1 00:00:00,280 --> 00:00:11,240 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,360 --> 00:00:13,440 Speaker 1: with Alex Steel and Paul Sweeney. 3 00:00:13,560 --> 00:00:16,759 Speaker 2: The real ap performance has been in US corporate high yield. 4 00:00:16,920 --> 00:00:19,320 Speaker 3: Are the companies lean enough? Have they trimmed all the fats? 5 00:00:19,360 --> 00:00:22,520 Speaker 2: The semiconductor business is a really cyclical. 6 00:00:22,120 --> 00:00:26,320 Speaker 1: Business, breaking market headlines and corporate news from across the globe. 7 00:00:26,360 --> 00:00:28,840 Speaker 3: Do investors like the M and A that we've seen? 8 00:00:29,000 --> 00:00:32,000 Speaker 2: These are two big time blue chip companies. 9 00:00:32,240 --> 00:00:35,640 Speaker 3: Window between the peak and cunt changing super fast. 10 00:00:35,880 --> 00:00:40,920 Speaker 1: Bloomberg Intelligence with Alex Steele and Paul Sweeney on Bloomberg Radio. 11 00:00:42,320 --> 00:00:44,720 Speaker 2: Of Today's Bloomberg Intelligence Show, we dig inside the big 12 00:00:44,760 --> 00:00:46,479 Speaker 2: business stories impacting Wall Street and. 13 00:00:46,440 --> 00:00:47,360 Speaker 4: The global markets. 14 00:00:47,600 --> 00:00:49,519 Speaker 2: Each and every week we provide in depth research and 15 00:00:49,600 --> 00:00:51,440 Speaker 2: data on some of the two thousand companies and one 16 00:00:51,479 --> 00:00:54,800 Speaker 2: hundred and thirty industries our analysts cover worldwide. Today, we'll 17 00:00:54,800 --> 00:00:57,360 Speaker 2: look at why the tech giant Microsoft says it will 18 00:00:57,360 --> 00:01:00,640 Speaker 2: cut six thousand workers. Plus we'll disc us how President 19 00:01:00,680 --> 00:01:03,960 Speaker 2: Donald Trump is looking to lower drug prices in the US. 20 00:01:04,280 --> 00:01:06,360 Speaker 4: But first we begin in the legal space. 21 00:01:06,640 --> 00:01:10,400 Speaker 2: Recent research by Bloomberg Intelligence maintains that the Supreme Court 22 00:01:10,440 --> 00:01:13,600 Speaker 2: would likely let President Donald Trump fire fed chair j 23 00:01:13,760 --> 00:01:18,120 Speaker 2: palell even if market reaction discourages it. It's a subject 24 00:01:18,160 --> 00:01:21,720 Speaker 2: of a bipiece titled scotus would likely let Trump fire 25 00:01:21,760 --> 00:01:24,320 Speaker 2: Pal even if the markets don't. For more, co hosts 26 00:01:24,319 --> 00:01:26,720 Speaker 2: Alex Steel and I were joined by the pieces author 27 00:01:26,800 --> 00:01:31,120 Speaker 2: Elliott Stein, Bloomberg Intelligence litigation analyst. Your first asked Elliott, 28 00:01:31,160 --> 00:01:35,160 Speaker 2: if it is constitutional for President Trump to fire fedchair Pal. 29 00:01:35,760 --> 00:01:38,800 Speaker 5: We're not sure yet, but wait, yeah, the way the 30 00:01:38,880 --> 00:01:41,000 Speaker 5: law is playing out is, you know, he's taken a 31 00:01:41,080 --> 00:01:45,759 Speaker 5: very expansive view of executive authority and what the president 32 00:01:46,000 --> 00:01:46,480 Speaker 5: can do. 33 00:01:47,200 --> 00:01:49,160 Speaker 6: You know, we see it with the tariffs. We sorry 34 00:01:49,200 --> 00:01:49,680 Speaker 6: with those. 35 00:01:49,760 --> 00:01:53,520 Speaker 5: He's taken a lot of actions that presidents haven't done before, 36 00:01:54,640 --> 00:01:56,800 Speaker 5: and they're being litigated. And one of the things that 37 00:01:56,840 --> 00:02:00,520 Speaker 5: he's done is that he has tied to exert control 38 00:02:00,600 --> 00:02:03,760 Speaker 5: over what used to be called independent agencies, so things 39 00:02:03,840 --> 00:02:07,240 Speaker 5: like the FTC, the Federal Trade Commission, the NRB, the 40 00:02:07,320 --> 00:02:13,000 Speaker 5: National Labor Relations Board. He has removed commissioners at these 41 00:02:13,040 --> 00:02:17,760 Speaker 5: agencies even though the statutes that govern these agencies have 42 00:02:17,840 --> 00:02:20,640 Speaker 5: what's called the fore cause removal restriction, meaning the president 43 00:02:20,680 --> 00:02:25,400 Speaker 5: can only remove them for cause, and he's just firing 44 00:02:25,480 --> 00:02:26,480 Speaker 5: them at will. 45 00:02:27,160 --> 00:02:28,919 Speaker 6: He's also obviously. 46 00:02:28,520 --> 00:02:31,600 Speaker 5: Threatened to do the same with Jerome Powell and maybe 47 00:02:31,639 --> 00:02:32,720 Speaker 5: other members. 48 00:02:32,360 --> 00:02:33,040 Speaker 6: Of the board. 49 00:02:33,240 --> 00:02:35,920 Speaker 5: And his view is that the four cause removal restriction 50 00:02:36,000 --> 00:02:40,880 Speaker 5: is unconstitutional because it impinges on his power as the 51 00:02:40,880 --> 00:02:44,960 Speaker 5: president under Article two of the Constitution to essentially oversee 52 00:02:45,000 --> 00:02:50,520 Speaker 5: the entire executive branch and the Supreme Court. In nineteen 53 00:02:50,560 --> 00:02:55,040 Speaker 5: thirty five, there's President saying those four cause removal restrictions 54 00:02:55,040 --> 00:02:59,639 Speaker 5: restrictions are constitutional. But more recently the Supreme Court has 55 00:02:59,680 --> 00:03:03,240 Speaker 5: sort of try to narrow that precedent and said, at 56 00:03:03,320 --> 00:03:05,800 Speaker 5: least with the couple agencies where you only have one director, 57 00:03:06,480 --> 00:03:10,040 Speaker 5: those four cause removal restrictions are unconstitutional. And so the president, 58 00:03:10,120 --> 00:03:13,480 Speaker 5: President Trump is sort of trying to expand that recent 59 00:03:13,520 --> 00:03:15,239 Speaker 5: precedent to these other agencies. 60 00:03:15,680 --> 00:03:19,360 Speaker 2: So do we have other I mean, so the Federal 61 00:03:19,360 --> 00:03:20,960 Speaker 2: Reserve is one thing. Do we have other agencies that 62 00:03:21,000 --> 00:03:23,480 Speaker 2: are in the legal process now where someone's been removed 63 00:03:23,480 --> 00:03:24,200 Speaker 2: and we have a suit? 64 00:03:24,400 --> 00:03:28,120 Speaker 5: Yeah, exactly. We actually have quite a few of them. So, 65 00:03:29,320 --> 00:03:32,320 Speaker 5: you know, he has fired two commissioners at the FTC. 66 00:03:32,560 --> 00:03:37,360 Speaker 5: Those are in litigation, sort of the most procedurally advanced 67 00:03:37,360 --> 00:03:40,800 Speaker 5: cases at this point are cases where he fired commissioners 68 00:03:40,880 --> 00:03:44,160 Speaker 5: at the NRB, the National Labor Relations Board, and also 69 00:03:44,360 --> 00:03:50,160 Speaker 5: the Merit Systems Protection Board, which sort of oversees federal 70 00:03:50,600 --> 00:03:53,000 Speaker 5: employees and whether you know they're being fired and hired 71 00:03:53,040 --> 00:03:58,520 Speaker 5: for merit or not. Those two cases, we have actually 72 00:03:58,600 --> 00:04:02,160 Speaker 5: several rulings, and what's interesting is that the judges in 73 00:04:02,240 --> 00:04:04,480 Speaker 5: those rulings sort of follow along. 74 00:04:04,400 --> 00:04:06,040 Speaker 6: Ideological lines and how they rule. 75 00:04:06,120 --> 00:04:09,480 Speaker 5: So the trial court judge was a judge appointed by 76 00:04:09,480 --> 00:04:13,320 Speaker 5: President Obama, and she said that the firing of those 77 00:04:13,320 --> 00:04:19,120 Speaker 5: commissioners was improper, that it was violated the four cause removal. 78 00:04:21,160 --> 00:04:22,720 Speaker 5: Those cases are now an appeal. It's going to be 79 00:04:22,720 --> 00:04:25,560 Speaker 5: an argument next week. But we have sort of preliminary 80 00:04:25,600 --> 00:04:29,599 Speaker 5: ruins from the appeals court. One panel of three judges, 81 00:04:29,800 --> 00:04:33,160 Speaker 5: two of whom were appointed by Republican presidents, said that 82 00:04:33,720 --> 00:04:36,719 Speaker 5: President Trump would have been likely to succeed on the merits. 83 00:04:37,800 --> 00:04:43,440 Speaker 5: But then that ruling was then revisited by the entire court, 84 00:04:43,440 --> 00:04:47,240 Speaker 5: which is dominated by judges is appointed by Democrats, who 85 00:04:47,240 --> 00:04:49,000 Speaker 5: said that the firing was improper. 86 00:04:49,080 --> 00:04:51,159 Speaker 6: So we're sort of seeing how this might play out. 87 00:04:51,279 --> 00:04:55,560 Speaker 3: Is this are we really in a truly partisan judicial 88 00:04:55,640 --> 00:04:59,360 Speaker 3: world or Is this just simply different interpretations or a 89 00:04:59,400 --> 00:05:01,760 Speaker 3: stricter looser interpretations of the Constitution. 90 00:05:02,440 --> 00:05:03,799 Speaker 6: It depends on the issue. 91 00:05:04,680 --> 00:05:07,039 Speaker 5: I would say on this issue, it seems like the 92 00:05:07,120 --> 00:05:12,480 Speaker 5: judges are essentially ruling along ideological lines, where you have 93 00:05:12,560 --> 00:05:15,279 Speaker 5: judges appointed by Republicans saying that the fore cause removal 94 00:05:15,320 --> 00:05:19,960 Speaker 5: restriction really does impinge on the president's executive authority, and 95 00:05:20,040 --> 00:05:22,360 Speaker 5: you have judges appointed by Democrats saying, you know. 96 00:05:22,839 --> 00:05:24,400 Speaker 6: It's okay to put some limits. 97 00:05:25,040 --> 00:05:27,640 Speaker 5: The four cause of removal restriction would still allow the 98 00:05:27,640 --> 00:05:30,400 Speaker 5: president to fire someone if they did something improper. 99 00:05:30,839 --> 00:05:33,040 Speaker 3: In terms of say the Supreme Court issuing and a 100 00:05:33,120 --> 00:05:37,040 Speaker 3: ruling that then is ignored, is there precedent for that 101 00:05:37,200 --> 00:05:38,400 Speaker 3: like over history? 102 00:05:38,880 --> 00:05:41,279 Speaker 5: Well, we've actually sort of seen it with TikTok right, 103 00:05:41,400 --> 00:05:44,520 Speaker 5: where the Supreme Court upheld the TikTok ban and the 104 00:05:44,520 --> 00:05:48,560 Speaker 5: president instructed his attorney general to ignore the ruling and 105 00:05:48,839 --> 00:05:53,040 Speaker 5: ignore the ban. So we have sort of started to 106 00:05:53,080 --> 00:05:56,840 Speaker 5: see some issues like that. But in this case I 107 00:05:56,920 --> 00:06:01,920 Speaker 5: expected Supreme Court to if if he fight well on 108 00:06:01,960 --> 00:06:04,200 Speaker 5: this issue, generally I expect the Supreme Court to side 109 00:06:04,240 --> 00:06:05,200 Speaker 5: with President Trump. 110 00:06:06,080 --> 00:06:09,400 Speaker 2: Interesting, so what happens when a president defies the Supreme Court. 111 00:06:09,440 --> 00:06:11,520 Speaker 4: Like in TikTok, nothing right so. 112 00:06:11,560 --> 00:06:17,720 Speaker 5: Far, nothing so TikTok's still The remedy is for Congress to. 113 00:06:17,720 --> 00:06:20,640 Speaker 6: Impeach the president and then the Senate. 114 00:06:20,320 --> 00:06:25,080 Speaker 5: To convict, highly unlikely in this political environment. 115 00:06:25,120 --> 00:06:28,320 Speaker 3: I also like pushed out the rights. It sort of 116 00:06:28,320 --> 00:06:31,560 Speaker 3: feels very blurry. It doesn't feel like he completely ignored 117 00:06:31,560 --> 00:06:32,880 Speaker 3: it's it's like we're working on it. 118 00:06:33,120 --> 00:06:34,400 Speaker 6: We're just pushing out the deadline. 119 00:06:34,440 --> 00:06:38,119 Speaker 5: That feels a little differense Yes, that's essentially what he's doing. 120 00:06:38,160 --> 00:06:41,080 Speaker 6: But at the same time, there's a law that Congress. 121 00:06:40,640 --> 00:06:44,359 Speaker 5: Passed that said, you know, it has to be banned 122 00:06:44,360 --> 00:06:47,680 Speaker 5: by a particular date. The Supreme Court upheld that law, 123 00:06:48,000 --> 00:06:51,400 Speaker 5: and he's essentially ignoring the details of the law. I 124 00:06:51,440 --> 00:06:54,159 Speaker 5: agree with you, he is using that as a reason, 125 00:06:54,640 --> 00:06:57,080 Speaker 5: but the law is still on the books and it's. 126 00:06:56,880 --> 00:07:01,160 Speaker 2: In violation our Thanks to Elliot Stein, Bloomberg Intelligence litigation analyst, 127 00:07:01,560 --> 00:07:03,080 Speaker 2: we moved next to some of the news in the 128 00:07:03,080 --> 00:07:06,280 Speaker 2: aerospace industry this week. We heard that China has removed 129 00:07:06,279 --> 00:07:09,520 Speaker 2: a month long ban on airlines taking delivery of Boeing planes, 130 00:07:09,720 --> 00:07:12,040 Speaker 2: and this follows a breakthrough in trade talks between the 131 00:07:12,120 --> 00:07:16,280 Speaker 2: US and China that temporarily slash terrace on each side. 132 00:07:16,320 --> 00:07:19,480 Speaker 2: The resumption of deliveries to China will be an immediate 133 00:07:19,480 --> 00:07:22,360 Speaker 2: boost of Boeing, with around fifty Boeing jets to be 134 00:07:22,400 --> 00:07:25,480 Speaker 2: delivered to China this year. For more co hosts, Alex 135 00:07:25,480 --> 00:07:28,160 Speaker 2: Steele and I were joined by George Ferguson, Bloomberg Intelligence 136 00:07:28,200 --> 00:07:32,000 Speaker 2: Senior Aerospace, Defense and Airlines analyst. We first asked George 137 00:07:32,000 --> 00:07:33,640 Speaker 2: for his take on this week's news. 138 00:07:34,240 --> 00:07:36,200 Speaker 7: I think what it means for Boeing in the short 139 00:07:36,320 --> 00:07:40,120 Speaker 7: term is that they could probably get you know, some 140 00:07:40,200 --> 00:07:43,600 Speaker 7: of these inventory airplanes they've built. They're sitting on Tarmax, 141 00:07:43,600 --> 00:07:46,280 Speaker 7: they're maintaining them, costs bowing a lot of money. They 142 00:07:46,360 --> 00:07:50,240 Speaker 7: can get them delivered into China. Serium expects thirty four 143 00:07:50,280 --> 00:07:53,080 Speaker 7: of those airplanes to go in through the remainder of 144 00:07:53,080 --> 00:07:56,000 Speaker 7: the year. They can get them delivered into China, get 145 00:07:56,000 --> 00:07:58,280 Speaker 7: them off their books, you know, sort of get rid 146 00:07:58,280 --> 00:08:01,240 Speaker 7: of that extra expense they it costs for them to 147 00:08:01,320 --> 00:08:03,960 Speaker 7: manage those airplanes, so they could just get onto focusing 148 00:08:04,040 --> 00:08:07,120 Speaker 7: on the factory floor and building brand new airplanes. So 149 00:08:07,400 --> 00:08:10,400 Speaker 7: I think it's a positive. We'll see how long it lasts. 150 00:08:10,400 --> 00:08:11,840 Speaker 7: But it's definitely a positive. 151 00:08:12,160 --> 00:08:14,760 Speaker 3: Was this sort of baked into the US China trade 152 00:08:14,760 --> 00:08:16,400 Speaker 3: talk or was this irrespective? 153 00:08:16,440 --> 00:08:18,760 Speaker 7: Do you have an idea now, I'm not sure what's 154 00:08:18,800 --> 00:08:21,680 Speaker 7: baked into the US China talks. I do think that 155 00:08:21,720 --> 00:08:25,360 Speaker 7: there's an opportunity here for Boeing to get to even 156 00:08:25,480 --> 00:08:28,880 Speaker 7: new orders from China. We haven't really seen material orders 157 00:08:28,880 --> 00:08:32,800 Speaker 7: out of China since twenty eighteen, so last decade before 158 00:08:32,800 --> 00:08:35,840 Speaker 7: the pandemic. Relations have been a bit strained since then. 159 00:08:37,160 --> 00:08:40,040 Speaker 7: I think it's just it's one more piece of evidence 160 00:08:40,080 --> 00:08:43,040 Speaker 7: that shows Look, as the US and China get into 161 00:08:43,120 --> 00:08:46,360 Speaker 7: negotiations over trade, you're going to see heavy fracturing, and 162 00:08:46,400 --> 00:08:48,360 Speaker 7: then you know, we see the markets kind of get 163 00:08:48,480 --> 00:08:53,200 Speaker 7: very distressed about that. But behind the scenes, there's discussions about, 164 00:08:53,360 --> 00:08:56,560 Speaker 7: you know, putting that relationship back together, and Boeing probably 165 00:08:57,080 --> 00:09:00,800 Speaker 7: factors in high in those negotiations, and so I think 166 00:09:00,840 --> 00:09:04,120 Speaker 7: you could go from a, hey there's despair here to hey, 167 00:09:04,120 --> 00:09:06,400 Speaker 7: look there's a three hundred airplane order for Boeing coming 168 00:09:06,440 --> 00:09:08,679 Speaker 7: out of China. So it's going to be a real 169 00:09:08,760 --> 00:09:10,520 Speaker 7: up and down I think roller coaster. 170 00:09:10,280 --> 00:09:14,679 Speaker 2: Here, George, are there airplane manufacturers in China? 171 00:09:14,880 --> 00:09:17,400 Speaker 4: Or do they depend solely on Boeing and Airbus. 172 00:09:18,280 --> 00:09:22,920 Speaker 7: They depend largely on Boeing and Airbus. There is a manufacturer, Comac, 173 00:09:24,040 --> 00:09:27,319 Speaker 7: you know, they've been building a seven thirty seven, a 174 00:09:27,440 --> 00:09:34,240 Speaker 7: three twenty you know, you know, substitute. They call it 175 00:09:34,280 --> 00:09:37,959 Speaker 7: the Comac C nine one nine. The airplane's rolling out slowly. 176 00:09:38,000 --> 00:09:41,320 Speaker 7: I think we saw fifteen or twenty deliveries last year. 177 00:09:41,640 --> 00:09:44,440 Speaker 7: This is a country that probably needs at least one 178 00:09:44,559 --> 00:09:47,880 Speaker 7: hundred and fifty narrow bodies a year, so not going 179 00:09:47,920 --> 00:09:51,160 Speaker 7: to fulfill all their needs. There's about that many flying 180 00:09:51,240 --> 00:09:53,680 Speaker 7: right now, and when we look at the flying statistics 181 00:09:54,080 --> 00:09:57,640 Speaker 7: for that airplane, it's not flying as frequently as a 182 00:09:57,760 --> 00:10:00,280 Speaker 7: three twenties and seven thirty seven. So it's it seems 183 00:10:00,320 --> 00:10:03,920 Speaker 7: to us that the maturity that product isn't there yet 184 00:10:03,960 --> 00:10:07,839 Speaker 7: for the Chinese to substitute out all of the Western jets. 185 00:10:07,760 --> 00:10:10,400 Speaker 3: Right, So there's still a little bit of a way 186 00:10:10,480 --> 00:10:12,320 Speaker 3: to go. You also have to wonder it's just in 187 00:10:12,360 --> 00:10:15,560 Speaker 3: general the comeback that Boeing has been able to make. George, 188 00:10:15,559 --> 00:10:17,720 Speaker 3: how would you describe the situation that Boeing is and 189 00:10:17,800 --> 00:10:19,600 Speaker 3: now versus a couple of years ago? 190 00:10:20,800 --> 00:10:22,440 Speaker 7: Yeah, you know, I think this year is what we 191 00:10:22,520 --> 00:10:24,960 Speaker 7: expected last year to be before the door came off 192 00:10:25,000 --> 00:10:28,240 Speaker 7: on the or the door plug and the Alaska chat. 193 00:10:28,640 --> 00:10:31,559 Speaker 7: We're starting to see good flow through the factory and 194 00:10:31,960 --> 00:10:34,600 Speaker 7: the delivery numbers we've seen. We're starting to see them 195 00:10:34,640 --> 00:10:38,400 Speaker 7: deliver that inventory of built airplanes. Like I said that 196 00:10:38,520 --> 00:10:43,000 Speaker 7: Dragon costs pretty heavily. We're hearing good things about supply chain. 197 00:10:43,360 --> 00:10:47,640 Speaker 7: They're going to pull Spirit Arrow Systems into Boeing by midyear. 198 00:10:47,640 --> 00:10:49,480 Speaker 7: That was a big part of their problems. I think 199 00:10:49,480 --> 00:10:53,560 Speaker 7: pulling them in helps them control quality at Spirit. So 200 00:10:53,600 --> 00:10:57,160 Speaker 7: I think this story this year should be all about 201 00:10:57,559 --> 00:11:01,720 Speaker 7: improving quality, improving throughput in the factory in the back half, 202 00:11:01,800 --> 00:11:04,720 Speaker 7: some good cash generation. So I think this year is 203 00:11:04,720 --> 00:11:07,240 Speaker 7: whatever they thought last year would be, which could be 204 00:11:07,240 --> 00:11:07,920 Speaker 7: a pretty good year. 205 00:11:08,520 --> 00:11:10,640 Speaker 2: So where are you, George and your model in terms 206 00:11:10,679 --> 00:11:13,840 Speaker 2: of seven thirty seven kind of build rate here? Because 207 00:11:13,880 --> 00:11:16,720 Speaker 2: I know that just with the economic model for Boeing 208 00:11:16,720 --> 00:11:19,600 Speaker 2: that just drives cash flow the more planes I can 209 00:11:19,640 --> 00:11:20,559 Speaker 2: get through the line. 210 00:11:21,240 --> 00:11:23,720 Speaker 7: We're looking for like a forty five to fifty percent 211 00:11:23,840 --> 00:11:27,480 Speaker 7: increase in seven thirty seven deliveries this year, so really 212 00:11:27,520 --> 00:11:29,200 Speaker 7: head a increase, and again that's going to be the 213 00:11:29,240 --> 00:11:32,520 Speaker 7: big driver of cash generation for the company over the 214 00:11:32,600 --> 00:11:35,800 Speaker 7: year and probably the ex the year. A build rate 215 00:11:35,840 --> 00:11:40,000 Speaker 7: per month of thirty eight, we think even maybe into 216 00:11:40,040 --> 00:11:43,000 Speaker 7: the forties kind of build rate, which is an area 217 00:11:43,000 --> 00:11:44,960 Speaker 7: where they should be able to generate some profits. 218 00:11:45,360 --> 00:11:48,720 Speaker 2: Our thanks to George ferguson Bloomberg Intelligence, senior Aerospace, Defense 219 00:11:48,760 --> 00:11:51,679 Speaker 2: and Airlines analysts. Coming up, we'll continue with more on 220 00:11:51,720 --> 00:11:55,920 Speaker 2: the planemaker Boeing, which landed its biggest ever aircraft order. 221 00:11:56,240 --> 00:11:59,080 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 222 00:11:59,120 --> 00:12:01,839 Speaker 2: different research on two thousand companies and one hundred and 223 00:12:01,840 --> 00:12:04,920 Speaker 2: thirty industries. You can access Bloomberg Intelligence. 224 00:12:04,520 --> 00:12:07,160 Speaker 4: Via b I go on the terminal. I'm Paul Sweeney. 225 00:12:07,400 --> 00:12:08,600 Speaker 4: This is Bloomberg. 226 00:12:12,880 --> 00:12:16,600 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 227 00:12:16,679 --> 00:12:19,760 Speaker 1: weekdays at ten am Eastern on Apple, Coarclay, and Android 228 00:12:19,800 --> 00:12:23,080 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 229 00:12:23,160 --> 00:12:26,679 Speaker 1: you get your podcasts, or watch us live on YouTube. 230 00:12:27,480 --> 00:12:31,160 Speaker 2: We continue with more news from the planemaker Boeing. This week, 231 00:12:31,200 --> 00:12:34,880 Speaker 2: Boeing landed its biggest ever aircraft order, with Qatar Airways 232 00:12:34,920 --> 00:12:37,360 Speaker 2: agreeing to purchase up to two hundred and ten wide 233 00:12:37,440 --> 00:12:40,880 Speaker 2: body aircraft, including the seven eighty seven Dreamlier and the 234 00:12:40,960 --> 00:12:44,360 Speaker 2: seven seventy seven X model. It's a deal worth ninety 235 00:12:44,480 --> 00:12:47,440 Speaker 2: six billion dollars and this comes a year after Boeing 236 00:12:47,480 --> 00:12:51,120 Speaker 2: slogged through a deep executive shakeup and prolonged crisis. 237 00:12:51,480 --> 00:12:51,800 Speaker 4: For more. 238 00:12:51,920 --> 00:12:54,600 Speaker 2: Co hosts Alex Steele and I were joined by Sid 239 00:12:54,600 --> 00:12:58,040 Speaker 2: Phillip Bloomberg, Deputy team leader for Global Aviation. You first 240 00:12:58,120 --> 00:13:00,440 Speaker 2: as Sid for his take on this week's news. 241 00:13:00,720 --> 00:13:03,800 Speaker 8: This is a significant deal for Boeing because they can 242 00:13:03,800 --> 00:13:07,439 Speaker 8: sort of add another sort of milestone order to their backlog, 243 00:13:07,600 --> 00:13:10,560 Speaker 8: especially for their seven eighty seven, which is the sort 244 00:13:10,559 --> 00:13:12,719 Speaker 8: of state of the art white body jet, as well 245 00:13:12,760 --> 00:13:16,160 Speaker 8: as potentially for the Triple seven X, which is again 246 00:13:16,200 --> 00:13:18,440 Speaker 8: a jet that Kataraways has ordered and is still to 247 00:13:18,440 --> 00:13:21,920 Speaker 8: be certified. And so as airlines sort of look to 248 00:13:21,920 --> 00:13:24,640 Speaker 8: the next generation of aircraft that they're looking to order, 249 00:13:24,880 --> 00:13:26,800 Speaker 8: this is sort of these are these You're going to 250 00:13:26,840 --> 00:13:29,800 Speaker 8: see these mega deals coming up where everyone's looking to 251 00:13:29,840 --> 00:13:33,280 Speaker 8: sort of replace their current generation planes with newer models 252 00:13:33,320 --> 00:13:35,960 Speaker 8: which are more advanced, better fuel economy, and that sort 253 00:13:35,960 --> 00:13:38,240 Speaker 8: of great for Boeing in terms of just building out 254 00:13:38,280 --> 00:13:41,080 Speaker 8: that backlog and sort of selling out aircraft past the 255 00:13:41,160 --> 00:13:41,840 Speaker 8: end of the decade. 256 00:13:42,920 --> 00:13:45,840 Speaker 3: What is the delivery time for something like this, But 257 00:13:46,040 --> 00:13:49,040 Speaker 3: it's clearly had some operational execution issues exactly. 258 00:13:49,040 --> 00:13:51,040 Speaker 8: I mean Boeing has been struggling to deliver both the 259 00:13:51,080 --> 00:13:53,520 Speaker 8: seven eight seven as well as seven three seven Max 260 00:13:53,600 --> 00:13:56,319 Speaker 8: and get the seven Triple seven X, which is their 261 00:13:56,400 --> 00:14:00,320 Speaker 8: latest sort of generation aircraft certified. So it is not 262 00:14:00,360 --> 00:14:02,960 Speaker 8: clear on the details for what the sort of intricacies 263 00:14:03,000 --> 00:14:05,959 Speaker 8: of this Qatar deal are, but potentially these will sort 264 00:14:06,000 --> 00:14:08,240 Speaker 8: of play out and go out into the next decade 265 00:14:08,280 --> 00:14:10,839 Speaker 8: because I mean, they've got a significant backlogo aircraft that 266 00:14:10,880 --> 00:14:13,280 Speaker 8: need to deliver in the short term. So this is 267 00:14:13,320 --> 00:14:15,040 Speaker 8: not going to be an aircraft deal that sort of 268 00:14:15,040 --> 00:14:17,440 Speaker 8: happens in the next two or three years. Is going 269 00:14:17,480 --> 00:14:20,080 Speaker 8: to be a drawn out deal that goes into the 270 00:14:20,120 --> 00:14:20,920 Speaker 8: next decade. 271 00:14:21,120 --> 00:14:23,600 Speaker 2: Well, Bombing's finally digging itself out of its hole from 272 00:14:23,600 --> 00:14:25,600 Speaker 2: the last several years, so that kind of goes to 273 00:14:25,640 --> 00:14:29,440 Speaker 2: the big question said, it's all about production, you know, 274 00:14:29,480 --> 00:14:31,920 Speaker 2: getting planes off that and people just like George Ferguson 275 00:14:31,920 --> 00:14:34,480 Speaker 2: fro Bloomberg Intelligence says, just focus on the seven thirty 276 00:14:34,520 --> 00:14:35,520 Speaker 2: seven Max deliveries. 277 00:14:35,560 --> 00:14:36,280 Speaker 4: That's the number. 278 00:14:37,320 --> 00:14:39,680 Speaker 2: Is there a sense that Boeing's making improvements there? 279 00:14:40,160 --> 00:14:43,000 Speaker 8: We have seen Boeing deliveries inch up, and we've sort 280 00:14:43,000 --> 00:14:45,800 Speaker 8: of they've had sort of milestone in terms of just 281 00:14:45,880 --> 00:14:49,320 Speaker 8: hitting better production levels on the sevent three seven, And 282 00:14:49,680 --> 00:14:52,680 Speaker 8: we've heard from Aline customers, including United Airlines, saying that 283 00:14:53,040 --> 00:14:55,080 Speaker 8: the deliveries of the seven three seven Max are sort 284 00:14:55,120 --> 00:14:57,800 Speaker 8: of going up and they're waiting on the seven eighty seven, 285 00:14:58,040 --> 00:15:01,000 Speaker 8: which is the aircraft that we understand that airways ordered. 286 00:15:01,560 --> 00:15:03,640 Speaker 8: That's also going to be sort of key to getting 287 00:15:03,640 --> 00:15:05,760 Speaker 8: production and profitability going. 288 00:15:06,840 --> 00:15:09,920 Speaker 3: What other kind of pitch can Boeing make to outstrip 289 00:15:09,960 --> 00:15:12,360 Speaker 3: air Bus? Because in a duopoly market, and I appreciate 290 00:15:12,400 --> 00:15:14,400 Speaker 3: that China has some smaller players, but if you're dealing 291 00:15:14,400 --> 00:15:17,560 Speaker 3: with a duopoly, like I mean, people got to buy 292 00:15:17,600 --> 00:15:19,320 Speaker 3: your planes exactly. 293 00:15:18,880 --> 00:15:21,280 Speaker 8: I mean, we have a duopoly in at the moment. 294 00:15:21,360 --> 00:15:24,000 Speaker 6: And does that change it all? You think? It sort 295 00:15:24,040 --> 00:15:24,560 Speaker 6: of has. 296 00:15:24,440 --> 00:15:27,320 Speaker 8: Changed a little bit in terms of just the sort 297 00:15:27,360 --> 00:15:29,880 Speaker 8: of there's a lot of politics behind aircraft orders again, 298 00:15:29,920 --> 00:15:32,960 Speaker 8: and that's something that we've seen where Trump's been announcing 299 00:15:33,000 --> 00:15:35,320 Speaker 8: these mega Boeing orders. I mean, he announced a deal 300 00:15:35,400 --> 00:15:38,760 Speaker 8: for thirty planes in Saudi Arabia, and so there is 301 00:15:38,800 --> 00:15:40,960 Speaker 8: a lot of politics attached to aircraft orders. But at 302 00:15:40,960 --> 00:15:43,680 Speaker 8: the same time, both Airbus and Boeing are sort of 303 00:15:43,680 --> 00:15:45,920 Speaker 8: sold out of aircraft until the end of the decades. 304 00:15:46,040 --> 00:15:48,840 Speaker 8: So it is sort of you you're notching up all 305 00:15:48,880 --> 00:15:51,400 Speaker 8: these orders. But at the same time, the biggest thing 306 00:15:51,400 --> 00:15:53,960 Speaker 8: that airlines are talking about is give me my plane, 307 00:15:53,960 --> 00:15:57,240 Speaker 8: because that's really the biggest question where airlines are saying 308 00:15:57,280 --> 00:16:00,000 Speaker 8: that plane deliveries are delayed, and the air Bus is 309 00:16:00,080 --> 00:16:03,320 Speaker 8: repeatedly talked about. How supply chain issues continue to haunt 310 00:16:03,360 --> 00:16:06,280 Speaker 8: the industry now five years after the pandemic. 311 00:16:06,400 --> 00:16:09,520 Speaker 2: Yeah, which I can't imagine, although again I'm what I 312 00:16:09,600 --> 00:16:13,720 Speaker 2: understand is one of the bottlenecks is labor getting the 313 00:16:13,960 --> 00:16:16,520 Speaker 2: This is not just you know, sitting then assembly line 314 00:16:16,520 --> 00:16:19,320 Speaker 2: and hitting a hammer. You've got to be really, really 315 00:16:19,400 --> 00:16:21,760 Speaker 2: skilled at labor, and during the pandemic you lost a 316 00:16:21,800 --> 00:16:24,600 Speaker 2: lot of that and retraining these folks are getting new 317 00:16:24,640 --> 00:16:25,080 Speaker 2: people in it. 318 00:16:25,080 --> 00:16:26,640 Speaker 4: I'm hearing that's a big problem. 319 00:16:26,760 --> 00:16:28,720 Speaker 8: Labor is a major problem as well as I mean, 320 00:16:29,200 --> 00:16:33,000 Speaker 8: just one part being shot holds up the entire production line. 321 00:16:33,000 --> 00:16:34,760 Speaker 8: I mean, you have millions of parts that go into 322 00:16:34,840 --> 00:16:37,760 Speaker 8: an aircraft, and e US and Boeing are the final 323 00:16:37,800 --> 00:16:40,280 Speaker 8: assembly lines where the plane actually gets put together. But 324 00:16:40,320 --> 00:16:43,880 Speaker 8: then if parts are not available in the sort of process, 325 00:16:44,280 --> 00:16:46,600 Speaker 8: you can't actually build those planes. And that's where the 326 00:16:46,600 --> 00:16:50,200 Speaker 8: bottlenecks come up because you have smaller suppliers who are 327 00:16:50,200 --> 00:16:54,160 Speaker 8: struggling financially. You've got parts in short supply. I mean 328 00:16:54,240 --> 00:16:56,480 Speaker 8: EBUS talks about it's sort of it's whack a mole 329 00:16:56,520 --> 00:16:59,680 Speaker 8: where you get one part sorted out and something else, 330 00:17:00,240 --> 00:17:03,320 Speaker 8: and so it's really a question of getting that supply 331 00:17:03,440 --> 00:17:05,719 Speaker 8: chain to sort of hum along nicely and then actually 332 00:17:05,720 --> 00:17:08,680 Speaker 8: building those planes and delivering those planes because you've now 333 00:17:08,680 --> 00:17:11,560 Speaker 8: got record backlogs at both Boeing and Airbus. 334 00:17:11,760 --> 00:17:14,400 Speaker 3: So are they exemmed from tariff issues? 335 00:17:14,920 --> 00:17:17,840 Speaker 8: That's again a really important question. No one's quite clear 336 00:17:17,880 --> 00:17:20,520 Speaker 8: about in terms of what the tariff impact is going 337 00:17:20,560 --> 00:17:23,440 Speaker 8: to be because for fifty years almost that the aviation 338 00:17:23,560 --> 00:17:27,159 Speaker 8: industry has largely functioned tariff free, and the tariffs added 339 00:17:27,200 --> 00:17:30,680 Speaker 8: complexity to it. Because now everyone's not quite sure if 340 00:17:30,720 --> 00:17:33,760 Speaker 8: aircraft are example, I mean, you've seen like all sorts 341 00:17:33,800 --> 00:17:37,679 Speaker 8: of industry players and industry groups making representations to President 342 00:17:37,760 --> 00:17:40,560 Speaker 8: Trump saying that we would like to have tariffs removed 343 00:17:40,600 --> 00:17:43,520 Speaker 8: on aircraft and aircraft parts, and so we're trying to 344 00:17:43,560 --> 00:17:47,160 Speaker 8: see where that sort of actually lands up, because potentially 345 00:17:47,359 --> 00:17:50,679 Speaker 8: aircraft manufacturers and airlines may have to pay tariffs on 346 00:17:50,720 --> 00:17:53,040 Speaker 8: planes depending on what they're in and what parts they have. 347 00:17:53,560 --> 00:17:56,159 Speaker 2: Our thanks to Sid Phillip, Bloomberg Deputy team leader for 348 00:17:56,200 --> 00:17:59,639 Speaker 2: Global Aviation. We move next to news in the tech space. 349 00:17:59,720 --> 00:18:02,560 Speaker 2: This we heard that the tech giant Microsoft will cut 350 00:18:02,640 --> 00:18:05,600 Speaker 2: six thousand workers across the company to reduce management layers. 351 00:18:05,680 --> 00:18:08,119 Speaker 2: This will amount to less than three percent of total 352 00:18:08,119 --> 00:18:10,520 Speaker 2: headcount for more. Co hosts Alex Deal and I were 353 00:18:10,560 --> 00:18:14,520 Speaker 2: joined by anorak Rana, Bloomberg Intelligence technology analyst. First ask 354 00:18:14,560 --> 00:18:17,800 Speaker 2: anarog for his take on why Microsoft is cutting jobs. 355 00:18:18,240 --> 00:18:21,560 Speaker 9: The numbers three percent does not say much, but at 356 00:18:21,560 --> 00:18:23,840 Speaker 9: the same time, Microsoft is not hiring at the same 357 00:18:23,880 --> 00:18:26,080 Speaker 9: pace that it used to. And I think one of 358 00:18:26,080 --> 00:18:29,520 Speaker 9: the areas where it is deploying AI is coding or 359 00:18:29,680 --> 00:18:31,919 Speaker 9: you know R and D. That's an area where we 360 00:18:31,960 --> 00:18:35,800 Speaker 9: anticipate productivity to improve because of the tools we have, 361 00:18:36,560 --> 00:18:38,360 Speaker 9: and not anticipating headcount to grow. 362 00:18:38,440 --> 00:18:38,600 Speaker 10: Up. 363 00:18:38,960 --> 00:18:42,640 Speaker 9: Second thing, Microsoft is investing very heavily in AI right now. 364 00:18:42,640 --> 00:18:45,359 Speaker 9: The capex is going to be extremely high, you know, 365 00:18:45,440 --> 00:18:49,120 Speaker 9: as North doos eighty billion dollars for the next twelve months, 366 00:18:49,320 --> 00:18:51,639 Speaker 9: and you know that they have to offset some of 367 00:18:51,680 --> 00:18:55,040 Speaker 9: that pressure on gross margins with cuts on the operating 368 00:18:55,080 --> 00:18:58,760 Speaker 9: expense side so that the net operating margin doesn't get impacted. 369 00:18:58,800 --> 00:19:00,280 Speaker 9: So I think I think it's a little bit of 370 00:19:00,320 --> 00:19:00,919 Speaker 9: all the above. 371 00:19:01,520 --> 00:19:04,200 Speaker 3: Was this a surprise, not really. 372 00:19:04,240 --> 00:19:06,800 Speaker 9: I mean three percent to me does not come as 373 00:19:06,840 --> 00:19:09,720 Speaker 9: a surprise. Three percent is you know, in an environment 374 00:19:09,840 --> 00:19:13,160 Speaker 9: like right now where companies are not anticipating the real 375 00:19:13,200 --> 00:19:16,160 Speaker 9: attrition rate. So on a normalized basis, if a company 376 00:19:16,240 --> 00:19:19,080 Speaker 9: is seeing attrition employee attrition between ten to fifteen percent, 377 00:19:19,440 --> 00:19:21,480 Speaker 9: we're not seeing that right now, so the company has 378 00:19:21,520 --> 00:19:23,399 Speaker 9: to do something in order to get back to the 379 00:19:23,400 --> 00:19:27,000 Speaker 9: normal range of their financial planning. So it is I said, 380 00:19:27,000 --> 00:19:29,840 Speaker 9: this doesn't sound too much to me, but they could 381 00:19:29,880 --> 00:19:32,400 Speaker 9: be a little bit more based on you know, who 382 00:19:32,480 --> 00:19:34,680 Speaker 9: they are trying to get rid of. If it's higher 383 00:19:34,680 --> 00:19:37,000 Speaker 9: on the paychecks, then I think it has to do with, 384 00:19:37,160 --> 00:19:38,840 Speaker 9: you know, protecting your margins. 385 00:19:40,160 --> 00:19:42,840 Speaker 2: An Rock talk to us about just what the investment 386 00:19:42,920 --> 00:19:45,920 Speaker 2: thesis is regarding Microsoft right now, given some of the 387 00:19:45,960 --> 00:19:49,520 Speaker 2: tariff uncertainty, given some of the geopolitics uncertainty, what's the 388 00:19:49,600 --> 00:19:51,080 Speaker 2: call on Microsoft these days? 389 00:19:51,720 --> 00:19:53,320 Speaker 9: You know, for the last few years we have been 390 00:19:53,359 --> 00:19:55,520 Speaker 9: saying this is probably one of the cleanest stories in 391 00:19:55,600 --> 00:19:59,920 Speaker 9: AI and software land because it doesn't get impacted with tariffs. 392 00:20:00,200 --> 00:20:03,840 Speaker 9: There is some marginal secondary impact because of you know, 393 00:20:03,840 --> 00:20:06,359 Speaker 9: if we do get into an economic slowdown, But the 394 00:20:06,400 --> 00:20:09,240 Speaker 9: real benefit for them is they are hosting chat GPT, 395 00:20:09,640 --> 00:20:13,120 Speaker 9: the most favorite app of consumers right now, and they 396 00:20:13,160 --> 00:20:15,320 Speaker 9: get the benefit of that. So if you have let's say, 397 00:20:15,560 --> 00:20:17,560 Speaker 9: you know that your user base goes from two hundred 398 00:20:17,600 --> 00:20:19,639 Speaker 9: million users to three hundred to four hundred now we 399 00:20:19,680 --> 00:20:23,200 Speaker 9: are close to about five hundred million users, it really 400 00:20:23,240 --> 00:20:26,240 Speaker 9: helps Microsoft because they are hosting that app, and as 401 00:20:26,320 --> 00:20:29,560 Speaker 9: those users use chapid chat GPT for a longer period 402 00:20:29,560 --> 00:20:31,399 Speaker 9: of time, they get to make more money. So I 403 00:20:31,440 --> 00:20:33,879 Speaker 9: think that's really one of the bigger benefits where they 404 00:20:33,880 --> 00:20:36,520 Speaker 9: are offsetting the risk that we are seeing that other 405 00:20:36,560 --> 00:20:39,920 Speaker 9: software vendors are seeing because of the lack of headcount 406 00:20:39,920 --> 00:20:42,600 Speaker 9: growth across the tech landscape. 407 00:20:43,200 --> 00:20:45,560 Speaker 3: So based on that, do we think that We're going 408 00:20:45,640 --> 00:20:48,840 Speaker 3: to see other tech companies follow suit in this particular way. 409 00:20:48,920 --> 00:20:51,880 Speaker 3: Like if Microsoft is a creme de la crem as 410 00:20:51,920 --> 00:20:54,480 Speaker 3: you see it, who's next. 411 00:20:54,240 --> 00:20:55,920 Speaker 9: So Alex, when you look at it, as I said, 412 00:20:55,960 --> 00:20:58,200 Speaker 9: three percent does not scare me as much. But if 413 00:20:58,440 --> 00:21:00,880 Speaker 9: over the next six months we start companies come out 414 00:21:00,880 --> 00:21:03,199 Speaker 9: and say, well, I'm laying off like seven to ten 415 00:21:03,240 --> 00:21:06,560 Speaker 9: percent of my workforce, that is concerning. Because that's concerning 416 00:21:06,560 --> 00:21:10,720 Speaker 9: for two reasons. One, these companies do buy software from 417 00:21:10,720 --> 00:21:13,560 Speaker 9: other companies, such as a work Day or a sales force, 418 00:21:13,920 --> 00:21:17,120 Speaker 9: because these two companies are seeing some pressure on their 419 00:21:17,160 --> 00:21:21,160 Speaker 9: seat growth because their end customers are not hiding as much. 420 00:21:21,320 --> 00:21:24,720 Speaker 9: So lack of hiding is concerned not just for the 421 00:21:24,760 --> 00:21:27,920 Speaker 9: companies or the industry themselves, but for the software landscape 422 00:21:27,920 --> 00:21:32,159 Speaker 9: as well, because primarily this is a seat based monetization model. 423 00:21:32,960 --> 00:21:35,800 Speaker 2: An RAG before we got all enmeshed in terroris. Over 424 00:21:35,800 --> 00:21:38,120 Speaker 2: the last few months, we seemingly talk to you every 425 00:21:38,200 --> 00:21:40,399 Speaker 2: day about the AI story that's kind of full off 426 00:21:40,400 --> 00:21:42,800 Speaker 2: the radar screen a little bit here for I think 427 00:21:42,840 --> 00:21:46,120 Speaker 2: Global Wall Street, not for you guys. What is the 428 00:21:46,160 --> 00:21:49,360 Speaker 2: AI story today? Where what's the feeling in the marketplace? 429 00:21:49,400 --> 00:21:51,480 Speaker 2: About AI as a growth story for tech. 430 00:21:52,280 --> 00:21:54,520 Speaker 9: Yeah, one of the things code one of the things 431 00:21:54,560 --> 00:21:58,040 Speaker 9: that deep Seat did for the entire space was divided 432 00:21:58,080 --> 00:22:01,720 Speaker 9: the space in two different buckets. Was the AI infrastructure 433 00:22:01,760 --> 00:22:04,080 Speaker 9: play and the other one is more on the application 434 00:22:04,200 --> 00:22:07,159 Speaker 9: side of it. And Microsoft I think guided them a 435 00:22:07,160 --> 00:22:10,919 Speaker 9: little bit towards that framework because when you look at it, 436 00:22:10,960 --> 00:22:14,280 Speaker 9: Microsoft is really focused on the inferencing side of it, 437 00:22:14,480 --> 00:22:17,240 Speaker 9: which is hosting the applications. Things that are more on 438 00:22:17,280 --> 00:22:20,520 Speaker 9: the infrastructure side, whether it's the chip building from Nvidia 439 00:22:20,640 --> 00:22:23,800 Speaker 9: or a recent hype like core Viv, they have not 440 00:22:23,920 --> 00:22:26,480 Speaker 9: done as well as some of the other vendors because 441 00:22:26,480 --> 00:22:28,719 Speaker 9: people are wondering, why do I need to play the 442 00:22:28,760 --> 00:22:31,760 Speaker 9: infrastructure game because it's CAPEX heavy and we do not 443 00:22:31,920 --> 00:22:33,679 Speaker 9: know how things are going to shape up in the 444 00:22:33,720 --> 00:22:36,119 Speaker 9: next two to three years. One thing is sort of 445 00:22:36,200 --> 00:22:39,680 Speaker 9: certain that even after three years, the number of use 446 00:22:39,760 --> 00:22:42,240 Speaker 9: cases on AI is going to rise. So you want 447 00:22:42,240 --> 00:22:44,760 Speaker 9: to be on the application or the inference side of 448 00:22:44,800 --> 00:22:47,280 Speaker 9: things rather than the infrastructure side of things. 449 00:22:47,160 --> 00:22:49,119 Speaker 3: Right, and that's going to be a lot more profitable 450 00:22:49,119 --> 00:22:52,760 Speaker 3: going forward, But it also requires something very different. At 451 00:22:52,760 --> 00:22:54,080 Speaker 3: the end of the day, Right, you're going to have 452 00:22:54,800 --> 00:22:58,600 Speaker 3: basically locations that are closer to the actual use point, right, 453 00:22:58,640 --> 00:23:00,679 Speaker 3: so closer to where I am and where you are, 454 00:23:00,840 --> 00:23:02,399 Speaker 3: versus out in the middle of nowhere, but you have 455 00:23:02,440 --> 00:23:05,080 Speaker 3: a huge heavy load. How does that change either investment 456 00:23:05,160 --> 00:23:06,399 Speaker 3: or power usage, et cetera. 457 00:23:07,359 --> 00:23:09,600 Speaker 9: That's probably one of the most important questions out there 458 00:23:09,720 --> 00:23:12,240 Speaker 9: right now. So somebody like a Microsoft is saying, for 459 00:23:12,359 --> 00:23:15,160 Speaker 9: my training needs, I will go to those data centers 460 00:23:15,200 --> 00:23:17,440 Speaker 9: that are out in the boondogs and middle of nowhere, 461 00:23:17,760 --> 00:23:20,280 Speaker 9: and that could be my data center, or I could 462 00:23:20,400 --> 00:23:22,960 Speaker 9: lease it from somebody like a Corview. But for the 463 00:23:23,000 --> 00:23:26,240 Speaker 9: application side, I'm going to create or lease smaller data 464 00:23:26,240 --> 00:23:28,600 Speaker 9: centers that are well connected where I'm going to do 465 00:23:28,640 --> 00:23:31,080 Speaker 9: the infrancing or from the user point of view. 466 00:23:31,320 --> 00:23:34,320 Speaker 2: Our thanks to Anna rog Rana Bloomberg Intelligence. Technology analysts 467 00:23:34,560 --> 00:23:36,439 Speaker 2: coming up on the program will break down how the 468 00:23:36,520 --> 00:23:40,560 Speaker 2: Chinese AI start a deep seek became world famous. You're 469 00:23:40,600 --> 00:23:43,440 Speaker 2: listening to Bloomberg Intelligence on Bloomberg Radio, providing in depth 470 00:23:43,520 --> 00:23:45,640 Speaker 2: research and data on two thousand companies and. 471 00:23:45,560 --> 00:23:46,920 Speaker 4: One hundred and thirty industries. 472 00:23:47,160 --> 00:23:49,879 Speaker 2: You can access Bloomberg Intelligence via bi go on the terminal. 473 00:23:50,080 --> 00:23:52,879 Speaker 4: I'm Paul Sweeney. This is bloomber. 474 00:24:00,080 --> 00:24:03,680 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 475 00:24:03,760 --> 00:24:07,240 Speaker 1: weekdays at ten am Eastern on Applecarplay and Android Auto 476 00:24:07,240 --> 00:24:10,320 Speaker 1: with the Bloomberg Business App. Listen on demand wherever you 477 00:24:10,359 --> 00:24:14,280 Speaker 1: get your podcasts, or watch us live on YouTube. 478 00:24:14,720 --> 00:24:16,760 Speaker 4: We move next to news in a pharmaceutical space. 479 00:24:16,880 --> 00:24:19,240 Speaker 2: This week, President Donald Trump sign an executive order to 480 00:24:19,240 --> 00:24:22,080 Speaker 2: lower drug prices in the US. You order as drugmakers 481 00:24:22,080 --> 00:24:26,440 Speaker 2: to lower prices voluntarily or face regulatory measures. Pharmaceutical companies 482 00:24:26,440 --> 00:24:30,119 Speaker 2: had expected and feared action on drug prices. However, the 483 00:24:30,240 --> 00:24:32,800 Speaker 2: order was far weaker than they anticipated. For more on 484 00:24:32,880 --> 00:24:34,920 Speaker 2: drug prices, co host Alex Steel and I were joined 485 00:24:34,920 --> 00:24:38,040 Speaker 2: by Sam Fazzelli, Bloomberg Intelligence, Director of Research for Global 486 00:24:38,040 --> 00:24:40,119 Speaker 2: Industries and senior pharmaceuticals analysts. 487 00:24:40,160 --> 00:24:41,680 Speaker 4: We first asked Sam to walk us. 488 00:24:41,560 --> 00:24:43,879 Speaker 2: Through why we pay more for drug pricing in the 489 00:24:44,000 --> 00:24:46,160 Speaker 2: US and whether that should be reduced. 490 00:24:46,440 --> 00:24:49,879 Speaker 10: The thing in the US is that it's a price 491 00:24:49,920 --> 00:24:55,880 Speaker 10: that's set by negotiation between farma companies and insurers, pharmacy 492 00:24:55,880 --> 00:24:59,800 Speaker 10: benefit managers, and payers. It is not dictated by the government, 493 00:25:00,080 --> 00:25:03,480 Speaker 10: still wasn't until recently, whereas in Europe and many other 494 00:25:03,480 --> 00:25:05,760 Speaker 10: countries you have a single payer, and that's the government, 495 00:25:06,200 --> 00:25:09,200 Speaker 10: and they play hardball so they can get lower prices 496 00:25:09,880 --> 00:25:12,399 Speaker 10: because that's the way they operate, and they had. There 497 00:25:12,440 --> 00:25:15,760 Speaker 10: are many situations where in the UK the National Institute 498 00:25:15,760 --> 00:25:19,359 Speaker 10: for Clinical Accident refuses to believe or accept that a 499 00:25:19,440 --> 00:25:22,480 Speaker 10: drug has got its value added, so it's not even 500 00:25:22,520 --> 00:25:26,960 Speaker 10: offered even at the negotiated lower price. So that's the 501 00:25:27,000 --> 00:25:29,600 Speaker 10: issue here, which means that as soon as innovation happens 502 00:25:29,680 --> 00:25:31,600 Speaker 10: is out in the market in the US, the day 503 00:25:31,680 --> 00:25:33,320 Speaker 10: that the product is approved, they can get on and 504 00:25:33,920 --> 00:25:36,000 Speaker 10: sell it. Over in Europe it could take a year 505 00:25:36,160 --> 00:25:38,399 Speaker 10: two years for a drug to come through, and maybe 506 00:25:38,400 --> 00:25:42,119 Speaker 10: with this new executive order sometimes the companies will go 507 00:25:42,160 --> 00:25:44,560 Speaker 10: where I'd rather not launch in Europe. 508 00:25:45,119 --> 00:25:49,320 Speaker 2: So is it realistic to expect that drug prices in 509 00:25:49,359 --> 00:25:51,359 Speaker 2: the US will come down? 510 00:25:51,400 --> 00:25:52,200 Speaker 4: Could come down? 511 00:25:53,560 --> 00:25:56,640 Speaker 10: See the thing is, Paul, if I'm having a tough 512 00:25:56,680 --> 00:25:59,320 Speaker 10: time to balance this out. If the intention is for 513 00:25:59,560 --> 00:26:01,600 Speaker 10: to get the rest of the world to pay more, 514 00:26:02,080 --> 00:26:05,080 Speaker 10: which of course helps the farmer companies, then I don't 515 00:26:05,080 --> 00:26:08,480 Speaker 10: see how that helps the US drug prices come down. 516 00:26:08,920 --> 00:26:09,120 Speaker 9: Right. 517 00:26:10,240 --> 00:26:14,840 Speaker 10: If they aren't able to increase prices in Europe, then 518 00:26:15,000 --> 00:26:17,920 Speaker 10: then the government in the US is saying, well, you 519 00:26:17,960 --> 00:26:21,080 Speaker 10: should give us the same prices to giving elsewhere. Then 520 00:26:21,119 --> 00:26:23,560 Speaker 10: innovation disappears because at the end of the day, if 521 00:26:23,560 --> 00:26:27,320 Speaker 10: you cut your revenue by half, you won't have and 522 00:26:27,480 --> 00:26:29,399 Speaker 10: you spend fifteen percent of that on R and D 523 00:26:29,800 --> 00:26:33,560 Speaker 10: as what your RND faults simple maths. So how this 524 00:26:33,680 --> 00:26:36,000 Speaker 10: plays out would be quite interesting to see. And of 525 00:26:36,000 --> 00:26:38,520 Speaker 10: course we have to also remember that there isn't a 526 00:26:38,560 --> 00:26:40,280 Speaker 10: price in Europe. So we just check the price of 527 00:26:40,400 --> 00:26:43,200 Speaker 10: we go V in the UK at the effective dose 528 00:26:43,440 --> 00:26:47,240 Speaker 10: two hundred and thirty dollars okay. In the US, literally 529 00:26:47,320 --> 00:26:50,160 Speaker 10: the Novo sells it for four hundred ninety nine dollars 530 00:26:50,200 --> 00:26:53,679 Speaker 10: to the direct to consumer. Okay, it's about twice as much, okay. 531 00:26:54,160 --> 00:26:56,400 Speaker 10: So let's say in Europe it goes up to three 532 00:26:56,480 --> 00:26:59,440 Speaker 10: hundred dur in the UK, and how does that change 533 00:26:59,840 --> 00:27:03,000 Speaker 10: the US number? And we don't even know whether the 534 00:27:03,680 --> 00:27:06,359 Speaker 10: company actually gives that price because then there's a little 535 00:27:06,359 --> 00:27:09,000 Speaker 10: small print. We have been doing a commer with the 536 00:27:09,119 --> 00:27:11,720 Speaker 10: NHS that are commercial deal, So what is the price? 537 00:27:12,160 --> 00:27:14,280 Speaker 10: How is the US government going to find that price 538 00:27:14,680 --> 00:27:17,680 Speaker 10: unless they incorporated into the trade agreements that they're doing, 539 00:27:17,920 --> 00:27:19,680 Speaker 10: which is what should be happening. 540 00:27:19,400 --> 00:27:23,960 Speaker 3: Here, So going for so what about the PBMs, So 541 00:27:24,200 --> 00:27:26,959 Speaker 3: that was singled out the pharmacy benefit managers as sort 542 00:27:26,960 --> 00:27:30,280 Speaker 3: of the middlemen between insurers and the government and the 543 00:27:30,359 --> 00:27:33,680 Speaker 3: drug makers, et cetera. What role do they really have 544 00:27:34,240 --> 00:27:36,240 Speaker 3: and are they the ones most at risk? 545 00:27:38,280 --> 00:27:40,120 Speaker 10: I would say they are the ones most at risk. 546 00:27:40,160 --> 00:27:42,400 Speaker 10: I would say they should have always been the ones 547 00:27:42,480 --> 00:27:47,240 Speaker 10: most at risk, because I don't understand the reason they 548 00:27:47,240 --> 00:27:49,800 Speaker 10: exist in the middle. I mean, that's the I can 549 00:27:49,880 --> 00:27:52,560 Speaker 10: show you a chart of how the US payment system works, 550 00:27:52,640 --> 00:27:55,840 Speaker 10: and it varies by insurance. You can go from one 551 00:27:55,880 --> 00:27:57,679 Speaker 10: job to another and the cost of your drug goes up, 552 00:27:58,359 --> 00:28:00,640 Speaker 10: right because you have a different insurance from one state 553 00:28:00,680 --> 00:28:04,320 Speaker 10: to another. So it's it's that just really isn't is untenable? 554 00:28:04,640 --> 00:28:07,639 Speaker 10: And it's nice to see that the government administration is 555 00:28:07,680 --> 00:28:11,040 Speaker 10: not talking about making it easier to sell directly to consumers. 556 00:28:11,160 --> 00:28:13,440 Speaker 10: So let me give you an example. We have data 557 00:28:13,480 --> 00:28:16,600 Speaker 10: on the terminal that gives you an ability to calculate 558 00:28:16,800 --> 00:28:19,240 Speaker 10: what a drug revenue should have been in the past 559 00:28:19,320 --> 00:28:23,560 Speaker 10: quarter volume times price, right, then you go and see 560 00:28:23,560 --> 00:28:26,960 Speaker 10: what they actually reported. Sometimes for Insulince, for instance, is 561 00:28:27,040 --> 00:28:31,280 Speaker 10: eighty percent lower eighty percent. Now the PBM say, I 562 00:28:31,320 --> 00:28:33,919 Speaker 10: know we passed that on back to the to medical party. 563 00:28:34,400 --> 00:28:36,320 Speaker 10: Why do you even take it? Why do we need 564 00:28:36,359 --> 00:28:38,239 Speaker 10: to pay an administrator in the middle here? 565 00:28:39,000 --> 00:28:39,560 Speaker 6: That's great. 566 00:28:39,600 --> 00:28:42,840 Speaker 2: So what are your companies within your food chain there 567 00:28:42,840 --> 00:28:43,840 Speaker 2: in a big farmer. 568 00:28:43,640 --> 00:28:45,000 Speaker 4: What are they sayings? 569 00:28:45,200 --> 00:28:47,840 Speaker 2: Because this issue is new, we've been plaining about high 570 00:28:47,880 --> 00:28:48,760 Speaker 2: drug prices forever. 571 00:28:49,520 --> 00:28:51,480 Speaker 4: What are they saying as to what is likely to 572 00:28:51,560 --> 00:28:52,160 Speaker 4: really happen. 573 00:28:53,560 --> 00:28:55,320 Speaker 10: They're not saying very much, Paul, you know, and in 574 00:28:55,320 --> 00:28:57,160 Speaker 10: this certal circumstances, the best thing to do is to 575 00:28:57,240 --> 00:29:00,480 Speaker 10: keep your head down and see how how things work out. 576 00:29:00,600 --> 00:29:02,840 Speaker 10: And of course today has only just come out, so 577 00:29:03,240 --> 00:29:06,600 Speaker 10: I think they all to a t say the PBMs 578 00:29:06,640 --> 00:29:08,080 Speaker 10: are a problem, we need to get rid of it. 579 00:29:08,280 --> 00:29:10,640 Speaker 10: That so let's say that. But that's not today, that's 580 00:29:10,640 --> 00:29:14,320 Speaker 10: been going on for ten years. They also say Europe 581 00:29:14,320 --> 00:29:16,800 Speaker 10: should pay more. But they also say that when you 582 00:29:16,880 --> 00:29:19,840 Speaker 10: do the net price calculation, in many instances I actually 583 00:29:19,920 --> 00:29:24,200 Speaker 10: sent you a code from Pascalsorio, the Astrisenica CEO, saying 584 00:29:24,240 --> 00:29:26,080 Speaker 10: that actually in many cases the prices end up being 585 00:29:26,080 --> 00:29:28,760 Speaker 10: about the same, maybe a little bit different. So all 586 00:29:28,760 --> 00:29:30,720 Speaker 10: of that needs to be taken into account, and we 587 00:29:30,800 --> 00:29:33,680 Speaker 10: need to decide if they're going to push Europe higher 588 00:29:34,000 --> 00:29:36,960 Speaker 10: to pay more. Is the US also happy to move 589 00:29:37,000 --> 00:29:38,920 Speaker 10: some of the R and D to Europe. Maybe that's 590 00:29:38,920 --> 00:29:41,640 Speaker 10: something that the European governments will ask for. Fine, will 591 00:29:41,680 --> 00:29:43,560 Speaker 10: pay more, but bring some of your R and D here, 592 00:29:43,680 --> 00:29:45,400 Speaker 10: Bring more manufacturing here. Oh. 593 00:29:45,440 --> 00:29:48,120 Speaker 2: Thanks to Sam Fazelli, Bloomberg Intelligence, Director of Research for 594 00:29:48,160 --> 00:29:50,880 Speaker 2: Global Industries and senior pharmaceuticals analyst. 595 00:29:51,240 --> 00:29:53,000 Speaker 4: This week we focused on a Bloomberg. 596 00:29:52,600 --> 00:29:55,480 Speaker 2: Big Takes story entitled deep Seeks Tech mad man is 597 00:29:55,520 --> 00:29:58,600 Speaker 2: threatening US dominance over AI. You can find it on 598 00:29:58,600 --> 00:30:01,520 Speaker 2: the Bloomberg dot com and the Terminal. The story looks 599 00:30:01,520 --> 00:30:04,560 Speaker 2: at how Chinese AI startup Deep Seek became world famous 600 00:30:04,760 --> 00:30:08,080 Speaker 2: and swallowed a trillion dollars of stock value from Nvidia 601 00:30:08,200 --> 00:30:10,920 Speaker 2: and other big tech companies. For more, co host Alex 602 00:30:10,960 --> 00:30:14,240 Speaker 2: Steele and I spoke with Austin Carr, Bloomberg Technology Reporter. 603 00:30:14,720 --> 00:30:17,360 Speaker 2: First asked Austin to give us more background on deep 604 00:30:17,400 --> 00:30:19,400 Speaker 2: Seek and the individual behind it. 605 00:30:19,760 --> 00:30:22,320 Speaker 11: Deep Seek, as you'll recall, gained a lot of attention 606 00:30:22,360 --> 00:30:24,480 Speaker 11: a few months ago when it released its new R 607 00:30:24,560 --> 00:30:27,320 Speaker 11: one reasoning model as well as another one called V three, 608 00:30:27,720 --> 00:30:30,120 Speaker 11: And the big deal about this was they sort of 609 00:30:30,160 --> 00:30:32,840 Speaker 11: claimed to have trained it without access to a lot 610 00:30:32,840 --> 00:30:35,479 Speaker 11: of the most cutting edge in Nvidia chips, and they 611 00:30:35,520 --> 00:30:38,920 Speaker 11: also trained their models for a lot less, at least 612 00:30:38,920 --> 00:30:41,760 Speaker 11: on the surface than a lot of the model's equivalent 613 00:30:41,840 --> 00:30:44,320 Speaker 11: models here have been trained in the US, and that 614 00:30:44,480 --> 00:30:47,640 Speaker 11: raised a lot of questions about some of the fundamental 615 00:30:47,680 --> 00:30:50,640 Speaker 11: notions about how many chips are needed to train these 616 00:30:50,680 --> 00:30:53,120 Speaker 11: big AI models and how much it's going to cost 617 00:30:53,160 --> 00:30:55,840 Speaker 11: to develop them. The reason it was also a big 618 00:30:55,840 --> 00:30:58,680 Speaker 11: deal was just because the startup was sort of very mysterious. 619 00:30:59,080 --> 00:31:01,600 Speaker 11: There had not been much written about them before sort 620 00:31:01,640 --> 00:31:04,880 Speaker 11: of January twenty twenty five, and we spent a lot 621 00:31:04,920 --> 00:31:07,240 Speaker 11: of time the last couple of months talking to about 622 00:31:07,280 --> 00:31:10,000 Speaker 11: a dozen former employees that worked for this company, it's 623 00:31:10,000 --> 00:31:13,440 Speaker 11: founder Leong win Feng, as well as forty sources closed 624 00:31:13,440 --> 00:31:16,400 Speaker 11: to the Chinese AI scene, to really just give you 625 00:31:16,440 --> 00:31:19,400 Speaker 11: a sense of how their sort of market is developing 626 00:31:19,560 --> 00:31:22,240 Speaker 11: very differently than what's happening in the US, which is 627 00:31:22,240 --> 00:31:24,719 Speaker 11: they're doing a lot more with less and sort of 628 00:31:24,800 --> 00:31:28,120 Speaker 11: just trying to innovate despite the constraints that the US 629 00:31:28,160 --> 00:31:30,920 Speaker 11: has placed on it with all its export controls. 630 00:31:31,160 --> 00:31:31,960 Speaker 6: How did he do it? 631 00:31:32,720 --> 00:31:35,720 Speaker 3: How is he able to undercut the price in such 632 00:31:35,720 --> 00:31:36,480 Speaker 3: a tremendous way. 633 00:31:37,640 --> 00:31:40,240 Speaker 11: Well, I think that's one of the big topics that 634 00:31:40,280 --> 00:31:42,440 Speaker 11: we explore in the story. We trace it actually back 635 00:31:42,440 --> 00:31:45,360 Speaker 11: to the history. Leong Win Fang had started a quant 636 00:31:45,400 --> 00:31:48,960 Speaker 11: fund back in about twenty fifteen twenty sixteen, and you 637 00:31:49,000 --> 00:31:51,800 Speaker 11: actually see a lot of those sort of quant sensibilities 638 00:31:52,160 --> 00:31:56,280 Speaker 11: looking for efficiencies in the market, which they later applied 639 00:31:56,280 --> 00:31:59,480 Speaker 11: to their AI models. It was sort of a sort 640 00:31:59,480 --> 00:32:01,080 Speaker 11: of spinoff off of what they were doing with that 641 00:32:01,160 --> 00:32:03,720 Speaker 11: quant fund that became Deep Seek. One of the big 642 00:32:03,760 --> 00:32:07,000 Speaker 11: things they did was called mixture of experts, basically a 643 00:32:07,000 --> 00:32:09,280 Speaker 11: lot of the sort of when you used CHATCHYPT back 644 00:32:09,280 --> 00:32:11,480 Speaker 11: in the day, you asked it a question, it activated 645 00:32:11,520 --> 00:32:14,560 Speaker 11: the entire model. So think of that like your entire 646 00:32:14,600 --> 00:32:17,040 Speaker 11: brain is activated just for the question two plus two. 647 00:32:17,560 --> 00:32:19,680 Speaker 11: What Deep Seat did that was a little bit different 648 00:32:19,720 --> 00:32:21,960 Speaker 11: was chop it up into what are called experts. So 649 00:32:22,080 --> 00:32:24,200 Speaker 11: every time you ask a question, it actually routes that 650 00:32:24,240 --> 00:32:26,960 Speaker 11: to the particular part of the brain that is expert 651 00:32:26,960 --> 00:32:29,520 Speaker 11: in math, or expert in physics, or expert in cooking 652 00:32:29,560 --> 00:32:32,040 Speaker 11: what have you. So it's a lot more efficient approach. 653 00:32:32,240 --> 00:32:35,000 Speaker 11: But the downside is it risks hallucinating more or perhaps 654 00:32:35,040 --> 00:32:36,760 Speaker 11: sending the question to the wrong part of the brain. 655 00:32:37,000 --> 00:32:38,760 Speaker 11: But that's one of the techniques that they use to 656 00:32:38,800 --> 00:32:41,200 Speaker 11: really bring this model to life for a lot cheaper 657 00:32:41,960 --> 00:32:44,080 Speaker 11: and according to the Deep Seek, with a lot less 658 00:32:44,200 --> 00:32:46,800 Speaker 11: or a lot fewer of in vidio chips than to 659 00:32:47,000 --> 00:32:49,800 Speaker 11: a lot of the Western players like open Ai have 660 00:32:49,920 --> 00:32:50,440 Speaker 11: access to. 661 00:32:51,160 --> 00:32:53,160 Speaker 2: I guess one of the things that surprised people, maybe 662 00:32:53,480 --> 00:32:56,200 Speaker 2: scared some people, is how deep Seak was able to 663 00:32:56,200 --> 00:33:00,960 Speaker 2: make so much progress when supposedly all these Western sanctions 664 00:33:01,000 --> 00:33:03,520 Speaker 2: on technology going to China didn't really seem to slow 665 00:33:03,520 --> 00:33:04,160 Speaker 2: it down at all. 666 00:33:04,520 --> 00:33:05,280 Speaker 4: What's the statistic? 667 00:33:05,360 --> 00:33:08,080 Speaker 11: I think that's that's one of the big ironies actually, 668 00:33:08,120 --> 00:33:11,360 Speaker 11: if incidentally, I had interviewed Jensen Huang from the CEO 669 00:33:11,440 --> 00:33:14,200 Speaker 11: of Vidia in May twenty twenty three, and that was 670 00:33:14,400 --> 00:33:17,280 Speaker 11: the very month, coincidentally that you know, on the other 671 00:33:17,320 --> 00:33:20,160 Speaker 11: side of the world, China was, you know, getting started 672 00:33:20,160 --> 00:33:22,560 Speaker 11: with deep Seek, and Jensen told me at the time, 673 00:33:22,840 --> 00:33:25,520 Speaker 11: you know, one of his big concerns about export controls 674 00:33:25,640 --> 00:33:28,719 Speaker 11: is it's actually just going to incentivive Chinese chip makers 675 00:33:28,760 --> 00:33:30,640 Speaker 11: to work a lot harder to develop chips that can 676 00:33:30,680 --> 00:33:34,000 Speaker 11: compete within videas. But the other nearer term threat is 677 00:33:34,040 --> 00:33:38,040 Speaker 11: actually software makers sort of their constraints from the sources 678 00:33:38,040 --> 00:33:40,200 Speaker 11: that we've talked too close to Deep Seek are actually 679 00:33:40,200 --> 00:33:43,480 Speaker 11: what really drove this company to develop workarounds do a 680 00:33:43,480 --> 00:33:45,480 Speaker 11: lot more with less, and that's become a sort of 681 00:33:45,680 --> 00:33:48,040 Speaker 11: founding DNA of a lot of the Chinese startups that 682 00:33:48,080 --> 00:33:49,920 Speaker 11: we've talked to, a lot of these what are called 683 00:33:49,960 --> 00:33:53,320 Speaker 11: Ai dragons across Beijing and Hangzhou that are doing a 684 00:33:53,360 --> 00:33:56,920 Speaker 11: lot more with a less access to compute. And I 685 00:33:56,960 --> 00:33:59,000 Speaker 11: think that that's, you know, I think the other sort 686 00:33:59,000 --> 00:34:01,520 Speaker 11: of component of that is they almost have this national 687 00:34:01,560 --> 00:34:05,040 Speaker 11: pride that we've heard resonate throughout our interviews that really 688 00:34:05,080 --> 00:34:07,280 Speaker 11: doesn't exist in the US in quite the same way. 689 00:34:07,320 --> 00:34:10,279 Speaker 11: No one's you know, quite as excited or you know, 690 00:34:10,280 --> 00:34:13,719 Speaker 11: we don't think of Sam Altman necessarily as a celebrity 691 00:34:13,920 --> 00:34:16,480 Speaker 11: as the same way as in China. People descend on 692 00:34:16,520 --> 00:34:19,640 Speaker 11: that campus, the Deep Seek office just to see pictures 693 00:34:19,680 --> 00:34:21,920 Speaker 11: of him. It seems like a national hero they are, 694 00:34:22,000 --> 00:34:25,440 Speaker 11: just because he's over able to overcome these constraints, and 695 00:34:25,480 --> 00:34:27,879 Speaker 11: I think that's a really interesting component of the AI 696 00:34:27,960 --> 00:34:30,280 Speaker 11: scene in China, which we don't have puite in the US. 697 00:34:30,600 --> 00:34:33,480 Speaker 3: So about forty five seconds here, what's to prevent the 698 00:34:33,520 --> 00:34:35,680 Speaker 3: AI companies here in the US doing the same thing 699 00:34:35,680 --> 00:34:36,520 Speaker 3: that Deep Seek does. 700 00:34:38,400 --> 00:34:40,600 Speaker 11: There's really not I mean, that's one of the things 701 00:34:40,600 --> 00:34:43,760 Speaker 11: that you know, they did make their models open source 702 00:34:43,800 --> 00:34:46,160 Speaker 11: for the so the most part they did, you know, 703 00:34:46,400 --> 00:34:48,479 Speaker 11: unveil some of their secret sauce, but they didn't quite 704 00:34:48,480 --> 00:34:51,200 Speaker 11: explain all the techniques and how they did them under 705 00:34:51,200 --> 00:34:53,040 Speaker 11: the hood. But that said, yes, I mean that was 706 00:34:53,080 --> 00:34:55,120 Speaker 11: part of the really compelling things. By making this model 707 00:34:55,160 --> 00:34:57,799 Speaker 11: open source, they really have this ethos. One of the 708 00:34:57,800 --> 00:35:01,799 Speaker 11: first quotes they put with their first public announcement was 709 00:35:01,800 --> 00:35:03,920 Speaker 11: takis cheap, show me the code. And so they have 710 00:35:04,000 --> 00:35:06,719 Speaker 11: this really aggressive, let me prove it to you attitude 711 00:35:06,760 --> 00:35:09,400 Speaker 11: that I think in the US space, with these proprietary models, 712 00:35:09,520 --> 00:35:12,320 Speaker 11: they're not willing to do. And I think the difference 713 00:35:12,360 --> 00:35:14,520 Speaker 11: here is that Deep Seek having an open source approach 714 00:35:14,600 --> 00:35:18,439 Speaker 11: is really putting the pressure on Google and Open Eye 715 00:35:18,520 --> 00:35:22,000 Speaker 11: to do something similar or at least undercut the costs 716 00:35:22,040 --> 00:35:24,400 Speaker 11: of their models. And you can get the same completing 717 00:35:24,440 --> 00:35:27,040 Speaker 11: power for thirty six dollars through deep Seek, as it 718 00:35:27,040 --> 00:35:28,839 Speaker 11: would cost one thousand dollars in Open AI. 719 00:35:29,400 --> 00:35:31,800 Speaker 2: All right, Thanks to Austin Carr, Bloomberg Technology Reporter. 720 00:35:32,200 --> 00:35:36,840 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 721 00:35:37,040 --> 00:35:41,000 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 722 00:35:41,239 --> 00:35:44,520 Speaker 1: ten am to noon Eastern on Bloomberg dot com, the 723 00:35:44,600 --> 00:35:48,480 Speaker 1: iHeartRadio app tune In, and the Bloomberg Business app. You 724 00:35:48,480 --> 00:35:51,799 Speaker 1: can also watch us live every weekday on YouTube and 725 00:35:52,000 --> 00:35:53,960 Speaker 1: always on the Bloomberg terminal