Speaker 1: Pushkin. Welcome back to Risky Business, a show about making
Speaker 1: better decisions. I'm Maria Kanakova.
Speaker 2: And I'm Nate Silver.
Speaker 1: So today Nate and I are going to be doing
Speaker 1: something slightly different. Today is Tuesday, as always, which is
Speaker 1: when we tape August thirteenth, which is a really important
Speaker 1: date because it's the day that Nate's book comes out.
Speaker 1: So today, instead of being co hosts, I am going
Speaker 1: to get in the interviewer's seat and put Nate in
Speaker 1: the hot seat where I get to ask him also
Speaker 1: some really hard questions about his book. How does that sound?
Speaker 3: N I have to be on your podcast, Maria. I'm
Speaker 3: glad that Nate guy is off this week.
Speaker 2: He's a fucking jerk.
Speaker 1: I really enjoyed reading On the Edge, which is out today.
Speaker 1: So by the time that everyone is listening, I hope
Speaker 1: you've already bought a copy, but if not, go ahead
Speaker 1: and buy it right now. So first of all, Nate, congratulations.
Speaker 1: I know that this was a long time coming labor
Speaker 1: of many many years.
Speaker 2: Yeah.
Speaker 3: No, I spent basically three years of my life where
Speaker 3: I'd say this is my main professional focus talk to
Speaker 3: two hundred people, and I know I just I would
Speaker 3: recommend email really smart people and ask to have a
Speaker 3: conversation with them and pretend it's for a book, and
Speaker 3: then don't write the book. That's probably still worth it
Speaker 3: in some sense. But no, I actually went ahead and
Speaker 3: wrote the.
Speaker 1: Book, and we're very glad you did. So I'm going
Speaker 1: to start with Nate. In the book, you make this
Speaker 1: distinction between two communities. You have the village and you
Speaker 1: have 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. Village
Speaker 3: 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.
Speaker 2: By contrast, the river is kind.
Speaker 3: Of the world that I suppose I inhabit, or I imagine
Speaker 3: myself inhabiting. It's the world of calculator risk taking, where
Speaker 3: people are some combination of being very analytical and extremely
Speaker 3: wildly competitive. So like poker is an archetypal maybe the
Speaker 3: archetypal river activity. But as I discovered this world in
Speaker 3: the book, you know, I found that the venture capitalist
Speaker 3: and the effective altruists, and the hedge fund people and
Speaker 3: the sports betters, even the crypto people, they all have
Speaker 3: a common vocabulary. They all talk about things in roughly
Speaker 3: the same ways, and so it's like it's not a place,
Speaker 3: it's a discrete community. I mean, in Las Vegas and
Speaker 3: Silicon Valley you might get closer to an actual network,
Speaker 3: but like they're just these people that I think are
Speaker 3: often poorly understood by the mainstream media and yet are
Speaker 3: incredibly wealthy and powerful. Many of the richest people in
Speaker 3: the world have some of these river tendencies, and like,
Speaker 3: surprisingly to me, would take my emails and my phone
Speaker 3: calls because they kind of recognize like a fellow traveler
Speaker 3: a little bit. And then what I discovered is that
Speaker 3: the kind of conceit of the proposal that actually there's
Speaker 3: a commonalities in how people think was more literally true
Speaker 3: than I had thought, and that there's more literal crossover
Speaker 3: poker players who become effective altruists or hedge funders who
Speaker 3: host poker games, or venture capitalists who do sports betting
Speaker 3: is a metaphor for some of these things. So it's
Speaker 3: a real place of a real group of people that
Speaker 3: I think, Maria are in our circles, maybe not the
Speaker 3: only circles to run in, but certainly one common type.
Speaker 3: And I felt like I had the opportunity tell a
Speaker 3: really good story about these people.
Speaker 2: 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 particularly Sam
Speaker 3: Bakman Free who had talked to first before the collapse
Speaker 3: 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 fund world in Silicon Valley, you go
Speaker 1: into 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, Wait, did you on purpose, like did you
Speaker 1: try 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: like is interesting to me. And when you're talking about
Speaker 1: risk takers, people often assume men, and so I was
Speaker 1: very curious if that was a deliberate choice or if
Speaker 1: it just so happens that these most interesting stories happened
Speaker 1: 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 a world that is Poker's
Speaker 3: a little bit more diverse, but the rest of the
Speaker 3: river 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 fifty to fifty. But like,
Speaker 3: but also the I call them characters, the people I
Speaker 3: talked to, like Catharine Sullivan, this astronaut's amazing and she
Speaker 3: has an amazing stories and she was studying like oceanography
Speaker 3: in Nova Scotia and beat out like ten thousand other people.
Speaker 3: I'm an astronaut and mean, it's an amazing story. Cataline Carico,
Speaker 3: I had to learn all the names pronounced correctly for
Speaker 3: the audiobook, but no, I've 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: come 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 tackled
Speaker 3: There's one section in the poker chapter about why aren't
Speaker 3: there more women in poker? And then one section in
Speaker 3: the VC chapter about why are there so few black
Speaker 3: and Hispanic and women founders? And so you have to
Speaker 3: take it on directly without it being awoke 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: x payout, that's an extraordinarily good bet. And if you
Speaker 3: can make a lot of those bets by aggregating different
Speaker 3: companies into a fund, then you're kind of guaranteed to
Speaker 3: make money.
Speaker 2: And maybe make a lot of money.
Speaker 3: And it also applies to things like evaluating nuclear war
Speaker 3: risk or AI risk, things that might be you know,
Speaker 3: a two percent chance in over some time frame or
Speaker 3: five percent chance whatever else, but have like very bad
Speaker 3: to infinitely bad negative payouts. The second thing that Silicon
Speaker 3: Valley knows is that having a longer time horizon is
Speaker 3: really worth it. We're a country that likes to make money,
Speaker 3: but we're a get rich quick country and not a
Speaker 3: get rich slowly country. When you're investing in startups, early
Speaker 3: stage startups, they often have a time horizon. You know,
Speaker 3: SpaceX took thirteen years I think to make a first
Speaker 3: profit nine years would actually even launched a rocket successfully
Speaker 3: or something like that. So I think you almost always
Speaker 3: benefit from having like a longer time horizon than other people.
Speaker 3: People just discount the long term future way too much,
Speaker 3: even in their personal lives, I think, And so therefore
Speaker 3: those I think are kind of the two foremost lessons
Speaker 3: and why the book is you know, kind of ambivalently
Speaker 3: to positive on that Silicon Valley ethos, even though the
Speaker 3: individual characters 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 also like, how do you balance
Speaker 1: that and how might that be 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 ev opportunities. So to
Speaker 3: some extent, this is related to the idea of opportunit 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 to risk averse
Speaker 3: about this kind of thing. This goes back to, as
Speaker 3: you'll know, like some of the connoment 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 poker player. Of course, she site studies
Speaker 3: like by Steve Levitt fre Economics that when they have
Speaker 3: people flip a coin to make a decision, and a
Speaker 3: major decision, not like am I getting Thai food or
Speaker 3: Indian for dinner tonight, which I do flip a coin
Speaker 3: about sometimes, by the way, but things like should I
Speaker 3: leave my partner, or should I quit my job, or
Speaker 3: should I move across the country to a different part
Speaker 3: of the world. People are happier on average when they
Speaker 3: make a change, and so you know, the ninety percent
Speaker 3: of people who are risk averse maybe could take some
Speaker 3: messages from that book then, but you also meet the
Speaker 3: 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 richest man in the world, and
Speaker 1: then SBF is going to jail. However, they do have
Speaker 1: this one characteristic in common, so I would I would
Speaker 1: love for you to expand on that alone.
Speaker 3: Yeah, and I should be I mean, I don't mean
Speaker 3: to I am very and the book is very.
Speaker 2: Unsympathetic to SBF.
Speaker 3: I'm not saying I'm sympathetic to Elon, but Elon has
Speaker 3: accomplished a lot of things, and I think Elon's a
Speaker 3: real person whose career wasn't based on I mean, I
Speaker 3: don't think Sam'shull career was based on fraud, but he didn't,
Speaker 3: you know, Elon has not committed ten billion dollars worth
Speaker 3: of stealing people's crypto deposits. Basically, Yeah, they are the
Speaker 3: two people that really, really, really go balls to the wall.
Speaker 3: I think we can use that phrase in terms of risk.
Speaker 2: 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 knowf is even
Speaker 3: more unambiguously I think irrational in taking a very literal
Speaker 3: approach of utilitarianism, where you know, he has said repeatedly
Speaker 3: he told his co founder Carolyn Ellison this. He told
Speaker 3: the economist Tyler Cowan this that if he could flip
Speaker 3: a coin to determine the fate of the world, and
Speaker 3: either half the time the world is destroyed, but the
Speaker 3: other half the time in 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 know,
Speaker 3: one point five is greater than one. Therefore you take
Speaker 3: 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: it is infinitely likely to leave you ruins.
Speaker 2: So what should you do? Probably not take the bet.
Speaker 2: It's not that hard a dilemma, actually, but to SBF
Speaker 2: it was.
Speaker 3: And in my interviews with him, you kind of find
Speaker 3: a pattern of him saying, I mean, the first thing
Speaker 3: that I did with him, when he's riding high January
Speaker 3: twenty twenty two, Bitcoin still near its all time peaks,
Speaker 3: He's like, if.
Speaker 2: You're not really literally willing to ruin.
Speaker 3: Yourself, not the fake ruined, Like if Elon Musk has
Speaker 3: to sell Twitter and he's only worth one hundred and
Speaker 3: seventy billion dollars instead of two hundred and twenty billion dollars,
Speaker 3: that's not what it would describe as being ruined. And
Speaker 3: Sam was very clear that I don't think you should
Speaker 3: protect your downside. You have to be so risk loving
Speaker 3: that you should literally be willing to like destroy your
Speaker 3: life and your reputation if it's positive expected value, which
Speaker 3: I think is insane. I think reflects I mean not
Speaker 3: a clinical diagnosis, but I think literally some I think
Speaker 3: he's a somewhat defective person emotionally in other ways. And
Speaker 3: you know, the fact that he was enabled by so
Speaker 3: many supposedly smart actors is a core question that's asked
Speaker 3: by the book. I mean, you know, the New York
Speaker 3: Times review understood this, which is that it's like the
Speaker 3: book is actually pretty unsparing to some of these characters
Speaker 3: in the River, even though like it's not a boo
Speaker 3: where I'm gonna be like Peter Teal is evil and
Speaker 3: Elon Musk is evil and a fascist, Like that's not
Speaker 3: my style of writing. And I think I think they're
Speaker 3: interesting people who deserve to have their stories told by
Speaker 3: someone who kind of understands the personality type. But SBF
Speaker 3: was a really dangerous figure and on top of everything else,
Speaker 3: like also a fairly bad calculator of risk. His decision
Speaker 3: to go take the witness stand at his trial after
Speaker 3: the government's testimony was just incredibly devastating, and they had
Speaker 3: him dead to rights and contradicting everything he told people publicly,
Speaker 3: everything he told me in interviews that were on the record,
Speaker 3: although with an embario, I mean, they couldn't be used
Speaker 3: until 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 awitted 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?
Speaker 2: As a question that I think has to be has
Speaker 2: 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 more than most people that you know,
Speaker 1: if you say sixty percent certain or like sixty percent
Speaker 1: chance of this, like you're not actually like that. There
Speaker 1: is a margin of error there, right, Like if you
Speaker 1: fifty one percent likely that you're going to have a
Speaker 1: 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'd love you to walk us through that thinking
Speaker 1: and through kind of the hubris that you both need
Speaker 1: to succeed in some ways in this area. But also
Speaker 1: that might make you over confident of these sorts of
Speaker 1: 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 I 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 kind 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 to Will mccaskell,
Speaker 3: who was kind of one of the founders or at
Speaker 3: least the brand name of EA, and wrote a book
Speaker 3: called What We Owe the Future. He calls himself a
Speaker 3: long term mispaning. He's concerned about the very very very
Speaker 3: far future. The first time to talk to him, he
Speaker 3: was like, okay, so how do we weigh like a
Speaker 3: human's life against an animal's life? And he's like, well,
Speaker 3: it should be based on the number of neurons in
Speaker 3: the animal's brain, or maybe the number of neurons to
Speaker 3: the one third power or something like that. It's a
Speaker 3: good approximation, but that leads to weird things like an
Speaker 3: elephant elephants.
Speaker 2: Elephants are actually.
Speaker 3: With more than human beings by that calculation, it turns out,
Speaker 3: but you also face some comfortable decisions. The book uses
Speaker 3: a story of a time when a dog named I
Speaker 3: don't I.
Speaker 1: Didn't know, Yeah I did it realize that the poodle
Speaker 1: like I didn't 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 out, put it
Speaker 3: in the book. If you had, like a human baby
Speaker 3: who had fallen onto the tracks, like, no one would
Speaker 3: dispute that we should halt every train till that baby
Speaker 3: is found. If it was a squirrel, then we would
Speaker 3: just run on the squirrel, right, no questions asked.
Speaker 1: We would we would? I mean, I hate squirrels.
Speaker 3: And the dog is intuitively kind of a close decision,
Speaker 3: and so and so and then. But the fact is
Speaker 3: that you have to make a decision, and we had
Speaker 3: to make these decisions in COVID, where you know, what's
Speaker 3: the cost of the shutting down schools versus the cost
Speaker 3: of preventing some degree of sickness and death, right, I
Speaker 3: mean that's a real that's a real trade off.
Speaker 2: Or things that are more intangible.
Speaker 3: What's the cost of the undermining people's happiness because they
Speaker 3: can't at least they're not supposed to see their friends
Speaker 3: in person during COVID, or they can't go to a
Speaker 3: baseball game, or go to a restaurant, or see their
Speaker 3: dying relative in a hospital or all, or go to church,
Speaker 3: all these other things, like like what's the cost of that?
Speaker 3: And we make these decisions with with you know, different
Speaker 3: heuristics and different rules of thumb that are often very
Speaker 3: clumsy and maybe maybe words of magnitude wrong. I think
Speaker 3: the decision to keep school shut down after vaccines were
Speaker 3: available is like wrong by like ten x or one
Speaker 3: hundred x, like unambiguously a terrible decision that some Blue
Speaker 3: states made even after vaccine availability. So you kind of
Speaker 3: get in this dilemma where you have to make you
Speaker 3: have to make decisions, you have to make some calculation.
Speaker 3: But I think people underestimate how back of the envelope
Speaker 3: these are. And when you start to put numbers to things,
Speaker 3: and people sometimes stop asking questions about models where they
Speaker 3: should ask you 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: Competer Teal, 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,
Speaker 1: how 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.
Speaker 1: You know.
Speaker 3: Jason Kuhn, for example, is an interesting poker player who
Speaker 3: has some very you know, had a rough upbringing, has
Speaker 3: some very aggressive tendencies, but I think has some type
Speaker 3: of perspective of there's this very competitive part of him
Speaker 3: that he understands can be unleashed, but has to be
Speaker 3: you know, meet it out in doses somehow. Look, there
Speaker 3: is some bias in the book in the following sense,
Speaker 3: which is that the people who are quote unquote crazy
Speaker 3: or the people who really you know, like to talk
Speaker 3: about themselves and take crazy risks, make for better stories.
Speaker 3: So yeah, I mean I'm thinking about like, you know,
Speaker 3: Michael Moritz is a Sequoia Capital venture capitalist who's been
Speaker 3: very successful early investor in Google of many of their companies.
Speaker 3: He's a guy who I think is probably pretty measured actually.
Speaker 3: A former journalist Michael Moritz ven know 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, overconfidence is a really big problem for
Speaker 3: people in the river because by definition, these are people
Speaker 3: who probably won their first couple of big bets, sometimes
Speaker 3: by skill, sometimes by luck. I'd say often more luck
Speaker 3: than skill, but usually some of both. And when you're
Speaker 3: on a winning streak, then you can think that your
Speaker 3: God's gift to whatever venture that you happen to be
Speaker 3: involved in, and you can be overconfident. And because there
Speaker 3: is like there is like fruitful territory. I mean, the
Speaker 3: village leaves a lot to be desired for how it
Speaker 3: 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 their 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 in. 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 didn't pan out. One
Speaker 1: of the things that really struck me as I was
Speaker 1: reading your book was when you actually did some of
Speaker 1: these simulations, right, and you actually wrote out what the
Speaker 1: numbers looked like if you had different strategies, and some
Speaker 1: of them I was like, holy shit, right, like this
Speaker 1: crazy risky strategy, Like, yes, you die ninety five percent
Speaker 1: 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? 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 narrative wise, I.
Speaker 2: Thought it was okay.
Speaker 3: I mean, there is one story about a person named
Speaker 3: run bow Lee who was a smart guy.
Speaker 2: You never see a trun that was.
Speaker 1: 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 these sucessful 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 option in
Speaker 3: particular getting into all the Wall Street bets 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 an myu athropologist 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 I didn't talk to that
Speaker 3: many problem gamblers, really you know, it's very dark about
Speaker 3: slot machine addiction.
Speaker 1: Yeah. Well, one of the things actually that really was
Speaker 1: interesting to me there because I hadn't read Schul'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 otherers 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 state.
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 reels and you know, I don't have much of
Speaker 3: a compulsion to play slots, but I've tossed in a
Speaker 3: few bucks.
Speaker 2: Here and there. When you're frustrated the Innovan Poker.
Speaker 3: Tournament, and some of the games are very fun and compelling,
Speaker 3: right you got like gorillas or what's the big one?
Speaker 3: Not Garia's but uh Buffalo Buffalo.
Speaker 1: I've never I've never played slots in my life, even I'm.
Speaker 2: Going to force you to.
Speaker 3: I'm going to give you money to play slots, I think, Maria,
Speaker 3: so you can no longer make that bras, which I
Speaker 3: find nitty by the way, but yeah, they they want
Speaker 3: to shut out all the other distractions of the world
Speaker 3: and just channel it into like one problem, which is
Speaker 3: problem part problem gambling, which is very different than like.
Speaker 2: The table games players.
Speaker 3: The Blackjack crabs player more often fits the stereotype of
Speaker 3: somebody who is bored because they don't have enough risk
Speaker 3: tolerance in their life, enough risk in their life, and
Speaker 3: so their useless as a way, and some of this
Speaker 3: is is gendered too, if you want to go there,
Speaker 3: but like you know, men feeling the need. If you've
Speaker 3: been enough craps tables, craps I do find fun, don't
Speaker 3: play it much. If you've been enough groups of guys
Speaker 3: at a at a craps table where they'll encourage one
Speaker 3: another to make bets at you know, every crafts bets
Speaker 3: negative EV, but also to play outside their means and
Speaker 3: gamble bigger, so they'll have stories to tell. I mean
Speaker 3: that's you know, that's a certain type of young man
Speaker 3: is more likely to do that kind of thing, and
Speaker 3: it might substitute for other types of ways to prove
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.
Speaker 2: Please do not try this yourself.
Speaker 3: There are conditions under witch playing slot machines can be
Speaker 3: plus ev because of contingencies the machines or bonus payouts
Speaker 3: or jackpots that becomes efficiently large. But those are those
Speaker 3: are one percent of the gambling world. I mean, sometimes
Speaker 3: I feel like you and I are use. You know,
Speaker 3: some of the prestige resorts, generally speaking, the prestige resorts
Speaker 3: in Las Vegas and elsewhere have the best poker rooms,
Speaker 3: the win that are et cetera resorts world Now, Alasio,
Speaker 3: I sometimes wonder if like we're used a little bit
Speaker 3: to whitewash the fact that like ninety nine percent of
Speaker 3: people in that casino are doing negative expected value gambling.
Speaker 1: Now, 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.
Speaker 3: Yeah. Look, I comfort myself by saying that poker, at
Speaker 3: least 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 situations
Speaker 3: a lot of the time.
Speaker 2: You know.
Speaker 3: Apart from that, I mean, I think there's a lot
Speaker 3: in the book about sports betting. I think there's more
Speaker 3: papers coming out saying that sports betting increases bankruptcies among
Speaker 3: exactly the classes of people that you would expect, Like
Speaker 3: you see oh, uptick in bankruptcies and males age twenty
Speaker 3: to thirty nine, And what if it could have caused
Speaker 3: that when sports Beenning went online in X and Y State.
Speaker 3: You know, look, I think the slot machine stuff is
Speaker 3: probably quite bad from a utilitarian standpoint. I think the
Speaker 3: state lottery is actually maybe the worst of all on
Speaker 3: a kind of a per capita basis, where the odds
Speaker 3: are very bad for the player and the socio economics
Speaker 3: of who plays the state lottery are It's basically attacks
Speaker 3: on like lower class people for the most part, so
Speaker 3: there's a lot of hipocrisy. There are also lots of
Speaker 3: arguments about the arms race that hey, if we don't
Speaker 3: legalize this game, then maybe somebody else would, right. I mean,
Speaker 3: if I had my you know, if I could perfectly
Speaker 3: calibrate it, then maybe i'd have something where you have
Speaker 3: a casino that offers poker and table games but not
Speaker 3: slot machines. And maybe you have sports bedding which can
Speaker 3: be done in person at a facility but not online
Speaker 3: where the addiction seems to be higher, and they have
Speaker 3: fair rules where you don't limit winning players, et cetera,
Speaker 3: and you have limits on how much can take from Wales,
Speaker 3: Like there's a world in which I would be I
Speaker 3: think more content with. And again I'm not a moralistic person.
Speaker 3: The book is not going to be scoldy, and I
Speaker 3: believe in showing and not telling. But like, it's not
Speaker 3: like the the gambling industry comes out looking great. It
Speaker 3: comes out looking smart, it comes out looking like it
Speaker 3: understands how to like model its customers out. And I
Speaker 3: would say one thing to say encounter to this is
Speaker 3: that if you go to the mid to high range
Speaker 3: resorts in Las Vegas, my subjective view is that people
Speaker 3: seem to be having a pretty good time, and Las
Speaker 3: Vegas gets a lot of repeat business, so there's some
Speaker 3: type of win win in that deal. But I might,
Speaker 3: I might draw the line a little bit higher than
Speaker 3: like a total free for all, which is kind of
Speaker 3: where we're headed. Now.
Speaker 2: We will 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 to twenty three NBA season, all the
Speaker 3: regular season, in about half the playoffs, and then five
Speaker 3: thirty eight blew up, and so I had other things
Speaker 3: to do, and I didn't bet the end of the playoffs,
Speaker 3: and I learned that, I mean, it's probably what I
Speaker 3: should have expected by I learned that it's pretty hard.
Speaker 3: I went on a huge heater at the start of
Speaker 3: the 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.
Speaker 2: But then but then things change.
Speaker 3: One thing that changes is you start to get limited
Speaker 3: by some of the sites, especially if you're trying to
Speaker 3: do things like, oh, I just suck. Because I'm a
Speaker 3: Twitter addict, I follow a lot of NBA writers. 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 here a this is a guy who we interview
Speaker 3: in the book. I have three guys who just track
Speaker 3: injury data for me. I mean, while I'm trying to
Speaker 3: like do some Twitter search for is this player in
Speaker 3: the warm up? You know, you're competing essentially against the
Speaker 3: best players, the best betters in the world. So I
Speaker 3: love that one point eight million dollars in bets and
Speaker 3: believe this is just like on average the bet size
Speaker 3: is like twelve hundred bucks, you're making fifteen hundred twelve
Speaker 3: hundred dollars bets or whatever. Over the course of the season,
Speaker 3: I made about five thousand bucks, probably spent about five
Speaker 3: thousand hours on this, so I made less than minimum
Speaker 3: wage actually with my with my sports betting side hustle.
Speaker 3: I love it.
Speaker 2: I love it.
Speaker 1: So I want to ask two more things before we
Speaker 1: wrap up. The first is kind of a theme that
Speaker 1: I and I think I picked up on this because
Speaker 1: you know, it's something that I've obviously thought about a lot,
Speaker 1: a lot, And it's the strand that is woven throughout
Speaker 1: the book, which is that even though the book is
Speaker 1: about skill, about finding edges, about how to make plus
Speaker 1: ev gambles, there is this thread throughout it of luck,
Speaker 1: right and of the fact that you also do need
Speaker 1: to get lucky, and you you actually you point to
Speaker 1: a lot of these moments where things could have turned
Speaker 1: out so differently, like, you know, to pick a random example,
Speaker 1: the car crash that Peter Tila and Elon Musk were in, right,
Speaker 1: Like what happens if the car lands differently. And I
Speaker 1: don't 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 in
Speaker 3: on and I think sand Hill Road or somewhere in
Speaker 3: Palo Alto, and Elon floor is the accelebrator while trying
Speaker 3: to change lanes, and it kind of spirals up into
Speaker 3: like doing a little helicopter motion and miraculously lands on
Speaker 3: its wheels, and Elon and Peter are are okay, and
Speaker 3: actually hitchhikes their VC meeting, And I guess keep PayPal
Speaker 3: alive by with that hitchhike. But so I asked Peter
Speaker 3: teel you know, I asked him in a way that
Speaker 3: was nerdier. I asked him, if you ran the world
Speaker 3: a thousand times, had a thousand simulations, then how often
Speaker 3: would you wind up in this position? And he objected
Speaker 3: to this question and went on a whole thing about
Speaker 3: determinism or probablism, which is interesting, but we don't have
Speaker 3: time for today, I don't think. But yeah, life is
Speaker 3: very contingent. We've talked on the show about the assassination
Speaker 3: attempt against President Trump where that was a very close call,
Speaker 3: or things like the butterfly ballot in the two thousand election,
Speaker 3: which is in one ballot in one county in Florida
Speaker 3: in two thousand, the ballot had a fun design where
Speaker 3: some people who would have wanted to vote for Al
Speaker 3: Gore wound up voting for Pat Buchanan instead, and that
Speaker 3: was enough to cost or the election. On top of
Speaker 3: the recount stuff in Florida wouldn't have mattered if this
Speaker 3: ballot design had been better.
Speaker 2: So life is.
Speaker 3: Whether it's literally metaphysically random or not is a philosophical question,
Speaker 3: But it's random to our ability to determine it in
Speaker 3: a lot of ways. And it's just it's just very
Speaker 3: hard to acknowledge the role that luck plays in your life.
Speaker 3: It's very hard even for me or for you. Maybe
Speaker 3: you're the exception, Maria, it's very hard not to tell
Speaker 3: yourself a clever narrative in which in which you know,
Speaker 3: even in writing the book I have. I think I've
Speaker 3: gotten very lucky with a lot of things to kind
Speaker 3: of have, like this SBF story, like fall into into
Speaker 3: My Lap, for example, was I think pretty lucky. And
Speaker 3: having a book that covered territory like AI that kind
Speaker 3: of became a much bigger deal, and sports betting really
Speaker 3: blew up. I mean, you know, so I think I've
Speaker 3: gotten very lucky in a lot of things recently, the
Speaker 3: way that, like, you know, I didn't know that actually
Speaker 3: having this independent newsletter is actually actually much better economically
Speaker 3: than being working for a big company. I didn't know that,
Speaker 3: And I could easily have taken a deal with another
Speaker 3: big company and wound up worse off for it, and
Speaker 3: a lot of respects, and so, you know, I know
Speaker 3: the role that luck has played in my life, including
Speaker 3: just things like being basically healthy and having a supportive
Speaker 3: partner and supportive friends, and being born into a country
Speaker 3: where it's okay to be a gay man and a
Speaker 3: country where where you have some downside protection. I mean,
Speaker 3: those are all things that are are very lucky. So
Speaker 3: you know, the next time that I take a cooler
Speaker 3: in poker or b louse set of or set or something,
Speaker 3: then I can't 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: something that you're thinking about as well, because I do
Speaker 1: 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 all of these endeavors, we do want
Speaker 1: to maximize our skill, but we do need to always
Speaker 1: remember that we're we are insanely lucky to even be
Speaker 1: in this position. So Nate, I hope that you get
Speaker 1: continue to get lucky, and that this book sells an
Speaker 1: insane number of copies and that it does incredibly well,
Speaker 1: and that you and I can continue making this show
Speaker 1: together because we're both continuing to be lucky and yet
Speaker 1: take 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 2: You know, I had a lot of fun. We have
Speaker 2: 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 2: Actually really fucking busy, but we'll make it work, all right.
Speaker 1: Sounds good, sounds good, Thanks so much, Nate. Oh, by
Speaker 1: the way, before you go, last week, Nate and I
Speaker 1: recorded a special video only podcast where we talked about
Speaker 1: Kamala's pick of Walls as her VP. So you can
Speaker 1: find that on our YouTube channel. We hope you enjoy it.
Speaker 3: Risky Business is hosted by me Nate Silver and me
Speaker 3: Maria Kanakova. The show was a co production of Pushkin
Speaker 3: Industries and iHeartMedia. This episode was produced by Isabel Carterer.
Speaker 3: Our associate producer is Gabriel hunter Chen. Our engineer is
Speaker 3: Sarah Bruger. Our executive producer is Jacob Goldstein.
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