1 00:00:02,520 --> 00:00:13,760 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is the Bloomberg 2 00:00:13,840 --> 00:00:17,920 Speaker 1: Surveillance Podcast. Catch us live weekdays at seven am Eastern 3 00:00:18,200 --> 00:00:22,000 Speaker 1: on Apple CarPlay or Android Auto with the Bloomberg Business App. 4 00:00:22,360 --> 00:00:25,680 Speaker 1: Listen on demand wherever you get your podcasts, or watch 5 00:00:25,760 --> 00:00:27,040 Speaker 1: us live on YouTube. 6 00:00:27,480 --> 00:00:30,720 Speaker 2: Constancena joins us right now, thrilled that she could be 7 00:00:30,760 --> 00:00:37,080 Speaker 2: with us today. I look at where we are, and BOYD, 8 00:00:37,120 --> 00:00:40,960 Speaker 2: does it reaffirm? I'm gonna call it Wallerian rate cuts? 9 00:00:41,640 --> 00:00:44,640 Speaker 2: Are we now generating rate cuts into the first quarter 10 00:00:44,680 --> 00:00:45,400 Speaker 2: of next year? 11 00:00:46,760 --> 00:00:49,319 Speaker 3: Well? I think I said in my note to you 12 00:00:49,560 --> 00:00:53,200 Speaker 3: that whatever we see in today's CPI is not going 13 00:00:53,240 --> 00:00:55,640 Speaker 3: to reflect what's coming down the pike. 14 00:00:55,920 --> 00:00:56,200 Speaker 4: Right. 15 00:00:56,320 --> 00:01:00,400 Speaker 3: We have seen companies absorb a lot of the tariffs 16 00:01:00,480 --> 00:01:03,200 Speaker 3: because there's so much uncertainty. And why would you pass 17 00:01:03,240 --> 00:01:05,319 Speaker 3: on tariffs if you think that they're going to be 18 00:01:05,360 --> 00:01:08,720 Speaker 3: removed or negotiated lower. You're just going to impair your 19 00:01:08,760 --> 00:01:11,840 Speaker 3: market share and impair your brand. And I think that 20 00:01:12,040 --> 00:01:14,479 Speaker 3: is about to come to an end, and we're going 21 00:01:14,520 --> 00:01:16,920 Speaker 3: to see that in future CPI reports. 22 00:01:17,160 --> 00:01:19,479 Speaker 2: With that said, the FED is going to. 23 00:01:19,400 --> 00:01:22,480 Speaker 3: Cut next week, and this certainly gives them cover for 24 00:01:22,600 --> 00:01:26,040 Speaker 3: next week and possibly even to cut again in December. 25 00:01:26,560 --> 00:01:29,720 Speaker 2: Are we anywhere near the territory of a fifty basis 26 00:01:29,720 --> 00:01:32,600 Speaker 2: point right and cut cool? We have a dearth of data, 27 00:01:33,440 --> 00:01:35,480 Speaker 2: But I mean, come on, if we were just to 28 00:01:35,520 --> 00:01:38,640 Speaker 2: make it up, folks, it's Friday helping her folks. If 29 00:01:38,680 --> 00:01:40,840 Speaker 2: we made it up Constance and we said we got 30 00:01:40,880 --> 00:01:44,639 Speaker 2: a fifty seven thousand non farm payroll and then maybe 31 00:01:44,640 --> 00:01:47,880 Speaker 2: next month we get a forty two thousand non farm payroll, 32 00:01:48,480 --> 00:01:50,520 Speaker 2: I mean, come on, they got to get it going, 33 00:01:50,680 --> 00:01:52,360 Speaker 2: don't they. 34 00:01:52,560 --> 00:01:56,040 Speaker 3: Well, it's interesting, and I'm actually sitting here in Michigan 35 00:01:57,240 --> 00:02:01,520 Speaker 3: where close to GM where the where you mentioned those layoffs, 36 00:02:01,840 --> 00:02:06,360 Speaker 3: And certainly if you look at at a state like Michigan, 37 00:02:06,440 --> 00:02:08,760 Speaker 3: it's got a higher rate of unemployment than the rest 38 00:02:08,800 --> 00:02:12,519 Speaker 3: of the country. It's definitely suffering. And it has been 39 00:02:12,560 --> 00:02:15,560 Speaker 3: our view that the FED is going to look at 40 00:02:15,639 --> 00:02:21,040 Speaker 3: labor markets more than inflation, because they're going to look 41 00:02:21,200 --> 00:02:24,919 Speaker 3: through that that CPI increase that we do eventually expect 42 00:02:24,960 --> 00:02:29,760 Speaker 3: from tariffs, and Paul to say, the private data suggests 43 00:02:29,800 --> 00:02:31,720 Speaker 3: that the job market is weakening as well. Right in 44 00:02:31,760 --> 00:02:34,680 Speaker 3: the absence of government data, we have the ism we 45 00:02:34,760 --> 00:02:36,720 Speaker 3: have ADP we have. 46 00:02:36,800 --> 00:02:40,840 Speaker 2: Indeed, Paul I just figured it out. Constance Hunter is 47 00:02:40,880 --> 00:02:44,280 Speaker 2: going with Diane Swack to the Michigan Michigan State. 48 00:02:44,120 --> 00:02:49,720 Speaker 5: Go compan Good, good, good, Constance. I mean again, I'm 49 00:02:49,760 --> 00:02:52,920 Speaker 5: just looking at the CPI data today. I'm just not 50 00:02:53,000 --> 00:02:56,840 Speaker 5: seeing this tariff induced inflation here. I know, seems like 51 00:02:56,840 --> 00:02:59,120 Speaker 5: maybe the companies are just kind of taking it in 52 00:02:59,200 --> 00:03:01,520 Speaker 5: a P and L and we are not going to 53 00:03:01,560 --> 00:03:03,120 Speaker 5: see tariff induced inflation. 54 00:03:04,560 --> 00:03:05,839 Speaker 2: Well, I don't think. 55 00:03:05,600 --> 00:03:09,280 Speaker 3: That that means we're never going to see it. On 56 00:03:09,320 --> 00:03:10,880 Speaker 3: the other hand, I mean, I guess there is the 57 00:03:10,919 --> 00:03:15,160 Speaker 3: possibility that firms find ways to be efficient and absorb 58 00:03:15,280 --> 00:03:18,720 Speaker 3: these tariff costs, but it seems somewhat unlikely, and firms, 59 00:03:18,760 --> 00:03:21,680 Speaker 3: if you survey them, have said they want to pass 60 00:03:21,760 --> 00:03:26,360 Speaker 3: on tariff price increases. But it's really challenging when you 61 00:03:26,400 --> 00:03:29,200 Speaker 3: think of what a CA shaped economy we are in, 62 00:03:29,320 --> 00:03:33,920 Speaker 3: right that top ten percent of consumers of wage earners 63 00:03:33,919 --> 00:03:36,800 Speaker 3: are also those that own equities and are participating in 64 00:03:36,840 --> 00:03:39,400 Speaker 3: this bowl market. Where's the bottom part of that k 65 00:03:39,640 --> 00:03:45,480 Speaker 3: We're starting to see increased delinquencies with credit cards and 66 00:03:45,600 --> 00:03:48,200 Speaker 3: auto loans. Those are levels that we saw coming out 67 00:03:48,240 --> 00:03:51,320 Speaker 3: of the global financial crisis, and that indicates that that 68 00:03:51,360 --> 00:03:53,600 Speaker 3: segment of the market is really struggling. 69 00:03:54,520 --> 00:03:57,360 Speaker 5: So where will we see that when we see that 70 00:03:57,400 --> 00:04:00,480 Speaker 5: in consumer spending? Will we see that in retail sales? 71 00:04:00,480 --> 00:04:01,400 Speaker 5: Where do you think we'll see that? 72 00:04:02,960 --> 00:04:04,640 Speaker 3: Well, we're not going to see it in retail sales 73 00:04:04,680 --> 00:04:07,520 Speaker 3: because it's not inflation adjusted. So if anything, we might 74 00:04:07,520 --> 00:04:09,880 Speaker 3: see retail sales go up a bit, but I think 75 00:04:10,000 --> 00:04:13,480 Speaker 3: we'll see it in the inflation adjusted consumer spending numbers. 76 00:04:14,520 --> 00:04:16,159 Speaker 3: Households are going to have to pull back. 77 00:04:16,560 --> 00:04:18,320 Speaker 2: And the other thing is, while it's not in. 78 00:04:18,240 --> 00:04:21,440 Speaker 3: This report, the anticipation is that we're going to see 79 00:04:21,520 --> 00:04:25,400 Speaker 3: upward pressure on utilities like electricity, which is going to 80 00:04:25,520 --> 00:04:28,440 Speaker 3: curb the ability of households to spend in other areas. 81 00:04:29,800 --> 00:04:33,400 Speaker 5: Labor market here, that's another mandate of this Federal Reserve. 82 00:04:34,480 --> 00:04:38,360 Speaker 5: The headline numbers look fine, but I know there are 83 00:04:38,480 --> 00:04:40,960 Speaker 5: definitely some concerns underneath the hood there. How do you 84 00:04:41,240 --> 00:04:42,599 Speaker 5: think about the US labor market? 85 00:04:44,360 --> 00:04:48,320 Speaker 3: Well, the labor market is weakening because we're seeing a 86 00:04:48,360 --> 00:04:51,120 Speaker 3: declining breadth that we have seen for some time now. 87 00:04:51,200 --> 00:04:53,600 Speaker 3: I'm looking at them talking about the August data. Of course, 88 00:04:53,600 --> 00:04:57,200 Speaker 3: we don't have that September data. And then if we 89 00:04:57,279 --> 00:05:00,720 Speaker 3: look at again alternative indicators. So so if you look 90 00:05:00,760 --> 00:05:04,440 Speaker 3: at the ISM employment indicator, that's been a very good 91 00:05:04,560 --> 00:05:07,520 Speaker 3: especially on service, is a very good predictor of overall 92 00:05:08,520 --> 00:05:11,760 Speaker 3: jobs growth, and that also is weak. And then we're 93 00:05:11,880 --> 00:05:16,760 Speaker 3: seeing a pullback in state and local hiring. The state 94 00:05:16,800 --> 00:05:21,120 Speaker 3: and local municipalities received huge windfall money from the rise 95 00:05:21,160 --> 00:05:25,560 Speaker 3: in real estate prices, and they were hiring a deficit 96 00:05:25,600 --> 00:05:27,640 Speaker 3: of workers that they didn't hire coming out of the 97 00:05:27,640 --> 00:05:29,440 Speaker 3: global financial crisis. 98 00:05:29,200 --> 00:05:32,560 Speaker 2: Comes as quickly. Here does a president deserve to be 99 00:05:32,720 --> 00:05:34,440 Speaker 2: impatient with the Fed? 100 00:05:35,360 --> 00:05:38,800 Speaker 3: Well, it depends on what you think the Fed's job is. 101 00:05:39,080 --> 00:05:39,279 Speaker 4: Right. 102 00:05:40,400 --> 00:05:42,520 Speaker 3: If you think the Fed's job is to move fast, 103 00:05:42,560 --> 00:05:47,000 Speaker 3: break things, be anticipatory, behave like a hedge fund, I 104 00:05:47,040 --> 00:05:52,160 Speaker 3: suppose yes. But by design, the FED is supposed. 105 00:05:51,600 --> 00:05:53,080 Speaker 6: To be a little late to. 106 00:05:53,120 --> 00:05:55,440 Speaker 2: React because if they react. 107 00:05:55,080 --> 00:05:59,640 Speaker 3: Preemptively and they're wrong, there's much more downside risk then 108 00:05:59,680 --> 00:06:03,279 Speaker 3: there than if they're fashionably late but still in the 109 00:06:03,360 --> 00:06:05,320 Speaker 3: right direction like that fashionly lady. 110 00:06:05,400 --> 00:06:09,159 Speaker 2: Yes, Constant, thank you so much. Constance Hunter. Coming to 111 00:06:09,240 --> 00:06:12,800 Speaker 2: us to the Economic Intelligence Unit today on this inflation 112 00:06:12,920 --> 00:06:16,600 Speaker 2: to review here REQUIESCE and report futures up twenty two 113 00:06:16,680 --> 00:06:20,080 Speaker 2: they double features up forty one, up sixtents of a percent, 114 00:06:20,400 --> 00:06:23,400 Speaker 2: Nazak one hundred and features up one percent, now a 115 00:06:23,600 --> 00:06:27,760 Speaker 2: point nine percent as well, the vics solidly under seventeen wow, 116 00:06:28,520 --> 00:06:31,560 Speaker 2: sixteen point sixty four. Speaking of well, let's do this 117 00:06:31,600 --> 00:06:35,000 Speaker 2: first Bloomberg surveillance. I'm so out of practice with their data, Paul, 118 00:06:35,160 --> 00:06:37,520 Speaker 2: I know, it's a shock to have data. He is 119 00:06:37,560 --> 00:06:40,640 Speaker 2: at the height of fashion at all time. It's good 120 00:06:40,640 --> 00:06:42,640 Speaker 2: to catch up and get a framework for the weekend 121 00:06:42,680 --> 00:06:46,200 Speaker 2: with Kit Jukes. He's had a foreign exchange strategy at 122 00:06:46,200 --> 00:06:50,040 Speaker 2: Society General. Kit just cut to the chase. What are 123 00:06:50,040 --> 00:06:53,760 Speaker 2: you going to write about for Monday within the geopolitics 124 00:06:53,760 --> 00:06:57,200 Speaker 2: that you see the litmus paper of the foreign exchange system? 125 00:06:57,480 --> 00:06:59,440 Speaker 2: What's your theme to get to Monday morning? 126 00:07:01,160 --> 00:07:03,880 Speaker 7: Going into Monday morning, I suspect it's going to be 127 00:07:04,640 --> 00:07:07,880 Speaker 7: the resilience of this strong dollar. In all honesty, even 128 00:07:08,000 --> 00:07:11,200 Speaker 7: just looking now over my shoulder at what's going on 129 00:07:11,560 --> 00:07:15,440 Speaker 7: since the release of a soft CPI print, the market 130 00:07:15,480 --> 00:07:17,160 Speaker 7: is not you know, it's going to price in some 131 00:07:17,560 --> 00:07:20,240 Speaker 7: rate cuts from the Fed with more enthusiasm at the 132 00:07:20,280 --> 00:07:22,840 Speaker 7: margin than before. We don't know much more than we did, 133 00:07:22,840 --> 00:07:25,880 Speaker 7: but we'll price a little bit more enthusiastically on easing. 134 00:07:26,400 --> 00:07:28,680 Speaker 7: And I'm not seeing the dollar backing off very much 135 00:07:28,680 --> 00:07:30,840 Speaker 7: at all on that because and it's. 136 00:07:31,080 --> 00:07:34,320 Speaker 2: Sorry, no, not continue, please please, I. 137 00:07:34,200 --> 00:07:36,880 Speaker 7: Was going to say, because the CPI numbers don't tell 138 00:07:36,920 --> 00:07:40,520 Speaker 7: us anything new about growth, and that's where the real 139 00:07:40,560 --> 00:07:42,600 Speaker 7: debate about the US economy is going to happen. 140 00:07:42,800 --> 00:07:45,320 Speaker 2: What you're so good at, kit, Is there a bet 141 00:07:45,360 --> 00:07:46,600 Speaker 2: in the market right now? 142 00:07:48,480 --> 00:07:51,640 Speaker 7: I think the market's betting on rate carts, but it's 143 00:07:51,760 --> 00:07:55,560 Speaker 7: also betting on resilient growth. That keeps me confused. And 144 00:07:55,600 --> 00:07:57,960 Speaker 7: I should add this is the most dollar centric, US 145 00:07:58,000 --> 00:08:00,840 Speaker 7: centric economic cycle that I can remember in ages because 146 00:08:01,040 --> 00:08:05,400 Speaker 7: the uncertainty around the US GDP numbers retail sales number 147 00:08:05,400 --> 00:08:08,120 Speaker 7: because sure prize numbers is much bigger than the uncertainty 148 00:08:08,120 --> 00:08:10,640 Speaker 7: about the lack of growth in Europe or in Asia. 149 00:08:10,760 --> 00:08:14,480 Speaker 7: So it's all about you, guys, and and we're getting 150 00:08:14,480 --> 00:08:17,000 Speaker 7: no information. So when we get some we think the 151 00:08:17,360 --> 00:08:20,800 Speaker 7: we think the labor market's weakening, but not knowing more 152 00:08:20,560 --> 00:08:25,160 Speaker 7: where I think collectively assuming that we shouldn't be revising 153 00:08:25,200 --> 00:08:28,280 Speaker 7: down GDP forecasts so that there's lots of productivity, there's 154 00:08:28,320 --> 00:08:30,280 Speaker 7: lots of ail, there's lots of good things. The equity 155 00:08:30,280 --> 00:08:33,960 Speaker 7: market's still strong. So US exceptionalism is still in place. 156 00:08:34,280 --> 00:08:38,640 Speaker 7: That bet is there. Today's soft CPI prints will encourage 157 00:08:38,920 --> 00:08:42,240 Speaker 7: rate cuts, but they won't change that optimism about the 158 00:08:42,280 --> 00:08:43,319 Speaker 7: economy is my bet. 159 00:08:43,600 --> 00:08:46,320 Speaker 5: And Kit it's interesting for me at least to hear 160 00:08:46,400 --> 00:08:48,160 Speaker 5: you say it's it's it's still all about the US, 161 00:08:48,280 --> 00:08:51,960 Speaker 5: US exceptionalism because the trade has been that. It's you know, 162 00:08:51,960 --> 00:08:53,959 Speaker 5: it's kind of about you guys over in Europe. I mean, 163 00:08:54,000 --> 00:08:58,520 Speaker 5: you're starting to spend some money on defense and infrastructure, 164 00:08:58,679 --> 00:09:01,160 Speaker 5: and you know you can't really depend upon the US 165 00:09:01,200 --> 00:09:03,679 Speaker 5: to the extent that you did maybe you did before. 166 00:09:04,800 --> 00:09:06,320 Speaker 5: How do you think about that dynamic? Because we do 167 00:09:06,400 --> 00:09:10,319 Speaker 5: have the euro again add at one sixteen here, well, the. 168 00:09:10,240 --> 00:09:12,360 Speaker 7: Europopped up to one sixteen on a move down in 169 00:09:12,480 --> 00:09:16,360 Speaker 7: US rate expectations that narrawed the gap significantly, on some 170 00:09:16,400 --> 00:09:19,480 Speaker 7: optimism in growth. Obviously the fiscal problems in France haven't 171 00:09:19,520 --> 00:09:23,600 Speaker 7: helped on that. And equally that there's a there's not 172 00:09:23,640 --> 00:09:26,520 Speaker 7: a huge amount of you know, there's hope, but if 173 00:09:26,520 --> 00:09:28,719 Speaker 7: we look at the if we look at the exceptional 174 00:09:28,760 --> 00:09:31,560 Speaker 7: spending that the Germans were getting ready to unleash. It's 175 00:09:31,600 --> 00:09:34,439 Speaker 7: not happening fast enough. So you see, the real performers 176 00:09:34,440 --> 00:09:36,160 Speaker 7: in Europe are the people that can be more fleet 177 00:09:36,200 --> 00:09:38,640 Speaker 7: of foot. The Swedish Chrona has been doing very well 178 00:09:38,679 --> 00:09:40,480 Speaker 7: because they are going to be they have a bigger 179 00:09:40,559 --> 00:09:44,040 Speaker 7: arms industry their economy, but they also are pushing forwards. 180 00:09:44,240 --> 00:09:47,600 Speaker 7: The Norwegian kroner is doing really well. The euro is 181 00:09:47,720 --> 00:09:50,439 Speaker 7: lagging those and the Swiss Frank is doing as well 182 00:09:50,440 --> 00:09:53,920 Speaker 7: as the euro is speaks volumes about safe Aden's that's right. 183 00:09:53,800 --> 00:09:56,320 Speaker 2: Where I wanted to go. Audrey Child Freeman with Bloomberg 184 00:09:56,360 --> 00:09:59,800 Speaker 2: at Queen Victoria's Street Kit was just brilliant yesterday. And 185 00:10:00,080 --> 00:10:02,720 Speaker 2: analyzing Swiss franc I mean, I guess it's out of 186 00:10:02,760 --> 00:10:06,760 Speaker 2: the purview of many of our listeners and viewers explain 187 00:10:06,840 --> 00:10:12,600 Speaker 2: the ramifications for America, for Zurich, for Geneva. If the 188 00:10:12,679 --> 00:10:15,600 Speaker 2: Swiss Frank breaks to new strength. 189 00:10:15,720 --> 00:10:17,720 Speaker 7: I think it causes I mean, I think people will 190 00:10:17,760 --> 00:10:21,360 Speaker 7: then turn around and see much more clearly that there's 191 00:10:21,480 --> 00:10:25,160 Speaker 7: money chasing Haven's. That the money that we see enthusiastically 192 00:10:25,160 --> 00:10:28,040 Speaker 7: going to gold, because we quite like that story that Actually, 193 00:10:28,080 --> 00:10:30,559 Speaker 7: this is a world where people want to put their 194 00:10:30,559 --> 00:10:34,160 Speaker 7: money somewhere safe and a vault deep underneath the alps. 195 00:10:34,880 --> 00:10:38,560 Speaker 7: That that's not animal spirits in the sense that we 196 00:10:38,600 --> 00:10:40,920 Speaker 7: know and understand them. So it tells people that although 197 00:10:40,920 --> 00:10:44,880 Speaker 7: the US economy is dominant, people aren't really confident in 198 00:10:44,920 --> 00:10:48,400 Speaker 7: the dollar, and they're not confident in the euroe. They're 199 00:10:48,440 --> 00:10:51,600 Speaker 7: not confident in the yet. And hey, hey, presto, we 200 00:10:51,640 --> 00:10:54,400 Speaker 7: don't mind if the rates of zero were coming to Switzerland. 201 00:10:54,600 --> 00:10:56,959 Speaker 2: Kitchooks, thanks for the brief wait too short, Kitchooks for 202 00:10:57,080 --> 00:10:59,800 Speaker 2: society general. Let us know kid, when you're in New 203 00:10:59,880 --> 00:11:03,240 Speaker 2: York City next. I'm honored to have you in the studio. 204 00:11:03,600 --> 00:11:07,800 Speaker 2: Stay with us. More from Bloomberg Surveillance coming up after this. 205 00:11:15,040 --> 00:11:18,600 Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us live 206 00:11:18,679 --> 00:11:21,840 Speaker 1: weekday afternoons from seven to ten am Eastern Listen on 207 00:11:21,920 --> 00:11:25,319 Speaker 1: Apple Karplay and Android Otto with the Bloomberg Business app, 208 00:11:25,480 --> 00:11:27,200 Speaker 1: or watch us live on YouTube. 209 00:11:27,240 --> 00:11:32,920 Speaker 2: Michael Darta joins some roth capital here steeped in Wisconsin economics. Michael, 210 00:11:32,920 --> 00:11:36,520 Speaker 2: I'm just going to cut to the chase. Everyone's head spinning, 211 00:11:37,240 --> 00:11:41,040 Speaker 2: and yet we just got a disinflationary vector. All in all, 212 00:11:41,960 --> 00:11:46,240 Speaker 2: like we got real GDP wrong and nominal GDP wrong 213 00:11:46,320 --> 00:11:49,760 Speaker 2: this year, are we getting massively wrong? The need for 214 00:11:49,800 --> 00:11:51,440 Speaker 2: the FED to cut where they're. 215 00:11:51,320 --> 00:11:54,959 Speaker 6: Going to have to pick it up, Well, that is 216 00:11:55,080 --> 00:11:55,840 Speaker 6: yet to be seen. 217 00:11:55,960 --> 00:11:58,480 Speaker 4: Tom, I think the FED is a little bit on 218 00:11:58,520 --> 00:12:01,240 Speaker 4: a preset course here at for the next two meetings, 219 00:12:01,280 --> 00:12:05,520 Speaker 4: and then we'll see what happens to this big divergence 220 00:12:05,640 --> 00:12:09,320 Speaker 4: between the labor market data which has been exceptionally weak 221 00:12:09,960 --> 00:12:13,160 Speaker 4: and the activity indicators. Uh, you know, looks like Q 222 00:12:13,240 --> 00:12:17,559 Speaker 4: three probably ended on a pretty strong note with underlying 223 00:12:17,800 --> 00:12:22,079 Speaker 4: you know, real growth pretty close to three percent and 224 00:12:22,360 --> 00:12:25,240 Speaker 4: you know, not nominal you know, up in the fives. 225 00:12:26,600 --> 00:12:29,079 Speaker 4: You know, that is not a picture of an economy 226 00:12:29,080 --> 00:12:32,720 Speaker 4: that needs a lot of monetary support. Yet the labor 227 00:12:32,760 --> 00:12:36,839 Speaker 4: market indicators that the fees watching have been have been weak, 228 00:12:37,200 --> 00:12:41,040 Speaker 4: and and so there's a bit of a divergence and puzzle. 229 00:12:40,640 --> 00:12:42,240 Speaker 6: That I think needs to be solved there. 230 00:12:43,280 --> 00:12:43,480 Speaker 2: You know. 231 00:12:44,080 --> 00:12:46,880 Speaker 5: Tom Michael is a native of Wisconsin. He did graduate 232 00:12:46,880 --> 00:12:50,760 Speaker 5: from the University Wisconsin at Whitewater, but it lives in Naples, Florida. 233 00:12:50,800 --> 00:12:54,720 Speaker 5: He bailed on the Wisconsin winters residents. 234 00:12:54,800 --> 00:12:56,480 Speaker 2: Yeah, I mean, you know, I mean. 235 00:12:56,400 --> 00:12:58,800 Speaker 5: I see these Wisconsin people that you know, they're all 236 00:12:58,840 --> 00:13:00,000 Speaker 5: they just they go to Florida. 237 00:13:00,160 --> 00:13:04,480 Speaker 2: A transportation of the dogs, keeping united in delting in chips. 238 00:13:04,559 --> 00:13:06,880 Speaker 5: Hey, Michael, So we're not I guess if youuld just 239 00:13:06,880 --> 00:13:09,480 Speaker 5: look at the CPI print today, we're not seeing any 240 00:13:09,880 --> 00:13:13,760 Speaker 5: real tariff induced inflation in this economy. 241 00:13:14,840 --> 00:13:17,520 Speaker 2: Does that surprise you, Well. 242 00:13:17,400 --> 00:13:20,680 Speaker 4: We definitely saw a lift, you know, mostly in goods 243 00:13:20,720 --> 00:13:23,280 Speaker 4: prices over the course of the last few months. 244 00:13:23,520 --> 00:13:25,839 Speaker 6: And that may be easing back now. 245 00:13:26,760 --> 00:13:28,880 Speaker 4: But I will say this, I mean, the bond market 246 00:13:28,920 --> 00:13:32,000 Speaker 4: does not look like it's really concerned about inflation. The 247 00:13:32,320 --> 00:13:34,920 Speaker 4: bond yields have fallen from a high of four point 248 00:13:34,960 --> 00:13:37,640 Speaker 4: eight percent early in the year. You know, we're just 249 00:13:37,679 --> 00:13:41,120 Speaker 4: a touch below four now. Inflation expectations in the bond 250 00:13:41,160 --> 00:13:43,840 Speaker 4: market have been coming down. That's a forward looking measure, 251 00:13:44,480 --> 00:13:47,120 Speaker 4: and so I think a lot of the tariff oriented 252 00:13:47,440 --> 00:13:51,760 Speaker 4: upward pressure on inflation is likely to be quite temporary. 253 00:13:52,760 --> 00:13:56,040 Speaker 4: And that's exactly what forward looking markets are saying. I'll 254 00:13:56,080 --> 00:13:59,679 Speaker 4: also throw this out. There's a private sector group called Trueflation. 255 00:14:00,040 --> 00:14:03,240 Speaker 4: The data is on the Bloomberg terminal and they update 256 00:14:03,480 --> 00:14:06,760 Speaker 4: daily in terms of year over year inflation readings, and 257 00:14:06,800 --> 00:14:10,240 Speaker 4: it's a much broader swath of prices. The CPI is 258 00:14:10,280 --> 00:14:14,040 Speaker 4: based on about ninety thousand prices. The True Inflation Index, 259 00:14:14,120 --> 00:14:19,520 Speaker 4: which is blockchain based, is driven by fifteen million or 260 00:14:19,520 --> 00:14:23,720 Speaker 4: so prices, and that's pretty close to two percent, And 261 00:14:23,920 --> 00:14:28,880 Speaker 4: historically it doesn't typically undershoot overall CPI inflation, So there's 262 00:14:28,960 --> 00:14:32,680 Speaker 4: not an obvious downward bias there. It's just higher frequency 263 00:14:32,720 --> 00:14:36,160 Speaker 4: and it's you know, it's measuring a much broader swath 264 00:14:36,280 --> 00:14:40,600 Speaker 4: of prices, and this kind of a divergence is exactly 265 00:14:40,640 --> 00:14:44,320 Speaker 4: what you'd expect with a one time relative price shock 266 00:14:44,400 --> 00:14:45,200 Speaker 4: from the tariffs. 267 00:14:45,200 --> 00:14:46,640 Speaker 6: It's very different. 268 00:14:46,280 --> 00:14:49,760 Speaker 4: From a monetary inflation, and so that definitely gives the 269 00:14:49,760 --> 00:14:52,800 Speaker 4: FED a bit more room to maneuver here to try 270 00:14:52,840 --> 00:14:55,440 Speaker 4: to support the labor market trueflation. 271 00:14:56,480 --> 00:14:59,280 Speaker 5: Tom, I've worked here at Bloomberg for sixteen years. This guy, 272 00:14:59,360 --> 00:15:01,640 Speaker 5: this Michael Dark guy, just taught me a Bloomberg function 273 00:15:01,680 --> 00:15:02,720 Speaker 5: I didn't even know existed. 274 00:15:04,040 --> 00:15:05,480 Speaker 2: The history here, I mean, we've got to get to 275 00:15:05,520 --> 00:15:07,760 Speaker 2: the market open here. But I really want to express 276 00:15:08,160 --> 00:15:11,360 Speaker 2: the originality of what young Darda did years ago. He 277 00:15:11,440 --> 00:15:14,600 Speaker 2: used to put out he worked for Jude Weninski. It 278 00:15:14,640 --> 00:15:18,120 Speaker 2: was legendary when he was like eighteen years old, and 279 00:15:18,160 --> 00:15:20,200 Speaker 2: then he went over and all of a sudden there 280 00:15:20,200 --> 00:15:24,640 Speaker 2: were these research reports melding the market and economics. And 281 00:15:24,640 --> 00:15:26,320 Speaker 2: the only reason we talked to him is he used 282 00:15:26,320 --> 00:15:29,840 Speaker 2: Bloomberg terminal screens boom, and it was like ed Heimens. 283 00:15:30,560 --> 00:15:34,520 Speaker 2: People were like stealing research from data over the years. 284 00:15:34,520 --> 00:15:36,440 Speaker 2: Now at rough Capital, Michael, I think I want to 285 00:15:36,480 --> 00:15:40,960 Speaker 2: focus on the equity markets. Here it's Friday, and I'm 286 00:15:40,960 --> 00:15:45,240 Speaker 2: going to get my angst of gloom crew rationalizing this 287 00:15:45,320 --> 00:15:48,880 Speaker 2: is going to end. What's your treatment? Speaking of Jude Weininsky, 288 00:15:49,360 --> 00:15:53,000 Speaker 2: of the history of great bull markets, how do they end? 289 00:15:53,280 --> 00:15:56,240 Speaker 4: Well, Tom, you know, if you want some efficient markets 290 00:15:56,320 --> 00:16:00,760 Speaker 4: hypothesis and theory here you know the market will end 291 00:16:00,800 --> 00:16:05,320 Speaker 4: when the profits expansion ends. So typically bear markets are 292 00:16:05,320 --> 00:16:10,040 Speaker 4: clustered around recessionary periods. But here, how's this for a statistic. 293 00:16:10,600 --> 00:16:13,440 Speaker 4: Ninety percent of the time post World War Two, the 294 00:16:13,480 --> 00:16:16,720 Speaker 4: economy is in an expansion phase, not a contraction phase, 295 00:16:17,280 --> 00:16:19,640 Speaker 4: and the equity market tends to go up about seventy 296 00:16:19,680 --> 00:16:23,040 Speaker 4: five percent of the time. So the default position should 297 00:16:23,080 --> 00:16:27,120 Speaker 4: be more optimistic than pessimistic. All of that said, people 298 00:16:27,200 --> 00:16:30,880 Speaker 4: are nervous because the valuations are quite high concentrated and 299 00:16:31,040 --> 00:16:34,680 Speaker 4: infotech and the AI driven story, but that's not going 300 00:16:34,720 --> 00:16:37,080 Speaker 4: to be enough to know, to cause the market to 301 00:16:37,160 --> 00:16:39,480 Speaker 4: crack here. I really think you'd need to have the 302 00:16:39,560 --> 00:16:43,240 Speaker 4: earning story evaporate. Q three earnings have been pretty good 303 00:16:43,280 --> 00:16:45,720 Speaker 4: so far, high single digits. It looks like we're probably 304 00:16:45,720 --> 00:16:48,520 Speaker 4: going to end up double digits year over year for 305 00:16:48,600 --> 00:16:52,760 Speaker 4: earnings growth. So as long as that support structures under 306 00:16:52,800 --> 00:16:56,160 Speaker 4: the equity market, you know, I think we'll probably hold 307 00:16:56,160 --> 00:16:57,720 Speaker 4: on to most of these games. 308 00:16:58,120 --> 00:17:01,120 Speaker 5: Michael, what's your view of the US labor market here? 309 00:17:01,160 --> 00:17:05,600 Speaker 5: The headline numbers seem pretty more than fine, more than good, 310 00:17:05,600 --> 00:17:07,480 Speaker 5: but I know there are concerns under the hood. 311 00:17:08,480 --> 00:17:11,800 Speaker 4: Yeah, there are some concerns, Paul. You know, the ADP 312 00:17:12,000 --> 00:17:15,600 Speaker 4: figures that we have through September showed actual job declines 313 00:17:15,640 --> 00:17:17,520 Speaker 4: in three out of the last four months. That's not 314 00:17:17,640 --> 00:17:20,320 Speaker 4: typically something that you would see in a healthy economy. 315 00:17:20,800 --> 00:17:24,480 Speaker 4: Yet the activity indicators I mentioned, GDP tracking and so 316 00:17:24,560 --> 00:17:28,480 Speaker 4: forth still look pretty strong. There's a Dallas Fed index 317 00:17:28,520 --> 00:17:31,840 Speaker 4: that is weekly that scaled to year to year GDP growth, 318 00:17:31,920 --> 00:17:33,280 Speaker 4: and that's still running up. 319 00:17:33,440 --> 00:17:33,679 Speaker 2: You know. 320 00:17:33,760 --> 00:17:35,840 Speaker 6: Above two percent, and. 321 00:17:35,840 --> 00:17:38,719 Speaker 4: So I think what could end up happening here is 322 00:17:38,760 --> 00:17:41,320 Speaker 4: that as we move into next year, we'll see some 323 00:17:41,400 --> 00:17:44,240 Speaker 4: of these jobs figures improve. I think the tariff and 324 00:17:44,320 --> 00:17:48,040 Speaker 4: uncertainty shock caused employers to pull back, But if the 325 00:17:48,040 --> 00:17:50,800 Speaker 4: top line growth is still there, the profitability story is 326 00:17:50,840 --> 00:17:53,680 Speaker 4: still intact, then you know pretty unlikely that you would 327 00:17:53,720 --> 00:17:56,760 Speaker 4: see the labor market completely fall out of bed. And 328 00:17:56,800 --> 00:17:59,520 Speaker 4: that is something you know, Tom asked about the FED. 329 00:17:59,600 --> 00:18:02,840 Speaker 4: And if the easing expectations were too aggressive. If we 330 00:18:02,920 --> 00:18:05,159 Speaker 4: do start to see a better tone and tenor to 331 00:18:05,240 --> 00:18:08,119 Speaker 4: the employment data, you know, the market will have to 332 00:18:08,200 --> 00:18:12,159 Speaker 4: reprice the the totality of the easing cycle that you 333 00:18:12,200 --> 00:18:13,720 Speaker 4: know is now pretty aggressive. 334 00:18:14,080 --> 00:18:16,840 Speaker 2: Michael Data. Thank you so much, Ruth Kapa, I really 335 00:18:16,840 --> 00:18:20,760 Speaker 2: appreciate it. Stay with us. More from Bloomberg Surveillance coming 336 00:18:20,840 --> 00:18:28,880 Speaker 2: up after this. 337 00:18:28,880 --> 00:18:32,800 Speaker 1: This is the Bloomberg Surveillance Podcast. Listen live each weekday 338 00:18:32,840 --> 00:18:36,240 Speaker 1: starting at seven am Eastern on Applecarplay and Android Auto 339 00:18:36,280 --> 00:18:39,240 Speaker 1: with the Bloomberg Business app. You can also listen live 340 00:18:39,320 --> 00:18:42,879 Speaker 1: on Amazon Alexa from our flagship New York station, Just 341 00:18:42,920 --> 00:18:45,560 Speaker 1: Say Alexa Play Bloomberg eleven thirty. 342 00:18:45,320 --> 00:18:48,399 Speaker 2: One LUs A Palti Guswin right now, founder Vista Energy. 343 00:18:48,440 --> 00:18:52,440 Speaker 2: She has been absolutely fabulous about the larger politics of it. 344 00:18:52,840 --> 00:18:57,479 Speaker 2: Just a Friday question, maybe the president meets with the president. 345 00:18:57,520 --> 00:19:03,119 Speaker 2: G Is China energy independent? They really rely an oil 346 00:19:03,200 --> 00:19:04,760 Speaker 2: coming through Singapore, right. 347 00:19:05,280 --> 00:19:09,520 Speaker 8: Yes, but China once, maybe one day to become independent. 348 00:19:09,560 --> 00:19:12,080 Speaker 8: They are producing a lot of hydrocarbons right now. I 349 00:19:12,080 --> 00:19:16,120 Speaker 8: mean they're producing their own coal and they are becoming 350 00:19:16,160 --> 00:19:20,320 Speaker 8: one of the largest natural gas producer. The amount of 351 00:19:20,320 --> 00:19:24,040 Speaker 8: gas they are producing now is but almost the same 352 00:19:24,119 --> 00:19:27,720 Speaker 8: as the whole African continent. But they are still importing. 353 00:19:27,920 --> 00:19:31,520 Speaker 8: They are still importers, and their strategy has been also 354 00:19:31,560 --> 00:19:34,920 Speaker 8: to create links and relationship between all those suppliers. 355 00:19:35,280 --> 00:19:39,399 Speaker 2: My head is spinning this morning with your geography. Which 356 00:19:39,520 --> 00:19:42,960 Speaker 2: geography are you most interested in? What you're writing about 357 00:19:42,960 --> 00:19:43,520 Speaker 2: for Monday? 358 00:19:45,359 --> 00:19:48,239 Speaker 8: My focus has always been global, as you can hear 359 00:19:48,320 --> 00:19:51,600 Speaker 8: with my accents. I was born and raised educated in France, 360 00:19:51,640 --> 00:19:54,200 Speaker 8: so Europe, I know it. But I've been based in 361 00:19:54,240 --> 00:19:57,440 Speaker 8: the US for twenty years now and I've been focusing 362 00:19:57,440 --> 00:20:02,439 Speaker 8: a lot on Asia. So the US maritime roots so 363 00:20:02,680 --> 00:20:08,119 Speaker 8: very global. But my sectorial is, you know, expertise is 364 00:20:08,320 --> 00:20:10,680 Speaker 8: energy commodities geologetics. 365 00:20:10,760 --> 00:20:14,760 Speaker 2: Yeah, it's not a job interview. More so, there's lots 366 00:20:14,800 --> 00:20:17,760 Speaker 2: of boats on the water that everything's floating around right now, 367 00:20:17,840 --> 00:20:18,560 Speaker 2: Is that true? 368 00:20:19,400 --> 00:20:21,000 Speaker 8: There are many boats, Yeah. 369 00:20:21,080 --> 00:20:21,439 Speaker 2: There is. 370 00:20:21,440 --> 00:20:24,240 Speaker 5: There a lot of oil in the marketplace these days. 371 00:20:25,280 --> 00:20:27,359 Speaker 8: So we have the same question on the gas side too. 372 00:20:27,880 --> 00:20:31,439 Speaker 8: There are you know, we are washed with oil and 373 00:20:31,440 --> 00:20:35,520 Speaker 8: gas potentially, but the key question for countries is how 374 00:20:35,560 --> 00:20:38,280 Speaker 8: to get it just on time in a safe manner. 375 00:20:38,320 --> 00:20:43,120 Speaker 8: Diversification of roots prices and I think we need more 376 00:20:43,119 --> 00:20:45,359 Speaker 8: oil c the countries exporting their supply. 377 00:20:45,520 --> 00:20:49,720 Speaker 5: Where do we where in reality does the Russia gas 378 00:20:49,760 --> 00:20:54,000 Speaker 5: and oil go and can that be curved or is 379 00:20:54,080 --> 00:20:56,200 Speaker 5: always going to be a way for that that those 380 00:20:56,359 --> 00:20:58,200 Speaker 5: hydrocarbons to find their way to a buyer. 381 00:20:58,720 --> 00:21:01,120 Speaker 8: Yeah, that's a great question because actually this week we've 382 00:21:01,119 --> 00:21:04,160 Speaker 8: seen a ramping up of sanctions on the European side 383 00:21:04,200 --> 00:21:11,040 Speaker 8: and on the US side. In Europe, the European Union 384 00:21:11,520 --> 00:21:17,320 Speaker 8: has agreed on phasing out Russian gas and Russian energy 385 00:21:18,200 --> 00:21:20,720 Speaker 8: by the first of January two Salon twenty six, with 386 00:21:20,800 --> 00:21:23,560 Speaker 8: a two year transition period. So they will start first 387 00:21:23,560 --> 00:21:27,880 Speaker 8: potentially with energy and then pipeline gas. Right now, there 388 00:21:27,920 --> 00:21:31,159 Speaker 8: is almost no I mean, you know, the share of 389 00:21:31,200 --> 00:21:34,320 Speaker 8: Russian gas in Europe we used to be forty five 390 00:21:34,400 --> 00:21:39,720 Speaker 8: percent is done to you know, a trivial amount and 391 00:21:39,880 --> 00:21:44,560 Speaker 8: zero into zero persons in twenty twenty eight. And so 392 00:21:44,600 --> 00:21:46,680 Speaker 8: it means that in Europe is going to be possible 393 00:21:48,840 --> 00:21:51,639 Speaker 8: with a cost potentially, So Europe is going to be 394 00:21:51,680 --> 00:21:55,679 Speaker 8: more dependent on other suppliers. Like the mix has really changed, 395 00:21:56,080 --> 00:21:59,159 Speaker 8: more Norwegian gas, more US energy into Europe. 396 00:22:01,160 --> 00:22:03,919 Speaker 5: Russian hydrocarbons. Are they going to go to India? Are 397 00:22:03,920 --> 00:22:05,639 Speaker 5: they going to go to China? Because that's kind of 398 00:22:05,680 --> 00:22:08,200 Speaker 5: where I think we Absolutely. 399 00:22:08,000 --> 00:22:13,640 Speaker 8: We've seen a reconfiguration of trid flows. The gas from Russia, 400 00:22:13,960 --> 00:22:18,320 Speaker 8: especially the sanctioned gas is going to China, like the 401 00:22:18,480 --> 00:22:21,560 Speaker 8: energy cargoes from Arctic two. Only China has been taking it. 402 00:22:22,000 --> 00:22:24,960 Speaker 2: Thank you, the sanction thing. And I'm not up to speed. 403 00:22:24,960 --> 00:22:29,760 Speaker 2: And as human Stenus is brilliant. Yesterday and Leslie, how 404 00:22:29,800 --> 00:22:35,639 Speaker 2: does India and MODI fit into successful sanctions? If Modi 405 00:22:35,720 --> 00:22:38,240 Speaker 2: doesn't play with Trump, it doesn't work, right. 406 00:22:38,240 --> 00:22:42,560 Speaker 8: Yeah, So yesterday was all about the oil sanctions, and 407 00:22:43,119 --> 00:22:46,160 Speaker 8: it's going to be a matter if they are successful, 408 00:22:46,200 --> 00:22:49,919 Speaker 8: it's going to be a matter of enforcement and whether 409 00:22:50,720 --> 00:22:56,520 Speaker 8: the US alliances are strong enough. Real mody still take 410 00:22:56,760 --> 00:22:59,840 Speaker 8: Luke oil or rost Nev oil right now after yesterday's sanction. 411 00:23:00,040 --> 00:23:04,119 Speaker 8: I'm not sure will China still take it? Probably maybe, 412 00:23:05,040 --> 00:23:08,280 Speaker 8: I think strong allies from the US, we're not there 413 00:23:08,359 --> 00:23:09,680 Speaker 8: taking this oil anymore. 414 00:23:09,880 --> 00:23:13,000 Speaker 5: Interesting so, but with Brent crew it's sixty six dollars 415 00:23:13,000 --> 00:23:16,600 Speaker 5: a barrow. The global energy complex can't be very happy 416 00:23:16,680 --> 00:23:19,480 Speaker 5: these days, don't they need oil seventy five eighty eighty 417 00:23:19,520 --> 00:23:20,480 Speaker 5: five dollars a barrel. 418 00:23:21,880 --> 00:23:24,879 Speaker 8: So it depends what you're talking to if you're a producer. 419 00:23:26,000 --> 00:23:29,240 Speaker 5: That's what I'm thinking about my friends in Texas and Oklahoma. 420 00:23:29,359 --> 00:23:30,920 Speaker 5: Not to mention the folks at Opek. 421 00:23:31,240 --> 00:23:35,960 Speaker 8: Yes, it's a fine balance. For the Trump administration, you 422 00:23:36,359 --> 00:23:39,560 Speaker 8: want the right equilibrium price. The same for Savudi Arabia, 423 00:23:39,640 --> 00:23:44,439 Speaker 8: they want the right price for their economy. In the US, 424 00:23:45,560 --> 00:23:48,800 Speaker 8: you know, we're lucky because the US has abundant oil 425 00:23:48,840 --> 00:23:53,360 Speaker 8: and gas. The producers need to see return on their investments. 426 00:23:54,680 --> 00:23:56,520 Speaker 8: What is going to be interesting to see on the 427 00:23:56,560 --> 00:24:00,680 Speaker 8: gas side is whether we're going to start using more 428 00:24:00,760 --> 00:24:03,439 Speaker 8: on the dry gas, which is less dependent from the 429 00:24:03,440 --> 00:24:08,280 Speaker 8: oil price. So just the shields that are producing only gas, 430 00:24:08,280 --> 00:24:11,240 Speaker 8: not associated oil, and in this case they are less 431 00:24:11,240 --> 00:24:12,440 Speaker 8: dependent from the oil price. 432 00:24:12,960 --> 00:24:13,800 Speaker 2: So gas. 433 00:24:13,960 --> 00:24:16,119 Speaker 5: I thought gas kind of natural gas came out of 434 00:24:16,640 --> 00:24:18,639 Speaker 5: oil wells and fracking and all that kind of stuff. 435 00:24:18,640 --> 00:24:21,760 Speaker 2: But there are separate sources of natural gas. 436 00:24:21,800 --> 00:24:26,120 Speaker 5: Civil engineering with Paul exactly, we're diving deep into this now. 437 00:24:26,520 --> 00:24:29,639 Speaker 8: So I'm talking mostly about shell gas right in the US, 438 00:24:29,760 --> 00:24:33,199 Speaker 8: and you have shell oil too some of the depending 439 00:24:33,240 --> 00:24:35,480 Speaker 8: on the bat sun the US, you get both shell 440 00:24:35,800 --> 00:24:38,399 Speaker 8: shell oil and shell gas. And then when you're moving 441 00:24:38,400 --> 00:24:40,720 Speaker 8: to different person you have only dry gas what we 442 00:24:40,760 --> 00:24:43,399 Speaker 8: call dry gas gas and it's only gas. 443 00:24:43,440 --> 00:24:47,400 Speaker 2: In ten years is the United States of America energy 444 00:24:47,480 --> 00:24:50,800 Speaker 2: independent ten years out fifteen years out? 445 00:24:52,040 --> 00:24:54,560 Speaker 8: So it depends the definition of energy in dependent. But 446 00:24:54,680 --> 00:24:57,520 Speaker 8: right now the US is the largest oil and gas 447 00:24:57,600 --> 00:24:59,560 Speaker 8: producer exporter globally. 448 00:25:01,040 --> 00:25:04,639 Speaker 5: See and that just happened like twenty sixteen. Up until 449 00:25:04,680 --> 00:25:08,320 Speaker 5: like that, we were energy importers. Great and then thanks 450 00:25:08,440 --> 00:25:12,760 Speaker 5: to the whole fracking technology, we're now net exporters. 451 00:25:12,840 --> 00:25:15,680 Speaker 2: Right. Yeah, that's awesome. This is thank you for this brief. 452 00:25:15,720 --> 00:25:18,760 Speaker 2: To have Ed Morrison from our Heart treat and to 453 00:25:18,800 --> 00:25:21,399 Speaker 2: have Leslie Palty Gozman and this has really been a 454 00:25:21,440 --> 00:25:22,840 Speaker 2: wonderful week in hydrog. 455 00:25:22,720 --> 00:25:25,639 Speaker 5: Tommy how to watch Lambmann. You'll just you'll be all 456 00:25:25,760 --> 00:25:26,480 Speaker 5: h I guess. 457 00:25:26,640 --> 00:25:30,160 Speaker 2: I'm watching slow horses. That's right, Okay, all right. Yeah. 458 00:25:30,440 --> 00:25:32,720 Speaker 2: One of my kids said, you look like Jackson Lamb. 459 00:25:32,760 --> 00:25:37,360 Speaker 2: I said, well, thank you. Uh, I'll watch Limban is it? 460 00:25:37,680 --> 00:25:39,840 Speaker 5: Yes, Yes, You're gonna love it. 461 00:25:39,880 --> 00:25:40,120 Speaker 2: Okay. 462 00:25:40,119 --> 00:25:41,200 Speaker 5: It's all about the energy. 463 00:25:41,160 --> 00:25:43,880 Speaker 2: Leslie Pault Gozman. Thank you so much, Vista Energy really 464 00:25:43,880 --> 00:25:48,200 Speaker 2: really appreciated. Stay with us. More from Bloomberg Surveillance coming 465 00:25:48,280 --> 00:25:56,359 Speaker 2: up after this. 466 00:25:56,359 --> 00:25:59,960 Speaker 1: This is the Bloomberg Surveillance Podcast. Listen live each week 467 00:26:00,119 --> 00:26:02,919 Speaker 1: day starting at seven am Eastern on Apple, Cocklay and 468 00:26:02,920 --> 00:26:05,920 Speaker 1: Android Auto with the Bloomberg Business App. You can also 469 00:26:06,040 --> 00:26:09,720 Speaker 1: listen live on Amazon Alexa from our flagship New York station. 470 00:26:10,240 --> 00:26:13,160 Speaker 1: Just say Alexa play Bloomberg. Eleven thirty we. 471 00:26:13,160 --> 00:26:15,639 Speaker 2: Cut to the chase, joining us now for with a 472 00:26:15,960 --> 00:26:21,159 Speaker 2: question our AI Conversation of the day, Jeffery Schumacher, I'm sorry, 473 00:26:21,280 --> 00:26:26,800 Speaker 2: Ernstin Winnie Erstin Young Ey Growth Platforms leader at Ey Parthenon. 474 00:26:27,359 --> 00:26:30,560 Speaker 2: You are in the depths of this and what I 475 00:26:30,600 --> 00:26:33,960 Speaker 2: love about what you do. It's not about stupid chat 476 00:26:34,000 --> 00:26:37,760 Speaker 2: bots and tech boys with eight dollars Latte, It's about 477 00:26:37,840 --> 00:26:41,679 Speaker 2: what are companies going to do? What is Target going 478 00:26:41,760 --> 00:26:47,560 Speaker 2: to do with your neuro symbolic ail Brooks? That nailed 479 00:26:47,680 --> 00:26:49,640 Speaker 2: nailed it, nailed it, nailed it. 480 00:26:49,640 --> 00:26:50,040 Speaker 9: That's good. 481 00:26:50,160 --> 00:26:50,720 Speaker 2: Nsai. 482 00:26:51,080 --> 00:26:53,119 Speaker 9: Yes, to tell me, Like, what we have is a 483 00:26:53,119 --> 00:26:56,960 Speaker 9: growth platform, right, so it takes enterprise data, it unifies 484 00:26:56,960 --> 00:27:01,159 Speaker 9: it with other external sources, and it allows predictions around 485 00:27:01,160 --> 00:27:04,600 Speaker 9: growth that enterprises and CEOs such as Target and Cornell 486 00:27:04,680 --> 00:27:07,200 Speaker 9: back in the day can bank the bank their business? 487 00:27:07,200 --> 00:27:12,760 Speaker 2: Ow are they the audience is scared stiff about this? 488 00:27:13,320 --> 00:27:15,080 Speaker 2: How many jobs are going to go? Do you guys 489 00:27:15,119 --> 00:27:18,239 Speaker 2: have an euy parthenon? Do you have some what's going 490 00:27:18,280 --> 00:27:19,960 Speaker 2: to do to the unemployment rate? Et cetera? 491 00:27:21,040 --> 00:27:23,080 Speaker 9: Will I think you got to look at the two 492 00:27:23,200 --> 00:27:25,720 Speaker 9: sides of that that the medallion, right, there's one side 493 00:27:25,760 --> 00:27:29,679 Speaker 9: around productivity where Jennai is there. The other side is growth. 494 00:27:29,720 --> 00:27:33,119 Speaker 9: That's where Neurosymbolic plates. The growth is where you're going 495 00:27:33,200 --> 00:27:35,639 Speaker 9: to activate a lot of jobs. Right. So if you 496 00:27:35,720 --> 00:27:38,000 Speaker 9: have both sides, I think you might lose some on 497 00:27:38,040 --> 00:27:39,440 Speaker 9: the productivity, but you're going to replace. 498 00:27:39,480 --> 00:27:41,679 Speaker 2: Okay, so Target's coming out with this headline. Do you 499 00:27:41,760 --> 00:27:44,240 Speaker 2: see buried in that that they're quietly going to do 500 00:27:44,320 --> 00:27:47,159 Speaker 2: what do you call it, Paul, synergies? Yeah, Duke, they 501 00:27:47,200 --> 00:27:49,800 Speaker 2: call it synergies. They're doing synergies, but they're going to 502 00:27:49,840 --> 00:27:51,359 Speaker 2: hire people on the other side of it. 503 00:27:51,440 --> 00:27:53,760 Speaker 9: I think I think you'll see the evolution of jobs 504 00:27:53,800 --> 00:27:57,600 Speaker 9: to where these growth opportunities lie, and corporations that embrace 505 00:27:57,680 --> 00:28:01,200 Speaker 9: the neuro symbolic side of this equation favors first movers. 506 00:28:01,280 --> 00:28:03,199 Speaker 2: Did I do? Okay, that's pretty good. That was all 507 00:28:03,240 --> 00:28:06,600 Speaker 2: the viral brief I know, all right. 508 00:28:06,680 --> 00:28:09,520 Speaker 5: I think most of our listeners and viewers have some 509 00:28:09,680 --> 00:28:12,239 Speaker 5: grasp of what AI is. It's an evolutionary thing. We're 510 00:28:12,280 --> 00:28:15,719 Speaker 5: still learning. What is neurosymbolic AI. 511 00:28:16,080 --> 00:28:18,760 Speaker 9: Yeah, so if you look at the analysts, they'll tell 512 00:28:18,760 --> 00:28:20,240 Speaker 9: you it's about two to five years away. 513 00:28:20,400 --> 00:28:21,240 Speaker 2: We have it today. 514 00:28:21,520 --> 00:28:23,920 Speaker 9: The neuro side of it is the unification of data 515 00:28:24,119 --> 00:28:26,960 Speaker 9: to understand behavior, the symbolic side of the rules to 516 00:28:27,000 --> 00:28:27,720 Speaker 9: which you look at. 517 00:28:27,600 --> 00:28:31,439 Speaker 2: That behavior, and how do companies use that? 518 00:28:31,800 --> 00:28:35,240 Speaker 9: So it focuses on their commercial model. Set another way 519 00:28:35,280 --> 00:28:37,520 Speaker 9: for your listeners, how they make money? Okay, so, how 520 00:28:37,520 --> 00:28:40,160 Speaker 9: they price, how they forecast, how they enter a market, 521 00:28:40,240 --> 00:28:42,400 Speaker 9: how they create a new product, new service, how they 522 00:28:42,400 --> 00:28:43,760 Speaker 9: buy something or divest. 523 00:28:44,120 --> 00:28:44,760 Speaker 2: Don't we hire? 524 00:28:44,800 --> 00:28:46,920 Speaker 5: Don't Those companies hire lots of MBAs to figure out 525 00:28:46,960 --> 00:28:48,760 Speaker 5: how to price a product, how to market a product, 526 00:28:48,800 --> 00:28:50,520 Speaker 5: how to account for that product. 527 00:28:50,680 --> 00:28:53,520 Speaker 9: Yes, and just like in pricing, you'll have MBAs that 528 00:28:53,560 --> 00:28:57,080 Speaker 9: do regression models, but you regression, which is how you 529 00:28:57,120 --> 00:28:57,720 Speaker 9: figure out what. 530 00:28:57,800 --> 00:28:58,520 Speaker 2: Is the right price? 531 00:28:58,640 --> 00:29:01,200 Speaker 9: Right, So are you pricing for margin or you're pricing 532 00:29:01,240 --> 00:29:04,800 Speaker 9: for growth? Neurosymbolic can take, well, what market are you in, 533 00:29:05,080 --> 00:29:07,840 Speaker 9: who are your competitive sets? What if the products don't 534 00:29:07,840 --> 00:29:10,720 Speaker 9: line up? All of those things, which that tree of 535 00:29:10,800 --> 00:29:13,920 Speaker 9: reasoning grows right, and neurosymbolic allows you to apply all 536 00:29:13,920 --> 00:29:16,560 Speaker 9: of that and grab margin that you otherwise wouldn't get. 537 00:29:16,560 --> 00:29:19,440 Speaker 9: We had one business. We created eight million an e 538 00:29:19,560 --> 00:29:21,440 Speaker 9: BIT on a fifty million dollar business, and. 539 00:29:21,480 --> 00:29:24,800 Speaker 2: How many jobs exited in that example? It's more how 540 00:29:24,840 --> 00:29:27,440 Speaker 2: many jobs created in that example on a net basis? 541 00:29:27,520 --> 00:29:28,520 Speaker 2: Jobs or created. 542 00:29:28,320 --> 00:29:29,400 Speaker 9: Jobs are created on a net. 543 00:29:29,600 --> 00:29:31,320 Speaker 2: I mean, I'm going to be honest, folks, I'm in 544 00:29:31,320 --> 00:29:33,560 Speaker 2: that camp, which is this is all going to end up? 545 00:29:33,600 --> 00:29:36,480 Speaker 2: I said this at the CFA, Sorry last night at 546 00:29:36,520 --> 00:29:40,440 Speaker 2: the Luzzetti building. I'm sorry. I'm in the camp where 547 00:29:40,440 --> 00:29:43,400 Speaker 2: this is going to be net positive. X percent of 548 00:29:43,440 --> 00:29:48,080 Speaker 2: my audience totally disagrees with what I said, Jeff, speak 549 00:29:48,120 --> 00:29:52,200 Speaker 2: to the people's scared stiff that fourteen point two percent 550 00:29:52,520 --> 00:29:55,160 Speaker 2: of every company's jobs are going to walk out the door. 551 00:29:55,320 --> 00:29:57,520 Speaker 9: Yes, So if you look at productivity, Tom, I agree 552 00:29:57,560 --> 00:29:59,080 Speaker 9: with you. There are things that you're going to do 553 00:29:59,120 --> 00:30:01,680 Speaker 9: faster and more. Now, if you're just doing that, you're 554 00:30:01,720 --> 00:30:04,320 Speaker 9: going to reduction and jobs on that side. But on 555 00:30:04,360 --> 00:30:06,120 Speaker 9: the other side, you're going to create jobs. I'll give 556 00:30:06,120 --> 00:30:10,680 Speaker 9: you another one example a manufacturer, big equipment manufacturer. 557 00:30:10,080 --> 00:30:14,200 Speaker 2: John Deere. Good example, like, very similar. It's only when 558 00:30:14,200 --> 00:30:16,520 Speaker 2: I know, so go with it, John Dear. What is 559 00:30:16,640 --> 00:30:20,240 Speaker 2: neuro what's it called neurosymbolic? Hey, the red sox the 560 00:30:20,640 --> 00:30:24,480 Speaker 2: neuro symbolic A. What is neuro symbolic? AI going to 561 00:30:24,520 --> 00:30:25,920 Speaker 2: do to Puia Illinois? 562 00:30:25,960 --> 00:30:27,720 Speaker 9: I love that, John, So if you take John Deere, 563 00:30:27,800 --> 00:30:30,200 Speaker 9: you can take telemetrics data, and you can take the 564 00:30:30,360 --> 00:30:33,080 Speaker 9: financing data when they sell a machine, and you can 565 00:30:33,120 --> 00:30:37,080 Speaker 9: apply that to OSHA data coming from the government. And 566 00:30:37,080 --> 00:30:38,720 Speaker 9: guess what I can do for John Deere, Well, they 567 00:30:38,760 --> 00:30:40,760 Speaker 9: finance all their products. Well, now I can create an 568 00:30:40,760 --> 00:30:43,040 Speaker 9: insurance on the bottom side or the backside of that, 569 00:30:43,440 --> 00:30:48,920 Speaker 9: so they have a better By taking in telemetrics data, 570 00:30:48,960 --> 00:30:51,160 Speaker 9: the machine data, OSHA data from the government telling you 571 00:30:51,240 --> 00:30:54,520 Speaker 9: cite location construction, and then the financing data, I can 572 00:30:54,520 --> 00:30:57,600 Speaker 9: build a better underwriting model than an insurance company. 573 00:30:57,640 --> 00:30:58,720 Speaker 2: I just created a whole. 574 00:30:58,520 --> 00:31:00,800 Speaker 9: New revenue line for John Deere, a whole set of 575 00:31:00,840 --> 00:31:04,240 Speaker 9: new employees for John Deere, and a whole changing their 576 00:31:04,280 --> 00:31:07,360 Speaker 9: margin mix and revenue mix of their business. Which if 577 00:31:07,360 --> 00:31:10,240 Speaker 9: you change your margin profile, you change your ebitdebt profile, 578 00:31:10,520 --> 00:31:13,520 Speaker 9: change the multiple, therefore your market capitalization. Right there, Tom, 579 00:31:13,800 --> 00:31:15,760 Speaker 9: I just probably added thirty percent of the market cap 580 00:31:15,760 --> 00:31:16,360 Speaker 9: at John Deere. 581 00:31:16,640 --> 00:31:17,120 Speaker 2: Nice. 582 00:31:17,360 --> 00:31:19,160 Speaker 5: Well, I'm just convinced that the first three or four 583 00:31:19,200 --> 00:31:22,800 Speaker 5: years of my investment backing career can be completely replaced 584 00:31:22,840 --> 00:31:25,000 Speaker 5: by AI. I mean, all I did is make pitch 585 00:31:25,040 --> 00:31:28,240 Speaker 5: books and proofrea prospectuses, and that can all be done. 586 00:31:28,360 --> 00:31:31,440 Speaker 5: When you go to the board of a company, I'm 587 00:31:31,440 --> 00:31:35,080 Speaker 5: sure the board's asking, what's this return I should be 588 00:31:35,160 --> 00:31:38,240 Speaker 5: getting on all these investments I've been making on AI 589 00:31:38,280 --> 00:31:40,520 Speaker 5: over the last two or three years. How do you 590 00:31:40,560 --> 00:31:42,280 Speaker 5: kind of frame that out for them? The return on 591 00:31:42,360 --> 00:31:43,200 Speaker 5: AI investments. 592 00:31:43,240 --> 00:31:44,880 Speaker 9: Well, I think there's you got to understand there's two 593 00:31:44,920 --> 00:31:47,600 Speaker 9: sides to that medallion. One side is productivity, there's your 594 00:31:47,640 --> 00:31:50,120 Speaker 9: Genai and your Augentic, and the other side is growth. 595 00:31:50,160 --> 00:31:52,680 Speaker 9: There is Neurosymbolic. So when you look at those two, 596 00:31:52,720 --> 00:31:55,160 Speaker 9: you're going to have your productivity gains, which means you're 597 00:31:55,160 --> 00:31:56,680 Speaker 9: going to be able to write emails faster, You're going 598 00:31:56,720 --> 00:31:58,400 Speaker 9: to be able to do call center scripts faster, You're 599 00:31:58,400 --> 00:32:01,080 Speaker 9: gonna be able to do all thosings faster. Neurosymbolic is 600 00:32:01,080 --> 00:32:04,160 Speaker 9: going to give you the precision to understand where the 601 00:32:04,240 --> 00:32:07,240 Speaker 9: value is in market. And what we've seen with Neurosymbolic 602 00:32:07,280 --> 00:32:09,840 Speaker 9: in the companies that we're working with using it, it 603 00:32:09,920 --> 00:32:13,480 Speaker 9: favors dramatically first movers and tom to your example, you're 604 00:32:13,480 --> 00:32:15,800 Speaker 9: going to go play moneyball, and you're going to when 605 00:32:15,800 --> 00:32:18,440 Speaker 9: everybody else is buying players, you're buying wins, and you're 606 00:32:18,440 --> 00:32:20,560 Speaker 9: buying wins by buying runs, and you're buying runs by 607 00:32:20,600 --> 00:32:23,120 Speaker 9: getting on base. And if you if you build that, 608 00:32:23,280 --> 00:32:25,440 Speaker 9: you'll build a team for a fraction of the dollars 609 00:32:25,520 --> 00:32:27,120 Speaker 9: and in two thousand and three, you win more games 610 00:32:27,120 --> 00:32:28,360 Speaker 9: thannybody else in American League. 611 00:32:29,000 --> 00:32:32,080 Speaker 2: This sounds like a Brewers fan. So what can you 612 00:32:32,120 --> 00:32:36,400 Speaker 2: mentioned medallion twice? If I get an Ey Parthenon medallion, 613 00:32:36,720 --> 00:32:40,400 Speaker 2: can I convince after thought to empty the dishwasher this weekend? 614 00:32:40,680 --> 00:32:42,720 Speaker 2: I mean, what side of them? What's a medalion? 615 00:32:42,880 --> 00:32:46,120 Speaker 9: Well, it's not a Medallion's just an example of the 616 00:32:46,120 --> 00:32:47,560 Speaker 9: way to think about it. If you want to branch 617 00:32:47,600 --> 00:32:50,800 Speaker 9: it into two, use me see another business term, mutually exclusive, 618 00:32:50,840 --> 00:32:54,280 Speaker 9: collectively exhausted. One side is productivity, Jennai. The other side 619 00:32:54,400 --> 00:32:56,280 Speaker 9: is growth in neurosymbolic. 620 00:32:56,320 --> 00:32:58,240 Speaker 2: Should we have them back, Lisa? What do you think? 621 00:32:58,400 --> 00:32:59,560 Speaker 6: I mean, my head is blown. 622 00:32:59,640 --> 00:33:05,360 Speaker 2: Yes, a successful visit and you know it would be 623 00:33:05,480 --> 00:33:09,240 Speaker 2: scary Schumacher and Greg doco together. Oh yeah, that would 624 00:33:09,280 --> 00:33:14,000 Speaker 2: be like scary, like the economics tied into the neurosymbolic 625 00:33:14,160 --> 00:33:18,000 Speaker 2: AI the mathematics. Thank this is brilliant, Jeff Schumacher, Thank 626 00:33:18,000 --> 00:33:22,080 Speaker 2: you so much. Outa Mauclair, Wisconsin and with ernstin Young Parthanon. 627 00:33:22,600 --> 00:33:27,440 Speaker 1: This is the Bloomberg Surveillance podcast, available on Apple, Spotify, 628 00:33:27,560 --> 00:33:31,360 Speaker 1: and anywhere else you get your podcasts. Listen live each 629 00:33:31,360 --> 00:33:35,200 Speaker 1: weekday seven to ten am Eastern on Bloomberg dot com 630 00:33:35,360 --> 00:33:39,160 Speaker 1: the iHeartRadio app tune In, and the Bloomberg Business app. 631 00:33:39,440 --> 00:33:42,560 Speaker 1: You can also watch us live every weekday on YouTube 632 00:33:42,880 --> 00:33:44,880 Speaker 1: and always on the Bloomberg terminal