1 00:00:02,400 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:11,680 --> 00:00:15,480 Speaker 2: This is the Bloomberg Surveillance Podcast. I'm Jonathan Ferrow, along 3 00:00:15,520 --> 00:00:18,720 Speaker 2: with Lisa Bromwitz and Amerie Hortenn. Join us each day 4 00:00:18,760 --> 00:00:22,280 Speaker 2: for insight from the best in markets, economics, and geopolitics 5 00:00:22,440 --> 00:00:24,880 Speaker 2: from our global headquarters in New York City. We are 6 00:00:24,960 --> 00:00:27,680 Speaker 2: live on Bloomberg Television weekday mornings from six to nine 7 00:00:27,720 --> 00:00:31,319 Speaker 2: am Eastern. Subscribe to the podcast on Apple, Spotify or 8 00:00:31,320 --> 00:00:33,960 Speaker 2: anywhere else you listen, and as always on the Bloomberg 9 00:00:34,040 --> 00:00:35,880 Speaker 2: Terminal and the Bloomberg Business App. 10 00:00:36,240 --> 00:00:38,680 Speaker 1: Peter Sheer of Academy Security is writing, now we can 11 00:00:38,720 --> 00:00:41,320 Speaker 1: watch the data and let the countdown to the Santa 12 00:00:41,440 --> 00:00:44,600 Speaker 1: Rally begin, and you put a little smiley emoji and 13 00:00:44,640 --> 00:00:47,479 Speaker 1: there is a real question here. Is this ultimately a 14 00:00:47,560 --> 00:00:49,800 Speaker 1: market that wants to go up and is going to 15 00:00:49,840 --> 00:00:52,320 Speaker 1: go up because there is a garth of other information 16 00:00:52,600 --> 00:00:54,120 Speaker 1: to really prevent it from doing otherwise. 17 00:00:54,280 --> 00:00:56,080 Speaker 3: Yeah, and I think you're seeing it. The Russell two 18 00:00:56,080 --> 00:00:58,280 Speaker 3: thousands doing very well. I think there's still questions around 19 00:00:58,320 --> 00:01:00,440 Speaker 3: and you know the big tech, the AI story, but 20 00:01:00,480 --> 00:01:02,480 Speaker 3: you're seeing the equal weight S and P five hundred 21 00:01:02,520 --> 00:01:05,280 Speaker 3: do better. Russell two thousand. I think it's helpful that 22 00:01:05,319 --> 00:01:07,040 Speaker 3: the FED is buying some T bills up to forty 23 00:01:07,080 --> 00:01:09,360 Speaker 3: billion dollars a month, so you're seeing all that. I think, 24 00:01:09,400 --> 00:01:11,200 Speaker 3: you know, the Oracle earnings yesterday kind of spook the 25 00:01:11,240 --> 00:01:13,319 Speaker 3: market a little bit, and I think that's maybe the 26 00:01:13,319 --> 00:01:16,000 Speaker 3: one susceptibility is there's a lot more questions around the 27 00:01:16,040 --> 00:01:18,600 Speaker 3: AI data center. But it rates are coming down. I 28 00:01:18,640 --> 00:01:20,959 Speaker 3: think we're going to get rate cuts faster than markets pricing. 29 00:01:21,120 --> 00:01:23,440 Speaker 3: That should be really good for small caps and the 30 00:01:23,440 --> 00:01:24,280 Speaker 3: broad economy. 31 00:01:24,440 --> 00:01:26,520 Speaker 1: How much better are you feeling about big tech given 32 00:01:26,560 --> 00:01:29,240 Speaker 1: the fact that you've seen such a lack of enthusiasm 33 00:01:29,319 --> 00:01:30,959 Speaker 1: following some of these earnings, you know. 34 00:01:30,920 --> 00:01:33,720 Speaker 3: I think there's still questions about valuations there. The one 35 00:01:33,720 --> 00:01:35,680 Speaker 3: thing that's I think coming up in every conversation we 36 00:01:35,720 --> 00:01:38,280 Speaker 3: have is more and more questions about the electricity bottleneck. 37 00:01:38,480 --> 00:01:39,880 Speaker 3: How are we going to run some of these things 38 00:01:39,920 --> 00:01:41,320 Speaker 3: if we can't produce the electricity? 39 00:01:41,520 --> 00:01:42,639 Speaker 4: So I think again we're. 40 00:01:42,520 --> 00:01:45,120 Speaker 3: Going to see a lot of investment into electricity next year. 41 00:01:45,240 --> 00:01:46,119 Speaker 4: That's going to be a story. 42 00:01:46,160 --> 00:01:50,160 Speaker 3: I still think that come the next election, electricity and 43 00:01:50,200 --> 00:01:52,440 Speaker 3: electricity costs are going to be an election issue. 44 00:01:52,480 --> 00:01:54,080 Speaker 5: Peter, how much do you look at a company like 45 00:01:54,120 --> 00:01:57,840 Speaker 5: Broadcom or Oracle as a proxy for this nascent industry 46 00:01:58,040 --> 00:01:58,560 Speaker 5: as a whole. 47 00:01:58,880 --> 00:02:01,000 Speaker 3: There's kind of that collection looking at what they're doing. 48 00:02:01,160 --> 00:02:03,040 Speaker 3: You're also trying to figure out how, you know, again 49 00:02:03,080 --> 00:02:05,800 Speaker 3: the Disney announcement yesterday, how are starks reacting to this? 50 00:02:05,920 --> 00:02:07,520 Speaker 3: And you know, it feels that we've kind of gone 51 00:02:07,520 --> 00:02:09,960 Speaker 3: through this period. If you raise your hand say anything AI, 52 00:02:10,040 --> 00:02:11,079 Speaker 3: your stock shoots higher. 53 00:02:11,320 --> 00:02:12,320 Speaker 4: Now there's a lot of questions. 54 00:02:12,360 --> 00:02:14,040 Speaker 3: People are trying to figure out where's this going to work, 55 00:02:14,040 --> 00:02:16,280 Speaker 3: how's it actually going to work? And you know, again, 56 00:02:16,360 --> 00:02:18,040 Speaker 3: you know going to be somewhat facetious. If you spend 57 00:02:18,080 --> 00:02:20,400 Speaker 3: fifty million dollars and you hire ten fewer people, how 58 00:02:20,440 --> 00:02:21,440 Speaker 3: much are you paying those people? 59 00:02:21,440 --> 00:02:22,520 Speaker 4: Maybe you shouldn't have done that. 60 00:02:22,560 --> 00:02:24,919 Speaker 3: So I think the AI story is growing and we're 61 00:02:24,919 --> 00:02:27,200 Speaker 3: seeing the use, but now it's kind of there's a 62 00:02:27,200 --> 00:02:29,400 Speaker 3: little bit more question are you spending it, are you 63 00:02:29,440 --> 00:02:30,079 Speaker 3: getting value? 64 00:02:30,160 --> 00:02:31,560 Speaker 4: How do you want to use it going forward? 65 00:02:31,600 --> 00:02:33,639 Speaker 5: At LISTA mentioned the high expectations that we've had for 66 00:02:33,680 --> 00:02:36,240 Speaker 5: a lot of these companies. Is this a moment to 67 00:02:36,280 --> 00:02:39,160 Speaker 5: recalibrate or rethink what those expectations are. So I look 68 00:02:39,160 --> 00:02:42,360 Speaker 5: at a company like WORKLID, look at a company like Broadcom. 69 00:02:42,520 --> 00:02:44,440 Speaker 5: There's been a lot of happy talk. It has performed 70 00:02:44,480 --> 00:02:46,440 Speaker 5: very well quarter after quarter after quarter. Now we're a 71 00:02:46,440 --> 00:02:48,600 Speaker 5: point where maybe we just can't reach those expectations that 72 00:02:48,600 --> 00:02:49,960 Speaker 5: have been in place for so long. 73 00:02:49,919 --> 00:02:51,600 Speaker 3: Right, it gets much more difficult. So again, one of 74 00:02:51,639 --> 00:02:54,440 Speaker 3: the trades we've liked is you are either underweight QQQ 75 00:02:54,680 --> 00:02:56,640 Speaker 3: or you know, the Nasdaq one hundred, and you overweight 76 00:02:56,720 --> 00:02:58,600 Speaker 3: either the S and P five hundred equal weight or 77 00:02:58,600 --> 00:03:00,640 Speaker 3: the Rustle two thousand. I think that's the performance is 78 00:03:00,639 --> 00:03:02,720 Speaker 3: going to come as we realize all these other companies 79 00:03:02,720 --> 00:03:05,040 Speaker 3: who are benefiting from AI, they should trade maybe at 80 00:03:05,080 --> 00:03:07,840 Speaker 3: better multiples the businesses here, And you're starting to see 81 00:03:07,919 --> 00:03:09,760 Speaker 3: rates come down again, which helps a lot of those 82 00:03:09,800 --> 00:03:11,880 Speaker 3: companies much more than it helps I think the big companies. 83 00:03:11,880 --> 00:03:13,320 Speaker 4: We're kind of walking in tyrope. Though. 84 00:03:13,400 --> 00:03:15,640 Speaker 1: I remember just a couple of months ago people saying 85 00:03:15,639 --> 00:03:17,600 Speaker 1: that if the AI trade doesn't work, it's going to 86 00:03:17,600 --> 00:03:21,160 Speaker 1: bring down everything, because ultimately the entire market and economy 87 00:03:21,160 --> 00:03:23,839 Speaker 1: has been propped up by AI hopes and dreams. Why 88 00:03:23,880 --> 00:03:27,960 Speaker 1: has that changed in terms of a narrative, so substantially well. 89 00:03:27,760 --> 00:03:29,680 Speaker 3: I think we're still seeing the investing going on in 90 00:03:29,720 --> 00:03:31,519 Speaker 3: that part, so the spend is still there. So I 91 00:03:31,560 --> 00:03:33,880 Speaker 3: think if that spend really drops, that's where you start 92 00:03:33,880 --> 00:03:35,720 Speaker 3: really getting the trouble. Then we start questioning, Okay, where's 93 00:03:35,760 --> 00:03:37,880 Speaker 3: this economy had? And I'm definitely not quite saying green 94 00:03:37,920 --> 00:03:39,760 Speaker 3: about the economy. I think there are some risks there. 95 00:03:39,920 --> 00:03:42,520 Speaker 3: I think the jobs data, I think a lot of 96 00:03:42,520 --> 00:03:45,119 Speaker 3: companies are actually going to budget less spending next year. 97 00:03:45,280 --> 00:03:47,040 Speaker 3: I think it's going to start flowing through the economy 98 00:03:47,040 --> 00:03:49,600 Speaker 3: where you slowly see the upticking tears, right, is this 99 00:03:49,680 --> 00:03:52,160 Speaker 3: terar for revenue keeps coming in slowly but surely two 100 00:03:52,520 --> 00:03:54,760 Speaker 3: and forty two hundred and fifty three hundred billion. That's 101 00:03:54,800 --> 00:03:57,160 Speaker 3: when you start feeling the impact on that. I think 102 00:03:57,200 --> 00:03:59,360 Speaker 3: the job market's a little bit sketchy. What I did 103 00:03:59,520 --> 00:04:01,119 Speaker 3: of all the day I saw this week. I still 104 00:04:01,120 --> 00:04:03,080 Speaker 3: look at the jolts quit very very closely. To me, 105 00:04:03,160 --> 00:04:05,440 Speaker 3: that's the closest we get to crowdsourcing. We're back to 106 00:04:05,520 --> 00:04:07,440 Speaker 3: levels in the quits rate. I think it was one 107 00:04:07,480 --> 00:04:10,160 Speaker 3: point eight or something. It goes back to twenty fifteen. 108 00:04:10,560 --> 00:04:13,800 Speaker 3: People aren't comfortable leaving their jobs. That tells you everyone 109 00:04:13,840 --> 00:04:16,200 Speaker 3: knows the job market's a little bit sketchy than maybe. 110 00:04:15,920 --> 00:04:17,680 Speaker 4: The data shows. I'm so glad you went there. 111 00:04:17,760 --> 00:04:20,279 Speaker 1: Next week we get the November jobs report, which of 112 00:04:20,320 --> 00:04:22,120 Speaker 1: course is delayed, and a lot of people in very 113 00:04:22,160 --> 00:04:24,359 Speaker 1: in particular, was really annoyed that we didn't get it 114 00:04:24,400 --> 00:04:26,640 Speaker 1: before the FED meeting, and we seem to get indication 115 00:04:26,760 --> 00:04:27,560 Speaker 1: from fedcher J. 116 00:04:27,680 --> 00:04:28,520 Speaker 4: Powell that there is. 117 00:04:28,440 --> 00:04:30,560 Speaker 1: This weakness in the labor market that you're talking about. 118 00:04:30,960 --> 00:04:34,240 Speaker 1: How pivotal is that report to highlight that things aren't 119 00:04:34,240 --> 00:04:36,520 Speaker 1: falling off a cliff. Yes, if that's going to cut rates, 120 00:04:36,520 --> 00:04:38,360 Speaker 1: but that the economy is actually still hanging in there. 121 00:04:38,400 --> 00:04:39,880 Speaker 3: Well, I hope it's pivotal and shows that we're not 122 00:04:39,920 --> 00:04:41,320 Speaker 3: falling off the cliff. I think there is a risk 123 00:04:41,360 --> 00:04:44,040 Speaker 3: that this data comes in much weaker than we're expecting, 124 00:04:44,080 --> 00:04:45,960 Speaker 3: and it's kind of this wake up call. Okay, we've 125 00:04:46,000 --> 00:04:48,440 Speaker 3: all been talking about this jobless recovery, and yeah, the 126 00:04:48,600 --> 00:04:50,800 Speaker 3: data center spend's been driving a lot of the economy. 127 00:04:51,080 --> 00:04:53,320 Speaker 3: Where are the jobs, who's getting the jobs? Where they come? 128 00:04:53,360 --> 00:04:55,960 Speaker 3: And again, everything I talked to and probably near and 129 00:04:55,960 --> 00:04:58,200 Speaker 3: dear to my heart your kids graduating college. The job 130 00:04:58,240 --> 00:05:00,440 Speaker 3: market looks very bleak right there, from compared to what 131 00:05:00,440 --> 00:05:03,160 Speaker 3: it's been a few years ago. So I think we 132 00:05:03,240 --> 00:05:05,239 Speaker 3: might be starting to get the data that really starts 133 00:05:05,240 --> 00:05:08,880 Speaker 3: confirming whoa something's not quite working, and I think everything's 134 00:05:08,920 --> 00:05:10,320 Speaker 3: been a little bit tall, and let's see how these 135 00:05:10,320 --> 00:05:10,760 Speaker 3: things play out. 136 00:05:10,800 --> 00:05:12,000 Speaker 4: Let's see how the trade deals play out. 137 00:05:12,080 --> 00:05:14,200 Speaker 3: Let's see now we're at that proof is in the pudding, 138 00:05:14,400 --> 00:05:16,400 Speaker 3: and I'm a little bit nervous that the job's data 139 00:05:16,440 --> 00:05:18,400 Speaker 3: is going to show this is not working the way. 140 00:05:18,240 --> 00:05:19,039 Speaker 4: We hoped it would be. 141 00:05:19,160 --> 00:05:21,600 Speaker 5: How are you thinking about FED timing and insurance? I 142 00:05:21,600 --> 00:05:23,440 Speaker 5: think Claire Jones are the fts to question a lot 143 00:05:23,480 --> 00:05:25,720 Speaker 5: of us had going into that meeting, which is, why 144 00:05:25,720 --> 00:05:26,600 Speaker 5: do this cut now? 145 00:05:26,920 --> 00:05:28,120 Speaker 6: Why not wait till January? 146 00:05:28,200 --> 00:05:30,520 Speaker 5: We would have the data that we're talking around because 147 00:05:30,520 --> 00:05:33,560 Speaker 5: we haven't had. Why not wait into Jai or did 148 00:05:33,600 --> 00:05:35,320 Speaker 5: he give you a satisfactory answer to that in that 149 00:05:35,320 --> 00:05:36,040 Speaker 5: press conference? 150 00:05:36,360 --> 00:05:36,600 Speaker 4: You know? 151 00:05:36,839 --> 00:05:40,120 Speaker 3: I think the ultimate reason is they are concerned about jobs, 152 00:05:40,120 --> 00:05:41,800 Speaker 3: and there's just enough out there, I think on the 153 00:05:41,880 --> 00:05:44,080 Speaker 3: jobs data and not just official data. I think it's 154 00:05:44,160 --> 00:05:47,000 Speaker 3: anecdotally right. There's no one I'm hearing talk about, oh, 155 00:05:47,040 --> 00:05:49,320 Speaker 3: there's some great underlying strength, and you. 156 00:05:49,320 --> 00:05:50,200 Speaker 4: Know, it was kind of weird. 157 00:05:50,240 --> 00:05:52,800 Speaker 3: They did raise the GDP forecast, but again they didn't 158 00:05:52,800 --> 00:05:55,160 Speaker 3: seem to know a company that with a big job growth, 159 00:05:55,279 --> 00:05:57,360 Speaker 3: So I think that's why they did it. And honestly, 160 00:05:57,360 --> 00:05:58,720 Speaker 3: I think you're going to see a lot of pressure. 161 00:05:58,760 --> 00:06:00,440 Speaker 3: I think the market is going to get to three 162 00:06:00,480 --> 00:06:02,720 Speaker 3: percent on FED funds way faster. I think we get 163 00:06:02,760 --> 00:06:04,480 Speaker 3: there by the summer, and market it's not pricing in 164 00:06:04,760 --> 00:06:06,359 Speaker 3: two cuts next year until September. 165 00:06:06,600 --> 00:06:08,240 Speaker 4: I think power will have to cave. I think we're 166 00:06:08,240 --> 00:06:08,920 Speaker 4: going to have to move. 167 00:06:09,000 --> 00:06:11,039 Speaker 3: And this is going to be an aggressive cycle where 168 00:06:11,320 --> 00:06:13,640 Speaker 3: we've been maybe a little bit tight too long, too 169 00:06:13,680 --> 00:06:17,320 Speaker 3: dependent on allowing that AI spend to maybe cloud or 170 00:06:17,360 --> 00:06:19,880 Speaker 3: cover up that there's an underlying weekly economy going on. 171 00:06:20,120 --> 00:06:21,719 Speaker 5: Curious if you noticed something that I did during that 172 00:06:21,720 --> 00:06:24,239 Speaker 5: press conference was the Fed cheer very willing to engage 173 00:06:24,240 --> 00:06:25,760 Speaker 5: with AI in a way that he hadn't been able 174 00:06:25,800 --> 00:06:27,920 Speaker 5: to before. So there were a number of colloquies with 175 00:06:28,160 --> 00:06:31,040 Speaker 5: Howard Schneider and Neil Erwin Steve Leisman. They asked about AI, 176 00:06:31,040 --> 00:06:34,240 Speaker 5: how much he's thinking about it, and he was pretty optimistic. 177 00:06:34,360 --> 00:06:36,279 Speaker 5: You know, he talked about the prospects of AI spend 178 00:06:36,320 --> 00:06:38,920 Speaker 5: continuing here at a moment when we are candidly kind 179 00:06:38,920 --> 00:06:41,720 Speaker 5: of wondering about how long that's likely to persist. What 180 00:06:41,760 --> 00:06:43,560 Speaker 5: did you make of that His kind of recognition of 181 00:06:43,600 --> 00:06:46,560 Speaker 5: the fact that this is going to be rather seismic 182 00:06:46,600 --> 00:06:47,080 Speaker 5: going forward. 183 00:06:47,920 --> 00:06:50,080 Speaker 4: I think it's good. I think it all makes sense. 184 00:06:50,080 --> 00:06:52,960 Speaker 3: Again, we are going to continue to use AI, people 185 00:06:53,000 --> 00:06:54,200 Speaker 3: are going to refine how they use AI. 186 00:06:54,240 --> 00:06:55,600 Speaker 6: They're going to admit it. He uses it, which I 187 00:06:55,600 --> 00:06:56,280 Speaker 6: thought was interesting. 188 00:06:56,320 --> 00:06:58,480 Speaker 4: I think, you know, I've used it multiple different ways. 189 00:06:58,480 --> 00:06:58,640 Speaker 7: You know. 190 00:06:58,640 --> 00:07:00,200 Speaker 4: If I'm on Twitter, I didend to use GROCK. 191 00:07:00,040 --> 00:07:02,080 Speaker 3: If I'm on my desktop, I tend to use chat 192 00:07:02,200 --> 00:07:05,080 Speaker 3: GTT or something like that. So I think it's important part. 193 00:07:05,200 --> 00:07:07,440 Speaker 3: But again, I think we also see some of the limitations. 194 00:07:07,560 --> 00:07:09,960 Speaker 3: And I'm still at this concern that we're basically paying 195 00:07:09,960 --> 00:07:13,040 Speaker 3: twenty thirty prices for twenty twenty five technology, and so 196 00:07:13,080 --> 00:07:14,840 Speaker 3: it's not quite where we want it to be, and 197 00:07:14,960 --> 00:07:16,600 Speaker 3: people are going to be a little bit more thoughtful 198 00:07:16,640 --> 00:07:19,040 Speaker 3: on their spend. And I keep coming back to whether 199 00:07:19,080 --> 00:07:22,080 Speaker 3: it's the dats of the digital acid treasury companies. You know, 200 00:07:22,080 --> 00:07:24,560 Speaker 3: when there was this you raise your hand, use raise 201 00:07:24,600 --> 00:07:26,920 Speaker 3: free money. It's going to continue as soon as you 202 00:07:26,960 --> 00:07:29,440 Speaker 3: start really questioning are we getting the value and what 203 00:07:29,480 --> 00:07:31,440 Speaker 3: are the limitations? Again, I keep thinking that it's going 204 00:07:31,480 --> 00:07:34,000 Speaker 3: to be electricity and power generation is the limitation. 205 00:07:34,160 --> 00:07:36,000 Speaker 4: People are going to have to think twice. That's where 206 00:07:36,000 --> 00:07:37,160 Speaker 4: it's going to stall out a little bit. 207 00:07:37,320 --> 00:07:39,080 Speaker 1: David, I'm so glad that Peter talked about this, the 208 00:07:39,120 --> 00:07:40,840 Speaker 1: idea of are we getting the value? I think about 209 00:07:40,920 --> 00:07:44,080 Speaker 1: chat EBT, I look up medical problems with GROCK. I say, 210 00:07:44,240 --> 00:07:46,400 Speaker 1: is this real? And that's what everybody does is real. 211 00:07:46,440 --> 00:07:47,280 Speaker 4: It's real there. 212 00:07:48,080 --> 00:07:49,520 Speaker 1: So it's sort of a certain point you can either 213 00:07:49,520 --> 00:07:51,280 Speaker 1: go to the mark manual or you could actually just 214 00:07:51,280 --> 00:07:54,400 Speaker 1: not have digital manufacturing of concepts, and then all of 215 00:07:54,400 --> 00:07:56,880 Speaker 1: a sudden you clarify those two issues. Are we actually 216 00:07:56,920 --> 00:07:59,520 Speaker 1: solving some of the issues that people think will create 217 00:07:59,520 --> 00:08:01,920 Speaker 1: the product boom that's being priced into the market. 218 00:08:02,000 --> 00:08:04,120 Speaker 5: I've heard skeptics say you can take out a calculator 219 00:08:04,120 --> 00:08:06,280 Speaker 5: and do two plus two is four. If you do 220 00:08:06,360 --> 00:08:07,960 Speaker 5: it through chet GPT, it's going to take I don't 221 00:08:07,960 --> 00:08:09,160 Speaker 5: know how much more energy to do that. Then it 222 00:08:09,160 --> 00:08:10,880 Speaker 5: will be my little solar powered calculator. 223 00:08:10,880 --> 00:08:11,920 Speaker 6: But this is a real issue. 224 00:08:11,960 --> 00:08:14,160 Speaker 5: I think people are kind of I think it's emblematic 225 00:08:14,240 --> 00:08:16,120 Speaker 5: the fact that we're trying to fumble through this figuring out. 226 00:08:16,040 --> 00:08:17,720 Speaker 6: Sort of what the best use case is. 227 00:08:17,760 --> 00:08:21,480 Speaker 5: We're doing that individually, you with your medical interests or whatever, 228 00:08:21,520 --> 00:08:23,720 Speaker 5: and you know, but just why broadst Then there's this 229 00:08:23,760 --> 00:08:25,520 Speaker 5: whole enterprise facet of this as well, which is I 230 00:08:25,520 --> 00:08:28,640 Speaker 5: think companies are still embracing this largely, but in a 231 00:08:28,720 --> 00:08:30,320 Speaker 5: kind of blind way, not knowing how it's going to 232 00:08:30,320 --> 00:08:31,400 Speaker 5: be applicable to what they're doing. 233 00:08:31,680 --> 00:08:34,480 Speaker 3: Yeah, and you know, I again have to jokingly say, 234 00:08:34,559 --> 00:08:36,320 Speaker 3: I think in some cases if they just ask their 235 00:08:36,320 --> 00:08:37,880 Speaker 3: employees have been there three or four years what they 236 00:08:37,880 --> 00:08:39,560 Speaker 3: should do, they'd probably get a really good answer. Maybe 237 00:08:39,559 --> 00:08:42,000 Speaker 3: we should go back to empowering our employees too. Like 238 00:08:42,120 --> 00:08:44,959 Speaker 3: people know the situation and some of this feels a 239 00:08:45,000 --> 00:08:46,719 Speaker 3: little bit like a crutch. And again, there's going to 240 00:08:46,800 --> 00:08:49,040 Speaker 3: be useful parts of it. I think the limitation is 241 00:08:49,040 --> 00:08:50,800 Speaker 3: how much data you have, How useful is your data? 242 00:08:50,800 --> 00:08:52,520 Speaker 3: You got to plug it into there so people are 243 00:08:52,600 --> 00:08:54,400 Speaker 3: using it. I think it's you know, makes people slightly 244 00:08:54,440 --> 00:08:56,599 Speaker 3: more productive. I don't think it's kind of this be 245 00:08:56,760 --> 00:08:58,640 Speaker 3: on end all that ultimately it should get to right. 246 00:08:58,800 --> 00:09:00,000 Speaker 3: It's going to do more and more, it's going to 247 00:09:00,040 --> 00:09:02,120 Speaker 3: get better and better. I just don't see the technology 248 00:09:02,200 --> 00:09:04,040 Speaker 3: quite at that level. And when I try and use it, 249 00:09:04,040 --> 00:09:07,160 Speaker 3: it's great until you realize, oh it hallucinated a ticker symbol, 250 00:09:07,160 --> 00:09:09,240 Speaker 3: and now I'm like, now what else do I have 251 00:09:09,280 --> 00:09:10,920 Speaker 3: to check? What else do I really need to go? 252 00:09:11,200 --> 00:09:12,880 Speaker 3: And also it's how do you learn if you kind 253 00:09:12,880 --> 00:09:14,040 Speaker 3: of rely on it for too much? 254 00:09:16,040 --> 00:09:19,520 Speaker 2: Stay with us multpleinpeg. Savannah's coming up off to this. 255 00:09:28,160 --> 00:09:30,520 Speaker 1: Here's the latest shoppers gearing up for what is expected 256 00:09:30,520 --> 00:09:34,760 Speaker 1: to be a record breaking holiday season, despite consumer confidence 257 00:09:34,800 --> 00:09:37,560 Speaker 1: tumbling to its lowest levels going back to April. Joining 258 00:09:37,600 --> 00:09:41,360 Speaker 1: us now is Elena shall get Cheva of the conference board. Lena, 259 00:09:41,679 --> 00:09:43,960 Speaker 1: could you give us some color around the retail sales 260 00:09:44,040 --> 00:09:45,719 Speaker 1: data that we're going to be getting next week as 261 00:09:45,720 --> 00:09:47,440 Speaker 1: well as this holiday shopping season? 262 00:09:48,120 --> 00:09:49,000 Speaker 4: Is it wonderful? 263 00:09:49,160 --> 00:09:51,560 Speaker 1: Is is people looking for deals and being picky and 264 00:09:51,640 --> 00:09:54,320 Speaker 1: just sort of back ending some of their purchases to 265 00:09:54,400 --> 00:09:56,160 Speaker 1: try to get the best bang for their buck. 266 00:09:57,120 --> 00:10:01,520 Speaker 7: I think so. I think the latest earnings results are 267 00:10:01,520 --> 00:10:06,960 Speaker 7: telling us that consumers are shifting towards value and essentials, 268 00:10:07,080 --> 00:10:12,160 Speaker 7: and you know, clubs and value retailers doing great means 269 00:10:12,200 --> 00:10:16,600 Speaker 7: that consumers are really concerned about what is going on 270 00:10:16,800 --> 00:10:20,880 Speaker 7: in terms of prices, and they are really shifting towards 271 00:10:21,280 --> 00:10:24,839 Speaker 7: those savings. So I think we are looking at a 272 00:10:24,840 --> 00:10:29,040 Speaker 7: healthy holiday season, but not necessarily the best one. So 273 00:10:29,280 --> 00:10:34,520 Speaker 7: I think, you know, probably the results of the Thanksgiving 274 00:10:34,880 --> 00:10:39,640 Speaker 7: holiday was we're a little bit overstating the health of 275 00:10:39,679 --> 00:10:40,240 Speaker 7: the consumer. 276 00:10:40,440 --> 00:10:41,960 Speaker 5: Jih and great to see you, And I'd love for 277 00:10:41,960 --> 00:10:43,560 Speaker 5: you to put in the context for us where we 278 00:10:43,600 --> 00:10:47,120 Speaker 5: are in the capacity the companies have their willingness to 279 00:10:47,200 --> 00:10:51,000 Speaker 5: absorb costs because of these tariffs. I might posit that 280 00:10:51,080 --> 00:10:54,280 Speaker 5: maybe this holiday Christmas season is kind of the last 281 00:10:54,320 --> 00:10:56,040 Speaker 5: gas for them to do this before maybe in the 282 00:10:56,040 --> 00:10:58,120 Speaker 5: new year we begin to see some of that trickling 283 00:10:58,160 --> 00:11:01,840 Speaker 5: down to consumers in anticipating that might be the case, 284 00:11:01,920 --> 00:11:04,840 Speaker 5: or what's the status of that sort their willingness to 285 00:11:04,840 --> 00:11:05,040 Speaker 5: do that. 286 00:11:05,760 --> 00:11:09,240 Speaker 7: Yeah, thanks, thanks for the question. I think you're right. 287 00:11:09,360 --> 00:11:13,600 Speaker 7: I think holidays is probably going to be okay. But 288 00:11:14,200 --> 00:11:18,640 Speaker 7: our modeling and the conference board shows that the bulk 289 00:11:18,720 --> 00:11:22,600 Speaker 7: of the tariffs impact will be evident in the beginning 290 00:11:22,640 --> 00:11:26,760 Speaker 7: of twenty twenty six, so Q one, Q two, and 291 00:11:26,880 --> 00:11:32,000 Speaker 7: that is where we see the biggest softening in consumer demand. Actually, 292 00:11:32,400 --> 00:11:35,280 Speaker 7: so I think despite the fact that you know, the 293 00:11:35,360 --> 00:11:39,839 Speaker 7: new cycle kind of moved away from tariffs a little 294 00:11:39,880 --> 00:11:43,959 Speaker 7: bit in recent months, consumers are still feeling the burden. 295 00:11:44,080 --> 00:11:49,400 Speaker 7: They still failing, prices are elevated, and they are shifting 296 00:11:50,360 --> 00:11:54,800 Speaker 7: their spending patterns. Actually, at the Conference Board, we survey 297 00:11:55,120 --> 00:12:01,120 Speaker 7: consumers by different types of income, so the whole spectrum 298 00:12:01,240 --> 00:12:06,760 Speaker 7: of income groups that we reach out to, and what 299 00:12:06,800 --> 00:12:10,040 Speaker 7: I see in our consumer confidence data is a broad 300 00:12:10,200 --> 00:12:14,760 Speaker 7: based decline in income expectations. I see a broad based 301 00:12:14,760 --> 00:12:19,160 Speaker 7: decline in consumer confidence over the course of twenty twenty five. 302 00:12:19,559 --> 00:12:21,880 Speaker 7: This is the time to kind of like look back 303 00:12:21,920 --> 00:12:24,920 Speaker 7: at the year and assess what's called what happened? And 304 00:12:24,960 --> 00:12:29,000 Speaker 7: there was a twenty four point decline in consumer confidence 305 00:12:29,120 --> 00:12:34,080 Speaker 7: over the course of twenty twenty five. Consumers earning making 306 00:12:34,480 --> 00:12:37,960 Speaker 7: more than one hundred and twenty five thousand dollars a year, 307 00:12:38,640 --> 00:12:43,200 Speaker 7: the decline in their confidence was also sizable, something like 308 00:12:43,240 --> 00:12:45,760 Speaker 7: minus seventeen points. 309 00:12:46,240 --> 00:12:48,440 Speaker 5: I'm still having a hard time kind of squaring the 310 00:12:48,520 --> 00:12:50,920 Speaker 5: sentiment data with the hard data. What we're seeing in 311 00:12:51,000 --> 00:12:53,120 Speaker 5: terms of people, yes out there spending money. I kind 312 00:12:53,120 --> 00:12:57,120 Speaker 5: of imagine Santa Claus filling is sack with gritted teeth. 313 00:12:57,160 --> 00:12:59,840 Speaker 5: He's doing it, but maybe he's not feeling good about 314 00:12:59,840 --> 00:13:02,200 Speaker 5: the crisis that he's paying. Is that reflective of what 315 00:13:02,240 --> 00:13:04,600 Speaker 5: we're seeing here? And what do you make of that 316 00:13:04,640 --> 00:13:07,600 Speaker 5: seeming disconnect between again the hard data and the sentiment 317 00:13:07,640 --> 00:13:08,800 Speaker 5: data that you watch so closely. 318 00:13:09,840 --> 00:13:13,600 Speaker 7: I think it's the timing issue, and you know, to 319 00:13:13,640 --> 00:13:17,880 Speaker 7: a certain degree, we did see some softening in consumer spending. 320 00:13:18,080 --> 00:13:22,559 Speaker 7: The actual data in September already, right, So look at 321 00:13:22,600 --> 00:13:26,359 Speaker 7: the personal income and spending reports for that month. Obviously 322 00:13:26,400 --> 00:13:30,200 Speaker 7: it's very stale, but that shows that, you know, there 323 00:13:30,280 --> 00:13:34,920 Speaker 7: was some softening in spending on non durable butos for example, right, 324 00:13:35,120 --> 00:13:39,520 Speaker 7: and as things are getting more and more expensive. I 325 00:13:39,559 --> 00:13:42,080 Speaker 7: think we do see that. We'll get another piece of 326 00:13:42,120 --> 00:13:46,960 Speaker 7: evidence next week when retail sales data comes out. I 327 00:13:47,000 --> 00:13:52,480 Speaker 7: think it's not a collapse, it's somewhat softer growth in 328 00:13:52,520 --> 00:13:57,080 Speaker 7: consumer spending. But obviously a big risk is the labor market. 329 00:13:57,200 --> 00:14:00,200 Speaker 7: So what happens to the labor market going into Twined 330 00:14:00,720 --> 00:14:05,360 Speaker 7: twenty six will matter the most for the pace of 331 00:14:05,400 --> 00:14:06,200 Speaker 7: consumer spending. 332 00:14:07,040 --> 00:14:10,360 Speaker 8: Elena, good to see you. So the labor differential which 333 00:14:10,360 --> 00:14:13,480 Speaker 8: you're highlighting, I think is key here where people if 334 00:14:13,520 --> 00:14:15,520 Speaker 8: you have a job, you're okay because they also are 335 00:14:15,520 --> 00:14:18,199 Speaker 8: not picking up. But do you think housing affordability is 336 00:14:18,240 --> 00:14:21,000 Speaker 8: another reason why consumer sentiment is that week? Because I'm 337 00:14:21,040 --> 00:14:23,200 Speaker 8: trying to see do the two hundred and one seventy 338 00:14:23,200 --> 00:14:26,800 Speaker 8: five basis points of rate cuts do they help housing affordability? 339 00:14:27,040 --> 00:14:29,840 Speaker 8: Do you think these rate cuts can actually improve consumer 340 00:14:29,880 --> 00:14:32,920 Speaker 8: sentiment or we continue to see this disconnect and sort 341 00:14:32,920 --> 00:14:34,320 Speaker 8: of wait for that timing to play out. 342 00:14:35,280 --> 00:14:38,440 Speaker 7: I apre Yeah, I think that you know, you're referring 343 00:14:38,520 --> 00:14:41,800 Speaker 7: to the level of interest rates, and even if interest 344 00:14:41,880 --> 00:14:47,760 Speaker 7: rates continue to edge lower in terms of mortgage rates, 345 00:14:48,240 --> 00:14:51,880 Speaker 7: that could improve affordability. But the big part of the 346 00:14:51,960 --> 00:14:57,920 Speaker 7: affordability calculation is prices, and prices are still elevated. Well 347 00:14:57,960 --> 00:15:01,320 Speaker 7: maybe they're growing at a slower pace, but they're still growing. 348 00:15:01,440 --> 00:15:05,040 Speaker 7: So you have that one million dollar house and it's 349 00:15:05,200 --> 00:15:09,880 Speaker 7: now a million and five thousand, so it's still it's 350 00:15:09,920 --> 00:15:13,480 Speaker 7: still very unaffordable to a lot of people out there. 351 00:15:14,640 --> 00:15:17,520 Speaker 7: Maybe at the margin it could help, but I think 352 00:15:18,000 --> 00:15:23,240 Speaker 7: we just need continued growth in real wages going forward, 353 00:15:23,880 --> 00:15:27,440 Speaker 7: as Chair Powell mentioned earlier this week, to kind of 354 00:15:27,560 --> 00:15:31,800 Speaker 7: outgrow from you know, the economy needs to grow into 355 00:15:31,840 --> 00:15:36,360 Speaker 7: that kind of state where consumers will be able to 356 00:15:36,440 --> 00:15:40,360 Speaker 7: afford a little bit more and you know, be happy 357 00:15:40,400 --> 00:15:43,120 Speaker 7: again about how they are faring. 358 00:15:44,560 --> 00:15:48,200 Speaker 2: Stay with us Mobilemberg Surveillance Coming up after this. 359 00:15:56,840 --> 00:16:00,400 Speaker 1: Sticking with AI and the application of it is inking 360 00:16:00,480 --> 00:16:03,680 Speaker 1: a billion dollar steak in open AI and licensing more 361 00:16:03,720 --> 00:16:06,840 Speaker 1: than two hundred of its characters to the startup. Jason 362 00:16:06,880 --> 00:16:09,840 Speaker 1: Mazine of City Writing, we suspect Disney views the use 363 00:16:09,880 --> 00:16:12,680 Speaker 1: of its IP as a free form of marketing. The 364 00:16:12,800 --> 00:16:16,120 Speaker 1: use of these characters should help sustain and potentially build 365 00:16:16,160 --> 00:16:19,040 Speaker 1: long term brand value. Jason has a buy rating on 366 00:16:19,160 --> 00:16:21,280 Speaker 1: shares of Disney with one hundred and forty five dollars 367 00:16:21,360 --> 00:16:23,880 Speaker 1: price ticket. These shares are up four tens percent in 368 00:16:23,920 --> 00:16:26,800 Speaker 1: pre market trading. Jason joins us now, Jason. 369 00:16:26,560 --> 00:16:27,560 Speaker 2: Thank you so much for being with us. 370 00:16:27,560 --> 00:16:29,280 Speaker 1: I thought this was a fascinating move and the part 371 00:16:29,320 --> 00:16:31,360 Speaker 1: of Disney, it kind of goes to the heart of 372 00:16:31,400 --> 00:16:34,880 Speaker 1: the anxiety for content creators. Is AI going to be 373 00:16:34,920 --> 00:16:36,960 Speaker 1: a partner? Is it going to be accounibal? Is it 374 00:16:36,960 --> 00:16:39,760 Speaker 1: going to make you obsolete. What do you take from 375 00:16:39,760 --> 00:16:40,840 Speaker 1: this approach from Disney? 376 00:16:42,480 --> 00:16:44,360 Speaker 9: Well, there's part of this deal we like, Lisa, in 377 00:16:44,400 --> 00:16:47,280 Speaker 9: part that we don't. The part that we like is 378 00:16:47,880 --> 00:16:51,680 Speaker 9: we'd rather see a commercial deal than litigation, and so 379 00:16:51,760 --> 00:16:54,200 Speaker 9: we like the idea that they've inked something. We think 380 00:16:54,200 --> 00:16:55,880 Speaker 9: Disney is going to get some cash for the use 381 00:16:55,880 --> 00:16:58,160 Speaker 9: of its IP and we like the idea that the 382 00:16:58,280 --> 00:17:01,800 Speaker 9: use case has been ring fenced these animated characters short 383 00:17:01,840 --> 00:17:05,240 Speaker 9: form video that makes a ton of sense. The part 384 00:17:05,280 --> 00:17:07,680 Speaker 9: I don't like is the billion dollar investment. 385 00:17:08,840 --> 00:17:11,680 Speaker 5: And explain why. I have another question. Let me taste 386 00:17:11,680 --> 00:17:13,080 Speaker 5: that out of here a little bit. Why are you 387 00:17:13,080 --> 00:17:14,560 Speaker 5: sitt down on this investment, which I think some could 388 00:17:14,600 --> 00:17:16,359 Speaker 5: argue is sort of them you know, a solidifying a 389 00:17:16,400 --> 00:17:18,800 Speaker 5: stake here in a company that has had more than 390 00:17:18,840 --> 00:17:20,600 Speaker 5: buzz over the last couple of years, and a lot 391 00:17:20,600 --> 00:17:22,360 Speaker 5: of people are thinking it's kind of the future of tech. 392 00:17:22,400 --> 00:17:26,359 Speaker 4: More broadly, well, Disney has. 393 00:17:26,520 --> 00:17:28,560 Speaker 9: First of all, I've never had an investor tell me 394 00:17:28,760 --> 00:17:31,000 Speaker 9: that they invest in Disney because they haven't a stud 395 00:17:31,080 --> 00:17:34,080 Speaker 9: venture capital arm. It's just not what investors care about 396 00:17:34,119 --> 00:17:36,920 Speaker 9: that are Disney shareholders. The reason I think that is 397 00:17:36,920 --> 00:17:40,560 Speaker 9: is Disney tends to have a propensity to invest it's capital. 398 00:17:40,640 --> 00:17:42,800 Speaker 9: It's sort of the peak of the mania. And so 399 00:17:42,840 --> 00:17:45,000 Speaker 9: I could go back to, you know, the Info Seek 400 00:17:45,000 --> 00:17:46,960 Speaker 9: investments that they made in the nineties. They never went 401 00:17:47,040 --> 00:17:51,160 Speaker 9: anywhere a bunch of video game developers. When video games 402 00:17:51,160 --> 00:17:53,679 Speaker 9: are growing very quickly, they shut all those down. A 403 00:17:53,720 --> 00:17:56,680 Speaker 9: couple of years ago, they invested in Epic Games. I 404 00:17:56,800 --> 00:17:58,560 Speaker 9: think it was a billion and a half dollar investment. 405 00:17:58,560 --> 00:17:59,960 Speaker 9: We'll see where that goes. But that was at the 406 00:18:00,040 --> 00:18:02,600 Speaker 9: height of the metaverse. And now we have the height 407 00:18:02,640 --> 00:18:05,680 Speaker 9: of AI and we see a billion dollar equity investment. 408 00:18:05,760 --> 00:18:09,160 Speaker 9: So you know, maybe this works out really well for Disney. 409 00:18:09,520 --> 00:18:11,480 Speaker 9: But call me a bit skeptical, poor. 410 00:18:11,280 --> 00:18:13,119 Speaker 6: One out for infras Seke. Haven't thought of that company 411 00:18:13,400 --> 00:18:14,000 Speaker 6: in a long time. 412 00:18:14,000 --> 00:18:15,600 Speaker 5: But let's get back to the use case here, Jason, 413 00:18:15,640 --> 00:18:17,520 Speaker 5: because I am very curious about this. It strikes me 414 00:18:17,800 --> 00:18:19,760 Speaker 5: maybe we're closer to Ann, Marie and John being out 415 00:18:19,760 --> 00:18:22,080 Speaker 5: and Micky, you'll be sitting at the table with Lesa 416 00:18:22,160 --> 00:18:24,840 Speaker 5: or Steamboat WILLI or Donald Duck. But I want to 417 00:18:24,840 --> 00:18:27,480 Speaker 5: ask about Sora, because my sense of Sora, this product 418 00:18:27,520 --> 00:18:29,480 Speaker 5: that Open Ai has made is that it hasn't gotten 419 00:18:29,520 --> 00:18:32,119 Speaker 5: a lot of widespread adoption or interest. There's that kind 420 00:18:32,119 --> 00:18:34,680 Speaker 5: of flash at the beginning when my Twitter feed was 421 00:18:34,720 --> 00:18:36,760 Speaker 5: just filled with these kind of insane ten second videos 422 00:18:36,760 --> 00:18:38,520 Speaker 5: that people were making, I think just to showcase or 423 00:18:38,560 --> 00:18:41,320 Speaker 5: show off the technology that OpenEye had made. But I 424 00:18:41,359 --> 00:18:42,840 Speaker 5: don't get the sense it has a lot of cultural 425 00:18:42,880 --> 00:18:45,439 Speaker 5: currency now. And I'm curious to sort of when you 426 00:18:45,440 --> 00:18:47,959 Speaker 5: think about the way in which open ai is going 427 00:18:48,000 --> 00:18:50,399 Speaker 5: to use this, do you have a sort of satisfactory 428 00:18:50,880 --> 00:18:52,600 Speaker 5: answer from the company, a sense of sort of how 429 00:18:52,680 --> 00:18:55,159 Speaker 5: much this is going to be you know, used for 430 00:18:55,280 --> 00:18:58,280 Speaker 5: PR or marketing employees, you say, or just use more generally. 431 00:19:00,040 --> 00:19:02,400 Speaker 9: I don't know, it's all very much TBD. I would 432 00:19:02,440 --> 00:19:05,080 Speaker 9: say that I had a number of investors paying me 433 00:19:05,160 --> 00:19:07,200 Speaker 9: yesterday and say that they believe that open ai is 434 00:19:07,240 --> 00:19:09,800 Speaker 9: sort of de emphasizing SOA and I would agree with 435 00:19:09,800 --> 00:19:12,119 Speaker 9: you it doesn't have a huge amount of cultural currency. 436 00:19:12,359 --> 00:19:14,520 Speaker 9: I think we're still in the early days of sort 437 00:19:14,520 --> 00:19:17,800 Speaker 9: of consumer adoption and embracing of all of these tools. 438 00:19:17,840 --> 00:19:20,840 Speaker 9: So TBD, But I can't I don't think it's going to. 439 00:19:20,760 --> 00:19:21,400 Speaker 4: Be a bad thing. 440 00:19:21,760 --> 00:19:24,920 Speaker 9: And you know, we've had a number of other companies 441 00:19:24,920 --> 00:19:28,119 Speaker 9: in our coverage universe that have licensed their IP and 442 00:19:28,200 --> 00:19:30,159 Speaker 9: receive checks that are you know, on the magnitude of 443 00:19:30,160 --> 00:19:32,960 Speaker 9: fifty million dollars a year. That's a that's a mix 444 00:19:33,040 --> 00:19:35,480 Speaker 9: of sort of a fixed payment and a usage based payment. 445 00:19:35,680 --> 00:19:38,280 Speaker 1: Jason, if I were an actor in Hollywood, would I 446 00:19:38,320 --> 00:19:40,439 Speaker 1: be excited about the steal or worried about the steal? 447 00:19:43,160 --> 00:19:48,960 Speaker 9: Well, I would say that you are, I guess excited 448 00:19:49,000 --> 00:19:52,080 Speaker 9: at one level, and that the you know, there's nothing 449 00:19:52,080 --> 00:19:55,680 Speaker 9: in here that sort of allows open AI to use 450 00:19:55,800 --> 00:20:00,480 Speaker 9: the likeness of actors or actresses. But in another level, 451 00:20:00,480 --> 00:20:04,320 Speaker 9: it doesn't answer the threshold question because this is an 452 00:20:04,359 --> 00:20:07,080 Speaker 9: easier deal for Disney to do because it has so 453 00:20:07,119 --> 00:20:10,199 Speaker 9: many animated characters, the two hundred or so that you 454 00:20:10,280 --> 00:20:13,240 Speaker 9: referred to, And so I think there's still an outstanding 455 00:20:13,280 --> 00:20:14,920 Speaker 9: question of how these AI tools are going to be 456 00:20:15,000 --> 00:20:18,119 Speaker 9: used for the bulk of the IP that exists in Hollywood. 457 00:20:20,080 --> 00:20:23,680 Speaker 2: Stay with us mulblindpeg Savannah's coming up off to this. 458 00:20:32,600 --> 00:20:35,440 Speaker 1: Global stocks pushing into record territory after the Federal Reserve's 459 00:20:35,520 --> 00:20:38,840 Speaker 1: latest interest rate cut. Lezanne Sounders of Charles Schwab remaining 460 00:20:38,840 --> 00:20:41,800 Speaker 1: cautious into twenty twenty six, saying we believe the macro 461 00:20:41,920 --> 00:20:44,960 Speaker 1: environment will continue to be unstable, but stocks can likely 462 00:20:45,040 --> 00:20:48,960 Speaker 1: churn higher given a firmer earnings backdrop. Lizanne joins us 463 00:20:49,000 --> 00:20:50,480 Speaker 1: now in Lizan, this is what a lot of people 464 00:20:50,520 --> 00:20:53,040 Speaker 1: are saying. Just watch the earnings, show me the money, 465 00:20:53,119 --> 00:20:55,000 Speaker 1: and the companies can do that, and then you'll start 466 00:20:55,040 --> 00:20:56,639 Speaker 1: to see gains. Just how do you think it's going 467 00:20:56,680 --> 00:20:58,960 Speaker 1: to play out in terms of a tale of two halves, 468 00:20:59,000 --> 00:21:00,480 Speaker 1: a tail of rotation, et cetera. 469 00:21:01,560 --> 00:21:03,680 Speaker 10: Well, you know, we've been talking a lot and writing 470 00:21:03,680 --> 00:21:07,280 Speaker 10: a lot about the case shape nature of this cycle. 471 00:21:07,320 --> 00:21:10,200 Speaker 10: It's become a bit ubiquitous. But I think one place 472 00:21:10,240 --> 00:21:13,760 Speaker 10: where you're actually already starting to see convergence is in 473 00:21:13,800 --> 00:21:18,480 Speaker 10: the earnings growth rates of the tech AI, megacap tech. 474 00:21:18,520 --> 00:21:22,320 Speaker 10: You know, different cohorts their earnings progress. So if you 475 00:21:22,520 --> 00:21:25,119 Speaker 10: look at any of those cohorts a year and a 476 00:21:25,160 --> 00:21:28,000 Speaker 10: half ago, you were running at earnings to your earnings 477 00:21:28,000 --> 00:21:31,560 Speaker 10: growth rates of about fifty percent, But those have been decelerating, 478 00:21:31,640 --> 00:21:35,480 Speaker 10: maybe into the twenty percent rain, where the other part 479 00:21:35,560 --> 00:21:39,399 Speaker 10: of the market is actually seeing accelerating rate of earnings growth. 480 00:21:39,480 --> 00:21:41,840 Speaker 10: So I'm a big believer in the old adage about 481 00:21:41,920 --> 00:21:43,879 Speaker 10: you know better or worse can often matter more than 482 00:21:43,880 --> 00:21:46,439 Speaker 10: good or bad. And I think it's the trajectory of 483 00:21:46,480 --> 00:21:49,720 Speaker 10: earnings that is one of the reasons why we've seen 484 00:21:49,800 --> 00:21:53,320 Speaker 10: some dislocations within these prior leadership areas, only two of 485 00:21:53,320 --> 00:21:56,800 Speaker 10: the mag seven outperforming the S and P and opportunities 486 00:21:57,040 --> 00:22:00,840 Speaker 10: that are being found outside of those prior leadership names. 487 00:22:00,960 --> 00:22:02,600 Speaker 5: Well, Zam, we've been having a lot of attention to 488 00:22:02,840 --> 00:22:05,080 Speaker 5: sentiment data, in part because we just haven't had the 489 00:22:05,080 --> 00:22:07,120 Speaker 5: hard data as a result of this government shut down. 490 00:22:07,119 --> 00:22:09,239 Speaker 5: But you and Kevin Gordon I have coined a new 491 00:22:09,240 --> 00:22:11,320 Speaker 5: word here. It's the vibe pression. You're warning about a 492 00:22:11,400 --> 00:22:13,040 Speaker 5: vibe pression, and I wonder if you kind of spell 493 00:22:13,119 --> 00:22:15,479 Speaker 5: out how much of a what it is first of all, 494 00:22:15,520 --> 00:22:16,720 Speaker 5: and then how much of a warrior it is for you? 495 00:22:16,760 --> 00:22:18,200 Speaker 6: Is we move ahead here to twenty. 496 00:22:18,000 --> 00:22:20,600 Speaker 10: Twenty six, I think you know it used to be 497 00:22:20,640 --> 00:22:23,160 Speaker 10: talked about just in the context of soft economic data 498 00:22:23,240 --> 00:22:25,679 Speaker 10: versus hard economic data. So soft data would be the 499 00:22:25,760 --> 00:22:29,440 Speaker 10: survey based data, and that's where you're still showing incredibly 500 00:22:29,560 --> 00:22:34,880 Speaker 10: dour outlooks. You look at New Michigan's consumer sentiment kind 501 00:22:34,880 --> 00:22:37,800 Speaker 10: of plumbing its cycle lows here at the same time, 502 00:22:37,960 --> 00:22:42,959 Speaker 10: as part of the Conference Board's index, their consumer confidence Index, 503 00:22:43,000 --> 00:22:46,960 Speaker 10: and they have different cohorts that represent the survey respond 504 00:22:47,000 --> 00:22:50,439 Speaker 10: It's Conference Board tends to skew a little bit wealthier. 505 00:22:50,520 --> 00:22:54,640 Speaker 10: But Conference Sport has a question about expectations for stock prices, 506 00:22:54,840 --> 00:22:57,879 Speaker 10: that's absolutely through the roof. You mish has a question 507 00:22:58,000 --> 00:23:01,879 Speaker 10: about expectations for the unemployment. That's through the roof. That 508 00:23:02,040 --> 00:23:05,879 Speaker 10: is one of the ultimate disconnects. That maybe is a 509 00:23:05,920 --> 00:23:10,040 Speaker 10: reference to this session vib pression or the way we 510 00:23:10,160 --> 00:23:12,560 Speaker 10: used to think of it as much weaker soft data 511 00:23:12,640 --> 00:23:15,600 Speaker 10: relative to hard data. You probably see a little bit 512 00:23:15,680 --> 00:23:19,840 Speaker 10: of convergence in twenty twenty six, and by convergence probably 513 00:23:19,880 --> 00:23:21,640 Speaker 10: moves in both directions. 514 00:23:22,720 --> 00:23:25,400 Speaker 5: I'm curious when you think about dispersion, where you see 515 00:23:25,440 --> 00:23:27,679 Speaker 5: things kind of moving here in twenty twenty six. So 516 00:23:27,840 --> 00:23:31,199 Speaker 5: if we're not going to be wagging our chins about 517 00:23:31,280 --> 00:23:34,040 Speaker 5: the magnificent seven and the hyperscalers in twenty twenty six 518 00:23:34,119 --> 00:23:35,760 Speaker 5: to the three which we have in twenty twenty five, 519 00:23:36,160 --> 00:23:38,440 Speaker 5: where do you see that conversation moving in the year ahead. 520 00:23:39,480 --> 00:23:41,520 Speaker 10: So I think as it relates to AI. The story 521 00:23:41,600 --> 00:23:43,479 Speaker 10: is certainly not in the review mirror, but I think 522 00:23:43,520 --> 00:23:47,400 Speaker 10: there's maybe an increasing focus even beyond data centers, which 523 00:23:47,560 --> 00:23:51,359 Speaker 10: was the more recent surge area relative to the hyperscalers 524 00:23:51,359 --> 00:23:53,960 Speaker 10: and the original picks and shovels companies, I think the 525 00:23:54,000 --> 00:23:58,240 Speaker 10: shift is more toward the adopters, the effective adopters, not 526 00:23:58,440 --> 00:24:01,240 Speaker 10: just in a very general set, but actually starting to 527 00:24:01,280 --> 00:24:04,040 Speaker 10: get meat put on the bones in terms of impact 528 00:24:04,040 --> 00:24:07,000 Speaker 10: on productivity, what it means for labor costs, can it 529 00:24:07,080 --> 00:24:10,440 Speaker 10: help improve profit margins? So I think that may be 530 00:24:11,160 --> 00:24:14,439 Speaker 10: one of the newer themes that develops within AI is 531 00:24:14,480 --> 00:24:19,760 Speaker 10: that adopter theme. But you're also seeing broader participation in 532 00:24:19,840 --> 00:24:22,840 Speaker 10: other segments of the market, healthcare having some of the 533 00:24:22,880 --> 00:24:24,080 Speaker 10: best breath right now. 534 00:24:24,119 --> 00:24:24,399 Speaker 4: Now. 535 00:24:24,680 --> 00:24:29,639 Speaker 10: We would also caution against simplistic, monolithic sector based investing 536 00:24:29,720 --> 00:24:33,640 Speaker 10: because there's a lot of dispersion within sectors, and that's 537 00:24:33,680 --> 00:24:37,679 Speaker 10: where we think you want to apply that factor based analysis, 538 00:24:37,720 --> 00:24:40,840 Speaker 10: at least as an add on to sector based analysis. 539 00:24:40,880 --> 00:24:44,520 Speaker 10: And the factors that we're focused on right now, to 540 00:24:44,800 --> 00:24:47,960 Speaker 10: use an old school acronym, are very garp like. So 541 00:24:48,040 --> 00:24:51,119 Speaker 10: you don't want to sacrifice growth just for value. You 542 00:24:51,160 --> 00:24:52,800 Speaker 10: want to look for value, but you want to have 543 00:24:52,800 --> 00:24:54,440 Speaker 10: those growth characteristics as well. 544 00:24:55,359 --> 00:24:58,720 Speaker 8: Lazan is bad news good news for stocks, I mean 545 00:24:58,760 --> 00:25:02,359 Speaker 8: ahead of next week, Meaning if you get weak economic data, 546 00:25:02,480 --> 00:25:04,200 Speaker 8: well then the Fed's going to cut a lot more. 547 00:25:04,320 --> 00:25:08,200 Speaker 8: Maybe do really qy and so equities can look through 548 00:25:08,200 --> 00:25:10,840 Speaker 8: it or you think either the rotation trade or actually 549 00:25:10,840 --> 00:25:14,000 Speaker 8: equy evaluations are at risk if the uneployment rate keeps rising. 550 00:25:14,960 --> 00:25:19,719 Speaker 10: Maybe marginally bad is okay from a FED reaction function perspective, 551 00:25:19,840 --> 00:25:23,960 Speaker 10: but really bad news, especially in the labor market, regardless 552 00:25:23,960 --> 00:25:26,800 Speaker 10: of what that means for the trajectory of monetary policy, 553 00:25:27,240 --> 00:25:30,760 Speaker 10: I think is bad news, particularly given that the support 554 00:25:30,840 --> 00:25:35,600 Speaker 10: for this economy, consumer spending resilience that has largely been 555 00:25:35,640 --> 00:25:38,600 Speaker 10: a function of not just health in the labor market, 556 00:25:38,680 --> 00:25:41,600 Speaker 10: but confidence about the labor market. You start to see 557 00:25:41,640 --> 00:25:45,240 Speaker 10: weaker than expected numbers to a significant degree that feeds 558 00:25:45,320 --> 00:25:48,240 Speaker 10: not just back into the consumption channels, but very quickly 559 00:25:48,280 --> 00:25:49,719 Speaker 10: back into the confidence channels. 560 00:25:49,840 --> 00:25:52,040 Speaker 1: So, Lezana, are you buying the idea of a jobless recovery? 561 00:25:52,200 --> 00:25:54,159 Speaker 1: Can we see that in twenty twenty six fueling the 562 00:25:54,240 --> 00:25:56,439 Speaker 1: rotation or is that kind of implausible given what you 563 00:25:56,480 --> 00:25:57,080 Speaker 1: just said. 564 00:25:57,880 --> 00:26:00,240 Speaker 10: Well, if you look at the ADP data of the 565 00:26:00,320 --> 00:26:04,400 Speaker 10: job growth has been concentrated recently in larger companies, companies 566 00:26:04,400 --> 00:26:07,840 Speaker 10: with five hundred employees or more. Anything below that is 567 00:26:07,880 --> 00:26:11,360 Speaker 10: really where you're seeing the compression. You see that divide 568 00:26:11,359 --> 00:26:15,160 Speaker 10: in terms of corporate profits to very strong SMP profits 569 00:26:15,240 --> 00:26:18,879 Speaker 10: thirteen fourteen percent. Yet NIPPA based version of profits National 570 00:26:18,920 --> 00:26:22,640 Speaker 10: Income and Product accounts, which is millions of companies, public companies, 571 00:26:22,680 --> 00:26:25,560 Speaker 10: private companies first half of the year and we don't 572 00:26:25,560 --> 00:26:27,960 Speaker 10: have the third quarter data yet was actually in slight 573 00:26:28,040 --> 00:26:31,679 Speaker 10: negative territory and that's being reflected in the labor market 574 00:26:31,840 --> 00:26:35,919 Speaker 10: as well. So that's where I do think the bifurcations persist. 575 00:26:36,080 --> 00:26:38,520 Speaker 10: I think the net is it looks just sluggish, but 576 00:26:38,600 --> 00:26:40,720 Speaker 10: I think you have to find tooth comment in terms 577 00:26:40,720 --> 00:26:42,440 Speaker 10: of size of company in particular. 578 00:26:44,320 --> 00:26:47,840 Speaker 2: This is the Bloomberg Svendans podcast, bringing you the best 579 00:26:47,880 --> 00:26:51,200 Speaker 2: in markets, economics, an giopolitics. You can watch the show 580 00:26:51,240 --> 00:26:54,199 Speaker 2: live on Bloomberg TV weekday mornings from six am to 581 00:26:54,320 --> 00:26:58,080 Speaker 2: nine am Eastern. Subscribe to the podcast on Apple, Spotify 582 00:26:58,240 --> 00:27:01,359 Speaker 2: or anywhere else you listen always on the Bloomberg terminal 583 00:27:01,560 --> 00:27:02,920 Speaker 2: and the Bloomberg Business app. 584 00:27:07,040 --> 00:27:07,480 Speaker 1: Mm hmm