1 00:00:02,520 --> 00:00:09,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news from the heart of 2 00:00:09,680 --> 00:00:15,400 Speaker 1: where innovation, money and power collide in Silicon Valley and beyond. 3 00:00:15,800 --> 00:00:21,079 Speaker 1: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:35,000 --> 00:00:37,040 Speaker 2: Live from New York. I'm Caroline Hyde. 5 00:00:37,159 --> 00:00:40,959 Speaker 3: And I'm Mike Sheppard in San Francisco. This is Bloomberg Technology, and. 6 00:00:40,920 --> 00:00:44,280 Speaker 4: We have special coverage of the Chinese AI competitor Deep 7 00:00:44,320 --> 00:00:47,880 Speaker 4: Seek and its impact on technanues the broader markets at large. 8 00:00:47,920 --> 00:00:50,360 Speaker 4: Let's look at the nastat, currently having its worst day 9 00:00:50,400 --> 00:00:52,800 Speaker 4: in more than a month, as we question the need 10 00:00:53,040 --> 00:00:56,080 Speaker 4: for compute power to get us ever more efficient, ever 11 00:00:56,200 --> 00:00:59,040 Speaker 4: more specific generative AI. 12 00:00:59,480 --> 00:01:00,720 Speaker 2: We're down too point six percent. 13 00:01:00,760 --> 00:01:02,560 Speaker 4: Move on and look at the individual names, because this 14 00:01:02,600 --> 00:01:05,360 Speaker 4: is when we question in video the compute power, the 15 00:01:05,440 --> 00:01:08,720 Speaker 4: necessity forever more efficient chips, and the cost of them. 16 00:01:08,959 --> 00:01:09,959 Speaker 2: In Vidia has its. 17 00:01:09,959 --> 00:01:14,080 Speaker 4: Worst route in history in terms of market capitalization ever 18 00:01:14,160 --> 00:01:16,280 Speaker 4: on the market. We have never seen almost half a 19 00:01:16,319 --> 00:01:19,440 Speaker 4: trillion dollars wiped out from one single name. This is 20 00:01:19,560 --> 00:01:23,240 Speaker 4: global ASML chip equipment maker in Europe down almost six percent, 21 00:01:23,480 --> 00:01:26,319 Speaker 4: But Hong Kong stocks do well as we start to 22 00:01:26,319 --> 00:01:30,360 Speaker 4: see AI as a real champion for China. Let's talk 23 00:01:30,360 --> 00:01:32,720 Speaker 4: about it all with Bloomberg Intelligence senior analyst man Leap 24 00:01:32,760 --> 00:01:35,880 Speaker 4: Singh who joins us some more. How does this change 25 00:01:35,880 --> 00:01:39,119 Speaker 4: the game for llm's in the US, for chip equipment 26 00:01:39,440 --> 00:01:40,040 Speaker 4: in the US. 27 00:01:40,400 --> 00:01:42,360 Speaker 5: Yeah, Look, I mean we look at it from a 28 00:01:42,400 --> 00:01:45,920 Speaker 5: stack perspective, and with generative AI, so far, all the 29 00:01:46,080 --> 00:01:50,320 Speaker 5: value was captured by the chip makers, the semicap equipment guys, 30 00:01:50,360 --> 00:01:54,120 Speaker 5: and the LLM companies like open Ai and Tropic, and 31 00:01:54,200 --> 00:01:57,560 Speaker 5: the software guys were really missing out because everyone was like, 32 00:01:57,600 --> 00:01:59,960 Speaker 5: their margins will be compressed, they have to pay more 33 00:02:00,080 --> 00:02:03,360 Speaker 5: for chips, pay for this LLM layer. Well, guess what 34 00:02:03,680 --> 00:02:06,800 Speaker 5: if the price can come down and what deep seek 35 00:02:06,840 --> 00:02:10,200 Speaker 5: has shown us is the price per token, price per 36 00:02:10,280 --> 00:02:14,960 Speaker 5: query can come down drastically, then suddenly the I think 37 00:02:14,960 --> 00:02:17,440 Speaker 5: the power goes back to the software guys because now 38 00:02:17,480 --> 00:02:20,800 Speaker 5: they can incorporate AI in everything they want, in their 39 00:02:20,840 --> 00:02:26,040 Speaker 5: app and their website, whatever functionality they have, they can 40 00:02:26,120 --> 00:02:29,720 Speaker 5: add a layer of AI without having to pay too much, 41 00:02:29,880 --> 00:02:32,560 Speaker 5: you know, and hurting their growth margins. And so I 42 00:02:32,600 --> 00:02:36,520 Speaker 5: think this really is a big development. And every cloud company, 43 00:02:36,520 --> 00:02:40,600 Speaker 5: whether it's Alphabet or Microsoft, they'll be looking to leverage 44 00:02:40,639 --> 00:02:43,800 Speaker 5: their compute more efficiently because they will benefit from the 45 00:02:43,800 --> 00:02:46,640 Speaker 5: cloud revenue on the infrastructure side, but also on the 46 00:02:46,680 --> 00:02:49,080 Speaker 5: application side, which is why I think Meta raise their 47 00:02:49,120 --> 00:02:50,480 Speaker 5: capex on Friday. 48 00:02:51,639 --> 00:02:55,160 Speaker 3: Mandep How does this undercut in Video in particular in 49 00:02:55,200 --> 00:03:00,080 Speaker 3: its argument for advances like the Blackwell product that it 50 00:02:59,960 --> 00:03:02,960 Speaker 3: is pushing out now and what may come after it? 51 00:03:03,000 --> 00:03:06,640 Speaker 3: Is it becoming much harder for Jensen want to sell 52 00:03:06,680 --> 00:03:08,919 Speaker 3: that to Microsoft and it's other customers. 53 00:03:08,919 --> 00:03:11,680 Speaker 5: Now all of a sudden, look, I mean the last 54 00:03:11,680 --> 00:03:14,520 Speaker 5: three months we were debating about the scaling laws and 55 00:03:14,600 --> 00:03:17,600 Speaker 5: how big of a cluster we need. We're talking about 56 00:03:17,600 --> 00:03:21,480 Speaker 5: one hundred kgpus in a cluster and possibly one million 57 00:03:22,160 --> 00:03:25,200 Speaker 5: GPUs in a cluster. I think this puts into question 58 00:03:25,280 --> 00:03:28,360 Speaker 5: mark how big of a cluster you need actually for 59 00:03:28,440 --> 00:03:31,919 Speaker 5: training your LLM And look, all these LLM companies have 60 00:03:32,000 --> 00:03:35,360 Speaker 5: to keep training their algorithms. You know, this is not 61 00:03:35,440 --> 00:03:38,360 Speaker 5: a one and done thing, so you need compute capacity. 62 00:03:38,480 --> 00:03:41,160 Speaker 5: But at the same time, and Video was accruing all 63 00:03:41,200 --> 00:03:45,120 Speaker 5: the benefits in terms of pricing because of the advances 64 00:03:45,120 --> 00:03:47,640 Speaker 5: in their chips. So if you can do more with 65 00:03:47,760 --> 00:03:50,440 Speaker 5: even the you know, the older version of their chips 66 00:03:50,680 --> 00:03:53,760 Speaker 5: and you don't need a cluster of one million GPUs 67 00:03:54,120 --> 00:03:57,320 Speaker 5: connected together, then probably I think you can do more 68 00:03:57,440 --> 00:04:00,280 Speaker 5: in terms of the distributed infrastructure. And I think that's 69 00:04:00,320 --> 00:04:02,880 Speaker 5: what a lot of people are trying to figure out today. 70 00:04:03,920 --> 00:04:06,720 Speaker 3: The guys Man Deep saying thank you. Let's bring in 71 00:04:06,800 --> 00:04:10,440 Speaker 3: Tony Wong, portfolio manager of the Science and Technology Fund 72 00:04:10,480 --> 00:04:13,480 Speaker 3: at t row Price and a key and video shareholder 73 00:04:13,520 --> 00:04:14,400 Speaker 3: for more on this. 74 00:04:15,000 --> 00:04:15,320 Speaker 6: Tony. 75 00:04:15,400 --> 00:04:18,520 Speaker 3: We've got a big week and month of earnings coming up. 76 00:04:18,720 --> 00:04:21,000 Speaker 3: How does this change the way you were looking at 77 00:04:21,000 --> 00:04:23,480 Speaker 3: companies as they prepare to deliver their results. 78 00:04:25,279 --> 00:04:27,919 Speaker 7: Yeah, I think it's a great question. And if you 79 00:04:27,920 --> 00:04:31,000 Speaker 7: look at the kind of trends and technology generally, when 80 00:04:31,040 --> 00:04:35,039 Speaker 7: things become more efficient, increases demand and there's more ubiquity. 81 00:04:35,080 --> 00:04:38,280 Speaker 7: And so it wasn't that long ago, you know, a 82 00:04:38,320 --> 00:04:41,200 Speaker 7: few months that we're all debating that, like the cost 83 00:04:41,360 --> 00:04:43,960 Speaker 7: was too great to deliver and so where is the ROI? 84 00:04:44,000 --> 00:04:47,640 Speaker 7: And so I actually feel like this is tam expansionary 85 00:04:47,760 --> 00:04:50,640 Speaker 7: for the market long term. I think that Man Deep, 86 00:04:50,720 --> 00:04:54,560 Speaker 7: you know, obviously has highlighted what the market is kind 87 00:04:54,560 --> 00:04:55,320 Speaker 7: of talking about today. 88 00:04:55,360 --> 00:04:56,919 Speaker 8: I think that's a very valid concern. 89 00:04:57,040 --> 00:05:01,560 Speaker 7: But Javon's paradox essentially is that when things become more efficient, 90 00:05:01,800 --> 00:05:04,599 Speaker 7: consumption goes up, demand goes up, and so I think 91 00:05:04,600 --> 00:05:08,560 Speaker 7: that's that's necessary, that's healthy. I do think that there's 92 00:05:08,600 --> 00:05:13,760 Speaker 7: a little bit of sensational reporting perhaps on Twitter and 93 00:05:13,839 --> 00:05:16,839 Speaker 7: just in terms of how deep seek only took you know, 94 00:05:16,880 --> 00:05:18,159 Speaker 7: six million dollars to train. 95 00:05:18,240 --> 00:05:20,559 Speaker 8: I think that's a that's a that's a nice marketing number. 96 00:05:20,560 --> 00:05:22,320 Speaker 7: But if you dig in that, you know, that doesn't 97 00:05:22,320 --> 00:05:25,159 Speaker 7: include R and D costs, experimental runs, and the market. 98 00:05:25,520 --> 00:05:28,840 Speaker 7: The model is a lot smaller than what chatchbt is 99 00:05:28,880 --> 00:05:31,320 Speaker 7: and it's only tech based, so there's a lot of 100 00:05:31,360 --> 00:05:34,920 Speaker 7: reasons to think that this is not exactly you know, 101 00:05:35,040 --> 00:05:38,680 Speaker 7: you know, a kind of direct comparison. They do have 102 00:05:38,720 --> 00:05:43,240 Speaker 7: a lot of great breakthroughs in the efficiency of the model, obviously, 103 00:05:43,360 --> 00:05:45,680 Speaker 7: but I think it's good for the industry, and it's 104 00:05:45,680 --> 00:05:49,000 Speaker 7: open source and the players will you know, the community 105 00:05:49,000 --> 00:05:53,120 Speaker 7: will adopt the innovations and then further kind of pursue 106 00:05:53,160 --> 00:05:55,200 Speaker 7: like kind of AI in a bigger way. 107 00:05:55,240 --> 00:05:58,880 Speaker 4: I think to that point, let's just reconfirm to our 108 00:05:58,920 --> 00:06:02,719 Speaker 4: audience what is that deep seek has done. Because Bloomberg 109 00:06:02,800 --> 00:06:06,360 Speaker 4: Intelligence Bloomberg News has been reporting on deep Seek four months. 110 00:06:06,360 --> 00:06:09,880 Speaker 4: Bloomberg Intelligence ranked it as the seventh most powerful LLM 111 00:06:09,960 --> 00:06:12,120 Speaker 4: all the way back in June of last year. But 112 00:06:12,240 --> 00:06:14,440 Speaker 4: now it gives us the fact that it's latest. Our 113 00:06:14,560 --> 00:06:18,280 Speaker 4: one model is as sophisticated. Money would say on the 114 00:06:18,279 --> 00:06:21,680 Speaker 4: internet and other experts out there is as sophisticated as 115 00:06:21,680 --> 00:06:24,800 Speaker 4: that of anthropics latest model, or of open A's open 116 00:06:24,800 --> 00:06:27,200 Speaker 4: AI's latest model. But they've done it at a fraction 117 00:06:27,240 --> 00:06:29,760 Speaker 4: of the cost. But six million is what is reported 118 00:06:29,800 --> 00:06:32,200 Speaker 4: to have been cost. We have been able to delve 119 00:06:32,240 --> 00:06:36,120 Speaker 4: into the veracity of that number, Tony. When it comes 120 00:06:36,160 --> 00:06:41,240 Speaker 4: to the rush to the bottom ever more cheap of 121 00:06:41,240 --> 00:06:45,800 Speaker 4: applications of generative AI, do we therefore see Microsoft have 122 00:06:45,920 --> 00:06:47,400 Speaker 4: to cut its prices. 123 00:06:47,480 --> 00:06:48,360 Speaker 2: Do we see. 124 00:06:48,320 --> 00:06:52,000 Speaker 4: Ultimately revenue and profitability of these LM providers having to 125 00:06:52,040 --> 00:06:52,920 Speaker 4: become less? 126 00:06:53,560 --> 00:06:54,920 Speaker 8: Yeah, I think that's a good question. 127 00:06:55,080 --> 00:06:58,280 Speaker 7: I would say that, you know, for why we don't 128 00:06:58,320 --> 00:07:00,800 Speaker 7: know why open ai in it Thropic are going to 129 00:07:00,839 --> 00:07:03,039 Speaker 7: release next and a lot of times, like you know, 130 00:07:03,080 --> 00:07:06,200 Speaker 7: there are so many innovations and marketing is moving very quickly, 131 00:07:06,360 --> 00:07:08,920 Speaker 7: so you can very well that they've got something that's 132 00:07:09,000 --> 00:07:10,200 Speaker 7: really exciting to be launched. 133 00:07:10,680 --> 00:07:11,600 Speaker 8: So that's number one. 134 00:07:11,600 --> 00:07:13,520 Speaker 7: I think like number two is that a lot of times, 135 00:07:13,680 --> 00:07:16,120 Speaker 7: you know, this is a fast follower bress model that 136 00:07:16,400 --> 00:07:17,920 Speaker 7: has a ceiling in terms of what it can do 137 00:07:18,080 --> 00:07:20,600 Speaker 7: versus larger models, and it's you know, open ai is 138 00:07:20,640 --> 00:07:24,120 Speaker 7: going to be multimodal with their model, and you know, 139 00:07:24,160 --> 00:07:24,560 Speaker 7: I think. 140 00:07:24,440 --> 00:07:26,559 Speaker 8: There's a lot of innovation that will happen. 141 00:07:26,640 --> 00:07:28,280 Speaker 7: And then I think that like at the end of 142 00:07:28,320 --> 00:07:30,280 Speaker 7: the day, like you know, there's a lot of kind 143 00:07:30,280 --> 00:07:35,400 Speaker 7: of ecosystem building, enterprise aspects that that also factor into it. 144 00:07:35,480 --> 00:07:37,680 Speaker 7: So I think that the market for oms can be 145 00:07:37,720 --> 00:07:40,000 Speaker 7: more diverse than people are giving credit for. 146 00:07:40,720 --> 00:07:43,120 Speaker 4: Is this a buying opportunity Tony to stack up on 147 00:07:43,240 --> 00:07:43,640 Speaker 4: end video? 148 00:07:43,680 --> 00:07:44,800 Speaker 2: Even more, I. 149 00:07:44,800 --> 00:07:48,280 Speaker 7: Think long term, you know, you think about the technology trends, 150 00:07:48,720 --> 00:07:52,480 Speaker 7: and I don't think that just because there's one model 151 00:07:52,520 --> 00:07:54,600 Speaker 7: that is more efficient that people are going to say, Okay, 152 00:07:54,600 --> 00:07:57,040 Speaker 7: we're just going to stop here. You know, at the 153 00:07:57,120 --> 00:08:00,440 Speaker 7: end of the day, everybody's racing towards hopefully AGI and 154 00:08:00,840 --> 00:08:03,440 Speaker 7: you know, looking to continue to invest. 155 00:08:03,440 --> 00:08:05,040 Speaker 8: And I think that you saw Mark. 156 00:08:04,920 --> 00:08:08,400 Speaker 7: Zuckerber, even though he knew about Deep Seek, you know, 157 00:08:08,440 --> 00:08:11,680 Speaker 7: since it was released over Christmas, you saw that he 158 00:08:11,720 --> 00:08:14,440 Speaker 7: actually increased CAPEC spend, and so, you know, I think 159 00:08:14,480 --> 00:08:16,760 Speaker 7: that there's it's not just the model, it's also the 160 00:08:16,800 --> 00:08:20,480 Speaker 7: compute as well, and so I think that as there's 161 00:08:20,520 --> 00:08:22,440 Speaker 7: more demand of the app layer, that's actually good for 162 00:08:23,200 --> 00:08:26,080 Speaker 7: filling up the compute capacity that we're building. 163 00:08:26,800 --> 00:08:30,560 Speaker 3: Tony, you talked about Mark Zuckerberg and his announcement last 164 00:08:30,600 --> 00:08:35,160 Speaker 3: week on capex. Is there any thought to the way 165 00:08:35,400 --> 00:08:38,760 Speaker 3: these companies should revisit those spending plans? 166 00:08:38,800 --> 00:08:39,920 Speaker 9: Are they going too big? 167 00:08:40,040 --> 00:08:43,240 Speaker 3: Is it too big and maybe not efficient enough? 168 00:08:44,000 --> 00:08:44,200 Speaker 6: Yeah? 169 00:08:44,240 --> 00:08:45,479 Speaker 8: Well, naturally, absolutely. 170 00:08:45,520 --> 00:08:47,840 Speaker 7: I think that everybody's going to the engineering teams and 171 00:08:47,880 --> 00:08:49,840 Speaker 7: saying like, hey, what can we do? This is open 172 00:08:49,840 --> 00:08:52,440 Speaker 7: source model, let's take it apart, let's figure out how 173 00:08:52,480 --> 00:08:55,080 Speaker 7: we can incorporate the best and you know, as cost 174 00:08:55,160 --> 00:08:57,760 Speaker 7: him down, Like, I think that actually increases the number 175 00:08:57,840 --> 00:08:59,920 Speaker 7: of use cases and the people say like, oh wow, 176 00:09:00,240 --> 00:09:04,120 Speaker 7: I wasn't able to afford perhaps the latest like equipment 177 00:09:04,200 --> 00:09:06,719 Speaker 7: because of the cost of doing so, we're not sure 178 00:09:06,720 --> 00:09:07,839 Speaker 7: about the ROI and so. 179 00:09:08,080 --> 00:09:10,600 Speaker 8: This I do think that it's it's absolutely natural. 180 00:09:10,640 --> 00:09:13,320 Speaker 7: But I don't think that companies are going to say like, oh, like, 181 00:09:13,600 --> 00:09:14,560 Speaker 7: let's just stop here. 182 00:09:15,480 --> 00:09:18,480 Speaker 8: And because there are so many like potential benefits, the 183 00:09:19,800 --> 00:09:21,439 Speaker 8: kind of goal at the end of the day is 184 00:09:21,800 --> 00:09:22,440 Speaker 8: pretty grand. 185 00:09:23,640 --> 00:09:23,920 Speaker 6: Tony. 186 00:09:23,920 --> 00:09:26,120 Speaker 3: I wanted to follow with a question about the potential 187 00:09:26,320 --> 00:09:31,440 Speaker 3: policy responses from Washington. Given the hawkish mood towards China 188 00:09:31,559 --> 00:09:35,559 Speaker 3: with the new administration. Do you see any risk of 189 00:09:35,640 --> 00:09:39,800 Speaker 3: a response that would pose a you know, a challenge 190 00:09:39,840 --> 00:09:41,960 Speaker 3: to the companies that you own and over and are 191 00:09:42,040 --> 00:09:42,840 Speaker 3: taking a look at. 192 00:09:43,520 --> 00:09:45,840 Speaker 7: Yeah, well, I think that definitely there might be some 193 00:09:45,880 --> 00:09:48,800 Speaker 7: geopolitical aspects of what's going on this week. You know, 194 00:09:48,840 --> 00:09:53,200 Speaker 7: I think President Trump announced the Stargate project and so 195 00:09:53,400 --> 00:09:56,040 Speaker 7: this was perhaps you know, deep seek is a response 196 00:09:56,080 --> 00:09:58,040 Speaker 7: to that, and that you know, the question is like 197 00:09:58,400 --> 00:10:03,040 Speaker 7: can you build AI more cheaply and openly versus like 198 00:10:03,080 --> 00:10:05,920 Speaker 7: building a bunch of AI infrastructure, And I think that, 199 00:10:06,440 --> 00:10:08,920 Speaker 7: you know, my view is it probably you need both, 200 00:10:09,400 --> 00:10:13,520 Speaker 7: and you know, each country will continue to race towards 201 00:10:14,160 --> 00:10:17,200 Speaker 7: you know, AI capabilities, and I think there could be 202 00:10:17,240 --> 00:10:21,240 Speaker 7: some export restrictions more more along the way, possibly, but 203 00:10:21,320 --> 00:10:26,319 Speaker 7: it does show that you know, perhaps like restricting isn't 204 00:10:26,400 --> 00:10:32,319 Speaker 7: particularly helpful because then you actually bore innovation out of scarcity. 205 00:10:32,720 --> 00:10:34,800 Speaker 7: I think that what we've seen is that there is 206 00:10:34,800 --> 00:10:37,200 Speaker 7: a lot of innovation we had and you know, I think, 207 00:10:37,400 --> 00:10:40,599 Speaker 7: you know, competition is good, and you know, at the 208 00:10:40,679 --> 00:10:42,280 Speaker 7: end of the day, like you want an open market. 209 00:10:42,920 --> 00:10:47,280 Speaker 4: Who will win from your portfolio from that innovation or 210 00:10:47,320 --> 00:10:50,959 Speaker 4: potentially from that shift from hardware more to the software side. 211 00:10:51,160 --> 00:10:52,760 Speaker 7: Yeah, I think it's a it's a really exciting time 212 00:10:52,800 --> 00:10:55,360 Speaker 7: because I do think that at the app layer this 213 00:10:55,600 --> 00:10:58,360 Speaker 7: improves kind of the r oy obviously, and you know, 214 00:10:58,559 --> 00:11:01,079 Speaker 7: anybody that has like a really taking customer base and 215 00:11:01,679 --> 00:11:04,959 Speaker 7: large kind of kind apps that they have a lot 216 00:11:05,000 --> 00:11:06,560 Speaker 7: of data from, I think that's good. 217 00:11:06,840 --> 00:11:08,280 Speaker 8: You know, I do like you know, the. 218 00:11:08,440 --> 00:11:11,880 Speaker 7: AI infrastructure still, I think that there's you know, still 219 00:11:12,000 --> 00:11:15,520 Speaker 7: steady kind of consistent demand there and you know, history 220 00:11:15,559 --> 00:11:19,640 Speaker 7: of technology would say that people take that innovation that 221 00:11:19,679 --> 00:11:23,160 Speaker 7: improved performance and they put into good use for developing 222 00:11:23,559 --> 00:11:25,640 Speaker 7: the next next here technologies. 223 00:11:26,280 --> 00:11:29,240 Speaker 4: Turning on Portfolio Manager at t ro Price, one of 224 00:11:29,360 --> 00:11:32,679 Speaker 4: the most significant in video shareholders, we so appreciate it. 225 00:11:32,720 --> 00:11:35,280 Speaker 4: Coming up will be joined by former White House Chief 226 00:11:35,280 --> 00:11:40,360 Speaker 4: Information Officer Theresa Paytent to discuss deepseeks, cybersecurity implications, and more. 227 00:11:40,880 --> 00:11:43,439 Speaker 4: But there are rather earnings upon us keep us a 228 00:11:43,480 --> 00:11:45,520 Speaker 4: float with AT and T. We'll bring you what AT 229 00:11:45,640 --> 00:11:47,800 Speaker 4: and T is currently doing on the market. We're higher 230 00:11:48,080 --> 00:11:51,040 Speaker 4: in that particular. Stop fourth quarter results came in better 231 00:11:51,120 --> 00:11:54,040 Speaker 4: than had been expected, driven by seasonal promotions for AT 232 00:11:54,160 --> 00:11:56,280 Speaker 4: and T and bundled product offerings. 233 00:11:56,600 --> 00:12:03,200 Speaker 2: This is bloot technology. 234 00:12:09,200 --> 00:12:12,440 Speaker 10: There's no question that this could be a potential game changer. 235 00:12:12,640 --> 00:12:14,880 Speaker 9: It's a game changer for the Max seven stocks. 236 00:12:15,120 --> 00:12:17,880 Speaker 10: Tech has to be okay simply because it's arch a 237 00:12:17,920 --> 00:12:20,520 Speaker 10: large weight in the market, and if Tech is not okay, 238 00:12:20,600 --> 00:12:21,720 Speaker 10: the entire market goes. 239 00:12:21,840 --> 00:12:24,120 Speaker 3: We're all tied to these seven stocks, and in particular, 240 00:12:24,120 --> 00:12:25,280 Speaker 3: we're all tied to video. 241 00:12:25,480 --> 00:12:27,920 Speaker 10: It looks like the story is China is not as 242 00:12:27,960 --> 00:12:29,320 Speaker 10: far back as what people thought. 243 00:12:29,360 --> 00:12:30,800 Speaker 9: They are much closer now. 244 00:12:30,880 --> 00:12:33,840 Speaker 11: China looks like a very viable competitor, and a competitor 245 00:12:33,840 --> 00:12:35,560 Speaker 11: that perhaps might catch the eyre of. 246 00:12:35,559 --> 00:12:36,520 Speaker 6: The Trump administration. 247 00:12:36,720 --> 00:12:37,400 Speaker 9: This has got to. 248 00:12:37,360 --> 00:12:42,360 Speaker 12: Be a core concern of not just the Trump administration, 249 00:12:42,400 --> 00:12:45,160 Speaker 12: but all the tech universe that has moved into the 250 00:12:45,200 --> 00:12:45,600 Speaker 12: West Way. 251 00:12:45,760 --> 00:12:49,600 Speaker 10: The US China trade wars and the restrictions on chips 252 00:12:49,600 --> 00:12:52,719 Speaker 10: and things like that could get even more heated. 253 00:12:52,520 --> 00:12:54,640 Speaker 9: Which will bring more volatility to this market. 254 00:12:56,240 --> 00:12:58,960 Speaker 3: That was what some of Bloomberg Television's guests had to 255 00:12:59,000 --> 00:13:03,040 Speaker 3: say about the deep impact earlier. Today, this is Nvidia 256 00:13:03,160 --> 00:13:07,240 Speaker 3: is hitting session lows now. Deepsek reminds us that technology 257 00:13:07,320 --> 00:13:10,480 Speaker 3: lies at the heart of geopolitical tensions between the US 258 00:13:10,520 --> 00:13:14,840 Speaker 3: and China, with disputes over AI TikTok and cybersecurity and 259 00:13:14,880 --> 00:13:17,160 Speaker 3: for more, and that we're joined by Teresa Payton. She 260 00:13:17,280 --> 00:13:19,760 Speaker 3: is the CEO of fort E Lee's Solutions and the 261 00:13:19,760 --> 00:13:23,280 Speaker 3: former White House Chief Performation Officer during the George W. 262 00:13:23,400 --> 00:13:27,480 Speaker 3: Bush administration. Teresa, thank you for joining us. Obviously this 263 00:13:27,640 --> 00:13:31,720 Speaker 3: is going to cause a significant impact m Washington. How 264 00:13:31,760 --> 00:13:34,680 Speaker 3: do you expect policy makers there we have taken a 265 00:13:34,880 --> 00:13:37,400 Speaker 3: hawkish approach towards China, will. 266 00:13:37,200 --> 00:13:41,520 Speaker 13: Respond, Yeah, I mean this tech route and massive market 267 00:13:41,559 --> 00:13:44,560 Speaker 13: sell off, it's a wake up call. It is for me, 268 00:13:45,120 --> 00:13:48,280 Speaker 13: and it underscores the urgent need for robust policies. We 269 00:13:48,400 --> 00:13:52,440 Speaker 13: have got to safeguard the United States leadership in AI. 270 00:13:52,600 --> 00:13:55,960 Speaker 13: I certainly don't want China setting the gold standard for 271 00:13:56,000 --> 00:14:01,000 Speaker 13: the rest of the world for privacy, safety, secure resiliency 272 00:14:01,559 --> 00:14:03,600 Speaker 13: and ethics around AI. 273 00:14:04,240 --> 00:14:05,360 Speaker 8: I believe this is. 274 00:14:05,360 --> 00:14:10,160 Speaker 13: Why you saw sort of the Technology Advisors Elon Musk included, 275 00:14:11,200 --> 00:14:17,480 Speaker 13: and President Trump last week speaking very boldly about AI 276 00:14:17,559 --> 00:14:21,360 Speaker 13: and the United States leadership position that is necessary. I mean, 277 00:14:21,720 --> 00:14:24,680 Speaker 13: we didn't even have a full week before the Trump 278 00:14:24,680 --> 00:14:28,040 Speaker 13: administration passed an executive order on AI, so this is 279 00:14:28,160 --> 00:14:28,840 Speaker 13: very serious. 280 00:14:29,880 --> 00:14:33,320 Speaker 3: They've set AI as a priority across Washington and including 281 00:14:33,400 --> 00:14:37,480 Speaker 3: from the White House itself, and yet previous measures, including 282 00:14:37,600 --> 00:14:41,880 Speaker 3: export controls by the Biden administration, don't appear to have worked. 283 00:14:41,920 --> 00:14:43,440 Speaker 6: So what is the. 284 00:14:43,320 --> 00:14:46,600 Speaker 3: Formula to achieve what Donald Trump is set as a 285 00:14:46,600 --> 00:14:49,160 Speaker 3: policy goal, that is US leadership in AI. 286 00:14:50,320 --> 00:14:51,280 Speaker 6: No, you're right about that. 287 00:14:51,320 --> 00:14:53,920 Speaker 13: This sort of this open source method in which deep 288 00:14:53,960 --> 00:14:58,400 Speaker 13: seek was released, it's now the top downloaded free app 289 00:14:58,880 --> 00:15:02,560 Speaker 13: at the Apple Store definitely got around sort of the 290 00:15:02,600 --> 00:15:04,680 Speaker 13: different protocols that were put in place by the US 291 00:15:04,760 --> 00:15:08,080 Speaker 13: government over the last couple of years. And so it 292 00:15:08,240 --> 00:15:12,600 Speaker 13: shows that although regulatory frameworks are incredibly helpful to set 293 00:15:12,960 --> 00:15:16,640 Speaker 13: the right standard in the right tone, that sometimes regulatory 294 00:15:16,640 --> 00:15:19,280 Speaker 13: frameworks don't service and they get in the way of innovation. 295 00:15:20,280 --> 00:15:23,200 Speaker 13: So we're going to have to figure out how do 296 00:15:23,280 --> 00:15:26,320 Speaker 13: we go back to the drawing board and look at 297 00:15:26,360 --> 00:15:30,520 Speaker 13: how we engineer AI. In the United States today and 298 00:15:31,720 --> 00:15:35,880 Speaker 13: ask ourselves what can we be doing differently? And it's 299 00:15:35,920 --> 00:15:39,440 Speaker 13: a race right now, It's a long race. Right now 300 00:15:39,600 --> 00:15:42,560 Speaker 13: we kind of lost one of the heats, so but 301 00:15:42,640 --> 00:15:45,360 Speaker 13: you know, there's time to make up the difference. 302 00:15:45,680 --> 00:15:48,960 Speaker 4: With your security expertise. We bring you this latest in 303 00:15:49,040 --> 00:15:52,280 Speaker 4: terms of breaking news that deep seek says it's subject 304 00:15:52,320 --> 00:15:54,840 Speaker 4: to a large scale malicious attack. If you've tried to 305 00:15:54,840 --> 00:15:59,120 Speaker 4: download it, you can't. Currently it seems to be offline 306 00:15:59,160 --> 00:16:01,960 Speaker 4: in many ways. How do you think this will be 307 00:16:02,040 --> 00:16:05,800 Speaker 4: able to be responded to? Is it right that we 308 00:16:05,840 --> 00:16:07,600 Speaker 4: can't access it here in the United States? 309 00:16:08,680 --> 00:16:10,800 Speaker 13: Yeah, I think this is a real challenge. I saw 310 00:16:10,800 --> 00:16:13,840 Speaker 13: that they were this is late breaking news that they 311 00:16:13,880 --> 00:16:19,920 Speaker 13: were having some resiliency and recoverability issues, ostensibly because there 312 00:16:20,000 --> 00:16:24,000 Speaker 13: was such heavy volume in traffic and interest. But if 313 00:16:24,000 --> 00:16:26,920 Speaker 13: they are under some type of a cyber attack, I 314 00:16:26,960 --> 00:16:30,080 Speaker 13: think that should give pause to everybody who's thinking about 315 00:16:30,120 --> 00:16:34,320 Speaker 13: testing out the app, perhaps on company data, perhaps using 316 00:16:34,360 --> 00:16:40,040 Speaker 13: your own personal data. This is an untested app. I 317 00:16:40,160 --> 00:16:45,360 Speaker 13: would caution people not to put too much proprietary company 318 00:16:45,400 --> 00:16:49,600 Speaker 13: information into it until there's been an opportunity to actually 319 00:16:49,680 --> 00:16:52,880 Speaker 13: do something called ethical hacking or red teaming, pen testing 320 00:16:53,440 --> 00:16:56,160 Speaker 13: of the actual app, and learning more about how is 321 00:16:56,200 --> 00:16:59,240 Speaker 13: your data treated, where is it stored, how do the 322 00:16:59,280 --> 00:17:02,680 Speaker 13: algorithms work. It is open source, but things do still 323 00:17:02,720 --> 00:17:05,040 Speaker 13: need to be put through their paces and theresa. 324 00:17:05,320 --> 00:17:07,920 Speaker 4: This comes at a time when an app that everyone 325 00:17:08,000 --> 00:17:11,600 Speaker 4: in the United States from a bipartisan perspective thought was 326 00:17:11,600 --> 00:17:15,160 Speaker 4: a national security threat that of TikTok doesn't get banned 327 00:17:15,200 --> 00:17:18,480 Speaker 4: for US users. We see this ongoing post to drive 328 00:17:18,560 --> 00:17:21,240 Speaker 4: in AI but actually perhaps pull away from some of 329 00:17:21,280 --> 00:17:23,560 Speaker 4: the commitments when it comes to ethics, when it comes 330 00:17:23,600 --> 00:17:24,720 Speaker 4: to safety. 331 00:17:25,320 --> 00:17:27,560 Speaker 2: Is that something that you're reading between the lines right now? 332 00:17:28,600 --> 00:17:34,120 Speaker 13: Yeah, I'm definitely watching this very closely and my interest 333 00:17:34,200 --> 00:17:39,120 Speaker 13: is peaked here for starters. Where is Siphius on this right? 334 00:17:39,200 --> 00:17:42,880 Speaker 13: So we're we've been upset about TikTok. We've demanded TikTok 335 00:17:42,960 --> 00:17:45,280 Speaker 13: do a lot of architecture changes. They did a lot 336 00:17:45,280 --> 00:17:49,080 Speaker 13: of architecture changes with Oracle. The United States still said 337 00:17:49,080 --> 00:17:52,760 Speaker 13: that there was some concerns there. Where does Cyphias stand 338 00:17:52,920 --> 00:17:55,200 Speaker 13: on this particular app. How do they feel about it? 339 00:17:55,240 --> 00:18:00,160 Speaker 13: For both US citizen data as well as US corporation data. 340 00:18:00,720 --> 00:18:04,359 Speaker 13: The origin story is China. It is headquartered in China, 341 00:18:05,240 --> 00:18:08,720 Speaker 13: and so again a lot of questions remain. Are we 342 00:18:08,840 --> 00:18:11,480 Speaker 13: going to allow this app to be downloaded in the 343 00:18:11,560 --> 00:18:15,159 Speaker 13: United States? And is it allowed to be downloaded just 344 00:18:15,280 --> 00:18:19,639 Speaker 13: because Siphius hasn't had the time to analyze it and 345 00:18:19,680 --> 00:18:20,760 Speaker 13: give a ruling on it. 346 00:18:22,880 --> 00:18:25,719 Speaker 3: Teresa Payden, CEO of for de Lee's Solutions and the 347 00:18:25,720 --> 00:18:36,360 Speaker 3: former CIO in the Bush White House, thanks for joining us. 348 00:18:38,800 --> 00:18:41,800 Speaker 3: Let's not bring in Bloomberg's Jackie Dabolos to discuss the 349 00:18:41,840 --> 00:18:45,840 Speaker 3: impact of Deep Seek on Open AI and Stargate. Jackie, 350 00:18:46,000 --> 00:18:49,400 Speaker 3: we worked on the coverage of this Stargate project unveiling 351 00:18:49,560 --> 00:18:52,359 Speaker 3: last week at the White House. This is not the 352 00:18:52,480 --> 00:18:55,960 Speaker 3: kind of moment that President Donald Trump and his administration 353 00:18:56,520 --> 00:19:00,520 Speaker 3: wanted to start this week, so soon after that big announcement, 354 00:19:00,760 --> 00:19:01,800 Speaker 3: where do they go from here? 355 00:19:03,400 --> 00:19:07,240 Speaker 14: Well, from here, we really have to assess how powerful 356 00:19:07,480 --> 00:19:11,119 Speaker 14: is Deep Seek? What makes these models so threatening to 357 00:19:11,160 --> 00:19:14,000 Speaker 14: the ones that we are building here in the United States. 358 00:19:14,440 --> 00:19:16,200 Speaker 2: One of the things that we can look to is. 359 00:19:16,119 --> 00:19:20,600 Speaker 14: The fact that this are one model family that is 360 00:19:21,080 --> 00:19:22,160 Speaker 14: really making a splash. 361 00:19:22,200 --> 00:19:23,240 Speaker 2: They're open weights. 362 00:19:23,359 --> 00:19:26,160 Speaker 14: What makes us so surprising is the fact that many 363 00:19:26,240 --> 00:19:29,600 Speaker 14: open weight models have actually kind of been lagging their 364 00:19:29,640 --> 00:19:34,560 Speaker 14: closed source peers like open AI, like and anthropics, and 365 00:19:34,600 --> 00:19:37,879 Speaker 14: so what the United States government really now has to 366 00:19:37,920 --> 00:19:40,960 Speaker 14: assess is what are we missing here? How are they 367 00:19:41,080 --> 00:19:45,040 Speaker 14: able to get past these export controls? 368 00:19:45,560 --> 00:19:46,760 Speaker 2: It might not even need them. 369 00:19:46,760 --> 00:19:50,600 Speaker 14: It seems like these models are really efficiently run because 370 00:19:50,600 --> 00:19:53,480 Speaker 14: they're far more computational in nature than what we might 371 00:19:53,520 --> 00:19:56,120 Speaker 14: see from an open AI. So this kind of turns 372 00:19:56,200 --> 00:19:59,679 Speaker 14: the whole premise that chips were really going to be 373 00:20:00,119 --> 00:20:03,200 Speaker 14: the key here to getting ahead in the AI race. 374 00:20:03,440 --> 00:20:05,119 Speaker 14: This really kind of flips. 375 00:20:04,720 --> 00:20:09,280 Speaker 4: That necessity is a mother of all invention. They had 376 00:20:09,320 --> 00:20:11,800 Speaker 4: to do it. They had lack of access to the 377 00:20:11,800 --> 00:20:14,919 Speaker 4: most sophisticated chips coming from Nvidia. And we understand from 378 00:20:14,960 --> 00:20:20,360 Speaker 4: Dooming Intelligence that this is about novel mixture of experts architecture, right. 379 00:20:20,480 --> 00:20:22,080 Speaker 4: This is how they lower the cost, This is how 380 00:20:22,080 --> 00:20:25,320 Speaker 4: they bring computational power is a different form of running 381 00:20:25,359 --> 00:20:27,560 Speaker 4: the models, but others have been bringing it on too. 382 00:20:27,600 --> 00:20:28,680 Speaker 2: Can you tell us a little. 383 00:20:28,520 --> 00:20:31,200 Speaker 4: Bit about how they're managing can compete and when out 384 00:20:31,240 --> 00:20:33,840 Speaker 4: when it comes to mass challenges, when it comes to 385 00:20:33,920 --> 00:20:37,960 Speaker 4: reasoning versus the lightest out of Claude or indeed open Ai. 386 00:20:39,119 --> 00:20:40,480 Speaker 15: You're absolutely rad, Caroline. 387 00:20:40,480 --> 00:20:43,520 Speaker 14: It's no surprise that the computational framework here really has. 388 00:20:43,400 --> 00:20:44,160 Speaker 2: Gone a long way. 389 00:20:44,240 --> 00:20:47,919 Speaker 14: It's a startup that has its roots in a quantitative 390 00:20:47,960 --> 00:20:50,320 Speaker 14: hedge fund, so this is really their bread and butter, 391 00:20:50,320 --> 00:20:52,120 Speaker 14: at least it used to be. But when we look 392 00:20:52,119 --> 00:20:55,960 Speaker 14: at what the numbers actually show their performance across several benchmarks, 393 00:20:55,960 --> 00:20:58,720 Speaker 14: the ones that stand out to me are in mas encoding. 394 00:20:59,200 --> 00:21:02,560 Speaker 14: These are the especially when you think about how widely 395 00:21:02,640 --> 00:21:04,679 Speaker 14: used it is in the coding community. The fact that 396 00:21:04,720 --> 00:21:09,840 Speaker 14: it outperformed Claude and open Ai on some benchmarks. Deep 397 00:21:09,880 --> 00:21:13,639 Speaker 14: Seek here really is a player that is contending with 398 00:21:13,760 --> 00:21:14,840 Speaker 14: these incumbents. 399 00:21:15,320 --> 00:21:17,080 Speaker 2: But you know kind of past that. 400 00:21:17,240 --> 00:21:20,560 Speaker 14: You're right, there is a difference here when it comes 401 00:21:20,600 --> 00:21:23,920 Speaker 14: to is this general purpose? How big are these parameters? 402 00:21:24,560 --> 00:21:27,600 Speaker 14: So far? The latest model has six hundred and seventy 403 00:21:27,640 --> 00:21:31,880 Speaker 14: one billion parameters compared to open Eyes, so it has 404 00:21:32,040 --> 00:21:35,320 Speaker 14: models that are smaller that they can handle customization, more 405 00:21:35,359 --> 00:21:40,320 Speaker 14: specific things. Quicker can can also you know, give you 406 00:21:40,480 --> 00:21:42,960 Speaker 14: how it's coming to that, similar to an open Eye's 407 00:21:43,520 --> 00:21:44,359 Speaker 14: reasoning feature. 408 00:21:44,760 --> 00:21:55,840 Speaker 4: Jackie davilos the detail on the technology. Welcome back to 409 00:21:55,920 --> 00:21:57,640 Speaker 4: new bag technology. I'm Cauline Hide to New. 410 00:21:57,600 --> 00:22:00,000 Speaker 3: York and I'm Mike Shepard in San Francisco. 411 00:22:00,240 --> 00:22:01,520 Speaker 2: We must get to these markets. 412 00:22:02,160 --> 00:22:06,679 Speaker 4: The Deep Seek impact royals across the board a clear 413 00:22:07,040 --> 00:22:11,800 Speaker 4: present concern about the cheap offering coming from China. Generative 414 00:22:11,800 --> 00:22:14,440 Speaker 4: AI are able to compare with that of Open AI 415 00:22:14,960 --> 00:22:17,440 Speaker 4: or indeed of Anthropic but with just six million dollars 416 00:22:17,520 --> 00:22:19,000 Speaker 4: spent on the latest model. 417 00:22:18,720 --> 00:22:19,320 Speaker 2: That's what we question. 418 00:22:19,400 --> 00:22:21,520 Speaker 4: Nas like one hundred off by three percent worse day 419 00:22:21,520 --> 00:22:23,600 Speaker 4: in at least a month. But Bitcoin also following down 420 00:22:23,680 --> 00:22:26,280 Speaker 4: risk assetsell off hard. Let's see what drags down the 421 00:22:26,320 --> 00:22:29,600 Speaker 4: NASZAK one hundred. The benchmark crumbles as in video, loses 422 00:22:30,000 --> 00:22:33,040 Speaker 4: sixteen percent. More than half a trillion dollars has been 423 00:22:33,040 --> 00:22:35,199 Speaker 4: wiped off in terms of market cap. That is a 424 00:22:35,400 --> 00:22:39,040 Speaker 4: record in terms of a suffering of a single name Apple. 425 00:22:39,520 --> 00:22:41,840 Speaker 4: On the higher side, earnings of course going to come 426 00:22:41,880 --> 00:22:44,440 Speaker 4: thick and fast this week the thirtieth or an Apple comes. 427 00:22:44,440 --> 00:22:47,400 Speaker 2: But the most downloaded app on your app. 428 00:22:47,320 --> 00:22:50,800 Speaker 4: Store right now is deep Seek, which is currently suffering. 429 00:22:51,160 --> 00:22:55,600 Speaker 4: They are saying a attack. Bloomberg Intelligence analysts Ana rag 430 00:22:55,680 --> 00:22:58,000 Speaker 4: Rana is here for more on the ripple effects. 431 00:22:58,760 --> 00:23:00,000 Speaker 2: Did you see this coming. 432 00:23:00,119 --> 00:23:02,640 Speaker 4: Deep Seek of course, has been written about plenty by 433 00:23:02,640 --> 00:23:04,560 Speaker 4: your colleagues in China. They said it's one of the 434 00:23:04,560 --> 00:23:08,160 Speaker 4: most powerful llms, and indeed have singled out how cost 435 00:23:08,200 --> 00:23:11,080 Speaker 4: efficient it is. But should it have such an impact 436 00:23:11,080 --> 00:23:13,040 Speaker 4: on the market capitalizations of US companies. 437 00:23:14,280 --> 00:23:17,240 Speaker 16: Yeah, I've been surprised about the massive impact it's having 438 00:23:17,280 --> 00:23:19,920 Speaker 16: on the chip guys. But at this point, I think 439 00:23:19,920 --> 00:23:22,560 Speaker 16: everybody is questioning that how is it that they can 440 00:23:22,640 --> 00:23:25,199 Speaker 16: run the model at such a cheap price while the 441 00:23:25,320 --> 00:23:27,199 Speaker 16: US company needs so much infrastructure. 442 00:23:27,200 --> 00:23:27,720 Speaker 15: So I think. 443 00:23:27,560 --> 00:23:31,160 Speaker 16: That that's the big question mark today. And again, as 444 00:23:31,200 --> 00:23:34,159 Speaker 16: you said, it is surprising. The follow three impact is 445 00:23:34,200 --> 00:23:36,000 Speaker 16: if you look at some of the software names, they 446 00:23:36,040 --> 00:23:39,600 Speaker 16: are actually responding positively to that because down the road 447 00:23:39,640 --> 00:23:42,920 Speaker 16: it means that he had an adoption rate could actually accelerate. 448 00:23:42,960 --> 00:23:47,760 Speaker 16: So lots going on, lots to digest today. 449 00:23:46,560 --> 00:23:50,080 Speaker 3: So anaag will demand for AI products make this in 450 00:23:50,119 --> 00:23:52,800 Speaker 3: the long run more of a uplip than anything else. 451 00:23:54,160 --> 00:23:56,800 Speaker 16: Yeah, but that's I mean, that was eventually going to 452 00:23:56,800 --> 00:23:59,359 Speaker 16: happen anyway, The question is are we actually at that 453 00:23:59,440 --> 00:24:02,199 Speaker 16: faster pace today? So you look at it, you know, 454 00:24:02,280 --> 00:24:06,520 Speaker 16: Microsoft's officeco Pilot product let's just take that as an example, 455 00:24:06,840 --> 00:24:09,320 Speaker 16: thirty dollars per user per month. They did, you know, 456 00:24:09,359 --> 00:24:12,280 Speaker 16: come up with some consumption related stuff as well, but 457 00:24:12,640 --> 00:24:14,720 Speaker 16: the adoption rate for that product, in our view has 458 00:24:14,720 --> 00:24:16,879 Speaker 16: not been at that same rate as it should have 459 00:24:16,960 --> 00:24:19,159 Speaker 16: been if it was only let's say, you know, Teams 460 00:24:19,320 --> 00:24:21,920 Speaker 16: Edition for five dollars a month or seven dollars a month. 461 00:24:22,119 --> 00:24:24,440 Speaker 16: So it does have an impact on the adoption rate 462 00:24:24,480 --> 00:24:28,640 Speaker 16: of people, you know, Adobe selling its Firefly services service. Now, 463 00:24:28,760 --> 00:24:30,840 Speaker 16: I mean you look at all these companies that are 464 00:24:31,040 --> 00:24:34,360 Speaker 16: you know, spending a lot of money to embed these features, 465 00:24:34,680 --> 00:24:37,280 Speaker 16: and if they can do it cheaply, that means faster 466 00:24:37,359 --> 00:24:39,800 Speaker 16: adoption you know in the long run. 467 00:24:40,880 --> 00:24:45,040 Speaker 3: And Agrana of Bloomberg Intelligence, thank you. Let's now bring 468 00:24:45,040 --> 00:24:48,560 Speaker 3: in Jordan client from the Zooho Americas for more Jordan, 469 00:24:48,600 --> 00:24:51,480 Speaker 3: thank you for joining us. We have to ask is 470 00:24:51,520 --> 00:24:53,720 Speaker 3: this the beginning of the end of the great AI 471 00:24:53,840 --> 00:24:55,359 Speaker 3: trade or maybe not so much. 472 00:24:57,800 --> 00:25:01,120 Speaker 12: Well, it's a beginning of probably a con solidation phase 473 00:25:01,320 --> 00:25:04,960 Speaker 12: and some profit taking for sure, But I don't think 474 00:25:05,000 --> 00:25:07,640 Speaker 12: it's the beginning of the end of the AI trade. 475 00:25:07,680 --> 00:25:11,000 Speaker 12: I just think we're up a lot after two massive 476 00:25:11,080 --> 00:25:14,320 Speaker 12: years about performance in both tech and these AI winners, 477 00:25:15,160 --> 00:25:18,240 Speaker 12: and it's really only one month into the year. So 478 00:25:19,000 --> 00:25:21,080 Speaker 12: I think the size and the scale of the pullback 479 00:25:21,160 --> 00:25:22,840 Speaker 12: is that who wants to be a hero if your 480 00:25:22,920 --> 00:25:27,080 Speaker 12: institutional money manager or a hedge fund, you know, and 481 00:25:27,119 --> 00:25:30,160 Speaker 12: buy the dip on the first day, I mean catch 482 00:25:30,200 --> 00:25:32,760 Speaker 12: the you know, proverbial falling knife, so to speak. 483 00:25:33,080 --> 00:25:34,760 Speaker 15: So I think, you know, people will wait and hear 484 00:25:34,760 --> 00:25:35,520 Speaker 15: from these companies. 485 00:25:35,560 --> 00:25:38,280 Speaker 12: That's the That's the positive is that we're going into 486 00:25:38,320 --> 00:25:40,639 Speaker 12: the meat of earning season where where you're going to 487 00:25:40,680 --> 00:25:44,560 Speaker 12: get Microsoft and Meta and Apple and eventually others to 488 00:25:44,680 --> 00:25:48,280 Speaker 12: talk about what they're seeing and what their CAPEX plans are. 489 00:25:48,480 --> 00:25:50,199 Speaker 12: So I think we'll know a little bit more in 490 00:25:50,240 --> 00:25:52,920 Speaker 12: the coming weeks, but for now, I think, yes, it's 491 00:25:52,960 --> 00:25:55,800 Speaker 12: a It's probably a healthy and needed consolidation phase. 492 00:25:56,680 --> 00:25:59,879 Speaker 4: A healthy consolidation phase that sees sixteen percent wipe to 493 00:26:00,080 --> 00:26:02,359 Speaker 4: of Nvidia, more than half a trillion dollars loft the 494 00:26:02,440 --> 00:26:05,560 Speaker 4: lowest it's trading app since October of last year, and 495 00:26:05,600 --> 00:26:08,040 Speaker 4: we don't get its earnings until February the twenty sixth. 496 00:26:08,320 --> 00:26:11,440 Speaker 4: How many cools have you had about in video Jordan, 497 00:26:11,720 --> 00:26:14,560 Speaker 4: and what do you think it means for the popularity 498 00:26:14,560 --> 00:26:16,320 Speaker 4: of it's very expensive chips. 499 00:26:17,400 --> 00:26:20,919 Speaker 12: Well, look, people are very concerned obviously because it's probably 500 00:26:20,960 --> 00:26:24,600 Speaker 12: the most owned stock in the market. I think you 501 00:26:24,640 --> 00:26:26,479 Speaker 12: have a lot of people that were you know, they 502 00:26:26,520 --> 00:26:28,840 Speaker 12: don't fully know what they own and they're just panicking 503 00:26:28,880 --> 00:26:32,520 Speaker 12: and selling. I think the real institutional money managers, we're 504 00:26:32,600 --> 00:26:35,920 Speaker 12: not seeing a wave of selling across our equity desk 505 00:26:36,359 --> 00:26:37,040 Speaker 12: at Mizuho. 506 00:26:37,160 --> 00:26:38,879 Speaker 15: I mean, we're seeing some profit taking. 507 00:26:38,960 --> 00:26:43,440 Speaker 12: We're seeing some investors, you know, rotate some money out 508 00:26:43,440 --> 00:26:47,680 Speaker 12: of all these tech names. That's to be expected, right, 509 00:26:48,200 --> 00:26:51,480 Speaker 12: But I'm not hearing from people that it's game over 510 00:26:51,560 --> 00:26:54,879 Speaker 12: for in Vidia, it's game over for Broadcom, Marvelle, Micron 511 00:26:55,400 --> 00:26:58,840 Speaker 12: or the big cloud hyperscalers, And if anything, I think 512 00:26:58,880 --> 00:27:02,200 Speaker 12: people are looking at that price point that deep seek 513 00:27:02,240 --> 00:27:06,280 Speaker 12: throughout of six million in questioning if that's even real. Again, 514 00:27:06,440 --> 00:27:08,959 Speaker 12: we need to I think the real money managers who 515 00:27:09,000 --> 00:27:12,439 Speaker 12: are here for the long term and think more you know, months, 516 00:27:12,560 --> 00:27:14,560 Speaker 12: not days, are going to wait and hear from the 517 00:27:14,560 --> 00:27:16,920 Speaker 12: companies before they do anything. 518 00:27:17,480 --> 00:27:20,000 Speaker 4: You echo what Tony u Wang of tro Price was 519 00:27:20,040 --> 00:27:22,919 Speaker 4: saying at the start. We called you up immediately that 520 00:27:22,960 --> 00:27:24,840 Speaker 4: we got these sorts of market sell off news on 521 00:27:24,880 --> 00:27:26,679 Speaker 4: a hands Jordan, because we saw your note had a 522 00:27:26,720 --> 00:27:29,040 Speaker 4: great title, of course, and the fact that we've got 523 00:27:29,440 --> 00:27:32,439 Speaker 4: a freak out happening deep seat creates a deep freak 524 00:27:32,880 --> 00:27:33,600 Speaker 4: across tech. 525 00:27:34,040 --> 00:27:38,360 Speaker 2: I'm interested though, about the calls you're fielding and as. 526 00:27:38,119 --> 00:27:40,120 Speaker 4: To whether or not you're starting to see a question 527 00:27:40,160 --> 00:27:42,159 Speaker 4: of buying into Chinese names. Is that something you have 528 00:27:42,200 --> 00:27:44,080 Speaker 4: access to, Is that something that people want to see 529 00:27:44,080 --> 00:27:44,760 Speaker 4: as a winner. 530 00:27:44,560 --> 00:27:46,920 Speaker 15: Hit Well, that's a great question. 531 00:27:47,000 --> 00:27:49,320 Speaker 12: I mean, I'm not seeing people call me and say 532 00:27:49,440 --> 00:27:51,760 Speaker 12: these are the list of Chinese names that I want 533 00:27:51,760 --> 00:27:55,200 Speaker 12: to own. I think this will bring up a discussion 534 00:27:55,280 --> 00:27:59,760 Speaker 12: point that's probably needed and was overlooked, as we can 535 00:27:59,840 --> 00:28:02,840 Speaker 12: all own the same trade, right if everyone owns the 536 00:28:02,880 --> 00:28:06,760 Speaker 12: same four or five six names, and then you get 537 00:28:06,800 --> 00:28:10,560 Speaker 12: news like this which questions the longevity of this sustainability 538 00:28:10,560 --> 00:28:14,800 Speaker 12: of this thesis, people just rush to sell and it's painful, right, 539 00:28:14,840 --> 00:28:18,760 Speaker 12: it's escalator up, elevator down. But the real thing is 540 00:28:18,760 --> 00:28:20,560 Speaker 12: is that people are getting a healthy wake up call. 541 00:28:20,680 --> 00:28:23,000 Speaker 12: Is like, look, I have to have a diversified portfolio. 542 00:28:23,359 --> 00:28:25,640 Speaker 12: The other thing that I'm really encouraged by is look 543 00:28:25,680 --> 00:28:27,680 Speaker 12: at a lot of the green on your screen as 544 00:28:27,680 --> 00:28:30,600 Speaker 12: it relates to software and some of these larger cap 545 00:28:30,640 --> 00:28:31,880 Speaker 12: tech names, they're not all. 546 00:28:31,720 --> 00:28:33,119 Speaker 15: Getting sold indiscriminately. 547 00:28:34,119 --> 00:28:35,879 Speaker 12: You know, a lot of areas of software are up, 548 00:28:35,880 --> 00:28:38,000 Speaker 12: and I think people that you've had on your show 549 00:28:38,080 --> 00:28:41,680 Speaker 12: say this could only increase the adoption over time and 550 00:28:42,040 --> 00:28:45,080 Speaker 12: make people want to deploy and invest more to catch 551 00:28:45,160 --> 00:28:47,400 Speaker 12: up with China or deploy some of these cost saving 552 00:28:47,880 --> 00:28:50,640 Speaker 12: measures to build out their own models. 553 00:28:50,680 --> 00:28:53,880 Speaker 15: So I think it's just too early to know, Sjordan. 554 00:28:54,000 --> 00:28:55,960 Speaker 3: We're going to hear a lot from companies over the 555 00:28:56,120 --> 00:28:59,920 Speaker 3: next several days and coming weeks about cap X. Does 556 00:29:00,080 --> 00:29:02,960 Speaker 3: is this news on deep seek unravel the argument that 557 00:29:03,040 --> 00:29:07,000 Speaker 3: Manhattan Project like spending is needed to maintain an edge 558 00:29:07,040 --> 00:29:07,560 Speaker 3: in AI? 559 00:29:09,120 --> 00:29:09,960 Speaker 15: I mean yes and no. 560 00:29:10,120 --> 00:29:12,880 Speaker 12: I think it raises a lot of questions that were 561 00:29:12,920 --> 00:29:16,160 Speaker 12: already there. So most of the meetings I have with investors, 562 00:29:16,520 --> 00:29:19,360 Speaker 12: the question comes up is are they going to ever 563 00:29:19,440 --> 00:29:24,080 Speaker 12: see a return on these tens of billions of CAPEC 564 00:29:24,120 --> 00:29:27,880 Speaker 12: spending that they're deploying. Will it ever monetize, what will 565 00:29:27,920 --> 00:29:30,800 Speaker 12: the returns be? Or is this just you know, throwing 566 00:29:30,840 --> 00:29:34,080 Speaker 12: money down a hole. I think that is going to 567 00:29:34,120 --> 00:29:38,600 Speaker 12: be an ongoing question and until we see these companies, 568 00:29:38,800 --> 00:29:42,720 Speaker 12: for example, Facebook or Meta and Microsoft this week talk 569 00:29:42,760 --> 00:29:46,440 Speaker 12: about what they're seeing in terms of monetizing AI, there's 570 00:29:46,440 --> 00:29:49,440 Speaker 12: going to be some questions and doubts. But again, I 571 00:29:49,720 --> 00:29:52,560 Speaker 12: don't think this is going to create We're cutting Capex, 572 00:29:52,680 --> 00:29:55,480 Speaker 12: we're you know, we're scaling back because they're all in 573 00:29:55,480 --> 00:29:58,840 Speaker 12: an arms race with one another, and and China's you know, 574 00:29:58,920 --> 00:30:01,080 Speaker 12: deep Seak initiative is going to change that. 575 00:30:01,080 --> 00:30:01,920 Speaker 15: That's why I think. 576 00:30:01,760 --> 00:30:05,800 Speaker 12: Microsoft talked eighty billion, Mark Zuckerberg at Facebook talk sixty 577 00:30:05,800 --> 00:30:09,320 Speaker 12: to sixty five billion, the stargate of one hundred dollars 578 00:30:09,400 --> 00:30:11,880 Speaker 12: five hundred billion. I don't think that Again, one new 579 00:30:11,960 --> 00:30:16,200 Speaker 12: Chinese app that makes some aggressive, audacious claims is going 580 00:30:16,240 --> 00:30:19,280 Speaker 12: to all of a sudden create this pullback effect. If 581 00:30:19,280 --> 00:30:21,320 Speaker 12: they start to see real savings and they can do 582 00:30:21,400 --> 00:30:24,160 Speaker 12: this faster, yeah, they might do that, but that that's 583 00:30:24,200 --> 00:30:26,160 Speaker 12: going to take time and we haven't seen that yet. 584 00:30:27,000 --> 00:30:28,760 Speaker 4: Jordan Klein, So good to have your voice in the 585 00:30:28,760 --> 00:30:31,080 Speaker 4: show The Missoho America's TMT analysts. 586 00:30:31,080 --> 00:30:32,240 Speaker 2: We appreciate it. 587 00:30:32,320 --> 00:30:35,280 Speaker 4: Coming up much more on China AI set up deep seats, 588 00:30:35,360 --> 00:30:37,880 Speaker 4: breakthrough model, what it means for the United States in 589 00:30:37,920 --> 00:30:41,120 Speaker 4: terms of supply chain. Former Congressman Ken Buck joins us. Next, 590 00:30:41,520 --> 00:30:43,160 Speaker 4: this is blue bag technology. 591 00:30:57,320 --> 00:31:00,440 Speaker 3: As Deep seeks AI potential continues to you at the 592 00:31:00,440 --> 00:31:03,920 Speaker 3: tech sector today. What does this mean for geopolitics and 593 00:31:04,000 --> 00:31:08,400 Speaker 3: President Donald Trump's agenda for AI dominance. Joining us now 594 00:31:08,440 --> 00:31:11,560 Speaker 3: to discuss all of this is Ken Buck, former congressman 595 00:31:11,600 --> 00:31:15,360 Speaker 3: from Colorado's fourth district. Congressman, we have to ask you 596 00:31:15,600 --> 00:31:18,360 Speaker 3: when it comes to AI, is Deep Seeks breakthrough a 597 00:31:18,400 --> 00:31:20,080 Speaker 3: sput Nick moment for Washington? 598 00:31:21,040 --> 00:31:21,240 Speaker 6: You know? 599 00:31:21,680 --> 00:31:23,800 Speaker 9: I think time will tell on that. I'm not sure. 600 00:31:24,000 --> 00:31:29,719 Speaker 11: I think that the critical factor is that America develops 601 00:31:29,760 --> 00:31:35,040 Speaker 11: its own ship manufacturing here. We don't rely on Taiwan's semiconductor. 602 00:31:35,080 --> 00:31:40,680 Speaker 11: We don't rely on others in this world marketplace. We 603 00:31:40,720 --> 00:31:44,800 Speaker 11: certainly could be behind. We recognized a few years ago 604 00:31:45,040 --> 00:31:49,680 Speaker 11: that we needed to incentivize chip manufacturing in the US. 605 00:31:50,160 --> 00:31:54,640 Speaker 11: We passed the Chips Act in Congress. It has been 606 00:31:54,680 --> 00:31:58,040 Speaker 11: a failure. Throwing a lot of money at a lot 607 00:31:58,040 --> 00:32:01,160 Speaker 11: of different companies has not worked. I think that Donald 608 00:32:01,200 --> 00:32:07,640 Speaker 11: Trump's policies involving a combination of tax incentives and perhaps tariffs, 609 00:32:07,680 --> 00:32:12,400 Speaker 11: will be more effective in trying to raise America's productivity 610 00:32:12,400 --> 00:32:13,000 Speaker 11: in this area. 611 00:32:13,720 --> 00:32:16,680 Speaker 3: Congressman, you brought up the Chips Act and your misgivings 612 00:32:16,720 --> 00:32:21,040 Speaker 3: and concerns there. How should the President adjust maybe the 613 00:32:21,080 --> 00:32:24,840 Speaker 3: implementation of this law and maybe the doling out of 614 00:32:24,880 --> 00:32:27,160 Speaker 3: some of the money that still remains in a way 615 00:32:27,240 --> 00:32:30,280 Speaker 3: that could meet the challenge that companies like Deep seek 616 00:32:30,320 --> 00:32:31,400 Speaker 3: opposing from abroad. 617 00:32:31,720 --> 00:32:34,560 Speaker 11: Well, one of the biggest benefactors of the Chips Act 618 00:32:34,600 --> 00:32:39,480 Speaker 11: has been the Taiwan Semiconductor and that's a mistake. They 619 00:32:39,480 --> 00:32:41,840 Speaker 11: are not producing chips in the US. They have a 620 00:32:41,880 --> 00:32:44,040 Speaker 11: plan to produce some in the future. We don't know 621 00:32:44,080 --> 00:32:45,400 Speaker 11: if those will be the high end. 622 00:32:45,360 --> 00:32:45,920 Speaker 9: Chips or not. 623 00:32:46,000 --> 00:32:49,240 Speaker 11: We don't know if Taiwan sim and Conductor will actually 624 00:32:50,600 --> 00:32:54,240 Speaker 11: mesh with the workforce here in the US. And so 625 00:32:54,320 --> 00:32:56,840 Speaker 11: I think what we've got to do, and what President Trump, 626 00:32:56,880 --> 00:32:59,920 Speaker 11: I'm sure his advisors are telling them right now, is 627 00:33:00,400 --> 00:33:03,640 Speaker 11: find those companies in the US that can produce the 628 00:33:03,880 --> 00:33:07,680 Speaker 11: high end chips that Taiwan semi Conductor. 629 00:33:07,280 --> 00:33:09,840 Speaker 9: Has a monopoly on. Look into the monopoly. 630 00:33:09,880 --> 00:33:12,120 Speaker 11: First of all, can we certainly have anti trust laws 631 00:33:12,120 --> 00:33:15,080 Speaker 11: in this country that prohibit a company from having a 632 00:33:15,160 --> 00:33:19,840 Speaker 11: ninety five percent market share like Taiwan Semiconductor does, but 633 00:33:19,920 --> 00:33:22,880 Speaker 11: find the US companies and make sure that we have 634 00:33:23,400 --> 00:33:27,720 Speaker 11: the tax structure in place and other incentives to make 635 00:33:28,000 --> 00:33:29,320 Speaker 11: high end ships in the US. 636 00:33:29,840 --> 00:33:31,800 Speaker 9: This whole issue of deep seek. 637 00:33:32,080 --> 00:33:37,400 Speaker 11: We don't know exactly whether someone violated our export controls to. 638 00:33:37,880 --> 00:33:38,520 Speaker 9: China or not. 639 00:33:39,120 --> 00:33:43,520 Speaker 4: I'm looking at Nvidia down almost seventeen percent, massive wipe 640 00:33:43,560 --> 00:33:47,720 Speaker 4: out of market capitalization by six hundred pillion dollars, Congressmen. 641 00:33:47,920 --> 00:33:51,680 Speaker 4: But to that point, did perhaps China circumvent some of 642 00:33:51,680 --> 00:33:55,400 Speaker 4: the limitations on Nvidia's exports. Is that something you're thinking 643 00:33:55,480 --> 00:33:58,600 Speaker 4: through rather than perhaps this was innovation because they couldn't 644 00:33:58,600 --> 00:34:00,640 Speaker 4: get the hands on the latest and greatest. 645 00:34:01,120 --> 00:34:03,840 Speaker 11: Well, this wouldn't be the first time that China stole 646 00:34:04,000 --> 00:34:07,560 Speaker 11: technology from the US or other countries, and then, with 647 00:34:07,640 --> 00:34:14,879 Speaker 11: their labor market and with their other production advantages, got 648 00:34:14,880 --> 00:34:16,080 Speaker 11: ahead of the curve on US. 649 00:34:16,120 --> 00:34:18,320 Speaker 9: And so I think that's certainly. 650 00:34:18,360 --> 00:34:20,480 Speaker 11: One of the issues that we've got to look at, 651 00:34:20,600 --> 00:34:23,280 Speaker 11: is whether they did violate export. 652 00:34:22,920 --> 00:34:23,719 Speaker 9: Controls or not. 653 00:34:23,880 --> 00:34:27,840 Speaker 11: But more importantly, what do we do in this country 654 00:34:27,400 --> 00:34:29,680 Speaker 11: that cat is out of the bag right now? What 655 00:34:29,719 --> 00:34:32,719 Speaker 11: do we do in this country to make sure and 656 00:34:33,239 --> 00:34:36,240 Speaker 11: Western Europe, frankly, to make sure that we stay ahead 657 00:34:36,320 --> 00:34:37,439 Speaker 11: of the AI race. 658 00:34:37,719 --> 00:34:39,839 Speaker 2: But the answer to thus far has been money. 659 00:34:39,880 --> 00:34:45,160 Speaker 4: Five hundred billion dollars unveiled by potentially Oracle, Open AI 660 00:34:45,440 --> 00:34:48,160 Speaker 4: and soft Bank into the future for AI infrastructure in 661 00:34:48,160 --> 00:34:52,160 Speaker 4: the United States, the creation of more three trillion dollar companies. 662 00:34:52,440 --> 00:34:55,960 Speaker 4: But ultimately that amount of money perhaps has slowed innovation 663 00:34:56,040 --> 00:34:56,680 Speaker 4: here in the US. 664 00:34:56,719 --> 00:34:57,680 Speaker 2: Would that be a concern. 665 00:34:58,320 --> 00:34:59,400 Speaker 9: I think it's a big concern. 666 00:35:00,000 --> 00:35:00,239 Speaker 6: Again. 667 00:35:00,280 --> 00:35:02,480 Speaker 9: I don't think we just throw money at an issue. 668 00:35:02,560 --> 00:35:05,000 Speaker 11: I think we've got to be very precise in how 669 00:35:05,080 --> 00:35:08,879 Speaker 11: in our strategy and how we develop AI. We all 670 00:35:08,920 --> 00:35:15,080 Speaker 11: recognize AI has these tremendous potential, wonderful benefits, positive benefits, 671 00:35:15,760 --> 00:35:19,360 Speaker 11: but when you're talking about a tatal's arean country like China, 672 00:35:20,000 --> 00:35:22,799 Speaker 11: you've also got to think about what that country can 673 00:35:22,840 --> 00:35:26,359 Speaker 11: do with AI advances that will put us at risk, 674 00:35:26,400 --> 00:35:27,680 Speaker 11: in our allies at risk. 675 00:35:28,440 --> 00:35:31,480 Speaker 4: Former Congressman Ken Buck, thank you very much for joining 676 00:35:31,520 --> 00:35:42,480 Speaker 4: the show today. SOFI shares, like the rest of the market, 677 00:35:42,760 --> 00:35:45,680 Speaker 4: slumping today, in fact, having its worst day since March 678 00:35:45,719 --> 00:35:49,680 Speaker 4: of last year. The fintech lender publishing perhaps a forecast 679 00:35:49,800 --> 00:35:52,560 Speaker 4: that seems to be below where the marketer wanted to see. 680 00:35:52,880 --> 00:35:54,719 Speaker 4: We've got a broad tech route, of course, linked to 681 00:35:54,760 --> 00:35:57,360 Speaker 4: deep seat more broadly in general, to AI over in China. 682 00:35:57,360 --> 00:35:59,719 Speaker 4: But here to discuss the earning, SOFI CEO Anthony Noto 683 00:35:59,760 --> 00:36:01,680 Speaker 4: a tough day to report earnings. 684 00:36:01,680 --> 00:36:03,360 Speaker 2: Anthony, and I ask you about. 685 00:36:03,160 --> 00:36:05,839 Speaker 4: The forecast, because look, your revenue was at record rate, 686 00:36:05,840 --> 00:36:08,280 Speaker 4: suggested revenue up twenty four percent for the fourth quarter. 687 00:36:08,520 --> 00:36:11,399 Speaker 4: But you're pushing us forward in a forecast that isn't 688 00:36:11,440 --> 00:36:12,720 Speaker 4: as high as the market wanted. 689 00:36:12,760 --> 00:36:15,520 Speaker 6: Why well, I think two things. 690 00:36:15,560 --> 00:36:15,799 Speaker 8: One. 691 00:36:16,239 --> 00:36:19,480 Speaker 17: Twenty twenty four, we took a pretty conservative approach to 692 00:36:19,560 --> 00:36:23,080 Speaker 17: the year in terms of our growth and our profitability. 693 00:36:23,120 --> 00:36:26,080 Speaker 17: We really wanted to make sure that we were able 694 00:36:26,120 --> 00:36:30,040 Speaker 17: to drive strong profitability in twenty four to achieve gap profitability, 695 00:36:30,239 --> 00:36:34,640 Speaker 17: to ensure we reinforced our balance sheet and a capital cushion, 696 00:36:34,680 --> 00:36:37,759 Speaker 17: and we did just that. Prior to twenty twenty four, 697 00:36:37,840 --> 00:36:40,799 Speaker 17: we had committed to a balanced approach to growth and 698 00:36:40,840 --> 00:36:44,880 Speaker 17: profitability where we would reinvest seventy cents of every incremental 699 00:36:44,920 --> 00:36:48,280 Speaker 17: revenue dollar, and we call that thirty percent incrementallybadam margins. 700 00:36:48,680 --> 00:36:51,279 Speaker 17: So we took our revenue forecast for twenty five up. 701 00:36:51,719 --> 00:36:53,759 Speaker 17: We're in the best position we've been since I've been 702 00:36:53,760 --> 00:36:55,960 Speaker 17: here at so far for the last seven years. We're 703 00:36:56,000 --> 00:36:59,520 Speaker 17: calling for twenty five percent revenue growth, strong margins at 704 00:36:59,520 --> 00:37:02,400 Speaker 17: twenty six percent, just not an expansion in the margins 705 00:37:02,640 --> 00:37:05,880 Speaker 17: because we want to invest in the massive opportunity that 706 00:37:05,920 --> 00:37:08,120 Speaker 17: still sits in front of us. And so we took 707 00:37:08,200 --> 00:37:11,040 Speaker 17: revenue guidance up and it's about ten percent higher than 708 00:37:11,080 --> 00:37:13,800 Speaker 17: the street, but that will require more investment and it 709 00:37:13,800 --> 00:37:16,240 Speaker 17: will help us ensure we have growth beyond twenty five. 710 00:37:16,600 --> 00:37:19,680 Speaker 17: And we also took our revenue guidance for twenty twenty 711 00:37:19,719 --> 00:37:22,480 Speaker 17: three at twenty twenty six on a compound and a 712 00:37:22,520 --> 00:37:24,120 Speaker 17: growth rate basis higher as well. 713 00:37:24,520 --> 00:37:26,799 Speaker 6: So the profitability of the business is there. 714 00:37:26,920 --> 00:37:28,800 Speaker 17: We could drive more to the bottom line, but we 715 00:37:28,880 --> 00:37:31,440 Speaker 17: think that's not the prudent thing to do because we 716 00:37:31,520 --> 00:37:34,319 Speaker 17: just see massive growth in front of us and the 717 00:37:34,360 --> 00:37:36,880 Speaker 17: ability for us to keep driving member growth and product 718 00:37:36,920 --> 00:37:39,480 Speaker 17: growth of more than thirty percent, which we've done, and 719 00:37:39,560 --> 00:37:43,320 Speaker 17: revenue growth of more than twenty five percent. Is you 720 00:37:43,360 --> 00:37:46,480 Speaker 17: know what we're calling for through twenty twenty. 721 00:37:46,239 --> 00:37:48,080 Speaker 6: Six, Anthony. 722 00:37:48,239 --> 00:37:51,080 Speaker 3: The investment that you just describe, what portion of it 723 00:37:51,120 --> 00:37:54,800 Speaker 3: will you be putting toward artificial intelligence. It's the topic 724 00:37:54,840 --> 00:37:57,480 Speaker 3: at the top of everybody's minds, and people are watching 725 00:37:57,520 --> 00:38:00,560 Speaker 3: how companies are deploying it. Do you see, for instance, 726 00:38:00,600 --> 00:38:04,000 Speaker 3: in agent driven service Perhaps at so far At some point. 727 00:38:04,800 --> 00:38:07,799 Speaker 17: The vast majority of our investment going into twenty twenty 728 00:38:07,800 --> 00:38:10,800 Speaker 17: five and twenty six, we'll be in building unaided brand awareness, 729 00:38:10,920 --> 00:38:13,080 Speaker 17: becoming a trusted household brand name. 730 00:38:13,120 --> 00:38:14,399 Speaker 6: We have great products. 731 00:38:14,760 --> 00:38:17,160 Speaker 17: The reason why we're driving such strong product and member 732 00:38:17,200 --> 00:38:20,359 Speaker 17: growth is because the products are very differentiated, and when 733 00:38:20,400 --> 00:38:23,160 Speaker 17: we make people aware of them and they use the products, 734 00:38:23,200 --> 00:38:25,520 Speaker 17: they not just use the first one and they gain 735 00:38:25,600 --> 00:38:27,840 Speaker 17: trust with us, they use the second and third one. 736 00:38:28,239 --> 00:38:29,879 Speaker 6: Thirty percent of our product. 737 00:38:29,560 --> 00:38:32,480 Speaker 17: Growth in the quarter of thirty four percent was from 738 00:38:32,520 --> 00:38:36,440 Speaker 17: our existing members. About forty percent of our members take 739 00:38:36,480 --> 00:38:39,400 Speaker 17: out a second product within thirty days. So we'll continue 740 00:38:39,440 --> 00:38:41,960 Speaker 17: to invest in differentiating the product. So FE money is 741 00:38:42,000 --> 00:38:44,560 Speaker 17: a great product that has a high apy. You can 742 00:38:44,560 --> 00:38:46,000 Speaker 17: do person to person payments. 743 00:38:46,040 --> 00:38:46,520 Speaker 6: You could do. 744 00:38:46,560 --> 00:38:50,319 Speaker 17: Zell auto pay, two day early paycheck, and so we'll 745 00:38:50,360 --> 00:38:52,520 Speaker 17: be investing in that. We'll be investing in our invest 746 00:38:52,560 --> 00:38:56,120 Speaker 17: product to expand the selection for invest in addition to 747 00:38:56,160 --> 00:38:59,040 Speaker 17: other product categories, like insurance and credit card and small 748 00:38:59,040 --> 00:38:59,960 Speaker 17: medium business lending. 749 00:39:00,680 --> 00:39:02,719 Speaker 4: You want to be able to offer crypto again, and 750 00:39:02,760 --> 00:39:04,640 Speaker 4: that's something that the administration has talked a lot about. 751 00:39:04,680 --> 00:39:06,520 Speaker 4: Look on a day where we see such volatility that 752 00:39:06,600 --> 00:39:09,000 Speaker 4: engulfs crypto too because of deep seek, is. 753 00:39:08,960 --> 00:39:10,680 Speaker 2: That really a product placement that you want to get 754 00:39:10,680 --> 00:39:11,200 Speaker 2: into again? 755 00:39:12,280 --> 00:39:16,640 Speaker 17: If the regulations changed so that cryptocurrency is permissible by 756 00:39:16,719 --> 00:39:19,640 Speaker 17: bank holding companies, we would absolutely provide not just what 757 00:39:19,640 --> 00:39:22,320 Speaker 17: we used to provide, which was the ability to safely 758 00:39:22,320 --> 00:39:26,879 Speaker 17: and securely trade bitcoin and other cryptocurrencies, but we'll also 759 00:39:26,960 --> 00:39:30,200 Speaker 17: go into other areas like custing and clearing in addition 760 00:39:30,239 --> 00:39:32,920 Speaker 17: to asset back lending, etc. But that will all be 761 00:39:33,000 --> 00:39:36,360 Speaker 17: gated by the regulators, which we think should be coming 762 00:39:36,400 --> 00:39:39,239 Speaker 17: over the next twenty four months, and we'll be ready. 763 00:39:38,960 --> 00:39:39,520 Speaker 6: When it does. 764 00:39:40,400 --> 00:39:44,640 Speaker 4: Ultimately, are you feeling more risk on in this environment 765 00:39:44,719 --> 00:39:47,160 Speaker 4: the new administration or way in which to develop your business? 766 00:39:47,160 --> 00:39:48,640 Speaker 2: Maybe M and A because it doesn't feel like a 767 00:39:48,719 --> 00:39:49,520 Speaker 2: risk on day to day. 768 00:39:50,600 --> 00:39:54,439 Speaker 17: We are definitely leaning into twenty twenty five. Twenty twenty 769 00:39:54,440 --> 00:39:58,200 Speaker 17: four was a record breaking year. We had record revenue, profits, returns, 770 00:39:58,520 --> 00:40:01,320 Speaker 17: member growth, Product growth couldn't be happier the year that 771 00:40:01,400 --> 00:40:04,200 Speaker 17: we had the most product've been of the company since 772 00:40:04,200 --> 00:40:06,480 Speaker 17: I've been here, but I think twenty twenty five will 773 00:40:06,480 --> 00:40:09,399 Speaker 17: be even better. We think the outlook is the best 774 00:40:09,480 --> 00:40:12,319 Speaker 17: environment we've operated in over the last seven years. Our 775 00:40:12,360 --> 00:40:14,960 Speaker 17: business is bigger, stronger, and more well known than it's 776 00:40:14,960 --> 00:40:16,919 Speaker 17: ever been, and we have more resources to go after 777 00:40:16,960 --> 00:40:19,960 Speaker 17: the opportunities. So we love our competitive positioning. We like 778 00:40:20,000 --> 00:40:22,600 Speaker 17: the macro backdrop. It's a very different outlook than when 779 00:40:22,600 --> 00:40:24,840 Speaker 17: we came in too twenty twenty four, and we're definitely 780 00:40:24,920 --> 00:40:28,960 Speaker 17: being more aggressive in innovation and driving durable growth and 781 00:40:29,080 --> 00:40:29,920 Speaker 17: strong returns. 782 00:40:31,239 --> 00:40:34,640 Speaker 3: Anthony, back to the regulatory side of things, What is 783 00:40:34,719 --> 00:40:37,440 Speaker 3: the one thing, one hurdle that you would like to 784 00:40:37,480 --> 00:40:40,920 Speaker 3: see the new administration and Congress clear for you? 785 00:40:42,520 --> 00:40:45,680 Speaker 17: The biggest question is what what will bank holding companies 786 00:40:45,680 --> 00:40:48,879 Speaker 17: be allowed to do with cryptocurrency? What will be permissible? 787 00:40:48,920 --> 00:40:52,640 Speaker 17: And that clarity is really critically important. The interest rate 788 00:40:52,719 --> 00:40:55,920 Speaker 17: cycle I think has pretty good visibility and transparency to that. 789 00:40:56,040 --> 00:40:56,520 Speaker 9: I think the. 790 00:40:56,480 --> 00:40:59,719 Speaker 17: Economy also has really strong economic indicators. 791 00:41:00,360 --> 00:41:02,280 Speaker 6: The big question is how much can we innovate? 792 00:41:02,360 --> 00:41:05,120 Speaker 17: How much can we invest in these different asset classes 793 00:41:05,320 --> 00:41:08,480 Speaker 17: that our members want to reinforce their ability to not 794 00:41:08,680 --> 00:41:11,520 Speaker 17: just borrow better and save better and protect better, but 795 00:41:11,600 --> 00:41:14,799 Speaker 17: to invest better. Investing is critical to reaching their long 796 00:41:14,880 --> 00:41:17,200 Speaker 17: term financial goals. We want to be there for every 797 00:41:17,280 --> 00:41:19,960 Speaker 17: one of the major financial decisions our members making their 798 00:41:19,960 --> 00:41:22,440 Speaker 17: lives in all the days in between. The key is 799 00:41:22,480 --> 00:41:24,560 Speaker 17: spending less than you make in investing the rest. So 800 00:41:24,640 --> 00:41:27,120 Speaker 17: the more opportunities we can give our members to invest, 801 00:41:27,360 --> 00:41:29,400 Speaker 17: the faster they'll get to their financial outcomes. 802 00:41:29,719 --> 00:41:31,279 Speaker 4: An can I notice so fi see you in the 803 00:41:31,320 --> 00:41:33,799 Speaker 4: day of your earnings. Great to have you on, Thank 804 00:41:33,840 --> 00:41:36,560 Speaker 4: you so much. Let's return to the story of the day, though, 805 00:41:36,600 --> 00:41:40,279 Speaker 4: which is of course Deep Seek and generative AI being 806 00:41:40,320 --> 00:41:43,680 Speaker 4: powered in China for much smaller amounts of money, managing 807 00:41:43,719 --> 00:41:47,279 Speaker 4: to circumvent perhaps in limitations on compute power MIC. We 808 00:41:47,320 --> 00:41:52,040 Speaker 4: see a whopping sixteen percent market cap erosion on Nvidia. 809 00:41:52,080 --> 00:41:53,520 Speaker 2: But we all thought we're going to be talking about 810 00:41:53,520 --> 00:41:54,520 Speaker 2: earnings this week. 811 00:41:54,600 --> 00:41:57,040 Speaker 4: We're looking towards Meta Microsoft coming in the twenty ninth, 812 00:41:57,040 --> 00:41:59,600 Speaker 4: Apple which is actually in the green coming on the thirtieth. 813 00:42:01,160 --> 00:42:04,480 Speaker 3: This is rewriting the narrative for earnings later this week 814 00:42:04,520 --> 00:42:06,799 Speaker 3: and through the rest of this month, and there is 815 00:42:06,840 --> 00:42:09,640 Speaker 3: also a wild card and that is the new president 816 00:42:09,680 --> 00:42:12,560 Speaker 3: in Washington and how he will react. We still have 817 00:42:12,680 --> 00:42:14,680 Speaker 3: not heard from him, and he does look at the 818 00:42:14,719 --> 00:42:16,600 Speaker 3: market as a benchmark of us success. 819 00:42:17,400 --> 00:42:20,240 Speaker 4: Also, we see how people try to continue to download 820 00:42:20,320 --> 00:42:22,600 Speaker 4: Deep Seek as of course an app. It was the 821 00:42:22,680 --> 00:42:25,960 Speaker 4: number one app on Apple and currently unavailable. They say 822 00:42:25,960 --> 00:42:30,000 Speaker 4: they are currently being afflicted by some sort of attack, 823 00:42:30,480 --> 00:42:32,839 Speaker 4: But that does it. From this edition of BLUEBG Technology, Mike, 824 00:42:32,880 --> 00:42:34,640 Speaker 4: do not forget to check out our podcast. You can 825 00:42:34,640 --> 00:42:37,799 Speaker 4: find it on the terminal as well as online on Apple, Spotify, Aniheart. 826 00:42:38,000 --> 00:42:39,160 Speaker 2: This is Blue meg Technology