1 00:00:02,520 --> 00:00:07,040 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:10,960 --> 00:00:14,280 Speaker 2: Welcome to the Bloomberg Daybreak Asia podcast. I'm Deck Chrisner. 3 00:00:14,640 --> 00:00:17,600 Speaker 2: On today's episode, we'll be taking a deep dive into 4 00:00:17,680 --> 00:00:21,920 Speaker 2: Deep Seek. This is a Chinese artificial intelligence startup and 5 00:00:22,040 --> 00:00:25,360 Speaker 2: its aimodel, known as R one, appears to rival the 6 00:00:25,440 --> 00:00:30,400 Speaker 2: performance of US products like Chat GPT. Perhaps its biggest 7 00:00:30,440 --> 00:00:33,279 Speaker 2: selling point is that the model was developed at a 8 00:00:33,320 --> 00:00:36,440 Speaker 2: fraction of the cost of competing products, and that could 9 00:00:36,440 --> 00:00:40,640 Speaker 2: make high valuations of some USAI related companies a little 10 00:00:40,640 --> 00:00:43,640 Speaker 2: tough to justify. Let me give you one example today 11 00:00:44,000 --> 00:00:47,559 Speaker 2: in US trading, shares in the AI chip leader in Nvidia, 12 00:00:47,600 --> 00:00:50,680 Speaker 2: we're down nearly seventeen percent, and that led to a 13 00:00:50,680 --> 00:00:53,960 Speaker 2: loss in the Nasdaq one hundred of three percent. In 14 00:00:54,000 --> 00:00:56,320 Speaker 2: a moment, we'll be speaking with Jamie Cox. He is 15 00:00:56,360 --> 00:00:59,720 Speaker 2: managing partner at Harris Financial Group. But let's begin in 16 00:00:59,800 --> 00:01:03,960 Speaker 2: home with Bloomberg Tech editor of Vlad Savov. It's always 17 00:01:04,000 --> 00:01:06,240 Speaker 2: a pleasure to chat with you've Lad. This feels like 18 00:01:06,280 --> 00:01:10,520 Speaker 2: a major inflection point in the development of AI models. First, 19 00:01:10,560 --> 00:01:13,199 Speaker 2: let me get your reaction and what you're hearing among 20 00:01:13,319 --> 00:01:15,760 Speaker 2: Hong Kongers. Now that follow the tech space. 21 00:01:16,319 --> 00:01:19,240 Speaker 3: Well, the interesting thing about deep Seek is it's a 22 00:01:19,280 --> 00:01:24,319 Speaker 3: company that has rocketed to global renown and recognition today 23 00:01:24,440 --> 00:01:26,640 Speaker 3: and over the past week, but it has been known 24 00:01:26,680 --> 00:01:29,880 Speaker 3: about ever since it's beginning in twenty twenty three. It's 25 00:01:29,920 --> 00:01:33,120 Speaker 3: one that our Bloomberg Intelligence colleagues have been taken a 26 00:01:33,160 --> 00:01:36,759 Speaker 3: close look at because it is developing such a hot 27 00:01:36,840 --> 00:01:40,440 Speaker 3: area of new software as AI. It has been developing 28 00:01:40,440 --> 00:01:44,320 Speaker 3: its own models, so people here have been aware of it. 29 00:01:44,319 --> 00:01:48,240 Speaker 3: It has been on the radar. But really the thing 30 00:01:48,280 --> 00:01:52,200 Speaker 3: that has happened is it hit a certain threshold of recognition. 31 00:01:53,200 --> 00:01:57,080 Speaker 3: Part of it really was Silicon Valley people, venture capitalists. 32 00:01:57,160 --> 00:02:00,520 Speaker 3: They took an interest, they tried it out. They found 33 00:02:00,560 --> 00:02:03,600 Speaker 3: out that it is, indeed, like you mentioned, as competitive, 34 00:02:03,640 --> 00:02:07,760 Speaker 3: as the company itself says. It has posted some benchmark 35 00:02:07,840 --> 00:02:10,800 Speaker 3: results on this website. It has claimed already that it 36 00:02:10,840 --> 00:02:14,440 Speaker 3: competes with open Ai, but frankly, every AI model developer 37 00:02:14,480 --> 00:02:17,360 Speaker 3: claims that they compete or they can rival open Ai. 38 00:02:17,840 --> 00:02:20,320 Speaker 3: The amazing thing, and the really really impressive thing is 39 00:02:20,320 --> 00:02:22,600 Speaker 3: that the people who know this stuff, which I can't 40 00:02:22,600 --> 00:02:25,320 Speaker 3: claim that I'm an expert in it, they all recognize 41 00:02:25,320 --> 00:02:27,720 Speaker 3: it as a breakthrough so we need to recognize that too. 42 00:02:28,240 --> 00:02:31,280 Speaker 3: And it's also worth mentioning here that the company has 43 00:02:31,280 --> 00:02:33,320 Speaker 3: put out a research paper of its own and they've 44 00:02:33,320 --> 00:02:36,680 Speaker 3: said that the costs of training this model are less 45 00:02:36,680 --> 00:02:39,320 Speaker 3: than six million dollars. Now, that excludes a whole bunch 46 00:02:39,360 --> 00:02:42,520 Speaker 3: of costs, so we can't be exactly sure about how 47 00:02:42,600 --> 00:02:46,160 Speaker 3: much more affordable it is for this small company to 48 00:02:46,200 --> 00:02:49,840 Speaker 3: develop it, but it is very clearly a difference maker 49 00:02:50,000 --> 00:02:52,160 Speaker 3: when it comes to our assessment of how much it 50 00:02:52,200 --> 00:02:54,560 Speaker 3: costs to develop a world leading AI model. 51 00:02:54,639 --> 00:02:57,200 Speaker 2: Yeah, I think to be fair, our analyst here in 52 00:02:57,240 --> 00:02:59,639 Speaker 2: New York, Man Deep Saying, pointed out that our one 53 00:02:59,760 --> 00:03:04,520 Speaker 2: was to developed using output from open Ai, Anthropic and 54 00:03:04,639 --> 00:03:07,320 Speaker 2: LAMA to train the models. So it's almost as though 55 00:03:07,480 --> 00:03:10,240 Speaker 2: AI is training AI a little bit. And I think 56 00:03:10,320 --> 00:03:14,120 Speaker 2: that what may have precipitated or at least engendered a 57 00:03:14,160 --> 00:03:16,880 Speaker 2: lot of the selling that we saw in the US 58 00:03:16,880 --> 00:03:19,600 Speaker 2: in the last session was the rating on the Apple 59 00:03:19,639 --> 00:03:22,959 Speaker 2: app Store. I think R one rose to the top level. 60 00:03:23,720 --> 00:03:28,160 Speaker 2: Can we agree though, that the current Western approach to AI. 61 00:03:28,800 --> 00:03:31,920 Speaker 2: I'm talking here about the reliance on those high end chips, 62 00:03:32,440 --> 00:03:37,120 Speaker 2: extensive computing power, and vast amounts of electricity. Is that 63 00:03:37,200 --> 00:03:39,200 Speaker 2: being challenged to some extent. 64 00:03:39,320 --> 00:03:42,080 Speaker 3: Yes, we can agree that it's challenged. We cannot agree 65 00:03:42,120 --> 00:03:45,200 Speaker 3: that it's a foregone conclusion that it's going to be 66 00:03:45,240 --> 00:03:47,920 Speaker 3: going away. One thing to bear in mind here, which 67 00:03:47,920 --> 00:03:51,240 Speaker 3: I think is very important, is that Deepsek's model has 68 00:03:51,280 --> 00:03:55,080 Speaker 3: delivered an impressive chatbot. It only works with text. So 69 00:03:55,440 --> 00:03:58,000 Speaker 3: what we have is a breakthrough and an impressive achievement, 70 00:03:58,120 --> 00:04:01,480 Speaker 3: but it's within one medium. One of the things that 71 00:04:01,520 --> 00:04:03,640 Speaker 3: you have to bear in mind is that the more 72 00:04:03,680 --> 00:04:06,000 Speaker 3: advanced chips, the ones that are making a video is 73 00:04:06,000 --> 00:04:09,040 Speaker 3: still a multi trillion dollar company, even with its massive 74 00:04:09,040 --> 00:04:12,080 Speaker 3: share price drop. The thing that drives it is that 75 00:04:12,240 --> 00:04:14,160 Speaker 3: you want those chips in order to get to the 76 00:04:14,200 --> 00:04:17,640 Speaker 3: next stage of AI, specifically generative AI. So at the 77 00:04:17,640 --> 00:04:21,680 Speaker 3: moment we're talking about generating text, but the next evolution 78 00:04:21,839 --> 00:04:24,599 Speaker 3: is going to be generating images. Generating video is stuff 79 00:04:24,600 --> 00:04:29,120 Speaker 3: that open ai and other services have already made major 80 00:04:29,120 --> 00:04:32,360 Speaker 3: advances in, and that's where deep seek is probably going 81 00:04:32,400 --> 00:04:34,400 Speaker 3: to need a lot more hardware than it's used just 82 00:04:34,440 --> 00:04:34,880 Speaker 3: for text. 83 00:04:34,920 --> 00:04:37,680 Speaker 2: So it may be too much to say that deep 84 00:04:37,760 --> 00:04:41,040 Speaker 2: seek has been able to engineer a way around the 85 00:04:41,080 --> 00:04:44,360 Speaker 2: export bands that were put in place during the Biden administration, 86 00:04:45,000 --> 00:04:48,279 Speaker 2: the bands that basically kept China from accessing some of 87 00:04:48,320 --> 00:04:53,559 Speaker 2: those advanced semiconductors manufactured by and Vidiot. We can't really 88 00:04:53,760 --> 00:04:55,000 Speaker 2: articulate that, can we? 89 00:04:55,560 --> 00:04:58,240 Speaker 3: Yes, this is correct. Now, there are two things to 90 00:04:58,320 --> 00:05:03,279 Speaker 3: say here. Firstly, those bands and those trade cubs. Everyone 91 00:05:03,480 --> 00:05:06,000 Speaker 3: in the industry said, this is only going to encourage 92 00:05:06,120 --> 00:05:10,120 Speaker 3: China and Chinese companies to focus on more efficient methods. 93 00:05:10,320 --> 00:05:13,279 Speaker 3: So this is exactly what is being predicted by the industry. 94 00:05:13,520 --> 00:05:16,200 Speaker 3: They said, China will focus on more light, more small 95 00:05:16,240 --> 00:05:19,839 Speaker 3: scale models, and Deep Seek is the fruits of those efforts. 96 00:05:20,440 --> 00:05:23,120 Speaker 3: That being said, we cannot say that the sanctions are 97 00:05:23,120 --> 00:05:27,400 Speaker 3: not effective because Deep Seek CEO and founder has himself said, 98 00:05:27,800 --> 00:05:30,560 Speaker 3: the thing that Deep Seak requires right now is not 99 00:05:30,640 --> 00:05:34,680 Speaker 3: more money, is not more investment, is actually access to 100 00:05:34,800 --> 00:05:37,560 Speaker 3: those most advanced ships. The embargo is e cosed, the 101 00:05:37,640 --> 00:05:40,200 Speaker 3: ban on accessing those video chips is the thing that's 102 00:05:40,200 --> 00:05:41,200 Speaker 3: holding Deep Sea back. 103 00:05:41,480 --> 00:05:44,599 Speaker 2: Are we likely to see some type of fragmentation within 104 00:05:44,680 --> 00:05:48,039 Speaker 2: the artificial intelligence industry, which is to say that you 105 00:05:48,120 --> 00:05:51,719 Speaker 2: have certain products that have been produced at low cost 106 00:05:51,880 --> 00:05:56,320 Speaker 2: being rolled out at lower prices, and maybe those products 107 00:05:56,360 --> 00:05:59,440 Speaker 2: will see greater adoption and some of the more sophisticated 108 00:05:59,480 --> 00:06:02,919 Speaker 2: elements of artificial intelligence you'll have to pay a premium for, 109 00:06:03,600 --> 00:06:06,240 Speaker 2: and that segment of the market will be controlled by 110 00:06:07,000 --> 00:06:08,200 Speaker 2: very few companies. 111 00:06:08,640 --> 00:06:09,480 Speaker 4: Yes, I can very. 112 00:06:09,400 --> 00:06:11,440 Speaker 3: Much see that. One of the things that has been 113 00:06:11,560 --> 00:06:14,560 Speaker 3: the trend over the past few months is actually opening 114 00:06:14,640 --> 00:06:17,800 Speaker 3: Ey's revenue generation has accelerated, and it's one of the 115 00:06:17,839 --> 00:06:20,240 Speaker 3: trends that we're seeing. And if you had asked me 116 00:06:20,320 --> 00:06:22,239 Speaker 3: to predict what would be the big trend in AI 117 00:06:22,240 --> 00:06:25,560 Speaker 3: in twenty twenty five, it is exactly this taking all 118 00:06:25,600 --> 00:06:29,200 Speaker 3: that investment, taking all that developed technology, and turning into 119 00:06:29,240 --> 00:06:32,520 Speaker 3: products that you can sell subscriptions or in other ways 120 00:06:32,680 --> 00:06:35,440 Speaker 3: entices people to spend money on them. That is still 121 00:06:35,480 --> 00:06:38,040 Speaker 3: going to continue to be the case the premium products. 122 00:06:38,080 --> 00:06:42,400 Speaker 3: Open ai sells a two hundred dollars per month pro product. 123 00:06:42,960 --> 00:06:45,640 Speaker 3: There is an audience for that. But that being said, 124 00:06:46,160 --> 00:06:50,240 Speaker 3: the deep seek approach, it releases the software as an 125 00:06:50,240 --> 00:06:53,360 Speaker 3: open source software, so you as an individual can take 126 00:06:53,400 --> 00:06:55,520 Speaker 3: that and piggyback on top of it and decide that 127 00:06:55,560 --> 00:06:58,240 Speaker 3: you want to build your own specific chatbook. And a 128 00:06:58,240 --> 00:07:00,839 Speaker 3: good analogy that I have for this is when you 129 00:07:00,839 --> 00:07:03,919 Speaker 3: think about banking. You have the traditional banks, the ones 130 00:07:03,920 --> 00:07:06,800 Speaker 3: with branches and all those overheads and costs, and then 131 00:07:06,839 --> 00:07:09,359 Speaker 3: you have the digit banks, the ones that are mobile only, 132 00:07:09,440 --> 00:07:12,520 Speaker 3: app only, and because of that efficiency that those other 133 00:07:12,600 --> 00:07:16,280 Speaker 3: banks have, they can offer you better rates and better office. 134 00:07:16,040 --> 00:07:19,400 Speaker 2: So to go back to the hardware software issue, it 135 00:07:19,480 --> 00:07:23,920 Speaker 2: seems like Chinese AI engineers have found a way to 136 00:07:24,160 --> 00:07:28,080 Speaker 2: work with the software given the limitations of the hardware 137 00:07:28,120 --> 00:07:30,560 Speaker 2: that they have been forced to use because of those 138 00:07:31,040 --> 00:07:34,920 Speaker 2: US export controls. But I'm wondering whether on the semiconductor 139 00:07:34,920 --> 00:07:38,320 Speaker 2: industry side of things in China that we may see 140 00:07:38,360 --> 00:07:41,200 Speaker 2: some sort of breakthrough when it comes to advanced AI 141 00:07:41,320 --> 00:07:43,960 Speaker 2: chips in the near future. Is that a possibility. 142 00:07:44,280 --> 00:07:46,880 Speaker 3: Yes, There's two things to say here. Firstly, from what 143 00:07:46,920 --> 00:07:49,200 Speaker 3: little we know about Deep Seek, and bear in mind 144 00:07:49,320 --> 00:07:52,080 Speaker 3: it's a very very young startup. It came out of 145 00:07:52,080 --> 00:07:55,800 Speaker 3: an AI hedge fund AI driven hedge fund, so we 146 00:07:55,840 --> 00:07:58,760 Speaker 3: still don't know that much about it. But from interviews 147 00:07:58,760 --> 00:08:00,840 Speaker 3: with the founder, he has said that most of his 148 00:08:00,960 --> 00:08:05,520 Speaker 3: engineers are graduates from China's universities. They are entirely domestic 149 00:08:05,720 --> 00:08:10,000 Speaker 3: Chinese engineers, so credit to the company is educational system 150 00:08:10,360 --> 00:08:14,400 Speaker 3: now on the semiconductor front, there are technical limitations and challenges. 151 00:08:14,520 --> 00:08:18,360 Speaker 3: The classic one is EUV machines that only ASML makes 152 00:08:18,520 --> 00:08:22,560 Speaker 3: and is prevented from selling to China. Solving that issue. 153 00:08:22,600 --> 00:08:25,040 Speaker 3: We haven't seen much evidence of China doing it, but 154 00:08:25,080 --> 00:08:27,400 Speaker 3: then again, it may be something along the lines of 155 00:08:27,440 --> 00:08:29,720 Speaker 3: deep Seek. We never saw deep Seek. We were only 156 00:08:29,760 --> 00:08:31,920 Speaker 3: aware that is building a model. We never saw it 157 00:08:32,320 --> 00:08:35,319 Speaker 3: doing a breakthrough until today, until it was actually ready 158 00:08:35,360 --> 00:08:36,280 Speaker 3: to come out there. 159 00:08:36,480 --> 00:08:38,440 Speaker 2: Flight, We'll leave it there, Thank you so much. Let's 160 00:08:38,480 --> 00:08:41,520 Speaker 2: have of Bloomberg Tech editor in Asia joining us from 161 00:08:41,559 --> 00:08:51,439 Speaker 2: Hong Kong here on the Daybreak Asia podcast. Welcome back 162 00:08:51,440 --> 00:08:55,040 Speaker 2: to the Daybreak Asia Podcast. I'm Doug Chrisner. We continue 163 00:08:55,040 --> 00:08:58,480 Speaker 2: with our deep dive on deep Seek. News of the 164 00:08:58,559 --> 00:09:01,960 Speaker 2: Chinese startups new Chat bought r one, sparked to sell 165 00:09:01,960 --> 00:09:05,600 Speaker 2: off in many AI related names globally, and it wiped 166 00:09:05,640 --> 00:09:08,600 Speaker 2: out nearly a trillion dollars in market cap from US 167 00:09:08,600 --> 00:09:12,120 Speaker 2: and European tech names. So now a key question is 168 00:09:12,200 --> 00:09:15,920 Speaker 2: on capex spending plans for some of America's biggest companies. 169 00:09:16,160 --> 00:09:18,480 Speaker 2: Joining me now for a closer look. As Jamie Cox 170 00:09:19,000 --> 00:09:22,520 Speaker 2: is managing partner at Harris Financial Group. Jamie, thank you 171 00:09:22,559 --> 00:09:25,000 Speaker 2: so much for being with us. Certainly was an interesting 172 00:09:25,080 --> 00:09:28,360 Speaker 2: day where AI was concerned. How do you make sense 173 00:09:28,600 --> 00:09:31,520 Speaker 2: of the market reaction to this Deep Seek story. 174 00:09:31,880 --> 00:09:34,960 Speaker 4: I think the market's reaction was quite positive. I mean, 175 00:09:35,000 --> 00:09:35,959 Speaker 4: there were only. 176 00:09:35,800 --> 00:09:39,040 Speaker 1: Eight dollstocks that were down today. Sixty percent of the 177 00:09:39,320 --> 00:09:41,960 Speaker 1: S and P five hundred companies were up. So this 178 00:09:42,120 --> 00:09:46,800 Speaker 1: was very much an isolated day for you know, chip stocks. 179 00:09:46,840 --> 00:09:50,040 Speaker 1: This was sort of the moment in time where you 180 00:09:50,120 --> 00:09:53,959 Speaker 1: see disruption take hold, and when you start to see 181 00:09:53,960 --> 00:10:01,520 Speaker 1: things like AI disruption and the possibility that maybe companies 182 00:10:01,559 --> 00:10:05,600 Speaker 1: are overinvesting in AI infrastructure, you have to sit back 183 00:10:05,600 --> 00:10:08,160 Speaker 1: and think a minute and ask yourself, you know, if 184 00:10:08,280 --> 00:10:11,200 Speaker 1: Nvidia can legitimately sell thirty thousand dollars chips or not. 185 00:10:11,600 --> 00:10:14,280 Speaker 1: So I think it made a lot of investors think. 186 00:10:14,559 --> 00:10:17,679 Speaker 1: But a lot of investors have been worried about overvaluation in. 187 00:10:17,640 --> 00:10:18,559 Speaker 4: These stocks anyway. 188 00:10:18,600 --> 00:10:23,000 Speaker 1: So I think that the Deep Seek situation sort of precipitated. 189 00:10:23,480 --> 00:10:26,560 Speaker 1: Was the precipitating event for things that may have happened anyway. 190 00:10:26,720 --> 00:10:28,800 Speaker 1: So I think that's one thing to consider. 191 00:10:28,880 --> 00:10:31,280 Speaker 4: The second is I actually think this is a good thing. 192 00:10:32,040 --> 00:10:36,480 Speaker 1: It's very important that we not get COMPLACENTECH in our 193 00:10:36,520 --> 00:10:39,720 Speaker 1: technology prowess in the United States. We need to realize 194 00:10:39,720 --> 00:10:42,920 Speaker 1: that these technologies can be created at scale all over 195 00:10:42,920 --> 00:10:43,360 Speaker 1: the world. 196 00:10:44,000 --> 00:10:45,360 Speaker 4: And what Deep. 197 00:10:45,160 --> 00:10:47,880 Speaker 1: Seek basically did was kick off what I believe will 198 00:10:47,920 --> 00:10:51,880 Speaker 1: be an arms race, maybe a spot Nick moment for 199 00:10:51,960 --> 00:10:55,000 Speaker 1: the United States where we get really serious in the 200 00:10:55,720 --> 00:10:58,760 Speaker 1: prospect of competition from around the world will actually propel 201 00:10:59,240 --> 00:11:02,960 Speaker 1: AI developed forward. So I'm actually very positive both on 202 00:11:03,600 --> 00:11:08,840 Speaker 1: the development of AI and actually, and ironically, even though 203 00:11:08,840 --> 00:11:10,959 Speaker 1: the stocks we took a beating today, I actually think 204 00:11:11,000 --> 00:11:14,920 Speaker 1: that it further illustrates the need to build out AI 205 00:11:15,040 --> 00:11:19,000 Speaker 1: infrastructure because what we're seeing now is disruption in basically 206 00:11:19,080 --> 00:11:21,839 Speaker 1: AI one point zero, which will bring about the possibility 207 00:11:21,880 --> 00:11:25,000 Speaker 1: of AI two point zero, which will make it universally 208 00:11:25,080 --> 00:11:28,280 Speaker 1: usable and much more widely adopted than what we see 209 00:11:28,360 --> 00:11:31,120 Speaker 1: right now. So I'm pretty excited about what we see. 210 00:11:31,280 --> 00:11:33,160 Speaker 1: This is just a moment in time in the market, 211 00:11:33,640 --> 00:11:35,840 Speaker 1: and we'll be looking back on this as one of 212 00:11:35,840 --> 00:11:38,680 Speaker 1: the best buying opportunities in these stocks at quite some time. 213 00:11:38,960 --> 00:11:41,480 Speaker 2: So one of the things that I've found very compelling 214 00:11:41,520 --> 00:11:44,760 Speaker 2: here is that deep Seek's chatbot, known as R one 215 00:11:45,840 --> 00:11:50,400 Speaker 2: was apparently trained using less than cutting edge semiconductors, and 216 00:11:50,480 --> 00:11:55,480 Speaker 2: that in turn required much less energy consumption. So I 217 00:11:55,520 --> 00:11:57,280 Speaker 2: hear what you're saying when it comes to the sell 218 00:11:57,320 --> 00:12:00,960 Speaker 2: off that we had in certain semiconductor names today. But 219 00:12:01,080 --> 00:12:04,640 Speaker 2: I was also struck by the fact that Constellation Energy 220 00:12:04,720 --> 00:12:08,520 Speaker 2: was down twenty percent. How does the Deep Seek story 221 00:12:08,679 --> 00:12:10,640 Speaker 2: change the conversation around power? 222 00:12:11,559 --> 00:12:14,200 Speaker 4: So there's two parts of that question. I'll address the 223 00:12:14,200 --> 00:12:16,319 Speaker 4: power one first. There have been. 224 00:12:17,720 --> 00:12:23,520 Speaker 1: Reports of inability for companies to get adequate power sources 225 00:12:23,559 --> 00:12:29,160 Speaker 1: to data centers. So companies like Microsoft Amazon are putting 226 00:12:29,160 --> 00:12:34,080 Speaker 1: in personal nuclear reactors to actually power data centers, and 227 00:12:34,160 --> 00:12:37,800 Speaker 1: companies like Constellation Energy provide those services. So if the 228 00:12:37,920 --> 00:12:39,880 Speaker 1: data centers are not going to be as plentible and 229 00:12:39,920 --> 00:12:43,520 Speaker 1: therefore the power sources will not be needed, that's why 230 00:12:43,520 --> 00:12:45,199 Speaker 1: you see these knock on effects. It's one of the 231 00:12:45,280 --> 00:12:49,319 Speaker 1: reasons why the power story had been one of the 232 00:12:49,679 --> 00:12:53,320 Speaker 1: sort of tertiary ways to play the AI story without 233 00:12:53,360 --> 00:12:56,120 Speaker 1: actually buying the tech stocks. So that's why they're sort 234 00:12:56,160 --> 00:12:59,600 Speaker 1: of connected together in a trade. 235 00:12:59,880 --> 00:13:03,120 Speaker 4: I don't believe that. 236 00:13:02,320 --> 00:13:05,160 Speaker 1: Either story will turn out to be, you know, the 237 00:13:05,200 --> 00:13:06,760 Speaker 1: sell off today will be the right one. 238 00:13:06,800 --> 00:13:08,600 Speaker 4: I think it's actually the wrong choice. 239 00:13:08,960 --> 00:13:12,480 Speaker 1: But to go back to talk about the clever technology 240 00:13:12,559 --> 00:13:17,360 Speaker 1: or the clever engineering that made this possible, where the 241 00:13:17,600 --> 00:13:21,000 Speaker 1: R one, you know, learning module is able to use. 242 00:13:22,440 --> 00:13:25,200 Speaker 4: Just a just a It's a very small change, but 243 00:13:25,240 --> 00:13:26,520 Speaker 4: it's very very impactful. 244 00:13:26,559 --> 00:13:29,840 Speaker 1: So if you think about like disruptive technologies, you have 245 00:13:30,000 --> 00:13:34,840 Speaker 1: incumbent players who basically iterate they have built this fantastic 246 00:13:35,440 --> 00:13:38,440 Speaker 1: black box, and they just iterate small and small pieces, 247 00:13:38,480 --> 00:13:41,560 Speaker 1: Whereas these disruptive players come come around and they actually 248 00:13:41,600 --> 00:13:44,480 Speaker 1: change the entire game. And that's what Deep Seek has done. 249 00:13:44,679 --> 00:13:47,720 Speaker 1: They basically took the AI engine which is ever present 250 00:13:47,800 --> 00:13:52,720 Speaker 1: in open ai, where the technology is ready for any 251 00:13:52,800 --> 00:13:56,920 Speaker 1: question on any topic, whereas in the in the deep 252 00:13:56,920 --> 00:14:01,240 Speaker 1: Seek infrastructure, it would basically take the input and only 253 00:14:01,320 --> 00:14:04,040 Speaker 1: turn on the pieces of the infrastructure that were needed 254 00:14:04,080 --> 00:14:04,800 Speaker 1: to answer. 255 00:14:04,480 --> 00:14:05,600 Speaker 4: The question or to iterate. 256 00:14:05,840 --> 00:14:09,600 Speaker 1: So it basically didn't have the entire system running, you know, 257 00:14:09,679 --> 00:14:13,440 Speaker 1: all the time. And that will be readily adopted by 258 00:14:13,520 --> 00:14:18,960 Speaker 1: all AI providers. For sure, Beta and for sure Open 259 00:14:19,000 --> 00:14:22,080 Speaker 1: AI will adopt that clever engineering and they too will 260 00:14:22,120 --> 00:14:24,160 Speaker 1: have it. And so the reason why I think that 261 00:14:24,240 --> 00:14:27,479 Speaker 1: this is just the beginning is that now that infrastructure 262 00:14:27,840 --> 00:14:32,280 Speaker 1: that was being built to handle an ever present AI 263 00:14:32,440 --> 00:14:36,280 Speaker 1: infrastructure now could be used for other things. And I 264 00:14:36,280 --> 00:14:39,720 Speaker 1: think that's why we're going to see this AI infrastructure 265 00:14:39,760 --> 00:14:43,160 Speaker 1: actually blossom as a result of this disruption, not be 266 00:14:43,280 --> 00:14:43,880 Speaker 1: torn apart. 267 00:14:44,040 --> 00:14:46,480 Speaker 2: It is a critical week for tech earnings. This week. 268 00:14:46,520 --> 00:14:50,800 Speaker 2: Four of the mag seven are reporting. That's Apple, Microsoft, Tesla, 269 00:14:51,040 --> 00:14:54,160 Speaker 2: and Meta Platforms. What are your expectations here and what 270 00:14:54,200 --> 00:14:57,560 Speaker 2: do these companies need to say about the future in 271 00:14:57,680 --> 00:15:01,360 Speaker 2: order for investors to really kind of not lose heart. 272 00:15:02,120 --> 00:15:04,560 Speaker 4: Well, two of the four of them had a very 273 00:15:04,560 --> 00:15:05,240 Speaker 4: good day to day. 274 00:15:05,280 --> 00:15:07,960 Speaker 1: I mean, Apple and Meta really bucked the trend, and 275 00:15:08,040 --> 00:15:11,760 Speaker 1: it was largely because they're consumers of these more expensive, 276 00:15:12,360 --> 00:15:16,000 Speaker 1: you know, chips, and I think if anything that lowers 277 00:15:16,000 --> 00:15:20,280 Speaker 1: the infrastructure costs a cruise directly to players like Meta 278 00:15:20,560 --> 00:15:24,520 Speaker 1: or Apple. Apple's unique story because there have been some 279 00:15:25,120 --> 00:15:29,720 Speaker 1: concerns about the inability to produce or sell the iPhone. 280 00:15:30,200 --> 00:15:32,960 Speaker 1: Maybe sales had declined, maybe people weren't upgrading at the 281 00:15:33,000 --> 00:15:36,720 Speaker 1: same rate. But the story on Apple is less hardware 282 00:15:36,800 --> 00:15:39,840 Speaker 1: these days and definitely more about. 283 00:15:39,200 --> 00:15:40,680 Speaker 4: You know, the ancillary services. 284 00:15:40,680 --> 00:15:42,400 Speaker 1: So I think Apple is going to do quite well 285 00:15:42,440 --> 00:15:45,440 Speaker 1: and I think the stock definitely had a fantastic day 286 00:15:45,480 --> 00:15:47,640 Speaker 1: to day on the prospects of them, you know, having 287 00:15:47,720 --> 00:15:50,800 Speaker 1: higher margins because they're spending less on their data infrastructure 288 00:15:51,080 --> 00:15:54,360 Speaker 1: meta the same thing. I think Microsoft kind of got 289 00:15:54,400 --> 00:15:57,080 Speaker 1: caught up in the AI given its investment in the 290 00:15:57,160 --> 00:16:01,280 Speaker 1: in the open AI platform. But I think that Microsoft 291 00:16:01,320 --> 00:16:04,800 Speaker 1: is actually, of the three, the best position to continue 292 00:16:04,800 --> 00:16:07,640 Speaker 1: to grow earnings year over year, and so I think 293 00:16:07,680 --> 00:16:09,920 Speaker 1: three of the four are doing well. I'm not I 294 00:16:09,920 --> 00:16:13,680 Speaker 1: don't follow Tesla that much, but I feel like Elon 295 00:16:13,800 --> 00:16:16,360 Speaker 1: Musk may be in a better position to understand whether 296 00:16:16,440 --> 00:16:20,680 Speaker 1: or not this deep seek technology is actually you know, 297 00:16:20,680 --> 00:16:23,320 Speaker 1: whether or not the technology being deployed or the things 298 00:16:23,360 --> 00:16:26,000 Speaker 1: that the company has said that they've been able to do, 299 00:16:26,120 --> 00:16:29,520 Speaker 1: or actual reality because Tesla has one of the largest 300 00:16:29,520 --> 00:16:33,680 Speaker 1: AI infrastructures powered by all the automobiles that Tesla makes, 301 00:16:33,880 --> 00:16:37,440 Speaker 1: so their neural net has been around for some time 302 00:16:37,520 --> 00:16:41,160 Speaker 1: and has been generating AI content for generation of software 303 00:16:41,240 --> 00:16:43,800 Speaker 1: with the cars. So I think maybe we might learn 304 00:16:43,840 --> 00:16:47,240 Speaker 1: a thing or two in the conference call that may 305 00:16:47,360 --> 00:16:50,200 Speaker 1: be related to deep seek in some of this AI disruption. 306 00:16:50,280 --> 00:16:52,720 Speaker 1: I think that will be the story for the for 307 00:16:52,880 --> 00:16:54,320 Speaker 1: the Tesla conference call this. 308 00:16:54,320 --> 00:16:57,160 Speaker 2: Year, Jamie. Before I let you go, We've also have 309 00:16:57,200 --> 00:17:00,920 Speaker 2: a FED decision on Wednesday, Powell news conference right after 310 00:17:00,960 --> 00:17:04,240 Speaker 2: we get the decision. Is it likely that we're going 311 00:17:04,280 --> 00:17:06,600 Speaker 2: to get kind of a hawkish message right now? 312 00:17:07,160 --> 00:17:10,680 Speaker 4: No, I think the FED is on hold. 313 00:17:11,400 --> 00:17:15,160 Speaker 1: They may discuss, as they did in the press conference 314 00:17:15,200 --> 00:17:19,399 Speaker 1: and December, the need to be cautious about lowering rates 315 00:17:19,480 --> 00:17:24,119 Speaker 1: too fast. But it's going to be interesting to watch 316 00:17:24,119 --> 00:17:26,320 Speaker 1: over the next couple of months because the President is 317 00:17:26,359 --> 00:17:28,720 Speaker 1: going to put enormous pressure on the FED to do 318 00:17:28,760 --> 00:17:32,960 Speaker 1: something about mortgage rates. That has been a particular concern 319 00:17:33,280 --> 00:17:36,200 Speaker 1: to President Trump, and he is going to start beating 320 00:17:36,200 --> 00:17:38,919 Speaker 1: the drum on that very loudly. So I have a 321 00:17:38,920 --> 00:17:41,120 Speaker 1: feeling we'll start to see the FED start to talk 322 00:17:41,200 --> 00:17:43,040 Speaker 1: about how they're going to deal with the balance sheet 323 00:17:43,560 --> 00:17:44,800 Speaker 1: and how they could. 324 00:17:44,680 --> 00:17:47,120 Speaker 4: Help consumers on the mortgage side. 325 00:17:47,840 --> 00:17:49,960 Speaker 1: So I don't think this FED meeting will be much 326 00:17:49,960 --> 00:17:52,480 Speaker 1: on the FED fund rate in terms of news. I mean, 327 00:17:52,480 --> 00:17:55,080 Speaker 1: the FED fund futures basically are ninety nine percent that 328 00:17:55,160 --> 00:17:59,840 Speaker 1: they hold. What is not built into the model is 329 00:18:00,040 --> 00:18:02,440 Speaker 1: what they talk about on the ballot sheet, and there 330 00:18:02,480 --> 00:18:06,439 Speaker 1: has been runoff at a fairly decent equipped over the 331 00:18:06,480 --> 00:18:08,480 Speaker 1: last year and a half, and I think that may 332 00:18:08,520 --> 00:18:11,600 Speaker 1: be ready to slow down. And if the FED would 333 00:18:12,080 --> 00:18:15,040 Speaker 1: you slow its mortgage backed securities runoff, I think that 334 00:18:15,119 --> 00:18:17,200 Speaker 1: might help mortgage rates some. So I think that's where 335 00:18:17,200 --> 00:18:19,119 Speaker 1: you're going to see some of the action. And it 336 00:18:19,200 --> 00:18:22,280 Speaker 1: would not surprise me at all if the President is out, 337 00:18:22,440 --> 00:18:25,440 Speaker 1: you know, with comments in and around the FED meeting, 338 00:18:25,440 --> 00:18:28,000 Speaker 1: that would you know, really encourage them to be more 339 00:18:28,160 --> 00:18:30,960 Speaker 1: aggressive in helping mortgage rates to. 340 00:18:30,920 --> 00:18:33,719 Speaker 2: Con Jamie will leave it there. Thank you so much 341 00:18:33,760 --> 00:18:36,720 Speaker 2: for being with us. Jimmy Cox there, managing partner at 342 00:18:36,760 --> 00:18:40,280 Speaker 2: Harris Financial Group, joining from Richmond, Virginia. Here on the 343 00:18:40,359 --> 00:18:46,000 Speaker 2: Daybreak Asia Podcast. Thanks for listening to today's episode of 344 00:18:46,040 --> 00:18:50,160 Speaker 2: the Bloomberg Daybreak Asia Edition podcast. Each weekday, we look 345 00:18:50,160 --> 00:18:53,960 Speaker 2: at the story shaping markets, finance, and geopolitics in the 346 00:18:54,000 --> 00:18:57,240 Speaker 2: Asia Pacific. You can find us on Apple, Spotify, the 347 00:18:57,280 --> 00:19:01,280 Speaker 2: Bloomberg Podcast YouTube channel, or anywhere else you listen. Join 348 00:19:01,400 --> 00:19:04,399 Speaker 2: us again tomorrow for insight on the market moves from 349 00:19:04,400 --> 00:19:08,879 Speaker 2: Hong Kong to Singapore and Australia. I'm Doug Prisoner and 350 00:19:09,000 --> 00:19:10,160 Speaker 2: this is Bloomberg