1 00:00:00,280 --> 00:00:11,560 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,720 --> 00:00:12,799 Speaker 1: with Paul Sweeney. 3 00:00:12,960 --> 00:00:16,160 Speaker 2: The real AP performance has been the US corporate high yield. 4 00:00:16,280 --> 00:00:18,720 Speaker 2: The market's telling me here's another AI play, Just bid 5 00:00:18,720 --> 00:00:22,240 Speaker 2: it by it. One person's cast is another person's animal spirits, 6 00:00:22,440 --> 00:00:23,600 Speaker 2: breaking market. 7 00:00:23,320 --> 00:00:26,120 Speaker 1: Headlines and corporate news from across the globe. 8 00:00:26,200 --> 00:00:29,360 Speaker 2: The semiconductor business is a really cyclical business. 9 00:00:29,400 --> 00:00:30,200 Speaker 3: These are two. 10 00:00:30,280 --> 00:00:34,240 Speaker 2: Big time blue chip companies, so people just too exuberant 11 00:00:34,240 --> 00:00:34,599 Speaker 2: out there. 12 00:00:34,840 --> 00:00:39,440 Speaker 1: Bloomberg Intelligence with Paul Sweeney on Bloomberg Radio, YouTube and 13 00:00:39,520 --> 00:00:40,760 Speaker 1: Bloomberg Originals. 14 00:00:41,400 --> 00:00:44,360 Speaker 2: On Today's Bloomberg Intelligence Show, we dig inside the big 15 00:00:44,400 --> 00:00:47,320 Speaker 2: business stories impacting Wall Street and the global markets. Each 16 00:00:47,360 --> 00:00:49,680 Speaker 2: and every week we provide in depth research and date 17 00:00:49,760 --> 00:00:51,720 Speaker 2: on some of the two thousand companies and one hundred 18 00:00:51,720 --> 00:00:55,319 Speaker 2: and thirty industries our animals cover worldwide. Today we'll look 19 00:00:55,360 --> 00:00:58,720 Speaker 2: at Oracle signing a cloud deal with thirty billion dollars 20 00:00:58,760 --> 00:01:01,600 Speaker 2: a year. Plus later on a big take story on 21 00:01:01,640 --> 00:01:05,680 Speaker 2: the Treasury Department's cybersecurity vulnerabilities. But first we begin with 22 00:01:05,800 --> 00:01:08,319 Speaker 2: an outlook into travel and leisure for the second half 23 00:01:08,360 --> 00:01:13,160 Speaker 2: of twenty twenty five. According to a bi US travel survey, tariffs, inflation, 24 00:01:13,240 --> 00:01:17,319 Speaker 2: and geopolitics could deter consumers from spending money on hotels 25 00:01:17,440 --> 00:01:21,280 Speaker 2: and flights. Jody Lourie, Bloomberg Intelligence Analysts breaks it down. 26 00:01:21,640 --> 00:01:23,759 Speaker 4: For a large part, the companies have been doing pretty well, 27 00:01:23,760 --> 00:01:27,680 Speaker 4: and from a credit perspective, we've seen pretty decent total 28 00:01:27,720 --> 00:01:32,360 Speaker 4: returns for the indices, and that's compared to high yield 29 00:01:32,440 --> 00:01:35,080 Speaker 4: and investment grade as a total. You know, we've seen 30 00:01:35,120 --> 00:01:40,120 Speaker 4: that for discretionary that these leisure related companies have actually 31 00:01:40,160 --> 00:01:42,600 Speaker 4: been doing better than most of discretionary and it makes 32 00:01:42,600 --> 00:01:46,080 Speaker 4: sense because other parts of discretionaries, such as retail, are 33 00:01:46,200 --> 00:01:50,680 Speaker 4: much more sensitive to know these tariffs and trade talks 34 00:01:50,720 --> 00:01:52,800 Speaker 4: that are really affecting a lot of our outlook. 35 00:01:53,080 --> 00:01:55,840 Speaker 5: Jody, I'm looking through your note where you talk about 36 00:01:55,840 --> 00:01:57,600 Speaker 5: the fact that there's a lot of uncertainties right now 37 00:01:57,640 --> 00:01:59,760 Speaker 5: that are a lot of these consumers are grappling with 38 00:01:59,800 --> 00:02:04,520 Speaker 5: as pertains to leisure traveling, with tariffs, inflation, China economy, 39 00:02:04,560 --> 00:02:07,400 Speaker 5: and geopolitics. Can you just give us a little bit 40 00:02:07,440 --> 00:02:09,720 Speaker 5: more of the backdrop right now? What is the consumer 41 00:02:09,800 --> 00:02:13,040 Speaker 5: thinking as it relates to travel right now during the summer. 42 00:02:14,320 --> 00:02:17,480 Speaker 4: So I think that consumers are still going along with 43 00:02:17,520 --> 00:02:20,640 Speaker 4: their plans for the most part, but maybe slightly tweaking it. 44 00:02:20,919 --> 00:02:23,560 Speaker 4: What we saw in our second half leisure survey that 45 00:02:23,600 --> 00:02:26,600 Speaker 4: we ran We run this every half a year, is 46 00:02:26,639 --> 00:02:29,720 Speaker 4: that consumers are not pulling back on experiences. They're not 47 00:02:29,720 --> 00:02:32,280 Speaker 4: pulling back on accommodations. What they might be pulling back 48 00:02:32,320 --> 00:02:35,880 Speaker 4: on is destination to do a cheaper destination if they 49 00:02:35,919 --> 00:02:38,679 Speaker 4: need to cut costs. But by and large, most consumers 50 00:02:38,720 --> 00:02:43,480 Speaker 4: are looking at spending on travel, spending on experiences as 51 00:02:43,520 --> 00:02:46,040 Speaker 4: a way to escape what's going on and sort of 52 00:02:46,120 --> 00:02:50,160 Speaker 4: the last party before maybe the chair gets pulled out 53 00:02:50,160 --> 00:02:50,680 Speaker 4: from under them. 54 00:02:51,080 --> 00:02:54,960 Speaker 2: How about my favorite, UH secrament, which is the casino business. 55 00:02:55,000 --> 00:02:57,520 Speaker 2: How does Zach perform in a time of maybe some 56 00:02:57,600 --> 00:02:58,520 Speaker 2: uncertainty out there? 57 00:02:59,240 --> 00:03:01,760 Speaker 4: So I think it's a question of what region we're 58 00:03:01,800 --> 00:03:05,760 Speaker 4: talking about. And so for Las Vegas, you're dealing with 59 00:03:05,800 --> 00:03:08,480 Speaker 4: the fact that we had really strong comps because we 60 00:03:08,520 --> 00:03:10,440 Speaker 4: had a couple of one time events such as the 61 00:03:10,480 --> 00:03:12,960 Speaker 4: Super Bowl, such as the first Step one that really 62 00:03:13,080 --> 00:03:15,160 Speaker 4: drove a lot of last year and the year before 63 00:03:15,160 --> 00:03:19,280 Speaker 4: it's performance. This year, we're not seeing that same momentum there, 64 00:03:19,560 --> 00:03:23,120 Speaker 4: So it's a little less exciting when you talk about regionals. 65 00:03:23,160 --> 00:03:25,120 Speaker 4: I mean, that's really my question. So a lot of 66 00:03:25,160 --> 00:03:27,840 Speaker 4: the companies like to talk about how regional casinos are 67 00:03:27,919 --> 00:03:31,959 Speaker 4: counter cyclical, meaning people will still go to regional casinos 68 00:03:31,960 --> 00:03:36,080 Speaker 4: to gamble even during a recession. But I think the 69 00:03:36,520 --> 00:03:39,880 Speaker 4: question I have is the last sort of data points 70 00:03:39,880 --> 00:03:42,760 Speaker 4: we have on that is before we had online gaming. Right. 71 00:03:42,960 --> 00:03:46,000 Speaker 4: Online gaming is definitely a game changer to the whole industry, 72 00:03:46,080 --> 00:03:48,560 Speaker 4: and my colleague Brian Egger, who covers on the equity side, 73 00:03:48,760 --> 00:03:51,520 Speaker 4: can talk at nauseum about online gaming, as you know. 74 00:03:52,320 --> 00:03:55,000 Speaker 4: But I think really the question is from more of 75 00:03:55,040 --> 00:03:58,839 Speaker 4: a macro standpoint, how that's going to affect these companies 76 00:03:59,440 --> 00:04:02,560 Speaker 4: as it really lates to their regional casinos. Now, when 77 00:04:02,560 --> 00:04:05,760 Speaker 4: we talk about Macau, there's a lot of sort of 78 00:04:05,840 --> 00:04:09,720 Speaker 4: questions about China and China's economy and how that might 79 00:04:09,760 --> 00:04:13,160 Speaker 4: trickle into the effects of a Macau. Never mind the 80 00:04:13,200 --> 00:04:14,880 Speaker 4: fact that we're now going to have competition in the 81 00:04:14,960 --> 00:04:17,680 Speaker 4: UAE with the Wind casino that's opening up in a 82 00:04:17,680 --> 00:04:22,440 Speaker 4: few years. You know, we're seeing Thailand has been not 83 00:04:22,600 --> 00:04:26,080 Speaker 4: so not so quick to get on the casino game, 84 00:04:26,360 --> 00:04:28,960 Speaker 4: but we've seen japan As starting to open up with MGM, 85 00:04:29,040 --> 00:04:32,520 Speaker 4: so there's a lot of competition globally that might change 86 00:04:32,560 --> 00:04:35,000 Speaker 4: the dynamics of casinos at large. 87 00:04:35,200 --> 00:04:38,800 Speaker 5: Judy, when you think of travel, I immediately think of airlines. 88 00:04:38,839 --> 00:04:40,080 Speaker 5: You know, how I'm I going to get from where 89 00:04:40,080 --> 00:04:41,600 Speaker 5: I need to go point A to point B. Able 90 00:04:41,680 --> 00:04:43,400 Speaker 5: we look at airlines, I'm looking at an index it's 91 00:04:43,440 --> 00:04:46,760 Speaker 5: not about eighteen percent. But then hotels you have to 92 00:04:46,839 --> 00:04:49,320 Speaker 5: need somewhere to stay. We're seeing them popping up about 93 00:04:49,400 --> 00:04:52,000 Speaker 5: nine percent so far this year. What's going on in 94 00:04:52,000 --> 00:04:53,080 Speaker 5: the hotel space right now? 95 00:04:54,080 --> 00:04:57,239 Speaker 4: So in the hotel space, they're still commanding these good 96 00:04:58,680 --> 00:05:01,560 Speaker 4: prices for the rooms, right and I think that's a 97 00:05:01,560 --> 00:05:04,080 Speaker 4: function of the fact that you're seeing business and conference 98 00:05:04,080 --> 00:05:08,120 Speaker 4: travel tick up. Now, the question is how are people traveling? 99 00:05:08,480 --> 00:05:11,239 Speaker 4: And are people doing more domestic travel? Are they driving 100 00:05:11,279 --> 00:05:13,880 Speaker 4: to places, are they taking the train, are they opting 101 00:05:14,000 --> 00:05:16,400 Speaker 4: out of airlines because it's just so uncomfortable, and are 102 00:05:16,400 --> 00:05:18,800 Speaker 4: they not doing international travel as much? And I think 103 00:05:18,839 --> 00:05:21,320 Speaker 4: that's really what's sort of playing out is is that 104 00:05:21,440 --> 00:05:24,839 Speaker 4: question of it is how are people getting to their destination? 105 00:05:24,960 --> 00:05:27,400 Speaker 4: What destination are they going to? You know, there's definitely 106 00:05:27,480 --> 00:05:29,640 Speaker 4: data points out there. I was just reading a report 107 00:05:29,640 --> 00:05:32,599 Speaker 4: this morning. I was talking about how July fourth is 108 00:05:32,640 --> 00:05:35,520 Speaker 4: going to be the busiest in terms of travel. But really, 109 00:05:35,600 --> 00:05:38,160 Speaker 4: I mean for July fourth, are we flying places? Are 110 00:05:38,160 --> 00:05:41,440 Speaker 4: we driving to barbecues? That's really the question. And I 111 00:05:41,480 --> 00:05:45,160 Speaker 4: think you know, with the sort of airline component, is 112 00:05:45,240 --> 00:05:48,640 Speaker 4: that rental car component, right? And so when you look 113 00:05:48,720 --> 00:05:52,120 Speaker 4: at what you know, the CPI, the Consumer Price Index 114 00:05:52,240 --> 00:05:54,760 Speaker 4: is for airlines what it is for rental cars. You know, 115 00:05:54,800 --> 00:05:57,040 Speaker 4: rental cars has shown a weakness of late, and I 116 00:05:57,080 --> 00:05:58,720 Speaker 4: think that's a function of the fact that you're seeing 117 00:05:58,720 --> 00:06:02,160 Speaker 4: consumers sort of not necessarily opting to rent a car, 118 00:06:02,240 --> 00:06:04,800 Speaker 4: especially when so many rental cars are tied to airlines. 119 00:06:05,240 --> 00:06:07,719 Speaker 2: I know you cover six Flags as in six Flags 120 00:06:07,720 --> 00:06:10,919 Speaker 2: Great Adventure in New Jersey. Haven't been since my junior 121 00:06:11,040 --> 00:06:11,720 Speaker 2: year in high school. 122 00:06:11,760 --> 00:06:12,640 Speaker 5: I went three years ago. 123 00:06:12,800 --> 00:06:15,520 Speaker 2: You did, great time, great time. It's awesome. How about 124 00:06:15,520 --> 00:06:17,840 Speaker 2: the theme park business, because a lot of listeners and 125 00:06:17,920 --> 00:06:21,400 Speaker 2: viewers are Disney shareholders, and that's the biggest part of 126 00:06:21,400 --> 00:06:23,960 Speaker 2: Disney's business. Are people still going to the high price 127 00:06:24,040 --> 00:06:24,560 Speaker 2: theme parks? 128 00:06:24,839 --> 00:06:27,000 Speaker 4: People are still going to theme parks, But I think 129 00:06:27,080 --> 00:06:32,159 Speaker 4: there's sort of a question of destination versus regional. Now 130 00:06:32,200 --> 00:06:36,440 Speaker 4: we've been sort of, you know, less constructive. I guess 131 00:06:36,440 --> 00:06:39,800 Speaker 4: on regional theme parks of late then say cruise lines, 132 00:06:39,800 --> 00:06:41,160 Speaker 4: And that's a function of the fact that I think 133 00:06:41,200 --> 00:06:44,799 Speaker 4: people were looking outside their general area. You know, somebody 134 00:06:44,839 --> 00:06:47,680 Speaker 4: in New Jersey isn't necessarily seeing six fives Great Adventure 135 00:06:47,680 --> 00:06:50,440 Speaker 4: as their big vacation. They're seeing, you know, the cruise 136 00:06:50,480 --> 00:06:53,679 Speaker 4: to the Hamas as their big vacation. But I think, 137 00:06:53,920 --> 00:06:57,000 Speaker 4: as you know, consumers, if and one consumers start really 138 00:06:57,040 --> 00:06:59,200 Speaker 4: feeling the straight on their wallet, we might see some 139 00:06:59,240 --> 00:07:00,920 Speaker 4: of them come back to theme parks. And we're still 140 00:07:00,960 --> 00:07:03,320 Speaker 4: trying to figure that out what that's going to look 141 00:07:03,400 --> 00:07:06,599 Speaker 4: like and what percentage. But for someone like United Parks, 142 00:07:06,600 --> 00:07:09,800 Speaker 4: which is formerly SeaWorld, you know, they're benefiting from the 143 00:07:09,800 --> 00:07:13,040 Speaker 4: fact that Universal is opening up a theme park in Florida. 144 00:07:13,080 --> 00:07:16,160 Speaker 4: You know, they have the large portion of their properties 145 00:07:16,600 --> 00:07:19,120 Speaker 4: are in Florida. You know, of course, they have the 146 00:07:19,160 --> 00:07:21,680 Speaker 4: bush gardens, they have the Sesame place, they have stuff 147 00:07:21,680 --> 00:07:25,880 Speaker 4: in San Diego. But a large percentage of their parks 148 00:07:25,920 --> 00:07:28,320 Speaker 4: and their revenue comes from Florida. And so when you 149 00:07:28,360 --> 00:07:31,560 Speaker 4: think about someone like a SeaWorld or United Parks, as 150 00:07:31,560 --> 00:07:35,120 Speaker 4: they go by. Now they are much more sensitive to 151 00:07:35,120 --> 00:07:37,280 Speaker 4: what's going on with Disney and Universal and if they're 152 00:07:37,280 --> 00:07:39,680 Speaker 4: building new parks, if they're getting people to come, they 153 00:07:39,760 --> 00:07:42,320 Speaker 4: might then accidentally also go to SeaWorld. 154 00:07:42,520 --> 00:07:45,960 Speaker 2: Oh thanks to Jody Lorie Bloomberg Intelligence Credit analysts, if 155 00:07:45,960 --> 00:07:48,280 Speaker 2: you choose to stay close to home this summer, no worries. 156 00:07:48,360 --> 00:07:50,800 Speaker 2: There are still plenty to do, like watching a movie 157 00:07:50,840 --> 00:07:54,440 Speaker 2: starring Brad Pitt on a Formula one racing track. That's 158 00:07:54,440 --> 00:07:57,559 Speaker 2: exactly what Apple wants you to do. Apple Original Film 159 00:07:57,680 --> 00:08:00,720 Speaker 2: f one. The movie is quickly becoming a some blockbuster 160 00:08:00,800 --> 00:08:04,280 Speaker 2: and could be the catalyst to supercharge bidding prospects for 161 00:08:04,680 --> 00:08:08,239 Speaker 2: Formula ones US TV rights. For more Onette Emli Grafeo 162 00:08:08,280 --> 00:08:10,800 Speaker 2: and I turned to geth It rang Anathan Bloomberg Intelligence 163 00:08:10,920 --> 00:08:12,680 Speaker 2: Senior US Media Analyst. 164 00:08:12,920 --> 00:08:15,520 Speaker 6: So this was obviously a big success story for Apple. 165 00:08:15,960 --> 00:08:19,160 Speaker 6: They've been dabbling in these theatrical films for quite a 166 00:08:19,160 --> 00:08:21,080 Speaker 6: bit now, but none of you know, none of their 167 00:08:21,120 --> 00:08:23,600 Speaker 6: movies have had this much of a success. So this 168 00:08:23,640 --> 00:08:25,080 Speaker 6: is really good news and I think it kind of 169 00:08:25,120 --> 00:08:28,920 Speaker 6: really emboldens them with respect to their strategy. So definitely, 170 00:08:28,960 --> 00:08:30,720 Speaker 6: movie theaters are happy. And the other point that I'd 171 00:08:30,760 --> 00:08:32,760 Speaker 6: like to make is, you know, more than twenty to 172 00:08:32,800 --> 00:08:36,120 Speaker 6: twenty five percent of the total box office actually came 173 00:08:36,120 --> 00:08:38,760 Speaker 6: in from Imax. So this is really good news for 174 00:08:38,760 --> 00:08:42,000 Speaker 6: the exhibiters because the premium large format, that whole thing 175 00:08:42,040 --> 00:08:45,920 Speaker 6: about going to the theater to experience the movie is 176 00:08:46,000 --> 00:08:46,600 Speaker 6: really working. 177 00:08:46,720 --> 00:08:50,040 Speaker 7: Okay, so your latest note is about how this F 178 00:08:50,120 --> 00:08:53,920 Speaker 7: one movie here from Apple is going to jolt you say, 179 00:08:54,040 --> 00:08:59,720 Speaker 7: lackluster lackluster bidding for the Formula one rights in the US. 180 00:09:00,200 --> 00:09:03,040 Speaker 7: Give us a little bit of context here the landscape 181 00:09:03,080 --> 00:09:06,240 Speaker 7: of rights for Formula one, Who currently has the rights, 182 00:09:06,400 --> 00:09:09,800 Speaker 7: when does that expire? And just put into context kind 183 00:09:09,840 --> 00:09:11,600 Speaker 7: of the current bidding right now? 184 00:09:12,040 --> 00:09:15,080 Speaker 6: Sure, Emily, so right now. So Formula one is, you know, 185 00:09:15,120 --> 00:09:19,080 Speaker 6: as a global sports, global media property in the US, 186 00:09:19,120 --> 00:09:22,640 Speaker 6: the rights to distribute Formula one content is owned by 187 00:09:22,679 --> 00:09:26,800 Speaker 6: Disney's ESPN, So they have rights that go through the 188 00:09:27,000 --> 00:09:29,000 Speaker 6: end of this year through the end of twenty twenty five. 189 00:09:29,360 --> 00:09:32,960 Speaker 6: They're currently paying about eighty five million dollars a year. 190 00:09:33,600 --> 00:09:36,840 Speaker 6: That's up from their last negotiation negotiation cycle when they 191 00:09:36,880 --> 00:09:40,080 Speaker 6: were paying only about five million dollars. So it's up 192 00:09:40,120 --> 00:09:42,400 Speaker 6: to eighty five million. But what F one is saying 193 00:09:42,480 --> 00:09:45,200 Speaker 6: is that this is such an interesting property, this is, 194 00:09:45,240 --> 00:09:47,200 Speaker 6: you know, a hot property, you should be paying up 195 00:09:47,200 --> 00:09:49,920 Speaker 6: for it. And they are really looking for something upwards 196 00:09:49,920 --> 00:09:52,480 Speaker 6: of about one hundred and fifty two hundred and sixty million, 197 00:09:52,840 --> 00:09:55,600 Speaker 6: and I'm not so sure ESPN wants to pay that 198 00:09:55,600 --> 00:09:58,480 Speaker 6: that much money. So it's really price is really a 199 00:09:58,480 --> 00:10:00,280 Speaker 6: sticking point here, and it looks like we've hit a 200 00:10:00,280 --> 00:10:03,199 Speaker 6: little bit of a stalemate. By the way, this problem, 201 00:10:03,280 --> 00:10:05,280 Speaker 6: you know, F one really kind of shot into the 202 00:10:05,280 --> 00:10:10,160 Speaker 6: limelight in the US with the Netflix drive to survive series. Now, 203 00:10:10,200 --> 00:10:13,000 Speaker 6: Netflix has been hugely actually instrumental in kind of driving 204 00:10:13,040 --> 00:10:15,920 Speaker 6: this Surgeon popularity. So everybody was kind of wondering whether 205 00:10:16,240 --> 00:10:19,360 Speaker 6: Netflix or you know, maybe another streamer like an Amazon 206 00:10:19,400 --> 00:10:21,080 Speaker 6: would kind of come in and bid for the rights. 207 00:10:21,360 --> 00:10:22,760 Speaker 6: But I think what we're seeing with a lot of 208 00:10:22,760 --> 00:10:26,360 Speaker 6: these streaming platforms is that they want global rights. These 209 00:10:26,400 --> 00:10:30,000 Speaker 6: are all global streaming platforms. They want rights to distribute 210 00:10:30,000 --> 00:10:31,839 Speaker 6: this content and all markets, and they're not going to 211 00:10:31,880 --> 00:10:32,920 Speaker 6: be able to get that right. 212 00:10:32,800 --> 00:10:36,920 Speaker 2: Now because Formula one, who's their European over is that 213 00:10:37,040 --> 00:10:39,320 Speaker 2: Warner Brothers Discovery, who distributes. 214 00:10:39,120 --> 00:10:45,600 Speaker 6: Ski Ski actually distributes them in most markets, so you know, Ireland, UK, Germany, 215 00:10:45,720 --> 00:10:49,320 Speaker 6: Italy again, France, Spain, they all have a little bit 216 00:10:49,360 --> 00:10:52,160 Speaker 6: different you know, distributors, but ski is a is a 217 00:10:52,320 --> 00:10:54,240 Speaker 6: very big one across most of Europe. 218 00:10:54,320 --> 00:10:58,760 Speaker 7: Wouldn't Apple be the obvious choice to partner up and 219 00:10:58,960 --> 00:11:01,040 Speaker 7: you know, get the rights to Formula one because they 220 00:11:01,120 --> 00:11:06,000 Speaker 7: just made this a blockbuster hit about Formula one. 221 00:11:05,800 --> 00:11:08,960 Speaker 6: You would think so, you know, but it's been a 222 00:11:08,960 --> 00:11:12,920 Speaker 6: little bit of you know, again, it's hard to actually 223 00:11:13,679 --> 00:11:15,920 Speaker 6: figure out what Apple is thinking here. One thing that 224 00:11:15,960 --> 00:11:20,600 Speaker 6: Apple doesn't have, which both Amazon and Netflix have, is scale. 225 00:11:20,640 --> 00:11:24,439 Speaker 6: So Netflix has well over three hundred million global subscribers. 226 00:11:24,480 --> 00:11:27,600 Speaker 6: Amazon probably has closed about three hundred million as well 227 00:11:27,640 --> 00:11:30,600 Speaker 6: on their video streaming service. Apple, on the other hand, 228 00:11:30,640 --> 00:11:33,640 Speaker 6: has probably less than fifteen million, so they don't necessarily 229 00:11:33,720 --> 00:11:35,880 Speaker 6: have the scale to support it. And remember there are 230 00:11:35,880 --> 00:11:37,959 Speaker 6: a few other things with the Formula one property. You 231 00:11:38,000 --> 00:11:41,400 Speaker 6: don't have a lot of advertising inventory necessarily, So again 232 00:11:41,440 --> 00:11:44,640 Speaker 6: that's something that doesn't necessarily appeal to streamers, especially because 233 00:11:44,640 --> 00:11:47,080 Speaker 6: all of these different streaming platforms are really trying to 234 00:11:47,120 --> 00:11:50,240 Speaker 6: build out their ad business. A Formula one doesn't necessarily 235 00:11:50,320 --> 00:11:53,400 Speaker 6: lend itself to that, but you're absolutely right. Apple could 236 00:11:53,480 --> 00:11:55,560 Speaker 6: be a very interested party down the road. 237 00:11:56,080 --> 00:11:59,800 Speaker 2: And for those who care, one can actually invest directly in. 238 00:12:00,000 --> 00:12:04,040 Speaker 2: Formula one is a public traded company FWNK. I like 239 00:12:04,160 --> 00:12:07,800 Speaker 2: Formula one K, it's owned by John Malone and Liberty Media. 240 00:12:07,880 --> 00:12:11,560 Speaker 2: There's a million different share classes out there, but Formula one, Keith, 241 00:12:11,720 --> 00:12:14,360 Speaker 2: I mean, under John Malone's leadership, they've done a good 242 00:12:14,440 --> 00:12:18,080 Speaker 2: job of increasing rights, sporting rights and sponsorships and all 243 00:12:18,120 --> 00:12:20,880 Speaker 2: that kind of stuff, making it actually profitable. 244 00:12:20,480 --> 00:12:23,560 Speaker 6: Very profitable, Paul, And they've they've done a fantastic job 245 00:12:23,559 --> 00:12:26,000 Speaker 6: with the media rights. Again, they've really capitalized on this 246 00:12:26,240 --> 00:12:29,240 Speaker 6: huge surgeon popularity because remember F one was really more 247 00:12:29,280 --> 00:12:32,760 Speaker 6: of a European really not so big in America, but 248 00:12:33,120 --> 00:12:37,360 Speaker 6: they've really kind of capitalized on this huge momentum in 249 00:12:37,400 --> 00:12:40,160 Speaker 6: the United States. And you're absolutely right. They've added a 250 00:12:40,160 --> 00:12:42,480 Speaker 6: whole lot of races, made it a year round calendar, 251 00:12:42,559 --> 00:12:45,200 Speaker 6: lots of sponsorship money. So they've done a very good 252 00:12:45,240 --> 00:12:47,520 Speaker 6: job with this new Liberty Media management team. 253 00:12:47,800 --> 00:12:50,520 Speaker 2: Oh thanks to the Keith ron Ganathan Bloomberg Intelligence senior 254 00:12:50,600 --> 00:12:53,600 Speaker 2: US media analyst, coming up a tech breakdown as we 255 00:12:53,640 --> 00:12:57,920 Speaker 2: talk Tesla sales, Oracle and Apple outsourcing some AI assistants. 256 00:12:58,280 --> 00:13:01,319 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 257 00:13:01,679 --> 00:13:04,120 Speaker 2: research and data on two thousand companies and one hundred 258 00:13:04,120 --> 00:13:07,559 Speaker 2: and thirty industries. You can access Bloomberg Intelligence via bi 259 00:13:07,600 --> 00:13:11,000 Speaker 2: go on the terminal. I'm Paul Sweeney. This is Bloomberg. 260 00:13:15,800 --> 00:13:21,040 Speaker 1: You're listening to Bloomberg Intelligence with Paul Sweeney on Bloomberg Radio. 261 00:13:21,840 --> 00:13:25,040 Speaker 2: This is the Bloomberg Intelligence Show. I'm Paul Sweeney. Tesla 262 00:13:25,120 --> 00:13:27,960 Speaker 2: this past week reported second quarter deliveries on its vehicles 263 00:13:28,080 --> 00:13:30,360 Speaker 2: and it was not good. Tesla now finds itself in 264 00:13:30,400 --> 00:13:33,000 Speaker 2: a deep hole to dig out of to avoid consecutive 265 00:13:33,080 --> 00:13:36,080 Speaker 2: annual declines. To help break down the numbers normally, Linda 266 00:13:36,120 --> 00:13:39,800 Speaker 2: Nice Book to Pierre Ferubu, head of Global Technology Infrastructure 267 00:13:39,800 --> 00:13:41,200 Speaker 2: at New Street Research. 268 00:13:41,000 --> 00:13:43,760 Speaker 3: There is a bit of a positive surprise, like people 269 00:13:43,800 --> 00:13:48,080 Speaker 3: tracking very precisely deliveries in various markets by expecting slightly 270 00:13:48,120 --> 00:13:52,319 Speaker 3: lower numbers. The surprise comes from the US. The US 271 00:13:52,360 --> 00:13:54,480 Speaker 3: did is a place where it's the most difficult to 272 00:13:54,520 --> 00:13:57,360 Speaker 3: track the number of Tesla being delivered, and the US 273 00:13:57,440 --> 00:14:01,800 Speaker 3: beat beat expectations that model while coming in doing actree 274 00:14:01,880 --> 00:14:05,080 Speaker 3: quite well, that's good. But then the at the end 275 00:14:05,080 --> 00:14:06,960 Speaker 3: of the day, you have to remember the big picture. 276 00:14:07,360 --> 00:14:10,800 Speaker 3: We are down thirteen persons here on air. So we 277 00:14:11,520 --> 00:14:14,800 Speaker 3: are facing a situation where Tesla is struggling to sell cars. 278 00:14:15,360 --> 00:14:19,840 Speaker 3: A gross margin is in the latins. To me, Tesla 279 00:14:19,880 --> 00:14:22,320 Speaker 3: facing good demons should be able to defend like mid 280 00:14:22,560 --> 00:14:25,960 Speaker 3: mid twenties gross margin. So we have like we are 281 00:14:26,000 --> 00:14:28,320 Speaker 3: facing like a core business and not a business that 282 00:14:28,440 --> 00:14:30,280 Speaker 3: is really at a very low point that is struggling. 283 00:14:31,320 --> 00:14:34,479 Speaker 3: And so that's the difficulty. You have, like one structural 284 00:14:34,520 --> 00:14:38,400 Speaker 3: difficulty that is relatively that we understand relatively well. It's 285 00:14:38,440 --> 00:14:42,120 Speaker 3: that with their current product, Tesla has kind of saturated 286 00:14:42,120 --> 00:14:46,600 Speaker 3: its opportunity. Tesla needs lower high points to keep growing 287 00:14:47,080 --> 00:14:49,680 Speaker 3: and that's in the plan they're working on it. And 288 00:14:49,720 --> 00:14:51,920 Speaker 3: then you have a number of other issues. You know, 289 00:14:52,040 --> 00:14:54,840 Speaker 3: electric cars are less fashionable than they were a couple 290 00:14:54,920 --> 00:14:59,720 Speaker 3: of years ago. The Tesla brand has suffered, particularly in 291 00:14:59,800 --> 00:15:02,680 Speaker 3: New Rope where numbers are down like almost forty percent 292 00:15:02,960 --> 00:15:08,160 Speaker 3: on air, and so it is definitely it's difficult to quantify, 293 00:15:08,400 --> 00:15:13,040 Speaker 3: but it is a significant headwind as well. And overall, 294 00:15:13,080 --> 00:15:15,960 Speaker 3: you know, the economy is not right, the best you 295 00:15:15,960 --> 00:15:18,640 Speaker 3: can imagine for auto dim Yeah. 296 00:15:18,640 --> 00:15:20,840 Speaker 5: I mean when we look at the mag seven stocks, 297 00:15:20,840 --> 00:15:23,760 Speaker 5: we see Tesla as the primary laggard this year, down 298 00:15:23,800 --> 00:15:26,720 Speaker 5: about twenty three percent. And of course we know that 299 00:15:26,720 --> 00:15:29,760 Speaker 5: investors have really been vying for Musk's attention as he's 300 00:15:29,800 --> 00:15:32,880 Speaker 5: been really politically involved. So as we're seeing these numbers 301 00:15:32,960 --> 00:15:36,520 Speaker 5: come through, what do things like moving forward? Are these 302 00:15:36,600 --> 00:15:40,400 Speaker 5: numbers a massive impact or are the Tesla loyalists still 303 00:15:40,400 --> 00:15:44,120 Speaker 5: out there and is there some further positivity to come. 304 00:15:44,320 --> 00:15:47,720 Speaker 3: Yes, you the Tesla stock has been on a royal cost. 305 00:15:48,440 --> 00:15:50,440 Speaker 3: It's like a very low performer this year, but it 306 00:15:50,600 --> 00:15:53,600 Speaker 3: was like a clear out performer last year last year. 307 00:15:53,640 --> 00:15:57,000 Speaker 3: And as you said, you know, it's following, it's following, 308 00:15:57,040 --> 00:15:59,600 Speaker 3: you know, the political carrier, if I may say, of 309 00:15:59,680 --> 00:16:06,000 Speaker 3: the sea in a Musk and like this has created 310 00:16:06,040 --> 00:16:09,680 Speaker 3: this situation. Now if you look at the stock, you know, 311 00:16:09,800 --> 00:16:13,720 Speaker 3: since you know, like April first, what you see that 312 00:16:13,880 --> 00:16:16,720 Speaker 3: what tends to really move the stock is actually the 313 00:16:16,760 --> 00:16:20,560 Speaker 3: news flow around how the company is now focused on 314 00:16:20,600 --> 00:16:25,200 Speaker 3: deploying fleets of robot taxes. They've just started that in 315 00:16:25,840 --> 00:16:29,240 Speaker 3: a couple of weeks ago, like late late last months 316 00:16:29,720 --> 00:16:31,720 Speaker 3: and I think that's going to be the primary driver 317 00:16:32,360 --> 00:16:35,840 Speaker 3: of the stock going forward. Of course, there is a 318 00:16:35,880 --> 00:16:38,880 Speaker 3: lot of uncertainty on you know, how fast THETA is 319 00:16:38,920 --> 00:16:41,280 Speaker 3: going to be able to deploy robotaxy or even make 320 00:16:41,320 --> 00:16:43,920 Speaker 3: a profits, like generate the profits out of the robot 321 00:16:43,920 --> 00:16:48,000 Speaker 3: taxi fleet. But the size of the price is so 322 00:16:48,200 --> 00:16:50,760 Speaker 3: gigantic and so much more than the ATOL business that 323 00:16:51,000 --> 00:16:53,400 Speaker 3: this is probably what drives the stock going forward. 324 00:16:53,760 --> 00:16:57,560 Speaker 2: Are thanks to Pierre Fair ahead of Global Technology Infrastructure 325 00:16:57,600 --> 00:17:01,720 Speaker 2: at New Street Research with tech Oracle this past week 326 00:17:01,840 --> 00:17:04,600 Speaker 2: said it had signed a single cloud deal worth thirty 327 00:17:04,640 --> 00:17:07,520 Speaker 2: billion dollars in annual revenue. It's more than the current 328 00:17:07,520 --> 00:17:11,080 Speaker 2: size of its entire cloud infrastructure business. For more on 329 00:17:11,119 --> 00:17:13,920 Speaker 2: this deal and what this means for the computer software company, 330 00:17:13,960 --> 00:17:17,240 Speaker 2: we turned to Anorak Rana, Bloomberg Intelligence technology analyst. 331 00:17:17,880 --> 00:17:20,199 Speaker 8: We think this is part of the Stargate contract. This 332 00:17:20,320 --> 00:17:23,280 Speaker 8: is something that's going to flow from open aie, you know, 333 00:17:23,320 --> 00:17:27,040 Speaker 8: the combination between soft Bank, Uticle and open AI. So 334 00:17:27,080 --> 00:17:29,679 Speaker 8: if you remember there was a big deal, you know, 335 00:17:29,840 --> 00:17:32,000 Speaker 8: just I think a day or two after the president 336 00:17:32,040 --> 00:17:34,840 Speaker 8: took office, where Laddie Allison and you know got on 337 00:17:35,160 --> 00:17:37,760 Speaker 8: TV and talked about it. I you know, we think 338 00:17:37,800 --> 00:17:39,600 Speaker 8: this is a byproduct of that. 339 00:17:41,080 --> 00:17:44,479 Speaker 5: How is Oracle matching up with its competitors? I mean, 340 00:17:44,520 --> 00:17:47,600 Speaker 5: I know it's gained traction for renting out computing power 341 00:17:47,640 --> 00:17:49,679 Speaker 5: over the Internet, and of course it's been targeting a 342 00:17:49,680 --> 00:17:52,880 Speaker 5: lot of its clients that are focused on AI there. 343 00:17:53,040 --> 00:17:55,719 Speaker 5: But how does it look in comparison to peers? 344 00:17:56,480 --> 00:17:58,800 Speaker 8: Yeah, I think you know, it was really I go 345 00:17:58,960 --> 00:18:01,159 Speaker 8: back a few years. It was, you know, to be honest, 346 00:18:01,200 --> 00:18:04,320 Speaker 8: non existent about three years ago. So the revenue was 347 00:18:04,400 --> 00:18:06,879 Speaker 8: you know, just a handful of billion dollars or so, 348 00:18:07,200 --> 00:18:09,960 Speaker 8: compared to you know, somebody like an AWS which is 349 00:18:10,040 --> 00:18:12,520 Speaker 8: now running at a rundred of over one hundred billion dollars. So, 350 00:18:12,960 --> 00:18:15,960 Speaker 8: but this, if you look at it, in last year alone, 351 00:18:16,760 --> 00:18:19,719 Speaker 8: oracles infrastructure as a service business, which is what we 352 00:18:19,760 --> 00:18:22,840 Speaker 8: look as the proxy for their cloud infrastructure business, had 353 00:18:22,880 --> 00:18:26,240 Speaker 8: revenues of about ten billion dollars. So and then here 354 00:18:26,320 --> 00:18:29,320 Speaker 8: comes a contract that in three years and you know, 355 00:18:29,400 --> 00:18:32,720 Speaker 8: they will have thirty billion dollars annually from one just 356 00:18:32,800 --> 00:18:35,960 Speaker 8: one customer. So you could see just the overall what's 357 00:18:36,000 --> 00:18:39,399 Speaker 8: happening with AI? What is really need so much of 358 00:18:39,440 --> 00:18:42,919 Speaker 8: computing power to run these models, and this is you 359 00:18:42,960 --> 00:18:45,960 Speaker 8: know again they are going everywhere, but Oracle is you know, 360 00:18:45,960 --> 00:18:47,760 Speaker 8: one of the bigger beneficiaries over here. 361 00:18:48,520 --> 00:18:52,240 Speaker 2: Rich go rich, the world's richest people. Larry Ellison of 362 00:18:52,400 --> 00:18:55,680 Speaker 2: Oracle clocks in at number four, two hundred and twenty 363 00:18:55,760 --> 00:19:00,600 Speaker 2: nine billion dollars, up thirty seven billion dollars year to date. 364 00:19:01,520 --> 00:19:03,879 Speaker 2: The stock is up fifty two week high on a 365 00:19:03,960 --> 00:19:08,560 Speaker 2: rockets at an all time high. What's the bookcase for Oracle? 366 00:19:08,600 --> 00:19:14,120 Speaker 2: Here's just just another way to play cloud slash AI. 367 00:19:14,880 --> 00:19:17,879 Speaker 8: Yeah, so that's the bookcase. Definitely on that end, you 368 00:19:17,880 --> 00:19:20,239 Speaker 8: could see their growth rates are going to pick up 369 00:19:20,280 --> 00:19:22,440 Speaker 8: so much more than any of the other cloud providers, 370 00:19:22,560 --> 00:19:25,440 Speaker 8: and partially because they're you know, the numbers are too small. 371 00:19:25,480 --> 00:19:27,720 Speaker 8: As I said, They're only at about ten billion run 372 00:19:27,840 --> 00:19:29,840 Speaker 8: rate or so in this case at this point. So 373 00:19:30,320 --> 00:19:32,439 Speaker 8: that's one. But on the other side, you know, they 374 00:19:32,480 --> 00:19:34,800 Speaker 8: will have to spend a lot on capex right now, 375 00:19:35,040 --> 00:19:37,840 Speaker 8: so that capex number is going to go up. Margins 376 00:19:37,880 --> 00:19:40,520 Speaker 8: are going to most likely take a hit. But frankly speaking, 377 00:19:40,560 --> 00:19:43,399 Speaker 8: in this market right now, it's all about market share 378 00:19:43,440 --> 00:19:46,879 Speaker 8: gains and which of the companies can actually benefit from 379 00:19:46,920 --> 00:19:51,040 Speaker 8: all these large language model training or inference basically, you know, 380 00:19:51,119 --> 00:19:53,000 Speaker 8: they are you know, they're going everywhere. I mean look 381 00:19:53,000 --> 00:19:56,000 Speaker 8: at for example, code WEEP their growth rate. The SAME's 382 00:19:56,040 --> 00:19:59,600 Speaker 8: the case with Microsoft, Azure or EWS. I mean, everybody 383 00:19:59,600 --> 00:20:02,280 Speaker 8: in this particular ecosystem is going to make lots of money. 384 00:20:03,000 --> 00:20:05,240 Speaker 5: We know, expectations remain high for a lot of these 385 00:20:05,240 --> 00:20:08,359 Speaker 5: tech firms, especially as we think about the AI push. 386 00:20:08,400 --> 00:20:10,280 Speaker 5: What do investors want to see right now? What's the 387 00:20:10,320 --> 00:20:11,640 Speaker 5: next step for Oracle? 388 00:20:12,840 --> 00:20:15,000 Speaker 8: I think a lot of this would become to execution 389 00:20:15,119 --> 00:20:17,199 Speaker 8: in this case, because you know, this is we are 390 00:20:17,240 --> 00:20:21,399 Speaker 8: assuming flawless execution in terms of data centered expansion. But 391 00:20:21,440 --> 00:20:23,160 Speaker 8: what if they are not able to get enough power 392 00:20:23,200 --> 00:20:25,280 Speaker 8: in there out there? I mean, what if we get 393 00:20:25,280 --> 00:20:27,240 Speaker 8: into a shortage in that area. So there's going to 394 00:20:27,240 --> 00:20:29,360 Speaker 8: be a lot to you know, I would say ups 395 00:20:29,400 --> 00:20:32,000 Speaker 8: and downs, not just for article, but everybody in the 396 00:20:32,040 --> 00:20:35,960 Speaker 8: equation where maybe the project gets delayed some funding issues. 397 00:20:36,359 --> 00:20:38,879 Speaker 8: Right now, everything is fine when it comes to funding 398 00:20:38,920 --> 00:20:41,720 Speaker 8: for these particular projects, but you know, those are the 399 00:20:41,760 --> 00:20:44,000 Speaker 8: things that we would watch for. But other than that, 400 00:20:44,119 --> 00:20:46,240 Speaker 8: I think the the you know, future is really bright 401 00:20:46,320 --> 00:20:49,520 Speaker 8: for all the cloud providers because of the workloads that 402 00:20:49,560 --> 00:20:50,160 Speaker 8: are coming. 403 00:20:49,960 --> 00:20:53,800 Speaker 2: In our thanks to anaag Rana Bloomberg Intelligence technology analysts. 404 00:20:54,359 --> 00:20:57,000 Speaker 2: Closing out our tech segment is a story this week 405 00:20:57,040 --> 00:21:00,440 Speaker 2: from Apple. The tech giant shocked investors when our Mark 406 00:21:00,480 --> 00:21:03,840 Speaker 2: German announced that the company was considering using artificial intelligence 407 00:21:03,840 --> 00:21:07,480 Speaker 2: technology from Anthropic or open AI to power a new 408 00:21:07,600 --> 00:21:11,520 Speaker 2: version of Siri, potentially sidelining its own in house models. 409 00:21:11,760 --> 00:21:15,080 Speaker 2: Man Deep Seeing, Bloomberg Intelligence senior tech oust breaks it down. 410 00:21:15,480 --> 00:21:19,359 Speaker 9: Yeah, I mean, if you remember at WWDC, all they 411 00:21:19,400 --> 00:21:23,560 Speaker 9: showcase was that new liquid glass interface. There was no 412 00:21:23,720 --> 00:21:27,440 Speaker 9: mention of AI, so it was pretty obvious that they 413 00:21:27,480 --> 00:21:31,840 Speaker 9: had nothing new to showcase from an LLM perspective. And look, 414 00:21:32,320 --> 00:21:36,840 Speaker 9: given the astronomical rise of chat GPT and how it's 415 00:21:37,080 --> 00:21:43,000 Speaker 9: taken the user and engagement share, I mean everyone is 416 00:21:43,040 --> 00:21:47,120 Speaker 9: looking to use that kind of functionality on the apps 417 00:21:47,920 --> 00:21:51,320 Speaker 9: that live within the Apple ecosystem, whether it's your email, 418 00:21:51,520 --> 00:21:56,000 Speaker 9: your calendar, your contacts, like the photos that are stored 419 00:21:56,040 --> 00:21:59,480 Speaker 9: on iCloud. You have to ask to yourself, why can't 420 00:21:59,640 --> 00:22:03,760 Speaker 9: Apple will deploy AI in a similar fashion where I 421 00:22:03,800 --> 00:22:07,280 Speaker 9: can engage in a chatchipt type format. And so that's 422 00:22:07,359 --> 00:22:12,239 Speaker 9: the use case here. Obviously, Apple's own LM efforts have 423 00:22:12,359 --> 00:22:16,080 Speaker 9: gone nowhere because they have, frankly speaking, underinvested. I mean, 424 00:22:16,160 --> 00:22:19,160 Speaker 9: look at their capex versus all the other hyperskillers. They're 425 00:22:19,200 --> 00:22:23,080 Speaker 9: spending eighty billion dollars. Apple is spending ten billion dollars. 426 00:22:23,119 --> 00:22:26,440 Speaker 9: So it's just not the same level of investment, and 427 00:22:26,480 --> 00:22:32,560 Speaker 9: they underappreciated how transformational this generative AI LLLM wave is 428 00:22:32,640 --> 00:22:35,040 Speaker 9: going to be. And now they are partnering with an 429 00:22:35,160 --> 00:22:39,000 Speaker 9: Entropic or open Ai. I think Entropic makes more sense 430 00:22:39,040 --> 00:22:42,520 Speaker 9: to me because OpenAI has clearly said they have their 431 00:22:42,520 --> 00:22:46,639 Speaker 9: own hardware ambitions, and to my mind, Entropic is an 432 00:22:46,800 --> 00:22:50,560 Speaker 9: enterprise focused player. They have already partnered with Amazon and 433 00:22:50,680 --> 00:22:53,760 Speaker 9: in this case they probably will partner with Apple as well. 434 00:22:54,200 --> 00:22:57,760 Speaker 7: As Paul mentioned the stock market, it wasn't disappointed in 435 00:22:57,840 --> 00:23:01,560 Speaker 7: this snooze. The stock was up yesterday when Bloomberg's Markerman 436 00:23:01,720 --> 00:23:03,439 Speaker 7: you know wrote the story, which I do think it 437 00:23:03,480 --> 00:23:06,560 Speaker 7: came out he breaks yes, he does break everything. I 438 00:23:06,600 --> 00:23:09,000 Speaker 7: think the story came after the close, but still the 439 00:23:09,040 --> 00:23:13,960 Speaker 7: stock is hired today, so this is good. I mean, 440 00:23:14,240 --> 00:23:18,040 Speaker 7: talk a little bit more about the partnerships, because if 441 00:23:18,080 --> 00:23:23,080 Speaker 7: Anthropic is partnering with Amazon as well, Like, this isn't 442 00:23:23,119 --> 00:23:26,440 Speaker 7: really an exclusive deal that they're doing. Apple is just 443 00:23:26,480 --> 00:23:28,800 Speaker 7: going to have the same AI as all the other times. 444 00:23:28,920 --> 00:23:33,800 Speaker 9: Yeah, but Apple controls the distribution across their iOS devices, 445 00:23:33,840 --> 00:23:38,000 Speaker 9: and whether it's your tablet or your smartphone or macpcs, 446 00:23:38,359 --> 00:23:42,879 Speaker 9: it's the Apple iOS ecosystem. So if Ententropics LLM is 447 00:23:42,960 --> 00:23:46,280 Speaker 9: natively integrated, I mean, look at how much Microsoft has 448 00:23:46,359 --> 00:23:51,240 Speaker 9: benefited from open AI's partnership. Microsoft doesn't have their own LLLM. 449 00:23:51,520 --> 00:23:54,679 Speaker 9: The fact that they have risen so much is because 450 00:23:54,720 --> 00:23:57,800 Speaker 9: of the open Ai partnership. So partnership isn't a bad 451 00:23:57,840 --> 00:24:02,560 Speaker 9: thing for Apple, and in this case, clearly Entropic has 452 00:24:02,600 --> 00:24:06,280 Speaker 9: a very good LLM. On the tech side, maybe you 453 00:24:06,320 --> 00:24:10,560 Speaker 9: could argue in terms of multi modality open Ai and 454 00:24:10,600 --> 00:24:16,040 Speaker 9: Google are ahead, But for a text LLM, Entropics performance 455 00:24:16,160 --> 00:24:19,200 Speaker 9: is comparable at part with the frontier models. And that's 456 00:24:19,240 --> 00:24:22,600 Speaker 9: where it makes a ton of sense that they use Anthropic. 457 00:24:22,160 --> 00:24:25,080 Speaker 2: Thirty seconds, Can I think about this potent This announcement 458 00:24:25,440 --> 00:24:29,240 Speaker 2: similar to Apple saying we're going to use Google for search. 459 00:24:30,040 --> 00:24:32,600 Speaker 9: Yeah, And part of the reason why there is no 460 00:24:32,760 --> 00:24:36,359 Speaker 9: mention of Google here is because of that anti trust 461 00:24:36,520 --> 00:24:40,640 Speaker 9: case that's pending. So clearly what Apple wants to signal 462 00:24:40,800 --> 00:24:43,400 Speaker 9: is they don't want to do anything with Google right now, 463 00:24:43,480 --> 00:24:47,240 Speaker 9: given that monopoly lawsuit and the twenty plus billion dollar 464 00:24:47,280 --> 00:24:50,119 Speaker 9: of payment from Google to Apple, and right now they 465 00:24:50,160 --> 00:24:54,800 Speaker 9: are really diversifying their exposure to these LLM players. They 466 00:24:54,800 --> 00:24:57,320 Speaker 9: could have very well used in LLLM, and I wouldn't 467 00:24:57,320 --> 00:25:00,879 Speaker 9: be surprised down the line once this ad BODA cases 468 00:25:00,960 --> 00:25:05,000 Speaker 9: resolved and if it's if it works out fine, they 469 00:25:05,119 --> 00:25:08,960 Speaker 9: may end up using Google's LM as well. Apple's main 470 00:25:09,040 --> 00:25:12,840 Speaker 9: goal here is to have as many LLLM partners and 471 00:25:13,359 --> 00:25:17,119 Speaker 9: really not depend on just one, whether it's opening oranthropic. 472 00:25:17,240 --> 00:25:19,440 Speaker 9: They want five players, and I think they would be 473 00:25:19,480 --> 00:25:21,320 Speaker 9: willing to use anyone. 474 00:25:20,960 --> 00:25:23,400 Speaker 2: Our Thanks to Man Deep saying, Bloomberg Intelligence senior tech 475 00:25:23,440 --> 00:25:26,280 Speaker 2: industry analyst coming up on the program A Trio Big 476 00:25:26,320 --> 00:25:28,760 Speaker 2: Takes stories from this week you do not want to miss. 477 00:25:28,960 --> 00:25:31,760 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 478 00:25:31,800 --> 00:25:34,639 Speaker 2: depth research and data on two thousand companies in one 479 00:25:34,720 --> 00:25:37,600 Speaker 2: hundred and thirty industries. You can access Bloomberg Intelligence via 480 00:25:37,720 --> 00:25:41,760 Speaker 2: Bigo on the terminal. I'm Paul Sweeney, and this is Bloomberg. 481 00:25:47,600 --> 00:25:47,840 Speaker 10: Now. 482 00:25:48,560 --> 00:25:53,800 Speaker 1: You're listening to Bloomberg Intelligence with Paul Sweeney on Bloomberg Radio. 483 00:25:54,440 --> 00:25:56,200 Speaker 2: We wrap up the show with a trio big takes 484 00:25:56,240 --> 00:25:59,000 Speaker 2: stories from this past week worth highlighting. First up as 485 00:25:59,040 --> 00:26:01,800 Speaker 2: a story on why the US Treasury keeps getting hacked. 486 00:26:01,960 --> 00:26:04,399 Speaker 2: For more, Emily Grafe and I spoke to Jordan robertson 487 00:26:04,400 --> 00:26:06,880 Speaker 2: Bloomberg News cybersecurity reporter. 488 00:26:07,280 --> 00:26:09,919 Speaker 10: So yeah, our Big Take, our Bloomberg Big Take investigation 489 00:26:10,359 --> 00:26:14,800 Speaker 10: is about a series of really impactful cybersecurity attacks at 490 00:26:14,840 --> 00:26:17,720 Speaker 10: the US Treasury Department. There was a major attack in 491 00:26:17,880 --> 00:26:22,080 Speaker 10: twenty twenty, major attack in December, a major attack that 492 00:26:22,160 --> 00:26:24,800 Speaker 10: was disclosed earlier this year, which news that was broken 493 00:26:24,840 --> 00:26:28,399 Speaker 10: by our some of our Bloomberg colleagues. And the question 494 00:26:28,480 --> 00:26:30,600 Speaker 10: we set out to answer was why does the US 495 00:26:30,640 --> 00:26:33,360 Speaker 10: Treasury Department keep getting hacked? And why does it keep 496 00:26:33,359 --> 00:26:37,880 Speaker 10: getting hacked so badly? Every organization faces attempted hacking attacks. 497 00:26:38,320 --> 00:26:42,639 Speaker 10: Many organizations experience successful hacking attacks. It's always about the 498 00:26:42,760 --> 00:26:46,200 Speaker 10: depth though, of the intrusion. How far did the hackers 499 00:26:46,240 --> 00:26:48,880 Speaker 10: get and what did they get where they wanted to go? 500 00:26:49,160 --> 00:26:51,800 Speaker 10: And in each of these three attacks, the hackers got 501 00:26:51,840 --> 00:26:54,959 Speaker 10: about as far deep into the organization as you could imagine, 502 00:26:55,640 --> 00:26:57,480 Speaker 10: and what we set out to answer was how is 503 00:26:57,520 --> 00:27:00,399 Speaker 10: that happening? And one of the answers we came up 504 00:27:00,440 --> 00:27:03,480 Speaker 10: with is that, you know, the Treasury Department is one agency, 505 00:27:03,600 --> 00:27:07,440 Speaker 10: but with the cybersecurity, the cybersecurity of it is run 506 00:27:07,480 --> 00:27:10,840 Speaker 10: effectively as a series of like a half dozen different countries. 507 00:27:10,880 --> 00:27:15,080 Speaker 10: Almost these are subdivisions of the Treasury Department that have 508 00:27:15,160 --> 00:27:19,080 Speaker 10: their own cybersecurity budget allocated by Congress, they have their 509 00:27:19,119 --> 00:27:23,080 Speaker 10: own cybersecurity management, they have essentially of their own IT infrastructure. 510 00:27:23,400 --> 00:27:25,800 Speaker 10: And so what that means is some of these divisions 511 00:27:25,800 --> 00:27:27,959 Speaker 10: have really good security. Some of these divisions don't have 512 00:27:28,000 --> 00:27:31,119 Speaker 10: such good security. And you know, this is an organization 513 00:27:31,240 --> 00:27:33,680 Speaker 10: that I think a lot of people focus on because 514 00:27:33,680 --> 00:27:36,439 Speaker 10: of the financial aspect of what Treasury does. But it 515 00:27:36,480 --> 00:27:40,119 Speaker 10: does so much more than that. Investigates sanctions, it investigates 516 00:27:40,200 --> 00:27:43,520 Speaker 10: terrorists financing. It does so much more than just kind 517 00:27:43,560 --> 00:27:47,119 Speaker 10: of collect tax revenue, you know, and issue treasury bonds. 518 00:27:47,440 --> 00:27:52,440 Speaker 7: What is the reporting show about who's actually committing these 519 00:27:52,480 --> 00:27:56,760 Speaker 7: hacks and what exactly are the hacks on the Treasury. 520 00:27:57,040 --> 00:28:00,520 Speaker 10: So these are primarily nation state attack. So these are 521 00:28:00,560 --> 00:28:03,639 Speaker 10: foreign governments attacking the US Treasury Department. Again, there are 522 00:28:03,680 --> 00:28:06,720 Speaker 10: so many different types of cyber attacks that organizations face. 523 00:28:07,000 --> 00:28:08,639 Speaker 10: A lot of them are very low level and kind 524 00:28:08,680 --> 00:28:12,639 Speaker 10: of easily deflected. Increasingly nowadays we're seeing criminal groups, ransomware 525 00:28:12,680 --> 00:28:18,080 Speaker 10: groups affecting, hijacking networks, and disrupting networks that way. That's 526 00:28:18,160 --> 00:28:20,080 Speaker 10: not what we're talking about here with Treasury. We're talking 527 00:28:20,080 --> 00:28:23,879 Speaker 10: about espionage. We're talking about kind of large, long range 528 00:28:24,000 --> 00:28:28,480 Speaker 10: cyber espionage where these are foreign government backed actors whose 529 00:28:28,520 --> 00:28:31,200 Speaker 10: purpose is spying on data. So in the latest breach, 530 00:28:31,480 --> 00:28:34,120 Speaker 10: the Office of the Comptroller of the Currency that's the 531 00:28:34,160 --> 00:28:38,360 Speaker 10: regulator of America's national banks, had its email system hacked 532 00:28:38,440 --> 00:28:41,800 Speaker 10: for almost two years. So for almost two years, a 533 00:28:41,840 --> 00:28:44,760 Speaker 10: cyber attack group which hasn't been identified yet, you know, 534 00:28:44,760 --> 00:28:48,560 Speaker 10: it was conducting very advanced cyber espionage on the emails 535 00:28:48,560 --> 00:28:52,440 Speaker 10: and the communications of America's national bank regulator. That's not 536 00:28:52,480 --> 00:28:55,600 Speaker 10: supposed to happen. It led to this really extraordinary series 537 00:28:55,640 --> 00:28:59,360 Speaker 10: of events where several of the biggest US banks JP, Morgan, 538 00:28:59,440 --> 00:29:04,160 Speaker 10: bny ME and others severed there temporarily paused their electronic 539 00:29:04,240 --> 00:29:07,720 Speaker 10: sharing of communicate of data with their regulator as a 540 00:29:07,760 --> 00:29:10,680 Speaker 10: result of this breach. That's really extraordinary to have a 541 00:29:10,720 --> 00:29:14,920 Speaker 10: regulated entity tell their regulator your network is not safe 542 00:29:15,000 --> 00:29:17,719 Speaker 10: enough for me to transmit data to That was the 543 00:29:17,720 --> 00:29:19,960 Speaker 10: incident that really compelled us to take a deeper look 544 00:29:19,960 --> 00:29:21,760 Speaker 10: at this and try to figure out what's going on 545 00:29:21,800 --> 00:29:24,120 Speaker 10: at Treasury. And I might add as well, this is 546 00:29:24,160 --> 00:29:26,959 Speaker 10: at a time that you know, Congress has allocated hundreds 547 00:29:27,000 --> 00:29:31,560 Speaker 10: of millions of additional dollars to Treasury to enhance their cybersecurity, 548 00:29:31,840 --> 00:29:34,640 Speaker 10: and they've spent that money. They have done things, but 549 00:29:34,680 --> 00:29:38,640 Speaker 10: they have still not been effective fully in preventing these attacks. 550 00:29:38,880 --> 00:29:42,600 Speaker 2: I noticing your reporting, Jordan, there's been a lot of 551 00:29:42,920 --> 00:29:47,080 Speaker 2: I guess turnover or departures by cybersecurity people within the 552 00:29:47,160 --> 00:29:49,160 Speaker 2: Treasury Department. Did that have any role in this? 553 00:29:49,200 --> 00:29:49,360 Speaker 11: Do? 554 00:29:49,360 --> 00:29:51,880 Speaker 10: We think that's right? So another interesting aspect of the 555 00:29:51,960 --> 00:29:55,600 Speaker 10: reporting is that you know, Treasury senior most cybersecurity officials, 556 00:29:55,640 --> 00:29:58,560 Speaker 10: from the Chief Technology Officer and the Chief Information Officer 557 00:29:58,600 --> 00:30:00,840 Speaker 10: and all their deputies, there have been you know, more 558 00:30:00,880 --> 00:30:03,600 Speaker 10: than a half dozen of these individuals who have left 559 00:30:03,640 --> 00:30:08,040 Speaker 10: Treasury since the Trump administration took office. A lot of 560 00:30:08,080 --> 00:30:11,080 Speaker 10: the attention with Elon Musk and the Doge group has 561 00:30:11,120 --> 00:30:14,280 Speaker 10: been focused you know, understandably on the layoffs. This is 562 00:30:14,320 --> 00:30:16,400 Speaker 10: not a case of layoffs. This is primarily a case 563 00:30:16,440 --> 00:30:19,720 Speaker 10: of buyouts. So when the Doge group, you know, has 564 00:30:19,800 --> 00:30:23,920 Speaker 10: offered buyouts to large numbers of government employees, what's happened 565 00:30:23,960 --> 00:30:27,360 Speaker 10: at Treasury is almost all of the senior most cybersecurity 566 00:30:27,400 --> 00:30:30,440 Speaker 10: officials have taken the buyout. And what we've been told 567 00:30:30,520 --> 00:30:32,600 Speaker 10: is that, in fact, some of those individuals may have 568 00:30:32,680 --> 00:30:36,120 Speaker 10: even supported the Doge effort and the idea that you know, 569 00:30:36,280 --> 00:30:40,640 Speaker 10: consolidating Treasury's IT infrastructure would help improve security. But all 570 00:30:40,680 --> 00:30:43,280 Speaker 10: of those folks are gone. So the cybersecurity leadership at 571 00:30:43,320 --> 00:30:46,920 Speaker 10: Treasury is almost entirely gone, you know, at a time 572 00:30:46,960 --> 00:30:50,240 Speaker 10: where it's facing kind of unprecedented levels of cyber attacks 573 00:30:50,280 --> 00:30:51,840 Speaker 10: and successful cyber attacks. 574 00:30:52,080 --> 00:30:56,440 Speaker 2: Our thanks to Jordan robertson Bloomberg News cybersecurity reporter. The 575 00:30:56,480 --> 00:31:00,800 Speaker 2: next Big Takes stories entitled Electronic Warfare Crashes Global Shipping's 576 00:31:00,880 --> 00:31:04,360 Speaker 2: navigation systems. It highlights how the Iran Israel war exposed 577 00:31:04,360 --> 00:31:06,920 Speaker 2: a critical flaw in the satellite based systems that makes 578 00:31:07,280 --> 00:31:10,600 Speaker 2: the industry hauling eighty percent of global trade vulnerable the 579 00:31:10,720 --> 00:31:14,120 Speaker 2: mass gymming. Jack Whittles is Bloomberg News Oil and Shipping reporter. 580 00:31:14,480 --> 00:31:18,640 Speaker 11: So we looked at shipping data around the Persian Gulf, 581 00:31:18,840 --> 00:31:22,280 Speaker 11: so obviously relevant to the israel around war, but also 582 00:31:22,640 --> 00:31:26,920 Speaker 11: the Red Sea, the Baltic and the Black Sea, and 583 00:31:26,960 --> 00:31:30,440 Speaker 11: we basically saw a big spike in the number of 584 00:31:30,840 --> 00:31:34,520 Speaker 11: strange signals from ships. And what I mean by strange 585 00:31:34,560 --> 00:31:38,920 Speaker 11: signals from ships is ships showing that they are in 586 00:31:39,000 --> 00:31:42,480 Speaker 11: impossible positions. So, for instance, we had a ship in 587 00:31:42,720 --> 00:31:45,440 Speaker 11: the Persian Gulf saying that it was on the edge 588 00:31:45,440 --> 00:31:49,080 Speaker 11: of a camel racetrack. In the UAE, we had a 589 00:31:49,120 --> 00:31:52,320 Speaker 11: ship jumping from off the coast of Saudi Arabia, which 590 00:31:52,400 --> 00:31:54,200 Speaker 11: is on the western side of the Persian Gulf, all 591 00:31:54,200 --> 00:31:56,840 Speaker 11: the way to Iran and then back again in one day, 592 00:31:57,400 --> 00:32:00,520 Speaker 11: which is obviously impossible, especially as the j to Iran 593 00:32:00,720 --> 00:32:02,200 Speaker 11: was on land in Iran. 594 00:32:02,680 --> 00:32:06,720 Speaker 5: Jack. Have we seen this type of signal interferences within 595 00:32:06,880 --> 00:32:09,400 Speaker 5: any other sort of geopolitical crises. 596 00:32:10,360 --> 00:32:14,600 Speaker 11: We have seen it before, but the number when we've 597 00:32:14,640 --> 00:32:17,760 Speaker 11: done our analysis, the number that we've done is massively 598 00:32:17,960 --> 00:32:20,840 Speaker 11: increased for ships appearing on land. In June, there was 599 00:32:20,880 --> 00:32:23,960 Speaker 11: a really huge spike for ships appearing on land in 600 00:32:24,000 --> 00:32:26,280 Speaker 11: the areas that we looked at. So that was again 601 00:32:26,400 --> 00:32:28,800 Speaker 11: the Red Sea, Black Sea, Baltic and push it off. 602 00:32:29,360 --> 00:32:32,920 Speaker 2: So Jack, is this just the standard performance of this 603 00:32:33,320 --> 00:32:36,479 Speaker 2: satellite technology, i e. It's not great? Or is it 604 00:32:36,520 --> 00:32:40,760 Speaker 2: in fact the subject of some type of electronic warfare maybe? 605 00:32:40,760 --> 00:32:41,080 Speaker 2: Do we know? 606 00:32:41,680 --> 00:32:46,080 Speaker 11: Yeah? So's there's two ways that this equipment can be 607 00:32:46,120 --> 00:32:50,080 Speaker 11: interfered with. There's what's called jamming, where the signals are 608 00:32:50,160 --> 00:32:54,520 Speaker 11: essentially overwhelmed, and there's what's called spoofing, where ships are 609 00:32:54,560 --> 00:32:57,960 Speaker 11: fed a false signal, so they're specifically told that there's 610 00:32:58,000 --> 00:33:00,440 Speaker 11: somewhere that they're not. So went on to about these 611 00:33:00,440 --> 00:33:03,520 Speaker 11: satellite navigation systems. They're using GPS, so you know, the 612 00:33:03,520 --> 00:33:05,080 Speaker 11: same sort of thing that's on your Google Maps. So 613 00:33:05,080 --> 00:33:06,800 Speaker 11: it'd be like if the blue dot on your Google 614 00:33:06,840 --> 00:33:09,040 Speaker 11: Maps that tells you where you are just suddenly told 615 00:33:09,080 --> 00:33:12,360 Speaker 11: you that you were somewhere you weren't. So that's obviously 616 00:33:12,880 --> 00:33:15,880 Speaker 11: quite a big problem when you consider that if you're 617 00:33:15,880 --> 00:33:18,560 Speaker 11: in charge of an oil tanker and there are lots 618 00:33:18,600 --> 00:33:20,680 Speaker 11: of oil tankers around you, and it can take you 619 00:33:20,840 --> 00:33:24,120 Speaker 11: kilometers to stop, and you're not sure where all the 620 00:33:24,120 --> 00:33:27,120 Speaker 11: other ships are. That can be a massive, massive problem. 621 00:33:27,360 --> 00:33:29,400 Speaker 5: I see in your story that alarm bells are also 622 00:33:29,560 --> 00:33:32,000 Speaker 5: starting to ring in the insurance industry. 623 00:33:32,840 --> 00:33:37,680 Speaker 11: Yes, you know, it's a real big problem. If it 624 00:33:37,720 --> 00:33:41,720 Speaker 11: wouldn't necessarily invalidate your insurance is my understanding. If you 625 00:33:41,760 --> 00:33:44,440 Speaker 11: know your ship, you know, god forbid, had to crash 626 00:33:45,200 --> 00:33:48,480 Speaker 11: as a result of jamming. But if the crew, if 627 00:33:48,520 --> 00:33:53,000 Speaker 11: their navigation system was jammed and they didn't do anything 628 00:33:53,040 --> 00:33:56,400 Speaker 11: about it, and then something happened, then I think that 629 00:33:56,400 --> 00:33:58,880 Speaker 11: that could cause problems from an insurance point of view. 630 00:33:59,320 --> 00:34:01,880 Speaker 2: Thanks to Jack Those, Bloomberg News Oil and Shipping reporter, 631 00:34:02,280 --> 00:34:05,640 Speaker 2: our final Big Takes story talks about America's hot garbage 632 00:34:05,680 --> 00:34:08,800 Speaker 2: problem and how overheating landfills across the country are spewing 633 00:34:08,880 --> 00:34:12,320 Speaker 2: toxic gases and geysers or trash juice. Yeah. Lar Bliss 634 00:34:12,360 --> 00:34:14,359 Speaker 2: is an editor for Bloomberg Business Week. 635 00:34:14,560 --> 00:34:16,400 Speaker 12: Maybe I'll just start with a little bit of background 636 00:34:16,480 --> 00:34:19,120 Speaker 12: about how I came across this story. I found some 637 00:34:19,239 --> 00:34:23,719 Speaker 12: data on LinkedIn sometime last year that suggested fires at 638 00:34:23,760 --> 00:34:27,720 Speaker 12: recycling facilities. We're skyrocketing, and we've probably all heard about 639 00:34:27,719 --> 00:34:31,279 Speaker 12: this as we all throw bait pens and electric toothbrushes 640 00:34:31,440 --> 00:34:34,160 Speaker 12: and even smartphones, you know, some of us into our 641 00:34:34,160 --> 00:34:37,120 Speaker 12: recycling bins or trash cans, and that can cause real 642 00:34:37,160 --> 00:34:40,640 Speaker 12: problems because of the lithium batteries. And that really made 643 00:34:40,680 --> 00:34:43,400 Speaker 12: me wonder what was happening in kind of the final 644 00:34:43,480 --> 00:34:46,960 Speaker 12: resting place for so much of our trash, which are landfills. 645 00:34:47,200 --> 00:34:51,080 Speaker 12: And it turns out a lot and as you mentioned, 646 00:34:51,239 --> 00:34:53,720 Speaker 12: my colleague Rachel and I really took a deep dive 647 00:34:53,880 --> 00:34:57,879 Speaker 12: into some of the problems that can arise as we 648 00:34:58,080 --> 00:35:01,239 Speaker 12: pile up our waist higher and deeper burb and this 649 00:35:01,360 --> 00:35:05,200 Speaker 12: landfill in Los Angeles County is called Chiquita Canyon, and 650 00:35:05,280 --> 00:35:09,720 Speaker 12: since twenty twenty two, temperatures have been climbing to above 651 00:35:09,920 --> 00:35:13,960 Speaker 12: two hundred degrees in pockets of waste. That's like forty 652 00:35:13,960 --> 00:35:17,880 Speaker 12: percent above the EPA safety standard. And this is a 653 00:35:17,880 --> 00:35:21,160 Speaker 12: big problem because what that's doing is the trash breaks 654 00:35:21,200 --> 00:35:25,359 Speaker 12: down deep below the surface at these really scorching temperatures. 655 00:35:25,400 --> 00:35:29,960 Speaker 12: It's releasing toxic gases, it's releasing geysers of trash juice 656 00:35:30,920 --> 00:35:34,120 Speaker 12: and these really noxious fumes, and it's making people sick 657 00:35:34,600 --> 00:35:35,840 Speaker 12: in the nearby community. 658 00:35:36,000 --> 00:35:36,480 Speaker 8: And what I. 659 00:35:36,480 --> 00:35:40,520 Speaker 12: Discovered is that there's debate about what is actually going 660 00:35:40,560 --> 00:35:43,279 Speaker 12: on and even kind of how to describe what I 661 00:35:43,440 --> 00:35:47,319 Speaker 12: just shared with you, the kind of basic facts. Regulators 662 00:35:47,320 --> 00:35:51,920 Speaker 12: in California suspect that the landfill is smoldering due to 663 00:35:52,080 --> 00:35:55,600 Speaker 12: the operator letting too much oxygen in, and this is 664 00:35:55,600 --> 00:35:58,360 Speaker 12: a problem that's known to start fires, which goes against 665 00:35:58,400 --> 00:36:02,399 Speaker 12: federal regulations. And the operator disputes that and says nothing 666 00:36:02,480 --> 00:36:04,960 Speaker 12: is on fire and says that this is an example 667 00:36:05,080 --> 00:36:07,640 Speaker 12: of a phenomenon the industry has seen a lot of 668 00:36:07,719 --> 00:36:11,560 Speaker 12: in the last twenty years, these elevated temperature landfills. 669 00:36:12,400 --> 00:36:15,000 Speaker 5: Or a really interesting story, what when you were doing 670 00:36:15,000 --> 00:36:18,040 Speaker 5: your reporting, what did you find out about what residents 671 00:36:18,080 --> 00:36:22,120 Speaker 5: are doing to rectify this issue? Who's to blame in 672 00:36:22,160 --> 00:36:22,840 Speaker 5: this situation? 673 00:36:23,480 --> 00:36:25,279 Speaker 12: Yeah, I mean, I think that is where a lot 674 00:36:25,320 --> 00:36:29,920 Speaker 12: of the debate comes down. You know, the waste industry 675 00:36:30,040 --> 00:36:32,600 Speaker 12: will say that, you know, these landfills that have overheated, 676 00:36:33,400 --> 00:36:37,000 Speaker 12: in some cases, they've been tied to you know, specific 677 00:36:37,600 --> 00:36:42,040 Speaker 12: reactive waste streams, right like aluminum dross, which is a 678 00:36:42,080 --> 00:36:45,000 Speaker 12: byproduct of smelting, you know, twenty years later or years 679 00:36:45,080 --> 00:36:49,320 Speaker 12: later reacting under the surface. You know, in other cases 680 00:36:49,360 --> 00:36:52,200 Speaker 12: like in Chikida Canyon. The regulators at the at the 681 00:36:52,239 --> 00:36:54,800 Speaker 12: state are saying, no, this is or they suspect I 682 00:36:54,840 --> 00:36:57,239 Speaker 12: should say that this is because of you know, the 683 00:36:57,280 --> 00:37:01,800 Speaker 12: operator basically mismanaging the gas collection system and their landfill. 684 00:37:02,320 --> 00:37:04,960 Speaker 12: And so this back and forth I think actually makes 685 00:37:05,520 --> 00:37:09,719 Speaker 12: you know, accountability somewhat challenging. But I will say in California, 686 00:37:10,040 --> 00:37:12,760 Speaker 12: you know, the state is is uh, you know, issuing 687 00:37:12,880 --> 00:37:16,160 Speaker 12: violations uh, you know, on a regular basis to this operator. 688 00:37:17,040 --> 00:37:20,040 Speaker 12: But for residents, you know, they're really kind of stuck 689 00:37:20,160 --> 00:37:23,400 Speaker 12: in their homes, you know, breathing in these these toxic 690 00:37:23,480 --> 00:37:27,320 Speaker 12: fumes and and these noxious odors, and they feel like 691 00:37:27,360 --> 00:37:31,120 Speaker 12: they can't sell their homes because of the financial barriers 692 00:37:31,239 --> 00:37:34,200 Speaker 12: and and you know even ethical kind of questions about 693 00:37:34,239 --> 00:37:37,160 Speaker 12: putting another family in the same situation, and so they 694 00:37:37,200 --> 00:37:41,840 Speaker 12: feel kind of stuck and and at a loss for 695 00:37:41,840 --> 00:37:43,040 Speaker 12: for kind of how to move forward. 696 00:37:43,239 --> 00:37:46,399 Speaker 2: Laura, who owns landfills across the US. Are these all 697 00:37:46,480 --> 00:37:47,600 Speaker 2: privately owned? 698 00:37:48,239 --> 00:37:51,640 Speaker 12: Not all of them are, but but many are. The 699 00:37:51,760 --> 00:37:54,719 Speaker 12: landfill I focused on in Los Angeles County is is 700 00:37:55,000 --> 00:37:59,000 Speaker 12: owned and managed by Waste Connections. There's certainly other you know, 701 00:37:59,080 --> 00:38:03,440 Speaker 12: large players in the industry Republic Services waste management, but 702 00:38:03,520 --> 00:38:06,960 Speaker 12: some of the affected landfalls I looked at were municipally 703 00:38:06,960 --> 00:38:11,520 Speaker 12: owned to Bristol Landfill in Virginia has been problematic in 704 00:38:11,560 --> 00:38:13,680 Speaker 12: the last several years, a lot of the same kind 705 00:38:13,719 --> 00:38:17,279 Speaker 12: of symptoms I've been describing affecting communities out there. 706 00:38:17,560 --> 00:38:20,160 Speaker 2: Our thanks to Laura Bliss, Bloomberg BusinessWeek editor, you can 707 00:38:20,239 --> 00:38:22,000 Speaker 2: check out all of the Big Takes stories by going 708 00:38:22,000 --> 00:38:24,879 Speaker 2: to Bloomberg dot com slash Big Take. That's this week's 709 00:38:25,000 --> 00:38:27,759 Speaker 2: edition of Bloomberg Intelligence on Bloomberg Radio, providing in depth 710 00:38:27,800 --> 00:38:30,080 Speaker 2: research and data on two thousand companies and one hundred 711 00:38:30,080 --> 00:38:33,040 Speaker 2: and thirty industries. And remember you can access Bloomberg Intelligence 712 00:38:33,120 --> 00:38:36,120 Speaker 2: via Big on the terminal. I'm Paul Sweeney. Stay with us. 713 00:38:36,120 --> 00:38:38,839 Speaker 2: Today's top stories and global business headlines are coming up 714 00:38:39,120 --> 00:38:41,920 Speaker 2: right now.