV And of course they

564
00:34:56,480 --> 00:34:59,759
Speaker 1: took the ultimate risk and it blew up. But they

565
00:35:00,040 --> 00:35:02,520
Speaker 1: seemed to think that these risks were worthwhile because in

566
00:35:02,560 --> 00:35:03,920
Speaker 1: part they wanted to make a lot of money, in

567
00:35:04,000 --> 00:35:06,840
Speaker 1: part because they thought it was important to save the world.

568
00:35:07,040 --> 00:35:10,280
Speaker 1: To make money to save the world from perceived threats,

569
00:35:10,320 --> 00:35:14,600
Speaker 1: whether it's AI or anything else. What did he misunderstand

570
00:35:14,800 --> 00:35:20,080
Speaker 1: about probabilistic thinking? Okay, thought, so this is it's great

571
00:35:20,120 --> 00:35:23,200
Speaker 1: to talk about him, not because it's sim but because

572
00:35:23,239 --> 00:35:26,880
Speaker 1: of that group of people. Yeah, we have this entire

573
00:35:26,920 --> 00:35:30,640
Speaker 1: collection of the young individuals who think that the past

574
00:35:30,680 --> 00:35:34,480
Speaker 1: does not exist. See, when I was a trader in

575
00:35:34,480 --> 00:35:37,640
Speaker 1: my twenties, I picked the brain every older trader who

576
00:35:37,680 --> 00:35:41,840
Speaker 1: had survived. And that was not just me. When I

577
00:35:41,880 --> 00:35:46,360
Speaker 1: look back, I see people who have survived, same attitude.

578
00:35:47,480 --> 00:35:52,720
Speaker 1: So these people make tabla raza. And I remember writing comments.

579
00:35:52,760 --> 00:35:55,520
Speaker 1: I think my friend Tom Holland who said the Romans

580
00:35:55,680 --> 00:35:58,960
Speaker 1: had no cult of the youth, and that was we

581
00:35:59,080 --> 00:36:00,799
Speaker 1: have a cult of the youth, and they have a

582
00:36:00,800 --> 00:36:04,440
Speaker 1: cult for themselves. And to me, being young and finance

583
00:36:04,600 --> 00:36:10,239
Speaker 1: is necessarily right bad thing. Ye, simply because of lack

584
00:36:10,280 --> 00:36:14,440
Speaker 1: of experience, but also that culture. So in the world

585
00:36:14,800 --> 00:36:18,160
Speaker 1: they thought that if they understood the blockchain, they did

586
00:36:18,200 --> 00:36:22,440
Speaker 1: not need to understand finance, and so that's the root

587
00:36:22,480 --> 00:36:24,440
Speaker 1: of the problem. The root of the problem is they

588
00:36:24,480 --> 00:36:28,480
Speaker 1: think that finances computer program finance has vastly more picture

589
00:36:28,560 --> 00:36:32,600
Speaker 1: than that requires a lot more as the introspection when

590
00:36:32,600 --> 00:36:34,960
Speaker 1: you when you when you will pick a decision to

591
00:36:35,040 --> 00:36:38,560
Speaker 1: consider many many, many more factors. Finances, as you know,

592
00:36:38,960 --> 00:36:41,680
Speaker 1: very complicated. This is like what say science is hard,

593
00:36:41,760 --> 00:36:45,280
Speaker 1: to say finance is harder. And there's a fortunate cookie

594
00:36:45,280 --> 00:36:49,520
Speaker 1: approach to finance simplified fortune Qikieff. And it will channelize

595
00:36:49,760 --> 00:36:53,840
Speaker 1: tons of people. And you know, many of them became

596
00:36:53,920 --> 00:36:58,640
Speaker 1: like him, paper billionaires, and now they're going will end

597
00:36:58,719 --> 00:37:03,600
Speaker 1: up like him, living in the basement because they have

598
00:37:03,680 --> 00:37:09,040
Speaker 1: no skills programming whose TRAGEDYDP is a skill that's not

599
00:37:09,120 --> 00:37:13,080
Speaker 1: longer going to be in high demand. So such a tragedy.

600
00:37:13,080 --> 00:37:15,759
Speaker 1: It's a tragedy. It's much more general than than what

601
00:37:15,880 --> 00:37:19,759
Speaker 1: he mists the general approach. We don't need this. I mean,

602
00:37:19,800 --> 00:37:23,240
Speaker 1: they're elementary mistakes these people make. When looking at time series,

603
00:37:23,280 --> 00:37:25,279
Speaker 1: for example, you look at the point if you bought

604
00:37:25,280 --> 00:37:27,880
Speaker 1: it four years ago, that these returns. Yet that's not

605
00:37:27,920 --> 00:37:30,319
Speaker 1: how we look at investments. We look at big two

606
00:37:30,400 --> 00:37:34,560
Speaker 1: valued drawdowns, the structure of drawdown, because you're not going

607
00:37:34,600 --> 00:37:36,600
Speaker 1: to go back in a time machine and buy four

608
00:37:36,680 --> 00:37:39,879
Speaker 1: years ago. You're buying now and you have to worry

609
00:37:39,880 --> 00:37:42,520
Speaker 1: about the next four years. Not the past four years.

610
00:37:42,600 --> 00:37:45,600
Speaker 1: So they don't understand how to present returns, how to

611
00:37:45,640 --> 00:37:49,360
Speaker 1: compare returns, how to discuss inflation. Basic things about monetary

612
00:37:49,400 --> 00:37:54,120
Speaker 1: policy are missed. Talking about someone you know, that age group, educated,

613
00:37:54,239 --> 00:37:58,440
Speaker 1: supposedly educated and making mistakes that I think a clerk

614
00:37:58,840 --> 00:38:01,760
Speaker 1: who you know, or trainee you know and you know trainees,

615
00:38:01,800 --> 00:38:04,560
Speaker 1: that's their main function is to serve often in the

616
00:38:04,640 --> 00:38:08,160
Speaker 1: training room that trainees you know would would would we

617
00:38:08,239 --> 00:38:12,400
Speaker 1: would know immediately mistake. So the world has lost in

618
00:38:12,440 --> 00:38:14,759
Speaker 1: some kind of sophistication and we're going to get it

619
00:38:14,800 --> 00:38:17,399
Speaker 1: back this evolution. These guys will go bust, and those

620
00:38:17,440 --> 00:38:22,560
Speaker 1: trains and was more respective for historical understanding will prevail.

621
00:38:23,680 --> 00:38:26,080
Speaker 1: So in the end the guys who last word and

622
00:38:26,160 --> 00:38:29,479
Speaker 1: the oldest investors around, like one buff It and Charlie Lander.

623
00:38:46,600 --> 00:38:50,040
Speaker 1: How is universal position nowadays? Talk to us about you

624
00:38:50,040 --> 00:38:53,760
Speaker 1: know all these ideas, how are you putting them into practice? Okay?

625
00:38:53,840 --> 00:38:56,279
Speaker 1: The only the thing I would say about universa is

626
00:38:56,320 --> 00:39:00,320
Speaker 1: that in twenty twenty three, the position in this donal

627
00:39:01,120 --> 00:39:04,759
Speaker 1: to the one in twenty thirteen and identical to the

628
00:39:04,800 --> 00:39:09,799
Speaker 1: one will have in twenty fifty one. Okay, So it's

629
00:39:09,880 --> 00:39:12,759
Speaker 1: my cycling allows me to survive that long. So in

630
00:39:12,800 --> 00:39:17,160
Speaker 1: other words, we are providing a structural service with portfolios

631
00:39:17,280 --> 00:39:20,600
Speaker 1: to prevent blow ups, to eliminate that tail and for us,

632
00:39:20,960 --> 00:39:23,840
Speaker 1: just like blocking noisy people on Twitter and has a Twitter,

633
00:39:24,400 --> 00:39:27,040
Speaker 1: eliminating your tail risk allow you to make any mistakes

634
00:39:27,040 --> 00:39:31,400
Speaker 1: you want for your investments. A lot of people seem

635
00:39:31,480 --> 00:39:36,480
Speaker 1: to intuitively like the idea of tail risk funds buying

636
00:39:36,640 --> 00:39:39,720
Speaker 1: protection and you know, for the probably for the reason

637
00:39:39,760 --> 00:39:41,719
Speaker 1: you like. It's like, yeah, we're all going to make mistakes,

638
00:39:42,080 --> 00:39:44,880
Speaker 1: but I want to sleep at night, and so I

639
00:39:45,040 --> 00:39:48,160
Speaker 1: want to have some sort of hedge or something like that,

640
00:39:48,320 --> 00:39:50,840
Speaker 1: and you know, buyputs or whatever it is. But that

641
00:39:50,960 --> 00:39:56,200
Speaker 1: doesn't seem obviously like that's that's costly in the short term. Yeah,

642
00:39:56,239 --> 00:39:58,279
Speaker 1: and there's no free lunch, right And there's no free

643
00:39:58,320 --> 00:40:00,640
Speaker 1: lunch right because no no, no, no, no, no, no.

644
00:40:01,280 --> 00:40:03,800
Speaker 1: That's our Our problem is that if you buy what

645
00:40:03,920 --> 00:40:05,920
Speaker 1: we call the suffers, but you might go give your

646
00:40:05,960 --> 00:40:08,320
Speaker 1: money to charities. And I can give you some charities.

647
00:40:09,160 --> 00:40:11,359
Speaker 1: There's one on level opally these money. I can give

648
00:40:11,360 --> 00:40:14,120
Speaker 1: you the name. The problem Taylor is scheduling is that

649
00:40:14,320 --> 00:40:17,680
Speaker 1: just like Sam Banking Street thought pay finance was easy

650
00:40:17,760 --> 00:40:22,560
Speaker 1: because he figured us with technical things. They had experient

651
00:40:22,640 --> 00:40:25,759
Speaker 1: let's do it. The devil is the new execution, very

652
00:40:25,880 --> 00:40:30,520
Speaker 1: very complicated, very very complicated, and it requires a lot

653
00:40:30,560 --> 00:40:37,479
Speaker 1: of experience. Conceptually though, setting a conceptually conceptually, how is it? Yeah,

654
00:40:38,880 --> 00:40:41,320
Speaker 1: it's a hugely difference because the return you're going to

655
00:40:41,400 --> 00:40:47,040
Speaker 1: have when you I mean, I'm not supposed to top return. Okay,

656
00:40:47,160 --> 00:40:50,400
Speaker 1: we don't have to. I'm going to say conceptually that

657
00:40:51,840 --> 00:40:55,120
Speaker 1: you can look at everything, but there is a huge

658
00:40:55,160 --> 00:40:58,840
Speaker 1: difference between the naive Dale Hedger and the Experiencedale heager.

659
00:40:59,000 --> 00:41:04,560
Speaker 1: And leave it at that. Because sizing there as a

660
00:41:04,600 --> 00:41:08,200
Speaker 1: lot of things involved, particular liquidity, that what how you

661
00:41:08,320 --> 00:41:10,840
Speaker 1: buy because a difference between lived and ask for options.

662
00:41:10,880 --> 00:41:17,240
Speaker 1: This bosphorus just practically you know, you mentioned the difference

663
00:41:17,280 --> 00:41:22,080
Speaker 1: between experienced and naive tail risk catchers. But practically is

664
00:41:22,120 --> 00:41:26,320
Speaker 1: it easier or harder, or cheaper or more expensive to

665
00:41:26,520 --> 00:41:30,360
Speaker 1: buy you know, really big tail risk insurance nowadays? Like

666
00:41:30,480 --> 00:41:34,080
Speaker 1: how has that process actually evolved since the nineteen eighties?

667
00:41:34,160 --> 00:41:37,120
Speaker 1: For instance, I think that people are even more naive

668
00:41:37,400 --> 00:41:41,239
Speaker 1: today than they were in native eighties or not. The

669
00:41:41,880 --> 00:41:45,080
Speaker 1: people are even more naive than that they were after

670
00:41:45,160 --> 00:41:48,279
Speaker 1: stuff market crash. I don't think that we have enough

671
00:41:48,360 --> 00:41:53,080
Speaker 1: financial certification. Maybe short term arbitragers disappear, but things are

672
00:41:53,160 --> 00:41:57,839
Speaker 1: more structural, like how the price tail risks. People are

673
00:41:57,960 --> 00:42:00,719
Speaker 1: very naive. Plus they quite don't quite people also don't

674
00:42:00,920 --> 00:42:03,520
Speaker 1: spend the following argument, it's your own money. You understand

675
00:42:03,640 --> 00:42:05,800
Speaker 1: very well that you don't want to you want to

676
00:42:05,800 --> 00:42:08,239
Speaker 1: sleep at night, and there's some things that we don't

677
00:42:08,239 --> 00:42:11,359
Speaker 1: want to lose all their money. And it's like white

678
00:42:11,400 --> 00:42:14,960
Speaker 1: people never buy a house unless they can have insurance

679
00:42:15,000 --> 00:42:16,960
Speaker 1: on it. To clip the house represents three four times

680
00:42:17,000 --> 00:42:20,280
Speaker 1: their networks. You don't buy a house you know before

681
00:42:20,400 --> 00:42:23,560
Speaker 1: making sure that if it's there's a fire, you're not

682
00:42:23,640 --> 00:42:26,560
Speaker 1: going to have a husual alability. And actually very often

683
00:42:27,000 --> 00:42:29,800
Speaker 1: when you drive, you're probably needed to have a tail insurance.

684
00:42:30,920 --> 00:42:35,600
Speaker 1: But then when it comes to portfolios, those who crede

685
00:42:35,640 --> 00:42:38,879
Speaker 1: their own money or who invest their own money will

686
00:42:39,360 --> 00:42:41,520
Speaker 1: find it natural to say I'm not going to invest

687
00:42:41,600 --> 00:42:43,920
Speaker 1: on this, and a stail risk is expensive vestment. I'm

688
00:42:43,920 --> 00:42:45,680
Speaker 1: not going to invest in that. And then you have

689
00:42:45,760 --> 00:42:50,879
Speaker 1: a second category. People are paid to invest and you say, oh, well,

690
00:42:51,000 --> 00:42:53,080
Speaker 1: this is expensive. I'm going to do that. Of course,

691
00:42:53,080 --> 00:42:55,759
Speaker 1: it's not your money, and that's that's the skill of

692
00:42:55,800 --> 00:42:58,919
Speaker 1: to be a problem. Speaking of tail risk, this week

693
00:42:59,040 --> 00:43:02,759
Speaker 1: that we're recording, several people sound an open letter saying

694
00:43:02,800 --> 00:43:06,719
Speaker 1: that we should halt development of technologies along the lines

695
00:43:06,760 --> 00:43:09,040
Speaker 1: of AI, and that there is an imminent risk, at

696
00:43:09,120 --> 00:43:12,719
Speaker 1: least some people believe of these computers becoming so powerful

697
00:43:12,800 --> 00:43:16,000
Speaker 1: that they wipe out all living things on Earth. Sounds

698
00:43:16,040 --> 00:43:18,520
Speaker 1: like the ultimate tail risk. I'm not going to ask

699
00:43:18,560 --> 00:43:20,399
Speaker 1: you how you would hedge against that, because I doubt

700
00:43:20,719 --> 00:43:23,719
Speaker 1: that would be a scenario worth hedging for. But is

701
00:43:23,760 --> 00:43:26,560
Speaker 1: that a tail risk in your view? Are are we

702
00:43:26,680 --> 00:43:29,960
Speaker 1: on track to develop computers that will eliminate life as

703
00:43:30,000 --> 00:43:32,759
Speaker 1: we know it? I don't think so. Number One is

704
00:43:34,040 --> 00:43:37,320
Speaker 1: people are worried that ship will put them out of business.

705
00:43:37,920 --> 00:43:42,040
Speaker 1: That's why to issue these calls. I'm worried about that. Yeah, well,

706
00:43:42,080 --> 00:43:46,080
Speaker 1: I mean GP is not running red lights, traffic lights,

707
00:43:46,320 --> 00:43:50,000
Speaker 1: it's not running things that are consequential. And when AI

708
00:43:50,080 --> 00:43:52,120
Speaker 1: starts running these things, and we'll talk about it, but

709
00:43:52,280 --> 00:43:55,719
Speaker 1: for the time being, we'll talking about development. And it

710
00:43:56,040 --> 00:43:59,960
Speaker 1: looks like it's a problistic machine no more nor less

711
00:44:00,040 --> 00:44:04,040
Speaker 1: with the defects of probabilistic machines. And the reason I

712
00:44:06,280 --> 00:44:09,640
Speaker 1: talk a little bit about AI because as the statistician,

713
00:44:10,560 --> 00:44:15,239
Speaker 1: it's just nothing but nonlinear statistics. That's what is right,

714
00:44:16,280 --> 00:44:19,919
Speaker 1: and it's the statistical device, and it worked a statistical device,

715
00:44:20,040 --> 00:44:23,120
Speaker 1: but we know the shortcomings and statistical machineries and it

716
00:44:23,280 --> 00:44:27,400
Speaker 1: has all the so I'm not even away nobody's going

717
00:44:27,480 --> 00:44:32,960
Speaker 1: to use that I for things beyond automated searches or

718
00:44:33,360 --> 00:44:35,759
Speaker 1: it just automates a lot of things that can be automated.

719
00:44:36,440 --> 00:44:39,920
Speaker 1: And unfortunately a lot of people feel threatened because they

720
00:44:40,000 --> 00:44:44,520
Speaker 1: see the discourse by by by the chat GDP very

721
00:44:44,560 --> 00:44:48,879
Speaker 1: similar to their own, because it's a bullshit. I think

722
00:44:48,920 --> 00:44:51,960
Speaker 1: this would be so far. I don't see anything as

723
00:44:52,000 --> 00:44:54,800
Speaker 1: far as society, I don't see It's not like with

724
00:44:54,920 --> 00:44:58,239
Speaker 1: the pandemic way to see something spreading. What's the tail

725
00:44:58,360 --> 00:45:03,640
Speaker 1: risk that you think investors are most underestimating nowadays? Okay,

726
00:45:03,800 --> 00:45:08,440
Speaker 1: that's the fact that zero interest rates are very unnatural

727
00:45:09,160 --> 00:45:11,719
Speaker 1: and if you raise rates to a normal level, and

728
00:45:11,840 --> 00:45:15,920
Speaker 1: what's normal levels, say between forms six percent said, you

729
00:45:16,000 --> 00:45:17,839
Speaker 1: know would like to have higher interest rates, but there

730
00:45:17,840 --> 00:45:20,640
Speaker 1: are some pressures you like to have a higher base

731
00:45:20,760 --> 00:45:23,200
Speaker 1: because if you're at four percent interest rate, then lower it.

732
00:45:23,239 --> 00:45:25,480
Speaker 1: If you have a crisis, you can you can go down,

733
00:45:25,640 --> 00:45:27,879
Speaker 1: you can go up. But if you interestrate at zero

734
00:45:27,920 --> 00:45:29,800
Speaker 1: and you have further crisis, you don't know what to do.

735
00:45:30,600 --> 00:45:33,239
Speaker 1: So or at least you can play with interest rates,

736
00:45:33,560 --> 00:45:35,880
Speaker 1: we have to look for something else suggesting on measures.

737
00:45:36,480 --> 00:45:39,400
Speaker 1: So I think that if you look at interest rates

738
00:45:39,719 --> 00:45:42,839
Speaker 1: higher than three percent of term as a discount rate,

739
00:45:43,280 --> 00:45:46,800
Speaker 1: then equities aren't trouble because I'm not priced for that.

740
00:45:49,000 --> 00:45:50,759
Speaker 1: So this is where where you're gonna look at it.

741
00:45:50,800 --> 00:45:53,200
Speaker 1: You can look at structurally, the equities aren't trouble. But

742
00:45:53,360 --> 00:45:57,200
Speaker 1: I think that many things will you know, the equity

743
00:45:57,239 --> 00:46:00,520
Speaker 1: would be the last you're all say, there's a lot

744
00:46:00,560 --> 00:46:03,480
Speaker 1: of things that would be in trouble. First, I have

745
00:46:03,880 --> 00:46:07,759
Speaker 1: two final questions. One is very short, do you still

746
00:46:08,040 --> 00:46:11,080
Speaker 1: eat squid ink pasta? And where's the best squid in pasta?

747
00:46:11,280 --> 00:46:14,879
Speaker 1: These are the important question. Where's the best important? Yeah? Yeah,

748
00:46:16,080 --> 00:46:19,480
Speaker 1: if you want good squil in pasta. If you want

749
00:46:19,560 --> 00:46:22,560
Speaker 1: good squitt in no pasta, you'll go Lebanon. There's no

750
00:46:22,880 --> 00:46:25,600
Speaker 1: the major recipes are the best. And if you want

751
00:46:25,960 --> 00:46:28,080
Speaker 1: a good squilling pasta. You got to go to sat

752
00:46:28,800 --> 00:46:32,920
Speaker 1: okay if you want squilling results though northern Italy. And

753
00:46:33,080 --> 00:46:35,640
Speaker 1: then if you you know, then I would say lower

754
00:46:35,760 --> 00:46:38,920
Speaker 1: on the list of Spain, you go for the arrows,

755
00:46:40,120 --> 00:46:43,719
Speaker 1: the bay. You know, the black so the Black rides

756
00:46:43,880 --> 00:46:48,320
Speaker 1: so so I New York yere I you know, I

757
00:46:48,680 --> 00:46:53,640
Speaker 1: don't recommend too much, but I can cook. I'm learning

758
00:46:53,680 --> 00:46:55,959
Speaker 1: to cook it. And it was in two three years

759
00:46:56,520 --> 00:47:00,879
Speaker 1: to produce the Descent dish Well, Tracy, and I would

760
00:47:00,920 --> 00:47:04,080
Speaker 1: love to do a live video episode coming over to

761
00:47:04,200 --> 00:47:07,600
Speaker 1: your house sometime and having you prepare us a squid

762
00:47:07,640 --> 00:47:12,120
Speaker 1: in dish in three years twenty see times. Yeah. Well,

763
00:47:12,640 --> 00:47:15,720
Speaker 1: so then one one last question, and I really appreciate

764
00:47:15,840 --> 00:47:18,239
Speaker 1: the time I have to say if I'm if I'm

765
00:47:18,280 --> 00:47:20,919
Speaker 1: being just like totally blunt. I know that you say

766
00:47:21,040 --> 00:47:24,840
Speaker 1: that many of your readers of Anti Fragile and some

767
00:47:24,960 --> 00:47:28,040
Speaker 1: of your other works misunderstood your work, and I get that,

768
00:47:28,640 --> 00:47:32,040
Speaker 1: but I also think, I mean, if I'm just being blunt,

769
00:47:32,080 --> 00:47:34,400
Speaker 1: I think like your tone has changed. You seem a

770
00:47:34,440 --> 00:47:38,040
Speaker 1: little less bombastic than previous times we have chatted. You

771
00:47:38,200 --> 00:47:42,160
Speaker 1: yourself have gotten let me no, maybe, but it's because

772
00:47:42,200 --> 00:47:47,640
Speaker 1: you agree with more because there's always a spile us.

773
00:47:47,719 --> 00:47:49,680
Speaker 1: If you agree with the message, all right, that's fair

774
00:47:49,800 --> 00:47:52,239
Speaker 1: to see you you you you give a lot of

775
00:47:52,239 --> 00:47:54,960
Speaker 1: flag to the messenger. And yeah, we're enough fair enough

776
00:47:55,080 --> 00:47:58,560
Speaker 1: that no perfect question. So that's what one thing is

777
00:47:58,600 --> 00:48:03,000
Speaker 1: that I had. Let it to go back to antifragile,

778
00:48:03,080 --> 00:48:06,919
Speaker 1: it's just a brief summary. You know what it means.

779
00:48:07,040 --> 00:48:09,840
Speaker 1: I means that we need stressors, we need low grade

780
00:48:09,880 --> 00:48:13,279
Speaker 1: a lot of low grade stresses, let's see. And and

781
00:48:13,840 --> 00:48:20,080
Speaker 1: the companies need to encounter a few problems because you

782
00:48:20,560 --> 00:48:24,880
Speaker 1: up regulate and you get stronger after that. But it

783
00:48:25,000 --> 00:48:28,000
Speaker 1: doesn't mean that we should tolerate tailor risk. It's all

784
00:48:28,040 --> 00:48:30,799
Speaker 1: conditional on a volume tail risk. And and and if

785
00:48:30,840 --> 00:48:34,680
Speaker 1: you get stronger jumping, you know, one foot Howard leaders

786
00:48:35,000 --> 00:48:37,319
Speaker 1: are going to kill you. So just just don't take

787
00:48:37,360 --> 00:48:41,719
Speaker 1: the idea too far. It's very local. That's the idea

788
00:48:41,719 --> 00:48:45,680
Speaker 1: of anti fragile. And an antifragial investment is now something

789
00:48:45,840 --> 00:48:50,160
Speaker 1: called antifragile by some web thinker. You know. Antifrassial investment

790
00:48:50,320 --> 00:48:54,640
Speaker 1: is something that we acts very well to the misfortunes

791
00:48:55,200 --> 00:48:59,799
Speaker 1: in the market. And then definitely a lot bitcoins, let's

792
00:48:59,800 --> 00:49:02,719
Speaker 1: see Sam Nicholas till This was a thrill. Thank you

793
00:49:02,840 --> 00:49:05,800
Speaker 1: so much for coming on. And I'm looking forward to

794
00:49:05,920 --> 00:49:08,280
Speaker 1: dinner at your house in twenty twenty six and another

795
00:49:08,360 --> 00:49:12,319
Speaker 1: episode in Yeah, and we'll come anywhere, Tracy, and I think,

796
00:49:12,360 --> 00:49:15,160
Speaker 1: Tracy and I would you would love to visit you

797
00:49:15,239 --> 00:49:17,399
Speaker 1: in Lebanon. We'll bring a crew, we'll film it. It'll

798
00:49:17,440 --> 00:49:19,439
Speaker 1: be great, and then we'll have you back on again

799
00:49:19,480 --> 00:49:22,399
Speaker 1: in fifteen years, you know, assuming the AI hasn't killed

800
00:49:22,440 --> 00:49:24,480
Speaker 1: us all. But uh, thank you so much. This was

801
00:49:24,520 --> 00:49:27,160
Speaker 1: a real, uh real pleasure. Thanks, thanks very much, very much,

802
00:49:27,400 --> 00:49:44,360
Speaker 1: Thank you. Thanks the same. I appreciate it so Tracy.

803
00:49:44,560 --> 00:49:48,680
Speaker 1: The big question is did the seam change or just

804
00:49:48,800 --> 00:49:53,000
Speaker 1: suddenly he says things that flatter our biases, so suddenly

805
00:49:53,080 --> 00:49:56,160
Speaker 1: we perceive him to have changed. I mean, I imagine

806
00:49:56,280 --> 00:49:59,000
Speaker 1: it's a bit of both. But I think my first

807
00:49:59,000 --> 00:50:01,040
Speaker 1: of all, I enjoyed that that was so fun a lot.

808
00:50:01,760 --> 00:50:05,600
Speaker 1: But secondly, I think my big like how I learned

809
00:50:05,680 --> 00:50:09,800
Speaker 1: to stop worrying and love the teleb moment, is you

810
00:50:09,920 --> 00:50:11,640
Speaker 1: kind of have to realize that a lot of the

811
00:50:11,920 --> 00:50:15,960
Speaker 1: criticisms and things he says about others kind of apply

812
00:50:16,400 --> 00:50:21,840
Speaker 1: to himself. Which doesn't necessarily which doesn't necessarily make them untrue.

813
00:50:21,960 --> 00:50:24,800
Speaker 1: They're still very valuable insights. But it's either you know,

814
00:50:24,920 --> 00:50:28,640
Speaker 1: you grasp that and it frustrates you enormously, or you

815
00:50:28,760 --> 00:50:32,480
Speaker 1: just roll with it and appreciate the insights nonetheless. And

816
00:50:32,560 --> 00:50:34,279
Speaker 1: I think I'm in the stage of my life where

817
00:50:34,320 --> 00:50:36,400
Speaker 1: I'm just going to roll with it. That's so funny,

818
00:50:36,520 --> 00:50:39,000
Speaker 1: especially because he specifically is like, no, no, no, I

819
00:50:39,160 --> 00:50:42,240
Speaker 1: published in academic journals. I am not a all purpose

820
00:50:43,040 --> 00:50:46,120
Speaker 1: bullshit or you know. One thing that I think throughout

821
00:50:46,200 --> 00:50:49,960
Speaker 1: and I have to say for years a point of

822
00:50:50,120 --> 00:50:54,319
Speaker 1: his that I've always whatever cycle he's in, whoever hates

823
00:50:54,360 --> 00:50:56,880
Speaker 1: him at a given moment, that I've always respected of

824
00:50:56,960 --> 00:50:58,880
Speaker 1: that I've always thought it was true. It is a

825
00:50:59,000 --> 00:51:02,160
Speaker 1: point about the difference between a plumber and an economist,

826
00:51:02,440 --> 00:51:06,200
Speaker 1: or the difference between a doctor and an epidemiologist, which

827
00:51:06,239 --> 00:51:09,200
Speaker 1: I think is like a really like insightful true point

828
00:51:09,320 --> 00:51:12,520
Speaker 1: that like, you know, a plumber is an expert on plumbing.

829
00:51:12,640 --> 00:51:16,120
Speaker 1: They they've fixed a pipe or a toilet, or a

830
00:51:16,200 --> 00:51:20,920
Speaker 1: sewer system or a shower system thousands and thousands of times.

831
00:51:21,239 --> 00:51:23,960
Speaker 1: There is very little new that you can ever show

832
00:51:24,040 --> 00:51:26,800
Speaker 1: a plumber that they haven't seen, and there is a

833
00:51:26,920 --> 00:51:30,600
Speaker 1: certain level of skill set and ability to solve things

834
00:51:30,880 --> 00:51:35,120
Speaker 1: that one can only get after having fixed a lot

835
00:51:35,200 --> 00:51:38,719
Speaker 1: of toilets or pipes, which and that's why there's the

836
00:51:38,719 --> 00:51:41,160
Speaker 1: whole apprenticeship thing. And I think that is like a

837
00:51:41,280 --> 00:51:44,759
Speaker 1: really useful heuristic to talking to anyone, which is like,

838
00:51:45,080 --> 00:51:47,960
Speaker 1: are they really an expert, have they done something that's

839
00:51:48,120 --> 00:51:50,440
Speaker 1: built up deep expertise, or are they just sort of

840
00:51:50,640 --> 00:51:54,160
Speaker 1: like kind of winging it right. I think that's a

841
00:51:54,480 --> 00:51:58,840
Speaker 1: completely valid point, and there's only so much expertise that

842
00:51:58,960 --> 00:52:01,960
Speaker 1: I think one person and can really have. You know,

843
00:52:02,080 --> 00:52:04,640
Speaker 1: you can't expect people to be an expert in everything.

844
00:52:04,960 --> 00:52:06,759
Speaker 1: But on the other hand, you know, he talked a

845
00:52:06,840 --> 00:52:10,480
Speaker 1: lot about the world becoming more complex, and I think

846
00:52:10,600 --> 00:52:14,960
Speaker 1: this is where the instinct comes to try to understand

847
00:52:15,440 --> 00:52:18,239
Speaker 1: more and more things, because yes, a plumber, he's seen

848
00:52:18,320 --> 00:52:22,160
Speaker 1: many many clog toilets and he can probably fix them

849
00:52:22,280 --> 00:52:26,520
Speaker 1: in his sleep. But then when something unexpected happens, like

850
00:52:26,840 --> 00:52:30,960
Speaker 1: for instance, COVID and a supply chain crisis, that impacts

851
00:52:31,160 --> 00:52:33,759
Speaker 1: his ability to get I don't know, those little like

852
00:52:33,920 --> 00:52:37,440
Speaker 1: toilet pump things that seems to like that's where the

853
00:52:37,560 --> 00:52:40,840
Speaker 1: instinct to try to understand the whole comes from, and

854
00:52:40,960 --> 00:52:43,239
Speaker 1: the irony and all of this is that, like, that's

855
00:52:43,280 --> 00:52:45,680
Speaker 1: a lot of what Teleb spends his time doing, right,

856
00:52:45,800 --> 00:52:50,279
Speaker 1: is trying to identify and presumably positioned for these sort

857
00:52:50,320 --> 00:52:53,600
Speaker 1: of unexpected risks. You know, it is interesting the difference

858
00:52:53,719 --> 00:52:57,040
Speaker 1: between in his view of the vaccine and GMOs, one

859
00:52:57,120 --> 00:53:01,560
Speaker 1: being systemic one bit, the other being the trials of

860
00:53:01,640 --> 00:53:05,080
Speaker 1: thousands and thousands of people. Is of course and intuitively

861
00:53:05,160 --> 00:53:08,600
Speaker 1: there's you know, statistics, but just a is a substitute

862
00:53:08,640 --> 00:53:13,480
Speaker 1: for time in the way that GMO crops. There's no

863
00:53:13,640 --> 00:53:15,880
Speaker 1: way to shortcut the process of like, well, what is

864
00:53:15,960 --> 00:53:20,759
Speaker 1: going to happen to the entire ecosystem of agriculture over

865
00:53:20,840 --> 00:53:22,880
Speaker 1: the next one hundred or a thousand years, because we

866
00:53:23,040 --> 00:53:27,120
Speaker 1: just literally haven't there's no way to substitute that time yet. Anyway,

867
00:53:27,320 --> 00:53:29,960
Speaker 1: I really liked that conversation. I was sad during that

868
00:53:30,080 --> 00:53:32,320
Speaker 1: period when to let block me and I'm glad we

869
00:53:32,360 --> 00:53:35,160
Speaker 1: are both I'm glad we are both unblocked because I

870
00:53:35,200 --> 00:53:37,960
Speaker 1: am very enjoying I'm watching I'm enjoying watching him his

871
00:53:38,120 --> 00:53:41,440
Speaker 1: cycling journey. I'm enjoying watching his fights, and I just

872
00:53:41,520 --> 00:53:44,680
Speaker 1: think he's an interesting guy absolutely, And I guess I'm

873
00:53:44,719 --> 00:53:48,560
Speaker 1: looking forward to having squid inc Yessa in three years time.

874
00:53:49,200 --> 00:53:51,279
Speaker 1: All I can say is there's gonna be a lot

875
00:53:51,320 --> 00:53:53,759
Speaker 1: of pressure though, because it better be good after three

876
00:53:53,840 --> 00:53:55,719
Speaker 1: years of study. Well, you know, the thing is he's

877
00:53:55,719 --> 00:53:57,840
Speaker 1: been talking about squidding forever and so the fact that

878
00:53:57,960 --> 00:54:00,839
Speaker 1: he himself says it's going to be another three years

879
00:54:00,960 --> 00:54:03,759
Speaker 1: before he's ready to like cook it, it's like, uh,

880
00:54:04,640 --> 00:54:07,279
Speaker 1: you know, he's a he's a journeyman. It sounds like

881
00:54:07,400 --> 00:54:11,640
Speaker 1: he is himself an apprentice or journeyman squid ink chef

882
00:54:11,920 --> 00:54:14,160
Speaker 1: who is not there yet. Maybe we should do a

883
00:54:14,320 --> 00:54:17,680
Speaker 1: come Dine with Me style cook off each of us,

884
00:54:17,840 --> 00:54:20,239
Speaker 1: each of us attempts to cook squid ink pasta in

885
00:54:20,320 --> 00:54:22,960
Speaker 1: a different location, and then we rate each other. All right,

886
00:54:23,200 --> 00:54:24,960
Speaker 1: I'm getting ahead of myself. Shall we leave it there?

887
00:54:25,080 --> 00:54:27,840
Speaker 1: Let's leave it there. This has been another episode of

888
00:54:27,960 --> 00:54:30,720
Speaker 1: the Odd Thoughts Podcast. I'm Tracy Alloway. You can follow

889
00:54:30,800 --> 00:54:33,880
Speaker 1: me on Twitter at Tracy Alloway and I'm Joe Wisent'tal.

890
00:54:33,960 --> 00:54:37,160
Speaker 1: You can follow me on Twitter at the Stalwart. Follow

891
00:54:37,239 --> 00:54:40,720
Speaker 1: our guest on Twitter if you're not blocked. His handle

892
00:54:40,920 --> 00:54:45,560
Speaker 1: is at n nd taleb. Follow our producers Kermen Rodriguez

893
00:54:45,680 --> 00:54:49,520
Speaker 1: at kerman Ermine and dash Bennett at dashbot. And check

894
00:54:49,560 --> 00:54:53,400
Speaker 1: out all of our podcasts under the handle at podcasts.

895
00:54:53,600 --> 00:54:57,040
Speaker 1: And if you want more odd Lots content, go to

896
00:54:57,080 --> 00:54:59,600
Speaker 1: Bloomberg dot com slash odd Lots, where you can find

897
00:54:59,640 --> 00:55:03,120
Speaker 1: trans scripts of blog newsletter. And if you want even more,

898
00:55:03,560 --> 00:55:06,319
Speaker 1: we have a really fun community online on discord where

899
00:55:06,320 --> 00:55:08,400
Speaker 1: a bunch of listeners hang out twenty four seven in

900
00:55:08,520 --> 00:55:12,720
Speaker 1: chat about things like markets, finance, economics, energy, water, AI

901
00:55:12,800 --> 00:55:15,040
Speaker 1: and all the things we talk about. Go check that

902
00:55:15,120 --> 00:55:19,000
Speaker 1: out discord dot gg slash oblocks of the invite link.

903
00:55:19,239 --> 00:55:21,319
Speaker 1: It's really fun. I've been spending a lot of time there.

904
00:55:21,560 --> 00:55:23,280
Speaker 1: Go hang out. Thanks for listening.