1 00:00:15,604 --> 00:00:27,844 Speaker 1: Pushkin. Welcome back to Risky Business, a show about making 2 00:00:27,924 --> 00:00:30,564 Speaker 1: better decisions. I'm Maria Kanakova. 3 00:00:30,364 --> 00:00:31,364 Speaker 2: And I'm Nate Silver. 4 00:00:32,084 --> 00:00:33,844 Speaker 1: So today Nate and I are going to be doing 5 00:00:33,924 --> 00:00:38,044 Speaker 1: something slightly different. Today is Tuesday, as always, which is 6 00:00:38,084 --> 00:00:41,124 Speaker 1: when we tape August thirteenth, which is a really important 7 00:00:41,164 --> 00:00:44,124 Speaker 1: date because it's the day that Nate's book comes out. 8 00:00:44,484 --> 00:00:47,404 Speaker 1: So today, instead of being co hosts, I am going 9 00:00:47,444 --> 00:00:50,204 Speaker 1: to get in the interviewer's seat and put Nate in 10 00:00:50,284 --> 00:00:52,564 Speaker 1: the hot seat where I get to ask him also 11 00:00:52,684 --> 00:00:55,804 Speaker 1: some really hard questions about his book. How does that sound? 12 00:00:55,884 --> 00:00:59,044 Speaker 3: N I have to be on your podcast, Maria. I'm 13 00:00:59,044 --> 00:01:00,364 Speaker 3: glad that Nate guy is off this week. 14 00:01:00,404 --> 00:01:01,524 Speaker 2: He's a fucking jerk. 15 00:01:05,924 --> 00:01:10,444 Speaker 1: I really enjoyed reading On the Edge, which is out today. 16 00:01:10,524 --> 00:01:13,364 Speaker 1: So by the time that everyone is listening, I hope 17 00:01:13,364 --> 00:01:16,484 Speaker 1: you've already bought a copy, but if not, go ahead 18 00:01:16,484 --> 00:01:19,924 Speaker 1: and buy it right now. So first of all, Nate, congratulations. 19 00:01:20,564 --> 00:01:23,644 Speaker 1: I know that this was a long time coming labor 20 00:01:23,764 --> 00:01:25,164 Speaker 1: of many many years. 21 00:01:25,564 --> 00:01:25,804 Speaker 2: Yeah. 22 00:01:25,964 --> 00:01:28,524 Speaker 3: No, I spent basically three years of my life where 23 00:01:28,524 --> 00:01:32,444 Speaker 3: I'd say this is my main professional focus talk to 24 00:01:32,524 --> 00:01:34,564 Speaker 3: two hundred people, and I know I just I would 25 00:01:34,604 --> 00:01:38,004 Speaker 3: recommend email really smart people and ask to have a 26 00:01:38,004 --> 00:01:39,924 Speaker 3: conversation with them and pretend it's for a book, and 27 00:01:39,964 --> 00:01:42,364 Speaker 3: then don't write the book. That's probably still worth it 28 00:01:42,404 --> 00:01:45,364 Speaker 3: in some sense. But no, I actually went ahead and 29 00:01:45,404 --> 00:01:45,684 Speaker 3: wrote the. 30 00:01:45,684 --> 00:01:48,244 Speaker 1: Book, and we're very glad you did. So I'm going 31 00:01:48,324 --> 00:01:53,044 Speaker 1: to start with Nate. In the book, you make this 32 00:01:53,124 --> 00:01:57,204 Speaker 1: distinction between two communities. You have the village and you 33 00:01:57,284 --> 00:02:01,324 Speaker 1: have the river. Talk me through what exactly those mean. 34 00:02:02,844 --> 00:02:04,804 Speaker 3: Let's start with the village, even though it's not the 35 00:02:04,804 --> 00:02:06,324 Speaker 3: focus of the book, just because it's a little bit 36 00:02:06,364 --> 00:02:09,724 Speaker 3: easier to define in a term that's more conventional. Village 37 00:02:09,724 --> 00:02:14,084 Speaker 3: is basically the kind of liberal, progressive East Coast establishment. 38 00:02:14,284 --> 00:02:17,924 Speaker 3: So it's the New York Times, it's Harvard University, it's 39 00:02:17,964 --> 00:02:21,804 Speaker 3: think tanks and nonprofits, it's you know, government when you 40 00:02:21,844 --> 00:02:23,964 Speaker 3: have a Democrat in office. At least, it's kind of 41 00:02:23,964 --> 00:02:27,604 Speaker 3: the expert class, the professional managerial class, that whole cluster 42 00:02:27,684 --> 00:02:28,364 Speaker 3: of attributes. 43 00:02:29,644 --> 00:02:31,444 Speaker 2: By contrast, the river is kind. 44 00:02:31,284 --> 00:02:34,324 Speaker 3: Of the world that I suppose I inhabit, or I imagine 45 00:02:34,244 --> 00:02:37,964 Speaker 3: myself inhabiting. It's the world of calculator risk taking, where 46 00:02:38,044 --> 00:02:42,244 Speaker 3: people are some combination of being very analytical and extremely 47 00:02:42,324 --> 00:02:45,964 Speaker 3: wildly competitive. So like poker is an archetypal maybe the 48 00:02:46,084 --> 00:02:50,244 Speaker 3: archetypal river activity. But as I discovered this world in 49 00:02:50,244 --> 00:02:53,884 Speaker 3: the book, you know, I found that the venture capitalist 50 00:02:53,924 --> 00:02:57,284 Speaker 3: and the effective altruists, and the hedge fund people and 51 00:02:57,884 --> 00:03:02,044 Speaker 3: the sports betters, even the crypto people, they all have 52 00:03:02,084 --> 00:03:04,604 Speaker 3: a common vocabulary. They all talk about things in roughly 53 00:03:04,604 --> 00:03:08,444 Speaker 3: the same ways, and so it's like it's not a place, 54 00:03:08,484 --> 00:03:11,284 Speaker 3: it's a discrete community. I mean, in Las Vegas and 55 00:03:11,284 --> 00:03:14,204 Speaker 3: Silicon Valley you might get closer to an actual network, 56 00:03:14,244 --> 00:03:16,644 Speaker 3: but like they're just these people that I think are 57 00:03:16,644 --> 00:03:20,404 Speaker 3: often poorly understood by the mainstream media and yet are 58 00:03:20,884 --> 00:03:26,004 Speaker 3: incredibly wealthy and powerful. Many of the richest people in 59 00:03:26,004 --> 00:03:28,884 Speaker 3: the world have some of these river tendencies, and like, 60 00:03:29,004 --> 00:03:32,484 Speaker 3: surprisingly to me, would take my emails and my phone 61 00:03:32,484 --> 00:03:36,004 Speaker 3: calls because they kind of recognize like a fellow traveler 62 00:03:36,084 --> 00:03:38,204 Speaker 3: a little bit. And then what I discovered is that 63 00:03:38,244 --> 00:03:40,724 Speaker 3: the kind of conceit of the proposal that actually there's 64 00:03:40,724 --> 00:03:44,644 Speaker 3: a commonalities in how people think was more literally true 65 00:03:45,164 --> 00:03:47,844 Speaker 3: than I had thought, and that there's more literal crossover 66 00:03:47,884 --> 00:03:51,604 Speaker 3: poker players who become effective altruists or hedge funders who 67 00:03:51,604 --> 00:03:55,764 Speaker 3: host poker games, or venture capitalists who do sports betting 68 00:03:55,844 --> 00:03:58,324 Speaker 3: is a metaphor for some of these things. So it's 69 00:03:58,364 --> 00:04:00,084 Speaker 3: a real place of a real group of people that 70 00:04:00,484 --> 00:04:03,444 Speaker 3: I think, Maria are in our circles, maybe not the 71 00:04:03,444 --> 00:04:06,084 Speaker 3: only circles to run in, but certainly one common type. 72 00:04:06,204 --> 00:04:09,884 Speaker 3: And I felt like I had the opportunity tell a 73 00:04:09,924 --> 00:04:11,404 Speaker 3: really good story about these people. 74 00:04:11,484 --> 00:04:13,564 Speaker 2: I did not. I think I got lucky. 75 00:04:13,644 --> 00:04:16,164 Speaker 3: I think I'm running in my like ninety eighth percentile 76 00:04:16,724 --> 00:04:19,244 Speaker 3: run good for how some of these topics really blew up. 77 00:04:19,284 --> 00:04:21,844 Speaker 3: I mean AI in particular, although that was a fairly 78 00:04:22,244 --> 00:04:26,604 Speaker 3: incidental thing in the book Effective Altruism, and particularly Sam 79 00:04:26,644 --> 00:04:30,324 Speaker 3: Bakman Free who had talked to first before the collapse 80 00:04:30,324 --> 00:04:33,924 Speaker 3: and then got to talk to you afterwards as well. 81 00:04:34,124 --> 00:04:40,284 Speaker 1: Yeah, so you draw some really interesting parallels between worlds 82 00:04:40,324 --> 00:04:44,324 Speaker 1: that I didn't necessarily connect, like obviously poker, sports, bedding, 83 00:04:44,604 --> 00:04:50,364 Speaker 1: Like yes, you know, I get why those things are related. 84 00:04:50,604 --> 00:04:53,484 Speaker 1: Then you go into finance, you go into venture capital 85 00:04:54,324 --> 00:04:57,484 Speaker 1: and the hedge fund world in Silicon Valley, you go 86 00:04:57,604 --> 00:05:01,524 Speaker 1: into AI and people who are kind of the AI researchers, 87 00:05:01,524 --> 00:05:04,604 Speaker 1: And I actually love your little side where you have 88 00:05:04,644 --> 00:05:07,324 Speaker 1: the physical risk taker, yes, where you have the astronauts, 89 00:05:07,364 --> 00:05:09,204 Speaker 1: and I love that you have a female astronaut. By 90 00:05:09,204 --> 00:05:11,284 Speaker 1: the way, Wait, did you on purpose, like did you 91 00:05:11,364 --> 00:05:14,364 Speaker 1: try to have two women or did that just so happen? 92 00:05:15,004 --> 00:05:17,124 Speaker 1: You know, because as you know, like it's something that 93 00:05:17,564 --> 00:05:20,964 Speaker 1: like is interesting to me. And when you're talking about 94 00:05:21,044 --> 00:05:25,164 Speaker 1: risk takers, people often assume men, and so I was 95 00:05:25,284 --> 00:05:27,324 Speaker 1: very curious if that was a deliberate choice or if 96 00:05:27,324 --> 00:05:30,444 Speaker 1: it just so happens that these most interesting stories happened 97 00:05:30,444 --> 00:05:30,964 Speaker 1: to be female. 98 00:05:31,044 --> 00:05:35,404 Speaker 3: No, there there was a deliberate choice made to I mean, look, 99 00:05:35,444 --> 00:05:37,604 Speaker 3: it's a careful balance to strike because it is a 100 00:05:37,644 --> 00:05:42,404 Speaker 3: world that's very male. Also a world that is Poker's 101 00:05:42,404 --> 00:05:44,084 Speaker 3: a little bit more diverse, but the rest of the 102 00:05:44,164 --> 00:05:48,564 Speaker 3: river is quite white and or Asian and or Asian sometimes, 103 00:05:49,804 --> 00:05:53,444 Speaker 3: so there was a deliberate choice to like not sugarcoat 104 00:05:53,444 --> 00:05:55,604 Speaker 3: this and pretend it's all fifty to fifty. But like, 105 00:05:56,084 --> 00:05:58,684 Speaker 3: but also the I call them characters, the people I 106 00:05:58,724 --> 00:06:02,884 Speaker 3: talked to, like Catharine Sullivan, this astronaut's amazing and she 107 00:06:02,884 --> 00:06:06,444 Speaker 3: has an amazing stories and she was studying like oceanography 108 00:06:06,484 --> 00:06:09,164 Speaker 3: in Nova Scotia and beat out like ten thousand other people. 109 00:06:09,404 --> 00:06:14,164 Speaker 3: I'm an astronaut and mean, it's an amazing story. Cataline Carico, 110 00:06:14,484 --> 00:06:16,964 Speaker 3: I had to learn all the names pronounced correctly for 111 00:06:17,004 --> 00:06:19,124 Speaker 3: the audiobook, but no, I've forgotten them. Who was this 112 00:06:19,164 --> 00:06:22,084 Speaker 3: woman who like smuggled money in a Teddy Bear to 113 00:06:22,204 --> 00:06:25,964 Speaker 3: move out of Hungary and then was just shot upon 114 00:06:26,084 --> 00:06:28,444 Speaker 3: in the establishment while she was working on MR and 115 00:06:28,524 --> 00:06:33,724 Speaker 3: A vaccines and eventually joined biointech and won the Nobel Prize, 116 00:06:33,724 --> 00:06:35,724 Speaker 3: come won the Nobel Prize for her work on MR 117 00:06:35,764 --> 00:06:37,804 Speaker 3: and A vaccines. I mean, they have amazing stories. Or 118 00:06:37,844 --> 00:06:40,844 Speaker 3: someone like our mutual friend but one of the best 119 00:06:40,844 --> 00:06:43,004 Speaker 3: poker players in the world. I think Maria ho who 120 00:06:43,044 --> 00:06:45,804 Speaker 3: has great stories to tell too, so so or Vanessa 121 00:06:45,804 --> 00:06:48,124 Speaker 3: Selps is another person that we know, but like it's 122 00:06:48,164 --> 00:06:50,844 Speaker 3: just an amazing person. So yeah, I mean when you 123 00:06:50,924 --> 00:06:53,484 Speaker 3: find people like that and you want to tell their stories. 124 00:06:53,484 --> 00:06:57,524 Speaker 3: And there was but there's also sections that directly tackled 125 00:06:57,524 --> 00:06:59,124 Speaker 3: There's one section in the poker chapter about why aren't 126 00:06:59,124 --> 00:07:01,084 Speaker 3: there more women in poker? And then one section in 127 00:07:01,124 --> 00:07:03,684 Speaker 3: the VC chapter about why are there so few black 128 00:07:03,924 --> 00:07:06,244 Speaker 3: and Hispanic and women founders? And so you have to 129 00:07:06,724 --> 00:07:09,884 Speaker 3: take it on directly without it being awoke book because 130 00:07:09,884 --> 00:07:11,844 Speaker 3: I'm not super woke, but you can't. But there are 131 00:07:11,844 --> 00:07:14,404 Speaker 3: real shortcomings here. I mean the VCS in particular, I think, 132 00:07:14,444 --> 00:07:18,724 Speaker 3: are they claim to want people who have overcome, who 133 00:07:18,764 --> 00:07:21,644 Speaker 3: have grit, who have overcome odds, and who are high variants, 134 00:07:21,644 --> 00:07:24,124 Speaker 3: who are different than the consensus, and yet they have 135 00:07:24,204 --> 00:07:26,724 Speaker 3: this prototype of like the on the spectrum, you know, 136 00:07:26,844 --> 00:07:29,924 Speaker 3: white or Asian twenty something nerd who dropped out of 137 00:07:30,044 --> 00:07:33,404 Speaker 3: Harvard or MIT as a sophomore, and Sam mcminfreed played 138 00:07:33,404 --> 00:07:36,564 Speaker 3: into that stereotype, and they're not actually, I think, trying 139 00:07:36,564 --> 00:07:38,724 Speaker 3: to diversify their portfolio, and it just in a purely 140 00:07:39,204 --> 00:07:41,644 Speaker 3: not moral but even like financial sense of wanting like 141 00:07:41,724 --> 00:07:43,284 Speaker 3: high variance founders. 142 00:07:44,884 --> 00:07:48,084 Speaker 1: Yeah. No, I think one of the things that I 143 00:07:48,164 --> 00:07:50,404 Speaker 1: enjoyed about the book is that it's about how to 144 00:07:50,404 --> 00:07:52,404 Speaker 1: take risk well. But you also talk about some of 145 00:07:52,404 --> 00:07:55,924 Speaker 1: the shortcomings of the of the people on the River 146 00:07:56,404 --> 00:07:59,004 Speaker 1: and how they think about risk. But first let's talk 147 00:07:59,084 --> 00:08:02,044 Speaker 1: about kind of the good elements. So what does the 148 00:08:02,164 --> 00:08:05,884 Speaker 1: River have in common when it comes to evaluating risk? 149 00:08:06,044 --> 00:08:09,084 Speaker 1: What can we take away from them when we're thinking 150 00:08:09,084 --> 00:08:14,924 Speaker 1: about Okay, I am you know, Maria everyday person? How 151 00:08:14,924 --> 00:08:17,564 Speaker 1: do I kind of think about risk in my life? 152 00:08:17,764 --> 00:08:20,804 Speaker 1: What do I take from these people who take risks 153 00:08:20,844 --> 00:08:25,444 Speaker 1: professionally so that I can just make better decisions and 154 00:08:25,644 --> 00:08:27,244 Speaker 1: better risk assessments myself. 155 00:08:27,524 --> 00:08:29,684 Speaker 3: So let me take the two axioms that you hear 156 00:08:29,724 --> 00:08:31,684 Speaker 3: over and over and over again in Silicon Valley and 157 00:08:31,724 --> 00:08:35,964 Speaker 3: make it very successful financially despite having all these flaws. 158 00:08:36,044 --> 00:08:38,724 Speaker 3: I mean, some of these guys are difficult people. It 159 00:08:38,764 --> 00:08:40,764 Speaker 3: is mostly guys. It's not very diverse. There are some 160 00:08:40,844 --> 00:08:43,884 Speaker 3: big flaws, right, I think their political instincts are often poor. 161 00:08:44,964 --> 00:08:46,964 Speaker 3: But they do two things, one of which is they 162 00:08:47,084 --> 00:08:49,564 Speaker 3: understand expected value, which is kind of the theme of 163 00:08:49,564 --> 00:08:52,164 Speaker 3: this show. They understand that if you can make a 164 00:08:52,244 --> 00:08:54,524 Speaker 3: bet that has a ten percent chance of one hundred 165 00:08:54,884 --> 00:08:58,084 Speaker 3: x payout, that's an extraordinarily good bet. And if you 166 00:08:58,124 --> 00:09:00,164 Speaker 3: can make a lot of those bets by aggregating different 167 00:09:00,204 --> 00:09:04,004 Speaker 3: companies into a fund, then you're kind of guaranteed to 168 00:09:04,084 --> 00:09:05,084 Speaker 3: make money. 169 00:09:04,804 --> 00:09:06,004 Speaker 2: And maybe make a lot of money. 170 00:09:06,804 --> 00:09:09,324 Speaker 3: And it also applies to things like evaluating nuclear war 171 00:09:09,524 --> 00:09:11,844 Speaker 3: risk or AI risk, things that might be you know, 172 00:09:11,884 --> 00:09:14,724 Speaker 3: a two percent chance in over some time frame or 173 00:09:14,764 --> 00:09:17,604 Speaker 3: five percent chance whatever else, but have like very bad 174 00:09:17,644 --> 00:09:22,764 Speaker 3: to infinitely bad negative payouts. The second thing that Silicon 175 00:09:22,844 --> 00:09:27,444 Speaker 3: Valley knows is that having a longer time horizon is 176 00:09:27,484 --> 00:09:30,124 Speaker 3: really worth it. We're a country that likes to make money, 177 00:09:30,164 --> 00:09:33,004 Speaker 3: but we're a get rich quick country and not a 178 00:09:33,004 --> 00:09:36,084 Speaker 3: get rich slowly country. When you're investing in startups, early 179 00:09:36,124 --> 00:09:38,524 Speaker 3: stage startups, they often have a time horizon. You know, 180 00:09:38,604 --> 00:09:41,524 Speaker 3: SpaceX took thirteen years I think to make a first 181 00:09:41,524 --> 00:09:44,284 Speaker 3: profit nine years would actually even launched a rocket successfully 182 00:09:44,404 --> 00:09:47,724 Speaker 3: or something like that. So I think you almost always 183 00:09:47,764 --> 00:09:50,764 Speaker 3: benefit from having like a longer time horizon than other people. 184 00:09:50,804 --> 00:09:55,244 Speaker 3: People just discount the long term future way too much, 185 00:09:55,284 --> 00:09:58,364 Speaker 3: even in their personal lives, I think, And so therefore 186 00:09:58,444 --> 00:10:00,364 Speaker 3: those I think are kind of the two foremost lessons 187 00:10:00,364 --> 00:10:03,084 Speaker 3: and why the book is you know, kind of ambivalently 188 00:10:03,364 --> 00:10:05,524 Speaker 3: to positive on that Silicon Valley ethos, even though the 189 00:10:05,524 --> 00:10:07,964 Speaker 3: individual characters there are are complex. 190 00:10:09,644 --> 00:10:13,644 Speaker 1: So one of the things that you stress over and 191 00:10:13,684 --> 00:10:18,564 Speaker 1: over is that people who evaluate risk correctly and make 192 00:10:19,204 --> 00:10:21,644 Speaker 1: risks that tend to pay out over the long term, 193 00:10:22,044 --> 00:10:26,604 Speaker 1: they not only can understand the upside, but they can 194 00:10:26,724 --> 00:10:29,804 Speaker 1: also protect against the risk of ruin. Right the risk 195 00:10:29,924 --> 00:10:33,844 Speaker 1: of not being able to take these risks going forward. 196 00:10:34,124 --> 00:10:36,404 Speaker 1: So first I'd love for you to talk a little 197 00:10:36,404 --> 00:10:38,884 Speaker 1: bit about this idea of risk of ruin because you 198 00:10:38,924 --> 00:10:41,084 Speaker 1: talk about people who do it correctly and then people 199 00:10:41,084 --> 00:10:45,684 Speaker 1: who might not, and also like, how do you balance 200 00:10:45,724 --> 00:10:48,324 Speaker 1: that and how might that be different from person to person. 201 00:10:49,244 --> 00:10:52,244 Speaker 3: Yeah, I mean, we have this abstract concept in poker 202 00:10:52,524 --> 00:10:56,124 Speaker 3: of a bank roll, which is and that is, how 203 00:10:56,244 --> 00:10:59,164 Speaker 3: much money can you spend on poker before your kind 204 00:10:59,164 --> 00:11:02,084 Speaker 3: of poker broke or at least you're severely limited in 205 00:11:02,164 --> 00:11:03,764 Speaker 3: the size of the games you might play and you 206 00:11:03,844 --> 00:11:07,444 Speaker 3: might have to pass up positive ev opportunities. So to 207 00:11:07,444 --> 00:11:10,004 Speaker 3: some extent, this is related to the idea of opportunit cost. 208 00:11:11,044 --> 00:11:14,604 Speaker 3: There's a famous formula from sports betting called the Kelly criterion, 209 00:11:14,644 --> 00:11:16,484 Speaker 3: which tells you how much can you bet to maximize 210 00:11:16,484 --> 00:11:22,164 Speaker 3: your expected value without enduring a very high risk of ruin. 211 00:11:22,684 --> 00:11:24,924 Speaker 3: Now I can get into the technicalities of why that 212 00:11:24,964 --> 00:11:27,364 Speaker 3: formula might make you a little bit more aggressive than 213 00:11:27,404 --> 00:11:31,284 Speaker 3: you should be. In general, though, like ninety percent of 214 00:11:31,284 --> 00:11:34,404 Speaker 3: people in the world are I think to risk averse 215 00:11:34,444 --> 00:11:37,444 Speaker 3: about this kind of thing. This goes back to, as 216 00:11:37,484 --> 00:11:40,044 Speaker 3: you'll know, like some of the connoment and diversity work 217 00:11:40,044 --> 00:11:43,204 Speaker 3: on prospect theory. It may have also an evolutionary base 218 00:11:43,204 --> 00:11:45,204 Speaker 3: where we weren't living in the time of abundance that 219 00:11:45,244 --> 00:11:49,084 Speaker 3: we are now and so and so, you know, you 220 00:11:49,204 --> 00:11:51,924 Speaker 3: only got one shot before it made sense to protect 221 00:11:51,964 --> 00:11:54,524 Speaker 3: your household. It made sense to be very cautious about 222 00:11:54,804 --> 00:11:57,044 Speaker 3: if you get an infection, you might die. And I 223 00:11:57,044 --> 00:12:01,044 Speaker 3: think people have not adapted to this world of abundant 224 00:12:01,164 --> 00:12:04,804 Speaker 3: but confusing opportunity that we have very much. Annie Duke 225 00:12:04,924 --> 00:12:06,964 Speaker 3: wrote a book called Quit, and she's in the book too, 226 00:12:06,964 --> 00:12:10,284 Speaker 3: also a former poker player. Of course, she site studies 227 00:12:10,324 --> 00:12:12,244 Speaker 3: like by Steve Levitt fre Economics that when they have 228 00:12:12,244 --> 00:12:14,804 Speaker 3: people flip a coin to make a decision, and a 229 00:12:14,804 --> 00:12:17,204 Speaker 3: major decision, not like am I getting Thai food or 230 00:12:17,204 --> 00:12:18,804 Speaker 3: Indian for dinner tonight, which I do flip a coin 231 00:12:18,844 --> 00:12:21,324 Speaker 3: about sometimes, by the way, but things like should I 232 00:12:21,404 --> 00:12:23,724 Speaker 3: leave my partner, or should I quit my job, or 233 00:12:23,764 --> 00:12:26,084 Speaker 3: should I move across the country to a different part 234 00:12:26,124 --> 00:12:28,684 Speaker 3: of the world. People are happier on average when they 235 00:12:28,684 --> 00:12:31,804 Speaker 3: make a change, and so you know, the ninety percent 236 00:12:31,844 --> 00:12:34,244 Speaker 3: of people who are risk averse maybe could take some 237 00:12:34,244 --> 00:12:36,284 Speaker 3: messages from that book then, but you also meet the 238 00:12:36,324 --> 00:12:38,044 Speaker 3: sbfs and the Elon Musks of the world. 239 00:12:39,964 --> 00:12:44,164 Speaker 1: And let's actually unwrap that last bit a little bit. 240 00:12:44,244 --> 00:12:48,604 Speaker 1: The Sbfs and the Elon Musks because you write about 241 00:12:49,324 --> 00:12:53,604 Speaker 1: the fact that you know, obviously they are people. Wouldn't 242 00:12:54,404 --> 00:12:57,564 Speaker 1: people don't necessarily lump them together, right, because Elon Musk 243 00:12:57,644 --> 00:13:00,844 Speaker 1: is someone who is richest man in the world, and 244 00:13:00,884 --> 00:13:04,044 Speaker 1: then SBF is going to jail. However, they do have 245 00:13:04,084 --> 00:13:07,444 Speaker 1: this one characteristic in common, so I would I would 246 00:13:07,444 --> 00:13:08,764 Speaker 1: love for you to expand on that alone. 247 00:13:08,804 --> 00:13:10,804 Speaker 3: Yeah, and I should be I mean, I don't mean 248 00:13:10,844 --> 00:13:13,884 Speaker 3: to I am very and the book is very. 249 00:13:13,804 --> 00:13:15,844 Speaker 2: Unsympathetic to SBF. 250 00:13:16,644 --> 00:13:18,724 Speaker 3: I'm not saying I'm sympathetic to Elon, but Elon has 251 00:13:18,764 --> 00:13:20,804 Speaker 3: accomplished a lot of things, and I think Elon's a 252 00:13:21,604 --> 00:13:24,724 Speaker 3: real person whose career wasn't based on I mean, I 253 00:13:24,724 --> 00:13:27,004 Speaker 3: don't think Sam'shull career was based on fraud, but he didn't, 254 00:13:27,004 --> 00:13:30,004 Speaker 3: you know, Elon has not committed ten billion dollars worth 255 00:13:30,044 --> 00:13:34,084 Speaker 3: of stealing people's crypto deposits. Basically, Yeah, they are the 256 00:13:34,164 --> 00:13:38,604 Speaker 3: two people that really, really, really go balls to the wall. 257 00:13:38,684 --> 00:13:40,324 Speaker 3: I think we can use that phrase in terms of risk. 258 00:13:40,444 --> 00:13:40,924 Speaker 2: Absolutely. 259 00:13:41,804 --> 00:13:44,244 Speaker 3: If you read the Walter Isaacson book on Elon Musk, 260 00:13:44,524 --> 00:13:47,804 Speaker 3: his poker strategy is literally going all in every hand 261 00:13:47,884 --> 00:13:49,884 Speaker 3: until he runs out of money or wins it all. 262 00:13:49,924 --> 00:13:51,884 Speaker 1: I didn't know that that was crazy to me. 263 00:13:52,764 --> 00:13:56,244 Speaker 3: It makes sense, so I've I've played in the all 264 00:13:56,284 --> 00:13:58,764 Speaker 3: in podcast poker game a couple of times with some 265 00:13:58,844 --> 00:14:02,764 Speaker 3: of Elon's buddies, and that's a very aggressive game, shall 266 00:14:02,804 --> 00:14:05,444 Speaker 3: we say. I'm forbidden to reveal details, but there's a 267 00:14:05,444 --> 00:14:11,084 Speaker 3: lot of blind, naked aggression there. You knowf is even 268 00:14:11,684 --> 00:14:15,964 Speaker 3: more unambiguously I think irrational in taking a very literal 269 00:14:16,524 --> 00:14:19,844 Speaker 3: approach of utilitarianism, where you know, he has said repeatedly 270 00:14:19,924 --> 00:14:23,644 Speaker 3: he told his co founder Carolyn Ellison this. He told 271 00:14:24,364 --> 00:14:26,964 Speaker 3: the economist Tyler Cowan this that if he could flip 272 00:14:27,004 --> 00:14:29,404 Speaker 3: a coin to determine the fate of the world, and 273 00:14:29,524 --> 00:14:34,964 Speaker 3: either half the time the world is destroyed, but the 274 00:14:35,004 --> 00:14:36,404 Speaker 3: other half the time in the world is like two 275 00:14:36,444 --> 00:14:39,804 Speaker 3: point oh one x as good, then he's calculating the 276 00:14:39,844 --> 00:14:42,724 Speaker 3: expected value. He's so committed to that that, hey, you know, 277 00:14:42,804 --> 00:14:47,084 Speaker 3: one point five is greater than one. Therefore you take 278 00:14:47,244 --> 00:14:49,244 Speaker 3: the coin flip, even though you might destroy the world, 279 00:14:49,284 --> 00:14:51,644 Speaker 3: and in fact, you take that coin flip repeatedly, over 280 00:14:51,684 --> 00:14:53,924 Speaker 3: and over again. This is called the Saint Petersburg paradox, 281 00:14:53,964 --> 00:14:57,004 Speaker 3: where it's a bet that has infinite expected value, but 282 00:14:57,044 --> 00:14:58,644 Speaker 3: it is infinitely likely to leave you ruins. 283 00:14:58,684 --> 00:15:00,724 Speaker 2: So what should you do? Probably not take the bet. 284 00:15:00,724 --> 00:15:03,844 Speaker 2: It's not that hard a dilemma, actually, but to SBF 285 00:15:03,884 --> 00:15:04,284 Speaker 2: it was. 286 00:15:04,444 --> 00:15:08,124 Speaker 3: And in my interviews with him, you kind of find 287 00:15:08,164 --> 00:15:09,964 Speaker 3: a pattern of him saying, I mean, the first thing 288 00:15:09,964 --> 00:15:12,164 Speaker 3: that I did with him, when he's riding high January 289 00:15:12,204 --> 00:15:14,724 Speaker 3: twenty twenty two, Bitcoin still near its all time peaks, 290 00:15:15,084 --> 00:15:15,684 Speaker 3: He's like, if. 291 00:15:15,604 --> 00:15:18,844 Speaker 2: You're not really literally willing to ruin. 292 00:15:18,604 --> 00:15:21,444 Speaker 3: Yourself, not the fake ruined, Like if Elon Musk has 293 00:15:21,484 --> 00:15:23,964 Speaker 3: to sell Twitter and he's only worth one hundred and 294 00:15:23,964 --> 00:15:26,724 Speaker 3: seventy billion dollars instead of two hundred and twenty billion dollars, 295 00:15:27,604 --> 00:15:31,364 Speaker 3: that's not what it would describe as being ruined. And 296 00:15:31,444 --> 00:15:33,484 Speaker 3: Sam was very clear that I don't think you should 297 00:15:33,484 --> 00:15:36,084 Speaker 3: protect your downside. You have to be so risk loving 298 00:15:36,444 --> 00:15:38,764 Speaker 3: that you should literally be willing to like destroy your 299 00:15:38,844 --> 00:15:42,364 Speaker 3: life and your reputation if it's positive expected value, which 300 00:15:42,484 --> 00:15:45,484 Speaker 3: I think is insane. I think reflects I mean not 301 00:15:45,564 --> 00:15:49,604 Speaker 3: a clinical diagnosis, but I think literally some I think 302 00:15:49,644 --> 00:15:55,124 Speaker 3: he's a somewhat defective person emotionally in other ways. And 303 00:15:56,684 --> 00:15:58,644 Speaker 3: you know, the fact that he was enabled by so 304 00:15:58,724 --> 00:16:02,324 Speaker 3: many supposedly smart actors is a core question that's asked 305 00:16:02,364 --> 00:16:05,404 Speaker 3: by the book. I mean, you know, the New York 306 00:16:05,444 --> 00:16:08,604 Speaker 3: Times review understood this, which is that it's like the 307 00:16:08,604 --> 00:16:12,844 Speaker 3: book is actually pretty unsparing to some of these characters 308 00:16:12,884 --> 00:16:15,084 Speaker 3: in the River, even though like it's not a boo 309 00:16:15,164 --> 00:16:17,204 Speaker 3: where I'm gonna be like Peter Teal is evil and 310 00:16:17,244 --> 00:16:19,284 Speaker 3: Elon Musk is evil and a fascist, Like that's not 311 00:16:19,644 --> 00:16:21,404 Speaker 3: my style of writing. And I think I think they're 312 00:16:21,444 --> 00:16:24,284 Speaker 3: interesting people who deserve to have their stories told by 313 00:16:24,284 --> 00:16:27,804 Speaker 3: someone who kind of understands the personality type. But SBF 314 00:16:27,884 --> 00:16:31,204 Speaker 3: was a really dangerous figure and on top of everything else, 315 00:16:31,244 --> 00:16:34,724 Speaker 3: like also a fairly bad calculator of risk. His decision 316 00:16:34,804 --> 00:16:37,404 Speaker 3: to go take the witness stand at his trial after 317 00:16:37,444 --> 00:16:42,564 Speaker 3: the government's testimony was just incredibly devastating, and they had 318 00:16:42,644 --> 00:16:46,124 Speaker 3: him dead to rights and contradicting everything he told people publicly, 319 00:16:46,124 --> 00:16:48,684 Speaker 3: everything he told me in interviews that were on the record, 320 00:16:48,724 --> 00:16:50,444 Speaker 3: although with an embario, I mean, they couldn't be used 321 00:16:50,484 --> 00:16:54,044 Speaker 3: until the book was released. You know, the willingness to lie, 322 00:16:54,204 --> 00:16:57,564 Speaker 3: plus the constant miscalculations he made where he probably could 323 00:16:57,604 --> 00:17:00,524 Speaker 3: have avoided jail time by conceding defeat and not taking 324 00:17:00,524 --> 00:17:02,564 Speaker 3: the witness stand. Not awitted all the jail time, but 325 00:17:03,124 --> 00:17:06,964 Speaker 3: ten years or five years and not twenty You know, 326 00:17:07,724 --> 00:17:10,284 Speaker 3: why did so many people vouch for him? 327 00:17:10,284 --> 00:17:11,964 Speaker 2: As a question that I think has to be has 328 00:17:12,004 --> 00:17:12,684 Speaker 2: to be pondered. 329 00:17:14,524 --> 00:17:26,404 Speaker 1: We'll be back after a quick break. You actually touched 330 00:17:27,084 --> 00:17:30,324 Speaker 1: on something there which I think is important to unwrap 331 00:17:30,364 --> 00:17:33,244 Speaker 1: a little bit more, which is that if you're taking 332 00:17:33,244 --> 00:17:36,484 Speaker 1: these huge gambles, you better be pretty damn sure your 333 00:17:36,524 --> 00:17:40,284 Speaker 1: calculations are accurate, that your percentages are accurate. But we're 334 00:17:40,284 --> 00:17:41,964 Speaker 1: living in a world where you can't do that, and 335 00:17:42,244 --> 00:17:45,524 Speaker 1: so I'm curious. You know, you build models, right that 336 00:17:47,004 --> 00:17:50,004 Speaker 1: try to kind of model things that are uncertain, that 337 00:17:50,044 --> 00:17:53,404 Speaker 1: are unknowable, that have this margin of error, And so 338 00:17:53,924 --> 00:17:57,564 Speaker 1: I think you understand more than most people that you know, 339 00:17:57,604 --> 00:18:00,724 Speaker 1: if you say sixty percent certain or like sixty percent 340 00:18:00,804 --> 00:18:03,884 Speaker 1: chance of this, like you're not actually like that. There 341 00:18:03,964 --> 00:18:06,124 Speaker 1: is a margin of error there, right, Like if you 342 00:18:06,524 --> 00:18:09,804 Speaker 1: fifty one percent likely that you're going to have a 343 00:18:09,804 --> 00:18:12,804 Speaker 1: plus EV and forty nine What if you're wrong, right, Like, 344 00:18:12,844 --> 00:18:16,484 Speaker 1: what if it's forty nine that you're that you're actually 345 00:18:16,484 --> 00:18:19,964 Speaker 1: negative TV and fifty one that you're going to destroy civilization? 346 00:18:20,364 --> 00:18:23,284 Speaker 1: So I'd love you to walk us through that thinking 347 00:18:23,444 --> 00:18:26,404 Speaker 1: and through kind of the hubris that you both need 348 00:18:26,444 --> 00:18:28,724 Speaker 1: to succeed in some ways in this area. But also 349 00:18:28,804 --> 00:18:31,364 Speaker 1: that might make you over confident of these sorts of 350 00:18:31,364 --> 00:18:33,644 Speaker 1: percentages where you really really can't afford to be. 351 00:18:34,484 --> 00:18:38,924 Speaker 3: Yeah, look, we've seen bad election models. We saw models 352 00:18:38,924 --> 00:18:42,164 Speaker 3: I gave Yeah, saw models I gave Trump a one 353 00:18:42,204 --> 00:18:44,564 Speaker 3: percent chance or zero point one percent chance of winning 354 00:18:44,564 --> 00:18:47,324 Speaker 3: in twenty sixteen. We saw models earlier this year that 355 00:18:47,404 --> 00:18:49,764 Speaker 3: gave Biden, even after he had tanked in the debate, 356 00:18:50,204 --> 00:18:52,044 Speaker 3: a fifty to fifty chance of winning, which didn't make 357 00:18:52,044 --> 00:18:55,484 Speaker 3: any sense. Even building models for these kind of close 358 00:18:55,644 --> 00:19:01,364 Speaker 3: problems like elections or sports forecasting is pretty tough. You 359 00:19:01,444 --> 00:19:03,164 Speaker 3: have a lot of choices to make as a modeler, 360 00:19:03,524 --> 00:19:08,884 Speaker 3: your biases might creep in and other things. So to 361 00:19:08,964 --> 00:19:12,004 Speaker 3: then kind of take these very back of the envelope 362 00:19:12,484 --> 00:19:16,284 Speaker 3: informal models and try to apply them to every problem 363 00:19:16,364 --> 00:19:19,444 Speaker 3: in the world is you know, obviously prone to going 364 00:19:19,484 --> 00:19:22,644 Speaker 3: wrong to some extent, and like in effective altruism, they're 365 00:19:22,764 --> 00:19:27,044 Speaker 3: very literal about this. I talked to to Will mccaskell, 366 00:19:27,124 --> 00:19:28,564 Speaker 3: who was kind of one of the founders or at 367 00:19:28,644 --> 00:19:30,764 Speaker 3: least the brand name of EA, and wrote a book 368 00:19:30,764 --> 00:19:33,084 Speaker 3: called What We Owe the Future. He calls himself a 369 00:19:33,604 --> 00:19:35,964 Speaker 3: long term mispaning. He's concerned about the very very very 370 00:19:35,964 --> 00:19:38,604 Speaker 3: far future. The first time to talk to him, he 371 00:19:38,724 --> 00:19:41,164 Speaker 3: was like, okay, so how do we weigh like a 372 00:19:41,244 --> 00:19:43,844 Speaker 3: human's life against an animal's life? And he's like, well, 373 00:19:43,844 --> 00:19:46,924 Speaker 3: it should be based on the number of neurons in 374 00:19:46,964 --> 00:19:49,764 Speaker 3: the animal's brain, or maybe the number of neurons to 375 00:19:49,844 --> 00:19:52,204 Speaker 3: the one third power or something like that. It's a 376 00:19:52,204 --> 00:19:55,324 Speaker 3: good approximation, but that leads to weird things like an 377 00:19:55,364 --> 00:19:56,724 Speaker 3: elephant elephants. 378 00:19:56,844 --> 00:19:58,124 Speaker 2: Elephants are actually. 379 00:19:57,804 --> 00:20:00,964 Speaker 3: With more than human beings by that calculation, it turns out, 380 00:20:01,684 --> 00:20:04,084 Speaker 3: but you also face some comfortable decisions. The book uses 381 00:20:04,124 --> 00:20:06,964 Speaker 3: a story of a time when a dog named I 382 00:20:06,964 --> 00:20:07,924 Speaker 3: don't I. 383 00:20:07,804 --> 00:20:10,564 Speaker 1: Didn't know, Yeah I did it realize that the poodle 384 00:20:10,684 --> 00:20:12,804 Speaker 1: like I didn't know this story, so please, yeah, please. 385 00:20:13,244 --> 00:20:16,564 Speaker 3: There's a poodle that name named Dakota that gets loose 386 00:20:16,604 --> 00:20:19,604 Speaker 3: in Prospect Park or some park in North Brooklyn from 387 00:20:19,644 --> 00:20:22,524 Speaker 3: its owner and dashes into the subways at the first 388 00:20:22,524 --> 00:20:27,404 Speaker 3: stop inbound on the F train from Manhattan to Brooklyn. So, 389 00:20:27,404 --> 00:20:29,724 Speaker 3: and this is happening at like three point thirty on 390 00:20:29,804 --> 00:20:32,444 Speaker 3: a weekday, so you're starting to get the rush hour commute. 391 00:20:32,444 --> 00:20:35,044 Speaker 3: And the question is should we shut down the entire 392 00:20:35,244 --> 00:20:39,764 Speaker 3: F line in order to search for and rescue Dakota, 393 00:20:39,804 --> 00:20:42,204 Speaker 3: which they did, and Dakota popped up at some other 394 00:20:42,284 --> 00:20:45,644 Speaker 3: subway station an hour or so later. But that's a 395 00:20:45,724 --> 00:20:47,644 Speaker 3: case where I'll put it this this out, put it 396 00:20:47,644 --> 00:20:50,844 Speaker 3: in the book. If you had, like a human baby 397 00:20:51,324 --> 00:20:53,884 Speaker 3: who had fallen onto the tracks, like, no one would 398 00:20:53,924 --> 00:20:58,044 Speaker 3: dispute that we should halt every train till that baby 399 00:20:58,124 --> 00:21:01,284 Speaker 3: is found. If it was a squirrel, then we would 400 00:21:01,284 --> 00:21:03,524 Speaker 3: just run on the squirrel, right, no questions asked. 401 00:21:03,844 --> 00:21:05,884 Speaker 1: We would we would? I mean, I hate squirrels. 402 00:21:06,364 --> 00:21:10,404 Speaker 3: And the dog is intuitively kind of a close decision, 403 00:21:10,484 --> 00:21:12,204 Speaker 3: and so and so and then. But the fact is 404 00:21:12,204 --> 00:21:14,804 Speaker 3: that you have to make a decision, and we had 405 00:21:14,804 --> 00:21:17,884 Speaker 3: to make these decisions in COVID, where you know, what's 406 00:21:17,924 --> 00:21:21,524 Speaker 3: the cost of the shutting down schools versus the cost 407 00:21:21,564 --> 00:21:24,844 Speaker 3: of preventing some degree of sickness and death, right, I 408 00:21:24,844 --> 00:21:26,764 Speaker 3: mean that's a real that's a real trade off. 409 00:21:26,804 --> 00:21:28,124 Speaker 2: Or things that are more intangible. 410 00:21:28,524 --> 00:21:33,764 Speaker 3: What's the cost of the undermining people's happiness because they 411 00:21:33,804 --> 00:21:36,324 Speaker 3: can't at least they're not supposed to see their friends 412 00:21:36,324 --> 00:21:38,724 Speaker 3: in person during COVID, or they can't go to a 413 00:21:38,724 --> 00:21:41,124 Speaker 3: baseball game, or go to a restaurant, or see their 414 00:21:41,204 --> 00:21:44,044 Speaker 3: dying relative in a hospital or all, or go to church, 415 00:21:44,084 --> 00:21:46,564 Speaker 3: all these other things, like like what's the cost of that? 416 00:21:46,724 --> 00:21:49,884 Speaker 3: And we make these decisions with with you know, different 417 00:21:49,884 --> 00:21:52,844 Speaker 3: heuristics and different rules of thumb that are often very 418 00:21:52,844 --> 00:21:56,324 Speaker 3: clumsy and maybe maybe words of magnitude wrong. I think 419 00:21:56,324 --> 00:21:59,284 Speaker 3: the decision to keep school shut down after vaccines were 420 00:21:59,284 --> 00:22:02,524 Speaker 3: available is like wrong by like ten x or one 421 00:22:02,564 --> 00:22:05,404 Speaker 3: hundred x, like unambiguously a terrible decision that some Blue 422 00:22:05,444 --> 00:22:09,844 Speaker 3: states made even after vaccine availability. So you kind of 423 00:22:09,844 --> 00:22:12,004 Speaker 3: get in this dilemma where you have to make you 424 00:22:12,004 --> 00:22:13,924 Speaker 3: have to make decisions, you have to make some calculation. 425 00:22:14,484 --> 00:22:18,364 Speaker 3: But I think people underestimate how back of the envelope 426 00:22:18,444 --> 00:22:20,764 Speaker 3: these are. And when you start to put numbers to things, 427 00:22:20,844 --> 00:22:24,364 Speaker 3: and people sometimes stop asking questions about models where they 428 00:22:24,364 --> 00:22:25,884 Speaker 3: should ask you more questions. 429 00:22:27,484 --> 00:22:29,684 Speaker 1: Is there something that you would recommend, because this is 430 00:22:29,724 --> 00:22:32,644 Speaker 1: something that I've come across, you know, many times at 431 00:22:32,724 --> 00:22:34,764 Speaker 1: a wall I hit in a lot of my research. 432 00:22:35,164 --> 00:22:38,644 Speaker 1: Is there something that you have seen in people who 433 00:22:38,684 --> 00:22:43,164 Speaker 1: are successful where they are able to modulate or at 434 00:22:43,244 --> 00:22:47,764 Speaker 1: least like self check their overconfidence and their hubris, because 435 00:22:47,764 --> 00:22:50,164 Speaker 1: it seems like most of the people on the river 436 00:22:50,484 --> 00:22:52,924 Speaker 1: don't do that, right, Like Elon Musk, absolutely not, Like 437 00:22:53,004 --> 00:22:56,684 Speaker 1: Competer Teal, absolutely not. Like all of these men who 438 00:22:56,724 --> 00:22:59,564 Speaker 1: you talk about, they don't seem to have a switch 439 00:22:59,604 --> 00:23:01,404 Speaker 1: where they can be like, Okay, you know what, let 440 00:23:01,444 --> 00:23:03,804 Speaker 1: me take a step back and self evaluate. Did you 441 00:23:03,884 --> 00:23:07,524 Speaker 1: find anyone who was able to do that? And if so, 442 00:23:07,684 --> 00:23:09,044 Speaker 1: how like how do you do that? 443 00:23:10,484 --> 00:23:12,924 Speaker 3: I mean, in some ways, some of the poker players 444 00:23:12,924 --> 00:23:15,604 Speaker 3: I talked to seem like they're more at ease with 445 00:23:15,724 --> 00:23:18,124 Speaker 3: themselves and can modulate it somehow. 446 00:23:18,164 --> 00:23:18,404 Speaker 1: You know. 447 00:23:18,524 --> 00:23:20,884 Speaker 3: Jason Kuhn, for example, is an interesting poker player who 448 00:23:20,884 --> 00:23:23,684 Speaker 3: has some very you know, had a rough upbringing, has 449 00:23:23,684 --> 00:23:26,244 Speaker 3: some very aggressive tendencies, but I think has some type 450 00:23:26,244 --> 00:23:29,324 Speaker 3: of perspective of there's this very competitive part of him 451 00:23:29,404 --> 00:23:32,404 Speaker 3: that he understands can be unleashed, but has to be 452 00:23:32,844 --> 00:23:36,604 Speaker 3: you know, meet it out in doses somehow. Look, there 453 00:23:36,644 --> 00:23:39,484 Speaker 3: is some bias in the book in the following sense, 454 00:23:39,484 --> 00:23:42,284 Speaker 3: which is that the people who are quote unquote crazy 455 00:23:42,804 --> 00:23:44,364 Speaker 3: or the people who really you know, like to talk 456 00:23:44,404 --> 00:23:47,724 Speaker 3: about themselves and take crazy risks, make for better stories. 457 00:23:47,764 --> 00:23:50,084 Speaker 3: So yeah, I mean I'm thinking about like, you know, 458 00:23:50,404 --> 00:23:54,964 Speaker 3: Michael Moritz is a Sequoia Capital venture capitalist who's been 459 00:23:55,004 --> 00:23:57,644 Speaker 3: very successful early investor in Google of many of their companies. 460 00:23:58,964 --> 00:24:01,564 Speaker 3: He's a guy who I think is probably pretty measured actually. 461 00:24:01,564 --> 00:24:06,124 Speaker 3: A former journalist Michael Moritz ven know Kosla is another 462 00:24:06,204 --> 00:24:09,964 Speaker 3: one who invests in kind of alternative things like alternative 463 00:24:10,084 --> 00:24:13,444 Speaker 3: energy or or you know, what's it called artificial meat 464 00:24:13,684 --> 00:24:15,404 Speaker 3: things like that. I mean, he seems to be like 465 00:24:15,484 --> 00:24:19,204 Speaker 3: reasonably well balanced. Patrick Collison, the founder of Strike. But 466 00:24:19,564 --> 00:24:22,004 Speaker 3: and these people are all in the book. But because 467 00:24:22,004 --> 00:24:25,324 Speaker 3: they're less bombastic, they maybe get like both a little 468 00:24:25,324 --> 00:24:26,964 Speaker 3: bit less of a write up from me and then 469 00:24:27,204 --> 00:24:30,604 Speaker 3: and then kind of having some lost in translation problem too. 470 00:24:30,644 --> 00:24:35,164 Speaker 3: But yeah, look, overconfidence is a really big problem for 471 00:24:35,204 --> 00:24:38,004 Speaker 3: people in the river because by definition, these are people 472 00:24:38,004 --> 00:24:43,324 Speaker 3: who probably won their first couple of big bets, sometimes 473 00:24:43,324 --> 00:24:46,844 Speaker 3: by skill, sometimes by luck. I'd say often more luck 474 00:24:46,884 --> 00:24:49,804 Speaker 3: than skill, but usually some of both. And when you're 475 00:24:49,804 --> 00:24:51,804 Speaker 3: on a winning streak, then you can think that your 476 00:24:52,124 --> 00:24:54,284 Speaker 3: God's gift to whatever venture that you happen to be 477 00:24:54,644 --> 00:24:57,644 Speaker 3: involved in, and you can be overconfident. And because there 478 00:24:57,724 --> 00:25:00,084 Speaker 3: is like there is like fruitful territory. I mean, the 479 00:25:00,164 --> 00:25:02,084 Speaker 3: village leaves a lot to be desired for how it 480 00:25:02,124 --> 00:25:05,324 Speaker 3: assesses risk and does other things, and so you know, 481 00:25:05,564 --> 00:25:08,164 Speaker 3: you can in some ways the critiques that these two 482 00:25:08,204 --> 00:25:11,764 Speaker 3: communities have of one another are both pretty smart. I mean, 483 00:25:11,764 --> 00:25:14,564 Speaker 3: I think the River's critique of the village that these 484 00:25:14,564 --> 00:25:18,324 Speaker 3: guys are too risk averse, that their partisan, that they're 485 00:25:18,324 --> 00:25:20,964 Speaker 3: too concerned with social perception. I think that's basically right. 486 00:25:21,004 --> 00:25:24,604 Speaker 3: But also some of the Silicon Valley founders and vcs 487 00:25:24,684 --> 00:25:26,964 Speaker 3: are are total arrogant pricks. 488 00:25:28,404 --> 00:25:31,484 Speaker 1: Yeah, that's one way of putting in. And I think 489 00:25:31,524 --> 00:25:33,724 Speaker 1: that was something that you're that you just hinted at, 490 00:25:34,404 --> 00:25:38,124 Speaker 1: which is I think going to necessarily be like a 491 00:25:38,164 --> 00:25:40,524 Speaker 1: problem when you're trying to figure out who to interview 492 00:25:40,604 --> 00:25:43,124 Speaker 1: for the book, and it's just in this industry, well 493 00:25:43,164 --> 00:25:47,084 Speaker 1: in the world in general, is we have this survivor bias, right, 494 00:25:47,204 --> 00:25:51,244 Speaker 1: which we see the people who are successful. How are 495 00:25:51,244 --> 00:25:53,324 Speaker 1: you going to go out and interview all the people 496 00:25:53,324 --> 00:25:56,564 Speaker 1: who took these risks, who where didn't pan out. One 497 00:25:56,604 --> 00:25:58,244 Speaker 1: of the things that really struck me as I was 498 00:25:58,284 --> 00:26:00,564 Speaker 1: reading your book was when you actually did some of 499 00:26:00,604 --> 00:26:03,724 Speaker 1: these simulations, right, and you actually wrote out what the 500 00:26:03,804 --> 00:26:07,444 Speaker 1: numbers looked like if you had different strategies, and some 501 00:26:07,484 --> 00:26:09,764 Speaker 1: of them I was like, holy shit, right, like this 502 00:26:10,004 --> 00:26:14,844 Speaker 1: crazy risky strategy, Like, yes, you die ninety five percent 503 00:26:14,844 --> 00:26:17,004 Speaker 1: of the time, but like, look at that five percent. 504 00:26:17,364 --> 00:26:20,204 Speaker 1: But that five percent is mostly who you end up interviewing, right, 505 00:26:20,204 --> 00:26:22,764 Speaker 1: because they become the elon Musks of the world. So 506 00:26:22,844 --> 00:26:24,924 Speaker 1: what does that do to kind of our perceptions of 507 00:26:24,964 --> 00:26:26,084 Speaker 1: some of these traits? You know? 508 00:26:26,244 --> 00:26:29,004 Speaker 3: Yeah, and so look, this became a little bit easier. 509 00:26:29,524 --> 00:26:32,124 Speaker 3: I'm just speaking kind of behind the scenes as a writer. 510 00:26:32,924 --> 00:26:36,764 Speaker 3: When the SBF thing blew up, then that became easier. 511 00:26:37,044 --> 00:26:41,044 Speaker 3: There's originally going to be like a chapter on failures, 512 00:26:41,084 --> 00:26:43,524 Speaker 3: but that meant more people that like you know, want 513 00:26:43,524 --> 00:26:45,444 Speaker 3: to poke a tournament and then developed some type of 514 00:26:45,444 --> 00:26:47,964 Speaker 3: problem afterward or whatever else. But like SBF kind of 515 00:26:48,564 --> 00:26:51,204 Speaker 3: served enough of that role where great narrative wise, I. 516 00:26:51,124 --> 00:26:51,724 Speaker 2: Thought it was okay. 517 00:26:51,804 --> 00:26:55,124 Speaker 3: I mean, there is one story about a person named 518 00:26:55,204 --> 00:26:57,724 Speaker 3: run bow Lee who was a smart guy. 519 00:26:57,764 --> 00:26:58,924 Speaker 2: You never see a trun that was. 520 00:26:59,324 --> 00:27:01,164 Speaker 1: That was kind of a heartbreaking story. Yeah, go ahead. 521 00:27:01,204 --> 00:27:02,444 Speaker 3: And he came to me and he said, you're writing 522 00:27:02,484 --> 00:27:04,084 Speaker 3: this book about these sucessful people. Just kind of cold 523 00:27:04,124 --> 00:27:06,364 Speaker 3: emailed me. And he's like, I'm somebody who got really 524 00:27:06,364 --> 00:27:09,564 Speaker 3: burned by by day trading, by day trading option in 525 00:27:09,564 --> 00:27:13,884 Speaker 3: particular getting into all the Wall Street bets kind of communities. 526 00:27:13,924 --> 00:27:17,484 Speaker 3: And he has lost a million dollars on options trading, 527 00:27:17,564 --> 00:27:20,404 Speaker 3: and he wanted to tell a story, and I'm like, 528 00:27:20,404 --> 00:27:22,084 Speaker 3: the story has to go in the book. There's also, 529 00:27:22,724 --> 00:27:27,164 Speaker 3: you know, the chapter on Las Vegas, which is chapter three, 530 00:27:27,604 --> 00:27:30,604 Speaker 3: is about how casinos, particularly through slot machines, kind of 531 00:27:30,644 --> 00:27:33,724 Speaker 3: manipulate their patrons into spending more. I mean, the stories 532 00:27:33,764 --> 00:27:37,164 Speaker 3: I heard from Natasha Sholl, who is a kind of 533 00:27:37,164 --> 00:27:40,044 Speaker 3: an amazing woman. She's an myu athropologist who had like 534 00:27:40,044 --> 00:27:41,724 Speaker 3: never been out of the East Coast and she's like, 535 00:27:42,124 --> 00:27:44,364 Speaker 3: Las Vegas is an exotic place. I want to study 536 00:27:44,404 --> 00:27:47,764 Speaker 3: Las Vegas as an anthropologist. Did her dissertation about it, 537 00:27:47,764 --> 00:27:49,844 Speaker 3: then wrote a book about it, and that's a story 538 00:27:49,884 --> 00:27:53,604 Speaker 3: about problem gambling. And although I didn't talk to that 539 00:27:53,604 --> 00:27:58,564 Speaker 3: many problem gamblers, really you know, it's very dark about 540 00:27:58,884 --> 00:27:59,764 Speaker 3: slot machine addiction. 541 00:27:59,844 --> 00:28:03,364 Speaker 1: Yeah. Well, one of the things actually that really was 542 00:28:03,444 --> 00:28:06,764 Speaker 1: interesting to me there because I hadn't read Schul's book, 543 00:28:07,724 --> 00:28:11,724 Speaker 1: was this insight that you that you go into, which 544 00:28:11,764 --> 00:28:14,484 Speaker 1: is that a lot of these problem gamblers that people 545 00:28:14,484 --> 00:28:17,124 Speaker 1: who play slot machines they know they're losing, and they 546 00:28:17,164 --> 00:28:20,844 Speaker 1: don't care that they're just like they enjoy, like they 547 00:28:20,964 --> 00:28:24,404 Speaker 1: enjoy that experience even though they lose. And to me, 548 00:28:25,204 --> 00:28:27,724 Speaker 1: like that was actually an alha moment because I always 549 00:28:27,764 --> 00:28:30,124 Speaker 1: thought that at least they thought they could win, right, 550 00:28:30,164 --> 00:28:31,804 Speaker 1: But like one of the otherers was that, like you 551 00:28:31,884 --> 00:28:34,004 Speaker 1: might be a winner, but you even said that some 552 00:28:34,044 --> 00:28:36,964 Speaker 1: of them don't like winning because it brings them out 553 00:28:36,964 --> 00:28:37,804 Speaker 1: of that flow state. 554 00:28:38,004 --> 00:28:41,484 Speaker 3: Yeah, you're in some machine zone. Natasha Shall calls it 555 00:28:41,564 --> 00:28:44,164 Speaker 3: the machine zone where it's just you and you're pressing 556 00:28:44,204 --> 00:28:47,124 Speaker 3: the reels and you know, I don't have much of 557 00:28:47,164 --> 00:28:49,164 Speaker 3: a compulsion to play slots, but I've tossed in a 558 00:28:49,164 --> 00:28:50,084 Speaker 3: few bucks. 559 00:28:50,004 --> 00:28:51,884 Speaker 2: Here and there. When you're frustrated the Innovan Poker. 560 00:28:51,684 --> 00:28:56,004 Speaker 3: Tournament, and some of the games are very fun and compelling, 561 00:28:56,084 --> 00:28:58,004 Speaker 3: right you got like gorillas or what's the big one? 562 00:28:58,084 --> 00:29:00,844 Speaker 3: Not Garia's but uh Buffalo Buffalo. 563 00:29:00,924 --> 00:29:04,164 Speaker 1: I've never I've never played slots in my life, even I'm. 564 00:29:04,044 --> 00:29:05,004 Speaker 2: Going to force you to. 565 00:29:05,124 --> 00:29:07,164 Speaker 3: I'm going to give you money to play slots, I think, Maria, 566 00:29:07,244 --> 00:29:10,124 Speaker 3: so you can no longer make that bras, which I 567 00:29:10,164 --> 00:29:15,204 Speaker 3: find nitty by the way, but yeah, they they want 568 00:29:15,244 --> 00:29:18,564 Speaker 3: to shut out all the other distractions of the world 569 00:29:18,604 --> 00:29:20,404 Speaker 3: and just channel it into like one problem, which is 570 00:29:20,484 --> 00:29:23,364 Speaker 3: problem part problem gambling, which is very different than like. 571 00:29:24,804 --> 00:29:26,204 Speaker 2: The table games players. 572 00:29:26,724 --> 00:29:30,844 Speaker 3: The Blackjack crabs player more often fits the stereotype of 573 00:29:30,884 --> 00:29:33,364 Speaker 3: somebody who is bored because they don't have enough risk 574 00:29:33,404 --> 00:29:35,844 Speaker 3: tolerance in their life, enough risk in their life, and 575 00:29:35,884 --> 00:29:38,444 Speaker 3: so their useless as a way, and some of this 576 00:29:38,564 --> 00:29:41,724 Speaker 3: is is gendered too, if you want to go there, 577 00:29:41,724 --> 00:29:45,324 Speaker 3: but like you know, men feeling the need. If you've 578 00:29:45,324 --> 00:29:47,524 Speaker 3: been enough craps tables, craps I do find fun, don't 579 00:29:47,524 --> 00:29:50,204 Speaker 3: play it much. If you've been enough groups of guys 580 00:29:50,284 --> 00:29:53,324 Speaker 3: at a at a craps table where they'll encourage one 581 00:29:53,324 --> 00:29:55,844 Speaker 3: another to make bets at you know, every crafts bets 582 00:29:55,884 --> 00:29:59,124 Speaker 3: negative EV, but also to play outside their means and 583 00:29:59,164 --> 00:30:01,284 Speaker 3: gamble bigger, so they'll have stories to tell. I mean 584 00:30:01,324 --> 00:30:04,484 Speaker 3: that's you know, that's a certain type of young man 585 00:30:04,524 --> 00:30:05,804 Speaker 3: is more likely to do that kind of thing, and 586 00:30:05,844 --> 00:30:08,364 Speaker 3: it might substitute for other types of ways to prove 587 00:30:08,404 --> 00:30:11,364 Speaker 3: their bravery. But like the slots machines players and then 588 00:30:11,404 --> 00:30:16,204 Speaker 3: the skill game players, the poker players, the sports betters, 589 00:30:16,244 --> 00:30:19,804 Speaker 3: the handful of advantage players, which means people who try 590 00:30:19,804 --> 00:30:22,804 Speaker 3: to count cards in blackjack, there are conditions. 591 00:30:22,884 --> 00:30:25,684 Speaker 2: Please do not try this yourself. 592 00:30:25,724 --> 00:30:29,324 Speaker 3: There are conditions under witch playing slot machines can be 593 00:30:29,404 --> 00:30:33,444 Speaker 3: plus ev because of contingencies the machines or bonus payouts 594 00:30:33,524 --> 00:30:36,644 Speaker 3: or jackpots that becomes efficiently large. But those are those 595 00:30:36,684 --> 00:30:39,284 Speaker 3: are one percent of the gambling world. I mean, sometimes 596 00:30:39,604 --> 00:30:42,884 Speaker 3: I feel like you and I are use. You know, 597 00:30:43,804 --> 00:30:47,484 Speaker 3: some of the prestige resorts, generally speaking, the prestige resorts 598 00:30:47,564 --> 00:30:50,164 Speaker 3: in Las Vegas and elsewhere have the best poker rooms, 599 00:30:50,204 --> 00:30:56,164 Speaker 3: the win that are et cetera resorts world Now, Alasio, 600 00:30:56,204 --> 00:30:58,164 Speaker 3: I sometimes wonder if like we're used a little bit 601 00:30:58,204 --> 00:31:01,804 Speaker 3: to whitewash the fact that like ninety nine percent of 602 00:31:01,844 --> 00:31:04,964 Speaker 3: people in that casino are doing negative expected value gambling. 603 00:31:06,084 --> 00:31:07,484 Speaker 1: Now, I mean that I think that's true, and I 604 00:31:07,564 --> 00:31:11,044 Speaker 1: do and I do overlook that on purpose because I 605 00:31:11,044 --> 00:31:15,084 Speaker 1: don't like paying attention to that, right, But yeah, I know, 606 00:31:15,404 --> 00:31:18,804 Speaker 1: I think you're right, And I don't want to judge 607 00:31:18,804 --> 00:31:21,524 Speaker 1: anyone for you know, playing slots or doing whatever they're doing. 608 00:31:21,604 --> 00:31:23,804 Speaker 1: But I do find it a little bit depressing when 609 00:31:23,844 --> 00:31:27,084 Speaker 1: I see people who don't have an edge and who 610 00:31:27,124 --> 00:31:29,164 Speaker 1: are just kind of pressing buttons. 611 00:31:29,884 --> 00:31:33,604 Speaker 3: Yeah. Look, I comfort myself by saying that poker, at 612 00:31:33,684 --> 00:31:39,164 Speaker 3: least I think is plus CV any utilitarian calculus for society, 613 00:31:39,244 --> 00:31:42,004 Speaker 3: just because I think it is such a unique teacher 614 00:31:42,644 --> 00:31:45,124 Speaker 3: of certain critical life skills in an environment where you 615 00:31:45,124 --> 00:31:47,644 Speaker 3: have something real at risk that like, I just I 616 00:31:47,724 --> 00:31:50,324 Speaker 3: just think that, like I just think that playing poker 617 00:31:50,364 --> 00:31:55,204 Speaker 3: makes you more adept at handling real life risky situations 618 00:31:55,204 --> 00:31:56,004 Speaker 3: a lot of the time. 619 00:31:56,924 --> 00:31:57,084 Speaker 2: You know. 620 00:31:57,164 --> 00:32:00,884 Speaker 3: Apart from that, I mean, I think there's a lot 621 00:32:00,924 --> 00:32:04,084 Speaker 3: in the book about sports betting. I think there's more 622 00:32:04,124 --> 00:32:08,084 Speaker 3: papers coming out saying that sports betting increases bankruptcies among 623 00:32:08,164 --> 00:32:10,404 Speaker 3: exactly the classes of people that you would expect, Like 624 00:32:10,444 --> 00:32:14,404 Speaker 3: you see oh, uptick in bankruptcies and males age twenty 625 00:32:14,404 --> 00:32:15,924 Speaker 3: to thirty nine, And what if it could have caused 626 00:32:15,924 --> 00:32:18,244 Speaker 3: that when sports Beenning went online in X and Y State. 627 00:32:19,684 --> 00:32:22,644 Speaker 3: You know, look, I think the slot machine stuff is 628 00:32:22,644 --> 00:32:26,324 Speaker 3: probably quite bad from a utilitarian standpoint. I think the 629 00:32:26,364 --> 00:32:28,524 Speaker 3: state lottery is actually maybe the worst of all on 630 00:32:28,524 --> 00:32:30,324 Speaker 3: a kind of a per capita basis, where the odds 631 00:32:30,324 --> 00:32:32,724 Speaker 3: are very bad for the player and the socio economics 632 00:32:32,724 --> 00:32:35,644 Speaker 3: of who plays the state lottery are It's basically attacks 633 00:32:35,684 --> 00:32:39,444 Speaker 3: on like lower class people for the most part, so 634 00:32:39,484 --> 00:32:41,284 Speaker 3: there's a lot of hipocrisy. There are also lots of 635 00:32:41,364 --> 00:32:44,084 Speaker 3: arguments about the arms race that hey, if we don't 636 00:32:44,164 --> 00:32:47,644 Speaker 3: legalize this game, then maybe somebody else would, right. I mean, 637 00:32:47,684 --> 00:32:51,004 Speaker 3: if I had my you know, if I could perfectly 638 00:32:51,044 --> 00:32:53,724 Speaker 3: calibrate it, then maybe i'd have something where you have 639 00:32:54,244 --> 00:32:57,364 Speaker 3: a casino that offers poker and table games but not 640 00:32:57,444 --> 00:33:00,564 Speaker 3: slot machines. And maybe you have sports bedding which can 641 00:33:00,604 --> 00:33:04,244 Speaker 3: be done in person at a facility but not online 642 00:33:04,284 --> 00:33:06,564 Speaker 3: where the addiction seems to be higher, and they have 643 00:33:06,724 --> 00:33:09,844 Speaker 3: fair rules where you don't limit winning players, et cetera, 644 00:33:09,844 --> 00:33:12,124 Speaker 3: and you have limits on how much can take from Wales, 645 00:33:12,564 --> 00:33:16,124 Speaker 3: Like there's a world in which I would be I 646 00:33:16,164 --> 00:33:18,444 Speaker 3: think more content with. And again I'm not a moralistic person. 647 00:33:18,484 --> 00:33:20,804 Speaker 3: The book is not going to be scoldy, and I 648 00:33:20,804 --> 00:33:23,004 Speaker 3: believe in showing and not telling. But like, it's not 649 00:33:23,044 --> 00:33:27,004 Speaker 3: like the the gambling industry comes out looking great. It 650 00:33:27,004 --> 00:33:28,804 Speaker 3: comes out looking smart, it comes out looking like it 651 00:33:28,924 --> 00:33:31,924 Speaker 3: understands how to like model its customers out. And I 652 00:33:31,964 --> 00:33:34,884 Speaker 3: would say one thing to say encounter to this is 653 00:33:34,924 --> 00:33:37,844 Speaker 3: that if you go to the mid to high range 654 00:33:37,884 --> 00:33:41,524 Speaker 3: resorts in Las Vegas, my subjective view is that people 655 00:33:41,604 --> 00:33:43,244 Speaker 3: seem to be having a pretty good time, and Las 656 00:33:43,324 --> 00:33:45,844 Speaker 3: Vegas gets a lot of repeat business, so there's some 657 00:33:45,924 --> 00:33:48,164 Speaker 3: type of win win in that deal. But I might, 658 00:33:48,844 --> 00:33:50,804 Speaker 3: I might draw the line a little bit higher than 659 00:33:50,844 --> 00:33:52,324 Speaker 3: like a total free for all, which is kind of 660 00:33:52,324 --> 00:33:53,164 Speaker 3: where we're headed. Now. 661 00:33:54,764 --> 00:33:58,484 Speaker 2: We will be back in just a minute. 662 00:34:12,244 --> 00:34:15,124 Speaker 1: I have a few questions, but first I'm just curious 663 00:34:15,244 --> 00:34:19,444 Speaker 1: are you still doing sports betting? So I loved that 664 00:34:19,524 --> 00:34:21,524 Speaker 1: you picked it up in a serious way for the book, 665 00:34:21,564 --> 00:34:23,884 Speaker 1: although holy shit, I had no idea that you'd wagered 666 00:34:23,924 --> 00:34:28,444 Speaker 1: over a million dollars for your research, Nate, I didn't know, 667 00:34:30,524 --> 00:34:33,844 Speaker 1: but yeah, tell us about that and tell us what 668 00:34:33,884 --> 00:34:34,764 Speaker 1: you're doing right now. 669 00:34:35,284 --> 00:34:37,924 Speaker 3: So I wanted to have skin in the game, to 670 00:34:37,924 --> 00:34:40,404 Speaker 3: borrow another author's phrase and see what it's actually like 671 00:34:40,444 --> 00:34:43,004 Speaker 3: when you're going through the grind of sports betting an 672 00:34:43,044 --> 00:34:45,844 Speaker 3: NBA season. So I bet almost the entirety of the 673 00:34:45,844 --> 00:34:48,044 Speaker 3: twenty twenty two to twenty three NBA season, all the 674 00:34:48,044 --> 00:34:50,484 Speaker 3: regular season, in about half the playoffs, and then five 675 00:34:50,644 --> 00:34:53,044 Speaker 3: thirty eight blew up, and so I had other things 676 00:34:53,044 --> 00:34:54,964 Speaker 3: to do, and I didn't bet the end of the playoffs, 677 00:34:56,404 --> 00:34:57,964 Speaker 3: and I learned that, I mean, it's probably what I 678 00:34:58,004 --> 00:35:00,564 Speaker 3: should have expected by I learned that it's pretty hard. 679 00:35:01,404 --> 00:35:04,204 Speaker 3: I went on a huge heater at the start of 680 00:35:04,204 --> 00:35:06,804 Speaker 3: the NBA season where I was up like seventy thousand bucks. 681 00:35:06,844 --> 00:35:10,524 Speaker 3: I'm like, man, I'm really fucking good at the sports 682 00:35:10,564 --> 00:35:11,124 Speaker 3: betting stuff. 683 00:35:11,204 --> 00:35:12,644 Speaker 2: But then but then things change. 684 00:35:12,724 --> 00:35:14,564 Speaker 3: One thing that changes is you start to get limited 685 00:35:14,884 --> 00:35:16,964 Speaker 3: by some of the sites, especially if you're trying to 686 00:35:16,964 --> 00:35:19,164 Speaker 3: do things like, oh, I just suck. Because I'm a 687 00:35:19,164 --> 00:35:22,484 Speaker 3: Twitter addict, I follow a lot of NBA writers. Oh, 688 00:35:22,964 --> 00:35:26,684 Speaker 3: Damian Lillard of the Portland Trailblazers is injured today, but 689 00:35:26,804 --> 00:35:29,444 Speaker 3: the line at sportsbook X does not yet account for that. 690 00:35:29,924 --> 00:35:31,924 Speaker 3: So let me go and bet three thousand dollars on 691 00:35:31,924 --> 00:35:34,564 Speaker 3: this bet which is now hugely positive. Ev you can 692 00:35:34,564 --> 00:35:37,164 Speaker 3: do it the first time, maybe the second time. The 693 00:35:37,204 --> 00:35:40,164 Speaker 3: third time, your hands are cut off basically kind of literally. 694 00:35:41,284 --> 00:35:43,524 Speaker 3: So when you aren't able to take advantage of things 695 00:35:43,564 --> 00:35:45,564 Speaker 3: that you know in principle, I think you should be. 696 00:35:45,604 --> 00:35:47,404 Speaker 3: I mean, if they're posting a line, they're taking action 697 00:35:47,484 --> 00:35:50,484 Speaker 3: on it. And the fact that, like in the NBA 698 00:35:50,684 --> 00:35:53,804 Speaker 3: regular season, it's all about is this player injured? Who 699 00:35:53,844 --> 00:35:56,724 Speaker 3: is trying to actually win and who's not. The models 700 00:35:56,804 --> 00:35:58,724 Speaker 3: that you have at the start of the season don't 701 00:35:58,724 --> 00:36:00,124 Speaker 3: work as well when you're kind of in this like 702 00:36:00,164 --> 00:36:02,804 Speaker 3: triage phase of the of the trenches of the NBA 703 00:36:02,884 --> 00:36:05,564 Speaker 3: regular season, and you know, and you talk to other 704 00:36:05,564 --> 00:36:08,004 Speaker 3: bears like, yeah, I have three guys whose entire job 705 00:36:08,084 --> 00:36:10,444 Speaker 3: Spanky here a this is a guy who we interview 706 00:36:10,444 --> 00:36:13,004 Speaker 3: in the book. I have three guys who just track 707 00:36:13,044 --> 00:36:15,004 Speaker 3: injury data for me. I mean, while I'm trying to 708 00:36:15,044 --> 00:36:16,924 Speaker 3: like do some Twitter search for is this player in 709 00:36:16,964 --> 00:36:21,444 Speaker 3: the warm up? You know, you're competing essentially against the 710 00:36:21,444 --> 00:36:25,684 Speaker 3: best players, the best betters in the world. So I 711 00:36:25,724 --> 00:36:28,404 Speaker 3: love that one point eight million dollars in bets and 712 00:36:28,404 --> 00:36:31,844 Speaker 3: believe this is just like on average the bet size 713 00:36:31,844 --> 00:36:34,484 Speaker 3: is like twelve hundred bucks, you're making fifteen hundred twelve 714 00:36:34,524 --> 00:36:36,644 Speaker 3: hundred dollars bets or whatever. Over the course of the season, 715 00:36:37,724 --> 00:36:40,924 Speaker 3: I made about five thousand bucks, probably spent about five 716 00:36:40,964 --> 00:36:43,724 Speaker 3: thousand hours on this, so I made less than minimum 717 00:36:43,724 --> 00:36:47,444 Speaker 3: wage actually with my with my sports betting side hustle. 718 00:36:49,004 --> 00:36:49,604 Speaker 3: I love it. 719 00:36:49,684 --> 00:36:50,204 Speaker 2: I love it. 720 00:36:50,484 --> 00:36:53,924 Speaker 1: So I want to ask two more things before we 721 00:36:53,964 --> 00:36:56,764 Speaker 1: wrap up. The first is kind of a theme that 722 00:36:57,044 --> 00:36:59,844 Speaker 1: I and I think I picked up on this because 723 00:37:00,244 --> 00:37:03,044 Speaker 1: you know, it's something that I've obviously thought about a lot, 724 00:37:03,604 --> 00:37:08,444 Speaker 1: a lot, And it's the strand that is woven throughout 725 00:37:08,484 --> 00:37:11,564 Speaker 1: the book, which is that even though the book is 726 00:37:11,644 --> 00:37:15,764 Speaker 1: about skill, about finding edges, about how to make plus 727 00:37:15,764 --> 00:37:19,284 Speaker 1: ev gambles, there is this thread throughout it of luck, 728 00:37:19,644 --> 00:37:22,604 Speaker 1: right and of the fact that you also do need 729 00:37:22,684 --> 00:37:25,764 Speaker 1: to get lucky, and you you actually you point to 730 00:37:25,804 --> 00:37:28,204 Speaker 1: a lot of these moments where things could have turned 731 00:37:28,204 --> 00:37:31,484 Speaker 1: out so differently, like, you know, to pick a random example, 732 00:37:31,644 --> 00:37:35,684 Speaker 1: the car crash that Peter Tila and Elon Musk were in, right, 733 00:37:35,804 --> 00:37:39,644 Speaker 1: Like what happens if the car lands differently. And I 734 00:37:39,644 --> 00:37:42,124 Speaker 1: don't know if you want to talk about that specific example, 735 00:37:42,364 --> 00:37:45,484 Speaker 1: but this is something that you're clearly very aware of, 736 00:37:45,564 --> 00:37:47,684 Speaker 1: and I just want to raise it and talk about 737 00:37:47,724 --> 00:37:48,324 Speaker 1: it a little bit. 738 00:37:49,044 --> 00:37:53,644 Speaker 3: Yeah, there is a survivorship bias problem when you're writing 739 00:37:53,644 --> 00:37:57,524 Speaker 3: a book about successful people, and literally kind of in 740 00:37:57,524 --> 00:37:59,604 Speaker 3: the case of Elon Musk and Peter Teal, Elon had 741 00:37:59,604 --> 00:38:02,684 Speaker 3: sold his first company, bought like a million dollar McLaren 742 00:38:03,164 --> 00:38:05,524 Speaker 3: F one or something sports car basically almost like a 743 00:38:05,844 --> 00:38:09,244 Speaker 3: you know, Formula one caliber car, and they're driving in 744 00:38:09,284 --> 00:38:11,244 Speaker 3: on and I think sand Hill Road or somewhere in 745 00:38:11,324 --> 00:38:15,084 Speaker 3: Palo Alto, and Elon floor is the accelebrator while trying 746 00:38:15,124 --> 00:38:18,484 Speaker 3: to change lanes, and it kind of spirals up into 747 00:38:18,524 --> 00:38:21,964 Speaker 3: like doing a little helicopter motion and miraculously lands on 748 00:38:22,004 --> 00:38:26,524 Speaker 3: its wheels, and Elon and Peter are are okay, and 749 00:38:26,564 --> 00:38:31,444 Speaker 3: actually hitchhikes their VC meeting, And I guess keep PayPal 750 00:38:31,564 --> 00:38:34,964 Speaker 3: alive by with that hitchhike. But so I asked Peter 751 00:38:35,084 --> 00:38:38,924 Speaker 3: teel you know, I asked him in a way that 752 00:38:39,044 --> 00:38:41,044 Speaker 3: was nerdier. I asked him, if you ran the world 753 00:38:41,124 --> 00:38:44,764 Speaker 3: a thousand times, had a thousand simulations, then how often 754 00:38:44,764 --> 00:38:47,244 Speaker 3: would you wind up in this position? And he objected 755 00:38:47,284 --> 00:38:48,804 Speaker 3: to this question and went on a whole thing about 756 00:38:48,804 --> 00:38:51,884 Speaker 3: determinism or probablism, which is interesting, but we don't have 757 00:38:51,964 --> 00:38:54,684 Speaker 3: time for today, I don't think. But yeah, life is 758 00:38:54,764 --> 00:38:58,124 Speaker 3: very contingent. We've talked on the show about the assassination 759 00:38:58,124 --> 00:39:00,764 Speaker 3: attempt against President Trump where that was a very close call, 760 00:39:00,884 --> 00:39:04,364 Speaker 3: or things like the butterfly ballot in the two thousand election, 761 00:39:04,404 --> 00:39:07,244 Speaker 3: which is in one ballot in one county in Florida 762 00:39:07,324 --> 00:39:10,204 Speaker 3: in two thousand, the ballot had a fun design where 763 00:39:10,244 --> 00:39:12,524 Speaker 3: some people who would have wanted to vote for Al 764 00:39:12,564 --> 00:39:14,924 Speaker 3: Gore wound up voting for Pat Buchanan instead, and that 765 00:39:15,084 --> 00:39:17,484 Speaker 3: was enough to cost or the election. On top of 766 00:39:17,484 --> 00:39:19,804 Speaker 3: the recount stuff in Florida wouldn't have mattered if this 767 00:39:19,844 --> 00:39:21,124 Speaker 3: ballot design had been better. 768 00:39:22,164 --> 00:39:22,924 Speaker 2: So life is. 769 00:39:23,324 --> 00:39:26,564 Speaker 3: Whether it's literally metaphysically random or not is a philosophical question, 770 00:39:26,684 --> 00:39:29,284 Speaker 3: But it's random to our ability to determine it in 771 00:39:29,324 --> 00:39:32,364 Speaker 3: a lot of ways. And it's just it's just very 772 00:39:32,404 --> 00:39:37,604 Speaker 3: hard to acknowledge the role that luck plays in your life. 773 00:39:37,644 --> 00:39:40,524 Speaker 3: It's very hard even for me or for you. Maybe 774 00:39:40,564 --> 00:39:44,164 Speaker 3: you're the exception, Maria, it's very hard not to tell 775 00:39:44,204 --> 00:39:47,924 Speaker 3: yourself a clever narrative in which in which you know, 776 00:39:47,964 --> 00:39:51,764 Speaker 3: even in writing the book I have. I think I've 777 00:39:51,804 --> 00:39:54,644 Speaker 3: gotten very lucky with a lot of things to kind 778 00:39:54,644 --> 00:39:57,804 Speaker 3: of have, like this SBF story, like fall into into 779 00:39:57,844 --> 00:40:00,404 Speaker 3: My Lap, for example, was I think pretty lucky. And 780 00:40:00,444 --> 00:40:02,884 Speaker 3: having a book that covered territory like AI that kind 781 00:40:02,884 --> 00:40:05,244 Speaker 3: of became a much bigger deal, and sports betting really 782 00:40:05,284 --> 00:40:07,444 Speaker 3: blew up. I mean, you know, so I think I've 783 00:40:07,484 --> 00:40:08,884 Speaker 3: gotten very lucky in a lot of things recently, the 784 00:40:08,964 --> 00:40:11,244 Speaker 3: way that, like, you know, I didn't know that actually 785 00:40:11,324 --> 00:40:14,924 Speaker 3: having this independent newsletter is actually actually much better economically 786 00:40:14,964 --> 00:40:16,764 Speaker 3: than being working for a big company. I didn't know that, 787 00:40:16,804 --> 00:40:18,884 Speaker 3: And I could easily have taken a deal with another 788 00:40:18,884 --> 00:40:20,804 Speaker 3: big company and wound up worse off for it, and 789 00:40:20,804 --> 00:40:22,764 Speaker 3: a lot of respects, and so, you know, I know 790 00:40:22,804 --> 00:40:25,724 Speaker 3: the role that luck has played in my life, including 791 00:40:25,764 --> 00:40:29,844 Speaker 3: just things like being basically healthy and having a supportive 792 00:40:29,844 --> 00:40:32,564 Speaker 3: partner and supportive friends, and being born into a country 793 00:40:32,604 --> 00:40:34,524 Speaker 3: where it's okay to be a gay man and a 794 00:40:34,524 --> 00:40:37,804 Speaker 3: country where where you have some downside protection. I mean, 795 00:40:37,804 --> 00:40:40,564 Speaker 3: those are all things that are are very lucky. So 796 00:40:40,724 --> 00:40:43,524 Speaker 3: you know, the next time that I take a cooler 797 00:40:43,844 --> 00:40:46,164 Speaker 3: in poker or b louse set of or set or something, 798 00:40:46,204 --> 00:40:47,924 Speaker 3: then I can't complain too much. 799 00:40:49,364 --> 00:40:51,484 Speaker 1: Well, I had more questions, but that seems like such 800 00:40:51,524 --> 00:40:55,004 Speaker 1: a beautiful place to end that I think I have 801 00:40:55,124 --> 00:40:59,844 Speaker 1: to not ask my remaining question. But yeah, I mean, 802 00:41:00,284 --> 00:41:03,124 Speaker 1: obviously this is something I've thought about a lot and 803 00:41:03,444 --> 00:41:05,764 Speaker 1: was kind of the big theme of the Biggest Bluff. 804 00:41:05,804 --> 00:41:09,204 Speaker 1: But it's nice to see that you know that this 805 00:41:09,524 --> 00:41:12,284 Speaker 1: something that you're thinking about as well, because I do 806 00:41:12,324 --> 00:41:15,844 Speaker 1: think that it's easy for us to get over confident. 807 00:41:15,884 --> 00:41:18,444 Speaker 1: And I think that as we you know, during risky 808 00:41:18,524 --> 00:41:22,124 Speaker 1: business and all all of these endeavors, we do want 809 00:41:22,164 --> 00:41:24,804 Speaker 1: to maximize our skill, but we do need to always 810 00:41:24,804 --> 00:41:27,164 Speaker 1: remember that we're we are insanely lucky to even be 811 00:41:27,204 --> 00:41:30,724 Speaker 1: in this position. So Nate, I hope that you get 812 00:41:30,804 --> 00:41:34,084 Speaker 1: continue to get lucky, and that this book sells an 813 00:41:34,124 --> 00:41:38,724 Speaker 1: insane number of copies and that it does incredibly well, 814 00:41:38,804 --> 00:41:40,964 Speaker 1: and that you and I can continue making this show 815 00:41:41,004 --> 00:41:45,244 Speaker 1: together because we're both continuing to be lucky and yet 816 00:41:45,644 --> 00:41:48,364 Speaker 1: take plus evy edges whenever we can. 817 00:41:49,404 --> 00:41:51,444 Speaker 3: Thank you so much, Maria, and you know maybe if 818 00:41:51,484 --> 00:41:53,084 Speaker 3: you want to have you on the show again sometime. 819 00:41:53,804 --> 00:41:55,404 Speaker 2: You know, I had a lot of fun. We have 820 00:41:55,444 --> 00:41:56,044 Speaker 2: good chemistry. 821 00:41:56,084 --> 00:41:59,604 Speaker 1: I think, I agree, I agree. So yeah, you're you're 822 00:41:59,644 --> 00:42:01,404 Speaker 1: welcome back. How's next week looking for you? 823 00:42:01,764 --> 00:42:04,644 Speaker 2: Actually really fucking busy, but we'll make it work, all right. 824 00:42:04,724 --> 00:42:09,604 Speaker 1: Sounds good, sounds good, Thanks so much, Nate. Oh, by 825 00:42:09,604 --> 00:42:12,084 Speaker 1: the way, before you go, last week, Nate and I 826 00:42:12,164 --> 00:42:17,124 Speaker 1: recorded a special video only podcast where we talked about 827 00:42:17,404 --> 00:42:20,924 Speaker 1: Kamala's pick of Walls as her VP. So you can 828 00:42:20,964 --> 00:42:24,324 Speaker 1: find that on our YouTube channel. We hope you enjoy it. 829 00:42:35,044 --> 00:42:37,844 Speaker 3: Risky Business is hosted by me Nate Silver and me 830 00:42:38,044 --> 00:42:40,764 Speaker 3: Maria Kanakova. The show was a co production of Pushkin 831 00:42:40,764 --> 00:42:45,124 Speaker 3: Industries and iHeartMedia. This episode was produced by Isabel Carterer. 832 00:42:45,484 --> 00:42:49,324 Speaker 3: Our associate producer is Gabriel hunter Chen. Our engineer is 833 00:42:49,364 --> 00:42:52,564 Speaker 3: Sarah Bruger. Our executive producer is Jacob Goldstein. 834 00:42:53,324 --> 00:42:55,084 Speaker 1: If you want to listen to an AD free version, 835 00:42:55,164 --> 00:42:57,844 Speaker 1: sign up for Pushkin Plus for six ninety nine a month. 836 00:42:57,964 --> 00:43:00,804 Speaker 1: You get access to ad free listening. Thanks for tuning 837 00:43:00,804 --> 00:43:00,924 Speaker 1: in 838 00:43:10,404 --> 00:43:12,564 Speaker 3: At don bom my FA