1 00:00:04,280 --> 00:00:08,680 Speaker 1: Bloomberg Tech is alive from coast to coast with Caroline 2 00:00:08,760 --> 00:00:11,920 Speaker 1: Hide in New York and ev Low in sentrances go. 3 00:00:14,920 --> 00:00:16,480 Speaker 2: This is Bloomberg Tech coming up. 4 00:00:16,520 --> 00:00:20,360 Speaker 3: Palenteer shares form concerns about the company's valuation and the 5 00:00:20,440 --> 00:00:22,800 Speaker 3: sustainability of the AI rally. 6 00:00:22,960 --> 00:00:26,160 Speaker 1: Thus we'll break down more tech earnings, Spotify, Uber grab 7 00:00:26,200 --> 00:00:28,560 Speaker 1: all out with results as well, and we push ahead 8 00:00:28,560 --> 00:00:29,880 Speaker 1: to am D tonight. 9 00:00:30,480 --> 00:00:34,479 Speaker 3: And the world's largest sovereign wealth fund vote against Tesla's 10 00:00:34,520 --> 00:00:37,880 Speaker 3: proposed pay package for CEO E Long. Musk will discuss 11 00:00:37,920 --> 00:00:39,840 Speaker 3: the impact on the company's shares. 12 00:00:39,840 --> 00:00:42,640 Speaker 1: But first we check out what's happening in these markets 13 00:00:42,840 --> 00:00:44,879 Speaker 1: more broadly. In and I'm looking at and now's that 14 00:00:44,920 --> 00:00:47,560 Speaker 1: one hundred that is under pressure. Look, we're only down 15 00:00:47,840 --> 00:00:51,240 Speaker 1: to levels well that we've seen this month, and indeed 16 00:00:51,320 --> 00:00:53,840 Speaker 1: it's the worst sell off since all Thursday. So this 17 00:00:53,880 --> 00:00:57,120 Speaker 1: isn't seismic, but there is a tension here about some 18 00:00:57,200 --> 00:01:00,360 Speaker 1: of the levels of valuations in certain people speaking out 19 00:01:00,520 --> 00:01:03,640 Speaker 1: about putting on some short bearish bets. I'm looking at Bitcoin, though, 20 00:01:03,680 --> 00:01:05,720 Speaker 1: has been under far more pressure. It's the lowest level 21 00:01:05,720 --> 00:01:07,200 Speaker 1: that we've seen since June. 22 00:01:06,959 --> 00:01:07,440 Speaker 4: Of this year. 23 00:01:07,440 --> 00:01:09,600 Speaker 1: Off by another three point three percent amid the risk 24 00:01:09,680 --> 00:01:12,240 Speaker 1: of tension. You're looking at some big movers underneath the herd. 25 00:01:13,000 --> 00:01:14,720 Speaker 2: Yeah, Tesla's down two point eight percent. 26 00:01:14,760 --> 00:01:17,600 Speaker 3: It had been down four percent of the open Norway's 27 00:01:17,680 --> 00:01:21,560 Speaker 3: Sovereign Wealth funds, Tessa's ninth largest shareholder voting against the 28 00:01:21,600 --> 00:01:23,080 Speaker 3: bait package. We're going to head out to Europe in 29 00:01:23,120 --> 00:01:25,199 Speaker 3: a minute and get more on that. And then there's 30 00:01:25,240 --> 00:01:30,039 Speaker 3: Palenteer strong beat in the third quarter, raising annual outlook 31 00:01:30,360 --> 00:01:34,319 Speaker 3: for revenue. But the cell side and investors and the 32 00:01:34,400 --> 00:01:37,520 Speaker 3: internet all going to valuation as a pointed discussion carro 33 00:01:38,080 --> 00:01:42,160 Speaker 3: commercial growth and government growth. But this isn't about fundamentals, 34 00:01:42,200 --> 00:01:42,840 Speaker 3: or maybe it is. 35 00:01:43,520 --> 00:01:46,479 Speaker 1: Let's talk about both fundamentals and valuations and talk about 36 00:01:46,520 --> 00:01:49,880 Speaker 1: Palenteer's earnings and the sustainability of these numbers. Of Marianna 37 00:01:49,960 --> 00:01:52,440 Speaker 1: Perez Mora, she has an aerospaceed defense analyst that be 38 00:01:52,480 --> 00:01:55,120 Speaker 1: Evasecurities with a BI rating two ondred and fifteen dollars 39 00:01:55,120 --> 00:01:57,800 Speaker 1: price target for Palenteer. More broadly, and I think you're 40 00:01:57,840 --> 00:02:01,800 Speaker 1: even upping it to two five five. Tell us about 41 00:02:01,840 --> 00:02:06,880 Speaker 1: why Palenteer is just outperforming. It is peerless in your perspective. 42 00:02:06,720 --> 00:02:09,000 Speaker 5: So first, thank you so much for having me here. 43 00:02:09,120 --> 00:02:12,880 Speaker 5: I'm happy to share this with you too. And I 44 00:02:12,919 --> 00:02:17,400 Speaker 5: think Polunteers has proven that has been the winner of 45 00:02:17,520 --> 00:02:22,160 Speaker 5: this AI implementation. And I mean not only about like investment, 46 00:02:22,320 --> 00:02:26,240 Speaker 5: but actually the growth they are unlocking the customers actually 47 00:02:26,240 --> 00:02:28,840 Speaker 5: go into them because they can prove that they can 48 00:02:28,919 --> 00:02:33,239 Speaker 5: actually extract value from these AI implementations. And it doesn't 49 00:02:33,240 --> 00:02:37,600 Speaker 5: only stop with those customers, but also other software peers 50 00:02:37,800 --> 00:02:40,639 Speaker 5: that are partnering with Palenteer because they also want to 51 00:02:40,680 --> 00:02:43,280 Speaker 5: be part of this. They have like really good software, 52 00:02:43,560 --> 00:02:46,160 Speaker 5: but they are still struggling to actually make that software work. 53 00:02:46,360 --> 00:02:51,160 Speaker 5: And Polunteering is like from a fundamental perspective, actually proving 54 00:02:51,200 --> 00:02:53,960 Speaker 5: that they can extract value from those implementations. 55 00:02:54,200 --> 00:02:56,520 Speaker 1: Your note is such a joy to read because you're 56 00:02:56,600 --> 00:02:59,680 Speaker 1: using wonderful analogies going back to the matrix, whether you're 57 00:02:59,680 --> 00:03:01,440 Speaker 1: going to be where you're going to red pell But 58 00:03:01,480 --> 00:03:04,400 Speaker 1: what's interesting is you're thinking that ultimately the red pillar 59 00:03:04,520 --> 00:03:07,640 Speaker 1: is there with Paneteer, and they are unlike the ninety 60 00:03:07,680 --> 00:03:11,120 Speaker 1: five percent of failed pilots that MIT drew a focus on, 61 00:03:11,480 --> 00:03:14,440 Speaker 1: they're managing to make AI work. But why what is 62 00:03:14,440 --> 00:03:18,160 Speaker 1: it different about dot cart Daddy Carp however you see 63 00:03:18,240 --> 00:03:21,520 Speaker 1: him and indeed the go to market focus that they have. 64 00:03:22,520 --> 00:03:25,640 Speaker 5: I think what is different first is that they have 65 00:03:25,760 --> 00:03:29,320 Speaker 5: been working on this infrastructure that is actually what makes 66 00:03:29,360 --> 00:03:34,200 Speaker 5: AI be operationable for more than twenty years. This is 67 00:03:34,240 --> 00:03:37,600 Speaker 5: something that today has more value than anything because of 68 00:03:37,640 --> 00:03:40,440 Speaker 5: like the acceleration and AI and the new software that 69 00:03:40,480 --> 00:03:43,120 Speaker 5: we have, but like this ontology that they have that 70 00:03:43,280 --> 00:03:46,240 Speaker 5: is like the data integration that can actually unlock value 71 00:03:46,280 --> 00:03:50,120 Speaker 5: from all these different like data parts and like have 72 00:03:50,240 --> 00:03:54,640 Speaker 5: the human to be able to interoperate with that that 73 00:03:55,120 --> 00:03:57,120 Speaker 5: has been in the works for more than twenty years, 74 00:03:57,320 --> 00:04:00,840 Speaker 5: and that's why they are a real winner. And from 75 00:04:00,880 --> 00:04:04,680 Speaker 5: a customer perspective, you have to take into account that 76 00:04:04,720 --> 00:04:06,880 Speaker 5: they work with the US government and they have worked 77 00:04:06,880 --> 00:04:10,200 Speaker 5: with the uscrment for so long, so they know how 78 00:04:10,240 --> 00:04:13,440 Speaker 5: to do complex operations and they are translating all that 79 00:04:13,640 --> 00:04:16,560 Speaker 5: know how into the commercial world. That is why I 80 00:04:16,600 --> 00:04:18,359 Speaker 5: think their winner. They were prepared for this. 81 00:04:20,080 --> 00:04:20,560 Speaker 2: Marianna. 82 00:04:20,600 --> 00:04:22,640 Speaker 3: A part of the pressure on the stock this morning, 83 00:04:22,640 --> 00:04:25,080 Speaker 3: and we need to acknowledge it is that Michael Berry 84 00:04:25,600 --> 00:04:28,440 Speaker 3: of the Big Short fame or Sian Asset Management his 85 00:04:28,520 --> 00:04:35,159 Speaker 3: firm have disclosed some various wages, including on Palenteer put options. 86 00:04:35,800 --> 00:04:40,039 Speaker 3: It's the form that they've taken, but there's clearly some 87 00:04:40,160 --> 00:04:43,720 Speaker 3: bigger picture worry about valuation and are we or are 88 00:04:43,760 --> 00:04:45,440 Speaker 3: we not in an AI bubble. The way that you 89 00:04:45,480 --> 00:04:48,000 Speaker 3: put it in your note is to discern what is 90 00:04:48,080 --> 00:04:50,560 Speaker 3: real and what is not real. What is it you 91 00:04:50,600 --> 00:04:54,320 Speaker 3: see in Palenteer that gives you the conviction it is real. 92 00:04:56,240 --> 00:04:59,800 Speaker 5: What makes me convinced about being real is what when 93 00:04:59,839 --> 00:05:02,560 Speaker 5: you hear about the customers and the transformation they are 94 00:05:02,560 --> 00:05:08,080 Speaker 5: seeing from implementing polunteers product. They are like actually saving money, 95 00:05:08,120 --> 00:05:14,040 Speaker 5: They are doing things faster, cheaper, better, smarter. Those real 96 00:05:14,120 --> 00:05:16,440 Speaker 5: changes are the ones that make me optimistic about it. 97 00:05:16,520 --> 00:05:21,280 Speaker 5: And we have said this over time. I'm really convinced 98 00:05:21,320 --> 00:05:25,120 Speaker 5: that even if THEI bubble were to burst, Polenteer will 99 00:05:25,160 --> 00:05:30,080 Speaker 5: survive because it's the structure to actually extract that value 100 00:05:30,279 --> 00:05:33,279 Speaker 5: and do more with AI, AI agents or whatever is 101 00:05:33,400 --> 00:05:36,960 Speaker 5: next from a stochastic models or anything in software and 102 00:05:37,000 --> 00:05:38,040 Speaker 5: computing and everything. 103 00:05:39,160 --> 00:05:43,280 Speaker 3: You're right that seeing is believing, Marianna. I've attended quite 104 00:05:43,279 --> 00:05:46,200 Speaker 3: a few AIP cons and what happens is you had 105 00:05:46,200 --> 00:05:50,000 Speaker 3: these customers go on stage and demo how they actually 106 00:05:50,080 --> 00:05:51,960 Speaker 3: use palented, Like, there are so many people out there 107 00:05:51,960 --> 00:05:53,800 Speaker 3: that are like, what does palenteer even too? 108 00:05:54,240 --> 00:05:55,000 Speaker 6: Do you know? 109 00:05:55,279 --> 00:05:57,120 Speaker 3: I've seen the demo and you get a sense for 110 00:05:57,160 --> 00:05:59,600 Speaker 3: how it works. So then that takes me to the 111 00:05:59,640 --> 00:06:02,440 Speaker 3: commercial growth one hundred and twenty one percent growth here 112 00:06:02,480 --> 00:06:05,320 Speaker 3: on year in the US. The nervousness out there is 113 00:06:05,320 --> 00:06:09,240 Speaker 3: that it's only the US that commercial business isn't growing internationally. 114 00:06:11,480 --> 00:06:14,320 Speaker 5: I think when you think about international, you have to 115 00:06:14,360 --> 00:06:17,000 Speaker 5: take into account two things. Number One, there are larger 116 00:06:17,040 --> 00:06:20,200 Speaker 5: customers there that like, we're already hardly like a lot 117 00:06:20,240 --> 00:06:24,800 Speaker 5: of penetrated, and that palenttary is putting their focus in 118 00:06:24,839 --> 00:06:28,680 Speaker 5: the US. So it's also demand is like amazing for 119 00:06:28,800 --> 00:06:31,040 Speaker 5: AI in general, and we see that with all the 120 00:06:31,120 --> 00:06:35,039 Speaker 5: capits being invested. But if you have to actually catch 121 00:06:35,080 --> 00:06:37,560 Speaker 5: up with that demand and execute on that, you have 122 00:06:37,600 --> 00:06:39,320 Speaker 5: to take into account where you put your efforts on 123 00:06:39,400 --> 00:06:41,560 Speaker 5: your resources, and I think it's aligned with that the 124 00:06:41,720 --> 00:06:45,240 Speaker 5: Palantear is putting most of their commercial resources focused in 125 00:06:45,279 --> 00:06:45,760 Speaker 5: the US. 126 00:06:47,480 --> 00:06:51,440 Speaker 3: Mariana perez More Bank of America Securities price target up 127 00:06:51,440 --> 00:06:53,839 Speaker 3: to two hundred and fifty five dollars, reiterating a buy 128 00:06:53,920 --> 00:06:56,400 Speaker 3: on Palenteer, appreciate it. That's get a lot of the 129 00:06:56,440 --> 00:06:59,960 Speaker 3: top story in Tesla shares down after the world's largest 130 00:07:00,000 --> 00:07:04,279 Speaker 3: sovereign wealth fund voted against Tesla's stock award proposal for 131 00:07:04,360 --> 00:07:07,800 Speaker 3: CEO Ela Musk, the biggest show of opposition yet by 132 00:07:07,839 --> 00:07:11,760 Speaker 3: a major shareholder in London, Bloomberg's Auto Zar crag Trudell 133 00:07:12,080 --> 00:07:14,840 Speaker 3: joins us as more, we're trying to tabulate in tally 134 00:07:15,080 --> 00:07:20,520 Speaker 3: who's voted no so far ahead of November sixth. Norway's 135 00:07:20,520 --> 00:07:24,920 Speaker 3: sovereign wealth fund is the ninth largest Tesla shareholder, I believe, 136 00:07:24,960 --> 00:07:27,960 Speaker 3: hence why we're saying this is the biggest no vote 137 00:07:28,000 --> 00:07:28,360 Speaker 3: so far. 138 00:07:29,920 --> 00:07:32,280 Speaker 7: Yeah, that's right. And they were pretty diplomatic about it. 139 00:07:32,320 --> 00:07:36,720 Speaker 7: They praised the value that Musk has has created over 140 00:07:36,760 --> 00:07:39,480 Speaker 7: the years. They refer to him as, you know, having 141 00:07:39,520 --> 00:07:42,520 Speaker 7: played a visionary role. And yet they're concerned just about 142 00:07:42,560 --> 00:07:46,120 Speaker 7: this sort of magnitude of of this award, about dilution 143 00:07:46,840 --> 00:07:49,800 Speaker 7: and also about the issue of sort of key man risk. 144 00:07:50,320 --> 00:07:52,520 Speaker 7: This is something that has been has come up time 145 00:07:52,560 --> 00:07:56,360 Speaker 7: and again with Musk. You know, the moment that he 146 00:07:56,400 --> 00:07:59,520 Speaker 7: has had someone who's emerged as sort of you know 147 00:07:59,720 --> 00:08:02,520 Speaker 7: who's sort of looks like a number two that person 148 00:08:02,520 --> 00:08:06,720 Speaker 7: doesn't seem to last very long. So in Tesla's defense, 149 00:08:06,760 --> 00:08:08,880 Speaker 7: I think the board has tried to take some steps 150 00:08:09,280 --> 00:08:12,000 Speaker 7: with this package to try and mitigate some of that 151 00:08:12,160 --> 00:08:15,960 Speaker 7: risk and sort of have Musk play a role in 152 00:08:15,960 --> 00:08:19,960 Speaker 7: in you know, succession, But the planning for that still 153 00:08:20,000 --> 00:08:23,720 Speaker 7: seems to be, you know, pretty much you know, sort 154 00:08:23,760 --> 00:08:26,040 Speaker 7: of to the side, as they really sort of focus 155 00:08:26,080 --> 00:08:29,440 Speaker 7: as a board on retaining him and on incentivizing him. 156 00:08:30,360 --> 00:08:34,240 Speaker 1: Can you take us to how they have voted in 157 00:08:34,360 --> 00:08:39,079 Speaker 1: prior votes on pay for Tesla or the relationship between 158 00:08:39,200 --> 00:08:42,040 Speaker 1: the Norwegian Weld Fund and Elon Musk going forward what 159 00:08:42,120 --> 00:08:44,760 Speaker 1: that means for well, really the retail base, which is 160 00:08:44,760 --> 00:08:46,120 Speaker 1: the big push here for many. 161 00:08:47,520 --> 00:08:51,040 Speaker 7: Yeah, I think as much as this no vote is concerning, 162 00:08:51,080 --> 00:08:53,920 Speaker 7: if if you want to see this measure pass, you know, 163 00:08:54,000 --> 00:08:56,680 Speaker 7: we only have to go back to last year to 164 00:08:56,880 --> 00:09:00,400 Speaker 7: an example of when this fund voted against a Musk 165 00:09:00,440 --> 00:09:03,679 Speaker 7: pay package. This was the vote on reratifying the twenty 166 00:09:03,720 --> 00:09:07,360 Speaker 7: eighteen award that a judge in Delaware threw out. And 167 00:09:07,840 --> 00:09:10,400 Speaker 7: there's there's kind of a juicy story to that in 168 00:09:10,440 --> 00:09:13,839 Speaker 7: that the CEO of this wealth fund, you know, kind 169 00:09:13,880 --> 00:09:16,120 Speaker 7: of got raked over the coals by Musk. We found 170 00:09:16,120 --> 00:09:19,560 Speaker 7: out later for the way that the fund voted on 171 00:09:19,640 --> 00:09:24,160 Speaker 7: that pay package. There was a freedom of information requests 172 00:09:23,960 --> 00:09:27,720 Speaker 7: for messages between the head of the fund, Nikolai Tangen 173 00:09:27,880 --> 00:09:31,920 Speaker 7: and Elon Musk, and Musk, you know, refer to this 174 00:09:32,040 --> 00:09:34,680 Speaker 7: notion that he needed to quote make amends for the 175 00:09:34,679 --> 00:09:38,440 Speaker 7: way that the fund voted. So there is a backstory here. 176 00:09:38,559 --> 00:09:41,320 Speaker 7: And as much as you know, this is maybe a 177 00:09:41,320 --> 00:09:44,200 Speaker 7: negative signal again if you want this to pass. We've 178 00:09:44,240 --> 00:09:47,240 Speaker 7: seen that it's been the case that even when this 179 00:09:47,360 --> 00:09:50,959 Speaker 7: fund has opposed a measure that the board has wanted 180 00:09:50,960 --> 00:09:53,720 Speaker 7: to see through, the board has managed to get its 181 00:09:53,760 --> 00:09:55,960 Speaker 7: way with its you know, in part thanks to its 182 00:09:56,000 --> 00:09:57,760 Speaker 7: very substantial retail investor base. 183 00:09:57,960 --> 00:10:00,959 Speaker 1: Great context is always pretty most creted out, so appreciate 184 00:10:01,000 --> 00:10:03,840 Speaker 1: you joining coming up. Grab well it Boost is early 185 00:10:03,880 --> 00:10:06,319 Speaker 1: forecast for the year. We speak with the CFO P 186 00:10:06,480 --> 00:10:08,880 Speaker 1: t Ui about the company's latest results as have been 187 00:10:08,920 --> 00:10:21,360 Speaker 1: their tech AI is headed for the grocery aisle. Instacart 188 00:10:21,400 --> 00:10:24,240 Speaker 1: is launching new AI tools, including an assistant they can 189 00:10:24,280 --> 00:10:27,200 Speaker 1: make product recommendations. As it really leans the e commerce 190 00:10:27,240 --> 00:10:30,280 Speaker 1: business into more profitable software those Natalie Lung joins us 191 00:10:30,280 --> 00:10:31,960 Speaker 1: to talk us through it, tell us a little bit 192 00:10:31,960 --> 00:10:34,720 Speaker 1: about how these actual instacar AI tools are working. 193 00:10:35,960 --> 00:10:39,200 Speaker 8: So instacart is building this AI chadboard as a white 194 00:10:39,280 --> 00:10:43,199 Speaker 8: label service for groceries, so they can actually have a 195 00:10:43,320 --> 00:10:47,280 Speaker 8: chatboard within let's say Kroger's own iOS app or the 196 00:10:47,320 --> 00:10:49,120 Speaker 8: Sprout's website app. 197 00:10:50,960 --> 00:10:53,120 Speaker 3: Natalie, let's head out to Uber, one of the big 198 00:10:53,160 --> 00:10:58,160 Speaker 3: decliners ride share good delivery, good, profit, not good. 199 00:10:58,440 --> 00:11:00,520 Speaker 2: What's the story then, Yes. 200 00:11:00,640 --> 00:11:04,360 Speaker 8: So Uber sort of reported some disappointing operating income and 201 00:11:04,400 --> 00:11:07,559 Speaker 8: adjusted EBITDA this morning out of the third quarter, as 202 00:11:07,559 --> 00:11:11,160 Speaker 8: well as some disappointing forecasts earning this forecast for the 203 00:11:11,640 --> 00:11:15,160 Speaker 8: four Q and so it's the profit has not been 204 00:11:15,200 --> 00:11:18,720 Speaker 8: catching up with some of the growth recovery. We're seeing 205 00:11:18,760 --> 00:11:22,840 Speaker 8: their growth reaccelerated to more than twenty percent on the 206 00:11:22,880 --> 00:11:27,240 Speaker 8: top line growth spookingymetric. And this is sort of attestament 207 00:11:27,320 --> 00:11:32,080 Speaker 8: to their strategy to go into new products, some of 208 00:11:32,120 --> 00:11:34,440 Speaker 8: which may not be proudable at the beginning, such as 209 00:11:34,480 --> 00:11:38,520 Speaker 8: autonomous investments, which they have been doing a lot of recently. 210 00:11:40,040 --> 00:11:41,920 Speaker 2: Most Natalie Lung, thank you very much. 211 00:11:42,200 --> 00:11:45,679 Speaker 3: Food delivery and right hailing company Grab raised its earnings 212 00:11:45,679 --> 00:11:49,000 Speaker 3: forecast for the year after carely profit top testaments. The 213 00:11:49,000 --> 00:11:53,200 Speaker 3: Singapore based Grab introduced new products like group food orders 214 00:11:53,400 --> 00:11:57,480 Speaker 3: and less expensive shared rights that helped drawing customers. Here 215 00:11:57,520 --> 00:12:01,640 Speaker 3: to discuss Peter Uey grabs CF. That's so interesting, right, 216 00:12:01,640 --> 00:12:04,400 Speaker 3: you just heard Natalie talking about Uber bringing in new 217 00:12:04,400 --> 00:12:08,720 Speaker 3: products that hurts profitability. Your strategy seems to me to 218 00:12:08,800 --> 00:12:13,040 Speaker 3: be like more affordable products for a wider audience based 219 00:12:13,120 --> 00:12:15,720 Speaker 3: customer base, but you're doing it in a way that 220 00:12:16,000 --> 00:12:18,199 Speaker 3: is accreative to your bottom line. 221 00:12:18,240 --> 00:12:20,880 Speaker 2: How have you done that? Let's try it. 222 00:12:21,360 --> 00:12:24,600 Speaker 9: We've been on point in terms of strategy making our 223 00:12:24,679 --> 00:12:28,160 Speaker 9: product more affordable and we really widened the top of 224 00:12:28,200 --> 00:12:30,520 Speaker 9: the funnel for us. If you look at the third quarter, 225 00:12:30,920 --> 00:12:34,600 Speaker 9: we have now forty eight monthly transacting users on our platform, 226 00:12:34,640 --> 00:12:37,840 Speaker 9: which is another rerecord for us. And we're also seeing 227 00:12:37,920 --> 00:12:41,280 Speaker 9: more you use spending on the platform at the same time, 228 00:12:41,320 --> 00:12:44,360 Speaker 9: so that it affordability also is pushing the average spend 229 00:12:44,360 --> 00:12:47,839 Speaker 9: also on our platform because they're transacting more on the platform. 230 00:12:48,040 --> 00:12:50,360 Speaker 9: If you look at the number of transactions on our platform, 231 00:12:50,360 --> 00:12:53,400 Speaker 9: we've grew twenty seven percent, which was actually growing faster 232 00:12:53,440 --> 00:12:56,200 Speaker 9: than our GMB also at the same time. So what 233 00:12:56,200 --> 00:12:58,640 Speaker 9: we're seeing is a number of things. One is people 234 00:12:58,679 --> 00:13:01,720 Speaker 9: are engaging on the platform. We're also cross selling more 235 00:13:01,760 --> 00:13:04,720 Speaker 9: also as they're coming in into certain funnels that we 236 00:13:04,800 --> 00:13:07,960 Speaker 9: have on the affordable side, they're also trying other products 237 00:13:08,000 --> 00:13:10,400 Speaker 9: that we have, whether it's the group order that you 238 00:13:10,520 --> 00:13:14,480 Speaker 9: just mentioned, whether it's the dine out features that we 239 00:13:14,559 --> 00:13:16,640 Speaker 9: have also for them to be able to go to restaurants. 240 00:13:16,920 --> 00:13:18,880 Speaker 9: So there's a lot more products that we've actually been 241 00:13:18,960 --> 00:13:21,400 Speaker 9: monetizing and that's producing some of the results that you've seen. 242 00:13:22,360 --> 00:13:25,559 Speaker 3: If you have a strategy where group food orders and 243 00:13:25,640 --> 00:13:29,920 Speaker 3: shared rides are a driver of demand, do you need 244 00:13:30,080 --> 00:13:34,400 Speaker 3: big volume in order to make this a profitable exercise? 245 00:13:34,440 --> 00:13:34,920 Speaker 2: For grab? 246 00:13:37,720 --> 00:13:41,400 Speaker 9: Obviously, scale is important. The more scale that we have, 247 00:13:42,040 --> 00:13:44,679 Speaker 9: the more users using the platform, the more we can 248 00:13:44,800 --> 00:13:48,160 Speaker 9: actually leverage the cost structure of our business and also 249 00:13:48,240 --> 00:13:51,320 Speaker 9: scale the big driver base that we have also at 250 00:13:51,320 --> 00:13:53,960 Speaker 9: the same time. So it does play a role, and 251 00:13:54,040 --> 00:13:56,640 Speaker 9: hence what you've been seeing is that now over a 252 00:13:56,720 --> 00:14:01,319 Speaker 9: third of our user base, especially in deliveries now are 253 00:14:01,400 --> 00:14:05,040 Speaker 9: coming into what we call more the affordable product stack, 254 00:14:05,480 --> 00:14:07,840 Speaker 9: and it's a great entry point for them to use. 255 00:14:08,520 --> 00:14:12,520 Speaker 9: You're looking at over twenty five million monthly transacting users 256 00:14:12,600 --> 00:14:15,400 Speaker 9: just interacting in those affordable products, just on the food 257 00:14:15,440 --> 00:14:18,320 Speaker 9: delivery side of the business. And that's a great way 258 00:14:18,360 --> 00:14:21,440 Speaker 9: for us because it enables us for those users who 259 00:14:21,440 --> 00:14:23,600 Speaker 9: are a little bit more price sensitive to try the 260 00:14:23,680 --> 00:14:26,520 Speaker 9: product first, get a taste of it. Also, they're a 261 00:14:26,560 --> 00:14:28,800 Speaker 9: little bit more price sensitive on the delivery fee, and 262 00:14:28,840 --> 00:14:31,000 Speaker 9: we've managed to be able to lower the delivery fee 263 00:14:31,040 --> 00:14:33,760 Speaker 9: for them. But they also get to experience the platform, 264 00:14:33,880 --> 00:14:36,240 Speaker 9: and once they've experienced the platform, they get to also 265 00:14:36,280 --> 00:14:39,360 Speaker 9: experience other products like grocery delivery, which is still a 266 00:14:39,440 --> 00:14:41,520 Speaker 9: very nascent business for us. It's about ten percent of 267 00:14:41,560 --> 00:14:44,560 Speaker 9: out delivery GMB. And at the same time, also we're 268 00:14:44,560 --> 00:14:47,040 Speaker 9: cross selling them to other products like dine now for 269 00:14:47,040 --> 00:14:49,160 Speaker 9: an example, for them to be able to go to restaurants. 270 00:14:49,360 --> 00:14:51,960 Speaker 1: At the same time, it's interesting that you also for 271 00:14:52,000 --> 00:14:53,440 Speaker 1: fintech as well and loans. 272 00:14:53,680 --> 00:14:55,880 Speaker 2: But I've just got to target. 273 00:14:55,960 --> 00:14:57,720 Speaker 1: The ennefit in the room is that we hear all 274 00:14:57,760 --> 00:15:01,000 Speaker 1: these growth stories, we see analyst raising price target on you, 275 00:15:01,080 --> 00:15:03,240 Speaker 1: but the shares are down on the day, Peter, and 276 00:15:03,560 --> 00:15:06,320 Speaker 1: from the conversations you had with investors and analysts. What 277 00:15:06,440 --> 00:15:08,600 Speaker 1: was it that perhaps was a little bit of concern. 278 00:15:08,320 --> 00:15:12,440 Speaker 9: About I would say any of concern. A lot of 279 00:15:12,480 --> 00:15:16,440 Speaker 9: the investors are really focused on how we are going 280 00:15:16,480 --> 00:15:19,000 Speaker 9: to finish the quarter, and I've reiterated them that were 281 00:15:19,000 --> 00:15:21,600 Speaker 9: on track to finish a very strong quarter. If anything, 282 00:15:21,640 --> 00:15:24,080 Speaker 9: that reflects on the guidance that we've given out. Also, 283 00:15:24,280 --> 00:15:27,760 Speaker 9: we've increased the EBD guidance now to four hundred and 284 00:15:27,840 --> 00:15:31,320 Speaker 9: ninety million dollars to five hundred and that's there also 285 00:15:31,400 --> 00:15:34,120 Speaker 9: shows that our confidence in terms of how we're exiting 286 00:15:34,200 --> 00:15:36,520 Speaker 9: the quarter. I've also told them that in terms of 287 00:15:36,560 --> 00:15:39,840 Speaker 9: our GM we growth, we're continuing to sustain this growth 288 00:15:39,880 --> 00:15:43,840 Speaker 9: acceleration in our business today. So we are looking to 289 00:15:43,920 --> 00:15:46,520 Speaker 9: finish strong in the quarter. One of the areas that 290 00:15:46,560 --> 00:15:49,880 Speaker 9: we're focusing also is the fintech business. We've given out 291 00:15:49,920 --> 00:15:52,880 Speaker 9: a one billion dollar loan outstanding by the end of 292 00:15:52,920 --> 00:15:54,920 Speaker 9: the year and we're on target to hit that also, 293 00:15:55,040 --> 00:15:57,720 Speaker 9: so all the different parts of the business are on 294 00:15:57,840 --> 00:15:59,680 Speaker 9: track for us to have a very strong finish of 295 00:15:59,720 --> 00:16:00,000 Speaker 9: the year. 296 00:16:00,240 --> 00:16:03,080 Speaker 1: What's also on track is early twenty twenty six Robotaxi 297 00:16:03,200 --> 00:16:06,240 Speaker 1: Chinese robotaxi company we Ride. You've been partnering there. How 298 00:16:06,280 --> 00:16:08,520 Speaker 1: are you seeing av as the key area of focus 299 00:16:08,640 --> 00:16:09,560 Speaker 1: just briefly. 300 00:16:10,240 --> 00:16:13,480 Speaker 9: Yeah, we're leaning it into av it the biggest platform 301 00:16:13,520 --> 00:16:16,920 Speaker 9: in Southeast Asia. We're leaning into the rarest part of 302 00:16:17,240 --> 00:16:20,440 Speaker 9: investments that were making. One is across learning the tech 303 00:16:20,520 --> 00:16:22,720 Speaker 9: and whether it's the wee Right partnership that we have 304 00:16:22,800 --> 00:16:26,040 Speaker 9: in Singapore. We're hoping to have the cast deployed on 305 00:16:26,080 --> 00:16:28,280 Speaker 9: the street in the first quarter to be able to 306 00:16:28,320 --> 00:16:31,200 Speaker 9: take on public passengers with the safety driver. And also 307 00:16:31,240 --> 00:16:34,240 Speaker 9: we're looking at US technology such as main Mobility. We 308 00:16:34,280 --> 00:16:37,120 Speaker 9: made an announcement we're honering with main Mobility also, So 309 00:16:37,120 --> 00:16:39,200 Speaker 9: we're looking at all the different tech and taking the 310 00:16:39,200 --> 00:16:41,600 Speaker 9: best of it and really bringing it to Southeast Asia. 311 00:16:42,480 --> 00:16:43,320 Speaker 2: Peter quickly. 312 00:16:43,560 --> 00:16:45,840 Speaker 3: You know, across the jurisdictions you operate in, do you 313 00:16:45,880 --> 00:16:49,880 Speaker 3: recognize one that is a better regulatory environment to deploy 314 00:16:49,960 --> 00:16:50,720 Speaker 3: robotaxi in. 315 00:16:52,600 --> 00:16:55,920 Speaker 9: You know, in all the countries, especially when we're studying 316 00:16:55,920 --> 00:16:59,880 Speaker 9: in Singapore, because really Singapore, when it comes to the 317 00:17:00,000 --> 00:17:03,600 Speaker 9: government regulations that the standard are very high here and 318 00:17:03,680 --> 00:17:05,879 Speaker 9: a lot of the other Southeast Asian countries tend to 319 00:17:05,920 --> 00:17:09,119 Speaker 9: look to Singapore as a proxy, So we've been working 320 00:17:09,240 --> 00:17:12,320 Speaker 9: very closely with them, and that's around safety, which is 321 00:17:12,359 --> 00:17:18,880 Speaker 9: really important, and also the customer adoption of autonomous vehicle itself. 322 00:17:19,040 --> 00:17:20,920 Speaker 9: But at the same time, also we're leaning it in 323 00:17:21,040 --> 00:17:23,880 Speaker 9: terms of how we can rescale the existing driver base 324 00:17:23,920 --> 00:17:27,040 Speaker 9: that we have here also, so we identifying new opportunities 325 00:17:27,040 --> 00:17:30,320 Speaker 9: for driver base where they become safety drivers, that become 326 00:17:30,359 --> 00:17:34,399 Speaker 9: remote drivers, fleet operators, etc. Which is really important. So 327 00:17:34,640 --> 00:17:37,280 Speaker 9: the way we're approaching AV is working with the government 328 00:17:37,480 --> 00:17:40,280 Speaker 9: but also working with the driver community and also the 329 00:17:40,400 --> 00:17:43,440 Speaker 9: end user also to be able to adopt these new technologies. 330 00:17:43,520 --> 00:17:45,680 Speaker 1: I mean, the labor impact of AI as front center. 331 00:17:45,720 --> 00:17:49,080 Speaker 1: It's interesting that you're really leaning into that, Peter, the 332 00:17:49,160 --> 00:17:52,359 Speaker 1: prioritization of innovation. How much you were able to do 333 00:17:52,440 --> 00:17:55,919 Speaker 1: that while still driving the bottom line because profitability is 334 00:17:55,960 --> 00:17:56,600 Speaker 1: being asked of you. 335 00:17:57,920 --> 00:18:01,120 Speaker 9: Yeah, we'll Actually AI is something which is very core 336 00:18:01,680 --> 00:18:02,399 Speaker 9: to grab itself. 337 00:18:02,440 --> 00:18:03,159 Speaker 2: It's not something new. 338 00:18:03,200 --> 00:18:05,600 Speaker 9: Also, we've been focusing a lot on AI about four 339 00:18:05,640 --> 00:18:08,560 Speaker 9: or five years ago. We've developed our own models actually 340 00:18:08,640 --> 00:18:11,960 Speaker 9: very early on. We now have over one thousand models 341 00:18:11,960 --> 00:18:14,640 Speaker 9: in production that we're running here. And the way we've 342 00:18:14,640 --> 00:18:17,359 Speaker 9: been operating in the last eighteen months since the technology 343 00:18:17,400 --> 00:18:20,680 Speaker 9: has advanced so much is really internally we're also using 344 00:18:20,720 --> 00:18:22,679 Speaker 9: a lot of AI of a ninety eight percent of 345 00:18:22,720 --> 00:18:25,879 Speaker 9: our engineers today use some sort of AI code assist. 346 00:18:26,600 --> 00:18:29,840 Speaker 9: What also we started to deploy is the products to 347 00:18:29,920 --> 00:18:33,560 Speaker 9: our customers. Also, we have voice activation now for the 348 00:18:33,680 --> 00:18:37,080 Speaker 9: visually impaired where they can actually just speak to our 349 00:18:37,119 --> 00:18:39,399 Speaker 9: app and able to order a food or order a 350 00:18:39,520 --> 00:18:44,040 Speaker 9: ride for an example. And also we've deployed copilot equivalent 351 00:18:44,080 --> 00:18:47,200 Speaker 9: to our driver base where they could really rely as 352 00:18:47,280 --> 00:18:50,320 Speaker 9: on with a really effective a pilot to be able 353 00:18:50,320 --> 00:18:53,160 Speaker 9: to navigate throughout their day. And also with the merchant 354 00:18:52,960 --> 00:18:55,520 Speaker 9: or merchant bot actually that we have also they can 355 00:18:55,560 --> 00:18:57,720 Speaker 9: interact with our merchants. 356 00:18:57,480 --> 00:18:59,719 Speaker 1: Staying up late for us. We so appreciate it you 357 00:18:59,720 --> 00:19:03,440 Speaker 1: do of grab the Chief Financial Officer. It's election day 358 00:19:03,440 --> 00:19:05,439 Speaker 1: in the US and millions of Americans heading to the 359 00:19:05,480 --> 00:19:08,320 Speaker 1: polls to cast ballots in local and state races. In 360 00:19:08,359 --> 00:19:10,960 Speaker 1: New York, the meyoral contest is in the spotlight Zauraen 361 00:19:11,000 --> 00:19:15,640 Speaker 1: Mamdani Andrew Cuomo Curtis leewa battle for city Hall, an 362 00:19:15,640 --> 00:19:18,560 Speaker 1: outcome that could have major implications of big tech footprint 363 00:19:18,640 --> 00:19:20,600 Speaker 1: right here in the city. Let's get the latest on 364 00:19:20,640 --> 00:19:22,840 Speaker 1: an extraordinary race for Miles Miller. What are you watching 365 00:19:22,840 --> 00:19:23,119 Speaker 1: out for? 366 00:19:26,040 --> 00:19:28,560 Speaker 6: Yeah, you know, this is going to be a race 367 00:19:28,680 --> 00:19:32,520 Speaker 6: that really is going to be either a landslide and 368 00:19:32,560 --> 00:19:36,199 Speaker 6: a mandate or a squeaker with Andrew Cuomo. But the 369 00:19:36,240 --> 00:19:38,640 Speaker 6: one thing that I think the tech community is looking 370 00:19:38,640 --> 00:19:42,040 Speaker 6: out for is if Mamdani is to win, will these 371 00:19:42,080 --> 00:19:44,959 Speaker 6: tech firms stay in New York. And the answer that 372 00:19:45,000 --> 00:19:48,280 Speaker 6: he's given is yes. He says that in a city 373 00:19:48,320 --> 00:19:51,679 Speaker 6: that is more affordable, tech firms will be able to 374 00:19:51,720 --> 00:19:55,800 Speaker 6: recruit and retain much quicker. And what he has said 375 00:19:55,960 --> 00:19:58,560 Speaker 6: is that that is what will be born out of 376 00:19:58,600 --> 00:20:03,240 Speaker 6: some of his big proposals, talking about everything from free childcare, 377 00:20:03,320 --> 00:20:07,200 Speaker 6: discounted childcare, to free buses, and he's saying that that'll 378 00:20:07,240 --> 00:20:10,879 Speaker 6: have a measurable effect on the tech community. You know, 379 00:20:11,000 --> 00:20:14,920 Speaker 6: as I speak to folks like Jeff blau At related 380 00:20:14,960 --> 00:20:17,880 Speaker 6: in some other real estate firms, they say that tech 381 00:20:18,000 --> 00:20:22,400 Speaker 6: is really starting to continue to work and stay in 382 00:20:22,440 --> 00:20:25,199 Speaker 6: New York. And you know, those were some of the 383 00:20:25,200 --> 00:20:29,320 Speaker 6: people who were backing Eric Adams and Andrew Cuomo's campaign. 384 00:20:29,720 --> 00:20:33,679 Speaker 6: Will they be able to move over and work with Mamdanni. 385 00:20:33,720 --> 00:20:37,840 Speaker 6: Mamdanni thinks yes if he is elected, because he will 386 00:20:38,080 --> 00:20:41,639 Speaker 6: have this affordability message and that will bore true. We 387 00:20:41,720 --> 00:20:44,199 Speaker 6: also know that if Andrew Cuomo were elected, you know, 388 00:20:44,240 --> 00:20:48,160 Speaker 6: he spent ten years as governor and really worked very 389 00:20:48,160 --> 00:20:51,560 Speaker 6: well with the tech community, and that's what he is 390 00:20:51,640 --> 00:20:52,920 Speaker 6: making his pitch as as well. 391 00:20:53,600 --> 00:20:55,600 Speaker 2: Bloomberg's Miles Miller, thank you very much. 392 00:20:55,640 --> 00:20:55,720 Speaker 10: Me. 393 00:20:55,760 --> 00:20:58,119 Speaker 3: While voters in New Jersey and Virginia are heading to 394 00:20:58,119 --> 00:21:01,520 Speaker 3: the polls and key gubinatorial races, contests that could shape 395 00:21:01,560 --> 00:21:05,600 Speaker 3: house states approached big tech, artificial intelligence, and data center development, 396 00:21:05,600 --> 00:21:08,359 Speaker 3: Bloomberg's remain bostic in New Jersey with the latest and 397 00:21:08,359 --> 00:21:11,240 Speaker 3: remain whether a Republican win or a Democratic win, the 398 00:21:11,320 --> 00:21:13,080 Speaker 3: considerations are very clear. 399 00:21:12,880 --> 00:21:17,480 Speaker 10: Here, yeah, ed. And in fact, this has actually become 400 00:21:17,520 --> 00:21:20,320 Speaker 10: a bit of a sleeper issue in this campaign. Remember 401 00:21:20,400 --> 00:21:22,399 Speaker 10: it was just about a year ago when one of 402 00:21:22,400 --> 00:21:26,879 Speaker 10: the main wholesalers in this region actually ended up delivering 403 00:21:26,920 --> 00:21:31,640 Speaker 10: to utilities a more than tenfold jump in wholesale electricity prices, 404 00:21:31,720 --> 00:21:34,639 Speaker 10: and that was driven almost entirely by a big build 405 00:21:34,640 --> 00:21:37,959 Speaker 10: out in data center demand earlier this year, just in 406 00:21:38,000 --> 00:21:40,480 Speaker 10: the summer of twenty twenty five, there was a fresh 407 00:21:40,480 --> 00:21:44,159 Speaker 10: auction and prices went up again more than twenty percent. 408 00:21:44,400 --> 00:21:47,240 Speaker 10: Remember these are wholesale prices, and these are prices that 409 00:21:47,280 --> 00:21:51,359 Speaker 10: are effectively a barometer on future demand for AI capacity. 410 00:21:51,560 --> 00:21:53,560 Speaker 10: And I want to put this in context for you ED. 411 00:21:53,840 --> 00:21:56,359 Speaker 10: As of right now, there is about five hundred megawatts 412 00:21:56,400 --> 00:21:59,920 Speaker 10: of AI data center capacity up and running right now, 413 00:22:00,000 --> 00:22:02,399 Speaker 10: so within a year that's likely to double. There are 414 00:22:02,400 --> 00:22:05,440 Speaker 10: two big projects under the works about one hundred plus 415 00:22:05,600 --> 00:22:08,040 Speaker 10: a mega white project being built by core Weave in 416 00:22:08,080 --> 00:22:10,760 Speaker 10: central New Jersey in the downtowntal Worth and one even 417 00:22:10,840 --> 00:22:14,320 Speaker 10: larger than that down south near Atlantic City, Carolina, and 418 00:22:14,320 --> 00:22:16,920 Speaker 10: there that's going to have a big impact on energy prices. 419 00:22:16,920 --> 00:22:19,600 Speaker 1: Great context Bimberg's remain Bostick. 420 00:22:19,440 --> 00:22:22,560 Speaker 3: And the earnings landscape. We're thinking a lot about Spotify. 421 00:22:22,680 --> 00:22:25,600 Speaker 3: This is kind of the Daniel Eck farewell tour. I 422 00:22:25,680 --> 00:22:27,800 Speaker 3: guess a little bit. Actually, Common's about to tell me 423 00:22:27,880 --> 00:22:29,720 Speaker 3: that I might be a bit wrong on that, but 424 00:22:30,200 --> 00:22:33,120 Speaker 3: the core data pretty good. Let's get more on what's 425 00:22:33,160 --> 00:22:35,640 Speaker 3: going on with Spotify Blue. It is actually common out 426 00:22:35,680 --> 00:22:38,480 Speaker 3: there on the East coast. You know, we've been through 427 00:22:38,520 --> 00:22:40,920 Speaker 3: this story with you, Daniel K. Taking a step back 428 00:22:41,040 --> 00:22:44,240 Speaker 3: the new co CEO structure, and I'm just looking at earnings. 429 00:22:44,640 --> 00:22:47,040 Speaker 3: The key metrics that matters seem to be good. They 430 00:22:47,080 --> 00:22:48,280 Speaker 3: seem to be okay. 431 00:22:48,240 --> 00:22:52,480 Speaker 11: Yeah, yeah, no great order. I think investors, you know, 432 00:22:52,520 --> 00:22:54,879 Speaker 11: there's a little bit of a loss at one point today. 433 00:22:56,080 --> 00:22:58,920 Speaker 11: I think investors really just want to hear that Spotify 434 00:22:58,960 --> 00:23:01,160 Speaker 11: is going to raise prices in the US, and they 435 00:23:01,240 --> 00:23:04,840 Speaker 11: didn't exactly hear that, so there was a little trepidation there. 436 00:23:04,920 --> 00:23:08,240 Speaker 11: But otherwise, broadly speaking, things are looking pretty good. 437 00:23:08,520 --> 00:23:10,639 Speaker 1: It does seem to be pricing power, doesn't it that 438 00:23:10,680 --> 00:23:13,800 Speaker 1: everyone's focusing in on and whether advertising can just go 439 00:23:13,920 --> 00:23:15,959 Speaker 1: up into the right In terms of that sort of oppo. 440 00:23:16,000 --> 00:23:18,520 Speaker 1: What are you hearing for the trajectory in twenty twenty six? 441 00:23:18,560 --> 00:23:21,920 Speaker 1: What are AMAS investors focusing on under the new co CEOs? 442 00:23:23,400 --> 00:23:26,240 Speaker 11: Yeah, so, well, they're focusing on a few things. AI 443 00:23:26,400 --> 00:23:29,520 Speaker 11: definitely is a big topic. Spotify is now partnering with 444 00:23:29,720 --> 00:23:33,360 Speaker 11: chat Chat GPT, so Spotify shows up in chat GPT. 445 00:23:33,840 --> 00:23:35,679 Speaker 11: They talked a lot on their earnings calls about this 446 00:23:35,760 --> 00:23:39,840 Speaker 11: idea of ubiquity, so their Apple TV app, Chat GPT 447 00:23:40,200 --> 00:23:42,480 Speaker 11: and other places where Spotify can show up to be 448 00:23:42,680 --> 00:23:45,800 Speaker 11: used everywhere people are so AI is a big conversation 449 00:23:45,880 --> 00:23:49,000 Speaker 11: for sure, but also again those prices, the investors and 450 00:23:49,040 --> 00:23:51,240 Speaker 11: the music rights holders want to see. 451 00:23:51,119 --> 00:23:55,159 Speaker 3: Higher prices actually want to go to a story that 452 00:23:55,240 --> 00:23:58,879 Speaker 3: you broke about Netflix, It's in talks to license video 453 00:23:59,000 --> 00:24:04,280 Speaker 3: podcasts from iHeartMedia. On the surface symbol headline, but it 454 00:24:04,320 --> 00:24:06,879 Speaker 3: was impactful in the moment. What have you learned in 455 00:24:06,920 --> 00:24:08,440 Speaker 3: the course of reporting about this deal? 456 00:24:09,800 --> 00:24:12,399 Speaker 11: Well, and this also relates to Spotify because Spotify was 457 00:24:12,440 --> 00:24:14,320 Speaker 11: the first one out of the gate. They announced that 458 00:24:14,320 --> 00:24:17,600 Speaker 11: they're licensing some of their video podcasts to Netflix starting 459 00:24:17,600 --> 00:24:20,679 Speaker 11: in the new year. Crucially, this means those video podcasts, 460 00:24:20,760 --> 00:24:24,320 Speaker 11: the full episodes will be removed from YouTube. So the 461 00:24:24,320 --> 00:24:27,639 Speaker 11: fact that Netflix is having conversations with additional networks like 462 00:24:27,760 --> 00:24:30,840 Speaker 11: iHeart is pretty significant. It shows that they really are 463 00:24:30,920 --> 00:24:34,240 Speaker 11: interested in at least testing out this video podcast space. 464 00:24:35,000 --> 00:24:38,040 Speaker 1: Ashley Carmen always breaking the news. We appreciate it, thanks 465 00:24:38,080 --> 00:24:41,040 Speaker 1: for joining. Let's just return to the macro picture now 466 00:24:41,200 --> 00:24:44,439 Speaker 1: around this investment landscape, because more Wall Street executives they 467 00:24:44,440 --> 00:24:47,280 Speaker 1: are sounding the alarm bell. They're saying investors should brace 468 00:24:47,400 --> 00:24:50,160 Speaker 1: for next market correction one ten percent in the next 469 00:24:50,200 --> 00:24:52,720 Speaker 1: twelve to twenty four months. Christina Hoop, a Man Group 470 00:24:52,760 --> 00:24:56,320 Speaker 1: chief market strategist, sees a mixed economic picture, saying it 471 00:24:56,400 --> 00:24:59,479 Speaker 1: is the tale of two cities, tech led success masking 472 00:24:59,480 --> 00:25:02,840 Speaker 1: broader week this elsewhere paces, say Christina Hooper joins us 473 00:25:02,880 --> 00:25:06,159 Speaker 1: now and we are on this day shining out and 474 00:25:06,200 --> 00:25:09,040 Speaker 1: palenteer for example, the sort of poster child or whether 475 00:25:09,119 --> 00:25:12,800 Speaker 1: fundamentals live up to what have been deemed nosebreed valuations 476 00:25:12,840 --> 00:25:17,239 Speaker 1: on nosebreed success stories. But how do you square that 477 00:25:17,440 --> 00:25:21,040 Speaker 1: with then the potential for a pullback in the act market. 478 00:25:21,040 --> 00:25:24,160 Speaker 12: More broadly, well, I think there is just a lot 479 00:25:24,200 --> 00:25:28,160 Speaker 12: of enthusiasm around anything that's related to AI. And I'm 480 00:25:28,200 --> 00:25:30,600 Speaker 12: old enough to remember the late nineteen nineties and how 481 00:25:30,680 --> 00:25:34,240 Speaker 12: much enthusiasm there was around any companies that were related 482 00:25:34,280 --> 00:25:37,320 Speaker 12: to the Internet. For example, everyone was scrambling to change 483 00:25:37,359 --> 00:25:40,480 Speaker 12: their name to have dot com at the end. Now 484 00:25:40,560 --> 00:25:43,639 Speaker 12: many companies are scrambling to say they are part of 485 00:25:43,680 --> 00:25:48,080 Speaker 12: this incredible AI food chain. And I think what is 486 00:25:48,240 --> 00:25:53,000 Speaker 12: happening though, is that investors may not be thinking about 487 00:25:53,040 --> 00:25:59,399 Speaker 12: what could be obstacles to the continued CAPEX boom. Around AI. 488 00:26:00,040 --> 00:26:02,720 Speaker 12: I think there are some pretty significant ones. First of all, 489 00:26:02,720 --> 00:26:05,960 Speaker 12: we knew and of course we got the deal done 490 00:26:06,080 --> 00:26:09,080 Speaker 12: on rare earth elements, but that was clearly an issue. 491 00:26:09,080 --> 00:26:11,560 Speaker 12: If you don't have access to enough rare earth elements, 492 00:26:11,800 --> 00:26:14,480 Speaker 12: that will certainly slow down an AI cap x boom. 493 00:26:14,800 --> 00:26:19,720 Speaker 12: But we also have concerns around how much productivity gains 494 00:26:19,760 --> 00:26:23,359 Speaker 12: companies will actually see. We had that MIT report that 495 00:26:23,400 --> 00:26:26,520 Speaker 12: came out last spring that suggested maybe not so much. 496 00:26:27,040 --> 00:26:28,879 Speaker 12: There could be a point where companies say, you know what, 497 00:26:28,920 --> 00:26:31,080 Speaker 12: we've thrown an enormous amount of money at this We're 498 00:26:31,119 --> 00:26:33,600 Speaker 12: not necessarily seeing all that we wanted to see. Will 499 00:26:33,640 --> 00:26:36,440 Speaker 12: slow down investment, and then finally we have the potential 500 00:26:36,520 --> 00:26:40,000 Speaker 12: for a NIMBI movement not in my backyard. In fact, 501 00:26:40,040 --> 00:26:43,920 Speaker 12: you just reported on how there are AI data centers, 502 00:26:44,200 --> 00:26:46,880 Speaker 12: and of course we're seeing a lot more news around 503 00:26:48,640 --> 00:26:52,000 Speaker 12: neighborhoods that are not thrilled to have AI there or 504 00:26:53,119 --> 00:26:55,680 Speaker 12: states that don't want to see it there because electricity 505 00:26:55,680 --> 00:26:56,879 Speaker 12: costs are going up a lot. 506 00:26:58,000 --> 00:27:01,160 Speaker 1: So there are a lot of macro elements and risk 507 00:27:01,280 --> 00:27:04,679 Speaker 1: factors to the endless money that's being thrown at the 508 00:27:04,960 --> 00:27:06,959 Speaker 1: desire to build out by the hyper scale as we've 509 00:27:06,960 --> 00:27:10,120 Speaker 1: seen in their earnings, but more about the valuation from 510 00:27:10,160 --> 00:27:12,080 Speaker 1: because I can understand how people. Is that really what 511 00:27:12,119 --> 00:27:14,040 Speaker 1: takes the wind out of the sales of valuations or 512 00:27:14,040 --> 00:27:16,399 Speaker 1: is it more of a Michael Berry perspective that you know, 513 00:27:16,440 --> 00:27:18,639 Speaker 1: there's just an awful lot of endless capital expenditure and 514 00:27:18,680 --> 00:27:21,840 Speaker 1: also cloud growth similarities that seem to be slowing down, 515 00:27:21,840 --> 00:27:24,000 Speaker 1: and he's trying to say that, you know, maybe it's 516 00:27:24,040 --> 00:27:26,440 Speaker 1: time to bet against just how far we've run rather 517 00:27:26,480 --> 00:27:27,520 Speaker 1: than the future risks. 518 00:27:28,080 --> 00:27:34,000 Speaker 12: Absolutely, I think it's both. There are certainly real question marks. 519 00:27:34,080 --> 00:27:37,440 Speaker 12: I think about how much more investment we'll see companies 520 00:27:37,520 --> 00:27:41,480 Speaker 12: make without seeing you know, sort of significant gains and 521 00:27:41,560 --> 00:27:45,440 Speaker 12: perhaps recognizing that there could be some overlap. There could 522 00:27:45,520 --> 00:27:52,840 Speaker 12: be there could be more discretion and more thoughtfulness around spending. 523 00:27:53,119 --> 00:27:55,880 Speaker 12: So I certainly think there's there's a little of both there. 524 00:27:56,440 --> 00:28:00,920 Speaker 12: I just as an outside observer seeing the credible, incredible 525 00:28:00,960 --> 00:28:04,119 Speaker 12: amount of money and excitement, there's nothing like it. I 526 00:28:04,119 --> 00:28:06,320 Speaker 12: think since what we've seen in the late nineteen nineties, 527 00:28:06,640 --> 00:28:08,800 Speaker 12: we know how that ended, so we should just think 528 00:28:08,800 --> 00:28:11,359 Speaker 12: of it as a cautionary tale and say, hey, maybe 529 00:28:11,359 --> 00:28:13,720 Speaker 12: we should be diversified. Maybe we should have some exposure 530 00:28:13,760 --> 00:28:18,080 Speaker 12: to China AI, given that the risks aren't the same, 531 00:28:18,119 --> 00:28:20,240 Speaker 12: the vulnerabilities aren't necessarily the same. 532 00:28:20,280 --> 00:28:24,879 Speaker 2: There, let's go back to the tail of two cities thesis. 533 00:28:24,880 --> 00:28:30,320 Speaker 3: So American technology at least good, but papering over some cracks. 534 00:28:30,920 --> 00:28:32,159 Speaker 2: What are those cracks? 535 00:28:32,200 --> 00:28:35,360 Speaker 3: Tell our audience more about the warning signs you're seeing 536 00:28:35,760 --> 00:28:37,240 Speaker 3: in other parts of this economy. 537 00:28:37,320 --> 00:28:39,560 Speaker 2: Christina, So, I certainly. 538 00:28:39,200 --> 00:28:45,040 Speaker 12: Think we are seeing consumer weakness, especially among lower income consumers, 539 00:28:45,120 --> 00:28:48,600 Speaker 12: but also some middle income consumers. And I think we 540 00:28:48,600 --> 00:28:51,719 Speaker 12: can just look to the Chipotle earnings call last week 541 00:28:52,200 --> 00:28:56,520 Speaker 12: for signs of that growing weakness. September and October were 542 00:28:56,640 --> 00:29:00,680 Speaker 12: difficult months where the frequency of visits went down for 543 00:29:00,920 --> 00:29:05,480 Speaker 12: a lot of customers, many of whom are young, and 544 00:29:05,720 --> 00:29:10,200 Speaker 12: so that could very well be giving us an inclination 545 00:29:10,440 --> 00:29:13,720 Speaker 12: of what could come and what could spread. Because keep 546 00:29:13,720 --> 00:29:17,200 Speaker 12: in mind, we are seeing a lot of white collar 547 00:29:17,320 --> 00:29:21,520 Speaker 12: job layoffs being announced. We could see more coming and 548 00:29:21,560 --> 00:29:27,280 Speaker 12: that will likely impact middle to higher income consumers, many 549 00:29:27,320 --> 00:29:32,120 Speaker 12: of whom are spending still spending quite robustly, and that 550 00:29:32,200 --> 00:29:36,840 Speaker 12: could create a much bigger problem for the economy. Thus far, 551 00:29:37,040 --> 00:29:40,440 Speaker 12: it's been a two legged stool, and it could very 552 00:29:40,440 --> 00:29:42,240 Speaker 12: well go down to one and a half legs. 553 00:29:42,840 --> 00:29:45,400 Speaker 3: So this is very interesting going into the holiday quarter. 554 00:29:45,640 --> 00:29:47,920 Speaker 3: Maybe this is not the right data set. That Apple 555 00:29:48,000 --> 00:29:51,600 Speaker 3: told us last week that revenue going into the holiday 556 00:29:51,640 --> 00:29:56,240 Speaker 3: quarters would be up ten to twelve percent, That indicating 557 00:29:56,320 --> 00:29:58,640 Speaker 3: that in the middle to hire income in is they'll 558 00:29:58,640 --> 00:30:01,400 Speaker 3: go out and spend money on iPhone. I guess that 559 00:30:01,560 --> 00:30:04,240 Speaker 3: how does that set us us up for the holiday 560 00:30:04,320 --> 00:30:06,680 Speaker 3: quarter and how we view this economy. 561 00:30:07,080 --> 00:30:09,720 Speaker 12: Well, we could still have a very strong holiday quarter 562 00:30:09,880 --> 00:30:14,400 Speaker 12: driven by those higher income consumers. My concern though, is 563 00:30:14,480 --> 00:30:17,000 Speaker 12: that there are vulnerabilities there, and especially as we go 564 00:30:17,040 --> 00:30:19,680 Speaker 12: into twenty twenty six, we could see more in the 565 00:30:19,680 --> 00:30:22,800 Speaker 12: way of white collar layoffs. We also know that the 566 00:30:23,000 --> 00:30:26,320 Speaker 12: higher income consumers are not so sensitive to inflation, but 567 00:30:26,360 --> 00:30:30,240 Speaker 12: they are very sensitive to the stock market, and so 568 00:30:30,280 --> 00:30:32,280 Speaker 12: if we were to see some kind of stock market 569 00:30:32,360 --> 00:30:35,320 Speaker 12: sell off, think that would be problematic and would certainly 570 00:30:35,360 --> 00:30:37,880 Speaker 12: reduce high end consumer spending as well. 571 00:30:38,120 --> 00:30:40,000 Speaker 1: Christina, I want to go back to something you said 572 00:30:40,360 --> 00:30:43,600 Speaker 1: that maybe you diversify into China. Ai had a great 573 00:30:43,600 --> 00:30:46,160 Speaker 1: conversation with Man Deep just on the side of the set. 574 00:30:46,200 --> 00:30:48,360 Speaker 1: He's oblom Bug intelligence analysts. He's just been to Asian 575 00:30:48,400 --> 00:30:50,440 Speaker 1: he said, they're doing it so differently there because they 576 00:30:50,480 --> 00:30:54,040 Speaker 1: cannot depend on in video chips being limitless. They are 577 00:30:54,200 --> 00:30:56,840 Speaker 1: underaware of the geopolitical risks, maybe in the way that 578 00:30:56,880 --> 00:30:59,800 Speaker 1: the US ones are putting rare rest to one side, 579 00:31:00,040 --> 00:31:02,800 Speaker 1: which China names and how does one get exposure to 580 00:31:02,840 --> 00:31:03,800 Speaker 1: that from your mindset? 581 00:31:04,520 --> 00:31:08,280 Speaker 12: So I can't name specific companies, but I can say 582 00:31:08,360 --> 00:31:10,719 Speaker 12: that there are so many that look very attractive, that 583 00:31:10,760 --> 00:31:14,040 Speaker 12: have much lower valuations and are part of that AI 584 00:31:14,440 --> 00:31:19,400 Speaker 12: food chain and that capex spending. I think we'll see 585 00:31:19,440 --> 00:31:24,640 Speaker 12: more dollars go there, and certainly it's in earlier innings 586 00:31:24,760 --> 00:31:27,360 Speaker 12: in Asia, so I think we have a longer runway there. 587 00:31:28,720 --> 00:31:30,880 Speaker 2: Christina Hooper of Man Group. Great to have you back 588 00:31:30,920 --> 00:31:32,640 Speaker 2: on Bloomberg Tech. Thank you very much. 589 00:31:32,680 --> 00:31:36,440 Speaker 3: Now coming up, Snowflake CEO Shwida Ramaswami joins us talk 590 00:31:36,440 --> 00:31:40,760 Speaker 3: about the company's latest partnerships to expand AI enterprise access. 591 00:31:40,840 --> 00:31:45,520 Speaker 3: This on a day when investors are broadly questioning AI valuations, 592 00:31:45,520 --> 00:31:47,400 Speaker 3: particularly in the software space. 593 00:31:47,720 --> 00:31:49,960 Speaker 2: That conversation's next. This is Bloomberg Tech. 594 00:32:00,160 --> 00:32:01,920 Speaker 1: Now for Talking Tech and first s up in video 595 00:32:01,960 --> 00:32:04,360 Speaker 1: and Deutsche telecom One have enveiled plans to build out 596 00:32:04,360 --> 00:32:06,880 Speaker 1: a one point two billion dollar data center in Germany, 597 00:32:06,960 --> 00:32:09,840 Speaker 1: boosting Europ's AI infrastructure. The facility will it's set to 598 00:32:09,880 --> 00:32:12,040 Speaker 1: be one of Europe's largest in the region, expected to 599 00:32:12,040 --> 00:32:15,000 Speaker 1: begin operations in the first quarter of twenty twenty six. 600 00:32:15,360 --> 00:32:18,160 Speaker 1: Plus a two hundred and forty percent rally in sgay 601 00:32:18,280 --> 00:32:21,960 Speaker 1: Heinez shares that's in this year alone explanted a warning 602 00:32:22,000 --> 00:32:24,200 Speaker 1: from the Career Exchange, signaling that the stock may have 603 00:32:24,320 --> 00:32:28,000 Speaker 1: been overheating. The exchange has issued an investment caution on 604 00:32:28,040 --> 00:32:31,120 Speaker 1: the chip maker following a surge driven by booming demand 605 00:32:31,160 --> 00:32:34,440 Speaker 1: for AI memory chips, and Nintendo is raising its sales 606 00:32:34,480 --> 00:32:37,400 Speaker 1: forecast for the switch to now expecting to sell nineteen 607 00:32:37,480 --> 00:32:39,680 Speaker 1: million units by March twenty twenty six, up from its 608 00:32:39,680 --> 00:32:43,280 Speaker 1: early projection of fifteen million. The upbeat outlook follows strong 609 00:32:43,400 --> 00:32:45,880 Speaker 1: early demand for the company, reporting of a ten million 610 00:32:45,960 --> 00:32:47,959 Speaker 1: units sold by the end of September. 611 00:32:48,160 --> 00:32:53,080 Speaker 3: Ed Okay Snowflake is announcing a series of new and 612 00:32:53,280 --> 00:32:56,440 Speaker 3: expanded partnerships part of the company's effort to become the 613 00:32:56,720 --> 00:33:00,680 Speaker 3: data center platform of choice for enterprise AI. Snowfla CEO 614 00:33:01,040 --> 00:33:04,200 Speaker 3: Shrida Ramaswami joins us to discuss each of them. The 615 00:33:04,240 --> 00:33:07,160 Speaker 3: one that caught my eye was the relationship with Google 616 00:33:07,200 --> 00:33:10,840 Speaker 3: Cloud and bringing the availability of the latest. 617 00:33:10,560 --> 00:33:12,920 Speaker 2: Gemini models to Snowflake. 618 00:33:13,360 --> 00:33:15,120 Speaker 3: And the reason I want to start with that is 619 00:33:15,880 --> 00:33:18,479 Speaker 3: you are all about choice, right because you can look 620 00:33:18,520 --> 00:33:21,760 Speaker 3: at anthropic OAI. But there must have been an indication 621 00:33:21,920 --> 00:33:25,480 Speaker 3: to U Shreda that those Gemini models are in demand 622 00:33:25,880 --> 00:33:27,120 Speaker 3: amongst your customer base. 623 00:33:28,360 --> 00:33:28,560 Speaker 10: Here. 624 00:33:28,680 --> 00:33:29,680 Speaker 2: It's great to be here. 625 00:33:29,840 --> 00:33:34,880 Speaker 13: Absolutely, Gemini models are among the best in the world 626 00:33:35,200 --> 00:33:38,280 Speaker 13: and a lot of our customers are asking for access 627 00:33:38,320 --> 00:33:41,000 Speaker 13: to these models. We are thrilled to be expanding our 628 00:33:41,120 --> 00:33:44,480 Speaker 13: partnership with Google Blogs similar to what we have done 629 00:33:44,680 --> 00:33:48,000 Speaker 13: with our big partners AWS and Azure. 630 00:33:48,040 --> 00:33:49,479 Speaker 2: Is a big step forward for us. 631 00:33:50,760 --> 00:33:53,680 Speaker 3: Can you just explain the basics of the Snowflake business model. 632 00:33:53,680 --> 00:33:57,040 Speaker 3: We always talk about Snowflake being a different layer above 633 00:33:57,080 --> 00:34:00,320 Speaker 3: the primary cloud. But why is it important that through 634 00:34:00,400 --> 00:34:05,040 Speaker 3: cotex AI your platform, any given enterprise customer can access 635 00:34:05,080 --> 00:34:06,000 Speaker 3: the underlying model. 636 00:34:07,040 --> 00:34:10,120 Speaker 13: This is a great question. Snowflake is the data layer 637 00:34:10,320 --> 00:34:14,720 Speaker 13: that sits above cloud service providers the AWS and Azure 638 00:34:14,840 --> 00:34:19,000 Speaker 13: and GCP. We are very much a data centric platform. 639 00:34:19,040 --> 00:34:23,400 Speaker 13: We are about making it really easy to ingest, clean 640 00:34:23,640 --> 00:34:25,800 Speaker 13: and be able to run analytics on top of the 641 00:34:25,920 --> 00:34:29,960 Speaker 13: data and AI, especially Snowflake Intelligence that we are launching, 642 00:34:30,160 --> 00:34:34,040 Speaker 13: is a game changer because it brings the access the 643 00:34:34,160 --> 00:34:37,880 Speaker 13: power of all the data directly to end users and 644 00:34:38,000 --> 00:34:42,359 Speaker 13: what required dashboards, what required analysis right now at the fingertips, 645 00:34:42,400 --> 00:34:46,040 Speaker 13: at the voices of every single person. And that's the 646 00:34:46,080 --> 00:34:50,480 Speaker 13: reason why Snowflake plays such a critical role. We are 647 00:34:50,640 --> 00:34:54,560 Speaker 13: among the best data platforms that run on top of 648 00:34:54,800 --> 00:34:59,319 Speaker 13: the hyperscalers and it is that that gives us incredible 649 00:34:59,360 --> 00:35:03,520 Speaker 13: ability to create value using AI with partnerships with the 650 00:35:03,560 --> 00:35:05,359 Speaker 13: best folks in the world. The open AI is an 651 00:35:05,360 --> 00:35:06,360 Speaker 13: anthropic and now. 652 00:35:06,239 --> 00:35:10,160 Speaker 1: Gemini creating value. I think that has been the proof point, 653 00:35:10,239 --> 00:35:13,200 Speaker 1: the watchword of this entire year. Strata. How can you 654 00:35:13,880 --> 00:35:16,560 Speaker 1: turn to our audience and say this is return on 655 00:35:16,640 --> 00:35:19,440 Speaker 1: AI investment, this is what my customers are experiencing. 656 00:35:20,680 --> 00:35:23,480 Speaker 13: This is a great question. First of all, our model 657 00:35:23,640 --> 00:35:28,520 Speaker 13: is a consumption model, meaning that Snowflake doesn't get paid. 658 00:35:28,520 --> 00:35:33,160 Speaker 13: We don't get to recognize revenue unless customers actually use products. 659 00:35:33,160 --> 00:35:37,320 Speaker 13: We don't sell subscriptions that automatically ties us to utility 660 00:35:37,560 --> 00:35:40,480 Speaker 13: that we create with our AI products. We work with 661 00:35:40,520 --> 00:35:44,759 Speaker 13: our customers, whether it is a ts Imagine or the 662 00:35:44,840 --> 00:35:49,360 Speaker 13: USA bobsled team, to create products that they get additional 663 00:35:49,440 --> 00:35:53,880 Speaker 13: value from, for example, often replacing an existing dashboarding solution 664 00:35:54,239 --> 00:35:59,640 Speaker 13: like we have done at Snowflake. You're confident that using 665 00:35:59,680 --> 00:36:03,600 Speaker 13: snowf like intelligence, we can replace a bunch of dashboards 666 00:36:03,640 --> 00:36:07,160 Speaker 13: and run the entire data access in a much more 667 00:36:07,200 --> 00:36:10,600 Speaker 13: flexible way for a fraction of the cost. We very 668 00:36:10,680 --> 00:36:14,880 Speaker 13: much believe in showing ROI return on investment for every 669 00:36:14,920 --> 00:36:18,480 Speaker 13: single project that we do, and the consumption model is 670 00:36:18,480 --> 00:36:21,080 Speaker 13: a huge help here because if the product isn't used, 671 00:36:21,520 --> 00:36:23,080 Speaker 13: there's no revenue on our site. 672 00:36:23,280 --> 00:36:26,560 Speaker 1: So should I. When we're talking in the market, writ 673 00:36:26,640 --> 00:36:29,759 Speaker 1: large about warriors of an AI bubble as people are 674 00:36:30,040 --> 00:36:32,880 Speaker 1: perhaps selling certain names because they feel that the fundamentals 675 00:36:32,920 --> 00:36:36,839 Speaker 1: have become dislocated with the actual valuation of companies. How 676 00:36:36,880 --> 00:36:39,080 Speaker 1: are you thinking in this your context? How are you 677 00:36:39,440 --> 00:36:42,880 Speaker 1: worrying perhaps that companies will stop their spending until they 678 00:36:42,920 --> 00:36:44,399 Speaker 1: get ROAI in the near term. 679 00:36:45,920 --> 00:36:48,000 Speaker 13: Well, this is where as I said, our model is 680 00:36:48,080 --> 00:36:52,600 Speaker 13: very very helpful because we don't ask for investments ahead 681 00:36:53,080 --> 00:36:56,720 Speaker 13: of the return. Very much we create pilots, we create 682 00:36:57,480 --> 00:37:01,280 Speaker 13: proofs of concept and show value to your to our customers, 683 00:37:01,280 --> 00:37:04,760 Speaker 13: and only then is it scaled. In our own example, 684 00:37:04,880 --> 00:37:08,320 Speaker 13: we launched a tool that indexed all of the enablement information, 685 00:37:08,440 --> 00:37:11,319 Speaker 13: the education information for our sales team, and then we 686 00:37:11,360 --> 00:37:13,600 Speaker 13: started putting more and more things. Now all of our 687 00:37:13,640 --> 00:37:16,600 Speaker 13: sales information lives in a single agent is used pretty 688 00:37:16,640 --> 00:37:20,640 Speaker 13: much by everyone in the salesfork, certainly me at Snowflake. 689 00:37:20,680 --> 00:37:22,960 Speaker 13: It is that step by step launch and not a 690 00:37:22,960 --> 00:37:26,719 Speaker 13: big bang launch that is also important. And keeping that 691 00:37:26,800 --> 00:37:31,480 Speaker 13: focus on what are projects that go about creating value? 692 00:37:31,560 --> 00:37:35,719 Speaker 13: Are they replacing existing systems? Are they lowering costs? That 693 00:37:35,800 --> 00:37:39,360 Speaker 13: kind of moniaical focus is what is helping us successful 694 00:37:39,680 --> 00:37:43,440 Speaker 13: even in the AI era, because we very carefully tally 695 00:37:43,560 --> 00:37:45,600 Speaker 13: all of this up and make sure that our customers 696 00:37:45,680 --> 00:37:48,200 Speaker 13: feel like we are creating value every step of the way. 697 00:37:49,560 --> 00:37:54,640 Speaker 3: STREETA, are we or are we not in an AI bubble? 698 00:37:55,080 --> 00:37:57,920 Speaker 3: Based on what you're saying every day through Snowflake. 699 00:37:59,000 --> 00:38:03,640 Speaker 13: Well, absolutely there's a lot of enthusiasm about it. But 700 00:38:03,880 --> 00:38:08,600 Speaker 13: at Snowflake, I and every employee at Snowflake is focused 701 00:38:08,680 --> 00:38:11,719 Speaker 13: on what does this mean for our customers? We are 702 00:38:11,760 --> 00:38:15,320 Speaker 13: back to basics. We created Snowflake Intelligence because we wanted 703 00:38:15,360 --> 00:38:19,720 Speaker 13: to bring the power of agentic AI to every single 704 00:38:20,040 --> 00:38:24,000 Speaker 13: user within a company in a meaningful way. We wanted 705 00:38:24,000 --> 00:38:26,799 Speaker 13: to make sure that data analysts who mind you get 706 00:38:26,800 --> 00:38:30,600 Speaker 13: paid a lot, are focused on helping create data agents 707 00:38:30,680 --> 00:38:33,400 Speaker 13: rather than writing endless equal queries as I've done in 708 00:38:34,480 --> 00:38:39,120 Speaker 13: my life, and that focus on what are projects that 709 00:38:39,200 --> 00:38:41,680 Speaker 13: can create value for our customers. How do we get 710 00:38:41,719 --> 00:38:45,120 Speaker 13: them to production fast? How do we show the return 711 00:38:45,200 --> 00:38:49,439 Speaker 13: to them is what we can do? The outside market valuations, 712 00:38:49,520 --> 00:38:52,080 Speaker 13: those things are distractions. I think the more we focus 713 00:38:52,160 --> 00:38:54,480 Speaker 13: on back to basics, the better off we are. 714 00:38:54,920 --> 00:38:57,520 Speaker 1: Should Ramswami, it's great to get back to basics with 715 00:38:57,560 --> 00:39:00,000 Speaker 1: the Snowflake CEO. We appreciate your time. 716 00:39:00,680 --> 00:39:03,680 Speaker 3: AMD sets report earnings after the closing bell, and the 717 00:39:03,719 --> 00:39:06,920 Speaker 3: results should give an indication of how the company's AI 718 00:39:07,000 --> 00:39:09,920 Speaker 3: push is going. Investor sentiment has been strong after the 719 00:39:10,000 --> 00:39:13,400 Speaker 3: chipmakers signed deals with open Ai and Oracle to deliver 720 00:39:13,520 --> 00:39:16,759 Speaker 3: massive amounts of its latest AI chips. Let's get more 721 00:39:16,800 --> 00:39:21,319 Speaker 3: with Bloomberg Semiconductor reporter Ian King, it's probably a good 722 00:39:21,320 --> 00:39:23,759 Speaker 3: moment to look at what the forecasted revenue is for 723 00:39:23,800 --> 00:39:26,400 Speaker 3: the period, and how much of that is data center 724 00:39:26,440 --> 00:39:29,960 Speaker 3: revenue because the reality is, for all the headlines and 725 00:39:30,000 --> 00:39:33,520 Speaker 3: that stock performance over the last two months, AMD is 726 00:39:33,719 --> 00:39:36,760 Speaker 3: just still a much smaller second player behind Video. 727 00:39:37,120 --> 00:39:37,319 Speaker 6: Yeah. 728 00:39:37,360 --> 00:39:42,040 Speaker 4: I mean, it's not getting in annual revenue what in 729 00:39:42,200 --> 00:39:44,120 Speaker 4: Vidia is getting in a year. And the story is 730 00:39:44,160 --> 00:39:46,400 Speaker 4: as simple as that. But what we've seen over the 731 00:39:46,440 --> 00:39:50,120 Speaker 4: last three months is maybe AMD has a seat at 732 00:39:50,160 --> 00:39:52,480 Speaker 4: the table now maybe you know, we've seen some validation 733 00:39:52,600 --> 00:39:55,840 Speaker 4: of its technology in these deals. Whether that translates in 734 00:39:55,880 --> 00:39:59,840 Speaker 4: the short term to stronger revenue and stronger revenue forecast 735 00:40:00,080 --> 00:40:03,120 Speaker 4: think importantly is going to be what determines the reaction today. 736 00:40:03,800 --> 00:40:06,040 Speaker 1: There's been a lot of people trying to interpret who 737 00:40:06,080 --> 00:40:08,480 Speaker 1: the key clients are. I think link secretary strategies really 738 00:40:08,480 --> 00:40:11,720 Speaker 1: saying the key client for MD is metter and actually 739 00:40:11,760 --> 00:40:14,640 Speaker 1: how real some of these longer term deals are in 740 00:40:14,760 --> 00:40:17,000 Speaker 1: how much vindication is Lisa's who's going to give tonight 741 00:40:17,080 --> 00:40:19,719 Speaker 1: or is it all about the analysts meeting that's coming 742 00:40:19,800 --> 00:40:20,640 Speaker 1: up in the next week. 743 00:40:22,040 --> 00:40:24,839 Speaker 4: It'll be both. I think up until now everybody has 744 00:40:24,880 --> 00:40:28,120 Speaker 4: sort of been AMD curious. Will you know we should 745 00:40:28,160 --> 00:40:30,880 Speaker 4: give them a chance, We should give them you know 746 00:40:31,280 --> 00:40:34,400 Speaker 4: a look and maybe see if they're a viable alternative. 747 00:40:34,719 --> 00:40:38,719 Speaker 4: These deals apparently tell people know their technology is real. 748 00:40:38,800 --> 00:40:40,960 Speaker 4: So what they will want from Lisa is the numbers. 749 00:40:41,000 --> 00:40:43,400 Speaker 4: If she doesn't give them the numbers the strong forecast, 750 00:40:43,680 --> 00:40:46,399 Speaker 4: they'll want a pretty good explanation of why not now 751 00:40:46,440 --> 00:40:48,000 Speaker 4: and if not why now then? 752 00:40:48,040 --> 00:40:52,799 Speaker 3: When AMD's traditional market is processes for personal computers and 753 00:40:52,840 --> 00:40:56,439 Speaker 3: servers away from the AI chip, what do we think 754 00:40:56,520 --> 00:40:57,239 Speaker 3: will learn there? 755 00:40:57,520 --> 00:40:59,680 Speaker 4: Yeah, I know there are very strong expectations there. You'll 756 00:40:59,719 --> 00:41:01,880 Speaker 4: remember Intel came out and said, hey, we were worried 757 00:41:01,880 --> 00:41:04,040 Speaker 4: that things were in trouble there and that we've got 758 00:41:04,080 --> 00:41:06,799 Speaker 4: a lot of inventory. Turns out demands really strong for 759 00:41:06,880 --> 00:41:10,920 Speaker 4: aipcs and for standard server parts. AMD is actually taking 760 00:41:11,000 --> 00:41:13,960 Speaker 4: market share or has been taking market share from Intel. 761 00:41:14,200 --> 00:41:16,600 Speaker 4: So the expectations there are very strong and that could 762 00:41:16,600 --> 00:41:17,840 Speaker 4: help the near term numbers. 763 00:41:18,520 --> 00:41:21,960 Speaker 1: It's relentless these earnings in inkings across it throughout for us, 764 00:41:22,040 --> 00:41:24,600 Speaker 1: we so appreciate it. Thank you all things AMD after 765 00:41:24,600 --> 00:41:26,600 Speaker 1: the bell, but that as I from this edition a 766 00:41:26,680 --> 00:41:28,839 Speaker 1: Bloomberg techn YEP. 767 00:41:28,920 --> 00:41:31,440 Speaker 3: Don't forget to check out the pod to recap the show. 768 00:41:31,880 --> 00:41:34,360 Speaker 3: There is so much going on in this earning season 769 00:41:34,600 --> 00:41:39,560 Speaker 3: and in the background anxiety about valuation and bubbles. Two 770 00:41:39,600 --> 00:41:43,240 Speaker 3: day's time of vote on Elon Musk's proposed one trillion 771 00:41:43,280 --> 00:41:46,600 Speaker 3: dollar pay package and earnings, no matter how good they 772 00:41:46,600 --> 00:41:51,880 Speaker 3: are carriers Impalent's case, valuation is what we're all concerned about. 773 00:41:52,440 --> 00:41:54,200 Speaker 3: I alluded to it and many of you use it. 774 00:41:54,400 --> 00:41:56,719 Speaker 3: Listen to the podcast. It's very good, if we say 775 00:41:56,760 --> 00:41:59,040 Speaker 3: so ourselves. You know where to find it. It's on 776 00:41:59,080 --> 00:42:02,400 Speaker 3: the Bloomberg platform as well as online on Apple, Spotify, 777 00:42:02,719 --> 00:42:05,239 Speaker 3: and iHeart this is Bloomberg Tech. 778 00:42:08,040 --> 00:42:08,080 Speaker 1: H