1 00:00:03,080 --> 00:00:07,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,320 --> 00:00:13,680 Speaker 2: Stacey, there's been so much talk here and elsewhere on 3 00:00:13,760 --> 00:00:18,000 Speaker 2: Wall Street in the business press about AI, your favorite subject, 4 00:00:18,360 --> 00:00:24,680 Speaker 2: saving the world, changing our lives, curing cancer, solving climate. 5 00:00:24,400 --> 00:00:26,439 Speaker 3: Change, talking to whales, talking. 6 00:00:26,200 --> 00:00:27,280 Speaker 4: To whales, or your favorite thing. 7 00:00:27,320 --> 00:00:30,800 Speaker 2: Almost everyone agrees AI is awesome as far as I 8 00:00:30,800 --> 00:00:34,080 Speaker 2: can tell. But there is starting to be quite a 9 00:00:34,120 --> 00:00:38,280 Speaker 2: lot of backlash as well, including some very upsetting incidents. 10 00:00:38,520 --> 00:00:42,120 Speaker 5: Yes, there's actually been some violence, including against Open AI 11 00:00:42,280 --> 00:00:45,800 Speaker 5: CEO Sam Alman recently. And you're right, it definitely seems 12 00:00:45,880 --> 00:00:48,720 Speaker 5: like a lot of the excitement and optimism around AI 13 00:00:49,280 --> 00:00:51,360 Speaker 5: has become at least more complicated. 14 00:00:51,600 --> 00:00:54,960 Speaker 1: There may be an erosion in our ability to learn 15 00:00:55,000 --> 00:00:57,880 Speaker 1: how to compromise with others, to learn how to collaborate 16 00:00:57,920 --> 00:01:02,000 Speaker 1: with others, like people who actually give us pushback, shape 17 00:01:02,040 --> 00:01:05,880 Speaker 1: how we feel, teach us things. Yeah, this is all existential. 18 00:01:06,319 --> 00:01:08,600 Speaker 2: We're going to hear about more of that from Bloomberg 19 00:01:08,600 --> 00:01:12,479 Speaker 2: reporter Sarah Fryer. We're talking about what these threats and 20 00:01:12,600 --> 00:01:15,640 Speaker 2: attempts at harm say about this current moment and the 21 00:01:15,680 --> 00:01:16,760 Speaker 2: conversation around AI. 22 00:01:17,200 --> 00:01:19,040 Speaker 5: And after that, Max, we are going to talk about 23 00:01:19,080 --> 00:01:23,000 Speaker 5: the job market. It is tough out there, especially for 24 00:01:23,120 --> 00:01:26,720 Speaker 5: young job seekers, especially if you just graduated. And Business 25 00:01:26,720 --> 00:01:28,959 Speaker 5: Week's latest issue is all about that, what it is 26 00:01:29,040 --> 00:01:31,639 Speaker 5: like to be young and just starting out in your career. 27 00:01:32,120 --> 00:01:34,600 Speaker 4: College graduates, recent graduates, we see you. 28 00:01:35,120 --> 00:01:38,800 Speaker 2: And even better, we have BusinessWeek Senior editor Julia Rubin 29 00:01:38,880 --> 00:01:40,959 Speaker 2: here to break it all down for us. 30 00:01:41,280 --> 00:01:44,319 Speaker 6: It's very interesting and this idea of Okay, if we 31 00:01:44,360 --> 00:01:48,560 Speaker 6: are sort of automating away the grunt work or the 32 00:01:48,600 --> 00:01:51,880 Speaker 6: sort of technical work, then perhaps like the work that 33 00:01:52,040 --> 00:01:53,440 Speaker 6: is more about. 34 00:01:53,600 --> 00:01:56,040 Speaker 3: Being a human is going to be much more important. 35 00:01:58,720 --> 00:02:01,120 Speaker 5: This is everybody's business from Bloomberg BusinessWeek. 36 00:02:01,200 --> 00:02:02,320 Speaker 3: I'm Stacy Vannox. 37 00:02:02,040 --> 00:02:06,280 Speaker 5: Smith and I'm Max Chafkin Jobs AI. It is all 38 00:02:06,320 --> 00:02:09,120 Speaker 5: happening this week, and it is a lot, but stick 39 00:02:09,160 --> 00:02:12,320 Speaker 5: around and Max, we can just all process this together. 40 00:02:18,080 --> 00:02:21,160 Speaker 7: This morning, dramatic new surveillance images of a man authorities 41 00:02:21,200 --> 00:02:25,000 Speaker 7: say tried to kill open AI CEO Sam Altman by 42 00:02:25,000 --> 00:02:28,720 Speaker 7: throwing a Molotov cocktail at his home. Investigators have charged 43 00:02:28,760 --> 00:02:33,040 Speaker 7: twenty year old Daniel Marino Gamma with attempted murder Stacy. 44 00:02:33,280 --> 00:02:36,480 Speaker 2: Just a few days ago, as we're talking now, there 45 00:02:36,639 --> 00:02:41,760 Speaker 2: was this attempted arson attack on the home of Sam Altman, 46 00:02:41,880 --> 00:02:45,399 Speaker 2: the CEO and co founder of Open Ai. And there's 47 00:02:45,440 --> 00:02:47,919 Speaker 2: a lot to say about this, and about to say 48 00:02:47,960 --> 00:02:51,560 Speaker 2: about the manifesto that this guy wrote, But I think 49 00:02:51,600 --> 00:02:54,960 Speaker 2: it also maybe says something about the current sentiment, the 50 00:02:54,960 --> 00:02:57,000 Speaker 2: broader sentiment about AI in general. 51 00:02:57,320 --> 00:02:59,799 Speaker 5: Yeah, I think so too. This attack, by the way, 52 00:02:59,880 --> 00:03:02,920 Speaker 5: is not the only one. Last week in Indianapolis, a 53 00:03:02,960 --> 00:03:06,040 Speaker 5: local councilman who had approved the construction of this big 54 00:03:06,160 --> 00:03:09,400 Speaker 5: new data center in the area had bullets fired at 55 00:03:09,440 --> 00:03:10,600 Speaker 5: his front door, along with. 56 00:03:10,560 --> 00:03:12,520 Speaker 3: This note that read no data centers. 57 00:03:12,960 --> 00:03:15,640 Speaker 5: It definitely seems like some of the kind of excitement 58 00:03:15,639 --> 00:03:19,200 Speaker 5: and optimism around AI has has turned or at least 59 00:03:19,400 --> 00:03:20,720 Speaker 5: gotten a little more complicated. 60 00:03:21,040 --> 00:03:23,720 Speaker 2: Yeah, and we've got a great person talked about this. 61 00:03:24,160 --> 00:03:28,720 Speaker 2: It is Sarah Fryar, Bloomberg Managing editor, a longtime reporter. 62 00:03:28,960 --> 00:03:31,840 Speaker 2: She wrote a book all about Facebook and kind of 63 00:03:31,880 --> 00:03:34,760 Speaker 2: the last time tech was under the microscope. It's called 64 00:03:34,760 --> 00:03:37,480 Speaker 2: No Filter, The Inside Story of Instagram. Sarah, Welcome to 65 00:03:37,480 --> 00:03:38,280 Speaker 2: everybody's business. 66 00:03:38,360 --> 00:03:39,160 Speaker 4: Thanks for having me. 67 00:03:39,800 --> 00:03:42,440 Speaker 2: All right, So, Sarah, I feel like just a couple 68 00:03:42,520 --> 00:03:46,240 Speaker 2: of weeks ago, we were talking about how the ais 69 00:03:46,280 --> 00:03:48,600 Speaker 2: were going to be our friends, and they were going 70 00:03:48,680 --> 00:03:50,400 Speaker 2: to do all our work and they were going to 71 00:03:50,480 --> 00:03:55,440 Speaker 2: usher in a glorious new future. Now we've got these 72 00:03:55,440 --> 00:03:58,360 Speaker 2: attacks we mentioned too, there was actually a second attack 73 00:03:58,440 --> 00:04:00,560 Speaker 2: on Sam Almon's house. And when you look the poll 74 00:04:00,680 --> 00:04:03,160 Speaker 2: numbers about this, there have been a bunch of polls 75 00:04:03,160 --> 00:04:06,120 Speaker 2: in and AI is pretty unpopular. 76 00:04:06,240 --> 00:04:08,480 Speaker 4: And I'm wondering what's your read on this. 77 00:04:08,760 --> 00:04:12,240 Speaker 2: It did something change or are people just paying attention 78 00:04:12,240 --> 00:04:13,560 Speaker 2: to something that was there the whole time. 79 00:04:14,040 --> 00:04:17,520 Speaker 1: Well, I think it's a big change when something goes 80 00:04:17,640 --> 00:04:21,000 Speaker 1: from a cool new tool you can play with to 81 00:04:22,000 --> 00:04:27,239 Speaker 1: the reason people thinks jobs are going away, the reason 82 00:04:27,320 --> 00:04:30,920 Speaker 1: young people can't find careers when they graduate from college 83 00:04:31,000 --> 00:04:35,000 Speaker 1: or grad school, the reason the environment is under stressed, 84 00:04:35,080 --> 00:04:39,039 Speaker 1: energy prices are high. There are these existential things that 85 00:04:39,120 --> 00:04:42,680 Speaker 1: are tied up in the progress of AI. It may 86 00:04:42,839 --> 00:04:47,040 Speaker 1: be of help to us for particular tasks or questions 87 00:04:47,240 --> 00:04:50,719 Speaker 1: or coding projects and what have you, But the vast 88 00:04:50,720 --> 00:04:56,240 Speaker 1: majority of people are not seeing yet the way that 89 00:04:56,279 --> 00:04:59,600 Speaker 1: AI is enriching them. Instead, they're seeing the way that 90 00:04:59,680 --> 00:05:04,840 Speaker 1: it's taking things away, whether that's employment or resources, or 91 00:05:04,880 --> 00:05:07,000 Speaker 1: just the threat that it might one day take something 92 00:05:07,040 --> 00:05:09,200 Speaker 1: away and part of that, I think is the company's 93 00:05:09,200 --> 00:05:12,200 Speaker 1: own ful the way that they have marketed this technology 94 00:05:12,240 --> 00:05:15,960 Speaker 1: as like, really, this is so powerful, we need to 95 00:05:16,080 --> 00:05:19,760 Speaker 1: roll this out slowly, take our time, because if we don't, 96 00:05:19,800 --> 00:05:21,400 Speaker 1: the results could be disastrous. 97 00:05:21,640 --> 00:05:24,680 Speaker 2: They've literally been saying our technology might end the world. 98 00:05:24,960 --> 00:05:27,400 Speaker 2: It might be that sam Altman, including the Samlman, might 99 00:05:27,440 --> 00:05:30,359 Speaker 2: be as bad as a nuclear weapon. And also it 100 00:05:30,560 --> 00:05:32,400 Speaker 2: very likely will take everyone's. 101 00:05:32,000 --> 00:05:33,920 Speaker 1: Jobs, which is why you should buy it from us, 102 00:05:33,960 --> 00:05:36,400 Speaker 1: because at least we're saying the truth right it is. 103 00:05:36,480 --> 00:05:38,400 Speaker 4: The matingment is that. 104 00:05:39,360 --> 00:05:42,960 Speaker 2: It is the strangest public relations strategy of all time. 105 00:05:43,240 --> 00:05:45,560 Speaker 3: Honesty, it's never a good idea in pr. 106 00:05:46,000 --> 00:05:49,120 Speaker 1: These companies that have that have spoken about the power 107 00:05:49,200 --> 00:05:55,880 Speaker 1: and danger and incredible uncontrollableness of AI may actually be 108 00:05:55,960 --> 00:06:00,080 Speaker 1: stoking real fear in regular people who think they have 109 00:06:00,120 --> 00:06:03,360 Speaker 1: to do something about it. And of course no condonement 110 00:06:03,520 --> 00:06:06,800 Speaker 1: of violence, but you don't have to go too far 111 00:06:06,839 --> 00:06:09,800 Speaker 1: in the logic to get somebody to that place where 112 00:06:09,839 --> 00:06:13,320 Speaker 1: they fear AI and think that they're doing the right 113 00:06:13,400 --> 00:06:17,279 Speaker 1: thing by trying to stop it. Obviously that's the wrong 114 00:06:17,320 --> 00:06:20,320 Speaker 1: way to do it, but there are people having these 115 00:06:20,360 --> 00:06:24,560 Speaker 1: philosophical even religious debates in Silicon Valley right now about 116 00:06:25,880 --> 00:06:29,800 Speaker 1: what AI is bringing to change our future and how 117 00:06:29,880 --> 00:06:32,800 Speaker 1: much of it we should be embracing. And then there's 118 00:06:32,839 --> 00:06:34,360 Speaker 1: the other side of that, which is like, how much 119 00:06:34,400 --> 00:06:36,480 Speaker 1: of that is just marketing talk? 120 00:06:36,920 --> 00:06:39,520 Speaker 5: Sarah, I feel like this is such a similar thing 121 00:06:39,640 --> 00:06:42,119 Speaker 5: that's happened throughout history, Like back in the eighteen eighties, 122 00:06:42,160 --> 00:06:45,440 Speaker 5: there were the Luddites and the saboteurs who threw their 123 00:06:45,440 --> 00:06:48,000 Speaker 5: shoes into the machines because they saw those machines as 124 00:06:48,040 --> 00:06:51,520 Speaker 5: threatening their jobs. The Luddites would go with tools and 125 00:06:51,680 --> 00:06:54,520 Speaker 5: smash the big textile machines apart. 126 00:06:55,440 --> 00:06:58,560 Speaker 3: Do you see this as like that or is this different? 127 00:06:58,760 --> 00:07:04,239 Speaker 5: Is this just the human reaction to an existential job 128 00:07:04,320 --> 00:07:05,919 Speaker 5: threat from a new technology. 129 00:07:06,640 --> 00:07:09,640 Speaker 1: I think even within the companies there's a lot of 130 00:07:09,680 --> 00:07:12,200 Speaker 1: this kind of debate, and Anthropic has made this part 131 00:07:12,200 --> 00:07:14,760 Speaker 1: of their brand, the fact that they're thinking all the 132 00:07:14,800 --> 00:07:20,400 Speaker 1: time about how do we not just enact AI progress, 133 00:07:20,720 --> 00:07:22,880 Speaker 1: but do it in a way that takes into account 134 00:07:22,920 --> 00:07:26,080 Speaker 1: everything that could go wrong, Because once we release it 135 00:07:26,720 --> 00:07:29,760 Speaker 1: on the world, we may not be able to control it. 136 00:07:29,800 --> 00:07:31,280 Speaker 1: So we have to make sure that what we release 137 00:07:32,000 --> 00:07:35,600 Speaker 1: is something that is at least understood, and I think 138 00:07:35,640 --> 00:07:39,120 Speaker 1: that's almost impossible because there's some level of complexity to 139 00:07:39,160 --> 00:07:41,920 Speaker 1: these models where we don't exactly know how they come 140 00:07:42,400 --> 00:07:45,120 Speaker 1: to the conclusions that they come to, and they're driving 141 00:07:45,640 --> 00:07:48,040 Speaker 1: their conclusions from a lot of different sources. They're building 142 00:07:48,120 --> 00:07:51,640 Speaker 1: upon the human knowledge that they've ingested, and they're really 143 00:07:51,640 --> 00:07:55,040 Speaker 1: only as good as how they've been trained. But I 144 00:07:55,080 --> 00:07:58,360 Speaker 1: think that does get us to an uncomfortable place where 145 00:07:58,920 --> 00:08:01,240 Speaker 1: on the one hand, you have people trying to do 146 00:08:01,320 --> 00:08:04,679 Speaker 1: whatever they can to build as quickly as they can 147 00:08:04,800 --> 00:08:08,360 Speaker 1: and take advantage of the new technology, and then you 148 00:08:08,440 --> 00:08:10,920 Speaker 1: have people who are too scared to even touch it. 149 00:08:12,000 --> 00:08:14,520 Speaker 2: Can I bring up a few sort of data points 150 00:08:14,520 --> 00:08:17,480 Speaker 2: and some polling here I alluded to it at the top. 151 00:08:17,600 --> 00:08:21,280 Speaker 2: But in September of twenty twenty five, Pew, the big 152 00:08:21,400 --> 00:08:26,480 Speaker 2: survey people, released their latest survey about how people feel 153 00:08:26,480 --> 00:08:30,080 Speaker 2: about AI, and the numbers are really stark, and I 154 00:08:30,080 --> 00:08:33,920 Speaker 2: think you go back to how people felt about smartphones 155 00:08:34,240 --> 00:08:38,440 Speaker 2: or cloud computing or any of these other recent technology developments, 156 00:08:38,480 --> 00:08:43,640 Speaker 2: it feels different. So in twenty twenty one, eighteen percent 157 00:08:43,760 --> 00:08:48,760 Speaker 2: of people were more excited than concerned about AI, and 158 00:08:49,000 --> 00:08:52,240 Speaker 2: thirty seven percent of the people were more concerned than excited. 159 00:08:51,920 --> 00:08:53,160 Speaker 4: So a lot of concern about AI. 160 00:08:53,640 --> 00:08:56,280 Speaker 2: Four years later, there's been a lot more AI, a 161 00:08:56,280 --> 00:08:58,520 Speaker 2: lot of people have had contact with this technology, and 162 00:08:58,559 --> 00:09:02,400 Speaker 2: the numbers have gotten way worse. Fifty percent more concerned 163 00:09:02,440 --> 00:09:06,720 Speaker 2: than excited, ten percent more excited than concerned, a huge, 164 00:09:07,440 --> 00:09:11,040 Speaker 2: huge majority. And then the by far the biggest thing 165 00:09:11,080 --> 00:09:12,640 Speaker 2: in the survey is more concerned. 166 00:09:12,880 --> 00:09:15,000 Speaker 4: And then the second part of this this is. 167 00:09:14,960 --> 00:09:18,240 Speaker 2: Showing up of course, in politics, people getting upset about 168 00:09:18,320 --> 00:09:21,400 Speaker 2: data centers in their backyard, Bernie Sanders saying, we need 169 00:09:21,400 --> 00:09:23,360 Speaker 2: to have a pause on data centers. But the thing 170 00:09:23,400 --> 00:09:26,560 Speaker 2: that really grabbed me in the survey was not the 171 00:09:26,559 --> 00:09:29,680 Speaker 2: top line. But then they went and asked people, Okay, 172 00:09:29,760 --> 00:09:32,360 Speaker 2: what are you concerned about, Sarah. You brought up a 173 00:09:32,400 --> 00:09:35,040 Speaker 2: bunch of things. You brought up the environment, you brought 174 00:09:35,120 --> 00:09:37,760 Speaker 2: up job loss, there was one other. When I heard 175 00:09:37,800 --> 00:09:39,760 Speaker 2: about this, I thought, okay, yeah, so people are probably 176 00:09:39,760 --> 00:09:42,320 Speaker 2: worried about it's probably economic anxiety, right, They've been hearing 177 00:09:42,360 --> 00:09:43,880 Speaker 2: these stories about job loss. It has got to be 178 00:09:43,920 --> 00:09:46,720 Speaker 2: the main thing. But you look at this survey. They 179 00:09:46,720 --> 00:09:49,760 Speaker 2: asked of the people who are most concerned, the top 180 00:09:50,240 --> 00:09:54,480 Speaker 2: problem was not job loss. It was erodes human abilities 181 00:09:54,480 --> 00:09:58,760 Speaker 2: and connections that was twenty seven percent eighteen negative impact 182 00:09:58,800 --> 00:10:03,200 Speaker 2: on accuracy of information. Next one concerns over human control 183 00:10:03,240 --> 00:10:05,960 Speaker 2: of AI, and then job losses all the way at 184 00:10:06,000 --> 00:10:09,720 Speaker 2: number five. The fifth most concern, the environment, which you 185 00:10:09,760 --> 00:10:11,760 Speaker 2: hear a lot about on the news, is way down there. 186 00:10:11,760 --> 00:10:13,480 Speaker 2: It's almost at the very bottom, like two percent. And 187 00:10:13,520 --> 00:10:16,240 Speaker 2: then then my favorite is general dislike, which is just 188 00:10:16,280 --> 00:10:17,720 Speaker 2: one percent. That's the lowest. 189 00:10:17,400 --> 00:10:21,200 Speaker 1: One's So it's so interesting. 190 00:10:21,280 --> 00:10:24,120 Speaker 2: I they're like spiritual concerns that people have, right, they 191 00:10:24,160 --> 00:10:26,200 Speaker 2: are worried about job loss, but they're also worried about 192 00:10:26,240 --> 00:10:27,720 Speaker 2: these more fundamental questions. 193 00:10:27,720 --> 00:10:31,840 Speaker 1: It's crazy loneliness, and you have people like Mark Zuckerberg 194 00:10:31,840 --> 00:10:34,599 Speaker 1: who are offering up AI as an antidote to loneliness, 195 00:10:34,640 --> 00:10:38,679 Speaker 1: while our social media feeds become full of AI slop. 196 00:10:39,200 --> 00:10:42,960 Speaker 1: One thing I think that is different about this era 197 00:10:43,120 --> 00:10:48,400 Speaker 1: of new tech compared to the past waves is this 198 00:10:48,480 --> 00:10:54,240 Speaker 1: is not as clear a game of disrupting the giants 199 00:10:54,920 --> 00:10:59,200 Speaker 1: as past eras of new tech have been. Normally, when 200 00:10:59,400 --> 00:11:02,960 Speaker 1: when you have this like big powerful new startup, they're 201 00:11:03,000 --> 00:11:06,840 Speaker 1: coming in to fight the incumbent. We've just come off 202 00:11:06,880 --> 00:11:11,560 Speaker 1: of a couple decades of sour feelings about big tech. 203 00:11:12,880 --> 00:11:17,000 Speaker 1: These AI giants don't exist without big tech. They're collaborating, 204 00:11:17,160 --> 00:11:21,679 Speaker 1: they're in deep business partnerships, deep financial partnerships. 205 00:11:21,920 --> 00:11:22,840 Speaker 4: They need each other. 206 00:11:23,440 --> 00:11:25,880 Speaker 1: And I think that means that a lot of the 207 00:11:25,960 --> 00:11:31,480 Speaker 1: sentiment and distrust that people have about Facebook, about Google, 208 00:11:31,480 --> 00:11:34,200 Speaker 1: about all these companies that they've just built up an 209 00:11:34,200 --> 00:11:39,360 Speaker 1: aversion to over antitrust over their personal data over all 210 00:11:39,360 --> 00:11:41,280 Speaker 1: of these things over the past few years, and yet 211 00:11:41,280 --> 00:11:43,960 Speaker 1: we keep using them because they're the best options. 212 00:11:44,000 --> 00:11:44,440 Speaker 4: We have. 213 00:11:45,040 --> 00:11:48,240 Speaker 1: A lot of that is trickling over to the AI era, 214 00:11:48,520 --> 00:11:50,720 Speaker 1: and people are thinking of these companies sort of in 215 00:11:50,760 --> 00:11:52,680 Speaker 1: the same boat. I think, if you're like a young 216 00:11:52,720 --> 00:11:56,640 Speaker 1: person today and you're being told you can't be on 217 00:11:56,720 --> 00:11:59,480 Speaker 1: social media or it's dangerous to be on social media, 218 00:12:00,080 --> 00:12:02,120 Speaker 1: what are you going to do with your time? You're 219 00:12:02,120 --> 00:12:06,920 Speaker 1: probably gonna talk to chat EBT. I just think that 220 00:12:07,240 --> 00:12:12,400 Speaker 1: as a substitute for human connection, it's going to be 221 00:12:12,720 --> 00:12:14,960 Speaker 1: a big enhancement in some ways because it's something that 222 00:12:15,000 --> 00:12:18,760 Speaker 1: will always respond, will always be there, will always give 223 00:12:18,800 --> 00:12:21,720 Speaker 1: you an answer. But it's but there may be an 224 00:12:21,720 --> 00:12:26,080 Speaker 1: erosion in our ability to learn how to compromise with others, 225 00:12:26,120 --> 00:12:28,520 Speaker 1: to learn how to collaborate with others, like people who 226 00:12:28,559 --> 00:12:34,040 Speaker 1: actually give us pushback shape how we feel, teach us things. Yeah, 227 00:12:34,080 --> 00:12:35,880 Speaker 1: this is all existential to. 228 00:12:35,840 --> 00:12:37,480 Speaker 3: Get a little more practical though. 229 00:12:38,040 --> 00:12:41,520 Speaker 5: AI is really, it feels like, in many ways, kind 230 00:12:41,520 --> 00:12:45,640 Speaker 5: of holding up our economy right now. Obviously the Magnificent 231 00:12:45,840 --> 00:12:49,480 Speaker 5: seven stocks, which are all very deeply invested, if not 232 00:12:49,600 --> 00:12:54,120 Speaker 5: making AI, are really carrying the markets, which has been 233 00:12:54,120 --> 00:12:57,120 Speaker 5: one of the real bright spots in the economy lately. 234 00:12:57,360 --> 00:12:59,720 Speaker 5: I mean, how big a part of our economy is 235 00:12:59,800 --> 00:13:03,319 Speaker 5: A Like, even if we're worried about it and there 236 00:13:03,360 --> 00:13:05,480 Speaker 5: are all these mixed feelings that are surfacing, do we 237 00:13:05,559 --> 00:13:05,880 Speaker 5: need it? 238 00:13:05,920 --> 00:13:06,000 Speaker 1: Oh? 239 00:13:06,080 --> 00:13:06,280 Speaker 4: Yeah? 240 00:13:06,320 --> 00:13:08,800 Speaker 1: The top four tech companies are spending six hundred and 241 00:13:08,840 --> 00:13:12,720 Speaker 1: fifty billion dollars on data centers this year. Like, that's insane. 242 00:13:13,120 --> 00:13:13,680 Speaker 4: It's insane. 243 00:13:13,679 --> 00:13:17,079 Speaker 1: It's not insane if you think about what the capacity 244 00:13:17,120 --> 00:13:20,840 Speaker 1: needs of AI are, it's just unprecedented. And yeah, that 245 00:13:20,920 --> 00:13:23,320 Speaker 1: is fueling the economy, But how much of that money 246 00:13:23,720 --> 00:13:27,160 Speaker 1: is trickling down to everyday people who are going to 247 00:13:27,200 --> 00:13:31,000 Speaker 1: the grocery store and seeing prices higher than It's just 248 00:13:31,280 --> 00:13:35,600 Speaker 1: there's a disconnect right now between the spending and who 249 00:13:36,240 --> 00:13:41,640 Speaker 1: is receiving the benefits of having an ai economy, And 250 00:13:41,679 --> 00:13:43,839 Speaker 1: I think that's going to show up in the midterms 251 00:13:43,920 --> 00:13:44,640 Speaker 1: later this year. 252 00:13:44,920 --> 00:13:47,240 Speaker 2: Sarah, before we let you go, I'm just curious. You're 253 00:13:47,240 --> 00:13:51,160 Speaker 2: talking to us from San Francisco. Big tech is your beat. 254 00:13:51,840 --> 00:13:54,920 Speaker 2: We haven't mentioned the name Luigi Mangioni, but I remember 255 00:13:54,920 --> 00:13:59,720 Speaker 2: when the United Health CEO was assassinated, there was a 256 00:13:59,720 --> 00:14:02,480 Speaker 2: lot of in talking to sources and stuff. They were afraid, 257 00:14:02,679 --> 00:14:05,880 Speaker 2: and I think are still afraid. And I'm curious whether 258 00:14:06,400 --> 00:14:09,559 Speaker 2: you're picking up that same level of anxiety, Like are 259 00:14:09,600 --> 00:14:14,480 Speaker 2: people aware of the potential for violence, for this kind 260 00:14:14,480 --> 00:14:19,040 Speaker 2: of generalized anxiety to spill into something scary. 261 00:14:19,360 --> 00:14:24,040 Speaker 1: I think that there has been a lot of thought 262 00:14:24,080 --> 00:14:28,000 Speaker 1: put into executive security in the wake of the United 263 00:14:28,040 --> 00:14:34,600 Speaker 1: Healthcare shooting, and even before that. Mark Zuckerberg he has 264 00:14:35,280 --> 00:14:38,040 Speaker 1: millions of dollars each year spent on the security of 265 00:14:38,120 --> 00:14:41,160 Speaker 1: him and his family. A lot of executives, including Sam Altman, 266 00:14:41,200 --> 00:14:45,080 Speaker 1: have spoken of building bunkers at their properties, maybe even 267 00:14:45,160 --> 00:14:45,880 Speaker 1: for more than. 268 00:14:45,760 --> 00:14:48,400 Speaker 3: Zuckerberg has a bunker on Kawaie. 269 00:14:48,400 --> 00:14:50,440 Speaker 1: I think, yeah, plenty of them have bunkers. 270 00:14:50,640 --> 00:14:55,120 Speaker 2: Again, not a great public relations strategy talking about your bunkers, right. 271 00:14:55,360 --> 00:14:58,000 Speaker 3: The logos might not love that, right. 272 00:14:58,360 --> 00:15:00,800 Speaker 1: And I don't know if the bunkers are to protect 273 00:15:00,800 --> 00:15:04,920 Speaker 1: from angry citizenry or to protect from the AI apocalypse. 274 00:15:04,960 --> 00:15:08,040 Speaker 4: I'm not quite sure. Sarah Fryar, thanks for being here, 275 00:15:08,320 --> 00:15:08,800 Speaker 4: Thanks for. 276 00:15:08,680 --> 00:15:09,160 Speaker 1: Having me. 277 00:15:16,960 --> 00:15:17,240 Speaker 3: Max. 278 00:15:17,280 --> 00:15:19,600 Speaker 5: One of the things I think we've talked about, maybe 279 00:15:19,600 --> 00:15:23,560 Speaker 5: more than almost anything else, is jobs, the job market, 280 00:15:23,640 --> 00:15:25,520 Speaker 5: which has been just the source of a lot of 281 00:15:25,560 --> 00:15:28,360 Speaker 5: distress and a lot of worry for years now. 282 00:15:28,920 --> 00:15:34,200 Speaker 2: Yeah, and it's been especially bad for young job seekers, 283 00:15:34,200 --> 00:15:39,000 Speaker 2: for people who are sort of leaving school and trying 284 00:15:39,040 --> 00:15:42,000 Speaker 2: to find their first job or their first internship. 285 00:15:42,120 --> 00:15:42,960 Speaker 4: It's tough out there. 286 00:15:43,280 --> 00:15:45,960 Speaker 5: It is tough out there, Yeah, for young job seekers. 287 00:15:46,000 --> 00:15:48,320 Speaker 5: The unemployment rate, I think is almost two times what 288 00:15:48,360 --> 00:15:51,720 Speaker 5: it is for overall job seekers. And the situation for 289 00:15:51,760 --> 00:15:54,560 Speaker 5: overall job seekers isn't great either. And we wanted to 290 00:15:54,640 --> 00:15:58,120 Speaker 5: see what it feels like for people who are young 291 00:15:58,280 --> 00:16:00,400 Speaker 5: and maybe just starting out in their careers or just 292 00:16:00,760 --> 00:16:04,360 Speaker 5: about to enter the job market. And so we sent 293 00:16:04,520 --> 00:16:07,960 Speaker 5: our producer Jasmine JT Green out onto the streets of 294 00:16:07,960 --> 00:16:10,720 Speaker 5: New York to talk to some young people and ask 295 00:16:10,760 --> 00:16:13,360 Speaker 5: them what their job situation is right now. 296 00:16:14,360 --> 00:16:17,320 Speaker 8: I think current job market in the US is really limited, 297 00:16:17,400 --> 00:16:22,520 Speaker 8: especially to like international students like me, especially who doesn't 298 00:16:22,560 --> 00:16:26,440 Speaker 8: have any like US citizenship or a green card or something. 299 00:16:26,200 --> 00:16:29,200 Speaker 9: That I'm a student, and what I've heard is it's 300 00:16:29,240 --> 00:16:31,360 Speaker 9: really difficult to get a job right now, depends what 301 00:16:31,400 --> 00:16:34,120 Speaker 9: worlds we're in. But I'm studying biology, hopefully to be 302 00:16:34,160 --> 00:16:36,600 Speaker 9: a dentist. And for me, it's been really difficult to 303 00:16:36,640 --> 00:16:39,680 Speaker 9: find like internship stuff like that boost my resume and 304 00:16:39,680 --> 00:16:40,840 Speaker 9: eventually get a better job. 305 00:16:41,400 --> 00:16:43,240 Speaker 10: For me, trying to get a job in New York City, 306 00:16:43,520 --> 00:16:50,000 Speaker 10: like a part time job, it's really hard and I 307 00:16:50,000 --> 00:16:52,000 Speaker 10: I've been unemployed for a few months now and it's 308 00:16:52,000 --> 00:16:53,080 Speaker 10: hard to get another. 309 00:16:52,880 --> 00:16:54,440 Speaker 4: Part time job. What would you say It's the most 310 00:16:54,440 --> 00:16:55,160 Speaker 4: difficult part. 311 00:16:56,360 --> 00:16:59,520 Speaker 9: Having connections in like knowing who to reach out to 312 00:16:59,520 --> 00:17:01,640 Speaker 9: to find what you want, because I feel like cold 313 00:17:01,680 --> 00:17:04,760 Speaker 9: emailing a lot of the time doesn't really work, So 314 00:17:04,840 --> 00:17:07,679 Speaker 9: I feel like having connections that can really get you 315 00:17:07,720 --> 00:17:09,159 Speaker 9: a job is definitely the hardest part. 316 00:17:09,800 --> 00:17:13,000 Speaker 8: English is my second language and kind of I think 317 00:17:13,040 --> 00:17:15,359 Speaker 8: it's really hard for me to engage like people in 318 00:17:15,400 --> 00:17:16,639 Speaker 8: a community. 319 00:17:16,280 --> 00:17:17,080 Speaker 9: And stuff like that. 320 00:17:17,320 --> 00:17:20,080 Speaker 10: I think that there's just a high demand for jobs 321 00:17:20,200 --> 00:17:24,080 Speaker 10: and there aren't enough. And also yeah. 322 00:17:25,160 --> 00:17:28,680 Speaker 4: High demand for jobs, and that tells the story. 323 00:17:28,760 --> 00:17:31,240 Speaker 3: You know what's interesting for me to hearing this. 324 00:17:31,359 --> 00:17:34,800 Speaker 5: I feel like, you know, all all the time, there 325 00:17:34,800 --> 00:17:36,000 Speaker 5: are things that give you a leg up in the 326 00:17:36,040 --> 00:17:38,439 Speaker 5: job market, right if you're looking for a job in 327 00:17:38,480 --> 00:17:42,160 Speaker 5: your native language, if you have citizenship in the place 328 00:17:42,160 --> 00:17:44,359 Speaker 5: where you're looking for a job, if you have connections, 329 00:17:45,080 --> 00:17:47,879 Speaker 5: all these things that are always an advantage in the 330 00:17:47,920 --> 00:17:50,399 Speaker 5: job market. I think when the job market gets tight, 331 00:17:50,720 --> 00:17:53,280 Speaker 5: it becomes like a really really hard to get a job. 332 00:17:53,320 --> 00:17:55,280 Speaker 5: If you don't have those things, I mean, they would 333 00:17:55,280 --> 00:17:57,080 Speaker 5: always be a leg up, but all of a sudden, 334 00:17:57,080 --> 00:17:58,879 Speaker 5: it's like almost impossible to get a job. If you 335 00:17:58,920 --> 00:18:02,320 Speaker 5: don't have like every conceivable advantage, then it becomes impossible. 336 00:18:02,960 --> 00:18:06,320 Speaker 2: We're talking about this, of course, because in the brand 337 00:18:06,320 --> 00:18:09,320 Speaker 2: new issue of Bloomberg Business Week, we have a whole 338 00:18:09,320 --> 00:18:11,399 Speaker 2: bunch of stories all about first jobs. 339 00:18:11,480 --> 00:18:12,879 Speaker 4: This is an awesome. 340 00:18:12,520 --> 00:18:16,920 Speaker 2: Collection of this combination of sort of trend stories and 341 00:18:17,200 --> 00:18:20,960 Speaker 2: first person and interviews. It's all about what things look 342 00:18:21,080 --> 00:18:23,280 Speaker 2: like right now for young people, what does it mean 343 00:18:23,320 --> 00:18:26,639 Speaker 2: for their careers, but also for all of us we 344 00:18:26,680 --> 00:18:30,879 Speaker 2: have Julia Rubin. She is a BusinessWeek editor. Our colleague 345 00:18:31,040 --> 00:18:33,320 Speaker 2: is here in the studio with us right now. Julia, 346 00:18:33,400 --> 00:18:34,600 Speaker 2: Welcome to everybody's business. 347 00:18:34,680 --> 00:18:35,440 Speaker 3: Thank you so much. 348 00:18:35,480 --> 00:18:38,240 Speaker 6: I feel this is like a real, you know, longtime listener, 349 00:18:38,240 --> 00:18:39,480 Speaker 6: first time caller situation. 350 00:18:39,760 --> 00:18:42,120 Speaker 4: So you know you're from the credit that's right. 351 00:18:43,240 --> 00:18:46,040 Speaker 2: Yeah, put together this show, and you also put together 352 00:18:46,080 --> 00:18:46,919 Speaker 2: this awesome issue. 353 00:18:46,920 --> 00:18:48,000 Speaker 3: Thank you, Yeah, I did. 354 00:18:48,119 --> 00:18:51,080 Speaker 5: I want to start out really broadly, what does the 355 00:18:51,160 --> 00:18:53,399 Speaker 5: job market look like for young people right now? 356 00:18:53,480 --> 00:18:57,040 Speaker 3: What are they up against? It's bad, It's not good. 357 00:18:57,240 --> 00:19:00,520 Speaker 6: The unemployment numbers for young people are higher than the 358 00:19:00,600 --> 00:19:05,119 Speaker 6: unemployment numbers for the general population. And we are in 359 00:19:05,160 --> 00:19:08,520 Speaker 6: a time of overlapping crises right now, and many things 360 00:19:08,520 --> 00:19:10,840 Speaker 6: feel really bad, but this in particular is not good. 361 00:19:10,920 --> 00:19:13,400 Speaker 6: There are fewer entry level jobs, there are more people 362 00:19:13,480 --> 00:19:15,960 Speaker 6: applying to them. Young people being out of work is 363 00:19:16,080 --> 00:19:18,639 Speaker 6: really bad for the long term health of the economy. 364 00:19:19,160 --> 00:19:22,320 Speaker 2: And not just young people, but particularly young people with 365 00:19:22,359 --> 00:19:23,160 Speaker 2: college degrees. 366 00:19:23,240 --> 00:19:23,400 Speaker 1: Right. 367 00:19:23,520 --> 00:19:26,800 Speaker 2: That's one of the big and strange and upsetting trends. 368 00:19:26,840 --> 00:19:28,879 Speaker 2: I guess, especially if you're one of these people with 369 00:19:28,960 --> 00:19:32,879 Speaker 2: a college degree, is that historically your job prospects are 370 00:19:32,920 --> 00:19:35,440 Speaker 2: better if you have a college degree, and right now 371 00:19:35,480 --> 00:19:37,760 Speaker 2: that is that's not true, right, Julia? 372 00:19:38,200 --> 00:19:42,560 Speaker 6: Yes, and no, Okay, there is an underemployment crisis, which 373 00:19:42,640 --> 00:19:47,879 Speaker 6: means that there are more degree hars than degree requiring jobs. 374 00:19:48,160 --> 00:19:51,800 Speaker 6: That said, degree requiring jobs are still going to make 375 00:19:51,840 --> 00:19:55,760 Speaker 6: you more money. It is still something that is incredibly desirable. 376 00:19:56,400 --> 00:19:58,680 Speaker 6: But if you are able to get a job right 377 00:19:58,720 --> 00:20:01,200 Speaker 6: now and you are young, it's very possible you're getting 378 00:20:01,200 --> 00:20:02,840 Speaker 6: a job that you did not need your two hundred 379 00:20:02,840 --> 00:20:04,320 Speaker 6: thousand dollars education for. 380 00:20:04,440 --> 00:20:06,520 Speaker 5: What are some examples of this? Did they talk to 381 00:20:06,560 --> 00:20:10,199 Speaker 5: anybody who was unde employed in the issue? Because I 382 00:20:10,240 --> 00:20:12,960 Speaker 5: feel like we see numbers for unemployed, and I believe 383 00:20:13,000 --> 00:20:15,560 Speaker 5: the unemployment numbers for eighteen to twenty four year olds 384 00:20:15,600 --> 00:20:17,919 Speaker 5: is I think it's like over eight percent. 385 00:20:17,960 --> 00:20:22,040 Speaker 3: It's really high. But underemployment the data is a little harder. Yeah. 386 00:20:22,119 --> 00:20:24,479 Speaker 6: I think the most interesting place and the most kind 387 00:20:24,480 --> 00:20:27,080 Speaker 6: of salient place to start is like engineers and computer 388 00:20:27,119 --> 00:20:31,040 Speaker 6: science grats, because from twenty fourteen to twenty twenty four, 389 00:20:31,680 --> 00:20:34,280 Speaker 6: entry level openings for those kinds of roles grew about 390 00:20:34,320 --> 00:20:38,520 Speaker 6: six percent. However, graduates of those fields or into those 391 00:20:38,520 --> 00:20:41,000 Speaker 6: fields grew by one hundred and ten percent, So there 392 00:20:41,040 --> 00:20:43,360 Speaker 6: is this huge mismatch between eighty set. 393 00:20:43,280 --> 00:20:46,040 Speaker 4: So learn to code right now, and they lie. 394 00:20:45,880 --> 00:20:48,120 Speaker 6: And you know what, I didn't learn to code, and look, 395 00:20:48,119 --> 00:20:49,520 Speaker 6: we are not recent college grads. 396 00:20:49,520 --> 00:20:51,240 Speaker 3: But there you go. But no, totally. 397 00:20:51,280 --> 00:20:53,720 Speaker 6: And I think this is a field where AI and 398 00:20:53,800 --> 00:20:55,399 Speaker 6: theory is starting to have some impact. 399 00:20:55,520 --> 00:20:57,160 Speaker 3: But also there's a lot of AI washing. 400 00:20:57,200 --> 00:21:00,320 Speaker 6: These companies hired too much during the pandemic, and now 401 00:21:00,359 --> 00:21:03,280 Speaker 6: they're blaming AI for layoffs when like, that's not really 402 00:21:03,440 --> 00:21:06,359 Speaker 6: the reason why yet at least that they are slashing jobs. 403 00:21:07,080 --> 00:21:10,240 Speaker 2: Juliet at the beginning of this issue of about first 404 00:21:10,320 --> 00:21:14,680 Speaker 2: jobs and early career workers, there's this really staggering chart 405 00:21:14,760 --> 00:21:19,520 Speaker 2: that compares essentially the unemployment rate for recent college graduates, 406 00:21:19,520 --> 00:21:22,480 Speaker 2: people between the ages of twenty two and twenty seven, 407 00:21:22,720 --> 00:21:27,320 Speaker 2: and everybody else. And until looking at it about what 408 00:21:27,440 --> 00:21:31,399 Speaker 2: like twenty twenty, for a very long time is fourteen 409 00:21:31,480 --> 00:21:36,600 Speaker 2: years or so, fifteen years, the recent college graduates had 410 00:21:36,600 --> 00:21:39,520 Speaker 2: a much lower unemployment rate than the rest of us. 411 00:21:39,760 --> 00:21:42,480 Speaker 2: And basically, if you were a recent grad, you were 412 00:21:42,480 --> 00:21:46,080 Speaker 2: golden compared to the average worker. And it flipped and 413 00:21:47,080 --> 00:21:50,119 Speaker 2: that I think maybe gets to the core of a 414 00:21:50,160 --> 00:21:53,040 Speaker 2: lot of the kind of anxiety you hear from young workers, 415 00:21:53,080 --> 00:21:57,320 Speaker 2: as well as conversations around AI. Is this a crisis 416 00:21:57,440 --> 00:21:58,280 Speaker 2: and how bad is it? 417 00:21:58,480 --> 00:22:01,240 Speaker 3: I mean, it's totally complicated. No one knows anything yet. 418 00:22:01,280 --> 00:22:04,679 Speaker 6: There are definitely people saying, okay, AI is going to 419 00:22:04,840 --> 00:22:07,840 Speaker 6: you know, wipe out the bottom bottom rung of the 420 00:22:08,680 --> 00:22:12,240 Speaker 6: ladder and the entry level jobs, the work that entry 421 00:22:12,280 --> 00:22:13,200 Speaker 6: level workers are doing. 422 00:22:13,240 --> 00:22:14,840 Speaker 3: That's the easiest stuff to be automated. 423 00:22:14,960 --> 00:22:17,119 Speaker 6: There are also people who are like, actually, this is 424 00:22:17,160 --> 00:22:19,600 Speaker 6: going to create all of these jobs, and it's actually 425 00:22:19,600 --> 00:22:22,199 Speaker 6: just going to sort of change the nature of what 426 00:22:22,240 --> 00:22:24,560 Speaker 6: these first jobs look like. They'll be way more valuable. 427 00:22:25,160 --> 00:22:28,840 Speaker 6: And there is this class of what we're calling AI natives, 428 00:22:28,880 --> 00:22:31,000 Speaker 6: though I don't really know if I totally agree with that, 429 00:22:31,119 --> 00:22:34,320 Speaker 6: because people who are graduating in the class of twenty 430 00:22:34,359 --> 00:22:37,160 Speaker 6: twenty six have had access to these large language models 431 00:22:37,200 --> 00:22:38,120 Speaker 6: their entire college career. 432 00:22:38,160 --> 00:22:41,400 Speaker 2: They've been cheating on their college classes fast years. 433 00:22:41,480 --> 00:22:43,680 Speaker 4: How they are ready to be management consultants. 434 00:22:43,920 --> 00:22:46,640 Speaker 6: But your children are actually the AI natives, right, because 435 00:22:46,640 --> 00:22:48,600 Speaker 6: it's like, if you're like an elementary school, you are 436 00:22:48,600 --> 00:22:50,600 Speaker 6: growing up with the technology in the same way that 437 00:22:50,640 --> 00:22:53,040 Speaker 6: we did with the internet, and then like social life, 438 00:22:53,160 --> 00:22:56,080 Speaker 6: you did that, I did that. I'm very young. I 439 00:22:56,080 --> 00:22:58,520 Speaker 6: don't know if you can sell. But yeah, so it's 440 00:22:58,520 --> 00:23:02,800 Speaker 6: there is some level of AI fluency, but there's also dependency. 441 00:23:02,840 --> 00:23:05,959 Speaker 6: You're also having these kind of recent grads or sometimes 442 00:23:06,080 --> 00:23:09,720 Speaker 6: college students going into these internships and they know how 443 00:23:09,720 --> 00:23:12,040 Speaker 6: to use these tools way better than anyone else in 444 00:23:12,080 --> 00:23:14,359 Speaker 6: the workplace does, but they know nothing about the subject 445 00:23:14,440 --> 00:23:16,440 Speaker 6: matter that they are actually, you. 446 00:23:16,320 --> 00:23:18,960 Speaker 3: Know, using the models for. There's also a lot of 447 00:23:18,960 --> 00:23:20,480 Speaker 3: soft skill stuff that they may not know. 448 00:23:20,560 --> 00:23:23,760 Speaker 6: They don't know how to present the slide deck that 449 00:23:23,840 --> 00:23:25,800 Speaker 6: is being made for them by the AI. 450 00:23:26,040 --> 00:23:26,240 Speaker 4: Yeah. 451 00:23:26,320 --> 00:23:28,720 Speaker 2: One of my favorite parts of this issue, there's a 452 00:23:28,720 --> 00:23:31,760 Speaker 2: story about management consultants, and the funny thing is it 453 00:23:31,800 --> 00:23:34,200 Speaker 2: seems like a lot of the anxiety among the people 454 00:23:34,200 --> 00:23:37,840 Speaker 2: who run management consultancies is not about the entry level 455 00:23:37,840 --> 00:23:41,320 Speaker 2: employees so much as the idea that AI slop might 456 00:23:41,400 --> 00:23:46,600 Speaker 2: be somewhat indistinguishable from the normal output of a management consultancy, 457 00:23:46,400 --> 00:23:49,240 Speaker 2: and that is manifesting in that they really want to 458 00:23:49,240 --> 00:23:53,840 Speaker 2: make sure that their junior associates whatever they're called, have 459 00:23:53,960 --> 00:23:56,320 Speaker 2: soft skills that they don't just sound like they're reciting 460 00:23:56,720 --> 00:23:59,960 Speaker 2: points on a PowerPoint created by a chatbot, and that's where. 461 00:23:59,800 --> 00:24:02,439 Speaker 3: We're hearing too. It's oh, like we really want English majors. 462 00:24:02,440 --> 00:24:04,560 Speaker 6: And that's like a cross across the spectrum now, which 463 00:24:04,600 --> 00:24:06,919 Speaker 6: is also like a joke, because when we graduated it 464 00:24:06,960 --> 00:24:09,280 Speaker 6: was like again, got to be an engineer, got to 465 00:24:09,320 --> 00:24:11,960 Speaker 6: be a business major, whatever, And so a lot of that. 466 00:24:11,920 --> 00:24:12,760 Speaker 3: I think is down to me. 467 00:24:12,920 --> 00:24:16,800 Speaker 6: I'm but learn to ode, learn to owde man. 468 00:24:16,920 --> 00:24:19,040 Speaker 3: I didn't even it wasn't even an English major. 469 00:24:19,040 --> 00:24:19,479 Speaker 9: I don't know. 470 00:24:20,119 --> 00:24:23,439 Speaker 6: But yeah, it's very interesting and this idea of Okay, 471 00:24:23,440 --> 00:24:27,320 Speaker 6: if we are sort of automating away the grunt work 472 00:24:27,600 --> 00:24:30,879 Speaker 6: or the sort of technical work, then perhaps like the 473 00:24:30,920 --> 00:24:34,320 Speaker 6: work that is more about being a human is going 474 00:24:34,320 --> 00:24:35,400 Speaker 6: to be much more important. 475 00:24:35,920 --> 00:24:36,320 Speaker 4: Julia. 476 00:24:36,440 --> 00:24:40,680 Speaker 5: A lot of times young people, especially in difficult job markets, 477 00:24:41,000 --> 00:24:42,320 Speaker 5: get the short end of the stick. 478 00:24:42,480 --> 00:24:43,879 Speaker 3: So a lot of times. 479 00:24:43,640 --> 00:24:48,080 Speaker 5: You will see unemployment much higher for younger people than 480 00:24:48,240 --> 00:24:48,680 Speaker 5: it is. 481 00:24:48,640 --> 00:24:50,040 Speaker 3: For the rest of the workforce. 482 00:24:51,560 --> 00:24:54,960 Speaker 5: Do you see this moment as different from previous downturns 483 00:24:55,000 --> 00:24:57,760 Speaker 5: in that way? Is this moment harder on young workers 484 00:24:57,840 --> 00:25:02,239 Speaker 5: than like the housing crisis, the financial crash were, or 485 00:25:02,640 --> 00:25:04,360 Speaker 5: is this sort of the same. 486 00:25:05,000 --> 00:25:07,000 Speaker 3: Young workers are always going to be the first impacted. 487 00:25:07,080 --> 00:25:08,960 Speaker 6: It's like when there is like a soft job market, 488 00:25:09,000 --> 00:25:11,280 Speaker 6: like that is who is going to have the most trouble. 489 00:25:11,400 --> 00:25:14,560 Speaker 6: But what's interesting is unemployment is way higher during two 490 00:25:14,600 --> 00:25:16,400 Speaker 6: thousand and eight, two thousand and nine, twenty ten when 491 00:25:16,440 --> 00:25:21,000 Speaker 6: I graduated, but the overall unemployment was higher than for 492 00:25:21,240 --> 00:25:23,760 Speaker 6: recent grads and that is flipped now, And I think 493 00:25:23,800 --> 00:25:26,239 Speaker 6: that is something that is like really interesting and like 494 00:25:26,320 --> 00:25:29,439 Speaker 6: really concerning because people are not able to enter the 495 00:25:29,480 --> 00:25:32,520 Speaker 6: market and then they're not able to build their careers. 496 00:25:32,560 --> 00:25:35,320 Speaker 6: There's also this like horrible research that is not being 497 00:25:35,320 --> 00:25:37,639 Speaker 6: able to get a first job is going to impact 498 00:25:38,200 --> 00:25:41,240 Speaker 6: your I think it's like your salary for the first 499 00:25:41,240 --> 00:25:43,920 Speaker 6: ten years. Also, higher rates of divorce, You're going to 500 00:25:43,960 --> 00:25:47,400 Speaker 6: have fewer kids, There's like worse health outcomes, there's higher mortality. 501 00:25:47,680 --> 00:25:51,560 Speaker 6: It's really bad when unemployment for young people is up 502 00:25:51,680 --> 00:25:53,760 Speaker 6: above the general population. 503 00:25:55,359 --> 00:25:58,120 Speaker 2: One thing I've wondered about at part this conversation has 504 00:25:58,160 --> 00:26:01,919 Speaker 2: made me sometimes a little bit on comfortable because so 505 00:26:02,040 --> 00:26:05,080 Speaker 2: much of it seems to be about class and about 506 00:26:05,480 --> 00:26:10,600 Speaker 2: the expectation that going to college will vault you above 507 00:26:10,760 --> 00:26:13,280 Speaker 2: people who don't go to college, and the fact that 508 00:26:13,280 --> 00:26:17,040 Speaker 2: that is breaking down feels like a violation of some 509 00:26:17,080 --> 00:26:21,439 Speaker 2: sort of social contract in the reporting for this, for 510 00:26:21,480 --> 00:26:24,720 Speaker 2: these stories, are you picking up anything like that any 511 00:26:24,840 --> 00:26:26,920 Speaker 2: young people being like, no, they should have not learned 512 00:26:26,920 --> 00:26:28,840 Speaker 2: a code. But maybe I shouldn't have gone to college. 513 00:26:29,040 --> 00:26:31,280 Speaker 6: Yeah, I mean, when I think about the cost of college, 514 00:26:31,359 --> 00:26:34,480 Speaker 6: I graduated sixteen years ago, I think college is now 515 00:26:34,920 --> 00:26:37,560 Speaker 6: twice It costs twice what it was when I was there, 516 00:26:37,600 --> 00:26:39,399 Speaker 6: and I remember even like five years out, it was 517 00:26:39,440 --> 00:26:43,479 Speaker 6: like fifty percent more, Like I couldn't how could college 518 00:26:43,520 --> 00:26:46,320 Speaker 6: possibly get any more expensive? So I would say there's 519 00:26:46,359 --> 00:26:48,800 Speaker 6: definitely a sense of like, how could this possibly be 520 00:26:48,920 --> 00:26:51,439 Speaker 6: worth it? That said, it doesn't change the kinds of 521 00:26:51,520 --> 00:26:54,120 Speaker 6: jobs that people want that still are paying more than 522 00:26:54,320 --> 00:26:57,200 Speaker 6: jobs that don't require degrees. But I do think there 523 00:26:57,359 --> 00:26:59,520 Speaker 6: is a feeling of like I have been failed. And 524 00:26:59,680 --> 00:27:01,520 Speaker 6: one thing that we did find is that this is 525 00:27:02,000 --> 00:27:06,720 Speaker 6: an education problem. This is like colleges having majors and 526 00:27:06,760 --> 00:27:09,639 Speaker 6: class sizes for those majors that are too big. It 527 00:27:09,720 --> 00:27:12,720 Speaker 6: is schools not working with each other to make sure 528 00:27:12,760 --> 00:27:15,919 Speaker 6: that each class is going to have x many graduates, 529 00:27:16,000 --> 00:27:18,679 Speaker 6: and across the country in this field where there's not 530 00:27:18,720 --> 00:27:21,760 Speaker 6: going to be jobs. Like the education system in this 531 00:27:21,760 --> 00:27:24,520 Speaker 6: way is like actually totally broken, just as like the 532 00:27:24,640 --> 00:27:26,080 Speaker 6: application process for. 533 00:27:26,200 --> 00:27:27,600 Speaker 3: Jobs is also totally broken. 534 00:27:27,640 --> 00:27:30,600 Speaker 6: So there's like these two very broken systems that young 535 00:27:30,640 --> 00:27:35,480 Speaker 6: people are dealing with. It is still true that the 536 00:27:35,560 --> 00:27:40,320 Speaker 6: unemployment rate for people with a college degree is lower 537 00:27:40,359 --> 00:27:44,439 Speaker 6: than people who haven't graduated from college, and even for 538 00:27:44,480 --> 00:27:46,600 Speaker 6: people with high school diploma, it's lower than people who 539 00:27:46,600 --> 00:27:48,600 Speaker 6: don't have a high school diploma. 540 00:27:48,760 --> 00:27:49,480 Speaker 3: Do you feel like. 541 00:27:51,240 --> 00:27:54,640 Speaker 5: The broken promise is more like a feeling about the 542 00:27:55,080 --> 00:27:58,560 Speaker 5: future prospects or is this an underemployment thing that's maybe 543 00:27:58,600 --> 00:27:59,960 Speaker 5: not being captured in those numbers. 544 00:28:00,320 --> 00:28:00,520 Speaker 8: You know. 545 00:28:00,600 --> 00:28:02,679 Speaker 6: One thing to note too is that it is not 546 00:28:03,800 --> 00:28:06,480 Speaker 6: like the jobs that these that you are taking when 547 00:28:06,480 --> 00:28:09,320 Speaker 6: you are under employed are great either. When you know, 548 00:28:09,359 --> 00:28:11,159 Speaker 6: we look at trades, I think there's been all this 549 00:28:11,200 --> 00:28:14,040 Speaker 6: conversation recently about tech leaders saying we're going to need 550 00:28:14,040 --> 00:28:17,280 Speaker 6: all these electricians and plumbers, but actually there's been this 551 00:28:17,480 --> 00:28:19,679 Speaker 6: total plateau. I think in the last year there's been 552 00:28:19,680 --> 00:28:21,959 Speaker 6: a net loss of one hundred and fifty thousand jobs 553 00:28:22,040 --> 00:28:24,399 Speaker 6: in the trades, and Trump said tariffs are going to 554 00:28:24,440 --> 00:28:27,080 Speaker 6: bring back blue collar jobs. But actually no, it's like 555 00:28:27,119 --> 00:28:30,919 Speaker 6: made manufacturing more expensive, it has made repair work more expensive, 556 00:28:31,119 --> 00:28:33,280 Speaker 6: and the data just really does not support that these 557 00:28:33,359 --> 00:28:36,399 Speaker 6: data center builds for AI are going to you know, 558 00:28:36,640 --> 00:28:39,640 Speaker 6: result in sort of this boom of electricians and plumbers. 559 00:28:40,280 --> 00:28:42,360 Speaker 5: So looking at the trends as they are now and 560 00:28:42,440 --> 00:28:45,400 Speaker 5: assuming that AI does indeed become a bigger part of 561 00:28:45,400 --> 00:28:47,800 Speaker 5: our economy and more jobs are displaced and things like that, 562 00:28:48,200 --> 00:28:52,960 Speaker 5: like where do you see this group of workers and 563 00:28:53,040 --> 00:28:56,560 Speaker 5: would be workers heading Like what does what does this 564 00:28:56,640 --> 00:28:59,400 Speaker 5: look like in the next ten years? 565 00:29:00,640 --> 00:29:01,200 Speaker 4: Hard question? 566 00:29:01,280 --> 00:29:03,000 Speaker 6: I mean, I think people really don't know because I 567 00:29:03,000 --> 00:29:05,640 Speaker 6: think again, there still is this open question on like 568 00:29:05,640 --> 00:29:08,280 Speaker 6: a I like, is AI going to decimate the entry 569 00:29:08,360 --> 00:29:11,400 Speaker 6: level you know, job market slash of the job market period? 570 00:29:11,480 --> 00:29:13,479 Speaker 6: Is it something that is really going to augment it? 571 00:29:14,000 --> 00:29:17,280 Speaker 6: You know, we've already seen like fewer computer science majors 572 00:29:17,280 --> 00:29:19,640 Speaker 6: in the last year. I do think what what kids 573 00:29:19,640 --> 00:29:22,680 Speaker 6: are deciding to study is going to change, But we 574 00:29:22,800 --> 00:29:25,800 Speaker 6: really we really just kind of don't know. I think, 575 00:29:25,840 --> 00:29:28,360 Speaker 6: you know, one thing brad Stone or editor in chief said, 576 00:29:28,360 --> 00:29:29,800 Speaker 6: it's like, you know, should there be sort of like 577 00:29:29,840 --> 00:29:31,520 Speaker 6: an AI apprenticeship program? 578 00:29:31,840 --> 00:29:32,800 Speaker 4: Wait, what does that mean? 579 00:29:32,880 --> 00:29:34,760 Speaker 2: I was wondering, is that does that like, does that 580 00:29:34,840 --> 00:29:36,920 Speaker 2: mean like you learn how to use AI or are. 581 00:29:36,840 --> 00:29:40,200 Speaker 3: You like on the job training or are you apprentice because. 582 00:29:40,160 --> 00:29:42,800 Speaker 4: A new major you just have to you just can't 583 00:29:42,840 --> 00:29:43,280 Speaker 4: get bad. 584 00:29:43,440 --> 00:29:45,040 Speaker 3: I think it's like training programs. 585 00:29:45,160 --> 00:29:47,320 Speaker 6: I think it is like, here is you know how 586 00:29:47,360 --> 00:29:49,560 Speaker 6: to use this technology for you know, X, y Z 587 00:29:49,720 --> 00:29:50,360 Speaker 6: line of work? 588 00:29:50,400 --> 00:29:50,920 Speaker 3: I mean, you know. 589 00:29:51,080 --> 00:29:54,000 Speaker 6: Another example he had is, you know, we are the 590 00:29:54,120 --> 00:29:57,800 Speaker 6: US subsidizes you know, med school right and all around 591 00:29:57,840 --> 00:30:01,760 Speaker 6: the world there are educational programs that are subsidized and 592 00:30:01,760 --> 00:30:04,320 Speaker 6: also really pushed, whether they be trade programs you know, 593 00:30:04,440 --> 00:30:07,000 Speaker 6: or other ways to really kind of like boost this 594 00:30:07,160 --> 00:30:10,120 Speaker 6: entry level workforce. So my very unsatisfying answer is like, 595 00:30:10,160 --> 00:30:12,000 Speaker 6: I don't think anyone knows what is going to happen. 596 00:30:12,320 --> 00:30:16,080 Speaker 3: This is often cyclical, but AI, if. 597 00:30:15,960 --> 00:30:21,160 Speaker 6: It really is this you know, paradigm shifting thing, what 598 00:30:21,280 --> 00:30:22,760 Speaker 6: will the cycles really look like? 599 00:30:23,760 --> 00:30:26,000 Speaker 2: Julia, you need to stick around because we're going to 600 00:30:26,040 --> 00:30:29,080 Speaker 2: talk about paradigm shifts even more can when we get to. 601 00:30:29,040 --> 00:30:30,040 Speaker 4: The underrated stories. 602 00:30:30,080 --> 00:30:42,760 Speaker 2: See you then, Stacy, Julia, I mentioned a paradigm shift, 603 00:30:42,960 --> 00:30:44,440 Speaker 2: and I've got a big one. 604 00:30:44,480 --> 00:30:46,800 Speaker 4: The underrated story some people have probably seen this. 605 00:30:46,840 --> 00:30:48,880 Speaker 2: I'm not even sure if it's underrated, but it's just 606 00:30:48,920 --> 00:30:51,360 Speaker 2: so stupid that I can't not talk about it. 607 00:30:51,760 --> 00:30:54,640 Speaker 4: Remember All Birds, those doors look. 608 00:30:54,600 --> 00:30:56,960 Speaker 3: Like the hippie shoes. Yeah, I bought them for my dad. 609 00:30:57,120 --> 00:30:58,720 Speaker 2: I was just gonna say they were the sneakers that 610 00:30:58,800 --> 00:31:04,320 Speaker 2: like your least cool friend was really school dad, or 611 00:31:04,400 --> 00:31:08,000 Speaker 2: like if you were like a white collar worker working 612 00:31:08,000 --> 00:31:10,680 Speaker 2: in a business casual workplace, like it's like a sensible 613 00:31:10,960 --> 00:31:13,240 Speaker 2: it's the floor sheime of sneakers. 614 00:31:13,440 --> 00:31:15,160 Speaker 4: Anyway, they were really big. 615 00:31:15,240 --> 00:31:17,520 Speaker 2: For like a hot second, I'm trying to remember what 616 00:31:17,640 --> 00:31:18,800 Speaker 2: the height of All Birds was. 617 00:31:18,840 --> 00:31:21,120 Speaker 4: I guess it was like covid era, yeah, or like. 618 00:31:21,200 --> 00:31:23,480 Speaker 6: Righte pre pandemic it was like because it was also 619 00:31:23,600 --> 00:31:25,440 Speaker 6: very much like direct to consumer. 620 00:31:25,040 --> 00:31:26,520 Speaker 4: Boom direct to consumer. 621 00:31:26,600 --> 00:31:29,480 Speaker 2: And then we all got cozy, and then All Birds 622 00:31:29,560 --> 00:31:33,360 Speaker 2: spacked and for a brief amount of time it was 623 00:31:33,520 --> 00:31:34,280 Speaker 2: very valuable. 624 00:31:34,720 --> 00:31:38,240 Speaker 4: And then this came up last week. It was sold. 625 00:31:38,640 --> 00:31:41,160 Speaker 2: It had been worth billions of dollars, it had been 626 00:31:41,160 --> 00:31:44,480 Speaker 2: worth four billion dollars. It was sold for thirty nine million. 627 00:31:44,840 --> 00:31:47,760 Speaker 2: But what I found out today on Bloomberg dot com 628 00:31:47,840 --> 00:31:50,040 Speaker 2: a great website that people should check out if they haven't. 629 00:31:50,520 --> 00:31:53,160 Speaker 2: Is that actually they did not sell All Birds. They 630 00:31:53,200 --> 00:31:56,200 Speaker 2: just sold the shoes part of the business and the 631 00:31:56,440 --> 00:31:59,080 Speaker 2: other part of the business is pivoting. Can you guess 632 00:31:59,160 --> 00:32:01,840 Speaker 2: what the pivot is too, Julia and Stacey? 633 00:32:02,440 --> 00:32:05,920 Speaker 3: I can I already write read the story. But it's 634 00:32:05,960 --> 00:32:06,959 Speaker 3: crazy AI. 635 00:32:07,440 --> 00:32:11,760 Speaker 4: They're going AI. They're gonna do GPUs or something. I 636 00:32:11,760 --> 00:32:14,880 Speaker 4: don't think anyone involved with this really knows. But it 637 00:32:14,960 --> 00:32:17,600 Speaker 4: is now called New Bird. AI. They got fifty million 638 00:32:17,640 --> 00:32:19,560 Speaker 4: bucks and the stock sword. 639 00:32:19,880 --> 00:32:23,640 Speaker 5: They were always running into the future, but now instead 640 00:32:23,680 --> 00:32:26,960 Speaker 5: of running with shoes, they're running metaphorically. 641 00:32:27,240 --> 00:32:28,680 Speaker 3: Well, I think it totally fits. 642 00:32:28,920 --> 00:32:31,120 Speaker 6: Makes this idea though, that you were like this part 643 00:32:31,120 --> 00:32:33,200 Speaker 6: of the company. The whole part of the company was shoes. 644 00:32:33,240 --> 00:32:36,320 Speaker 6: This is not the name of the name, so it's 645 00:32:36,360 --> 00:32:36,720 Speaker 6: not like. 646 00:32:36,640 --> 00:32:38,360 Speaker 4: It's not even the name actually because now it's called 647 00:32:38,400 --> 00:32:38,760 Speaker 4: New Bird. 648 00:32:38,840 --> 00:32:40,760 Speaker 6: They were never one of these companies that were actually 649 00:32:40,800 --> 00:32:43,160 Speaker 6: a tech company. Also, wasn't their whole thing, like were 650 00:32:43,200 --> 00:32:45,240 Speaker 6: sustainable and you know it's not sustainable. 651 00:32:45,640 --> 00:32:46,000 Speaker 3: AI. 652 00:32:47,240 --> 00:32:48,680 Speaker 2: I never had a pair of All Birds, but I 653 00:32:48,720 --> 00:32:51,880 Speaker 2: knew many people who did. And I guess the thinking 654 00:32:51,920 --> 00:32:55,760 Speaker 2: here is that brand that really strong All Birds brand 655 00:32:56,040 --> 00:32:59,560 Speaker 2: it could be the AI company that those all Birds 656 00:32:59,640 --> 00:33:01,680 Speaker 2: enthusias patronize. 657 00:33:01,800 --> 00:33:02,720 Speaker 4: That's my best guess. 658 00:33:02,760 --> 00:33:05,120 Speaker 3: I don't know I did learn the term. I mean, 659 00:33:05,160 --> 00:33:06,760 Speaker 3: I don't even know if I can say it out loud. 660 00:33:07,160 --> 00:33:12,120 Speaker 6: GPU ass like SaaS, but for GPUs, GPUs. 661 00:33:11,720 --> 00:33:17,719 Speaker 2: Does IPUs as a cert GPUs wos W that's correct. 662 00:33:18,320 --> 00:33:20,840 Speaker 6: That is correct, so you know, and like their whole 663 00:33:20,840 --> 00:33:23,200 Speaker 6: thing is going to be like hardware, which I don't know. 664 00:33:23,240 --> 00:33:25,680 Speaker 6: Maybe makes sense because it's like they're making a physical product, 665 00:33:25,720 --> 00:33:27,440 Speaker 6: but in every other regard it does not make any 666 00:33:27,440 --> 00:33:27,920 Speaker 6: sense at all. 667 00:33:28,240 --> 00:33:30,640 Speaker 4: Yeah, stocks up three hundred and seventy three percent today, 668 00:33:30,640 --> 00:33:33,720 Speaker 4: That's all I know. That's real. Market is never wrong. 669 00:33:34,040 --> 00:33:34,959 Speaker 4: We will follow this. 670 00:33:35,240 --> 00:33:37,680 Speaker 2: I can only imagine this could really we may have 671 00:33:37,760 --> 00:33:39,600 Speaker 2: to retrack the early part of the show when we 672 00:33:39,640 --> 00:33:42,200 Speaker 2: talk about AI because All Birds could ship new bird 673 00:33:42,320 --> 00:33:44,080 Speaker 2: AI could shake it up potentially. 674 00:33:44,440 --> 00:33:47,040 Speaker 4: Stacy, what's your underrated story? Okay? 675 00:33:48,040 --> 00:33:51,120 Speaker 5: Mine is it's a little bit of a conglomeration of 676 00:33:51,200 --> 00:33:54,920 Speaker 5: all the news that we've been hearing. But it is 677 00:33:54,960 --> 00:33:57,720 Speaker 5: this new term that I started to see this week 678 00:33:57,800 --> 00:34:02,360 Speaker 5: that I found very illuminating. It is from Nate Hagen's 679 00:34:02,360 --> 00:34:05,440 Speaker 5: a podcaster and he said, we are now living in 680 00:34:05,480 --> 00:34:13,200 Speaker 5: the Mortor economy, or for the uninitiated, is in Lord 681 00:34:13,239 --> 00:34:17,640 Speaker 5: of the Rings. It is the dystopian kingdom that tries 682 00:34:17,680 --> 00:34:21,120 Speaker 5: to take over everything. But what he specifically meant, and 683 00:34:21,360 --> 00:34:24,640 Speaker 5: why I think this is actually really interesting, is that 684 00:34:24,680 --> 00:34:28,480 Speaker 5: it's an economy that's all about energy, and energy prices 685 00:34:28,520 --> 00:34:31,719 Speaker 5: get more expensive, so the economy has to make more 686 00:34:31,840 --> 00:34:34,799 Speaker 5: energy in order to feed the energy needs, and it 687 00:34:34,800 --> 00:34:37,640 Speaker 5: becomes like a snake swallowing its own tail. And I 688 00:34:37,719 --> 00:34:41,880 Speaker 5: feel like this moment is very much the Mortor economy. 689 00:34:41,960 --> 00:34:45,520 Speaker 5: Between electricity prices going nuts because of data centers, and 690 00:34:45,600 --> 00:34:49,320 Speaker 5: also because Canada cut us off, oil prices going nuts, 691 00:34:49,440 --> 00:34:52,279 Speaker 5: natural gas prices going nuts, fertilizers made of a lot 692 00:34:52,280 --> 00:34:54,759 Speaker 5: of natural gas, so that means food prices are going 693 00:34:54,840 --> 00:34:57,720 Speaker 5: to go up, and our whole economy is becoming about 694 00:34:58,120 --> 00:35:02,920 Speaker 5: making energy the it is then consumed and becomes more expensive. 695 00:35:03,120 --> 00:35:05,560 Speaker 3: The mortar economy. I mean, it just feels like something 696 00:35:05,560 --> 00:35:06,880 Speaker 3: that didn't have to happen. 697 00:35:08,960 --> 00:35:13,399 Speaker 2: Okay, My critique here is more with the term is. 698 00:35:13,360 --> 00:35:15,799 Speaker 4: That the only thing going on with mortar that they're 699 00:35:15,880 --> 00:35:16,840 Speaker 4: using energy. 700 00:35:16,920 --> 00:35:20,799 Speaker 2: I feel like this is like a fundamentally confusing analogy. 701 00:35:20,800 --> 00:35:22,680 Speaker 3: What do you think was going on with Mortar? 702 00:35:22,840 --> 00:35:25,120 Speaker 2: It's like a totalitarian stake. They're trying to take over 703 00:35:25,160 --> 00:35:26,920 Speaker 2: the world and get the one ring of power. 704 00:35:26,960 --> 00:35:28,600 Speaker 4: I don't know. I'm not a Lord of the Rings. 705 00:35:28,360 --> 00:35:30,160 Speaker 6: Guy, but it may be a better analogy than you 706 00:35:30,280 --> 00:35:30,759 Speaker 6: even thought. 707 00:35:31,920 --> 00:35:35,800 Speaker 4: Who's sore on and who are the trees? And I'm like, 708 00:35:35,840 --> 00:35:36,800 Speaker 4: where are the hobbits? 709 00:35:36,840 --> 00:35:39,399 Speaker 2: I just I'm having written a lot about guys who 710 00:35:39,400 --> 00:35:40,720 Speaker 2: are really into Lord of the Rings. 711 00:35:40,719 --> 00:35:42,000 Speaker 4: I'm afraid I may have, like. 712 00:35:42,280 --> 00:35:44,880 Speaker 5: You may be very sensitive to this Stophore. It doesn't 713 00:35:44,920 --> 00:35:48,239 Speaker 5: line up at all. That is possibly true, But I 714 00:35:48,280 --> 00:35:51,880 Speaker 5: do think there is something really interesting about an economy 715 00:35:51,920 --> 00:35:55,760 Speaker 5: where energy starts to get so expensive. There's some really 716 00:35:55,880 --> 00:35:58,799 Speaker 5: chilling statistics, including that for low income people, they're now 717 00:35:58,840 --> 00:36:02,919 Speaker 5: spending about twenty percent of their income on energy. That's 718 00:36:03,200 --> 00:36:06,960 Speaker 5: gas and electricity and everything, twenty percent of their income. 719 00:36:07,840 --> 00:36:09,920 Speaker 5: And I agree with you, Max, And maybe the finer 720 00:36:09,960 --> 00:36:14,200 Speaker 5: points of Mortar are like and that's difficult. 721 00:36:14,600 --> 00:36:16,120 Speaker 4: The eye of sourn. 722 00:36:16,880 --> 00:36:19,680 Speaker 3: Is it was with you too, the eye of Soren's 723 00:36:19,760 --> 00:36:22,520 Speaker 3: Our economy was about so much more than energy. 724 00:36:24,120 --> 00:36:27,120 Speaker 4: Listeners, if you have a Lord of the Rings related take. 725 00:36:27,400 --> 00:36:32,560 Speaker 2: Yes, Oh my god, all everybody at Everybody's at Bloomberg 726 00:36:32,560 --> 00:36:36,000 Speaker 2: dot that's everybody's with an S. Is it the mortar economy? 727 00:36:36,120 --> 00:36:38,360 Speaker 2: And if so, who are the orcs? 728 00:36:38,520 --> 00:36:48,279 Speaker 4: Let us know. This show is produced by Jasmine, J. T. 729 00:36:48,400 --> 00:36:49,439 Speaker 4: Green and Stacy Wong. 730 00:36:49,920 --> 00:36:53,920 Speaker 2: Magnus Hendrickson is our supervising producer, Sam Rogich handles engineering, 731 00:36:54,120 --> 00:36:57,320 Speaker 2: and Dave per Cell factions. Special thanks to Jeff Muscus, 732 00:36:57,440 --> 00:37:02,239 Speaker 2: Julia Rubin, Reeling, Rachel Lewis, and Angel Recchio. If you 733 00:37:02,239 --> 00:37:04,000 Speaker 2: have a minute, please rate and review the show. 734 00:37:04,120 --> 00:37:04,920 Speaker 4: It means a lot to us. 735 00:37:04,960 --> 00:37:06,720 Speaker 2: And if you have a story that should be our business, 736 00:37:07,000 --> 00:37:09,879 Speaker 2: send us an email at Everybody's at Bloomberg dot net. 737 00:37:09,880 --> 00:37:12,160 Speaker 2: That's everybody with an S at the end at Bloomberg 738 00:37:12,160 --> 00:37:14,480 Speaker 2: dot net. Thank you for listening and we will see 739 00:37:14,480 --> 00:37:14,919 Speaker 2: you next week.