1 00:00:00,280 --> 00:00:11,440 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,560 --> 00:00:13,680 Speaker 1: with Alex Steel and Paul Sweeney. 3 00:00:13,760 --> 00:00:17,000 Speaker 2: The real app performance has been in US corporate high yield. 4 00:00:17,160 --> 00:00:19,520 Speaker 3: Are the companies lean enough? Have they trimmed all the fats? 5 00:00:19,600 --> 00:00:22,800 Speaker 2: The semiconductor business is a really cyclical. 6 00:00:22,320 --> 00:00:26,560 Speaker 1: Business, breaking market headlines and corporate news from across the globe. 7 00:00:26,600 --> 00:00:29,040 Speaker 3: Do investors like the M and A that we've seen? 8 00:00:29,240 --> 00:00:32,199 Speaker 2: These are two big time blue chip companies. 9 00:00:32,479 --> 00:00:36,199 Speaker 3: Window between the peak and cunt changing super fast. 10 00:00:36,120 --> 00:00:41,160 Speaker 1: Bloomberg Intelligence with Alex Steel and Paul Sweeney on Bloomberg Radio. 11 00:00:42,680 --> 00:00:44,959 Speaker 2: On Today's Boomberg Intelligence Show, we dig inside the big 12 00:00:44,960 --> 00:00:47,240 Speaker 2: business stories impacting Wall Street and the global markets. 13 00:00:47,440 --> 00:00:49,320 Speaker 3: Each and every week we provide in depth research and 14 00:00:49,400 --> 00:00:51,159 Speaker 3: data on some of the two thousand companies and one 15 00:00:51,240 --> 00:00:53,800 Speaker 3: hundred and thirty industries our analysts cover worldwide. 16 00:00:53,880 --> 00:00:55,160 Speaker 2: Today, we'll look at a big deal in the M 17 00:00:55,160 --> 00:00:57,279 Speaker 2: and A space that will create a mortgage behemoth. 18 00:00:57,560 --> 00:00:59,720 Speaker 3: Plus we're going to discuss the rising costs of food 19 00:00:59,760 --> 00:01:01,400 Speaker 3: and wow that's effecting the consumer. 20 00:01:01,600 --> 00:01:04,600 Speaker 2: But first we begin with research. Bloomberg Intelligence recently put 21 00:01:04,640 --> 00:01:06,840 Speaker 2: out on ten companies to watch for in the second 22 00:01:06,880 --> 00:01:08,759 Speaker 2: quarter of twenty twenty five and for more on this. 23 00:01:08,840 --> 00:01:10,600 Speaker 3: Liz Paul and I were joined by Tim Craig, head 24 00:01:10,600 --> 00:01:13,440 Speaker 3: Bloomberg Intelligence Global Chief Content Officer. 25 00:01:13,720 --> 00:01:15,959 Speaker 2: We first asked him to give us a broad scope 26 00:01:16,000 --> 00:01:17,120 Speaker 2: of the company he's looking at. 27 00:01:17,720 --> 00:01:20,800 Speaker 4: So just as a reminder, these are all based off 28 00:01:20,840 --> 00:01:25,000 Speaker 4: of focus ideas, which for us in Bloomberg Intelligence are 29 00:01:25,240 --> 00:01:29,520 Speaker 4: high conviction out of consensus views where we see catalysts 30 00:01:29,600 --> 00:01:34,040 Speaker 4: ahead that can actually change the market's mindset around these companies. 31 00:01:34,959 --> 00:01:40,280 Speaker 4: So you've talked about the weight on consumer sentiment and 32 00:01:40,319 --> 00:01:45,240 Speaker 4: things along those lines. Well, Dollar Rama, which is a 33 00:01:45,360 --> 00:01:51,560 Speaker 4: dollar store up in Canada has yeah, exactly, They're going 34 00:01:51,640 --> 00:01:57,080 Speaker 4: to face slower wage growth, higher inflation. We've not seen 35 00:01:57,280 --> 00:02:00,080 Speaker 4: estimate cuts yet for Dollar Rama, like we've seen a 36 00:02:00,200 --> 00:02:04,760 Speaker 4: dollar Tree and dollar stores and whatnot. We think that's coming. 37 00:02:05,280 --> 00:02:08,920 Speaker 4: Another one that is interesting if you want to throw 38 00:02:09,000 --> 00:02:12,400 Speaker 4: on as well the whole tariff concept is PDD. You 39 00:02:12,480 --> 00:02:14,240 Speaker 4: might not have heard of this, but this is one 40 00:02:14,280 --> 00:02:17,640 Speaker 4: of the big Chinese companies that trade tech companies that 41 00:02:17,760 --> 00:02:23,120 Speaker 4: trades in New York. They own Timu, which is that 42 00:02:23,480 --> 00:02:28,840 Speaker 4: ultra low priced e commerce platform in the States. There's 43 00:02:29,520 --> 00:02:34,600 Speaker 4: their bigger platform is one in China, they're going to 44 00:02:34,680 --> 00:02:37,200 Speaker 4: have constraints on what they can bring in at the 45 00:02:37,320 --> 00:02:41,000 Speaker 4: right price, and they're also investing pretty heavily outside the 46 00:02:41,120 --> 00:02:44,680 Speaker 4: US to grow their non US platform beyond China. So 47 00:02:45,000 --> 00:02:47,360 Speaker 4: again we think that there's estimate cuts there to come. 48 00:02:47,960 --> 00:02:49,600 Speaker 2: Hey, Tim, One of the more looking at this list, 49 00:02:49,680 --> 00:02:53,400 Speaker 2: one of the more controversial calls, I think is Tesla. 50 00:02:53,520 --> 00:02:57,120 Speaker 2: Steve Manner, the analyst for Bloomberg Intelligence covering the auto business, 51 00:02:57,240 --> 00:03:01,040 Speaker 2: he's pretty positive on Tesla here despite the stock price 52 00:03:01,080 --> 00:03:04,040 Speaker 2: in there. I guess the concern about Elon Muskin is focus. 53 00:03:04,720 --> 00:03:08,040 Speaker 4: Yeah, you know, you could throw this in with tariffs, 54 00:03:08,080 --> 00:03:11,640 Speaker 4: with policy, et cetera, and clearly it's taken a shellacking 55 00:03:11,720 --> 00:03:15,400 Speaker 4: from a stock price perspective. We see two things going 56 00:03:15,440 --> 00:03:19,280 Speaker 4: on here. Number One, there's been a lot of talk 57 00:03:19,320 --> 00:03:22,200 Speaker 4: about how their sales have been disappointing as of Leyden. 58 00:03:22,360 --> 00:03:26,760 Speaker 4: Is that because of politics, We think, frankly it's because 59 00:03:26,760 --> 00:03:30,600 Speaker 4: of the model transition with the new Model Y coming out, 60 00:03:30,919 --> 00:03:34,200 Speaker 4: orders even in China have been good, and we would 61 00:03:34,200 --> 00:03:36,520 Speaker 4: expect to see sales to pick up as we proceed 62 00:03:36,520 --> 00:03:40,119 Speaker 4: through the second quarter and on ind to midyear. You've 63 00:03:40,160 --> 00:03:46,160 Speaker 4: also got a really underappreciated battery storage business that is 64 00:03:46,280 --> 00:03:49,360 Speaker 4: also starting to ramp up. So we see two catalysts 65 00:03:49,400 --> 00:03:52,360 Speaker 4: here to play out that people aren't focused on because 66 00:03:52,400 --> 00:03:53,480 Speaker 4: of all the other noise. 67 00:03:53,920 --> 00:03:57,280 Speaker 3: And here's one for Paul here Cracker Barrel, Oh yeah, 68 00:03:57,440 --> 00:03:59,560 Speaker 3: is on your list again. I've never been. 69 00:04:00,120 --> 00:04:01,800 Speaker 2: Now, Tim, he lives in London. 70 00:04:02,000 --> 00:04:04,640 Speaker 4: You go to New. 71 00:04:04,560 --> 00:04:06,560 Speaker 3: York City, man like, I don't know what I'm going 72 00:04:06,600 --> 00:04:06,920 Speaker 3: to see. 73 00:04:06,960 --> 00:04:10,360 Speaker 2: Tim grew up in southwest Virginia. He knows. 74 00:04:10,920 --> 00:04:12,800 Speaker 3: I mean I'll eat it. If you buy it and 75 00:04:12,880 --> 00:04:14,360 Speaker 3: eat it, I mean give it to me, I'll eat it. 76 00:04:14,440 --> 00:04:16,480 Speaker 3: Let's put it that way, all right, talk to us about. 77 00:04:16,279 --> 00:04:24,440 Speaker 4: Really good I've crack. Cracker Barrel falls into the restructuring 78 00:04:25,000 --> 00:04:28,719 Speaker 4: reorganization bucket. And there's another one on the list as 79 00:04:28,760 --> 00:04:32,200 Speaker 4: well that actually may be good for you, Alex. I'll 80 00:04:32,200 --> 00:04:35,880 Speaker 4: come back to But cracker Barrel three years of estimate cuts. 81 00:04:36,320 --> 00:04:39,440 Speaker 4: There's next to no buys on this on this stock. 82 00:04:39,880 --> 00:04:44,560 Speaker 4: And from an underlying business perspective, they've made some wholesale revamps, 83 00:04:44,960 --> 00:04:49,680 Speaker 4: the menu, their approach to service. Uh, they're remodeling stores, 84 00:04:49,920 --> 00:04:52,440 Speaker 4: and we think that there's an inflection to come from 85 00:04:52,520 --> 00:04:57,560 Speaker 4: the standpoint of earnings trends. Notwithstanding the economy, this one's 86 00:04:57,560 --> 00:04:59,760 Speaker 4: already been beaten down, and we think that there's an 87 00:04:59,800 --> 00:05:04,560 Speaker 4: up decided to come Carrying's the other one carrying? Yeah? 88 00:05:04,720 --> 00:05:07,600 Speaker 4: So again three years of estimate cuts, you think who 89 00:05:07,680 --> 00:05:11,960 Speaker 4: is carrying? Well, their biggest label is Gucci. There you go, 90 00:05:12,040 --> 00:05:13,560 Speaker 4: Alex and if. 91 00:05:13,640 --> 00:05:15,760 Speaker 3: I only discount him, let's be clear. 92 00:05:15,960 --> 00:05:21,520 Speaker 4: Yeah, So new CEO at Gucci, a new head designer 93 00:05:21,560 --> 00:05:25,400 Speaker 4: at Gucci. We think there's a rejuvenation at hand, and 94 00:05:25,440 --> 00:05:26,800 Speaker 4: it's not in the estimates. 95 00:05:27,480 --> 00:05:30,000 Speaker 2: So again, Tim, let's just step back a little bit. 96 00:05:30,640 --> 00:05:34,359 Speaker 2: European markets really performing well, certainly relative to the US market. 97 00:05:34,400 --> 00:05:35,440 Speaker 2: What are your clients saying here? 98 00:05:36,839 --> 00:05:41,760 Speaker 4: So I think there are a couple of things coalescing. Three. 99 00:05:42,080 --> 00:05:45,920 Speaker 4: In fact, you take tariffs and all the concern that's 100 00:05:45,960 --> 00:05:50,440 Speaker 4: going on. You take deep seek and that raising the 101 00:05:50,520 --> 00:05:53,599 Speaker 4: specter of g Is there a different dynamic in the 102 00:05:53,640 --> 00:05:58,000 Speaker 4: world of tech that's been driving US markets and US exceptionalism. 103 00:05:58,440 --> 00:06:02,479 Speaker 4: And then you have a big European defense initiative and 104 00:06:02,560 --> 00:06:07,560 Speaker 4: imperative with Germany coming out with what people around here 105 00:06:07,560 --> 00:06:11,520 Speaker 4: are calling the Bazooka. And you add these together, there's 106 00:06:11,560 --> 00:06:16,200 Speaker 4: a growth idea developing in Europe. There's concern about what's 107 00:06:16,200 --> 00:06:18,000 Speaker 4: going on in the US. And if you look at 108 00:06:19,320 --> 00:06:22,240 Speaker 4: our economists, they see as much of a hit on 109 00:06:22,520 --> 00:06:27,080 Speaker 4: US growth and inflation as they see it anywhere outside 110 00:06:27,120 --> 00:06:29,760 Speaker 4: of Mexico and Canada. So you know, all of that 111 00:06:30,080 --> 00:06:34,560 Speaker 4: weighs on the US and Europe is an alternative, and 112 00:06:35,040 --> 00:06:38,680 Speaker 4: obviously we've talked a lot about how it's valued relative 113 00:06:38,720 --> 00:06:41,720 Speaker 4: to the States. I think that creates a money flow issue, 114 00:06:42,040 --> 00:06:44,360 Speaker 4: a money flow opportunity, just like it does for China. 115 00:06:44,720 --> 00:06:45,000 Speaker 5: All right. 116 00:06:45,000 --> 00:06:47,599 Speaker 3: Thanks to Tim Craig had Bloomberg Intelligence, a global Chief 117 00:06:47,680 --> 00:06:51,039 Speaker 3: Content Officer, Each week we look at research from Bloomberg 118 00:06:51,240 --> 00:06:54,279 Speaker 3: n EF previously known as New Energy Finance. They're the 119 00:06:54,279 --> 00:06:57,120 Speaker 3: team at Bloomberg that tracks and analyzes the energy transition 120 00:06:57,279 --> 00:07:01,240 Speaker 3: from commodities to power, transport, industries, building, and ag sectors. 121 00:07:01,520 --> 00:07:05,000 Speaker 3: This week we looked at the growing enthusiasm for nuclear 122 00:07:05,120 --> 00:07:06,120 Speaker 3: power for more. 123 00:07:06,279 --> 00:07:09,360 Speaker 2: Guest hosts Isabelle Lee and I were joined by Chris Gaddomski, 124 00:07:09,480 --> 00:07:12,600 Speaker 2: Bloomberg and EF lead nuclear analysts. We first to ask 125 00:07:12,680 --> 00:07:14,960 Speaker 2: Chris what his thoughts are on nuclear power as a 126 00:07:15,040 --> 00:07:17,000 Speaker 2: source to fuel AI expansion. 127 00:07:17,280 --> 00:07:24,240 Speaker 6: It's a great technology for supplying clean, carbon free twenty 128 00:07:24,240 --> 00:07:29,080 Speaker 6: four to seven base load power. However, there's a mismatch 129 00:07:29,360 --> 00:07:33,920 Speaker 6: between the demand for the electricity. Data centers say they 130 00:07:33,920 --> 00:07:37,760 Speaker 6: would like to have the electricity right away tomorrow, if 131 00:07:37,800 --> 00:07:42,840 Speaker 6: not sooner, and any new nuclear capacity in the US 132 00:07:42,960 --> 00:07:46,560 Speaker 6: is not going to come online until after twenty thirty. 133 00:07:46,720 --> 00:07:48,920 Speaker 6: I mean you may have one or two outliers come 134 00:07:48,960 --> 00:07:53,880 Speaker 6: on beforehand, but before you have a reliable demonstration of 135 00:07:53,960 --> 00:07:57,080 Speaker 6: the technology. Because these are all new technologies being built, 136 00:07:58,080 --> 00:08:01,280 Speaker 6: we'll be looking for after twenty thirty, So possibly we 137 00:08:01,320 --> 00:08:05,920 Speaker 6: could miss the first wave of advanced reactor contribution to 138 00:08:05,960 --> 00:08:08,600 Speaker 6: the AI developments. 139 00:08:09,080 --> 00:08:12,360 Speaker 5: Why is nuclear construction in the West barely budgeting, whereas, 140 00:08:12,440 --> 00:08:15,000 Speaker 5: in for instance, Asia Pacific region it's booming. 141 00:08:15,720 --> 00:08:19,760 Speaker 6: There's two big reasons for that. One, the price at 142 00:08:19,880 --> 00:08:23,400 Speaker 6: which the Chinese, for example, can build a nuclear power 143 00:08:23,440 --> 00:08:26,920 Speaker 6: plant is much lower than we can in the US, 144 00:08:26,960 --> 00:08:29,360 Speaker 6: and that's the function the fact that China right now 145 00:08:29,400 --> 00:08:33,439 Speaker 6: is building twenty eight nuclear power plants. We have built 146 00:08:33,600 --> 00:08:37,000 Speaker 6: two in the last ten or fifteen years, and so 147 00:08:37,360 --> 00:08:40,800 Speaker 6: they will build six nuclear power plants at the same site, 148 00:08:40,880 --> 00:08:43,920 Speaker 6: and the construction team moves from one reactor to the 149 00:08:43,920 --> 00:08:47,640 Speaker 6: next reactor to the next reactive reactor h reactor subsequently 150 00:08:47,679 --> 00:08:51,120 Speaker 6: costs less than the first. So to China to build 151 00:08:51,160 --> 00:08:53,840 Speaker 6: reactors for maybe one fifth to one quarter of what 152 00:08:53,920 --> 00:08:56,280 Speaker 6: the US can build, it makes sense for them to 153 00:08:56,280 --> 00:08:58,920 Speaker 6: build a lot of nuclear power plants. We've lost the 154 00:08:59,760 --> 00:09:03,160 Speaker 6: x tease and the desire to build a lot of 155 00:09:03,240 --> 00:09:07,720 Speaker 6: large nuclear power plants. To cite some advantages and significant 156 00:09:08,120 --> 00:09:10,680 Speaker 6: benefits to the technology, talk to. 157 00:09:10,720 --> 00:09:14,840 Speaker 2: Us about small modular reactors. Is that a solution. 158 00:09:16,559 --> 00:09:20,160 Speaker 6: Yeah, it is a solution because the not dynamic has 159 00:09:20,360 --> 00:09:24,120 Speaker 6: has has changed in the past. We've seen let's build 160 00:09:24,200 --> 00:09:27,640 Speaker 6: large reactors to get economies of scale in size. The 161 00:09:27,679 --> 00:09:30,679 Speaker 6: conversations talk, well, let's build a lot of small reactors 162 00:09:30,679 --> 00:09:33,720 Speaker 6: and get down the learning curve as quickly as we can. 163 00:09:34,120 --> 00:09:38,600 Speaker 6: So there are only two small modi reactors operating modern 164 00:09:38,640 --> 00:09:41,400 Speaker 6: ones operating in the world, one in China, one in Russia. 165 00:09:41,760 --> 00:09:44,880 Speaker 6: China is building another one. Russia is planning to build 166 00:09:44,880 --> 00:09:47,839 Speaker 6: another one, but they haven't yet broken ground on that. 167 00:09:48,800 --> 00:09:52,440 Speaker 6: So it's a technology that presents a lot less risk 168 00:09:52,720 --> 00:09:56,839 Speaker 6: to the utility or the hyperscaler who is buying it. 169 00:09:57,080 --> 00:10:02,560 Speaker 6: There's no big deployment risk. There is technology risk because 170 00:10:02,720 --> 00:10:05,160 Speaker 6: these are all first of a kind, and so with 171 00:10:05,240 --> 00:10:07,440 Speaker 6: the first of a kind project, especially in the nucle 172 00:10:07,520 --> 00:10:10,680 Speaker 6: power business, there's a lot of uncertainty with regards to 173 00:10:10,720 --> 00:10:14,400 Speaker 6: schedule and cost, and so people are thinking about this. 174 00:10:14,960 --> 00:10:17,520 Speaker 6: A lot of the utilities of are making plans to 175 00:10:17,600 --> 00:10:20,680 Speaker 6: go forward. The US and Canada the UK are leading 176 00:10:20,720 --> 00:10:23,240 Speaker 6: the effort, and so it's going to take some time 177 00:10:23,360 --> 00:10:27,080 Speaker 6: before those first reactors, small, major reactors, and advanced reactors 178 00:10:27,440 --> 00:10:28,760 Speaker 6: across the finish line. 179 00:10:29,640 --> 00:10:33,160 Speaker 5: We know that new nuclear projects are coming online slower, 180 00:10:33,559 --> 00:10:35,920 Speaker 5: But do you think this administration will change that or 181 00:10:36,000 --> 00:10:37,240 Speaker 5: is it going to be status quo. 182 00:10:37,840 --> 00:10:44,160 Speaker 6: The Trump administration in its first time first term was 183 00:10:44,360 --> 00:10:49,120 Speaker 6: very very pro developing uranium the equivalent of drill baby 184 00:10:49,200 --> 00:10:54,240 Speaker 6: drill from the uranium sector, and to a certain extent 185 00:10:54,320 --> 00:10:58,320 Speaker 6: that uranium has bounced back up. The current administration now 186 00:10:58,800 --> 00:11:02,720 Speaker 6: is favorable towards nuclear. Chris Wright, the Secretary of Energy, 187 00:11:02,760 --> 00:11:05,720 Speaker 6: has a pedigree from Berkeley and MIT to the leading 188 00:11:06,120 --> 00:11:09,959 Speaker 6: academic institutions regarding nuclear, and I think that they are 189 00:11:10,080 --> 00:11:14,720 Speaker 6: very pro nuclear. However, the big question is how much 190 00:11:14,880 --> 00:11:19,600 Speaker 6: government funding will be used to support nuclear reactor construction. 191 00:11:19,760 --> 00:11:22,000 Speaker 6: That's something that we're still working out and trying to 192 00:11:22,120 --> 00:11:23,920 Speaker 6: understand Chris. 193 00:11:23,920 --> 00:11:26,680 Speaker 2: I finished the first season of the TV show Landman, 194 00:11:26,800 --> 00:11:29,439 Speaker 2: so I now consider myself an expert in the oil 195 00:11:29,440 --> 00:11:33,080 Speaker 2: and gas business, and I think my takeaway is, you know, 196 00:11:33,160 --> 00:11:36,520 Speaker 2: listening to energy providers and energy users, we're going to 197 00:11:36,600 --> 00:11:39,480 Speaker 2: need everything. We're going to need fossil fuels, We're gonna 198 00:11:39,480 --> 00:11:41,720 Speaker 2: need the wind, We're going to need solar, all that 199 00:11:41,800 --> 00:11:45,360 Speaker 2: kind of stuff. Maybe nuclear. How do you think about that? 200 00:11:47,040 --> 00:11:50,439 Speaker 6: Yeah, my take is that we certainly do need nuclear. 201 00:11:50,480 --> 00:11:53,960 Speaker 6: If you look on a geopolitical basis, you have China 202 00:11:54,000 --> 00:11:57,720 Speaker 6: building twenty eight reactors, large reactors. Also, as soon as 203 00:11:57,760 --> 00:12:01,559 Speaker 6: it passes are post twenty third, they'll have more nuclear 204 00:12:01,679 --> 00:12:04,640 Speaker 6: power plants operating than we do. The Russians are very, 205 00:12:04,720 --> 00:12:07,560 Speaker 6: very aggressively building nuclear power plants not only in Russia 206 00:12:07,600 --> 00:12:10,559 Speaker 6: but in Middle East and establishing one hundred year relations 207 00:12:10,600 --> 00:12:12,679 Speaker 6: with that, and I think that has caused a lot 208 00:12:12,679 --> 00:12:15,160 Speaker 6: of concern for the US government saying, hey, listen, we 209 00:12:15,240 --> 00:12:18,840 Speaker 6: need to sort of offer a viable alternative option for 210 00:12:20,040 --> 00:12:22,760 Speaker 6: new nuclear in the years ahead. And I think it's 211 00:12:22,800 --> 00:12:27,440 Speaker 6: a very very solid ambition. Biden dedministration called for two 212 00:12:27,480 --> 00:12:31,320 Speaker 6: hundred gigawats of extra nuclear capacity. That's tripling what we 213 00:12:31,440 --> 00:12:35,120 Speaker 6: have right now, and so there is a tremendous need 214 00:12:35,160 --> 00:12:37,240 Speaker 6: for it, and I think there has a place. It's 215 00:12:37,280 --> 00:12:40,040 Speaker 6: not a solution for all the problems, but it works 216 00:12:40,160 --> 00:12:42,280 Speaker 6: very well for many types of applications. 217 00:12:42,880 --> 00:12:47,000 Speaker 2: Our thanks to Chris Gadomski, Bloomberg and EF lead nuclear analyst. 218 00:12:47,160 --> 00:12:48,520 Speaker 3: All right, coming up, we're going to break down the 219 00:12:48,520 --> 00:12:50,760 Speaker 3: future of AI empowering data centers. 220 00:12:50,960 --> 00:12:53,719 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 221 00:12:53,800 --> 00:12:55,880 Speaker 2: depth research and data on two thousand companies and one 222 00:12:55,960 --> 00:12:58,840 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 223 00:12:58,920 --> 00:13:00,839 Speaker 2: bi go in the terminal Paul Sweeney and. 224 00:13:00,800 --> 00:13:02,840 Speaker 3: I'm Alex Steele, and this is Bloomberg. 225 00:13:07,520 --> 00:13:11,200 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 226 00:13:11,280 --> 00:13:14,360 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 227 00:13:14,400 --> 00:13:17,680 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 228 00:13:17,760 --> 00:13:20,880 Speaker 1: you get your podcasts, or watch us live on YouTube. 229 00:13:21,840 --> 00:13:23,560 Speaker 3: We move now to the M and A space. 230 00:13:23,960 --> 00:13:26,839 Speaker 2: This week we heard that the online mortgage provider Rocket 231 00:13:26,880 --> 00:13:31,560 Speaker 2: is acquiring mister Cooper Group, the country's largest mortgage servicer it's. 232 00:13:31,400 --> 00:13:34,120 Speaker 3: An all stock deal valued about nine point four billion dollars, 233 00:13:34,120 --> 00:13:36,800 Speaker 3: and this will create a mortgage behemoth that handles one 234 00:13:36,920 --> 00:13:39,360 Speaker 3: in every six mortgages in the US. 235 00:13:39,559 --> 00:13:41,440 Speaker 2: For more, guest host Isabelle Lee and I were joined 236 00:13:41,440 --> 00:13:45,160 Speaker 2: by Page Smith, Bloomberg News consumer finance reporter. We first 237 00:13:45,160 --> 00:13:47,560 Speaker 2: asked Page to explain the latest deal and what mister 238 00:13:47,679 --> 00:13:48,280 Speaker 2: Cooper is. 239 00:13:48,960 --> 00:13:51,680 Speaker 7: Mister Cooper is more is best known, I would say, 240 00:13:51,679 --> 00:13:54,080 Speaker 7: in the mortgage servicing side of things, So it's sort 241 00:13:54,080 --> 00:13:56,680 Speaker 7: of after the fact, you've got your mortgage, but who 242 00:13:56,720 --> 00:13:59,720 Speaker 7: were you actually interacting with, you know, gears down the line, 243 00:13:59,720 --> 00:14:02,400 Speaker 7: it's actually going to be mister Cooper. But this is 244 00:14:02,440 --> 00:14:05,680 Speaker 7: a big deal for Rocket because they've kind of they've 245 00:14:05,720 --> 00:14:08,280 Speaker 7: been trying to position themselves as sort of a one 246 00:14:08,400 --> 00:14:13,480 Speaker 7: stop shop for consumers finance offerings, so think credit cards 247 00:14:13,559 --> 00:14:18,960 Speaker 7: in addition to your mortgage and you know, now theoretically 248 00:14:19,000 --> 00:14:22,520 Speaker 7: your mortgage servicing rights. So it's it's a pretty big 249 00:14:22,520 --> 00:14:25,080 Speaker 7: deal for this company based in Detroit. Dan Gilbert a 250 00:14:25,120 --> 00:14:29,240 Speaker 7: pretty prominent person in the finance yep, pretty big in 251 00:14:29,280 --> 00:14:32,920 Speaker 7: sports as well. But nine point four billion dollars, it's 252 00:14:33,200 --> 00:14:34,320 Speaker 7: a big deal for these folks. 253 00:14:34,680 --> 00:14:37,400 Speaker 5: We have Rocket also striking a deal to acquire Redfin, 254 00:14:37,480 --> 00:14:40,560 Speaker 5: which is a real estate brokerage. How will that build 255 00:14:40,760 --> 00:14:43,480 Speaker 5: into this one or if they're even connected at all. 256 00:14:43,680 --> 00:14:46,760 Speaker 7: Certainly it's it is basically sort of from a sort 257 00:14:46,800 --> 00:14:49,360 Speaker 7: of a nose to nose to toe, if you will, 258 00:14:49,600 --> 00:14:53,520 Speaker 7: of the home buying experience. Everything will sort of be 259 00:14:53,640 --> 00:14:58,040 Speaker 7: under this Rocket umbrella. It you know Redfinn as you 260 00:14:58,400 --> 00:15:00,200 Speaker 7: as I think a lot of folks will you know 261 00:15:00,320 --> 00:15:04,800 Speaker 7: quite well, is this platform for buying and selling homes 262 00:15:04,840 --> 00:15:07,040 Speaker 7: and it would be sort of a starting point for 263 00:15:07,080 --> 00:15:11,320 Speaker 7: the home buying journey. Rocket is well known for originating, 264 00:15:11,360 --> 00:15:15,000 Speaker 7: getting or creating mortgages, and now mister Cooper is well 265 00:15:15,040 --> 00:15:17,320 Speaker 7: known for sort of the servicing of mortgages. So it's 266 00:15:17,360 --> 00:15:19,760 Speaker 7: it is kind of from start to finish of the 267 00:15:19,760 --> 00:15:20,680 Speaker 7: home buying process. 268 00:15:20,680 --> 00:15:23,480 Speaker 2: Theoretically, they got a lot of debt on their bouncy 269 00:15:23,480 --> 00:15:25,480 Speaker 2: because I being a former banker, I don't look at 270 00:15:25,520 --> 00:15:28,280 Speaker 2: stock market values. I look at enterprise value to include 271 00:15:28,280 --> 00:15:30,880 Speaker 2: the debt. It's eighteen point five billion dollars because that's 272 00:15:30,880 --> 00:15:32,800 Speaker 2: what you get paid on. You get paid on enterprise 273 00:15:32,840 --> 00:15:37,480 Speaker 2: a so that is a big, big number there. Talk 274 00:15:37,480 --> 00:15:39,600 Speaker 2: to us about the mortgage business. I mean, is it 275 00:15:39,600 --> 00:15:41,440 Speaker 2: doesn't seem like there's a lot of deals happening in 276 00:15:41,840 --> 00:15:42,680 Speaker 2: the business. 277 00:15:42,960 --> 00:15:45,400 Speaker 7: Well, I mean, I think we can look at interest rates. 278 00:15:45,400 --> 00:15:48,760 Speaker 7: It's certainly been a tough time for home buyers and sellers, 279 00:15:48,800 --> 00:15:52,880 Speaker 7: and that also weighs on these home on these home lenders. 280 00:15:53,520 --> 00:15:56,160 Speaker 7: It's a tough time for folks who kind of have 281 00:15:56,280 --> 00:15:58,800 Speaker 7: put their eggs all in one basket, which is the 282 00:15:58,840 --> 00:16:02,080 Speaker 7: home lending business. But that's kind of what Rocket has 283 00:16:02,120 --> 00:16:04,560 Speaker 7: been aiming to do, is to really branch out and 284 00:16:04,640 --> 00:16:09,040 Speaker 7: diversify their business so they're not so interest rate reliant 285 00:16:09,720 --> 00:16:12,480 Speaker 7: or exposed rather which they have been in the past. 286 00:16:12,520 --> 00:16:14,880 Speaker 7: And frankly that's showed up in their earnings over the 287 00:16:14,880 --> 00:16:18,800 Speaker 7: past couple of years. So I chatted with CEO U 288 00:16:18,800 --> 00:16:22,000 Speaker 7: and Krishna over the summer. Actually we did kind of 289 00:16:22,000 --> 00:16:24,840 Speaker 7: a deeper dive into Rocket and the company, and one 290 00:16:24,840 --> 00:16:27,920 Speaker 7: thing that he told me then was that artificial intelligence 291 00:16:28,000 --> 00:16:30,440 Speaker 7: is actually a big bet that they're making to really 292 00:16:30,480 --> 00:16:33,240 Speaker 7: try to boost the business and make that home buying 293 00:16:33,280 --> 00:16:36,400 Speaker 7: process truly as smooth as possible for the customers, to 294 00:16:36,560 --> 00:16:40,280 Speaker 7: kind of amp up their offerings and we'll see how 295 00:16:40,280 --> 00:16:41,760 Speaker 7: it works out. With for them in the end, But 296 00:16:41,880 --> 00:16:43,760 Speaker 7: so far numbers look pretty good. 297 00:16:44,400 --> 00:16:47,600 Speaker 5: What's this deal expected? Were you hearing chatter before? Did 298 00:16:47,600 --> 00:16:48,760 Speaker 5: it's completely shock you? 299 00:16:49,080 --> 00:16:51,960 Speaker 7: I was not hearing chatter before, and it seems like 300 00:16:52,000 --> 00:16:54,320 Speaker 7: the market is responding in kind of an interesting way. 301 00:16:54,360 --> 00:16:57,040 Speaker 7: But we're going to continue to follow the story and 302 00:16:57,080 --> 00:17:00,000 Speaker 7: see how this kind of fits into the broader rockets. 303 00:17:00,320 --> 00:17:02,400 Speaker 2: Who do they compete against? Do they compete against the 304 00:17:03,160 --> 00:17:05,119 Speaker 2: banks that make the loans or the banks that make 305 00:17:05,119 --> 00:17:07,159 Speaker 2: the loans usually just syndicate them away, right. 306 00:17:07,280 --> 00:17:09,359 Speaker 7: Well, I kind of take a different take on that 307 00:17:09,640 --> 00:17:12,280 Speaker 7: since I cover consumer and finance kind of broadly. I 308 00:17:12,280 --> 00:17:14,480 Speaker 7: think of folks who are kind of trying to be 309 00:17:14,960 --> 00:17:19,480 Speaker 7: that one one stop shop for consumer the consumer finance experience. 310 00:17:19,520 --> 00:17:21,960 Speaker 7: So for me, I think of you know, so FI 311 00:17:22,119 --> 00:17:26,280 Speaker 7: technologies for example, that is like a they have a 312 00:17:26,320 --> 00:17:29,879 Speaker 7: lot of lending opportunities for consumers, but they do a 313 00:17:29,880 --> 00:17:33,000 Speaker 7: heck of a lot of other business as well. Robinhood 314 00:17:33,040 --> 00:17:35,520 Speaker 7: even coming at it from the investing side of things 315 00:17:35,680 --> 00:17:39,520 Speaker 7: now offering banking products for folks, That's kind of how 316 00:17:39,560 --> 00:17:42,080 Speaker 7: I think about it in terms of competitors. But that's 317 00:17:42,200 --> 00:17:45,240 Speaker 7: just from a fintech consumer finance perspective when it comes 318 00:17:45,240 --> 00:17:48,200 Speaker 7: to home lenders. You know, other big folks in the space, 319 00:17:48,440 --> 00:17:50,400 Speaker 7: the space. I was just looking at some data by 320 00:17:50,760 --> 00:17:55,240 Speaker 7: Inside Mortgage Finance that placed United Wholesale Mortgagees number one, 321 00:17:55,600 --> 00:17:58,640 Speaker 7: Pennymac is number two, and Rocket as number three. 322 00:17:58,720 --> 00:18:02,399 Speaker 2: Penny Mac is what I use. Anything that rings a bell? Okay, 323 00:18:02,760 --> 00:18:05,280 Speaker 2: Dan Gilbert just for what it's worth. Kids on the 324 00:18:05,359 --> 00:18:08,000 Speaker 2: rich top list of Bloomberg all the wealthy people, he 325 00:18:08,080 --> 00:18:10,680 Speaker 2: comes in at number sixty two. Well they're not bad. 326 00:18:10,960 --> 00:18:13,879 Speaker 2: Net worth of twenty eight point seven billion dollars up 327 00:18:13,880 --> 00:18:14,960 Speaker 2: two point six billion. 328 00:18:14,760 --> 00:18:15,159 Speaker 8: Year to date. 329 00:18:15,359 --> 00:18:18,439 Speaker 7: Yeah, he's a force in Detroit. I would say some 330 00:18:18,560 --> 00:18:20,760 Speaker 7: of our colleagues out of the Detroit Bureau did a 331 00:18:20,800 --> 00:18:22,960 Speaker 7: great story on him last year. If you're interested in 332 00:18:23,040 --> 00:18:24,000 Speaker 7: the terminal, All. 333 00:18:23,960 --> 00:18:26,760 Speaker 3: Right, thanks to Paige Smith, Bloomberg News Consumer Finance Reporter, 334 00:18:27,280 --> 00:18:29,560 Speaker 3: we move next to the artificial intelligence sector. 335 00:18:29,920 --> 00:18:32,600 Speaker 2: Guest host Normal Linda and I were joined by John Lynn, 336 00:18:32,680 --> 00:18:34,440 Speaker 2: chief business officer at Equinox. 337 00:18:34,760 --> 00:18:37,399 Speaker 3: Equinex is the largest global data center provider and is 338 00:18:37,400 --> 00:18:40,160 Speaker 3: listed on the Nasdaq Stock Exchange under the ticker symbol. 339 00:18:39,880 --> 00:18:41,159 Speaker 4: E q i X. 340 00:18:41,520 --> 00:18:44,160 Speaker 2: John lenn joined to discuss the future of AI empowering 341 00:18:44,240 --> 00:18:46,800 Speaker 2: data centers, and I began the conversation by asking John 342 00:18:46,840 --> 00:18:48,720 Speaker 2: to explain what Equinox does. 343 00:18:49,080 --> 00:18:52,679 Speaker 8: We're really the fundamental digital infrastructure provider of the world, 344 00:18:52,800 --> 00:18:55,479 Speaker 8: building two hundred and sixty eight data centers across seventy 345 00:18:55,480 --> 00:18:57,520 Speaker 8: four market If you're the guys, we are the guys 346 00:18:57,600 --> 00:19:01,119 Speaker 8: building and connecting all of these cloud provide and enterprises, 347 00:19:01,400 --> 00:19:03,240 Speaker 8: making all of that data available for AI. 348 00:19:03,760 --> 00:19:05,840 Speaker 9: And it's really interesting because I was just speaking with 349 00:19:05,920 --> 00:19:09,200 Speaker 9: him during the break saying that I cover US real 350 00:19:09,280 --> 00:19:11,719 Speaker 9: estate stocks, I cover real estate investment trusts, and that's 351 00:19:11,760 --> 00:19:14,880 Speaker 9: exactly what falls in that patch. This is my guy. 352 00:19:15,880 --> 00:19:18,439 Speaker 9: So wonderful to have this conversation. But I think that 353 00:19:18,480 --> 00:19:21,160 Speaker 9: there's often people don't really when you think about data centers, 354 00:19:21,160 --> 00:19:23,600 Speaker 9: they don't often think about the people that are actually 355 00:19:23,720 --> 00:19:25,960 Speaker 9: providing the real estate for data centers. So can you 356 00:19:26,000 --> 00:19:29,280 Speaker 9: talk a little bit about how equinics differs from maybe 357 00:19:29,280 --> 00:19:31,119 Speaker 9: for thinking about a data center itself, but more or 358 00:19:31,200 --> 00:19:33,399 Speaker 9: less the fact that you guys are acquiring properties and 359 00:19:33,440 --> 00:19:34,240 Speaker 9: doing it that way. 360 00:19:34,440 --> 00:19:36,560 Speaker 8: Yeah, you can think about it as full scope development, 361 00:19:36,560 --> 00:19:38,800 Speaker 8: where I mean we're going from raw land getting their 362 00:19:38,920 --> 00:19:41,360 Speaker 8: entitlements and then building the entire data center and then 363 00:19:41,520 --> 00:19:45,399 Speaker 8: operating that for perpetuity essentially. And our focus is around 364 00:19:45,400 --> 00:19:48,200 Speaker 8: making sure we're getting as many customers as possible into 365 00:19:48,240 --> 00:19:51,480 Speaker 8: the facilities and really interconnecting their data flows together, which 366 00:19:51,520 --> 00:19:54,120 Speaker 8: is pretty unique in the data center space, which has 367 00:19:54,200 --> 00:19:56,280 Speaker 8: also been a great opportunity for us to participate in 368 00:19:56,280 --> 00:19:57,080 Speaker 8: the AI growth. 369 00:19:57,720 --> 00:19:59,320 Speaker 2: What are you guys seeing here? What are you seeing 370 00:19:59,320 --> 00:20:02,840 Speaker 2: from your client and the people you talk to about 371 00:20:02,960 --> 00:20:05,080 Speaker 2: kind of their needs going forward? Because right now, I 372 00:20:05,119 --> 00:20:07,240 Speaker 2: think in the marketplace, if you look at like Nvidious 373 00:20:07,240 --> 00:20:09,200 Speaker 2: stock and some of the other stocks that trade around 374 00:20:09,280 --> 00:20:11,639 Speaker 2: the AI theme, twenty twenty five has not been a 375 00:20:11,640 --> 00:20:14,560 Speaker 2: good year, after obviously phenomenal extraordinary growth in twenty three 376 00:20:14,600 --> 00:20:18,520 Speaker 2: twenty four, maybe before that. How are you viewing the 377 00:20:18,600 --> 00:20:20,840 Speaker 2: growth here in AI and from your end of the business, 378 00:20:20,840 --> 00:20:21,600 Speaker 2: the real estate side. 379 00:20:21,760 --> 00:20:23,679 Speaker 8: Yeah, First, I'd just say, you know, AI is a 380 00:20:23,720 --> 00:20:26,240 Speaker 8: portion of the demand for data centers, but data center 381 00:20:26,240 --> 00:20:28,720 Speaker 8: as a whole are powering everything that everybody is doing, 382 00:20:28,800 --> 00:20:31,600 Speaker 8: right like listening to this broadcast, you know, ordering food 383 00:20:31,600 --> 00:20:35,040 Speaker 8: for lunch, you know, trading, trading on the exchange, et cetera. 384 00:20:35,280 --> 00:20:38,080 Speaker 8: I mean, you need computers for everything nowadays, and that's 385 00:20:38,080 --> 00:20:40,840 Speaker 8: still continuing. I think, you know, digital transformation is not 386 00:20:40,880 --> 00:20:43,320 Speaker 8: in the early stages anymore, but we're far from done, 387 00:20:43,320 --> 00:20:46,080 Speaker 8: and so that is just the secular driver that will continue. 388 00:20:46,680 --> 00:20:49,600 Speaker 8: From the eye landscape, I'd say obviously a huge amount 389 00:20:49,640 --> 00:20:52,080 Speaker 8: of interest and excitement and I think the it caught 390 00:20:52,080 --> 00:20:55,399 Speaker 8: the imagination of everyone, and I'd say, right now what 391 00:20:55,440 --> 00:20:59,320 Speaker 8: we're seeing is like exciting use cases that are really 392 00:20:59,359 --> 00:21:02,160 Speaker 8: providing durable value, right. And I think it's still early 393 00:21:02,240 --> 00:21:05,240 Speaker 8: stages for many of that across the general business landscape, 394 00:21:05,320 --> 00:21:07,480 Speaker 8: but that's what gets me fundamentally excited. You look at 395 00:21:07,520 --> 00:21:09,960 Speaker 8: a company like a Bristol Meyers squib a customer of ours. 396 00:21:10,080 --> 00:21:14,240 Speaker 8: They're doing drug discovery using videogpus and like being able 397 00:21:14,280 --> 00:21:18,560 Speaker 8: to increase and accelerate their time to therapeutics. That's fundamentally 398 00:21:18,600 --> 00:21:20,560 Speaker 8: going to improve like human life, right, and I think 399 00:21:20,600 --> 00:21:22,800 Speaker 8: that there's so many different aspects that AI can improve 400 00:21:23,000 --> 00:21:23,640 Speaker 8: based on that. 401 00:21:23,840 --> 00:21:25,879 Speaker 9: So run us through some of your biggest customers, who 402 00:21:25,920 --> 00:21:26,560 Speaker 9: do you all work with. 403 00:21:26,960 --> 00:21:29,320 Speaker 8: Certainly the cloud providers are some of our top customers. 404 00:21:29,480 --> 00:21:32,119 Speaker 8: We've got over two thousand different network providers as well. 405 00:21:32,280 --> 00:21:35,000 Speaker 8: The Genesis of the company was really around how do 406 00:21:35,040 --> 00:21:37,480 Speaker 8: we help the Internet scale? And that ended up being, well, 407 00:21:37,480 --> 00:21:41,600 Speaker 8: how do we help the globes telecommunications and data flows scale. 408 00:21:41,840 --> 00:21:43,920 Speaker 8: And so when you think about all of the cloud providers, 409 00:21:44,040 --> 00:21:46,680 Speaker 8: how do they connect to the end customers, that's through 410 00:21:46,720 --> 00:21:50,200 Speaker 8: our facilities. And then as we've built that landscape, we've 411 00:21:50,280 --> 00:21:53,800 Speaker 8: ended up basically becoming the place where enterprises put their 412 00:21:53,840 --> 00:21:56,760 Speaker 8: most trusted assets. When you think about then, whether they 413 00:21:56,800 --> 00:21:59,040 Speaker 8: have some workloads that are in the public cloud, well, 414 00:21:59,040 --> 00:22:00,520 Speaker 8: they're going to have some that are going to have 415 00:22:00,560 --> 00:22:03,159 Speaker 8: ownership and control of themselves. When they put those in 416 00:22:03,160 --> 00:22:06,399 Speaker 8: our facilities, it lets them glue that infrastructure together and 417 00:22:06,440 --> 00:22:09,640 Speaker 8: become like one super powerful environment. 418 00:22:09,960 --> 00:22:14,399 Speaker 2: And folks, Equinics is a publicly traded company. Eqix is 419 00:22:14,640 --> 00:22:16,880 Speaker 2: the ticker. It's got a market cap of eighty one 420 00:22:17,119 --> 00:22:20,520 Speaker 2: billion dollars. And if you want some research on it 421 00:22:20,560 --> 00:22:22,720 Speaker 2: and you're on the Bloomberg terminal, Jeffrey Langbaum was my 422 00:22:22,880 --> 00:22:25,840 Speaker 2: reat analyst. He covers eqix. You can go big and 423 00:22:25,840 --> 00:22:30,200 Speaker 2: that's where you find the research on Equinox. John talk 424 00:22:30,240 --> 00:22:31,879 Speaker 2: to us about the global formfront and we know you 425 00:22:31,880 --> 00:22:36,440 Speaker 2: guys are global here, where are you seeing growth stronger 426 00:22:36,480 --> 00:22:37,679 Speaker 2: growth versus weaker growth. 427 00:22:38,200 --> 00:22:41,080 Speaker 8: Yeah, I'd say across the landscape, there's still quite a 428 00:22:41,080 --> 00:22:43,760 Speaker 8: bit of demand for data center activity. You know, we're 429 00:22:43,800 --> 00:22:46,680 Speaker 8: particularly excited about some of the emerging markets Southeast Asia 430 00:22:46,720 --> 00:22:50,000 Speaker 8: for instance. It's certainly growing quite a bit. But you know, 431 00:22:50,320 --> 00:22:52,520 Speaker 8: based off of a lot of the recent Surgeon AI 432 00:22:52,760 --> 00:22:54,520 Speaker 8: and kind of the use cases set up for that, 433 00:22:54,880 --> 00:22:57,000 Speaker 8: just a tremendous amount of growth in the US over 434 00:22:57,000 --> 00:22:58,200 Speaker 8: the course of the last two years. 435 00:22:58,720 --> 00:23:00,560 Speaker 9: So, I mean, when we think about your creditors in 436 00:23:00,640 --> 00:23:03,520 Speaker 9: this broader landscape, there's obviously digital realty trust when we're 437 00:23:03,520 --> 00:23:06,359 Speaker 9: thinking about publicly traded routes here in the data center space, 438 00:23:06,680 --> 00:23:09,080 Speaker 9: and if you look over the past five years, we 439 00:23:09,160 --> 00:23:12,360 Speaker 9: have equinic shares that have risen forty percent, but that's 440 00:23:12,400 --> 00:23:15,199 Speaker 9: compared to digital realty that's risen about eleven percent. What 441 00:23:15,240 --> 00:23:16,840 Speaker 9: do you think that you all are doing differently than 442 00:23:16,880 --> 00:23:17,800 Speaker 9: your competitors. 443 00:23:18,800 --> 00:23:23,280 Speaker 8: I think one, it's our focus around really driving diversity 444 00:23:23,320 --> 00:23:26,560 Speaker 8: of customer and like kind of having an ecosystem that 445 00:23:26,600 --> 00:23:28,760 Speaker 8: we've built around the value that we're doing, and so 446 00:23:28,840 --> 00:23:31,440 Speaker 8: that's incredibly important for us. Like for the AI trade, 447 00:23:31,440 --> 00:23:34,199 Speaker 8: for instance, we're focusing not just on capturing some of 448 00:23:34,200 --> 00:23:36,719 Speaker 8: these large training footprints, but really, how do we make 449 00:23:36,720 --> 00:23:39,040 Speaker 8: sure we're getting all of these AI players and exposing 450 00:23:39,080 --> 00:23:40,920 Speaker 8: them to the rest of our customer base and really, 451 00:23:40,960 --> 00:23:46,000 Speaker 8: again that fuel becomes additional growth across our entire portfolio. 452 00:23:46,800 --> 00:23:49,919 Speaker 2: Do you develop and build data centers or do you 453 00:23:49,960 --> 00:23:53,040 Speaker 2: just buy existing we develop and build? Where are you 454 00:23:53,400 --> 00:23:56,520 Speaker 2: developing and building these days? And if you say Texas. 455 00:23:56,280 --> 00:23:59,480 Speaker 8: Or Florida, Well, we're building all around the world. I 456 00:23:59,520 --> 00:24:02,560 Speaker 8: think we've got sixty eight current like major construction projects 457 00:24:02,640 --> 00:24:05,040 Speaker 8: across it. Yeah, so it's we're very active. 458 00:24:05,760 --> 00:24:07,760 Speaker 2: Wow, how about it? 459 00:24:07,760 --> 00:24:10,360 Speaker 9: It's really it's a big company in this. I mean, yeah, 460 00:24:10,600 --> 00:24:13,840 Speaker 9: people think about you guys have to come to my path. 461 00:24:15,080 --> 00:24:16,000 Speaker 8: It's a beautiful space. 462 00:24:17,040 --> 00:24:18,600 Speaker 9: So what is your you know, what are your thoughts 463 00:24:18,600 --> 00:24:20,520 Speaker 9: for people who are saying that, you know, the tech 464 00:24:20,600 --> 00:24:22,919 Speaker 9: rally has run too far? You know, maybe we have 465 00:24:23,080 --> 00:24:25,439 Speaker 9: CAPEC spend that's just you know, bloated. There's so much 466 00:24:25,480 --> 00:24:27,879 Speaker 9: spending in this space. Is this a place to be 467 00:24:27,960 --> 00:24:30,560 Speaker 9: investing right now? When we think about AI and places 468 00:24:30,600 --> 00:24:31,000 Speaker 9: of that. 469 00:24:31,280 --> 00:24:34,960 Speaker 8: Regard, I think the long term trend around this is 470 00:24:35,000 --> 00:24:36,920 Speaker 8: going to be inevitable, right, I think it's certainly we're 471 00:24:36,960 --> 00:24:39,480 Speaker 8: creating durable value, not just for you know, kind of 472 00:24:39,480 --> 00:24:42,199 Speaker 8: the planet and all of our customers, but but for shareholders. 473 00:24:42,240 --> 00:24:44,600 Speaker 8: I think the the amount of investment in the space, 474 00:24:44,640 --> 00:24:47,320 Speaker 8: and like the numbers are candidly like eyewatering right now. 475 00:24:47,359 --> 00:24:48,879 Speaker 8: And so but a lot of that I think is 476 00:24:48,920 --> 00:24:52,760 Speaker 8: just capital accumulation rather than deployment. And you know, compared 477 00:24:52,800 --> 00:24:55,000 Speaker 8: to a lot of other markets in the real estate side, 478 00:24:55,080 --> 00:24:58,159 Speaker 8: it's actually a little hard to kind of overbuild just 479 00:24:58,200 --> 00:25:01,679 Speaker 8: because there's so many natural like limiters in terms of 480 00:25:01,680 --> 00:25:04,640 Speaker 8: the way we want to scale, from utility power availability, 481 00:25:04,680 --> 00:25:07,560 Speaker 8: to supply chain to you know kind of just the 482 00:25:07,600 --> 00:25:09,920 Speaker 8: amount of trades you need to be able to build 483 00:25:09,920 --> 00:25:12,159 Speaker 8: and operate these facilities. And so I think that that 484 00:25:12,280 --> 00:25:15,159 Speaker 8: helps kind of provide more rationality than you know, in 485 00:25:15,200 --> 00:25:17,120 Speaker 8: some some real estate markets where you know, you can 486 00:25:17,119 --> 00:25:19,040 Speaker 8: throw up a shell pretty easily, you can you can 487 00:25:19,119 --> 00:25:21,760 Speaker 8: kind of just like convert and overbuild. In this case, 488 00:25:22,040 --> 00:25:24,480 Speaker 8: it's a very long development cycle, and so I think 489 00:25:24,520 --> 00:25:26,560 Speaker 8: you'll you'll see kind of some self metering there. 490 00:25:26,880 --> 00:25:29,760 Speaker 2: Our thanks to John Lynn, chief business officer Equinics. 491 00:25:29,640 --> 00:25:31,199 Speaker 3: Coming up in the program, we're going to break down 492 00:25:31,240 --> 00:25:33,640 Speaker 3: the rising cost of food and how that's affecting the consumer. 493 00:25:33,760 --> 00:25:36,439 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 494 00:25:36,480 --> 00:25:38,639 Speaker 2: depth research and data on two thousand companies and one 495 00:25:38,760 --> 00:25:41,680 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 496 00:25:41,760 --> 00:25:43,840 Speaker 2: b I go on the terminal. I'm Paul Sweeney and. 497 00:25:43,800 --> 00:25:45,760 Speaker 3: Am Alex Steele, and this is Bloomberg. 498 00:25:53,640 --> 00:25:57,560 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch the program 499 00:25:57,640 --> 00:26:00,560 Speaker 1: live weekdays at ten a m. Eastern on app Cocklay 500 00:26:00,560 --> 00:26:03,320 Speaker 1: and Android Auto with the Bloomberg Business App. You can 501 00:26:03,400 --> 00:26:06,840 Speaker 1: also listen live on Amazon Alexa from our flagship New 502 00:26:06,920 --> 00:26:10,639 Speaker 1: York station. Just say Alexa play Bloomberg eleven thirty. 503 00:26:11,720 --> 00:26:14,080 Speaker 2: We turned out to the restaurant industry this week. Guess 504 00:26:14,119 --> 00:26:16,119 Speaker 2: so was normal? Lindon I were joined by Michael Halen, 505 00:26:16,119 --> 00:26:18,879 Speaker 2: Bloomberg Intelligence senior restaurant and food service analysts. 506 00:26:18,960 --> 00:26:21,240 Speaker 3: He joined to discuss the rise and costs of food 507 00:26:21,280 --> 00:26:22,840 Speaker 3: and how that's affecting the consumer. 508 00:26:23,200 --> 00:26:25,680 Speaker 2: First asked Michael to break down his most recent research 509 00:26:25,760 --> 00:26:27,000 Speaker 2: on restaurant spending. 510 00:26:27,359 --> 00:26:30,000 Speaker 10: Restaurant space is unique. We were in a restaurant recession 511 00:26:30,080 --> 00:26:34,320 Speaker 10: last year. Higher prices, Yeah, higher prices really kind of 512 00:26:34,400 --> 00:26:37,200 Speaker 10: you know, the you know, the low income consumer kind 513 00:26:37,200 --> 00:26:38,840 Speaker 10: of pushed back against higher prices. 514 00:26:38,920 --> 00:26:39,080 Speaker 4: Right. 515 00:26:39,160 --> 00:26:42,359 Speaker 10: QUSR has been raising their prices since twenty twenty. The 516 00:26:42,400 --> 00:26:44,760 Speaker 10: rest of the restaurant industry has been raising prices since 517 00:26:44,800 --> 00:26:46,919 Speaker 10: twenty twenty one. So last year was kind of that 518 00:26:47,000 --> 00:26:51,520 Speaker 10: restaurant recession. This year, you know, we see you know, 519 00:26:51,600 --> 00:26:55,720 Speaker 10: higher income consumers with a better balance sheet. Right, Crypto's 520 00:26:55,760 --> 00:26:59,119 Speaker 10: up significantly. Home prices are still rising, right, and so 521 00:26:59,720 --> 00:27:02,080 Speaker 10: you know, for that reason, we think that this is 522 00:27:02,119 --> 00:27:03,800 Speaker 10: going to be a better year. And we still think 523 00:27:03,840 --> 00:27:06,600 Speaker 10: so in the data that we've seen and from a 524 00:27:06,640 --> 00:27:09,440 Speaker 10: lot of the CEOs that we've spoken to. The weakness 525 00:27:09,440 --> 00:27:14,400 Speaker 10: in February was broad based, and to me that's less concerning. 526 00:27:14,440 --> 00:27:16,520 Speaker 10: I would be more concerned if they said, listen, low 527 00:27:16,560 --> 00:27:20,200 Speaker 10: income consumer pulled back even harder, the middle income consumer 528 00:27:20,240 --> 00:27:22,960 Speaker 10: started to pull back harder, that would be more concerning 529 00:27:23,000 --> 00:27:25,639 Speaker 10: to me. Broad Based tells me that, well, it was 530 00:27:25,720 --> 00:27:29,840 Speaker 10: really cold. We got snow all over the country, including Sarasota, right, 531 00:27:30,119 --> 00:27:35,400 Speaker 10: and New Orleans. It was the coldest January in thirteen 532 00:27:35,520 --> 00:27:38,879 Speaker 10: years or so, right, and the flu was really bad. 533 00:27:38,960 --> 00:27:42,040 Speaker 10: So for me, it really seems that people were just 534 00:27:42,320 --> 00:27:44,560 Speaker 10: sick of the weather and sitting on the couch waiting 535 00:27:44,560 --> 00:27:46,680 Speaker 10: for things to open up. We have some weekly data 536 00:27:46,720 --> 00:27:49,600 Speaker 10: for early March, and data got better. 537 00:27:49,920 --> 00:27:51,880 Speaker 9: So what data are you looking at when it comes 538 00:27:51,880 --> 00:27:53,520 Speaker 9: to consumer health? I know you said there's some that 539 00:27:53,520 --> 00:27:54,919 Speaker 9: you don't really pay as much attention to. 540 00:27:55,160 --> 00:27:55,400 Speaker 6: Yeah. 541 00:27:55,400 --> 00:27:58,960 Speaker 10: For the restaurant stuff, we use black box intelligence. We 542 00:27:59,040 --> 00:28:02,960 Speaker 10: get very good industry level and sub segment level seems 543 00:28:02,960 --> 00:28:06,000 Speaker 10: source sales traffic and check data. When I'm looking at 544 00:28:06,000 --> 00:28:08,560 Speaker 10: the consumer, you know, it's kind of dated right now 545 00:28:08,600 --> 00:28:10,760 Speaker 10: because it comes out quarterly, But I'm looking very closely 546 00:28:10,800 --> 00:28:13,720 Speaker 10: at credit card balances and so credit card balances, credit 547 00:28:13,800 --> 00:28:17,439 Speaker 10: card delinquencies, autal on delinquencies. They are still rising, but 548 00:28:17,560 --> 00:28:21,320 Speaker 10: at a much lower slower pace than they were early 549 00:28:21,400 --> 00:28:25,800 Speaker 10: last year, so it's a rate of change improvement. We're 550 00:28:25,840 --> 00:28:29,640 Speaker 10: also looking at CPI declining, real income's rising, and savings 551 00:28:29,720 --> 00:28:33,800 Speaker 10: rates rising, right, and so to me, those are all 552 00:28:33,840 --> 00:28:37,280 Speaker 10: good things for the low income consumer, right, So yeah, 553 00:28:37,320 --> 00:28:41,160 Speaker 10: we're not so concerned about that that consumer sentiment data. 554 00:28:41,280 --> 00:28:44,280 Speaker 2: How about terraffs, How does that impact the average restaurant 555 00:28:44,320 --> 00:28:46,880 Speaker 2: if they're buying food and that type of stuff. 556 00:28:46,960 --> 00:28:49,800 Speaker 10: Yeah, Listen, if you're a mom and pop shop and 557 00:28:49,840 --> 00:28:54,680 Speaker 10: you're importing you know, Italian or Japanese items, stuff like that, 558 00:28:55,120 --> 00:28:55,720 Speaker 10: it could hurt. 559 00:28:55,840 --> 00:28:56,000 Speaker 4: Right. 560 00:28:56,040 --> 00:28:59,920 Speaker 10: For most our chains, they're sourcing a very large majority 561 00:29:00,120 --> 00:29:03,160 Speaker 10: of their products in the United States. You know, Chipotle 562 00:29:03,320 --> 00:29:05,520 Speaker 10: was one everyone was worried about their saying, it's gonna 563 00:29:05,520 --> 00:29:07,880 Speaker 10: be like thirty basis points impact to their food. 564 00:29:08,160 --> 00:29:10,160 Speaker 2: So the block prices aren't going to go crazy. 565 00:29:10,400 --> 00:29:12,040 Speaker 10: Yeah, it's gonna be like thirty basis points for the 566 00:29:12,080 --> 00:29:15,040 Speaker 10: Alvocado prices, right, And they've for years they've been you know, 567 00:29:15,120 --> 00:29:18,200 Speaker 10: expanding beyond Mexico in terms of sourcing. One of the 568 00:29:18,680 --> 00:29:21,360 Speaker 10: companies that we cover that has probably the most exposure 569 00:29:21,520 --> 00:29:25,520 Speaker 10: overseas is Darden. They said they actually import about twenty 570 00:29:25,560 --> 00:29:28,240 Speaker 10: percent of their items. Part of that is they have 571 00:29:28,280 --> 00:29:31,840 Speaker 10: an Italian chain, right, but also it's just cheaper for them, 572 00:29:32,280 --> 00:29:36,200 Speaker 10: and so they're working on sourcing domestically and in other 573 00:29:36,240 --> 00:29:38,400 Speaker 10: places to try to ease some of that pain. So 574 00:29:38,640 --> 00:29:40,719 Speaker 10: even though it's a twenty percent number, it can be 575 00:29:40,880 --> 00:29:43,000 Speaker 10: much lower than that, So the restaurants aren't going to 576 00:29:43,040 --> 00:29:44,800 Speaker 10: be impacted that harshly. 577 00:29:45,080 --> 00:29:48,720 Speaker 9: How are consumers adjusting to the price increases. Are people 578 00:29:48,760 --> 00:29:50,800 Speaker 9: just saying, Hey, I want to go out, I want 579 00:29:50,800 --> 00:29:52,520 Speaker 9: to eat, so I'm just going to pay more even 580 00:29:52,600 --> 00:29:54,520 Speaker 9: sell or are they a bit more resistant? 581 00:29:54,600 --> 00:29:57,880 Speaker 2: If so, witch groups, it's a case shaped recovery, right. 582 00:29:57,960 --> 00:30:01,200 Speaker 10: And so chains like Chilis, I mean, they just posted 583 00:30:01,200 --> 00:30:04,160 Speaker 10: a thirty percent comp in the United States over a 584 00:30:04,280 --> 00:30:07,000 Speaker 10: five percent comp. We've never seen that before for a 585 00:30:07,080 --> 00:30:09,640 Speaker 10: chain that's been around as long as But they're bringing 586 00:30:09,680 --> 00:30:14,400 Speaker 10: in younger consumers, wealthier consumers that are willing to spend, right. 587 00:30:14,440 --> 00:30:18,040 Speaker 10: And so the top slant of the K, people with money, 588 00:30:18,160 --> 00:30:20,840 Speaker 10: they're doing just fine, and they're still spending at restaurants, right. 589 00:30:20,880 --> 00:30:22,560 Speaker 10: It's the people on the bottom slant of the K. 590 00:30:22,720 --> 00:30:26,240 Speaker 10: It's these chains that are catering to low income consumers, right, 591 00:30:26,240 --> 00:30:29,400 Speaker 10: that are getting that pushback. Either they're you know, going 592 00:30:29,400 --> 00:30:31,760 Speaker 10: to restaurants less frequently, they're doing more shopping at the 593 00:30:31,760 --> 00:30:34,560 Speaker 10: grocery store, or and when they do go to the restaurants, 594 00:30:34,600 --> 00:30:38,680 Speaker 10: oftentimes they're ordering off the menu, or they're ordering less drinks, apptizers, 595 00:30:38,680 --> 00:30:40,080 Speaker 10: desserts and stuff like that. 596 00:30:40,280 --> 00:30:44,480 Speaker 2: Hey, Mike, how about labor real quick? Thirty seconds, bus boys, dishwashers, 597 00:30:44,600 --> 00:30:46,880 Speaker 2: migrant labor migrant labors cut off here is that can 598 00:30:46,960 --> 00:30:48,200 Speaker 2: be a problem for some of these restaurants. 599 00:30:48,280 --> 00:30:52,000 Speaker 10: Well, it's a concern for the restaurant and industry in general. 600 00:30:52,200 --> 00:30:55,560 Speaker 10: Most of our companies are compliant, they are right, and 601 00:30:55,640 --> 00:30:57,760 Speaker 10: so they're not really too worried about it. 602 00:30:57,880 --> 00:31:00,520 Speaker 2: Our thanks to Michael Halen, Bloomberg Intelligence scene restaurant and 603 00:31:00,560 --> 00:31:01,479 Speaker 2: food service analyst. 604 00:31:01,920 --> 00:31:05,800 Speaker 3: This week, Bloomberg Intelligence hosted its fourth Generative Artificial Intelligence 605 00:31:05,840 --> 00:31:08,440 Speaker 3: Conference and there were some great lineups in terms of 606 00:31:08,480 --> 00:31:11,200 Speaker 3: how you apply and mag jen AI for more. 607 00:31:11,320 --> 00:31:14,400 Speaker 2: Alex and I were joined by Julie Choice, CMO of 608 00:31:14,560 --> 00:31:17,400 Speaker 2: Sarah Bros. We first to ask Julie where she thinks 609 00:31:17,440 --> 00:31:19,479 Speaker 2: we are with the changing views of AI. 610 00:31:20,040 --> 00:31:23,320 Speaker 11: Well, I think that AI is really kind of hitting 611 00:31:23,400 --> 00:31:28,320 Speaker 11: the mainstream more. I think that you know, chatchipt surfaced 612 00:31:28,360 --> 00:31:32,280 Speaker 11: at the end of was that twenty two? I mean technically, 613 00:31:32,320 --> 00:31:34,760 Speaker 11: I think the first model came out in twenty twenty two, 614 00:31:35,560 --> 00:31:37,880 Speaker 11: and it's been almost two and a half years since then, 615 00:31:38,520 --> 00:31:42,360 Speaker 11: and chatchipt has found its way into so much of 616 00:31:42,400 --> 00:31:45,560 Speaker 11: our lives, and so people are more comfortable with AI, right, 617 00:31:45,960 --> 00:31:48,960 Speaker 11: and so now I think we're just shifting into okay, 618 00:31:49,280 --> 00:31:52,560 Speaker 11: AI can be a part of my life. But it's 619 00:31:52,600 --> 00:31:53,840 Speaker 11: still early days. 620 00:31:54,080 --> 00:31:56,360 Speaker 3: So where do you sit then, in the in the 621 00:31:56,360 --> 00:31:57,480 Speaker 3: pyramid for AI? 622 00:31:58,280 --> 00:32:01,760 Speaker 11: So we are Cerebras makes this beautiful chip which is 623 00:32:01,800 --> 00:32:06,840 Speaker 11: basically AI infrastructure. We are the equivalent of Nvidia, so 624 00:32:06,880 --> 00:32:10,920 Speaker 11: in video GPUs, it's just an alternative type of processor 625 00:32:11,000 --> 00:32:15,360 Speaker 11: for AI. So we are the underlying compute that's powering 626 00:32:15,440 --> 00:32:18,800 Speaker 11: the training and running of models like CHET GPT. 627 00:32:19,760 --> 00:32:21,600 Speaker 2: I guess one of the questions now is people are 628 00:32:21,640 --> 00:32:23,760 Speaker 2: trying to just get a better handle on what the 629 00:32:23,880 --> 00:32:28,440 Speaker 2: compute needs are going forward. Gensen Wong at Nvidia remains 630 00:32:28,480 --> 00:32:31,200 Speaker 2: extraordinary bullish about that, and he has been, you know, 631 00:32:31,360 --> 00:32:35,360 Speaker 2: the voice of AI for many investors for the past 632 00:32:35,360 --> 00:32:36,600 Speaker 2: couple of years. Where do you think we are in 633 00:32:36,640 --> 00:32:37,400 Speaker 2: that compute need? 634 00:32:37,520 --> 00:32:41,720 Speaker 11: Oh my goodness, I completely agree with Jensen Hang. I've 635 00:32:41,720 --> 00:32:45,680 Speaker 11: been following Jensen kind of unofficially as a mentor. I 636 00:32:45,720 --> 00:32:50,080 Speaker 11: find him to be an incredibly inspiring leader. I agree. 637 00:32:50,280 --> 00:32:53,080 Speaker 11: I think that the compute needs for AI are ever 638 00:32:53,280 --> 00:32:58,840 Speaker 11: increasing now with models like GPT four O which and 639 00:32:59,000 --> 00:33:02,960 Speaker 11: the whole one series. These are like these reasoning models. Right. 640 00:33:03,160 --> 00:33:05,440 Speaker 11: I'm here in New York to go to the generative 641 00:33:05,480 --> 00:33:09,480 Speaker 11: AI scaling event that Bloomberg is hosting, and it's all 642 00:33:09,520 --> 00:33:14,120 Speaker 11: about this like inference time. Compute. Inference time requires a 643 00:33:14,200 --> 00:33:19,040 Speaker 11: tremendous amount of compute, and so we're still just scratching 644 00:33:19,040 --> 00:33:22,440 Speaker 11: the surface of how much compute is really needed for 645 00:33:22,560 --> 00:33:24,600 Speaker 11: this extra level of intelligence. 646 00:33:24,720 --> 00:33:26,960 Speaker 3: And do you play in the inference and the LM 647 00:33:27,240 --> 00:33:30,240 Speaker 3: space like the training and the usage you do both? 648 00:33:30,480 --> 00:33:30,760 Speaker 4: Yes? 649 00:33:30,840 --> 00:33:33,760 Speaker 11: Okay, yes, so cerebversus in both training and inference. 650 00:33:34,000 --> 00:33:38,320 Speaker 3: What is has demand for your products changed at all 651 00:33:38,440 --> 00:33:40,760 Speaker 3: since deep Seed came out in terms of pricing or 652 00:33:40,800 --> 00:33:41,720 Speaker 3: in terms of demand. 653 00:33:42,120 --> 00:33:44,920 Speaker 11: Yeah, I mean deep Seak happened in January and it 654 00:33:45,000 --> 00:33:48,720 Speaker 11: was such a big moment for the industry and we 655 00:33:49,120 --> 00:33:52,200 Speaker 11: immediately within forty eight hours of the news that big day, 656 00:33:52,680 --> 00:33:55,680 Speaker 11: we added deep Seek to our catalog of models. And 657 00:33:55,720 --> 00:33:59,000 Speaker 11: so what we do is we offer Lama models, Deep 658 00:33:59,040 --> 00:34:02,400 Speaker 11: Seek models, and other kinds of models to the developer 659 00:34:02,440 --> 00:34:06,960 Speaker 11: community right in our cloud. And our differentiation from GPUs 660 00:34:07,040 --> 00:34:10,360 Speaker 11: is that we're like twenty to seventy times faster in 661 00:34:10,480 --> 00:34:14,440 Speaker 11: terms of like that response time when you put in 662 00:34:14,480 --> 00:34:17,960 Speaker 11: a query. Our responses are about twenty to seventy times 663 00:34:18,000 --> 00:34:21,880 Speaker 11: faster because of the architecture being bigger, So deep Seek 664 00:34:22,000 --> 00:34:25,640 Speaker 11: really created this moment of developers coming at us saying, 665 00:34:25,719 --> 00:34:28,000 Speaker 11: oh my god, you guys have the fastest deep Seek. 666 00:34:28,040 --> 00:34:30,279 Speaker 11: We want to you know, we want we want that, 667 00:34:30,719 --> 00:34:32,960 Speaker 11: we want to see what that can do. And that's 668 00:34:33,000 --> 00:34:35,920 Speaker 11: led to a lot of prototyping. But what we do 669 00:34:35,960 --> 00:34:38,000 Speaker 11: see is that when it comes to developers that are 670 00:34:38,000 --> 00:34:41,960 Speaker 11: building AI businesses, there's a lot more usage of Lama. 671 00:34:42,120 --> 00:34:46,240 Speaker 11: So Meta Lama is probably still the most popular open 672 00:34:46,280 --> 00:34:48,880 Speaker 11: source AI model that is downloaded at least from the 673 00:34:48,920 --> 00:34:52,520 Speaker 11: Cerebraus cloud. And then we have customers like Perplexity and 674 00:34:52,640 --> 00:34:55,959 Speaker 11: mistral Alpha Sense based here in New York that are 675 00:34:56,040 --> 00:34:59,680 Speaker 11: kind of serving their adapted Lama models as well as 676 00:34:59,719 --> 00:35:01,040 Speaker 11: their own custom models. 677 00:35:01,320 --> 00:35:06,279 Speaker 2: So the deep Seak issue for the industry was, Hey, 678 00:35:06,320 --> 00:35:11,800 Speaker 2: here's this Chinese company coming out with an AI solution 679 00:35:11,920 --> 00:35:15,840 Speaker 2: at a much lower cost. Is that good or bad 680 00:35:15,960 --> 00:35:18,960 Speaker 2: or neither good or bad for the AI evolution? 681 00:35:19,440 --> 00:35:19,640 Speaker 7: Oh? 682 00:35:19,719 --> 00:35:23,440 Speaker 11: I think it's very good because deep Seek was a 683 00:35:23,520 --> 00:35:26,160 Speaker 11: state of the art model when it came out. It's 684 00:35:26,200 --> 00:35:28,840 Speaker 11: still one of the best models in the world, especially 685 00:35:28,880 --> 00:35:32,000 Speaker 11: I think with their at V three. And what it 686 00:35:32,160 --> 00:35:35,720 Speaker 11: proves is that when you can open source this level 687 00:35:35,760 --> 00:35:40,400 Speaker 11: of intelligence, You're bringing down the cost to developers and 688 00:35:40,480 --> 00:35:45,040 Speaker 11: it increases developer creativity. And so one of the things 689 00:35:45,040 --> 00:35:48,319 Speaker 11: that Cerebras is super passionate about is providing developers with 690 00:35:48,400 --> 00:35:52,680 Speaker 11: the best models open source or custom you know, fastest 691 00:35:52,800 --> 00:35:55,480 Speaker 11: at the best price. And so we were just very 692 00:35:55,480 --> 00:35:59,080 Speaker 11: excited when Deepseak open sourced, and clearly like the developers 693 00:35:59,080 --> 00:36:01,520 Speaker 11: were very excited as well. It was definitely kind of 694 00:36:01,520 --> 00:36:03,200 Speaker 11: an inflection moment. 695 00:36:03,360 --> 00:36:06,279 Speaker 3: In terms of what you're most excited about right now, Like, 696 00:36:06,280 --> 00:36:09,359 Speaker 3: what's the coolest use case you've seen when it comes 697 00:36:09,400 --> 00:36:11,160 Speaker 3: to training and when it comes to in fronts. 698 00:36:11,520 --> 00:36:14,279 Speaker 11: Fantastic question. So for training, I'm going to bring up 699 00:36:14,320 --> 00:36:19,239 Speaker 11: a healthcare use case. So one of our favorite partners, 700 00:36:19,360 --> 00:36:22,040 Speaker 11: and you know, we shouldn't have favorites, but I really 701 00:36:22,080 --> 00:36:25,680 Speaker 11: appreciate the work that Mayo Clinic has done in Cerebras. 702 00:36:26,000 --> 00:36:29,640 Speaker 11: They have been able to train world leading foundation model 703 00:36:29,840 --> 00:36:34,480 Speaker 11: using genomic data, and the point of their genomic model 704 00:36:34,600 --> 00:36:38,440 Speaker 11: is to help patients of rheumatoid arthritis. I have a 705 00:36:38,480 --> 00:36:41,040 Speaker 11: family member who's been struggling with this terrible disease for 706 00:36:41,080 --> 00:36:45,000 Speaker 11: a decade, and this AI model can help her find 707 00:36:45,040 --> 00:36:49,760 Speaker 11: the medicine that actually works much more quickly. I didn't 708 00:36:49,800 --> 00:36:55,080 Speaker 11: realize that actually rheumatoid arthritis generally forty percent fail rate 709 00:36:55,400 --> 00:36:58,280 Speaker 11: in terms of aligning the right medicine to the patient. 710 00:36:58,680 --> 00:37:01,279 Speaker 11: And so really proud of the work that the mail 711 00:37:01,320 --> 00:37:04,960 Speaker 11: Clinic team has done in partnership with Cerebris, training that 712 00:37:05,080 --> 00:37:08,680 Speaker 11: model super fast on our chip, releasing it in probably 713 00:37:08,760 --> 00:37:11,239 Speaker 11: less than nine months of development time. Wow on the 714 00:37:11,280 --> 00:37:14,719 Speaker 11: training side. On the inference side, I'm very excited to 715 00:37:14,760 --> 00:37:17,920 Speaker 11: be working with companies like Perplexity as well as Mistral. 716 00:37:18,239 --> 00:37:22,000 Speaker 11: These are some very very AI forward companies leading the 717 00:37:22,080 --> 00:37:26,839 Speaker 11: way in terms of disruptive search experiences for consumers and 718 00:37:27,640 --> 00:37:32,680 Speaker 11: amazing chat assistance, especially in Europe, and we're powering their 719 00:37:32,760 --> 00:37:33,960 Speaker 11: super fast results. 720 00:37:34,520 --> 00:37:37,719 Speaker 2: Your company, are you seeing any impacts of just the 721 00:37:37,840 --> 00:37:41,080 Speaker 2: uncertainty surrounding how this TWERFF policy will evolve? Is it 722 00:37:41,320 --> 00:37:43,200 Speaker 2: and your customers saying, you know, we're just going to 723 00:37:43,239 --> 00:37:44,920 Speaker 2: wait a little bit before we place an order or 724 00:37:44,920 --> 00:37:45,480 Speaker 2: something like that. 725 00:37:45,640 --> 00:37:49,320 Speaker 11: No, Actually, it's really all systems go Okay. Our customers 726 00:37:49,400 --> 00:37:52,640 Speaker 11: want the fastest inference speeds, they want the smartest models 727 00:37:52,640 --> 00:37:56,560 Speaker 11: in their applications. We're not waiting to find out. There's 728 00:37:56,600 --> 00:37:59,000 Speaker 11: no time to wait. We have to deliver the fastest 729 00:37:59,000 --> 00:38:00,640 Speaker 11: compute to the best Custos commers in the world. 730 00:38:01,120 --> 00:38:04,000 Speaker 3: Our thanks to Julie Choi, CMO of Sarah Bross. 731 00:38:04,600 --> 00:38:09,319 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 732 00:38:09,480 --> 00:38:13,440 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 733 00:38:13,680 --> 00:38:16,960 Speaker 1: ten am to noon Eastern on Bloomberg dot com, the 734 00:38:17,040 --> 00:38:20,880 Speaker 1: iHeartRadio app tune In, and the Bloomberg Business app. You 735 00:38:20,920 --> 00:38:24,200 Speaker 1: can also watch us live every weekday on YouTube and 736 00:38:24,440 --> 00:38:26,400 Speaker 1: always on the Bloomberg terminal