1 00:00:02,520 --> 00:00:12,600 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. Bloomberg Tech is a 2 00:00:12,640 --> 00:00:16,360 Speaker 1: live from coast to coast with Caroline Hyde in New 3 00:00:16,440 --> 00:00:20,120 Speaker 1: York and ed Lo Loow in San Francisco. 4 00:00:22,320 --> 00:00:23,920 Speaker 2: This is Bloomberg Tech coming up. 5 00:00:23,960 --> 00:00:27,000 Speaker 3: In video, CEO Jensen Wong makes a big forecast for 6 00:00:27,040 --> 00:00:28,520 Speaker 3: the AI buildout. 7 00:00:30,280 --> 00:00:36,400 Speaker 4: Writ your rice down, I see through twenty twenty seven 8 00:00:39,040 --> 00:00:42,199 Speaker 4: at least one trillion dollars. 9 00:00:43,120 --> 00:00:46,239 Speaker 5: As we discussed the AI and MNA landscape with the 10 00:00:46,280 --> 00:00:50,199 Speaker 5: CEO of IBM hot off the company's own GtC announcement. 11 00:00:50,560 --> 00:00:54,040 Speaker 3: And Gecko Robotics works to deploy its AI powered robots 12 00:00:54,080 --> 00:00:58,400 Speaker 3: to assess the condition and readiness of the US Navy's warships. 13 00:00:58,920 --> 00:01:00,920 Speaker 6: A first we turn around ten two. 14 00:01:01,040 --> 00:01:04,440 Speaker 5: Well, once again a market that is focused on geopolitics, 15 00:01:04,680 --> 00:01:07,039 Speaker 5: on war, but also on the eve of the federal 16 00:01:07,080 --> 00:01:08,320 Speaker 5: reserve rate decision. 17 00:01:08,360 --> 00:01:11,160 Speaker 6: We're currently seeing some buying of stocks. In fact, eighty. 18 00:01:11,000 --> 00:01:13,480 Speaker 5: Nine of these one hundred names in the green for 19 00:01:13,520 --> 00:01:15,640 Speaker 5: the NASAK one hundred. We're up for a second straight date. 20 00:01:15,720 --> 00:01:18,080 Speaker 5: We're up eight ten percent, and we're seeing a little 21 00:01:18,120 --> 00:01:19,880 Speaker 5: bit more of a risk on attitude there and you're 22 00:01:19,880 --> 00:01:22,479 Speaker 5: going to dig into well, perhaps one of the catalysts. 23 00:01:23,080 --> 00:01:26,040 Speaker 3: As I stand here right now in video, shares are 24 00:01:26,040 --> 00:01:27,960 Speaker 3: completely flat in the session, but they've been on a 25 00:01:28,040 --> 00:01:32,520 Speaker 3: roller coaster across two sessions. Late Monday, when jensenmong had 26 00:01:32,520 --> 00:01:35,319 Speaker 3: his keynote, you can see the big spike almost five 27 00:01:35,319 --> 00:01:39,360 Speaker 3: percent in the session of what was extended visibility into 28 00:01:39,400 --> 00:01:41,080 Speaker 3: the demand for invidious products. 29 00:01:41,120 --> 00:01:43,840 Speaker 2: Here's what he said, right, you're rise down. 30 00:01:45,760 --> 00:01:54,480 Speaker 4: I see through twenty twenty seven at least one trillion dollars. 31 00:01:54,600 --> 00:01:58,040 Speaker 4: A trillion dollars is an enormous amount of infrastructure. That 32 00:01:58,560 --> 00:02:01,880 Speaker 4: infrastructure investment you could make on Nvidia, you could make 33 00:02:01,960 --> 00:02:05,400 Speaker 4: with complete confidence. We have now proven that. 34 00:02:07,520 --> 00:02:10,960 Speaker 3: Let's get right into it with Bloomberg's Equities reporter Rhyan Vslica. 35 00:02:11,000 --> 00:02:13,560 Speaker 3: I mean you've summarized what the cell sized reaction has 36 00:02:13,600 --> 00:02:15,880 Speaker 3: been to that one trillion dollar number. 37 00:02:16,360 --> 00:02:18,799 Speaker 2: It's mixed, but on the whole bullish. 38 00:02:18,840 --> 00:02:21,880 Speaker 7: What do you say, Yeah, absolutely, I would say people 39 00:02:21,960 --> 00:02:25,280 Speaker 7: are in general pretty positive on this target. It really 40 00:02:25,280 --> 00:02:29,080 Speaker 7: speaks to how much visibility in Vidia has growing forward. 41 00:02:29,120 --> 00:02:31,880 Speaker 7: It really speaks to how they expect all this AI 42 00:02:31,960 --> 00:02:35,320 Speaker 7: infrastructure spending to remain pretty durable, which is something that 43 00:02:35,360 --> 00:02:37,840 Speaker 7: has come under question this year. And led to a 44 00:02:37,880 --> 00:02:41,120 Speaker 7: lot of volatility across the tech space. However, I think 45 00:02:41,120 --> 00:02:43,600 Speaker 7: it also just speaks to just a certain amount of 46 00:02:43,960 --> 00:02:47,160 Speaker 7: uncertainty and a certain amount of hesitancy when it comes 47 00:02:47,240 --> 00:02:50,079 Speaker 7: to this company and this stock. Now we wrote about 48 00:02:50,120 --> 00:02:52,240 Speaker 7: this earlier this week, but Nvidia has been stuck in 49 00:02:52,280 --> 00:02:55,760 Speaker 7: a pretty tight trading range for months. It's been unable 50 00:02:55,760 --> 00:02:58,480 Speaker 7: to break out, even on the back of strong earnings. 51 00:02:58,680 --> 00:03:01,120 Speaker 7: Even on the back of yes today's announcement, we did 52 00:03:01,120 --> 00:03:03,080 Speaker 7: see a brief spike, but then as people sort of 53 00:03:03,120 --> 00:03:06,239 Speaker 7: dissected the target, it came back down again. So I 54 00:03:06,240 --> 00:03:08,720 Speaker 7: think there's still a lot of skepticism out there. Now, 55 00:03:08,760 --> 00:03:11,040 Speaker 7: I would flag that it is hosting an analyst Q 56 00:03:11,160 --> 00:03:12,680 Speaker 7: and A. I think it starts in about an hour. 57 00:03:12,919 --> 00:03:14,240 Speaker 7: That is something. I think people are going to be 58 00:03:14,240 --> 00:03:17,840 Speaker 7: really drilling into this one trillion number trying to understand it, 59 00:03:18,160 --> 00:03:20,400 Speaker 7: and maybe that's something that will spur a little bit 60 00:03:20,440 --> 00:03:22,320 Speaker 7: more gaining. But so far it seems like there's been 61 00:03:22,360 --> 00:03:23,840 Speaker 7: nothing that has been able to lift the stock. 62 00:03:23,960 --> 00:03:25,760 Speaker 5: Yeah, because of those analysts, So it's going to be 63 00:03:25,840 --> 00:03:29,280 Speaker 5: tuning in seventy five, say by the stock only one 64 00:03:29,480 --> 00:03:33,000 Speaker 5: as ever, says stell Cell no, then to see more 65 00:03:33,040 --> 00:03:35,080 Speaker 5: broadly at price target in the next twelve months of 66 00:03:35,080 --> 00:03:37,320 Speaker 5: two hundred and sixty nine dollars, well above where we 67 00:03:37,400 --> 00:03:39,680 Speaker 5: currently trade Ryan. So they're still just waiting on some 68 00:03:39,800 --> 00:03:40,560 Speaker 5: other catalyst. 69 00:03:41,920 --> 00:03:43,760 Speaker 7: Yeah, it's hard to know what the catalyst is going 70 00:03:43,800 --> 00:03:43,920 Speaker 7: to be. 71 00:03:44,000 --> 00:03:44,520 Speaker 2: At this point. 72 00:03:44,520 --> 00:03:47,080 Speaker 7: They've really talked about their product lineup, they've talked about 73 00:03:47,560 --> 00:03:50,720 Speaker 7: their growth expectations. The multiple has really come in. It's 74 00:03:50,760 --> 00:03:53,040 Speaker 7: i think one of the cheapest stocks in the mag seven. 75 00:03:53,680 --> 00:03:55,480 Speaker 7: There is still a lot of questions about what is 76 00:03:55,520 --> 00:03:57,640 Speaker 7: growth going to look like, you know, years out, what 77 00:03:57,720 --> 00:04:00,600 Speaker 7: kind of deceleration could we see? And people are factoring that. 78 00:04:00,880 --> 00:04:02,920 Speaker 7: But certainly when you talk to a lot of people, 79 00:04:03,000 --> 00:04:05,480 Speaker 7: everybody still likes n Video. Everybody still thinks it's a 80 00:04:05,600 --> 00:04:08,800 Speaker 7: very well run stock. They don't really see demand drawing 81 00:04:08,920 --> 00:04:10,880 Speaker 7: up anytime soon. But it does seem like there's a 82 00:04:10,920 --> 00:04:14,640 Speaker 7: certain amount of hesitancy. You know, it's almost like they've 83 00:04:14,640 --> 00:04:16,320 Speaker 7: built up a tolerance for the kinds of games that 84 00:04:16,360 --> 00:04:18,120 Speaker 7: we've seen in quarters past, where they would give some 85 00:04:18,200 --> 00:04:19,960 Speaker 7: kind of big target and we'd see the stock jump 86 00:04:20,000 --> 00:04:22,800 Speaker 7: twenty percent. It's a lot harder to imagine anything like 87 00:04:22,839 --> 00:04:24,000 Speaker 7: that happening today. 88 00:04:24,480 --> 00:04:26,320 Speaker 3: Roan, what else you're seeing on your desk this morning 89 00:04:26,320 --> 00:04:28,839 Speaker 3: as it relates to GtC, Like, is there a broader 90 00:04:28,920 --> 00:04:32,240 Speaker 3: market digestion of the two and a half hour speech 91 00:04:32,279 --> 00:04:34,920 Speaker 3: that Jensen Wong gave yesterday? 92 00:04:35,040 --> 00:04:35,840 Speaker 2: Yeah, two and a half hour. 93 00:04:35,880 --> 00:04:37,240 Speaker 7: I did see a lot of people saying, you know, 94 00:04:37,240 --> 00:04:38,640 Speaker 7: this could have been a lot shorter, so that was 95 00:04:38,680 --> 00:04:40,560 Speaker 7: kind of funny. But beyond that, we are seeing a 96 00:04:40,600 --> 00:04:42,640 Speaker 7: lot of people just digesting this. We saw some moves 97 00:04:42,720 --> 00:04:46,800 Speaker 7: yesterday and IBM and optical companies and Intel, so there 98 00:04:46,839 --> 00:04:48,760 Speaker 7: are a lot of companies. Like it hasn't quite been 99 00:04:48,800 --> 00:04:50,600 Speaker 7: the same way that it was in years past, where 100 00:04:50,600 --> 00:04:53,159 Speaker 7: they would mention a company and we'd see that stock rally. 101 00:04:53,320 --> 00:04:55,240 Speaker 7: Everything is a bit more I muted right now. People 102 00:04:55,279 --> 00:04:57,880 Speaker 7: are really trying to understand this and trying to understand 103 00:04:57,920 --> 00:05:00,600 Speaker 7: where this trade goes from here. I've seen the sort 104 00:05:00,600 --> 00:05:02,800 Speaker 7: of excitement that we saw really at the onset of 105 00:05:02,839 --> 00:05:03,719 Speaker 7: the AI era. 106 00:05:04,240 --> 00:05:07,159 Speaker 5: Most Roun Vasilica with all the technicals on trading this stock. Now, 107 00:05:07,240 --> 00:05:10,320 Speaker 5: let's get you the fundamentals the market perspective. With Daniel Filling, 108 00:05:10,320 --> 00:05:13,719 Speaker 5: his senior research analyst and portfolio manager over it, sounds capital, Daniel. 109 00:05:14,200 --> 00:05:17,520 Speaker 5: I mean, let's talk about the demand for in videos products. 110 00:05:17,600 --> 00:05:20,599 Speaker 5: You still see them as insatiable. We've got GPU CPU 111 00:05:20,720 --> 00:05:24,279 Speaker 5: help you. What drove your optimism from yesterday's speech. 112 00:05:25,640 --> 00:05:28,440 Speaker 8: Yes, so, I think the proof is in the pudding, 113 00:05:28,480 --> 00:05:30,120 Speaker 8: I might say so when we look at sort of 114 00:05:30,160 --> 00:05:33,840 Speaker 8: the labs and the revenue growth this year today, it 115 00:05:33,880 --> 00:05:37,160 Speaker 8: has been massively accelerating. Right Zanthropics public's numbers went from 116 00:05:37,240 --> 00:05:41,360 Speaker 8: fourteen to nineteen billion literally within a few weeks. And 117 00:05:41,400 --> 00:05:43,279 Speaker 8: I think the crux of what is going on here 118 00:05:43,400 --> 00:05:47,200 Speaker 8: is that agentic ai is here. It is as viral 119 00:05:47,279 --> 00:05:49,640 Speaker 8: as zoom was at the beginning of COVID. And what 120 00:05:49,680 --> 00:05:52,000 Speaker 8: I mean with that is that once somebody starts using it, 121 00:05:52,000 --> 00:05:56,200 Speaker 8: it starts doing amazing things. Every colleague will follow over time, 122 00:05:56,560 --> 00:05:59,000 Speaker 8: and we just don't have enough computer to really satiate this. 123 00:05:59,279 --> 00:06:02,080 Speaker 9: And this is like a multi year process of sort. 124 00:06:01,880 --> 00:06:04,880 Speaker 8: Of everybody is starting to use agentic ai within their 125 00:06:04,880 --> 00:06:08,120 Speaker 8: own workflows. We have a billion people as knowledge workers, 126 00:06:08,160 --> 00:06:11,080 Speaker 8: and we maybe have a few million agents as of today, 127 00:06:11,400 --> 00:06:13,280 Speaker 8: so to me and to us, so this feels like 128 00:06:13,680 --> 00:06:15,680 Speaker 8: sort of the iPhone moment of two thousand and seven 129 00:06:15,720 --> 00:06:18,120 Speaker 8: and eight where everybody will want to buy an iPhone 130 00:06:18,160 --> 00:06:20,560 Speaker 8: and everybody will want to run an agent, which means 131 00:06:20,560 --> 00:06:22,880 Speaker 8: the numbers will likely continue to be much, much much 132 00:06:22,880 --> 00:06:23,640 Speaker 8: bigger over time. 133 00:06:23,760 --> 00:06:26,359 Speaker 5: And Jensen wants to be the infrastructure for that, but 134 00:06:26,440 --> 00:06:29,720 Speaker 5: also not just the chips, but the operating system that 135 00:06:30,279 --> 00:06:32,800 Speaker 5: goes with. Talk to us about openclore and how they're 136 00:06:32,839 --> 00:06:35,680 Speaker 5: developing sort of their own operating system for that and 137 00:06:35,720 --> 00:06:36,479 Speaker 5: for a gendki. 138 00:06:37,320 --> 00:06:38,720 Speaker 9: Yeah, so it's very interesting. 139 00:06:39,480 --> 00:06:43,599 Speaker 8: So Nvidia created the security framework to wrap these open 140 00:06:43,600 --> 00:06:46,880 Speaker 8: source agents within an enterprise, right And why do they 141 00:06:46,880 --> 00:06:49,000 Speaker 8: do that, right, Because then an agent will be able 142 00:06:49,040 --> 00:06:53,800 Speaker 8: to access your calendar, your payment mechanisms, your documents, so 143 00:06:53,800 --> 00:06:56,280 Speaker 8: it's got to be very, very safe and secure. So 144 00:06:56,320 --> 00:06:58,480 Speaker 8: this is one of the key bottlenecks that people have 145 00:06:58,560 --> 00:07:02,120 Speaker 8: to overcome to install all agents within the enterprise space. 146 00:07:02,560 --> 00:07:04,800 Speaker 8: And then, as n Video always does, they sort of 147 00:07:04,839 --> 00:07:07,480 Speaker 8: create their own markets. So they created this framework called 148 00:07:07,520 --> 00:07:10,400 Speaker 8: Nemo Claw, which means that everybody, every enterprise in the 149 00:07:10,400 --> 00:07:13,920 Speaker 8: world now can implement an agent locally on their devices, 150 00:07:13,960 --> 00:07:16,360 Speaker 8: which means that this growth can really start to accelerate 151 00:07:16,640 --> 00:07:19,040 Speaker 8: within the enterprise because the security is there. 152 00:07:20,320 --> 00:07:23,080 Speaker 3: Daniel my Bloomberg Terminal tells me SANS Capital has about 153 00:07:23,080 --> 00:07:24,680 Speaker 3: twenty million in video shares. 154 00:07:24,720 --> 00:07:25,160 Speaker 2: Is that right? 155 00:07:26,720 --> 00:07:27,920 Speaker 9: Yes, Ballparker. 156 00:07:29,680 --> 00:07:32,720 Speaker 3: Let's go back to the number twenty twenty five. Through 157 00:07:32,720 --> 00:07:35,600 Speaker 3: the end of calendar twenty twenty six five fiscal quarters 158 00:07:36,040 --> 00:07:39,920 Speaker 3: was five hundred billion dollars of demand just for Blackwell 159 00:07:39,960 --> 00:07:43,560 Speaker 3: and Reuben. I think that Bernstein were able to confirm 160 00:07:43,600 --> 00:07:47,600 Speaker 3: with in VIDEOSCFO that the one trillion is also Blackwell 161 00:07:47,600 --> 00:07:51,360 Speaker 3: and Rubin, including associated networking. But there was a bit 162 00:07:51,440 --> 00:07:55,760 Speaker 3: that Jensen said in passing right after that sentence, which 163 00:07:55,960 --> 00:07:59,480 Speaker 3: was we will be short. In other words, I think 164 00:07:59,480 --> 00:08:03,200 Speaker 3: they're still massively supply constrained relative to demand. How are 165 00:08:03,200 --> 00:08:04,000 Speaker 3: you interpreting that? 166 00:08:05,080 --> 00:08:07,480 Speaker 9: Yeah, so I would agree. I think there's two things 167 00:08:07,480 --> 00:08:08,280 Speaker 9: we need to see here. 168 00:08:08,960 --> 00:08:12,920 Speaker 8: One, the hyperscaler revenue growth has to accelerate to get 169 00:08:13,200 --> 00:08:16,400 Speaker 8: you know, to basically maybe get a market away from 170 00:08:16,480 --> 00:08:19,640 Speaker 8: focusing on some sort of peak cycle concern and back 171 00:08:19,680 --> 00:08:21,840 Speaker 8: to focusing on sort of this growth and tokens that 172 00:08:21,920 --> 00:08:24,960 Speaker 8: Jensen is talking about. So once we start seeing hyper 173 00:08:25,000 --> 00:08:28,600 Speaker 8: scalers really reaccelerate because they're buying all these chips, I 174 00:08:28,640 --> 00:08:31,240 Speaker 8: think that concern goes away. And then the second piece 175 00:08:31,280 --> 00:08:36,480 Speaker 8: is the demand is truly viral, right, so everybody. 176 00:08:35,960 --> 00:08:36,440 Speaker 9: In the world. 177 00:08:36,520 --> 00:08:38,559 Speaker 8: I think we'll start seeing these agents as a massive 178 00:08:38,559 --> 00:08:41,800 Speaker 8: productivity tool. If your colleagues are five to ten times 179 00:08:41,840 --> 00:08:44,640 Speaker 8: more productive than you, then you have to implement it 180 00:08:44,640 --> 00:08:47,920 Speaker 8: as well. And we think the penetration of agents today 181 00:08:47,960 --> 00:08:51,000 Speaker 8: is within single digits percentages, right, So as we go 182 00:08:51,040 --> 00:08:53,760 Speaker 8: from single digits tow one hundred percent, it's a billion people. 183 00:08:54,160 --> 00:08:56,959 Speaker 8: That means you need so much more canacter works. 184 00:08:57,760 --> 00:09:01,120 Speaker 3: The associated data point with the one trillion bar chart 185 00:09:01,320 --> 00:09:04,160 Speaker 3: was the pie chart that sixty percent of that demand 186 00:09:04,240 --> 00:09:06,600 Speaker 3: is still coming from the hyperscalers. It wasn't that long 187 00:09:06,640 --> 00:09:10,000 Speaker 3: ago that we were very focused on how much in 188 00:09:10,040 --> 00:09:14,080 Speaker 3: Nvidia diversified away from the hyperscalers. It seems like that 189 00:09:14,240 --> 00:09:16,280 Speaker 3: hyperscalar portions just going up. 190 00:09:18,200 --> 00:09:18,640 Speaker 9: Yes and no. 191 00:09:18,840 --> 00:09:21,640 Speaker 8: Right, So hyperscalers are by far the biggest purchases of 192 00:09:21,679 --> 00:09:23,840 Speaker 8: these things. But there's also the neo clouds, which are 193 00:09:23,880 --> 00:09:27,880 Speaker 8: investing heavily and actually also very interesting enough governments will 194 00:09:27,880 --> 00:09:29,840 Speaker 8: be able to invest more. 195 00:09:29,480 --> 00:09:31,640 Speaker 9: Going forward as well. And in Video is playing a 196 00:09:31,679 --> 00:09:32,679 Speaker 9: key role in that, right. 197 00:09:32,679 --> 00:09:36,720 Speaker 8: They're one of the leading open source providers of these models, 198 00:09:36,720 --> 00:09:39,120 Speaker 8: which means as a government, you can take the open 199 00:09:39,160 --> 00:09:43,400 Speaker 8: source nemotron model from Nvideo implemented locally, you can buy 200 00:09:43,480 --> 00:09:45,880 Speaker 8: all the chips from Nvidia at the same time and 201 00:09:46,000 --> 00:09:49,000 Speaker 8: sort of run your own local cloud within your country. 202 00:09:49,360 --> 00:09:51,199 Speaker 8: So I think those three will be quite important, but 203 00:09:51,320 --> 00:09:53,679 Speaker 8: hyperscalers will always be very important in all of us. 204 00:09:53,960 --> 00:09:56,520 Speaker 5: Daniel, It's interesting that really where Jensen always tries to 205 00:09:56,600 --> 00:09:59,160 Speaker 5: lean in is differentiation, the fact that he's got the 206 00:09:59,240 --> 00:10:01,240 Speaker 5: right chip for the right workload at the right time, 207 00:10:01,280 --> 00:10:02,320 Speaker 5: and that's why they started. 208 00:10:02,080 --> 00:10:04,240 Speaker 6: To talk a lot more about inference and growth for example. 209 00:10:04,280 --> 00:10:07,719 Speaker 5: But I'm interested about he usually pushes us forward a 210 00:10:07,760 --> 00:10:08,480 Speaker 5: little bit more. 211 00:10:08,760 --> 00:10:10,520 Speaker 6: Yes, we've already actually heard a. 212 00:10:10,440 --> 00:10:13,160 Speaker 5: Lot about Vera Rubin, we know a lot about Blackwell, 213 00:10:13,360 --> 00:10:15,760 Speaker 5: but why not even push us into feyn minimal. Why 214 00:10:15,800 --> 00:10:18,160 Speaker 5: not hear about the next situation, because it's like every 215 00:10:18,200 --> 00:10:21,480 Speaker 5: single year we've got a new architecture coming for us. 216 00:10:21,840 --> 00:10:24,600 Speaker 8: Yes, I would actually say what happened yesterday is quiet 217 00:10:24,760 --> 00:10:29,080 Speaker 8: revolution area. So Nvidia is the first company that we'll 218 00:10:29,120 --> 00:10:32,320 Speaker 8: have two different types of chips for inference. So for 219 00:10:32,480 --> 00:10:35,400 Speaker 8: calling on the AI models to get an answer, there's 220 00:10:35,440 --> 00:10:37,680 Speaker 8: a new LPU chip which is super fast. 221 00:10:37,880 --> 00:10:41,000 Speaker 9: Nobody else has that, and that's combined. 222 00:10:40,520 --> 00:10:43,280 Speaker 8: Together with sort of the old sort of the generalized 223 00:10:43,320 --> 00:10:45,760 Speaker 8: GPUs that they always had in the past. And then 224 00:10:46,200 --> 00:10:50,040 Speaker 8: he did give an astonishing quote that LPU are the 225 00:10:50,080 --> 00:10:53,359 Speaker 8: fast chips. There will be thirty five times more performant 226 00:10:53,400 --> 00:10:55,640 Speaker 8: on the performance per work basis, and anything that was 227 00:10:55,679 --> 00:10:58,199 Speaker 8: there before thirty five times. So I do think those 228 00:10:58,280 --> 00:11:01,400 Speaker 8: jumps are just as good, maybe even better than in 229 00:11:01,440 --> 00:11:02,319 Speaker 8: the past. 230 00:11:03,200 --> 00:11:05,960 Speaker 3: Daniel, you clearly have a command of the spec right 231 00:11:06,040 --> 00:11:10,960 Speaker 3: generations generation on the chips. I'll take it back to 232 00:11:11,040 --> 00:11:13,920 Speaker 3: the stock. You know, Carr outlined where the cell side 233 00:11:13,960 --> 00:11:16,520 Speaker 3: stands on this name. The question for you and for 234 00:11:16,600 --> 00:11:19,800 Speaker 3: sans capital, do you buy more based on what you 235 00:11:19,920 --> 00:11:21,319 Speaker 3: heard on stage in San Jose? 236 00:11:22,720 --> 00:11:23,040 Speaker 9: Yes. 237 00:11:23,400 --> 00:11:27,320 Speaker 8: So I think this event itself has been very reaffirming 238 00:11:27,400 --> 00:11:30,559 Speaker 8: of the future, but the entire debate about the stock 239 00:11:30,600 --> 00:11:33,960 Speaker 8: I think will be resolved truly by this. The market 240 00:11:34,200 --> 00:11:36,720 Speaker 8: is looking at n Video as a business that is. 241 00:11:36,760 --> 00:11:39,160 Speaker 9: At peak revenue and peak earnings. 242 00:11:40,040 --> 00:11:42,800 Speaker 8: We disagree, and the reason why we disagree is everything 243 00:11:42,800 --> 00:11:44,439 Speaker 8: we discussed before agentic. 244 00:11:44,080 --> 00:11:45,840 Speaker 9: Ai is exploding in terms of demand. 245 00:11:46,120 --> 00:11:49,520 Speaker 8: Now this all will get resolved once the free cash 246 00:11:49,559 --> 00:11:52,440 Speaker 8: flow and the revenue growth of the buyers of these chips. 247 00:11:52,440 --> 00:11:54,480 Speaker 8: Are you that sixty percent of the hyperskillers, for example, 248 00:11:54,520 --> 00:11:57,960 Speaker 8: will start growing again, And whenever you start seeing this, 249 00:11:57,960 --> 00:12:00,640 Speaker 8: this entire debate will be resolved because the market will 250 00:12:00,640 --> 00:12:04,240 Speaker 8: say we're going from peak concerns to actually this is 251 00:12:04,240 --> 00:12:07,760 Speaker 8: a very structural, durable growth trajectory the next five to 252 00:12:07,800 --> 00:12:11,160 Speaker 8: ten years. I don't know precisely when this will be solved, 253 00:12:11,360 --> 00:12:12,880 Speaker 8: but my guess is that we're going to see a 254 00:12:12,920 --> 00:12:16,719 Speaker 8: significant acceleration hyper skill and revenue growth within the next 255 00:12:16,800 --> 00:12:18,920 Speaker 8: one or two years because they're buying hundreds of billions 256 00:12:18,920 --> 00:12:22,160 Speaker 8: of dollars of these chips. And then maybe equally important, 257 00:12:22,480 --> 00:12:25,400 Speaker 8: the return or the payback periods off these chips is 258 00:12:25,440 --> 00:12:28,160 Speaker 8: actually improved in the past twelve months because we're completely 259 00:12:28,200 --> 00:12:31,839 Speaker 8: sold out, so basically you're buying a GPU and you're 260 00:12:31,840 --> 00:12:34,480 Speaker 8: getting your money back in record time, which again then 261 00:12:34,559 --> 00:12:36,960 Speaker 8: leads to the point of the hyper skills. We'll start 262 00:12:37,000 --> 00:12:39,880 Speaker 8: seeing more growth, better free cash for growth, which should 263 00:12:39,880 --> 00:12:42,120 Speaker 8: translate then hopefully to a positive outcome. 264 00:12:42,160 --> 00:12:42,880 Speaker 9: Fund video. 265 00:12:44,160 --> 00:12:46,680 Speaker 2: Daniel Pelling from SANSKAF, so, thank you very much. 266 00:12:52,679 --> 00:12:54,320 Speaker 5: Let's check in on the sheds of Uber and Lyft 267 00:12:54,360 --> 00:12:57,800 Speaker 5: right now in the green, as we'll see significantly, and 268 00:12:57,840 --> 00:13:01,240 Speaker 5: that's after both companies announced deepening partnerships within Video, which 269 00:13:01,280 --> 00:13:02,640 Speaker 5: is currently down three tens percent. 270 00:13:02,800 --> 00:13:03,000 Speaker 2: Look. 271 00:13:03,080 --> 00:13:05,600 Speaker 5: Uber says it plans to drull out a global fleet 272 00:13:05,600 --> 00:13:08,200 Speaker 5: of in Vidia powered self driving vehicles across twenty eight. 273 00:13:08,120 --> 00:13:11,400 Speaker 6: Cities by twenty twenty eight. Lift Meanwhile, we'll use in. 274 00:13:11,440 --> 00:13:13,959 Speaker 5: Video's AI to strengthen machine learning systems across it's on 275 00:13:14,200 --> 00:13:17,520 Speaker 5: operations for more blue most consumer apps and gig economy, 276 00:13:17,559 --> 00:13:20,400 Speaker 5: reported Natalie Lang joints this. Now, what's interesting with Uber 277 00:13:20,760 --> 00:13:23,839 Speaker 5: is that we've had some big, bold ambitions articulated back 278 00:13:23,880 --> 00:13:26,200 Speaker 5: in February was it and one hundred thousand cars are 279 00:13:26,200 --> 00:13:28,920 Speaker 5: going to be on the road in this partnership within video. 280 00:13:29,040 --> 00:13:31,120 Speaker 6: But what's the timeline now looking like? 281 00:13:31,760 --> 00:13:35,200 Speaker 10: So the updated timeline is that these vehicles will be 282 00:13:35,679 --> 00:13:38,880 Speaker 10: scaled across twenty eight studies by twenty twenty eight, and 283 00:13:38,920 --> 00:13:41,760 Speaker 10: that will really start in earnest in twenty twenty seven 284 00:13:41,800 --> 00:13:43,479 Speaker 10: in Los Angeles and San Francisco. 285 00:13:45,000 --> 00:13:47,280 Speaker 3: I mean, Uber is about a percentage point of having 286 00:13:47,320 --> 00:13:49,920 Speaker 3: its best days since June of last year. So like 287 00:13:49,960 --> 00:13:52,160 Speaker 3: at first You're like, this is a name check that's 288 00:13:52,240 --> 00:13:56,040 Speaker 3: driving the stock, but really it's it's more updates to. 289 00:13:56,000 --> 00:13:57,240 Speaker 2: An existing partnership. 290 00:13:57,480 --> 00:14:00,240 Speaker 3: What about Lift then, I mean this really is by 291 00:14:00,440 --> 00:14:03,320 Speaker 3: name association because it's more about internal use. 292 00:14:03,960 --> 00:14:07,040 Speaker 10: Yeah, it's about internal use. But part of the release 293 00:14:07,280 --> 00:14:11,880 Speaker 10: also mentions future possible deployments of n video powered vehicles 294 00:14:11,880 --> 00:14:13,679 Speaker 10: on the Lift platform, but we don't have a lot 295 00:14:13,720 --> 00:14:17,839 Speaker 10: of details on the manufacturer partner yet. Whereas for Uber, 296 00:14:18,080 --> 00:14:22,120 Speaker 10: they have a couple of partnerships already announced, such as 297 00:14:22,240 --> 00:14:26,200 Speaker 10: Lucid and neuro vehicles, wave vehicles or with Nissan that 298 00:14:26,240 --> 00:14:30,080 Speaker 10: will that will be powered by video chips and technology. 299 00:14:30,560 --> 00:14:33,080 Speaker 5: I think it sort of goes to what RBC's analyst 300 00:14:33,080 --> 00:14:35,600 Speaker 5: Brad Erickson was spelling out that all of this news 301 00:14:35,680 --> 00:14:40,200 Speaker 5: vindicates perhaps both Uber and Lift's role as platforms in 302 00:14:40,280 --> 00:14:43,360 Speaker 5: the av era. We'd all been worried about competition, but 303 00:14:43,440 --> 00:14:45,680 Speaker 5: actually they're the ones that can bring it all together. 304 00:14:45,480 --> 00:14:47,880 Speaker 10: Right, Yeah, and in this further shows it's not just 305 00:14:47,920 --> 00:14:50,320 Speaker 10: a demand generation platform. It's not just going to be 306 00:14:50,360 --> 00:14:53,680 Speaker 10: an app that will allow people to hail a robotaxi. 307 00:14:54,120 --> 00:14:56,400 Speaker 10: But here we can see that Uber wants to be 308 00:14:56,480 --> 00:14:59,560 Speaker 10: the fleet partner. It wants to support some remote assistance 309 00:14:59,640 --> 00:15:04,360 Speaker 10: operation for these fleets that they run themselves with partners 310 00:15:04,360 --> 00:15:04,800 Speaker 10: as well. 311 00:15:06,520 --> 00:15:08,800 Speaker 3: Blubos Natalie Lung across all the movies in the G 312 00:15:08,880 --> 00:15:12,760 Speaker 3: economy space. After GtC Thank You, another deal announced Amidia's 313 00:15:12,800 --> 00:15:16,160 Speaker 3: GtC conference. IBM will collaborate with the ship maker on 314 00:15:16,200 --> 00:15:19,640 Speaker 3: an open source project aim to help enterprises use AI 315 00:15:19,680 --> 00:15:22,880 Speaker 3: at scale. We sat down with IBM CEO Avin Krishna 316 00:15:23,040 --> 00:15:25,040 Speaker 3: for more in the deal and on his take about 317 00:15:25,040 --> 00:15:26,400 Speaker 3: the wider M and A landscape. 318 00:15:26,440 --> 00:15:26,920 Speaker 2: Listen to this. 319 00:15:27,880 --> 00:15:32,080 Speaker 11: I think the regulatory environment is definitely friendlier where we 320 00:15:32,160 --> 00:15:32,560 Speaker 11: got this. 321 00:15:32,640 --> 00:15:35,920 Speaker 2: Done in just under four months. 322 00:15:35,560 --> 00:15:38,240 Speaker 11: Where you used to take a lot longer a few 323 00:15:38,280 --> 00:15:38,840 Speaker 11: years back. 324 00:15:39,320 --> 00:15:43,320 Speaker 6: If regulatory environment is friendlier, should we be doing more 325 00:15:43,320 --> 00:15:43,520 Speaker 6: of it? 326 00:15:43,560 --> 00:15:45,360 Speaker 5: Should there be more M and A particularly with some 327 00:15:45,440 --> 00:15:49,000 Speaker 5: beaten up overall valuations of software companies at the moment. 328 00:15:49,440 --> 00:15:52,640 Speaker 11: I'll just say watch the space, Oh watch. 329 00:15:52,440 --> 00:15:54,080 Speaker 5: This space, okay, But where would you want to add 330 00:15:54,080 --> 00:15:55,960 Speaker 5: on in this moment and what would make sense to 331 00:15:55,960 --> 00:15:57,119 Speaker 5: be adding to your portfolio? 332 00:15:57,800 --> 00:16:01,160 Speaker 11: So we are very focused hybrid class and AI and 333 00:16:01,240 --> 00:16:04,520 Speaker 11: the intersection. The work we're doing together with Nvidia was 334 00:16:04,560 --> 00:16:08,160 Speaker 11: a five times speed up, so five times, not five percent, 335 00:16:08,320 --> 00:16:11,760 Speaker 11: not a little amount, but five times. So there we 336 00:16:12,000 --> 00:16:15,200 Speaker 11: began to leverage the Nvidia GPUs together with some of 337 00:16:15,280 --> 00:16:20,440 Speaker 11: their cudf or software, combining with our watsonex dot data 338 00:16:21,000 --> 00:16:24,920 Speaker 11: and the example we used was our client Nestley, where 339 00:16:25,000 --> 00:16:27,520 Speaker 11: together we managed to get that speed up across their 340 00:16:27,600 --> 00:16:31,680 Speaker 11: massive amounts of data. And that really is important in 341 00:16:31,720 --> 00:16:34,520 Speaker 11: that case, combining some of the technologies we work on 342 00:16:34,640 --> 00:16:38,080 Speaker 11: also an open source with the Presta Presto data engine 343 00:16:38,680 --> 00:16:41,960 Speaker 11: Nvidia and the example at NESLE. But then we're very 344 00:16:41,960 --> 00:16:44,400 Speaker 11: excited we're going to do more work on that and 345 00:16:44,440 --> 00:16:46,360 Speaker 11: then take it into the market and take it out 346 00:16:46,400 --> 00:16:48,360 Speaker 11: to hundreds of clients from there. 347 00:16:49,800 --> 00:16:53,280 Speaker 3: That was IBN CEO of Increationna Karen. Many more headlines 348 00:16:53,320 --> 00:16:54,000 Speaker 3: out there there is. 349 00:16:54,040 --> 00:16:56,000 Speaker 6: It's time to talk talking tech. 350 00:16:56,120 --> 00:16:58,720 Speaker 5: First up, the UK is ramping up it push into 351 00:16:58,760 --> 00:17:01,560 Speaker 5: quantum computing, committing well the one boy three billion dollars 352 00:17:01,600 --> 00:17:02,680 Speaker 5: over the next four years. 353 00:17:02,720 --> 00:17:05,399 Speaker 6: It's a major bet on a technology. 354 00:17:04,880 --> 00:17:09,320 Speaker 5: Increasingly seen as critical to national security, future economic competitiveness 355 00:17:09,480 --> 00:17:12,880 Speaker 5: for US. Don't expect memoryship shortages to ease anytime soon. 356 00:17:13,119 --> 00:17:15,960 Speaker 6: Sk Heinex well, it says that the crunch could last 357 00:17:16,000 --> 00:17:17,280 Speaker 6: another four to five years. 358 00:17:17,359 --> 00:17:20,880 Speaker 5: Sk Group chairman that's Anthony J notes that while chipmakers 359 00:17:20,880 --> 00:17:21,720 Speaker 5: have already. 360 00:17:21,440 --> 00:17:22,960 Speaker 6: Ramped up, capacity may. 361 00:17:22,800 --> 00:17:25,639 Speaker 5: Not be enough to satisfy demand until around twenty thirty. 362 00:17:26,200 --> 00:17:29,760 Speaker 5: And Samsung, while is already pulling back on its Galaxy 363 00:17:29,960 --> 00:17:32,760 Speaker 5: Z trifled just three months after the launch. So the 364 00:17:32,800 --> 00:17:35,680 Speaker 5: South Korean company plans to halt sales and the nearly 365 00:17:35,720 --> 00:17:38,359 Speaker 5: three thousand dollars device in its home market, and the 366 00:17:38,440 --> 00:17:40,560 Speaker 5: US discontinuation expected to follow it. 367 00:17:41,640 --> 00:17:44,439 Speaker 3: Okay, Coming up amid the chaos of the Iron conflict, 368 00:17:44,840 --> 00:17:49,280 Speaker 3: Bitcoin is emerging as an unlikely oasis for some investors. 369 00:17:49,280 --> 00:17:50,240 Speaker 2: We have more on that. Next. 370 00:17:50,640 --> 00:18:00,320 Speaker 3: This is Bloomberg Tech, one of the world's best known 371 00:18:00,359 --> 00:18:04,240 Speaker 3: investors in Nvidia rc CEO Kathy Woods, so she's optimistic 372 00:18:04,320 --> 00:18:08,119 Speaker 3: about the return on investment from Frontier AI model providers. 373 00:18:08,119 --> 00:18:10,479 Speaker 3: She spoke with Bloomberg's Anda Edwards in London. 374 00:18:10,600 --> 00:18:11,119 Speaker 2: Take a listen. 375 00:18:11,400 --> 00:18:16,080 Speaker 12: We are seeing a revenue generation exploding from the frontier 376 00:18:16,200 --> 00:18:23,320 Speaker 12: model providers. Athropics annualized revenue run rate so ARR went 377 00:18:23,440 --> 00:18:29,240 Speaker 12: from nine billion in December to nineteen billion today. So 378 00:18:29,720 --> 00:18:34,000 Speaker 12: annualizing revenue at nineteen billion that's that's astonishing growth open 379 00:18:34,040 --> 00:18:39,040 Speaker 12: AI from twenty to twenty five billion. The productivity that 380 00:18:39,720 --> 00:18:43,920 Speaker 12: we are enjoying from these large language models is astonishing. 381 00:18:44,000 --> 00:18:48,800 Speaker 12: Even within our own firm. I'm even former skeptics that 382 00:18:49,000 --> 00:18:51,600 Speaker 12: this was going to amount to very much, you know, 383 00:18:51,760 --> 00:18:57,200 Speaker 12: very New York skepticism. They're blown away by what they 384 00:18:57,240 --> 00:18:57,640 Speaker 12: can do. 385 00:18:57,960 --> 00:18:59,920 Speaker 13: Just to take a detail to geopolitics, because it's the 386 00:19:00,040 --> 00:19:02,919 Speaker 13: I'm such a dominant market driver, and I wonder how. 387 00:19:02,760 --> 00:19:04,600 Speaker 6: It influences your thinking. 388 00:19:04,880 --> 00:19:07,440 Speaker 13: We are week three of a wall that's taking place 389 00:19:07,440 --> 00:19:10,399 Speaker 13: in the Middle East. Many tech businesses, of course probably 390 00:19:10,480 --> 00:19:13,040 Speaker 13: quite insulated from everything that is happening there. But I wonder, 391 00:19:13,160 --> 00:19:16,800 Speaker 13: does it what rethinking? Does it prompt at arc? What 392 00:19:19,119 --> 00:19:22,320 Speaker 13: shift in in focus or shift in strategy if any 393 00:19:22,440 --> 00:19:23,639 Speaker 13: does this kind of thing. 394 00:19:23,480 --> 00:19:26,680 Speaker 12: Prompt Yes, well, of course it depends how long term 395 00:19:26,760 --> 00:19:30,240 Speaker 12: this is, and we are thinking it will be short term. 396 00:19:30,320 --> 00:19:34,399 Speaker 12: We do have midterm elections this year and other considerations. 397 00:19:35,359 --> 00:19:38,800 Speaker 12: But of course energy price is going up. Any supply 398 00:19:38,960 --> 00:19:43,640 Speaker 12: shock to the extent it slows unit growth down, it 399 00:19:43,680 --> 00:19:47,560 Speaker 12: will slow down the learning curves associated with various technologies. 400 00:19:47,920 --> 00:19:50,840 Speaker 12: If this is you know, a month or two months 401 00:19:51,800 --> 00:19:53,560 Speaker 12: it's not going to have a big impact at all 402 00:19:53,680 --> 00:19:55,040 Speaker 12: if this is extended. 403 00:19:55,280 --> 00:19:55,760 Speaker 2: COVID. 404 00:19:56,240 --> 00:20:00,800 Speaker 12: We did not understand that the supply shock would reverberate 405 00:20:00,840 --> 00:20:03,840 Speaker 12: for three years and that inflation would take off, and 406 00:20:03,880 --> 00:20:06,960 Speaker 12: that monetary policy would accommodate the inflation the way it did. 407 00:20:07,640 --> 00:20:10,760 Speaker 12: We're not in that situation right now. Monetary policy is 408 00:20:10,800 --> 00:20:15,399 Speaker 12: not accommodating inflation. We're at four point three percent M 409 00:20:15,440 --> 00:20:18,200 Speaker 12: two growth on a year over year basis, so nothing 410 00:20:18,359 --> 00:20:21,080 Speaker 12: like the high twenties, low thirties in COVID. 411 00:20:21,400 --> 00:20:25,080 Speaker 5: Oksen mkathy Wood there alongside Bloomberg's Anna Edwards, and let's 412 00:20:25,080 --> 00:20:26,840 Speaker 5: talk more about the war in Iraq because it is 413 00:20:26,880 --> 00:20:31,240 Speaker 5: fueling volatility across global markets. But cryptocurrencies I'm actually imagining 414 00:20:31,400 --> 00:20:34,439 Speaker 5: as an unexpected bright spot of late Bitcoin and its. 415 00:20:34,280 --> 00:20:35,919 Speaker 6: Peers of rally during these times geo. 416 00:20:35,800 --> 00:20:38,639 Speaker 5: Political stress for more Bloemberg cross ass that reports as well. 417 00:20:38,720 --> 00:20:40,879 Speaker 5: Lee joins us look on the day a little bit 418 00:20:40,920 --> 00:20:41,440 Speaker 5: of pullback. 419 00:20:41,680 --> 00:20:42,720 Speaker 6: Over the last few. 420 00:20:42,560 --> 00:20:46,920 Speaker 5: Weeks, we've actually finally seen somewhat more buying or less parishless. 421 00:20:47,119 --> 00:20:47,840 Speaker 6: I think it's both. 422 00:20:47,840 --> 00:20:50,240 Speaker 14: Bitcoin has been resilient, it's now on a sixth week 423 00:20:50,320 --> 00:20:53,639 Speaker 14: high and unlike it's other asset classes, gold equities or 424 00:20:53,680 --> 00:20:56,960 Speaker 14: even other ASEC classes, it's seen a relative calm. The 425 00:20:57,040 --> 00:20:59,840 Speaker 14: volatility of bitcoin has been kind of flat and really 426 00:21:00,080 --> 00:21:02,920 Speaker 14: can point to it to many things. Maybe it's institutional buying, 427 00:21:03,040 --> 00:21:05,040 Speaker 14: or maybe it's just a narrative. Although what a lot 428 00:21:05,040 --> 00:21:07,000 Speaker 14: of my analysts are saying, at least when they talk 429 00:21:07,000 --> 00:21:10,240 Speaker 14: to me, it's really more institutions, especially corporate treasuries. For 430 00:21:10,280 --> 00:21:12,679 Speaker 14: every fall they absorb it and they buy more of 431 00:21:12,680 --> 00:21:13,119 Speaker 14: the supply. 432 00:21:14,160 --> 00:21:17,160 Speaker 3: There is a technical or i guess a better phrase 433 00:21:17,240 --> 00:21:20,240 Speaker 3: rate transactional part of this story which you write about. 434 00:21:20,800 --> 00:21:24,320 Speaker 3: The rally is driven in parts by traders unwinding their 435 00:21:24,320 --> 00:21:25,080 Speaker 3: options bets. 436 00:21:25,080 --> 00:21:26,200 Speaker 2: Could you explain that quickly? 437 00:21:26,320 --> 00:21:28,359 Speaker 14: Is about this is the thing with bitcoin. It's interesting 438 00:21:28,359 --> 00:21:31,120 Speaker 14: because more than the sentiment, it's really all about this mechanic. 439 00:21:31,240 --> 00:21:34,440 Speaker 14: So we have traders unwinding their options bets, those that 440 00:21:34,480 --> 00:21:36,800 Speaker 14: bet that bitcoin will continue to fall, and so when 441 00:21:36,800 --> 00:21:39,480 Speaker 14: they underwind that, traders close out their negative positions. 442 00:21:39,600 --> 00:21:40,479 Speaker 6: Bitcoin rallies. 443 00:21:40,480 --> 00:21:42,560 Speaker 14: And we have about one point five billion dollars of 444 00:21:42,640 --> 00:21:45,479 Speaker 14: bitcoin puts clustered around the sixty thousand level, and now 445 00:21:45,480 --> 00:21:48,280 Speaker 14: we're around seventy five thousand level, which is again a 446 00:21:48,359 --> 00:21:50,520 Speaker 14: six month high, so which is really impressive. And there 447 00:21:50,520 --> 00:21:52,960 Speaker 14: are one point three billions of calls at seventy five thousand, 448 00:21:53,200 --> 00:21:55,800 Speaker 14: So that's why you see bitcoin rally. But again it's 449 00:21:55,800 --> 00:21:58,880 Speaker 14: more about narrative. We're moving beyond that now, it's about mechanics. 450 00:21:58,920 --> 00:22:00,920 Speaker 14: I mean, you look at etfight. One and a half 451 00:22:00,960 --> 00:22:03,040 Speaker 14: billion dollars have flown a month to day, so that's 452 00:22:03,040 --> 00:22:04,120 Speaker 14: really a sign of confidence. 453 00:22:04,119 --> 00:22:08,160 Speaker 3: Then Bloomberg Zazabellie, thank you, Carol. What you're looking at, well. 454 00:22:08,000 --> 00:22:09,439 Speaker 5: I just want to look at what's happening in the 455 00:22:09,440 --> 00:22:12,440 Speaker 5: world of stable coins because a bit of an acquisition 456 00:22:12,520 --> 00:22:16,280 Speaker 5: has happened with MasterCard and b the NK. Look, this 457 00:22:16,320 --> 00:22:18,040 Speaker 5: is a company that they're buying for one point eight 458 00:22:18,080 --> 00:22:21,000 Speaker 5: billion dollars to really ride into the infrastructure play of 459 00:22:21,000 --> 00:22:24,119 Speaker 5: stable coins. Remember Coinbase were looking at that asset and 460 00:22:24,119 --> 00:22:25,080 Speaker 5: they decided to walk. 461 00:22:24,960 --> 00:22:26,720 Speaker 6: Away when it cost them two billion. That were the 462 00:22:26,920 --> 00:22:27,600 Speaker 6: reports at. 463 00:22:27,560 --> 00:22:37,760 Speaker 3: Least, welcome back to Bloomberg Tech. One of the other 464 00:22:37,800 --> 00:22:40,879 Speaker 3: stories out this morning is Qualcomm buying back another twenty 465 00:22:40,920 --> 00:22:44,240 Speaker 3: billion dollars of shares, also boosting its dividend from eighty 466 00:22:44,320 --> 00:22:47,280 Speaker 3: nine cents to ninety two cents. The story is pretty simple, 467 00:22:47,520 --> 00:22:51,120 Speaker 3: as Qualcomm tells it, they want to bolster shareholder returns 468 00:22:51,320 --> 00:22:54,600 Speaker 3: while also continuing to try and diversify this business from 469 00:22:54,680 --> 00:22:58,440 Speaker 3: smartphone to things like automotive and increasingly and more recently 470 00:22:58,720 --> 00:23:01,359 Speaker 3: data centered. That's what the CEA, Christiano amon is talking 471 00:23:01,440 --> 00:23:04,000 Speaker 3: about in the statement, stock up two percent and then 472 00:23:04,080 --> 00:23:06,840 Speaker 3: another check on in video we're now modestly lower four 473 00:23:06,880 --> 00:23:10,400 Speaker 3: tenths of one percent. The peak of Monday session during 474 00:23:10,480 --> 00:23:13,360 Speaker 3: genson One's keynote was a gain of five percent, which 475 00:23:13,600 --> 00:23:17,840 Speaker 3: very quickly faded as the market interpreted the twenty twenty 476 00:23:17,840 --> 00:23:21,159 Speaker 3: five to twenty twenty seven outlook or forecast of a 477 00:23:21,240 --> 00:23:24,280 Speaker 3: trillion dollars of demand for its AI compute. We're now 478 00:23:24,440 --> 00:23:25,760 Speaker 3: down half percentage. 479 00:23:25,359 --> 00:23:27,800 Speaker 5: Point carac Yeah, and two and a half hours of 480 00:23:27,880 --> 00:23:31,919 Speaker 5: speeching it would feel Gensenhan didn't actually only just forcus 481 00:23:32,000 --> 00:23:35,200 Speaker 5: that trillion dollars, of course, said he also talked about 482 00:23:35,200 --> 00:23:37,920 Speaker 5: what the company will need to get there, including more 483 00:23:37,960 --> 00:23:41,240 Speaker 5: copper and optics capacity. That those comments really rattle shares 484 00:23:41,240 --> 00:23:44,480 Speaker 5: the companies that make data center optical components. Let's talk 485 00:23:44,480 --> 00:23:46,639 Speaker 5: about it overly most common rhinikey, we're off by one 486 00:23:46,720 --> 00:23:48,880 Speaker 5: and a half percent on Canning and some other suppliers 487 00:23:48,880 --> 00:23:52,280 Speaker 5: at the moment. But what's been interesting is Lamentum and Coherent. 488 00:23:52,480 --> 00:23:55,320 Speaker 5: They've been running up into this announcement on the anticipation 489 00:23:55,359 --> 00:23:58,320 Speaker 5: that we're all in on optics, and it looks as. 490 00:23:58,200 --> 00:24:00,000 Speaker 6: Though there's a double barreled strategy. 491 00:24:00,640 --> 00:24:03,120 Speaker 15: Yeah, totally. And it's so interesting because we see those 492 00:24:03,200 --> 00:24:05,359 Speaker 15: run ups and then we just have one little comment 493 00:24:05,640 --> 00:24:08,560 Speaker 15: from Jensen Wong and it just you know, unraveled right away. 494 00:24:08,720 --> 00:24:11,400 Speaker 15: So yeah, I mean what he said was basically that 495 00:24:11,640 --> 00:24:13,440 Speaker 15: you know, copper is still going to be or is 496 00:24:13,480 --> 00:24:16,560 Speaker 15: going to remain important in these data center buildouts, which 497 00:24:16,880 --> 00:24:19,359 Speaker 15: just I guess made some of the investors a little 498 00:24:19,359 --> 00:24:23,400 Speaker 15: bit concerned about all of these you know, optics components. 499 00:24:23,720 --> 00:24:26,040 Speaker 15: The thing that's interesting, a lot of analysts did say 500 00:24:26,080 --> 00:24:28,840 Speaker 15: this morning in some notes following GtC that you know, 501 00:24:28,880 --> 00:24:31,880 Speaker 15: both are definitely going to remain important in these buildouts 502 00:24:31,960 --> 00:24:36,000 Speaker 15: going forward, So there's probably not huge cause for concern here. 503 00:24:36,040 --> 00:24:38,000 Speaker 15: And we did see some some of those stocks were 504 00:24:38,000 --> 00:24:40,760 Speaker 15: covered today. I think Lumentum did kind of go back up, 505 00:24:40,800 --> 00:24:42,760 Speaker 15: and so is in positive territory. 506 00:24:42,400 --> 00:24:45,720 Speaker 3: Now common what's the experience of a GtC like for 507 00:24:45,760 --> 00:24:49,119 Speaker 3: the equities team. Every single time on stage there was 508 00:24:49,160 --> 00:24:52,600 Speaker 3: a name check of any given company, you'd notice a 509 00:24:52,640 --> 00:24:56,439 Speaker 3: little tick higher in some of those stocks, which were 510 00:24:56,480 --> 00:24:59,760 Speaker 3: the ones of substance and which were just literally name checks. 511 00:25:00,320 --> 00:25:02,199 Speaker 15: Yeah, I mean it ends up being such a like 512 00:25:02,240 --> 00:25:04,520 Speaker 15: a flurry of excitement as we're all rushing to you know, 513 00:25:04,560 --> 00:25:07,320 Speaker 15: send headlines and watch these shares move. I mean we 514 00:25:07,400 --> 00:25:09,919 Speaker 15: did see some things, you know, move quite materially, you know, 515 00:25:10,600 --> 00:25:15,320 Speaker 15: outside of Lumentum Coherent. We saw shares of Uber and 516 00:25:15,400 --> 00:25:20,399 Speaker 15: Lyft jump on partnerships that were announced. IBM shares also jumped, 517 00:25:20,440 --> 00:25:22,600 Speaker 15: and then there were some other little, smaller ones where 518 00:25:22,600 --> 00:25:24,840 Speaker 15: things quickly faded. I mean, one that was actually kind 519 00:25:24,880 --> 00:25:27,879 Speaker 15: of interesting is Nvidia itself had a very big spike, 520 00:25:28,240 --> 00:25:30,560 Speaker 15: you know, around that one trillion figure, and then actually 521 00:25:30,640 --> 00:25:32,919 Speaker 15: paired most of those gains sort of as the market 522 00:25:32,920 --> 00:25:36,439 Speaker 15: digested it. But yeah, overall, we really are seeing again 523 00:25:36,480 --> 00:25:40,000 Speaker 15: that you know, Jensen's comments have the ability to move markets, 524 00:25:40,040 --> 00:25:42,560 Speaker 15: to move these stocks, and it actually is a little 525 00:25:42,600 --> 00:25:44,960 Speaker 15: bit of a flip from last year. We didn't see 526 00:25:45,000 --> 00:25:47,880 Speaker 15: a ton of movement around GtC investors were really sort 527 00:25:47,880 --> 00:25:50,320 Speaker 15: of concerned about the macro and you know, had a 528 00:25:50,320 --> 00:25:51,560 Speaker 15: lot of AI skepticism. 529 00:25:51,640 --> 00:25:53,200 Speaker 6: So seeing a little bit of. 530 00:25:53,119 --> 00:25:55,760 Speaker 15: A trend kind of maybe back towards normal this year 531 00:25:55,920 --> 00:25:58,800 Speaker 15: where you know, they have the power to move stocks 532 00:25:58,800 --> 00:25:59,560 Speaker 15: of other companies. 533 00:26:00,400 --> 00:26:03,000 Speaker 3: Bloomber's Cullen Rhinike, thank you very much. Let's stick with 534 00:26:03,040 --> 00:26:05,879 Speaker 3: what's going on broader markets today, particularly for tech and 535 00:26:05,920 --> 00:26:09,640 Speaker 3: bringing Carosh Life, chief market strategists at BEMO Wealth Management, 536 00:26:09,720 --> 00:26:12,200 Speaker 3: and on a very serious note, like with everything going on, 537 00:26:13,200 --> 00:26:16,359 Speaker 3: you know, the war in Iran, considerations around trade, a 538 00:26:16,440 --> 00:26:20,040 Speaker 3: number of headlines relayings trade this morning, even a decline 539 00:26:20,040 --> 00:26:23,119 Speaker 3: of four tenths of a percent for Nvidia. It's the 540 00:26:23,160 --> 00:26:25,919 Speaker 3: second biggest points drag at the index level for then 541 00:26:25,960 --> 00:26:29,160 Speaker 3: as that one hundred, Carol, you know how closely were 542 00:26:29,200 --> 00:26:32,159 Speaker 3: you watching GtC of the last twenty four hours for 543 00:26:32,200 --> 00:26:34,240 Speaker 3: some macro level impact. 544 00:26:34,840 --> 00:26:39,080 Speaker 16: I think not necessarily a macro level impact. And it's 545 00:26:39,320 --> 00:26:41,600 Speaker 16: really important to zoom out from what's going on in 546 00:26:41,680 --> 00:26:44,240 Speaker 16: these day to day basises and try to think intermediate, 547 00:26:44,320 --> 00:26:47,399 Speaker 16: longer term. Clearly markets are trying to get beyond that, 548 00:26:47,480 --> 00:26:49,200 Speaker 16: and I think it's one of the reasons why you've 549 00:26:49,240 --> 00:26:54,320 Speaker 16: seen the aggregate indexes hold in so tightly because realistically, 550 00:26:54,400 --> 00:26:58,080 Speaker 16: given all the news we've had since basically the first 551 00:26:58,160 --> 00:27:04,480 Speaker 16: weekend before January, we've had several different wars, conflicts started here, 552 00:27:04,640 --> 00:27:06,680 Speaker 16: lots of stuff that have hit the markets, and yet 553 00:27:06,720 --> 00:27:11,240 Speaker 16: the aggregate averages are still hugging that close to all 554 00:27:11,280 --> 00:27:14,920 Speaker 16: time highs even though there's been a lot of turbulence underneath. 555 00:27:15,240 --> 00:27:17,640 Speaker 16: But I think that indicative of the fact that people 556 00:27:17,680 --> 00:27:21,159 Speaker 16: are really leaning into You've still got growth stories going on. 557 00:27:21,280 --> 00:27:24,080 Speaker 16: You had GtC reaffirm that growth story, I mean a 558 00:27:24,200 --> 00:27:28,000 Speaker 16: trillion dollars if you will out over the next couple 559 00:27:28,040 --> 00:27:30,920 Speaker 16: of years, and so you've got a lot of momentum underneath, 560 00:27:30,920 --> 00:27:33,600 Speaker 16: and investors don't want to be out of the market 561 00:27:33,800 --> 00:27:36,280 Speaker 16: when some of that macro clears. 562 00:27:37,400 --> 00:27:38,360 Speaker 2: I find that interesting. 563 00:27:38,400 --> 00:27:40,960 Speaker 3: You know, as Jensen Wong tells it, the global economy 564 00:27:41,040 --> 00:27:44,520 Speaker 3: is in the early phase of a transition spanning agentic 565 00:27:44,600 --> 00:27:47,120 Speaker 3: AI through to robotics in the physical AI space. 566 00:27:47,600 --> 00:27:48,360 Speaker 2: You know, if you've. 567 00:27:48,320 --> 00:27:52,359 Speaker 3: Started classic economics, how does one prepare for that as 568 00:27:52,400 --> 00:27:55,600 Speaker 3: an investor to understand where in that transition the global 569 00:27:55,640 --> 00:27:56,240 Speaker 3: economy is? 570 00:27:57,080 --> 00:27:59,000 Speaker 16: Well, I think one of the things you do is 571 00:27:59,119 --> 00:28:01,439 Speaker 16: understand we are in the middle of a transition. They 572 00:28:01,520 --> 00:28:04,720 Speaker 16: tend to be really muddy where you're in the very 573 00:28:04,720 --> 00:28:08,080 Speaker 16: short term or the new phases of a technology. I 574 00:28:08,119 --> 00:28:10,720 Speaker 16: was rolling it back thinking to when we first got 575 00:28:10,840 --> 00:28:16,439 Speaker 16: Excel and word perfect in some of the other early software, 576 00:28:16,480 --> 00:28:18,159 Speaker 16: and everyone was trying to figure out how do I 577 00:28:18,240 --> 00:28:18,520 Speaker 16: use it? 578 00:28:18,520 --> 00:28:19,120 Speaker 10: Do I have to. 579 00:28:19,080 --> 00:28:21,960 Speaker 16: Put everything into a spreadsheet or just some things into 580 00:28:22,000 --> 00:28:26,840 Speaker 16: a spreadsheet? And when you have that sort of reconfiguration, 581 00:28:27,000 --> 00:28:29,480 Speaker 16: if you will, it takes some period of time to 582 00:28:29,560 --> 00:28:32,879 Speaker 16: figure out what the impacts are. But rolling it back 583 00:28:33,000 --> 00:28:38,840 Speaker 16: and looking at company after company, country after country reinvesting 584 00:28:38,960 --> 00:28:41,280 Speaker 16: or investing for the first time in a long time 585 00:28:41,600 --> 00:28:45,520 Speaker 16: in some of those really important infrastructure and capital investments, 586 00:28:45,800 --> 00:28:48,600 Speaker 16: and that has a long, long, live and long tail 587 00:28:48,760 --> 00:28:51,200 Speaker 16: to it, if you will. But it's also important to 588 00:28:51,240 --> 00:28:54,720 Speaker 16: remember from economics one oh one that a lot of 589 00:28:54,720 --> 00:28:57,720 Speaker 16: the economic stats we have were meant to measure a 590 00:28:57,840 --> 00:29:01,760 Speaker 16: very different society than we have now or are emerging too, 591 00:29:01,960 --> 00:29:05,440 Speaker 16: and so that'll be part of the challenge. So as investors, 592 00:29:05,520 --> 00:29:09,120 Speaker 16: I think part of it is stay diversified, stay long, 593 00:29:09,440 --> 00:29:14,239 Speaker 16: lean into it, and don't get too don't hyperventilate too 594 00:29:14,360 --> 00:29:17,360 Speaker 16: much about day to day activity. 595 00:29:17,960 --> 00:29:21,320 Speaker 5: Should you be leaning more into the compute buildout, the 596 00:29:21,360 --> 00:29:22,640 Speaker 5: AI infrastructure build out. 597 00:29:22,680 --> 00:29:24,120 Speaker 6: That's what's worked for the last few years. 598 00:29:24,120 --> 00:29:27,320 Speaker 5: And I'm looking at Nevies today for example, it's selling 599 00:29:27,760 --> 00:29:31,360 Speaker 5: well convertible debt to be able to continue to build out. 600 00:29:31,400 --> 00:29:33,480 Speaker 5: It's so neocloud offering. We know it's going to deal 601 00:29:33,520 --> 00:29:35,600 Speaker 5: with meta. Should you belong that space? 602 00:29:36,560 --> 00:29:39,240 Speaker 16: Well, I think a piece of it is to understand 603 00:29:39,320 --> 00:29:44,080 Speaker 16: not only the buildout, but to look far and wide 604 00:29:44,080 --> 00:29:46,840 Speaker 16: for those companies that are leaning into using the new 605 00:29:46,880 --> 00:29:50,200 Speaker 16: technology as well, because there's lots of different applications, different 606 00:29:50,240 --> 00:29:57,760 Speaker 16: places that it has the chance to supplement if you will, 607 00:29:57,800 --> 00:30:01,360 Speaker 16: not necessarily replace everybody. But and so where are those 608 00:30:01,440 --> 00:30:06,200 Speaker 16: leaders that are encouraging in their employees, if you will, 609 00:30:06,240 --> 00:30:08,440 Speaker 16: and prepping their employees to be able to lean in 610 00:30:08,800 --> 00:30:10,640 Speaker 16: and those new technology. 611 00:30:10,680 --> 00:30:13,200 Speaker 5: I mean they've got to lean in because many of 612 00:30:13,240 --> 00:30:15,080 Speaker 5: them are going to be forced out. Look, there's another 613 00:30:15,120 --> 00:30:18,120 Speaker 5: headline in your space of a banking NUDEA, which is 614 00:30:18,120 --> 00:30:20,280 Speaker 5: a NAUDIC bank saying they're gonna be laying off some 615 00:30:20,320 --> 00:30:23,760 Speaker 5: five percent of staff because of AI productivity. 616 00:30:23,800 --> 00:30:27,280 Speaker 6: Now you can call it AI washing. But when someone. 617 00:30:27,000 --> 00:30:29,520 Speaker 5: Like Jack Dorsey is laying off forty percent of staff, 618 00:30:29,520 --> 00:30:31,640 Speaker 5: and he's thinking he can do that because of AI. 619 00:30:32,680 --> 00:30:34,000 Speaker 5: We just going to see more and more of that 620 00:30:34,240 --> 00:30:35,320 Speaker 5: impacting the labor force. 621 00:30:36,600 --> 00:30:39,800 Speaker 16: I think it's interesting because I do think there's a 622 00:30:40,960 --> 00:30:44,040 Speaker 16: a if you will have AI washing to some of it. 623 00:30:44,200 --> 00:30:47,480 Speaker 16: But there's also the issue that these leaders are looking 624 00:30:47,520 --> 00:30:50,360 Speaker 16: for people in their organizations who and they're giving them 625 00:30:50,400 --> 00:30:52,160 Speaker 16: seats at the table, the ones that are rolling up 626 00:30:52,200 --> 00:30:54,880 Speaker 16: their sleeves, getting messy trying this stuff out and figuring 627 00:30:54,920 --> 00:30:58,280 Speaker 16: it out. But also there was a reporter out recently 628 00:30:58,320 --> 00:31:01,160 Speaker 16: that talked about the bulk of the space being done 629 00:31:01,200 --> 00:31:03,960 Speaker 16: in AI is done on the technology, not on teaching 630 00:31:04,000 --> 00:31:06,320 Speaker 16: people how to use it. So there's a piece of 631 00:31:06,360 --> 00:31:08,560 Speaker 16: it where we have to create an environment where it's 632 00:31:08,600 --> 00:31:11,440 Speaker 16: okay for people to experiment with it. We're not going 633 00:31:11,480 --> 00:31:15,320 Speaker 16: to replace overnight some of these macro systems, and especially 634 00:31:15,320 --> 00:31:19,200 Speaker 16: in highly regulated industries like banking, it's hard to believe 635 00:31:19,200 --> 00:31:22,640 Speaker 16: that you're necessarily going to totally displace an old software 636 00:31:23,040 --> 00:31:26,080 Speaker 16: and allow agentic AI to take over all of it. 637 00:31:26,280 --> 00:31:28,760 Speaker 16: But it is going to supplement what each of us. 638 00:31:28,680 --> 00:31:31,320 Speaker 9: Are doing and how we're doing it. 639 00:31:31,360 --> 00:31:34,880 Speaker 16: And you go back and look just in my business 640 00:31:34,920 --> 00:31:38,480 Speaker 16: in analysis, where we started with hand plotted charts and 641 00:31:38,520 --> 00:31:42,000 Speaker 16: hand calculated moving averages to deploying new technology all the 642 00:31:42,000 --> 00:31:44,920 Speaker 16: way along makes me a lot more productive day to day. 643 00:31:45,400 --> 00:31:48,000 Speaker 16: And I think that's what companies are looking for. But 644 00:31:49,240 --> 00:31:51,200 Speaker 16: each of us individually is going to have to figure 645 00:31:51,200 --> 00:31:52,440 Speaker 16: out how to lean into that. 646 00:31:52,400 --> 00:31:56,120 Speaker 5: Too well, said Cowash Life, Chief Market Strutches. That be 647 00:31:56,200 --> 00:31:59,320 Speaker 5: my wealth management, who is appreciate your time. Let's talk 648 00:31:59,320 --> 00:32:02,160 Speaker 5: about that disrupt little bit more Now for labor, because China. 649 00:32:01,960 --> 00:32:03,320 Speaker 6: It faces a key test. 650 00:32:03,400 --> 00:32:06,040 Speaker 5: It's the nation's AI boom really reshaped industries while putting 651 00:32:06,080 --> 00:32:07,200 Speaker 5: millions of jobs at risk. 652 00:32:07,400 --> 00:32:08,600 Speaker 6: Now analysts worn that up. 653 00:32:08,560 --> 00:32:11,120 Speaker 5: To one hundred and forty two million urban jobs could 654 00:32:11,200 --> 00:32:14,360 Speaker 5: vanish by twenty forty nine due to rapid AI driven automation. 655 00:32:15,000 --> 00:32:17,840 Speaker 6: China correspondent Min Minla reports in Beijing. 656 00:32:19,080 --> 00:32:20,320 Speaker 2: Juju Hi. 657 00:32:21,200 --> 00:32:25,280 Speaker 17: You know financial tribium that almost looks like me, but 658 00:32:25,360 --> 00:32:28,120 Speaker 17: it's not. It's an AI generated video based on a 659 00:32:28,160 --> 00:32:32,480 Speaker 17: single screenshot. Tools from Earli, Bubba, ten Cent, Kwaisho are 660 00:32:32,480 --> 00:32:37,120 Speaker 17: making AI video generation accessible to millions, sometimes for free. 661 00:32:37,520 --> 00:32:40,760 Speaker 18: Our company, I can't imagine without AI, how can we survive? 662 00:32:41,320 --> 00:32:42,000 Speaker 6: At the beginning. 663 00:32:43,040 --> 00:32:46,360 Speaker 18: Without AI, I don't think our game can actually be 664 00:32:46,440 --> 00:32:47,720 Speaker 18: done in one year. 665 00:32:49,080 --> 00:32:52,600 Speaker 17: It's brought huge productivity gains for this game developer, but 666 00:32:52,760 --> 00:32:57,400 Speaker 17: also raised alarms in the entertainment industry. Disney and Paramount 667 00:32:57,360 --> 00:33:00,640 Speaker 17: to have accused by dons of IP infringement after its 668 00:33:00,720 --> 00:33:05,120 Speaker 17: video generator produced near cinematic scenes from just a text prompt. 669 00:33:05,360 --> 00:33:08,720 Speaker 17: The fear is that AI could replace not just mundane tasks, 670 00:33:08,800 --> 00:33:10,920 Speaker 17: but jobs across creative industries. 671 00:33:11,360 --> 00:33:14,960 Speaker 18: The painting, if we use like labor works, it's like 672 00:33:15,080 --> 00:33:19,480 Speaker 18: two thousands to four thousand for one piece, but with AI, 673 00:33:19,560 --> 00:33:24,080 Speaker 18: we only use like probably two R and B for 674 00:33:24,400 --> 00:33:24,920 Speaker 18: one piece. 675 00:33:25,520 --> 00:33:29,760 Speaker 17: For policymakers, the challenge is balancing growth will. 676 00:33:29,440 --> 00:33:32,320 Speaker 9: Nurture emerging industries and industries of the. 677 00:33:32,280 --> 00:33:36,920 Speaker 18: Future with disruption jingle jinin The rapid development of AI 678 00:33:37,200 --> 00:33:39,600 Speaker 18: is having a profound impact on employment. 679 00:33:40,040 --> 00:33:42,920 Speaker 17: The China Economic Journal projects that more than thirty percent 680 00:33:42,960 --> 00:33:45,280 Speaker 17: of urban jobs in China could be lost to AI 681 00:33:45,440 --> 00:33:48,280 Speaker 17: by twenty forty nine. That's one hundred and forty two 682 00:33:48,400 --> 00:33:52,080 Speaker 17: million jobs. That's a scenario that could threaten social stability. 683 00:33:52,480 --> 00:33:55,320 Speaker 17: Beijing is aiming to create another twelve million jobs this 684 00:33:55,520 --> 00:33:58,200 Speaker 17: year with just as many graduates set to enter an 685 00:33:58,240 --> 00:34:00,200 Speaker 17: already select labor market. 686 00:34:01,200 --> 00:34:03,680 Speaker 19: In finance and it if they're warn't for these kind 687 00:34:03,680 --> 00:34:06,520 Speaker 19: of regulatory barriers, about thirty to forty percent of the 688 00:34:06,640 --> 00:34:09,120 Speaker 19: job could have been lost right away. If you're thinking 689 00:34:09,160 --> 00:34:13,319 Speaker 19: about the automation AI replacement of a junior rows, it's 690 00:34:13,360 --> 00:34:15,720 Speaker 19: already happening massively in China. 691 00:34:16,160 --> 00:34:19,759 Speaker 17: That leads Seating Ping's government with a dilemma. China counterford 692 00:34:19,800 --> 00:34:22,520 Speaker 17: to fall behind, and it's tachways with the United States. 693 00:34:22,719 --> 00:34:26,000 Speaker 17: But the implications of a tech revolution are far from clear. 694 00:34:26,840 --> 00:34:31,360 Speaker 20: If we let AI replace the jobs without taxing the 695 00:34:31,360 --> 00:34:34,480 Speaker 20: AI appropriately, then then it can really get to the 696 00:34:34,520 --> 00:34:36,200 Speaker 20: core of the consumer economy. And we got to think 697 00:34:36,200 --> 00:34:39,359 Speaker 20: about the sort of tax policy that is targeted specifically 698 00:34:39,360 --> 00:34:41,240 Speaker 20: at what is driving those job job losses. 699 00:34:41,560 --> 00:34:45,240 Speaker 19: It will be in the regulatory framework at some point. 700 00:34:45,480 --> 00:34:49,880 Speaker 19: I just don't see it as a choice yet because 701 00:34:49,920 --> 00:34:52,640 Speaker 19: we're at the very early stage of promoting the application 702 00:34:52,719 --> 00:34:56,840 Speaker 19: of AI. So the great area must be caps actually 703 00:34:56,880 --> 00:34:58,840 Speaker 19: by a very wide margin. 704 00:34:59,320 --> 00:35:03,000 Speaker 17: Last de State Media published a landmark arbitration case in 705 00:35:03,040 --> 00:35:06,600 Speaker 17: Beijing that sets some early guard rails. Dismissing an employee 706 00:35:06,600 --> 00:35:10,320 Speaker 17: because of AI is illegal because it's a business decision 707 00:35:10,360 --> 00:35:14,320 Speaker 17: for profit, not an uncontrollable event. That means companies must 708 00:35:14,320 --> 00:35:19,200 Speaker 17: prioritize retraining and reassignment before dismissing an employee. But for now, 709 00:35:19,280 --> 00:35:21,719 Speaker 17: all signs that the two sessions suggest the government is 710 00:35:21,760 --> 00:35:24,920 Speaker 17: going all in on tech at the risk of leaving 711 00:35:24,960 --> 00:35:26,160 Speaker 17: some people behind. 712 00:35:28,680 --> 00:35:29,960 Speaker 2: That was Bloomberg's mem in Lau. 713 00:35:30,120 --> 00:35:32,799 Speaker 3: Coming up, Gecko Robotics makes a deal with the US 714 00:35:32,960 --> 00:35:36,400 Speaker 3: Navy to monitor and maintain it's warships. We speak with 715 00:35:36,520 --> 00:35:40,560 Speaker 3: CEO Jake Lucerian. That's next. This is Bloomberg Tech. 716 00:35:51,760 --> 00:35:52,480 Speaker 6: US government. 717 00:35:52,600 --> 00:35:55,600 Speaker 5: Well, it's stepping up its use of AI to monitor aging, 718 00:35:55,640 --> 00:35:58,720 Speaker 5: infrastructure and modernized military systems and a new push. Gecko 719 00:35:58,800 --> 00:36:01,719 Speaker 5: Robotics has announced seventy one million dollar partnership with the 720 00:36:01,800 --> 00:36:05,279 Speaker 5: US Navy, deploying its AI powered robots to assess the 721 00:36:05,320 --> 00:36:09,480 Speaker 5: condition the readiness of American warships joining US now. Jake 722 00:36:09,640 --> 00:36:14,480 Speaker 5: lucis Arian, Gecko Robotics CEO and co founder Jake can 723 00:36:14,520 --> 00:36:17,719 Speaker 5: you measure what your robots are able to achieve in 724 00:36:17,840 --> 00:36:20,040 Speaker 5: terms of real term military readiness? 725 00:36:20,320 --> 00:36:23,719 Speaker 21: We're all about measuring the real information and details on 726 00:36:23,760 --> 00:36:26,080 Speaker 21: the ground, and so we swarm our robots all over 727 00:36:26,080 --> 00:36:27,759 Speaker 21: these ships as you see in the videos, and what 728 00:36:27,760 --> 00:36:30,040 Speaker 21: they're doing is they're gathering ground truth and information that 729 00:36:30,080 --> 00:36:32,520 Speaker 21: would typically take three or four months to be done 730 00:36:32,520 --> 00:36:34,680 Speaker 21: and gather one one millionth of the amount of information 731 00:36:34,800 --> 00:36:37,640 Speaker 21: data set that is not filtered to any source of 732 00:36:37,680 --> 00:36:41,000 Speaker 21: truth and software that could be gathered and then and 733 00:36:41,040 --> 00:36:43,960 Speaker 21: then in perpetuity be evaluated to help with planning into 734 00:36:43,960 --> 00:36:47,040 Speaker 21: the future. What we're providing to the Navy, and kudos 735 00:36:47,040 --> 00:36:49,480 Speaker 21: to the Navy for adopting this technology, you know, in 736 00:36:49,480 --> 00:36:52,600 Speaker 21: a way that's giving them an advantage over others that 737 00:36:52,719 --> 00:36:55,680 Speaker 21: deal with the same problem every single day. They're taking 738 00:36:55,800 --> 00:36:58,760 Speaker 21: very seriously this demands of getting to eighty percent readiness 739 00:36:58,800 --> 00:37:01,600 Speaker 21: of their fleets and to collect all this information data 740 00:37:01,760 --> 00:37:04,560 Speaker 21: that can help perpetuate goodness both now saving three to 741 00:37:04,600 --> 00:37:08,120 Speaker 21: four months of cycle time in maintenance cycles, but also 742 00:37:08,239 --> 00:37:10,279 Speaker 21: to the future to plan smarter so that we have 743 00:37:10,360 --> 00:37:12,280 Speaker 21: less and less days of downtime for the vessels. 744 00:37:12,440 --> 00:37:14,920 Speaker 5: I mean, you're seeing them flying, we're seeing them climbing, 745 00:37:14,960 --> 00:37:18,640 Speaker 5: we're seeing the hardware, but you're also about the interpretation 746 00:37:18,719 --> 00:37:20,920 Speaker 5: of the data as well. How are you developing your 747 00:37:20,920 --> 00:37:23,680 Speaker 5: own models to ensure that the right information is getting 748 00:37:23,800 --> 00:37:25,000 Speaker 5: to the end user. 749 00:37:25,239 --> 00:37:29,040 Speaker 2: Yeah, well, it's this example. And when we say at. 750 00:37:28,680 --> 00:37:30,480 Speaker 21: The company, a lot is if it's not ready, it 751 00:37:30,480 --> 00:37:32,920 Speaker 21: doesn't count. And that's very important as it relates to 752 00:37:32,960 --> 00:37:34,680 Speaker 21: the readiness of our fleets, as it relates to the 753 00:37:34,719 --> 00:37:37,160 Speaker 21: Turing conflict, what's going on right now in the Middle East, 754 00:37:37,160 --> 00:37:39,360 Speaker 21: and then also on the Pacific side. And you know, 755 00:37:39,360 --> 00:37:41,000 Speaker 21: we've been doing this for thirteen years or sort of 756 00:37:41,000 --> 00:37:43,120 Speaker 21: the company of a college dorm with this idea of 757 00:37:43,120 --> 00:37:44,480 Speaker 21: what if you could diagnose. 758 00:37:44,120 --> 00:37:45,200 Speaker 2: The health of the built world? 759 00:37:45,360 --> 00:37:47,360 Speaker 21: You know, what could that enable as it relates to 760 00:37:47,400 --> 00:37:49,879 Speaker 21: predicting what the future structures, how they should be built, 761 00:37:49,920 --> 00:37:52,320 Speaker 21: and then what we're going to what's going to occur, 762 00:37:53,040 --> 00:37:55,759 Speaker 21: and how to prevent that from occurring. And so we've 763 00:37:55,760 --> 00:37:59,759 Speaker 21: been improving these models and believing that if you can 764 00:37:59,760 --> 00:38:02,239 Speaker 21: gather the information in data using robots, turn atoms and 765 00:38:02,280 --> 00:38:05,359 Speaker 21: the bits, you can have such an incredible advantage as 766 00:38:05,440 --> 00:38:08,319 Speaker 21: related to how to make infrastructure smarter, make it, make 767 00:38:08,360 --> 00:38:11,560 Speaker 21: it more efficiently, and make even new structures. And so 768 00:38:11,680 --> 00:38:13,959 Speaker 21: these models are all being fed into a central source 769 00:38:13,960 --> 00:38:17,560 Speaker 21: of truth, a digital thread for critical infrastructure called Candileer. 770 00:38:18,840 --> 00:38:19,440 Speaker 2: Jake youa. 771 00:38:19,640 --> 00:38:23,200 Speaker 3: You presented basically at the Winning the AI Race summer 772 00:38:23,280 --> 00:38:26,279 Speaker 3: in DC last year, which was very much focused on 773 00:38:26,680 --> 00:38:30,960 Speaker 3: this administration's policy platforms for AI. I'm curious in the 774 00:38:30,960 --> 00:38:32,880 Speaker 3: time that that since past you've done a deal with 775 00:38:32,920 --> 00:38:35,279 Speaker 3: the Navy. You got it done, and I wonder what 776 00:38:35,320 --> 00:38:38,120 Speaker 3: that's like, you know, to see a project through to 777 00:38:38,400 --> 00:38:41,439 Speaker 3: fruition with some of the military apparatus of this nation. 778 00:38:42,080 --> 00:38:44,400 Speaker 21: It is so exciting and so incredible to see the 779 00:38:44,480 --> 00:38:47,080 Speaker 21: Navy being a leader in the world as relates to 780 00:38:47,120 --> 00:38:51,440 Speaker 21: giving our warfighters an advantage and advantage that no other 781 00:38:51,560 --> 00:38:54,000 Speaker 21: navy around the world, including China, has, and that's the 782 00:38:54,040 --> 00:38:57,440 Speaker 21: ability to have this incredible advantage with robotics in AI 783 00:38:57,760 --> 00:38:59,880 Speaker 21: to speed up how quickly we're gathering information and then 784 00:38:59,880 --> 00:39:01,640 Speaker 21: to deploying it. We're not just doing that, you know, 785 00:39:01,640 --> 00:39:04,160 Speaker 21: for the US government, We're doing that for the largest 786 00:39:04,160 --> 00:39:07,640 Speaker 21: companies in the world, companies like ADOC ADNOC, who's giving 787 00:39:07,680 --> 00:39:11,279 Speaker 21: this AI and robotics native approach. It's such an incredibly 788 00:39:11,640 --> 00:39:14,680 Speaker 21: advanced way. We're bringing that same technology and beyond to 789 00:39:14,760 --> 00:39:17,960 Speaker 21: the Navy and ensuring as well that it's not just 790 00:39:18,000 --> 00:39:20,839 Speaker 21: about talk about five years or ten years where you're 791 00:39:20,840 --> 00:39:24,279 Speaker 21: going to see autonomous systems and robots and AI affecting 792 00:39:24,360 --> 00:39:27,400 Speaker 21: the Navy in our and helping our warfighters. No, this 793 00:39:27,440 --> 00:39:29,840 Speaker 21: is actually happening today, and that is something that the 794 00:39:29,840 --> 00:39:33,600 Speaker 21: administration cares deeply about. That's something that Secretary Phalan of 795 00:39:33,680 --> 00:39:36,040 Speaker 21: the Navy cares deeply about. If you want to have 796 00:39:36,160 --> 00:39:39,200 Speaker 21: the best Navy in the world, the best assets and 797 00:39:39,239 --> 00:39:42,080 Speaker 21: programs in the world, most robust, you need impact today 798 00:39:42,120 --> 00:39:45,200 Speaker 21: and as you're seeing today, matters, and it matters more 799 00:39:45,239 --> 00:39:45,640 Speaker 21: than ever. 800 00:39:46,239 --> 00:39:50,160 Speaker 3: Right, Jake, what is your core competence at Gecko Robotics. 801 00:39:50,400 --> 00:39:52,920 Speaker 3: Are you good at hardware or are you good at software? 802 00:39:53,440 --> 00:39:56,560 Speaker 21: We're good at building robotics systems that go out and 803 00:39:56,600 --> 00:40:00,239 Speaker 21: gather information and data sets that no one want knew 804 00:40:00,440 --> 00:40:03,000 Speaker 21: how to ever capture before, but then to interpreting it 805 00:40:03,080 --> 00:40:05,279 Speaker 21: to allow for decisions to be made, to give you 806 00:40:05,320 --> 00:40:09,279 Speaker 21: advantages that can speed up times to bring critical infrastructure 807 00:40:09,320 --> 00:40:12,040 Speaker 21: that's you know, constantly delayed, constantly over budget because of 808 00:40:12,080 --> 00:40:14,400 Speaker 21: how hard it is to find out where are things broken, 809 00:40:14,440 --> 00:40:16,719 Speaker 21: how to fix it. The robots are able to gather 810 00:40:16,760 --> 00:40:19,480 Speaker 21: all the information, not how to destroy the infrastructure and 811 00:40:19,520 --> 00:40:22,080 Speaker 21: assets to gather it and then provide the ability to 812 00:40:22,160 --> 00:40:25,000 Speaker 21: optimize where to make the repairs, the replacements, and then 813 00:40:25,040 --> 00:40:27,400 Speaker 21: into the future how to plan for that to potentially 814 00:40:27,400 --> 00:40:29,279 Speaker 21: never even have a shutdown. You know, we've been able 815 00:40:29,280 --> 00:40:32,359 Speaker 21: to prevent shutdowns at oil and gas facilities that were 816 00:40:32,360 --> 00:40:34,960 Speaker 21: going to occur that would have cause big explosions, and 817 00:40:35,000 --> 00:40:37,080 Speaker 21: for the Navy be able to accelerate and give them 818 00:40:37,120 --> 00:40:39,359 Speaker 21: advantages in terms of how to get our ships at 819 00:40:39,360 --> 00:40:42,200 Speaker 21: a dry dock and defending our values. And so we're 820 00:40:42,239 --> 00:40:45,800 Speaker 21: just so incredibly proud to be serving the Navy in 821 00:40:45,840 --> 00:40:48,279 Speaker 21: this way, and just kudos to the Navy for taking 822 00:40:48,280 --> 00:40:50,799 Speaker 21: a big, bold step as related to giving us this advantage. 823 00:40:51,520 --> 00:40:54,200 Speaker 3: J Luceerrarian of Gecko Robotics great to heavy back on 824 00:40:54,239 --> 00:40:54,560 Speaker 3: the show. 825 00:40:54,640 --> 00:40:55,399 Speaker 2: Thank you very much,