1 00:00:00,840 --> 00:00:05,080 Speaker 1: From the heart of where innovation, money and power collide 2 00:00:05,360 --> 00:00:10,640 Speaker 1: in Silicon Valley and beyond. This is Bloomberg Technology with 3 00:00:10,800 --> 00:00:13,360 Speaker 1: Caroline Hyde and Ed Ludlow. 4 00:00:27,760 --> 00:00:30,760 Speaker 2: Live from San Francisco. This is Bloomberg Technology coming up. 5 00:00:30,800 --> 00:00:35,559 Speaker 2: Big Tech volatile after Moody's downgrades, America's credit rating, and 6 00:00:35,680 --> 00:00:39,320 Speaker 2: Nvidia allowing customers to use rival chip makers in an 7 00:00:39,360 --> 00:00:44,120 Speaker 2: effort to expand its AI ecosystem, And why Apple has 8 00:00:44,159 --> 00:00:47,600 Speaker 2: been spending billions of dollars on AI and still hasn't 9 00:00:47,640 --> 00:00:51,479 Speaker 2: managed to crack the code. This is what financial markets 10 00:00:51,520 --> 00:00:54,760 Speaker 2: look like across major averages. We've paired some of the 11 00:00:54,800 --> 00:00:58,200 Speaker 2: early declines in Monday's session, but the story broadly is 12 00:00:58,360 --> 00:01:01,240 Speaker 2: tech is pulling us down and leading us lower, and 13 00:01:01,280 --> 00:01:04,520 Speaker 2: that center is particularly on semiconductors. 14 00:01:03,840 --> 00:01:05,360 Speaker 3: And AI infrastructure names. 15 00:01:05,400 --> 00:01:07,360 Speaker 2: In terms of the single names that we're watching, you 16 00:01:07,440 --> 00:01:11,200 Speaker 2: have stocks like Tesla under pressure, and Video has actually 17 00:01:11,280 --> 00:01:11,920 Speaker 2: paid a lot. 18 00:01:11,840 --> 00:01:12,920 Speaker 3: Of it's declined from the open. 19 00:01:12,959 --> 00:01:15,040 Speaker 2: It was down two percent at one point in Video 20 00:01:15,120 --> 00:01:16,959 Speaker 2: is at the heart of the news cycle and it's 21 00:01:16,959 --> 00:01:18,960 Speaker 2: going to be at the heart of today's program as well. 22 00:01:19,080 --> 00:01:20,520 Speaker 3: With a lot of news out of Taiwan. 23 00:01:20,600 --> 00:01:23,679 Speaker 2: Let's get to the big picture on technology and financial markets. 24 00:01:23,680 --> 00:01:27,600 Speaker 2: Here to break down the tech moves, Bloomberg's Ryan Vostelica, Ryan, 25 00:01:27,640 --> 00:01:28,200 Speaker 2: what's going on? 26 00:01:29,319 --> 00:01:31,280 Speaker 4: Hey, good morning, Thanks for having me. So there is 27 00:01:31,319 --> 00:01:34,160 Speaker 4: a lot of uncertainty, a lot of volatility in the 28 00:01:34,240 --> 00:01:36,720 Speaker 4: market today. You mentioned the Moody's downgrade. I think that 29 00:01:36,880 --> 00:01:39,880 Speaker 4: just speaks to the level of political uncertainty. 30 00:01:39,959 --> 00:01:40,679 Speaker 3: In particular. 31 00:01:40,720 --> 00:01:42,959 Speaker 4: Of this, of course comes in the back of all 32 00:01:43,000 --> 00:01:45,360 Speaker 4: the back and forth on tariffs in the trade war. 33 00:01:45,640 --> 00:01:48,120 Speaker 4: There's just so much uncertainty in terms of policy, and 34 00:01:48,160 --> 00:01:50,480 Speaker 4: the implications of this, I think are for a lot 35 00:01:50,480 --> 00:01:52,680 Speaker 4: of people to just continue sort of selling the US 36 00:01:52,680 --> 00:01:55,920 Speaker 4: look for defensive areas, look for safety. In that case, 37 00:01:55,960 --> 00:01:58,400 Speaker 4: that means a lot of big tech names, especially since 38 00:01:58,440 --> 00:02:01,480 Speaker 4: tech has been pretty over the past couple of weeks. 39 00:02:01,520 --> 00:02:03,320 Speaker 4: So this could just be some profit taking, but I 40 00:02:03,360 --> 00:02:06,760 Speaker 4: do think it underlines how uncertain people feel about the 41 00:02:06,840 --> 00:02:07,800 Speaker 4: economic outlook. 42 00:02:08,600 --> 00:02:12,440 Speaker 2: The mag seven have a sort of disproportionate impact to 43 00:02:12,520 --> 00:02:16,280 Speaker 2: the index level, but it's AI, infrastructure and semiconductors where 44 00:02:16,320 --> 00:02:17,800 Speaker 2: I see most weakness. 45 00:02:17,880 --> 00:02:19,280 Speaker 3: Basically this Monday. Why is that? 46 00:02:20,680 --> 00:02:22,200 Speaker 4: Yeah, So, I think you know, this is an area 47 00:02:22,240 --> 00:02:25,560 Speaker 4: that is very much tied to the trade situation given 48 00:02:25,560 --> 00:02:27,240 Speaker 4: to sort of the nature of the products that we're 49 00:02:27,240 --> 00:02:29,800 Speaker 4: talking about here, there's still a lot of uncertainty about 50 00:02:30,520 --> 00:02:32,960 Speaker 4: you know, chip restrictions and all these other kinds of things. 51 00:02:32,960 --> 00:02:34,480 Speaker 4: And this also comes on the back of, you know, 52 00:02:34,480 --> 00:02:37,880 Speaker 4: people still continue to want to see more return on 53 00:02:38,080 --> 00:02:41,080 Speaker 4: all this AI related spending that is going on. There's 54 00:02:41,160 --> 00:02:43,200 Speaker 4: just a lot of concerns maybe that you know, has 55 00:02:43,240 --> 00:02:46,400 Speaker 4: there been sort of a bubble in AI infrastructure. So 56 00:02:46,440 --> 00:02:48,240 Speaker 4: I think this is an area where because of these 57 00:02:48,240 --> 00:02:50,880 Speaker 4: group has done so well because the outlook is so 58 00:02:51,080 --> 00:02:53,720 Speaker 4: debated right now, it's just a very natural place for 59 00:02:53,760 --> 00:02:56,760 Speaker 4: people to be taking profits, especially on any kind of 60 00:02:56,800 --> 00:02:59,359 Speaker 4: sort of negative headline that you see cross the wire. 61 00:03:00,160 --> 00:03:03,160 Speaker 2: Bloomboas round Vastelica with the market moves this Monday, thank you, 62 00:03:03,240 --> 00:03:06,440 Speaker 2: let's get more on markets. Hillary Frisch, senior Research analysts 63 00:03:06,440 --> 00:03:09,919 Speaker 2: for Software and IT services at Clearbridge Investments. 64 00:03:09,480 --> 00:03:10,000 Speaker 3: Is with us. 65 00:03:10,600 --> 00:03:13,920 Speaker 2: Last week, the tailwind was in all of the headlines 66 00:03:14,120 --> 00:03:17,440 Speaker 2: about the billions and billions of dollars of AI infrastructure 67 00:03:17,480 --> 00:03:22,399 Speaker 2: deals the United States and the Gulf Nations, Nvidia, super Micro, Dell. 68 00:03:22,800 --> 00:03:24,399 Speaker 3: But that's the area of weakness. 69 00:03:24,440 --> 00:03:26,920 Speaker 2: This Monday morning, is it just a little bit of 70 00:03:27,160 --> 00:03:30,320 Speaker 2: a reality check on how soon some of that investment 71 00:03:30,360 --> 00:03:30,959 Speaker 2: will be felt. 72 00:03:31,360 --> 00:03:32,440 Speaker 5: It's a good question, ed. 73 00:03:32,600 --> 00:03:35,440 Speaker 6: It may be a reality check, it may just also 74 00:03:35,480 --> 00:03:39,560 Speaker 6: be profit ticking. And we went from overbought to severely 75 00:03:39,600 --> 00:03:43,000 Speaker 6: oversold to overbought again within just about the span of 76 00:03:43,000 --> 00:03:46,560 Speaker 6: a month. So the tech sector in general has had 77 00:03:46,600 --> 00:03:48,840 Speaker 6: a very strong run recently, and we can talk more 78 00:03:48,880 --> 00:03:52,000 Speaker 6: about why that's the case, but I think investors are 79 00:03:52,080 --> 00:03:54,640 Speaker 6: just looking seeing, okay, we can take some profits at 80 00:03:54,640 --> 00:03:55,200 Speaker 6: this point. 81 00:03:55,920 --> 00:03:59,040 Speaker 2: Well, in part of why that's the case is earnings, right, 82 00:03:59,080 --> 00:04:02,080 Speaker 2: I think we're still a little bit digesting what the 83 00:04:02,120 --> 00:04:06,200 Speaker 2: net takeaways from the calendar first quarter earnings were. What 84 00:04:06,240 --> 00:04:08,000 Speaker 2: were your main takeaways from that period? 85 00:04:08,240 --> 00:04:11,680 Speaker 6: Sure, well, first of all, it was I think what 86 00:04:11,800 --> 00:04:15,560 Speaker 6: was notable was just how resilient Q one results were. 87 00:04:16,080 --> 00:04:18,120 Speaker 6: And it wasn't just in Q one, it was in 88 00:04:18,160 --> 00:04:21,839 Speaker 6: the subsequent weeks through to the quarterly reports, and that 89 00:04:22,040 --> 00:04:25,719 Speaker 6: encompassed a period of Teriff related headlines and uncertainties. So 90 00:04:25,800 --> 00:04:29,560 Speaker 6: that was refreshing and encouraging. And it wasn't just AI 91 00:04:29,839 --> 00:04:32,839 Speaker 6: or a series of AI proof of concepts at the 92 00:04:32,839 --> 00:04:35,320 Speaker 6: expense of all else, which is largely what it felt 93 00:04:35,360 --> 00:04:39,599 Speaker 6: like last year. It was AI strength plus real core 94 00:04:39,760 --> 00:04:43,680 Speaker 6: business strength, and that was refreshing. Now we're coming up 95 00:04:43,760 --> 00:04:47,800 Speaker 6: upon April quarter earnings reports, and I could imagine that 96 00:04:47,880 --> 00:04:49,760 Speaker 6: some of those companies who would have had to close 97 00:04:49,800 --> 00:04:52,880 Speaker 6: their quarters in the midst of tariff uncertainty might see 98 00:04:52,880 --> 00:04:56,039 Speaker 6: some variability, might have seen some pausing and spend, but 99 00:04:56,200 --> 00:04:58,520 Speaker 6: overall the underlying results are very encouraging. 100 00:04:59,320 --> 00:05:02,800 Speaker 2: What I found super interesting about that earnings period. In technologies, 101 00:05:02,880 --> 00:05:06,120 Speaker 2: you have like primary data and secondary data. The primary 102 00:05:06,200 --> 00:05:08,719 Speaker 2: data like the commentary and what's in the earning statement, 103 00:05:09,160 --> 00:05:11,919 Speaker 2: but some of the secondary data we were tracking, for example, 104 00:05:12,120 --> 00:05:16,120 Speaker 2: in the AI infrastructure space, was leases, data center leases, 105 00:05:16,640 --> 00:05:19,159 Speaker 2: and that caused all kinds of noise going into the 106 00:05:19,200 --> 00:05:23,120 Speaker 2: prints and we had to decipher what it was that 107 00:05:23,279 --> 00:05:25,840 Speaker 2: was the truth, you know, based on executive comments, How 108 00:05:25,839 --> 00:05:26,840 Speaker 2: did you navigate that? 109 00:05:27,200 --> 00:05:30,159 Speaker 6: Yes, set of data, tons of noise in the space. 110 00:05:30,240 --> 00:05:33,200 Speaker 6: That was probably maximum noise that we've seen in a 111 00:05:33,279 --> 00:05:33,800 Speaker 6: long time. 112 00:05:34,200 --> 00:05:35,280 Speaker 5: But it's fascinating. 113 00:05:36,560 --> 00:05:41,680 Speaker 6: Starting with the underlying demand trends, you saw hyperscale vendors 114 00:05:41,720 --> 00:05:44,640 Speaker 6: reaffirming CAPEX plans. You saw some of them increase them. 115 00:05:44,880 --> 00:05:49,360 Speaker 6: Microsoft in particular talked about rising near and long term 116 00:05:49,400 --> 00:05:53,200 Speaker 6: demand signals, which could actually cause them to prolong their 117 00:05:53,240 --> 00:05:56,719 Speaker 6: investment in long live assets, meaning land and buildings, which 118 00:05:56,720 --> 00:05:58,880 Speaker 6: they've been engaging in our over the course of the 119 00:05:58,960 --> 00:06:01,800 Speaker 6: last eighteen months. So how do we marry that up 120 00:06:01,839 --> 00:06:06,599 Speaker 6: with data center lease cancelations. Well, it's interesting Microsoft in 121 00:06:06,640 --> 00:06:09,880 Speaker 6: particular makes a lot of use of leases. They came 122 00:06:09,920 --> 00:06:14,040 Speaker 6: from behind relative to the strong demand signals they're seen 123 00:06:14,680 --> 00:06:17,719 Speaker 6: in getting that requisite capacity to be able to fulfill 124 00:06:17,800 --> 00:06:20,200 Speaker 6: AI and other demand for the next five ten years, 125 00:06:20,640 --> 00:06:25,040 Speaker 6: So they were building rapidly. Within that, they signed a 126 00:06:25,160 --> 00:06:29,160 Speaker 6: ton of leases across the globe, which was a brilliant strategy, 127 00:06:29,200 --> 00:06:32,560 Speaker 6: by the way, because it froze it froze some of 128 00:06:32,560 --> 00:06:35,520 Speaker 6: those opportunities to competitors. And then within that, I think 129 00:06:35,560 --> 00:06:37,960 Speaker 6: they decided that they didn't want to be the sole 130 00:06:38,040 --> 00:06:40,760 Speaker 6: source of training for open AI. And also within that 131 00:06:41,040 --> 00:06:44,359 Speaker 6: there were power constraints, and I think they realized that 132 00:06:44,640 --> 00:06:47,159 Speaker 6: these leased facilities wouldn't be up and running until twenty 133 00:06:47,200 --> 00:06:49,760 Speaker 6: seven to twenty twenty nine, and in that vein they 134 00:06:49,800 --> 00:06:52,600 Speaker 6: could actually shift to some degree to own data centers 135 00:06:52,680 --> 00:06:55,479 Speaker 6: as well as least data centers and you're seeing that 136 00:06:55,560 --> 00:06:58,680 Speaker 6: kind of across the board. But in the end, some 137 00:06:58,720 --> 00:07:00,960 Speaker 6: of the data center lease capacit we were hearing was 138 00:07:01,040 --> 00:07:04,080 Speaker 6: five x what you were seeing signed on for by 139 00:07:04,200 --> 00:07:06,600 Speaker 6: an Amazon, for instance, for the rest of the ecosystem. 140 00:07:06,640 --> 00:07:09,720 Speaker 6: And last time I choked, Microsoft wasn't five x the 141 00:07:09,760 --> 00:07:13,640 Speaker 6: size of an Amazon. So basically they were giving themselves optionality. 142 00:07:14,040 --> 00:07:16,640 Speaker 6: And in retrospect, they're still building and they're still demand. 143 00:07:17,400 --> 00:07:20,280 Speaker 2: And again a part of the higher capex reflects the 144 00:07:20,360 --> 00:07:23,080 Speaker 2: higher cost of doing business because of the impact of 145 00:07:23,160 --> 00:07:27,280 Speaker 2: tariffs to supply chain. I've been thinking a lot, as 146 00:07:27,280 --> 00:07:30,680 Speaker 2: one does, about year to date performance of the hyperscalers. 147 00:07:30,800 --> 00:07:34,840 Speaker 2: And it's interesting, right if you just say hyperscalers Microsoft, 148 00:07:35,000 --> 00:07:38,920 Speaker 2: Amazon alphabet. Microsoft stock is up more than eight percent 149 00:07:39,040 --> 00:07:41,720 Speaker 2: year to day. The other two names have been under pressure. 150 00:07:41,760 --> 00:07:47,000 Speaker 2: We of course kickoff with Microsoft Build, but we also 151 00:07:47,040 --> 00:07:49,800 Speaker 2: talked last week about some of the difficulty Microsoft has 152 00:07:49,920 --> 00:07:53,560 Speaker 2: in reaching its own goals because of its relationship with 153 00:07:53,600 --> 00:07:57,400 Speaker 2: open AI. This is something you've been thinking about as well. 154 00:07:57,680 --> 00:08:01,280 Speaker 6: Yes, Well, first of all, with respect to relative to performance, 155 00:08:01,320 --> 00:08:04,560 Speaker 6: I think you're almost seeing from a cloud perspective in 156 00:08:04,600 --> 00:08:06,960 Speaker 6: the other two what we saw with Microsoft for the 157 00:08:07,040 --> 00:08:09,560 Speaker 6: end of last year and early this year, which is 158 00:08:09,880 --> 00:08:13,040 Speaker 6: that they're behind a getting capacity and that is constraining 159 00:08:13,120 --> 00:08:16,160 Speaker 6: cloud growth. So it hasn't been a demand issue, it's 160 00:08:16,200 --> 00:08:19,880 Speaker 6: been a supply issue. Second of all, Microsoft has been 161 00:08:20,000 --> 00:08:22,480 Speaker 6: very tethered to open Ai. They've been diversifying, but the 162 00:08:22,520 --> 00:08:26,200 Speaker 6: two are quite aligned. I'm not sure I view it 163 00:08:26,240 --> 00:08:28,360 Speaker 6: at all in terms of their not being able to 164 00:08:28,480 --> 00:08:30,880 Speaker 6: realize their goals. I think their goals are to focus 165 00:08:30,920 --> 00:08:35,880 Speaker 6: on their own compute capabilities for their customers and to 166 00:08:35,920 --> 00:08:39,400 Speaker 6: add in open ai and other LLLM vendors, but especially 167 00:08:39,440 --> 00:08:41,880 Speaker 6: open Ai as part of that equation. I think it 168 00:08:41,920 --> 00:08:44,920 Speaker 6: was a deliberate decision not to focus solely on training 169 00:08:44,960 --> 00:08:46,880 Speaker 6: that would have crowded out a lot of the other 170 00:08:47,000 --> 00:08:50,960 Speaker 6: exciting and high margin endeavors Microsoft can engage in an 171 00:08:51,000 --> 00:08:53,480 Speaker 6: as core customer base, and in the meantime it's a 172 00:08:53,520 --> 00:08:58,400 Speaker 6: highly symbiotic relationship. Microsoft's infrastructure was originally built in AI 173 00:08:58,559 --> 00:09:02,400 Speaker 6: to around open Ai and the related infrastructure it has 174 00:09:02,400 --> 00:09:05,319 Speaker 6: in finnabaum throughout it has some tremendous capabilities and I 175 00:09:05,360 --> 00:09:07,520 Speaker 6: believe the two companies want to continue to work together. 176 00:09:08,480 --> 00:09:11,720 Speaker 2: Hillary Frish, Senior Research Analystic Clearbridge Investments. 177 00:09:11,760 --> 00:09:12,800 Speaker 3: Thank you for joining us. 178 00:09:13,240 --> 00:09:16,640 Speaker 2: Now coming up, Video announces new AI products as the 179 00:09:16,640 --> 00:09:21,720 Speaker 2: computing giants descend. On Compute's twenty twenty five, We'll go 180 00:09:21,800 --> 00:09:24,360 Speaker 2: out to Taiwan next this Spoomberg Technology. 181 00:09:35,640 --> 00:09:38,880 Speaker 7: We're outside the venue here where Jensen Wang just kicked 182 00:09:38,880 --> 00:09:41,439 Speaker 7: off Kombu Techs for twenty twenty five. This is one 183 00:09:41,480 --> 00:09:44,360 Speaker 7: of the biggest tech events in Asia and Jensen Wang, 184 00:09:44,400 --> 00:09:46,640 Speaker 7: the founder and CEO of Nvidia, was on stage for 185 00:09:46,760 --> 00:09:50,160 Speaker 7: ninety minutes at talking through his vision for AI and 186 00:09:50,200 --> 00:09:52,280 Speaker 7: the products that are going to be needed to sustain it. 187 00:09:52,360 --> 00:09:57,960 Speaker 8: Today we're announcing the Fox Kind Taiwan. The Taiwanese government 188 00:09:58,440 --> 00:10:05,040 Speaker 8: and VideA. We're going to build the first giant AI supercomputer. 189 00:10:05,320 --> 00:10:09,040 Speaker 7: The focus was on greater AI applications, so specifically the 190 00:10:09,040 --> 00:10:13,520 Speaker 7: integration of AI into products like humanoid robots, also autonomous 191 00:10:13,600 --> 00:10:16,320 Speaker 7: driving vehicles. On the product front, we did get an 192 00:10:16,400 --> 00:10:19,560 Speaker 7: update on the next generation GB three hundred system that 193 00:10:19,720 --> 00:10:22,760 Speaker 7: is expected to come in the third quarter of this year. Also, 194 00:10:22,880 --> 00:10:25,000 Speaker 7: Nvideo is going to be offering a new version of 195 00:10:25,040 --> 00:10:29,000 Speaker 7: the complete computers that it provides to its data center customers. 196 00:10:29,040 --> 00:10:30,720 Speaker 7: Now what that means in a nutshell is that it's 197 00:10:30,760 --> 00:10:33,480 Speaker 7: going to be opening its AI servers to chips from 198 00:10:33,520 --> 00:10:37,439 Speaker 7: other companies. This is its names like Microsoft, Amazon, they 199 00:10:37,480 --> 00:10:38,880 Speaker 7: try to design their own. 200 00:10:38,760 --> 00:10:40,400 Speaker 5: Processors and accelerators. 201 00:10:40,720 --> 00:10:46,200 Speaker 7: In Taipei, I'm Annabel Drawlers Bloomberg News for more. 202 00:10:46,040 --> 00:10:49,440 Speaker 2: On computext and Nvidea's latest revealed Bloombozine. King joins us 203 00:10:49,440 --> 00:10:52,480 Speaker 2: here in San Francisco. What we're talking about is envy 204 00:10:52,520 --> 00:10:55,199 Speaker 2: link Fusion. You and I have sat through many briefings 205 00:10:55,520 --> 00:10:58,280 Speaker 2: about envy link Fusion and read and written about a 206 00:10:58,280 --> 00:10:58,560 Speaker 2: lot of it. 207 00:10:58,600 --> 00:11:00,400 Speaker 3: But it's a high speed into connect. 208 00:11:00,440 --> 00:11:03,040 Speaker 2: You can connect lots of GPUs and in this case 209 00:11:03,080 --> 00:11:04,080 Speaker 2: also CPUs. 210 00:11:04,440 --> 00:11:06,800 Speaker 3: The main point here is in Vidia is. 211 00:11:06,760 --> 00:11:11,079 Speaker 2: Saying if you are a hyperscaler, perhaps with custom Silicon, 212 00:11:11,480 --> 00:11:15,080 Speaker 2: then we can allow you into data centers where we dominate, 213 00:11:15,480 --> 00:11:18,280 Speaker 2: and it kind of opens up this place where everyone 214 00:11:18,360 --> 00:11:21,439 Speaker 2: is not only and solely reliant on one single name, 215 00:11:21,440 --> 00:11:22,160 Speaker 2: which is in video. 216 00:11:22,760 --> 00:11:24,360 Speaker 9: I think the other way to look at this would 217 00:11:24,400 --> 00:11:27,880 Speaker 9: be it's a business decision, right if you have your 218 00:11:27,880 --> 00:11:32,920 Speaker 9: biggest customers who are essentially creating rival efforts, rival efforts 219 00:11:32,920 --> 00:11:34,720 Speaker 9: that may one day mean that they need less of 220 00:11:34,760 --> 00:11:37,920 Speaker 9: you why not make it easier to actually work with 221 00:11:37,960 --> 00:11:40,000 Speaker 9: you say hey, great, yeah you've got a great CPU, 222 00:11:40,000 --> 00:11:42,080 Speaker 9: come work with us. Hey you've got a great GPU, 223 00:11:42,120 --> 00:11:44,360 Speaker 9: Come work with us. This is a way to keep 224 00:11:44,440 --> 00:11:48,640 Speaker 9: people in in Video's ecosystem and to stop them going elsewhere. 225 00:11:48,920 --> 00:11:49,640 Speaker 3: That's the pitch. 226 00:11:49,960 --> 00:11:53,920 Speaker 2: Computexts is a really big deal, and in VideA seems 227 00:11:53,960 --> 00:11:56,520 Speaker 2: to have dominated it in more recent years. What else 228 00:11:56,520 --> 00:11:59,440 Speaker 2: do we learn about things of substance that they're actually 229 00:11:59,480 --> 00:12:00,400 Speaker 2: doing at all? 230 00:12:00,640 --> 00:12:03,680 Speaker 9: Anything of substance, Well, computext wasn't a big deal up 231 00:12:03,720 --> 00:12:05,920 Speaker 9: until in Video basically made it a big deal again 232 00:12:05,920 --> 00:12:08,400 Speaker 9: when they started using it on this worldwide trip that 233 00:12:08,440 --> 00:12:08,760 Speaker 9: they do. 234 00:12:09,200 --> 00:12:10,120 Speaker 3: To keep the. 235 00:12:10,120 --> 00:12:13,439 Speaker 9: Air momentum going as a show, it was definitely fading away. 236 00:12:13,480 --> 00:12:16,800 Speaker 9: So they put a lot of updates of things out there, robotics, 237 00:12:18,240 --> 00:12:21,400 Speaker 9: new servers, new designs for data centers, and basically just 238 00:12:21,440 --> 00:12:25,000 Speaker 9: trying to keep the momentum of this massive AI infrastructure 239 00:12:25,000 --> 00:12:25,720 Speaker 9: build out going. 240 00:12:26,080 --> 00:12:28,360 Speaker 2: Is there anything else happening at computext that is not 241 00:12:28,640 --> 00:12:29,520 Speaker 2: in Video related? 242 00:12:29,760 --> 00:12:33,679 Speaker 9: Well, our old friend Christiano Amon actually appeared on stage 243 00:12:33,720 --> 00:12:38,320 Speaker 9: with Qualcom right the Corecom CEO. As we reported a 244 00:12:38,360 --> 00:12:41,000 Speaker 9: long time ago, they want to get into the data 245 00:12:41,040 --> 00:12:44,600 Speaker 9: center with their technology. This is part of his diversification 246 00:12:44,760 --> 00:12:47,319 Speaker 9: and if you can't beat them, join them. So he's 247 00:12:47,360 --> 00:12:50,480 Speaker 9: there saying we'll work with in video as a step 248 00:12:50,520 --> 00:12:53,840 Speaker 9: towards the data center strategy, and that is very pragmatic 249 00:12:53,880 --> 00:12:54,920 Speaker 9: and we'll see how it works out. 250 00:12:55,000 --> 00:12:58,640 Speaker 2: Okay, Bloombozie and King, who leads our coverage of semiconductors, 251 00:12:58,960 --> 00:13:00,640 Speaker 2: Thank you very much. I want to stay on that 252 00:13:00,720 --> 00:13:04,440 Speaker 2: news out of Computex in Taiwan and bringing Michelle Geider. 253 00:13:04,520 --> 00:13:07,040 Speaker 2: She's the CEO of the Crash Institute for Tech Diplomacy 254 00:13:07,280 --> 00:13:10,920 Speaker 2: at Purdue. She's also served as Assistant Secretary of State 255 00:13:10,960 --> 00:13:14,120 Speaker 2: for Global Public Affairs under the first Trump administration between 256 00:13:14,120 --> 00:13:18,640 Speaker 2: twenty eighteen and twenty twenty. For me away from the 257 00:13:18,679 --> 00:13:24,840 Speaker 2: technology news, the absolute key piece of importance was Jensen 258 00:13:24,880 --> 00:13:28,200 Speaker 2: one talking about his relationship with Taiwan and in Vidia's 259 00:13:28,320 --> 00:13:29,319 Speaker 2: relationship with Taiwan. 260 00:13:29,679 --> 00:13:31,080 Speaker 3: The basic plan is to. 261 00:13:30,960 --> 00:13:33,760 Speaker 2: Build a supercomputer for Taiwan, but he was kind of 262 00:13:33,760 --> 00:13:35,920 Speaker 2: at pains to point out that this is in partnership 263 00:13:35,960 --> 00:13:41,040 Speaker 2: with TSMC and other leading names that are Taiwanese champions. 264 00:13:41,360 --> 00:13:43,800 Speaker 3: Purely from a geopolitical perspective. 265 00:13:43,720 --> 00:13:46,840 Speaker 2: How crucial was that or how high level was that 266 00:13:47,320 --> 00:13:48,880 Speaker 2: for Jensen Wong to say such a thing. 267 00:13:50,440 --> 00:13:54,400 Speaker 10: It's very important and in video with Jensen Wang at 268 00:13:54,400 --> 00:13:57,040 Speaker 10: the Helm has been on quite the tech diplomacy tour, 269 00:13:57,120 --> 00:14:00,320 Speaker 10: the chip diplomacy tour really over the past few in 270 00:14:00,320 --> 00:14:03,120 Speaker 10: the past week, including in Taiwan, as you reference where 271 00:14:03,120 --> 00:14:05,640 Speaker 10: they're talking about building a supercomputer. Taiwan is a very 272 00:14:05,640 --> 00:14:08,240 Speaker 10: strategic partner and trusted partner of the United States. 273 00:14:08,240 --> 00:14:10,359 Speaker 11: It's very important for our shared security. 274 00:14:11,080 --> 00:14:14,199 Speaker 10: And that's right off the heels of some major deals 275 00:14:14,200 --> 00:14:17,560 Speaker 10: announced in the Golf over the weekend and late last 276 00:14:17,600 --> 00:14:20,760 Speaker 10: week with Saudi Arabia with a UAE who also want 277 00:14:20,760 --> 00:14:23,640 Speaker 10: to be global capitals when it comes to artificial intelligence, 278 00:14:23,760 --> 00:14:26,200 Speaker 10: and there's going to be more sharing of advanced chips there. 279 00:14:26,280 --> 00:14:29,160 Speaker 10: So all of that is really important as the United 280 00:14:29,200 --> 00:14:34,200 Speaker 10: States seeks to secure trusted partnerships with important partners across 281 00:14:34,200 --> 00:14:37,360 Speaker 10: the globe when it comes to advance semiconductors. 282 00:14:39,000 --> 00:14:43,280 Speaker 2: Michelle, those deals with Golf nations, how concerned are you 283 00:14:43,760 --> 00:14:46,920 Speaker 2: that they can act as sort of backdoor indirect access 284 00:14:46,960 --> 00:14:49,320 Speaker 2: for China to US technology. 285 00:14:50,960 --> 00:14:54,280 Speaker 10: It's definitely a concern among some in the US government. 286 00:14:54,400 --> 00:14:57,760 Speaker 10: Including in Congress. But I'll say the big picture is 287 00:14:57,800 --> 00:15:01,359 Speaker 10: we have to do two things. One is maximum proliferation 288 00:15:01,680 --> 00:15:05,160 Speaker 10: of American technology, including our semiconductors, because if it's not 289 00:15:05,240 --> 00:15:08,440 Speaker 10: our technology, it's going to be our adversaries technology like China, 290 00:15:08,520 --> 00:15:11,480 Speaker 10: So max proliferation is really important. At the same time, 291 00:15:11,520 --> 00:15:14,720 Speaker 10: we also have to have maximum security controls to make 292 00:15:14,760 --> 00:15:18,240 Speaker 10: sure that those technologies, including chips, don't get diverted toward 293 00:15:18,280 --> 00:15:19,520 Speaker 10: adversaries like China. 294 00:15:19,600 --> 00:15:20,760 Speaker 12: Those two things have to happen. 295 00:15:20,880 --> 00:15:24,440 Speaker 10: It can be done, but it requires that companies and 296 00:15:24,640 --> 00:15:27,000 Speaker 10: the US government and our allies by the way, work 297 00:15:27,080 --> 00:15:28,120 Speaker 10: really closely together. 298 00:15:28,160 --> 00:15:30,000 Speaker 12: And that's what tech diplomacy is all about. 299 00:15:30,120 --> 00:15:32,480 Speaker 10: And I think you see the White House, the Commerce Department, 300 00:15:32,560 --> 00:15:35,280 Speaker 10: as well as leaders in Congress with the new Chip 301 00:15:35,360 --> 00:15:38,640 Speaker 10: Security Act making sure that those security controls are in place. 302 00:15:39,920 --> 00:15:43,240 Speaker 2: Jensen Wong's text diplomacy, I think it is worth lingering 303 00:15:43,240 --> 00:15:49,520 Speaker 2: on Taiwan is clearly important to Nvidia, and we remind 304 00:15:49,520 --> 00:15:53,040 Speaker 2: ourselves that the mainstay of TSMC's lead edge fabrication is 305 00:15:53,040 --> 00:15:56,920 Speaker 2: in Taiwan. But China is also important to Jensen one 306 00:15:57,040 --> 00:16:00,120 Speaker 2: whether that market is closed to him or not, is 307 00:16:00,160 --> 00:16:01,360 Speaker 2: he getting the balance right. 308 00:16:03,080 --> 00:16:05,680 Speaker 10: Well, it's interesting that you mentioned that because on this 309 00:16:05,720 --> 00:16:08,800 Speaker 10: tech diplomacy tour there were the deals announced in the 310 00:16:08,800 --> 00:16:11,240 Speaker 10: golf there's the news in Taiwan today. But let's also 311 00:16:11,280 --> 00:16:14,240 Speaker 10: not forget that he announced a new research center in 312 00:16:14,280 --> 00:16:17,080 Speaker 10: Shanghai just a few days ago. And it's no coincidence 313 00:16:17,120 --> 00:16:18,800 Speaker 10: that all of these things are happening in the same time. 314 00:16:18,840 --> 00:16:21,960 Speaker 10: And look, he's got a global business to run. He 315 00:16:22,000 --> 00:16:24,680 Speaker 10: definitely has multiple interests that he's trying to meet. But 316 00:16:24,800 --> 00:16:27,280 Speaker 10: we have to make sure from the US national security 317 00:16:27,360 --> 00:16:31,360 Speaker 10: standpoint is that all of that doesn't endanger American national 318 00:16:31,360 --> 00:16:34,360 Speaker 10: security and give some of our most advanced technologies to China. 319 00:16:34,800 --> 00:16:37,640 Speaker 10: So the role that he plays in working together with 320 00:16:37,720 --> 00:16:40,520 Speaker 10: our government, we have to make sure that business objectives 321 00:16:40,800 --> 00:16:43,120 Speaker 10: meet national security objectives. And it can be done, but 322 00:16:43,120 --> 00:16:44,400 Speaker 10: we have to be working together in. 323 00:16:44,400 --> 00:16:45,040 Speaker 11: Order to do it. 324 00:16:46,480 --> 00:16:50,080 Speaker 2: In January of this year, Anderill's co founder Pamelucky told 325 00:16:50,080 --> 00:16:54,320 Speaker 2: me point blank that they are preparing for scenario where 326 00:16:54,480 --> 00:16:58,280 Speaker 2: China invades Taiwan at some point in the coming years. 327 00:16:58,600 --> 00:17:04,440 Speaker 2: It is one severe come take that argument and extrapolate 328 00:17:04,520 --> 00:17:09,520 Speaker 2: it out to ai infrastructure and semiconductive manufacturing and Taiwan's 329 00:17:09,760 --> 00:17:12,679 Speaker 2: role in everything that we've just discussed. 330 00:17:13,880 --> 00:17:16,040 Speaker 10: Well, it's a reason that we have to make sure 331 00:17:16,080 --> 00:17:19,760 Speaker 10: Taiwan's resilience and the US resilience when it comes to 332 00:17:19,800 --> 00:17:24,239 Speaker 10: these critical sectors like semiconductors is really strong, and so 333 00:17:24,359 --> 00:17:28,359 Speaker 10: partnerships like with Nvidia TSMC is investing another one hundred 334 00:17:28,359 --> 00:17:30,520 Speaker 10: billion dollars year in the United States to build out 335 00:17:30,520 --> 00:17:33,040 Speaker 10: its FABS, to build out advanced packaging facilities. 336 00:17:33,240 --> 00:17:35,439 Speaker 12: All of that has to scale so. 337 00:17:35,320 --> 00:17:38,160 Speaker 10: That we have some more strategic partners and it's also 338 00:17:38,160 --> 00:17:40,879 Speaker 10: with other allies like Japan, like Korea s Khiiniz is 339 00:17:40,920 --> 00:17:45,840 Speaker 10: investing in an advanced manufacturing facility for semiconductors at Predue 340 00:17:45,840 --> 00:17:48,320 Speaker 10: Research part where the CROC Institute is. All of that 341 00:17:48,359 --> 00:17:50,600 Speaker 10: has to happen more deeply in its scale in order 342 00:17:50,640 --> 00:17:53,600 Speaker 10: to make sure our shared resilience and security, because. 343 00:17:53,359 --> 00:17:55,240 Speaker 12: We have to be prepared for a scenario like that. 344 00:17:55,600 --> 00:17:58,399 Speaker 10: But rather than just try to slow China down, we 345 00:17:58,520 --> 00:18:01,080 Speaker 10: have to make sure that we are turbot our own 346 00:18:01,119 --> 00:18:04,600 Speaker 10: supply chains, our own resilience in our collaboration with our allies. 347 00:18:05,480 --> 00:18:08,360 Speaker 2: Michelle Guided, CEO of the Cracked Institute for Tech Diplomacy 348 00:18:08,400 --> 00:18:18,200 Speaker 2: at Purdue, Thank you very much. Okay, take a quick 349 00:18:18,240 --> 00:18:20,800 Speaker 2: look at shares of Ali Barber and Apple. The New 350 00:18:20,880 --> 00:18:24,800 Speaker 2: York Times reporting that the Trump administration has raised concerns 351 00:18:24,840 --> 00:18:27,679 Speaker 2: over a potential AI deal between Apple and Ali Barba 352 00:18:27,880 --> 00:18:30,320 Speaker 2: that would allow the Chinese firm to make it's AI 353 00:18:30,720 --> 00:18:32,680 Speaker 2: available to use on iPhones in China. 354 00:18:32,720 --> 00:18:34,800 Speaker 3: We will track that story as we go, and we 355 00:18:34,920 --> 00:18:35,639 Speaker 3: stay on Apple. 356 00:18:36,160 --> 00:18:40,000 Speaker 2: The tech giant promised a bold leap into AI, but 357 00:18:40,160 --> 00:18:43,880 Speaker 2: instead Siri has been left stumbling and the company's AI 358 00:18:44,000 --> 00:18:46,879 Speaker 2: ambitions are lagging behind rivals. It's the subject of the 359 00:18:46,960 --> 00:18:51,120 Speaker 2: latest Bloomberg Big Take, and its author Mark German joins us. Now, 360 00:18:51,160 --> 00:18:54,760 Speaker 2: this is a deeply reported piece about the people inside 361 00:18:54,800 --> 00:18:58,960 Speaker 2: Apple working on AI, the rollout of Siri and Apple Intelligence. 362 00:18:59,040 --> 00:19:02,280 Speaker 2: But if you could just summ arise the pace, how 363 00:19:02,320 --> 00:19:03,359 Speaker 2: would you summarize it? 364 00:19:03,600 --> 00:19:05,720 Speaker 13: I think the best way to summarize it is that 365 00:19:05,800 --> 00:19:09,120 Speaker 13: Apple has a history of coming into emerging areas late. 366 00:19:09,280 --> 00:19:13,280 Speaker 13: You saw that with tablets, smart watches and B three players, 367 00:19:13,400 --> 00:19:16,919 Speaker 13: even smartphones. But they've come in and then they've destroyed 368 00:19:17,000 --> 00:19:20,040 Speaker 13: or vanquished the competition. We've seen that with the iPod, 369 00:19:20,119 --> 00:19:21,280 Speaker 13: the iPhone, the iPad, and. 370 00:19:21,200 --> 00:19:23,280 Speaker 5: The Apple Watch with AI. 371 00:19:23,440 --> 00:19:27,399 Speaker 13: Not only were they late compared to many of the competition, right, 372 00:19:27,640 --> 00:19:29,760 Speaker 13: but they're not the best. In fact, it's fair to 373 00:19:29,800 --> 00:19:33,120 Speaker 13: say all the big technology companies, they're behind more than 374 00:19:33,119 --> 00:19:39,280 Speaker 13: anyone else. And there are philosophical issues, management issues, prioritization issues, 375 00:19:39,320 --> 00:19:44,199 Speaker 13: financial issues, perhaps even innovation issues, and many questions that 376 00:19:44,280 --> 00:19:48,239 Speaker 13: led to this point. Apple was caught off guard when 377 00:19:48,320 --> 00:19:51,280 Speaker 13: chat GPT launched in twenty twenty two. There are questions 378 00:19:51,320 --> 00:19:54,760 Speaker 13: about how a company of Apple scale and expertise could 379 00:19:54,760 --> 00:19:57,680 Speaker 13: have not seen this coming. Then there are questions of 380 00:19:57,960 --> 00:20:01,080 Speaker 13: how they released, with all their resources products that appeared 381 00:20:01,080 --> 00:20:03,440 Speaker 13: so rush to market, And this story takes a deep 382 00:20:03,480 --> 00:20:05,560 Speaker 13: look at the how and why about all of that. 383 00:20:06,520 --> 00:20:10,840 Speaker 2: I remember, not necessarily twenty eleven specifically, but twenty eleven 384 00:20:10,840 --> 00:20:14,800 Speaker 2: and twelve thirteen early editions of Syria and being like, Wow, Siri, 385 00:20:14,960 --> 00:20:18,240 Speaker 2: what is this? This is astonishing, and then quickly it 386 00:20:18,320 --> 00:20:19,119 Speaker 2: just fades away. 387 00:20:19,880 --> 00:20:22,160 Speaker 3: At the heart of your reporting. 388 00:20:21,760 --> 00:20:25,200 Speaker 2: Is the hiring of a key executive in twenty sixteen. 389 00:20:25,560 --> 00:20:28,280 Speaker 2: Who is that person and why do you follow that 390 00:20:28,400 --> 00:20:30,080 Speaker 2: executive's passage through Apple? 391 00:20:30,480 --> 00:20:31,680 Speaker 5: Yes, John g Andrea. 392 00:20:31,760 --> 00:20:34,280 Speaker 13: He was the head of search and AI at Google 393 00:20:34,520 --> 00:20:37,520 Speaker 13: until Apple hired him in twenty eighteen, so about seven 394 00:20:37,600 --> 00:20:40,280 Speaker 13: years ago. And he runs the show when it comes 395 00:20:40,280 --> 00:20:42,080 Speaker 13: to AI, or at least he did run the show 396 00:20:42,119 --> 00:20:44,520 Speaker 13: when it came to AI. For the last several years, 397 00:20:44,520 --> 00:20:47,120 Speaker 13: he was in charge of SIRI, in charge of foundation 398 00:20:47,280 --> 00:20:53,440 Speaker 13: models LMS, AI testing AI infrastructure teams designed to annotate 399 00:20:53,520 --> 00:20:56,600 Speaker 13: in train data to determine how well llms and the 400 00:20:56,640 --> 00:21:00,159 Speaker 13: different AI technologies are working. And him come right to 401 00:21:00,400 --> 00:21:02,840 Speaker 13: Apple was very exciting for the company. They thought they 402 00:21:02,840 --> 00:21:06,200 Speaker 13: would turn into an AI powerhouse. Instead, the one person 403 00:21:06,240 --> 00:21:09,840 Speaker 13: in charge of AI oversaw as the company completely missed 404 00:21:09,840 --> 00:21:11,920 Speaker 13: the boat on this technology. But as they get into 405 00:21:11,960 --> 00:21:15,439 Speaker 13: the piece, it's not him alone. Marketing issues, finance issues 406 00:21:15,440 --> 00:21:16,360 Speaker 13: and software issues. 407 00:21:17,560 --> 00:21:20,560 Speaker 2: Blue most Mark German with the latest Bloomberg Big Take. 408 00:21:20,600 --> 00:21:23,399 Speaker 2: It is an absolutely must read on what's happening inside 409 00:21:23,440 --> 00:21:33,639 Speaker 2: Apple with AI. Thank you very much, Welcome back to 410 00:21:33,680 --> 00:21:37,840 Speaker 2: Bloomberg Technology Ed Lovelow here in San Francisco. So stocks 411 00:21:37,880 --> 00:21:39,960 Speaker 2: have pared some of the declines, but the story in 412 00:21:40,000 --> 00:21:43,680 Speaker 2: the session has very much been AI infrastructure, semiconductor names 413 00:21:44,000 --> 00:21:47,720 Speaker 2: under pressure. There was the impact of Moody's credit rating 414 00:21:47,800 --> 00:21:51,200 Speaker 2: downgrade for America from Friday Night but actually I think 415 00:21:51,280 --> 00:21:53,320 Speaker 2: a lot of it is a reality check from all 416 00:21:53,359 --> 00:21:55,600 Speaker 2: of the headlines of last week about all of the 417 00:21:55,720 --> 00:21:59,040 Speaker 2: billions of dollars of investment in AI infrastructure Microsoft, the 418 00:21:59,119 --> 00:22:02,280 Speaker 2: exceptions of the rule of up half of percent, There 419 00:22:02,320 --> 00:22:05,600 Speaker 2: is a lot going on right now in the technology industry. 420 00:22:05,920 --> 00:22:09,240 Speaker 2: There are several events either underway or due to take 421 00:22:09,280 --> 00:22:12,719 Speaker 2: place this week. Computext we talked about in video focus 422 00:22:12,760 --> 00:22:16,040 Speaker 2: of that Google iOS coming up, Dell Technology World coming up, 423 00:22:16,200 --> 00:22:17,719 Speaker 2: and then there's Microsoft Build. 424 00:22:17,960 --> 00:22:19,600 Speaker 3: And I think that's a good place to start. 425 00:22:19,680 --> 00:22:22,280 Speaker 2: Let's get to what we can expect from Microsoft Build 426 00:22:22,320 --> 00:22:25,920 Speaker 2: and bring in anarag Rana of Bloomberg Intelligence. You've done 427 00:22:26,000 --> 00:22:29,399 Speaker 2: a little preview piece of research for Microsoft Build, and 428 00:22:29,440 --> 00:22:32,399 Speaker 2: I think that you, like everyone, are kind of expecting 429 00:22:32,480 --> 00:22:34,919 Speaker 2: Microsoft to talk about how we're actually going to be 430 00:22:35,000 --> 00:22:37,920 Speaker 2: using some of the AI tools that they charge us for. 431 00:22:39,280 --> 00:22:42,280 Speaker 14: Yeah, you know, when you look at the air infrastructure space, 432 00:22:42,640 --> 00:22:45,119 Speaker 14: Microsoft has really made a name for themselves to saying, 433 00:22:45,440 --> 00:22:47,760 Speaker 14: you know, we will focus a lot more on inferencing 434 00:22:47,920 --> 00:22:50,920 Speaker 14: rather than then training, and this is where a lot 435 00:22:50,960 --> 00:22:53,280 Speaker 14: of the innovation I think it's going to come from. 436 00:22:53,480 --> 00:22:56,040 Speaker 14: They're going to come from new products that are going 437 00:22:56,080 --> 00:22:59,320 Speaker 14: to be launched throughout the ecosystem, more focus on small 438 00:22:59,400 --> 00:23:02,639 Speaker 14: language mores and that you know may or may not 439 00:23:02,840 --> 00:23:06,119 Speaker 14: use that much of GPU use out there, And I 440 00:23:06,160 --> 00:23:08,239 Speaker 14: think that's the way they want to focus on. Is 441 00:23:08,280 --> 00:23:11,359 Speaker 14: the company that you know is more focused on inferencing 442 00:23:11,680 --> 00:23:12,480 Speaker 14: than then training. 443 00:23:13,520 --> 00:23:16,480 Speaker 2: Well, so important about Bloomberg technology this show and RAGU 444 00:23:16,560 --> 00:23:18,320 Speaker 2: is like lots of people that watch it are either 445 00:23:18,359 --> 00:23:20,760 Speaker 2: already at one of those events on their way to it, 446 00:23:20,840 --> 00:23:23,960 Speaker 2: or they'll be tracking it closely their developers or their 447 00:23:24,280 --> 00:23:27,880 Speaker 2: workers in the technology industry. And part of your research 448 00:23:27,960 --> 00:23:32,240 Speaker 2: is focusing on the lower cost large language model. Why 449 00:23:32,320 --> 00:23:34,520 Speaker 2: is that important? Why does it impact all of the 450 00:23:34,560 --> 00:23:37,080 Speaker 2: people across the world of technology that are watching this 451 00:23:37,160 --> 00:23:38,000 Speaker 2: program right now. 452 00:23:39,040 --> 00:23:41,159 Speaker 14: So one of the most important things for us is 453 00:23:41,480 --> 00:23:44,320 Speaker 14: how do you get this technology to be used, you know, 454 00:23:44,320 --> 00:23:47,199 Speaker 14: throughout the masses, whether it's on the consumer applications or 455 00:23:47,240 --> 00:23:50,080 Speaker 14: on the enterprise applications. So when you look at even 456 00:23:50,119 --> 00:23:53,639 Speaker 14: Microsoft co Pilot, you know, subscription it's at thirty dollars 457 00:23:53,680 --> 00:23:56,600 Speaker 14: per user per month, and with that high rate, we 458 00:23:56,680 --> 00:23:58,800 Speaker 14: think the adoption rate is going to be a lot 459 00:23:59,119 --> 00:24:02,600 Speaker 14: lower than if they bring it down. You can extrapolate 460 00:24:02,680 --> 00:24:05,760 Speaker 14: that on the enterprise use cases. Also. Currently we're in 461 00:24:05,800 --> 00:24:10,240 Speaker 14: a phase where running a large language model costs a 462 00:24:10,280 --> 00:24:13,080 Speaker 14: lot of money. Now, over time the cost of computing 463 00:24:13,160 --> 00:24:16,520 Speaker 14: will go down or broken usage, and I think this 464 00:24:16,560 --> 00:24:19,479 Speaker 14: is where one of the ways you can do that 465 00:24:19,640 --> 00:24:22,560 Speaker 14: is by using a small language model that does one 466 00:24:22,600 --> 00:24:25,600 Speaker 14: specific task and one specific task very well. And for 467 00:24:25,680 --> 00:24:28,600 Speaker 14: that you really don't need that sophisticated orf A hardware 468 00:24:28,640 --> 00:24:29,160 Speaker 14: to run it. 469 00:24:30,160 --> 00:24:31,800 Speaker 3: All, right, Ran or Bloomberg Intelligence. 470 00:24:31,880 --> 00:24:33,480 Speaker 2: Let's catch up at the end of the week, when 471 00:24:33,520 --> 00:24:35,480 Speaker 2: all of these events are out the way, we can 472 00:24:35,480 --> 00:24:38,320 Speaker 2: assess what was said. There is more AI news out 473 00:24:38,320 --> 00:24:40,760 Speaker 2: of the Middle East and Europe in video and Abadabi 474 00:24:40,840 --> 00:24:45,240 Speaker 2: Investment Vehicle MNGX partnering with French firms to build Europe's 475 00:24:45,320 --> 00:24:49,000 Speaker 2: largest AI data center campus. The moves follow a week 476 00:24:49,040 --> 00:24:51,480 Speaker 2: of AI announcements. We've been over them during the hour 477 00:24:51,880 --> 00:24:54,359 Speaker 2: during President Trump's visit to the Middle East, and that 478 00:24:54,440 --> 00:24:59,040 Speaker 2: includes plans by Saudi investment fund STV to launch a 479 00:24:59,080 --> 00:25:01,480 Speaker 2: one hundred million dollar like AI Focus fund. 480 00:25:01,760 --> 00:25:03,320 Speaker 3: We're backing from Google. 481 00:25:03,560 --> 00:25:07,280 Speaker 2: STV General partner and COO Luca Barbie joins us now 482 00:25:07,280 --> 00:25:11,399 Speaker 2: from Riad. Congrats on the fund. You're one of the 483 00:25:11,440 --> 00:25:17,160 Speaker 2: biggest sort of private growth equity venture firms out there 484 00:25:17,160 --> 00:25:19,879 Speaker 2: in terms of money under management that's focused on like 485 00:25:19,920 --> 00:25:22,439 Speaker 2: a very specific domain. What do you want to do 486 00:25:22,480 --> 00:25:25,440 Speaker 2: with this fund and what is the unlock of everything 487 00:25:25,480 --> 00:25:29,320 Speaker 2: that happened last week because your region is so under 488 00:25:29,320 --> 00:25:32,639 Speaker 2: the microscope with all of the infrastructure investment that's taking place. 489 00:25:35,200 --> 00:25:38,600 Speaker 15: Thanks for letting me. STV Indeed is a povate technology company. 490 00:25:38,640 --> 00:25:41,280 Speaker 15: We invest in technology in the Middle East, so we 491 00:25:41,359 --> 00:25:44,119 Speaker 15: have a one point five billion of asset under the 492 00:25:44,200 --> 00:25:47,199 Speaker 15: management and last week we announced. 493 00:25:46,840 --> 00:25:48,920 Speaker 12: A thematic fund for AI. 494 00:25:50,160 --> 00:25:53,520 Speaker 15: So our thesis is that this time to invest in 495 00:25:53,520 --> 00:25:56,760 Speaker 15: the application layer. The Middle East has tool in itself 496 00:25:57,320 --> 00:26:00,240 Speaker 15: being able to build a technology company. Today we have 497 00:26:00,320 --> 00:26:04,199 Speaker 15: fifteen unicorns in the region and more in the pipeline. 498 00:26:04,560 --> 00:26:07,240 Speaker 15: In our portfolio, we have nine companies that are preparing 499 00:26:07,240 --> 00:26:09,960 Speaker 15: for IPO and so there is enough knowledge, there is 500 00:26:10,080 --> 00:26:14,240 Speaker 15: enough momentus, there are enough infrastructure to compete and play 501 00:26:14,280 --> 00:26:18,159 Speaker 15: a role in the AI arena. The fund will be 502 00:26:18,160 --> 00:26:21,159 Speaker 15: focused on the application area. As I mentioned, there are 503 00:26:21,160 --> 00:26:24,800 Speaker 15: a number of use caes that are immediate relevant and 504 00:26:24,920 --> 00:26:30,720 Speaker 15: can find tangible application in the market, especially with the 505 00:26:30,960 --> 00:26:35,680 Speaker 15: customer service or enterprise, automension or legal This is where 506 00:26:35,720 --> 00:26:36,760 Speaker 15: we are focusing today. 507 00:26:38,200 --> 00:26:40,639 Speaker 2: We talked about Google backing this fund. Which are the 508 00:26:40,720 --> 00:26:44,119 Speaker 2: notable LPs and investors? Have you managed to get behind you? 509 00:26:46,760 --> 00:26:48,359 Speaker 15: Most of the things that are still in the making, 510 00:26:48,480 --> 00:26:50,840 Speaker 15: So we disclosed name in the new course, But we 511 00:26:50,920 --> 00:26:53,359 Speaker 15: have a number of institutional investors that have been with 512 00:26:53,440 --> 00:26:56,360 Speaker 15: us before, and we'll come also in the iFun and 513 00:26:56,440 --> 00:27:00,520 Speaker 15: I think the time is right to attract international investors 514 00:27:00,560 --> 00:27:02,680 Speaker 15: in our fund and in general in the vision. 515 00:27:04,760 --> 00:27:09,560 Speaker 2: A big factor in the Gulf is the activity of 516 00:27:09,680 --> 00:27:13,040 Speaker 2: the sovereign wealth funds. So you think about Saudi Arabia 517 00:27:13,040 --> 00:27:15,040 Speaker 2: for example, we covered a lot last week on the 518 00:27:15,080 --> 00:27:16,120 Speaker 2: show Humane. 519 00:27:16,640 --> 00:27:19,440 Speaker 3: How does that impact your ability to do deals? 520 00:27:19,480 --> 00:27:22,159 Speaker 2: Do you kind of get muscled out by either the 521 00:27:22,160 --> 00:27:25,280 Speaker 2: public investment fund or another vehicle like Humane? 522 00:27:25,560 --> 00:27:26,600 Speaker 3: Or are you looking at. 523 00:27:26,480 --> 00:27:30,359 Speaker 2: Different company sizes and targets for your investments? 524 00:27:31,720 --> 00:27:35,120 Speaker 15: What's the foundership puting prim and public sectors so far 525 00:27:35,200 --> 00:27:38,840 Speaker 15: has worked quite well, even in the most recent announcement 526 00:27:38,960 --> 00:27:40,200 Speaker 15: that we had last week. 527 00:27:40,480 --> 00:27:44,200 Speaker 12: Most of these money and capital will. 528 00:27:44,040 --> 00:27:48,359 Speaker 15: Be deployed in the infrastructure layer, so large project costs 529 00:27:48,560 --> 00:27:52,280 Speaker 15: billions of dollars to develop data center, to develop also 530 00:27:52,480 --> 00:27:54,200 Speaker 15: and localize and models. 531 00:27:54,480 --> 00:27:55,040 Speaker 12: So this is. 532 00:27:55,000 --> 00:27:59,120 Speaker 15: Actually favoring the initiative of the private sector. And as 533 00:27:59,119 --> 00:28:01,880 Speaker 15: I mentioned before, we focus more on the application layers. 534 00:28:01,920 --> 00:28:05,520 Speaker 12: So this is actually enforcing our teases that investing in 535 00:28:05,560 --> 00:28:07,199 Speaker 12: AI in the application layer. 536 00:28:07,320 --> 00:28:10,120 Speaker 15: In this vision we make more and more sense as 537 00:28:10,160 --> 00:28:12,879 Speaker 15: the infrastructure will be comparable to what we see in 538 00:28:12,880 --> 00:28:14,240 Speaker 15: the most developed markets. 539 00:28:15,840 --> 00:28:20,480 Speaker 2: President Trump's visit kind of put Saudi's name out there, 540 00:28:20,680 --> 00:28:23,719 Speaker 2: even if you forget the infrastructure layer right now, are 541 00:28:23,720 --> 00:28:26,640 Speaker 2: you worried about evaluations a little bit? Given how much 542 00:28:26,880 --> 00:28:31,480 Speaker 2: interest and energy there is into looking at Saudi Arabia 543 00:28:31,520 --> 00:28:35,720 Speaker 2: as an investment opportunity, There's. 544 00:28:35,600 --> 00:28:36,760 Speaker 12: Always a challenging job. 545 00:28:37,320 --> 00:28:39,600 Speaker 15: There is a lot of volatility that we have observed 546 00:28:39,640 --> 00:28:42,240 Speaker 15: across different cycles, so we need to deal with it. 547 00:28:42,720 --> 00:28:46,720 Speaker 15: We need to put forward our reputation as a long 548 00:28:46,800 --> 00:28:50,360 Speaker 15: term credible investors that can add on a number of 549 00:28:50,440 --> 00:28:54,960 Speaker 15: value adding activity, in particular to integrate company in our ecosystem, 550 00:28:55,120 --> 00:28:58,440 Speaker 15: talking to government, reguletto large layers. 551 00:28:58,000 --> 00:29:01,000 Speaker 12: And make things happen. So we believe that smart and 552 00:29:01,120 --> 00:29:03,080 Speaker 12: experienced entrepreneur we value that. 553 00:29:04,040 --> 00:29:07,160 Speaker 15: Actually, we welcome the Trump visit I think the world 554 00:29:07,320 --> 00:29:09,160 Speaker 15: is more and more noticing what's. 555 00:29:08,880 --> 00:29:12,320 Speaker 12: Happening in our region, in the Middle East. So far 556 00:29:12,560 --> 00:29:13,880 Speaker 12: things have been under the RADA. 557 00:29:14,360 --> 00:29:17,600 Speaker 15: But I think, you know, especially in our sector, in technology, 558 00:29:17,640 --> 00:29:21,480 Speaker 15: the growth is exponential and I would expect in the 559 00:29:21,560 --> 00:29:24,480 Speaker 15: next three five years to become you know, the success 560 00:29:24,480 --> 00:29:27,040 Speaker 15: case of the Middle East intact to become more visible, 561 00:29:27,640 --> 00:29:32,080 Speaker 15: and so we'll be more more more global capital and 562 00:29:32,080 --> 00:29:35,320 Speaker 15: more global investors. I see we enter the market and 563 00:29:35,360 --> 00:29:36,920 Speaker 15: play a role next to us. 564 00:29:38,360 --> 00:29:40,480 Speaker 2: Luca, you talked about some of the unicorns in the 565 00:29:40,520 --> 00:29:43,760 Speaker 2: region and some of what's in your portfolio. You must 566 00:29:43,800 --> 00:29:47,360 Speaker 2: be about excited about some exits this year. Any case 567 00:29:47,400 --> 00:29:50,320 Speaker 2: studies where you see an IPO or even M and 568 00:29:50,400 --> 00:29:51,760 Speaker 2: A giving you a nice exit. 569 00:29:53,560 --> 00:29:54,320 Speaker 12: Absolutely so. 570 00:29:54,440 --> 00:29:57,640 Speaker 15: Look, I mentioned fifteen unicorns so far, right, five of 571 00:29:57,720 --> 00:29:59,040 Speaker 15: those fifteen. 572 00:29:59,080 --> 00:30:01,400 Speaker 12: Being materialized the next eighteen months. 573 00:30:01,680 --> 00:30:04,720 Speaker 15: This is why I was talking about the exponential shape 574 00:30:04,760 --> 00:30:08,200 Speaker 15: of how things happen in technology. You know, our portfolio, 575 00:30:08,200 --> 00:30:10,320 Speaker 15: we have other four candidates. 576 00:30:10,960 --> 00:30:13,880 Speaker 12: One of it is actually already unicorner. It's Toby by 577 00:30:14,000 --> 00:30:14,680 Speaker 12: Now paying later. 578 00:30:14,880 --> 00:30:19,080 Speaker 15: They raised recently around that three point two billion's dollar 579 00:30:19,280 --> 00:30:22,200 Speaker 15: valuation and as a credible path to be listed in 580 00:30:22,240 --> 00:30:25,080 Speaker 15: the Middle East Socket Change in the next eighteen months. 581 00:30:25,440 --> 00:30:27,560 Speaker 15: So we need more of this success case to hove 582 00:30:27,600 --> 00:30:30,720 Speaker 15: ourself and the rest of the global audience that the 583 00:30:30,800 --> 00:30:36,200 Speaker 15: market is big enough, entrepreneurs are innovative enough, and the 584 00:30:36,240 --> 00:30:41,320 Speaker 15: capital can be you know, put a play with attractive. 585 00:30:41,640 --> 00:30:47,080 Speaker 2: Y Luca Barbie, general partner and COO of STV. Great 586 00:30:47,080 --> 00:30:49,560 Speaker 2: to have you here on Bloomberg Technology. Thank you very much. 587 00:30:50,000 --> 00:30:52,200 Speaker 2: Now coming up, we're going to talk to Luxe Capitalist 588 00:30:52,240 --> 00:30:57,080 Speaker 2: Josh Wolf about the firm's helpline for academics and scientists 589 00:30:57,680 --> 00:31:00,680 Speaker 2: and the state of research in this country. 590 00:31:01,400 --> 00:31:02,720 Speaker 3: This is Bloomberg Technology. 591 00:31:14,680 --> 00:31:17,760 Speaker 2: As the US public sector pulls back on funding for 592 00:31:17,880 --> 00:31:21,400 Speaker 2: research and development, some big name private sector names are 593 00:31:21,440 --> 00:31:25,200 Speaker 2: stepping in. That includes Lux Capital Managing partner Josh Wolf 594 00:31:25,240 --> 00:31:28,600 Speaker 2: is here. Lux Capital's portfolio includes names like Anderil and 595 00:31:28,680 --> 00:31:32,040 Speaker 2: drug discovery company Icon Therapeutics. What you were doing, Josh, 596 00:31:32,480 --> 00:31:34,960 Speaker 2: is a one hundred million dollar commitment for basically a 597 00:31:35,000 --> 00:31:41,320 Speaker 2: helpline for American scientists, American researchers whose work might otherwise 598 00:31:41,400 --> 00:31:44,520 Speaker 2: be taken from underneath them because of the lack of funding. 599 00:31:46,800 --> 00:31:47,640 Speaker 11: That's absolutely right. 600 00:31:47,680 --> 00:31:51,280 Speaker 16: You go back eighty years American competitiveness started with that 601 00:31:51,400 --> 00:31:53,480 Speaker 16: of the Bush. Yeah, this great thing, the Endless Frontier. 602 00:31:53,800 --> 00:31:56,080 Speaker 16: That is what gave us competitive advantage. We attracted the 603 00:31:56,080 --> 00:31:58,360 Speaker 16: best and the brightest from German Jews that came here 604 00:31:58,400 --> 00:32:00,640 Speaker 16: and made sure that we had the atomic bond, went 605 00:32:00,640 --> 00:32:03,080 Speaker 16: to the Institute for Advanced Studies at Princeton. You go 606 00:32:03,160 --> 00:32:06,160 Speaker 16: through the Cold War in the eighties, attracting Russian emigree 607 00:32:06,160 --> 00:32:07,440 Speaker 16: so that we can have the best in the brightest 608 00:32:07,440 --> 00:32:10,800 Speaker 16: sciences in the world, all of our academic institutions being funded. 609 00:32:11,160 --> 00:32:12,720 Speaker 11: Then you fast forward to two thousand and five. 610 00:32:12,720 --> 00:32:15,320 Speaker 16: You've got Normal Augustine, previously head of major defense company, 611 00:32:15,480 --> 00:32:18,280 Speaker 16: writing the Gathering Storm, which was basically like the clouds 612 00:32:18,320 --> 00:32:21,520 Speaker 16: are gathering and we are starting to lose, particularly to China. Today, 613 00:32:21,560 --> 00:32:24,280 Speaker 16: I would call it a perfect storm. You've got politicization 614 00:32:24,400 --> 00:32:27,240 Speaker 16: at universities. You've got massive federal cuts and that is 615 00:32:27,280 --> 00:32:30,120 Speaker 16: not a political thing between the Republicans and the Democrats. 616 00:32:30,160 --> 00:32:33,840 Speaker 16: That is the past twenty two years we've had major 617 00:32:33,880 --> 00:32:36,400 Speaker 16: funding cuts in federal government. We are at the lowest 618 00:32:36,720 --> 00:32:39,920 Speaker 16: level in science funding from the government since nineteen ninety seven. 619 00:32:40,320 --> 00:32:42,760 Speaker 16: Why does this all matter? We are competing with China, 620 00:32:42,960 --> 00:32:45,520 Speaker 16: This is not a left or right, or red or blue. 621 00:32:45,680 --> 00:32:48,320 Speaker 16: It is a past verse future thing. And with those 622 00:32:48,360 --> 00:32:51,560 Speaker 16: funding cuts and the politicization on the problems that scientists 623 00:32:51,600 --> 00:32:53,960 Speaker 16: are facing, the lifeblood of venture capital, the life blood 624 00:32:53,960 --> 00:32:55,280 Speaker 16: of innovation is this. 625 00:32:55,360 --> 00:32:56,640 Speaker 11: So we're stepping forward and seeing at. 626 00:32:56,600 --> 00:32:58,800 Speaker 16: Least one hundred million dollars, we're going to be doubling 627 00:32:58,800 --> 00:33:02,200 Speaker 16: down on early stage venture creation around scientists. 628 00:33:03,280 --> 00:33:07,040 Speaker 2: Josh I discussed this exact issue with Michael Kratzios, recently 629 00:33:07,160 --> 00:33:09,800 Speaker 2: Director of the White House Office for the Science Technology 630 00:33:09,880 --> 00:33:13,000 Speaker 2: Right just listen to how he summarized funding right. 631 00:33:12,880 --> 00:33:15,600 Speaker 17: Now, if you spend a lot of money on the 632 00:33:15,600 --> 00:33:18,040 Speaker 17: wrong things, it's worse for spending a little money on 633 00:33:18,080 --> 00:33:20,040 Speaker 17: the right things. So we're making sure of that the 634 00:33:20,080 --> 00:33:23,440 Speaker 17: biggest priorities for the nation, things like leading artificial intelligence, 635 00:33:23,720 --> 00:33:26,520 Speaker 17: leading in quantum information science. Can we continue to fund 636 00:33:26,560 --> 00:33:28,760 Speaker 17: those areas and make sure that our universities are a 637 00:33:28,760 --> 00:33:30,880 Speaker 17: big part of that agenda. 638 00:33:31,440 --> 00:33:34,840 Speaker 2: How mister Kratzios would summarize it is that it's how 639 00:33:34,920 --> 00:33:37,440 Speaker 2: and where you spend money, spend but. 640 00:33:37,760 --> 00:33:40,080 Speaker 3: Research funds rather than the volume. 641 00:33:40,680 --> 00:33:43,640 Speaker 2: Your reaction to that, and I guess that the lack 642 00:33:43,720 --> 00:33:47,280 Speaker 2: of knowledge right now and the direction that this administration 643 00:33:47,360 --> 00:33:49,440 Speaker 2: is going in terms of committing to growing. 644 00:33:49,200 --> 00:33:50,120 Speaker 3: R and D budgets. 645 00:33:51,400 --> 00:33:52,760 Speaker 16: Well, first of all, I have a lot of respect 646 00:33:52,800 --> 00:33:54,680 Speaker 16: for Kratzios. I think he's doing a great job across 647 00:33:54,720 --> 00:33:56,760 Speaker 16: many things. I would say as it relates to funding 648 00:33:56,840 --> 00:33:59,720 Speaker 16: the things that are evident and obvious science and the 649 00:33:59,720 --> 00:34:03,120 Speaker 16: fundoing for it is not a slot machine for strategy returns. 650 00:34:03,680 --> 00:34:06,280 Speaker 16: This is about the slow burn of serendipity. We never 651 00:34:06,360 --> 00:34:08,759 Speaker 16: knew that COVID was coming, but thank god we were 652 00:34:08,800 --> 00:34:13,359 Speaker 16: able to have messenger RNA mRNA vaccine that was there. 653 00:34:13,960 --> 00:34:15,400 Speaker 16: We would have been late to the game if we 654 00:34:15,400 --> 00:34:17,640 Speaker 16: were trying to fund it when the moment happened. So 655 00:34:17,640 --> 00:34:19,800 Speaker 16: when we were talking about the things today that mattered, 656 00:34:20,040 --> 00:34:23,080 Speaker 16: AI and quantum, which I'm super skeptical about, and some 657 00:34:23,120 --> 00:34:25,600 Speaker 16: of the cutting edge areas and robotics and aerospace and defense, 658 00:34:26,040 --> 00:34:28,160 Speaker 16: those kinds of things are the things that we've been 659 00:34:28,160 --> 00:34:30,400 Speaker 16: funding for ten years. So the venture industry has been 660 00:34:30,400 --> 00:34:32,440 Speaker 16: funding it for ten years, which means that the breakthroughs 661 00:34:32,600 --> 00:34:35,040 Speaker 16: have come from science and academia, and that's. 662 00:34:34,920 --> 00:34:35,640 Speaker 11: Fifteen years ago. 663 00:34:35,719 --> 00:34:37,640 Speaker 16: So the things that we should be funding now are 664 00:34:37,680 --> 00:34:40,759 Speaker 16: this slow burn of serendipity that five, ten, fifteen years. 665 00:34:40,800 --> 00:34:43,080 Speaker 16: Hence folks like us from the private sector come in, 666 00:34:43,400 --> 00:34:46,240 Speaker 16: identify and take the risk and put in risk capital 667 00:34:46,680 --> 00:34:49,200 Speaker 16: behind the human capital, behind the human capital and the 668 00:34:49,239 --> 00:34:51,960 Speaker 16: intellectual capital of intellectual property and patents owned by the 669 00:34:52,080 --> 00:34:54,920 Speaker 16: US and owned by our government and licensed to the 670 00:34:55,000 --> 00:34:57,080 Speaker 16: universities and to the researchers, and then ultimately to the 671 00:34:57,080 --> 00:34:59,960 Speaker 16: startups that can create multi billion dollar companies doing exact 672 00:35:00,000 --> 00:35:03,160 Speaker 16: actly what great cratios are saying. So I do not 673 00:35:03,280 --> 00:35:05,000 Speaker 16: agree that we should be funding the here and now. 674 00:35:05,320 --> 00:35:07,360 Speaker 16: We want to own the future. We should be funding 675 00:35:07,400 --> 00:35:09,239 Speaker 16: the future, and you never know where it's going to 676 00:35:09,280 --> 00:35:11,120 Speaker 16: come from. If we did, it wouldn't be science. 677 00:35:12,440 --> 00:35:17,400 Speaker 2: Josh, you this conversation pretty clear. This is about competition 678 00:35:17,440 --> 00:35:20,759 Speaker 2: with China, right, And I record conversation I had with 679 00:35:20,840 --> 00:35:24,720 Speaker 2: Jensen Wong in March where he basically said, look, fifty 680 00:35:24,760 --> 00:35:29,240 Speaker 2: percent of AI research is being done either in China 681 00:35:29,719 --> 00:35:35,920 Speaker 2: or by Chinese researchers in other domains. It is simple 682 00:35:35,920 --> 00:35:39,320 Speaker 2: and clear cut as the Chinese government just being willing 683 00:35:39,400 --> 00:35:42,080 Speaker 2: to commit capital and do what it takes kind of 684 00:35:42,120 --> 00:35:44,799 Speaker 2: attitude for them to come out on top. Or is 685 00:35:44,840 --> 00:35:49,680 Speaker 2: it different to that, what is the domain for the 686 00:35:49,719 --> 00:35:50,640 Speaker 2: competition here? 687 00:35:51,840 --> 00:35:54,759 Speaker 16: It is a geopolitical domean. It is a cultural competition. 688 00:35:55,440 --> 00:35:58,200 Speaker 16: Cultures get what they celebrate. When we're celebrating celebrities like 689 00:35:58,280 --> 00:36:02,239 Speaker 16: Kardashians and Paris Hillmen, TikTok and nonsense, and they are 690 00:36:02,280 --> 00:36:05,080 Speaker 16: directing their youth to go after the cutting edge that 691 00:36:05,120 --> 00:36:10,120 Speaker 16: will define competitive advantage, to let them dominate in biotech, 692 00:36:10,280 --> 00:36:14,560 Speaker 16: in aerospace, in breakthrough materials, in space itself, in life 693 00:36:14,560 --> 00:36:17,640 Speaker 16: saving drugs. Today most of the world depends on the 694 00:36:17,640 --> 00:36:21,040 Speaker 16: American biotech industry. You go back to Conan Boyer on 695 00:36:21,080 --> 00:36:24,000 Speaker 16: the West Coast that launched Genetech and helped to create 696 00:36:24,040 --> 00:36:29,080 Speaker 16: this vibrant industry unleashing cancer diagnostics and drugs, immunotherapies and 697 00:36:29,160 --> 00:36:32,480 Speaker 16: JLP ones for weight loss. All of that came from basic, 698 00:36:32,680 --> 00:36:36,000 Speaker 16: undirected science. It's absolutely critical. You go to aerospace and 699 00:36:36,040 --> 00:36:38,839 Speaker 16: again the atomic bomb, thank god it was built here 700 00:36:39,000 --> 00:36:39,239 Speaker 16: and not. 701 00:36:39,320 --> 00:36:40,880 Speaker 11: Somewhere else through our Manhattan Project. 702 00:36:40,880 --> 00:36:44,880 Speaker 16: That was massive directed scientific research with lots of unknowns, 703 00:36:44,880 --> 00:36:47,879 Speaker 16: where we had Germans and Austrians and British all coming 704 00:36:47,920 --> 00:36:51,680 Speaker 16: here to work so that the Allies could have superior advantage. 705 00:36:51,800 --> 00:36:54,400 Speaker 16: So we are fighting and losing right now in nearly 706 00:36:54,520 --> 00:36:58,880 Speaker 16: forty to forty four critical technology areas where China is dominating, 707 00:36:58,880 --> 00:37:02,000 Speaker 16: and Jensen from Nvidia one hundred percent correct, fifty percent 708 00:37:02,040 --> 00:37:04,799 Speaker 16: of all AI graduates today are coming from China. That's 709 00:37:04,840 --> 00:37:07,120 Speaker 16: not a twenty twenty three or twenty twenty four decision. 710 00:37:07,320 --> 00:37:09,560 Speaker 16: We're in twenty twenty five. This was a decision from 711 00:37:09,600 --> 00:37:12,440 Speaker 16: fifteen years ago. So the decisions we're making today about 712 00:37:12,480 --> 00:37:15,560 Speaker 16: long horizon science is what is going to give venture 713 00:37:15,600 --> 00:37:18,120 Speaker 16: capitalists like us the ability to go in to early 714 00:37:18,160 --> 00:37:20,560 Speaker 16: stage science and do what we do. We've done over 715 00:37:20,640 --> 00:37:24,240 Speaker 16: twenty companies Denovo from university research, but that wasn't research 716 00:37:24,280 --> 00:37:26,160 Speaker 16: that was done a year ago. It was research that 717 00:37:26,239 --> 00:37:28,759 Speaker 16: was funded ten to fifteen plus years ago. Being able 718 00:37:28,800 --> 00:37:31,200 Speaker 16: to back Nobel Prize winners like Richard Axel, Josh where 719 00:37:31,200 --> 00:37:35,120 Speaker 16: we in Caliobe, or or all kinds of other companies 720 00:37:35,440 --> 00:37:39,040 Speaker 16: critically important that the science research funding is there now 721 00:37:39,160 --> 00:37:41,239 Speaker 16: so that US venture capitalists in the private sector can 722 00:37:41,280 --> 00:37:42,000 Speaker 16: step in tomorrow. 723 00:37:42,719 --> 00:37:44,319 Speaker 2: I sorry to interrupt you, Josh, I just want to 724 00:37:44,320 --> 00:37:46,560 Speaker 2: go back to the lux Science Helpline before we run 725 00:37:46,600 --> 00:37:48,400 Speaker 2: out of time. And it used to be that you 726 00:37:48,440 --> 00:37:53,040 Speaker 2: would require those researchers to have their work ninety ninety 727 00:37:53,040 --> 00:37:57,040 Speaker 2: five percent scientifically proven before you'd commit capital. You've lowered 728 00:37:57,080 --> 00:38:00,520 Speaker 2: the bar to fifty percent scientifically proven. What are the 729 00:38:00,600 --> 00:38:03,040 Speaker 2: risks with that and the motivation for doing so? We 730 00:38:03,080 --> 00:38:04,040 Speaker 2: have about a minute left. 731 00:38:05,000 --> 00:38:07,920 Speaker 16: Lowering the bar for the science risk is raising the stakes. 732 00:38:08,560 --> 00:38:11,399 Speaker 16: Before we would want to only take market risk, technology risk, 733 00:38:11,440 --> 00:38:14,439 Speaker 16: people risk, product risk, financing risk. Science risk we wanted 734 00:38:14,480 --> 00:38:17,000 Speaker 16: funded by the US taxpayer and by the university. Now 735 00:38:17,040 --> 00:38:19,120 Speaker 16: we're saying, if it's not fully baked, it's okay. If 736 00:38:19,120 --> 00:38:21,240 Speaker 16: it's half baked, if it's down to fifty percent ready, 737 00:38:21,360 --> 00:38:23,239 Speaker 16: and it still needs money from the private sector, in 738 00:38:23,280 --> 00:38:26,399 Speaker 16: a private lab, in a venture supported company, We're willing 739 00:38:26,440 --> 00:38:29,320 Speaker 16: to take that risk because the stakes are that high, 740 00:38:29,560 --> 00:38:31,960 Speaker 16: the profits for the scientists, for us, for our limited 741 00:38:32,000 --> 00:38:34,439 Speaker 16: partners are that high. It's worth doing. We're not talking 742 00:38:34,440 --> 00:38:36,600 Speaker 16: about a huge amount of money. We can't do it alone. 743 00:38:36,719 --> 00:38:39,279 Speaker 16: We're leading this effort. We're inviting our venture peers who 744 00:38:39,280 --> 00:38:42,880 Speaker 16: can be scientifically minded to create syndicates of American greatness 745 00:38:42,880 --> 00:38:44,520 Speaker 16: here to come along for the ride. 746 00:38:45,840 --> 00:38:48,799 Speaker 2: Lux Capital Managing partner Josh Wolf, thank you very much 747 00:38:48,880 --> 00:39:00,000 Speaker 2: coming back on the program, and thropt pic has cultivated 748 00:39:00,200 --> 00:39:05,200 Speaker 2: a reputation for taking issues such as safety and responsibility seriously, 749 00:39:05,760 --> 00:39:08,560 Speaker 2: more seriously than the company from which it is sprung, 750 00:39:08,880 --> 00:39:12,279 Speaker 2: open Ai. But can it keep up while remaining so 751 00:39:12,440 --> 00:39:15,719 Speaker 2: focused on safety? Bloomberg Shering Gafari joins us for more 752 00:39:16,080 --> 00:39:19,160 Speaker 2: on her Business Week's story. I look at Anithropic and 753 00:39:19,160 --> 00:39:20,399 Speaker 2: as you know, like you spend a lot of time 754 00:39:20,400 --> 00:39:24,239 Speaker 2: talking with Anthropic as well over several years, I feel 755 00:39:24,239 --> 00:39:27,759 Speaker 2: like they have managed to move quickly, but it's this 756 00:39:27,880 --> 00:39:32,520 Speaker 2: kind of repetition of safety. What are you talking about 757 00:39:32,560 --> 00:39:33,520 Speaker 2: in the Business Week piece? 758 00:39:34,040 --> 00:39:34,200 Speaker 3: Right? 759 00:39:34,239 --> 00:39:36,560 Speaker 18: If you think about just even a few years ago, right, 760 00:39:36,640 --> 00:39:39,680 Speaker 18: Dario Miday, the CEO of Anthropic, was someone who really 761 00:39:39,719 --> 00:39:40,960 Speaker 18: had an academic background. 762 00:39:41,000 --> 00:39:43,000 Speaker 5: He had worked at open Ai and buy New Google, 763 00:39:43,040 --> 00:39:46,520 Speaker 5: but largely spent his time as. 764 00:39:46,400 --> 00:39:50,680 Speaker 18: A biophysics researcher right running experiments. And so now he's 765 00:39:50,719 --> 00:39:53,160 Speaker 18: in this position where he's actually running a sixty one 766 00:39:53,239 --> 00:39:56,480 Speaker 18: billion dollar startup while trying to balance that sort of 767 00:39:56,520 --> 00:39:59,440 Speaker 18: academic commitment that he has to his ideals around safety. 768 00:40:00,080 --> 00:40:02,919 Speaker 3: In the piece, what do you report on how they're 769 00:40:02,920 --> 00:40:03,840 Speaker 3: getting that balance? 770 00:40:03,920 --> 00:40:04,080 Speaker 16: Right? 771 00:40:04,200 --> 00:40:06,880 Speaker 2: Running a business versus the safety responsibility. 772 00:40:07,040 --> 00:40:08,760 Speaker 5: Right, So they have some impressive numbers. 773 00:40:08,800 --> 00:40:12,400 Speaker 18: I mean the latest annualized revenue projections they have are 774 00:40:12,400 --> 00:40:15,960 Speaker 18: two billion, and that's double what it was at the 775 00:40:15,960 --> 00:40:19,640 Speaker 18: beginning of the year. So it's nothing to you beat 776 00:40:19,640 --> 00:40:22,799 Speaker 18: an eyelash at. And at the same time they are 777 00:40:22,920 --> 00:40:25,120 Speaker 18: going to have to keep racing it this very fast 778 00:40:25,160 --> 00:40:27,000 Speaker 18: feed to keep up with deep Seek, to keep up 779 00:40:27,040 --> 00:40:29,600 Speaker 18: with open Ai, with Google, and so how do you 780 00:40:29,640 --> 00:40:31,400 Speaker 18: sort of balance it when sometimes you do have to 781 00:40:31,440 --> 00:40:32,319 Speaker 18: take a step back. 782 00:40:33,520 --> 00:40:35,000 Speaker 5: That's going to be the challenge I think in the 783 00:40:35,080 --> 00:40:35,640 Speaker 5: years ahead. 784 00:40:36,040 --> 00:40:38,799 Speaker 2: When I speak to computer scientists or even people in 785 00:40:38,800 --> 00:40:42,080 Speaker 2: the enterprise software space, I think what and frop It 786 00:40:42,080 --> 00:40:43,719 Speaker 2: gets a lot of credit for, and what they're good 787 00:40:43,719 --> 00:40:48,719 Speaker 2: at is coding agents and those sorts of domains. How 788 00:40:48,760 --> 00:40:51,200 Speaker 2: have they done well there? And what are their products? 789 00:40:51,200 --> 00:40:54,240 Speaker 2: What is it that the anthropic cells to the world. 790 00:40:54,520 --> 00:40:56,359 Speaker 18: Yeah, So another nugget in this piece is it one 791 00:40:56,360 --> 00:40:59,640 Speaker 18: of the biggest sources of growth for them recently has 792 00:40:59,640 --> 00:41:02,839 Speaker 18: been through their coding agents and sort of coding technology, 793 00:41:02,840 --> 00:41:06,840 Speaker 18: and that's essentially software that helps engineers write their code. 794 00:41:07,040 --> 00:41:09,879 Speaker 18: And that is, on the one hand, a huge boon 795 00:41:09,920 --> 00:41:11,960 Speaker 18: to them because if they have the best software for that, 796 00:41:12,000 --> 00:41:14,160 Speaker 18: they're going to get big contracts. On the other hand, 797 00:41:14,800 --> 00:41:17,080 Speaker 18: it also provides a complication in terms of what does 798 00:41:17,080 --> 00:41:18,279 Speaker 18: that mean for the future of work? 799 00:41:18,320 --> 00:41:19,520 Speaker 5: What does that mean for employees? 800 00:41:19,520 --> 00:41:21,920 Speaker 18: Even with an anthropic whose jobs are going to change, 801 00:41:22,440 --> 00:41:24,520 Speaker 18: you know, and A told me, we don't want to 802 00:41:24,560 --> 00:41:26,480 Speaker 18: slow down. We don't want to have to fire our 803 00:41:26,520 --> 00:41:30,319 Speaker 18: employees because of our product claud code, but that means 804 00:41:30,360 --> 00:41:32,960 Speaker 18: we may have to slow down hiring in the future, right, 805 00:41:33,040 --> 00:41:36,600 Speaker 18: So I think he's being quite open about the trade offs. 806 00:41:36,600 --> 00:41:39,879 Speaker 2: Here very quickly, just twenty seconds. Where are Anthropic? Were's 807 00:41:39,920 --> 00:41:41,680 Speaker 2: home for them? And who are their biggest investors? 808 00:41:42,160 --> 00:41:45,120 Speaker 18: So they are based here in San Francisco actually, where 809 00:41:45,200 --> 00:41:48,640 Speaker 18: Drio Meda is from. And their biggest investors are folks 810 00:41:48,760 --> 00:41:50,279 Speaker 18: like light Speed. 811 00:41:50,920 --> 00:41:52,319 Speaker 5: You know. Eric Schmidt was. 812 00:41:52,320 --> 00:41:55,440 Speaker 18: An early investor who I talked to Google and Amazon 813 00:41:55,480 --> 00:41:58,120 Speaker 18: of course, who will also partner with them on the Infrat. 814 00:41:58,160 --> 00:42:01,400 Speaker 2: By Sharen Gafari with Frankly must read in BusinessWeek. 815 00:42:01,480 --> 00:42:02,279 Speaker 3: Thank you very much. 816 00:42:02,800 --> 00:42:05,680 Speaker 2: That does it for this edition of Bloomberg Technology. Don't 817 00:42:05,680 --> 00:42:08,160 Speaker 2: forget check out our podcast. So many of you listen 818 00:42:08,200 --> 00:42:10,640 Speaker 2: to this show as a podcast. You know where to 819 00:42:10,680 --> 00:42:12,840 Speaker 2: find it. It's on all of the Bloomberg platforms, as 820 00:42:12,880 --> 00:42:17,040 Speaker 2: well as on Apple, Spotify, and online on iHeart from 821 00:42:17,080 --> 00:42:17,839 Speaker 2: San Francisco. 822 00:42:18,640 --> 00:42:19,840 Speaker 3: This is Bloomberg Technology.