1 00:00:00,280 --> 00:00:11,400 Speaker 1: Bloomberg Audio, Studios, podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,480 --> 00:00:13,560 Speaker 1: with Scarletfoo and Paul Sweeney. 3 00:00:13,640 --> 00:00:15,720 Speaker 2: How do you think the FED is looking at tariffs? 4 00:00:15,920 --> 00:00:18,119 Speaker 3: The uncertainty of terriffs. Let's take a look at the 5 00:00:18,239 --> 00:00:20,119 Speaker 3: sectors and how they performed. 6 00:00:19,680 --> 00:00:21,720 Speaker 4: A lot of investors getting whip saled every day by 7 00:00:21,760 --> 00:00:22,759 Speaker 4: news events. 8 00:00:22,440 --> 00:00:26,200 Speaker 1: Breaking market headlines, and corporate news from across the globe. 9 00:00:26,200 --> 00:00:28,840 Speaker 3: Could we see a market disruption of market events? 10 00:00:28,880 --> 00:00:30,920 Speaker 2: So people just too exuberant out there? 11 00:00:31,040 --> 00:00:33,600 Speaker 3: You see some so called low quality stocks driving this 12 00:00:33,680 --> 00:00:34,440 Speaker 3: short term rally. 13 00:00:34,479 --> 00:00:39,280 Speaker 1: Bloomberg Intelligence with Scarletfoo and Paul Sweeney on Bloomberg Radio, 14 00:00:39,479 --> 00:00:41,360 Speaker 1: YouTube and Bloomberg Originals. 15 00:00:42,760 --> 00:00:45,360 Speaker 3: I'm Scarletfoo and I'm normal Inda, filling in for Paul 16 00:00:45,360 --> 00:00:48,320 Speaker 3: Sweeney on today's Bloomberg Intelligence Show. We dig inside the 17 00:00:48,320 --> 00:00:51,080 Speaker 3: big business stories impacting Wall Street and the global markets. 18 00:00:51,240 --> 00:00:53,479 Speaker 5: Each and every week, we provide in depth research and 19 00:00:53,600 --> 00:00:55,840 Speaker 5: data on some of the two thousand companies and one 20 00:00:55,920 --> 00:00:58,560 Speaker 5: hundred and thirty industries are analysts cover worldwide. 21 00:00:58,600 --> 00:01:00,680 Speaker 3: Today, we look at one of the biggest mining deals 22 00:01:00,680 --> 00:01:01,520 Speaker 3: in over a decade. 23 00:01:01,680 --> 00:01:03,800 Speaker 5: Plaus Well look at why the Danish drug maker Novo 24 00:01:03,880 --> 00:01:06,600 Speaker 5: Nordist is cutting about eleven percent of its workforce. 25 00:01:06,680 --> 00:01:09,080 Speaker 3: But first we begin with earnings from the computer tech 26 00:01:09,080 --> 00:01:10,000 Speaker 3: company Oracle. 27 00:01:10,200 --> 00:01:12,440 Speaker 5: This week, the company gave an aggressive outlook for its 28 00:01:12,480 --> 00:01:14,800 Speaker 5: cloud business, causing the stock to surge the most since 29 00:01:14,920 --> 00:01:15,960 Speaker 5: nineteen ninety two. 30 00:01:16,319 --> 00:01:18,920 Speaker 3: For more, guest host Alexis Christophers and I were joined 31 00:01:18,959 --> 00:01:22,959 Speaker 3: by anor Agrana, Bloomberg Intelligence technology analyst. We first asked 32 00:01:22,959 --> 00:01:25,520 Speaker 3: on Aroan to break down Oracles earnings and the company's 33 00:01:25,560 --> 00:01:26,400 Speaker 3: major stock jump. 34 00:01:26,720 --> 00:01:28,960 Speaker 6: Oracle is a very unique company because it has your 35 00:01:29,000 --> 00:01:32,200 Speaker 6: traditional software but also a few years ago it started 36 00:01:32,600 --> 00:01:36,479 Speaker 6: a cloud business called Oracle Cloud Infrastructure, something very similar 37 00:01:36,480 --> 00:01:39,959 Speaker 6: to what Aws Tells or Microsoft Azure. And one of 38 00:01:39,959 --> 00:01:42,440 Speaker 6: the advantages it has is it just rents out it's 39 00:01:42,800 --> 00:01:44,680 Speaker 6: you know, servers for people to go out and train 40 00:01:44,760 --> 00:01:48,320 Speaker 6: their AI models or you know, run their applications on it. 41 00:01:48,400 --> 00:01:51,200 Speaker 6: So this is really a new business for them, you know, 42 00:01:51,320 --> 00:01:53,520 Speaker 6: just came up the last few years, and that's where 43 00:01:53,560 --> 00:01:55,680 Speaker 6: the biggest growth is and that's what's driving the stock. 44 00:01:55,920 --> 00:01:59,360 Speaker 3: What's happening with the rest of the company, its operations, 45 00:01:59,360 --> 00:02:02,120 Speaker 3: it's software bil business. Is it still cutting costs for instance, 46 00:02:02,160 --> 00:02:03,880 Speaker 3: and reducing headcount. 47 00:02:04,480 --> 00:02:07,280 Speaker 6: The advantage Oracle has compared to let's say a company 48 00:02:07,280 --> 00:02:10,720 Speaker 6: like core v for Nebus, is that it has a 49 00:02:10,919 --> 00:02:15,200 Speaker 6: very high margin database business and very high margin application business. 50 00:02:15,520 --> 00:02:18,239 Speaker 6: These businesses have gross margins, you know, let's say north 51 00:02:18,280 --> 00:02:21,240 Speaker 6: of ninety percent. Now when you marry that with a 52 00:02:21,320 --> 00:02:24,280 Speaker 6: massive AI infrastructure business where you need to spend a 53 00:02:24,280 --> 00:02:28,080 Speaker 6: lot of money, that's problematic for somebody that doesn't have that. 54 00:02:28,280 --> 00:02:31,440 Speaker 6: But Oracle has that luxury that it can spend billions 55 00:02:31,520 --> 00:02:33,840 Speaker 6: of dollars. You know, before we were thinking they're going 56 00:02:33,880 --> 00:02:36,880 Speaker 6: to spend you know, somewhere in the low thirty billion range, 57 00:02:36,960 --> 00:02:39,880 Speaker 6: which was already higher than what they had guided, and 58 00:02:40,400 --> 00:02:42,839 Speaker 6: you know, even the street was expecting. They came out 59 00:02:42,840 --> 00:02:44,639 Speaker 6: and said they're going to spend over thirty five billion 60 00:02:45,280 --> 00:02:47,880 Speaker 6: and you know the next financial year. So that's a 61 00:02:47,919 --> 00:02:50,880 Speaker 6: lot of money going into expanding data center, buying chips 62 00:02:51,160 --> 00:02:54,200 Speaker 6: by you know, creating a new infrastructure to fulfill this demand. 63 00:02:54,840 --> 00:02:59,480 Speaker 7: When you look at the competition here, Microsoft, Amazon, Alphabet, 64 00:02:59,639 --> 00:03:03,000 Speaker 7: what does Oracle or how does Oracle differentiate itself not 65 00:03:03,040 --> 00:03:06,960 Speaker 7: only for the customer, the client, but for investors. 66 00:03:08,200 --> 00:03:09,760 Speaker 6: Yeah, and one of the things I would say is 67 00:03:10,040 --> 00:03:12,520 Speaker 6: everybody is having a good time in this particular party. 68 00:03:12,560 --> 00:03:15,000 Speaker 8: It's not just you know, one company that's scaling it. 69 00:03:15,000 --> 00:03:17,639 Speaker 6: It's just the size of their business is far smaller 70 00:03:17,680 --> 00:03:21,480 Speaker 6: than it is for let's say AWS or Microsoft. You know, 71 00:03:21,880 --> 00:03:23,959 Speaker 6: in the last financial year, the revenue was you know, 72 00:03:24,080 --> 00:03:27,120 Speaker 6: roughly about ten billion or so. Aws's rund rate is 73 00:03:27,200 --> 00:03:30,359 Speaker 6: one hundred and twenty five billion. Microsoft's business is seventy 74 00:03:30,360 --> 00:03:33,840 Speaker 6: five billion. Google's business is fifty billion. So you know, please, 75 00:03:34,200 --> 00:03:36,040 Speaker 6: you know, you've got to take that with the context 76 00:03:36,080 --> 00:03:38,960 Speaker 6: of how big Oracle's cloud infrastructure is compared to some 77 00:03:39,000 --> 00:03:40,560 Speaker 6: of the bigger vendors out there. 78 00:03:41,360 --> 00:03:43,320 Speaker 3: You know, honrog it feels like Oracle kind of came 79 00:03:43,360 --> 00:03:47,680 Speaker 3: from out of nowhere with its you know, contributions to 80 00:03:47,920 --> 00:03:50,640 Speaker 3: the AI space and being such a big player. Certainly, 81 00:03:50,720 --> 00:03:54,240 Speaker 3: the bookings of almost half a trillion dollars caught my eye, 82 00:03:54,240 --> 00:03:55,880 Speaker 3: and it's more than four times what it was the 83 00:03:55,880 --> 00:03:58,360 Speaker 3: same time last year. Are there any other kind of 84 00:03:58,440 --> 00:04:01,400 Speaker 3: dark horses out there that you would be keeping your 85 00:04:01,400 --> 00:04:03,640 Speaker 3: eye on that could be the next Oracle, which is 86 00:04:03,680 --> 00:04:05,080 Speaker 3: already feels like it's the next in. 87 00:04:05,160 --> 00:04:08,000 Speaker 6: VideA, Yeah, I think one of the things you have 88 00:04:08,040 --> 00:04:09,720 Speaker 6: to think about it is it's not easy to come 89 00:04:09,800 --> 00:04:12,160 Speaker 6: up with this structure. A lot of business like this 90 00:04:12,280 --> 00:04:14,760 Speaker 6: because you need billions of dollars to create a data 91 00:04:14,800 --> 00:04:17,440 Speaker 6: center or to lease out a data center, then buy 92 00:04:17,480 --> 00:04:21,720 Speaker 6: equipment like in Vidia chips and computing equipment. There are 93 00:04:21,760 --> 00:04:24,800 Speaker 6: a handful of companies like you know, core beav of Nebeus, 94 00:04:24,800 --> 00:04:28,520 Speaker 6: which are smaller neo cloud players that specialize in renting 95 00:04:28,520 --> 00:04:32,120 Speaker 6: and VideA GPUs to customers. Now, the bigger vendors have 96 00:04:32,279 --> 00:04:34,840 Speaker 6: the capital that they are also expanding it. But again, 97 00:04:34,920 --> 00:04:37,159 Speaker 6: as I said, given the size of the increment that 98 00:04:37,200 --> 00:04:39,240 Speaker 6: they are seeing, you know, you're not getting that same 99 00:04:39,279 --> 00:04:39,839 Speaker 6: growth rate. 100 00:04:40,640 --> 00:04:42,719 Speaker 7: Do you think there's a little over exuberance though in 101 00:04:42,760 --> 00:04:45,160 Speaker 7: the market, especially with so much talk about whether or 102 00:04:45,240 --> 00:04:48,760 Speaker 7: not we are in an AI bubble and what happens 103 00:04:48,760 --> 00:04:49,880 Speaker 7: if it starts to deflate. 104 00:04:51,120 --> 00:04:51,320 Speaker 8: Yeah. 105 00:04:51,440 --> 00:04:53,039 Speaker 6: See, one of the things you have to remember is 106 00:04:53,040 --> 00:04:56,000 Speaker 6: for Oracle, at least they have this backlock of ordos 107 00:04:56,040 --> 00:04:58,640 Speaker 6: coming in and it's coming you know. For example, one 108 00:04:58,680 --> 00:05:01,120 Speaker 6: of the biggest customers right now is open Ai. If 109 00:05:01,160 --> 00:05:04,200 Speaker 6: Opening Eye continues to expand revenue at a good pace 110 00:05:04,400 --> 00:05:06,479 Speaker 6: over the next several years, they are going to be 111 00:05:06,480 --> 00:05:09,320 Speaker 6: spending a lot of money to train their models in 112 00:05:09,400 --> 00:05:11,320 Speaker 6: order to stay ahead. So I think there is a 113 00:05:11,360 --> 00:05:13,719 Speaker 6: lot of relationship over there. If let's say you and 114 00:05:13,760 --> 00:05:16,640 Speaker 6: I don't use chat GPT as much, that would have 115 00:05:16,680 --> 00:05:20,039 Speaker 6: an impact on open Aiy's revenue, That would have impact 116 00:05:20,160 --> 00:05:22,800 Speaker 6: downstream revenue on all the other players, you know, whether 117 00:05:22,839 --> 00:05:23,840 Speaker 6: that's in Verdiao Oracle. 118 00:05:24,000 --> 00:05:26,039 Speaker 8: But frankly speaking, we are not there yet. 119 00:05:26,960 --> 00:05:29,120 Speaker 3: It doesn't feel like this is a situation where it's 120 00:05:29,120 --> 00:05:33,039 Speaker 3: a zero sum game Oracle wins and Microsoft, Google and 121 00:05:33,240 --> 00:05:34,680 Speaker 3: Amazon lose, or is it. 122 00:05:36,160 --> 00:05:38,760 Speaker 6: So there are a few companies that fall into this pocket, 123 00:05:38,800 --> 00:05:44,080 Speaker 6: you know, and those include Amazon Web Services, Microsoft, Google 124 00:05:44,120 --> 00:05:48,560 Speaker 6: Cloud Code, viv Oracle, and Nabius for example. So these 125 00:05:48,600 --> 00:05:50,920 Speaker 6: are the ones that are seeing extra spending right now. 126 00:05:51,000 --> 00:05:53,039 Speaker 6: But when you look at on the application side, you know, 127 00:05:53,040 --> 00:05:56,000 Speaker 6: whether that's Salesforce or Workday, those guys are not seeing 128 00:05:56,000 --> 00:05:58,599 Speaker 6: that benefit because they're not in that infrastructure game. So 129 00:05:58,640 --> 00:06:01,480 Speaker 6: there's a huge difference between two vendors, or between the 130 00:06:01,520 --> 00:06:02,760 Speaker 6: two category of vendors. 131 00:06:03,120 --> 00:06:03,520 Speaker 8: Right now. 132 00:06:03,520 --> 00:06:05,719 Speaker 6: We are more in the built phase of AI, not 133 00:06:05,800 --> 00:06:07,640 Speaker 6: the implementation phase of AI. 134 00:06:08,240 --> 00:06:12,800 Speaker 7: You know, it's hard to believe, Scarlett, but this stock 135 00:06:13,160 --> 00:06:16,679 Speaker 7: and is now worth more than has a larger market 136 00:06:16,720 --> 00:06:21,120 Speaker 7: cap than Walmart, Eli, Lilly, JP, Morgan Chase. 137 00:06:21,560 --> 00:06:23,560 Speaker 3: Yeah, that is pretty remarkable, and again it feels like 138 00:06:23,600 --> 00:06:25,080 Speaker 3: it kind of came from out of nowhere. 139 00:06:25,160 --> 00:06:27,720 Speaker 7: You're right on that, which is why I brought up, 140 00:06:27,760 --> 00:06:29,880 Speaker 7: you know, Amazon, Microsoft, right on your rog I mean, 141 00:06:29,920 --> 00:06:31,920 Speaker 7: the fact that those were the names we were really 142 00:06:31,920 --> 00:06:34,839 Speaker 7: talking about when it came to AI and then sort 143 00:06:34,880 --> 00:06:36,760 Speaker 7: of Oracle comes out of out of the blue. 144 00:06:38,000 --> 00:06:41,120 Speaker 6: Yeah, but that's the whole point this particular infrastructure. If 145 00:06:41,160 --> 00:06:43,920 Speaker 6: you're going to run large models, it's very unlikely you 146 00:06:44,040 --> 00:06:45,360 Speaker 6: going to do it in house. I mean, you can 147 00:06:45,440 --> 00:06:47,520 Speaker 6: do it, but you required a lot of investment going in. 148 00:06:47,880 --> 00:06:51,839 Speaker 6: You're better off renting out data center capacity or computing 149 00:06:51,839 --> 00:06:55,159 Speaker 6: capacity from one of the cloud vendors. And Oracle has 150 00:06:55,200 --> 00:06:57,600 Speaker 6: the money to spend it, and they have the relationship 151 00:06:57,640 --> 00:07:00,520 Speaker 6: with Nvidia to get those GPUs. And on top of that, 152 00:07:00,520 --> 00:07:04,160 Speaker 6: they're offering this to customers and saying here's my equipment, 153 00:07:04,279 --> 00:07:06,920 Speaker 6: come and test it out the way you like it. Amazon, 154 00:07:06,920 --> 00:07:10,160 Speaker 6: on the other hand, thought example sells it as a package, 155 00:07:10,440 --> 00:07:13,160 Speaker 6: and that's where you're you're seeing some distinction. And also 156 00:07:13,200 --> 00:07:15,160 Speaker 6: the size, as I mentioned, before our. 157 00:07:15,000 --> 00:07:17,920 Speaker 5: Thanks to Honor Agrana, Bloomberg Intelligence technology analysts. 158 00:07:18,160 --> 00:07:20,600 Speaker 3: We move next to news in the telecommunications space. 159 00:07:20,800 --> 00:07:23,680 Speaker 5: This week, the space tech company SpaceX agreed to buy 160 00:07:23,720 --> 00:07:27,480 Speaker 5: wireless spectrum from EchoStar for about seventeen billion dollars in 161 00:07:27,560 --> 00:07:28,400 Speaker 5: cash and stock. 162 00:07:28,520 --> 00:07:31,160 Speaker 3: And this comes weeks after EchoStar agreed to sell spectrum 163 00:07:31,240 --> 00:07:33,680 Speaker 3: licenses to AT and T for about twenty three billion 164 00:07:33,720 --> 00:07:35,800 Speaker 3: dollars in an all cash transaction. 165 00:07:36,280 --> 00:07:36,640 Speaker 8: For more. 166 00:07:36,760 --> 00:07:40,320 Speaker 5: Guest host Alexis Christopherus was joined by John Butler, Bloomberg 167 00:07:40,360 --> 00:07:43,240 Speaker 5: Intelligence Senior telecom analysts. We first asked John how this 168 00:07:43,360 --> 00:07:44,600 Speaker 5: deal will help SpaceX. 169 00:07:45,280 --> 00:07:48,800 Speaker 9: So the key to this deal is a spectrum called 170 00:07:48,920 --> 00:07:54,400 Speaker 9: AWS four. So if you look at Starlink and it's 171 00:07:54,480 --> 00:07:58,280 Speaker 9: sprivals in the satellite space, they all hold spectrum, but 172 00:07:58,320 --> 00:08:02,960 Speaker 9: they're not allowed to offer wireless spectrum, I'm sorry, wireless 173 00:08:03,000 --> 00:08:09,880 Speaker 9: services over that spectrum. AWS four spectrum, according to the FCC, 174 00:08:10,800 --> 00:08:14,760 Speaker 9: can be used flexibly. It's called flexible use spectrum, meaning 175 00:08:15,040 --> 00:08:19,600 Speaker 9: the satellite providers can offer anything from mobile services to 176 00:08:19,800 --> 00:08:25,680 Speaker 9: satellite data, downlinked services, video, whatever. It's open use spectrum 177 00:08:25,760 --> 00:08:28,080 Speaker 9: if you want to think of it that way. And 178 00:08:28,200 --> 00:08:32,400 Speaker 9: so Starlink getting its hands on this key spectrum will 179 00:08:32,440 --> 00:08:35,800 Speaker 9: allow them at some point in the future to potentially 180 00:08:35,960 --> 00:08:39,240 Speaker 9: enter the wireless market as a competitor to AT and T, 181 00:08:39,480 --> 00:08:41,440 Speaker 9: Verizon and T Mobile. 182 00:08:42,200 --> 00:08:45,200 Speaker 7: So EchoStar's shareholders seem to like this deal. What are 183 00:08:45,240 --> 00:08:48,160 Speaker 7: they going to do with the proceeds? What will Echo 184 00:08:48,160 --> 00:08:49,640 Speaker 7: Star do with the proceeds of the sale? 185 00:08:50,760 --> 00:08:53,439 Speaker 9: So for EchoStar, it's a very good deal as well. 186 00:08:53,480 --> 00:08:57,240 Speaker 9: They have really struggled to get their wireless service off 187 00:08:57,280 --> 00:09:00,600 Speaker 9: the ground. They've been building this five G wireless network 188 00:09:01,080 --> 00:09:04,839 Speaker 9: and have a relatively weak prepaid brand called Boost and 189 00:09:05,240 --> 00:09:09,680 Speaker 9: they've really struggled there. So this brings in much needed 190 00:09:09,760 --> 00:09:13,720 Speaker 9: funds for them to pay down debt. They've been a 191 00:09:13,760 --> 00:09:18,199 Speaker 9: bit stretched on that front, over leveraged, and you know, 192 00:09:18,559 --> 00:09:21,240 Speaker 9: this deal, together with a deal a couple of weeks 193 00:09:21,240 --> 00:09:23,080 Speaker 9: ago with AT and T, is going to bring in 194 00:09:23,160 --> 00:09:27,000 Speaker 9: billions of dollars to reduce debt. That's number one. And 195 00:09:27,080 --> 00:09:29,840 Speaker 9: number two, it brings in fresh capital for them to 196 00:09:30,000 --> 00:09:33,640 Speaker 9: invest in the business. And I have a feeling they're 197 00:09:33,679 --> 00:09:36,440 Speaker 9: probably going to take a step back and reevaluate the 198 00:09:36,520 --> 00:09:40,400 Speaker 9: strategy here based on the fresh funding and decide what 199 00:09:40,600 --> 00:09:43,439 Speaker 9: the best avenues of growth may be going forward. 200 00:09:44,040 --> 00:09:46,760 Speaker 7: I'm wondering if this is going to satisfy the FCC 201 00:09:46,920 --> 00:09:48,760 Speaker 7: this deal, because I know that they were sort of 202 00:09:49,200 --> 00:09:52,160 Speaker 7: on EchoStar's case, if you will. They wanted them to 203 00:09:52,280 --> 00:09:55,040 Speaker 7: sell some of their airwaves as the company looks to 204 00:09:55,160 --> 00:09:57,640 Speaker 7: roll out it's five G technology. I know they sold 205 00:09:58,400 --> 00:10:01,160 Speaker 7: some spectrum licenses to at and recently. So do you 206 00:10:01,160 --> 00:10:03,440 Speaker 7: think this is going to sort of take the FCC 207 00:10:03,559 --> 00:10:04,320 Speaker 7: off their backs? 208 00:10:06,000 --> 00:10:06,440 Speaker 2: I do. 209 00:10:06,559 --> 00:10:12,239 Speaker 9: I mean, I think Starlink was working with the FCC. 210 00:10:12,520 --> 00:10:16,360 Speaker 9: They had written a letter about Echo Stars under use 211 00:10:16,400 --> 00:10:20,840 Speaker 9: of this spectrum. Starlink obviously really wants to get their 212 00:10:20,880 --> 00:10:23,520 Speaker 9: hands on it, and I think the FCC really took 213 00:10:23,600 --> 00:10:26,080 Speaker 9: their side on this one. I think they understood that 214 00:10:27,120 --> 00:10:31,400 Speaker 9: echo Star, being the only owner of this flexible use spectrum, 215 00:10:32,040 --> 00:10:34,400 Speaker 9: had not really put it to use to date, so 216 00:10:34,640 --> 00:10:37,920 Speaker 9: in their view it was being underutilized and being such 217 00:10:37,960 --> 00:10:43,800 Speaker 9: a critical asset, felt that, you know, sale might make sense, 218 00:10:43,880 --> 00:10:47,240 Speaker 9: and so they really EchoStar really got into it with 219 00:10:47,280 --> 00:10:51,080 Speaker 9: the FCC trying to protect those licenses. But in the 220 00:10:51,200 --> 00:10:53,000 Speaker 9: end here I think, if you want to think of 221 00:10:53,040 --> 00:10:57,439 Speaker 9: it this way, the FCC won the day and net. 222 00:10:57,640 --> 00:10:59,960 Speaker 9: I think it's a good deal for both Echo Stars 223 00:11:00,600 --> 00:11:02,400 Speaker 9: and Starlink and the FCC. 224 00:11:03,360 --> 00:11:06,480 Speaker 7: Now I know SpaceX or Starlink had a deal with 225 00:11:06,559 --> 00:11:09,680 Speaker 7: T Mobile, right, how is this spectrum acquisition with EchoStar 226 00:11:09,800 --> 00:11:10,640 Speaker 7: different if at all? 227 00:11:11,440 --> 00:11:15,880 Speaker 9: So? I think ultimately Starlink could go from being a 228 00:11:15,920 --> 00:11:21,200 Speaker 9: partner with T Mobile to being a competitor of T Mobiles. 229 00:11:21,840 --> 00:11:24,800 Speaker 9: As I said a moment ago, this spectrum opens the 230 00:11:24,840 --> 00:11:28,880 Speaker 9: door for them to get into wireless directly without having 231 00:11:28,920 --> 00:11:32,640 Speaker 9: to partner with a terrestrial carrier. The current deal with 232 00:11:32,760 --> 00:11:36,880 Speaker 9: T Mobile basically has Starlink. I think the best way 233 00:11:36,880 --> 00:11:40,240 Speaker 9: to think about it is they're almost like a tower company. 234 00:11:40,360 --> 00:11:44,480 Speaker 9: They're providing the space based cell sites if you will, 235 00:11:44,640 --> 00:11:48,160 Speaker 9: to carry T Mobile traffic, and they get paid a 236 00:11:48,200 --> 00:11:52,120 Speaker 9: fee for that, but they're not earning the level of 237 00:11:52,240 --> 00:11:55,920 Speaker 9: profits that you could as as a direct carrier. So 238 00:11:56,080 --> 00:12:00,079 Speaker 9: down the road, Starlink, understanding this, wants to enter the 239 00:12:00,760 --> 00:12:03,800 Speaker 9: cellular business directly. They have a lot to do before 240 00:12:03,880 --> 00:12:08,640 Speaker 9: that that happens, but I think it longer term puts 241 00:12:08,720 --> 00:12:11,599 Speaker 9: the deal with T Mobile in jeopardy. 242 00:12:11,880 --> 00:12:14,600 Speaker 7: The seventeen billion dollars a fair price for this for 243 00:12:14,640 --> 00:12:15,640 Speaker 7: these spectrum licenses. 244 00:12:15,640 --> 00:12:17,600 Speaker 6: Do you think you know? 245 00:12:17,720 --> 00:12:22,560 Speaker 9: Spectrums a very liquid market, so it's tough to gauge valuations. 246 00:12:22,600 --> 00:12:25,679 Speaker 9: But in my mind, I look at it as a 247 00:12:25,720 --> 00:12:30,959 Speaker 9: fair price given the potential again for Starlink to enter 248 00:12:31,040 --> 00:12:33,280 Speaker 9: this huge market. Down the road. 249 00:12:33,559 --> 00:12:37,000 Speaker 3: Our thanks to John Butler, Bloomberg Intelligence, senior telecom analyst. 250 00:12:37,320 --> 00:12:39,319 Speaker 5: Coming up, Well, look at how the US's ability to 251 00:12:39,360 --> 00:12:42,439 Speaker 5: assess data on climate change is being impacted by the 252 00:12:42,440 --> 00:12:43,280 Speaker 5: Trump administration. 253 00:12:43,600 --> 00:12:46,760 Speaker 3: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 254 00:12:46,800 --> 00:12:49,280 Speaker 3: depth research and data on two thousand companies at one 255 00:12:49,360 --> 00:12:50,440 Speaker 3: hundred and thirty industries. 256 00:12:50,480 --> 00:12:53,400 Speaker 5: You can access Bloomberg Intelligence via Bigo on the terminal. 257 00:12:53,520 --> 00:12:54,480 Speaker 5: I'm Normal Indo. 258 00:12:54,400 --> 00:12:56,880 Speaker 3: And I'm Scarlet Food. This is Bloomberg. 259 00:13:00,440 --> 00:13:04,840 Speaker 1: Is Bloomberg Intelligence with Scarlet Foo and Paul Sweeney on 260 00:13:05,040 --> 00:13:06,079 Speaker 1: Bloomberg Radio. 261 00:13:06,800 --> 00:13:09,120 Speaker 5: I'm Scarlet Foo and I'm normal in a filling in 262 00:13:09,160 --> 00:13:11,839 Speaker 5: for Paul Sweeney. We move next to a major deal 263 00:13:11,920 --> 00:13:12,880 Speaker 5: in the mining industry. 264 00:13:13,080 --> 00:13:15,800 Speaker 3: This week, the global mining company Anglo American agree to 265 00:13:15,840 --> 00:13:18,920 Speaker 3: acquire Canada's natural resource company tech Resources. 266 00:13:19,040 --> 00:13:21,560 Speaker 5: It's a so called zero premium deal and this deal 267 00:13:21,559 --> 00:13:24,400 Speaker 5: will create a more than fifty billion dollar company and 268 00:13:24,440 --> 00:13:26,960 Speaker 5: one of the biggest mining deals in over a decade. 269 00:13:27,040 --> 00:13:29,520 Speaker 3: So for more guest host Alexis Christophers and I were 270 00:13:29,559 --> 00:13:33,280 Speaker 3: joined by Richard Burke, Bloomberg Intelligence senior basic materials analysts. 271 00:13:33,480 --> 00:13:36,960 Speaker 3: We first asked Richard what a zero premium transaction actually is. 272 00:13:37,720 --> 00:13:40,240 Speaker 4: So what they've done is it's a merger of equals, 273 00:13:40,400 --> 00:13:43,360 Speaker 4: and it's kind of interesting even though it's it's labeled 274 00:13:43,440 --> 00:13:48,120 Speaker 4: mergers equal that American Angle shareholders are going to end 275 00:13:48,240 --> 00:13:53,600 Speaker 4: up owning Butts two thirds of company roughly, and tech 276 00:13:53,720 --> 00:13:58,080 Speaker 4: is shareholders going to end up owning a third company. Well, 277 00:13:58,120 --> 00:14:00,960 Speaker 4: it's gonna be headquarter in Canada, the board is going 278 00:14:01,040 --> 00:14:04,079 Speaker 4: to be fifty to fifty the city, and the management's 279 00:14:04,080 --> 00:14:06,760 Speaker 4: gonna be fifty to fifty. So even though it's kind 280 00:14:06,760 --> 00:14:11,280 Speaker 4: of you know, no premium merger equals kind of you know, 281 00:14:11,360 --> 00:14:14,959 Speaker 4: the the governance issues kind of point to merger of 282 00:14:15,000 --> 00:14:18,920 Speaker 4: equals and that's really probably structured in order to get 283 00:14:19,120 --> 00:14:23,640 Speaker 4: approved by the Investment Act Kada Investment Act. And also 284 00:14:23,800 --> 00:14:28,200 Speaker 4: the Kevil family that owns Tech has two classes shore olders, 285 00:14:28,200 --> 00:14:31,520 Speaker 4: class Class A and Class B. Class A is super 286 00:14:31,600 --> 00:14:36,000 Speaker 4: voting rights. He's owned, he owns majority of those shares, 287 00:14:36,280 --> 00:14:39,600 Speaker 4: so he has to approve this deal. In the past, 288 00:14:39,640 --> 00:14:42,080 Speaker 4: he you know, Glenn Corter, when Glencourt tried to buy 289 00:14:42,120 --> 00:14:45,840 Speaker 4: them about two years ago, he kind of vetoed the deal. 290 00:14:45,960 --> 00:14:47,920 Speaker 4: So the deal and go through. So there's kind of 291 00:14:47,960 --> 00:14:51,440 Speaker 4: a unique structure this deal to kind of you know, 292 00:14:51,560 --> 00:14:54,160 Speaker 4: fitted through final approval makes sense. 293 00:14:54,360 --> 00:14:57,120 Speaker 3: Okay. So there was also language in the statement of 294 00:14:57,120 --> 00:14:59,200 Speaker 3: this so called merger of equals that would allow both 295 00:14:59,240 --> 00:15:02,920 Speaker 3: firms to con that are unsolicited proposals and for the 296 00:15:03,000 --> 00:15:05,160 Speaker 3: deal to end if there's a better offer. Does that 297 00:15:05,200 --> 00:15:09,200 Speaker 3: mean they're angling for some counter offer from someone else. 298 00:15:09,200 --> 00:15:13,800 Speaker 4: Well, I think it would be a hard press, I doubt, 299 00:15:14,080 --> 00:15:16,760 Speaker 4: in order to get it through the Canadian Investment Act, 300 00:15:16,800 --> 00:15:20,160 Speaker 4: which you know, copper is now being considered a critical 301 00:15:20,200 --> 00:15:24,360 Speaker 4: material critical mineral. Canada has said they don't want to 302 00:15:24,360 --> 00:15:27,840 Speaker 4: see any of their mining companies go away. So would 303 00:15:27,880 --> 00:15:30,920 Speaker 4: somebody come in who can pay that much, be willing 304 00:15:31,080 --> 00:15:34,320 Speaker 4: or to PHP or Rio Tinto, be willing to move 305 00:15:34,400 --> 00:15:38,440 Speaker 4: their headquarters from where they're at to Canada. I highly 306 00:15:38,520 --> 00:15:41,040 Speaker 4: doubt it. So, but I think you have to put 307 00:15:41,080 --> 00:15:43,840 Speaker 4: that language in the deal that hey, we're open to. 308 00:15:44,360 --> 00:15:47,640 Speaker 10: So it's like boilerplate, language plate and also prevent that 309 00:15:47,720 --> 00:15:50,240 Speaker 10: you know't get sued by shareholders that you have to 310 00:15:50,280 --> 00:15:52,080 Speaker 10: go out and looked at for all deals. 311 00:15:52,160 --> 00:15:52,400 Speaker 8: Right. 312 00:15:52,640 --> 00:15:55,160 Speaker 7: Do you think that this deal though, between Anglo American 313 00:15:55,200 --> 00:15:57,440 Speaker 7: and Tech is going to be the thing that sort 314 00:15:57,440 --> 00:16:01,160 Speaker 7: of ignites more M and A in this industry? 315 00:16:01,520 --> 00:16:04,000 Speaker 4: I think you know, they used to be saying I 316 00:16:04,080 --> 00:16:06,800 Speaker 4: used to cover oil and gas industry years ago before 317 00:16:07,800 --> 00:16:09,960 Speaker 4: more focused on Melson mining. There's you used to be 318 00:16:10,040 --> 00:16:12,520 Speaker 4: on saying it's cheaper draw on Wall Street and then 319 00:16:12,760 --> 00:16:16,560 Speaker 4: drill up in the field. And that seems to be 320 00:16:16,640 --> 00:16:21,320 Speaker 4: happening in If you look at how much it costs 321 00:16:21,360 --> 00:16:25,200 Speaker 4: to you know, get a new copper, deposit up and 322 00:16:25,200 --> 00:16:28,360 Speaker 4: writing also how many years were permitting and all the 323 00:16:28,440 --> 00:16:33,840 Speaker 4: other things that happens, you can see that maybe you 324 00:16:33,920 --> 00:16:36,400 Speaker 4: sit down and look at this and say, Okay, maybe 325 00:16:36,560 --> 00:16:39,680 Speaker 4: cheaper draw out, you know, mine on Wall Street, then 326 00:16:39,760 --> 00:16:40,440 Speaker 4: go out and make the. 327 00:16:40,480 --> 00:16:44,080 Speaker 5: Mine our Thanks to Richard Bork Bloomberg Intelligence Senior Basic 328 00:16:44,120 --> 00:16:45,840 Speaker 5: materials analysts. 329 00:16:45,280 --> 00:16:47,600 Speaker 3: We now moved to some news in the pharmaceutical space. 330 00:16:47,880 --> 00:16:50,280 Speaker 5: This week, the Danish drug maker Novo Nordis announces it's 331 00:16:50,280 --> 00:16:53,720 Speaker 5: cutting nine thousand jobs globally. This equals about eleven percent 332 00:16:53,720 --> 00:16:56,440 Speaker 5: of its workforce. The company also cut its profit forecast 333 00:16:56,520 --> 00:16:58,560 Speaker 5: for the third time this year as it fights to 334 00:16:58,560 --> 00:17:00,440 Speaker 5: recover ground in the obesity dry market. 335 00:17:00,720 --> 00:17:03,200 Speaker 3: For more on this, guest host Alexis Christophers and I 336 00:17:03,280 --> 00:17:06,240 Speaker 3: turned to Sam Fazzelli, Bloomberg Intelligence, Director of Research for 337 00:17:06,280 --> 00:17:10,320 Speaker 3: Global Industries and senior pharmaceuticals analysts. We began by asking 338 00:17:10,359 --> 00:17:13,240 Speaker 3: Sam to talk about the larger changes taking place at NOVO. 339 00:17:13,640 --> 00:17:15,520 Speaker 11: The story here is that this is not the first 340 00:17:15,520 --> 00:17:17,840 Speaker 11: time they've lowered guidance for the year. I mean this 341 00:17:17,920 --> 00:17:20,840 Speaker 11: time there's an impact on the EPs front from obviously 342 00:17:20,840 --> 00:17:24,040 Speaker 11: the cost of restructuring, assuming they can get it all 343 00:17:24,080 --> 00:17:26,400 Speaker 11: done this year, but it's a pretty chunky one. Nine 344 00:17:26,440 --> 00:17:28,800 Speaker 11: thousand people, about eleven percent of the workforce, and a 345 00:17:28,840 --> 00:17:29,320 Speaker 11: lot of that. 346 00:17:29,240 --> 00:17:30,600 Speaker 8: Coming from Denmark. 347 00:17:31,400 --> 00:17:33,960 Speaker 11: If you read the essence of the press release that 348 00:17:33,960 --> 00:17:35,320 Speaker 11: they put at there's a couple of things that they 349 00:17:35,320 --> 00:17:35,639 Speaker 11: bring out. 350 00:17:35,640 --> 00:17:38,000 Speaker 8: They want to invest more on R and D Well. 351 00:17:38,520 --> 00:17:40,160 Speaker 11: What I think this is telling you is that the 352 00:17:40,200 --> 00:17:42,960 Speaker 11: growth that they had planned for the next five years, 353 00:17:43,200 --> 00:17:45,320 Speaker 11: they may be thinking maybe that won't come as test 354 00:17:45,359 --> 00:17:48,480 Speaker 11: as we were expecting it because of competition from Lily, 355 00:17:49,000 --> 00:17:52,399 Speaker 11: compounders and other products potentially coming to market to the 356 00:17:52,440 --> 00:17:54,119 Speaker 11: pace that they were hoping. So if you want to 357 00:17:54,200 --> 00:17:55,919 Speaker 11: keep investing in R and D, you need to liberate 358 00:17:55,960 --> 00:17:58,920 Speaker 11: some cash. And also it looks like that they're also 359 00:17:58,920 --> 00:18:01,560 Speaker 11: thinking about changing the world that they're doing their marketing 360 00:18:02,040 --> 00:18:04,359 Speaker 11: because of the different channels that they're getting involved with 361 00:18:04,400 --> 00:18:07,119 Speaker 11: now direct to consumer, that is not something that Prauma 362 00:18:07,200 --> 00:18:09,719 Speaker 11: has done very often before. In fact, only Lily had 363 00:18:09,760 --> 00:18:13,160 Speaker 11: done it prior to this, also for obesity drugs, So 364 00:18:13,240 --> 00:18:15,280 Speaker 11: maybe the costs of that are different. And there's an 365 00:18:15,320 --> 00:18:17,320 Speaker 11: element that they keep that they've referred to in there, 366 00:18:17,359 --> 00:18:24,879 Speaker 11: which is a culture of performance related reward, and you 367 00:18:24,880 --> 00:18:27,119 Speaker 11: think to yourself, well, what was it before? So it 368 00:18:27,200 --> 00:18:30,719 Speaker 11: seems like that they want to make it much harder 369 00:18:30,760 --> 00:18:33,840 Speaker 11: focus on you've got to deliver because we need the growth. 370 00:18:34,080 --> 00:18:35,280 Speaker 8: So that's the interesting wle to. 371 00:18:35,240 --> 00:18:37,679 Speaker 7: Watch, you know, Sam, I think to a lot of 372 00:18:37,720 --> 00:18:41,800 Speaker 7: casual investors who don't deep dive into you know, Novo 373 00:18:41,880 --> 00:18:44,439 Speaker 7: Nordisk the way you might say, would look at this 374 00:18:44,480 --> 00:18:47,679 Speaker 7: and say, listen, they have the most popular weight loss drugs, right, 375 00:18:47,720 --> 00:18:51,280 Speaker 7: will go vi Ozempek. Why is this company having these 376 00:18:51,320 --> 00:18:55,280 Speaker 7: problems when you drill down or is it just a 377 00:18:55,359 --> 00:18:58,399 Speaker 7: problem with having to scale too quickly? Is it that 378 00:18:58,400 --> 00:19:02,159 Speaker 7: the company isn't nimble enough, just sort of change with 379 00:19:02,200 --> 00:19:03,280 Speaker 7: the times. 380 00:19:04,000 --> 00:19:07,239 Speaker 11: Well, Nobodordisk has been a poster child of how you 381 00:19:07,280 --> 00:19:11,560 Speaker 11: grow from a small company selling to a relatively basic 382 00:19:11,600 --> 00:19:14,840 Speaker 11: set of products twenty thirty years ago to one that's 383 00:19:14,880 --> 00:19:17,840 Speaker 11: become essentially one of the innovators in this space. They 384 00:19:17,920 --> 00:19:19,960 Speaker 11: brought the first JL we want to market, so we 385 00:19:20,040 --> 00:19:22,680 Speaker 11: can't fault them for that. It's just that I think 386 00:19:22,720 --> 00:19:25,399 Speaker 11: this growth that they had to go through where the 387 00:19:25,560 --> 00:19:27,359 Speaker 11: planning was done, you know a lot of I mean, 388 00:19:27,400 --> 00:19:30,200 Speaker 11: we have one hundred billion dollar market potential for more 389 00:19:30,320 --> 00:19:34,800 Speaker 11: obesity as a whole, right, So it's just difficult, I think, 390 00:19:34,840 --> 00:19:37,600 Speaker 11: to judge into this space. And maybe the previous CEO 391 00:19:37,640 --> 00:19:39,919 Speaker 11: had looked and said, I'm pretty sure we're going to 392 00:19:39,920 --> 00:19:42,200 Speaker 11: be fifty percent of that market. We need to plan 393 00:19:42,280 --> 00:19:46,320 Speaker 11: for that growth. And then two things changed. Competition from 394 00:19:46,359 --> 00:19:49,359 Speaker 11: really is superintense. They have what I would call it 395 00:19:49,359 --> 00:19:51,760 Speaker 11: a better drug in terms of the efficacy profile that's 396 00:19:51,800 --> 00:19:54,960 Speaker 11: been shown in head to her trials. And then of 397 00:19:55,000 --> 00:19:57,480 Speaker 11: course you've got on top of that compounders that don't 398 00:19:57,520 --> 00:20:00,760 Speaker 11: seem to be don't seem to be going way, they 399 00:20:00,840 --> 00:20:04,560 Speaker 11: keep taking cheaper generics or generics manufactured, they'll try to 400 00:20:04,600 --> 00:20:08,320 Speaker 11: making the drugs. So that's where I think the new 401 00:20:08,400 --> 00:20:11,920 Speaker 11: Area issue is in that we all expected the FDA 402 00:20:11,960 --> 00:20:14,400 Speaker 11: to really clump hard down on that and they haven't 403 00:20:16,040 --> 00:20:19,280 Speaker 11: made issue, you know, statements, but they're allowing them. They 404 00:20:19,280 --> 00:20:23,280 Speaker 11: even found areas outside the US where these compound compounders 405 00:20:23,280 --> 00:20:26,040 Speaker 11: can get their drugs from, which is counterintuitive. 406 00:20:26,440 --> 00:20:29,760 Speaker 5: Our thanks to Sam Fazelli, Bloomberg Intelligence, Director of Research 407 00:20:29,760 --> 00:20:32,240 Speaker 5: for Global Industries and senior pharmaceuticals analysts. 408 00:20:32,520 --> 00:20:34,680 Speaker 3: This week we focus on a Bloomberg Big Take story 409 00:20:34,840 --> 00:20:38,280 Speaker 3: entitled Trump is Unwinding Climate Science at a Dangerous Pace. 410 00:20:38,440 --> 00:20:40,920 Speaker 3: You can find it on Bloomberg dot Com and the Terminal. 411 00:20:41,040 --> 00:20:44,640 Speaker 5: The story looks at how mass firings, regulatory rollbacks, program closures, 412 00:20:44,680 --> 00:20:48,040 Speaker 5: and funding cuts across agencies have threatened the US's ability 413 00:20:48,080 --> 00:20:50,800 Speaker 5: to gather and says data on climate change, and this 414 00:20:50,840 --> 00:20:52,200 Speaker 5: is under the current Trump administration. 415 00:20:52,640 --> 00:20:55,320 Speaker 3: For more guestos, Alexis Christophers and I were joined by 416 00:20:55,440 --> 00:20:59,040 Speaker 3: Eric Roston, Bloomberg Climate reporter. We began by asking Eric 417 00:20:59,200 --> 00:21:02,480 Speaker 3: to explain how President Trump's policies are impacting the ability 418 00:21:02,600 --> 00:21:03,440 Speaker 3: to gather data. 419 00:21:03,960 --> 00:21:09,200 Speaker 2: Whether you are a mayor of a city, or head 420 00:21:09,240 --> 00:21:13,400 Speaker 2: of an insurance company or head of an agriculture company, 421 00:21:14,119 --> 00:21:19,560 Speaker 2: everyone throughout the economy, public and private sector needs good 422 00:21:19,680 --> 00:21:24,399 Speaker 2: information to make good decisions, and the US in recent 423 00:21:24,480 --> 00:21:29,439 Speaker 2: decades has had the global leadership role in developing the 424 00:21:29,560 --> 00:21:33,120 Speaker 2: kinds of data that everybody needs in order to see 425 00:21:33,160 --> 00:21:37,000 Speaker 2: what's coming, to set insurance rates, to understand whether we 426 00:21:37,040 --> 00:21:40,560 Speaker 2: should build a house here, to understand if this floodplane 427 00:21:40,680 --> 00:21:43,680 Speaker 2: is going to be like a bad place to build houses. 428 00:21:45,520 --> 00:21:49,000 Speaker 2: And so that's the data we're talking about here, is 429 00:21:49,200 --> 00:21:53,520 Speaker 2: actionable data that people in the economy have come to 430 00:21:53,600 --> 00:21:58,560 Speaker 2: rely on to make decisions. To cite one of the 431 00:21:58,560 --> 00:22:00,800 Speaker 2: many examples we talk about it in in the story, 432 00:22:02,520 --> 00:22:06,520 Speaker 2: the US produces it's supposed to be not more than 433 00:22:06,600 --> 00:22:11,800 Speaker 2: every four years national Climate Assessment, which is a huge 434 00:22:12,520 --> 00:22:14,960 Speaker 2: amount of time, a huge amount of time, and they take, 435 00:22:15,320 --> 00:22:18,000 Speaker 2: you know, years to develop, hundreds of people to write, 436 00:22:18,080 --> 00:22:22,000 Speaker 2: and thousands of papers that they're based on. And you 437 00:22:22,080 --> 00:22:26,720 Speaker 2: see these reports. Cite it in Travelers earning calls and 438 00:22:26,880 --> 00:22:30,840 Speaker 2: Chipotle earning calls and reports in Marriott, you know, brands 439 00:22:30,880 --> 00:22:34,280 Speaker 2: we see every day understand the material and understand the 440 00:22:34,280 --> 00:22:36,520 Speaker 2: needs of this material. I'm not putting words in their mouth. 441 00:22:36,560 --> 00:22:38,840 Speaker 2: I'm just pointing out that they say things like this, 442 00:22:39,960 --> 00:22:44,040 Speaker 2: those were taken away from public view. So I think 443 00:22:44,080 --> 00:22:47,879 Speaker 2: that's an interesting place to start on because like, yeah, 444 00:22:47,960 --> 00:22:52,800 Speaker 2: the government, like any government, can be cut right and 445 00:22:52,800 --> 00:22:57,040 Speaker 2: save money, and it's just the nature of bureaucracy. But 446 00:22:57,200 --> 00:23:02,760 Speaker 2: why are some of the most authoritative and well vetted 447 00:23:02,920 --> 00:23:08,760 Speaker 2: scientific reports ever written being taken away from public view 448 00:23:08,760 --> 00:23:10,040 Speaker 2: where they've been for many years? 449 00:23:11,000 --> 00:23:13,720 Speaker 3: All good questions, and I'm sure that we won't get 450 00:23:13,760 --> 00:23:16,240 Speaker 3: answers to them right away either. So what does it 451 00:23:16,280 --> 00:23:18,760 Speaker 3: mean if you're a company like Travelers and you're relying 452 00:23:18,800 --> 00:23:20,879 Speaker 3: on that data? Do you can you go to private 453 00:23:20,920 --> 00:23:23,440 Speaker 3: sources for that kind of information? Does that exist? 454 00:23:24,640 --> 00:23:28,159 Speaker 2: It's that's a great question. And if we had like 455 00:23:28,280 --> 00:23:30,920 Speaker 2: another two hours, I'd love to, you know, because we've 456 00:23:30,920 --> 00:23:33,520 Speaker 2: spent a lot of time in the last few years 457 00:23:33,680 --> 00:23:39,159 Speaker 2: on the private sectors, you know, filling a hole in 458 00:23:39,480 --> 00:23:44,359 Speaker 2: useful climate data that that businesses the businesses needs. So 459 00:23:44,400 --> 00:23:49,600 Speaker 2: the short answer is, yes, some of this this this data, 460 00:23:50,400 --> 00:23:52,439 Speaker 2: A lot of it is like translated into the language 461 00:23:52,440 --> 00:23:58,359 Speaker 2: of risk for companies, it does exist. The bigger concern is, like, 462 00:23:58,440 --> 00:24:01,600 Speaker 2: that's a tiny fraction of what we're talking about here. 463 00:24:02,440 --> 00:24:06,880 Speaker 2: You know, we're talking about, you know, very sophisticated computer 464 00:24:07,000 --> 00:24:10,200 Speaker 2: models that like the entire world relies on to make 465 00:24:10,320 --> 00:24:14,159 Speaker 2: sense of you know, observations we see and weather stations 466 00:24:14,240 --> 00:24:19,960 Speaker 2: and in you know, in other sources and a particularly 467 00:24:20,560 --> 00:24:22,280 Speaker 2: you know, I don't know, I'll get in trouble for 468 00:24:22,320 --> 00:24:25,359 Speaker 2: saying this, but this could be described as like the 469 00:24:25,359 --> 00:24:28,320 Speaker 2: most boring kind of data is also some of the 470 00:24:28,320 --> 00:24:33,560 Speaker 2: most important, and that is unsexy data. Unsexy data. It's 471 00:24:33,600 --> 00:24:38,600 Speaker 2: like continuous monitoring data. It's you know, it's very important 472 00:24:38,640 --> 00:24:42,760 Speaker 2: for some kinds of data that you have a complete, 473 00:24:42,960 --> 00:24:47,440 Speaker 2: unbroken record. The most iconic one in this space is 474 00:24:47,760 --> 00:24:51,959 Speaker 2: the CO two measurements from Hawaii and Mauna Loa that 475 00:24:52,000 --> 00:24:55,439 Speaker 2: have been going on since the late nineteen fifties and 476 00:24:55,920 --> 00:24:59,919 Speaker 2: are largely responsible for the discovery of global warming. Like 477 00:25:00,359 --> 00:25:03,919 Speaker 2: that data set, one of the most famous data sets 478 00:25:03,920 --> 00:25:10,160 Speaker 2: ever collected, was slated for closure in the recent White 479 00:25:10,200 --> 00:25:10,920 Speaker 2: House budget. 480 00:25:11,440 --> 00:25:14,520 Speaker 5: Hard thanks to Eric Roston Bloomberg Climate Reporter, Coming up, 481 00:25:14,720 --> 00:25:17,000 Speaker 5: look Ahead to a potentially big m and a deal 482 00:25:17,080 --> 00:25:18,000 Speaker 5: in the banking sector. 483 00:25:18,160 --> 00:25:21,320 Speaker 3: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 484 00:25:21,359 --> 00:25:23,840 Speaker 3: depth research and data on two thousand companies and one 485 00:25:23,920 --> 00:25:25,120 Speaker 3: hundred and thirty industries. 486 00:25:25,200 --> 00:25:28,120 Speaker 5: You can access Bloomberg Intelligence via Bigo on the terminal. 487 00:25:28,280 --> 00:25:31,040 Speaker 3: I'm normal Linda, and I'm Scarletfoo. This is Bloomberg. 488 00:25:38,240 --> 00:25:43,159 Speaker 1: This is Bloomberg Intelligence with Scarlettfoo and Paul Sweeney on 489 00:25:43,359 --> 00:25:44,399 Speaker 1: Bloomberg Radio. 490 00:25:45,320 --> 00:25:48,360 Speaker 3: I'm Scarletfoo and I'm normal Linda, filling in for Paul Sweeney. 491 00:25:48,560 --> 00:25:51,040 Speaker 3: We moved to some news in the banking sector this week. 492 00:25:51,119 --> 00:25:53,960 Speaker 3: PNC Financial Services agreed to buy First Bank for about 493 00:25:53,960 --> 00:25:56,960 Speaker 3: four point one billion dollars, adding more than twenty six 494 00:25:57,000 --> 00:26:00,600 Speaker 3: billion dollars in assets and branches in Colorado and Araa, Arizona. 495 00:26:00,640 --> 00:26:02,879 Speaker 5: The acquisition will triple the number of P and C 496 00:26:03,000 --> 00:26:05,560 Speaker 5: branches in Colorado, and it will give the combined company 497 00:26:05,600 --> 00:26:08,200 Speaker 5: more than fifteen percent of deposits in the state. 498 00:26:08,400 --> 00:26:11,240 Speaker 3: Guest hosts Alexis Christophers and I were joined by Herman Chen, 499 00:26:11,359 --> 00:26:15,160 Speaker 3: Bloomberg Intelligence, senior analysts for US regional banks. We first 500 00:26:15,160 --> 00:26:17,760 Speaker 3: asked Herman if bank regulators are going to be okay 501 00:26:17,800 --> 00:26:18,960 Speaker 3: with the deal of this size. 502 00:26:19,160 --> 00:26:22,880 Speaker 10: The regular toory landscape has been really great for banking 503 00:26:23,600 --> 00:26:26,600 Speaker 10: and getting bank deals done. That's why you've seen some 504 00:26:26,680 --> 00:26:29,399 Speaker 10: more bank deals over the past couple of months. You 505 00:26:29,480 --> 00:26:32,760 Speaker 10: saw Huntington, which is based in Ohio, go into Texas 506 00:26:32,760 --> 00:26:35,639 Speaker 10: for Vera Techs. And you've see an emerging of vehicles 507 00:26:35,640 --> 00:26:38,960 Speaker 10: in the Southeast with so Novas and Pinnacle. And this 508 00:26:39,080 --> 00:26:41,760 Speaker 10: is sort of like the third large regional bank deal 509 00:26:41,800 --> 00:26:44,119 Speaker 10: over the past couple months. So it just sort of 510 00:26:44,200 --> 00:26:47,040 Speaker 10: signals that the bank the Trump administration is really open 511 00:26:47,240 --> 00:26:48,120 Speaker 10: it for a bank of an am. 512 00:26:48,640 --> 00:26:52,040 Speaker 3: And this is not PNC's first attempt at real expanding 513 00:26:52,080 --> 00:26:54,960 Speaker 3: its Colorado presence. It had previously sought to open sixteen 514 00:26:55,000 --> 00:26:58,520 Speaker 3: locations in Colorado. Why is Colorado such a hot market 515 00:26:58,520 --> 00:26:58,920 Speaker 3: for P ANDC? 516 00:26:59,200 --> 00:27:02,560 Speaker 10: Right, I would take step back and sort of highlight 517 00:27:02,640 --> 00:27:07,240 Speaker 10: that the P and C, which is mostly in the Northeast, 518 00:27:07,280 --> 00:27:12,680 Speaker 10: mid Atlantic Midwest, that they've expanded across the Sun Belt 519 00:27:12,680 --> 00:27:15,280 Speaker 10: states in Colorado with the twenty twenty one deal for 520 00:27:15,760 --> 00:27:18,880 Speaker 10: BBVA USA, and they sort of double down on those 521 00:27:18,920 --> 00:27:22,920 Speaker 10: markets with this expansion plan to grow organically by opening 522 00:27:23,000 --> 00:27:25,679 Speaker 10: up new branches and this deal that they announced really 523 00:27:25,800 --> 00:27:29,320 Speaker 10: sort of supports that growth and accelerates it in Colorado 524 00:27:29,359 --> 00:27:30,440 Speaker 10: and in Arizona. 525 00:27:30,560 --> 00:27:32,879 Speaker 7: So I'm just wondering, as you look out onto the 526 00:27:32,960 --> 00:27:36,600 Speaker 7: landscape now, who might be the next takeover candidate. Who 527 00:27:36,640 --> 00:27:38,439 Speaker 7: might we be talking about in the not too just 528 00:27:38,480 --> 00:27:38,919 Speaker 7: in future. 529 00:27:39,040 --> 00:27:41,840 Speaker 10: Yeah, So you've seen some of these deals where these 530 00:27:42,359 --> 00:27:49,400 Speaker 10: slower growth Midwest banks grow into areas like Texas, Florida, Colorado. 531 00:27:49,600 --> 00:27:53,520 Speaker 10: So the banks are sort of smaller in size, and 532 00:27:53,720 --> 00:27:57,440 Speaker 10: we're talking about the first banks about twenty billion in assets, 533 00:27:57,480 --> 00:28:01,000 Speaker 10: so banks of slimmer of that size, these higher growth 534 00:28:01,040 --> 00:28:05,600 Speaker 10: markets are really attractive to some of these larger institutions. 535 00:28:06,520 --> 00:28:10,119 Speaker 3: So with this purchase, PNC's total assets approaches six hundred 536 00:28:10,119 --> 00:28:12,879 Speaker 3: billion dollars. It will bring it closer to the number 537 00:28:12,880 --> 00:28:15,840 Speaker 3: one regional bank, which is US Bank Corp. Is Uanrup 538 00:28:15,920 --> 00:28:17,920 Speaker 3: still a regional bank, Yeah. 539 00:28:18,040 --> 00:28:21,760 Speaker 10: I would say there are three super regional banks, and 540 00:28:21,800 --> 00:28:24,760 Speaker 10: that will put p and C, US Bank Corp and 541 00:28:24,840 --> 00:28:28,880 Speaker 10: Truest where they're sort of below that seven P fifty mark, 542 00:28:28,960 --> 00:28:32,879 Speaker 10: but larger than your typical regional bank. And they PNC 543 00:28:33,080 --> 00:28:35,960 Speaker 10: now is a coast to coast lender, so and have 544 00:28:36,080 --> 00:28:41,520 Speaker 10: operations all from the Northeast all the way down to Florida, Texas, California, 545 00:28:41,640 --> 00:28:44,800 Speaker 10: So you can't really call them a regional bank at 546 00:28:44,800 --> 00:28:48,200 Speaker 10: this point. I would view them as more super regional. 547 00:28:48,960 --> 00:28:50,520 Speaker 7: And I want to tie it back to the FED 548 00:28:50,560 --> 00:28:53,560 Speaker 7: in interest rates, because don't all roads really lead there 549 00:28:53,560 --> 00:28:56,320 Speaker 7: at this point, And I want to talk about what again, 550 00:28:56,360 --> 00:28:58,120 Speaker 7: if this is going to be an environment of lower 551 00:28:58,160 --> 00:29:01,040 Speaker 7: interest rates for the foreseeable future, what is this going 552 00:29:01,080 --> 00:29:03,680 Speaker 7: to mean for the banks, but especially these smaller regional banks, 553 00:29:03,680 --> 00:29:06,480 Speaker 7: because I'm thinking it may even squeeze their profitability more. 554 00:29:06,720 --> 00:29:06,960 Speaker 8: Yeah. 555 00:29:07,040 --> 00:29:10,760 Speaker 10: Yeah, it's interesting because we had a few FED rate 556 00:29:10,840 --> 00:29:13,240 Speaker 10: cuts at the end of last year, and the regional 557 00:29:13,240 --> 00:29:15,880 Speaker 10: banks that I cover were able to navigate that pretty well. 558 00:29:15,920 --> 00:29:18,160 Speaker 10: They're able to keep their net interest margins, which is 559 00:29:18,160 --> 00:29:22,600 Speaker 10: a key metric for profitability, fairly stable. And that's one 560 00:29:22,640 --> 00:29:24,600 Speaker 10: of the reasons why they've been able to do that 561 00:29:24,680 --> 00:29:27,920 Speaker 10: is they've been able to lower their deposit costs, which 562 00:29:28,160 --> 00:29:31,400 Speaker 10: was one of the things that really is a key 563 00:29:31,480 --> 00:29:36,000 Speaker 10: metric for banks to really show top line growth. On 564 00:29:36,120 --> 00:29:40,920 Speaker 10: top of that, lower interest rates could help loan growth. 565 00:29:41,280 --> 00:29:48,120 Speaker 10: You've seen some companies like Rockets Mortgage be very abuiliant 566 00:29:48,160 --> 00:29:51,160 Speaker 10: for lower interest rates, and so that could help improve 567 00:29:51,280 --> 00:29:54,000 Speaker 10: some of the demand for lending, and that's something that 568 00:29:54,040 --> 00:29:56,760 Speaker 10: we're seeing so far over the first half of the year. 569 00:29:57,000 --> 00:29:58,680 Speaker 3: So they'll make it up in volume what they lose 570 00:29:58,720 --> 00:30:00,000 Speaker 3: in profitability, they plan. 571 00:29:59,880 --> 00:30:02,680 Speaker 10: To we can make it in volume. You'll see some 572 00:30:02,840 --> 00:30:06,720 Speaker 10: dents in like the rates that they charge for loans, 573 00:30:06,800 --> 00:30:09,320 Speaker 10: but they'll also make it up in volume and lowering 574 00:30:09,360 --> 00:30:10,400 Speaker 10: their funding costs. 575 00:30:10,640 --> 00:30:12,800 Speaker 3: When you look at the landscape overall of M and 576 00:30:12,840 --> 00:30:14,680 Speaker 3: A in the banking sector, it feels like we've been 577 00:30:14,680 --> 00:30:16,800 Speaker 3: waiting for something to happen for a long time, and 578 00:30:16,880 --> 00:30:19,040 Speaker 3: maybe this is kind of the domino, the first domino 579 00:30:19,120 --> 00:30:22,440 Speaker 3: that falls. Yeah, well, the big banks and I'm here, 580 00:30:22,440 --> 00:30:24,440 Speaker 3: I'm talking about the JP Morgans, the Bank of America's, 581 00:30:24,440 --> 00:30:27,200 Speaker 3: Wells Fargo's. Will they play any role in this consolidation. 582 00:30:27,720 --> 00:30:33,800 Speaker 10: They're already too big, So there's that they are prohibited 583 00:30:34,040 --> 00:30:37,600 Speaker 10: from from doing such deals because they just have dominant 584 00:30:37,600 --> 00:30:40,360 Speaker 10: market share in their markets. One thing that we did 585 00:30:40,400 --> 00:30:43,440 Speaker 10: see during the height of the regional banking crisis a 586 00:30:43,480 --> 00:30:46,440 Speaker 10: couple of years back is that JP Morgan bought First 587 00:30:46,440 --> 00:30:50,800 Speaker 10: Republic in an FDIC failed bank acquisition. So if we 588 00:30:51,560 --> 00:30:54,880 Speaker 10: in the sense that if there we do get to 589 00:30:55,120 --> 00:30:58,240 Speaker 10: a place like that, that's when the big banks can play, 590 00:30:58,320 --> 00:31:01,680 Speaker 10: but in a straight man a type scenario. 591 00:31:01,360 --> 00:31:02,080 Speaker 3: To clean up messes. 592 00:31:02,120 --> 00:31:02,880 Speaker 2: They can clean They can. 593 00:31:02,920 --> 00:31:06,560 Speaker 10: Clean up messes, but they can't outworthy buy a bank 594 00:31:06,720 --> 00:31:08,320 Speaker 10: at this point, they're just way too big. 595 00:31:08,920 --> 00:31:11,840 Speaker 7: And just what about the consumer out there only shopping 596 00:31:11,840 --> 00:31:13,120 Speaker 7: for the best deal. You know, it used to be 597 00:31:13,160 --> 00:31:15,160 Speaker 7: that these smaller regional banks would be the place to 598 00:31:15,200 --> 00:31:17,680 Speaker 7: go for the better CD rates, right or a more 599 00:31:17,720 --> 00:31:20,960 Speaker 7: friendly friendly mortgage terms. Is that still the case? 600 00:31:21,280 --> 00:31:21,480 Speaker 8: Yeah? 601 00:31:21,560 --> 00:31:24,760 Speaker 10: Sure, So that's what really great about these markets that 602 00:31:25,080 --> 00:31:27,280 Speaker 10: P and C is going to where they can have 603 00:31:27,360 --> 00:31:31,560 Speaker 10: really dominant market share in Denver and Arizona, and just 604 00:31:31,640 --> 00:31:35,920 Speaker 10: having that marketing and having the branches really helps with 605 00:31:36,840 --> 00:31:40,080 Speaker 10: deposit taking and helping your consumer. 606 00:31:39,680 --> 00:31:43,040 Speaker 5: Out our Thanks to Herman Chan, Bloomberg Intelligence senior analysts 607 00:31:43,040 --> 00:31:44,160 Speaker 5: for US regional banks. 608 00:31:44,520 --> 00:31:46,960 Speaker 3: This week we focus on another Bloomberg Big Take story 609 00:31:47,080 --> 00:31:51,160 Speaker 3: entitled Tesla's dangerous store design can trap people inside. You 610 00:31:51,200 --> 00:31:53,520 Speaker 3: can find it on Bloomberg dot Com and The Terminal. 611 00:31:53,760 --> 00:31:56,080 Speaker 5: This story looks at how some of Tesla's design features, 612 00:31:56,120 --> 00:31:59,760 Speaker 5: like door handles are confusing occupants and first responders, and 613 00:31:59,800 --> 00:32:03,040 Speaker 5: this worsening injuries and damage caused by survivable crashes. 614 00:32:03,440 --> 00:32:05,880 Speaker 3: So for more on this guest host Alexis Christophers and 615 00:32:05,920 --> 00:32:08,960 Speaker 3: I were joined by Craig Tdrudell, Bloomberg Global Autos Editor. 616 00:32:09,240 --> 00:32:11,480 Speaker 3: We began by asking Craig to give us his main 617 00:32:11,480 --> 00:32:12,719 Speaker 3: takeaways from this story. 618 00:32:13,080 --> 00:32:15,320 Speaker 12: This is a company that you know from the from 619 00:32:15,360 --> 00:32:18,360 Speaker 12: the beginning, you've heard Musk talk about, you know, just 620 00:32:18,400 --> 00:32:21,280 Speaker 12: how safe his vehicles are, and you know, even just 621 00:32:21,400 --> 00:32:25,360 Speaker 12: very recently, the company has posted on X if you 622 00:32:25,480 --> 00:32:27,960 Speaker 12: love them, put them in a Tesla. That's the kind 623 00:32:27,960 --> 00:32:31,520 Speaker 12: of language that you see and hear from from Tesla 624 00:32:31,520 --> 00:32:35,200 Speaker 12: and from Musk. I think what happened here with the 625 00:32:35,240 --> 00:32:37,520 Speaker 12: doors is, you know, sort of from the beginning, an 626 00:32:37,560 --> 00:32:41,280 Speaker 12: attempt to kind of innovate what I think Tesla's critics 627 00:32:41,280 --> 00:32:44,720 Speaker 12: would say didn't need innovating. We didn't need to change 628 00:32:44,760 --> 00:32:47,960 Speaker 12: the way that you know, a door opens and closes. 629 00:32:48,040 --> 00:32:50,080 Speaker 12: It's it's intuitive. Everyone knows how to do it. You 630 00:32:50,120 --> 00:32:51,800 Speaker 12: sort of know how to do it when you're three 631 00:32:51,840 --> 00:32:56,200 Speaker 12: or four years old and you know the company wanted 632 00:32:56,240 --> 00:32:58,880 Speaker 12: to go to this, you know, new different sort of 633 00:32:58,880 --> 00:33:03,400 Speaker 12: path breaking approach of handles that would be flushed to 634 00:33:03,440 --> 00:33:07,360 Speaker 12: the to the doors for better aerodynamics. And and also 635 00:33:07,760 --> 00:33:10,360 Speaker 12: you know, if you think about a battery powered car, 636 00:33:10,760 --> 00:33:12,960 Speaker 12: it's it's very quiet, right, you don't have engine noise 637 00:33:13,000 --> 00:33:15,120 Speaker 12: to drown out any sort of you know, whistles or 638 00:33:15,120 --> 00:33:19,120 Speaker 12: anything like that. So the smoother the surfaces, the flatter everything, 639 00:33:19,520 --> 00:33:23,000 Speaker 12: the better in terms of not just aerodynamics, but also uh, 640 00:33:23,080 --> 00:33:26,600 Speaker 12: you know, this sort of getting rid of any any noise. 641 00:33:26,680 --> 00:33:30,440 Speaker 12: And so while while those all you know, are understandable 642 00:33:30,640 --> 00:33:34,360 Speaker 12: reasons to sort of make some design changes, what we 643 00:33:34,360 --> 00:33:36,880 Speaker 12: found in the course of reporting of this story is just, 644 00:33:37,000 --> 00:33:40,040 Speaker 12: you know, some sort of tragic consequences of those decisions 645 00:33:40,440 --> 00:33:42,760 Speaker 12: in terms of, you know, how difficult it is both 646 00:33:43,120 --> 00:33:45,440 Speaker 12: for you as an occupant to get out and also 647 00:33:45,520 --> 00:33:48,240 Speaker 12: for first responders to get to you, to open the 648 00:33:48,280 --> 00:33:51,960 Speaker 12: doors and get to you, and in situations where maybe 649 00:33:51,960 --> 00:33:55,840 Speaker 12: you're incapacitated or you're having trouble with the doors. 650 00:33:56,240 --> 00:33:58,920 Speaker 7: Yeah, I mean the stories that you highlight, Craig were 651 00:33:58,960 --> 00:34:03,200 Speaker 7: really popping and disturbing. I mean, one a little eighteen 652 00:34:03,240 --> 00:34:07,280 Speaker 7: month old girl was stuck in the car her parents 653 00:34:07,360 --> 00:34:09,560 Speaker 7: couldn't get her out, and that was one of the 654 00:34:09,560 --> 00:34:11,399 Speaker 7: better stories. It had a better ending. They were able 655 00:34:11,400 --> 00:34:13,200 Speaker 7: to get her out. I mean, I'm surprised that the 656 00:34:13,280 --> 00:34:17,600 Speaker 7: National Highway Traffic Safety Administration isn't really cracking down on Tesla. 657 00:34:17,719 --> 00:34:19,319 Speaker 7: What are they saying about all of this? 658 00:34:20,040 --> 00:34:22,600 Speaker 12: I think that was one of the more interesting, you know, 659 00:34:23,160 --> 00:34:26,600 Speaker 12: issues with this story is is that Nit's a you know, 660 00:34:27,040 --> 00:34:29,799 Speaker 12: a lot of complaints, a lot of safety concerns come 661 00:34:29,840 --> 00:34:34,120 Speaker 12: across their their proverbial desk, if you will, and at 662 00:34:34,160 --> 00:34:37,360 Speaker 12: times it will take all of a single complaint or 663 00:34:37,760 --> 00:34:43,040 Speaker 12: you know, a handful of owner surveys that that will 664 00:34:43,040 --> 00:34:46,480 Speaker 12: sort of get its attention and lead to communication with 665 00:34:46,600 --> 00:34:49,120 Speaker 12: manufacturers of hey, you need to take a look at this, 666 00:34:49,560 --> 00:34:52,680 Speaker 12: and sort of, you know, the agency will sort of 667 00:34:52,680 --> 00:34:55,319 Speaker 12: put its thumb on the scale in terms of, you know, 668 00:34:55,560 --> 00:34:59,799 Speaker 12: a potential recall. In the case of this story, what 669 00:35:00,080 --> 00:35:03,520 Speaker 12: NITSA told us is that they are in communication with 670 00:35:03,600 --> 00:35:07,840 Speaker 12: Tesla to gather additional data and determine whether a quote 671 00:35:07,880 --> 00:35:12,080 Speaker 12: full investigation unquote is warranted. That does allude to this 672 00:35:12,200 --> 00:35:16,360 Speaker 12: idea that there may be some sort of initial investigating 673 00:35:16,480 --> 00:35:19,520 Speaker 12: going on, but in terms of a formal probe that 674 00:35:19,880 --> 00:35:23,200 Speaker 12: has not been initiated at least as of yet. 675 00:35:24,200 --> 00:35:27,839 Speaker 3: Okay, so that's where the regulators stand. What's interesting to 676 00:35:27,880 --> 00:35:31,799 Speaker 3: me as well is that other ev makers have kind 677 00:35:31,840 --> 00:35:34,800 Speaker 3: of taken Tesla's design and copied it. They are also 678 00:35:34,880 --> 00:35:38,040 Speaker 3: doing the same thing. So this now goes beyond Tesla vehicles, 679 00:35:38,040 --> 00:35:38,440 Speaker 3: doesn't it. 680 00:35:39,200 --> 00:35:42,120 Speaker 12: Yeah. And I think what was interesting to us and 681 00:35:42,239 --> 00:35:44,560 Speaker 12: sort of you know, kind of trying to get a 682 00:35:44,600 --> 00:35:49,120 Speaker 12: sense for just how problematic this is for other manufacturers. 683 00:35:49,200 --> 00:35:51,239 Speaker 12: I think, you know, there was nothing that stood out 684 00:35:51,280 --> 00:35:55,960 Speaker 12: more for me was, you know, looking at JD Powers 685 00:35:56,719 --> 00:35:59,320 Speaker 12: study that they do every year. It's called the Initial 686 00:35:59,400 --> 00:36:03,560 Speaker 12: Qualities the IQs. You might be familiar from with you know, 687 00:36:03,680 --> 00:36:07,319 Speaker 12: car companies you know, put making this, you know, a 688 00:36:07,360 --> 00:36:10,480 Speaker 12: point of emphasis in their ads or their commercials when 689 00:36:10,520 --> 00:36:13,040 Speaker 12: they do well in that. In that study, and in 690 00:36:13,080 --> 00:36:16,240 Speaker 12: twenty twenty three, uh, they had a record high number 691 00:36:16,239 --> 00:36:20,680 Speaker 12: of problems per US vehicle in the US market and 692 00:36:21,000 --> 00:36:22,400 Speaker 12: one of the things that they pointed to as a 693 00:36:22,440 --> 00:36:26,279 Speaker 12: reason for this was was doors. And they kind of 694 00:36:26,320 --> 00:36:28,319 Speaker 12: poked fun a little bit at the industry that they 695 00:36:28,440 --> 00:36:31,120 Speaker 12: you know, researched by saying, this wasn't something that we 696 00:36:31,200 --> 00:36:33,320 Speaker 12: needed to change, you know it was working. We weren't 697 00:36:33,320 --> 00:36:36,520 Speaker 12: getting complaints from people that we survey for this study, 698 00:36:37,400 --> 00:36:40,279 Speaker 12: and you know, now this is a percolating problem area 699 00:36:40,440 --> 00:36:43,160 Speaker 12: was the phrasing that they used. And seven of the 700 00:36:43,200 --> 00:36:46,560 Speaker 12: ten most problematic models in this respect were EV. So 701 00:36:46,640 --> 00:36:49,440 Speaker 12: what we're seeing is a lot of sort of you know, 702 00:36:49,520 --> 00:36:52,600 Speaker 12: taking after Tesla in terms of door design. And for 703 00:36:52,640 --> 00:36:55,200 Speaker 12: the most part, what we're seeing is is sort of 704 00:36:55,200 --> 00:36:58,759 Speaker 12: you know, copycatting or taking after EV for EV right, 705 00:36:59,000 --> 00:37:02,080 Speaker 12: you know, trying to feel a model Wi Fighter that 706 00:37:02,160 --> 00:37:04,520 Speaker 12: also has you know, doors that are similar to the 707 00:37:04,560 --> 00:37:05,000 Speaker 12: model Y. 708 00:37:05,640 --> 00:37:08,000 Speaker 7: I'm wondering, I don't think your story got into the 709 00:37:08,120 --> 00:37:11,600 Speaker 7: story got into this, Craig, But what's the legal ramifications 710 00:37:11,600 --> 00:37:14,680 Speaker 7: here for for Tesla? You know, I would imagine the 711 00:37:14,760 --> 00:37:16,960 Speaker 7: lawsuits are piling up. I mean if certainly, if my 712 00:37:17,120 --> 00:37:19,600 Speaker 7: child was stuck in a Tesla and I couldn't get 713 00:37:19,680 --> 00:37:21,400 Speaker 7: him out, I might be suing Tesla. 714 00:37:21,640 --> 00:37:24,160 Speaker 12: We are aware of a handful of lawsuits and I 715 00:37:24,200 --> 00:37:27,480 Speaker 12: think you know, one of the complicating factors here is 716 00:37:27,480 --> 00:37:31,879 Speaker 12: is Tesla uh pushes its its customers into arbitration for 717 00:37:31,880 --> 00:37:34,600 Speaker 12: for these sorts of of suits that this is part 718 00:37:34,640 --> 00:37:36,640 Speaker 12: of the sort of purchase agreement when you buy it 719 00:37:36,760 --> 00:37:40,400 Speaker 12: a Tesla, that is something that you know, liigans are 720 00:37:40,400 --> 00:37:43,359 Speaker 12: going to have to sort of overcome because of that 721 00:37:43,680 --> 00:37:45,799 Speaker 12: sort of policy on the part of the company. I 722 00:37:45,800 --> 00:37:48,840 Speaker 12: think another sort of complicating factor here is that you know, 723 00:37:48,920 --> 00:37:53,200 Speaker 12: one of the uh you know, uh a couple that 724 00:37:53,200 --> 00:37:55,440 Speaker 12: that we speak with in the story, they had no 725 00:37:55,480 --> 00:37:59,200 Speaker 12: inclination to sue Tesla immediately after the crash. It took 726 00:37:59,400 --> 00:38:01,680 Speaker 12: one of the first responders coming to them after the 727 00:38:01,760 --> 00:38:04,799 Speaker 12: fact and saying, hey, we really had a hard time 728 00:38:04,840 --> 00:38:06,640 Speaker 12: getting to you, and it was because of your vehicle. 729 00:38:07,640 --> 00:38:10,480 Speaker 12: That is part of the issue here. You don't necessarily 730 00:38:10,560 --> 00:38:14,040 Speaker 12: know right off the bat, Hey something wasn't right here. 731 00:38:14,440 --> 00:38:17,160 Speaker 12: I need to do some investigating hard. 732 00:38:17,200 --> 00:38:19,880 Speaker 5: Thanks to Craig Trudell, Bloomberg Global Autos Editor. 733 00:38:20,120 --> 00:38:23,360 Speaker 3: That is this week's edition of Bloomberg Intelligence on Bloomberg Radio, 734 00:38:23,440 --> 00:38:26,480 Speaker 3: providing in depth research and data on two thousand companies 735 00:38:26,600 --> 00:38:28,080 Speaker 3: and one hundred and thirty industries. 736 00:38:28,200 --> 00:38:30,920 Speaker 5: And remember you can access Bloomberg Intelligence via Bigo on 737 00:38:31,040 --> 00:38:33,480 Speaker 5: the terminal. I'm normal Inda, and I'm Scarlet Foo. 738 00:38:33,600 --> 00:38:36,160 Speaker 3: Stay with us. Today's top stories and global business headlines 739 00:38:36,160 --> 00:38:37,160 Speaker 3: are coming up right now.