1 00:00:00,800 --> 00:00:05,040 Speaker 1: From the heart of where innovation, money and power collide 2 00:00:05,320 --> 00:00:10,639 Speaker 1: in Silicon Valley and beyond. This is Bloomberg Technology with 3 00:00:10,760 --> 00:00:13,320 Speaker 1: Caroline Hyde and Ed Ludlow. 4 00:00:26,880 --> 00:00:29,480 Speaker 2: Live from New York. This is BlueBag Technology coming up. 5 00:00:29,640 --> 00:00:32,400 Speaker 2: In Vidia takes center stage at CES with new hardware, 6 00:00:32,440 --> 00:00:35,440 Speaker 2: software and services. For the stock falls, We sit down 7 00:00:35,479 --> 00:00:38,360 Speaker 2: with it's CEO, Jas and Wang. Later this hour plus, 8 00:00:38,479 --> 00:00:41,800 Speaker 2: Metad cuts its team of fact checkers, saying content moderation 9 00:00:41,920 --> 00:00:45,240 Speaker 2: went quote too far, and the US places some of 10 00:00:45,360 --> 00:00:49,879 Speaker 2: China's biggest tech giants on its military blacklist. But first 11 00:00:50,159 --> 00:00:52,839 Speaker 2: we check in on the key stop. You have to 12 00:00:52,880 --> 00:00:55,800 Speaker 2: watch today. It is the most valuable three point six 13 00:00:55,840 --> 00:00:58,120 Speaker 2: trillion dollars and it dies by four and a half percent, 14 00:00:58,160 --> 00:01:01,720 Speaker 2: in Vidia losing its pre market gains. We had touched 15 00:01:01,720 --> 00:01:03,440 Speaker 2: a new record high and now we sink the worst 16 00:01:03,480 --> 00:01:06,760 Speaker 2: days at September. Why we got a whole raft of products, 17 00:01:06,800 --> 00:01:09,920 Speaker 2: whether it's hardware, software, services, whether it's the future of 18 00:01:10,360 --> 00:01:14,520 Speaker 2: robotics from autonomous driving, from PCs and gaming. But is 19 00:01:14,560 --> 00:01:18,280 Speaker 2: it the here and now, the Blackwell program and platform 20 00:01:18,360 --> 00:01:20,880 Speaker 2: that we don't get enough detail on. Let's head out 21 00:01:20,920 --> 00:01:23,680 Speaker 2: to Las Vegas. Big story, of course, is in Vidio 22 00:01:23,760 --> 00:01:27,039 Speaker 2: and the CEO in veiling these products, we've got none 23 00:01:27,080 --> 00:01:31,039 Speaker 2: other than a key focus on the gamer, the original 24 00:01:31,080 --> 00:01:33,320 Speaker 2: customer from Jensen Wang. Have a listen. 25 00:01:34,480 --> 00:01:38,720 Speaker 3: We used g force to enable artificial intelligence, and now 26 00:01:38,800 --> 00:01:44,480 Speaker 3: artificial intelligence is revolutionizing g Force everyone. Today we're announcing 27 00:01:45,040 --> 00:01:49,840 Speaker 3: our next generation, the RTX Blackwell family. 28 00:01:51,720 --> 00:01:54,880 Speaker 2: Let's go to our gamer in residence, Ed Ludlow on 29 00:01:54,920 --> 00:01:58,040 Speaker 2: the ground at CES. There was going back to their roots, 30 00:01:58,080 --> 00:02:00,400 Speaker 2: the GPU for the gamer. There was so much much more, 31 00:02:00,400 --> 00:02:01,480 Speaker 2: but the stock doesn't like it. 32 00:02:04,200 --> 00:02:07,600 Speaker 4: Yeah, I mean, we've become so accustomed to the nvidious 33 00:02:07,640 --> 00:02:10,760 Speaker 4: story of being dominant in a market for high performance 34 00:02:10,840 --> 00:02:14,800 Speaker 4: GPUs that go into data centers and train AI models, 35 00:02:14,800 --> 00:02:18,200 Speaker 4: foundation models. But it isn't going back to its roots story, right. 36 00:02:18,240 --> 00:02:21,000 Speaker 4: It's taken the work that it's done on those AI 37 00:02:21,080 --> 00:02:24,320 Speaker 4: accelerators and put them into a factor that is more 38 00:02:24,360 --> 00:02:28,200 Speaker 4: relevant to the consumer developers in particular, and maybe the 39 00:02:28,240 --> 00:02:30,840 Speaker 4: market this morning doesn't quite understand that story. There was 40 00:02:30,880 --> 00:02:34,440 Speaker 4: plenty of big picture last night from Jensen One. Blackwell 41 00:02:34,480 --> 00:02:37,600 Speaker 4: is in full production, a kind of timeline or a 42 00:02:37,680 --> 00:02:41,280 Speaker 4: move into physical AI, which we'll get into robotics autonomous driving. 43 00:02:42,400 --> 00:02:46,160 Speaker 4: But maybe also a conversation to be had around diversifying revenue. 44 00:02:46,280 --> 00:02:49,280 Speaker 4: One hundred billion dollars of revenue is still data centerships, 45 00:02:49,520 --> 00:02:54,519 Speaker 4: but increasingly those high performance graphics cards that go into 46 00:02:54,520 --> 00:02:57,400 Speaker 4: the desktop laptops and something called project digits. 47 00:02:57,440 --> 00:02:58,840 Speaker 5: There's a lot to discuss. 48 00:02:58,880 --> 00:03:01,880 Speaker 2: That PC or on the three thousand dollars PC for 49 00:03:01,960 --> 00:03:04,639 Speaker 2: AI developers. And look, there is a macro context to 50 00:03:04,680 --> 00:03:07,360 Speaker 2: the share move today. We of course got some ism data, 51 00:03:07,400 --> 00:03:09,800 Speaker 2: we got yield spiking higher, and no wonder some of 52 00:03:09,800 --> 00:03:12,400 Speaker 2: the big tech names arounder pressure. What's interesting is some 53 00:03:12,440 --> 00:03:15,480 Speaker 2: of the suppliers, for example a Micron still leading the 54 00:03:15,600 --> 00:03:17,960 Speaker 2: charge here as its memory is going to be within 55 00:03:18,000 --> 00:03:20,519 Speaker 2: the Nvidia offering. There was a lot to be had 56 00:03:20,520 --> 00:03:22,040 Speaker 2: for some of the partnerships to play. 57 00:03:24,960 --> 00:03:27,520 Speaker 4: Yeah, and as you know, Caro, it's Bloomberg's policy not 58 00:03:27,560 --> 00:03:29,240 Speaker 4: to give questions an advance of interview, and there are 59 00:03:29,240 --> 00:03:30,320 Speaker 4: all these Nvidia. 60 00:03:30,040 --> 00:03:31,520 Speaker 6: Staff over my shoulder. 61 00:03:31,760 --> 00:03:35,320 Speaker 4: But probably the one big question is the sustainability of 62 00:03:35,360 --> 00:03:39,840 Speaker 4: the investment into AI. Those other chip names are pushing 63 00:03:39,920 --> 00:03:42,320 Speaker 4: higher because when an investment is made into a high 64 00:03:42,320 --> 00:03:45,600 Speaker 4: performance GPU, it goes in combination with the memory chips, 65 00:03:45,640 --> 00:03:48,960 Speaker 4: other CPUs and all of the telematics from those other providers. 66 00:03:49,200 --> 00:03:52,480 Speaker 4: That probably is the key question for Jensen, particularly in 67 00:03:52,520 --> 00:03:55,240 Speaker 4: a year where things run certain economically and I see 68 00:03:55,240 --> 00:03:57,640 Speaker 4: a bit of a concern about inflation in the market 69 00:03:57,640 --> 00:03:58,240 Speaker 4: this morning. 70 00:03:58,920 --> 00:04:00,400 Speaker 5: Jensen has a lot of world views. 71 00:04:00,440 --> 00:04:02,160 Speaker 4: Whether he'll talk to me about inflation, we'll have to 72 00:04:02,200 --> 00:04:03,000 Speaker 4: wait and see. 73 00:04:03,400 --> 00:04:06,920 Speaker 2: At Ludlow, we cannot wait for the conversation. We'll see 74 00:04:06,960 --> 00:04:10,000 Speaker 2: you a little bit later. Let's get an investor's take 75 00:04:10,080 --> 00:04:13,240 Speaker 2: on the here and now lack of visibility on Blackwell 76 00:04:13,360 --> 00:04:16,240 Speaker 2: or or indeed the future proofing of the business model. 77 00:04:16,320 --> 00:04:19,040 Speaker 2: Cebi's Investment Advisory Services CIO Anna Rathbun. 78 00:04:19,400 --> 00:04:20,280 Speaker 7: We tuned in. 79 00:04:20,240 --> 00:04:22,640 Speaker 2: At six thirty Pacific time as nine thirty pm New 80 00:04:22,720 --> 00:04:26,160 Speaker 2: York time. When we heard Jensen on stage, what did 81 00:04:26,200 --> 00:04:28,440 Speaker 2: you make you? In line with some of these analysts 82 00:04:28,440 --> 00:04:30,640 Speaker 2: who are out there saying, look not enough on the 83 00:04:30,680 --> 00:04:32,360 Speaker 2: Blackwell platform. 84 00:04:33,279 --> 00:04:36,720 Speaker 8: Yeah, there weren't that many details that investors were hoping for. 85 00:04:36,800 --> 00:04:39,159 Speaker 8: But if you have to talk for ninety minutes plus 86 00:04:39,200 --> 00:04:42,080 Speaker 8: almost two hours, I'm not sure if you can fill 87 00:04:42,160 --> 00:04:46,120 Speaker 8: those that timeline with a lot of details about the business. 88 00:04:46,160 --> 00:04:47,680 Speaker 7: And plus you know he's kicking. 89 00:04:47,480 --> 00:04:49,480 Speaker 8: Off a conference, you sort of want to be a 90 00:04:49,560 --> 00:04:53,480 Speaker 8: visionary and there is this a little slab of romanticism 91 00:04:53,520 --> 00:04:56,279 Speaker 8: about AI thrown in there, So you know, I think 92 00:04:56,320 --> 00:04:59,320 Speaker 8: maybe it was the wrong platform to really wait for 93 00:04:59,400 --> 00:05:01,919 Speaker 8: some of those details. But I know that the excitement 94 00:05:02,000 --> 00:05:04,960 Speaker 8: was built into the stock market certainly yesterday, so I 95 00:05:05,240 --> 00:05:08,200 Speaker 8: can understand the disappointment. But at the same time, maybe 96 00:05:08,880 --> 00:05:11,320 Speaker 8: maybe we need to wait for the earnings report. 97 00:05:11,480 --> 00:05:14,440 Speaker 2: And we're currently showing a five day shot of in video, 98 00:05:14,440 --> 00:05:16,960 Speaker 2: which gives us the context. It is ratherly eleven percent 99 00:05:17,000 --> 00:05:19,720 Speaker 2: in January. We're maybe seeing some profit being taken off 100 00:05:19,720 --> 00:05:21,520 Speaker 2: the table a little bit, hair Ona, But what did 101 00:05:21,560 --> 00:05:23,919 Speaker 2: you make of what it means for its mote, for 102 00:05:24,000 --> 00:05:28,440 Speaker 2: its ultimate leadership and technology? Does AMD do others even stand. 103 00:05:28,200 --> 00:05:28,880 Speaker 6: A chance here? 104 00:05:30,040 --> 00:05:32,520 Speaker 8: Well, you know, I think the leadership part of the 105 00:05:32,640 --> 00:05:37,159 Speaker 8: visionary talk was establishing that leadership. I think in Video 106 00:05:37,440 --> 00:05:39,640 Speaker 8: wants to be a leader and wants to continue to 107 00:05:39,680 --> 00:05:42,800 Speaker 8: be a leader. So you're taking things like you know, 108 00:05:42,880 --> 00:05:46,760 Speaker 8: self driving cars and partnership with Toyota and media attach. 109 00:05:46,839 --> 00:05:48,960 Speaker 7: Those are real things that can turn into cash flow. 110 00:05:49,160 --> 00:05:51,640 Speaker 8: But then you know, you have visionary things like humanoid 111 00:05:51,760 --> 00:05:56,120 Speaker 8: and chance gypt turning into you know, robotics, right, turning 112 00:05:56,120 --> 00:05:58,440 Speaker 8: into but robotics having a moment, right. 113 00:05:58,520 --> 00:05:59,840 Speaker 7: So these are the things that. 114 00:05:59,800 --> 00:06:05,279 Speaker 8: I think establish Nvidia, at least in rhetoric, as a 115 00:06:05,320 --> 00:06:09,039 Speaker 8: continued leader in the space. And whether or not AMDs 116 00:06:09,080 --> 00:06:10,760 Speaker 8: and other players can catch up. 117 00:06:11,240 --> 00:06:13,240 Speaker 7: I do think that Nvidia still. 118 00:06:13,080 --> 00:06:17,239 Speaker 8: Has a large, you know, sort of monopoly in the space. 119 00:06:17,520 --> 00:06:20,000 Speaker 8: The reality of catching up to Nvidia, I think is 120 00:06:20,040 --> 00:06:21,400 Speaker 8: still a difficult. 121 00:06:20,920 --> 00:06:24,320 Speaker 2: Feat and that's why i'mdphaps leaning into the PC side 122 00:06:24,360 --> 00:06:26,600 Speaker 2: of the business with its Dell announcement rather than trying 123 00:06:26,600 --> 00:06:29,520 Speaker 2: to talk up AI accelerate as in the data center part. 124 00:06:30,000 --> 00:06:33,160 Speaker 2: And to that point, are you feeling comfortable that we're 125 00:06:33,200 --> 00:06:37,000 Speaker 2: seeing diversification or at least that the demand for infrastructure 126 00:06:37,000 --> 00:06:39,719 Speaker 2: from a data center perspective is still there. Microsoft eighty 127 00:06:39,800 --> 00:06:41,640 Speaker 2: billion that they're going to spend in twenty twenty five 128 00:06:41,680 --> 00:06:42,680 Speaker 2: seems to signal. 129 00:06:42,400 --> 00:06:47,400 Speaker 8: That, yeah, I don't think this AI venture is going 130 00:06:47,440 --> 00:06:50,159 Speaker 8: to stop. And certainly, you know, likes of Microsoft and 131 00:06:50,200 --> 00:06:53,520 Speaker 8: some of these big companies are putting big dollars into it. 132 00:06:53,720 --> 00:06:56,320 Speaker 8: But we can't forget about the private side either. There 133 00:06:56,320 --> 00:06:58,440 Speaker 8: are a lot of venture companies that are going to 134 00:06:58,480 --> 00:07:02,400 Speaker 8: require as their develop of being applications for real businesses 135 00:07:02,400 --> 00:07:05,359 Speaker 8: on the user end, they're going to have to lean 136 00:07:05,520 --> 00:07:09,520 Speaker 8: into some of that infrastructure like data centers and ultimately 137 00:07:09,640 --> 00:07:12,600 Speaker 8: energy too. But that's not a conversation for today. So 138 00:07:12,680 --> 00:07:14,640 Speaker 8: I think I think the demand for this is not 139 00:07:14,720 --> 00:07:15,560 Speaker 8: going to ease up. 140 00:07:16,280 --> 00:07:18,920 Speaker 2: Can you give us your macro perspective here though as well? 141 00:07:19,200 --> 00:07:22,040 Speaker 2: We were just talking how on a macro perspective, maybe 142 00:07:22,080 --> 00:07:24,560 Speaker 2: we are seeing that feed into the stock weakness. Do 143 00:07:24,600 --> 00:07:28,120 Speaker 2: you still your mind be thinking about inflationary pressure? Does 144 00:07:28,160 --> 00:07:30,600 Speaker 2: that have any weight on a company that just is 145 00:07:30,640 --> 00:07:34,720 Speaker 2: so in focus for its innovation, not just the macro headwinds. 146 00:07:35,880 --> 00:07:36,240 Speaker 7: Yeah. 147 00:07:36,280 --> 00:07:38,760 Speaker 8: So you know, when I think about macro headwinds such 148 00:07:38,760 --> 00:07:41,520 Speaker 8: as inflation and prices, I think about who has the 149 00:07:41,560 --> 00:07:42,280 Speaker 8: pricing power? 150 00:07:42,480 --> 00:07:44,720 Speaker 7: Right, So if you're in Nvidia and you. 151 00:07:44,840 --> 00:07:49,040 Speaker 8: Have dominance in a marketplace, you have the pricing power. 152 00:07:49,120 --> 00:07:52,120 Speaker 8: So I don't think Nvidia is necessarily, you know, thinking 153 00:07:52,760 --> 00:07:55,600 Speaker 8: about whether or not they have they can pass down 154 00:07:56,280 --> 00:07:58,920 Speaker 8: any kind of inflation or price increases. 155 00:07:59,600 --> 00:08:01,400 Speaker 7: I think it's really on the consumer end. 156 00:08:01,440 --> 00:08:04,000 Speaker 8: I think it's on the end of the other max 157 00:08:04,040 --> 00:08:07,280 Speaker 8: evens that need to develop the R and D aspect 158 00:08:07,320 --> 00:08:09,080 Speaker 8: of it, they're going to have to worry about the 159 00:08:09,080 --> 00:08:09,920 Speaker 8: inflation portion. 160 00:08:10,560 --> 00:08:13,520 Speaker 2: Interesting. So for you, if you're looking at portfolio at 161 00:08:13,520 --> 00:08:15,360 Speaker 2: the start of twenty twenty five and you're thinking how 162 00:08:15,360 --> 00:08:17,000 Speaker 2: you're going to be changing up, do you make any 163 00:08:17,040 --> 00:08:19,840 Speaker 2: key decisions of the back of the latest inflation three points? 164 00:08:20,680 --> 00:08:23,560 Speaker 8: You know, our assumption has always been that it's going 165 00:08:23,600 --> 00:08:26,800 Speaker 8: to be higher for longer, both on the inflation area 166 00:08:26,920 --> 00:08:28,960 Speaker 8: as well as rates. 167 00:08:28,600 --> 00:08:30,600 Speaker 7: So our portfolio is already positioned for it. 168 00:08:30,680 --> 00:08:33,360 Speaker 8: We never really thought that inflation could go down to 169 00:08:33,400 --> 00:08:35,319 Speaker 8: two percent it you know, I'm going to use the 170 00:08:35,320 --> 00:08:38,720 Speaker 8: word safely without causing some kind of a slowdown in 171 00:08:38,760 --> 00:08:42,240 Speaker 8: the economy. And frankly, even if it does, it's really 172 00:08:42,240 --> 00:08:45,160 Speaker 8: in the cyclical areas of the economy, and I think 173 00:08:45,240 --> 00:08:49,360 Speaker 8: tech has some immunity from that cyclicality simply because there's 174 00:08:49,400 --> 00:08:52,920 Speaker 8: just way too much momentum going into this AI venture. 175 00:08:53,240 --> 00:08:56,400 Speaker 2: And raf fan so good to have you, CIOC BIZ 176 00:08:56,559 --> 00:09:00,439 Speaker 2: Investment Advisory Services stay well. Coming up, META looks back 177 00:09:00,440 --> 00:09:03,520 Speaker 2: on fact checking across its social media platforms or on that. 178 00:09:03,600 --> 00:09:30,120 Speaker 2: Next this is Bloomberg Technology. Yet more big news auta Meta. 179 00:09:30,240 --> 00:09:33,520 Speaker 2: Today it's announced that it will end third party fact 180 00:09:33,559 --> 00:09:36,360 Speaker 2: checking on its social media platforms, saying content moderation went 181 00:09:36,440 --> 00:09:39,200 Speaker 2: too far, so instead, the company will allow users to 182 00:09:39,240 --> 00:09:42,079 Speaker 2: comment on our post's accuracy, adopting a kind of format 183 00:09:42,120 --> 00:09:44,840 Speaker 2: similar to excess community notes. This, of course follows some 184 00:09:45,080 --> 00:09:48,360 Speaker 2: notable changes to its board as well. For more bloombergs 185 00:09:48,400 --> 00:09:51,880 Speaker 2: Lin Duine joins us for more fascinating moves. It's moving 186 00:09:51,880 --> 00:09:54,800 Speaker 2: safety and content moderation teams from California to Texas and 187 00:09:54,880 --> 00:09:56,800 Speaker 2: ultimately relying on its userbase now. 188 00:09:57,040 --> 00:10:00,400 Speaker 9: It is very fascinating and obviously, as you said, further 189 00:10:00,520 --> 00:10:05,280 Speaker 9: aligns Meta to the x approach of content moderation and 190 00:10:05,320 --> 00:10:09,320 Speaker 9: fact checking, which is to sort of crowdsource. One cannot 191 00:10:09,559 --> 00:10:14,080 Speaker 9: ignore the underlying trend here. It's clear that over the 192 00:10:14,120 --> 00:10:19,040 Speaker 9: past several months, Mark Zuckerberg has tried to repair, if 193 00:10:19,080 --> 00:10:23,520 Speaker 9: you will, his relationship with president incoming President Donald Trump. 194 00:10:23,960 --> 00:10:26,880 Speaker 9: That has been a relationship that has in the past 195 00:10:27,000 --> 00:10:30,560 Speaker 9: been fairly adversarial. I don't need to remind you that 196 00:10:30,600 --> 00:10:35,720 Speaker 9: at one point Trump was suspended on Meta's platforms briefly 197 00:10:35,800 --> 00:10:39,800 Speaker 9: and has since been reinstated, and since then Zuckerberg has 198 00:10:39,840 --> 00:10:45,720 Speaker 9: personally contributed to his inauguration fund and has made efforts, 199 00:10:45,800 --> 00:10:48,679 Speaker 9: as you have said, to make appointments that would be 200 00:10:48,960 --> 00:10:52,760 Speaker 9: more conducive to a relationship with the Trump administration coming in. 201 00:10:52,880 --> 00:10:54,720 Speaker 2: I mean, it's interesting that this is basically the first 202 00:10:54,720 --> 00:10:58,160 Speaker 2: announcement coming from Jill Kaplan, who is now the chief 203 00:10:58,160 --> 00:11:03,080 Speaker 2: Global Affairs Officer as Clegg moved stage right. And we're 204 00:11:03,080 --> 00:11:06,280 Speaker 2: not only getting a more right leaning global affairs officer, 205 00:11:06,320 --> 00:11:09,480 Speaker 2: but we're also getting Dana White UFC coming to the 206 00:11:09,480 --> 00:11:10,360 Speaker 2: board of MATA. 207 00:11:10,400 --> 00:11:13,760 Speaker 9: That's right, and as you probably recall, Dana White was 208 00:11:13,800 --> 00:11:17,520 Speaker 9: a very vocal Trump supporter. He was actually featured in 209 00:11:17,600 --> 00:11:21,560 Speaker 9: Trump's first video on TikTok, another social media platform. So 210 00:11:22,840 --> 00:11:25,800 Speaker 9: a good fit because you know, Dana White and Mark 211 00:11:25,880 --> 00:11:29,000 Speaker 9: Zuckerberg have a pre existing relationship. 212 00:11:29,040 --> 00:11:30,720 Speaker 5: They for a long time. 213 00:11:30,840 --> 00:11:34,680 Speaker 9: Right, They share a very strong interest in the same sport. 214 00:11:35,480 --> 00:11:37,920 Speaker 9: So in a lot of ways, it makes sense for 215 00:11:38,040 --> 00:11:40,520 Speaker 9: Mark and for what he's trying to do with that 216 00:11:40,600 --> 00:11:44,480 Speaker 9: relationship with Trump. I think that migration for the fact 217 00:11:44,520 --> 00:11:48,880 Speaker 9: checking team from California to Texas, as you mentioned, is 218 00:11:48,880 --> 00:11:50,120 Speaker 9: a really interesting one. 219 00:11:50,200 --> 00:11:50,440 Speaker 6: Two. 220 00:11:50,880 --> 00:11:54,560 Speaker 9: Whether that's politically motivated, I don't personally know, And I 221 00:11:54,600 --> 00:11:56,880 Speaker 9: think that the bigger question is, like will there be 222 00:11:56,920 --> 00:11:59,559 Speaker 9: a broader pullback at Meta and some of the other 223 00:11:59,600 --> 00:12:03,480 Speaker 9: social media platforms with this incoming Trump administration across all 224 00:12:03,600 --> 00:12:06,880 Speaker 9: content moderation, not necessarily just fact checking, but you know, 225 00:12:07,520 --> 00:12:09,600 Speaker 9: will there be a pullback in like the monitoring of 226 00:12:09,760 --> 00:12:14,840 Speaker 9: disturbing content CSM content. I think that's the broader question. 227 00:12:14,640 --> 00:12:17,120 Speaker 2: Ask and that leads us perfectly to our next conversation. 228 00:12:17,200 --> 00:12:19,760 Speaker 2: It's as if she knows it by Magic lindaan on 229 00:12:19,920 --> 00:12:22,400 Speaker 2: All Things Meta and look as metapairs back its fact 230 00:12:22,480 --> 00:12:25,280 Speaker 2: check is there's exclusive reporting today showing that the FBI 231 00:12:25,320 --> 00:12:28,120 Speaker 2: and the Department of Homeland Security will also scale back 232 00:12:28,120 --> 00:12:30,920 Speaker 2: efforts over the past two years to disrupt violent extremists 233 00:12:30,960 --> 00:12:34,400 Speaker 2: online activities. That's according to current and former US officials 234 00:12:34,440 --> 00:12:37,439 Speaker 2: and internet radicalization experts actually fear that the trend will 235 00:12:37,480 --> 00:12:41,040 Speaker 2: accelerate under Trump. That reporting all comes fro bloombergs Jeffstone 236 00:12:41,280 --> 00:12:45,320 Speaker 2: extraordinary timing. So you're working out here that basically demotment 237 00:12:45,360 --> 00:12:49,520 Speaker 2: of honand security and indeed other agencies have not wanted 238 00:12:49,520 --> 00:12:52,000 Speaker 2: to tip off the social media companies as much. 239 00:12:53,040 --> 00:12:56,560 Speaker 10: That's right, Caroline, since the social media companies Meta being 240 00:12:56,600 --> 00:13:00,200 Speaker 10: one of them, formerly Twitter being one of them, had 241 00:13:00,200 --> 00:13:04,520 Speaker 10: a long history now of flagging false COVID related information 242 00:13:05,320 --> 00:13:07,800 Speaker 10: misinformation around the vaccines. There's been a lot of political 243 00:13:07,800 --> 00:13:12,400 Speaker 10: pushback in Washington encouraging them not to do that. The 244 00:13:12,400 --> 00:13:14,680 Speaker 10: Department of Homeland Security and the FBI have kind of 245 00:13:14,679 --> 00:13:17,400 Speaker 10: become collateral damage in that larger political fight, and they 246 00:13:17,440 --> 00:13:22,400 Speaker 10: are now incredibly reluctant and prevented in some cases by 247 00:13:22,480 --> 00:13:26,720 Speaker 10: law from dealing with social media firms to remove some 248 00:13:26,840 --> 00:13:30,000 Speaker 10: of this really incendiary violent misinformation. 249 00:13:30,840 --> 00:13:35,319 Speaker 2: Yet this comes but days after the New Year's Day 250 00:13:35,360 --> 00:13:38,720 Speaker 2: attack on a Trump hotel, what happened in New Orleans. 251 00:13:38,840 --> 00:13:42,360 Speaker 2: I mean, the idea that extremist content isn't going to 252 00:13:42,400 --> 00:13:47,480 Speaker 2: be as monitored feels pretty scary, that's right. 253 00:13:47,520 --> 00:13:49,720 Speaker 10: I mean, there's certainly a trend where we are seeing 254 00:13:49,720 --> 00:13:52,760 Speaker 10: these extremist attacks in almost every case. You know, it 255 00:13:52,840 --> 00:13:54,600 Speaker 10: used to be a small fraction, but now in almost 256 00:13:54,640 --> 00:13:58,280 Speaker 10: every case, the perpetrators or suspected perpetrators who are carrying 257 00:13:58,280 --> 00:14:01,160 Speaker 10: out if these attacks are often taught talking about them online, 258 00:14:01,200 --> 00:14:03,480 Speaker 10: whether it be on sites like Meta in this case 259 00:14:03,960 --> 00:14:06,920 Speaker 10: or on Discord as in the case with the mass 260 00:14:06,920 --> 00:14:10,120 Speaker 10: shooter in Buffalo. The FBI and DHS are still going 261 00:14:10,160 --> 00:14:13,080 Speaker 10: to continue to investigate based on some of those leads 262 00:14:13,080 --> 00:14:15,080 Speaker 10: that they're seeing online, but they're not going to interact 263 00:14:15,120 --> 00:14:19,040 Speaker 10: with social media companies to remove that content proactively. They're 264 00:14:19,040 --> 00:14:21,600 Speaker 10: going to leave that to the social media firms. 265 00:14:21,880 --> 00:14:24,400 Speaker 2: What's interesting within your story, you really build out the 266 00:14:24,440 --> 00:14:28,280 Speaker 2: case that since so key court ruling here that as 267 00:14:28,280 --> 00:14:31,240 Speaker 2: you articulated, has pushed the DHS and the FBI, it's 268 00:14:31,600 --> 00:14:34,280 Speaker 2: less weighed in, but also there's less money going to 269 00:14:34,440 --> 00:14:36,760 Speaker 2: external third party researchers as well. 270 00:14:36,800 --> 00:14:38,640 Speaker 5: Right, that's right. 271 00:14:38,880 --> 00:14:42,000 Speaker 10: External third party researchers are a key component in this 272 00:14:42,080 --> 00:14:44,240 Speaker 10: larger ecosystem. They do a lot of the work on 273 00:14:44,320 --> 00:14:47,600 Speaker 10: behalf of social media companies to kind of proactively flag 274 00:14:47,800 --> 00:14:52,320 Speaker 10: and detect and remove some of this really again extremist, 275 00:14:52,440 --> 00:14:55,840 Speaker 10: violent material. It goes beyond a lot of misinformation. They 276 00:14:55,880 --> 00:14:59,360 Speaker 10: are now taking significantly less funding from the government because 277 00:15:00,360 --> 00:15:05,480 Speaker 10: of some of the political connotations and disputes around this. Specifically, 278 00:15:06,080 --> 00:15:08,240 Speaker 10: some of these researchers used to receive tens of millions 279 00:15:08,280 --> 00:15:11,120 Speaker 10: of dollars, whereas last year the Department of Homeland Security 280 00:15:11,160 --> 00:15:15,960 Speaker 10: gave them zero dollars for Internet related research exclusive reporting. 281 00:15:16,080 --> 00:15:18,120 Speaker 2: Jeff Stone, we thank you so much. 282 00:15:26,200 --> 00:15:26,400 Speaker 6: Now. 283 00:15:26,520 --> 00:15:30,480 Speaker 2: The US has just added China's most valuable company, Tencent, 284 00:15:30,640 --> 00:15:33,680 Speaker 2: as well as Tesla battery supply Caatl to its list 285 00:15:33,680 --> 00:15:37,280 Speaker 2: of quote Chinese military companies. It's a move which could 286 00:15:37,280 --> 00:15:39,640 Speaker 2: accelerate decoupling of the world's biggest economies. 287 00:15:39,760 --> 00:15:40,040 Speaker 5: For more. 288 00:15:40,040 --> 00:15:41,760 Speaker 2: We got to get out to Bloombergs Mike Shepard, and 289 00:15:41,760 --> 00:15:44,560 Speaker 2: this seemed to come out of nowhere yesterday, and even 290 00:15:44,600 --> 00:15:46,880 Speaker 2: Tencent itself thought it might be a mistake. 291 00:15:48,280 --> 00:15:50,440 Speaker 11: And a lot of the other companies thought so too. 292 00:15:50,520 --> 00:15:52,520 Speaker 11: They were really caught off guard, as a lot of 293 00:15:52,560 --> 00:15:55,840 Speaker 11: us here in Washington were. It showed up without fanfare 294 00:15:55,920 --> 00:15:59,440 Speaker 11: in a publication known as the Federal Register. They Pentagon 295 00:15:59,560 --> 00:16:02,040 Speaker 11: just dropped it in there. But there was a purpose 296 00:16:02,120 --> 00:16:05,400 Speaker 11: to this. It dates back to twenty twenty when President 297 00:16:05,440 --> 00:16:08,720 Speaker 11: Donald Trump was still in office and under an executive 298 00:16:08,880 --> 00:16:12,360 Speaker 11: order from him, he asked the Defense Department to look 299 00:16:12,400 --> 00:16:16,760 Speaker 11: for companies in China that had ties or actual control 300 00:16:16,920 --> 00:16:20,280 Speaker 11: by China's military. And the concern is that this nexus 301 00:16:20,360 --> 00:16:24,920 Speaker 11: between China's military and the business community in China post 302 00:16:25,040 --> 00:16:28,640 Speaker 11: a risk to global security, and as a result, we 303 00:16:28,680 --> 00:16:31,080 Speaker 11: see this listening now. It's important to note that there 304 00:16:31,200 --> 00:16:35,280 Speaker 11: is no sanction attached to it. However, there is something 305 00:16:35,320 --> 00:16:39,160 Speaker 11: that would discourage US firms from doing business with anyone 306 00:16:39,160 --> 00:16:40,200 Speaker 11: who is on this list. 307 00:16:40,560 --> 00:16:42,280 Speaker 2: I think there was initially a worry that they might 308 00:16:42,280 --> 00:16:45,440 Speaker 2: be forced to delist as well, Mike, is there any 309 00:16:45,560 --> 00:16:48,840 Speaker 2: reality in that. Certainly ten Cent came out with a 310 00:16:48,880 --> 00:16:51,120 Speaker 2: statement saying this doesn't affect our business in the here 311 00:16:51,200 --> 00:16:51,600 Speaker 2: and now. 312 00:16:53,000 --> 00:16:55,560 Speaker 11: That is a really good question, Caroly, and it was 313 00:16:55,640 --> 00:16:58,440 Speaker 11: one that we were chasing down yesterday. And as far 314 00:16:58,480 --> 00:17:01,400 Speaker 11: as we can tell, there is no immediate threat of 315 00:17:01,480 --> 00:17:04,719 Speaker 11: the listening directly as a result of this, but it 316 00:17:04,760 --> 00:17:09,320 Speaker 11: does prompt eight companies here, as I said, to revisit 317 00:17:09,359 --> 00:17:13,440 Speaker 11: and rethink relationships with ten Cent and others. And there 318 00:17:13,480 --> 00:17:16,840 Speaker 11: are now one hundred and twenty one Chinese companies on 319 00:17:16,920 --> 00:17:21,639 Speaker 11: this Pentagon list that subject them to some additional scrutiny 320 00:17:21,720 --> 00:17:26,040 Speaker 11: about their relationship with China's military, and the list also 321 00:17:26,080 --> 00:17:29,680 Speaker 11: includes CATL, which is one of the world's biggest battery makers. 322 00:17:29,960 --> 00:17:34,760 Speaker 11: They're critical to powering the electric vehicle revolution globally, but 323 00:17:34,800 --> 00:17:37,520 Speaker 11: they've also been the focus of a lot of attention, 324 00:17:37,800 --> 00:17:42,199 Speaker 11: especially from Congress, including incoming Secretary of State Mark or Rubio, 325 00:17:42,359 --> 00:17:44,800 Speaker 11: who when he was in the Senate had really zeroed 326 00:17:44,840 --> 00:17:48,240 Speaker 11: in on CATL as a potential national security risk through 327 00:17:48,240 --> 00:17:48,680 Speaker 11: the US. 328 00:17:48,880 --> 00:17:51,200 Speaker 2: So read those tea leaves for US, Mike, because on 329 00:17:51,200 --> 00:17:53,720 Speaker 2: one side, my immediate reaction is, well, Coatl is a 330 00:17:53,720 --> 00:17:56,479 Speaker 2: big supply to Tesla, and you know, Mosk isn't going 331 00:17:56,520 --> 00:17:59,439 Speaker 2: to like that. He has the air of Trump for 332 00:17:59,480 --> 00:18:03,159 Speaker 2: all some purposes, we understand. But then Marco Rubio coming in, 333 00:18:03,359 --> 00:18:05,560 Speaker 2: he doesn't much like c ATL. So what does the 334 00:18:05,600 --> 00:18:07,040 Speaker 2: next administration mean for this? 335 00:18:08,280 --> 00:18:10,960 Speaker 11: Well, the tea leaves really are tough to read here, Caroline, 336 00:18:11,040 --> 00:18:13,720 Speaker 11: and that's in part because we do see this kind. 337 00:18:13,600 --> 00:18:15,280 Speaker 10: Of division of loyalties. 338 00:18:15,640 --> 00:18:18,320 Speaker 11: This is also playing out with the TikTok band that 339 00:18:18,400 --> 00:18:21,960 Speaker 11: is about to go into effect on January nineteenth unless 340 00:18:22,000 --> 00:18:25,000 Speaker 11: the company, which has a Chinese parent, is able to 341 00:18:25,240 --> 00:18:29,480 Speaker 11: persuade the Supreme Court to overturn this devest or ban law. 342 00:18:29,760 --> 00:18:32,639 Speaker 11: Now President of Like Donald Trump, has made clear he 343 00:18:32,720 --> 00:18:36,520 Speaker 11: would like to see TikTok survive as its own entity, 344 00:18:36,760 --> 00:18:39,639 Speaker 11: to exist on its own and not be banned to 345 00:18:39,760 --> 00:18:43,439 Speaker 11: somehow evade this ultimate sanction as a result of a 346 00:18:43,480 --> 00:18:47,200 Speaker 11: new law. One also that Marco Rubio have helped push 347 00:18:47,240 --> 00:18:50,040 Speaker 11: through so we will see these kinds of tensions playing 348 00:18:50,040 --> 00:18:53,159 Speaker 11: out between some of the business interests, including from Elon 349 00:18:53,240 --> 00:18:56,840 Speaker 11: Musk and others, playing out right there in the White House. 350 00:18:56,920 --> 00:18:59,920 Speaker 11: They're trying to get tough on China, yet need also 351 00:19:00,119 --> 00:19:02,480 Speaker 11: this connection to China for business themselves. 352 00:19:02,640 --> 00:19:04,359 Speaker 2: And as shocking is it might be for many that 353 00:19:04,480 --> 00:19:07,720 Speaker 2: a big gaming and social network platform Tencent might be 354 00:19:07,760 --> 00:19:11,399 Speaker 2: in any way deemed a military related organization. There is 355 00:19:11,440 --> 00:19:15,520 Speaker 2: also past tens We've seen Shaomi, for example, as a phonemaker, 356 00:19:15,520 --> 00:19:18,240 Speaker 2: but also now a carmaker is managed to get itself 357 00:19:18,280 --> 00:19:20,800 Speaker 2: off the list, right, So there's potential hope. 358 00:19:20,640 --> 00:19:24,680 Speaker 11: Here, Yes, there is. These companies do have a way 359 00:19:24,800 --> 00:19:27,800 Speaker 11: to appeal this. They can even take it to court 360 00:19:27,840 --> 00:19:30,879 Speaker 11: and contest the listing, and a number of companies like 361 00:19:30,920 --> 00:19:33,800 Speaker 11: you just mentioned show Me especially, have been able to 362 00:19:33,840 --> 00:19:37,640 Speaker 11: get themselves removed and struck from the list. However, when 363 00:19:37,640 --> 00:19:40,280 Speaker 11: we talk about ten Cent, let's turn back the clock 364 00:19:40,320 --> 00:19:44,119 Speaker 11: again to twenty twenty, when Donald Trump as president initially 365 00:19:44,160 --> 00:19:47,320 Speaker 11: tried to ban TikTok, he also went after ten cents 366 00:19:47,400 --> 00:19:51,639 Speaker 11: we chat app, which is this all inclusive communications and 367 00:19:51,760 --> 00:19:54,919 Speaker 11: payments platform that the US government had viewed as a 368 00:19:55,000 --> 00:19:58,639 Speaker 11: risk in the US by anybody who was using it 369 00:19:58,680 --> 00:20:01,600 Speaker 11: here now there is not nearly as much use of 370 00:20:01,720 --> 00:20:05,400 Speaker 11: we chair here as by TikTok, but we do see 371 00:20:05,440 --> 00:20:10,200 Speaker 11: the same sort of argument being played out with tencent 372 00:20:10,760 --> 00:20:13,520 Speaker 11: back then and perhaps the echo of it now as well. 373 00:20:13,960 --> 00:20:16,640 Speaker 2: Mike Shepard, we thank you so much on all things 374 00:20:16,800 --> 00:20:19,600 Speaker 2: US China. We can go just onto data centers now 375 00:20:19,680 --> 00:20:21,359 Speaker 2: and get back to see yes where our own ed 376 00:20:21,440 --> 00:20:23,920 Speaker 2: Ludlow is standing by with a very special guest who's 377 00:20:23,920 --> 00:20:26,120 Speaker 2: probably going to benefit from that spending in the United States. 378 00:20:26,320 --> 00:20:31,840 Speaker 4: D Yeah, Good morning from Las Vegas, Caroline and Jensen one, 379 00:20:32,080 --> 00:20:35,320 Speaker 4: CEO of Nvidia, fresh off his keynote last night on stage. 380 00:20:35,760 --> 00:20:36,320 Speaker 6: Great to see you. 381 00:20:36,720 --> 00:20:39,760 Speaker 5: Welcome to Las Vegas. Thank you every new year, familiar territory. 382 00:20:39,880 --> 00:20:41,439 Speaker 6: Congratulations on your new baby. 383 00:20:41,560 --> 00:20:42,480 Speaker 5: Thank you very much. 384 00:20:43,040 --> 00:20:45,480 Speaker 4: A lot's changed since we last spoke, actually, but of 385 00:20:45,600 --> 00:20:48,360 Speaker 4: the broad spectrum of what you announced last night. New 386 00:20:48,359 --> 00:20:52,879 Speaker 4: graphics cards, new chips actually technically in the automotive space, 387 00:20:52,960 --> 00:20:56,199 Speaker 4: products and services on the software side, which is the 388 00:20:56,240 --> 00:20:59,520 Speaker 4: single most important for in video's future. 389 00:20:59,600 --> 00:21:00,919 Speaker 6: There are important you know. 390 00:21:00,960 --> 00:21:01,600 Speaker 5: It's hard. 391 00:21:01,760 --> 00:21:05,760 Speaker 6: It's it's hard. It's hard. It's hard to pick your favorites. 392 00:21:05,880 --> 00:21:09,280 Speaker 12: You know, we announced three chips, We announced a brand 393 00:21:09,359 --> 00:21:13,720 Speaker 12: new AI, a world foundation model and first of its kind, 394 00:21:14,200 --> 00:21:18,840 Speaker 12: and we announced our work in three areas and robotics, right, 395 00:21:19,200 --> 00:21:22,760 Speaker 12: and they're all important. And you know the thing of course, 396 00:21:23,280 --> 00:21:28,159 Speaker 12: we announced a brand new Blackwell RTX and has a 397 00:21:28,200 --> 00:21:36,240 Speaker 12: new AI technology called neuro Neuroshaters, and we're combining artificial 398 00:21:36,280 --> 00:21:38,920 Speaker 12: intelligence and classical. 399 00:21:38,440 --> 00:21:40,880 Speaker 5: Computers RTX and U RTX. 400 00:21:40,960 --> 00:21:44,560 Speaker 4: Yeah, we've become so accustomed to your story being dominant 401 00:21:44,600 --> 00:21:48,280 Speaker 4: in a market for high performance GPUs server ACS data centers. 402 00:21:48,560 --> 00:21:51,000 Speaker 4: This is going back to your roots. It's in the 403 00:21:51,000 --> 00:21:54,520 Speaker 4: desktop context. There's one right there later in the year laptop. 404 00:21:54,920 --> 00:21:59,000 Speaker 4: But for a target base that is developers, a nerdy, 405 00:21:59,080 --> 00:22:01,879 Speaker 4: hardcore gamment, what's the future of that business for you? 406 00:22:03,720 --> 00:22:05,800 Speaker 6: Computer graphics is going to be here forever, forever. 407 00:22:06,160 --> 00:22:10,720 Speaker 12: And what we've done is we've fused artificial intelligence and 408 00:22:10,760 --> 00:22:14,960 Speaker 12: computer graphics together. And the images that we're generating today 409 00:22:15,000 --> 00:22:17,160 Speaker 12: wouldn't be possible if not for the fact that we're 410 00:22:17,240 --> 00:22:21,560 Speaker 12: using computer graphics to create beautiful pixels and then use 411 00:22:21,640 --> 00:22:27,840 Speaker 12: artificial intelligence to amplify that capability. For example, out of 412 00:22:27,840 --> 00:22:30,600 Speaker 12: four frames I was talking about yesterday, thirty three million 413 00:22:30,600 --> 00:22:31,800 Speaker 12: pixels to SEW and four K. 414 00:22:32,400 --> 00:22:33,800 Speaker 6: Now. Out of that thirty three. 415 00:22:33,600 --> 00:22:37,359 Speaker 12: Million pixels, two million pixels were computed. The other thirty 416 00:22:37,359 --> 00:22:39,680 Speaker 12: one million pixels were generated by AI. 417 00:22:39,880 --> 00:22:42,159 Speaker 5: In other words, the AI predicts what it thinks the 418 00:22:42,200 --> 00:22:42,800 Speaker 5: pixtels should. 419 00:22:42,920 --> 00:22:45,200 Speaker 6: That's right, Yeah, the ultimate generator of AI. 420 00:22:45,280 --> 00:22:47,800 Speaker 4: But what was interesting for me is the focus again 421 00:22:47,960 --> 00:22:51,159 Speaker 4: was away from the graphics cards, away from blackweld, undependents 422 00:22:51,160 --> 00:22:55,119 Speaker 4: and Cosmos. We probably don't have time to explain in 423 00:22:55,160 --> 00:22:57,880 Speaker 4: full detailed Cosmos, but you call it a world foundation. 424 00:22:58,040 --> 00:23:02,240 Speaker 12: Model Cosmos is for or the physical world what chat 425 00:23:02,480 --> 00:23:05,160 Speaker 12: BT is for words and text. 426 00:23:05,800 --> 00:23:07,359 Speaker 6: That's the easiest way to think about that. 427 00:23:07,560 --> 00:23:12,760 Speaker 4: Okay, so model text input, but can generate synthetic data 428 00:23:12,800 --> 00:23:13,840 Speaker 4: in multiple mediums. 429 00:23:14,640 --> 00:23:17,960 Speaker 12: It understands the physical world. So for example, if I 430 00:23:18,040 --> 00:23:21,120 Speaker 12: ask it a question, if I ask it to generate 431 00:23:21,520 --> 00:23:25,919 Speaker 12: multiple futures of a car driving down the road, it 432 00:23:25,960 --> 00:23:29,800 Speaker 12: would understand the dynamics of the world, it would understand 433 00:23:30,440 --> 00:23:34,440 Speaker 12: the object permanence, it would under understand geometry and space, 434 00:23:34,800 --> 00:23:38,840 Speaker 12: and it would create a driving scenario for the car 435 00:23:39,560 --> 00:23:41,520 Speaker 12: that is plausible and so. 436 00:23:41,800 --> 00:23:42,960 Speaker 5: And you open sourced it. 437 00:23:43,080 --> 00:23:45,120 Speaker 4: So I don't really think about it as a product 438 00:23:45,200 --> 00:23:48,920 Speaker 4: or a go to market. It's more about what Cosmos enables. 439 00:23:49,760 --> 00:23:51,159 Speaker 4: Is that how we should think about it. 440 00:23:51,280 --> 00:23:55,320 Speaker 12: Yeah, Well, the automous vehicle industry and the rovice industry 441 00:23:55,359 --> 00:23:57,080 Speaker 12: is a really point to us, and we offer three 442 00:23:57,080 --> 00:23:58,000 Speaker 12: computers for them. 443 00:23:58,359 --> 00:24:01,240 Speaker 6: We offer it, of course, the training computer through DGX. 444 00:24:01,280 --> 00:24:04,440 Speaker 12: Through DGX, the robotics computer that's inside the car or 445 00:24:04,520 --> 00:24:07,080 Speaker 12: inside of robot and now we have this new computer 446 00:24:07,160 --> 00:24:12,760 Speaker 12: call Omniverse with Cosmos that is the digital twin or 447 00:24:12,800 --> 00:24:15,760 Speaker 12: the playground where these robots can learn how to be robots. 448 00:24:16,119 --> 00:24:18,879 Speaker 12: And so if we could accelerate the development of an 449 00:24:18,960 --> 00:24:22,959 Speaker 12: artificial intelligence for avs and for robotics, it brings in 450 00:24:23,000 --> 00:24:23,800 Speaker 12: a lot of business for us. 451 00:24:23,840 --> 00:24:26,280 Speaker 4: Of course, there's an academic point of tension here. If 452 00:24:26,320 --> 00:24:29,520 Speaker 4: I may Elon Musk is a customer of yours and Tesla, 453 00:24:30,720 --> 00:24:34,280 Speaker 4: their theory or practice is based on real world data. 454 00:24:34,080 --> 00:24:35,040 Speaker 5: Gathered through vision. 455 00:24:35,280 --> 00:24:39,520 Speaker 4: Yeah, does the synthetic data underpinning of Cosmos kind of 456 00:24:39,720 --> 00:24:41,000 Speaker 4: contradict that it. 457 00:24:40,960 --> 00:24:43,879 Speaker 12: Doesn't replace it augments, And so you're going to you 458 00:24:43,880 --> 00:24:46,320 Speaker 12: should collect as much world data as you can. Of course, 459 00:24:46,320 --> 00:24:49,320 Speaker 12: collecting world data is very expensive and Elon has a 460 00:24:49,320 --> 00:24:53,240 Speaker 12: great advantage because the number one his AI factory for 461 00:24:53,320 --> 00:24:56,280 Speaker 12: his cars is fantastic, has a lot of video gear 462 00:24:56,320 --> 00:25:01,400 Speaker 12: in it. His av algorithms is incredible, it's the best 463 00:25:01,400 --> 00:25:04,840 Speaker 12: in the world. And he has a very large fleet 464 00:25:04,880 --> 00:25:07,640 Speaker 12: of cars on the road that allows him to collect. 465 00:25:07,359 --> 00:25:07,960 Speaker 6: A lot of data. 466 00:25:08,000 --> 00:25:11,360 Speaker 12: And so so I think he has just a phenomenal position. 467 00:25:11,600 --> 00:25:13,320 Speaker 12: And he's been working on this for a long time, 468 00:25:13,359 --> 00:25:15,040 Speaker 12: and so he's he's going to be in a great 469 00:25:15,040 --> 00:25:16,639 Speaker 12: position to take advantage of it. 470 00:25:16,840 --> 00:25:18,360 Speaker 5: Well, may I ask you that's juncture. 471 00:25:18,400 --> 00:25:22,680 Speaker 4: He's clearly influential in this upcoming administration, but he also 472 00:25:22,800 --> 00:25:25,479 Speaker 4: positions Tesla as a leading AI and robotics company. 473 00:25:25,600 --> 00:25:27,760 Speaker 5: Yeah, how does that code for Nvidia? 474 00:25:27,880 --> 00:25:32,560 Speaker 4: Elon Musk's influence the President Electrump and also thecoming administration's 475 00:25:32,600 --> 00:25:34,600 Speaker 4: kind of attitude towards AI. 476 00:25:36,359 --> 00:25:40,119 Speaker 6: I don't know that the attitude towards AI. 477 00:25:40,520 --> 00:25:43,520 Speaker 12: I know Elon's attitude towards AI, and and he's very 478 00:25:43,520 --> 00:25:44,760 Speaker 12: optimistic about his future. 479 00:25:44,880 --> 00:25:47,800 Speaker 6: And obviously he's working on some of the most important 480 00:25:48,359 --> 00:25:49,200 Speaker 6: AI areas. 481 00:25:50,119 --> 00:25:57,800 Speaker 12: XAI is working on foundation cognitive Intelligence AI, his Tesla 482 00:25:57,880 --> 00:26:03,200 Speaker 12: is working on Thomas vehicles, and optimists were humanoid robotics. 483 00:26:03,400 --> 00:26:06,040 Speaker 12: These three areas of AI are the three most important 484 00:26:06,040 --> 00:26:08,879 Speaker 12: areas of AI, and so I think he's working on 485 00:26:08,960 --> 00:26:09,960 Speaker 12: exactly the right things. 486 00:26:10,119 --> 00:26:12,720 Speaker 4: You kind of positioned AI and video's position in the 487 00:26:13,160 --> 00:26:17,480 Speaker 4: supply chain for physical AI, robotics, autonomous driving. 488 00:26:17,640 --> 00:26:19,840 Speaker 5: Explain it a bit more, the role you see in 489 00:26:19,920 --> 00:26:20,440 Speaker 5: video play. 490 00:26:20,760 --> 00:26:23,359 Speaker 12: Well, we're a core technology company, and so we build 491 00:26:23,400 --> 00:26:25,160 Speaker 12: the foundational computing platforms. 492 00:26:25,880 --> 00:26:28,320 Speaker 6: We're also full stack, and so we developed. 493 00:26:27,960 --> 00:26:31,400 Speaker 12: The necessary algorithms and necessary AI technologies and. 494 00:26:31,359 --> 00:26:32,000 Speaker 6: Then we put it. 495 00:26:32,200 --> 00:26:34,240 Speaker 12: We put it out to the industry for them to 496 00:26:34,720 --> 00:26:37,639 Speaker 12: adopt it and turn it into in market solutions. 497 00:26:38,359 --> 00:26:39,719 Speaker 6: We're computing platform companies. 498 00:26:39,720 --> 00:26:41,880 Speaker 4: So you're on stage and you're surrounded by I think 499 00:26:41,920 --> 00:26:44,720 Speaker 4: a dozen humanoid robots, which. 500 00:26:44,520 --> 00:26:45,200 Speaker 5: Is nice for you. 501 00:26:45,640 --> 00:26:48,359 Speaker 4: When will I be here at CS and Las Vegas 502 00:26:48,400 --> 00:26:51,080 Speaker 4: and actually have there are some robots right now? In 503 00:26:51,440 --> 00:26:54,639 Speaker 4: real terms, you must have a timeline that you see 504 00:26:55,440 --> 00:26:58,960 Speaker 4: real world commercial deployment of the technology you outlined last night. 505 00:26:59,520 --> 00:27:03,600 Speaker 12: It depends on use case, I would say first use. Yeah, 506 00:27:03,640 --> 00:27:05,840 Speaker 12: the first use case will probably be in manufacturing. You know, 507 00:27:05,880 --> 00:27:11,720 Speaker 12: there's estimates tends, if not one hundred million jobs that 508 00:27:11,760 --> 00:27:14,840 Speaker 12: are workers that are the world is short of workers 509 00:27:15,560 --> 00:27:23,000 Speaker 12: and aging population, declining, declining birth rates, and so I 510 00:27:23,040 --> 00:27:27,240 Speaker 12: think the world needs a lot more workers. Robotics is 511 00:27:27,240 --> 00:27:30,520 Speaker 12: one of the best ways for us to supplement all 512 00:27:30,560 --> 00:27:33,639 Speaker 12: of that and help companies recover the lost revenues on 513 00:27:33,680 --> 00:27:38,000 Speaker 12: the one hand and drive productivity which reduces inflation for 514 00:27:38,040 --> 00:27:40,479 Speaker 12: the world on the other hand. And so I think 515 00:27:40,560 --> 00:27:42,919 Speaker 12: robotics is going to be very important to that in 516 00:27:42,960 --> 00:27:46,840 Speaker 12: different different areas. You could have probably deploy into manufacturing 517 00:27:46,840 --> 00:27:49,120 Speaker 12: first because they obviously need it most. 518 00:27:49,119 --> 00:27:52,560 Speaker 4: Which you see is a ginormous potential market addressable lots. 519 00:27:52,560 --> 00:27:55,720 Speaker 6: It's a fifty trillion dollar industry that wants to. 520 00:27:55,600 --> 00:27:58,080 Speaker 4: Grow, and it thinks to grow. Sorry to interrupt you, Jensen. 521 00:27:58,080 --> 00:28:00,200 Speaker 4: I think that something that wool Street struggling with this morning, 522 00:28:00,440 --> 00:28:05,000 Speaker 4: among many things, is dg X. They understand the investment 523 00:28:05,040 --> 00:28:08,680 Speaker 4: there that trains the foundation models in terms of Nvidia's 524 00:28:08,680 --> 00:28:13,960 Speaker 4: business model what you outlined last night, Cosmos, and then 525 00:28:14,160 --> 00:28:17,960 Speaker 4: later on on the inference side, how physical AI helps 526 00:28:18,000 --> 00:28:19,640 Speaker 4: guare your business from the drives. 527 00:28:19,720 --> 00:28:21,560 Speaker 5: D GX growth just simple as that. 528 00:28:21,680 --> 00:28:23,400 Speaker 12: Yeah, as simple as that, I think if you if 529 00:28:23,440 --> 00:28:25,760 Speaker 12: you just look at simply like that, we have three computers, 530 00:28:26,400 --> 00:28:29,920 Speaker 12: and two of the computers d GX and Omniverse drives 531 00:28:29,920 --> 00:28:32,800 Speaker 12: an enormous amount of data that is necessary to train 532 00:28:32,880 --> 00:28:36,440 Speaker 12: the AI models right, and so Omniverse creates the data 533 00:28:36,760 --> 00:28:39,680 Speaker 12: that we then use to train AI models. The training 534 00:28:39,760 --> 00:28:43,680 Speaker 12: is what drives DGX sales. And the more robots that 535 00:28:43,720 --> 00:28:46,400 Speaker 12: are that are available, the more data we can create, 536 00:28:46,440 --> 00:28:46,880 Speaker 12: the more. 537 00:28:46,760 --> 00:28:48,720 Speaker 6: AI models we have to we have to go train. 538 00:28:49,400 --> 00:28:53,920 Speaker 12: That's that cycle is ultimately what we're striving for. All 539 00:28:53,960 --> 00:28:56,760 Speaker 12: of that drives consumption from data center growth. 540 00:28:56,840 --> 00:29:00,360 Speaker 4: There is a surge in AI spending data center growth. 541 00:29:01,600 --> 00:29:03,640 Speaker 4: Some of our audience are a bit concerned about how 542 00:29:03,680 --> 00:29:07,160 Speaker 4: sustainable that is, short, medium, and long term. 543 00:29:07,520 --> 00:29:11,840 Speaker 12: Well, at the limit, Artificial intelligence is a single most 544 00:29:11,880 --> 00:29:15,520 Speaker 12: important technology force of our time, and it's really about 545 00:29:16,040 --> 00:29:18,440 Speaker 12: we're at the beginning of that and in the future, 546 00:29:18,960 --> 00:29:23,040 Speaker 12: every single data center will be driven by AI and 547 00:29:23,080 --> 00:29:25,640 Speaker 12: the type of computing that we build today. And so 548 00:29:25,680 --> 00:29:28,640 Speaker 12: if you look at the world today, we're about a 549 00:29:28,800 --> 00:29:32,080 Speaker 12: year and a half into the remodeling, if you will, 550 00:29:32,920 --> 00:29:36,720 Speaker 12: the modernization, the reinvention of computing, and so I think 551 00:29:36,760 --> 00:29:38,840 Speaker 12: that over the next several years you're going to see 552 00:29:39,200 --> 00:29:40,760 Speaker 12: the transition from the old. 553 00:29:40,560 --> 00:29:44,080 Speaker 6: Way of doing computing, general purpose computing. There's a new way. 554 00:29:43,920 --> 00:29:47,320 Speaker 12: Of doing computing, artificial intelligence and accelerated computing, and so 555 00:29:47,360 --> 00:29:48,640 Speaker 12: we have a lot of growth to go do. 556 00:29:49,160 --> 00:29:52,080 Speaker 4: Let's go back to your and nvidious routes, and therein 557 00:29:52,160 --> 00:29:56,120 Speaker 4: lies the complication, right and the story accelerated computing. You 558 00:29:56,160 --> 00:30:00,240 Speaker 4: spent four years redefining the computer for me, and we 559 00:30:00,320 --> 00:30:05,120 Speaker 4: get rtx Blackwell single black Well in that form factor. 560 00:30:05,520 --> 00:30:09,800 Speaker 4: The target audience is hardcore gamers but also developers, and 561 00:30:09,800 --> 00:30:12,120 Speaker 4: I wondered if you could give me any early examples 562 00:30:12,520 --> 00:30:15,600 Speaker 4: or evidence of how you see the gaming industry adopting 563 00:30:15,640 --> 00:30:16,440 Speaker 4: your technology. 564 00:30:17,640 --> 00:30:23,560 Speaker 12: Well, AI is going to reinvigorate the video game industry. 565 00:30:23,880 --> 00:30:26,680 Speaker 12: On the one hand, for developers, it's going to reduce 566 00:30:26,680 --> 00:30:29,920 Speaker 12: the cost of creating the content. On the other hand, 567 00:30:30,200 --> 00:30:32,360 Speaker 12: all of the characters that are in the games are 568 00:30:32,400 --> 00:30:34,960 Speaker 12: going to be smart characters in the future, so every 569 00:30:34,960 --> 00:30:36,800 Speaker 12: time you interact with them, they're going to be interacting 570 00:30:36,840 --> 00:30:39,080 Speaker 12: with you in a much more intelligent way. And so 571 00:30:39,160 --> 00:30:41,479 Speaker 12: the games are going to be more interesting, the characters 572 00:30:41,520 --> 00:30:45,240 Speaker 12: are going to be more interesting. The content development cost 573 00:30:45,360 --> 00:30:47,320 Speaker 12: is going to decline, and that's going to be really 574 00:30:47,320 --> 00:30:49,600 Speaker 12: great for the industry, and so I think that the 575 00:30:49,640 --> 00:30:53,520 Speaker 12: future is really bright for video games and these virtual 576 00:30:53,560 --> 00:30:57,240 Speaker 12: worlds and artificial intelligence is going to really reinvigorate it. 577 00:30:57,760 --> 00:31:00,920 Speaker 5: Project digits, may I pick it up? Oh yeah, yeah, 578 00:31:01,080 --> 00:31:06,680 Speaker 5: three thousand dollars digits? Yeah, a supercomputer costing. 579 00:31:06,520 --> 00:31:09,320 Speaker 12: Exactly How could you imagine you're just sitting there just 580 00:31:09,360 --> 00:31:10,920 Speaker 12: like that, You're working on your PC? 581 00:31:11,480 --> 00:31:14,880 Speaker 5: Well, here, why would I need one of these? Probably 582 00:31:14,920 --> 00:31:15,240 Speaker 5: not me? 583 00:31:15,920 --> 00:31:18,959 Speaker 4: But how big is the addressable market for this? What 584 00:31:19,080 --> 00:31:20,280 Speaker 4: is the addressable market? 585 00:31:20,400 --> 00:31:24,880 Speaker 12: There are thirty million software developers. They're probably something along 586 00:31:24,880 --> 00:31:29,400 Speaker 12: the lines of ten million designers around the world. Probably 587 00:31:29,440 --> 00:31:33,720 Speaker 12: another twenty million creative artists. Hard to say exactly how 588 00:31:33,720 --> 00:31:36,920 Speaker 12: many students. I'm going to guess probably a couple one 589 00:31:37,000 --> 00:31:39,400 Speaker 12: hundred million students around the world. 590 00:31:40,040 --> 00:31:41,560 Speaker 6: Everybody is going to have to board it. 591 00:31:41,680 --> 00:31:45,160 Speaker 12: Everybody's going to have to Well, they can afford computers, 592 00:31:45,640 --> 00:31:47,760 Speaker 12: and so here's if they can afford computers and they 593 00:31:47,800 --> 00:31:51,840 Speaker 12: would like to have a companion that helps them do AI, 594 00:31:52,840 --> 00:31:53,800 Speaker 12: this is the way to do it. 595 00:31:53,800 --> 00:31:54,960 Speaker 5: Can I just clarify something? 596 00:31:55,000 --> 00:31:55,560 Speaker 6: Yeah? On it? 597 00:31:55,600 --> 00:31:59,760 Speaker 4: I think you said on stage mac os, Linux and Windows. 598 00:32:00,080 --> 00:32:02,240 Speaker 5: No, No, the location said just Linux, No. 599 00:32:03,600 --> 00:32:07,800 Speaker 12: Whatever computer you use, you're literally enjoying how it's going 600 00:32:07,840 --> 00:32:10,520 Speaker 12: to be used. It's sitting right there and you'll just 601 00:32:10,520 --> 00:32:12,880 Speaker 12: connect to what wirelessly like it's at your personal cloud. 602 00:32:13,640 --> 00:32:15,959 Speaker 4: I promised the audience I'd garify that because very excited. 603 00:32:16,040 --> 00:32:19,320 Speaker 4: Oh yeah, we're running short on time. President Electrump has 604 00:32:19,360 --> 00:32:23,680 Speaker 4: been speaking during the course of conversation. How imperative is 605 00:32:23,720 --> 00:32:25,960 Speaker 4: it that you go to mar A Lago and meet 606 00:32:25,960 --> 00:32:29,680 Speaker 4: with him? If Nvidia is America's leading AI company, and will. 607 00:32:29,480 --> 00:32:31,400 Speaker 6: You I would be delighted to go see him. Have 608 00:32:31,480 --> 00:32:32,240 Speaker 6: you been invited? 609 00:32:32,880 --> 00:32:34,800 Speaker 12: Not yet, but I would be delighted to go see 610 00:32:34,840 --> 00:32:40,520 Speaker 12: him and congratulate him and do everything we can to 611 00:32:40,520 --> 00:32:42,240 Speaker 12: help this administration succeed. 612 00:32:42,600 --> 00:32:44,520 Speaker 4: A lot of what you outlined on stage last night 613 00:32:44,560 --> 00:32:47,920 Speaker 4: in the remophysical AI. You know, I saw x Pong, 614 00:32:48,000 --> 00:32:51,800 Speaker 4: for example, in the autonomous driving context that's happening in China, 615 00:32:51,920 --> 00:32:53,520 Speaker 4: like they are doing a lot on robotics. 616 00:32:53,720 --> 00:32:55,520 Speaker 5: So I'm going to ask you about tariffs. 617 00:32:56,080 --> 00:32:59,000 Speaker 4: You know, it's likely this coming administration will be as 618 00:32:59,040 --> 00:33:02,520 Speaker 4: restrictive on technology export and tarifs will be a function. 619 00:33:02,640 --> 00:33:04,160 Speaker 5: How have you prepared for that? Jensen? 620 00:33:07,080 --> 00:33:11,920 Speaker 12: Whatever the administration ultimately decides, will give them as much 621 00:33:12,400 --> 00:33:16,040 Speaker 12: insight as we can from our perspective, and I'm sure 622 00:33:16,040 --> 00:33:18,680 Speaker 12: that the administration will make the right moves that's the 623 00:33:18,720 --> 00:33:19,880 Speaker 12: best interest of our country. 624 00:33:20,520 --> 00:33:21,400 Speaker 5: Twenty twenty five. 625 00:33:21,800 --> 00:33:23,520 Speaker 4: I kind of started the conversation by saying, a lot 626 00:33:23,520 --> 00:33:26,880 Speaker 4: has changed since we last spoke in the summer. You 627 00:33:27,000 --> 00:33:29,800 Speaker 4: said that the age of general robotics is just around 628 00:33:29,800 --> 00:33:33,440 Speaker 4: the corner. Is twenty twenty five the year of general 629 00:33:33,520 --> 00:33:35,720 Speaker 4: robotics or is that a little premature. 630 00:33:35,200 --> 00:33:36,720 Speaker 6: To your mind? 631 00:33:36,960 --> 00:33:40,680 Speaker 12: The development is going gangbusters, as you can see all 632 00:33:40,680 --> 00:33:42,600 Speaker 12: the different robots that are going to be around here, 633 00:33:43,080 --> 00:33:47,520 Speaker 12: and the enabling technology necessary for general robotics is coming together. 634 00:33:47,600 --> 00:33:50,719 Speaker 12: All the pieces are coming together. The industry still has 635 00:33:50,760 --> 00:33:54,120 Speaker 12: a lot of engineering to do. If you looked long term, 636 00:33:54,480 --> 00:33:57,720 Speaker 12: you know, pick your horizon in ten or twenty years, 637 00:33:58,040 --> 00:34:01,200 Speaker 12: the number of robots that's to be on Earth that's 638 00:34:01,240 --> 00:34:03,880 Speaker 12: going to be measured and probably tens of nine hundreds 639 00:34:03,920 --> 00:34:08,080 Speaker 12: and not potentially billions of robots, and so those days 640 00:34:08,120 --> 00:34:11,680 Speaker 12: are clearly coming. Is it going to happen in the 641 00:34:11,719 --> 00:34:13,520 Speaker 12: next couple of years or the next five years, hard 642 00:34:13,520 --> 00:34:16,279 Speaker 12: to say, but the development of robotics is going to 643 00:34:16,280 --> 00:34:19,240 Speaker 12: be all over the world now and we're seeing startup companies, 644 00:34:19,320 --> 00:34:24,040 Speaker 12: large companies, industrial companies, consumer electronics companies all getting involved 645 00:34:24,040 --> 00:34:26,520 Speaker 12: in the future of robotics. And our offering to the 646 00:34:26,560 --> 00:34:30,000 Speaker 12: industry is a three computer system. And so whether they're 647 00:34:30,640 --> 00:34:33,560 Speaker 12: developing the robot, training the robot, we have DJX systems 648 00:34:33,560 --> 00:34:36,360 Speaker 12: for them and DJX clouds for them. If they're simulating 649 00:34:36,400 --> 00:34:38,960 Speaker 12: the robots, we have omnivers for them, and if they 650 00:34:39,080 --> 00:34:39,759 Speaker 12: want to deploy them. 651 00:34:39,800 --> 00:34:41,759 Speaker 6: When they're ready to deploy the robots, we have. 652 00:34:43,560 --> 00:34:47,160 Speaker 12: Little computers that basically is the computer brain of the 653 00:34:47,280 --> 00:34:49,720 Speaker 12: robot that they can put inside the robot. 654 00:34:50,000 --> 00:34:50,920 Speaker 6: And so we'll work with. 655 00:34:50,880 --> 00:34:54,000 Speaker 12: The industry across the board from the development of the 656 00:34:54,080 --> 00:34:57,040 Speaker 12: robots to the deployment of the robot. And we have 657 00:34:57,120 --> 00:35:00,399 Speaker 12: computer systems for them, algorithms for them, ais for them, 658 00:35:00,760 --> 00:35:03,400 Speaker 12: and we'll partner with the industry and make this feature happen. 659 00:35:03,320 --> 00:35:04,120 Speaker 6: Very very quick. 660 00:35:04,560 --> 00:35:07,560 Speaker 4: Which line of business grows fast? Is this year, data center, 661 00:35:07,760 --> 00:35:08,759 Speaker 4: gaming or other. 662 00:35:09,719 --> 00:35:12,239 Speaker 6: They're all going to grow fast. I think gaming is 663 00:35:12,239 --> 00:35:13,080 Speaker 6: continuing to grow. 664 00:35:13,800 --> 00:35:18,000 Speaker 12: Our automas vehicle business is already on its way to 665 00:35:18,040 --> 00:35:20,799 Speaker 12: be a five billion dollar business this year, and so 666 00:35:21,360 --> 00:35:24,880 Speaker 12: right and sometimes right run rate, and so the autonomous 667 00:35:25,000 --> 00:35:27,760 Speaker 12: vehicle business is just starting to get off the ground, 668 00:35:28,040 --> 00:35:30,279 Speaker 12: and that tells you something about how we address it. 669 00:35:30,320 --> 00:35:32,560 Speaker 12: And the reason for that is we get the benefit 670 00:35:32,760 --> 00:35:35,759 Speaker 12: from the beginning of the development of the AIS all 671 00:35:35,800 --> 00:35:38,799 Speaker 12: the way to the deployment of the cars. Because a 672 00:35:38,840 --> 00:35:43,000 Speaker 12: car company needs two factories, a car factory that builds 673 00:35:43,000 --> 00:35:44,960 Speaker 12: the cars and an AI factory that builds the AI 674 00:35:45,040 --> 00:35:47,919 Speaker 12: sport the cars, and both of these, both of these 675 00:35:48,239 --> 00:35:49,640 Speaker 12: areas we could participate. 676 00:35:49,719 --> 00:35:51,600 Speaker 6: And so I think it's going to be a very 677 00:35:51,640 --> 00:35:52,160 Speaker 6: large business. 678 00:35:52,160 --> 00:35:54,279 Speaker 4: Send I've made you late for your next appointment. Yeah, 679 00:35:54,280 --> 00:35:56,400 Speaker 4: it's crazy, literally grateful a few times. It's good to 680 00:35:56,400 --> 00:35:59,200 Speaker 4: see you as well. Jensen one, the Nvidia CEO. 681 00:35:59,040 --> 00:36:05,080 Speaker 2: Caroline, what a conversation, ed Ludlow live from Las Vegas. 682 00:36:05,120 --> 00:36:07,080 Speaker 2: So good to have you back. We'll let you go 683 00:36:07,120 --> 00:36:09,319 Speaker 2: and return to paternity leave from New York. 684 00:36:09,840 --> 00:36:11,040 Speaker 6: This is blue Bag technology.