1 00:00:00,280 --> 00:00:07,200 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,119 --> 00:00:12,440 Speaker 2: Perhaps no company embodies the promise of AI more than 3 00:00:12,480 --> 00:00:12,960 Speaker 2: in Vidia. 4 00:00:13,640 --> 00:00:16,280 Speaker 1: Very quick story of in Nvidia is that it used 5 00:00:16,320 --> 00:00:18,960 Speaker 1: to make video game chips that gamers really liked. It 6 00:00:19,040 --> 00:00:20,920 Speaker 1: turned out those things we're good for AI, and now 7 00:00:21,000 --> 00:00:22,520 Speaker 1: it's one of the biggest companies in the world. 8 00:00:22,760 --> 00:00:26,439 Speaker 2: Joshua Breustein is a technology editor at Bloomberg BusinessWeek. He's 9 00:00:26,480 --> 00:00:29,720 Speaker 2: covered in Videa's stunning rise and says the company is 10 00:00:29,800 --> 00:00:32,040 Speaker 2: now on the cusp of its next chapter. 11 00:00:32,080 --> 00:00:35,040 Speaker 1: And video doesn't a funny place because, on the one hand, 12 00:00:35,200 --> 00:00:39,080 Speaker 1: it is in a great position. It's very good at 13 00:00:39,080 --> 00:00:41,640 Speaker 1: making these chips that everyone wants. 14 00:00:42,040 --> 00:00:45,920 Speaker 2: Microsoft, Meta, Amazon, pretty much all the big tech companies 15 00:00:45,960 --> 00:00:49,320 Speaker 2: have collectively announced hundreds of billions of dollars of investment 16 00:00:49,520 --> 00:00:52,440 Speaker 2: in AI, and much of that investment will go straight 17 00:00:52,479 --> 00:00:55,480 Speaker 2: to buying in Vidia's product. In some ways, it's a 18 00:00:55,600 --> 00:00:58,480 Speaker 2: dream position for a company, but being at the top 19 00:00:58,680 --> 00:01:01,880 Speaker 2: also means there's a law way to fall. In Vidia 20 00:01:01,920 --> 00:01:03,640 Speaker 2: got a glimpse of what that could look like in 21 00:01:03,720 --> 00:01:07,280 Speaker 2: late January, when the Chinese AI startup deep Sea released 22 00:01:07,319 --> 00:01:11,160 Speaker 2: its R one chatbot. Deep Seek's developers say their model 23 00:01:11,280 --> 00:01:15,280 Speaker 2: uses a fraction of the resources of American competitors. That 24 00:01:15,360 --> 00:01:18,399 Speaker 2: news rattled assumptions about how much computing power and how 25 00:01:18,400 --> 00:01:21,920 Speaker 2: many in Nvidia chips the AI revolution will actually need. 26 00:01:22,080 --> 00:01:24,920 Speaker 3: The main event yesterday one single name in video in 27 00:01:25,040 --> 00:01:28,800 Speaker 3: video getting absolutely hammered yesterday session down by seventeen percent, 28 00:01:28,880 --> 00:01:32,160 Speaker 3: a record five hundred and ninety three billion dollars taken 29 00:01:32,160 --> 00:01:33,000 Speaker 3: off the market cap. 30 00:01:33,319 --> 00:01:36,720 Speaker 2: It was the largest route in market history, and Joshua 31 00:01:36,720 --> 00:01:39,520 Speaker 2: says the risk of something like that happening again is 32 00:01:39,560 --> 00:01:42,759 Speaker 2: something in Vidia's founder, Jensen Huang takes seriously. 33 00:01:43,160 --> 00:01:46,840 Speaker 1: Huang looks at the history of semiconductors and knows that 34 00:01:46,880 --> 00:01:50,840 Speaker 1: when you see a big transition in tech, the infrastructure 35 00:01:50,840 --> 00:01:53,160 Speaker 1: companies do really well at the beginning of it, and 36 00:01:53,240 --> 00:02:00,680 Speaker 1: then their products become commoditized and they crash. 37 00:02:01,280 --> 00:02:03,080 Speaker 2: I'm David Gera, and this is the big take from 38 00:02:03,120 --> 00:02:06,320 Speaker 2: Bloomberg News today. On the show, in Vidia's scrambled to 39 00:02:06,440 --> 00:02:14,320 Speaker 2: future proof it's technology and hold on to its top spot. Today, 40 00:02:14,320 --> 00:02:17,520 Speaker 2: the semiconductor manufacturer in Vidia is one of the largest 41 00:02:17,560 --> 00:02:21,280 Speaker 2: companies in the world by market cap. Bloomberg's Joshua Brustein 42 00:02:21,360 --> 00:02:24,520 Speaker 2: says its story starts more than thirty years ago with 43 00:02:24,600 --> 00:02:26,120 Speaker 2: its CEO, Jensen Huang. 44 00:02:26,880 --> 00:02:31,680 Speaker 1: He just really is known within Nvidia in the wider 45 00:02:32,000 --> 00:02:34,760 Speaker 1: industry as kind of a force of nature. He's a 46 00:02:34,840 --> 00:02:40,520 Speaker 1: very hard charging boss. He is very enthusiastic about the 47 00:02:40,680 --> 00:02:43,440 Speaker 1: technical aspects of running a semiconductor company. 48 00:02:43,720 --> 00:02:46,080 Speaker 2: To put it simply, he's a nerd. 49 00:02:46,080 --> 00:02:48,160 Speaker 1: But not in the way that I think a lot 50 00:02:48,200 --> 00:02:50,520 Speaker 1: of people think of tech nerds, where it's like a 51 00:02:50,520 --> 00:02:53,520 Speaker 1: lot of talk about our computer is gonna become sentient, 52 00:02:53,680 --> 00:02:56,080 Speaker 1: and you know, will they lead to the end of 53 00:02:56,160 --> 00:02:59,600 Speaker 1: human existence. It's more kind of like how many transistors 54 00:02:59,600 --> 00:03:01,480 Speaker 1: can we fit on this chip? And is there a 55 00:03:01,480 --> 00:03:03,760 Speaker 1: new architecture we can use to do it even better? 56 00:03:04,200 --> 00:03:06,880 Speaker 1: So he's not doing the science fiction thing. He's doing 57 00:03:06,880 --> 00:03:07,720 Speaker 1: the engineering thing. 58 00:03:08,160 --> 00:03:11,080 Speaker 2: Joshua says. Doing the engineering thing has been at the 59 00:03:11,120 --> 00:03:13,360 Speaker 2: core of Nvidia's business since the beginning. 60 00:03:13,600 --> 00:03:16,920 Speaker 1: So the founders thought that they saw a niche for 61 00:03:17,000 --> 00:03:20,360 Speaker 1: making a specific kind of chip that would be really 62 00:03:20,360 --> 00:03:24,600 Speaker 1: good at rendering graphics, primarily for video games. The idea 63 00:03:24,720 --> 00:03:26,639 Speaker 1: was that there's general use chips, but if we focus 64 00:03:26,639 --> 00:03:28,800 Speaker 1: on this one use, we can find a good niche 65 00:03:28,800 --> 00:03:31,040 Speaker 1: and maybe we can you know, make a couple of 66 00:03:31,080 --> 00:03:32,200 Speaker 1: tens of millions of dollars. 67 00:03:32,320 --> 00:03:34,359 Speaker 2: And it was in pursuit of that goal that in 68 00:03:34,440 --> 00:03:38,040 Speaker 2: Vidia develop was a key breakthrough in chip technology. 69 00:03:38,160 --> 00:03:41,480 Speaker 1: The basic way to put this is that Intel makes 70 00:03:41,560 --> 00:03:45,280 Speaker 1: chips that are general use chips that are very powerful 71 00:03:45,600 --> 00:03:49,000 Speaker 1: and that do things sequentially. They do a task by 72 00:03:49,000 --> 00:03:51,080 Speaker 1: doing one part of the task, then the next part 73 00:03:51,080 --> 00:03:54,200 Speaker 1: of the task. In Vidia's chips take a task, they 74 00:03:54,280 --> 00:03:55,960 Speaker 1: break it up into a bunch of little parts, and 75 00:03:56,000 --> 00:03:58,600 Speaker 1: they do it all at once. There's a forthcoming book 76 00:03:58,640 --> 00:04:01,040 Speaker 1: about in Vidia where they describe it as instead of 77 00:04:01,080 --> 00:04:04,080 Speaker 1: having a truck that drives around the city making deliveries, 78 00:04:04,560 --> 00:04:08,040 Speaker 1: they buy a bunch of motorcycles. Each motorcycle carries one package, 79 00:04:08,080 --> 00:04:10,520 Speaker 1: and they go to everyone's house all at once. So 80 00:04:11,240 --> 00:04:13,520 Speaker 1: that turns out to be a very good way to 81 00:04:13,680 --> 00:04:18,200 Speaker 1: do modern AI, which is based on these things called 82 00:04:18,200 --> 00:04:22,080 Speaker 1: neural networks, which pass information through a whole bunch of 83 00:04:22,120 --> 00:04:23,240 Speaker 1: nodes in parallel. 84 00:04:23,560 --> 00:04:26,359 Speaker 2: But the wider use and mass market appeal of Nvidia 85 00:04:26,680 --> 00:04:30,000 Speaker 2: was still years away, and Joshua says the company went 86 00:04:30,040 --> 00:04:32,320 Speaker 2: through some rocky times, like. 87 00:04:32,279 --> 00:04:36,640 Speaker 1: A lot of companies in the semiconductor industry. It went 88 00:04:36,640 --> 00:04:39,320 Speaker 1: through a series of existential crises. It would release a chip, 89 00:04:39,320 --> 00:04:40,920 Speaker 1: there'll be something wrong with it. It almost went out of 90 00:04:40,920 --> 00:04:43,840 Speaker 1: business several times early in its existence. This is part 91 00:04:43,839 --> 00:04:47,719 Speaker 1: of company lore. Jensen likes to say that over always 92 00:04:47,720 --> 00:04:49,560 Speaker 1: thirty days from going out of business. 93 00:04:49,440 --> 00:04:52,640 Speaker 2: In Video sought to widen its customer base. The goal, 94 00:04:52,720 --> 00:04:55,919 Speaker 2: Joshua says, was to take its video game graphics cards, 95 00:04:56,040 --> 00:04:59,160 Speaker 2: which were getting faster and faster, and sell them to 96 00:04:59,279 --> 00:05:00,719 Speaker 2: non video game clients. 97 00:05:01,120 --> 00:05:04,000 Speaker 1: The problem was doing this required making the chips more expensive, 98 00:05:04,360 --> 00:05:06,920 Speaker 1: so it started doing things that made its core product 99 00:05:06,920 --> 00:05:09,520 Speaker 1: worse in the hopes that someone would find some use 100 00:05:09,560 --> 00:05:10,000 Speaker 1: for it. 101 00:05:10,040 --> 00:05:14,320 Speaker 2: And eventually someone did. In Nvidia built its own computer 102 00:05:14,440 --> 00:05:18,400 Speaker 2: language called Pudha, which allowed developers to maximize the potential 103 00:05:18,400 --> 00:05:21,640 Speaker 2: of their GPU chips, and in Vidia's coding language and 104 00:05:21,680 --> 00:05:25,240 Speaker 2: in Vidia's chips became the standard hardware for early pioneers 105 00:05:25,240 --> 00:05:28,520 Speaker 2: of what's called deep learning. That's a branch of computer 106 00:05:28,640 --> 00:05:31,880 Speaker 2: science that relies on those neural networks modeled after the 107 00:05:31,880 --> 00:05:35,560 Speaker 2: neurons in the human brain. Early on, Joshua says, the 108 00:05:35,600 --> 00:05:39,760 Speaker 2: commercial applications of deep learning weren't yet clear, but Nvidia 109 00:05:40,200 --> 00:05:41,480 Speaker 2: saw an opportunity. 110 00:05:41,520 --> 00:05:46,440 Speaker 1: It just happened that in Vidia saw the seeds of 111 00:05:46,960 --> 00:05:51,440 Speaker 1: its product being good for this use and really pursued 112 00:05:51,440 --> 00:05:54,080 Speaker 1: it from a very early point and was very effective 113 00:05:54,080 --> 00:05:55,280 Speaker 1: at building this technology. 114 00:05:55,560 --> 00:05:59,760 Speaker 2: Then OpenAI's chat GPT took the world by storm, and 115 00:05:59,800 --> 00:06:03,200 Speaker 2: so suddenly every player looking to invest in AI was 116 00:06:03,240 --> 00:06:05,440 Speaker 2: in need of Nvidia's chips. 117 00:06:05,680 --> 00:06:09,640 Speaker 1: In Nvidia now finds itself having the thing, maybe not 118 00:06:09,760 --> 00:06:12,200 Speaker 1: that customers want the most, but it has a thing 119 00:06:12,279 --> 00:06:15,640 Speaker 1: that Meta wants the most, Google wants the most, Microsoft 120 00:06:15,680 --> 00:06:17,960 Speaker 1: wants the most, and that puts it in an incredibly 121 00:06:18,040 --> 00:06:18,880 Speaker 1: powerful position. 122 00:06:19,480 --> 00:06:22,120 Speaker 2: For the last few years, the tech Titan seems certain 123 00:06:22,200 --> 00:06:24,799 Speaker 2: that the AI future would be powered by massive data 124 00:06:24,839 --> 00:06:28,479 Speaker 2: centers filled with Nvidia chips, and the Frenzy showed just 125 00:06:28,520 --> 00:06:33,000 Speaker 2: how quickly the fortunes of semiconductor manufacturers can change. At 126 00:06:33,000 --> 00:06:36,160 Speaker 2: the start of twenty twenty, Intel's market cap was roughly 127 00:06:36,240 --> 00:06:40,080 Speaker 2: twice the size of Nvidia. Today, in Vidia is around 128 00:06:40,120 --> 00:06:44,120 Speaker 2: thirty times more valuable, and Joshua says the precariousness of 129 00:06:44,200 --> 00:06:46,880 Speaker 2: being on the cutting edge of tech is something that 130 00:06:47,000 --> 00:06:51,360 Speaker 2: weighs heavily on Nvidia's CEO, especially after the deep Seek 131 00:06:51,440 --> 00:06:52,600 Speaker 2: route in late January. 132 00:06:52,960 --> 00:06:57,120 Speaker 1: There's some jitteriness now, not only within video but throughout 133 00:06:57,120 --> 00:07:00,240 Speaker 1: the economy that like, are we reaching the end of 134 00:07:00,279 --> 00:07:02,600 Speaker 1: this stage of the AI boom? I mean, people who 135 00:07:02,600 --> 00:07:04,400 Speaker 1: are pessimized it will tell you we're reaching the point 136 00:07:04,400 --> 00:07:07,000 Speaker 1: where the bubble pops. Other people will say it's time 137 00:07:07,040 --> 00:07:10,920 Speaker 1: to move on from building the foundation to actually creating 138 00:07:11,400 --> 00:07:16,360 Speaker 1: something more than that. And because expectations are so high, 139 00:07:16,960 --> 00:07:19,440 Speaker 1: if this isn't a really really big success, even if 140 00:07:19,440 --> 00:07:22,120 Speaker 1: it's just like a pretty good success, then in Vidia 141 00:07:22,200 --> 00:07:24,840 Speaker 1: could be in kind of a tricky spot. And that's 142 00:07:24,880 --> 00:07:26,800 Speaker 1: sort of the water that it's navigating right now. 143 00:07:27,000 --> 00:07:30,400 Speaker 2: Joshua Sayshuog is now scrambling to find new customers and 144 00:07:30,520 --> 00:07:32,280 Speaker 2: markets for Nvidia's technology. 145 00:07:32,720 --> 00:07:35,600 Speaker 1: His plan for doing that is to do more than 146 00:07:35,760 --> 00:07:38,640 Speaker 1: just make the hardware that everyone builds their tech on 147 00:07:38,680 --> 00:07:41,360 Speaker 1: top of, but to actually be involved in building a 148 00:07:41,360 --> 00:07:45,440 Speaker 1: little bit more of that tech, both which brings that 149 00:07:45,480 --> 00:07:49,360 Speaker 1: tech along faster, increasing the demand for the company, and 150 00:07:49,480 --> 00:07:52,120 Speaker 1: which makes it harder to get rid of Nvidia and 151 00:07:52,200 --> 00:07:53,120 Speaker 1: go with something else. 152 00:07:56,200 --> 00:07:59,560 Speaker 2: So what is the company planning for its future? That is, 153 00:07:59,600 --> 00:08:09,160 Speaker 2: after the rate, Even though in Vidia is the world's 154 00:08:09,160 --> 00:08:13,360 Speaker 2: most valuable chip maker. It's CEO Jensen Huang is always 155 00:08:13,440 --> 00:08:17,960 Speaker 2: selling as he was back in January Welcome to CUS 156 00:08:18,520 --> 00:08:22,520 Speaker 2: at the industry's biggest trade show in his trademark leather jacket. 157 00:08:23,000 --> 00:08:28,000 Speaker 2: Do you like my jacket? Blimber's Joshua Brustein says one 158 00:08:28,040 --> 00:08:30,440 Speaker 2: of the markets Swang is trying to develop is what 159 00:08:30,520 --> 00:08:33,640 Speaker 2: he refers to as physical AI, and. 160 00:08:33,559 --> 00:08:36,640 Speaker 1: That's basically just instead of chatbots that operate on the Internet, 161 00:08:36,720 --> 00:08:38,360 Speaker 1: it's stuff that operates in the real world. 162 00:08:38,520 --> 00:08:42,280 Speaker 3: With this physical AI, there are many downstream things that 163 00:08:42,320 --> 00:08:44,360 Speaker 3: we could do as a result. 164 00:08:44,400 --> 00:08:48,840 Speaker 1: We could do synthetic so you have robotics factories that 165 00:08:48,880 --> 00:08:52,240 Speaker 1: would be automated self driving cars. Obviously, that's been a 166 00:08:52,240 --> 00:08:56,079 Speaker 1: big thing for Nvidia for about a decade, and if 167 00:08:56,120 --> 00:08:58,800 Speaker 1: they can get all of those things to happen sooner, 168 00:08:59,120 --> 00:09:01,640 Speaker 1: then those are enormous markets that they can. 169 00:09:01,600 --> 00:09:05,880 Speaker 2: Unlock, Joshua says. Another big strategy is developing new software 170 00:09:06,040 --> 00:09:09,400 Speaker 2: technology in Vidia could sell not only to their existing customers, 171 00:09:09,640 --> 00:09:12,320 Speaker 2: but tech that also could be used to unlock AI 172 00:09:12,360 --> 00:09:16,040 Speaker 2: applications in some of the world's biggest industries, like healthcare. 173 00:09:16,280 --> 00:09:20,000 Speaker 1: That would be not only hospital systems but also pharmaceutical 174 00:09:20,040 --> 00:09:23,839 Speaker 1: companies doing drug discovery, maybe robotic surgery, but also just 175 00:09:23,920 --> 00:09:25,760 Speaker 1: kind of the administration of hospitals. 176 00:09:26,040 --> 00:09:29,599 Speaker 2: Joshua Siswang is pursuing this with a lot of urgency. 177 00:09:29,760 --> 00:09:33,520 Speaker 1: So what he's doing now is really trying to almost 178 00:09:33,760 --> 00:09:37,240 Speaker 1: will into existence all these AI applications that you'll hear 179 00:09:37,320 --> 00:09:40,360 Speaker 1: people say are coming soon, are coming down the road. 180 00:09:40,720 --> 00:09:42,440 Speaker 1: He wants those things to come in six months, not 181 00:09:42,520 --> 00:09:43,360 Speaker 1: in seven years. 182 00:09:43,440 --> 00:09:46,920 Speaker 2: It's not just will, it's also money, Joshua says. In 183 00:09:47,000 --> 00:09:49,679 Speaker 2: Vidia is investing in all kinds of companies that are 184 00:09:49,679 --> 00:09:52,600 Speaker 2: trying to realize new commercial applications for AI. 185 00:09:53,120 --> 00:09:54,800 Speaker 1: Not so much in the hopes that they'll invest in 186 00:09:54,800 --> 00:09:56,800 Speaker 1: a startup that's now worth a million dollars that will 187 00:09:56,800 --> 00:09:58,920 Speaker 1: be worth a billion dollars, although they wouldn't mind that great, 188 00:10:00,120 --> 00:10:02,760 Speaker 1: but that they want to make sure that they are 189 00:10:03,520 --> 00:10:06,599 Speaker 1: having as many irons in the fire of future in 190 00:10:06,720 --> 00:10:10,080 Speaker 1: Vidia customers, Like they just want a startup that is 191 00:10:10,080 --> 00:10:12,400 Speaker 1: now small to be an enormous in video customer, and 192 00:10:12,480 --> 00:10:15,960 Speaker 1: so they invest in and work with lots of startups 193 00:10:15,960 --> 00:10:20,080 Speaker 1: in many different fields to try to form those relationships 194 00:10:20,080 --> 00:10:22,720 Speaker 1: and to have them like working on in video hardware 195 00:10:22,760 --> 00:10:23,880 Speaker 1: from the very beginning. 196 00:10:23,800 --> 00:10:27,760 Speaker 2: You mentioned that in Vidia makes its own software. It's 197 00:10:27,760 --> 00:10:31,040 Speaker 2: not just making chips, but it's serving dips. 198 00:10:31,520 --> 00:10:32,640 Speaker 1: There are kind of other. 199 00:10:32,520 --> 00:10:35,120 Speaker 2: Things augmenting the kind of core of its business model. 200 00:10:35,840 --> 00:10:37,600 Speaker 2: What is the appetite so much as we know it 201 00:10:37,679 --> 00:10:39,760 Speaker 2: for that other stuff. Are we at a point at 202 00:10:39,800 --> 00:10:44,800 Speaker 2: which tech companies AI companies really just want the chips 203 00:10:45,080 --> 00:10:48,920 Speaker 2: they don't really care about that stuff. Or is the 204 00:10:49,000 --> 00:10:51,680 Speaker 2: other stuff that in video makes appealing to them? Do 205 00:10:51,720 --> 00:10:53,680 Speaker 2: they need it? Do they want it? Is that catching on? 206 00:10:54,160 --> 00:10:56,600 Speaker 1: I think that that question is like a core dynamic 207 00:10:56,640 --> 00:10:59,880 Speaker 1: to watch within video right now. There's a metaphor that 208 00:11:00,080 --> 00:11:02,000 Speaker 1: computer scientists use a lot, which is like a stack 209 00:11:02,040 --> 00:11:04,000 Speaker 1: of technologies. You start at the bottom, that's the chips, 210 00:11:04,040 --> 00:11:05,600 Speaker 1: all the way at the top is like whatever software 211 00:11:05,640 --> 00:11:09,240 Speaker 1: the user's using, and in videos, building upwards on the 212 00:11:09,280 --> 00:11:12,000 Speaker 1: stack and talking to a lot of startups who are 213 00:11:12,000 --> 00:11:14,679 Speaker 1: in video customers, the enthusiasm starts very high at the 214 00:11:14,720 --> 00:11:17,280 Speaker 1: bottom and diminishes as they go up. So they really 215 00:11:17,280 --> 00:11:19,520 Speaker 1: want the chips, and like the software that makes the 216 00:11:19,600 --> 00:11:22,200 Speaker 1: chips do the basic functions they're really into. When it 217 00:11:22,240 --> 00:11:26,600 Speaker 1: gets to like the NVIDIAI models, they'll kind of politely 218 00:11:26,640 --> 00:11:28,920 Speaker 1: acknowledge that in video makes those but like they kind 219 00:11:28,960 --> 00:11:31,360 Speaker 1: of wouldn't build that stuff, So it's hard to tell 220 00:11:31,360 --> 00:11:33,839 Speaker 1: where that line is. And in video will say, well, 221 00:11:33,840 --> 00:11:35,720 Speaker 1: we're happy as long as they're you know, like we 222 00:11:35,760 --> 00:11:38,160 Speaker 1: want them to use whatever is useful to them. But 223 00:11:38,160 --> 00:11:40,560 Speaker 1: they're also aggressively trying to sell you the whole computer. 224 00:11:40,760 --> 00:11:42,959 Speaker 1: So it's interesting to watch how that plays out. 225 00:11:43,480 --> 00:11:45,240 Speaker 2: I think in VideA is a proxy for a lot 226 00:11:45,240 --> 00:11:48,600 Speaker 2: of people for AI generally. I think for investors, it's 227 00:11:48,600 --> 00:11:51,040 Speaker 2: a way to get into this this sector, this part 228 00:11:51,080 --> 00:11:54,960 Speaker 2: of the tech sector. What is its success or failure 229 00:11:55,000 --> 00:11:58,160 Speaker 2: navigating say these next twelve months going to say about 230 00:11:58,520 --> 00:12:01,000 Speaker 2: the industry more broadly, it's in other words, how good 231 00:12:01,040 --> 00:12:03,199 Speaker 2: is it as a proxy for where AI is and 232 00:12:03,200 --> 00:12:03,800 Speaker 2: where it's heading. 233 00:12:04,720 --> 00:12:08,480 Speaker 1: So I think that on the one end, if you 234 00:12:09,120 --> 00:12:14,520 Speaker 1: saw like a real retrenchment in the construction of data centers, 235 00:12:14,840 --> 00:12:18,120 Speaker 1: a real lack of demand in video chips, then that 236 00:12:18,120 --> 00:12:20,480 Speaker 1: would be really bad for in video. It'd be bad 237 00:12:20,480 --> 00:12:22,080 Speaker 1: for video share price and all those investors who are 238 00:12:22,120 --> 00:12:23,600 Speaker 1: hoping to get in AI, and it would indicate that 239 00:12:23,640 --> 00:12:26,959 Speaker 1: maybe AI has been overhyped over the last couple of years. 240 00:12:27,520 --> 00:12:30,960 Speaker 1: On the other end, you could see full speed ahead, 241 00:12:31,040 --> 00:12:33,040 Speaker 1: all of these things are getting built. In video is 242 00:12:33,040 --> 00:12:35,760 Speaker 1: still doing really well, and there would still be the 243 00:12:35,840 --> 00:12:38,400 Speaker 1: question of when we get past the building out phase, 244 00:12:38,920 --> 00:12:42,120 Speaker 1: what happens next? And I think even in video will 245 00:12:42,160 --> 00:12:43,520 Speaker 1: tell you like, that's not going to We're not going 246 00:12:43,600 --> 00:12:46,000 Speaker 1: to know that in twelve months. It will tell you 247 00:12:46,080 --> 00:12:49,920 Speaker 1: that it will be everywhere, but the world will change 248 00:12:49,960 --> 00:12:54,160 Speaker 1: more slowly than maybe the next year, and so you'll 249 00:12:54,160 --> 00:12:59,000 Speaker 1: have to just continually watch, like is AI progressing, and 250 00:12:59,080 --> 00:13:01,320 Speaker 1: if it is in video will likely be in a 251 00:13:01,360 --> 00:13:05,000 Speaker 1: central role, and if it's not, then you know, we 252 00:13:05,040 --> 00:13:06,520 Speaker 1: may have to reassess some things. 253 00:13:07,160 --> 00:13:08,840 Speaker 2: Is there a moment? Are there going to be moments 254 00:13:08,840 --> 00:13:11,480 Speaker 2: in the near future when we're likely to learn more 255 00:13:11,880 --> 00:13:13,000 Speaker 2: about what he has planned? 256 00:13:13,520 --> 00:13:16,000 Speaker 1: Yeah? One is coming up actually next week and Vidia 257 00:13:16,120 --> 00:13:18,679 Speaker 1: has an annual conference in which it sort of takes 258 00:13:18,679 --> 00:13:23,160 Speaker 1: over downtown San Jose. Almost a thousand other tech companies 259 00:13:23,200 --> 00:13:25,640 Speaker 1: come and present how they interact with Nvidia. Jensen will 260 00:13:25,640 --> 00:13:28,120 Speaker 1: give a big keynote in which he will lay out 261 00:13:28,240 --> 00:13:30,520 Speaker 1: the vision. I'm sure we'll hear a lot about his 262 00:13:30,559 --> 00:13:35,680 Speaker 1: pet projects at that point, and given the potential moment 263 00:13:35,679 --> 00:13:39,480 Speaker 1: of inflection. We're in for AI. Generally, it will be 264 00:13:39,520 --> 00:13:41,960 Speaker 1: an interesting time to kind of take in video's temperature 265 00:13:42,000 --> 00:13:44,760 Speaker 1: and see how the company responds to what could be 266 00:13:44,800 --> 00:13:45,439 Speaker 1: a tricky moment. 267 00:13:45,679 --> 00:13:48,360 Speaker 2: He'll don the leather jacket and yes, always done to 268 00:13:48,360 --> 00:13:49,120 Speaker 2: the leather jacket. 269 00:13:49,840 --> 00:13:51,760 Speaker 1: If he has on a different jacket, then we'll know 270 00:13:51,840 --> 00:13:52,920 Speaker 1: something is big and changed. 271 00:13:56,400 --> 00:13:58,839 Speaker 2: This is the Big Take from Bloomberg News. I'm David Gera. 272 00:13:59,280 --> 00:14:02,000 Speaker 2: This episode is produced by Alex Tye and David Fox. 273 00:14:02,360 --> 00:14:05,360 Speaker 2: It was edited by Tracy Samuelson, Mark Million, and our 274 00:14:05,400 --> 00:14:08,560 Speaker 2: senior editor, Elizabeth Ponso. It was fact checked by our 275 00:14:08,679 --> 00:14:11,720 Speaker 2: editorial team and mixed and sound designed by Alex Sigura. 276 00:14:12,200 --> 00:14:15,439 Speaker 2: Our senior producer is Naomi Shaven. Our executive producer is 277 00:14:15,520 --> 00:14:18,920 Speaker 2: Nicole Beamster. Board Sage Bauman is Bloomberg's head of Podcasts. 278 00:14:19,440 --> 00:14:21,520 Speaker 2: If you liked this episode, make sure to subscribe and 279 00:14:21,560 --> 00:14:24,120 Speaker 2: review The Big Take wherever you listen to podcasts. It 280 00:14:24,120 --> 00:14:26,840 Speaker 2: helps people find the show. Thanks for listening. We'll be 281 00:14:26,880 --> 00:14:27,520 Speaker 2: back next week.