1 00:00:00,840 --> 00:00:04,000 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, alongside 2 00:00:04,040 --> 00:00:05,280 Speaker 1: my co host Matt Miller. 3 00:00:05,640 --> 00:00:09,600 Speaker 2: Every business day, we bring you interviews from CEOs, market pros, 4 00:00:09,720 --> 00:00:13,600 Speaker 2: and Bloomberg experts, along with essential market moving news. 5 00:00:14,160 --> 00:00:17,279 Speaker 1: Find the Bloomberg Markets podcast called Apple Podcasts or wherever 6 00:00:17,400 --> 00:00:20,520 Speaker 1: you listen to podcasts, and at Bloomberg dot com slash podcast. 7 00:00:21,440 --> 00:00:25,480 Speaker 1: Darkening the Door, I gotta describe here what's going on here? 8 00:00:25,600 --> 00:00:28,440 Speaker 1: Pretty this is a little bit I'm thrown unnerving here. 9 00:00:28,440 --> 00:00:31,720 Speaker 1: All right, let's start with anaag Rana. He walks from 10 00:00:31,760 --> 00:00:34,479 Speaker 1: the door freshly shaven. Now, the reason this is important 11 00:00:34,479 --> 00:00:37,120 Speaker 1: because he's had a scraggly beard. I'm calling it for 12 00:00:37,200 --> 00:00:38,720 Speaker 1: a couple three years. I don't know if it was 13 00:00:38,760 --> 00:00:42,000 Speaker 1: a pandemic thing, but I was petitioning from the shave 14 00:00:42,040 --> 00:00:44,320 Speaker 1: it off since the first time I saw it. That's 15 00:00:44,360 --> 00:00:47,280 Speaker 1: gone looks a thousand times younger. I would say, he 16 00:00:47,280 --> 00:00:48,440 Speaker 1: looks him ten years younger. 17 00:00:48,560 --> 00:00:50,720 Speaker 3: I gotta say, I saw him walking towards the radio 18 00:00:50,720 --> 00:00:51,720 Speaker 3: studio and I see a double take. 19 00:00:51,720 --> 00:00:52,600 Speaker 4: I didn't even recognize it. 20 00:00:52,640 --> 00:00:54,840 Speaker 1: I know. Sorry, which we got that going for us? Yeah, 21 00:00:54,880 --> 00:00:57,200 Speaker 1: and then we've got Dan ives he covers technology stuff 22 00:00:57,200 --> 00:01:01,120 Speaker 1: for web Bush Security. So I think I'm scruff are you? Okay, 23 00:01:01,160 --> 00:01:03,760 Speaker 1: it's just not it's not even a choice here with Ana. 24 00:01:03,960 --> 00:01:05,600 Speaker 1: You gotta go. And then he's got the haircut. He's like, 25 00:01:05,600 --> 00:01:07,679 Speaker 1: it's a whole new on ruck, all right. So dan 26 00:01:07,800 --> 00:01:10,600 Speaker 1: Ives comes in with an summer outfit that is right 27 00:01:10,680 --> 00:01:14,119 Speaker 1: on the edge of being summer cool chic. 28 00:01:14,319 --> 00:01:16,440 Speaker 4: It's like fresh back from Bermuda. What's going on here? 29 00:01:16,560 --> 00:01:18,800 Speaker 1: Or it's right on that line of being cool and 30 00:01:18,840 --> 00:01:20,920 Speaker 1: being obscene. I don't know where we are. 31 00:01:21,080 --> 00:01:24,119 Speaker 3: It's it's like a Picasso painting in a very strange way, 32 00:01:24,120 --> 00:01:25,520 Speaker 3: like I can't even I don't even know what's saying. 33 00:01:25,520 --> 00:01:27,680 Speaker 1: And you brought that look over to Asia because I 34 00:01:27,720 --> 00:01:29,680 Speaker 1: saw your postings on social media Daan when you're over 35 00:01:29,760 --> 00:01:32,000 Speaker 1: on your marketing trip to Asia. You weren't holding back, 36 00:01:32,040 --> 00:01:32,440 Speaker 1: were you. 37 00:01:32,440 --> 00:01:32,560 Speaker 5: No? 38 00:01:32,600 --> 00:01:35,200 Speaker 6: I mean we brought it from from two to one 39 00:01:35,280 --> 00:01:36,399 Speaker 6: two over to Asia. 40 00:01:36,480 --> 00:01:38,040 Speaker 4: Oh that's where the color is coming from. Okay, this 41 00:01:38,120 --> 00:01:39,039 Speaker 4: makes sense two one. 42 00:01:38,959 --> 00:01:40,720 Speaker 6: Two color over to Asia, and I think, yeah, I 43 00:01:40,720 --> 00:01:42,960 Speaker 6: think it worked well. It could be a media a trend. 44 00:01:43,040 --> 00:01:46,600 Speaker 6: You've started seeing some tech investers throughout Asia. It's because 45 00:01:47,240 --> 00:01:50,280 Speaker 6: I mean it could besides AI and some of the themes, 46 00:01:50,280 --> 00:01:51,400 Speaker 6: you could have a all. 47 00:01:51,440 --> 00:01:54,160 Speaker 1: Right, so let me here's all right, Let let me 48 00:01:54,200 --> 00:01:56,840 Speaker 1: just go to anaack here, what does Nvidia do and 49 00:01:56,880 --> 00:02:00,360 Speaker 1: why are they seemingly getting all these all this chip 50 00:02:00,440 --> 00:02:04,360 Speaker 1: demand for AIS? Is this high bitch or is it real? 51 00:02:04,800 --> 00:02:07,400 Speaker 7: Well, the high pot is will always be driven by 52 00:02:07,680 --> 00:02:09,840 Speaker 7: you know, a little bit of valuation. But I think 53 00:02:09,880 --> 00:02:11,480 Speaker 7: one of the things you have to think about when 54 00:02:11,520 --> 00:02:13,880 Speaker 7: you run some of the very large workloads, even on 55 00:02:13,919 --> 00:02:17,040 Speaker 7: the cloud, you need really fast computers and you really 56 00:02:17,080 --> 00:02:21,040 Speaker 7: need faster chips. And Nvidia is the leader in those 57 00:02:21,120 --> 00:02:23,480 Speaker 7: chips out there, and they really posted good numbers, which 58 00:02:23,520 --> 00:02:27,280 Speaker 7: basically tells us that the world is investing a lot 59 00:02:27,320 --> 00:02:30,440 Speaker 7: more in high powered computers right now because of all 60 00:02:30,440 --> 00:02:31,320 Speaker 7: the AI frenzy. 61 00:02:32,160 --> 00:02:32,840 Speaker 4: Before I go to. 62 00:02:32,880 --> 00:02:34,840 Speaker 3: Dan, can I just point out that the reason an 63 00:02:35,040 --> 00:02:37,600 Speaker 3: rag has haircut and fresh shave and all that because 64 00:02:37,600 --> 00:02:41,280 Speaker 3: there's an event happening this evening, afternoon, afternoon, slush evening 65 00:02:41,600 --> 00:02:42,520 Speaker 3: it is, I argue. 66 00:02:42,320 --> 00:02:44,359 Speaker 7: Went to tell them, Yeah, there'll be a big semiconductory 67 00:02:44,400 --> 00:02:47,839 Speaker 7: event we are having in the one twenty park about well, 68 00:02:47,840 --> 00:02:51,960 Speaker 7: have Micron, Intel and Global founderies. They're talking about, you know, 69 00:02:52,080 --> 00:02:54,799 Speaker 7: the the making of the chips in the US from 70 00:02:54,880 --> 00:02:57,240 Speaker 7: an executive from Department of Commerce. 71 00:02:57,400 --> 00:02:58,760 Speaker 1: Yeah, perfect suite of. 72 00:02:58,760 --> 00:03:00,480 Speaker 4: Some of the Yeah, some of the some the biggest 73 00:03:00,480 --> 00:03:03,280 Speaker 4: and some of the best. Very excited about that. Yeah, 74 00:03:03,320 --> 00:03:05,400 Speaker 4: they are on top of it. Somean deep thing. Bloomberg 75 00:03:05,400 --> 00:03:07,560 Speaker 4: Intelligence will be there on ROG of course leading the charge. 76 00:03:08,800 --> 00:03:09,120 Speaker 1: Yeah. 77 00:03:09,280 --> 00:03:10,960 Speaker 4: Good, good lineup, Dan, talk. 78 00:03:10,880 --> 00:03:12,959 Speaker 3: To us a little bit about this trade here the 79 00:03:13,080 --> 00:03:17,680 Speaker 3: idea that kind of AI is driving in video shares 80 00:03:17,720 --> 00:03:21,440 Speaker 3: and video isn't a pure play on AI necessarily. So 81 00:03:22,040 --> 00:03:24,720 Speaker 3: how quickly does this thirty percent move in the stock 82 00:03:24,800 --> 00:03:25,560 Speaker 3: get walked back? 83 00:03:26,000 --> 00:03:29,280 Speaker 6: Look, this is historic. I mean in covering tech stocks 84 00:03:29,320 --> 00:03:32,560 Speaker 6: twenty two years, I've never seen anything in my career 85 00:03:32,960 --> 00:03:35,760 Speaker 6: where you have a guidance raise sixty percent in the quarter. 86 00:03:35,840 --> 00:03:38,560 Speaker 6: And I think the difference and why this is I 87 00:03:38,600 --> 00:03:40,960 Speaker 6: think the ripple effects here in terms of this goal 88 00:03:41,080 --> 00:03:42,080 Speaker 6: rushers happen in AI. 89 00:03:42,520 --> 00:03:43,480 Speaker 1: It validates it. 90 00:03:43,600 --> 00:03:47,080 Speaker 6: Demand is Eshian's real because ultimately, as that Aerog talks about, 91 00:03:47,240 --> 00:03:50,160 Speaker 6: they're the ones that see at first. I believe, in 92 00:03:50,200 --> 00:03:50,920 Speaker 6: my view. 93 00:03:50,920 --> 00:03:51,840 Speaker 1: This is just a start. 94 00:03:51,840 --> 00:03:54,720 Speaker 6: I think it's a revolution that's playing out and in 95 00:03:55,040 --> 00:03:59,400 Speaker 6: my view, Microsoft, Google, across the board. It's just the 96 00:03:59,440 --> 00:04:02,640 Speaker 6: start of what I view is probably the biggest transformational 97 00:04:02,720 --> 00:04:04,480 Speaker 6: theme that I've ever seen. 98 00:04:04,520 --> 00:04:05,800 Speaker 1: Cortentions, You've been. 99 00:04:05,680 --> 00:04:08,280 Speaker 3: A techable for ages, so this is this is pretty 100 00:04:08,680 --> 00:04:10,280 Speaker 3: This makes a lot of sense that you would say 101 00:04:10,280 --> 00:04:11,640 Speaker 3: that what flips your view? 102 00:04:11,640 --> 00:04:12,760 Speaker 4: What gets in the way of us? 103 00:04:12,800 --> 00:04:14,560 Speaker 6: It's when I talked and I can tell you the 104 00:04:14,640 --> 00:04:17,160 Speaker 6: last three weeks, like whether it's asier whether us when 105 00:04:17,160 --> 00:04:20,960 Speaker 6: I talk to CIOs, when I talked to product managers, 106 00:04:21,120 --> 00:04:23,440 Speaker 6: and now they're looking to spend for every one hundred 107 00:04:23,480 --> 00:04:27,960 Speaker 6: dollars in cloud, spend thirty five to forty dollars incremental 108 00:04:28,000 --> 00:04:31,040 Speaker 6: for AI. To me, that that's the game changing. That's 109 00:04:31,080 --> 00:04:35,920 Speaker 6: why like, look those if your valuation centric and we've 110 00:04:35,920 --> 00:04:38,880 Speaker 6: talked about this, you would have missed the Facebook's Netflix, 111 00:04:38,960 --> 00:04:41,880 Speaker 6: has it? I feel like this is one where we're 112 00:04:41,920 --> 00:04:45,400 Speaker 6: talking conservatively eight hundred billion over the next decade incremental 113 00:04:45,440 --> 00:04:49,640 Speaker 6: spend and mebe aggressively in the trillions with the amount 114 00:04:49,680 --> 00:04:52,920 Speaker 6: of names you count on two hands, all. 115 00:04:52,839 --> 00:04:57,480 Speaker 1: Right, so Anurag explain to me, I mean every company 116 00:04:57,480 --> 00:04:59,960 Speaker 1: in yester p. Five hundred and the last quarterly earnings 117 00:05:01,440 --> 00:05:06,280 Speaker 1: mentioned AI Yeah, for the average company, average person, what 118 00:05:06,400 --> 00:05:06,839 Speaker 1: is AI. 119 00:05:07,480 --> 00:05:09,839 Speaker 7: So it's you get trying to get a lot more 120 00:05:09,839 --> 00:05:12,840 Speaker 7: insights from what you're doing already and try to make 121 00:05:12,880 --> 00:05:15,200 Speaker 7: better decisions either on the revenue side or the cost site. 122 00:05:15,200 --> 00:05:18,520 Speaker 1: Because you have data, You have data, another way to analyze, 123 00:05:18,760 --> 00:05:20,640 Speaker 1: organize and analyze data. 124 00:05:20,800 --> 00:05:23,760 Speaker 7: So you know, it's much easier to do on the 125 00:05:23,760 --> 00:05:26,120 Speaker 7: consumer front because it's you know, we have all the 126 00:05:26,120 --> 00:05:28,640 Speaker 7: consumer data, you have the Internet, you can feed off that. 127 00:05:29,000 --> 00:05:31,200 Speaker 7: It's a little bit harder on the enterprise side because 128 00:05:31,200 --> 00:05:34,600 Speaker 7: your data is extremely disaggregated in multiple systems, so it's 129 00:05:34,640 --> 00:05:36,880 Speaker 7: a little bit of a longer process. Means you have 130 00:05:36,920 --> 00:05:39,560 Speaker 7: to buy the computers which we we're looking at, or 131 00:05:39,600 --> 00:05:41,560 Speaker 7: the chips. Then you've got to figure out how to 132 00:05:41,600 --> 00:05:44,280 Speaker 7: put the data in a common data platform where you're 133 00:05:44,279 --> 00:05:46,480 Speaker 7: going to bring all the data together from let's say 134 00:05:46,560 --> 00:05:49,000 Speaker 7: hundreds of systems. Then you got to analyze it, and 135 00:05:49,040 --> 00:05:51,000 Speaker 7: then you put the algorithm on top of it to 136 00:05:51,040 --> 00:05:53,120 Speaker 7: get you insights. So it's a little bit more longer 137 00:05:53,440 --> 00:05:56,240 Speaker 7: term view when it comes to enterprises, but a lot 138 00:05:56,320 --> 00:05:58,160 Speaker 7: faster when it's it's a consumer application. 139 00:05:58,720 --> 00:06:01,320 Speaker 3: Well, explain to me, like put put some numbers on it. 140 00:06:01,360 --> 00:06:03,880 Speaker 3: For us, if we're looking at what do you say 141 00:06:03,920 --> 00:06:04,440 Speaker 3: in it was an eight. 142 00:06:04,480 --> 00:06:08,120 Speaker 6: Hundred eight hundred billion or eight hundred billion an incremental 143 00:06:08,200 --> 00:06:10,320 Speaker 6: spend that basically six months ago, wasn't there? 144 00:06:10,400 --> 00:06:12,720 Speaker 3: Okay, eight hundred billion over the next decade. And Dan, 145 00:06:12,720 --> 00:06:14,240 Speaker 3: I'm going to come to you next on this, but 146 00:06:15,040 --> 00:06:17,240 Speaker 3: explain how the math works here, because if you're looking 147 00:06:17,240 --> 00:06:19,440 Speaker 3: at Nvidia, I think that this data right or something 148 00:06:19,520 --> 00:06:21,000 Speaker 3: was like the share price a loan or the market 149 00:06:21,040 --> 00:06:23,720 Speaker 3: cap alone off this rally has risen like one am 150 00:06:23,800 --> 00:06:25,360 Speaker 3: D or something like that. 151 00:06:25,720 --> 00:06:26,960 Speaker 4: It's most direct competitor. 152 00:06:27,320 --> 00:06:32,400 Speaker 3: How do you price in eight hundred million worth of 153 00:06:32,520 --> 00:06:34,680 Speaker 3: market over the next ten years. 154 00:06:34,920 --> 00:06:36,480 Speaker 7: So one of the things you have to see is 155 00:06:36,520 --> 00:06:38,839 Speaker 7: you know what kind of applications are going to be created. 156 00:06:38,839 --> 00:06:41,320 Speaker 7: Who's going to be the big beneficiary? Now we talked 157 00:06:41,360 --> 00:06:44,200 Speaker 7: about a chip manufacturer. For our point of view, the 158 00:06:44,279 --> 00:06:46,880 Speaker 7: cloud players are going to be the biggest beneficiary because 159 00:06:47,320 --> 00:06:49,359 Speaker 7: you have to build this thing on a cloud. You 160 00:06:49,400 --> 00:06:51,680 Speaker 7: can't really run it in your basement and come up 161 00:06:51,720 --> 00:06:55,440 Speaker 7: with applications. These are things that require very fast computation 162 00:06:55,520 --> 00:06:58,640 Speaker 7: and very large memory needs. So you need very very 163 00:06:58,720 --> 00:07:02,120 Speaker 7: large you know, cloud hyperscale cloud wider, So that's one 164 00:07:02,160 --> 00:07:05,520 Speaker 7: big beneficiary. We also think, you know areas where such 165 00:07:05,520 --> 00:07:07,520 Speaker 7: as you have a lot of consulting companies are going 166 00:07:07,560 --> 00:07:10,520 Speaker 7: to come out and help out to create these applications. 167 00:07:10,520 --> 00:07:13,840 Speaker 7: So that's very downstream work. But it really starts with 168 00:07:14,280 --> 00:07:16,840 Speaker 7: the chip and the hardware level. Then it trickles down 169 00:07:16,840 --> 00:07:19,880 Speaker 7: to software, then it trickles down to services. Everybody in 170 00:07:19,920 --> 00:07:22,720 Speaker 7: the ecosystem will get some piece of it. And we've 171 00:07:22,760 --> 00:07:25,200 Speaker 7: seen every company service now just had their analysts day. 172 00:07:25,200 --> 00:07:27,960 Speaker 7: They spent entires analysts day talking about, you know, how 173 00:07:27,960 --> 00:07:31,680 Speaker 7: they're going to improve their product portfolio through it. Adobe 174 00:07:31,760 --> 00:07:33,560 Speaker 7: did the same thing, you know, a few weeks ago. 175 00:07:33,760 --> 00:07:36,960 Speaker 7: So everybody is spending crazy amount of money trying to 176 00:07:37,120 --> 00:07:39,920 Speaker 7: make their products a lot more smarter for the users 177 00:07:39,920 --> 00:07:40,480 Speaker 7: to use it. 178 00:07:40,640 --> 00:07:42,600 Speaker 4: All right, Dan, your turn. What's your price target? By 179 00:07:42,600 --> 00:07:43,080 Speaker 4: the way, on in. 180 00:07:43,160 --> 00:07:46,200 Speaker 6: Video, Yes, well, I mean we think the video right here, 181 00:07:46,200 --> 00:07:48,880 Speaker 6: I mean five hundred hours when you start to work 182 00:07:48,880 --> 00:07:50,920 Speaker 6: at where this could be fat to six hundred hours 183 00:07:51,000 --> 00:07:54,600 Speaker 6: could be fair value based on the income and opportunity, 184 00:07:54,720 --> 00:07:58,120 Speaker 6: and then you even stream it to Microsoft Poe case. 185 00:07:58,240 --> 00:07:59,920 Speaker 6: I mean you could have a start with a foreign 186 00:08:00,040 --> 00:08:04,200 Speaker 6: front of it, because now all that incremental spend, it's 187 00:08:04,240 --> 00:08:06,960 Speaker 6: going to Microsoft, it's going to Alphabet, it's going to 188 00:08:06,960 --> 00:08:10,320 Speaker 6: potential to Amazon and others. In my view to your question, 189 00:08:10,960 --> 00:08:15,120 Speaker 6: I view it that now, look execution is clearly there 190 00:08:15,120 --> 00:08:18,080 Speaker 6: will be issues. Now there's gonna be losers. There's gonna 191 00:08:18,080 --> 00:08:22,200 Speaker 6: be AI roadkill as well. Yeah, but this goes back 192 00:08:22,240 --> 00:08:25,000 Speaker 6: to basically a late nineties type feel in terms of 193 00:08:25,040 --> 00:08:28,640 Speaker 6: trying trying to identify the winners. There will be losers. 194 00:08:29,120 --> 00:08:32,719 Speaker 6: From evaluation perspective, I think it's sort of the it's 195 00:08:32,760 --> 00:08:35,760 Speaker 6: a goal rush because now it really changes the whole 196 00:08:35,800 --> 00:08:38,480 Speaker 6: paradigm in terms of what the revenue opportunity Paul. 197 00:08:38,520 --> 00:08:40,520 Speaker 3: For a radio audience, it's worth saying, he said, a 198 00:08:40,559 --> 00:08:43,080 Speaker 3: four handle on Microsoft shares Microsoft shares are training at 199 00:08:43,080 --> 00:08:45,520 Speaker 3: three twenty one right now, and a five handle on 200 00:08:45,679 --> 00:08:48,199 Speaker 3: end Video training about three eighty a share. 201 00:08:48,400 --> 00:08:50,480 Speaker 1: Here's what I see, having been in this market for 202 00:08:50,520 --> 00:08:53,720 Speaker 1: more than thirty years, I see the average portfolio manager 203 00:08:53,840 --> 00:08:58,439 Speaker 1: anywhere in the world saying I have to have exposure 204 00:08:58,480 --> 00:09:02,080 Speaker 1: to AI, calls up as tech analyst. Tech anist comes 205 00:09:02,120 --> 00:09:05,240 Speaker 1: back with the NVDA. That's what we're seeing here today. 206 00:09:05,320 --> 00:09:07,480 Speaker 1: I don't see. I don't see a seven hundred and 207 00:09:07,480 --> 00:09:10,560 Speaker 1: fifty billion dollar market cap stock up twenty five percent 208 00:09:10,920 --> 00:09:14,400 Speaker 1: on anything more than just panic buying. 209 00:09:14,440 --> 00:09:16,960 Speaker 6: Almost But to your point, I mean, you call it FOMA, 210 00:09:17,000 --> 00:09:19,760 Speaker 6: but so many investadors that sit there in debt ceiling, 211 00:09:19,920 --> 00:09:25,040 Speaker 6: macro evaluation, you're you missed these moves in the video 212 00:09:25,160 --> 00:09:29,520 Speaker 6: in Microsoft and Alphabet in terms of institutional speaking, you're 213 00:09:29,520 --> 00:09:31,240 Speaker 6: trying to figure out what fond to use on the 214 00:09:31,240 --> 00:09:34,199 Speaker 6: resume this year. And I think that's that's sort of 215 00:09:34,240 --> 00:09:36,040 Speaker 6: the issue. You could sit there, well, you could talk 216 00:09:36,160 --> 00:09:39,120 Speaker 6: valuation till you're blue in the face. That in three 217 00:09:39,160 --> 00:09:40,960 Speaker 6: bucks gets you. He is not a coffee don't get 218 00:09:40,960 --> 00:09:42,439 Speaker 6: all right, So when. 219 00:09:42,120 --> 00:09:45,320 Speaker 1: You're in Asia talking to clients and they say I 220 00:09:45,360 --> 00:09:47,440 Speaker 1: want to get exposure to AI, what do you tell them? 221 00:09:47,760 --> 00:09:50,640 Speaker 6: So on the China side, clearly there's there's bob about 222 00:09:50,720 --> 00:09:53,520 Speaker 6: ten cent by do you know from a China perspective, 223 00:09:53,520 --> 00:09:55,760 Speaker 6: But more and more they're looking for us, and I 224 00:09:55,840 --> 00:09:59,400 Speaker 6: viewed as the basket. It's it's Microsoft, it's Alphabet. I 225 00:09:59,520 --> 00:10:03,960 Speaker 6: view powervolunteers and name that's a play here Salesforce dot Com. 226 00:10:04,280 --> 00:10:07,280 Speaker 6: You have the chip players navidian a MD and then 227 00:10:07,280 --> 00:10:10,520 Speaker 6: you kind of go down stream data dog, Snowflake, Mango dB. 228 00:10:10,679 --> 00:10:13,360 Speaker 6: I mean you're really now starting to look at baskets 229 00:10:13,960 --> 00:10:17,000 Speaker 6: and that's why today it all changes. I mean, I 230 00:10:17,120 --> 00:10:20,800 Speaker 6: view this as historic. If there was an earnings Hall 231 00:10:20,800 --> 00:10:23,000 Speaker 6: of Fame, this would be historic in terms of what 232 00:10:23,080 --> 00:10:24,440 Speaker 6: it means for broader tech. 233 00:10:24,920 --> 00:10:29,000 Speaker 3: An a quick question here on the infrastructure investment dan 234 00:10:29,040 --> 00:10:30,600 Speaker 3: Way and if you have any thoughts. One of the 235 00:10:30,640 --> 00:10:33,200 Speaker 3: things that overnight, I promise they're connected, but overnight one 236 00:10:33,200 --> 00:10:34,640 Speaker 3: of the things that was so shocking was kind of 237 00:10:34,640 --> 00:10:38,680 Speaker 3: the DeSantis going on Twitter thing. Trust me this connects, 238 00:10:39,040 --> 00:10:41,480 Speaker 3: but Twitter essentially crashed and a lot of people said, look, 239 00:10:41,760 --> 00:10:44,240 Speaker 3: they were preparing for a bigger fallout without having the 240 00:10:44,240 --> 00:10:47,199 Speaker 3: infrastructure to do it. Could a similar thought be applied 241 00:10:47,240 --> 00:10:49,760 Speaker 3: to this kind of AI boom that everyone's ready for AI, 242 00:10:49,840 --> 00:10:51,520 Speaker 3: but the infrastructure can't catch up. 243 00:10:51,880 --> 00:10:53,920 Speaker 7: See that's why I'm saying the hyper schedus love provider 244 00:10:53,960 --> 00:10:56,319 Speaker 7: us at the right place to be because you don't 245 00:10:56,320 --> 00:10:58,640 Speaker 7: have a bigger computer than Amazon across the world. 246 00:10:58,800 --> 00:10:59,000 Speaker 5: Yeah. 247 00:10:59,040 --> 00:11:01,800 Speaker 7: Now Amazon doesn't having you know, partnerships with some of 248 00:11:01,800 --> 00:11:04,560 Speaker 7: these leading AI. I'll goithm companies right now. But I 249 00:11:04,559 --> 00:11:06,160 Speaker 7: can bet you in the next twelve months all of 250 00:11:06,200 --> 00:11:09,600 Speaker 7: that changes. It is just going to happen. So you 251 00:11:09,640 --> 00:11:12,440 Speaker 7: can't run this thing, you know, in somebody's back office. 252 00:11:12,480 --> 00:11:15,760 Speaker 7: It just doesn't work that way. Now, we will over time, 253 00:11:16,160 --> 00:11:18,560 Speaker 7: at any given time, we will see crashes in the 254 00:11:18,559 --> 00:11:21,400 Speaker 7: cloud infrastructure, just because that's the nature of it. It's 255 00:11:21,400 --> 00:11:24,680 Speaker 7: a bunch of computers connected together. You know, somebody somewhere 256 00:11:24,679 --> 00:11:26,959 Speaker 7: it is going to do something stupid that will crash 257 00:11:27,000 --> 00:11:27,319 Speaker 7: this thing. 258 00:11:27,559 --> 00:11:31,040 Speaker 6: I'd also say it is Andy Jasey right now has 259 00:11:31,120 --> 00:11:34,079 Speaker 6: a number of people in some room somewhere in Seattle, 260 00:11:34,640 --> 00:11:37,400 Speaker 6: and they're looking at it like they see Microsoft, they 261 00:11:37,440 --> 00:11:41,119 Speaker 6: see Alphabet. They got to figure out their AI strategy, 262 00:11:41,160 --> 00:11:43,320 Speaker 6: and that right now, I think is probably the biggest 263 00:11:43,320 --> 00:11:44,120 Speaker 6: person need there. 264 00:11:44,160 --> 00:11:46,800 Speaker 1: All right, guys on ock ten seconds. What's your conference 265 00:11:46,800 --> 00:11:47,360 Speaker 1: again today? 266 00:11:47,760 --> 00:11:50,120 Speaker 7: Semigindert conference. You can see it on Life Go. 267 00:11:50,480 --> 00:11:52,920 Speaker 1: Live, Go okay, And two of the best dressed tech 268 00:11:52,960 --> 00:11:55,560 Speaker 1: analysts on Wall Street right here on a rag Rana 269 00:11:55,559 --> 00:11:58,280 Speaker 1: from Bloomberg Intelligence, Dan Eyes from what Bush Securities joining 270 00:11:58,360 --> 00:12:00,280 Speaker 1: us here to help us put in perspective kind of 271 00:12:00,280 --> 00:12:02,480 Speaker 1: what we're seeing out there on as Dan was suggesting 272 00:12:02,559 --> 00:12:04,320 Speaker 1: kind of a historic day here for tech, and you've 273 00:12:04,320 --> 00:12:07,000 Speaker 1: got it. Seems like the markets is opening its eyes 274 00:12:07,160 --> 00:12:10,200 Speaker 1: today to what AI could mean across the tech stack, 275 00:12:10,200 --> 00:12:11,800 Speaker 1: and that's what we're seeing. So we thank those gentlemen 276 00:12:12,040 --> 00:12:13,320 Speaker 1: coming in. This is Bloomberg. 277 00:12:15,080 --> 00:12:17,840 Speaker 8: You're listening to the team. Ken's a our live program, 278 00:12:17,960 --> 00:12:21,920 Speaker 8: Bloomberg Markets weekdays at ten am Eastern on Bloomberg dot com, 279 00:12:22,000 --> 00:12:25,120 Speaker 8: the iHeartRadio app and the Bloomberg Business app, or listen 280 00:12:25,200 --> 00:12:27,520 Speaker 8: on demand wherever you get your podcasts. 281 00:12:29,559 --> 00:12:32,520 Speaker 1: Let's get off the Nvidia trail for just a second, 282 00:12:32,520 --> 00:12:34,520 Speaker 1: go little macro here and we could do that with 283 00:12:34,559 --> 00:12:37,080 Speaker 1: one of our all time faves here, Danielle di Martino, Booth, 284 00:12:37,520 --> 00:12:41,560 Speaker 1: CEO and chief strategists at QI Research. She was also 285 00:12:41,559 --> 00:12:44,319 Speaker 1: a former advisor at the Federal Reserve Bank of Dallas, 286 00:12:44,320 --> 00:12:47,520 Speaker 1: so we always appreciate getting her thoughts and her insight there. Danielle, 287 00:12:47,559 --> 00:12:50,280 Speaker 1: Let's just start with our good friends Dan in Washington, 288 00:12:50,400 --> 00:12:53,160 Speaker 1: d C. Trying to figure out a way to pay 289 00:12:53,160 --> 00:12:55,800 Speaker 1: the bills and everything. How does that hold I guess 290 00:12:55,920 --> 00:12:58,520 Speaker 1: uncertainty factory and kind of to your outlook and your. 291 00:12:58,400 --> 00:13:03,920 Speaker 5: Work, Well, it factors in pretty largely given I was 292 00:13:03,920 --> 00:13:07,160 Speaker 5: around in twenty eleven, and you know now that we're 293 00:13:07,200 --> 00:13:11,000 Speaker 5: talking about Fitch possibly downgrading the nation's credit it's kind 294 00:13:11,000 --> 00:13:14,040 Speaker 5: of getting real. And you know, for all of the 295 00:13:14,120 --> 00:13:18,520 Speaker 5: headlines that speak of progress, that's the one thing that 296 00:13:18,559 --> 00:13:22,000 Speaker 5: doesn't seem to be getting made is progress. So you 297 00:13:22,080 --> 00:13:23,120 Speaker 5: have to stuff in wonder right. 298 00:13:24,040 --> 00:13:27,160 Speaker 3: Well, Danille, talk to us a little bit about the 299 00:13:27,200 --> 00:13:32,839 Speaker 3: fundamental agreement here, because the formula for GDP is to 300 00:13:32,960 --> 00:13:35,640 Speaker 3: econ one oh one. A big part of that is 301 00:13:35,679 --> 00:13:40,319 Speaker 3: government spending. If you were looking at even the flatline 302 00:13:40,320 --> 00:13:43,040 Speaker 3: offer from the Biden adminstiration, which is just to keep 303 00:13:43,080 --> 00:13:45,920 Speaker 3: spending the same as it is right now, that's still 304 00:13:45,960 --> 00:13:49,640 Speaker 3: got to take some sort of toll on economic growth. 305 00:13:49,880 --> 00:13:51,079 Speaker 3: Why aren't we worried about that? 306 00:13:52,760 --> 00:13:56,160 Speaker 5: Well, you have a really fundamental question, and we should 307 00:13:56,240 --> 00:14:00,600 Speaker 5: be worried about that, because once you dig into the 308 00:14:00,280 --> 00:14:04,080 Speaker 5: the guts of the GDP math, we haven't been getting 309 00:14:04,120 --> 00:14:10,120 Speaker 5: a lot from the biggest traditional inputs of business investment 310 00:14:10,280 --> 00:14:14,439 Speaker 5: and consumption. In fact, we've been seeing just the opposite. 311 00:14:14,480 --> 00:14:16,839 Speaker 5: And if you look at Bank of America proprietary credit 312 00:14:16,840 --> 00:14:19,800 Speaker 5: card debit card data, that's trending in the same way 313 00:14:19,960 --> 00:14:24,320 Speaker 5: as well, so these are the times when your economy 314 00:14:24,400 --> 00:14:28,320 Speaker 5: is slowing into recession that you need the offset of 315 00:14:28,480 --> 00:14:31,440 Speaker 5: government spending the most exactly. 316 00:14:31,480 --> 00:14:34,280 Speaker 1: So we had some a little bit of economic data today. 317 00:14:34,280 --> 00:14:37,600 Speaker 1: We're gonna get some more tomorrow with the PC deflator data, 318 00:14:37,640 --> 00:14:40,160 Speaker 1: which is obviously real key with the Fed. But just 319 00:14:40,200 --> 00:14:41,720 Speaker 1: on the job was front. I mean, I don't know 320 00:14:41,800 --> 00:14:42,960 Speaker 1: how you want to look at I know we had 321 00:14:42,960 --> 00:14:46,120 Speaker 1: some squirmy numbers coming out of Massachusetts, kind of stuff 322 00:14:46,120 --> 00:14:49,560 Speaker 1: I usually expect to see in New Jersey. But anyway, 323 00:14:49,640 --> 00:14:51,440 Speaker 1: jobs claims came in a little bit better and expected 324 00:14:51,480 --> 00:14:54,120 Speaker 1: at the labor market still looks pretty solid. How do 325 00:14:54,160 --> 00:14:54,760 Speaker 1: you think about it? 326 00:14:55,880 --> 00:14:58,080 Speaker 5: So I don't look at the labor market in absolute 327 00:14:58,200 --> 00:15:02,400 Speaker 5: numbers I research, we haven't for some time. What we 328 00:15:02,440 --> 00:15:04,960 Speaker 5: look at is the number of states that we have 329 00:15:05,120 --> 00:15:07,040 Speaker 5: with rising claims, and that really does take all the 330 00:15:07,080 --> 00:15:10,760 Speaker 5: Massachusetts noise out of the mass and we've had at 331 00:15:10,840 --> 00:15:13,280 Speaker 5: least two thirds of the states with rising initial and 332 00:15:13,800 --> 00:15:18,440 Speaker 5: continuing claims here for a persistent period of time. And 333 00:15:18,520 --> 00:15:21,720 Speaker 5: that tells you that while off, it is off a 334 00:15:21,760 --> 00:15:25,200 Speaker 5: small base, a low base historically speaking, as it would 335 00:15:25,240 --> 00:15:28,600 Speaker 5: be coming out of a pandemic that re employed people 336 00:15:28,600 --> 00:15:31,600 Speaker 5: who wanted to be re employed and left people on 337 00:15:31,640 --> 00:15:34,160 Speaker 5: the sidelines who were making enough from the government for 338 00:15:34,800 --> 00:15:37,240 Speaker 5: a good long time. Think of how long the government 339 00:15:37,280 --> 00:15:40,960 Speaker 5: was paying household rent and still is in states like California. 340 00:15:42,040 --> 00:15:44,920 Speaker 5: But given the breath of states be R, E. A. 341 00:15:45,080 --> 00:15:50,040 Speaker 5: D H that are seeing rising initial and continuing jobless claims, 342 00:15:50,480 --> 00:15:55,560 Speaker 5: we are at or passed recessionary levels. 343 00:15:55,800 --> 00:15:59,400 Speaker 3: Danille, Is there a way here to still get that 344 00:15:59,680 --> 00:16:03,320 Speaker 3: shall recession or is the hard landing still the growing 345 00:16:03,360 --> 00:16:03,880 Speaker 3: base case? 346 00:16:05,400 --> 00:16:07,720 Speaker 5: Well, I think it grows with every day that the 347 00:16:07,760 --> 00:16:11,080 Speaker 5: media tells us there's been quote unquote progress on the 348 00:16:11,080 --> 00:16:13,760 Speaker 5: debt ceiling front, and we up at the end of 349 00:16:13,800 --> 00:16:17,080 Speaker 5: the day without that progress. So once we can take 350 00:16:17,080 --> 00:16:19,520 Speaker 5: the air quotes off progress and we actually see a 351 00:16:19,520 --> 00:16:23,440 Speaker 5: deal done. And even in that case, the Terminal had 352 00:16:23,440 --> 00:16:26,000 Speaker 5: a story out a few days ago that said that 353 00:16:26,000 --> 00:16:28,720 Speaker 5: that Wells Fargo figured that if the debt ceiling was 354 00:16:28,760 --> 00:16:31,800 Speaker 5: resolved by Labor Day, call it, which seems like a 355 00:16:32,680 --> 00:16:35,240 Speaker 5: we're not even a Memorial day, so Labor Day seems 356 00:16:35,240 --> 00:16:37,560 Speaker 5: a long ways away. But Wells Fargo said that even 357 00:16:37,560 --> 00:16:39,720 Speaker 5: if the debt ceiling was resolved by a memorial day 358 00:16:39,960 --> 00:16:42,640 Speaker 5: that the Treasury Department would have to sell somewhere around 359 00:16:42,680 --> 00:16:46,720 Speaker 5: one point five trillion dollars in treasuries between then and 360 00:16:47,360 --> 00:16:49,840 Speaker 5: the end of the first quarter twenty twenty four. That's 361 00:16:49,880 --> 00:16:51,840 Speaker 5: a lot of liquidity to pull out of a system 362 00:16:52,240 --> 00:16:56,720 Speaker 5: that's already contending with US households pulling money out of 363 00:16:56,720 --> 00:17:00,600 Speaker 5: banks and putting them into investments that pay five percent 364 00:17:00,680 --> 00:17:01,000 Speaker 5: or more. 365 00:17:02,000 --> 00:17:04,359 Speaker 1: Hey, Danielle. When I when I type into FED go 366 00:17:04,560 --> 00:17:07,320 Speaker 1: function into the Bloomberg terminal FAD, I get a lot 367 00:17:07,320 --> 00:17:09,840 Speaker 1: of information about the FED and what's going on there. 368 00:17:09,880 --> 00:17:12,479 Speaker 1: One of the easiest piece of data is I got 369 00:17:12,480 --> 00:17:15,680 Speaker 1: another appointment on June fourteenth, which is not only Flag Day, 370 00:17:15,720 --> 00:17:17,680 Speaker 1: but it's going to be a big day for the markets. 371 00:17:18,280 --> 00:17:20,960 Speaker 1: I'm guessing the Fed's going to know something about this 372 00:17:21,040 --> 00:17:24,720 Speaker 1: whole debt ceiling by June fourteenth. One would think, how 373 00:17:24,720 --> 00:17:26,800 Speaker 1: do you think the Fed's looking at this news every day? 374 00:17:26,840 --> 00:17:29,040 Speaker 1: Are they hanging on every word like we are, or 375 00:17:29,040 --> 00:17:32,000 Speaker 1: are they just maybe working under the assumption that things 376 00:17:32,000 --> 00:17:32,600 Speaker 1: will be okay. 377 00:17:33,720 --> 00:17:36,080 Speaker 5: So, given I used to work at the FED, I 378 00:17:36,200 --> 00:17:38,840 Speaker 5: kind of know who's who. And let's just say that 379 00:17:38,920 --> 00:17:42,160 Speaker 5: Christopher Waller is as close to if you can't get Waller, 380 00:17:42,560 --> 00:17:44,639 Speaker 5: if you can't get Powell at a microphone, then you 381 00:17:44,680 --> 00:17:48,280 Speaker 5: can substitute in Christopher Waller. He's about palace closest confidant. 382 00:17:48,760 --> 00:17:51,840 Speaker 5: And yesterday he said he didn't like the position that 383 00:17:51,920 --> 00:17:55,280 Speaker 5: the US government had put FED policymakers in, but that 384 00:17:55,280 --> 00:17:58,920 Speaker 5: that was not formulating into his calculus. I think that's 385 00:17:58,960 --> 00:18:00,800 Speaker 5: a lot of the reason that we're being the probability 386 00:18:00,840 --> 00:18:04,200 Speaker 5: of a June fourteenth rate hike the highest since it's 387 00:18:04,240 --> 00:18:08,160 Speaker 5: been Now we're north of forty percent there for June fourteenth. 388 00:18:08,240 --> 00:18:10,720 Speaker 5: Yesterday we were talking about, oh, will we get twenty 389 00:18:10,720 --> 00:18:14,040 Speaker 5: five basis points by July. At this point, we're getting 390 00:18:14,080 --> 00:18:16,960 Speaker 5: really close to a toss up on June, and we've 391 00:18:17,000 --> 00:18:20,520 Speaker 5: got some critical data between now and that meeting, And 392 00:18:20,560 --> 00:18:23,399 Speaker 5: again Waller's saying, we are data dependent. So as we 393 00:18:23,440 --> 00:18:27,320 Speaker 5: get into their hot employment report in who knows, who 394 00:18:27,320 --> 00:18:29,639 Speaker 5: knows that they don't go right then on June fourteenth 395 00:18:30,000 --> 00:18:31,720 Speaker 5: and raise the flag. 396 00:18:31,920 --> 00:18:35,600 Speaker 3: Danielle connect those dots for us though, how does the 397 00:18:35,720 --> 00:18:42,040 Speaker 3: debt ceiling affect FED policy? How much of it is inflationary? 398 00:18:42,600 --> 00:18:46,320 Speaker 5: It's not inflationary, and well back in twenty eleven, the 399 00:18:46,440 --> 00:18:52,520 Speaker 5: tenure bond. The benchmark tenure yield came down quite dramatically. 400 00:18:53,320 --> 00:18:55,920 Speaker 5: It was a massive rally in the bond market, and 401 00:18:56,040 --> 00:19:00,600 Speaker 5: immediately the stock market shaved seventeen percentage point. It's not 402 00:19:00,640 --> 00:19:04,879 Speaker 5: a small move. So in that sense, it makes the 403 00:19:04,880 --> 00:19:09,440 Speaker 5: Fed's job easier, But it is the disruptive effect of 404 00:19:09,520 --> 00:19:15,680 Speaker 5: the financial markets that plays into how policymakers approach FED policy, 405 00:19:15,720 --> 00:19:19,600 Speaker 5: and if there's something that's highly disruptive, very difficult, and 406 00:19:19,840 --> 00:19:23,800 Speaker 5: again we forget that. It's like the stock market does 407 00:19:23,880 --> 00:19:28,840 Speaker 5: yoga before every FED meeting and financial conditions ease. Things 408 00:19:28,880 --> 00:19:33,680 Speaker 5: always seem to be nicer, more complacent. But again that's 409 00:19:33,800 --> 00:19:35,840 Speaker 5: just how it always seems to be. Right at the 410 00:19:35,880 --> 00:19:40,200 Speaker 5: cusp of FED meetings. If there's a massive disruption in 411 00:19:40,240 --> 00:19:42,959 Speaker 5: the financial markets because of this debt ceiling stand up, 412 00:19:43,080 --> 00:19:44,920 Speaker 5: makes it much more difficult for the FED to go. 413 00:19:46,080 --> 00:19:49,240 Speaker 1: Danielle, you know, I've got Eco go on my terminal, 414 00:19:49,240 --> 00:19:51,960 Speaker 1: But that's basically all I have for economic indicators, and 415 00:19:52,000 --> 00:19:54,960 Speaker 1: it's pretty high, high level market stuff. I always know 416 00:19:55,080 --> 00:19:57,360 Speaker 1: you always quote these really obscure things that for you 417 00:19:57,440 --> 00:20:00,679 Speaker 1: are really important, and you think that really tell it 418 00:20:00,720 --> 00:20:04,199 Speaker 1: maybe a deeper story, what are you looking at now 419 00:20:04,640 --> 00:20:06,600 Speaker 1: these days? And kind of what's it telling you? 420 00:20:08,200 --> 00:20:11,000 Speaker 5: So two things this week, in particular, because it's been 421 00:20:11,040 --> 00:20:17,280 Speaker 5: a pretty quiet economic data docket, we did see purchase 422 00:20:17,320 --> 00:20:20,560 Speaker 5: applications for homes did right back down to kind of 423 00:20:20,600 --> 00:20:24,000 Speaker 5: that thirty year low. And at the same time, I've 424 00:20:24,119 --> 00:20:27,640 Speaker 5: put what my trader buddies on Wall Street se there, 425 00:20:27,640 --> 00:20:30,520 Speaker 5: like Danielle, We've got teflation up on our screens, watched 426 00:20:30,600 --> 00:20:34,960 Speaker 5: tru True inflation since it's like checking a billion prices 427 00:20:35,000 --> 00:20:39,040 Speaker 5: in real time. And again, it was kind of a moment. 428 00:20:39,160 --> 00:20:41,000 Speaker 5: It was a threshold. It was a rubicon that was 429 00:20:41,000 --> 00:20:44,879 Speaker 5: crossed in yesterday's trading that it traded south of the 430 00:20:44,920 --> 00:20:48,639 Speaker 5: three percent mark. So in the real world, I understand 431 00:20:48,760 --> 00:20:52,879 Speaker 5: the beautiful models that come out of agencies and the 432 00:20:52,960 --> 00:20:55,920 Speaker 5: Census Bureau and the BA and the BLS and all 433 00:20:55,960 --> 00:20:58,919 Speaker 5: of those happy acronyms in the statistical agencies. But in 434 00:20:58,960 --> 00:21:02,359 Speaker 5: the real world, inflation is breaking below three percent, which 435 00:21:02,400 --> 00:21:05,600 Speaker 5: is pretty close to the FEDS two percent targets. So 436 00:21:05,920 --> 00:21:09,159 Speaker 5: and I'm seeing it. My grocery circular is thicker than 437 00:21:09,160 --> 00:21:11,960 Speaker 5: it used to be. There are more things on sale, 438 00:21:12,240 --> 00:21:14,959 Speaker 5: and I'm sure as Heck, don't see any help wanted 439 00:21:15,000 --> 00:21:17,800 Speaker 5: signs where I am. These are the telltale signs. 440 00:21:18,040 --> 00:21:20,639 Speaker 1: Okay, Danielle DeMartino Booth, thank you so much for joining 441 00:21:20,680 --> 00:21:24,040 Speaker 1: us on this Thursday before Memorial Day weekend. We appreciated 442 00:21:24,119 --> 00:21:28,000 Speaker 1: Danielle de Martino Booth. She's the CEO and chief strategistic 443 00:21:28,080 --> 00:21:31,160 Speaker 1: QI Research, and she a former advisor at the Federal 444 00:21:31,160 --> 00:21:33,080 Speaker 1: Reserve Bank of Dallas, so really has a unique view 445 00:21:33,080 --> 00:21:34,879 Speaker 1: into the FED and we always appreciate getting some of 446 00:21:34,880 --> 00:21:37,200 Speaker 1: her time. And again, one of the many reasons we 447 00:21:37,440 --> 00:21:40,159 Speaker 1: like talking to Danielle is because she looks at the 448 00:21:40,400 --> 00:21:42,960 Speaker 1: economic data sets that I didn't even know existed, but 449 00:21:43,040 --> 00:21:45,640 Speaker 1: they do. And she looks at the data and she's 450 00:21:45,640 --> 00:21:47,639 Speaker 1: able to put that data into context for us and 451 00:21:47,680 --> 00:21:49,760 Speaker 1: tell us why it's important and why we should focus 452 00:21:49,760 --> 00:21:49,960 Speaker 1: on it. 453 00:21:50,280 --> 00:21:53,359 Speaker 8: You're listening to the tape. Ken's are Live program Bloomberg 454 00:21:53,440 --> 00:21:57,320 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, tune 455 00:21:57,359 --> 00:22:00,439 Speaker 8: in app, Bloomberg dot Com, and The Bloomberg Business. You 456 00:22:00,440 --> 00:22:03,680 Speaker 8: can also listen live on Amazon Alexa from our flagship 457 00:22:03,760 --> 00:22:08,800 Speaker 8: New York station, Just Say Alexa, play Bloomberg eleven thirty. 458 00:22:09,840 --> 00:22:13,240 Speaker 1: Let's step back a little bit shit gears and talk geopolitics. 459 00:22:13,320 --> 00:22:15,440 Speaker 1: Let's head over to Europe. A lot of news coming 460 00:22:15,440 --> 00:22:17,360 Speaker 1: out out of Europe, of course, and where you want 461 00:22:17,359 --> 00:22:19,919 Speaker 1: to get the latest on the war in Ukraine and 462 00:22:19,960 --> 00:22:23,479 Speaker 1: some other topics over there. Let's go to Maria today. Oh, 463 00:22:23,520 --> 00:22:26,919 Speaker 1: she's a European correspondent for Bloomberg News. She is based 464 00:22:26,960 --> 00:22:31,400 Speaker 1: in our Brussels office over there, but she's all over 465 00:22:31,440 --> 00:22:33,920 Speaker 1: Europe covering the big stories over there. So Maria, thank 466 00:22:33,920 --> 00:22:35,520 Speaker 1: you so much for taking some time out of your 467 00:22:36,040 --> 00:22:42,240 Speaker 1: busy afternoon and evening. Let's start with Ukraine here, the counteroffensive. 468 00:22:42,920 --> 00:22:44,879 Speaker 1: I kind of thought we might have seen it start 469 00:22:44,960 --> 00:22:48,560 Speaker 1: by Now, what's the feeling in Europe about when this 470 00:22:48,640 --> 00:22:51,920 Speaker 1: may start, what it may mean, and how we should 471 00:22:51,920 --> 00:22:52,480 Speaker 1: think about it? 472 00:22:53,600 --> 00:22:56,040 Speaker 9: Yes, and it's a free, good question. And today I 473 00:22:56,280 --> 00:22:59,280 Speaker 9: happen to speak with one of the senior advisors to 474 00:22:59,440 --> 00:23:02,119 Speaker 9: presidents and I key on this matter. Now, what the 475 00:23:02,240 --> 00:23:05,360 Speaker 9: Ukrainians will say, and you hear two sides of the story. 476 00:23:05,400 --> 00:23:09,480 Speaker 9: They say, we're still waiting for more weapons and the 477 00:23:09,520 --> 00:23:12,320 Speaker 9: counter offensive will be successful, but obviously they do not 478 00:23:12,400 --> 00:23:15,280 Speaker 9: want to put a date on it. And then on 479 00:23:15,280 --> 00:23:17,440 Speaker 9: the other hand, you also have other officials also from 480 00:23:17,480 --> 00:23:21,600 Speaker 9: the Ukrainian government who say, when you launch a counter offensive, 481 00:23:21,720 --> 00:23:23,400 Speaker 9: this is not going to be like d D when 482 00:23:23,400 --> 00:23:25,359 Speaker 9: it all happens on one day and this is a 483 00:23:25,359 --> 00:23:27,960 Speaker 9: make or break. This will be a serious of events 484 00:23:28,240 --> 00:23:31,120 Speaker 9: that will happen. So it's very difficult and obviously for 485 00:23:31,160 --> 00:23:33,639 Speaker 9: them too, it's not something that they would do to 486 00:23:33,800 --> 00:23:37,840 Speaker 9: publicize a date or to say it has started already. 487 00:23:37,880 --> 00:23:40,560 Speaker 9: But what I can't say, specially when you look to 488 00:23:40,680 --> 00:23:44,200 Speaker 9: European officials, is that this counter offensive will be key. 489 00:23:44,240 --> 00:23:47,640 Speaker 9: There's so much going on for Ukraine in terms of 490 00:23:47,960 --> 00:23:50,080 Speaker 9: the momentum for the war, in terms of whether or 491 00:23:50,080 --> 00:23:54,200 Speaker 9: not they can make significant gains, and the West has 492 00:23:54,200 --> 00:23:56,600 Speaker 9: provided money, they have provided weapons. 493 00:23:56,600 --> 00:23:57,320 Speaker 10: Obviously it's the. 494 00:23:57,320 --> 00:24:00,440 Speaker 9: Ukrainian people that suffer the war. Is the Ukraine army 495 00:24:00,480 --> 00:24:03,680 Speaker 9: that fights the war and ultimately also dies in battle. 496 00:24:03,720 --> 00:24:05,920 Speaker 9: I mean, that's the grim reality of this. But there 497 00:24:06,000 --> 00:24:08,720 Speaker 9: is a sense that a lot is going on with 498 00:24:08,760 --> 00:24:10,520 Speaker 9: its kind of friends. It means a lot. If it 499 00:24:10,560 --> 00:24:13,480 Speaker 9: doesn't go well, it could remove some of the momentum. 500 00:24:13,560 --> 00:24:16,720 Speaker 9: This idea that Ukraine can win if they are successful 501 00:24:16,760 --> 00:24:18,920 Speaker 9: and they're able to claim some of the land back, 502 00:24:19,080 --> 00:24:20,680 Speaker 9: it could change a lot of things in terms of 503 00:24:20,760 --> 00:24:23,040 Speaker 9: what the future piece may look like. 504 00:24:23,680 --> 00:24:26,320 Speaker 3: So let's go from the war in Ukraine Maria to 505 00:24:26,880 --> 00:24:32,120 Speaker 3: the economics of Europe broadly Germany and during its first recession. 506 00:24:32,680 --> 00:24:35,760 Speaker 3: Talk us through the numbers here and essentially how the 507 00:24:35,760 --> 00:24:39,399 Speaker 3: biggest economy in Europe is really being interpreted by its 508 00:24:39,440 --> 00:24:40,120 Speaker 3: peers right now. 509 00:24:40,880 --> 00:24:43,520 Speaker 9: Well, Germany we always care about for two reasons. Obviously, 510 00:24:43,520 --> 00:24:46,280 Speaker 9: just the biggest economy in the euro area. So obviously 511 00:24:46,320 --> 00:24:49,679 Speaker 9: the cloud and the just kind of say they have 512 00:24:50,280 --> 00:24:53,000 Speaker 9: in talks and negotiations when it comes to anything that 513 00:24:53,040 --> 00:24:56,760 Speaker 9: gets done in Europe is tremendous. Obviously, this is a 514 00:24:56,800 --> 00:25:01,760 Speaker 9: country that has the biggest pockets, also a big industrial 515 00:25:01,840 --> 00:25:04,520 Speaker 9: power for Europe too, on their husband's idea that we 516 00:25:04,600 --> 00:25:08,120 Speaker 9: have to rebuild the European industry and that Germany will 517 00:25:08,160 --> 00:25:11,200 Speaker 9: be key because of the well just simply the fact 518 00:25:11,200 --> 00:25:13,040 Speaker 9: that they know how to make things and they make 519 00:25:13,119 --> 00:25:16,200 Speaker 9: them very well and also export them very well. When 520 00:25:16,200 --> 00:25:18,520 Speaker 9: you look at the number today the recession, it is 521 00:25:18,960 --> 00:25:22,760 Speaker 9: significant in some ways if you look at the political 522 00:25:22,840 --> 00:25:25,959 Speaker 9: narrative all of Schultz, the Chancellor had repeated many times. 523 00:25:26,119 --> 00:25:28,399 Speaker 9: Remember he did an interview with our editor in chief 524 00:25:28,720 --> 00:25:31,040 Speaker 9: about two months ago, and he said, We're not going 525 00:25:31,080 --> 00:25:33,800 Speaker 9: to have this recession. We're going to escape this recession 526 00:25:33,800 --> 00:25:36,280 Speaker 9: that we're trying to flip the momentum of it. Today 527 00:25:36,280 --> 00:25:39,760 Speaker 9: we see the economic reality is different. Having said that, 528 00:25:39,920 --> 00:25:43,920 Speaker 9: are we shocked surprise? Well, no, because the PMIS had 529 00:25:43,960 --> 00:25:46,959 Speaker 9: been painting a bad picture for Germany. We knew that 530 00:25:47,080 --> 00:25:50,840 Speaker 9: manufacturing was already in contraction. We knew that some of 531 00:25:50,840 --> 00:25:53,280 Speaker 9: the implications of the energy crisis. This was not the 532 00:25:53,359 --> 00:25:56,359 Speaker 9: winter from Hell that we were expecting. This was not 533 00:25:56,680 --> 00:26:00,600 Speaker 9: the terrible winter that would break Europe, but obviously lasting 534 00:26:00,640 --> 00:26:03,520 Speaker 9: effects on the industry, and now we see them play 535 00:26:03,520 --> 00:26:06,600 Speaker 9: out and beyond Germany. To answer your question a bigger picture, 536 00:26:06,800 --> 00:26:08,920 Speaker 9: I think it also reflects some of the fears that 537 00:26:09,080 --> 00:26:11,840 Speaker 9: I hear and see in every panel that I do, 538 00:26:12,200 --> 00:26:14,440 Speaker 9: in every conference that I've done over the past months, 539 00:26:14,720 --> 00:26:18,320 Speaker 9: this idea that Europe is losing attractiveness when it comes 540 00:26:18,359 --> 00:26:21,320 Speaker 9: to the industry. And there was this survey just very briefly, 541 00:26:21,320 --> 00:26:23,560 Speaker 9: but I think this is so key that came out 542 00:26:23,680 --> 00:26:26,560 Speaker 9: this week from a very influential European saying tank that 543 00:26:26,680 --> 00:26:31,359 Speaker 9: said fifty two percent of CEOs would consider changing investments 544 00:26:31,440 --> 00:26:34,800 Speaker 9: and operations and moving them to North America. That is 545 00:26:34,960 --> 00:26:37,520 Speaker 9: terrible means for main in Germany. But beyond that, and 546 00:26:37,560 --> 00:26:40,399 Speaker 9: here's another stat and this is terrible, eighty percent of 547 00:26:40,440 --> 00:26:43,760 Speaker 9: the CEOs that were surveyed they said that they believe, yes, 548 00:26:43,880 --> 00:26:47,520 Speaker 9: Europe is losing competitiveness when it comes to the industry. 549 00:26:47,680 --> 00:26:50,800 Speaker 9: So eighty percent of respond that number is really it 550 00:26:50,840 --> 00:26:52,439 Speaker 9: is worrying, it is, it is. 551 00:26:52,480 --> 00:26:55,280 Speaker 1: And in Germany, just following up there in Germany, you know, 552 00:26:55,400 --> 00:26:57,240 Speaker 1: when I think of Germany, I think some of these 553 00:26:57,280 --> 00:27:00,199 Speaker 1: great industrial companies Semens and so on, and I think 554 00:27:00,240 --> 00:27:04,960 Speaker 1: about them making big, big stuff and exporting it to China. 555 00:27:05,680 --> 00:27:08,280 Speaker 1: So they must have a very difficult political and economic, 556 00:27:08,800 --> 00:27:11,480 Speaker 1: you know, tightrope to walk in terms of the relationships, 557 00:27:11,760 --> 00:27:14,679 Speaker 1: the relationship with China. How do they phrase it? How 558 00:27:14,680 --> 00:27:15,960 Speaker 1: does the German government phrase it? 559 00:27:16,400 --> 00:27:19,720 Speaker 9: Yes, for sure, and it's not just the big companies 560 00:27:19,760 --> 00:27:22,080 Speaker 9: in Germany that we all know of, and obviously they 561 00:27:22,080 --> 00:27:23,760 Speaker 9: make things that are very well and then very well 562 00:27:23,800 --> 00:27:27,120 Speaker 9: done and that they sell, but it's also the atames. Remember, 563 00:27:27,440 --> 00:27:29,919 Speaker 9: Germany is a country that is built on small to 564 00:27:30,000 --> 00:27:33,439 Speaker 9: medium companies too, that that are interconnected. 565 00:27:32,800 --> 00:27:33,440 Speaker 11: With the big ones. 566 00:27:33,440 --> 00:27:35,919 Speaker 9: But it doesn't stop there because Germany is also a 567 00:27:36,000 --> 00:27:39,959 Speaker 9: country that buys components and parts to countries like Italy, 568 00:27:40,200 --> 00:27:43,119 Speaker 9: So a recession in Germany has trickled down effects on 569 00:27:43,160 --> 00:27:45,679 Speaker 9: countries like Italy. So overall this is a very heavily 570 00:27:45,680 --> 00:27:49,320 Speaker 9: connected economy when you look at China. Look, this is 571 00:27:49,359 --> 00:27:52,800 Speaker 9: a difficult question for Germany, but also the European Union overall. 572 00:27:53,280 --> 00:27:56,040 Speaker 9: When you see the G seven statement and all of 573 00:27:56,080 --> 00:27:59,399 Speaker 9: Scheltz obviously participates representing Germany, they all agree in the 574 00:27:59,440 --> 00:28:03,879 Speaker 9: communication that they have to de risk the economy, not decouple, 575 00:28:04,000 --> 00:28:07,800 Speaker 9: and they mentioned China in that statement twenty times. I 576 00:28:07,800 --> 00:28:10,119 Speaker 9: have never seen that in the GET statement, So that 577 00:28:10,160 --> 00:28:13,399 Speaker 9: shows the political jitters are obviously playing out. They have 578 00:28:13,440 --> 00:28:15,959 Speaker 9: been accelerated by the war in Ukraine. But for Germany 579 00:28:15,960 --> 00:28:18,760 Speaker 9: this is a very very difficult question because on the 580 00:28:18,760 --> 00:28:21,359 Speaker 9: one hand, the relationship with Russia on the energy is 581 00:28:21,720 --> 00:28:24,560 Speaker 9: over the North Triam blew up. But also a major 582 00:28:24,600 --> 00:28:27,639 Speaker 9: recipient of your experts. You're now having to rethink is 583 00:28:27,680 --> 00:28:30,359 Speaker 9: this country a partner, is there a revival? Is it 584 00:28:30,440 --> 00:28:33,200 Speaker 9: a challenge? And now what do you do after years 585 00:28:33,200 --> 00:28:35,919 Speaker 9: in which China was a huge market for Germany and 586 00:28:35,960 --> 00:28:38,400 Speaker 9: continues to be Maria. 587 00:28:38,560 --> 00:28:41,240 Speaker 3: In the absence of, let's say, some sort of transition 588 00:28:41,440 --> 00:28:45,120 Speaker 3: or diversification away from China, who's the benefactor from Germany's perspective. 589 00:28:46,560 --> 00:28:50,520 Speaker 9: From Germany's I look again, it's a good question because 590 00:28:51,040 --> 00:28:53,800 Speaker 9: and it goes back to the initial point that I 591 00:28:53,880 --> 00:28:58,160 Speaker 9: made on European CEOs being gloomy. What they feel is like, 592 00:28:58,200 --> 00:29:01,680 Speaker 9: if we lose business with China, if we also have 593 00:29:02,040 --> 00:29:04,320 Speaker 9: a lot of the regulation that kicks in, especially when 594 00:29:04,320 --> 00:29:06,719 Speaker 9: it comes to the greening the economy, there's all these 595 00:29:06,720 --> 00:29:08,800 Speaker 9: transitions that are going on, and we don't have the 596 00:29:08,880 --> 00:29:12,160 Speaker 9: money and the type of subsidies that perhaps a transformation 597 00:29:12,360 --> 00:29:15,920 Speaker 9: like this would entail. Then one of the big beneficiaries 598 00:29:16,040 --> 00:29:19,080 Speaker 9: is obviously the United States, and we know that for 599 00:29:19,160 --> 00:29:21,200 Speaker 9: the Europeans there have been a lot of concerns now 600 00:29:21,240 --> 00:29:24,040 Speaker 9: for the Inflatient Reduction Act. What they mean when obviously 601 00:29:24,040 --> 00:29:26,640 Speaker 9: they see a survey, we're one in two CEOs essentially 602 00:29:27,040 --> 00:29:29,880 Speaker 9: say I would consider relocated to North America. 603 00:29:30,240 --> 00:29:31,120 Speaker 5: That is problematic. 604 00:29:31,200 --> 00:29:32,880 Speaker 9: But at the same time, you find yourself in a 605 00:29:32,920 --> 00:29:37,440 Speaker 9: situation where again politically and diplomatically, the United States and 606 00:29:37,440 --> 00:29:39,840 Speaker 9: the European Union are very close now because of the 607 00:29:39,840 --> 00:29:40,840 Speaker 9: war in Ukraine. 608 00:29:41,040 --> 00:29:42,600 Speaker 1: Maria, thank you so much for joining us. I really 609 00:29:42,640 --> 00:29:46,320 Speaker 1: appreciate getting your perspective about all things Europe. Lots to 610 00:29:46,320 --> 00:29:51,080 Speaker 1: think about there. Maria Today, European correspondent for Bloomberg News. 611 00:29:51,080 --> 00:29:55,720 Speaker 1: She does fantastic reporting from all of the hotspots around Europe, 612 00:29:56,000 --> 00:29:59,640 Speaker 1: including Ukraine. So again some great color there from Maria 613 00:29:59,680 --> 00:30:03,080 Speaker 1: today on the ground in Europe. 614 00:30:03,960 --> 00:30:07,360 Speaker 8: You're listening to the Team Ken's are Live program Bloomberg 615 00:30:07,400 --> 00:30:10,800 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg dot Com, 616 00:30:10,880 --> 00:30:14,000 Speaker 8: the iHeartRadio app, and the Bloomberg Business App, or listen 617 00:30:14,080 --> 00:30:16,200 Speaker 8: on demand wherever you get your podcasts. 618 00:30:18,480 --> 00:30:20,960 Speaker 1: Next guest, this is kind of stuff right in my wheelhouse. 619 00:30:20,960 --> 00:30:24,440 Speaker 1: We're talking leverage finance, you know, putting loan. I had 620 00:30:24,440 --> 00:30:26,760 Speaker 1: the first when I was at Chaseman, a bank. We 621 00:30:26,840 --> 00:30:29,840 Speaker 1: lent to a company called god I can't remember it 622 00:30:29,920 --> 00:30:32,920 Speaker 1: became next Star, became oh, it's fleet Call. I was 623 00:30:32,960 --> 00:30:34,920 Speaker 1: asking for a two hundred fifty million dollar loan on 624 00:30:35,160 --> 00:30:39,040 Speaker 1: no assets, wow, no cash flow. We were lending against 625 00:30:39,080 --> 00:30:42,720 Speaker 1: air and we got the loan. Our committee we finally 626 00:30:42,800 --> 00:30:44,600 Speaker 1: took us like three weeks of meetings, but we finally 627 00:30:44,640 --> 00:30:46,120 Speaker 1: convinced her and it turned out to be fleet call. 628 00:30:46,200 --> 00:30:48,360 Speaker 1: We made a ga jillion dollars on the warrants. Ted Swimmer, 629 00:30:48,520 --> 00:30:50,160 Speaker 1: he knows what I'm talking about. He's head of Capital 630 00:30:50,200 --> 00:30:53,280 Speaker 1: Markets and Citizens Financial. Ted. You guys at Citizens Financial 631 00:30:53,400 --> 00:30:56,880 Speaker 1: kind of mid market upper mid market financing deals getting 632 00:30:56,920 --> 00:31:00,520 Speaker 1: deals done. Tell us about that part of the market today, 633 00:31:00,520 --> 00:31:02,320 Speaker 1: because here's sman and I we just sit around and 634 00:31:02,320 --> 00:31:04,240 Speaker 1: we talk about the debt ceiling and the FED and 635 00:31:04,240 --> 00:31:07,360 Speaker 1: stuff like that. You're on the ground helping companies raise 636 00:31:07,440 --> 00:31:10,520 Speaker 1: capital to grow and to buy stuff and build stuff. 637 00:31:10,520 --> 00:31:11,160 Speaker 1: What do you see? 638 00:31:11,520 --> 00:31:14,320 Speaker 11: You know, it feels a lot better out there than 639 00:31:14,360 --> 00:31:16,960 Speaker 11: it has in about a year year and a half. 640 00:31:17,720 --> 00:31:20,160 Speaker 11: You know, a year ago, we saw M and A 641 00:31:20,320 --> 00:31:22,760 Speaker 11: kind of cease, a lot of people concerned about how 642 00:31:22,840 --> 00:31:26,200 Speaker 11: high rates we're going to go, People uncomfortable with trying 643 00:31:26,240 --> 00:31:29,440 Speaker 11: to raise debt in the public markets and the private markets. 644 00:31:29,840 --> 00:31:31,680 Speaker 11: And we saw a real pause in M and A 645 00:31:31,960 --> 00:31:36,280 Speaker 11: and in debt financing. If you've just looked the last 646 00:31:36,280 --> 00:31:39,280 Speaker 11: two months, a fair amount of transactions that started to 647 00:31:39,320 --> 00:31:42,720 Speaker 11: be announced, underwritten by banks, underwritten by debt direct lenders. 648 00:31:43,000 --> 00:31:45,520 Speaker 11: We're starting to see the market unfreeze a little bit, 649 00:31:45,640 --> 00:31:48,280 Speaker 11: and we're starting to see, especially for really good companies, 650 00:31:48,600 --> 00:31:51,160 Speaker 11: a very very active m and a market, so things 651 00:31:51,160 --> 00:31:52,680 Speaker 11: feel a heck of a lot better than they did. 652 00:31:53,440 --> 00:31:55,480 Speaker 12: That kind of sounds surprising to me because at the 653 00:31:55,560 --> 00:31:58,640 Speaker 12: same time, you know, the Fed's talking about tightening lending conditions. 654 00:31:58,920 --> 00:32:01,200 Speaker 12: You know, that's what we're hearing from money managers as well. 655 00:32:01,240 --> 00:32:05,280 Speaker 12: And you're in this upper middle market space. Shouldn't this 656 00:32:05,360 --> 00:32:08,880 Speaker 12: regional banking stress kind of take away from some of 657 00:32:08,920 --> 00:32:10,680 Speaker 12: these things have the opposite effect. 658 00:32:10,720 --> 00:32:13,680 Speaker 11: Perhaps, Well, there's there's a lot of different parts in 659 00:32:13,720 --> 00:32:16,480 Speaker 11: the lending market. There's a leverage finance market which is 660 00:32:16,560 --> 00:32:19,440 Speaker 11: funded a lot by colos and things of that nature, 661 00:32:20,040 --> 00:32:22,880 Speaker 11: and by direct lenders, and you know, as banks sell 662 00:32:22,920 --> 00:32:25,880 Speaker 11: a fair amount of these exposures off the markets to do, 663 00:32:25,960 --> 00:32:29,560 Speaker 11: those have unfrozen. You've seen the secondary levels trade up, 664 00:32:29,560 --> 00:32:32,000 Speaker 11: both on the bank and the bond side, which are 665 00:32:32,000 --> 00:32:35,040 Speaker 11: giving and banks a lot more comfort in underwriting the 666 00:32:35,040 --> 00:32:40,080 Speaker 11: transactions which they didn't have this time last year. I mean, 667 00:32:40,120 --> 00:32:42,520 Speaker 11: you saw how the bank the bank index kind of 668 00:32:42,560 --> 00:32:44,760 Speaker 11: went down about ten points, bond index trading in the 669 00:32:44,760 --> 00:32:47,960 Speaker 11: low eighties. That's all reversed and if we're still not 670 00:32:48,080 --> 00:32:50,760 Speaker 11: back to where we were twenty twenty one, but it 671 00:32:50,800 --> 00:32:53,400 Speaker 11: feels a heck a lot better than it did recently. 672 00:32:53,480 --> 00:32:54,680 Speaker 11: But let's go ahead. 673 00:32:54,760 --> 00:32:56,160 Speaker 1: You know, I just want to ask you about one 674 00:32:56,160 --> 00:32:57,920 Speaker 1: of the big topics that I like here, and it 675 00:32:58,080 --> 00:32:59,800 Speaker 1: seems like a real growth business on Wall Street over 676 00:32:59,840 --> 00:33:03,719 Speaker 1: the several years has been private credit, not private equity, 677 00:33:03,760 --> 00:33:06,920 Speaker 1: private credit, and coming down the Great Financial Crisis, a 678 00:33:06,960 --> 00:33:09,400 Speaker 1: lot of the banks really were curtailed in their ability 679 00:33:09,440 --> 00:33:12,040 Speaker 1: to take to type of lending they could do, with 680 00:33:12,160 --> 00:33:13,680 Speaker 1: the risks they could take on. And I kind of 681 00:33:13,680 --> 00:33:16,320 Speaker 1: created this private credit business and it seems like they're 682 00:33:16,360 --> 00:33:18,880 Speaker 1: raising money hand over fist. How do you deal with 683 00:33:19,400 --> 00:33:22,080 Speaker 1: how do you view private credit in the marketplace as 684 00:33:22,080 --> 00:33:23,480 Speaker 1: a competitor to you guys. 685 00:33:23,440 --> 00:33:27,160 Speaker 11: It's a competitor, and it's also it's also somebody we 686 00:33:27,200 --> 00:33:31,600 Speaker 11: work with, so as we underwrite transactions, sometimes we'll move 687 00:33:31,640 --> 00:33:34,000 Speaker 11: it over to the CLO market more of a public 688 00:33:34,080 --> 00:33:38,480 Speaker 11: not public, but more diversified market, and sometimes we'll work 689 00:33:38,480 --> 00:33:41,440 Speaker 11: directly with private credit to help place deals. If you 690 00:33:41,920 --> 00:33:43,720 Speaker 11: look at a lot of the deals that are coming out, 691 00:33:43,720 --> 00:33:46,080 Speaker 11: some of the larger deals, you'll see a combination of 692 00:33:46,240 --> 00:33:51,640 Speaker 11: bank underwriting and private credit both underwriting the transaction together now, 693 00:33:52,040 --> 00:33:54,240 Speaker 11: so you're starting to see these markets I think, start 694 00:33:54,240 --> 00:33:56,520 Speaker 11: to merge a little bit more. And it's not just 695 00:33:56,680 --> 00:33:59,560 Speaker 11: private credit or bank you're seeing a combination of both. 696 00:34:00,160 --> 00:34:03,000 Speaker 11: Blackstone did a deal last year where you saw them 697 00:34:03,000 --> 00:34:05,480 Speaker 11: both use the bank market and the private credit market 698 00:34:05,520 --> 00:34:08,680 Speaker 11: to get something done. So you've seen a combination of 699 00:34:08,680 --> 00:34:09,880 Speaker 11: those markets come together. 700 00:34:10,800 --> 00:34:14,440 Speaker 12: Well, we talked about this enthusiasm for private credit. I mean, 701 00:34:15,360 --> 00:34:17,120 Speaker 12: has I feel like that's the only thing we read 702 00:34:17,160 --> 00:34:20,120 Speaker 12: about on top on the Bloomberg terminal? Has this gotten 703 00:34:20,160 --> 00:34:22,640 Speaker 12: over as skis? Has too much money been raised? Or 704 00:34:23,400 --> 00:34:26,160 Speaker 12: how does this How does this money enter the economy 705 00:34:26,320 --> 00:34:28,120 Speaker 12: over the next couple of years. 706 00:34:28,200 --> 00:34:30,600 Speaker 11: Well, it enters the economy with an increase in M 707 00:34:30,640 --> 00:34:32,320 Speaker 11: and A flow. I mean a lot of the deals 708 00:34:32,360 --> 00:34:36,440 Speaker 11: that are being h that are that are being a 709 00:34:36,480 --> 00:34:38,960 Speaker 11: lot of the financing is going into these new M 710 00:34:39,000 --> 00:34:40,959 Speaker 11: and A transactions. And I think you've seen a period 711 00:34:41,000 --> 00:34:43,880 Speaker 11: of time over the last year where there hasn't been 712 00:34:43,920 --> 00:34:45,719 Speaker 11: a lot of M and A and you've seen the 713 00:34:45,719 --> 00:34:49,640 Speaker 11: private credit funds build up capacity. So there's a lot 714 00:34:49,640 --> 00:34:52,520 Speaker 11: of unused capacity which I think will help fund as 715 00:34:52,800 --> 00:34:55,920 Speaker 11: people get more and more comfortable with valuations and whatever, 716 00:34:55,960 --> 00:34:59,000 Speaker 11: this new rate environment looks like you'll see an avenue 717 00:34:59,040 --> 00:35:02,440 Speaker 11: for both private credit and for colo credit put to 718 00:35:02,520 --> 00:35:05,040 Speaker 11: work in order to finance these transactions. 719 00:35:05,719 --> 00:35:09,160 Speaker 1: So you guys at Citizens hosted a conference in Atlanta recently, 720 00:35:09,200 --> 00:35:12,600 Speaker 1: and presumably that's where you bring companies together, sponsors together, 721 00:35:12,640 --> 00:35:15,080 Speaker 1: and you guys try to generate some conversations and hopefully 722 00:35:15,120 --> 00:35:17,520 Speaker 1: some some trades down the line. What was kind of 723 00:35:17,360 --> 00:35:20,840 Speaker 1: the conversation when he brought these folks together. 724 00:35:20,960 --> 00:35:23,600 Speaker 11: Sounds like you were there, Paul, you described I described 725 00:35:24,080 --> 00:35:27,160 Speaker 11: many of them. Yeah, you know, it was a very 726 00:35:27,200 --> 00:35:30,680 Speaker 11: optimistic group. We've we've had we've hosted this conference in 727 00:35:30,719 --> 00:35:36,160 Speaker 11: the last four or five years, and we saw more optimism. 728 00:35:36,600 --> 00:35:38,560 Speaker 11: We we had a record amount of attendance, a market 729 00:35:38,560 --> 00:35:41,840 Speaker 11: amount of sponsors, financial at private equity firms, and a 730 00:35:41,880 --> 00:35:44,680 Speaker 11: record amount of companies looking to try to transact. 731 00:35:44,719 --> 00:35:44,959 Speaker 6: Now. 732 00:35:45,840 --> 00:35:48,440 Speaker 11: I think what we're what we're hearing is for really 733 00:35:48,560 --> 00:35:51,560 Speaker 11: solid companies, there's a very very active market and an 734 00:35:51,560 --> 00:35:55,120 Speaker 11: aggressive market to get those transactions done. For a little 735 00:35:55,120 --> 00:35:58,120 Speaker 11: bit more storied credits. We're still seeing it'll be a 736 00:35:58,160 --> 00:36:01,640 Speaker 11: little slow, but our pipelines right now at a record amount, 737 00:36:01,880 --> 00:36:03,480 Speaker 11: and it's just a question of can we get a 738 00:36:03,480 --> 00:36:05,080 Speaker 11: buyer and a seller together. 739 00:36:04,840 --> 00:36:08,000 Speaker 1: To how about you? You bring it? Host this conference, 740 00:36:08,040 --> 00:36:11,080 Speaker 1: you get you get some conversations going boom, you think 741 00:36:11,080 --> 00:36:13,160 Speaker 1: you got a deal, you get them together, you go 742 00:36:13,200 --> 00:36:15,759 Speaker 1: to your credit committee, or you go to whoever. Can 743 00:36:15,800 --> 00:36:18,600 Speaker 1: you get deals done? Leverage deals or with a little 744 00:36:18,600 --> 00:36:21,400 Speaker 1: hair on them? What's it like going to try to 745 00:36:21,400 --> 00:36:22,879 Speaker 1: get some some of these deals done right now? 746 00:36:26,040 --> 00:36:29,600 Speaker 11: It's not easy ran Obviously, banks are thinking about a 747 00:36:29,600 --> 00:36:31,960 Speaker 11: lot of different things right now as they're putting capital 748 00:36:32,040 --> 00:36:33,759 Speaker 11: to work. But again, a lot of these deals are 749 00:36:33,760 --> 00:36:38,600 Speaker 11: distributed to markets where we can, which is very active 750 00:36:38,600 --> 00:36:41,440 Speaker 11: and very liquid right now. I mean, to Simon's point earlier, 751 00:36:41,920 --> 00:36:45,040 Speaker 11: you've seen a ton of private credit raised, there's a 752 00:36:45,040 --> 00:36:47,000 Speaker 11: lot of there's a lot of desire to put money 753 00:36:47,040 --> 00:36:50,239 Speaker 11: to work. You've seen very little loan issuance over the 754 00:36:50,320 --> 00:36:52,480 Speaker 11: last year, so there's a really pent up demand on 755 00:36:52,520 --> 00:36:56,920 Speaker 11: the COLO side. So although banks are not necessarily desirous 756 00:36:56,960 --> 00:36:59,960 Speaker 11: to hold big capital commitments right now, there are other 757 00:37:00,160 --> 00:37:03,920 Speaker 11: avenues of investors that are and our ability to put 758 00:37:03,960 --> 00:37:07,279 Speaker 11: the buyer and the seller together take some distribution risk 759 00:37:07,360 --> 00:37:09,360 Speaker 11: has never I wouldn't say never been as good, but 760 00:37:09,480 --> 00:37:12,719 Speaker 11: it is certainly improving over the last twenty for twelve months. 761 00:37:13,200 --> 00:37:17,399 Speaker 12: When you look down to I guess lower middle kind 762 00:37:17,440 --> 00:37:21,160 Speaker 12: of credits, is that somewhere you see a little bit 763 00:37:21,320 --> 00:37:22,759 Speaker 12: more anxiety. 764 00:37:22,880 --> 00:37:26,759 Speaker 11: Yes, absolutely, the lower And just to clarify what we're 765 00:37:26,800 --> 00:37:29,880 Speaker 11: talking about, I would say upper middle market is epita 766 00:37:30,200 --> 00:37:33,320 Speaker 11: sizes of the fifty million dollars or greater, so enterprise 767 00:37:33,400 --> 00:37:37,320 Speaker 11: value somewhere between four hundred and a billion dollars type things. 768 00:37:37,560 --> 00:37:40,080 Speaker 11: When you start looking at lower that's become a market 769 00:37:40,120 --> 00:37:43,399 Speaker 11: that has been more dominated by direct lenders as they've 770 00:37:43,440 --> 00:37:46,320 Speaker 11: been able to do things that are banks would have 771 00:37:46,400 --> 00:37:49,600 Speaker 11: a tough time during the due to a regulatory environment 772 00:37:49,680 --> 00:37:53,719 Speaker 11: to get done, but the direct lenders can certainly do 773 00:37:53,840 --> 00:37:56,440 Speaker 11: as they're not obviously regulated by the bank. So we're 774 00:37:56,440 --> 00:37:59,160 Speaker 11: seeing a lot of appetite from that for those type 775 00:37:59,239 --> 00:38:01,640 Speaker 11: transactions getting done with direct lenders. 776 00:38:01,719 --> 00:38:05,440 Speaker 12: Because it was really interesting, the CEO of Citizens Financial 777 00:38:05,480 --> 00:38:07,760 Speaker 12: was on Bloomberg Markets yesterday and he was talking about 778 00:38:07,800 --> 00:38:12,600 Speaker 12: how restricting reining in credit by two to three percent 779 00:38:13,040 --> 00:38:15,799 Speaker 12: was going to essentially help the FED do its job, 780 00:38:16,239 --> 00:38:21,040 Speaker 12: but it does seem to really disadvantage smaller businesses. 781 00:38:21,080 --> 00:38:23,080 Speaker 11: It's a great point. I think a lot of that 782 00:38:23,200 --> 00:38:25,600 Speaker 11: was done prior right after, as Paul mentioned that the 783 00:38:25,640 --> 00:38:29,239 Speaker 11: Great Recession, when the regulatory environment became harder, we really 784 00:38:29,239 --> 00:38:31,400 Speaker 11: saw direct lenders pick up some of the slack. But 785 00:38:31,480 --> 00:38:35,000 Speaker 11: I think as you get to more corporate driven transactions 786 00:38:35,000 --> 00:38:37,840 Speaker 11: that don't necessarily have tradition relied on the bank, I 787 00:38:37,840 --> 00:38:41,000 Speaker 11: think that's where you're going to see some issues if 788 00:38:41,080 --> 00:38:43,880 Speaker 11: things get tighter from a lending perspective, because banks are 789 00:38:43,960 --> 00:38:46,440 Speaker 11: used to holding very large dollar amounts of those and 790 00:38:46,440 --> 00:38:49,520 Speaker 11: obviously that's going to become more expensive as capital becomes 791 00:38:49,560 --> 00:38:50,280 Speaker 11: more precious. 792 00:38:50,760 --> 00:38:54,399 Speaker 1: What are some of the sectors you guys like right here? 793 00:38:54,440 --> 00:38:57,600 Speaker 1: Some sectors you like investing in and business with right now. 794 00:38:57,680 --> 00:39:01,160 Speaker 11: So we're doing a fair amount with data Center's. Digital 795 00:39:01,200 --> 00:39:04,120 Speaker 11: infrastructure is a great space for us right now. We're 796 00:39:04,120 --> 00:39:06,799 Speaker 11: seeing We just purchased an m and a firm called 797 00:39:06,840 --> 00:39:10,120 Speaker 11: DH Capital that specializes in that, and we've had a 798 00:39:10,160 --> 00:39:13,160 Speaker 11: great run in that business and we see that as 799 00:39:13,200 --> 00:39:16,160 Speaker 11: a real growth engine over the next couple of years. 800 00:39:17,000 --> 00:39:23,960 Speaker 11: A lot of business technology, business outsourcing, things of that nature. 801 00:39:24,040 --> 00:39:28,759 Speaker 11: We've been showing a commercial industrial industrial flows stuff for 802 00:39:28,920 --> 00:39:32,120 Speaker 11: doing a fair amount in there. So those areas have 803 00:39:32,239 --> 00:39:36,279 Speaker 11: really grown and seem to be somewhat especially the digital infrastructure, 804 00:39:36,320 --> 00:39:39,520 Speaker 11: somewhat immune to concern around the overall economy that seems 805 00:39:39,560 --> 00:39:41,520 Speaker 11: to be a never ending need for the capital and 806 00:39:41,600 --> 00:39:42,800 Speaker 11: now business. 807 00:39:43,400 --> 00:39:45,520 Speaker 1: So all right, Ted, thanks so much for coming in. 808 00:39:45,560 --> 00:39:48,280 Speaker 1: Appreciate it as always, Ted Swimmer, you set of capital 809 00:39:48,280 --> 00:39:51,960 Speaker 1: markets at Citizens Financial, joining us live in our Bloomberg 810 00:39:51,960 --> 00:39:54,800 Speaker 1: Interactive Brokers Studio. None of this phony and in garbage 811 00:39:54,800 --> 00:39:57,160 Speaker 1: coming in live to the Bloomberg Term studio. 812 00:39:57,520 --> 00:40:00,640 Speaker 8: You're listening to the tape Cancher Line program. I'm Bloomberg 813 00:40:00,680 --> 00:40:04,279 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the 814 00:40:04,360 --> 00:40:07,560 Speaker 8: tune in app, Bloomberg dot Com, and the Bloomberg Business App. 815 00:40:07,600 --> 00:40:10,440 Speaker 8: You can also listen live on Amazon Alexa from our 816 00:40:10,440 --> 00:40:14,799 Speaker 8: flagship New York station Just Say Alexa playing Bloomberg eleven thirty. 817 00:40:16,320 --> 00:40:20,200 Speaker 1: Marie Driscoll. She's a luxury retail analyst for Core Site Research. 818 00:40:20,280 --> 00:40:23,560 Speaker 1: Got lots of experience in the retail space, and let's 819 00:40:23,600 --> 00:40:26,399 Speaker 1: start with with the luxury space here. I'm just looking 820 00:40:26,400 --> 00:40:28,600 Speaker 1: at LVMH. That's kind of one of the names I 821 00:40:28,800 --> 00:40:31,960 Speaker 1: kind of know, and it's big. It's four hundred billion 822 00:40:31,960 --> 00:40:34,439 Speaker 1: market cap. I'm looking at the French shares that trade 823 00:40:34,480 --> 00:40:37,719 Speaker 1: in France up twenty percent year to date of fifty 824 00:40:37,760 --> 00:40:40,200 Speaker 1: percent on a troilling twelve month basis. Talk to us 825 00:40:40,200 --> 00:40:43,439 Speaker 1: about retail space. What are you seeing out there these days? 826 00:40:44,040 --> 00:40:49,440 Speaker 10: So if we're talking about luxury in LVNH, you know 827 00:40:49,560 --> 00:40:54,040 Speaker 10: there's still pockets of money. The aspirational shopper is definitely 828 00:40:55,400 --> 00:41:01,160 Speaker 10: more purposeful and making the purchases, you know, with thought. 829 00:41:01,320 --> 00:41:04,319 Speaker 10: And we heard that from the likes of Walmart. 830 00:41:05,800 --> 00:41:06,520 Speaker 13: On up. 831 00:41:07,200 --> 00:41:11,520 Speaker 10: But for a company like LVMH that has seventy five 832 00:41:11,600 --> 00:41:16,839 Speaker 10: brands in the luxury space, and brands that are powerfully 833 00:41:16,840 --> 00:41:22,320 Speaker 10: supported throughout various economic cycles, these brands hold a lore 834 00:41:22,520 --> 00:41:27,560 Speaker 10: and they never, they really rarely marked down. Their key 835 00:41:27,640 --> 00:41:32,640 Speaker 10: brands like a Christian Dior or the Louis Duitan. They're 836 00:41:32,719 --> 00:41:37,600 Speaker 10: not marked down, and because of that, many consumers look 837 00:41:37,640 --> 00:41:41,000 Speaker 10: at them as like storage value, like you know that 838 00:41:41,080 --> 00:41:43,440 Speaker 10: when you buy it, you're not making a mistake. It 839 00:41:43,480 --> 00:41:47,560 Speaker 10: won't be marked down next week. Luxury shoppers are still shopping. 840 00:41:47,880 --> 00:41:50,799 Speaker 10: People are being more purposeful, and you do see a 841 00:41:50,840 --> 00:41:55,839 Speaker 10: bifurcated market. The people that have are spending, and they're 842 00:41:55,840 --> 00:42:00,520 Speaker 10: not spending flagrantly, but they're still buying luxury products. Has 843 00:42:00,560 --> 00:42:04,000 Speaker 10: also benefited from price increases for the last few years. 844 00:42:04,360 --> 00:42:07,719 Speaker 10: China is coming back, so we expect to see a 845 00:42:07,840 --> 00:42:11,640 Speaker 10: slow down in US consumption of luxury as the US 846 00:42:11,760 --> 00:42:16,400 Speaker 10: consumer deals with the impacts of inflation and also reverts 847 00:42:16,440 --> 00:42:23,440 Speaker 10: back to a pre COVID lifestyle of increased experiences. Experiences 848 00:42:23,480 --> 00:42:29,319 Speaker 10: are where the consumer is encountering severe inflation. You know, 849 00:42:29,480 --> 00:42:33,080 Speaker 10: twenty to forty percent increases in the price of going 850 00:42:33,120 --> 00:42:36,480 Speaker 10: out to eat or staying in a hotel airfare, and 851 00:42:36,560 --> 00:42:39,719 Speaker 10: yet consumers want that. There's pent up demand for that, 852 00:42:40,880 --> 00:42:45,520 Speaker 10: and so as consumers spend on those experiences in America, 853 00:42:45,760 --> 00:42:50,360 Speaker 10: they're less likely to be spending on another expensive handbag. 854 00:42:51,160 --> 00:42:53,920 Speaker 10: Though the handbag is the handbag that remains of choice 855 00:42:53,960 --> 00:42:56,680 Speaker 10: and they'll spend on it when they when they decide 856 00:42:56,719 --> 00:43:00,560 Speaker 10: to do so. But this year luxury will see the 857 00:43:00,600 --> 00:43:03,799 Speaker 10: benefits of the Chinese returning to the market with the 858 00:43:03,840 --> 00:43:07,920 Speaker 10: opening of China again, and other UAE will be strong. 859 00:43:08,000 --> 00:43:11,960 Speaker 10: And also you have gen Z supporting you know, a 860 00:43:12,000 --> 00:43:15,840 Speaker 10: younger a younger luxury shopper that is supporting luxury. 861 00:43:16,080 --> 00:43:18,600 Speaker 12: All right, Well, you know we have been hearing from 862 00:43:18,719 --> 00:43:25,280 Speaker 12: CEOs across the consumer space about this softening of US demand. 863 00:43:25,719 --> 00:43:30,160 Speaker 12: Take me through the details of that. Which demographics are 864 00:43:30,160 --> 00:43:35,960 Speaker 12: feeling the pinch most, and that who are the recipients 865 00:43:36,080 --> 00:43:40,720 Speaker 12: or of this potential cynicism and you know, inflation tightening 866 00:43:40,760 --> 00:43:42,120 Speaker 12: of belts from consumers. 867 00:43:42,160 --> 00:43:46,520 Speaker 10: Yeah, so you see like people are tightening their belts. 868 00:43:46,800 --> 00:43:51,280 Speaker 10: Like if you just listen to the Walmart Paul, people 869 00:43:51,360 --> 00:43:57,160 Speaker 10: are purposeful, they're looking for bargains Walmart. During COVID, Walmart 870 00:43:57,640 --> 00:44:02,840 Speaker 10: attracted many consumers earning one hundred thousand a year or more, 871 00:44:03,320 --> 00:44:07,120 Speaker 10: and of course they're trying to retain them as customers 872 00:44:07,480 --> 00:44:11,800 Speaker 10: this year. This year, you know, higher end consumers. Everybody 873 00:44:11,800 --> 00:44:17,040 Speaker 10: feels inflation. And you know, except for the one percent, 874 00:44:17,880 --> 00:44:21,080 Speaker 10: most people feel inflation. They feel it in their growth 875 00:44:21,120 --> 00:44:23,320 Speaker 10: rey bills, they feel it in going out to eat, 876 00:44:24,280 --> 00:44:28,640 Speaker 10: and in their travel and it's and various services and 877 00:44:28,719 --> 00:44:33,880 Speaker 10: experience it and so there while there's demands for selected 878 00:44:34,040 --> 00:44:38,800 Speaker 10: items like if you recently cold, which is very Middle 879 00:44:38,840 --> 00:44:42,120 Speaker 10: America reported, and where did they see strength? They saw 880 00:44:42,280 --> 00:44:46,080 Speaker 10: incredible strength and beauty so people are still saying, I'm 881 00:44:46,160 --> 00:44:49,680 Speaker 10: going to buy I'm going to have this cheap taste 882 00:44:49,719 --> 00:44:53,200 Speaker 10: of luxury and with a fragrance or a lipstick. And 883 00:44:53,280 --> 00:44:56,439 Speaker 10: guess what, you can use ellipstick every day. So it's 884 00:44:56,480 --> 00:45:01,000 Speaker 10: got efficacy which is similar to using luxury hand you know, 885 00:45:01,040 --> 00:45:04,040 Speaker 10: so you can there's room for that. So the last 886 00:45:04,040 --> 00:45:08,200 Speaker 10: few years, we've spent a lot on casual attire calls, 887 00:45:08,719 --> 00:45:15,040 Speaker 10: a big increase in both men's and women's more occasion 888 00:45:15,120 --> 00:45:18,560 Speaker 10: the attire or address the. 889 00:45:18,640 --> 00:45:22,480 Speaker 12: Attire right, you know, with calls though, I mean, you 890 00:45:23,400 --> 00:45:27,160 Speaker 12: saw shares pop up seven and a half percent, but 891 00:45:27,200 --> 00:45:29,560 Speaker 12: I think you know during the day that was quite 892 00:45:29,600 --> 00:45:34,359 Speaker 12: a bit higher. Is it enough for these companies to 893 00:45:34,400 --> 00:45:37,680 Speaker 12: just sort of like beat the estimates that their executives 894 00:45:37,719 --> 00:45:41,440 Speaker 12: put out. Is that what we're seeing in the market 895 00:45:41,440 --> 00:45:45,680 Speaker 12: reaction or are we seeing some fundamental sense from investors 896 00:45:45,760 --> 00:45:48,719 Speaker 12: that the consumer is a little bit better off than 897 00:45:48,719 --> 00:45:51,040 Speaker 12: they had anticipated going into earning season. 898 00:45:52,160 --> 00:45:56,920 Speaker 10: So you know, you know, reading enough and listening to 899 00:45:57,120 --> 00:46:01,480 Speaker 10: enough call during the last week or two, A few 900 00:46:01,480 --> 00:46:06,239 Speaker 10: of the big takeaways are that while sales may not 901 00:46:06,360 --> 00:46:09,839 Speaker 10: be meeting ejectives, and the consumer is more purposeful and 902 00:46:10,280 --> 00:46:13,920 Speaker 10: there's winners and there's losers. Here I'm going to quote 903 00:46:14,000 --> 00:46:20,280 Speaker 10: what the Dick Hayne's urban outfitter said. The hostile operating 904 00:46:20,360 --> 00:46:24,160 Speaker 10: environment of the last few years has finally abated. Freight 905 00:46:24,320 --> 00:46:29,480 Speaker 10: rates are have normalized, Supply chain speed and reliability have returned. 906 00:46:30,040 --> 00:46:35,600 Speaker 10: Our initial merchandise markup. Improvement initiatives have begun to bear fruit, 907 00:46:36,000 --> 00:46:39,640 Speaker 10: and total inventories are down and we're once again growing 908 00:46:39,719 --> 00:46:45,520 Speaker 10: inventories that have slower rates themselves. That kind of the 909 00:46:45,600 --> 00:46:48,640 Speaker 10: way he characterized his business is what I was seeing 910 00:46:48,719 --> 00:46:52,640 Speaker 10: across many businesses. While the top line isn't there. The 911 00:46:52,840 --> 00:46:57,680 Speaker 10: erratic business operating environment we've dealt with in the last 912 00:46:57,840 --> 00:47:01,680 Speaker 10: three years with peaks of this man not enough, not 913 00:47:01,880 --> 00:47:05,040 Speaker 10: enough supply, then the rush of supply and too much 914 00:47:05,800 --> 00:47:10,120 Speaker 10: and not enough demand is abating, and we're entering a 915 00:47:10,239 --> 00:47:14,319 Speaker 10: time when now the costs of getting your products to 916 00:47:14,440 --> 00:47:18,200 Speaker 10: the store are less. You're seeing a return to stores. 917 00:47:19,040 --> 00:47:22,000 Speaker 10: Digital is strolling down, people are in the stores. 918 00:47:22,560 --> 00:47:26,960 Speaker 12: So the fundamental post pandemic improvements, you know, that's what 919 00:47:27,000 --> 00:47:30,120 Speaker 12: we're seeing rather than yeah, I think consumer. 920 00:47:30,480 --> 00:47:33,399 Speaker 10: And if we can get through a quarter or two 921 00:47:33,719 --> 00:47:38,680 Speaker 10: of you know, like the consume, like these are the 922 00:47:38,719 --> 00:47:41,319 Speaker 10: summer is the week quarter. The summer is when the 923 00:47:41,440 --> 00:47:44,319 Speaker 10: US consumer is out traveling. If we can get through that, 924 00:47:45,400 --> 00:47:48,240 Speaker 10: the second half of the year should be a better time. 925 00:47:48,480 --> 00:47:50,480 Speaker 10: And I think investors should be looking at that. 926 00:47:50,840 --> 00:47:55,239 Speaker 1: Hey, Marie, China reopening. As I walk through Midtown and 927 00:47:55,280 --> 00:47:57,799 Speaker 1: down Fifth Avenue and Madison Avenue, I see tons of 928 00:47:57,840 --> 00:48:01,400 Speaker 1: European shoppers. I don't see Chinese. When are they coming back? 929 00:48:02,400 --> 00:48:05,640 Speaker 10: Yeah, so, you know, of course that is very close 930 00:48:05,640 --> 00:48:08,120 Speaker 10: to the Chinese consumer. We have offices there, we have 931 00:48:08,200 --> 00:48:10,719 Speaker 10: analysts on the ground, and we've been following them for 932 00:48:10,800 --> 00:48:14,960 Speaker 10: probably eight years and more, and but we're you know, 933 00:48:15,080 --> 00:48:17,960 Speaker 10: it's going to take six to twelve months for them 934 00:48:18,040 --> 00:48:21,560 Speaker 10: to come back. They just opened up in February, basically, 935 00:48:21,960 --> 00:48:25,560 Speaker 10: and it takes a while to get your travel plants 936 00:48:25,680 --> 00:48:29,560 Speaker 10: in order. We don't really see it coming end of 937 00:48:29,600 --> 00:48:34,440 Speaker 10: this year, next year, but the Europeans are here, and 938 00:48:34,480 --> 00:48:39,399 Speaker 10: the Chinese are starting to travel domestically within within the 939 00:48:39,440 --> 00:48:44,040 Speaker 10: Asia region, and so many American brands will benefit from that, 940 00:48:45,239 --> 00:48:46,440 Speaker 10: you know, the European brand. 941 00:48:46,560 --> 00:48:49,880 Speaker 1: Yeah, absolutely, absolutely well, they're welcome on Fifth Avenue, the 942 00:48:49,920 --> 00:48:52,040 Speaker 1: walkome on Madison Avenue. I'm sure I speak for all 943 00:48:52,040 --> 00:48:55,239 Speaker 1: the retailers there, because when you talk luxury retail, a big, 944 00:48:55,239 --> 00:48:58,640 Speaker 1: big component, a big driver is the Chinese Chinese consumer. 945 00:48:59,160 --> 00:49:02,480 Speaker 1: Marie Driscoll, luxury retail analysts at Corsite Research, joining us 946 00:49:02,480 --> 00:49:05,799 Speaker 1: here to talk to us all things retail. Despite some 947 00:49:05,880 --> 00:49:07,880 Speaker 1: of the tough headwinds out there in terms of inflation 948 00:49:08,000 --> 00:49:10,080 Speaker 1: and economic concern NEI, there are certain pockets of some 949 00:49:10,120 --> 00:49:13,280 Speaker 1: retail strength, and we're seeing other areas where some consumers 950 00:49:13,280 --> 00:49:14,399 Speaker 1: are trading down a little bit. 951 00:49:14,560 --> 00:49:17,640 Speaker 8: You're listening to the tape. Can's are live program Bloomberg 952 00:49:17,680 --> 00:49:21,279 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the 953 00:49:21,320 --> 00:49:24,560 Speaker 8: tune in app, Bloomberg dot Com, and the Bloomberg Business App. 954 00:49:24,600 --> 00:49:27,440 Speaker 8: You can also listen live on Amazon Alexa from our 955 00:49:27,440 --> 00:49:32,480 Speaker 8: flagship New York station, Just say Alexa play Bloomberg eleven thirty. 956 00:49:33,440 --> 00:49:35,400 Speaker 1: I think one of my favorite stories coming out of 957 00:49:35,440 --> 00:49:39,960 Speaker 1: Bloomberg News this week was won by John Gittelson about 958 00:49:40,160 --> 00:49:44,200 Speaker 1: Downtown LA's office distress. So's the pain coming for cities A. 959 00:49:44,400 --> 00:49:47,920 Speaker 1: This is an incredibly well sourced and reported story, and 960 00:49:47,960 --> 00:49:51,080 Speaker 1: it's got just killer graphics by Kyle Kim, and it 961 00:49:51,160 --> 00:49:53,719 Speaker 1: really brings home some of the problems we're going to 962 00:49:53,800 --> 00:49:55,840 Speaker 1: see in some of these big US cities that you 963 00:49:55,880 --> 00:49:59,200 Speaker 1: wouldn't necessarily think but like LA, go figure. So anyway, 964 00:49:59,920 --> 00:50:02,799 Speaker 1: us on. He's a reporter, real estate reporter for Bloomberg News. John, 965 00:50:02,840 --> 00:50:04,480 Speaker 1: thanks so much for joining us here. I said to 966 00:50:04,560 --> 00:50:06,400 Speaker 1: our producer Eric, we got to get John on here 967 00:50:06,400 --> 00:50:08,480 Speaker 1: because this is a great story. Here. Talk to us 968 00:50:08,520 --> 00:50:13,279 Speaker 1: about LA and some of the primo building's iconic been 969 00:50:13,320 --> 00:50:15,680 Speaker 1: in buildings in LA. They're in a lot of trouble, 970 00:50:15,719 --> 00:50:16,120 Speaker 1: aren't they. 971 00:50:17,360 --> 00:50:21,239 Speaker 14: Yeah, they really are. I mean, downtown LA has not 972 00:50:21,480 --> 00:50:24,760 Speaker 14: really been a magnet for office workers for quite a while, 973 00:50:25,239 --> 00:50:29,400 Speaker 14: but the pandemic really gave it this punch to the stomach. 974 00:50:30,760 --> 00:50:38,239 Speaker 14: And so the biggest landlord, Brookfield, is defaulted on three 975 00:50:38,320 --> 00:50:42,360 Speaker 14: buildings and it's got more troubled dead out there. So 976 00:50:42,840 --> 00:50:46,160 Speaker 14: one point one billion dollars worth of loans it's not 977 00:50:46,520 --> 00:50:50,600 Speaker 14: been paying and uh more trouble on the way trying 978 00:50:50,600 --> 00:50:51,760 Speaker 14: to refinance those buildings. 979 00:50:51,920 --> 00:50:55,440 Speaker 12: Yeah, talk to me about what happens exactly to those buildings. 980 00:50:55,480 --> 00:50:58,960 Speaker 12: I'm sure some of it we don't know. So Brookfield 981 00:50:59,200 --> 00:51:02,120 Speaker 12: in some cases has said we'll step away from this 982 00:51:02,160 --> 00:51:06,480 Speaker 12: building completely. I believe based on the story, but it's 983 00:51:06,520 --> 00:51:08,399 Speaker 12: not necessarily true for all of them. 984 00:51:08,520 --> 00:51:11,400 Speaker 14: Take me through the process, that's right, Well, I mean 985 00:51:11,400 --> 00:51:15,640 Speaker 14: there's a lot of options. Basically, though, these buildings are underwater, 986 00:51:16,040 --> 00:51:20,360 Speaker 14: so that means their debt is bigger than the value 987 00:51:20,400 --> 00:51:23,040 Speaker 14: of the buildings themselves. So what can happen is the 988 00:51:23,120 --> 00:51:26,960 Speaker 14: lenders can say, Okay, well take a haircut and you 989 00:51:27,000 --> 00:51:29,920 Speaker 14: can keep running the building. You can keep managing it. 990 00:51:30,640 --> 00:51:36,239 Speaker 14: Or the owner can walk away and somebody else could 991 00:51:36,320 --> 00:51:39,279 Speaker 14: take over the building could sell for a loss to 992 00:51:39,400 --> 00:51:44,200 Speaker 14: a new owner. Theoretically, down the road, maybe some of 993 00:51:44,239 --> 00:51:48,400 Speaker 14: these buildings could be converted to another use, like an 994 00:51:48,440 --> 00:51:50,920 Speaker 14: apartment building. But a lot of these are kind of 995 00:51:50,960 --> 00:51:54,759 Speaker 14: like nineteen eighties nineteen nineties office towers that would be 996 00:51:54,880 --> 00:51:56,440 Speaker 14: very hard to convert to apartments. 997 00:51:56,520 --> 00:51:58,360 Speaker 1: The eighties were very good for me, John. 998 00:52:00,840 --> 00:52:04,720 Speaker 14: Well, they weren't necessarily good buildings in today's world. You're 999 00:52:04,800 --> 00:52:08,720 Speaker 14: in your prime, so. 1000 00:52:07,239 --> 00:52:10,120 Speaker 1: So it's interesting, John, like I noticed, you know what 1001 00:52:10,160 --> 00:52:12,680 Speaker 1: my experience with downtown la is. I would go see 1002 00:52:12,719 --> 00:52:15,160 Speaker 1: Capital Research Group and the Trust Company of the West, 1003 00:52:15,800 --> 00:52:17,640 Speaker 1: and that was kind of it. Every all the other 1004 00:52:17,960 --> 00:52:20,760 Speaker 1: asset managers and financial firms were kind of scattered throughout 1005 00:52:20,760 --> 00:52:24,680 Speaker 1: the Greater LA area. Who is in downtown before, Well, 1006 00:52:24,719 --> 00:52:25,400 Speaker 1: that's a good point. 1007 00:52:26,320 --> 00:52:28,439 Speaker 14: Yeah, I mean there's a lot of law firms. There's 1008 00:52:28,440 --> 00:52:31,120 Speaker 14: a lot of accounting firms. One of the two of 1009 00:52:31,160 --> 00:52:33,640 Speaker 14: the towers actually that Brookfield owns at the top of 1010 00:52:33,680 --> 00:52:36,920 Speaker 14: the building once says ey Plaza, which is Ernest and Young. 1011 00:52:37,040 --> 00:52:40,960 Speaker 14: The other one is Deloitte, another accounting firm. A lot 1012 00:52:40,960 --> 00:52:44,680 Speaker 14: of law firms. Those businesses are classic like we don't 1013 00:52:44,680 --> 00:52:49,080 Speaker 14: need all this office space because our workers maybe work 1014 00:52:49,120 --> 00:52:51,879 Speaker 14: in somebody else's office, or they can work remotely so 1015 00:52:52,000 --> 00:52:54,320 Speaker 14: they don't necessarily need to go downtown. Then there's a 1016 00:52:54,360 --> 00:52:57,640 Speaker 14: lot of government workers downtown. About a third of the 1017 00:52:57,680 --> 00:53:01,560 Speaker 14: employment based downtown is like city, county, state, federal court 1018 00:53:02,160 --> 00:53:06,000 Speaker 14: type of government workers, many of whom continue to work remotely. 1019 00:53:07,120 --> 00:53:12,239 Speaker 12: You mentioned potentially converting these buildings into you know, perhaps 1020 00:53:12,360 --> 00:53:16,520 Speaker 12: apartments or something like that. Kyle bass Back, I believe 1021 00:53:16,600 --> 00:53:19,040 Speaker 12: last month was talking about how, you know, maybe these 1022 00:53:19,080 --> 00:53:21,239 Speaker 12: buildings are just going to have to come down wholesale 1023 00:53:21,320 --> 00:53:25,680 Speaker 12: because they're not necessarily built for people to live in. 1024 00:53:25,800 --> 00:53:28,520 Speaker 12: I think he was speaking specifically about warehouses and that 1025 00:53:28,600 --> 00:53:33,480 Speaker 12: sort of office space at least in you know, smaller cities, 1026 00:53:34,000 --> 00:53:36,760 Speaker 12: but in LA is that a realistic thing that could 1027 00:53:36,840 --> 00:53:40,080 Speaker 12: happen these buildings get converted into residential real estate. 1028 00:53:41,360 --> 00:53:45,920 Speaker 14: Well, I think the sort of trophy office buildings that 1029 00:53:45,960 --> 00:53:49,719 Speaker 14: you see in the skyline photos of downtown LA, those 1030 00:53:49,760 --> 00:53:53,480 Speaker 14: ones would be very hard to convert. But actually, there 1031 00:53:53,560 --> 00:53:57,480 Speaker 14: are dozens of older buildings built in early nineteen twenties 1032 00:53:59,000 --> 00:54:05,560 Speaker 14: scattered around that are now already converted to apartments, lofts, condos, 1033 00:54:06,000 --> 00:54:10,600 Speaker 14: and they were before the pandemic, very attractive places to live. 1034 00:54:11,239 --> 00:54:15,680 Speaker 14: Downtown LA is relatively affordable and has a lot of 1035 00:54:15,760 --> 00:54:20,400 Speaker 14: new multifamily offerings compared to like the West Side or 1036 00:54:20,440 --> 00:54:24,040 Speaker 14: other parts of LA where there's it's very expensive to 1037 00:54:24,120 --> 00:54:29,120 Speaker 14: live and it's also kind of difficult to build new products. 1038 00:54:29,120 --> 00:54:32,560 Speaker 14: So there is a possibility that people will move into 1039 00:54:32,600 --> 00:54:36,880 Speaker 14: some of these buildings. The glass and steel late twentieth 1040 00:54:36,880 --> 00:54:40,800 Speaker 14: century ones are going to have a very hard path forward, though. 1041 00:54:41,000 --> 00:54:43,040 Speaker 1: Hey, John, just about I don't know, thirty two minutes, 1042 00:54:43,040 --> 00:54:45,480 Speaker 1: I'm going to begin my walk from Bloomberg at fifty 1043 00:54:45,520 --> 00:54:47,520 Speaker 1: eight and Lex to Penn Station at thirty fourth and 1044 00:54:47,600 --> 00:54:49,919 Speaker 1: let's call it seventh Avenue, So right through the heart 1045 00:54:49,920 --> 00:54:52,160 Speaker 1: of midtown Manhattan, and I'm going to see a lot 1046 00:54:52,160 --> 00:54:55,759 Speaker 1: of empty buildings, a lot of dark floors. But it's 1047 00:54:56,160 --> 00:54:58,200 Speaker 1: bad here. But it's a lot worse than other big 1048 00:54:58,239 --> 00:55:01,200 Speaker 1: cities like Houston and Los Angeles that your reporting brings out. 1049 00:55:01,560 --> 00:55:03,120 Speaker 1: So what are those cities thinking about? 1050 00:55:04,320 --> 00:55:07,800 Speaker 14: Yeah, well, I mean part of the problem with cities 1051 00:55:07,880 --> 00:55:13,600 Speaker 14: like Houston, La, Atlanta, Denver, Dallas, they're car centric. So 1052 00:55:14,320 --> 00:55:16,880 Speaker 14: people have a lot of opportunities to go outside of 1053 00:55:16,880 --> 00:55:20,400 Speaker 14: the city to offices to they have big houses that 1054 00:55:20,440 --> 00:55:23,360 Speaker 14: they can work from. Commuting to downtown is very hard. 1055 00:55:23,560 --> 00:55:26,920 Speaker 14: So yeah, those cities have a problem. They're facing falling revenue, 1056 00:55:27,000 --> 00:55:32,840 Speaker 14: They're facing retailers who are not making street life very attractive. 1057 00:55:33,080 --> 00:55:37,640 Speaker 14: So urban planners are really kind of trying to figure 1058 00:55:37,640 --> 00:55:39,760 Speaker 14: out what to do with all of this real estate. 1059 00:55:40,320 --> 00:55:43,160 Speaker 14: Downtown could be potentially a great place to live in 1060 00:55:43,320 --> 00:55:48,480 Speaker 14: La for example, there's great transportation downtown. It's a good 1061 00:55:48,480 --> 00:55:51,720 Speaker 14: place to leave, not necessarily just a good place to 1062 00:55:51,960 --> 00:55:55,000 Speaker 14: go into nine to five during the day, so people 1063 00:55:55,000 --> 00:55:57,520 Speaker 14: who live downtown can get to a lot of other 1064 00:55:57,560 --> 00:56:02,520 Speaker 14: parts of this metro area and jobs. For example, in 1065 00:56:02,560 --> 00:56:06,680 Speaker 14: the entertainment industry, you may work in Hollywood one six 1066 00:56:06,719 --> 00:56:09,040 Speaker 14: month period, and then you're going out to Burbank for 1067 00:56:09,200 --> 00:56:12,240 Speaker 14: a job for another six month period. So living downtown 1068 00:56:12,400 --> 00:56:14,960 Speaker 14: can be very central and easy to commute from. 1069 00:56:15,239 --> 00:56:17,120 Speaker 1: Hey, John, One of the things that I you know, 1070 00:56:17,160 --> 00:56:19,719 Speaker 1: maybe the next shoe to drop in this whole commercial 1071 00:56:19,719 --> 00:56:22,800 Speaker 1: real estate, office building real estate story is we start 1072 00:56:22,840 --> 00:56:26,600 Speaker 1: seeing some transactions, some sales of buildings, and I think 1073 00:56:26,640 --> 00:56:29,279 Speaker 1: the write down that we'll see in some of these 1074 00:56:29,360 --> 00:56:30,960 Speaker 1: is going to be shocking to a lot of people. 1075 00:56:31,000 --> 00:56:34,480 Speaker 1: Have we seen anything change hands in Los Angeles to 1076 00:56:34,480 --> 00:56:36,520 Speaker 1: give us a sense of how far down that market's fallen. 1077 00:56:37,120 --> 00:56:42,000 Speaker 14: Well, yeah, in LA they're Union Bank building sold in 1078 00:56:42,160 --> 00:56:45,320 Speaker 14: March for one hundred and four million dollars. It lasts 1079 00:56:45,360 --> 00:56:49,359 Speaker 14: sold in twenty ten for two hundred and eight million dollars. 1080 00:56:49,680 --> 00:56:55,240 Speaker 14: So there's an exam there. Yeah, fifty percent price drop. 1081 00:56:55,760 --> 00:56:58,600 Speaker 14: There was a motivated seller, and there was a little 1082 00:56:58,600 --> 00:57:02,280 Speaker 14: bit of timing. If you did the deal before March thirty, first, 1083 00:57:02,480 --> 00:57:04,120 Speaker 14: you didn't have to pay a five and a half 1084 00:57:04,200 --> 00:57:08,440 Speaker 14: percent transfer tax. That's a new kind of disincentive to 1085 00:57:08,680 --> 00:57:13,120 Speaker 14: real estate investors in LA. So anyway, there are multiple 1086 00:57:13,920 --> 00:57:18,280 Speaker 14: which hauld I say, factors at play here. But basically, 1087 00:57:19,440 --> 00:57:20,840 Speaker 14: you know, it's going to have to get a lot 1088 00:57:20,880 --> 00:57:23,120 Speaker 14: worse before it gets better for offices here in LA. 1089 00:57:23,640 --> 00:57:27,560 Speaker 12: Now, does this have a ripple effect on the residential 1090 00:57:27,720 --> 00:57:32,960 Speaker 12: space in LA that downtown is suffering? How does this 1091 00:57:33,760 --> 00:57:37,120 Speaker 12: emanate from that central area? 1092 00:57:37,560 --> 00:57:42,880 Speaker 14: Yeah, well, if the offices are vacant, if people aren't 1093 00:57:42,880 --> 00:57:46,680 Speaker 14: on the streets. What's happened in LA is the sort 1094 00:57:46,680 --> 00:57:52,840 Speaker 14: of ratio between homeless and you know, housed normal people 1095 00:57:53,280 --> 00:57:57,080 Speaker 14: has fallen out of whack. So there are fewer people 1096 00:57:57,640 --> 00:58:01,840 Speaker 14: going to offices, there are fewer commuters, and it seems 1097 00:58:01,840 --> 00:58:06,360 Speaker 14: like the population of people who are unhoused, who are 1098 00:58:06,400 --> 00:58:11,680 Speaker 14: not necessarily very how to state this anyway, they can 1099 00:58:11,720 --> 00:58:15,400 Speaker 14: be scary, and so it's a deterrent to people, you 1100 00:58:15,440 --> 00:58:19,200 Speaker 14: know there, it's a deterrent to businesses. There's just a 1101 00:58:19,240 --> 00:58:23,320 Speaker 14: lot of factors at play. Safety is a key issue 1102 00:58:23,440 --> 00:58:28,840 Speaker 14: being downtown that has really come to the forefront as 1103 00:58:29,120 --> 00:58:31,600 Speaker 14: office vacancies have risen. 1104 00:58:31,760 --> 00:58:34,800 Speaker 1: Yeah, it's a challenge for LA, as you're reporting indicates, 1105 00:58:34,800 --> 00:58:37,240 Speaker 1: but lots of other cities around the country be very 1106 00:58:37,240 --> 00:58:40,000 Speaker 1: interesting to see how this plays out over the coming years. 1107 00:58:40,080 --> 00:58:41,480 Speaker 1: Is probably how it's going to play out. 1108 00:58:41,840 --> 00:58:44,360 Speaker 14: Again, it's going to be a slow melting ice cube. 1109 00:58:44,440 --> 00:58:47,760 Speaker 1: Yeah, I think you're right, John. So that's John ghettos On. 1110 00:58:47,840 --> 00:58:50,840 Speaker 1: He is the Bloomberg News real estate reporter. He's got 1111 00:58:50,840 --> 00:58:53,200 Speaker 1: this great story out. Check it out Bloomberg dot Com. 1112 00:58:53,480 --> 00:58:56,200 Speaker 1: Check it out. Downtown LA's office distress shows the pains 1113 00:58:56,240 --> 00:58:58,320 Speaker 1: coming for cities. I also want to call out the 1114 00:58:58,360 --> 00:59:01,240 Speaker 1: graphics because I like pictures. They me understand things. So 1115 00:59:01,280 --> 00:59:04,160 Speaker 1: the graphics here by Kyle Kim were outstanding, really blended 1116 00:59:04,360 --> 00:59:07,080 Speaker 1: really well into this story, and it just gives you 1117 00:59:07,080 --> 00:59:08,600 Speaker 1: a sense of how tough things are out there in 1118 00:59:08,640 --> 00:59:09,800 Speaker 1: the commercial real estate business. 1119 00:59:09,880 --> 00:59:12,040 Speaker 12: Yeah, making real these numbers. 1120 00:59:11,600 --> 00:59:14,800 Speaker 1: Exactly exactly, and what it means for the lenders, you know. 1121 00:59:14,880 --> 00:59:16,440 Speaker 1: I mean, that's that's the next wave that a lot 1122 00:59:16,480 --> 00:59:18,439 Speaker 1: of people are concerned about. Are we gonna start seeing 1123 00:59:18,480 --> 00:59:20,640 Speaker 1: banks take some big write downs on commercial real estate? 1124 00:59:20,720 --> 00:59:21,280 Speaker 1: So good story. 1125 00:59:21,600 --> 00:59:25,200 Speaker 8: You're listening to the tape Cansur Live program Bloomberg Markets 1126 00:59:25,240 --> 00:59:28,640 Speaker 8: weekdays at ten am Eastern on Bloomberg Radio, the tune 1127 00:59:28,680 --> 00:59:31,640 Speaker 8: in app, Bloomberg dot Com, and the Bloomberg Business App. 1128 00:59:31,680 --> 00:59:34,480 Speaker 8: You can also listen live on Amazon Alexa from our 1129 00:59:34,520 --> 00:59:43,560 Speaker 8: flagship New York station just say Alexa playing Bloomberg eleven thirty. 1130 00:59:41,080 --> 00:59:44,480 Speaker 1: Small Foxman Paul Sweety here in the Bloomberg Interactive Brokers studio. 1131 00:59:45,000 --> 00:59:47,920 Speaker 1: Seems like for months all we've been talking about is AI, 1132 00:59:47,960 --> 00:59:49,920 Speaker 1: and then of course in the last twenty four hours 1133 00:59:49,920 --> 00:59:53,960 Speaker 1: with uh in Nvidia even more to the front and center. 1134 00:59:54,440 --> 00:59:58,240 Speaker 1: Let's bring AI to investing. People are doing that. Our 1135 00:59:58,280 --> 01:00:00,400 Speaker 1: next guest is part of that whole wave frame O. 1136 01:00:01,080 --> 01:00:04,800 Speaker 1: He is the APEX CEO and head of AI E 1137 01:00:04,920 --> 01:00:08,680 Speaker 1: t FS and the firm's name is Craft Technologies APAC 1138 01:00:08,880 --> 01:00:12,400 Speaker 1: with a Q, Craft with a Q. Francis you know 1139 01:00:12,520 --> 01:00:16,400 Speaker 1: we're applying AI, or at least people believe we're applying 1140 01:00:16,440 --> 01:00:18,480 Speaker 1: AI to just about everything we do in life. I 1141 01:00:18,520 --> 01:00:20,480 Speaker 1: think it feels overdone to me. But what do I know? 1142 01:00:21,120 --> 01:00:24,480 Speaker 1: How about for in the business of investing? How can 1143 01:00:24,560 --> 01:00:28,560 Speaker 1: artificial intelligence help individual investors just kind of navigate the 1144 01:00:28,600 --> 01:00:30,280 Speaker 1: whole investment process? 1145 01:00:30,600 --> 01:00:30,760 Speaker 8: Right? 1146 01:00:30,960 --> 01:00:33,880 Speaker 13: First of all, thanks for having me here. It's great 1147 01:00:33,920 --> 01:00:37,400 Speaker 13: to speak more about how the Craft Technologies applying the 1148 01:00:37,400 --> 01:00:42,400 Speaker 13: AI into the investment practices we have been developing our 1149 01:00:42,400 --> 01:00:45,400 Speaker 13: AM all since twenty sixteen. Two different area one is 1150 01:00:45,440 --> 01:00:48,080 Speaker 13: stock selection based AI. The out one is the sl 1151 01:00:48,120 --> 01:00:50,160 Speaker 13: location tope of model because. 1152 01:00:49,840 --> 01:00:53,360 Speaker 1: We want to stock selection. Two important things you've got 1153 01:00:53,400 --> 01:00:53,880 Speaker 1: to get right. 1154 01:00:53,800 --> 01:00:57,640 Speaker 13: As yes, okay, yes exactly. And the technology we are 1155 01:00:57,720 --> 01:01:00,000 Speaker 13: using you see something similar to the chechipt as well. 1156 01:01:00,160 --> 01:01:05,120 Speaker 13: CHAPT is the the core AI engine is a transformer 1157 01:01:05,200 --> 01:01:08,440 Speaker 13: engine and the transform engine is combination of attention layer. 1158 01:01:08,880 --> 01:01:11,520 Speaker 13: Attention is a difficult tom but attention layer yourself. What 1159 01:01:11,560 --> 01:01:14,440 Speaker 13: it does is the trying to analyze the relationship between 1160 01:01:14,480 --> 01:01:18,760 Speaker 13: the input data. So when we type something, the CHPTPT 1161 01:01:18,840 --> 01:01:22,280 Speaker 13: is trying to analyzing the wordings or the sentences or 1162 01:01:22,440 --> 01:01:26,160 Speaker 13: paragraph to analyzing relationship, trying to get the context what 1163 01:01:26,320 --> 01:01:28,840 Speaker 13: we are asking to them so they delivered answer back 1164 01:01:28,880 --> 01:01:33,120 Speaker 13: to us. Where we are applying that attention layer to 1165 01:01:33,240 --> 01:01:35,960 Speaker 13: our Stacks election model is trying to analyze input data, 1166 01:01:36,600 --> 01:01:40,600 Speaker 13: price data, fundamental data and macro data and relationship of 1167 01:01:40,680 --> 01:01:44,160 Speaker 13: that to the future expected return of the stack within 1168 01:01:44,200 --> 01:01:46,960 Speaker 13: the universe. So we've been using that for since twenty nineteen. 1169 01:01:47,640 --> 01:01:50,240 Speaker 13: That for our Stacks election model. But yesterday we launched 1170 01:01:50,280 --> 01:01:53,600 Speaker 13: our AI t B a power that's a location ATS. 1171 01:01:53,280 --> 01:01:56,240 Speaker 1: So it check us AIDB for yes okayes. 1172 01:01:56,560 --> 01:01:59,240 Speaker 13: But that is thes a location type of btfs. It 1173 01:01:59,440 --> 01:02:00,680 Speaker 13: first lunch in here. 1174 01:02:01,120 --> 01:02:03,040 Speaker 12: Well, congratulations on the launch. 1175 01:02:03,520 --> 01:02:05,800 Speaker 1: I've been looking back. They rang the opening bell today. 1176 01:02:05,600 --> 01:02:07,760 Speaker 13: In your Yeah, it was so owner. 1177 01:02:08,200 --> 01:02:11,560 Speaker 12: Yes, you know, look, I'm looking back at the performance 1178 01:02:11,560 --> 01:02:14,200 Speaker 12: of some of the ETFs that you have out there. 1179 01:02:14,880 --> 01:02:20,280 Speaker 12: Pretty spectacular twenty twenty thirty seven point six percent for 1180 01:02:20,480 --> 01:02:24,479 Speaker 12: the US large capt Yeah. But you know what's clear 1181 01:02:24,520 --> 01:02:28,040 Speaker 12: is it's not always the market beating, at least against 1182 01:02:28,040 --> 01:02:33,000 Speaker 12: the S and P total return. When does AI perform better? 1183 01:02:33,080 --> 01:02:38,320 Speaker 12: When do AI generated investment decisions perform better than the 1184 01:02:38,360 --> 01:02:42,600 Speaker 12: overall picture? Is there any sort of common threads that 1185 01:02:42,640 --> 01:02:43,120 Speaker 12: go into this? 1186 01:02:43,360 --> 01:02:49,680 Speaker 13: Okay, show is very the cancussion, yes to we lalid 1187 01:02:49,720 --> 01:02:52,080 Speaker 13: a lot to Don twenty. First, it start to decrease 1188 01:02:52,120 --> 01:02:56,919 Speaker 13: your bit. Remember the moment of the Alpha Go when 1189 01:02:57,000 --> 01:02:59,880 Speaker 13: the Alpha Go was a batting the professional good players. 1190 01:03:00,920 --> 01:03:03,720 Speaker 13: It beat the human most of the time, but only 1191 01:03:03,760 --> 01:03:06,560 Speaker 13: the one game it was losing the game as well, 1192 01:03:06,800 --> 01:03:09,920 Speaker 13: there was unexpected the play was happened by the uh, 1193 01:03:10,000 --> 01:03:12,360 Speaker 13: the professional goal player, and it could be similar to 1194 01:03:12,920 --> 01:03:16,000 Speaker 13: our AM model as well. Something unexpected happened for you, 1195 01:03:16,240 --> 01:03:21,920 Speaker 13: like or the very sharp the regime reversion or regime changes. 1196 01:03:22,000 --> 01:03:24,640 Speaker 13: It could be make our AM model or confused on 1197 01:03:24,760 --> 01:03:26,760 Speaker 13: the stock selection, but that could be also happening for 1198 01:03:26,800 --> 01:03:30,520 Speaker 13: the human perform manager. And the main difference is or 1199 01:03:30,680 --> 01:03:34,320 Speaker 13: the possible a distinctive advantage. You see, AM model at 1200 01:03:34,400 --> 01:03:38,320 Speaker 13: least has doesn't have ego like or the big perform manager. 1201 01:03:38,680 --> 01:03:42,120 Speaker 13: It didn't attach it to the previous investment decison making 1202 01:03:42,160 --> 01:03:42,920 Speaker 13: to the next one. 1203 01:03:43,080 --> 01:03:45,640 Speaker 12: So I mean does that make moments like game stop 1204 01:03:46,200 --> 01:03:49,240 Speaker 12: and the meme crazes. Does that make it a little 1205 01:03:49,240 --> 01:03:51,720 Speaker 12: bit easier for you guys to capture that? 1206 01:03:52,200 --> 01:03:55,680 Speaker 13: Uh, there was the moment, Yes, but we are are 1207 01:03:55,840 --> 01:03:58,439 Speaker 13: using atfs are mostly the last v. It didn't get 1208 01:03:58,560 --> 01:04:00,960 Speaker 13: to have a chance for the GM or the other 1209 01:04:01,000 --> 01:04:02,720 Speaker 13: memes that yeah, project could be Yes. 1210 01:04:03,360 --> 01:04:09,240 Speaker 1: So to what extent, Francis, are retail investors or retail advisors, 1211 01:04:09,280 --> 01:04:12,720 Speaker 1: retail investors, institution investors. To what extent are they using 1212 01:04:12,840 --> 01:04:17,400 Speaker 1: AI today? Is it happening? Is the typical broker out 1213 01:04:17,440 --> 01:04:19,800 Speaker 1: there or fund manager using artificial intelligence? 1214 01:04:20,120 --> 01:04:23,000 Speaker 13: It's started to start boling in the market right now. 1215 01:04:23,080 --> 01:04:25,520 Speaker 13: So we are having so we are the B to 1216 01:04:25,600 --> 01:04:28,960 Speaker 13: B solution provider and then lucky enough get the size 1217 01:04:28,960 --> 01:04:31,080 Speaker 13: of funding from sub bank group beginning of the laws 1218 01:04:31,480 --> 01:04:34,440 Speaker 13: one hundred for six million. So we currently providing we 1219 01:04:34,480 --> 01:04:36,640 Speaker 13: have our AA t ETF, but at the same time 1220 01:04:36,680 --> 01:04:40,360 Speaker 13: providing our solution to twenty different financial institutions for PUFLO 1221 01:04:40,440 --> 01:04:43,960 Speaker 13: signals or trading execution mode less TRU. And right now, 1222 01:04:44,000 --> 01:04:47,520 Speaker 13: the big agenda I keep hearing from our potential patnus 1223 01:04:47,640 --> 01:04:52,240 Speaker 13: is the how to leveraging AI to deliver a personalized 1224 01:04:52,280 --> 01:04:55,720 Speaker 13: investment solution or advice back to the clients the like 1225 01:04:55,720 --> 01:04:58,040 Speaker 13: a freeom like a banking or the lost managers. The 1226 01:04:58,600 --> 01:05:00,720 Speaker 13: top tier of the clients is covered by the human 1227 01:05:01,000 --> 01:05:04,760 Speaker 13: private bankers, but those who are under are still uh 1228 01:05:04,800 --> 01:05:08,680 Speaker 13: and the dead areas could be probably lossouped by the AI. 1229 01:05:10,080 --> 01:05:13,560 Speaker 12: As a market participant that uses a lot of the 1230 01:05:13,600 --> 01:05:17,640 Speaker 12: AI technology. And we're looking at you know, this massive 1231 01:05:17,680 --> 01:05:21,400 Speaker 12: gain and Nvidia on the verge of maybe, I mean 1232 01:05:21,400 --> 01:05:25,040 Speaker 12: maybe it's surpassed at this point a trillion dollar company. 1233 01:05:26,040 --> 01:05:30,360 Speaker 12: How do you see some of this demand for chips 1234 01:05:30,480 --> 01:05:33,760 Speaker 12: for these various different components because you're putting together these 1235 01:05:33,800 --> 01:05:35,240 Speaker 12: models that use that stuff, right. 1236 01:05:35,200 --> 01:05:39,919 Speaker 13: Yes, we use a lot of chips from them video, Yes, 1237 01:05:40,040 --> 01:05:40,959 Speaker 13: we spent a lot of money. 1238 01:05:40,960 --> 01:05:43,880 Speaker 4: Well, I guess is this is this over Is this overplayed? 1239 01:05:44,520 --> 01:05:47,400 Speaker 13: Yes, it's true, but at the same time I believe 1240 01:05:47,440 --> 01:05:50,000 Speaker 13: the watch it is literally changing the game for the 1241 01:05:50,040 --> 01:05:53,040 Speaker 13: AI in the public space or the private market. Is 1242 01:05:53,080 --> 01:05:58,280 Speaker 13: the It gives A the two A the experiences and 1243 01:05:58,600 --> 01:06:01,080 Speaker 13: stop to building up the trust of the This is 1244 01:06:01,120 --> 01:06:04,240 Speaker 13: the level of the AI can deliver back to the 1245 01:06:04,240 --> 01:06:07,720 Speaker 13: the client solution services rust. So there will be more 1246 01:06:07,720 --> 01:06:10,280 Speaker 13: and more demand. And one funny thing for the not 1247 01:06:10,320 --> 01:06:13,880 Speaker 13: funny thing, one interesting for the m VIDA is the 1248 01:06:13,880 --> 01:06:16,080 Speaker 13: beginning of dismay. The one of our A A t 1249 01:06:16,160 --> 01:06:18,440 Speaker 13: F A M O m us last momentum MEDIAF is 1250 01:06:18,520 --> 01:06:22,880 Speaker 13: dumbed the Apple and then pick the Nvidia as the things. 1251 01:06:23,200 --> 01:06:27,720 Speaker 13: So you're seeing the huge gain. Uh today, I'm excited 1252 01:06:27,720 --> 01:06:28,200 Speaker 13: about it. 1253 01:06:28,280 --> 01:06:31,600 Speaker 1: How about your just thirty seconds left the demand for 1254 01:06:31,640 --> 01:06:33,520 Speaker 1: your ETFs? What are you seeing out there? Who's who's 1255 01:06:33,560 --> 01:06:34,640 Speaker 1: interested in your A t FS? 1256 01:06:34,760 --> 01:06:38,000 Speaker 13: Yes, mostly from these who have the savvy understanding of 1257 01:06:38,040 --> 01:06:41,600 Speaker 13: the tech background or who wants to feel is lee 1258 01:06:41,880 --> 01:06:44,040 Speaker 13: that I want to experience the new adoption of the 1259 01:06:44,080 --> 01:06:47,480 Speaker 13: AI could be helpful. What I want to envision into 1260 01:06:47,480 --> 01:06:50,160 Speaker 13: five to ten years later is the if one of 1261 01:06:50,160 --> 01:06:53,120 Speaker 13: our goal is trying to deliver a sustainable alpha generative 1262 01:06:53,200 --> 01:06:56,080 Speaker 13: solution by the AI. And if that is happening, I 1263 01:06:56,120 --> 01:06:58,360 Speaker 13: believe there will be a moment that AI power investment 1264 01:06:58,440 --> 01:07:01,520 Speaker 13: solution become in the new bat you baykind the market. 1265 01:07:02,000 --> 01:07:05,000 Speaker 1: Great stuff, exciting stuff. Francis Oh, I'm glad there's somebody 1266 01:07:05,080 --> 01:07:07,920 Speaker 1: doing this stuff. Francis Oh APAX CEO and head of 1267 01:07:08,040 --> 01:07:13,240 Speaker 1: AI ETFs, Man Technology Changes Everything, Craft Technologies APAC. That's 1268 01:07:13,280 --> 01:07:15,800 Speaker 1: the name of the firm. They've got some ETFs out 1269 01:07:15,800 --> 01:07:20,760 Speaker 1: they're really using to integrate artificial intelligence into the investment process. 1270 01:07:20,760 --> 01:07:23,920 Speaker 1: And why not. You're using AI for just about everything 1271 01:07:23,920 --> 01:07:26,000 Speaker 1: else in life. So we've learned just maybe in the 1272 01:07:26,040 --> 01:07:28,440 Speaker 1: last six months, it seems to have been front and center. 1273 01:07:31,040 --> 01:07:34,120 Speaker 2: Thanks for listening to the Bloomberg Markets podcasts. You can 1274 01:07:34,160 --> 01:07:37,960 Speaker 2: subscribe and listen to interviews at Apple Podcasts or whatever 1275 01:07:38,040 --> 01:07:39,560 Speaker 2: podcast platform you prefer. 1276 01:07:39,920 --> 01:07:40,720 Speaker 1: I'm Matt Miller. 1277 01:07:40,960 --> 01:07:43,880 Speaker 2: I'm on Twitter at Matt Miller nineteen seventy three. 1278 01:07:44,320 --> 01:07:46,720 Speaker 1: And I'm Paul Sweeney. I'm on Twitter at pt Sweeney. 1279 01:07:46,840 --> 01:07:49,480 Speaker 1: Before the podcast, you can always catch us worldwide at 1280 01:07:49,520 --> 01:07:50,520 Speaker 1: Bloomberg Radio