1 00:00:02,400 --> 00:00:09,240 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news from Marhart. 2 00:00:09,240 --> 00:00:13,640 Speaker 2: We're Innovation, Money and Power Collie in Silicon Valley, NBN. 3 00:00:14,000 --> 00:00:29,240 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:31,840 --> 00:00:34,919 Speaker 3: I'm Caroline Hyde at Bloomberg's world headquarters in New York and. 5 00:00:34,880 --> 00:00:38,320 Speaker 4: I met Lovelow in San Francisco. This is Bloomberg Technology. 6 00:00:38,000 --> 00:00:41,239 Speaker 5: Coming up full coverage on guess what in video? You 7 00:00:41,280 --> 00:00:41,599 Speaker 5: know it. 8 00:00:41,640 --> 00:00:45,720 Speaker 3: Pushing ahead to that all important earnings after the closing. 9 00:00:45,440 --> 00:00:49,320 Speaker 4: Bell, Plus Apple looks to get its doj antitrust case 10 00:00:49,400 --> 00:00:49,960 Speaker 4: tossed out. 11 00:00:50,280 --> 00:00:52,120 Speaker 5: We'll bring you the details and scale. 12 00:00:52,159 --> 00:00:55,280 Speaker 3: AI excures one billion dollars in funding as its valuation 13 00:00:55,440 --> 00:00:59,800 Speaker 3: nearly doubles to almost fourteen billion. CEO Alexander Wang going 14 00:00:59,840 --> 00:01:01,640 Speaker 3: to be joining us later in the hour, but first 15 00:01:01,840 --> 00:01:04,360 Speaker 3: and let's check in on these markets because there's a 16 00:01:04,360 --> 00:01:07,640 Speaker 3: macro bit of data. We were waiting for the FED 17 00:01:07,680 --> 00:01:09,880 Speaker 3: minutes coming a little bit later in the afternoon, and 18 00:01:09,920 --> 00:01:13,640 Speaker 3: we're basically flat. Ahead of that, we're still digesting some 19 00:01:13,720 --> 00:01:16,080 Speaker 3: really strong earnings that have come from the tech ecosystem. 20 00:01:16,120 --> 00:01:18,319 Speaker 3: We Bracewort and Video brings us and we're up just 21 00:01:18,520 --> 00:01:21,000 Speaker 3: five points on the Nasdaq. But remember new record highs 22 00:01:21,000 --> 00:01:23,200 Speaker 3: after record highs, we're looking at twenty a yield just 23 00:01:23,240 --> 00:01:25,319 Speaker 3: flat ahead of an all important auction. We had seen 24 00:01:25,360 --> 00:01:27,959 Speaker 3: bond selling off, yields rising. Now we managed to go 25 00:01:28,040 --> 00:01:30,920 Speaker 3: back into the green just a moment. Pound I look 26 00:01:30,920 --> 00:01:33,800 Speaker 3: at because inflation, yes, it is cooling, but it is 27 00:01:33,800 --> 00:01:34,679 Speaker 3: not cooling as much. 28 00:01:34,600 --> 00:01:35,720 Speaker 5: As the market wanted to see. 29 00:01:35,800 --> 00:01:38,119 Speaker 3: The pound is higher events as the US dollar, as 30 00:01:38,160 --> 00:01:40,080 Speaker 3: potentially we see the Bank of England not being able 31 00:01:40,120 --> 00:01:42,920 Speaker 3: to cut at the anticipated rate that the market had 32 00:01:42,959 --> 00:01:44,680 Speaker 3: wanted to see. Let's move on and see what's happening 33 00:01:44,720 --> 00:01:48,000 Speaker 3: in the world of crypto, because suddenly, out of nowhere 34 00:01:48,080 --> 00:01:51,440 Speaker 3: we regalvanize ourselves. Potentially the latest spot etf this one 35 00:01:51,480 --> 00:01:54,960 Speaker 3: being an ether an ethereum related one. Thoughts and prayers 36 00:01:54,960 --> 00:01:56,920 Speaker 3: to all those who are the lawyers or the compliance 37 00:01:56,920 --> 00:01:58,720 Speaker 3: folks having to get the data, having to get the 38 00:01:58,720 --> 00:02:00,640 Speaker 3: documents ready. We're currently down by a tenth of a 39 00:02:00,680 --> 00:02:02,840 Speaker 3: percent on ether as we speak, bened What are you watching? 40 00:02:04,000 --> 00:02:07,200 Speaker 4: It has been called the mother of all earnings. It's 41 00:02:07,200 --> 00:02:10,720 Speaker 4: been labeled as the single most important stock on the planet, 42 00:02:11,080 --> 00:02:14,440 Speaker 4: and that when you say AI industry, you are talking 43 00:02:14,480 --> 00:02:17,480 Speaker 4: about in video in video reports earnings after the bell. 44 00:02:17,560 --> 00:02:21,520 Speaker 4: The stakes are incredibly high, and we want to continue 45 00:02:21,520 --> 00:02:24,440 Speaker 4: to see year on year top line growth in excess 46 00:02:24,440 --> 00:02:27,600 Speaker 4: of two hundred percent. I also look at the market 47 00:02:27,680 --> 00:02:31,040 Speaker 4: capitalization of this company. When you think about how high 48 00:02:31,120 --> 00:02:36,280 Speaker 4: the stakes are, it's a lot, right, and so what's 49 00:02:36,320 --> 00:02:37,800 Speaker 4: going to happen? I know a lot of you in 50 00:02:37,800 --> 00:02:40,840 Speaker 4: our bloombog technology audience look at demand signals, You look 51 00:02:40,840 --> 00:02:43,240 Speaker 4: at the commentary from the hyperscalers, you look at all 52 00:02:43,280 --> 00:02:46,520 Speaker 4: the news stories about the AI startups training models, whether 53 00:02:46,639 --> 00:02:49,200 Speaker 4: or not they secured h one hundred clusters, and you 54 00:02:49,240 --> 00:02:51,600 Speaker 4: look at some of the sovereign AI discussion that's happening. 55 00:02:52,360 --> 00:02:55,119 Speaker 4: There is a lot at stake, Carrow. We're very excited 56 00:02:55,120 --> 00:02:57,960 Speaker 4: about it. The expectation, though, is probably that in Vidia 57 00:02:58,000 --> 00:02:59,760 Speaker 4: and Jensen knock it out of the park. 58 00:03:00,120 --> 00:03:02,119 Speaker 3: Yeah, and how often do you get to see two 59 00:03:02,240 --> 00:03:05,480 Speaker 3: hundred percent increases in a quarterly revenue? 60 00:03:05,560 --> 00:03:06,240 Speaker 5: It ain't much. 61 00:03:06,360 --> 00:03:09,760 Speaker 3: Let's talk about Nvidia now reporting earnings after that bell. 62 00:03:10,480 --> 00:03:12,799 Speaker 3: Here's what in Vidia CEO Jenson Wang had to say 63 00:03:12,840 --> 00:03:15,280 Speaker 3: about his outlook for AI just earlier this week with 64 00:03:15,360 --> 00:03:15,639 Speaker 3: you ed. 65 00:03:16,720 --> 00:03:21,280 Speaker 6: We've re engineered and reinvented every layer of computing, from 66 00:03:21,480 --> 00:03:25,040 Speaker 6: the chip to the operating system, to the system servers, 67 00:03:25,080 --> 00:03:27,280 Speaker 6: to the way that these data centers are put together. 68 00:03:27,360 --> 00:03:30,040 Speaker 6: We want to bring this generaltive AI capability to every 69 00:03:30,040 --> 00:03:31,239 Speaker 6: company in the world. 70 00:03:33,200 --> 00:03:34,240 Speaker 5: From the man himself. 71 00:03:34,480 --> 00:03:36,000 Speaker 3: So yeah, the key voice we want to bring on 72 00:03:36,080 --> 00:03:38,440 Speaker 3: right now, Blue meg Intelligence senior analyst Man Deep saying, 73 00:03:38,520 --> 00:03:42,680 Speaker 3: because there is a lot of hope and market valuation 74 00:03:42,840 --> 00:03:44,160 Speaker 3: riding on these set of numbers. 75 00:03:44,600 --> 00:03:48,280 Speaker 7: Yeah, look, I think they probably will guide to twenty 76 00:03:48,320 --> 00:03:51,400 Speaker 7: five billion in data center revenue because that's what everyone 77 00:03:51,680 --> 00:03:54,560 Speaker 7: cares about right now, and it's higher than consensus. So 78 00:03:54,760 --> 00:03:58,360 Speaker 7: you know, the byside bogie here is higher than consensus. 79 00:03:58,520 --> 00:04:01,560 Speaker 7: Anything in line with consense would have a negative knee 80 00:04:01,600 --> 00:04:05,200 Speaker 7: jerk reaction, and they'll do it. The question to me 81 00:04:05,520 --> 00:04:09,400 Speaker 7: is how much of their new black Quall chip is 82 00:04:09,520 --> 00:04:13,000 Speaker 7: used for training and then what are the existing chips 83 00:04:13,360 --> 00:04:15,760 Speaker 7: in terms of you know, the inferencing side of the equation, 84 00:04:15,920 --> 00:04:19,279 Speaker 7: because once you pass through that lens, you will get 85 00:04:19,320 --> 00:04:22,039 Speaker 7: a sense of, you know, is this sustainable in terms 86 00:04:22,120 --> 00:04:24,799 Speaker 7: of triple digit growth? Probably that's not going to happen 87 00:04:24,839 --> 00:04:27,880 Speaker 7: given the comps get tougher in the second half. But 88 00:04:27,960 --> 00:04:31,599 Speaker 7: in terms of sustaining high double digit growth, it comes 89 00:04:31,640 --> 00:04:35,120 Speaker 7: down to whether Nvidia will be a big player in inferencing, 90 00:04:35,200 --> 00:04:38,159 Speaker 7: and last quarter Jensen said they have a forty percent 91 00:04:38,400 --> 00:04:42,440 Speaker 7: share in inferencing. Everyone questions that, so we will be 92 00:04:42,520 --> 00:04:45,880 Speaker 7: looking for update around inferencing because look, Microsoft announced a 93 00:04:45,920 --> 00:04:49,640 Speaker 7: new PC with Qualcomm chip. Intel is probably doing something 94 00:04:49,680 --> 00:04:53,919 Speaker 7: around their own chip. Apple will announce something around on 95 00:04:54,080 --> 00:04:59,040 Speaker 7: device GENII. That's all inferencing. So Nvidia having inferencing share, 96 00:04:59,360 --> 00:05:01,919 Speaker 7: it comes down to do they have a play with 97 00:05:02,000 --> 00:05:06,200 Speaker 7: the hyperscale cloud vendors Amazon, Microsoft and Google using in 98 00:05:06,279 --> 00:05:08,960 Speaker 7: Vidia for inferencing And the answer is they'll probably end 99 00:05:09,040 --> 00:05:12,039 Speaker 7: up using their own chips for inferencing. Why would they 100 00:05:12,160 --> 00:05:15,880 Speaker 7: use in video for inferencing given the SP's you know, 101 00:05:15,920 --> 00:05:17,280 Speaker 7: in video commend expensive. 102 00:05:17,480 --> 00:05:21,960 Speaker 4: Yeah, Monday, I appreciate that answer so much. I've been 103 00:05:21,960 --> 00:05:25,479 Speaker 4: thinking a lot about generations of technology. So all of 104 00:05:25,480 --> 00:05:28,320 Speaker 4: this is built on H one hundred, right, and a 105 00:05:28,360 --> 00:05:31,360 Speaker 4: lot of people worry there's this air pocket where H 106 00:05:31,480 --> 00:05:34,560 Speaker 4: two hundred, the next generation with high bandwidth memory is 107 00:05:34,640 --> 00:05:37,560 Speaker 4: ramping up, and then you're Blackwell later in the year, 108 00:05:38,560 --> 00:05:41,159 Speaker 4: you're an analyst that looks at this company. Are you 109 00:05:41,279 --> 00:05:44,440 Speaker 4: concerned that the end market say, why would I buy 110 00:05:44,560 --> 00:05:46,960 Speaker 4: H one hundred like the next best things coming? 111 00:05:47,240 --> 00:05:47,920 Speaker 1: I'll just wait. 112 00:05:48,920 --> 00:05:52,080 Speaker 7: I mean, there were rumors around that with Amazon yesterday. 113 00:05:52,120 --> 00:05:55,560 Speaker 7: But look right now the market is undersupplied and everyone 114 00:05:55,680 --> 00:05:58,240 Speaker 7: wants to get a hold of whatever they can for 115 00:05:58,400 --> 00:06:02,359 Speaker 7: training their llms. It comes down to who are the 116 00:06:02,400 --> 00:06:05,520 Speaker 7: big LLLM players. And we know that field is consolidating 117 00:06:05,560 --> 00:06:07,800 Speaker 7: a number of them have actually thrown in the towel. 118 00:06:08,120 --> 00:06:11,640 Speaker 7: They're getting acquired by larger you know, hyperscalers, So that 119 00:06:11,720 --> 00:06:15,599 Speaker 7: field is narrowing. The second vector that drives that is 120 00:06:15,640 --> 00:06:16,640 Speaker 7: the size of the model. 121 00:06:16,680 --> 00:06:17,400 Speaker 1: If the size of. 122 00:06:17,360 --> 00:06:20,680 Speaker 7: The model is growing, then you need more a bigger 123 00:06:20,720 --> 00:06:23,520 Speaker 7: cluster and probably H two hundred or a black belt 124 00:06:23,600 --> 00:06:27,120 Speaker 7: series will have more performance. In fact, Google said their 125 00:06:27,160 --> 00:06:31,200 Speaker 7: TPU six is five times faster than their TPU five. 126 00:06:31,520 --> 00:06:33,720 Speaker 7: What does it tell you that, you know, the performance 127 00:06:33,720 --> 00:06:37,479 Speaker 7: increases are there, And if you're a hyperscaler that's buying 128 00:06:37,520 --> 00:06:40,520 Speaker 7: ten billion dollars of Invidia chips, you would want to 129 00:06:40,520 --> 00:06:42,800 Speaker 7: see that higher performance chip as opposed to you know, 130 00:06:42,839 --> 00:06:45,839 Speaker 7: the last generation. So all that is a risk, I 131 00:06:45,839 --> 00:06:48,799 Speaker 7: mean in the end, in Nvidia has a fifty percent 132 00:06:49,120 --> 00:06:52,760 Speaker 7: you know, exposure to hyperscalers and these are very concentrated customers, 133 00:06:52,760 --> 00:06:55,040 Speaker 7: five to six customers making a fifty percent of their 134 00:06:55,080 --> 00:06:56,240 Speaker 7: data center revenue. 135 00:06:56,440 --> 00:06:59,839 Speaker 3: They've still got some dependency on China. China can't have 136 00:07:00,240 --> 00:07:03,680 Speaker 3: the most sophisticated chips. Will we hear anything about exposure there? 137 00:07:03,880 --> 00:07:06,560 Speaker 7: I mean, we know that's going down. It's almost mid 138 00:07:06,600 --> 00:07:10,000 Speaker 7: single digit of their revenue, but that's a market where 139 00:07:10,040 --> 00:07:13,160 Speaker 7: they could have seen an upside. Surprises. Every company in 140 00:07:13,240 --> 00:07:16,320 Speaker 7: China is training their own llms. Again, it comes down 141 00:07:16,360 --> 00:07:19,560 Speaker 7: to training market. How big is the training market and 142 00:07:19,600 --> 00:07:22,280 Speaker 7: how many players are there. I would say, you know, 143 00:07:22,400 --> 00:07:25,360 Speaker 7: the top six llms are based out of United States 144 00:07:25,480 --> 00:07:27,920 Speaker 7: and then the next five are in China. But if 145 00:07:28,400 --> 00:07:31,520 Speaker 7: in Video can't sell their latest chips to the China market, 146 00:07:31,880 --> 00:07:34,480 Speaker 7: that kind of takes some of the upside out at 147 00:07:34,560 --> 00:07:36,320 Speaker 7: least in the near term. And we don't know how 148 00:07:36,360 --> 00:07:38,360 Speaker 7: this geopolitical situation will pan out. 149 00:07:39,680 --> 00:07:42,200 Speaker 4: This is a really big finale to what it has 150 00:07:42,240 --> 00:07:44,640 Speaker 4: been an incredible earning season and Video after Bell and 151 00:07:44,640 --> 00:07:48,240 Speaker 4: Bloomberg Intelligence senior analyst Man keep saying, great setup for 152 00:07:48,360 --> 00:07:50,440 Speaker 4: us coming up on the program. We're going to talk 153 00:07:50,440 --> 00:07:53,760 Speaker 4: about optimism in the crypto industry amid science of Ether 154 00:07:53,880 --> 00:07:58,120 Speaker 4: ETF approval. Look, we've got Jack Mallard's Strike CEO on 155 00:07:58,200 --> 00:07:59,920 Speaker 4: set New York City, Caro. What are you looking at? 156 00:08:00,280 --> 00:08:02,640 Speaker 3: IM just having a kicken eye on what else is 157 00:08:02,680 --> 00:08:06,080 Speaker 3: reporting after the ballad. Snowflake shares actually up about half 158 00:08:06,080 --> 00:08:09,000 Speaker 3: a percentage point, but there has been anxiety that revenue 159 00:08:09,040 --> 00:08:11,960 Speaker 3: growth for this particular software business is expected to slow 160 00:08:12,080 --> 00:08:14,280 Speaker 3: to twenty six percent from forty eight percent last year. 161 00:08:14,560 --> 00:08:17,440 Speaker 3: It says companies look prioritized, of course jenerator AI over 162 00:08:17,480 --> 00:08:22,040 Speaker 3: traditional data warehousing. But where does that competitive landscape evolve 163 00:08:22,200 --> 00:08:25,440 Speaker 3: for Snowflake. We wait to see if revenue can indeed 164 00:08:25,520 --> 00:08:28,640 Speaker 3: hit the seven hundred and eighty six million dollars anticipated. 165 00:08:28,760 --> 00:08:37,240 Speaker 5: This is Bloomberg Technology. 166 00:08:46,040 --> 00:08:48,880 Speaker 4: Okay, So I'm looking at Ether and there's been a 167 00:08:48,880 --> 00:08:52,120 Speaker 4: lot of energy and ether over the last five days, 168 00:08:52,200 --> 00:08:54,880 Speaker 4: just like bitcoin trading. Twenty four to seven, Bloomberg reported, 169 00:08:54,920 --> 00:08:58,559 Speaker 4: citing sources, that the SEC had instructed the New York 170 00:08:58,559 --> 00:09:02,360 Speaker 4: Stock Exchange and the cbo WE to update regulatory filings 171 00:09:02,400 --> 00:09:07,040 Speaker 4: basically that relate to changing rules that would make the 172 00:09:07,120 --> 00:09:12,480 Speaker 4: approval of a spot ether ETF more likely, and the 173 00:09:12,520 --> 00:09:14,880 Speaker 4: market kind of got caught by surprise as if it 174 00:09:14,920 --> 00:09:17,079 Speaker 4: wasn't priced in. And like Caro said at the top 175 00:09:17,120 --> 00:09:19,360 Speaker 4: of the show, you've got compliance officers and chief legal 176 00:09:19,360 --> 00:09:22,360 Speaker 4: officers everywhere going, wait, this isn't supposed to happen this quickly. 177 00:09:22,559 --> 00:09:25,240 Speaker 4: And we learned from our bitcoin experience it ain't linear, 178 00:09:26,240 --> 00:09:28,320 Speaker 4: it doesn't go in a straight line, but people are 179 00:09:28,320 --> 00:09:30,720 Speaker 4: now excited about it. And you look at the performance 180 00:09:30,760 --> 00:09:35,680 Speaker 4: of the two right ether totalized version of the Ethereum blockchain, 181 00:09:35,679 --> 00:09:39,120 Speaker 4: the kind of underpinnings of this industry, the gaps closing 182 00:09:39,160 --> 00:09:41,079 Speaker 4: with Bitcoin a little bit, and Cara, I guess the 183 00:09:41,120 --> 00:09:43,600 Speaker 4: point I'm making is that the market's kind of surprised, 184 00:09:43,600 --> 00:09:45,520 Speaker 4: and I guess you and I might be a bit 185 00:09:45,559 --> 00:09:48,200 Speaker 4: surprised that we're talking about this in this context. And 186 00:09:48,200 --> 00:09:49,960 Speaker 4: looking at the chart behind me, tell. 187 00:09:49,800 --> 00:09:51,719 Speaker 3: You what many people feel really moved the market. It 188 00:09:51,800 --> 00:09:56,360 Speaker 3: was actually Bluemberg intelligence about tunis raising its probability that 189 00:09:56,800 --> 00:09:59,640 Speaker 3: the spot ether ETF was going to come and the 190 00:09:59,679 --> 00:10:01,880 Speaker 3: market we've done that amazing. Well, we're going to talk 191 00:10:01,880 --> 00:10:04,360 Speaker 3: about someone who's we need to the bitcoin scene and 192 00:10:04,400 --> 00:10:06,760 Speaker 3: what he thinks about the Ethereo METF as well. Strike 193 00:10:06,800 --> 00:10:08,520 Speaker 3: CEO Jack Maner is always great to have you on 194 00:10:08,559 --> 00:10:11,720 Speaker 3: the show. Thanks coming in to the studio. You, of 195 00:10:11,760 --> 00:10:15,439 Speaker 3: course very much focused on functionality and building upon Bitcoin. 196 00:10:16,160 --> 00:10:18,520 Speaker 3: What do you think though of a ETF yet more 197 00:10:18,520 --> 00:10:21,400 Speaker 3: institution money potentially coming into the smart contract side of 198 00:10:21,400 --> 00:10:21,800 Speaker 3: the equation. 199 00:10:22,400 --> 00:10:25,480 Speaker 8: Yeah, I'm not a fan of any other cryptocurrency outside 200 00:10:25,520 --> 00:10:28,400 Speaker 8: of Bitcoin, but I think there is a hilarious story 201 00:10:28,400 --> 00:10:30,480 Speaker 8: as to why this is happening. I mean, I was 202 00:10:30,520 --> 00:10:33,160 Speaker 8: laughing out loud. It looks like someone went to Gary 203 00:10:33,160 --> 00:10:35,640 Speaker 8: Gensler and said, hey, buddy, you're not in charge anymore. 204 00:10:36,080 --> 00:10:38,640 Speaker 8: And you'd have to think why that was. It's because 205 00:10:38,679 --> 00:10:40,320 Speaker 8: banks and Wall Street are making money. 206 00:10:40,760 --> 00:10:40,960 Speaker 2: Right. 207 00:10:41,480 --> 00:10:44,800 Speaker 8: Banks get a really bad deal in today's market, is 208 00:10:44,840 --> 00:10:48,320 Speaker 8: they have to buy bonds that are performing awfully, and 209 00:10:48,400 --> 00:10:50,480 Speaker 8: all of a sudden they get a free market in 210 00:10:50,520 --> 00:10:54,280 Speaker 8: this independent crypto thing and they're actually making money. These 211 00:10:54,280 --> 00:10:57,000 Speaker 8: markets have life, These markets go up when governments debase 212 00:10:57,040 --> 00:10:59,760 Speaker 8: their currency. These things perform well. And they got to 213 00:10:59,760 --> 00:11:02,640 Speaker 8: take of bitcoin and it was the best performing product 214 00:11:02,640 --> 00:11:05,199 Speaker 8: that they've ever had, and they're like, oh man, how 215 00:11:05,240 --> 00:11:07,240 Speaker 8: many other pieces of crap are there out there? That 216 00:11:07,280 --> 00:11:09,920 Speaker 8: we can list as ETFs, and I literally think that's 217 00:11:09,960 --> 00:11:12,800 Speaker 8: what's happening. It's a way better business to launch crypto 218 00:11:12,840 --> 00:11:16,760 Speaker 8: ETFs than it is to buy bonds right now based 219 00:11:16,760 --> 00:11:20,280 Speaker 8: on the macro environment. So I think someone said, hey, Gary, sorry, buddy, 220 00:11:20,800 --> 00:11:23,360 Speaker 8: despite you thinking that these things are securities, we need 221 00:11:23,400 --> 00:11:24,720 Speaker 8: to make money, and that's what happened. 222 00:11:24,880 --> 00:11:26,520 Speaker 3: It's funny that you bring up Gary against because, of 223 00:11:26,559 --> 00:11:28,600 Speaker 3: course the House of Representatives currently taking on a bill 224 00:11:28,640 --> 00:11:30,120 Speaker 3: at the moment as to whether it should be the 225 00:11:30,160 --> 00:11:33,440 Speaker 3: sec at AOL overseeing crypto products more generally, or whether 226 00:11:33,480 --> 00:11:37,120 Speaker 3: it should be shifted. But to your point, whether or 227 00:11:37,120 --> 00:11:41,000 Speaker 3: not Ethereum does indeed manage to become spot traded with 228 00:11:41,080 --> 00:11:41,839 Speaker 3: an ETF and. 229 00:11:41,800 --> 00:11:46,160 Speaker 5: Money coming in. There's a real organization. 230 00:11:45,600 --> 00:11:46,920 Speaker 3: Of some of the documents because they don't want to 231 00:11:46,920 --> 00:11:49,600 Speaker 3: see staking involved in any way. How do you think 232 00:11:49,640 --> 00:11:52,679 Speaker 3: about what that means for an investment opportunity? 233 00:11:53,000 --> 00:11:56,480 Speaker 8: I listen again, I'm a bitcoin guy. I think all 234 00:11:56,520 --> 00:11:58,960 Speaker 8: of this is a distraction and a load and nonsense. 235 00:11:59,000 --> 00:12:00,640 Speaker 8: I think what they're trying to do is walk the 236 00:12:00,679 --> 00:12:03,120 Speaker 8: fine line of how can we justify banks and Wall 237 00:12:03,160 --> 00:12:06,200 Speaker 8: Street making more revenue on this industry? Because with all 238 00:12:06,280 --> 00:12:10,199 Speaker 8: passive investing and central banks really trying to price control everything, 239 00:12:10,559 --> 00:12:12,880 Speaker 8: this market has life, and if it has life, it 240 00:12:12,960 --> 00:12:15,680 Speaker 8: means there's an opportunity to make money. Volatility is a 241 00:12:15,679 --> 00:12:18,160 Speaker 8: good thing. People on Wall Street like volatility, so they're 242 00:12:18,160 --> 00:12:20,280 Speaker 8: trying to find a way to justify and make happy 243 00:12:20,320 --> 00:12:24,480 Speaker 8: in peace with existing securities laws while giving these Wall 244 00:12:24,480 --> 00:12:27,840 Speaker 8: Street and these big banks like JP Morgan an ability 245 00:12:27,840 --> 00:12:30,719 Speaker 8: to monetize this space and actually make money because the 246 00:12:30,760 --> 00:12:33,880 Speaker 8: business they're in right now is awful. I mean, you're 247 00:12:34,000 --> 00:12:36,360 Speaker 8: needing to buy bonds, lend money to the government and 248 00:12:36,360 --> 00:12:38,400 Speaker 8: not get paid the growth that they're e's eve been. 249 00:12:38,360 --> 00:12:40,120 Speaker 3: Doing quite well. I mean, there are certain parts of 250 00:12:40,120 --> 00:12:43,120 Speaker 3: the finance. I get your point. The financial institutions still 251 00:12:43,160 --> 00:12:44,760 Speaker 3: been doing quite well in the rest of the markets. 252 00:12:44,800 --> 00:12:47,280 Speaker 3: But there's yeah, ebrtually a role for banks to play here, 253 00:12:47,280 --> 00:12:48,840 Speaker 3: which could also be going through bigulary. 254 00:12:48,880 --> 00:12:51,360 Speaker 8: Yeah, but I think listen, the S and P five 255 00:12:51,440 --> 00:12:53,880 Speaker 8: hundred is dominated by what like the top seven companies, 256 00:12:53,880 --> 00:12:56,280 Speaker 8: and they're all tech companies. Tech companies don't care about 257 00:12:56,320 --> 00:12:59,520 Speaker 8: the cost of capital. They don't need business loans, Facebook, Apple, 258 00:12:59,600 --> 00:13:02,040 Speaker 8: these company copanies can finance their business with their existing 259 00:13:02,040 --> 00:13:04,720 Speaker 8: cash flows, and so the rest of the world is 260 00:13:04,760 --> 00:13:07,200 Speaker 8: in a pain of hurt and banks right now their 261 00:13:07,240 --> 00:13:09,600 Speaker 8: whole job is to take our deposits and buy bonds, 262 00:13:09,600 --> 00:13:11,679 Speaker 8: and these bonds are getting slaughtered, and all of a 263 00:13:11,720 --> 00:13:14,320 Speaker 8: sudden they're like, Wow, here's a market with life. Here's 264 00:13:14,360 --> 00:13:16,600 Speaker 8: a market with retail flows. Here's a market that has 265 00:13:16,720 --> 00:13:20,040 Speaker 8: volatility and that is actually reactive to the outside world. Hey, 266 00:13:20,080 --> 00:13:21,640 Speaker 8: how many of these crypto things exist? 267 00:13:21,720 --> 00:13:21,920 Speaker 5: Hey? 268 00:13:21,920 --> 00:13:24,640 Speaker 8: Gary, except them all? So I wouldn't be surprised if 269 00:13:24,640 --> 00:13:27,480 Speaker 8: we got a Subaduchie coin. What did I say on here? 270 00:13:27,679 --> 00:13:30,720 Speaker 8: We should get a Subaduchie ETF soon. Seriously, I really 271 00:13:30,760 --> 00:13:33,200 Speaker 8: think they're just trying to actize it. Jack. 272 00:13:33,600 --> 00:13:35,640 Speaker 4: Let's go back to basics here. There will be members 273 00:13:35,640 --> 00:13:38,240 Speaker 4: of the Bloomberg tetlas the audience that push back on 274 00:13:38,400 --> 00:13:43,520 Speaker 4: I'm paraphrasing a spot eth ETF being the next listing 275 00:13:43,559 --> 00:13:46,720 Speaker 4: of a piece of crap. There's a standard tagline in 276 00:13:46,760 --> 00:13:50,000 Speaker 4: our Bloomberg Cabaret coverage, which is the ether is a 277 00:13:50,080 --> 00:13:54,400 Speaker 4: native token of ethereum, and ethereum is the most widely 278 00:13:54,520 --> 00:13:59,000 Speaker 4: commercially used blockchain. What would be your response to that 279 00:13:59,200 --> 00:14:00,120 Speaker 4: standard line? 280 00:14:00,400 --> 00:14:04,080 Speaker 8: Sure, okay, and I apologize to all those I offended. 281 00:14:04,280 --> 00:14:07,680 Speaker 8: Here's my point. Bitcoin is the only money within the 282 00:14:07,679 --> 00:14:11,840 Speaker 8: cryptocurrency space. It's designed and treated as a monetary asset. 283 00:14:12,080 --> 00:14:13,880 Speaker 8: So then what do I think ethereum is. I think 284 00:14:13,880 --> 00:14:17,480 Speaker 8: ethereum is a technology, which you actually just displayed in 285 00:14:17,520 --> 00:14:20,880 Speaker 8: your introduction and definition of it. Ethereum appeals to developers. 286 00:14:20,920 --> 00:14:23,480 Speaker 8: They change the monetary policy and the rules all of 287 00:14:23,560 --> 00:14:27,280 Speaker 8: the time. However, the market often conflates it as a money, 288 00:14:27,360 --> 00:14:30,760 Speaker 8: conflates it as a commodity, strike as a technology, and 289 00:14:30,840 --> 00:14:33,480 Speaker 8: video is a technology. These things are regulated by the SEC. 290 00:14:33,520 --> 00:14:36,239 Speaker 8: These things have cash flows, These things have founders, directors, 291 00:14:36,320 --> 00:14:38,800 Speaker 8: people that set the roadmap. And so when I say 292 00:14:38,840 --> 00:14:41,120 Speaker 8: a piece of crap, maybe I'm being a bit aggressive, 293 00:14:41,160 --> 00:14:43,600 Speaker 8: but I think it's distinctly different from bitcoin. I think 294 00:14:43,640 --> 00:14:46,400 Speaker 8: bitcoin can be the world reserve asset, the one money 295 00:14:46,400 --> 00:14:48,840 Speaker 8: that we use to store our time and energy. And 296 00:14:48,920 --> 00:14:51,680 Speaker 8: I think ethereum can be a technology which competes within video. 297 00:14:51,720 --> 00:14:53,560 Speaker 8: But then it gets confusing. It's like, well, what's their 298 00:14:53,560 --> 00:14:56,640 Speaker 8: cash flows? Who founded it, who sets the direction? Why 299 00:14:56,680 --> 00:14:58,960 Speaker 8: does it change so much? And when it does change, 300 00:14:58,960 --> 00:15:00,920 Speaker 8: why does it change? I think that there's a lot 301 00:15:00,960 --> 00:15:04,360 Speaker 8: of intentional confusion going on in the story, and as 302 00:15:04,360 --> 00:15:06,440 Speaker 8: a bitcoiner, it frustrates me because I'm trying to fix 303 00:15:06,480 --> 00:15:09,080 Speaker 8: the money, fix the world, and so I think that 304 00:15:09,120 --> 00:15:11,040 Speaker 8: there's a lot the market has to sort out as 305 00:15:11,040 --> 00:15:13,000 Speaker 8: to what it actually is and how to value it. 306 00:15:13,720 --> 00:15:17,440 Speaker 3: Jack Man has come back with your messaging and come 307 00:15:17,480 --> 00:15:19,920 Speaker 3: on the show from San Francisco as well. Strike CEO 308 00:15:20,080 --> 00:15:21,840 Speaker 3: Jack maners on all things crypto. I mean, while we 309 00:15:21,840 --> 00:15:23,920 Speaker 3: do have some breaking news coming from the UK, there 310 00:15:23,920 --> 00:15:26,040 Speaker 3: has been a lot of rumor swirling about whether or 311 00:15:26,080 --> 00:15:28,440 Speaker 3: not we'll see a summer election called by Rishie Sunak, 312 00:15:28,720 --> 00:15:31,560 Speaker 3: the current Prime Minister UK. Sunac will call a summer 313 00:15:31,600 --> 00:15:35,040 Speaker 3: election this afternoon, that's being reported by The Guardian. He 314 00:15:35,240 --> 00:15:38,360 Speaker 3: is going to call that election for July, so the 315 00:15:38,400 --> 00:15:39,600 Speaker 3: Guardian reports now. 316 00:15:39,640 --> 00:15:40,960 Speaker 5: Thus far, Bloomberg has. 317 00:15:40,840 --> 00:15:43,840 Speaker 3: Been reporting that there has been repeated affirmation that there 318 00:15:43,840 --> 00:15:46,520 Speaker 3: will be an election in twenty twenty four in the 319 00:15:46,600 --> 00:15:48,400 Speaker 3: second half of the year. 320 00:15:48,760 --> 00:15:51,200 Speaker 5: The nuance is exactly when that second half. 321 00:15:51,040 --> 00:16:02,280 Speaker 3: Is politics out of the United Kingdom, Rishi Sunac, the 322 00:16:02,360 --> 00:16:04,880 Speaker 3: Prime Minister, likely to call an election for as soon 323 00:16:04,920 --> 00:16:07,840 Speaker 3: as July, as being reported by the Guardian, now we 324 00:16:07,920 --> 00:16:11,160 Speaker 3: know that there's been plenty of rumors, particularly accentuated by 325 00:16:11,160 --> 00:16:13,400 Speaker 3: the inflation report that came out of the UK today, 326 00:16:13,440 --> 00:16:15,400 Speaker 3: which showed, even though it was higher than the market 327 00:16:15,400 --> 00:16:18,600 Speaker 3: and anticipated, inflation is slowing closer to where the Bank 328 00:16:18,640 --> 00:16:20,880 Speaker 3: of England's key rate of two percent. 329 00:16:21,000 --> 00:16:22,520 Speaker 5: Level target is ed. 330 00:16:22,680 --> 00:16:26,120 Speaker 3: This is notable because there has been much frustration with 331 00:16:26,160 --> 00:16:29,080 Speaker 3: a way in which Rissi Sunac, leader of the Conservative Party, 332 00:16:29,080 --> 00:16:32,040 Speaker 3: Prime Minister of the UK, has been able to inject 333 00:16:32,120 --> 00:16:36,560 Speaker 3: or not the economic recovery into over the UK. The 334 00:16:36,640 --> 00:16:41,160 Speaker 3: labor the alternative party in the UK needs by many 335 00:16:41,240 --> 00:16:45,520 Speaker 3: points in terms of how perhaps voters would lean, and 336 00:16:45,600 --> 00:16:48,520 Speaker 3: notable that he wants to well accelerate any sort of 337 00:16:48,960 --> 00:16:51,280 Speaker 3: putting them to the polls well. 338 00:16:51,320 --> 00:16:54,760 Speaker 4: According to the Guardians reporting July would be sooner than 339 00:16:54,800 --> 00:16:57,920 Speaker 4: the sort of general wisdom which it was that Prime 340 00:16:57,960 --> 00:17:02,160 Speaker 4: Minister Sunak would wait until the autumn. His consistent line 341 00:17:02,160 --> 00:17:03,560 Speaker 4: has been that there would be a vote in the 342 00:17:03,600 --> 00:17:06,760 Speaker 4: second half of the year. But the rationale or thinking 343 00:17:06,800 --> 00:17:09,159 Speaker 4: that he'd wait to the autumn is that there is 344 00:17:09,200 --> 00:17:12,399 Speaker 4: a cost of living crisis in the United Kingdom. Basically 345 00:17:12,920 --> 00:17:18,000 Speaker 4: and strategically, he and the Conservative Party the thinking was 346 00:17:18,480 --> 00:17:22,119 Speaker 4: would hold into the autumn to let that cost of 347 00:17:22,160 --> 00:17:25,520 Speaker 4: living crisis dissipate somewhat ease off and then have more 348 00:17:25,560 --> 00:17:28,359 Speaker 4: favorable environment. But you're completely right that if you go 349 00:17:28,400 --> 00:17:31,880 Speaker 4: off the polls, which one always approaches with caution, then 350 00:17:31,920 --> 00:17:36,880 Speaker 4: the Conservatives do lead trail the opposition Labor Party. 351 00:17:37,080 --> 00:17:38,960 Speaker 3: We can go out to Lizzie Burdon, who's over in 352 00:17:39,000 --> 00:17:42,879 Speaker 3: the United Kingdom covering the political storytelling there, and at 353 00:17:42,880 --> 00:17:45,639 Speaker 3: the moment, Lizzie, it looks as though the Guardian is 354 00:17:45,640 --> 00:17:48,240 Speaker 3: giving us a month at least that we could see 355 00:17:48,359 --> 00:17:51,879 Speaker 3: a general election. It's being cited by some senior those 356 00:17:51,920 --> 00:17:53,960 Speaker 3: close to Rashid Zunac himself. 357 00:17:55,240 --> 00:17:59,879 Speaker 9: And lead up downing too. They always are being rightly 358 00:18:00,200 --> 00:18:04,200 Speaker 9: takes in Oxford twenty twenty two when Boris Johnson said 359 00:18:04,359 --> 00:18:08,119 Speaker 9: down and Ricky see that came in put somewhere in 360 00:18:08,160 --> 00:18:11,119 Speaker 9: between that look all day, which you see how this 361 00:18:11,240 --> 00:18:14,359 Speaker 9: has the opportunity to deny that you're going before the 362 00:18:14,520 --> 00:18:17,879 Speaker 9: July elected. He's had unto opportunity and he's reflected at 363 00:18:18,000 --> 00:18:22,520 Speaker 9: the champ having It is ongoing currently the Letios administ 364 00:18:22,760 --> 00:18:24,600 Speaker 9: and has been for the past twenty a few minutes 365 00:18:25,040 --> 00:18:27,639 Speaker 9: it's old executive thing. We've come when he put a 366 00:18:27,720 --> 00:18:30,399 Speaker 9: Pilar Bailer who is only on the grounds for about 367 00:18:30,480 --> 00:18:35,720 Speaker 9: two hours, and the defense granted that. The gold tip 368 00:18:35,800 --> 00:18:41,240 Speaker 9: states for I've been twenty to a big announcement, and 369 00:18:41,760 --> 00:18:42,720 Speaker 9: here he had to gude. 370 00:18:42,640 --> 00:18:45,720 Speaker 4: Yeah, Lizzie, Sorry, sorry to interrupts you. Here we're getting Lizzie, 371 00:18:45,800 --> 00:18:47,560 Speaker 4: give me a second. Here, we're getting some more headlines 372 00:18:47,600 --> 00:18:51,000 Speaker 4: on the Bloomberg terminal, this time Sky News reporting that 373 00:18:51,040 --> 00:18:54,359 Speaker 4: a UK general election will be on July fourth, a 374 00:18:54,440 --> 00:18:57,880 Speaker 4: more specific date than the Guardian was giving. We now 375 00:18:57,920 --> 00:19:01,160 Speaker 4: have two reports from two different U outlets or news 376 00:19:01,280 --> 00:19:04,280 Speaker 4: organizations that there will be a general election in July. 377 00:19:04,920 --> 00:19:06,879 Speaker 4: I want to bring in Bonnie Quinn and talking a 378 00:19:06,880 --> 00:19:09,719 Speaker 4: little bit more about the UK economy here, Vonnie, because 379 00:19:09,960 --> 00:19:13,119 Speaker 4: this is an issue of timing and strategy. July is 380 00:19:13,160 --> 00:19:16,720 Speaker 4: sooner than expected, and it's sooner than expected because Rishi 381 00:19:16,800 --> 00:19:21,439 Speaker 4: Sunac faces a difficult domestic economic picture that will inform 382 00:19:21,520 --> 00:19:23,120 Speaker 4: voters minds exactly. 383 00:19:23,280 --> 00:19:23,359 Speaker 9: Ed. 384 00:19:23,359 --> 00:19:25,840 Speaker 10: Well, let's talk about that inflation data that came in earlier, 385 00:19:25,880 --> 00:19:28,520 Speaker 10: coming into five point three percent, which was higher than 386 00:19:28,520 --> 00:19:32,960 Speaker 10: most analysts and economists were anticipating. Services inflation two point 387 00:19:33,000 --> 00:19:36,200 Speaker 10: three services inflation at five point nine percent, which really 388 00:19:36,200 --> 00:19:39,240 Speaker 10: shows that inflation is entrenched. Rishi Sunac would have been 389 00:19:39,240 --> 00:19:41,119 Speaker 10: hoping for that to come down and for there to 390 00:19:41,160 --> 00:19:44,840 Speaker 10: have been a few more months of data showing better 391 00:19:44,880 --> 00:19:46,040 Speaker 10: inflation figures. 392 00:19:46,240 --> 00:19:47,240 Speaker 5: He's not getting that. 393 00:19:47,760 --> 00:19:50,960 Speaker 10: Interestingly, the Bank of England, which had been expected to 394 00:19:51,119 --> 00:19:54,080 Speaker 10: maybe make it cut in June. Banks have been pushing 395 00:19:54,080 --> 00:19:54,920 Speaker 10: their estimates for. 396 00:19:54,960 --> 00:19:56,000 Speaker 5: That first cut out. 397 00:19:56,200 --> 00:19:58,560 Speaker 10: In fact, just the most recent bank to do that 398 00:19:58,600 --> 00:20:02,000 Speaker 10: was Goldman sachsing that's most likely August now for there 399 00:20:02,080 --> 00:20:04,640 Speaker 10: to be a snap election, that makes life a lot easier, 400 00:20:04,720 --> 00:20:06,919 Speaker 10: right because if there were to be a snap election 401 00:20:07,280 --> 00:20:10,080 Speaker 10: in July, as some outlets are reporting, that would mean 402 00:20:10,119 --> 00:20:12,680 Speaker 10: that June would be a very contentious time to make 403 00:20:12,720 --> 00:20:16,360 Speaker 10: a raid cut. If the cut isn't going to happen anyway, 404 00:20:16,400 --> 00:20:18,920 Speaker 10: as most banks are now anticipating, well, that makes it 405 00:20:18,960 --> 00:20:21,359 Speaker 10: maybe easier to get an election out of the way. However, 406 00:20:21,440 --> 00:20:24,720 Speaker 10: Chancellor Jeremy Hunt has a difficult job in that position then, 407 00:20:24,800 --> 00:20:27,480 Speaker 10: because if he's not going to be the Chancellor anymore 408 00:20:27,520 --> 00:20:30,800 Speaker 10: than it falls to possibly Labor's Rachel Reeves. That's the 409 00:20:30,840 --> 00:20:34,399 Speaker 10: person that markets are anticipating would enter a number eleven 410 00:20:34,520 --> 00:20:36,560 Speaker 10: in the event of a snap election with a better 411 00:20:36,680 --> 00:20:39,760 Speaker 10: Labor result, and then it's her bag, right, And the IMF, 412 00:20:39,800 --> 00:20:42,080 Speaker 10: as we know, has been calling for raid cuts this 413 00:20:42,160 --> 00:20:45,239 Speaker 10: year and more next year, trying to bring to that 414 00:20:45,320 --> 00:20:47,280 Speaker 10: bank rate all the way down to about three and 415 00:20:47,320 --> 00:20:49,240 Speaker 10: a half percent by the end of twenty twenty five 416 00:20:49,320 --> 00:20:52,320 Speaker 10: in order to relieve this cost of living crisis which 417 00:20:52,320 --> 00:20:54,359 Speaker 10: is ongoing now three years ed. 418 00:20:55,359 --> 00:20:57,680 Speaker 3: And let's just talk about Rachel Reeves, who of course 419 00:20:57,760 --> 00:21:01,399 Speaker 3: is the Shadow Chancellor, meaning she's in the opposition party 420 00:21:01,440 --> 00:21:04,600 Speaker 3: of Labor led by Seki Stama, and they have been 421 00:21:04,960 --> 00:21:07,680 Speaker 3: well ahead in terms of the polls thus far, and 422 00:21:07,720 --> 00:21:10,440 Speaker 3: the Labor leader has indeed been recently launching his six 423 00:21:10,480 --> 00:21:13,160 Speaker 3: Pledges first steps to guiding the UK going forward. 424 00:21:13,160 --> 00:21:15,800 Speaker 5: And it's not just all about here and now of inflation. 425 00:21:15,880 --> 00:21:19,440 Speaker 3: It's a lot about healthcare, access to NHS waiting lists, 426 00:21:19,480 --> 00:21:21,159 Speaker 3: and of course that comes down to funding. 427 00:21:21,240 --> 00:21:21,520 Speaker 5: Vonny. 428 00:21:21,800 --> 00:21:26,240 Speaker 3: More broadly, would it be the viewpoint from the economy 429 00:21:26,280 --> 00:21:28,920 Speaker 3: here that Labour would in any way change. 430 00:21:28,640 --> 00:21:32,280 Speaker 10: The trajectory, Well, changing the trajectory is one thing. 431 00:21:32,359 --> 00:21:33,880 Speaker 5: Promises are another thing, Caroline. 432 00:21:33,960 --> 00:21:37,280 Speaker 10: So for sure Rachel Reeves has constantly said that you 433 00:21:37,320 --> 00:21:40,480 Speaker 10: would increase government spending in order to help along the 434 00:21:40,520 --> 00:21:44,600 Speaker 10: lines of healthcare, national insurance and pensions in particular, which 435 00:21:44,640 --> 00:21:46,879 Speaker 10: is going to be a very contentious issue for the 436 00:21:46,920 --> 00:21:50,200 Speaker 10: next chancellor. That said, the IMF has said you can't 437 00:21:50,200 --> 00:21:54,359 Speaker 10: do it. You can neither decrease taxes nor increase government spending. 438 00:21:54,440 --> 00:21:56,199 Speaker 10: The economy won't be able to take it. You are 439 00:21:56,280 --> 00:21:59,240 Speaker 10: risking growth. And remember the UK just came out of recession. 440 00:21:59,240 --> 00:21:59,960 Speaker 5: In the first quarter. 441 00:22:00,200 --> 00:22:02,080 Speaker 10: It grew point six percent and that was a big 442 00:22:02,119 --> 00:22:04,760 Speaker 10: relief to the authorities, but that could be in jeopardy 443 00:22:04,800 --> 00:22:05,800 Speaker 10: if there were to be more spending. 444 00:22:05,800 --> 00:22:07,520 Speaker 5: At least according to the IMF. 445 00:22:07,440 --> 00:22:12,159 Speaker 3: Buddy Quinn, there were the latest. 446 00:22:17,280 --> 00:22:19,600 Speaker 4: Welcome back to Blindow Technology, Ed love Low in San 447 00:22:19,640 --> 00:22:20,480 Speaker 4: Francisco and. 448 00:22:20,480 --> 00:22:21,399 Speaker 5: Caroline Hied in New York. 449 00:22:21,480 --> 00:22:23,480 Speaker 3: Let's check in on these markets because there is a 450 00:22:23,480 --> 00:22:26,280 Speaker 3: wait and see feel to the broader indexes. Today we've 451 00:22:26,320 --> 00:22:29,119 Speaker 3: hit record high after record high on the SMP, on 452 00:22:29,160 --> 00:22:32,000 Speaker 3: the Dow, and indeed on our trusted tech benchmarks. I'm 453 00:22:32,000 --> 00:22:34,159 Speaker 3: looking at the NASDAT one hundred currently holding on to 454 00:22:34,200 --> 00:22:36,400 Speaker 3: gains up two tens percent as the all important set 455 00:22:36,400 --> 00:22:38,880 Speaker 3: of numbers come out after the mall in Nvidia, key 456 00:22:38,960 --> 00:22:42,639 Speaker 3: to market capitalization, key to the optimism around AI. 457 00:22:43,000 --> 00:22:44,760 Speaker 5: For the rest of the benchmarks, I'm looking. 458 00:22:44,520 --> 00:22:47,840 Speaker 3: What's happening with the US tenure yield basically training Flattenman. 459 00:22:47,880 --> 00:22:50,160 Speaker 3: We've got a big twenty year auction and also fed 460 00:22:50,200 --> 00:22:52,920 Speaker 3: minutes coming to really pass through for the market. That's 461 00:22:52,920 --> 00:22:55,919 Speaker 3: at lunchtime basically up about a tenth of a percent. 462 00:22:56,119 --> 00:22:59,960 Speaker 3: That's as we do potentially anticipate yet another spot ETF, 463 00:23:00,080 --> 00:23:03,320 Speaker 3: this one linked to ethereum. We'll see if that continues. 464 00:23:03,480 --> 00:23:05,040 Speaker 3: Let's move on and have a look at what's happening 465 00:23:05,040 --> 00:23:06,520 Speaker 3: from a macro perspecture. 466 00:23:06,119 --> 00:23:08,399 Speaker 5: Across the pond. Great British pound. 467 00:23:08,359 --> 00:23:11,160 Speaker 3: Currently still holding onto gains. Why was it up versus 468 00:23:11,200 --> 00:23:13,960 Speaker 3: the US dollar because the CPI print the consumer price 469 00:23:14,000 --> 00:23:16,960 Speaker 3: in next coming in actually hotter than was anticipated. Yes, 470 00:23:16,960 --> 00:23:18,960 Speaker 3: it's down from an excess of three percent, but two 471 00:23:19,000 --> 00:23:20,800 Speaker 3: point three percent was more than the market wanted to 472 00:23:20,840 --> 00:23:23,600 Speaker 3: see for anticipating some sort of Bank of England rate cut. 473 00:23:23,800 --> 00:23:25,440 Speaker 5: So the pound is up on some macro data. 474 00:23:25,440 --> 00:23:28,160 Speaker 3: But there's also the points that being driven home from 475 00:23:28,160 --> 00:23:30,600 Speaker 3: a political perspective that we could see a general election 476 00:23:30,640 --> 00:23:33,320 Speaker 3: held as soon as July fourth, currently being reported by 477 00:23:33,359 --> 00:23:35,160 Speaker 3: sky News. We'll have more on that in a minute, 478 00:23:35,200 --> 00:23:36,520 Speaker 3: but what we got on the. 479 00:23:36,480 --> 00:23:41,040 Speaker 4: Tech Another top story on the Bloomberg terminon dot com 480 00:23:41,119 --> 00:23:43,520 Speaker 4: is Apple. It plans to ask a court to throw 481 00:23:43,560 --> 00:23:46,960 Speaker 4: out the DOJ's case against the iPhone maker, making a 482 00:23:46,960 --> 00:23:49,679 Speaker 4: long shot bid to ward off what promises to be 483 00:23:49,800 --> 00:23:53,960 Speaker 4: a lengthy legal battle, joining US Bloomberg Intelligence, Sandalist Santa Agrana. 484 00:23:54,040 --> 00:23:57,440 Speaker 4: You know, this is a case followed closely, largely procedural 485 00:23:57,560 --> 00:24:01,480 Speaker 4: update that Apple saying this is what we believe help 486 00:24:01,560 --> 00:24:02,440 Speaker 4: us out. 487 00:24:03,000 --> 00:24:04,119 Speaker 1: How do you price that anrag? 488 00:24:05,320 --> 00:24:07,520 Speaker 11: Yeah. I think this is one of the more important things, 489 00:24:07,520 --> 00:24:10,560 Speaker 11: and people don't talk about it because frankly speaking, you know, 490 00:24:10,600 --> 00:24:13,280 Speaker 11: services revenue, as you know, is the one that's driving 491 00:24:13,480 --> 00:24:16,159 Speaker 11: Apple's growth right now, and a large portion of that 492 00:24:16,359 --> 00:24:19,160 Speaker 11: is the app store revenue. And if there are things 493 00:24:19,200 --> 00:24:22,159 Speaker 11: that will be done to damage that and it's going 494 00:24:22,200 --> 00:24:24,320 Speaker 11: to have an impact on Apple, so far we haven't 495 00:24:24,359 --> 00:24:27,040 Speaker 11: seen it because right now it's only a little bit 496 00:24:27,160 --> 00:24:29,800 Speaker 11: changes in the EU, but any changes in the US, 497 00:24:30,520 --> 00:24:32,760 Speaker 11: you know, could could have a material impact on that number. 498 00:24:32,800 --> 00:24:35,520 Speaker 11: So I think Apple will do whatever it can to 499 00:24:35,560 --> 00:24:38,960 Speaker 11: protect its ecosystem, and as you said, it's going to 500 00:24:38,960 --> 00:24:40,439 Speaker 11: be a lengthy legal battle. 501 00:24:41,080 --> 00:24:44,320 Speaker 3: Yeah, we're not on how it seems as though it 502 00:24:44,359 --> 00:24:46,720 Speaker 3: is rare for these sorts of things to be thrown 503 00:24:46,760 --> 00:24:49,159 Speaker 3: out at the behest of certain companies. Em though we 504 00:24:49,200 --> 00:24:52,600 Speaker 3: did see Meta indeed manage to persuade on that FDC 505 00:24:53,640 --> 00:24:56,960 Speaker 3: focused previously. But ad more broadly, is Apple doing enough 506 00:24:57,000 --> 00:24:59,399 Speaker 3: to change the tune when it comes to AI? For example, 507 00:24:59,440 --> 00:25:02,360 Speaker 3: we've actually seen shares do particularly well post earnings. 508 00:25:03,400 --> 00:25:05,600 Speaker 11: Yeah, I think, you know, I would give a little 509 00:25:05,600 --> 00:25:08,280 Speaker 11: bit of that credit to Mark Government for breaking news 510 00:25:08,320 --> 00:25:11,280 Speaker 11: on you know, their partnership with open ai or potential 511 00:25:11,280 --> 00:25:14,400 Speaker 11: partnership with open Ai and the ongoing talks that they 512 00:25:14,400 --> 00:25:16,399 Speaker 11: have and even Google, So you know, one of the 513 00:25:16,440 --> 00:25:19,080 Speaker 11: things we I think it's it's pretty Most people will 514 00:25:19,080 --> 00:25:21,199 Speaker 11: agree that Apple has not been at the forefront of 515 00:25:21,840 --> 00:25:24,800 Speaker 11: any new GENI developments. So the question is, well, what 516 00:25:24,840 --> 00:25:26,840 Speaker 11: are they going to do when they when they have 517 00:25:26,920 --> 00:25:29,120 Speaker 11: to and when when the June tenth event comes in, 518 00:25:29,520 --> 00:25:32,360 Speaker 11: if they are going to announce a partnership with open ai, 519 00:25:32,520 --> 00:25:35,600 Speaker 11: with with Google that can help them to change their 520 00:25:35,640 --> 00:25:38,399 Speaker 11: software make it a little bit better, and based on 521 00:25:38,520 --> 00:25:42,080 Speaker 11: that we see a refresh cycle improved for the iPhone. 522 00:25:42,119 --> 00:25:44,040 Speaker 11: I think it's going to be good for Apple, So 523 00:25:44,119 --> 00:25:45,920 Speaker 11: I think I think it is. It is going in 524 00:25:45,960 --> 00:25:47,760 Speaker 11: the right direction, but we have a long way to 525 00:25:47,800 --> 00:25:50,480 Speaker 11: go before we can say that they have they have 526 00:25:50,520 --> 00:25:53,520 Speaker 11: a good position in that market, An. 527 00:25:53,600 --> 00:25:57,680 Speaker 3: Rag Rana, We always appreciate it coming from Boston glmeg Intelligence. Meanwhile, 528 00:25:57,760 --> 00:26:00,399 Speaker 3: let's focus in on what companies are driving forward. AI 529 00:26:00,560 --> 00:26:04,600 Speaker 3: Salesforce launching Einstein co Pilot for merchants and marketers to 530 00:26:04,720 --> 00:26:10,360 Speaker 3: help companies basically personalized customer engagement across marketing commerce sales services. Well, 531 00:26:10,359 --> 00:26:13,359 Speaker 3: thanks of course, Generator AI. Let's bring in Salesforce AI 532 00:26:13,480 --> 00:26:17,520 Speaker 3: CEO Clara she who is traveling around post the VivaTech 533 00:26:17,600 --> 00:26:20,320 Speaker 3: conference in Paris, and I'm really interested as to what 534 00:26:20,320 --> 00:26:22,960 Speaker 3: you're setting forth a Clara, how are you trying to 535 00:26:22,960 --> 00:26:26,040 Speaker 3: pit yourself against the competition of being able for your 536 00:26:26,040 --> 00:26:29,639 Speaker 3: clients to use their own data in a more sophisticated manner. 537 00:26:31,320 --> 00:26:32,520 Speaker 1: Thank you so much for having me. 538 00:26:32,680 --> 00:26:34,720 Speaker 12: First of all, we are thrilled to be hosting our 539 00:26:34,760 --> 00:26:39,840 Speaker 12: Salesforce Connections event this week in Chicago for commerce merchants 540 00:26:39,920 --> 00:26:43,920 Speaker 12: and marketers and to be announcing just amazing innovation around 541 00:26:44,000 --> 00:26:48,359 Speaker 12: Einstein Copilot as well as Einstein Personalization and now data 542 00:26:48,359 --> 00:26:51,520 Speaker 12: cloud for commerce. I mean, if you think about it, Caroline, 543 00:26:51,560 --> 00:26:54,159 Speaker 12: you know, for the last twenty five years, customers have 544 00:26:54,240 --> 00:26:58,720 Speaker 12: been bringing their trusted data to salesforce and building their campaigns, 545 00:26:58,960 --> 00:27:02,600 Speaker 12: their transaction data, all of their business logic and salesforce, 546 00:27:02,880 --> 00:27:04,560 Speaker 12: and now they can unlock. 547 00:27:04,160 --> 00:27:05,840 Speaker 8: That data through AI. 548 00:27:06,040 --> 00:27:10,080 Speaker 12: And it's very different than going to chat GBT, going 549 00:27:10,080 --> 00:27:13,679 Speaker 12: to another AI system that may not be secure, that 550 00:27:13,800 --> 00:27:16,919 Speaker 12: may not have their trusted data, and getting results that 551 00:27:17,000 --> 00:27:19,600 Speaker 12: may not make sense for their business, versus one that's 552 00:27:19,640 --> 00:27:22,840 Speaker 12: completely grounded in their organization's data and processes. 553 00:27:24,200 --> 00:27:27,440 Speaker 4: Clara, Hey, it's dead in San Francisco. I'm really interested 554 00:27:27,440 --> 00:27:29,560 Speaker 4: in the co pilot for merchants. Right, we seem to 555 00:27:29,560 --> 00:27:34,119 Speaker 4: be increasingly moving toward basically the AI agent where the 556 00:27:34,240 --> 00:27:38,600 Speaker 4: human being or several human beings interact with this agent 557 00:27:38,720 --> 00:27:41,840 Speaker 4: just in the normal course of their roles. How quickly 558 00:27:41,880 --> 00:27:44,360 Speaker 4: do you see the adoption happening and where is it happening? 559 00:27:45,359 --> 00:27:47,840 Speaker 12: Well, it's just amazing to see the response so far 560 00:27:48,160 --> 00:27:51,640 Speaker 12: for Einstein Copilot in both sales and in customer service, 561 00:27:51,720 --> 00:27:55,000 Speaker 12: and for merchants. And marketers, it's going to be equally powerful. 562 00:27:55,160 --> 00:27:58,520 Speaker 12: What we're seeing is that AI is able to augment 563 00:27:58,920 --> 00:28:02,119 Speaker 12: employees and you know, think about a commerce manager that 564 00:28:02,280 --> 00:28:05,199 Speaker 12: has to set up a digital storefront or wants to 565 00:28:05,280 --> 00:28:09,040 Speaker 12: launch a personalized commerce campaign. Now they're able to do 566 00:28:09,119 --> 00:28:12,240 Speaker 12: so with the help of this powerful copilot that has 567 00:28:12,320 --> 00:28:15,520 Speaker 12: all of their inventory data, that understands all of the 568 00:28:15,560 --> 00:28:19,520 Speaker 12: best practices and is able to personalize to that specific 569 00:28:19,560 --> 00:28:22,600 Speaker 12: customer their transaction history and their preferences. 570 00:28:22,800 --> 00:28:25,919 Speaker 3: Clara, it's really interesting you brought up chat GPT is 571 00:28:26,000 --> 00:28:28,119 Speaker 3: the potential of what someone else could be typing into 572 00:28:28,200 --> 00:28:31,200 Speaker 3: because ultimately Einstein GBT correct me if I'm wrong is 573 00:28:31,240 --> 00:28:33,879 Speaker 3: a mixture of public private AI models, and you actually 574 00:28:33,880 --> 00:28:36,520 Speaker 3: have a partnership with open AI. So talk to us 575 00:28:36,560 --> 00:28:40,800 Speaker 3: about the competitive or friendome situation you have with Microsoft 576 00:28:40,800 --> 00:28:42,120 Speaker 3: and open AIS partnership. 577 00:28:43,160 --> 00:28:46,040 Speaker 12: Well, we really believe in customer choice, and it's also 578 00:28:46,320 --> 00:28:50,120 Speaker 12: very early in the LM in the large language model game, 579 00:28:50,200 --> 00:28:52,400 Speaker 12: so it's too soon to call a winner. And so 580 00:28:52,640 --> 00:28:55,719 Speaker 12: the way that we've built our Einstein one platform is 581 00:28:56,400 --> 00:28:59,920 Speaker 12: it's an open architecture and so customers can choose their 582 00:29:00,080 --> 00:29:03,400 Speaker 12: favorite LM, whether it's one of our own Salesforce homegrown 583 00:29:03,520 --> 00:29:07,760 Speaker 12: domain specific llms like Flogen and Cogen, or it's from 584 00:29:07,800 --> 00:29:12,800 Speaker 12: one of our trusted model partners including Google, Anthropic, Amazon, 585 00:29:13,400 --> 00:29:16,440 Speaker 12: Microsoft Open Ai, and then customers can also bring their 586 00:29:16,440 --> 00:29:20,160 Speaker 12: own model. So it's really really about adding value on top. 587 00:29:20,200 --> 00:29:24,280 Speaker 12: It's our Einstein trust layer, providing data security, data privacy, 588 00:29:24,800 --> 00:29:28,600 Speaker 12: zero retention prompts to ensure that none of the context 589 00:29:28,840 --> 00:29:31,800 Speaker 12: that gets put into a prompt is ever stored or 590 00:29:31,920 --> 00:29:34,320 Speaker 12: learned by a model, whether that's ours or a third 591 00:29:34,360 --> 00:29:38,480 Speaker 12: party model. It's providing an audit, trail, citations, and really 592 00:29:38,520 --> 00:29:42,000 Speaker 12: that trust that customers need as a price of admission 593 00:29:42,000 --> 00:29:43,320 Speaker 12: for AI in the enterprise. 594 00:29:44,920 --> 00:29:49,120 Speaker 4: Clara media reports suggest that the Informatica talks have broken 595 00:29:49,160 --> 00:29:53,080 Speaker 4: down with Salesforce. Maybe Salesforce is back on the hunt. 596 00:29:53,240 --> 00:29:55,880 Speaker 4: You know, from an M and A perspective, you lead 597 00:29:55,960 --> 00:29:59,640 Speaker 4: the AI teams at Salesforce. How are you thinking about 598 00:30:00,120 --> 00:30:03,040 Speaker 4: using acquisitions to build out your AI offering? 599 00:30:04,280 --> 00:30:06,560 Speaker 12: Now I'm not able to comment on that, but what 600 00:30:06,640 --> 00:30:10,760 Speaker 12: I can say is that we're seeing phenomenal explosive growth 601 00:30:10,880 --> 00:30:13,520 Speaker 12: in our Salesforce data cloud and what that does is 602 00:30:13,520 --> 00:30:17,280 Speaker 12: it allows customers to bring together all of their trapped 603 00:30:17,360 --> 00:30:20,320 Speaker 12: siloed data from across the enterprise. You think about you know, 604 00:30:20,360 --> 00:30:23,920 Speaker 12: for any business of even a small business, has multiple 605 00:30:23,920 --> 00:30:28,560 Speaker 12: different systems, multiple databases, data warehouses, and data lakes. And 606 00:30:28,600 --> 00:30:32,000 Speaker 12: now we're able to connect that data in a seamless manner. 607 00:30:32,200 --> 00:30:32,560 Speaker 1: And if a. 608 00:30:32,560 --> 00:30:36,960 Speaker 12: Customer is using a Snowflake or a Microsoft Fabric or 609 00:30:37,000 --> 00:30:40,680 Speaker 12: an Amazon Google Big Query, they're able to bring all 610 00:30:40,720 --> 00:30:44,239 Speaker 12: of that data, harmonize it, cleanse it, and activate that 611 00:30:44,320 --> 00:30:47,880 Speaker 12: for use by all of their sales, service marketing and 612 00:30:47,920 --> 00:30:48,640 Speaker 12: merchant teams. 613 00:30:49,560 --> 00:30:52,520 Speaker 4: Salesforce AI CEO Clarocy. Great to have you back on 614 00:30:52,800 --> 00:30:55,280 Speaker 4: the program. Thank you. Let's get back to the breaking 615 00:30:55,320 --> 00:30:58,160 Speaker 4: news from the United Kingdom. The BBC is reporting that 616 00:30:58,200 --> 00:31:01,640 Speaker 4: Prime Minister Rihi soun Act is currently informing his cabinet 617 00:31:01,920 --> 00:31:06,160 Speaker 4: of his intention to hold a general election on July fourth, 618 00:31:06,480 --> 00:31:10,120 Speaker 4: and that Parliament will be dissolved next week. Of course, 619 00:31:10,360 --> 00:31:14,040 Speaker 4: when the parliament is dissolved, it's the official term we 620 00:31:14,160 --> 00:31:16,280 Speaker 4: use for the end of a parliament, and legally a 621 00:31:16,360 --> 00:31:19,920 Speaker 4: general election must follow. The pound in the currency markets 622 00:31:19,960 --> 00:31:23,360 Speaker 4: relatively stable following all these headlines. In the bomb market's 623 00:31:23,480 --> 00:31:26,720 Speaker 4: guilts also steady. The expectation you're looking at a live 624 00:31:26,760 --> 00:31:30,400 Speaker 4: shot of number ten Downing Street. Many media outlets reporting 625 00:31:30,440 --> 00:31:32,880 Speaker 4: that we will get a statement from Prime Minister Rishi 626 00:31:32,960 --> 00:31:36,680 Speaker 4: Sunak of the Conservative Party at some point today. Stay 627 00:31:36,680 --> 00:31:38,440 Speaker 4: with us, we will have all of the latest. This 628 00:31:38,480 --> 00:31:52,160 Speaker 4: is Bloomberg Technology Scaled AI, as startup that helps top 629 00:31:52,160 --> 00:31:55,880 Speaker 4: tech companies improve data used to build AI products, is 630 00:31:55,960 --> 00:31:59,160 Speaker 4: raising one billion dollars in one of the largest financing 631 00:31:59,160 --> 00:32:01,720 Speaker 4: deals of the year. The funding round now values the 632 00:32:01,760 --> 00:32:05,520 Speaker 4: company nearly fourteen billion dollars. Joining us is Alexander Wang, 633 00:32:05,560 --> 00:32:08,320 Speaker 4: Scales founder and CEO. And I look at the new investors, 634 00:32:09,120 --> 00:32:12,520 Speaker 4: the venture arms of AMD, Intel, Cisco's in their Amazon. 635 00:32:13,360 --> 00:32:16,440 Speaker 4: What does that tell me? That's very interesting, this sort 636 00:32:16,440 --> 00:32:19,200 Speaker 4: of strategic backing you've got beyond just the size and 637 00:32:19,240 --> 00:32:20,040 Speaker 4: scope of the round. 638 00:32:21,720 --> 00:32:22,920 Speaker 1: First of all, thanks for having me. 639 00:32:23,400 --> 00:32:26,320 Speaker 13: I think one of the key things of our role 640 00:32:26,480 --> 00:32:28,840 Speaker 13: in the AI industry is that we really are an 641 00:32:28,880 --> 00:32:34,480 Speaker 13: infrastructure provider. The three pillars of AI ultimately are data, compute, 642 00:32:34,480 --> 00:32:35,280 Speaker 13: and algorithms. 643 00:32:35,560 --> 00:32:37,800 Speaker 1: Folks like open Ai solve. 644 00:32:37,600 --> 00:32:40,320 Speaker 13: The algorithmic piece, looks like in Nvidia solve the compute piece, 645 00:32:40,480 --> 00:32:43,400 Speaker 13: and our role at scale is to solve the data pillar. 646 00:32:43,560 --> 00:32:44,120 Speaker 1: For AI. 647 00:32:44,520 --> 00:32:48,640 Speaker 13: Our data foundry today powers nearly every leading large iguage model, 648 00:32:48,760 --> 00:32:52,040 Speaker 13: including those from Open Ei, Meta, Microsoft, and Video. 649 00:32:52,440 --> 00:32:55,440 Speaker 1: And so ultimately, one of our goals with this. 650 00:32:55,760 --> 00:32:58,200 Speaker 13: Financing ground was really to ensure that we can continue 651 00:32:58,280 --> 00:33:01,200 Speaker 13: serving the entire AI eCos system. So you look at 652 00:33:01,200 --> 00:33:03,920 Speaker 13: a lot of the strategic and corporate investors that we 653 00:33:04,000 --> 00:33:06,400 Speaker 13: brought on board, it really was to sort of bring 654 00:33:06,440 --> 00:33:09,600 Speaker 13: together the entire ecosystem and multiple layers of the stack. 655 00:33:09,680 --> 00:33:11,720 Speaker 1: So this includes other folks. 656 00:33:11,400 --> 00:33:15,080 Speaker 13: In the kind of infrastructure layer folks like Nvidia and 657 00:33:15,280 --> 00:33:18,520 Speaker 13: the Intel, as well as folks in the model layer, 658 00:33:18,680 --> 00:33:21,520 Speaker 13: folks like Amazon or Meta, and then lastly folks of 659 00:33:21,560 --> 00:33:24,280 Speaker 13: the application layer, folks like Cisco or service Now. And 660 00:33:24,320 --> 00:33:27,200 Speaker 13: so our goal ultimately was to ensure that we can 661 00:33:27,240 --> 00:33:29,000 Speaker 13: continue serving the entirety of the. 662 00:33:28,960 --> 00:33:31,800 Speaker 1: AI ecosystem as a level infrastructure. 663 00:33:31,320 --> 00:33:35,600 Speaker 13: Provider by sort of like bringing together the entire cobord. 664 00:33:36,360 --> 00:33:37,960 Speaker 5: What about Google or Microsoft? 665 00:33:37,960 --> 00:33:40,800 Speaker 3: Are they interested is that for the later dates or 666 00:33:41,040 --> 00:33:43,080 Speaker 3: can we only go with a certain number of each 667 00:33:43,080 --> 00:33:45,000 Speaker 3: player on each system? 668 00:33:45,600 --> 00:33:48,440 Speaker 13: You know, it's always it's always like hurting cats with 669 00:33:48,520 --> 00:33:51,160 Speaker 13: these with these with these corporations. But we're you know, 670 00:33:51,600 --> 00:33:54,680 Speaker 13: our goal is to continue serving the entire ecosystem. We 671 00:33:54,720 --> 00:33:57,480 Speaker 13: want to ensure that artificial intelligence on the whole is 672 00:33:57,520 --> 00:34:02,040 Speaker 13: able to is able to accomplish the incredible potential you know, 673 00:34:02,120 --> 00:34:04,960 Speaker 13: our data engine. Our goal with our data engine is 674 00:34:05,000 --> 00:34:07,800 Speaker 13: to generate all the frontier data needed to fuel us 675 00:34:07,840 --> 00:34:10,000 Speaker 13: to AGI and potentially even beyond. 676 00:34:10,040 --> 00:34:11,920 Speaker 1: You know, our view, one of the ways we think. 677 00:34:11,760 --> 00:34:14,120 Speaker 13: About it is, what are all the problems in data 678 00:34:14,160 --> 00:34:16,080 Speaker 13: then need you be solved to get us from GPD 679 00:34:16,160 --> 00:34:18,480 Speaker 13: four to GPD ten, And how do we ensure that 680 00:34:18,480 --> 00:34:20,160 Speaker 13: we have the needs of production to do all of that? 681 00:34:20,640 --> 00:34:23,239 Speaker 3: And let's talk about the means of production, about the 682 00:34:23,400 --> 00:34:26,399 Speaker 3: action of labeling such data, because I know that you've 683 00:34:26,560 --> 00:34:29,239 Speaker 3: really been thinking a lot about AI safety, the development 684 00:34:29,320 --> 00:34:32,080 Speaker 3: the biases, ensuring that that's something that's thought about within 685 00:34:32,120 --> 00:34:33,600 Speaker 3: your business and the moods that you serve. 686 00:34:34,000 --> 00:34:35,640 Speaker 5: But there was a lot of concern back. 687 00:34:35,520 --> 00:34:38,440 Speaker 3: In twenty twenty three about who you employ to label 688 00:34:38,560 --> 00:34:40,799 Speaker 3: data and a lot of that work being done in 689 00:34:40,840 --> 00:34:43,120 Speaker 3: the so called global self. How you're paying them, at 690 00:34:43,120 --> 00:34:45,520 Speaker 3: what rate you're paying them. How is that being solved 691 00:34:45,520 --> 00:34:46,560 Speaker 3: now by technology? 692 00:34:46,560 --> 00:34:51,920 Speaker 13: Alexander, Yeah, Ultimately, you know, we believe that the future 693 00:34:51,920 --> 00:34:56,560 Speaker 13: of AI data rests on three principles data abundance, frontier data, 694 00:34:56,600 --> 00:34:59,440 Speaker 13: and measurement and evaluation. In terms of abundance, I mean, 695 00:34:59,440 --> 00:35:02,640 Speaker 13: I think this is one of the clearer areas. We 696 00:35:02,680 --> 00:35:04,560 Speaker 13: need to ensure that we are able to build a 697 00:35:04,600 --> 00:35:06,399 Speaker 13: data foundry that ushers. 698 00:35:06,080 --> 00:35:10,280 Speaker 1: In an era of data abundance. You know, these models are. 699 00:35:10,200 --> 00:35:13,879 Speaker 13: Becoming increasingly data hungry due to the scaling laws. Every 700 00:35:13,880 --> 00:35:17,560 Speaker 13: successive generation models requires exponentially more data, and we need 701 00:35:17,600 --> 00:35:21,080 Speaker 13: to ensure that we are able to build the systems 702 00:35:21,080 --> 00:35:23,520 Speaker 13: and the means of production that allows to not resign 703 00:35:23,520 --> 00:35:26,759 Speaker 13: ourselves to data scarcity. A lot of the key for 704 00:35:26,840 --> 00:35:29,520 Speaker 13: us comes into the second bullet point, though, frontier data. 705 00:35:29,640 --> 00:35:33,560 Speaker 13: As we develop progressively more and more powerful AI systems, 706 00:35:33,800 --> 00:35:37,160 Speaker 13: we need to be building frontier data, which is always 707 00:35:37,200 --> 00:35:41,200 Speaker 13: pushing the boundaries of AI capabilities towards you know, more 708 00:35:41,239 --> 00:35:46,280 Speaker 13: advanced areas such as complex reasoning agents, multimodality, multi linguality, 709 00:35:46,320 --> 00:35:51,360 Speaker 13: and more. This production of frontier data requires human experts 710 00:35:51,440 --> 00:35:53,600 Speaker 13: all around the world, and so you know, our view 711 00:35:53,640 --> 00:35:57,680 Speaker 13: is that humans and expertise are critical component of this 712 00:35:57,719 --> 00:35:58,680 Speaker 13: production process. 713 00:35:59,360 --> 00:36:02,680 Speaker 4: Alex Aram But August twenty nineteen, you were on a 714 00:36:02,800 --> 00:36:05,560 Speaker 4: previous iteration of this show, you were twenty two years 715 00:36:05,600 --> 00:36:08,160 Speaker 4: old and you've just done a one hundred million dollar 716 00:36:08,239 --> 00:36:11,800 Speaker 4: Series C. Fast forward to today, you've just announced the 717 00:36:11,840 --> 00:36:15,320 Speaker 4: European HQ in London, you've valued at fourteen billion dollars. 718 00:36:15,640 --> 00:36:16,279 Speaker 5: What's that like? 719 00:36:16,840 --> 00:36:17,520 Speaker 4: How do you feel? 720 00:36:19,600 --> 00:36:23,880 Speaker 13: You know, I think ultimately the most gratifying piece for 721 00:36:24,000 --> 00:36:26,080 Speaker 13: me and I think for the entire company has really 722 00:36:26,120 --> 00:36:28,799 Speaker 13: been how far AI has come in that timeframe. You know, 723 00:36:28,840 --> 00:36:33,080 Speaker 13: in twenty nineteen, it was the very primordial early days 724 00:36:33,080 --> 00:36:35,720 Speaker 13: of what's now called generator of AI. You know, even 725 00:36:36,000 --> 00:36:38,520 Speaker 13: on that program, we didn't talk at all about about 726 00:36:38,520 --> 00:36:40,680 Speaker 13: the exciting things that OPENING or other companies were doing. 727 00:36:40,719 --> 00:36:42,240 Speaker 1: We were talking about self driving cars. 728 00:36:42,400 --> 00:36:45,200 Speaker 13: If you fast forward today, we've gone from GPT two 729 00:36:45,520 --> 00:36:48,160 Speaker 13: to GPD four and beyond work four and four to 730 00:36:48,239 --> 00:36:48,960 Speaker 13: zero and beyond. 731 00:36:49,560 --> 00:36:51,600 Speaker 1: We have AI. 732 00:36:51,400 --> 00:36:54,279 Speaker 13: Systems that are significantly more capable, and I think we 733 00:36:54,320 --> 00:36:58,400 Speaker 13: see a path to AI really improving everyone's lives in 734 00:36:58,400 --> 00:37:00,640 Speaker 13: a way that was more of a pectoring back in 735 00:37:00,640 --> 00:37:03,040 Speaker 13: twenty nineteen. And so my hope is that, you know, 736 00:37:03,160 --> 00:37:05,600 Speaker 13: five years later, if I'm back on the show, that 737 00:37:05,760 --> 00:37:08,520 Speaker 13: you know, we can we're looking back on the technology 738 00:37:08,600 --> 00:37:11,640 Speaker 13: and seeing even further development in art fial intelligence, even 739 00:37:11,719 --> 00:37:17,240 Speaker 13: further application of the technology, even further impact from ultimately. 740 00:37:16,800 --> 00:37:19,359 Speaker 1: What we view is the most exciting technology of our time. 741 00:37:20,120 --> 00:37:22,920 Speaker 3: I have it sooner than five years time SCALEAI founder 742 00:37:22,960 --> 00:37:25,960 Speaker 3: and CEO Alexander Wang, thank you for joining us on 743 00:37:26,000 --> 00:37:28,600 Speaker 3: the latest funding news. Meanwhile, we've got more funding news 744 00:37:28,640 --> 00:37:31,759 Speaker 3: for you. And also in the world of artificial intelligence, 745 00:37:32,000 --> 00:37:35,000 Speaker 3: deep l announcing a three hundred million dollar investment today. 746 00:37:35,280 --> 00:37:37,640 Speaker 3: There's a two billion dollar valuation for the company that 747 00:37:37,680 --> 00:37:41,000 Speaker 3: provides AI language solutions and it's doing that too more 748 00:37:41,040 --> 00:37:41,399 Speaker 3: than one. 749 00:37:41,320 --> 00:37:42,480 Speaker 5: Hundred thousand customers. 750 00:37:42,680 --> 00:37:46,160 Speaker 3: H CEO and founder Jarek Kutilowski joins us now and 751 00:37:46,680 --> 00:37:49,560 Speaker 3: Eric tell us about DPL and what you're. 752 00:37:49,400 --> 00:37:50,920 Speaker 5: Providing to your enterprise clients. 753 00:37:50,920 --> 00:37:53,239 Speaker 3: Because there's been a lot of excitement about the translation 754 00:37:53,560 --> 00:37:56,279 Speaker 3: capabilities of the latest chat GVT four. 755 00:37:56,320 --> 00:37:57,360 Speaker 5: Oh how are you different? 756 00:37:59,000 --> 00:38:01,439 Speaker 14: I think thank you for having me. I think it's 757 00:38:01,480 --> 00:38:06,959 Speaker 14: amazing how translation actually is the first frontier at which 758 00:38:07,040 --> 00:38:09,759 Speaker 14: AI has been excelling since since quite a few years. 759 00:38:09,800 --> 00:38:13,160 Speaker 14: And DEEPEL has been found in twenty seventeen and we've 760 00:38:13,200 --> 00:38:17,400 Speaker 14: been kind of researching on how this translation technology can 761 00:38:17,480 --> 00:38:21,160 Speaker 14: be as reliable and accurate and on the point all 762 00:38:21,200 --> 00:38:24,120 Speaker 14: of the time so that it suits the needs of 763 00:38:24,200 --> 00:38:27,719 Speaker 14: the enterprises. It's really not about kind of translating this 764 00:38:27,920 --> 00:38:31,880 Speaker 14: menu in in a restaurant where you're in a foreign country. 765 00:38:32,400 --> 00:38:37,600 Speaker 14: It's really about enabling companies to just go and send 766 00:38:37,640 --> 00:38:41,440 Speaker 14: the emails to their customers in the respective languages of 767 00:38:41,800 --> 00:38:45,760 Speaker 14: those which those are speaking, and kind of really going 768 00:38:45,880 --> 00:38:49,680 Speaker 14: global as quickly as possible as they can with as 769 00:38:49,760 --> 00:38:51,400 Speaker 14: much efficiency as possible. 770 00:38:52,120 --> 00:38:55,480 Speaker 3: And so you have been managing to scale your business clients, 771 00:38:55,520 --> 00:38:58,839 Speaker 3: your government clients with this offering, and I'm interested as 772 00:38:58,880 --> 00:39:00,640 Speaker 3: to what they're for the new round the money will 773 00:39:00,640 --> 00:39:03,319 Speaker 3: be doing. How will you be beefing up? Is it 774 00:39:03,400 --> 00:39:06,080 Speaker 3: talent that you need right now? Is it marketing and 775 00:39:06,080 --> 00:39:09,040 Speaker 3: getting other companies to know your story? 776 00:39:09,480 --> 00:39:11,560 Speaker 14: I think it's it's it's basically all of that. I mean, 777 00:39:11,600 --> 00:39:14,160 Speaker 14: the company has been investing on all of those fronts. 778 00:39:14,560 --> 00:39:17,440 Speaker 14: We are really a company that is investing very heavily 779 00:39:17,480 --> 00:39:20,000 Speaker 14: on the research side. We're running our own models and 780 00:39:20,040 --> 00:39:22,920 Speaker 14: has been have been doing so since since the inception 781 00:39:23,040 --> 00:39:23,720 Speaker 14: of the company. 782 00:39:23,880 --> 00:39:25,399 Speaker 1: But at the same time, the. 783 00:39:25,360 --> 00:39:28,680 Speaker 14: Type of customers that we're working with all of those 784 00:39:28,800 --> 00:39:33,080 Speaker 14: enterprises that actually need this kind of technology, they need 785 00:39:34,120 --> 00:39:37,080 Speaker 14: to we need to work with them, We need to 786 00:39:37,239 --> 00:39:41,400 Speaker 14: enable them to use this brand new AI uh and 787 00:39:41,400 --> 00:39:44,439 Speaker 14: and help them and in doing so. And that's that's 788 00:39:44,480 --> 00:39:47,840 Speaker 14: that's a very large part of where our investments go to. 789 00:39:50,239 --> 00:39:54,279 Speaker 4: What are the challenges with building language AI. You know 790 00:39:54,480 --> 00:39:57,080 Speaker 4: you're keen underscore it's not just sort of simple translation, 791 00:39:57,760 --> 00:40:00,880 Speaker 4: but it must mean training based on multiple data sets 792 00:40:01,320 --> 00:40:04,359 Speaker 4: and calling on multiple data sets and the inference side. 793 00:40:04,440 --> 00:40:05,719 Speaker 4: Just explain the challenge. 794 00:40:06,920 --> 00:40:09,480 Speaker 14: Yeah, it's it's it's first and foremost, it's all about 795 00:40:09,480 --> 00:40:12,160 Speaker 14: the accuracy and the quality. I mean, you want to 796 00:40:12,160 --> 00:40:14,800 Speaker 14: be sure and you want to be confident that whenever 797 00:40:14,840 --> 00:40:17,960 Speaker 14: you're communicating, like the right point comes across. And it's 798 00:40:18,120 --> 00:40:21,360 Speaker 14: and it's really not only about this accuracy itself, but 799 00:40:21,400 --> 00:40:23,960 Speaker 14: it's also about the fluency of the of the language 800 00:40:24,160 --> 00:40:28,000 Speaker 14: you want to convince. You want to make sure that 801 00:40:28,040 --> 00:40:31,560 Speaker 14: you're being understood as a professional. And for that you 802 00:40:31,640 --> 00:40:34,840 Speaker 14: don't only need models that are great at getting the 803 00:40:34,920 --> 00:40:37,360 Speaker 14: facts right, but you also have to go beyond that 804 00:40:37,560 --> 00:40:40,480 Speaker 14: and have all of that language fluency in that. And 805 00:40:40,520 --> 00:40:43,840 Speaker 14: that is then that makes really of the research work 806 00:40:43,880 --> 00:40:47,720 Speaker 14: that goes both on the data side that goes into 807 00:40:47,719 --> 00:40:52,720 Speaker 14: the algorithms, but also into the feedback loop and making 808 00:40:52,760 --> 00:41:00,279 Speaker 14: sure that that our human editors, our translators are during 809 00:41:00,320 --> 00:41:02,799 Speaker 14: the quality of of whatever comes out of the of 810 00:41:02,840 --> 00:41:03,839 Speaker 14: the I models. 811 00:41:04,480 --> 00:41:07,400 Speaker 4: You're joining us from Cologne, Germany. We've just shown a 812 00:41:07,440 --> 00:41:10,360 Speaker 4: graphic with all of the customers that you have, thousands 813 00:41:10,440 --> 00:41:11,319 Speaker 4: and thousands of them. 814 00:41:11,360 --> 00:41:12,840 Speaker 1: Where are you growing fastest? 815 00:41:13,200 --> 00:41:14,360 Speaker 4: Are you making money? 816 00:41:14,360 --> 00:41:15,400 Speaker 1: What does growth look like? 817 00:41:16,840 --> 00:41:17,680 Speaker 2: Yeah, we are. 818 00:41:17,719 --> 00:41:20,000 Speaker 14: We are a global company from the very beginning on, 819 00:41:20,080 --> 00:41:22,959 Speaker 14: and I think that's that's that's pretty understandable given given 820 00:41:23,000 --> 00:41:25,239 Speaker 14: the product it is. 821 00:41:25,280 --> 00:41:27,200 Speaker 1: It is really truly one. 822 00:41:27,120 --> 00:41:30,000 Speaker 14: Of the one of the very international products out there, 823 00:41:30,440 --> 00:41:35,320 Speaker 14: and key markets for US are Germany, Japan, and the US, 824 00:41:35,520 --> 00:41:39,920 Speaker 14: so it can't be more distributed than that. But the 825 00:41:40,040 --> 00:41:43,640 Speaker 14: US is a is a pretty strong foothold for us 826 00:41:44,000 --> 00:41:47,239 Speaker 14: right now. We've we've just opened an office a few 827 00:41:47,239 --> 00:41:51,560 Speaker 14: months ago in Austin and and will be working more 828 00:41:51,600 --> 00:41:54,359 Speaker 14: and more with WES companies that want to expand their 829 00:41:54,360 --> 00:41:55,360 Speaker 14: footprint globally. 830 00:41:57,080 --> 00:42:00,320 Speaker 4: Dep l C and founder yere At Katilovski. Great to 831 00:42:00,360 --> 00:42:10,400 Speaker 4: have you on the program here on Bloomberg Technology. Do 832 00:42:10,480 --> 00:42:12,160 Speaker 4: you have a place in the AIPC market. 833 00:42:12,239 --> 00:42:13,040 Speaker 14: I'm back next year. 834 00:42:14,040 --> 00:42:16,400 Speaker 6: There's exactly there are a bunch of bunch of the 835 00:42:16,520 --> 00:42:21,920 Speaker 6: video GPUs and delpcs, del workstations. All of our GPUs 836 00:42:22,400 --> 00:42:25,239 Speaker 6: have the same tensor cores that are running in h 837 00:42:25,360 --> 00:42:28,160 Speaker 6: one hundreds in the cloud, and so every one of 838 00:42:28,200 --> 00:42:29,800 Speaker 6: our GPUs use. 839 00:42:29,760 --> 00:42:31,080 Speaker 14: AI to do its work. 840 00:42:31,719 --> 00:42:36,879 Speaker 8: AI, of course is going to transform gaming in video. 841 00:42:36,960 --> 00:42:40,319 Speaker 4: CEO Jensen Huang being saved by his mate Michael Dell 842 00:42:40,680 --> 00:42:44,080 Speaker 4: and I guess, Caro, the question is move beyond data centers. 843 00:42:44,080 --> 00:42:46,640 Speaker 4: When does a video getting the CPU game in PCs 844 00:42:46,680 --> 00:42:47,920 Speaker 4: and go for Intel's market. 845 00:42:48,360 --> 00:42:49,680 Speaker 5: That was just what I heard. 846 00:42:50,520 --> 00:42:52,600 Speaker 3: Come back next year to hear ed and I know 847 00:42:52,640 --> 00:42:54,239 Speaker 3: you will be back there next year. But now we 848 00:42:54,320 --> 00:42:56,520 Speaker 3: brace ourselves on what the earnings are like after the 849 00:42:56,560 --> 00:42:58,879 Speaker 3: bell today, Then can they eclipse what the market wants 850 00:42:58,920 --> 00:43:02,400 Speaker 3: to see and more than two hundred percent increase in revenue? 851 00:43:02,560 --> 00:43:03,359 Speaker 5: We wait, we watch. 852 00:43:03,440 --> 00:43:07,040 Speaker 3: We also anticipate a statement coming from UK Prime Minister 853 00:43:07,120 --> 00:43:09,800 Speaker 3: Rishi Sunak as soon as the next five minutes. 854 00:43:09,920 --> 00:43:13,520 Speaker 5: Tune in. This is the Bloomberg technology