1 00:00:02,720 --> 00:00:07,200 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,280 --> 00:00:09,520 Speaker 2: Do you guys know what today is? 3 00:00:10,720 --> 00:00:15,120 Speaker 3: It's our tenure anniversary? Because everyone I know, we only 4 00:00:15,200 --> 00:00:16,640 Speaker 3: we knew, we only knew that. 5 00:00:16,680 --> 00:00:18,840 Speaker 1: I didn't realize that today was the actual ten year 6 00:00:18,840 --> 00:00:21,560 Speaker 1: anniversary until I started getting emails from random people this morning. 7 00:00:21,560 --> 00:00:22,680 Speaker 1: It's like, oh, happy ten yures? 8 00:00:22,680 --> 00:00:24,480 Speaker 3: Like what random Bloomberg people too? 9 00:00:25,079 --> 00:00:26,279 Speaker 2: I thought I would be the first three. 10 00:00:26,400 --> 00:00:30,400 Speaker 3: Oh yeah, sorry, in our hearts you were the first. 11 00:00:30,560 --> 00:00:32,879 Speaker 1: Yes, ten years ago today the first episode. 12 00:00:32,479 --> 00:00:35,040 Speaker 2: Of Do You Have I forget who said this to me? 13 00:00:35,440 --> 00:00:36,080 Speaker 2: I have a mug. 14 00:00:36,760 --> 00:00:40,040 Speaker 1: Yeah, so, happy anniversary to us, Happy birthday, Happy birthday. 15 00:00:40,120 --> 00:00:40,800 Speaker 3: What did you get me? 16 00:00:40,880 --> 00:00:42,800 Speaker 1: I guess this is a little weird. Happy anniversary to us, 17 00:00:42,920 --> 00:00:47,280 Speaker 1: Happy birthday to odd lots and uh, you know what, 18 00:00:47,320 --> 00:00:48,920 Speaker 1: I didn't get you anything. I know you don't. 19 00:00:50,120 --> 00:00:52,600 Speaker 3: I wait, I wait with baited breath for a gift 20 00:00:52,640 --> 00:00:53,840 Speaker 3: from Joe one of these days. 21 00:00:53,880 --> 00:00:56,320 Speaker 1: You know what, No, that's not true. I've gotten your 22 00:00:56,320 --> 00:01:00,360 Speaker 1: gift name one. Well, I always bring back sweet when 23 00:01:00,400 --> 00:01:03,840 Speaker 1: I travel. I guess that. No, I guess that doesn't count, right, 24 00:01:03,920 --> 00:01:07,520 Speaker 1: You know it would be a really wonderful gift. Fresher 25 00:01:07,640 --> 00:01:12,840 Speaker 1: Jobs Data Oh yes, I did a deadlist. 26 00:01:12,920 --> 00:01:16,440 Speaker 3: I'm both the most popular trader and most successful trader 27 00:01:16,640 --> 00:01:19,680 Speaker 3: at Citadel. That is going viral, uh barges. 28 00:01:19,760 --> 00:01:21,800 Speaker 1: This isn't after school Special, except I've. 29 00:01:21,720 --> 00:01:24,400 Speaker 3: Decided I'm going to base my entire personality going forward 30 00:01:24,520 --> 00:01:27,360 Speaker 3: on campaigning for a strategic pork reserve in the US. 31 00:01:27,560 --> 00:01:28,240 Speaker 1: Black goals. 32 00:01:28,400 --> 00:01:31,000 Speaker 3: These are the important question. Is that robots taking over 33 00:01:31,040 --> 00:01:31,400 Speaker 3: the world. 34 00:01:31,480 --> 00:01:31,640 Speaker 2: No. 35 00:01:31,680 --> 00:01:34,560 Speaker 1: I think that, like in a couple of years, the 36 00:01:34,640 --> 00:01:36,880 Speaker 1: AI will do a really good job of making the 37 00:01:36,880 --> 00:01:39,920 Speaker 1: Odd Lots podcast. One day that person will have the 38 00:01:39,959 --> 00:01:40,880 Speaker 1: mandate of heaven. 39 00:01:41,040 --> 00:01:43,240 Speaker 3: How do I get more popular and successful? 40 00:01:43,560 --> 00:01:44,480 Speaker 2: We do have. 41 00:01:46,760 --> 00:01:49,120 Speaker 3: You're listening to lots More where we catch up with 42 00:01:49,160 --> 00:01:51,920 Speaker 3: friends about what's going on right now, because. 43 00:01:51,640 --> 00:01:54,560 Speaker 1: Even when the Odd Lots is over, there's always lots. 44 00:01:54,360 --> 00:02:00,600 Speaker 3: More and we really do have the perfect guest. I 45 00:02:00,600 --> 00:02:02,560 Speaker 3: don't know about you, guys, but I did not expect 46 00:02:02,600 --> 00:02:06,360 Speaker 3: to miss the NFP as much as I am at 47 00:02:06,360 --> 00:02:08,160 Speaker 3: the moment. I just thought, you know, we have all 48 00:02:08,200 --> 00:02:10,239 Speaker 3: the alt data. Things will be fine. 49 00:02:10,360 --> 00:02:12,079 Speaker 1: It doesn't hit the same, does it, Connor? 50 00:02:13,200 --> 00:02:16,400 Speaker 2: It doesn't not ADP Like having to care about ADPs 51 00:02:16,600 --> 00:02:17,160 Speaker 2: is the worst. 52 00:02:17,520 --> 00:02:20,280 Speaker 1: You're like all these estimates or whatever. It's like, it 53 00:02:20,400 --> 00:02:22,480 Speaker 1: just doesn't even if you know it's coming out, it 54 00:02:22,520 --> 00:02:25,000 Speaker 1: doesn't hit the same. There's nothing like uh. 55 00:02:25,120 --> 00:02:28,200 Speaker 2: Chicago Fed estimate of unemployment, Like, all right, Austin goles 56 00:02:28,440 --> 00:02:30,239 Speaker 2: with you, that's not the same. 57 00:02:30,680 --> 00:02:33,120 Speaker 1: Well wait, we gotta get him on here. Okay. 58 00:02:33,160 --> 00:02:35,919 Speaker 3: So the one upside of not having the official data, 59 00:02:36,000 --> 00:02:38,880 Speaker 3: the official jobs data because of the government shutdown is 60 00:02:39,000 --> 00:02:41,880 Speaker 3: everyone gets to be really mean about the alt data 61 00:02:42,040 --> 00:02:45,000 Speaker 3: right and say like really harsh things about why they 62 00:02:45,000 --> 00:02:48,160 Speaker 3: don't like ADP. But could someone just remind me why 63 00:02:48,200 --> 00:02:49,000 Speaker 3: we hate ADP? 64 00:02:49,280 --> 00:02:50,280 Speaker 1: Oh, yeah, what's wrong with it? 65 00:02:50,360 --> 00:02:50,640 Speaker 3: Connor? 66 00:02:51,200 --> 00:02:54,040 Speaker 2: I mean, it's always they revise it to the BLS data, 67 00:02:54,080 --> 00:02:55,920 Speaker 2: so it's like, here's our estimate, and then six months 68 00:02:56,000 --> 00:02:58,560 Speaker 2: lady revise it. So then the historical data looks fine, 69 00:02:58,639 --> 00:03:00,560 Speaker 2: but in the mind it's actually not the same at all. 70 00:03:00,840 --> 00:03:03,320 Speaker 1: It's interesting because you know, people always say, like ADP, 71 00:03:03,960 --> 00:03:06,119 Speaker 1: oh it missed again. But it is a little weird, 72 00:03:06,120 --> 00:03:08,320 Speaker 1: and it gets to I think some philosophical things which 73 00:03:08,320 --> 00:03:11,120 Speaker 1: aren't necessarily worth diving into for a conversation about what 74 00:03:11,160 --> 00:03:13,280 Speaker 1: does it even mean to be right or wrong? I mean, 75 00:03:13,520 --> 00:03:16,280 Speaker 1: NFP gets revised all the time NFPH at the model 76 00:03:16,320 --> 00:03:19,320 Speaker 1: for the real economy, you know, a little confusing the 77 00:03:19,320 --> 00:03:22,440 Speaker 1: map and the territory stuff. Anyway, when people are listening 78 00:03:22,440 --> 00:03:24,800 Speaker 1: to this, it should have been jobs Day, and so 79 00:03:24,919 --> 00:03:26,519 Speaker 1: I don't really think there's gonna be a report and 80 00:03:26,560 --> 00:03:27,959 Speaker 1: list on how the government gets hoped. 81 00:03:27,960 --> 00:03:29,200 Speaker 2: I don't think that's happening. 82 00:03:29,240 --> 00:03:31,680 Speaker 3: This is our gift to Odd Lots listeners. Yes, in 83 00:03:31,760 --> 00:03:34,680 Speaker 3: celebration of our tenure anniversary, we are bringing you this 84 00:03:34,880 --> 00:03:38,120 Speaker 3: labor market episode of Lots More, brought to you by 85 00:03:38,160 --> 00:03:41,400 Speaker 3: Odd Lots. And it's a poor substitute perhaps, but it's 86 00:03:41,400 --> 00:03:42,280 Speaker 3: the best that we can do. 87 00:03:42,960 --> 00:03:46,360 Speaker 1: So this morning there was a headline Challenger, which has 88 00:03:46,440 --> 00:03:49,440 Speaker 1: never been a particularly like top shelf data point for me, 89 00:03:49,840 --> 00:03:52,680 Speaker 1: Like they said, the worst month for layoffs in twenty years. 90 00:03:52,800 --> 00:03:56,080 Speaker 1: On the other hand, in initial claims have been steady. Also, 91 00:03:56,080 --> 00:03:59,280 Speaker 1: there was a headline from Cleveland Fed's Hammock, she's more 92 00:03:59,320 --> 00:04:02,560 Speaker 1: concerned about inflation than employment right now in terms of 93 00:04:02,640 --> 00:04:05,240 Speaker 1: risk to the dual mandate. Connor, you've sort of pretty 94 00:04:06,200 --> 00:04:08,840 Speaker 1: taken the opposite view. You think the labor market situation 95 00:04:09,200 --> 00:04:12,440 Speaker 1: is pretty serious and urgent, maybe underappreciated as a risk 96 00:04:13,160 --> 00:04:14,400 Speaker 1: you'd probably disagree with. 97 00:04:15,080 --> 00:04:17,800 Speaker 2: You've had this low hires, low fires labor market for 98 00:04:17,839 --> 00:04:19,960 Speaker 2: at least eighteen to twenty four months now, and I 99 00:04:20,000 --> 00:04:21,920 Speaker 2: think we have evidence over the past few months that 100 00:04:21,960 --> 00:04:24,760 Speaker 2: at least there's no reason for low fires to still 101 00:04:24,800 --> 00:04:27,520 Speaker 2: be happening in corporate America. They don't have to hoard 102 00:04:27,600 --> 00:04:30,040 Speaker 2: labor because if nothing else, if you really want to hire, 103 00:04:30,360 --> 00:04:32,280 Speaker 2: there's plenty of young people. There's plenty of long term 104 00:04:32,360 --> 00:04:35,440 Speaker 2: unemployee people to hire. Unemployment rates kind of low, but 105 00:04:35,640 --> 00:04:37,560 Speaker 2: there's plenty of slack out there to hire people. So 106 00:04:38,360 --> 00:04:40,359 Speaker 2: to the extent that Amazon has shown the way of 107 00:04:40,440 --> 00:04:42,880 Speaker 2: we don't need to hold onto our COVID workers anymore, 108 00:04:43,279 --> 00:04:45,359 Speaker 2: that could get permission to other companies to do the same. 109 00:04:45,440 --> 00:04:47,719 Speaker 2: And at a time when everybody's trying to cut costs, 110 00:04:48,240 --> 00:04:49,520 Speaker 2: that could snowball a little bit. 111 00:04:50,520 --> 00:04:52,680 Speaker 3: So just going back to the ADP data for a second. 112 00:04:52,760 --> 00:04:55,640 Speaker 3: So sorry, I hate to do this, but according to ADP, 113 00:04:56,040 --> 00:04:56,880 Speaker 3: payrolls were up. 114 00:04:56,920 --> 00:04:58,600 Speaker 1: We're going to get a call from No. I'm sure 115 00:04:58,600 --> 00:05:00,240 Speaker 1: we're gonna want to come on and defend, which is fun. 116 00:05:00,480 --> 00:05:03,039 Speaker 3: Well, I'm trying to get to some of the tension here, 117 00:05:03,080 --> 00:05:06,520 Speaker 3: but Okay, jobs up forty two thousand for October, and 118 00:05:06,520 --> 00:05:08,679 Speaker 3: then we get the Challenger data for that same month, 119 00:05:08,839 --> 00:05:11,200 Speaker 3: and as Joe said, it's like the worst job cuts 120 00:05:11,240 --> 00:05:13,279 Speaker 3: in twenty years, I think, over one hundred and fifty 121 00:05:13,320 --> 00:05:17,120 Speaker 3: thousand cut. Why does it seem like we are getting 122 00:05:17,200 --> 00:05:21,160 Speaker 3: these two very different streams of jobs data at the moment, 123 00:05:21,200 --> 00:05:23,680 Speaker 3: where we do have some alternative data points that are 124 00:05:23,680 --> 00:05:26,440 Speaker 3: coming in better than expected, and then we have some 125 00:05:26,600 --> 00:05:28,719 Speaker 3: that are just coming in that look almost I don't 126 00:05:28,720 --> 00:05:31,320 Speaker 3: want to say depression level, but like certainly worse than 127 00:05:31,360 --> 00:05:32,480 Speaker 3: it feels at the moment. 128 00:05:32,520 --> 00:05:35,320 Speaker 2: Well, the way the Challenger data works is it's announced layoffs, 129 00:05:35,480 --> 00:05:38,040 Speaker 2: So that could be anything from UPS saying we've laid 130 00:05:38,040 --> 00:05:40,520 Speaker 2: off fifty thousand people over the past twelve months or 131 00:05:40,680 --> 00:05:44,080 Speaker 2: announced job cuts that haven't happened yet. So they're just headlines, 132 00:05:44,160 --> 00:05:45,760 Speaker 2: not actual job cuts that month. 133 00:05:45,960 --> 00:05:49,679 Speaker 3: Oh I see, Okay, So it's aggregating just people saying stuff. 134 00:05:49,720 --> 00:05:53,640 Speaker 3: And we're still not entirely sure whether we have on nagregation. 135 00:05:53,279 --> 00:05:55,080 Speaker 2: Not confirmed layoffs that month. 136 00:05:55,839 --> 00:05:57,960 Speaker 1: What do we know about initial claims? 137 00:05:59,760 --> 00:06:02,599 Speaker 2: So the initial claims are still pretty low. I think 138 00:06:02,640 --> 00:06:04,760 Speaker 2: one thing we're dealing with right now is that the 139 00:06:04,839 --> 00:06:08,320 Speaker 2: year over year is incorporating the Hurricane Halene situation from 140 00:06:08,360 --> 00:06:10,800 Speaker 2: last year. So if you remember that was in a September, 141 00:06:11,200 --> 00:06:14,040 Speaker 2: then you saw claims in North Carolina and Florida in 142 00:06:14,080 --> 00:06:17,080 Speaker 2: the southeast as people were you know, there was flooding 143 00:06:17,080 --> 00:06:19,240 Speaker 2: and so companies were closed for a while. So you 144 00:06:19,279 --> 00:06:22,200 Speaker 2: saw spiking claims in October. So we're kind of lapping 145 00:06:22,200 --> 00:06:24,440 Speaker 2: that period. And so to the extent that the year 146 00:06:24,440 --> 00:06:26,200 Speaker 2: every years don't look bad right now, that could be 147 00:06:26,279 --> 00:06:27,440 Speaker 2: partially a hurricane impact. 148 00:06:27,960 --> 00:06:30,800 Speaker 3: Do you have like a shadow NFP figure in your 149 00:06:30,839 --> 00:06:32,160 Speaker 3: mind that you're working on. 150 00:06:33,320 --> 00:06:35,600 Speaker 2: For me, it's just sort of I don't think jobs 151 00:06:35,600 --> 00:06:38,240 Speaker 2: are really growing in the aggregate right now, certainly ex healthcare. 152 00:06:38,680 --> 00:06:40,799 Speaker 2: And so to the extent that we think corporate earnings 153 00:06:40,839 --> 00:06:42,760 Speaker 2: are going to grow ten percent over the next twelve months, 154 00:06:42,800 --> 00:06:44,719 Speaker 2: how do you grow earnings ten percent if there's no 155 00:06:44,839 --> 00:06:47,120 Speaker 2: job growth. It just seems like you'd need a real 156 00:06:47,160 --> 00:06:50,400 Speaker 2: productivity miracle or some sort of compositional dynamic to get there. 157 00:06:50,839 --> 00:06:54,840 Speaker 1: If the labor market is substantially weakening, we are going 158 00:06:54,880 --> 00:06:58,360 Speaker 1: to revisit the cyclical versus structural debate, except this time 159 00:06:58,720 --> 00:07:01,760 Speaker 1: the structural argument would be that it has something to 160 00:07:01,800 --> 00:07:04,120 Speaker 1: do with AI. And if the call in that if 161 00:07:04,160 --> 00:07:07,560 Speaker 1: AI is driving job loss, which I'm not really convinced by, 162 00:07:07,720 --> 00:07:10,840 Speaker 1: but if AI is driving job loss, then rate cuts 163 00:07:10,880 --> 00:07:12,239 Speaker 1: aren't going to do much. 164 00:07:12,280 --> 00:07:14,160 Speaker 2: Like what is it going to give you? Yeah, to 165 00:07:14,200 --> 00:07:16,320 Speaker 2: give you my conundrum of what I think is going on. 166 00:07:16,920 --> 00:07:20,040 Speaker 2: I think about Craig Fuller and free commodity markets and 167 00:07:20,120 --> 00:07:22,800 Speaker 2: all the supply chain episodes you guys did, and I 168 00:07:22,840 --> 00:07:27,000 Speaker 2: think about there's sort of the contracted rate of promotions 169 00:07:27,000 --> 00:07:28,960 Speaker 2: and jobs that people got in twenty twenty two, home 170 00:07:29,000 --> 00:07:31,640 Speaker 2: prices people committed to in twenty twenty two, and then 171 00:07:31,720 --> 00:07:34,760 Speaker 2: like the spot market, which is long term, unemployee young people, 172 00:07:35,400 --> 00:07:38,640 Speaker 2: resale housing inventory, and I feel like the spot market 173 00:07:38,720 --> 00:07:41,400 Speaker 2: wants to get back to twenty nineteen affordability, and then 174 00:07:41,440 --> 00:07:44,440 Speaker 2: the contracted market wants to stay at twenty twenty two prices, 175 00:07:44,480 --> 00:07:47,880 Speaker 2: and there's this really growing tension between the two as 176 00:07:47,920 --> 00:07:50,120 Speaker 2: the spot market's trying to drag down the contracted market, 177 00:07:50,200 --> 00:07:50,720 Speaker 2: so to speak. 178 00:07:50,760 --> 00:07:53,360 Speaker 1: So to understand this to maybe the way to think 179 00:07:53,360 --> 00:07:56,480 Speaker 1: about this is that there is a seat that a 180 00:07:56,520 --> 00:08:01,000 Speaker 1: company has a role, and the person sitting in that 181 00:08:01,200 --> 00:08:03,560 Speaker 1: seat might be taking in one hundred and fifty thousand 182 00:08:03,600 --> 00:08:06,160 Speaker 1: dollars currently. But if that see we're open and they 183 00:08:06,240 --> 00:08:08,280 Speaker 1: had to hire for it, maybe it would only be 184 00:08:08,400 --> 00:08:10,360 Speaker 1: they could hire for that role for one hundred thousand 185 00:08:10,440 --> 00:08:12,600 Speaker 1: or ninety thousand or a hundred and ten thousand, and 186 00:08:12,640 --> 00:08:16,040 Speaker 1: thus the gap between contract and spot right. 187 00:08:16,080 --> 00:08:18,120 Speaker 2: And I think you know it's you think about like 188 00:08:18,120 --> 00:08:20,440 Speaker 2: a bank analyst program where you hire a bunch of 189 00:08:20,440 --> 00:08:22,520 Speaker 2: twenty one year olds. Maybe they were making one hundred 190 00:08:22,520 --> 00:08:25,560 Speaker 2: thousand dollars in twenty twenty, and then that got raised 191 00:08:25,560 --> 00:08:27,680 Speaker 2: at one hundred and fifty and twenty twenty two because 192 00:08:27,880 --> 00:08:30,840 Speaker 2: the job market was so strong. Those analysts then become associates, 193 00:08:31,000 --> 00:08:33,360 Speaker 2: and then there's kind of an expectation they're going to 194 00:08:33,440 --> 00:08:35,880 Speaker 2: leave for business school or private equity or whatever, but 195 00:08:35,960 --> 00:08:38,440 Speaker 2: because the labor market's so bad, they don't want to leave. 196 00:08:38,920 --> 00:08:41,440 Speaker 2: And they might be perfectly fine people, but at some 197 00:08:41,440 --> 00:08:42,800 Speaker 2: point you're like, well, we need to kind of kick 198 00:08:42,800 --> 00:08:44,240 Speaker 2: you out to make room for the next twenty one 199 00:08:44,320 --> 00:08:47,479 Speaker 2: year olds. Almost like if a college like University of Texas, 200 00:08:47,720 --> 00:08:49,400 Speaker 2: if the seniors were like, we're not leaving because the 201 00:08:49,440 --> 00:08:51,920 Speaker 2: job markets, We're just going to stay and at some 202 00:08:51,960 --> 00:08:54,520 Speaker 2: point Texas is like, well, we have freshmen that need beds. 203 00:08:54,600 --> 00:08:56,600 Speaker 2: You have to leave, Yeah, and I worry we're kind 204 00:08:56,600 --> 00:08:57,120 Speaker 2: of getting there. 205 00:08:57,200 --> 00:08:58,280 Speaker 1: That's a great analogy. 206 00:08:58,360 --> 00:09:00,960 Speaker 3: There's a whole movie about Ryan Reynolds trying to stay 207 00:09:01,000 --> 00:09:04,720 Speaker 3: at university because of a lackluster job market. I presume anyway, 208 00:09:05,360 --> 00:09:07,840 Speaker 3: the other thing that everyone seems to be debating at 209 00:09:07,840 --> 00:09:11,840 Speaker 3: the moment is the impact of immigration or lack thereof, 210 00:09:12,240 --> 00:09:14,760 Speaker 3: on the total labor market and what that means for supply. 211 00:09:14,880 --> 00:09:17,880 Speaker 3: And depending on where you come out on this particular debate, 212 00:09:17,880 --> 00:09:20,440 Speaker 3: you might have very different impressions of what's going on 213 00:09:20,679 --> 00:09:23,400 Speaker 3: at the moment. What side of it are you sort 214 00:09:23,400 --> 00:09:24,240 Speaker 3: of landing on. 215 00:09:25,400 --> 00:09:27,160 Speaker 2: I think it's fair to say that the break even 216 00:09:27,559 --> 00:09:29,840 Speaker 2: job's rate is much lower, like maybe it's thirty thousand, 217 00:09:30,320 --> 00:09:33,560 Speaker 2: But also supply kind of generates demand as well, and 218 00:09:33,600 --> 00:09:35,160 Speaker 2: I think you can look at housing to show that. 219 00:09:35,800 --> 00:09:37,439 Speaker 2: I don't know if he'll appreciate me calling him out, 220 00:09:37,440 --> 00:09:39,160 Speaker 2: but Lee Everett, who you've had on to talk about 221 00:09:39,200 --> 00:09:41,360 Speaker 2: multi family a couple of times, I asked him, do 222 00:09:41,400 --> 00:09:44,920 Speaker 2: you think that reduce immigration is hurting multi family performance? 223 00:09:44,960 --> 00:09:47,880 Speaker 2: Because Q three was pretty softer apartments and his view 224 00:09:47,960 --> 00:09:51,160 Speaker 2: is it's not like undocumented migrants are living in Class 225 00:09:51,160 --> 00:09:53,240 Speaker 2: A and Class B apartments. But if you don't have 226 00:09:53,280 --> 00:09:55,360 Speaker 2: population growth, you don't need job growth, and if you 227 00:09:55,400 --> 00:09:57,720 Speaker 2: don't have job growth, you don't need to resign an 228 00:09:57,720 --> 00:10:02,400 Speaker 2: apartment lease. So might not be directly leading to weakness 229 00:10:02,679 --> 00:10:05,040 Speaker 2: and whatever, but it's sort of that demand weakness is 230 00:10:05,040 --> 00:10:08,880 Speaker 2: showing up elsewhere in housing, in consumer staples, things like that. 231 00:10:09,520 --> 00:10:11,880 Speaker 1: Let's get back to the AI question. Do you think 232 00:10:11,880 --> 00:10:16,640 Speaker 1: it's playing some role in cut because yeah, I'll. 233 00:10:16,559 --> 00:10:19,200 Speaker 2: Leave it at that. I think in two ways. Yes, 234 00:10:19,280 --> 00:10:22,160 Speaker 2: I don't think it's the technologies displacing workers, but I 235 00:10:22,200 --> 00:10:25,240 Speaker 2: think that companies are first cost constrained and they feel 236 00:10:25,240 --> 00:10:26,920 Speaker 2: like they have to invest in AI. So if you 237 00:10:26,960 --> 00:10:29,640 Speaker 2: have to increase here your budget somewhere, you've got to 238 00:10:29,640 --> 00:10:31,520 Speaker 2: cut it somewhere else, and labor's a good way to 239 00:10:31,559 --> 00:10:33,560 Speaker 2: do that. And then I just think the vibes in 240 00:10:33,600 --> 00:10:35,560 Speaker 2: general of well, if you're hiring a bunch of people, 241 00:10:35,559 --> 00:10:37,840 Speaker 2: you're probably a loser that you get AI, and so 242 00:10:38,080 --> 00:10:39,840 Speaker 2: you can't look like a loser, so you're just not 243 00:10:39,920 --> 00:10:40,360 Speaker 2: doing it. 244 00:10:40,640 --> 00:10:42,920 Speaker 3: Yeah, this is what I worry about with the optics, 245 00:10:42,960 --> 00:10:45,080 Speaker 3: which is if there are a bunch of companies recently 246 00:10:45,120 --> 00:10:48,200 Speaker 3: who have seen their share prices go down, and we're 247 00:10:48,200 --> 00:10:51,240 Speaker 3: recording this at lunchtime, so all the food ones are 248 00:10:51,240 --> 00:10:55,120 Speaker 3: on my mind. But for instance, McDonald's, Chipotle right came 249 00:10:55,120 --> 00:10:58,040 Speaker 3: out with disappointing earnings and shares are going down. If 250 00:10:58,080 --> 00:11:00,559 Speaker 3: you're a company watching your stock price go down, you're 251 00:11:00,600 --> 00:11:02,560 Speaker 3: thinking about the levers you can pull to make it 252 00:11:02,600 --> 00:11:06,360 Speaker 3: go up in the future. Price increases probably aren't going 253 00:11:06,400 --> 00:11:10,439 Speaker 3: to work when everyone's already complaining that. You know, cheeseburger 254 00:11:10,480 --> 00:11:12,480 Speaker 3: and fries over at McDonald's are like more than ten 255 00:11:12,559 --> 00:11:16,400 Speaker 3: dollars now. But one thing you can do is say, well, 256 00:11:16,400 --> 00:11:18,920 Speaker 3: we're going to cut workers, and by cutting workers, look 257 00:11:18,960 --> 00:11:21,679 Speaker 3: at us, we really understand AI and we're in on 258 00:11:21,760 --> 00:11:23,360 Speaker 3: like the current trend or craze. 259 00:11:24,240 --> 00:11:27,240 Speaker 2: Something McDonald's did eighteen months ago is they finally when 260 00:11:27,360 --> 00:11:29,560 Speaker 2: everything was slumping in twenty three and twenty four, their 261 00:11:29,559 --> 00:11:31,839 Speaker 2: cop sales in the US went negative, and they had 262 00:11:31,840 --> 00:11:35,440 Speaker 2: this big earnings call saying, we've always prided ourself on value. 263 00:11:35,520 --> 00:11:38,280 Speaker 2: Our value gap versus our peers has really compressed, but 264 00:11:38,400 --> 00:11:40,360 Speaker 2: we are going to win at value whatever it takes. 265 00:11:40,720 --> 00:11:43,839 Speaker 2: Almost like a droggy moment for past food, and they 266 00:11:43,840 --> 00:11:47,320 Speaker 2: introduced this five dollars value meal. This they cotton makevalue 267 00:11:47,320 --> 00:11:49,840 Speaker 2: their new program that they launched earlier this year, and 268 00:11:49,880 --> 00:11:52,560 Speaker 2: they've really clawed that value gap back, and so it's 269 00:11:52,600 --> 00:11:54,559 Speaker 2: sort of like they're going to claim their market share 270 00:11:54,679 --> 00:11:57,320 Speaker 2: and then everybody else is going to lose traffic to McDonald's. 271 00:11:57,440 --> 00:12:00,640 Speaker 2: So I think they're kind of fine. And that kind 272 00:12:00,640 --> 00:12:03,160 Speaker 2: of gets to that contracted versus spot economy framework where 273 00:12:03,520 --> 00:12:05,640 Speaker 2: McDonald's got back to where they need to be. Everybody 274 00:12:05,640 --> 00:12:08,000 Speaker 2: else isn't there, and they're all trying to figure out 275 00:12:08,040 --> 00:12:09,959 Speaker 2: how do we deal with this environment where demand is 276 00:12:10,000 --> 00:12:11,960 Speaker 2: weak and consumers are very price pressured. 277 00:12:12,240 --> 00:12:14,280 Speaker 3: I want to say, Joe, I have yet to experience 278 00:12:14,360 --> 00:12:17,520 Speaker 3: the rebound of value at McDonald's. I had a moment 279 00:12:17,559 --> 00:12:18,560 Speaker 3: of weakness on Monday. 280 00:12:18,640 --> 00:12:20,240 Speaker 1: Yeah, and I did, and it was costly. 281 00:12:20,440 --> 00:12:21,880 Speaker 3: It was costly. I went through the drive through. I 282 00:12:21,880 --> 00:12:24,640 Speaker 3: didn't use the app, so that was probably the problem. 283 00:12:24,760 --> 00:12:27,120 Speaker 3: But like, it is not nothing to get a meal 284 00:12:27,160 --> 00:12:28,480 Speaker 3: from McDonald's nowadays. 285 00:12:28,720 --> 00:12:29,040 Speaker 2: Would do. 286 00:12:29,160 --> 00:12:31,880 Speaker 1: My favorite thing is whenever like a sector comes up, 287 00:12:31,920 --> 00:12:34,000 Speaker 1: I started like pulling up stock charts on the terminal. 288 00:12:34,200 --> 00:12:39,240 Speaker 1: Sweet Green that was a forty five dollars stock last November. 289 00:12:39,559 --> 00:12:41,600 Speaker 1: This is maybe the biggest trump You're a loser. It 290 00:12:41,640 --> 00:12:44,000 Speaker 1: was forty four dollars. Now it's a six dollars stock. 291 00:12:44,679 --> 00:12:51,600 Speaker 1: Cova was a one hundred and fifty dollars stock also 292 00:12:51,600 --> 00:12:53,840 Speaker 1: in late twenty twenty four. Now it's a forty seven 293 00:12:53,920 --> 00:12:55,960 Speaker 1: dollars stock. I mean these are like, you know, these 294 00:12:56,000 --> 00:12:58,080 Speaker 1: are the tip of the sphere, the most cutting edge 295 00:12:58,120 --> 00:12:59,400 Speaker 1: slot bowls you can get. 296 00:12:59,480 --> 00:13:01,480 Speaker 2: And there I feel like if you were the kind 297 00:13:01,480 --> 00:13:05,079 Speaker 2: of person working for like Jiggrshaws group and like eating 298 00:13:05,160 --> 00:13:07,240 Speaker 2: lunch in DC, the things you're eating, that's like in 299 00:13:07,280 --> 00:13:09,360 Speaker 2: a bad recession. Now those types of work I do. 300 00:13:09,720 --> 00:13:12,760 Speaker 1: Yeah, I mean that's not funny, it's it's true. I also, Tracy, 301 00:13:12,960 --> 00:13:16,120 Speaker 1: like when I I love our DC listeners, so I 302 00:13:16,120 --> 00:13:18,640 Speaker 1: don't want to insult people in DC. But when I 303 00:13:18,679 --> 00:13:21,079 Speaker 1: think of, like, what is the city which I'm certain 304 00:13:21,200 --> 00:13:23,920 Speaker 1: has the highest percentage of people that sort of eat 305 00:13:23,920 --> 00:13:26,920 Speaker 1: a bowl lunch, I always think in terms of the workforce, 306 00:13:26,960 --> 00:13:27,560 Speaker 1: it must. 307 00:13:27,320 --> 00:13:29,480 Speaker 2: Be DC, And I do think it's actually founded. 308 00:13:29,440 --> 00:13:33,120 Speaker 1: Well and sweet both Tava and Sweet Green are like 309 00:13:33,200 --> 00:13:36,120 Speaker 1: this is true innovation serving the local market, and so 310 00:13:36,200 --> 00:13:38,400 Speaker 1: I do think it's interesting that DC is an industrial 311 00:13:38,480 --> 00:13:40,040 Speaker 1: hutbed for these kind of lunches. 312 00:13:40,360 --> 00:13:42,240 Speaker 3: I'll just say I see a lot of sad salad 313 00:13:42,240 --> 00:13:46,520 Speaker 3: eaters here, including Americ including Joe. But I am willing 314 00:13:46,559 --> 00:13:49,040 Speaker 3: to say that Kava and Sweet Green are certainly that 315 00:13:49,160 --> 00:13:53,880 Speaker 3: sort of I guess, liberal bureaucratic government official code in 316 00:13:54,000 --> 00:13:54,400 Speaker 3: your meal? 317 00:13:54,559 --> 00:13:56,440 Speaker 1: Yeah, your rations they. 318 00:13:56,280 --> 00:13:58,560 Speaker 3: Are, yeah, they are. Here's your bowl of salad. 319 00:13:59,400 --> 00:14:02,400 Speaker 2: Oh man, when you're hearing like Chipotle talked about weakness 320 00:14:02,440 --> 00:14:04,320 Speaker 2: among twenty five to thirty five year old ye, yeah, 321 00:14:04,360 --> 00:14:05,840 Speaker 2: not just twenty to twenty four year olds. So it 322 00:14:05,840 --> 00:14:08,160 Speaker 2: does seem like that weakness is kind of creeping up 323 00:14:08,200 --> 00:14:10,960 Speaker 2: the income scale the age ladder, and you know, maybe 324 00:14:10,960 --> 00:14:13,120 Speaker 2: the AMEX consumer is still fine, but it just seems 325 00:14:13,120 --> 00:14:14,720 Speaker 2: like the weakness is moving up the income curve. 326 00:14:28,320 --> 00:14:30,560 Speaker 3: We were actually talking about the labor monent. Yeah, and 327 00:14:30,600 --> 00:14:33,040 Speaker 3: I know this is related. But one thing that kind 328 00:14:33,040 --> 00:14:36,440 Speaker 3: of worries me at the moment is that even if 329 00:14:36,480 --> 00:14:41,560 Speaker 3: the government opens up tomorrow and NFP gets released soon 330 00:14:41,640 --> 00:14:45,080 Speaker 3: after that, it feels like it's just going to be messy, 331 00:14:45,360 --> 00:14:47,600 Speaker 3: and even if we get the official number, it's not 332 00:14:47,760 --> 00:14:50,000 Speaker 3: actually going to be that insightful, and we're still going 333 00:14:50,040 --> 00:14:52,520 Speaker 3: to be spending all our time having the debates that 334 00:14:52,520 --> 00:14:53,400 Speaker 3: we're having right now. 335 00:14:53,880 --> 00:14:55,120 Speaker 2: I think we're not going to have a clean read 336 00:14:55,120 --> 00:14:57,960 Speaker 2: on the data until least January, just because October is 337 00:14:57,960 --> 00:14:59,320 Speaker 2: gonna be a mess. Who knows when we're going to 338 00:14:59,320 --> 00:15:01,720 Speaker 2: get the data. Then even if the government reopens, then 339 00:15:01,760 --> 00:15:03,920 Speaker 2: you have the November data is impacted by the shutdown, 340 00:15:03,920 --> 00:15:06,400 Speaker 2: which so yeah, yeah, we're kind of just twiddling our 341 00:15:06,400 --> 00:15:07,280 Speaker 2: thumbs until your end. 342 00:15:07,720 --> 00:15:09,640 Speaker 1: So let's talk about you know, let's put it in 343 00:15:09,760 --> 00:15:12,160 Speaker 1: the stakes for the FED. If you're the FED share, 344 00:15:12,600 --> 00:15:16,160 Speaker 1: which you know you probably play fantasy, there's. 345 00:15:16,000 --> 00:15:18,640 Speaker 3: A non zero chance that Connor could one day yeah. 346 00:15:18,600 --> 00:15:22,000 Speaker 1: Or certainly yeah, or certainly governor, Like, what do you like? 347 00:15:22,240 --> 00:15:24,080 Speaker 1: Is there of an effective move here? Is is it 348 00:15:24,200 --> 00:15:26,480 Speaker 1: keep cutting raids? And how effective would they be? Play 349 00:15:26,480 --> 00:15:26,720 Speaker 1: it out? 350 00:15:26,760 --> 00:15:29,680 Speaker 2: From the FED framework, What I think they're thinking is 351 00:15:29,720 --> 00:15:32,160 Speaker 2: that we keep missing on inflation. We don't know if 352 00:15:32,200 --> 00:15:36,080 Speaker 2: tariffs are going to lead to unanchored and placed and expectations. 353 00:15:36,080 --> 00:15:38,160 Speaker 2: Even though they don't take the stock market into account, 354 00:15:38,560 --> 00:15:40,160 Speaker 2: I do think on some vibes level, if the stock 355 00:15:40,160 --> 00:15:43,120 Speaker 2: market's high, that doesn't force their hand and they they're 356 00:15:43,120 --> 00:15:46,480 Speaker 2: really anchoring to the unemployment rate is historically low, which 357 00:15:46,760 --> 00:15:49,080 Speaker 2: kind of but again if you look at the long 358 00:15:49,160 --> 00:15:52,200 Speaker 2: term unemployment numbers at age twenty twenty four, kind of 359 00:15:52,200 --> 00:15:55,840 Speaker 2: that spot labor market groups that's actually quite weak. That's 360 00:15:55,880 --> 00:15:57,880 Speaker 2: more of like a twenty fifteen type of labor market, 361 00:15:58,280 --> 00:16:00,360 Speaker 2: And I just think they don't want to do anything 362 00:16:00,600 --> 00:16:03,520 Speaker 2: until their hand is forced. But again for me, I 363 00:16:03,560 --> 00:16:05,640 Speaker 2: see howsing getting worse. We've the Fed's cut rates one 364 00:16:05,680 --> 00:16:08,040 Speaker 2: hundred fifty basis points, and housing is worse now than 365 00:16:08,040 --> 00:16:09,440 Speaker 2: it was a year and a half ago. So I 366 00:16:09,440 --> 00:16:11,640 Speaker 2: think the whole when the FED cuts rates, housing is 367 00:16:11,640 --> 00:16:13,440 Speaker 2: going to be fixed. It has not proven to be true, 368 00:16:14,040 --> 00:16:16,320 Speaker 2: and the labor market doesn't seem to be getting better yet. 369 00:16:16,560 --> 00:16:18,760 Speaker 2: So outside of AI, it's just hard to see any 370 00:16:18,840 --> 00:16:20,800 Speaker 2: upward momentum heading into twenty twenty six. 371 00:16:21,600 --> 00:16:23,800 Speaker 3: If you were at the FED dealing with an AI 372 00:16:23,920 --> 00:16:27,880 Speaker 3: economy versus the economy of ten years ago, let alone 373 00:16:28,040 --> 00:16:30,200 Speaker 3: thirty or forty years ago. But let's say ten years ago, 374 00:16:31,080 --> 00:16:33,440 Speaker 3: does that make you think about monetary policy and the 375 00:16:33,440 --> 00:16:37,000 Speaker 3: transmission mechanism different at all? Joe kind of got up 376 00:16:37,040 --> 00:16:38,760 Speaker 3: this earlier, but yeah. 377 00:16:38,560 --> 00:16:40,720 Speaker 2: It's I think, you know, we all lived through the 378 00:16:40,760 --> 00:16:43,280 Speaker 2: mid two thousands and then the bust and the bailouts, 379 00:16:43,320 --> 00:16:46,160 Speaker 2: and it's like, should the FED have prevented Open Eye 380 00:16:46,160 --> 00:16:48,640 Speaker 2: from making these commitments? Should the FED have prevented Nvidia 381 00:16:48,680 --> 00:16:51,960 Speaker 2: from doing vendor finance on the scale they have? Maybe, 382 00:16:52,000 --> 00:16:54,080 Speaker 2: we'll say in hindsight, yes, but like in the moment, 383 00:16:54,160 --> 00:16:56,440 Speaker 2: which is right, now, come on, like that's not going 384 00:16:56,520 --> 00:16:59,080 Speaker 2: to happen. So I'm a little more sympathetic now to 385 00:16:59,080 --> 00:17:01,920 Speaker 2: the FED being blind certain things in the boom, especially 386 00:17:01,920 --> 00:17:04,280 Speaker 2: when it's sort of outside of their band aid, just. 387 00:17:04,240 --> 00:17:08,440 Speaker 3: Because it's right. They're not AI regulators, no exactly. 388 00:17:08,440 --> 00:17:10,399 Speaker 2: And it's also with Trump, it's not like they're going 389 00:17:10,440 --> 00:17:12,880 Speaker 2: to be telling Sam Altman he can't do what he's doing. 390 00:17:13,040 --> 00:17:14,520 Speaker 2: So I feel like they're going to probably have a 391 00:17:14,600 --> 00:17:16,679 Speaker 2: role in the cleanup, but in the moment, there's not 392 00:17:16,680 --> 00:17:17,439 Speaker 2: a whole lot they can do. 393 00:17:17,800 --> 00:17:21,640 Speaker 1: It's funny that we're already like projecting it so far forward, 394 00:17:21,800 --> 00:17:23,800 Speaker 1: but it is. I mean, this is and I really 395 00:17:23,840 --> 00:17:26,160 Speaker 1: want to do more on this. I think the politics 396 00:17:26,240 --> 00:17:28,520 Speaker 1: of AI are going to be huge in twenty twenty eight, 397 00:17:28,560 --> 00:17:31,720 Speaker 1: and I expect I don't know, it's very possible to 398 00:17:31,760 --> 00:17:34,399 Speaker 1: me that the AI industry at the same time is 399 00:17:34,440 --> 00:17:37,120 Speaker 1: a very big deal in the US touch and sort 400 00:17:37,160 --> 00:17:41,040 Speaker 1: of friendless in DC. It seems very plausible. Seems very 401 00:17:41,040 --> 00:17:43,720 Speaker 1: plausible to me that in twenty twenty eight, the story 402 00:17:43,960 --> 00:17:48,400 Speaker 1: is most people again, twenty twenty eight, it's a lifetime 403 00:17:48,400 --> 00:17:51,840 Speaker 1: from now, both politics and technology. But it could be 404 00:17:51,880 --> 00:17:54,000 Speaker 1: a story where it's like, here's this thing, it's really important, 405 00:17:54,240 --> 00:17:56,760 Speaker 1: there's tons of investment in it, but people for the 406 00:17:56,760 --> 00:18:00,520 Speaker 1: most part either mostly see it as a job killers 407 00:18:00,560 --> 00:18:05,320 Speaker 1: slash and electricity price booster, and there are some you know, 408 00:18:05,440 --> 00:18:07,560 Speaker 1: the stakes are going to be very high, and I 409 00:18:07,600 --> 00:18:11,080 Speaker 1: expect sort of like especially on the Republican primary, I 410 00:18:11,080 --> 00:18:13,280 Speaker 1: would expect I would expect the Democrats to sort of 411 00:18:13,280 --> 00:18:17,639 Speaker 1: be more comfortable. Their antagonism towards big tech has obviously 412 00:18:18,080 --> 00:18:20,080 Speaker 1: been you know, several years. I think it's going to 413 00:18:20,160 --> 00:18:22,720 Speaker 1: be some interesting divisions on the geop side in terms 414 00:18:22,760 --> 00:18:24,439 Speaker 1: of which lanes. And I think there is going to 415 00:18:24,440 --> 00:18:27,160 Speaker 1: be an AI lane or an anti a line AI lane. 416 00:18:27,200 --> 00:18:29,880 Speaker 1: You already see Ron DeSantis, he's been tweeting a lot 417 00:18:29,920 --> 00:18:31,679 Speaker 1: about this. I think that's very telling. 418 00:18:32,000 --> 00:18:35,160 Speaker 2: Well, it's also in twenty twenty four, the MAGA coalition 419 00:18:35,280 --> 00:18:37,800 Speaker 2: was kind of working class voters of all races and 420 00:18:37,840 --> 00:18:40,720 Speaker 2: ethnicities and then tech and VC. Yeah, and it's just 421 00:18:40,760 --> 00:18:43,160 Speaker 2: hard to see how their interests are truly aligned on AI. 422 00:18:44,080 --> 00:18:46,560 Speaker 1: Let's talk a little bit more just about the job 423 00:18:46,640 --> 00:18:49,479 Speaker 1: market right now, Like, you know, let's say we were 424 00:18:49,520 --> 00:18:52,920 Speaker 1: getting data, like do you think you know some role 425 00:18:53,000 --> 00:18:55,000 Speaker 1: like we're in sort of it's sort of come down, 426 00:18:55,040 --> 00:18:57,400 Speaker 1: But how close do you think we are to the snowball? 427 00:18:57,440 --> 00:18:59,480 Speaker 1: Like how urgent? How far behind do you think the 428 00:18:59,480 --> 00:19:00,320 Speaker 1: FED could get here? 429 00:19:01,080 --> 00:19:03,960 Speaker 2: I remember when Liberation Day happened and people were wondering 430 00:19:03,960 --> 00:19:05,399 Speaker 2: how bad it was going to get. And my thought 431 00:19:05,440 --> 00:19:08,080 Speaker 2: was that companies don't like to change their capex plans 432 00:19:08,080 --> 00:19:09,639 Speaker 2: into April and May. They're going to try to get 433 00:19:09,680 --> 00:19:11,479 Speaker 2: through the end of the year and then figure out 434 00:19:11,480 --> 00:19:13,960 Speaker 2: twenty twenty six and Q four. Yeah, and as they're 435 00:19:13,960 --> 00:19:15,680 Speaker 2: doing that right now, it's hard to think that they're 436 00:19:15,680 --> 00:19:17,480 Speaker 2: going to feel like we need outside of AI, like 437 00:19:17,520 --> 00:19:19,280 Speaker 2: we need to step up investment in hiring. 438 00:19:19,480 --> 00:19:21,920 Speaker 3: We feel really good about twenty twenty six when the 439 00:19:21,960 --> 00:19:23,240 Speaker 3: government's shut down, And. 440 00:19:23,240 --> 00:19:25,600 Speaker 2: Yeah, exat share price is going on, and so I 441 00:19:25,680 --> 00:19:27,760 Speaker 2: worry a bit that it's like we all freaked out 442 00:19:27,760 --> 00:19:29,679 Speaker 2: in April and May, and then it was kind of 443 00:19:29,680 --> 00:19:32,080 Speaker 2: fine for six months, and then now is when people 444 00:19:32,080 --> 00:19:33,800 Speaker 2: are going to make their investment in spending plans for 445 00:19:33,840 --> 00:19:35,680 Speaker 2: next year, and those are going to come in lower 446 00:19:35,720 --> 00:19:37,960 Speaker 2: than they did a year ago, and we're going to 447 00:19:37,960 --> 00:19:39,479 Speaker 2: start to see that show up in Q one. So 448 00:19:39,560 --> 00:19:41,960 Speaker 2: I don't think it's a next week, next month thing. 449 00:19:42,080 --> 00:19:45,960 Speaker 2: But could the unemployment rate go certainly into the high fours, Yes, 450 00:19:45,960 --> 00:19:47,800 Speaker 2: and I think we probably will get there. Does it 451 00:19:47,800 --> 00:19:50,120 Speaker 2: get beyond that? I don't know, But I just think 452 00:19:50,320 --> 00:19:51,720 Speaker 2: three to six months from now it's still gonna be 453 00:19:51,720 --> 00:19:52,600 Speaker 2: worse than it is today. 454 00:19:53,200 --> 00:19:56,159 Speaker 1: How concerned are you about, like just lower here and 455 00:19:56,200 --> 00:20:00,160 Speaker 1: have you somebody first brand tree Clore some of these 456 00:20:00,320 --> 00:20:05,719 Speaker 1: dodgy credits, you know, Jamie Diamond cockroaches right, like, you know, 457 00:20:06,359 --> 00:20:07,040 Speaker 1: what's your sense? 458 00:20:07,440 --> 00:20:09,400 Speaker 2: I feel like the whole private asset thing is that 459 00:20:09,680 --> 00:20:11,439 Speaker 2: it was small in two thousand and eight, so there 460 00:20:11,440 --> 00:20:13,400 Speaker 2: weren't really any problems there, and then in the early 461 00:20:13,400 --> 00:20:15,880 Speaker 2: twenty tens you had zup and then very cheap assets 462 00:20:15,880 --> 00:20:17,960 Speaker 2: and they had a great decade of performance based on that, 463 00:20:18,440 --> 00:20:20,160 Speaker 2: and then the asset class kind of just kept getting 464 00:20:20,160 --> 00:20:22,280 Speaker 2: bigger just because, like I don't know if there's a 465 00:20:22,320 --> 00:20:24,800 Speaker 2: real fundamental reason for it. It just became like a 466 00:20:24,840 --> 00:20:27,679 Speaker 2: thing that institutions do. And so it's like, are they 467 00:20:27,720 --> 00:20:29,800 Speaker 2: really underwriting very well? Like you look at the stock 468 00:20:29,840 --> 00:20:33,560 Speaker 2: prices of a firm and up work or all these 469 00:20:33,600 --> 00:20:36,400 Speaker 2: like AI fintech lenders are getting destroyed and you've had 470 00:20:37,080 --> 00:20:39,800 Speaker 2: that's the treaty guy talked about Blue Owl, which is just 471 00:20:39,800 --> 00:20:42,680 Speaker 2: hard to think underwriting is really really great right now. 472 00:20:42,800 --> 00:20:45,680 Speaker 2: And so if anything works in the economy, I assume 473 00:20:45,720 --> 00:20:46,879 Speaker 2: that those guys are gonna be in trouble. 474 00:20:47,040 --> 00:20:50,399 Speaker 1: The Blue Owl things we're talking about more, and I 475 00:20:50,400 --> 00:20:52,280 Speaker 1: think we have an episode coming up that we'll touch 476 00:20:52,280 --> 00:20:55,959 Speaker 1: on that. You know what I should say. We've been 477 00:20:56,000 --> 00:21:00,159 Speaker 1: speaking with Bloomberg opinion columnist Connor sund Oh. Yes are 478 00:21:00,200 --> 00:21:02,320 Speaker 1: important that I wanted to make sure I knew there 479 00:21:02,400 --> 00:21:04,560 Speaker 1: was one more thing I wanted to get in the conversation, 480 00:21:04,800 --> 00:21:07,439 Speaker 1: and so I wanted to establish who the guest we 481 00:21:07,480 --> 00:21:08,920 Speaker 1: are actually talking to was. 482 00:21:09,040 --> 00:21:12,680 Speaker 3: I guess Connorson person with non zero chance of becoming fed. 483 00:21:12,520 --> 00:21:14,280 Speaker 1: Shaer non zero chance of becoming fed. 484 00:21:14,600 --> 00:21:17,199 Speaker 3: She said, that's that's the real title, just the real title. 485 00:21:17,520 --> 00:21:19,320 Speaker 3: We should leave it there, We should go. I kind 486 00:21:19,320 --> 00:21:20,280 Speaker 3: of want McDonald's again. 487 00:21:20,359 --> 00:21:28,240 Speaker 1: Yeah, it's Lunchhime lots More is produced by Carmen Rodriguez 488 00:21:28,280 --> 00:21:30,600 Speaker 1: and dash Ol Bennett, with help from Moses Onam and 489 00:21:30,640 --> 00:21:31,320 Speaker 1: Kill Brooks. 490 00:21:31,720 --> 00:21:34,879 Speaker 3: Our sound engineer is Blake Maples. Sage Bauman is the 491 00:21:34,920 --> 00:21:36,280 Speaker 3: head of Bloomberg Podcasts. 492 00:21:36,760 --> 00:21:40,080 Speaker 1: Please rate, review, and subscribe to Odd Lots and lots 493 00:21:40,119 --> 00:21:43,080 Speaker 1: More on your favorite podcast platforms. 494 00:21:42,760 --> 00:21:45,520 Speaker 3: And remember that Bloomberg subscribers can listen to all our 495 00:21:45,560 --> 00:21:50,199 Speaker 3: podcasts at free by connecting through Apple Podcasts. Thanks for listening.