1 00:00:02,520 --> 00:00:13,399 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive 2 00:00:13,440 --> 00:00:17,239 Speaker 1: from coast to coast with Caroline Hide in New York 3 00:00:17,560 --> 00:00:19,520 Speaker 1: and ever Low in sentrancs go. 4 00:00:22,680 --> 00:00:25,480 Speaker 2: This is Bloomberg Tech coming up. Google agrees to create 5 00:00:25,520 --> 00:00:28,640 Speaker 2: an AI cloud business with Blackstone, which will run on 6 00:00:28,680 --> 00:00:30,520 Speaker 2: Google's homegrown AI chips. 7 00:00:30,600 --> 00:00:32,880 Speaker 3: Plus, the legal battle between Elon Musk and his Open 8 00:00:32,880 --> 00:00:36,120 Speaker 3: Ai co founders ends with a jewelry ruling Musk waited 9 00:00:36,200 --> 00:00:38,600 Speaker 3: too long to soon. We'll dive into the details of 10 00:00:38,640 --> 00:00:39,120 Speaker 3: the verdict. 11 00:00:39,920 --> 00:00:43,560 Speaker 2: A Meta Betts Big on Louisiana for the world's biggest 12 00:00:43,600 --> 00:00:48,080 Speaker 2: AI facility. This is It plans AI related layoffs this week. 13 00:00:48,720 --> 00:00:52,400 Speaker 3: AI related layoffs a theme for the market, but AI 14 00:00:53,159 --> 00:00:56,240 Speaker 3: lack of exuberance pounding the market once again with actually 15 00:00:56,280 --> 00:00:59,160 Speaker 3: yields pushing higher Riskill focused on geopolitics, said, but we're 16 00:00:59,160 --> 00:01:01,080 Speaker 3: looking at a third straight day of losses on the 17 00:01:01,120 --> 00:01:03,000 Speaker 3: laws that one hundred or off five out a percentage point. 18 00:01:03,000 --> 00:01:05,560 Speaker 3: We're off about three percent in those three training days. 19 00:01:05,600 --> 00:01:07,399 Speaker 3: As we just get a little bit of worry that 20 00:01:07,440 --> 00:01:09,440 Speaker 3: we went too far, too farst on the hardware side 21 00:01:09,440 --> 00:01:11,800 Speaker 3: of things, I shine a light on the Semiconductor Index. 22 00:01:12,040 --> 00:01:13,920 Speaker 3: The SOCKS is coming off by two percent. In fact, 23 00:01:14,160 --> 00:01:16,560 Speaker 3: we're down by eight nine percent. We're on the cusp 24 00:01:16,800 --> 00:01:19,680 Speaker 3: of some sort of well recalibration of where we've been 25 00:01:19,720 --> 00:01:21,759 Speaker 3: in this run up. Yes, it was about seventy percent 26 00:01:21,760 --> 00:01:23,480 Speaker 3: from the end of March, but now are we on 27 00:01:23,520 --> 00:01:25,640 Speaker 3: the verge of a correction. We're down almost eight or 28 00:01:25,720 --> 00:01:28,520 Speaker 3: nine percent, as I say, but a rotation. Some money 29 00:01:28,520 --> 00:01:30,680 Speaker 3: moving into the software stocks. You've got key earnings coming 30 00:01:30,680 --> 00:01:34,320 Speaker 3: from software and hardware over the next few days. 31 00:01:34,440 --> 00:01:35,839 Speaker 4: Neo clouds down. 32 00:01:36,080 --> 00:01:40,039 Speaker 2: The news Blackstone and Google forming a new company to 33 00:01:40,120 --> 00:01:44,120 Speaker 2: be a neocloud. Blackstone brings five billion dollars. There's debt financing. 34 00:01:44,400 --> 00:01:48,680 Speaker 2: Five hundred megawats is the target. Google's TPUs tensor processing 35 00:01:48,760 --> 00:01:51,280 Speaker 2: units are the chips. And look at those names that 36 00:01:51,320 --> 00:01:54,920 Speaker 2: are down Core weave, Ironebuus in particular, all very Nvidia 37 00:01:55,000 --> 00:01:58,560 Speaker 2: focused architectures. But neoclouds all the same. Competition seems to 38 00:01:58,560 --> 00:01:59,640 Speaker 2: be coming it does. 39 00:01:59,680 --> 00:02:02,200 Speaker 3: That's scuss the impact of Google blackstory. The partnership ed 40 00:02:02,360 --> 00:02:05,200 Speaker 3: Brie Big Intelligence says this pact could be a major 41 00:02:05,200 --> 00:02:08,760 Speaker 3: boost for the tech giants. Chips business Managsing, who wrote 42 00:02:08,840 --> 00:02:11,720 Speaker 3: that React joins us now. And what's so interesting is, yes, 43 00:02:11,760 --> 00:02:13,799 Speaker 3: the equity comes from Blackstone five billion, it could be 44 00:02:13,840 --> 00:02:15,920 Speaker 3: leveraged up to twenty five billion dollars. But this is 45 00:02:15,960 --> 00:02:19,040 Speaker 3: just another way for Alphabet to get its hardware out 46 00:02:19,040 --> 00:02:21,000 Speaker 3: there but not actually have to build the data centers. 47 00:02:21,000 --> 00:02:21,600 Speaker 3: It feels like. 48 00:02:22,800 --> 00:02:26,720 Speaker 5: Yeah, and look, when it comes to Nvidia, their ecosystem 49 00:02:26,840 --> 00:02:29,680 Speaker 5: of neo cloud players is pretty big. I mean, you 50 00:02:29,760 --> 00:02:32,120 Speaker 5: had a few of those on the on your screen. 51 00:02:32,200 --> 00:02:34,360 Speaker 5: And when it comes to Google, you know, you have 52 00:02:34,480 --> 00:02:37,880 Speaker 5: got terrible cipher mining, but there aren't a lot of 53 00:02:38,800 --> 00:02:41,919 Speaker 5: you know, TPU providers. And that's where you know, when 54 00:02:41,960 --> 00:02:45,920 Speaker 5: Anthropics says they want to use almost two hundred billion 55 00:02:46,000 --> 00:02:50,400 Speaker 5: dollars of capacity from Google, Google has to find a 56 00:02:50,440 --> 00:02:53,000 Speaker 5: way to you know, wrap that up outside of their 57 00:02:53,080 --> 00:02:56,760 Speaker 5: own cloud. And that's where I think this sort of 58 00:02:56,960 --> 00:03:01,040 Speaker 5: a transaction is quite interesting. Where Blackstone is putting the 59 00:03:01,080 --> 00:03:04,280 Speaker 5: money and also has got live capacity that they can 60 00:03:04,320 --> 00:03:07,640 Speaker 5: bring online by twenty twenty seven, which is what makes 61 00:03:07,639 --> 00:03:09,880 Speaker 5: this very attractive from a Google standpoint. 62 00:03:10,680 --> 00:03:13,919 Speaker 2: Hey, Mandy, what's the Bloomberg intelligence thesis on the business 63 00:03:13,960 --> 00:03:16,560 Speaker 2: model of setting up a neocloud for the TPU? 64 00:03:17,000 --> 00:03:17,080 Speaker 6: Right? 65 00:03:17,120 --> 00:03:20,440 Speaker 2: Because Google Cloud, you know, they have capacity that they 66 00:03:20,440 --> 00:03:23,120 Speaker 2: could get to third party customers. Why set up a 67 00:03:23,160 --> 00:03:24,480 Speaker 2: new standalone new code. 68 00:03:25,880 --> 00:03:26,120 Speaker 7: Yeah. 69 00:03:26,160 --> 00:03:28,560 Speaker 5: So in the case of Google Cloud, you know, they 70 00:03:28,600 --> 00:03:32,560 Speaker 5: are serving that traffic from Google's data centers. I mean 71 00:03:32,600 --> 00:03:36,160 Speaker 5: in this case, you know, having a neocloud provider use 72 00:03:36,680 --> 00:03:41,080 Speaker 5: Google TPUs and then everything else is taken care of 73 00:03:41,480 --> 00:03:43,960 Speaker 5: by a blackstown in this case, whether it's you know, 74 00:03:44,120 --> 00:03:47,120 Speaker 5: the power or the cooling or the you know, even 75 00:03:47,160 --> 00:03:50,960 Speaker 5: getting the land for that data center. So from that perspective, 76 00:03:51,200 --> 00:03:55,440 Speaker 5: having that live capacity is what you know, makes this 77 00:03:55,600 --> 00:03:58,800 Speaker 5: different from Google having to do it on their own, 78 00:03:58,840 --> 00:04:01,040 Speaker 5: which will be a lot more aggreed in terms of 79 00:04:01,040 --> 00:04:04,040 Speaker 5: how much gigawad they can bring online in twenty twenty six, 80 00:04:04,320 --> 00:04:07,440 Speaker 5: twenty twenty seven and beyond, whereas this would come life 81 00:04:07,840 --> 00:04:10,760 Speaker 5: much faster given the involvement of blacksture. 82 00:04:11,240 --> 00:04:14,240 Speaker 2: Man deep Seeing, who leads technology research at Bloomberg Intelligence. 83 00:04:14,240 --> 00:04:16,280 Speaker 2: Thank you very much. Let's take it today. A look 84 00:04:16,320 --> 00:04:19,960 Speaker 2: at today's big number. Seven thousand. That's how many workers 85 00:04:20,520 --> 00:04:24,239 Speaker 2: Medas resigning to new jobs related to AI. That's according 86 00:04:24,240 --> 00:04:26,440 Speaker 2: to an internal memo. This comes on the eve of 87 00:04:26,440 --> 00:04:30,120 Speaker 2: a ten percent staff cut tomorrow to improve efficiency and 88 00:04:30,160 --> 00:04:34,080 Speaker 2: quote offset metas investments in AI. AAR related job cuts 89 00:04:34,160 --> 00:04:36,120 Speaker 2: are the talk of Wall Street right now. Is Standard 90 00:04:36,200 --> 00:04:39,520 Speaker 2: Chartered CEO Bill Winters also saying his company plans to 91 00:04:39,560 --> 00:04:42,720 Speaker 2: cut more than fifteen percent of its supports staff by 92 00:04:42,760 --> 00:04:47,160 Speaker 2: twenty thirty, aiming to replace quote lower value human capital 93 00:04:47,560 --> 00:04:51,080 Speaker 2: with AI. Martin Norton is MPOWER Chief Investment Strategists, and 94 00:04:51,360 --> 00:04:54,320 Speaker 2: I'm going to ask you straight up, it's an interesting 95 00:04:54,440 --> 00:04:59,160 Speaker 2: balancing act cutting rolls and saying that this is not 96 00:04:59,200 --> 00:05:04,320 Speaker 2: necessarily a trim downsize, it's a reallocation a priority to AI. 97 00:05:04,920 --> 00:05:07,640 Speaker 2: As an investor, what signal does that give you? 98 00:05:08,640 --> 00:05:11,640 Speaker 8: Well, it's not the only piece of data that we 99 00:05:11,760 --> 00:05:14,000 Speaker 8: have as companies do that. We hear a lot of 100 00:05:14,040 --> 00:05:18,680 Speaker 8: different announcements of companies making reductions to their workforce related 101 00:05:18,720 --> 00:05:22,720 Speaker 8: to AI. I think a lot of these are idiosyncratic. 102 00:05:23,000 --> 00:05:25,320 Speaker 8: When we're looking at the broad economy at the moment, 103 00:05:25,760 --> 00:05:29,479 Speaker 8: we know that the broad economy added labor upon labor 104 00:05:29,640 --> 00:05:32,159 Speaker 8: and the wake of the pandemic, and now there's a 105 00:05:32,200 --> 00:05:34,719 Speaker 8: bit of right sizing, and so in some cases, I 106 00:05:34,800 --> 00:05:38,760 Speaker 8: do think companies are adding the AI moniker to make 107 00:05:38,800 --> 00:05:42,839 Speaker 8: it feel very forward looking to maybe AI whitewash it, 108 00:05:43,760 --> 00:05:46,960 Speaker 8: but not necessarily a sign of the times in terms 109 00:05:46,960 --> 00:05:49,960 Speaker 8: of very broad layoffs across the economy. In fact, one 110 00:05:49,960 --> 00:05:52,560 Speaker 8: of the areas that I'm looking more closely for signs 111 00:05:52,600 --> 00:05:54,839 Speaker 8: of an AI impact is more on the hiring side, 112 00:05:54,920 --> 00:05:57,279 Speaker 8: where people are maybe slower to higher than they otherwise 113 00:05:57,320 --> 00:05:59,839 Speaker 8: would because of AI. But yes, there are these idiosyncratic 114 00:06:00,120 --> 00:06:02,640 Speaker 8: reports that suggest we are seeing a little bit of 115 00:06:02,640 --> 00:06:03,919 Speaker 8: movement on that front. 116 00:06:04,160 --> 00:06:09,200 Speaker 3: I'm sorry, language matters, lower value, human capital, the extraordinary 117 00:06:09,279 --> 00:06:13,239 Speaker 3: choice of words, but Marta putting that to one side, 118 00:06:13,400 --> 00:06:16,640 Speaker 3: there is anxiety out there of labor and people who 119 00:06:16,640 --> 00:06:21,279 Speaker 3: are affected. But when you're thinking about how we see 120 00:06:21,600 --> 00:06:25,200 Speaker 3: actual businesses lean into this moment, are we still seeing 121 00:06:25,240 --> 00:06:28,320 Speaker 3: adoption taking root in the way you anticipated? Are we 122 00:06:28,400 --> 00:06:31,440 Speaker 3: seeing the people the label that is able to use 123 00:06:31,480 --> 00:06:33,280 Speaker 3: AI being more productive with it? 124 00:06:34,240 --> 00:06:36,760 Speaker 8: You know, I think those early signs that are pretty 125 00:06:36,760 --> 00:06:40,400 Speaker 8: positive in terms of adoption by current employees. So I 126 00:06:40,400 --> 00:06:43,240 Speaker 8: think everyone at this point is using the chatbots on 127 00:06:43,279 --> 00:06:47,080 Speaker 8: a regular basis to do, you know, low value research. 128 00:06:47,520 --> 00:06:51,239 Speaker 8: But I think the bigger question is this AI agent 129 00:06:51,400 --> 00:06:54,799 Speaker 8: deployment what that looks like? And you are seeing companies 130 00:06:55,360 --> 00:06:58,440 Speaker 8: talking about that in real time and beginning to structure 131 00:06:58,520 --> 00:07:01,280 Speaker 8: workflows around it. I think we're very early obviously on 132 00:07:01,320 --> 00:07:03,320 Speaker 8: the adoption of that. But that is where I think 133 00:07:03,360 --> 00:07:10,040 Speaker 8: a lot of this AI productivity can stem from. 134 00:07:08,640 --> 00:07:12,000 Speaker 2: Modern the market's trading, in part in anticipation of Nvidia. 135 00:07:12,400 --> 00:07:16,200 Speaker 2: After the closing bell Wednesday. Yesterday, I spoke to Nvidia 136 00:07:16,280 --> 00:07:19,040 Speaker 2: CEO Jensen Wang. This is how he frames the state 137 00:07:19,080 --> 00:07:19,480 Speaker 2: of play. 138 00:07:20,440 --> 00:07:23,000 Speaker 9: We have the largest supply chain in the world. Our 139 00:07:23,040 --> 00:07:27,040 Speaker 9: partners have done a great job securing supply for us, 140 00:07:27,360 --> 00:07:30,200 Speaker 9: and so all of the pieces go together. The silicon, 141 00:07:30,240 --> 00:07:33,520 Speaker 9: photonics is lined up, Everything is all lined up. It's 142 00:07:33,600 --> 00:07:38,080 Speaker 9: just that the demand is much greater than the overall capacity. 143 00:07:37,560 --> 00:07:38,000 Speaker 3: Of the world. 144 00:07:39,240 --> 00:07:43,600 Speaker 2: The demand is far out pacing this industry's ability to supply. 145 00:07:43,680 --> 00:07:46,840 Speaker 2: When you go into an earnings how do you decide 146 00:07:46,880 --> 00:07:49,800 Speaker 2: therefore the bar in terms of what is positive and 147 00:07:49,800 --> 00:07:53,720 Speaker 2: what is negative. If you're in a supply constraint world. 148 00:07:54,520 --> 00:07:56,680 Speaker 8: Well, I think you do want to see signs that 149 00:07:56,720 --> 00:07:59,080 Speaker 8: they are monetizing it. Right, If you're in a supply 150 00:07:59,240 --> 00:08:02,480 Speaker 8: constrained world, that demand should be moving earnings and you 151 00:08:02,520 --> 00:08:05,240 Speaker 8: want to see some evidence of that. But of course, 152 00:08:05,280 --> 00:08:07,920 Speaker 8: when we're talking about the supply constraint. We're also talking 153 00:08:07,960 --> 00:08:12,040 Speaker 8: about supply constraint and areas that would infect the likes 154 00:08:12,080 --> 00:08:14,280 Speaker 8: of n videos so memory checks, for example, And what 155 00:08:14,320 --> 00:08:16,920 Speaker 8: does that mean for gross margins and how are they 156 00:08:17,080 --> 00:08:19,960 Speaker 8: navigating that? I think those are important questions at the moment, 157 00:08:20,200 --> 00:08:21,960 Speaker 8: but I think the bigger picture, and I caught that 158 00:08:22,200 --> 00:08:25,000 Speaker 8: that interview yesterday. I think the bigger picture that Jensen 159 00:08:25,040 --> 00:08:28,280 Speaker 8: Wong is pointing to is this massive supply and demand 160 00:08:28,320 --> 00:08:31,600 Speaker 8: mismatch that we'll carry through not just this earnings report, 161 00:08:31,720 --> 00:08:35,400 Speaker 8: but for several quarters years to come as this all 162 00:08:35,400 --> 00:08:38,079 Speaker 8: plays out. So I really agree with his comment that 163 00:08:38,160 --> 00:08:41,199 Speaker 8: this is inning one, Inning two on the AI build. 164 00:08:42,040 --> 00:08:43,760 Speaker 3: Luckily we all call that into you. It was a 165 00:08:43,760 --> 00:08:45,840 Speaker 3: great one with Ed and Mountain Norton. We so appreciate 166 00:08:45,880 --> 00:08:47,920 Speaker 3: having you on the show today of Empower. Thank you 167 00:08:47,960 --> 00:08:51,720 Speaker 3: for your time. Coming up a bitter feud, personal grievances, 168 00:08:51,760 --> 00:08:54,520 Speaker 3: billions of dollars. The decision comes down to timing. We'll 169 00:08:54,520 --> 00:08:57,120 Speaker 3: discuss the jury's rejection of El Musk's claims against his 170 00:08:57,200 --> 00:09:14,680 Speaker 3: Open AI co founders, and what comes next the decision 171 00:09:14,760 --> 00:09:17,080 Speaker 3: in that closely watched legal battle between Ela Musk and 172 00:09:17,080 --> 00:09:20,480 Speaker 3: his open ai co founders came down to timing. A 173 00:09:20,559 --> 00:09:22,800 Speaker 3: jury rule that Musk waited too long to sue of 174 00:09:22,880 --> 00:09:26,080 Speaker 3: open AI's transition to a for profit business. Mugs Madelon 175 00:09:26,080 --> 00:09:28,400 Speaker 3: and Macklburg has been following the case every step of 176 00:09:28,440 --> 00:09:30,360 Speaker 3: the way. You join us now, Madeline. What did the 177 00:09:30,440 --> 00:09:33,120 Speaker 3: jury decide? What didn't it decide on? 178 00:09:34,840 --> 00:09:34,960 Speaker 6: Right? 179 00:09:35,080 --> 00:09:37,480 Speaker 10: It was a bit of an anti climactic verdict, if 180 00:09:37,480 --> 00:09:40,199 Speaker 10: I can say that, because the jury didn't actually make 181 00:09:40,240 --> 00:09:43,360 Speaker 10: a decision on any of the claims that Elon Musk 182 00:09:43,440 --> 00:09:46,720 Speaker 10: presented over the course of this lawsuit. What they said 183 00:09:46,760 --> 00:09:50,400 Speaker 10: instead was that he had not proven his case within 184 00:09:50,480 --> 00:09:53,600 Speaker 10: the statute of limitations set by law for the specific 185 00:09:53,640 --> 00:09:57,240 Speaker 10: claims that he brought. So essentially, they found that he 186 00:09:57,320 --> 00:10:00,160 Speaker 10: had concerns about this conduct and should have known about 187 00:10:00,200 --> 00:10:04,000 Speaker 10: this conduct years before he actually filed his lawsuit, and 188 00:10:04,040 --> 00:10:06,600 Speaker 10: so therefore the case that the jury was hearing should 189 00:10:06,640 --> 00:10:08,520 Speaker 10: be tossed out, which is what happened. 190 00:10:09,840 --> 00:10:12,440 Speaker 2: We're going to get an appeal, according to mister Mask, 191 00:10:12,480 --> 00:10:14,520 Speaker 2: who posted on x that he would appeal. I believe 192 00:10:14,640 --> 00:10:17,840 Speaker 2: like his legal team outside the courtroom said that we 193 00:10:17,880 --> 00:10:20,800 Speaker 2: would appeal. If that's the case, what do we know 194 00:10:21,120 --> 00:10:23,679 Speaker 2: about what that appeal would be based on From a 195 00:10:23,760 --> 00:10:25,320 Speaker 2: legal perspective. 196 00:10:26,280 --> 00:10:28,800 Speaker 10: Right, it should come as no surprise to anybody who 197 00:10:28,960 --> 00:10:31,520 Speaker 10: follows Elon Musk as a litigant that he plans to 198 00:10:31,640 --> 00:10:35,439 Speaker 10: appeal this and fight this vigorously. We saw him celebrating 199 00:10:35,520 --> 00:10:39,160 Speaker 10: on X afterwards saying that, you know, just because they 200 00:10:39,160 --> 00:10:42,040 Speaker 10: decided on the statute of limitations, they never actually touched 201 00:10:42,080 --> 00:10:44,520 Speaker 10: the merits of the case. I think an appeal on 202 00:10:44,800 --> 00:10:47,200 Speaker 10: this ground is a lot more narrow than if the 203 00:10:47,280 --> 00:10:49,480 Speaker 10: jury had actually ruled on the. 204 00:10:49,480 --> 00:10:50,920 Speaker 6: Merits of his claims. 205 00:10:51,240 --> 00:10:53,040 Speaker 10: So I think a lot It still kind of remains 206 00:10:53,040 --> 00:10:55,120 Speaker 10: to be seen of how this is going to play out, 207 00:10:55,120 --> 00:10:56,720 Speaker 10: but it's going to be an issue that the Ninth 208 00:10:56,720 --> 00:10:58,720 Speaker 10: Circuit Court of Appeals is going to have to sort 209 00:10:58,760 --> 00:11:00,200 Speaker 10: through when he eventually files that. 210 00:11:01,160 --> 00:11:04,480 Speaker 2: Bloomberg's Madeline Meckelberg, thank you very much. Let's dive more 211 00:11:04,480 --> 00:11:07,480 Speaker 2: into the legal considerations. Dorothy Lund is with us, a 212 00:11:07,520 --> 00:11:11,240 Speaker 2: professor at Columbia Law School focused on corporate law, governance 213 00:11:11,280 --> 00:11:13,960 Speaker 2: and contracts, returning to the show and has been covering 214 00:11:13,960 --> 00:11:17,000 Speaker 2: this trial with us. In our Bloomberg News story, we 215 00:11:17,160 --> 00:11:21,559 Speaker 2: write that mister Musk was too late in his suit, 216 00:11:21,679 --> 00:11:25,439 Speaker 2: and we later down in the story explain the idea 217 00:11:25,559 --> 00:11:28,560 Speaker 2: that he had knowledge in twenty twenty four and so 218 00:11:28,679 --> 00:11:31,400 Speaker 2: basically should have filed his suit sooner. But I've got 219 00:11:31,440 --> 00:11:33,320 Speaker 2: a lot of sympathy with people around the world that 220 00:11:33,360 --> 00:11:37,120 Speaker 2: don't understand the legal technicality of that. The Statute of 221 00:11:37,120 --> 00:11:40,559 Speaker 2: limitations seems to play a role here. Is this codified 222 00:11:41,360 --> 00:11:45,040 Speaker 2: or is this the jury's discretion in deciding that mister 223 00:11:45,120 --> 00:11:46,200 Speaker 2: Musk ran out of time? 224 00:11:47,679 --> 00:11:48,320 Speaker 6: Yeah, the jury. 225 00:11:48,440 --> 00:11:52,720 Speaker 11: So the jury had to basically answer this question of 226 00:11:52,760 --> 00:11:55,439 Speaker 11: whether or not the Statute of limitations applied to the 227 00:11:55,480 --> 00:11:57,520 Speaker 11: two claims that were brought here. So the two claims 228 00:11:57,559 --> 00:12:01,840 Speaker 11: is to remind everybody or there was a violation that 229 00:12:02,000 --> 00:12:06,400 Speaker 11: open ai, Brockman and Altman violated a promise that was 230 00:12:06,440 --> 00:12:08,880 Speaker 11: made to Musk that it would have a permanent charitable 231 00:12:09,160 --> 00:12:13,040 Speaker 11: charitable mission to develop safe open source AI technology, and 232 00:12:13,080 --> 00:12:17,000 Speaker 11: that they received undeserved benefits because of those broken promises. 233 00:12:17,600 --> 00:12:20,760 Speaker 11: And so then open ai said, well, you waited too 234 00:12:20,840 --> 00:12:25,080 Speaker 11: long to sue. You know, you brought your case on 235 00:12:25,240 --> 00:12:29,440 Speaker 11: August twenty twenty four, and the statute of limitations was 236 00:12:29,480 --> 00:12:32,719 Speaker 11: only three It was three years, which meant the things 237 00:12:32,760 --> 00:12:35,160 Speaker 11: that you're complaining about that took place in twenty eighteen, 238 00:12:35,320 --> 00:12:39,520 Speaker 11: twenty nineteen, you're too late. 239 00:12:40,120 --> 00:12:40,880 Speaker 7: Well, I think that is. 240 00:12:41,240 --> 00:12:44,840 Speaker 2: Surely, did Elon Musk and his team of very expensive 241 00:12:44,920 --> 00:12:48,240 Speaker 2: lawyers not understand the law or something when they followed 242 00:12:48,280 --> 00:12:48,640 Speaker 2: the suit? 243 00:12:49,480 --> 00:12:50,120 Speaker 6: Nosk? 244 00:12:50,200 --> 00:12:53,400 Speaker 11: You know, so Musk of course had these fabulous lawyers, 245 00:12:53,960 --> 00:12:57,960 Speaker 11: and you know that the statute of limitations is told 246 00:12:58,080 --> 00:13:01,440 Speaker 11: if you've concealed, if harm has been concealed, or you 247 00:13:01,480 --> 00:13:05,240 Speaker 11: haven't discovered the harm. And so his argument was, well, 248 00:13:05,280 --> 00:13:07,559 Speaker 11: you know, Sam Altman and open Ai have been lying 249 00:13:07,600 --> 00:13:10,439 Speaker 11: to me. They concealed that they were doing all this stuff. 250 00:13:10,440 --> 00:13:14,160 Speaker 11: It wasn't until twenty twenty three that I really understood 251 00:13:14,280 --> 00:13:16,640 Speaker 11: what was going on. And so this and this gets 252 00:13:16,640 --> 00:13:19,160 Speaker 11: back to your original question, which is why did this 253 00:13:19,240 --> 00:13:20,000 Speaker 11: go to a jury? 254 00:13:20,120 --> 00:13:20,360 Speaker 7: Right? 255 00:13:20,760 --> 00:13:24,000 Speaker 11: The jury had to sift through testimony from both parties, 256 00:13:24,520 --> 00:13:27,760 Speaker 11: documents and to try to understand this timing question. Okay, 257 00:13:27,800 --> 00:13:31,120 Speaker 11: so when was it that Musk knew or reasonably should 258 00:13:31,120 --> 00:13:34,400 Speaker 11: have known about the harm that he was claiming happened. 259 00:13:36,200 --> 00:13:40,720 Speaker 3: And instead we all get consumed with billionaire versus billionaire 260 00:13:41,040 --> 00:13:43,680 Speaker 3: and how much equity they own in the underlying company 261 00:13:43,960 --> 00:13:46,240 Speaker 3: and what they're like in terms of managers or how 262 00:13:46,280 --> 00:13:48,920 Speaker 3: trustworthy they are at the end of the day, when 263 00:13:48,920 --> 00:13:52,000 Speaker 3: we misguided to think that when they go back to 264 00:13:52,040 --> 00:13:54,600 Speaker 3: an appeals court, will it be argued in a different way. 265 00:13:56,240 --> 00:14:00,199 Speaker 11: Yeah, So it's a little disappointing that we didn't get 266 00:14:00,200 --> 00:14:03,360 Speaker 11: an answer on the big question here, right, that the 267 00:14:04,200 --> 00:14:07,200 Speaker 11: claims that I was telling you about just now are 268 00:14:07,400 --> 00:14:10,960 Speaker 11: are unaddressed, and in this appeal we won't get any 269 00:14:11,000 --> 00:14:13,679 Speaker 11: resolution on this either. So you know, this is going 270 00:14:13,720 --> 00:14:17,679 Speaker 11: to be a narrow appeal on this decision of the 271 00:14:17,720 --> 00:14:21,440 Speaker 11: statute of limitations. And because that statute of limitations question 272 00:14:22,000 --> 00:14:26,000 Speaker 11: turned on a factual determination, you know that is going 273 00:14:26,040 --> 00:14:29,440 Speaker 11: to be really hard to be overturned. Public courts rarely 274 00:14:29,560 --> 00:14:34,080 Speaker 11: overturn a jury's fact intensive findings, especially in this case 275 00:14:34,120 --> 00:14:37,280 Speaker 11: where the judge also reviewed the evidence and said I 276 00:14:37,320 --> 00:14:40,520 Speaker 11: came to the same conclusion. And so the only path 277 00:14:40,560 --> 00:14:43,600 Speaker 11: four from USK is going to be arguing a different 278 00:14:43,600 --> 00:14:46,280 Speaker 11: set of things about you know, the argument would have 279 00:14:46,320 --> 00:14:54,080 Speaker 11: to be some error that proud come here, improper instructions 280 00:14:54,240 --> 00:14:56,760 Speaker 11: or improper evidentiary rulings. 281 00:14:57,080 --> 00:14:59,360 Speaker 2: We're having some technical issues, or if you with your zoom. 282 00:14:59,440 --> 00:15:03,240 Speaker 2: So give the Internet gremlins a second to reset. But 283 00:15:03,280 --> 00:15:05,160 Speaker 2: my question to you is exactly that, I don't want 284 00:15:05,200 --> 00:15:06,040 Speaker 2: you to speculate. 285 00:15:06,720 --> 00:15:09,240 Speaker 4: But what options does mister. 286 00:15:09,080 --> 00:15:11,320 Speaker 2: Musk have if he needs to go to the appeals 287 00:15:11,360 --> 00:15:14,600 Speaker 2: court but argue something completely different to the original case. 288 00:15:16,360 --> 00:15:19,360 Speaker 11: Yeah, his argument is going to have to be that 289 00:15:20,040 --> 00:15:25,120 Speaker 11: there were significant legal or procedural errors that prejudice the outcome. 290 00:15:25,600 --> 00:15:29,080 Speaker 11: You know that there the instructions to the jury were improper, 291 00:15:29,960 --> 00:15:33,840 Speaker 11: their evidentiary rulings were wrongly decided. There won't be any 292 00:15:34,280 --> 00:15:39,880 Speaker 11: discussion on the substance of this larger question of whether 293 00:15:39,960 --> 00:15:43,080 Speaker 11: or not Opening I violated its charitable purpose when it 294 00:15:43,120 --> 00:15:45,920 Speaker 11: restructured to create a for profit affiliate. 295 00:15:46,080 --> 00:15:49,400 Speaker 3: Called a public opinion. Therefore, in many ways, Dorothy Lund, 296 00:15:49,480 --> 00:15:52,720 Speaker 3: Professor at Columbia Law School, fantastic to have you. Thank you. 297 00:15:52,800 --> 00:15:56,240 Speaker 3: Coming up, Parallel is moving past the era of flat 298 00:15:56,280 --> 00:16:00,160 Speaker 3: f AI licensing deals. The CEO Arak Agrawal is me 299 00:16:00,280 --> 00:16:02,640 Speaker 3: joining us. Next this is blue bag Tech. 300 00:16:16,760 --> 00:16:20,000 Speaker 2: Parallel, a web API purpose built for AI and led 301 00:16:20,000 --> 00:16:23,400 Speaker 2: by the FORMACYO of Twitter, is launching Index today, a 302 00:16:23,440 --> 00:16:27,600 Speaker 2: new marketplace that pays publishers and data providers based on 303 00:16:27,640 --> 00:16:31,000 Speaker 2: how much their content actually helps an AI agent completed task. 304 00:16:31,400 --> 00:16:34,960 Speaker 2: Joining us is Parallel, founder and CEO Para Agrowl. So, 305 00:16:35,000 --> 00:16:37,200 Speaker 2: in answer to the question what did you get done 306 00:16:37,240 --> 00:16:43,160 Speaker 2: this week? You launched Index in this era of agentic right, 307 00:16:43,720 --> 00:16:48,120 Speaker 2: the agents need the web data and the proprietary data sets. 308 00:16:49,200 --> 00:16:52,520 Speaker 2: I guess saw a gap where the marketplace for that 309 00:16:52,600 --> 00:16:54,560 Speaker 2: data doesn't exist. Then now you think it does. 310 00:16:55,520 --> 00:17:00,520 Speaker 12: Yeah, we've been linked towards this content marketplace for the 311 00:17:00,560 --> 00:17:02,760 Speaker 12: last two and a half years. We've started Parallel with 312 00:17:02,800 --> 00:17:05,919 Speaker 12: the notion that agents will use all of the content 313 00:17:05,920 --> 00:17:09,280 Speaker 12: on the web thousand x more than humans ever have. 314 00:17:09,840 --> 00:17:13,200 Speaker 12: And when that happens, the technology that we've built over 315 00:17:13,240 --> 00:17:16,640 Speaker 12: the last couple of decades isn't enough. But the business 316 00:17:16,680 --> 00:17:20,760 Speaker 12: models also break down. Adds, subscriptions, these business models no 317 00:17:20,840 --> 00:17:25,199 Speaker 12: longer work, and that I think is the opportunity for 318 00:17:25,280 --> 00:17:27,960 Speaker 12: us to figure out how to align the interests of 319 00:17:28,280 --> 00:17:32,320 Speaker 12: AI agents doing real work along with the content owners 320 00:17:32,359 --> 00:17:35,680 Speaker 12: who are creating valuable content that feeds these agents. 321 00:17:36,400 --> 00:17:38,040 Speaker 7: And that's what Index is all about. 322 00:17:37,920 --> 00:17:40,159 Speaker 2: To help the Boomberg Tech audience out. The question what 323 00:17:40,240 --> 00:17:41,879 Speaker 2: did you get done this week? Was posed to you 324 00:17:41,920 --> 00:17:45,919 Speaker 2: by mister musk just before you left Twitter. How do 325 00:17:45,920 --> 00:17:48,560 Speaker 2: you assign a value then to the data? You've called 326 00:17:48,560 --> 00:17:52,480 Speaker 2: this index very interesting. Right on the one side you 327 00:17:52,520 --> 00:17:56,280 Speaker 2: have the agent or the entity the agent is acting. 328 00:17:56,080 --> 00:17:56,760 Speaker 4: On behalf of. 329 00:17:57,200 --> 00:17:58,640 Speaker 2: And then on the other side you have the source 330 00:17:58,640 --> 00:18:01,439 Speaker 2: of the data who sets up price or value. 331 00:18:01,640 --> 00:18:04,160 Speaker 12: Yeah, So if you think about other deals that are 332 00:18:04,160 --> 00:18:08,520 Speaker 12: being done, these deals are typically between a large lab 333 00:18:08,880 --> 00:18:13,639 Speaker 12: or a hyperscaler or Google alongside a large publishing house. 334 00:18:13,680 --> 00:18:17,600 Speaker 12: And these are flat feed deals where over time, I 335 00:18:17,640 --> 00:18:20,840 Speaker 12: don't believe content owners get to participate in the growing 336 00:18:21,000 --> 00:18:24,760 Speaker 12: economy of agents. Now, what's really happening with agents is 337 00:18:25,200 --> 00:18:29,040 Speaker 12: they are starting to do real work, real knowledge work 338 00:18:29,080 --> 00:18:31,480 Speaker 12: in the enterprise. So if you think about our customers, 339 00:18:31,480 --> 00:18:35,719 Speaker 12: our customers span people building AI scientists. Our customers are 340 00:18:35,720 --> 00:18:40,200 Speaker 12: building air lawyers, customers are building for finance using agents, 341 00:18:40,920 --> 00:18:44,159 Speaker 12: and all of these agents that our customers built are 342 00:18:44,160 --> 00:18:48,920 Speaker 12: doing real work when they produce value. Content owners don't 343 00:18:48,920 --> 00:18:52,720 Speaker 12: get to participate in that economy of the value being. 344 00:18:52,600 --> 00:18:54,320 Speaker 7: Created with index. 345 00:18:54,359 --> 00:18:57,280 Speaker 12: What we're trying to do is buil a system where 346 00:18:57,840 --> 00:19:02,480 Speaker 12: if very valuable work is done using somebody's data, they 347 00:19:03,040 --> 00:19:07,960 Speaker 12: get more money. If someone has very high quality data, 348 00:19:08,359 --> 00:19:11,840 Speaker 12: they get paid more. And we've built a model to 349 00:19:12,000 --> 00:19:15,320 Speaker 12: figure out what this value is, what this marginal contribution is, 350 00:19:15,840 --> 00:19:18,920 Speaker 12: and it draws from this game theorty concept called Shapi values. 351 00:19:19,600 --> 00:19:22,639 Speaker 3: Let's talk about the shapely value. It's contribution to the 352 00:19:22,680 --> 00:19:25,159 Speaker 3: work performed by an AI agent. So basically, instead of 353 00:19:25,680 --> 00:19:29,280 Speaker 3: content access crawls or citations, you consider the value. I mean, 354 00:19:29,880 --> 00:19:32,560 Speaker 3: but where's the monetary number coming from? What are you 355 00:19:32,640 --> 00:19:36,639 Speaker 3: pegging it against? And how how do you work out what? 356 00:19:36,760 --> 00:19:39,720 Speaker 3: Then ADII agent has eventually learned itself. 357 00:19:39,359 --> 00:19:41,040 Speaker 7: Off for well. 358 00:19:41,119 --> 00:19:43,879 Speaker 12: Shappie values has been my obsession for the last three 359 00:19:43,960 --> 00:19:46,719 Speaker 12: years as we've been working on this. So just in 360 00:19:46,720 --> 00:19:49,200 Speaker 12: a sentence, what shappily values is is about like if 361 00:19:49,560 --> 00:19:53,399 Speaker 12: a few people decide to collaborate on building something in 362 00:19:53,440 --> 00:19:55,600 Speaker 12: a positive some way where the whole is bigger than 363 00:19:55,640 --> 00:19:58,359 Speaker 12: the sum of parts. The question is how do you 364 00:19:58,400 --> 00:20:01,080 Speaker 12: divide up this value? And schappely value is the concept 365 00:20:01,119 --> 00:20:03,679 Speaker 12: that tells us the way to divide this value so 366 00:20:03,720 --> 00:20:09,199 Speaker 12: that everyone is incentivized to participate. The net outcome of 367 00:20:09,400 --> 00:20:13,920 Speaker 12: using this concept two reward content owners. It creates three 368 00:20:14,000 --> 00:20:18,000 Speaker 12: really nice properties. One high quality content gets paid more. 369 00:20:18,480 --> 00:20:22,399 Speaker 12: Number two, content that gets used in high value work 370 00:20:22,640 --> 00:20:26,600 Speaker 12: gets paid more. And most importantly, as agents do more 371 00:20:26,680 --> 00:20:31,320 Speaker 12: work and create more value, content owners grow alongside them, 372 00:20:31,680 --> 00:20:35,800 Speaker 12: which we believe allows content creation on the web to 373 00:20:35,800 --> 00:20:36,520 Speaker 12: be sustainable. 374 00:20:37,280 --> 00:20:39,639 Speaker 3: There are a lot of content creators out there right now, 375 00:20:40,320 --> 00:20:42,480 Speaker 3: So how do they sign up to INEX? How do 376 00:20:42,520 --> 00:20:45,120 Speaker 3: you make sure that it's not just quite large either? 377 00:20:45,200 --> 00:20:46,480 Speaker 6: Individuals who are. 378 00:20:46,440 --> 00:20:48,720 Speaker 3: Writing I think of alex Etho is actually those that 379 00:20:48,760 --> 00:20:51,560 Speaker 3: have got a minor substack, but with really valuable content 380 00:20:51,560 --> 00:20:53,280 Speaker 3: that somehow the agent has managed to access. 381 00:20:53,280 --> 00:20:54,199 Speaker 6: How do they access that? 382 00:20:55,440 --> 00:20:55,720 Speaker 7: Yeah? 383 00:20:55,760 --> 00:21:00,000 Speaker 12: With Index Today we're announcing collection of launch partners. These 384 00:21:00,080 --> 00:21:04,480 Speaker 12: span premium news publishers like The Atlantic and Fortune to 385 00:21:05,160 --> 00:21:09,080 Speaker 12: really raw, factual, amazing content from the pr Newswire, business 386 00:21:09,160 --> 00:21:14,119 Speaker 12: intelligence and data providers like pitch Bork and Traction and 387 00:21:14,200 --> 00:21:18,800 Speaker 12: zoom Info, as well as independent creators like Alex Heath, 388 00:21:19,440 --> 00:21:23,879 Speaker 12: Azimazer as several others. The really interesting part about this 389 00:21:24,160 --> 00:21:30,640 Speaker 12: is that we want everyone, all content owners big and small, 390 00:21:31,280 --> 00:21:33,439 Speaker 12: to be able to participate in this economy, and we 391 00:21:33,480 --> 00:21:36,640 Speaker 12: want all agents, not just once built by large providers, 392 00:21:36,680 --> 00:21:38,560 Speaker 12: to be able to get access to all of this 393 00:21:38,600 --> 00:21:39,280 Speaker 12: amazing content. 394 00:21:39,359 --> 00:21:42,240 Speaker 2: Correct, We just have sixty seconds, but can ask you 395 00:21:42,280 --> 00:21:45,040 Speaker 2: about x the platform known as Twitter. Of course, still 396 00:21:45,119 --> 00:21:48,399 Speaker 2: using it, your view of it and as part of 397 00:21:48,520 --> 00:21:49,800 Speaker 2: XAI is part of SpaceX. 398 00:21:51,200 --> 00:21:53,240 Speaker 7: I'm still using it. I'm still calling it Twitter. 399 00:21:54,280 --> 00:21:57,399 Speaker 12: I think we built a product over a decade that 400 00:21:57,480 --> 00:22:03,680 Speaker 12: I was there which endures in value forever, and I'm 401 00:22:03,720 --> 00:22:06,000 Speaker 12: really proud of the platform that we created. 402 00:22:06,040 --> 00:22:07,560 Speaker 7: It allows the theme of. 403 00:22:08,400 --> 00:22:11,960 Speaker 12: Content access and democratization out in the open versus and 404 00:22:12,000 --> 00:22:15,400 Speaker 12: permission groups, and that theme is what carries forward into 405 00:22:15,480 --> 00:22:18,280 Speaker 12: my current company, Parallel, where the goal is to have 406 00:22:18,520 --> 00:22:21,879 Speaker 12: as much content as possible out in the open, for 407 00:22:22,119 --> 00:22:25,479 Speaker 12: everyone to have access to, everyone's agent to have access 408 00:22:25,520 --> 00:22:27,880 Speaker 12: to instead of just the. 409 00:22:27,800 --> 00:22:32,200 Speaker 3: Few, and be compensated Paral Else, CEO Aragagowel is great 410 00:22:32,240 --> 00:22:33,719 Speaker 3: to have some time with you. Thank you for coming on. 411 00:22:34,160 --> 00:22:34,360 Speaker 6: Now. 412 00:22:34,359 --> 00:22:37,480 Speaker 3: Coming out, we dive into metas mega data center plans 413 00:22:37,480 --> 00:22:40,720 Speaker 3: in Louisiana and some the hiccups along the way from 414 00:22:40,720 --> 00:22:43,200 Speaker 3: New York from San Francisco, there's a Bluema. 415 00:22:42,960 --> 00:23:03,800 Speaker 4: Tech welcome back to Bloomberg Tech. 416 00:23:03,840 --> 00:23:07,160 Speaker 2: There's still a semiconductor story in financial markets and looking 417 00:23:07,160 --> 00:23:10,160 Speaker 2: at the socks or the Philadelphia Semiconductor index. We're down 418 00:23:10,160 --> 00:23:11,240 Speaker 2: for a third straight day. 419 00:23:11,880 --> 00:23:13,040 Speaker 4: A part of this is. 420 00:23:13,080 --> 00:23:15,240 Speaker 2: Kind of a breather after what we were calling the 421 00:23:15,280 --> 00:23:18,399 Speaker 2: melt up. The performance in chip stocks since the end 422 00:23:18,400 --> 00:23:22,199 Speaker 2: of March was astonishing. But there's also this element that 423 00:23:22,240 --> 00:23:25,440 Speaker 2: we are really close to correction territory in three days. 424 00:23:25,480 --> 00:23:27,200 Speaker 2: At one point in the session, we were down almost 425 00:23:27,200 --> 00:23:29,600 Speaker 2: ten percent. Its karra Of pointed out earlier. In Vidia 426 00:23:29,760 --> 00:23:32,080 Speaker 2: is a part of it, right, and the idea that 427 00:23:32,160 --> 00:23:35,359 Speaker 2: posts President Trump's visit to China. We don't really have 428 00:23:35,400 --> 00:23:37,800 Speaker 2: a net conclusion of what's going on, but we get 429 00:23:37,840 --> 00:23:41,280 Speaker 2: in Video earnings on Wednesday, so fresh off his trip 430 00:23:41,320 --> 00:23:43,880 Speaker 2: to Beijing and Video CEO Jensen Wong says he's confident 431 00:23:44,240 --> 00:23:46,760 Speaker 2: the Chinese market will eventually open back up. 432 00:23:46,760 --> 00:23:47,480 Speaker 4: To his company. 433 00:23:47,520 --> 00:23:51,359 Speaker 2: Delceo and Michael Dell shared that optimism for economic collaboration 434 00:23:51,440 --> 00:23:53,520 Speaker 2: as well. Here's what both leaders told me at Del 435 00:23:53,560 --> 00:23:55,280 Speaker 2: World in Las Vegas yesterday. 436 00:23:56,760 --> 00:24:01,280 Speaker 9: The President once America the wind everywhere right. The President 437 00:24:01,359 --> 00:24:05,760 Speaker 9: wants America to lead the Ai revolution, and so h 438 00:24:05,840 --> 00:24:08,639 Speaker 9: two hundreds are licensed to sell to China. 439 00:24:09,480 --> 00:24:11,560 Speaker 7: The Chinese. The Chinese government. 440 00:24:11,240 --> 00:24:14,359 Speaker 9: Has to decide how much of their local market do 441 00:24:14,400 --> 00:24:16,480 Speaker 9: they want to protect and how much of their local 442 00:24:16,520 --> 00:24:19,000 Speaker 9: market do they want to expand with more a more 443 00:24:19,040 --> 00:24:22,399 Speaker 9: AI capacity. My sense is that the demand in China 444 00:24:22,520 --> 00:24:26,280 Speaker 9: is so incredible, just like it is here. Agentic Ai 445 00:24:26,480 --> 00:24:29,960 Speaker 9: is also making enormous progress there. My sense is that 446 00:24:30,080 --> 00:24:34,760 Speaker 9: over time the market will open. President she was very 447 00:24:34,760 --> 00:24:38,399 Speaker 9: clear that he wants China to be an even wider 448 00:24:38,520 --> 00:24:44,359 Speaker 9: open market. Premier Lee Chang was very straightforward and to 449 00:24:44,520 --> 00:24:48,399 Speaker 9: explain very eloquently that that China will be an open market. 450 00:24:48,440 --> 00:24:50,240 Speaker 7: So I'm looking forward to China be in a more. 451 00:24:50,119 --> 00:24:52,800 Speaker 2: Open clarified as you were able to meet with those 452 00:24:52,840 --> 00:24:56,440 Speaker 2: officials directly to discuss whether or not you can sell 453 00:24:56,520 --> 00:24:58,320 Speaker 2: to those Chinese tech companies. 454 00:24:58,440 --> 00:24:58,680 Speaker 7: I did. 455 00:24:58,760 --> 00:25:01,920 Speaker 9: I didn't discuss directly with them about age two hundred, right. 456 00:25:01,960 --> 00:25:03,960 Speaker 9: I was there to represent the United States and I 457 00:25:04,040 --> 00:25:05,959 Speaker 9: was honored to do so. I was there to support 458 00:25:06,000 --> 00:25:07,240 Speaker 9: President Trump and. 459 00:25:07,520 --> 00:25:08,560 Speaker 7: Really glad to do so. 460 00:25:09,320 --> 00:25:12,800 Speaker 9: But that was really the focus of my trip. President 461 00:25:12,880 --> 00:25:17,240 Speaker 9: Trump had some conversations with the leaders, and I'm looking 462 00:25:17,240 --> 00:25:18,879 Speaker 9: forward to what they decided. 463 00:25:19,200 --> 00:25:21,240 Speaker 2: Michael, you did not go to China, but I think 464 00:25:21,280 --> 00:25:23,680 Speaker 2: what's interesting is you are a member of the President's 465 00:25:23,680 --> 00:25:28,440 Speaker 2: Council advises for science and technology, as is Jensen, your 466 00:25:28,600 --> 00:25:31,840 Speaker 2: net conclusion on whether or not China will become open 467 00:25:32,240 --> 00:25:35,240 Speaker 2: to American technology companies to do business there. 468 00:25:36,840 --> 00:25:39,119 Speaker 4: You know, we have a bus in China. 469 00:25:39,200 --> 00:25:44,000 Speaker 13: Obviously we comply with all the restrictions and you know, 470 00:25:44,640 --> 00:25:46,639 Speaker 13: various controls that are in place. 471 00:25:47,400 --> 00:25:49,320 Speaker 4: But I hope that there's. 472 00:25:49,240 --> 00:25:52,800 Speaker 13: More economic collaboration between the United States and China that 473 00:25:52,920 --> 00:25:57,840 Speaker 13: ultimately is will lead to greater outcomes and prosperity for everyone. 474 00:25:58,080 --> 00:26:02,199 Speaker 13: And you know, a greater likely heard of a successful 475 00:26:02,240 --> 00:26:06,359 Speaker 13: relationship between the countries and around the world. 476 00:26:07,240 --> 00:26:09,560 Speaker 3: Dell CEO Michael Dale, of course, and Video CEO Jensen, 477 00:26:09,560 --> 00:26:12,840 Speaker 3: And I'm speaking there with Ed yesterday. And let's just 478 00:26:12,920 --> 00:26:18,040 Speaker 3: revisit today's big number with Meta reassigning seven thousand workers 479 00:26:18,080 --> 00:26:20,800 Speaker 3: to new jobs. Of course they're related to AI. An 480 00:26:20,800 --> 00:26:24,320 Speaker 3: internal memo from Chief people Officer Janelle Gale, viewed by Bloomberg, 481 00:26:24,440 --> 00:26:27,399 Speaker 3: Reveal's employees will move into one of several new groups 482 00:26:27,440 --> 00:26:30,240 Speaker 3: focused on AI related products, including agents and apps. The 483 00:26:30,280 --> 00:26:33,960 Speaker 3: new corporate structure will quote be flatter and have smaller teams. 484 00:26:34,400 --> 00:26:36,280 Speaker 3: Now this comes as the company is getting ready, of course, 485 00:26:36,280 --> 00:26:38,639 Speaker 3: to cup ten percent of its staff this week as 486 00:26:38,680 --> 00:26:41,800 Speaker 3: part of an effort to, as they say, improve efficiency ed. 487 00:26:42,520 --> 00:26:42,720 Speaker 1: Yeah. 488 00:26:42,760 --> 00:26:46,080 Speaker 2: Sticking with Meta's big AI plans, Mark Zuckerberg's plan to 489 00:26:46,119 --> 00:26:50,640 Speaker 2: build the world's biggest AI facility has enmeshed his company 490 00:26:50,680 --> 00:26:55,000 Speaker 2: deeply in Louisiana's politics and economy, the projects providing an 491 00:26:55,000 --> 00:26:58,080 Speaker 2: economic boost to one of the nation's poorest regions, but 492 00:26:58,200 --> 00:27:02,359 Speaker 2: also bringing a host of growing Bloomberg's Riley Griffin joins 493 00:27:02,440 --> 00:27:05,840 Speaker 2: us more with the big take, and there's big readership 494 00:27:05,880 --> 00:27:09,080 Speaker 2: on this, a lot of interest. You did what every 495 00:27:09,119 --> 00:27:12,440 Speaker 2: good Beat reporter does. You go there, you see what's 496 00:27:12,440 --> 00:27:16,000 Speaker 2: happening on the ground. The headline is two hundred billion dollars, right, 497 00:27:16,040 --> 00:27:19,800 Speaker 2: that's the commitment. What is happening with Meta in Louisiana. 498 00:27:19,920 --> 00:27:22,240 Speaker 14: Yeah, A really important point here is that two hundred 499 00:27:22,320 --> 00:27:25,080 Speaker 14: billion dollars spend that Meta is thinking about with Richland 500 00:27:25,160 --> 00:27:26,879 Speaker 14: Parish and its data center. 501 00:27:26,920 --> 00:27:29,520 Speaker 6: There is largely about the chips. 502 00:27:29,840 --> 00:27:32,520 Speaker 14: This is going to be a five gigawatt of compute 503 00:27:32,560 --> 00:27:35,679 Speaker 14: capacity data center, another two point five gigawatts to support 504 00:27:35,680 --> 00:27:39,520 Speaker 14: the broader campus, which is spanning nearly four thousand acres. 505 00:27:39,680 --> 00:27:41,639 Speaker 14: President Donald Trump has said that this is going to 506 00:27:41,680 --> 00:27:45,119 Speaker 14: be a Manhattan sized project after speaking with Mark Zuckerberg. 507 00:27:45,440 --> 00:27:47,240 Speaker 14: But a lot of that spend isn't going directly into 508 00:27:47,280 --> 00:27:50,120 Speaker 14: the community. It's going into the chips inside the facilities. 509 00:27:50,520 --> 00:27:52,720 Speaker 14: And we still have to ask the question, what does 510 00:27:52,720 --> 00:27:55,399 Speaker 14: this mean for the people of Richland Parish in the 511 00:27:55,440 --> 00:27:59,600 Speaker 14: Louisiana Delta, a region that has struggled for decades and 512 00:27:59,640 --> 00:28:03,680 Speaker 14: where farmers are really losing four hundred dollars on every 513 00:28:03,680 --> 00:28:04,680 Speaker 14: acre of cotton they sell. 514 00:28:04,760 --> 00:28:07,679 Speaker 3: Right now, Well, let's go to Dustin Morris, because you 515 00:28:07,720 --> 00:28:11,680 Speaker 3: paint a picture of how his economic reality is currently. 516 00:28:11,720 --> 00:28:15,440 Speaker 3: He's a soybean, a corn farmer, and he's flying around 517 00:28:16,080 --> 00:28:18,439 Speaker 3: his own land trying to work out whether it's the 518 00:28:18,520 --> 00:28:20,359 Speaker 3: right time to be selling out right. 519 00:28:21,440 --> 00:28:24,680 Speaker 14: Yeah, it's a story of expected land rush right as 520 00:28:24,760 --> 00:28:27,159 Speaker 14: with all of the data center stories of this size 521 00:28:27,160 --> 00:28:30,840 Speaker 14: around the United States, many of them shifting into rural regions, 522 00:28:30,880 --> 00:28:34,400 Speaker 14: particularly in the South, and people are looking around and wondering, 523 00:28:34,720 --> 00:28:36,840 Speaker 14: you know, do I trade this history, Do I trade 524 00:28:36,880 --> 00:28:40,440 Speaker 14: this land? Do I trade this crop for a data center? 525 00:28:41,360 --> 00:28:43,320 Speaker 14: And it was I mean, it was really an honor 526 00:28:43,360 --> 00:28:45,680 Speaker 14: to be able to go multiple times to Richland Parish 527 00:28:45,680 --> 00:28:48,640 Speaker 14: and hear their concerns and think about the way they're 528 00:28:48,800 --> 00:28:51,960 Speaker 14: looking at Meta as a lifeline, and I over. 529 00:28:52,280 --> 00:28:55,200 Speaker 2: Was jumping what a Meta saying about that? Like was 530 00:28:55,240 --> 00:28:58,240 Speaker 2: Meta's response to it? And what do people genuinely feel 531 00:28:58,240 --> 00:29:00,560 Speaker 2: about the company even where it's made a building there. 532 00:29:01,040 --> 00:29:01,960 Speaker 6: Well, it took some time. 533 00:29:02,040 --> 00:29:04,000 Speaker 14: Meta hashed out this deal over the course of a 534 00:29:04,080 --> 00:29:07,200 Speaker 14: year in private. Meta is certainly saying they want to 535 00:29:07,240 --> 00:29:09,280 Speaker 14: be there in the thick of it with the community. 536 00:29:09,280 --> 00:29:12,200 Speaker 14: They're investing in the schools. There's actually a school at 537 00:29:12,200 --> 00:29:15,560 Speaker 14: the corner of the data center. There are discussions about 538 00:29:15,560 --> 00:29:17,800 Speaker 14: whether it should be moved. I have heard that Meta 539 00:29:17,880 --> 00:29:22,840 Speaker 14: is reviewing plans, so they're in the trenches. But notably, 540 00:29:22,840 --> 00:29:25,080 Speaker 14: Meta is only bringing five hundred jobs. This is a 541 00:29:25,080 --> 00:29:27,880 Speaker 14: community that's looked for five thousand over the years from 542 00:29:27,920 --> 00:29:31,040 Speaker 14: an auto manufacturer and have been unable to court that. 543 00:29:31,200 --> 00:29:34,000 Speaker 14: So will five hundred jobs be enough to provide that 544 00:29:34,080 --> 00:29:35,800 Speaker 14: economic lifeline long term? 545 00:29:35,960 --> 00:29:36,600 Speaker 6: I'm not sure. 546 00:29:37,360 --> 00:29:40,360 Speaker 2: To the's Riley Griffin with the big take top of 547 00:29:40,400 --> 00:29:42,959 Speaker 2: the website and the Bloomberg terminal carry there's plenty more 548 00:29:43,000 --> 00:29:43,800 Speaker 2: news out there today. 549 00:29:43,920 --> 00:29:45,760 Speaker 3: There is It's time now for talking tech ed and 550 00:29:45,800 --> 00:29:49,880 Speaker 3: first up Chinese AI pioneer Moonshot is restructuring to secure 551 00:29:49,920 --> 00:29:52,120 Speaker 3: its path to public markets now. The developer of the 552 00:29:52,120 --> 00:29:55,440 Speaker 3: popular kidney chatbot has informed investors it will revamp its 553 00:29:55,440 --> 00:29:58,160 Speaker 3: corporate structure to comply with Beijing's rules and pave the 554 00:29:58,200 --> 00:30:01,440 Speaker 3: way for a highly anticipated Hong Kong ipo. Speaking of which, 555 00:30:01,640 --> 00:30:05,560 Speaker 3: Chinese robotics Unicorn Lingobot is exploring a Hong Kong ipo 556 00:30:05,680 --> 00:30:07,800 Speaker 3: that could launch as early as this year, and the 557 00:30:07,840 --> 00:30:11,160 Speaker 3: maker of highly dexterous robotic hands is targeting a six 558 00:30:11,160 --> 00:30:14,640 Speaker 3: billion dollar evaluation and counts Samsung ten Cent among its clients. 559 00:30:15,360 --> 00:30:21,320 Speaker 3: And China's demographic time clock is triggering an unprecedented automation boom. 560 00:30:21,560 --> 00:30:24,880 Speaker 3: A new report from Barkley's shows humanoid robots could offset 561 00:30:24,920 --> 00:30:27,480 Speaker 3: as much as sixty percent of that labor decline by 562 00:30:27,480 --> 00:30:29,360 Speaker 3: twenty thirty five, turning. 563 00:30:29,040 --> 00:30:32,080 Speaker 6: What is a structural crisis into a hardware gold rush. 564 00:30:32,440 --> 00:30:35,520 Speaker 2: Ed Coming up, we're going to go live from the 565 00:30:35,600 --> 00:30:38,480 Speaker 2: JP Morgan Tech Media and Communications Conference, and we'll hear 566 00:30:38,520 --> 00:30:42,320 Speaker 2: from Lori Beer, JP Morgan Global c Io that conversation. 567 00:30:42,440 --> 00:30:44,000 Speaker 4: Next this is Bloomberg Tech. 568 00:30:56,800 --> 00:30:59,400 Speaker 2: Let's get out to the JP Morgan Tech Media and 569 00:30:59,400 --> 00:31:03,120 Speaker 2: Communication Conference in Boston. Where Bloomberg Surveillance co host Lisa 570 00:31:03,240 --> 00:31:06,280 Speaker 2: Vonovitz is standing by Lisa. 571 00:31:07,760 --> 00:31:09,160 Speaker 6: Thank you so much. Ed. 572 00:31:09,240 --> 00:31:12,800 Speaker 15: I am here at a JP Morgan conference at four Technology, 573 00:31:12,840 --> 00:31:15,240 Speaker 15: Media and Communications, and I'm here with Lori Beer, the 574 00:31:15,320 --> 00:31:17,240 Speaker 15: chief information officer for JP Morgan. 575 00:31:17,280 --> 00:31:19,680 Speaker 6: Laurie, wonderful to speak with you. In many ways, you 576 00:31:19,760 --> 00:31:21,440 Speaker 6: are the woman of the moment. 577 00:31:21,440 --> 00:31:25,360 Speaker 15: Because you have to understand how to oversee change that 578 00:31:25,440 --> 00:31:27,680 Speaker 15: seems to be happening at an accelerating pace. Can you 579 00:31:27,680 --> 00:31:30,520 Speaker 15: give a sense of just how quickly it has accelerated 580 00:31:30,760 --> 00:31:33,400 Speaker 15: from say, six months ago, a year ago to right now. 581 00:31:33,720 --> 00:31:36,960 Speaker 16: Yeah, I think it's an amazing moment, first of all, 582 00:31:36,960 --> 00:31:39,560 Speaker 16: to be in technology and to be in financial services. 583 00:31:40,040 --> 00:31:42,080 Speaker 6: And we definitely saw the. 584 00:31:41,880 --> 00:31:45,120 Speaker 16: Gen one tools come out a couple of years ago. 585 00:31:45,600 --> 00:31:49,280 Speaker 16: The last six months have rapidly accelerated, not only new 586 00:31:49,320 --> 00:31:52,040 Speaker 16: models getting created, but how we're able to apply them. 587 00:31:52,400 --> 00:31:55,600 Speaker 6: And so it really ties back to how. 588 00:31:55,520 --> 00:31:57,800 Speaker 16: Do we think about driving and adapting. 589 00:31:57,400 --> 00:31:58,280 Speaker 6: Change so quickly? 590 00:31:58,600 --> 00:32:01,240 Speaker 15: Yeah, one thing is wered a lot of nervousness, a 591 00:32:01,240 --> 00:32:03,920 Speaker 15: lot of anxiety. There was a story that was out 592 00:32:03,920 --> 00:32:06,800 Speaker 15: this morning about Standard Charter CEO coming out and saying 593 00:32:06,800 --> 00:32:08,880 Speaker 15: they was going to cut fifty percent of staff support 594 00:32:08,960 --> 00:32:14,560 Speaker 15: staff and removing lower value human capital and replacing it 595 00:32:14,880 --> 00:32:18,640 Speaker 15: with AI tools and investment in AI. What's real what's 596 00:32:18,680 --> 00:32:20,280 Speaker 15: not when it comes to sort of the mass job 597 00:32:20,320 --> 00:32:21,520 Speaker 15: cause what's been your experience? 598 00:32:21,720 --> 00:32:26,240 Speaker 16: Yeah, So overall we've seen definitely productivity opportunities. 599 00:32:26,320 --> 00:32:27,200 Speaker 6: So even when we. 600 00:32:27,120 --> 00:32:30,880 Speaker 16: Think about technology, we've seen ten to twenty to thirty 601 00:32:30,920 --> 00:32:34,600 Speaker 16: percent improvement just from the initial generation one tools. As 602 00:32:34,640 --> 00:32:37,480 Speaker 16: you look to agentic you're going to see even more. 603 00:32:37,680 --> 00:32:38,840 Speaker 6: But there's a huge demand. 604 00:32:39,000 --> 00:32:42,120 Speaker 16: There's a huge demand to create new products and services 605 00:32:42,120 --> 00:32:45,440 Speaker 16: and experiences for our customers. We're using AI to figure 606 00:32:45,480 --> 00:32:48,680 Speaker 16: out new products to offer and frankly, when you think 607 00:32:48,720 --> 00:32:51,080 Speaker 16: about the other side of it, the AI is also 608 00:32:51,160 --> 00:32:54,240 Speaker 16: creating increased cybersecurity risks. So how do we think about 609 00:32:54,400 --> 00:32:59,680 Speaker 16: also the accelerating the investments to protect our customers and 610 00:32:59,720 --> 00:33:03,400 Speaker 16: client too. So the demand remains large, you know, significant, 611 00:33:03,400 --> 00:33:05,680 Speaker 16: and we're able to get a lot more done than 612 00:33:05,680 --> 00:33:09,680 Speaker 16: we have historically. How's very is mythos so with mythos 613 00:33:10,520 --> 00:33:14,800 Speaker 16: AI in general, but it's really good on both sides 614 00:33:14,800 --> 00:33:19,800 Speaker 16: of the equation. One to understand vulnerabilities and so the 615 00:33:20,200 --> 00:33:23,719 Speaker 16: key with that and any other AI is how do 616 00:33:23,760 --> 00:33:26,440 Speaker 16: we capture those, how do we clearing house those, how 617 00:33:26,440 --> 00:33:28,160 Speaker 16: do we update those in. 618 00:33:28,120 --> 00:33:30,680 Speaker 6: A safe and secure way. But on the other. 619 00:33:30,560 --> 00:33:33,080 Speaker 16: Side of this, as you apply these models into the 620 00:33:33,080 --> 00:33:36,680 Speaker 16: software development process, we're going to have safer and more 621 00:33:36,720 --> 00:33:37,720 Speaker 16: secure software. 622 00:33:37,920 --> 00:33:39,480 Speaker 6: On the flip side, or cyber. 623 00:33:39,200 --> 00:33:42,440 Speaker 16: Teams can use the power of these models to be 624 00:33:42,480 --> 00:33:47,280 Speaker 16: able to protect, defend, identify fraud, identify. 625 00:33:46,720 --> 00:33:47,960 Speaker 6: Issues more quickly. 626 00:33:48,240 --> 00:33:50,800 Speaker 16: So there's really two sides of this equation to think about. 627 00:33:50,920 --> 00:33:54,440 Speaker 15: Based on how quickly you see technology shifting and what 628 00:33:54,520 --> 00:33:57,200 Speaker 15: tools are really coming to the fore, how does the 629 00:33:57,240 --> 00:34:00,000 Speaker 15: mindset have to shift of the people who work for you. 630 00:34:00,120 --> 00:34:03,360 Speaker 16: Yeah, I think it's a great question. The technology is amazing. 631 00:34:03,680 --> 00:34:08,320 Speaker 16: I think this is truly a shift, a transformational shift 632 00:34:08,320 --> 00:34:11,720 Speaker 16: in technology where this is getting embedded and horizontally across 633 00:34:11,800 --> 00:34:14,040 Speaker 16: all layers of the stack. The harder part is the 634 00:34:14,120 --> 00:34:17,360 Speaker 16: change management in the leadership, and I think really thinking 635 00:34:17,400 --> 00:34:21,040 Speaker 16: about where are things going. We don't know all the answers, 636 00:34:21,080 --> 00:34:24,799 Speaker 16: so how do you navigate change in an uncertainty? It's 637 00:34:24,840 --> 00:34:28,120 Speaker 16: still very early innings, so it's really had to shift 638 00:34:28,200 --> 00:34:30,799 Speaker 16: from how do we help guide our teams, how do 639 00:34:30,840 --> 00:34:34,520 Speaker 16: we make sure our software engineers understand what's coming. 640 00:34:34,840 --> 00:34:36,000 Speaker 6: My management team. 641 00:34:35,840 --> 00:34:37,920 Speaker 16: Spends a lot of time on the weekends building their 642 00:34:37,960 --> 00:34:42,319 Speaker 16: own apps, pick your favorite coding adentic coding tool, but 643 00:34:42,400 --> 00:34:44,960 Speaker 16: it helps them understand what's coming. And I think this 644 00:34:45,040 --> 00:34:48,280 Speaker 16: is a moment where leaders, in addition to software engineers 645 00:34:48,320 --> 00:34:50,840 Speaker 16: truly have to understand the change. And we have to 646 00:34:50,960 --> 00:34:55,280 Speaker 16: understand how do we actually create an environment that allows 647 00:34:55,360 --> 00:34:59,279 Speaker 16: us to pivot and navigate when times are uncertain and 648 00:34:59,360 --> 00:35:01,279 Speaker 16: really make sure don't lose track. 649 00:35:01,080 --> 00:35:02,480 Speaker 6: Of what's really important. 650 00:35:02,880 --> 00:35:06,160 Speaker 16: How do we deliver products and services and experience. 651 00:35:05,800 --> 00:35:07,160 Speaker 6: From our customers and clients. 652 00:35:07,320 --> 00:35:11,400 Speaker 15: Howty balance innovation with risk and the idea of the 653 00:35:11,440 --> 00:35:13,840 Speaker 15: tried and true you need to do this project versus 654 00:35:13,960 --> 00:35:14,960 Speaker 15: moonshot projects. 655 00:35:15,120 --> 00:35:16,799 Speaker 16: Yeah, you know, I think this is one of the 656 00:35:16,800 --> 00:35:19,440 Speaker 16: most interesting times when you have to think about the 657 00:35:19,600 --> 00:35:22,600 Speaker 16: balance of innovation and risk taking. We wouldn't be in 658 00:35:22,719 --> 00:35:24,960 Speaker 16: business as long as we have been if we haven't 659 00:35:25,080 --> 00:35:27,920 Speaker 16: driven a lot of innovation historically. But we're also in 660 00:35:27,960 --> 00:35:30,719 Speaker 16: the business of managing risk, and so I think in 661 00:35:30,760 --> 00:35:34,359 Speaker 16: this moment you have to really think about going at 662 00:35:34,400 --> 00:35:37,279 Speaker 16: the appropriate speed of innovation. Some of these moonshots are 663 00:35:37,280 --> 00:35:41,880 Speaker 16: becoming real and so you have to move aggressively at 664 00:35:41,920 --> 00:35:42,920 Speaker 16: the innovation front. 665 00:35:43,160 --> 00:35:45,480 Speaker 6: But we obviously are a. 666 00:35:45,400 --> 00:35:47,520 Speaker 16: Business of trust, and we have to make sure our 667 00:35:47,520 --> 00:35:50,920 Speaker 16: customers are clients, the bank is protected every day, and 668 00:35:51,000 --> 00:35:54,680 Speaker 16: so finding this balance and actually again using these capabilities 669 00:35:54,719 --> 00:35:56,120 Speaker 16: to also protect ourselves. 670 00:35:56,280 --> 00:35:59,719 Speaker 6: And so it's this really interesting time while along with 671 00:35:59,760 --> 00:36:02,480 Speaker 6: that you don't necessarily know who's going to win in 672 00:36:02,520 --> 00:36:03,279 Speaker 6: the end, and. 673 00:36:03,280 --> 00:36:06,759 Speaker 16: So making sure we're not locking ourselves in to one 674 00:36:06,840 --> 00:36:13,960 Speaker 16: provider or one scenario and we're constantly sandboxing what's on 675 00:36:14,000 --> 00:36:15,200 Speaker 16: the horizon in the future. 676 00:36:15,880 --> 00:36:18,919 Speaker 15: Does it change your decision whether to build or buy 677 00:36:19,200 --> 00:36:21,480 Speaker 15: in terms of different software solutions. 678 00:36:21,560 --> 00:36:23,680 Speaker 16: You know, it's been interesting because over the last several 679 00:36:23,760 --> 00:36:27,760 Speaker 16: years we've made sure that we're really focusing our engineering community, 680 00:36:27,760 --> 00:36:30,200 Speaker 16: and we have a large engineering community on building the 681 00:36:30,280 --> 00:36:33,759 Speaker 16: things that are competitively differentiating and great value for our 682 00:36:33,760 --> 00:36:37,360 Speaker 16: customers and clients. And over time you have to balance 683 00:36:37,520 --> 00:36:41,359 Speaker 16: where that still makes a lot of sense and where 684 00:36:41,400 --> 00:36:44,920 Speaker 16: we focus our energy on those unique products and services 685 00:36:44,960 --> 00:36:48,200 Speaker 16: for our customers and clients. And so when you think 686 00:36:48,280 --> 00:36:50,840 Speaker 16: about the balance of trade and the fact that software 687 00:36:50,880 --> 00:36:55,160 Speaker 16: engineering overall is increasing in speed and it is more 688 00:36:55,160 --> 00:36:57,920 Speaker 16: cost effective when you look at the end to end 689 00:36:57,960 --> 00:37:02,000 Speaker 16: product development life cycle. You know, there are different trade 690 00:37:02,040 --> 00:37:05,640 Speaker 16: offs you would consider in making those build versus buy decisions, 691 00:37:06,000 --> 00:37:08,440 Speaker 16: but you're always going to have some of those, you know, 692 00:37:08,640 --> 00:37:14,480 Speaker 16: enterprise software solutions frankly that aren't competitively differentiated that sort 693 00:37:14,520 --> 00:37:17,680 Speaker 16: of help us run the bank without delivering those unique 694 00:37:17,680 --> 00:37:18,719 Speaker 16: products and services. 695 00:37:18,800 --> 00:37:19,880 Speaker 6: Just twenty seconds. 696 00:37:20,000 --> 00:37:21,040 Speaker 15: Do you think that we're going to be able to 697 00:37:21,080 --> 00:37:23,920 Speaker 15: recognize what banking looks like, say, in ten years. 698 00:37:24,000 --> 00:37:24,560 Speaker 6: I think we're. 699 00:37:24,440 --> 00:37:28,000 Speaker 16: Watching it transform as we speak right now. At the 700 00:37:28,120 --> 00:37:30,120 Speaker 16: end of the day, we have to maintain that trust 701 00:37:30,160 --> 00:37:32,319 Speaker 16: with customers and clients and we have to make sure 702 00:37:32,360 --> 00:37:34,799 Speaker 16: that their unique needs are served across the over one 703 00:37:34,840 --> 00:37:37,759 Speaker 16: hundred countries we operate in. But how we do it 704 00:37:37,800 --> 00:37:40,480 Speaker 16: and the products and services will absolutely evolve over the 705 00:37:40,480 --> 00:37:41,200 Speaker 16: next several years. 706 00:37:41,520 --> 00:37:45,000 Speaker 15: Caroline, that is Lori Beer, the chief information officer at 707 00:37:45,239 --> 00:37:46,000 Speaker 15: GENP Market. 708 00:37:46,040 --> 00:37:46,759 Speaker 6: I'll set it back to you. 709 00:37:47,160 --> 00:37:49,240 Speaker 3: What a great day, what a great set of interviews. 710 00:37:49,239 --> 00:37:52,400 Speaker 3: Thank you New Mexsavana's co hosting ROUMWA. It's there, and 711 00:37:52,680 --> 00:37:54,160 Speaker 3: we've got some bocking news in the meantime. 712 00:37:54,800 --> 00:37:57,240 Speaker 2: Yeah, it's some breaking news on the tunnel about Apple. 713 00:37:57,320 --> 00:38:02,719 Speaker 2: Apple chief hardware Officer Johnny Shreuji, reorganizing hardware development and 714 00:38:02,760 --> 00:38:07,000 Speaker 2: shifting oversight of key functions such as product design partner 715 00:38:07,080 --> 00:38:10,600 Speaker 2: effort to speed up work on future devices. Sources say 716 00:38:10,760 --> 00:38:13,960 Speaker 2: the hardware shakeup is also meant to better integrate teams 717 00:38:13,960 --> 00:38:17,800 Speaker 2: working on in house silicon with those that are creating products. 718 00:38:17,840 --> 00:38:20,160 Speaker 2: And of course Bloomberg's Mark Gum and the one carrier 719 00:38:20,160 --> 00:38:23,560 Speaker 2: that broke the story some other detailed Shelly Goldberg Dave 720 00:38:23,600 --> 00:38:26,759 Speaker 2: Pecula are now going to oversee product design. Maybe not 721 00:38:26,880 --> 00:38:30,279 Speaker 2: names as familiar to the Bloomberg Tech audience, but in 722 00:38:30,320 --> 00:38:32,520 Speaker 2: this new era for Apple, a lot of focus on 723 00:38:32,600 --> 00:38:34,680 Speaker 2: who's right at the top and running the show. 724 00:38:35,040 --> 00:38:37,960 Speaker 3: It will become known ed thanks mean while coming up, Look, 725 00:38:37,960 --> 00:38:40,240 Speaker 3: we're going to be speaking with Armada CEO Dan Wright 726 00:38:40,560 --> 00:38:42,439 Speaker 3: as the company raises to one of thirty million dollars 727 00:38:42,440 --> 00:38:44,919 Speaker 3: in a series be funding boosting his valuation to two 728 00:38:45,000 --> 00:38:48,800 Speaker 3: billion dollars, and some new strategic investors and big projects 729 00:38:48,800 --> 00:38:51,640 Speaker 3: for the hyperscaler for the Edge as a Bloomberg Tech. 730 00:38:58,320 --> 00:39:00,680 Speaker 4: Some more breaking news crossing the Blue terminal. 731 00:39:00,719 --> 00:39:04,680 Speaker 2: Open AI founding member Andre Carpaty says he's joined rival 732 00:39:05,080 --> 00:39:07,719 Speaker 2: and Thropic. In a post on x Carpappy says he 733 00:39:07,760 --> 00:39:10,319 Speaker 2: will be working on R and D with Anthropic and 734 00:39:10,360 --> 00:39:13,920 Speaker 2: be focused on helping train new AI models. Cope previously 735 00:39:13,960 --> 00:39:17,480 Speaker 2: helped lead the team behind Tesla's autopilot system before then 736 00:39:17,520 --> 00:39:20,520 Speaker 2: returning to Open Ai. He left the chat gp maker 737 00:39:20,760 --> 00:39:23,400 Speaker 2: for a second time last year and had launched a 738 00:39:23,440 --> 00:39:26,880 Speaker 2: startup focused on AI and education. In that post, carry 739 00:39:26,880 --> 00:39:29,719 Speaker 2: he says that he's still passionate about education plans to 740 00:39:29,719 --> 00:39:32,040 Speaker 2: resume his work on that, but in the market of 741 00:39:32,080 --> 00:39:34,680 Speaker 2: AI talent, this is a bit of a wow. This 742 00:39:34,719 --> 00:39:37,040 Speaker 2: is a big story that I didn't see coming breaking 743 00:39:37,080 --> 00:39:37,520 Speaker 2: this morning. 744 00:39:38,000 --> 00:39:40,359 Speaker 3: It certainly is. The wows keep coming. Meanwhile, let's talk 745 00:39:40,400 --> 00:39:43,160 Speaker 3: about a fundraise now. Amada has just raised two hundred 746 00:39:43,160 --> 00:39:46,360 Speaker 3: thirty million dollars in an oversubscribed Series B round, bosting 747 00:39:46,400 --> 00:39:48,279 Speaker 3: the company's valuation to a call two billion dollars. But 748 00:39:48,320 --> 00:39:50,880 Speaker 3: the company is also announcing a partnership with Johnson Controls 749 00:39:51,200 --> 00:39:54,919 Speaker 3: to produce modular data centers and they're at scale joining 750 00:39:55,000 --> 00:39:58,200 Speaker 3: us now. I'm oada CEO Dan Wright alsome modular data center? 751 00:39:58,239 --> 00:39:59,160 Speaker 3: How are we going to be making it? 752 00:39:59,600 --> 00:39:59,680 Speaker 4: Ye? 753 00:40:00,040 --> 00:40:02,120 Speaker 17: So much for having me on a modular data center 754 00:40:02,320 --> 00:40:06,400 Speaker 17: is a full stack AI factory that you can deploy anywhere, 755 00:40:06,800 --> 00:40:08,560 Speaker 17: and we can do it with the three s's. We 756 00:40:08,600 --> 00:40:11,399 Speaker 17: call it speed, scale, and sovereignty. You can deploy these 757 00:40:11,440 --> 00:40:15,000 Speaker 17: within weeks. You can scale up with demand versus traditional 758 00:40:15,040 --> 00:40:17,360 Speaker 17: data center sometimes you end up overbuilding or building the 759 00:40:17,360 --> 00:40:20,200 Speaker 17: wrong thing. And then modular data centers also have the 760 00:40:20,239 --> 00:40:22,600 Speaker 17: benefit that you can get sovereignty down to the site level. 761 00:40:22,600 --> 00:40:25,160 Speaker 17: People used to talk about data sovereignty in terms of countries, 762 00:40:25,520 --> 00:40:28,640 Speaker 17: but with increasingly sophisticated cyber attacks, now you want data 763 00:40:28,680 --> 00:40:29,759 Speaker 17: sovereignty at the site level. 764 00:40:31,600 --> 00:40:36,560 Speaker 2: This is all about speed, movement, building funding. This has 765 00:40:36,600 --> 00:40:38,680 Speaker 2: helps you move even quicker or what's the plan here? 766 00:40:39,400 --> 00:40:42,879 Speaker 17: Yeah, So this round was led by co ed by 767 00:40:43,000 --> 00:40:45,960 Speaker 17: Overmatch eighty ninety industries as well as black Rock, and 768 00:40:46,000 --> 00:40:48,640 Speaker 17: really what it allows us to do is scale up 769 00:40:48,680 --> 00:40:52,200 Speaker 17: to accelerate deployment of the USAI stack. As part of this, 770 00:40:52,320 --> 00:40:55,880 Speaker 17: we're also announcing a global agreement with John's Controls, where 771 00:40:55,920 --> 00:40:58,960 Speaker 17: starting with Galleon Forge one in Arizona, we're going to 772 00:40:58,960 --> 00:41:02,879 Speaker 17: do continuous manufacturing of these modular data centers. And if 773 00:41:02,880 --> 00:41:05,080 Speaker 17: you look at them, they kind of look like shipping containers, 774 00:41:05,080 --> 00:41:08,400 Speaker 17: so they're easily transportable anywhere in the world. You can 775 00:41:08,440 --> 00:41:11,360 Speaker 17: deploy them whereever you want. And the nice thing is 776 00:41:11,560 --> 00:41:14,560 Speaker 17: you can use any source of power, and that's obviously important. 777 00:41:14,800 --> 00:41:17,120 Speaker 17: Power is a big concerned when it comes to data centers. 778 00:41:17,280 --> 00:41:20,360 Speaker 17: Energy is distributed. We're saying compute should also be distributed. 779 00:41:20,960 --> 00:41:24,200 Speaker 3: Where's the demand coming from? Exactly who wants these? 780 00:41:25,280 --> 00:41:27,560 Speaker 17: Yeah, so energy companies. We're working with many of the 781 00:41:27,640 --> 00:41:30,440 Speaker 17: largest energy companies in the world. All of them have 782 00:41:30,520 --> 00:41:33,640 Speaker 17: a hard segregation between IT and OT, meaning that the 783 00:41:33,680 --> 00:41:35,640 Speaker 17: only way to do anything with AI is to deploy 784 00:41:35,680 --> 00:41:38,799 Speaker 17: the infrastructure on the rigs, on the refineries and run 785 00:41:38,840 --> 00:41:41,560 Speaker 17: those models behind the firewall at the edge. We're enabling that. 786 00:41:41,800 --> 00:41:43,799 Speaker 17: They're all trying to be fully autonomous over the next 787 00:41:43,800 --> 00:41:47,120 Speaker 17: few years. You need compute locally in order to enable that. 788 00:41:47,560 --> 00:41:50,399 Speaker 17: And then also defense is a huge driver for us. 789 00:41:50,880 --> 00:41:53,319 Speaker 17: For example, with the conflict in the Middle East. We 790 00:41:53,400 --> 00:41:54,920 Speaker 17: got a call from an ally. They said, hey, we 791 00:41:54,960 --> 00:41:57,040 Speaker 17: need one of these like immediately. We were able to 792 00:41:57,040 --> 00:41:59,359 Speaker 17: deploy within days. They called back said we need more. 793 00:41:59,360 --> 00:42:00,440 Speaker 7: We were able to share more. 794 00:42:00,280 --> 00:42:03,360 Speaker 3: Out and who your partners within this data center? I 795 00:42:03,360 --> 00:42:05,600 Speaker 3: can understand that you're helping with the construction of it all, 796 00:42:05,600 --> 00:42:06,879 Speaker 3: but still you need the chips from. 797 00:42:06,840 --> 00:42:08,240 Speaker 7: Someone, you need the service of someone. 798 00:42:08,400 --> 00:42:10,960 Speaker 3: How are you bringing this all together and how open 799 00:42:11,239 --> 00:42:12,680 Speaker 3: are you to different types of partners? 800 00:42:12,719 --> 00:42:15,160 Speaker 17: Well, well, I think that's actually part of the magic 801 00:42:15,200 --> 00:42:17,800 Speaker 17: of Armada is that we built this ecosystem of amazing 802 00:42:17,880 --> 00:42:20,840 Speaker 17: partners and it's kind of like, actually, what the hyperscalers did. 803 00:42:20,719 --> 00:42:21,759 Speaker 7: You know twenty years ago? 804 00:42:22,200 --> 00:42:24,719 Speaker 17: Is you're bringing all of these different services and you're 805 00:42:24,760 --> 00:42:27,880 Speaker 17: making them accessible to more people. That's what Armada is 806 00:42:27,920 --> 00:42:29,799 Speaker 17: doing for the edge is the Hyperscaler for the edge. 807 00:42:29,800 --> 00:42:31,800 Speaker 17: And a good example of this we just announced a 808 00:42:31,800 --> 00:42:36,840 Speaker 17: partnership recently with Microsoft to bring Foundry, bring Azure Local, 809 00:42:37,160 --> 00:42:39,960 Speaker 17: allow them to run these customers to run services that 810 00:42:39,960 --> 00:42:43,080 Speaker 17: they love from Microsoft models that they run from Microsoft 811 00:42:43,480 --> 00:42:44,680 Speaker 17: at the edge air Gap. 812 00:42:44,760 --> 00:42:45,120 Speaker 7: Same thing. 813 00:42:45,120 --> 00:42:47,759 Speaker 17: We announced a partnership with Opening Eye. We announced on 814 00:42:47,840 --> 00:42:52,680 Speaker 17: with Nvidia, Dell as well as Pallenteer bringing all those capabilities. 815 00:42:52,040 --> 00:42:52,520 Speaker 7: To the edge. 816 00:42:52,560 --> 00:42:54,520 Speaker 2: So Dan real quick. I was with Michael Dell and 817 00:42:54,560 --> 00:42:58,800 Speaker 2: jensmong yesterday. The bigger theme is on prem people want 818 00:42:58,840 --> 00:43:01,799 Speaker 2: at the edge facility close to them. Is that an 819 00:43:01,800 --> 00:43:03,880 Speaker 2: addressable market for you. We just have thirty seconds. 820 00:43:03,960 --> 00:43:04,759 Speaker 7: Yeah, absolutely. 821 00:43:04,800 --> 00:43:06,520 Speaker 17: I mean that's what I believe is going to happen, 822 00:43:06,600 --> 00:43:09,359 Speaker 17: is you're going to have sovereign AI factories. And really 823 00:43:09,360 --> 00:43:12,160 Speaker 17: what that means is that every country and every company 824 00:43:12,520 --> 00:43:15,279 Speaker 17: is going to have its own AI factory. And I 825 00:43:15,360 --> 00:43:18,120 Speaker 17: know that Michael and Jensen would agree with me on 826 00:43:18,160 --> 00:43:21,160 Speaker 17: that because they've been saying that publicly. And in order 827 00:43:21,200 --> 00:43:23,399 Speaker 17: to do that, you need both the chips, you need 828 00:43:23,400 --> 00:43:25,640 Speaker 17: the servers, and then you also need the infrastructure, and 829 00:43:25,640 --> 00:43:28,399 Speaker 17: that's what are enabling as the hyperscaler. 830 00:43:27,840 --> 00:43:28,520 Speaker 7: For the edge. 831 00:43:29,280 --> 00:43:32,200 Speaker 2: Then right, I'm out a CEO back on Bloomberg Tech 832 00:43:32,480 --> 00:43:34,279 Speaker 2: after a big fundraise. Great to have you back. Thank 833 00:43:34,320 --> 00:43:37,440 Speaker 2: you very much. That does it for this edition of 834 00:43:37,440 --> 00:43:38,399 Speaker 2: Bloomberg Tech Carrot. 835 00:43:38,719 --> 00:43:41,440 Speaker 3: Yeah, you don't want to forget to check out this podcast. 836 00:43:41,520 --> 00:43:42,440 Speaker 6: What a rich podcast. 837 00:43:42,480 --> 00:43:44,560 Speaker 3: We're going to be having all eyes as we build 838 00:43:44,600 --> 00:43:46,640 Speaker 3: up towards the videos earnings after the bal tomorrow. 839 00:43:46,640 --> 00:43:47,399 Speaker 7: You can find it on. 840 00:43:47,360 --> 00:43:49,799 Speaker 3: The terminal as well as online on Apple, Spotify and 841 00:43:49,880 --> 00:43:53,239 Speaker 3: iHeart ed still a phenomenal interview from yesterday as well. 842 00:43:54,160 --> 00:43:57,239 Speaker 2: Yeah, and you know the market is waiting anticipation for 843 00:43:57,280 --> 00:44:01,640 Speaker 2: the Nvidia Print Wednesday night post melt up in chip stocks. 844 00:44:02,040 --> 00:44:06,600 Speaker 4: Wow, this is Bloomberg Tech. H