1 00:00:02,480 --> 00:00:13,240 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Welcome to the Daybreak 2 00:00:13,280 --> 00:00:16,920 Speaker 1: Asia podcast. I'm Doug Krisner. We begin with the simmering 3 00:00:17,000 --> 00:00:20,400 Speaker 1: tensions between China and Japan, the trace back to late 4 00:00:20,480 --> 00:00:24,520 Speaker 1: last year with comments from Japanese Prime Minister Sana atuki 5 00:00:24,520 --> 00:00:28,120 Speaker 1: Ichi regarding Taiwan. For a closer look, I'm joined by 6 00:00:28,160 --> 00:00:32,479 Speaker 1: Bloomberg's Garfield Reynolds. He is team leader for Asia Markets 7 00:00:32,479 --> 00:00:36,400 Speaker 1: live on the Bloomberg terminal. Garfield joins from Sydney. Thank 8 00:00:36,440 --> 00:00:38,680 Speaker 1: you so much for being with us. Happy New Year 9 00:00:38,720 --> 00:00:41,600 Speaker 1: to you. Can you begin by giving me a little 10 00:00:41,600 --> 00:00:46,120 Speaker 1: bit of understanding the context of how we got here 11 00:00:46,200 --> 00:00:50,320 Speaker 1: with relation to the tension between China and Japan. 12 00:00:51,360 --> 00:00:55,200 Speaker 2: Yeah, Well, how we got here is that Japanese Prime 13 00:00:55,200 --> 00:01:00,600 Speaker 2: Minister Sana Takaichi, soon after she won her way to 14 00:01:00,680 --> 00:01:07,160 Speaker 2: offers through a parliamentary vote, commented about Taiwan and the 15 00:01:07,240 --> 00:01:12,160 Speaker 2: idea that Japan might be willing to play a role 16 00:01:12,600 --> 00:01:18,440 Speaker 2: in as matters developed there, and that brought on an 17 00:01:18,480 --> 00:01:24,839 Speaker 2: immediate response from China. You're recalling that, of course China. 18 00:01:25,120 --> 00:01:30,559 Speaker 2: China's official stance is that Taiwan is part of China 19 00:01:30,760 --> 00:01:33,840 Speaker 2: and will always be that it's not in any ways 20 00:01:33,880 --> 00:01:36,920 Speaker 2: a separate entity. It has no sovereignty, and of course 21 00:01:36,959 --> 00:01:40,840 Speaker 2: these days there are very few countries that recognize Taiwan 22 00:01:40,959 --> 00:01:44,960 Speaker 2: is a sovereign nation. So that's how we got here, 23 00:01:45,760 --> 00:01:50,240 Speaker 2: and to some extent, the heat came out of the issue. 24 00:01:50,600 --> 00:01:55,760 Speaker 2: But now China's come out with this again, and this 25 00:01:55,960 --> 00:02:00,560 Speaker 2: ramps up what's been going on, and that that's part 26 00:02:00,560 --> 00:02:02,640 Speaker 2: of the reason, a big part of the reason, I 27 00:02:02,680 --> 00:02:06,560 Speaker 2: would argue, why we're seeing Japanese shares quoted lower today, 28 00:02:07,080 --> 00:02:12,359 Speaker 2: even as Asian shares elsewhere, in particular South Korea continue 29 00:02:12,440 --> 00:02:16,760 Speaker 2: what has been a really stunning start through the year 30 00:02:17,040 --> 00:02:18,799 Speaker 2: for equities here in the region. 31 00:02:19,160 --> 00:02:22,200 Speaker 1: I was struck by the fact that Beijing recently banned 32 00:02:22,200 --> 00:02:26,520 Speaker 1: exports of what they are calling dual use goods to 33 00:02:26,600 --> 00:02:30,240 Speaker 1: the Japanese military. So there seems to be implicit in 34 00:02:30,280 --> 00:02:32,320 Speaker 1: all of this the real risk of some type of 35 00:02:32,639 --> 00:02:35,440 Speaker 1: confrontation or is it merely saber rattling do you think? 36 00:02:37,919 --> 00:02:41,679 Speaker 2: I think it's more saber rattling, and very much Beijing 37 00:02:43,919 --> 00:02:45,919 Speaker 2: seems to be acting on the basis that it doesn't 38 00:02:46,080 --> 00:02:51,880 Speaker 2: need to sort of directly physically as it were, threatened Japan, 39 00:02:52,480 --> 00:02:57,000 Speaker 2: rather it can hit Japan through its economy. Last year, 40 00:02:57,680 --> 00:03:04,639 Speaker 2: it discourages Chinese tourists from going to Japan, and that 41 00:03:04,800 --> 00:03:08,959 Speaker 2: was definitely something that could do some damage to Japan's economy. 42 00:03:09,560 --> 00:03:14,480 Speaker 2: We saw a number of tourism and also retail related 43 00:03:14,639 --> 00:03:19,400 Speaker 2: equities in Japan underperform, precisely because of that. 44 00:03:19,480 --> 00:03:22,280 Speaker 1: You mentioned a moment Ago Garfield, the positivity that we're 45 00:03:22,320 --> 00:03:25,120 Speaker 1: seeing in the South Korean equity market, I would be 46 00:03:25,200 --> 00:03:27,560 Speaker 1: remiss if we didn't kind of come back to the 47 00:03:27,600 --> 00:03:32,520 Speaker 1: meeting between Chinese President Chi Jinping and South Korean leader 48 00:03:32,720 --> 00:03:37,600 Speaker 1: Lee j Jung in Beijing. Helped me understand the importance 49 00:03:37,640 --> 00:03:40,080 Speaker 1: of this meeting and how it may be reflected in 50 00:03:40,120 --> 00:03:42,360 Speaker 1: some of the price action that we're seeing in markets. 51 00:03:43,160 --> 00:03:48,200 Speaker 2: South Korea's new president, relatively newly minted president, took over 52 00:03:48,320 --> 00:03:52,600 Speaker 2: after the previous president was ousted over what was seen 53 00:03:52,640 --> 00:03:57,280 Speaker 2: as some authoritarian moves there. So the new president's been 54 00:03:57,280 --> 00:04:02,240 Speaker 2: doing a pretty decent job of navigating some difficult waters 55 00:04:02,320 --> 00:04:05,600 Speaker 2: between the US on one side and China on the other. 56 00:04:06,240 --> 00:04:08,680 Speaker 2: On the one hand, the US is a lot term 57 00:04:08,720 --> 00:04:14,920 Speaker 2: ally of South Korea, and yet President Trump had imposed tariffs, 58 00:04:15,440 --> 00:04:18,919 Speaker 2: complained about the trade surplus, so Korea mostly seemed to 59 00:04:18,960 --> 00:04:22,280 Speaker 2: have gotten him on side. Meantime, they've also been working 60 00:04:22,320 --> 00:04:25,560 Speaker 2: hard to get China back on side. There was a 61 00:04:25,680 --> 00:04:29,839 Speaker 2: Chinese I think there still is a Chinese ban on 62 00:04:30,240 --> 00:04:36,400 Speaker 2: some cultural exports as it were from Korea movies, videos, music, 63 00:04:36,920 --> 00:04:40,320 Speaker 2: which doesn't necessarily do a lot of economic damage, but 64 00:04:40,880 --> 00:04:46,040 Speaker 2: is not a good look from the Korean's point of view. 65 00:04:46,120 --> 00:04:50,400 Speaker 2: So they're very eager to work with China to get 66 00:04:50,440 --> 00:04:55,120 Speaker 2: them on side along with the US. And although there's 67 00:04:55,160 --> 00:04:59,799 Speaker 2: no sort of direct idea that Korean shares are rallying 68 00:05:00,000 --> 00:05:04,440 Speaker 2: because of china diplomatic efforts, there is an argument to 69 00:05:04,480 --> 00:05:07,799 Speaker 2: be made that part of what's helping South Korean equities 70 00:05:07,800 --> 00:05:12,480 Speaker 2: outperform is that they are seen as being a strong 71 00:05:13,160 --> 00:05:19,440 Speaker 2: AI slash tech play that is not necessarily tied to 72 00:05:19,640 --> 00:05:26,560 Speaker 2: one or other of the US AI slash LLM streams. 73 00:05:26,839 --> 00:05:31,680 Speaker 2: So in Nvidia versus Google, as it were, architecture, there, 74 00:05:32,200 --> 00:05:36,039 Speaker 2: South Korean companies plug into both. They also have some 75 00:05:36,160 --> 00:05:40,159 Speaker 2: exposure to China's AI boom and what's going on with 76 00:05:40,240 --> 00:05:45,560 Speaker 2: deep Seek, so we're seeing the costs be dramatically outperformed 77 00:05:45,680 --> 00:05:51,600 Speaker 2: US equities as the AI related boom for global stocks extends. 78 00:05:51,960 --> 00:05:54,080 Speaker 1: I want to change gears. Garfield, talk a little bit 79 00:05:54,120 --> 00:05:57,840 Speaker 1: about the oil market today. WTI crude oil right now 80 00:05:57,920 --> 00:06:00,800 Speaker 1: is down more than one point eight percent, and in 81 00:06:00,839 --> 00:06:05,360 Speaker 1: the US Tuesday evening, President Trump said that interim authorities 82 00:06:05,400 --> 00:06:09,760 Speaker 1: in Venezuela will be turning over between thirty to fifty 83 00:06:09,839 --> 00:06:14,800 Speaker 1: million barrels of high qualitied sanctioned oil to the United States. Now, 84 00:06:14,839 --> 00:06:17,359 Speaker 1: we know that China is a big probably the biggest 85 00:06:17,480 --> 00:06:20,560 Speaker 1: consumer of Venezuelan and crude. Talk to me a little 86 00:06:20,560 --> 00:06:24,000 Speaker 1: bit about what the reaction has been to the developments 87 00:06:24,000 --> 00:06:27,240 Speaker 1: that we have seen over the last several days in Venezuela, 88 00:06:27,279 --> 00:06:30,960 Speaker 1: and whether or not the US involvement now in the 89 00:06:30,960 --> 00:06:35,760 Speaker 1: crude oil industry in Venezuela could be a significant issue 90 00:06:35,800 --> 00:06:39,840 Speaker 1: for the Chinese. 91 00:06:38,160 --> 00:06:42,400 Speaker 2: Impact for the crude markets of what's gone in Venezuela. 92 00:06:42,800 --> 00:06:46,440 Speaker 2: What's gone on in Venezuela should be a lower pricing 93 00:06:46,520 --> 00:06:52,240 Speaker 2: crude and a lack of any scope for serious rallies, 94 00:06:52,440 --> 00:06:55,919 Speaker 2: sustained rallies because whatever may or may not go on 95 00:06:56,040 --> 00:06:59,960 Speaker 2: and reviving Venezuela's oil industry would take plenty of time 96 00:07:00,120 --> 00:07:03,760 Speaker 2: and money. The simple fact that you have a bias 97 00:07:03,800 --> 00:07:07,359 Speaker 2: towards more supplies coming out of Venezuela than the trickle 98 00:07:07,720 --> 00:07:11,800 Speaker 2: that has been coming recently. That adds to the potential 99 00:07:11,800 --> 00:07:17,040 Speaker 2: for a supply glut, So lower crude prices globally is 100 00:07:17,120 --> 00:07:19,400 Speaker 2: part of what you would expect with the way the 101 00:07:19,480 --> 00:07:22,600 Speaker 2: Venezuelan situation has been developing. From the point of view 102 00:07:22,640 --> 00:07:25,560 Speaker 2: of what that means for China, on the one hand, 103 00:07:25,880 --> 00:07:29,240 Speaker 2: it has been the biggest buyer of Venezuelan crude and 104 00:07:29,400 --> 00:07:33,760 Speaker 2: it's Venezuela's biggest creditor, so it's a little surprise that 105 00:07:33,880 --> 00:07:38,240 Speaker 2: China was very critical of the US moves in Venezuela. 106 00:07:38,360 --> 00:07:43,840 Speaker 2: But it is noticeable that there hasn't been any really 107 00:07:44,200 --> 00:07:49,640 Speaker 2: heated rhetoric or moves out of China. There's been no 108 00:07:49,680 --> 00:07:53,520 Speaker 2: sense that, for example, they might impose breash restrictions on 109 00:07:53,640 --> 00:07:56,240 Speaker 2: exports to the US, such as those they've just toweded 110 00:07:56,240 --> 00:07:59,920 Speaker 2: for Japan. So that signal was a couple of things. 111 00:08:00,000 --> 00:08:02,160 Speaker 2: I think one is that, of course China can get 112 00:08:02,280 --> 00:08:08,520 Speaker 2: crued from elsewhere, and if global crude prices are falling, 113 00:08:09,200 --> 00:08:12,920 Speaker 2: that means that the cost for China to get that 114 00:08:13,080 --> 00:08:17,880 Speaker 2: crude is not going to be significantly up, and China 115 00:08:17,920 --> 00:08:22,200 Speaker 2: had also been stockpiling some crude, so I think the 116 00:08:22,280 --> 00:08:26,640 Speaker 2: hit to China might not be too huge. China also, 117 00:08:26,680 --> 00:08:32,559 Speaker 2: of course, has famously boosted its usage of renewables, precisely 118 00:08:32,800 --> 00:08:37,480 Speaker 2: because that's a way of increasing its energy independence. So 119 00:08:37,840 --> 00:08:41,000 Speaker 2: there is a potential modest hit to China from what 120 00:08:41,040 --> 00:08:42,960 Speaker 2: the US is doing on the as far as ven 121 00:08:43,000 --> 00:08:45,760 Speaker 2: as well as crude goes, but as I said, probably 122 00:08:45,880 --> 00:08:49,640 Speaker 2: just a modest one. Of course, it also has no 123 00:08:49,720 --> 00:08:52,600 Speaker 2: problems getting crued out of Russia, so that could be 124 00:08:52,600 --> 00:08:55,880 Speaker 2: a help. And there's also from the point of view 125 00:08:55,920 --> 00:08:59,760 Speaker 2: of China coming back to where we started with regards 126 00:08:59,760 --> 00:09:05,679 Speaker 2: to Taiwan. China does want very much to ultimately absorb Taiwan. 127 00:09:06,240 --> 00:09:08,240 Speaker 2: And if it looks at what the US has done 128 00:09:08,800 --> 00:09:13,040 Speaker 2: in saying, well, the western hemisphere, Monroe doctrine says that 129 00:09:13,120 --> 00:09:16,040 Speaker 2: the western hemisphere is the US's backyard, and we get 130 00:09:16,080 --> 00:09:20,120 Speaker 2: to say what happens here all China that turns around 131 00:09:20,160 --> 00:09:23,679 Speaker 2: and says, we agree with this sort of thing in fact, 132 00:09:24,080 --> 00:09:26,120 Speaker 2: or you know, you certainly can't tell us to lay 133 00:09:26,160 --> 00:09:33,640 Speaker 2: off Taiwan when you're busy, you know, running roughshod over Venezuela. 134 00:09:34,040 --> 00:09:36,760 Speaker 2: I wouldn't expand it affect China to do anything as 135 00:09:36,840 --> 00:09:40,360 Speaker 2: dramatic as what's been occurring the last couple of weeks 136 00:09:40,440 --> 00:09:47,040 Speaker 2: or so with Venezuela, but it does enhance China's capacity 137 00:09:47,120 --> 00:09:49,600 Speaker 2: to put pressure on Taiwan and to see a path 138 00:09:49,640 --> 00:09:56,000 Speaker 2: toward at some stage absorbing it. So any pain from 139 00:09:56,040 --> 00:10:00,880 Speaker 2: the Venezuelan crude side of things, it might be well 140 00:10:00,880 --> 00:10:03,360 Speaker 2: and truly okay with China when it looks at the 141 00:10:03,360 --> 00:10:05,760 Speaker 2: longer term as it usually does and say, well, this 142 00:10:05,920 --> 00:10:09,040 Speaker 2: opens up a path for us to have access at 143 00:10:09,040 --> 00:10:14,760 Speaker 2: some stage to the extremely valuable Taiwanese economy, and we. 144 00:10:14,760 --> 00:10:19,320 Speaker 1: Know that Chinese President Chi Jinping has set unification with 145 00:10:19,559 --> 00:10:23,200 Speaker 1: Taiwan as one of his goals. Garfield, thank you so much. 146 00:10:23,320 --> 00:10:27,440 Speaker 1: Bloomberg's Garfield Reynolds, team leader for Asia Markets, Live on 147 00:10:27,440 --> 00:10:30,559 Speaker 1: the Bloomberg Terminal. Garfield joining from Sydney here on the 148 00:10:30,640 --> 00:10:41,160 Speaker 1: Daybreak Asia podcast. Welcome back to the Daybreak Asia Podcast. 149 00:10:41,200 --> 00:10:44,360 Speaker 1: I'm Doug Krisner. We go to Las Vegas Next and 150 00:10:44,640 --> 00:10:48,520 Speaker 1: cees the annual event focused on consumer tech. That's where 151 00:10:48,520 --> 00:10:52,000 Speaker 1: we caught up with Nvidia CEO Jensen Huang today he 152 00:10:52,160 --> 00:10:55,640 Speaker 1: joined the CEO of Siemens, Roland Bush to discuss the 153 00:10:55,679 --> 00:11:00,000 Speaker 1: partnership of these two companies. They spoke with Bloomberg's at Ludlow. 154 00:11:00,120 --> 00:11:03,880 Speaker 3: And we've discussed the five layer cake often. You know 155 00:11:03,960 --> 00:11:08,360 Speaker 3: within video. It started with the GPUs, but it's now software, 156 00:11:08,400 --> 00:11:12,000 Speaker 3: the element of simulation. On stage, you talked about integrating 157 00:11:12,400 --> 00:11:15,800 Speaker 3: the software side, in particular into DA Again, a similar 158 00:11:15,840 --> 00:11:17,880 Speaker 3: question for you, more of a timeline. What is it 159 00:11:18,000 --> 00:11:20,040 Speaker 3: you think you'll do first in how you guys work 160 00:11:20,080 --> 00:11:20,679 Speaker 3: closer together. 161 00:11:20,840 --> 00:11:23,560 Speaker 4: The first thing, let me just say very quickly, we're 162 00:11:23,600 --> 00:11:26,440 Speaker 4: announcing a big partnership between us. We've known each other 163 00:11:26,520 --> 00:11:29,280 Speaker 4: for a long time, but the partnership we're announcing is 164 00:11:29,320 --> 00:11:30,040 Speaker 4: really a big deal. 165 00:11:30,800 --> 00:11:31,240 Speaker 5: One. 166 00:11:31,400 --> 00:11:37,120 Speaker 4: We're accelerating their EDA software. We're accelerating their simulation software. 167 00:11:37,520 --> 00:11:42,880 Speaker 4: We're integrating AI technology, physical AI and agentic AI into 168 00:11:43,000 --> 00:11:47,600 Speaker 4: their team center and their factory automation operating system, and 169 00:11:47,679 --> 00:11:51,480 Speaker 4: so we're working together across this entire spectrum. When we 170 00:11:51,559 --> 00:11:54,040 Speaker 4: accelerate the software, then we'll get to use it to 171 00:11:54,080 --> 00:11:58,160 Speaker 4: design our chips and systems. When we accelerate their simulation software, 172 00:11:58,320 --> 00:12:01,120 Speaker 4: we'll use it in our AI factories to simulate the 173 00:12:01,120 --> 00:12:07,320 Speaker 4: thermal properties of our AI factories. When we integrate our 174 00:12:07,360 --> 00:12:12,960 Speaker 4: automation and agentic systems into their AI industrial operating system, 175 00:12:13,280 --> 00:12:16,000 Speaker 4: we can then use it in our factory floors with. 176 00:12:16,000 --> 00:12:17,720 Speaker 5: Our partners, for example fox Con. 177 00:12:18,160 --> 00:12:21,319 Speaker 4: And so we're working across this entire spectrum together and 178 00:12:21,360 --> 00:12:24,079 Speaker 4: we're going to put the technology to use basically as 179 00:12:24,080 --> 00:12:24,800 Speaker 4: soon as we can. 180 00:12:24,880 --> 00:12:28,199 Speaker 3: What's the net effect for you, Jensen? Is it improves 181 00:12:28,240 --> 00:12:31,320 Speaker 3: margins efficient capital allocation. I know that might sound a 182 00:12:31,320 --> 00:12:33,880 Speaker 3: bit dry, but actually, right now, that's the answer everyone's 183 00:12:33,880 --> 00:12:37,559 Speaker 3: searching for. How is this investment in AI and use 184 00:12:37,600 --> 00:12:40,160 Speaker 3: of the technology actually change things in the real world? 185 00:12:40,160 --> 00:12:42,440 Speaker 5: For me, now's yesterday Vera Rubin. 186 00:12:42,640 --> 00:12:46,959 Speaker 4: It takes six different chips to integrate into this incredible 187 00:12:47,000 --> 00:12:50,839 Speaker 4: system called Vera Rubin. And when you're done, each one 188 00:12:50,840 --> 00:12:55,000 Speaker 4: of these Vera Rubin GPUs is two hundred and forty 189 00:12:55,320 --> 00:12:59,679 Speaker 4: thousand wants and it is ten times more energy efficient 190 00:12:59,679 --> 00:13:03,120 Speaker 4: than all generation. It is ten times more cost efficient 191 00:13:03,160 --> 00:13:07,200 Speaker 4: than a last generation. But still the technology is insanely complicated. 192 00:13:07,440 --> 00:13:12,000 Speaker 4: One hundred fifteen thousand engineering years came together to build 193 00:13:12,000 --> 00:13:15,360 Speaker 4: this system. And so when we accelerate EDA tools, when 194 00:13:15,400 --> 00:13:19,560 Speaker 4: we accelerate stimulation tools and when we can eventually and 195 00:13:19,600 --> 00:13:24,119 Speaker 4: I'm hoping very soon design entire vera Ruben systems inside 196 00:13:24,120 --> 00:13:28,480 Speaker 4: a Semen's digital twin, the chance the ability for us 197 00:13:28,520 --> 00:13:32,680 Speaker 4: to create much much more complex systems will scale, will 198 00:13:32,720 --> 00:13:35,240 Speaker 4: do it much more efficiently, and so this is really 199 00:13:35,280 --> 00:13:38,160 Speaker 4: about being able to do the impossible and being able 200 00:13:38,200 --> 00:13:40,840 Speaker 4: to do it the impossible right. 201 00:13:40,880 --> 00:13:47,439 Speaker 5: The first time. And once they then realize that AI 202 00:13:47,679 --> 00:13:48,600 Speaker 5: creates real. 203 00:13:48,400 --> 00:13:52,480 Speaker 6: World impact, this is where it really deploys the full power. 204 00:13:52,320 --> 00:13:53,840 Speaker 5: And also the economic power. 205 00:13:54,400 --> 00:13:56,600 Speaker 6: It's not only in the data centers on any efactories 206 00:13:56,600 --> 00:13:58,720 Speaker 6: which we see, but also on the edge because once 207 00:13:58,760 --> 00:14:01,920 Speaker 6: we start influencing law latency, you bring this technology to 208 00:14:02,000 --> 00:14:05,040 Speaker 6: the edge. This is a huge potential for our customers 209 00:14:05,080 --> 00:14:07,360 Speaker 6: too to deploy this technology. 210 00:14:07,480 --> 00:14:10,719 Speaker 5: And this includes of course includes hardware where we come 211 00:14:10,760 --> 00:14:11,680 Speaker 5: from from the jibs. 212 00:14:12,400 --> 00:14:15,160 Speaker 6: It goes into the controllers, some of our controllers run 213 00:14:15,240 --> 00:14:18,160 Speaker 6: on GPUs and then it goes all the. 214 00:14:18,120 --> 00:14:24,000 Speaker 5: Way to the industrial PC exactly and are AI industry. 215 00:14:24,400 --> 00:14:28,640 Speaker 6: We supercharge it and they can now run algorithms trained 216 00:14:28,640 --> 00:14:29,200 Speaker 6: in the cloud. 217 00:14:29,240 --> 00:14:31,400 Speaker 5: They can run it on the shop floor and do 218 00:14:31,560 --> 00:14:34,200 Speaker 5: all that trick. What we talked about it. 219 00:14:34,200 --> 00:14:37,560 Speaker 6: In real time optimization and running in a plant, and 220 00:14:37,600 --> 00:14:38,600 Speaker 6: that makes a huge difference. 221 00:14:38,720 --> 00:14:40,120 Speaker 5: It drives the economy growth. 222 00:14:40,240 --> 00:14:42,280 Speaker 3: The question that's being searched for is how is this 223 00:14:42,360 --> 00:14:43,880 Speaker 3: going to manifest in the real world. You know, the 224 00:14:43,960 --> 00:14:46,920 Speaker 3: emphasis that this is cs I think is physical AI 225 00:14:47,600 --> 00:14:50,680 Speaker 3: is not the manifestation of the final stage of physical 226 00:14:50,720 --> 00:14:52,960 Speaker 3: AI just one giant robot that you guys call a 227 00:14:52,960 --> 00:14:56,480 Speaker 3: factory In the manufacturing context, is that where you're seeing 228 00:14:56,520 --> 00:14:59,240 Speaker 3: demand from actual customers, you know that they need a 229 00:14:59,280 --> 00:15:03,720 Speaker 3: factory that is automated and genuinely autonomous in some degree. 230 00:15:04,080 --> 00:15:06,480 Speaker 6: Number one is if you want to build a factory, 231 00:15:06,640 --> 00:15:10,360 Speaker 6: and very often you're missing out on labor. And we 232 00:15:10,520 --> 00:15:12,960 Speaker 6: talk about the own skilled labor, it's hard to find 233 00:15:13,080 --> 00:15:16,720 Speaker 6: number one. Number two is once you build it autonomous 234 00:15:16,760 --> 00:15:20,080 Speaker 6: and automated, then you have a much much higher yield, 235 00:15:20,200 --> 00:15:23,040 Speaker 6: but you can generate to use lesser energy, by the way, 236 00:15:23,040 --> 00:15:25,440 Speaker 6: at the same time before you optimize it in a way. 237 00:15:25,720 --> 00:15:27,440 Speaker 5: So therefore there's a lot of benefits. 238 00:15:28,000 --> 00:15:30,160 Speaker 6: And if you want to talk about the United States 239 00:15:30,840 --> 00:15:34,480 Speaker 6: up manufacturing United States, you need to go as digital 240 00:15:34,720 --> 00:15:38,000 Speaker 6: and as automated, and AI is supercharged as possible. 241 00:15:38,680 --> 00:15:44,880 Speaker 7: A factory is robotic, and it is orchestrating robots that 242 00:15:44,960 --> 00:15:48,600 Speaker 7: are building systems that are also robotic, like for example, 243 00:15:48,600 --> 00:15:50,120 Speaker 7: self dragging cars a robotic system. 244 00:15:50,840 --> 00:15:54,320 Speaker 4: And the reason why it's so hard to deploy robots 245 00:15:54,360 --> 00:15:57,840 Speaker 4: today is because it's hard to program these robotic systems. 246 00:15:58,560 --> 00:16:03,000 Speaker 4: The software expertise next necessary to customization necessary is really intense. 247 00:16:03,040 --> 00:16:06,240 Speaker 4: It's just too much. And so the fact that we 248 00:16:06,280 --> 00:16:11,280 Speaker 4: could now apply artificial intelligence physical AI technology to these 249 00:16:11,360 --> 00:16:16,240 Speaker 4: robotic systems make them easier to teach. You show it 250 00:16:16,320 --> 00:16:19,160 Speaker 4: a few demonstrations, and the AI learned it by itself. 251 00:16:19,520 --> 00:16:21,200 Speaker 3: I think, would you, guys far As say Jensen, that 252 00:16:21,240 --> 00:16:24,000 Speaker 3: you've solved for that that software limitation. 253 00:16:24,040 --> 00:16:27,480 Speaker 4: That's offtware limitation is the chat GPT moment of it 254 00:16:27,560 --> 00:16:30,040 Speaker 4: is now here. I think over the course of the 255 00:16:30,040 --> 00:16:31,920 Speaker 4: next couple of two three years, we're going to make 256 00:16:32,000 --> 00:16:33,240 Speaker 4: some really big breakthroughs. 257 00:16:33,760 --> 00:16:37,040 Speaker 3: Let's please talk about energy, electricity, power supply, call it 258 00:16:37,080 --> 00:16:39,960 Speaker 3: what you will. In turn, for each of you, how 259 00:16:40,000 --> 00:16:42,600 Speaker 3: worried are you about it as a bottleneck and what 260 00:16:42,760 --> 00:16:45,120 Speaker 3: is your experience date staying running both companies. 261 00:16:45,520 --> 00:16:48,360 Speaker 4: In that respect, you should always be a bottleneck for 262 00:16:48,400 --> 00:16:51,640 Speaker 4: any industry, and this is a new industry that's growing 263 00:16:51,760 --> 00:16:55,720 Speaker 4: incredibly fast. As you know, AI is both the technology 264 00:16:55,720 --> 00:16:58,360 Speaker 4: that's going to revolutionize many applications, and we're talking about 265 00:16:58,400 --> 00:17:03,400 Speaker 4: some of them here. The AI industry itself, the manufacturing 266 00:17:03,440 --> 00:17:06,840 Speaker 4: of the artificial intelligence takes energy. It takes energy, it 267 00:17:06,880 --> 00:17:10,920 Speaker 4: takes AI factories. It's exactly the reason why from Hopper 268 00:17:11,000 --> 00:17:16,200 Speaker 4: to Blackwell we increased energy efficiency by ten x. From 269 00:17:16,240 --> 00:17:20,879 Speaker 4: Blackwell to Reuben, we increased energy efficiency again by tenx. 270 00:17:21,560 --> 00:17:25,840 Speaker 4: And that translates directly to our customers' revenues because in 271 00:17:25,880 --> 00:17:31,080 Speaker 4: the case of an AI factory, whatever factory size you have, 272 00:17:31,160 --> 00:17:34,359 Speaker 4: you're limited by the power and that power within that 273 00:17:34,400 --> 00:17:37,440 Speaker 4: power constraint, you want to have the most tokens or 274 00:17:37,480 --> 00:17:41,600 Speaker 4: most AI per whte that you can possibly generate. And 275 00:17:41,640 --> 00:17:45,760 Speaker 4: so every time we improve energy efficiency, we're effectively improving 276 00:17:46,320 --> 00:17:50,159 Speaker 4: both the AI capabilities for our customers and their revenues 277 00:17:50,680 --> 00:17:52,439 Speaker 4: because they're always constrained by power. 278 00:17:52,600 --> 00:17:56,440 Speaker 1: That was in Nvidia CEO Jensen Huang and Siemens CEO 279 00:17:56,640 --> 00:18:01,119 Speaker 1: Roland Bush speaking with Bloomberg zed Ludlow at CEES and 280 00:18:01,160 --> 00:18:04,920 Speaker 1: turned to artificial intelligence in China, we had the chance 281 00:18:04,960 --> 00:18:07,520 Speaker 1: to visit with Lisa Sue, the CEO of a m D. 282 00:18:08,160 --> 00:18:11,800 Speaker 1: She told us demand for AI in Beijing is high. 283 00:18:11,920 --> 00:18:14,360 Speaker 1: Here is Sue speaking with Bloomberg's at Ludlow. 284 00:18:14,680 --> 00:18:16,240 Speaker 8: China is an important market for us. 285 00:18:16,680 --> 00:18:18,600 Speaker 9: You know, We actually sell a broad range of chips 286 00:18:18,600 --> 00:18:22,040 Speaker 9: into China, including our you know, our PCs as well 287 00:18:22,080 --> 00:18:24,600 Speaker 9: as you know, other embedded chips as. 288 00:18:24,520 --> 00:18:27,600 Speaker 5: In the data sets context, sorry, in the data. 289 00:18:27,440 --> 00:18:30,520 Speaker 9: Center context, we are you know, certainly we see China 290 00:18:30,560 --> 00:18:31,720 Speaker 9: as an important market. 291 00:18:32,160 --> 00:18:34,760 Speaker 8: We were We did get some licenses. 292 00:18:34,240 --> 00:18:37,240 Speaker 9: From the US government, you know, late last year as 293 00:18:37,280 --> 00:18:40,119 Speaker 9: it relates to some of our previous generation our m 294 00:18:40,160 --> 00:18:43,000 Speaker 9: I three oh eight you know chips, and we are 295 00:18:43,040 --> 00:18:46,360 Speaker 9: in the process of applying for new licenses with our 296 00:18:46,880 --> 00:18:48,800 Speaker 9: m I three twenty five chips that. 297 00:18:48,760 --> 00:18:54,040 Speaker 8: Were recently allowed to apply for licenses. 298 00:18:54,040 --> 00:18:57,080 Speaker 9: We haven't gotten those licenses yet, but we continue to 299 00:18:57,160 --> 00:18:58,800 Speaker 9: view China as an important market for us. 300 00:18:58,920 --> 00:19:00,560 Speaker 3: The reason I asked about it is is in part 301 00:19:00,640 --> 00:19:03,199 Speaker 3: because a lot of the work that's being done in 302 00:19:03,240 --> 00:19:06,239 Speaker 3: open source models and bridging the gap between open and 303 00:19:06,280 --> 00:19:08,800 Speaker 3: closed it is being done in China. To some extent, 304 00:19:09,880 --> 00:19:11,800 Speaker 3: There's been a lot of discussion about the demands being 305 00:19:11,840 --> 00:19:14,120 Speaker 3: there in China, But could you reflect a little bit 306 00:19:14,119 --> 00:19:17,520 Speaker 3: on that demand, but also what the Chinese government's attitude 307 00:19:17,600 --> 00:19:22,800 Speaker 3: is to you taking a later generation of tech to 308 00:19:22,840 --> 00:19:23,320 Speaker 3: the country. 309 00:19:23,680 --> 00:19:26,840 Speaker 8: Well, I do think the demand for AI in. 310 00:19:26,760 --> 00:19:30,000 Speaker 9: General and in China is high for all the reasons 311 00:19:30,000 --> 00:19:32,200 Speaker 9: that we talked about. I think we are in a 312 00:19:32,240 --> 00:19:36,879 Speaker 9: demand environment where more compute is beneficial across the world. 313 00:19:37,320 --> 00:19:39,880 Speaker 9: We think China is an important market for US, and 314 00:19:39,960 --> 00:19:43,120 Speaker 9: it's a place where we certainly would have been very 315 00:19:43,160 --> 00:19:45,879 Speaker 9: active in having our solutions deployed, so, you know, we 316 00:19:45,960 --> 00:19:48,800 Speaker 9: continue to view it as something that's important. We're working 317 00:19:48,800 --> 00:19:51,399 Speaker 9: with the US government as well as our Chinese customers, 318 00:19:51,920 --> 00:19:53,840 Speaker 9: you know, to find good solutions there. 319 00:19:53,720 --> 00:19:56,320 Speaker 3: And there are signs from both governments that the licensed 320 00:19:56,359 --> 00:19:59,320 Speaker 3: process is moving. Commerce is kind of notorious for things 321 00:19:59,320 --> 00:20:00,960 Speaker 3: sitting on a desk for quite a long time. 322 00:20:01,040 --> 00:20:04,600 Speaker 9: I think we are optimistic that we'll have an opportunity 323 00:20:04,760 --> 00:20:06,400 Speaker 9: to get some of those licenses. 324 00:20:06,440 --> 00:20:10,280 Speaker 1: Granted, that was AMD CEO Lisa Sue speaking with Bloomberg's 325 00:20:10,359 --> 00:20:14,359 Speaker 1: Ed Ludlow at cees here on the Daybreak Asia podcast. 326 00:20:17,720 --> 00:20:21,080 Speaker 1: Thanks for listening to today's episode of the Bloomberg Daybreak 327 00:20:21,240 --> 00:20:24,600 Speaker 1: Asia Edition podcast. Each weekday, we look at the story 328 00:20:24,680 --> 00:20:29,040 Speaker 1: shaping markets, finance, and geopolitics in the Asia Pacific. You 329 00:20:29,080 --> 00:20:33,159 Speaker 1: can find us on Apple, Spotify, the Bloomberg Podcast YouTube channel, 330 00:20:33,280 --> 00:20:36,320 Speaker 1: or anywhere else you listen. Join us again tomorrow for 331 00:20:36,440 --> 00:20:39,919 Speaker 1: insight on the market moves from Hong Kong to Singapore 332 00:20:40,320 --> 00:20:44,080 Speaker 1: and Australia. I'm Doug Chrisner, and this is Bloomberg