1 00:00:13,840 --> 00:00:17,480 Speaker 1: Welcome to text stuff. I'm's Volocian here with Cara Price, 2 00:00:17,600 --> 00:00:21,440 Speaker 1: and this is the story. Hello Oz, Hello Cara. One 3 00:00:21,440 --> 00:00:25,639 Speaker 1: of my favorite memories that we have together was a 4 00:00:25,760 --> 00:00:29,400 Speaker 1: trip to the Consumer Electronics Show in Vegas in January 5 00:00:29,400 --> 00:00:32,560 Speaker 1: twenty twenty, and we went to the panels. We spoke 6 00:00:32,640 --> 00:00:35,040 Speaker 1: on a panel, but where I really saw you could 7 00:00:35,040 --> 00:00:38,400 Speaker 1: come alive was on the casino floor. 8 00:00:38,240 --> 00:00:39,920 Speaker 2: Not at CES. On the casino. 9 00:00:40,000 --> 00:00:43,200 Speaker 1: From the casino floor, I hadn't known until that point 10 00:00:43,280 --> 00:00:46,360 Speaker 1: that you were quite a you know, passionate gambler. 11 00:00:46,520 --> 00:00:49,440 Speaker 2: That's a euphemism for degenerate, but yes I am. I'm 12 00:00:49,440 --> 00:00:53,240 Speaker 2: not a degenerate gambler. I dabble in gambling and blackjack 13 00:00:53,400 --> 00:00:54,080 Speaker 2: is my sport. 14 00:00:54,280 --> 00:00:55,320 Speaker 1: When did you get into it? 15 00:00:55,400 --> 00:00:58,720 Speaker 2: My mom liked to gamble, and so she taught me 16 00:00:58,760 --> 00:01:01,280 Speaker 2: how to play blackjack. And I've always loved blackjack, just 17 00:01:01,320 --> 00:01:04,120 Speaker 2: for the high highs and the low lows of it. 18 00:01:04,280 --> 00:01:06,920 Speaker 1: I only get to supervise your behavior with more I pusson. 19 00:01:07,240 --> 00:01:09,440 Speaker 1: But how much do you play on your phone? 20 00:01:09,520 --> 00:01:12,360 Speaker 2: I do play online blackjack, but not for money. 21 00:01:12,360 --> 00:01:16,039 Speaker 1: Just for fun, just for fun. Well, today's story is 22 00:01:16,120 --> 00:01:22,319 Speaker 1: actually all about how digital blackjack poker other casino games 23 00:01:22,760 --> 00:01:26,200 Speaker 1: have just gone crazy and been booming online for the 24 00:01:26,319 --> 00:01:30,440 Speaker 1: last few years, and our guest today, Bloomberg reporter Kitchillel, 25 00:01:30,840 --> 00:01:32,520 Speaker 1: has been looking into why in the. 26 00:01:32,560 --> 00:01:36,160 Speaker 3: Last five, six, seven years it has taken over the 27 00:01:36,319 --> 00:01:38,600 Speaker 3: entire world, and the reason for that is a combination 28 00:01:38,920 --> 00:01:42,840 Speaker 3: of obviously enormous demand, the idea that you can just 29 00:01:43,040 --> 00:01:46,240 Speaker 3: gamble by reaching into your pocket and pulling out a 30 00:01:46,240 --> 00:01:49,920 Speaker 3: smartphone is quite appealing, but also a regulatory changes. I 31 00:01:49,920 --> 00:01:54,960 Speaker 3: think online gambling gambling anywhere fifty years ago was seen 32 00:01:55,000 --> 00:01:57,680 Speaker 3: as a kind of fringe, shady, wrong side of the 33 00:01:57,720 --> 00:02:02,760 Speaker 3: tracks activity, and a combination of massive lobbying by big 34 00:02:02,800 --> 00:02:07,640 Speaker 3: gambling companies and just societal changes, really ethical changes have 35 00:02:07,720 --> 00:02:10,320 Speaker 3: meant that what used to be a shady thing to 36 00:02:10,320 --> 00:02:12,000 Speaker 3: do is now entirely normal. 37 00:02:12,600 --> 00:02:15,200 Speaker 2: Yeah, this is very much in line with my experience 38 00:02:15,200 --> 00:02:19,720 Speaker 2: of seeing sports betting ads everywhere. Besides it moving online, 39 00:02:19,800 --> 00:02:21,800 Speaker 2: what does it actually have to do with our show. 40 00:02:22,160 --> 00:02:24,760 Speaker 1: Well, Kit actually wrote this piece for Bloomberg that really 41 00:02:24,840 --> 00:02:28,720 Speaker 1: caught my eye with the headline the Russian bot army 42 00:02:28,919 --> 00:02:30,520 Speaker 1: that conquered online poker. 43 00:02:30,720 --> 00:02:35,040 Speaker 2: All right, so a Russian bot army is not necessarily 44 00:02:35,040 --> 00:02:38,440 Speaker 2: a phrase we see put together with poker. 45 00:02:38,919 --> 00:02:41,960 Speaker 1: Normally we're thinking more about Russian bot armies trying to 46 00:02:41,960 --> 00:02:45,639 Speaker 1: influence elections or also, the Russian bot army is kind 47 00:02:45,680 --> 00:02:48,920 Speaker 1: of like a little bit of internet boogeyman. 48 00:02:48,760 --> 00:02:51,120 Speaker 2: Very much like if I imagine what a Russian bot 49 00:02:51,200 --> 00:02:53,880 Speaker 2: army looks like in real life, it's it's very different 50 00:02:53,919 --> 00:02:56,320 Speaker 2: than what it probably is in reality. 51 00:02:56,840 --> 00:03:00,760 Speaker 1: Well, Kit was also slightly dubious, but he got to 52 00:03:00,840 --> 00:03:03,080 Speaker 1: meet a real life Russian bot army. 53 00:03:03,520 --> 00:03:05,960 Speaker 3: I'll be honest, I was very suspicious about the idea 54 00:03:06,040 --> 00:03:08,560 Speaker 3: that that all this spot activity in the poker world 55 00:03:08,560 --> 00:03:12,000 Speaker 3: emanated from Russia. I think there was a tendency here 56 00:03:11,360 --> 00:03:15,560 Speaker 3: to see any sort of perception of nefarious online activity 57 00:03:15,560 --> 00:03:18,160 Speaker 3: to go as the Russians. And you know, when I 58 00:03:18,200 --> 00:03:21,079 Speaker 3: went through the available material when I started this project, 59 00:03:21,960 --> 00:03:25,040 Speaker 3: none of it seemed to be based on any real evidence, 60 00:03:25,080 --> 00:03:27,519 Speaker 3: on knowledge that something was happening in Russia. It was 61 00:03:27,560 --> 00:03:30,239 Speaker 3: all smoke and mirrors. So I approached it with a 62 00:03:30,320 --> 00:03:33,560 Speaker 3: very skeptical eye. But then after doing six months to 63 00:03:33,600 --> 00:03:36,120 Speaker 3: a year worth of reporting and finally meeting the people 64 00:03:36,120 --> 00:03:38,800 Speaker 3: behind it, you know, it turns out the rumor was 65 00:03:38,920 --> 00:03:39,880 Speaker 3: essentially correct. 66 00:03:40,320 --> 00:03:41,520 Speaker 2: So it was the Russians. 67 00:03:41,680 --> 00:03:42,640 Speaker 1: It was the Russians. 68 00:03:42,720 --> 00:03:44,480 Speaker 2: What are the Russians doing? Well? 69 00:03:44,640 --> 00:03:48,160 Speaker 3: I'll let Kit explain that everyone who plays poker knows 70 00:03:48,480 --> 00:03:51,720 Speaker 3: that there's software that can play poker very well. So 71 00:03:52,040 --> 00:03:55,320 Speaker 3: I was curious about who was using this new technology 72 00:03:55,320 --> 00:03:58,880 Speaker 3: to actually make money from online poker. And there were 73 00:03:58,920 --> 00:04:02,480 Speaker 3: all sorts of rumors of out genius Russian poker bots. 74 00:04:02,520 --> 00:04:05,440 Speaker 3: You could log onto the poker chat rooms and everyone 75 00:04:05,480 --> 00:04:08,240 Speaker 3: was complaining about Russian bots, but there was no information 76 00:04:08,280 --> 00:04:11,280 Speaker 3: about who they were, who was making them. So I 77 00:04:11,400 --> 00:04:14,120 Speaker 3: went looking for the people who made those bots, and 78 00:04:14,160 --> 00:04:17,400 Speaker 3: that led me to this operation based out of Omskin, Siberia. 79 00:04:18,120 --> 00:04:20,200 Speaker 2: Nothing I have ever done in life has led me 80 00:04:20,279 --> 00:04:23,040 Speaker 2: to OMPs in Siberia, and I don't know if anything 81 00:04:23,120 --> 00:04:23,640 Speaker 2: ever will. 82 00:04:23,839 --> 00:04:26,240 Speaker 1: You never watched a Bond movie from Russia with love. 83 00:04:26,760 --> 00:04:30,400 Speaker 2: From Ompsk with love, Now it has a different ring 84 00:04:30,480 --> 00:04:33,960 Speaker 2: to it. This is really interesting and I just want 85 00:04:33,960 --> 00:04:34,760 Speaker 2: to know more about it. 86 00:04:35,000 --> 00:04:37,159 Speaker 1: Yeah. So it turns out that not only is this 87 00:04:37,240 --> 00:04:40,960 Speaker 1: Russian group making lots of money doing this, but also 88 00:04:41,000 --> 00:04:45,360 Speaker 1: that the technology they've built has fundamentally changed the way 89 00:04:45,400 --> 00:04:49,880 Speaker 1: poka is played online and in real life. So it's 90 00:04:49,880 --> 00:04:53,400 Speaker 1: one of these interesting kind of human machine interaction stories 91 00:04:53,440 --> 00:04:56,360 Speaker 1: about the ways we may not be even conscious of 92 00:04:56,400 --> 00:04:59,080 Speaker 1: the fact that we're interacting with machines all around us 93 00:04:59,520 --> 00:05:01,320 Speaker 1: and they're fluencing our behavior. 94 00:05:01,839 --> 00:05:04,200 Speaker 3: I think I was fascinated by the idea that these 95 00:05:04,200 --> 00:05:06,440 Speaker 3: are very human games. I mean, poker is the most 96 00:05:06,520 --> 00:05:12,440 Speaker 3: human of games. It involves bluffing, lying, dishonesty, deception, reading 97 00:05:12,600 --> 00:05:17,320 Speaker 3: human visual clues. It's the archetypal game that machines shouldn't 98 00:05:17,320 --> 00:05:20,520 Speaker 3: be able to play, and the things that humans used 99 00:05:20,560 --> 00:05:23,800 Speaker 3: to exclusively dominate. Now machines more and more are taking over. 100 00:05:24,080 --> 00:05:26,120 Speaker 3: And that's true in gambling as much as anywhere else. 101 00:05:26,640 --> 00:05:29,480 Speaker 3: And when we talk about gambling and we talk about 102 00:05:29,560 --> 00:05:32,560 Speaker 3: the people using technology to do it, I still think 103 00:05:32,600 --> 00:05:35,640 Speaker 3: most people today who place online bets or who bet 104 00:05:35,720 --> 00:05:38,560 Speaker 3: on sports or play poker, they don't know that this 105 00:05:38,680 --> 00:05:41,599 Speaker 3: world exists. They don't know there's a whole subculture of 106 00:05:41,600 --> 00:05:43,800 Speaker 3: players who are using state of the art technology to 107 00:05:43,800 --> 00:05:46,720 Speaker 3: play these games in a very inhuman way. And I 108 00:05:46,760 --> 00:05:48,840 Speaker 3: was fascinated by the fact that it's kind of there, 109 00:05:48,880 --> 00:05:51,080 Speaker 3: in secret but affecting games we all know and love. 110 00:05:51,520 --> 00:05:53,480 Speaker 2: I'm really excited to hear the rest of your interview 111 00:05:53,520 --> 00:05:56,960 Speaker 2: because I want to know more about how machines are 112 00:05:57,040 --> 00:05:59,839 Speaker 2: actually influencing the way human players. 113 00:06:00,640 --> 00:06:03,640 Speaker 3: I'll let explain that here he is the standard for 114 00:06:03,720 --> 00:06:07,560 Speaker 3: machine intelligence really is is always creative thinking, which is 115 00:06:07,760 --> 00:06:10,240 Speaker 3: which is a very human thing to do. And one 116 00:06:10,279 --> 00:06:13,000 Speaker 3: of the simplest and most binary ways to test the 117 00:06:13,040 --> 00:06:15,680 Speaker 3: machine's ability to think like a person is to play 118 00:06:15,720 --> 00:06:20,039 Speaker 3: a game that involves deduction and logic. The gamification of 119 00:06:20,120 --> 00:06:23,400 Speaker 3: this technology allows researchers to test how good it is, 120 00:06:23,880 --> 00:06:26,400 Speaker 3: and poker is very actually a very good proxy for 121 00:06:26,480 --> 00:06:29,200 Speaker 3: real life. You know, it's a game of imperfect information. 122 00:06:29,600 --> 00:06:33,080 Speaker 3: The players don't really know what the other players might hold. 123 00:06:33,080 --> 00:06:35,599 Speaker 3: They have to guess, they have to do the best 124 00:06:35,640 --> 00:06:37,599 Speaker 3: they can with the limited information they have. That's what 125 00:06:37,680 --> 00:06:39,920 Speaker 3: real life is like. And in the world of computing, 126 00:06:40,240 --> 00:06:43,160 Speaker 3: you deal with ones and zeros and certainties. What computers 127 00:06:43,160 --> 00:06:45,960 Speaker 3: have had traditionally been quite bad at is dealing with 128 00:06:46,000 --> 00:06:48,960 Speaker 3: these very uncertain situations where you're guessing and you're reading. 129 00:06:49,480 --> 00:06:51,800 Speaker 3: So Poker is a very good proxy for how human 130 00:06:52,000 --> 00:06:55,600 Speaker 3: like a machine can be. And I think it's interesting 131 00:06:55,680 --> 00:06:59,040 Speaker 3: that it took until about twenty fifteen, which is extremely 132 00:06:59,120 --> 00:07:02,520 Speaker 3: late in the world of artificial intelligence for the best 133 00:07:02,520 --> 00:07:05,039 Speaker 3: poker bots to be able to beat really good poker pros, 134 00:07:05,640 --> 00:07:09,760 Speaker 3: and ever since, really the best poker blots are essentially unbeatable. 135 00:07:10,000 --> 00:07:12,680 Speaker 3: In practical terms. It's really a question of how slowly 136 00:07:13,080 --> 00:07:14,360 Speaker 3: or quickly you will lose to them. 137 00:07:14,720 --> 00:07:16,520 Speaker 1: And this brings us back to the story which has 138 00:07:16,560 --> 00:07:19,800 Speaker 1: the headline the Russian bot Army that conquered online Poker. 139 00:07:20,240 --> 00:07:24,560 Speaker 1: The piece opens with two online poker players engaged in 140 00:07:24,600 --> 00:07:28,480 Speaker 1: a standoff. Tell us about the standoff and the match 141 00:07:28,640 --> 00:07:29,239 Speaker 1: that followed. 142 00:07:29,600 --> 00:07:31,960 Speaker 3: The situation was one that very typically happens in an 143 00:07:31,960 --> 00:07:35,720 Speaker 3: online poker where the people who are online poker professionals, 144 00:07:35,760 --> 00:07:39,640 Speaker 3: they tend to compete exclusively online. That means they can 145 00:07:39,720 --> 00:07:41,880 Speaker 3: be challenged and challenge anyone in the world. So the 146 00:07:41,920 --> 00:07:45,520 Speaker 3: top poker professionals online will routinely open up and say 147 00:07:45,560 --> 00:07:47,120 Speaker 3: come and play me and see how good you are. 148 00:07:47,960 --> 00:07:51,080 Speaker 3: This guy Farewell was one of the world's best heads 149 00:07:51,160 --> 00:07:54,120 Speaker 3: up poker players. He had an elite level skill set, 150 00:07:54,200 --> 00:07:56,240 Speaker 3: and he got into a kind of an argument online 151 00:07:56,240 --> 00:07:58,920 Speaker 3: with another Russian and challenged him to a game of 152 00:07:58,960 --> 00:08:02,800 Speaker 3: poker and very surprisingly lost that contest and lost a 153 00:08:02,800 --> 00:08:06,640 Speaker 3: lot of money and the way he got beaten. He 154 00:08:06,720 --> 00:08:09,360 Speaker 3: was immediately suspicious. And I know poker players when they 155 00:08:09,400 --> 00:08:11,760 Speaker 3: lose game of poker will always tend to say I've 156 00:08:11,760 --> 00:08:14,920 Speaker 3: been conned, I've been cheated. In this case, someone with 157 00:08:15,200 --> 00:08:17,600 Speaker 3: his abilities losing a match like that, it kind of 158 00:08:17,680 --> 00:08:20,280 Speaker 3: was suspicious, but it was also the way he lost. 159 00:08:20,840 --> 00:08:23,840 Speaker 3: His opponent was making decisions that human beings don't normally make. 160 00:08:24,440 --> 00:08:27,200 Speaker 3: He complained and said they was cheating involved, and the 161 00:08:27,240 --> 00:08:29,600 Speaker 3: other guy on the other side denied it. But my 162 00:08:29,760 --> 00:08:33,400 Speaker 3: research led me to evidence that his opponent had access 163 00:08:33,440 --> 00:08:36,600 Speaker 3: to this state of the art poker pot system that 164 00:08:36,720 --> 00:08:41,040 Speaker 3: was built in Siberia, And I think what I discovered 165 00:08:41,360 --> 00:08:44,400 Speaker 3: was that this kind of matchup happens a lot in poker. 166 00:08:44,800 --> 00:08:47,040 Speaker 3: You're playing someone online and you don't know who they are, 167 00:08:47,120 --> 00:08:49,000 Speaker 3: or where they come from, or what they're using, and 168 00:08:49,000 --> 00:08:51,320 Speaker 3: it's genuinely very hard to tell if someone is using 169 00:08:51,360 --> 00:08:54,200 Speaker 3: a solver, if someone has a computer that's not necessarily 170 00:08:54,240 --> 00:08:57,600 Speaker 3: the one they're using running in the background, it's very 171 00:08:57,600 --> 00:09:00,640 Speaker 3: difficult to tell. And I think what most people who 172 00:09:00,640 --> 00:09:03,439 Speaker 3: play the game online don't realize is just how widespread 173 00:09:03,520 --> 00:09:06,960 Speaker 3: this is. It's almost impossible to stop what did. 174 00:09:06,840 --> 00:09:09,040 Speaker 1: This moment crystallize for you and why did you start 175 00:09:09,080 --> 00:09:09,680 Speaker 1: the story here. 176 00:09:10,040 --> 00:09:12,800 Speaker 3: This was early days in the poker bot era, and 177 00:09:13,040 --> 00:09:15,960 Speaker 3: I think back then very few people knew poker bots existed. 178 00:09:16,760 --> 00:09:19,360 Speaker 3: I liked the idea that one of the world's best 179 00:09:19,520 --> 00:09:22,480 Speaker 3: proponents of this particular game of poker could be defeated 180 00:09:22,520 --> 00:09:24,880 Speaker 3: by a complete nobody in the poker space. This guy 181 00:09:24,920 --> 00:09:27,880 Speaker 3: had no reputation. If anything, his reputation online was all 182 00:09:27,880 --> 00:09:29,920 Speaker 3: sorts of things that had nothing to do with poker. 183 00:09:30,200 --> 00:09:33,960 Speaker 3: He had a kind of clownish, cartoonish online persona. You know, 184 00:09:34,000 --> 00:09:37,000 Speaker 3: it's the idea of Novak Djokovic losing to someone from 185 00:09:37,000 --> 00:09:39,400 Speaker 3: your local tennis club at tennis. It's just so improbable, 186 00:09:39,640 --> 00:09:41,320 Speaker 3: and you have to look at why that would happen. 187 00:09:41,520 --> 00:09:43,520 Speaker 3: And the answer led me to pokabots. 188 00:09:43,360 --> 00:09:47,880 Speaker 1: And in particular the bot farm Corporation. Yeah, paint of 189 00:09:47,920 --> 00:09:50,920 Speaker 1: a picture of their lives in Siberia, these young men 190 00:09:51,280 --> 00:09:53,200 Speaker 1: and how they got started. 191 00:09:53,360 --> 00:09:55,240 Speaker 3: Yeah, this was fascinating to learn about. I mean, I 192 00:09:55,280 --> 00:09:57,319 Speaker 3: had a sort of specific image in my mind of 193 00:09:57,880 --> 00:10:00,720 Speaker 3: what bot farmers would look like. She I think the 194 00:10:00,760 --> 00:10:02,760 Speaker 3: reality of it was slightly different to what I expected. 195 00:10:03,400 --> 00:10:07,080 Speaker 3: I'd imagined one sort of big poker kingpin with a 196 00:10:07,080 --> 00:10:09,240 Speaker 3: garage full of computers and a bunch of employees, but 197 00:10:09,679 --> 00:10:11,560 Speaker 3: in reality it was a bunch of sort of nerdy 198 00:10:11,600 --> 00:10:14,960 Speaker 3: students that two technical colleges in this remote region of 199 00:10:15,000 --> 00:10:19,360 Speaker 3: Siberia where they have very good computing courses. So these 200 00:10:19,440 --> 00:10:23,679 Speaker 3: kids were spending all day doing very advanced courses in 201 00:10:23,760 --> 00:10:26,680 Speaker 3: software and programming and machine logic and mathematics and physics. 202 00:10:26,840 --> 00:10:30,320 Speaker 3: And this happened at around the time that online poker 203 00:10:30,360 --> 00:10:32,560 Speaker 3: was really getting going, it was really becoming bigger businesses. 204 00:10:32,600 --> 00:10:34,160 Speaker 3: It was two thousand and one, two thousand and two, 205 00:10:34,800 --> 00:10:37,000 Speaker 3: for the first time, really you could have a game 206 00:10:37,040 --> 00:10:39,640 Speaker 3: of poker against someone anywhere in the world online, any 207 00:10:39,640 --> 00:10:42,200 Speaker 3: time of day and night, for real money. And these 208 00:10:42,280 --> 00:10:45,319 Speaker 3: kids started playing. They realized they were good, and they 209 00:10:45,360 --> 00:10:47,840 Speaker 3: got better, and they realized also that they could make 210 00:10:47,880 --> 00:10:51,400 Speaker 3: a lot of money, mainly playing American people who thought 211 00:10:51,400 --> 00:10:54,000 Speaker 3: they were fantastic at poker and logged on after a 212 00:10:54,000 --> 00:10:56,080 Speaker 3: hard day at work and didn't realize they were playing. 213 00:10:56,120 --> 00:10:59,719 Speaker 3: A math PhD who had studied game theory was using 214 00:10:59,720 --> 00:11:02,560 Speaker 3: an on training tool to play nearly perfect poker. So 215 00:11:03,000 --> 00:11:04,960 Speaker 3: initially they made a lot of money, just a bunch 216 00:11:05,000 --> 00:11:07,240 Speaker 3: of students getting together on the top floor of a campus. 217 00:11:07,360 --> 00:11:08,800 Speaker 1: They're just playing. They were playing as players. 218 00:11:08,840 --> 00:11:11,920 Speaker 3: They as human the human but they but they had 219 00:11:12,000 --> 00:11:15,040 Speaker 3: learnt the science of poker, and there is a mathematical 220 00:11:15,120 --> 00:11:17,440 Speaker 3: science behind poker. If you're willing to put in the 221 00:11:17,440 --> 00:11:19,400 Speaker 3: house and you have that kind of brain, it's a 222 00:11:19,400 --> 00:11:23,839 Speaker 3: distinct advantage, especially playing everyday folks who don't know those things. 223 00:11:24,280 --> 00:11:26,520 Speaker 3: So they were making good money just playing human to human. 224 00:11:26,640 --> 00:11:31,480 Speaker 3: But these Russian colleges, they are excellent farms for very 225 00:11:31,520 --> 00:11:34,920 Speaker 3: tech minded people, and so it wasn't long before they 226 00:11:34,920 --> 00:11:38,480 Speaker 3: started applying that that technology to the game of poker. 227 00:11:38,640 --> 00:11:41,600 Speaker 3: And it didn't take long to realize that you can 228 00:11:41,640 --> 00:11:44,800 Speaker 3: have a piece of software running. It can play twenty 229 00:11:44,800 --> 00:11:46,840 Speaker 3: four hours a day. It doesn't need a break, it 230 00:11:46,840 --> 00:11:48,720 Speaker 3: doesn't need food, you don't need to pay it, and 231 00:11:48,760 --> 00:11:50,839 Speaker 3: it will play much better poker than the average human. 232 00:11:51,400 --> 00:11:53,760 Speaker 3: And you can scale it almost infinitely. As long as 233 00:11:53,800 --> 00:11:56,560 Speaker 3: you have the ability to create accounts. You can have 234 00:11:56,600 --> 00:11:59,040 Speaker 3: as many bots playing as you want. And so they 235 00:11:59,080 --> 00:12:04,520 Speaker 3: thought transitioned greatly from humans playing less capable humans online 236 00:12:05,000 --> 00:12:08,480 Speaker 3: to having lots of computers run this software automatically and 237 00:12:08,600 --> 00:12:11,040 Speaker 3: only being sort of broadly supervised by a few tech 238 00:12:11,080 --> 00:12:13,520 Speaker 3: operators who kept on yet things to make sure nothing 239 00:12:13,600 --> 00:12:15,920 Speaker 3: was going wrong. And they made millions and millions and 240 00:12:15,960 --> 00:12:17,440 Speaker 3: millions of dollars in those early years. 241 00:12:17,720 --> 00:12:19,840 Speaker 1: And at what point did they start to attract the 242 00:12:19,960 --> 00:12:24,240 Speaker 1: notice of investors and also local police. 243 00:12:24,600 --> 00:12:27,280 Speaker 3: Well, I think they attracted the attention of local police 244 00:12:27,320 --> 00:12:32,160 Speaker 3: because the dorm was busy all hours of day and night. 245 00:12:32,240 --> 00:12:34,640 Speaker 3: They were playing mostly people on the side of the world. 246 00:12:34,920 --> 00:12:39,239 Speaker 3: Lots of students would arrive wearing their hoodies and marching upstairs, 247 00:12:39,280 --> 00:12:42,120 Speaker 3: and you know, eleven o'clock at night, and there was 248 00:12:42,160 --> 00:12:44,920 Speaker 3: activity all through the night. So the neighbors complained and 249 00:12:44,960 --> 00:12:47,600 Speaker 3: the local police came round, and I guess they assumed 250 00:12:47,600 --> 00:12:50,440 Speaker 3: it with sun criminal operation, and so they invited the 251 00:12:50,440 --> 00:12:52,520 Speaker 3: cops in and just showed them the computers with the 252 00:12:52,559 --> 00:12:55,000 Speaker 3: poker on the screen, saying, we're just playing poker. 253 00:12:54,760 --> 00:12:56,880 Speaker 1: Which is ripping off Americans, how about it. 254 00:12:57,080 --> 00:12:59,320 Speaker 3: I mean, I'm sure the Russian police were quite work 255 00:13:00,040 --> 00:13:02,040 Speaker 3: well unhappy about what was going on, but it was 256 00:13:02,040 --> 00:13:04,800 Speaker 3: so easy to scale it. And I think the key 257 00:13:04,800 --> 00:13:08,000 Speaker 3: thing to remember is that their starting point was a 258 00:13:08,080 --> 00:13:10,720 Speaker 3: very high level of knowledge and a very scientific approach 259 00:13:10,760 --> 00:13:13,280 Speaker 3: to poker. And you know, over the course of six, seven, 260 00:13:13,360 --> 00:13:16,120 Speaker 3: eight years, they just got stronger and stronger and stronger. 261 00:13:16,400 --> 00:13:19,720 Speaker 3: You know, every single month this system operated, it got 262 00:13:19,760 --> 00:13:24,960 Speaker 3: better at playing poker. By twenty ten, twenty eleven, the 263 00:13:25,000 --> 00:13:29,600 Speaker 3: system was able to play an individual human opponent, analyze 264 00:13:29,760 --> 00:13:32,839 Speaker 3: that human's track record in poker, every game they've ever 265 00:13:32,840 --> 00:13:36,520 Speaker 3: played online because you can buy game records. So if 266 00:13:36,559 --> 00:13:38,080 Speaker 3: you're a habitual poker player and you. 267 00:13:38,200 --> 00:13:40,040 Speaker 1: Pay perfect knowledge of your opponent. 268 00:13:39,800 --> 00:13:42,120 Speaker 3: Perfect knowledge of your opponent's games, all the mistakes they've 269 00:13:42,120 --> 00:13:43,959 Speaker 3: made over the last couple of years, all the weaknesses 270 00:13:44,040 --> 00:13:47,160 Speaker 3: in their game, and the system could spot those weaknesses 271 00:13:47,160 --> 00:13:50,120 Speaker 3: and taylor of its own approach to the specific weaknesses 272 00:13:50,120 --> 00:13:53,000 Speaker 3: of its opponent. And that's really that's where the real 273 00:13:53,040 --> 00:13:56,520 Speaker 3: mind is in poker. I mean, all human poker players 274 00:13:56,559 --> 00:13:58,760 Speaker 3: have winknesses in the game. It's natural that will happen. 275 00:13:58,880 --> 00:14:02,480 Speaker 3: If you're able to algorithmically analyze them and exploit them, 276 00:14:03,000 --> 00:14:05,160 Speaker 3: then you know you will win vastly more money than 277 00:14:05,160 --> 00:14:05,880 Speaker 3: you lose. And they did. 278 00:14:06,840 --> 00:14:09,360 Speaker 1: This was back in twenty eleven. Was kind of a 279 00:14:09,360 --> 00:14:11,480 Speaker 1: heyday of this. But your story obviously came out in 280 00:14:11,480 --> 00:14:14,480 Speaker 1: twenty twenty four, So I want to kind of get 281 00:14:14,480 --> 00:14:16,320 Speaker 1: through all the steps that take us from twenty eleven 282 00:14:16,320 --> 00:14:18,360 Speaker 1: to twenty twenty four. What was the kind of urgency 283 00:14:18,480 --> 00:14:20,920 Speaker 1: that you and your editors wanted to run it now 284 00:14:21,200 --> 00:14:22,000 Speaker 1: rather than back then. 285 00:14:22,400 --> 00:14:25,320 Speaker 3: The main reason to do the story when we did 286 00:14:25,320 --> 00:14:27,640 Speaker 3: it was that no one had done this before. I 287 00:14:27,720 --> 00:14:30,560 Speaker 3: had never been had someone show me where the poker 288 00:14:30,600 --> 00:14:33,560 Speaker 3: blots come from. I've had read lots of stories about 289 00:14:33,640 --> 00:14:37,400 Speaker 3: academics in Pittsburgh and Alberta, Canada, sitting in their offices 290 00:14:37,600 --> 00:14:42,160 Speaker 3: and you know, crafting these ingenious, artificially intelligent poker programs. 291 00:14:42,160 --> 00:14:43,840 Speaker 3: But they didn't do it to make money. They did 292 00:14:43,880 --> 00:14:45,920 Speaker 3: it to sort of test the boundaries of what machines 293 00:14:45,960 --> 00:14:49,160 Speaker 3: could do. Anyone who's played online poker knows there are 294 00:14:49,200 --> 00:14:51,720 Speaker 3: bots out there, So I just I was curious to 295 00:14:51,760 --> 00:14:54,760 Speaker 3: know what kind of person does it purely for the money? 296 00:14:54,840 --> 00:14:56,240 Speaker 3: How good do you have to be? How do you 297 00:14:56,280 --> 00:14:58,480 Speaker 3: get into that job? And so there was a big 298 00:14:58,520 --> 00:15:00,920 Speaker 3: hole in our knowledge of this, and so I went 299 00:15:00,960 --> 00:15:04,920 Speaker 3: looking trying to fill it. And botting Artificial intelligence has 300 00:15:04,960 --> 00:15:07,680 Speaker 3: been a reality of poker for a long time, and 301 00:15:07,720 --> 00:15:09,920 Speaker 3: I don't think that that has ever changed, and it 302 00:15:09,960 --> 00:15:11,120 Speaker 3: probably will never change. 303 00:15:11,640 --> 00:15:13,720 Speaker 1: I thought, you know, poker is obviously a game of 304 00:15:14,040 --> 00:15:17,840 Speaker 1: incentives and motives and reading your opponent and direction and misdirection. 305 00:15:18,400 --> 00:15:20,440 Speaker 1: So I'm very attracted to this quote and your piece, 306 00:15:20,440 --> 00:15:25,000 Speaker 1: which was neither professionals nor poker providers want to acknowledge 307 00:15:25,040 --> 00:15:29,120 Speaker 1: the presence of intelligent machines for fear of deterring the 308 00:15:29,200 --> 00:15:33,880 Speaker 1: new players whose money keeps the game afloat. Explain that, well, yeah, 309 00:15:33,880 --> 00:15:35,320 Speaker 1: this was the big challenge of the story. 310 00:15:35,400 --> 00:15:38,520 Speaker 3: I mean, no one involved in the poker industry wants 311 00:15:38,520 --> 00:15:39,600 Speaker 3: to talk about poker bots. 312 00:15:40,200 --> 00:15:40,360 Speaker 2: Now. 313 00:15:40,440 --> 00:15:44,440 Speaker 3: Arguably, the new reality of poker is dominated by technology. 314 00:15:44,480 --> 00:15:47,280 Speaker 3: It has changed the way even human professionels play the game. 315 00:15:47,880 --> 00:15:49,680 Speaker 3: I think you'd be a fool to think that most 316 00:15:49,720 --> 00:15:53,400 Speaker 3: online markets aren't absolutely crawling with bots. Yet if you 317 00:15:53,480 --> 00:15:55,440 Speaker 3: talk to the big providers, they just don't want to 318 00:15:55,480 --> 00:15:58,440 Speaker 3: talk about it. They don't want to acknowledge the presence 319 00:15:58,480 --> 00:16:01,080 Speaker 3: of bots on their system. They don't want to talk 320 00:16:01,080 --> 00:16:04,080 Speaker 3: about where bots come from. And it's a simple reason 321 00:16:04,080 --> 00:16:06,320 Speaker 3: for that is that they rely on new money. They 322 00:16:06,320 --> 00:16:08,880 Speaker 3: rely on people like me and you deciding we want 323 00:16:08,880 --> 00:16:10,960 Speaker 3: to have a go online poker and logging on investing 324 00:16:10,960 --> 00:16:14,680 Speaker 3: our hard earned dollars in a game of poker. If 325 00:16:14,720 --> 00:16:17,760 Speaker 3: they sort of acknowledge the existence of these unbeatabule machines 326 00:16:17,880 --> 00:16:22,200 Speaker 3: in their ecosystem, it doesn't help them commercially. So there's 327 00:16:22,240 --> 00:16:25,720 Speaker 3: just no information out there. Conversely that people who make 328 00:16:25,760 --> 00:16:27,520 Speaker 3: money from poker bots don't really want to talk about 329 00:16:27,560 --> 00:16:30,040 Speaker 3: it either because they kind of their business model relies 330 00:16:30,080 --> 00:16:33,680 Speaker 3: on secrecy. If people know who they are every platform 331 00:16:33,720 --> 00:16:37,160 Speaker 3: they play on, they'll be excluded, they'll be banned, their 332 00:16:37,200 --> 00:16:40,280 Speaker 3: winnings will be seized. So with secrecy on both sides, 333 00:16:40,320 --> 00:16:42,400 Speaker 3: So what that letter is a big void of information 334 00:16:42,920 --> 00:16:46,000 Speaker 3: and lots of sort of crazy online speculation and conspiracy 335 00:16:46,000 --> 00:16:48,440 Speaker 3: theories that weren't based in reality at all. 336 00:16:48,600 --> 00:16:50,600 Speaker 1: What would it be like as a normal if you're 337 00:16:50,600 --> 00:16:53,680 Speaker 1: listening to this podcast and you're playing online poker. Would 338 00:16:53,680 --> 00:16:55,480 Speaker 1: you be able to tell if you're playing against a bot? 339 00:16:55,520 --> 00:16:57,840 Speaker 1: Are there only tells that bots have? I don't think 340 00:16:57,840 --> 00:16:58,480 Speaker 1: you can tell. 341 00:16:58,560 --> 00:16:58,640 Speaker 2: No. 342 00:16:59,080 --> 00:17:02,000 Speaker 3: I think the experience of losing to a very good 343 00:17:02,080 --> 00:17:06,640 Speaker 3: game theory optimal poker bot, it's a series of situations 344 00:17:06,640 --> 00:17:09,520 Speaker 3: that just go against you inexplicably. It feels like you're 345 00:17:09,520 --> 00:17:11,600 Speaker 3: getting rotten luck over and over and over and over 346 00:17:11,640 --> 00:17:14,000 Speaker 3: and over and over again. When you have an advantage 347 00:17:14,040 --> 00:17:16,400 Speaker 3: in a game like poker, it doesn't manifest as one big, 348 00:17:16,480 --> 00:17:19,439 Speaker 3: dramatic win. It's not the big, showy hand that gets you. 349 00:17:19,680 --> 00:17:21,879 Speaker 3: It's the one hundred hands, it's the two hundred hands. 350 00:17:22,240 --> 00:17:24,399 Speaker 3: And if he speak to people who work with poker bots, 351 00:17:24,720 --> 00:17:26,680 Speaker 3: they're not worried about a single hand of poker. It's 352 00:17:26,720 --> 00:17:29,720 Speaker 3: not where interests them. They're interested in the plus minus 353 00:17:29,800 --> 00:17:32,160 Speaker 3: dollar value after a thousand hands. That's where the real 354 00:17:32,240 --> 00:17:34,239 Speaker 3: value is to them. So actually, I don't think it's 355 00:17:34,320 --> 00:17:37,479 Speaker 3: very fun playing poker bots. One of the characteristics of 356 00:17:37,600 --> 00:17:40,159 Speaker 3: good game theory oatical poker is that there's loads of 357 00:17:40,200 --> 00:17:43,439 Speaker 3: action pre flop. That means that when you're playing poker, 358 00:17:43,440 --> 00:17:46,000 Speaker 3: before the community cards have even been put down the table, 359 00:17:46,320 --> 00:17:48,800 Speaker 3: before you see what the possible hands might be, you 360 00:17:49,280 --> 00:17:52,639 Speaker 3: get dealt your two whole cards. Poker bots game theory 361 00:17:52,680 --> 00:17:55,800 Speaker 3: tells you you must bet aggressively before the community cards 362 00:17:55,840 --> 00:17:58,240 Speaker 3: are down, which works. You know you're more likely to 363 00:17:58,280 --> 00:18:01,240 Speaker 3: win or lose hands if you do that effective, but 364 00:18:01,359 --> 00:18:04,080 Speaker 3: it's boring. It means you don't get those dramatic moments 365 00:18:04,080 --> 00:18:07,200 Speaker 3: of one card, two card, three card, what have I got? 366 00:18:07,400 --> 00:18:10,400 Speaker 3: You don't get those big showdowns. So I think it's 367 00:18:10,400 --> 00:18:13,919 Speaker 3: boring and frustrating, quite frankly, to lose slowly to a 368 00:18:13,960 --> 00:18:14,960 Speaker 3: poker bot online. 369 00:18:15,160 --> 00:18:17,680 Speaker 1: So the providers didn't want to talk, the poker bot 370 00:18:18,359 --> 00:18:22,760 Speaker 1: makers didn't want to talk, and yet you got the story. 371 00:18:23,080 --> 00:18:25,240 Speaker 3: Yeah, it was a tough one. It tried all sorts 372 00:18:25,240 --> 00:18:27,360 Speaker 3: of things to get to the bottom of it, and eventually, 373 00:18:27,400 --> 00:18:30,040 Speaker 3: I think it was just persistence paid off. I had 374 00:18:30,080 --> 00:18:32,920 Speaker 3: bothered enough people that would eventually reached the guys who 375 00:18:32,920 --> 00:18:34,679 Speaker 3: built this software that I was doing it, and they 376 00:18:34,680 --> 00:18:36,800 Speaker 3: got in touch with me, and that was pure luck. 377 00:18:37,080 --> 00:18:38,760 Speaker 3: And it just so happened that they had reached a 378 00:18:38,800 --> 00:18:40,680 Speaker 3: stage in their careers where they were ready to talk. 379 00:18:40,760 --> 00:18:45,160 Speaker 3: You know, their business model had changed somewhat, and they 380 00:18:45,200 --> 00:18:47,520 Speaker 3: seemed quite keen to tell me what they were looking 381 00:18:47,560 --> 00:18:49,359 Speaker 3: at doing next and tell me their big plans for 382 00:18:50,000 --> 00:18:52,919 Speaker 3: the next poker revolution that they wanted to oversee. I 383 00:18:52,920 --> 00:18:54,880 Speaker 3: think if I'd approached them ten years ago, I don't 384 00:18:54,880 --> 00:18:55,960 Speaker 3: think they would have talked. 385 00:19:03,359 --> 00:19:06,680 Speaker 1: After the break kip me to the humans behind the 386 00:19:06,760 --> 00:19:24,120 Speaker 1: Russian bots. Stay with us, So how did they approach 387 00:19:24,200 --> 00:19:27,560 Speaker 1: you and did you have any red flags when you 388 00:19:27,600 --> 00:19:32,920 Speaker 1: got inbound from purported Russian poker bot Manager Farm Manager. 389 00:19:33,520 --> 00:19:36,119 Speaker 3: I had a few red flags. I mean, there was 390 00:19:36,200 --> 00:19:38,719 Speaker 3: some quite scary stuff online about who they were and 391 00:19:38,720 --> 00:19:41,760 Speaker 3: what they were about, and of course the mental associations 392 00:19:41,800 --> 00:19:44,840 Speaker 3: that go with a black market Russian poker operation. You 393 00:19:44,840 --> 00:19:47,879 Speaker 3: can imagine there might be some scary people involved in that. 394 00:19:48,640 --> 00:19:52,560 Speaker 3: I'd spend possibly a year, I think, trying to talk 395 00:19:52,600 --> 00:19:56,000 Speaker 3: to these guys, trying to find them, researching every aspect 396 00:19:56,000 --> 00:19:58,520 Speaker 3: of their businesses. I knew that there were six or 397 00:19:58,560 --> 00:20:01,320 Speaker 3: seven Russian company needs that they were linked to, but 398 00:20:01,400 --> 00:20:04,520 Speaker 3: there was no online information about them. So I basically 399 00:20:04,800 --> 00:20:07,439 Speaker 3: just called everyone that I could find that had the 400 00:20:07,440 --> 00:20:10,359 Speaker 3: loosest connection to those companies and ask them about what 401 00:20:10,400 --> 00:20:12,080 Speaker 3: they did and how they did it. After I've been 402 00:20:12,080 --> 00:20:15,240 Speaker 3: doing this for a few months, one of my encrypted 403 00:20:15,280 --> 00:20:18,560 Speaker 3: messages services popped up and it was, you know, a 404 00:20:18,600 --> 00:20:22,160 Speaker 3: message from someone claiming to be from Neo Cores, which 405 00:20:22,200 --> 00:20:25,879 Speaker 3: is the bot farm operation, and they told me they 406 00:20:25,880 --> 00:20:28,479 Speaker 3: were thinking about breaking their silence and talking about their 407 00:20:28,520 --> 00:20:30,520 Speaker 3: business for the first time, and I had to meet 408 00:20:30,520 --> 00:20:33,000 Speaker 3: them face to face and the only place we could 409 00:20:33,000 --> 00:20:35,280 Speaker 3: do that was Armenia. Journalists can't really go to Russia 410 00:20:35,320 --> 00:20:38,040 Speaker 3: these days, and there's limited places they can go, but 411 00:20:38,119 --> 00:20:41,400 Speaker 3: Armenia is a friendly neighboring country, and so I flew 412 00:20:41,440 --> 00:20:43,560 Speaker 3: out to Armenia and they showed up in a couple 413 00:20:43,560 --> 00:20:47,600 Speaker 3: of big SUVs with tinted windows, and we were sitting 414 00:20:47,800 --> 00:20:50,640 Speaker 3: in the shadow of Mount araw Rat in a vineyard, 415 00:20:51,320 --> 00:20:52,960 Speaker 3: which they chose as the place to have our big 416 00:20:52,960 --> 00:20:55,680 Speaker 3: sit down interview. I think the quite what to expect, 417 00:20:55,680 --> 00:20:57,960 Speaker 3: but you know, as always, the reality of things is 418 00:20:58,000 --> 00:21:01,159 Speaker 3: never as intimidating as your imagination of it. And what 419 00:21:01,240 --> 00:21:04,200 Speaker 3: I met was a bunch of very smart guys, kind 420 00:21:04,200 --> 00:21:07,040 Speaker 3: of nerdy guys who are fascinated by maths and game 421 00:21:07,080 --> 00:21:10,600 Speaker 3: theory and technology, who had built, actually, you know, whatever 422 00:21:10,640 --> 00:21:14,760 Speaker 3: you think about poker bots, an ingenious system for exploiting 423 00:21:14,760 --> 00:21:17,119 Speaker 3: winkses in the poker market, and they'd be doing it 424 00:21:17,160 --> 00:21:19,960 Speaker 3: longer than anyone else. And I went through all the 425 00:21:19,960 --> 00:21:22,600 Speaker 3: different evolutions of this poke bot business, from the start 426 00:21:22,640 --> 00:21:25,480 Speaker 3: in Siberia all the way through to the most modern one, 427 00:21:25,480 --> 00:21:28,240 Speaker 3: which is Deplay, which is kind of an artificial intelligence company. 428 00:21:28,359 --> 00:21:30,199 Speaker 3: At this point, no one had connected them all, and 429 00:21:30,480 --> 00:21:32,919 Speaker 3: as I went through all of those different iterations of 430 00:21:32,920 --> 00:21:35,359 Speaker 3: the business, they said, yes, that was us, so the 431 00:21:35,400 --> 00:21:38,280 Speaker 3: same like three or four guys were the architects of 432 00:21:38,280 --> 00:21:41,480 Speaker 3: the whole system, and that's like two decades worth of 433 00:21:41,480 --> 00:21:44,960 Speaker 3: poker experience there. It was fascinating to hear them talk 434 00:21:45,000 --> 00:21:47,639 Speaker 3: so openly about what they did. Their oversight of the 435 00:21:47,840 --> 00:21:51,040 Speaker 3: online poker gave them a unique view of how the 436 00:21:51,040 --> 00:21:54,240 Speaker 3: whole thing works. You know, they weren't providers, they weren't 437 00:21:54,320 --> 00:21:57,560 Speaker 3: running poker sides, they weren't players, they weren't just playing 438 00:21:57,600 --> 00:21:59,960 Speaker 3: themselves for their own profit. They had enormous reams of 439 00:22:00,119 --> 00:22:02,280 Speaker 3: data in how this whole market operates. 440 00:22:02,840 --> 00:22:05,719 Speaker 1: What has changed in their business model that makes them, 441 00:22:06,160 --> 00:22:08,720 Speaker 1: timing wise, want to speak to you last year. 442 00:22:09,320 --> 00:22:13,080 Speaker 3: I think they've they've been through an evolution of their 443 00:22:13,200 --> 00:22:15,920 Speaker 3: view of poker. I mean, obviously they've always made money 444 00:22:15,920 --> 00:22:18,800 Speaker 3: from poker, and they still are, but I think they've 445 00:22:18,880 --> 00:22:22,200 Speaker 3: recognized that the online poker market the way it exists 446 00:22:22,240 --> 00:22:25,679 Speaker 3: now isn't sustainable. At least that's what they believe because 447 00:22:25,680 --> 00:22:28,639 Speaker 3: they are essentially involved thousands of times a day in 448 00:22:28,760 --> 00:22:32,639 Speaker 3: playing online poker against opponents on various different platforms. They know, 449 00:22:32,760 --> 00:22:36,680 Speaker 3: for example, that the average player who joins online poker 450 00:22:37,080 --> 00:22:40,000 Speaker 3: an online poker site and invests money will leave much 451 00:22:40,040 --> 00:22:42,560 Speaker 3: more quickly than was the case ten years ago. So 452 00:22:42,560 --> 00:22:44,919 Speaker 3: people are logging on to play online poker and try it, 453 00:22:45,000 --> 00:22:47,000 Speaker 3: thinking that it's going to be fun, and they're quickly 454 00:22:47,000 --> 00:22:50,520 Speaker 3: discovering that it's not very fun, losing their money and leaving. Now, 455 00:22:50,800 --> 00:22:53,199 Speaker 3: the whole of the online poker economy is structured like 456 00:22:53,200 --> 00:22:56,240 Speaker 3: a pyramid. At that bottom rung of new money coming 457 00:22:56,280 --> 00:22:58,600 Speaker 3: in funds the whole thing right up to the very 458 00:22:58,600 --> 00:23:00,919 Speaker 3: top of the Chris money makers who you know, the 459 00:23:00,920 --> 00:23:02,840 Speaker 3: guys that you see in the World Series of Poker 460 00:23:02,840 --> 00:23:05,679 Speaker 3: in Las Vegas on the final table. None of it 461 00:23:05,720 --> 00:23:07,840 Speaker 3: works unless you have that bottom rung of new money 462 00:23:07,880 --> 00:23:10,760 Speaker 3: coming in funding the whole thing. And I think they 463 00:23:10,840 --> 00:23:14,320 Speaker 3: recognize that the game really may not exist anymore if 464 00:23:14,960 --> 00:23:17,600 Speaker 3: it becomes too easy for machines to compete against humans 465 00:23:17,640 --> 00:23:18,200 Speaker 3: and beat them. 466 00:23:18,440 --> 00:23:21,000 Speaker 1: Too many poker bots have killed the golden goose of 467 00:23:21,040 --> 00:23:23,840 Speaker 1: the bottom layer of the pyramid. Essentially, that's exactly it. 468 00:23:24,040 --> 00:23:27,000 Speaker 3: And actually it's not just poker bots either, it's the 469 00:23:27,080 --> 00:23:31,280 Speaker 3: whole atmosphere around the poker economy. It's, you know, it's 470 00:23:31,320 --> 00:23:34,280 Speaker 3: how intimidating it can be, how unfriendly it can be, 471 00:23:34,480 --> 00:23:37,119 Speaker 3: how competitive it can be. If you're not losing to 472 00:23:37,119 --> 00:23:39,520 Speaker 3: a Russian poker bot, you know, there's every chance you'll 473 00:23:39,560 --> 00:23:44,160 Speaker 3: be losing to a Brazilian PhD student who is simply 474 00:23:44,520 --> 00:23:48,040 Speaker 3: better at processing information than you are. But the point is, 475 00:23:48,080 --> 00:23:50,840 Speaker 3: it's it's just not fun to lose so quickly. So 476 00:23:50,960 --> 00:23:53,840 Speaker 3: they recognize that there is a love of poker. People 477 00:23:53,880 --> 00:23:56,280 Speaker 3: are very passionate about the enjoy playing it. But I 478 00:23:56,280 --> 00:23:59,240 Speaker 3: think their view is that that the new poker economy 479 00:23:59,320 --> 00:24:01,840 Speaker 3: needs to be more welcoming. It needs to be more friendly, 480 00:24:02,119 --> 00:24:04,080 Speaker 3: and it needs to be more tailored to welcome in 481 00:24:04,160 --> 00:24:07,359 Speaker 3: new people rather than just driving them away. You know, 482 00:24:07,920 --> 00:24:10,520 Speaker 3: to use the tennis example again, if you want to 483 00:24:10,520 --> 00:24:12,880 Speaker 3: expand the game of tennis, you don't just pit your 484 00:24:12,880 --> 00:24:15,760 Speaker 3: club players against the local pro who beat them six love, 485 00:24:15,800 --> 00:24:17,639 Speaker 3: six love, six love. Then they go home and they 486 00:24:17,640 --> 00:24:20,840 Speaker 3: never play again. But that's what happens in the poker market. 487 00:24:21,040 --> 00:24:23,360 Speaker 1: So looks the business models. Let them win a bit 488 00:24:23,400 --> 00:24:26,119 Speaker 1: and sacrifice a bit of margin in favor of more volume. 489 00:24:26,240 --> 00:24:29,639 Speaker 3: Essentially, the business model is the margin is people playing, 490 00:24:30,440 --> 00:24:32,560 Speaker 3: so they should be playing in a way that makes 491 00:24:32,560 --> 00:24:35,560 Speaker 3: them want to play more, and the poker coonry hasn't 492 00:24:35,600 --> 00:24:38,800 Speaker 3: been particularly good at that so far, and they are 493 00:24:38,800 --> 00:24:42,119 Speaker 3: looking for a new way to sort of structure the 494 00:24:42,119 --> 00:24:44,960 Speaker 3: payment system and make sure that you're not playing against 495 00:24:45,000 --> 00:24:47,719 Speaker 3: people who are twenty thirty levels above you make it 496 00:24:47,800 --> 00:24:50,760 Speaker 3: more fair, more fun. And it's ironic really that the 497 00:24:50,760 --> 00:24:52,320 Speaker 3: people who are trying to do this are the same 498 00:24:52,320 --> 00:24:55,760 Speaker 3: ones that have helped make poker such an inhospitable place. 499 00:24:56,160 --> 00:24:57,919 Speaker 1: Well, so they're really I mean, is that the spiers 500 00:24:57,920 --> 00:25:00,560 Speaker 1: who came in from the cold, who are based trying 501 00:25:00,600 --> 00:25:07,080 Speaker 1: to remake poker into a more equitable and slightly fairer arena, 502 00:25:07,240 --> 00:25:09,440 Speaker 1: perhaps so that more people play. I mean, that is 503 00:25:09,440 --> 00:25:13,520 Speaker 1: an intro is a very interesting and strange and unexpected inversion, 504 00:25:13,600 --> 00:25:15,560 Speaker 1: and that was kind of why they wanted to talk 505 00:25:15,560 --> 00:25:16,359 Speaker 1: to you at this moment. 506 00:25:16,400 --> 00:25:20,160 Speaker 3: You think, look, they're business people. They don't care about poker. 507 00:25:20,200 --> 00:25:22,600 Speaker 3: They don't even play poker anymore. These guys, you know, 508 00:25:22,600 --> 00:25:24,440 Speaker 3: they played in their youth, but it's it's a job 509 00:25:24,480 --> 00:25:26,840 Speaker 3: to them, you know, it's it's their career. 510 00:25:27,160 --> 00:25:29,520 Speaker 1: You mentioned the beginning that poker is a kind of 511 00:25:30,480 --> 00:25:35,240 Speaker 1: unusual subset of the kind of wider world of online gambling. 512 00:25:35,760 --> 00:25:39,600 Speaker 1: Looking at the wider world, how has technology changed an 513 00:25:39,640 --> 00:25:41,840 Speaker 1: AI in particular, changed gambling more broadly. 514 00:25:42,400 --> 00:25:44,600 Speaker 3: I mean, I really believe we're we're in the middle 515 00:25:44,600 --> 00:25:47,240 Speaker 3: of a revolution of online vetting, which has been led 516 00:25:47,240 --> 00:25:49,640 Speaker 3: by AI. I just don't think most people have realized 517 00:25:49,640 --> 00:25:52,239 Speaker 3: it yet, because most people who use these games for 518 00:25:52,400 --> 00:25:56,440 Speaker 3: leisure purposes, who bet on sports or horses, or play 519 00:25:56,440 --> 00:25:59,719 Speaker 3: online slot machines, they haven't yet realized that there's this 520 00:25:59,760 --> 00:26:02,720 Speaker 3: sub set of players out there who were using artificially 521 00:26:02,720 --> 00:26:06,120 Speaker 3: intelligent software to play the games in an optimal way. 522 00:26:06,800 --> 00:26:10,840 Speaker 3: This whole story goes back decades and there's a subculture. 523 00:26:10,880 --> 00:26:14,600 Speaker 3: There's a kind of subset of professional gamblers who are 524 00:26:14,680 --> 00:26:19,359 Speaker 3: very tech minded, very scientific, very mathematical, very bloodless in 525 00:26:19,400 --> 00:26:21,600 Speaker 3: the way they play the game. And they all emerge 526 00:26:21,640 --> 00:26:24,160 Speaker 3: from Las Vegas in the nineteen eighties, from the carve 527 00:26:24,240 --> 00:26:28,400 Speaker 3: counting circuit, and they applied that sort of advantage mindset 528 00:26:28,440 --> 00:26:31,680 Speaker 3: to technology, and they asked, you, what would happen if 529 00:26:31,720 --> 00:26:34,920 Speaker 3: we took our mathematical skills and we added the advantage 530 00:26:35,160 --> 00:26:39,280 Speaker 3: of terabytes of processing power? How much money could we make. 531 00:26:39,320 --> 00:26:41,840 Speaker 3: And it turns out what they discovered and what they've 532 00:26:41,840 --> 00:26:44,159 Speaker 3: been doing ever since the last forty years, is the 533 00:26:44,200 --> 00:26:46,960 Speaker 3: only profitable way to bet. They've been doing this in 534 00:26:47,000 --> 00:26:49,800 Speaker 3: secret for decades. They never talk about it because it's 535 00:26:49,840 --> 00:26:52,440 Speaker 3: their livelihood, and the casinos are quite happy not to 536 00:26:52,480 --> 00:26:54,359 Speaker 3: talk about it either, because they don't want anyone to 537 00:26:54,359 --> 00:26:57,920 Speaker 3: know their games are broken and vulnerable. But that's the reality. 538 00:26:58,240 --> 00:27:00,640 Speaker 3: It's like a secret arms race. And it was that 539 00:27:01,040 --> 00:27:04,040 Speaker 3: I found that whole world so fascinating that, you know, 540 00:27:04,080 --> 00:27:06,359 Speaker 3: I spent the last five years of my life writing 541 00:27:06,359 --> 00:27:08,800 Speaker 3: about it for Bloomberg, And I've got a book coming 542 00:27:08,800 --> 00:27:11,000 Speaker 3: out next year which is the sort of origin story 543 00:27:11,280 --> 00:27:14,080 Speaker 3: for how this new generation of tech gamblers came to exist. 544 00:27:14,840 --> 00:27:16,680 Speaker 1: The book's called Lucky Devil's. 545 00:27:16,560 --> 00:27:18,800 Speaker 3: That's correct, Yes, Simon and Schuster our next year. 546 00:27:19,280 --> 00:27:22,760 Speaker 1: There's a lot of movies, I mean, obviously the Ocean's franchise, 547 00:27:23,040 --> 00:27:25,720 Speaker 1: you know, front and center, but plenty of others as 548 00:27:25,760 --> 00:27:30,600 Speaker 1: well about gambling and beating the house and stuff. What 549 00:27:30,680 --> 00:27:36,560 Speaker 1: do you think makes the sort of human gambling winning 550 00:27:36,560 --> 00:27:40,359 Speaker 1: against the odds, ingenious ways to beat the system so 551 00:27:40,760 --> 00:27:44,000 Speaker 1: compelling to audiences and readers everywhere. 552 00:27:44,320 --> 00:27:47,760 Speaker 3: I think people recognize what it feels like to play 553 00:27:47,800 --> 00:27:52,879 Speaker 3: a rigged game. It's like modern capitalism encapsulated in a 554 00:27:52,920 --> 00:27:56,640 Speaker 3: kind of easily understandable format, in a relatable format. Most 555 00:27:56,720 --> 00:27:59,280 Speaker 3: people know what it feels like to spend your whole 556 00:27:59,320 --> 00:28:01,520 Speaker 3: life trying to play a game fairly and still lose, 557 00:28:01,920 --> 00:28:05,320 Speaker 3: whether it's your job, whether it's your love life. This 558 00:28:05,520 --> 00:28:07,399 Speaker 3: feeling that the world that the cards are kind of 559 00:28:07,440 --> 00:28:10,320 Speaker 3: against you, I think is very familiar, and that the 560 00:28:10,359 --> 00:28:14,920 Speaker 3: house the casino has become sort of metaphor for entrenched 561 00:28:15,080 --> 00:28:20,440 Speaker 3: wealth and power that basically makes the rules, changes the rules, 562 00:28:20,880 --> 00:28:23,480 Speaker 3: and acts for its own benefit in a way that's 563 00:28:23,560 --> 00:28:25,199 Speaker 3: very unfair for the majority of people. 564 00:28:25,520 --> 00:28:25,680 Speaker 1: Now. 565 00:28:25,680 --> 00:28:28,400 Speaker 3: I think that's that's undeniably true of the gambling business. 566 00:28:28,440 --> 00:28:31,480 Speaker 3: I think it's arguably true of modern politics and modern 567 00:28:31,480 --> 00:28:34,600 Speaker 3: economies around the world. And I think it's very relatable 568 00:28:34,640 --> 00:28:36,959 Speaker 3: to sort of get one over on the man. And 569 00:28:37,000 --> 00:28:39,520 Speaker 3: these guys are sort of in the minority that actually 570 00:28:39,520 --> 00:28:42,040 Speaker 3: were able to do it, and they didn't do it 571 00:28:42,040 --> 00:28:44,680 Speaker 3: by cheating. They did it just by their smarts. They 572 00:28:44,720 --> 00:28:47,080 Speaker 3: did it just by hard work. And that's what I 573 00:28:47,080 --> 00:28:50,960 Speaker 3: find compelling. I think is that it's a lifetime worth 574 00:28:50,960 --> 00:28:55,480 Speaker 3: of effort and grinding away to get that tiny advantage 575 00:28:55,480 --> 00:28:56,400 Speaker 3: and turn it into money. 576 00:28:56,720 --> 00:28:58,600 Speaker 1: Kitchen Well, thank you for joining us on tech Stuff. 577 00:28:58,720 --> 00:28:59,560 Speaker 3: Thanks Ausin. 578 00:29:24,200 --> 00:29:27,000 Speaker 1: For Tech Stuff. I'm as Voloscian and I'm Cara Price. 579 00:29:27,240 --> 00:29:30,600 Speaker 1: This episode was produced by Eliza Dennis, Melissa Slaughter, and 580 00:29:30,680 --> 00:29:34,360 Speaker 1: Tyler Hill. It was executive produced by me, Karra Price, 581 00:29:34,440 --> 00:29:39,000 Speaker 1: and Kate Osborne for Kaleidoscope and Katrian Norvel for iHeart Podcasts. 582 00:29:39,640 --> 00:29:42,640 Speaker 1: Kyle Murdoch mixed this episode and also wrote our theme song. 583 00:29:43,280 --> 00:29:45,760 Speaker 1: Join us on Friday for the Week in Tech. Karen 584 00:29:45,800 --> 00:29:47,520 Speaker 1: and I will run through all the biggest stories in 585 00:29:47,600 --> 00:29:51,240 Speaker 1: tech and the headlines you may have missed. Please do rate, 586 00:29:51,320 --> 00:29:54,160 Speaker 1: review and reach out to us at tech Stuff podcast 587 00:29:54,240 --> 00:30:05,520 Speaker 1: at gmail dot com.