1 00:00:02,560 --> 00:00:07,400 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,760 --> 00:00:12,880 Speaker 2: Max Chafkin, Stacy VANX Smith. I have a question for you, Shoot, 3 00:00:13,160 --> 00:00:14,400 Speaker 2: have you seen the Odyssey? 4 00:00:14,640 --> 00:00:15,200 Speaker 3: Stacy? 5 00:00:15,280 --> 00:00:16,880 Speaker 4: It is so hard to see the Odyssey. 6 00:00:16,920 --> 00:00:21,720 Speaker 5: I want to see the but I can. It's the 7 00:00:21,720 --> 00:00:23,840 Speaker 5: theaters are all sold out. There's the question of do 8 00:00:23,880 --> 00:00:25,599 Speaker 5: you see it in Imax? Do you see it on 9 00:00:25,640 --> 00:00:29,040 Speaker 5: a normal screen? Is the Imax screen you are watching 10 00:00:29,080 --> 00:00:31,520 Speaker 5: it on a real Imax screen, or is it Limax 11 00:00:31,560 --> 00:00:32,800 Speaker 5: which is like a fake Imax. 12 00:00:32,840 --> 00:00:36,000 Speaker 4: There's a whole bunch of stuff. But yes, there's also. 13 00:00:35,800 --> 00:00:39,680 Speaker 2: A huge secondary market for tickets, especially for Imax tickets, 14 00:00:39,760 --> 00:00:42,280 Speaker 2: and this is just the hottest ticket in town right now. 15 00:00:42,440 --> 00:00:45,000 Speaker 5: My favorite thing about the Odyssey, Stacey, is that the 16 00:00:45,040 --> 00:00:48,160 Speaker 5: Odyssey metaphors because it's such a universal story. We could 17 00:00:48,280 --> 00:00:51,440 Speaker 5: use it absolutely an economy. 18 00:00:50,920 --> 00:00:56,600 Speaker 2: Of twists and turns, from skylet a Sirens, It's all there. 19 00:00:57,120 --> 00:01:00,680 Speaker 2: So I propose that today, for this episode, we go 20 00:01:00,800 --> 00:01:03,480 Speaker 2: on a little bit of an odyssey of our own 21 00:01:04,120 --> 00:01:06,080 Speaker 2: into the US economy. 22 00:01:06,280 --> 00:01:08,360 Speaker 4: Okay, I feel like we do that every week, Stacey, 23 00:01:08,480 --> 00:01:09,840 Speaker 4: But I'm with you. 24 00:01:10,040 --> 00:01:18,280 Speaker 2: I'm branding it. That's the difference. This is everybody's business. 25 00:01:18,319 --> 00:01:19,400 Speaker 2: From Bloomberg Business Week. 26 00:01:19,400 --> 00:01:20,399 Speaker 6: I'm Stacey Mannix Smith. 27 00:01:20,560 --> 00:01:23,480 Speaker 2: I'm Max Schafkin, and today on the show Max, we 28 00:01:24,160 --> 00:01:26,640 Speaker 2: are going on an odyssey of our own into the 29 00:01:26,640 --> 00:01:29,240 Speaker 2: heart of the economy. We have got Rusher Sharma talking 30 00:01:29,280 --> 00:01:31,960 Speaker 2: about where we are in our economy right now and 31 00:01:32,160 --> 00:01:35,399 Speaker 2: why maybe the stock market is the economy. 32 00:01:35,760 --> 00:01:37,200 Speaker 5: Yeah, and we're going to be talking about one of 33 00:01:37,200 --> 00:01:42,200 Speaker 5: my favorite topics, how AI companies brand themselves. We've got 34 00:01:42,200 --> 00:01:45,600 Speaker 5: Sarah Fryer, Bloomberg Managing editor, author of a great book 35 00:01:45,640 --> 00:01:47,440 Speaker 5: on Instagram here to talk about. 36 00:01:47,240 --> 00:01:50,080 Speaker 6: That and how they are getting into the fashion business. 37 00:01:50,240 --> 00:01:51,520 Speaker 4: Yeah, they're one of my they are. 38 00:01:51,640 --> 00:01:54,760 Speaker 5: They are fashion houses now AI companies if you didn't know. 39 00:01:55,040 --> 00:01:55,680 Speaker 7: Absolutely. 40 00:01:55,760 --> 00:01:58,440 Speaker 2: We also have some great underrated stories. We have got 41 00:01:58,680 --> 00:02:01,720 Speaker 2: Python Pizza, and the new rat Dollar. 42 00:02:02,200 --> 00:02:06,680 Speaker 5: And a sequel, a potential sequel to the Odyssey. You 43 00:02:06,720 --> 00:02:08,400 Speaker 5: may not have seen it yet, but there's another one 44 00:02:08,480 --> 00:02:09,919 Speaker 5: coming by the end. 45 00:02:09,880 --> 00:02:12,360 Speaker 2: Of the year, maybe ever so slightly less great than 46 00:02:12,360 --> 00:02:24,920 Speaker 2: this one. So Max, Americans feel awful about the economy 47 00:02:25,000 --> 00:02:27,840 Speaker 2: right now, which is kind of strange because in a 48 00:02:27,840 --> 00:02:32,240 Speaker 2: lot of ways the economy is actually doing pretty well. 49 00:02:32,360 --> 00:02:35,960 Speaker 2: But you know, I think people are feeling like we're 50 00:02:36,000 --> 00:02:37,400 Speaker 2: not going in a good direction. 51 00:02:38,400 --> 00:02:38,760 Speaker 4: Yeah. 52 00:02:38,880 --> 00:02:41,840 Speaker 5: I mean, on one hand, you have the stock market 53 00:02:42,080 --> 00:02:45,399 Speaker 5: is ripping, you have the job market is okay. 54 00:02:45,440 --> 00:02:46,160 Speaker 4: We've talked about this. 55 00:02:46,200 --> 00:02:49,240 Speaker 5: On the other hand, even today, oil prices going above 56 00:02:49,280 --> 00:02:51,760 Speaker 5: one hundred today as we record this amid a new 57 00:02:52,120 --> 00:02:53,680 Speaker 5: you know, sort of back and forth with a ran, 58 00:02:54,560 --> 00:02:55,320 Speaker 5: a lot uncertainty. 59 00:02:55,320 --> 00:02:56,320 Speaker 4: I don't know, it's a weird time. 60 00:02:56,840 --> 00:02:58,720 Speaker 2: So here to help us make sense of this moment 61 00:02:58,960 --> 00:03:02,840 Speaker 2: is Rusher Sharmat, chairman of Rockefeller International and author of 62 00:03:02,880 --> 00:03:04,360 Speaker 2: What Went Wrong with Capitalism? 63 00:03:04,480 --> 00:03:05,240 Speaker 6: Welcomer Share. 64 00:03:05,440 --> 00:03:07,639 Speaker 3: Thanks so much, Stacye for having me so. 65 00:03:07,880 --> 00:03:10,520 Speaker 2: Before we kind of dive into the particulars, i'd love 66 00:03:10,520 --> 00:03:13,360 Speaker 2: to get your overview of the economy right now, because 67 00:03:13,360 --> 00:03:15,560 Speaker 2: in a lot of ways it's quite strong, but also 68 00:03:15,919 --> 00:03:17,920 Speaker 2: what I consider to be kind of two of the 69 00:03:17,960 --> 00:03:20,640 Speaker 2: main pillars of it, that being the job market and 70 00:03:20,680 --> 00:03:25,400 Speaker 2: inflation have been kind of middling and seemingly going in 71 00:03:25,440 --> 00:03:29,240 Speaker 2: a circle of sorts for years now. When you look 72 00:03:29,240 --> 00:03:30,720 Speaker 2: at the economy, what do you see? 73 00:03:31,040 --> 00:03:34,000 Speaker 8: Well, I see a very big disconnect, a big disconnect 74 00:03:34,040 --> 00:03:37,160 Speaker 8: which has never been there before, between what the sentiment 75 00:03:37,200 --> 00:03:40,400 Speaker 8: surveys are telling you and what economic growth is actually doing. 76 00:03:40,840 --> 00:03:43,880 Speaker 8: And there's a perfectly logical explanation for why this is happening, 77 00:03:44,480 --> 00:03:48,400 Speaker 8: because the economic growth is being entirely driven by the 78 00:03:48,440 --> 00:03:51,480 Speaker 8: top ten or twenty percent. If you look at all 79 00:03:51,520 --> 00:03:55,400 Speaker 8: the spending, all the wealth creation which is accruing to 80 00:03:55,480 --> 00:03:59,160 Speaker 8: them from the stock market, it's all concentrated. 81 00:03:58,440 --> 00:03:59,320 Speaker 3: In that top cohort. 82 00:04:00,040 --> 00:04:04,400 Speaker 8: So now the surveys which people take, those are asking 83 00:04:04,440 --> 00:04:07,600 Speaker 8: the average person how they're feeling, and the average person 84 00:04:07,840 --> 00:04:10,280 Speaker 8: is not doing that well. If you look at what 85 00:04:10,400 --> 00:04:14,160 Speaker 8: rail wages are doing, particularly after inflation, and then also 86 00:04:14,240 --> 00:04:16,960 Speaker 8: if you look at basic stuff that can they afford 87 00:04:16,960 --> 00:04:22,240 Speaker 8: a home, the ownership of equities, it's all concentrated in 88 00:04:22,279 --> 00:04:26,600 Speaker 8: the older generation. Very few of the young generation have 89 00:04:26,760 --> 00:04:30,120 Speaker 8: that much equity wealth, and that's what's generating so much 90 00:04:30,120 --> 00:04:33,040 Speaker 8: of the buzz in the economy today, the wealth creation coming. 91 00:04:32,920 --> 00:04:33,960 Speaker 3: From the stock market. 92 00:04:34,200 --> 00:04:36,279 Speaker 8: And then there is the relative argument, which is that 93 00:04:36,360 --> 00:04:39,679 Speaker 8: as you know that even today, surveys show that people 94 00:04:39,720 --> 00:04:42,960 Speaker 8: with three hundred thousand or four hundred thousand of earnings 95 00:04:43,000 --> 00:04:47,160 Speaker 8: a year, they are also feeling miserable, partly because of 96 00:04:47,240 --> 00:04:50,520 Speaker 8: social media and other things where we're always hearing about 97 00:04:50,520 --> 00:04:53,839 Speaker 8: how the billionaires or the multi billionaires are doing so well, 98 00:04:54,040 --> 00:04:56,400 Speaker 8: and so that sort of feeds even more insecurity. So 99 00:04:56,440 --> 00:04:59,599 Speaker 8: as you know, so much of how you feel is 100 00:04:59,640 --> 00:05:01,960 Speaker 8: a really game, yeah, and so we look at the 101 00:05:02,000 --> 00:05:04,359 Speaker 8: other people who are doing really well, and then we 102 00:05:04,400 --> 00:05:07,120 Speaker 8: look at ourselves and we're comparing our insights to their 103 00:05:07,160 --> 00:05:09,919 Speaker 8: outside right, and so we are feeling miserable about it. 104 00:05:09,960 --> 00:05:12,640 Speaker 8: So I think therefore this massive gap is opened up 105 00:05:13,000 --> 00:05:16,159 Speaker 8: between how the real economy is doing and how the 106 00:05:16,760 --> 00:05:20,200 Speaker 8: average person is feeling. And so the average person, they're 107 00:05:20,200 --> 00:05:23,320 Speaker 8: not feeling that great about the economy for understandable reasons. 108 00:05:24,200 --> 00:05:26,599 Speaker 2: So one thing I wanted to ask about, what within 109 00:05:26,640 --> 00:05:30,200 Speaker 2: the gains of the stock market is almost a reflection 110 00:05:30,440 --> 00:05:33,320 Speaker 2: of how there's a top cohort of consumers driving all 111 00:05:33,320 --> 00:05:36,159 Speaker 2: consumer spending. There are a few stocks that are really 112 00:05:36,320 --> 00:05:37,880 Speaker 2: driving the gains in the stock market. 113 00:05:38,760 --> 00:05:40,880 Speaker 6: Does this put us in a precarious position? 114 00:05:40,920 --> 00:05:42,640 Speaker 2: You've written about this a little bit also in What 115 00:05:42,680 --> 00:05:45,440 Speaker 2: Went Wrong with Capitalism? You talk about the dangers of 116 00:05:45,480 --> 00:05:48,640 Speaker 2: companies getting so big they're kind of tangled up with 117 00:05:48,760 --> 00:05:49,279 Speaker 2: the government. 118 00:05:49,839 --> 00:05:52,200 Speaker 6: Do you see that moment happening right now? 119 00:05:52,240 --> 00:05:55,159 Speaker 2: How do you look at this moment where we have 120 00:05:55,320 --> 00:05:58,479 Speaker 2: a few companies driving you know, gains that we've never 121 00:05:58,560 --> 00:05:59,720 Speaker 2: seen before in the stock market. 122 00:05:59,800 --> 00:06:03,160 Speaker 8: Yeah, that's the problem of the entire economy today, which 123 00:06:03,240 --> 00:06:05,400 Speaker 8: is that it's not just stock market, but this concentration 124 00:06:05,440 --> 00:06:07,760 Speaker 8: which is happening is happening at a corporate level as well. 125 00:06:08,279 --> 00:06:10,320 Speaker 8: In three out of four industries. We have seen much 126 00:06:10,320 --> 00:06:15,120 Speaker 8: greater concentration over the last few decades take place, and 127 00:06:15,160 --> 00:06:17,640 Speaker 8: the stock market has never been this concentrated. And of 128 00:06:17,640 --> 00:06:20,719 Speaker 8: course this is a unique moment because we're seeing this 129 00:06:20,839 --> 00:06:24,080 Speaker 8: incredible AI boom and that's what's driving a lot of 130 00:06:24,080 --> 00:06:26,360 Speaker 8: the returns in the stock market as well. 131 00:06:26,440 --> 00:06:28,000 Speaker 3: So it's I think the two factors. 132 00:06:28,040 --> 00:06:31,000 Speaker 8: One that over time we have seen that the gains 133 00:06:31,000 --> 00:06:33,760 Speaker 8: have become increasingly concentrated. That's reflected today in the S 134 00:06:33,800 --> 00:06:36,680 Speaker 8: and P. Five hundred that it's never been this concentrated, 135 00:06:36,839 --> 00:06:40,640 Speaker 8: that the top ten companies are, you know whatever, nearly 136 00:06:40,680 --> 00:06:43,240 Speaker 8: forty percent of the entire market cap or so. But 137 00:06:43,320 --> 00:06:45,200 Speaker 8: the other thing which is going on also is that 138 00:06:45,520 --> 00:06:47,680 Speaker 8: and this is happening across the world, which is that 139 00:06:47,680 --> 00:06:51,119 Speaker 8: there's only one factor driving returns today, which is AI. 140 00:06:51,760 --> 00:06:53,720 Speaker 8: So it's like across the world, what you're seeing is 141 00:06:54,120 --> 00:06:57,360 Speaker 8: AI or bye bye, right, which either you have AI 142 00:06:57,880 --> 00:07:00,520 Speaker 8: or you don't if you don't have AI stock any 143 00:07:00,560 --> 00:07:04,880 Speaker 8: stock market around the world. The positive returns this year 144 00:07:05,040 --> 00:07:09,560 Speaker 8: have almost all been driven by which countries are doing 145 00:07:09,560 --> 00:07:11,680 Speaker 8: well on AI and which and which countries are not 146 00:07:11,720 --> 00:07:14,720 Speaker 8: doing well on AI they in fact have negative returns. 147 00:07:14,960 --> 00:07:17,560 Speaker 8: So that's the sort of world order we have been 148 00:07:17,880 --> 00:07:19,640 Speaker 8: for the first six seven months of this year. 149 00:07:19,720 --> 00:07:22,480 Speaker 5: When you say it's being driven by AI, I'm kind 150 00:07:22,480 --> 00:07:24,360 Speaker 5: of curious what you mean, because I almost would say 151 00:07:24,360 --> 00:07:28,200 Speaker 5: it's being driven by AI hype or AI enthusiasm, like, 152 00:07:28,360 --> 00:07:31,440 Speaker 5: because there is this, there's a story we could tell. 153 00:07:31,920 --> 00:07:34,440 Speaker 4: It's not happened yet. I don't think where AI. 154 00:07:35,080 --> 00:07:37,760 Speaker 5: Everyone you know buys a bunch of GPUs and we 155 00:07:37,800 --> 00:07:41,040 Speaker 5: all get large language models and we do our job 156 00:07:41,160 --> 00:07:41,720 Speaker 5: so much better. 157 00:07:41,760 --> 00:07:44,160 Speaker 4: We make twice as many podcast episodes. 158 00:07:43,760 --> 00:07:46,400 Speaker 5: In the economy dooms, and it's amazing that has not 159 00:07:46,480 --> 00:07:49,480 Speaker 5: happened yet. I don't think we're still making the same 160 00:07:49,560 --> 00:07:52,880 Speaker 5: number of podcast episodes each week. But so what is 161 00:07:52,920 --> 00:07:55,160 Speaker 5: when you say AI is driving and what are we 162 00:07:55,240 --> 00:07:55,960 Speaker 5: actually saying? 163 00:07:56,160 --> 00:07:58,320 Speaker 3: Well, it's the AI spend which is driving it, right. 164 00:07:58,320 --> 00:08:01,200 Speaker 4: So like people buying stuff to do AI. 165 00:08:01,440 --> 00:08:03,840 Speaker 8: That's right, sure, yeah, So this is a massive investment 166 00:08:03,920 --> 00:08:06,200 Speaker 8: cycle which is going on around the world. In fact, 167 00:08:06,560 --> 00:08:09,880 Speaker 8: this is the fastest increase we have seen in any 168 00:08:10,080 --> 00:08:14,120 Speaker 8: investment cycle, in any technological breakthrough. And this is where 169 00:08:14,120 --> 00:08:16,800 Speaker 8: I've been writing a lot about this, which is that 170 00:08:16,840 --> 00:08:21,880 Speaker 8: the greater the technological breakthrough, the greater the financial bubble 171 00:08:21,920 --> 00:08:24,880 Speaker 8: tends to be. That's the three hundred year history of bubbles. 172 00:08:25,160 --> 00:08:27,520 Speaker 8: Because the excitement tends to be huge, you end up 173 00:08:27,560 --> 00:08:31,000 Speaker 8: spending a lot, the lot of overinvestment, which happens, and 174 00:08:31,040 --> 00:08:33,800 Speaker 8: then what happens typically is that after a while, when 175 00:08:33,800 --> 00:08:37,400 Speaker 8: this investment goes on, you typically get up an increase 176 00:08:37,400 --> 00:08:40,120 Speaker 8: in interest rates for some reason, and that's what ends 177 00:08:40,200 --> 00:08:40,600 Speaker 8: the bubble. 178 00:08:41,040 --> 00:08:42,520 Speaker 3: So I'm very clear about this, which is. 179 00:08:42,520 --> 00:08:46,000 Speaker 8: That the technology is great, and you're totally correct that 180 00:08:46,040 --> 00:08:48,320 Speaker 8: there's a lot of hype around it as well. It's 181 00:08:48,400 --> 00:08:52,400 Speaker 8: driving this massive investment boom around the world. That spend 182 00:08:52,440 --> 00:08:54,719 Speaker 8: is showing up in the GDP numbers also going up. 183 00:08:55,200 --> 00:08:58,079 Speaker 8: But the fault line here is that this is happening 184 00:08:58,120 --> 00:09:01,560 Speaker 8: at a time when money is relatively easy available that 185 00:09:01,640 --> 00:09:04,880 Speaker 8: if you look at interest rates today, by any measure, 186 00:09:05,240 --> 00:09:10,000 Speaker 8: interest rates are relatively low after adjusting for inflation. Uh So, 187 00:09:10,080 --> 00:09:12,120 Speaker 8: if you end up getting any any increase in interest rates, 188 00:09:12,160 --> 00:09:14,800 Speaker 8: I think this AI hype and this AI bubble comes 189 00:09:14,840 --> 00:09:15,280 Speaker 8: to an end. 190 00:09:16,160 --> 00:09:19,319 Speaker 2: You're saying, there is an AI bubble. Yeah, what happens 191 00:09:19,320 --> 00:09:20,840 Speaker 2: when this bubble pops? 192 00:09:21,240 --> 00:09:23,840 Speaker 8: I think then be a left with a massive amount 193 00:09:23,880 --> 00:09:27,560 Speaker 8: of overinvestment and it will be a long clean out 194 00:09:27,600 --> 00:09:29,600 Speaker 8: of the cycle and a bubble. You typically tend to 195 00:09:29,640 --> 00:09:35,760 Speaker 8: get over investment, you get overvaluation, you get over leverage, 196 00:09:36,280 --> 00:09:37,840 Speaker 8: and you end up getting over ownership. 197 00:09:38,360 --> 00:09:40,000 Speaker 3: So those are the four ownership. Yeah. 198 00:09:40,000 --> 00:09:42,520 Speaker 8: So that's a very interesting uh, which is that a 199 00:09:42,640 --> 00:09:46,200 Speaker 8: lot of people own the same stocks and they own 200 00:09:47,080 --> 00:09:49,400 Speaker 8: it in big size. Now, one very interesting thing which 201 00:09:49,400 --> 00:09:52,160 Speaker 8: has happened here, going back to the top of our discussion, 202 00:09:52,720 --> 00:09:56,480 Speaker 8: is that today, for the first time in America's history, 203 00:09:57,240 --> 00:10:02,400 Speaker 8: more of America's wealth is in the stock market then 204 00:10:02,440 --> 00:10:04,599 Speaker 8: in property. 205 00:10:04,400 --> 00:10:06,880 Speaker 2: Oh wow, rather than in their homes, which is in 206 00:10:06,920 --> 00:10:09,439 Speaker 2: the traditional story, rather than their own homes or even 207 00:10:09,480 --> 00:10:10,160 Speaker 2: a second home. 208 00:10:10,640 --> 00:10:12,160 Speaker 8: More of the wealth today, So if you look at 209 00:10:12,160 --> 00:10:17,000 Speaker 8: the total bucket today, more than thirty percent of America's 210 00:10:17,000 --> 00:10:19,840 Speaker 8: wealth today is in the stock market. And that is 211 00:10:19,840 --> 00:10:22,520 Speaker 8: slightly above for the first time crossed over how much 212 00:10:22,600 --> 00:10:23,400 Speaker 8: is in the property. 213 00:10:23,559 --> 00:10:27,480 Speaker 5: That's crazy, especially given how home prices have been gone 214 00:10:27,559 --> 00:10:30,120 Speaker 5: up a lot. And so you brought this up at 215 00:10:30,160 --> 00:10:34,320 Speaker 5: the beginning of our conversation, the kind of disconnect between 216 00:10:34,360 --> 00:10:38,120 Speaker 5: the way people feel and the way the economy actually is. 217 00:10:38,400 --> 00:10:41,960 Speaker 5: And I'm curious you have on one hand, AI kind 218 00:10:42,000 --> 00:10:45,080 Speaker 5: of causing the economy to go berserk, you know, firing 219 00:10:45,080 --> 00:10:47,720 Speaker 5: on all cylinders, and then you also have this story 220 00:10:47,720 --> 00:10:50,360 Speaker 5: that's happening at the same time about the same technology 221 00:10:50,559 --> 00:10:52,920 Speaker 5: that's like it's going to take your jobs. It's going 222 00:10:52,960 --> 00:10:54,440 Speaker 5: to be scary. I mean, we'll get to this in 223 00:10:54,480 --> 00:10:56,360 Speaker 5: the next segment, but I'm kind of curious how you 224 00:10:56,400 --> 00:10:58,280 Speaker 5: see those things playing against each other. 225 00:10:58,360 --> 00:11:01,520 Speaker 8: This is what's unique about this book that historically, with 226 00:11:01,800 --> 00:11:04,920 Speaker 8: the Internet or other technological progresses, there was a lot 227 00:11:04,920 --> 00:11:08,240 Speaker 8: of excitement even amongst the average person. This is possibly 228 00:11:08,320 --> 00:11:12,200 Speaker 8: the most hated technological innovation of all time, you know, 229 00:11:12,240 --> 00:11:15,040 Speaker 8: like as a boom, partly because the tech bros and 230 00:11:15,080 --> 00:11:17,280 Speaker 8: everyone is going out there had been telling you that, 231 00:11:17,720 --> 00:11:20,920 Speaker 8: you know, like you're gonna have ten twenty percent unemployment, 232 00:11:21,480 --> 00:11:23,720 Speaker 8: and you're sitting there at home and you're hearing about 233 00:11:23,720 --> 00:11:26,240 Speaker 8: all these cases of how the data centers these boxes 234 00:11:26,240 --> 00:11:29,240 Speaker 8: are coming up somewhere, and that your electricity bill is 235 00:11:29,280 --> 00:11:29,760 Speaker 8: going up. 236 00:11:30,160 --> 00:11:30,360 Speaker 2: You know. 237 00:11:30,480 --> 00:11:33,240 Speaker 8: So now, while none of this has really happened as 238 00:11:33,240 --> 00:11:35,480 Speaker 8: far as job losses are concerned in any size, the 239 00:11:35,800 --> 00:11:39,560 Speaker 8: employment rate is still relatively healthy. But I'd say that 240 00:11:39,720 --> 00:11:42,840 Speaker 8: the feeling which has been conveyed is that this is 241 00:11:42,880 --> 00:11:43,760 Speaker 8: coming for you. 242 00:11:43,800 --> 00:11:45,840 Speaker 4: No, noan you better get with it now. Yeah, you're 243 00:11:45,880 --> 00:11:46,640 Speaker 4: gonna get left behind. 244 00:11:46,679 --> 00:11:47,559 Speaker 3: Yeah. And it's also like. 245 00:11:47,520 --> 00:11:49,840 Speaker 8: I think there's also like also been a bit of arrogance, 246 00:11:49,880 --> 00:11:51,559 Speaker 8: you know, like in some of these closed door meetings 247 00:11:51,559 --> 00:11:54,319 Speaker 8: you know, of some of these top tech people, they 248 00:11:54,400 --> 00:11:56,240 Speaker 8: sort of you know, they're sort of attitude. And I've 249 00:11:56,240 --> 00:11:59,440 Speaker 8: seen this is the fact that listen, this is happening. 250 00:11:59,679 --> 00:12:02,079 Speaker 8: We have to win this arms race. You better get 251 00:12:02,120 --> 00:12:04,199 Speaker 8: in line. And if we don't get in line, you. 252 00:12:04,160 --> 00:12:04,800 Speaker 3: Know, too bad. 253 00:12:05,360 --> 00:12:08,720 Speaker 2: I wanted to ask you, like relating back to your book, 254 00:12:09,360 --> 00:12:14,560 Speaker 2: is this capitalism sort of succeeding until it fails? Do 255 00:12:14,600 --> 00:12:18,040 Speaker 2: you see this as a moment of capitalism success and 256 00:12:18,120 --> 00:12:21,960 Speaker 2: promise or why capitalism isn't working? 257 00:12:22,280 --> 00:12:22,319 Speaker 5: No? 258 00:12:22,480 --> 00:12:25,000 Speaker 8: For me, as I said that, I am a huge 259 00:12:25,000 --> 00:12:28,560 Speaker 8: believer in capitalism. Okay, but I feel but I'm very 260 00:12:28,600 --> 00:12:32,080 Speaker 8: distressed by the fact that today most Americans, young Americans 261 00:12:32,080 --> 00:12:35,440 Speaker 8: in particular, have more of a positive view of socialism 262 00:12:35,440 --> 00:12:36,160 Speaker 8: than capitalism. 263 00:12:36,400 --> 00:12:38,360 Speaker 2: I mean, look at the city we're in in our 264 00:12:38,400 --> 00:12:40,200 Speaker 2: mayor who is a socialist democrat. 265 00:12:40,440 --> 00:12:43,120 Speaker 8: And that's why, like I wrote this book to show 266 00:12:43,120 --> 00:12:46,000 Speaker 8: the history of capitalism and to argue in the book 267 00:12:46,200 --> 00:12:50,240 Speaker 8: that capitalism hasn't failed. The current system we have is broken, 268 00:12:50,320 --> 00:12:53,640 Speaker 8: and that what we have today is not capitalism, that 269 00:12:53,720 --> 00:12:57,400 Speaker 8: it is a very distorted form of capitalism which favors 270 00:12:57,400 --> 00:13:00,640 Speaker 8: the incumbents, where the government keeps on in devening to 271 00:13:00,720 --> 00:13:05,640 Speaker 8: help the establishment out there. So capitalism did not fail 272 00:13:05,800 --> 00:13:08,920 Speaker 8: big government which has failed it. And then the government 273 00:13:08,920 --> 00:13:12,199 Speaker 8: policies progressively over the last forty to fifty years, whether 274 00:13:12,240 --> 00:13:16,480 Speaker 8: it's regulation or its monetary policy, or it's the culture 275 00:13:16,480 --> 00:13:20,240 Speaker 8: of bailouts, all these have helped the established players. And 276 00:13:20,280 --> 00:13:23,160 Speaker 8: that's what's fueling the resentment amongst the average person. They're like, 277 00:13:23,600 --> 00:13:27,040 Speaker 8: you know that the government seems to only help like 278 00:13:27,080 --> 00:13:30,559 Speaker 8: the rich, you know, like they socialize their losses when 279 00:13:30,559 --> 00:13:33,880 Speaker 8: they're in trouble, and for us, we don't get that 280 00:13:34,000 --> 00:13:35,440 Speaker 8: much benefit out of the system. 281 00:13:35,600 --> 00:13:37,840 Speaker 6: All right, Well, Ruchier, thank you so much for talking 282 00:13:37,880 --> 00:13:38,280 Speaker 6: with us. 283 00:13:38,360 --> 00:13:41,080 Speaker 2: It's been great having you on this journey with us 284 00:13:41,120 --> 00:13:42,600 Speaker 2: into the part of the economy. 285 00:13:42,760 --> 00:13:47,280 Speaker 5: We'll have you back when we bail out those aiss. 286 00:13:48,200 --> 00:13:51,439 Speaker 8: Yeah, we'll justify it saying China, you know, is doing X. 287 00:13:51,440 --> 00:13:51,960 Speaker 4: Of course we. 288 00:13:52,040 --> 00:13:52,480 Speaker 3: Got to do it. 289 00:13:53,280 --> 00:13:56,960 Speaker 2: It's a matter of national security. Yeah, thanks for share. 290 00:13:57,080 --> 00:14:05,120 Speaker 4: Thank you, Stacy. 291 00:14:05,320 --> 00:14:08,200 Speaker 5: You know that I consider myself to be a fashionable guy, 292 00:14:08,280 --> 00:14:11,240 Speaker 5: somebody who's always trying to look look, did not know, 293 00:14:11,440 --> 00:14:15,920 Speaker 5: constantly updating my look, checking out what the new designers. 294 00:14:16,520 --> 00:14:19,040 Speaker 5: I'm kind of kidding, but I bring this up because 295 00:14:19,040 --> 00:14:22,000 Speaker 5: there's some hot new looks that have come out from 296 00:14:22,080 --> 00:14:26,360 Speaker 5: some very exciting new designers. Yes, tell us about these 297 00:14:26,760 --> 00:14:31,400 Speaker 5: these companies, the big new fashion firms, Open AI, the 298 00:14:31,440 --> 00:14:34,000 Speaker 5: AI company, anthropic. 299 00:14:34,480 --> 00:14:37,200 Speaker 6: Companies, fashion, housing and talent. 300 00:14:37,360 --> 00:14:41,240 Speaker 5: Here, the the slightly big brother is surveillance thing that's 301 00:14:41,360 --> 00:14:44,800 Speaker 5: AI coded. I bring this all up because AI is 302 00:14:44,800 --> 00:14:47,480 Speaker 5: in kind of a weird moment where you have people 303 00:14:47,480 --> 00:14:50,200 Speaker 5: with very strong feelings about it, and the AI companies 304 00:14:50,280 --> 00:14:53,360 Speaker 5: kind of frantically trying to figure out how to turn 305 00:14:53,400 --> 00:14:55,480 Speaker 5: those strong feelings around. We have a great person to 306 00:14:55,480 --> 00:14:58,920 Speaker 5: talk about this. It's Sarah Fryer. She is Bloomberg's managing 307 00:14:59,000 --> 00:15:02,840 Speaker 5: editor for Technology. She wrote the book No Filter, which 308 00:15:02,840 --> 00:15:05,960 Speaker 5: is about Instagram. She's kind of an expert in how 309 00:15:06,360 --> 00:15:09,720 Speaker 5: big tech companies market themselves, how they sell themselves to 310 00:15:09,760 --> 00:15:10,160 Speaker 5: the world. 311 00:15:10,480 --> 00:15:13,280 Speaker 4: Hey, Sarah, Hello, all right, I don't know if you've 312 00:15:13,280 --> 00:15:13,640 Speaker 4: seen this. 313 00:15:13,720 --> 00:15:15,400 Speaker 5: I don't know if Stacey's seen it, and I'm guessing 314 00:15:15,440 --> 00:15:17,280 Speaker 5: some listeners haven't seen it. So we should just pull 315 00:15:17,360 --> 00:15:19,800 Speaker 5: up a couple of these Jasmine. Can you just just 316 00:15:19,880 --> 00:15:23,240 Speaker 5: cycle through some of these hot new looks that I'm 317 00:15:23,280 --> 00:15:24,239 Speaker 5: talking about. 318 00:15:24,880 --> 00:15:28,640 Speaker 4: So this is a beautiful fleece like a pullover go over, 319 00:15:28,680 --> 00:15:29,600 Speaker 4: it says research. 320 00:15:29,920 --> 00:15:32,960 Speaker 5: There's some really good looks from Palentier, as I mentioned 321 00:15:33,280 --> 00:15:34,400 Speaker 5: Drop sixteen. 322 00:15:34,680 --> 00:15:37,120 Speaker 4: Yeah, Palenteer, I guess white is the thing. 323 00:15:37,160 --> 00:15:39,720 Speaker 6: They've got all these tennis white tennis whites. 324 00:15:39,880 --> 00:15:42,720 Speaker 4: There's a tennis ball with the Palenteer stock ticker. 325 00:15:42,960 --> 00:15:44,840 Speaker 6: Why are they associated with tennis? 326 00:15:45,240 --> 00:15:49,120 Speaker 2: They have like a tennis ball, a tennis skirt, tennis 327 00:15:49,160 --> 00:15:51,600 Speaker 2: patches and oh this one I knew know about. 328 00:15:51,600 --> 00:15:52,240 Speaker 6: The work cooat. 329 00:15:52,600 --> 00:15:54,880 Speaker 2: The work Cooat, I've heard is quite a hot item. 330 00:15:55,040 --> 00:15:57,200 Speaker 2: It's a couple hundred dollars and it keeps selling out 331 00:15:57,240 --> 00:15:59,840 Speaker 2: and at a couple points I think crashed the site. 332 00:16:00,200 --> 00:16:03,800 Speaker 5: Yeah, and Anthropic not to be outdone, has their own 333 00:16:04,280 --> 00:16:06,400 Speaker 5: kind of fashiony thing. 334 00:16:06,880 --> 00:16:09,680 Speaker 4: Here's a hat is thinking. 335 00:16:09,400 --> 00:16:10,600 Speaker 6: On a thinking cap. 336 00:16:11,600 --> 00:16:15,320 Speaker 5: They open like a pop up store, which I think 337 00:16:15,360 --> 00:16:16,200 Speaker 5: we'll see in a second. 338 00:16:16,240 --> 00:16:17,680 Speaker 7: Oh yeah, it's in the West Village. 339 00:16:17,760 --> 00:16:18,720 Speaker 4: Yeah, in the West Village. 340 00:16:18,840 --> 00:16:21,640 Speaker 6: What, oh my gosh, a clad clothing store. 341 00:16:21,720 --> 00:16:23,560 Speaker 4: So, Sarah, what's going on here? 342 00:16:23,880 --> 00:16:30,040 Speaker 5: Like, why are these companies that are saying, on one hand, 343 00:16:30,200 --> 00:16:32,480 Speaker 5: we need to spend all our money to buy these 344 00:16:32,680 --> 00:16:35,840 Speaker 5: very expensive GPU chips, so we can, you know, basically 345 00:16:35,880 --> 00:16:39,800 Speaker 5: lock ourselves in a closet, do prompts all day and eventually, 346 00:16:39,840 --> 00:16:42,000 Speaker 5: I don't know, cure cancer or make the world a 347 00:16:42,040 --> 00:16:42,480 Speaker 5: better place. 348 00:16:42,520 --> 00:16:44,160 Speaker 4: Like why are they selling all this merch. 349 00:16:44,080 --> 00:16:47,160 Speaker 1: If you're building an AI, one of the biggest things 350 00:16:47,200 --> 00:16:50,640 Speaker 1: you're running up against is this idea that AI doesn't 351 00:16:50,640 --> 00:16:53,720 Speaker 1: have taste. It might have information, it might have facts, 352 00:16:53,720 --> 00:16:56,800 Speaker 1: It might have resources for you, advice. It might be 353 00:16:56,840 --> 00:17:00,400 Speaker 1: able to code you a personal website or you know, 354 00:17:00,480 --> 00:17:02,960 Speaker 1: give you tips on how to declutter. 355 00:17:02,520 --> 00:17:04,040 Speaker 7: Your home, do your practice for you. 356 00:17:04,280 --> 00:17:07,760 Speaker 1: Yeah, it does not have fashion sense, it does not 357 00:17:07,960 --> 00:17:13,800 Speaker 1: have coolness. It doesn't have the stuff we're looking for 358 00:17:13,880 --> 00:17:17,359 Speaker 1: from humans, which is creativity, new ideas, like a new 359 00:17:17,359 --> 00:17:21,000 Speaker 1: way of living that is inspirational. It might inspire me 360 00:17:22,000 --> 00:17:26,439 Speaker 1: in you know, very convenient ways, but not in ways 361 00:17:26,440 --> 00:17:29,600 Speaker 1: that like will light my brain up. So I think 362 00:17:29,600 --> 00:17:32,479 Speaker 1: what they're trying to do here is they're trying to say, 363 00:17:32,880 --> 00:17:35,880 Speaker 1: if people buy this stuff, they'll go around and give 364 00:17:35,920 --> 00:17:39,879 Speaker 1: it a cool factor. Honestly, it feels to me like 365 00:17:39,960 --> 00:17:42,840 Speaker 1: people might be buying it. Ironically, it might be a 366 00:17:42,840 --> 00:17:47,359 Speaker 1: conversation starter. Honestly, if I'm wearing a hat that says 367 00:17:47,359 --> 00:17:48,600 Speaker 1: thinking that is from Claude. 368 00:17:48,760 --> 00:17:52,520 Speaker 2: Do you think there is an image problem among AI companies? 369 00:17:52,640 --> 00:17:56,080 Speaker 2: Is this an attempt to deal with that or is 370 00:17:56,119 --> 00:17:56,879 Speaker 2: this something else? 371 00:17:57,000 --> 00:17:59,160 Speaker 1: This is an attempt to deal with the blandness problem. 372 00:17:59,200 --> 00:18:02,920 Speaker 1: I think that is it's very predictable. I mean, people 373 00:18:02,960 --> 00:18:06,160 Speaker 1: make fun of like the M dashes or the it's 374 00:18:06,200 --> 00:18:08,159 Speaker 1: not this, it's that, you know, all these things that 375 00:18:08,240 --> 00:18:11,720 Speaker 1: come as like the way of talking that is so 376 00:18:11,960 --> 00:18:17,280 Speaker 1: recognizably from a chatbot, that they're trying to say that 377 00:18:17,680 --> 00:18:19,520 Speaker 1: there's a different flavor if you go to this one 378 00:18:19,640 --> 00:18:23,320 Speaker 1: versus that one. People might stand chat TPT or they 379 00:18:23,400 --> 00:18:27,360 Speaker 1: might be more of a cloud person, and the more 380 00:18:27,520 --> 00:18:31,359 Speaker 1: they can connect it to personal identity, the more staying 381 00:18:31,400 --> 00:18:33,639 Speaker 1: power they have. Look at the market right now, like 382 00:18:33,680 --> 00:18:36,159 Speaker 1: everyone's got a chatbot. They have to build stuff on 383 00:18:36,240 --> 00:18:39,320 Speaker 1: top of the chatbot to make it a desirable chatbot 384 00:18:39,800 --> 00:18:41,919 Speaker 1: that you want to keep coming back to instead of 385 00:18:41,920 --> 00:18:45,159 Speaker 1: going to Gemini, instead of going to Grock, Like, you 386 00:18:45,720 --> 00:18:48,760 Speaker 1: want to have people stay with the one that they 387 00:18:48,840 --> 00:18:54,159 Speaker 1: identify with and build perhaps a relationship with a dependability. 388 00:18:53,440 --> 00:18:56,400 Speaker 5: With I will say that a lot of these slides 389 00:18:56,400 --> 00:18:59,439 Speaker 5: that I just showed, they sort of look derivative in 390 00:18:59,480 --> 00:19:00,800 Speaker 5: their own way, almost like. 391 00:19:00,760 --> 00:19:04,520 Speaker 4: A chat GPT version of fashion or something. 392 00:19:04,640 --> 00:19:08,960 Speaker 5: I mean, it's like the anthropic one particularly looks like 393 00:19:09,720 --> 00:19:12,600 Speaker 5: a store that was cool around the time that the 394 00:19:12,640 --> 00:19:16,919 Speaker 5: average anthropic engineer locked themselves in and started you know, 395 00:19:17,040 --> 00:19:18,879 Speaker 5: vibe coding or whatever. 396 00:19:18,960 --> 00:19:19,080 Speaker 4: Like. 397 00:19:19,240 --> 00:19:20,879 Speaker 5: But putting that aside, and a bunch of people have 398 00:19:20,880 --> 00:19:23,800 Speaker 5: criticized these, They're getting dunked on for being kind of 399 00:19:23,840 --> 00:19:25,560 Speaker 5: like try to trying. 400 00:19:27,560 --> 00:19:30,200 Speaker 1: When it comes to like increasing awareness of the brand. 401 00:19:30,280 --> 00:19:32,359 Speaker 5: You know, Okay, but just put all that aside, because 402 00:19:32,400 --> 00:19:34,679 Speaker 5: I do think some people think these are cool. Stacy 403 00:19:34,760 --> 00:19:37,600 Speaker 5: mentioned the chore code, and it does seem like, Okay, 404 00:19:37,640 --> 00:19:39,840 Speaker 5: there's probably some people are buying that ironically because it's 405 00:19:39,880 --> 00:19:43,520 Speaker 5: funny to buy a chortcoat from a surveillance company. But 406 00:19:43,640 --> 00:19:45,960 Speaker 5: also I think some people are buying it cute coat 407 00:19:46,040 --> 00:19:49,919 Speaker 5: because it's a cute coat. Yes, and I wonder Stacy 408 00:19:49,960 --> 00:19:52,720 Speaker 5: brought up this this issue, but you know, AI is 409 00:19:52,760 --> 00:19:56,399 Speaker 5: just ridiculously unpopular, and it almost feels like these companies 410 00:19:56,760 --> 00:20:00,800 Speaker 5: are just trying to meme themselves into some kind of 411 00:20:00,920 --> 00:20:04,040 Speaker 5: popularity here, like maybe if they sell you a basketball 412 00:20:04,119 --> 00:20:06,040 Speaker 5: or something, you won't focus on the data center. 413 00:20:06,119 --> 00:20:06,520 Speaker 4: I don't know. 414 00:20:06,720 --> 00:20:09,119 Speaker 1: I think is this where there is a lot of 415 00:20:09,200 --> 00:20:12,679 Speaker 1: unpopularity of AI they have to try to figure out 416 00:20:12,720 --> 00:20:13,920 Speaker 1: a way to turn it around. 417 00:20:14,280 --> 00:20:15,320 Speaker 7: I don't know if this is the way. 418 00:20:15,480 --> 00:20:19,720 Speaker 1: I think some of the work they've done with studios 419 00:20:19,840 --> 00:20:24,240 Speaker 1: or with influencers or celebrities it might help. But then 420 00:20:24,560 --> 00:20:28,399 Speaker 1: I feel like with those groups it's playing with fire 421 00:20:28,640 --> 00:20:34,320 Speaker 1: because they don't want to outsource their livelihoods to AI. 422 00:20:34,440 --> 00:20:37,120 Speaker 1: I mean, like you said, it's bland advice because it's 423 00:20:37,160 --> 00:20:41,439 Speaker 1: the compilation of advice from the entire internet, right, like 424 00:20:41,520 --> 00:20:47,000 Speaker 1: whatever you're getting from AI comes from from every bit 425 00:20:47,040 --> 00:20:49,720 Speaker 1: of documentation it can possibly ingest and then bring back 426 00:20:49,760 --> 00:20:51,520 Speaker 1: to you. It's going to be bland. It's going to 427 00:20:51,560 --> 00:20:53,360 Speaker 1: be very basic. 428 00:20:54,160 --> 00:20:56,280 Speaker 2: So you say the AI is kind of known for 429 00:20:56,320 --> 00:20:59,040 Speaker 2: being bland, and I think that's true. But also it's known, 430 00:20:59,119 --> 00:21:02,120 Speaker 2: I think for being a little sinister. I think there's 431 00:21:02,160 --> 00:21:05,840 Speaker 2: a sinister element, particularly in regards to jobs. I think 432 00:21:05,840 --> 00:21:09,840 Speaker 2: people are so worried about jobs going away and ais 433 00:21:10,400 --> 00:21:14,199 Speaker 2: impact on our relationships, on our work, all these things, 434 00:21:14,400 --> 00:21:16,520 Speaker 2: and it being kind of a force for the not good. 435 00:21:17,040 --> 00:21:19,400 Speaker 2: And it occurs to me that all of these fashions 436 00:21:19,520 --> 00:21:23,720 Speaker 2: are sort of car heart like, which is a brand 437 00:21:23,720 --> 00:21:27,040 Speaker 2: that I grew up with in Idaho, like a farming brand, 438 00:21:27,280 --> 00:21:30,240 Speaker 2: and then now it's all over Brooklyn, which is interesting, 439 00:21:30,240 --> 00:21:32,439 Speaker 2: But it seems like, yeah, work were it seems like 440 00:21:32,720 --> 00:21:36,000 Speaker 2: kind of coded for workers. Are they trying to like, 441 00:21:36,320 --> 00:21:40,000 Speaker 2: is this like a trojan horse situation where they're kind 442 00:21:40,000 --> 00:21:43,080 Speaker 2: of trying to introduce their brand as like the friend 443 00:21:43,080 --> 00:21:47,080 Speaker 2: of the worker while possibly taking workers' jobs. 444 00:21:47,200 --> 00:21:50,160 Speaker 1: I don't think a two hundred dollars coat is what 445 00:21:50,320 --> 00:21:55,120 Speaker 1: I can what I associate with the average American worker. 446 00:21:55,520 --> 00:21:57,520 Speaker 1: I see where you're going with that. But like I 447 00:21:57,560 --> 00:22:00,239 Speaker 1: see this more as like somet someone who's like a 448 00:22:00,280 --> 00:22:04,080 Speaker 1: fanboy of this company would maybe buy it, or an 449 00:22:04,080 --> 00:22:09,480 Speaker 1: employee or an investor would want to like present these 450 00:22:09,760 --> 00:22:11,800 Speaker 1: like I wouldn't wear it if I were a college professor. 451 00:22:11,960 --> 00:22:14,199 Speaker 5: Let me just play one more thing. This is a 452 00:22:14,240 --> 00:22:17,719 Speaker 5: commercial that Anthropic is running. All the AI companies are 453 00:22:17,760 --> 00:22:21,119 Speaker 5: advertising now, and they're they're average. They're actually starting to 454 00:22:21,320 --> 00:22:23,840 Speaker 5: talk to these specific issues like some images and. 455 00:22:27,119 --> 00:22:30,040 Speaker 6: A burning house is an interesting can AIV trust it? 456 00:22:31,200 --> 00:22:31,800 Speaker 7: Graveyard? 457 00:22:32,160 --> 00:22:33,240 Speaker 1: Who's going to hit the brakes? 458 00:22:33,280 --> 00:22:33,919 Speaker 3: If we need to? 459 00:22:34,160 --> 00:22:36,160 Speaker 6: Oh my gosh, how would really endure that? 460 00:22:36,440 --> 00:22:40,160 Speaker 3: Or we're aiming to achieve really does benefit the majority 461 00:22:40,160 --> 00:22:42,560 Speaker 3: of people? What is this? 462 00:22:43,160 --> 00:22:45,320 Speaker 5: If it ends up taking like almost all the jobs, 463 00:22:45,440 --> 00:22:47,440 Speaker 5: then what does it mean to work? 464 00:22:49,560 --> 00:22:52,840 Speaker 3: Wait a minute, why do we have to have this stuff? 465 00:22:55,320 --> 00:22:58,880 Speaker 6: Could AI help people stop feeling misunderstood? 466 00:23:00,280 --> 00:23:03,280 Speaker 5: Could AI help me build more connections? 467 00:23:04,040 --> 00:23:05,159 Speaker 6: You're getting more inspiring? 468 00:23:06,080 --> 00:23:09,679 Speaker 5: Can AI help me be a better teacher, a better mom? 469 00:23:10,080 --> 00:23:12,200 Speaker 3: Maybe it'll cure some great things? 470 00:23:12,600 --> 00:23:16,680 Speaker 6: Okay, Okay, there's hope questions. 471 00:23:16,760 --> 00:23:22,639 Speaker 4: Okay, yeah, the tagline keep thinking thinking, So Sarah, I 472 00:23:22,680 --> 00:23:23,760 Speaker 4: mean this is anthropic. 473 00:23:24,200 --> 00:23:27,840 Speaker 5: Their brand is kind of like we are the AI 474 00:23:28,040 --> 00:23:32,200 Speaker 5: safety guys. But I find it pretty interesting that they're 475 00:23:32,720 --> 00:23:37,800 Speaker 5: just how explicitly they are in acknowledging that many people 476 00:23:37,800 --> 00:23:38,520 Speaker 5: find the scary. 477 00:23:38,880 --> 00:23:40,480 Speaker 7: The images in the beginning are disturbing. 478 00:23:40,520 --> 00:23:43,920 Speaker 5: It's so melodramatics that I, I mean, I find it 479 00:23:44,040 --> 00:23:46,960 Speaker 5: very cringey. It's just like I was joking, it makes 480 00:23:47,000 --> 00:23:48,720 Speaker 5: me want to be an AI accelerationist. 481 00:23:48,760 --> 00:23:52,359 Speaker 4: It's so like over the top. And what is this 482 00:23:52,440 --> 00:23:53,680 Speaker 4: ad trying to say? 483 00:23:54,359 --> 00:23:56,680 Speaker 1: I think I just trying to acknowledge the fear, the 484 00:23:57,400 --> 00:24:01,679 Speaker 1: apocalyptic sense that people have around AI, the fact that 485 00:24:01,720 --> 00:24:04,240 Speaker 1: they think it's gonna take all the jobs, it's gonna 486 00:24:04,720 --> 00:24:08,280 Speaker 1: start the contents to the planet. It's trying to get 487 00:24:08,359 --> 00:24:12,640 Speaker 1: people to think about the possibilities too, Like, Okay, maybe 488 00:24:13,280 --> 00:24:17,360 Speaker 1: we'll acknowledge that that's a concern, but what about curing disease? 489 00:24:17,600 --> 00:24:21,840 Speaker 1: What about becoming a better mom? Like I think it's 490 00:24:21,880 --> 00:24:25,960 Speaker 1: trying to ask you to change your framing. Whether that's 491 00:24:26,080 --> 00:24:29,480 Speaker 1: gonna work. I think people a lot of people watching 492 00:24:29,480 --> 00:24:32,920 Speaker 1: this would probably just have like a like an emotional 493 00:24:32,960 --> 00:24:34,560 Speaker 1: swimming with it and then feel manipulated. 494 00:24:34,600 --> 00:24:38,080 Speaker 2: I don't know, does it matter if we have a 495 00:24:38,080 --> 00:24:39,679 Speaker 2: bad association with AI. 496 00:24:39,880 --> 00:24:43,360 Speaker 6: It's being adopted everywhere. It's very useful. 497 00:24:43,720 --> 00:24:46,359 Speaker 2: It kind of helps us, even if we don't have 498 00:24:46,400 --> 00:24:50,360 Speaker 2: a great feeling about it. Do these companies care if 499 00:24:50,400 --> 00:24:53,200 Speaker 2: they are liked or feared? Is this a big deal 500 00:24:53,240 --> 00:24:54,200 Speaker 2: to them economically? 501 00:24:55,080 --> 00:24:55,639 Speaker 6: Politically? 502 00:24:56,400 --> 00:24:59,440 Speaker 1: I think that it's it's a big deal for them 503 00:24:59,600 --> 00:25:06,160 Speaker 1: in part because the boom for AI depends on communities 504 00:25:06,320 --> 00:25:11,080 Speaker 1: letting companies build data centers in their in their local area. 505 00:25:11,200 --> 00:25:17,040 Speaker 1: It depends on states not passing moratoriums on data centers centers, 506 00:25:15,840 --> 00:25:19,320 Speaker 1: and it's just happening to some extent. It depends on 507 00:25:19,800 --> 00:25:23,680 Speaker 1: communities being willing to share their energy resources, water resources, 508 00:25:24,359 --> 00:25:26,879 Speaker 1: land as of now. You know, unless we have some 509 00:25:26,960 --> 00:25:30,200 Speaker 1: breakthrough that doesn't require transform er architecture. As of now, 510 00:25:30,280 --> 00:25:34,120 Speaker 1: we really do need people to want the build out 511 00:25:34,320 --> 00:25:37,280 Speaker 1: because we live in a democracy where people get to 512 00:25:37,359 --> 00:25:41,119 Speaker 1: vote on these things, and politicians know that it's unpopular 513 00:25:41,760 --> 00:25:45,240 Speaker 1: and it is likely going to be a big factor 514 00:25:45,400 --> 00:25:50,040 Speaker 1: in the midterms as to whether people will will embrace 515 00:25:50,400 --> 00:25:51,600 Speaker 1: the growth of AI or. 516 00:25:51,600 --> 00:25:53,000 Speaker 4: Not stick around. 517 00:25:53,119 --> 00:25:55,000 Speaker 5: We are going to get to our underrated story and 518 00:25:55,040 --> 00:25:58,120 Speaker 5: we have something special just for you, Sarah. 519 00:25:58,160 --> 00:25:58,520 Speaker 7: For me. 520 00:26:05,640 --> 00:26:07,920 Speaker 4: All right, Stacey, it's that time. It's the time we love. 521 00:26:07,920 --> 00:26:10,959 Speaker 5: We've got a special guest, Sarah Fryar here underrated story. 522 00:26:11,000 --> 00:26:13,720 Speaker 2: Why don't you start my underrated story? I think could 523 00:26:13,720 --> 00:26:16,760 Speaker 2: solve a lot of problems. It has to do with 524 00:26:17,080 --> 00:26:20,960 Speaker 2: a pizza place in Florida, in Everglade City, and the 525 00:26:21,000 --> 00:26:24,840 Speaker 2: owner of the pizza place in lieu of payment will 526 00:26:24,880 --> 00:26:29,840 Speaker 2: accept Burmese pythons. So let me explain. The Everglade has 527 00:26:29,880 --> 00:26:33,280 Speaker 2: a Burmese python problem. It's an invasive species. They're eating 528 00:26:33,320 --> 00:26:36,480 Speaker 2: all the native species, and so Florida's trying all these 529 00:26:36,480 --> 00:26:38,560 Speaker 2: things to get rid of the pythons, including it has 530 00:26:38,600 --> 00:26:42,120 Speaker 2: this big contest that just wrapped up where they send 531 00:26:42,119 --> 00:26:44,399 Speaker 2: a snake hunters out to see who can collect the 532 00:26:44,440 --> 00:26:45,280 Speaker 2: most pythons, and they. 533 00:26:45,280 --> 00:26:46,119 Speaker 6: Have a prize. 534 00:26:46,280 --> 00:26:49,400 Speaker 2: But this pizza place will sell you a pizza for 535 00:26:49,800 --> 00:26:54,040 Speaker 2: a dead python, and the owner uses the python skins 536 00:26:54,080 --> 00:26:57,800 Speaker 2: to make products uses not in the pizza, also a 537 00:26:57,840 --> 00:26:58,680 Speaker 2: pizza topping. 538 00:26:59,280 --> 00:27:00,520 Speaker 7: So this is this story. 539 00:27:00,600 --> 00:27:04,399 Speaker 2: It's very funny and delightful, but I think this is 540 00:27:04,440 --> 00:27:07,880 Speaker 2: something that we could use in other places too. Here 541 00:27:07,880 --> 00:27:10,960 Speaker 2: in New York, we have a major rat problem. We 542 00:27:11,000 --> 00:27:14,040 Speaker 2: have a ratsar. There is a big rat infestation in 543 00:27:14,080 --> 00:27:16,600 Speaker 2: our city. We have an affordability crisis happening. 544 00:27:16,720 --> 00:27:20,119 Speaker 4: Oh my god, are you pitching putting rats on pizza 545 00:27:20,240 --> 00:27:21,040 Speaker 4: to solved. 546 00:27:20,720 --> 00:27:23,199 Speaker 6: The rat putting rats on pizza? 547 00:27:23,200 --> 00:27:25,240 Speaker 2: But I was thinking we could have like a like 548 00:27:25,440 --> 00:27:28,400 Speaker 2: rat currency, like a rat dollar here, and we could 549 00:27:28,440 --> 00:27:30,840 Speaker 2: have like coinstar machines. It's so they could accept rat 550 00:27:30,880 --> 00:27:31,840 Speaker 2: carcasses rats. 551 00:27:32,160 --> 00:27:33,880 Speaker 1: And then you know, I mean you're going to say, 552 00:27:34,280 --> 00:27:37,480 Speaker 1: introduce pythons to New York City to eat all the rats. 553 00:27:37,520 --> 00:27:39,960 Speaker 4: Well, the coming rat's also. 554 00:27:39,720 --> 00:27:43,840 Speaker 1: An interesting idea, so many pythons by the end of it, 555 00:27:43,840 --> 00:27:46,000 Speaker 1: they'd be feasting in the subways for a long time. 556 00:27:46,240 --> 00:27:48,560 Speaker 2: But I just thought this was such an interesting solution 557 00:27:48,720 --> 00:27:53,040 Speaker 2: to turn something you don't want into a currency, because 558 00:27:53,160 --> 00:27:55,680 Speaker 2: I do think it could solve an affordability problem, it 559 00:27:55,720 --> 00:27:59,720 Speaker 2: could solve a pest problem, and it's like a win. 560 00:27:59,600 --> 00:28:03,280 Speaker 5: Win win in I'm just glad that innovators like this 561 00:28:03,359 --> 00:28:06,359 Speaker 5: pizza guy are are trying to come up with unique 562 00:28:06,359 --> 00:28:08,480 Speaker 5: market based solutions to this uffle. 563 00:28:08,640 --> 00:28:11,600 Speaker 6: Would you guys kill rats and converted into city for. 564 00:28:13,160 --> 00:28:14,640 Speaker 7: A total health hazard? 565 00:28:14,840 --> 00:28:16,680 Speaker 6: Okay, well I didn't think of that, but. 566 00:28:17,040 --> 00:28:21,960 Speaker 5: Yeah, okay, My underrated story is kind of in keeping 567 00:28:22,040 --> 00:28:25,000 Speaker 5: with what we're talking about before. It's about AI executives 568 00:28:25,240 --> 00:28:28,680 Speaker 5: embracing their creative sides. And of course I'm talking about 569 00:28:28,760 --> 00:28:33,080 Speaker 5: Elon Musk's plan to remake The Odyssey to prove that 570 00:28:33,160 --> 00:28:35,479 Speaker 5: Christopher Nolan's the Odyssey sucks. 571 00:28:35,760 --> 00:28:37,240 Speaker 4: Are you familiar with this, Sarah. 572 00:28:38,520 --> 00:28:38,720 Speaker 3: Plan? 573 00:28:38,880 --> 00:28:40,120 Speaker 4: So let's just pull this up. 574 00:28:40,200 --> 00:28:43,440 Speaker 5: I should say Elon Musk as you know, Sarah, because 575 00:28:43,480 --> 00:28:45,720 Speaker 5: you know we were on the Elon Ink podcast together 576 00:28:45,800 --> 00:28:46,320 Speaker 5: all the time. 577 00:28:46,560 --> 00:28:47,880 Speaker 4: He loves side quests. 578 00:28:47,960 --> 00:28:51,040 Speaker 5: He also, you know, not averse to kind of right 579 00:28:51,080 --> 00:28:55,400 Speaker 5: wing cultural signaling. He has been mad, along with many, many, 580 00:28:55,440 --> 00:28:57,840 Speaker 5: many people on the internet for like, I don't know, 581 00:28:57,880 --> 00:29:00,880 Speaker 5: a year or so or however long ago it was 582 00:29:00,920 --> 00:29:05,000 Speaker 5: when Christopher Nolan announced the casting of the Odyssey, essentially 583 00:29:05,080 --> 00:29:07,760 Speaker 5: mad that they cast a black woman as Helen of Troy, 584 00:29:08,160 --> 00:29:11,400 Speaker 5: cast a trans man in a role Elliott Page. And 585 00:29:11,480 --> 00:29:14,960 Speaker 5: he has been posting up a storm about how terrible 586 00:29:15,000 --> 00:29:17,240 Speaker 5: the Odyssey is gonna be, how it's all a siop 587 00:29:17,280 --> 00:29:21,000 Speaker 5: and so on, And unfortunately, for that point of view, 588 00:29:21,320 --> 00:29:24,720 Speaker 5: the Odyssey has been doing really, really well. Elon Musk 589 00:29:24,800 --> 00:29:29,360 Speaker 5: responded yesterday, vowing to make his own odyssey to prove 590 00:29:29,400 --> 00:29:31,360 Speaker 5: once and for all that this is bad. 591 00:29:31,480 --> 00:29:33,960 Speaker 4: He's gonna use Grock to do it. And he even 592 00:29:34,640 --> 00:29:35,240 Speaker 4: sort of re. 593 00:29:35,720 --> 00:29:37,640 Speaker 5: Posted kind of like, I guess what is sort of 594 00:29:37,680 --> 00:29:40,160 Speaker 5: like an early trailer or something we can we can give. 595 00:29:40,040 --> 00:29:40,360 Speaker 4: It a lot. 596 00:29:40,440 --> 00:29:41,840 Speaker 6: It's an AI generated trailer. 597 00:29:41,880 --> 00:29:42,720 Speaker 4: It's more like a. 598 00:29:44,440 --> 00:29:45,120 Speaker 6: Sketch. 599 00:29:45,160 --> 00:29:47,600 Speaker 4: I guess you'd call it. It's it's a Grock creation. 600 00:29:47,800 --> 00:29:51,480 Speaker 5: Grock is the AI chatbot, and I guess this is 601 00:29:51,520 --> 00:29:53,120 Speaker 5: the idea is that this would be a. 602 00:29:53,120 --> 00:29:55,880 Speaker 4: More palatable version of the Odyssey to Elon Musk. 603 00:29:57,040 --> 00:29:59,920 Speaker 6: The sea will take you from me, I will. 604 00:30:01,600 --> 00:30:02,840 Speaker 3: I left my one love. 605 00:30:04,480 --> 00:30:05,440 Speaker 1: An oath called me. 606 00:30:07,600 --> 00:30:13,200 Speaker 5: Troy Sarah, do you think this could be Elon Musk's 607 00:30:13,440 --> 00:30:16,920 Speaker 5: next big venture after SpaceX, Tesla and so on, to 608 00:30:17,000 --> 00:30:17,720 Speaker 5: make movies? 609 00:30:18,440 --> 00:30:22,680 Speaker 1: I hear it's really I fear it's really profitable, great business. 610 00:30:22,960 --> 00:30:23,320 Speaker 4: Stacey. 611 00:30:23,360 --> 00:30:26,800 Speaker 2: What do you think it's interesting the idea of the 612 00:30:26,840 --> 00:30:30,400 Speaker 2: odyssey too. I feel like it's kind of a beautiful 613 00:30:30,720 --> 00:30:35,920 Speaker 2: end to the journey of our show to have another odyssey, right, 614 00:30:35,960 --> 00:30:39,520 Speaker 2: because the whole point is Ulysses coming home, right, But 615 00:30:39,680 --> 00:30:42,920 Speaker 2: now Ulysses has come home, everybody else wants to send 616 00:30:43,000 --> 00:30:45,600 Speaker 2: him out on this journey again. We're like right back 617 00:30:45,640 --> 00:30:50,480 Speaker 2: where we started, in another odyssey with angels apparently. 618 00:30:51,360 --> 00:30:56,120 Speaker 5: Sarah Fryar, author of No Filter, Bloomberg Technology Managing Editor, 619 00:30:56,160 --> 00:30:56,520 Speaker 5: thank you. 620 00:30:56,480 --> 00:30:57,680 Speaker 7: For being here, Thanks for having me. 621 00:30:57,720 --> 00:31:01,040 Speaker 6: Thanks Sarah. 622 00:31:04,400 --> 00:31:06,560 Speaker 4: This show is produced by Jasmine, J. T. Green and 623 00:31:06,600 --> 00:31:07,280 Speaker 4: Stacy Wong. 624 00:31:07,680 --> 00:31:11,480 Speaker 5: Mangus Hendrickson is our supervising producer, Sam Rogitch, Chandles Engineering, 625 00:31:11,560 --> 00:31:14,600 Speaker 5: and Dave Pricell fact checks. Special thanks to Jeff Muscus, 626 00:31:14,680 --> 00:31:17,040 Speaker 5: Julia Rubin and Maria Ling. If you have a minute, 627 00:31:17,040 --> 00:31:18,719 Speaker 5: please rate and review the show. It'll mean a lot 628 00:31:18,760 --> 00:31:20,280 Speaker 5: to us. And if you have a story that should 629 00:31:20,280 --> 00:31:23,000 Speaker 5: be our business. If you have a mystery that you 630 00:31:23,040 --> 00:31:25,720 Speaker 5: want us to investigate like we did last week. 631 00:31:25,560 --> 00:31:26,240 Speaker 4: Email us. 632 00:31:26,440 --> 00:31:29,160 Speaker 5: Everybody's at Bloomberg dot net. That's everybody with ans at 633 00:31:29,200 --> 00:31:31,520 Speaker 5: Bloomberg dot net. Thank you for listening and we will 634 00:31:31,520 --> 00:31:33,080 Speaker 5: see you next week.