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Speaker 1: Pushkin.

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Speaker 2: Hey everyone, we are off for the holidays, so we

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Speaker 2: wanted to reshare one of our favorite episodes last year,

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Speaker 2: when Nate's book On the Edge came out, I had

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Speaker 2: a chance to interview him about it on the podcast,

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Speaker 2: and I thought it would be really fun to take

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Speaker 2: a look back at that interview where Nate was in

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Speaker 2: the interviewee seat. Then I wrote a little review recap

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Speaker 2: of the book on my sub stock. If somehow you

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Speaker 2: are listening to Risky Business and you have not read

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Speaker 2: the book, please read the book. It's wonderful, Nate. You

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Speaker 2: did such a great job and I really enjoyed that conversation.

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Speaker 3: Yeah, no, look, maybe I should go back and read

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Speaker 3: that book is pression. I think it's talking about all

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Speaker 3: the kind of topics from you know, prediction markets to

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Speaker 3: AI to crypto that are our only you know, to

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Speaker 3: gambling scandals right that are only becoming more pertinent today.

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Speaker 3: But thank you for that interview, Maria, And yeah, maybe

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Speaker 3: I'll take a listen to myself.

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Speaker 2: Yeah, so enjoy everyone.

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Speaker 1: So, first of all, Nate, congratulations. I know that this

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Speaker 1: was a long time coming labor of many many years.

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Speaker 3: Yeah no, I spent basically three years of my life

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Speaker 3: where I'd say this is my main professional focus, talk

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Speaker 3: to two hundred people, and I know, I just I

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Speaker 3: would recommend email really smart people and ask to have

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Speaker 3: a conversation with them and pretend it's for a book,

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Speaker 3: and then don't write the book. That's probably still worth

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Speaker 3: it in some sense. But no, I actually went ahead

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Speaker 3: and wrote the book.

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Speaker 1: And we're very glad you did. So I'm going to

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Speaker 1: start with Nate. In the book, you make this distinction

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Speaker 1: between two community. You have the village and you have

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Speaker 1: the river. Talk me through what exactly those mean.

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Speaker 3: Let's start with the village, even though it's not the

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Speaker 3: focus of the book, just because it's a little bit

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Speaker 3: easier to define in a term that's more conventional. The

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Speaker 3: village is basically the kind of liberal, progressive East Coast establishment.

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Speaker 3: So it's the New York Times, it's Harvard University, it's

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Speaker 3: think tanks and nonprofits, it's you know, government when you

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Speaker 3: have a Democrat in office at least, it's kind of

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Speaker 3: the expert class, the professional managerial class, that whole cluster

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Speaker 3: of attributes. By contrast, the river is kind of the

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Speaker 3: world that I suppose I inhabit, or I imagine myself inhabiting.

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Speaker 3: It's the world of calculator risk taking, where people are

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Speaker 3: some combination of being very analytical and extremely wildly competitive.

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Speaker 3: So like poker is an archetypal maybe the archetypal river activity.

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Speaker 3: But as I discovered this world in the book, you know,

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Speaker 3: I found that the venture capitalist and the effective at

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Speaker 3: and the hedge fund people, and the sports betters, even

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Speaker 3: the crypto people, they all have a common vocabulary. They

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Speaker 3: all talk about things in roughly the same ways, and

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Speaker 3: so it's like it's not a place, it's a discrete community.

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Speaker 3: I mean, in Las Vegas and Silicon Valley you might

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Speaker 3: get closer to an actual network, but like they're just

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Speaker 3: these people that I think are often poorly understood by

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Speaker 3: the mainstream media and yet are incredibly wealthy and powerful.

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Speaker 3: Many of the richest people in the world have some

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Speaker 3: of these river tendencies, and like, surprisingly to me, would

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Speaker 3: take my emails and my phone calls because they kind

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Speaker 3: of recognize like a fellow traveler a little bit. And

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Speaker 3: then what I discovered is that the kind of conceit

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Speaker 3: of the proposal that actually there's a commonalities in how

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Speaker 3: people think more was more literally true than I had thought,

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Speaker 3: and that there's more literal crossover poker players who become

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Speaker 3: effective altruists, or hedge funders who host poker games, or

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Speaker 3: venture capitalists who do sports betting is a metaphor for

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Speaker 3: some of these things. So it's a real place of

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Speaker 3: a real group of people that I think, Maria are

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Speaker 3: in our circles, maybe not the only circle to run in,

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Speaker 3: but certainly one common type. And I felt like I

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Speaker 3: had the opportunity to tell a really good story about

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Speaker 3: these people. I did not. I think I got lucky.

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Speaker 3: I think I'm running in my like ninety eighth percentile

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Speaker 3: run good for how some of these topics really blew up.

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Speaker 3: I mean Ai in particular, although that was a fairly

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Speaker 3: incidental thing in the book Effective Altruism and its particularly

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Speaker 3: Sam Bakman Free who had talked to first before the

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Speaker 3: collapse and then got to talk to you afterwards as well.

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Speaker 1: Yeah, so you draw some really interesting parallels between worlds

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Speaker 1: that I didn't necessarily connect, like obviously poker sports bedding,

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Speaker 1: Like yes, you know, I get why those things are related.

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Speaker 1: Then you go into finance, you go into venture capital

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Speaker 1: and the hedge world in Silicon Valley you go into

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Speaker 1: AI and people who are kind of the AI researchers,

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Speaker 1: and I actually love your little side where you have

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Speaker 1: the physical risk taker. Yes, where you have the astronauts,

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Speaker 1: and I love that you have a female astronaut. By

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Speaker 1: the way, did you on purpose, like, did you try

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Speaker 1: to have two women or did that just so happen?

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Speaker 1: You know, because as you know, like it's something that

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Speaker 1: is interesting to me, And when you're talking about risk takers,

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Speaker 1: people often assume men, and so I was very curious

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Speaker 1: if that was a deliberate choice or if it just

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Speaker 1: so happens that these most interesting stories happen to be female.

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Speaker 3: No, there there was a deliberate choice made to I mean, look,

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Speaker 3: it's a careful balance to strike because it is a

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Speaker 3: world that's very male. Also world that is pokers a

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Speaker 3: little bit more diverse, but the rest of the river

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Speaker 3: is quite white and or Asian and or Asian sometimes.

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Speaker 3: So there was a deliberate choice to like not sugarcoat

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Speaker 3: this and pretend it's all a fifty fifty like. But

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Speaker 3: also the I call them characters. The people I talked to,

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Speaker 3: like Katharine Sullivan, this astronaut's amazing, and she has an

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Speaker 3: amazing stories and she was studying like oceanography in Nova

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Speaker 3: Scotia and beat out like ten thousand other people to

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Speaker 3: become an astronaut. I mean, it's an amazing story. Cataline Kaiko,

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Speaker 3: I had to learn all the names pronounced correctly for

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Speaker 3: the audiobook and no I'd forgotten them. Who was this

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Speaker 3: woman who like smuggled money in a Teddy Bear to

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Speaker 3: move out of Hungary and then was just shot upon

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Speaker 3: in the establishment while she was working on MR and

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Speaker 3: A vaccines and eventually joined biointech and won the Nobel Prize.

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Speaker 3: Co won the Nobel Prize for her work on MR

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Speaker 3: and A vaccines. I mean, they have amazing stories. Or

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Speaker 3: someone like our mutual friend but one of the best

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Speaker 3: poker players in the world. I think Maria Ho who

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Speaker 3: has great stories to tell too so so or Vanessa

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Speaker 3: Selps is another person that we know but like it's

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Speaker 3: just an amazing person. So yeah, I mean, when you

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Speaker 3: find people like that and you want to tell their stories.

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Speaker 3: And there was, but there's also sections that directly tackle

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Speaker 3: There's one section the poker chapter about why an't there

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Speaker 3: more women in poker? And then one section in the

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Speaker 3: VC chapter about why are there so few black and

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Speaker 3: Hispanic and women founders, and so you have to take

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Speaker 3: it on directly without it being a woke book because

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Speaker 3: I'm not super woke, but you can't. But there are

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Speaker 3: real shortcomings here. I mean the VCS in particular, I think,

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Speaker 3: are they claim to want people who have overcome, who

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Speaker 3: have grit, who have overcome odds, and who are high variants,

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Speaker 3: who are different than the consensus, and yet they have

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Speaker 3: this prototype of like the on the spectrum, you know,

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Speaker 3: white or Asian twenty something nerd who dropped out of

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Speaker 3: Harvard or MIT as a sophomore and Sam mcminfreed played

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Speaker 3: into that stereotype, and they're not actually, I think, trying

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Speaker 3: to diversify their portfolio, and it just in a purely

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Speaker 3: not moral but even like financial sense of wanting like

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Speaker 3: high variance founders.

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Speaker 1: Yeah. No, I think one of the things that I

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Speaker 1: enjoyed about the book is that it's about how to

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Speaker 1: take risk well. But you also talk about some of

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Speaker 1: the shortcomings of the of the people on the River

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Speaker 1: and how they think about risk. But first, let's talk

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Speaker 1: about kind of the good elements. So what does the

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Speaker 1: river have in common when it comes to evaluating risk.

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Speaker 1: What can we take away from them when we're thinking

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Speaker 1: about Okay, I am, you know, Maria, everyday person. How

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Speaker 1: do I kind of think about risk in my life?

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Speaker 1: What do I take from these people who take risks

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Speaker 1: professionally so that I can just make better decisions and

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Speaker 1: better risk assessments myself.

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Speaker 3: So let me take the two axioms that you hear

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Speaker 3: over and over and over again in Silicon Valley and

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Speaker 3: make it very successful financially despite having all these flaws.

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Speaker 3: I mean, some of these guys are difficult people. It

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Speaker 3: is mostly guys. It's not very diverse. There are some

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Speaker 3: big flaws, right, I think their political instincts are often poor.

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Speaker 3: But they do two things, one of which is they

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Speaker 3: understand expected value, which is kind of the theme of

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Speaker 3: this show. They understand that if you can make a

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Speaker 3: bet that has a ten percent chance of one hundred

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Speaker 3: act payout, that's an extraordinarily good bet. And then you

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Speaker 3: can make a lot of those bits by aggregating different

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Speaker 3: companies into a fund, then you're kind of guaranteed to

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Speaker 3: make money and maybe make a lot of money. And

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Speaker 3: it also applies to things like evaluating nuclear war risk

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Speaker 3: or AI risk. Things that might be you know, a

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Speaker 3: two percent chance in over some timeframe, or five percent

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Speaker 3: chance whatever else, but have like very bad to infinitely

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Speaker 3: bad negative payouts. The second thing that Silicon Valley knows

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Speaker 3: is that having a longer time horizon is really worth it.

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Speaker 3: We're a country that likes to make money, but we're

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Speaker 3: a get rich quick country and not to get rich

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Speaker 3: slowly country. When you're investing in startups, early stage startups,

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Speaker 3: they often have a time horizon. You know, SpaceX took

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Speaker 3: thirteen years I think to make a first profit nine

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Speaker 3: year would actually even launched a rocket successfully or something

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Speaker 3: like that. So I think you almost always benefit from

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Speaker 3: having like a longer time horizon than other people. People

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Speaker 3: just discount the long term future wait much even in

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Speaker 3: their personal lives, I think. And so therefore those I

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Speaker 3: think are kind of the two foremost lessons and why

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Speaker 3: the book is you know, kind of ambivalently to positive

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Speaker 3: on that Silicon Valley ethos, even though the individual characters

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Speaker 3: there are are complex.

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Speaker 1: So one of the things that you stress over and

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Speaker 1: over is that people who evaluate risk correctly and make

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Speaker 1: risks that tend to pay out over the long term,

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Speaker 1: they not only can understand the upside, but they can

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Speaker 1: also protect against the risk of ruin, right, the risk

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Speaker 1: of not being able to take these risks going forward.

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Speaker 1: So first, I'd love for you to talk a little

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Speaker 1: bit about this idea of risk of ruin because you

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Speaker 1: talk about people who do it correctly and then people

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Speaker 1: who might not, and and also like how do you

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Speaker 1: how do you balance that, and how might that be

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Speaker 1: different from person to person.

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Speaker 3: Yeah, I mean, we have this abstract concept in poker

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Speaker 3: of a bank roll, which is and that is, how

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Speaker 3: much money can you spend on poker before your kind

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Speaker 3: of poker broke or at least you're severely limited in

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Speaker 3: the size of the games you might play, and you

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Speaker 3: might have to pass up positive evy opportunities. So to

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Speaker 3: some extent, this is related to the idea of opportunity cost.

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Speaker 3: There's a famous formula from sports betting called the Kelly criterion,

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Speaker 3: which tells you how much can you bet to maximize

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Speaker 3: your expected value without enduring a very high risk of ruin.

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Speaker 3: Now I can get into the technicalities of why that

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Speaker 3: formula might make you a little bit more aggressive than

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Speaker 3: you should be. In general, though like ninety percent of

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Speaker 3: people in the world are I think too risk averse

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Speaker 3: about this kind of thing. This goes back to, as

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Speaker 3: you'll know, like some of the condoment and diversity work

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Speaker 3: on prospect theory, it may have also an evolutionary base

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Speaker 3: where we weren't living in the time of abundance that

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Speaker 3: we are now and so and so. You know, you

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Speaker 3: only got one shot before it made sense to protect

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Speaker 3: your household. It made sense to be very cautious about

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Speaker 3: if you get an infection, you might die. And I

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Speaker 3: think people have not adapted to this world of abundant

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Speaker 3: but confusing opportunity that we have very much. Annie Duke

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Speaker 3: wrote a book called Quit, and she's in the book too,

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Speaker 3: also a former hooker player. Of course, she cites studies

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Speaker 3: like by Steve Levitt of fre Economics that when they

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Speaker 3: have people flip a coin to make a decision, and

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Speaker 3: a major decision, not like am I getting Thai food

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Speaker 3: or Indian for dinner tonight, which I do flip a

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Speaker 3: coin about sometimes, by the way, but things like should

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Speaker 3: I leave my partner, or should I quit my job,

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Speaker 3: or should I move across the country to a different

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Speaker 3: part of the world. People are happier on average when

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Speaker 3: they make a change and so you know, the ninety

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Speaker 3: percent of people who are risk averse maybe could take

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Speaker 3: some messages from that book. Then, but you also meet

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Speaker 3: the sbfs and the Elon Musks of the world.

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Speaker 1: And let's actually unwrap that last bit a little bit.

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Speaker 1: The sbfs and the Elon Musks because you write about

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Speaker 1: the fact that you know, obviously they are people. Wouldn't

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Speaker 1: people don't necessarily lump them together, right, because Elon Musk

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Speaker 1: is someone who is the richest man in the world,

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Speaker 1: and then SBF is going to jail. However, they do

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Speaker 1: have this one characteristic in common. So would I would

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Speaker 1: love for you to expand on that alone.

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Speaker 3: Yeah, and I should be clear, I mean I don't

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Speaker 3: mean to I am very and the book is very

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Speaker 3: unsympathetic to SBF. I'm not saying I'm sympathetic to Elon.

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Speaker 3: But Elon has accomplished a lot of things, and I

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Speaker 3: think Elon's a real person whose career wasn't based on

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Speaker 3: I mean, I don't think Sam's ull career was based

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Speaker 3: on fraud, but he didn't. You know, Elon has not

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Speaker 3: committed ten billion dollars worth of stealing people's crypto deposits. Basically, Yeah,

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Speaker 3: they are the two people that really, really really go

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Speaker 3: Baalse to the wall. I think we can use that

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Speaker 3: phrase in terms of risk.

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Speaker 1: Absolutely.

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Speaker 3: If you read the Walter Isaacson book on Elon Musk,

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Speaker 3: his poker strategy is literally going all in every hand

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Speaker 3: until he runs out of money or wins it all.

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Speaker 1: I didn't know that that was crazy to me.

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Speaker 3: It makes sense, so I've I've played in the all

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Speaker 3: in podcast poker game a couple of times with some

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Speaker 3: of Elon's buddies, and that's a very aggressive game. Shall

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Speaker 3: we say. I'm forbidden to reveal details, but there's a

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Speaker 3: lot of blind naked aggression there. You know. SBF is

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Speaker 3: even more unambiguously, I think irrational in taking a very

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Speaker 3: literal approach of utilitarianism, where you know, he has said

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Speaker 3: repeatedly he told his co founder Carolyn Ellison this. He

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Speaker 3: told the economist Tyler Cowan this that if he could

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Speaker 3: flip a coin to determine the fate of the world,

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Speaker 3: and either half the time the world is destroyed, but

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Speaker 3: the other half the time the world is like two

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Speaker 3: point oh one x as good, then he's calculating the

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Speaker 3: expected value. He's so committed to that that hey you

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Speaker 3: know one point five is greater than one. Therefore you

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Speaker 3: take the coin flip, even though you might destroy the world,

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Speaker 3: And in fact, you take that coin flip repeatedly, over

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Speaker 3: and over again. This is called the Saint Petersburg paradox,

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Speaker 3: where it's a bet that has infinite expected value, but

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Speaker 3: its infinitely likely to leave you ruins. So what should

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Speaker 3: you do? Probably not take the bet. It's not that

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Speaker 3: hard a dilemma, actually, but to SBF it was. And

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Speaker 3: in my interviews with him, you kind of find a

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Speaker 3: pattern of him saying, I mean the first thing who

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Speaker 3: I did with him when he's riding high January twenty

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Speaker 3: twenty two, Bitcoin still near its all time peaks. He's like,

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Speaker 3: if you're not really literally willing to ruin yourself, not

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Speaker 3: the fake ruined, Like if Elon Musk has to sell

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Speaker 3: Twitter and he's only worth one hundred and seventy billion

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Speaker 3: dollars instead of two hundred and twenty billion dollars, that's

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Speaker 3: not what it would describe as being ruined. And Sam

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Speaker 3: was very clear that I don't think you should protect

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Speaker 3: your downside. You have to be so risk loving that

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Speaker 3: you should literally be willing to destroy your life and

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Speaker 3: your reputation if it's positive expected value, which I think

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Speaker 3: is insane. I think reflects I mean, not a clinical diagnosis,

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Speaker 3: but I think literally some I think he's a somewhat

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Speaker 3: defective person emotionally in another way. And you know, the

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Speaker 3: fact that he was enabled by so many supposedly smart

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Speaker 3: actors is a core question that's asked by the book.

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Speaker 3: I mean, I you know, the New York Times review

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Speaker 3: understood this, which is that it's like the book is

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Speaker 3: actually pretty unsparing to some of these characters in the River,

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Speaker 3: even though like it's not a book where I'm gonna

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Speaker 3: be like Peter Teal is evil and Elon Musk is

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Speaker 3: evil and a fascist, Like that's not my style of writing.

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Speaker 3: And I think I think they're interesting people who deserve

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Speaker 3: to have their stories told by someone who kind of

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Speaker 3: understands the personality type. But SBF was a really dangerous

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Speaker 3: figure and on top of everything else, like also a

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Speaker 3: fairly bad calculator of risk. His decision to go take

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Speaker 3: the witness stand at his trial after the government's testimony

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Speaker 3: was just incredibly devastating, and they had him dead to

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Speaker 3: rights and contradicting everything he told people publicly, everything he

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Speaker 3: told me in interviews that were on the record, although

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Speaker 3: with an embario, I mean they couldn't be used until

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Speaker 3: the book was released. You know, the willingness to lie,

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Speaker 3: plus the constant miscalculations he made where he probably could

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Speaker 3: have avoided jail time by conceding defeat and not taking

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Speaker 3: the witness stand. Not at all the jail time, but

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Speaker 3: ten years or five years and not twenty You know,

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Speaker 3: why did so many people vouch for him? As a

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Speaker 3: question that I think has to be has to be pondered.

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Speaker 1: We'll be back after a quick break. You actually touched

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Speaker 1: on something there which I think is important to unwrap

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Speaker 1: a little bit more, which is that if you're taking

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Speaker 1: these huge gambles, you better be pretty damn sure your

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Speaker 1: calculations are accurate, that your percentages are accurate. But we're

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Speaker 1: living in a world where you can't do that, and

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Speaker 1: so I'm curious. You know, you build models, right that

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Speaker 1: try to kind of model things that are uncertain, that

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Speaker 1: are unknowable, that have this margin of error, And so

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Speaker 1: I think you understand and more than most people that

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Speaker 1: you know, if you say sixty percent certain or like

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Speaker 1: sixty percent chance of this, like you're not actually like

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Speaker 1: that there is a margin of error there, right, Like,

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Speaker 1: if you fifty one percent likely that you're going to

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Speaker 1: have a plus EV and forty nine, what if you're wrong? Right, Like,

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Speaker 1: what if it's forty nine that you're that you're actually

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Speaker 1: negative TV and fifty one that you're going to destroy civilization?

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Speaker 1: So I'm I'd love you to walk us through that

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Speaker 1: thinking and through kind of the hubris that you both

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Speaker 1: need to succeed in some ways in this area. But

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Speaker 1: also that might make you over confident of these sorts

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Speaker 1: of percentages where you really really can't afford to be.

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Speaker 3: Yeah, look, we've seen bad election models. We saw models

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Speaker 3: I gave. Yeah, saw models that gave Trump a one

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Speaker 3: percent chance or zero point one percent chance of winning

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Speaker 3: in twenty sixteen. We saw models earlier this year that

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Speaker 3: gave Biden, even after he had tanked in the debate,

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Speaker 3: a fifty to fifty chance of winning, which didn't make

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Speaker 3: any sense. Even building models for these kinds of close

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Speaker 3: problems like elections or sports forecasting is pretty tough. You

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Speaker 3: have a lot of choices to make as a modeler,

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Speaker 3: your biases might creep in and other things. So to

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Speaker 3: then kind of take these very back of the envelope

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Speaker 3: informal models and try to apply them to every problem

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Speaker 3: in the world. Is, you know, obviously prone to going

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Speaker 3: wrong to some extent, and like in effective altruism, they're

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Speaker 3: very literal about this. I talked to Will mccaskell, who

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Speaker 3: was kind of one of the founders or at least

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Speaker 3: the brand name of EA and wrote a book called

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Speaker 3: What We Owe the Future. He calls himself a long

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Speaker 3: term'speaning he's concerned about the very very very far future.

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Speaker 3: The first time to talk to him, he was like, okay,

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Speaker 3: so how do we weigh like a human's life against

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Speaker 3: an animal's life? And he's like, well, it should be

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Speaker 3: based on the number of neurons in the animal's brain,

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Speaker 3: or maybe the number of neurons to the one third

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Speaker 3: power or something like that. It's a good approximation, but

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Speaker 3: that leads to weird things like an elephant elephants. Elephants

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Speaker 3: are actually with more than human beings by that calculation,

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Speaker 3: it turns out, but you also face some comfortable decisions.

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Speaker 3: The book uses a story of a time when a

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Speaker 3: dog named I didn't know.

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Speaker 1: Yeah, I didn't realize that the poodle like I didn't

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Speaker 1: know this story, so please, yeah, please.

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Speaker 3: There's a poodle that name named Dakota that gets loose

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Speaker 3: in Prospect Park or some park in North Brooklyn from

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Speaker 3: its owner and dashes into the subways at the first

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Speaker 3: stop inbound on the F train from Manhattan to Brooklyn. So,

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Speaker 3: and this is happening at like three point thirty on

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Speaker 3: a weekday, so you're starting to get the rush hour commute.

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Speaker 3: And the question is should we shut down the entire

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Speaker 3: F line in order to search for and rescue Dakota,

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Speaker 3: which they did, and Dakota popped up at some other

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Speaker 3: subway station an hour or so later. But that's a

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Speaker 3: case where I'll put it this. This is how I

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Speaker 3: put it in the book. If you had, like a

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Speaker 3: human baby who had fallen onto the tracks, like, no

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Speaker 3: one would dispute that we should halt every train until

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Speaker 3: that baby is found. If it was a squirrel, then

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Speaker 3: we would just run under the squirrel, right, no questions.

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Speaker 1: Asked, We would we would.

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Speaker 3: I mean, I hate squirrels, and the dog is intuitively

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Speaker 3: kind of a close decision, and so and so and

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Speaker 3: but the fact is that you have to make a decision,

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Speaker 3: and we had to make these decisions in COVID, where

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Speaker 3: you know, what's the cost of the shutting down schools

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Speaker 3: versus the cost of preventing some degree of sickness and death? Right,

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00:21:39,284 --> 00:21:41,284
Speaker 3: I mean, that's a real that's a real trade off,

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Speaker 3: or things that are more intangible. What's the cost of

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Speaker 3: the undermining people's happiness because they can't at least they're

404
00:21:49,724 --> 00:21:52,444
Speaker 3: not supposed to see their friends in person during COVID,

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Speaker 3: or they can't go to a baseball game, or go

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Speaker 3: to a restaurant, or see their dying relative in a

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Speaker 3: hospital or all, or go to church, all these other things,

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Speaker 3: like like what's the cost of that? And we make

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Speaker 3: these decisions with with you know, different heuristics and different

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Speaker 3: rules of thumb that are often very clumsy and maybe

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Speaker 3: maybe words of magnitude wrong. I think the decision to

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Speaker 3: keep school shut down after vaccines were available is like

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Speaker 3: wrong by like ten x or one hundred x, like

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Speaker 3: unambiguously a terrible decision that some Blue states made even

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Speaker 3: after vaccine availability. So you kind of get this dilemma

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Speaker 3: where you have to make you have to make decisions,

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Speaker 3: you have to make some calculation. But I think people

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Speaker 3: underestimate how back of the envelope. These are and when

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Speaker 3: you start to put numbers to things, and people sometimes

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Speaker 3: stop asking questions about models where they should ask more questions.

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Speaker 1: Is there something that you would recommend, because this is

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Speaker 1: something that I've come across, you know, many times at

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Speaker 1: a wall I hit in a lot of my research.

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Speaker 1: Is there something that you have seen in people who

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Speaker 1: are successful where they are able to modulate or at

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Speaker 1: least like self check their overconfidence and their hubris, because

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Speaker 1: it seems like most of the people on the river

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Speaker 1: don't do that, right, Like Elon Musk, absolutely not. Like

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Speaker 1: Computer Tel absolutely not. Like all of these men who

430
00:23:11,244 --> 00:23:14,084
Speaker 1: you talk about, they don't seem to have a switch

431
00:23:14,124 --> 00:23:15,884
Speaker 1: where they can be like, Okay, you know what, let

432
00:23:15,964 --> 00:23:18,324
Speaker 1: me take a step back and self evaluate. Did you

433
00:23:18,404 --> 00:23:22,404
Speaker 1: find anyone who was able to do that? And if so, how,

434
00:23:22,524 --> 00:23:23,524
Speaker 1: like how do you do that?

435
00:23:25,004 --> 00:23:27,444
Speaker 3: I mean, in some ways, some of the poker players

436
00:23:27,484 --> 00:23:30,124
Speaker 3: I talked to seem like they're more at ease with

437
00:23:30,244 --> 00:23:33,724
Speaker 3: themselves and can modulate it somehow. You know, Jason Kuhn,

438
00:23:33,764 --> 00:23:35,684
Speaker 3: for example, is an interesting poker player who has some

439
00:23:35,804 --> 00:23:38,484
Speaker 3: very you know, had a rough upbringing, has some very

440
00:23:38,524 --> 00:23:42,124
Speaker 3: aggressive tendencies. But I think has some type of perspective

441
00:23:42,124 --> 00:23:44,684
Speaker 3: of there's this very competitive part of him that he

442
00:23:44,764 --> 00:23:47,604
Speaker 3: understands can be unleashed, but has to be you know,

443
00:23:48,604 --> 00:23:51,524
Speaker 3: meet it out in doses somehow. Look, there is some

444
00:23:51,844 --> 00:23:54,284
Speaker 3: bias in the book in the following sense, which is

445
00:23:54,284 --> 00:23:57,444
Speaker 3: that the people who are quote unquote crazy or the

446
00:23:57,484 --> 00:23:59,724
Speaker 3: people who really you know, like to talk about themselves

447
00:23:59,724 --> 00:24:02,644
Speaker 3: and take crazy risks, make for better stories. So yeah,

448
00:24:02,644 --> 00:24:05,764
Speaker 3: I mean I'm thinking about like, you know, Michael Moritz

449
00:24:05,964 --> 00:24:10,244
Speaker 3: is a Sequoia capital venture capitalist who's been very successful

450
00:24:10,284 --> 00:24:13,684
Speaker 3: early investor in Google of many of other companies. He's

451
00:24:13,724 --> 00:24:16,084
Speaker 3: a guy who I think is probably pretty measured actually.

452
00:24:16,084 --> 00:24:20,684
Speaker 3: A former journalist Michael Moritz ven Knowed Kosla is another

453
00:24:20,724 --> 00:24:24,484
Speaker 3: one who invests in kind of alternative things like alternative

454
00:24:24,644 --> 00:24:27,964
Speaker 3: energy or or you know, what's it called artificial meat,

455
00:24:28,204 --> 00:24:29,924
Speaker 3: things like that. I mean, he seems to be like

456
00:24:30,004 --> 00:24:33,724
Speaker 3: reasonably well balanced. Patrick Collison, the founder of Strike. But

457
00:24:34,084 --> 00:24:36,524
Speaker 3: and these people are all in the book, but because

458
00:24:36,564 --> 00:24:39,844
Speaker 3: they're less bombastic, they maybe get like both a little

459
00:24:39,844 --> 00:24:41,644
Speaker 3: bit less of a write up from me and then

460
00:24:41,724 --> 00:24:45,124
Speaker 3: and then kind of having some lost in translation problem too.

461
00:24:45,164 --> 00:24:48,724
Speaker 3: But yeah, look, over competence is a really big problem

462
00:24:49,604 --> 00:24:52,244
Speaker 3: for people in the River because by definition, these are

463
00:24:52,284 --> 00:24:56,204
Speaker 3: people who probably won their first couple of big bets,

464
00:24:57,364 --> 00:25:01,164
Speaker 3: sometimes by skill, sometimes by luck. I'd say often more

465
00:25:01,244 --> 00:25:04,124
Speaker 3: luck than skill, but usually some of both. And when

466
00:25:04,164 --> 00:25:05,924
Speaker 3: you're on a winning streak, then you can think that

467
00:25:06,004 --> 00:25:08,684
Speaker 3: your God's gift to whatever venture that you happen to

468
00:25:08,724 --> 00:25:11,724
Speaker 3: be involved in, and you can be overconfident and that

469
00:25:11,764 --> 00:25:14,524
Speaker 3: because there is like there is like fruitful territory. I mean,

470
00:25:14,564 --> 00:25:16,564
Speaker 3: the village leaves a lot to be desired for how

471
00:25:16,564 --> 00:25:19,844
Speaker 3: it assesses risk and does other things, and so you know,

472
00:25:20,084 --> 00:25:22,724
Speaker 3: you can in some ways the critiques that these two

473
00:25:22,724 --> 00:25:26,284
Speaker 3: communities have of one another are both pretty smart. I mean,

474
00:25:26,284 --> 00:25:29,084
Speaker 3: I think the River's critique of the village that these

475
00:25:29,084 --> 00:25:32,844
Speaker 3: guys are too risk averse, that they're partisan, that they're

476
00:25:32,844 --> 00:25:35,484
Speaker 3: too concerned with social perception. I think that's basically right.

477
00:25:35,524 --> 00:25:39,124
Speaker 3: But also some of the Silicon Valley founders and vcs

478
00:25:39,204 --> 00:25:41,484
Speaker 3: are are total arrogant pricks.

479
00:25:42,924 --> 00:25:45,964
Speaker 1: Yeah, that's one way of putting it. And I think

480
00:25:46,044 --> 00:25:48,244
Speaker 1: that was something that you're that you just hinted at,

481
00:25:48,924 --> 00:25:52,684
Speaker 1: which is I think going to necessarily be like a

482
00:25:52,724 --> 00:25:55,084
Speaker 1: problem when you're trying to figure out who to interview

483
00:25:55,124 --> 00:25:57,644
Speaker 1: for the book. And it's just in this industry, well

484
00:25:57,684 --> 00:26:01,644
Speaker 1: in the world in general, is we have this survivor bias, right,

485
00:26:01,724 --> 00:26:05,764
Speaker 1: which we see the people who are successful. How are

486
00:26:05,764 --> 00:26:07,884
Speaker 1: you going to go out and interview all the people

487
00:26:07,884 --> 00:26:10,804
Speaker 1: who took these risks, who where it didn't pan out.

488
00:26:10,964 --> 00:26:12,684
Speaker 1: One of the things that really struck me as I

489
00:26:12,724 --> 00:26:14,924
Speaker 1: was reading your book was when you actually did some

490
00:26:15,004 --> 00:26:18,124
Speaker 1: of these simulations, right, and you actually wrote out what

491
00:26:18,164 --> 00:26:21,604
Speaker 1: the numbers looked like if you had different strategies, and

492
00:26:21,804 --> 00:26:24,164
Speaker 1: some of them I was like, holy shit, right, like

493
00:26:24,204 --> 00:26:29,004
Speaker 1: this crazy risky strategy, Like, yes, you die ninety five

494
00:26:29,044 --> 00:26:31,524
Speaker 1: percent of the time, but like, look at that five percent.

495
00:26:31,884 --> 00:26:34,724
Speaker 1: But that five percent is mostly who you end up interviewing, right,

496
00:26:34,764 --> 00:26:37,284
Speaker 1: because they become the elon Musks of the world. So

497
00:26:37,404 --> 00:26:39,444
Speaker 1: what does that do to kind of our perceptions of

498
00:26:39,484 --> 00:26:40,364
Speaker 1: some of these traits?

499
00:26:40,404 --> 00:26:40,604
Speaker 2: You know?

500
00:26:40,764 --> 00:26:43,524
Speaker 3: Yeah, and so look, this became a little bit easier.

501
00:26:44,044 --> 00:26:46,644
Speaker 3: I'm just speaking kind of behind the scenes as a writer.

502
00:26:47,444 --> 00:26:51,284
Speaker 3: When the SBF thing blew up, then that became easier.

503
00:26:51,564 --> 00:26:55,564
Speaker 3: There's originally going to be like a chapter on failures,

504
00:26:55,604 --> 00:26:58,084
Speaker 3: but that meant more people that like you know, want

505
00:26:58,084 --> 00:26:59,964
Speaker 3: to poke a tournament and then developed some type of

506
00:26:59,964 --> 00:27:02,484
Speaker 3: problem afterward or whatever else. But like SBF kind of

507
00:27:03,084 --> 00:27:05,604
Speaker 3: served enough of that role where great meretive wise, I

508
00:27:05,644 --> 00:27:07,324
Speaker 3: thought it was okay. I mean, there is one story

509
00:27:07,324 --> 00:27:11,324
Speaker 3: about a person named run bow Lee who was a

510
00:27:11,364 --> 00:27:13,124
Speaker 3: smart guy. You never see it Toronto.

511
00:27:13,204 --> 00:27:15,684
Speaker 1: How that was kind of a heartbreaking story. Yeah, go ahead.

512
00:27:15,724 --> 00:27:16,964
Speaker 3: And he came to me and he said, you're writing

513
00:27:17,004 --> 00:27:18,644
Speaker 3: this book about the successful people just kind of cold

514
00:27:18,644 --> 00:27:20,884
Speaker 3: emailed me, and he's like, I'm somebody who got really

515
00:27:20,924 --> 00:27:24,044
Speaker 3: burned by by day trading, by day trading options, in

516
00:27:24,084 --> 00:27:28,444
Speaker 3: particular getting into all the Wall Street Beth's kind of communities,

517
00:27:28,444 --> 00:27:32,004
Speaker 3: and he has lost a million dollars on options trading,

518
00:27:32,084 --> 00:27:34,924
Speaker 3: and he wanted to tell a story, and I'm like,

519
00:27:34,924 --> 00:27:36,604
Speaker 3: the story has to go in the book. There's also,

520
00:27:37,244 --> 00:27:41,684
Speaker 3: you know, the chapter on Las Vegas, which is chapter three,

521
00:27:42,124 --> 00:27:45,164
Speaker 3: is about how casinos, particularly through slot machines, kind of

522
00:27:45,164 --> 00:27:48,284
Speaker 3: manipulate their patrons into spending more. I mean, the stories

523
00:27:48,284 --> 00:27:51,684
Speaker 3: I heard from Natasha Sholl, who is a kind of

524
00:27:51,684 --> 00:27:54,564
Speaker 3: an amazing woman. She's a NYU ethropologist who had like

525
00:27:54,564 --> 00:27:56,284
Speaker 3: never been out of the East Coast, and she's like

526
00:27:56,644 --> 00:27:58,884
Speaker 3: Las Vegas is an exotic place. I want to study

527
00:27:58,924 --> 00:28:02,284
Speaker 3: Las Vegas as an anthropologist, did her dissertation about it,

528
00:28:02,324 --> 00:28:04,364
Speaker 3: then wrote a book about it, and that's a story

529
00:28:04,404 --> 00:28:08,284
Speaker 3: about problem gambling. And although didn't talk to that many

530
00:28:08,324 --> 00:28:13,644
Speaker 3: problem gamblers, really you know, it's very dark about slot

531
00:28:13,724 --> 00:28:14,244
Speaker 3: machine edication.

532
00:28:14,364 --> 00:28:17,884
Speaker 1: Yeah. Well, one of the things actually that really was

533
00:28:17,964 --> 00:28:21,164
Speaker 1: interesting to me there because I hadn't read Schell's book,

534
00:28:22,284 --> 00:28:26,244
Speaker 1: was this insight that you that you go into, which

535
00:28:26,324 --> 00:28:29,004
Speaker 1: is that a lot of these problem gamblers that people

536
00:28:29,004 --> 00:28:31,644
Speaker 1: who play slot machines they know they're losing and they

537
00:28:31,684 --> 00:28:35,364
Speaker 1: don't care that. They're just like they enjoy, like they

538
00:28:35,484 --> 00:28:38,924
Speaker 1: enjoy that experience even though they lose. And to me,

539
00:28:39,724 --> 00:28:42,284
Speaker 1: like that was actually an alha moment because I always

540
00:28:42,284 --> 00:28:44,644
Speaker 1: thought that at least they thought they could win, right,

541
00:28:44,684 --> 00:28:46,364
Speaker 1: But like one of the allers was that, like you

542
00:28:46,404 --> 00:28:48,524
Speaker 1: might be a winner, but you even said that some

543
00:28:48,564 --> 00:28:51,484
Speaker 1: of them don't like winning because it brings them out

544
00:28:51,484 --> 00:28:52,364
Speaker 1: of that flow stage.

545
00:28:52,524 --> 00:28:56,044
Speaker 3: Yeah, you're in some machine zone Natasha Shall calls it

546
00:28:56,084 --> 00:28:58,724
Speaker 3: the machine zone where it's just you and you're pressing

547
00:28:58,724 --> 00:29:01,684
Speaker 3: the reeles and you know, I don't have much of

548
00:29:01,684 --> 00:29:03,684
Speaker 3: a compulsion to flay slots, but I've tossed in a

549
00:29:03,684 --> 00:29:05,924
Speaker 3: few bucks here and there when you're frustrated the Innovan

550
00:29:05,924 --> 00:29:09,844
Speaker 3: Foker tournament, and some of the games are very fun

551
00:29:09,924 --> 00:29:12,244
Speaker 3: and compelling, right you got like gorillas or what's the

552
00:29:12,244 --> 00:29:14,364
Speaker 3: big one? Not garillas, but uh, Buffalo.

553
00:29:15,484 --> 00:29:18,444
Speaker 1: I've never I've never played slots in my life.

554
00:29:18,164 --> 00:29:19,964
Speaker 3: Even I'm going to force you to. I'm going to

555
00:29:20,044 --> 00:29:21,964
Speaker 3: give you money to play slots. I think, Maria, you

556
00:29:22,004 --> 00:29:25,244
Speaker 3: can no longer make that drag, which I find nitty

557
00:29:25,284 --> 00:29:30,404
Speaker 3: by the way, but but yeah, they they want to

558
00:29:30,444 --> 00:29:33,284
Speaker 3: shut out all the other distractions of the world and

559
00:29:33,324 --> 00:29:35,484
Speaker 3: just channel it into like one problem which is problem

560
00:29:35,644 --> 00:29:39,404
Speaker 3: part problem gambling, which is very different than like the

561
00:29:39,564 --> 00:29:44,484
Speaker 3: table games players. The blackjack crabs player more often fits

562
00:29:44,484 --> 00:29:47,244
Speaker 3: the stereotype of somebody who is bored because they don't

563
00:29:47,244 --> 00:29:49,844
Speaker 3: have enough risk tolerance in their life, enough risk in

564
00:29:49,884 --> 00:29:52,244
Speaker 3: their life, and so they're useless as a way, and

565
00:29:52,284 --> 00:29:55,444
Speaker 3: this but some of this is is gendered too, if

566
00:29:55,484 --> 00:29:57,484
Speaker 3: you want to go there, but like you know, men

567
00:29:57,604 --> 00:30:01,204
Speaker 3: feeling the need. If you've been enough craps, tables, craps,

568
00:30:01,204 --> 00:30:03,284
Speaker 3: I do find fun, don't play it much. If you've

569
00:30:03,284 --> 00:30:05,884
Speaker 3: been enough groups of guys at a at a craps

570
00:30:05,924 --> 00:30:09,644
Speaker 3: table where they'll encourage one another to make bets. You know,

571
00:30:09,644 --> 00:30:13,044
Speaker 3: every crafts bets negative ev. But also to play outside

572
00:30:13,044 --> 00:30:15,524
Speaker 3: their means and gamble bigger still have stories to tell.

573
00:30:15,564 --> 00:30:18,524
Speaker 3: I mean, that's you know, that's a certain type of

574
00:30:18,604 --> 00:30:20,284
Speaker 3: young man is more likely to do that kind of thing,

575
00:30:20,284 --> 00:30:22,884
Speaker 3: and it might substitute for other types of ways approve

576
00:30:22,924 --> 00:30:25,884
Speaker 3: their bravery. But like the slots machines players and then

577
00:30:25,924 --> 00:30:30,724
Speaker 3: the skill game players, the poker players, the sports betters,

578
00:30:30,764 --> 00:30:34,324
Speaker 3: the handful of advantage players, which means people who try

579
00:30:34,324 --> 00:30:37,964
Speaker 3: to count cards in blackjack, there are conditions. Please do

580
00:30:38,044 --> 00:30:42,164
Speaker 3: not try this yourself. There are conditions under witch playing.

581
00:30:42,324 --> 00:30:46,604
Speaker 3: Slot machines can be plus ev because of contingencies the

582
00:30:46,604 --> 00:30:49,884
Speaker 3: machines or bonus payouts or jackpots that becomes efficiently large.

583
00:30:50,284 --> 00:30:53,324
Speaker 3: But those are those are one percent of the gambling world.

584
00:30:53,364 --> 00:30:55,324
Speaker 3: I mean, sometimes I feel like you and I are

585
00:30:55,644 --> 00:31:00,884
Speaker 3: use you know, some of the prestige resorts, generally speaking,

586
00:31:00,924 --> 00:31:03,844
Speaker 3: the prestige resorts in Las Vegas and elsewhere have the

587
00:31:03,884 --> 00:31:08,884
Speaker 3: best poker rooms, the win that are et cetera. Resorts

588
00:31:08,884 --> 00:31:12,004
Speaker 3: world Now, Allas, you, I sometimes wonder if, like we're

589
00:31:12,084 --> 00:31:14,884
Speaker 3: used a little bit to whitewash the fact that like

590
00:31:15,484 --> 00:31:17,804
Speaker 3: ninety nine percent of people in that casino are doing

591
00:31:17,844 --> 00:31:19,524
Speaker 3: negative expected value gambling.

592
00:31:20,604 --> 00:31:22,004
Speaker 1: No, I mean that I think that's true, and I

593
00:31:22,084 --> 00:31:25,564
Speaker 1: do and I do overlook that on purpose because I

594
00:31:25,564 --> 00:31:29,644
Speaker 1: don't like paying attention to that, right, But yeah, I know,

595
00:31:29,924 --> 00:31:33,284
Speaker 1: I think you're right, And I don't want to judge

596
00:31:33,324 --> 00:31:36,124
Speaker 1: anyone for you know, playing slots or doing whatever they're doing.

597
00:31:36,124 --> 00:31:38,324
Speaker 1: But I do find it a little bit depressing when

598
00:31:38,364 --> 00:31:41,604
Speaker 1: I see people who don't have an edge and who

599
00:31:41,644 --> 00:31:44,644
Speaker 1: are just kind of pressing buttons. Yeah.

600
00:31:44,724 --> 00:31:48,444
Speaker 3: Look, I comfort myself by saying that poker, at least

601
00:31:48,484 --> 00:31:53,684
Speaker 3: I think is plus CV any utilitarian calculus for society,

602
00:31:53,764 --> 00:31:56,564
Speaker 3: just because I think it is such a unique teacher

603
00:31:57,164 --> 00:31:59,644
Speaker 3: of certain critical life skills in an environment where you

604
00:31:59,644 --> 00:32:02,204
Speaker 3: have something real at risk that like, I just I

605
00:32:02,244 --> 00:32:04,844
Speaker 3: just think that, like I just think that playing poker

606
00:32:04,884 --> 00:32:09,604
Speaker 3: makes you more adept at handling real life risky situation

607
00:32:09,764 --> 00:32:12,364
Speaker 3: a lot of the time, you know. Apart from that,

608
00:32:12,484 --> 00:32:15,804
Speaker 3: I mean, I think there's a lot in the book

609
00:32:15,804 --> 00:32:19,404
Speaker 3: about sports betting. I think there's more papers coming out

610
00:32:19,444 --> 00:32:23,524
Speaker 3: saying that sports betting increases bankruptcies, among exactly the classes

611
00:32:23,564 --> 00:32:25,924
Speaker 3: of people that you would expect, like you see oh

612
00:32:25,964 --> 00:32:29,524
Speaker 3: in uptick and bankruptcies and males age twenty to thirty nine.

613
00:32:29,524 --> 00:32:31,284
Speaker 3: I wonder what could have caused that when sports betting

614
00:32:31,324 --> 00:32:35,324
Speaker 3: went online in X and Y State. You know, look,

615
00:32:35,364 --> 00:32:38,644
Speaker 3: I think the slot machine stuff is probably quite bad

616
00:32:38,684 --> 00:32:42,084
Speaker 3: from utilitarian standpoint. I think the state lottery is actually

617
00:32:42,124 --> 00:32:43,444
Speaker 3: maybe the worst of all on a kind of a

618
00:32:43,444 --> 00:32:45,404
Speaker 3: per capita basis, where the odds are very bad for

619
00:32:45,444 --> 00:32:48,204
Speaker 3: the player and the socio economics of who plays the

620
00:32:48,244 --> 00:32:51,604
Speaker 3: state lottery are It's basically attacks on like lower class

621
00:32:51,604 --> 00:32:55,044
Speaker 3: people for the most part, so there's a lot of hypocrisy.

622
00:32:55,164 --> 00:32:57,604
Speaker 3: There are also lots of arguments about the arms race

623
00:32:57,684 --> 00:33:00,524
Speaker 3: that hey, if we don't legalize this game, then maybe

624
00:33:00,804 --> 00:33:02,804
Speaker 3: somebody else would, right. I mean, if I had my

625
00:33:04,404 --> 00:33:06,484
Speaker 3: you know, if I could perfectly calibrate it, then maybe

626
00:33:06,484 --> 00:33:10,364
Speaker 3: i'd have something where you have a casino that offers

627
00:33:10,404 --> 00:33:13,684
Speaker 3: poker and table games but not slot machines. And maybe

628
00:33:13,724 --> 00:33:16,084
Speaker 3: you have sports bedding which can be done in person

629
00:33:16,484 --> 00:33:19,804
Speaker 3: at a facility but not online, where the addiction seems

630
00:33:19,804 --> 00:33:22,164
Speaker 3: to be higher, and they have fair rules where you

631
00:33:22,164 --> 00:33:24,924
Speaker 3: don't limit winning players et cetera. And you have limits

632
00:33:24,964 --> 00:33:27,524
Speaker 3: on how much can take from Wales. Like there's a

633
00:33:27,564 --> 00:33:31,564
Speaker 3: world in which I would be I think more content with.

634
00:33:31,604 --> 00:33:33,444
Speaker 3: And again I'm not a moralistic person. The book is

635
00:33:33,444 --> 00:33:36,124
Speaker 3: not going to be scoldy, and I believe in showing

636
00:33:36,124 --> 00:33:38,964
Speaker 3: and not telling. But like, it's not like the the

637
00:33:39,004 --> 00:33:42,404
Speaker 3: gambling industry comes out looking great. It comes out looking smart.

638
00:33:42,444 --> 00:33:44,484
Speaker 3: It comes out looking like it understands how to like

639
00:33:45,244 --> 00:33:47,244
Speaker 3: model its customers out. And I would say one thing

640
00:33:47,284 --> 00:33:50,284
Speaker 3: to say encounter to this is that if you go

641
00:33:50,364 --> 00:33:53,684
Speaker 3: to the mid to high range resorts in Las Vegas,

642
00:33:54,644 --> 00:33:56,684
Speaker 3: my subjective view is that people seem to be having

643
00:33:56,724 --> 00:33:58,564
Speaker 3: a pretty good time, and Las Vegas gets a lot

644
00:33:58,564 --> 00:34:01,204
Speaker 3: of repeat business, so there's some type of win win

645
00:34:01,324 --> 00:34:04,044
Speaker 3: in that deal. But I might I might draw the

646
00:34:04,084 --> 00:34:06,364
Speaker 3: line a little bit higher than like a total freefer all,

647
00:34:06,364 --> 00:34:07,404
Speaker 3: which is kind of where we're headed.

648
00:34:07,444 --> 00:34:07,684
Speaker 2: Now.

649
00:34:09,444 --> 00:34:15,724
Speaker 3: We'll be back in just a minute.

650
00:34:26,764 --> 00:34:29,644
Speaker 1: I have a few questions, But first I'm just curious

651
00:34:29,764 --> 00:34:34,004
Speaker 1: are you still doing sports betting? So I loved that

652
00:34:34,044 --> 00:34:36,044
Speaker 1: you picked it up in a serious way for the book,

653
00:34:36,084 --> 00:34:38,404
Speaker 1: although holy shit, I had no idea that you'd wagered

654
00:34:38,444 --> 00:34:43,004
Speaker 1: over a million dollars for your research, Nate, I didn't know,

655
00:34:45,044 --> 00:34:48,364
Speaker 1: but yeah, tell us about that, and tell us what

656
00:34:48,404 --> 00:34:49,324
Speaker 1: you're doing right now.

657
00:34:49,804 --> 00:34:52,444
Speaker 3: So I wanted to have skin in the game, to

658
00:34:52,444 --> 00:34:54,964
Speaker 3: borrow another author's phrase and see what it's actually like

659
00:34:54,964 --> 00:34:57,524
Speaker 3: when you're going through the grind of sports betting an

660
00:34:57,604 --> 00:35:00,364
Speaker 3: NBA season. So I bet almost the entirety of the

661
00:35:00,364 --> 00:35:03,124
Speaker 3: twenty twenty two twenty three NBA season, all the regular season,

662
00:35:03,204 --> 00:35:05,604
Speaker 3: in about half the playoffs, and then five thirty eight

663
00:35:06,124 --> 00:35:07,764
Speaker 3: blew up, and so I had other things to do,

664
00:35:07,804 --> 00:35:11,004
Speaker 3: and I didn't bet the end of the playoffs, and

665
00:35:11,044 --> 00:35:12,764
Speaker 3: I learned that, I mean, it's probably what I should

666
00:35:12,764 --> 00:35:16,044
Speaker 3: have expected by I learned that it's pretty hard. I

667
00:35:16,164 --> 00:35:18,804
Speaker 3: went on a huge heater at the start of the

668
00:35:18,884 --> 00:35:21,364
Speaker 3: NBA season where I was up like seventy thousand bucks.

669
00:35:21,364 --> 00:35:25,084
Speaker 3: I'm like, man, I'm really fucking good at the sports

670
00:35:25,084 --> 00:35:27,924
Speaker 3: betting stuff. But then things change. One thing that changed

671
00:35:27,964 --> 00:35:30,364
Speaker 3: is you start to get limited by some of the sites,

672
00:35:30,604 --> 00:35:32,884
Speaker 3: especially if you're trying to do things like, oh, I

673
00:35:32,924 --> 00:35:35,684
Speaker 3: just suck. Because I'm a Twitter addict, I follow a

674
00:35:35,684 --> 00:35:36,724
Speaker 3: lot of NBA writers.

675
00:35:36,804 --> 00:35:37,004
Speaker 1: Oh.

676
00:35:37,484 --> 00:35:41,204
Speaker 3: Damian Lillard of the Portland Trailblazers is injured today, but

677
00:35:41,324 --> 00:35:43,964
Speaker 3: the line at sportsbook X does not yet account for that.

678
00:35:44,444 --> 00:35:46,444
Speaker 3: So let me go and bet three thousand dollars on

679
00:35:46,444 --> 00:35:49,044
Speaker 3: this bet, which is now hugely positive. Ev you can

680
00:35:49,084 --> 00:35:51,684
Speaker 3: do it the first time, maybe the second time. The

681
00:35:51,724 --> 00:35:54,684
Speaker 3: third time, your hands are cut off basically kind of literally.

682
00:35:55,804 --> 00:35:58,044
Speaker 3: So when you aren't able to take advantage of things

683
00:35:58,084 --> 00:36:00,084
Speaker 3: that you know in principle, I think you should be.

684
00:36:00,124 --> 00:36:01,924
Speaker 3: I mean, if they're posting a line, they're taking action

685
00:36:02,044 --> 00:36:05,004
Speaker 3: on it. And the fact that, like in the NBA

686
00:36:05,204 --> 00:36:08,324
Speaker 3: regular season, it's all about is this player injured? Who

687
00:36:08,364 --> 00:36:11,284
Speaker 3: is trying to actually win and who's not. The models

688
00:36:11,324 --> 00:36:13,244
Speaker 3: that you have at the start of the season don't

689
00:36:13,284 --> 00:36:14,644
Speaker 3: work as well when you're kind of in this like

690
00:36:14,724 --> 00:36:17,324
Speaker 3: triage phase of the of the trenches of the NBA

691
00:36:17,404 --> 00:36:20,084
Speaker 3: regular season, and you know, and you talk to other

692
00:36:20,084 --> 00:36:22,564
Speaker 3: bears like, yeah, I have three guys whose entire job

693
00:36:22,604 --> 00:36:25,044
Speaker 3: Spanky kiro Lois is a guy who we interview in

694
00:36:25,044 --> 00:36:27,884
Speaker 3: the book. I have three guys who just track injury

695
00:36:27,964 --> 00:36:29,604
Speaker 3: data for me. I mean, while I'm trying to like

696
00:36:29,644 --> 00:36:31,604
Speaker 3: do some Twitter search for is this player in the

697
00:36:31,644 --> 00:36:36,724
Speaker 3: warm up? You know you're competing essentially against the best players,

698
00:36:36,884 --> 00:36:40,604
Speaker 3: the best betters in the world. So I love that

699
00:36:41,284 --> 00:36:43,284
Speaker 3: one point eight million dollars in bets and believe this

700
00:36:43,404 --> 00:36:46,524
Speaker 3: is just like on average the bet size is like

701
00:36:46,564 --> 00:36:49,524
Speaker 3: twelve hundred bucks, you're making fifteen hundred twelve hundred dollars

702
00:36:49,524 --> 00:36:52,364
Speaker 3: bets or whatever. Over the course of the season, I

703
00:36:52,444 --> 00:36:55,844
Speaker 3: made about five thousand bucks, probably spent about five thousand

704
00:36:55,924 --> 00:36:58,564
Speaker 3: hours on this, so I made less than minimum wage

705
00:36:58,604 --> 00:37:01,964
Speaker 3: actually with my with my sports betting side hustle.

706
00:37:03,524 --> 00:37:06,044
Speaker 1: I love it. I love it. So I want to

707
00:37:06,084 --> 00:37:10,164
Speaker 1: ask two more things before we wrap up. The first

708
00:37:10,244 --> 00:37:12,764
Speaker 1: is kind of a theme that I and I think

709
00:37:12,804 --> 00:37:15,444
Speaker 1: I picked up on this because you know, it's something

710
00:37:15,484 --> 00:37:19,484
Speaker 1: that I've obviously thought about a lot a lot, And

711
00:37:19,644 --> 00:37:24,164
Speaker 1: it's the strand that is woven throughout the book, which

712
00:37:24,244 --> 00:37:27,604
Speaker 1: is that even though the book is about skill, about

713
00:37:27,644 --> 00:37:32,004
Speaker 1: finding edges, about how to make plus ev gambles, there

714
00:37:32,084 --> 00:37:34,644
Speaker 1: is this thread throughout it of luck, right and of

715
00:37:34,644 --> 00:37:37,884
Speaker 1: the fact that you also do need to get lucky,

716
00:37:38,204 --> 00:37:41,044
Speaker 1: and you actually you point to a lot of these

717
00:37:41,124 --> 00:37:44,524
Speaker 1: moments where things could have turned out so differently, like,

718
00:37:44,564 --> 00:37:47,404
Speaker 1: you know, to pick a random example, the car crash

719
00:37:47,524 --> 00:37:50,404
Speaker 1: that Peter Tila and Elon Musk were in right, like

720
00:37:50,684 --> 00:37:54,404
Speaker 1: what happens if the car lands differently? And I don't

721
00:37:54,604 --> 00:37:56,644
Speaker 1: know if you want to talk about that specific example,

722
00:37:56,884 --> 00:38:00,004
Speaker 1: but this is something that you're clearly very aware of,

723
00:38:00,084 --> 00:38:02,244
Speaker 1: and I just want to raise it and talk about

724
00:38:02,244 --> 00:38:02,844
Speaker 1: it a little bit.

725
00:38:03,564 --> 00:38:08,164
Speaker 3: Yeah, there is a survivorship bias problem when you're writing

726
00:38:08,164 --> 00:38:12,084
Speaker 3: a book about successful people, and literally kind of in

727
00:38:12,084 --> 00:38:14,124
Speaker 3: the case of Elon Musk and Peter Teal, Elon had

728
00:38:14,124 --> 00:38:17,204
Speaker 3: sold his first company, bought like a million dollar McLaren

729
00:38:17,684 --> 00:38:20,044
Speaker 3: F one or something sports car basically almost like a

730
00:38:20,364 --> 00:38:23,844
Speaker 3: you know, Formula one caliber car, and they're driving it

731
00:38:23,884 --> 00:38:26,524
Speaker 3: on I think sand Hill Road or somewhere in Palo Alto,

732
00:38:27,684 --> 00:38:30,564
Speaker 3: and Elon floor is the accelebrator while trying to change lanes,

733
00:38:30,924 --> 00:38:33,524
Speaker 3: and it kind of spirals up into like doing a

734
00:38:33,524 --> 00:38:37,404
Speaker 3: little helicopter motion and miraculously lands on its wheels, and

735
00:38:38,404 --> 00:38:42,764
Speaker 3: Elon and Peter are are okay, and actually hitchhikes their

736
00:38:42,804 --> 00:38:46,884
Speaker 3: VC meeting, And I guess keep PayPal alive by with

737
00:38:46,964 --> 00:38:51,804
Speaker 3: that hitchhike. But so I asked Peter Teal, you know,

738
00:38:51,924 --> 00:38:54,404
Speaker 3: I asked him in a way that was nerdier. I

739
00:38:54,404 --> 00:38:56,564
Speaker 3: asked him if you ran the world a thousand times,

740
00:38:56,924 --> 00:38:59,724
Speaker 3: had a thousand simulations, then how often would you wind

741
00:38:59,804 --> 00:39:02,284
Speaker 3: up in this position? And he objected to this question

742
00:39:02,324 --> 00:39:04,804
Speaker 3: and went on the whole thing about determinism or probablism,

743
00:39:04,804 --> 00:39:07,084
Speaker 3: which is interesting, but we don't have time for today,

744
00:39:07,124 --> 00:39:10,044
Speaker 3: I don't think. But yeah, life is very contingent. We've

745
00:39:10,044 --> 00:39:13,604
Speaker 3: talked on the show about the assassination attempt against President

746
00:39:13,644 --> 00:39:15,844
Speaker 3: Trump where that was a very close call, or things

747
00:39:15,924 --> 00:39:19,124
Speaker 3: like the butterfly ballot in the two thousand election, which

748
00:39:19,164 --> 00:39:21,964
Speaker 3: is in one ballot in one county in Florida in

749
00:39:22,084 --> 00:39:24,924
Speaker 3: two thousand, the ballot had a funky design where some

750
00:39:24,964 --> 00:39:27,324
Speaker 3: people who would have wanted to vote for Al Gore

751
00:39:27,404 --> 00:39:29,684
Speaker 3: wound up voting for Pat Buchanan instead, and that was

752
00:39:29,804 --> 00:39:32,044
Speaker 3: enough to cost or the election. On top of the

753
00:39:32,084 --> 00:39:34,684
Speaker 3: recount stuff in Florida wouldn't have mattered if this ballot

754
00:39:34,724 --> 00:39:38,644
Speaker 3: design had been better. So life is. Whether it's literally

755
00:39:38,644 --> 00:39:41,564
Speaker 3: metaphysically random or not is a philosophical question, But it's

756
00:39:42,044 --> 00:39:44,084
Speaker 3: random to our ability to determine it in a lot

757
00:39:44,084 --> 00:39:49,044
Speaker 3: of ways. And it's just it's just very hard to

758
00:39:49,204 --> 00:39:52,324
Speaker 3: acknowledge the role that luck plays in your life. It's

759
00:39:52,444 --> 00:39:55,244
Speaker 3: very hard, even for me or for you. Maybe you're

760
00:39:55,524 --> 00:39:59,124
Speaker 3: the exception, Maria, it's very hard not to tell yourself

761
00:39:59,124 --> 00:40:02,644
Speaker 3: a clever narrative in which in which you know, even

762
00:40:02,724 --> 00:40:06,524
Speaker 3: in writing the book I have, I think I've gotten

763
00:40:06,644 --> 00:40:09,204
Speaker 3: very lucky with a lot of things to kind of

764
00:40:09,204 --> 00:40:12,764
Speaker 3: have like this SBF story, like fall into My Lap,

765
00:40:12,804 --> 00:40:15,244
Speaker 3: for example, was I think pretty lucky. And having a

766
00:40:15,244 --> 00:40:17,684
Speaker 3: book that covered territory like AI that kind of became

767
00:40:17,724 --> 00:40:20,164
Speaker 3: a much bigger deal, and sports betting really blew up.

768
00:40:20,244 --> 00:40:22,364
Speaker 3: I mean, you know, so I think I've gotten very

769
00:40:22,444 --> 00:40:23,964
Speaker 3: lucky in a lot of things recently, the way that like,

770
00:40:24,204 --> 00:40:26,644
Speaker 3: you know, I didn't know that actually having this independent

771
00:40:26,684 --> 00:40:30,004
Speaker 3: newsletter is actually actually much better economically than being working

772
00:40:30,044 --> 00:40:31,444
Speaker 3: for a big company. I didn't know that, And I

773
00:40:31,444 --> 00:40:33,964
Speaker 3: could easily have taken a deal with another big company

774
00:40:34,004 --> 00:40:35,564
Speaker 3: and wound up worse off for it, and a lot

775
00:40:35,564 --> 00:40:37,644
Speaker 3: of respects, and so, you know, I know the role

776
00:40:37,684 --> 00:40:40,764
Speaker 3: that luck has played in my life, including just things

777
00:40:40,844 --> 00:40:44,764
Speaker 3: like being basically healthy and having a supportive partner and

778
00:40:44,764 --> 00:40:47,844
Speaker 3: supportive friends, and being born into a country where it's

779
00:40:47,884 --> 00:40:49,684
Speaker 3: okay to be a gay man, and a country where

780
00:40:50,524 --> 00:40:52,604
Speaker 3: where you have some downside protection. I mean, those are

781
00:40:52,604 --> 00:40:55,644
Speaker 3: all things that are very lucky. So you know, the

782
00:40:55,644 --> 00:40:59,124
Speaker 3: next time that I take a cooler in poker or

783
00:40:59,204 --> 00:41:01,684
Speaker 3: blue set of or set or something, then I can't

784
00:41:01,684 --> 00:41:02,484
Speaker 3: complain too much.

785
00:41:03,884 --> 00:41:06,004
Speaker 1: Well, I had more questions, but that seems like such

786
00:41:06,044 --> 00:41:09,604
Speaker 1: a beautiful place to end that I think I have

787
00:41:09,684 --> 00:41:14,364
Speaker 1: to not ask my remaining question. But yeah, I mean,

788
00:41:14,804 --> 00:41:17,644
Speaker 1: obviously this is something I've thought about a lot and

789
00:41:17,964 --> 00:41:20,284
Speaker 1: was kind of the big theme of the Biggest Bluff.

790
00:41:20,324 --> 00:41:23,804
Speaker 1: But it's nice to see that you know that this

791
00:41:23,924 --> 00:41:26,604
Speaker 1: is something that you're thinking about as well, because I

792
00:41:26,644 --> 00:41:30,364
Speaker 1: do think that it's easy for us to get over confident,

793
00:41:30,444 --> 00:41:33,004
Speaker 1: and I think that as we you know, during risky

794
00:41:33,044 --> 00:41:36,764
Speaker 1: business and all of these endeavors, we do want to

795
00:41:36,804 --> 00:41:39,684
Speaker 1: maximize our skill, but we do need to always remember

796
00:41:39,724 --> 00:41:41,804
Speaker 1: that we're we are insanely lucky to even be in

797
00:41:41,844 --> 00:41:45,804
Speaker 1: this position. So Nate, I hope that you get continue

798
00:41:45,844 --> 00:41:49,084
Speaker 1: to get lucky, and that this book sells an insane

799
00:41:49,164 --> 00:41:53,444
Speaker 1: number of copies and that it does incredibly well, and

800
00:41:53,484 --> 00:41:55,844
Speaker 1: that you and I can continue making this show together

801
00:41:55,964 --> 00:42:00,404
Speaker 1: because we're both continuing to be lucky and yet take

802
00:42:00,484 --> 00:42:02,884
Speaker 1: plus evy edges whenever we can.

803
00:42:03,924 --> 00:42:06,004
Speaker 3: Thank you so much, Maria, and you know, maybe if

804
00:42:06,004 --> 00:42:07,644
Speaker 3: you want to have you on the show again sometime.

805
00:42:08,364 --> 00:42:10,964
Speaker 3: You know, of fun, we have a good chemistry.

806
00:42:10,604 --> 00:42:14,124
Speaker 1: I think, I agree, I agree. So yeah, you're you're

807
00:42:14,164 --> 00:42:15,924
Speaker 1: welcome back. How's next week looking for you?

808
00:42:16,284 --> 00:42:18,244
Speaker 3: Actually really fucking busy, but we'll make it work.

809
00:42:30,244 --> 00:42:32,044
Speaker 1: Let us know what you think of the show. Reach

810
00:42:32,084 --> 00:42:35,844
Speaker 1: out to us at Risky Business at pushkin dot fm.

811
00:42:36,004 --> 00:42:39,084
Speaker 1: Risky Business is hosted by me Maria Kanakova.

812
00:42:38,804 --> 00:42:41,564
Speaker 3: And by me Nate Silver. The show was a cool

813
00:42:41,564 --> 00:42:45,524
Speaker 3: production of Pushing Industries and iHeartMedia. This episode was produced

814
00:42:45,564 --> 00:42:49,924
Speaker 3: by Isaac Carter. Our associate producer is Sonya gerwit Lydia,

815
00:42:50,004 --> 00:42:52,884
Speaker 3: Jean Kott and Daphne Chen are our editors, and our

816
00:42:52,924 --> 00:42:56,764
Speaker 3: executive producer is Jacob Goldstein. Mixing by Sarah Bruger.

817
00:42:57,324 --> 00:42:59,564
Speaker 1: If you like the show, please rate and review us

818
00:42:59,564 --> 00:43:02,244
Speaker 1: so other people can find us too, But once again,

819
00:43:02,364 --> 00:43:04,164
Speaker 1: only if you like us. We don't want those bad

820
00:43:04,204 --> 00:43:06,444
Speaker 1: reviews out there. Thanks for tuning in.

821
00:43:10,084 --> 00:43:11,684
Speaker 2: From the call.

822
00:43:12,044 --> 00:43:12,084
Speaker 1: W