1 00:00:15,960 --> 00:00:19,280 Speaker 1: Welcome to tech stuff. I'm os Volosen. Today we're asking 2 00:00:19,320 --> 00:00:22,799 Speaker 1: the question what is going on with Palenteer, one of 3 00:00:22,840 --> 00:00:27,560 Speaker 1: America's most notorious and also profitable tech companies. Since the 4 00:00:27,560 --> 00:00:31,400 Speaker 1: beginning of the Trump administration, Palenteer has taken center stage 5 00:00:31,640 --> 00:00:35,239 Speaker 1: more and more, from their polarizing partnership with immigration and 6 00:00:35,240 --> 00:00:40,199 Speaker 1: customs enforcement to advocating for nationwide military service, to their 7 00:00:40,240 --> 00:00:44,360 Speaker 1: CEO stating that artificial intelligence will disrupt democracy as we 8 00:00:44,440 --> 00:00:47,599 Speaker 1: know it. Join Yesterday to unpack the story is journalist 9 00:00:47,680 --> 00:00:50,839 Speaker 1: and author Michael Steinberger, who has written a biography of 10 00:00:50,920 --> 00:00:55,720 Speaker 1: Palenteers polarizing CEO, Alex kap It's called The Philosopher in 11 00:00:55,760 --> 00:00:59,600 Speaker 1: the Valley Alex Carp Palenteer and the Rise of the 12 00:00:59,640 --> 00:01:02,480 Speaker 1: Surveyor and State. Michael, welcome, It's a pleasure to be 13 00:01:02,600 --> 00:01:05,240 Speaker 1: with you. Great to have you. I believe you first 14 00:01:05,319 --> 00:01:08,240 Speaker 1: wrote about Palteer in twenty twenty for the New York 15 00:01:08,240 --> 00:01:09,440 Speaker 1: Times magazine. Is that right? 16 00:01:09,920 --> 00:01:11,479 Speaker 2: That is correct? 17 00:01:11,200 --> 00:01:14,640 Speaker 1: What was it, you know, six years ago that sparked 18 00:01:14,680 --> 00:01:17,160 Speaker 1: your interest and why did it deepen over time? 19 00:01:17,640 --> 00:01:20,520 Speaker 2: Well? What got my interest was two things. One, Pallenteer 20 00:01:20,840 --> 00:01:24,120 Speaker 2: was already then making a lot of news it was 21 00:01:24,959 --> 00:01:28,320 Speaker 2: involved to a certain degree with the first immigration crackdown 22 00:01:28,440 --> 00:01:31,560 Speaker 2: during Trump's first presidency. But there was also a personal 23 00:01:31,600 --> 00:01:34,080 Speaker 2: reason that Palenteer was on my radar. It's because Alex 24 00:01:34,160 --> 00:01:37,120 Speaker 2: Carpets CEO and I had gone to college together. We 25 00:01:37,200 --> 00:01:40,960 Speaker 2: both attended a small college outside of Philadelphia called Haverford College. 26 00:01:41,640 --> 00:01:44,360 Speaker 2: We were in the same graduating class. It's a quite 27 00:01:44,400 --> 00:01:48,040 Speaker 2: small school, fewer than fifteen hundred students. Yet amazingly, in 28 00:01:48,040 --> 00:01:50,480 Speaker 2: those four years on that tiny campus, we never once 29 00:01:50,520 --> 00:01:53,760 Speaker 2: exchanged a word, so there was no conflict of interest. 30 00:01:53,840 --> 00:01:56,240 Speaker 2: So they gave me the green light to do this story. 31 00:01:56,320 --> 00:02:00,680 Speaker 2: And so for those two reasons, because Pallenteers seemed even 32 00:02:00,720 --> 00:02:03,800 Speaker 2: then to be such an interesting and powerful company, and 33 00:02:03,840 --> 00:02:06,919 Speaker 2: because of this personal connection that carp and I shared, 34 00:02:06,960 --> 00:02:10,520 Speaker 2: I had this I was interested in writing about them. 35 00:02:10,320 --> 00:02:13,160 Speaker 1: And he went on to spend six years interviewing for 36 00:02:13,200 --> 00:02:13,600 Speaker 1: the book. 37 00:02:13,880 --> 00:02:17,880 Speaker 2: Effectively, I mean the story we first spoke in twenty nineteen. 38 00:02:17,960 --> 00:02:19,960 Speaker 2: Then the pandemic came along, and the pandemic made it 39 00:02:20,000 --> 00:02:24,080 Speaker 2: an even more interesting story because Pallenteer was very involved 40 00:02:24,080 --> 00:02:26,560 Speaker 2: in the pandemic response in the United States and the 41 00:02:26,639 --> 00:02:29,800 Speaker 2: UK and some other countries. The magazine story was published 42 00:02:29,840 --> 00:02:33,680 Speaker 2: in October twenty twenty, just after Pallenteer went public, and 43 00:02:34,000 --> 00:02:36,480 Speaker 2: it was a very comprehensive magazine piece. It was almost 44 00:02:36,560 --> 00:02:40,600 Speaker 2: nine thousand words. That's how the magazine story gave rise 45 00:02:40,680 --> 00:02:43,080 Speaker 2: to a book, and Carp and I kept in touch, 46 00:02:43,160 --> 00:02:45,920 Speaker 2: kept talking, and the book is the product of essentially 47 00:02:46,000 --> 00:02:48,880 Speaker 2: six years of ongoing conversation. 48 00:02:50,000 --> 00:02:52,440 Speaker 1: Now, is this like that scene in Harry Potter where 49 00:02:52,440 --> 00:02:54,800 Speaker 1: he goes to the one shop for the first time 50 00:02:55,200 --> 00:02:58,200 Speaker 1: and his one kind of leaps off the shelf into 51 00:02:58,200 --> 00:03:00,359 Speaker 1: his hand. I mean, what is the what is the 52 00:03:00,440 --> 00:03:03,920 Speaker 1: relationship between a biographer and a subject? And how do 53 00:03:04,000 --> 00:03:06,320 Speaker 1: you how do you and come together? And are you 54 00:03:06,440 --> 00:03:08,919 Speaker 1: some kind of strange inin and yang? What is the 55 00:03:09,280 --> 00:03:12,200 Speaker 1: what is the what is there beyond the alumni connection. 56 00:03:12,720 --> 00:03:15,440 Speaker 2: Well, it's a great question, because you know, it's often 57 00:03:15,480 --> 00:03:19,000 Speaker 2: the case that biographers, by the time they reached the 58 00:03:19,080 --> 00:03:22,000 Speaker 2: end of their reporting and writing, have come to abstually 59 00:03:22,040 --> 00:03:26,440 Speaker 2: hate their subjects. I did not reach that point with Karp. 60 00:03:26,720 --> 00:03:28,679 Speaker 2: I enjoy his company, I enjoyed talking to him. We 61 00:03:28,760 --> 00:03:31,240 Speaker 2: disagree about a lot of stuff, but argument was, you know, 62 00:03:31,400 --> 00:03:34,000 Speaker 2: sort of the key to making the relationship work. At 63 00:03:34,040 --> 00:03:36,760 Speaker 2: one point during my reporting, just one day, I was 64 00:03:36,760 --> 00:03:40,000 Speaker 2: down in Washington with him and he was being driven 65 00:03:40,040 --> 00:03:42,480 Speaker 2: to a meeting and I just hopped along for the ride, 66 00:03:42,520 --> 00:03:45,160 Speaker 2: and we were just talking about the book, and he 67 00:03:45,200 --> 00:03:48,200 Speaker 2: said that people had asked him why he'd agreed to 68 00:03:48,200 --> 00:03:50,720 Speaker 2: cooperate with me, and he said one reason for that 69 00:03:50,800 --> 00:03:53,400 Speaker 2: was because he knew that I would never be jealous 70 00:03:53,480 --> 00:03:56,480 Speaker 2: or resentful of him. He was already very wealthy and 71 00:03:56,520 --> 00:03:59,320 Speaker 2: has become even more wealthy now. But it was true. 72 00:03:59,360 --> 00:04:02,480 Speaker 2: I was I was not going to be envious of 73 00:04:02,520 --> 00:04:04,120 Speaker 2: either his power or his wealth. 74 00:04:04,680 --> 00:04:06,360 Speaker 1: How did he? How did he know that about you? Why? 75 00:04:06,400 --> 00:04:06,600 Speaker 2: Why? 76 00:04:06,680 --> 00:04:07,440 Speaker 1: Why is that the case? 77 00:04:07,760 --> 00:04:10,760 Speaker 2: He's just he's got a very good sense of what 78 00:04:10,880 --> 00:04:13,280 Speaker 2: makes people tick. It's one of the it's one of 79 00:04:13,320 --> 00:04:15,760 Speaker 2: the things that I think has made him quite effective CEO. 80 00:04:15,960 --> 00:04:20,960 Speaker 2: He's he's very perceptive about you know, about people. He 81 00:04:21,040 --> 00:04:23,480 Speaker 2: can figure them out. He's not always right, He's been. 82 00:04:23,680 --> 00:04:26,159 Speaker 2: He was wrong about some things with me. He was 83 00:04:26,240 --> 00:04:29,240 Speaker 2: convinced that I was a trust fund kid and would 84 00:04:29,320 --> 00:04:31,719 Speaker 2: not believe me when I told him it's not true. 85 00:04:32,360 --> 00:04:35,160 Speaker 2: It's not true. So he was absolutely wrong about that. 86 00:04:35,200 --> 00:04:37,640 Speaker 2: But you couldn't I couldn't dissuade him otherwise. 87 00:04:37,520 --> 00:04:40,719 Speaker 1: Talk about Palenteer. I mean, it's sort of like a 88 00:04:41,560 --> 00:04:44,040 Speaker 1: sorry for another magic reference, but the word is almost 89 00:04:44,080 --> 00:04:45,920 Speaker 1: like a spell, and of course it comes from Tolkien. 90 00:04:46,240 --> 00:04:49,919 Speaker 1: But what what what are most people like behind the 91 00:04:50,040 --> 00:04:53,719 Speaker 1: kind of dark sorcery branding of Palenteer, or at least 92 00:04:53,720 --> 00:04:56,599 Speaker 1: perception of Paleteer in public, Like what do most people 93 00:04:56,640 --> 00:04:59,679 Speaker 1: miss about the story? And and what does your book reveal? 94 00:05:00,200 --> 00:05:03,839 Speaker 2: Well, I think there's a fundamental misconception that colors a 95 00:05:03,839 --> 00:05:07,159 Speaker 2: lot of the debate about this company the work they do. 96 00:05:07,920 --> 00:05:10,800 Speaker 2: There's reason enough to be concerned, but a lot of 97 00:05:10,839 --> 00:05:14,280 Speaker 2: people labor under the misconception that this is a company 98 00:05:14,279 --> 00:05:17,680 Speaker 2: that collects data itself and uses the data itself, and 99 00:05:17,720 --> 00:05:21,360 Speaker 2: that's not true. It's technology that allows organizations to make 100 00:05:21,440 --> 00:05:23,839 Speaker 2: better use of their own data. They store it, they 101 00:05:23,960 --> 00:05:27,520 Speaker 2: use it, they oversee its use. So this is, as 102 00:05:27,520 --> 00:05:29,440 Speaker 2: I said, this has colored a lot of the discussion 103 00:05:29,480 --> 00:05:33,000 Speaker 2: about Palenteer. There are legitimate reasons to be concerned about 104 00:05:33,480 --> 00:05:36,520 Speaker 2: the service this company provides and some of the organizations 105 00:05:36,560 --> 00:05:39,520 Speaker 2: it works with, But a lot of people believe that 106 00:05:39,640 --> 00:05:44,039 Speaker 2: Palenteer is using the data itself. Is collecting and using it, 107 00:05:44,080 --> 00:05:45,200 Speaker 2: and that's just not the case. 108 00:05:45,240 --> 00:05:47,600 Speaker 1: But the subtimes in your book is alex Cart Palenteer 109 00:05:47,640 --> 00:05:51,159 Speaker 1: and the rise of the surveillance state. So that's you 110 00:05:51,200 --> 00:05:53,360 Speaker 1: will be forgiven for the misunderstanding. 111 00:05:53,480 --> 00:05:55,720 Speaker 2: Well, yeah, no, that's that's you know, they work with 112 00:05:55,760 --> 00:05:58,640 Speaker 2: a lot of organizations and among the organizations they work 113 00:05:58,720 --> 00:06:03,400 Speaker 2: for are those who individuals and particular populations. And this 114 00:06:03,440 --> 00:06:05,680 Speaker 2: goes back to the very beginning. They were started in 115 00:06:05,720 --> 00:06:09,080 Speaker 2: part with funding from the CIA, and the first government 116 00:06:09,120 --> 00:06:11,880 Speaker 2: client was the CIA. But the software itself is not 117 00:06:11,920 --> 00:06:15,200 Speaker 2: surveillance technology. What it does is it enables those who 118 00:06:15,320 --> 00:06:19,000 Speaker 2: use surveillance technology and other things to make better use 119 00:06:19,080 --> 00:06:22,560 Speaker 2: of all the information that they're gathering. Palanteer is you 120 00:06:22,560 --> 00:06:24,440 Speaker 2: can think of it as like a wood shipper. It's, 121 00:06:24,480 --> 00:06:26,880 Speaker 2: you know, all this information is being fed into it 122 00:06:26,960 --> 00:06:29,720 Speaker 2: and it's it's in this case not spinning out what 123 00:06:30,000 --> 00:06:34,000 Speaker 2: it's spinning out, you know, patterns, connections, trends. It finds 124 00:06:34,040 --> 00:06:37,360 Speaker 2: these things in the data. But it's not surveillance technology 125 00:06:37,400 --> 00:06:41,279 Speaker 2: per se. But it enables those who, for instance, are 126 00:06:42,760 --> 00:06:46,160 Speaker 2: trying to track down terrorists or in the context of 127 00:06:46,200 --> 00:06:48,440 Speaker 2: the current moment, trying to find people who are in 128 00:06:48,440 --> 00:06:52,160 Speaker 2: the United States illegally. It makes it a lot easier 129 00:06:52,200 --> 00:06:54,600 Speaker 2: for them to use the data they're collecting. And it's 130 00:06:54,640 --> 00:06:56,599 Speaker 2: all sorts of data. It can be bank records, it 131 00:06:56,600 --> 00:07:00,159 Speaker 2: could be information gathered from automatical license plate readers. All 132 00:07:00,240 --> 00:07:03,320 Speaker 2: this is fed into Palenteer and the software merges it 133 00:07:03,880 --> 00:07:06,919 Speaker 2: and it finds, as I said, patterns, connections, trends that 134 00:07:06,960 --> 00:07:10,480 Speaker 2: would take human analysts days, hours, weeks, sometimes forever to find. 135 00:07:11,120 --> 00:07:14,240 Speaker 1: And most people hear about Plenteer and read about it, 136 00:07:14,280 --> 00:07:16,400 Speaker 1: but they don't get to experience the product, in part 137 00:07:16,440 --> 00:07:19,720 Speaker 1: because it's very expensive, and also because most people don't 138 00:07:19,720 --> 00:07:22,400 Speaker 1: work for the CIA or for very deep corporations. But 139 00:07:23,000 --> 00:07:25,200 Speaker 1: I assume, and you're reporting you saw it working. 140 00:07:25,760 --> 00:07:28,120 Speaker 2: Oh yeah, No, they have demos and they'll show them. 141 00:07:28,120 --> 00:07:30,840 Speaker 2: And then I would also say this, the company has 142 00:07:31,120 --> 00:07:35,440 Speaker 2: a reputation for being very secretive, and in some in 143 00:07:35,480 --> 00:07:38,360 Speaker 2: some ways that's true. There are relationships they have that 144 00:07:38,400 --> 00:07:41,440 Speaker 2: they can't really discuss publicly, and work that they help 145 00:07:41,520 --> 00:07:44,920 Speaker 2: with that they can't discuss publicly. But there is a 146 00:07:44,960 --> 00:07:47,000 Speaker 2: fair amount of transparency with this company. I mean you 147 00:07:47,040 --> 00:07:49,760 Speaker 2: just go to their website and just the amount of 148 00:07:49,760 --> 00:07:53,240 Speaker 2: detail the amount of information they're sharing there is quite extraordinary, 149 00:07:53,240 --> 00:07:56,840 Speaker 2: I think, and the idea that it is this opaque 150 00:07:56,840 --> 00:08:00,960 Speaker 2: black box is not really entirely case. That's a bit 151 00:08:01,000 --> 00:08:02,160 Speaker 2: of a misconception as well. 152 00:08:02,680 --> 00:08:05,920 Speaker 1: How much fear is there within Paleteer about being a 153 00:08:05,960 --> 00:08:09,760 Speaker 1: victim of the saaspocalypse, in other words, of readily available 154 00:08:09,840 --> 00:08:14,640 Speaker 1: AI tools preventing them from charging this huge price to 155 00:08:14,680 --> 00:08:15,640 Speaker 1: deploy their software. 156 00:08:15,720 --> 00:08:18,480 Speaker 2: Well, that's a great question, and there's obviously a lot 157 00:08:18,520 --> 00:08:21,960 Speaker 2: of debate about that now. And you know, Palenteer, the 158 00:08:22,080 --> 00:08:26,040 Speaker 2: argument there is that their product is sufficiently differentiated and 159 00:08:28,280 --> 00:08:32,160 Speaker 2: does things that other software doesn't do, and that therefore 160 00:08:32,280 --> 00:08:35,640 Speaker 2: they enjoy, as they say in the in the industry, 161 00:08:35,679 --> 00:08:39,800 Speaker 2: a mote. But you know, there are a number of people, 162 00:08:39,920 --> 00:08:41,760 Speaker 2: perhaps a growing number of people, who think that's not 163 00:08:41,880 --> 00:08:44,960 Speaker 2: the case, and that Pallenteer is under as much threat 164 00:08:45,559 --> 00:08:49,280 Speaker 2: as other companies. And obviously, you know, look at the 165 00:08:49,320 --> 00:08:52,480 Speaker 2: stock had a massive run up in the year after 166 00:08:52,520 --> 00:08:56,240 Speaker 2: Trump was like the second time, which mostly reflected investor 167 00:08:56,400 --> 00:08:59,080 Speaker 2: belief that the company was uniquely positioned to benefit from 168 00:08:59,120 --> 00:09:01,679 Speaker 2: a second Trump president. And see, and I think that's proven. 169 00:09:01,720 --> 00:09:02,120 Speaker 2: It hasn't. 170 00:09:02,240 --> 00:09:04,600 Speaker 1: Certainly, it certainly has in terms of its revenue. I 171 00:09:04,600 --> 00:09:07,360 Speaker 1: think government revenue, government contact revenues are up one hundred 172 00:09:07,400 --> 00:09:10,120 Speaker 1: and two percent this, you know, the last quarter. But 173 00:09:10,160 --> 00:09:12,079 Speaker 1: the stop market is not responding very positively. 174 00:09:12,240 --> 00:09:14,080 Speaker 2: But it's also it's gotten you know. I mean, there's 175 00:09:14,080 --> 00:09:16,480 Speaker 2: been this pullback, but it also got pulled into this 176 00:09:16,679 --> 00:09:19,959 Speaker 2: selloff software starts because of exactly what you were talking about, 177 00:09:19,960 --> 00:09:22,480 Speaker 2: a belief that AI is coming for them too. So 178 00:09:22,640 --> 00:09:25,520 Speaker 2: you know, the party line, if you will, is that 179 00:09:25,720 --> 00:09:29,679 Speaker 2: Palenteer is different, It's product is sufficiently differentiated that they 180 00:09:29,720 --> 00:09:32,079 Speaker 2: should be Okay. I don't have a view on that, 181 00:09:32,120 --> 00:09:34,080 Speaker 2: because I have no idea. We're going to find out. 182 00:09:36,120 --> 00:09:39,400 Speaker 1: So they say often that every every great book begins 183 00:09:39,400 --> 00:09:42,240 Speaker 1: with a great question. What was your question? What? What 184 00:09:42,280 --> 00:09:44,960 Speaker 1: didn't satisfy you in your reporting for the nine thousand 185 00:09:45,000 --> 00:09:47,360 Speaker 1: word magazine piece that made you want to spend five 186 00:09:47,440 --> 00:09:48,400 Speaker 1: years writing this book. 187 00:09:49,360 --> 00:09:51,600 Speaker 2: I still was trying to figure out what karp is 188 00:09:51,600 --> 00:09:56,640 Speaker 2: all about. He's quite different than other CEOs, looks different, 189 00:09:57,040 --> 00:10:02,120 Speaker 2: and certainly talks differently. He is at very candid in 190 00:10:02,160 --> 00:10:05,880 Speaker 2: a way that is disarming and also quite refreshing for 191 00:10:06,040 --> 00:10:09,240 Speaker 2: someone in the business world. Were any any any public figure, 192 00:10:09,600 --> 00:10:14,720 Speaker 2: but he's also a very canny operator. And and you know, 193 00:10:14,800 --> 00:10:17,160 Speaker 2: trying to make sense of how much of this was 194 00:10:17,200 --> 00:10:21,160 Speaker 2: conviction and how much of this was expedients, and what 195 00:10:21,280 --> 00:10:23,200 Speaker 2: was good for the business and and and and what 196 00:10:23,400 --> 00:10:25,880 Speaker 2: was the exact balance That was very interesting to me. 197 00:10:25,960 --> 00:10:28,880 Speaker 2: And I'm still not sure I know the answer. People 198 00:10:29,840 --> 00:10:32,079 Speaker 2: who read the book will arrive at different conclusions, and 199 00:10:32,160 --> 00:10:32,719 Speaker 2: I know they have. 200 00:10:33,000 --> 00:10:35,520 Speaker 1: You're talking specifically about the political shape shifting. 201 00:10:35,960 --> 00:10:38,760 Speaker 2: With political shape shifting and and you know just how 202 00:10:38,880 --> 00:10:40,800 Speaker 2: you know, I mean, this is a guy who, you know, 203 00:10:40,880 --> 00:10:44,560 Speaker 2: during Trump's first presidency, made very clear that he was 204 00:10:44,600 --> 00:10:46,760 Speaker 2: not a fan of Donald Trump. Peter Tiel, his co 205 00:10:46,800 --> 00:10:49,520 Speaker 2: founder and very close friend and the chairman of Pallenteers board, 206 00:10:50,559 --> 00:10:53,360 Speaker 2: was Trump's biggest supporter in Silicon Valley and maybe the 207 00:10:53,360 --> 00:10:56,439 Speaker 2: biggest supporter in the business community. So you had this 208 00:10:56,440 --> 00:10:59,480 Speaker 2: this internal tension, though it was not an issue between 209 00:10:59,480 --> 00:11:01,719 Speaker 2: the two of them, but cart made very clear during 210 00:11:01,760 --> 00:11:04,319 Speaker 2: Trump's first presidency that he was not a fan and 211 00:11:06,320 --> 00:11:10,280 Speaker 2: a number of issues, including immigration. And then now here 212 00:11:10,280 --> 00:11:13,120 Speaker 2: we are Trump back in the White House, and carp 213 00:11:13,240 --> 00:11:16,320 Speaker 2: has I wouldn't say gone full maga, but he has 214 00:11:16,520 --> 00:11:20,360 Speaker 2: expressed a lot of support for Trump. Not everything, but 215 00:11:20,400 --> 00:11:22,480 Speaker 2: I mean he says that the issues that matter to him, 216 00:11:23,040 --> 00:11:26,120 Speaker 2: Trump is the guy. And so, you know, was what 217 00:11:26,200 --> 00:11:29,280 Speaker 2: he said during Trump's first presidency the real axe Carper? 218 00:11:29,360 --> 00:11:30,960 Speaker 2: Or are we seeing the real one now? 219 00:11:30,960 --> 00:11:33,760 Speaker 1: But that's that's broadly true throughout the tech community, right 220 00:11:33,800 --> 00:11:37,200 Speaker 1: Like I think about Mark Pinkers, Mark Andreson, Like a 221 00:11:37,200 --> 00:11:40,400 Speaker 1: lot of these formerly you know, somewhat vocal opponents of 222 00:11:40,440 --> 00:11:42,160 Speaker 1: Trump are now like loud. 223 00:11:42,440 --> 00:11:46,480 Speaker 2: Yeah, but they weren't as they weren't as specific in 224 00:11:46,520 --> 00:11:50,199 Speaker 2: their criticisms. They hadn't spent you know, the previous twenty 225 00:11:50,280 --> 00:11:53,920 Speaker 2: years shouting from every rooftop I'm a progressive the way carpad. 226 00:11:54,480 --> 00:11:58,520 Speaker 2: So you know, his about face has been quite a 227 00:11:58,559 --> 00:12:01,440 Speaker 2: bit more jarring. And even though many people in the 228 00:12:01,480 --> 00:12:04,920 Speaker 2: tech community have gotten on board with Trump this time around, 229 00:12:05,040 --> 00:12:08,360 Speaker 2: if you have been as outspoken about it or as 230 00:12:08,440 --> 00:12:11,520 Speaker 2: vituperative towards the other side as karp has been, so 231 00:12:11,679 --> 00:12:14,520 Speaker 2: you know, you've got this quite violent pivot with him. 232 00:12:15,320 --> 00:12:17,680 Speaker 2: And then he's just you know, he's pissing a lot 233 00:12:17,679 --> 00:12:19,959 Speaker 2: of people off with the stuff he's saying. And when 234 00:12:20,000 --> 00:12:22,760 Speaker 2: he goes out and says, you know, AI is is 235 00:12:22,800 --> 00:12:24,800 Speaker 2: coming from white collar jobs, and it's principally going to 236 00:12:24,800 --> 00:12:28,079 Speaker 2: affect women and they vote democratic. It's just, you know 237 00:12:29,200 --> 00:12:32,400 Speaker 2: a lot of people reacted, Okay, you're a misogynist too. Now, 238 00:12:32,640 --> 00:12:35,120 Speaker 2: whether that's true or not, I'm not going to say. 239 00:12:35,120 --> 00:12:38,240 Speaker 2: But he's really put himself out there in a way 240 00:12:38,280 --> 00:12:41,640 Speaker 2: that you know, maybe only Elon has other than in 241 00:12:42,000 --> 00:12:42,480 Speaker 2: that world. 242 00:12:42,960 --> 00:12:44,680 Speaker 1: You have a quote from Copp on the back of 243 00:12:44,679 --> 00:12:46,880 Speaker 1: the book, which I actually never seen a quote from 244 00:12:46,920 --> 00:12:51,400 Speaker 1: the subject on the back of but Off tell us 245 00:12:51,400 --> 00:12:57,840 Speaker 1: the quote, well, I have in front of me saving 246 00:12:57,960 --> 00:13:01,800 Speaker 1: lives and on occasion taking lives is super interesting. 247 00:13:04,440 --> 00:13:09,160 Speaker 2: Yeah, that's that's that's carp and he he is a provocateur. 248 00:13:09,520 --> 00:13:13,160 Speaker 2: He likes to cause a little shock. But he has 249 00:13:13,240 --> 00:13:17,280 Speaker 2: also been very clear that Palenteer has been used for 250 00:13:17,760 --> 00:13:21,880 Speaker 2: deadly purposes, For lethal purposes. Part of this is, you know, 251 00:13:22,240 --> 00:13:24,120 Speaker 2: has always he's always done this to kind of feed 252 00:13:24,120 --> 00:13:28,720 Speaker 2: the mystique. But I think it's also true because used 253 00:13:28,720 --> 00:13:32,560 Speaker 2: by intelligence services, used extensively by the US military. We 254 00:13:32,679 --> 00:13:35,199 Speaker 2: know it is being used now for lethal purposes because 255 00:13:35,280 --> 00:13:39,240 Speaker 2: Palenteer's software is the backbone for Maven smart system, which 256 00:13:39,280 --> 00:13:43,079 Speaker 2: is the Pentagon's AI targeting program. So yeah, its software 257 00:13:43,200 --> 00:13:49,040 Speaker 2: helps conduct lethal activities, and carp makes no bones about that. 258 00:13:49,280 --> 00:13:51,280 Speaker 2: It's an interesting thing. You know, if you saw him 259 00:13:51,280 --> 00:13:53,679 Speaker 2: around the office, he's kind of a he's a very 260 00:13:53,760 --> 00:13:58,000 Speaker 2: cheerful presence. He's funny, he's he's basically conducting a pep 261 00:13:58,080 --> 00:14:01,199 Speaker 2: rally for his people, and then of the Palenteer offices 262 00:14:01,360 --> 00:14:04,080 Speaker 2: is in on in a given day. And yet he's 263 00:14:04,080 --> 00:14:06,240 Speaker 2: got a very dark view of the world and always 264 00:14:06,280 --> 00:14:09,160 Speaker 2: has and this is the view that became Palenteer's worldview. 265 00:14:09,160 --> 00:14:12,480 Speaker 2: It's it's, you know, the world is inclined to violence, chaos, 266 00:14:12,520 --> 00:14:16,560 Speaker 2: that is humanity's natural condition. We have lots of people 267 00:14:16,559 --> 00:14:19,160 Speaker 2: who want to kill us, and I think his view 268 00:14:19,280 --> 00:14:22,000 Speaker 2: is we should do them harm before they do us harm. 269 00:14:22,400 --> 00:14:23,960 Speaker 1: I want to come back to this kind of West 270 00:14:24,040 --> 00:14:26,600 Speaker 1: versus the rest. Well, before that, I want to I know, 271 00:14:26,600 --> 00:14:28,920 Speaker 1: I understand a little bit more about the origin story 272 00:14:29,080 --> 00:14:34,360 Speaker 1: of Palenteer you've written. Although Copp was Palenteer's CEO, the 273 00:14:34,440 --> 00:14:37,720 Speaker 1: press often referred to the company as Peter Teel's Palenteer 274 00:14:38,520 --> 00:14:40,880 Speaker 1: and that that part was why he wants to do 275 00:14:40,920 --> 00:14:41,360 Speaker 1: this book. 276 00:14:41,680 --> 00:14:45,520 Speaker 2: Yeah. During the first Trump presidency, TiO, of course was 277 00:14:45,520 --> 00:14:48,360 Speaker 2: a lightning rod. He still is, but he was very 278 00:14:48,440 --> 00:14:50,640 Speaker 2: much so during the first Trump presidency because of the 279 00:14:50,680 --> 00:14:53,720 Speaker 2: support he had given Trump and the kind of center 280 00:14:53,760 --> 00:14:56,600 Speaker 2: stage role he had taken, at least during the transition. 281 00:14:56,800 --> 00:14:59,560 Speaker 2: When you saw him around Trump Tower, the famous meeting 282 00:14:59,600 --> 00:15:02,440 Speaker 2: with all the tech executives, that was Teal who had 283 00:15:02,560 --> 00:15:06,040 Speaker 2: organized that. And you know stories about Palenteer during the 284 00:15:06,040 --> 00:15:09,280 Speaker 2: first Trump presidency, I mean, the headlines invariably read Teal's 285 00:15:09,360 --> 00:15:13,200 Speaker 2: Pallenteer even though he was not running the company and. 286 00:15:13,120 --> 00:15:16,840 Speaker 1: Never had larger shareholder or largest larger individual Shoholder. 287 00:15:16,720 --> 00:15:19,400 Speaker 2: Largest individual shareholder and chairman of the board, so very 288 00:15:19,400 --> 00:15:23,080 Speaker 2: involved with this, but his idea or so it was 289 00:15:23,120 --> 00:15:25,640 Speaker 2: his idea. It was his idea, you know this. The 290 00:15:25,960 --> 00:15:29,840 Speaker 2: origin story is that this grew out of PayPal. PayPal 291 00:15:30,160 --> 00:15:33,800 Speaker 2: had run into serious trouble with fraudsters and they designed 292 00:15:33,840 --> 00:15:38,040 Speaker 2: some anti fraud algorithms that ultimately saved the business. That again, 293 00:15:38,200 --> 00:15:41,840 Speaker 2: pattern recognition software, finding those needles in the haystack. They 294 00:15:41,840 --> 00:15:44,880 Speaker 2: were generating a massive amount of data. Human analysts at 295 00:15:44,880 --> 00:15:48,040 Speaker 2: PayPal couldn't figure out what's going on, so they developed 296 00:15:48,120 --> 00:15:51,200 Speaker 2: software that could do that. After nine to eleven. It 297 00:15:51,200 --> 00:15:54,120 Speaker 2: occurs to te at some point you know that same 298 00:15:54,160 --> 00:15:56,800 Speaker 2: idea could be applied to the war on terrorism, and 299 00:15:56,880 --> 00:16:01,200 Speaker 2: those anti fraud algorithms could perhaps be repurposed help intelligence 300 00:16:01,200 --> 00:16:05,160 Speaker 2: analysts find potential terrorists and prevent future attacks. And that 301 00:16:05,360 --> 00:16:08,440 Speaker 2: gave rise to Palenteer, so he was it was his idea. 302 00:16:08,800 --> 00:16:12,440 Speaker 2: He brought karp into Palenteer first to help raise money 303 00:16:12,480 --> 00:16:17,120 Speaker 2: for this startup company, and then in consultation with the 304 00:16:17,120 --> 00:16:21,080 Speaker 2: other co founders, brought him in a CEO. But it 305 00:16:21,160 --> 00:16:24,960 Speaker 2: was Karpoo from that point on ran it. So these 306 00:16:24,960 --> 00:16:26,840 Speaker 2: things can be put to all sorts of uses, and 307 00:16:26,880 --> 00:16:30,680 Speaker 2: that's why a lot of the discussion about Palenteer is tricky. 308 00:16:30,720 --> 00:16:33,240 Speaker 2: And part of what I've been trying to do and 309 00:16:33,360 --> 00:16:36,560 Speaker 2: talking about the book is to to get across the 310 00:16:36,600 --> 00:16:40,240 Speaker 2: point that this is a really complicated company, and because 311 00:16:40,240 --> 00:16:43,080 Speaker 2: they're doing good stuff in some areas. So for instance, 312 00:16:43,160 --> 00:16:45,800 Speaker 2: during the pandemic, they were being used by the World 313 00:16:45,880 --> 00:16:51,040 Speaker 2: Food Program to help manage logistics, and Palenters technology people 314 00:16:51,080 --> 00:16:53,400 Speaker 2: at the WFP will tell you helped save a lot 315 00:16:53,400 --> 00:16:56,040 Speaker 2: of lives during the pandemic, So that's good, But then 316 00:16:56,200 --> 00:16:58,440 Speaker 2: you look at the work they're doing with ice, and 317 00:16:58,480 --> 00:17:00,000 Speaker 2: I think a lot of people say, well that's not 318 00:17:00,040 --> 00:17:03,320 Speaker 2: that's so good. So it's a complicated picture, a complicated company. 319 00:17:03,360 --> 00:17:07,119 Speaker 2: Karp embraces those complications in their complexity. But what do 320 00:17:07,160 --> 00:17:09,920 Speaker 2: you think about Palenteer? It depends on what issues are 321 00:17:09,960 --> 00:17:13,200 Speaker 2: dominating the headlines. But I think there is ambiguity and 322 00:17:13,240 --> 00:17:16,440 Speaker 2: complexity of the story that maybe don't find with you 323 00:17:16,840 --> 00:17:17,920 Speaker 2: all that many companies. 324 00:17:33,560 --> 00:17:35,639 Speaker 1: Let's talk a bit more about him. I mean he 325 00:17:35,680 --> 00:17:40,159 Speaker 1: embodies certain contradictions, right, I mean, he's talked about how 326 00:17:40,200 --> 00:17:43,840 Speaker 1: his greatest fear is fascism, but he's often accused of 327 00:17:43,880 --> 00:17:47,720 Speaker 1: techno fascism. He has an unusual background for a Silicon 328 00:17:47,800 --> 00:17:52,280 Speaker 1: Valley mogul. He's biracial, black and Jewish, you know, went 329 00:17:52,400 --> 00:17:54,960 Speaker 1: his early part of his career was not working in tech, 330 00:17:55,040 --> 00:17:58,160 Speaker 1: but studying, you know, in the Frankfurt School and studying 331 00:17:58,359 --> 00:18:02,640 Speaker 1: you know, leftist politics essentially. I mean, who is he? 332 00:18:02,920 --> 00:18:05,480 Speaker 2: Well, it's a great question. I mean, the unlikeliest tech 333 00:18:05,600 --> 00:18:09,439 Speaker 2: mogul I think you could find raised in an astaunchly 334 00:18:09,520 --> 00:18:13,920 Speaker 2: left wing household in Philadelphia. A father was a pediatrician, 335 00:18:14,080 --> 00:18:17,520 Speaker 2: mother and artist. I think they took a quite dim 336 00:18:17,640 --> 00:18:21,240 Speaker 2: view of the of the business world in general. It 337 00:18:21,359 --> 00:18:25,239 Speaker 2: was not their ambition for their son, and given that 338 00:18:25,640 --> 00:18:27,879 Speaker 2: much of his early childhood was spent going to anti 339 00:18:27,920 --> 00:18:30,399 Speaker 2: war protests, I don't think if you told him at 340 00:18:30,400 --> 00:18:32,560 Speaker 2: that time that your son would be running a major 341 00:18:32,640 --> 00:18:35,639 Speaker 2: defense contractor they would have been particularly happy. So he 342 00:18:35,680 --> 00:18:37,520 Speaker 2: came out of a very different background. He stayed this 343 00:18:37,640 --> 00:18:39,679 Speaker 2: was the world that he grew up in. He remained 344 00:18:39,720 --> 00:18:42,720 Speaker 2: in that world, having for college, which we both attended. 345 00:18:42,760 --> 00:18:46,439 Speaker 2: It was a traditional a Quaker school, so a tradition 346 00:18:46,480 --> 00:18:52,800 Speaker 2: of pacifism, and you know, very I would say, not 347 00:18:52,920 --> 00:18:56,439 Speaker 2: wildly left wing, but certainly left leaning, left of center. 348 00:18:56,680 --> 00:18:59,639 Speaker 2: Then he went to stand For law school, which he hated. 349 00:19:00,040 --> 00:19:02,159 Speaker 2: The only good thing he took away from that is 350 00:19:02,200 --> 00:19:04,960 Speaker 2: that that's where he and Teo met and became friends. 351 00:19:05,320 --> 00:19:08,600 Speaker 2: They were political opposites. Then Karp decided that he didn't 352 00:19:08,600 --> 00:19:11,000 Speaker 2: want to pursue a career in law and went off 353 00:19:11,040 --> 00:19:13,960 Speaker 2: to Germany to you a doctor at a got University 354 00:19:14,000 --> 00:19:17,800 Speaker 2: of Frankfurt, and you know, then decided he didn't want 355 00:19:17,840 --> 00:19:19,760 Speaker 2: to be a professor either. So he was in his 356 00:19:19,840 --> 00:19:23,840 Speaker 2: mid thirties kind of at loose ends, and again no 357 00:19:23,960 --> 00:19:27,080 Speaker 2: background in technology, no background really a business, had never 358 00:19:27,119 --> 00:19:30,720 Speaker 2: run an organization, and he ended up as the CEO 359 00:19:30,760 --> 00:19:34,000 Speaker 2: of this startup. But what tea recognized that Karp was 360 00:19:34,119 --> 00:19:36,240 Speaker 2: very good at working with people and was really good 361 00:19:36,240 --> 00:19:39,639 Speaker 2: at raising money. He got people to sign checks. But 362 00:19:39,720 --> 00:19:42,199 Speaker 2: the other co founders also saw that there was a 363 00:19:42,280 --> 00:19:45,320 Speaker 2: kind of charisma to Karp that that would be very 364 00:19:45,520 --> 00:19:48,399 Speaker 2: helpful to this company. And and and he is in 365 00:19:48,520 --> 00:19:52,600 Speaker 2: his very idiosyncratic way of very charismatic figures. So despite 366 00:19:52,600 --> 00:19:55,639 Speaker 2: this unlikely background, he ended up a CEO of this 367 00:19:55,680 --> 00:19:56,320 Speaker 2: tech startup. 368 00:19:57,119 --> 00:19:59,760 Speaker 1: Did he express any doubt to you about his political conversion? 369 00:20:00,320 --> 00:20:02,440 Speaker 2: Well, I don't think he. I mean, he wouldn't say 370 00:20:02,480 --> 00:20:04,360 Speaker 2: that he's had a political conversion. He's one of those 371 00:20:04,400 --> 00:20:06,679 Speaker 2: who say I didn't leave the Democrats, they left me. 372 00:20:07,280 --> 00:20:09,480 Speaker 2: So that's kind of his view. He's never put it 373 00:20:09,560 --> 00:20:12,720 Speaker 2: quite so directly, but that is clearly what he's saying, 374 00:20:12,920 --> 00:20:15,159 Speaker 2: you know, in public, and kind of what he has 375 00:20:15,200 --> 00:20:18,920 Speaker 2: said in private, that he thinks that you know, he didn't. 376 00:20:18,960 --> 00:20:23,639 Speaker 2: It's not that he's portrayed his progressive values. He thinks 377 00:20:23,840 --> 00:20:26,840 Speaker 2: progressives have betrayed their own values. By I mean, he's 378 00:20:27,000 --> 00:20:29,520 Speaker 2: the critique has just kind of snowballed over the last 379 00:20:29,520 --> 00:20:32,480 Speaker 2: two years, but it was a lot of it was 380 00:20:32,480 --> 00:20:35,440 Speaker 2: about immigration, which you know, during the first Drump presidency 381 00:20:35,440 --> 00:20:37,560 Speaker 2: he said, look at you know, I'm totally fine with 382 00:20:37,600 --> 00:20:40,760 Speaker 2: the demographics of this country changing, but people do not 383 00:20:41,040 --> 00:20:43,560 Speaker 2: like an open border. They don't they do not like 384 00:20:43,640 --> 00:20:47,120 Speaker 2: scenes of chast at the border. And Democrats didn't take 385 00:20:47,160 --> 00:20:50,080 Speaker 2: these concerns seriously, and they got President Trump. And then 386 00:20:50,200 --> 00:20:54,119 Speaker 2: after Trump lost in twenty twenty, he said during the 387 00:20:54,520 --> 00:20:57,120 Speaker 2: Biden years, he said, if they don't take the border seriously, 388 00:20:57,160 --> 00:20:59,040 Speaker 2: you're going to have President Trump again. And he was 389 00:20:59,119 --> 00:21:02,000 Speaker 2: right about that. But I think one of many reasons 390 00:21:02,000 --> 00:21:05,679 Speaker 2: he grew frustrated with the Democrats and eventually split with 391 00:21:05,800 --> 00:21:09,400 Speaker 2: them is because they didn't listen to him. So there 392 00:21:09,480 --> 00:21:10,920 Speaker 2: is there is that they'd listened to. 393 00:21:11,000 --> 00:21:13,760 Speaker 1: Hilan not being invited to the to the EV summing 394 00:21:13,800 --> 00:21:14,360 Speaker 1: at the White. 395 00:21:14,200 --> 00:21:17,760 Speaker 2: House exactly exactly. I mean, you know, if Kamala had 396 00:21:17,760 --> 00:21:21,280 Speaker 2: brought him into runner campaign, maybe he'd feel different. 397 00:21:22,880 --> 00:21:25,240 Speaker 1: But twits fascism word which he has, which he has 398 00:21:25,280 --> 00:21:29,160 Speaker 1: made a centerpiece of who he is, is wanting to, 399 00:21:29,359 --> 00:21:34,400 Speaker 1: you know, avoid fascism returning. And yet you know, if 400 00:21:34,440 --> 00:21:38,280 Speaker 1: you read nineteen eighty four, The kind of techno surveillance 401 00:21:38,520 --> 00:21:42,959 Speaker 1: and predictive algorithmic infrastructure that he is building for governments 402 00:21:43,600 --> 00:21:46,800 Speaker 1: can be certainly used and arguably is being used, depending 403 00:21:46,800 --> 00:21:50,400 Speaker 1: on perspective, for something approaching fascism. 404 00:21:50,640 --> 00:21:55,520 Speaker 2: Yes, well, he's got a multi part answer to this, 405 00:21:56,160 --> 00:21:59,119 Speaker 2: which is that oh, no, no, no. Our technology is 406 00:21:59,119 --> 00:22:01,800 Speaker 2: the stuff that prevent and prevents it in a couple 407 00:22:01,840 --> 00:22:04,480 Speaker 2: of ways. First of all, it's got these guardrails built 408 00:22:04,520 --> 00:22:08,640 Speaker 2: into it, these privacy controls, these robust audit logs, so 409 00:22:09,280 --> 00:22:12,760 Speaker 2: you can't be used for nefarious purposes, which isn't really 410 00:22:12,800 --> 00:22:15,920 Speaker 2: true because Palenteer doesn't monitor how its technology is being 411 00:22:16,040 --> 00:22:19,479 Speaker 2: used the clients use it. So if the people using it, 412 00:22:19,560 --> 00:22:24,160 Speaker 2: say at ICE or another government agency, actually don't give 413 00:22:24,200 --> 00:22:28,240 Speaker 2: a crap about legal niceties and don't fear any consequences, 414 00:22:28,359 --> 00:22:30,800 Speaker 2: you have a problem. So that's always been the kind 415 00:22:30,800 --> 00:22:32,960 Speaker 2: of fly in the ointment, if you will. But you know, 416 00:22:33,359 --> 00:22:36,120 Speaker 2: Karp's argument is like, no, no, no, our technology which 417 00:22:36,160 --> 00:22:39,399 Speaker 2: allows organizations to be much more targeted. They allow, for instance, 418 00:22:39,440 --> 00:22:41,959 Speaker 2: ICE to be much more targeted and going after people. 419 00:22:42,480 --> 00:22:47,080 Speaker 2: That's actually protecting cimiliberties in privacy and ultimately upholding democracy. 420 00:22:47,800 --> 00:22:50,080 Speaker 2: And then in the context of the current moment, he 421 00:22:50,200 --> 00:22:54,119 Speaker 2: just flat out denies that Trump is authoritarian. He dismisses 422 00:22:54,119 --> 00:22:57,600 Speaker 2: that argument out of hand. And so that's kind of 423 00:22:57,600 --> 00:23:01,480 Speaker 2: how he answers all these concerns, and that's Yes, fascism 424 00:23:01,520 --> 00:23:04,439 Speaker 2: is his fear, but no, this isn't fascism. So my 425 00:23:04,520 --> 00:23:05,720 Speaker 2: fear is not being realized. 426 00:23:06,240 --> 00:23:07,840 Speaker 1: Did you find his own sepersuasive? 427 00:23:08,520 --> 00:23:14,879 Speaker 2: No, No, we disagree about Trump and who he is 428 00:23:15,440 --> 00:23:18,760 Speaker 2: and what this is we're looking at. He has his 429 00:23:18,920 --> 00:23:22,679 Speaker 2: reasons there even if he even if he agreed with me, 430 00:23:22,880 --> 00:23:25,960 Speaker 2: he's running a company that's a major government contractor what's 431 00:23:25,960 --> 00:23:29,199 Speaker 2: he going to say? So, yeah, got to dance for 432 00:23:29,240 --> 00:23:30,879 Speaker 2: his dinner in a way that I don't. So I 433 00:23:31,280 --> 00:23:33,040 Speaker 2: cut him some slack in that regard. But I do 434 00:23:33,080 --> 00:23:37,960 Speaker 2: think that Palenter recently posted that twenty two point manifesto that. 435 00:23:37,560 --> 00:23:41,760 Speaker 1: The British politician describe it as the ramblings of a supervillain. 436 00:23:41,440 --> 00:23:44,199 Speaker 2: Exactly, and so you know, and and then this freaked 437 00:23:44,200 --> 00:23:46,800 Speaker 2: a lot of people out because you know, he has 438 00:23:47,000 --> 00:23:49,480 Speaker 2: express views. I mean, he published a he co authored 439 00:23:49,480 --> 00:23:51,960 Speaker 2: a book with a colleague from Pallenteer last year called 440 00:23:51,960 --> 00:23:55,600 Speaker 2: the Technological Republic that many people saw as a as 441 00:23:55,640 --> 00:24:00,240 Speaker 2: a thinly veiled manifesto for techno authoritarianism. That twenty two 442 00:24:00,240 --> 00:24:03,360 Speaker 2: point manifesto that they posted on x, which was kind 443 00:24:03,359 --> 00:24:06,199 Speaker 2: of a distillation of the main points of the book, 444 00:24:06,600 --> 00:24:09,679 Speaker 2: definitely came across too many people as an argument for 445 00:24:09,840 --> 00:24:14,080 Speaker 2: techno authoritarianism or techno fascism. So, you know, Karp again, 446 00:24:14,160 --> 00:24:16,600 Speaker 2: this is a way in which he's a complicated figure, 447 00:24:16,800 --> 00:24:19,159 Speaker 2: and some people would say, no, he's not complicated. It's 448 00:24:19,240 --> 00:24:21,879 Speaker 2: very clear what he is. But I've been following this 449 00:24:21,960 --> 00:24:25,320 Speaker 2: guy for a very long time. I've had pretty extensive conversations, 450 00:24:25,320 --> 00:24:27,119 Speaker 2: and I think it is a complicated picture. 451 00:24:27,720 --> 00:24:29,639 Speaker 1: A few of the points Silicon from the twenty two 452 00:24:29,640 --> 00:24:32,240 Speaker 1: point manifesto. Silicon value owes a moral debt to the 453 00:24:32,280 --> 00:24:35,479 Speaker 1: country that made its rise possible. The engineeringly to Silicon 454 00:24:35,560 --> 00:24:39,399 Speaker 1: Valley has an affirmative obligation to participate in the defense 455 00:24:39,440 --> 00:24:42,880 Speaker 1: of the nation. The atomic age is ending, one age 456 00:24:42,880 --> 00:24:44,639 Speaker 1: of the terrants. The atomic age is ending in a 457 00:24:44,680 --> 00:24:47,120 Speaker 1: new era of the terrance built on AI is said 458 00:24:47,160 --> 00:24:50,600 Speaker 1: to begin. Silicon Valley must play a role in addressing 459 00:24:50,760 --> 00:24:55,760 Speaker 1: violent crime. How much do these personal beliefs or Palenteers 460 00:24:55,760 --> 00:24:59,560 Speaker 1: corporate strategy and how much has palentiers corporate strategy affect 461 00:24:59,560 --> 00:25:01,800 Speaker 1: the line of everyday Americans. 462 00:25:02,200 --> 00:25:05,679 Speaker 2: Well, this is integral to what Palenteer is. From the start, 463 00:25:05,840 --> 00:25:09,199 Speaker 2: its goal was to work with the government, and it 464 00:25:09,240 --> 00:25:11,680 Speaker 2: believes that if you choose to work with the government, 465 00:25:11,800 --> 00:25:16,520 Speaker 2: you have an obligation to fulfill your contractual commitments, regardless 466 00:25:16,560 --> 00:25:19,480 Speaker 2: of who's president or what policies they choose to implement 467 00:25:19,600 --> 00:25:22,000 Speaker 2: within a reason. I mean, that's the problem with that 468 00:25:22,119 --> 00:25:24,560 Speaker 2: argument is you can take it to extreme. Certainly there 469 00:25:24,600 --> 00:25:27,080 Speaker 2: are some policies that might be pursued where you, as 470 00:25:27,080 --> 00:25:29,800 Speaker 2: a corporation would say, well, we can't actually do that. 471 00:25:30,119 --> 00:25:32,920 Speaker 2: And that's kind of the big question right now with Palenteer, 472 00:25:32,960 --> 00:25:36,480 Speaker 2: and it's worked with this second Trump administration, is okay, 473 00:25:36,560 --> 00:25:38,879 Speaker 2: what are the red lines? So, for instance, with ICE, 474 00:25:39,119 --> 00:25:42,119 Speaker 2: how extensive do the reports need to be of what 475 00:25:42,160 --> 00:25:45,080 Speaker 2: our effectively human rights abuses before Palenteer might say, you 476 00:25:45,119 --> 00:25:48,360 Speaker 2: know what, we can't be part of this. If, as 477 00:25:49,119 --> 00:25:53,440 Speaker 2: people in the Tromp administration have suggested, ICE agents are 478 00:25:53,440 --> 00:25:55,919 Speaker 2: deployed at polling stations this November, is that going to 479 00:25:55,920 --> 00:25:58,199 Speaker 2: be okay? With Palenteer, is that work they're going to 480 00:25:58,200 --> 00:26:00,639 Speaker 2: want to help facilitate or just even working with that 481 00:26:00,800 --> 00:26:06,240 Speaker 2: agency in an effort to tamper with the election. You know, 482 00:26:06,359 --> 00:26:08,639 Speaker 2: they don't answer that question. It's not their interest to 483 00:26:08,680 --> 00:26:10,840 Speaker 2: answer it. But it is a very real question. What 484 00:26:10,920 --> 00:26:12,200 Speaker 2: are the red lines here? 485 00:26:12,800 --> 00:26:15,840 Speaker 1: I mean the standard kind of cry as we build 486 00:26:15,880 --> 00:26:19,719 Speaker 1: the tools, not the rules. But they're also building the architecture, right, 487 00:26:19,720 --> 00:26:22,560 Speaker 1: an architect when you build something, the architecture has its 488 00:26:22,600 --> 00:26:24,800 Speaker 1: own set of rules baked into it, right, or like 489 00:26:25,760 --> 00:26:28,680 Speaker 1: using an architecture puts you in the rules of the 490 00:26:28,720 --> 00:26:32,280 Speaker 1: person who designs the architecture. And so I personally just 491 00:26:32,920 --> 00:26:35,680 Speaker 1: I don't understand what they're saying, but it rings a 492 00:26:35,720 --> 00:26:36,280 Speaker 1: little hollow. 493 00:26:37,000 --> 00:26:39,639 Speaker 2: No, absolutely, I mean, you know, forget even ice. But 494 00:26:39,680 --> 00:26:41,360 Speaker 2: I mean, so for instance, you know they were they 495 00:26:41,520 --> 00:26:44,280 Speaker 2: tried for a number of years to break in with 496 00:26:44,600 --> 00:26:46,760 Speaker 2: police departments in the United States. I had some success. 497 00:26:47,040 --> 00:26:49,840 Speaker 2: Those relationships have all ended for various reasons now, but 498 00:26:50,280 --> 00:26:53,919 Speaker 2: they were being used in Los Angeles for predictive policing. 499 00:26:54,359 --> 00:26:56,640 Speaker 2: At the heart of the controversy is the idea that 500 00:26:57,200 --> 00:27:02,040 Speaker 2: the algorithms reflect human biases and that you're targeting minority 501 00:27:02,040 --> 00:27:06,440 Speaker 2: communities because that's the data. The technology is not neutral 502 00:27:06,760 --> 00:27:10,360 Speaker 2: because it's designed by human beings, and so you know this, 503 00:27:10,800 --> 00:27:13,679 Speaker 2: you know, we just build it, they use it. Explanation 504 00:27:13,880 --> 00:27:17,560 Speaker 2: is not very satisfying, not in that context certainly. And 505 00:27:17,600 --> 00:27:21,119 Speaker 2: I think this is where they're running into some real trouble. 506 00:27:21,200 --> 00:27:23,760 Speaker 2: It's not necessarily reflected in the share price that's come off, 507 00:27:23,800 --> 00:27:26,439 Speaker 2: but not because the political controversy. But yeah, it's not 508 00:27:26,480 --> 00:27:28,959 Speaker 2: reflected in the share price, not reflected in the in 509 00:27:29,000 --> 00:27:31,359 Speaker 2: the in the financials, it's not hitting the bottom line. 510 00:27:31,400 --> 00:27:34,000 Speaker 2: But they have become the bogeyman, and in ways that 511 00:27:34,600 --> 00:27:38,360 Speaker 2: were not true during the first Trump presidency. No company 512 00:27:38,400 --> 00:27:41,359 Speaker 2: I think has been more closely associated with Trump this 513 00:27:41,480 --> 00:27:44,480 Speaker 2: time around, and pollenteer and we'll see if it has 514 00:27:44,520 --> 00:27:45,920 Speaker 2: implications for them. 515 00:27:46,200 --> 00:27:48,879 Speaker 1: I mean you mentioned possible implications. There was this kind 516 00:27:48,920 --> 00:27:54,000 Speaker 1: of reported internal mutiny last year by employees concerned about 517 00:27:54,119 --> 00:27:58,040 Speaker 1: normalizing authoritarianism. I mean you talked about Karp as someone 518 00:27:58,040 --> 00:28:00,560 Speaker 1: who's kind of like a cheerleader in chief levels around 519 00:28:00,720 --> 00:28:03,399 Speaker 1: the paleteer offices. Has he been able to cheer lead 520 00:28:03,480 --> 00:28:06,240 Speaker 1: his way out of this mutiny? 521 00:28:06,600 --> 00:28:09,560 Speaker 2: Well, I would, I would, I would politely correct you 522 00:28:09,600 --> 00:28:10,960 Speaker 2: in that. I don't think it was a mutiny. You 523 00:28:11,040 --> 00:28:16,040 Speaker 2: had pretty robust internal descent during the first Trump presidency 524 00:28:16,560 --> 00:28:20,520 Speaker 2: over the work with ICE, which you know, that issue 525 00:28:21,000 --> 00:28:24,439 Speaker 2: was somewhat overblown because their contract at the time was 526 00:28:24,480 --> 00:28:27,920 Speaker 2: with a division of ICE that was not really involved 527 00:28:27,960 --> 00:28:32,440 Speaker 2: in the immigration crackdown. This is during the first Trump presidency. Now, 528 00:28:32,480 --> 00:28:35,320 Speaker 2: they of course are intimately involved in it. But back 529 00:28:35,359 --> 00:28:37,000 Speaker 2: you know, during the first Trump presidency you had several 530 00:28:37,080 --> 00:28:40,600 Speaker 2: hundred employees sign a letter asking, you know, Kark to 531 00:28:40,600 --> 00:28:43,480 Speaker 2: break the contract with ICE. I would say the difference 532 00:28:43,520 --> 00:28:46,480 Speaker 2: between now and then because the internal descent has been 533 00:28:46,560 --> 00:28:48,240 Speaker 2: quite a bit more muted this time. And I would 534 00:28:48,280 --> 00:28:50,880 Speaker 2: say the difference between now and then is that tech 535 00:28:50,960 --> 00:28:52,640 Speaker 2: jobs are a lot harder to come by right now, 536 00:28:53,240 --> 00:28:57,920 Speaker 2: So you know, it's it's yeah. In twenty eighteen, twenty nineteen, 537 00:28:58,040 --> 00:29:02,120 Speaker 2: you could protest Internallyalenteer, and if you end up pissing 538 00:29:02,200 --> 00:29:05,480 Speaker 2: off the up rachel On enough that they fired you, 539 00:29:05,480 --> 00:29:07,680 Speaker 2: you were pretty well assured that with your with your 540 00:29:07,840 --> 00:29:10,200 Speaker 2: background at Palenteer, you could get a job at another 541 00:29:10,520 --> 00:29:12,880 Speaker 2: major tech company. Now it's very hard, So I think 542 00:29:12,880 --> 00:29:15,240 Speaker 2: that has kind of kept a lid on the internal 543 00:29:15,280 --> 00:29:18,040 Speaker 2: dissent because there are not very many places you can 544 00:29:18,080 --> 00:29:20,080 Speaker 2: go to if you get shown in the door Palenteer. 545 00:29:20,840 --> 00:29:22,960 Speaker 1: Well, so what are the potential consequence? Is it a 546 00:29:22,960 --> 00:29:26,880 Speaker 1: Democrat administration who come in basically saying, you know, our 547 00:29:26,920 --> 00:29:29,920 Speaker 1: first policy is to cancel our contracts with Palenteer. We're 548 00:29:29,920 --> 00:29:32,480 Speaker 1: seeing in the UK that the UK government is underpression 549 00:29:32,560 --> 00:29:34,160 Speaker 1: not to work with Palenteer with the NHS. 550 00:29:34,600 --> 00:29:37,320 Speaker 2: Yeah, no, that's it. And they would never say this 551 00:29:37,400 --> 00:29:41,160 Speaker 2: publicly and they would probably deny it privately, but I 552 00:29:41,280 --> 00:29:45,040 Speaker 2: think I know that the view internally is that this 553 00:29:45,160 --> 00:29:48,760 Speaker 2: technology is mission critical for the US military. The number 554 00:29:48,760 --> 00:29:51,440 Speaker 2: of contracts they've received from US military, it's become Maven 555 00:29:51,440 --> 00:29:54,000 Speaker 2: has now become you know, the AI targeting program, of 556 00:29:54,040 --> 00:29:57,080 Speaker 2: which again Palneers software is the is the backbone. It's 557 00:29:57,120 --> 00:29:59,280 Speaker 2: just been made a program of record, which is a 558 00:29:59,320 --> 00:30:04,959 Speaker 2: very important day. So what Democrat coming in would say, Okay, yeah, Maven, 559 00:30:05,120 --> 00:30:06,680 Speaker 2: you know, is great and all, but you know what, 560 00:30:06,880 --> 00:30:09,280 Speaker 2: screw Pallenteer. So sorry you guys, you can't use this. 561 00:30:09,400 --> 00:30:11,760 Speaker 2: No one's going to do that. So so you know, 562 00:30:11,920 --> 00:30:16,320 Speaker 2: I think the calculus internally is like, Okay, we can 563 00:30:16,400 --> 00:30:18,719 Speaker 2: ride this out. And yeah, we're going to take some 564 00:30:18,760 --> 00:30:22,080 Speaker 2: stick from Democrats on Capitol Hill and we're not popular. 565 00:30:22,240 --> 00:30:24,720 Speaker 2: But are they going to really go through the trouble 566 00:30:24,920 --> 00:30:27,280 Speaker 2: of pulling us out of all the sockets, if you will? 567 00:30:27,640 --> 00:30:29,800 Speaker 2: And are they really going to sit here and say, oh, 568 00:30:30,040 --> 00:30:32,160 Speaker 2: this is the best product in the market, but because 569 00:30:32,160 --> 00:30:34,160 Speaker 2: we don't like Karp, we're not going to use it, 570 00:30:34,160 --> 00:30:36,200 Speaker 2: So you're going to have to use some a lesser product. 571 00:30:36,280 --> 00:30:38,080 Speaker 2: Is that how they Is that what they want to tell, 572 00:30:38,160 --> 00:30:39,840 Speaker 2: you know, voters, Is that what they want to tell 573 00:30:39,880 --> 00:30:42,600 Speaker 2: people on Capitol Hill? And so the answer is no. 574 00:30:42,720 --> 00:30:45,600 Speaker 2: And and look at I mean, there was concern after 575 00:30:45,640 --> 00:30:48,360 Speaker 2: the first Trump presidency that they would face blowback, that 576 00:30:48,400 --> 00:30:51,920 Speaker 2: the Biden administration would somehow punish them, and then that 577 00:30:52,000 --> 00:30:55,560 Speaker 2: really didn't happen. They have some reason to believe I 578 00:30:55,600 --> 00:30:58,640 Speaker 2: think that they can ride this out and that for 579 00:30:58,760 --> 00:31:02,960 Speaker 2: all the all the outcry, for all the approbrium heaped 580 00:31:02,960 --> 00:31:06,920 Speaker 2: at them, that it won't really materially impact their government 581 00:31:07,000 --> 00:31:08,560 Speaker 2: business in the long run. 582 00:31:09,000 --> 00:31:11,440 Speaker 1: Because the product is the product is too good, or 583 00:31:11,480 --> 00:31:13,480 Speaker 1: because it's just baked in them. I guess my. 584 00:31:13,480 --> 00:31:15,760 Speaker 2: Question is a combination. It's a combination of both and 585 00:31:15,800 --> 00:31:19,760 Speaker 2: I mean this, I mean what car clearly recognized and 586 00:31:19,760 --> 00:31:22,960 Speaker 2: then certainly one reason he became such a Trump enthusiast 587 00:31:22,960 --> 00:31:25,680 Speaker 2: this time around is he recognized that this was an 588 00:31:25,720 --> 00:31:29,600 Speaker 2: opportunity to even more deeply entrench the technology in government. 589 00:31:29,760 --> 00:31:32,440 Speaker 2: And that's what's happening. I mean, it's all across the 590 00:31:32,480 --> 00:31:35,560 Speaker 2: executive branch the federal landscape right now. So it was 591 00:31:35,600 --> 00:31:39,640 Speaker 2: already extensive use across many government agencies, across the military, 592 00:31:39,680 --> 00:31:42,360 Speaker 2: but now it's even more so. So, Yeah, they are 593 00:31:42,360 --> 00:31:44,680 Speaker 2: in a sense creating facts on the ground. And so 594 00:31:44,760 --> 00:31:47,640 Speaker 2: it's a combination of it is the incumbent and if 595 00:31:47,640 --> 00:31:51,160 Speaker 2: a normal bureaucratic inertia, who's going to replace it? And 596 00:31:51,200 --> 00:31:54,000 Speaker 2: then there's their view, which is that it can't be 597 00:31:54,080 --> 00:31:58,640 Speaker 2: replaced because it is materially better than other products available 598 00:31:58,680 --> 00:31:59,000 Speaker 2: to them. 599 00:31:59,240 --> 00:32:02,200 Speaker 1: What's your view? Is it a digital crystal ball or 600 00:32:02,280 --> 00:32:02,640 Speaker 1: is it. 601 00:32:03,280 --> 00:32:06,320 Speaker 2: The people who use it swear by it. They find 602 00:32:06,360 --> 00:32:08,920 Speaker 2: it much more effective, and it does have its issues. 603 00:32:08,960 --> 00:32:11,160 Speaker 2: There are things about it that they'll love, but you know, 604 00:32:11,480 --> 00:32:16,200 Speaker 2: they find that it is worth the premium that they're charged. 605 00:32:16,320 --> 00:32:18,840 Speaker 2: That's what you hear from people at Airbus. They say, 606 00:32:18,880 --> 00:32:22,800 Speaker 2: look at if we could find something better and something cheaper. 607 00:32:23,160 --> 00:32:26,400 Speaker 2: We would, but they have not been able to do 608 00:32:26,480 --> 00:32:29,480 Speaker 2: so in their view, so it does seem to be 609 00:32:29,760 --> 00:32:30,400 Speaker 2: very effective. 610 00:32:30,440 --> 00:32:33,480 Speaker 1: Indeed, have you spoken to cops since publishing the book. 611 00:32:33,960 --> 00:32:36,240 Speaker 2: Yeah, we spoke a couple times after. Haven't spoken in 612 00:32:36,240 --> 00:32:38,440 Speaker 2: a few months, but you know, we were in touch 613 00:32:38,480 --> 00:32:39,600 Speaker 2: after the book came out. 614 00:32:39,600 --> 00:32:42,000 Speaker 1: And he was okay with with how it all came out. 615 00:32:42,480 --> 00:32:44,080 Speaker 2: Oh yeah, yeah, No, I mean I think you know. 616 00:32:44,680 --> 00:32:47,120 Speaker 2: The position he took in public was that the book 617 00:32:47,240 --> 00:32:52,480 Speaker 2: was marred by my TDS, my Trump derangement syndrome, which 618 00:32:52,520 --> 00:32:55,760 Speaker 2: I would say was his Trump denial syndrome. Apart from that, 619 00:32:55,840 --> 00:32:58,680 Speaker 2: he thought it was I think he thought it was 620 00:32:58,800 --> 00:33:01,080 Speaker 2: very fair and I think he like being the subject 621 00:33:01,120 --> 00:33:04,280 Speaker 2: of a book, having his story told. And he said 622 00:33:04,280 --> 00:33:07,000 Speaker 2: that people who read it, people he knew who read it, 623 00:33:07,400 --> 00:33:08,080 Speaker 2: thought well of it. 624 00:33:08,200 --> 00:33:12,560 Speaker 1: So final question for you, what do you hope readers 625 00:33:12,600 --> 00:33:13,680 Speaker 1: take away from this book. 626 00:33:14,040 --> 00:33:17,400 Speaker 2: I think they need to understand where this technology is 627 00:33:17,440 --> 00:33:19,840 Speaker 2: taking us. They need to understand something about the people 628 00:33:19,840 --> 00:33:23,800 Speaker 2: who are taking us towards this new world. Karp is 629 00:33:23,840 --> 00:33:27,600 Speaker 2: someone who's at the forefront now of the AI revolution, 630 00:33:27,880 --> 00:33:29,920 Speaker 2: and I think you need to understand what makes these 631 00:33:29,920 --> 00:33:31,920 Speaker 2: people tick. You need to understand how they think, how 632 00:33:31,960 --> 00:33:35,760 Speaker 2: they see the world, and I think you understanding Karp, 633 00:33:35,840 --> 00:33:38,920 Speaker 2: understanding Palenteer is important if you want to understand the 634 00:33:38,960 --> 00:33:39,760 Speaker 2: moment we're living through. 635 00:33:40,240 --> 00:33:42,680 Speaker 1: The book is The Philosopher in the Valley Alex Carp 636 00:33:42,720 --> 00:33:47,200 Speaker 1: Pallentier and the Rise of the Savannah State. Michael Steinberger, 637 00:33:47,240 --> 00:34:12,480 Speaker 1: thank you, thank you for tech stuff. I'mos Voloshin. This 638 00:34:12,600 --> 00:34:16,200 Speaker 1: episode was produced by Eliza Dennis. It was executive produced 639 00:34:16,200 --> 00:34:20,040 Speaker 1: by me and Julian Nutter for Kaleidoscope and Katrina norvelve 640 00:34:20,120 --> 00:34:24,040 Speaker 1: iHeart Podcasts. Jack Instantly mixed this episode and Kyle Murdoch 641 00:34:24,080 --> 00:34:24,960 Speaker 1: wrote alph theme song