1 00:00:01,440 --> 00:00:06,720 Speaker 1: From Marhard where Innovation, Money and power Collie in Silicon Valley, NBN. 2 00:00:07,040 --> 00:00:11,080 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed loved Love. 3 00:00:24,920 --> 00:00:27,880 Speaker 3: I'm Kroene Heindeer Bloomberg's world headquarters in New York, and 4 00:00:28,000 --> 00:00:29,440 Speaker 3: I'm Med Lovelow in San Francisco. 5 00:00:29,640 --> 00:00:31,680 Speaker 4: This is Bloomberg Technology. 6 00:00:31,280 --> 00:00:32,400 Speaker 5: Coming up Intel. 7 00:00:32,600 --> 00:00:36,080 Speaker 3: The shares they pop as the company's turnaround effort gains momentum. 8 00:00:36,440 --> 00:00:38,680 Speaker 5: We'll discuss with the CEO, Pat Gelsinger. 9 00:00:39,520 --> 00:00:42,560 Speaker 6: Plus we'll continue with the earnings coverage. Is Amazon's results 10 00:00:42,600 --> 00:00:45,000 Speaker 6: show a boost in its cloud computing unit, We'll break 11 00:00:45,040 --> 00:00:45,760 Speaker 6: down those numbers. 12 00:00:46,080 --> 00:00:48,320 Speaker 3: And one year on since Elon must truck over, the 13 00:00:48,400 --> 00:00:51,600 Speaker 3: company formerly known as Twitter and the Billionaire announces plans 14 00:00:51,640 --> 00:00:54,200 Speaker 3: to take on the likes of YouTube, LinkedIn and push 15 00:00:54,280 --> 00:00:57,319 Speaker 3: into payments more. We'll discuss that and so much more 16 00:00:57,360 --> 00:00:59,040 Speaker 3: throughout the hour, but first has. 17 00:00:59,040 --> 00:01:01,120 Speaker 5: Check in and what is a to finish up the week. 18 00:01:01,440 --> 00:01:04,639 Speaker 3: It's notable, though, we're seeing a rally across stocks and bonds, 19 00:01:04,720 --> 00:01:06,480 Speaker 3: which are usually at odds. And that's even as we 20 00:01:06,520 --> 00:01:09,840 Speaker 3: see those inflationary pressures build. From a macro perspective, Certainly, 21 00:01:09,920 --> 00:01:13,240 Speaker 3: consumers anticipated to see more inflation, so says Umiss, the 22 00:01:13,680 --> 00:01:16,920 Speaker 3: University of Michigan numbers and indeed that all important PC 23 00:01:17,160 --> 00:01:19,800 Speaker 3: number coming in strong. So we do though see tech 24 00:01:19,920 --> 00:01:21,560 Speaker 3: on top. Is that all to do with earnings? We'll 25 00:01:21,600 --> 00:01:23,480 Speaker 3: to get into it with you. Ed Nasdaq at one 26 00:01:23,520 --> 00:01:25,759 Speaker 3: point two percent, but still down on the week. I'm 27 00:01:25,800 --> 00:01:27,400 Speaker 3: looking at the two year yield just pulling in on 28 00:01:27,440 --> 00:01:28,960 Speaker 3: the longer round, it's pulling in a little bit more. 29 00:01:29,160 --> 00:01:31,160 Speaker 3: We're flat, but we're still above that five percent level. 30 00:01:31,240 --> 00:01:33,480 Speaker 3: But is there like a risk aversion as we head 31 00:01:33,520 --> 00:01:36,240 Speaker 3: towards the week and amid of course ongoing geo political concerns, 32 00:01:36,280 --> 00:01:38,920 Speaker 3: particularly with the Middle East, Bloomberg Dollar Index is actually 33 00:01:38,959 --> 00:01:41,600 Speaker 3: off by a quarter of percent despite that inflation gauge. 34 00:01:41,880 --> 00:01:43,800 Speaker 3: Let's have a little look at what's happening in terms 35 00:01:43,800 --> 00:01:45,520 Speaker 3: of the world a bitcoin, because over the last five 36 00:01:45,600 --> 00:01:47,920 Speaker 3: days it has been on a tear. We're up some 37 00:01:48,080 --> 00:01:51,960 Speaker 3: fourteen percent. Is this desire for haven Probably not. This 38 00:01:52,120 --> 00:01:55,160 Speaker 3: is still more a focus, a hope, a prayer, a 39 00:01:55,240 --> 00:01:57,840 Speaker 3: reality ed that maybe an ETF and spot bit coin 40 00:01:57,920 --> 00:01:58,440 Speaker 3: is coming soon. 41 00:01:59,360 --> 00:02:01,279 Speaker 6: Yeah, it's kind of like a broad risk on sentiment. 42 00:02:01,360 --> 00:02:03,720 Speaker 6: And you talked about tech being on top and earnings 43 00:02:03,880 --> 00:02:06,000 Speaker 6: is kind of the driver of that. Intel gave a 44 00:02:06,080 --> 00:02:08,359 Speaker 6: sales forecast for the current period that at the top 45 00:02:08,440 --> 00:02:11,480 Speaker 6: end of the range was way above street expectations, and 46 00:02:11,560 --> 00:02:13,840 Speaker 6: the story that they're trying to tell is that the 47 00:02:13,919 --> 00:02:17,000 Speaker 6: PC market is bottomed out. PC chips are their biggest 48 00:02:17,480 --> 00:02:20,160 Speaker 6: sort of volume sales, and that's been important. We will 49 00:02:20,240 --> 00:02:22,639 Speaker 6: speak to Pat Gelsinger later in the show. One point 50 00:02:22,680 --> 00:02:25,320 Speaker 6: in the session where I'm pointing to over there, biggest 51 00:02:25,400 --> 00:02:27,520 Speaker 6: jump on the stock since January of twenty twenty one. 52 00:02:27,560 --> 00:02:30,239 Speaker 6: But it's also the third straight quarter where investors have 53 00:02:30,320 --> 00:02:32,400 Speaker 6: looked at the story of Intel and said, yeah, we 54 00:02:32,639 --> 00:02:35,200 Speaker 6: like it. We believe you that the turnaround's happening. That's 55 00:02:35,240 --> 00:02:37,560 Speaker 6: what we're going to discuss. The other big, big name 56 00:02:37,800 --> 00:02:40,639 Speaker 6: is Amazon, and with Amazon right now, we've paired some 57 00:02:40,760 --> 00:02:43,520 Speaker 6: of the sessions gains. We were up a little more 58 00:02:43,600 --> 00:02:46,840 Speaker 6: than that, but we're still up significantly. The story was 59 00:02:46,919 --> 00:02:50,400 Speaker 6: actually beats across the board apart from aws in the 60 00:02:50,480 --> 00:02:53,600 Speaker 6: top line relative to expectations, but if you look at 61 00:02:53,639 --> 00:02:56,360 Speaker 6: the bottom line, the cloud unit brought in almost one 62 00:02:56,400 --> 00:02:59,200 Speaker 6: point five billion dollars of operating and come more than 63 00:02:59,200 --> 00:03:01,840 Speaker 6: the street was expect they've added customers in the quarter, 64 00:03:02,160 --> 00:03:04,480 Speaker 6: and generative AI is what's going to be the driver 65 00:03:04,600 --> 00:03:07,280 Speaker 6: of growth for AWS, the cash cow of Amazon's business 66 00:03:07,560 --> 00:03:08,240 Speaker 6: for years to come. 67 00:03:08,320 --> 00:03:10,560 Speaker 4: That's the story that Andy Jasse wanted to tell. 68 00:03:10,760 --> 00:03:13,680 Speaker 6: Let's get more with Bloomberg's Poonam goil Bloomberg Intelligence. 69 00:03:14,400 --> 00:03:16,880 Speaker 4: That's the story. But what did the numbers tell you? Poonam? 70 00:03:20,800 --> 00:03:23,239 Speaker 7: The numbers actually looked really good. 71 00:03:24,680 --> 00:03:27,560 Speaker 8: From a results perspective. Three Q was strong both for 72 00:03:27,639 --> 00:03:30,280 Speaker 8: AWS in our mind, the twelve percent game was in 73 00:03:30,400 --> 00:03:33,320 Speaker 8: line with last quarter, and the retail business did much 74 00:03:33,360 --> 00:03:36,200 Speaker 8: better than expected, which bodes well going into the all 75 00:03:36,240 --> 00:03:37,960 Speaker 8: important holiday season coming up. 76 00:03:39,160 --> 00:03:41,520 Speaker 5: So consumer looking more resilient. 77 00:03:42,080 --> 00:03:44,160 Speaker 3: What was it that Andy Jasse was able to get 78 00:03:44,200 --> 00:03:47,160 Speaker 3: across on the cool poonam that really studded nerves around 79 00:03:47,200 --> 00:03:48,920 Speaker 3: AWS and cloud in particular. 80 00:03:51,200 --> 00:03:54,960 Speaker 8: I think for EWUS, you know, the real important thing 81 00:03:55,080 --> 00:03:57,280 Speaker 8: was his tone and the fact that he said the 82 00:03:57,360 --> 00:04:01,040 Speaker 8: deal books were good and that things weren't any further 83 00:04:01,280 --> 00:04:04,240 Speaker 8: So it's like the trend is kind of just continuing 84 00:04:04,360 --> 00:04:07,440 Speaker 8: to stabilize, which was very encouraging, and it was in 85 00:04:07,680 --> 00:04:10,680 Speaker 8: contrast to what we heard from the other tech companies 86 00:04:10,680 --> 00:04:13,840 Speaker 8: are cloud providers earlier so I think that really was 87 00:04:13,920 --> 00:04:16,720 Speaker 8: what took the stock up during the call, and I 88 00:04:16,800 --> 00:04:19,200 Speaker 8: think that's what you're seeing today, just the confidence and 89 00:04:19,279 --> 00:04:22,160 Speaker 8: the cloud business near term and of course for the 90 00:04:22,279 --> 00:04:25,520 Speaker 8: longer term. We remain very optimistic on the cloud business, 91 00:04:25,839 --> 00:04:27,960 Speaker 8: both on the margin side and the top line. 92 00:04:29,200 --> 00:04:32,680 Speaker 6: All right, Pounam Goyle of Bloomberg Intelligence, thank you very much, Caroline. 93 00:04:33,200 --> 00:04:35,920 Speaker 6: Some news crossing the Bloomberg terminal that I've broken with 94 00:04:36,080 --> 00:04:39,200 Speaker 6: colleagues around the world in the last few minutes. ASC, 95 00:04:39,920 --> 00:04:43,440 Speaker 6: which is a Japanese battery maker, has just closed a 96 00:04:43,520 --> 00:04:48,640 Speaker 6: one billion dollar Series B led by GIC, Singapore's wealth fund. 97 00:04:49,160 --> 00:04:50,440 Speaker 4: But what I'm hearing from. 98 00:04:50,400 --> 00:04:53,520 Speaker 6: Sources is that they're also working already on a Series 99 00:04:53,680 --> 00:04:56,840 Speaker 6: C with their bankers and advisors that would value them 100 00:04:56,880 --> 00:04:59,200 Speaker 6: in the billions of dollars. This is a battery company 101 00:04:59,520 --> 00:05:01,960 Speaker 6: that makes the seals that go into evs. It has 102 00:05:02,080 --> 00:05:04,440 Speaker 6: deals with the likes of BMW and Mercedes here in 103 00:05:04,480 --> 00:05:08,240 Speaker 6: the US, some of the automakers in the UK. But 104 00:05:08,320 --> 00:05:11,120 Speaker 6: here's the next step that sources tell us they're looking 105 00:05:11,120 --> 00:05:13,719 Speaker 6: at a USIPO down the line as well. 106 00:05:14,080 --> 00:05:14,800 Speaker 4: And it's interesting. 107 00:05:14,880 --> 00:05:18,080 Speaker 6: It started life as a JV between Nissan and NEC 108 00:05:18,320 --> 00:05:20,800 Speaker 6: back in two thousand and seven. But in twenty eighteen, 109 00:05:20,920 --> 00:05:25,680 Speaker 6: a Chinese energy technology company called Envision bought a controlling 110 00:05:25,839 --> 00:05:29,960 Speaker 6: steak and Jang Lai, who is the founder CEO of Envision, 111 00:05:30,440 --> 00:05:32,520 Speaker 6: is also the chairman of ASC. So a lot of 112 00:05:32,760 --> 00:05:37,120 Speaker 6: questions from investors and insiders about how that ownership and 113 00:05:37,200 --> 00:05:40,680 Speaker 6: the Chinese relationships perceived, especially in the context of the IRA. 114 00:05:41,040 --> 00:05:42,760 Speaker 4: Check that story out on Bloomberg dot com. 115 00:05:43,120 --> 00:05:45,479 Speaker 3: Yeah, a great big school coming from ued. I don't 116 00:05:45,480 --> 00:05:47,640 Speaker 3: know where you find the time. Meanwhile, someone else is 117 00:05:47,680 --> 00:05:49,920 Speaker 3: busy at the moment, and it's one Sam mag Munfried. 118 00:05:50,040 --> 00:05:52,640 Speaker 3: He has taken a stand again today in the FDx 119 00:05:52,680 --> 00:05:55,320 Speaker 3: flaud trial, now this time in front of a jury. Yesterday, 120 00:05:55,360 --> 00:05:58,880 Speaker 3: of course we understood that there was a focus where 121 00:05:59,040 --> 00:06:02,000 Speaker 3: the judges being allowed, in particular for sanbackmcfree to testify 122 00:06:02,080 --> 00:06:05,680 Speaker 3: on ftx' data retention before this jury. At the moment, 123 00:06:06,160 --> 00:06:08,800 Speaker 3: he is currently discussing really the terms of service that 124 00:06:08,839 --> 00:06:11,640 Speaker 3: were created, he says back in May of twenty twenty 125 00:06:11,720 --> 00:06:14,760 Speaker 3: two documents currently being displayed on the monitor. Actually in 126 00:06:15,000 --> 00:06:17,120 Speaker 3: that particular court, he said he skimmed over a few 127 00:06:17,160 --> 00:06:19,920 Speaker 3: times overall, and that he went through parts of it 128 00:06:20,000 --> 00:06:22,640 Speaker 3: in more detail when they were released, But his understanding 129 00:06:23,040 --> 00:06:25,080 Speaker 3: was that this was referring to two different features on 130 00:06:25,160 --> 00:06:28,080 Speaker 3: the platform, first look coordation of user accounts, and second 131 00:06:28,160 --> 00:06:32,120 Speaker 3: clawbacks or socializing losses Bagmenfred is talking about. This is 132 00:06:32,200 --> 00:06:37,920 Speaker 3: basically all about explaining how maybe funds were used, particularly 133 00:06:37,960 --> 00:06:40,560 Speaker 3: when it comes in between Alameda Research, the Hedge Fund, 134 00:06:40,920 --> 00:06:41,760 Speaker 3: and FTX. 135 00:06:42,720 --> 00:06:44,920 Speaker 6: Part of what he was trying to explain was kind 136 00:06:44,960 --> 00:06:47,320 Speaker 6: of the role that FTX lawyers had in the lead 137 00:06:47,400 --> 00:06:49,400 Speaker 6: up to the collapse, that, you know, just drawing on 138 00:06:49,480 --> 00:06:52,360 Speaker 6: my own experience. As a parallel, I covered Trevor Milton, 139 00:06:52,400 --> 00:06:55,880 Speaker 6: the Nikola founder's trial on securities fraud, and I raised 140 00:06:55,920 --> 00:06:59,520 Speaker 6: that because Milton chose not to testify or his lawyer's 141 00:06:59,520 --> 00:07:02,120 Speaker 6: advice not to, it's highly unusual. 142 00:07:02,240 --> 00:07:03,680 Speaker 4: Not only the SBF is. 143 00:07:03,720 --> 00:07:07,480 Speaker 6: Testifying, but he's actually going to do so twice and 144 00:07:07,600 --> 00:07:09,040 Speaker 6: put it all on the line on the stand. 145 00:07:09,120 --> 00:07:10,679 Speaker 4: So we'll continue to cover that one character. 146 00:07:10,720 --> 00:07:13,960 Speaker 3: And of course no cameras inside the courtroom. That's why 147 00:07:13,960 --> 00:07:15,840 Speaker 3: I was thinking so much drawing of all of this. 148 00:07:16,080 --> 00:07:18,240 Speaker 3: But what's notable is we have got our reporters on 149 00:07:18,320 --> 00:07:20,640 Speaker 3: the ground. We are getting a tea live blog chee 150 00:07:20,720 --> 00:07:22,480 Speaker 3: throughout so go. Of course, that's one of the most 151 00:07:22,520 --> 00:07:25,240 Speaker 3: red stories on the Bloomberg terminal today Tea live if 152 00:07:25,240 --> 00:07:27,600 Speaker 3: you're lucky enough to have a Bloomberg. But overall, the 153 00:07:27,640 --> 00:07:29,800 Speaker 3: blow by blow blogging account of really what's going on 154 00:07:29,920 --> 00:07:30,640 Speaker 3: within that trial. 155 00:07:32,800 --> 00:07:36,480 Speaker 6: Welcome to our Bloomberg TV and radio audiences worldwide. Shares 156 00:07:36,480 --> 00:07:39,360 Speaker 6: of Intel popping today as the chip maker predicted a 157 00:07:39,480 --> 00:07:42,360 Speaker 6: return to sales growth in the fourth quarter, fueled by 158 00:07:42,360 --> 00:07:45,280 Speaker 6: a rebound in the personal computer market but also a 159 00:07:45,360 --> 00:07:49,440 Speaker 6: more competitive product lineup. Joining us now is Intel CEO 160 00:07:49,560 --> 00:07:52,200 Speaker 6: Pat Gelsinger. And look, Pat, there was quite a long 161 00:07:52,280 --> 00:07:54,960 Speaker 6: list of things that investors cheered, right. This is the 162 00:07:55,080 --> 00:07:58,600 Speaker 6: third quarter where the market's basically said, Pat, we believe 163 00:07:58,680 --> 00:08:03,240 Speaker 6: you on the turnaround story, on your to do list, 164 00:08:03,640 --> 00:08:05,240 Speaker 6: where do you think you are and what do you 165 00:08:05,280 --> 00:08:08,040 Speaker 6: think you have left to do on that turnaround story? 166 00:08:08,960 --> 00:08:09,160 Speaker 4: Yeah? 167 00:08:09,280 --> 00:08:11,360 Speaker 9: Thanks Ed, And you know, overall, hey, it was a 168 00:08:11,440 --> 00:08:14,559 Speaker 9: great as we say, just a clean beat and raise 169 00:08:15,080 --> 00:08:18,120 Speaker 9: on all the financial metrics, but even more important was 170 00:08:18,200 --> 00:08:21,160 Speaker 9: the operational performance. And you know, as we said, hey, 171 00:08:21,240 --> 00:08:25,520 Speaker 9: getting back to process leadership, and we delivered key milestones 172 00:08:25,560 --> 00:08:27,480 Speaker 9: and we still have at least another year or two 173 00:08:27,560 --> 00:08:31,320 Speaker 9: to go on getting that done, but major milestones starting 174 00:08:31,360 --> 00:08:33,040 Speaker 9: to deliver foundry customers. 175 00:08:33,320 --> 00:08:35,000 Speaker 2: You know, I promised one at the start of. 176 00:08:35,000 --> 00:08:36,840 Speaker 9: The year, and now we have three on our most 177 00:08:36,880 --> 00:08:41,280 Speaker 9: advanced process technology, major packaging wins, and of course the 178 00:08:41,520 --> 00:08:46,600 Speaker 9: product execution you know, clean launch of the AI PC generation, 179 00:08:46,960 --> 00:08:50,439 Speaker 9: but also the server business getting back to profitability ahead 180 00:08:50,480 --> 00:08:53,679 Speaker 9: of schedule and a little bit better performance there and 181 00:08:54,080 --> 00:08:58,600 Speaker 9: delivering the AI Everywhere message with our accelerators our server 182 00:08:58,760 --> 00:09:01,839 Speaker 9: product line. So a really excellent quarter, and I'm just 183 00:09:01,920 --> 00:09:04,280 Speaker 9: so grateful for the Intel team. It's been a journey 184 00:09:04,600 --> 00:09:06,359 Speaker 9: and we are clearly coming. 185 00:09:06,160 --> 00:09:09,640 Speaker 6: Back a pat You've talked to investors and indeed to 186 00:09:09,760 --> 00:09:14,000 Speaker 6: Caroline and I about these new customers for the foundry business. 187 00:09:14,760 --> 00:09:16,679 Speaker 6: When do we get names? When are you going to 188 00:09:16,720 --> 00:09:18,120 Speaker 6: announce who these customers are? 189 00:09:19,520 --> 00:09:21,959 Speaker 9: Well, you two things there, ed, One is it's not 190 00:09:22,240 --> 00:09:25,840 Speaker 9: generally the practice of the foundry industry for the foundry 191 00:09:25,960 --> 00:09:28,200 Speaker 9: to be declaring their customer names. 192 00:09:28,559 --> 00:09:30,680 Speaker 2: So one, it's not practice, and also many of. 193 00:09:30,679 --> 00:09:35,720 Speaker 9: The customers consider it confidential and part of their competitive advantage. 194 00:09:35,240 --> 00:09:37,280 Speaker 2: And how and what technologies they choose. 195 00:09:37,400 --> 00:09:40,719 Speaker 9: So I can't promise names but we're going to characterize 196 00:09:40,760 --> 00:09:43,040 Speaker 9: them as best we can. And as we said, these 197 00:09:43,080 --> 00:09:46,840 Speaker 9: are high performance and AI customers. And we've really seen 198 00:09:46,880 --> 00:09:50,240 Speaker 9: the surge of interest in using the Intel technologies and 199 00:09:50,400 --> 00:09:54,360 Speaker 9: foundry for different AI offerings in the marketplace. And that's 200 00:09:54,400 --> 00:09:58,040 Speaker 9: both a wafer but also a packaging and this idea 201 00:09:58,080 --> 00:10:00,760 Speaker 9: of advanced packaging. In addition into the three on the 202 00:10:00,800 --> 00:10:04,080 Speaker 9: wafer side, we had two advanced packaging customers in AI 203 00:10:04,520 --> 00:10:08,000 Speaker 9: and that revenue materializes more rapidly and six more in 204 00:10:08,040 --> 00:10:12,040 Speaker 9: the pipeline, so overall a really substantial quarter. And the 205 00:10:12,200 --> 00:10:14,800 Speaker 9: AI space in particular has been the customers that have 206 00:10:14,880 --> 00:10:16,160 Speaker 9: seen the most enthusiasm. 207 00:10:16,440 --> 00:10:19,440 Speaker 3: And let's talk about the AI space because the running 208 00:10:19,480 --> 00:10:21,839 Speaker 3: of AI models is where you see your future. It's 209 00:10:21,840 --> 00:10:24,240 Speaker 3: not all just about the foundation models. It's actually the 210 00:10:24,360 --> 00:10:27,599 Speaker 3: running of them, not the building. But can you just 211 00:10:27,679 --> 00:10:29,959 Speaker 3: relieve some of the anxiety coming from investors about a 212 00:10:30,040 --> 00:10:32,839 Speaker 3: lack of clarity over data center future and indeed, what 213 00:10:33,040 --> 00:10:34,599 Speaker 3: is it that they need to hear from you? What 214 00:10:34,920 --> 00:10:37,439 Speaker 3: more can you articulate that really makes it clear to 215 00:10:37,480 --> 00:10:38,760 Speaker 3: them that you're going to be front of center in 216 00:10:38,800 --> 00:10:39,440 Speaker 3: the AI race. 217 00:10:40,360 --> 00:10:42,360 Speaker 2: Yeah, thanks Carolyn, And you know, really. 218 00:10:42,559 --> 00:10:44,679 Speaker 9: You know, first, let's characterize what we're talking about in 219 00:10:44,720 --> 00:10:49,520 Speaker 9: this idea of creating you know, these frontier or foundation models. 220 00:10:49,559 --> 00:10:53,000 Speaker 9: This is described versus using right and the training and 221 00:10:53,160 --> 00:10:56,640 Speaker 9: the inferencing against those models, and I sort of compare 222 00:10:56,679 --> 00:10:59,400 Speaker 9: it to like weather models. Not that many people generate 223 00:10:59,480 --> 00:11:01,839 Speaker 9: weather models, but a lot of people use them. And 224 00:11:01,960 --> 00:11:04,360 Speaker 9: that's how we think about this next phase of AI. 225 00:11:04,760 --> 00:11:07,160 Speaker 9: How do we make this inferencing or the use of 226 00:11:07,280 --> 00:11:10,199 Speaker 9: the models broadly available. And that's going to be in 227 00:11:10,360 --> 00:11:13,840 Speaker 9: the client, right we talked about the AIPC. It's going 228 00:11:13,880 --> 00:11:17,160 Speaker 9: to be at the edge right in retail and manufacturing 229 00:11:17,640 --> 00:11:20,360 Speaker 9: and supply chains. But it's also going to be an 230 00:11:20,480 --> 00:11:23,079 Speaker 9: on premise data centers. And as we've said, instead of 231 00:11:23,440 --> 00:11:26,000 Speaker 9: taking my data to the cloud, I want to bring 232 00:11:26,120 --> 00:11:29,240 Speaker 9: the AI to my data center where the data is 233 00:11:29,600 --> 00:11:32,520 Speaker 9: already and finally work in the cloud. 234 00:11:32,679 --> 00:11:34,240 Speaker 2: And for the data center proper. 235 00:11:34,320 --> 00:11:37,040 Speaker 9: As your question talks about, hey, you know, we knew 236 00:11:37,120 --> 00:11:39,120 Speaker 9: we were going to lose some market share here, right. 237 00:11:39,240 --> 00:11:41,839 Speaker 9: Those losses happened last year and they're sort of playing 238 00:11:41,880 --> 00:11:45,480 Speaker 9: out in the marketplace. But our roadmap is strong and 239 00:11:45,600 --> 00:11:49,559 Speaker 9: we over executed in the quarter on our next gen 240 00:11:49,679 --> 00:11:51,679 Speaker 9: R Gen four product that did a bit better than 241 00:11:51,720 --> 00:11:53,920 Speaker 9: we thought in a lot of AI use cases in 242 00:11:54,000 --> 00:11:57,480 Speaker 9: this area of inferencing. The next generation Gen five, we're 243 00:11:57,600 --> 00:11:59,960 Speaker 9: already ramping that in production and that's going to get 244 00:12:00,200 --> 00:12:03,680 Speaker 9: announced in December. But next year's products, we're already seeing 245 00:12:03,720 --> 00:12:05,760 Speaker 9: great health and we're ahead of schedule in those and 246 00:12:06,080 --> 00:12:09,320 Speaker 9: those really improve our competitiveness. And the twenty five products 247 00:12:09,360 --> 00:12:12,439 Speaker 9: will go into fab in the first quarter of next year. 248 00:12:12,559 --> 00:12:15,880 Speaker 9: So our whole roadmap and execution has really improved, and 249 00:12:16,000 --> 00:12:18,760 Speaker 9: we start to see ourselves regaining market share in twenty 250 00:12:18,840 --> 00:12:20,679 Speaker 9: four in that area. And I think that will be 251 00:12:20,760 --> 00:12:23,959 Speaker 9: sort of the final piece of the turnaround story. When 252 00:12:24,240 --> 00:12:27,559 Speaker 9: you know the market sees okay, data center is backstrong, 253 00:12:27,640 --> 00:12:30,079 Speaker 9: they're winning in the AI space. You know that'll be 254 00:12:30,320 --> 00:12:32,760 Speaker 9: the end of the turnaround story and people say, Okay, 255 00:12:32,920 --> 00:12:33,319 Speaker 9: they did it. 256 00:12:33,880 --> 00:12:36,839 Speaker 3: We'll go back to that building that training of data. 257 00:12:37,320 --> 00:12:39,680 Speaker 3: Can you talk a little about stability AI. Of course 258 00:12:39,920 --> 00:12:42,520 Speaker 3: you've got to deal to build their AI supercomputer. Was 259 00:12:42,559 --> 00:12:46,360 Speaker 3: that them really going to you for ultimately what GOUDI 260 00:12:46,400 --> 00:12:48,760 Speaker 3: can provide over what in video would or is it 261 00:12:49,480 --> 00:12:52,400 Speaker 3: that you wanted to really be sort of offering them 262 00:12:52,480 --> 00:12:54,559 Speaker 3: some carrots in the situation to be able to be 263 00:12:54,679 --> 00:12:56,000 Speaker 3: helping with the training of the models. 264 00:12:56,840 --> 00:12:57,680 Speaker 2: Yeah, great question. 265 00:12:57,800 --> 00:13:01,000 Speaker 9: And you know, now with Goudi, we're now deliver performance 266 00:13:01,080 --> 00:13:03,199 Speaker 9: and benchmarks that are as good as the best in 267 00:13:03,280 --> 00:13:06,120 Speaker 9: the industry. So we've gotten our performance there. You know, 268 00:13:06,160 --> 00:13:07,959 Speaker 9: there was also some of this work that you know, 269 00:13:08,040 --> 00:13:11,640 Speaker 9: the models were created and much of the software industry 270 00:13:11,800 --> 00:13:14,280 Speaker 9: was working, you know, on the n video platform, so 271 00:13:14,360 --> 00:13:16,079 Speaker 9: we had to do some of the software work to 272 00:13:16,160 --> 00:13:19,440 Speaker 9: get those running on the Gaudy platform. And they're looking 273 00:13:19,520 --> 00:13:22,360 Speaker 9: for more cost effective choices and ones that are supply 274 00:13:22,520 --> 00:13:25,880 Speaker 9: chain available in the industry. And as we're ramping our 275 00:13:25,960 --> 00:13:28,640 Speaker 9: Goudy product line, we're getting that software work done. 276 00:13:28,960 --> 00:13:30,680 Speaker 2: You know, they're priced more competitively. 277 00:13:31,000 --> 00:13:34,160 Speaker 9: Customers are saying, wow, I can do that work and 278 00:13:34,320 --> 00:13:37,280 Speaker 9: do it at a much lower power performance envelope than 279 00:13:37,320 --> 00:13:41,240 Speaker 9: the alternatives and have a much more cost effective model 280 00:13:41,320 --> 00:13:45,079 Speaker 9: training and inferencing at scale. Okay, you were seeing a 281 00:13:45,200 --> 00:13:47,720 Speaker 9: real surge of interest, and as I said, we doubled 282 00:13:47,760 --> 00:13:51,040 Speaker 9: our pipeline of customers this quarter, you know, and we 283 00:13:51,480 --> 00:13:53,400 Speaker 9: you know, like others in the industry, are now supply 284 00:13:53,559 --> 00:13:56,000 Speaker 9: chain constrained and we're racing to catch up to that 285 00:13:56,120 --> 00:13:58,160 Speaker 9: demand on our gaudy product. 286 00:13:57,880 --> 00:14:02,360 Speaker 6: Line throppening by radio and television audience worldwide. We're speaking 287 00:14:02,440 --> 00:14:05,720 Speaker 6: to the CEO of Intel, Pat Gelsinger. Pat, the story 288 00:14:05,800 --> 00:14:08,920 Speaker 6: of this week has been chip companies entering the PC 289 00:14:09,120 --> 00:14:14,640 Speaker 6: processor market on architecture, how do you hold off those newcomers? 290 00:14:15,040 --> 00:14:18,520 Speaker 6: You know, attention for example one Apple this coming Monday, 291 00:14:18,640 --> 00:14:20,720 Speaker 6: and they have done well in that domain. 292 00:14:22,520 --> 00:14:26,720 Speaker 9: Yeah, and I think of the AI PC as an 293 00:14:26,760 --> 00:14:30,720 Speaker 9: exciting category and this is one that we announced. We've 294 00:14:31,000 --> 00:14:33,520 Speaker 9: been the first company on that and we're now ramping 295 00:14:33,880 --> 00:14:37,680 Speaker 9: our first generation AI PC products called the Core Ultra. 296 00:14:38,080 --> 00:14:40,560 Speaker 9: So others are talking about what they might do in 297 00:14:40,640 --> 00:14:43,880 Speaker 9: a year or two years, we're ramping products in the marketplace. 298 00:14:44,400 --> 00:14:47,640 Speaker 9: Today we announced over one hundred is svs in our 299 00:14:47,880 --> 00:14:51,720 Speaker 9: AI Acceleration program, so they're coming on board, and before 300 00:14:51,840 --> 00:14:54,880 Speaker 9: others have their products even shipping in the marketplace, we'll 301 00:14:54,880 --> 00:14:58,200 Speaker 9: be launching our next generation, our lunar Lake product, which 302 00:14:58,240 --> 00:15:01,240 Speaker 9: we've already demonstrated for next year, and panther La like 303 00:15:01,320 --> 00:15:04,440 Speaker 9: our twenty five product. We're sending that into fab on 304 00:15:04,600 --> 00:15:08,600 Speaker 9: our leadership Intel eighteen A process Technology and Q one 305 00:15:08,960 --> 00:15:11,200 Speaker 9: so I feel like we have a very strong roadmap, 306 00:15:11,480 --> 00:15:15,000 Speaker 9: and Hey, the idea of an ARM based PC, you know, 307 00:15:15,040 --> 00:15:17,320 Speaker 9: they've always been sort of niche and low end, with 308 00:15:17,400 --> 00:15:19,800 Speaker 9: the exception of Apple, and there it's not ARM, it's 309 00:15:19,840 --> 00:15:24,040 Speaker 9: Apple and their ecosystem. So for the broader windows are market, 310 00:15:24,160 --> 00:15:27,840 Speaker 9: you know, it's always been pretty low end and insignificant 311 00:15:27,880 --> 00:15:30,240 Speaker 9: in the bigger context. And as long as we deliver 312 00:15:30,400 --> 00:15:33,360 Speaker 9: our roadmap, I feel very confident that it's other surge 313 00:15:33,440 --> 00:15:36,360 Speaker 9: into the AIPC space. You know, this is a lift 314 00:15:36,680 --> 00:15:40,640 Speaker 9: to the overall PC market and will be uniquely positioned 315 00:15:40,680 --> 00:15:41,400 Speaker 9: to benefit from that. 316 00:15:42,680 --> 00:15:45,120 Speaker 6: Pat going back just a second to Stability in the 317 00:15:45,160 --> 00:15:48,520 Speaker 6: AI supercomputer. That's kind of in the assembled component domain. 318 00:15:49,000 --> 00:15:51,240 Speaker 6: But are you saying or are you able to confirm 319 00:15:51,320 --> 00:15:55,040 Speaker 6: that's a paid relationship with Stability to praise you for 320 00:15:55,240 --> 00:15:55,960 Speaker 6: use of Goudy. 321 00:15:56,880 --> 00:15:59,120 Speaker 9: Oh yeah, yeah, this is a is a major customer 322 00:15:59,560 --> 00:16:02,680 Speaker 9: and we'll be building that with them, of course, working 323 00:16:02,760 --> 00:16:06,000 Speaker 9: closely with them, but this is a paid customer relationship. 324 00:16:06,280 --> 00:16:06,440 Speaker 10: You know. 325 00:16:06,520 --> 00:16:10,720 Speaker 2: We also see quite another set around our OEMs. 326 00:16:10,760 --> 00:16:14,200 Speaker 9: We announced the major partnership with Dell right for not 327 00:16:14,360 --> 00:16:17,800 Speaker 9: only Xeon's but also Goudy's as they come on premise 328 00:16:17,880 --> 00:16:20,040 Speaker 9: and their cloud offerings. You know, we've seen a big 329 00:16:20,200 --> 00:16:24,040 Speaker 9: upsurge in Goudy interest in the Intel Developer Cloud. We 330 00:16:24,120 --> 00:16:27,200 Speaker 9: had a five x increase in the developers on our 331 00:16:27,280 --> 00:16:30,600 Speaker 9: developer cloud, much of that on the Goudi platform. And 332 00:16:30,640 --> 00:16:33,760 Speaker 9: as I said, we saw you well over a billion 333 00:16:33,840 --> 00:16:37,920 Speaker 9: dollars last quarter. We've approximately doubled that to this quarter 334 00:16:38,040 --> 00:16:43,400 Speaker 9: of Goudy demand worldwide, and those are largely paid customer engagements. 335 00:16:43,760 --> 00:16:47,280 Speaker 9: So overall, we're just seeing a surge of interest with stability, AI, 336 00:16:47,480 --> 00:16:48,520 Speaker 9: Dell and many others. 337 00:16:50,160 --> 00:16:54,280 Speaker 6: Pat we every earnings look to your forecast for the 338 00:16:54,400 --> 00:16:58,560 Speaker 6: PC market and you're slightly more positive than consensus in 339 00:16:58,720 --> 00:17:01,600 Speaker 6: terms of literally how many PCs you think we'll ship 340 00:17:01,640 --> 00:17:04,520 Speaker 6: around the world this year. I guess part of that 341 00:17:04,760 --> 00:17:07,720 Speaker 6: is baked into your sales forecast for the current period 342 00:17:07,760 --> 00:17:11,440 Speaker 6: as well. What gives you that confidence and why is 343 00:17:11,520 --> 00:17:14,280 Speaker 6: it that consumers will return to buying PCs? 344 00:17:15,240 --> 00:17:17,239 Speaker 2: Yeah, and there's probably three different factors there. 345 00:17:17,359 --> 00:17:19,280 Speaker 9: You know. One is I can say, hey, we gave 346 00:17:19,359 --> 00:17:23,920 Speaker 9: this two hundred and seventy million ish PCs being sold 347 00:17:24,040 --> 00:17:26,560 Speaker 9: through this year, and we said that earlier in the year. 348 00:17:26,720 --> 00:17:28,440 Speaker 2: Many thought that we were too optimistic. 349 00:17:28,800 --> 00:17:31,320 Speaker 9: Hey, we look at it today and we're almost spot 350 00:17:31,400 --> 00:17:35,920 Speaker 9: on with our accuracy on that forecast. Second, we've seen 351 00:17:36,000 --> 00:17:39,640 Speaker 9: the industry, you know, not just Intel, but the industry overall. 352 00:17:40,080 --> 00:17:43,160 Speaker 9: Inventory levels are now healthy, you know, and we look 353 00:17:43,160 --> 00:17:46,200 Speaker 9: at our selling rate versus sellout rate, you know, the 354 00:17:46,280 --> 00:17:49,280 Speaker 9: product is selling through. I'd also say, hey, we're off 355 00:17:49,320 --> 00:17:51,240 Speaker 9: to a good start in Q four. We're a couple 356 00:17:51,280 --> 00:17:52,960 Speaker 9: of weeks into the quarter, and as I said on 357 00:17:53,000 --> 00:17:55,800 Speaker 9: the earnings call, really good start. 358 00:17:55,640 --> 00:17:56,760 Speaker 2: To Q four as well. 359 00:17:56,840 --> 00:17:59,520 Speaker 9: And you know, seasonality is a bit above in Q 360 00:17:59,760 --> 00:18:02,920 Speaker 9: four or historical levels. We also have things like Windows 361 00:18:03,040 --> 00:18:07,000 Speaker 9: ten end of service coming from Microsoft. Microsoft's about to 362 00:18:07,560 --> 00:18:11,399 Speaker 9: release their copilot products. But I'd say the sizzle in 363 00:18:11,480 --> 00:18:16,119 Speaker 9: the marketplaces around this AIPC brad new use cases for 364 00:18:16,200 --> 00:18:19,359 Speaker 9: the PC. And I've compared it to the Centrino moment 365 00:18:19,760 --> 00:18:23,119 Speaker 9: of twenty years ago when Centrino really ushered Wi Fi 366 00:18:23,320 --> 00:18:26,280 Speaker 9: at scale into the industry. And we think that's exactly 367 00:18:26,359 --> 00:18:28,520 Speaker 9: what's going to happen with the AIPC. It will be 368 00:18:28,600 --> 00:18:32,240 Speaker 9: a driver of new applications and use cases for the 369 00:18:32,320 --> 00:18:35,720 Speaker 9: PC and bringing a bit more excitement, a bit acceleration 370 00:18:36,000 --> 00:18:38,600 Speaker 9: more users coming into the marketplace because it's going to 371 00:18:38,680 --> 00:18:41,800 Speaker 9: give significant new capabilities to PC users. 372 00:18:42,160 --> 00:18:43,920 Speaker 3: Is that what gives you your gross margin level of 373 00:18:43,960 --> 00:18:46,280 Speaker 3: sixty percent? Again, is that where the confidence comes from. 374 00:18:47,760 --> 00:18:50,919 Speaker 9: Well, to get our overall gross margins up above sixty percent, 375 00:18:51,080 --> 00:18:54,480 Speaker 9: I need the whole business to improve, Carolyn. Obviously we're 376 00:18:54,840 --> 00:18:57,600 Speaker 9: making good progress in the PC. I also need to 377 00:18:57,640 --> 00:19:01,520 Speaker 9: improve my factory network. And as we get back to leadership, 378 00:19:01,640 --> 00:19:05,520 Speaker 9: we finished this super aggressive five nodes in four years. 379 00:19:05,560 --> 00:19:08,600 Speaker 9: You know, I'm churning through capital very rapidly to get 380 00:19:08,640 --> 00:19:11,680 Speaker 9: back to leadership. That's a big factor getting the data 381 00:19:11,760 --> 00:19:14,560 Speaker 9: center business healthy. Going back to one of your earlier questions, 382 00:19:14,560 --> 00:19:18,840 Speaker 9: another factor in getting back to our margin structures. One 383 00:19:18,840 --> 00:19:20,760 Speaker 9: of the other things we did this quarter was also 384 00:19:20,920 --> 00:19:24,520 Speaker 9: have great operational success on our cost saving initiatives, and 385 00:19:24,600 --> 00:19:27,640 Speaker 9: we said, you know, we would result in three billion savings. 386 00:19:28,119 --> 00:19:29,560 Speaker 2: We've also cleaned up the company. 387 00:19:29,600 --> 00:19:33,080 Speaker 9: I've exited ten businesses since I've been here, and now 388 00:19:33,200 --> 00:19:34,960 Speaker 9: we think we're finished with that phase and we just 389 00:19:35,040 --> 00:19:37,919 Speaker 9: get focused on growing the company again to the future. 390 00:19:38,320 --> 00:19:40,520 Speaker 9: So part of its growth, part of it's this focus 391 00:19:40,640 --> 00:19:43,520 Speaker 9: areas across the businesses and part of it's just increased 392 00:19:43,560 --> 00:19:46,840 Speaker 9: operational discipline. But this quarter's results were well on our 393 00:19:46,880 --> 00:19:47,920 Speaker 9: way to accomplishing that. 394 00:19:48,320 --> 00:19:50,760 Speaker 3: And you talk of operations there, and we didn't get 395 00:19:50,840 --> 00:19:52,359 Speaker 3: time to talk about it, but we know that you do, 396 00:19:52,440 --> 00:19:55,800 Speaker 3: indeed have operations in Israel, and we think of your 397 00:19:55,880 --> 00:19:59,520 Speaker 3: own employees and your infrastructure there at this time, Pats, 398 00:19:59,600 --> 00:20:01,520 Speaker 3: so thank you, thank you very much for spending some 399 00:20:01,640 --> 00:20:03,200 Speaker 3: time with us and walking us through your numbers. 400 00:20:03,240 --> 00:20:05,120 Speaker 5: Intel CEO Pat Gelsinger. 401 00:20:04,680 --> 00:20:14,679 Speaker 4: There time for talking tech. First up. 402 00:20:14,680 --> 00:20:17,240 Speaker 6: Apple enthusiasts are getting ready for the company to real 403 00:20:17,280 --> 00:20:20,399 Speaker 6: it's latest Imax and MacBook pros. Bloomberg's reported the new 404 00:20:20,480 --> 00:20:24,600 Speaker 6: computers will likely include the new M three three nanometer processors. 405 00:20:24,800 --> 00:20:27,200 Speaker 6: The product reveal will be Apple's last of twenty twenty 406 00:20:27,240 --> 00:20:31,120 Speaker 6: three this upcoming Monday, plus Huawei's Mate sixty smartphone breakthrough 407 00:20:31,240 --> 00:20:34,040 Speaker 6: driving sales to the company as profit doubles in the 408 00:20:34,080 --> 00:20:36,640 Speaker 6: most recent quart, Adding to signs the Chinese tech leader 409 00:20:36,920 --> 00:20:40,560 Speaker 6: is steadying a business rocked by US sanctions and Elon 410 00:20:40,680 --> 00:20:44,119 Speaker 6: Musk setting his sites on YouTube and LinkedIn ex executives 411 00:20:44,160 --> 00:20:46,560 Speaker 6: whole staff at a company meeting. They see the two 412 00:20:46,680 --> 00:20:50,480 Speaker 6: sites as future competitors Bloomberg reports as must also planning 413 00:20:50,520 --> 00:20:53,480 Speaker 6: to create a new service called x wire that would 414 00:20:53,520 --> 00:20:57,720 Speaker 6: rither scisions, pr newswir and interesting one from us overnight Carrot. 415 00:20:57,800 --> 00:20:59,840 Speaker 5: Yeah, I mean from you ed overnight. 416 00:21:00,040 --> 00:21:03,280 Speaker 3: And really, ultimately this is coming down to wanting to 417 00:21:03,400 --> 00:21:05,760 Speaker 3: be that global town hall, wanting stilled to be the 418 00:21:05,840 --> 00:21:08,600 Speaker 3: place where you go to X for news in particular, 419 00:21:08,680 --> 00:21:11,040 Speaker 3: I mean really got a lot of convincing of business 420 00:21:11,119 --> 00:21:13,320 Speaker 3: or corporates there to want to be filing them press 421 00:21:13,359 --> 00:21:14,240 Speaker 3: releases for example. 422 00:21:14,440 --> 00:21:15,600 Speaker 4: Yeah, there's a lot of battlegrounds. 423 00:21:15,600 --> 00:21:18,080 Speaker 6: Remember today is the one year anniversary of Musk closing 424 00:21:18,119 --> 00:21:20,639 Speaker 6: the deal to take Twitter private now X and this 425 00:21:20,840 --> 00:21:22,800 Speaker 6: was the first time the Acarina and Musk had done 426 00:21:22,840 --> 00:21:25,520 Speaker 6: a joint all hands together, I'm told by sources. 427 00:21:25,600 --> 00:21:27,320 Speaker 4: So some interesting data coming out of that one. 428 00:21:34,760 --> 00:21:36,960 Speaker 5: Welcome back to Bloomberg Technology. I'm Caroline Hired in. 429 00:21:36,960 --> 00:21:40,600 Speaker 6: New York and I'm Loavelo in San Francisco. Look, we 430 00:21:40,680 --> 00:21:43,360 Speaker 6: made it to the end of a megaweek for megacat 431 00:21:43,400 --> 00:21:46,440 Speaker 6: Tech earnings and it's interesting to see holistically how. 432 00:21:46,359 --> 00:21:47,520 Speaker 4: The market looks right now. 433 00:21:47,600 --> 00:21:49,520 Speaker 6: And that's that one hundred on a five day basis 434 00:21:49,880 --> 00:21:52,920 Speaker 6: down for a second consecutive week, down two percent. We're 435 00:21:52,960 --> 00:21:56,080 Speaker 6: up one percent in Friday session. Amazon a big part 436 00:21:56,119 --> 00:21:58,280 Speaker 6: of the points moving to the upside driving that, but 437 00:21:58,760 --> 00:22:01,800 Speaker 6: on the whole into kind of we're waiting for the 438 00:22:01,960 --> 00:22:05,239 Speaker 6: next two Apple and Nvidia. Remember Carroe that we went 439 00:22:05,320 --> 00:22:08,159 Speaker 6: into this earning season saying the big five are going 440 00:22:08,240 --> 00:22:10,119 Speaker 6: to bring all of the EPs growth for the S 441 00:22:10,200 --> 00:22:12,240 Speaker 6: and P five hundred. And if you took out those 442 00:22:12,280 --> 00:22:14,600 Speaker 6: big five, the three of which reported this week, and 443 00:22:14,640 --> 00:22:17,000 Speaker 6: then in Vidia and Apple to come, the SMP five 444 00:22:17,119 --> 00:22:20,560 Speaker 6: hundred would have an earning's growth decline or lack of 445 00:22:20,680 --> 00:22:23,800 Speaker 6: growth down around five percent. So we've been waiting and 446 00:22:23,840 --> 00:22:26,239 Speaker 6: there's more to come. Dig in on one specific name, 447 00:22:26,240 --> 00:22:29,280 Speaker 6: which is Amazon going gangbusters this Friday. We've got the 448 00:22:29,320 --> 00:22:31,800 Speaker 6: perfect guests to talk about Amazon as well, and bring 449 00:22:31,880 --> 00:22:36,080 Speaker 6: in a Yoko Yoshioka portfolio manager, a wealth enhancement group. 450 00:22:36,440 --> 00:22:39,760 Speaker 6: You heard my pre amble on the big five tech names. 451 00:22:40,320 --> 00:22:42,560 Speaker 6: Was your scorecard for the three that have gone so far? 452 00:22:44,240 --> 00:22:48,520 Speaker 10: Well, clearly Microsoft is a big winner during earning season, 453 00:22:48,640 --> 00:22:52,680 Speaker 10: but Amazon wasn't too shabby either, And it's really you 454 00:22:52,720 --> 00:22:55,720 Speaker 10: know the story of AI and. 455 00:22:55,840 --> 00:22:58,600 Speaker 7: The impact that AI is going to have going forward. 456 00:22:59,560 --> 00:23:03,040 Speaker 10: Amazon did a great job on their conference call talking 457 00:23:03,119 --> 00:23:05,639 Speaker 10: about the impact of AI and how that's going to 458 00:23:05,720 --> 00:23:10,160 Speaker 10: add tens of billions of dollars of revenue for AWS, 459 00:23:10,280 --> 00:23:12,880 Speaker 10: which was something that investors really like to hear. 460 00:23:13,800 --> 00:23:18,320 Speaker 3: What they don't like to hear, IAKO is consumer concern 461 00:23:18,560 --> 00:23:22,440 Speaker 3: or macro concerns, And that was a business macro perspective 462 00:23:22,480 --> 00:23:24,480 Speaker 3: that we got that let us down on the metafront, 463 00:23:24,520 --> 00:23:26,760 Speaker 3: for example, and maybe a little bit on Alphabet as well. 464 00:23:27,119 --> 00:23:29,000 Speaker 3: What do you think about the resiliency of the macro 465 00:23:29,119 --> 00:23:30,439 Speaker 3: pitch for these tech names right now? 466 00:23:31,960 --> 00:23:35,440 Speaker 10: Sure, you know, from a overall standpoint, all of these 467 00:23:35,480 --> 00:23:39,480 Speaker 10: companies have just very strong balance sheets, and so you know, 468 00:23:39,720 --> 00:23:42,680 Speaker 10: it's it's been a flight to quality to a certain 469 00:23:42,720 --> 00:23:45,719 Speaker 10: degree for many investors who are concerned about the overall 470 00:23:45,840 --> 00:23:50,520 Speaker 10: macro environment. Having companies that have high quality balance sheets, 471 00:23:50,560 --> 00:23:53,920 Speaker 10: that have cash that is now generating you know, five 472 00:23:54,000 --> 00:23:57,680 Speaker 10: percent plus, that's a huge relief for a lot of 473 00:23:57,800 --> 00:24:01,320 Speaker 10: investors that they can rely on despite the valuations being 474 00:24:01,359 --> 00:24:03,440 Speaker 10: a little bit elevated, I think in the near term. 475 00:24:04,080 --> 00:24:05,960 Speaker 3: And what we've seen over the course of this week 476 00:24:06,160 --> 00:24:09,719 Speaker 3: is the idiosyncratic nature of big tech some of them 477 00:24:09,800 --> 00:24:12,280 Speaker 3: doing well on earnings and leading gages high, and then 478 00:24:12,320 --> 00:24:14,880 Speaker 3: the macro perspective just pushing back and over the course 479 00:24:14,920 --> 00:24:17,879 Speaker 3: of the week, the Nazark is lower going for is AI. 480 00:24:18,040 --> 00:24:21,000 Speaker 3: Do you think the silver line necessary to allow these 481 00:24:21,040 --> 00:24:23,200 Speaker 3: companies to continue to go up into the right. 482 00:24:24,800 --> 00:24:25,359 Speaker 7: Absolutely? 483 00:24:25,440 --> 00:24:27,919 Speaker 10: And I think one of the things that Andy Jassey 484 00:24:28,040 --> 00:24:31,480 Speaker 10: highlighted on the Amazon call yesterday was just the impact 485 00:24:31,640 --> 00:24:35,160 Speaker 10: of some of these AI technologies. You know, whether it's 486 00:24:35,320 --> 00:24:41,000 Speaker 10: code Whisper for Amazon's AWS or duet for Google and 487 00:24:41,800 --> 00:24:44,080 Speaker 10: GitHub Copilot for Microsoft. 488 00:24:44,320 --> 00:24:47,280 Speaker 7: You know, all of these are going to produce productivity gains, 489 00:24:47,560 --> 00:24:49,000 Speaker 7: you know, just removing some of. 490 00:24:49,040 --> 00:24:52,080 Speaker 10: That repetitive code for developers, and I think that's going 491 00:24:52,119 --> 00:24:54,399 Speaker 10: to be a big game changer and really speed up 492 00:24:54,720 --> 00:24:58,359 Speaker 10: the acceleration of the changes and the impact that AI 493 00:24:58,520 --> 00:25:02,960 Speaker 10: can bring a you know, not just for these three companies, 494 00:25:03,040 --> 00:25:04,280 Speaker 10: but across. 495 00:25:03,920 --> 00:25:05,000 Speaker 7: The board for every company. 496 00:25:07,640 --> 00:25:10,840 Speaker 6: It's hard to believe, but we're already basically in November, 497 00:25:11,520 --> 00:25:13,760 Speaker 6: and Caroline and I have been thinking about the end 498 00:25:13,800 --> 00:25:16,760 Speaker 6: of the year. We started the year with just mass 499 00:25:16,840 --> 00:25:21,160 Speaker 6: layoffs across big tech and cutting back costs. How much 500 00:25:21,240 --> 00:25:23,480 Speaker 6: of that is what's showing up now, particularly on the 501 00:25:23,520 --> 00:25:25,760 Speaker 6: bottom line with tech that all the pain that they 502 00:25:25,840 --> 00:25:29,040 Speaker 6: went through, actually it seems to have benefited them at 503 00:25:29,080 --> 00:25:30,439 Speaker 6: least financially. 504 00:25:32,119 --> 00:25:32,760 Speaker 7: Absolutely. 505 00:25:32,880 --> 00:25:35,800 Speaker 10: You know, it was you heard all of these companies 506 00:25:35,880 --> 00:25:40,239 Speaker 10: talking about cloud optimization, and that's really their customers. 507 00:25:39,760 --> 00:25:41,720 Speaker 7: Sort of dialing back a little. 508 00:25:41,520 --> 00:25:44,760 Speaker 10: Bit in terms of how much cloud usage they're going 509 00:25:44,840 --> 00:25:49,159 Speaker 10: to have with these big providers, and you know, you 510 00:25:49,640 --> 00:25:52,760 Speaker 10: because of that, I think a lot of these Microsoft, 511 00:25:53,160 --> 00:25:56,600 Speaker 10: Meta Amazon, they all pulled back on their own spend 512 00:25:57,000 --> 00:26:00,119 Speaker 10: and I think you saw them see the benefit of 513 00:26:00,440 --> 00:26:01,879 Speaker 10: you know, the lower expenses. 514 00:26:02,000 --> 00:26:03,400 Speaker 7: Especially with Amazon. 515 00:26:03,480 --> 00:26:07,320 Speaker 10: They talked about the lower headcount you know in AWS 516 00:26:07,440 --> 00:26:10,440 Speaker 10: and just you know, fewer engineers there. So I think 517 00:26:10,480 --> 00:26:12,960 Speaker 10: that was a huge positive for them and continue to 518 00:26:13,040 --> 00:26:15,720 Speaker 10: sort of strengthen their overall financial position. 519 00:26:17,280 --> 00:26:19,800 Speaker 6: It may seem premature, but I'm going to do it anyway. 520 00:26:20,200 --> 00:26:23,000 Speaker 6: What does twenty twenty four look like for big tech 521 00:26:23,040 --> 00:26:23,640 Speaker 6: in your mind? 522 00:26:25,480 --> 00:26:27,240 Speaker 10: You know a lot of it is going to depend 523 00:26:27,400 --> 00:26:30,320 Speaker 10: on the overall macro environment. But I think it is 524 00:26:30,520 --> 00:26:35,800 Speaker 10: difficult to you know, slow down the impact of AI 525 00:26:36,520 --> 00:26:40,600 Speaker 10: and you know, cloud is still relatively early and it's journey. 526 00:26:40,920 --> 00:26:43,880 Speaker 10: There's still workloads that need to migrate to the cloud, 527 00:26:44,240 --> 00:26:45,760 Speaker 10: and it's just going to accelerate. 528 00:26:45,880 --> 00:26:46,920 Speaker 7: I think because of AI. 529 00:26:47,840 --> 00:26:50,879 Speaker 10: You know, twenty twenty four will be determined by you know, 530 00:26:51,080 --> 00:26:54,159 Speaker 10: other factors other than just AI. But I think a 531 00:26:54,200 --> 00:26:56,840 Speaker 10: lot of these companies can whether through that over the 532 00:26:56,920 --> 00:26:57,360 Speaker 10: long term. 533 00:26:58,119 --> 00:27:00,280 Speaker 3: The reason why you're such a group group for to 534 00:27:00,320 --> 00:27:02,360 Speaker 3: have on is not only how you're having to navigate 535 00:27:02,440 --> 00:27:06,400 Speaker 3: what I think Wealth Enhancement Group has thousands, forty nine 536 00:27:06,560 --> 00:27:09,520 Speaker 3: thousand households of wealthy individuals that you're helping manage their money, 537 00:27:09,600 --> 00:27:13,000 Speaker 3: but your relationship having build up all the research analysis 538 00:27:13,040 --> 00:27:14,960 Speaker 3: and they're moving into portfolio management. You've got a whole 539 00:27:15,000 --> 00:27:18,080 Speaker 3: host of tech companies that you own, Io Cohen, are 540 00:27:18,160 --> 00:27:20,199 Speaker 3: there any that are overlooked or how do you think 541 00:27:20,240 --> 00:27:22,280 Speaker 3: about the Kathy Woods of this world that feel that 542 00:27:22,680 --> 00:27:24,720 Speaker 3: in videos and some of the big cap names just 543 00:27:24,800 --> 00:27:26,679 Speaker 3: too obvious when it comes to the aiplay. 544 00:27:28,600 --> 00:27:28,879 Speaker 4: Sure. 545 00:27:29,000 --> 00:27:31,639 Speaker 10: I mean we like a lot of the sort of 546 00:27:31,960 --> 00:27:36,320 Speaker 10: value oriented tech names as well. Oracles been in that 547 00:27:36,920 --> 00:27:39,640 Speaker 10: space as well, and Oracle has been talked about their 548 00:27:39,760 --> 00:27:42,720 Speaker 10: cloud evolution has really come a long way. 549 00:27:43,560 --> 00:27:46,680 Speaker 7: And so there are other names outside of just the 550 00:27:47,040 --> 00:27:48,320 Speaker 7: big megacat tech that. 551 00:27:48,400 --> 00:27:52,240 Speaker 10: We like Accenture is another name that we like in 552 00:27:52,359 --> 00:27:56,119 Speaker 10: the space in technology. They have consultants who are going 553 00:27:56,200 --> 00:28:00,240 Speaker 10: to help all of these companies adopt newer technologies like 554 00:28:00,359 --> 00:28:04,040 Speaker 10: AI and so you know there there are other technology 555 00:28:04,160 --> 00:28:08,440 Speaker 10: names outside of just the big five that we constantly 556 00:28:08,520 --> 00:28:09,040 Speaker 10: talk about. 557 00:28:09,040 --> 00:28:14,440 Speaker 6: It wouldn't be a week if we didn't speak FED speak, 558 00:28:14,680 --> 00:28:17,720 Speaker 6: and you know, for our global audience and Bloomberg Technology, 559 00:28:18,320 --> 00:28:21,960 Speaker 6: just give your perspective on why rates and the FED 560 00:28:22,040 --> 00:28:26,640 Speaker 6: and the outlook for rates influences your decision about your 561 00:28:26,960 --> 00:28:29,919 Speaker 6: allocation to technology names in a portfolio. 562 00:28:31,320 --> 00:28:33,280 Speaker 10: It's a great question, ed. You know, we get this 563 00:28:33,400 --> 00:28:35,440 Speaker 10: a lot, and you know, why do we focus so 564 00:28:35,560 --> 00:28:38,200 Speaker 10: much on the FED and interest rates? And I go 565 00:28:38,360 --> 00:28:42,200 Speaker 10: back to just sort of valuation one O one discounted 566 00:28:42,280 --> 00:28:45,200 Speaker 10: cash flows. You know, you have to use the risk 567 00:28:45,320 --> 00:28:47,840 Speaker 10: free rate, which tends to be the ten year, right, 568 00:28:48,000 --> 00:28:50,840 Speaker 10: so the tenure treasury rate, and what we see there 569 00:28:51,080 --> 00:28:53,080 Speaker 10: is what's used in order to discount all of those 570 00:28:53,160 --> 00:28:55,800 Speaker 10: long term cash flows back to the present. And that's 571 00:28:55,880 --> 00:28:59,120 Speaker 10: why there's such a huge focus on what the FED 572 00:28:59,200 --> 00:29:02,080 Speaker 10: will do and how the treasury curve will. 573 00:29:02,080 --> 00:29:03,920 Speaker 7: React to FED action. 574 00:29:04,560 --> 00:29:07,720 Speaker 10: So yes, it is something that we continuously talk about 575 00:29:07,800 --> 00:29:12,120 Speaker 10: but it impacts not just tech valuations, but valuations across 576 00:29:12,200 --> 00:29:15,440 Speaker 10: the board, inequities as well as real estate and other markets. 577 00:29:15,800 --> 00:29:18,360 Speaker 3: IACO great to have some time with you, Iico Yeshioka 578 00:29:18,720 --> 00:29:21,040 Speaker 3: of Wealth Enhancement Group. We thank you coming to us 579 00:29:21,040 --> 00:29:23,440 Speaker 3: from LA. Meanwhile, no, we're going to keep on talking 580 00:29:23,440 --> 00:29:26,920 Speaker 3: about AI and indeed how machine learning can have kind 581 00:29:26,960 --> 00:29:29,520 Speaker 3: of major impact on how companies and governments handle climate 582 00:29:29,640 --> 00:29:33,120 Speaker 3: change questions since Witherspoon, head of Climate and Sustainability of 583 00:29:33,320 --> 00:29:36,880 Speaker 3: Google deep Mind, spoke about it the use cases in particular, 584 00:29:37,240 --> 00:29:39,760 Speaker 3: but also it's orangines and gaming take listen. 585 00:29:41,280 --> 00:29:42,960 Speaker 11: Go was a really offha Go was a system that 586 00:29:43,040 --> 00:29:46,920 Speaker 11: we developed that hopefully folks are familiar with. But games 587 00:29:47,080 --> 00:29:50,800 Speaker 11: are a really interesting place to test AI because they 588 00:29:50,880 --> 00:29:56,360 Speaker 11: have very clear goals, success metrics, clear benchmarks, and the 589 00:29:56,640 --> 00:29:58,760 Speaker 11: end lots of data. And so that's the kind of 590 00:29:58,840 --> 00:30:01,520 Speaker 11: system that is really interesting for AI because it's a 591 00:30:01,560 --> 00:30:04,120 Speaker 11: perfect test bed. And one of the things that was 592 00:30:04,160 --> 00:30:06,360 Speaker 11: really interesting about Alpha Go is that even though it's 593 00:30:06,400 --> 00:30:09,640 Speaker 11: a game we've been playing for hundreds of years, using 594 00:30:09,680 --> 00:30:13,000 Speaker 11: an AI system actually taught us new information that we 595 00:30:13,120 --> 00:30:17,120 Speaker 11: didn't know about the game. Before, and we've really expanded 596 00:30:17,520 --> 00:30:20,840 Speaker 11: this idea into other systems like Alpha fold. For instance. 597 00:30:21,240 --> 00:30:25,560 Speaker 11: Alpha fold is a system that takes in amino acid 598 00:30:25,640 --> 00:30:32,000 Speaker 11: sequences and tells us how proteins are folded. We developed 599 00:30:32,000 --> 00:30:34,840 Speaker 11: a database that basically works like Google Search for proteins, 600 00:30:35,840 --> 00:30:38,400 Speaker 11: and we were able to give that to the research 601 00:30:38,440 --> 00:30:42,040 Speaker 11: community in order to further their research. So if you 602 00:30:42,160 --> 00:30:46,800 Speaker 11: look at kind of the development of systems like alpha fold, 603 00:30:47,120 --> 00:30:50,040 Speaker 11: you can see that some of the scientists that might 604 00:30:50,160 --> 00:30:54,360 Speaker 11: use that would understand proteins, for instance, if they're engineering enzymes. 605 00:30:54,720 --> 00:30:57,280 Speaker 11: We actually do work with a partner who is looking 606 00:30:57,400 --> 00:31:01,440 Speaker 11: into enzymes that biodietegrade plastics and are able to do 607 00:31:01,560 --> 00:31:03,680 Speaker 11: their work more quickly because they have access to the 608 00:31:03,760 --> 00:31:05,240 Speaker 11: library that Alpha fold provides. 609 00:31:05,480 --> 00:31:07,680 Speaker 8: Deep Mind CEO is a big guitar you fan, so 610 00:31:08,320 --> 00:31:09,960 Speaker 8: stories let your kids play video games. 611 00:31:10,040 --> 00:31:11,760 Speaker 12: I mean the answer to how do you go from 612 00:31:12,040 --> 00:31:14,680 Speaker 12: Alpha go to climate science is where you just go 613 00:31:14,880 --> 00:31:18,600 Speaker 12: via you know, two hundred million protein Yeah, you know 614 00:31:18,680 --> 00:31:20,560 Speaker 12: which is which is which is great? So you know 615 00:31:21,400 --> 00:31:23,440 Speaker 12: in the episode that you are in next month, we 616 00:31:23,800 --> 00:31:26,400 Speaker 12: take a very global view because it's a global show, 617 00:31:26,600 --> 00:31:28,600 Speaker 12: but we're here in the UK with. 618 00:31:28,720 --> 00:31:30,960 Speaker 4: Our fantastic climate and weather. 619 00:31:31,120 --> 00:31:33,000 Speaker 2: You know, it's lovely today. 620 00:31:34,280 --> 00:31:36,720 Speaker 12: So can you tell us about something you've done here, 621 00:31:38,000 --> 00:31:41,200 Speaker 12: that you've been, that you've seen and that is going 622 00:31:41,240 --> 00:31:42,240 Speaker 12: to excite everybody. 623 00:31:42,840 --> 00:31:45,880 Speaker 11: Yeah, we'll talk about the weather, since the UK loves 624 00:31:45,920 --> 00:31:48,680 Speaker 11: talking about especially rainfall, so maybe i'll go with that example. 625 00:31:49,160 --> 00:31:52,040 Speaker 11: We did a partnership with the Met Office, particularly working 626 00:31:52,080 --> 00:31:54,800 Speaker 11: with some of their expert meteorologists looking at heavy rainfall 627 00:31:54,840 --> 00:31:59,120 Speaker 11: because heavy rainfall is what causes damage to people and property, 628 00:31:59,600 --> 00:32:02,280 Speaker 11: and so we developed a system that was a generative 629 00:32:02,320 --> 00:32:04,520 Speaker 11: AI system so effectively you can think about it, you 630 00:32:04,520 --> 00:32:06,680 Speaker 11: know when you look at the weather and you see 631 00:32:06,920 --> 00:32:09,040 Speaker 11: those you know, beautiful pictures on screen with lots of 632 00:32:09,120 --> 00:32:11,480 Speaker 11: colors of like where where rain is going to happen, 633 00:32:11,680 --> 00:32:13,040 Speaker 11: like where it's going to be heavy, where it's going 634 00:32:13,080 --> 00:32:14,600 Speaker 11: to be light, how it's going to move throughout the UK. 635 00:32:15,480 --> 00:32:18,560 Speaker 11: And we basically fed that radar data to our system 636 00:32:19,280 --> 00:32:22,600 Speaker 11: and it watched it like a movie effectively and predicted 637 00:32:22,680 --> 00:32:24,880 Speaker 11: the next frame. So we were able to This is 638 00:32:24,920 --> 00:32:28,720 Speaker 11: something we call precipitation now casting, so it's the forecasting 639 00:32:29,040 --> 00:32:31,080 Speaker 11: for two hours ahead of time, so very short term 640 00:32:31,160 --> 00:32:31,959 Speaker 11: for forecasting. 641 00:32:32,360 --> 00:32:33,880 Speaker 13: And we worked with the Met Office to do this. 642 00:32:33,960 --> 00:32:37,880 Speaker 11: They were an incredible partner because actually ninety nine percent 643 00:32:37,920 --> 00:32:40,320 Speaker 11: of the country here in the UK is covered by 644 00:32:40,440 --> 00:32:41,200 Speaker 11: the radar data. 645 00:32:42,760 --> 00:32:46,600 Speaker 6: That was Sims Witherspoon, Climate and Sustainability lead at Google 646 00:32:46,680 --> 00:32:48,960 Speaker 6: Deep Mind and now come out here on Bloomberg Technology 647 00:32:49,080 --> 00:32:53,440 Speaker 6: ion CUE shift into commercialization is fully underway. We're going 648 00:32:53,480 --> 00:32:56,920 Speaker 6: to talk it to the CEO, Peter Chapman, about recent partnership, 649 00:32:57,040 --> 00:32:59,720 Speaker 6: some departures and one roller coaster. 650 00:33:00,800 --> 00:33:02,680 Speaker 4: This has been bot Technology. 651 00:33:11,400 --> 00:33:14,600 Speaker 3: Quantum computing company ion Q, when it's placing some big 652 00:33:14,680 --> 00:33:17,320 Speaker 3: bets on it shift into national security. How Q is 653 00:33:17,320 --> 00:33:19,800 Speaker 3: actually teaming up with the US Air Force Research Lab 654 00:33:20,040 --> 00:33:23,320 Speaker 3: to develop and apply quantum systems. Now, this news comes 655 00:33:23,400 --> 00:33:25,200 Speaker 3: in the wake of the co found at Chris Monroe's 656 00:33:25,200 --> 00:33:27,640 Speaker 3: departure from the company. Now all of this rattle the 657 00:33:27,640 --> 00:33:30,040 Speaker 3: stock and we want to really get clarity the picture 658 00:33:30,080 --> 00:33:32,960 Speaker 3: forward with none other than well the CEO Peter Chapman, 659 00:33:33,000 --> 00:33:35,240 Speaker 3: who joins us. Now and Peter, going back to basics, 660 00:33:35,320 --> 00:33:37,640 Speaker 3: like your aim at the company is to build the 661 00:33:37,760 --> 00:33:40,800 Speaker 3: best quantum computers and basically solve some of the most 662 00:33:40,800 --> 00:33:41,920 Speaker 3: complex tasks out of there. 663 00:33:41,960 --> 00:33:44,800 Speaker 5: You came out of universities, out of the labs into 664 00:33:44,800 --> 00:33:47,320 Speaker 5: the real world. But your chief science officer is sort of. 665 00:33:47,360 --> 00:33:49,560 Speaker 3: Going back into the labs, back into the research, back 666 00:33:49,600 --> 00:33:52,440 Speaker 3: into the universities. And some analysts are saying, look, they're 667 00:33:52,440 --> 00:33:55,160 Speaker 3: worried about the timing and the reasoning. Explain the timing 668 00:33:55,200 --> 00:33:56,000 Speaker 3: and the reasoning for us. 669 00:33:56,680 --> 00:33:56,880 Speaker 8: Yeah. 670 00:33:56,960 --> 00:33:57,160 Speaker 10: Sure. 671 00:33:58,280 --> 00:34:03,160 Speaker 14: So the company was co founded by two college professors 672 00:34:03,560 --> 00:34:07,800 Speaker 14: originally from the University of Maryland and Duke, although both 673 00:34:07,840 --> 00:34:14,200 Speaker 14: are today at Duke University. They in Chris's case, he 674 00:34:14,320 --> 00:34:21,399 Speaker 14: still teaches classes at Duke and they run Duke's Quantum Lab, 675 00:34:21,680 --> 00:34:25,600 Speaker 14: which is a significant undertaking unto itself. And we have 676 00:34:25,760 --> 00:34:29,640 Speaker 14: a relationship with Duke University where the work that they 677 00:34:29,920 --> 00:34:36,279 Speaker 14: do there, funded by the government, is exclusively licensed and 678 00:34:36,400 --> 00:34:42,160 Speaker 14: royalty free to the company. So Chris is going back 679 00:34:42,239 --> 00:34:47,879 Speaker 14: to Duke University to lead that program. But the relationship 680 00:34:47,960 --> 00:34:51,400 Speaker 14: between Duke and and Chris is really not It's not 681 00:34:51,600 --> 00:34:54,800 Speaker 14: changed in any way, shape or form. Chris would be 682 00:34:54,880 --> 00:34:58,000 Speaker 14: the first one to tell you that the physics for 683 00:34:58,200 --> 00:35:01,919 Speaker 14: what it is that we're doing was solved long, long ago. 684 00:35:02,400 --> 00:35:05,239 Speaker 14: If you'll go back and listen to Chris, you know, 685 00:35:05,360 --> 00:35:08,600 Speaker 14: from several years ago, you will find tapes where he 686 00:35:08,760 --> 00:35:12,840 Speaker 14: says exactly that where the company is today is no 687 00:35:12,960 --> 00:35:17,759 Speaker 14: longer about academic research. It's not about fundamental physics. And 688 00:35:17,880 --> 00:35:21,120 Speaker 14: that's actually quite a difference between I and Q and 689 00:35:21,239 --> 00:35:24,080 Speaker 14: many of our kind of competitors. They're still working on 690 00:35:24,760 --> 00:35:29,680 Speaker 14: fundamental physics sometimes, you know, and we're we're not. We're 691 00:35:29,719 --> 00:35:34,520 Speaker 14: actually migrating out of an academic phase and now into 692 00:35:34,560 --> 00:35:38,239 Speaker 14: an engineering and product phase, and so we're in the 693 00:35:38,280 --> 00:35:40,919 Speaker 14: middle of that transition, Peter. 694 00:35:41,000 --> 00:35:43,759 Speaker 6: One of the analysts that that Caroline reference was Benchmark's 695 00:35:43,800 --> 00:35:47,000 Speaker 6: David Williams, and he basically says that the timing and 696 00:35:47,120 --> 00:35:52,080 Speaker 6: motivation of Chris's departure is curious. Just on the timing part, 697 00:35:53,160 --> 00:35:56,800 Speaker 6: could you just explain why now, why the process that 698 00:35:56,840 --> 00:35:59,120 Speaker 6: you just outlined is being announced this week? 699 00:36:00,080 --> 00:36:02,439 Speaker 14: Well, I think I'm not sure I'd want to speak 700 00:36:02,480 --> 00:36:05,840 Speaker 14: for Chris, but I think there's a number of likely factors. 701 00:36:06,160 --> 00:36:08,400 Speaker 14: One is, he did state that he wants to go 702 00:36:08,560 --> 00:36:13,120 Speaker 14: back to his academic roots. He seems to enjoy that, 703 00:36:13,360 --> 00:36:16,319 Speaker 14: and and that's exactly what it is that he's doing. 704 00:36:17,760 --> 00:36:22,840 Speaker 14: The other aspects just in terms I think you know, 705 00:36:23,000 --> 00:36:29,160 Speaker 14: the companies often outgrow their founders. The You know, we're 706 00:36:29,200 --> 00:36:32,160 Speaker 14: at a place today where it's no longer an academic endeavor. 707 00:36:32,760 --> 00:36:37,360 Speaker 14: It really is a engineering and product based endeavor. And 708 00:36:37,440 --> 00:36:41,600 Speaker 14: I think that the Chris felt that his skill set 709 00:36:41,840 --> 00:36:44,160 Speaker 14: is better in the academic. 710 00:36:43,800 --> 00:36:44,200 Speaker 4: Side of it. 711 00:36:45,160 --> 00:36:48,239 Speaker 3: So let's talk about the application of the technology now, Peter, 712 00:36:48,400 --> 00:36:49,600 Speaker 3: where do you feel. 713 00:36:49,360 --> 00:36:52,840 Speaker 5: The reward of your investors? Start to push in on. 714 00:36:52,960 --> 00:36:56,360 Speaker 3: We were just talking about applications in national security for example. 715 00:36:57,160 --> 00:36:57,359 Speaker 4: Yep. 716 00:36:58,440 --> 00:37:02,200 Speaker 14: So we're just on the verge of We just announced 717 00:37:02,400 --> 00:37:05,880 Speaker 14: just recently a news system which is going to have 718 00:37:07,080 --> 00:37:13,040 Speaker 14: enough we call them algorithmic cubits sixty four algorithmic cubits, 719 00:37:13,640 --> 00:37:16,960 Speaker 14: and this will allow you to explore our computational space 720 00:37:17,520 --> 00:37:20,719 Speaker 14: of two to the sixty four in parallel and so 721 00:37:21,239 --> 00:37:29,160 Speaker 14: two to the sixty four is eighteen quintillion different possibilities 722 00:37:29,280 --> 00:37:33,080 Speaker 14: states in a single instruction in a fraction of a second, 723 00:37:33,239 --> 00:37:36,640 Speaker 14: similar to the instruction speeds you have on your laptop. 724 00:37:37,320 --> 00:37:41,640 Speaker 14: This will allow now applications to be developed which can 725 00:37:41,719 --> 00:37:45,719 Speaker 14: take on the world's largest supercomputers. Quantum is not good 726 00:37:45,800 --> 00:37:49,480 Speaker 14: for everything. It strangely has a tough time adding one 727 00:37:49,560 --> 00:37:52,600 Speaker 14: plus one, and at the same time, maybe is good 728 00:37:52,719 --> 00:37:57,320 Speaker 14: for solving differential equations. In terms of applications that we 729 00:37:57,640 --> 00:38:01,759 Speaker 14: have seen so far, machine learning is certainly one area 730 00:38:01,840 --> 00:38:06,320 Speaker 14: where quantum seems to have a huge advantage. My guess 731 00:38:06,360 --> 00:38:09,880 Speaker 14: would be that strong AI will be another area. The 732 00:38:10,000 --> 00:38:14,760 Speaker 14: original premise of quantum is that exploring the natural world, 733 00:38:15,400 --> 00:38:19,279 Speaker 14: which is not digital, it's not analog, it's quantum, is 734 00:38:19,320 --> 00:38:21,520 Speaker 14: that you need a quantum computer to be able to 735 00:38:21,800 --> 00:38:26,319 Speaker 14: model mother nature. So you know, it has been thought 736 00:38:26,400 --> 00:38:31,240 Speaker 14: that at about ninety six good enough cubits, algorithmic cubits, 737 00:38:31,640 --> 00:38:36,160 Speaker 14: you could model the photosynthesis and be able to unlock 738 00:38:36,280 --> 00:38:40,919 Speaker 14: how plants can turn sunlight into energy, and that would 739 00:38:41,000 --> 00:38:44,640 Speaker 14: produce the next generation of solar cells that would be 740 00:38:44,719 --> 00:38:48,760 Speaker 14: four times more efficient than what they are today. 741 00:38:49,800 --> 00:38:52,640 Speaker 6: Peter, much of our global audience does think in floating 742 00:38:52,960 --> 00:38:55,400 Speaker 6: point operations the second that are just unimaginable as the 743 00:38:55,480 --> 00:38:59,240 Speaker 6: human brain. But just quickly, in a layman's perspective, explain 744 00:38:59,320 --> 00:39:01,800 Speaker 6: what's proprie tree about your technology quickly. 745 00:39:02,760 --> 00:39:06,080 Speaker 14: So what we do is we use atomic clocks. Actually 746 00:39:06,200 --> 00:39:10,520 Speaker 14: have one of them here, and it's a chip which 747 00:39:10,600 --> 00:39:13,320 Speaker 14: is sitting in a little vacuum chamber, and it's using 748 00:39:14,920 --> 00:39:18,200 Speaker 14: thirty two to sixty four atoms, and it uses those 749 00:39:18,239 --> 00:39:23,600 Speaker 14: atoms for computation. And what's unusual about it is that 750 00:39:23,680 --> 00:39:26,640 Speaker 14: it can run at room temperature. We're down at point 751 00:39:26,760 --> 00:39:31,160 Speaker 14: zero two nanometers, so you know, the standard silicon at 752 00:39:31,200 --> 00:39:34,239 Speaker 14: seven nanometers is way up from where we are. And 753 00:39:34,400 --> 00:39:38,080 Speaker 14: we use white to program the atoms. We shine little 754 00:39:38,160 --> 00:39:41,040 Speaker 14: lasers onto these atoms and put a spin on them 755 00:39:41,400 --> 00:39:44,400 Speaker 14: and entangle them to be able to do the computation. 756 00:39:45,880 --> 00:39:48,680 Speaker 6: All right, Pizza Chapman, CEO, I Cut, thank you very 757 00:39:48,800 --> 00:39:49,399 Speaker 6: much for your time. 758 00:39:58,280 --> 00:40:04,719 Speaker 13: It's official. Tailor's is a billionaire. Welcome to the Aristour. 759 00:40:05,239 --> 00:40:09,160 Speaker 13: Her Aristour alone is a party generating hundreds of millions 760 00:40:09,200 --> 00:40:12,799 Speaker 13: of dollars. It's a multinational conglomerate with the world's most 761 00:40:12,880 --> 00:40:18,200 Speaker 13: devoted customer base and an ultra charismatic CEO. Swift has 762 00:40:18,239 --> 00:40:22,640 Speaker 13: made her fortune almost exclusively from her music. Bloomberg estimates 763 00:40:22,640 --> 00:40:25,160 Speaker 13: Swift has made one hundred and twenty five million dollars 764 00:40:25,200 --> 00:40:26,640 Speaker 13: over the years from record sales. 765 00:40:27,320 --> 00:40:30,440 Speaker 1: We estimate that her total income from streaming is one 766 00:40:30,520 --> 00:40:31,680 Speaker 1: hundred and seventy five million. 767 00:40:31,880 --> 00:40:35,680 Speaker 13: The biggest part of our earnings is undoubtedly her concert revenue. 768 00:40:36,120 --> 00:40:38,720 Speaker 1: We estimated that Taylor, and that's about thirty five percent 769 00:40:38,960 --> 00:40:42,040 Speaker 1: of the ticket sales. Is profit about five hundred million 770 00:40:42,200 --> 00:40:43,880 Speaker 1: from touring over the years. 771 00:40:44,600 --> 00:40:48,280 Speaker 13: Her tours, her record sales, and streaming royalties are all earnings, 772 00:40:48,480 --> 00:40:51,480 Speaker 13: but she also has assets, including her recording catalog. 773 00:40:52,360 --> 00:40:55,160 Speaker 1: We estimate that her catalog is worth about four hundred million. 774 00:40:55,680 --> 00:40:58,360 Speaker 13: Then, of course there's her actual property. 775 00:40:59,280 --> 00:41:03,360 Speaker 1: That include a condo in the state in Nashville, in 776 00:41:03,440 --> 00:41:07,680 Speaker 1: a state in Los Angeles, a large apartment in Tribeca 777 00:41:07,800 --> 00:41:10,840 Speaker 1: in New York City, as well as a summer house 778 00:41:10,920 --> 00:41:14,239 Speaker 1: in Rhode Island. The total value of her properties is 779 00:41:14,239 --> 00:41:15,560 Speaker 1: about one hundred and ten billion. 780 00:41:17,360 --> 00:41:21,400 Speaker 13: Subtract her expenses, taxes, staff costs, and so on, and 781 00:41:21,520 --> 00:41:25,919 Speaker 13: you get a net worth of one point one billion dollars. 782 00:41:30,960 --> 00:41:33,440 Speaker 6: Some reporting from the Bloomberg Big Tight team there on 783 00:41:33,520 --> 00:41:36,880 Speaker 6: Swift's influence, reach and wealth and Karen never in her 784 00:41:37,000 --> 00:41:39,640 Speaker 6: wildest dreams, which she probably thinks she'd be where she. 785 00:41:39,760 --> 00:41:42,000 Speaker 5: Is now, Well never know. She might have thought that 786 00:41:42,040 --> 00:41:44,160 Speaker 5: from very young, age ed, big ambious man. 787 00:41:44,320 --> 00:41:47,000 Speaker 3: But what's so interesting is, well, maybe her success isn't 788 00:41:47,040 --> 00:41:50,239 Speaker 3: always paying off completely. To Universal Music Group, of course, 789 00:41:50,400 --> 00:41:53,400 Speaker 3: she is the biggest seller in terms of artists, but 790 00:41:53,440 --> 00:41:57,200 Speaker 3: actually their numbers came out amsonam based company and actually underwhelmed. 791 00:41:57,239 --> 00:42:00,480 Speaker 3: They saw a lot of growth but did missanish estimate overall, 792 00:42:00,560 --> 00:42:04,160 Speaker 3: but Midnight album the release was the biggest impact and 793 00:42:04,280 --> 00:42:06,640 Speaker 3: indeed perhaps she's outformed too much in previous years. 794 00:42:07,200 --> 00:42:08,560 Speaker 5: That's it from Bloomberg Technology. 795 00:42:09,680 --> 00:42:12,080 Speaker 6: Check out the podcast wherever you get your podcast from 796 00:42:12,200 --> 00:42:13,000 Speaker 6: SF in New York. 797 00:42:13,239 --> 00:42:14,520 Speaker 4: This is Bloomberg Technology.