Speaker 1: Pushkin.
Speaker 2: Hey everyone, we are off for the holidays, so we
Speaker 2: wanted to reshare one of our favorite episodes last year,
Speaker 2: when Nate's book On the Edge came out, I had
Speaker 2: a chance to interview him about it on the podcast,
Speaker 2: and I thought it would be really fun to take
Speaker 2: a look back at that interview where Nate was in
Speaker 2: the interviewee seat. Then I wrote a little review recap
Speaker 2: of the book on my sub stock. If somehow you
Speaker 2: are listening to Risky Business and you have not read
Speaker 2: the book, please read the book. It's wonderful, Nate. You
Speaker 2: did such a great job and I really enjoyed that conversation.
Speaker 3: Yeah, no, look, maybe I should go back and read
Speaker 3: that book is pression. I think it's talking about all
Speaker 3: the kind of topics from you know, prediction markets to
Speaker 3: AI to crypto that are our only you know, to
Speaker 3: gambling scandals right that are only becoming more pertinent today.
Speaker 3: But thank you for that interview, Maria, And yeah, maybe
Speaker 3: I'll take a listen to myself.
Speaker 2: Yeah, so enjoy everyone.
Speaker 1: So, first of all, Nate, congratulations. I know that this
Speaker 1: was a long time coming labor of many many years.
Speaker 3: Yeah no, I spent basically three years of my life
Speaker 3: where I'd say this is my main professional focus, talk
Speaker 3: to two hundred people, and I know, I just I
Speaker 3: would recommend email really smart people and ask to have
Speaker 3: a conversation with them and pretend it's for a book,
Speaker 3: and then don't write the book. That's probably still worth
Speaker 3: it in some sense. But no, I actually went ahead
Speaker 3: and wrote the book.
Speaker 1: And we're very glad you did. So I'm going to
Speaker 1: start with Nate. In the book, you make this distinction
Speaker 1: between two community. You have the village and you have
Speaker 1: the river. Talk me through what exactly those mean.
Speaker 3: Let's start with the village, even though it's not the
Speaker 3: focus of the book, just because it's a little bit
Speaker 3: easier to define in a term that's more conventional. The
Speaker 3: village is basically the kind of liberal, progressive East Coast establishment.
Speaker 3: So it's the New York Times, it's Harvard University, it's
Speaker 3: think tanks and nonprofits, it's you know, government when you
Speaker 3: have a Democrat in office at least, it's kind of
Speaker 3: the expert class, the professional managerial class, that whole cluster
Speaker 3: of attributes. By contrast, the river is kind of the
Speaker 3: world that I suppose I inhabit, or I imagine myself inhabiting.
Speaker 3: It's the world of calculator risk taking, where people are
Speaker 3: some combination of being very analytical and extremely wildly competitive.
Speaker 3: So like poker is an archetypal maybe the archetypal river activity.
Speaker 3: But as I discovered this world in the book, you know,
Speaker 3: I found that the venture capitalist and the effective at
Speaker 3: and the hedge fund people, and the sports betters, even
Speaker 3: the crypto people, they all have a common vocabulary. They
Speaker 3: all talk about things in roughly the same ways, and
Speaker 3: so it's like it's not a place, it's a discrete community.
Speaker 3: I mean, in Las Vegas and Silicon Valley you might
Speaker 3: get closer to an actual network, but like they're just
Speaker 3: these people that I think are often poorly understood by
Speaker 3: the mainstream media and yet are incredibly wealthy and powerful.
Speaker 3: Many of the richest people in the world have some
Speaker 3: of these river tendencies, and like, surprisingly to me, would
Speaker 3: take my emails and my phone calls because they kind
Speaker 3: of recognize like a fellow traveler a little bit. And
Speaker 3: then what I discovered is that the kind of conceit
Speaker 3: of the proposal that actually there's a commonalities in how
Speaker 3: people think more was more literally true than I had thought,
Speaker 3: and that there's more literal crossover poker players who become
Speaker 3: effective altruists, or hedge funders who host poker games, or
Speaker 3: venture capitalists who do sports betting is a metaphor for
Speaker 3: some of these things. So it's a real place of
Speaker 3: a real group of people that I think, Maria are
Speaker 3: in our circles, maybe not the only circle to run in,
Speaker 3: but certainly one common type. And I felt like I
Speaker 3: had the opportunity to tell a really good story about
Speaker 3: these people. I did not. I think I got lucky.
Speaker 3: I think I'm running in my like ninety eighth percentile
Speaker 3: run good for how some of these topics really blew up.
Speaker 3: I mean Ai in particular, although that was a fairly
Speaker 3: incidental thing in the book Effective Altruism and its particularly
Speaker 3: Sam Bakman Free who had talked to first before the
Speaker 3: collapse and then got to talk to you afterwards as well.
Speaker 1: Yeah, so you draw some really interesting parallels between worlds
Speaker 1: that I didn't necessarily connect, like obviously poker sports bedding,
Speaker 1: Like yes, you know, I get why those things are related.
Speaker 1: Then you go into finance, you go into venture capital
Speaker 1: and the hedge world in Silicon Valley you go into
Speaker 1: AI and people who are kind of the AI researchers,
Speaker 1: and I actually love your little side where you have
Speaker 1: the physical risk taker. Yes, where you have the astronauts,
Speaker 1: and I love that you have a female astronaut. By
Speaker 1: the way, did you on purpose, like, did you try
Speaker 1: to have two women or did that just so happen?
Speaker 1: You know, because as you know, like it's something that
Speaker 1: is interesting to me, And when you're talking about risk takers,
Speaker 1: people often assume men, and so I was very curious
Speaker 1: if that was a deliberate choice or if it just
Speaker 1: so happens that these most interesting stories happen to be female.
Speaker 3: No, there there was a deliberate choice made to I mean, look,
Speaker 3: it's a careful balance to strike because it is a
Speaker 3: world that's very male. Also world that is pokers a
Speaker 3: little bit more diverse, but the rest of the river
Speaker 3: is quite white and or Asian and or Asian sometimes.
Speaker 3: So there was a deliberate choice to like not sugarcoat
Speaker 3: this and pretend it's all a fifty fifty like. But
Speaker 3: also the I call them characters. The people I talked to,
Speaker 3: like Katharine Sullivan, this astronaut's amazing, and she has an
Speaker 3: amazing stories and she was studying like oceanography in Nova
Speaker 3: Scotia and beat out like ten thousand other people to
Speaker 3: become an astronaut. I mean, it's an amazing story. Cataline Kaiko,
Speaker 3: I had to learn all the names pronounced correctly for
Speaker 3: the audiobook and no I'd forgotten them. Who was this
Speaker 3: woman who like smuggled money in a Teddy Bear to
Speaker 3: move out of Hungary and then was just shot upon
Speaker 3: in the establishment while she was working on MR and
Speaker 3: A vaccines and eventually joined biointech and won the Nobel Prize.
Speaker 3: Co won the Nobel Prize for her work on MR
Speaker 3: and A vaccines. I mean, they have amazing stories. Or
Speaker 3: someone like our mutual friend but one of the best
Speaker 3: poker players in the world. I think Maria Ho who
Speaker 3: has great stories to tell too so so or Vanessa
Speaker 3: Selps is another person that we know but like it's
Speaker 3: just an amazing person. So yeah, I mean, when you
Speaker 3: find people like that and you want to tell their stories.
Speaker 3: And there was, but there's also sections that directly tackle
Speaker 3: There's one section the poker chapter about why an't there
Speaker 3: more women in poker? And then one section in the
Speaker 3: VC chapter about why are there so few black and
Speaker 3: Hispanic and women founders, and so you have to take
Speaker 3: it on directly without it being a woke book because
Speaker 3: I'm not super woke, but you can't. But there are
Speaker 3: real shortcomings here. I mean the VCS in particular, I think,
Speaker 3: are they claim to want people who have overcome, who
Speaker 3: have grit, who have overcome odds, and who are high variants,
Speaker 3: who are different than the consensus, and yet they have
Speaker 3: this prototype of like the on the spectrum, you know,
Speaker 3: white or Asian twenty something nerd who dropped out of
Speaker 3: Harvard or MIT as a sophomore and Sam mcminfreed played
Speaker 3: into that stereotype, and they're not actually, I think, trying
Speaker 3: to diversify their portfolio, and it just in a purely
Speaker 3: not moral but even like financial sense of wanting like
Speaker 3: high variance founders.
Speaker 1: Yeah. No, I think one of the things that I
Speaker 1: enjoyed about the book is that it's about how to
Speaker 1: take risk well. But you also talk about some of
Speaker 1: the shortcomings of the of the people on the River
Speaker 1: and how they think about risk. But first, let's talk
Speaker 1: about kind of the good elements. So what does the
Speaker 1: river have in common when it comes to evaluating risk.
Speaker 1: What can we take away from them when we're thinking
Speaker 1: about Okay, I am, you know, Maria, everyday person. How
Speaker 1: do I kind of think about risk in my life?
Speaker 1: What do I take from these people who take risks
Speaker 1: professionally so that I can just make better decisions and
Speaker 1: better risk assessments myself.
Speaker 3: So let me take the two axioms that you hear
Speaker 3: over and over and over again in Silicon Valley and
Speaker 3: make it very successful financially despite having all these flaws.
Speaker 3: I mean, some of these guys are difficult people. It
Speaker 3: is mostly guys. It's not very diverse. There are some
Speaker 3: big flaws, right, I think their political instincts are often poor.
Speaker 3: But they do two things, one of which is they
Speaker 3: understand expected value, which is kind of the theme of
Speaker 3: this show. They understand that if you can make a
Speaker 3: bet that has a ten percent chance of one hundred
Speaker 3: act payout, that's an extraordinarily good bet. And then you
Speaker 3: can make a lot of those bits by aggregating different
Speaker 3: companies into a fund, then you're kind of guaranteed to
Speaker 3: make money and maybe make a lot of money. And
Speaker 3: it also applies to things like evaluating nuclear war risk
Speaker 3: or AI risk. Things that might be you know, a
Speaker 3: two percent chance in over some timeframe, or five percent
Speaker 3: chance whatever else, but have like very bad to infinitely
Speaker 3: bad negative payouts. The second thing that Silicon Valley knows
Speaker 3: is that having a longer time horizon is really worth it.
Speaker 3: We're a country that likes to make money, but we're
Speaker 3: a get rich quick country and not to get rich
Speaker 3: slowly country. When you're investing in startups, early stage startups,
Speaker 3: they often have a time horizon. You know, SpaceX took
Speaker 3: thirteen years I think to make a first profit nine
Speaker 3: year would actually even launched a rocket successfully or something
Speaker 3: like that. So I think you almost always benefit from
Speaker 3: having like a longer time horizon than other people. People
Speaker 3: just discount the long term future wait much even in
Speaker 3: their personal lives, I think. And so therefore those I
Speaker 3: think are kind of the two foremost lessons and why
Speaker 3: the book is you know, kind of ambivalently to positive
Speaker 3: on that Silicon Valley ethos, even though the individual characters
Speaker 3: there are are complex.
Speaker 1: So one of the things that you stress over and
Speaker 1: over is that people who evaluate risk correctly and make
Speaker 1: risks that tend to pay out over the long term,
Speaker 1: they not only can understand the upside, but they can
Speaker 1: also protect against the risk of ruin, right, the risk
Speaker 1: of not being able to take these risks going forward.
Speaker 1: So first, I'd love for you to talk a little
Speaker 1: bit about this idea of risk of ruin because you
Speaker 1: talk about people who do it correctly and then people
Speaker 1: who might not, and and also like how do you
Speaker 1: how do you balance that, and how might that be
Speaker 1: different from person to person.
Speaker 3: Yeah, I mean, we have this abstract concept in poker
Speaker 3: of a bank roll, which is and that is, how
Speaker 3: much money can you spend on poker before your kind
Speaker 3: of poker broke or at least you're severely limited in
Speaker 3: the size of the games you might play, and you
Speaker 3: might have to pass up positive evy opportunities. So to
Speaker 3: some extent, this is related to the idea of opportunity cost.
Speaker 3: There's a famous formula from sports betting called the Kelly criterion,
Speaker 3: which tells you how much can you bet to maximize
Speaker 3: your expected value without enduring a very high risk of ruin.
Speaker 3: Now I can get into the technicalities of why that
Speaker 3: formula might make you a little bit more aggressive than
Speaker 3: you should be. In general, though like ninety percent of
Speaker 3: people in the world are I think too risk averse
Speaker 3: about this kind of thing. This goes back to, as
Speaker 3: you'll know, like some of the condoment and diversity work
Speaker 3: on prospect theory, it may have also an evolutionary base
Speaker 3: where we weren't living in the time of abundance that
Speaker 3: we are now and so and so. You know, you
Speaker 3: only got one shot before it made sense to protect
Speaker 3: your household. It made sense to be very cautious about
Speaker 3: if you get an infection, you might die. And I
Speaker 3: think people have not adapted to this world of abundant
Speaker 3: but confusing opportunity that we have very much. Annie Duke
Speaker 3: wrote a book called Quit, and she's in the book too,
Speaker 3: also a former hooker player. Of course, she cites studies
Speaker 3: like by Steve Levitt of fre Economics that when they
Speaker 3: have people flip a coin to make a decision, and
Speaker 3: a major decision, not like am I getting Thai food
Speaker 3: or Indian for dinner tonight, which I do flip a
Speaker 3: coin about sometimes, by the way, but things like should
Speaker 3: I leave my partner, or should I quit my job,
Speaker 3: or should I move across the country to a different
Speaker 3: part of the world. People are happier on average when
Speaker 3: they make a change and so you know, the ninety
Speaker 3: percent of people who are risk averse maybe could take
Speaker 3: some messages from that book. Then, but you also meet
Speaker 3: the sbfs and the Elon Musks of the world.
Speaker 1: And let's actually unwrap that last bit a little bit.
Speaker 1: The sbfs and the Elon Musks because you write about
Speaker 1: the fact that you know, obviously they are people. Wouldn't
Speaker 1: people don't necessarily lump them together, right, because Elon Musk
Speaker 1: is someone who is the richest man in the world,
Speaker 1: and then SBF is going to jail. However, they do
Speaker 1: have this one characteristic in common. So would I would
Speaker 1: love for you to expand on that alone.
Speaker 3: Yeah, and I should be clear, I mean I don't
Speaker 3: mean to I am very and the book is very
Speaker 3: unsympathetic to SBF. I'm not saying I'm sympathetic to Elon.
Speaker 3: But Elon has accomplished a lot of things, and I
Speaker 3: think Elon's a real person whose career wasn't based on
Speaker 3: I mean, I don't think Sam's ull career was based
Speaker 3: on fraud, but he didn't. You know, Elon has not
Speaker 3: committed ten billion dollars worth of stealing people's crypto deposits. Basically, Yeah,
Speaker 3: they are the two people that really, really really go
Speaker 3: Baalse to the wall. I think we can use that
Speaker 3: phrase in terms of risk.
Speaker 1: Absolutely.
Speaker 3: If you read the Walter Isaacson book on Elon Musk,
Speaker 3: his poker strategy is literally going all in every hand
Speaker 3: until he runs out of money or wins it all.
Speaker 1: I didn't know that that was crazy to me.
Speaker 3: It makes sense, so I've I've played in the all
Speaker 3: in podcast poker game a couple of times with some
Speaker 3: of Elon's buddies, and that's a very aggressive game. Shall
Speaker 3: we say. I'm forbidden to reveal details, but there's a
Speaker 3: lot of blind naked aggression there. You know. SBF is
Speaker 3: even more unambiguously, I think irrational in taking a very
Speaker 3: literal approach of utilitarianism, where you know, he has said
Speaker 3: repeatedly he told his co founder Carolyn Ellison this. He
Speaker 3: told the economist Tyler Cowan this that if he could
Speaker 3: flip a coin to determine the fate of the world,
Speaker 3: and either half the time the world is destroyed, but
Speaker 3: the other half the time the world is like two
Speaker 3: point oh one x as good, then he's calculating the
Speaker 3: expected value. He's so committed to that that hey you
Speaker 3: know one point five is greater than one. Therefore you
Speaker 3: take the coin flip, even though you might destroy the world,
Speaker 3: And in fact, you take that coin flip repeatedly, over
Speaker 3: and over again. This is called the Saint Petersburg paradox,
Speaker 3: where it's a bet that has infinite expected value, but
Speaker 3: its infinitely likely to leave you ruins. So what should
Speaker 3: you do? Probably not take the bet. It's not that
Speaker 3: hard a dilemma, actually, but to SBF it was. And
Speaker 3: in my interviews with him, you kind of find a
Speaker 3: pattern of him saying, I mean the first thing who
Speaker 3: I did with him when he's riding high January twenty
Speaker 3: twenty two, Bitcoin still near its all time peaks. He's like,
Speaker 3: if you're not really literally willing to ruin yourself, not
Speaker 3: the fake ruined, Like if Elon Musk has to sell
Speaker 3: Twitter and he's only worth one hundred and seventy billion
Speaker 3: dollars instead of two hundred and twenty billion dollars, that's
Speaker 3: not what it would describe as being ruined. And Sam
Speaker 3: was very clear that I don't think you should protect
Speaker 3: your downside. You have to be so risk loving that
Speaker 3: you should literally be willing to destroy your life and
Speaker 3: your reputation if it's positive expected value, which I think
Speaker 3: is insane. I think reflects I mean, not a clinical diagnosis,
Speaker 3: but I think literally some I think he's a somewhat
Speaker 3: defective person emotionally in another way. And you know, the
Speaker 3: fact that he was enabled by so many supposedly smart
Speaker 3: actors is a core question that's asked by the book.
Speaker 3: I mean, I you know, the New York Times review
Speaker 3: understood this, which is that it's like the book is
Speaker 3: actually pretty unsparing to some of these characters in the River,
Speaker 3: even though like it's not a book where I'm gonna
Speaker 3: be like Peter Teal is evil and Elon Musk is
Speaker 3: evil and a fascist, Like that's not my style of writing.
Speaker 3: And I think I think they're interesting people who deserve
Speaker 3: to have their stories told by someone who kind of
Speaker 3: understands the personality type. But SBF was a really dangerous
Speaker 3: figure and on top of everything else, like also a
Speaker 3: fairly bad calculator of risk. His decision to go take
Speaker 3: the witness stand at his trial after the government's testimony
Speaker 3: was just incredibly devastating, and they had him dead to
Speaker 3: rights and contradicting everything he told people publicly, everything he
Speaker 3: told me in interviews that were on the record, although
Speaker 3: with an embario, I mean they couldn't be used until
Speaker 3: the book was released. You know, the willingness to lie,
Speaker 3: plus the constant miscalculations he made where he probably could
Speaker 3: have avoided jail time by conceding defeat and not taking
Speaker 3: the witness stand. Not at all the jail time, but
Speaker 3: ten years or five years and not twenty You know,
Speaker 3: why did so many people vouch for him? As a
Speaker 3: question that I think has to be has to be pondered.
Speaker 1: We'll be back after a quick break. You actually touched
Speaker 1: on something there which I think is important to unwrap
Speaker 1: a little bit more, which is that if you're taking
Speaker 1: these huge gambles, you better be pretty damn sure your
Speaker 1: calculations are accurate, that your percentages are accurate. But we're
Speaker 1: living in a world where you can't do that, and
Speaker 1: so I'm curious. You know, you build models, right that
Speaker 1: try to kind of model things that are uncertain, that
Speaker 1: are unknowable, that have this margin of error, And so
Speaker 1: I think you understand and more than most people that
Speaker 1: you know, if you say sixty percent certain or like
Speaker 1: sixty percent chance of this, like you're not actually like
Speaker 1: that there is a margin of error there, right, Like,
Speaker 1: if you fifty one percent likely that you're going to
Speaker 1: have a plus EV and forty nine, what if you're wrong? Right, Like,
Speaker 1: what if it's forty nine that you're that you're actually
Speaker 1: negative TV and fifty one that you're going to destroy civilization?
Speaker 1: So I'm I'd love you to walk us through that
Speaker 1: thinking and through kind of the hubris that you both
Speaker 1: need to succeed in some ways in this area. But
Speaker 1: also that might make you over confident of these sorts
Speaker 1: of percentages where you really really can't afford to be.
Speaker 3: Yeah, look, we've seen bad election models. We saw models
Speaker 3: I gave. Yeah, saw models that gave Trump a one
Speaker 3: percent chance or zero point one percent chance of winning
Speaker 3: in twenty sixteen. We saw models earlier this year that
Speaker 3: gave Biden, even after he had tanked in the debate,
Speaker 3: a fifty to fifty chance of winning, which didn't make
Speaker 3: any sense. Even building models for these kinds of close
Speaker 3: problems like elections or sports forecasting is pretty tough. You
Speaker 3: have a lot of choices to make as a modeler,
Speaker 3: your biases might creep in and other things. So to
Speaker 3: then kind of take these very back of the envelope
Speaker 3: informal models and try to apply them to every problem
Speaker 3: in the world. Is, you know, obviously prone to going
Speaker 3: wrong to some extent, and like in effective altruism, they're
Speaker 3: very literal about this. I talked to Will mccaskell, who
Speaker 3: was kind of one of the founders or at least
Speaker 3: the brand name of EA and wrote a book called
Speaker 3: What We Owe the Future. He calls himself a long
Speaker 3: term'speaning he's concerned about the very very very far future.
Speaker 3: The first time to talk to him, he was like, okay,
Speaker 3: so how do we weigh like a human's life against
Speaker 3: an animal's life? And he's like, well, it should be
Speaker 3: based on the number of neurons in the animal's brain,
Speaker 3: or maybe the number of neurons to the one third
Speaker 3: power or something like that. It's a good approximation, but
Speaker 3: that leads to weird things like an elephant elephants. Elephants
Speaker 3: are actually with more than human beings by that calculation,
Speaker 3: it turns out, but you also face some comfortable decisions.
Speaker 3: The book uses a story of a time when a
Speaker 3: dog named I didn't know.
Speaker 1: Yeah, I didn't realize that the poodle like I didn't
Speaker 1: know this story, so please, yeah, please.
Speaker 3: There's a poodle that name named Dakota that gets loose
Speaker 3: in Prospect Park or some park in North Brooklyn from
Speaker 3: its owner and dashes into the subways at the first
Speaker 3: stop inbound on the F train from Manhattan to Brooklyn. So,
Speaker 3: and this is happening at like three point thirty on
Speaker 3: a weekday, so you're starting to get the rush hour commute.
Speaker 3: And the question is should we shut down the entire
Speaker 3: F line in order to search for and rescue Dakota,
Speaker 3: which they did, and Dakota popped up at some other
Speaker 3: subway station an hour or so later. But that's a
Speaker 3: case where I'll put it this. This is how I
Speaker 3: put it in the book. If you had, like a
Speaker 3: human baby who had fallen onto the tracks, like, no
Speaker 3: one would dispute that we should halt every train until
Speaker 3: that baby is found. If it was a squirrel, then
Speaker 3: we would just run under the squirrel, right, no questions.
Speaker 1: Asked, We would we would.
Speaker 3: I mean, I hate squirrels, and the dog is intuitively
Speaker 3: kind of a close decision, and so and so and
Speaker 3: but the fact is that you have to make a decision,
Speaker 3: and we had to make these decisions in COVID, where
Speaker 3: you know, what's the cost of the shutting down schools
Speaker 3: versus the cost of preventing some degree of sickness and death? Right,
Speaker 3: I mean, that's a real that's a real trade off,
Speaker 3: or things that are more intangible. What's the cost of
Speaker 3: the undermining people's happiness because they can't at least they're
Speaker 3: not supposed to see their friends in person during COVID,
Speaker 3: or they can't go to a baseball game, or go
Speaker 3: to a restaurant, or see their dying relative in a
Speaker 3: hospital or all, or go to church, all these other things,
Speaker 3: like like what's the cost of that? And we make
Speaker 3: these decisions with with you know, different heuristics and different
Speaker 3: rules of thumb that are often very clumsy and maybe
Speaker 3: maybe words of magnitude wrong. I think the decision to
Speaker 3: keep school shut down after vaccines were available is like
Speaker 3: wrong by like ten x or one hundred x, like
Speaker 3: unambiguously a terrible decision that some Blue states made even
Speaker 3: after vaccine availability. So you kind of get this dilemma
Speaker 3: where you have to make you have to make decisions,
Speaker 3: you have to make some calculation. But I think people
Speaker 3: underestimate how back of the envelope. These are and when
Speaker 3: you start to put numbers to things, and people sometimes
Speaker 3: stop asking questions about models where they should ask more questions.
Speaker 1: Is there something that you would recommend, because this is
Speaker 1: something that I've come across, you know, many times at
Speaker 1: a wall I hit in a lot of my research.
Speaker 1: Is there something that you have seen in people who
Speaker 1: are successful where they are able to modulate or at
Speaker 1: least like self check their overconfidence and their hubris, because
Speaker 1: it seems like most of the people on the river
Speaker 1: don't do that, right, Like Elon Musk, absolutely not. Like
Speaker 1: Computer Tel absolutely not. Like all of these men who
Speaker 1: you talk about, they don't seem to have a switch
Speaker 1: where they can be like, Okay, you know what, let
Speaker 1: me take a step back and self evaluate. Did you
Speaker 1: find anyone who was able to do that? And if so, how,
Speaker 1: like how do you do that?
Speaker 3: I mean, in some ways, some of the poker players
Speaker 3: I talked to seem like they're more at ease with
Speaker 3: themselves and can modulate it somehow. You know, Jason Kuhn,
Speaker 3: for example, is an interesting poker player who has some
Speaker 3: very you know, had a rough upbringing, has some very
Speaker 3: aggressive tendencies. But I think has some type of perspective
Speaker 3: of there's this very competitive part of him that he
Speaker 3: understands can be unleashed, but has to be you know,
Speaker 3: meet it out in doses somehow. Look, there is some
Speaker 3: bias in the book in the following sense, which is
Speaker 3: that the people who are quote unquote crazy or the
Speaker 3: people who really you know, like to talk about themselves
Speaker 3: and take crazy risks, make for better stories. So yeah,
Speaker 3: I mean I'm thinking about like, you know, Michael Moritz
Speaker 3: is a Sequoia capital venture capitalist who's been very successful
Speaker 3: early investor in Google of many of other companies. He's
Speaker 3: a guy who I think is probably pretty measured actually.
Speaker 3: A former journalist Michael Moritz ven Knowed Kosla is another
Speaker 3: one who invests in kind of alternative things like alternative
Speaker 3: energy or or you know, what's it called artificial meat,
Speaker 3: things like that. I mean, he seems to be like
Speaker 3: reasonably well balanced. Patrick Collison, the founder of Strike. But
Speaker 3: and these people are all in the book, but because
Speaker 3: they're less bombastic, they maybe get like both a little
Speaker 3: bit less of a write up from me and then
Speaker 3: and then kind of having some lost in translation problem too.
Speaker 3: But yeah, look, over competence is a really big problem
Speaker 3: for people in the River because by definition, these are
Speaker 3: people who probably won their first couple of big bets,
Speaker 3: sometimes by skill, sometimes by luck. I'd say often more
Speaker 3: luck than skill, but usually some of both. And when
Speaker 3: you're on a winning streak, then you can think that
Speaker 3: your God's gift to whatever venture that you happen to
Speaker 3: be involved in, and you can be overconfident and that
Speaker 3: because there is like there is like fruitful territory. I mean,
Speaker 3: the village leaves a lot to be desired for how
Speaker 3: it assesses risk and does other things, and so you know,
Speaker 3: you can in some ways the critiques that these two
Speaker 3: communities have of one another are both pretty smart. I mean,
Speaker 3: I think the River's critique of the village that these
Speaker 3: guys are too risk averse, that they're partisan, that they're
Speaker 3: too concerned with social perception. I think that's basically right.
Speaker 3: But also some of the Silicon Valley founders and vcs
Speaker 3: are are total arrogant pricks.
Speaker 1: Yeah, that's one way of putting it. And I think
Speaker 1: that was something that you're that you just hinted at,
Speaker 1: which is I think going to necessarily be like a
Speaker 1: problem when you're trying to figure out who to interview
Speaker 1: for the book. And it's just in this industry, well
Speaker 1: in the world in general, is we have this survivor bias, right,
Speaker 1: which we see the people who are successful. How are
Speaker 1: you going to go out and interview all the people
Speaker 1: who took these risks, who where it didn't pan out.
Speaker 1: One of the things that really struck me as I
Speaker 1: was reading your book was when you actually did some
Speaker 1: of these simulations, right, and you actually wrote out what
Speaker 1: the numbers looked like if you had different strategies, and
Speaker 1: some of them I was like, holy shit, right, like
Speaker 1: this crazy risky strategy, Like, yes, you die ninety five
Speaker 1: percent of the time, but like, look at that five percent.
Speaker 1: But that five percent is mostly who you end up interviewing, right,
Speaker 1: because they become the elon Musks of the world. So
Speaker 1: what does that do to kind of our perceptions of
Speaker 1: some of these traits?
Speaker 2: You know?
Speaker 3: Yeah, and so look, this became a little bit easier.
Speaker 3: I'm just speaking kind of behind the scenes as a writer.
Speaker 3: When the SBF thing blew up, then that became easier.
Speaker 3: There's originally going to be like a chapter on failures,
Speaker 3: but that meant more people that like you know, want
Speaker 3: to poke a tournament and then developed some type of
Speaker 3: problem afterward or whatever else. But like SBF kind of
Speaker 3: served enough of that role where great meretive wise, I
Speaker 3: thought it was okay. I mean, there is one story
Speaker 3: about a person named run bow Lee who was a
Speaker 3: smart guy. You never see it Toronto.
Speaker 1: How that was kind of a heartbreaking story. Yeah, go ahead.
Speaker 3: And he came to me and he said, you're writing
Speaker 3: this book about the successful people just kind of cold
Speaker 3: emailed me, and he's like, I'm somebody who got really
Speaker 3: burned by by day trading, by day trading options, in
Speaker 3: particular getting into all the Wall Street Beth's kind of communities,
Speaker 3: and he has lost a million dollars on options trading,
Speaker 3: and he wanted to tell a story, and I'm like,
Speaker 3: the story has to go in the book. There's also,
Speaker 3: you know, the chapter on Las Vegas, which is chapter three,
Speaker 3: is about how casinos, particularly through slot machines, kind of
Speaker 3: manipulate their patrons into spending more. I mean, the stories
Speaker 3: I heard from Natasha Sholl, who is a kind of
Speaker 3: an amazing woman. She's a NYU ethropologist who had like
Speaker 3: never been out of the East Coast, and she's like
Speaker 3: Las Vegas is an exotic place. I want to study
Speaker 3: Las Vegas as an anthropologist, did her dissertation about it,
Speaker 3: then wrote a book about it, and that's a story
Speaker 3: about problem gambling. And although didn't talk to that many
Speaker 3: problem gamblers, really you know, it's very dark about slot
Speaker 3: machine edication.
Speaker 1: Yeah. Well, one of the things actually that really was
Speaker 1: interesting to me there because I hadn't read Schell's book,
Speaker 1: was this insight that you that you go into, which
Speaker 1: is that a lot of these problem gamblers that people
Speaker 1: who play slot machines they know they're losing and they
Speaker 1: don't care that. They're just like they enjoy, like they
Speaker 1: enjoy that experience even though they lose. And to me,
Speaker 1: like that was actually an alha moment because I always
Speaker 1: thought that at least they thought they could win, right,
Speaker 1: But like one of the allers was that, like you
Speaker 1: might be a winner, but you even said that some
Speaker 1: of them don't like winning because it brings them out
Speaker 1: of that flow stage.
Speaker 3: Yeah, you're in some machine zone Natasha Shall calls it
Speaker 3: the machine zone where it's just you and you're pressing
Speaker 3: the reeles and you know, I don't have much of
Speaker 3: a compulsion to flay slots, but I've tossed in a
Speaker 3: few bucks here and there when you're frustrated the Innovan
Speaker 3: Foker tournament, and some of the games are very fun
Speaker 3: and compelling, right you got like gorillas or what's the
Speaker 3: big one? Not garillas, but uh, Buffalo.
Speaker 1: I've never I've never played slots in my life.
Speaker 3: Even I'm going to force you to. I'm going to
Speaker 3: give you money to play slots. I think, Maria, you
Speaker 3: can no longer make that drag, which I find nitty
Speaker 3: by the way, but but yeah, they they want to
Speaker 3: shut out all the other distractions of the world and
Speaker 3: just channel it into like one problem which is problem
Speaker 3: part problem gambling, which is very different than like the
Speaker 3: table games players. The blackjack crabs player more often fits
Speaker 3: the stereotype of somebody who is bored because they don't
Speaker 3: have enough risk tolerance in their life, enough risk in
Speaker 3: their life, and so they're useless as a way, and
Speaker 3: this but some of this is is gendered too, if
Speaker 3: you want to go there, but like you know, men
Speaker 3: feeling the need. If you've been enough craps, tables, craps,
Speaker 3: I do find fun, don't play it much. If you've
Speaker 3: been enough groups of guys at a at a craps
Speaker 3: table where they'll encourage one another to make bets. You know,
Speaker 3: every crafts bets negative ev. But also to play outside
Speaker 3: their means and gamble bigger still have stories to tell.
Speaker 3: I mean, that's you know, that's a certain type of
Speaker 3: young man is more likely to do that kind of thing,
Speaker 3: and it might substitute for other types of ways approve
Speaker 3: their bravery. But like the slots machines players and then
Speaker 3: the skill game players, the poker players, the sports betters,
Speaker 3: the handful of advantage players, which means people who try
Speaker 3: to count cards in blackjack, there are conditions. Please do
Speaker 3: not try this yourself. There are conditions under witch playing.
Speaker 3: Slot machines can be plus ev because of contingencies the
Speaker 3: machines or bonus payouts or jackpots that becomes efficiently large.
Speaker 3: But those are those are one percent of the gambling world.
Speaker 3: I mean, sometimes I feel like you and I are
Speaker 3: use you know, some of the prestige resorts, generally speaking,
Speaker 3: the prestige resorts in Las Vegas and elsewhere have the
Speaker 3: best poker rooms, the win that are et cetera. Resorts
Speaker 3: world Now, Allas, you, I sometimes wonder if, like we're
Speaker 3: used a little bit to whitewash the fact that like
Speaker 3: ninety nine percent of people in that casino are doing
Speaker 3: negative expected value gambling.
Speaker 1: No, I mean that I think that's true, and I
Speaker 1: do and I do overlook that on purpose because I
Speaker 1: don't like paying attention to that, right, But yeah, I know,
Speaker 1: I think you're right, And I don't want to judge
Speaker 1: anyone for you know, playing slots or doing whatever they're doing.
Speaker 1: But I do find it a little bit depressing when
Speaker 1: I see people who don't have an edge and who
Speaker 1: are just kind of pressing buttons. Yeah.
Speaker 3: Look, I comfort myself by saying that poker, at least
Speaker 3: I think is plus CV any utilitarian calculus for society,
Speaker 3: just because I think it is such a unique teacher
Speaker 3: of certain critical life skills in an environment where you
Speaker 3: have something real at risk that like, I just I
Speaker 3: just think that, like I just think that playing poker
Speaker 3: makes you more adept at handling real life risky situation
Speaker 3: a lot of the time, you know. Apart from that,
Speaker 3: I mean, I think there's a lot in the book
Speaker 3: about sports betting. I think there's more papers coming out
Speaker 3: saying that sports betting increases bankruptcies, among exactly the classes
Speaker 3: of people that you would expect, like you see oh
Speaker 3: in uptick and bankruptcies and males age twenty to thirty nine.
Speaker 3: I wonder what could have caused that when sports betting
Speaker 3: went online in X and Y State. You know, look,
Speaker 3: I think the slot machine stuff is probably quite bad
Speaker 3: from utilitarian standpoint. I think the state lottery is actually
Speaker 3: maybe the worst of all on a kind of a
Speaker 3: per capita basis, where the odds are very bad for
Speaker 3: the player and the socio economics of who plays the
Speaker 3: state lottery are It's basically attacks on like lower class
Speaker 3: people for the most part, so there's a lot of hypocrisy.
Speaker 3: There are also lots of arguments about the arms race
Speaker 3: that hey, if we don't legalize this game, then maybe
Speaker 3: somebody else would, right. I mean, if I had my
Speaker 3: you know, if I could perfectly calibrate it, then maybe
Speaker 3: i'd have something where you have a casino that offers
Speaker 3: poker and table games but not slot machines. And maybe
Speaker 3: you have sports bedding which can be done in person
Speaker 3: at a facility but not online, where the addiction seems
Speaker 3: to be higher, and they have fair rules where you
Speaker 3: don't limit winning players et cetera. And you have limits
Speaker 3: on how much can take from Wales. Like there's a
Speaker 3: world in which I would be I think more content with.
Speaker 3: And again I'm not a moralistic person. The book is
Speaker 3: not going to be scoldy, and I believe in showing
Speaker 3: and not telling. But like, it's not like the the
Speaker 3: gambling industry comes out looking great. It comes out looking smart.
Speaker 3: It comes out looking like it understands how to like
Speaker 3: model its customers out. And I would say one thing
Speaker 3: to say encounter to this is that if you go
Speaker 3: to the mid to high range resorts in Las Vegas,
Speaker 3: my subjective view is that people seem to be having
Speaker 3: a pretty good time, and Las Vegas gets a lot
Speaker 3: of repeat business, so there's some type of win win
Speaker 3: in that deal. But I might I might draw the
Speaker 3: line a little bit higher than like a total freefer all,
Speaker 3: which is kind of where we're headed.
Speaker 2: Now.
Speaker 3: We'll be back in just a minute.
Speaker 1: I have a few questions, But first I'm just curious
Speaker 1: are you still doing sports betting? So I loved that
Speaker 1: you picked it up in a serious way for the book,
Speaker 1: although holy shit, I had no idea that you'd wagered
Speaker 1: over a million dollars for your research, Nate, I didn't know,
Speaker 1: but yeah, tell us about that, and tell us what
Speaker 1: you're doing right now.
Speaker 3: So I wanted to have skin in the game, to
Speaker 3: borrow another author's phrase and see what it's actually like
Speaker 3: when you're going through the grind of sports betting an
Speaker 3: NBA season. So I bet almost the entirety of the
Speaker 3: twenty twenty two twenty three NBA season, all the regular season,
Speaker 3: in about half the playoffs, and then five thirty eight
Speaker 3: blew up, and so I had other things to do,
Speaker 3: and I didn't bet the end of the playoffs, and
Speaker 3: I learned that, I mean, it's probably what I should
Speaker 3: have expected by I learned that it's pretty hard. I
Speaker 3: went on a huge heater at the start of the
Speaker 3: NBA season where I was up like seventy thousand bucks.
Speaker 3: I'm like, man, I'm really fucking good at the sports
Speaker 3: betting stuff. But then things change. One thing that changed
Speaker 3: is you start to get limited by some of the sites,
Speaker 3: especially if you're trying to do things like, oh, I
Speaker 3: just suck. Because I'm a Twitter addict, I follow a
Speaker 3: lot of NBA writers.
Speaker 1: Oh.
Speaker 3: Damian Lillard of the Portland Trailblazers is injured today, but
Speaker 3: the line at sportsbook X does not yet account for that.
Speaker 3: So let me go and bet three thousand dollars on
Speaker 3: this bet, which is now hugely positive. Ev you can
Speaker 3: do it the first time, maybe the second time. The
Speaker 3: third time, your hands are cut off basically kind of literally.
Speaker 3: So when you aren't able to take advantage of things
Speaker 3: that you know in principle, I think you should be.
Speaker 3: I mean, if they're posting a line, they're taking action
Speaker 3: on it. And the fact that, like in the NBA
Speaker 3: regular season, it's all about is this player injured? Who
Speaker 3: is trying to actually win and who's not. The models
Speaker 3: that you have at the start of the season don't
Speaker 3: work as well when you're kind of in this like
Speaker 3: triage phase of the of the trenches of the NBA
Speaker 3: regular season, and you know, and you talk to other
Speaker 3: bears like, yeah, I have three guys whose entire job
Speaker 3: Spanky kiro Lois is a guy who we interview in
Speaker 3: the book. I have three guys who just track injury
Speaker 3: data for me. I mean, while I'm trying to like
Speaker 3: do some Twitter search for is this player in the
Speaker 3: warm up? You know you're competing essentially against the best players,
Speaker 3: the best betters in the world. So I love that
Speaker 3: one point eight million dollars in bets and believe this
Speaker 3: is just like on average the bet size is like
Speaker 3: twelve hundred bucks, you're making fifteen hundred twelve hundred dollars
Speaker 3: bets or whatever. Over the course of the season, I
Speaker 3: made about five thousand bucks, probably spent about five thousand
Speaker 3: hours on this, so I made less than minimum wage
Speaker 3: actually with my with my sports betting side hustle.
Speaker 1: I love it. I love it. So I want to
Speaker 1: ask two more things before we wrap up. The first
Speaker 1: is kind of a theme that I and I think
Speaker 1: I picked up on this because you know, it's something
Speaker 1: that I've obviously thought about a lot a lot, And
Speaker 1: it's the strand that is woven throughout the book, which
Speaker 1: is that even though the book is about skill, about
Speaker 1: finding edges, about how to make plus ev gambles, there
Speaker 1: is this thread throughout it of luck, right and of
Speaker 1: the fact that you also do need to get lucky,
Speaker 1: and you actually you point to a lot of these
Speaker 1: moments where things could have turned out so differently, like,
Speaker 1: you know, to pick a random example, the car crash
Speaker 1: that Peter Tila and Elon Musk were in right, like
Speaker 1: what happens if the car lands differently? And I don't
Speaker 1: know if you want to talk about that specific example,
Speaker 1: but this is something that you're clearly very aware of,
Speaker 1: and I just want to raise it and talk about
Speaker 1: it a little bit.
Speaker 3: Yeah, there is a survivorship bias problem when you're writing
Speaker 3: a book about successful people, and literally kind of in
Speaker 3: the case of Elon Musk and Peter Teal, Elon had
Speaker 3: sold his first company, bought like a million dollar McLaren
Speaker 3: F one or something sports car basically almost like a
Speaker 3: you know, Formula one caliber car, and they're driving it
Speaker 3: on I think sand Hill Road or somewhere in Palo Alto,
Speaker 3: and Elon floor is the accelebrator while trying to change lanes,
Speaker 3: and it kind of spirals up into like doing a
Speaker 3: little helicopter motion and miraculously lands on its wheels, and
Speaker 3: Elon and Peter are are okay, and actually hitchhikes their
Speaker 3: VC meeting, And I guess keep PayPal alive by with
Speaker 3: that hitchhike. But so I asked Peter Teal, you know,
Speaker 3: I asked him in a way that was nerdier. I
Speaker 3: asked him if you ran the world a thousand times,
Speaker 3: had a thousand simulations, then how often would you wind
Speaker 3: up in this position? And he objected to this question
Speaker 3: and went on the whole thing about determinism or probablism,
Speaker 3: which is interesting, but we don't have time for today,
Speaker 3: I don't think. But yeah, life is very contingent. We've
Speaker 3: talked on the show about the assassination attempt against President
Speaker 3: Trump where that was a very close call, or things
Speaker 3: like the butterfly ballot in the two thousand election, which
Speaker 3: is in one ballot in one county in Florida in
Speaker 3: two thousand, the ballot had a funky design where some
Speaker 3: people who would have wanted to vote for Al Gore
Speaker 3: wound up voting for Pat Buchanan instead, and that was
Speaker 3: enough to cost or the election. On top of the
Speaker 3: recount stuff in Florida wouldn't have mattered if this ballot
Speaker 3: design had been better. So life is. Whether it's literally
Speaker 3: metaphysically random or not is a philosophical question, But it's
Speaker 3: random to our ability to determine it in a lot
Speaker 3: of ways. And it's just it's just very hard to
Speaker 3: acknowledge the role that luck plays in your life. It's
Speaker 3: very hard, even for me or for you. Maybe you're
Speaker 3: the exception, Maria, it's very hard not to tell yourself
Speaker 3: a clever narrative in which in which you know, even
Speaker 3: in writing the book I have, I think I've gotten
Speaker 3: very lucky with a lot of things to kind of
Speaker 3: have like this SBF story, like fall into My Lap,
Speaker 3: for example, was I think pretty lucky. And having a
Speaker 3: book that covered territory like AI that kind of became
Speaker 3: a much bigger deal, and sports betting really blew up.
Speaker 3: I mean, you know, so I think I've gotten very
Speaker 3: lucky in a lot of things recently, the way that like,
Speaker 3: you know, I didn't know that actually having this independent
Speaker 3: newsletter is actually actually much better economically than being working
Speaker 3: for a big company. I didn't know that, And I
Speaker 3: could easily have taken a deal with another big company
Speaker 3: and wound up worse off for it, and a lot
Speaker 3: of respects, and so, you know, I know the role
Speaker 3: that luck has played in my life, including just things
Speaker 3: like being basically healthy and having a supportive partner and
Speaker 3: supportive friends, and being born into a country where it's
Speaker 3: okay to be a gay man, and a country where
Speaker 3: where you have some downside protection. I mean, those are
Speaker 3: all things that are very lucky. So you know, the
Speaker 3: next time that I take a cooler in poker or
Speaker 3: blue set of or set or something, then I can't
Speaker 3: complain too much.
Speaker 1: Well, I had more questions, but that seems like such
Speaker 1: a beautiful place to end that I think I have
Speaker 1: to not ask my remaining question. But yeah, I mean,
Speaker 1: obviously this is something I've thought about a lot and
Speaker 1: was kind of the big theme of the Biggest Bluff.
Speaker 1: But it's nice to see that you know that this
Speaker 1: is something that you're thinking about as well, because I
Speaker 1: do think that it's easy for us to get over confident,
Speaker 1: and I think that as we you know, during risky
Speaker 1: business and all of these endeavors, we do want to
Speaker 1: maximize our skill, but we do need to always remember
Speaker 1: that we're we are insanely lucky to even be in
Speaker 1: this position. So Nate, I hope that you get continue
Speaker 1: to get lucky, and that this book sells an insane
Speaker 1: number of copies and that it does incredibly well, and
Speaker 1: that you and I can continue making this show together
Speaker 1: because we're both continuing to be lucky and yet take
Speaker 1: plus evy edges whenever we can.
Speaker 3: Thank you so much, Maria, and you know, maybe if
Speaker 3: you want to have you on the show again sometime.
Speaker 3: You know, of fun, we have a good chemistry.
Speaker 1: I think, I agree, I agree. So yeah, you're you're
Speaker 1: welcome back. How's next week looking for you?
Speaker 3: Actually really fucking busy, but we'll make it work.
Speaker 1: Let us know what you think of the show. Reach
Speaker 1: out to us at Risky Business at pushkin dot fm.
Speaker 1: Risky Business is hosted by me Maria Kanakova.
Speaker 3: And by me Nate Silver. The show was a cool
Speaker 3: production of Pushing Industries and iHeartMedia. This episode was produced
Speaker 3: by Isaac Carter. Our associate producer is Sonya gerwit Lydia,
Speaker 3: Jean Kott and Daphne Chen are our editors, and our
Speaker 3: executive producer is Jacob Goldstein. Mixing by Sarah Bruger.
Speaker 1: If you like the show, please rate and review us
Speaker 1: so other people can find us too, But once again,
Speaker 1: only if you like us. We don't want those bad
Speaker 1: reviews out there. Thanks for tuning in.
Speaker 2: From the call.
Speaker 1: W