1 00:00:00,080 --> 00:00:13,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is alive 2 00:00:13,640 --> 00:00:17,440 Speaker 1: from coast to coast, with Caroline Hyde in New York 3 00:00:17,720 --> 00:00:19,680 Speaker 1: and Eva Low in Sent Francisco. 4 00:00:22,600 --> 00:00:24,280 Speaker 2: This is Bloomberg Tech coming up. 5 00:00:24,320 --> 00:00:29,080 Speaker 3: Microsoft accuses Chinese hackers of exploiting vulnerabilities in its SharePoint 6 00:00:29,200 --> 00:00:31,440 Speaker 3: software that have led to breaches worldwide. 7 00:00:31,560 --> 00:00:34,720 Speaker 4: Plus Open Ai and Oracle deepen their Stargate Data Center 8 00:00:34,800 --> 00:00:38,360 Speaker 4: partnership to power AI work lanes, but no sign of 9 00:00:38,400 --> 00:00:41,400 Speaker 4: soft Bank to help finance the US infrastructure, and. 10 00:00:41,479 --> 00:00:45,159 Speaker 3: NXP gives a not so bullish third quarter forecast, suggesting 11 00:00:45,159 --> 00:00:48,440 Speaker 3: the chip makers still contending with a turbulent industry in 12 00:00:48,479 --> 00:00:51,159 Speaker 3: the face of tariffs. Microsoft our top story in a 13 00:00:51,200 --> 00:00:55,080 Speaker 3: blog post, saying that state sponsored Chinese hackers are behind 14 00:00:55,120 --> 00:00:58,920 Speaker 3: what's happening, and in particular the stock reacting level to 15 00:00:58,960 --> 00:01:01,720 Speaker 3: the early in the session car a normalizing a little bit, 16 00:01:01,840 --> 00:01:02,800 Speaker 3: but we've got to get into it. 17 00:01:02,920 --> 00:01:04,640 Speaker 5: We do. Let's stick with that Microsoft story. 18 00:01:04,640 --> 00:01:07,080 Speaker 4: But in Magistrate dreiberg joins us for the latest and 19 00:01:07,120 --> 00:01:09,920 Speaker 4: these are known state actors, two of them and then 20 00:01:10,000 --> 00:01:12,199 Speaker 4: one Chinese based actor. 21 00:01:12,240 --> 00:01:13,120 Speaker 5: It would seem. 22 00:01:13,360 --> 00:01:14,840 Speaker 2: Yes, that's exactly right. 23 00:01:14,920 --> 00:01:19,160 Speaker 6: Microsoft said this morning that two groups backed by the 24 00:01:19,319 --> 00:01:25,560 Speaker 6: Chinese government, silk Linen Typhoon and another group have been 25 00:01:25,600 --> 00:01:29,039 Speaker 6: responsible for some of the exploitation of this vulnerability in 26 00:01:29,120 --> 00:01:34,200 Speaker 6: its SharePoint software. We've also heard from other cybersecurity firms 27 00:01:34,440 --> 00:01:38,720 Speaker 6: that they're seeing multiple different organizations sort of hack into 28 00:01:38,840 --> 00:01:41,840 Speaker 6: businesses and governments based on this vulnerability. 29 00:01:42,560 --> 00:01:45,880 Speaker 3: Jake, the top priority right now seems to be identifying 30 00:01:45,920 --> 00:01:48,600 Speaker 3: the victims. Do we know what the kind of net 31 00:01:48,600 --> 00:01:51,600 Speaker 3: result of this this hacking activity is? Who's been impacted? 32 00:01:52,040 --> 00:01:57,080 Speaker 6: We know that the impacted organizations are ones that hosted 33 00:01:57,240 --> 00:02:02,000 Speaker 6: SharePoint on their own servers. Seems to be a real 34 00:02:02,160 --> 00:02:08,440 Speaker 6: smattering of different groups, government, agencies, businesses, other organizations all 35 00:02:08,480 --> 00:02:11,519 Speaker 6: over the globe. We know that governments have been hit 36 00:02:11,800 --> 00:02:14,440 Speaker 6: in the Middle East and in Europe and here in 37 00:02:14,440 --> 00:02:18,200 Speaker 6: the United States. We also know of businesses and state 38 00:02:18,280 --> 00:02:20,200 Speaker 6: level agencies that have also been hacked into. 39 00:02:20,880 --> 00:02:23,840 Speaker 4: Microsoft for its part, say investigations into other actors are 40 00:02:23,840 --> 00:02:26,160 Speaker 4: also ongoing. When it comes to the exploits, we could 41 00:02:26,160 --> 00:02:28,920 Speaker 4: currently see what they've put out in a blog post, 42 00:02:29,280 --> 00:02:32,560 Speaker 4: but more broadly, they acted swiftly to try and patch 43 00:02:32,680 --> 00:02:34,680 Speaker 4: the issue for once in a server. 44 00:02:34,919 --> 00:02:37,600 Speaker 5: That is the key concern exactly. 45 00:02:37,600 --> 00:02:41,560 Speaker 6: We've seen warnings from cybersecurity companies that you don't just 46 00:02:41,639 --> 00:02:44,480 Speaker 6: need to patch, you need to sort of hunt for 47 00:02:44,720 --> 00:02:47,840 Speaker 6: whether your systems were penetrated and what they might have 48 00:02:47,880 --> 00:02:52,520 Speaker 6: been taken. And a source told Bloomberg yesterday that the hackers, 49 00:02:52,520 --> 00:02:56,680 Speaker 6: once getting in in some instances, are stealing log in credentials, 50 00:02:56,760 --> 00:03:00,640 Speaker 6: user names, passwords, tokens, and that suggests that they may 51 00:03:00,680 --> 00:03:03,840 Speaker 6: be trying to exploit that information to watch other attacks. 52 00:03:04,520 --> 00:03:06,920 Speaker 3: Bloomboags Jake Bleiberg out of New York City, thank you 53 00:03:07,040 --> 00:03:10,680 Speaker 3: very much. Meanwhile, shares an NXP semiconductor or trading lower today, 54 00:03:10,720 --> 00:03:14,520 Speaker 3: the company posting third quarter forecasts that were less bullish 55 00:03:14,880 --> 00:03:17,280 Speaker 3: than invested in anticipated. Then they went on to talk 56 00:03:17,320 --> 00:03:20,000 Speaker 3: about the state of play in the automotive industry, which 57 00:03:20,040 --> 00:03:23,079 Speaker 3: is fifty percent or more of their revenue. Bloombozy and King, 58 00:03:23,120 --> 00:03:25,800 Speaker 3: who leads our chip coverages here, that'tually a pretty simple 59 00:03:25,880 --> 00:03:28,960 Speaker 3: story right away from all the AI stuff, there's been 60 00:03:28,960 --> 00:03:33,120 Speaker 3: a glut in inventory on automotive chips. NXP saying, look, 61 00:03:33,480 --> 00:03:35,880 Speaker 3: it takes time to work through them, but for that 62 00:03:36,000 --> 00:03:38,240 Speaker 3: industry and that key customer, the world's a bit uncertain 63 00:03:38,280 --> 00:03:38,880 Speaker 3: as well. 64 00:03:39,080 --> 00:03:41,840 Speaker 7: It absolutely is, and we'll hear from Texas Instruments in 65 00:03:41,840 --> 00:03:45,160 Speaker 7: a similar vein later today. The cross currents are everything's 66 00:03:45,160 --> 00:03:48,120 Speaker 7: supposed to be getting better. This kind of glow that 67 00:03:48,160 --> 00:03:50,360 Speaker 7: we've had over the last couple of years is supposed 68 00:03:50,400 --> 00:03:52,520 Speaker 7: to be going away. So we want you know, if 69 00:03:52,520 --> 00:03:54,200 Speaker 7: you're an investor, you want them to be more bullish, 70 00:03:54,240 --> 00:03:57,400 Speaker 7: you want them to be a bit more reassuring. That's 71 00:03:57,440 --> 00:03:59,640 Speaker 7: not what happened for man XP. They said things are 72 00:03:59,640 --> 00:04:00,960 Speaker 7: still had hanging around. 73 00:04:00,760 --> 00:04:03,680 Speaker 5: And ian almost bullish is the wrong word for it. 74 00:04:03,720 --> 00:04:07,120 Speaker 4: When revenue is down six percent for n XP and 75 00:04:07,160 --> 00:04:10,360 Speaker 4: actually revenues have been down quarter after quarter. 76 00:04:10,160 --> 00:04:13,120 Speaker 7: Right, Yeah, I mean what we'll have to look at. 77 00:04:13,160 --> 00:04:14,640 Speaker 7: And this is why I think it's going to be 78 00:04:14,760 --> 00:04:17,680 Speaker 7: useful to have Texas Instruments reporting today sort of back 79 00:04:17,720 --> 00:04:21,919 Speaker 7: to back. Is this a customer related thing? Is NXP 80 00:04:22,400 --> 00:04:26,360 Speaker 7: basically supplying automotive companies that are struggling more than others. 81 00:04:27,080 --> 00:04:31,000 Speaker 7: Texas Instruments is a much broader supplier, has a bigger 82 00:04:31,040 --> 00:04:34,840 Speaker 7: customer list and a bigger set of products, So that 83 00:04:34,920 --> 00:04:36,960 Speaker 7: will give us a sense of whether this is a 84 00:04:37,080 --> 00:04:40,719 Speaker 7: general automotive malaise, whether this is China related, whether this 85 00:04:40,800 --> 00:04:42,960 Speaker 7: is Europe related, and we'll have a much clearer picture 86 00:04:43,040 --> 00:04:43,599 Speaker 7: later today. 87 00:04:43,720 --> 00:04:45,279 Speaker 3: I am going to dig into this a bit later 88 00:04:45,279 --> 00:04:48,800 Speaker 3: in the program, but the Philadelphia Semiconductor Industry Index, sorry, 89 00:04:49,480 --> 00:04:52,520 Speaker 3: is underperforming generally in NVIDEO and am D A big 90 00:04:52,600 --> 00:04:55,760 Speaker 3: drags on that. The read through between NXP and those 91 00:04:55,760 --> 00:04:59,920 Speaker 3: two is difficult for me to see. But generally speaking, 92 00:05:00,839 --> 00:05:04,320 Speaker 3: away from those kind of ultra specialized and even analog chips, 93 00:05:04,360 --> 00:05:05,719 Speaker 3: things are quite robust still. 94 00:05:06,120 --> 00:05:08,320 Speaker 7: Yeah, I mean we've seen I mean you talk about 95 00:05:08,320 --> 00:05:10,280 Speaker 7: the index, the index has done pretty well this year. 96 00:05:10,600 --> 00:05:12,440 Speaker 7: There's a lot of money has been put to work. 97 00:05:12,720 --> 00:05:15,400 Speaker 7: Everything we've heard so far and we'll hear more on 98 00:05:15,400 --> 00:05:18,039 Speaker 7: on that vein as the inning season unfolds, is there's 99 00:05:18,080 --> 00:05:21,080 Speaker 7: a lot of spending still going on on these large 100 00:05:21,160 --> 00:05:25,680 Speaker 7: AI infrastructure systems, and we've heard nothing that's telling us 101 00:05:25,680 --> 00:05:28,560 Speaker 7: that that's not going to continue. And that's really what's 102 00:05:28,560 --> 00:05:30,400 Speaker 7: going to be the most important thing, and that's what's 103 00:05:30,440 --> 00:05:33,360 Speaker 7: driven the socks so far. Analog has been a slightly 104 00:05:33,400 --> 00:05:36,200 Speaker 7: different story, but again analog benefits from the server build 105 00:05:36,200 --> 00:05:38,800 Speaker 7: out as well. You need these power related chips, you 106 00:05:38,839 --> 00:05:40,600 Speaker 7: need these micro controllers as well. 107 00:05:40,960 --> 00:05:44,080 Speaker 4: In king always with all the context, so appreciate it. 108 00:05:44,120 --> 00:05:47,640 Speaker 4: Thank you. Let's dive deeper into the tech earnings, the 109 00:05:47,760 --> 00:05:50,320 Speaker 4: vibe in the market right now, Anna Rathens with us 110 00:05:50,320 --> 00:05:54,480 Speaker 4: founder and CEO of Grenna Dilla Advisory. Anna, your take 111 00:05:54,560 --> 00:05:57,359 Speaker 4: here is that, yes, we're perhaps pulling back on the 112 00:05:57,440 --> 00:06:02,560 Speaker 4: day somewhat nxp not resounding vealing of optimism, but we're 113 00:06:02,600 --> 00:06:04,880 Speaker 4: still near all time highs and we've still got a 114 00:06:04,880 --> 00:06:06,760 Speaker 4: four trillion dollar in video in our hands. 115 00:06:07,400 --> 00:06:08,240 Speaker 5: Yeah, good morning. 116 00:06:08,920 --> 00:06:12,839 Speaker 8: Markets are definitely the valuations are high. There's no slicing 117 00:06:12,880 --> 00:06:15,440 Speaker 8: that in a different way. But I guess you know, 118 00:06:15,480 --> 00:06:18,159 Speaker 8: you have to look at tech differently than we used to. 119 00:06:18,279 --> 00:06:21,520 Speaker 8: So a lot of allocators and wealth advisors out there, 120 00:06:21,560 --> 00:06:24,839 Speaker 8: we've been taught to look at tech as a growth sector. 121 00:06:25,279 --> 00:06:28,640 Speaker 8: Perhaps it's more than that. So the AI component is 122 00:06:28,680 --> 00:06:32,560 Speaker 8: definitely tech, but maybe there's also a quality factor there 123 00:06:32,600 --> 00:06:36,080 Speaker 8: as well. Got healthy balance sheets, great cash flow, especially 124 00:06:36,080 --> 00:06:40,760 Speaker 8: those mega companies, megacap companies. It's also got a defensive 125 00:06:40,800 --> 00:06:44,719 Speaker 8: sector of characteristic as well. We learned that we cannot 126 00:06:44,760 --> 00:06:49,000 Speaker 8: operate this economy and our society without tech infrastructure as 127 00:06:49,000 --> 00:06:52,440 Speaker 8: well as software. So I think that when we're looking 128 00:06:52,560 --> 00:06:55,120 Speaker 8: at tech, we have to look at it from multiple 129 00:06:55,200 --> 00:06:58,080 Speaker 8: angles in order to perhaps try to explain some of 130 00:06:58,120 --> 00:07:01,680 Speaker 8: this valuation. It's not just about AI froup. It's much 131 00:07:01,720 --> 00:07:02,159 Speaker 8: more than that. 132 00:07:02,440 --> 00:07:05,920 Speaker 4: But what's been interesting is the AI winners have powered 133 00:07:06,120 --> 00:07:08,280 Speaker 4: forward on the twenty twenty five, and those winners have 134 00:07:08,400 --> 00:07:11,880 Speaker 4: been deemed well the metas of this, Wells in videos 135 00:07:11,880 --> 00:07:14,760 Speaker 4: of this, Wells Apple left for dust. In some ways, 136 00:07:14,800 --> 00:07:17,880 Speaker 4: Alphabet has been a lagged too. How do we set 137 00:07:17,960 --> 00:07:19,800 Speaker 4: up into Wednesday when we start to get a couple 138 00:07:19,840 --> 00:07:21,240 Speaker 4: of those MACS seven names. 139 00:07:22,120 --> 00:07:25,640 Speaker 8: Yeah, I think there's definitely a vifurcation in those MAC 140 00:07:25,720 --> 00:07:28,960 Speaker 8: seven names. And right now it looks like the people 141 00:07:28,960 --> 00:07:32,240 Speaker 8: who have really invested and gone forward with AIS are 142 00:07:32,280 --> 00:07:36,120 Speaker 8: definitely winning. Now Alphabet, for example, will have different challenges, 143 00:07:36,240 --> 00:07:38,960 Speaker 8: right Not only do they have some legal challenges, but 144 00:07:39,080 --> 00:07:40,720 Speaker 8: you know, I don't know about you, but when I 145 00:07:40,760 --> 00:07:43,200 Speaker 8: talk to my friends, they stopped using Google as a 146 00:07:43,200 --> 00:07:46,320 Speaker 8: search engine. They're using chat, GPT. And I know that's 147 00:07:46,400 --> 00:07:49,120 Speaker 8: just a few people around me and perhaps you, but 148 00:07:49,560 --> 00:07:52,200 Speaker 8: those things can actually catch up. And I know Chetman 149 00:07:52,200 --> 00:07:55,040 Speaker 8: and I is improving, but I think that's a real 150 00:07:55,720 --> 00:07:59,240 Speaker 8: competitor there, and so AI is going to be more 151 00:07:59,240 --> 00:08:01,680 Speaker 8: important going for work for some of these Max seven names, 152 00:08:01,720 --> 00:08:07,600 Speaker 8: and I wouldn't expect disappointing report from them, but certainly 153 00:08:07,640 --> 00:08:10,000 Speaker 8: I think investors can read between the lines. 154 00:08:10,640 --> 00:08:14,160 Speaker 3: And I what's curious about this week is that on Wednesday, 155 00:08:14,200 --> 00:08:20,280 Speaker 3: Alphabet and Tesla posts their earnings results right somewhat analogous companies, 156 00:08:20,600 --> 00:08:24,360 Speaker 3: some differences, and on the same day we'll hear from 157 00:08:24,400 --> 00:08:28,960 Speaker 3: the President about his vision for AI and American AI. 158 00:08:29,960 --> 00:08:31,920 Speaker 3: It's going to be a wild ride for investors this week, 159 00:08:32,000 --> 00:08:32,360 Speaker 3: isn't it. 160 00:08:33,600 --> 00:08:37,400 Speaker 8: Well. You know, anytime anyone from the government talks about 161 00:08:37,440 --> 00:08:41,480 Speaker 8: technology and AI and even bitcoin or blockchain, I get 162 00:08:41,520 --> 00:08:44,959 Speaker 8: a little nervous because I think we have to tread 163 00:08:45,040 --> 00:08:47,760 Speaker 8: very carefully here. We don't want to get in the 164 00:08:47,800 --> 00:08:50,880 Speaker 8: way of innovation, and yet we want to protect consumers, right, 165 00:08:50,920 --> 00:08:53,640 Speaker 8: So it's going to be a very It's going to 166 00:08:53,720 --> 00:08:56,760 Speaker 8: be a balancing act for anybody who from the government, 167 00:08:56,800 --> 00:09:01,280 Speaker 8: from the White House talking about AI and how America 168 00:09:01,360 --> 00:09:03,720 Speaker 8: is going to be involved in the development of AI. 169 00:09:04,520 --> 00:09:06,680 Speaker 8: I would go in with a little bit of a 170 00:09:06,720 --> 00:09:11,880 Speaker 8: cautious view, and frankly, I'm expecting a lot of lofty statements, 171 00:09:11,880 --> 00:09:14,680 Speaker 8: but the question always is how does this actually translate 172 00:09:14,800 --> 00:09:17,640 Speaker 8: down to the day to day and actual software and 173 00:09:17,760 --> 00:09:20,400 Speaker 8: make difference in our productivity. 174 00:09:21,320 --> 00:09:23,200 Speaker 2: Let's bring it back to today's news. 175 00:09:23,480 --> 00:09:28,240 Speaker 3: What did you learn about the near term and quantifileble 176 00:09:28,320 --> 00:09:31,679 Speaker 3: impact of tariffs? You names like General Motors and NXP 177 00:09:32,440 --> 00:09:34,600 Speaker 3: show us that actually something's happening here. 178 00:09:35,840 --> 00:09:38,200 Speaker 8: Yeah, I mean something is happening there. We're talking about 179 00:09:38,240 --> 00:09:41,480 Speaker 8: actual money, right. I mean, so for last week, if 180 00:09:41,520 --> 00:09:45,400 Speaker 8: you think about the CPI and consumer sentiment, you didn't 181 00:09:45,400 --> 00:09:48,560 Speaker 8: really see it, right, but you are seeing it in earnings. 182 00:09:48,559 --> 00:09:51,520 Speaker 8: This is actual cash flow impacts. So I think this 183 00:09:51,600 --> 00:09:54,120 Speaker 8: is going to trickle in as the quarter goes and 184 00:09:54,120 --> 00:09:57,000 Speaker 8: give us a better view of exactly what areas of 185 00:09:57,040 --> 00:10:00,360 Speaker 8: the economy are being impacted by tariffs. Who is is 186 00:10:00,440 --> 00:10:04,319 Speaker 8: actually assuming some of those costs because GM actually came 187 00:10:04,360 --> 00:10:06,040 Speaker 8: out and said we don't want to pass down all 188 00:10:06,080 --> 00:10:08,719 Speaker 8: the costs to the consumers. So it'll be interesting to 189 00:10:08,840 --> 00:10:12,199 Speaker 8: see how those things get I guess diversified into the 190 00:10:12,240 --> 00:10:15,560 Speaker 8: supply chain and how GM and other companies actually manage 191 00:10:15,600 --> 00:10:16,400 Speaker 8: those challenges. 192 00:10:16,679 --> 00:10:19,360 Speaker 4: And going back to taras tarvis is all about the 193 00:10:19,440 --> 00:10:23,559 Speaker 4: geopolitics that is currently involved in technology investing. I think 194 00:10:23,559 --> 00:10:25,880 Speaker 4: of the geopolitics between US and China. I think of 195 00:10:25,920 --> 00:10:28,520 Speaker 4: the latest news out of Microsoft blaming China for a hack. 196 00:10:28,760 --> 00:10:31,520 Speaker 4: How much do you factor that into your current investment thesis? 197 00:10:31,520 --> 00:10:33,840 Speaker 4: How much can you price that sort of geopolitics in? 198 00:10:35,240 --> 00:10:38,080 Speaker 8: Yeah, I mean I never thought that this trade talk 199 00:10:38,120 --> 00:10:40,559 Speaker 8: with China is going to be simple or smooth, right, 200 00:10:40,600 --> 00:10:44,600 Speaker 8: and this is yet another twist in the plot. I 201 00:10:44,600 --> 00:10:49,080 Speaker 8: would hold that August first deadline date really loosely in 202 00:10:49,120 --> 00:10:52,200 Speaker 8: your hands, because you know, it was April first, and 203 00:10:52,240 --> 00:10:55,640 Speaker 8: then or July ninth, and then now it's August first. 204 00:10:56,120 --> 00:10:59,480 Speaker 8: I think that the Trump administration as well as China, 205 00:10:59,520 --> 00:11:01,840 Speaker 8: I mean, they have a lot at stake here, so 206 00:11:02,000 --> 00:11:04,600 Speaker 8: I don't think the deadline really matters. I think we're 207 00:11:04,640 --> 00:11:08,400 Speaker 8: going to be negotiating until we actually get to a 208 00:11:08,480 --> 00:11:12,960 Speaker 8: good place for both countries, and August first might coming. 209 00:11:12,800 --> 00:11:18,080 Speaker 3: Go Anna Rathban, founder and CEO of Grenadilla Advisory, thank 210 00:11:18,120 --> 00:11:20,920 Speaker 3: you very much for coming up. Oracle and open Ai 211 00:11:21,040 --> 00:11:24,160 Speaker 3: is set to expand their partnership, opening up more data 212 00:11:24,240 --> 00:11:26,600 Speaker 3: centers to satiate AI demand. 213 00:11:26,600 --> 00:11:29,480 Speaker 2: We had the details next. This is Bloomberg Tech. 214 00:11:41,840 --> 00:11:44,360 Speaker 4: Oracle along with open Ai have announced that they're set 215 00:11:44,360 --> 00:11:47,600 Speaker 4: to develop four and a half gigawatts of additional US 216 00:11:47,679 --> 00:11:51,600 Speaker 4: data center capacity. Let's bringing Bloomberg's Brodie Ford now on 217 00:11:52,000 --> 00:11:55,400 Speaker 4: how the evolution of stargate is going. 218 00:11:55,200 --> 00:11:57,560 Speaker 5: And who's involved and who isn't, because we'll get. 219 00:11:57,480 --> 00:11:58,680 Speaker 4: To sort of bank in a moment the fact they 220 00:11:58,720 --> 00:12:00,640 Speaker 4: don't seem to be on the page, but this is 221 00:12:00,679 --> 00:12:03,200 Speaker 4: this component to what have ten giga? 222 00:12:03,400 --> 00:12:05,200 Speaker 5: What's by four years time? 223 00:12:05,200 --> 00:12:07,160 Speaker 4: They really seem to be building it out now, right, and. 224 00:12:07,120 --> 00:12:08,560 Speaker 2: We can for get a giggle lot. 225 00:12:08,559 --> 00:12:11,320 Speaker 9: I mean we're throwing these numbers around now, but you know, 226 00:12:11,360 --> 00:12:13,240 Speaker 9: a couple of years ago you would never hear about 227 00:12:13,240 --> 00:12:15,120 Speaker 9: a giggle wat data center, and now we're talking about 228 00:12:15,160 --> 00:12:16,800 Speaker 9: four and a half. I mean each one of those 229 00:12:16,840 --> 00:12:19,640 Speaker 9: about equivalent to a nuclear reactor. And so a four 230 00:12:19,679 --> 00:12:22,600 Speaker 9: and a half gigawatt data center deal that's across the country. 231 00:12:23,040 --> 00:12:25,120 Speaker 9: This is an unprecedented cloud deal. I mean, this is 232 00:12:25,240 --> 00:12:28,560 Speaker 9: likely the largest single cloud deal ever signed. And so right, 233 00:12:28,600 --> 00:12:30,839 Speaker 9: if your oracle, if you've been trying to tell people 234 00:12:30,880 --> 00:12:32,800 Speaker 9: for many years, you're going to be a real cloud 235 00:12:32,800 --> 00:12:33,840 Speaker 9: infrastructure player. 236 00:12:34,440 --> 00:12:35,720 Speaker 2: This is a pretty. 237 00:12:35,400 --> 00:12:39,560 Speaker 9: Resounding sign of confidence from the most important AI company 238 00:12:39,600 --> 00:12:41,440 Speaker 9: at the moment, Berdie. 239 00:12:41,480 --> 00:12:44,120 Speaker 3: The way we look at this is it it's confirmation 240 00:12:44,240 --> 00:12:47,880 Speaker 3: of our prior reporting on the projects. But also, as 241 00:12:47,960 --> 00:12:52,040 Speaker 3: Cara alluded to, some clarity on the bits that SoftBank 242 00:12:52,400 --> 00:12:55,079 Speaker 3: is and is not financing absolutely. 243 00:12:55,200 --> 00:12:58,040 Speaker 9: So it's very interesting because in January we saw you know, 244 00:12:58,120 --> 00:13:02,040 Speaker 9: Larry Ellison, Sam Altman, and soft Bank sitting at the 245 00:13:02,040 --> 00:13:05,600 Speaker 9: White House. Today, SoftBank doesn't appear to be too involved. 246 00:13:05,640 --> 00:13:07,880 Speaker 9: They're not saying we're out for good. But this four 247 00:13:07,880 --> 00:13:10,560 Speaker 9: and a half gigle walks, which likely gets those to 248 00:13:10,600 --> 00:13:13,800 Speaker 9: around thirty billion a year, soft Bank is not involved 249 00:13:13,800 --> 00:13:16,920 Speaker 9: in financing. Where does the rest of that money come from? 250 00:13:17,000 --> 00:13:19,360 Speaker 9: That's going to be a big question in the following months, 251 00:13:19,440 --> 00:13:22,800 Speaker 9: right because open Ai makes a lot of money, they're 252 00:13:22,800 --> 00:13:24,680 Speaker 9: not making that much money to be able to just 253 00:13:24,720 --> 00:13:26,000 Speaker 9: spend all this themselves. 254 00:13:26,400 --> 00:13:28,959 Speaker 4: We're going to dig into more news miracle in a minute, 255 00:13:28,960 --> 00:13:31,439 Speaker 4: but I just want to understand where we think these 256 00:13:31,520 --> 00:13:33,560 Speaker 4: data centers are going to be. Do we have much 257 00:13:33,760 --> 00:13:35,959 Speaker 4: detail on the timescale here as well? 258 00:13:36,200 --> 00:13:38,440 Speaker 9: Every time I ask a source where the next data 259 00:13:38,480 --> 00:13:40,080 Speaker 9: center is going to be, they tell me a different state. 260 00:13:41,160 --> 00:13:43,720 Speaker 9: They're evaluating a lot of different states, a lot of 261 00:13:43,720 --> 00:13:46,720 Speaker 9: different sites. If you've read an article that says, hey, 262 00:13:46,800 --> 00:13:49,240 Speaker 9: they want to develop a multi giggle data center here, 263 00:13:49,720 --> 00:13:52,559 Speaker 9: odds are that starting. It has evaluated this site, so 264 00:13:52,640 --> 00:13:53,760 Speaker 9: I'm keeping my eyes out. 265 00:13:55,000 --> 00:13:57,560 Speaker 3: Slightly smaller scale, but Oracle's also in talks around one 266 00:13:57,600 --> 00:14:01,320 Speaker 3: hundred million dollar a year paramoun Skydanceteel, What do we 267 00:14:01,360 --> 00:14:02,480 Speaker 3: need to know about that one? 268 00:14:02,679 --> 00:14:04,280 Speaker 9: What you need to know is if your dad is 269 00:14:04,360 --> 00:14:06,880 Speaker 9: Larry Ellis said, and he helps you buy something, you're 270 00:14:06,920 --> 00:14:08,920 Speaker 9: going to have to kind of help buy some stuff 271 00:14:08,920 --> 00:14:12,280 Speaker 9: from him as well, right, I mean Larry Ellison's son, 272 00:14:12,360 --> 00:14:15,440 Speaker 9: David Ellison has been in the works to buy Paramount 273 00:14:15,440 --> 00:14:19,280 Speaker 9: for a while and they're already having negotiations that assuming 274 00:14:19,320 --> 00:14:23,160 Speaker 9: that deal goes through, Paramount will be a major OCI customer. 275 00:14:23,200 --> 00:14:25,680 Speaker 9: They're going to put all of their CBS, NTV, it's 276 00:14:25,720 --> 00:14:27,640 Speaker 9: a lot of assets, a lot of gigabytes, and they're 277 00:14:27,680 --> 00:14:30,920 Speaker 9: going to be on Oracle servers doing best. 278 00:14:30,920 --> 00:14:33,000 Speaker 3: Brady Ford, thank you really on top of this story. 279 00:14:33,040 --> 00:14:35,120 Speaker 3: Thank you very much. Okay, we have another story. Some 280 00:14:35,240 --> 00:14:38,240 Speaker 3: updates in the four billion dollar fraud case against the 281 00:14:38,400 --> 00:14:42,600 Speaker 3: estate of deceased Autonomy founder Mike Lynch. A London judge 282 00:14:42,640 --> 00:14:46,320 Speaker 3: ruled that HPE lost close to one billion dollars after 283 00:14:46,440 --> 00:14:50,320 Speaker 3: buying Autonomy. Lynch's estate will face a claim for much 284 00:14:50,360 --> 00:14:52,880 Speaker 3: of that sum after he lost a London fraud case 285 00:14:53,000 --> 00:14:56,480 Speaker 3: along with the former CFO of Autonomy. The ruling suggests 286 00:14:56,840 --> 00:15:01,120 Speaker 3: HPE will likely recain just a fraction of its total claim. 287 00:15:01,200 --> 00:15:04,080 Speaker 3: The ruling comes less than a year after Lynch drowned 288 00:15:04,120 --> 00:15:06,600 Speaker 3: when his yacht sank in a storm off the coast 289 00:15:06,760 --> 00:15:17,000 Speaker 3: of Sicily. Poseidon has announced its raised fifteen million dollars 290 00:15:17,040 --> 00:15:19,800 Speaker 3: in a seed round led by a sixteen Z crypto, 291 00:15:19,960 --> 00:15:23,520 Speaker 3: as the company looks to tackle bottlenecks in AI training data, 292 00:15:23,560 --> 00:15:28,200 Speaker 3: including IP safe resources. This particularly aimed at training data 293 00:15:28,240 --> 00:15:32,560 Speaker 3: for robotics, multimodal models, and physical AI. Here with more 294 00:15:32,720 --> 00:15:36,280 Speaker 3: is s y Le Poseidon President and CEO story The 295 00:15:36,360 --> 00:15:40,000 Speaker 3: incubator behind Poseidon state of players really clear, right. AI 296 00:15:40,080 --> 00:15:44,240 Speaker 3: models have become commoditized, compute costs have come down. But 297 00:15:44,280 --> 00:15:47,119 Speaker 3: the bottleneck, as you see it and many others see it, 298 00:15:47,160 --> 00:15:47,840 Speaker 3: is the data. 299 00:15:48,360 --> 00:15:51,960 Speaker 10: You have a solution, yes, as you just wanted to 300 00:15:52,360 --> 00:15:56,200 Speaker 10: put that into context again because now you know the 301 00:15:56,240 --> 00:15:59,680 Speaker 10: previous generation of models. We're scraping the internets data, which 302 00:15:59,720 --> 00:16:01,600 Speaker 10: is you know, you know you can scrape it all. 303 00:16:01,720 --> 00:16:05,160 Speaker 10: But now AI is going to the physical world. It's 304 00:16:05,200 --> 00:16:08,680 Speaker 10: the multimodel models, right, it's a robotics model, it's a 305 00:16:08,760 --> 00:16:12,720 Speaker 10: video model, audio model, and this really real bottleleg is 306 00:16:12,720 --> 00:16:14,760 Speaker 10: the data, as you said, because you know, you can't 307 00:16:14,800 --> 00:16:18,200 Speaker 10: ask rate it illegally on the Internet, and a lot 308 00:16:18,240 --> 00:16:21,320 Speaker 10: of times this data is very private. You know, there's 309 00:16:21,360 --> 00:16:25,320 Speaker 10: a lot of IP safety concerns. And in that world, 310 00:16:25,480 --> 00:16:30,800 Speaker 10: how do you coordinate the collection, labeling, curating of the data. 311 00:16:30,800 --> 00:16:35,520 Speaker 10: And we're using actually crypto incentives to actually coordinate that 312 00:16:35,680 --> 00:16:37,080 Speaker 10: massively label coordinator. 313 00:16:37,080 --> 00:16:40,080 Speaker 3: So my next question was why is Andreson's crypto team 314 00:16:40,160 --> 00:16:43,880 Speaker 3: backing you and not an AI specialist. 315 00:16:45,800 --> 00:16:47,400 Speaker 2: Partner or a different firm. 316 00:16:47,880 --> 00:16:50,760 Speaker 10: First of all, micro founder is a you know, robotics 317 00:16:50,760 --> 00:16:54,680 Speaker 10: at UT Austin, and he's he's a big AI specialist 318 00:16:54,840 --> 00:16:57,120 Speaker 10: who's been kind of working on this data licensing and 319 00:16:57,240 --> 00:16:59,400 Speaker 10: data collection for the last ten years and even he 320 00:16:59,480 --> 00:17:02,560 Speaker 10: thought about how do how do you incentivize data collection 321 00:17:02,680 --> 00:17:05,439 Speaker 10: with data dividends? So you know, he he he's an 322 00:17:05,440 --> 00:17:08,520 Speaker 10: AI expert in in this in this problem. But the 323 00:17:08,600 --> 00:17:11,359 Speaker 10: reason why we are kind of they we think this 324 00:17:11,440 --> 00:17:14,520 Speaker 10: is so so timely, is you know forty days ago 325 00:17:14,640 --> 00:17:18,639 Speaker 10: actually meta semi a quiet actually as as you know, uh, 326 00:17:18,840 --> 00:17:22,280 Speaker 10: scaling on as a data left a very huge vacuum 327 00:17:22,320 --> 00:17:25,199 Speaker 10: in the space to actually for competitors and new players 328 00:17:25,240 --> 00:17:27,560 Speaker 10: to come in. And if you look at scaleing on 329 00:17:27,840 --> 00:17:29,760 Speaker 10: D I mean, I'm not trying to criticize, but they 330 00:17:29,800 --> 00:17:32,280 Speaker 10: have to you know, make a lot of shell companies, 331 00:17:32,280 --> 00:17:34,800 Speaker 10: work with shell companies and developing countries to actually coordinate 332 00:17:34,840 --> 00:17:37,719 Speaker 10: this kind of digital sweatshop to make that happen. But 333 00:17:37,760 --> 00:17:40,320 Speaker 10: the beauty of crypto is that it's still really good 334 00:17:40,320 --> 00:17:43,800 Speaker 10: at coordinating incentives in a at a global scale and 335 00:17:43,880 --> 00:17:46,000 Speaker 10: pay them instantaneously with stable coins or. 336 00:17:46,000 --> 00:17:47,240 Speaker 5: Other type of cryptotokens. 337 00:17:47,320 --> 00:17:50,840 Speaker 10: Right, And that is a very very huge advantage number one, right, 338 00:17:51,160 --> 00:17:54,920 Speaker 10: And number two, not only that, what crypto and blockchain 339 00:17:54,960 --> 00:17:58,000 Speaker 10: does is it is good at you know, putting this 340 00:17:58,160 --> 00:18:00,959 Speaker 10: data and IP on an immedable ledger and track and 341 00:18:01,000 --> 00:18:03,960 Speaker 10: attribute it and license it with small contracts. And that's 342 00:18:04,240 --> 00:18:08,119 Speaker 10: those two are very very important advantage of crypto. Even 343 00:18:08,160 --> 00:18:12,879 Speaker 10: where my co founder, Ai Specialist wants to actually use thistagnomogy. 344 00:18:12,480 --> 00:18:14,399 Speaker 4: S ry, let's just go to the basics of the 345 00:18:14,480 --> 00:18:17,600 Speaker 4: data that is needed. Here, I can see the crypto 346 00:18:17,640 --> 00:18:20,160 Speaker 4: read across. I think of Vaccineinfinity, the game that when 347 00:18:20,600 --> 00:18:23,639 Speaker 4: bananas in the Philippines years ago, how people wanted to 348 00:18:23,640 --> 00:18:26,720 Speaker 4: play because they earned money in tokens via playing this 349 00:18:27,000 --> 00:18:30,960 Speaker 4: crypto native game. You're going to use people, incentivize people 350 00:18:31,320 --> 00:18:34,000 Speaker 4: to sort of video themselves in the real world, then 351 00:18:34,160 --> 00:18:37,359 Speaker 4: ultimately helps Tesla build optimists in the real world. 352 00:18:38,440 --> 00:18:43,400 Speaker 10: Yes, actually you hit on the right comparable. Ex Infinity 353 00:18:43,760 --> 00:18:47,320 Speaker 10: was an example of coordinating this massive number of people 354 00:18:47,359 --> 00:18:49,840 Speaker 10: in the Philippines and other types of developing country, other 355 00:18:49,920 --> 00:18:52,600 Speaker 10: other developing countries to actually play games to ourn' tokens 356 00:18:52,680 --> 00:18:53,320 Speaker 10: right in. 357 00:18:53,280 --> 00:18:54,000 Speaker 5: The same way. 358 00:18:54,560 --> 00:18:57,400 Speaker 10: Now, you know, when Tessa wants to train as human 359 00:18:57,440 --> 00:19:00,920 Speaker 10: aid robot, they you know, even if you script the 360 00:19:01,040 --> 00:19:03,959 Speaker 10: entire Internet's data, you can't actually have a testa robot 361 00:19:04,480 --> 00:19:07,639 Speaker 10: holding a coffee cup, right because you know, you need 362 00:19:07,680 --> 00:19:11,960 Speaker 10: a million angles of data to ecocentric video footage and 363 00:19:11,960 --> 00:19:14,960 Speaker 10: a million different types of settings and that kind of data. 364 00:19:15,760 --> 00:19:19,280 Speaker 10: Instead of actually hiring a shell company and they're having 365 00:19:19,320 --> 00:19:22,119 Speaker 10: their share company hired, you know, tens of thousands of people. 366 00:19:22,520 --> 00:19:24,520 Speaker 10: You can coordinate this at a mass skill and an 367 00:19:24,520 --> 00:19:27,720 Speaker 10: instantaneously with a with crypto incentive. That's what we're doing. 368 00:19:28,240 --> 00:19:32,240 Speaker 4: And let's talk about the end demand here. How many companies? 369 00:19:32,880 --> 00:19:36,280 Speaker 4: Who is your ultimate provider? How do you then sell 370 00:19:36,320 --> 00:19:40,359 Speaker 4: this aip this ip safe AI training data. 371 00:19:41,240 --> 00:19:44,119 Speaker 10: Yeah, the reason why it's IP safe is that everyone 372 00:19:44,200 --> 00:19:47,399 Speaker 10: who are actually contributing data, anyone can contribute this data 373 00:19:47,440 --> 00:19:50,320 Speaker 10: anywhere around the world, right, and then actually set their 374 00:19:50,359 --> 00:19:54,160 Speaker 10: licensing usage terms on the blockchain. And we are at 375 00:19:54,160 --> 00:19:57,080 Speaker 10: the moment we're not completely centralized, and we are now 376 00:19:57,200 --> 00:20:00,119 Speaker 10: going to launch a decentralize a deep in app in 377 00:20:00,119 --> 00:20:03,960 Speaker 10: the center physical infrastructure app and fall that actually collects data, 378 00:20:04,760 --> 00:20:07,520 Speaker 10: you know, puts this on the blockchain with the licensing 379 00:20:07,600 --> 00:20:10,080 Speaker 10: terms in place. But at the moment, we're now actually 380 00:20:10,480 --> 00:20:15,440 Speaker 10: acting as an agent to actually secure these contracts, and 381 00:20:16,040 --> 00:20:18,080 Speaker 10: we already have a you know, we've already signed a 382 00:20:18,119 --> 00:20:19,720 Speaker 10: contract and we cannot discuss. 383 00:20:19,400 --> 00:20:20,560 Speaker 2: It at the moment, right, you know. 384 00:20:20,600 --> 00:20:23,480 Speaker 10: And then I mean in the robotics relevant. 385 00:20:23,160 --> 00:20:27,760 Speaker 3: Well, quickly, if scale AI, the scenario left a vacuum 386 00:20:27,800 --> 00:20:30,640 Speaker 3: and you look to occupy that vacuum, how many phone 387 00:20:30,680 --> 00:20:34,040 Speaker 3: calls have you had from the other frontier model makers 388 00:20:34,080 --> 00:20:36,440 Speaker 3: that you could fit into their organization in the same 389 00:20:36,480 --> 00:20:37,720 Speaker 3: way just thirty seconds. 390 00:20:37,880 --> 00:20:41,400 Speaker 10: You know, actually many many organizations, And I was really 391 00:20:41,440 --> 00:20:44,359 Speaker 10: surprised as C Stage company, they were willing to work 392 00:20:44,400 --> 00:20:46,640 Speaker 10: with us because a lot of them were telling us 393 00:20:46,640 --> 00:20:48,880 Speaker 10: that they were dropping contracts from SCALEI. 394 00:20:49,320 --> 00:20:51,520 Speaker 3: But I meant in terms of you being acquired or 395 00:20:51,560 --> 00:20:52,680 Speaker 3: you joining a bigger group. 396 00:20:54,280 --> 00:20:55,280 Speaker 5: We don't have any plan. 397 00:20:55,640 --> 00:20:58,440 Speaker 10: We don't have any plan. And we think the advantage 398 00:20:58,440 --> 00:20:59,960 Speaker 10: of this open system is that we can work with 399 00:21:00,000 --> 00:21:00,520 Speaker 10: any AI. 400 00:21:00,480 --> 00:21:04,800 Speaker 5: Farmers, s y Lee, a Poseidon and Story. Great to 401 00:21:04,800 --> 00:21:05,520 Speaker 5: have you on today. 402 00:21:05,840 --> 00:21:08,080 Speaker 4: Now, let's just turn back a minute for what's going 403 00:21:08,200 --> 00:21:11,840 Speaker 4: viral because Tesma's futuristic diner in la is complete with 404 00:21:11,880 --> 00:21:15,160 Speaker 4: a drive in charging experience. Now, the sleek retro Vibe 405 00:21:15,160 --> 00:21:19,199 Speaker 4: diner is also replete with two sixty six foot megascreens 406 00:21:19,480 --> 00:21:22,600 Speaker 4: and a rooftop skypad and a prototype of Optimist. 407 00:21:22,680 --> 00:21:24,120 Speaker 5: The robot is serving popcorn. 408 00:21:24,480 --> 00:21:26,560 Speaker 4: Apparently that was at the pre launch event for the 409 00:21:26,560 --> 00:21:30,000 Speaker 4: Hollywood venue. Tesa claims it's the largest urban supercharger in 410 00:21:30,040 --> 00:21:30,480 Speaker 4: the world. 411 00:21:37,040 --> 00:21:37,920 Speaker 5: Let's just talk. 412 00:21:37,800 --> 00:21:41,640 Speaker 4: About another key player that's got earnings later this week. Alphabet, Well, Google, 413 00:21:41,800 --> 00:21:45,639 Speaker 4: we understand is seeking to recruit news organizations for a 414 00:21:45,680 --> 00:21:47,440 Speaker 4: new licensing project related to AI. 415 00:21:47,560 --> 00:21:50,080 Speaker 5: It's all, according to sources, which could be a big 416 00:21:50,160 --> 00:21:52,080 Speaker 5: win for the struggling media companies. 417 00:21:52,160 --> 00:21:54,920 Speaker 4: Bloomberg's Hannah Miller joins us, now, is this some sort 418 00:21:54,920 --> 00:21:58,000 Speaker 4: of Google capitulation here, because we've seen some deals done 419 00:21:58,119 --> 00:22:00,880 Speaker 4: by other AI players, but Google's always set on the side. 420 00:22:00,960 --> 00:22:03,159 Speaker 11: I think Google is playing catch up here. You know, 421 00:22:03,359 --> 00:22:07,240 Speaker 11: we've seen big companies like open ai and Microsoft strike 422 00:22:07,320 --> 00:22:12,280 Speaker 11: these massive licensing deals with news organizations, and Google, yeah, 423 00:22:12,280 --> 00:22:13,560 Speaker 11: they've been sitting on the sidelines. 424 00:22:13,600 --> 00:22:14,880 Speaker 5: They haven't been doing that as much. 425 00:22:14,960 --> 00:22:17,520 Speaker 11: So this could be them, you know, testing the waters, 426 00:22:17,560 --> 00:22:20,880 Speaker 11: you know, trying to build better relationships with media companies 427 00:22:20,960 --> 00:22:22,600 Speaker 11: as the AI race heats up. 428 00:22:23,720 --> 00:22:23,960 Speaker 2: Kind of. 429 00:22:23,960 --> 00:22:26,200 Speaker 3: One of our sources said that in one case, Google 430 00:22:26,320 --> 00:22:28,960 Speaker 3: launched this kind of pilot with twenty different news orgs. 431 00:22:29,320 --> 00:22:33,520 Speaker 3: You cover TMT right from the news perspective, So it's 432 00:22:33,560 --> 00:22:36,479 Speaker 3: like a cash cow, a great gold mine just to 433 00:22:36,520 --> 00:22:39,840 Speaker 3: sell data and bring in some money through a new mechanism. 434 00:22:40,600 --> 00:22:41,240 Speaker 5: A lot of people in. 435 00:22:41,240 --> 00:22:44,119 Speaker 11: The industry are saying this is a temporary band aid, 436 00:22:44,280 --> 00:22:46,320 Speaker 11: you know, yes, we can get some money from these 437 00:22:46,320 --> 00:22:51,160 Speaker 11: big companies at the start, but looking long term, of 438 00:22:51,200 --> 00:22:54,880 Speaker 11: many journalists feel like AI is a threat to their business. 439 00:22:55,640 --> 00:22:57,199 Speaker 11: You have people in the arts, you have people at 440 00:22:57,200 --> 00:23:00,639 Speaker 11: major media companies concerned about the effect AI will have 441 00:23:01,000 --> 00:23:01,800 Speaker 11: on their jobs. 442 00:23:02,960 --> 00:23:05,960 Speaker 3: Bloombog's Hannah Miller out in New York, thank you very much. Now, 443 00:23:06,000 --> 00:23:09,600 Speaker 3: Netflix and Disney are both taking steps into the somewhat 444 00:23:09,680 --> 00:23:14,120 Speaker 3: controversial world of AI video creation with software from Runway 445 00:23:14,160 --> 00:23:18,520 Speaker 3: AI Blueboks. Rachel Mets brings us the story. Interesting one 446 00:23:18,520 --> 00:23:20,600 Speaker 3: about this is that in the earnings context, this has 447 00:23:20,640 --> 00:23:24,080 Speaker 3: already come up, particularly for Netflix right in recent weeks. 448 00:23:24,119 --> 00:23:28,439 Speaker 3: But what's the controversy and what is the actual tool here? Like, 449 00:23:28,480 --> 00:23:31,440 Speaker 3: what is it that a Netflix would need AI to do? 450 00:23:33,040 --> 00:23:36,640 Speaker 12: So as you as you mentioned, Netflix said on their 451 00:23:36,760 --> 00:23:41,840 Speaker 12: earnings call just last week that they're using AI, and 452 00:23:41,880 --> 00:23:44,040 Speaker 12: they also mentioned a production that they'd used it in. 453 00:23:44,320 --> 00:23:46,320 Speaker 12: So companies like Netflix, what they could do with this 454 00:23:46,440 --> 00:23:50,040 Speaker 12: software in theory is they could maybe lower some of 455 00:23:50,080 --> 00:23:52,359 Speaker 12: the costs for special effects, so they'd use a model 456 00:23:52,440 --> 00:23:56,000 Speaker 12: like Runway. Runways top line model is JEN four. They 457 00:23:56,040 --> 00:24:00,960 Speaker 12: also have some models that aim to reproduce motion capture 458 00:24:01,280 --> 00:24:05,360 Speaker 12: software and without using all the fancy and clunky hardware 459 00:24:05,359 --> 00:24:06,919 Speaker 12: that comes with it. So the idea would be to 460 00:24:06,920 --> 00:24:10,280 Speaker 12: save costs here and to do some shots that perhaps 461 00:24:10,280 --> 00:24:15,760 Speaker 12: it would have been impossible previously without using AI software. 462 00:24:16,040 --> 00:24:18,600 Speaker 4: I think we learned from Ted surroundos over Netflix that 463 00:24:18,640 --> 00:24:23,639 Speaker 4: it was previously using to sort of simulate a building collapse, 464 00:24:24,080 --> 00:24:27,320 Speaker 4: and I think it was an Argentinian particular visual effects company, 465 00:24:27,359 --> 00:24:31,440 Speaker 4: but it wasn't Runway usually we understand according to people 466 00:24:31,480 --> 00:24:35,320 Speaker 4: familiar who are Runway, have they worked with and what 467 00:24:35,400 --> 00:24:37,840 Speaker 4: have they been able to produce thus far? Because they're 468 00:24:37,880 --> 00:24:42,760 Speaker 4: actually out competing Hollywood or West Coast based visual effects companies. 469 00:24:44,080 --> 00:24:48,400 Speaker 12: Yeah, so Runway works with a number of different companies. 470 00:24:48,440 --> 00:24:53,840 Speaker 12: We also know that Disney has been testing out those software. 471 00:24:53,440 --> 00:24:57,960 Speaker 12: They also Runway has a deal with Lionsgate. They're working 472 00:24:58,000 --> 00:25:00,439 Speaker 12: in a field that's increasingly crowded and there are so 473 00:25:00,480 --> 00:25:04,080 Speaker 12: much larger players. Open Ai has its own video software. 474 00:25:04,119 --> 00:25:07,040 Speaker 12: Google does as well, And what I would expect to 475 00:25:07,040 --> 00:25:10,960 Speaker 12: see is companies like Netflix. Probably they said they did 476 00:25:10,960 --> 00:25:13,240 Speaker 12: not create that scene with Runway, Well that implies there's 477 00:25:13,240 --> 00:25:15,919 Speaker 12: at least one other company that they're working with the 478 00:25:15,960 --> 00:25:18,080 Speaker 12: software from, right, so I would expect to see that 479 00:25:18,400 --> 00:25:21,280 Speaker 12: actually As these companies dip their toes into the water 480 00:25:21,480 --> 00:25:23,840 Speaker 12: of AI video software more and more, you'll probably see 481 00:25:23,880 --> 00:25:25,760 Speaker 12: them try out a bunch of different ones, and maybe 482 00:25:25,760 --> 00:25:28,160 Speaker 12: they will end up using a bunch of different tools 483 00:25:28,200 --> 00:25:31,199 Speaker 12: for different things. I think it really just depends on 484 00:25:31,280 --> 00:25:34,080 Speaker 12: what they find most useful and what their production teams do. 485 00:25:34,240 --> 00:25:36,359 Speaker 12: Is it live action, is an animation, and what tools 486 00:25:36,600 --> 00:25:38,040 Speaker 12: work the best for those sorts of things. 487 00:25:38,600 --> 00:25:40,160 Speaker 2: Let's try and get the big picture vision. 488 00:25:40,200 --> 00:25:43,359 Speaker 3: This is what Ted Sarandos actually said on Netflix's earning score. 489 00:25:44,440 --> 00:25:48,240 Speaker 13: We remain convinced that AI represents an incredible opportunity to 490 00:25:48,240 --> 00:25:52,040 Speaker 13: help creators make films and series better, not just cheaper. 491 00:25:53,240 --> 00:25:56,480 Speaker 13: There are AI powered creator tools, so this is real 492 00:25:56,520 --> 00:25:59,960 Speaker 13: people doing real work with better tools. Our creators are 493 00:25:59,880 --> 00:26:04,119 Speaker 13: already seeing the benefits and production through pre visualization and 494 00:26:04,160 --> 00:26:07,160 Speaker 13: shot planning work and certainly visual effects. 495 00:26:08,760 --> 00:26:10,920 Speaker 2: So Rachel, that's how Netflix sees it. 496 00:26:11,000 --> 00:26:14,640 Speaker 3: If you are a Runway or another AI company that's 497 00:26:14,680 --> 00:26:18,160 Speaker 3: working on text to video tools, right is the movie 498 00:26:18,200 --> 00:26:22,720 Speaker 3: industry or television. What they're working toward is that who 499 00:26:22,760 --> 00:26:24,000 Speaker 3: they want to serve. 500 00:26:24,920 --> 00:26:28,360 Speaker 12: Absolutely, I mean they want to work with professionals who 501 00:26:28,400 --> 00:26:31,120 Speaker 12: are working in these fields, and they want to work 502 00:26:31,119 --> 00:26:34,000 Speaker 12: with the studios. I think what's also important to keep 503 00:26:34,000 --> 00:26:39,560 Speaker 12: in mind is this technology is really controversial because there 504 00:26:39,560 --> 00:26:43,320 Speaker 12: are a lot of people people that perhaps would or 505 00:26:43,359 --> 00:26:46,720 Speaker 12: would not be wanting to use this software, that are 506 00:26:46,920 --> 00:26:49,080 Speaker 12: concerned that this is going to cut into their livelihos, 507 00:26:49,080 --> 00:26:51,480 Speaker 12: and I think that's a really real concern. So that's 508 00:26:51,560 --> 00:26:55,280 Speaker 12: part of why the studios are being I think they're 509 00:26:55,320 --> 00:26:57,919 Speaker 12: a little bit hesitant to be public right now about 510 00:26:58,119 --> 00:27:00,639 Speaker 12: how and if they're using this software because the stakes 511 00:27:00,680 --> 00:27:02,440 Speaker 12: are really high. 512 00:27:02,840 --> 00:27:05,960 Speaker 2: Bloomberg's Rachel Metz, thank you very much, Cara, what you. 513 00:27:05,920 --> 00:27:08,880 Speaker 4: Got as time now for Talking Tech and first stop 514 00:27:09,040 --> 00:27:13,360 Speaker 4: ed in video. The chip challenger Furiosa AI has finalized 515 00:27:13,400 --> 00:27:16,320 Speaker 4: a deal with LGAI Research. Now the South Korean chip 516 00:27:16,359 --> 00:27:19,640 Speaker 4: designer one final approval for its AI chip Renegade after 517 00:27:19,680 --> 00:27:22,679 Speaker 4: seven months of evaluations. Now in March, remember, the company 518 00:27:22,720 --> 00:27:26,359 Speaker 4: rejected an eight hundred million dollar takeover from Meta, choosing 519 00:27:26,400 --> 00:27:28,240 Speaker 4: instead to grow the business as. 520 00:27:28,080 --> 00:27:29,119 Speaker 5: An independent company. 521 00:27:29,400 --> 00:27:31,919 Speaker 4: Plast black Rock has told staff not to bring company 522 00:27:31,920 --> 00:27:34,959 Speaker 4: devices to China, including iPhones and iPads. That's all according 523 00:27:34,960 --> 00:27:37,800 Speaker 4: to a memo seen by bluembg News, and then move underscools, 524 00:27:37,840 --> 00:27:40,880 Speaker 4: growing concern among some global firms about employees working there 525 00:27:41,160 --> 00:27:45,040 Speaker 4: and data security and glade Brook Capital Partners has raised 526 00:27:45,080 --> 00:27:47,200 Speaker 4: five hundred and fifty million dollars for investors for its 527 00:27:47,240 --> 00:27:50,840 Speaker 4: full fund. The firm has Stakes and SpaceX Perplexity Stripe, 528 00:27:50,840 --> 00:27:54,040 Speaker 4: among other startups, and it prows to continue backing fintech, AI, 529 00:27:54,080 --> 00:27:56,280 Speaker 4: E commerce, space and defense companies. 530 00:27:56,680 --> 00:28:00,000 Speaker 3: Ed okay, coming up, tech and energy leaders head to 531 00:28:00,240 --> 00:28:03,879 Speaker 3: Washington to discuss how the US can win in the 532 00:28:03,920 --> 00:28:06,320 Speaker 3: AI race. Or take a look at where global AI 533 00:28:06,400 --> 00:28:10,520 Speaker 3: competition stands now, Caroline, you pointed this one out earlier today. 534 00:28:11,040 --> 00:28:12,760 Speaker 2: It's a name what I've not looked at in a while. 535 00:28:12,880 --> 00:28:13,520 Speaker 2: Open Door. 536 00:28:13,560 --> 00:28:15,960 Speaker 3: Okay, the stock's down one point six percent in the 537 00:28:16,000 --> 00:28:17,560 Speaker 3: hearing now, but look at some of the swings in 538 00:28:17,600 --> 00:28:21,199 Speaker 3: the session. Bloomberg writing about some of the activity, some 539 00:28:21,240 --> 00:28:24,439 Speaker 3: of the halts. Open Door is the latest memes stock. 540 00:28:24,840 --> 00:28:27,760 Speaker 3: This says it on the Bloomberg terminal. No, seriously, go 541 00:28:27,920 --> 00:28:28,440 Speaker 3: check it out. 542 00:28:28,520 --> 00:28:30,320 Speaker 2: We'll be right back. This is Bloomberg Tech. 543 00:28:39,800 --> 00:28:42,680 Speaker 4: President Trump in a bilateral meeting with President of the 544 00:28:42,720 --> 00:28:45,120 Speaker 4: Republic of the Philippines. I want to go straight out 545 00:28:45,160 --> 00:28:47,880 Speaker 4: to Mike Shepard there was a lot discussed there DOJ 546 00:28:48,080 --> 00:28:53,720 Speaker 4: investigations being queried, and some referring to potential as Trump 547 00:28:53,880 --> 00:28:57,840 Speaker 4: continues to see it of an election rigging, as he said, 548 00:28:57,880 --> 00:28:59,960 Speaker 4: but we've also had so much more when it comes 549 00:29:00,240 --> 00:29:02,680 Speaker 4: to the trade, when we think about China, when we 550 00:29:02,680 --> 00:29:06,360 Speaker 4: think about magnets, as it says, flowing out succinctly from 551 00:29:06,400 --> 00:29:08,720 Speaker 4: the country. Can you just wrap up what we were 552 00:29:08,720 --> 00:29:09,440 Speaker 4: hearing from there? 553 00:29:10,280 --> 00:29:13,040 Speaker 14: Well, First, the very first question the President took, of course, 554 00:29:13,200 --> 00:29:16,640 Speaker 14: was on the FED, something very close interest to our audience, 555 00:29:16,880 --> 00:29:19,800 Speaker 14: and the President stopped short of calling for Jerome Powell 556 00:29:19,880 --> 00:29:22,520 Speaker 14: to resign as FED chair. He did keep up his 557 00:29:22,560 --> 00:29:26,600 Speaker 14: criticism of Powell's handling of interest rate policy, saying that 558 00:29:26,640 --> 00:29:29,720 Speaker 14: he's been too late and too slow to lower interest rates. 559 00:29:30,000 --> 00:29:32,720 Speaker 14: And this is a theme he has returned to again 560 00:29:32,840 --> 00:29:37,200 Speaker 14: and again in these moments when visiting foreign leaders join 561 00:29:37,320 --> 00:29:39,440 Speaker 14: him in the Oval Office. Just last week we saw 562 00:29:39,520 --> 00:29:43,800 Speaker 14: him hit these notes with the visiting Crown Prince at Bahrain. 563 00:29:44,120 --> 00:29:47,560 Speaker 14: We heard it again today with the President of the Philippines, 564 00:29:47,600 --> 00:29:50,800 Speaker 14: Ferdinand Marcos. We also heard the President talk about China, 565 00:29:50,840 --> 00:29:53,560 Speaker 14: which of course is a crucial topic between the US 566 00:29:53,600 --> 00:29:58,200 Speaker 14: and the Philippines. China's one of China's closest neighbors. The 567 00:29:58,200 --> 00:30:01,600 Speaker 14: Philippines has to balance this relatelationship, and we heard Marcos 568 00:30:01,640 --> 00:30:04,320 Speaker 14: and Trump both talk about this, and Trump also indicated 569 00:30:04,560 --> 00:30:09,000 Speaker 14: he had accepted taken an invitation from President's Shushin Ping 570 00:30:09,040 --> 00:30:11,280 Speaker 14: of China and was looking at possibly accepting it and 571 00:30:11,280 --> 00:30:13,400 Speaker 14: would make a decision on that in the near future. 572 00:30:13,520 --> 00:30:17,080 Speaker 3: Carol Okay, Bloombergs, Mike Shephard, thank you very much. We 573 00:30:17,080 --> 00:30:19,280 Speaker 3: also expect to hear from the President later this week 574 00:30:19,320 --> 00:30:21,640 Speaker 3: in a speech on AI. Let's get from DC to 575 00:30:21,680 --> 00:30:25,000 Speaker 3: the global perspective of how the AI race is unfolding. 576 00:30:25,080 --> 00:30:28,200 Speaker 3: Nicolaus Lang is with us for that, managing director, senior 577 00:30:28,240 --> 00:30:31,680 Speaker 3: partner Boston Consulting Group and the global leader of BCG's 578 00:30:31,720 --> 00:30:35,880 Speaker 3: think tank, the BCG Henderson Institute. We've covered a lot 579 00:30:35,880 --> 00:30:40,840 Speaker 3: in this program, how codified Europe's framework is around AI. 580 00:30:41,560 --> 00:30:45,080 Speaker 3: We're waiting on America to follow suit in many respects. 581 00:30:45,160 --> 00:30:48,160 Speaker 3: The President will outline a vision on Wednesday. How would 582 00:30:48,200 --> 00:30:52,280 Speaker 3: you summarize the differences of approach between those two jurisdictions. 583 00:30:54,000 --> 00:30:55,960 Speaker 15: Yeah, well, thank you for having me, and I think 584 00:30:56,000 --> 00:31:00,080 Speaker 15: when we look at the global geopolitic SOFII. What we 585 00:31:00,120 --> 00:31:03,280 Speaker 15: have seen here in our research is that we see 586 00:31:03,320 --> 00:31:05,840 Speaker 15: two superpowers, Yeah, on one side, the US. On the 587 00:31:05,840 --> 00:31:10,640 Speaker 15: other side, China, I think, leading in talent, compute, power, 588 00:31:11,640 --> 00:31:14,760 Speaker 15: foundation models. And then you have Europe and the Middle East, 589 00:31:14,760 --> 00:31:19,240 Speaker 15: which are these middle powers challenging the superpowers. And I 590 00:31:19,280 --> 00:31:22,239 Speaker 15: think that's where we will see the dynamic unfold in 591 00:31:22,280 --> 00:31:27,760 Speaker 15: the years to come. You just mentioned the approach to regulation. 592 00:31:27,920 --> 00:31:29,800 Speaker 15: I think this is different. I think in the US, 593 00:31:29,840 --> 00:31:32,479 Speaker 15: it's different in Europe, it's different in China. And the 594 00:31:32,520 --> 00:31:36,840 Speaker 15: realities that we need to adapt to these three constituencies 595 00:31:36,840 --> 00:31:38,440 Speaker 15: and these three approaches to regulation. 596 00:31:39,040 --> 00:31:42,920 Speaker 4: Very briefly, Niklaus, the companies you advise, where are they 597 00:31:43,080 --> 00:31:45,840 Speaker 4: trying to navigate? They're looking at expanding data centers within 598 00:31:45,880 --> 00:31:47,840 Speaker 4: the Middle East, within the US. Where are people tending 599 00:31:47,840 --> 00:31:49,520 Speaker 4: to default right now for their expertise? 600 00:31:51,080 --> 00:31:53,080 Speaker 15: Yeah, Well, I think what we see in AI is 601 00:31:53,080 --> 00:31:55,360 Speaker 15: a fragmented world as we see it also in other 602 00:31:55,400 --> 00:31:58,760 Speaker 15: parts of geopolitics, and I think companies realize that they 603 00:31:58,800 --> 00:32:01,040 Speaker 15: need to be present in the different regions, both with 604 00:32:01,280 --> 00:32:04,880 Speaker 15: data centers, with AI talent, and also with capital and investment. 605 00:32:05,280 --> 00:32:07,960 Speaker 15: And I think that what makes this jubiled pigs of 606 00:32:08,000 --> 00:32:10,440 Speaker 15: AI space so interesting because I think we have a 607 00:32:10,480 --> 00:32:13,800 Speaker 15: huge dynamic, a regional dynamic which is very different from 608 00:32:13,840 --> 00:32:14,959 Speaker 15: what we see in other areas. 609 00:32:15,520 --> 00:32:17,320 Speaker 4: We're going to be hearing much more about that regional 610 00:32:17,360 --> 00:32:20,400 Speaker 4: dynamic and an event later this week, of course, over 611 00:32:20,480 --> 00:32:23,840 Speaker 4: in DC Nicolaus Lang fascinating from the Boston Consulting Group. 612 00:32:23,880 --> 00:32:26,000 Speaker 4: We wish we had had more time. Meanwhile, though, that 613 00:32:26,040 --> 00:32:28,000 Speaker 4: does it for this edition of Bloomberg Tech Ed. 614 00:32:28,960 --> 00:32:31,080 Speaker 3: Yeah, don't forget check out the podcast where we can 615 00:32:31,120 --> 00:32:33,520 Speaker 3: retap all of today's Bloomberg stories and a lot more 616 00:32:33,560 --> 00:32:34,240 Speaker 3: that was going on. 617 00:32:34,400 --> 00:32:35,160 Speaker 2: You know where to find it. 618 00:32:35,160 --> 00:32:37,760 Speaker 3: It's on all the Bloomberg platforms, the Bloomberg terminal and 619 00:32:37,920 --> 00:32:41,880 Speaker 3: online on Apple, Spotify, and iHeart this is Bloomberg Tech