1 00:00:02,759 --> 00:00:11,760 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,880 --> 00:00:13,000 Speaker 1: with Paul Sweeney. 3 00:00:13,119 --> 00:00:16,360 Speaker 2: The real app performance has been the US corporate high yield. 4 00:00:16,440 --> 00:00:20,079 Speaker 2: These are two big time blue chip companies. One person's 5 00:00:20,120 --> 00:00:23,840 Speaker 2: cast is another person's animal spirits breaking market. 6 00:00:23,520 --> 00:00:26,320 Speaker 1: Headlines and corporate news from across the globe. 7 00:00:26,360 --> 00:00:28,440 Speaker 3: Our view is, if the economy is slowing down. 8 00:00:28,400 --> 00:00:30,560 Speaker 1: There is the possibility of the debt spirals. 9 00:00:30,640 --> 00:00:33,160 Speaker 4: Both putum competing and AI are going to power the future. 10 00:00:33,240 --> 00:00:36,080 Speaker 2: People are just buying everything with tax Bloomberg. 11 00:00:35,680 --> 00:00:40,240 Speaker 1: Intelligence with Paul Sweeney on Bloomberg Radio, YouTube and Bloomberg 12 00:00:40,280 --> 00:00:42,640 Speaker 1: Originals on Paul. 13 00:00:42,479 --> 00:00:45,920 Speaker 4: Sweeney and I'm Lis Matteo filling in on Bloomberg Intelligence. 14 00:00:46,000 --> 00:00:48,000 Speaker 2: On today's show, we dig inside the big business stories 15 00:00:48,040 --> 00:00:50,680 Speaker 2: impacting Wall Street and the global markets. Each and every 16 00:00:50,680 --> 00:00:52,680 Speaker 2: week we provide in dept with research and date on 17 00:00:52,720 --> 00:00:54,520 Speaker 2: some of the two thousand companies in one hundred and 18 00:00:54,520 --> 00:00:57,840 Speaker 2: thirty industries our animals cover worldwide. Today, we'll look at 19 00:00:57,880 --> 00:01:01,680 Speaker 2: Bloomberg Intelligence deep dive into aiding apps and how gen 20 00:01:01,800 --> 00:01:02,959 Speaker 2: Z is using them. 21 00:01:02,840 --> 00:01:05,800 Speaker 4: Plus a look at how Trump administration policies are impacting 22 00:01:05,840 --> 00:01:07,480 Speaker 4: the transition to clean power. 23 00:01:07,840 --> 00:01:09,440 Speaker 2: First, we begin with the tech sector. 24 00:01:09,840 --> 00:01:12,560 Speaker 4: Well, this week we heard that tech giants Nvidia and 25 00:01:12,640 --> 00:01:16,000 Speaker 4: AMD they plan to resume sales of some AI chips 26 00:01:16,040 --> 00:01:19,120 Speaker 4: in China. This came after US government officials told the 27 00:01:19,160 --> 00:01:21,720 Speaker 4: companies these shipments would get approved for. 28 00:01:21,680 --> 00:01:23,640 Speaker 2: More at least than I were joined by Mandeep seeing 29 00:01:23,640 --> 00:01:26,040 Speaker 2: Bloomberg Intelligence senior tech industry analysts. 30 00:01:26,480 --> 00:01:29,000 Speaker 4: We first asked Mandeep to explain the policies of the 31 00:01:29,080 --> 00:01:30,959 Speaker 4: US government as it relates to chips. 32 00:01:31,440 --> 00:01:36,880 Speaker 5: In mid April, the current administration banned the sale of 33 00:01:37,280 --> 00:01:41,880 Speaker 5: Nvidia H twenty chips. And remember H twenty is a 34 00:01:41,920 --> 00:01:47,000 Speaker 5: deprecated version of the Hopper two hundred chip that Nvidia 35 00:01:47,080 --> 00:01:49,640 Speaker 5: sells in the US and other markets. So it's not 36 00:01:49,720 --> 00:01:54,120 Speaker 5: the highest hip in terms of performance and power consumption. 37 00:01:54,880 --> 00:01:58,200 Speaker 5: But at the same time, what they were able to 38 00:01:58,240 --> 00:02:00,520 Speaker 5: do is to sell this deprecated verd and for a 39 00:02:00,600 --> 00:02:04,320 Speaker 5: while and then since mid April it was completely banned. 40 00:02:04,560 --> 00:02:07,880 Speaker 5: And so what Nvidia did was they took down the 41 00:02:07,920 --> 00:02:11,519 Speaker 5: guidance and they pretty much rode off. The inventory of 42 00:02:11,639 --> 00:02:16,160 Speaker 5: the EDH twenty chips for their one Q party was 43 00:02:16,280 --> 00:02:19,079 Speaker 5: around two point one billion for one queue and then 44 00:02:19,120 --> 00:02:23,000 Speaker 5: for two Q they guided for an eight billion dollar 45 00:02:23,840 --> 00:02:27,600 Speaker 5: sort of headwind from the lack of sales to the 46 00:02:27,680 --> 00:02:33,480 Speaker 5: China region. Now with this new guideline around Nvidia being 47 00:02:33,520 --> 00:02:36,880 Speaker 5: able to sell those edged twenty chips, I believe it's 48 00:02:36,919 --> 00:02:41,840 Speaker 5: a similar version to what they were selling before. I 49 00:02:41,880 --> 00:02:46,080 Speaker 5: think you could pretty much add you know, I would 50 00:02:46,120 --> 00:02:49,720 Speaker 5: say up to a twenty five billion dollar run rate 51 00:02:49,919 --> 00:02:52,960 Speaker 5: annually in terms of sales to China. So think of 52 00:02:54,000 --> 00:02:57,000 Speaker 5: Nvidia sales next year are going to be around two 53 00:02:57,040 --> 00:03:00,960 Speaker 5: hundred and fifty billion. That's the current consensus US add 54 00:03:01,000 --> 00:03:04,960 Speaker 5: twenty five billion dollars to that because that's how much 55 00:03:05,040 --> 00:03:07,640 Speaker 5: they are able to sell to China as of right now. 56 00:03:07,919 --> 00:03:10,480 Speaker 5: It could be much higher, but at least a ten 57 00:03:10,520 --> 00:03:14,240 Speaker 5: percent lift from being able to sell into China with 58 00:03:14,360 --> 00:03:18,639 Speaker 5: these kind of lighter version of their highest end chips. 59 00:03:18,639 --> 00:03:21,680 Speaker 5: So I think it's a net positive. Same thing for 60 00:03:21,800 --> 00:03:26,120 Speaker 5: AMDMD the market is much smaller for them given their 61 00:03:26,160 --> 00:03:29,920 Speaker 5: market share in GPUs, but I would say AMD would 62 00:03:29,919 --> 00:03:33,400 Speaker 5: get a similar lift at least ten percent of their 63 00:03:33,480 --> 00:03:34,639 Speaker 5: sales expectations. 64 00:03:34,760 --> 00:03:36,960 Speaker 4: So is there a difference between what Nvidia can sell 65 00:03:36,960 --> 00:03:39,400 Speaker 4: and what AMD can sell in China? Is it about 66 00:03:40,520 --> 00:03:42,440 Speaker 4: the same as far what the white House would say. 67 00:03:43,080 --> 00:03:48,120 Speaker 5: So. AMD also has a deprecated version of their highest 68 00:03:48,120 --> 00:03:51,520 Speaker 5: stand chips that they're selling into the US market as well, 69 00:03:51,960 --> 00:03:57,120 Speaker 5: just because the administration doesn't want either AMD or Nvidia 70 00:03:57,200 --> 00:04:00,360 Speaker 5: to sell their highest and chip that's made on three 71 00:04:00,440 --> 00:04:03,760 Speaker 5: nanometers note that has got a search to performance PEC 72 00:04:04,200 --> 00:04:06,640 Speaker 5: and so they don't want the latest technology to be 73 00:04:06,720 --> 00:04:10,880 Speaker 5: sold into the China region. What they're okay with is 74 00:04:11,680 --> 00:04:15,640 Speaker 5: an older version of Nvidia chips that doesn't have the 75 00:04:15,680 --> 00:04:18,640 Speaker 5: same level of performance as the current version, and that 76 00:04:19,120 --> 00:04:22,480 Speaker 5: they can sell into these Chinese companies, and the administration 77 00:04:22,600 --> 00:04:23,720 Speaker 5: is okay with that, all. 78 00:04:23,720 --> 00:04:25,880 Speaker 2: Right, man, Deep, I have no idea why the government's 79 00:04:25,920 --> 00:04:30,400 Speaker 2: changing its policy, but it's good for these companies negotiations certainly, 80 00:04:30,440 --> 00:04:32,240 Speaker 2: so we'll keep it on that. Do we know just 81 00:04:32,279 --> 00:04:34,360 Speaker 2: off and why they change the policy. 82 00:04:34,920 --> 00:04:38,120 Speaker 5: I mean, it's part of the bigger negotiation. The administration 83 00:04:38,279 --> 00:04:41,680 Speaker 5: is focused on building more data centers here in the 84 00:04:41,800 --> 00:04:44,960 Speaker 5: US for them for the companies to be able to 85 00:04:45,000 --> 00:04:48,000 Speaker 5: do that meta talking about superintelligence and you know all 86 00:04:48,040 --> 00:04:51,720 Speaker 5: the capex spen they need some stuff from China as well. 87 00:04:52,120 --> 00:04:55,560 Speaker 5: So this is more about you have the rare earth stuff. 88 00:04:56,040 --> 00:05:00,240 Speaker 5: We have the chips and it's part of the bigger negotiation. 89 00:05:00,160 --> 00:05:02,920 Speaker 4: Our thanks to man deep seeing Bloomberg Intelligence senior tech 90 00:05:02,960 --> 00:05:03,920 Speaker 4: industry analyst. 91 00:05:04,279 --> 00:05:07,839 Speaker 2: We recently focused on a Bloomberg story entitled Stealth Steak 92 00:05:08,080 --> 00:05:10,440 Speaker 2: Sales Health, United Health beat Wall Street Targets. 93 00:05:10,560 --> 00:05:12,880 Speaker 4: You can find it on the Bloomberg Terminal and Bloomberg 94 00:05:12,960 --> 00:05:16,520 Speaker 4: dot com. It discusses how until this year, United Health 95 00:05:16,520 --> 00:05:19,200 Speaker 4: Group had managed to pull off an impressive feat more 96 00:05:19,240 --> 00:05:23,200 Speaker 4: than sixty consecutive quarters of earnings that beat Wall Street estimates, but. 97 00:05:23,160 --> 00:05:25,760 Speaker 2: Now analysts are starting to question how United Health is 98 00:05:25,920 --> 00:05:28,160 Speaker 2: arriving at these numbers for more. Atleasta and I were 99 00:05:28,200 --> 00:05:31,000 Speaker 2: joined by Michelle Davis, Bloomberg senior deals reporter. 100 00:05:31,360 --> 00:05:33,839 Speaker 4: We first asked Michelle about what she found out when 101 00:05:33,839 --> 00:05:35,640 Speaker 4: she looked into United Health's earnings. 102 00:05:36,040 --> 00:05:38,440 Speaker 6: They had kind of a pristine record for more than 103 00:05:38,520 --> 00:05:42,960 Speaker 6: sixty quarters. They beat estimates. Analysts love them, and at 104 00:05:42,960 --> 00:05:45,120 Speaker 6: the end of last year that got a bit harder 105 00:05:45,120 --> 00:05:47,840 Speaker 6: for them to keep up. You know, medical costs arising, 106 00:05:48,040 --> 00:05:51,080 Speaker 6: the government's been cracking down on reinforcements and that was 107 00:05:51,120 --> 00:05:54,160 Speaker 6: eating into profits. And so what we reported is that 108 00:05:54,200 --> 00:05:57,159 Speaker 6: at the end of last year they approached several private 109 00:05:57,160 --> 00:06:00,240 Speaker 6: equity firms and asked them if they wanted to buy 110 00:06:00,400 --> 00:06:03,839 Speaker 6: stakes off of them of their businesses, and a couple 111 00:06:03,880 --> 00:06:07,480 Speaker 6: interesting things happen here. Not only did they quietly do this, 112 00:06:07,720 --> 00:06:11,240 Speaker 6: but the deals were structured such that United Health can 113 00:06:11,279 --> 00:06:14,200 Speaker 6: be forced to buy the businesses back down the road, 114 00:06:14,279 --> 00:06:19,000 Speaker 6: so it's temporary in nature. And United booked the gains 115 00:06:19,080 --> 00:06:21,159 Speaker 6: in an interesting place. They booked them as part of 116 00:06:21,240 --> 00:06:24,440 Speaker 6: operating earnings, which is or adjusted earnings, which is normally 117 00:06:24,760 --> 00:06:27,040 Speaker 6: where you look to see kind of how a business 118 00:06:27,080 --> 00:06:29,600 Speaker 6: is doing, you know, excluding one off gains or one 119 00:06:29,600 --> 00:06:32,280 Speaker 6: off events like this, And so it was just interesting 120 00:06:32,320 --> 00:06:34,440 Speaker 6: to see not only the fact that they stealthily did this, 121 00:06:34,560 --> 00:06:37,560 Speaker 6: but where they put them. And without these gains, they 122 00:06:37,560 --> 00:06:40,599 Speaker 6: would have missed estimates and profit would have dropped in 123 00:06:40,640 --> 00:06:41,839 Speaker 6: the last quarter of last year. 124 00:06:42,000 --> 00:06:44,760 Speaker 4: So, as I'm going through this article, this certain quote 125 00:06:44,800 --> 00:06:46,560 Speaker 4: stands out for me From an analyst that you spoke to. 126 00:06:46,600 --> 00:06:48,960 Speaker 4: He said, if the company is manufacturing earnings by chopping 127 00:06:49,080 --> 00:06:52,080 Speaker 4: up their furniture or selling their assets, that's not exactly 128 00:06:52,160 --> 00:06:55,280 Speaker 4: a great business model. Okay, so is the risk that 129 00:06:55,320 --> 00:06:58,400 Speaker 4: it might be kind of masking this weakness in the operations. 130 00:06:58,880 --> 00:07:01,159 Speaker 6: That's the concern, and to be clear, you know, there's 131 00:07:01,200 --> 00:07:04,120 Speaker 6: nothing illegal about what they're doing, per se. You know, 132 00:07:04,160 --> 00:07:07,160 Speaker 6: they disclosed that they did this, most people didn't see it. 133 00:07:07,160 --> 00:07:09,000 Speaker 6: It was in a footnote in their ten K that 134 00:07:09,160 --> 00:07:11,000 Speaker 6: kind of went under the radar, and there are no 135 00:07:11,080 --> 00:07:13,880 Speaker 6: details about what exactly they sold. That's what we're trying 136 00:07:13,880 --> 00:07:16,320 Speaker 6: to you know, report on here. But yeah, the concern 137 00:07:16,400 --> 00:07:19,200 Speaker 6: is it clouds, you know, your ability to see how 138 00:07:19,240 --> 00:07:22,400 Speaker 6: the business is actually performing. And we heard from sources 139 00:07:22,440 --> 00:07:25,640 Speaker 6: that there's a culture inside United Health of you know, 140 00:07:26,040 --> 00:07:28,400 Speaker 6: kind of there being this pressure to do whatever you 141 00:07:28,480 --> 00:07:30,880 Speaker 6: can to meet targets every quarter. 142 00:07:31,120 --> 00:07:31,320 Speaker 7: Yep. 143 00:07:31,360 --> 00:07:34,200 Speaker 2: It's a huge company, huge player in the managed care business. 144 00:07:34,400 --> 00:07:37,360 Speaker 2: What's the underlying concern for investors out there? Do you 145 00:07:37,360 --> 00:07:38,280 Speaker 2: think around this company? 146 00:07:39,160 --> 00:07:42,360 Speaker 6: United Health has been dealing with a lot of things. 147 00:07:42,440 --> 00:07:45,480 Speaker 6: I mean, even before we knew about this, there was 148 00:07:45,560 --> 00:07:48,240 Speaker 6: obviously the tragedy of you know, one of their executives 149 00:07:48,280 --> 00:07:52,000 Speaker 6: being murdered last year, and then you know, there was 150 00:07:52,000 --> 00:07:56,120 Speaker 6: a Wall Street Journal investigation this year about potential medicare fraud, 151 00:07:56,200 --> 00:08:00,000 Speaker 6: which they have denied. They also, in the first courts 152 00:08:00,240 --> 00:08:02,760 Speaker 6: reported their first earnings miss in you know, more than 153 00:08:02,800 --> 00:08:05,559 Speaker 6: sixty quarters, so that shoe finally dropped. They outed their CEO. 154 00:08:05,640 --> 00:08:09,480 Speaker 6: So investors are just concerned about the story here, you know, what, 155 00:08:09,480 --> 00:08:11,480 Speaker 6: what is the United Health story? I think that's what 156 00:08:11,600 --> 00:08:12,440 Speaker 6: the big concern is. 157 00:08:12,720 --> 00:08:15,679 Speaker 4: So are there any other companies, like maybe other health 158 00:08:15,720 --> 00:08:18,680 Speaker 4: companies or something like that where this same story that 159 00:08:18,720 --> 00:08:20,240 Speaker 4: you've been talking about kind of plays out? 160 00:08:20,840 --> 00:08:23,640 Speaker 6: Not that we could find it. Seems like United Health 161 00:08:23,760 --> 00:08:28,760 Speaker 6: is really, you know, unique in its ability and history 162 00:08:28,800 --> 00:08:33,560 Speaker 6: of you know, really carefully managing its reporting every quarter. 163 00:08:33,800 --> 00:08:37,160 Speaker 2: Our thanks to Michelle Davis, Bloomberg Senior Deals reporter, we. 164 00:08:37,040 --> 00:08:39,520 Speaker 4: Move next to the IT space. This week, we heard 165 00:08:39,520 --> 00:08:42,640 Speaker 4: that the IT company, Hewlett Packard Enterprise is creating a 166 00:08:42,679 --> 00:08:45,880 Speaker 4: new strategy committee and HPA is agreeing to work with 167 00:08:45,960 --> 00:08:49,480 Speaker 4: activist investor Elliott Investment Management on ways to help the 168 00:08:49,520 --> 00:08:51,640 Speaker 4: software company boost value for more. 169 00:08:51,760 --> 00:08:54,480 Speaker 2: Lisa and I were joined by Wujin Hope, Bloomberg Intelligence 170 00:08:54,520 --> 00:08:58,000 Speaker 2: senior technology analysts. You're first to ask Wugent about the 171 00:08:58,080 --> 00:09:00,680 Speaker 2: new committee HPE is creating. 172 00:09:01,240 --> 00:09:06,400 Speaker 8: It is a handpicked board member by Elliott Management. Keep 173 00:09:06,440 --> 00:09:10,360 Speaker 8: in mind, Elliott is an activist investor that has a 174 00:09:10,360 --> 00:09:14,240 Speaker 8: ten percent plus shareholding of HPE. I think I believe 175 00:09:14,240 --> 00:09:18,040 Speaker 8: they made their position earlier this year, and who they 176 00:09:18,080 --> 00:09:24,360 Speaker 8: appointed was Bob Calderoni, who has done similar type of 177 00:09:24,400 --> 00:09:27,640 Speaker 8: deals in terms of taking companies private or more importantly 178 00:09:27,679 --> 00:09:31,760 Speaker 8: operational streamlining. So any changes, I think Elliott is going 179 00:09:31,800 --> 00:09:35,560 Speaker 8: to try through Bob to affect a lot of changes 180 00:09:35,559 --> 00:09:36,920 Speaker 8: at HPE. 181 00:09:37,000 --> 00:09:41,320 Speaker 2: Which just remind us what HP is today. Number one 182 00:09:41,320 --> 00:09:44,240 Speaker 2: and number two, what do they need to fix to 183 00:09:44,320 --> 00:09:45,160 Speaker 2: move this thing forward? 184 00:09:45,960 --> 00:09:49,640 Speaker 8: Yeah, hey, Paul, so, HPE is one of the leading 185 00:09:51,000 --> 00:09:57,800 Speaker 8: enterprise IT infrastructure vendors. They provide servers, storage, now a 186 00:09:57,840 --> 00:10:02,680 Speaker 8: bigger networking presence with the June acquisition, and actually, quite 187 00:10:02,760 --> 00:10:05,320 Speaker 8: quite frankly, there's quite a bit to fix from an 188 00:10:05,360 --> 00:10:11,920 Speaker 8: operational standpoint. They fumbled the first quarter results by mispricing 189 00:10:12,000 --> 00:10:15,880 Speaker 8: their server business and it seems to be reconciled now, 190 00:10:16,480 --> 00:10:19,720 Speaker 8: but it flags some of the operational issues. Number one, 191 00:10:19,800 --> 00:10:22,240 Speaker 8: Number two, they have made a lot of M and 192 00:10:22,320 --> 00:10:25,040 Speaker 8: A that just didn't create the synergies that they had hoped. So, 193 00:10:25,679 --> 00:10:32,040 Speaker 8: you know, they did hire the former HPCFO to streamline 194 00:10:32,160 --> 00:10:38,719 Speaker 8: the financials, and with the Juniper merger starting underway, I 195 00:10:39,120 --> 00:10:43,560 Speaker 8: suspect that you know, the new strategy board, it will 196 00:10:43,559 --> 00:10:47,440 Speaker 8: try to find more operational synergies between the two networking businesses. 197 00:10:47,640 --> 00:10:50,400 Speaker 4: WIJ and can you dig more into how it's under 198 00:10:50,440 --> 00:10:53,640 Speaker 4: pressure kind of like lagging behind Dell and growing AI. 199 00:10:53,840 --> 00:10:55,720 Speaker 4: What kind of pressure is it facing in that market? 200 00:10:56,520 --> 00:11:01,200 Speaker 8: Yeah, so if we think about AI, right, you know, 201 00:11:01,920 --> 00:11:07,400 Speaker 8: HPE actually owns Cray, and we think about high performance compute, 202 00:11:08,400 --> 00:11:11,800 Speaker 8: they are the market leaders there. They weren't able. HPE 203 00:11:11,840 --> 00:11:16,040 Speaker 8: has not been able to transition that Cray business elegantly 204 00:11:16,960 --> 00:11:19,880 Speaker 8: to UH the to the AI side of things. So 205 00:11:20,040 --> 00:11:24,400 Speaker 8: let me put this into context. I think HPE should 206 00:11:24,440 --> 00:11:27,200 Speaker 8: be on track for about three to four billion dollars 207 00:11:27,240 --> 00:11:31,560 Speaker 8: in AI sales UH this year, possibly you know four 208 00:11:31,559 --> 00:11:35,360 Speaker 8: to five, right, Dell is going to be on track 209 00:11:35,360 --> 00:11:38,240 Speaker 8: for about fifteen billion dollars this year, and super Micro 210 00:11:38,440 --> 00:11:40,640 Speaker 8: is probably on track for about twenty billion this year. 211 00:11:41,160 --> 00:11:43,760 Speaker 8: So you know, they've lagged on the AI front even 212 00:11:43,800 --> 00:11:46,360 Speaker 8: though they have some of the leading technologies on high 213 00:11:46,400 --> 00:11:49,680 Speaker 8: performance compute. That's an area that you know could be 214 00:11:49,720 --> 00:11:51,760 Speaker 8: fixable under the right hands. 215 00:11:52,480 --> 00:11:55,560 Speaker 2: One more here before we let you go, which is 216 00:11:55,679 --> 00:11:58,120 Speaker 2: just you mentioned they owned Cray, they owned Juniper. If 217 00:11:58,160 --> 00:12:01,439 Speaker 2: i'm Elliott Management, am I thinking about maybe they can 218 00:12:01,480 --> 00:12:03,640 Speaker 2: be selling or spinning off some of these businesses to 219 00:12:03,800 --> 00:12:04,680 Speaker 2: enhance value. 220 00:12:05,360 --> 00:12:07,920 Speaker 8: Well, you know, Bob Calderoni used to be on the 221 00:12:08,000 --> 00:12:11,439 Speaker 8: Juniper board and they actually helped with the operational stitch 222 00:12:11,520 --> 00:12:15,720 Speaker 8: lining of Juniper. Right, so I think that you want 223 00:12:15,760 --> 00:12:20,440 Speaker 8: to keep Juniper. Okay, there are other aspects of the 224 00:12:20,480 --> 00:12:25,000 Speaker 8: networking business that you could probably probably parse away. Right, 225 00:12:25,520 --> 00:12:28,920 Speaker 8: the crab business you'd want to keep because AI is 226 00:12:28,960 --> 00:12:31,160 Speaker 8: going to be the growth engine and also help you 227 00:12:31,200 --> 00:12:33,600 Speaker 8: transition to the enterprise AI our. 228 00:12:33,559 --> 00:12:36,559 Speaker 2: Thanks to Wujinell, Bloomberg Intelligence Senior Technology Analyst. 229 00:12:36,840 --> 00:12:39,280 Speaker 4: Coming up, we'll look at Bloomberg Intelligence to this deep 230 00:12:39,360 --> 00:12:40,800 Speaker 4: dive into dating apps. 231 00:12:41,080 --> 00:12:43,960 Speaker 2: Listening to Bloomberg Intelligence on Bloomberg Radio, providing in depth 232 00:12:43,960 --> 00:12:46,240 Speaker 2: research and data on two thousand companies and one hundred 233 00:12:46,240 --> 00:12:47,040 Speaker 2: and thirty industries. 234 00:12:47,160 --> 00:12:52,120 Speaker 4: You can access Bloomberg Intelligence big. I'm the Terminal, I'm LISMITTEO. 235 00:12:51,520 --> 00:12:53,400 Speaker 2: And Paul Sweingy and this is Bloomberg. 236 00:12:57,400 --> 00:13:01,120 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 237 00:13:01,200 --> 00:13:04,280 Speaker 1: weekdays at ten am Easterned on Apple, Cocklay and Android 238 00:13:04,280 --> 00:13:07,600 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 239 00:13:07,640 --> 00:13:11,480 Speaker 1: you get your podcasts or watch us live on YouTube. 240 00:13:11,520 --> 00:13:13,960 Speaker 4: I'm Paul Sweeney and I'm Lisa Matteo filling in on 241 00:13:14,000 --> 00:13:15,439 Speaker 4: Bloomberg Intelligence The. 242 00:13:15,440 --> 00:13:17,520 Speaker 2: Next move, So the world of online dating. 243 00:13:17,640 --> 00:13:20,880 Speaker 4: Bloomberg Intelligence recently did a survey on dating apps and 244 00:13:20,880 --> 00:13:23,400 Speaker 4: how gen Z is using them, and the survey found 245 00:13:23,400 --> 00:13:26,280 Speaker 4: that gen Z is dating less than most other generations 246 00:13:26,400 --> 00:13:26,720 Speaker 4: from are. 247 00:13:26,760 --> 00:13:29,199 Speaker 2: On this Lisa and I were joined by Nicole Desuza, 248 00:13:29,400 --> 00:13:32,160 Speaker 2: Bloomberg Intelligence Internet and software equity analysts. 249 00:13:32,320 --> 00:13:34,720 Speaker 4: We first asked Nicole to explain her findings. 250 00:13:35,160 --> 00:13:38,400 Speaker 3: We have Bloomberg Intelligence conducted the survey to better understand 251 00:13:38,440 --> 00:13:42,080 Speaker 3: how people are navigating dating, how they use dating apps, 252 00:13:42,120 --> 00:13:44,920 Speaker 3: and then also how they feel about AI within dating apps. 253 00:13:45,600 --> 00:13:49,599 Speaker 3: And so some really interesting findings. First that you know, specifically, 254 00:13:49,679 --> 00:13:53,120 Speaker 3: gen Z tends to be single but not dating. Gen 255 00:13:53,200 --> 00:13:56,360 Speaker 3: Z's what age again, sixteen to twenty eight, sixteen. 256 00:13:55,920 --> 00:13:58,280 Speaker 2: To twenty al right, so yeah, single, two of my 257 00:13:58,520 --> 00:14:01,400 Speaker 2: four into that one, okay, And so they're not using 258 00:14:01,440 --> 00:14:02,160 Speaker 2: the apps. 259 00:14:02,000 --> 00:14:06,000 Speaker 3: They're not even dating. Single and not dating. So this 260 00:14:06,200 --> 00:14:09,160 Speaker 3: is I mean, there's a few reasons. Studies have kind 261 00:14:09,160 --> 00:14:11,840 Speaker 3: of shown they do have higher rates of loneliness, but 262 00:14:11,960 --> 00:14:16,520 Speaker 3: they are also prioritizing independence. They are also you know, 263 00:14:16,559 --> 00:14:20,560 Speaker 3: feeling a reduced stigma around being single. So it could 264 00:14:20,600 --> 00:14:22,680 Speaker 3: really change dating patterns generationally. 265 00:14:22,800 --> 00:14:24,120 Speaker 4: I can kind of say my son was on a 266 00:14:24,200 --> 00:14:26,120 Speaker 4: dating app and then he stopped because he got tired 267 00:14:26,160 --> 00:14:28,160 Speaker 4: of it and it just and he's in that gen 268 00:14:28,280 --> 00:14:31,800 Speaker 4: Z kind of group and they're not going to pay 269 00:14:31,800 --> 00:14:35,280 Speaker 4: for them too. So how does that change for these 270 00:14:35,480 --> 00:14:37,840 Speaker 4: different you know, dating apps out there. How do they 271 00:14:37,840 --> 00:14:38,800 Speaker 4: have to change their approach? 272 00:14:39,480 --> 00:14:43,680 Speaker 3: So right now a lot of what we're seeing from 273 00:14:43,760 --> 00:14:46,280 Speaker 3: gen Z is that even though they are dating less, 274 00:14:46,280 --> 00:14:49,040 Speaker 3: they are looking for long term relationships. Those that are dating, 275 00:14:49,240 --> 00:14:52,560 Speaker 3: they are looking to form meaningful connections. So, you know, 276 00:14:52,640 --> 00:14:55,000 Speaker 3: some of the products we've seen from these dating apps 277 00:14:55,080 --> 00:14:57,320 Speaker 3: that really introduce AI are more around how to create 278 00:14:57,320 --> 00:14:59,960 Speaker 3: a profile, how to make it easier to talk to people, 279 00:15:00,240 --> 00:15:02,720 Speaker 3: you know, using AI to generate prompts that might not 280 00:15:02,760 --> 00:15:05,440 Speaker 3: necessarily you know, correlate with what gen Z is looking 281 00:15:05,480 --> 00:15:07,240 Speaker 3: for in terms of forming a meaningful connection. 282 00:15:08,080 --> 00:15:10,680 Speaker 2: I'll tell you Lisa go to the Parker House and 283 00:15:10,720 --> 00:15:14,800 Speaker 2: seeing her at New Jersey on a summer Saturday, thousands 284 00:15:14,880 --> 00:15:19,360 Speaker 2: and thousands of kids of gen Z type are there 285 00:15:20,000 --> 00:15:21,840 Speaker 2: at like four o'clock in the afternoon. They're not on 286 00:15:21,880 --> 00:15:24,720 Speaker 2: the beach. They're all made up, dressed to the nines. 287 00:15:25,120 --> 00:15:27,640 Speaker 2: I think they're looking to hook to you meet somebody 288 00:15:27,720 --> 00:15:28,560 Speaker 2: right now. I've been there. 289 00:15:28,600 --> 00:15:29,080 Speaker 3: I had to wait. 290 00:15:30,600 --> 00:15:33,120 Speaker 2: I mean, I don't know what's going on. How about millennials? 291 00:15:33,120 --> 00:15:34,400 Speaker 2: How did they fare? 292 00:15:34,800 --> 00:15:37,640 Speaker 3: So millennials are they kind of came of age during 293 00:15:37,640 --> 00:15:39,520 Speaker 3: the time of dating apps, so a lot of dating 294 00:15:39,560 --> 00:15:43,800 Speaker 3: apps are really created to target dating patterns of millennials. 295 00:15:43,880 --> 00:15:46,960 Speaker 3: So millennials have a more favorable relationship with dating apps. 296 00:15:47,000 --> 00:15:50,480 Speaker 3: And they also are much more like comfortable with AI 297 00:15:50,680 --> 00:15:53,840 Speaker 3: in dating apps versus gen Z, which was surprising to us. 298 00:15:54,120 --> 00:15:56,760 Speaker 4: Now what about Okay, people always forget about gen X, 299 00:15:57,640 --> 00:16:01,680 Speaker 4: the gen X folks, So what about gen X? And 300 00:16:01,720 --> 00:16:05,120 Speaker 4: then you know my mom, you know, single, like she 301 00:16:05,280 --> 00:16:07,600 Speaker 4: wants to find out about these apps. I'm telling you, 302 00:16:08,320 --> 00:16:09,880 Speaker 4: what about the older generation? 303 00:16:10,080 --> 00:16:13,000 Speaker 3: They're on dating apps, so they're on it. There are Yeah, 304 00:16:13,120 --> 00:16:15,560 Speaker 3: there are a lot of gen X and baby boomers 305 00:16:15,560 --> 00:16:18,040 Speaker 3: on dating apps, and there are a wide variety of 306 00:16:18,080 --> 00:16:20,480 Speaker 3: dating apps to kind of address different age groups, different 307 00:16:20,680 --> 00:16:22,760 Speaker 3: you know, things that people are looking for. So they're 308 00:16:22,960 --> 00:16:23,680 Speaker 3: they're available. 309 00:16:24,000 --> 00:16:27,120 Speaker 2: How does AI I have to ask the AI questions 310 00:16:27,680 --> 00:16:29,680 Speaker 2: Because we had a guest on earlier about it. I 311 00:16:29,720 --> 00:16:31,800 Speaker 2: walked out of there thinking AI is going to take 312 00:16:31,800 --> 00:16:34,920 Speaker 2: over Wall Street? How about AI and dating apps that 313 00:16:35,600 --> 00:16:38,840 Speaker 2: I would think could be helpful to better select somebody 314 00:16:38,880 --> 00:16:40,480 Speaker 2: who might be a good match or something. 315 00:16:40,800 --> 00:16:43,160 Speaker 3: So we've seen dating app companies roll out a lot 316 00:16:43,160 --> 00:16:47,160 Speaker 3: of AI products. I would say, right now, it seems, 317 00:16:47,160 --> 00:16:49,400 Speaker 3: at least based on our survey, that they haven't been 318 00:16:49,480 --> 00:16:52,880 Speaker 3: that well received. It seems like people don't necessarily need 319 00:16:52,920 --> 00:16:55,640 Speaker 3: AI to build a better profile, they don't need AI 320 00:16:55,760 --> 00:16:58,440 Speaker 3: to help them engage in conversation. I think where it 321 00:16:58,440 --> 00:17:01,120 Speaker 3: has been helpful is user safety, so to weed out 322 00:17:01,160 --> 00:17:04,800 Speaker 3: profiles that are fake or you know, potentially sent people 323 00:17:04,800 --> 00:17:08,000 Speaker 3: who are sending harmful messages. And that is a common complaint. 324 00:17:08,320 --> 00:17:12,320 Speaker 3: But as of right now, it doesn't seem that you know, 325 00:17:12,720 --> 00:17:15,520 Speaker 3: at least gen Z and even some millennials are really 326 00:17:16,480 --> 00:17:18,760 Speaker 3: really adopting these new AI products. 327 00:17:18,840 --> 00:17:21,439 Speaker 4: So, and there are certain a dating apps that are 328 00:17:21,480 --> 00:17:23,520 Speaker 4: more popular than others, I mean, which are the hot 329 00:17:23,560 --> 00:17:24,160 Speaker 4: ones right now? 330 00:17:24,560 --> 00:17:28,680 Speaker 3: So right now, at least by users, Tinder has by 331 00:17:28,720 --> 00:17:31,320 Speaker 3: far the most users that's owned by match Group. And 332 00:17:31,359 --> 00:17:33,920 Speaker 3: then Hinge is actually one of the few dating apps 333 00:17:33,920 --> 00:17:37,080 Speaker 3: that is continuing to grow users, and that's probably because 334 00:17:37,119 --> 00:17:38,919 Speaker 3: Hinge focus is a little bit more on kind of 335 00:17:39,000 --> 00:17:42,480 Speaker 3: long term relationships, building meaningful connections. Tinder still has a 336 00:17:42,520 --> 00:17:44,280 Speaker 3: reputation of kind of a hookup app. 337 00:17:44,440 --> 00:17:46,400 Speaker 2: Yes, which one. 338 00:17:47,520 --> 00:17:49,040 Speaker 5: Yeah, that's how my son was on. 339 00:17:52,080 --> 00:17:56,320 Speaker 2: Our thanks to Nicole Desuza, Bloomberg Intelligence, Internet and Software analysts. 340 00:17:56,520 --> 00:17:59,280 Speaker 4: We move next to news from the ev giant Tesla. 341 00:17:59,560 --> 00:18:01,800 Speaker 2: This week, I heard that Tesla's shareholders will vote on 342 00:18:01,840 --> 00:18:05,480 Speaker 2: whether to invest in CEO Elon Musk's artificial intelligence startup 343 00:18:05,840 --> 00:18:08,280 Speaker 2: x Ai. That's according to Elon Musk. 344 00:18:08,480 --> 00:18:10,679 Speaker 4: Musk made the statement in response to an account on 345 00:18:10,960 --> 00:18:13,520 Speaker 4: x that said the Carmaker must be able to invest 346 00:18:13,520 --> 00:18:17,280 Speaker 4: in x Ai to be fair to Tesla retail investors for. 347 00:18:17,200 --> 00:18:18,919 Speaker 2: More at least and I were joined by Craig Trudell, 348 00:18:19,000 --> 00:18:20,840 Speaker 2: Bloomberg Global Autos Editor. 349 00:18:21,240 --> 00:18:23,520 Speaker 4: We first asked Craig what kind of message he thinks 350 00:18:23,600 --> 00:18:25,359 Speaker 4: Musk's recent statement is sending. 351 00:18:25,680 --> 00:18:30,000 Speaker 7: It's sending that, you know, Elon's Elon's business empire is 352 00:18:30,640 --> 00:18:34,159 Speaker 7: becoming all the more intertwined. So this has been, you know, 353 00:18:34,280 --> 00:18:38,080 Speaker 7: sort of a case on and off for years. We 354 00:18:38,119 --> 00:18:41,320 Speaker 7: can think back to say, you know, the Tesla Solar 355 00:18:41,359 --> 00:18:45,919 Speaker 7: City acquisition, you know, and that's going back quite a ways. 356 00:18:45,960 --> 00:18:49,800 Speaker 7: But you know, in this case, it would be Tesla, 357 00:18:50,760 --> 00:18:54,240 Speaker 7: you know, his most valuable company in investing in in 358 00:18:54,359 --> 00:18:56,600 Speaker 7: sort of his up and comer and one where he's 359 00:18:56,760 --> 00:19:00,840 Speaker 7: you know, spent an awful lot of time and energy, folks, Gustan. Lately, 360 00:19:01,440 --> 00:19:05,040 Speaker 7: as Tesla's electric vehicle sales have been slowing, he's been 361 00:19:05,480 --> 00:19:08,440 Speaker 7: really sort of dead set on taking on Open AI 362 00:19:08,560 --> 00:19:11,200 Speaker 7: and and sort of standing up a competitor to chat 363 00:19:11,240 --> 00:19:15,159 Speaker 7: GPT and as with all the companies in the space, 364 00:19:15,760 --> 00:19:18,280 Speaker 7: spending an awful lot of money to try and compete 365 00:19:18,320 --> 00:19:19,399 Speaker 7: in that realm. 366 00:19:20,200 --> 00:19:24,160 Speaker 2: So where do we stand here in terms of kind 367 00:19:24,160 --> 00:19:27,200 Speaker 2: of how investors are viewing their investment in Tesla. Are 368 00:19:27,200 --> 00:19:30,240 Speaker 2: they invested in a auto company or are they invested in 369 00:19:30,280 --> 00:19:33,880 Speaker 2: an AI company or they invested just an Elon Inc? 370 00:19:34,320 --> 00:19:35,640 Speaker 2: How did they think about it these days? 371 00:19:36,640 --> 00:19:36,840 Speaker 5: Yeah? 372 00:19:36,840 --> 00:19:39,359 Speaker 7: I mean, I think Musk has has been trying for 373 00:19:39,520 --> 00:19:43,479 Speaker 7: for several years now to sell and position Tesla as 374 00:19:43,600 --> 00:19:46,879 Speaker 7: much more than just a car company, right and you know, 375 00:19:46,960 --> 00:19:52,000 Speaker 7: it's it's definitely in the energy space and and you know, 376 00:19:52,080 --> 00:19:56,439 Speaker 7: has a battery business to show for that. And in Ai, 377 00:19:56,840 --> 00:19:59,760 Speaker 7: you know, he's he's talked a big game about you know, 378 00:20:00,560 --> 00:20:04,040 Speaker 7: developing self driving technology. He's not he's not gotten there yet. 379 00:20:04,640 --> 00:20:07,359 Speaker 7: Uh as as we've seen with the company, you know, 380 00:20:07,480 --> 00:20:11,399 Speaker 7: starting to offer rides in in Tesla's around Austin, Texas, 381 00:20:11,840 --> 00:20:14,760 Speaker 7: uh they still have uh, you know, Tesla employees in 382 00:20:15,040 --> 00:20:17,800 Speaker 7: the front passenger seat to sort of take over in 383 00:20:17,920 --> 00:20:20,600 Speaker 7: events where you know, the cars have not been able 384 00:20:20,640 --> 00:20:23,720 Speaker 7: to handle uh, you know, navigating the streets on their own. 385 00:20:24,800 --> 00:20:28,520 Speaker 7: And yet with with uh SpaceX, I think interest interestingly, 386 00:20:28,760 --> 00:20:31,400 Speaker 7: you know, for them to have just pumped two billion 387 00:20:31,920 --> 00:20:34,919 Speaker 7: into x Ai, I think there's a little bit of 388 00:20:34,960 --> 00:20:38,000 Speaker 7: fomo on the part of some in the Tesla shareholder 389 00:20:38,040 --> 00:20:41,639 Speaker 7: base where they've seen you know, x AI go from 390 00:20:42,240 --> 00:20:45,200 Speaker 7: you know, not all that valuable a startup to suddenly, 391 00:20:45,560 --> 00:20:49,040 Speaker 7: you know, an extremely valuable startup and they feel like 392 00:20:49,080 --> 00:20:52,320 Speaker 7: they've missed out on some of this appreciation and one 393 00:20:52,400 --> 00:20:52,800 Speaker 7: in on that. 394 00:20:53,119 --> 00:20:55,200 Speaker 4: So with all that said, Craig, I mean, does must 395 00:20:55,240 --> 00:20:59,440 Speaker 4: support a merger between x AI and Tesla or maybe 396 00:20:59,520 --> 00:21:03,560 Speaker 4: x a hind SpaceX You mentioned space X two, He says. 397 00:21:03,359 --> 00:21:05,400 Speaker 7: He does not. And and I think there's been talk 398 00:21:05,480 --> 00:21:08,760 Speaker 7: about this for years, right that, you know, and on 399 00:21:08,840 --> 00:21:11,040 Speaker 7: and off again there's been you know, questions as to 400 00:21:11,080 --> 00:21:13,120 Speaker 7: whether or not Musk is you know, sort of stretched 401 00:21:13,119 --> 00:21:16,040 Speaker 7: too thin and doing too much. Would it make more 402 00:21:16,080 --> 00:21:20,280 Speaker 7: sense to turn his companies into into one and and 403 00:21:20,359 --> 00:21:23,760 Speaker 7: sort of you know, he's sort of the think about 404 00:21:23,800 --> 00:21:26,320 Speaker 7: it as sort of the general electric of of the 405 00:21:26,400 --> 00:21:29,879 Speaker 7: New age, right, which is kind of fascinating because of 406 00:21:29,880 --> 00:21:32,960 Speaker 7: course that didn't work out too well for Ge. And 407 00:21:32,960 --> 00:21:37,240 Speaker 7: and yet you know, even some of Musk's own allies 408 00:21:37,240 --> 00:21:42,120 Speaker 7: have sort of alluded to, you know, viewing him and 409 00:21:42,200 --> 00:21:44,880 Speaker 7: his empire as as sort of you know, modern day 410 00:21:45,160 --> 00:21:45,960 Speaker 7: general electric. 411 00:21:46,119 --> 00:21:47,040 Speaker 5: So uh. 412 00:21:47,280 --> 00:21:50,600 Speaker 7: He he did take pains to say on an x 413 00:21:50,680 --> 00:21:54,040 Speaker 7: that he does not support a full blown merger of 414 00:21:54,119 --> 00:21:57,920 Speaker 7: Tesla and x Ai, but he has you know, signals 415 00:21:57,960 --> 00:22:00,399 Speaker 7: on there multiple times now that he would be in 416 00:22:00,440 --> 00:22:04,080 Speaker 7: favor of of Tesla putting money in and you know, 417 00:22:04,320 --> 00:22:08,119 Speaker 7: that's that's of course, you know, a proposition that could 418 00:22:08,800 --> 00:22:13,280 Speaker 7: be helpful if Xai continues to grow its valuation. We 419 00:22:13,280 --> 00:22:16,359 Speaker 7: should note, however, that our colleagues have reported just in 420 00:22:16,400 --> 00:22:19,000 Speaker 7: the last month or so that Xai has been burning 421 00:22:19,040 --> 00:22:21,560 Speaker 7: through about a billion dollars a month. So this is 422 00:22:21,600 --> 00:22:25,240 Speaker 7: a company that is really sort of cash hungry and 423 00:22:26,400 --> 00:22:30,040 Speaker 7: needy as it's trying to stand up a business, you know, 424 00:22:30,119 --> 00:22:31,600 Speaker 7: with all of this capability. 425 00:22:31,920 --> 00:22:33,520 Speaker 2: Yeah, and Craig, it's been I don't know a month 426 00:22:33,600 --> 00:22:36,919 Speaker 2: or two since Elon Musk left the US government. Doje 427 00:22:37,240 --> 00:22:40,720 Speaker 2: is there any evidence that he is meaningfully engaging with Tesla? 428 00:22:42,480 --> 00:22:46,800 Speaker 7: You know what, I think he's messaging more about, you know, 429 00:22:46,880 --> 00:22:49,400 Speaker 7: sort of what Tesla's up to than he was while 430 00:22:49,400 --> 00:22:53,399 Speaker 7: he was in Washington. He did also post that, you know, 431 00:22:53,440 --> 00:22:57,160 Speaker 7: he was in Tesla's design studio and sort of hyped 432 00:22:57,240 --> 00:23:01,359 Speaker 7: up how excited he was about what he saw. But 433 00:23:01,600 --> 00:23:04,280 Speaker 7: you know, the company is is, you know, sort of 434 00:23:04,400 --> 00:23:08,719 Speaker 7: in this really sort of challenging state from a sales perspective, 435 00:23:09,240 --> 00:23:12,160 Speaker 7: and you've you've sort of gotten indications that, well, maybe 436 00:23:12,200 --> 00:23:15,199 Speaker 7: he doesn't necessarily fully have his his finger on the 437 00:23:15,200 --> 00:23:18,160 Speaker 7: pulse in the sense that he told us just within 438 00:23:18,200 --> 00:23:21,359 Speaker 7: the last few months that sales have turned around, you know. 439 00:23:21,560 --> 00:23:23,880 Speaker 7: A couple of months later, the company reports that its 440 00:23:23,920 --> 00:23:27,280 Speaker 7: vehicle deliveries had fallen thirteen percent in the second quarter. 441 00:23:28,040 --> 00:23:31,320 Speaker 7: So either he was you know, not necessarily up to 442 00:23:31,359 --> 00:23:33,879 Speaker 7: speed on the state of sales, or perhaps he was 443 00:23:34,160 --> 00:23:36,800 Speaker 7: more optimistic that they were able to going to be 444 00:23:36,840 --> 00:23:39,000 Speaker 7: able to turn things around then they ultimately were able 445 00:23:39,040 --> 00:23:40,040 Speaker 7: to last quarter. 446 00:23:40,480 --> 00:23:43,359 Speaker 2: Oh thanks to Craig Trudell and Bloomberg Global Auto's editor. 447 00:23:43,720 --> 00:23:46,879 Speaker 4: Coming up a conversation with nuclear electricity influence or an 448 00:23:46,920 --> 00:23:48,399 Speaker 4: author Isabelle bo Mecki. 449 00:23:48,680 --> 00:23:51,680 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing indes 450 00:23:51,760 --> 00:23:54,240 Speaker 2: research and data on two thousand companies and one hundred 451 00:23:54,240 --> 00:23:55,040 Speaker 2: and thirty industries. 452 00:23:55,119 --> 00:23:57,719 Speaker 4: You can access Bloomberg Intelligence via b I Go, I'm 453 00:23:57,720 --> 00:24:00,360 Speaker 4: the Terminal, I'm Lisa Matteo and on paulse Need. 454 00:24:00,480 --> 00:24:01,640 Speaker 2: This is Bloomberg. 455 00:24:09,160 --> 00:24:12,879 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 456 00:24:12,960 --> 00:24:16,040 Speaker 1: weekdays at ten am Eastern on Apple Corplay and Android 457 00:24:16,040 --> 00:24:19,360 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 458 00:24:19,400 --> 00:24:22,520 Speaker 1: you get your podcasts, or watch us live on YouTube. 459 00:24:23,160 --> 00:24:25,520 Speaker 4: I'm Paul Sweeney and I'm Lisa Matteo. Filly in on 460 00:24:25,560 --> 00:24:26,679 Speaker 4: Bloomberg Intelligence. 461 00:24:26,960 --> 00:24:29,560 Speaker 2: Each week we will get research from Bloomberg and EF 462 00:24:29,640 --> 00:24:32,520 Speaker 2: previously known as New Energy Finance. They're the team at 463 00:24:32,520 --> 00:24:36,120 Speaker 2: Bloomberg that tracks and analyzes the energy transition from commodities 464 00:24:36,200 --> 00:24:40,080 Speaker 2: to power, transport, industries, buildings, and agricultural sectors. 465 00:24:40,280 --> 00:24:42,719 Speaker 4: This week, we looked at the recent policies of President 466 00:24:42,760 --> 00:24:46,040 Speaker 4: Donald Trump and how they may impact clean power moving forward. 467 00:24:46,400 --> 00:24:48,680 Speaker 2: For more, Lisa and I were joined by Meredith Annex 468 00:24:48,840 --> 00:24:50,880 Speaker 2: b n EF, head of Clean Power. 469 00:24:51,000 --> 00:24:54,080 Speaker 4: We first asked Meredith how the Trump administration is impacting 470 00:24:54,119 --> 00:24:55,560 Speaker 4: the transition to clean power. 471 00:24:55,960 --> 00:24:57,359 Speaker 9: I don't know if I've called it so much a 472 00:24:57,440 --> 00:25:00,199 Speaker 9: new policy as a reversal of the policy that's been 473 00:25:00,200 --> 00:25:03,000 Speaker 9: in place for about two years. For the last couple 474 00:25:03,040 --> 00:25:06,840 Speaker 9: of years, the big flagship policy for renewable energy in 475 00:25:06,840 --> 00:25:09,760 Speaker 9: the United States has been the Inflation Reduction Act. What 476 00:25:10,119 --> 00:25:14,639 Speaker 9: the One Big Beautiful Act now has effectively done is 477 00:25:14,640 --> 00:25:17,919 Speaker 9: is remove those tax credits on a set schedule. Now, 478 00:25:18,880 --> 00:25:21,280 Speaker 9: in the text of the bill, that schedule would be 479 00:25:21,400 --> 00:25:25,080 Speaker 9: mid next year. However, with an executive order that came out, 480 00:25:25,200 --> 00:25:27,600 Speaker 9: there's a possibility that these tax credits has been very 481 00:25:27,640 --> 00:25:31,479 Speaker 9: fundamental for the build up and expansion of renewable energy, 482 00:25:32,480 --> 00:25:35,640 Speaker 9: that those might actually become very difficult to find at 483 00:25:35,640 --> 00:25:36,400 Speaker 9: the end of this year. 484 00:25:37,280 --> 00:25:41,280 Speaker 4: All since that announcement of the change, what's changed actually 485 00:25:41,320 --> 00:25:42,680 Speaker 4: since then, since that announcement. 486 00:25:44,200 --> 00:25:46,399 Speaker 9: Yeah, what we saw was an executive order from the 487 00:25:46,440 --> 00:25:49,639 Speaker 9: Trump administration essentially saying that they're going to really look 488 00:25:49,720 --> 00:25:53,320 Speaker 9: at how companies claim these tax credits. Now, the details 489 00:25:53,320 --> 00:25:55,440 Speaker 9: on that are still pretty opaque. A lot of that's 490 00:25:55,480 --> 00:25:58,119 Speaker 9: going to come from IRS guidance that we're expecting to 491 00:25:58,119 --> 00:26:00,960 Speaker 9: see in the coming weeks or months. However, there's a 492 00:26:01,000 --> 00:26:05,520 Speaker 9: couple of things that could be open to debate. For instance, 493 00:26:05,560 --> 00:26:09,000 Speaker 9: the interpretation of what it means to safe harbor these 494 00:26:09,040 --> 00:26:11,479 Speaker 9: tax credits, which means proving that you're far enough along 495 00:26:11,560 --> 00:26:13,600 Speaker 9: that you would still merit the tax credits once you 496 00:26:13,800 --> 00:26:17,320 Speaker 9: become in service. Those definitions could change at the moment. 497 00:26:17,880 --> 00:26:21,120 Speaker 9: Usually there's a look at five percent of capex commitment 498 00:26:21,440 --> 00:26:25,159 Speaker 9: or some amount of equipment being procured, that sort of 499 00:26:25,240 --> 00:26:28,720 Speaker 9: thing as an indicator. If that changes, that could change 500 00:26:28,840 --> 00:26:31,680 Speaker 9: which projects are eligible. We could also see differences in 501 00:26:31,720 --> 00:26:35,200 Speaker 9: the timelines around how long you can have to become 502 00:26:35,240 --> 00:26:37,000 Speaker 9: in service in order to claim the tax credit. 503 00:26:37,480 --> 00:26:40,880 Speaker 2: Any sense at this early stage, Meredith, how this will 504 00:26:41,160 --> 00:26:45,760 Speaker 2: affect this overall transition to cleaner energy? Is this a 505 00:26:45,920 --> 00:26:48,359 Speaker 2: material bump in the timeline? 506 00:26:50,040 --> 00:26:53,280 Speaker 9: Unfortunately? I think it is, and that's hard because renewables 507 00:26:53,280 --> 00:26:56,000 Speaker 9: are the fastest technology to deploy in the US right now. 508 00:26:56,080 --> 00:26:59,040 Speaker 9: So the segment's been talking a lot about data centers 509 00:26:59,119 --> 00:27:02,280 Speaker 9: around the need from power in the United States. That 510 00:27:02,320 --> 00:27:04,600 Speaker 9: power is not going to come from nuclear tomorrow. It's 511 00:27:04,640 --> 00:27:07,080 Speaker 9: going to come from solar, a project that takes six 512 00:27:07,160 --> 00:27:11,440 Speaker 9: months to deploy. What we're finding is that there's sort 513 00:27:11,440 --> 00:27:14,280 Speaker 9: of a couple of sectors that are particularly impacted. On 514 00:27:14,440 --> 00:27:17,160 Speaker 9: Shore wind, for instance, is affected a lot because it's 515 00:27:17,240 --> 00:27:20,639 Speaker 9: quite location sensitive. What the tax credits did was it 516 00:27:20,640 --> 00:27:23,280 Speaker 9: allowed more projects to be able to break even at 517 00:27:23,280 --> 00:27:26,159 Speaker 9: the same rate of return, even with the change in 518 00:27:26,200 --> 00:27:29,040 Speaker 9: the quality of the wind speed that was in that area. 519 00:27:29,560 --> 00:27:31,199 Speaker 9: Now you're going to have to be a lot more picky. 520 00:27:32,320 --> 00:27:36,440 Speaker 9: Even with solar, especially things like rooftop solar. The tax 521 00:27:36,480 --> 00:27:38,879 Speaker 9: credits that are available for homeowners are disappearing by the 522 00:27:38,960 --> 00:27:41,560 Speaker 9: end of this year. When you're looking at more utility 523 00:27:41,600 --> 00:27:44,240 Speaker 9: scale solar, we think that is more robust, just because 524 00:27:44,240 --> 00:27:48,280 Speaker 9: it's so cheap. But there are concerns around what's called 525 00:27:48,320 --> 00:27:53,399 Speaker 9: the Foreign Entities of Concern FBOC categorization and how that 526 00:27:53,440 --> 00:27:57,280 Speaker 9: will get calculated, how that exposure will get calculated once 527 00:27:57,320 --> 00:28:00,440 Speaker 9: IRS guidance comes out. That could be complex because a 528 00:28:00,520 --> 00:28:03,520 Speaker 9: lot of solar modules, despite an increase in local manufacturing, 529 00:28:03,520 --> 00:28:04,360 Speaker 9: are still imported. 530 00:28:04,720 --> 00:28:08,800 Speaker 4: What changes could the act mean for data centers for 531 00:28:09,160 --> 00:28:10,240 Speaker 4: manufacturing sites. 532 00:28:11,840 --> 00:28:14,720 Speaker 9: So as of right now, a data center is much 533 00:28:14,760 --> 00:28:16,960 Speaker 9: more likely to be operational. It's connected to the grid. 534 00:28:17,080 --> 00:28:19,080 Speaker 9: So at the end of the day, the biggest thing 535 00:28:19,320 --> 00:28:21,879 Speaker 9: that the US needs, which wasn't addressed at all in 536 00:28:21,920 --> 00:28:25,880 Speaker 9: this budget bill, is more invested in transmission distribution CRAD 537 00:28:25,920 --> 00:28:29,320 Speaker 9: networks so that you can actually connect outside of that. 538 00:28:30,200 --> 00:28:32,560 Speaker 9: Renewables are the easiest thing to deploy, So if you're 539 00:28:32,680 --> 00:28:36,080 Speaker 9: talking about more power demand being on the system, you 540 00:28:36,119 --> 00:28:39,480 Speaker 9: need to generate more power. It's one thing to produce 541 00:28:39,560 --> 00:28:42,520 Speaker 9: natural gass, another thing to make that into electricity that 542 00:28:42,560 --> 00:28:46,040 Speaker 9: a data center can use. That requires a turbine. We're 543 00:28:46,080 --> 00:28:48,800 Speaker 9: seeing very long wait times on the supply chain for 544 00:28:48,880 --> 00:28:53,840 Speaker 9: new gas turbines for power plants nuclear capacity. Similarly, if 545 00:28:53,880 --> 00:28:57,040 Speaker 9: it's not a restart, can take maybe a decade before 546 00:28:57,080 --> 00:29:00,280 Speaker 9: it'll be online. If you want something online now, the 547 00:29:00,360 --> 00:29:03,000 Speaker 9: vast majority of planned and permittive projects in the US 548 00:29:03,080 --> 00:29:04,880 Speaker 9: are wind, solar, and storage. 549 00:29:05,240 --> 00:29:08,200 Speaker 4: Our thanks to Meredith adds bn EF head of Clean Power. 550 00:29:08,600 --> 00:29:11,080 Speaker 2: Staying in the Energy space, guest host Normal Linda and 551 00:29:11,080 --> 00:29:14,600 Speaker 2: I recently spoke with Isabelle Bomecki. Isabelle is a nuclear 552 00:29:14,640 --> 00:29:16,719 Speaker 2: electricity influencer and author. 553 00:29:16,880 --> 00:29:19,480 Speaker 4: She's also a former model who is built a following 554 00:29:19,520 --> 00:29:22,800 Speaker 4: discussing the role of nuclear energy to fight climate change. 555 00:29:22,960 --> 00:29:25,959 Speaker 4: Her upcoming book, rad Future will be released this summer. 556 00:29:26,200 --> 00:29:29,640 Speaker 4: The book focuses on nuclear electricity and its effectiveness. 557 00:29:29,920 --> 00:29:32,520 Speaker 2: I began the conversation with isabel by asking about some 558 00:29:32,600 --> 00:29:34,400 Speaker 2: of the points she's trying to get across to our 559 00:29:34,440 --> 00:29:36,320 Speaker 2: audience when it comes to nuclear energy. 560 00:29:36,840 --> 00:29:39,040 Speaker 10: Well, first of all, I think when we talk about 561 00:29:39,160 --> 00:29:42,280 Speaker 10: nuclear energy, it's very important to highlight so this is 562 00:29:42,560 --> 00:29:46,080 Speaker 10: the most reliable source of clean energy. And so when 563 00:29:46,120 --> 00:29:49,040 Speaker 10: we're talking about a transition to clean energy, which is 564 00:29:49,080 --> 00:29:52,640 Speaker 10: what we all know are aiming to do, we have 565 00:29:52,720 --> 00:29:55,640 Speaker 10: to make sure we remain with the stable grid, and 566 00:29:55,720 --> 00:29:58,440 Speaker 10: nuclear really helps with that. We've seen around the world 567 00:29:58,480 --> 00:30:02,000 Speaker 10: that places that turn of from nuclear end up with 568 00:30:02,160 --> 00:30:05,200 Speaker 10: thirtier and less reliable grids. But there are so many 569 00:30:05,280 --> 00:30:09,120 Speaker 10: other benefits. For instance, a nuclear power plant employees up 570 00:30:09,160 --> 00:30:12,840 Speaker 10: to one thousand people who have very high being stable jobs, 571 00:30:13,040 --> 00:30:15,880 Speaker 10: union jobs. It's the source of energy that uses the 572 00:30:15,960 --> 00:30:19,400 Speaker 10: least materials, the least amount of land, and more importantly, 573 00:30:19,720 --> 00:30:22,440 Speaker 10: as we're witnessing right now, it's a source of energy 574 00:30:22,440 --> 00:30:25,320 Speaker 10: that has bipartisan support. In the United States. The Biding 575 00:30:25,360 --> 00:30:29,360 Speaker 10: administration was extremely pro nuclear and so is the Trump administration. 576 00:30:29,440 --> 00:30:31,080 Speaker 10: So I think those are all things that we need 577 00:30:31,120 --> 00:30:34,000 Speaker 10: to take into consideration when we're talking about a clean 578 00:30:34,120 --> 00:30:35,960 Speaker 10: energy future as well. 579 00:30:36,000 --> 00:30:38,800 Speaker 11: So back in twenty nineteen, there was the Aulstragian bush 580 00:30:39,040 --> 00:30:43,600 Speaker 11: fire season, and then additionally we had the Amazon rainforest wildfires. 581 00:30:43,640 --> 00:30:46,400 Speaker 11: Both of these events really helped to inspire you to 582 00:30:46,440 --> 00:30:48,360 Speaker 11: become a part of the solution. When we think about 583 00:30:48,720 --> 00:30:52,440 Speaker 11: fighting climate change. How much progress has been made in 584 00:30:52,520 --> 00:30:55,320 Speaker 11: that fight against climate change since then? 585 00:30:55,840 --> 00:31:00,800 Speaker 10: Well, some progress, but as always, our energy and you 586 00:31:00,920 --> 00:31:03,960 Speaker 10: were talking about this beforehand, our energy demand just keeps 587 00:31:04,000 --> 00:31:07,800 Speaker 10: increasing because we keep inventing newer technologies that use a 588 00:31:07,800 --> 00:31:10,040 Speaker 10: lot of energy. Right now, you know, ten years ago 589 00:31:10,080 --> 00:31:13,160 Speaker 10: it was cryptocurrencies. Right now we're talking about AI and 590 00:31:13,200 --> 00:31:17,800 Speaker 10: the huge increasing energy demand, and so I think unfortunately 591 00:31:18,040 --> 00:31:20,360 Speaker 10: we just you know that everyone out of way is 592 00:31:20,360 --> 00:31:22,840 Speaker 10: to use more and more energy. So while some progress 593 00:31:22,880 --> 00:31:25,840 Speaker 10: has been made, our demand has also increased, so we're 594 00:31:25,840 --> 00:31:28,600 Speaker 10: still burning a lot of fossil fuels. About eighty percent 595 00:31:28,680 --> 00:31:32,400 Speaker 10: of the world's energy is still provided by by fossil fuels. 596 00:31:33,360 --> 00:31:36,320 Speaker 10: So we have to keep investing in these technologies, and 597 00:31:36,360 --> 00:31:39,000 Speaker 10: this is where nuclear can play a really big role. 598 00:31:39,080 --> 00:31:42,160 Speaker 10: It's already the second largest source of clean energy in 599 00:31:42,200 --> 00:31:45,200 Speaker 10: the world, just behind hydropower. It is the largest source 600 00:31:45,200 --> 00:31:48,280 Speaker 10: of clean energy in the United States, and I think 601 00:31:48,440 --> 00:31:51,920 Speaker 10: for you know, over three decades it was completely ignored 602 00:31:52,560 --> 00:31:55,000 Speaker 10: for a variety of reasons, and some of them is 603 00:31:55,480 --> 00:31:58,000 Speaker 10: the fears around accidents, which we can talk about a 604 00:31:58,040 --> 00:32:01,120 Speaker 10: little bit as well. So it's very important that we're 605 00:32:01,160 --> 00:32:04,320 Speaker 10: now investing in this technology again so that we can 606 00:32:04,360 --> 00:32:06,640 Speaker 10: speed up this transition to clean energy. 607 00:32:06,640 --> 00:32:10,120 Speaker 2: Isabelle talk to us about modular plants. We don't have 608 00:32:10,160 --> 00:32:12,840 Speaker 2: to go and build these monster plants that we see occasionally. 609 00:32:12,840 --> 00:32:14,800 Speaker 2: I know one most recent one was built years ago 610 00:32:15,000 --> 00:32:18,480 Speaker 2: in Georgia. Talked just about the smaller modular plants, how 611 00:32:18,520 --> 00:32:19,560 Speaker 2: that technology could. 612 00:32:19,440 --> 00:32:22,520 Speaker 10: That be used. So I think we actually do need 613 00:32:22,560 --> 00:32:26,040 Speaker 10: to build some of the large plants, because as much 614 00:32:26,040 --> 00:32:29,720 Speaker 10: as small modular reactors or these new technologies that people 615 00:32:29,720 --> 00:32:33,200 Speaker 10: are very very excited about. Unfortunately, if I'm a utility 616 00:32:33,600 --> 00:32:35,960 Speaker 10: or a data center, I cannot go and buy one 617 00:32:36,000 --> 00:32:38,520 Speaker 10: of those off the market just yet. None of those 618 00:32:38,560 --> 00:32:42,400 Speaker 10: companies have built prototypes, so we're still pretty far from 619 00:32:42,440 --> 00:32:45,600 Speaker 10: them being commercially available. In the meantime, we have to 620 00:32:45,640 --> 00:32:49,600 Speaker 10: build the existing technologies that we have. But some of 621 00:32:49,280 --> 00:32:53,560 Speaker 10: the benefits that the small modular companies claim they will 622 00:32:53,560 --> 00:32:56,920 Speaker 10: have is they'll be able to reduce costs, time to build, 623 00:32:57,080 --> 00:32:59,360 Speaker 10: and so on. But again, all of this has yet 624 00:32:59,400 --> 00:33:01,720 Speaker 10: to be proven, and so I think we cannot put 625 00:33:01,760 --> 00:33:04,360 Speaker 10: all of our eggs in that one basket. We have 626 00:33:04,440 --> 00:33:07,440 Speaker 10: to invest in technology that we already know works. 627 00:33:08,040 --> 00:33:09,959 Speaker 11: So it's about you have a book called rad Future. 628 00:33:10,360 --> 00:33:11,360 Speaker 11: What's that all about? 629 00:33:11,800 --> 00:33:14,600 Speaker 10: So Red Future was inspired really by my own need 630 00:33:15,160 --> 00:33:19,480 Speaker 10: to have an accessible book about nuclear electricity. To your point, 631 00:33:19,520 --> 00:33:22,120 Speaker 10: In twenty nineteen, I saw the fires in Australia and 632 00:33:22,160 --> 00:33:25,640 Speaker 10: the Amazon and that inspired me to do something using 633 00:33:25,680 --> 00:33:28,520 Speaker 10: my platform that I had built as a fashion model, 634 00:33:29,320 --> 00:33:32,520 Speaker 10: to do something about climate. And at the time I 635 00:33:32,600 --> 00:33:35,920 Speaker 10: tried buying books that explained how nuclear worked, and I 636 00:33:35,960 --> 00:33:39,040 Speaker 10: couldn't find anything that was accessible. It took me many, 637 00:33:39,040 --> 00:33:40,880 Speaker 10: many years of research to get to a point where 638 00:33:40,920 --> 00:33:44,640 Speaker 10: I could break down this information about nuclear, the history, 639 00:33:44,680 --> 00:33:46,960 Speaker 10: the history of the anti nuclear movement, and the technology 640 00:33:46,960 --> 00:33:51,000 Speaker 10: itself into something that was extremely accessible. And so Red 641 00:33:51,040 --> 00:33:53,720 Speaker 10: Future was inspired by that, by a need to provide 642 00:33:53,800 --> 00:33:57,000 Speaker 10: accessible material. And I think it's such an important topic 643 00:33:57,080 --> 00:34:01,200 Speaker 10: that right now is being discussed everywhere, and I think 644 00:34:01,320 --> 00:34:03,920 Speaker 10: people should be able to learn about the technology and 645 00:34:03,960 --> 00:34:05,040 Speaker 10: answer their questions. 646 00:34:05,480 --> 00:34:07,680 Speaker 2: Is it well with the Trump administration to used inse 647 00:34:07,760 --> 00:34:12,239 Speaker 2: that nuclear power may get more support, less support, or 648 00:34:12,440 --> 00:34:13,440 Speaker 2: no real change. 649 00:34:13,840 --> 00:34:17,560 Speaker 10: No, the Trump administration is extremely supportive of nuclear. He 650 00:34:17,680 --> 00:34:22,600 Speaker 10: passed several executive orders mandating the acceleration of nuclear in 651 00:34:22,640 --> 00:34:27,120 Speaker 10: the United States to be determined what's going to happen. Also, 652 00:34:27,400 --> 00:34:30,879 Speaker 10: in the One Big Beautiful Bill, they basically maintained all 653 00:34:30,960 --> 00:34:35,440 Speaker 10: the support, the credits, the credit techs, and the LPO 654 00:34:35,480 --> 00:34:38,760 Speaker 10: guarantees for nuclear as well. So the administration is extremely 655 00:34:38,800 --> 00:34:42,680 Speaker 10: supportive of nuclear. Hopefully this will play out in the 656 00:34:42,719 --> 00:34:45,279 Speaker 10: real world, but it's still to be determined, depending on 657 00:34:45,960 --> 00:34:47,960 Speaker 10: some of the nuances of the text credits. 658 00:34:48,480 --> 00:34:52,360 Speaker 4: Our thanks to nuclear electricity influencer and author Isabelle bo Mecki. 659 00:34:53,160 --> 00:34:57,840 Speaker 1: This is the Bloomberg Intelligence Podcast available on Apple, Spotify, 660 00:34:58,040 --> 00:35:02,000 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 661 00:35:02,200 --> 00:35:05,480 Speaker 1: ten am to noon Eastern on Bloomberg dot Com, the 662 00:35:05,560 --> 00:35:09,440 Speaker 1: iHeartRadio app, tune In, and the Bloomberg Business app. You 663 00:35:09,480 --> 00:35:12,759 Speaker 1: can also watch us live every weekday on YouTube and 664 00:35:13,000 --> 00:35:14,920 Speaker 1: always on the Bloomberg terminal