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 --> 00:00:21,240 Speaker 1: on Apple car Play or Android Auto with the Bloomberg 4 00:00:21,320 --> 00:00:24,840 Speaker 1: Business app. Listen on demand wherever you get your podcasts, 5 00:00:25,280 --> 00:00:27,200 Speaker 1: or watch us live on YouTube. 6 00:00:27,560 --> 00:00:33,120 Speaker 2: There is no other grind in engineering like Annapolis in 7 00:00:33,360 --> 00:00:38,400 Speaker 2: the fire Holes program. There of nuclear power school or 8 00:00:38,440 --> 00:00:43,680 Speaker 2: applied engineering and technology. Ted Mortonsons survived this. He's out 9 00:00:43,720 --> 00:00:46,800 Speaker 2: at Baird right now and joins us on. Ted, what 10 00:00:46,840 --> 00:00:50,920 Speaker 2: was it like your first day at our Naval Academy? 11 00:00:51,360 --> 00:00:52,159 Speaker 2: It was brutal. 12 00:00:52,360 --> 00:00:56,279 Speaker 3: I mean I would say, you know, I look back 13 00:00:56,320 --> 00:00:57,960 Speaker 3: and I try to figure out how I got through 14 00:00:58,040 --> 00:01:01,480 Speaker 3: four years of that, going to going to class through Saturday. 15 00:01:02,280 --> 00:01:04,880 Speaker 3: It was a real challenge. And I wasn't the smartest 16 00:01:05,520 --> 00:01:06,440 Speaker 3: attach in the group. 17 00:01:06,680 --> 00:01:10,520 Speaker 2: That's what Stravina said. He did pretty good leaving the Navy. 18 00:01:10,560 --> 00:01:13,039 Speaker 2: Where did you serve in the US Navy for this nation? 19 00:01:14,080 --> 00:01:19,280 Speaker 3: I flew for thirteen years really kind of the surveillance 20 00:01:19,480 --> 00:01:22,319 Speaker 3: ani submarine. What the Pights are doing right now in 21 00:01:22,360 --> 00:01:27,720 Speaker 3: the Straits of harm moves so grape flying pretty much 22 00:01:27,760 --> 00:01:28,520 Speaker 3: all over the world. 23 00:01:28,600 --> 00:01:30,480 Speaker 2: We'd love to get you back on. We'd love to 24 00:01:30,480 --> 00:01:32,560 Speaker 2: get you back on to talk about that. Sometimes I 25 00:01:32,560 --> 00:01:35,960 Speaker 2: guess right now we'll be forced to do technology. You 26 00:01:36,000 --> 00:01:38,199 Speaker 2: look at Nvidia and a lot of the research notes 27 00:01:38,200 --> 00:01:41,760 Speaker 2: today say, look, it's all concentrated in two three five 28 00:01:42,000 --> 00:01:46,759 Speaker 2: vendors as well. How bad does Google? How bad does Microsoft? 29 00:01:47,000 --> 00:01:49,720 Speaker 2: How bad does some company? I don't know, how bad 30 00:01:49,760 --> 00:01:51,720 Speaker 2: do they not want to use? In Nvidia? 31 00:01:53,320 --> 00:01:56,680 Speaker 3: There's a huge Navidia attacks on once you you know 32 00:01:56,880 --> 00:02:01,120 Speaker 3: they have this whole system approach where you're buying essentially 33 00:02:01,280 --> 00:02:04,080 Speaker 3: everything in a box and it's all in the video 34 00:02:04,200 --> 00:02:10,520 Speaker 3: generated through architectures. Not only does that represent a price tax, 35 00:02:11,000 --> 00:02:14,160 Speaker 3: but it also represents a fair degree of lock in. 36 00:02:15,200 --> 00:02:17,040 Speaker 3: And I think if you look at where Google and 37 00:02:17,160 --> 00:02:22,640 Speaker 3: specifically Amazon with trying to do their own Silicon in inference, 38 00:02:24,160 --> 00:02:27,520 Speaker 3: they just don't want this lock in. And that's one 39 00:02:27,520 --> 00:02:29,680 Speaker 3: of the reasons why you're seeing all this other co 40 00:02:29,840 --> 00:02:34,360 Speaker 3: development on silicon going on through broad common others. 41 00:02:35,280 --> 00:02:37,320 Speaker 4: Ted, what do you make and what's the conversation you're 42 00:02:37,320 --> 00:02:41,120 Speaker 4: having with your clients about software and the AI threat 43 00:02:41,200 --> 00:02:43,880 Speaker 4: to software? It becomes such a dominant theme just over 44 00:02:43,919 --> 00:02:46,840 Speaker 4: the last three four weeks here what's the conversation you're having. 45 00:02:47,880 --> 00:02:50,000 Speaker 2: It's for adults only. 46 00:02:50,040 --> 00:02:54,000 Speaker 3: To be totally honest with you, I cover all of techs, 47 00:02:54,000 --> 00:02:57,000 Speaker 3: so I see it, you know, from a not a 48 00:02:57,080 --> 00:03:02,040 Speaker 3: siloed approach, but the whole tech eco structure. And if 49 00:03:02,040 --> 00:03:06,840 Speaker 3: you look at the IGV, which is you know, pretty 50 00:03:06,880 --> 00:03:10,079 Speaker 3: much of a software index, it's down twenty four percent 51 00:03:10,400 --> 00:03:13,720 Speaker 3: year to date and basically in two months. This has 52 00:03:14,080 --> 00:03:18,240 Speaker 3: absolutely annihilated attribution for a lot of my big big 53 00:03:18,280 --> 00:03:22,880 Speaker 3: pms Number one, they can't get out. Number two. This 54 00:03:23,120 --> 00:03:26,799 Speaker 3: pivot and Jensen alluded to it last night on the 55 00:03:26,919 --> 00:03:31,280 Speaker 3: videos call. The move to agentic or agents has just 56 00:03:31,560 --> 00:03:36,000 Speaker 3: exploded exponentially, and that's the adjective he used in the 57 00:03:36,080 --> 00:03:39,720 Speaker 3: last two months. And this is affecting all of software. 58 00:03:39,840 --> 00:03:44,040 Speaker 3: It's a if you look what's happening with Entropic and 59 00:03:44,080 --> 00:03:47,200 Speaker 3: I would do work on Opus four point five and 60 00:03:47,280 --> 00:03:51,720 Speaker 3: even with chat GPT on their codex offering, you now 61 00:03:51,760 --> 00:03:55,280 Speaker 3: can code an agent by not being a developer, by 62 00:03:55,440 --> 00:03:59,200 Speaker 3: just either talking into your laptop or typing in and 63 00:03:59,240 --> 00:04:03,760 Speaker 3: getting an agent. So agents are absolutely exploding out there. 64 00:04:04,120 --> 00:04:07,760 Speaker 3: And when you explode agents, you need a tremendous amount 65 00:04:07,800 --> 00:04:10,920 Speaker 3: of compute in the video's right in the in the 66 00:04:10,960 --> 00:04:13,600 Speaker 3: middle of that, and you need a lot of memory 67 00:04:13,640 --> 00:04:16,400 Speaker 3: and we're short on memory. So this has been a 68 00:04:16,440 --> 00:04:20,840 Speaker 3: pivot that I haven't seen in three decades of Nope. 69 00:04:20,640 --> 00:04:22,479 Speaker 4: I agree with you. I've never seen anything like it. 70 00:04:22,520 --> 00:04:25,080 Speaker 4: And I guess the question for a lot of investors 71 00:04:25,200 --> 00:04:28,120 Speaker 4: is just pick a name. A Salesforce dot Com doesn't 72 00:04:28,160 --> 00:04:31,600 Speaker 4: get any more embedded than that. I mean, if you're 73 00:04:31,640 --> 00:04:35,120 Speaker 4: a salesperson anywhere in any business, Salesforce dot com is 74 00:04:35,120 --> 00:04:38,839 Speaker 4: your buddy. Is Salesforce dot Com at risk? Just to 75 00:04:38,880 --> 00:04:39,960 Speaker 4: pick a name? 76 00:04:41,440 --> 00:04:41,800 Speaker 2: I think? 77 00:04:42,240 --> 00:04:45,440 Speaker 3: And I listened to that call last night, and you know, 78 00:04:45,960 --> 00:04:48,839 Speaker 3: the large camp companies most likely will make it over 79 00:04:48,920 --> 00:04:54,159 Speaker 3: the chasm on transitioning from a SAS model. And Jensen 80 00:04:54,200 --> 00:04:57,760 Speaker 3: was pretty funny last night. He basically said software is changing, 81 00:04:57,839 --> 00:05:00,240 Speaker 3: and you know, Navidia has a huge sw for a 82 00:05:00,320 --> 00:05:04,479 Speaker 3: component that nobody really talks about, and it's these Kuda 83 00:05:04,560 --> 00:05:10,200 Speaker 3: libraries are very disruptive on traditional SAX. So he called 84 00:05:10,240 --> 00:05:14,680 Speaker 3: software as a service a pre recorded software where eight 85 00:05:14,839 --> 00:05:17,600 Speaker 3: or Agentic or what he's working on on Kuda libraries 86 00:05:17,680 --> 00:05:21,920 Speaker 3: is real time. Wow, that's a big, big statement. And 87 00:05:22,000 --> 00:05:25,040 Speaker 3: where my clients are having trouble is they can't model 88 00:05:25,080 --> 00:05:28,960 Speaker 3: these companies. In the new world, it was easy to 89 00:05:29,000 --> 00:05:33,640 Speaker 3: model like a CRM on SaaS revenue because it was reoccurring. 90 00:05:33,720 --> 00:05:37,239 Speaker 3: Right now you introduced an agent, which is assumption views, 91 00:05:37,440 --> 00:05:39,920 Speaker 3: and nobody can get to free cash flow. So how 92 00:05:39,960 --> 00:05:41,680 Speaker 3: do you put a multiple on free cash flow? 93 00:05:41,720 --> 00:05:43,599 Speaker 5: You can't model. 94 00:05:43,520 --> 00:05:47,000 Speaker 2: Ted Morn said, rob Ship's got a rob Shift has 95 00:05:47,040 --> 00:05:49,800 Speaker 2: got a question, and that is simply, are they setting 96 00:05:49,880 --> 00:05:53,000 Speaker 2: us up for revenue increases out there? I'm doing this 97 00:05:53,120 --> 00:05:56,200 Speaker 2: or that for twenty dollars a month and it's great, 98 00:05:56,360 --> 00:05:58,560 Speaker 2: And is it going to be like twelve months from now? Oh, 99 00:05:58,600 --> 00:06:01,280 Speaker 2: I'm sorry, we've just lifted to fifty dollars a month. 100 00:06:01,360 --> 00:06:02,960 Speaker 2: Is that where all this is heading? 101 00:06:04,760 --> 00:06:05,159 Speaker 5: I don't. 102 00:06:05,920 --> 00:06:09,640 Speaker 3: I personally think that Navidia is going to use technology innovation. 103 00:06:10,320 --> 00:06:12,719 Speaker 3: This reuben cycle that they have in front of them 104 00:06:13,240 --> 00:06:18,880 Speaker 3: is staggerly good. You can reduce a token costs, you know, 105 00:06:19,000 --> 00:06:22,440 Speaker 3: the cost of per token by ten x and that's 106 00:06:22,440 --> 00:06:25,880 Speaker 3: a paradigm shift on system design. I think they're just 107 00:06:25,960 --> 00:06:30,520 Speaker 3: going for massive share across training and inference, and yes, 108 00:06:30,839 --> 00:06:34,360 Speaker 3: you know they'll charge a premium for availability and functionality. 109 00:06:34,800 --> 00:06:36,800 Speaker 3: But I don't think they're out to gouge anybody. I 110 00:06:36,800 --> 00:06:41,840 Speaker 3: think they're out to basically rule the THEAI infrastructure world globally. 111 00:06:42,360 --> 00:06:45,200 Speaker 2: Ted, thanks so much, greatly appreciated this morning. He's a Baird. 112 00:06:47,000 --> 00:06:51,200 Speaker 2: Stay with us. More from Bloomberg Surveillance coming up after this. 113 00:06:58,440 --> 00:07:01,720 Speaker 1: You're listening to The bloombergs our Villain's podcast. Catch us 114 00:07:01,760 --> 00:07:04,640 Speaker 1: Live weekday afternoons from seven to ten am. E's durn 115 00:07:04,800 --> 00:07:08,760 Speaker 1: Listen on Applecarplay and Android Otto with the Bloomberg Business app, 116 00:07:08,920 --> 00:07:10,640 Speaker 1: or watch US live on YouTube. 117 00:07:10,960 --> 00:07:14,720 Speaker 2: John Bilton lizsu He's had a global multi asset strategy 118 00:07:15,120 --> 00:07:18,480 Speaker 2: at JP Morgan Asset Management. My good friend John Farroh 119 00:07:18,640 --> 00:07:21,600 Speaker 2: is adamant that all annual market outlooks should be written 120 00:07:21,680 --> 00:07:26,000 Speaker 2: March thirty first, because there's something always wacko that happens. 121 00:07:26,080 --> 00:07:29,240 Speaker 2: Q one. Clearly, the tech pullback is there. How does 122 00:07:29,360 --> 00:07:34,560 Speaker 2: JP Morgan reallocate for the tech tobaccle we're living right now? 123 00:07:35,320 --> 00:07:37,160 Speaker 6: Well, I think we've got to actually ask is it 124 00:07:37,200 --> 00:07:39,880 Speaker 6: a tech debarcle or is it something which actually the 125 00:07:39,880 --> 00:07:41,680 Speaker 6: market is doing what it should do, which is it's 126 00:07:41,760 --> 00:07:45,240 Speaker 6: actually looking at what's happening at disruption at the stock level. 127 00:07:45,840 --> 00:07:47,680 Speaker 6: One of the things that you know, colleagues of might 128 00:07:47,760 --> 00:07:50,040 Speaker 6: often say is, remember this is a market of stocks. 129 00:07:50,040 --> 00:07:52,200 Speaker 6: It's not a single entity. It's not a stock market, 130 00:07:52,320 --> 00:07:54,080 Speaker 6: and I think we often forget that. What we've seen 131 00:07:54,200 --> 00:07:57,680 Speaker 6: is huge rotation. We've seen, i think an episode in 132 00:07:57,720 --> 00:08:02,200 Speaker 6: the tech trade which is suddenly a realization that this 133 00:08:02,280 --> 00:08:06,080 Speaker 6: disruption that tech is causing generally actually they're going to 134 00:08:06,320 --> 00:08:08,040 Speaker 6: do some of that to their own sector as well. 135 00:08:08,640 --> 00:08:10,520 Speaker 6: And as a result, I think the market is struggling 136 00:08:10,600 --> 00:08:12,600 Speaker 6: or attempting to reprice that. But if we actually look 137 00:08:12,600 --> 00:08:16,040 Speaker 6: at the sector overall, and remember we allocate off top down, 138 00:08:16,080 --> 00:08:18,520 Speaker 6: not at the stock level. In my particular line of business, 139 00:08:18,920 --> 00:08:21,480 Speaker 6: actually tech and comm services are down one to two 140 00:08:21,480 --> 00:08:24,960 Speaker 6: percent this year as sectors, after having risen one hundred 141 00:08:25,000 --> 00:08:27,679 Speaker 6: and twenty and sixty percent, respectively in the last five 142 00:08:28,160 --> 00:08:31,160 Speaker 6: So yes, we see single names, you know, getting hit 143 00:08:31,240 --> 00:08:34,400 Speaker 6: quite hard as their business models are being questioned. That 144 00:08:34,480 --> 00:08:37,400 Speaker 6: the overall tech trade, we think is actually becoming a 145 00:08:37,440 --> 00:08:41,000 Speaker 6: little bit more resilient if anything, because you're getting this 146 00:08:41,080 --> 00:08:44,440 Speaker 6: pricing differentiation happening, which is what the stock market's supposed 147 00:08:44,440 --> 00:08:44,880 Speaker 6: to be doing. 148 00:08:45,040 --> 00:08:48,600 Speaker 4: So from the sector level, the asset allocation level. Have 149 00:08:48,760 --> 00:08:52,240 Speaker 4: you increased, decreased, or kept your tech exposure the same here? 150 00:08:52,480 --> 00:08:55,480 Speaker 6: So we continue to like holding tech and comm services 151 00:08:56,320 --> 00:08:58,160 Speaker 6: for a couple of reasons. I think first and foremost, 152 00:08:58,240 --> 00:09:00,480 Speaker 6: if we actually look at the revue when you hit 153 00:09:00,600 --> 00:09:03,640 Speaker 6: that might come through software. We took a look at 154 00:09:03,679 --> 00:09:05,840 Speaker 6: that and we said, okay, if we took that sector 155 00:09:05,840 --> 00:09:08,680 Speaker 6: and we took some of these worst case scenarios in 156 00:09:08,760 --> 00:09:12,520 Speaker 6: terms of the losses around revenue from software, what would 157 00:09:12,520 --> 00:09:15,920 Speaker 6: that do to the overall revenue for the sectors? It 158 00:09:15,960 --> 00:09:18,560 Speaker 6: would take it down five six percent. Markets pricing at 159 00:09:18,559 --> 00:09:21,199 Speaker 6: around about three times that level, So we think, if anything, 160 00:09:21,440 --> 00:09:23,120 Speaker 6: actually it's cheapened up a long way. And the second 161 00:09:23,160 --> 00:09:24,920 Speaker 6: thing I'd point out, if we take the MAG six 162 00:09:25,160 --> 00:09:27,719 Speaker 6: or MAG seven I'm sorry, and we look at their 163 00:09:27,840 --> 00:09:30,960 Speaker 6: valuation relative to the rest of the market, it's below 164 00:09:31,240 --> 00:09:33,160 Speaker 6: the trough that it was out on Liberation Day, the 165 00:09:33,240 --> 00:09:35,880 Speaker 6: last time we had a big market turmoil event. So 166 00:09:35,920 --> 00:09:39,520 Speaker 6: if anything relative to its average valuation to the rest 167 00:09:39,559 --> 00:09:41,840 Speaker 6: of the market, it's looking pretty cheap at the moment. 168 00:09:42,360 --> 00:09:46,079 Speaker 4: How about us versus rest of the world, Because you know, 169 00:09:46,120 --> 00:09:48,680 Speaker 4: we're patting ourselves on the shoulder on the back last 170 00:09:48,720 --> 00:09:51,080 Speaker 4: year with the US SMP and the NASDAG, but Boil 171 00:09:51,160 --> 00:09:54,280 Speaker 4: lots of markets outside of the US did appreciably better, 172 00:09:54,600 --> 00:09:58,000 Speaker 4: and that's continuing into this year. How do you think 173 00:09:58,040 --> 00:09:59,600 Speaker 4: about US versus the rest of the world? 174 00:10:00,000 --> 00:10:02,480 Speaker 6: For me, it's as much as anything else, a currency story. Okay, 175 00:10:02,640 --> 00:10:06,040 Speaker 6: If I'm a European based investor and I bought a 176 00:10:06,480 --> 00:10:10,080 Speaker 6: Global sixty forty priced in dollars, I did about three 177 00:10:10,080 --> 00:10:12,520 Speaker 6: percent last year. If I talked to someone based here 178 00:10:12,520 --> 00:10:15,079 Speaker 6: in the US, of course they haven't had that currency translation, 179 00:10:15,320 --> 00:10:18,400 Speaker 6: They've done about seventeen percent on the same portfolio. That 180 00:10:18,720 --> 00:10:21,920 Speaker 6: dollar hit is being significant, and I think, you know, 181 00:10:21,960 --> 00:10:24,240 Speaker 6: if people talk a lot about this whole Cell America idea, 182 00:10:24,320 --> 00:10:26,480 Speaker 6: we just don't see it in the data. What we 183 00:10:26,640 --> 00:10:29,880 Speaker 6: do see is a repricing downwards gradually of the US dollar, 184 00:10:29,880 --> 00:10:31,320 Speaker 6: and we think it's got a little bit further to go. 185 00:10:31,520 --> 00:10:33,760 Speaker 6: So I think we're getting two things going on. First 186 00:10:33,760 --> 00:10:36,640 Speaker 6: and foremost, stimulus light we've not seen before coming through 187 00:10:36,679 --> 00:10:38,800 Speaker 6: in Europe at long last, a bit more to come 188 00:10:38,840 --> 00:10:41,600 Speaker 6: in China and Japan as well, which supports the growth 189 00:10:41,600 --> 00:10:45,360 Speaker 6: in those regions. But secondly, we've got the dollar gradually weakening, 190 00:10:45,559 --> 00:10:49,400 Speaker 6: which of course accentuates interested in having that international exposure. 191 00:10:49,600 --> 00:10:51,240 Speaker 2: John Bilton with us. We're going to come back and 192 00:10:51,280 --> 00:10:54,360 Speaker 2: really take advantage of his twisted education here to talk 193 00:10:54,400 --> 00:10:59,280 Speaker 2: about others. AI uproar and had a global ALTESI strategy 194 00:10:59,280 --> 00:11:03,439 Speaker 2: at Jpmore Asset Management features up six. Nice green to 195 00:11:03,480 --> 00:11:05,320 Speaker 2: the screen right now, there's been a lift to the 196 00:11:05,360 --> 00:11:08,160 Speaker 2: market after all. Of course, back and forth on Nvidia. 197 00:11:08,559 --> 00:11:10,880 Speaker 2: Young eys will join us in the nine o'clock hour. 198 00:11:11,440 --> 00:11:15,200 Speaker 2: The conversations on Nvidia today, Paul have has been absolutely spectacular. 199 00:11:15,320 --> 00:11:17,840 Speaker 4: Yeah, I mean again, a solid quarter. It's a beatn 200 00:11:17,920 --> 00:11:19,680 Speaker 4: raise as the tech kids like to talk about. 201 00:11:19,840 --> 00:11:20,240 Speaker 2: It's good. 202 00:11:21,360 --> 00:11:23,360 Speaker 4: Is it on the magnitude that we've seen in the past. No, 203 00:11:23,480 --> 00:11:26,120 Speaker 4: but I mean the larger law, the law of large 204 00:11:26,160 --> 00:11:28,640 Speaker 4: numbers that nothing else would make that kind of difficult 205 00:11:28,640 --> 00:11:28,760 Speaker 4: to do. 206 00:11:28,920 --> 00:11:31,360 Speaker 2: Well, we'll have to see here. I mean it is 207 00:11:31,480 --> 00:11:33,800 Speaker 2: eight thirty and it's you know, in an hour we'll 208 00:11:33,800 --> 00:11:36,720 Speaker 2: open the market as well. Let me get this up 209 00:11:36,800 --> 00:11:39,320 Speaker 2: right now. Yeah, and now sweet ahead, it is now, 210 00:11:39,360 --> 00:11:43,000 Speaker 2: but you know, claims come in below and a revision 211 00:11:43,080 --> 00:11:45,200 Speaker 2: is two hundred and eight thousand. I mean I'm sorry, 212 00:11:45,240 --> 00:11:48,800 Speaker 2: it's what we're hearing across the board. I mean, the 213 00:11:48,880 --> 00:11:53,000 Speaker 2: labor market just hasn't cracked and at the same time 214 00:11:53,120 --> 00:11:54,880 Speaker 2: the struggle of so many Americans. 215 00:11:55,000 --> 00:11:55,800 Speaker 5: Yep, absolutely so. 216 00:11:55,840 --> 00:11:58,720 Speaker 4: Again, the initial jobasclaims came into two hundred and twelve 217 00:11:58,800 --> 00:12:02,560 Speaker 4: thousand concent as was two hundred and sixteen thousand. Last 218 00:12:02,600 --> 00:12:05,280 Speaker 4: period was revised a little bit higher to two hundred 219 00:12:05,280 --> 00:12:05,800 Speaker 4: and eight thousand. 220 00:12:05,840 --> 00:12:07,480 Speaker 2: So there you go. Talk so much of this AI 221 00:12:07,559 --> 00:12:11,640 Speaker 2: debate that we're having, and what great informed conversations is 222 00:12:11,720 --> 00:12:16,880 Speaker 2: about this strange word productivity, which comes down to efficiency. 223 00:12:17,600 --> 00:12:20,959 Speaker 2: John Bilton of JP Morgan is exquisite on this. He's 224 00:12:21,000 --> 00:12:24,160 Speaker 2: got legit chemistry chops out of the United Kingdom and 225 00:12:24,240 --> 00:12:26,760 Speaker 2: enjoyed a tour of duty on the Charles at River. 226 00:12:26,880 --> 00:12:33,640 Speaker 2: It solos MIT. So in chemistry, the iconic nineteenth century shift, 227 00:12:33,679 --> 00:12:37,600 Speaker 2: including the statue of Faraday on the River Thames, is 228 00:12:37,640 --> 00:12:42,320 Speaker 2: to move from batch chemistry to flow chemistry and pipes. 229 00:12:42,720 --> 00:12:46,640 Speaker 2: The world changed. We went from medieval Sir Isaac Newton 230 00:12:46,760 --> 00:12:51,280 Speaker 2: alchemy to like DuPont and other gen C other giant 231 00:12:51,320 --> 00:12:54,240 Speaker 2: chemistry companies. Then you go over to MIT where you 232 00:12:54,320 --> 00:12:59,560 Speaker 2: learn productivity with the troops there. How does AI become 233 00:12:59,679 --> 00:13:02,920 Speaker 2: a process where we win down the road. 234 00:13:03,960 --> 00:13:06,200 Speaker 6: So you're talking the Haber process of course, which takes 235 00:13:06,200 --> 00:13:08,320 Speaker 6: me right back to my undergrad days. But how do 236 00:13:08,320 --> 00:13:12,440 Speaker 6: we think about productivity? Stock and flow is incredibly important 237 00:13:12,480 --> 00:13:15,040 Speaker 6: in Eliot any I knew you were. 238 00:13:14,920 --> 00:13:17,680 Speaker 2: Going here, and I set them up for this discuss 239 00:13:17,800 --> 00:13:19,000 Speaker 2: the So. 240 00:13:19,960 --> 00:13:23,040 Speaker 6: What allows either to be more efficient? What allows the 241 00:13:23,040 --> 00:13:25,560 Speaker 6: flow to be more efficient is a better idea of 242 00:13:25,600 --> 00:13:30,040 Speaker 6: what the dynamics and the data are describing that flow. 243 00:13:30,480 --> 00:13:33,439 Speaker 6: And that's something which AI can do. What it doesn't 244 00:13:33,480 --> 00:13:37,520 Speaker 6: do is make the decisions around how to influence how 245 00:13:37,520 --> 00:13:39,560 Speaker 6: that flow is going through. That's what a human being does. 246 00:13:39,960 --> 00:13:42,439 Speaker 6: But where AI, I think has something which we can 247 00:13:42,480 --> 00:13:45,640 Speaker 6: really lean into is that ability to help us quickly 248 00:13:45,720 --> 00:13:48,840 Speaker 6: pass through data and understand the problem. Seting it doesn't 249 00:13:49,679 --> 00:13:53,560 Speaker 6: mean that suddenly it educates us, but it informs us, 250 00:13:53,559 --> 00:13:55,040 Speaker 6: and I think that's an important subtlety. 251 00:13:56,000 --> 00:13:59,760 Speaker 2: No good. If you're helping Jennie Diamond right in your 252 00:13:59,800 --> 00:14:01,200 Speaker 2: love and we know that are you're going to get 253 00:14:01,240 --> 00:14:04,319 Speaker 2: two paragraphs in it this year. The stock is the 254 00:14:04,360 --> 00:14:07,760 Speaker 2: water in the bathtub, I would suggest, and this is 255 00:14:07,800 --> 00:14:11,000 Speaker 2: all the heritage of my family that we will see 256 00:14:11,040 --> 00:14:15,360 Speaker 2: the water in the bathtub clearer and better with all 257 00:14:15,400 --> 00:14:18,280 Speaker 2: this innovation. Do you agree with some of that idea? 258 00:14:18,400 --> 00:14:20,280 Speaker 6: I would agree with that. I'd also agree that what 259 00:14:20,320 --> 00:14:22,240 Speaker 6: you can do is you can optimize how it flows. 260 00:14:22,240 --> 00:14:23,760 Speaker 6: So if you've got the plug out of the bath, 261 00:14:23,800 --> 00:14:25,600 Speaker 6: you've got the tap running into the bath. That's the 262 00:14:25,800 --> 00:14:29,240 Speaker 6: classic old stock and flow view of the economy. You're 263 00:14:29,360 --> 00:14:32,560 Speaker 6: able to see both what's in the bathtub much better. 264 00:14:33,000 --> 00:14:35,600 Speaker 6: You're also able to see an optimal way to have 265 00:14:35,680 --> 00:14:38,320 Speaker 6: it flow through the bathtub. And I think that's if 266 00:14:38,320 --> 00:14:39,960 Speaker 6: we want to use an analogy, that to me is 267 00:14:40,000 --> 00:14:41,960 Speaker 6: what productivity is about. And that's where AI can be 268 00:14:42,000 --> 00:14:45,360 Speaker 6: a helpful tool. It doesn't replace the person stood there 269 00:14:45,440 --> 00:14:47,720 Speaker 6: looking at and this is beef I have with the 270 00:14:47,800 --> 00:14:49,720 Speaker 6: some of the moves we've seen in software, the idea 271 00:14:49,760 --> 00:14:51,920 Speaker 6: that all of a sudden AI can do everything. 272 00:14:52,320 --> 00:14:52,560 Speaker 2: We know. 273 00:14:52,640 --> 00:14:55,920 Speaker 6: AI has its limitations in its prone to hallucination. It 274 00:14:55,960 --> 00:14:59,400 Speaker 6: requires that interface, but it can create a clearer picture 275 00:15:00,080 --> 00:15:02,520 Speaker 6: in combination with that human expertise, and that, to me 276 00:15:03,000 --> 00:15:06,920 Speaker 6: is why we're seeing this interesting situation two hundred and 277 00:15:06,960 --> 00:15:10,760 Speaker 6: ten thousand on Jobless claims a few minutes ago below expectations. 278 00:15:11,600 --> 00:15:14,240 Speaker 6: Yet we're not seeing wage pressure and that's because jobs 279 00:15:14,240 --> 00:15:18,080 Speaker 6: are becoming more productive using the AI inputs. 280 00:15:18,640 --> 00:15:20,480 Speaker 2: Can you come back the second week in March. Paul 281 00:15:20,520 --> 00:15:24,480 Speaker 2: wants us to do thermodynamics. Can we do some entreprise 282 00:15:25,120 --> 00:15:28,480 Speaker 2: Jennie Diamonds. People are listening to this right now. How 283 00:15:28,520 --> 00:15:33,320 Speaker 2: do we get a bathtub? The letter folks, that was 284 00:15:33,400 --> 00:15:40,800 Speaker 2: a clinic on the chemistry dynamics, the nineteenth century models 285 00:15:40,840 --> 00:15:43,640 Speaker 2: that we're all struggling with here as we look forward 286 00:15:44,040 --> 00:15:47,200 Speaker 2: to twenty and thirty and beyond. John Bilton with a 287 00:15:47,240 --> 00:15:51,400 Speaker 2: clinic their global Manta asset strategy. JP Morgan, he is 288 00:15:51,920 --> 00:15:57,560 Speaker 2: a chemist. Stay with us. More from Bloomberg Surveillance coming 289 00:15:57,640 --> 00:15:58,640 Speaker 2: up after this. 290 00:16:05,880 --> 00:16:09,480 Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us live 291 00:16:09,520 --> 00:16:12,680 Speaker 1: weekday afternoons from seven to ten am Eastern. Listen on 292 00:16:12,760 --> 00:16:16,440 Speaker 1: Applecarplay and Android Auto with the Bloomberg Business app, or 293 00:16:16,600 --> 00:16:18,080 Speaker 1: watch us live on YouTube. 294 00:16:18,280 --> 00:16:21,760 Speaker 2: True madis of course, with equity and strategy at met Life, 295 00:16:22,120 --> 00:16:25,080 Speaker 2: pulling in all of his first class economics at ubs 296 00:16:25,440 --> 00:16:28,720 Speaker 2: over the years, writes a blistering short note today from 297 00:16:28,760 --> 00:16:33,080 Speaker 2: MetLife clients on AI in your job. We're thrilled that 298 00:16:33,120 --> 00:16:37,160 Speaker 2: dru Madis could join us this morning. Our chief market strategist, Drew, 299 00:16:37,200 --> 00:16:40,360 Speaker 2: thank you for this note. It's almost calming what Angelo 300 00:16:40,480 --> 00:16:43,040 Speaker 2: said there at the back end. Paul, I didn't understand 301 00:16:43,040 --> 00:16:46,200 Speaker 2: a word of it, Drew. I mean, just to cut 302 00:16:46,240 --> 00:16:50,240 Speaker 2: to the chase. There's this fear out there off of 303 00:16:50,320 --> 00:16:55,120 Speaker 2: a briefing from Sutrini, which is getting a lot of criticism. AI. 304 00:16:55,280 --> 00:16:59,160 Speaker 2: Can you fold it into our macroeconomics. Can you fold 305 00:16:59,160 --> 00:17:02,960 Speaker 2: it into our guest of it of core economic data 306 00:17:03,400 --> 00:17:05,240 Speaker 2: out one year or five years? 307 00:17:07,119 --> 00:17:09,719 Speaker 5: Well, I think you can, and I think you know. 308 00:17:09,840 --> 00:17:12,640 Speaker 7: Just to dispel a few things, one is like, this 309 00:17:12,720 --> 00:17:15,840 Speaker 7: is not the AI jobs apocalypse. You know, we are 310 00:17:15,840 --> 00:17:18,760 Speaker 7: thinking about this the wrong way. When I came into 311 00:17:18,760 --> 00:17:20,560 Speaker 7: work today, I've got fifty things I want to do, 312 00:17:21,160 --> 00:17:24,440 Speaker 7: and if AI can help me do thirty of them, right, 313 00:17:24,560 --> 00:17:26,840 Speaker 7: my constraint is still how many people I have working 314 00:17:27,040 --> 00:17:29,280 Speaker 7: for me and with me who can kind of engage 315 00:17:29,280 --> 00:17:32,280 Speaker 7: with me and discuss things with me, And how much 316 00:17:32,320 --> 00:17:33,560 Speaker 7: technology I have available? 317 00:17:33,680 --> 00:17:35,480 Speaker 5: Right, And that's anyone anywhere. 318 00:17:36,880 --> 00:17:40,520 Speaker 7: If I somehow use AI to discover fifty new things 319 00:17:40,560 --> 00:17:44,159 Speaker 7: about the universe or my job, right, I'm going to 320 00:17:44,320 --> 00:17:47,399 Speaker 7: get one hundred new questions from that knowledge, right that 321 00:17:47,560 --> 00:17:49,359 Speaker 7: I then have to answer. And then when I answer 322 00:17:49,320 --> 00:17:50,960 Speaker 7: those one hundred, I have a thousand I have to 323 00:17:51,000 --> 00:17:55,600 Speaker 7: answer off of those. And so you know, we're competitive species, right, 324 00:17:55,680 --> 00:17:57,080 Speaker 7: and so like you know, I work at a firm 325 00:17:57,119 --> 00:17:59,360 Speaker 7: that competes with other firms and everyone else does too, 326 00:18:00,240 --> 00:18:02,399 Speaker 7: and you're trying to make your firm win, right, and 327 00:18:02,440 --> 00:18:05,400 Speaker 7: so you're going to be constantly pushing for that knowledge 328 00:18:05,480 --> 00:18:08,359 Speaker 7: they kind of be useful and then learn the next thing. 329 00:18:08,800 --> 00:18:11,439 Speaker 7: And the best way to do that is obviously, you know, 330 00:18:11,880 --> 00:18:14,720 Speaker 7: technology and then people, and the people was a key 331 00:18:14,760 --> 00:18:17,119 Speaker 7: part of it. I think it's actually kind of you know, 332 00:18:17,240 --> 00:18:19,560 Speaker 7: I came from a liberal arts background, and I think, 333 00:18:19,640 --> 00:18:21,600 Speaker 7: you know, those kids who chose liberal arts are going 334 00:18:21,680 --> 00:18:23,280 Speaker 7: to be really happy in the coming future. 335 00:18:24,280 --> 00:18:26,840 Speaker 4: Drew, what do you say to a shareholder and I 336 00:18:26,840 --> 00:18:29,240 Speaker 4: don't know Salesforce dot Com or any of these other 337 00:18:29,720 --> 00:18:33,080 Speaker 4: software company or software as a service companies that Senior 338 00:18:33,119 --> 00:18:37,080 Speaker 4: stock's really beaten down over the last several months, and 339 00:18:37,160 --> 00:18:39,720 Speaker 4: yet their fundamentals are great, their earnings are great, the 340 00:18:39,800 --> 00:18:41,800 Speaker 4: cash flows great. Everybody loves software. 341 00:18:42,000 --> 00:18:43,280 Speaker 6: What do you say to those shareholders. 342 00:18:44,800 --> 00:18:45,679 Speaker 5: You know, I don't know. 343 00:18:45,760 --> 00:18:48,239 Speaker 7: I don't so I don't follow equity, so I'll say that, 344 00:18:48,280 --> 00:18:50,800 Speaker 7: and I don't certainly don't follow individual companies. But what 345 00:18:50,880 --> 00:18:52,320 Speaker 7: I will say is that there is a bit of 346 00:18:52,359 --> 00:18:54,960 Speaker 7: a panic motif that anyone who's bought a house knows 347 00:18:54,960 --> 00:18:56,639 Speaker 7: that you buy a house and then someone shows up 348 00:18:56,640 --> 00:18:59,000 Speaker 7: at your house, they knock on your door and they're like, oh, 349 00:18:59,040 --> 00:19:00,639 Speaker 7: I used to work for the people who own this 350 00:19:00,680 --> 00:19:03,399 Speaker 7: house before you know you bought it. And if you 351 00:19:03,440 --> 00:19:05,760 Speaker 7: don't change this one thing really quickly, your whole house 352 00:19:05,800 --> 00:19:08,240 Speaker 7: will burn down. You all die, right, And then a 353 00:19:08,280 --> 00:19:11,000 Speaker 7: lot of people end up hiring that contractor to do 354 00:19:11,000 --> 00:19:14,359 Speaker 7: whatever useless piece of work it is. And it's just 355 00:19:15,320 --> 00:19:17,399 Speaker 7: it's not to say that everything that's happening is useless, 356 00:19:17,400 --> 00:19:19,480 Speaker 7: but I think that there's first of all, got to 357 00:19:19,480 --> 00:19:26,080 Speaker 7: be a recognition that the pace is important and you 358 00:19:26,119 --> 00:19:28,200 Speaker 7: don't want to be left behind. But you also don't 359 00:19:28,240 --> 00:19:30,240 Speaker 7: want to be pressured into kind of moving before you're 360 00:19:30,240 --> 00:19:34,200 Speaker 7: reading right, And there's nothing wrong with thinking about things 361 00:19:34,240 --> 00:19:36,240 Speaker 7: and how to kind of use technology in the best 362 00:19:36,280 --> 00:19:37,320 Speaker 7: way for your company. 363 00:19:38,040 --> 00:19:39,960 Speaker 5: And I do think, you know, I think in terms 364 00:19:39,960 --> 00:19:40,800 Speaker 5: of using AI. 365 00:19:41,320 --> 00:19:42,960 Speaker 7: You know, one of the main constraints is going to 366 00:19:43,000 --> 00:19:47,680 Speaker 7: be the organizational structures of companies rather than finding things 367 00:19:47,680 --> 00:19:52,480 Speaker 7: for AI to do or you know, I just think 368 00:19:52,520 --> 00:19:54,639 Speaker 7: there's a lot more benefit to kind of expanding the 369 00:19:54,680 --> 00:19:57,439 Speaker 7: knowledge base, and people are ignoring that part and the 370 00:19:57,480 --> 00:20:00,760 Speaker 7: fact that the more knowledge each company has, the more 371 00:20:00,800 --> 00:20:04,520 Speaker 7: competitive they are going to be, and the more wealth 372 00:20:04,560 --> 00:20:07,480 Speaker 7: they will generate for the economy, the more productive the 373 00:20:07,480 --> 00:20:09,040 Speaker 7: economy will be, and the higher the growth rate in 374 00:20:09,080 --> 00:20:12,240 Speaker 7: the economy will be. And that doesn't happen without. 375 00:20:11,960 --> 00:20:17,600 Speaker 4: People drew what's the overall kind of backdrop here as 376 00:20:17,640 --> 00:20:19,720 Speaker 4: we think about tariffs. 377 00:20:19,920 --> 00:20:21,080 Speaker 5: We kind of put that the rest. 378 00:20:21,080 --> 00:20:23,280 Speaker 4: It seemed like the market had at least come to 379 00:20:23,320 --> 00:20:25,640 Speaker 4: groups with the kind of the tariff regime. That kind 380 00:20:25,640 --> 00:20:27,639 Speaker 4: of changed in the last couple of weeks with the 381 00:20:27,680 --> 00:20:31,360 Speaker 4: Supreme Court ruling and President Trump's response to that. Now 382 00:20:31,440 --> 00:20:33,639 Speaker 4: I guess as investors we all have to think about 383 00:20:34,240 --> 00:20:37,320 Speaker 4: tariffs again. Is that impacted kind of your outlook for 384 00:20:37,760 --> 00:20:38,520 Speaker 4: the markets here? 385 00:20:41,119 --> 00:20:43,560 Speaker 7: It hasn't, because, you know, we were kind of back 386 00:20:43,600 --> 00:20:46,040 Speaker 7: at the same tariff rate, and you know, we kind 387 00:20:46,040 --> 00:20:47,560 Speaker 7: of felt like we were living in a period of 388 00:20:47,640 --> 00:20:49,720 Speaker 7: uncertainty and so that hasn't changed at all. 389 00:20:51,160 --> 00:20:51,359 Speaker 6: You know. 390 00:20:52,240 --> 00:20:54,640 Speaker 7: That being said, if you stay at a consistent level 391 00:20:54,640 --> 00:20:58,760 Speaker 7: of uncertainty, that uncertainty becomes a feature, right, and so 392 00:20:58,880 --> 00:21:00,919 Speaker 7: you can actually almost be in the plan for it. 393 00:21:01,760 --> 00:21:05,440 Speaker 7: And so the more consistent things are in the universe, 394 00:21:05,760 --> 00:21:08,280 Speaker 7: the easier they are to kind of forecast everything else. 395 00:21:09,200 --> 00:21:14,680 Speaker 7: And that consistency can also be achieved with consistently uncertain true. 396 00:21:15,320 --> 00:21:17,440 Speaker 2: To bring it back to the market strategy. Right now, 397 00:21:17,440 --> 00:21:19,879 Speaker 2: we haven't even had a correction a lot of inks 398 00:21:19,920 --> 00:21:22,080 Speaker 2: in the market. Maybe we can say there's a flatness 399 00:21:22,119 --> 00:21:27,280 Speaker 2: inequities out here on a portfolio basis, people retirees looking 400 00:21:27,359 --> 00:21:30,000 Speaker 2: at two years, three years, five years, Do you make 401 00:21:30,040 --> 00:21:33,359 Speaker 2: adjustments here or is it basically steady as should go 402 00:21:33,680 --> 00:21:36,160 Speaker 2: from the plan of twenty twenty five. 403 00:21:37,320 --> 00:21:39,560 Speaker 7: I mean, as we think about our asset allocation. You know, 404 00:21:39,560 --> 00:21:42,679 Speaker 7: we've been moving up in quality for some time, and so, 405 00:21:44,359 --> 00:21:47,960 Speaker 7: you know, because spreads are so tight across so many sectors, 406 00:21:48,040 --> 00:21:50,359 Speaker 7: you know, moving up in quality just kind of if 407 00:21:50,400 --> 00:21:51,800 Speaker 7: I'm not going to get paid to take the risk, 408 00:21:51,840 --> 00:21:54,360 Speaker 7: why wouldn't I just not get paid to take no risk? 409 00:21:54,480 --> 00:21:54,640 Speaker 5: Right? 410 00:21:54,680 --> 00:21:57,720 Speaker 7: And so you move towards a lower risk environment there, 411 00:21:58,200 --> 00:22:00,840 Speaker 7: and you look for opportunities where you can risks that 412 00:22:00,960 --> 00:22:04,640 Speaker 7: actually benefit you as opposed to just kind of generic risk. 413 00:22:05,520 --> 00:22:07,560 Speaker 7: And I think that's what people should be thinking about, 414 00:22:07,680 --> 00:22:09,200 Speaker 7: is you know what kind of risk am I willing 415 00:22:09,240 --> 00:22:09,600 Speaker 7: to take? 416 00:22:10,040 --> 00:22:12,680 Speaker 5: And adjusting their portfolios accordingly du. 417 00:22:12,720 --> 00:22:17,240 Speaker 2: Manus, thank you so much, appreciated, uh with met Life. 418 00:22:18,119 --> 00:22:22,280 Speaker 2: Stay with us. More from Bloomberg Surveillance coming up after this. 419 00:22:29,520 --> 00:22:33,120 Speaker 1: You're listening to the Bloomberg Surveillance podcast. Catch us Live 420 00:22:33,200 --> 00:22:36,280 Speaker 1: weekday afternoons from seven to ten am Eastern Listen on 421 00:22:36,440 --> 00:22:39,840 Speaker 1: Apple Karplay and Android Auto with the Bloomberg Business app, 422 00:22:40,000 --> 00:22:41,680 Speaker 1: or watch us Live on YouTube. 423 00:22:41,880 --> 00:22:44,080 Speaker 2: Let's get right to the newspapers. We've got a special 424 00:22:44,160 --> 00:22:47,639 Speaker 2: treat at the end with Paul Sweeney, Alexis Christophers is 425 00:22:47,760 --> 00:22:48,960 Speaker 2: all newspapers. 426 00:22:49,200 --> 00:22:51,720 Speaker 8: So front page of the Wall Street Journal today, big 427 00:22:51,800 --> 00:22:55,359 Speaker 8: story here on how Americans are leaving the US in. 428 00:22:55,359 --> 00:22:57,679 Speaker 2: Record right, every word of it, Thank you, right. 429 00:22:57,840 --> 00:23:00,239 Speaker 8: Fascinating story here, and it's not all to do right 430 00:23:00,320 --> 00:23:03,640 Speaker 8: with the immigration crackdown. So they're leaving for a few reasons. 431 00:23:03,880 --> 00:23:05,720 Speaker 8: The rise of remote work, which we saw we can 432 00:23:05,760 --> 00:23:09,400 Speaker 8: do post pandemic, the high cost of living so affordability right, 433 00:23:09,400 --> 00:23:13,280 Speaker 8: the key and this appetite for foreign lifestyles, especially in 434 00:23:13,320 --> 00:23:16,680 Speaker 8: Europe where people find it more affordable and safer. 435 00:23:17,160 --> 00:23:18,320 Speaker 2: Yeah, but it's not just Europe. 436 00:23:18,520 --> 00:23:22,440 Speaker 8: Locals in places like Bali, Columbia and Thailand now protesting 437 00:23:22,600 --> 00:23:24,720 Speaker 8: against the wave of gentrification. 438 00:23:25,520 --> 00:23:27,960 Speaker 2: Absolutely, and Cyper shut it down. They had so many 439 00:23:28,000 --> 00:23:30,840 Speaker 2: Russians and Chinese coming in. Cypers shut it on program. 440 00:23:31,119 --> 00:23:33,919 Speaker 2: In that article, what I noted was Portugal is still 441 00:23:34,359 --> 00:23:38,320 Speaker 2: giarmis hot. I mean I was in Prague twenty years 442 00:23:38,440 --> 00:23:41,560 Speaker 2: ago at nine o'clock at night, and everyone around me 443 00:23:41,600 --> 00:23:44,879 Speaker 2: in a little cafe out of a movie was American. 444 00:23:45,240 --> 00:23:46,879 Speaker 6: And this is the cusp. 445 00:23:47,280 --> 00:23:51,040 Speaker 8: Yeah, you don't want to sit there hearing English being 446 00:23:51,080 --> 00:23:53,600 Speaker 8: spoken all around you. That's not why you moved there. 447 00:23:53,440 --> 00:23:55,439 Speaker 4: The number four offsprings heading to Japan. 448 00:23:55,440 --> 00:23:57,000 Speaker 6: I don't know if he's coming back. We'll see that. 449 00:23:57,280 --> 00:24:01,239 Speaker 2: Yeah, I mean I have kids abroad, and actually, now 450 00:24:01,280 --> 00:24:04,040 Speaker 2: that I think about it, they're almost all. 451 00:24:03,960 --> 00:24:07,040 Speaker 4: Is Los Angeles, bro, Yes, exactly, I've got one there 452 00:24:07,040 --> 00:24:07,440 Speaker 4: as well. 453 00:24:07,440 --> 00:24:10,440 Speaker 2: It's a great article. Just you know, the two guys 454 00:24:10,480 --> 00:24:12,880 Speaker 2: that did it over the journalist killed good stuff. Yeah, 455 00:24:12,880 --> 00:24:13,640 Speaker 2: what do you got next? 456 00:24:13,760 --> 00:24:14,120 Speaker 6: All right? 457 00:24:14,160 --> 00:24:16,800 Speaker 8: The race is on for companies to get their tariff 458 00:24:16,880 --> 00:24:19,720 Speaker 8: money back. So it's been less than a week since 459 00:24:19,720 --> 00:24:23,359 Speaker 8: the Supreme Court struck down many of Trump's global terrors, 460 00:24:23,400 --> 00:24:26,840 Speaker 8: and already nearly two thousand companies have filed lawsuits seeking 461 00:24:26,840 --> 00:24:29,240 Speaker 8: their tariff money back. That's on top of the hundreds 462 00:24:29,280 --> 00:24:32,560 Speaker 8: who filed suits and anticipation of that court ruling. And 463 00:24:32,600 --> 00:24:35,479 Speaker 8: these are well known companies, Costco, Barnes and Noble, Goodyear, 464 00:24:35,520 --> 00:24:38,600 Speaker 8: Tire and Rubber. Before the High Court ruling, the lawyers 465 00:24:38,600 --> 00:24:42,040 Speaker 8: for the Trump administration assured the lower courts that companies 466 00:24:42,040 --> 00:24:44,960 Speaker 8: would be made whole and they would also be paid interest. 467 00:24:45,480 --> 00:24:47,520 Speaker 8: They haven't said anything about that though, you know. 468 00:24:47,600 --> 00:24:50,040 Speaker 4: I saw some reporting recently and says, hey, do you 469 00:24:50,080 --> 00:24:53,320 Speaker 4: want to incur potential the wrath of President Trump if 470 00:24:53,359 --> 00:24:56,600 Speaker 4: you're a big company, public company and say you want 471 00:24:56,640 --> 00:24:58,719 Speaker 4: your money back? Or do you just say that special 472 00:24:58,720 --> 00:25:01,040 Speaker 4: eat at the bridge. I don't know, but now, but 473 00:25:01,080 --> 00:25:02,200 Speaker 4: the companies they are in fact. 474 00:25:02,400 --> 00:25:05,920 Speaker 8: This could be miired in the sports for years to come, 475 00:25:06,160 --> 00:25:06,640 Speaker 8: but there'll. 476 00:25:06,440 --> 00:25:10,399 Speaker 2: Be a point where they will win. I'm editorializing, and 477 00:25:10,440 --> 00:25:13,440 Speaker 2: then somebody's got to write a check to FedEx like that. 478 00:25:13,760 --> 00:25:16,000 Speaker 8: Basically FedEx is one of the companies suing exactly. 479 00:25:16,000 --> 00:25:17,720 Speaker 4: They're going to issue te bills and government is what. 480 00:25:17,800 --> 00:25:19,959 Speaker 8: And this could happen well after there you go, This 481 00:25:20,000 --> 00:25:21,760 Speaker 8: could happen well after Trump is out of office. 482 00:25:21,880 --> 00:25:22,800 Speaker 2: Next one one all right. 483 00:25:22,840 --> 00:25:25,560 Speaker 8: New York Times talks about how women are making their 484 00:25:25,600 --> 00:25:28,800 Speaker 8: mark on the world of art collecting. So this is interesting. 485 00:25:28,800 --> 00:25:31,320 Speaker 8: Women now control more than a third of global wealth, 486 00:25:31,440 --> 00:25:33,880 Speaker 8: and recent data finds they are spending more on art 487 00:25:33,960 --> 00:25:37,600 Speaker 8: than men. And guess what that's influencing what museums are buying. 488 00:25:38,280 --> 00:25:41,480 Speaker 8: So it shows that women often buy works from little 489 00:25:41,520 --> 00:25:42,560 Speaker 8: known artists more. 490 00:25:42,440 --> 00:25:43,000 Speaker 4: Than men do. 491 00:25:43,040 --> 00:25:47,040 Speaker 8: They buy artwork made by women. And Christie's over here 492 00:25:47,040 --> 00:25:49,240 Speaker 8: in New York City, the auction house said that its 493 00:25:49,280 --> 00:25:52,439 Speaker 8: female client base has grown ten percent. New crop of 494 00:25:52,600 --> 00:25:55,120 Speaker 8: young female collectors as well. Let's hear it from them, 495 00:25:55,160 --> 00:25:58,560 Speaker 8: gen Z and millennial women outspent men last year when 496 00:25:58,600 --> 00:25:59,520 Speaker 8: it came to artwork. 497 00:26:00,080 --> 00:26:03,680 Speaker 2: I do a plug, sure, Amy ing enng Amy ing 498 00:26:03,880 --> 00:26:06,200 Speaker 2: is the curator over at the Frick. She's an act 499 00:26:06,240 --> 00:26:10,720 Speaker 2: of God. Her show that's there just opening Gainsborough. It's 500 00:26:10,760 --> 00:26:13,320 Speaker 2: all the pretty pictures of people in the eighteenth Center. 501 00:26:13,400 --> 00:26:15,880 Speaker 2: We know all the and I went in. I'm like, yeah, yeah, 502 00:26:16,280 --> 00:26:19,359 Speaker 2: you know, okay, this is a punishment before lunchhowcake right. 503 00:26:20,040 --> 00:26:24,000 Speaker 2: I was blown away about what the Frick Museum in 504 00:26:24,040 --> 00:26:27,520 Speaker 2: New York has done with something is conventional. Is Gainesboro 505 00:26:27,720 --> 00:26:30,320 Speaker 2: of the eighteenth century. I'm so happy to hear did 506 00:26:30,720 --> 00:26:33,520 Speaker 2: what Amy did was. It's just shocking how good it is. 507 00:26:33,840 --> 00:26:35,560 Speaker 8: Well, if you haven't gone to the Frick, guys, and 508 00:26:35,600 --> 00:26:39,119 Speaker 8: you're able to go because post the renovation, it is 509 00:26:39,320 --> 00:26:40,000 Speaker 8: really a treat. 510 00:26:40,240 --> 00:26:40,600 Speaker 6: Awesome. 511 00:26:40,640 --> 00:26:43,439 Speaker 2: Okay, thanks so much, alexis the newspapers here. 512 00:26:43,680 --> 00:26:52,760 Speaker 1: This is the Bloomberg Surveillance podcast, available on apples, Spotify, 513 00:26:53,200 --> 00:26:55,680 Speaker 1: and anywhere else you. 514 00:26:55,600 --> 00:26:57,080 Speaker 2: Get your podcasts. 515 00:26:58,560 --> 00:27:05,119 Speaker 1: Listen live each weekday, seven to ten am Eastern on 516 00:27:05,480 --> 00:27:12,920 Speaker 1: Bloomberg dot com, the iHeartRadio app, tune In, and the 517 00:27:13,040 --> 00:27:19,760 Speaker 1: Bloomberg Business app. You can also watch us live every 518 00:27:19,920 --> 00:27:26,159 Speaker 1: weekday on YouTube and always on the Bloomberg terminal.