1 00:00:02,600 --> 00:00:11,559 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,480 --> 00:00:13,160 Speaker 2: From the Heart where Innovation, money and power. Collie in 3 00:00:13,240 --> 00:00:14,720 Speaker 2: Silicon Valley, Nbon. 4 00:00:15,080 --> 00:00:19,600 Speaker 3: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 5 00:00:33,600 --> 00:00:34,199 Speaker 2: Live from New York. 6 00:00:34,200 --> 00:00:36,960 Speaker 4: There's the Bloomberg Technology coming up. Nissan in talks to 7 00:00:37,040 --> 00:00:40,879 Speaker 4: merge with Honda. Why tech giant Fox con catalyze the 8 00:00:40,920 --> 00:00:45,120 Speaker 4: autodial discussions plus data bricks. Valuation rises to sixty two 9 00:00:45,159 --> 00:00:47,400 Speaker 4: billion dollars. We'll sit down with the CEO to discuss 10 00:00:47,440 --> 00:00:51,440 Speaker 4: the company's AI strategy and its expansion, and all eyes 11 00:00:51,479 --> 00:00:55,840 Speaker 4: on micron earnings after the closing bell, Can AI demand deliver? First, 12 00:00:55,920 --> 00:00:57,440 Speaker 4: let's get to this key story of the day, the 13 00:00:57,520 --> 00:01:01,240 Speaker 4: merger discussions Nissan and talks with Honda, and it would 14 00:01:01,240 --> 00:01:03,880 Speaker 4: be a deal that could create the world's third largest 15 00:01:03,920 --> 00:01:06,240 Speaker 4: automaker with a better chance to compete with the like 16 00:01:06,360 --> 00:01:09,320 Speaker 4: Toyota and even Tesla in the EV space for example. Now, 17 00:01:09,360 --> 00:01:12,759 Speaker 4: according to reports, the discussion between the two they accelerated 18 00:01:13,000 --> 00:01:16,120 Speaker 4: off the Fox Cohn, the Taiwan based producer of iPhones, 19 00:01:16,280 --> 00:01:18,280 Speaker 4: actually approached Nissan about taking a stake. 20 00:01:18,720 --> 00:01:20,399 Speaker 2: Bloomberg's Crotodel joins us. 21 00:01:20,440 --> 00:01:23,640 Speaker 4: Some more so interestingly, once again, is Japanese companies trying 22 00:01:23,640 --> 00:01:25,440 Speaker 4: to fend off international investors. 23 00:01:26,880 --> 00:01:29,800 Speaker 5: Yeah, I think you know this, This is a case 24 00:01:29,840 --> 00:01:32,680 Speaker 5: of a company in Nissan that has been challenged for 25 00:01:32,760 --> 00:01:36,600 Speaker 5: years now, and really since the moment that Carlos Gohn 26 00:01:36,760 --> 00:01:43,119 Speaker 5: was unceremoniously taken out and arrested actually, you know, by 27 00:01:43,160 --> 00:01:46,600 Speaker 5: folks in Japan. There was really concern within the government 28 00:01:46,640 --> 00:01:50,080 Speaker 5: there about you know, the long term viability of this company, 29 00:01:50,080 --> 00:01:52,560 Speaker 5: and I think a desire to kind of drive them 30 00:01:52,600 --> 00:01:57,040 Speaker 5: into the arms of Honda. I think Honda was reluctant, 31 00:01:57,200 --> 00:02:01,000 Speaker 5: and perhaps Fox Cohn with its you know, interest in 32 00:02:01,360 --> 00:02:05,600 Speaker 5: entering the auto industry, it's interests in particular in electric vehicles, 33 00:02:06,440 --> 00:02:08,920 Speaker 5: you know, knocking on Nissan's doors, I think, you know, 34 00:02:09,320 --> 00:02:12,520 Speaker 5: it is sort of indicative of the sort of of 35 00:02:12,880 --> 00:02:17,320 Speaker 5: prospect that would only you know, further alarm the Japanese government. 36 00:02:17,560 --> 00:02:19,960 Speaker 4: I remember back in the day talking to carloscon about 37 00:02:19,960 --> 00:02:23,120 Speaker 4: the Nissan Leaf. He was really upbeat about the scale 38 00:02:23,160 --> 00:02:26,000 Speaker 4: of the market they had in electric vehicles. But now 39 00:02:26,000 --> 00:02:29,200 Speaker 4: it pales into comparison to a Tesla, for example, How 40 00:02:29,200 --> 00:02:31,600 Speaker 4: would the combined units be beneficial? 41 00:02:33,000 --> 00:02:36,760 Speaker 5: I think importantly it would help Nissan in a real 42 00:02:36,840 --> 00:02:39,320 Speaker 5: sort of blank space, which is hybrids. And you know, 43 00:02:39,360 --> 00:02:42,079 Speaker 5: a few years ago we would have you know, sort 44 00:02:42,080 --> 00:02:45,920 Speaker 5: of been talking about, you know, would hybrids have a 45 00:02:45,960 --> 00:02:50,960 Speaker 5: future our Tesla's the way of the industry going forward. 46 00:02:51,800 --> 00:02:54,840 Speaker 5: I think we've seen this real renaissance, particularly in the 47 00:02:54,919 --> 00:02:57,440 Speaker 5: US market, but also in other parts of the world 48 00:02:57,520 --> 00:03:00,440 Speaker 5: where you know, it's clear that consumers are still not 49 00:03:00,520 --> 00:03:03,160 Speaker 5: ready yet to pull the trigger on a Nissan Leaf 50 00:03:03,520 --> 00:03:07,000 Speaker 5: or a Tesla. And you know, it's been the case 51 00:03:07,080 --> 00:03:09,440 Speaker 5: that Nissan has had a lot of issues, but one 52 00:03:09,480 --> 00:03:12,520 Speaker 5: of them has been the complete lack of hybrids in 53 00:03:12,520 --> 00:03:15,960 Speaker 5: the US market. Honda really has some strength and that 54 00:03:16,040 --> 00:03:20,160 Speaker 5: regard and would help Nissan to better compete if these 55 00:03:20,200 --> 00:03:22,160 Speaker 5: two were to combine. 56 00:03:22,320 --> 00:03:23,120 Speaker 2: Well, I'm not sure. 57 00:03:23,000 --> 00:03:25,320 Speaker 4: Hybrids will help in California in the future. Just talk 58 00:03:25,360 --> 00:03:27,760 Speaker 4: us through what's coming from the Biden administration in terms 59 00:03:27,800 --> 00:03:33,519 Speaker 4: of California regulations compelling people to have ev zero emission cars. 60 00:03:33,280 --> 00:03:33,840 Speaker 2: In the future. 61 00:03:35,040 --> 00:03:39,600 Speaker 5: Yeah, California you know, announced intentions quite a while ago 62 00:03:40,040 --> 00:03:43,920 Speaker 5: to phase out combustion cars from twenty thirty five in 63 00:03:44,040 --> 00:03:46,720 Speaker 5: order to actually you know, sort of en trine that 64 00:03:46,800 --> 00:03:50,560 Speaker 5: and really protect that the state needs to go to 65 00:03:50,600 --> 00:03:53,120 Speaker 5: the federal government and get a waiver. And there's a 66 00:03:53,360 --> 00:03:56,840 Speaker 5: precedent for this going back decades where California, because of 67 00:03:56,920 --> 00:03:59,920 Speaker 5: the issues unique issues that's had with air quality, going 68 00:04:00,000 --> 00:04:03,360 Speaker 5: going back, you know, many years ago, there's precedent for 69 00:04:03,400 --> 00:04:04,920 Speaker 5: it for the state to be able to set its 70 00:04:04,960 --> 00:04:08,680 Speaker 5: own standards separate from the federal government. And there are 71 00:04:08,680 --> 00:04:13,080 Speaker 5: many states that follow and adopt California's standards. So this 72 00:04:13,120 --> 00:04:17,000 Speaker 5: is a case of the Biden administration kind of making 73 00:04:17,040 --> 00:04:20,159 Speaker 5: more difficult on its way out the door. Any effort 74 00:04:20,160 --> 00:04:23,120 Speaker 5: that the Trump administration, I think is inevitably going to 75 00:04:23,200 --> 00:04:25,880 Speaker 5: try and and you know, take a stab at the 76 00:04:26,200 --> 00:04:29,919 Speaker 5: various waivers that California has had to try and set 77 00:04:30,160 --> 00:04:31,919 Speaker 5: tougher policies for the auto industry. 78 00:04:32,120 --> 00:04:34,120 Speaker 2: Yeah, we'll see how it devised the next administration. 79 00:04:34,200 --> 00:04:37,919 Speaker 4: Crotrudelle just a brilliant roundup of all things autos and evs. 80 00:04:38,360 --> 00:04:41,000 Speaker 4: Let's get back though, to the semiconductor space right now, 81 00:04:41,040 --> 00:04:43,799 Speaker 4: because that's really where we're looking at what's happening underneath 82 00:04:43,800 --> 00:04:46,719 Speaker 4: the benchmarks. Mike cron of course reporting after the bell 83 00:04:46,760 --> 00:04:48,760 Speaker 4: as well today, and that's really looking for growth in 84 00:04:48,800 --> 00:04:51,320 Speaker 4: the company's high manwidth memory. As I mentioned the products there, 85 00:04:51,360 --> 00:04:54,520 Speaker 4: it's all about AI, infrastructure, surprise price. Denny Fish as 86 00:04:54,520 --> 00:04:56,200 Speaker 4: a man who knows exactly about all these things, put 87 00:04:56,200 --> 00:04:59,000 Speaker 4: folio manager on the Global Tech and Innovation team. And 88 00:04:59,120 --> 00:05:01,560 Speaker 4: Rick Janis Henderson Bess, you own all the big names 89 00:05:01,560 --> 00:05:04,039 Speaker 4: when it comes to semiconductors, and cutos to you because 90 00:05:04,080 --> 00:05:07,040 Speaker 4: you've had exposure to the biggest rallies of the year. Denny, 91 00:05:07,480 --> 00:05:10,279 Speaker 4: what do you make of high bandwidth memory the focus 92 00:05:10,279 --> 00:05:12,240 Speaker 4: on US chip makers to get into that space. 93 00:05:13,520 --> 00:05:19,320 Speaker 6: Yeah, I mean, we've witnessed probably the largest deployment of 94 00:05:19,520 --> 00:05:23,960 Speaker 6: advanced semiconductor content ever in our history. And the reason 95 00:05:24,160 --> 00:05:27,640 Speaker 6: that's happened is because we've advanced AI over the last 96 00:05:27,680 --> 00:05:32,960 Speaker 6: couple of years and those advancements continue. I mean, we've 97 00:05:33,000 --> 00:05:36,320 Speaker 6: really laid the tracks the last couple of years with 98 00:05:36,760 --> 00:05:40,720 Speaker 6: pre training of all these models from open AI, from Google, 99 00:05:40,800 --> 00:05:45,480 Speaker 6: with Gemini, Anthropic and others. And what's really interesting now 100 00:05:46,040 --> 00:05:51,880 Speaker 6: is we're actually making the transition from from what we've 101 00:05:51,880 --> 00:05:54,640 Speaker 6: seen with these models to reasoning. And that's a really 102 00:05:54,680 --> 00:05:57,080 Speaker 6: big deal because now we're going to start seeing the 103 00:05:57,120 --> 00:06:00,960 Speaker 6: handoff in a pretty material way for from pre training 104 00:06:01,000 --> 00:06:04,360 Speaker 6: and training to actually inference and time based inference, and 105 00:06:04,440 --> 00:06:10,200 Speaker 6: so that should continue to be quite good for accelerated computing. 106 00:06:10,640 --> 00:06:13,120 Speaker 6: It's just the mix might get a little different over time. 107 00:06:13,560 --> 00:06:17,920 Speaker 6: But as it relates to just you know, GPUs customasics 108 00:06:17,960 --> 00:06:22,480 Speaker 6: from the hyperscalers, high bandwidth memory optics, you know, it 109 00:06:22,480 --> 00:06:24,200 Speaker 6: should continue to be quite strong. 110 00:06:24,440 --> 00:06:26,800 Speaker 4: Jonath Henderson has some exposure to Micron, but I'm not 111 00:06:26,800 --> 00:06:29,600 Speaker 4: seeing it, for example, in your Global Technology Fund for example. 112 00:06:30,160 --> 00:06:31,480 Speaker 2: I'm interested, therefore. 113 00:06:31,120 --> 00:06:32,840 Speaker 4: More about what you think about the shift that we've 114 00:06:32,839 --> 00:06:35,640 Speaker 4: seen going on from all in on picks and shovels, 115 00:06:35,720 --> 00:06:38,280 Speaker 4: all in on the infrastructure build out when it comes 116 00:06:38,320 --> 00:06:41,000 Speaker 4: to semiconductors, but now people moving across the software, now 117 00:06:41,040 --> 00:06:43,360 Speaker 4: people thinking that it's the application lay that people are 118 00:06:43,360 --> 00:06:44,160 Speaker 4: more interested in. 119 00:06:45,160 --> 00:06:47,880 Speaker 6: Yeah, that's absolutely right. And you know, I actually think 120 00:06:48,360 --> 00:06:50,359 Speaker 6: twenty twenty five is going to be a really interesting 121 00:06:50,480 --> 00:06:54,720 Speaker 6: year because you can't put the investment into data centers 122 00:06:54,720 --> 00:06:56,440 Speaker 6: that we put in over the last couple of years 123 00:06:56,839 --> 00:06:59,680 Speaker 6: unless you start to monetize it. And we're actually starting 124 00:06:59,680 --> 00:07:02,560 Speaker 6: to see breadcrumbs, particularly over the last couple of quarters 125 00:07:02,800 --> 00:07:04,840 Speaker 6: from a lot of companies, and you know, you hit 126 00:07:04,880 --> 00:07:07,719 Speaker 6: it on the head, you know, the application software ecosystem, 127 00:07:07,839 --> 00:07:11,080 Speaker 6: whether that's publicly traded vendors that are starting to actually 128 00:07:11,120 --> 00:07:16,280 Speaker 6: talk about specific contributions to their revenue models, or a 129 00:07:16,480 --> 00:07:19,560 Speaker 6: ton of you know, startups obviously that are you know, 130 00:07:19,760 --> 00:07:24,280 Speaker 6: racing to you know, disrupt the existing vendors and take 131 00:07:24,280 --> 00:07:28,000 Speaker 6: advantage of this multi trillion dollar opportunity. And what's really 132 00:07:28,000 --> 00:07:31,000 Speaker 6: interesting is, you know, the software industry, you know, over 133 00:07:31,000 --> 00:07:35,040 Speaker 6: the last twenty years, you know, growed you know, I 134 00:07:35,080 --> 00:07:38,560 Speaker 6: don't know, six seven hundred billion dollars in aggregate as 135 00:07:38,600 --> 00:07:41,800 Speaker 6: it expanded the software TAM. But as you really start 136 00:07:41,840 --> 00:07:45,800 Speaker 6: thinking about what AI can do, this jump to reasoning 137 00:07:46,280 --> 00:07:48,800 Speaker 6: and where we're going from here, and it brings not 138 00:07:48,880 --> 00:07:51,840 Speaker 6: only the software TAM into play, but this you know, 139 00:07:52,040 --> 00:07:55,680 Speaker 6: larger you know services TAM that can be attacked by AI. 140 00:07:56,240 --> 00:07:59,160 Speaker 6: And that's that should be really good for the software ecosystem. 141 00:08:00,200 --> 00:08:02,680 Speaker 4: How are we going to start to see the wheek 142 00:08:02,720 --> 00:08:03,320 Speaker 4: from the chaff? 143 00:08:03,320 --> 00:08:03,480 Speaker 1: Here? 144 00:08:03,520 --> 00:08:07,560 Speaker 4: Though, Denny, throughout twenty twenty four, in many ways, all 145 00:08:07,600 --> 00:08:10,000 Speaker 4: boats have risen until the second half and then we 146 00:08:10,040 --> 00:08:11,880 Speaker 4: started to pit winners against losers. 147 00:08:12,320 --> 00:08:14,120 Speaker 2: Are we going to see that continue? Look, we take 148 00:08:14,160 --> 00:08:14,679 Speaker 2: a case. 149 00:08:14,480 --> 00:08:16,800 Speaker 4: Study of Adobe just not managing to deliver when it 150 00:08:16,840 --> 00:08:18,640 Speaker 4: comes to how much generative AI is going. 151 00:08:18,520 --> 00:08:20,600 Speaker 2: To get to their bottom line compared. 152 00:08:20,200 --> 00:08:22,600 Speaker 4: To paneteer that has just exploded ever higher because the 153 00:08:22,640 --> 00:08:23,520 Speaker 4: AI opportunity. 154 00:08:24,840 --> 00:08:27,720 Speaker 6: Yeah, well that's where we're at. And I mean, we've 155 00:08:27,760 --> 00:08:31,600 Speaker 6: actually seen a narrowing in the semiconductor ecosystem in terms 156 00:08:31,600 --> 00:08:33,760 Speaker 6: of the winners as we look at picks and shovels, 157 00:08:34,120 --> 00:08:36,559 Speaker 6: and that's to be expected. Every time there's a big 158 00:08:36,600 --> 00:08:38,840 Speaker 6: theme like this, you get a rising tide lifts all boats, 159 00:08:39,040 --> 00:08:40,400 Speaker 6: and then you get a narrowing of. 160 00:08:40,360 --> 00:08:41,480 Speaker 7: It, you know. 161 00:08:41,679 --> 00:08:44,000 Speaker 6: And I think that's what we're going to see across 162 00:08:44,360 --> 00:08:47,160 Speaker 6: the rest of the tech ecosystem, including software and twenty five. 163 00:08:48,160 --> 00:08:50,680 Speaker 6: As you said, we'll separate the wheat from the chaff. 164 00:08:51,480 --> 00:08:54,720 Speaker 6: The companies that are actually having meaningful contribution from AI, 165 00:08:55,080 --> 00:08:57,480 Speaker 6: they're going to get higher multiples, their growth is going 166 00:08:57,520 --> 00:09:00,160 Speaker 6: to accelerate, and it's going to be goodness. And then 167 00:09:00,200 --> 00:09:03,479 Speaker 6: there are going to be companies that are clearly struggling 168 00:09:04,280 --> 00:09:06,360 Speaker 6: to get to the right side of time, as you 169 00:09:06,400 --> 00:09:09,000 Speaker 6: mentioned and Adobe's and you know, it's an interesting case 170 00:09:09,000 --> 00:09:11,640 Speaker 6: study because it's been a heavily debated stock in the 171 00:09:11,640 --> 00:09:15,960 Speaker 6: investment community for the last couple of years, as investors 172 00:09:15,960 --> 00:09:18,160 Speaker 6: have gone from Adobe being on the right side of 173 00:09:18,160 --> 00:09:20,800 Speaker 6: time as it relates to AI, to potentially being on 174 00:09:20,840 --> 00:09:21,720 Speaker 6: the wrong side of time. 175 00:09:22,600 --> 00:09:24,679 Speaker 4: Talking of wrong side of time, many trying to pass 176 00:09:24,720 --> 00:09:27,280 Speaker 4: what the right or wrong side of the next administration 177 00:09:27,360 --> 00:09:29,880 Speaker 4: is going to be. This is going to affect, for example, 178 00:09:29,880 --> 00:09:31,800 Speaker 4: some Internet names. I know that you focus in on 179 00:09:31,880 --> 00:09:33,719 Speaker 4: Denny And at the moment, we've got the breaking news 180 00:09:33,720 --> 00:09:35,920 Speaker 4: that the US Supreme Court is going to hear arguments 181 00:09:36,160 --> 00:09:38,240 Speaker 4: over a TikTok divestment. We're going to dig into that 182 00:09:38,280 --> 00:09:41,880 Speaker 4: in a moment, but we do see regulatory implications for 183 00:09:41,920 --> 00:09:43,040 Speaker 4: a lot of companies coming. 184 00:09:43,120 --> 00:09:44,040 Speaker 2: How do you navigate that? 185 00:09:45,160 --> 00:09:48,760 Speaker 6: Yeah, well, not too differently than how we've navigated it 186 00:09:48,840 --> 00:09:51,079 Speaker 6: for the last handful of years. And what I mean 187 00:09:51,120 --> 00:09:54,920 Speaker 6: by that, we've had a very hostile backdrop for the 188 00:09:54,960 --> 00:09:57,200 Speaker 6: last four years. So is it going to get worse? 189 00:09:57,240 --> 00:09:59,240 Speaker 6: It's probably hard to get worse. Is it going to 190 00:09:59,240 --> 00:10:03,000 Speaker 6: get better? Might get a little bit better in certain areas, 191 00:10:03,040 --> 00:10:05,439 Speaker 6: but I expect it to continue to be a difficult 192 00:10:05,520 --> 00:10:09,240 Speaker 6: environment for large cap tech. You know, that diminishes the 193 00:10:09,240 --> 00:10:11,480 Speaker 6: opportunity for them to do M and A and then 194 00:10:11,679 --> 00:10:15,920 Speaker 6: it also you know, we will continue to see aggressive 195 00:10:16,000 --> 00:10:19,480 Speaker 6: postures from the DOJ like we've seen with the Google case. 196 00:10:20,320 --> 00:10:24,160 Speaker 4: Dannie Fish, portfolio manager at Janis Henderson Investors really taking 197 00:10:24,200 --> 00:10:26,720 Speaker 4: us across pulp Brett to the tech ecosystem. We thank you, 198 00:10:26,760 --> 00:10:28,719 Speaker 4: and look, let's just dig into that. Breaking news now 199 00:10:28,760 --> 00:10:31,640 Speaker 4: on TikTok. The Supreme Court has announced that it will 200 00:10:31,679 --> 00:10:35,439 Speaker 4: hear TikTok's challenge to its US ban on January the tenth, 201 00:10:35,600 --> 00:10:38,320 Speaker 4: just days of course before it's potential ban or divest 202 00:10:38,440 --> 00:10:42,400 Speaker 4: argument coming January the nineteenth. We're seeing the implications for Snap. 203 00:10:42,480 --> 00:10:45,880 Speaker 4: For example, snapshares currently falling, Meta shares. 204 00:10:45,720 --> 00:10:46,720 Speaker 2: Have impairing their gain. 205 00:10:46,880 --> 00:10:50,640 Speaker 4: On the news now coming up cracking down on China 206 00:10:50,800 --> 00:10:52,840 Speaker 4: made hips the latest action being taken by the Biden 207 00:10:52,920 --> 00:10:57,360 Speaker 4: administration to combat national security and competition concerns. That's next, 208 00:10:58,080 --> 00:11:12,800 Speaker 4: bring their technology. Get back to the breaking TikTok news. 209 00:11:13,160 --> 00:11:15,720 Speaker 4: Acrossing this plumemberg terminal is the news that the Supreme 210 00:11:15,760 --> 00:11:20,160 Speaker 4: Court will indeed hear TikTok's arguments on January tenth, before 211 00:11:20,200 --> 00:11:22,680 Speaker 4: a potential ban which is likely to come into place 212 00:11:22,920 --> 00:11:26,559 Speaker 4: on January the nineteenth Bluemog's Kaylee lines Josephs for more. 213 00:11:26,640 --> 00:11:29,880 Speaker 4: Of course, this is all regarding it's either to divest 214 00:11:30,160 --> 00:11:32,640 Speaker 4: or be banned on January the nineteenth, but they managed 215 00:11:32,640 --> 00:11:34,400 Speaker 4: to get the Supreme Court to hear the argument. 216 00:11:35,520 --> 00:11:38,120 Speaker 8: Yeah, of course, TikTok, since this law was first passed 217 00:11:38,120 --> 00:11:40,360 Speaker 8: in a bipartisan manner by Congress and then signed into 218 00:11:40,480 --> 00:11:43,160 Speaker 8: law by President Biden earlier this year, has contended that 219 00:11:43,200 --> 00:11:45,839 Speaker 8: it violates the right to free speech embedded in the 220 00:11:45,880 --> 00:11:48,400 Speaker 8: Bill of Rights in the US Constitution. So that is 221 00:11:48,440 --> 00:11:51,000 Speaker 8: the grounds with which TikTok is pursuing this appeal at 222 00:11:51,040 --> 00:11:54,320 Speaker 8: the Supreme Court, after having lost an appeal in the DC. 223 00:11:54,240 --> 00:11:56,040 Speaker 2: Circuit Federal Court of Appeals. 224 00:11:55,640 --> 00:11:59,000 Speaker 8: Which voted three to zero against the company's arguments, saying 225 00:11:59,000 --> 00:12:01,920 Speaker 8: that Congress as well with and its rates within concerns 226 00:12:01,920 --> 00:12:05,120 Speaker 8: over national security to put into place this band. So 227 00:12:05,200 --> 00:12:07,520 Speaker 8: we'll see how this goes on January tenth, as you say, 228 00:12:07,559 --> 00:12:09,920 Speaker 8: Carolina's just nine days before this band is set to 229 00:12:09,960 --> 00:12:12,720 Speaker 8: go into effect. But keep in mind this Supreme Court 230 00:12:13,000 --> 00:12:15,960 Speaker 8: does have a conservative majority, three justices of which were 231 00:12:16,000 --> 00:12:19,280 Speaker 8: appointed by then President and now President elect Donald Trump, 232 00:12:19,320 --> 00:12:21,520 Speaker 8: who earlier this week at a press conference in mar 233 00:12:21,520 --> 00:12:24,000 Speaker 8: A Lago, when asked about the TikTok Ban said that 234 00:12:24,040 --> 00:12:26,720 Speaker 8: he has a warm place in his hurt for TikTok, 235 00:12:26,760 --> 00:12:28,360 Speaker 8: talking about how he does think it's one of the 236 00:12:28,400 --> 00:12:30,400 Speaker 8: reasons why he was able to pick up more of 237 00:12:30,440 --> 00:12:32,200 Speaker 8: the youth vote, and at marra A Lago on that 238 00:12:32,320 --> 00:12:35,000 Speaker 8: very same day this week he met with TikTok CEO 239 00:12:35,360 --> 00:12:37,120 Speaker 8: Show Choo. So it'll be interesting to see if we 240 00:12:37,160 --> 00:12:40,240 Speaker 8: hear from the President elect on the Supreme Court's decision 241 00:12:40,280 --> 00:12:42,880 Speaker 8: to hear TikTok's arguments on January. 242 00:12:42,440 --> 00:12:46,440 Speaker 4: Tenth's having market implications. We see Snap, for example, losing 243 00:12:46,520 --> 00:12:48,640 Speaker 4: some market value on the back of this as people 244 00:12:48,720 --> 00:12:50,560 Speaker 4: try to price in whether or not TikTok will be 245 00:12:50,600 --> 00:12:53,920 Speaker 4: remaining as a competitor. Meta also pairing some of its 246 00:12:53,960 --> 00:12:54,720 Speaker 4: earlier gains. 247 00:12:55,120 --> 00:12:55,400 Speaker 2: Katie. 248 00:12:55,440 --> 00:12:58,640 Speaker 4: All of this comes in the context of US versus China. 249 00:12:58,840 --> 00:13:01,280 Speaker 4: We've had the breaking news today that Biden is going 250 00:13:01,360 --> 00:13:05,160 Speaker 4: to be announcing well a Chinese semiconductor probe here in 251 00:13:05,200 --> 00:13:06,000 Speaker 4: the coming days. 252 00:13:06,120 --> 00:13:07,240 Speaker 2: Another area of tip. 253 00:13:07,120 --> 00:13:11,000 Speaker 8: For tat absolutely and this one is interesting, Caroline, because 254 00:13:11,000 --> 00:13:14,240 Speaker 8: this probe will specifically be looking at foundational chips. The 255 00:13:14,280 --> 00:13:16,599 Speaker 8: older generation ones that of course are the backbone to 256 00:13:16,640 --> 00:13:20,240 Speaker 8: everything from cars to smartphones. Knowing this administration already for 257 00:13:20,240 --> 00:13:22,680 Speaker 8: the last several years has been putting into place export 258 00:13:22,679 --> 00:13:26,320 Speaker 8: controls around advanced semiconductor technology, so the things that go 259 00:13:26,360 --> 00:13:30,200 Speaker 8: into say artificial intelligence, trying to restrain China's ability to 260 00:13:30,320 --> 00:13:32,960 Speaker 8: control that technology. But the concern that this White House 261 00:13:33,000 --> 00:13:35,280 Speaker 8: still has in its waning days is that China, because 262 00:13:35,320 --> 00:13:37,920 Speaker 8: it's still producing these older chips, is going to be 263 00:13:37,960 --> 00:13:40,200 Speaker 8: able to flood the market with them and therefore makes 264 00:13:40,200 --> 00:13:44,520 Speaker 8: say American companies less competitive. Already, this administration had announced 265 00:13:44,520 --> 00:13:46,800 Speaker 8: that they would like to raise the tariff on older 266 00:13:46,880 --> 00:13:49,920 Speaker 8: chips from twenty five percent to fifty percent by twenty 267 00:13:49,960 --> 00:13:52,080 Speaker 8: twenty five, but according to people familiar with the matter, 268 00:13:52,120 --> 00:13:54,280 Speaker 8: the feeling of the administration is that is not enough 269 00:13:54,559 --> 00:13:58,160 Speaker 8: to actually adequately protect competition. Hence the launching of this 270 00:13:58,280 --> 00:14:01,439 Speaker 8: investigation the rub Caroline and is while this investigation will 271 00:14:01,440 --> 00:14:04,320 Speaker 8: be launching, the inauguration where Joe Biden will leave the 272 00:14:04,320 --> 00:14:06,720 Speaker 8: office and Donald Trump will take it is just over 273 00:14:06,760 --> 00:14:09,720 Speaker 8: thirty days away, So ultimately the conclusion of the probe 274 00:14:09,720 --> 00:14:12,480 Speaker 8: the findings will be left up to the discretion of 275 00:14:12,559 --> 00:14:14,400 Speaker 8: Donald Trump as to how to handle them, whether that 276 00:14:14,400 --> 00:14:17,480 Speaker 8: will be tariffs or other restrictions around trade. We do 277 00:14:17,600 --> 00:14:19,960 Speaker 8: know though that the President elect is not shy when 278 00:14:19,960 --> 00:14:21,760 Speaker 8: it comes to tariff's in China, Caroline, he. 279 00:14:21,760 --> 00:14:25,720 Speaker 2: Loves the word m Kaylee. Just set the context then. 280 00:14:25,600 --> 00:14:29,000 Speaker 4: Because also we know that he did invite President She 281 00:14:29,200 --> 00:14:30,920 Speaker 4: we understand, to the inauguration. 282 00:14:31,240 --> 00:14:32,520 Speaker 2: So could yes, a. 283 00:14:32,440 --> 00:14:35,280 Speaker 4: Lot of this end not being implemented because deals are done. 284 00:14:36,360 --> 00:14:37,600 Speaker 2: Well, that's entirely possible. 285 00:14:37,600 --> 00:14:39,920 Speaker 8: And it's a live conversation here in Washington as to 286 00:14:39,960 --> 00:14:42,360 Speaker 8: whether or not the tariffs that Donald Trump has threatened, 287 00:14:42,760 --> 00:14:45,520 Speaker 8: not just technology but all goods coming from China are 288 00:14:45,640 --> 00:14:49,280 Speaker 8: just a negotiating tactic, a way to start the conversation 289 00:14:49,360 --> 00:14:52,360 Speaker 8: to extract more from China that he would see, basically, 290 00:14:52,360 --> 00:14:54,600 Speaker 8: a way to use leverage that remains to be seen. 291 00:14:54,640 --> 00:14:56,240 Speaker 8: But it will be up to his discretion if he 292 00:14:56,280 --> 00:14:58,480 Speaker 8: does want to act if these findings do conclude that 293 00:14:58,520 --> 00:15:01,440 Speaker 8: there are unfair trade practices China is involved in when 294 00:15:01,440 --> 00:15:03,880 Speaker 8: it comes to this very critical technology that the US 295 00:15:03,920 --> 00:15:07,200 Speaker 8: has already heavily invested in developing here in the States. 296 00:15:07,240 --> 00:15:09,000 Speaker 8: I would not as well, Caroline, that while a lot 297 00:15:09,000 --> 00:15:11,320 Speaker 8: of this is the discretion of the president. Congress is 298 00:15:11,360 --> 00:15:15,600 Speaker 8: consistently acting hawkishly toward China as well. It is Congress 299 00:15:15,600 --> 00:15:18,120 Speaker 8: where the TikTok band originated, where we've seen a lot 300 00:15:18,120 --> 00:15:21,160 Speaker 8: of restrictions pass through law, and in the continuing funding 301 00:15:21,200 --> 00:15:23,760 Speaker 8: resolution that was just released last night, it also includes 302 00:15:23,840 --> 00:15:28,000 Speaker 8: language related to outbound investment into China restrictions. So this 303 00:15:28,080 --> 00:15:29,800 Speaker 8: is something we're seeing really across the board. 304 00:15:30,240 --> 00:15:33,760 Speaker 4: Kayley lines, thank you very much. Indeed, all things US China. 305 00:15:33,840 --> 00:15:36,160 Speaker 4: Now it's time for talking tech. And first up, the 306 00:15:36,240 --> 00:15:37,320 Speaker 4: risk of blackouts. 307 00:15:37,480 --> 00:15:38,000 Speaker 2: They're going to. 308 00:15:37,960 --> 00:15:41,040 Speaker 4: Increase for most of North America over the coming decade, 309 00:15:41,160 --> 00:15:44,160 Speaker 4: according to a report by a grid reliability regulator. Then, 310 00:15:44,200 --> 00:15:48,200 Speaker 4: the report underschool's utility challenges as demand is forecast arise 311 00:15:48,240 --> 00:15:51,040 Speaker 4: the most in decades, of course, driven by AI and 312 00:15:51,120 --> 00:15:52,520 Speaker 4: the electrification of the economy. 313 00:15:53,200 --> 00:15:54,720 Speaker 2: Plus, a new court order. 314 00:15:54,560 --> 00:15:58,440 Speaker 4: Issued Monday says TikTok must turnover its source code and 315 00:15:58,520 --> 00:16:01,400 Speaker 4: financial data. This after plays from the Chinese tech company 316 00:16:01,480 --> 00:16:05,160 Speaker 4: Beijing Machine Network accuse the social media platform of copyright 317 00:16:05,160 --> 00:16:09,840 Speaker 4: infringement and misappropriation of trade secrets. A Meta's Instagram is 318 00:16:09,840 --> 00:16:12,680 Speaker 4: expected to account for half of the company's and revenue 319 00:16:12,720 --> 00:16:15,320 Speaker 4: in the United States. That's according to estimates from research 320 00:16:15,360 --> 00:16:18,320 Speaker 4: firm E Marketer, Instagram is now expected to top thirty 321 00:16:18,360 --> 00:16:21,160 Speaker 4: two billion dollars in US advertising revenue in twenty twenty five. 322 00:16:21,080 --> 00:16:23,720 Speaker 2: Up more than twenty four percent from the current year. 323 00:16:24,560 --> 00:16:27,960 Speaker 4: Coming up, sam MoOx aq and announces a major new 324 00:16:27,960 --> 00:16:28,520 Speaker 4: funding round. 325 00:16:28,600 --> 00:16:30,440 Speaker 2: CEO Jack Hidari is with us. 326 00:16:30,840 --> 00:16:33,120 Speaker 4: The next we're going to be discussing the company's plans 327 00:16:33,160 --> 00:16:36,240 Speaker 4: to use the course AI and innovate everything from medical 328 00:16:36,240 --> 00:16:40,400 Speaker 4: devices to cybersecurity, AQ AI and quantum. 329 00:16:40,480 --> 00:16:51,160 Speaker 2: This is remote technology. 330 00:16:52,280 --> 00:16:56,080 Speaker 4: AI solutions company sandmox Aq has closed its latest round 331 00:16:56,120 --> 00:16:58,920 Speaker 4: of funding, raising three hundred million dollars funding. The company, 332 00:16:58,920 --> 00:17:01,600 Speaker 4: now a five point six big company, says the investment 333 00:17:01,640 --> 00:17:03,720 Speaker 4: will be used to further its development in areas like 334 00:17:03,800 --> 00:17:08,000 Speaker 4: drug discovery, cybersecurity, navigation, and medical devices, or using its 335 00:17:08,000 --> 00:17:12,120 Speaker 4: own AI techniques. Sunbox aqco Jack Henry is here with us. 336 00:17:12,480 --> 00:17:15,680 Speaker 2: Let's talk large quantitative models. 337 00:17:15,880 --> 00:17:18,560 Speaker 4: How is what you're building different from the large language 338 00:17:18,560 --> 00:17:20,040 Speaker 4: models we've all had to wrap our head around. 339 00:17:20,119 --> 00:17:20,399 Speaker 2: Karling. 340 00:17:20,440 --> 00:17:21,840 Speaker 7: Good to see you. First of all, great to be 341 00:17:21,880 --> 00:17:24,520 Speaker 7: here live on set in the world of Zoom. It's 342 00:17:24,560 --> 00:17:28,199 Speaker 7: great to be in person for once. This is a 343 00:17:28,240 --> 00:17:31,240 Speaker 7: fundamental new area of AI. So I think we're familiar 344 00:17:31,240 --> 00:17:34,280 Speaker 7: with chat GPT, we're familiar with large language models, wonderful 345 00:17:34,280 --> 00:17:37,200 Speaker 7: set of applications, millions of people using it around the 346 00:17:37,200 --> 00:17:41,080 Speaker 7: world as it should be. Now comes large quantitative models, 347 00:17:41,400 --> 00:17:43,679 Speaker 7: and when we look at the majority of the business 348 00:17:43,720 --> 00:17:48,720 Speaker 7: sectors the economy, we look at biopharma, chemicals, energy, financial services. 349 00:17:49,119 --> 00:17:53,159 Speaker 7: These are all quantitatively driven, non language driven, and so 350 00:17:53,200 --> 00:17:56,800 Speaker 7: the majority of our economy is actually depending on quantitative relationships. 351 00:17:56,960 --> 00:17:59,680 Speaker 7: An example would be in biopharma. Let's say we want 352 00:17:59,680 --> 00:18:02,119 Speaker 7: to create a new drug for a certain cancer or 353 00:18:02,160 --> 00:18:05,359 Speaker 7: Alzheimer's or Parkinson's. We probably don't want to take the 354 00:18:05,440 --> 00:18:08,160 Speaker 7: drug of the language model trained on Reddit and cat 355 00:18:08,200 --> 00:18:12,160 Speaker 7: pictures probably not recommend it. But we can use large 356 00:18:12,200 --> 00:18:15,560 Speaker 7: language models to summarize scientific literature for example. 357 00:18:15,600 --> 00:18:16,200 Speaker 2: That's wonderful. 358 00:18:16,440 --> 00:18:19,040 Speaker 7: But when it comes to actually the quantitative relationship of 359 00:18:19,440 --> 00:18:21,960 Speaker 7: creating the drug, seeing if it fits at that receptor, 360 00:18:22,160 --> 00:18:25,280 Speaker 7: and developing that all throughout the clinical trial, that's a 361 00:18:25,359 --> 00:18:29,199 Speaker 7: job for large quantitative models. The data is fundamentally different, 362 00:18:29,359 --> 00:18:30,399 Speaker 7: the models are different. 363 00:18:30,640 --> 00:18:31,639 Speaker 2: The whole field is. 364 00:18:31,640 --> 00:18:34,719 Speaker 4: A new area, and how practical is it in its 365 00:18:34,720 --> 00:18:37,399 Speaker 4: application right here, right now, when you're raising three hundred 366 00:18:37,400 --> 00:18:37,880 Speaker 4: million dollars. 367 00:18:37,920 --> 00:18:39,240 Speaker 2: What is that for? Is that fat talent? 368 00:18:39,359 --> 00:18:43,640 Speaker 4: Is that to actually drive the necessary. 369 00:18:42,480 --> 00:18:44,159 Speaker 2: Improvements in the models? What is it that you're going 370 00:18:44,200 --> 00:18:44,879 Speaker 2: to be fine tuning? 371 00:18:45,000 --> 00:18:47,479 Speaker 7: Well, the goodness is, over the last two three years 372 00:18:47,920 --> 00:18:51,159 Speaker 7: since our spinout, we've now proven with customer after customer 373 00:18:51,240 --> 00:18:54,919 Speaker 7: that it actually is having impact. For example, UCSF University 374 00:18:54,960 --> 00:18:57,399 Speaker 7: of California, San Francisco, one of the best places for 375 00:18:57,440 --> 00:19:01,239 Speaker 7: biomedical work. Stamprusner is a Nobel laureate there using our 376 00:19:01,320 --> 00:19:05,760 Speaker 7: software to advance his drugs for NEURODJN diseases right now. 377 00:19:06,000 --> 00:19:08,919 Speaker 7: So this is having impact today. It's running today on 378 00:19:09,000 --> 00:19:12,480 Speaker 7: the GPUs on Nvidia and TPUs from Alphabet. 379 00:19:12,720 --> 00:19:13,960 Speaker 2: It's running right now. 380 00:19:14,240 --> 00:19:17,320 Speaker 7: And these are quantitative models again, not trained on the 381 00:19:17,320 --> 00:19:20,439 Speaker 7: corpus of words of the Internet, but trained on the 382 00:19:20,480 --> 00:19:24,720 Speaker 7: relationships of chemicals and atoms and those kind of dynamic relationships. 383 00:19:24,840 --> 00:19:26,639 Speaker 7: So it's having impact right now, Carolina. 384 00:19:26,640 --> 00:19:28,920 Speaker 4: Because what's interesting, of course he's talked about a spinoff. 385 00:19:28,920 --> 00:19:31,479 Speaker 2: It was a spinoff from Google. Your chair is Eric Schmidt. 386 00:19:31,480 --> 00:19:33,760 Speaker 4: We understand the sort of way in which the company 387 00:19:33,760 --> 00:19:36,159 Speaker 4: first was born and then we look at what's happening 388 00:19:36,160 --> 00:19:39,280 Speaker 4: with Google Quantum, the Willow Chip. Everyone's deeply excited about 389 00:19:39,400 --> 00:19:42,679 Speaker 4: real applications to quantum in the next five ten years. 390 00:19:42,840 --> 00:19:45,119 Speaker 2: Is that realistic? Well, it's a great question. 391 00:19:45,480 --> 00:19:48,320 Speaker 7: Why do we call our company sambox AQ? What's the 392 00:19:48,359 --> 00:19:51,960 Speaker 7: AQ A for AIQ for quantum and so years ago 393 00:19:52,080 --> 00:19:55,400 Speaker 7: we already saw that advanced compute really has two engines. 394 00:19:55,720 --> 00:19:58,720 Speaker 7: It has the AI engine of large language Models, Large 395 00:19:58,800 --> 00:20:03,040 Speaker 7: quantitative Models lqms, but it now also has the queue 396 00:20:03,040 --> 00:20:07,000 Speaker 7: the quantum and the breakthrough that Google announced just last week, 397 00:20:07,119 --> 00:20:10,159 Speaker 7: our colleague hartmand Nevin, who announced that at the Santa 398 00:20:10,160 --> 00:20:13,439 Speaker 7: Barbara campus of Google, is a wonderful example of the 399 00:20:13,480 --> 00:20:16,760 Speaker 7: milestones being achieved in the quantum hardware space. We don't 400 00:20:16,800 --> 00:20:21,119 Speaker 7: build quantum hardware, We use quantum hardware as it progresses 401 00:20:21,160 --> 00:20:25,000 Speaker 7: to business impact. Today quantum computers are very early, but 402 00:20:25,040 --> 00:20:27,639 Speaker 7: what Google showed last week with the Willow chap is 403 00:20:27,640 --> 00:20:30,080 Speaker 7: that as you scale the number of cubics, you can 404 00:20:30,119 --> 00:20:33,600 Speaker 7: actually decrease the number of errors. That was a critical 405 00:20:33,640 --> 00:20:37,560 Speaker 7: milestone Caroline, in terms of how we can advance quantum computing. 406 00:20:37,800 --> 00:20:38,760 Speaker 2: So the future that. 407 00:20:38,680 --> 00:20:43,080 Speaker 7: We see is one that has GPUs and QPUs quantum 408 00:20:43,080 --> 00:20:46,480 Speaker 7: processing units together working together in a mesh in the 409 00:20:46,520 --> 00:20:49,400 Speaker 7: hybrid cloud that I think is the future, and we're 410 00:20:49,400 --> 00:20:51,720 Speaker 7: going to see I think, very significant progress over the 411 00:20:51,760 --> 00:20:54,560 Speaker 7: next five six years in the quantum hardware itself. 412 00:20:55,240 --> 00:21:01,680 Speaker 4: All of that means your applications become well, how quickly 413 00:21:01,760 --> 00:21:02,920 Speaker 4: are you going to be making money. 414 00:21:02,760 --> 00:21:04,520 Speaker 2: From this for your investors that we've just been seeing. 415 00:21:04,880 --> 00:21:07,240 Speaker 7: Well, the good news is we're already generating revenue today. 416 00:21:07,400 --> 00:21:09,600 Speaker 7: We're creating a lot of value for our customers today. 417 00:21:09,880 --> 00:21:12,040 Speaker 7: And so be it in biropharma, be it in the 418 00:21:12,119 --> 00:21:15,399 Speaker 7: chemicals and material space, be it in battery chemistry. We 419 00:21:15,480 --> 00:21:18,560 Speaker 7: know the importance of batteries, not just for electric vehicles. Actually, 420 00:21:18,560 --> 00:21:22,240 Speaker 7: the bigger market for batteries are stationary batteries to store 421 00:21:22,400 --> 00:21:25,679 Speaker 7: energy from renewable sources. That's actually the bigger market. But 422 00:21:25,720 --> 00:21:29,560 Speaker 7: we must leap frog beyond lithium ion technology. This again 423 00:21:29,640 --> 00:21:32,640 Speaker 7: is a job for an l QM, a large quantitative 424 00:21:32,640 --> 00:21:35,520 Speaker 7: model versus an l LM, and so I think that 425 00:21:35,560 --> 00:21:38,880 Speaker 7: the number of applications are really numerous, and we raise 426 00:21:38,920 --> 00:21:41,359 Speaker 7: this money in order to advance our investment in the 427 00:21:41,400 --> 00:21:44,600 Speaker 7: product development. We do want to hire more PhDs, more 428 00:21:44,600 --> 00:21:46,960 Speaker 7: software engineers. We have quite a few ready, but it's 429 00:21:47,000 --> 00:21:49,920 Speaker 7: time to hire more. So those listening who have those 430 00:21:49,960 --> 00:21:51,479 Speaker 7: kind of skills please contact us. 431 00:21:51,960 --> 00:22:03,399 Speaker 4: Hey, well, Jack Hnary is Samox AQCEO. Welcome back to 432 00:22:03,400 --> 00:22:05,719 Speaker 4: BLOEMG Technology. I'm Karen Hide in New York. Let's dig 433 00:22:05,760 --> 00:22:07,640 Speaker 4: into what's happening in the crypto side of things, because 434 00:22:07,640 --> 00:22:10,000 Speaker 4: another risk asset of choice that's trading in a sensetive 435 00:22:10,000 --> 00:22:12,160 Speaker 4: manner ahead of the federal reserve is bitcoin. We're now 436 00:22:12,160 --> 00:22:14,600 Speaker 4: one hundred three thousand. Remember yesterday we managed to peak 437 00:22:14,640 --> 00:22:17,879 Speaker 4: above one hundred and eight thousand dollars per token, so 438 00:22:18,119 --> 00:22:19,719 Speaker 4: we're just seeing a little bit of money coming off 439 00:22:19,800 --> 00:22:22,120 Speaker 4: ahead of also the holidays as well. Mike's strategy down 440 00:22:22,320 --> 00:22:24,879 Speaker 4: as of course, it's basically a bitcoin proxy here, but 441 00:22:25,000 --> 00:22:27,239 Speaker 4: interestingly we were hearing from Michael Sailor a little bit 442 00:22:27,240 --> 00:22:31,040 Speaker 4: earlier Washington considering a potential cryptoregulation. How it's going to 443 00:22:31,320 --> 00:22:34,760 Speaker 4: basically iterate and micro Strategy is continuing to be buying bitcoin, 444 00:22:34,840 --> 00:22:37,760 Speaker 4: bringing its total investments to roughly forty five billion dollars. 445 00:22:38,200 --> 00:22:41,480 Speaker 4: The chairman, Michael Saylor, spoke to Bloomberg earlier on why 446 00:22:41,480 --> 00:22:43,880 Speaker 4: the company is investing in the digital asset and continuing 447 00:22:43,960 --> 00:22:44,280 Speaker 4: to do so. 448 00:22:44,320 --> 00:22:47,400 Speaker 2: When whether he'd consider a role in shaping new crypto. 449 00:22:47,119 --> 00:22:50,040 Speaker 9: Policy, take listen, I've met with a lot of people 450 00:22:50,880 --> 00:22:55,280 Speaker 9: in the incoming administration, but I couldn't comment further than that. 451 00:22:55,720 --> 00:22:58,240 Speaker 10: Okay, I can't comment further than that. I was going 452 00:22:58,280 --> 00:23:00,800 Speaker 10: to ask you about plans, but said I'll ask you 453 00:23:00,920 --> 00:23:03,159 Speaker 10: about whether or not you would be willing to serve 454 00:23:03,200 --> 00:23:07,120 Speaker 10: in the Trump administration in any capacity. Bloomberg has reported 455 00:23:07,160 --> 00:23:11,000 Speaker 10: that there potentially could be a crypto advisory council. Is 456 00:23:11,040 --> 00:23:13,000 Speaker 10: that something that you might be interested in? 457 00:23:14,400 --> 00:23:19,199 Speaker 9: You know, I'm always willing to provide, you know, thoughts 458 00:23:18,880 --> 00:23:24,520 Speaker 9: on constructive digital assets policy, either in confidence or publicly, 459 00:23:25,200 --> 00:23:27,320 Speaker 9: And if I'm asked to serve on some sort of 460 00:23:27,359 --> 00:23:32,320 Speaker 9: digital assets advisory counseling, probably would do so. 461 00:23:32,480 --> 00:23:35,400 Speaker 11: Yes, Michael, I also want to talk to you about 462 00:23:35,760 --> 00:23:39,280 Speaker 11: micro strategy, your own plans. It's amazing you have announced 463 00:23:39,320 --> 00:23:42,000 Speaker 11: plans to raise forty two billion dollars over three years. 464 00:23:42,000 --> 00:23:44,720 Speaker 11: You announced that back in October. But the rate you're 465 00:23:44,760 --> 00:23:48,520 Speaker 11: going at, and this is through confortable debt offerings, stock sales, 466 00:23:48,560 --> 00:23:50,480 Speaker 11: the rate you're going at you could fulfill that target 467 00:23:50,520 --> 00:23:54,560 Speaker 11: by January. Are you planning to lift that cap and 468 00:23:54,640 --> 00:23:57,280 Speaker 11: line up more facilities in the pursuit of more bitcoin. 469 00:23:59,000 --> 00:24:02,119 Speaker 9: Yeah. When we bounced it, it wasn't clear how enthusiastic the 470 00:24:02,119 --> 00:24:05,440 Speaker 9: capital markets would be, but we got a very enthusiastic reception. 471 00:24:05,560 --> 00:24:08,920 Speaker 9: And then after November fifth with the Red Wave, we 472 00:24:08,960 --> 00:24:12,800 Speaker 9: saw a big sea change in the political environment, and 473 00:24:12,840 --> 00:24:15,760 Speaker 9: so we went faster than we had thought we were 474 00:24:15,800 --> 00:24:19,320 Speaker 9: going to go. On October thirtieth, our goal is to 475 00:24:20,320 --> 00:24:24,920 Speaker 9: is to continue to raise capital, primarily through fixed income markets. 476 00:24:24,920 --> 00:24:31,960 Speaker 9: So we'll pursue preferred stock or convertible bond or other 477 00:24:32,280 --> 00:24:36,600 Speaker 9: equity linked financings as long as it's creative for our shareholders. 478 00:24:37,400 --> 00:24:40,200 Speaker 9: When we get through the twenty one twenty one plan, 479 00:24:40,320 --> 00:24:45,120 Speaker 9: which is forty two billion in capital, we'll revisit revisit 480 00:24:45,160 --> 00:24:47,600 Speaker 9: our capital plan, and we'll put in place a new plan, 481 00:24:47,880 --> 00:24:49,719 Speaker 9: subject to market conditions at the time. 482 00:24:50,760 --> 00:24:54,080 Speaker 4: Micro Strategy Chairman Michael Sailor and political roles in the 483 00:24:54,080 --> 00:24:56,840 Speaker 4: future of his bitcoin buying. Let's talk a little bit 484 00:24:56,840 --> 00:24:59,960 Speaker 4: more now with the Defiance etfco and CEO Sylvia Jablunsk. 485 00:25:00,080 --> 00:25:03,200 Speaker 4: You've got Defiance Daily target two x long micro strategy. 486 00:25:03,320 --> 00:25:05,600 Speaker 2: ETF always demand. 487 00:25:05,160 --> 00:25:07,400 Speaker 4: For those sorts of leverage products on MicroStrategy. 488 00:25:07,400 --> 00:25:10,040 Speaker 3: At the moment, demand is on fire. That's the best 489 00:25:10,080 --> 00:25:11,879 Speaker 3: way I can put it. You know, we've seen billions 490 00:25:11,920 --> 00:25:14,360 Speaker 3: of dollars of assets flowing into this ETF. It's by 491 00:25:14,400 --> 00:25:16,320 Speaker 3: far the fastest growing ETF. 492 00:25:15,960 --> 00:25:16,760 Speaker 2: That we've ever seen. 493 00:25:17,160 --> 00:25:19,480 Speaker 3: And I think the reason is that, you know, Bitcoin 494 00:25:19,520 --> 00:25:21,439 Speaker 3: has just taken off, and whether that's because of the 495 00:25:21,440 --> 00:25:26,560 Speaker 3: commercialization of bitcoin, ETF's holding bitcoin, you know, favorable policy 496 00:25:26,600 --> 00:25:29,360 Speaker 3: with the new government coming into office. I just think 497 00:25:29,400 --> 00:25:32,840 Speaker 3: that there's this huge tailwind and who is the best 498 00:25:32,840 --> 00:25:33,760 Speaker 3: proxy for bitcoin. 499 00:25:33,800 --> 00:25:36,200 Speaker 2: It's micro strategy. So stock listed on an. 500 00:25:36,080 --> 00:25:39,199 Speaker 3: Exchange as a single name, it trades as a leverage 501 00:25:39,560 --> 00:25:41,879 Speaker 3: you know, version of bitcoin, and we've now leveraged it 502 00:25:41,920 --> 00:25:45,320 Speaker 3: two times and so this you know, short term volatility. 503 00:25:45,359 --> 00:25:48,160 Speaker 3: Traders love the ETF and it's just been wildly popular. 504 00:25:48,200 --> 00:25:50,920 Speaker 4: And from an institutional perspective as well as a retail. 505 00:25:50,640 --> 00:25:54,160 Speaker 3: Perspective, absolutely both institutions in retail. I think what's great 506 00:25:54,160 --> 00:25:57,200 Speaker 3: about the institutional angle is that some institutions are still 507 00:25:57,240 --> 00:25:59,560 Speaker 3: sort of weary about holding bitcoin itself, whether it's through 508 00:25:59,560 --> 00:26:02,080 Speaker 3: the digital wallet or using an ETF for it. But 509 00:26:02,760 --> 00:26:04,840 Speaker 3: you know, micro strategy is just a sock, right that 510 00:26:05,119 --> 00:26:07,199 Speaker 3: trades on an exchange, and so they are able to 511 00:26:07,200 --> 00:26:09,399 Speaker 3: put that in their portfolios. And I think that shows 512 00:26:09,480 --> 00:26:10,600 Speaker 3: up a lot of demand. 513 00:26:10,760 --> 00:26:14,959 Speaker 4: A very volatile holding in portfolios taught to us about. Therefore, 514 00:26:15,320 --> 00:26:18,560 Speaker 4: when you're looking at the hype cycles around, whether it's 515 00:26:18,560 --> 00:26:21,720 Speaker 4: crypto or AI parent contrast, how much you's still seeing 516 00:26:21,760 --> 00:26:24,000 Speaker 4: people wanting to pour into the infrastructure slide of the 517 00:26:24,040 --> 00:26:25,040 Speaker 4: AI bet and other. 518 00:26:24,920 --> 00:26:26,199 Speaker 2: Ets you have exposure to that. 519 00:26:26,800 --> 00:26:29,040 Speaker 3: Yeah, I think that the demand is that it's all 520 00:26:29,080 --> 00:26:31,399 Speaker 3: time high, right ever since the Chat Shiepyte moment and 521 00:26:31,400 --> 00:26:34,199 Speaker 3: now the Google Willow moment and you know crypto and 522 00:26:34,280 --> 00:26:36,280 Speaker 3: kind of all the buzz around that. You know, there's 523 00:26:36,520 --> 00:26:39,040 Speaker 3: trillions of dollars of cash sitting on the sidelines. There's 524 00:26:39,040 --> 00:26:42,200 Speaker 3: the biggest wealth transfer of our generation happening as we speak. 525 00:26:42,480 --> 00:26:45,480 Speaker 3: And you know gen Z, you know gen Z, Millennial 526 00:26:45,560 --> 00:26:46,880 Speaker 3: gen X kind of you know. 527 00:26:46,840 --> 00:26:47,800 Speaker 2: The younger traders are. 528 00:26:47,840 --> 00:26:50,040 Speaker 3: This is where they're allocating their funds too. And you know, 529 00:26:50,119 --> 00:26:53,400 Speaker 3: retail has definitely spoken, institutions have definitely spoken, and they're 530 00:26:53,440 --> 00:26:57,080 Speaker 3: looking for that Fourth Industrial Revolution allocation, and you know, 531 00:26:57,080 --> 00:26:59,959 Speaker 3: the biggest trades right now are basically quantum and micro strategy. 532 00:27:00,240 --> 00:27:02,439 Speaker 4: Let's go into quantum because we just had Jack Hetery 533 00:27:02,480 --> 00:27:05,280 Speaker 4: on from sam max AQ and talking about how we're 534 00:27:05,320 --> 00:27:08,639 Speaker 4: starting to see quantum theory. 535 00:27:08,320 --> 00:27:09,360 Speaker 2: Be put into practice. 536 00:27:09,440 --> 00:27:12,240 Speaker 4: You just mentioned Willow and their breakthroughs over at Google. 537 00:27:12,520 --> 00:27:14,120 Speaker 2: Which other stock should people be looking at? 538 00:27:14,160 --> 00:27:16,520 Speaker 4: Are they only dominating on the big companies that have 539 00:27:16,560 --> 00:27:18,560 Speaker 4: got exposure to quantum. 540 00:27:18,160 --> 00:27:19,880 Speaker 2: Like an IBM or Google. Are they're going smaller? 541 00:27:20,280 --> 00:27:22,040 Speaker 3: Yeah, And so we kind of looked at this space 542 00:27:22,080 --> 00:27:24,200 Speaker 3: and what I liked about what Jack explained. His company 543 00:27:24,240 --> 00:27:26,000 Speaker 3: has the A and the Q, the AI and the quantum. 544 00:27:26,119 --> 00:27:26,280 Speaker 8: Right. 545 00:27:26,359 --> 00:27:28,800 Speaker 3: So, so chat gpt AI is you know, kind of 546 00:27:28,840 --> 00:27:31,720 Speaker 3: version one. Quantum is taking everything to the next level. 547 00:27:31,760 --> 00:27:34,240 Speaker 2: So you need quantum in order for AI to be efficient. 548 00:27:34,280 --> 00:27:35,920 Speaker 2: You need to process that data quickly. 549 00:27:36,480 --> 00:27:40,880 Speaker 3: It will help you know, essentially healthcare, cryptography, aerospace, and defense, 550 00:27:41,600 --> 00:27:44,120 Speaker 3: you know, blockchain technology, anything you can think of will 551 00:27:44,119 --> 00:27:47,320 Speaker 3: be bettered with quantum supercomputing power. And so the most 552 00:27:47,320 --> 00:27:51,040 Speaker 3: popular stocks right now are are a combination of large 553 00:27:51,080 --> 00:27:53,359 Speaker 3: cabs and small caps. Right, so you have like the ibms, 554 00:27:53,359 --> 00:27:55,959 Speaker 3: the Googles, the n videos. Some of these companies are 555 00:27:55,960 --> 00:27:59,720 Speaker 3: actually very relevant hp IBM. And then the smaller names 556 00:27:59,720 --> 00:28:02,000 Speaker 3: that are taking off hundreds of you know, one hundreds 557 00:28:02,040 --> 00:28:05,240 Speaker 3: percentage points of performance are companies like d Wave ion 558 00:28:05,320 --> 00:28:08,280 Speaker 3: Cue for Gratty Computing. We actually married them in our 559 00:28:08,280 --> 00:28:10,280 Speaker 3: ETF Quantum. So you have the large caps and the 560 00:28:10,320 --> 00:28:12,560 Speaker 3: small caps, so the ballast and the quality earnings with 561 00:28:12,840 --> 00:28:15,080 Speaker 3: the high flyers and the movers that are just winning. 562 00:28:15,280 --> 00:28:17,119 Speaker 4: Just look at these high flies for a moment, Like, 563 00:28:17,240 --> 00:28:20,520 Speaker 4: we're not basically one thousand percent on DWave Quantum over 564 00:28:20,560 --> 00:28:21,639 Speaker 4: the last year to date. 565 00:28:21,520 --> 00:28:22,479 Speaker 2: Two hundred percent. 566 00:28:22,720 --> 00:28:26,359 Speaker 4: How how cautious have been people getting into these wildly 567 00:28:27,160 --> 00:28:28,639 Speaker 4: volatile names, even if they are. 568 00:28:28,520 --> 00:28:29,200 Speaker 2: Open to the right. 569 00:28:29,560 --> 00:28:31,879 Speaker 3: Yeah, I think I think that people have been doing 570 00:28:31,920 --> 00:28:33,760 Speaker 3: a combination of both. Right, So they're getting into these 571 00:28:33,760 --> 00:28:36,280 Speaker 3: smaller names, but they're also looking at things like the 572 00:28:36,280 --> 00:28:37,240 Speaker 3: ETFs at all that. 573 00:28:37,880 --> 00:28:40,480 Speaker 2: Yeah, and you know, you want to kind of like balance. 574 00:28:40,160 --> 00:28:42,280 Speaker 3: It with the Googles, right, I think Google is a 575 00:28:42,280 --> 00:28:44,960 Speaker 3: company that does many, many things that can perform quite well, 576 00:28:44,960 --> 00:28:46,760 Speaker 3: and so it kind of like balances your risk on 577 00:28:47,080 --> 00:28:48,960 Speaker 3: the small caps. But also again it comes back to 578 00:28:49,000 --> 00:28:52,240 Speaker 3: the you know, administration, administration will be very favorable of 579 00:28:52,360 --> 00:28:56,360 Speaker 3: quantum you know, rate cuts, you know kind of we're 580 00:28:56,440 --> 00:28:58,560 Speaker 3: freedom for M and A activity and theory, and so 581 00:28:58,720 --> 00:29:01,680 Speaker 3: that also helped the backdrop pubs along with just the 582 00:29:01,760 --> 00:29:03,959 Speaker 3: super high fining technology that's moving forward. 583 00:29:04,360 --> 00:29:06,120 Speaker 2: All lies on the FED in about an hour or 584 00:29:06,160 --> 00:29:06,720 Speaker 2: so's time. 585 00:29:06,880 --> 00:29:09,120 Speaker 4: Thank you, Thank you for taking it back to macro 586 00:29:09,160 --> 00:29:13,960 Speaker 4: policy too. Defiance ETF CEO Silvi Jablonski. Happy holidays. Now, 587 00:29:14,040 --> 00:29:16,040 Speaker 4: let's just talk about Eno Musk and SpaceX for a moment, 588 00:29:16,080 --> 00:29:19,920 Speaker 4: because they've repeatedly failed to comply with federal reporting protocols 589 00:29:20,080 --> 00:29:23,320 Speaker 4: aimed at protecting state secrets, triggering several federal reviews. 590 00:29:23,320 --> 00:29:25,480 Speaker 2: This is all being cited by the New York Times. 591 00:29:25,520 --> 00:29:26,800 Speaker 2: They're looking at sources there now. 592 00:29:26,880 --> 00:29:30,040 Speaker 4: The Air Force also recently denied mask a high level 593 00:29:30,080 --> 00:29:34,760 Speaker 4: security access, citing potential security risks. Meanwhile, here's what SpaceX 594 00:29:34,800 --> 00:29:37,760 Speaker 4: president when Shotwell said about the company's relationship with the 595 00:29:37,800 --> 00:29:40,160 Speaker 4: Air Force and an event with a Center of Strategic 596 00:29:40,200 --> 00:29:41,880 Speaker 4: and International Studies just yesterday. 597 00:29:42,600 --> 00:29:46,640 Speaker 12: We have, we had a rough relationship early on with 598 00:29:46,840 --> 00:29:54,080 Speaker 12: the Air Force, but we have over the last probably 599 00:29:54,080 --> 00:29:59,000 Speaker 12: eleven ten or eleven years, I think, developed a really 600 00:30:00,280 --> 00:30:03,040 Speaker 12: a really great relationship. Frankly, I think there's do you know, 601 00:30:03,040 --> 00:30:05,480 Speaker 12: it takes a long time to build trust, and I 602 00:30:05,520 --> 00:30:07,520 Speaker 12: think we're we're I think we're finally there. 603 00:30:08,440 --> 00:30:12,200 Speaker 4: Elsewhere in space, NASA announcing another delay in the mission 604 00:30:12,200 --> 00:30:14,760 Speaker 4: to bring back two US astronauts who are stranded for 605 00:30:14,840 --> 00:30:17,640 Speaker 4: months on the International Space Station and now saying they 606 00:30:17,680 --> 00:30:20,960 Speaker 4: will remain there at least until late March Tuesday. 607 00:30:21,600 --> 00:30:25,160 Speaker 2: That's it. Time currently for home coming up. 608 00:30:25,240 --> 00:30:27,960 Speaker 4: Data Bricks sixty two billion dollar valuation. We speak with 609 00:30:28,040 --> 00:30:30,320 Speaker 4: Ali Godzi's Data Bricks CEO. 610 00:30:30,400 --> 00:30:41,120 Speaker 2: That's next. This is Blue meg Technology. 611 00:30:45,440 --> 00:30:49,280 Speaker 4: Data Bricks raising ten billion dollars in new funding bring 612 00:30:49,320 --> 00:30:52,040 Speaker 4: the software makers valuation to sixty two billion dollars. 613 00:30:52,080 --> 00:30:54,520 Speaker 2: The company says it intends to invest this capital towards 614 00:30:54,600 --> 00:30:56,320 Speaker 2: new AI products. 615 00:30:55,760 --> 00:30:59,720 Speaker 4: To acquisitions, significant expansion in its international go to market 616 00:30:59,720 --> 00:31:03,479 Speaker 4: operation for more Data Bricks CEO Aligotzi joins us. 617 00:31:03,480 --> 00:31:04,720 Speaker 2: Now, boy, it's been busy. 618 00:31:05,080 --> 00:31:07,200 Speaker 4: Let's just talk a little bit about the AI applications 619 00:31:07,200 --> 00:31:09,560 Speaker 4: and just where you see the wide open space at 620 00:31:09,560 --> 00:31:10,600 Speaker 4: the moment for Data Bricks. 621 00:31:11,800 --> 00:31:14,200 Speaker 1: Yeah, thank you for having me on. There's so many 622 00:31:14,200 --> 00:31:17,600 Speaker 1: applications across many different protocols, you know, lots and lots 623 00:31:17,600 --> 00:31:19,480 Speaker 1: of customers that are using us for AI and then 624 00:31:19,480 --> 00:31:22,360 Speaker 1: a processing I should say, starting with Rivan. For instance, 625 00:31:22,440 --> 00:31:25,360 Speaker 1: the carmaker uses us to optimize the batteries of their 626 00:31:25,360 --> 00:31:29,680 Speaker 1: electric vehicles. You know, Walgreens uses us to figure out 627 00:31:29,760 --> 00:31:31,840 Speaker 1: how much should they stock up the inventory and their 628 00:31:31,840 --> 00:31:34,720 Speaker 1: supply chain. It's super important price for them to be 629 00:31:34,760 --> 00:31:37,400 Speaker 1: able to optimize their prices and lower their CEOs. And 630 00:31:37,440 --> 00:31:40,760 Speaker 1: then square or block. They used to have these app 631 00:31:40,760 --> 00:31:42,760 Speaker 1: where you swipe your credit cards, but to set that 632 00:31:42,840 --> 00:31:44,600 Speaker 1: up in a store used to be pretty complicated a 633 00:31:44,600 --> 00:31:46,680 Speaker 1: few years ago. Now it's super simple because you can 634 00:31:46,760 --> 00:31:48,880 Speaker 1: use generative AI and just tell it, hey, I have 635 00:31:49,080 --> 00:31:51,000 Speaker 1: these five products, this is the price, and it sets 636 00:31:51,000 --> 00:31:52,880 Speaker 1: itself up just using generive AI. 637 00:31:53,480 --> 00:31:56,520 Speaker 4: So before you were unifying to moockratizing data. Now it's 638 00:31:56,520 --> 00:31:57,720 Speaker 4: about the applications of AI. 639 00:31:57,800 --> 00:31:59,000 Speaker 2: But for that also you. 640 00:31:59,000 --> 00:32:02,160 Speaker 4: Need talent ALI and I'm interested as to where the 641 00:32:02,240 --> 00:32:04,640 Speaker 4: next layer of talent is coming from. Where you need 642 00:32:04,640 --> 00:32:06,400 Speaker 4: to hone in on how you get them on board. 643 00:32:07,600 --> 00:32:10,200 Speaker 1: Yeah, I mean there is a crazy war for AI 644 00:32:10,280 --> 00:32:12,440 Speaker 1: talent right now. I've never seen anything like it before. 645 00:32:12,520 --> 00:32:14,880 Speaker 1: I mean, it's a you know talent war was already 646 00:32:14,920 --> 00:32:17,520 Speaker 1: crazy last ten years for software engineers. Now in AI 647 00:32:17,800 --> 00:32:20,920 Speaker 1: it's just completely insane. So we are going to use 648 00:32:20,960 --> 00:32:23,720 Speaker 1: a lot of the proceeds towards that to be competitive. 649 00:32:23,920 --> 00:32:26,560 Speaker 1: There's a lot of talent you know, was hired by 650 00:32:26,720 --> 00:32:30,120 Speaker 1: Open AI and Bianthrofic. What's happened is we've hit this 651 00:32:30,160 --> 00:32:31,400 Speaker 1: thing called the scaling wall. 652 00:32:31,480 --> 00:32:33,560 Speaker 4: Now you know, oh you think there is a wall, 653 00:32:33,640 --> 00:32:35,800 Speaker 4: do because some altman's determine there isn't one. 654 00:32:36,600 --> 00:32:38,640 Speaker 1: There is a wall. I think it's it's pretty clear 655 00:32:38,760 --> 00:32:41,600 Speaker 1: that most even those big companies, many of them are 656 00:32:41,640 --> 00:32:46,240 Speaker 1: now investing in other techniques, not the classic scaling laws. 657 00:32:47,120 --> 00:32:49,480 Speaker 1: So that actually also has opened up the opportunity where 658 00:32:49,480 --> 00:32:51,720 Speaker 1: people are now going There are more companies now that 659 00:32:51,800 --> 00:32:55,920 Speaker 1: can participate in this next chapter after the you know, 660 00:32:56,000 --> 00:32:59,640 Speaker 1: scaling wall or the scaling laws. So that's also very 661 00:32:59,680 --> 00:33:02,200 Speaker 1: exciting times. But you got to pay up for that talent. 662 00:33:03,080 --> 00:33:04,840 Speaker 2: You've got to pay up. Do you pay up for 663 00:33:05,080 --> 00:33:05,760 Speaker 2: us talent? 664 00:33:05,880 --> 00:33:08,600 Speaker 4: I'm going to make it personal, Ali, because yourself are 665 00:33:08,600 --> 00:33:11,560 Speaker 4: an immigrant originally Iran, moved over to Sweden, come over 666 00:33:11,600 --> 00:33:13,800 Speaker 4: to the United States, and I'm interested in how much 667 00:33:13,840 --> 00:33:16,560 Speaker 4: you look abroad particularly in the context of the next administration. 668 00:33:17,960 --> 00:33:21,239 Speaker 1: Yeah, well look, not because of the administration, but when 669 00:33:21,280 --> 00:33:24,360 Speaker 1: there is this crazy talent war going on, you of 670 00:33:24,400 --> 00:33:26,400 Speaker 1: course start looking at you know, where can we go 671 00:33:26,800 --> 00:33:30,320 Speaker 1: where it's not so intense, right, where the competition is 672 00:33:30,360 --> 00:33:32,160 Speaker 1: not so intense, So we look everywhere. You know, I 673 00:33:32,200 --> 00:33:34,800 Speaker 1: think Eastern Europe is super interesting, very smart people that 674 00:33:34,880 --> 00:33:38,320 Speaker 1: live there, and you know, it's just different. You know, 675 00:33:38,520 --> 00:33:41,440 Speaker 1: you don't have that intensity of a competition around the 676 00:33:41,480 --> 00:33:44,280 Speaker 1: talent Europe and of course Asia. I'm in the location, 677 00:33:44,360 --> 00:33:45,080 Speaker 1: so we're looking at. 678 00:33:44,960 --> 00:33:45,520 Speaker 6: All of those. 679 00:33:45,920 --> 00:33:47,920 Speaker 1: But still you don't want to you know, you don't 680 00:33:47,920 --> 00:33:50,720 Speaker 1: want to ignore Silicon Valley or United States. It's still 681 00:33:50,760 --> 00:33:54,000 Speaker 1: where you have the highest talent density. It's absolutely necessary, 682 00:33:54,000 --> 00:33:55,360 Speaker 1: So we're going to compete here as well. 683 00:33:55,760 --> 00:33:56,880 Speaker 2: Is it about acqui hiring? 684 00:33:57,080 --> 00:33:59,520 Speaker 4: I mean you've been inquisitive and some of that in 685 00:34:00,040 --> 00:34:02,560 Speaker 4: els based music mL for example, some of that in 686 00:34:02,600 --> 00:34:06,280 Speaker 4: the data side tabula for example. You're going to make 687 00:34:06,520 --> 00:34:09,600 Speaker 4: acquisitions because of the people or because of the products 688 00:34:09,640 --> 00:34:10,120 Speaker 4: being built. 689 00:34:11,280 --> 00:34:12,839 Speaker 1: Well both, you know, there's three things you could get. 690 00:34:12,840 --> 00:34:14,480 Speaker 1: You could also get revenue, right, but there's a lot 691 00:34:14,520 --> 00:34:18,640 Speaker 1: of startups that started super talented people amazing ip, but 692 00:34:18,680 --> 00:34:21,880 Speaker 1: they quite didn't get to the revenue. So it's, you know, 693 00:34:22,120 --> 00:34:24,799 Speaker 1: two birds with one stone. You get that talent and 694 00:34:24,840 --> 00:34:27,279 Speaker 1: you get their intellectual property, and in this race, you 695 00:34:27,360 --> 00:34:29,400 Speaker 1: need that right, you need both of those things. So 696 00:34:29,520 --> 00:34:31,759 Speaker 1: absolutely we're going to actually double down on that even 697 00:34:31,760 --> 00:34:32,960 Speaker 1: more now that we have this funding. 698 00:34:33,560 --> 00:34:36,040 Speaker 4: It's a race, but there's also a race versus your 699 00:34:36,040 --> 00:34:38,520 Speaker 4: competition at the moment to be getting those clients who 700 00:34:38,600 --> 00:34:40,719 Speaker 4: are your key competitors Because I know on LinkedIn you've 701 00:34:40,719 --> 00:34:42,640 Speaker 4: been throwing some shade at Snowflake for example. 702 00:34:42,640 --> 00:34:43,880 Speaker 2: We all think about Microsoft. 703 00:34:44,080 --> 00:34:47,680 Speaker 4: Are they the two competitors for you? 704 00:34:47,680 --> 00:34:49,440 Speaker 1: You know it fact I would say that was in 705 00:34:49,520 --> 00:34:51,040 Speaker 1: previous years. You know, I would say a couple of 706 00:34:51,080 --> 00:34:53,760 Speaker 1: years ago we were really intensely focused on competing with them. 707 00:34:53,880 --> 00:34:57,440 Speaker 1: Now in this AI era, that's kind of shifted and 708 00:34:57,480 --> 00:34:58,200 Speaker 1: Microsoft is. 709 00:34:58,080 --> 00:34:58,879 Speaker 6: Super important partner. 710 00:34:58,920 --> 00:35:02,360 Speaker 1: In fact, Microsoft on and Google our investors in data bricks. 711 00:35:02,520 --> 00:35:05,480 Speaker 1: There is some competition. They have products that sometimes compete 712 00:35:05,520 --> 00:35:08,759 Speaker 1: as well, but all uh, you know those companies the 713 00:35:08,800 --> 00:35:12,120 Speaker 1: majority of the revenue in the cloud is storage, compute, networking, 714 00:35:12,560 --> 00:35:14,720 Speaker 1: and you know that might be seventy percent of their revenue. 715 00:35:14,719 --> 00:35:17,520 Speaker 1: I'm speculating, and we're a killer app for that. We 716 00:35:17,600 --> 00:35:20,799 Speaker 1: drive so much storage, compute, and networking. In fact, we 717 00:35:20,880 --> 00:35:24,879 Speaker 1: drive close to thirty million machines in the cloud every 718 00:35:24,960 --> 00:35:28,400 Speaker 1: day that we launch. So we're super close partners and 719 00:35:28,440 --> 00:35:30,719 Speaker 1: they love us as customers and vice versa. But there's 720 00:35:30,800 --> 00:35:33,719 Speaker 1: that little coopetition happening as well, and probably that's going 721 00:35:33,800 --> 00:35:36,240 Speaker 1: to expand over time. As you know, AI is becoming 722 00:35:36,239 --> 00:35:37,040 Speaker 1: more and more important. 723 00:35:37,360 --> 00:35:39,440 Speaker 4: There's one big game of frenemies out there. And when 724 00:35:39,480 --> 00:35:43,560 Speaker 4: it comes to technology, Ali, I'm interested in particular when 725 00:35:43,600 --> 00:35:46,000 Speaker 4: you're thinking about how you're raising money. Ten billion is 726 00:35:46,040 --> 00:35:48,239 Speaker 4: a lot for live capital front and center there. 727 00:35:48,280 --> 00:35:51,040 Speaker 2: You've also got who's who of other investors. 728 00:35:50,520 --> 00:35:53,200 Speaker 4: With a sixteen Z for example. What's interesting, you're also 729 00:35:53,239 --> 00:35:56,040 Speaker 4: going to the debt markets. We understand how is that going? 730 00:35:56,120 --> 00:35:57,920 Speaker 4: Because that seems to be trying to offset the tax 731 00:35:58,000 --> 00:35:59,799 Speaker 4: implications to keep your employees. 732 00:36:01,680 --> 00:36:04,359 Speaker 1: Yeah, I mean that's a great point. I think let's 733 00:36:04,360 --> 00:36:07,080 Speaker 1: take them one by one. Let's take the equities. You know, 734 00:36:07,120 --> 00:36:09,040 Speaker 1: the equity raise for us, it's super important to get 735 00:36:09,080 --> 00:36:12,400 Speaker 1: partners that are long term oriented. We want investors that 736 00:36:12,440 --> 00:36:13,600 Speaker 1: are going to say, you know, we're going to be 737 00:36:13,680 --> 00:36:15,320 Speaker 1: with you probably ten years, twenty years. 738 00:36:15,440 --> 00:36:16,400 Speaker 10: This is a great price. 739 00:36:16,480 --> 00:36:18,440 Speaker 1: We'll buy again more and later in the ideo will 740 00:36:18,480 --> 00:36:20,640 Speaker 1: buy even more, and we'll stick you know, to your plan, 741 00:36:20,960 --> 00:36:23,000 Speaker 1: and we'll let you implement that long term vision and 742 00:36:23,040 --> 00:36:26,040 Speaker 1: not become too short term oriented, you know, the ninety 743 00:36:26,040 --> 00:36:28,920 Speaker 1: to quarterly sort of mindset. So that was the thing 744 00:36:28,920 --> 00:36:30,920 Speaker 1: we optimized for, and we were pretty picky. We had 745 00:36:31,040 --> 00:36:34,160 Speaker 1: nineteen billion of interests in this round and basically cut 746 00:36:34,200 --> 00:36:36,239 Speaker 1: people back to half of that, and we said no 747 00:36:36,320 --> 00:36:40,239 Speaker 1: to most investors. And then on the death financing, that 748 00:36:40,360 --> 00:36:42,240 Speaker 1: makes a lot of sense as well. If the company 749 00:36:42,280 --> 00:36:45,560 Speaker 1: is growing in valuation so much, then actually you're better 750 00:36:45,640 --> 00:36:48,640 Speaker 1: off just getting a big loan. Even if interest rates 751 00:36:48,640 --> 00:36:50,360 Speaker 1: are high and you're paying let's say just make that 752 00:36:50,440 --> 00:36:54,000 Speaker 1: number round number ten percent interest on that, you're better 753 00:36:54,040 --> 00:36:55,839 Speaker 1: off taking that as a loan and paying that back 754 00:36:55,840 --> 00:36:57,719 Speaker 1: in a few years because you can pay that back 755 00:36:57,719 --> 00:37:01,600 Speaker 1: in equity then, and presumably the company has doubled in valuation. 756 00:37:01,680 --> 00:37:03,960 Speaker 1: Let's call it into three years. 757 00:37:03,600 --> 00:37:06,160 Speaker 4: Okay, one hundred and twenty billion, looking at it for 758 00:37:06,200 --> 00:37:08,120 Speaker 4: the next couple of years, and you've given us the 759 00:37:08,160 --> 00:37:10,960 Speaker 4: timescale potentially for an IPO as well, Ali Gotzi. Always 760 00:37:10,960 --> 00:37:13,440 Speaker 4: great to have you on data. Rick CEO, congrats on 761 00:37:13,480 --> 00:37:17,560 Speaker 4: the fundraise. Meanwhile, let's talk about concerns over AI's global 762 00:37:17,600 --> 00:37:21,920 Speaker 4: boom receiving Bipowerisan support in Congress in a framework report 763 00:37:21,960 --> 00:37:26,839 Speaker 4: released on Tuesday recommending guardrails and regulations on AI really 764 00:37:26,840 --> 00:37:29,400 Speaker 4: being detailed in an effort to protect Americans and maintain 765 00:37:29,480 --> 00:37:32,800 Speaker 4: national security. We spent with Senator Amy Klobershaw of Minnesota, 766 00:37:32,800 --> 00:37:34,560 Speaker 4: who had this to say on regulating AI. 767 00:37:35,920 --> 00:37:38,880 Speaker 13: My view has been that we should have rules of 768 00:37:38,920 --> 00:37:42,080 Speaker 13: the road in place, by the way, for all platforms. 769 00:37:42,120 --> 00:37:43,880 Speaker 13: I have been way out there, as I think, you know, 770 00:37:44,400 --> 00:37:47,960 Speaker 13: in terms of getting not just pornography off the Internet, 771 00:37:48,480 --> 00:37:53,640 Speaker 13: but other very very difficult things that are on there. 772 00:37:53,760 --> 00:37:57,640 Speaker 4: Right now, let's go back to those regulatory proposals, Bloomberg 773 00:37:57,640 --> 00:38:01,239 Speaker 4: Government reporter Omer City joins us. Now, it was a 774 00:38:01,280 --> 00:38:03,920 Speaker 4: comprehensive list of recommendations. 775 00:38:04,160 --> 00:38:05,400 Speaker 2: What were the key takeaways for you? 776 00:38:07,120 --> 00:38:07,279 Speaker 7: Right? 777 00:38:07,360 --> 00:38:10,920 Speaker 14: Congress released this major policy paper this week addressing AI 778 00:38:11,360 --> 00:38:17,919 Speaker 14: on various areas. They touched on education, healthcare, financial services, agriculture, 779 00:38:18,040 --> 00:38:21,920 Speaker 14: national security. Because they're trying to examine what AI's impacts 780 00:38:21,960 --> 00:38:25,080 Speaker 14: are on each of these areas and how Congress should respond. 781 00:38:25,440 --> 00:38:28,640 Speaker 14: The key takeaway is that Congress is seeking to strike 782 00:38:28,719 --> 00:38:31,680 Speaker 14: this balance. They want to promote AI because they believe 783 00:38:31,719 --> 00:38:35,480 Speaker 14: it has tremendous opportunity, you know, to unleash human productivity 784 00:38:35,560 --> 00:38:38,399 Speaker 14: to solve complex issues. But at the same time, they're 785 00:38:38,440 --> 00:38:40,759 Speaker 14: well aware of AI's risks, and so they want to 786 00:38:40,800 --> 00:38:43,800 Speaker 14: protect the American people and uphold their civil liberties. 787 00:38:44,920 --> 00:38:49,000 Speaker 4: Very briefly, urgent is what we expect to come next 788 00:38:49,040 --> 00:38:50,200 Speaker 4: from Congress. 789 00:38:51,360 --> 00:38:53,880 Speaker 14: Right, So that's the key question. How do they translate 790 00:38:53,920 --> 00:38:56,000 Speaker 14: these recommendations into action. 791 00:38:56,360 --> 00:38:58,080 Speaker 2: We saw Senator Amy Kolochar there. 792 00:38:58,160 --> 00:39:00,919 Speaker 14: She's proposing, you know, to combat this issue of non 793 00:39:00,960 --> 00:39:04,239 Speaker 14: consensual explicit deep fakes or deep fake pornography, which has 794 00:39:04,280 --> 00:39:06,960 Speaker 14: been around for a while, but AI has only accelerated 795 00:39:06,960 --> 00:39:09,759 Speaker 14: the spread, which has alarmed many many lawmakers. And so 796 00:39:09,760 --> 00:39:12,840 Speaker 14: that's an area where Congress is eager tacked quickly. Another 797 00:39:12,880 --> 00:39:15,640 Speaker 14: area that Congress is eager tacked is to increase you know, 798 00:39:15,719 --> 00:39:19,799 Speaker 14: scientific research and development in AI, expand energy infrastructure to 799 00:39:19,840 --> 00:39:23,920 Speaker 14: me AI demands, train the workforce, educate folks on AI, 800 00:39:24,600 --> 00:39:27,880 Speaker 14: and even you know, clarify existing intellectual property law. And 801 00:39:27,920 --> 00:39:30,400 Speaker 14: so there are many, many key recommendations that are proposing 802 00:39:30,480 --> 00:39:33,440 Speaker 14: this report. But it's just a matter of how Congress 803 00:39:33,480 --> 00:39:37,560 Speaker 14: is then transitions into translating that into legislation that gets 804 00:39:37,600 --> 00:39:38,399 Speaker 14: signed into law. 805 00:39:38,680 --> 00:39:40,759 Speaker 4: We'll see how it happens on the next administration. I'm 806 00:39:40,760 --> 00:39:49,880 Speaker 4: a city. Thanks for breaking it down. Let's get back 807 00:39:49,920 --> 00:39:52,879 Speaker 4: to Micron now schedule to report this fiscal first code 808 00:39:52,920 --> 00:39:54,279 Speaker 4: results after the market close today. 809 00:39:54,440 --> 00:39:56,000 Speaker 2: Remegs Inking joins. 810 00:39:55,760 --> 00:39:58,520 Speaker 4: Us some more all about high bandwidth memory. 811 00:39:59,080 --> 00:40:02,520 Speaker 15: That's right. Obviously, the biggest story in chips has been 812 00:40:02,920 --> 00:40:05,719 Speaker 15: in video. Guess what in videos? And chips need a 813 00:40:05,719 --> 00:40:09,640 Speaker 15: lot of memory to train those AI models. And really 814 00:40:09,640 --> 00:40:11,719 Speaker 15: it's a case of how much can Skhi nix how 815 00:40:11,800 --> 00:40:15,400 Speaker 15: much can Microns supply in video with and this has 816 00:40:15,440 --> 00:40:17,879 Speaker 15: been a big story for them, a lot of high 817 00:40:17,920 --> 00:40:21,120 Speaker 15: margin business coming their way. But on the flip side, 818 00:40:21,200 --> 00:40:23,600 Speaker 15: the markets that there depend on, there's a lot of 819 00:40:23,600 --> 00:40:25,080 Speaker 15: debate about how well they're doing. 820 00:40:25,600 --> 00:40:27,880 Speaker 4: Yeah, I mean, we're expecting about an eighty percent increase 821 00:40:27,920 --> 00:40:29,839 Speaker 4: in revenues for example, we're up on the day. 822 00:40:29,880 --> 00:40:32,880 Speaker 2: But it's been a turbulent time for Micron stock. 823 00:40:33,080 --> 00:40:35,600 Speaker 4: Still it feels as though most analysts say buy it 824 00:40:35,840 --> 00:40:36,440 Speaker 4: at the moment. 825 00:40:37,000 --> 00:40:38,880 Speaker 2: Is it gonna be about the forecasting? What are the 826 00:40:38,880 --> 00:40:39,719 Speaker 2: pitfalls here in? 827 00:40:40,480 --> 00:40:44,040 Speaker 15: Yeah, No, that's exactly it. I mean people are kind 828 00:40:44,040 --> 00:40:48,120 Speaker 15: of tentatively positive, and the concern there is that, look, 829 00:40:48,600 --> 00:40:51,359 Speaker 15: still a lot of money that this company gets comes 830 00:40:51,400 --> 00:40:54,000 Speaker 15: from the smartphone market, comes from the PC market, and 831 00:40:54,040 --> 00:40:57,120 Speaker 15: guess what, they're not that great. They're still inventory around. 832 00:40:57,200 --> 00:40:59,719 Speaker 15: So let's not get too carried away with this AI 833 00:40:59,800 --> 00:41:03,440 Speaker 15: thing until it really shows that it can take over completely. 834 00:41:03,840 --> 00:41:06,960 Speaker 4: But meanwhile, everyone's desperate to get totally in on stocks 835 00:41:07,000 --> 00:41:09,880 Speaker 4: like Broadcom, even if they're just giving us total addressable markets. 836 00:41:09,920 --> 00:41:11,799 Speaker 4: Do you think we will get a signal of what 837 00:41:11,840 --> 00:41:13,279 Speaker 4: the market will be like for mi cron. 838 00:41:14,400 --> 00:41:16,840 Speaker 15: I mean, they have been talking about this for a 839 00:41:16,880 --> 00:41:19,279 Speaker 15: long time, and I'm sure we'll continue to bang that 840 00:41:19,360 --> 00:41:22,319 Speaker 15: drum and talk about how this is a different opportunity 841 00:41:22,360 --> 00:41:24,719 Speaker 15: for them once they're designed in, they're designed in, it's 842 00:41:24,760 --> 00:41:27,200 Speaker 15: not the old commodity market. They'll give us all of that, 843 00:41:27,280 --> 00:41:30,839 Speaker 15: I'm sure, and they're probably right about that particular area. 844 00:41:31,000 --> 00:41:33,160 Speaker 15: The key is whether it's big enough to offset what's 845 00:41:33,200 --> 00:41:34,000 Speaker 15: going on elsewhere. 846 00:41:34,160 --> 00:41:36,920 Speaker 4: Yeah, just briefly, China an issue. 847 00:41:38,160 --> 00:41:41,160 Speaker 15: China is always an issue. There's a concern there that 848 00:41:41,880 --> 00:41:44,160 Speaker 15: the access to that market becomes an issue. And also 849 00:41:44,920 --> 00:41:50,080 Speaker 15: local competitors given the competition between the two countries and 850 00:41:50,560 --> 00:41:53,680 Speaker 15: the bands and this, you know, and all of the 851 00:41:53,719 --> 00:41:56,640 Speaker 15: trade ward, that's clearly something that's on people's minds. 852 00:41:57,120 --> 00:41:59,479 Speaker 4: And King if I'd be a busy man later, thanks 853 00:41:59,520 --> 00:42:01,760 Speaker 4: so much stopping in ahead of that set of earnings 854 00:42:01,800 --> 00:42:02,520 Speaker 4: later in the day. 855 00:42:03,239 --> 00:42:05,800 Speaker 2: Meanwhile, that does it for this edition of Bloomberg Technology. 856 00:42:05,840 --> 00:42:07,680 Speaker 4: You do not want to forget to focus in on 857 00:42:07,680 --> 00:42:10,120 Speaker 4: the FED in a minute, but also check out our podcast. 858 00:42:10,320 --> 00:42:12,280 Speaker 4: You'll find it on the terminal as well as online 859 00:42:12,320 --> 00:42:15,960 Speaker 4: on Apple, Spotify, and iHeart this is Bloomberg Technology.