1 00:00:02,720 --> 00:00:10,559 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:17,880 Speaker 1: Eastern on Apple Coarclay and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,040 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,520 Speaker 1: or watch us live on YouTube. 6 00:00:24,120 --> 00:00:25,919 Speaker 2: Looking at a lot of tech news today, I'm going 7 00:00:26,000 --> 00:00:26,920 Speaker 2: to start with just Samsung's. 8 00:00:26,920 --> 00:00:29,080 Speaker 3: I'm looking at the SMP, I'm the Nasdaq ROF. 9 00:00:29,160 --> 00:00:31,560 Speaker 2: You know, one and a half percent here, the socks 10 00:00:31,880 --> 00:00:36,640 Speaker 2: index trading off today. Samsung reported the numbers. I don't know, 11 00:00:36,680 --> 00:00:39,040 Speaker 2: to my own trained they look good. I don't know, 12 00:00:39,080 --> 00:00:41,080 Speaker 2: but apparently the market's not digging it. So let me 13 00:00:41,120 --> 00:00:43,160 Speaker 2: go to the guy who does this stuff for living 14 00:00:43,200 --> 00:00:45,280 Speaker 2: at ludlow b Tech co anchor, talk to us about 15 00:00:45,280 --> 00:00:49,040 Speaker 2: Samsung and get the spillover maybe for the broader tech market. 16 00:00:49,080 --> 00:00:50,839 Speaker 4: Well, there is a lot of spillover, right, there's a 17 00:00:50,880 --> 00:00:53,640 Speaker 4: lot of downward pressure on chip stocks in particular, but 18 00:00:53,760 --> 00:00:56,480 Speaker 4: genuine generally the AI trade in the US. You are right, 19 00:00:56,560 --> 00:01:01,480 Speaker 4: numbers good, you know, revenue more than double profit nineteenfold. 20 00:01:01,920 --> 00:01:06,400 Speaker 4: And what's so interesting about it is fundamentally very deliberate 21 00:01:06,440 --> 00:01:08,920 Speaker 4: choice of word, nothing in the memory markets change from 22 00:01:08,920 --> 00:01:10,600 Speaker 4: when you and I went to bed last night. You 23 00:01:10,640 --> 00:01:14,200 Speaker 4: know it. There is still tight supply pricing of de 24 00:01:14,400 --> 00:01:17,280 Speaker 4: RAM and nand the key markets for memory is going up, 25 00:01:17,680 --> 00:01:20,880 Speaker 4: and Samsung has no line of sight when that improves. 26 00:01:21,280 --> 00:01:24,120 Speaker 4: It's really a stock story, where like Samsung was a 27 00:01:24,160 --> 00:01:26,720 Speaker 4: stock that was up one hundred and fifty percent year 28 00:01:26,720 --> 00:01:28,240 Speaker 4: to stay going into it. And I think everyone's just 29 00:01:28,240 --> 00:01:31,160 Speaker 4: taking a pause and saying, you know, this isn't the 30 00:01:31,200 --> 00:01:33,360 Speaker 4: right trade right now, you know, let's do something different. 31 00:01:34,160 --> 00:01:36,479 Speaker 5: It seems like a lot of AI and tech stocks 32 00:01:36,480 --> 00:01:38,800 Speaker 5: are having sell offs this morning, and like you say, 33 00:01:38,840 --> 00:01:40,880 Speaker 5: there doesn't seem to be a real reason behind it, 34 00:01:41,040 --> 00:01:44,080 Speaker 5: just animal spirits. Is that what's going on? Or people 35 00:01:44,200 --> 00:01:46,840 Speaker 5: just taking stock at this moment and worried we're out 36 00:01:46,840 --> 00:01:49,200 Speaker 5: over our skis or something else going on. 37 00:01:49,280 --> 00:01:52,360 Speaker 4: Yeah, I mean if there was a place with Samsung 38 00:01:52,440 --> 00:01:56,400 Speaker 4: you might have some concern, And with memory generally, you 39 00:01:56,440 --> 00:02:00,400 Speaker 4: could point to margins and say how do we feel 40 00:02:00,400 --> 00:02:04,680 Speaker 4: about this? Historically memory is a very cyclical market boom 41 00:02:04,720 --> 00:02:08,960 Speaker 4: and bust. It's highly commoditized. Right now, what's happening doesn't 42 00:02:09,000 --> 00:02:13,440 Speaker 4: reflect it reflect historical norms, but in those periods where 43 00:02:13,600 --> 00:02:15,800 Speaker 4: the memory names have had very high margins, they've never 44 00:02:15,840 --> 00:02:18,680 Speaker 4: been maintained. They ever flow, they crash to a floor, 45 00:02:19,080 --> 00:02:20,840 Speaker 4: and I think everyone might be a little bit worried 46 00:02:20,880 --> 00:02:26,280 Speaker 4: about that maybe, But generally speaking, you know, we've had 47 00:02:26,800 --> 00:02:29,919 Speaker 4: a melt up, We've not quite had a melt down. 48 00:02:30,200 --> 00:02:31,840 Speaker 4: And if you just look at the last month of 49 00:02:31,840 --> 00:02:34,360 Speaker 4: trading on the socks, it's so normal to have a 50 00:02:34,400 --> 00:02:37,080 Speaker 4: swing of five to eight percent. I think, you know, Paul, 51 00:02:37,080 --> 00:02:39,440 Speaker 4: you know this. Right in the equity space, there is 52 00:02:39,480 --> 00:02:44,320 Speaker 4: a distinction between volatility and a swing in price, but 53 00:02:44,440 --> 00:02:47,400 Speaker 4: you know, probably both are true in that market right now. 54 00:02:48,960 --> 00:02:50,959 Speaker 2: The folks on Wall Street, my friends on the debt 55 00:02:50,960 --> 00:02:53,799 Speaker 2: capital market says they have to be going crazy busy. 56 00:02:53,960 --> 00:02:55,600 Speaker 3: No summer vacation for those people. 57 00:02:56,080 --> 00:03:00,200 Speaker 2: Say every day there's another mega, mega tech deok to 58 00:03:00,240 --> 00:03:02,919 Speaker 2: the investment grade bond market today, to Amazon looking to 59 00:03:03,000 --> 00:03:05,400 Speaker 2: raise at least twenty five billion dollars in a US 60 00:03:05,440 --> 00:03:07,960 Speaker 2: dollar year, to. 61 00:03:07,800 --> 00:03:10,120 Speaker 3: Just what's going on? What is Amazon doing? Is this 62 00:03:10,280 --> 00:03:11,519 Speaker 3: just more ai capeck? 63 00:03:11,960 --> 00:03:15,000 Speaker 4: The latest is that in quick succession for the second time, 64 00:03:15,040 --> 00:03:18,359 Speaker 4: it's looking at the USIG market and Bloomberg's reporting twenty 65 00:03:18,360 --> 00:03:22,160 Speaker 4: five billion across eight tranches maturities three to forty years. 66 00:03:22,360 --> 00:03:24,200 Speaker 4: You know, corporate credit is not my strong suit, but 67 00:03:24,600 --> 00:03:28,960 Speaker 4: Robert Schiffman of BI would tell you that on a 68 00:03:29,000 --> 00:03:32,000 Speaker 4: weighted cost of capital basis, it makes total sense for 69 00:03:32,080 --> 00:03:35,920 Speaker 4: these hyperscalers to use the debt market to finance massive capex. 70 00:03:36,280 --> 00:03:40,120 Speaker 4: Amazon's a really simple story. Capex is growing at an 71 00:03:40,240 --> 00:03:44,160 Speaker 4: enormous rate, much faster than revenue is growing. The reason 72 00:03:44,200 --> 00:03:46,920 Speaker 4: capex is growing is also because building data centers is 73 00:03:46,960 --> 00:03:51,080 Speaker 4: getting more expensive with inflation. There is labor and construction 74 00:03:51,360 --> 00:03:54,160 Speaker 4: labor inflation. There is memory chip pricing inflation. We just 75 00:03:54,200 --> 00:03:57,480 Speaker 4: talked about that, and so right now, you know, BIC 76 00:03:57,640 --> 00:03:59,560 Speaker 4: is a path of capex for Amazon, going from two 77 00:03:59,600 --> 00:04:02,440 Speaker 4: hundred bis into three hundred billion dollars in full year 78 00:04:02,480 --> 00:04:05,080 Speaker 4: twenty seven. I've got to find the money somewhere. But 79 00:04:05,400 --> 00:04:09,040 Speaker 4: like you know, bondholders love Amazone, the krem de la 80 00:04:09,080 --> 00:04:13,080 Speaker 4: creme of credit profile, you know, so exactly that's the story. 81 00:04:13,160 --> 00:04:13,560 Speaker 3: Bring it on. 82 00:04:14,080 --> 00:04:15,680 Speaker 5: Well, all kinds of companies are going to have to 83 00:04:15,680 --> 00:04:18,240 Speaker 5: be raising money for AI, for data centers and things 84 00:04:18,240 --> 00:04:20,400 Speaker 5: like that. Like you say, Amazon is krem de la 85 00:04:20,400 --> 00:04:23,000 Speaker 5: crem So there will definitely be a market for this step. 86 00:04:23,040 --> 00:04:25,120 Speaker 5: But what about for other companies that are wanting to 87 00:04:25,160 --> 00:04:26,760 Speaker 5: expand into AAR They're going to have trouble. 88 00:04:26,839 --> 00:04:32,000 Speaker 4: So there is there are distinctions, right, So something happened. 89 00:04:32,360 --> 00:04:34,480 Speaker 4: There are two case sies where we can compare and contrast. 90 00:04:35,240 --> 00:04:38,520 Speaker 4: There was a time where Amazon flipping into negative free 91 00:04:38,560 --> 00:04:40,840 Speaker 4: cash flow. I one would imagine the market would have 92 00:04:40,880 --> 00:04:44,640 Speaker 4: freaked out several years ago, but now that's okay, you know, 93 00:04:44,720 --> 00:04:48,800 Speaker 4: negative free cash flow Amazon. Andy Jasse explained that a 94 00:04:48,839 --> 00:04:50,640 Speaker 4: way that you know, when you have a period of 95 00:04:50,680 --> 00:04:53,600 Speaker 4: time where you have cafex and whatever you build takes 96 00:04:53,600 --> 00:04:55,520 Speaker 4: time to come online. Like data centers are real things, 97 00:04:55,640 --> 00:04:57,360 Speaker 4: it takes a couple of years for them to actually 98 00:04:57,400 --> 00:04:58,920 Speaker 4: like do something and get some revenues. 99 00:05:00,040 --> 00:05:01,120 Speaker 3: Paragrapas with Oracle. 100 00:05:01,320 --> 00:05:04,200 Speaker 4: You know, Oracle flip to negative cash flow free cashwoaw 101 00:05:04,200 --> 00:05:05,840 Speaker 4: for the first time in the nineties and the market 102 00:05:05,960 --> 00:05:09,280 Speaker 4: melted down because you know, you have to look holistically 103 00:05:09,320 --> 00:05:12,240 Speaker 4: at the strength of the balance sheet that that company's 104 00:05:12,360 --> 00:05:15,200 Speaker 4: power in the real world market of AI, like Amazon 105 00:05:15,600 --> 00:05:19,560 Speaker 4: and aws's cloud business are highly important in what's happening 106 00:05:19,560 --> 00:05:22,360 Speaker 4: in AI compute. So those are the two things I'd 107 00:05:22,360 --> 00:05:24,719 Speaker 4: point to that not all companies are created equal in 108 00:05:24,760 --> 00:05:25,479 Speaker 4: that sense, you. 109 00:05:25,480 --> 00:05:26,560 Speaker 3: Know, talking about Amazon. 110 00:05:26,920 --> 00:05:29,280 Speaker 2: You know what surprised me, What's that the extent to 111 00:05:29,279 --> 00:05:32,760 Speaker 2: which Jeff Bezos has stepped back. You never hear from 112 00:05:32,760 --> 00:05:35,200 Speaker 2: this dude other than you see in Powder Boring, Padda Borning, 113 00:05:35,520 --> 00:05:36,240 Speaker 2: you know somewhere. 114 00:05:38,279 --> 00:05:41,039 Speaker 4: Yeah, that's certainly how I think about it, though, you 115 00:05:41,080 --> 00:05:42,520 Speaker 4: know he's a busy guy elsewhere. 116 00:05:42,680 --> 00:05:45,360 Speaker 3: Yeah, but boy, I just don't as it related to Amazon. 117 00:05:45,560 --> 00:05:48,760 Speaker 2: You know, he's just he really has step back and 118 00:05:48,839 --> 00:05:51,640 Speaker 2: just kind of bracing the chair and letting his management 119 00:05:51,640 --> 00:05:54,800 Speaker 2: team go, which oftentimes with founders you just don't see 120 00:05:54,839 --> 00:05:56,760 Speaker 2: it that they can turn it off that quickly. 121 00:05:56,880 --> 00:05:58,080 Speaker 3: So yeah, it depends. 122 00:05:58,120 --> 00:06:00,839 Speaker 4: Like in the Valley, like founder mode On is still 123 00:06:00,880 --> 00:06:03,799 Speaker 4: that the mantre, you know, like Airbnb has Brancheski found 124 00:06:03,800 --> 00:06:07,040 Speaker 4: a CEO, Mark Zockerberg found Azeo. They lean into that, 125 00:06:07,480 --> 00:06:09,479 Speaker 4: you know, Jeff's running a space company and an AI 126 00:06:09,560 --> 00:06:11,880 Speaker 4: company somewhere else, that he's kind of busy exactly. Still 127 00:06:11,960 --> 00:06:13,440 Speaker 4: ends not eight percent of the company. 128 00:06:14,640 --> 00:06:15,320 Speaker 1: Stay with us. 129 00:06:15,360 --> 00:06:17,560 Speaker 2: More from Bloomberg Intelligence coming up after this. 130 00:06:21,360 --> 00:06:25,040 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us Live 131 00:06:25,120 --> 00:06:28,200 Speaker 1: weekdays at ten am easterned on Apple, Cocklay and Android 132 00:06:28,240 --> 00:06:31,560 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 133 00:06:31,600 --> 00:06:34,719 Speaker 1: you get your podcasts, or watch us live on YouTube 134 00:06:35,400 --> 00:06:35,880 Speaker 1: the Mag. 135 00:06:35,800 --> 00:06:37,719 Speaker 3: Seven and I was looking at some Bloomberg News reporting 136 00:06:37,760 --> 00:06:37,919 Speaker 3: on this. 137 00:06:38,000 --> 00:06:41,080 Speaker 2: The MAGS seven tech giants are no longer dominating the 138 00:06:41,080 --> 00:06:43,840 Speaker 2: stock market, with their index gaining just one point seven 139 00:06:43,920 --> 00:06:45,800 Speaker 2: percent in twenty twenty six. 140 00:06:45,839 --> 00:06:48,480 Speaker 3: Let's break it down. Our next guest, Ryan Lastelica. 141 00:06:48,240 --> 00:06:52,640 Speaker 2: Equities reporter for Bloomberg News talk was Ryan about this 142 00:06:53,200 --> 00:06:54,640 Speaker 2: rotation we're seeing in the stock worker. 143 00:06:54,640 --> 00:06:55,200 Speaker 3: What are you seeing? 144 00:06:56,040 --> 00:06:59,240 Speaker 6: Yeah, it's been pretty remarkable. After years of really being 145 00:06:59,320 --> 00:07:03,120 Speaker 6: the driver four stocks the MAG seven have really started 146 00:07:03,120 --> 00:07:06,480 Speaker 6: to become something of an afterthought. Momentum has shifted away 147 00:07:06,520 --> 00:07:09,520 Speaker 6: from them and into other parts of the market, even 148 00:07:09,560 --> 00:07:13,160 Speaker 6: though the momentum is still within tech. But we've seen 149 00:07:13,240 --> 00:07:16,360 Speaker 6: a lot more interest in the semiconductor side of things, 150 00:07:16,520 --> 00:07:19,880 Speaker 6: especially the memory and storage area, which have become the 151 00:07:19,920 --> 00:07:24,120 Speaker 6: real bottleneck for this stage of the AI infrastructure buildout. 152 00:07:24,200 --> 00:07:28,360 Speaker 6: So we've seen huge gains in stocks like Micron, sand Disk, 153 00:07:28,440 --> 00:07:34,080 Speaker 6: Western Digital, Seagate, and we've seen basically flat performance overall 154 00:07:34,120 --> 00:07:36,240 Speaker 6: in the names that have otherwise been sort of the 155 00:07:36,280 --> 00:07:39,240 Speaker 6: market leaders for like I said, for years, this. 156 00:07:39,120 --> 00:07:41,600 Speaker 5: Doesn't seem like such a huge shift. It seems like 157 00:07:41,760 --> 00:07:45,400 Speaker 5: more of a semantics issue, like shifting money around within 158 00:07:46,560 --> 00:07:50,000 Speaker 5: the AI space. Still, do you see it as a 159 00:07:50,040 --> 00:07:50,960 Speaker 5: significant shift? 160 00:07:52,040 --> 00:07:54,119 Speaker 6: Well, I think what it really shows is that people 161 00:07:54,160 --> 00:07:57,400 Speaker 6: are really trying to go where the AI trade is going. 162 00:07:57,640 --> 00:07:59,640 Speaker 6: So right now we have all of these sort of 163 00:07:59,680 --> 00:08:03,760 Speaker 6: trational mag seven companies, and I'm talking particularly about the hyperscalers, 164 00:08:03,800 --> 00:08:08,240 Speaker 6: which is Amazon, Alphabet, Microsoft, Meta, companies that are really 165 00:08:08,320 --> 00:08:12,280 Speaker 6: spending aggressively to build out their AI infrastructure. People have 166 00:08:12,360 --> 00:08:14,640 Speaker 6: sort of moved away from those. Now there's been sort 167 00:08:14,640 --> 00:08:17,680 Speaker 6: of a mixed bag even within that group. Alphabet's doing 168 00:08:17,680 --> 00:08:21,160 Speaker 6: pretty well because people remain pretty positive about its AI position. 169 00:08:21,640 --> 00:08:24,840 Speaker 6: Microsoft has really struggled this year because people aren't sure 170 00:08:24,880 --> 00:08:28,080 Speaker 6: about its AI position or what it's really getting out 171 00:08:28,080 --> 00:08:30,720 Speaker 6: of the tens of billions of dollars that it's spending 172 00:08:30,800 --> 00:08:33,480 Speaker 6: on this infrastructure. So right now there is a lot 173 00:08:33,520 --> 00:08:37,440 Speaker 6: of questioning and concern about what is all this money 174 00:08:37,480 --> 00:08:39,880 Speaker 6: really going to, what kind of return are they going 175 00:08:39,920 --> 00:08:42,200 Speaker 6: to see from it? And at least for the time being, 176 00:08:42,360 --> 00:08:45,240 Speaker 6: people feel a lot more comfortable being in the companies 177 00:08:45,280 --> 00:08:48,200 Speaker 6: that are the beneficiaries of all that spending, which, like 178 00:08:48,240 --> 00:08:52,840 Speaker 6: I said, is the infrastructure complex, memory other parts of chips. 179 00:08:53,360 --> 00:08:55,400 Speaker 2: So, Ryan, I'm old enough to remember the day when 180 00:08:55,679 --> 00:08:57,600 Speaker 2: a stock would go up, when a company would take 181 00:08:57,600 --> 00:08:58,520 Speaker 2: its CAPEX. 182 00:08:58,240 --> 00:09:01,080 Speaker 3: Numbers up here, maybe like three or four quarters ago. 183 00:09:01,720 --> 00:09:04,920 Speaker 3: That game seems to be out of fashion. Is that 184 00:09:04,920 --> 00:09:05,439 Speaker 3: what you're saying. 185 00:09:06,360 --> 00:09:08,600 Speaker 6: Yeah, I think there's been a real change in centiment 186 00:09:08,640 --> 00:09:11,520 Speaker 6: in terms of how investors are viewing these kinds of 187 00:09:11,600 --> 00:09:15,640 Speaker 6: aggressive capbex plans. Now. I think people, you know, you 188 00:09:15,720 --> 00:09:17,280 Speaker 6: talk to a lot of investors, they say, you know, 189 00:09:17,320 --> 00:09:19,160 Speaker 6: I still feel pretty positive. I think they know what 190 00:09:19,160 --> 00:09:21,160 Speaker 6: they're doing. But at least for the time being, we're 191 00:09:21,200 --> 00:09:23,800 Speaker 6: really seeing an impact on free cash flow, on their 192 00:09:23,840 --> 00:09:27,960 Speaker 6: cash piles. Some companies are raising equity or tapping debt markets. 193 00:09:28,000 --> 00:09:30,840 Speaker 6: There's been a lot of, you know, complicating factors that 194 00:09:30,880 --> 00:09:34,160 Speaker 6: have just gone beyond you know, you know, spend the money. 195 00:09:34,200 --> 00:09:36,040 Speaker 6: You know, spend a dollar on AI infrastructure, you get 196 00:09:36,080 --> 00:09:38,240 Speaker 6: a dollar back and revenue growth or something like that. 197 00:09:38,280 --> 00:09:40,760 Speaker 6: It's not nearly that simple anymore. I think people are 198 00:09:40,920 --> 00:09:43,720 Speaker 6: taking a much more jaundice look on this type of strategy, 199 00:09:44,080 --> 00:09:46,160 Speaker 6: even though I think they remain pretty positive that over 200 00:09:46,320 --> 00:09:49,040 Speaker 6: a longer term period it will eventually start paying out. 201 00:09:49,920 --> 00:09:50,520 Speaker 4: Stay with us. 202 00:09:50,640 --> 00:09:52,839 Speaker 3: More from Bloomberg Intelligence coming up after this. 203 00:09:56,640 --> 00:10:00,960 Speaker 1: You're listening to the Bloomberg Intelligence podcast live weekdays at 204 00:10:01,000 --> 00:10:03,720 Speaker 1: ten am. He's done on Apple, Coarclay and Android Auto 205 00:10:03,840 --> 00:10:06,880 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 206 00:10:06,920 --> 00:10:09,880 Speaker 1: get your podcasts, or watch us live on YouTube. 207 00:10:10,400 --> 00:10:13,439 Speaker 2: All right, here's a pretty good deal, I think Sam Altman, 208 00:10:13,520 --> 00:10:16,199 Speaker 2: you know, the open ai guy. He's offered the US 209 00:10:16,320 --> 00:10:19,360 Speaker 2: a five percent stake in open Ai, which he said 210 00:10:19,400 --> 00:10:24,480 Speaker 2: would allow Americans to share in the benefits of artificial intelligence. 211 00:10:24,840 --> 00:10:26,360 Speaker 2: Seems like a good deal to me. Let's suit our 212 00:10:26,400 --> 00:10:29,520 Speaker 2: next guest, Thanks got Kunda. He's a lecturer at the 213 00:10:29,559 --> 00:10:32,520 Speaker 2: Yale School of Management and he's a Bloomberg Opinion contributor, 214 00:10:32,880 --> 00:10:35,360 Speaker 2: joining us via zoom from Sweden. 215 00:10:35,559 --> 00:10:38,600 Speaker 3: Okay, why not? It's a nice place to hear Gotham, 216 00:10:38,600 --> 00:10:40,000 Speaker 3: What do you mean here? How do you view this 217 00:10:40,320 --> 00:10:41,520 Speaker 3: potential offer. 218 00:10:41,240 --> 00:10:44,800 Speaker 2: From mister Altman as it relates to ai open Ai. 219 00:10:45,960 --> 00:10:47,680 Speaker 3: I think it's a bit of a trojan horse, Paul. 220 00:10:49,160 --> 00:10:51,679 Speaker 7: If he gives fifty five percent of the equity of 221 00:10:52,240 --> 00:10:54,520 Speaker 7: open Ai to the government. He's going to get the 222 00:10:54,559 --> 00:10:57,400 Speaker 7: government committed to the success of open Ai, and since 223 00:10:57,480 --> 00:10:59,880 Speaker 7: the government has a lot of ability to determine that success, 224 00:11:01,120 --> 00:11:03,160 Speaker 7: that's probably worth more than five percent to him. But 225 00:11:03,200 --> 00:11:04,719 Speaker 7: I'm not sure it's worth more than five percent the 226 00:11:04,760 --> 00:11:05,480 Speaker 7: American public. 227 00:11:06,640 --> 00:11:08,880 Speaker 5: Is this I mean, is this an attempt to make 228 00:11:08,920 --> 00:11:11,800 Speaker 5: the company kind of too big to fail? And what 229 00:11:11,960 --> 00:11:15,120 Speaker 5: is the case he's making to the government for why 230 00:11:15,679 --> 00:11:16,760 Speaker 5: this would be a good idea? 231 00:11:17,840 --> 00:11:18,040 Speaker 3: Right? 232 00:11:18,080 --> 00:11:20,240 Speaker 7: I think it's exactly that, right, And it wouldn't be 233 00:11:20,280 --> 00:11:22,800 Speaker 7: too big to fail economically. That argument just doesn't make 234 00:11:22,840 --> 00:11:26,640 Speaker 7: any sense opening eye, however, bigot is not systemically economically important. 235 00:11:27,000 --> 00:11:29,360 Speaker 7: What it would become is too big to fail politically, 236 00:11:29,520 --> 00:11:32,680 Speaker 7: right that because there's this huge chunk of equity that 237 00:11:32,840 --> 00:11:35,120 Speaker 7: people would be able to track, it would go back 238 00:11:35,160 --> 00:11:38,839 Speaker 7: if you think back to like in twenty twelve, the 239 00:11:40,080 --> 00:11:42,800 Speaker 7: Department of Energy had a loan program during the Obama 240 00:11:42,840 --> 00:11:45,360 Speaker 7: administration that gave out loans to lots of companies, one 241 00:11:45,440 --> 00:11:48,160 Speaker 7: of which was Cylindra, a solar sol company that's failed. 242 00:11:48,160 --> 00:11:49,960 Speaker 7: It was a sort of a catastrophe. They lost all 243 00:11:49,960 --> 00:11:52,240 Speaker 7: the money and Mitt Romney made a lot of hay 244 00:11:52,280 --> 00:11:54,040 Speaker 7: out of that. And you you know, if the Obama 245 00:11:54,080 --> 00:11:56,120 Speaker 7: administration had the ability, you think they might have gone 246 00:11:56,160 --> 00:11:57,559 Speaker 7: back and been like, maybe we should have propped that 247 00:11:57,600 --> 00:12:02,200 Speaker 7: company up. That that would be open AI times like 248 00:12:02,400 --> 00:12:04,720 Speaker 7: a million, right, because the scale of the open Aie 249 00:12:04,760 --> 00:12:07,440 Speaker 7: investment is so large, and it would put so much 250 00:12:07,480 --> 00:12:09,520 Speaker 7: pressure on the government to kind of lean on the 251 00:12:09,600 --> 00:12:12,200 Speaker 7: levers and keep this company propped up for no other 252 00:12:12,240 --> 00:12:14,920 Speaker 7: reason than to stop people from saying, hey, you cost 253 00:12:15,000 --> 00:12:17,240 Speaker 7: us you know, eighty billion or one hundred million dollars 254 00:12:17,320 --> 00:12:18,280 Speaker 7: or whatever it ends up being. 255 00:12:19,400 --> 00:12:23,760 Speaker 2: So we have seen this administration take investments in other 256 00:12:23,960 --> 00:12:27,679 Speaker 2: companies a put give us some context around that. I 257 00:12:27,679 --> 00:12:30,839 Speaker 2: don't recall our country doing our government doing that too 258 00:12:30,920 --> 00:12:33,280 Speaker 2: much before, maybe the auto industry, and I don't know, 259 00:12:33,280 --> 00:12:34,760 Speaker 2: how do we think about that in general? 260 00:12:35,520 --> 00:12:39,680 Speaker 7: Yes, this administration alternates between launching those sufference attacks on 261 00:12:39,720 --> 00:12:43,600 Speaker 7: socialism and communism and taking ownership of private companies. So 262 00:12:43,760 --> 00:12:45,800 Speaker 7: I guess today we're on a taking ownership of private 263 00:12:45,800 --> 00:12:49,240 Speaker 7: companies day, and tomorrow we'll be back to attacking communism. 264 00:12:49,360 --> 00:12:50,960 Speaker 6: And yeah, this is this. 265 00:12:50,840 --> 00:12:52,880 Speaker 7: Is a new thing, and they're very good reasons we 266 00:12:52,960 --> 00:12:55,040 Speaker 7: don't do it right, and they're good reasons on both 267 00:12:55,080 --> 00:12:58,040 Speaker 7: sides of the equation. We've talked on the government side 268 00:12:58,080 --> 00:13:00,280 Speaker 7: how this is going to lead to bad policy and 269 00:13:00,320 --> 00:13:02,920 Speaker 7: to bad political outcomes, and it is just right. It 270 00:13:03,000 --> 00:13:06,160 Speaker 7: is corrosive on free markets and free enterprise to have 271 00:13:06,240 --> 00:13:08,920 Speaker 7: government puts a heavy hand in like that. But it's 272 00:13:08,960 --> 00:13:11,480 Speaker 7: also really bad for the companies, right, So they may 273 00:13:11,480 --> 00:13:14,160 Speaker 7: think of themselves as buying insurance against failure by getting 274 00:13:14,160 --> 00:13:16,640 Speaker 7: the government on their side. But these are companies that 275 00:13:16,679 --> 00:13:20,080 Speaker 7: have a global market. And if you are you know, 276 00:13:20,200 --> 00:13:24,000 Speaker 7: the government of France or Germany, or you know Denmark, 277 00:13:24,040 --> 00:13:26,440 Speaker 7: which is just seeing the United's just a few just 278 00:13:26,520 --> 00:13:29,200 Speaker 7: a few minutes ago, right, Donald Trump issued more threats 279 00:13:29,200 --> 00:13:33,960 Speaker 7: to Greenland. Then the idea of having your economy or 280 00:13:33,960 --> 00:13:38,080 Speaker 7: your government run on a company's software when that company 281 00:13:38,120 --> 00:13:41,480 Speaker 7: is functionally part owned by the United States government, you know, 282 00:13:41,520 --> 00:13:43,319 Speaker 7: a few years ago that wouldn't have been a problem, 283 00:13:43,400 --> 00:13:45,600 Speaker 7: but now it's a huge problem. And it means that 284 00:13:45,640 --> 00:13:47,640 Speaker 7: these companies are going to limit their global market in 285 00:13:47,679 --> 00:13:48,640 Speaker 7: a really profound way. 286 00:13:49,240 --> 00:13:53,160 Speaker 5: I mean, the hazard seem quite clear here. Is there 287 00:13:53,280 --> 00:13:56,320 Speaker 5: potentially a case to be made for government investment in AI, 288 00:13:56,520 --> 00:14:00,040 Speaker 5: just because the economy has become so dependent on it, 289 00:14:00,200 --> 00:14:03,880 Speaker 5: such an expensive thing to try to build out. Is 290 00:14:03,960 --> 00:14:06,760 Speaker 5: there possibly a case to be made for an investment 291 00:14:06,840 --> 00:14:07,160 Speaker 5: like this? 292 00:14:08,120 --> 00:14:10,640 Speaker 7: I think there's an extremely strong case to be made 293 00:14:10,720 --> 00:14:14,200 Speaker 7: for the government. You for the government profiting from gains 294 00:14:14,240 --> 00:14:17,360 Speaker 7: in AI, and for being taking those gains and using 295 00:14:17,360 --> 00:14:19,800 Speaker 7: them to sort of provide public goods for the whole, 296 00:14:20,000 --> 00:14:21,720 Speaker 7: for the whole the United States, and that benefit the 297 00:14:21,720 --> 00:14:24,240 Speaker 7: economy of the siety as a whole. That case is overwhelming. 298 00:14:24,560 --> 00:14:28,240 Speaker 7: But the mechanism for executing that is taxes. And if 299 00:14:28,280 --> 00:14:30,720 Speaker 7: the United States government wants to benefit from AI, it 300 00:14:30,760 --> 00:14:33,960 Speaker 7: should tax profitable AI companies. That's the way we should 301 00:14:33,960 --> 00:14:36,720 Speaker 7: do it. And to tax the individuals who make billions 302 00:14:36,720 --> 00:14:39,080 Speaker 7: of dollars off AI, that's the right way to do it. 303 00:14:39,240 --> 00:14:40,240 Speaker 7: This equity thing. 304 00:14:40,120 --> 00:14:40,400 Speaker 3: Is not. 305 00:14:42,000 --> 00:14:46,320 Speaker 2: Where where is open AI in its IPO process? I think, 306 00:14:46,440 --> 00:14:48,600 Speaker 2: you know, the feeling on the street is maybe it 307 00:14:48,680 --> 00:14:51,680 Speaker 2: might slip into next year, maybe Anthroomic might get out first. 308 00:14:51,720 --> 00:14:52,600 Speaker 3: Is that your understanding? 309 00:14:53,520 --> 00:14:55,680 Speaker 7: You know, That's what I'm hearing. And I'll just say 310 00:14:55,720 --> 00:14:58,160 Speaker 7: that the more you look at the finances of open AI, 311 00:14:58,360 --> 00:15:00,520 Speaker 7: you know, as a front of the Financial Times just 312 00:15:00,560 --> 00:15:03,440 Speaker 7: were able to get some of them. These look really bad, 313 00:15:03,640 --> 00:15:06,000 Speaker 7: Like I mean, I know that what with SpaceX, we've 314 00:15:06,040 --> 00:15:09,800 Speaker 7: sort of decided that traditional metrics for an IPO don't 315 00:15:09,840 --> 00:15:13,280 Speaker 7: apply anymore. But it's really kind of hard to make 316 00:15:13,320 --> 00:15:15,680 Speaker 7: a case for how these companies are going to some 317 00:15:15,920 --> 00:15:19,760 Speaker 7: pay back, you know, pay the gigantic capital investments they're making. 318 00:15:19,760 --> 00:15:21,720 Speaker 7: And I think a lot of people are starting to go, 319 00:15:22,120 --> 00:15:26,640 Speaker 7: you know, you can have an extraordinary revolutionary technology, one 320 00:15:26,640 --> 00:15:28,520 Speaker 7: that changes the world, that changes the way we do 321 00:15:28,880 --> 00:15:31,120 Speaker 7: the way we do work, and the peak companies that 322 00:15:31,160 --> 00:15:33,560 Speaker 7: are doing it don't make money. Because we saw that 323 00:15:33,680 --> 00:15:36,800 Speaker 7: with the airlines and with biotech for two generations. These 324 00:15:36,800 --> 00:15:40,280 Speaker 7: were incredibly important technologies and they just weren't profitable for 325 00:15:40,320 --> 00:15:41,440 Speaker 7: the companies that were doing it. 326 00:15:42,560 --> 00:15:44,800 Speaker 5: Do you see Opening Eye as in a good position 327 00:15:44,880 --> 00:15:46,600 Speaker 5: right now? It seems like it was way out in 328 00:15:46,680 --> 00:15:51,160 Speaker 5: front at first and had all of the public's imagination 329 00:15:51,280 --> 00:15:53,240 Speaker 5: and a lot of money, and now it seems like 330 00:15:53,280 --> 00:15:55,440 Speaker 5: it's losing its shine a little bit. 331 00:15:56,640 --> 00:15:56,840 Speaker 3: Yeah. 332 00:15:56,880 --> 00:15:58,520 Speaker 7: I mean, as I'm one of the people who sort 333 00:15:58,560 --> 00:16:01,560 Speaker 7: of switched from using using chat GPT to claude just 334 00:16:01,600 --> 00:16:04,320 Speaker 7: because you know, the experience was so much better, and 335 00:16:04,360 --> 00:16:06,280 Speaker 7: I think a lot of people are having are sort 336 00:16:06,320 --> 00:16:09,880 Speaker 7: of saying that where the the in terms of usage, 337 00:16:09,920 --> 00:16:12,000 Speaker 7: open ai is still on ahead, but in terms of 338 00:16:12,040 --> 00:16:14,840 Speaker 7: the I would say the more sophisticated an AI user is, 339 00:16:15,400 --> 00:16:17,160 Speaker 7: the less likely it seems to be that they are 340 00:16:17,440 --> 00:16:20,640 Speaker 7: that they are open ai dependent. But beyond that, I 341 00:16:20,680 --> 00:16:23,320 Speaker 7: think that the questions about their leader are also just 342 00:16:23,520 --> 00:16:27,480 Speaker 7: you know, their questions about is this sustainable, the level 343 00:16:27,560 --> 00:16:30,600 Speaker 7: of the commitments that they've made for data centers and 344 00:16:31,000 --> 00:16:33,440 Speaker 7: sort of the spending commitments they've made, aired with the 345 00:16:33,440 --> 00:16:36,720 Speaker 7: fact that the construction of these data centers is not, 346 00:16:36,920 --> 00:16:40,080 Speaker 7: from what I can tell, going nearly as quickly as 347 00:16:40,080 --> 00:16:41,840 Speaker 7: it would have to be to make sort of these 348 00:16:41,840 --> 00:16:44,920 Speaker 7: compute these demands are commute, these contracts full out. I 349 00:16:44,960 --> 00:16:46,400 Speaker 7: think a lot of people are starting to look at 350 00:16:46,400 --> 00:16:49,000 Speaker 7: this and going, wait a sec, how does you know, 351 00:16:49,160 --> 00:16:51,800 Speaker 7: how does this story end? Does it really end with 352 00:16:51,840 --> 00:16:54,360 Speaker 7: you spending one and a half trillion dollars on data 353 00:16:54,360 --> 00:16:56,680 Speaker 7: centers when no one of the United States sort of 354 00:16:56,840 --> 00:16:59,080 Speaker 7: wants you to build them in your neighborhood, when there's 355 00:16:59,080 --> 00:17:02,520 Speaker 7: so many construction bottlenecks, and when you know when the 356 00:17:02,560 --> 00:17:04,240 Speaker 7: demand for them is starting to get a little iffy. 357 00:17:04,320 --> 00:17:05,360 Speaker 7: That's it's not clear to right. 358 00:17:06,520 --> 00:17:11,200 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 359 00:17:11,400 --> 00:17:14,880 Speaker 1: and anywhere else you get your podcasts. 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