1 00:00:02,640 --> 00:00:05,320 Speaker 1: Welcome to the Bloomberg Penl Podcast. I'm Paul swing you 2 00:00:05,360 --> 00:00:07,680 Speaker 1: along with my co host Lisa Brahma wits. Each day 3 00:00:07,720 --> 00:00:10,240 Speaker 1: we bring you the most noteworthy and useful interviews for 4 00:00:10,280 --> 00:00:12,520 Speaker 1: you and your money, whether at the grocery store or 5 00:00:12,560 --> 00:00:15,480 Speaker 1: the trading floor. Find a Bloomberg Penl podcast on Apple 6 00:00:15,520 --> 00:00:17,959 Speaker 1: podcast or wherever you listen to podcasts, as well as 7 00:00:17,960 --> 00:00:27,520 Speaker 1: at Bloomberg dot com. Qualcom is really a mysterious story 8 00:00:27,600 --> 00:00:29,520 Speaker 1: right now, Shares down more than two and a half 9 00:00:29,560 --> 00:00:33,680 Speaker 1: percent today after a nearly eleven percent tumble yesterday. A 10 00:00:33,840 --> 00:00:38,919 Speaker 1: question is whether their business model is being fundamentally challenged 11 00:00:39,000 --> 00:00:42,519 Speaker 1: right now in court and whether basically, uh, they have 12 00:00:42,600 --> 00:00:45,760 Speaker 1: an existential threat that is looming down on them. I'm 13 00:00:45,800 --> 00:00:48,200 Speaker 1: sure overday joining us here at our Bloomberg Interactive Broker 14 00:00:48,280 --> 00:00:51,800 Speaker 1: Studios Technology calumnists with Bloomberg Opinion. So just lay out 15 00:00:51,800 --> 00:00:53,920 Speaker 1: the issue here. There was a core case that was 16 00:00:53,960 --> 00:00:58,280 Speaker 1: particularly punitive why Yes. So this was a court case 17 00:00:58,440 --> 00:01:01,120 Speaker 1: that the US Federal Trade Mission brought. It was an 18 00:01:01,120 --> 00:01:06,280 Speaker 1: antitrust case and then um overnight on Tuesday, uh U 19 00:01:06,360 --> 00:01:10,000 Speaker 1: S District Court judge ruled that basically, um, the way 20 00:01:10,000 --> 00:01:13,840 Speaker 1: that qualcomn has conducted business is in fact a violation 21 00:01:13,920 --> 00:01:17,600 Speaker 1: of antitrust law. The way that the company basically, if 22 00:01:17,640 --> 00:01:22,640 Speaker 1: you want Qualcomms chips, you need to license a set 23 00:01:22,720 --> 00:01:26,399 Speaker 1: of of patent technologies from the company. So there's no license, 24 00:01:26,480 --> 00:01:29,959 Speaker 1: no chips. And also, even if you're not using qualcoms chips, 25 00:01:30,240 --> 00:01:32,720 Speaker 1: you still need to pay the company for these kind 26 00:01:32,720 --> 00:01:35,040 Speaker 1: of collection of patents that are really essential to the 27 00:01:35,040 --> 00:01:38,600 Speaker 1: functioning of modern smartphones and other computing devices. And the 28 00:01:38,640 --> 00:01:41,080 Speaker 1: judge that that's not legal. Well, I had to admit 29 00:01:41,120 --> 00:01:43,679 Speaker 1: I was very confused by the judges ruling today and surprised, 30 00:01:43,680 --> 00:01:45,559 Speaker 1: and I guess so was the market. The stock sold 31 00:01:45,600 --> 00:01:48,480 Speaker 1: off so much yesterday because when they had their Apple 32 00:01:48,680 --> 00:01:51,960 Speaker 1: resolution um a couple of weeks ago, the stock rallied 33 00:01:52,000 --> 00:01:54,720 Speaker 1: and I thought, Okay, that's it, the qual tom story. 34 00:01:54,720 --> 00:01:57,040 Speaker 1: It's off to the races. But there was still the 35 00:01:57,040 --> 00:01:59,360 Speaker 1: FTC issue out there, So this is something that's still 36 00:01:59,400 --> 00:02:02,480 Speaker 1: fundamentally on the radar for this company. Yes, and frankly 37 00:02:02,480 --> 00:02:05,080 Speaker 1: I thought the same thing about the Apple case. So 38 00:02:05,120 --> 00:02:08,840 Speaker 1: the Apple brought suit against qualcom over almost exactly the 39 00:02:08,840 --> 00:02:12,320 Speaker 1: same issues that the judge ruled on this week, and 40 00:02:12,400 --> 00:02:15,399 Speaker 1: so you're right. When Qualcom and Apple reached a legal settlement, 41 00:02:15,560 --> 00:02:17,720 Speaker 1: I think a lot of investors thought, well, that's behind them. 42 00:02:17,760 --> 00:02:22,000 Speaker 1: But look, this issue about Qualcom's dual sided business model, 43 00:02:22,040 --> 00:02:24,760 Speaker 1: the selling of chips and the licensing of its patents, 44 00:02:25,080 --> 00:02:28,200 Speaker 1: and the links between them. It's been an issue over 45 00:02:28,320 --> 00:02:32,160 Speaker 1: and over and over again and repeated litigation, in repeated 46 00:02:32,240 --> 00:02:36,440 Speaker 1: regulatory um investigations and finds all over the world. It 47 00:02:36,639 --> 00:02:39,640 Speaker 1: is kind of a fundamental aspect of Qualcoms business, but 48 00:02:39,680 --> 00:02:43,760 Speaker 1: it's also under constant assault. Well, and so I guess 49 00:02:43,800 --> 00:02:48,040 Speaker 1: that then the larger question here is what is Qualcom 50 00:02:48,240 --> 00:02:51,800 Speaker 1: as a business without the stream of patent revenues. Because 51 00:02:51,800 --> 00:02:54,360 Speaker 1: you were saying, we were speaking earlier this morning, that 52 00:02:54,440 --> 00:02:58,040 Speaker 1: those patent revenues account for more than fifty percent of 53 00:02:58,520 --> 00:03:03,320 Speaker 1: the firm's income. Well, I guess we may see what 54 00:03:03,320 --> 00:03:06,040 Speaker 1: what Qualcom looks like if if you sort of untangle 55 00:03:06,160 --> 00:03:08,840 Speaker 1: the two halves of the company's business. But we should 56 00:03:08,919 --> 00:03:11,400 Speaker 1: say that, look, this may not be kind of the 57 00:03:11,520 --> 00:03:15,560 Speaker 1: nuclear option in this in this legal ruling that Qualcom 58 00:03:15,560 --> 00:03:19,280 Speaker 1: hasn't disagrees with the judges' decision. Of course, the company 59 00:03:19,320 --> 00:03:23,079 Speaker 1: has asked for an expedited appeal. I assume the litigation 60 00:03:23,240 --> 00:03:25,239 Speaker 1: is going to the appeal process is going to take 61 00:03:25,240 --> 00:03:29,440 Speaker 1: many years to resolve. They are there may be ways 62 00:03:29,440 --> 00:03:33,600 Speaker 1: for Qualcom to sort of adjust its relationships with customers 63 00:03:33,840 --> 00:03:37,640 Speaker 1: in a way that doesn't mean dumping one or or 64 00:03:37,760 --> 00:03:39,920 Speaker 1: or the other half of its business model. So there 65 00:03:39,960 --> 00:03:42,560 Speaker 1: could be some kind of middle ground that is not 66 00:03:42,720 --> 00:03:45,760 Speaker 1: kind of Qualcom blowing up how it does business. Is 67 00:03:45,800 --> 00:03:48,920 Speaker 1: there any chance for a settlement or the fact or 68 00:03:48,960 --> 00:03:51,240 Speaker 1: have we just passed that point now? Between the you 69 00:03:51,280 --> 00:03:55,520 Speaker 1: know Qualcom in the FTC, I I mean, I think 70 00:03:55,560 --> 00:03:57,800 Speaker 1: the f you know since the FTC one this case, 71 00:03:57,960 --> 00:04:01,680 Speaker 1: I can't. I mean, I don't know, is the short answer. 72 00:04:01,720 --> 00:04:03,280 Speaker 1: But I find it hard to believe there would be 73 00:04:03,280 --> 00:04:06,120 Speaker 1: a settlement. But again, there could be um some kind 74 00:04:06,120 --> 00:04:10,200 Speaker 1: of resolution assuming the judges judges decision holds up on appeal, 75 00:04:10,560 --> 00:04:13,560 Speaker 1: that falls short of Qualcom having to blow up everything 76 00:04:13,600 --> 00:04:17,480 Speaker 1: and subject itself to government scrutiny, government oversight for seven years. 77 00:04:17,560 --> 00:04:20,560 Speaker 1: So we were talking about how Apple had their lawsuit 78 00:04:20,640 --> 00:04:24,080 Speaker 1: against Qualcom. Who is the biggest sort of I don't 79 00:04:24,080 --> 00:04:26,320 Speaker 1: want to use the word victims since that's loaded, But 80 00:04:26,360 --> 00:04:28,719 Speaker 1: who's on the other side of some of these suits. 81 00:04:28,720 --> 00:04:34,440 Speaker 1: Who's most angry, Well, certainly the customers, including UM, including 82 00:04:35,240 --> 00:04:40,120 Speaker 1: smartphone makers like Samsung and Apple and Huawei. The latter 83 00:04:40,160 --> 00:04:46,800 Speaker 1: two had various litigation pending against Qualcom. And look, I 84 00:04:46,800 --> 00:04:50,160 Speaker 1: think what the FTC argued in its litigation was that 85 00:04:50,279 --> 00:04:55,440 Speaker 1: the ultimate victim is the competitiveness of of the US 86 00:04:55,520 --> 00:04:59,000 Speaker 1: chip industry. That the judge agreed that the way the 87 00:04:59,080 --> 00:05:01,080 Speaker 1: Qualcom does bus nous of the way was able to 88 00:05:01,080 --> 00:05:04,520 Speaker 1: sort of leverage its power in the chip market to 89 00:05:04,720 --> 00:05:09,120 Speaker 1: sort of overcharge basically for its patents. It forced other 90 00:05:09,240 --> 00:05:13,960 Speaker 1: companies either out of business, other potential competitors to Qualcom 91 00:05:14,040 --> 00:05:16,960 Speaker 1: force them out of business, or kind of hindered their 92 00:05:17,000 --> 00:05:20,479 Speaker 1: ability to compete. So ultimately, you know, the competitive the 93 00:05:20,560 --> 00:05:24,600 Speaker 1: competitiveness of US technology may have been the ultimate victim here. 94 00:05:24,800 --> 00:05:26,200 Speaker 1: And one of the things I remember, if I can 95 00:05:26,240 --> 00:05:28,200 Speaker 1: think all the way back to the Apple settlement just 96 00:05:28,240 --> 00:05:30,480 Speaker 1: a few weeks ago, Uh, they were hailing it as 97 00:05:30,560 --> 00:05:34,479 Speaker 1: you know, this kind of pro five G or pro technology. UM. 98 00:05:35,160 --> 00:05:37,080 Speaker 1: Is that an argument you think Qualcom is gonna make 99 00:05:37,120 --> 00:05:39,880 Speaker 1: here as they try to, you know, fight back against 100 00:05:39,920 --> 00:05:42,640 Speaker 1: this ruling. Yeah, I think the five G argument is 101 00:05:42,680 --> 00:05:45,320 Speaker 1: going to be very interesting. So we saw last year 102 00:05:45,920 --> 00:05:52,240 Speaker 1: the US government stepped into essentially block Broadcoms proposed um 103 00:05:52,360 --> 00:05:54,880 Speaker 1: takeover offer of Qualcom that would have been a hundred 104 00:05:54,880 --> 00:05:57,760 Speaker 1: billion dollar plus deal, and the government. The US government's 105 00:05:57,800 --> 00:06:02,080 Speaker 1: reasoning was, if Broadcom succeeds in this takeover, Qualcom is 106 00:06:02,120 --> 00:06:05,840 Speaker 1: gonna be gutted. They're gonna slash costs, and that's gonna 107 00:06:05,920 --> 00:06:09,680 Speaker 1: hurt qual Comms competitiveness in five G, this next generation 108 00:06:09,760 --> 00:06:14,120 Speaker 1: of wireless standard that is considered sort of this essential 109 00:06:14,360 --> 00:06:18,360 Speaker 1: national security priority, which is weird because I mean, look, 110 00:06:18,400 --> 00:06:22,200 Speaker 1: it's a technology standard, but the US government and the 111 00:06:22,279 --> 00:06:26,120 Speaker 1: Chinese government to have now basically made five G this 112 00:06:26,240 --> 00:06:29,719 Speaker 1: political football. And so one thing that I wonder is 113 00:06:29,720 --> 00:06:33,200 Speaker 1: will the US government step in uh in this litigation 114 00:06:33,240 --> 00:06:36,400 Speaker 1: that the FTC one and basically seek to blunt it 115 00:06:37,080 --> 00:06:40,400 Speaker 1: against citing the ability of Qualcom to remain competitive in 116 00:06:40,720 --> 00:06:44,280 Speaker 1: five G. I have to wonder what qual Comms side 117 00:06:44,320 --> 00:06:46,400 Speaker 1: of this is, because I hear what you're saying, and 118 00:06:46,440 --> 00:06:48,359 Speaker 1: I thought that it was a really salient comment that 119 00:06:48,400 --> 00:06:50,760 Speaker 1: you made. The competitiveness of US technology may have been 120 00:06:50,760 --> 00:06:53,720 Speaker 1: the biggest victim here. What does Qualcomms say to that 121 00:06:54,000 --> 00:06:56,839 Speaker 1: what qual Comms argument is the opposite that it says, 122 00:06:57,279 --> 00:07:00,839 Speaker 1: look that their business model is not unusual, that the 123 00:07:00,920 --> 00:07:04,719 Speaker 1: chip industry is competitive, and if you look at some 124 00:07:04,720 --> 00:07:08,240 Speaker 1: some estimates of market share, Qualcom is losing some market share, 125 00:07:08,240 --> 00:07:12,320 Speaker 1: which shows that there is competition out there. And Qualcom's 126 00:07:12,360 --> 00:07:16,360 Speaker 1: basic fundamental point is, look the way that we have 127 00:07:16,440 --> 00:07:20,040 Speaker 1: set up our business model, including this patent licensing stream, 128 00:07:20,320 --> 00:07:24,520 Speaker 1: we plow that back into tens of billions of dollars 129 00:07:24,560 --> 00:07:27,840 Speaker 1: in research and development spending to come up with the 130 00:07:27,880 --> 00:07:31,120 Speaker 1: next big technologies in the future. And look, it is 131 00:07:31,160 --> 00:07:36,840 Speaker 1: true that particularly in areas around cellular internet technology, Qualcom 132 00:07:36,840 --> 00:07:39,840 Speaker 1: has been a leader, and there would be no smartphone 133 00:07:39,880 --> 00:07:43,520 Speaker 1: industry without Qualcom. It's interesting. I think if I'm a guess, 134 00:07:43,520 --> 00:07:45,280 Speaker 1: if I'm a Qualcom investor, I just have to live 135 00:07:45,320 --> 00:07:47,280 Speaker 1: with this litigation risk going forward. I guess kind of 136 00:07:47,280 --> 00:07:50,400 Speaker 1: the business model just licensing technology, and you're always gonna 137 00:07:50,400 --> 00:07:52,200 Speaker 1: have that issue. I think, are you're gonna bett in 138 00:07:52,240 --> 00:07:54,320 Speaker 1: the Trump put with five G and that I think 139 00:07:54,360 --> 00:07:56,920 Speaker 1: it's really going to be a big bet underpitting some 140 00:07:56,920 --> 00:07:59,440 Speaker 1: people who might dive in and buy the shares as 141 00:07:59,480 --> 00:08:01,680 Speaker 1: they fall to day. Yeah. Interesting share of a day. 142 00:08:01,680 --> 00:08:04,000 Speaker 1: Thank you so much for joining us once again. Shearer's 143 00:08:04,040 --> 00:08:06,720 Speaker 1: Technology Comms for Bloomberg Opinion. Joining us here in our 144 00:08:06,720 --> 00:08:26,920 Speaker 1: Bloomberg Interactive Broker studio, Paul. We like to laugh when 145 00:08:27,000 --> 00:08:31,800 Speaker 1: company after company blames the weather for disappointing earnings. But 146 00:08:31,880 --> 00:08:34,400 Speaker 1: it turns out they may not have that same excuse 147 00:08:34,480 --> 00:08:37,240 Speaker 1: going forward, at least if Cameron Clayton has anything to 148 00:08:37,280 --> 00:08:39,240 Speaker 1: do with it. He joins us here in our Blooberator 149 00:08:39,240 --> 00:08:42,559 Speaker 1: Active broker studios. He's the general manager for the IBM 150 00:08:42,720 --> 00:08:47,480 Speaker 1: Wattson Media and Weather Unit with IBM and Cameron. Can 151 00:08:47,520 --> 00:08:51,360 Speaker 1: you just first start by how IBM and the Weather 152 00:08:51,440 --> 00:08:56,000 Speaker 1: Channel are using artificial intelligence to help these executives get 153 00:08:56,040 --> 00:09:00,600 Speaker 1: away from just simply blaming the whims of weather. Well, 154 00:09:00,640 --> 00:09:06,280 Speaker 1: thanks for having me, Watson. An artificial intelligence is really 155 00:09:06,280 --> 00:09:09,360 Speaker 1: about a big data and so just give you a 156 00:09:09,440 --> 00:09:13,400 Speaker 1: sense of the scale of the data. So on Wednesday, uh, 157 00:09:13,760 --> 00:09:18,000 Speaker 1: we had thirty four billion data requests of our infrastructure 158 00:09:18,200 --> 00:09:21,760 Speaker 1: asking about weather. And the reason that number is so 159 00:09:21,880 --> 00:09:24,000 Speaker 1: large is because of all the tornadic activity we had 160 00:09:24,320 --> 00:09:27,520 Speaker 1: you know this week in the in the Midwest. But 161 00:09:27,640 --> 00:09:31,560 Speaker 1: when there is that much data being generated every single day. 162 00:09:32,120 --> 00:09:35,240 Speaker 1: The only way to sort of get insights out of 163 00:09:35,240 --> 00:09:38,360 Speaker 1: that data to help people make better decisions, to help 164 00:09:38,360 --> 00:09:42,400 Speaker 1: integrate into their supply chain, et cetera, is using artificial intelligence. 165 00:09:42,440 --> 00:09:45,680 Speaker 1: And so that's where Watson has come to bear and 166 00:09:45,679 --> 00:09:48,920 Speaker 1: and it's just something that wouldn't be possible without official intelligence. 167 00:09:48,920 --> 00:09:51,920 Speaker 1: Al Right, So let's talk about this weather signals product. 168 00:09:52,040 --> 00:09:55,680 Speaker 1: What is it and who are you trying to sell 169 00:09:55,720 --> 00:09:58,920 Speaker 1: it to? I guess yeah, So we're basically selling it 170 00:09:59,000 --> 00:10:03,920 Speaker 1: to prizes around the world by industry, and so whether 171 00:10:03,960 --> 00:10:08,440 Speaker 1: you're a retailer trying to sell inventory, So I'll use 172 00:10:08,480 --> 00:10:11,200 Speaker 1: home depot as an example, right, and and the torn 173 00:10:11,240 --> 00:10:15,120 Speaker 1: netic activity this week. Uh, they've got inventory of top holands, 174 00:10:15,720 --> 00:10:18,360 Speaker 1: but they might be in Los Angeles or in Florida. 175 00:10:18,600 --> 00:10:21,280 Speaker 1: They're not in Kansas and Oklahoma where they need to be, 176 00:10:21,640 --> 00:10:23,280 Speaker 1: and so we can tell them a hit of time 177 00:10:23,640 --> 00:10:27,600 Speaker 1: where to deploy their stock. Uh. Similarly, you know, we 178 00:10:27,679 --> 00:10:31,120 Speaker 1: help drive consumption, right, so weeks in advance, we can say, 179 00:10:31,520 --> 00:10:36,040 Speaker 1: you know, beer sales are going to increase by when 180 00:10:36,120 --> 00:10:41,239 Speaker 1: weather conditions are X and so that ends up integrating 181 00:10:41,240 --> 00:10:44,960 Speaker 1: into all kinds of companies into the supply chain. For 182 00:10:45,000 --> 00:10:47,920 Speaker 1: the logistics as well as the stock but also how 183 00:10:47,920 --> 00:10:51,320 Speaker 1: to staff your company. Right, So I guess that one 184 00:10:51,360 --> 00:10:54,040 Speaker 1: thing that as you talk sort of surprises to me. 185 00:10:54,240 --> 00:10:57,360 Speaker 1: It surprises me. How is this so much different than 186 00:10:57,440 --> 00:10:59,559 Speaker 1: looking at the weather and saying, oh, there's going to 187 00:10:59,600 --> 00:11:01,679 Speaker 1: be a big storm, it's going to be forecast to come. 188 00:11:01,800 --> 00:11:04,040 Speaker 1: Everyone's going to go to the local grocery store and 189 00:11:04,080 --> 00:11:06,839 Speaker 1: buy off, you know, everything that they possibly can. I mean, 190 00:11:06,920 --> 00:11:10,400 Speaker 1: haven't companies been able to do that forever or not forever, 191 00:11:10,520 --> 00:11:14,480 Speaker 1: but for you know decades. So companies have had access 192 00:11:14,520 --> 00:11:17,840 Speaker 1: to weather data from US and others for you know, decades. 193 00:11:18,600 --> 00:11:22,000 Speaker 1: But this is really about making very specific recommendations of 194 00:11:22,080 --> 00:11:26,160 Speaker 1: decisions that they should make specifically for their business. And 195 00:11:26,280 --> 00:11:30,280 Speaker 1: so it integrates into the different software they use. Already 196 00:11:30,440 --> 00:11:33,360 Speaker 1: in retails, a lot of retailers use Tableau as a 197 00:11:33,360 --> 00:11:36,320 Speaker 1: as a software tool. Uh, it integrates into that and 198 00:11:36,360 --> 00:11:39,880 Speaker 1: it makes specific recommendations of exactly what to do. How 199 00:11:39,920 --> 00:11:43,240 Speaker 1: many people should you have come into work in your 200 00:11:43,320 --> 00:11:48,320 Speaker 1: office tomorrow, how many people should not come to work 201 00:11:48,679 --> 00:11:52,480 Speaker 1: on Saturday? Um, and so you know, use this weekend 202 00:11:52,559 --> 00:11:55,440 Speaker 1: is a long weekend for for travel, for example, So 203 00:11:55,800 --> 00:11:59,840 Speaker 1: we will actually in advance, help airlines change their flights 204 00:12:00,000 --> 00:12:02,720 Speaker 1: a jules to accommodate the what we know is an 205 00:12:02,760 --> 00:12:06,200 Speaker 1: increase in traffic UH that's coming. Tie that to the 206 00:12:06,240 --> 00:12:09,600 Speaker 1: weather data and make it very prescriptive. Right, this flight 207 00:12:09,760 --> 00:12:13,720 Speaker 1: going out of this airport to this destination needs to 208 00:12:13,720 --> 00:12:16,319 Speaker 1: be adjusted by thirty minutes, you know, a week or 209 00:12:16,360 --> 00:12:18,800 Speaker 1: two in advance. So give us a sense that this 210 00:12:18,880 --> 00:12:21,480 Speaker 1: weather signals product that you're talking about that incorporates AI, 211 00:12:21,559 --> 00:12:24,080 Speaker 1: it's a relatively new product for you guys. What's been 212 00:12:24,080 --> 00:12:26,280 Speaker 1: the uptake And just give us some examples of maybe 213 00:12:26,280 --> 00:12:29,200 Speaker 1: some companies who who have you know, chosen go this 214 00:12:29,280 --> 00:12:32,240 Speaker 1: round and kind of what they use it for specifically. Yeah, 215 00:12:32,280 --> 00:12:37,840 Speaker 1: so you know, uh, the biggest retailers uh the probably 216 00:12:38,000 --> 00:12:43,720 Speaker 1: shouldn't name, but uh essentially using it today. Uh. And 217 00:12:43,760 --> 00:12:46,440 Speaker 1: so retail is probably the largest category for where the 218 00:12:46,440 --> 00:12:52,120 Speaker 1: signals specifically right now. The second big category is aviation, right, 219 00:12:52,160 --> 00:12:55,440 Speaker 1: so they're adding it to existing solutions that we provide 220 00:12:55,440 --> 00:12:58,640 Speaker 1: an aviation uh. And then you know, we're also bringing 221 00:12:58,640 --> 00:13:00,960 Speaker 1: it all the way up all the way down the 222 00:13:01,000 --> 00:13:04,160 Speaker 1: supply chain, if you like, uh, to agriculture as well. 223 00:13:04,559 --> 00:13:07,360 Speaker 1: And so we're starting to see large scale farms UH 224 00:13:07,600 --> 00:13:11,760 Speaker 1: using it to help make decisions on the farm UH 225 00:13:11,880 --> 00:13:16,400 Speaker 1: in addition to retailers and transportation company. It's so interesting 226 00:13:16,440 --> 00:13:19,160 Speaker 1: to me because most recently you're a chief executive officer 227 00:13:19,200 --> 00:13:23,120 Speaker 1: and general manager of the Weather Company UH and including 228 00:13:23,160 --> 00:13:25,880 Speaker 1: Weather Underground, which I check every morning when I have 229 00:13:26,000 --> 00:13:28,160 Speaker 1: to take the kids to school and have to figure 230 00:13:28,160 --> 00:13:30,360 Speaker 1: out how painful it's going to be. And I guess 231 00:13:30,400 --> 00:13:34,880 Speaker 1: that I'm wondering whether you see the future of weather 232 00:13:34,960 --> 00:13:38,440 Speaker 1: forecasts as being part of this is a sort of 233 00:13:38,559 --> 00:13:43,640 Speaker 1: generating revenue from actually helping the prescriptive to businesses UH 234 00:13:43,679 --> 00:13:46,120 Speaker 1: in a way that perhaps is different than just looking 235 00:13:46,120 --> 00:13:49,080 Speaker 1: at the hourly weather forecast. What I think one of 236 00:13:49,120 --> 00:13:51,960 Speaker 1: the one of the things that's you know, interesting to 237 00:13:52,000 --> 00:13:54,800 Speaker 1: me and I asked about all the time, is accuracy, right, 238 00:13:55,240 --> 00:13:59,640 Speaker 1: And so fifteen years ago, where the forecasting was about 239 00:13:59,679 --> 00:14:03,959 Speaker 1: a not much better than a coin flip, it's about accuracy. 240 00:14:04,480 --> 00:14:09,360 Speaker 1: Jump forward to today, it's about accurate. In the last 241 00:14:09,400 --> 00:14:11,640 Speaker 1: year with the accuracy is improved more than in ten 242 00:14:11,720 --> 00:14:15,120 Speaker 1: years prior. AI is a huge part of that, and 243 00:14:15,240 --> 00:14:17,600 Speaker 1: so we hope that we're gonna be able to take 244 00:14:17,679 --> 00:14:21,120 Speaker 1: these recommendations for decisions all the way down to individuals 245 00:14:21,200 --> 00:14:23,720 Speaker 1: like you write is literal leagu practice is going to 246 00:14:23,760 --> 00:14:26,280 Speaker 1: be canceled. And you know this in the morning. You 247 00:14:26,320 --> 00:14:29,400 Speaker 1: don't find out thirty minutes before you You have to 248 00:14:29,440 --> 00:14:33,640 Speaker 1: adjust your schedule h and every decision in between. Cameron Clayton, 249 00:14:33,640 --> 00:14:35,880 Speaker 1: thanks so much for joining us. Cameron's a general manager 250 00:14:36,040 --> 00:14:39,040 Speaker 1: IBM Watson Media and Weather. Joining us here in our 251 00:14:39,040 --> 00:14:58,600 Speaker 1: Bloomberg Interactive Broker studio. Well, I really can't get over 252 00:14:58,600 --> 00:15:02,480 Speaker 1: the statistics that this statistic that automakers globally have it 253 00:15:02,760 --> 00:15:06,440 Speaker 1: announced at least thirty eight thousand job cuts in the 254 00:15:06,480 --> 00:15:09,760 Speaker 1: past six months, and I really I wonder what this 255 00:15:09,840 --> 00:15:12,800 Speaker 1: says about auto sales going forward and how much they 256 00:15:12,800 --> 00:15:15,920 Speaker 1: are poised to decline. Joining us now, Michelle Krabs, executive 257 00:15:15,920 --> 00:15:20,120 Speaker 1: analyst at auto trader dot Com based in Detroit. Michelle, 258 00:15:20,160 --> 00:15:22,520 Speaker 1: thank you so much for joining us. Let's start there. 259 00:15:22,880 --> 00:15:26,080 Speaker 1: How much do you expect auto sales to decline going forward? 260 00:15:26,080 --> 00:15:29,400 Speaker 1: And we're talking on a global basis, Um, Well, we 261 00:15:29,560 --> 00:15:32,280 Speaker 1: expect them to slip a little bit. We do not 262 00:15:32,480 --> 00:15:35,560 Speaker 1: expect any kind of collapse in the market, but we 263 00:15:35,640 --> 00:15:38,360 Speaker 1: do expect it to edge downward. You know, We've been 264 00:15:38,400 --> 00:15:41,720 Speaker 1: on a great run here, almost ten years of a 265 00:15:41,800 --> 00:15:45,280 Speaker 1: year over year increases, and you know it's the market's 266 00:15:45,320 --> 00:15:49,040 Speaker 1: kind of peaked. We expect sales for in the US 267 00:15:49,120 --> 00:15:53,040 Speaker 1: to be down two or three percent. So, Michelle, what's 268 00:15:53,080 --> 00:15:55,200 Speaker 1: the impact. I know it's probably early, but what are 269 00:15:55,200 --> 00:15:57,760 Speaker 1: you hearing from some of the manufacturers and some of 270 00:15:57,760 --> 00:16:00,240 Speaker 1: the dealers that you talk with about terror of It 271 00:16:00,280 --> 00:16:04,120 Speaker 1: seemed to be on again, off again for the automobile sector. Well, yes, 272 00:16:04,480 --> 00:16:07,160 Speaker 1: you know, I think everyone would like certainty and they 273 00:16:07,160 --> 00:16:10,240 Speaker 1: would like certainty that there there are no terroriffs. Um, 274 00:16:10,280 --> 00:16:13,000 Speaker 1: we see interesting patterns when there's a lot of talk 275 00:16:13,080 --> 00:16:15,160 Speaker 1: leading up to it, like last summer, we saw a 276 00:16:15,200 --> 00:16:19,080 Speaker 1: little bit of stockpiling of inventory, stockpiling of used cars 277 00:16:19,120 --> 00:16:22,920 Speaker 1: by dealers as they thought that tariffs would happen, and 278 00:16:22,920 --> 00:16:24,840 Speaker 1: then they didn't. And again here we are in a 279 00:16:24,840 --> 00:16:28,160 Speaker 1: waiting period for six months, and we if it really 280 00:16:28,200 --> 00:16:31,480 Speaker 1: becomes looks like they will happen, we expect we'll see 281 00:16:31,520 --> 00:16:34,480 Speaker 1: some more of that stockpiling of inventory, both new and 282 00:16:34,640 --> 00:16:40,360 Speaker 1: used vehicles going forward. So Memorial Day weekend is definitely 283 00:16:40,600 --> 00:16:44,000 Speaker 1: a big one for selling cars. And I'm just wondering 284 00:16:44,360 --> 00:16:47,040 Speaker 1: what kind of discounts you're expecting this year because this 285 00:16:47,080 --> 00:16:50,160 Speaker 1: has sort of been one big point of contention for 286 00:16:50,400 --> 00:16:52,760 Speaker 1: the big automakers. How much do you give incentives to 287 00:16:52,840 --> 00:16:55,760 Speaker 1: people to get in the door versus give up profits 288 00:16:56,520 --> 00:16:58,800 Speaker 1: and rather than just sort of sell things at full 289 00:16:58,800 --> 00:17:03,320 Speaker 1: price your right. Memorial Day weekend traditionally has been a 290 00:17:03,320 --> 00:17:06,119 Speaker 1: big sales lift weekend for automakers. We don't think it 291 00:17:06,119 --> 00:17:08,840 Speaker 1: will be as much so this year because of that 292 00:17:08,960 --> 00:17:12,320 Speaker 1: overall decline in the market and the fact that car 293 00:17:12,400 --> 00:17:16,040 Speaker 1: prices are at their highest level. UM, auto loan rates 294 00:17:16,040 --> 00:17:18,640 Speaker 1: are at a nine year high, and UM we are 295 00:17:18,720 --> 00:17:22,400 Speaker 1: not seeing a lot of uh laboring on of incentives. 296 00:17:22,400 --> 00:17:26,200 Speaker 1: They're very targeted, and automakers have been showing a lot 297 00:17:26,240 --> 00:17:30,720 Speaker 1: of restraint in terms of going for profit versus incentives. 298 00:17:30,720 --> 00:17:34,200 Speaker 1: So there aren't going to be fabulous deals out there, 299 00:17:34,560 --> 00:17:39,240 Speaker 1: uh for consumers. So we'll see how the weekend ends up. So, Michelle, 300 00:17:39,240 --> 00:17:41,240 Speaker 1: talk to us a little bit about the used car market. 301 00:17:41,240 --> 00:17:43,119 Speaker 1: I'm always surprised when I see, like you know, the 302 00:17:43,200 --> 00:17:46,200 Speaker 1: number of like sixty or six people are leaning towards 303 00:17:46,200 --> 00:17:48,600 Speaker 1: a used car versus a new car. What's the status 304 00:17:48,600 --> 00:17:51,520 Speaker 1: of that market? Well, it is an interesting market. We 305 00:17:51,720 --> 00:17:54,640 Speaker 1: focus all our energy on the new car market, which 306 00:17:54,680 --> 00:17:58,680 Speaker 1: is about seventeen million sales a year. Typically used car 307 00:17:58,720 --> 00:18:01,919 Speaker 1: sales are almost four million every year and a pretty 308 00:18:01,920 --> 00:18:05,159 Speaker 1: consistent UM. There's a lot of interest in used cars 309 00:18:05,200 --> 00:18:07,640 Speaker 1: for some of the reasons I mentioned before. New car 310 00:18:07,720 --> 00:18:11,520 Speaker 1: prices are at record highs uh and there are a 311 00:18:11,600 --> 00:18:16,040 Speaker 1: huge number of fabulous used cars because we had record leasing, 312 00:18:16,240 --> 00:18:18,679 Speaker 1: those cars are coming back onto the used car market, 313 00:18:19,080 --> 00:18:22,000 Speaker 1: and there's a richer mix of the kinds of vehicles, 314 00:18:22,040 --> 00:18:24,399 Speaker 1: so there's not only cars which used to be the 315 00:18:24,440 --> 00:18:27,560 Speaker 1: only thing that was leased released and then UH sport 316 00:18:27,640 --> 00:18:30,560 Speaker 1: utili vehicles which are really in favor with consumers and 317 00:18:30,560 --> 00:18:34,240 Speaker 1: and a hugely discounted prices because they're three years old. 318 00:18:34,560 --> 00:18:36,480 Speaker 1: That's what I was going to ask, is the used 319 00:18:36,480 --> 00:18:38,720 Speaker 1: car values. I know this was something that was really 320 00:18:39,080 --> 00:18:42,680 Speaker 1: UH sinking Hurts and Avis a couple of years ago 321 00:18:42,760 --> 00:18:45,119 Speaker 1: because of the glut of leasing and then the cars 322 00:18:45,160 --> 00:18:47,880 Speaker 1: that went into the used car market. I'm just wondering 323 00:18:48,080 --> 00:18:50,480 Speaker 1: whether we've seen a real firming up there of those 324 00:18:50,520 --> 00:18:54,760 Speaker 1: resale values. Yes, we have and UH and interestingly, you know, 325 00:18:54,840 --> 00:18:56,920 Speaker 1: cars are kind of about a favor on the new 326 00:18:56,920 --> 00:18:59,560 Speaker 1: car side, uh, and so we've seen a lot of 327 00:18:59,560 --> 00:19:02,040 Speaker 1: discount there, but if you look at the used car side, 328 00:19:02,040 --> 00:19:06,479 Speaker 1: there's that used car prices I've actually risen higher because 329 00:19:06,480 --> 00:19:08,760 Speaker 1: there's such high demand. And of course some of the 330 00:19:08,760 --> 00:19:11,359 Speaker 1: automakers have gotten out of the traditional car business and 331 00:19:11,440 --> 00:19:16,040 Speaker 1: so there's uh, there's less availability there, so tremendous interest 332 00:19:16,080 --> 00:19:19,959 Speaker 1: and use cars and that keeps prices strong. So, Michelle, 333 00:19:19,960 --> 00:19:22,440 Speaker 1: you mentioned that the you know, the market has really moved, 334 00:19:22,480 --> 00:19:24,600 Speaker 1: certainly in the US and new car markets and trucks 335 00:19:24,640 --> 00:19:27,760 Speaker 1: and SUVs, and UM, it give us a sense of 336 00:19:27,880 --> 00:19:32,679 Speaker 1: how sensitive those sales are too. Changes in gasoline prices 337 00:19:32,680 --> 00:19:35,200 Speaker 1: were just noting earlier today that oil is down about 338 00:19:35,240 --> 00:19:38,200 Speaker 1: five percent today, So it's kind of fluctuating. But boy, 339 00:19:38,200 --> 00:19:41,400 Speaker 1: it just seems like the U s consumers trucks and SUVs. 340 00:19:41,400 --> 00:19:44,280 Speaker 1: But that's not just the US. Globally, the shift is 341 00:19:44,320 --> 00:19:47,640 Speaker 1: more towards sport utility vehicles. That's true in China, that's 342 00:19:47,640 --> 00:19:51,280 Speaker 1: true true in uh Europe as well. UM. I think 343 00:19:51,600 --> 00:19:54,280 Speaker 1: the automakers don't get enough credit that they have done 344 00:19:54,320 --> 00:19:59,280 Speaker 1: an amazing job of improving fuel economy of sport utility vehicles. 345 00:19:59,320 --> 00:20:02,000 Speaker 1: It used to be that was the reason people didn't 346 00:20:02,000 --> 00:20:04,800 Speaker 1: buy them. That's kind of gone away. There's there's not 347 00:20:04,960 --> 00:20:10,000 Speaker 1: this huge penalty for in terms of fuel price at 348 00:20:10,000 --> 00:20:13,080 Speaker 1: fuel efficiency, and yet there's a ton of other advantage 349 00:20:13,080 --> 00:20:19,760 Speaker 1: of you know, various combinations of passengers and cargo and 350 00:20:19,800 --> 00:20:22,840 Speaker 1: that higher seating position, which is what everybody likes because 351 00:20:22,840 --> 00:20:26,080 Speaker 1: they feel safer, they can see better, So it offsets 352 00:20:26,119 --> 00:20:29,239 Speaker 1: the advantages understood. Michelle Krebs, thanks so much for joining us. 353 00:20:29,240 --> 00:20:47,560 Speaker 1: Michelle's executive analyst at auto trader dot com based in Detroit. Well, 354 00:20:47,600 --> 00:20:50,439 Speaker 1: the FED seemingly has navigated the US economy to what 355 00:20:50,560 --> 00:20:54,040 Speaker 1: can be described as perhaps a soft landing with modest 356 00:20:54,280 --> 00:20:57,160 Speaker 1: economic growth and limited inflation. To get some insight as 357 00:20:57,160 --> 00:21:00,000 Speaker 1: to the FEDS thinking, returned to Danielle di Martino Booth. 358 00:21:00,320 --> 00:21:03,920 Speaker 1: Danielle CEO and director of Intelligence for Quill Intelligence. She's 359 00:21:03,960 --> 00:21:06,679 Speaker 1: also a former advisor that Dallas Fed to Reserve, and 360 00:21:06,720 --> 00:21:09,520 Speaker 1: she's a Bloomberg opinion columnist. Danielle, thanks so much for 361 00:21:09,680 --> 00:21:13,879 Speaker 1: being with us. What is your sense of what you 362 00:21:13,920 --> 00:21:16,080 Speaker 1: think the FED will do next? I'm looking at the 363 00:21:16,280 --> 00:21:18,520 Speaker 1: w I RP function and we just chatted about briefly 364 00:21:18,680 --> 00:21:22,400 Speaker 1: before again a seventy nine, a chance for a rate 365 00:21:22,600 --> 00:21:24,359 Speaker 1: rate cut by the end of the year. Do you 366 00:21:24,480 --> 00:21:27,000 Speaker 1: agree with that, Well, it doesn't really matter if I 367 00:21:27,040 --> 00:21:29,600 Speaker 1: agree with it. What matters is FED history, and FED 368 00:21:29,640 --> 00:21:32,560 Speaker 1: history tells us really anytime you get over the line, 369 00:21:33,000 --> 00:21:35,760 Speaker 1: they really start talking about whatever direction it's going to be, 370 00:21:35,760 --> 00:21:38,520 Speaker 1: whether it's it's going to be easy or tightening. Once 371 00:21:38,600 --> 00:21:42,439 Speaker 1: you get past sixty probability, it's kind of in the bag. 372 00:21:42,840 --> 00:21:46,119 Speaker 1: So I would be surprised if between here and blackout, 373 00:21:46,240 --> 00:21:49,320 Speaker 1: before the June fo MC, we didn't start to see 374 00:21:49,880 --> 00:21:52,399 Speaker 1: a little bit more descent in terms of FED speak, 375 00:21:52,480 --> 00:21:57,080 Speaker 1: because they have been so consistent across the entire committee 376 00:21:57,080 --> 00:22:00,520 Speaker 1: they've had. They've all been towing the Clarita line about 377 00:22:01,040 --> 00:22:03,639 Speaker 1: we're gonna let inflation run too hot. Right now, the 378 00:22:03,680 --> 00:22:06,520 Speaker 1: market is telling us that there's there's nothing hot about it. 379 00:22:07,119 --> 00:22:10,240 Speaker 1: Inflation needs a pachmina. It's running so cold, and it's 380 00:22:10,400 --> 00:22:12,520 Speaker 1: looking more and more like and I think the bond 381 00:22:12,560 --> 00:22:14,840 Speaker 1: market is telling us break evens are telling us that 382 00:22:15,000 --> 00:22:17,679 Speaker 1: this is not transient and that the core PC is 383 00:22:17,680 --> 00:22:20,359 Speaker 1: going to be pulled down further. So let's dig into 384 00:22:20,400 --> 00:22:23,200 Speaker 1: that issue. Because according to the minutes that came out 385 00:22:23,320 --> 00:22:26,680 Speaker 1: yesterday of the last for MC meeting. It does seem 386 00:22:26,760 --> 00:22:30,720 Speaker 1: like members do believe that this is a transient sort 387 00:22:30,720 --> 00:22:35,800 Speaker 1: of dipping inflation in a weakening Why does the market disagree. Well, 388 00:22:35,800 --> 00:22:39,280 Speaker 1: it's interesting because not all participants on the Federal Open 389 00:22:39,320 --> 00:22:43,199 Speaker 1: Market Committee agree. There was there's some fine print in 390 00:22:43,200 --> 00:22:46,879 Speaker 1: the minutes yesterday that said several participants were concerned that 391 00:22:47,119 --> 00:22:49,879 Speaker 1: inflation was not transitory, and then in fact CORPC was 392 00:22:50,000 --> 00:22:53,920 Speaker 1: going to be pulled lower. But decoding FETE speak, participants 393 00:22:53,960 --> 00:22:58,840 Speaker 1: mean non voting federals or of district presidents. So again, 394 00:22:59,000 --> 00:23:03,840 Speaker 1: I think that the Fed wants for inflation to be transitory, 395 00:23:03,960 --> 00:23:06,439 Speaker 1: but wanting and getting are two different things. When you 396 00:23:06,480 --> 00:23:09,960 Speaker 1: start to see things like this morning's market composite data, 397 00:23:10,200 --> 00:23:13,399 Speaker 1: it's a composite. It's at fifty point nine new orders 398 00:23:13,440 --> 00:23:16,680 Speaker 1: in the manufacturing went negative for the first time. I mean, 399 00:23:16,680 --> 00:23:18,600 Speaker 1: that's about the clearest signal you can get. We'll see 400 00:23:18,600 --> 00:23:21,800 Speaker 1: if it's validated when the m numbers hit um. But 401 00:23:21,880 --> 00:23:24,840 Speaker 1: if you start to see bleeding from this kind of 402 00:23:25,000 --> 00:23:28,119 Speaker 1: isolated weakness that we've had in the factory sector sector 403 00:23:28,320 --> 00:23:32,439 Speaker 1: into services, you will have a downward poll on that 404 00:23:32,520 --> 00:23:35,480 Speaker 1: core PC. Let's talk about I want to talk about 405 00:23:35,520 --> 00:23:37,399 Speaker 1: one of your colums that you wrote just recently about 406 00:23:37,400 --> 00:23:40,000 Speaker 1: the consumers, because the consumers such an obviously a big 407 00:23:40,000 --> 00:23:43,280 Speaker 1: part of the economy, a big part of the growth story, um, 408 00:23:43,320 --> 00:23:47,040 Speaker 1: but you arguing that perhaps the consumer isn't as strong 409 00:23:47,080 --> 00:23:49,400 Speaker 1: as maybe we're led to believe. What are your thoughts 410 00:23:49,400 --> 00:23:51,440 Speaker 1: on that? Well, I think what you need to do 411 00:23:51,600 --> 00:23:53,679 Speaker 1: is to look at the composition of jobs that have 412 00:23:53,760 --> 00:23:59,400 Speaker 1: been created so last October, for example, the vast preponderance 413 00:23:59,440 --> 00:24:02,800 Speaker 1: of jobs that we're in high paying industries, and by 414 00:24:02,880 --> 00:24:05,040 Speaker 1: time we rolled around to the April data, you know, 415 00:24:05,080 --> 00:24:08,080 Speaker 1: we're seeing sixty pc of the jobs being created in 416 00:24:08,160 --> 00:24:12,040 Speaker 1: low paying industries. So it's not going to show up 417 00:24:12,200 --> 00:24:16,680 Speaker 1: yet in something like initial jobless claims. Employers have worked 418 00:24:16,800 --> 00:24:21,200 Speaker 1: so hard to source skilled workers that they're holding onto 419 00:24:21,200 --> 00:24:23,240 Speaker 1: them for dear life. But we're seeing weakness in the 420 00:24:23,320 --> 00:24:26,160 Speaker 1: number of hours that are worked. We're seeing a weakening 421 00:24:26,160 --> 00:24:30,439 Speaker 1: trend and temporary employment. And again, wage inflation has started 422 00:24:30,480 --> 00:24:33,080 Speaker 1: to come back in. As opposed to what most economists 423 00:24:33,080 --> 00:24:36,439 Speaker 1: were predicting, say six months ago, that we were finally 424 00:24:36,440 --> 00:24:39,520 Speaker 1: seeing traction and wage inflation. That is not the case. 425 00:24:39,960 --> 00:24:43,240 Speaker 1: But couldn't you argue, if you look at the consumers balance, 426 00:24:43,320 --> 00:24:46,600 Speaker 1: she looks pretty good. Interest payments are still fairly low. 427 00:24:47,000 --> 00:24:50,520 Speaker 1: The actual amount of debt relative to GDP hasn't climbed 428 00:24:50,520 --> 00:24:53,320 Speaker 1: that much. When you talk about the consumer, this isn't 429 00:24:53,320 --> 00:24:55,800 Speaker 1: a bad picture. And yes, we have seen dilinguacies take 430 00:24:55,880 --> 00:24:58,119 Speaker 1: up with credit card and auto loans, but not necessarily 431 00:24:58,160 --> 00:25:00,000 Speaker 1: to the extent where you start to get really concerned 432 00:25:00,080 --> 00:25:02,760 Speaker 1: about something. Couldn't you say this has been a very 433 00:25:02,880 --> 00:25:06,440 Speaker 1: unusual economic cycle, but one that is actually allowed things 434 00:25:06,560 --> 00:25:10,480 Speaker 1: to grow and to possibly soften at a slow and 435 00:25:10,560 --> 00:25:14,879 Speaker 1: steady pace. That's kind of healthy. It has been. But 436 00:25:15,560 --> 00:25:18,880 Speaker 1: I tend to think about the delta and the rate 437 00:25:18,960 --> 00:25:25,040 Speaker 1: at which servicing household credit has come up is extremely problematic. 438 00:25:25,040 --> 00:25:27,439 Speaker 1: Deutsche Banks done some good work on this, Torsten slock 439 00:25:28,000 --> 00:25:32,120 Speaker 1: And and right now you're getting to where households are 440 00:25:32,160 --> 00:25:35,680 Speaker 1: spending so much more of their income to service their debt, 441 00:25:36,280 --> 00:25:40,000 Speaker 1: and that's coming so quickly that that is becoming It's 442 00:25:40,000 --> 00:25:43,080 Speaker 1: not the absolute level of household debt, it's it's it's 443 00:25:43,280 --> 00:25:46,000 Speaker 1: it's the rate of which the increases is happening. I 444 00:25:46,040 --> 00:25:48,120 Speaker 1: have to wonder, though, how much is this an income 445 00:25:48,200 --> 00:25:51,600 Speaker 1: inequality story? Because how much is this uh the very 446 00:25:51,640 --> 00:25:54,480 Speaker 1: low end incurring debt and then sort of trying to 447 00:25:54,560 --> 00:25:58,720 Speaker 1: cycle and make payments versus an average story, a story 448 00:25:58,720 --> 00:26:02,080 Speaker 1: of averages. Well, you know, that's a good point, but 449 00:26:02,160 --> 00:26:05,800 Speaker 1: I would I would go back to a the the 450 00:26:05,840 --> 00:26:09,800 Speaker 1: economy that we have today, um and that in many ways. 451 00:26:10,000 --> 00:26:11,960 Speaker 1: Moodies did an interesting study at the end of last 452 00:26:12,040 --> 00:26:15,160 Speaker 1: year that said initial jobless claims are understated by about 453 00:26:16,520 --> 00:26:18,880 Speaker 1: because we have so many people who were self employed, 454 00:26:18,960 --> 00:26:21,480 Speaker 1: We have so many people who who drive for a living, 455 00:26:22,200 --> 00:26:27,160 Speaker 1: that particular area the self employed is getting crushed right now. 456 00:26:27,680 --> 00:26:30,280 Speaker 1: But if you work for yourself a, you're not gonna 457 00:26:30,359 --> 00:26:32,560 Speaker 1: put an ad out to hire yourself and be you're 458 00:26:32,560 --> 00:26:36,280 Speaker 1: not going to file unemployment insurance against yourself. So there's 459 00:26:36,320 --> 00:26:38,639 Speaker 1: an element I think that's not being picked up in 460 00:26:38,680 --> 00:26:42,000 Speaker 1: the averages right now that's harder to see, harder to discern. 461 00:26:42,400 --> 00:26:44,760 Speaker 1: And you put on top of that the tax season 462 00:26:44,800 --> 00:26:47,359 Speaker 1: that we've just come through and the fact that I 463 00:26:47,359 --> 00:26:51,520 Speaker 1: think a lot of middle income American families ended up 464 00:26:51,800 --> 00:26:54,240 Speaker 1: not getting a refund but actually having to pay taxes 465 00:26:54,720 --> 00:26:57,240 Speaker 1: that was unexpected as well. We've seen what a court 466 00:26:57,240 --> 00:27:00,160 Speaker 1: of American families are not have have decided against taking 467 00:27:00,160 --> 00:27:04,560 Speaker 1: a vacation this summer. Those types of anecdotes will show 468 00:27:04,640 --> 00:27:06,520 Speaker 1: up in the data. I mean, you're not canceling the 469 00:27:06,560 --> 00:27:09,919 Speaker 1: trip to Disney for no reason unless the taxes you 470 00:27:10,000 --> 00:27:13,359 Speaker 1: paid that you weren't expecting to pay were in fact 471 00:27:13,440 --> 00:27:17,280 Speaker 1: your vacation fund. Daniel, we're seeing wage inflation over the 472 00:27:17,359 --> 00:27:19,399 Speaker 1: last couple of reports of you know, low three percent 473 00:27:19,560 --> 00:27:22,800 Speaker 1: kind of range. To me, that seems low given where 474 00:27:22,840 --> 00:27:25,639 Speaker 1: I think we're really full of employment. What do you 475 00:27:25,640 --> 00:27:29,399 Speaker 1: think I think wage inflation should be much hotter than 476 00:27:29,600 --> 00:27:33,320 Speaker 1: what it is. Uh. And in fact, we've seen average 477 00:27:33,320 --> 00:27:36,320 Speaker 1: weekly earnings. I prefer to steer clear of average hourly 478 00:27:36,320 --> 00:27:38,639 Speaker 1: earnings because I don't take home and hours pay. I 479 00:27:38,920 --> 00:27:42,080 Speaker 1: take them a paycheck which I earn over a weekly period. 480 00:27:42,400 --> 00:27:47,360 Speaker 1: That particular number, again, since October, has shown serious deterioration 481 00:27:47,400 --> 00:27:51,639 Speaker 1: and weakening. So thank you so much for being here. Honestly, 482 00:27:51,680 --> 00:27:53,879 Speaker 1: you raised really good points, and especially right now as 483 00:27:53,920 --> 00:27:56,280 Speaker 1: we look at the market activity with a nearly eight 484 00:27:56,760 --> 00:27:58,919 Speaker 1: chance of a rate cut being priced into markets, a 485 00:27:59,000 --> 00:28:02,600 Speaker 1: real disagreement right now between markets and FED participants. Where 486 00:28:02,600 --> 00:28:05,959 Speaker 1: you've got fed saying you know what, inflation is just 487 00:28:06,160 --> 00:28:09,520 Speaker 1: simply cooling off as a temporary factor. The market saying 488 00:28:10,320 --> 00:28:13,760 Speaker 1: we're not buying it. Danielle di Martino Booth. She, of course, 489 00:28:13,840 --> 00:28:16,720 Speaker 1: is a Bloomberg opinion columnist. She's chief executive officer of 490 00:28:16,800 --> 00:28:20,719 Speaker 1: Quill Intelligence, former advisor to the Dallas Fed. Joining us 491 00:28:20,720 --> 00:28:24,640 Speaker 1: here in our Bloomberg Interactive Broker's Studios. Thanks for listening 492 00:28:24,680 --> 00:28:27,080 Speaker 1: to the Bloomberg P and L podcast. You can subscribe 493 00:28:27,080 --> 00:28:29,920 Speaker 1: and listen to interviews at Apple Podcasts or whatever podcast 494 00:28:29,920 --> 00:28:33,480 Speaker 1: platform you prefer. Paul Sweeney, I'm on Twitter at pt Sweeney. 495 00:28:33,520 --> 00:28:36,040 Speaker 1: I'm Lisa abram Woit's I'm on Twitter at Lisa abram 496 00:28:36,040 --> 00:28:38,640 Speaker 1: woits one before the podcast. You can always catch us 497 00:28:38,720 --> 00:28:40,280 Speaker 1: worldwide on Bloomberg Radio.