WEBVTT - SoftBank’s Purchase of Ampere, Future of Ed-Tech 0:00:02.520 --> 0:00:09.840 Bloomberg Audio Studios, podcasts, radio news from the heart of 0:00:09.920 --> 0:00:15.800 where innovation, money and power collide in Silicon Valley and beyond. 0:00:16.040 --> 0:00:21.320 This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 0:00:35.720 --> 0:00:36.880 Live from San Francisco. 0:00:37.040 --> 0:00:40.879 I'm Tim Stenebeck and this is Bloomberg Technology. Coming up 0:00:41.040 --> 0:00:44.919 today is Quantum Day at Nvidia GtC, and Ed Ludlow 0:00:45.080 --> 0:00:47.559 is there. We'll have the main takeaways from the conference 0:00:47.600 --> 0:00:51.640 so far and discuss what to expect from today. Plus, 0:00:51.720 --> 0:00:55.040 soft Bank is acquiring the chip designer Ampier in an 0:00:55.040 --> 0:00:58.320 all cash transaction died at six point five billion dollars. 0:00:58.720 --> 0:01:01.760 We'll sit down with Amper's CEO to learn more about 0:01:01.760 --> 0:01:05.039 the deal. And Campus, a for profit community college, has 0:01:05.120 --> 0:01:08.360 raised forty six million dollars from investors including Sam Altman, 0:01:08.440 --> 0:01:12.160 Peter Tiele's Founder Fund, and Joe Lonsdale's eight VC. The 0:01:12.200 --> 0:01:15.800 Founder and Joe Lonsdale join us on the future of 0:01:16.000 --> 0:01:19.200 ed tech. First though, a check of the markets, and 0:01:19.560 --> 0:01:21.320 we're seeing some green across the screen. 0:01:21.360 --> 0:01:23.080 Stocks opening higher. 0:01:22.840 --> 0:01:25.920 After yesterday's FED fueled rally or should say lower, but 0:01:26.000 --> 0:01:27.800 then it was the best FED day going. 0:01:27.640 --> 0:01:28.199 Back to July. 0:01:28.360 --> 0:01:30.480 Then about a half hour into the trading session, we 0:01:30.560 --> 0:01:33.920 got some surprisingly strong housing data which turned around the trade. 0:01:34.080 --> 0:01:36.440 At last check, about sixty five stocks in the Nasdaq 0:01:36.480 --> 0:01:39.360 one hundred moving higher right now, up about four tens 0:01:39.400 --> 0:01:41.479 of one percent off its best levels of the day 0:01:41.560 --> 0:01:44.280 so far, but still in the green. One of the 0:01:44.280 --> 0:01:46.759 stocks that's helping to push the Nasdaq one hundred higher 0:01:47.120 --> 0:01:50.400 is Meta Platforms, investors cheering the news that Meta will 0:01:50.480 --> 0:01:54.440 roll out Meta AI across forty one European countries this week. 0:01:54.720 --> 0:01:57.960 It's up right now by about four percent. Meta's intelligent 0:01:58.040 --> 0:02:00.440 chat function will also be rolled out across twenty one 0:02:00.440 --> 0:02:03.760 overseas territories and available in six European languages. The company 0:02:03.840 --> 0:02:05.520 said in a statement. It's going to be free too 0:02:05.800 --> 0:02:09.519 across its apps, including Facebook, Instagram, WhatsApp, and Messenger. 0:02:10.120 --> 0:02:10.800 Also watching. 0:02:10.840 --> 0:02:13.760 Shares of Intel down about seven tenths of one percent. 0:02:14.040 --> 0:02:17.200 It did fall by nearly seven percent yesterday after a 0:02:17.200 --> 0:02:20.799 TSMC board member dismissed report that the company has pitched 0:02:20.800 --> 0:02:23.680 a major US shipmakers about taking stakes in it JV 0:02:23.840 --> 0:02:26.720 to operate intels and factories. Shares we're bouncing back earlier, 0:02:26.720 --> 0:02:29.960 but lower now, and in Vidia is higher today as 0:02:30.000 --> 0:02:33.720 the company continues its GTCAI conference in San Jose today. 0:02:33.760 --> 0:02:36.520 Of course, Quantum day and video shares hired by one 0:02:36.720 --> 0:02:40.040 point three percent. And that's exactly where we go now 0:02:40.320 --> 0:02:43.200 live from the DCC event, our own ed Ludlow joining 0:02:43.240 --> 0:02:45.320 us at I want you to set the scene for us, 0:02:45.320 --> 0:02:47.240 but I also want you to clear something up for us, 0:02:47.240 --> 0:02:49.960 because there's lots of chatter this morning about in Nvidia 0:02:50.000 --> 0:02:52.120 in the context of money being spent in the US. 0:02:52.160 --> 0:02:56.920 What's going on, Yeah, it's based on an interview that 0:02:57.040 --> 0:03:00.040 Jensen Wong, the CEO, gave the Financial Times, and he 0:03:00.200 --> 0:03:04.880 quoted as saying is that in Vidia will procure half 0:03:04.880 --> 0:03:07.359 a trillion dollars worth of electronics in the next four 0:03:07.440 --> 0:03:09.960 or five years. But what he's not saying is that 0:03:09.960 --> 0:03:12.920 that is capital expenditures. Right, this is a company that 0:03:13.520 --> 0:03:17.680 has seventy percent market share in the market for AI accelerators, 0:03:17.760 --> 0:03:21.040 high performance GPUs that go into data centers. He spent 0:03:21.080 --> 0:03:22.840 a lot of time yesterday. We were with him for 0:03:22.880 --> 0:03:25.400 an hour explaining the mechanics of that. Right, when you 0:03:25.440 --> 0:03:29.079 build a data center from scratch or you upgrade its technology, 0:03:29.320 --> 0:03:31.720 that is a tens of billions of dollars or hundreds 0:03:31.760 --> 0:03:34.280 of billions of dollars project. If you have seventy percent 0:03:34.320 --> 0:03:36.960 market share for the brain that goes into it, Inevitably 0:03:37.000 --> 0:03:40.080 you're going to have to pay TSMC to manufacture the chips. 0:03:40.080 --> 0:03:42.480 You're going to work with Dell in HPE on the 0:03:42.600 --> 0:03:47.600 server IRAQ assembly and packaging. That's what he's referencing, essentially 0:03:47.640 --> 0:03:50.080 the cost of doing business. It's interesting because this is 0:03:50.120 --> 0:03:52.440 what Gensen one wants us to be talking about. He 0:03:52.560 --> 0:03:56.560 sees in video as foundational to all AI companies. In 0:03:56.600 --> 0:03:59.600 other words, companies are being created because of what in 0:03:59.680 --> 0:04:02.480 Video is doing. In the context of it calling itself 0:04:02.520 --> 0:04:06.120 an AI infrastructure company, an AI factory company. 0:04:06.800 --> 0:04:10.440 Okay AI infrastructure a AI factory today though is quantum 0:04:10.560 --> 0:04:14.360 day and in Nvidia doesn't actually make sure quantum computers 0:04:14.440 --> 0:04:15.000 what's going on? 0:04:16.640 --> 0:04:17.039 Correct? 0:04:17.720 --> 0:04:23.040 Yeah, so exactly the same point as with AAI data centers. 0:04:23.560 --> 0:04:25.920 In Vida does not make quantum computers. What it does 0:04:26.000 --> 0:04:29.880 is sell its existing technology to the quantum computing industry 0:04:30.160 --> 0:04:32.560 to help them make their own machines better. You can 0:04:32.640 --> 0:04:36.919 use an AI supercomputer for error correction and calibration of 0:04:36.920 --> 0:04:40.520 a quantum computer, but they're essentially two distinct field. Of course, 0:04:40.560 --> 0:04:44.400 quantum computers follow quantum mechanics and are coded in cubits, 0:04:44.720 --> 0:04:47.520 not in bits, ones and zeros. But we're all here 0:04:47.560 --> 0:04:51.039 today because what happened in January, right, Jensen one was 0:04:51.080 --> 0:04:53.040 asked basically at point blank, what do you think of 0:04:53.120 --> 0:04:55.960 quantum computers, and he said they're more than a decade 0:04:56.000 --> 0:04:59.599 away from being useful. The net result that day, January eighth, 0:04:59.760 --> 0:05:03.359 was the the publicly traded quantum computing stocks all sank 0:05:03.480 --> 0:05:06.560 thirty forty percent. And so we're all assembling today and 0:05:06.640 --> 0:05:08.640 Jensen's going to be on stage with all of the 0:05:08.720 --> 0:05:13.320 quantum computing CEOs who are basically his customers. He just 0:05:13.360 --> 0:05:16.120 sells the existing gear to them, and maybe we'll get 0:05:16.120 --> 0:05:18.880 an update on how Jensen feels on quantum computing. But 0:05:18.960 --> 0:05:22.000 from in Vidia's perspective, it's an arm of research, and 0:05:22.040 --> 0:05:26.279 it's where they sell existing tech GPUs principally to that industry. 0:05:26.320 --> 0:05:27.839 They do not make quantum computers. 0:05:28.960 --> 0:05:32.680 Bloomberg's Ed Ludlow ed at Invidio GtC ed, good to 0:05:32.680 --> 0:05:34.440 see you. We'll see you a little later too, Thanks 0:05:34.480 --> 0:05:37.640 so much. Stay with us, though, because Bloomberg this Afternoon 0:05:37.640 --> 0:05:41.279 has a special edition of Bloomberg Technology on quantum Computing. 0:05:41.600 --> 0:05:44.360 Ed Lodlow will return live from in Video's GtC event 0:05:44.440 --> 0:05:47.919 starting at four to thirty pm Eastern Time. Four on 0:05:47.960 --> 0:05:50.240 the broader tech market and in Video, let's bring in 0:05:50.279 --> 0:05:52.720 Sylvia Jablonski, Defiance ETF's CEO. 0:05:52.920 --> 0:05:53.600 She joins us. 0:05:53.760 --> 0:05:57.920 Now, Sylvia, I'm wondering how you're watching everything happening at 0:05:57.920 --> 0:06:01.599 in Video GtC. You do argue that in Vidio is 0:06:01.640 --> 0:06:04.360 a buy right now off of its highs. How are 0:06:04.400 --> 0:06:06.359 you watching everything come out of San Jose this week? 0:06:07.600 --> 0:06:10.560 Yes, good morning, Thanks for having me here. I think 0:06:10.600 --> 0:06:12.880 it's all very exciting. You know, what we're what we've 0:06:12.880 --> 0:06:14.240 been seeing over the last. 0:06:14.040 --> 0:06:16.320 Year or two is just you know, so much news 0:06:16.360 --> 0:06:19.920 around the growth of AI, the potential for quantum computing, 0:06:19.960 --> 0:06:20.960 the buildout. 0:06:20.560 --> 0:06:21.080 Of six G. 0:06:21.680 --> 0:06:23.760 And you know who's the star of that show. It's 0:06:23.880 --> 0:06:26.640 it's Nvidia. And you're talking about a stock that you know, 0:06:26.760 --> 0:06:29.839 was trading up over above one forty pretty recently before 0:06:29.880 --> 0:06:31.320 this this pullback that. 0:06:31.279 --> 0:06:33.200 We see here at these levels. 0:06:33.279 --> 0:06:35.320 I mean, I love the stock, I love the company 0:06:35.360 --> 0:06:39.880 you're talking about potentially, you know, a fourth Industrial Revolution compounded. 0:06:39.880 --> 0:06:42.640 I know, growth rate themes, of AI and quantum and 0:06:42.680 --> 0:06:45.280 things like this of thirty five to forty percent per year. 0:06:45.680 --> 0:06:48.760 As an investor, I'm patient with technology. It takes time 0:06:48.800 --> 0:06:50.600 for things to play out, but I want to get 0:06:50.600 --> 0:06:52.760 in early. And this is still you know, first innings. 0:06:52.760 --> 0:06:54.560 A lot of people are saying that, but you know, 0:06:54.640 --> 0:06:57.080 we're seeing that it's it's it's true as we get 0:06:57.120 --> 0:06:58.359 more news from this conference. 0:06:59.160 --> 0:07:02.680 So do you think that in Nvidia investors right now 0:07:02.760 --> 0:07:06.159 have it wrong? Does the market have it wrong? To 0:07:06.200 --> 0:07:08.599 what extent do you think this stock is undervalued right now? 0:07:09.760 --> 0:07:12.480 Well, we can always say that the market is you know, 0:07:12.480 --> 0:07:14.520 the market's a little bit emotional, right, So I think 0:07:14.560 --> 0:07:16.360 that there are a lot of reactions in the market, 0:07:16.520 --> 0:07:20.520 and when the market becomes emotional and people panic, usually 0:07:20.520 --> 0:07:22.720 they sell the macseven or they sell kind of like 0:07:22.720 --> 0:07:25.000 the high flying names that have done well for that year. 0:07:25.040 --> 0:07:27.400 We've seen it happen with Tesla, we saw it happen 0:07:27.440 --> 0:07:29.960 with Apple during COVID, you know, all these different sorts 0:07:30.000 --> 0:07:32.160 of things. And then video is kind of the poster 0:07:32.240 --> 0:07:33.480 child of the market this year. 0:07:33.600 --> 0:07:35.200 So when we have fear. 0:07:34.920 --> 0:07:38.320 And panics about tariffs and things like this, people tend 0:07:38.320 --> 0:07:40.000 to run for the hills and sell you know, the 0:07:40.040 --> 0:07:42.840 most profitable stock. So I just think that there's a 0:07:42.880 --> 0:07:45.600 lot of liquidity on the sidelines. There's still this consumer 0:07:45.680 --> 0:07:47.400 consumer sentiment that is uneasy. 0:07:47.720 --> 0:07:49.520 But eventually, you know, when. 0:07:49.480 --> 0:07:51.600 Some of the tariff things shake out, and then we 0:07:51.680 --> 0:07:55.480 get you know, the tailwinds of lower regulation and tax policy, 0:07:55.560 --> 0:07:57.000 things that are favorable to the market. 0:07:57.320 --> 0:07:59.400 These are the names that also rally first. 0:07:59.560 --> 0:08:02.280 Right, It's kind of like the buy the dips and 0:08:02.800 --> 0:08:04.720 you know, sell the rips or hold on to the 0:08:04.800 --> 0:08:05.520 rip scenario. 0:08:05.600 --> 0:08:08.400 Here, well, we're still down on the NAZAC one hundred 0:08:08.400 --> 0:08:12.400 and ten percent, so officially in correction territory. You said, 0:08:12.440 --> 0:08:18.520 when we get the regulatory clarity, when we get tax cuts, 0:08:18.840 --> 0:08:21.360 when the tariff stuff shakes out, how are you so 0:08:21.400 --> 0:08:23.040 certain that stuff is going to shake out? 0:08:24.440 --> 0:08:27.520 Well, I think, you know, all of these things take time, right, 0:08:27.560 --> 0:08:29.800 and the only information we have is the information that 0:08:29.840 --> 0:08:32.400 we actually get from Washington, and that information seems to 0:08:32.440 --> 0:08:35.240 be that, you know, the tariff policy is inactive because 0:08:35.240 --> 0:08:39.199 of these fensanyl issues, immigration issues, cybersecurity, these other types 0:08:39.240 --> 0:08:42.000 of things that have to be sorted out. We have 0:08:42.120 --> 0:08:45.760 information that the president plans to you know, cut taxes 0:08:45.840 --> 0:08:49.560 to support your regulation and businesses. So I think to 0:08:49.600 --> 0:08:51.560 your point, it's a fair question, right, we actually have 0:08:51.600 --> 0:08:54.160 to see these things pan out. But even if nothing 0:08:54.200 --> 0:08:56.600 else happens, right, you have an economy that. 0:08:56.600 --> 0:08:59.920 Is still growing positive, you know, positive GDP. 0:09:00.160 --> 0:09:01.760 It's a little bit lower, but we're still above that 0:09:01.840 --> 0:09:05.280 two percent range. Jobs are fine, wages are fine. Corporate 0:09:05.280 --> 0:09:07.840 earnings are estimated to be in the high single digits, 0:09:07.840 --> 0:09:10.360 you know, even up to ten percent by some analyst estimates. 0:09:10.800 --> 0:09:14.520 The earning season was very good. There are still strong balance. 0:09:14.200 --> 0:09:17.080 Sheets, and you know what we're hearing out of corporate 0:09:17.120 --> 0:09:19.960 America is that it's you know, there's soll cap bax. 0:09:20.040 --> 0:09:20.199 Right. 0:09:20.320 --> 0:09:23.280 I don't see a recession either way. So maybe you 0:09:23.280 --> 0:09:26.040 don't get hyperbolic growth. But when you have these themes 0:09:26.040 --> 0:09:27.079 like quantum computing and. 0:09:27.040 --> 0:09:30.800 AI that are on sale, I think it's worth taking, 0:09:30.880 --> 0:09:34.520 potentially taking a risk for long term returns, regardless of 0:09:34.520 --> 0:09:36.440 what happens in the next year or so with policy. 0:09:37.200 --> 0:09:39.600 Hey, Sylvia, before we let you go, just twenty seconds 0:09:39.640 --> 0:09:42.920 on Broadcom another soccer bullet round, but down twenty percent 0:09:43.040 --> 0:09:44.120 from all time highs. 0:09:45.160 --> 0:09:46.920 Yeah, I mean I think Broadcom is going to be 0:09:46.960 --> 0:09:49.400 one of the leaders in AI and videos, the poster 0:09:49.520 --> 0:09:52.199 child there, but Broadcom should be a second winner there. 0:09:52.240 --> 0:09:54.360 You know, they're in software, they're in VM sales. 0:09:54.400 --> 0:09:58.040 They've had over fifty seven percent growth in AI revenue 0:09:58.600 --> 0:10:00.320 per the last earnings call. I just I think that 0:10:00.400 --> 0:10:02.800 this is a name that did sell off a little bit. 0:10:02.840 --> 0:10:04.559 It might be good to get in for the long run. 0:10:05.480 --> 0:10:09.079 Sylvia Jablonski of Defiance ETF's always good to see you, Sylvia, 0:10:09.080 --> 0:10:11.920 thanks so much for joining us. Well, coming up, we're 0:10:11.960 --> 0:10:14.280 going to talk about soft bank six point five billion 0:10:14.320 --> 0:10:17.959 dollar acquisition of Chip Designer and Peer Computing and Peer 0:10:18.120 --> 0:10:19.960 CEO Renee James joins us. 0:10:20.000 --> 0:10:20.240 Next. 0:10:20.400 --> 0:10:21.520 This is Bloomberg. 0:10:36.080 --> 0:10:38.960 Soft bank six point five billion dollar acquisition of Chip 0:10:39.000 --> 0:10:42.360 Designer and Peer Computing is highlighting how the increasing demand 0:10:42.400 --> 0:10:46.760 for compute is crashing up against infrastructure constraints. Renee James, 0:10:46.880 --> 0:10:49.160 founder and CEO of and Pierre joins us to talk 0:10:49.240 --> 0:10:53.280 more about this deal. Renee, good to see you, congratulations 0:10:53.520 --> 0:10:54.000 on this. 0:10:54.200 --> 0:10:54.600 Thank you. 0:10:54.640 --> 0:10:56.760 I just want to know how are you feeling this morning. 0:10:59.160 --> 0:11:01.920 I'm feeling I think it hasn't sunk in. 0:11:02.160 --> 0:11:06.840 I'm of course thrilled with this outcome for you know, 0:11:06.880 --> 0:11:11.200 my employees, my investors, and most importantly, we're a group 0:11:11.200 --> 0:11:15.280 of inventors and innovators who are very excited about the 0:11:15.360 --> 0:11:19.200 vision that Masa has for AI and our ability to 0:11:19.280 --> 0:11:22.160 continue innovation as part of SoftBank. 0:11:23.080 --> 0:11:24.160 What is that vision? 0:11:24.200 --> 0:11:27.160 Because Ampier will operate as this wholly owned subsidiary of 0:11:27.280 --> 0:11:30.800 soft Bank, of course it is a majority owner of ARM. 0:11:31.360 --> 0:11:33.880 How do you fit into that vision? What is that vision? 0:11:35.360 --> 0:11:37.720 You know, Massa has talked a lot, even the Stargate 0:11:37.720 --> 0:11:41.959 announcement that was recently done in the White House, about 0:11:42.120 --> 0:11:46.920 AI and the the role AI will play in everybody's 0:11:46.960 --> 0:11:52.640 lives and building super chips, and he's talked a lot 0:11:52.679 --> 0:11:53.080 about that. 0:11:53.480 --> 0:11:55.880 So I think our role is to make that come 0:11:55.920 --> 0:11:56.440 to life. 0:11:57.120 --> 0:12:02.839 We are the leading supplier of high performance, very power 0:12:02.840 --> 0:12:07.760 efficient processors for data centers on the our architecture, so 0:12:07.800 --> 0:12:12.040 it's a very synergistic for us to join into the 0:12:12.040 --> 0:12:16.680 SoftBank family and continue working on the Silicon roadmap that 0:12:16.720 --> 0:12:20.640 we have which includes AI acceleration, and now we'll have 0:12:20.679 --> 0:12:21.520 a broader mandate. 0:12:21.600 --> 0:12:23.680 So I'm very excited about that. 0:12:24.679 --> 0:12:28.000 What happens to your existing customers and your existing product 0:12:28.000 --> 0:12:30.400 line when this deal does close in the second half 0:12:30.400 --> 0:12:30.760 of the year. 0:12:31.840 --> 0:12:33.800 Yeah, we continue as is. 0:12:33.840 --> 0:12:36.400 We continue with the product line that we've worked on 0:12:36.440 --> 0:12:39.439 for the last eight years are very low power, high 0:12:39.440 --> 0:12:43.720 efficiency microprocessors, and now we've announced that we have AI 0:12:43.760 --> 0:12:46.920 acceleration in our products. So I think that's the future 0:12:46.960 --> 0:12:48.720 of where we're going in the data center. We're going 0:12:48.760 --> 0:12:53.160 to see compute and AI start to come together, especially 0:12:53.240 --> 0:12:57.480 as inference becomes the larger part of the market, and 0:12:57.600 --> 0:13:02.040 so our customers continue with us and hopefully we'll be 0:13:02.160 --> 0:13:04.800 excited about a broader set of products from us. 0:13:05.440 --> 0:13:09.160 Well, Y, you recognize something really early on that there's 0:13:09.160 --> 0:13:10.800 this need and there's going to be this need, and 0:13:10.800 --> 0:13:13.840 indeed we're seeing it right now for super high performance 0:13:14.400 --> 0:13:18.440 that required lower power. When you look across the landscape 0:13:18.880 --> 0:13:21.880 right now and where we are in AI, what do 0:13:21.960 --> 0:13:24.240 you see that perhaps other people don't see right now. 0:13:25.600 --> 0:13:30.080 Well, as you know, Tim, I've been doing this a 0:13:30.080 --> 0:13:30.600 long time. 0:13:30.679 --> 0:13:33.400 I've been in semis a long time, and power is 0:13:34.040 --> 0:13:38.760 always been a variable in semiconductors for how you get 0:13:38.840 --> 0:13:42.720 performance or a limited performance. And so one of the 0:13:42.760 --> 0:13:44.760 things that I didn't get to work on in my 0:13:45.040 --> 0:13:48.080 long career at Intel was working on how to do 0:13:48.160 --> 0:13:52.800 the highest performance possible in the most constrained power envelope. 0:13:52.800 --> 0:13:56.400 That was a portion of the spectrum of computing that 0:13:56.440 --> 0:13:59.680 we didn't really work on. And the reason the thesis 0:13:59.840 --> 0:14:02.080 was we knew that power would be the biggest limited 0:14:02.080 --> 0:14:03.240 to growth long term. 0:14:03.400 --> 0:14:06.640 There wouldn't be enough of it. You need increasing amounts 0:14:06.640 --> 0:14:07.400 of compute. 0:14:07.480 --> 0:14:11.080 We've talked a lot about AI, especially with GtC this week, 0:14:11.520 --> 0:14:14.600 taking nothing away from that, we're also having a massive 0:14:14.600 --> 0:14:18.560 growth in compute. It's going alongside this massive growth in 0:14:18.600 --> 0:14:20.480 AI AI compute. 0:14:20.680 --> 0:14:22.320 So those two things. 0:14:22.080 --> 0:14:25.000 Are just taking you know, a tremendous amount of growth 0:14:25.000 --> 0:14:27.320 and power and AIS a ten x. 0:14:27.520 --> 0:14:28.360 You know, if you. 0:14:28.320 --> 0:14:33.400 Will a function growth in power required and I think 0:14:33.840 --> 0:14:36.560 we knew that you could know that from the workloads, 0:14:36.640 --> 0:14:39.560 and we decided that, you know, one of the things 0:14:39.560 --> 0:14:44.080 we should go pioneer is is this sufficiency. Our architecture 0:14:44.200 --> 0:14:46.920 is very efficient and we preserve that efficiency, but we 0:14:47.080 --> 0:14:51.200 used our experience in high performance and building high performance 0:14:51.280 --> 0:14:54.760 microprocessors to really, you know, get us to this level. 0:14:55.880 --> 0:14:57.800 You know, we've heard a lot from Jensen this week 0:14:57.960 --> 0:15:03.360 about physical A and I'm wondering from your perspective. When 0:15:03.400 --> 0:15:05.600 you think about the compute that will be needed in 0:15:05.640 --> 0:15:08.480 the years to come, what does that world actually look 0:15:08.600 --> 0:15:10.800 like for the normal person. What are the products and 0:15:10.840 --> 0:15:13.360 services and tools that we are all going to be 0:15:13.440 --> 0:15:15.280 using that will require this compute. 0:15:16.480 --> 0:15:19.520 You know, I used to think this is funny. 0:15:19.600 --> 0:15:22.240 You know, every wave of computing, whether it was the 0:15:22.440 --> 0:15:28.359 wave of PCs, the waves of mobile phones and laptops, 0:15:28.400 --> 0:15:30.720 we thought, this is it. We're going to have computers 0:15:30.720 --> 0:15:32.440 that you know here, you have a computer in your pocket, 0:15:32.440 --> 0:15:34.680 You're going to have a computer here at computer. I 0:15:34.720 --> 0:15:38.360 think that in this next phase, you know, as was 0:15:38.400 --> 0:15:42.880 discussed at GtC, we begin to really crest over into 0:15:42.960 --> 0:15:44.720 integrated computing and everything. 0:15:44.760 --> 0:15:46.600 And it really is transparent. 0:15:47.080 --> 0:15:50.480 It's a background activity that happens in your life that's 0:15:50.520 --> 0:15:52.440 assistive in different ways. 0:15:52.320 --> 0:15:53.720 Whether it's robotic or not. 0:15:54.200 --> 0:15:56.800 All of your appliances are smart now, all of your 0:15:56.840 --> 0:15:58.440 homes have become smart, your. 0:15:58.320 --> 0:15:59.240 Car is smart. 0:15:59.520 --> 0:16:03.760 So the experience of computing that used to be isolated 0:16:03.800 --> 0:16:07.080 to your computer or your phone or what have you, 0:16:07.440 --> 0:16:10.560 is now integrated into your life and you have I think, 0:16:10.960 --> 0:16:13.680 you know what we'll see. This is why I'm very 0:16:13.720 --> 0:16:15.120 positive on semi conductors. 0:16:15.280 --> 0:16:17.840 Semiconductors have fueled every single. 0:16:17.560 --> 0:16:20.720 One of these waves of growth, and the base technology 0:16:20.920 --> 0:16:26.840 to go into any kind of AI is basic computational semiconductors. 0:16:27.240 --> 0:16:31.560 That's why, despite you know semis are always cyclical, we 0:16:31.600 --> 0:16:35.640 do see this, we see these downturns. I'm very confident 0:16:35.680 --> 0:16:37.560 that we have another growth cycle ahead of us in 0:16:37.600 --> 0:16:39.160 semis and. 0:16:39.400 --> 0:16:42.560 Peter founder and CEO Renee James Renee, thanks so much 0:16:42.560 --> 0:16:45.160 for joining us on what is certainly a really big day. 0:16:45.160 --> 0:16:52.440 I really appreciate it. 0:16:55.040 --> 0:16:58.160 Deep Seeks Innovation made ripples across the AI industry when 0:16:58.160 --> 0:17:00.840 it announced that it's models performed as well or better 0:17:00.920 --> 0:17:04.119 than its American counterparts and at a cheaper price. 0:17:04.680 --> 0:17:05.840 That was back in January. 0:17:06.200 --> 0:17:09.040 Since then, China and many other companies have been raising 0:17:09.080 --> 0:17:12.600 to integrate that model throughout the country. Bloomberg's Daviting glaz 0:17:12.680 --> 0:17:16.120 sat down with one AI c Kaifu Lead to discuss 0:17:16.160 --> 0:17:17.680 their adoption of Deep Seek. 0:17:18.640 --> 0:17:22.240 Well, I think China had its Chatgibet moment when Deep 0:17:22.280 --> 0:17:24.399 Seak came out. We can call it deep Seek moment. 0:17:25.000 --> 0:17:29.240 Everyone's aware of it over the Chinese holidays, everyone's talking 0:17:29.320 --> 0:17:31.720 about it, and the CEOs came back to work saying, 0:17:32.280 --> 0:17:35.399 put put deep Seek to work and my company, and 0:17:35.440 --> 0:17:38.280 what they found out was deep Seek is a fantastic model, 0:17:38.359 --> 0:17:42.280 amazing AI, but it doesn't have the middleware and the 0:17:42.359 --> 0:17:45.879 user interface that it takes to connect to corporate databases 0:17:46.119 --> 0:17:49.440 to build applications to make it useful for HR finance 0:17:49.720 --> 0:17:50.760 and customer service. 0:17:51.200 --> 0:17:52.400 So what zero one Dot. 0:17:52.359 --> 0:17:55.120 Day I did was we saw deep Seak has been 0:17:55.400 --> 0:17:58.679 making great momentum, and we decided to really bet on 0:17:58.720 --> 0:18:02.600 deep Seek and build a missing middleware and UI so 0:18:02.720 --> 0:18:04.320 that deep Sea can be made. 0:18:04.160 --> 0:18:05.840 Useful in corporations. 0:18:06.080 --> 0:18:09.119 That's the product we announced this Monday, and we're getting 0:18:09.160 --> 0:18:12.000 fantastic reception in China and. 0:18:12.000 --> 0:18:12.880 Also in Hong Kong. 0:18:13.320 --> 0:18:14.399 Tell us more about that launch. 0:18:14.600 --> 0:18:17.880 What we talked about was many of you have deep 0:18:17.920 --> 0:18:21.399 Seak now you love to use it. In fact, one 0:18:21.720 --> 0:18:24.080 CEO friend of mine asked his employees what do you 0:18:24.160 --> 0:18:24.520 use it for? 0:18:25.080 --> 0:18:28.080 And good question and the answer was fortune telling. 0:18:28.320 --> 0:18:30.120 By the way, that's a great thing to try for you, 0:18:30.600 --> 0:18:35.480 but it's not very deep into the industry the company. 0:18:35.560 --> 0:18:39.760 You know, every company has ERP and the CRM databases, 0:18:39.920 --> 0:18:45.000 they have employee records, they have their internal information, and 0:18:45.040 --> 0:18:47.560 they want the model to be more a generalist. They 0:18:47.560 --> 0:18:52.080 wanted to be knowledgeable. Bloomberg would want a finance knowledgeable model, 0:18:52.359 --> 0:18:55.640 right Ping I would want an insurance knowledgeable model. So 0:18:55.720 --> 0:18:59.440 our job is to really build that layer for that purpose, 0:19:00.119 --> 0:19:02.680 sort of like if I gave a If I gave 0:19:02.800 --> 0:19:06.600 you a Windows kernel that is the core operating system, 0:19:06.720 --> 0:19:08.760 you wouldn't know what to do with it. You need 0:19:08.800 --> 0:19:13.399 all the Windows layer, the application interfaces, so that the 0:19:13.440 --> 0:19:16.080 Windows kernel can be useful. And we like to think 0:19:16.119 --> 0:19:20.360 that zero one dot AI is providing that layer for deepseak, 0:19:20.400 --> 0:19:23.280 which is the underlying model in technology. 0:19:24.960 --> 0:19:27.720 K That was Kai fu Lee. There's you know, Innovation 0:19:27.840 --> 0:19:31.440 Ventures and also one AI. Meanwhile, with the advent of 0:19:31.480 --> 0:19:34.439 the AI boom, many of the manufacturers involved are becoming 0:19:34.480 --> 0:19:38.639 amopolistic and only ever growing companies such as Nvidia and 0:19:38.680 --> 0:19:41.680 it's partners who make the semiconductors just keep getting richer 0:19:41.920 --> 0:19:44.720 every time you use your favorite chatbot. That's the story 0:19:44.760 --> 0:19:47.919 in today's quick Take, and Bloomberg's Peter Elstrom joins us 0:19:47.920 --> 0:19:51.040 now Peter the team over a quick take, writing that 0:19:51.119 --> 0:19:56.320 every time we use chat GBT or Claude or Lama. 0:19:56.760 --> 0:20:00.040 We're making a handful of companies wealthier take us through it. 0:20:01.000 --> 0:20:03.800 That's right, or even deep Seek for that matter, they 0:20:03.840 --> 0:20:06.320 also use this supply chain. So we took a look 0:20:06.359 --> 0:20:10.359 at is this really unusual dominance that we've seen in 0:20:10.400 --> 0:20:13.280 the supply chain of AI technologies. It begins with the 0:20:13.320 --> 0:20:16.399 in Nvidia, which is probably the highest profile player here 0:20:16.920 --> 0:20:19.760 in the supply chain, but it's also TSMC, the company 0:20:19.760 --> 0:20:23.040 that makes the chips, s Kehaynix, which makes the high 0:20:23.080 --> 0:20:27.000 bandwidth memory that is paired with invidious chips, and then ASML, 0:20:27.119 --> 0:20:30.879 the maker of these lithography machines that are really the 0:20:32.160 --> 0:20:34.320 necessary to be able to make the highest end chips 0:20:34.320 --> 0:20:34.840 in the business. 0:20:35.000 --> 0:20:36.360 So what we've seen is this really. 0:20:36.160 --> 0:20:39.560 Consolidation of power in the AI supply chain with these 0:20:39.600 --> 0:20:43.320 four companies where they wield tremendous power over how companies 0:20:43.359 --> 0:20:46.200 are able to get these technologies and then deploy them. 0:20:46.200 --> 0:20:52.720 That's true for all the hyperscalers, Meta, Microsoft, Open Ai, etc. 0:20:53.160 --> 0:20:55.119 But also the companies in China have been trying to 0:20:55.119 --> 0:20:58.639 buy these Now there are limitations on which chips Chinese 0:20:58.640 --> 0:21:01.040 companies are able to buy, and deep Seek and even 0:21:01.119 --> 0:21:02.600 Kaifu les zero. 0:21:02.480 --> 0:21:03.320 One Dot AI. 0:21:03.680 --> 0:21:05.560 But they want to be able to get those Nvidia 0:21:05.680 --> 0:21:08.400 chips and the rest of the technologies from these companies 0:21:08.400 --> 0:21:10.199 to be able to build the AI models that are 0:21:10.200 --> 0:21:12.960 now going to be marketed to companies and to individuals. 0:21:13.720 --> 0:21:16.480 What is the moat that these companies have, Peter, Because 0:21:16.680 --> 0:21:20.119 typically when we think about innovation and technology such as this, 0:21:21.080 --> 0:21:22.679 we think about it from the perspective of, Okay, if 0:21:22.720 --> 0:21:25.720 these companies are making money, a rush of competitors are 0:21:25.720 --> 0:21:26.960 going to come in and they're going to try to 0:21:26.960 --> 0:21:27.560 do the same thing. 0:21:27.600 --> 0:21:28.960 What's the moat that these companies have. 0:21:30.080 --> 0:21:32.880 Yeah, that's a very important question. And just to take 0:21:32.880 --> 0:21:34.880 a step back, I'd say that when you look at 0:21:34.920 --> 0:21:38.359 monopolies over time, especially monopolies in tech, they tend to 0:21:38.440 --> 0:21:40.639 last for quite a while. We saw it with Windows 0:21:40.680 --> 0:21:43.560 and Intel in the PC era. Before that, we saw 0:21:43.560 --> 0:21:46.159 it with IBM, which got sued three different times for 0:21:46.400 --> 0:21:49.359 and I trust allegations. But they tend to last for 0:21:49.400 --> 0:21:51.840 a very long period of time, and they tend to 0:21:52.200 --> 0:21:55.960 fade not just because of competition, but because of government 0:21:56.000 --> 0:22:00.720 intervention too. Now, these AI dominating players, let's call monopolies 0:22:00.760 --> 0:22:05.520 for now, four players that are effectively monopolies in their categories. 0:22:05.680 --> 0:22:07.800 They've only been in place for a very short period 0:22:07.800 --> 0:22:10.119 of time at this point. When it comes to Nvidia, 0:22:10.160 --> 0:22:13.280 they have lots of competition. TSMC and ASML have quite 0:22:13.280 --> 0:22:13.840 a bit less. 0:22:15.080 --> 0:22:28.280 Bloomberg's Peter Elstrom joining us from London today. Welcome back 0:22:28.280 --> 0:22:31.600 to Bloomberg Technology. I'm Tim Seneveek in San Francisco. Let's 0:22:31.600 --> 0:22:34.760 get a quick check of the markets. We do see 0:22:35.000 --> 0:22:38.480 stocks off their best levels of the day. We did 0:22:38.520 --> 0:22:41.560 see a lower open, but then we got some surprisingly 0:22:41.600 --> 0:22:44.160 strong housing data which turned around the trade. Our last 0:22:44.240 --> 0:22:47.679 check just about sixty five stocks in the Nasdaq one hundred. 0:22:47.760 --> 0:22:49.560 We're moving higher. A couple of. 0:22:49.560 --> 0:22:52.040 Individual equities I do want to check in on. Check 0:22:52.040 --> 0:22:54.280 out what's going on with pdd Hired by about two 0:22:54.280 --> 0:22:56.640 percent right now. These are shares listed in the US. 0:22:56.720 --> 0:22:59.119 They erased that earlier decline, this coming after the company 0:22:59.160 --> 0:23:04.400 reported fourth quarter results. Sales did misestimates for a third consecutive. 0:23:04.000 --> 0:23:06.240 Quarter, but earnings were better than expected. 0:23:06.680 --> 0:23:09.960 And look at Tesla down about eight tenths of one percent. 0:23:10.359 --> 0:23:13.520 The company is recalling all cyber trucks produced and sold 0:23:13.600 --> 0:23:15.800 in the US over the past fifteen months. This due 0:23:15.800 --> 0:23:18.920 to a safety issue with steel trim pieces that can 0:23:18.960 --> 0:23:22.040 detach from the vehicle. The company's recalling them all, but 0:23:22.080 --> 0:23:24.399 it estimates that only about one percent of the recalled 0:23:24.480 --> 0:23:27.400 vehicles have the defect. It can actually create a road 0:23:27.400 --> 0:23:31.200 hazard and increase the risk of injury or collision. Now, 0:23:31.240 --> 0:23:34.359 let's head back to in video GtC where Bloomberg's Ed 0:23:34.440 --> 0:23:35.800 Ludlow is standing back. 0:23:35.840 --> 0:23:40.679 Hey, Ed, Yeah, there's just such a large volume of 0:23:40.800 --> 0:23:44.199 news and data about in video out GtC. If you 0:23:44.200 --> 0:23:46.320 look at the stock over the first four days of 0:23:46.359 --> 0:23:50.320 this week. There's also skepticism in the market about the 0:23:50.480 --> 0:23:53.480 understanding for demand long term. That's all many care about. 0:23:53.520 --> 0:23:56.359 And I've got a brilliant guest to unpick it with me. 0:23:56.640 --> 0:24:00.640 Flad Galibov is research director at Omdia. Stick that caught 0:24:00.680 --> 0:24:06.360 my eye is that compute demand, particularly from agentic and reasoning, 0:24:06.800 --> 0:24:10.159 is one hundred x today one hundred x what it 0:24:10.280 --> 0:24:13.080 was one year ago. And to many people that doesn't 0:24:13.080 --> 0:24:15.320 actually mean anything. But the way that it was explained 0:24:15.359 --> 0:24:17.879 to me by in video is that they just counted 0:24:17.920 --> 0:24:18.640 all the tokens. 0:24:18.720 --> 0:24:18.880 Right. 0:24:18.920 --> 0:24:22.960 In a tokenization context, you basically take a token three 0:24:23.000 --> 0:24:26.760 bytes of data and you say, okay, what our companies 0:24:26.760 --> 0:24:29.560 beyond the hyperscalers doing right now today it's one hundred 0:24:29.680 --> 0:24:30.919 x more than it was a year ago. 0:24:31.160 --> 0:24:32.040 How do you model that? 0:24:32.119 --> 0:24:35.240 I mean, it's a very very difficult forward looking metric. 0:24:35.440 --> 0:24:36.840 So there's two ways to do that. 0:24:36.960 --> 0:24:39.600 So and by the way, there is a misunderstanding and 0:24:39.760 --> 0:24:44.160 because it's complex, right, that's why people struggle. So one 0:24:44.200 --> 0:24:48.080 part of my team tests models. So you know, what 0:24:48.200 --> 0:24:50.560 they tried to find out is how good a model 0:24:50.600 --> 0:24:53.480 can behave and they found out there is any models 0:24:53.520 --> 0:24:56.560 behave better. The reason why they behave better is that 0:24:56.840 --> 0:25:00.399 extra tokens is the extra computing. By being able to 0:25:01.000 --> 0:25:04.399 in essence think, they actually end up getting a better result. 0:25:04.520 --> 0:25:07.560 So my team got very excellent results from that. But 0:25:07.640 --> 0:25:09.760 I have a different part of my team that actually 0:25:09.840 --> 0:25:13.320 tries to understand exactly how many GPUs the different companies bought. 0:25:13.800 --> 0:25:16.960 So my team in China was able. 0:25:16.720 --> 0:25:18.720 To you're going to bring us to deep Sea, can't you? 0:25:18.880 --> 0:25:19.639 Yes, I'll bring it to. 0:25:19.640 --> 0:25:22.159 Deep Sek because my team in China found out that 0:25:22.200 --> 0:25:26.280 Deep Seek bought a huge amount of GPUs. So imagine 0:25:26.280 --> 0:25:29.040 then they release we have given that information to our 0:25:29.040 --> 0:25:32.440 clients they release their paper, and in the paper they say, 0:25:32.480 --> 0:25:35.080 we don't use many GPUs for training. So my clients 0:25:35.119 --> 0:25:37.399 immediately came in they said, why did you tell us 0:25:37.400 --> 0:25:39.120 they bought so many GPUs And. 0:25:39.080 --> 0:25:40.040 I said, well, they did. 0:25:40.440 --> 0:25:43.320 I have the receipts, and we know now that they 0:25:43.359 --> 0:25:47.159 bought them because their inference is so compute intensive, because 0:25:47.160 --> 0:25:49.640 their infance uses, as you said, one hundred x small 0:25:49.680 --> 0:25:54.480 tokens than a traditional knowledge model. But that's a good 0:25:54.480 --> 0:25:56.920 thing for us. It's actually better for us to train 0:25:57.040 --> 0:26:00.840 quickly and simply and have a better out put through 0:26:00.920 --> 0:26:05.240 more tokens, through more reasoning. People are, especially the environmentally 0:26:05.240 --> 0:26:08.160 conscious people, which we all should be, are very concerned 0:26:08.160 --> 0:26:15.400 about that extra tokenization during the inference stage. But actually, 0:26:15.440 --> 0:26:18.919 if you can get the right answer once with one question, 0:26:19.440 --> 0:26:22.520 that stops you from having to prompt many times. You 0:26:22.640 --> 0:26:25.840 might have got a good answer from CHGBT, but you 0:26:25.880 --> 0:26:27.919 would have needed to ask it one hundred questions, so 0:26:27.960 --> 0:26:30.639 you're doing the reasoning for it now. If you use 0:26:30.640 --> 0:26:34.200 a reasoning model with you know, what they call a 0:26:34.280 --> 0:26:37.440 gender GAI, you end up having the right answers traded. 0:26:37.600 --> 0:26:39.800 This is the core of Jensen Wong's argument right. If 0:26:39.800 --> 0:26:42.640 you were at GtC in twenty twenty two, twenty twenty three, 0:26:42.720 --> 0:26:45.879 twenty twenty four, maybe the fixation was on H one 0:26:45.960 --> 0:26:48.800 hundred and training the next frontier model. But the world's 0:26:48.880 --> 0:26:50.919 very different now. I think in videos really focus on 0:26:50.960 --> 0:26:54.359 its enterprise customers. What Gensen one did outline was a 0:26:54.440 --> 0:26:59.520 roadmap four years and four generations worth of hardware. Electronics 0:26:59.560 --> 0:27:02.800 company the consumer electronics technology companies they don't do that. 0:27:03.040 --> 0:27:05.199 They don't say here's what I'm doing this year all 0:27:05.240 --> 0:27:07.240 the way through to twenty twenty seven. What did you 0:27:07.320 --> 0:27:07.880 make of that? 0:27:08.640 --> 0:27:12.560 So I disagree that electronics companies don't share their roadmap, 0:27:12.800 --> 0:27:15.679 I'll be honest, because I think if you look at AMD, 0:27:15.800 --> 0:27:17.600 they share their roadmap pretty broadly. 0:27:18.119 --> 0:27:19.719 I think this is very transparent. 0:27:20.359 --> 0:27:22.920 You know, I come from Intel and Intel we've always 0:27:22.920 --> 0:27:24.320 shared a roadmap pretty broadly. 0:27:24.520 --> 0:27:26.159 Well maybe not in the le nasty this have been 0:27:26.160 --> 0:27:26.560 a bit shick. 0:27:26.600 --> 0:27:29.040 Well, there's a difference between sharing a roadmap and executing 0:27:29.040 --> 0:27:29.480 on it. 0:27:29.480 --> 0:27:30.560 It is very big difference. 0:27:30.760 --> 0:27:34.200 I think what's amazing about Nvidia is an extreme laser 0:27:34.320 --> 0:27:39.920 focus in this incredible culture, and they understand, they understand 0:27:40.119 --> 0:27:42.560 the hardware stack. They understand their software stack, they understand 0:27:42.560 --> 0:27:45.240 the services, they have a strategy, and they understand that 0:27:45.280 --> 0:27:47.840 the world is getting tokenized, so they're focused on that. 0:27:47.840 --> 0:27:50.680 Their laser focused on how do we make the most 0:27:50.680 --> 0:27:53.520 efficient token processing engine. 0:27:53.560 --> 0:27:56.600 The analogy that gents one gave was Louis Vuitton bagged. 0:27:57.119 --> 0:27:59.480 So for what it's worth, you argued, Louis Vuitton comes 0:27:59.480 --> 0:28:02.080 out of this twenty five bag. But at the same day, 0:28:02.119 --> 0:28:03.560 it doesn't tell you what it's going to be doing 0:28:03.600 --> 0:28:06.159 in twenty twenty eight or twenty twenty nine. Whether you 0:28:06.200 --> 0:28:08.840 agree with that that analogy or not remains to be seen. 0:28:09.840 --> 0:28:12.080 What is different is you get a sense then videos 0:28:12.119 --> 0:28:16.920 move beyond the hyperscalers the demand side of that equation. 0:28:17.040 --> 0:28:18.359 What do you see? 0:28:18.920 --> 0:28:22.080 So I do think that let me just kind of 0:28:22.640 --> 0:28:25.000 just touch on enterprises in the world for a second. 0:28:25.480 --> 0:28:30.720 Enterprises need predictability and they like it so and actually 0:28:30.720 --> 0:28:33.680 they've been looking ever since Intel stop delivering, they've been 0:28:33.720 --> 0:28:36.840 looking for more partners to be honest, to give them 0:28:37.119 --> 0:28:39.959 a roadmap, to explain things and to then deliver. So 0:28:40.000 --> 0:28:42.520 I think it's actually the best way to address the enterprise. 0:28:42.600 --> 0:28:47.120 Does it protect the enterprise's ability to commit spending if 0:28:47.120 --> 0:28:49.920 they know what employer signed the technology bar it does. 0:28:49.960 --> 0:28:52.120 In my discussion with Jensen, that's exactly what we got 0:28:52.120 --> 0:28:56.640 into this protection of enterprise spend, this guarantee because the 0:28:56.720 --> 0:28:59.560 investments these days are huge. But it also helps to 0:28:59.600 --> 0:29:03.640 create an ecosystem. So what you need to make it 0:29:03.680 --> 0:29:07.000 in the enterprise is you need an ecosystem. So over 0:29:07.080 --> 0:29:10.360 seventy percent of it is purchased through partners, through channel partners, 0:29:10.560 --> 0:29:13.560 but in the enterprise, if you zoom into just the enterprise, 0:29:13.960 --> 0:29:19.000 it's virtually every transaction, So you really need to have 0:29:20.120 --> 0:29:23.280 trust in partners. You need to have trust that you 0:29:23.320 --> 0:29:25.479 know there'll be people who will help you to have 0:29:25.520 --> 0:29:26.680 a hard ar ecosystem. 0:29:27.040 --> 0:29:27.880 And video. 0:29:29.120 --> 0:29:31.440 May make a GPU, but they work with the cooling 0:29:31.520 --> 0:29:34.960 vendors on this exact specification of how the codeplate that 0:29:35.000 --> 0:29:36.200 will cool it will look like. 0:29:36.920 --> 0:29:38.600 That's very impressive, right. 0:29:38.800 --> 0:29:41.200 So then when you go to the software layer, you 0:29:41.240 --> 0:29:44.280 want to get the developers behind you. And on top 0:29:44.320 --> 0:29:46.560 of developers, you want to also users that might. 0:29:46.400 --> 0:29:47.200 Not be experts. 0:29:47.520 --> 0:29:51.560 So by having both you know, a language, by having 0:29:51.720 --> 0:29:54.959 a platform, by having models, that means that the different 0:29:55.040 --> 0:29:57.680 level of skills you know, people can work with you, 0:29:58.160 --> 0:30:01.480 and then you need to have a services prior enterprises. 0:30:02.480 --> 0:30:05.320 Some enterprises like to do stuff in house. Other enterprises 0:30:05.600 --> 0:30:07.000 like to have a partner. 0:30:07.680 --> 0:30:10.200 And we were short on time, and I've got to 0:30:10.240 --> 0:30:14.600 mention quantum day. That's why we're here in this room. 0:30:14.800 --> 0:30:18.360 And Video does not make quantum computers. Yes, we're having 0:30:18.440 --> 0:30:19.800 quantum day. How do you approach it? 0:30:20.280 --> 0:30:23.360 So I think you know, big speculation of quantum computing. 0:30:23.480 --> 0:30:24.280 When is it coming? 0:30:24.400 --> 0:30:27.280 So I'll just tell you one very quick story. Arm 0:30:27.440 --> 0:30:30.200 CPUs Right, arm CPUs are now a really big part 0:30:30.240 --> 0:30:32.160 of the ecosystem. We use it, and Video has them, 0:30:32.200 --> 0:30:34.240 Amazon has them, many people have them. 0:30:34.760 --> 0:30:35.760 But when it was. 0:30:35.800 --> 0:30:38.720 When the first kind of data center ARMCPU was launched 0:30:38.840 --> 0:30:42.320 in about twenty eleven twenty thirteen. You know, I was 0:30:42.360 --> 0:30:44.400 at Intel when we were very worried about it. But 0:30:44.440 --> 0:30:47.440 at the time it lacked performance, so it took another 0:30:47.520 --> 0:30:50.760 five years for performance to happen. But then it lacked 0:30:50.760 --> 0:30:54.480 software ecosystem, it lacked programmability, it lacked libraries, and it 0:30:54.560 --> 0:30:57.160 lacked you know, being able to use enterprise software out 0:30:57.200 --> 0:31:00.400 of the box. So it took another five years. Huge 0:31:00.440 --> 0:31:04.160 investment from Amazon. Actually, if we're honest for codes to 0:31:04.160 --> 0:31:07.560 be rewritten to work. So we're now in the place 0:31:08.040 --> 0:31:11.440 where Armor was in twenty eleven. So I think that 0:31:11.480 --> 0:31:14.720 we need at least another five years for the hardware 0:31:14.920 --> 0:31:18.640 to get to a place where it's highly reliable. But 0:31:18.760 --> 0:31:23.880 then the programmability of it, how easy it can be popularized, 0:31:24.040 --> 0:31:26.600 that's the difficulty. So in many ways there might be 0:31:26.680 --> 0:31:30.040 true behind truth behind everyone sayings. Pad Guessinger thinks it's 0:31:30.080 --> 0:31:32.440 going to take five years for the hardware, yes, but 0:31:32.520 --> 0:31:35.240 I think Jensen thinks about it very practically. It takes 0:31:35.280 --> 0:31:36.760 longer for the programma BOS. 0:31:36.600 --> 0:31:40.320 System, and Nvidia's argument would be their AI supercomputer separate 0:31:40.360 --> 0:31:43.880 technology can help in the development. Flag Alobov of Ondia 0:31:43.920 --> 0:31:45.640 really great to catch up here in San Jose. 0:31:46.040 --> 0:31:49.360 Turn back to UNSF no great stuff, big thank you 0:31:49.400 --> 0:31:51.920 to add lod though out there in San Jose. Tune 0:31:51.960 --> 0:31:54.320 in at four thirty pm Eastern today for a special 0:31:54.440 --> 0:31:57.880 edition of Bloomberg Technology, hosted by our very own the 0:31:57.960 --> 0:31:59.720 Live from Videos GtC. 0:32:01.120 --> 0:32:02.480 Now coming up, investor. 0:32:02.200 --> 0:32:05.920 Joe Lonsdale and Campus CEO today O Rende are going 0:32:05.960 --> 0:32:08.840 to join us to discuss the startup Series B funding 0:32:08.880 --> 0:32:10.360 round and changes to higher ed. 0:32:10.760 --> 0:32:25.120 This is Bloomberg, a tech startup. 0:32:25.160 --> 0:32:27.720 Campus has just raised forty three million dollars in a 0:32:27.760 --> 0:32:30.440 Series B funding round. The company aims to give students 0:32:30.480 --> 0:32:33.800 a more affordable path to a college degree. Bloomberg Beta, 0:32:33.840 --> 0:32:35.960 the venture capital arm of Bloomberg LP, is one of 0:32:36.000 --> 0:32:39.320 Campus's five largest shareholders. We should note joining us now 0:32:39.400 --> 0:32:42.320 is campus CEO Toddy o ya Rende and one of 0:32:42.320 --> 0:32:45.280 its investors, Joe Lonsdale. Toddy, I want to start with 0:32:45.320 --> 0:32:48.880 you because you studied aerospace engineering in the UK and 0:32:48.880 --> 0:32:52.040 at Embry Riddle in Florida. Was it your experience with 0:32:52.200 --> 0:32:54.080 education that led you to start this company? 0:32:54.320 --> 0:32:57.440 Hey, Tim, thanks for having me. Hey Joe, definitely, I 0:32:57.440 --> 0:33:00.720 mean before college, I was homeschooled until high school. My 0:33:00.840 --> 0:33:04.160 paternal grandfather was a college dean. My paternal grandfather was 0:33:04.160 --> 0:33:07.680 a high school principal. My mother is a college dean. 0:33:08.240 --> 0:33:10.480 My older sister is a professor. So probably I was 0:33:10.560 --> 0:33:12.760 brainwashed from birth to get really excited about education. 0:33:13.840 --> 0:33:16.800 Well, a school's reputation when it comes to academics is everything. 0:33:16.840 --> 0:33:19.400 How do you build that reputation today from the ground up, 0:33:19.480 --> 0:33:22.479 especially in the early years and when for profit schools 0:33:22.520 --> 0:33:23.760 have had such a checkered past. 0:33:24.000 --> 0:33:26.760 Look, it's about elite education for all and so that's 0:33:26.760 --> 0:33:28.960 what we're doing at Campus. We're sort of rethinking the 0:33:29.000 --> 0:33:31.280 first two years of college. We're building a new kind 0:33:31.320 --> 0:33:33.640 of two year college where students get to learned from 0:33:33.720 --> 0:33:36.520 a the best professors from the top schools in the 0:33:36.520 --> 0:33:40.520 country Princeton, Stanford, UCLA knock out the first years of 0:33:40.520 --> 0:33:43.160 college with us, and there not just learning theoretical nonsense, 0:33:43.320 --> 0:33:46.640 learning like really useful skills. And then they transferred to 0:33:46.640 --> 0:33:48.320 the four year school of their dreams to complete their 0:33:48.360 --> 0:33:50.720 bachelors with no debt. And I think that's the key. 0:33:51.120 --> 0:33:52.080 No debt. 0:33:52.200 --> 0:33:54.400 Student loan debt's about to pass two trillion dollars in 0:33:54.400 --> 0:33:57.360 this country. We were hearing crazy stories students taking out 0:33:57.360 --> 0:33:59.680 one hundred thousand dollars two hundred thousand dollar loans that 0:33:59.720 --> 0:34:02.040 are graduating. They can't even get jobs. It makes no sense. 0:34:02.080 --> 0:34:03.720 It's got to stop. But now there's actually a better way. 0:34:04.600 --> 0:34:06.360 Hey, well, speaking of that, I want to bring in 0:34:06.520 --> 0:34:07.520 Joe to this conversation. 0:34:07.600 --> 0:34:08.279 Joe Lonsdale. 0:34:08.680 --> 0:34:10.759 Look, you've already invested in Campus, but this isn't your 0:34:10.800 --> 0:34:14.080 first foray into education. You co founded the University of 0:34:14.120 --> 0:34:16.880 Boston a few years ago. What in your view is 0:34:16.920 --> 0:34:20.440 wrong with higher education? You went to Stanford, you seem 0:34:20.440 --> 0:34:21.600 to be doing pretty well. 0:34:23.239 --> 0:34:23.600 Well. 0:34:23.640 --> 0:34:25.319 Of course, there's a lot of issues with the very 0:34:25.320 --> 0:34:27.400 top of our education, which is what the University of 0:34:27.440 --> 0:34:29.640 Austin's focus on. But you know, I'd argue that our 0:34:29.640 --> 0:34:32.640 community college is unfortunately, are even more troubled in this country. 0:34:32.640 --> 0:34:35.480 There are many have very low graduation rates. A lot 0:34:35.520 --> 0:34:38.560 of them also are focused more on ideology than skills, sadly. 0:34:38.960 --> 0:34:41.319 And so what today and campus represents to me, It 0:34:41.360 --> 0:34:44.560 represents excellence, it represents merit. And you know, our economy 0:34:44.600 --> 0:34:47.319 is changing drastically ais you guys are talking about on 0:34:47.360 --> 0:34:50.080 other segments today. You know, it's changing everything how it's 0:34:50.080 --> 0:34:51.640 going to work, and we need to get the right 0:34:51.680 --> 0:34:54.800 skills and the right frameworks, you know, to millions of 0:34:55.080 --> 0:34:57.200 young adults. And you know, I'm hoping Todd I could 0:34:57.200 --> 0:34:59.880 scale this to a million students, kepture timers, sent the community, 0:34:59.880 --> 0:35:02.319 call colledge market and really help all of those live 0:35:02.360 --> 0:35:05.000 better lives and succeed more on the economy that it's coming. 0:35:05.280 --> 0:35:09.359 Let's go, well, Joe, what's your input on the curriculum, 0:35:09.640 --> 0:35:13.200 because You're hiring a lot of folks, your portfolio companies 0:35:13.239 --> 0:35:17.719 are hiring a lot of folks who have diverse backgrounds, 0:35:18.120 --> 0:35:22.120 who have skills that are arguably not necessarily taught in 0:35:22.160 --> 0:35:24.920 some schools and universities. What's the input that we're getting 0:35:24.960 --> 0:35:26.160 him on the curriculum? 0:35:26.760 --> 0:35:29.120 You know, my push from my side is let's do 0:35:29.600 --> 0:35:32.080 let's add some in more, some more courses in that 0:35:32.160 --> 0:35:33.439 reflect what you need to know for AI. 0:35:33.520 --> 0:35:36.000 You know, there's people like Sam Altman involved. 0:35:35.600 --> 0:35:38.040 As well, who build open AI of course, and others 0:35:38.040 --> 0:35:40.239 who are invested here. And the idea is, how can 0:35:40.280 --> 0:35:43.480 we help hundreds of thousands, millions of young Americans, you know, 0:35:43.520 --> 0:35:46.399 obtain the skills necessary to work in an economy where 0:35:46.440 --> 0:35:48.200 AI is going to be involved in a lot more So, 0:35:48.239 --> 0:35:50.959 that's not the immediate focus today. The immediate focus today 0:35:51.000 --> 0:35:53.600 is on a lot of basic skills needed in today's economy. 0:35:53.680 --> 0:35:56.120 But what's really fun is today's talking a lot and 0:35:56.160 --> 0:35:57.600 thinking a lot about what else can we add in 0:35:57.640 --> 0:35:59.840 here to really make sure we get people ready for 0:35:59.880 --> 0:36:00.640 the twenty thirties. 0:36:01.239 --> 0:36:03.080 Well, Toddy, you were talking about the cost of college 0:36:03.120 --> 0:36:06.000 getting out of control. You were sharing some pretty staggering 0:36:06.000 --> 0:36:08.359 statistics about the trillions of dollars of student loan debt 0:36:08.400 --> 0:36:13.160 that exists in this country. Nobody argues with that. How 0:36:13.160 --> 0:36:16.359 do you make the economics of campus work though, when 0:36:16.760 --> 0:36:21.719 other colleges and other even junior colleges community colleges. 0:36:21.400 --> 0:36:22.080 Are more expensive. 0:36:22.800 --> 0:36:25.200 Yeah, I think the key is, like the completion rates 0:36:25.320 --> 0:36:27.480 actually have to go up for the economics to work. 0:36:27.520 --> 0:36:30.879 So the traditional community college has an average completion rate 0:36:31.040 --> 0:36:34.279 of about twenty seven percent graduation rate, and so when 0:36:34.320 --> 0:36:36.719 you lose students when they drop out, you actually earn 0:36:36.800 --> 0:36:40.239 less tuition revenue per student. So if you actually it's 0:36:40.320 --> 0:36:42.799 sort of paradoxical. But if you actually keep students longer 0:36:42.840 --> 0:36:45.200 because you help them graduate, then guess what, you earn 0:36:45.239 --> 0:36:48.279 more tuition revenue, which makes the economics more healthy. And 0:36:48.320 --> 0:36:50.759 so that's like the sort of the beautiful symmetry in 0:36:50.840 --> 0:36:53.280 terms of what's best for the student, what's best for campus, 0:36:53.360 --> 0:36:56.160 and what's best for our country. Driving up graduation rates 0:36:56.160 --> 0:36:57.640 is actually how you make the economics work. 0:36:59.200 --> 0:37:01.400 Joe, come on in here, because I'm curious about your 0:37:01.480 --> 0:37:05.840 view of the federal government's involvement in education. The government 0:37:05.920 --> 0:37:08.960 is frozen, suspended, or cut more than a billion dollars 0:37:09.000 --> 0:37:12.520 from universities In a recent week, we're talking about reports 0:37:12.520 --> 0:37:16.399 from Columbia, Johns Hopkins ten and more. And I'm wondering, 0:37:16.440 --> 0:37:18.719 as an entrepreneur, as somebody who's hired a lot of 0:37:18.719 --> 0:37:22.960 folks who founded successful companies, somebody who has founded successful companies, 0:37:23.200 --> 0:37:26.520 are you concerned about the American talent pipeline being cut 0:37:26.560 --> 0:37:27.960 off as a result of these cuts? 0:37:29.160 --> 0:37:31.720 You know, I'm more concerned about making sure we spend 0:37:31.760 --> 0:37:34.600 money effectively and efficiently, and so I really like what 0:37:34.640 --> 0:37:36.239 Toddy is doing along those lines. A lot of the 0:37:36.239 --> 0:37:39.120 policy I'm pushing, you know, coming from my side of things, 0:37:39.160 --> 0:37:41.480 is how do we make this spend accountable. So, for example, 0:37:41.640 --> 0:37:44.040 if you're going to do vocational education, Unfortunately, just like 0:37:44.080 --> 0:37:46.560 our community colleges, a lot of the vocational programs, low 0:37:46.640 --> 0:37:49.879 graduation rates, wrong skills, not helpful if you can spend 0:37:49.880 --> 0:37:51.719 the money effectively, if you say these money is going 0:37:51.719 --> 0:37:54.080 to be tied to results. For example, when you tie 0:37:54.080 --> 0:37:56.239 the money to the salaries of students coming out. 0:37:56.160 --> 0:37:58.160 Of vocational schools, it doubles those results. 0:37:58.320 --> 0:38:00.600 Those are types of policies I think be popular on 0:38:00.680 --> 0:38:02.399 both the left and the right, And what I love 0:38:02.400 --> 0:38:04.480 about what today is doing is it's not really playing 0:38:04.480 --> 0:38:05.560 the ideological games. 0:38:05.680 --> 0:38:06.840 There's people of all backgrounds. 0:38:06.840 --> 0:38:10.800 There's people involved from the left, from the right, black, white, Hispanic, everything, 0:38:10.840 --> 0:38:12.879 and it's just about merit and excellence and getting good 0:38:12.880 --> 0:38:15.040 results for very small spend. So I think this is 0:38:15.080 --> 0:38:17.160 this sort of thing that is going to remain popular 0:38:17.160 --> 0:38:19.400 with everyone, regardless of some of the other fights going on. 0:38:20.200 --> 0:38:21.799 Well, today, how do you watch what's happening in the 0:38:21.800 --> 0:38:25.680 federal government because Predesident Trump today is expected to sign 0:38:25.719 --> 0:38:29.280 an executive action that formally asked officials to take steps 0:38:29.320 --> 0:38:32.400 to dismantle the Department of Education. According to our reporting, 0:38:32.920 --> 0:38:35.080 what happens to your business then, because forty percent of 0:38:35.080 --> 0:38:37.759 your students qualify for pelgrants and those are administered by 0:38:37.800 --> 0:38:39.280 the Department of Education. 0:38:39.760 --> 0:38:41.799 Yeah, Look, the vast and jort of our students use 0:38:41.840 --> 0:38:43.799 PEL grants to cover their twition and so they don't 0:38:43.800 --> 0:38:46.000 have to pay anything out of pocket for twition expenses. 0:38:46.200 --> 0:38:48.640 Paul Grant is not going away. Even if the Department 0:38:48.680 --> 0:38:51.520 of Education is dismantled, some of these key programs that 0:38:51.560 --> 0:38:53.720 are mandated by Congress are going to be split across. 0:38:53.840 --> 0:38:56.759 You know, maybe Treasury or the IRS or other organizations. 0:38:56.800 --> 0:38:58.880 The way I look at what's happening in Washington right 0:38:58.880 --> 0:39:01.960 now is, hey, look, obviously everyone's looking at this and saying, 0:39:02.000 --> 0:39:03.759 we need to be more accountable. As show talked about, 0:39:03.880 --> 0:39:06.080 we definitely need to be more efficient with taxpayer dollars. 0:39:06.280 --> 0:39:09.000 It's really early days. Secaturmic Man's been in there for 0:39:09.080 --> 0:39:10.920 less than three weeks. I think you know we're watching 0:39:10.960 --> 0:39:12.160 it closely, but we're going to have to let this 0:39:12.200 --> 0:39:12.759 one play out. 0:39:13.920 --> 0:39:17.279 Hey, Joe, last one to you, speaking of efficiencies in 0:39:17.280 --> 0:39:20.399 the federal government, You've been supportive of Dog this week, 0:39:20.440 --> 0:39:23.239 though a federal judge ruled that Elon Musk's actions to 0:39:23.239 --> 0:39:27.520 shut down USAID violated likely violated the Constitution in multiple ways. 0:39:27.760 --> 0:39:29.839 Are you concerned that the courts are going to prevent 0:39:29.880 --> 0:39:32.160 Elon from being able to do the cuts that you 0:39:32.200 --> 0:39:32.759 want to see him? 0:39:32.800 --> 0:39:35.879 Do you know that particular ruling. I'm glad you mentioned 0:39:35.920 --> 0:39:38.680 it because it was so ridiculous. So actually the ruling 0:39:39.000 --> 0:39:41.400 was so misguided that it thought Congress had created USA, 0:39:41.400 --> 0:39:43.360 which is not correct. It was actually created by executive 0:39:43.400 --> 0:39:46.040 action the USA. It is just such a great example 0:39:46.480 --> 0:39:49.759 of just complete waste, right they're just all sorts of 0:39:49.800 --> 0:39:51.680 scams and fraud that we've uncovered. I think no matter 0:39:51.680 --> 0:39:53.200 what your party background, if you look at the details, 0:39:53.239 --> 0:39:54.520 you'll agree this should have been turned off. 0:39:54.719 --> 0:39:55.439 And there are. 0:39:55.360 --> 0:39:57.799 Activist judges they are going to try to slow it down. 0:39:58.160 --> 0:40:00.000 I personally hope the Supreme Court is going to step 0:40:00.080 --> 0:40:02.720 in and make some sound rulings here and stop activist 0:40:02.760 --> 0:40:05.520 judges from violating the Constitution by their interference. And it 0:40:05.800 --> 0:40:07.040 is going to be a big issue. 0:40:07.920 --> 0:40:10.080 So what have you spoken to Elon about this? Have 0:40:10.120 --> 0:40:12.280 you spoken to Elon since he's been a doge. 0:40:13.480 --> 0:40:16.239 He's a friend and I am in touch, and he's 0:40:16.280 --> 0:40:18.759 working really hard with a lot of smart people. They're 0:40:18.800 --> 0:40:20.560 being very aggressive. A lot of my friends are involved 0:40:20.600 --> 0:40:23.319 in DOGE and listen, there's there's I think. I don't 0:40:23.320 --> 0:40:25.360 think everything they're doing is going to always be perfect, 0:40:25.600 --> 0:40:27.600 but there's so many crazy things that have to be 0:40:27.600 --> 0:40:29.719 turned off and have to be confronted that overall, I'm 0:40:29.840 --> 0:40:31.600 very very happy for the work they're doing, and I 0:40:31.600 --> 0:40:34.080 think they're kind of shocked about some of the ridiculous 0:40:34.120 --> 0:40:37.239 things they're finding as well as they're publishing all right. 0:40:37.239 --> 0:40:39.480 Well, really appreciate both of you guys joining us. That's 0:40:39.600 --> 0:40:43.680 Joe Lonsdale from a VC also campus CEO today. Oh 0:40:43.760 --> 0:40:48.120 Yareen Day, thanks so much for joining us. If you 0:40:48.160 --> 0:40:50.239 wanted to grind the world to a complete hall, you 0:40:50.280 --> 0:40:51.239 could achieve. 0:40:50.920 --> 0:40:52.239 That by removing magnets. 0:40:52.280 --> 0:40:55.840 They're crucial to basically all tech, including EVS and the 0:40:55.880 --> 0:41:00.000 next nuclear breakthrough fusion Energy Primer or the latest Bloomberg 0:41:00.000 --> 0:41:02.640 original series takes a deep dive into all of this. 0:41:03.520 --> 0:41:05.840 It takes a lot of work to build something big. 0:41:06.120 --> 0:41:09.000 You also gets very expensive, like the amount of money 0:41:09.000 --> 0:41:11.120 you need to spend on something to get just the 0:41:11.160 --> 0:41:13.520 first one can get very very expensive. 0:41:14.480 --> 0:41:18.440 That's exactly what's happening with ETA, a giant fusion reactor 0:41:18.480 --> 0:41:22.239 currently under construction in France that uses super conducting magnets. 0:41:22.760 --> 0:41:25.680 Look at this thing. It's huge and as a result, 0:41:25.800 --> 0:41:28.880 it's projected to cost as much as sixty five billion dollars. 0:41:30.400 --> 0:41:34.319 So to make fusion smaller, cheaper and more practical, Commonwealth's 0:41:34.360 --> 0:41:37.120 founders needed a whole new kind of magnet. 0:41:38.600 --> 0:41:41.640 The question was like would that material ever happen? And 0:41:41.719 --> 0:41:44.600 it wasn't until the early two thousands we could really 0:41:44.640 --> 0:41:48.759 see that that material was going to happen that there 0:41:48.760 --> 0:41:51.759 would be a new type of superconductor. And it's a 0:41:51.760 --> 0:41:53.799 weird it's not a wire, it's a weird thing. It's 0:41:53.800 --> 0:41:56.640 a film and it won the Nobel Prize like months 0:41:56.640 --> 0:41:57.600 after it was discovered. 0:41:59.200 --> 0:42:03.040 So this is a material called HTS or high temperature superconductor. 0:42:03.400 --> 0:42:05.480 Is it is Literally it comes down a tape. It's 0:42:05.480 --> 0:42:07.200 probably kind of hard to see on the camera. It's 0:42:07.320 --> 0:42:10.839 very thin. It's actually mostly copper and steel, but there's 0:42:10.880 --> 0:42:14.560 a very very very thin layer inside of this that 0:42:14.640 --> 0:42:16.680 is high temperature superconducting material. 0:42:17.800 --> 0:42:21.399 High temperature in this case is still wildly cold, but 0:42:21.480 --> 0:42:24.640 not quite out of space cold. And that was the 0:42:24.680 --> 0:42:29.120 breakthrough that Commonwealth needed. Magnets made with this material can 0:42:29.160 --> 0:42:32.239 create a stronger magnetic field, so they don't have to 0:42:32.239 --> 0:42:33.320 be so massive. 0:42:34.719 --> 0:42:37.400 You can shrink the size of the device by a 0:42:37.440 --> 0:42:41.320 factor of ten. Basically allows us to make things smaller, 0:42:41.320 --> 0:42:44.200 which makes things cheaper and makes things faster to get 0:42:44.239 --> 0:42:46.920 fusion to a spot where we can make energy from it. 0:42:50.040 --> 0:42:52.560 And that was the voice of Caroline High. Tune into 0:42:52.560 --> 0:42:55.080 the first episode of Primer tonight on a Bloomberg TV 0:42:55.160 --> 0:42:58.400 at six pm Eastern time. That is going to do 0:42:58.440 --> 0:43:00.960 it for Bloomberg Technology. Tune in later today at four 0:43:01.120 --> 0:43:04.480 thirty pm Eastern one thirty pm Pacific for a special 0:43:04.600 --> 0:43:07.839 edition of Bloomberg Technology Live from Nvidia's GtC event. 0:43:07.880 --> 0:43:09.640 Also check out our podcast. 0:43:09.719 --> 0:43:11.680 You can do that on the terminal, as well as 0:43:11.719 --> 0:43:15.560 online at Apple, Spotify, and iHeart This is Bloomberg.