1 00:00:02,560 --> 00:00:07,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:10,240 --> 00:00:11,320 Speaker 2: This is Everybody's business. 3 00:00:11,320 --> 00:00:15,200 Speaker 3: I'm Max Chapkins and I'm Stacey Vnnix Smith. Hey, Stacy, Hello, Max, 4 00:00:15,640 --> 00:00:16,079 Speaker 3: how are you. 5 00:00:16,480 --> 00:00:17,120 Speaker 2: I'm good. 6 00:00:18,040 --> 00:00:21,840 Speaker 4: I've just been thinking that all the world is a 7 00:00:21,880 --> 00:00:26,520 Speaker 4: stage and the economy is no exception. We have got 8 00:00:26,600 --> 00:00:30,720 Speaker 4: this week the congressional theater around the tax cut extension, 9 00:00:30,840 --> 00:00:32,240 Speaker 4: the big beautiful bill. 10 00:00:32,680 --> 00:00:35,800 Speaker 2: Yeah, they've got a lot packed into this bill, tax 11 00:00:35,880 --> 00:00:38,920 Speaker 2: cuts that are gonna potentially save people a lot of money, 12 00:00:38,920 --> 00:00:42,159 Speaker 2: but also trillions of dollars that could be added to 13 00:00:42,520 --> 00:00:43,360 Speaker 2: our country's debt. 14 00:00:43,560 --> 00:00:45,760 Speaker 4: But why think about that, Max, when you could think 15 00:00:45,760 --> 00:00:47,879 Speaker 4: about WrestleMania. 16 00:00:48,720 --> 00:00:50,760 Speaker 2: Oh wow. 17 00:00:51,520 --> 00:00:54,000 Speaker 4: We have got economic writer Kylo Scanlon on to talk 18 00:00:54,040 --> 00:00:56,680 Speaker 4: about how much you can learn about Trump's approach to 19 00:00:56,720 --> 00:01:00,080 Speaker 4: the economy by watching WWE. 20 00:00:59,760 --> 00:01:03,320 Speaker 2: And I've read this everywhere. AI is coming for your job, abraded. 21 00:01:03,480 --> 00:01:06,240 Speaker 2: We're going to actually try to get into what that means. 22 00:01:06,440 --> 00:01:10,040 Speaker 4: And we have a little actual magic in our underrated 23 00:01:10,080 --> 00:01:10,920 Speaker 4: story of the week. 24 00:01:11,160 --> 00:01:15,320 Speaker 2: So, Stacy, this is the week many college graduations happen. 25 00:01:15,360 --> 00:01:19,560 Speaker 2: We have four million college students graduating in the United States. 26 00:01:20,040 --> 00:01:21,800 Speaker 2: Many of those folks, of course going to be getting 27 00:01:21,840 --> 00:01:24,880 Speaker 2: their first jobs really soon, they hope their parents hope. 28 00:01:25,160 --> 00:01:29,720 Speaker 4: Yes, exactly, and this is a strange, possibly difficult time 29 00:01:29,760 --> 00:01:31,360 Speaker 4: to be coming onto the job market. A lot of 30 00:01:31,400 --> 00:01:34,880 Speaker 4: people economists are calling this a frozen job market, meaning 31 00:01:34,959 --> 00:01:38,280 Speaker 4: unemployment is near historic lows, but hiring levels are too. 32 00:01:38,600 --> 00:01:41,240 Speaker 2: You know, look at the unemployment level for college graduates. 33 00:01:41,280 --> 00:01:43,319 Speaker 2: It's you know, it's almost six percent, which is low, 34 00:01:43,400 --> 00:01:45,759 Speaker 2: but it's not it's not that low. 35 00:01:45,840 --> 00:01:48,080 Speaker 3: Yeah, it's always a little higher for recent college graduate. 36 00:01:48,120 --> 00:01:49,640 Speaker 3: It's a little bit of a tougher. 37 00:01:49,320 --> 00:01:52,000 Speaker 2: Game, Stacy. This of course is a part of the 38 00:01:52,040 --> 00:01:55,320 Speaker 2: show where we go out into the world talk to 39 00:01:55,360 --> 00:01:58,360 Speaker 2: some people, ask them how the world of business, the 40 00:01:58,400 --> 00:02:01,800 Speaker 2: economy is affecting them. Where did you go this week? 41 00:02:02,080 --> 00:02:02,680 Speaker 2: I wanted to. 42 00:02:02,640 --> 00:02:05,400 Speaker 4: Talk to these recent college grads, see what was going on, 43 00:02:05,440 --> 00:02:07,440 Speaker 4: how many of them had jobs, how they were feeling 44 00:02:07,440 --> 00:02:10,800 Speaker 4: about the job market. We're pretty close to Columbia University. 45 00:02:10,880 --> 00:02:13,760 Speaker 4: Their graduation was this week and I went to the 46 00:02:13,800 --> 00:02:23,399 Speaker 4: campus congratulations to the twenty twenty five class of Columbia Journalism. 47 00:02:23,639 --> 00:02:26,680 Speaker 4: Everyone was running around and graduation robes. They were taking 48 00:02:26,680 --> 00:02:29,360 Speaker 4: pictures with their parents. They were like holding big bouquets 49 00:02:29,360 --> 00:02:30,040 Speaker 4: and hugging each other. 50 00:02:30,080 --> 00:02:31,080 Speaker 3: It was really sweet. 51 00:02:31,120 --> 00:02:33,080 Speaker 2: They're all hungover also, by the way. 52 00:02:33,040 --> 00:02:35,919 Speaker 4: I did not think about that, but possibly I went 53 00:02:35,960 --> 00:02:38,480 Speaker 4: to ask them, like, how do you feel venturing out 54 00:02:38,600 --> 00:02:40,359 Speaker 4: into the job market and what is going on? 55 00:02:40,919 --> 00:02:43,440 Speaker 3: I'm a graduate student in computer science. Do you have 56 00:02:43,480 --> 00:02:43,920 Speaker 3: a job? 57 00:02:44,360 --> 00:02:45,040 Speaker 2: Oh? Yeah, I do. 58 00:02:45,480 --> 00:02:46,480 Speaker 3: I got like cape to. 59 00:02:46,440 --> 00:02:49,239 Speaker 4: Be honest among your friends, like how many have jobs? 60 00:02:49,280 --> 00:02:50,400 Speaker 4: How many are still looking? 61 00:02:50,800 --> 00:02:51,680 Speaker 2: In computer science? 62 00:02:51,680 --> 00:02:54,000 Speaker 3: Approximately sixty to seventy percent have jobs. 63 00:02:54,120 --> 00:02:55,959 Speaker 5: I studied epudymiology. 64 00:02:56,360 --> 00:02:57,680 Speaker 3: Do you have a job right now? 65 00:02:57,800 --> 00:02:57,880 Speaker 2: No? 66 00:02:58,000 --> 00:03:01,440 Speaker 3: I did not. How's the job market fell extremely hard? 67 00:03:01,760 --> 00:03:05,440 Speaker 1: I was mainly aiming for labs in different universities, but 68 00:03:05,840 --> 00:03:07,720 Speaker 1: universities are cutting phones. 69 00:03:08,200 --> 00:03:10,160 Speaker 3: How's your job market for electrical engineering? 70 00:03:10,600 --> 00:03:11,200 Speaker 6: Just terrible? 71 00:03:11,360 --> 00:03:14,200 Speaker 2: The market is super terrible right now. Everyone will search. 72 00:03:14,760 --> 00:03:16,079 Speaker 3: AI affect your field at all. 73 00:03:16,400 --> 00:03:20,880 Speaker 5: AI is a tricky subject because it really does help 74 00:03:20,919 --> 00:03:24,960 Speaker 5: a lot, and in other ways it's detrimental to a 75 00:03:25,000 --> 00:03:28,280 Speaker 5: lot of job security. It's kind of fifty to fifty. 76 00:03:28,720 --> 00:03:31,280 Speaker 4: I use AIA every day, fly the way, So you're 77 00:03:31,360 --> 00:03:32,519 Speaker 4: using AI to code? 78 00:03:32,800 --> 00:03:36,080 Speaker 2: Yeah, almost every day. Actually a lot of people are 79 00:03:36,120 --> 00:03:38,960 Speaker 2: doing these days. I just want to know how many 80 00:03:39,000 --> 00:03:41,880 Speaker 2: his kids are using chat GPT to write their essays, 81 00:03:41,920 --> 00:03:44,680 Speaker 2: because you know that guy's like I use it to code. 82 00:03:44,840 --> 00:03:45,240 Speaker 2: I don't know. 83 00:03:45,400 --> 00:03:48,160 Speaker 3: Well, you know, he was a software engineer. He has 84 00:03:48,200 --> 00:03:48,560 Speaker 3: a job. 85 00:03:48,600 --> 00:03:51,880 Speaker 4: But I was pretty shocked that like thirty forty percent 86 00:03:51,920 --> 00:03:55,000 Speaker 4: of software engineering grads did not have jobs that he 87 00:03:55,040 --> 00:03:57,960 Speaker 4: talked to when a lot of people were really pessimistic. 88 00:03:58,000 --> 00:03:59,280 Speaker 4: A lot of people did not want to talk to 89 00:03:59,280 --> 00:03:59,800 Speaker 4: me about this. 90 00:04:00,200 --> 00:04:03,160 Speaker 2: You know, I feel like it's always like this for 91 00:04:03,240 --> 00:04:06,440 Speaker 2: college graduates, or maybe this is just my adulthood or whatever, 92 00:04:06,560 --> 00:04:10,160 Speaker 2: but like, you know, the economy's changing. You're coming in 93 00:04:10,200 --> 00:04:12,400 Speaker 2: there with a limited network. I mean, even even these 94 00:04:12,400 --> 00:04:14,880 Speaker 2: Columbia students, who of course have a huge leg up 95 00:04:14,880 --> 00:04:18,360 Speaker 2: from a normal person. But yeah, I mean it's it's scary, 96 00:04:18,520 --> 00:04:20,000 Speaker 2: but you know, I'm excited for them. 97 00:04:20,360 --> 00:04:22,839 Speaker 3: I'm excited for them too. Of course, we wish them well. 98 00:04:23,240 --> 00:04:27,200 Speaker 2: We're gonna hopefully later in the show put some data 99 00:04:27,279 --> 00:04:29,760 Speaker 2: to this question of job loss, really get to specifics, 100 00:04:30,000 --> 00:04:32,359 Speaker 2: but we definitely want to hear what you all think. 101 00:04:32,400 --> 00:04:35,520 Speaker 2: How big an impact is technology having on the job market, 102 00:04:35,560 --> 00:04:39,320 Speaker 2: Like are you using AI in your job? Do you 103 00:04:39,320 --> 00:04:42,120 Speaker 2: think AI can replace you? Do you like me think 104 00:04:42,160 --> 00:04:45,560 Speaker 2: it's a total bunch of bs and like, oh my god, whatever, Like, 105 00:04:46,080 --> 00:04:48,640 Speaker 2: let us know, Stacey, we had so many good emails 106 00:04:48,640 --> 00:04:49,120 Speaker 2: this week. 107 00:04:49,360 --> 00:04:49,960 Speaker 3: Yes, we did. 108 00:04:50,000 --> 00:04:51,920 Speaker 4: We had great emails this week, and we're going to 109 00:04:51,960 --> 00:04:54,200 Speaker 4: be talking about them in future shows. 110 00:04:54,440 --> 00:04:56,520 Speaker 3: But please keep the emails coming. We want to know 111 00:04:56,600 --> 00:04:57,320 Speaker 3: what you want to hear about. 112 00:04:57,440 --> 00:05:01,039 Speaker 2: Yeah, that address, everybody's at Bloomberg dot that's everybody's with 113 00:05:01,080 --> 00:05:03,320 Speaker 2: an s at Bloomberg dot net. Just real quick, Stacy, 114 00:05:03,360 --> 00:05:05,640 Speaker 2: I got to tell you we had a couple requests 115 00:05:05,760 --> 00:05:08,960 Speaker 2: in in terms of future episodes about insurance. Do you 116 00:05:08,960 --> 00:05:09,479 Speaker 2: believe that? So? 117 00:05:09,680 --> 00:05:12,680 Speaker 3: Alex a listener insurance is fascinating, wants to. 118 00:05:12,640 --> 00:05:15,920 Speaker 2: Know why insurance is getting so expensive, why car insurance 119 00:05:15,920 --> 00:05:18,560 Speaker 2: is getting so expensive? Will wants to hear about the 120 00:05:18,680 --> 00:05:23,400 Speaker 2: kind of voting rights control at Berkshire Hathaway. Marita asked 121 00:05:23,400 --> 00:05:25,719 Speaker 2: about the bond markets. I know we'll hit that, and 122 00:05:26,040 --> 00:05:29,640 Speaker 2: a listener named josh gave us a note that I'd say, 123 00:05:29,720 --> 00:05:33,240 Speaker 2: is are bordering on NC seventeen rated that I'm not 124 00:05:33,279 --> 00:05:37,120 Speaker 2: going to give, but without business, we do appreciate it. Joshua. 125 00:05:37,520 --> 00:05:43,600 Speaker 4: Yeah, apparently glazing does not mean what we think it means. 126 00:05:45,160 --> 00:05:48,599 Speaker 2: The big story this week is this tax bill. The 127 00:05:48,720 --> 00:05:51,719 Speaker 2: House worked hard to pass a version of the bill, 128 00:05:51,839 --> 00:05:55,000 Speaker 2: the Big Beautiful Bill, just before the long weekend. It's 129 00:05:55,040 --> 00:05:58,200 Speaker 2: an extension essentially of the twenty seventeen tax cuts that 130 00:05:58,240 --> 00:06:01,479 Speaker 2: Trump put in place during his administration and that we're 131 00:06:01,520 --> 00:06:02,359 Speaker 2: said to expire. 132 00:06:02,760 --> 00:06:05,640 Speaker 4: Yeah, the amounts of money that we're talking about are huge. 133 00:06:05,839 --> 00:06:08,960 Speaker 4: We're talking around four trillion dollars over the next ten years. 134 00:06:09,360 --> 00:06:11,880 Speaker 4: And to put that in perspective, our entire budget, what 135 00:06:11,960 --> 00:06:15,280 Speaker 4: it costs to run the entire country, is about seven 136 00:06:15,360 --> 00:06:18,920 Speaker 4: trillion dollars a year. So it's just the amounts of 137 00:06:18,960 --> 00:06:24,880 Speaker 4: money aren't enormous and even potentially quite destabilizing. Obviously, it's 138 00:06:24,880 --> 00:06:27,120 Speaker 4: going to add to our debt, which is already pretty substantial. 139 00:06:27,279 --> 00:06:31,240 Speaker 2: Yeah, this is confusing, particularly because we were talking in 140 00:06:31,279 --> 00:06:33,240 Speaker 2: this country just a couple of weeks ago about all 141 00:06:33,240 --> 00:06:34,880 Speaker 2: the cuts we're making, and now all of a sudden, 142 00:06:34,920 --> 00:06:38,880 Speaker 2: there's a huge deficit. There are some cuts in the bill, 143 00:06:39,040 --> 00:06:42,800 Speaker 2: cuts to medicaid, food aid, clean energy. It's a lot 144 00:06:42,800 --> 00:06:45,719 Speaker 2: of really important stuff, stuff that people care about. 145 00:06:46,080 --> 00:06:48,360 Speaker 4: The problem I think with a lot of these issues 146 00:06:48,560 --> 00:06:51,720 Speaker 4: is that it gets a little bit dry and boring. 147 00:06:52,040 --> 00:06:53,760 Speaker 4: I hate to say that, because it is really important, 148 00:06:53,760 --> 00:06:55,960 Speaker 4: but it's a little bit boring tax policy. When you're 149 00:06:55,960 --> 00:06:59,040 Speaker 4: talking about pass throughs, tax brackets, state and local policy, 150 00:06:59,400 --> 00:07:01,640 Speaker 4: it's a lot less exciting to talk about. 151 00:07:01,360 --> 00:07:04,000 Speaker 2: Than doge or than professional wrestling. 152 00:07:04,560 --> 00:07:08,600 Speaker 4: Everything's less exciting to talk about them professional wrestling. And 153 00:07:08,720 --> 00:07:12,280 Speaker 4: according to Kyla Scanlon, author of In This Economy and 154 00:07:12,440 --> 00:07:17,480 Speaker 4: ECON blogger Extraordinaire, WrestleMania has taken over the US economy 155 00:07:17,520 --> 00:07:20,520 Speaker 4: and the tax cut is the perfect example of what 156 00:07:20,720 --> 00:07:21,160 Speaker 4: is going on. 157 00:07:21,200 --> 00:07:24,440 Speaker 3: She joins us, Now, Hi, Kyla, Hey, how's it good? Good? 158 00:07:24,560 --> 00:07:28,880 Speaker 3: Thanks for talking with us. So, Kyla, you talk in. 159 00:07:28,840 --> 00:07:30,800 Speaker 4: One of your latest sub stacks and videos that you 160 00:07:30,880 --> 00:07:35,440 Speaker 4: made about this economic policy aligning with something called k 161 00:07:35,680 --> 00:07:36,960 Speaker 4: fabe in wrestling. 162 00:07:37,880 --> 00:07:42,000 Speaker 3: What is k fabe and how does this apply to taxes? 163 00:07:42,760 --> 00:07:42,960 Speaker 2: Yeah? 164 00:07:43,000 --> 00:07:45,880 Speaker 1: So, k fabe is staged reality. So if you ever 165 00:07:45,920 --> 00:07:49,320 Speaker 1: watched WrestleMania or anything like that, the wrestlers have these 166 00:07:49,320 --> 00:07:53,120 Speaker 1: personas that they take on and they act out these 167 00:07:53,160 --> 00:07:56,720 Speaker 1: really elaborate like wrestle moves, pile drivers, things like that, 168 00:07:57,080 --> 00:07:59,800 Speaker 1: and they even act like that off the wrestle matt 169 00:08:00,200 --> 00:08:03,920 Speaker 1: and it's just this idea that the whole reality is 170 00:08:04,720 --> 00:08:07,800 Speaker 1: fake more or less, but it looks incredibly real. The 171 00:08:07,800 --> 00:08:11,160 Speaker 1: world of wrestling by Roland Bars was the big inspiration 172 00:08:11,320 --> 00:08:14,080 Speaker 1: behind that piece, and he sort of talks about. 173 00:08:13,960 --> 00:08:16,120 Speaker 3: Roland Bars like the philosopher linguist. 174 00:08:16,760 --> 00:08:19,280 Speaker 1: Yeah, so he wrote this incredible piece that talks all 175 00:08:19,320 --> 00:08:21,960 Speaker 1: about how you know, wrestling and this sense of justice 176 00:08:22,000 --> 00:08:24,240 Speaker 1: and the sense of morality, and like, in order to 177 00:08:24,400 --> 00:08:26,400 Speaker 1: really have a narrative, like you have to have these 178 00:08:26,400 --> 00:08:28,160 Speaker 1: good guys and these bad guys, and you have to 179 00:08:28,200 --> 00:08:33,720 Speaker 1: have k fabe. And so the analogy comparing WrestleMania essentially 180 00:08:33,880 --> 00:08:36,640 Speaker 1: to the US government, where we have a lot of posturing, 181 00:08:37,040 --> 00:08:40,400 Speaker 1: big chair slams on people's heads, Like a lot of 182 00:08:40,440 --> 00:08:43,800 Speaker 1: it seems staged in order to make some sort of point, 183 00:08:44,040 --> 00:08:47,080 Speaker 1: specifically with regards to tariffs. But the tax bill could 184 00:08:47,080 --> 00:08:50,160 Speaker 1: definitely be k fabe too. And so I think that 185 00:08:50,240 --> 00:08:53,000 Speaker 1: the current administration just because of the background of Trump 186 00:08:53,080 --> 00:08:56,839 Speaker 1: in WWE, in reality TV, like the guy is an 187 00:08:56,840 --> 00:09:00,120 Speaker 1: attention merchant, right, He's really good at it, and I 188 00:09:00,120 --> 00:09:01,000 Speaker 1: think it's carrying over. 189 00:09:01,240 --> 00:09:03,280 Speaker 2: Speaking of that, we got to play this clip for 190 00:09:03,360 --> 00:09:04,720 Speaker 2: people who haven't heard it. 191 00:09:04,840 --> 00:09:06,800 Speaker 3: Oh my god, So I was looking around. 192 00:09:06,880 --> 00:09:10,280 Speaker 4: I vaguely remembered I used to watch wrestling back in 193 00:09:10,320 --> 00:09:13,000 Speaker 4: the day, and I remembered vaguely that Trump had been on. 194 00:09:13,120 --> 00:09:14,920 Speaker 4: But of course, with the magic of the Internet, I 195 00:09:14,960 --> 00:09:16,520 Speaker 4: looked it up, and I have to say I was 196 00:09:16,520 --> 00:09:18,600 Speaker 4: like a little bit mind blown. We have a clip 197 00:09:19,280 --> 00:09:22,280 Speaker 4: of Donald Trump. Basically it's part of the something called 198 00:09:22,320 --> 00:09:24,440 Speaker 4: the Battle of the Billionaires, but it was a wrestling 199 00:09:24,520 --> 00:09:25,840 Speaker 4: match where Trump was in it. 200 00:09:30,360 --> 00:09:30,680 Speaker 1: Often. 201 00:09:31,360 --> 00:09:33,119 Speaker 2: So Trump is like walking around. 202 00:09:32,880 --> 00:09:35,560 Speaker 4: The ring, yeah, and he's like on the edge of 203 00:09:35,600 --> 00:09:38,160 Speaker 4: the ring, and then he jumps in and starts beating 204 00:09:38,240 --> 00:09:41,640 Speaker 4: up this this other man, this other fighter, Vince McMahon. 205 00:09:41,720 --> 00:09:44,360 Speaker 2: Yeah, he's beating up Vince McMahon, who was the CEO 206 00:09:44,720 --> 00:09:48,520 Speaker 2: of WWE also, by the way, the husband of the 207 00:09:48,520 --> 00:09:51,560 Speaker 2: current sectarification. Yeah. 208 00:09:51,559 --> 00:09:53,120 Speaker 3: I mean, it's it's it's wild. 209 00:09:55,320 --> 00:09:57,720 Speaker 4: I mean, one thing that that is coming into my mind, 210 00:09:57,800 --> 00:10:00,280 Speaker 4: especially around the tax us, is all of the theater 211 00:10:00,400 --> 00:10:04,120 Speaker 4: with Doge and the government cuts. And I read that 212 00:10:04,440 --> 00:10:06,880 Speaker 4: all of the cuts that have been made by Elon 213 00:10:06,960 --> 00:10:10,520 Speaker 4: Musk and Doge amount to just a fraction, a tiny, 214 00:10:10,559 --> 00:10:14,080 Speaker 4: tiny fraction of one percent of the budget. Meanwhile, the 215 00:10:14,120 --> 00:10:17,080 Speaker 4: tax bill, which has had some drama around it, but 216 00:10:17,520 --> 00:10:21,000 Speaker 4: way less than Doge. We've just talked about it, way less. 217 00:10:21,559 --> 00:10:25,880 Speaker 4: You know, the implications economically are enormous. I mean that 218 00:10:26,040 --> 00:10:29,560 Speaker 4: also seems like it kind of falls under this k 219 00:10:29,760 --> 00:10:31,880 Speaker 4: fabe philosophy. 220 00:10:32,360 --> 00:10:34,000 Speaker 1: Yeah, I mean I think there's an element of that. 221 00:10:34,160 --> 00:10:36,520 Speaker 1: Like Doge was very public, Like they had their own 222 00:10:36,520 --> 00:10:39,480 Speaker 1: Twitter account, They had those guys on the front lines 223 00:10:39,559 --> 00:10:42,280 Speaker 1: doing interviews all over the place. Elon Musk was in 224 00:10:42,360 --> 00:10:44,800 Speaker 1: charge of Doze, even though I think he says he isn't, 225 00:10:45,240 --> 00:10:48,000 Speaker 1: and so they were very you know, intentionally in the 226 00:10:48,000 --> 00:10:50,720 Speaker 1: public eye, which is probably why they got more pressed 227 00:10:50,720 --> 00:10:51,160 Speaker 1: in media. 228 00:10:51,559 --> 00:10:54,280 Speaker 2: I like the analogy for Doge too, because you know, 229 00:10:54,360 --> 00:10:57,040 Speaker 2: Silicon Valley, you guys both probably know this, but like 230 00:10:57,200 --> 00:10:59,840 Speaker 2: Silicon Valley is super into k fabe as a term. 231 00:11:00,160 --> 00:11:01,760 Speaker 2: I spent spent a lot of time when I was 232 00:11:01,800 --> 00:11:04,040 Speaker 2: writing my book listening to Peter Teele and Teal talks 233 00:11:04,080 --> 00:11:06,600 Speaker 2: about kfab all the time. I feel like with Doge, 234 00:11:06,800 --> 00:11:10,199 Speaker 2: they were in costumes and the crazy thing to me, though, 235 00:11:10,559 --> 00:11:14,800 Speaker 2: is it worked right. There were like budget experts saying like, hey, 236 00:11:14,960 --> 00:11:17,559 Speaker 2: these guys are never going to get anywhere near two 237 00:11:17,640 --> 00:11:21,760 Speaker 2: trillion dollars Nonetheless, you had like Jamie Diamond and people 238 00:11:21,960 --> 00:11:25,319 Speaker 2: I think who are very smart and very sophisticated, essentially saying, oh, 239 00:11:25,360 --> 00:11:28,640 Speaker 2: this is great, like basically talking as if we'd entered 240 00:11:28,640 --> 00:11:31,840 Speaker 2: this era of austerity. Meanwhile, over here in the middle 241 00:11:31,840 --> 00:11:34,360 Speaker 2: of the night, they're planning this tax cut that's going 242 00:11:34,440 --> 00:11:36,360 Speaker 2: to add a huge amount of money to the federal budgets, 243 00:11:36,400 --> 00:11:39,680 Speaker 2: doing the exact opposite thing that if you were watching 244 00:11:39,720 --> 00:11:42,319 Speaker 2: the show that was on TV, that was the story 245 00:11:42,360 --> 00:11:43,000 Speaker 2: you were being told. 246 00:11:43,400 --> 00:11:46,640 Speaker 1: I think that that distraction is the ultimate tool. And 247 00:11:46,679 --> 00:11:47,880 Speaker 1: I know I sound like a little bit of a 248 00:11:47,920 --> 00:11:50,880 Speaker 1: conspiracy theorist story when I talk about this stuff, but 249 00:11:51,360 --> 00:11:53,360 Speaker 1: I think hidden in this tax bill is a bunch 250 00:11:53,360 --> 00:11:55,760 Speaker 1: of stuff that can really hurt a lot of people, 251 00:11:56,080 --> 00:11:59,240 Speaker 1: especially in terms of healthcare access and in terms of 252 00:11:59,280 --> 00:12:02,760 Speaker 1: just affordability. And so I think for the government, like 253 00:12:02,840 --> 00:12:06,800 Speaker 1: having this sort of dramatic sparring, you know, Trump tweeted 254 00:12:07,160 --> 00:12:11,360 Speaker 1: or truth whatever about you know, the government selling Freddie 255 00:12:11,400 --> 00:12:13,520 Speaker 1: May and Fannie mack on, you know, the morning of 256 00:12:13,559 --> 00:12:16,840 Speaker 1: this beautiful, big tax bill getting passed. And so I 257 00:12:16,840 --> 00:12:20,320 Speaker 1: think there's always some element of distraction that comes up 258 00:12:20,400 --> 00:12:24,000 Speaker 1: when they want to have stuff slide under the rug 259 00:12:24,000 --> 00:12:24,800 Speaker 1: A bit kyla. 260 00:12:24,920 --> 00:12:29,360 Speaker 2: I'm wondering. During the last Trump administration, the Republicans did 261 00:12:29,400 --> 00:12:33,720 Speaker 2: this thing where they drastically dialed back the deductions for 262 00:12:33,840 --> 00:12:36,839 Speaker 2: people who live in high tax states, and in certain ways, right, 263 00:12:36,880 --> 00:12:41,600 Speaker 2: that was like a understandable, sort of sensible move if 264 00:12:41,600 --> 00:12:44,080 Speaker 2: you were going to cut taxes, and now it appears 265 00:12:44,080 --> 00:12:46,960 Speaker 2: like they're poised to raise it huge amount. That seems 266 00:12:46,960 --> 00:12:49,200 Speaker 2: like a big part of the problem here. 267 00:12:49,640 --> 00:12:51,520 Speaker 1: Yeah, I mean, the bill gives out a lot of 268 00:12:51,559 --> 00:12:54,800 Speaker 1: tax cuts and doesn't really think through how to pay 269 00:12:54,840 --> 00:12:58,080 Speaker 1: for them. A lot of the ideas that like, you know, 270 00:12:58,120 --> 00:13:01,560 Speaker 1: maybe turiffs will cover some of those costs, or the 271 00:13:01,559 --> 00:13:05,760 Speaker 1: cuts to Medicaid and Medicare will cover some of those costs. 272 00:13:06,280 --> 00:13:09,520 Speaker 1: I think it's a pretty short term look at that, 273 00:13:09,760 --> 00:13:11,960 Speaker 1: because like, sure, you can cut costs in the meantime, 274 00:13:12,000 --> 00:13:15,440 Speaker 1: but the repercussions down the road could be massively expensive. 275 00:13:15,480 --> 00:13:18,520 Speaker 1: They're also increasing taxes on like Carverard and maybeat'll cover it, 276 00:13:18,880 --> 00:13:23,240 Speaker 1: but it's incredibly expensive and it's incredibly short sighted because 277 00:13:23,280 --> 00:13:25,720 Speaker 1: the last thing that we need is more tax cuts. 278 00:13:26,280 --> 00:13:29,400 Speaker 2: So Trump has tried to raise taxes on rich people 279 00:13:29,440 --> 00:13:31,200 Speaker 2: a few times or sort of floated it, and he 280 00:13:31,320 --> 00:13:35,600 Speaker 2: just always gets smushed basically by the Republican establishment and 281 00:13:35,679 --> 00:13:38,319 Speaker 2: big donors and stuff. I almost feel like it's because 282 00:13:38,360 --> 00:13:41,600 Speaker 2: the k fabe stuff like doesn't work on private equity managers. 283 00:13:41,880 --> 00:13:44,079 Speaker 2: Like you can't just like k fave your way into 284 00:13:44,160 --> 00:13:47,280 Speaker 2: like cutting carried interests because there are very very rich 285 00:13:47,320 --> 00:13:49,480 Speaker 2: people who really really don't want that to. 286 00:13:49,440 --> 00:13:51,640 Speaker 3: Happen, and they're going to fight back. You can't really 287 00:13:51,679 --> 00:13:53,840 Speaker 3: hit them with a chair. They can afford a bigger chair. 288 00:13:54,679 --> 00:13:57,600 Speaker 4: So just to do a little bit of counterprogramming, Kylo, 289 00:13:57,679 --> 00:14:00,600 Speaker 4: where should we be looking? Like, what should we be 290 00:14:00,640 --> 00:14:04,320 Speaker 4: focused on? With all of these performances happening? Where should 291 00:14:04,320 --> 00:14:04,680 Speaker 4: we look? 292 00:14:05,320 --> 00:14:08,160 Speaker 1: I mean, I think the ar border of truth right now, 293 00:14:08,160 --> 00:14:10,560 Speaker 1: in my opinion, is the bond market. And so I 294 00:14:10,600 --> 00:14:14,040 Speaker 1: think that if you want sort of a story about 295 00:14:14,120 --> 00:14:17,360 Speaker 1: how the world is thinking about the United States, like 296 00:14:17,400 --> 00:14:20,200 Speaker 1: looking at the bond market is pretty useful because it 297 00:14:20,240 --> 00:14:24,120 Speaker 1: doesn't mess around, right like it's going to usually tell 298 00:14:24,200 --> 00:14:25,960 Speaker 1: some element of the truth. The bond market is not 299 00:14:26,000 --> 00:14:26,720 Speaker 1: happy about this. 300 00:14:26,720 --> 00:14:28,520 Speaker 3: Big beautiful bill because of the debt. 301 00:14:28,760 --> 00:14:32,480 Speaker 1: Yeah, Moody's downgraded the US. I think that that's pretty 302 00:14:32,600 --> 00:14:35,800 Speaker 1: useful sort of using that as a gauge. 303 00:14:35,880 --> 00:14:38,200 Speaker 3: What signals should we be on the lookout for with 304 00:14:38,200 --> 00:14:38,920 Speaker 3: the bond market. 305 00:14:39,120 --> 00:14:41,280 Speaker 1: Yeah, so if people are looking at the situation in 306 00:14:41,280 --> 00:14:43,280 Speaker 1: the US government and they're like, oh my gosh, I 307 00:14:43,320 --> 00:14:45,760 Speaker 1: don't know for the thirty year bond, for example, I'm 308 00:14:45,800 --> 00:14:47,680 Speaker 1: not sure how the US government's going to look in 309 00:14:47,680 --> 00:14:49,520 Speaker 1: the next thirty years. You're gonna have to pay me 310 00:14:49,600 --> 00:14:52,000 Speaker 1: so much money in order for me to buy these bonds. 311 00:14:52,120 --> 00:14:54,520 Speaker 1: Fields are going up. That's a good way to gauge 312 00:14:54,640 --> 00:14:58,760 Speaker 1: how the market is thinking about the long term viability 313 00:14:59,080 --> 00:15:01,880 Speaker 1: of the US government and how they're looking at that outlook. 314 00:15:02,040 --> 00:15:03,720 Speaker 4: Yeah, it's kind of like if I'm in a lot 315 00:15:03,720 --> 00:15:05,360 Speaker 4: a lot of debt and go to the bank and 316 00:15:05,400 --> 00:15:06,760 Speaker 4: they might give me a loan, but they're going to 317 00:15:06,840 --> 00:15:08,600 Speaker 4: charge me a really high interest rate for it because 318 00:15:08,600 --> 00:15:10,280 Speaker 4: they're going to look at me as a really risky 319 00:15:10,320 --> 00:15:11,280 Speaker 4: person to lend money to. 320 00:15:11,840 --> 00:15:14,840 Speaker 1: Yeah, and right now, the youth government is seeming increasingly 321 00:15:14,960 --> 00:15:17,320 Speaker 1: risky to people, especially investors. 322 00:15:18,000 --> 00:15:19,840 Speaker 2: You hinted at this, Kyla, but like a lot of 323 00:15:19,840 --> 00:15:24,240 Speaker 2: this stuff may not survive the Senate bill. Is your 324 00:15:24,520 --> 00:15:29,280 Speaker 2: assumption that the Republicans will figure out a way to 325 00:15:30,560 --> 00:15:32,760 Speaker 2: get this passed, adding a huge amount to the debt, 326 00:15:33,120 --> 00:15:34,600 Speaker 2: or is there some chance that there are lots of 327 00:15:34,600 --> 00:15:36,480 Speaker 2: different forces pushing against this, one of which is the 328 00:15:36,480 --> 00:15:39,600 Speaker 2: bomb market that we may you know, be talking in 329 00:15:39,640 --> 00:15:41,880 Speaker 2: two months or three months and this thing hasn't really 330 00:15:41,880 --> 00:15:44,920 Speaker 2: gone anywhere, or they're still fighting over this or something. Yeah. 331 00:15:45,000 --> 00:15:47,480 Speaker 1: What's sort of funny is everybody thinks they're big and 332 00:15:47,520 --> 00:15:49,480 Speaker 1: in charge until the bond market comes knocking. 333 00:15:49,760 --> 00:15:51,800 Speaker 2: It's James Carvill, right, I want to come back as 334 00:15:51,800 --> 00:15:55,040 Speaker 2: the bomb market when I die, because everybody, everybody's afraid 335 00:15:55,040 --> 00:15:55,240 Speaker 2: of you. 336 00:15:55,560 --> 00:15:58,800 Speaker 1: Yes, And I think it's really simple to like past 337 00:15:58,800 --> 00:16:00,640 Speaker 1: these big things and be like it's going to pay 338 00:16:00,640 --> 00:16:03,400 Speaker 1: for itself and we're going to replace income taxes the tariffs, 339 00:16:03,400 --> 00:16:07,320 Speaker 1: which is just not mathematically possible. But I absolutely think 340 00:16:07,360 --> 00:16:09,880 Speaker 1: that the bond market will let its thoughts be known 341 00:16:10,360 --> 00:16:13,880 Speaker 1: and the US government, the Treasury specifically, is going to 342 00:16:13,920 --> 00:16:16,320 Speaker 1: have to do things to appease the bond market in 343 00:16:16,360 --> 00:16:18,560 Speaker 1: the meantime, things that they didn't want to do that 344 00:16:18,600 --> 00:16:21,480 Speaker 1: they don't want to do, and it could get complicated 345 00:16:21,520 --> 00:16:23,920 Speaker 1: really fast if they keep on being irresponsible. 346 00:16:24,280 --> 00:16:29,160 Speaker 4: The biggest wrestler of all the bond market. Kyla Scanlon 347 00:16:29,360 --> 00:16:32,000 Speaker 4: is the author of In This Economy. You can check 348 00:16:32,000 --> 00:16:36,600 Speaker 4: her out on substack, TikTok Instagram. She makes content every 349 00:16:36,680 --> 00:16:39,240 Speaker 4: day talks about all kinds of aspects of the economy. 350 00:16:39,320 --> 00:16:41,280 Speaker 3: There's definitely a lot to learn. 351 00:16:41,120 --> 00:16:44,760 Speaker 4: And it's always really fun, really unique takes much like 352 00:16:44,880 --> 00:16:49,480 Speaker 4: WrestleMania as an explanation for our economy. 353 00:16:53,040 --> 00:16:56,000 Speaker 2: Stacey, we heard at the top of the show that 354 00:16:56,080 --> 00:16:59,000 Speaker 2: the graduates of today, the young people who will lead 355 00:16:59,080 --> 00:17:03,400 Speaker 2: us in the future, concerned about artificial intelligence. They're worried 356 00:17:03,400 --> 00:17:06,159 Speaker 2: about what it means for their jobs. And you know, 357 00:17:06,280 --> 00:17:10,000 Speaker 2: it has gotten me thinking, like, how much is this 358 00:17:10,160 --> 00:17:13,159 Speaker 2: actually happening right now? I mean, and we've seen a 359 00:17:13,200 --> 00:17:16,960 Speaker 2: lot of this. There's BusinessWeek has a great issue out, 360 00:17:17,000 --> 00:17:19,480 Speaker 2: the AI Issue. There's some interesting stuff in there on this. 361 00:17:19,520 --> 00:17:23,240 Speaker 2: There's also just an amazing overview of what's happening in 362 00:17:23,280 --> 00:17:25,560 Speaker 2: the market. To kind of break this down, I wanted 363 00:17:25,560 --> 00:17:29,280 Speaker 2: to bring in Sarah Fryar, who's Bloomberg's big tech editor. 364 00:17:29,640 --> 00:17:34,080 Speaker 2: She's following all of these big companies and these big investments, 365 00:17:34,440 --> 00:17:36,160 Speaker 2: and I think also just brings a lot of good 366 00:17:36,200 --> 00:17:36,880 Speaker 2: perspective here. 367 00:17:36,960 --> 00:17:41,400 Speaker 7: Hey, Sarah, hi Sarah, Hi, thanks for having me so, Sarah, 368 00:17:41,480 --> 00:17:44,919 Speaker 7: there's a new genre in social media that I've been noticing, 369 00:17:44,960 --> 00:17:47,880 Speaker 7: a kind of a new form of bro atry, which 370 00:17:47,920 --> 00:17:49,720 Speaker 7: is the poetry. 371 00:17:49,160 --> 00:17:54,320 Speaker 2: That executives right on LinkedIn, and that is the memo 372 00:17:55,000 --> 00:18:00,080 Speaker 2: warning your employees about AI. Toby Lucky of shopify I 373 00:18:00,119 --> 00:18:04,159 Speaker 2: wrote one essentially saying that everyone had to use AI 374 00:18:04,520 --> 00:18:07,040 Speaker 2: and you weren't going to be allowed to hire unless 375 00:18:07,320 --> 00:18:10,520 Speaker 2: you show that AI couldn't do it first. Mikah Kaufman 376 00:18:10,760 --> 00:18:14,120 Speaker 2: of Fiver wrote, AI is coming for your job. I'm 377 00:18:14,119 --> 00:18:16,320 Speaker 2: not talking about your job at five or talking aboute 378 00:18:16,359 --> 00:18:20,000 Speaker 2: that to his employees, your ability to stay in your profession. 379 00:18:20,040 --> 00:18:21,760 Speaker 2: He not only wrote it to his employees, he posted 380 00:18:21,800 --> 00:18:25,240 Speaker 2: it publicly. Sarah, can you just give us a sense? 381 00:18:25,640 --> 00:18:29,479 Speaker 2: Is this basically like conventional wisdom among the kind of 382 00:18:29,520 --> 00:18:33,119 Speaker 2: like tech CEO class that basically like, hey, we just 383 00:18:33,160 --> 00:18:35,639 Speaker 2: need to buckle up. The AI is going to like 384 00:18:35,960 --> 00:18:38,600 Speaker 2: replace half my staff or something. You know. 385 00:18:38,720 --> 00:18:41,840 Speaker 5: I think it's for real that it's changing a lot 386 00:18:41,880 --> 00:18:45,040 Speaker 5: of the workforce. And I think the sentiment from the 387 00:18:45,040 --> 00:18:47,840 Speaker 5: shopif I ceo. He got really slammed on the internet 388 00:18:47,880 --> 00:18:50,439 Speaker 5: for that, for telling employees he wasn't going to give 389 00:18:50,480 --> 00:18:53,439 Speaker 5: them headcount unless he could prove that AI couldn't do 390 00:18:53,560 --> 00:18:57,520 Speaker 5: that job. That's pretty widespread right now in tech companies, 391 00:18:57,560 --> 00:19:00,960 Speaker 5: and I think that it's more of a forcing factors 392 00:19:01,000 --> 00:19:04,240 Speaker 5: to get their employees to fully embrace the new technology 393 00:19:04,280 --> 00:19:07,640 Speaker 5: and understand what they can do with it. Now, there 394 00:19:07,640 --> 00:19:11,359 Speaker 5: are downsides. There are obvious downsides. If you have so 395 00:19:11,480 --> 00:19:14,840 Speaker 5: much work done by AI, it gets things wrong. 396 00:19:15,200 --> 00:19:18,080 Speaker 4: Yeah, there was just that booklist this week where like 397 00:19:18,119 --> 00:19:21,480 Speaker 4: a bunch of newspapers published like the fifteen Great Summer Reads, 398 00:19:21,680 --> 00:19:23,720 Speaker 4: and it had been generated by AI, and ten of 399 00:19:23,720 --> 00:19:25,240 Speaker 4: them weren't real books. 400 00:19:25,760 --> 00:19:29,320 Speaker 2: There are so many examples like this, so many cautionary tails. 401 00:19:29,359 --> 00:19:31,800 Speaker 2: The one that Stacy mentioned though very bad. 402 00:19:32,200 --> 00:19:34,800 Speaker 5: Then you have companies like Microsoft who do major rounds 403 00:19:34,800 --> 00:19:38,000 Speaker 5: of layoffs, and we looked at the numbers and Bloomberg 404 00:19:38,000 --> 00:19:40,600 Speaker 5: found that the majority of those positions the hardest hit 405 00:19:41,119 --> 00:19:46,520 Speaker 5: were software engineers. So those people graduating from college that 406 00:19:46,560 --> 00:19:50,000 Speaker 5: you noted who are worried about the workforce, of course, 407 00:19:50,000 --> 00:19:52,400 Speaker 5: they're worried because they grew up in this era of 408 00:19:53,119 --> 00:19:57,960 Speaker 5: everyone of our generation telling them to learn to code. 409 00:19:58,320 --> 00:20:01,760 Speaker 5: You know, they all grew up learning that STEM was 410 00:20:01,840 --> 00:20:06,200 Speaker 5: everything and that you had to be a quantitative person 411 00:20:06,320 --> 00:20:10,639 Speaker 5: in order to succeed in the modern world. Now that 412 00:20:10,680 --> 00:20:14,880 Speaker 5: may have been true ten years ago, but now there 413 00:20:15,400 --> 00:20:19,000 Speaker 5: are fewer entry level software jobs because so much of 414 00:20:19,160 --> 00:20:21,399 Speaker 5: entry level software can be done with AI. It's not 415 00:20:21,440 --> 00:20:24,800 Speaker 5: replacing the senior jobs, and in fact, a lot of 416 00:20:24,840 --> 00:20:26,760 Speaker 5: the code still has to be checked by someone who 417 00:20:26,840 --> 00:20:30,159 Speaker 5: actually knows what they're doing. It's going to make it 418 00:20:30,240 --> 00:20:34,520 Speaker 5: easier for someone who's an experienced developer to develop faster, 419 00:20:35,160 --> 00:20:37,560 Speaker 5: But for those entry level jobs, for those people who 420 00:20:37,560 --> 00:20:40,040 Speaker 5: are going to come in and learn the basics and 421 00:20:40,119 --> 00:20:43,159 Speaker 5: be put to work before before growing up, it's going 422 00:20:43,200 --> 00:20:43,879 Speaker 5: to be harder for that. 423 00:20:44,480 --> 00:20:47,520 Speaker 2: I got to say. The New York Federal Reserve Bank 424 00:20:48,000 --> 00:20:51,440 Speaker 2: recently published a list of undergraduate majors with the highest 425 00:20:51,480 --> 00:20:57,119 Speaker 2: and lowest unemployment rates, and as English major, I have 426 00:20:57,240 --> 00:21:00,440 Speaker 2: never felt so smug in my entire life because for 427 00:21:00,520 --> 00:21:03,080 Speaker 2: decades I have been hearing this learned the code thing. 428 00:21:03,640 --> 00:21:07,520 Speaker 2: And let me tell you art history majors. Art history 429 00:21:07,560 --> 00:21:11,119 Speaker 2: that's like even more like irrelevant from that point of 430 00:21:11,200 --> 00:21:13,320 Speaker 2: view than you know, like. 431 00:21:13,480 --> 00:21:16,000 Speaker 3: I mean objection, but continue I do. 432 00:21:16,320 --> 00:21:20,400 Speaker 2: Like when somebody when somebody from the shape Rotator class 433 00:21:20,640 --> 00:21:23,680 Speaker 2: tries to make fun of us word cells. They always 434 00:21:23,680 --> 00:21:27,080 Speaker 2: pick on art history majors and unemployment rate for art 435 00:21:27,080 --> 00:21:30,800 Speaker 2: history majors three percent unemployment rate. 436 00:21:31,160 --> 00:21:33,679 Speaker 5: Right, if you're putting your kid through college now or 437 00:21:33,720 --> 00:21:37,320 Speaker 5: through high school now, you know, forget about those coding hamps. 438 00:21:37,400 --> 00:21:41,400 Speaker 5: Teach some critical thinking, like so much about Yeah. 439 00:21:41,240 --> 00:21:44,760 Speaker 2: You gotta know the difference between a doric column and 440 00:21:44,800 --> 00:21:47,320 Speaker 2: an ionic column or you will not get by in 441 00:21:47,359 --> 00:21:47,800 Speaker 2: this world. 442 00:21:48,359 --> 00:21:50,760 Speaker 5: I love STEM. I think that that stuff is really 443 00:21:51,080 --> 00:21:53,679 Speaker 5: you know, engineering is really important, but you need to 444 00:21:53,680 --> 00:21:57,040 Speaker 5: be able to look at what the AI produces and 445 00:21:57,600 --> 00:22:00,600 Speaker 5: correct it. I mean that's like a key skill, and 446 00:22:00,640 --> 00:22:02,959 Speaker 5: you need to be able to use the AI in 447 00:22:03,000 --> 00:22:06,959 Speaker 5: a way that is productive. And we're seeing it's not 448 00:22:07,000 --> 00:22:10,879 Speaker 5: just Microsoft. Googles CEO said on their earnings call that 449 00:22:11,359 --> 00:22:16,160 Speaker 5: thirty percent of the code that's submitted now is maybe 450 00:22:16,240 --> 00:22:19,560 Speaker 5: more than thirty percent created by AI, So it's really 451 00:22:19,600 --> 00:22:22,640 Speaker 5: across the board. And I think that, you know, young 452 00:22:22,640 --> 00:22:25,280 Speaker 5: people need to just figure out how to be how 453 00:22:25,320 --> 00:22:29,159 Speaker 5: to learn creativity, critical thinking, independence, originality. 454 00:22:29,280 --> 00:22:29,919 Speaker 3: I mean, all. 455 00:22:29,880 --> 00:22:35,520 Speaker 5: These extremely human skills are going to become more in 456 00:22:35,640 --> 00:22:41,000 Speaker 5: demand because rote memorization basic math, like all that stuff is, 457 00:22:42,200 --> 00:22:44,440 Speaker 5: we got it handled by the machines. 458 00:22:44,720 --> 00:22:45,920 Speaker 3: It's not just coding either. 459 00:22:46,000 --> 00:22:48,240 Speaker 4: There was a big study this week from the UN, 460 00:22:48,280 --> 00:22:51,359 Speaker 4: from their International Labor Organization kind of looking at the 461 00:22:51,400 --> 00:22:54,199 Speaker 4: impact AI would have on jobs, and they looked at 462 00:22:54,200 --> 00:22:58,239 Speaker 4: thirty thousand different occupations and they found that overall, in 463 00:22:58,720 --> 00:23:02,800 Speaker 4: wealthy countries, about one in three jobs would be impacted distressingly. 464 00:23:03,560 --> 00:23:07,920 Speaker 4: Way more jobs that are traditionally considered female jobs would 465 00:23:07,960 --> 00:23:11,879 Speaker 4: be what they called transformed. That'd be like clerical jobs, assistants, 466 00:23:11,960 --> 00:23:12,520 Speaker 4: things like that. 467 00:23:12,600 --> 00:23:13,520 Speaker 3: But it's huge. 468 00:23:13,600 --> 00:23:16,359 Speaker 2: Can I just say as much as like part of 469 00:23:16,400 --> 00:23:18,720 Speaker 2: me wants to believe this, and I want to believe that, 470 00:23:18,920 --> 00:23:21,200 Speaker 2: like being an art history major is like a great 471 00:23:21,280 --> 00:23:24,480 Speaker 2: career choice. And I was comp lit and you know 472 00:23:24,640 --> 00:23:28,640 Speaker 2: I should say, like the the computer engineering major seven 473 00:23:28,680 --> 00:23:31,240 Speaker 2: point five percent unemployment computer science. 474 00:23:30,960 --> 00:23:32,640 Speaker 3: Wait, seven point five percent unemployment. 475 00:23:32,640 --> 00:23:35,879 Speaker 2: It's point one double more than double both more than 476 00:23:35,920 --> 00:23:38,720 Speaker 2: double art history. But you know what, there are other 477 00:23:38,920 --> 00:23:41,919 Speaker 2: things going on here, and I am somewhat skeptical of 478 00:23:41,960 --> 00:23:44,000 Speaker 2: this narrative, to be honest, and I'll tell you why. 479 00:23:44,480 --> 00:23:48,119 Speaker 2: Number One, these companies are all very, very motivated to 480 00:23:48,119 --> 00:23:52,560 Speaker 2: be pushing this idea. So like Microsoft has invested tens 481 00:23:52,600 --> 00:23:57,360 Speaker 2: of billions of dollars, its entire business is essentially predicated 482 00:23:57,440 --> 00:24:00,760 Speaker 2: on this working. If this didn't work, if you weren't 483 00:24:00,800 --> 00:24:05,439 Speaker 2: able to replace large numbers of computer scientists like that 484 00:24:05,600 --> 00:24:08,439 Speaker 2: is gonna be a big problem for Microsoft. The second 485 00:24:08,480 --> 00:24:13,040 Speaker 2: thing is all of these tech companies hired huge numbers 486 00:24:13,160 --> 00:24:17,560 Speaker 2: of engineers and computer scientists, often overpaying, during the pandemic 487 00:24:17,600 --> 00:24:20,399 Speaker 2: and immediately afterward there was this war for talent. And 488 00:24:20,480 --> 00:24:23,520 Speaker 2: I don't think we know for sure whether these layoffs 489 00:24:23,560 --> 00:24:28,119 Speaker 2: are actually layoffs that are related to AI, or are 490 00:24:28,119 --> 00:24:31,480 Speaker 2: they layoffs that are related because these companies like basically 491 00:24:31,720 --> 00:24:32,720 Speaker 2: stapped up too much. 492 00:24:33,320 --> 00:24:36,520 Speaker 5: Well, they definitely stapped up too much, and that calling 493 00:24:36,640 --> 00:24:40,639 Speaker 5: started around twenty twenty three that you know, Mark Zuckerberg's 494 00:24:40,680 --> 00:24:43,720 Speaker 5: Year of Efficiency, a bunch of other companies did. They 495 00:24:43,760 --> 00:24:48,120 Speaker 5: sort of broke the seal in terms of tech layoffs. Previously, 496 00:24:48,640 --> 00:24:51,439 Speaker 5: it was considered like a really bad sign if your 497 00:24:51,480 --> 00:24:53,880 Speaker 5: company was laying people off, and these tech jobs were 498 00:24:53,920 --> 00:24:57,800 Speaker 5: considered like the most stable careers you could have. But 499 00:24:58,440 --> 00:25:01,840 Speaker 5: what I'm hearing from everyone who who's working in tech 500 00:25:01,920 --> 00:25:04,680 Speaker 5: right now, even big tech, is that you don't get 501 00:25:04,720 --> 00:25:08,760 Speaker 5: tenure anymore. You have to actually be working, you have 502 00:25:08,840 --> 00:25:12,080 Speaker 5: to be submitting code, you have to be doing things 503 00:25:12,119 --> 00:25:14,720 Speaker 5: that have an impact. And I think that that's a 504 00:25:14,800 --> 00:25:20,159 Speaker 5: reaction to this sort of complacency that developed from just 505 00:25:20,280 --> 00:25:23,960 Speaker 5: the growing forever idea and all of the big tech 506 00:25:23,960 --> 00:25:26,920 Speaker 5: companies across the board if you look at their products, 507 00:25:26,960 --> 00:25:30,280 Speaker 5: I mean, their legacy products are getting to the point 508 00:25:30,320 --> 00:25:32,680 Speaker 5: where they're not going to fuel the next ten years 509 00:25:32,680 --> 00:25:35,679 Speaker 5: the way they fueled the last twenty. Right Google Search, 510 00:25:36,040 --> 00:25:42,600 Speaker 5: Facebook's Instagram and WhatsApp, Amazon's marketplace, Microsoft's products. How much 511 00:25:42,640 --> 00:25:44,119 Speaker 5: bigger can those things get? 512 00:25:44,320 --> 00:25:44,560 Speaker 2: Right? 513 00:25:44,840 --> 00:25:47,280 Speaker 5: And so there has to be a lighting of a 514 00:25:47,400 --> 00:25:50,359 Speaker 5: fire under all of these companies to push them to 515 00:25:50,440 --> 00:25:53,439 Speaker 5: the next era, whatever that may be. And look at Apple, 516 00:25:53,520 --> 00:25:56,560 Speaker 5: like what's coming after the iPhone? They have no idea, 517 00:25:56,640 --> 00:26:00,000 Speaker 5: they don't have a solution yet. So these tech workers 518 00:26:00,080 --> 00:26:03,400 Speaker 5: who used to kind of have a famously cushy lifestyle 519 00:26:03,480 --> 00:26:05,560 Speaker 5: are being put to work in a way that they 520 00:26:05,560 --> 00:26:06,239 Speaker 5: are not used to. 521 00:26:06,680 --> 00:26:08,440 Speaker 4: I mean, I find it really interesting what Max was 522 00:26:08,440 --> 00:26:11,200 Speaker 4: saying about the CEOs kind of scaring everybody or sort 523 00:26:11,240 --> 00:26:13,159 Speaker 4: of using the you know, the robots are coming the 524 00:26:13,200 --> 00:26:16,840 Speaker 4: robots are coming be very afraid to maybe scare workers, 525 00:26:17,440 --> 00:26:21,160 Speaker 4: Like who should be scared? What jobs do you see 526 00:26:21,520 --> 00:26:25,080 Speaker 4: AI really threatening versus. 527 00:26:24,680 --> 00:26:26,160 Speaker 3: Maybe not as much as we thought. 528 00:26:26,640 --> 00:26:29,560 Speaker 5: I mean you kind of think about the secretarial jobs 529 00:26:30,400 --> 00:26:33,320 Speaker 5: when nobody really knew how to type except for secretaries. 530 00:26:33,400 --> 00:26:36,639 Speaker 5: Right soon, everyone's going to know how to use AI 531 00:26:37,119 --> 00:26:42,239 Speaker 5: in ways that are integrated with their jobs. And you know, 532 00:26:42,280 --> 00:26:45,800 Speaker 5: there's no career that's not going to be using that 533 00:26:46,040 --> 00:26:50,280 Speaker 5: a little bit. It's just going to become so infused 534 00:26:50,320 --> 00:26:52,879 Speaker 5: with our every day the way the internet is, so 535 00:26:54,080 --> 00:26:58,680 Speaker 5: whether people will become totally unemployed, I mean long hauled 536 00:26:58,680 --> 00:27:02,720 Speaker 5: truck drivers that's going away. Customer service I think is 537 00:27:02,760 --> 00:27:03,920 Speaker 5: going away, is it? 538 00:27:03,960 --> 00:27:04,200 Speaker 7: Though? 539 00:27:04,560 --> 00:27:08,639 Speaker 2: I don't think that's clear. Self drive trucks, trucking is 540 00:27:08,680 --> 00:27:10,600 Speaker 2: going to be. That's gonna be one of the hardest ones. 541 00:27:10,720 --> 00:27:12,720 Speaker 5: I know, you're a self driving. 542 00:27:12,440 --> 00:27:15,840 Speaker 2: Skeptic, show me, show me a computer driving a truck, 543 00:27:15,880 --> 00:27:17,200 Speaker 2: and I'll believe you. Max. 544 00:27:17,600 --> 00:27:20,120 Speaker 5: I live in the city of Waimo, and I see 545 00:27:20,200 --> 00:27:24,680 Speaker 5: I see Waimo become ubiquitous here in San Francisco. 546 00:27:25,400 --> 00:27:27,880 Speaker 2: The thing about trucking that is hard is that they 547 00:27:27,960 --> 00:27:31,119 Speaker 2: drive really fast and and like if it crashes, it 548 00:27:31,200 --> 00:27:35,119 Speaker 2: kills people. I really think it's important to have humility here, Like. 549 00:27:35,000 --> 00:27:37,359 Speaker 5: Max, here's what I know is going to happen. People 550 00:27:37,480 --> 00:27:41,280 Speaker 5: are going to overdo it before they correct. Right, Say 551 00:27:41,320 --> 00:27:45,479 Speaker 5: you're you're outsourcing editing of research papers or something, and 552 00:27:45,560 --> 00:27:47,919 Speaker 5: you decide, oh, we don't need any copy editors anymore, 553 00:27:48,000 --> 00:27:50,600 Speaker 5: we'll just use AI. And then you realize that the 554 00:27:50,640 --> 00:27:54,119 Speaker 5: AI mess things up. That'll happen afterwards. Right, there's going 555 00:27:54,200 --> 00:27:58,440 Speaker 5: to be like a mass embrace before there's a backlash 556 00:27:58,600 --> 00:28:02,440 Speaker 5: and people figure out the balance like a pendulum swinging 557 00:28:02,640 --> 00:28:04,960 Speaker 5: right and finally get to a point where we understand 558 00:28:05,600 --> 00:28:08,720 Speaker 5: what works and what doesn't work. And what's happening right 559 00:28:08,760 --> 00:28:11,240 Speaker 5: now is we're getting to this point in the industry 560 00:28:11,560 --> 00:28:15,560 Speaker 5: where we're kind of realizing this pace that we've had 561 00:28:15,560 --> 00:28:17,240 Speaker 5: for the past couple of years of like, Okay, the 562 00:28:17,280 --> 00:28:19,239 Speaker 5: better model comes out, and the better model comes out, 563 00:28:19,280 --> 00:28:21,320 Speaker 5: and the better and the more specific model comes out. 564 00:28:21,480 --> 00:28:24,320 Speaker 5: I think if we just like stop and take stock 565 00:28:24,359 --> 00:28:27,920 Speaker 5: of what has already been built and then figure out 566 00:28:27,960 --> 00:28:31,040 Speaker 5: how to apply it, that's kind of the next step, right, 567 00:28:31,200 --> 00:28:34,680 Speaker 5: And you're seeing companies invest in that open AI, hiring 568 00:28:35,000 --> 00:28:38,640 Speaker 5: people like Fijismo. That's because they need to just take 569 00:28:38,680 --> 00:28:42,160 Speaker 5: what they've already done and put it to work, figure 570 00:28:42,160 --> 00:28:43,880 Speaker 5: out what is useful about it. 571 00:28:44,560 --> 00:28:47,200 Speaker 2: And Sarah just you're saying that people are going to 572 00:28:47,320 --> 00:28:49,680 Speaker 2: overdo it at some point, which I think is like 573 00:28:49,840 --> 00:28:53,280 Speaker 2: one hundred percent of great prediction. It's even happening now. 574 00:28:53,320 --> 00:28:55,560 Speaker 2: This this thing that Stacy brought up at the top 575 00:28:55,600 --> 00:28:59,000 Speaker 2: of the show where a bunch of major newspapers printed 576 00:28:59,040 --> 00:29:03,880 Speaker 2: a whole page of book recommendations books by famous authors. 577 00:29:03,920 --> 00:29:06,959 Speaker 2: The thing was, ten of the fifteen books were just 578 00:29:07,280 --> 00:29:09,520 Speaker 2: entirely made up. And this went in the like I 579 00:29:09,600 --> 00:29:12,240 Speaker 2: know that, you know, newspapers are maybe not as big 580 00:29:12,240 --> 00:29:13,480 Speaker 2: as they used to be, but it went in the 581 00:29:13,480 --> 00:29:17,360 Speaker 2: printed newspaper like some you know, old lady in like 582 00:29:17,400 --> 00:29:21,040 Speaker 2: Philadelphia is hearing about an Isabelle Ion date book that 583 00:29:21,120 --> 00:29:24,280 Speaker 2: does not exist, wondering dreams and wondering why you can't 584 00:29:24,320 --> 00:29:26,000 Speaker 2: find tidewater dreams. 585 00:29:26,720 --> 00:29:31,360 Speaker 5: Yeah, I mean AI is built upon an approximation of 586 00:29:31,400 --> 00:29:33,920 Speaker 5: everything that it's that has happened in the past, right, 587 00:29:34,040 --> 00:29:38,520 Speaker 5: So it's a remixer of knowledge from so many different sources, 588 00:29:39,000 --> 00:29:42,440 Speaker 5: and sometimes that's gonna come out looking funky and that's 589 00:29:42,440 --> 00:29:44,960 Speaker 5: what I'm talking about critical thinking, Like you have to 590 00:29:45,080 --> 00:29:45,880 Speaker 5: check this work. 591 00:29:46,000 --> 00:29:46,400 Speaker 2: You can't. 592 00:29:46,720 --> 00:29:52,400 Speaker 5: It looks so so perfect that you know, you just 593 00:29:52,400 --> 00:29:53,080 Speaker 5: just trust it. 594 00:29:53,280 --> 00:29:55,880 Speaker 2: This is why you need your long haul trucker, because 595 00:29:55,920 --> 00:29:57,360 Speaker 2: somebody's got to check that work. 596 00:29:57,680 --> 00:29:59,400 Speaker 5: Yeah, someone's got to check that work. 597 00:29:59,560 --> 00:30:02,880 Speaker 3: And you're history majors and your art history. 598 00:30:03,640 --> 00:30:07,440 Speaker 5: Try making up an idiom in asking Google what it means. 599 00:30:07,520 --> 00:30:09,520 Speaker 5: It'll just make up an answer if you ask it, 600 00:30:09,600 --> 00:30:12,760 Speaker 5: Like for recipes using gasoline, it'll tell you about how 601 00:30:12,760 --> 00:30:16,160 Speaker 5: to use gasoline to make spaghetti. It's it's just like 602 00:30:16,800 --> 00:30:21,480 Speaker 5: early days in the world of AI in its application, 603 00:30:21,960 --> 00:30:26,480 Speaker 5: which is why, Like you can see how transformational it's 604 00:30:26,560 --> 00:30:31,920 Speaker 5: going to be, but you also have to understand that 605 00:30:31,960 --> 00:30:35,240 Speaker 5: you're dealing with something that still needs to be trained 606 00:30:35,360 --> 00:30:37,920 Speaker 5: and can't fully replace a human. 607 00:30:42,640 --> 00:30:44,840 Speaker 4: So Max, we are now to the part of the 608 00:30:44,880 --> 00:30:46,960 Speaker 4: show where we talk about. 609 00:30:46,600 --> 00:30:50,400 Speaker 3: Our underrated story of the week. This is story we 610 00:30:50,520 --> 00:30:52,479 Speaker 3: think did not get enough attention. 611 00:30:52,760 --> 00:30:55,120 Speaker 2: Yeah, and you've got one for us today. 612 00:30:55,320 --> 00:30:57,200 Speaker 4: Not only is this an underrated story of the week, 613 00:30:57,280 --> 00:31:00,120 Speaker 4: I think this is the underrated story of like the century. 614 00:31:00,320 --> 00:31:00,880 Speaker 3: So get this. 615 00:31:01,440 --> 00:31:07,160 Speaker 4: Scientists at Europe's large had drawn collider they successfully transformed 616 00:31:07,880 --> 00:31:08,800 Speaker 4: lead into gold. 617 00:31:09,440 --> 00:31:14,480 Speaker 2: Stacey, are you saying that the thousand year old dream? Yeah, Alc, 618 00:31:14,640 --> 00:31:16,840 Speaker 2: I don't think it's the story of the century. Could 619 00:31:16,880 --> 00:31:19,000 Speaker 2: be the story of the millennium. We're probably talking about 620 00:31:19,000 --> 00:31:22,160 Speaker 2: a large quantity of lead turning into a large quantity 621 00:31:22,160 --> 00:31:22,440 Speaker 2: of gold. 622 00:31:22,440 --> 00:31:24,400 Speaker 3: This is something you could say, Okay, that's semantics. 623 00:31:25,080 --> 00:31:26,840 Speaker 4: I think what we need to look at is that 624 00:31:26,880 --> 00:31:30,600 Speaker 4: this is a quest that humankind has been on since 625 00:31:30,640 --> 00:31:34,160 Speaker 4: like three thousand Beasty Egyptians were looking into this building 626 00:31:34,200 --> 00:31:37,800 Speaker 4: blocks of matter, transforming matter. It's been linked to eternal life, 627 00:31:37,880 --> 00:31:40,840 Speaker 4: it's been linked to balancing the universe. I mean Isaac 628 00:31:40,880 --> 00:31:42,240 Speaker 4: Newton was into this. 629 00:31:42,240 --> 00:31:44,800 Speaker 6: This is like on the level into this fountain of youth, 630 00:31:44,960 --> 00:31:48,920 Speaker 6: basically one hundred percent thousands of years. 631 00:31:48,800 --> 00:31:51,160 Speaker 2: And some dorks in Switzerland did this. 632 00:31:51,240 --> 00:31:53,880 Speaker 4: Yere say, they figured it out. They figured it out. 633 00:31:53,880 --> 00:31:58,240 Speaker 4: They turned lead into gold. Wow, eighty nine thousand atoms 634 00:31:58,680 --> 00:31:59,920 Speaker 4: is apparently microscopic. 635 00:32:00,120 --> 00:32:03,320 Speaker 3: It only was gold for like a couple of millisones. 636 00:32:03,400 --> 00:32:05,120 Speaker 2: So wait, what did they do? They just like shot 637 00:32:05,160 --> 00:32:08,280 Speaker 2: it through the collider or whatever, shot it through the tube, 638 00:32:08,320 --> 00:32:12,200 Speaker 2: and it like somewhere along the way, it just it transformed. 639 00:32:12,160 --> 00:32:14,960 Speaker 4: That's actually pretty close from what I understand, which is 640 00:32:15,040 --> 00:32:17,880 Speaker 4: granted not a lot. So they had this big particle 641 00:32:17,880 --> 00:32:20,840 Speaker 4: accelerator that smashes atoms together at really high speeds, and 642 00:32:20,880 --> 00:32:23,560 Speaker 4: they found a way to kind of knock these particles 643 00:32:23,600 --> 00:32:27,280 Speaker 4: together out of lead atoms and the like briefly turn 644 00:32:27,320 --> 00:32:27,960 Speaker 4: them into gold. 645 00:32:28,240 --> 00:32:31,960 Speaker 2: Now, this is obviously very cool, but like, do you 646 00:32:32,040 --> 00:32:34,440 Speaker 2: have any sense of like what we're gonna do with this, 647 00:32:34,600 --> 00:32:37,000 Speaker 2: like turning a little piece of lead into a little 648 00:32:37,000 --> 00:32:38,360 Speaker 2: piece of gold and then having it turned. 649 00:32:38,160 --> 00:32:40,080 Speaker 3: Back in other word, like kind of why do we care? 650 00:32:40,320 --> 00:32:42,719 Speaker 2: Yeah, well, just there must be some sort of use, right. 651 00:32:42,720 --> 00:32:45,120 Speaker 2: They must have been doing this for a reason besides 652 00:32:45,200 --> 00:32:46,560 Speaker 2: just the Here's. 653 00:32:46,280 --> 00:32:48,920 Speaker 4: Why I think this is the business story that I 654 00:32:48,920 --> 00:32:49,440 Speaker 4: think it is. 655 00:32:49,600 --> 00:32:51,920 Speaker 3: The gold market Max. The gold market. 656 00:32:51,960 --> 00:32:56,520 Speaker 4: Gold has been hitting record highs because in this topsy 657 00:32:56,600 --> 00:33:00,320 Speaker 4: turvy world of inflation of crypto node even knows what 658 00:33:00,440 --> 00:33:04,040 Speaker 4: money is, people still trust gold. Gold is probably one 659 00:33:04,080 --> 00:33:08,640 Speaker 4: of the oldest stores of value in the world. For centuries, 660 00:33:08,720 --> 00:33:10,760 Speaker 4: people have been putting value into gold. 661 00:33:10,880 --> 00:33:12,360 Speaker 2: Are you telling me I need to short the gold 662 00:33:12,400 --> 00:33:15,680 Speaker 2: market right now? This is not investment advice, by the way. 663 00:33:16,040 --> 00:33:19,160 Speaker 4: I mean short the gold market no, this is the 664 00:33:19,240 --> 00:33:22,600 Speaker 4: last anchor that we have to anything real involving money, 665 00:33:23,200 --> 00:33:26,400 Speaker 4: Like what happens to us when that goes away? When 666 00:33:26,400 --> 00:33:28,840 Speaker 4: you can just when just a bunch of people in 667 00:33:28,880 --> 00:33:32,480 Speaker 4: Switzerland with a particle accelerator can just crank out gold 668 00:33:33,120 --> 00:33:35,080 Speaker 4: thirty nine thousand atoms at a time. 669 00:33:35,160 --> 00:33:37,200 Speaker 2: I didn't hear an answer to the question there, but 670 00:33:37,840 --> 00:33:38,840 Speaker 2: I do hope that's the. 671 00:33:38,800 --> 00:33:41,040 Speaker 3: Physical sency market's disrupted forever. 672 00:33:41,240 --> 00:33:43,520 Speaker 2: The physicists who listen to the show will write us 673 00:33:43,520 --> 00:33:44,720 Speaker 2: at everybody's at. 674 00:33:44,680 --> 00:33:49,280 Speaker 6: And they're gonna what we're gonna do with the twenty 675 00:33:49,720 --> 00:33:54,160 Speaker 6: picogram piece of material that is briefly gold, But that's 676 00:33:54,160 --> 00:33:54,720 Speaker 6: really cool. 677 00:33:59,000 --> 00:34:02,120 Speaker 2: The show is produced by Stacy Wong. Magnus Hendrickson is 678 00:34:02,120 --> 00:34:06,560 Speaker 2: our supervising producer, Blake Maples handles engineering, Amy Keen is 679 00:34:06,600 --> 00:34:09,880 Speaker 2: our editor, and Brendan Francis Newdam is our executive producer. 680 00:34:10,360 --> 00:34:16,400 Speaker 2: Sage Bauman heads Bloomberg Podcast Special thanks to Jeff Muscus, Annamasarakis, 681 00:34:16,600 --> 00:34:19,040 Speaker 2: Robert Smith, and Aaron Casper. If you have a minute, 682 00:34:19,160 --> 00:34:21,239 Speaker 2: please rate and review the show. It means a lot 683 00:34:21,280 --> 00:34:23,640 Speaker 2: to us. And if you have a story or an 684 00:34:23,719 --> 00:34:27,080 Speaker 2: idea or something that should be our business email us 685 00:34:27,120 --> 00:34:30,520 Speaker 2: at Everybody's at Bloomberg dot net. That's everybody with an 686 00:34:30,520 --> 00:34:33,480 Speaker 2: AS at Bloomberg dot net. Thank you for listening, and 687 00:34:33,560 --> 00:34:36,160 Speaker 2: we'll see you next week.