1 00:00:02,520 --> 00:00:15,040 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. Bloomberg Tech is live 2 00:00:15,160 --> 00:00:18,599 Speaker 1: from the heart of Silicon Valley with Ed la Though 3 00:00:18,800 --> 00:00:20,480 Speaker 1: in San Francisco. 4 00:00:22,680 --> 00:00:26,120 Speaker 2: This is Bloomberg Tech coming up China's latest AI breakthrough 5 00:00:26,160 --> 00:00:29,400 Speaker 2: shaking up the industry. What moonshots Kimmy K three means 6 00:00:29,440 --> 00:00:32,200 Speaker 2: for the race with open AI Andthropic and the chip 7 00:00:32,240 --> 00:00:35,800 Speaker 2: makers powering it, plus SpaceX gears up for a second 8 00:00:35,840 --> 00:00:38,760 Speaker 2: attempt at a starship test flight later this week. The 9 00:00:38,800 --> 00:00:43,159 Speaker 2: Stog's underperforming most of the other big nazac IPOs and 10 00:00:43,159 --> 00:00:46,760 Speaker 2: andresen Horowitz signed defense investor and military vet Connor Love 11 00:00:47,000 --> 00:00:51,440 Speaker 2: to the firm's American dynamism team. Love joins the show. 12 00:00:52,120 --> 00:00:55,920 Speaker 2: Good morning from San Francisco. The surprise release of Kimmy 13 00:00:55,960 --> 00:00:59,520 Speaker 2: came K three from China's moonshot. It's still everything that 14 00:00:59,560 --> 00:01:03,680 Speaker 2: people are talking about reading about. We are rebounding in 15 00:01:03,720 --> 00:01:06,040 Speaker 2: technology stocks modestly. 16 00:01:06,080 --> 00:01:08,040 Speaker 3: We're off session highs where we opened earlier. 17 00:01:08,480 --> 00:01:12,360 Speaker 2: Remember, chip stocks entered a bear market Friday in part 18 00:01:12,720 --> 00:01:15,680 Speaker 2: because of all of the questions pose by what Kimmy 19 00:01:15,760 --> 00:01:19,160 Speaker 2: K three a two point eight trillion parameters model represents. 20 00:01:19,480 --> 00:01:21,480 Speaker 2: Some of those were like, well, if we have more 21 00:01:21,520 --> 00:01:23,760 Speaker 2: efficient AI, do we need to spend as much on 22 00:01:24,160 --> 00:01:28,119 Speaker 2: commute compute? Nvidia is an example of a name that's rebounding. 23 00:01:28,319 --> 00:01:30,920 Speaker 2: A lot of focus has also shifted to the intense 24 00:01:31,040 --> 00:01:35,560 Speaker 2: memory requirements for what is happening in open weighted models. 25 00:01:36,080 --> 00:01:40,039 Speaker 2: Moonshop's gone from AI upstart to global contender almost overnight, 26 00:01:40,120 --> 00:01:44,000 Speaker 2: Kimmy K three drawing praise form rivaling the best models 27 00:01:44,000 --> 00:01:46,000 Speaker 2: from open AI and Antropic. But demand has been so 28 00:01:46,080 --> 00:01:50,720 Speaker 2: strong that the company says it's temporarily limiting new subscriptions 29 00:01:51,120 --> 00:01:54,320 Speaker 2: as it adds to work works that add more capacity. 30 00:01:54,400 --> 00:01:57,760 Speaker 2: Bloombergs executive editor for Asia tech piece elstroms with us 31 00:01:57,760 --> 00:02:02,280 Speaker 2: from DC. There are so many reports on the Bloomberg 32 00:02:02,320 --> 00:02:05,400 Speaker 2: this morning, so many places we could start, but I 33 00:02:05,440 --> 00:02:07,240 Speaker 2: want to go to one of the pieces that the 34 00:02:07,280 --> 00:02:11,440 Speaker 2: team did about Okay, it's Monday morning. We probably went 35 00:02:11,440 --> 00:02:13,760 Speaker 2: into the weekend thinking, oh no, we don't need to 36 00:02:13,800 --> 00:02:17,760 Speaker 2: spend as much on compute hardware in that context. Now 37 00:02:17,880 --> 00:02:20,000 Speaker 2: we have a piece on the Bloomberg saying this, there's 38 00:02:20,040 --> 00:02:21,520 Speaker 2: a lot of emphasis on memory. 39 00:02:21,840 --> 00:02:23,359 Speaker 3: What's the team and age you're writing about. 40 00:02:25,080 --> 00:02:28,720 Speaker 4: Well, yeah, let's broaden up the lens A little bit here. 41 00:02:29,160 --> 00:02:32,160 Speaker 4: What's going on is China is holding this World AI 42 00:02:32,240 --> 00:02:36,040 Speaker 4: conference in Shanghai, and we're seeing their companies come forward 43 00:02:36,120 --> 00:02:39,639 Speaker 4: with new offerings in all sorts of different areas. Moonshot 44 00:02:39,680 --> 00:02:43,119 Speaker 4: is certainly the one that got the most attention over 45 00:02:43,160 --> 00:02:46,000 Speaker 4: the weekend. We saw this company that was probably not 46 00:02:46,080 --> 00:02:48,880 Speaker 4: that well known outside of China if at all, and 47 00:02:48,960 --> 00:02:50,720 Speaker 4: all of a sudden people are paying attention to it. 48 00:02:50,760 --> 00:02:53,560 Speaker 4: As you mentioned, its performance is very close to the 49 00:02:53,560 --> 00:02:59,559 Speaker 4: frontier models two point eight trillion parameters. Essentially the synapses 50 00:02:59,560 --> 00:03:03,640 Speaker 4: of this the brain synapses of this model are very significant. 51 00:03:03,760 --> 00:03:05,800 Speaker 4: But also you saw Ali Baba come out with a 52 00:03:05,840 --> 00:03:08,520 Speaker 4: new version of their Glen model that is very close 53 00:03:08,600 --> 00:03:11,359 Speaker 4: behind it's two point four trillion parameters. It's a very 54 00:03:11,400 --> 00:03:13,840 Speaker 4: powerful model too. So I think the big lesson, the 55 00:03:13,880 --> 00:03:17,639 Speaker 4: big takeaway here that China AI is much closer behind 56 00:03:17,680 --> 00:03:21,160 Speaker 4: the US in terms of AI capabilities than we really realized. 57 00:03:21,440 --> 00:03:24,840 Speaker 4: If you thought China was six to twelve months behind before, 58 00:03:25,160 --> 00:03:27,000 Speaker 4: now it looks like maybe they're only a couple of 59 00:03:27,000 --> 00:03:29,080 Speaker 4: months behind, and they're catching. 60 00:03:28,880 --> 00:03:29,720 Speaker 3: Up quite quickly. 61 00:03:29,840 --> 00:03:33,160 Speaker 4: And it's a broader selection of competitors that we really anticipated. 62 00:03:33,320 --> 00:03:36,360 Speaker 4: People have been focusing mostly on Deep Seek in particular, 63 00:03:36,600 --> 00:03:38,440 Speaker 4: maybe a little bit on Ali Baba and then a 64 00:03:38,440 --> 00:03:41,040 Speaker 4: couple of the smaller models. But now we're seeing companies 65 00:03:41,040 --> 00:03:43,880 Speaker 4: like Moonshot come forward and they're very competitive and a 66 00:03:43,920 --> 00:03:45,600 Speaker 4: lot of people didn't even realize that they were going 67 00:03:45,680 --> 00:03:46,360 Speaker 4: to be in this race. 68 00:03:47,280 --> 00:03:50,080 Speaker 2: There's some really important Bloomberg reporting on what Moonshot's looking 69 00:03:50,120 --> 00:03:52,120 Speaker 2: at financially. We're going to get to that with Bloomberg's 70 00:03:52,120 --> 00:03:54,680 Speaker 2: baty dip shots in just a moment. But away from 71 00:03:54,760 --> 00:03:56,960 Speaker 2: sort of the numbers, what do we know about the 72 00:03:57,040 --> 00:04:01,160 Speaker 2: people behind Moonshot, Like, what do we actually know about 73 00:04:01,200 --> 00:04:04,920 Speaker 2: this startup? And it's team that have been just thrust 74 00:04:05,000 --> 00:04:08,520 Speaker 2: into the limelight in such a quick and short space 75 00:04:08,600 --> 00:04:09,000 Speaker 2: of time. 76 00:04:10,320 --> 00:04:13,320 Speaker 4: Well, the founder of Moonshot is a thirty three year 77 00:04:13,360 --> 00:04:17,200 Speaker 4: old Yang Jie Lynn. He graduated from Carnegie Mellen. He 78 00:04:17,200 --> 00:04:19,599 Speaker 4: got his degree in the United States. He decided to 79 00:04:19,600 --> 00:04:21,960 Speaker 4: go back to China because of the possibilities that they 80 00:04:22,000 --> 00:04:24,920 Speaker 4: had there and really before Deep Seek, he was kind 81 00:04:24,920 --> 00:04:27,920 Speaker 4: of seen as the rising star within the AI universe. 82 00:04:28,240 --> 00:04:31,159 Speaker 4: He's a big music fan. He named the company in 83 00:04:31,240 --> 00:04:34,280 Speaker 4: Chinese after Dark Side of the Moon, The Pink Floyd album, 84 00:04:34,279 --> 00:04:37,320 Speaker 4: which apparently is his favorite. They have conference rooms. But 85 00:04:37,440 --> 00:04:40,400 Speaker 4: beyond being this geek and this music fan, at the 86 00:04:40,440 --> 00:04:42,640 Speaker 4: same time, he's been at kind of the cutting edge 87 00:04:42,640 --> 00:04:46,279 Speaker 4: of AI developments there and he made a very conscious choice. 88 00:04:46,360 --> 00:04:49,360 Speaker 4: Rather than trying to make a lean, efficient model, he 89 00:04:49,480 --> 00:04:52,480 Speaker 4: decided to go very big with this version of Kimmy 90 00:04:52,520 --> 00:04:55,360 Speaker 4: Kimmy three and try to make something that was very 91 00:04:55,360 --> 00:04:57,680 Speaker 4: close to the frontier edge that we're seeing in the US. 92 00:04:57,720 --> 00:05:00,120 Speaker 4: So I think Yang is somebody that we're going to 93 00:05:00,200 --> 00:05:02,200 Speaker 4: hear more about the same way that we talk about 94 00:05:02,440 --> 00:05:04,960 Speaker 4: deep seeks found early on. I think we're going to 95 00:05:05,000 --> 00:05:07,919 Speaker 4: see more breakthroughs, more innovations, and people are going to 96 00:05:07,920 --> 00:05:10,120 Speaker 4: start watching him much more closely than we've seen in 97 00:05:10,120 --> 00:05:12,400 Speaker 4: the past. We had a nice scoop over the weekend 98 00:05:12,480 --> 00:05:15,560 Speaker 4: also that Moonshad is planning an ongoing public so we're 99 00:05:15,600 --> 00:05:17,440 Speaker 4: going to be able to look at the financials of 100 00:05:17,480 --> 00:05:19,640 Speaker 4: their company in the way that we've seen some of 101 00:05:19,680 --> 00:05:21,919 Speaker 4: the other Chinese AA models, and by the way, we 102 00:05:21,960 --> 00:05:23,880 Speaker 4: haven't seen that from the US players so far. 103 00:05:24,560 --> 00:05:26,680 Speaker 2: A lot of key figures critical to the Bloomberg Tech 104 00:05:26,720 --> 00:05:29,760 Speaker 2: audience just kind of remarked over the weekend that this 105 00:05:29,839 --> 00:05:32,080 Speaker 2: could have been an American startup. You know, there's a 106 00:05:32,080 --> 00:05:36,560 Speaker 2: lot of focus on the US's own policy with regards 107 00:05:36,560 --> 00:05:38,480 Speaker 2: to talent immigration. We'll get to all of that later 108 00:05:38,520 --> 00:05:40,960 Speaker 2: in the program. Bloomboa, speede Elstrom, thank you very much. 109 00:05:40,960 --> 00:05:43,039 Speaker 2: We'll stay with moonshop you They just mentioned it. The 110 00:05:43,080 --> 00:05:45,599 Speaker 2: company isn't just making waves with its latest AI model, 111 00:05:45,640 --> 00:05:48,599 Speaker 2: it's now setting its sites on the public markets. The 112 00:05:48,640 --> 00:05:51,280 Speaker 2: startup is said to be preparing for a Hong Kong 113 00:05:51,360 --> 00:05:54,400 Speaker 2: IPO that could come in the next six months following 114 00:05:54,440 --> 00:05:58,799 Speaker 2: an absolute surge in demand. Its valuation above thirty billion 115 00:05:58,880 --> 00:06:01,240 Speaker 2: dollars right now, according to source, has been expated at 116 00:06:01,279 --> 00:06:04,760 Speaker 2: Schultz All Things IPO. Was so interesting to read through 117 00:06:04,760 --> 00:06:08,159 Speaker 2: this one. Let's start with the basics. What are we reporting, 118 00:06:08,240 --> 00:06:10,920 Speaker 2: what do we know about how quickly they're moving to 119 00:06:11,360 --> 00:06:13,560 Speaker 2: do a Hong Kong listing, and any of the sort 120 00:06:13,560 --> 00:06:15,560 Speaker 2: of valuation considerations around that. 121 00:06:15,839 --> 00:06:17,279 Speaker 3: Yeah, and you kind of nailed it on the head 122 00:06:17,279 --> 00:06:17,760 Speaker 3: with the intro. 123 00:06:18,000 --> 00:06:21,240 Speaker 5: So closing up around right now about thirty billion dollars, again, 124 00:06:21,279 --> 00:06:23,360 Speaker 5: a small drop in the bucket relative to what we've 125 00:06:23,400 --> 00:06:26,200 Speaker 5: been reporting around the Anthropics and open eyes of the 126 00:06:26,200 --> 00:06:28,719 Speaker 5: world here in the US and the expectation that in 127 00:06:28,760 --> 00:06:31,479 Speaker 5: the next six months they could be pricing that IPO 128 00:06:31,680 --> 00:06:33,599 Speaker 5: in Hong Kong. The main thing to keep in mind, 129 00:06:33,720 --> 00:06:35,960 Speaker 5: Bloomberg is reported a deep Seek is looking at a 130 00:06:36,000 --> 00:06:39,800 Speaker 5: mainland listing sometime in twenty twenty seven. So these two 131 00:06:39,960 --> 00:06:44,320 Speaker 5: now premiere companies in Asia in China could actually be 132 00:06:44,600 --> 00:06:47,920 Speaker 5: racing to go public, kind of similar to what we're 133 00:06:47,920 --> 00:06:50,479 Speaker 5: seeing with Anthropic and Open Eye. But again, mainland China 134 00:06:50,600 --> 00:06:54,120 Speaker 5: versus Hong Kong versus US listings open up completely different 135 00:06:54,160 --> 00:06:55,159 Speaker 5: investing universes. 136 00:06:56,080 --> 00:06:58,320 Speaker 3: So go into that in a little bit more. 137 00:06:58,360 --> 00:07:01,400 Speaker 2: De So I find that really interesting, Right, A big 138 00:07:01,480 --> 00:07:03,880 Speaker 2: pulse of the story over the weekend is that this 139 00:07:04,080 --> 00:07:07,839 Speaker 2: is a Chinese AI startup. Right, we are saying that 140 00:07:07,920 --> 00:07:10,160 Speaker 2: looking at Hong Kong as a listing, what are the 141 00:07:10,280 --> 00:07:13,840 Speaker 2: kind of political considerations around that as opposed to the US. 142 00:07:14,040 --> 00:07:16,680 Speaker 2: Where do the pools of capital tend to come from? 143 00:07:16,760 --> 00:07:18,040 Speaker 2: If you do an HK listing. 144 00:07:18,320 --> 00:07:22,160 Speaker 5: HK listing opens up an entirely different investing universe, So 145 00:07:22,560 --> 00:07:24,800 Speaker 5: you have the rest of really all of Asia. So 146 00:07:24,840 --> 00:07:26,800 Speaker 5: you think about Hong Kong based investors, do you think 147 00:07:26,800 --> 00:07:30,280 Speaker 5: about people in Australia and Singapore and other really countries 148 00:07:30,280 --> 00:07:33,880 Speaker 5: that could tap that listing. Again, US investors sometimes do 149 00:07:34,040 --> 00:07:35,840 Speaker 5: invest in Hong Kong, but similar story to what we 150 00:07:35,920 --> 00:07:38,400 Speaker 5: track with sk Heiniz, it's a bit of a higher 151 00:07:38,440 --> 00:07:41,000 Speaker 5: bar to clear and there had been some consternation around that, 152 00:07:41,080 --> 00:07:44,320 Speaker 5: whereas on the flip side, a deep seek listing in 153 00:07:44,400 --> 00:07:48,240 Speaker 5: mainland China, completely different investing universe. It's a really kind 154 00:07:48,280 --> 00:07:52,320 Speaker 5: of local driven investing community. You do have retail numbers 155 00:07:52,320 --> 00:07:54,200 Speaker 5: that are kind of eyepopping. It looks a bit kind 156 00:07:54,200 --> 00:07:56,400 Speaker 5: of like gambling and trying to get in on some 157 00:07:56,440 --> 00:07:58,600 Speaker 5: of those options. So the big push in actually the 158 00:07:58,600 --> 00:08:01,440 Speaker 5: big news is the fact that Moonshot wants to tap 159 00:08:01,720 --> 00:08:04,280 Speaker 5: the HK market just again because that does open an 160 00:08:04,440 --> 00:08:07,720 Speaker 5: entirely different investing universe, and we kind of opening up 161 00:08:07,760 --> 00:08:09,800 Speaker 5: more similarly to an Ali Barber or some of those 162 00:08:09,800 --> 00:08:12,720 Speaker 5: other companies JD dot Com that are listed in Hong Kong. 163 00:08:12,840 --> 00:08:16,480 Speaker 2: And also let's sit here in the US Bloombex Beley Lipschutz, 164 00:08:16,520 --> 00:08:19,640 Speaker 2: thank you very much. Indeed, in competition among Chinese AI 165 00:08:19,680 --> 00:08:24,040 Speaker 2: developers also continues to intensify. Ali Barber just unveiled its 166 00:08:24,120 --> 00:08:28,040 Speaker 2: latest flagship Queen Model which it says is second only 167 00:08:28,120 --> 00:08:31,440 Speaker 2: to Anthropics latest system, which sent Hong Kong shares of 168 00:08:31,520 --> 00:08:35,120 Speaker 2: Vali Barber rising on that news. The Sunday release came 169 00:08:35,160 --> 00:08:39,080 Speaker 2: only days, of course, after Moonshot unveiled its latest K 170 00:08:39,200 --> 00:08:42,200 Speaker 2: three model. We have some breaking news crossing the terminal 171 00:08:42,200 --> 00:08:46,040 Speaker 2: in the last few minutes. UK's Rachel Reeves has resigned 172 00:08:46,440 --> 00:08:49,600 Speaker 2: as Chancellor of the Exchequer. In a post on X, 173 00:08:49,920 --> 00:08:52,480 Speaker 2: she says it's been quote the privilege of my life 174 00:08:52,720 --> 00:08:55,600 Speaker 2: to serve as the Chancellor of the Exchequer, the UK's 175 00:08:55,640 --> 00:08:59,640 Speaker 2: equivalent of a finance minister or Treasury secretary. You can 176 00:08:59,640 --> 00:09:03,319 Speaker 2: see that as an immediate response in the pound dropping 177 00:09:03,360 --> 00:09:08,160 Speaker 2: against the dollar one thirty four, where it currently stands. Meanwhile, 178 00:09:08,320 --> 00:09:12,240 Speaker 2: new Prime Minister Andy Burnham just told reporters the Labor 179 00:09:12,280 --> 00:09:16,200 Speaker 2: government will use quote any flexibility within its fiscal rules. 180 00:09:16,200 --> 00:09:18,880 Speaker 2: He said he would stick to the self imposed rules 181 00:09:18,880 --> 00:09:22,560 Speaker 2: which were adjusted by Rachel Reeves two years ago to 182 00:09:22,640 --> 00:09:24,440 Speaker 2: allow for greater capital spending. 183 00:09:24,480 --> 00:09:26,160 Speaker 3: It's a dynamic situation in the UK. 184 00:09:26,280 --> 00:09:29,600 Speaker 2: Government will keep you up to date as things change. 185 00:09:29,640 --> 00:09:34,480 Speaker 2: Coming up the countdowns back on SpaceX targets Thursday for 186 00:09:34,679 --> 00:09:38,240 Speaker 2: another attempt for Starship's thirteenth test flight. 187 00:09:38,480 --> 00:09:39,559 Speaker 3: We've got more on that next. 188 00:09:39,880 --> 00:10:20,359 Speaker 6: This is Bloomberg TECHX. 189 00:10:00,640 --> 00:10:03,679 Speaker 2: Is gearing up for another attempt at a thirteenth Starship 190 00:10:03,720 --> 00:10:04,120 Speaker 2: test flight. 191 00:10:04,160 --> 00:10:05,080 Speaker 3: The company is set. 192 00:10:04,880 --> 00:10:08,199 Speaker 2: Thursday as the new launch date after last week's attempt 193 00:10:08,520 --> 00:10:12,240 Speaker 2: was scrubbed and right at the last moment, shares we're 194 00:10:12,280 --> 00:10:15,720 Speaker 2: down about one and a half percent. This has been 195 00:10:15,800 --> 00:10:19,760 Speaker 2: a broader story post IPO on SpaceX kind of underperforming. 196 00:10:19,800 --> 00:10:23,080 Speaker 2: For more on SpaceX, Brett Lindsay ma Zoi, managing director 197 00:10:23,120 --> 00:10:26,599 Speaker 2: and head of Industrial sector Research joins US, recently initiated 198 00:10:26,600 --> 00:10:30,760 Speaker 2: on SpaceX has an outperformed rating with the stock price 199 00:10:30,800 --> 00:10:34,120 Speaker 2: target of two hundred dollars per share. You know, Brett, 200 00:10:34,120 --> 00:10:38,080 Speaker 2: I appreciate that Jas there's a clear thesis at the 201 00:10:38,080 --> 00:10:40,920 Speaker 2: core of your coverage of SpaceX, right, but I wanted 202 00:10:40,960 --> 00:10:42,800 Speaker 2: to get to the idea that I'm sat here at 203 00:10:42,800 --> 00:10:47,520 Speaker 2: this desk during the Starship attempt last week, and it 204 00:10:47,600 --> 00:10:51,280 Speaker 2: was amazing to see how quickly in after hours trading 205 00:10:51,320 --> 00:10:55,160 Speaker 2: the stock dropped right four percent in the moment because 206 00:10:55,360 --> 00:10:58,880 Speaker 2: the test was aborted. Right at the final moments, all 207 00:10:58,920 --> 00:11:01,960 Speaker 2: of these future business lines on the AI side, NEO 208 00:11:02,040 --> 00:11:07,360 Speaker 2: cloud side, starlink orbsort data center. They're all predicated on 209 00:11:07,520 --> 00:11:12,160 Speaker 2: Starship not just working, but being rapidly reusable. So can 210 00:11:12,200 --> 00:11:15,120 Speaker 2: we just start there and sort of your response to 211 00:11:15,640 --> 00:11:18,160 Speaker 2: tracking the cadence of Starships development. 212 00:11:19,200 --> 00:11:20,719 Speaker 3: Yeah, you bet, Thanks for having me on. 213 00:11:20,760 --> 00:11:24,040 Speaker 7: So, yeah, this was like thirteen recent delay was some 214 00:11:25,000 --> 00:11:29,720 Speaker 7: minor technical issues that were identified pre launch. And I 215 00:11:29,800 --> 00:11:32,320 Speaker 7: remind you this is version three, so it is a bigger, 216 00:11:32,400 --> 00:11:36,240 Speaker 7: more powerful vehicle they've debuted about two months ago. The 217 00:11:36,280 --> 00:11:39,640 Speaker 7: automatic abart ad ignition was really the function of for 218 00:11:39,840 --> 00:11:43,000 Speaker 7: the thirty three Rapptor engines on the booster didn't immediately 219 00:11:43,480 --> 00:11:46,360 Speaker 7: light up and the onboard system caught it. It stood 220 00:11:46,360 --> 00:11:49,400 Speaker 7: the vehicle down before the liftoff, and that's a safety 221 00:11:49,400 --> 00:11:51,600 Speaker 7: system really doing, you know what it's supposed to do. 222 00:11:51,760 --> 00:11:55,120 Speaker 7: Musk in the team, you know they've identified the root cause. 223 00:11:55,160 --> 00:11:58,360 Speaker 7: Within a few hours, two engines are being swapped out. 224 00:11:58,400 --> 00:12:01,680 Speaker 7: They're already back on the pad targeting as you mentioned 225 00:12:01,679 --> 00:12:05,839 Speaker 7: this Thursday for relaunch. That's about a week of turnaround 226 00:12:05,880 --> 00:12:09,040 Speaker 7: on a pretty complex rocket. I think these development programs 227 00:12:09,679 --> 00:12:13,600 Speaker 7: that you've seen historically at this scale, you know, from 228 00:12:13,640 --> 00:12:17,160 Speaker 7: a diagnose and fixed cycle that's that's relatively quick versus 229 00:12:17,200 --> 00:12:19,319 Speaker 7: anything that's structurally wrong with you know, with the ship. 230 00:12:20,280 --> 00:12:24,839 Speaker 2: So Brett, here's the thing I'm reading your research investment thesis. 231 00:12:25,240 --> 00:12:29,319 Speaker 2: SpaceX is not a rocket company. We're just talking about rockets. 232 00:12:31,120 --> 00:12:32,040 Speaker 2: Explain your thesis. 233 00:12:33,880 --> 00:12:37,640 Speaker 7: Yeah, No, absolutely, I would say that the you know, 234 00:12:37,679 --> 00:12:43,040 Speaker 7: the enabler of the starlink opportunity and the connectivity opportunity 235 00:12:43,080 --> 00:12:47,160 Speaker 7: longer term is going to be dependent on the success 236 00:12:47,200 --> 00:12:52,040 Speaker 7: in the reusability cadence of of of Starship. Falcon nine 237 00:12:52,120 --> 00:12:55,200 Speaker 7: is the current vehicle they use this year. They've got 238 00:12:55,240 --> 00:12:57,280 Speaker 7: really good visibility on some of the you know, the 239 00:12:57,320 --> 00:13:01,320 Speaker 7: payload to orbit densifying the constant relation on starlink for 240 00:13:01,400 --> 00:13:04,520 Speaker 7: starlink on the actual Facon nine. But we do think 241 00:13:04,600 --> 00:13:08,280 Speaker 7: long term it will hinge upon the success of Starship. 242 00:13:09,080 --> 00:13:11,640 Speaker 7: You know, right now they do have a very you know, 243 00:13:11,840 --> 00:13:14,959 Speaker 7: big running start relative to the other competitive set. About 244 00:13:15,000 --> 00:13:18,680 Speaker 7: eighty five percent of all mass to orbit was you know, 245 00:13:18,800 --> 00:13:23,160 Speaker 7: was enabled by SpaceX over the last three years. And 246 00:13:23,960 --> 00:13:26,520 Speaker 7: you know, operational cads will be important. These launches this 247 00:13:26,640 --> 00:13:29,240 Speaker 7: year will be important, and they're you know, they're making 248 00:13:29,320 --> 00:13:32,520 Speaker 7: inroads in there. You know, well, we'll see on Thursday. 249 00:13:32,520 --> 00:13:34,680 Speaker 7: But yeah, that will be a big enabler and gating 250 00:13:34,720 --> 00:13:37,000 Speaker 7: factor for the success over the next couple of years. 251 00:13:37,559 --> 00:13:40,120 Speaker 2: Within your two hundred dollars price target, how do you 252 00:13:40,160 --> 00:13:44,800 Speaker 2: sort of break out each business line within that price target. 253 00:13:46,520 --> 00:13:48,480 Speaker 7: Yeah, no, it's a great question. So when we look 254 00:13:48,480 --> 00:13:52,160 Speaker 7: at the you know, the next three year roadmap, we're 255 00:13:52,200 --> 00:13:56,200 Speaker 7: really taking a large discount relative to the AI platform. 256 00:13:56,760 --> 00:13:59,079 Speaker 7: So near term, a lot of our price target is 257 00:13:59,080 --> 00:14:02,040 Speaker 7: going to hinge upon this success of star Link and 258 00:14:02,080 --> 00:14:03,960 Speaker 7: then some of what they're doing on space with the 259 00:14:04,000 --> 00:14:07,480 Speaker 7: government contracts and some of the enterprise most recently some 260 00:14:07,559 --> 00:14:10,679 Speaker 7: of the contracts with the airlines. We do think there 261 00:14:10,720 --> 00:14:13,079 Speaker 7: is pretty good line of site here in the near term, 262 00:14:13,480 --> 00:14:16,520 Speaker 7: and we anchored our evaluation on twenty twenty nine and 263 00:14:16,600 --> 00:14:20,320 Speaker 7: actually took a pretty large aircut relative to what the 264 00:14:20,360 --> 00:14:24,960 Speaker 7: internal plant is. And we're you know, thinking that connectivity 265 00:14:25,000 --> 00:14:27,480 Speaker 7: is more optionality around the stock and underwriting it on 266 00:14:27,560 --> 00:14:30,520 Speaker 7: some of the near term fundamentals versus a long upshot 267 00:14:30,560 --> 00:14:33,720 Speaker 7: on orbital data center in twenty thirty and beyond. 268 00:14:35,360 --> 00:14:39,560 Speaker 2: Gret do you model at all for any key man 269 00:14:39,680 --> 00:14:42,840 Speaker 2: risk with Elon Musk, You know, how do you kind 270 00:14:42,840 --> 00:14:45,680 Speaker 2: of factor that into how you value the company as 271 00:14:45,760 --> 00:14:46,480 Speaker 2: much as anything. 272 00:14:48,320 --> 00:14:51,800 Speaker 7: Well, that's that's certainly a consideration, right, He's a visionary, 273 00:14:51,840 --> 00:14:56,320 Speaker 7: he's you know, very involved in the operations and really 274 00:14:56,360 --> 00:15:00,120 Speaker 7: across every single segment and division within the organization. And 275 00:15:00,440 --> 00:15:02,240 Speaker 7: you know, all that being said, you know, there is 276 00:15:02,280 --> 00:15:05,560 Speaker 7: a there is a blueprint that's that's in place the 277 00:15:06,680 --> 00:15:08,440 Speaker 7: you know, i'd say the mostest, they've been able to 278 00:15:08,440 --> 00:15:11,920 Speaker 7: develop some of the secular strengths across the business. And 279 00:15:12,040 --> 00:15:14,680 Speaker 7: really what they have is you know, ownership within you know, 280 00:15:14,680 --> 00:15:18,120 Speaker 7: the launch capabilities in the US and across the globe. 281 00:15:18,480 --> 00:15:21,000 Speaker 7: That is the key differentiator as we're thinking about some 282 00:15:21,120 --> 00:15:24,840 Speaker 7: of the optionality with the other segments down the road. 283 00:15:25,600 --> 00:15:28,000 Speaker 7: So yeah, something always we consider. But you know, he's 284 00:15:28,000 --> 00:15:31,440 Speaker 7: built something from what was really a you know, you know, 285 00:15:31,480 --> 00:15:34,720 Speaker 7: a vision you know years ago, you know, over decades ago. Uh, 286 00:15:34,800 --> 00:15:36,480 Speaker 7: and it's a you know, it's a real company with 287 00:15:36,560 --> 00:15:38,640 Speaker 7: real motes and defensibility in our view. 288 00:15:39,840 --> 00:15:42,720 Speaker 2: Brett lindsay a Massur on Bloomberg Tech, Thank you very much. 289 00:15:42,840 --> 00:15:45,040 Speaker 3: Indeed, now coming up, this. 290 00:15:45,080 --> 00:15:48,800 Speaker 2: Space infrastructure startup just found a way to address a 291 00:15:48,840 --> 00:15:53,120 Speaker 2: bottleneck in scaling quantum computing in lind CEO and co 292 00:15:53,160 --> 00:16:05,080 Speaker 2: founder Rob Mason joins us next this is Bloomberg Tech. 293 00:16:08,160 --> 00:16:10,520 Speaker 8: It's time now for talking tech on yahira on on 294 00:16:10,600 --> 00:16:14,000 Speaker 8: First Up. CUSP, AAI of Bezos back to British AI startup, 295 00:16:14,040 --> 00:16:16,840 Speaker 8: has raised four hundred and fifty million dollars to accelerate 296 00:16:17,080 --> 00:16:21,280 Speaker 8: the developments of next generation chip materials using AI. The 297 00:16:21,320 --> 00:16:25,960 Speaker 8: company is also launching the AI Materials Foundry, bringing together 298 00:16:26,040 --> 00:16:29,760 Speaker 8: more than forty eight organizations including Nvidia, Meta, Hyundai, the 299 00:16:29,880 --> 00:16:33,840 Speaker 8: Speed Up Chip Innovation plus Deep Seek founder Li Ang. 300 00:16:33,840 --> 00:16:38,200 Speaker 8: When Fund's investment firm fell sixteen percent after China's quant 301 00:16:38,200 --> 00:16:41,360 Speaker 8: firms were hit with an AI route, The volatility and 302 00:16:41,520 --> 00:16:45,360 Speaker 8: Chinese hedge funds highlight the wild swings LAI related stocks 303 00:16:45,360 --> 00:16:49,920 Speaker 8: as bubble concerns intensify, and Boeing says it will need 304 00:16:50,040 --> 00:16:53,320 Speaker 8: until the end of the next decade to bring its 305 00:16:53,400 --> 00:16:56,360 Speaker 8: next generation jet to market as it continues to repair 306 00:16:56,360 --> 00:17:00,480 Speaker 8: its finances for the huge investment and as customers keep 307 00:17:00,560 --> 00:17:03,800 Speaker 8: buying its current model. CEO Kelly Orberg spoke with our 308 00:17:03,800 --> 00:17:06,879 Speaker 8: own Guy Johnson earlier today at this year's firm both 309 00:17:07,200 --> 00:17:08,880 Speaker 8: air show, OH. 310 00:17:08,840 --> 00:17:11,800 Speaker 9: The market does not quite ready for the new airplane, 311 00:17:11,840 --> 00:17:14,520 Speaker 9: and the customers are telling us, let's focus on the 312 00:17:14,560 --> 00:17:17,720 Speaker 9: existing product. We'd like to see better durability of that 313 00:17:17,800 --> 00:17:21,040 Speaker 9: product in the marketplace. And as I mentioned, we've got 314 00:17:21,040 --> 00:17:23,320 Speaker 9: big backlogs and they're looking for us to deliver on 315 00:17:23,400 --> 00:17:27,120 Speaker 9: those backlogs. We are studying a new airplane and we'll 316 00:17:27,160 --> 00:17:28,480 Speaker 9: be ready when the market's ready. 317 00:17:30,880 --> 00:17:35,240 Speaker 2: This startup is tackling a bottleneck facing quantum computing. After 318 00:17:35,280 --> 00:17:40,200 Speaker 2: President Trump made the technology a strategic National priority. Interlun 319 00:17:40,320 --> 00:17:43,520 Speaker 2: says it's discovered a new way to harvest helium three, 320 00:17:44,080 --> 00:17:48,040 Speaker 2: essential to cool the computers and to scale the industry. 321 00:17:48,160 --> 00:17:51,920 Speaker 2: Joining us now is Interlun CEO and co founder Rob Myerson. 322 00:17:52,000 --> 00:17:54,880 Speaker 2: I have to say, when this idea hit my inbox, 323 00:17:54,920 --> 00:17:59,800 Speaker 2: I was intrigued. Interlune's goal is to harvest helium three 324 00:18:00,080 --> 00:18:04,040 Speaker 2: in industrial quantities from the Moon. But in the here 325 00:18:04,119 --> 00:18:07,520 Speaker 2: and now, there just isn't enough helium three for the 326 00:18:07,560 --> 00:18:11,160 Speaker 2: quantum computing industry on Earth to scale. So you seem 327 00:18:11,240 --> 00:18:15,000 Speaker 2: to have found a stopgap. What have you done? 328 00:18:15,440 --> 00:18:19,400 Speaker 10: That's right, and thanks for having me. We've we've devised 329 00:18:19,560 --> 00:18:22,440 Speaker 10: a technique. Our team has gone back to first principles, 330 00:18:23,160 --> 00:18:27,480 Speaker 10: the crack the textbooks and invented a way to separate 331 00:18:27,560 --> 00:18:31,160 Speaker 10: helium three using cryogenic distillation from grade A helium here 332 00:18:31,200 --> 00:18:31,639 Speaker 10: on Earth. 333 00:18:32,080 --> 00:18:32,320 Speaker 5: Uh. 334 00:18:32,440 --> 00:18:34,320 Speaker 10: This is an idea that's been around for a while 335 00:18:34,359 --> 00:18:37,760 Speaker 10: but never pursued commercially, and as far as we know what, 336 00:18:37,800 --> 00:18:41,919 Speaker 10: we're the first to demonstrate the ability to separate helium 337 00:18:41,960 --> 00:18:45,320 Speaker 10: three from commercial grade A helium. 338 00:18:45,800 --> 00:18:48,440 Speaker 2: The Bloombirtek audience is familiar with the use of helium 339 00:18:48,480 --> 00:18:51,840 Speaker 2: and cooling and stabilization in the chip industry, for example, 340 00:18:52,200 --> 00:18:54,520 Speaker 2: and so with the war in Iran and the straight 341 00:18:54,560 --> 00:18:57,840 Speaker 2: of for Mu's bottleneck, we came to understand that that 342 00:18:57,920 --> 00:18:59,120 Speaker 2: there was a shortage there. 343 00:18:59,640 --> 00:19:00,800 Speaker 3: How was this for you? 344 00:19:00,880 --> 00:19:04,280 Speaker 2: Now, Rob, what's your solution looking like on a timeline basis? 345 00:19:05,119 --> 00:19:08,120 Speaker 10: Well, so, the shortage we're talking about is helium three, 346 00:19:08,119 --> 00:19:11,760 Speaker 10: which is an isotope of helium that's extremely rare on Earth. 347 00:19:11,800 --> 00:19:14,560 Speaker 10: It comes from the decay of tritium and it's used 348 00:19:14,560 --> 00:19:20,520 Speaker 10: to chill down superconducting quantum computing chips down to milliclvin temperatures. 349 00:19:21,000 --> 00:19:25,479 Speaker 10: We've for decades secured our helium three from tretium decay 350 00:19:26,280 --> 00:19:29,919 Speaker 10: in the US and overseas, including Canada, but there's not 351 00:19:30,040 --> 00:19:34,360 Speaker 10: much treatium made anymore, and tritium has a twelve point 352 00:19:34,400 --> 00:19:37,320 Speaker 10: three year half life, so it decays to helium three, 353 00:19:37,359 --> 00:19:40,960 Speaker 10: and we're just not producing helium three anymore, and so 354 00:19:41,520 --> 00:19:46,199 Speaker 10: interlun Is identified the Moon as a target market in 355 00:19:46,240 --> 00:19:50,920 Speaker 10: the future. The Sun produces helium three and large quantities. 356 00:19:50,920 --> 00:19:53,840 Speaker 10: There's over a million metric tons of helium three on 357 00:19:53,880 --> 00:19:59,920 Speaker 10: the Moon and it's lunar regular. In developing that plan 358 00:20:00,080 --> 00:20:03,000 Speaker 10: to go to the Moon and extract helien three, we 359 00:20:03,080 --> 00:20:07,760 Speaker 10: devised this technology we call cold capture to healium to 360 00:20:08,760 --> 00:20:10,320 Speaker 10: separate helium three here on Earth. 361 00:20:11,960 --> 00:20:14,600 Speaker 2: So it's the same technique we are Yes, you heard right, 362 00:20:14,600 --> 00:20:17,800 Speaker 2: blinbotech audience, We're talking about harvesting helium three from the Moon. 363 00:20:17,920 --> 00:20:19,400 Speaker 3: That's the ultimate goal, right Rob. 364 00:20:19,760 --> 00:20:23,320 Speaker 2: But I guess from an economics perspective, you know that 365 00:20:23,560 --> 00:20:27,160 Speaker 2: technique it has a higher yield on the Moon. Get 366 00:20:27,240 --> 00:20:29,080 Speaker 2: us to the point where this is real. 367 00:20:30,119 --> 00:20:32,600 Speaker 10: Yeah, it absolutely will have a higher yield on the Moon. 368 00:20:33,640 --> 00:20:35,760 Speaker 10: We plan with our go to market in the early 369 00:20:35,800 --> 00:20:39,400 Speaker 10: twenty thirties to produce ten kilograms of helium three on 370 00:20:39,480 --> 00:20:42,960 Speaker 10: the Moon here on Earth by deploying cold capture to 371 00:20:43,240 --> 00:20:46,600 Speaker 10: greater a helium plants on Earth. We expect to produce 372 00:20:46,640 --> 00:20:49,760 Speaker 10: about two and a half kilograms of helium three annually, 373 00:20:50,480 --> 00:20:53,240 Speaker 10: and we have customers that are already signed up to 374 00:20:53,320 --> 00:20:57,280 Speaker 10: buy helium three from US over well just under five 375 00:20:57,359 --> 00:21:03,400 Speaker 10: hundred million dollars in purchase or binding contracts that extend 376 00:21:03,400 --> 00:21:05,720 Speaker 10: from twenty twenty eight out to seven to ten years 377 00:21:05,760 --> 00:21:07,960 Speaker 10: in the future. So we had to find a way 378 00:21:08,400 --> 00:21:12,560 Speaker 10: to service those customers earlier, and by applying this technology 379 00:21:12,600 --> 00:21:15,560 Speaker 10: that we've developed for the Moon to this Earth application, 380 00:21:15,640 --> 00:21:16,439 Speaker 10: we're able to do that. 381 00:21:17,840 --> 00:21:18,960 Speaker 3: Rob just very quickly. 382 00:21:19,240 --> 00:21:22,440 Speaker 2: Have you a pathway or plan in place to get 383 00:21:22,440 --> 00:21:25,159 Speaker 2: your technology to the Moon's surface. Who is the launch 384 00:21:25,200 --> 00:21:28,000 Speaker 2: provider who's going to help you do that operationally? 385 00:21:28,840 --> 00:21:30,439 Speaker 10: Well, well, we have a little bit of time to 386 00:21:30,440 --> 00:21:33,840 Speaker 10: decide on that, ed, but we are looking to the 387 00:21:33,960 --> 00:21:38,520 Speaker 10: Artemis program and the launch companies like SpaceX and Blue Origin, 388 00:21:39,200 --> 00:21:43,200 Speaker 10: but other companies that are building landers, like Firefly, Intuitive 389 00:21:43,200 --> 00:21:48,240 Speaker 10: Machines and others. We plan to invest alongside the Artemis 390 00:21:48,320 --> 00:21:51,359 Speaker 10: program the NASA program to go back to the Moon 391 00:21:51,520 --> 00:21:54,199 Speaker 10: and then on to Mars, and we will use that 392 00:21:54,240 --> 00:21:58,120 Speaker 10: infrastructure that they developed now of course, add the infrastructure 393 00:21:58,160 --> 00:22:01,880 Speaker 10: we need to harvest large volumes of healium three from 394 00:22:01,960 --> 00:22:05,639 Speaker 10: regular is infrastructure that also can help now, so build 395 00:22:05,640 --> 00:22:09,240 Speaker 10: a moon base, build roads and build do construction on 396 00:22:09,320 --> 00:22:11,879 Speaker 10: the moon that will support it right, large scale and 397 00:22:11,880 --> 00:22:14,360 Speaker 10: long term a lunar economy. 398 00:22:15,320 --> 00:22:17,960 Speaker 2: Into lun CEO and co founder Rob Us and thank 399 00:22:17,960 --> 00:22:20,200 Speaker 2: you very much for your time. Now coming up, It's 400 00:22:20,280 --> 00:22:23,080 Speaker 2: been a fascinating weekend in AI and there is a 401 00:22:23,080 --> 00:22:26,600 Speaker 2: lot to unpack yep, Kimmy K three, but also bigger 402 00:22:26,680 --> 00:22:30,040 Speaker 2: questions about the future of American AI. Mary Dino Frio, 403 00:22:30,160 --> 00:22:33,200 Speaker 2: partner at Crosslink Capital, joins us to discuss the next 404 00:22:33,280 --> 00:22:35,280 Speaker 2: frontier in AI investing. 405 00:22:35,680 --> 00:22:48,119 Speaker 11: This is Bloomberg Tech. Welcome back to Bloomberg Tech. 406 00:22:48,200 --> 00:22:50,960 Speaker 2: We're rebounding in the technology sector when it comes to 407 00:22:50,960 --> 00:22:54,719 Speaker 2: public markets. Remember Chip stocks fell into a bear market 408 00:22:54,800 --> 00:22:58,399 Speaker 2: Friday in part because of questions posed by the release 409 00:22:58,400 --> 00:23:01,879 Speaker 2: of Chemy K three by China's Moonshot. We're off session 410 00:23:01,920 --> 00:23:03,840 Speaker 2: highs right now, but then as that one hundred adding 411 00:23:03,880 --> 00:23:06,960 Speaker 2: almost a percentage point, the socks up two percent. And 412 00:23:07,000 --> 00:23:09,320 Speaker 2: it's a lot of the compute names and video but 413 00:23:09,359 --> 00:23:12,800 Speaker 2: also memory names that are driving some upside in those 414 00:23:12,800 --> 00:23:15,600 Speaker 2: public markets since Moonshot. 415 00:23:15,160 --> 00:23:16,760 Speaker 3: Released' Kimmi K three model last week. 416 00:23:16,800 --> 00:23:20,000 Speaker 2: Everyone's debating what it really means. Does more efficient AI 417 00:23:20,160 --> 00:23:24,080 Speaker 2: mean less hardware? Is China closing the gap faster than 418 00:23:24,240 --> 00:23:29,080 Speaker 2: we expected? Where does AI's real value ultimately accrue? Joining 419 00:23:29,160 --> 00:23:33,119 Speaker 2: us now is Crosslink Capital partner Mary donofrio focused on 420 00:23:33,240 --> 00:23:37,880 Speaker 2: venture growth investments, but across software, AI and infrastructure. And 421 00:23:39,160 --> 00:23:41,440 Speaker 2: for the last three days, I've said Kimmy K three 422 00:23:41,480 --> 00:23:44,800 Speaker 2: many times, but it was interesting to take a sort 423 00:23:44,800 --> 00:23:49,480 Speaker 2: of straw pole of VC's founders, people in industry of 424 00:23:49,560 --> 00:23:52,680 Speaker 2: how they react to it so differently. Some people are 425 00:23:52,760 --> 00:23:56,359 Speaker 2: very fearful, defensive. Others say this is very good for 426 00:23:56,440 --> 00:23:59,000 Speaker 2: AI at a whole, very difficult job, first thing on a Monday. 427 00:23:59,040 --> 00:24:00,920 Speaker 3: But your reaction to what happened. 428 00:24:01,200 --> 00:24:04,399 Speaker 12: Well, Kimmy K three, as you said, was phenomenal. And 429 00:24:04,440 --> 00:24:07,360 Speaker 12: it's similar to the deep seek moment we had last year. 430 00:24:07,880 --> 00:24:11,639 Speaker 12: You know, two point eight trillion parameter model, and what 431 00:24:11,680 --> 00:24:14,240 Speaker 12: it demonstrates, I think is that you know, China and 432 00:24:14,280 --> 00:24:17,439 Speaker 12: Frontier pardon me, China and open weight models are closing 433 00:24:17,480 --> 00:24:20,400 Speaker 12: the gap with frontier models. You know, obviously it's done, 434 00:24:20,520 --> 00:24:23,640 Speaker 12: it was done based on their own benchmarking. But according 435 00:24:23,680 --> 00:24:27,399 Speaker 12: to the benchmarks. You know they're fourth and while you 436 00:24:27,400 --> 00:24:30,240 Speaker 12: know you can debate the merits of those benchmarks themselves, 437 00:24:30,520 --> 00:24:34,359 Speaker 12: the productivity gains are tremendous, and I think that in 438 00:24:34,400 --> 00:24:39,119 Speaker 12: general it is part of the pathway towards more widespread 439 00:24:39,200 --> 00:24:42,240 Speaker 12: open weight model usage, and in the context of enterprise 440 00:24:42,280 --> 00:24:45,520 Speaker 12: budgets ballooning in AI, I actually think that it's probably 441 00:24:45,520 --> 00:24:47,440 Speaker 12: a boon for AI ubiquity over time. 442 00:24:47,960 --> 00:24:51,880 Speaker 2: When you think about your your existing portfolio of companies, 443 00:24:52,720 --> 00:24:54,840 Speaker 2: were you sort of like, Okay, how does this impact 444 00:24:54,920 --> 00:24:58,879 Speaker 2: the trajectory? Is anything actually fundamentally change for the different 445 00:24:59,760 --> 00:25:02,760 Speaker 2: piece is the AI five layer cake that you're invested in. 446 00:25:03,280 --> 00:25:07,080 Speaker 12: I don't know if it changes the cake itself, but 447 00:25:07,119 --> 00:25:09,840 Speaker 12: I do think it's another demonstration that models keep leap 448 00:25:09,880 --> 00:25:13,560 Speaker 12: frogging one another, and if companies don't out innovate, they're 449 00:25:13,560 --> 00:25:15,959 Speaker 12: going to be leap frog two. Whether it's you know, 450 00:25:16,920 --> 00:25:19,840 Speaker 12: every single model seems to outperform the last, and it's 451 00:25:19,880 --> 00:25:23,399 Speaker 12: a matter of out innovating by creating modes of data, 452 00:25:23,640 --> 00:25:30,239 Speaker 12: of performance, of customer resonance and otherwise. Unfortunately, you know, 453 00:25:30,359 --> 00:25:35,080 Speaker 12: the innovations in both frontier and open weight models is tremendous. 454 00:25:35,440 --> 00:25:38,320 Speaker 2: You're on the crossover investment team, but basically focused on 455 00:25:38,400 --> 00:25:43,240 Speaker 2: private and maybe later stage or venture growth investments. Seven 456 00:25:43,320 --> 00:25:45,920 Speaker 2: years at Bessemer as well prior to being at cross 457 00:25:45,920 --> 00:25:49,560 Speaker 2: Link sort of introduce us to your overall investment focus, 458 00:25:49,600 --> 00:25:50,720 Speaker 2: your investment thesis. 459 00:25:51,240 --> 00:25:51,879 Speaker 3: Yeah, of course. 460 00:25:51,920 --> 00:25:55,239 Speaker 12: So, as you mentioned, I'm a partner Crosslink Capital. I 461 00:25:55,280 --> 00:25:58,000 Speaker 12: helped to lead the mid stage venture practice. I spend 462 00:25:58,040 --> 00:26:01,760 Speaker 12: an asymmetric amount of time in vertical AI and AI infrastructure. 463 00:26:02,040 --> 00:26:05,120 Speaker 2: What are some examples of what you call vertical AI 464 00:26:05,200 --> 00:26:06,280 Speaker 2: and AI infrastructure? 465 00:26:06,359 --> 00:26:08,760 Speaker 12: Yeah, of course, So AI infrastructure is a place I 466 00:26:08,760 --> 00:26:10,920 Speaker 12: spend a lot of time. That's you know, the physical 467 00:26:10,920 --> 00:26:14,080 Speaker 12: and software systems required to build, train and run models. 468 00:26:14,480 --> 00:26:16,800 Speaker 12: And in the first wave it was about how to 469 00:26:16,800 --> 00:26:19,399 Speaker 12: build a scalable model, and now we have those as 470 00:26:19,440 --> 00:26:22,480 Speaker 12: we're talking about right now, and so in the next wave, 471 00:26:22,520 --> 00:26:26,639 Speaker 12: it's how to deploy them quickly, fastly, cheaply agents of 472 00:26:26,640 --> 00:26:28,760 Speaker 12: course being one of one of the ways to do 473 00:26:28,800 --> 00:26:31,639 Speaker 12: so in an efficient way. You know, taking models is 474 00:26:31,640 --> 00:26:34,919 Speaker 12: the reasoning layer, adding tools in infrastructure around it to 475 00:26:35,040 --> 00:26:39,320 Speaker 12: connect outside data, data and apps, and using memory in 476 00:26:39,400 --> 00:26:43,359 Speaker 12: order to do it in a way that produces outcomes, 477 00:26:43,840 --> 00:26:47,600 Speaker 12: and agent infrastructure itself has become a sector that I've 478 00:26:47,600 --> 00:26:49,879 Speaker 12: spent a lot of time in, whether it's agent harnesses 479 00:26:50,240 --> 00:26:53,840 Speaker 12: or the identity layer around it. One of my portfolio companies, Teleport, 480 00:26:53,880 --> 00:26:55,520 Speaker 12: is a leader in that space, and it's a place 481 00:26:55,520 --> 00:26:57,080 Speaker 12: that I'm spending an asymmetric amount of time. 482 00:26:57,480 --> 00:27:00,240 Speaker 2: I wanted to get to that kind of back ground 483 00:27:00,440 --> 00:27:02,040 Speaker 2: on your focus to try and answer some of the 484 00:27:02,119 --> 00:27:04,240 Speaker 2: questions that the Communicay three moment posed. 485 00:27:04,720 --> 00:27:06,280 Speaker 3: One idea is that. 486 00:27:07,800 --> 00:27:11,960 Speaker 2: Basically, by lowering the margins at the model layer, it's 487 00:27:11,960 --> 00:27:14,920 Speaker 2: an enabler, it opens up the market a little bit, 488 00:27:14,960 --> 00:27:17,400 Speaker 2: it's a driver of demand essentially. 489 00:27:17,840 --> 00:27:20,840 Speaker 3: Do you agree with that kind of thinking In response. 490 00:27:20,520 --> 00:27:23,560 Speaker 12: To this, I do think that it demonstrates what you 491 00:27:23,560 --> 00:27:27,399 Speaker 12: know a lot of even the inference infrastructure layer is demonstrating, 492 00:27:27,400 --> 00:27:29,239 Speaker 12: which is, you know, as cost has gone down by 493 00:27:29,320 --> 00:27:33,719 Speaker 12: ninety five percent, does that displace the total dollar value 494 00:27:34,680 --> 00:27:37,520 Speaker 12: expended by customers? And I would argue it probably won't, 495 00:27:37,800 --> 00:27:41,159 Speaker 12: because the migration will just be from workloads that are 496 00:27:41,200 --> 00:27:43,879 Speaker 12: at the frontier, that are most expensive and of course 497 00:27:43,920 --> 00:27:47,320 Speaker 12: that require the most reasoning capabilities and are the most advanced. 498 00:27:47,359 --> 00:27:52,920 Speaker 12: But they'll go to cheaper models that you know, still 499 00:27:52,960 --> 00:27:56,159 Speaker 12: provide the same amount the requisite amount of compute for 500 00:27:56,359 --> 00:27:59,840 Speaker 12: the question being posed, but do so at much cheaper 501 00:28:00,400 --> 00:28:03,200 Speaker 12: while not sacrificing on things like latenccene performance. 502 00:28:03,760 --> 00:28:08,119 Speaker 2: It's interesting because like kim K three kind of pricing 503 00:28:08,720 --> 00:28:11,280 Speaker 2: three dollar per input fifteen dollars per output on the 504 00:28:11,280 --> 00:28:16,000 Speaker 2: token side, Like it's it's quite in line with what 505 00:28:16,119 --> 00:28:18,399 Speaker 2: a lot of leading models are also priced. That I 506 00:28:18,400 --> 00:28:20,399 Speaker 2: found that interesting. Maybe we should talk a little bit 507 00:28:20,400 --> 00:28:24,520 Speaker 2: about valuations. It's not a sophisticated argument, but when this 508 00:28:24,560 --> 00:28:26,840 Speaker 2: will happened, a lot of people came out and said, well, 509 00:28:27,280 --> 00:28:31,600 Speaker 2: if a twenty billion dollar Chinese startup can do a 510 00:28:31,600 --> 00:28:35,200 Speaker 2: two point eight trillion parameter model with these economics, why 511 00:28:35,240 --> 00:28:38,640 Speaker 2: are we signing open AI and anthropic evaluation of a trillion? 512 00:28:38,880 --> 00:28:41,520 Speaker 2: You know, they looked at the sort of fundamentals of that. 513 00:28:42,080 --> 00:28:45,040 Speaker 2: Is that the right way to think about it? 514 00:28:45,400 --> 00:28:48,280 Speaker 12: Frankly, I don't know yet, and I don't think anybody 515 00:28:48,320 --> 00:28:52,040 Speaker 12: knows yet, because even in the context of those questions, 516 00:28:52,160 --> 00:28:55,280 Speaker 12: Anthropic and open AI are still producing billions and billions 517 00:28:55,280 --> 00:28:58,520 Speaker 12: of revenue. And it's not just from their you know, 518 00:28:58,560 --> 00:29:01,360 Speaker 12: they have first party businesses, they have third party businesses, 519 00:29:01,800 --> 00:29:06,000 Speaker 12: and I think you know, obviously the API opened for 520 00:29:06,480 --> 00:29:08,360 Speaker 12: a moonshot, but the open weights have not yet. 521 00:29:08,440 --> 00:29:09,840 Speaker 3: Yeah, twenty seventh of July. 522 00:29:09,960 --> 00:29:12,520 Speaker 12: And it's worth noting that because it's open weight doesn't 523 00:29:12,520 --> 00:29:15,000 Speaker 12: mean it's free to run. And so I think that's 524 00:29:15,040 --> 00:29:18,360 Speaker 12: a dynamic to these models that people potentially underappreciate as well. 525 00:29:18,840 --> 00:29:22,120 Speaker 2: Say the argument you just outlined, the same that graylocks 526 00:29:22,120 --> 00:29:24,760 Speaker 2: on mod of Many made on Friday on the show, 527 00:29:24,800 --> 00:29:27,160 Speaker 2: that you know, these are very robust businesses that have 528 00:29:27,280 --> 00:29:32,000 Speaker 2: multiple revenue streams. You are focused on the private markets, 529 00:29:32,040 --> 00:29:36,040 Speaker 2: but we've had some really interesting IPOs of late, you know, 530 00:29:36,560 --> 00:29:39,480 Speaker 2: not just SpaceX, but also s k Heinez doing its ADRs. 531 00:29:39,480 --> 00:29:41,320 Speaker 3: I was in New York City for that. What does 532 00:29:41,320 --> 00:29:44,600 Speaker 3: that signal to you? I think it's two things. 533 00:29:45,320 --> 00:29:48,120 Speaker 12: Obviously, those are fantastic businesses, and I think when it 534 00:29:48,160 --> 00:29:50,880 Speaker 12: comes to what it says for the markets is that 535 00:29:51,000 --> 00:29:53,520 Speaker 12: investors are really looking for anything having to do with 536 00:29:53,560 --> 00:29:56,320 Speaker 12: the AI build out AI infrastructure. Obviously you made the 537 00:29:56,360 --> 00:30:00,240 Speaker 12: reference to the performance of public market stocks today. Also 538 00:30:00,240 --> 00:30:02,640 Speaker 12: think it's one other thing for the capital markets, which 539 00:30:02,680 --> 00:30:05,120 Speaker 12: is that you know, we have this narrative, but potentially 540 00:30:05,160 --> 00:30:07,880 Speaker 12: the IPO window is reopening. But it's doing so with 541 00:30:07,920 --> 00:30:10,880 Speaker 12: these long standing businesses with billions of dollars of revenue 542 00:30:11,040 --> 00:30:14,560 Speaker 12: that are or frankly older. You know, they're at the 543 00:30:14,600 --> 00:30:17,320 Speaker 12: cutting edge of innovation, but they've been around for decades, 544 00:30:17,600 --> 00:30:20,360 Speaker 12: and so I think it's saying, you know, there is 545 00:30:20,560 --> 00:30:24,120 Speaker 12: a desire for things having to do with AI, but nonetheless, 546 00:30:24,120 --> 00:30:27,440 Speaker 12: investors are still they're hardly speculative. Investors are looking for 547 00:30:27,480 --> 00:30:28,719 Speaker 12: things that are relatively secure. 548 00:30:29,400 --> 00:30:32,480 Speaker 2: Cross Link partner married and for making the debut on 549 00:30:32,480 --> 00:30:33,240 Speaker 2: Bloomberg Techs. 550 00:30:33,280 --> 00:30:34,840 Speaker 3: Really great to have you here, and Alisa, thank you 551 00:30:34,960 --> 00:30:35,440 Speaker 3: very much. 552 00:30:35,800 --> 00:30:40,080 Speaker 2: TSMC is spending at an unprecedented pace, committing a record 553 00:30:40,280 --> 00:30:43,880 Speaker 2: two hundred and sixty five billion dollars to its US expansion. 554 00:30:43,960 --> 00:30:47,520 Speaker 2: So with AI fueling enormous capital needs across the chip industry, 555 00:30:47,520 --> 00:30:50,560 Speaker 2: the question becomes, how will it pay for it? Bloomberg 556 00:30:50,560 --> 00:30:54,000 Speaker 2: Stephen Engel our CFO Wendel Wang whether another trip to 557 00:30:54,040 --> 00:30:56,080 Speaker 2: the bond market could be on the table. 558 00:30:57,280 --> 00:31:00,760 Speaker 13: Our fundamental belief is that WEISH should be able to 559 00:31:00,880 --> 00:31:05,160 Speaker 13: self sustain using our operating cash row to support the 560 00:31:05,240 --> 00:31:10,200 Speaker 13: KPEX and leave enough free cash flow to return cash 561 00:31:10,400 --> 00:31:16,320 Speaker 13: to shareholder through a sustainable and steadily increasing cash dividends, 562 00:31:16,560 --> 00:31:19,920 Speaker 13: which we have been doing that in the past several years. 563 00:31:19,960 --> 00:31:23,200 Speaker 13: We have very good track record of it. Now from 564 00:31:23,280 --> 00:31:26,000 Speaker 13: time to time we can go out to the market, 565 00:31:26,320 --> 00:31:30,880 Speaker 13: to the bone market. If the timing is right. We've 566 00:31:30,920 --> 00:31:35,880 Speaker 13: been doing it. We've done thirty billion dollars fundraising in 567 00:31:36,000 --> 00:31:39,440 Speaker 13: bond market a few years back, about half of it 568 00:31:39,560 --> 00:31:45,400 Speaker 13: in US dollar, half of it in NT. NT is very. 569 00:31:47,000 --> 00:31:47,280 Speaker 3: Cheap. 570 00:31:47,320 --> 00:31:52,160 Speaker 13: Funding for US US dollar is becoming more and more expensive, 571 00:31:52,240 --> 00:31:56,760 Speaker 13: so we'll see if the timing is correct. If the 572 00:31:56,800 --> 00:31:59,360 Speaker 13: timing is right, we do not rule out that we 573 00:31:59,440 --> 00:32:01,160 Speaker 13: can go to the bomb market again. 574 00:32:01,640 --> 00:32:04,560 Speaker 3: Well, given what you just laid out, would maybe a 575 00:32:04,600 --> 00:32:08,200 Speaker 3: mix of more empty dollar denominated bonds be more likely 576 00:32:09,680 --> 00:32:10,760 Speaker 3: or yields to a high. 577 00:32:10,720 --> 00:32:15,400 Speaker 13: ANTY dollar bomb market is much shallower than the US 578 00:32:15,440 --> 00:32:18,800 Speaker 13: dollar bomb market, so it has its limitations. 579 00:32:20,320 --> 00:32:24,000 Speaker 2: That was tsmc CFO Wendl Juan with Bloomberg Stephen Engel 580 00:32:24,080 --> 00:32:26,800 Speaker 2: in Taipei. Now coming up, Silicon Valley is still betting 581 00:32:26,800 --> 00:32:29,720 Speaker 2: big on defense. We're gonna speak with Andres and Horowitz 582 00:32:30,280 --> 00:32:33,760 Speaker 2: new signing Connor Love about the startup shaping the future 583 00:32:34,040 --> 00:32:37,840 Speaker 2: of national security kind of just joined the American Dynamism. 584 00:32:37,320 --> 00:32:39,240 Speaker 3: Team that's next. This is Bloomberg Tech. 585 00:32:45,600 --> 00:32:48,360 Speaker 2: Venture firm and recent Horowitz is doubling down on its 586 00:32:48,400 --> 00:32:52,920 Speaker 2: American Dynamism thesis and practice, adding defense and space investor 587 00:32:52,960 --> 00:32:55,520 Speaker 2: and former Army captain Connor Love to the team and 588 00:32:55,520 --> 00:32:58,240 Speaker 2: Connor joins us now joining from light Speed. It's great 589 00:32:58,240 --> 00:33:01,840 Speaker 2: to have you on the show day one. You're very 590 00:33:01,840 --> 00:33:06,760 Speaker 2: welcome Day one on American Dynamism. You, you know, reading 591 00:33:06,800 --> 00:33:11,080 Speaker 2: about your investments at the last firm and. 592 00:33:11,000 --> 00:33:12,400 Speaker 3: Like how you've approached your career. 593 00:33:13,000 --> 00:33:17,320 Speaker 2: You would call it American exceptionalism, right, but there's a 594 00:33:17,360 --> 00:33:20,720 Speaker 2: close alignment between those two ideas. Just reflect on what 595 00:33:20,760 --> 00:33:23,280 Speaker 2: it was that prompted you to join this firm. 596 00:33:23,400 --> 00:33:27,440 Speaker 14: Yeah, well, first of all, thanks for having me. We're 597 00:33:27,760 --> 00:33:30,400 Speaker 14: in a pretty amazing opportunity right now to invest really 598 00:33:30,440 --> 00:33:33,160 Speaker 14: in the fabric of America. And when Mark and Ben 599 00:33:33,200 --> 00:33:35,800 Speaker 14: and team kind of approached me, look, this is a 600 00:33:35,840 --> 00:33:38,920 Speaker 14: category that four years ago, they didn't jump on the bandwagon. 601 00:33:39,000 --> 00:33:41,320 Speaker 14: They said, hey, this is important to the fabric of America. 602 00:33:41,920 --> 00:33:44,640 Speaker 14: This is important for the entrepreneurs and the technology future 603 00:33:44,640 --> 00:33:48,760 Speaker 14: for our nation. And when given that opportunity. You know, 604 00:33:48,120 --> 00:33:50,240 Speaker 14: you kind of get on you have to get on 605 00:33:50,280 --> 00:33:52,960 Speaker 14: board and join the team. So it's been amazing, you know, 606 00:33:53,040 --> 00:33:54,600 Speaker 14: you know so far just to you know, dive in 607 00:33:54,600 --> 00:33:56,360 Speaker 14: and get to know everyone. I think in the end, 608 00:33:56,400 --> 00:33:57,760 Speaker 14: when you take a step back and just look at 609 00:33:57,800 --> 00:34:00,960 Speaker 14: the opportunity here. You know, as I said before, what's 610 00:34:01,000 --> 00:34:03,800 Speaker 14: happening is a once in a generation opportunity where we're 611 00:34:03,800 --> 00:34:06,840 Speaker 14: redefining what it really means to build in America. It's 612 00:34:06,880 --> 00:34:09,360 Speaker 14: not just software primitives. I mean, it's it's things that 613 00:34:09,440 --> 00:34:12,480 Speaker 14: are core to the fabric of America. It's defense technology, 614 00:34:12,680 --> 00:34:15,880 Speaker 14: it's energy, it's many things. And so to join a 615 00:34:15,920 --> 00:34:18,319 Speaker 14: team like Indres and Horowitz and kind of continue to 616 00:34:18,360 --> 00:34:21,360 Speaker 14: invest in that back entrepreneurs in this ecosystem, I'm just 617 00:34:21,400 --> 00:34:22,719 Speaker 14: really thrilled and excited to be. 618 00:34:22,640 --> 00:34:24,439 Speaker 3: Able to do it conin. 619 00:34:24,440 --> 00:34:25,919 Speaker 2: Do you mind if I ask a bit more about 620 00:34:25,960 --> 00:34:28,680 Speaker 2: the backstory. You know, you mentioned Mark and Drees and 621 00:34:28,719 --> 00:34:31,000 Speaker 2: Ben Horowitz that they approached you. 622 00:34:31,000 --> 00:34:32,800 Speaker 3: You know, how did this all come together. 623 00:34:33,160 --> 00:34:35,799 Speaker 14: It's actually funny. One of the partners in, you know, 624 00:34:35,840 --> 00:34:39,160 Speaker 14: founders of American Dynamism, Catherine Boyle. I actually spoke to 625 00:34:39,200 --> 00:34:41,719 Speaker 14: her about seven years ago, I had the privilege of 626 00:34:42,000 --> 00:34:44,920 Speaker 14: serving in the military and was actually for deployed in Iraq, 627 00:34:44,960 --> 00:34:47,000 Speaker 14: you know, looking for what the next opportunity was in 628 00:34:47,280 --> 00:34:50,080 Speaker 14: my professional career. And I connected with her really and 629 00:34:50,239 --> 00:34:52,640 Speaker 14: just kind of a you know, haphazard out of the 630 00:34:52,640 --> 00:34:54,839 Speaker 14: blue way, and she said, Hey, there's this company called 631 00:34:54,880 --> 00:34:57,319 Speaker 14: Androl you should look at andrel Obviously everyone knows what 632 00:34:57,400 --> 00:35:00,759 Speaker 14: Andreil is these days, but I think importantly I just 633 00:35:00,800 --> 00:35:04,239 Speaker 14: got exposed to Silicon Valley and I saw then what 634 00:35:04,360 --> 00:35:07,239 Speaker 14: I know now that really technology will be a force 635 00:35:07,320 --> 00:35:10,160 Speaker 14: for good, not just for the defense fabric of our 636 00:35:10,239 --> 00:35:13,239 Speaker 14: nation as I mentioned, but also just you can't have 637 00:35:13,280 --> 00:35:15,480 Speaker 14: an industrial base in America if you don't have a 638 00:35:15,560 --> 00:35:18,239 Speaker 14: vision in defense base. So it's to me a once 639 00:35:18,280 --> 00:35:20,840 Speaker 14: in a lifetime opportunity that is very much rooted in 640 00:35:20,920 --> 00:35:24,000 Speaker 14: my background and just happens to connect at you know, 641 00:35:24,040 --> 00:35:27,160 Speaker 14: three in the morning with Catherine Boyle at you know, 642 00:35:27,520 --> 00:35:29,400 Speaker 14: when she was then at her previous firm. 643 00:35:30,760 --> 00:35:34,360 Speaker 2: You've explained it right that it's not exclusively defense technology. 644 00:35:34,360 --> 00:35:37,359 Speaker 3: American dynamism is broad in that bit right now. 645 00:35:37,400 --> 00:35:41,560 Speaker 2: Anderil is the best known case study, yeah, right, seronic. 646 00:35:41,760 --> 00:35:44,480 Speaker 2: More recently in the news cycle, what is the next 647 00:35:44,480 --> 00:35:47,000 Speaker 2: phase of American dynamism, do you think Conna. 648 00:35:47,160 --> 00:35:49,560 Speaker 14: Yeah, well I put it in that lens, which is 649 00:35:49,920 --> 00:35:53,560 Speaker 14: you really cannot have a defense industrial base without an 650 00:35:53,560 --> 00:35:56,319 Speaker 14: industrial base. And when you look into what goes into 651 00:35:56,320 --> 00:36:00,000 Speaker 14: these autonomous systems, these new platforms, it's really the subcomet, 652 00:36:00,320 --> 00:36:02,480 Speaker 14: it's really the parts, it's really the you know, metal 653 00:36:02,560 --> 00:36:05,200 Speaker 14: and the end components of what's going on. So when 654 00:36:05,239 --> 00:36:06,879 Speaker 14: I think, you take a step back and you look 655 00:36:06,920 --> 00:36:10,640 Speaker 14: at hey, obviously defense, we're creating a new opportunity for 656 00:36:10,760 --> 00:36:13,880 Speaker 14: neoprimes to really capture and serve not just the United 657 00:36:13,880 --> 00:36:17,200 Speaker 14: States but also our allies. You see things like energy, 658 00:36:17,280 --> 00:36:21,080 Speaker 14: you see things like core manufacturing. Importantly, you see things 659 00:36:21,080 --> 00:36:23,920 Speaker 14: like space. Yes, there's a huge defense component to space, 660 00:36:23,960 --> 00:36:26,160 Speaker 14: but as we put more kilograms of mass up to 661 00:36:26,200 --> 00:36:29,160 Speaker 14: space in a lot cheaper way, you know, really it is, 662 00:36:29,239 --> 00:36:30,839 Speaker 14: you know, kind of the next frontier of where we're 663 00:36:30,880 --> 00:36:32,719 Speaker 14: going to be building not just for defense and kind 664 00:36:32,760 --> 00:36:36,440 Speaker 14: of government use cases, but all other parts of the economy. 665 00:36:36,520 --> 00:36:38,400 Speaker 14: So I think when I take a step back and 666 00:36:38,400 --> 00:36:40,920 Speaker 14: I look at American dynamism as we call it now, 667 00:36:41,360 --> 00:36:43,640 Speaker 14: I actually just viewed as it's a proxy for how 668 00:36:43,680 --> 00:36:46,360 Speaker 14: some of the most valuable companies in the world are created. 669 00:36:46,560 --> 00:36:48,960 Speaker 14: I mean, historically you look at Boeing, and historically you 670 00:36:49,000 --> 00:36:50,640 Speaker 14: look at you know, all these these companies that have 671 00:36:50,680 --> 00:36:54,239 Speaker 14: been important to America. They've been very important to you know, 672 00:36:54,480 --> 00:36:57,880 Speaker 14: the American individual, they necessarily have not always been the 673 00:36:57,880 --> 00:37:01,919 Speaker 14: most valuable companies. Technology is changing that. Technology is saying 674 00:37:01,960 --> 00:37:04,360 Speaker 14: you can be both important to America and our allies 675 00:37:04,480 --> 00:37:06,720 Speaker 14: and be some of the most valuable companies in the world. 676 00:37:06,960 --> 00:37:09,040 Speaker 14: And that's what we and myself at Andrews and Horwitz 677 00:37:09,040 --> 00:37:10,399 Speaker 14: are really excited to invest into. 678 00:37:11,640 --> 00:37:14,720 Speaker 2: And Reil is a big focus for the Bloomberg Tech audience. 679 00:37:14,760 --> 00:37:17,320 Speaker 2: You would have seen the news this morning with Archer 680 00:37:17,840 --> 00:37:21,719 Speaker 2: finally showing the autonomous Vitol platform. I think what'd be 681 00:37:21,800 --> 00:37:25,360 Speaker 2: interesting is to you know, you participated in light speeds 682 00:37:25,320 --> 00:37:28,920 Speaker 2: and aero investment and a real clearly important part of 683 00:37:28,960 --> 00:37:30,399 Speaker 2: the American dynamism in. 684 00:37:30,600 --> 00:37:31,399 Speaker 3: A sixteen team. 685 00:37:31,400 --> 00:37:35,239 Speaker 2: More broadly, portfolio, how are you going to approach that? 686 00:37:35,840 --> 00:37:38,160 Speaker 14: Yeah, well, I think I'll say a couple of things 687 00:37:38,160 --> 00:37:41,120 Speaker 14: about Andreil. The first thing is when you think they've 688 00:37:41,160 --> 00:37:44,920 Speaker 14: got to their last product, you're wrong. There's another product. 689 00:37:44,960 --> 00:37:47,120 Speaker 14: And I think the beauty is when you have such 690 00:37:47,120 --> 00:37:49,759 Speaker 14: a complex use case that is the kind of you know, 691 00:37:50,000 --> 00:37:53,000 Speaker 14: Department of War and the American warfighter. There are a 692 00:37:53,040 --> 00:37:55,440 Speaker 14: lot of problems that need to be solved. So you know, 693 00:37:55,520 --> 00:37:57,960 Speaker 14: to see this kind of partnership with Archer, I think 694 00:37:58,080 --> 00:37:59,880 Speaker 14: is only not good for the American warfighter and the 695 00:38:00,000 --> 00:38:02,600 Speaker 14: American public. I think when you take a step back 696 00:38:02,640 --> 00:38:05,239 Speaker 14: and say, you know, really this market is massive. I 697 00:38:05,280 --> 00:38:07,440 Speaker 14: mean there are a lot of problems to be solved 698 00:38:07,480 --> 00:38:09,920 Speaker 14: in the defense space. I mean there's everything's from missiles 699 00:38:09,960 --> 00:38:13,480 Speaker 14: and what Castilian's doing to ground autonomy systems. I mean 700 00:38:13,560 --> 00:38:16,880 Speaker 14: it is really a massively large market where there's actually 701 00:38:16,920 --> 00:38:19,560 Speaker 14: a ton of technology problems to be solved below it. 702 00:38:19,960 --> 00:38:23,000 Speaker 14: I think the underlying substrate that I think is important 703 00:38:23,000 --> 00:38:24,840 Speaker 14: and what we look at, Andresa and I've looked at 704 00:38:24,880 --> 00:38:27,920 Speaker 14: in the past is really what's happening in defense is 705 00:38:27,960 --> 00:38:31,719 Speaker 14: this low cost autonomy thesis that is just you know, 706 00:38:31,760 --> 00:38:33,600 Speaker 14: it's playing out in the battlefield. Go look at what 707 00:38:33,680 --> 00:38:36,759 Speaker 14: Saronic's done in kind of the conflict in Iran, Go 708 00:38:36,800 --> 00:38:40,160 Speaker 14: look at the front battlefield of Ukraine. When you can 709 00:38:40,280 --> 00:38:43,919 Speaker 14: introduce something that is literally ten times cheaper and as 710 00:38:44,040 --> 00:38:47,440 Speaker 14: performance and in mass it really changes the game in 711 00:38:47,520 --> 00:38:49,520 Speaker 14: terms of deterrence and what you can do for our nation. 712 00:38:49,920 --> 00:38:52,239 Speaker 14: So I think, again, you take a step back. The 713 00:38:52,280 --> 00:38:53,719 Speaker 14: market's big, and I think there's going to be a 714 00:38:53,719 --> 00:38:55,240 Speaker 14: ton of opportunity. 715 00:38:55,520 --> 00:38:56,200 Speaker 3: Kind of really quick. 716 00:38:56,239 --> 00:38:59,480 Speaker 2: You know, you mentioned Cafrine and airin regular contributors to 717 00:38:59,800 --> 00:39:02,600 Speaker 2: the show, but with you, somebody joined the team that 718 00:39:02,680 --> 00:39:04,719 Speaker 2: is a veteran just in the thirty seconds we have. 719 00:39:04,800 --> 00:39:06,960 Speaker 2: Would you reflect on that a little bit, the skill 720 00:39:07,000 --> 00:39:08,319 Speaker 2: set and experience you bring. 721 00:39:08,600 --> 00:39:11,360 Speaker 14: Yeah, I mean there are more impressive people that have 722 00:39:11,440 --> 00:39:13,880 Speaker 14: done some pretty amazing things for our country that I 723 00:39:13,880 --> 00:39:16,439 Speaker 14: want to put in front of me. I do think, though, 724 00:39:16,520 --> 00:39:19,680 Speaker 14: that time of service really just grounds me and why 725 00:39:19,760 --> 00:39:22,799 Speaker 14: we are doing this. Like I said at the start, importantly, 726 00:39:23,360 --> 00:39:26,239 Speaker 14: this is a category where you're not just doing good economically, 727 00:39:26,320 --> 00:39:28,120 Speaker 14: but I do believe you're doing good for the world 728 00:39:28,120 --> 00:39:30,440 Speaker 14: and for the American public. So having worn that uniform 729 00:39:30,680 --> 00:39:32,400 Speaker 14: puts me in a position just to take a step 730 00:39:32,400 --> 00:39:35,080 Speaker 14: back and say, hey, this means a lot, not just 731 00:39:35,120 --> 00:39:38,600 Speaker 14: for our shareholders and for everyone you know backing in dreason, 732 00:39:38,719 --> 00:39:41,239 Speaker 14: but also for America. So I'm deeply thankful and I'm 733 00:39:41,239 --> 00:39:42,440 Speaker 14: going to keep doing it for decades. 734 00:39:43,680 --> 00:39:46,360 Speaker 2: Connor Love now on the American Dynamison team at A 735 00:39:46,480 --> 00:39:48,040 Speaker 2: sixteen S great to have you on the show. 736 00:39:48,160 --> 00:39:49,120 Speaker 3: Thank thank you very much. 737 00:39:49,120 --> 00:39:54,280 Speaker 2: Indeed, coming up Alphabet, Tesla, Intel and more. Posting earnings. 738 00:39:54,320 --> 00:39:56,279 Speaker 2: This week, we're going to look ahead. We're going to 739 00:39:56,280 --> 00:40:07,600 Speaker 2: discuss what to expect. This is Bloomberg Tech Okay, tech 740 00:40:07,680 --> 00:40:10,880 Speaker 2: Earning's kicking off and we're expecting some big names this week, 741 00:40:10,920 --> 00:40:15,239 Speaker 2: including Alphabet, Tesla and Intel. Investors will be looking for 742 00:40:15,440 --> 00:40:19,000 Speaker 2: proof that billions in AI spending are starting to pay off, 743 00:40:19,239 --> 00:40:22,960 Speaker 2: even as cheap AI models from China have raised some questions. 744 00:40:22,960 --> 00:40:26,600 Speaker 2: Bloomberg Intelligence Senior analyst man Deep Singh joins us on 745 00:40:26,680 --> 00:40:29,160 Speaker 2: what to expect, and you know, it's been so interesting 746 00:40:29,239 --> 00:40:30,840 Speaker 2: to read the team's research. 747 00:40:31,800 --> 00:40:32,720 Speaker 3: There will be short. 748 00:40:32,600 --> 00:40:36,080 Speaker 2: Term a focus on CAPEX, but I think looking past that, 749 00:40:36,239 --> 00:40:38,319 Speaker 2: what is it that you want to see? What's the 750 00:40:38,440 --> 00:40:40,560 Speaker 2: data point that shows this AI things real? 751 00:40:41,640 --> 00:40:44,359 Speaker 15: I mean, cloud revenue is still the key metric when 752 00:40:44,360 --> 00:40:48,759 Speaker 15: it comes to hyperscalers who are at the forefront of kapex, 753 00:40:48,840 --> 00:40:52,279 Speaker 15: and everyone is expecting those CAPEX numbers to go up. 754 00:40:52,320 --> 00:40:54,960 Speaker 15: I mean, the question is is next year going to 755 00:40:54,960 --> 00:40:58,000 Speaker 15: be thirty to forty percent, or we are going to 756 00:40:58,040 --> 00:41:01,440 Speaker 15: have another year of fifty percent plus increase in capex. 757 00:41:01,520 --> 00:41:04,560 Speaker 15: I mean, to my mind, the numbers are getting very 758 00:41:04,600 --> 00:41:08,560 Speaker 15: big in terms of trillion dollars in analyzed capex from 759 00:41:08,600 --> 00:41:12,239 Speaker 15: the hyperscaler. So there is a limit to how far 760 00:41:12,360 --> 00:41:15,879 Speaker 15: they can go. But clearly you know twenty twenty seven 761 00:41:15,960 --> 00:41:19,320 Speaker 15: numbers will be higher and we are expecting positive upward 762 00:41:19,360 --> 00:41:25,200 Speaker 15: revisions justified by accelerating cloud growth from these hyperscalers. 763 00:41:26,080 --> 00:41:28,800 Speaker 2: Let's stick with the Google parent is the case study. 764 00:41:28,880 --> 00:41:31,279 Speaker 2: You know, overall revenue growth is expected to be about 765 00:41:31,320 --> 00:41:34,680 Speaker 2: twenty percent, but Google Cloud I think sixty five percent. 766 00:41:34,760 --> 00:41:36,880 Speaker 3: You're only a growth you know, that's. 767 00:41:36,680 --> 00:41:40,040 Speaker 2: The story, right that Google Cloud is where the traction 768 00:41:40,160 --> 00:41:41,359 Speaker 2: with AI shows up. 769 00:41:43,680 --> 00:41:46,799 Speaker 15: Yeah, no, you're absolutely right, And that's where you know, 770 00:41:46,840 --> 00:41:51,080 Speaker 15: the comparisons would be drawn to Azure and the fact 771 00:41:51,080 --> 00:41:54,720 Speaker 15: that Google Cloud is growing at least fifteen percentage faster 772 00:41:55,640 --> 00:42:00,480 Speaker 15: points faster than Azure and AWLUSS. I mean, seems to 773 00:42:00,520 --> 00:42:03,080 Speaker 15: be things seem to be picking up. And now OpenAI 774 00:42:03,760 --> 00:42:08,319 Speaker 15: is deploying on Amazon Ablus as well as Azure. So 775 00:42:08,800 --> 00:42:11,480 Speaker 15: the impact of that on Microsoft that's going to be 776 00:42:11,480 --> 00:42:13,560 Speaker 15: a front end center, but there is no doubt that 777 00:42:14,000 --> 00:42:18,400 Speaker 15: cloud infrastructure is where the monetization will be evident. And look, 778 00:42:18,760 --> 00:42:22,239 Speaker 15: all the frontier labs are monetizing tokens, but you're not 779 00:42:22,320 --> 00:42:25,200 Speaker 15: going to be able to glean that from the earnings 780 00:42:25,200 --> 00:42:29,799 Speaker 15: of companies. It's too early to see that token monetization 781 00:42:29,920 --> 00:42:30,560 Speaker 15: being visible. 782 00:42:31,520 --> 00:42:34,560 Speaker 3: This is what the week looks like. All kicks off Wednesday. 783 00:42:34,560 --> 00:42:38,839 Speaker 2: Alphabet and tesla IBM of course pre released. But maybe 784 00:42:38,920 --> 00:42:40,880 Speaker 2: we'll learn a bit more than Intel on Thursday. Bloomberg 785 00:42:40,920 --> 00:42:44,480 Speaker 2: intelligencing around this, Mandip Singh, thank you very much. Indeed, 786 00:42:44,520 --> 00:42:47,160 Speaker 2: a busy week form mendy two. As we head out, 787 00:42:47,200 --> 00:42:50,120 Speaker 2: let's take a look at today's big number. Two hundred 788 00:42:50,160 --> 00:42:53,799 Speaker 2: and sixty four point one million dollars. That's how much 789 00:42:53,840 --> 00:42:57,680 Speaker 2: The Odyssey, the new movie from Blockbuster director Christopher Nolan, 790 00:42:57,960 --> 00:43:01,840 Speaker 2: took in sales globally opening weekend. It was the number 791 00:43:01,880 --> 00:43:04,920 Speaker 2: one film in the US and Canada over the weekend, 792 00:43:05,200 --> 00:43:07,640 Speaker 2: with one hundred and twenty four point five million dollars 793 00:43:07,800 --> 00:43:10,719 Speaker 2: in ticket sales in North America, a large portion of 794 00:43:10,760 --> 00:43:16,400 Speaker 2: which was generated from screenings in IMAX's giant auditoriums, many 795 00:43:16,440 --> 00:43:21,160 Speaker 2: of them were sold out many months in advance. I'm 796 00:43:21,239 --> 00:43:25,000 Speaker 2: still yet to see it. That does it for this 797 00:43:25,160 --> 00:43:29,360 Speaker 2: edition of Bloomberg Tech. A really big focus still on 798 00:43:29,480 --> 00:43:33,560 Speaker 2: Moonshot's Kimmy K three model two point eight trillion parameters, 799 00:43:33,800 --> 00:43:36,320 Speaker 2: and a lot of questions posed for the world for AI, 800 00:43:36,560 --> 00:43:38,960 Speaker 2: for markets for investors. We got through all of that 801 00:43:39,000 --> 00:43:40,920 Speaker 2: in the program, so recap it on the podcast. You 802 00:43:40,960 --> 00:43:43,480 Speaker 2: know where to find it on the Bloomberg terminal. As 803 00:43:43,520 --> 00:43:46,279 Speaker 2: one audience member reminded me, we call it the Blue Meet, 804 00:43:46,320 --> 00:43:47,279 Speaker 2: but it's online too. 805 00:43:47,800 --> 00:43:49,680 Speaker 3: Have a great week. This is Bloomberg Tech.