1 00:00:02,520 --> 00:00:13,079 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Bloomberg Tech is a 2 00:00:13,160 --> 00:00:16,920 Speaker 1: live from coast to coast with Carolline Hyde in New 3 00:00:17,000 --> 00:00:19,480 Speaker 1: York and Ed Lovelow in Sent Francisco. 4 00:00:24,600 --> 00:00:27,640 Speaker 2: Welcome to a special edition of Bloomberg Tech Live from 5 00:00:27,680 --> 00:00:30,480 Speaker 2: CES in Las Vegas. We've been bringing you conversations from 6 00:00:30,480 --> 00:00:32,800 Speaker 2: the biggest names in the industry in today we have 7 00:00:32,840 --> 00:00:35,400 Speaker 2: another great lineup coming up. First, we sit down with 8 00:00:35,479 --> 00:00:38,680 Speaker 2: Jim Johnson, an Intel's client computing group, to discuss the 9 00:00:38,760 --> 00:00:42,080 Speaker 2: chipmaker's efforts to make its products competitive again. 10 00:00:42,159 --> 00:00:45,400 Speaker 3: Plus Warner Brothers. It rejects an amended takeover offer from 11 00:00:45,440 --> 00:00:48,159 Speaker 3: Paramount Skuydouts. We'll break it all down, and Paul Pastor 12 00:00:48,280 --> 00:00:48,800 Speaker 3: of quick. 13 00:00:48,640 --> 00:00:51,479 Speaker 2: Pay will be joined by Mobilized CEO to discuss the 14 00:00:51,520 --> 00:00:55,360 Speaker 2: company's acquisition of Israeli startup Mentee Robotics in a cash 15 00:00:55,400 --> 00:00:58,480 Speaker 2: and stock deal valued at nine hundred million dollars. 16 00:00:58,560 --> 00:01:00,160 Speaker 3: The deals still getting done, but let's check it on 17 00:01:00,200 --> 00:01:03,040 Speaker 3: these markets, which are being ruled by geopolitics once more 18 00:01:03,080 --> 00:01:04,800 Speaker 3: than aw's that managing to brush that off a little 19 00:01:04,800 --> 00:01:06,920 Speaker 3: bit more than the SMP. Remember we're still now record 20 00:01:07,000 --> 00:01:09,600 Speaker 3: highs on the SMP. We're up another three tenser percent 21 00:01:09,640 --> 00:01:12,280 Speaker 3: on the main benchmark. When it comes to the tech names, 22 00:01:12,560 --> 00:01:15,600 Speaker 3: maybe less affected by what's happening in terms of geopolitics 23 00:01:15,680 --> 00:01:18,640 Speaker 3: and Russia and the US at the moment out in 24 00:01:18,840 --> 00:01:20,959 Speaker 3: the seas, but we're currently holding on to gaines. 25 00:01:21,360 --> 00:01:24,240 Speaker 2: Geopolitics is always there I'm looking at in video. It 26 00:01:24,360 --> 00:01:27,440 Speaker 2: is the mainstay of this CES week, with Gensen one 27 00:01:27,520 --> 00:01:30,120 Speaker 2: speaking multiple times. The stock is up almost one and 28 00:01:30,200 --> 00:01:33,200 Speaker 2: a half percent in the current session. You know, there 29 00:01:33,200 --> 00:01:36,480 Speaker 2: are headlines out right now from the information that China 30 00:01:36,640 --> 00:01:38,600 Speaker 2: is asking US tech companies to hold orders of H 31 00:01:38,640 --> 00:01:41,920 Speaker 2: two hundred. Gens One's been peppered with questions about H 32 00:01:41,959 --> 00:01:44,040 Speaker 2: two hundred for the last three days straight and you 33 00:01:44,080 --> 00:01:46,360 Speaker 2: can see over three days this was not the blockbuster 34 00:01:46,680 --> 00:01:48,440 Speaker 2: that we thought it would be to start the year. 35 00:01:48,600 --> 00:01:50,760 Speaker 2: In video's up half a percentage point. 36 00:01:50,880 --> 00:01:51,200 Speaker 4: Now. 37 00:01:51,520 --> 00:01:55,160 Speaker 2: The big data point was the forecast for the next 38 00:01:55,160 --> 00:01:58,840 Speaker 2: few quarters Blackwell and Rubin, and this is what Gensen 39 00:01:58,840 --> 00:02:00,680 Speaker 2: one had to say about that forecast. 40 00:02:01,440 --> 00:02:05,840 Speaker 5: Harbor pricing is actually going up in the cloud. All 41 00:02:05,880 --> 00:02:08,799 Speaker 5: of the harbors are consumed in the cloud and now 42 00:02:08,880 --> 00:02:11,360 Speaker 5: pricing spark pricing is starting to go up, and so 43 00:02:11,400 --> 00:02:16,000 Speaker 5: that tells you about the demand that's been generated all 44 00:02:16,040 --> 00:02:18,160 Speaker 5: over the world. So age two hundreds, I think is 45 00:02:18,160 --> 00:02:21,400 Speaker 5: going to contribute also to that, and I think, all told, 46 00:02:21,800 --> 00:02:25,520 Speaker 5: all told, I think we should have a very good year. 47 00:02:27,720 --> 00:02:31,200 Speaker 2: That question was posed to Jenum won by Bloomberg Sean King, 48 00:02:31,320 --> 00:02:35,320 Speaker 2: who's covered semiconductors here since nineteen ninety eight, and the 49 00:02:35,360 --> 00:02:37,760 Speaker 2: context is that you said to him in October, you 50 00:02:37,880 --> 00:02:41,280 Speaker 2: told us over five fiscal quarters, five hundred billion dollars 51 00:02:41,280 --> 00:02:44,600 Speaker 2: of sales across Blackwell. And then Reuben, is it possible 52 00:02:44,639 --> 00:02:47,040 Speaker 2: that that number could go up? And as we just heard, 53 00:02:47,200 --> 00:02:49,600 Speaker 2: there are various reasons why Jensen thinks that number could 54 00:02:49,600 --> 00:02:49,960 Speaker 2: go up. 55 00:02:50,200 --> 00:02:50,399 Speaker 6: Yeah. 56 00:02:50,440 --> 00:02:53,320 Speaker 7: I mean fundamentally here, all our audiences care about is 57 00:02:53,360 --> 00:02:55,000 Speaker 7: are we in a bubble or not? And they just 58 00:02:55,040 --> 00:02:59,360 Speaker 7: want any piece of information, any hint to explain, well, 59 00:02:59,400 --> 00:03:01,919 Speaker 7: why we're not. Obviously, what he was saying was there's 60 00:03:02,000 --> 00:03:04,640 Speaker 7: lots of things going on. There's new models, there's new 61 00:03:04,960 --> 00:03:07,240 Speaker 7: areas of the economy that are coming into AI, and 62 00:03:07,280 --> 00:03:09,600 Speaker 7: all of this means that demand is actually better than 63 00:03:09,639 --> 00:03:13,040 Speaker 7: I thought it was, so that number is looking like 64 00:03:13,120 --> 00:03:15,480 Speaker 7: it's a bit low. So that was good news, but 65 00:03:15,560 --> 00:03:17,280 Speaker 7: nobody got that excited about it. 66 00:03:17,480 --> 00:03:20,880 Speaker 3: Where they did get excited is memory storage. Oh my goodness, 67 00:03:20,880 --> 00:03:23,040 Speaker 3: Sandusk is up one thousand percent from its lows. It's 68 00:03:23,120 --> 00:03:25,200 Speaker 3: up fifty percent in the first three trading days of 69 00:03:25,200 --> 00:03:27,720 Speaker 3: the year. In what are you hearing in terms of 70 00:03:27,760 --> 00:03:30,079 Speaker 3: this demand for memory and the price point that we're 71 00:03:30,080 --> 00:03:30,680 Speaker 3: going to see. 72 00:03:30,800 --> 00:03:34,640 Speaker 7: I mean, as always with the memory ship industry, you 73 00:03:34,760 --> 00:03:36,720 Speaker 7: either got a fee storre of famine at the moment. 74 00:03:36,760 --> 00:03:37,520 Speaker 8: We're in a famine. 75 00:03:37,560 --> 00:03:40,320 Speaker 7: When we're in a famine, the price goes up, right, 76 00:03:40,360 --> 00:03:42,400 Speaker 7: And that is exactly what's happening right now. 77 00:03:42,440 --> 00:03:45,720 Speaker 3: My demand is basically always has been. 78 00:03:45,760 --> 00:03:48,280 Speaker 7: They can't build factories fast enough, they can't build production 79 00:03:48,360 --> 00:03:50,880 Speaker 7: fast enough to meet these short term surges in demand. 80 00:03:51,040 --> 00:03:52,720 Speaker 7: So right now the price is going up. 81 00:03:52,680 --> 00:03:54,720 Speaker 2: And we're actually taking storage as well, not just the 82 00:03:54,760 --> 00:03:58,600 Speaker 2: high bandwidth memory that goes around the GPU. A point 83 00:03:58,680 --> 00:04:03,720 Speaker 2: of discussion on the internet has been autonomous driving. Yeah, 84 00:04:04,080 --> 00:04:06,560 Speaker 2: once again, in Video came out strong with autono striving. 85 00:04:06,600 --> 00:04:07,680 Speaker 2: You and I was sat next to each other in 86 00:04:07,680 --> 00:04:10,200 Speaker 2: the keynote, and you can tell us about Nvideo's offering. 87 00:04:10,520 --> 00:04:12,400 Speaker 2: But he's also caught the attention of Elon Musk and 88 00:04:12,440 --> 00:04:13,120 Speaker 2: Tesla of course. 89 00:04:13,280 --> 00:04:13,480 Speaker 6: Yeah. 90 00:04:13,520 --> 00:04:14,920 Speaker 7: I mean there was a little bit of a backwards 91 00:04:14,960 --> 00:04:18,080 Speaker 7: and forwards between Jensen and Elon about you know, whose 92 00:04:18,279 --> 00:04:21,239 Speaker 7: system is better, and most of them ending up praising 93 00:04:21,320 --> 00:04:23,200 Speaker 7: what the others doing in the end, because, let's face it, 94 00:04:23,240 --> 00:04:23,880 Speaker 7: they need each other. 95 00:04:23,920 --> 00:04:25,600 Speaker 8: But this is the point that we're at. 96 00:04:25,640 --> 00:04:28,640 Speaker 7: We've been promised cars that are robots that can do 97 00:04:28,680 --> 00:04:31,320 Speaker 7: their own thing for a very long time. Hasn't happened 98 00:04:31,360 --> 00:04:34,560 Speaker 7: as fast as we were promised. And what we're being 99 00:04:34,560 --> 00:04:36,280 Speaker 7: told now is we're at the cusp of this. And 100 00:04:36,320 --> 00:04:38,520 Speaker 7: this is great if you're making chips, if you're making 101 00:04:38,520 --> 00:04:40,360 Speaker 7: the components for these vehicles. 102 00:04:40,160 --> 00:04:42,359 Speaker 3: Cars, robots. Seen a lot of them. I'm going to 103 00:04:42,360 --> 00:04:44,480 Speaker 3: continue to see a lot of them. In King we 104 00:04:44,520 --> 00:04:46,800 Speaker 3: thank him so much for his decades of work in 105 00:04:46,800 --> 00:04:49,360 Speaker 3: the chip sector as well. But let's stay with Nvideo 106 00:04:49,440 --> 00:04:52,160 Speaker 3: because of course, ed you sat down with Nvidia CEO 107 00:04:52,240 --> 00:04:55,039 Speaker 3: Jensen one and Zeeman CEO Ronn Busch. Do you discuss 108 00:04:55,080 --> 00:04:57,840 Speaker 3: their latest partnership the expansion of AI efforts. Just take 109 00:04:57,839 --> 00:04:58,160 Speaker 3: a listen. 110 00:04:59,000 --> 00:05:01,960 Speaker 9: We're announcing a big partnership between us. We've known each 111 00:05:01,960 --> 00:05:04,760 Speaker 9: other for a long time, but the partnership we're announcing 112 00:05:04,839 --> 00:05:05,680 Speaker 9: is really a big deal. 113 00:05:06,440 --> 00:05:06,680 Speaker 2: One. 114 00:05:07,040 --> 00:05:12,840 Speaker 9: We're accelerating their EDA software, We're accelerating their simulation software. 115 00:05:13,160 --> 00:05:18,440 Speaker 9: We're integrating AI technology, physical AI and agentic AI into 116 00:05:18,600 --> 00:05:23,240 Speaker 9: their team center and their factory automation operating system. And 117 00:05:23,279 --> 00:05:27,120 Speaker 9: so we're working together across this entire spectrum. When we 118 00:05:27,160 --> 00:05:29,680 Speaker 9: accelerate the software, then we'll get to use it to 119 00:05:29,720 --> 00:05:33,799 Speaker 9: design our chips and systems. When we accelerate their simulation software, 120 00:05:33,920 --> 00:05:36,760 Speaker 9: we'll use it in our AI factories to simulate the 121 00:05:36,760 --> 00:05:42,960 Speaker 9: thermal properties of our AI factories. When we integrate our 122 00:05:43,000 --> 00:05:48,600 Speaker 9: automation and agentic systems into their AI industrial operating system, 123 00:05:48,920 --> 00:05:51,560 Speaker 9: we can then use it in our factory floors with 124 00:05:51,640 --> 00:05:55,320 Speaker 9: our partners, for example fox Con. And so we're working 125 00:05:55,320 --> 00:05:57,480 Speaker 9: across this entire spectrum together, and we're going to put 126 00:05:57,520 --> 00:06:00,440 Speaker 9: the technology to use basically as soon as we can. 127 00:06:00,520 --> 00:06:03,839 Speaker 2: What's the net effect for you, Jensen, Is it improves 128 00:06:03,880 --> 00:06:06,920 Speaker 2: margins efficient capital allocation. I know that might sound a 129 00:06:06,920 --> 00:06:09,520 Speaker 2: bit dry, but actually, right now, that's the answer everyone's 130 00:06:09,520 --> 00:06:13,200 Speaker 2: searching for. How is this investment in AI and use 131 00:06:13,240 --> 00:06:15,800 Speaker 2: of the technology actually changed things in the real world 132 00:06:15,800 --> 00:06:16,040 Speaker 2: for me? 133 00:06:16,080 --> 00:06:20,720 Speaker 9: Well, announce yesterday Vera Rubin it takes six different chips 134 00:06:20,880 --> 00:06:24,720 Speaker 9: to integrate into this incredible system called Vera Rubin. And 135 00:06:25,080 --> 00:06:28,560 Speaker 9: when you're done, each one of these Vera Rubin GPUs 136 00:06:28,880 --> 00:06:33,640 Speaker 9: is two hundred and forty thousand wants and it is 137 00:06:33,760 --> 00:06:36,840 Speaker 9: ten times more energy efficient than a last generation. It 138 00:06:36,880 --> 00:06:39,760 Speaker 9: is ten times more cost efficient than a last generation. 139 00:06:40,200 --> 00:06:43,559 Speaker 9: But it's still the technology is insanely complicated. One hundred 140 00:06:44,400 --> 00:06:48,240 Speaker 9: fifteen thousand engineering years came together to build this system. 141 00:06:48,600 --> 00:06:51,719 Speaker 9: And so when we accelerate EDA tools, when we accelerate 142 00:06:51,720 --> 00:06:55,719 Speaker 9: simulation tools, and when we can eventually and I'm hoping 143 00:06:55,839 --> 00:07:00,280 Speaker 9: very soon design entire Vera Ruben systems inside a even's 144 00:07:00,360 --> 00:07:05,160 Speaker 9: digital twin, the chance the ability for us to create 145 00:07:05,320 --> 00:07:08,600 Speaker 9: much much more complex systems, will scale, will do it 146 00:07:08,680 --> 00:07:11,360 Speaker 9: much more efficiently. And so this is really about being 147 00:07:11,400 --> 00:07:14,000 Speaker 9: able to do the impossible, and being able to do 148 00:07:14,040 --> 00:07:17,200 Speaker 9: it impossible the impossible right the first. 149 00:07:16,960 --> 00:07:22,640 Speaker 10: Time, and and and and once they then realize that 150 00:07:22,760 --> 00:07:26,440 Speaker 10: AI creates real world impact, this is where it really 151 00:07:26,440 --> 00:07:28,600 Speaker 10: deploys the full power and also. 152 00:07:28,400 --> 00:07:29,480 Speaker 6: The economic power. 153 00:07:30,000 --> 00:07:31,640 Speaker 10: It's not only in the data centers are any I 154 00:07:31,800 --> 00:07:34,120 Speaker 10: factories which we see, but also on the edge because 155 00:07:34,160 --> 00:07:36,440 Speaker 10: once you start influencing with low latency. 156 00:07:36,480 --> 00:07:38,040 Speaker 11: You bring this technology to the edge. 157 00:07:38,400 --> 00:07:41,680 Speaker 10: This is a huge potential for our customers to to 158 00:07:41,760 --> 00:07:45,280 Speaker 10: deploy this technology. And this includes of course includes hardware 159 00:07:45,720 --> 00:07:48,400 Speaker 10: and where we come from from the gits, it goes 160 00:07:48,440 --> 00:07:49,440 Speaker 10: into the controllers. 161 00:07:49,600 --> 00:07:51,040 Speaker 11: Some of our controllers. 162 00:07:50,600 --> 00:07:53,840 Speaker 12: Run on GPUs and then it goes all. 163 00:07:53,680 --> 00:07:55,520 Speaker 4: The way to the industrial PC. 164 00:07:55,680 --> 00:08:01,360 Speaker 12: Exactly, and and the andes are aides supercharge it and 165 00:08:01,520 --> 00:08:04,880 Speaker 12: they can now run algorithms trained in the cloud that 166 00:08:05,000 --> 00:08:07,360 Speaker 12: can run it on the shop floor and do all 167 00:08:07,440 --> 00:08:11,080 Speaker 12: that trick what we talked about it in retime optimization 168 00:08:11,120 --> 00:08:14,320 Speaker 12: and running in a planned and that makes a use difference. 169 00:08:17,000 --> 00:08:20,720 Speaker 2: Nvidia CEO Jensen Wang and Semen CEO Roland Bush. They're 170 00:08:20,720 --> 00:08:22,920 Speaker 2: starting their own industrial revolution, is how they put it. 171 00:08:22,920 --> 00:08:25,920 Speaker 2: Coming up, Intel looks to gain a competitive edge with 172 00:08:26,080 --> 00:08:28,400 Speaker 2: new computers and redesigned processors. 173 00:08:28,520 --> 00:08:30,040 Speaker 4: We speak with Jim Johnson. 174 00:08:29,720 --> 00:08:33,080 Speaker 2: Intel Client Computing Group SVP and General Manager. That's coming 175 00:08:33,160 --> 00:08:35,520 Speaker 2: up next, This is Bloomberg Tech. 176 00:08:43,679 --> 00:08:46,040 Speaker 3: Intel is here at CES with a slew of new 177 00:08:46,040 --> 00:08:49,000 Speaker 3: products from laptops and newly designed chips and it's part 178 00:08:49,000 --> 00:08:52,080 Speaker 3: of an effort to be more competitive across its product lineup. 179 00:08:52,559 --> 00:08:55,280 Speaker 3: Joining us now Jim Johnson, Intel Client Computing Group SVP 180 00:08:55,400 --> 00:08:58,439 Speaker 3: and general Manager. And the way you describe it as 181 00:08:58,640 --> 00:09:01,840 Speaker 3: the vibes are good, feeling positive about what you're unveiling. 182 00:09:01,880 --> 00:09:04,439 Speaker 13: Yeah, it's been a long journey to get here and 183 00:09:04,720 --> 00:09:07,360 Speaker 13: finally launching. You know, what we think is the most 184 00:09:07,400 --> 00:09:12,360 Speaker 13: powerful mobile processor for mobile computing with our new process 185 00:09:12,440 --> 00:09:15,120 Speaker 13: Note eighteen A is something the industry has been waiting 186 00:09:15,160 --> 00:09:16,880 Speaker 13: for and wondering if we would pull off. 187 00:09:16,920 --> 00:09:19,960 Speaker 3: Okay, for those that aren't deeply within the industry and 188 00:09:20,000 --> 00:09:22,320 Speaker 3: coming to your foundry and understanding what's going into this 189 00:09:22,360 --> 00:09:26,520 Speaker 3: new processor, the Core Alter series three. What makes them different. 190 00:09:27,240 --> 00:09:31,680 Speaker 13: We took our most powerful mobile processor from last year 191 00:09:32,400 --> 00:09:35,720 Speaker 13: and built it on the most power efficient mobile processor 192 00:09:35,720 --> 00:09:39,959 Speaker 13: from last year, combining those two capabilities, and so we 193 00:09:40,120 --> 00:09:44,560 Speaker 13: run workloads from last year at forty percent lower power 194 00:09:45,320 --> 00:09:46,760 Speaker 13: this year on this processor. 195 00:09:47,240 --> 00:09:48,280 Speaker 8: And it's I asked. 196 00:09:48,320 --> 00:09:50,240 Speaker 13: I've been asking, knowing I've been coming here, I've been 197 00:09:50,280 --> 00:09:54,280 Speaker 13: asking our customers what would you say? They'd say, super powerful, surprisingly, 198 00:09:54,320 --> 00:09:54,920 Speaker 13: power efficient. 199 00:09:55,000 --> 00:09:56,040 Speaker 6: That's what they told me last night. 200 00:09:56,080 --> 00:09:59,480 Speaker 2: So here's the thing, Jim, and with respect AMD and 201 00:09:59,559 --> 00:10:02,199 Speaker 2: qual customers would say the same thing. You know, AMD 202 00:10:02,280 --> 00:10:04,920 Speaker 2: and qualcom have already been out there this week saying 203 00:10:04,920 --> 00:10:08,720 Speaker 2: that their processors and parts, best battery life, best performance, 204 00:10:09,160 --> 00:10:11,480 Speaker 2: and they both argue that they're taking share. 205 00:10:11,960 --> 00:10:13,479 Speaker 4: Right, What data. 206 00:10:13,200 --> 00:10:16,040 Speaker 2: Points can you point me to that would evidence Intel's 207 00:10:16,120 --> 00:10:19,239 Speaker 2: back in the lead in that market based on this technology. 208 00:10:19,360 --> 00:10:21,520 Speaker 13: I get asked quite often what's the killer app for 209 00:10:21,559 --> 00:10:25,280 Speaker 13: an AIPC, And up until this part I didn't have 210 00:10:25,600 --> 00:10:30,440 Speaker 13: an answer. But if you're into mobile gaming, we built 211 00:10:30,640 --> 00:10:34,840 Speaker 13: a GPU in Series three with an AI capability called 212 00:10:34,920 --> 00:10:38,720 Speaker 13: multi frame generation. So you would typically render a frame 213 00:10:38,760 --> 00:10:40,880 Speaker 13: and then render a frame in between. You can now 214 00:10:40,960 --> 00:10:45,600 Speaker 13: use AI called multi frame Generation. It's quadruple in the 215 00:10:45,640 --> 00:10:49,640 Speaker 13: frame rate and super smooth play. So I will take 216 00:10:49,679 --> 00:10:52,319 Speaker 13: that system up against any of our competitors. If you're into. 217 00:10:52,120 --> 00:10:54,320 Speaker 4: Mobile games, it is Intel in the lead, I believe. 218 00:10:54,320 --> 00:10:58,360 Speaker 2: So, yes, twenty twenty six is fascinating. Bigger picture, you 219 00:10:58,440 --> 00:11:01,559 Speaker 2: have some fore Karta's forecasts. Is I think IDC saying 220 00:11:01,559 --> 00:11:03,839 Speaker 2: that the market will shrink nine percent this year? We 221 00:11:03,920 --> 00:11:07,520 Speaker 2: have others saying it will grow literally because of AIPC. 222 00:11:08,080 --> 00:11:09,560 Speaker 4: What is your kind of forecast? 223 00:11:09,960 --> 00:11:12,000 Speaker 2: And you know it comes back to the same question 224 00:11:12,000 --> 00:11:13,640 Speaker 2: we've had at CES for a couple of years now 225 00:11:13,679 --> 00:11:17,800 Speaker 2: is does the AIPC resonate with anyone at the corporate level, 226 00:11:17,800 --> 00:11:18,839 Speaker 2: at the consumer level. 227 00:11:19,000 --> 00:11:20,480 Speaker 13: So a couple things. So that is part of the 228 00:11:20,520 --> 00:11:24,320 Speaker 13: conversation we're having this week. But the feedback I keep 229 00:11:24,360 --> 00:11:28,000 Speaker 13: getting from our customers is keep ramping supply of these 230 00:11:28,040 --> 00:11:31,080 Speaker 13: new processors because they believe they're going to win with them. 231 00:11:31,760 --> 00:11:35,120 Speaker 13: And from an AI standpoint, another thing that's happened because 232 00:11:35,440 --> 00:11:38,920 Speaker 13: we've been deploying aipcs now for two years, we have 233 00:11:39,160 --> 00:11:42,360 Speaker 13: the equivalent of forty data center's worth of compute on 234 00:11:42,440 --> 00:11:45,199 Speaker 13: the edge, and now we have like Arvin, the CEO 235 00:11:45,200 --> 00:11:47,920 Speaker 13: of Perplexity, came and did a talk with me and 236 00:11:47,960 --> 00:11:51,120 Speaker 13: he's now figuring out with us how to run portions 237 00:11:51,160 --> 00:11:53,960 Speaker 13: of his comment browser directly on the AIPC. 238 00:11:54,120 --> 00:11:54,960 Speaker 6: For four reasons. 239 00:11:55,240 --> 00:12:00,000 Speaker 13: He sees better performance, he sees better security and privacy, 240 00:12:00,080 --> 00:12:04,800 Speaker 13: see better cost, but for more importantly, more control. 241 00:12:04,960 --> 00:12:06,200 Speaker 6: So for business they have more control. 242 00:12:06,280 --> 00:12:09,840 Speaker 13: So I'm seeing these big AI companies actually start to 243 00:12:09,920 --> 00:12:13,600 Speaker 13: tap into what can they do more locally. Same with 244 00:12:14,240 --> 00:12:17,760 Speaker 13: so I guess another thing that happened is edge computing. 245 00:12:17,760 --> 00:12:20,120 Speaker 13: I know you've been talking about edge computing between cloud 246 00:12:20,160 --> 00:12:23,599 Speaker 13: and PC. We used to sell these parts maybe a 247 00:12:23,679 --> 00:12:26,360 Speaker 13: year or two after we launch them into factories and 248 00:12:26,400 --> 00:12:29,880 Speaker 13: so forth. But because the Series three process is really 249 00:12:29,920 --> 00:12:33,480 Speaker 13: good at perceiving the environment with our visual processor and 250 00:12:33,520 --> 00:12:37,280 Speaker 13: doing motor control of factory equipment and robotics, we have 251 00:12:37,400 --> 00:12:41,200 Speaker 13: demand on day one for Series three from edge customers. 252 00:12:41,280 --> 00:12:43,800 Speaker 3: So let's talk about the supply. Because you have got 253 00:12:43,920 --> 00:12:47,800 Speaker 3: vertical integration, you're actually using your foundries, you are printing 254 00:12:47,800 --> 00:12:48,640 Speaker 3: this silicon. 255 00:12:48,559 --> 00:12:54,040 Speaker 13: Yes, and we're building capacity specifically for client and that's 256 00:12:54,080 --> 00:12:56,440 Speaker 13: a real value proposition for our customers because they can 257 00:12:56,520 --> 00:12:59,280 Speaker 13: depend on us to keep supporting our product lines for them. 258 00:12:59,559 --> 00:13:02,000 Speaker 3: In a way, you are the current of your own 259 00:13:02,440 --> 00:13:05,640 Speaker 3: OWL boundary. Why did you go with them not t SMC? 260 00:13:06,440 --> 00:13:08,920 Speaker 3: What turned you to think, Okay, these are the ones, 261 00:13:08,960 --> 00:13:10,200 Speaker 3: this is the supplier I need. 262 00:13:10,280 --> 00:13:12,520 Speaker 13: There's three things that they did with eighteen A that 263 00:13:12,559 --> 00:13:15,720 Speaker 13: it's really impressive. They use the latest UV technology you can. 264 00:13:15,640 --> 00:13:19,080 Speaker 6: Buy tag yousml YEAP, Yeah, that's right, that's right. And 265 00:13:19,160 --> 00:13:20,880 Speaker 6: they did two new unique innovations. 266 00:13:20,880 --> 00:13:23,600 Speaker 13: We call it ribon fet or gate all around and 267 00:13:23,679 --> 00:13:26,840 Speaker 13: backside power delivery so you can separate power from signals. 268 00:13:27,320 --> 00:13:31,600 Speaker 13: We get fifteen percent better performance per w and multiply 269 00:13:31,640 --> 00:13:33,959 Speaker 13: that times twenty or twenty five watts in a PC 270 00:13:34,640 --> 00:13:37,520 Speaker 13: and we have thirty percent better chip density, and so 271 00:13:37,559 --> 00:13:40,840 Speaker 13: that allows us to compact our design, which allows our 272 00:13:40,880 --> 00:13:44,400 Speaker 13: customers to compact their design and put in bigger batteries, 273 00:13:44,559 --> 00:13:47,200 Speaker 13: and so you get they're now driving all day battery 274 00:13:47,200 --> 00:13:48,560 Speaker 13: life to multi day battery life. 275 00:13:48,640 --> 00:13:51,040 Speaker 2: Jim, I think we have to end by talking about pricing. 276 00:13:51,559 --> 00:13:56,000 Speaker 2: You know, the background is that memory is an issue 277 00:13:56,040 --> 00:13:58,720 Speaker 2: at the moment. How do you manage it? Do you 278 00:13:58,800 --> 00:14:02,360 Speaker 2: pass on the cost or whether the storm feast and famine, 279 00:14:02,400 --> 00:14:05,920 Speaker 2: sicklical memory problems. You're you've been around right with. 280 00:14:05,920 --> 00:14:08,040 Speaker 6: Respect, it's a conversation we're having this. 281 00:14:08,000 --> 00:14:10,439 Speaker 2: Week, conversation or an acknowledgement that it's a problem. 282 00:14:10,559 --> 00:14:13,240 Speaker 13: Well, it definitely is an issue in the industry. Our 283 00:14:13,280 --> 00:14:15,760 Speaker 13: customers are struggling on what they're going to do with it, 284 00:14:16,400 --> 00:14:19,240 Speaker 13: and they haven't decided yet. But what they're asking us 285 00:14:19,240 --> 00:14:22,080 Speaker 13: to do is keep our build plans solid because in 286 00:14:22,120 --> 00:14:26,240 Speaker 13: twenty twenty six they see a chance to really gain 287 00:14:26,320 --> 00:14:28,280 Speaker 13: share on the back of Series three. 288 00:14:28,400 --> 00:14:29,640 Speaker 4: We just have a few seconds. 289 00:14:29,760 --> 00:14:33,040 Speaker 2: Does the PC market grow, stay flat or shrink in 290 00:14:33,080 --> 00:14:33,880 Speaker 2: twenty twenty six. 291 00:14:34,240 --> 00:14:37,040 Speaker 13: I think it's going to be plus or minus where 292 00:14:37,040 --> 00:14:39,320 Speaker 13: it's at now. Some people are much more dire than that. 293 00:14:39,920 --> 00:14:42,280 Speaker 13: But we have the same conversation when Terris came up, 294 00:14:42,800 --> 00:14:46,760 Speaker 13: and all the predictions, whether really drastic or big pull ins, 295 00:14:47,160 --> 00:14:49,680 Speaker 13: they kind of didn't come true. There was something more 296 00:14:49,720 --> 00:14:51,880 Speaker 13: in the middle. So we're planning for more in the middle. 297 00:14:52,160 --> 00:14:53,920 Speaker 13: But of course we'll react with our customers as we 298 00:14:53,960 --> 00:14:54,200 Speaker 13: need to. 299 00:14:54,840 --> 00:14:59,000 Speaker 2: Jim Johnson, Intel Client Computing Group SVP and general Manager, 300 00:14:59,080 --> 00:15:03,000 Speaker 2: thank you so much. Up a conversation with Qualcom Cristianamon 301 00:15:03,320 --> 00:15:06,120 Speaker 2: on why robotics is the next big thing in AI 302 00:15:06,240 --> 00:15:07,320 Speaker 2: and they're not alone in that. 303 00:15:07,880 --> 00:15:08,880 Speaker 4: This is Bloomberg Tech. 304 00:15:17,160 --> 00:15:20,720 Speaker 2: Robotics is the next big opportunity in AI. That's according 305 00:15:20,760 --> 00:15:23,840 Speaker 2: to Qualcom CEO Christiano I'm on Carra sat down with 306 00:15:23,920 --> 00:15:26,000 Speaker 2: him here at CES in Las Vegas yesterday. 307 00:15:26,000 --> 00:15:26,440 Speaker 4: Listen to this. 308 00:15:27,680 --> 00:15:31,040 Speaker 14: Yes, look, we're incredibly excited about this is a new chapter. 309 00:15:31,360 --> 00:15:34,480 Speaker 14: I think of the Qualcom expansion and diversification, I think 310 00:15:34,480 --> 00:15:38,440 Speaker 14: we're going to robotics. We like robotics a lot because 311 00:15:38,640 --> 00:15:42,640 Speaker 14: by definition, is an edge AI problem to solve, not 312 00:15:42,800 --> 00:15:45,840 Speaker 14: different than what we did in automotive. You cannot put 313 00:15:45,840 --> 00:15:48,360 Speaker 14: a server in a robot. You need bettery life, you 314 00:15:48,400 --> 00:15:51,520 Speaker 14: need a lot of integration as sensors and physical AI 315 00:15:51,760 --> 00:15:54,840 Speaker 14: is a massive opportunity and it's an EDGAI opportunity. So 316 00:15:56,120 --> 00:15:58,560 Speaker 14: as we look at this transition of qualcom into a 317 00:15:58,600 --> 00:16:01,560 Speaker 14: new industry, we went to automore, motive, to PC to industrial, 318 00:16:02,000 --> 00:16:05,840 Speaker 14: UH to data center. Now robotics this is the next opportunity. 319 00:16:06,280 --> 00:16:09,320 Speaker 14: We actually have a number of robots here at our 320 00:16:09,360 --> 00:16:15,920 Speaker 14: booth at CS demonstrating training humanoids Industrial. I think we 321 00:16:16,080 --> 00:16:18,800 Speaker 14: started UH the year working with some of you know, 322 00:16:18,920 --> 00:16:23,440 Speaker 14: great companies German Kouka, Figure AI, and I think it's 323 00:16:23,440 --> 00:16:27,560 Speaker 14: going to be a great opportunity and robotics it's perfect 324 00:16:28,040 --> 00:16:31,160 Speaker 14: for you to have high performance computing and low power 325 00:16:31,240 --> 00:16:34,880 Speaker 14: connectivity is an edge AI problem and I think it's 326 00:16:34,960 --> 00:16:38,080 Speaker 14: going to be the next big wave of AI physical AI. 327 00:16:38,400 --> 00:16:41,840 Speaker 3: How soon is that reality? Already we have robots in 328 00:16:41,960 --> 00:16:45,960 Speaker 3: manufacturing and industrials, but how soon is it fully autonomous robots? 329 00:16:45,960 --> 00:16:47,920 Speaker 3: How soon do we start to have the humanoid versions 330 00:16:47,920 --> 00:16:48,600 Speaker 3: in our houses. 331 00:16:48,760 --> 00:16:52,560 Speaker 14: Look the way we think about this is things that 332 00:16:52,640 --> 00:16:55,560 Speaker 14: you didn't find were possible to do with a robot, 333 00:16:55,600 --> 00:16:56,040 Speaker 14: you can do it. 334 00:16:56,120 --> 00:16:57,040 Speaker 6: Right now, use an AI. 335 00:16:57,800 --> 00:17:01,960 Speaker 14: I think industrial robot is the big largest opportunity that 336 00:17:02,000 --> 00:17:03,880 Speaker 14: we see in front of us, and it's probably starting 337 00:17:04,280 --> 00:17:09,640 Speaker 14: as early as twenty twenty six. When you train use 338 00:17:09,720 --> 00:17:14,080 Speaker 14: AI to train a robot on one given task, it's 339 00:17:14,160 --> 00:17:17,080 Speaker 14: it's a very well defined problem, and you can do 340 00:17:17,119 --> 00:17:20,960 Speaker 14: this and you put into production consumer robot, the one 341 00:17:20,960 --> 00:17:22,199 Speaker 14: that is going to be in your house and do 342 00:17:22,240 --> 00:17:24,119 Speaker 14: everything for you. It's gonna take a little bit of time, 343 00:17:24,440 --> 00:17:26,200 Speaker 14: but it's going to happen and it's going to be 344 00:17:26,240 --> 00:17:28,560 Speaker 14: a big opportunity. I like to do this parallel that 345 00:17:28,640 --> 00:17:32,639 Speaker 14: we saw with automotive when we start talking about autonomous cars, 346 00:17:33,960 --> 00:17:35,800 Speaker 14: a lot of companies went in and said, we're just 347 00:17:35,840 --> 00:17:39,000 Speaker 14: going to get this full autonomo robotax in, but until 348 00:17:39,040 --> 00:17:42,119 Speaker 14: you get there, you can do assist the driving to 349 00:17:42,320 --> 00:17:44,879 Speaker 14: every car on the road, assuming that the driver is 350 00:17:44,960 --> 00:17:46,800 Speaker 14: there to pick it up. And we've seen in ads 351 00:17:46,880 --> 00:17:50,920 Speaker 14: level two, level three highway highway autopilot that's. 352 00:17:50,840 --> 00:17:51,320 Speaker 6: What we're doing. 353 00:17:51,520 --> 00:17:55,639 Speaker 14: I think the same parallel applies to robotics. First, you 354 00:17:55,720 --> 00:17:59,040 Speaker 14: have a lot of enterprise in the industrial applications. We 355 00:17:59,080 --> 00:18:02,679 Speaker 14: see companies form when retail at night robot go to 356 00:18:02,720 --> 00:18:05,200 Speaker 14: the aisles of the supermarket and restuck the shelves. 357 00:18:05,200 --> 00:18:06,080 Speaker 6: Something very simple. 358 00:18:07,200 --> 00:18:11,280 Speaker 14: That opportunity is happening right now. Over time, we're gonna 359 00:18:11,280 --> 00:18:12,920 Speaker 14: have the domestic robots that would do. 360 00:18:12,880 --> 00:18:13,480 Speaker 6: Everything for you. 361 00:18:15,520 --> 00:18:18,240 Speaker 3: Carl Concio, Cristiano, I'm on there trying to be a 362 00:18:18,280 --> 00:18:20,040 Speaker 3: little bit more realistic about how soon we're gonna have 363 00:18:20,080 --> 00:18:22,960 Speaker 3: humanoid robots in our homes. But robotics is one of 364 00:18:22,960 --> 00:18:26,600 Speaker 3: the key themes that everywhere here in ces right now book. 365 00:18:26,640 --> 00:18:29,280 Speaker 3: Sam Kelly should have been tracking them on the shop 366 00:18:29,320 --> 00:18:31,160 Speaker 3: floor should we call it in the time, I mean 367 00:18:31,240 --> 00:18:34,160 Speaker 3: everywhere you look as a humanoid, is that your vibe? 368 00:18:34,320 --> 00:18:36,080 Speaker 3: And is everyone taking that form factor? 369 00:18:36,240 --> 00:18:38,399 Speaker 15: Yeah, it's so funny. Yesterday I was walking around. I 370 00:18:38,400 --> 00:18:41,000 Speaker 15: saw one playing ping pong, one. 371 00:18:40,840 --> 00:18:41,760 Speaker 3: Made me a latte. 372 00:18:42,160 --> 00:18:44,480 Speaker 15: There was a chess robot, you know, and some of 373 00:18:44,520 --> 00:18:46,879 Speaker 15: them were on the enterprise side too, showing what it 374 00:18:46,920 --> 00:18:47,400 Speaker 15: could be like. 375 00:18:47,359 --> 00:18:48,359 Speaker 16: On the assembly line. 376 00:18:48,440 --> 00:18:51,640 Speaker 15: But yeah, humanoids in particular are really big. This year 377 00:18:51,640 --> 00:18:54,240 Speaker 15: we saw during a Quino, LG had its Cloyd robot 378 00:18:54,320 --> 00:18:58,080 Speaker 15: come out folding laundry. Lots of laundry folding robots as well, 379 00:18:58,240 --> 00:19:00,600 Speaker 15: and I think it shows one thing that's really interesting 380 00:19:00,760 --> 00:19:04,200 Speaker 15: is before a lot of these were in controlled experiments. 381 00:19:04,359 --> 00:19:07,240 Speaker 15: Of course that's still happening, but companies are now coming 382 00:19:07,240 --> 00:19:09,400 Speaker 15: out doing more live demos, which shows a little bit 383 00:19:09,400 --> 00:19:12,399 Speaker 15: more confidence, a lot of growth. Also, we've seen some 384 00:19:12,520 --> 00:19:16,200 Speaker 15: challenges around hand finger movements. Legs is another challenge. 385 00:19:16,200 --> 00:19:17,800 Speaker 16: So certainly challenges. 386 00:19:17,359 --> 00:19:18,400 Speaker 4: Ahead, dexterity. 387 00:19:18,640 --> 00:19:21,120 Speaker 2: I mean, when we were speaking to Jensen Wong, you say, 388 00:19:21,200 --> 00:19:25,040 Speaker 2: you know he labels this the chat GPT moment for robotics, right, 389 00:19:25,040 --> 00:19:26,240 Speaker 2: but there are still challenges. 390 00:19:26,359 --> 00:19:27,120 Speaker 16: Oh yeah, Is there. 391 00:19:27,040 --> 00:19:30,520 Speaker 2: Any acknowledgement here on the showroom floor that we aren't 392 00:19:30,800 --> 00:19:32,840 Speaker 2: going to see these robots in our homes tomorrow? 393 00:19:33,400 --> 00:19:33,640 Speaker 16: Yeah? 394 00:19:33,760 --> 00:19:35,880 Speaker 15: I mean I think we can see that just by 395 00:19:35,920 --> 00:19:38,359 Speaker 15: sometimes looking at a demo and it might take a while, 396 00:19:38,560 --> 00:19:41,200 Speaker 15: or even with LG the robot was doing the laundry, 397 00:19:41,200 --> 00:19:43,320 Speaker 15: but it took a long time. It was a little 398 00:19:43,359 --> 00:19:46,240 Speaker 15: painfully slow. So we're certainly not there yet. And also 399 00:19:46,400 --> 00:19:49,240 Speaker 15: just to have it in the home, you have cluttered homes. 400 00:19:49,320 --> 00:19:52,479 Speaker 15: Homes are unpredictable. You have to be really careful, especially 401 00:19:52,520 --> 00:19:55,159 Speaker 15: with humanoids with legs. You don't want them falling. So 402 00:19:55,200 --> 00:19:59,359 Speaker 15: we are certainly very this is not happening anytime too soon, And. 403 00:19:59,280 --> 00:20:01,520 Speaker 3: That's what this is about. This is about pushing forward 404 00:20:01,600 --> 00:20:03,800 Speaker 3: the innovations, thinking what's coming in the next decade, maybe 405 00:20:03,800 --> 00:20:06,480 Speaker 3: not the next ten months, but some What are you 406 00:20:06,520 --> 00:20:09,120 Speaker 3: seeing in terms of health and wellness? Because I'm hearing 407 00:20:09,160 --> 00:20:12,800 Speaker 3: a lot about robots, perhaps therefore mental health, full caring 408 00:20:12,800 --> 00:20:14,840 Speaker 3: for the elderlady, but you'll see a lot of other 409 00:20:14,880 --> 00:20:16,280 Speaker 3: areas what health and wellness. 410 00:20:16,080 --> 00:20:18,879 Speaker 15: Is, Yeah, exactly different form factors. So we're all familiar 411 00:20:18,880 --> 00:20:21,159 Speaker 15: with the smart watches and the smart rings, of course, 412 00:20:21,600 --> 00:20:25,760 Speaker 15: but now yesterday I saw a toothbrush that predicts signs 413 00:20:25,760 --> 00:20:29,400 Speaker 15: of longevity, whether you might be more likely to get 414 00:20:29,400 --> 00:20:32,000 Speaker 15: a certain type of disease or kidney issues from a 415 00:20:32,040 --> 00:20:35,160 Speaker 15: toothbrush because of saliva is actually a little bit more 416 00:20:35,240 --> 00:20:38,639 Speaker 15: of a predictor than just like the skin. Yes, so 417 00:20:38,920 --> 00:20:41,840 Speaker 15: the different form factors like that. I saw a longevity 418 00:20:41,960 --> 00:20:46,080 Speaker 15: mirror that analyzed my blood flow to predict maybe perhaps 419 00:20:46,160 --> 00:20:49,200 Speaker 15: signs of either hypertension down the line or other heart 420 00:20:49,240 --> 00:20:53,000 Speaker 15: health things like that. Also a smart night guard, so 421 00:20:53,320 --> 00:20:56,120 Speaker 15: people who grind their teeth at night. In addition, it'll 422 00:20:56,119 --> 00:20:57,840 Speaker 15: like nudge you so you step doing that. But it 423 00:20:57,920 --> 00:21:01,560 Speaker 15: also again with the saliva you're sleep and it'll predict 424 00:21:02,480 --> 00:21:05,879 Speaker 15: issues long term issues with your health, but also it'll 425 00:21:06,720 --> 00:21:09,719 Speaker 15: tell you how well you're sleeping, sleep tracking, sleep apnea, 426 00:21:09,800 --> 00:21:13,440 Speaker 15: things like that. So different types of health is coming 427 00:21:13,440 --> 00:21:14,560 Speaker 15: into different form factors. 428 00:21:14,600 --> 00:21:15,399 Speaker 6: That's super quick. 429 00:21:15,880 --> 00:21:17,560 Speaker 16: You've been to many cees. 430 00:21:17,280 --> 00:21:19,440 Speaker 4: Yeah, the vibe relative to prior. 431 00:21:19,320 --> 00:21:22,919 Speaker 15: Years, Yeah, it feels like exciting, you know, to the 432 00:21:22,920 --> 00:21:25,400 Speaker 15: point of like a chat GBT moment. People are really 433 00:21:25,440 --> 00:21:28,639 Speaker 15: excited to be here and show off different new things. 434 00:21:28,720 --> 00:21:31,159 Speaker 15: And I think there's you know, who knows what you know, 435 00:21:31,359 --> 00:21:33,199 Speaker 15: this is a testing ground. Who knows what really is 436 00:21:33,200 --> 00:21:35,560 Speaker 15: going to happen, But there's definitely a level of excitement. 437 00:21:35,720 --> 00:21:37,560 Speaker 2: You're trying to check out what Sund's been writing about 438 00:21:37,560 --> 00:21:39,880 Speaker 2: on Bloomberg dot com write character. 439 00:21:40,160 --> 00:21:42,800 Speaker 3: Yeah, you're going to have a big conversation later. It's 440 00:21:42,840 --> 00:21:46,480 Speaker 3: all going to be about health wellness, AURA and particularly 441 00:21:46,520 --> 00:21:49,040 Speaker 3: about what well how this goes in sync with the FDA. 442 00:21:49,160 --> 00:21:51,080 Speaker 2: Right when you think about help Tom Hale or a 443 00:21:51,160 --> 00:21:54,480 Speaker 2: CEO the ring wearable. I'm not an AURA awarer, but 444 00:21:54,880 --> 00:21:56,399 Speaker 2: you know right now they have a lot of questions 445 00:21:56,400 --> 00:21:59,720 Speaker 2: about how they go beyond, particularly a female user base. 446 00:22:00,119 --> 00:22:01,560 Speaker 4: To come Bluemosum Kelly, thank you. 447 00:22:01,960 --> 00:22:04,360 Speaker 2: Coming up, we've got a big conversation with Mobile Ized 448 00:22:04,400 --> 00:22:10,040 Speaker 2: CEO and non Shahu after the acquisition of a robotics company, Mentee. 449 00:22:10,160 --> 00:22:12,399 Speaker 2: It is halftime. We are in Las Vegas. This is 450 00:22:12,480 --> 00:22:15,000 Speaker 2: CS stay with us, this is Bloomberg Tech. 451 00:22:23,520 --> 00:22:26,359 Speaker 3: Welcome back to Bloomberg Tech, a very special edition live 452 00:22:26,400 --> 00:22:28,320 Speaker 3: in Las Vegas for CES. Let's take a look at 453 00:22:28,359 --> 00:22:30,480 Speaker 3: these markets, because look, we are being ruled to this 454 00:22:30,520 --> 00:22:34,040 Speaker 3: can extent by fresh geopolitical tension. But not then as 455 00:22:34,080 --> 00:22:36,560 Speaker 3: that one hundred we remain in gains up a quarter 456 00:22:36,600 --> 00:22:39,520 Speaker 3: of a percentage point. Yes, there's all eyes on Memory makers, 457 00:22:39,600 --> 00:22:41,720 Speaker 3: Yes there's all eyes on Video and AMD post some 458 00:22:41,720 --> 00:22:43,639 Speaker 3: of their announcements, but we want to put all our 459 00:22:43,640 --> 00:22:45,920 Speaker 3: eyes on one particular stock right now. Ed because Mobile 460 00:22:45,920 --> 00:22:49,240 Speaker 3: I yesterday had a pretty key announcement a new deal, 461 00:22:49,760 --> 00:22:52,000 Speaker 3: a deal to the tune of nine hundred million dollars 462 00:22:52,080 --> 00:22:56,320 Speaker 3: combining cash mobilized shares to buy Menty Robotics, and the 463 00:22:56,320 --> 00:22:58,120 Speaker 3: dealers expect to be closing the first quarter of twenty 464 00:22:58,160 --> 00:23:00,000 Speaker 3: twenty six, we're still in gains up three times percent. 465 00:23:00,160 --> 00:23:03,920 Speaker 3: Were significantly off of our hives as the market digests 466 00:23:04,040 --> 00:23:06,280 Speaker 3: what this deal really means. We've got a perfect person. 467 00:23:06,320 --> 00:23:08,600 Speaker 3: I'm asking what it means. I'm not Shashure has here 468 00:23:08,640 --> 00:23:11,360 Speaker 3: with us Mobile I CEO is wonderful TOI some time with. 469 00:23:11,320 --> 00:23:13,159 Speaker 11: You, Sam, wonderful to be with your Carolina Ed. 470 00:23:13,440 --> 00:23:17,000 Speaker 3: Why is it right for you to go from autonomous 471 00:23:17,080 --> 00:23:18,840 Speaker 3: driving into the world of robotics. 472 00:23:19,080 --> 00:23:22,879 Speaker 17: Well, Mobilize, an AI company in the field of autonomous 473 00:23:23,160 --> 00:23:27,520 Speaker 17: driving driving, says the full spectrum of a computer vision 474 00:23:27,760 --> 00:23:33,640 Speaker 17: and AI to control cars and humanoid robotics has been 475 00:23:33,640 --> 00:23:35,879 Speaker 17: recognized in the past two or three years as a 476 00:23:35,920 --> 00:23:40,800 Speaker 17: complementary domain and Mobiliz wants to it's not only another 477 00:23:40,880 --> 00:23:44,600 Speaker 17: growth engine, but from a technological point of view, if 478 00:23:44,640 --> 00:23:47,600 Speaker 17: you are an actor in this area of physical AI, 479 00:23:47,720 --> 00:23:50,159 Speaker 17: you want to then extend it to the full scope 480 00:23:50,600 --> 00:23:54,800 Speaker 17: of physical AI. And I believe that robotics, humanoids, they 481 00:23:54,800 --> 00:23:57,440 Speaker 17: have a great future. Now maybe it's a long term 482 00:23:57,440 --> 00:23:59,840 Speaker 17: plate that it's not, you know, a year from now, 483 00:24:00,000 --> 00:24:04,440 Speaker 17: but I believe it has a significant, exciting future. And 484 00:24:04,920 --> 00:24:12,080 Speaker 17: from Mobilize, there's synergetic areas in infrastructure, in AI, in software, 485 00:24:12,119 --> 00:24:13,280 Speaker 17: in AI talents. 486 00:24:13,400 --> 00:24:15,280 Speaker 11: So from Mobiliz, this is a great move. 487 00:24:15,600 --> 00:24:18,159 Speaker 3: I mean of course, in many ways, people say you 488 00:24:18,440 --> 00:24:22,920 Speaker 3: think that because you helped found menty your son's work 489 00:24:23,440 --> 00:24:28,119 Speaker 3: at MENTI help me get comfortable with that because many 490 00:24:28,160 --> 00:24:31,959 Speaker 3: have thrown such accusations. Elon must sway sometimes previous acquisitions 491 00:24:31,960 --> 00:24:32,680 Speaker 3: and not liked it. 492 00:24:32,920 --> 00:24:35,800 Speaker 17: Well, it's called the related party transaction. Has been done 493 00:24:35,840 --> 00:24:37,640 Speaker 17: in the past and will be done in the future, 494 00:24:38,320 --> 00:24:41,399 Speaker 17: and there are ways to handle it, like being recused 495 00:24:41,400 --> 00:24:44,680 Speaker 17: from decision, which I did and so forth. And you know, 496 00:24:44,840 --> 00:24:47,480 Speaker 17: my son has a zero point zero zero zero zero 497 00:24:47,560 --> 00:24:50,159 Speaker 17: one percent is just an employee, so it's not a 498 00:24:50,160 --> 00:24:54,240 Speaker 17: big deal. You know, he graduated from computer science and 499 00:24:54,320 --> 00:24:58,240 Speaker 17: he wanted to do some work and go work for 500 00:24:58,280 --> 00:25:01,040 Speaker 17: a company that is very very strong and AI working 501 00:25:01,080 --> 00:25:04,200 Speaker 17: on the future of physical AI. So it makes sense. 502 00:25:04,840 --> 00:25:08,560 Speaker 17: But you know that is really immaterial. The material part 503 00:25:08,680 --> 00:25:14,480 Speaker 17: is the scope of AI. You know, physical AI includes humanoids, 504 00:25:14,520 --> 00:25:17,080 Speaker 17: includes autonomous driving, and it makes a lot a lot 505 00:25:17,119 --> 00:25:20,840 Speaker 17: of sense. Now, Mobili over the years was always looking 506 00:25:20,880 --> 00:25:23,879 Speaker 17: for a new growth engine, right for example, take computer 507 00:25:24,000 --> 00:25:27,879 Speaker 17: vision and applied maybe to security areas in cameras and 508 00:25:27,880 --> 00:25:30,199 Speaker 17: so forth, But we never found something that really clicked, 509 00:25:30,720 --> 00:25:33,919 Speaker 17: found something that has significant modes, that has a significant 510 00:25:34,000 --> 00:25:39,679 Speaker 17: teram and humanoids is becoming that click is becoming this area. 511 00:25:39,800 --> 00:25:40,920 Speaker 11: Where are you coming to growth? 512 00:25:40,920 --> 00:25:43,760 Speaker 2: You know, I've been talking for years about you know, 513 00:25:43,840 --> 00:25:46,800 Speaker 2: mobilized existing footprint and then it's go to market in 514 00:25:46,840 --> 00:25:47,320 Speaker 2: the future. 515 00:25:47,520 --> 00:25:48,920 Speaker 4: What's the go to market for mente? 516 00:25:49,800 --> 00:25:51,760 Speaker 17: Well, we said to go to market in two phases. 517 00:25:52,240 --> 00:25:58,240 Speaker 17: The first phase is structured environments like warehouses, assembly plants, 518 00:25:58,760 --> 00:26:04,199 Speaker 17: retail where the number of tasks are finite are also 519 00:26:04,560 --> 00:26:07,400 Speaker 17: understood in advance. What are the tasks that the customer 520 00:26:07,480 --> 00:26:10,560 Speaker 17: is interested. A customer will buy a fleet of robots, 521 00:26:10,600 --> 00:26:14,920 Speaker 17: not just one robot, and you can customize per customer 522 00:26:15,480 --> 00:26:19,120 Speaker 17: that I think based on pocs that the mentee has 523 00:26:19,160 --> 00:26:21,879 Speaker 17: been doing. If you look at the demonstrations that have 524 00:26:21,920 --> 00:26:24,720 Speaker 17: been showing like robots picking boxes, moving them from place 525 00:26:25,000 --> 00:26:25,679 Speaker 17: to place. 526 00:26:25,480 --> 00:26:27,400 Speaker 4: You wouldessentially just sell the hardware. 527 00:26:27,160 --> 00:26:29,480 Speaker 11: A seller robot, either sell or Lisa robot. 528 00:26:31,640 --> 00:26:35,320 Speaker 17: We believe that the manufacturing costs in a reasonable volume 529 00:26:35,359 --> 00:26:37,760 Speaker 17: of tens of thousands of robots would be twenty thousand dollars. 530 00:26:37,800 --> 00:26:41,600 Speaker 17: So this gives a lot of flexibility in a business model. 531 00:26:41,640 --> 00:26:43,720 Speaker 17: So this is phase one. We believe it will start 532 00:26:43,720 --> 00:26:48,000 Speaker 17: in twenty twenty eight. Phase two is unstructured environments like 533 00:26:48,040 --> 00:26:52,159 Speaker 17: home use that is much more complicated because the tasks 534 00:26:52,160 --> 00:26:53,000 Speaker 17: are open ended. 535 00:26:54,080 --> 00:26:55,640 Speaker 11: You need to continuously learn. 536 00:26:55,840 --> 00:26:58,200 Speaker 17: So it's not that you can prepare yourself to all 537 00:26:58,200 --> 00:27:02,480 Speaker 17: the possible tasks in a home home use, so you 538 00:27:02,560 --> 00:27:05,119 Speaker 17: need new technology. So it's not that the same technology 539 00:27:05,160 --> 00:27:07,480 Speaker 17: you use for structured environments. And this is one of 540 00:27:07,480 --> 00:27:10,360 Speaker 17: the things that I showed in my keynote, a technology 541 00:27:10,480 --> 00:27:12,639 Speaker 17: that Minty and this is why Minty is called Minty 542 00:27:12,840 --> 00:27:16,840 Speaker 17: from mentory. The robot is watching passively watching a human 543 00:27:17,000 --> 00:27:19,240 Speaker 17: showing a new task, and. 544 00:27:19,600 --> 00:27:21,320 Speaker 11: The video goes up to the cloud. 545 00:27:21,359 --> 00:27:23,720 Speaker 17: In the cloud, there's a foundation model that takes the video, 546 00:27:24,280 --> 00:27:28,320 Speaker 17: moves it into a simulator, trains over the simulator. Today 547 00:27:28,359 --> 00:27:30,160 Speaker 17: it's a few hours in the future, a few minutes 548 00:27:30,240 --> 00:27:30,760 Speaker 17: goes back. 549 00:27:30,600 --> 00:27:32,280 Speaker 11: To the robot and the robots perform the test. 550 00:27:32,480 --> 00:27:34,439 Speaker 2: Let's go back to your core business and on this 551 00:27:34,560 --> 00:27:37,639 Speaker 2: week in video showed us a full stack solution for 552 00:27:37,760 --> 00:27:42,320 Speaker 2: autonomous driving. You know, they make arguments that their software 553 00:27:42,320 --> 00:27:46,520 Speaker 2: and hardware competencies will make them win your evaluation of 554 00:27:46,520 --> 00:27:49,200 Speaker 2: what they're offering against what you plan to do. Costs 555 00:27:49,200 --> 00:27:52,159 Speaker 2: for a mile basis, how worried you are. 556 00:27:52,240 --> 00:27:55,919 Speaker 17: I'm not worried competition. I believe it's always good. It 557 00:27:56,040 --> 00:28:00,480 Speaker 17: kind of ignites the market. In my keynote, I had 558 00:28:00,840 --> 00:28:06,320 Speaker 17: Christian Zenger, the CEO of Folkswagen Autonomously Cars, chairman of Moya, 559 00:28:06,640 --> 00:28:08,879 Speaker 17: come to the stage and talk about one hundred thousand 560 00:28:09,000 --> 00:28:12,879 Speaker 17: vehicles the next eight years, one hundred thousand robotaxes in 561 00:28:12,920 --> 00:28:16,840 Speaker 17: the next eight years. Talked about that we are almost 562 00:28:16,880 --> 00:28:20,200 Speaker 17: there in removing the driver, going driver less in Q three. 563 00:28:20,600 --> 00:28:22,600 Speaker 2: Yeah, but are they using mobiliz Yeah, of course this 564 00:28:22,720 --> 00:28:25,920 Speaker 2: is clad iron, claud this is a mobilize. 565 00:28:25,920 --> 00:28:29,359 Speaker 17: There are one hundred id bus vehicles in multiple locales. 566 00:28:29,800 --> 00:28:33,080 Speaker 11: All of them is mobilized. 567 00:28:31,920 --> 00:28:36,800 Speaker 17: Tack and we are really on the way of removing 568 00:28:36,800 --> 00:28:41,080 Speaker 17: the driver and a very cost effective solution. Our sensor 569 00:28:41,160 --> 00:28:45,840 Speaker 17: set is, you know, ten thousand dollars twelve thousand dollars. 570 00:28:46,120 --> 00:28:51,000 Speaker 17: Compute is very very cost cost optimized. Mobilized chips are 571 00:28:51,560 --> 00:28:55,480 Speaker 17: one tenth of the cost of competing chips. We developed 572 00:28:55,480 --> 00:28:58,600 Speaker 17: our imaging radars which are the best on the planet, 573 00:28:58,600 --> 00:29:01,880 Speaker 17: those imaging radars. I feel very confident that we are 574 00:29:01,960 --> 00:29:03,880 Speaker 17: on our way to a new growth engine. 575 00:29:03,920 --> 00:29:06,080 Speaker 16: With the Robotax your striking deals. 576 00:29:06,560 --> 00:29:08,120 Speaker 3: We understand as us all to make it that you 577 00:29:08,120 --> 00:29:11,360 Speaker 3: struck a deal with the nine million cars for safety software. 578 00:29:12,160 --> 00:29:16,240 Speaker 17: Okay, US automaker. There are three to four no pick one. 579 00:29:19,640 --> 00:29:24,640 Speaker 17: But I think that this nine million units is a 580 00:29:24,640 --> 00:29:27,720 Speaker 17: big news because we are talking about the evolution of AIDAS. 581 00:29:27,880 --> 00:29:30,320 Speaker 17: So AIDAS today is a front facing camera. Maybe you 582 00:29:30,400 --> 00:29:33,960 Speaker 17: have a front facing radar. This nine million units is 583 00:29:34,000 --> 00:29:37,240 Speaker 17: called surround aidas. You are taking a front facing camera, 584 00:29:37,400 --> 00:29:41,920 Speaker 17: four parking cameras, multiple radars, a very strong value proposition 585 00:29:42,040 --> 00:29:46,800 Speaker 17: to the customer, and it replaces multiple ECUs in the car. 586 00:29:47,040 --> 00:29:49,440 Speaker 17: It replaces the aco A parking a CEO. 587 00:29:49,240 --> 00:29:52,040 Speaker 11: Of aed driving monitoring system, hands free. 588 00:29:51,920 --> 00:29:57,440 Speaker 17: Driving on highways, safety functions for the next five years. 589 00:29:57,520 --> 00:30:00,560 Speaker 17: So this is the evolution of AIDAS, and it's high volume. 590 00:30:00,920 --> 00:30:03,680 Speaker 17: We have two customers in the past year, Volkswagen and 591 00:30:03,680 --> 00:30:06,360 Speaker 17: this US customer nineteen million units. 592 00:30:06,880 --> 00:30:09,120 Speaker 4: This is big AI twenty one. 593 00:30:09,800 --> 00:30:13,200 Speaker 2: We're hearing that in Vidia, mister Fong is quite interested 594 00:30:13,240 --> 00:30:16,240 Speaker 2: in another one of your companies. He's here this week. 595 00:30:16,360 --> 00:30:18,280 Speaker 2: What can you tell us about how talks are going. 596 00:30:18,680 --> 00:30:20,520 Speaker 2: Did you manage to speak to Jensen while here? 597 00:30:20,840 --> 00:30:23,480 Speaker 17: You know the Israeli press is not as tight as 598 00:30:23,760 --> 00:30:26,040 Speaker 17: the US press, So don't believe anything you read in 599 00:30:26,040 --> 00:30:28,040 Speaker 17: the Israeli press media. 600 00:30:28,200 --> 00:30:29,440 Speaker 11: No that there are talks, are talks. 601 00:30:29,280 --> 00:30:32,400 Speaker 17: On Vidia, talks with others, but no, it's nothing even 602 00:30:32,480 --> 00:30:35,680 Speaker 17: close to talking about it in the press. 603 00:30:36,320 --> 00:30:39,400 Speaker 3: How do you think Mobile I investors would feel about 604 00:30:39,400 --> 00:30:42,880 Speaker 3: an acquisition of AI twenty one by and. 605 00:30:43,640 --> 00:30:46,640 Speaker 17: It has nothing to do or the I twenty one 606 00:30:46,760 --> 00:30:50,960 Speaker 17: is working on foundation models and agents, has nothing to 607 00:30:51,000 --> 00:30:54,920 Speaker 17: do with activity of Mobilize, activity of Minty Mobili is 608 00:30:55,040 --> 00:30:59,000 Speaker 17: physical AI busy man by man man. 609 00:30:59,040 --> 00:31:01,560 Speaker 2: Yeah, the time here at the ces Las Vegas amnon 610 00:31:01,800 --> 00:31:05,000 Speaker 2: Shashua Mobili CEO, thank you very much. 611 00:31:05,240 --> 00:31:07,160 Speaker 4: Coming up, we have a whole lot more we speak 612 00:31:07,200 --> 00:31:07,880 Speaker 4: with the quick. 613 00:31:07,640 --> 00:31:11,880 Speaker 2: Play CEO Paul Pasta, as Warner Bros. Says paramounts amended 614 00:31:11,920 --> 00:31:14,640 Speaker 2: offer remains quote inadequate. 615 00:31:15,360 --> 00:31:26,160 Speaker 4: This is Bloomberg Tech. Warner Bros. 616 00:31:26,240 --> 00:31:30,360 Speaker 2: Is rejected Paramounts Guidancewer's latest amended offer, calling the company's 617 00:31:30,360 --> 00:31:34,160 Speaker 2: revised bid quote inadequate and urging shareholders to stay the 618 00:31:34,280 --> 00:31:37,520 Speaker 2: course with Netflix. Here discussed the state of media and 619 00:31:37,520 --> 00:31:40,880 Speaker 2: streaming is Paul Pastor Quick Play Chief business Officer Paul 620 00:31:40,880 --> 00:31:45,000 Speaker 2: also previously held senior executive positions the Discovery Communications and 621 00:31:45,040 --> 00:31:46,040 Speaker 2: the Walt Disney Company. 622 00:31:46,720 --> 00:31:48,560 Speaker 4: There's a lot in the sort of structure of. 623 00:31:48,480 --> 00:31:52,600 Speaker 2: The deal that the Warner Brothers Discovery board points out. 624 00:31:52,600 --> 00:31:53,640 Speaker 2: A lot of it has to do with the debt 625 00:31:53,640 --> 00:31:56,239 Speaker 2: load of the Paramount deal. So there's something interesting in 626 00:31:56,280 --> 00:31:58,720 Speaker 2: it that I wanted to pick out, which is, we 627 00:31:58,800 --> 00:32:02,200 Speaker 2: don't agree how valuable table networks are. So the street 628 00:32:02,240 --> 00:32:05,280 Speaker 2: and the investors would say, actually, they're much more valuable 629 00:32:05,320 --> 00:32:08,040 Speaker 2: than you're giving them credit for. Paramount Skuydiance would say, 630 00:32:08,120 --> 00:32:11,240 Speaker 2: in our thirty dollars share offer, we don't think they're valuable. 631 00:32:11,280 --> 00:32:14,200 Speaker 2: Therefore the thirty dollars a share offer is a great 632 00:32:14,240 --> 00:32:15,760 Speaker 2: premium because we want the whole thing. 633 00:32:16,360 --> 00:32:20,040 Speaker 18: How do we value those networks? Gusha was another great question. 634 00:32:20,080 --> 00:32:22,120 Speaker 18: I mean, it really is a difficulty to manage. At 635 00:32:22,120 --> 00:32:24,240 Speaker 18: this moment, we're talking about a really big shift in 636 00:32:24,280 --> 00:32:24,920 Speaker 18: the ecosystem. 637 00:32:24,960 --> 00:32:25,080 Speaker 2: Right. 638 00:32:25,120 --> 00:32:28,280 Speaker 18: We've had these traditional channels around broadcast cable that have 639 00:32:28,360 --> 00:32:31,240 Speaker 18: been almost cash cass for these businesses for a long time. 640 00:32:31,960 --> 00:32:34,080 Speaker 18: Then the shift of streaming and the ability to build 641 00:32:34,080 --> 00:32:37,240 Speaker 18: those businesses with lower margins. Right, but now we're seeing 642 00:32:37,240 --> 00:32:40,440 Speaker 18: with price increases, advertising dollars moving in that it's actually 643 00:32:40,680 --> 00:32:43,840 Speaker 18: a great value and now a huge shift into this 644 00:32:43,960 --> 00:32:47,120 Speaker 18: creator economy and thinking about shormcom content, And then you 645 00:32:47,160 --> 00:32:50,680 Speaker 18: have to evaluate those assets against those big shifts in 646 00:32:50,720 --> 00:32:51,440 Speaker 18: the ecosystem. 647 00:32:52,080 --> 00:32:53,480 Speaker 16: They still have value, they still. 648 00:32:53,280 --> 00:32:55,480 Speaker 18: Spin off cash, They still can be the home for 649 00:32:55,800 --> 00:32:59,040 Speaker 18: reach and advertising, but the real value has shifted the 650 00:32:59,080 --> 00:33:00,360 Speaker 18: advertising dollars of did in. 651 00:33:00,360 --> 00:33:00,880 Speaker 16: A new direction. 652 00:33:00,960 --> 00:33:02,840 Speaker 2: Well, we're going to remind our audiences a state of play, 653 00:33:02,840 --> 00:33:04,640 Speaker 2: because in a deal like this you get some fatigued. 654 00:33:04,680 --> 00:33:07,840 Speaker 2: But Netflix is basically saying we want the streaming business 655 00:33:07,840 --> 00:33:11,160 Speaker 2: in the studios, will spin out the legacy networks, paramounts, guidance, 656 00:33:11,160 --> 00:33:15,080 Speaker 2: wants the whole enchilada. In your mind, which result would 657 00:33:15,080 --> 00:33:17,800 Speaker 2: make most sense for the consumer, you know, and what 658 00:33:17,840 --> 00:33:20,400 Speaker 2: they would actually use those platforms for sure? 659 00:33:20,520 --> 00:33:23,080 Speaker 18: Well, listen, I think that there's absolute value for the 660 00:33:23,080 --> 00:33:26,120 Speaker 18: consumer and for the shareholders most certainly in a Netflix 661 00:33:26,240 --> 00:33:27,560 Speaker 18: Warner Brothers combination. 662 00:33:27,720 --> 00:33:30,240 Speaker 16: Right, you have superior storytelling. 663 00:33:29,640 --> 00:33:32,720 Speaker 18: Capabilities coming from the HBO Max and the Warner Legacy 664 00:33:32,720 --> 00:33:36,000 Speaker 18: and their entire library, coupled with what is superior tech. 665 00:33:36,560 --> 00:33:40,880 Speaker 18: Netflix has led the industry in personalization, in driving, new 666 00:33:40,920 --> 00:33:45,240 Speaker 18: features right and algorithms. That combination is certainly unique. Now 667 00:33:45,600 --> 00:33:47,520 Speaker 18: does that mean that's the best consumer I don't know. 668 00:33:47,920 --> 00:33:52,000 Speaker 18: Does the storytelling capabilities that are combined with Paramount coupled 669 00:33:52,040 --> 00:33:55,000 Speaker 18: with Warner deliver more value into the ecosystem in general 670 00:33:55,040 --> 00:33:58,080 Speaker 18: and for consumers overall who get great enjoyment out. 671 00:33:57,960 --> 00:34:01,120 Speaker 16: Of those stories. That's a difficult equation to value. 672 00:34:01,120 --> 00:34:03,160 Speaker 18: But I can most certainly see there's value in the 673 00:34:03,200 --> 00:34:05,240 Speaker 18: greatest tech and greatest storytellers coming together. 674 00:34:05,360 --> 00:34:07,800 Speaker 3: Well, perhaps that isn't value for for consumers. Right now 675 00:34:08,160 --> 00:34:10,640 Speaker 3: is three companies getting caught up in a lot of 676 00:34:10,680 --> 00:34:14,319 Speaker 3: admin and fighting and regulatory discussions rather than focusing on 677 00:34:14,360 --> 00:34:16,799 Speaker 3: the business and innovating. How much is that a risk? 678 00:34:16,880 --> 00:34:18,399 Speaker 18: I think this is the biggest risk of any big 679 00:34:18,440 --> 00:34:21,600 Speaker 18: transaction and M and A activity. Right, the focus becomes 680 00:34:21,640 --> 00:34:24,879 Speaker 18: how do these companies potentially come together? Everybody internally thinks 681 00:34:24,920 --> 00:34:27,920 Speaker 18: about what is my role in this transition? And no 682 00:34:27,920 --> 00:34:30,319 Speaker 18: one's focused on the fact that they're competing with an 683 00:34:30,360 --> 00:34:33,279 Speaker 18: ecosystem that is evolving around them. Right, we saw that 684 00:34:33,360 --> 00:34:36,520 Speaker 18: social media platforms are taking over not only viewership but 685 00:34:36,600 --> 00:34:39,400 Speaker 18: total ad dollars this year. There is a huge shift 686 00:34:39,440 --> 00:34:42,239 Speaker 18: into the creator economy. And if you're just focused on 687 00:34:42,400 --> 00:34:44,640 Speaker 18: how do we put these two companies together versus the 688 00:34:44,640 --> 00:34:48,120 Speaker 18: competition that is outpacing you with tiktoking, YouTube and others, 689 00:34:48,320 --> 00:34:50,560 Speaker 18: you're going to be further behind by the time you 690 00:34:50,560 --> 00:34:51,520 Speaker 18: complete this transaction. 691 00:34:51,640 --> 00:34:53,839 Speaker 3: And we haven't even discussed AI yet. And you think 692 00:34:53,880 --> 00:34:56,120 Speaker 3: about the big deal that was announced between sous of 693 00:34:56,239 --> 00:34:59,080 Speaker 3: Disney and open Ai at the tail end of last year, 694 00:34:59,880 --> 00:35:01,680 Speaker 3: is that's my viewing experience is going to be like 695 00:35:01,760 --> 00:35:04,480 Speaker 3: going forward? When I turn on Disney Plus, what does 696 00:35:04,520 --> 00:35:05,680 Speaker 3: it look like as a screen? 697 00:35:06,040 --> 00:35:07,960 Speaker 18: So I think this is going to be the biggest 698 00:35:07,960 --> 00:35:10,239 Speaker 18: piece of innovation we see in twenty twenty six. So 699 00:35:10,800 --> 00:35:13,680 Speaker 18: AI for the last year has been about doing discreete projects. 700 00:35:13,719 --> 00:35:15,440 Speaker 16: How do I create a vertical video? 701 00:35:15,520 --> 00:35:18,840 Speaker 18: How do I think about leveraging it to help improve 702 00:35:19,760 --> 00:35:22,800 Speaker 18: the algorithm. But what we've really got to be focusing 703 00:35:22,840 --> 00:35:24,440 Speaker 18: on today, right is how do we think about the 704 00:35:24,560 --> 00:35:27,080 Speaker 18: entire ecosystem I'm playing in multiple universes? 705 00:35:27,280 --> 00:35:28,800 Speaker 16: What is that consumer experience? 706 00:35:28,920 --> 00:35:31,560 Speaker 18: Like I believe this year, instead of when you download 707 00:35:31,600 --> 00:35:34,160 Speaker 18: the app and you hit a wall that says pay now, 708 00:35:34,360 --> 00:35:36,720 Speaker 18: you'll see in the future people pushing short form content 709 00:35:36,719 --> 00:35:39,440 Speaker 18: as a way to discover it's a way to engage consumers, 710 00:35:39,560 --> 00:35:43,239 Speaker 18: a new way to engage a conversation. That data from 711 00:35:43,280 --> 00:35:44,759 Speaker 18: there begins to inform how I get. 712 00:35:44,680 --> 00:35:45,360 Speaker 16: A cold start? 713 00:35:45,560 --> 00:35:47,880 Speaker 18: So how do when I first go into that experience, 714 00:35:48,040 --> 00:35:49,319 Speaker 18: how is it customized for me? 715 00:35:49,560 --> 00:35:49,719 Speaker 6: Right? 716 00:35:49,760 --> 00:35:51,080 Speaker 18: How do I ensure that I'm going to be able 717 00:35:51,120 --> 00:35:54,560 Speaker 18: to retain that consumer? Then all those interactions over time 718 00:35:54,880 --> 00:35:57,279 Speaker 18: also become part of this greater ecosystem that says, now 719 00:35:57,320 --> 00:35:59,680 Speaker 18: I know what to serve you, how to engage you, 720 00:35:59,719 --> 00:36:01,600 Speaker 18: how to retain you, and how to reach you on 721 00:36:01,640 --> 00:36:02,280 Speaker 18: other platforms. 722 00:36:02,320 --> 00:36:03,399 Speaker 16: And that's really when we. 723 00:36:03,400 --> 00:36:05,759 Speaker 18: Begin to think about the value of what AI can 724 00:36:05,800 --> 00:36:07,759 Speaker 18: deliver when it's managed as an ecosystem. 725 00:36:08,239 --> 00:36:12,440 Speaker 2: We've talked a lot about chips and robots and consumer gadgets. 726 00:36:12,680 --> 00:36:14,399 Speaker 2: I think it would be really helpful to give your 727 00:36:14,480 --> 00:36:16,440 Speaker 2: kind of media and entertainment. 728 00:36:16,000 --> 00:36:17,880 Speaker 4: Experience of what it's been like at CES. 729 00:36:18,280 --> 00:36:20,759 Speaker 2: Very conscious, a lot of the advertisers are here very 730 00:36:20,760 --> 00:36:24,280 Speaker 2: conscious that actually the mode which you consume that content 731 00:36:24,320 --> 00:36:25,080 Speaker 2: is on display. 732 00:36:25,440 --> 00:36:26,400 Speaker 4: What has it been like for you? 733 00:36:26,800 --> 00:36:29,920 Speaker 18: Yes, for us, as I scussed a little bit earlier, 734 00:36:30,600 --> 00:36:32,920 Speaker 18: it used to be very discrete investments that we were 735 00:36:32,920 --> 00:36:35,680 Speaker 18: seeing across the AI spectrum. I think today people are 736 00:36:35,680 --> 00:36:38,080 Speaker 18: thinking about how do I look at AI as foundational 737 00:36:38,200 --> 00:36:40,719 Speaker 18: to the way I operate with my technology and with 738 00:36:40,800 --> 00:36:43,360 Speaker 18: my consumers. And that's the big shift I think in 739 00:36:43,400 --> 00:36:45,200 Speaker 18: the mindset. And now they're coming to us and looking 740 00:36:45,239 --> 00:36:48,480 Speaker 18: for solutions at scale, at huge enterprise, across. 741 00:36:48,200 --> 00:36:50,640 Speaker 16: The multiple ecosystems. That's been the big shift for us. 742 00:36:50,840 --> 00:36:52,600 Speaker 2: We probably should have done this at the start, which 743 00:36:52,600 --> 00:36:54,759 Speaker 2: I apologize, but just what is quick play? 744 00:36:54,880 --> 00:36:55,080 Speaker 8: Yeah? 745 00:36:55,160 --> 00:36:57,279 Speaker 4: Yeah, give us the quick play some absolutely. 746 00:36:57,280 --> 00:37:01,200 Speaker 18: So We're at a company that basically builds out OTT services, 747 00:37:01,200 --> 00:37:03,719 Speaker 18: so over the top television services, all the back end, 748 00:37:03,760 --> 00:37:05,359 Speaker 18: everything from in jests of the time it ends up 749 00:37:05,360 --> 00:37:07,800 Speaker 18: on your device, and where we have pivoted right is 750 00:37:07,840 --> 00:37:09,560 Speaker 18: how to actually enable that through AI? 751 00:37:09,920 --> 00:37:11,759 Speaker 16: How do you leverage those AI tools to. 752 00:37:11,760 --> 00:37:14,560 Speaker 18: Ensure not only can I create short format a long 753 00:37:14,600 --> 00:37:17,840 Speaker 18: form respond to social signals that are happening in the universe. 754 00:37:17,840 --> 00:37:20,280 Speaker 18: To be able to say, hey, there's a conversation happening 755 00:37:20,320 --> 00:37:23,719 Speaker 18: around bad Bunny appearing on SNL, how do we participate 756 00:37:23,920 --> 00:37:27,320 Speaker 18: in that conversation with our content to drive new engagement, 757 00:37:27,360 --> 00:37:30,799 Speaker 18: to drive new retention strategies, to drive new acquisition strategies, 758 00:37:31,000 --> 00:37:32,120 Speaker 18: but we're also thinking. 759 00:37:31,880 --> 00:37:35,200 Speaker 16: About that entire data fabric to create those experiences that 760 00:37:35,200 --> 00:37:36,239 Speaker 16: are hyper personalized. 761 00:37:36,520 --> 00:37:39,440 Speaker 3: Well pastor great speaking with you quick play chief business 762 00:37:39,440 --> 00:37:43,520 Speaker 3: officer on all things media and tech. But coming up, 763 00:37:43,719 --> 00:37:45,200 Speaker 3: we're going to bring you part of our conversation with 764 00:37:45,239 --> 00:37:48,440 Speaker 3: a General Catalyst CEO comment to Nager and how he 765 00:37:48,560 --> 00:37:51,440 Speaker 3: sees AI impacting the consumer experience. That's next. This is 766 00:37:51,480 --> 00:38:03,960 Speaker 3: brettly BG Tech. AI valuations tricky topic, so says General 767 00:38:03,960 --> 00:38:07,320 Speaker 3: Capitalist CEO IM in Teenasia. That's one way that he 768 00:38:07,400 --> 00:38:09,400 Speaker 3: put it. We sit down with him here at CES 769 00:38:09,480 --> 00:38:11,839 Speaker 3: to discuss his views on AI valuations and whether it's 770 00:38:11,880 --> 00:38:14,480 Speaker 3: all hype or not, and some potential ipeos on the 771 00:38:14,480 --> 00:38:15,240 Speaker 3: line take listen. 772 00:38:16,120 --> 00:38:18,200 Speaker 19: Valuations are a tricky topic and I feel like they 773 00:38:18,200 --> 00:38:21,200 Speaker 19: always provide me right wrong in hindsight. But if you 774 00:38:21,360 --> 00:38:24,240 Speaker 19: break down the kinds of companies that get funded today, 775 00:38:24,520 --> 00:38:26,640 Speaker 19: there's a ton of dollars going into a handful of 776 00:38:26,640 --> 00:38:30,440 Speaker 19: big research projects. Yeah there the valuation. 777 00:38:30,520 --> 00:38:31,520 Speaker 8: Your guess is as good as mine. 778 00:38:31,560 --> 00:38:34,839 Speaker 19: It really is about you know, ownership, and that's enough 779 00:38:34,880 --> 00:38:37,799 Speaker 19: to incentivize the teams because they're research projects. You don't 780 00:38:37,840 --> 00:38:39,719 Speaker 19: know when they're going to become businesses or if they're 781 00:38:39,719 --> 00:38:43,799 Speaker 19: going to become businesses. And then there's companies where they're 782 00:38:43,880 --> 00:38:48,400 Speaker 19: real use cases applied AI use cases. There the valuations 783 00:38:48,440 --> 00:38:51,239 Speaker 19: sound extraordinary at the moment, but the progress happens so 784 00:38:51,360 --> 00:38:53,920 Speaker 19: fast in some of these companies that they grow into 785 00:38:53,960 --> 00:38:54,600 Speaker 19: it fast as well. 786 00:38:54,600 --> 00:38:56,760 Speaker 8: So I think it's a nuanced dynamic. 787 00:38:56,840 --> 00:38:59,880 Speaker 19: And what's What's the thing that is confusing for me 788 00:39:00,000 --> 00:39:01,200 Speaker 19: A lot of times when I look at these companies 789 00:39:01,200 --> 00:39:03,640 Speaker 19: that are growing, is not about are we pricing it 790 00:39:03,719 --> 00:39:06,000 Speaker 19: right in the context of their revenue or their revenue growth, 791 00:39:06,080 --> 00:39:06,800 Speaker 19: but much more. 792 00:39:06,640 --> 00:39:07,920 Speaker 8: About are they going to be around? 793 00:39:08,880 --> 00:39:10,560 Speaker 19: Are there models going to be smart enough to do 794 00:39:10,600 --> 00:39:12,200 Speaker 19: a lot of what some of these companies are doing 795 00:39:12,280 --> 00:39:15,080 Speaker 19: and just do it themselves, or are there applied to 796 00:39:15,360 --> 00:39:17,640 Speaker 19: which applied the AI companies will be durable on top 797 00:39:17,680 --> 00:39:19,680 Speaker 19: of the LLLM innovation that's going on. 798 00:39:20,080 --> 00:39:22,280 Speaker 3: Anthropic you're in that's durable. 799 00:39:22,400 --> 00:39:26,440 Speaker 19: Yes, yes, look first of all topics, you know, I 800 00:39:26,480 --> 00:39:27,520 Speaker 19: have a lot of respect for Daria. 801 00:39:27,520 --> 00:39:30,760 Speaker 8: What is accomplished and a team there, it's durable. 802 00:39:30,960 --> 00:39:34,319 Speaker 19: They've built a great set of models, They've been very 803 00:39:34,320 --> 00:39:36,920 Speaker 19: capital efficient, and the thing that's interesting is they also 804 00:39:37,719 --> 00:39:40,000 Speaker 19: have an amazing set of use cases on top that 805 00:39:40,040 --> 00:39:40,719 Speaker 19: are taking hold. 806 00:39:40,840 --> 00:39:41,719 Speaker 8: So it's not just the model. 807 00:39:41,760 --> 00:39:45,640 Speaker 19: Companies also applied AI right, Claude, it's really redefining the 808 00:39:45,680 --> 00:39:48,720 Speaker 19: engineering department and just think about the size of that market. 809 00:39:48,880 --> 00:39:51,759 Speaker 19: You might use spend on tools and engineers to be 810 00:39:51,760 --> 00:39:55,360 Speaker 19: able to build products, and you know, the amount of 811 00:39:55,400 --> 00:39:59,920 Speaker 19: progress is like Silicon value companies now code self right 812 00:40:00,200 --> 00:40:01,840 Speaker 19: for most of what they do. I think it's a 813 00:40:01,880 --> 00:40:03,600 Speaker 19: real transformation with clouds really enabled. 814 00:40:03,680 --> 00:40:06,960 Speaker 3: I know you're very passionate about, like the democratization of 815 00:40:07,040 --> 00:40:09,560 Speaker 3: access to some of these companies. Anthropic might go public? 816 00:40:09,920 --> 00:40:12,560 Speaker 3: Is that should it be going public sooner? Should the 817 00:40:12,560 --> 00:40:15,680 Speaker 3: next round be one that comes to the masses rather 818 00:40:15,760 --> 00:40:19,040 Speaker 3: than perhaps going to let another venture or another crossover 819 00:40:19,480 --> 00:40:20,800 Speaker 3: round where they remain private. 820 00:40:20,880 --> 00:40:22,920 Speaker 19: Yeah, look, I think the company has to decide that. 821 00:40:23,760 --> 00:40:25,400 Speaker 19: But do they have the size and scale to go 822 00:40:25,480 --> 00:40:28,080 Speaker 19: public in the next twelve to eighteen months. I think 823 00:40:28,080 --> 00:40:30,560 Speaker 19: they certainly can. I think there's going to be a 824 00:40:30,640 --> 00:40:33,520 Speaker 19: variety factor they'll have to weigh to decide, you know, 825 00:40:33,560 --> 00:40:35,560 Speaker 19: when they think it makes sense to be public, when 826 00:40:35,600 --> 00:40:38,160 Speaker 19: they're predictable enough. Because so much is changing in the 827 00:40:38,280 --> 00:40:39,680 Speaker 19: market itself and the dynamics. 828 00:40:40,200 --> 00:40:41,319 Speaker 8: It's actually hard to credict growth. 829 00:40:41,320 --> 00:40:44,000 Speaker 19: When we invest in Anthropic, we model a certain amount 830 00:40:44,000 --> 00:40:46,719 Speaker 19: of growth. The company grew three times faster than that, 831 00:40:47,200 --> 00:40:49,080 Speaker 19: so you know, we look good in hindsight. But we 832 00:40:49,120 --> 00:40:52,080 Speaker 19: thought we were just doing a reasonable and in my opinion, 833 00:40:52,080 --> 00:40:55,120 Speaker 19: reasonably expensive pricing for that round and end up being 834 00:40:55,239 --> 00:40:57,840 Speaker 19: you know, we would look look great at hindsight. So 835 00:40:57,880 --> 00:40:59,640 Speaker 19: it's hard to predict, you know, how these companies are 836 00:40:59,640 --> 00:41:00,960 Speaker 19: going to scale for a little bit longer. 837 00:41:03,960 --> 00:41:07,839 Speaker 2: That was General Catalysts CEO him On Tenaga, Speaking of IPOs, 838 00:41:08,120 --> 00:41:11,719 Speaker 2: Discord filed confidentially for an IPO, according to our sources, 839 00:41:11,960 --> 00:41:15,440 Speaker 2: adding to a rapidly growing pipeline of bench capital backtech listings. 840 00:41:15,520 --> 00:41:18,280 Speaker 4: Carol, you broke the story with our mate Bailey Lipshaltz. 841 00:41:18,280 --> 00:41:21,440 Speaker 2: The details are important, right, Like Discord, it's a platform 842 00:41:21,480 --> 00:41:22,520 Speaker 2: that I've used, But what do we. 843 00:41:22,440 --> 00:41:22,920 Speaker 4: Need to know here? 844 00:41:23,000 --> 00:41:24,920 Speaker 3: Yeah, because you're a gamer and it all started a 845 00:41:24,960 --> 00:41:27,600 Speaker 3: decade ago, and it's really a gaming chat platform that's 846 00:41:27,640 --> 00:41:29,840 Speaker 3: morphed into much more than That's got two hundred million 847 00:41:30,320 --> 00:41:32,759 Speaker 3: users on average on a monthly basis. They have an 848 00:41:32,840 --> 00:41:35,880 Speaker 3: interesting executive leadership reshuffle of last year. They've now got 849 00:41:35,920 --> 00:41:38,799 Speaker 3: an Activision Blizzard alumni as the CEO of someone who 850 00:41:38,800 --> 00:41:41,320 Speaker 3: worked at King, someone who can really steer the monetization 851 00:41:41,440 --> 00:41:43,520 Speaker 3: part of the business now because ez Citra and the 852 00:41:43,600 --> 00:41:45,640 Speaker 3: co found has gone over to the board level. But 853 00:41:45,719 --> 00:41:48,799 Speaker 3: this really does build this kind of fire feeling of 854 00:41:48,880 --> 00:41:51,440 Speaker 3: IPO pipeline. We've got motive waiting in the wings. We 855 00:41:51,520 --> 00:41:54,839 Speaker 3: understand there's plenty of others the smaller nature, but we're 856 00:41:54,840 --> 00:41:57,560 Speaker 3: talking about anthropic, we're talking about maybe opening. 857 00:41:57,719 --> 00:41:58,280 Speaker 4: It's interesting. 858 00:41:58,320 --> 00:42:00,399 Speaker 2: They're like they have Goldman and JP Moore been working 859 00:42:00,440 --> 00:42:03,040 Speaker 2: on it and we understand, right, but last year was 860 00:42:03,080 --> 00:42:05,239 Speaker 2: better for IPOs in tech than twenty twenty four. 861 00:42:05,400 --> 00:42:08,040 Speaker 3: Was fifteen billion dollars was raised on US exchanges. That 862 00:42:08,120 --> 00:42:10,520 Speaker 3: was book four times the amount that was raised in 863 00:42:10,520 --> 00:42:14,200 Speaker 3: twenty twenty four. So can we still teck in to 864 00:42:14,400 --> 00:42:16,879 Speaker 3: basically a higher valuation point inn s and P five 865 00:42:16,960 --> 00:42:19,040 Speaker 3: hundred and nearer a record high. People want to see 866 00:42:19,040 --> 00:42:21,040 Speaker 3: these companies come, but we've got to work out what 867 00:42:21,120 --> 00:42:23,080 Speaker 3: valuations they come at. This is a company that raised 868 00:42:23,080 --> 00:42:25,720 Speaker 3: in about fifteen billion in twenty twenty one. Twenty twenty 869 00:42:25,719 --> 00:42:27,800 Speaker 3: one was a very nice year for valuations and a 870 00:42:27,800 --> 00:42:29,920 Speaker 3: lot of companies are having to swallow some think of 871 00:42:29,960 --> 00:42:31,880 Speaker 3: Figma in many ways it managed to have a blockbus 872 00:42:31,920 --> 00:42:34,319 Speaker 3: for IPO, but still it was always being marked. 873 00:42:34,040 --> 00:42:36,160 Speaker 4: Into previous run versus public right. 874 00:42:36,840 --> 00:42:39,280 Speaker 3: That does it for this edition of Bloomberg Tech Tomorrow, 875 00:42:39,400 --> 00:42:41,919 Speaker 3: We've got so many other guests coming on ces. Let's 876 00:42:41,920 --> 00:42:44,920 Speaker 3: talk about them. Kind adventures. Joby Aviation is going to 877 00:42:44,920 --> 00:42:47,160 Speaker 3: be joining us, and Jacob Helberg of course, a little 878 00:42:47,200 --> 00:42:48,120 Speaker 3: bit of government perspective. 879 00:42:48,520 --> 00:42:50,320 Speaker 4: It's fun to be in Vegas. Recap. 880 00:42:50,400 --> 00:42:52,360 Speaker 2: There is a lot to recap. Do it on the pod. 881 00:42:52,440 --> 00:42:55,279 Speaker 2: You know where to find it. We keep going. This 882 00:42:55,719 --> 00:42:56,600 Speaker 2: is Bloomberg Tech