1 00:00:02,720 --> 00:00:10,559 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:17,840 Speaker 1: Eastern on Apple, Cocklay and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,040 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,480 Speaker 1: or watch us live on YouTube. 6 00:00:24,160 --> 00:00:26,360 Speaker 2: Let's go back to the tech sector. We can't stray 7 00:00:26,440 --> 00:00:28,080 Speaker 2: too far from it ever, when we talk about the 8 00:00:28,160 --> 00:00:30,320 Speaker 2: market and take a look at some of the mag 9 00:00:30,400 --> 00:00:34,200 Speaker 2: seven names. Alphabet this week has moved ahead of Apple 10 00:00:34,240 --> 00:00:36,960 Speaker 2: in terms of market cap, and Met us signing this big, 11 00:00:37,040 --> 00:00:42,519 Speaker 2: big power generation deal with this company called Vistra, which 12 00:00:42,560 --> 00:00:44,320 Speaker 2: has had a really futile week. Let's bring in Man 13 00:00:44,320 --> 00:00:46,400 Speaker 2: Deep sing right now. Man Deep is our global tech 14 00:00:46,440 --> 00:00:48,839 Speaker 2: research head at Bloomberg Intelligence, and Man Deep just put 15 00:00:48,880 --> 00:00:52,639 Speaker 2: into context for us this multi gigawatt nuclear deal that 16 00:00:52,760 --> 00:00:55,400 Speaker 2: Meta has signed for its AI data centers. How should 17 00:00:55,400 --> 00:00:56,120 Speaker 2: we think about this? 18 00:00:57,560 --> 00:00:59,680 Speaker 3: Yeah, I mean, the best way to frame it is 19 00:00:59,800 --> 00:01:02,960 Speaker 3: how how these companies are spending their capex? And we 20 00:01:03,160 --> 00:01:07,440 Speaker 3: know roughly, you know, one gigawa of AI data center 21 00:01:07,680 --> 00:01:12,319 Speaker 3: capacity costs about fifty billion dollars. Again depends on the region, 22 00:01:12,480 --> 00:01:15,720 Speaker 3: the source of energy and land et cetera. But that's 23 00:01:15,760 --> 00:01:18,720 Speaker 3: the rough ballpark. So when you think about six point 24 00:01:18,800 --> 00:01:22,640 Speaker 3: six gigawat, and we already know open Ai has committed 25 00:01:22,840 --> 00:01:26,640 Speaker 3: to you know, about twenty six gigawatts, So clearly these 26 00:01:26,680 --> 00:01:30,399 Speaker 3: companies have big ambitions. And what it suggests is a 27 00:01:30,480 --> 00:01:33,880 Speaker 3: company like Meta, which will very well spend over one 28 00:01:33,959 --> 00:01:37,720 Speaker 3: hundred billion dollars in capex this year in twenty twenty six, 29 00:01:38,319 --> 00:01:40,920 Speaker 3: may stay on that path for at least the next 30 00:01:40,959 --> 00:01:45,200 Speaker 3: three to four years because they believe, you know, they 31 00:01:45,240 --> 00:01:48,280 Speaker 3: have a lot of applications when it comes to their 32 00:01:48,320 --> 00:01:52,680 Speaker 3: own consumption of AI data centers, and they seem to 33 00:01:52,680 --> 00:01:55,680 Speaker 3: be confident about their own model, which has so far 34 00:01:55,800 --> 00:01:59,200 Speaker 3: trailed the likes of open Ai and Tropic and Gemini 35 00:01:59,240 --> 00:02:02,560 Speaker 3: in terms of abilities. But it sounds like they want 36 00:02:02,600 --> 00:02:05,440 Speaker 3: to make sure they have the capacity to deploy AI. 37 00:02:05,600 --> 00:02:09,160 Speaker 3: And that's where nuclear is an interesting choice because a 38 00:02:09,200 --> 00:02:13,040 Speaker 3: lot of the other hyperscalers have gone for more natural 39 00:02:13,080 --> 00:02:16,040 Speaker 3: gas turbines, but we know there is a big backlog 40 00:02:16,120 --> 00:02:19,959 Speaker 3: with someone like ge Vernola for their natural gas turbines, 41 00:02:20,040 --> 00:02:24,200 Speaker 3: So from that perspective, nuclear is an interesting choice, you know, 42 00:02:24,240 --> 00:02:24,960 Speaker 3: as an alternate. 43 00:02:25,800 --> 00:02:29,079 Speaker 4: What are the big tech companies, these big AI companies 44 00:02:29,160 --> 00:02:33,440 Speaker 4: saying about their confidence in the reliability of power in 45 00:02:33,480 --> 00:02:36,400 Speaker 4: the next five, ten, twenty years, because a lot of 46 00:02:36,440 --> 00:02:40,079 Speaker 4: folks are saying that really could be the gating issue 47 00:02:40,120 --> 00:02:42,960 Speaker 4: for the development and evolution of AI. 48 00:02:44,480 --> 00:02:47,960 Speaker 3: Yeah, I mean, look, I was at CEES where Jensen highlighted, 49 00:02:48,200 --> 00:02:52,000 Speaker 3: you know, the reason why companies would upgrade quickly to 50 00:02:52,120 --> 00:02:56,959 Speaker 3: the latest Rubin architecture is because they give I mean, 51 00:02:57,080 --> 00:03:01,360 Speaker 3: Ruben will give them more tokens per unit of power, 52 00:03:01,400 --> 00:03:05,320 Speaker 3: which is really a way to emphasize the efficiency of 53 00:03:05,480 --> 00:03:10,120 Speaker 3: how you utilize your available power. And so from that perspective, 54 00:03:10,600 --> 00:03:13,960 Speaker 3: everyone sees very long lead times when it comes to 55 00:03:14,080 --> 00:03:17,680 Speaker 3: adding new power, and they want to maximize, you know, 56 00:03:17,720 --> 00:03:21,160 Speaker 3: the usage and utilization of whatever they have right now. 57 00:03:21,520 --> 00:03:25,360 Speaker 3: And look, you could argue, you know, there are some 58 00:03:25,680 --> 00:03:29,200 Speaker 3: other sources which may have shorter lead times, like solar 59 00:03:29,760 --> 00:03:33,560 Speaker 3: or battery packs. But in this case, given the size 60 00:03:33,639 --> 00:03:37,040 Speaker 3: of power that these companies need for running we're talking 61 00:03:37,080 --> 00:03:40,720 Speaker 3: about one gigawatt data center, it's very hard to think 62 00:03:40,720 --> 00:03:44,640 Speaker 3: about too many sources of energy that will give you 63 00:03:44,760 --> 00:03:47,200 Speaker 3: that sort of power, and you know that's where the 64 00:03:47,280 --> 00:03:48,400 Speaker 3: lead times are so long. 65 00:03:50,760 --> 00:03:53,840 Speaker 5: Stay with us. More from Bloomberg Intelligence coming up after this. 66 00:03:57,480 --> 00:04:01,160 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 67 00:04:01,240 --> 00:04:04,320 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 68 00:04:04,360 --> 00:04:07,640 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 69 00:04:07,720 --> 00:04:11,280 Speaker 1: you get your podcasts, or watch us live on YouTube. 70 00:04:11,560 --> 00:04:12,960 Speaker 5: Let's get to the restaurant business here. 71 00:04:13,000 --> 00:04:15,600 Speaker 4: You know, the flu season which is upon us, and 72 00:04:15,600 --> 00:04:18,479 Speaker 4: it's you know, a little bit more harsh than average. 73 00:04:18,720 --> 00:04:20,080 Speaker 5: It's impacting a lot of folks. 74 00:04:20,160 --> 00:04:23,719 Speaker 4: Obviously, it's also impacting some businesses, like the restaurant business. 75 00:04:23,839 --> 00:04:24,719 Speaker 5: I didn't think of that. 76 00:04:24,760 --> 00:04:27,360 Speaker 4: Michael Halen, He thinks of it. Senior restaurant and food 77 00:04:27,360 --> 00:04:31,599 Speaker 4: service analysts for Bloomberg Intelligence Mike talk to us about 78 00:04:32,000 --> 00:04:34,320 Speaker 4: recent trends in the restaurant bizz. 79 00:04:36,320 --> 00:04:39,600 Speaker 6: Yeah, it was a tough fourth quarter, you know, especially December. 80 00:04:39,640 --> 00:04:44,599 Speaker 6: December was hurt by cold weather, snow, and an earlier 81 00:04:44,720 --> 00:04:49,440 Speaker 6: flu season. It's the worst flu season so far in 82 00:04:49,520 --> 00:04:53,800 Speaker 6: twenty five years. This mad some issues with you know, 83 00:04:53,839 --> 00:04:57,440 Speaker 6: the vaccines not covering the current trains that. 84 00:04:57,360 --> 00:05:00,840 Speaker 5: Are out there, and so was pretty rough. 85 00:05:00,960 --> 00:05:05,320 Speaker 6: You know. Quick service and fast casual did better. Quick 86 00:05:05,360 --> 00:05:09,360 Speaker 6: service sales were down slightly. You know, fast food chains 87 00:05:09,360 --> 00:05:12,839 Speaker 6: like McDonald's and were as fast casual like Chipotle, Shakeshack, 88 00:05:12,880 --> 00:05:16,400 Speaker 6: they were up slightly in December. It was the full 89 00:05:16,440 --> 00:05:20,280 Speaker 6: service chain, so casual diners like Chili's and family dining 90 00:05:20,760 --> 00:05:23,320 Speaker 6: like ihop that struggled the most, Which makes sense. If 91 00:05:23,360 --> 00:05:26,000 Speaker 6: it's really cold or if it's snowing, you're less likely 92 00:05:26,040 --> 00:05:29,240 Speaker 6: to go out to a restaurant and dine in. You're 93 00:05:29,240 --> 00:05:35,400 Speaker 6: more likely to order dominoes, and so it all kinds 94 00:05:35,400 --> 00:05:38,640 Speaker 6: of makes sense. But you know, these trends are gonna 95 00:05:38,680 --> 00:05:42,680 Speaker 6: flip pretty pretty nicely into January. We've seen a huge 96 00:05:42,680 --> 00:05:47,240 Speaker 6: squeeze in restaurant stocks to open up this year, and 97 00:05:47,279 --> 00:05:48,479 Speaker 6: we think it's going to continue. 98 00:05:49,760 --> 00:05:53,000 Speaker 2: So what do these casual dining chains do to change 99 00:05:53,000 --> 00:05:55,840 Speaker 2: the trajectory of what seems like a pretty stet trend 100 00:05:55,880 --> 00:05:56,400 Speaker 2: at this point. 101 00:05:57,520 --> 00:06:00,680 Speaker 6: Well, the casual dining chains had a really nice year 102 00:06:00,839 --> 00:06:01,960 Speaker 6: in twenty twenty five. 103 00:06:03,600 --> 00:06:04,880 Speaker 5: We you know, they have. 104 00:06:05,000 --> 00:06:09,760 Speaker 6: But because of that, they have tougher comparisons to LAP right, 105 00:06:09,800 --> 00:06:12,719 Speaker 6: and so for that reason, we think, you know, and 106 00:06:12,800 --> 00:06:17,440 Speaker 6: we think USR stands to benefit more from from tax 107 00:06:17,440 --> 00:06:21,479 Speaker 6: reform as well as like a ten percent ish decline in. 108 00:06:21,400 --> 00:06:24,400 Speaker 5: Oil prices, So we don't expect a. 109 00:06:24,360 --> 00:06:27,799 Speaker 6: Bad year at a casual dining. We think they'll continue 110 00:06:28,000 --> 00:06:31,200 Speaker 6: to do pretty well. They're gonna you know, they're not 111 00:06:31,200 --> 00:06:33,080 Speaker 6: gonna receive as much of a boost. 112 00:06:32,839 --> 00:06:34,520 Speaker 5: From Chili's, which had a, you. 113 00:06:34,400 --> 00:06:38,560 Speaker 6: Know, an unbelievable year in twenty twenty five. But we 114 00:06:38,640 --> 00:06:42,120 Speaker 6: think casual dining can have a solid year here in 115 00:06:42,200 --> 00:06:44,880 Speaker 6: twenty six. But it's the casual dining names and some 116 00:06:44,920 --> 00:06:48,000 Speaker 6: of the fast casual names we cover, like Cava and Winkstop, 117 00:06:48,040 --> 00:06:50,320 Speaker 6: that we think can have a really nice bounce here, 118 00:06:50,600 --> 00:06:52,760 Speaker 6: especially in the first half of twenty twenty six. 119 00:06:54,160 --> 00:06:56,160 Speaker 4: Mike, we got some labor data, takes some jobs data, 120 00:06:56,320 --> 00:06:58,200 Speaker 4: unemployment rate ticks down a little bit here. 121 00:06:58,320 --> 00:07:00,599 Speaker 5: I got to think that's that's important for US companies. 122 00:07:02,400 --> 00:07:07,720 Speaker 6: Yeah, you know, labor has been tough for restaurants. You know, 123 00:07:08,000 --> 00:07:12,520 Speaker 6: we've seen a four percent ish wage rate inflation in 124 00:07:12,600 --> 00:07:16,040 Speaker 6: our in this industry, you know, going back to before 125 00:07:16,560 --> 00:07:22,760 Speaker 6: the pandemic, you know, so labor continues to be kind 126 00:07:22,800 --> 00:07:25,640 Speaker 6: of an issue. There's still some talk about restaurants being 127 00:07:26,600 --> 00:07:32,440 Speaker 6: understaffed right now, so that obviously impacts service levels negatively 128 00:07:32,480 --> 00:07:37,240 Speaker 6: and hurts the customer experience. So obviously something we look 129 00:07:37,280 --> 00:07:38,520 Speaker 6: at pretty closely. 130 00:07:39,960 --> 00:07:42,200 Speaker 2: In terms of names that you like, and I know 131 00:07:42,240 --> 00:07:44,800 Speaker 2: we don't do buy hold cell recommendations at Bloomberg Intelligence, 132 00:07:44,880 --> 00:07:48,160 Speaker 2: but the kinds of companies that are best positioned in 133 00:07:48,240 --> 00:07:50,320 Speaker 2: twenty twenty six. What are you looking for? 134 00:07:52,480 --> 00:07:55,080 Speaker 6: Oh yeah, so you know, for us, we're looking at 135 00:07:55,320 --> 00:07:59,080 Speaker 6: chains that we think can outperform you know, same store 136 00:07:59,160 --> 00:08:01,760 Speaker 6: sales estimates on the street with some chains that we think, 137 00:08:03,760 --> 00:08:06,600 Speaker 6: you know, other analysts are just not bullish enough on. 138 00:08:06,960 --> 00:08:09,880 Speaker 6: You know, McDonald's is coming up against some really easy 139 00:08:10,280 --> 00:08:13,440 Speaker 6: comparisons due to e Coli once they report the four 140 00:08:13,520 --> 00:08:16,880 Speaker 6: Q and the one Q, Winstock, Brinker. These are some 141 00:08:16,960 --> 00:08:19,040 Speaker 6: of the names that we've written about. 142 00:08:19,680 --> 00:08:22,840 Speaker 5: Stay with us. More from Bloomberg Intelligence coming up after this. 143 00:08:26,680 --> 00:08:30,360 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 144 00:08:30,440 --> 00:08:33,559 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 145 00:08:33,559 --> 00:08:36,880 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 146 00:08:36,920 --> 00:08:40,240 Speaker 1: you get your podcasts, or watch us live on YouTube. 147 00:08:40,840 --> 00:08:45,640 Speaker 4: By this, transition to electric vehicles is proving very costly 148 00:08:45,760 --> 00:08:48,679 Speaker 4: for automakers around the world. We had the big twenty 149 00:08:48,679 --> 00:08:50,439 Speaker 4: plus billion dollar right off from four to a couple 150 00:08:50,480 --> 00:08:53,120 Speaker 4: of weeks ago. Now General Motors will take another six 151 00:08:53,200 --> 00:08:57,000 Speaker 4: billion dollars in charges tied to production cutbacks. 152 00:08:56,559 --> 00:08:58,960 Speaker 5: In its electric vehicle and battery operations. 153 00:08:59,040 --> 00:09:02,280 Speaker 4: Let's break it down with Craig Trudell, gloom Global Autos 154 00:09:02,400 --> 00:09:07,360 Speaker 4: editor for Bloomberg News. He joins us from our London office. Craig, 155 00:09:07,880 --> 00:09:11,119 Speaker 4: what's GM telling us here about kind of their transition 156 00:09:11,840 --> 00:09:12,480 Speaker 4: to evs. 157 00:09:14,640 --> 00:09:14,880 Speaker 1: Yeah. 158 00:09:15,200 --> 00:09:18,000 Speaker 7: I think, you know, this is like a sort of 159 00:09:18,120 --> 00:09:22,120 Speaker 7: ongoing toll that they're reporting, and I don't know that 160 00:09:22,160 --> 00:09:27,080 Speaker 7: they're necessarily done. We heard them report actually some months 161 00:09:27,120 --> 00:09:31,319 Speaker 7: back that you know, sort of rethinking their production capacity 162 00:09:31,559 --> 00:09:36,160 Speaker 7: and pairing that back in response to the way electric 163 00:09:36,280 --> 00:09:39,840 Speaker 7: vehicle demand and you know, battery supply was shaking out, 164 00:09:40,520 --> 00:09:42,000 Speaker 7: that that was going to set them back more than 165 00:09:42,040 --> 00:09:44,720 Speaker 7: a billion dollars. So this is you know, an incremental 166 00:09:44,760 --> 00:09:47,400 Speaker 7: amount of money on top of that, and there was 167 00:09:47,400 --> 00:09:50,560 Speaker 7: some warning in the AK last night that actually, you know, 168 00:09:50,600 --> 00:09:52,640 Speaker 7: there may be more to come. So you know, I 169 00:09:52,640 --> 00:09:55,640 Speaker 7: think this is part of a broader sort of rationalization 170 00:09:55,920 --> 00:09:59,000 Speaker 7: taking place in the US, where you know, an industry 171 00:09:59,200 --> 00:10:03,240 Speaker 7: was trying to to respond to an administration that was 172 00:10:03,800 --> 00:10:05,719 Speaker 7: you know, banging the drum in a much different way 173 00:10:05,760 --> 00:10:07,360 Speaker 7: than the Trump administration is now. 174 00:10:07,600 --> 00:10:09,800 Speaker 4: You know, I look at this trailing twelve month performance 175 00:10:09,840 --> 00:10:12,680 Speaker 4: of General Motors. It's up sixty one percent, and that 176 00:10:12,760 --> 00:10:15,360 Speaker 4: kind of tells me you can take as many charges 177 00:10:15,400 --> 00:10:17,679 Speaker 4: as you want here, I want you to pair back 178 00:10:18,240 --> 00:10:21,880 Speaker 4: your transitions to ev Is that kind of what you're hearing. 179 00:10:22,800 --> 00:10:25,319 Speaker 7: You know, it's it's fascinating because on one hand, you're 180 00:10:25,360 --> 00:10:30,120 Speaker 7: seeing you know, electric car leader or at least former 181 00:10:30,160 --> 00:10:34,760 Speaker 7: electric car leader Tesla taking off and doing so in 182 00:10:34,800 --> 00:10:36,920 Speaker 7: spite of the fact that their sales are slowing down. 183 00:10:37,200 --> 00:10:40,120 Speaker 7: The outlook for EVS and their home market is pretty bleak. 184 00:10:40,520 --> 00:10:44,319 Speaker 7: And yet you also in Unison, have you know, sort 185 00:10:44,360 --> 00:10:48,040 Speaker 7: of the contrarian play taking off in GM. I think, 186 00:10:48,280 --> 00:10:50,480 Speaker 7: you know, it's maybe not quite that simple, and that 187 00:10:51,040 --> 00:10:54,360 Speaker 7: you know, GM does have I think David Welch's story 188 00:10:54,400 --> 00:10:57,120 Speaker 7: on last night the News does have, you know, sort 189 00:10:57,160 --> 00:10:59,400 Speaker 7: of the context that they have quite quite a lot 190 00:10:59,400 --> 00:11:02,920 Speaker 7: of electric vehicles available. They're going to continue to have 191 00:11:03,200 --> 00:11:06,840 Speaker 7: a pretty broad range of cars, and Mary Barrow has 192 00:11:06,880 --> 00:11:10,679 Speaker 7: talked about how EVS is their quote north star and 193 00:11:10,720 --> 00:11:13,120 Speaker 7: that that's not going to change. So I think there's 194 00:11:13,160 --> 00:11:15,320 Speaker 7: going to be a little bit more stick toitiveness on 195 00:11:15,440 --> 00:11:18,920 Speaker 7: GM's part than Ford, and yet I think this is 196 00:11:19,000 --> 00:11:21,600 Speaker 7: also an indication that the market is saying, you know what, 197 00:11:21,960 --> 00:11:24,280 Speaker 7: the way this company makes money is it's full sized 198 00:11:24,280 --> 00:11:26,679 Speaker 7: pickups and SUVs. The more of those that they can 199 00:11:26,720 --> 00:11:29,880 Speaker 7: make in this new paradigm, the merrier. 200 00:11:30,360 --> 00:11:34,560 Speaker 4: So I guess give us a sense of In the US, 201 00:11:34,600 --> 00:11:36,520 Speaker 4: I think we have an idea that this transition to 202 00:11:36,559 --> 00:11:39,800 Speaker 4: EV's is going to take longer than maybe we we 203 00:11:39,840 --> 00:11:42,839 Speaker 4: initially thought that's not the case, it seems like in Europe. 204 00:11:42,880 --> 00:11:44,920 Speaker 5: Tell us how the adoption is going in Europe. 205 00:11:46,120 --> 00:11:50,239 Speaker 7: Yeah, I think actually last year it may be surprising 206 00:11:50,240 --> 00:11:52,520 Speaker 7: to people because we ended the year with these headlines 207 00:11:52,559 --> 00:11:56,120 Speaker 7: about the EUS sort of backing off of its combustion 208 00:11:56,240 --> 00:12:00,360 Speaker 7: engine PAN for twenty thirty five. That being said, we 209 00:12:00,400 --> 00:12:03,720 Speaker 7: actually had a nice pickup in momentum for electric vehicles 210 00:12:03,800 --> 00:12:06,000 Speaker 7: last year. A lot of that was driven by the 211 00:12:06,040 --> 00:12:08,960 Speaker 7: Chinese manufacturers. I think that is part of where the 212 00:12:09,000 --> 00:12:13,000 Speaker 7: concern is here that you know, the the you know, 213 00:12:13,160 --> 00:12:17,120 Speaker 7: local manufacturers in Europe. Some of them are doing better 214 00:12:17,440 --> 00:12:20,160 Speaker 7: than they were, but it's it is patchy. I think 215 00:12:20,200 --> 00:12:23,480 Speaker 7: it's also patchy, you know, sort of country by country, 216 00:12:23,520 --> 00:12:26,240 Speaker 7: but we did see, you know, a roughly thirty percent 217 00:12:26,360 --> 00:12:30,360 Speaker 7: increase in battery electric vehicles sales last year in spite 218 00:12:30,360 --> 00:12:32,400 Speaker 7: of the fact that a big player in Tesla had 219 00:12:32,880 --> 00:12:34,320 Speaker 7: a really tough twenty twenty five. 220 00:12:34,760 --> 00:12:37,079 Speaker 5: That's interesting, you know, talk to those about hybrids here. 221 00:12:37,120 --> 00:12:39,440 Speaker 4: I just caved at least a hybrid form my number 222 00:12:39,480 --> 00:12:41,959 Speaker 4: four offspring because he goes to school in California where 223 00:12:41,960 --> 00:12:44,280 Speaker 4: the gas prices are just ridiculously high. 224 00:12:44,520 --> 00:12:46,440 Speaker 5: A the technology seemed really cool to me. 225 00:12:46,520 --> 00:12:49,839 Speaker 4: The car drove great, and it seems like a nice 226 00:12:50,600 --> 00:12:53,920 Speaker 4: mix between or a nice compromise between going full EV 227 00:12:54,160 --> 00:12:55,079 Speaker 4: and full ice. 228 00:12:55,480 --> 00:12:56,199 Speaker 5: How do you think about it? 229 00:12:57,000 --> 00:12:59,719 Speaker 7: Yeah, I mean this was the supposed bridge technology, and 230 00:13:00,120 --> 00:13:03,280 Speaker 7: the bridge is going for a lot longer than I think. 231 00:13:03,280 --> 00:13:06,560 Speaker 7: A lot of people reckoned, you know, I remember, uh, 232 00:13:06,760 --> 00:13:10,200 Speaker 7: you know, uh covering Toyota being based in Japan for 233 00:13:10,240 --> 00:13:12,760 Speaker 7: a few years. Uh, you know, Toyota taking a lot 234 00:13:12,760 --> 00:13:15,920 Speaker 7: of heat for sort of clinging to uh. You know, 235 00:13:16,000 --> 00:13:18,960 Speaker 7: this technology that it's sort of pioneered with the Prius 236 00:13:19,280 --> 00:13:21,920 Speaker 7: and you know, not sort of jumping with both feet 237 00:13:22,000 --> 00:13:25,439 Speaker 7: in fully electric vehicles that has very much turned out 238 00:13:25,480 --> 00:13:28,480 Speaker 7: to be to be the play and I think we're seeing, uh, 239 00:13:28,520 --> 00:13:31,160 Speaker 7: you know, a lot of the excess battery capacity that 240 00:13:31,200 --> 00:13:34,920 Speaker 7: got built in response to Biden administration's push for uh, 241 00:13:34,960 --> 00:13:37,360 Speaker 7: you know, more of a battery uh you know sector 242 00:13:37,400 --> 00:13:39,120 Speaker 7: to be built in the US. A lot of that 243 00:13:39,240 --> 00:13:41,480 Speaker 7: is going to get soaked up by these hybrids, whether 244 00:13:41,840 --> 00:13:45,000 Speaker 7: they're whether they're plug in vehicles or not. If you 245 00:13:45,000 --> 00:13:48,439 Speaker 7: can make you know, the the bigger pickups and SUVs 246 00:13:48,480 --> 00:13:51,240 Speaker 7: that are already popular in the US more efficient, you 247 00:13:51,320 --> 00:13:53,080 Speaker 7: get sort of the both the best of both worlds. 248 00:13:53,200 --> 00:13:55,520 Speaker 7: Even if there's a little bit of incremental costs on 249 00:13:55,559 --> 00:13:58,120 Speaker 7: the front end, these vehicles are going to be much 250 00:13:58,200 --> 00:14:01,960 Speaker 7: cheaper to to refuel and and you know, better to run. 251 00:14:02,920 --> 00:14:07,599 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 252 00:14:07,800 --> 00:14:11,280 Speaker 1: and anywhere else you get your podcasts. Listen live each 253 00:14:11,280 --> 00:14:15,040 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 254 00:14:15,160 --> 00:14:18,720 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 255 00:14:19,120 --> 00:14:22,080 Speaker 1: You can also watch us live every weekday on YouTube 256 00:14:22,440 --> 00:14:24,720 Speaker 1: and always on the Bloomberg terminal