1 00:00:01,440 --> 00:00:04,960 Speaker 1: From the heart where innovation, money and power co lie 2 00:00:05,040 --> 00:00:09,760 Speaker 1: in Silicon Valley, NBN. This is Bloomberg Technology with Caroline 3 00:00:09,800 --> 00:00:10,560 Speaker 1: Hyde and Ed. 4 00:00:10,640 --> 00:00:27,760 Speaker 2: Luvedlove live from New York and San Francisco. This is 5 00:00:27,760 --> 00:00:31,520 Speaker 2: Bloomberg Technology coming up. Tesl posts its first increase in 6 00:00:31,600 --> 00:00:35,320 Speaker 2: quarterly sales this year, but it disappoints investors. 7 00:00:35,640 --> 00:00:38,840 Speaker 3: And Apple preparing to announce a new low end iPhone. 8 00:00:38,880 --> 00:00:41,279 Speaker 4: We have the Bloomberg exclusive. 9 00:00:40,880 --> 00:00:42,200 Speaker 5: US we talk to tech. Have you hitters? 10 00:00:42,280 --> 00:00:45,520 Speaker 2: Later in the our CEO of Pinterest coming from its 11 00:00:45,600 --> 00:00:48,720 Speaker 2: advertising event, Ed, Let's. 12 00:00:48,520 --> 00:00:50,080 Speaker 4: Get strike to our top story. 13 00:00:50,159 --> 00:00:54,000 Speaker 3: Tesla just posted its first increase in quarterly sales this year, 14 00:00:54,280 --> 00:00:57,960 Speaker 3: but for the quarter, deliveries came in just below street estimates. 15 00:00:58,000 --> 00:01:01,520 Speaker 3: Shares are down sharply to the company delivered four hundred 16 00:01:01,520 --> 00:01:04,440 Speaker 3: and sixty two thousand, eight hundred and ninety ebs in 17 00:01:04,520 --> 00:01:07,400 Speaker 3: the third quarter, just shy of the four hundred and 18 00:01:07,440 --> 00:01:10,760 Speaker 3: sixty three thousand, eight hundred and ninety seven estimate. And 19 00:01:10,800 --> 00:01:13,839 Speaker 3: this all ahead of course of next week's robotaxi event. 20 00:01:13,840 --> 00:01:17,880 Speaker 3: Bloomberg's Global Auto's editor Craig Trudell joins us from London. 21 00:01:17,959 --> 00:01:18,960 Speaker 4: Going into the data. 22 00:01:19,000 --> 00:01:24,039 Speaker 3: It's interesting because three and y the volume products beat estimates, 23 00:01:24,280 --> 00:01:28,839 Speaker 3: the other bucket which is XS and cyber truck missed estimates. 24 00:01:29,040 --> 00:01:29,840 Speaker 4: What else do we know? 25 00:01:31,319 --> 00:01:33,240 Speaker 6: Yeah, I think this is a case of, you know, 26 00:01:33,959 --> 00:01:36,680 Speaker 6: a narrowbeat giveth and a narrowmiss take it away. This 27 00:01:36,760 --> 00:01:39,040 Speaker 6: is a company that, you know, its shares are known 28 00:01:39,080 --> 00:01:42,240 Speaker 6: to be quite volidle. Just a quarter ago, they just 29 00:01:42,400 --> 00:01:45,880 Speaker 6: barely exceeded expectations and the stock went kind of crazy. 30 00:01:46,160 --> 00:01:48,880 Speaker 6: I don't think we necessarily learn a ton from these numbers. 31 00:01:48,920 --> 00:01:52,640 Speaker 6: I think, you know, we do generally see a nice 32 00:01:52,680 --> 00:01:56,640 Speaker 6: upward trend, both sequentially and year over year. Having said that, 33 00:01:56,680 --> 00:01:58,400 Speaker 6: if you kind of you know, pan out a little 34 00:01:58,400 --> 00:02:00,920 Speaker 6: bit and look at the trend, you know, going a 35 00:02:00,920 --> 00:02:03,160 Speaker 6: little further back, we really just see a sort of 36 00:02:03,240 --> 00:02:06,800 Speaker 6: general kind of flattening and lack of growth from this company. 37 00:02:07,720 --> 00:02:09,800 Speaker 6: You know, you know, year on a year to day basis, 38 00:02:09,840 --> 00:02:13,760 Speaker 6: we may actually see a decline if if trends hold up. 39 00:02:13,800 --> 00:02:17,080 Speaker 6: We've seen them drop a little over two percent for 40 00:02:17,120 --> 00:02:19,520 Speaker 6: the year. So this is a company that you know, 41 00:02:19,600 --> 00:02:21,840 Speaker 6: is priced as a growth stock that that is having 42 00:02:21,840 --> 00:02:22,839 Speaker 6: trouble growing. 43 00:02:22,960 --> 00:02:24,040 Speaker 5: And particularly in China. 44 00:02:24,120 --> 00:02:28,359 Speaker 6: Craig, Yeah, and I think a lot of the expectations 45 00:02:28,400 --> 00:02:30,640 Speaker 6: going into these numbers was that this company was going 46 00:02:30,720 --> 00:02:34,239 Speaker 6: to get a big boost from China increasing subsidies for 47 00:02:34,480 --> 00:02:38,040 Speaker 6: people to trade in older cars and get an electric vehicle. 48 00:02:38,120 --> 00:02:41,800 Speaker 6: We did see Tesla's wholesale numbers tick up as a 49 00:02:41,800 --> 00:02:46,360 Speaker 6: result of that boosted incentive. But you know, those numbers 50 00:02:46,440 --> 00:02:49,320 Speaker 6: also are are wholesales. That means, you know, they're they're 51 00:02:49,400 --> 00:02:52,160 Speaker 6: kind of closer to shipments as opposed to actual you know, 52 00:02:52,280 --> 00:02:55,560 Speaker 6: vehicle deliveries, and so you know, we may actually see 53 00:02:55,560 --> 00:02:59,040 Speaker 6: some of that you know, support coming into play in 54 00:02:59,080 --> 00:03:00,400 Speaker 6: the fourth quarter of this yere. 55 00:03:01,800 --> 00:03:04,720 Speaker 3: As always, you go straight to social media. I posted 56 00:03:04,760 --> 00:03:06,919 Speaker 3: the numbers there, and there this big body of people 57 00:03:06,919 --> 00:03:10,320 Speaker 3: that say the cell side doesn't know what it's talking about. 58 00:03:10,639 --> 00:03:14,080 Speaker 3: This isn't about delivering evs. It's about a future where 59 00:03:14,120 --> 00:03:17,480 Speaker 3: Tesla own and operates a proprietary ride hailing app Craig, 60 00:03:17,880 --> 00:03:21,640 Speaker 3: they run and operate robotaxis, and I think next week 61 00:03:21,880 --> 00:03:23,959 Speaker 3: we will find out whether or not that's true. 62 00:03:25,720 --> 00:03:28,720 Speaker 6: Yeah, I think, you know, the counter to that argument 63 00:03:28,760 --> 00:03:30,640 Speaker 6: would of course, just be that this is also a 64 00:03:30,680 --> 00:03:33,799 Speaker 6: company that's talked about, you know, putting self driving cars 65 00:03:33,800 --> 00:03:36,920 Speaker 6: on the road and offering a you know, a service 66 00:03:36,920 --> 00:03:38,840 Speaker 6: that would be sort of uber like where you could 67 00:03:38,840 --> 00:03:42,440 Speaker 6: hail one of their self driving cars. For for years now. 68 00:03:42,480 --> 00:03:44,720 Speaker 6: I mean it's it's been a half decade at least 69 00:03:44,720 --> 00:03:48,320 Speaker 6: since Elon Musk was openly musing about this. So you know, 70 00:03:48,360 --> 00:03:50,640 Speaker 6: whether or not they're actually able to deliver that is 71 00:03:50,680 --> 00:03:53,119 Speaker 6: another question. I do think that, you know, if they're 72 00:03:53,160 --> 00:03:56,080 Speaker 6: able to execute and sort of do what others haven't 73 00:03:56,600 --> 00:03:58,480 Speaker 6: you know, pulled off, which is real they do this 74 00:03:58,560 --> 00:04:02,200 Speaker 6: at scale, of course, that would be a massive for 75 00:04:02,280 --> 00:04:05,760 Speaker 6: this company, but it's already a pretty massive valuation for 76 00:04:06,960 --> 00:04:10,720 Speaker 6: you know, a manufacturer that is really struggling to increase sales. 77 00:04:11,120 --> 00:04:14,840 Speaker 5: All Eyes a little later this month, Creatudell. Thank you. 78 00:04:15,440 --> 00:04:17,560 Speaker 5: Now let's get the investor perspective. 79 00:04:17,720 --> 00:04:20,680 Speaker 2: Multi team and global Chief investment Officer Nancy Kurton joins 80 00:04:20,760 --> 00:04:23,599 Speaker 2: us with your view more broadly than just one single stock. 81 00:04:23,920 --> 00:04:27,360 Speaker 2: But the AI play that is so key to Tesla 82 00:04:27,520 --> 00:04:30,280 Speaker 2: is so key to the market, and we still question it. 83 00:04:31,800 --> 00:04:33,839 Speaker 1: Well, I think there's a couple of things to think about. 84 00:04:33,920 --> 00:04:37,599 Speaker 1: We are strong believers that this is early innings and 85 00:04:37,680 --> 00:04:41,960 Speaker 1: it's long term, medium term, hugely disinflationary, will bring great 86 00:04:42,000 --> 00:04:46,440 Speaker 1: efficiency and productivity benefits. But near term, let's just look 87 00:04:46,480 --> 00:04:49,360 Speaker 1: at some of the facts here. The hyperscalers are having 88 00:04:49,360 --> 00:04:53,200 Speaker 1: to spend billions of dollars probably we'll go into the trillions. 89 00:04:53,279 --> 00:04:56,120 Speaker 1: We're doing, you know, a one time shift from CPUs 90 00:04:56,200 --> 00:05:00,760 Speaker 1: to GPUs for extending data center capacity. But we also 91 00:05:00,760 --> 00:05:04,160 Speaker 1: have to build power power not just for GENAI, but 92 00:05:04,240 --> 00:05:08,200 Speaker 1: we just touched on autonomous vehicles and power demand looks 93 00:05:08,200 --> 00:05:10,800 Speaker 1: like it's going to double over the next three to 94 00:05:10,839 --> 00:05:14,880 Speaker 1: five years. These are huge, huge numbers, huge CAPEX investment, 95 00:05:15,200 --> 00:05:19,120 Speaker 1: So it may be near term that actually this investment 96 00:05:19,160 --> 00:05:20,760 Speaker 1: in capex, which ry the way we think is an 97 00:05:20,880 --> 00:05:25,160 Speaker 1: interesting investment opportunity to infrastructure. But this investment in capex 98 00:05:25,200 --> 00:05:29,640 Speaker 1: and maybe shortages that it can bring near term if 99 00:05:29,680 --> 00:05:32,680 Speaker 1: the monetization doesn't come as quickly, and could bring some 100 00:05:32,800 --> 00:05:33,719 Speaker 1: inflation as well. 101 00:05:34,279 --> 00:05:37,039 Speaker 2: So where in the stack does one invest Do you 102 00:05:37,120 --> 00:05:40,640 Speaker 2: go still picks and shovels, chips, Do you go infrastructure? 103 00:05:40,640 --> 00:05:42,920 Speaker 2: When it comes to energy, do you go to applications 104 00:05:42,920 --> 00:05:43,280 Speaker 2: of AI? 105 00:05:44,920 --> 00:05:47,080 Speaker 1: We think you go a little bit to all of them. So, 106 00:05:47,160 --> 00:05:50,320 Speaker 1: first of all, we are invested in infrastructure. We think 107 00:05:50,320 --> 00:05:54,480 Speaker 1: it's super interesting digital infrastructure and power, and the combination, 108 00:05:54,839 --> 00:05:58,960 Speaker 1: we think we can earn equity like returns essential services, 109 00:05:59,080 --> 00:06:04,440 Speaker 1: less correlation to the market, with some great counter parties here, 110 00:06:04,720 --> 00:06:08,360 Speaker 1: and with revenue escalation and inflation protection over time. That's 111 00:06:08,400 --> 00:06:12,120 Speaker 1: a pretty powerful story. But we also have investments at 112 00:06:12,160 --> 00:06:15,160 Speaker 1: the other end of the spectrum with venture companies that 113 00:06:15,200 --> 00:06:18,600 Speaker 1: are doing some things that are incredibly disruptive. We think 114 00:06:18,600 --> 00:06:21,040 Speaker 1: there'll be winners and losers from Jenai, and we want 115 00:06:21,080 --> 00:06:23,560 Speaker 1: to make sure we have exposure to that as well. 116 00:06:23,960 --> 00:06:28,040 Speaker 1: And then finally, we have a specialist technology manager because 117 00:06:28,040 --> 00:06:31,520 Speaker 1: that allows us in the public markets to look where 118 00:06:31,600 --> 00:06:35,520 Speaker 1: Jenai is not yet priced in, and I would say 119 00:06:35,560 --> 00:06:40,360 Speaker 1: all valuations, particularly in data heavy industries, jena is not 120 00:06:40,480 --> 00:06:43,839 Speaker 1: priced in today, and there's some really interesting opportunities there. 121 00:06:45,279 --> 00:06:45,599 Speaker 4: Nancy. 122 00:06:45,640 --> 00:06:48,080 Speaker 3: The problem for our Bloomberg technology audience, if they're just 123 00:06:48,160 --> 00:06:51,240 Speaker 3: tuning in, is that Tesla's down five percent on a 124 00:06:51,360 --> 00:06:53,880 Speaker 3: data set that comes out once a quarter. Even though 125 00:06:53,880 --> 00:06:57,240 Speaker 3: if there's this big body of people that believe in 126 00:06:57,279 --> 00:07:00,960 Speaker 3: this long term story that's very analogous with what you've 127 00:07:01,040 --> 00:07:03,200 Speaker 3: just outlined. I think I'm right in saying that ALTI 128 00:07:03,279 --> 00:07:07,440 Speaker 3: has some small exposures Tesla, but use that as a 129 00:07:07,440 --> 00:07:11,320 Speaker 3: case study. What wins out here, this multi year horizon 130 00:07:11,360 --> 00:07:14,840 Speaker 3: belief in AI or the short term pain of having 131 00:07:14,880 --> 00:07:15,440 Speaker 3: to get there. 132 00:07:16,960 --> 00:07:20,600 Speaker 1: Well, we have a strong view that technology may underperform 133 00:07:20,640 --> 00:07:23,480 Speaker 1: a little bit here. They're very high expectations. These stocks 134 00:07:23,480 --> 00:07:26,680 Speaker 1: are trading at pretty high multiples thirty two times on 135 00:07:26,760 --> 00:07:30,600 Speaker 1: average for the Magnificent seven. And meanwhile, the FED and 136 00:07:30,640 --> 00:07:33,560 Speaker 1: other central banks in the world are easing interest rates, 137 00:07:33,560 --> 00:07:35,680 Speaker 1: which we think will help the other four hundred and 138 00:07:35,800 --> 00:07:39,200 Speaker 1: ninety three shares, which straight up more attractive valuations, and 139 00:07:39,320 --> 00:07:43,480 Speaker 1: with the easing should support our earning's growth going forward. 140 00:07:43,840 --> 00:07:46,800 Speaker 1: So there are things to do in the market. You 141 00:07:46,920 --> 00:07:50,520 Speaker 1: don't just have to play technology at the moment to 142 00:07:50,680 --> 00:07:53,640 Speaker 1: benefit from what's happening with the soft landing and the 143 00:07:53,640 --> 00:07:56,440 Speaker 1: FED easing. And as I said, we think it's going 144 00:07:56,520 --> 00:08:00,280 Speaker 1: to be a little time here for monetization to take call. 145 00:08:00,800 --> 00:08:03,320 Speaker 1: Maybe it's twenty six, maybe it's twenty seven. You know, 146 00:08:03,400 --> 00:08:06,040 Speaker 1: Baying believes, you know, by twenty seven we'll see a 147 00:08:06,040 --> 00:08:09,080 Speaker 1: trillion in revenue. Let's see, but it's going to take 148 00:08:09,120 --> 00:08:12,240 Speaker 1: more time. And remember in the enterprise, when you use 149 00:08:12,320 --> 00:08:15,000 Speaker 1: jen Ai, it's not like the consumer, you have to 150 00:08:15,080 --> 00:08:18,400 Speaker 1: embed it in business processes, you have to industrialize it. 151 00:08:18,800 --> 00:08:21,400 Speaker 1: You've got to make sure that it doesn't have cyber threats, 152 00:08:21,440 --> 00:08:24,000 Speaker 1: and so on and so forth. So this all takes time, 153 00:08:24,080 --> 00:08:26,400 Speaker 1: so you know, a note of caution here near term 154 00:08:26,440 --> 00:08:28,720 Speaker 1: on jen Ai. We can do other things in the 155 00:08:28,760 --> 00:08:32,320 Speaker 1: marketplace and benefit from the digital picks and shovel build 156 00:08:32,880 --> 00:08:35,760 Speaker 1: while we're waiting, but we do ultimately think it will 157 00:08:35,800 --> 00:08:36,800 Speaker 1: be transformational. 158 00:08:37,960 --> 00:08:41,600 Speaker 3: Nancy, is there a corner of the technology sector that's 159 00:08:41,720 --> 00:08:43,800 Speaker 3: underappreciated by investors? 160 00:08:45,320 --> 00:08:48,960 Speaker 1: I think beyond the Magnificent seven as you look more broadly, 161 00:08:49,800 --> 00:08:52,600 Speaker 1: you know, to other companies that will participate. You know, 162 00:08:52,760 --> 00:08:55,960 Speaker 1: Nvidia is not going to be alone here. Maybe it's 163 00:08:56,040 --> 00:08:58,600 Speaker 1: a winner take most as opposed to a winner take all, 164 00:08:58,880 --> 00:09:02,640 Speaker 1: so there will be competition. There are other companies that 165 00:09:02,760 --> 00:09:06,840 Speaker 1: also benefit. You know, think about data centers. They need cooling, 166 00:09:07,440 --> 00:09:10,359 Speaker 1: so you know there are industrial companies that are providing 167 00:09:10,440 --> 00:09:15,040 Speaker 1: cooling services to data centers. The trade at really attract evaluations, 168 00:09:15,360 --> 00:09:19,360 Speaker 1: So there are other ways of playing GENI in this infrastructure. 169 00:09:19,600 --> 00:09:22,280 Speaker 1: But take a look at utilities, probably one of the 170 00:09:22,320 --> 00:09:25,600 Speaker 1: best performing sectors year to date. Who would have thunk 171 00:09:25,640 --> 00:09:29,360 Speaker 1: it right, So there are other ways to think about this. 172 00:09:29,679 --> 00:09:30,720 Speaker 7: You just have to think. 173 00:09:30,559 --> 00:09:35,280 Speaker 1: More broadly, where is JENI not priced in? Particularly in 174 00:09:35,320 --> 00:09:38,520 Speaker 1: this period where we're in the CAPEX build. The monetization 175 00:09:38,679 --> 00:09:41,840 Speaker 1: will take some time, and at the moment, I think 176 00:09:41,880 --> 00:09:44,680 Speaker 1: investors may be getting ahead of what may be delivered 177 00:09:44,760 --> 00:09:45,360 Speaker 1: next quarter. 178 00:09:46,080 --> 00:09:49,680 Speaker 2: What about where it's underpriced globally, Nancy, I think of 179 00:09:49,960 --> 00:09:51,720 Speaker 2: the latest news that we'll go into a little bit 180 00:09:51,800 --> 00:09:54,840 Speaker 2: later about KKR looking to take private Hong Kong based 181 00:09:55,200 --> 00:09:56,920 Speaker 2: or traded chip company. 182 00:09:57,440 --> 00:09:58,720 Speaker 5: What about global. 183 00:09:58,400 --> 00:10:03,080 Speaker 1: Exposure, Well, I think there's some really interesting things happening globally. 184 00:10:03,120 --> 00:10:06,320 Speaker 1: You might have seen as well, something like Blackstone just 185 00:10:06,400 --> 00:10:10,480 Speaker 1: did a sixteen billion dollar deal in Australia called air Trunk, 186 00:10:10,520 --> 00:10:15,880 Speaker 1: which is the largest Asia Pacific data center. Europe is 187 00:10:16,080 --> 00:10:20,440 Speaker 1: further behind the US build. Asia Pacific is further behind 188 00:10:20,440 --> 00:10:24,040 Speaker 1: the US build. So we think in our national exposure 189 00:10:24,360 --> 00:10:28,000 Speaker 1: to data center infrastructure, power and build out is a 190 00:10:28,080 --> 00:10:32,079 Speaker 1: hugely interesting opportunity. They are much further behind the United 191 00:10:32,160 --> 00:10:36,720 Speaker 1: States and prices are more reasonable on a relative basis. 192 00:10:36,760 --> 00:10:39,880 Speaker 1: So yeah, there are opportunities here beyond the United States, 193 00:10:39,960 --> 00:10:41,280 Speaker 1: and they are internationally. 194 00:10:42,440 --> 00:10:45,320 Speaker 3: Nancy Kahr and chief investment officer at ALLTT and then 195 00:10:45,400 --> 00:10:48,840 Speaker 3: global really sizeable advisory. 196 00:10:48,200 --> 00:10:50,120 Speaker 4: And investment firm. Thank you very much. 197 00:10:50,160 --> 00:10:53,080 Speaker 3: Now coming up, Apple prepares for a new slate of 198 00:10:53,120 --> 00:10:57,319 Speaker 3: iPhones and iPads as and this raised concerns about iPhone 199 00:10:57,360 --> 00:10:59,960 Speaker 3: sales growth. We have a bluemeg exclusive coming out now. 200 00:11:00,520 --> 00:11:07,400 Speaker 3: This is Blue Day Technology. 201 00:11:13,040 --> 00:11:16,560 Speaker 2: Apple is preparing to announce a new low end iPhone 202 00:11:16,640 --> 00:11:19,880 Speaker 2: early next year alongside upgraded iPads as all. According to 203 00:11:19,960 --> 00:11:22,760 Speaker 2: sources bluem Eggs Mark German broke, the news brings us 204 00:11:22,800 --> 00:11:26,200 Speaker 2: the details. Is this about India and China and the 205 00:11:26,240 --> 00:11:26,880 Speaker 2: consumer there? 206 00:11:28,679 --> 00:11:31,679 Speaker 8: This is not about India and China specifically. 207 00:11:31,720 --> 00:11:33,120 Speaker 7: This is more of a global thing. 208 00:11:33,200 --> 00:11:35,480 Speaker 8: Right, if you look at how the iPhone sixteen has 209 00:11:35,520 --> 00:11:39,880 Speaker 8: been performing across the world, right, it's not meeting expectations 210 00:11:39,880 --> 00:11:42,520 Speaker 8: according to some analysts. But if the company comes out 211 00:11:42,559 --> 00:11:45,400 Speaker 8: with the phone, obviously they'd been planning this for multiple years. 212 00:11:45,640 --> 00:11:48,400 Speaker 8: That brings down the price point of features like an 213 00:11:48,480 --> 00:11:51,760 Speaker 8: edge to edge stream face ID this thinner luck with 214 00:11:51,840 --> 00:11:55,319 Speaker 8: the swirred off edges globally at a sub five hundred 215 00:11:55,320 --> 00:11:58,959 Speaker 8: dollars price point, they're going to be bringing something quite compelling. 216 00:11:58,520 --> 00:11:59,240 Speaker 5: To the market. 217 00:11:59,559 --> 00:12:02,120 Speaker 1: Now, the reason this is not so much about. 218 00:12:01,840 --> 00:12:05,400 Speaker 8: India is because in India the market share leaders, they're 219 00:12:05,400 --> 00:12:07,880 Speaker 8: not sub five hundred dollars phones, they're sub two hundred 220 00:12:07,880 --> 00:12:11,480 Speaker 8: dollars phones, right, so they're still not playing in that 221 00:12:11,480 --> 00:12:15,439 Speaker 8: that particular market. That would boost market share significantly in India, 222 00:12:15,760 --> 00:12:18,760 Speaker 8: but this would help them make additional straws globally. 223 00:12:19,559 --> 00:12:23,440 Speaker 3: Mark project code name V fifty nine. What a source 224 00:12:23,520 --> 00:12:26,480 Speaker 3: is telling you about a refreshed iPhone SE. 225 00:12:28,000 --> 00:12:30,000 Speaker 8: Yeah, so that's the iPhone SE. That's the new low 226 00:12:30,080 --> 00:12:32,480 Speaker 8: end iPhone and this is going to look similar to 227 00:12:32,520 --> 00:12:35,560 Speaker 8: the iPhone fourteen from a few years ago. It's going 228 00:12:35,640 --> 00:12:39,360 Speaker 8: to have the internal specifications to support Apple Intelligence. There's 229 00:12:39,400 --> 00:12:42,720 Speaker 8: going to be upgraded cameras over the prior SSE. The 230 00:12:42,760 --> 00:12:45,600 Speaker 8: current SE launched in twenty two, that's when they added 231 00:12:45,640 --> 00:12:48,160 Speaker 8: five G to it, but it still has the home button, right, 232 00:12:48,200 --> 00:12:51,440 Speaker 8: it's the only iPhone left with the home button. In fact, 233 00:12:51,520 --> 00:12:55,240 Speaker 8: it's the only Apple product that hasn't been updated to 234 00:12:55,240 --> 00:12:59,439 Speaker 8: remove the home button. They've already transitioned the low end iPad, right, 235 00:12:59,480 --> 00:13:01,120 Speaker 8: they cut the price of that and made that a 236 00:13:01,120 --> 00:13:03,680 Speaker 8: full screen design. So this was the laggard in the 237 00:13:03,679 --> 00:13:06,120 Speaker 8: product line, and that's going to change now. And this 238 00:13:06,160 --> 00:13:08,080 Speaker 8: is going to make things easier for Apple. They don't 239 00:13:08,080 --> 00:13:11,560 Speaker 8: have to support older operating systems for that much longer 240 00:13:12,240 --> 00:13:14,560 Speaker 8: that support a home button. They're able to move to 241 00:13:14,600 --> 00:13:17,040 Speaker 8: a user interface that's consistent across all. 242 00:13:17,000 --> 00:13:17,800 Speaker 4: Of its devices. 243 00:13:17,880 --> 00:13:20,240 Speaker 8: So this is a big deal for the consumer and 244 00:13:20,320 --> 00:13:21,960 Speaker 8: for Apple as a company. And I think this is 245 00:13:21,960 --> 00:13:24,080 Speaker 8: going to be an extremely strong seller. 246 00:13:24,400 --> 00:13:26,760 Speaker 2: How big a deal, How strong a seller is the 247 00:13:26,840 --> 00:13:29,199 Speaker 2: new iPad egg going to be in the keyboards? 248 00:13:30,240 --> 00:13:32,760 Speaker 8: Yeah, The iPad air update's going to be quite minor, right. 249 00:13:32,800 --> 00:13:35,480 Speaker 8: The iPad Air was less updated alongside the iPad Pro 250 00:13:35,840 --> 00:13:39,400 Speaker 8: in May. What they did last May is they introduced 251 00:13:39,400 --> 00:13:41,520 Speaker 8: a new larger size with the iPad Air. So what 252 00:13:41,559 --> 00:13:43,720 Speaker 8: they were doing was they were bringing iPad pro size 253 00:13:43,720 --> 00:13:47,800 Speaker 8: display down market and that actually is pretty significant for 254 00:13:47,920 --> 00:13:50,360 Speaker 8: schools and businesses where you're buying these things in bulk 255 00:13:50,440 --> 00:13:52,839 Speaker 8: and you may want to buy something with that bigger 256 00:13:52,880 --> 00:13:55,360 Speaker 8: display but not spend the typical extra four or five 257 00:13:55,440 --> 00:13:58,360 Speaker 8: hundred dollars. So that was quite interesting. And then they're 258 00:13:58,400 --> 00:14:01,560 Speaker 8: also manufacturing new keyboards for those new iPad airs. The 259 00:14:01,600 --> 00:14:04,880 Speaker 8: current models actually used the old iPad pro keyboard. They 260 00:14:04,920 --> 00:14:07,199 Speaker 8: didn't create a new iPad Air keyboard just for that 261 00:14:07,320 --> 00:14:10,040 Speaker 8: device when they launched the new models earlier this year, 262 00:14:10,360 --> 00:14:12,360 Speaker 8: and so by doing that, they're able to bring down 263 00:14:12,400 --> 00:14:14,720 Speaker 8: market some of the new features of the iPad pro 264 00:14:14,840 --> 00:14:17,760 Speaker 8: keyboard to that iPad Air family and create more of 265 00:14:18,120 --> 00:14:21,960 Speaker 8: a unified accessory in product ecosystem. So I think that's 266 00:14:22,000 --> 00:14:24,200 Speaker 8: going to be a strong seller potentially as well. 267 00:14:25,440 --> 00:14:28,400 Speaker 3: Name based Mark Gunman with another big breaking story on Apple, 268 00:14:28,520 --> 00:14:29,000 Speaker 3: Thank you. 269 00:14:37,000 --> 00:14:37,400 Speaker 5: Google. 270 00:14:37,640 --> 00:14:39,800 Speaker 2: Well, it's looking to take on open ai with new 271 00:14:39,840 --> 00:14:43,920 Speaker 2: AI software that resembles the human ability to reason, similar 272 00:14:44,000 --> 00:14:46,680 Speaker 2: of course to open AI's O one sort, according to 273 00:14:46,720 --> 00:14:49,440 Speaker 2: sources who say multiple teams at Google have been making 274 00:14:49,520 --> 00:14:52,640 Speaker 2: some progress in that area. Bloomberg's Rachel Metz has more, 275 00:14:52,720 --> 00:14:55,560 Speaker 2: how much are they chasing open ai? How much were 276 00:14:55,560 --> 00:14:57,760 Speaker 2: they already building this chain of thought? 277 00:14:58,920 --> 00:15:02,440 Speaker 9: Well, Google has been a pioneer actually in building the 278 00:15:02,520 --> 00:15:04,480 Speaker 9: kind of technology that is used in a lot of 279 00:15:04,480 --> 00:15:07,600 Speaker 9: these systems. As you mentioned chain of thought. The idea 280 00:15:07,680 --> 00:15:12,160 Speaker 9: behind that technique is that you're basically when a person 281 00:15:12,240 --> 00:15:15,880 Speaker 9: gives an AI system a prompt, like a really hard 282 00:15:15,880 --> 00:15:18,920 Speaker 9: math problem, let's say, with this kind of technique, the 283 00:15:18,960 --> 00:15:21,960 Speaker 9: AI system takes the prompt and in the background, unseen 284 00:15:22,000 --> 00:15:23,840 Speaker 9: to the user, it's kind of coming up with a 285 00:15:23,880 --> 00:15:28,440 Speaker 9: bunch of different similar prompts and sorting through the problem 286 00:15:28,560 --> 00:15:31,120 Speaker 9: sort of step by step, and then presenting what seems 287 00:15:31,200 --> 00:15:34,880 Speaker 9: like the best answer to the user. So Google, Google 288 00:15:34,920 --> 00:15:37,200 Speaker 9: has a history here. But as we know, open Ai 289 00:15:37,320 --> 00:15:40,200 Speaker 9: may this flashed recently with their O one model, and 290 00:15:40,320 --> 00:15:42,600 Speaker 9: Google tends to be more cautious. I mean, is it 291 00:15:42,680 --> 00:15:44,960 Speaker 9: is a huge company. There's a lot of moving parts there, 292 00:15:45,320 --> 00:15:47,080 Speaker 9: so we'll see what happens. 293 00:15:47,960 --> 00:15:49,640 Speaker 4: It tends to be more cautious. 294 00:15:49,680 --> 00:15:54,160 Speaker 3: But since chat GPT's release in November twenty twenty two, 295 00:15:54,360 --> 00:15:57,160 Speaker 3: it's been a it's been a wild ride. Since there's 296 00:15:57,280 --> 00:16:01,320 Speaker 3: pressure there, right, and I think the competition, they're cognizant 297 00:16:01,360 --> 00:16:04,200 Speaker 3: of it. So what does that look like, the pushback 298 00:16:04,320 --> 00:16:07,600 Speaker 3: to catch up I suppose against open Ai. 299 00:16:09,040 --> 00:16:10,840 Speaker 9: Yeah, I mean, I think it looks like a few 300 00:16:10,840 --> 00:16:12,000 Speaker 9: different things happening there. 301 00:16:12,080 --> 00:16:12,280 Speaker 7: Right. 302 00:16:12,520 --> 00:16:16,320 Speaker 9: Initially, when chat GPT was so popular with people, I 303 00:16:16,360 --> 00:16:20,440 Speaker 9: think Google felt caught flat footed, but as and felt, 304 00:16:20,520 --> 00:16:22,240 Speaker 9: you know, like, oh, we have to put something out 305 00:16:22,280 --> 00:16:24,840 Speaker 9: so we can be competitive with that. But as more 306 00:16:24,880 --> 00:16:27,880 Speaker 9: time has passed, I think they want to be really cautious. 307 00:16:27,920 --> 00:16:29,560 Speaker 9: I mean, they have a lot of things to think about. 308 00:16:29,560 --> 00:16:31,680 Speaker 9: They have so many users, and they have so many 309 00:16:31,720 --> 00:16:33,880 Speaker 9: people working on different things within their company. So I 310 00:16:33,880 --> 00:16:35,960 Speaker 9: think with this they're really trying to take their time 311 00:16:36,080 --> 00:16:39,320 Speaker 9: and get it right. And they have some amazingly smart 312 00:16:39,320 --> 00:16:41,360 Speaker 9: people that have been working on this kind of technology 313 00:16:41,400 --> 00:16:43,280 Speaker 9: for a very long time, so it'll be really interesting 314 00:16:43,280 --> 00:16:44,360 Speaker 9: to see what comes out of there. 315 00:16:44,840 --> 00:16:47,440 Speaker 2: Haven't even discussed notebook LM yet, which is a lot 316 00:16:47,440 --> 00:16:47,720 Speaker 2: of fun. 317 00:16:47,840 --> 00:16:49,520 Speaker 5: Ratro Metz, thank you so much. 318 00:16:49,640 --> 00:16:53,840 Speaker 2: Meanwhile, well, not everyone is bullish on aidur own a 319 00:16:53,920 --> 00:16:56,960 Speaker 2: small group. Is a renowned professor at MIT, and it's 320 00:16:57,000 --> 00:16:59,160 Speaker 2: not so sure that AI can deliver on the promise 321 00:16:59,200 --> 00:17:01,920 Speaker 2: of an economic He wants to make clear that he 322 00:17:02,280 --> 00:17:06,480 Speaker 2: does get the potential of artificial intelligence, but by his calculation, 323 00:17:06,840 --> 00:17:10,120 Speaker 2: only a small percent of all jobs, a mere five percent, 324 00:17:10,760 --> 00:17:13,120 Speaker 2: is right to be taken over or at least heavily 325 00:17:13,160 --> 00:17:16,040 Speaker 2: aided by AI over the next decade. 326 00:17:16,400 --> 00:17:17,120 Speaker 5: And what have you got. 327 00:17:17,760 --> 00:17:20,280 Speaker 3: Okay, it's time for today's AI and action, and today 328 00:17:20,280 --> 00:17:23,320 Speaker 3: we're taking a look at Codium, which aims to utilize 329 00:17:23,320 --> 00:17:27,240 Speaker 3: AI to accelerate software development. Just last month, they raise 330 00:17:27,280 --> 00:17:29,480 Speaker 3: one hundred and fifty million dollars in a series C 331 00:17:29,760 --> 00:17:33,639 Speaker 3: round with evaluation of one point twenty five billion dollars. 332 00:17:33,680 --> 00:17:37,280 Speaker 3: Codium CEO and co founder Varun Mohan joins us for more. 333 00:17:37,320 --> 00:17:39,399 Speaker 3: Welcome to the program. A lot of people phone me 334 00:17:39,440 --> 00:17:41,280 Speaker 3: and said that you've got to check out Codium, So 335 00:17:41,359 --> 00:17:44,320 Speaker 3: let's start with the basics of what is Codium. 336 00:17:44,600 --> 00:17:46,399 Speaker 4: Thanks for having me ed so. 337 00:17:46,680 --> 00:17:50,200 Speaker 10: Codium is an AI software development toolkit that fundamentally enables 338 00:17:50,200 --> 00:17:54,360 Speaker 10: developers to write software faster and review software faster. Right now, 339 00:17:54,400 --> 00:17:57,720 Speaker 10: the platform has over seven hundred thousand individual developers that 340 00:17:57,800 --> 00:18:00,560 Speaker 10: use the product in over one thousand enterprises, and one 341 00:18:00,560 --> 00:18:03,040 Speaker 10: of the key things that we focus on is deep 342 00:18:03,040 --> 00:18:04,760 Speaker 10: personalization based. 343 00:18:04,480 --> 00:18:05,520 Speaker 4: On the rest of the codebase. 344 00:18:05,800 --> 00:18:08,960 Speaker 10: Code is a very interdependent sort of knowledge and data source, 345 00:18:09,160 --> 00:18:12,159 Speaker 10: and we try to make the responses as personalized to 346 00:18:12,200 --> 00:18:12,879 Speaker 10: the private. 347 00:18:12,640 --> 00:18:13,600 Speaker 4: Data inside companies. 348 00:18:13,920 --> 00:18:16,720 Speaker 10: Right now, they're seeing around fifty percent of all their 349 00:18:16,760 --> 00:18:20,240 Speaker 10: private code that's committed generated by Codium, as well as 350 00:18:20,240 --> 00:18:23,680 Speaker 10: a twenty percent reduction in total application development time when 351 00:18:23,760 --> 00:18:24,400 Speaker 10: using Codium. 352 00:18:24,440 --> 00:18:27,760 Speaker 3: I'm going to make an observation and you respond to it. 353 00:18:28,320 --> 00:18:32,280 Speaker 3: You have seven hundred thousand users and a thousand enterprise customers. 354 00:18:33,320 --> 00:18:37,679 Speaker 3: You've raised relatively little money compared to a number of 355 00:18:37,680 --> 00:18:40,879 Speaker 3: companies that raised quite a lot of money that haven't 356 00:18:40,920 --> 00:18:44,160 Speaker 3: started selling anything yet or anyone using their tech. 357 00:18:44,440 --> 00:18:45,440 Speaker 4: What do you make of that? 358 00:18:46,119 --> 00:18:46,199 Speaker 7: So? 359 00:18:46,240 --> 00:18:47,760 Speaker 4: I don't have a lot of comments on this. 360 00:18:48,480 --> 00:18:51,720 Speaker 10: I previously worked in an industry autonomous vehicles, where companies 361 00:18:51,720 --> 00:18:55,199 Speaker 10: were raising larger and larger amounts of money without actually 362 00:18:55,200 --> 00:18:58,120 Speaker 10: developing a product, So I think this is not uncommon 363 00:18:58,160 --> 00:18:59,359 Speaker 10: in a bunch of categories. 364 00:19:00,040 --> 00:19:01,439 Speaker 7: What we believe in is we're in. 365 00:19:01,440 --> 00:19:04,280 Speaker 10: A space that is moving incredibly fast, and the only 366 00:19:04,320 --> 00:19:07,520 Speaker 10: thing that would guarantee us the ability to actually earn 367 00:19:07,560 --> 00:19:10,680 Speaker 10: the right to work with large enterprises is actually shipping 368 00:19:10,880 --> 00:19:13,600 Speaker 10: incredibly quickly, staying really close to the technology. 369 00:19:14,520 --> 00:19:20,760 Speaker 2: Bern It's a relatively crowded space though. You've got GitHub Copilot, 370 00:19:20,840 --> 00:19:22,919 Speaker 2: You've got Sam Altman telling me that his favorite thing 371 00:19:22,960 --> 00:19:25,399 Speaker 2: to do on the latest version of chatchubt is to code. 372 00:19:25,440 --> 00:19:29,080 Speaker 2: So how do you ensure you take market share? 373 00:19:30,320 --> 00:19:30,600 Speaker 7: Yeah? 374 00:19:30,880 --> 00:19:33,520 Speaker 10: I think we believe the space is going to be crowded, 375 00:19:33,600 --> 00:19:37,600 Speaker 10: largely because the prize is quite large. If a tool 376 00:19:37,680 --> 00:19:40,640 Speaker 10: is able to actually generate or reduce application development time 377 00:19:40,680 --> 00:19:42,639 Speaker 10: by twenty percent, It would be surprising if there are 378 00:19:42,680 --> 00:19:46,360 Speaker 10: only one player in the category. The way we sort 379 00:19:46,359 --> 00:19:47,960 Speaker 10: of like to look at it is what can we 380 00:19:47,960 --> 00:19:52,119 Speaker 10: do differentially better than other companies and compared to our 381 00:19:52,160 --> 00:19:55,159 Speaker 10: biggest competitor being gethub copilot. The big thing we do 382 00:19:55,280 --> 00:19:57,760 Speaker 10: is care a lot about security as well as being 383 00:19:57,760 --> 00:20:01,560 Speaker 10: a platform agnostic tool. Right no, noticing a very small 384 00:20:01,560 --> 00:20:06,200 Speaker 10: fraction of large enterprises standardizing on getub Enterprise Cloud, and we, 385 00:20:06,280 --> 00:20:09,480 Speaker 10: on the other hand, don't really tie ourselves to any 386 00:20:09,520 --> 00:20:12,240 Speaker 10: one of these platforms. In addition to that, we don't 387 00:20:12,280 --> 00:20:15,720 Speaker 10: actually require a company to send their IP outside of 388 00:20:15,760 --> 00:20:17,840 Speaker 10: their firewall, which is one of the things that get 389 00:20:17,920 --> 00:20:19,080 Speaker 10: up copile that actually requires. 390 00:20:19,119 --> 00:20:21,800 Speaker 2: Right now, Okay, so you feel you've got a unique 391 00:20:21,880 --> 00:20:24,840 Speaker 2: selling point there, but you're going to keep on innovating. 392 00:20:25,200 --> 00:20:28,800 Speaker 2: You told previously that you raise a load of money 393 00:20:29,240 --> 00:20:31,440 Speaker 2: before and haven't actually even spent that. 394 00:20:31,640 --> 00:20:33,600 Speaker 5: Why do you need this money right now? 395 00:20:35,040 --> 00:20:37,119 Speaker 10: I think as we're starting to work with some of 396 00:20:37,160 --> 00:20:40,639 Speaker 10: the world's largest enterprises companies like Dell, we want to 397 00:20:40,680 --> 00:20:43,560 Speaker 10: make sure we're great sort of vendors to our customers, 398 00:20:44,000 --> 00:20:46,720 Speaker 10: and that means continuing to invest in innovation. As you 399 00:20:46,720 --> 00:20:50,800 Speaker 10: can imagine GPU costs are fairly high, but also beyond 400 00:20:50,800 --> 00:20:53,840 Speaker 10: that increasing the amount of the size of our research 401 00:20:53,880 --> 00:20:56,560 Speaker 10: and development organization. We just don't want to be in 402 00:20:56,560 --> 00:20:59,879 Speaker 10: a position where we are working and with large companies 403 00:21:00,080 --> 00:21:03,159 Speaker 10: that trust us and we actually cannot provide support to 404 00:21:03,200 --> 00:21:05,080 Speaker 10: them in the best way possible. 405 00:21:05,440 --> 00:21:07,320 Speaker 2: We raised sixty five million in a Series B and 406 00:21:07,320 --> 00:21:09,240 Speaker 2: now just raise one hundred and fifty million a series See, 407 00:21:09,240 --> 00:21:10,720 Speaker 2: you've got some money to spend on R and D. 408 00:21:10,960 --> 00:21:13,240 Speaker 2: Cody and the CEO from in Mohan, thank you very 409 00:21:13,320 --> 00:21:20,520 Speaker 2: much for joining us. 410 00:21:21,640 --> 00:21:22,920 Speaker 5: Welcome back to Bluemotechnology. 411 00:21:22,920 --> 00:21:25,280 Speaker 3: I'm Carolin Hide in New York and Imed Loveo in 412 00:21:25,320 --> 00:21:26,000 Speaker 3: San Francisco. 413 00:21:26,480 --> 00:21:28,119 Speaker 5: Check on these markets which have turned around here. 414 00:21:28,200 --> 00:21:30,840 Speaker 2: We were trading lower at the start of this program, 415 00:21:30,880 --> 00:21:33,399 Speaker 2: and now we're up some four tenths percent. You've got 416 00:21:33,400 --> 00:21:35,160 Speaker 2: some of the key chip makers to thank for that. 417 00:21:35,280 --> 00:21:38,280 Speaker 2: We got broad coom and video leading from our points perspective. 418 00:21:38,600 --> 00:21:42,720 Speaker 2: Also digesting the latest economic data, but largely Bitcoin, also bouncing. 419 00:21:42,320 --> 00:21:43,560 Speaker 5: Back from yesterday's sell off. 420 00:21:43,600 --> 00:21:45,879 Speaker 2: Of course, that all about geopolitical tension to search for 421 00:21:45,960 --> 00:21:48,600 Speaker 2: safety amid what was, of course the key concerns of 422 00:21:48,840 --> 00:21:52,640 Speaker 2: Iran missill attacks on Israel. We now await the comeback, 423 00:21:52,960 --> 00:21:56,520 Speaker 2: but we're currently improving from a market sentiment perspective. 424 00:21:56,640 --> 00:21:59,720 Speaker 3: Ed some of the other single names we are watching. 425 00:21:59,800 --> 00:22:03,399 Speaker 3: Ten slid down after the company posted quarterly sales coming 426 00:22:03,600 --> 00:22:07,200 Speaker 3: just below estimates, but it's off its session lows. Apple's 427 00:22:07,240 --> 00:22:09,800 Speaker 3: now flat. It had been down as much as one 428 00:22:09,800 --> 00:22:12,920 Speaker 3: and a half percent after Bloomberg reported the company's near 429 00:22:12,960 --> 00:22:15,720 Speaker 3: production of a new low end iPhone, which have come 430 00:22:15,920 --> 00:22:17,240 Speaker 3: as early as next year. 431 00:22:17,280 --> 00:22:18,480 Speaker 4: Let's talk about the markets. 432 00:22:18,640 --> 00:22:21,719 Speaker 3: Bloomberg's jessmentton is here, I guess on a daylight today 433 00:22:21,800 --> 00:22:25,080 Speaker 3: where at the index level we were treading water. Some 434 00:22:25,200 --> 00:22:28,480 Speaker 3: of those megacap names, the moves are more notable DC 435 00:22:28,680 --> 00:22:31,800 Speaker 3: a story when it comes to tech in markets today. 436 00:22:31,720 --> 00:22:33,879 Speaker 11: There is because if you look at the MLV function, 437 00:22:33,960 --> 00:22:35,960 Speaker 11: which y'all were just alluding to you, when you're looking 438 00:22:36,000 --> 00:22:38,160 Speaker 11: at obviously these big tech and growth companies and their 439 00:22:38,200 --> 00:22:40,080 Speaker 11: waitings in the S and P five hundred, when you 440 00:22:40,080 --> 00:22:42,879 Speaker 11: do have C and Vidia as well as broadcome among 441 00:22:42,920 --> 00:22:45,960 Speaker 11: the biggest point contributors to the S and P five hundred. 442 00:22:46,440 --> 00:22:48,359 Speaker 11: The other side of that, though, of course, Tesla and 443 00:22:48,400 --> 00:22:50,000 Speaker 11: you all have been talking about how to miss those 444 00:22:50,119 --> 00:22:53,200 Speaker 11: third quarter delivery numbers there, especially some of the issues 445 00:22:53,200 --> 00:22:54,160 Speaker 11: potentially with China. 446 00:22:54,200 --> 00:22:56,400 Speaker 7: But that's actually the biggest point decliner. 447 00:22:56,000 --> 00:22:57,639 Speaker 11: Here in the S and P five hundred, as well 448 00:22:57,640 --> 00:22:59,880 Speaker 11: as some of the other names like Apple of Core, 449 00:23:00,440 --> 00:23:03,119 Speaker 11: as well as Meta and Alphabet. So when you have 450 00:23:03,200 --> 00:23:05,880 Speaker 11: more of the chip shares, because the socks was actually 451 00:23:05,960 --> 00:23:08,680 Speaker 11: down last quarter, it snapped a streak of gains here 452 00:23:08,680 --> 00:23:11,040 Speaker 11: and Evercore actually made a call for the fourth quarter 453 00:23:11,080 --> 00:23:13,360 Speaker 11: going into year in that they did anticipate to see 454 00:23:13,359 --> 00:23:15,960 Speaker 11: a potential rebound in those chip stocks, and you're beginning 455 00:23:16,000 --> 00:23:17,119 Speaker 11: to see some of that this morning. 456 00:23:17,160 --> 00:23:17,440 Speaker 5: Guys. 457 00:23:17,960 --> 00:23:19,639 Speaker 2: Yeah, look at that Socks up more than two and 458 00:23:19,640 --> 00:23:22,000 Speaker 2: a quarter of percent. You type in sent functions to 459 00:23:22,000 --> 00:23:24,280 Speaker 2: see when that is the biggest moves in September the 460 00:23:24,280 --> 00:23:27,240 Speaker 2: twenty sixth, we understand, is this just the knee jug 461 00:23:27,280 --> 00:23:30,399 Speaker 2: reaction to yesterday's sell off or is this thesis changing 462 00:23:30,440 --> 00:23:30,920 Speaker 2: in any way. 463 00:23:31,240 --> 00:23:33,560 Speaker 11: It looks like it's basically a knee jerk reaction because 464 00:23:33,600 --> 00:23:35,520 Speaker 11: that was the biggest decline in almost a month for 465 00:23:35,560 --> 00:23:37,680 Speaker 11: the S and P five hundred, and then especially a 466 00:23:37,720 --> 00:23:40,760 Speaker 11: lot of these chip makers, even Micron Technology ahead of 467 00:23:40,760 --> 00:23:43,480 Speaker 11: its earnings report last week. Obviously it boosted a lot 468 00:23:43,480 --> 00:23:45,600 Speaker 11: of other chip companies after it had reported and had 469 00:23:45,600 --> 00:23:48,360 Speaker 11: that optimistic outlook. We still have a lot of time 470 00:23:48,400 --> 00:23:50,320 Speaker 11: before we would hear from Video though, that's going to 471 00:23:50,320 --> 00:23:52,800 Speaker 11: be well into November by that point. But when you're 472 00:23:52,840 --> 00:23:54,840 Speaker 11: looking at that, a lot of those companies like Micron 473 00:23:54,880 --> 00:23:58,399 Speaker 11: were trading at quite a discount there, and especially because 474 00:23:58,400 --> 00:24:00,119 Speaker 11: of that, as will some of these other names in 475 00:24:00,600 --> 00:24:03,040 Speaker 11: Video for instance, even once it had that stock splid 476 00:24:03,160 --> 00:24:06,040 Speaker 11: back in June, I mean, it was actually underperforming Apple. 477 00:24:06,119 --> 00:24:08,160 Speaker 11: So the question is moving forward, a lot of those 478 00:24:08,200 --> 00:24:10,280 Speaker 11: that looked a little bit cheaper, maybe people are going 479 00:24:10,320 --> 00:24:12,320 Speaker 11: back in and stepping up to buy those types of names. 480 00:24:12,359 --> 00:24:16,280 Speaker 2: Now, Caroline best menton, we thank you chip companies. Thinking 481 00:24:16,320 --> 00:24:19,200 Speaker 2: about something else at the moment, Global Semiconductors Make is 482 00:24:19,359 --> 00:24:23,679 Speaker 2: really monitoring supplies of quartz. That's after Hurricane Helene halted 483 00:24:23,720 --> 00:24:27,120 Speaker 2: production of two North Carolina mines that provide basically. 484 00:24:26,760 --> 00:24:29,720 Speaker 5: Most of the world supplies for all Bloombergs. Daniel Woolman 485 00:24:29,840 --> 00:24:30,399 Speaker 5: joins us to. 486 00:24:30,400 --> 00:24:34,040 Speaker 2: Explain why high purity courts matters to chip makers. 487 00:24:34,640 --> 00:24:37,919 Speaker 12: So I'm really glad to be here geeking out about 488 00:24:37,920 --> 00:24:42,119 Speaker 12: this with you, courts is a prehistoric material. We're talking 489 00:24:42,160 --> 00:24:44,600 Speaker 12: a mineral that is hundreds of millions of years old 490 00:24:44,720 --> 00:24:46,920 Speaker 12: that developed at a time when there was very little 491 00:24:46,960 --> 00:24:52,560 Speaker 12: water around and on the planet. And because that scarcity 492 00:24:52,560 --> 00:24:55,960 Speaker 12: of water means fewer imperfections in the material, and fewer 493 00:24:55,960 --> 00:25:00,119 Speaker 12: imperfections means that when it's used in this chip making process, 494 00:25:00,160 --> 00:25:03,399 Speaker 12: it imparts fewer imperfections onto the eventual chip. 495 00:25:04,960 --> 00:25:08,359 Speaker 3: Its role in the manufacturing process is multifaceted because you 496 00:25:08,359 --> 00:25:11,959 Speaker 3: have quartz wafers, it's used in frequency control. But if 497 00:25:11,960 --> 00:25:14,639 Speaker 3: there's one thing I've learned about the industry in the 498 00:25:14,680 --> 00:25:17,600 Speaker 3: last few years, it's that it's quite good at stockpiling 499 00:25:17,720 --> 00:25:21,720 Speaker 3: the stuff that it needs. Is that the scenario that's 500 00:25:21,720 --> 00:25:22,520 Speaker 3: playing out here. 501 00:25:22,920 --> 00:25:25,399 Speaker 12: Indeed, our reporters reached out to a whole bunch of 502 00:25:25,440 --> 00:25:30,280 Speaker 12: major chip makers on different continents, TSMC, Samsung, sk Heinez, 503 00:25:30,800 --> 00:25:34,159 Speaker 12: and all of them delivered comments to us that seemed 504 00:25:34,400 --> 00:25:38,119 Speaker 12: very calm, unbothered. They all seem to for now be 505 00:25:38,240 --> 00:25:41,080 Speaker 12: thriving off of these stockpiles they have. That said, to 506 00:25:41,119 --> 00:25:44,520 Speaker 12: your point in the intro, these two mines in North 507 00:25:44,560 --> 00:25:47,320 Speaker 12: Carolina account for about eighty percent or maybe even a 508 00:25:47,359 --> 00:25:50,399 Speaker 12: little more of the global supply of courts. So really 509 00:25:50,480 --> 00:25:53,639 Speaker 12: their calmness in this situation depends on the assumption that 510 00:25:53,720 --> 00:25:57,119 Speaker 12: these minds will eventually and hopefully soon be able to 511 00:25:57,119 --> 00:25:58,440 Speaker 12: reopen after the hurricane. 512 00:25:59,040 --> 00:26:03,520 Speaker 2: It feels almost very painful that something that was focused 513 00:26:03,520 --> 00:26:06,560 Speaker 2: on because of a lack of water is now prevented 514 00:26:06,600 --> 00:26:09,000 Speaker 2: because of too much water and flooding in a certain area. 515 00:26:09,800 --> 00:26:12,919 Speaker 2: Do we have any timeframe anything from the manufacturers in 516 00:26:12,960 --> 00:26:15,879 Speaker 2: North Carolina as to when they might get back online. 517 00:26:16,440 --> 00:26:20,720 Speaker 12: Our reporting doesn't really nail down a specific timeline. I 518 00:26:20,760 --> 00:26:23,800 Speaker 12: think the tone right now is rosy enough. I don't 519 00:26:23,800 --> 00:26:27,080 Speaker 12: want to say rosie given the circumstances, but I think 520 00:26:27,200 --> 00:26:29,840 Speaker 12: the manufacturer seem confident enough at this moment that the 521 00:26:29,880 --> 00:26:32,080 Speaker 12: plants will eventually reopen. 522 00:26:33,119 --> 00:26:35,320 Speaker 5: Danil woman, we thank you so much. 523 00:26:35,400 --> 00:26:37,639 Speaker 2: Meanwhile, it is time for talking tech and first up, 524 00:26:37,720 --> 00:26:41,080 Speaker 2: KKR considering a takeover bid for chips and electronics equipment 525 00:26:41,160 --> 00:26:45,080 Speaker 2: maker ASMPT It's at According to sources, KKR has made 526 00:26:45,119 --> 00:26:48,120 Speaker 2: a non binding preliminary approach for the company. The move 527 00:26:48,160 --> 00:26:51,800 Speaker 2: follows similar attempts in the past by other bidders. Plus Oracle, 528 00:26:51,960 --> 00:26:53,919 Speaker 2: set to spend six and a half billion dollars to 529 00:26:53,960 --> 00:26:57,480 Speaker 2: build out cloud service centers in Malaysia. The company hopes 530 00:26:57,480 --> 00:26:59,760 Speaker 2: to establish a cloud region in the country, providing data 531 00:26:59,760 --> 00:27:03,119 Speaker 2: center do corporate clients are called didn't offer timeframes or 532 00:27:03,200 --> 00:27:06,359 Speaker 2: specifics of the build out, and a new law signed 533 00:27:06,359 --> 00:27:09,200 Speaker 2: in the Philippines will impose a twelve percent value added 534 00:27:09,240 --> 00:27:13,040 Speaker 2: tax on non resident digital service providers. Sectors set to 535 00:27:13,080 --> 00:27:17,719 Speaker 2: be impacted include online search engines, advertising platforms, digital marketplaces, 536 00:27:17,720 --> 00:27:21,879 Speaker 2: cloud services, and even media like Netflix, HBO and Disney. 537 00:27:22,800 --> 00:27:24,879 Speaker 5: Meanwhile, coming up, we're going to be joined by the. 538 00:27:24,880 --> 00:27:28,400 Speaker 2: Former president and CFO of SoftBank Group International. We want 539 00:27:28,440 --> 00:27:31,160 Speaker 2: to get behind the scenes to look at how tech 540 00:27:31,280 --> 00:27:34,359 Speaker 2: VC firms were behind some of the biggest deals. What 541 00:27:34,400 --> 00:27:36,600 Speaker 2: does he make of ARM, What does he make of 542 00:27:36,640 --> 00:27:39,240 Speaker 2: the open AI valuation? This subruebeg technology. 543 00:27:51,880 --> 00:27:55,560 Speaker 3: We all know the powerhouse that is SoftBank, the tech investor, 544 00:27:55,600 --> 00:27:58,879 Speaker 3: the backtor likes of ARM in video TikTok, Uber, Ali 545 00:27:58,960 --> 00:28:01,959 Speaker 3: Barber and work, but we don't know as much about 546 00:28:02,200 --> 00:28:05,159 Speaker 3: what goes on behind the scenes of these major deals. 547 00:28:05,160 --> 00:28:08,240 Speaker 3: A new book gives us a peek into the dealings 548 00:28:08,240 --> 00:28:11,919 Speaker 3: and into soft Bank's iconic founder, Masaoshi's son. It's called 549 00:28:12,040 --> 00:28:15,119 Speaker 3: The Money Trap written by a Look Sama, a veteran 550 00:28:15,160 --> 00:28:18,440 Speaker 3: Morgan Stanley banker and a former chief deal maker at 551 00:28:18,480 --> 00:28:21,840 Speaker 3: soft Bank, and he joins us now in San Francisco. 552 00:28:22,040 --> 00:28:24,280 Speaker 4: Let's start by asking, now that the book is written 553 00:28:24,320 --> 00:28:27,040 Speaker 4: and out, yea, what is soft Bank? 554 00:28:27,920 --> 00:28:29,960 Speaker 7: Well, first of all, thanks for having me. Great to 555 00:28:29,960 --> 00:28:31,520 Speaker 7: be here. What is soft Bank? 556 00:28:31,640 --> 00:28:33,720 Speaker 4: I mean, I you must know you just wrote a 557 00:28:33,720 --> 00:28:35,320 Speaker 4: book about the Well, the. 558 00:28:35,359 --> 00:28:38,440 Speaker 13: Name itself is a bank for software and gets back 559 00:28:38,480 --> 00:28:42,680 Speaker 13: to Marsa ruts as a software distributor for Microsoft software. 560 00:28:44,000 --> 00:28:47,320 Speaker 13: But soft Bank is really about Masa Yoshi Son and 561 00:28:47,360 --> 00:28:49,720 Speaker 13: I consider it one of the great privileges of my 562 00:28:49,880 --> 00:28:54,640 Speaker 13: life to have worked alongside him for a while, you know, 563 00:28:54,720 --> 00:28:57,360 Speaker 13: kind of fancy titled aside. You know, my role really 564 00:28:57,400 --> 00:28:59,800 Speaker 13: there was was chief you know, kind of deal maker 565 00:28:59,800 --> 00:29:03,000 Speaker 13: in chiefs, so to speak. And U and I think 566 00:29:03,080 --> 00:29:06,440 Speaker 13: the one thing I would really highlight about him, the 567 00:29:06,520 --> 00:29:11,120 Speaker 13: standout characteristic is uh is he's a futurist in the 568 00:29:11,280 --> 00:29:15,400 Speaker 13: true sense of the word. And and yeah that that 569 00:29:15,400 --> 00:29:19,120 Speaker 13: that term visionary genius. It gets thrown around a lot, 570 00:29:19,200 --> 00:29:21,560 Speaker 13: but he's pulling a time and time. 571 00:29:21,680 --> 00:29:23,800 Speaker 3: It's an interesting point in sort of the mechanics of 572 00:29:23,800 --> 00:29:26,320 Speaker 3: how soft Bank works. Right, it is a conglomerate. Then 573 00:29:26,360 --> 00:29:28,400 Speaker 3: you have the vision fun part vision from one and 574 00:29:28,440 --> 00:29:32,080 Speaker 3: two each has an investment committee. My point being that 575 00:29:32,200 --> 00:29:35,640 Speaker 3: ultimately it's still in many senses, comes down to Massa 576 00:29:36,200 --> 00:29:39,520 Speaker 3: making an instinctive call on whether or not to invest 577 00:29:39,600 --> 00:29:40,000 Speaker 3: in something. 578 00:29:40,040 --> 00:29:40,840 Speaker 4: Is that what you found? 579 00:29:41,040 --> 00:29:43,800 Speaker 13: It's more than just instinctive and the answer is yes. 580 00:29:43,920 --> 00:29:47,120 Speaker 13: For me at least, self Bank is all about Massa, 581 00:29:47,480 --> 00:29:51,600 Speaker 13: and the standout thing about self Bank is he gets 582 00:29:51,680 --> 00:29:55,280 Speaker 13: the big bits spectacularly, right, I mean, I mean stories 583 00:29:55,360 --> 00:29:57,800 Speaker 13: like we work that kind of fun amusing. I talk 584 00:29:57,840 --> 00:30:02,840 Speaker 13: about it too, But nobody's the movie about about for example, 585 00:30:02,960 --> 00:30:05,640 Speaker 13: which is just a remarkable success story, right, and we 586 00:30:05,680 --> 00:30:09,640 Speaker 13: can talk about that. And soft Bank Japan is a 587 00:30:09,800 --> 00:30:13,880 Speaker 13: lesser known, low profile up. There's no reason a US audience, 588 00:30:13,880 --> 00:30:16,040 Speaker 13: so US investors would be familiar with that. But it's 589 00:30:16,040 --> 00:30:18,880 Speaker 13: a remarkable story. I mean, you know, people think that 590 00:30:18,880 --> 00:30:22,240 Speaker 13: that Massa has come back in the in the in 591 00:30:22,280 --> 00:30:24,440 Speaker 13: two thousand and six, two thousand and seven had to 592 00:30:24,440 --> 00:30:27,560 Speaker 13: do with Ali Baba and Alibaba is phenomenal. But soft 593 00:30:27,560 --> 00:30:31,400 Speaker 13: Bank Japan, which is a mobile phone company Voteraphone, was 594 00:30:31,480 --> 00:30:34,160 Speaker 13: so desperate to get rid of it they actually lent 595 00:30:34,280 --> 00:30:37,160 Speaker 13: Masa money to take it off their hands. And you know, 596 00:30:37,240 --> 00:30:39,720 Speaker 13: he bought that company on the strength of a handshake 597 00:30:39,760 --> 00:30:43,480 Speaker 13: from Steve Jobs to give him an exclusive for smartphones, 598 00:30:43,480 --> 00:30:45,000 Speaker 13: and he kind of saw that coming. 599 00:30:45,080 --> 00:30:47,480 Speaker 7: So he's made these huge bets. 600 00:30:47,520 --> 00:30:49,480 Speaker 13: And by the way, these gain on that on a 601 00:30:49,520 --> 00:30:53,720 Speaker 13: mark to markets basic basis on SoftBank Japan, sorry, on 602 00:30:53,760 --> 00:30:55,720 Speaker 13: Votafone Japan is to the order of forty billion. 603 00:30:55,840 --> 00:30:57,200 Speaker 7: On arm it's one hundred billion. 604 00:30:57,560 --> 00:31:01,480 Speaker 13: So he gets these big bets spectacularly alert. 605 00:31:01,760 --> 00:31:04,920 Speaker 2: You have got some spectacular misses as well, and that's 606 00:31:04,960 --> 00:31:09,360 Speaker 2: the VC way you bet big. Some of them hugely payoff, 607 00:31:09,800 --> 00:31:13,440 Speaker 2: others don't. But there was this moment where money being 608 00:31:13,480 --> 00:31:16,360 Speaker 2: thrown at we work, money being thrown at improbable in 609 00:31:16,400 --> 00:31:20,160 Speaker 2: the UK, money being thrown at wag which rent horribly wrong, 610 00:31:20,160 --> 00:31:24,800 Speaker 2: a dog walking service. What makes the open Ai potential 611 00:31:24,920 --> 00:31:28,280 Speaker 2: bet that we're hearing about soft Bank bringing in five 612 00:31:28,360 --> 00:31:30,840 Speaker 2: hundred million, is that the right one to be throwing 613 00:31:30,880 --> 00:31:31,680 Speaker 2: money at right now? 614 00:31:32,720 --> 00:31:37,240 Speaker 13: Well, I'm not sure trying money is the way I 615 00:31:37,360 --> 00:31:39,600 Speaker 13: described it. I think you have to keep in mind 616 00:31:39,640 --> 00:31:43,000 Speaker 13: that in the Masters is he's been fascinated by AI. 617 00:31:43,120 --> 00:31:44,480 Speaker 7: He was talking about. 618 00:31:44,160 --> 00:31:47,040 Speaker 13: This when I first got to know him back in 619 00:31:47,080 --> 00:31:50,200 Speaker 13: twenty fourteen, and I described this in that book, sitting 620 00:31:50,200 --> 00:31:53,520 Speaker 13: on his porch and his home in Tokyo, and you know, 621 00:31:54,200 --> 00:31:58,440 Speaker 13: he was talking about obsessively about AI, about singularity and 622 00:31:58,560 --> 00:32:03,360 Speaker 13: what would mean from a technology perspective, and I honestly 623 00:32:03,520 --> 00:32:06,120 Speaker 13: I didn't take him seriously at the time. So it's 624 00:32:06,160 --> 00:32:08,520 Speaker 13: it's and he's been very clear now it is. 625 00:32:08,600 --> 00:32:09,080 Speaker 7: I think in. 626 00:32:09,120 --> 00:32:11,960 Speaker 13: June at his AGM, he announced that everything we've seen 627 00:32:12,040 --> 00:32:15,239 Speaker 13: before is a warm up deck, so he's ready to 628 00:32:15,240 --> 00:32:18,080 Speaker 13: make some some He's ready to make some big moves. 629 00:32:18,080 --> 00:32:19,800 Speaker 7: And that's a long winded way of answer your question. 630 00:32:19,840 --> 00:32:21,440 Speaker 13: I mean, you look at soft Bank and the scale 631 00:32:21,480 --> 00:32:24,920 Speaker 13: on which it operates. I think it's sitting on certainly 632 00:32:25,120 --> 00:32:28,000 Speaker 13: north of thirty billion, probably close it a forty billion 633 00:32:28,080 --> 00:32:31,719 Speaker 13: in cash, so A I don't I don't think of 634 00:32:31,760 --> 00:32:34,920 Speaker 13: a five hundred million in open AI as a big 635 00:32:34,960 --> 00:32:38,440 Speaker 13: bet by soft Bank standards. I think it's it's a 636 00:32:38,600 --> 00:32:40,280 Speaker 13: it's it's it's it's sort of. 637 00:32:40,280 --> 00:32:41,960 Speaker 7: Being in the game, so to speak. 638 00:32:42,000 --> 00:32:44,320 Speaker 13: I mean, I'm kind of waiting for Sonson to make 639 00:32:44,400 --> 00:32:48,160 Speaker 13: his next big move. And it will be big. Knowing him, 640 00:32:48,200 --> 00:32:51,360 Speaker 13: that's just just that's just the way he wrotes to what. 641 00:32:51,560 --> 00:32:54,520 Speaker 2: End paints us the vision Therefore, because we too have 642 00:32:54,640 --> 00:32:58,600 Speaker 2: heard the AI thesis, this is why the deal that 643 00:32:58,640 --> 00:33:01,240 Speaker 2: you helped orchestrate was first the target. 644 00:33:01,920 --> 00:33:02,760 Speaker 4: But to what end? 645 00:33:02,760 --> 00:33:04,160 Speaker 5: What does he build eventually? 646 00:33:04,200 --> 00:33:07,280 Speaker 2: Because sometimes he's purchasing with soft Bank, sometimes he's purchasing 647 00:33:07,320 --> 00:33:09,680 Speaker 2: with the vc ARM. 648 00:33:10,040 --> 00:33:13,920 Speaker 13: Well, let's be clear, the first vision fund was a 649 00:33:13,960 --> 00:33:17,160 Speaker 13: true fund inasmuch as it involved third party. 650 00:33:16,880 --> 00:33:18,400 Speaker 7: Money mainly from the Middle East. 651 00:33:18,960 --> 00:33:22,320 Speaker 13: The other Vision funds are on balance sheet, so you know, 652 00:33:22,440 --> 00:33:25,160 Speaker 13: the the subtlety of soft Bank versus the vc ARM 653 00:33:25,800 --> 00:33:30,680 Speaker 13: to me, it's it's it's it's not meaningful in that context, right, 654 00:33:30,720 --> 00:33:32,720 Speaker 13: I mean, so, for example, when you read about soft 655 00:33:32,760 --> 00:33:37,240 Speaker 13: Bank buying Graphcore, my understanding is that's a strategic deal. 656 00:33:37,440 --> 00:33:40,360 Speaker 13: In what Sonson intends to do with that in conjunction 657 00:33:40,560 --> 00:33:42,840 Speaker 13: with his ownership of ARM, you know, I think, I 658 00:33:42,880 --> 00:33:46,160 Speaker 13: think remains to be seen. But talking about vision again, 659 00:33:46,200 --> 00:33:48,200 Speaker 13: I mean I speak is I've been out a soft 660 00:33:48,240 --> 00:33:51,240 Speaker 13: Bank for five years now, so I'm obviously not an insider. 661 00:33:51,800 --> 00:33:54,560 Speaker 13: But but you know, in terms of what I see, 662 00:33:54,800 --> 00:33:57,680 Speaker 13: first of all this ARM ARM is and I don't 663 00:33:57,720 --> 00:34:00,360 Speaker 13: follow stocks. I'm not a research analyst, so I'm not 664 00:34:00,400 --> 00:34:02,960 Speaker 13: going to comment on the valuation. But ARM for me 665 00:34:03,120 --> 00:34:06,720 Speaker 13: is remarkably well positioned as AI moves to the edge 666 00:34:06,720 --> 00:34:09,920 Speaker 13: of the network. ARM has always been about blueprints for 667 00:34:10,040 --> 00:34:13,600 Speaker 13: chip design. Energy efficiency has always been their secret source. 668 00:34:14,000 --> 00:34:15,920 Speaker 13: And when you get to the edge, you know AI, 669 00:34:16,200 --> 00:34:19,640 Speaker 13: you know, with Apple Intelligence and smartphones and autonomous cars, 670 00:34:20,360 --> 00:34:23,040 Speaker 13: that gets to be really really important. I think that, 671 00:34:23,160 --> 00:34:25,440 Speaker 13: you know, I think the graph Core acquisition is a 672 00:34:25,440 --> 00:34:28,160 Speaker 13: signal at and I think the company has been pretty 673 00:34:28,160 --> 00:34:31,200 Speaker 13: explicit about it that that that ARM intends to be 674 00:34:31,239 --> 00:34:35,800 Speaker 13: a challenger to to in video in terms of Individia's 675 00:34:35,800 --> 00:34:39,600 Speaker 13: dominances in data centers, So ARM intends to to challenge 676 00:34:39,640 --> 00:34:42,600 Speaker 13: in video in that regard to So there's a lot 677 00:34:42,640 --> 00:34:43,359 Speaker 13: of that going on. 678 00:34:44,040 --> 00:34:45,680 Speaker 7: My own view of AI. 679 00:34:46,120 --> 00:34:49,120 Speaker 13: Uh, you know, I do spend a fair bit of 680 00:34:49,120 --> 00:34:51,440 Speaker 13: time with Warber Pinkers as a senior advisor, and what 681 00:34:51,480 --> 00:34:54,360 Speaker 13: they're seeing They've avoided getting into the you know, l 682 00:34:54,480 --> 00:34:57,600 Speaker 13: l ms, but but they've been you know, what they're 683 00:34:57,640 --> 00:35:01,920 Speaker 13: seeing is AI very folk push on their portfolio and 684 00:35:03,320 --> 00:35:06,839 Speaker 13: the implications for deploying AI margin Improumus. Jensen want talked 685 00:35:06,840 --> 00:35:08,480 Speaker 13: about it in his last earnings release. I mean he 686 00:35:08,480 --> 00:35:11,400 Speaker 13: gave the example of m ducts and a thirty percent 687 00:35:11,440 --> 00:35:15,800 Speaker 13: reduction in customer service cars by deploying an AI agent, 688 00:35:15,880 --> 00:35:17,480 Speaker 13: So that that stuff is very, very real. 689 00:35:17,560 --> 00:35:19,880 Speaker 4: Let me ask you saying end conversation. 690 00:35:21,400 --> 00:35:23,960 Speaker 3: They were in early in nvideo SoftBank, but then they 691 00:35:23,960 --> 00:35:27,240 Speaker 3: got out early and threw that, Yeah, five hundred million 692 00:35:27,280 --> 00:35:29,520 Speaker 3: in open AI may not be big. You said that 693 00:35:29,560 --> 00:35:32,880 Speaker 3: they will make their next big bet. How do you 694 00:35:33,000 --> 00:35:35,920 Speaker 3: know that he'll time it right and that in the 695 00:35:36,040 --> 00:35:38,880 Speaker 3: end with AI, whatever happens, he'll be a part of whatever. 696 00:35:38,920 --> 00:35:40,480 Speaker 4: The conversation we're having is. 697 00:35:41,280 --> 00:35:44,080 Speaker 7: What Because the track record speaks for itself. He's done 698 00:35:44,120 --> 00:35:45,799 Speaker 7: it over and over again, right. 699 00:35:45,880 --> 00:35:48,279 Speaker 13: I mean, I think with smartphones he was at least 700 00:35:48,320 --> 00:35:51,640 Speaker 13: two to three years ahead of it. I don't know 701 00:35:51,800 --> 00:35:54,759 Speaker 13: what his next big move will be, but I do 702 00:35:54,880 --> 00:35:57,040 Speaker 13: know it's going to be big, and I think he's 703 00:35:57,040 --> 00:35:58,759 Speaker 13: come out and said it will be something to do 704 00:35:58,840 --> 00:36:01,800 Speaker 13: with AI and it may well involve me. Look, I 705 00:36:01,800 --> 00:36:04,239 Speaker 13: mean energy is another space by soft Bank Energy. Enough 706 00:36:04,320 --> 00:36:07,520 Speaker 13: money people get this, but soft Bank Energy is one 707 00:36:07,520 --> 00:36:10,600 Speaker 13: of the leading solo bar generators and that, as we know, 708 00:36:10,920 --> 00:36:13,440 Speaker 13: is a huge bottleneck in terms of the built out 709 00:36:13,480 --> 00:36:16,360 Speaker 13: of these data centers, I mean the hugely power hungry, 710 00:36:16,440 --> 00:36:18,880 Speaker 13: so could be something in that space, for example. 711 00:36:18,800 --> 00:36:19,720 Speaker 5: Alo Summer. 712 00:36:20,040 --> 00:36:21,600 Speaker 2: We thank you so much for spending some time with 713 00:36:21,680 --> 00:36:24,320 Speaker 2: our senior advisor at Wilbert Pinker's former presidency of a 714 00:36:24,440 --> 00:36:25,040 Speaker 2: SoftBank Group. 715 00:36:25,120 --> 00:36:25,680 Speaker 5: International. 716 00:36:33,080 --> 00:36:36,200 Speaker 2: Interest has just launched Performance Plus ahead of the holiday season. 717 00:36:36,280 --> 00:36:38,520 Speaker 2: It's a new suite of AI and jen Ai tools 718 00:36:38,800 --> 00:36:41,640 Speaker 2: to help advertisers optimize targeting, drive results. 719 00:36:41,960 --> 00:36:44,680 Speaker 5: Hit explain as pinterest CEO Bill Ready, And I'm assuming 720 00:36:45,080 --> 00:36:45,600 Speaker 5: the better. 721 00:36:45,440 --> 00:36:48,840 Speaker 2: Efficiency and targeting for advertisers means more repeat advertisers on 722 00:36:48,920 --> 00:36:49,440 Speaker 2: your platform. 723 00:36:50,480 --> 00:36:51,880 Speaker 14: Yeah, that's exactly right, Caroline. 724 00:36:52,760 --> 00:36:56,640 Speaker 15: You know, we just had pinterest Presents yesterday, which is 725 00:36:56,640 --> 00:37:01,200 Speaker 15: our annual marketing event for advertisers, and a record turnout 726 00:37:01,239 --> 00:37:03,120 Speaker 15: for that. And really at the core of that is 727 00:37:03,160 --> 00:37:05,560 Speaker 15: that over the last two years, we have transformed Pinterest 728 00:37:05,640 --> 00:37:09,000 Speaker 15: from a place that was just digital window shopping to 729 00:37:09,080 --> 00:37:12,360 Speaker 15: a place where people are actually clicking and buying and shopping, 730 00:37:13,440 --> 00:37:16,560 Speaker 15: you know, directly with retailers, and that's given us our 731 00:37:16,640 --> 00:37:19,319 Speaker 15: best product market fit ever with users. But it's also 732 00:37:19,400 --> 00:37:22,000 Speaker 15: cutting through for advertisers where we have more than double 733 00:37:22,040 --> 00:37:23,920 Speaker 15: the number of clicks to advertisers a year on year 734 00:37:24,000 --> 00:37:27,040 Speaker 15: for three consecutive quarters. And as we're bringing them great 735 00:37:27,120 --> 00:37:30,600 Speaker 15: AI driven ad tools, it's like more and more advertisers 736 00:37:30,640 --> 00:37:31,320 Speaker 15: take advantage of that. 737 00:37:31,960 --> 00:37:32,520 Speaker 4: You're brought on. 738 00:37:32,640 --> 00:37:35,719 Speaker 2: Because your expertise and commerce and payments and a whole 739 00:37:35,760 --> 00:37:39,520 Speaker 2: wealth at Google. I'm interested Bill as to where the 740 00:37:39,600 --> 00:37:44,680 Speaker 2: clicks are being made, which products, which demographic you serve well. 741 00:37:44,760 --> 00:37:48,520 Speaker 15: So that's a great question, Caroline, because we have really 742 00:37:48,600 --> 00:37:51,200 Speaker 15: become the place that gen Z goes to shop. So 743 00:37:51,560 --> 00:37:54,120 Speaker 15: we have more than forty percent of our users our 744 00:37:54,239 --> 00:37:58,160 Speaker 15: gen Z and it's our largest, fastest growing demographic. And 745 00:37:58,400 --> 00:38:00,960 Speaker 15: gen Z states that the number one reason they go 746 00:38:01,040 --> 00:38:03,600 Speaker 15: to pinterest is for shopping, and more than sixty percent 747 00:38:03,640 --> 00:38:06,320 Speaker 15: of them say that they're always shopping. So when you 748 00:38:06,400 --> 00:38:09,000 Speaker 15: think about the rise of gen Z, where they're buying 749 00:38:09,080 --> 00:38:11,359 Speaker 15: power is just growing tremendously. In fact, over the next 750 00:38:11,440 --> 00:38:16,360 Speaker 15: five years, gen Z is predicted to bypass Baby boomers 751 00:38:16,480 --> 00:38:19,400 Speaker 15: in buying power over the next five years. And Pinterest 752 00:38:19,920 --> 00:38:21,840 Speaker 15: is where gen Z goes to shop, and so obviously 753 00:38:21,840 --> 00:38:24,080 Speaker 15: a great place for advertisers to meet that really important 754 00:38:24,160 --> 00:38:24,800 Speaker 15: growing audience. 755 00:38:25,840 --> 00:38:28,040 Speaker 3: Bill, do you have a sense for this upcoming holiday 756 00:38:28,160 --> 00:38:30,759 Speaker 3: season if what the biggest funnel. 757 00:38:30,600 --> 00:38:32,759 Speaker 4: For you will be, or at least who you will 758 00:38:32,840 --> 00:38:33,319 Speaker 4: help the most. 759 00:38:33,400 --> 00:38:35,160 Speaker 3: You and I have talked a lot about the relationship 760 00:38:35,200 --> 00:38:38,799 Speaker 3: with Amazon, for example, but also some of the other 761 00:38:38,880 --> 00:38:41,680 Speaker 3: names from China are becoming increasingly important for you. 762 00:38:43,000 --> 00:38:46,800 Speaker 15: Yeah, So we're really broadening our reach with advertisers. So 763 00:38:47,400 --> 00:38:50,440 Speaker 15: as we opened up the stores on our platform, we 764 00:38:50,560 --> 00:38:53,560 Speaker 15: saw the largest, most sophisticated advertisers lean in first, which 765 00:38:53,600 --> 00:38:56,000 Speaker 15: is great because they're the hardest to please, the most discerning. 766 00:38:56,719 --> 00:38:58,680 Speaker 15: But what we're seeing now is that that's really widening, 767 00:38:58,800 --> 00:39:02,080 Speaker 15: and when with Performance Plus and those AI driven tools, 768 00:39:02,160 --> 00:39:04,120 Speaker 15: it's really making it easier for a broader set of 769 00:39:04,160 --> 00:39:08,160 Speaker 15: advertisers to take advantage where it gives them automated campaign creation, 770 00:39:08,320 --> 00:39:12,760 Speaker 15: automated creative and so we're seeing big advertisers win. For example, 771 00:39:12,800 --> 00:39:15,919 Speaker 15: Walgreens is seeing a fifty five percent improvement and click 772 00:39:15,960 --> 00:39:20,000 Speaker 15: through rate by using our generated backgrounds so the products 773 00:39:20,000 --> 00:39:23,840 Speaker 15: look more attractive, so you can see that shampoo or 774 00:39:23,920 --> 00:39:25,960 Speaker 15: that hand soap and what your bathroom might look like. 775 00:39:26,360 --> 00:39:29,440 Speaker 15: But we're also seeing as we go down to smaller 776 00:39:29,520 --> 00:39:32,480 Speaker 15: up and coming advertisers like Ruggables, which is a really 777 00:39:32,520 --> 00:39:35,920 Speaker 15: fantastic home goods company. We are now their largest traffic 778 00:39:36,040 --> 00:39:40,520 Speaker 15: source by volume and efficiency, literally driving three times the 779 00:39:40,560 --> 00:39:43,839 Speaker 15: clicks at half the costs. So these AI driven tools 780 00:39:43,880 --> 00:39:46,440 Speaker 15: are really broadening out the set of advertisers on our platform. 781 00:39:48,560 --> 00:39:51,200 Speaker 3: Bill we've been taking a lot about augmented reality. Check 782 00:39:51,239 --> 00:39:54,239 Speaker 3: out my piece on metas o Ryan, will Pinterest make 783 00:39:54,360 --> 00:39:58,520 Speaker 3: investments in hardware for AR or partner with meta snaping 784 00:39:58,600 --> 00:39:59,399 Speaker 3: span what you're doing? 785 00:40:00,600 --> 00:40:04,200 Speaker 15: We're not a hardware platform, but we are seeing that 786 00:40:04,320 --> 00:40:09,839 Speaker 15: we have become increasingly a destination for our users. More 787 00:40:09,920 --> 00:40:13,080 Speaker 15: than eighty percent of our usage is people coming to 788 00:40:13,160 --> 00:40:18,000 Speaker 15: our mobile app directly, and that usage increasingly is more 789 00:40:18,000 --> 00:40:19,759 Speaker 15: than half our easier here to shop, This even more 790 00:40:19,840 --> 00:40:23,000 Speaker 15: so with gen z. So I think as you look 791 00:40:23,040 --> 00:40:26,240 Speaker 15: at these visual exploration journeys, this is really where pinterest 792 00:40:26,360 --> 00:40:28,400 Speaker 15: is winning that on visual exploration. 793 00:40:29,120 --> 00:40:32,160 Speaker 14: Visual search shopping is inherently a visual journey. 794 00:40:32,200 --> 00:40:35,000 Speaker 15: So these kinds of things are really where Pinterest stands out, 795 00:40:35,400 --> 00:40:37,920 Speaker 15: and we see users coming to us directly for that increasingly. 796 00:40:38,400 --> 00:40:40,160 Speaker 5: Are you llm agnostic? 797 00:40:40,239 --> 00:40:43,719 Speaker 2: What's the underlying technology driving these innovations. 798 00:40:43,840 --> 00:40:46,279 Speaker 14: Yeah, great question. So you know, AI has become a 799 00:40:46,360 --> 00:40:47,560 Speaker 14: core competency for us. 800 00:40:48,040 --> 00:40:50,880 Speaker 15: I've talked about previously how when we move to GPU 801 00:40:51,000 --> 00:40:54,719 Speaker 15: serving in large models, you have models one hundred times 802 00:40:54,800 --> 00:40:57,200 Speaker 15: larger than what they were two years ago. That gave 803 00:40:57,280 --> 00:41:00,440 Speaker 15: us a ten percentage point lift in real andy of 804 00:41:00,480 --> 00:41:03,839 Speaker 15: our recommendations. But the AI is only as good as 805 00:41:03,840 --> 00:41:05,960 Speaker 15: a signal upon which it's acting, and what we have 806 00:41:06,160 --> 00:41:10,080 Speaker 15: is really unique signal where, unlike other platforms, users come 807 00:41:10,120 --> 00:41:13,600 Speaker 15: to Pinterest and curate their taste, how's that handbag going 808 00:41:13,640 --> 00:41:15,600 Speaker 15: to look with that dress, with this pair of shoes, 809 00:41:15,840 --> 00:41:18,759 Speaker 15: and hundreds of millions of users do that every day, 810 00:41:19,440 --> 00:41:21,160 Speaker 15: and so that gives us really unique signal to say, 811 00:41:21,200 --> 00:41:23,239 Speaker 15: what's a great recommendation not just for that user but 812 00:41:23,360 --> 00:41:26,600 Speaker 15: for other users. So when we're training models, we're training 813 00:41:26,680 --> 00:41:29,399 Speaker 15: on completely unique signal to our platform. We actually see 814 00:41:29,440 --> 00:41:33,040 Speaker 15: that when we test llms off the shelf from major 815 00:41:33,160 --> 00:41:36,879 Speaker 15: LM providers, we actually see that when that same LLM 816 00:41:37,040 --> 00:41:40,160 Speaker 15: is tuned on our signal, we get three hundred basis 817 00:41:40,200 --> 00:41:43,560 Speaker 15: points of additional engagement from our unique signal, which really 818 00:41:43,600 --> 00:41:45,880 Speaker 15: makes tangible how unique our signal is relative to what 819 00:41:45,960 --> 00:41:47,520 Speaker 15: these lms have elsewhere. 820 00:41:47,520 --> 00:41:50,239 Speaker 5: Interesting CEO Phil Ready, We thank you for your time. 821 00:41:50,880 --> 00:41:53,320 Speaker 5: Now that does it for this edition of BlueBag Technology ED. 822 00:41:54,160 --> 00:41:55,840 Speaker 4: Check out the podcast you know where to find it. 823 00:41:56,120 --> 00:41:57,240 Speaker 4: This has been bad technology