1 00:00:01,720 --> 00:00:02,600 Speaker 1: From Mahart. 2 00:00:02,600 --> 00:00:07,000 Speaker 2: We're Innovation of Money and Power Collie in Silicon Valley, NBN. 3 00:00:07,360 --> 00:00:20,880 Speaker 2: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 4 00:00:25,720 --> 00:00:28,240 Speaker 3: I'm Caroline Heind at Bloomberg's world headquarters in New York. 5 00:00:28,760 --> 00:00:32,360 Speaker 4: Ed Ludlow here in San Francisco. This is Bloomberg Technology. 6 00:00:32,000 --> 00:00:35,159 Speaker 3: Coming up this hour the AI hypercycle. 7 00:00:35,159 --> 00:00:38,760 Speaker 5: It's helping TSMC, the company reporting better than expected sales 8 00:00:38,800 --> 00:00:42,320 Speaker 5: on that boom in artificial intelligence applications demand. But can 9 00:00:42,360 --> 00:00:46,760 Speaker 5: it support the second half? We break down the numbers, plus. 10 00:00:46,479 --> 00:00:49,640 Speaker 4: The fate of microsoft sixty nine billion dollar takeover of 11 00:00:49,720 --> 00:00:52,479 Speaker 4: Activision Blizzard. It's in the hands of a judge, but 12 00:00:52,560 --> 00:00:55,680 Speaker 4: it was merger arbitrage specialist glued to the core in 13 00:00:55,760 --> 00:00:58,880 Speaker 4: San Francisco, some even paying line sitters to snag a 14 00:00:58,920 --> 00:01:00,760 Speaker 4: seat at high stakes hearing. 15 00:01:01,160 --> 00:01:03,960 Speaker 5: We have the story and Meta's newest social app, Threads 16 00:01:04,000 --> 00:01:06,160 Speaker 5: topping one hundred million users. 17 00:01:06,120 --> 00:01:07,240 Speaker 3: Less than five days. 18 00:01:07,800 --> 00:01:12,600 Speaker 5: Can this hype cycle hold for first TSMC, Let's bring 19 00:01:12,600 --> 00:01:15,160 Speaker 5: in our in house expert and another than bloom Legs, 20 00:01:15,240 --> 00:01:17,639 Speaker 5: Ian King, another brit to add to the mix today. 21 00:01:17,959 --> 00:01:21,440 Speaker 5: And Ian is it because we've seen the stock already 22 00:01:21,480 --> 00:01:23,360 Speaker 5: outperform so much that we're seeing a little bit of 23 00:01:23,360 --> 00:01:25,080 Speaker 5: weakness or is it more that we're worried about the 24 00:01:25,120 --> 00:01:25,720 Speaker 5: second half. 25 00:01:26,800 --> 00:01:28,760 Speaker 1: I mean it's a mixture of both there. 26 00:01:28,800 --> 00:01:31,880 Speaker 6: I mean, the way to look at this performances really well, 27 00:01:31,920 --> 00:01:34,680 Speaker 6: it's not as bad as people thought it might be. 28 00:01:35,120 --> 00:01:36,920 Speaker 1: I mean that there are no runaway. 29 00:01:36,520 --> 00:01:39,199 Speaker 6: Expectations of things getting better here. It's just like, well, 30 00:01:39,520 --> 00:01:41,800 Speaker 6: they kind of did better in a way that we 31 00:01:41,880 --> 00:01:44,960 Speaker 6: may have expected, and things really for the majority of 32 00:01:45,000 --> 00:01:48,360 Speaker 6: their business, which is really the smartphone industry, just still 33 00:01:48,400 --> 00:01:49,280 Speaker 6: doesn't look that good. 34 00:01:49,800 --> 00:01:53,760 Speaker 4: If we simplify the story, TSNC is a contract manufacturer. 35 00:01:53,800 --> 00:01:57,360 Speaker 4: It makes chips for others who design them. The story 36 00:01:57,480 --> 00:02:01,600 Speaker 4: smartphone PC bad it basically. 37 00:02:01,520 --> 00:02:03,240 Speaker 1: Yeah, no, that's absolutely it. 38 00:02:03,280 --> 00:02:06,120 Speaker 6: I mean forty percent of around forty percent of their 39 00:02:06,120 --> 00:02:09,000 Speaker 6: sales are from the smartphone industry, and that's going an 40 00:02:09,000 --> 00:02:11,680 Speaker 6: industry that's going to be down roughly ten percent this year, 41 00:02:12,120 --> 00:02:14,320 Speaker 6: so not not brilliant at all, and that obviously has 42 00:02:14,360 --> 00:02:18,440 Speaker 6: wider implications. On the other side, high performance computing, which 43 00:02:18,480 --> 00:02:20,639 Speaker 6: is PCs, but also the data center. 44 00:02:21,040 --> 00:02:22,360 Speaker 1: And as we've seen, if you've. 45 00:02:22,160 --> 00:02:24,959 Speaker 6: Got people like in video giving you orders, giving you 46 00:02:25,040 --> 00:02:27,280 Speaker 6: orders for very expensive chips and that's a good place 47 00:02:27,320 --> 00:02:27,520 Speaker 6: to be. 48 00:02:27,840 --> 00:02:30,840 Speaker 5: Yeah, it is, and for many the warriors and constraints 49 00:02:30,880 --> 00:02:32,919 Speaker 5: have been on the supply side. 50 00:02:33,120 --> 00:02:37,040 Speaker 3: How much is that t SMC is concerned right now? 51 00:02:37,639 --> 00:02:38,720 Speaker 1: Not so much anymore. 52 00:02:38,760 --> 00:02:41,480 Speaker 6: I mean, I think everybody would like to see, you know, 53 00:02:41,760 --> 00:02:45,240 Speaker 6: a lot more volume. What we're seeing in terms of 54 00:02:45,280 --> 00:02:49,000 Speaker 6: this better than expected performance is really a value story, 55 00:02:49,080 --> 00:02:51,519 Speaker 6: right you know, you're looking at a smartphone chip which 56 00:02:51,560 --> 00:02:55,480 Speaker 6: is thirty forty, you know, thirty four dollars ago compared 57 00:02:55,520 --> 00:02:58,440 Speaker 6: to some of Invideo's chips which are in the thousands. Obviously, 58 00:02:58,720 --> 00:03:01,560 Speaker 6: if you're selling chips that cost the thousands, then your 59 00:03:01,600 --> 00:03:04,200 Speaker 6: margins are really good and that helps your revenue where 60 00:03:04,200 --> 00:03:06,519 Speaker 6: really you want those factories full as well, and that's 61 00:03:06,560 --> 00:03:08,240 Speaker 6: going to get you, you know, your gross margins. 62 00:03:09,040 --> 00:03:11,320 Speaker 4: We've been talking a lot about an AI hype cycle, 63 00:03:11,840 --> 00:03:14,560 Speaker 4: but in Caroline this is interesting. Behind the scenes, Ian 64 00:03:14,639 --> 00:03:18,160 Speaker 4: has been teaching me things which sometimes goes well, sometimes 65 00:03:18,160 --> 00:03:21,080 Speaker 4: does in h one hundred a one hundred. These are 66 00:03:21,160 --> 00:03:25,080 Speaker 4: high power gpups, very specific use case to the training 67 00:03:25,120 --> 00:03:28,240 Speaker 4: and inference side of large language models. You still need 68 00:03:28,240 --> 00:03:29,360 Speaker 4: the memory chips. 69 00:03:29,400 --> 00:03:29,800 Speaker 1: Correct. 70 00:03:29,840 --> 00:03:34,040 Speaker 4: Why explain why being on the same board GPU, CPU 71 00:03:34,600 --> 00:03:35,800 Speaker 4: DRAM is important. 72 00:03:36,040 --> 00:03:38,600 Speaker 6: It's all about data sets, right. The bigger the data set, 73 00:03:38,640 --> 00:03:42,640 Speaker 6: the more parameters, the more information, the better your calculations are, 74 00:03:42,680 --> 00:03:44,320 Speaker 6: the better you can train it, and the better your 75 00:03:44,320 --> 00:03:47,800 Speaker 6: influences down the line. What do you need to handle 76 00:03:47,840 --> 00:03:51,200 Speaker 6: those larger data sets? Better storage and bigger lumps of 77 00:03:51,240 --> 00:03:56,480 Speaker 6: memory that sit next to those processes. So in theory, Micron, SAMs, Heinix, 78 00:03:56,520 --> 00:04:00,000 Speaker 6: all of those other companies should be benefiting from this. Unfortunately, 79 00:04:00,560 --> 00:04:02,440 Speaker 6: smartphones PC's not great. 80 00:04:03,000 --> 00:04:06,160 Speaker 4: Bloombo c and King mister Chip we call him, thank 81 00:04:06,160 --> 00:04:08,920 Speaker 4: you very much. Now, coming up, what the fate of 82 00:04:08,960 --> 00:04:12,119 Speaker 4: the Microsoft Activision deal in the video game space has 83 00:04:12,160 --> 00:04:14,280 Speaker 4: in store for that industry at large. We're going to 84 00:04:14,280 --> 00:04:17,240 Speaker 4: discuss all that with Convoy Ventures Josh Chapman, a VC 85 00:04:17,640 --> 00:04:21,560 Speaker 4: whose singular focus is the video games industry. Speaking of 86 00:04:21,600 --> 00:04:24,160 Speaker 4: take a look at shares of both Microsoft and Activision. 87 00:04:24,560 --> 00:04:29,240 Speaker 4: We expect imminently the judge to decide an outcome in 88 00:04:29,320 --> 00:04:34,160 Speaker 4: the FTC hearing or trial for a temporary block on 89 00:04:34,200 --> 00:04:37,560 Speaker 4: that deal. Activision off half a percentage point, a soft 90 00:04:37,640 --> 00:04:41,359 Speaker 4: proxy for the direction of travel where investors see this going. 91 00:04:41,520 --> 00:04:44,159 Speaker 4: But as we've discussed Microsoft down two point six percent, 92 00:04:44,440 --> 00:04:46,960 Speaker 4: really big points drag on the nasat one hundred. This 93 00:04:47,640 --> 00:05:04,279 Speaker 4: is Bloombog Technology. The Microsoft Activision deal is the biggest 94 00:05:04,360 --> 00:05:07,560 Speaker 4: video gaming and tech deal ever, and it's pretty important 95 00:05:07,920 --> 00:05:12,159 Speaker 4: for the arbitrage community too. Portfolio managers, analysts, and specialized 96 00:05:12,200 --> 00:05:15,720 Speaker 4: brokers who bet on whether mergers will go through flew 97 00:05:15,760 --> 00:05:17,800 Speaker 4: in from all over the country to sit in on 98 00:05:17,839 --> 00:05:21,400 Speaker 4: the hearing over Microsoft's takeover deal here in San Francisco. 99 00:05:21,600 --> 00:05:24,520 Speaker 4: Bloomberg's Maltin Nayak is here for more. So you've spent 100 00:05:24,720 --> 00:05:26,600 Speaker 4: days and days in that court room. 101 00:05:27,200 --> 00:05:28,920 Speaker 3: It was a five day hearing, yes. 102 00:05:29,520 --> 00:05:32,719 Speaker 4: Alongside some suited and booted individuals. Who are they and 103 00:05:32,720 --> 00:05:33,320 Speaker 4: why they're there? 104 00:05:33,520 --> 00:05:37,400 Speaker 7: So, you know, I was interested to see some linstitters 105 00:05:37,520 --> 00:05:40,720 Speaker 7: and then I noticed that they made way for folks 106 00:05:40,200 --> 00:05:43,880 Speaker 7: in suits and formal clothes and they really stood out 107 00:05:43,920 --> 00:05:46,080 Speaker 7: in San Francisco. So I was wondering, you know, who 108 00:05:46,120 --> 00:05:47,839 Speaker 7: they were, And it turns out that they were merger 109 00:05:47,920 --> 00:05:51,279 Speaker 7: arbitragers and analysts, you know who were there to just 110 00:05:51,320 --> 00:05:53,760 Speaker 7: see what was going on, and they were taking notes, 111 00:05:53,839 --> 00:05:57,520 Speaker 7: you know, feverishly and keeping a very close eye on, 112 00:05:57,920 --> 00:06:00,400 Speaker 7: you know, all the little grimaces and and sort of 113 00:06:00,400 --> 00:06:02,560 Speaker 7: the body language of the judge. 114 00:06:02,279 --> 00:06:05,880 Speaker 5: New Yorkers be busted and go to herself in your suits. 115 00:06:06,520 --> 00:06:10,120 Speaker 5: Malthe what's interesting is, of course they're trying to discern 116 00:06:10,560 --> 00:06:15,640 Speaker 5: any frowns, any facial movements, basically how this court case 117 00:06:15,720 --> 00:06:19,440 Speaker 5: is going. From your writing, it seems that perhaps not 118 00:06:19,600 --> 00:06:22,720 Speaker 5: to or the FDC's advantage. 119 00:06:23,080 --> 00:06:25,760 Speaker 7: It definitely seems like from the last round of questioning 120 00:06:25,800 --> 00:06:28,279 Speaker 7: by the judge in the last day, she really grilled 121 00:06:28,320 --> 00:06:30,680 Speaker 7: the FTC and asked for a lot of evidence that 122 00:06:30,880 --> 00:06:33,680 Speaker 7: wasn't really produced in court. So it definitely seems like 123 00:06:33,920 --> 00:06:36,800 Speaker 7: right now the companies have the upper hand. But you 124 00:06:36,920 --> 00:06:39,800 Speaker 7: never know with judges, some of them, you know, they 125 00:06:39,839 --> 00:06:41,680 Speaker 7: may ask a lot of questions in court, but they 126 00:06:41,720 --> 00:06:44,320 Speaker 7: won't go very far in terms of taking drastic steps 127 00:06:44,360 --> 00:06:46,480 Speaker 7: like blocking a deal. So we'll have to see how 128 00:06:46,480 --> 00:06:49,120 Speaker 7: it all plays out. But definitely the sentiment now is 129 00:06:49,200 --> 00:06:52,719 Speaker 7: among the ubtraged community even that perhaps the companies right 130 00:06:52,720 --> 00:06:53,960 Speaker 7: now things are looking. 131 00:06:53,760 --> 00:06:54,400 Speaker 3: Good for them. 132 00:06:54,520 --> 00:06:56,440 Speaker 4: So there are some things we need to consider about 133 00:06:56,880 --> 00:07:00,960 Speaker 4: outcomes and scenarios if this goes in Microsoft and acts 134 00:07:01,000 --> 00:07:03,760 Speaker 4: visions favor. There is a deadline on the deal. We've 135 00:07:03,760 --> 00:07:06,479 Speaker 4: got to think about that. The UK has a completely 136 00:07:06,480 --> 00:07:09,159 Speaker 4: separate regulatory process going bring us up to speed on 137 00:07:09,200 --> 00:07:10,280 Speaker 4: what might happen next. 138 00:07:10,520 --> 00:07:13,480 Speaker 7: So we have this July eighteen deadline that's looming and 139 00:07:13,520 --> 00:07:16,040 Speaker 7: the companies have to move quickly to close the deal. 140 00:07:16,320 --> 00:07:19,040 Speaker 7: So everyone is waiting for this ruling to come down 141 00:07:19,040 --> 00:07:21,400 Speaker 7: from the judge. That will sort of be the first 142 00:07:21,480 --> 00:07:23,720 Speaker 7: round in the fight with the FTC, which is trying 143 00:07:23,720 --> 00:07:27,240 Speaker 7: to temporarily block its deal. Block the deal right now. 144 00:07:27,600 --> 00:07:32,640 Speaker 7: So also UK regulators have said that this deal won't 145 00:07:32,840 --> 00:07:34,920 Speaker 7: you know, have said that this deal can't go through. 146 00:07:35,200 --> 00:07:38,160 Speaker 7: So we have Microsoft which is appealed that decision. We'll 147 00:07:38,160 --> 00:07:40,040 Speaker 7: have to see what happens in terms of how they 148 00:07:40,040 --> 00:07:43,000 Speaker 7: plan to close the deal around that. There could be 149 00:07:43,080 --> 00:07:45,560 Speaker 7: a scenario where they just decide to go ahead and 150 00:07:45,560 --> 00:07:48,400 Speaker 7: close the deal, but you know, keep the board the 151 00:07:48,520 --> 00:07:49,880 Speaker 7: entity separate for a while. 152 00:07:50,120 --> 00:07:51,040 Speaker 3: Perhaps they could do. 153 00:07:51,040 --> 00:07:53,160 Speaker 7: That globally or only in the UK. So we'll have 154 00:07:53,200 --> 00:07:55,440 Speaker 7: to see how that works out in terms of how 155 00:07:55,640 --> 00:07:58,680 Speaker 7: you know, they work around the UK decision and sort 156 00:07:58,680 --> 00:08:00,240 Speaker 7: of the appeals process around that. 157 00:08:00,640 --> 00:08:03,560 Speaker 5: July eighteenth clock is ticking. Mauthy Nayak, thank you so 158 00:08:03,680 --> 00:08:06,280 Speaker 5: much for bringing us, well the legal expertise, what's happening 159 00:08:06,320 --> 00:08:08,560 Speaker 5: in the courtroom, let's look at how it affects the ecosystem. 160 00:08:08,560 --> 00:08:10,920 Speaker 5: When please to welcome Josh Chapman, managing partner at Convoy 161 00:08:10,920 --> 00:08:14,560 Speaker 5: It's and Denver based VC firm investing in all things gaming. Josh, 162 00:08:14,600 --> 00:08:16,760 Speaker 5: great to have you back on. And does this affect 163 00:08:17,080 --> 00:08:18,080 Speaker 5: the ecosystem? 164 00:08:18,160 --> 00:08:21,800 Speaker 3: If it doesn't go through, it does affect the ecosystem. 165 00:08:21,880 --> 00:08:25,440 Speaker 8: So right now there's about forty five billion that's sitting 166 00:08:25,480 --> 00:08:28,680 Speaker 8: in cash on the gaming corporates, and then secondarily there's 167 00:08:28,680 --> 00:08:31,040 Speaker 8: about one hundred and seventy five billion that's sitting on 168 00:08:31,120 --> 00:08:35,000 Speaker 8: the balance sheets of tech companies that have gaming divisions, 169 00:08:35,040 --> 00:08:39,280 Speaker 8: such as Meta or Microsoft. So this ruling certainly does 170 00:08:39,360 --> 00:08:41,680 Speaker 8: have quite an impact on what's going to happen next 171 00:08:41,679 --> 00:08:45,760 Speaker 8: around vertical integration and m and A in the gaming industry. 172 00:08:46,200 --> 00:08:48,560 Speaker 5: And of course ed the argument, for example in the 173 00:08:48,640 --> 00:08:53,359 Speaker 5: UK has been about the cloud element is gaming in particular. 174 00:08:54,320 --> 00:08:56,319 Speaker 4: Yeah, And what's so interesting is when I had Bobby 175 00:08:56,360 --> 00:08:58,560 Speaker 4: Kotik on the show a few weeks ago, he said, 176 00:08:58,559 --> 00:09:01,520 Speaker 4: this isn't anything to do with cloud. It's a tiny market. 177 00:09:01,679 --> 00:09:04,440 Speaker 4: We're not there yet. We write time and again Josh 178 00:09:04,520 --> 00:09:06,680 Speaker 4: and all blame bag news stories that this deal will 179 00:09:06,720 --> 00:09:10,520 Speaker 4: reshape the landscape for video games as an industry. 180 00:09:11,080 --> 00:09:14,880 Speaker 8: Does it in part, It doesn't reshape it in the 181 00:09:14,920 --> 00:09:18,520 Speaker 8: sense of sort of upending the entire console market. If anything, 182 00:09:18,559 --> 00:09:23,120 Speaker 8: this is a continuation of very competitive and healthy competitive markets. 183 00:09:23,160 --> 00:09:23,319 Speaker 9: Right. 184 00:09:23,400 --> 00:09:25,800 Speaker 8: I personally, I'm on the side of I don't believe 185 00:09:25,840 --> 00:09:29,200 Speaker 8: that this acquisition is anti competitive, and so from a 186 00:09:29,200 --> 00:09:34,120 Speaker 8: cloud gaming perspective, Microsoft actually is the perfect cloud infrastructure 187 00:09:34,160 --> 00:09:37,360 Speaker 8: platform to build an entirely new market. In addition to 188 00:09:37,400 --> 00:09:40,720 Speaker 8: not only cloud gaming seeing more competition, and you also 189 00:09:40,800 --> 00:09:43,560 Speaker 8: see the rise of more competition in the mobile market 190 00:09:43,559 --> 00:09:46,240 Speaker 8: the Xbox game Pass mobile store that they want to launch. 191 00:09:46,320 --> 00:09:50,480 Speaker 8: So this adds a ton of competition already to the industry, 192 00:09:51,200 --> 00:09:53,480 Speaker 8: and so it certainly, you know, they could actually be 193 00:09:53,559 --> 00:09:56,400 Speaker 8: responsible for creating an entire cloud gaming market. 194 00:09:57,800 --> 00:10:01,000 Speaker 4: When you think about some of the compromises offering Call 195 00:10:01,040 --> 00:10:05,079 Speaker 4: of Duty on other platforms as an example, what kind 196 00:10:05,080 --> 00:10:07,920 Speaker 4: of disruption does that give the industry? Because it changes 197 00:10:09,360 --> 00:10:13,400 Speaker 4: how players have historically accessed those games. There's a lot 198 00:10:13,440 --> 00:10:17,079 Speaker 4: of loyalty, right you're either PlayStation, Xbox, you're a PC gamer, 199 00:10:17,440 --> 00:10:18,719 Speaker 4: does that mess things up a bit? 200 00:10:20,080 --> 00:10:21,080 Speaker 1: I don't think it messes it up. 201 00:10:21,080 --> 00:10:23,559 Speaker 8: If anything, I think it actually keeps the plane field 202 00:10:23,559 --> 00:10:26,320 Speaker 8: across gaming, specifically with Call of Duty, which is really 203 00:10:26,360 --> 00:10:29,280 Speaker 8: the number one topic right now. Right now, Call of 204 00:10:29,360 --> 00:10:32,920 Speaker 8: Duty is played by about a million people on PlayStation, 205 00:10:33,080 --> 00:10:37,240 Speaker 8: So from an economic incentive perspective, Xbox has no economic 206 00:10:37,720 --> 00:10:40,920 Speaker 8: incentive to remove that game from Sony, and so Sony 207 00:10:41,280 --> 00:10:44,080 Speaker 8: wants Call of Dude to succeed, and so does Microsoft 208 00:10:44,120 --> 00:10:47,000 Speaker 8: and definitely obviously the activision Blizzard. So when it comes 209 00:10:47,080 --> 00:10:50,320 Speaker 8: to what's next, I think that you could see the 210 00:10:50,440 --> 00:10:53,959 Speaker 8: rise of cloud gaming being a sort of more affordable 211 00:10:54,000 --> 00:10:56,720 Speaker 8: way for people to gain which would be great for 212 00:10:56,760 --> 00:10:59,080 Speaker 8: people who don't want to buy, you know, and invest 213 00:10:59,360 --> 00:11:02,320 Speaker 8: five hundred to fifteen hundred dollars into an Xbox or 214 00:11:02,480 --> 00:11:05,520 Speaker 8: the latest PlayStation. So I think this is a netwind 215 00:11:05,520 --> 00:11:07,480 Speaker 8: for the consumer. I think over time you're going to 216 00:11:07,480 --> 00:11:12,000 Speaker 8: see increasingly more cross platform games like Fortnite, like Call 217 00:11:12,040 --> 00:11:14,839 Speaker 8: of Duty, and I think that that will continue and 218 00:11:15,040 --> 00:11:18,319 Speaker 8: gaming is going to thrive under the approval of this acquisition. 219 00:11:19,040 --> 00:11:22,440 Speaker 5: Okay, Josh, if it doesn't get approved, how does that 220 00:11:22,559 --> 00:11:24,600 Speaker 5: change your own investment thesis? 221 00:11:24,679 --> 00:11:26,720 Speaker 3: Do you hold back in terms of writing checks At 222 00:11:26,720 --> 00:11:27,280 Speaker 3: the moment. 223 00:11:28,440 --> 00:11:30,959 Speaker 8: We wouldn't hold back, So we focus on seeds series 224 00:11:30,960 --> 00:11:33,960 Speaker 8: of investing, writing one to five million dollar checks primarily 225 00:11:34,000 --> 00:11:37,800 Speaker 8: into technologies, platforms, and infrastructure of the gaming industry. So 226 00:11:37,960 --> 00:11:40,520 Speaker 8: we will continue investing in at early stage, which is 227 00:11:40,840 --> 00:11:43,719 Speaker 8: proveing to be quite vibrant right now. But from an 228 00:11:43,960 --> 00:11:46,199 Speaker 8: m and a landscape for our companies once they get 229 00:11:46,200 --> 00:11:49,160 Speaker 8: to later stages that is actually where this could have 230 00:11:49,200 --> 00:11:53,760 Speaker 8: an impact. Is are vertical integrations and mergers and acquisitions 231 00:11:53,880 --> 00:11:56,480 Speaker 8: going to be approved? And I think, as I mentioned earlier, 232 00:11:56,480 --> 00:11:58,440 Speaker 8: with some of the cash that's out there waiting to 233 00:11:58,480 --> 00:12:04,000 Speaker 8: acquire great companies, they might be more hesitant because of 234 00:12:04,040 --> 00:12:07,680 Speaker 8: regulatory concerns versus pure economic deal making. 235 00:12:08,720 --> 00:12:11,240 Speaker 4: Josh, this is the first opportunity I've had to ask 236 00:12:11,320 --> 00:12:14,640 Speaker 4: you about Apple Vision pro and gaming. What do you 237 00:12:14,679 --> 00:12:15,160 Speaker 4: make of it? 238 00:12:16,360 --> 00:12:18,280 Speaker 8: I think we're a long way off from the vision 239 00:12:18,280 --> 00:12:22,240 Speaker 8: that we saw pitched. I think that it is very cool. 240 00:12:22,480 --> 00:12:26,400 Speaker 8: This is going to be great for very solo experiences. 241 00:12:26,920 --> 00:12:29,120 Speaker 8: But I view this as sort of one of the 242 00:12:29,920 --> 00:12:31,800 Speaker 8: just like the first Oculis that came out. If you 243 00:12:31,800 --> 00:12:34,720 Speaker 8: remember that ed everyone was very excited about it, but 244 00:12:34,760 --> 00:12:38,280 Speaker 8: true adoption didn't happen until five six, eight years later, 245 00:12:38,640 --> 00:12:42,080 Speaker 8: so I think that the augmented reality world is coming 246 00:12:42,280 --> 00:12:46,360 Speaker 8: and will absolutely be here. We actually got excited about 247 00:12:46,360 --> 00:12:48,439 Speaker 8: one of the other announcements they made where they are 248 00:12:48,480 --> 00:12:53,079 Speaker 8: actually launching a gaming operating system to turn their PC 249 00:12:53,280 --> 00:12:57,280 Speaker 8: industry with the Mac into a gaming platform. That actually 250 00:12:57,400 --> 00:12:59,000 Speaker 8: kind of went under the radar but was one of 251 00:12:59,000 --> 00:13:01,560 Speaker 8: the most exciting things you were watching is that they're 252 00:13:01,600 --> 00:13:05,160 Speaker 8: opening up the ability to run high performing games on 253 00:13:05,240 --> 00:13:08,280 Speaker 8: top of Max. That will be much more impactful. I 254 00:13:08,280 --> 00:13:10,480 Speaker 8: think in four or five six years the Vision pro 255 00:13:10,920 --> 00:13:14,400 Speaker 8: will enter certainly a more affordable price entry point for 256 00:13:14,440 --> 00:13:18,200 Speaker 8: the consumer. But like many things, this is about wow 257 00:13:18,400 --> 00:13:21,680 Speaker 8: now and then leader CEMTS Adoption. 258 00:13:22,559 --> 00:13:26,679 Speaker 4: Josh Chapman, managing partner at Convoy also talking all things Activision, Microsoft, 259 00:13:26,679 --> 00:13:29,240 Speaker 4: and we are expecting any day now the judge to 260 00:13:29,280 --> 00:13:41,520 Speaker 4: make a decision in that FTC pause request time for 261 00:13:41,600 --> 00:13:44,200 Speaker 4: talking tech First up and ton Ali Barbara are paying 262 00:13:44,240 --> 00:13:47,800 Speaker 4: the price for Jackmar's clash with the Chinese government. On Friday, 263 00:13:48,080 --> 00:13:51,360 Speaker 4: Beijing announced a nearly one billion dollar fine four ANC Group, 264 00:13:51,480 --> 00:13:53,880 Speaker 4: but it's signaled an end to a regulatory crackdown. Now, 265 00:13:53,920 --> 00:13:57,120 Speaker 4: Jack Mars, Ali Barba and out Group have collectively lost 266 00:13:57,120 --> 00:14:00,600 Speaker 4: eight hundred and fifty billion dollars in value since twenty 267 00:14:00,760 --> 00:14:03,400 Speaker 4: with Ali Barber's market value off by about six hundred 268 00:14:03,400 --> 00:14:06,200 Speaker 4: and twenty billion from its peak, according to data compiled 269 00:14:06,200 --> 00:14:09,600 Speaker 4: by Bloomberg. And Foxcott has determined it will not move 270 00:14:09,640 --> 00:14:13,280 Speaker 4: forward on a semiconductor joint venture with India's the Dana. 271 00:14:13,440 --> 00:14:16,600 Speaker 4: Fox Conn said the company aims at exploring more diverse 272 00:14:16,640 --> 00:14:19,800 Speaker 4: development opportunities and that the company is working to remove 273 00:14:19,840 --> 00:14:22,280 Speaker 4: the fox Conn name from what is now a fully 274 00:14:22,280 --> 00:14:26,080 Speaker 4: owned entity of the Danta. Plus. Amazon's annual Prime Day 275 00:14:26,120 --> 00:14:28,280 Speaker 4: isn't as popular as it used to be in the 276 00:14:28,320 --> 00:14:31,680 Speaker 4: past four years. The stock's actually fallen in the week 277 00:14:31,720 --> 00:14:34,920 Speaker 4: of the two dal day sale as investors' focus has 278 00:14:34,920 --> 00:14:38,520 Speaker 4: shifted to aws It's cloud computing business. One to watch 279 00:14:38,560 --> 00:14:39,400 Speaker 4: Carol it. 280 00:14:39,400 --> 00:14:41,880 Speaker 5: Is and let's stick on how Amazon is clearly a 281 00:14:41,920 --> 00:14:45,040 Speaker 5: company that's been embracing the world of AI like everything, 282 00:14:45,240 --> 00:14:48,120 Speaker 5: but also we'll slowly been trying to embrace healthcare offerings too. 283 00:14:48,360 --> 00:14:49,360 Speaker 3: We want to talk about. 284 00:14:49,120 --> 00:14:51,320 Speaker 5: That insect a little bit more, particularly as we recently 285 00:14:51,320 --> 00:14:54,920 Speaker 5: saw in Connecticut, consumers getting more control over how businesses 286 00:14:55,000 --> 00:14:57,840 Speaker 5: treat information about their health and data, particularly for those 287 00:14:57,960 --> 00:15:00,720 Speaker 5: aged under eighteen. Lawmakers added the new LO for example, 288 00:15:00,720 --> 00:15:03,120 Speaker 5: to the Connecticut Data Privacy Act, which to affect July. 289 00:15:03,200 --> 00:15:06,720 Speaker 5: The first joining us now about insect AI and healthcare 290 00:15:06,840 --> 00:15:08,200 Speaker 5: is an expert in the field. 291 00:15:08,360 --> 00:15:09,360 Speaker 3: When it's Sony. 292 00:15:09,200 --> 00:15:13,680 Speaker 5: SUKEAI founder and CEO, you're really trying to basically give 293 00:15:13,720 --> 00:15:18,320 Speaker 5: advantages to clinicians, in particular AI helping streamline their tasks. 294 00:15:18,440 --> 00:15:21,040 Speaker 5: How much has data and privacy been one that you've had. 295 00:15:20,840 --> 00:15:23,400 Speaker 4: To navigate significant? 296 00:15:23,480 --> 00:15:25,920 Speaker 10: I mean, you know, to be honest, the biggest public 297 00:15:25,920 --> 00:15:28,720 Speaker 10: health crisis in this country that few people talk about 298 00:15:28,800 --> 00:15:31,640 Speaker 10: is the fact that clinicians are burning out. I would 299 00:15:31,640 --> 00:15:34,800 Speaker 10: say eighty eight percent of all clinicians, by some recent 300 00:15:34,840 --> 00:15:38,520 Speaker 10: study don't recommend their own profession to their kids anymore. 301 00:15:38,880 --> 00:15:41,120 Speaker 10: For every hour of clinical work you do, you spend 302 00:15:41,160 --> 00:15:42,960 Speaker 10: two hours doing administrative work. 303 00:15:43,200 --> 00:15:44,400 Speaker 1: So it's super important to. 304 00:15:44,320 --> 00:15:47,680 Speaker 10: Actually figure out how to use AI in healthcare. And 305 00:15:47,720 --> 00:15:49,760 Speaker 10: the first place where you will start is probably an 306 00:15:49,800 --> 00:15:53,040 Speaker 10: automating clinician workflows. If you're going to do that, then 307 00:15:53,080 --> 00:15:56,360 Speaker 10: privacy data, how do you actually retain it and take 308 00:15:56,400 --> 00:15:58,000 Speaker 10: care of it becomes paramount. 309 00:15:58,560 --> 00:16:01,280 Speaker 4: We are learning on blibag technology and we have been 310 00:16:01,400 --> 00:16:04,200 Speaker 4: for a year now that when you're building a large 311 00:16:04,280 --> 00:16:08,080 Speaker 4: language model or foundation model, the quality and volume of 312 00:16:08,160 --> 00:16:12,480 Speaker 4: data is key. So how do you reconcile the need 313 00:16:12,520 --> 00:16:15,400 Speaker 4: for privacy with the need for massive volumes of data 314 00:16:15,960 --> 00:16:18,040 Speaker 4: to develop next generation technology? 315 00:16:18,560 --> 00:16:20,640 Speaker 10: You know, first of all, it's actually important to actually 316 00:16:20,640 --> 00:16:21,440 Speaker 10: talk about what. 317 00:16:21,320 --> 00:16:22,120 Speaker 9: AI really is. 318 00:16:22,160 --> 00:16:23,040 Speaker 1: The truth is here. 319 00:16:23,240 --> 00:16:25,880 Speaker 10: You know, even a dog walking app has AI these days, 320 00:16:25,880 --> 00:16:27,320 Speaker 10: and so it's a little bit of a hype cycle 321 00:16:27,360 --> 00:16:30,000 Speaker 10: that's going on. AI is a very broad term and 322 00:16:30,440 --> 00:16:34,520 Speaker 10: it goes all the way from speech technologies, speech transcription technologies, 323 00:16:34,960 --> 00:16:39,200 Speaker 10: understanding your commands, so intent extractors, large language models is 324 00:16:39,240 --> 00:16:41,960 Speaker 10: just the next generation of language model work that's happened 325 00:16:41,960 --> 00:16:45,080 Speaker 10: in AI. You can actually make a big dent in 326 00:16:45,120 --> 00:16:48,280 Speaker 10: clinical workflows by using all of these in some variety 327 00:16:48,320 --> 00:16:52,480 Speaker 10: of fashions. The truth is that the data in healthcare 328 00:16:52,560 --> 00:16:55,440 Speaker 10: is very discrete and very specific, and so therefore you 329 00:16:55,480 --> 00:16:59,280 Speaker 10: don't need tremendously large bodies of data to be able 330 00:16:59,320 --> 00:17:01,320 Speaker 10: to create that actually works in this way. 331 00:17:01,600 --> 00:17:03,840 Speaker 4: Caroline, I think we have so many founders on this show, 332 00:17:04,359 --> 00:17:06,679 Speaker 4: so many bench cats, lists and we do talk broadly 333 00:17:06,680 --> 00:17:10,720 Speaker 4: about AI. To be fair, we also find vcs that 334 00:17:10,760 --> 00:17:14,720 Speaker 4: are very specifically focused on where they're putting money thematically 335 00:17:15,040 --> 00:17:16,040 Speaker 4: or in a narrow field. 336 00:17:16,280 --> 00:17:18,879 Speaker 5: So right, think about the applications in the legal field, 337 00:17:18,880 --> 00:17:21,560 Speaker 5: we think about your application in the healthcare field, pun it. 338 00:17:21,680 --> 00:17:24,320 Speaker 5: And to that end, have you been taking a lot 339 00:17:24,359 --> 00:17:26,520 Speaker 5: more calls from people wanting to put money to work 340 00:17:26,520 --> 00:17:28,520 Speaker 5: in your own company? I know you raised funds, it's 341 00:17:28,520 --> 00:17:31,080 Speaker 5: a significant round what back in twenty twenty one, but 342 00:17:31,160 --> 00:17:34,240 Speaker 5: have you been looking at adding on in this particular realm? 343 00:17:34,920 --> 00:17:37,800 Speaker 10: Yeah, I mean the right time, that will actually become 344 00:17:37,880 --> 00:17:40,320 Speaker 10: very important, and you know, as you scale the company 345 00:17:40,320 --> 00:17:42,160 Speaker 10: you have to do that. I think the primary question 346 00:17:42,240 --> 00:17:44,399 Speaker 10: to ask is what problem are you really trying to solve? 347 00:17:45,080 --> 00:17:47,800 Speaker 10: First and foremost, we want to make sure that clinicians 348 00:17:47,840 --> 00:17:51,919 Speaker 10: don't have to really focus on these screens and typing 349 00:17:51,920 --> 00:17:53,920 Speaker 10: and checking boxes when they're actually talking. 350 00:17:53,720 --> 00:17:54,280 Speaker 1: To the patient. 351 00:17:54,520 --> 00:17:56,520 Speaker 10: I mean, imagine a world when you walk in and 352 00:17:56,560 --> 00:17:59,159 Speaker 10: you just ask, you know, Suki, which is kind of 353 00:17:59,200 --> 00:18:02,760 Speaker 10: like a SERI for doctors, what's my day like? And 354 00:18:02,800 --> 00:18:05,280 Speaker 10: it tells you here's the next few patients you're coming in, said, 355 00:18:05,280 --> 00:18:07,040 Speaker 10: give me a summary of the next patient, show me 356 00:18:07,080 --> 00:18:09,600 Speaker 10: the chest text ray, summarize the findings for me, and 357 00:18:09,640 --> 00:18:11,600 Speaker 10: then hey, let's just edit it a little bit and 358 00:18:11,640 --> 00:18:14,000 Speaker 10: push it into the underlying system of record well quickly. 359 00:18:14,080 --> 00:18:17,440 Speaker 4: The idea is that Suki does not replace human doctors, 360 00:18:17,600 --> 00:18:21,760 Speaker 4: but it is an assistant, an aid. Yes, mechanically, how 361 00:18:21,800 --> 00:18:22,399 Speaker 4: does that work? 362 00:18:22,480 --> 00:18:25,960 Speaker 10: Well, I mean, you know, imagine having something that's always 363 00:18:26,000 --> 00:18:29,440 Speaker 10: with you everywhere you go, and when you walk into 364 00:18:29,480 --> 00:18:31,639 Speaker 10: an exam room you ask it to give a summary 365 00:18:31,640 --> 00:18:34,240 Speaker 10: of the patient. When you are talking to the patient, 366 00:18:34,280 --> 00:18:36,560 Speaker 10: what about you ask Suki to pay attention and if 367 00:18:36,560 --> 00:18:38,760 Speaker 10: you'll take the doctor, patient and counter and create a 368 00:18:38,760 --> 00:18:41,119 Speaker 10: clinical note out of it. What if you edit it, 369 00:18:41,200 --> 00:18:43,680 Speaker 10: add some orders and then you say, Suki, just submit 370 00:18:43,720 --> 00:18:47,000 Speaker 10: that to the system of record. That is an assistant 371 00:18:47,160 --> 00:18:49,280 Speaker 10: for the doctor. And I think that is the future 372 00:18:49,280 --> 00:18:50,640 Speaker 10: of where healthcare text is going to be going. 373 00:18:50,840 --> 00:18:54,320 Speaker 4: Suk I founder and CEO PUNIT Sony Caroline. This is 374 00:18:54,320 --> 00:18:55,960 Speaker 4: a topic on the show that we are going to 375 00:18:55,960 --> 00:18:59,320 Speaker 4: continue talking about health in artificial intelligence. 376 00:19:06,440 --> 00:19:08,520 Speaker 5: Welcome back to lu Meg Technology. I'm Karine Hide in 377 00:19:08,520 --> 00:19:10,280 Speaker 5: New York and let's get you a quick update on 378 00:19:10,359 --> 00:19:12,719 Speaker 5: the public markets, right now halfway through the trading day, 379 00:19:12,760 --> 00:19:14,600 Speaker 5: and look a little bit more caution here in the 380 00:19:14,680 --> 00:19:17,360 Speaker 5: United States, particularly in big tech look than NASDAK more 381 00:19:17,400 --> 00:19:19,919 Speaker 5: broadly as a benchmark off by some three tenths and percent. 382 00:19:19,960 --> 00:19:22,240 Speaker 3: We've got well a very healthy jobs data that was 383 00:19:22,280 --> 00:19:23,200 Speaker 3: clear from last week. 384 00:19:23,400 --> 00:19:26,120 Speaker 5: Now we look ahead to what the CPI print inflation data. 385 00:19:25,920 --> 00:19:26,640 Speaker 3: Is going to show US. 386 00:19:26,560 --> 00:19:29,280 Speaker 5: And the Federal reserves speak ahead of them going silent 387 00:19:29,320 --> 00:19:32,400 Speaker 5: before they make the latest rate hike decision as many 388 00:19:32,440 --> 00:19:34,800 Speaker 5: are bracing for. I'm looking at the MASCI or Country 389 00:19:34,840 --> 00:19:35,320 Speaker 5: World Index. 390 00:19:35,320 --> 00:19:36,560 Speaker 3: So interestingly Europe. 391 00:19:36,240 --> 00:19:38,560 Speaker 5: Managed to pick itself up after a few days of 392 00:19:38,600 --> 00:19:41,480 Speaker 5: sell off. The macro data really out there showing that 393 00:19:41,640 --> 00:19:44,000 Speaker 5: Europe could do with a little bit more purchasing power. 394 00:19:44,000 --> 00:19:45,240 Speaker 5: On the least on the day, were seeing that the 395 00:19:45,280 --> 00:19:48,440 Speaker 5: Golden Dragon index in China showing actually there was a 396 00:19:48,440 --> 00:19:51,480 Speaker 5: little bit of a lift in Asia trading Chinese stocks 397 00:19:51,520 --> 00:19:53,199 Speaker 5: on the higher side, at least those traded in the 398 00:19:53,200 --> 00:19:55,280 Speaker 5: tech world here in the United States are one point 399 00:19:55,320 --> 00:19:58,119 Speaker 5: two percent. As we understand, the Chinese government in particular 400 00:19:58,240 --> 00:20:00,439 Speaker 5: is going to be showing some support to its property sector. 401 00:20:00,480 --> 00:20:01,560 Speaker 3: But the all. 402 00:20:01,760 --> 00:20:04,960 Speaker 5: Encompassing meaning is that maybe the Chinese economy gets a 403 00:20:04,960 --> 00:20:06,640 Speaker 5: little bit of a boost. I'm looking at the individual 404 00:20:06,640 --> 00:20:09,560 Speaker 5: movers Microsoft on the downside. Look, the Nazak one hundred 405 00:20:09,840 --> 00:20:13,119 Speaker 5: is under scrutiny because the Magnificent seven on which Microsoft 406 00:20:13,160 --> 00:20:15,360 Speaker 5: is one of them, and indeed so is Amazon. Look, 407 00:20:15,400 --> 00:20:17,879 Speaker 5: it's got two heavier waitings towards them. The outperformance of 408 00:20:17,880 --> 00:20:20,320 Speaker 5: those seven stocks means that maybe we get a rebalancing 409 00:20:20,359 --> 00:20:22,520 Speaker 5: coming July the fourteenth. What does that mean for how 410 00:20:22,520 --> 00:20:25,280 Speaker 5: these stocks are traded in the ETF so QQQ For example, 411 00:20:25,400 --> 00:20:26,360 Speaker 5: we're currently trading. 412 00:20:26,160 --> 00:20:26,760 Speaker 3: Down by Amazon. 413 00:20:26,760 --> 00:20:28,560 Speaker 5: We look ahead to of course that all important prime 414 00:20:28,640 --> 00:20:31,080 Speaker 5: data Moorrow tomorrow kickoff and Rivia and I do this 415 00:20:31,119 --> 00:20:33,200 Speaker 5: for you, Ed because I know that you've been all 416 00:20:33,240 --> 00:20:36,040 Speaker 5: over this stock after that exclusive conversation last week. 417 00:20:36,320 --> 00:20:38,680 Speaker 3: Now nine straight days potentially a record run. 418 00:20:40,200 --> 00:20:41,760 Speaker 4: Hi, there's a good stock to watch. Thank you. 419 00:20:41,920 --> 00:20:42,120 Speaker 9: Now. 420 00:20:42,240 --> 00:20:46,199 Speaker 4: Global Data Analytics Analytics Software make a quantexa SO to 421 00:20:46,240 --> 00:20:49,640 Speaker 4: invest two hundred and fifty million US dollars into new 422 00:20:49,720 --> 00:20:52,560 Speaker 4: AI research over the next three years. The firm helps 423 00:20:52,720 --> 00:20:56,880 Speaker 4: to uncover hidden risks and opportunities for financial services businesses. 424 00:20:56,960 --> 00:21:02,240 Speaker 4: Joining us now is CONTEXTA CEO Vishal Marrier from London, Vishaw. 425 00:21:02,280 --> 00:21:06,119 Speaker 4: Is this a case of invest in AI or get 426 00:21:06,200 --> 00:21:06,800 Speaker 4: left behind? 427 00:21:08,640 --> 00:21:12,080 Speaker 11: Great question, Edain, thanks for having me on. I think 428 00:21:12,119 --> 00:21:14,280 Speaker 11: there's a couple of points here. So first and foremost, 429 00:21:14,400 --> 00:21:17,240 Speaker 11: AI machine learning has been in the DNA of context 430 00:21:17,480 --> 00:21:20,280 Speaker 11: since we started it in twenty sixteen, on the way 431 00:21:20,320 --> 00:21:24,399 Speaker 11: we unify data, contextualized data and support those insights for 432 00:21:24,440 --> 00:21:27,800 Speaker 11: our clients to make better trusted decision. Machine learning in 433 00:21:27,840 --> 00:21:30,040 Speaker 11: AI has been through the platform. 434 00:21:29,640 --> 00:21:30,360 Speaker 1: End to end. 435 00:21:30,760 --> 00:21:33,560 Speaker 11: The investments we're doing today and what we've announced this 436 00:21:33,640 --> 00:21:37,120 Speaker 11: morning is to catapult further investment in all things AI 437 00:21:37,119 --> 00:21:39,720 Speaker 11: and machine learning to better serve some of the more 438 00:21:40,000 --> 00:21:43,120 Speaker 11: harder applications when it comes down to regulated markets. 439 00:21:43,760 --> 00:21:46,680 Speaker 5: You are based in London, of course, but also here 440 00:21:46,680 --> 00:21:48,959 Speaker 5: in New York, Boston, Toronto, Malaga, you name it. You're 441 00:21:49,000 --> 00:21:51,960 Speaker 5: across Europe in particular, for shout, Where do you allocate 442 00:21:52,119 --> 00:21:54,560 Speaker 5: some of this money this investment? Is it London or 443 00:21:54,560 --> 00:21:55,000 Speaker 5: else where? 444 00:21:56,760 --> 00:21:57,760 Speaker 1: Great question, Caroline. 445 00:21:57,840 --> 00:22:00,840 Speaker 11: So we are hq'ed here in the UK, and as 446 00:22:00,880 --> 00:22:03,960 Speaker 11: you craigly state, we're in twenty plus cities worldwide and 447 00:22:04,119 --> 00:22:07,960 Speaker 11: you mentioned a few, and from the engineering perspective, our 448 00:22:08,320 --> 00:22:10,560 Speaker 11: hard of our engineering is done here in the UK. 449 00:22:11,000 --> 00:22:14,760 Speaker 11: If that's data engineers, data scientists working with the core platform, 450 00:22:14,880 --> 00:22:19,119 Speaker 11: verticalizing those applications in areas like data management, customer or 451 00:22:19,160 --> 00:22:22,800 Speaker 11: Patient three sixty, areas like KYC and AML. Many of 452 00:22:22,800 --> 00:22:26,160 Speaker 11: those applications are built here in the UK, working closely 453 00:22:26,200 --> 00:22:29,520 Speaker 11: with many of our strategic clients. And those clients, as 454 00:22:29,600 --> 00:22:32,119 Speaker 11: you said, are globally so if they're in the US 455 00:22:32,160 --> 00:22:34,359 Speaker 11: with large banks such as Banking New York and Melon, 456 00:22:34,720 --> 00:22:38,400 Speaker 11: or ensure as companies like Alians Group in Germany, these 457 00:22:38,400 --> 00:22:40,560 Speaker 11: are major clients of context, so that we work very 458 00:22:40,560 --> 00:22:42,640 Speaker 11: closely with base here in the UK. 459 00:22:42,920 --> 00:22:45,320 Speaker 5: I mean, ed we sit here as three Brits. But 460 00:22:45,480 --> 00:22:48,440 Speaker 5: it's notable that the UK has had a rich history 461 00:22:48,440 --> 00:22:51,359 Speaker 5: with AI. We know that DeepMind of course really built 462 00:22:51,400 --> 00:22:53,600 Speaker 5: there and it's something that the UK is trying to 463 00:22:53,600 --> 00:22:54,879 Speaker 5: show what particularly with the US. 464 00:22:54,800 --> 00:22:58,280 Speaker 4: Visit, there's a history of research in the UK. But 465 00:22:58,440 --> 00:23:02,240 Speaker 4: also President by on his visit to UK, I'm told 466 00:23:02,359 --> 00:23:05,359 Speaker 4: that the Prime Minister brought up this kind of cooperation 467 00:23:05,920 --> 00:23:08,879 Speaker 4: fishow between the US and UK. What's your assessment of 468 00:23:08,920 --> 00:23:12,080 Speaker 4: that cooperative work in the field of AI. 469 00:23:14,040 --> 00:23:15,560 Speaker 1: So, I think the field of AI. 470 00:23:16,200 --> 00:23:19,720 Speaker 11: It is an ecosystem coming together when it's to do 471 00:23:19,840 --> 00:23:23,520 Speaker 11: with academia, when it's to do with regulation or government, 472 00:23:23,800 --> 00:23:27,879 Speaker 11: but also the enterprise and obviously as tech providers like Quantexa, 473 00:23:28,080 --> 00:23:31,600 Speaker 11: working in a broader ecosystem is critical for the success 474 00:23:31,800 --> 00:23:35,000 Speaker 11: of AI. But one thing I would also add ed 475 00:23:35,040 --> 00:23:38,439 Speaker 11: and Karen to your point of the visit today of 476 00:23:38,520 --> 00:23:43,159 Speaker 11: the AI regulations and AI standards is really important to 477 00:23:43,200 --> 00:23:46,040 Speaker 11: put in play because one thing that's critical here is 478 00:23:46,080 --> 00:23:49,480 Speaker 11: AI is here to stay. I studied AI twenty plus 479 00:23:49,560 --> 00:23:52,320 Speaker 11: years ago. This is not necessarily a new technology. It's 480 00:23:52,359 --> 00:23:54,800 Speaker 11: a technology which is at the forefront today. It's in 481 00:23:54,840 --> 00:23:58,600 Speaker 11: primetime today. But ensuring that is treated carefully in a 482 00:23:58,680 --> 00:24:03,120 Speaker 11: trusted and transparent fashion is really critical. And if that's 483 00:24:03,160 --> 00:24:06,960 Speaker 11: any regulation or any standard or any collaboration between governments 484 00:24:07,000 --> 00:24:09,159 Speaker 11: as a key topic, I think is only important for 485 00:24:09,320 --> 00:24:10,520 Speaker 11: organizations to be doing that. 486 00:24:11,320 --> 00:24:13,640 Speaker 4: But I do go back to the point Caroline made 487 00:24:13,640 --> 00:24:17,040 Speaker 4: about your global footprint. When you listen to President Biden 488 00:24:17,480 --> 00:24:20,960 Speaker 4: and you listen to Prime Minister Sunak, who is more 489 00:24:21,040 --> 00:24:24,679 Speaker 4: convincing in the leadership of driving AI forward, where do 490 00:24:24,720 --> 00:24:27,280 Speaker 4: you want to put your money and find your talent 491 00:24:27,560 --> 00:24:29,280 Speaker 4: The UK or the United States. 492 00:24:31,280 --> 00:24:34,880 Speaker 11: Great, great question ed, And I think from a talent perspective, 493 00:24:35,080 --> 00:24:37,960 Speaker 11: we must always put our customers, our clients, at the 494 00:24:38,000 --> 00:24:41,199 Speaker 11: center to everything we do. When I started Context in 495 00:24:41,240 --> 00:24:44,560 Speaker 11: twenty sixteen, it was with that sole mission to ensure 496 00:24:44,600 --> 00:24:47,639 Speaker 11: that we put our clients, if those are large banks, 497 00:24:48,080 --> 00:24:53,000 Speaker 11: government organizations, insurers, or healthcare organizations, putting them at the 498 00:24:53,040 --> 00:24:56,840 Speaker 11: center to our innovation is critical. So working closely with 499 00:24:56,920 --> 00:25:01,040 Speaker 11: our clients, working closely with academia obviously a critical part 500 00:25:01,080 --> 00:25:02,800 Speaker 11: to any CEO strategy. 501 00:25:02,400 --> 00:25:03,680 Speaker 1: Who is running a tech company. 502 00:25:03,840 --> 00:25:06,320 Speaker 11: And we've obviously done incredibly well both here in the 503 00:25:06,440 --> 00:25:08,880 Speaker 11: UK as well as in the US and other countries 504 00:25:09,200 --> 00:25:11,600 Speaker 11: in recruiting some of the best talent. You know, we 505 00:25:11,640 --> 00:25:14,840 Speaker 11: work very closely with a number of universities to ensure 506 00:25:14,880 --> 00:25:16,919 Speaker 11: we're working with some of the hardest working with some 507 00:25:16,960 --> 00:25:19,399 Speaker 11: of the hardest problems with those universities, and looking at 508 00:25:19,440 --> 00:25:20,880 Speaker 11: the talent from that perspective too. 509 00:25:21,160 --> 00:25:23,840 Speaker 5: Vishaw, what's interesting, of course, when you're in the intersection 510 00:25:23,920 --> 00:25:27,240 Speaker 5: of finance and AI and tech, you're going to be 511 00:25:27,359 --> 00:25:30,480 Speaker 5: very used to trying to navigate regulation when it comes 512 00:25:30,520 --> 00:25:35,159 Speaker 5: to financial institutions. What about regulation of AI I mean 513 00:25:35,200 --> 00:25:37,600 Speaker 5: I'm looking at the EU or in Ireland or in 514 00:25:37,640 --> 00:25:41,560 Speaker 5: Amsterdam Brussels, and clearly they're trying to act quickly on 515 00:25:41,600 --> 00:25:42,280 Speaker 5: an AI act. 516 00:25:44,160 --> 00:25:44,360 Speaker 1: Yeah. 517 00:25:44,400 --> 00:25:47,320 Speaker 11: No, absolutely So coming back to my earlier point current, So, 518 00:25:47,720 --> 00:25:51,040 Speaker 11: everything we have done here at CONTEXTA from an AI 519 00:25:51,160 --> 00:25:56,719 Speaker 11: machine learning trans perspective, is fully transparent, fully scalable, and 520 00:25:56,760 --> 00:26:00,440 Speaker 11: most importantly within our platform, we've adopted a wide box 521 00:26:00,760 --> 00:26:04,000 Speaker 11: set of algorithms that are very clear to explain to 522 00:26:04,080 --> 00:26:08,040 Speaker 11: any regulator, to any model risk management department in a 523 00:26:08,119 --> 00:26:12,400 Speaker 11: bank on a how you stitch that data? But more importantly, 524 00:26:12,640 --> 00:26:15,120 Speaker 11: why did you come to an outcome to say this 525 00:26:15,160 --> 00:26:18,160 Speaker 11: person's of interest or this party is not of interest? 526 00:26:18,520 --> 00:26:20,439 Speaker 11: And that is at the common to most of the 527 00:26:20,480 --> 00:26:26,040 Speaker 11: regulations globally, the transparency, the explainability, and the ethical use 528 00:26:26,119 --> 00:26:30,119 Speaker 11: of such data. 529 00:26:28,560 --> 00:26:30,920 Speaker 4: When you're going to make a return on your big 530 00:26:30,960 --> 00:26:31,840 Speaker 4: investment in AI. 531 00:26:34,040 --> 00:26:36,119 Speaker 11: So, just like with many investments, is backed by a 532 00:26:36,119 --> 00:26:41,040 Speaker 11: business case and you know this investment is completely follows 533 00:26:41,080 --> 00:26:43,919 Speaker 11: that path. Now, we obviously just close our Series E 534 00:26:44,080 --> 00:26:47,240 Speaker 11: at the beginning of April, where we raised just over 535 00:26:47,240 --> 00:26:49,800 Speaker 11: one hundred and thirty million at evaluation a one point 536 00:26:49,800 --> 00:26:53,840 Speaker 11: eight billion led by GIC. Now as part of that investment, 537 00:26:54,040 --> 00:26:56,600 Speaker 11: there is an investment case on where we deploy our funds. 538 00:26:56,720 --> 00:26:59,080 Speaker 11: But we will take a long term strategic view on 539 00:26:59,119 --> 00:27:02,119 Speaker 11: this and so am I investors. As I mentioned, AI 540 00:27:02,200 --> 00:27:04,720 Speaker 11: is not just here today and gone tomorrow. AI is 541 00:27:04,800 --> 00:27:07,080 Speaker 11: here and has been here before, and it will stay 542 00:27:07,119 --> 00:27:11,760 Speaker 11: here continually. But it's very important that organizations like Quantexa 543 00:27:11,920 --> 00:27:14,520 Speaker 11: take a much more long term view when it comes 544 00:27:14,520 --> 00:27:17,399 Speaker 11: down to investments and what I'm delighted my clients, my 545 00:27:17,520 --> 00:27:20,199 Speaker 11: partners as well as my investors are all on that 546 00:27:20,320 --> 00:27:23,439 Speaker 11: journey with us to ensure we're making those making that 547 00:27:23,480 --> 00:27:24,439 Speaker 11: impact in the market. 548 00:27:24,720 --> 00:27:27,240 Speaker 5: Michelle Maria, great to have some time with you and 549 00:27:27,440 --> 00:27:30,760 Speaker 5: Texa there based over in London. Meanwhile, coming up, look, 550 00:27:30,760 --> 00:27:32,480 Speaker 5: we're going to be taking the pulse of the crypto 551 00:27:32,640 --> 00:27:33,440 Speaker 5: VC industry. 552 00:27:33,520 --> 00:27:36,080 Speaker 3: Remember when we only used to talk about crypto. Now 553 00:27:36,160 --> 00:27:37,639 Speaker 3: it's all talking about AI. 554 00:27:37,720 --> 00:27:40,080 Speaker 5: Where's the intersection in that pitchfook crypto and it's going 555 00:27:40,080 --> 00:27:42,639 Speaker 5: to be with us of course, I'm at they and 556 00:27:42,720 --> 00:27:43,320 Speaker 5: what are you watching? 557 00:27:43,400 --> 00:27:46,160 Speaker 4: Yeah, yeah, snowflake, I'm looking to shares the snowflake down 558 00:27:46,200 --> 00:27:48,959 Speaker 4: one point six six percent another name kind of at 559 00:27:48,960 --> 00:27:52,160 Speaker 4: the intersection of Cloud Enterprise and AI not a lot 560 00:27:52,160 --> 00:27:54,560 Speaker 4: in the news flow, but I would note that Frank 561 00:27:54,600 --> 00:27:58,680 Speaker 4: sloopmanco and chairman did put a regulatory filing in Friday 562 00:27:58,760 --> 00:28:02,879 Speaker 4: night to exis size options and some insider selling going 563 00:28:02,920 --> 00:28:06,040 Speaker 4: on in that name, So that could be one of 564 00:28:06,080 --> 00:28:09,000 Speaker 4: the factors impacting the stock to the downside. We keep looking. 565 00:28:09,280 --> 00:28:28,000 Speaker 4: This is Bloomberg Technology. Global VC funding into crypto in 566 00:28:28,080 --> 00:28:30,560 Speaker 4: the second quarter of twenty twenty three was the lowest 567 00:28:30,560 --> 00:28:34,639 Speaker 4: amount since twenty twenty. Meanwhile, AI is the green shoe. 568 00:28:34,760 --> 00:28:38,040 Speaker 4: Every single VC here on Bloomberg Technology is talking about 569 00:28:38,120 --> 00:28:41,800 Speaker 4: let's bringing bloomberg'snnah Mila. Who's been going off over the data? 570 00:28:42,040 --> 00:28:45,000 Speaker 4: This is data from pitchbook largely. What does it tell us? 571 00:28:45,200 --> 00:28:48,920 Speaker 12: Yeah, so we've seen a continued steep decline in crypto 572 00:28:49,040 --> 00:28:53,280 Speaker 12: venture funding, especially compared to last year. So it's the 573 00:28:53,320 --> 00:28:54,200 Speaker 12: worst quarter. 574 00:28:54,040 --> 00:28:55,040 Speaker 3: Since twenty twenty. 575 00:28:56,200 --> 00:28:58,720 Speaker 12: We thought maybe it had bottomed out, that you know, 576 00:28:58,840 --> 00:28:59,960 Speaker 12: funding would pop back up. 577 00:29:00,080 --> 00:29:01,280 Speaker 3: That has not happened. 578 00:29:02,240 --> 00:29:04,320 Speaker 12: So, you know, I think vcs are looking for a 579 00:29:04,360 --> 00:29:07,320 Speaker 12: way to reignite interests in the crypto industry, and founders 580 00:29:07,360 --> 00:29:09,960 Speaker 12: are scrambling to get you know funding when they. 581 00:29:09,840 --> 00:29:13,600 Speaker 5: Can okay, Hanna, So where's the legit intersection of AI 582 00:29:13,680 --> 00:29:14,200 Speaker 5: and crypto? 583 00:29:15,320 --> 00:29:17,440 Speaker 12: You know, it's hard to tell because it's so nascent. 584 00:29:17,520 --> 00:29:20,000 Speaker 12: You know, you have people arguing that crypto and blockchain 585 00:29:20,040 --> 00:29:23,760 Speaker 12: can bring more decentralization to AI, which is really dominated 586 00:29:23,760 --> 00:29:26,560 Speaker 12: by a few companies right now. But the companies that 587 00:29:26,640 --> 00:29:29,360 Speaker 12: are building in the intersection of crypto and AI and 588 00:29:29,440 --> 00:29:32,680 Speaker 12: AI are still very nason They're still working on stuff, 589 00:29:32,720 --> 00:29:35,760 Speaker 12: they haven't released anything yet, so it's kind of hard 590 00:29:35,760 --> 00:29:38,560 Speaker 12: to tell whether this will carry through and whether crypto 591 00:29:38,640 --> 00:29:40,320 Speaker 12: and AI can go hand in hand. 592 00:29:40,520 --> 00:29:42,680 Speaker 4: I want to show the data just for the United 593 00:29:42,720 --> 00:29:46,760 Speaker 4: States focused on AI because when we consider global investments 594 00:29:47,840 --> 00:29:49,800 Speaker 4: the US and what we're seeing on our screen now, 595 00:29:49,840 --> 00:29:53,000 Speaker 4: thirty one billion dollars a year today is most of 596 00:29:53,080 --> 00:29:53,960 Speaker 4: global spend. 597 00:29:55,680 --> 00:29:59,680 Speaker 12: Yeah, So with AI, you know people are interested in 598 00:29:59,680 --> 00:30:02,120 Speaker 12: invest thing in it that there are these deals that 599 00:30:02,200 --> 00:30:04,120 Speaker 12: are happening in the space. You know, there was a 600 00:30:04,120 --> 00:30:09,360 Speaker 12: slight declined quarter over quarter, but overall, you know, things 601 00:30:09,400 --> 00:30:10,680 Speaker 12: are happening here in the space. 602 00:30:10,960 --> 00:30:13,400 Speaker 5: How do you talk about how the startups themselves are 603 00:30:13,440 --> 00:30:16,480 Speaker 5: pivoting trying to sort of show that prowess in the 604 00:30:16,520 --> 00:30:19,480 Speaker 5: intersection of AI and crypto. But what about the dedicated 605 00:30:19,520 --> 00:30:23,760 Speaker 5: funds to crypto investing the vcs, how are they allocating 606 00:30:23,800 --> 00:30:25,000 Speaker 5: at the moment because a lot of them have a 607 00:30:25,320 --> 00:30:28,600 Speaker 5: lot of overhang from money lost in previous rounds or thought. 608 00:30:29,920 --> 00:30:32,240 Speaker 12: Yeah, so a lot of these crypto venture funds they 609 00:30:32,240 --> 00:30:34,960 Speaker 12: have mandates that require them to back the space, so 610 00:30:34,960 --> 00:30:37,920 Speaker 12: they're obviously still going to be deploying capital and they're 611 00:30:37,960 --> 00:30:41,000 Speaker 12: looking for ways, you know, to spend their money wisely, 612 00:30:41,120 --> 00:30:43,120 Speaker 12: and some of them are being drawn to this intersection 613 00:30:43,160 --> 00:30:46,360 Speaker 12: of crypto and AI and looking at companies like you know, 614 00:30:46,480 --> 00:30:49,760 Speaker 12: Tools for Humanity, which developed world coin, as well as Jensen, 615 00:30:49,800 --> 00:30:53,120 Speaker 12: which is developing a decentralized marketplace based on blockchain for 616 00:30:53,200 --> 00:30:55,200 Speaker 12: compute power world coin. 617 00:30:55,280 --> 00:30:57,160 Speaker 5: If in doubt back some outmen, it seems to be 618 00:30:57,240 --> 00:31:00,480 Speaker 5: the takeaway, and I thank you very much. Indeed really 619 00:31:00,520 --> 00:31:02,280 Speaker 5: great to have a perspective to build up to what 620 00:31:02,400 --> 00:31:06,200 Speaker 5: is now an integral conversation with Pitchbooks, cryptoanalyst from it Lay, 621 00:31:06,240 --> 00:31:07,920 Speaker 5: who I'm pleased to say is right here in New York. 622 00:31:08,000 --> 00:31:10,240 Speaker 3: And but we were hearing there about. 623 00:31:09,960 --> 00:31:12,960 Speaker 5: What a dial quarter it was for crypto VC and 624 00:31:13,000 --> 00:31:14,280 Speaker 5: for crypto allocations. 625 00:31:15,280 --> 00:31:17,480 Speaker 3: Does any of it look better going into Q three. 626 00:31:17,560 --> 00:31:20,240 Speaker 13: Do you think honestly, we think it will look better. 627 00:31:20,840 --> 00:31:23,840 Speaker 13: Like Hannah just mentioned, a lot of these crypto VC funds, 628 00:31:23,880 --> 00:31:26,680 Speaker 13: they have to deploy capitals into space. Part of the 629 00:31:26,760 --> 00:31:30,280 Speaker 13: slowdown is that the diligence cycles just slow down. So 630 00:31:30,320 --> 00:31:32,680 Speaker 13: you look at in twenty twenty one and twenty twenty two, 631 00:31:33,040 --> 00:31:35,200 Speaker 13: some of those deals were happening in a one week 632 00:31:35,280 --> 00:31:36,520 Speaker 13: or two week time frame. 633 00:31:36,720 --> 00:31:37,320 Speaker 9: And that i e. 634 00:31:37,520 --> 00:31:39,800 Speaker 4: Not much utilis exactly right. 635 00:31:39,600 --> 00:31:42,960 Speaker 13: And so now investors are taking the time meeting the teams, 636 00:31:43,040 --> 00:31:46,479 Speaker 13: meeting the founders, understanding the business model, understand their product, 637 00:31:46,480 --> 00:31:49,000 Speaker 13: product market fit, and then they're going to deploy capital. 638 00:31:49,160 --> 00:31:51,840 Speaker 13: What we're looking at and talking about those crypto venture 639 00:31:51,880 --> 00:31:55,440 Speaker 13: funds last year alone, there's twenty five billion dollars raised 640 00:31:55,440 --> 00:31:58,720 Speaker 13: for crypton EATA funds. That tabital has to be deployed 641 00:31:58,760 --> 00:32:01,800 Speaker 13: over the next two to four years year. So investors 642 00:32:01,800 --> 00:32:04,600 Speaker 13: have some time because they just raised those fresh funds 643 00:32:04,640 --> 00:32:07,840 Speaker 13: in twenty twenty two. But we think that as investors 644 00:32:07,880 --> 00:32:10,680 Speaker 13: start to get comfortable with the different business models, the 645 00:32:10,760 --> 00:32:13,200 Speaker 13: different opportunities in the crypto space, they're going to start 646 00:32:13,200 --> 00:32:16,680 Speaker 13: to deploying more and then our outlook at the beginning 647 00:32:16,720 --> 00:32:18,360 Speaker 13: of the years that we're going to see an uptick 648 00:32:18,440 --> 00:32:20,800 Speaker 13: in the second half of twenty twenty three, and it's 649 00:32:20,800 --> 00:32:22,720 Speaker 13: going to bottom up around sometime in the summer. And 650 00:32:22,800 --> 00:32:23,920 Speaker 13: we're seeing that right now. 651 00:32:25,040 --> 00:32:29,000 Speaker 4: It's so interesting to compare and contrast what's happening right 652 00:32:29,040 --> 00:32:32,000 Speaker 4: now in AI and what happened in crypto. Ashton Kutcher 653 00:32:32,080 --> 00:32:34,480 Speaker 4: was on Bloomberg Technology a few weeks ago saying he 654 00:32:34,640 --> 00:32:37,440 Speaker 4: raised his two hundred and thirty two million dollar fund 655 00:32:37,440 --> 00:32:41,360 Speaker 4: in just five weeks. And we're basically talking about crypto 656 00:32:41,520 --> 00:32:45,040 Speaker 4: so broadly, just like we talk about AI broadly. Are 657 00:32:45,040 --> 00:32:49,040 Speaker 4: there any bright spots, specific areas in the underlying technology 658 00:32:49,440 --> 00:32:52,480 Speaker 4: or marketplaces where there is still activity for crypto. 659 00:32:53,120 --> 00:32:55,760 Speaker 13: Yeah, there are a few areas that investors are still 660 00:32:55,840 --> 00:32:58,840 Speaker 13: really excited about and deploying capital in right now. And 661 00:32:59,120 --> 00:33:02,360 Speaker 13: it's really three categories. Infrastructure, so you look at the 662 00:33:02,440 --> 00:33:06,520 Speaker 13: underlying blockchains and the scaling solutions to make the blockchains 663 00:33:06,560 --> 00:33:09,800 Speaker 13: faster and having higher throughput, the service providers. 664 00:33:09,800 --> 00:33:11,360 Speaker 9: So you think about a lot of. 665 00:33:11,480 --> 00:33:15,720 Speaker 13: Institutional traditional financial institutions are still moving into. 666 00:33:15,520 --> 00:33:16,280 Speaker 9: The cryptal space. 667 00:33:16,280 --> 00:33:19,600 Speaker 13: And we just heard about black Rock and Fidelity refiling 668 00:33:19,720 --> 00:33:23,280 Speaker 13: for the Bitcoin spot ETF. So there's a lot of 669 00:33:23,360 --> 00:33:26,920 Speaker 13: institutional interests. So what are the technologies and software and 670 00:33:27,000 --> 00:33:30,640 Speaker 13: services you can provide to help institutions move into the space. 671 00:33:30,880 --> 00:33:34,800 Speaker 13: Custodiums is one type of technology, or prime brokerage or 672 00:33:34,840 --> 00:33:38,600 Speaker 13: trading services, anything that can help them better facilitate their 673 00:33:38,640 --> 00:33:41,920 Speaker 13: interests into the space and providing service to their customers. 674 00:33:42,120 --> 00:33:44,920 Speaker 13: And the last area is what we call when we 675 00:33:45,000 --> 00:33:47,600 Speaker 13: just wrote research on this space, it's called decentralized physical 676 00:33:47,600 --> 00:33:50,200 Speaker 13: infrastructure networks. I know, it's a really long term. We 677 00:33:50,280 --> 00:33:52,840 Speaker 13: call it deep in, and it's any kind of real 678 00:33:52,920 --> 00:33:58,120 Speaker 13: world infrastructure that can facilitate token to be able to 679 00:33:58,120 --> 00:34:02,600 Speaker 13: provide services. So you can think about decentralized servers, mobile networks, 680 00:34:02,640 --> 00:34:03,719 Speaker 13: or decentralized servers. 681 00:34:04,000 --> 00:34:05,600 Speaker 9: Hannah just mentioned Jensen. 682 00:34:05,680 --> 00:34:10,799 Speaker 13: They do a decentralized computing for training AI models. That's 683 00:34:10,840 --> 00:34:12,960 Speaker 13: just an example, and they can use tokens to have 684 00:34:13,320 --> 00:34:17,480 Speaker 13: anyone that has a computer can help facilitate and contribute 685 00:34:17,520 --> 00:34:19,200 Speaker 13: power and compute to that. 686 00:34:19,120 --> 00:34:22,440 Speaker 5: Network, kind of like an AI version of file coin exactly. 687 00:34:22,440 --> 00:34:23,600 Speaker 9: That's exactly what they're doing. 688 00:34:23,800 --> 00:34:27,120 Speaker 5: I'm interested in where these things are getting built because 689 00:34:27,640 --> 00:34:30,840 Speaker 5: crypto is decentralized. It's also global, and there was a 690 00:34:30,880 --> 00:34:33,719 Speaker 5: bit of sort of regulatory arbitrage going on at one point. 691 00:34:33,880 --> 00:34:35,839 Speaker 5: Is that still happening as some of the founders moving 692 00:34:35,880 --> 00:34:37,320 Speaker 5: off going out of the US. 693 00:34:37,680 --> 00:34:40,560 Speaker 13: Honestly, we that is the narrative. We just don't see 694 00:34:40,560 --> 00:34:44,400 Speaker 13: that happening. We think that the talent largely is still 695 00:34:44,440 --> 00:34:47,440 Speaker 13: here in the US. A lot of the founders are 696 00:34:47,480 --> 00:34:51,080 Speaker 13: still building here and they're recruiting talent here in the US. 697 00:34:51,680 --> 00:34:54,160 Speaker 13: You know, a lot of folks talk about the strict 698 00:34:54,200 --> 00:34:57,360 Speaker 13: regulations that is happening right now with you know, the sec, CFTC, 699 00:34:58,040 --> 00:35:02,719 Speaker 13: the FED all coming after crypto, that I think is 700 00:35:02,760 --> 00:35:05,080 Speaker 13: not going to push founders to move abroad. 701 00:35:05,120 --> 00:35:06,200 Speaker 9: And I give you a quick example. 702 00:35:06,760 --> 00:35:09,799 Speaker 13: We know in Europe typically their regulations are a lot 703 00:35:09,840 --> 00:35:12,239 Speaker 13: more progressive. You know, even with fintech. You let a 704 00:35:12,280 --> 00:35:14,640 Speaker 13: PSC two and open banking that happened a few years ago. 705 00:35:15,040 --> 00:35:17,040 Speaker 13: A lot of folks that that fintech innovation is going 706 00:35:17,080 --> 00:35:17,880 Speaker 13: to move to Europe. 707 00:35:18,040 --> 00:35:19,040 Speaker 9: We never saw that happen. 708 00:35:19,120 --> 00:35:21,040 Speaker 13: Still a lot of the fintech innovations happening here in 709 00:35:21,040 --> 00:35:22,680 Speaker 13: the US, so we expect the same for crypto. 710 00:35:23,000 --> 00:35:25,120 Speaker 5: Now we have an end so all our talk of 711 00:35:25,640 --> 00:35:27,120 Speaker 5: the founders leaving not happening. 712 00:35:26,880 --> 00:35:29,520 Speaker 4: But things can move so quickly, right, Yeah, I guess 713 00:35:29,560 --> 00:35:32,000 Speaker 4: the question I have, Robert, is when's the rebound for 714 00:35:32,040 --> 00:35:34,440 Speaker 4: crypto come? What moves the needle? 715 00:35:35,680 --> 00:35:40,840 Speaker 13: Yeah, it's going to be mostly the funding, Okay, because 716 00:35:40,840 --> 00:35:43,520 Speaker 13: scarps are now they need funding to grow and scale. 717 00:35:43,680 --> 00:35:46,680 Speaker 9: So what point will investors get comfortable? 718 00:35:47,000 --> 00:35:49,759 Speaker 13: And our call still holds is that it's going to 719 00:35:49,800 --> 00:35:52,280 Speaker 13: be sometime towards the end of this year. So as 720 00:35:53,040 --> 00:35:56,040 Speaker 13: what investors are looking at, what projects are building the 721 00:35:56,200 --> 00:36:00,279 Speaker 13: strongest products out there, and what are their under line 722 00:36:00,320 --> 00:36:02,839 Speaker 13: business models and how are they getting product market fit? 723 00:36:03,280 --> 00:36:05,279 Speaker 13: And then the ones that are going to get that 724 00:36:05,719 --> 00:36:07,360 Speaker 13: are going to get the funding. And we're going to 725 00:36:07,360 --> 00:36:09,080 Speaker 13: expect to see that towards the end of this year. 726 00:36:09,160 --> 00:36:12,000 Speaker 13: So you know, the rebound's hard to say, but we're 727 00:36:12,000 --> 00:36:14,440 Speaker 13: hoping sometime next year, early twenty twenty five, we're going 728 00:36:14,480 --> 00:36:17,800 Speaker 13: to start seeing a lot more capital and more founders 729 00:36:17,840 --> 00:36:18,839 Speaker 13: coming into space to build. 730 00:36:20,040 --> 00:36:23,040 Speaker 4: Pagebild crypto analyst Robert lay with the banqward looking data 731 00:36:23,080 --> 00:36:25,879 Speaker 4: but also the forward looking forcust Thank you very much. 732 00:36:33,719 --> 00:36:35,000 Speaker 3: Time now we're going viral. 733 00:36:35,239 --> 00:36:38,600 Speaker 5: It still is one hundred million people are saying it's Threads, 734 00:36:38,640 --> 00:36:39,360 Speaker 5: It's going viral. 735 00:36:39,520 --> 00:36:40,440 Speaker 3: There has been. 736 00:36:40,320 --> 00:36:42,759 Speaker 5: Much hype over metas newly launched social media app. 737 00:36:42,800 --> 00:36:44,000 Speaker 3: That has of course Twitter. 738 00:36:44,440 --> 00:36:47,800 Speaker 5: I'm pretty high alert analysts over at ever CORESI estimate 739 00:36:47,960 --> 00:36:51,320 Speaker 5: that Threads could actually need two hundred million daily active 740 00:36:51,400 --> 00:36:54,440 Speaker 5: users and generate about eight billion dollars in revenue by 741 00:36:54,440 --> 00:36:58,560 Speaker 5: twenty twenty five. Now, no eight billion per year for 742 00:36:58,640 --> 00:36:59,680 Speaker 5: the next couple of years. 743 00:36:59,840 --> 00:36:59,880 Speaker 4: Ed. 744 00:37:00,360 --> 00:37:01,799 Speaker 3: That is about a drop in the ocean of. 745 00:37:01,719 --> 00:37:04,080 Speaker 5: The one hundred and fifty billion in revenue that Meta 746 00:37:04,080 --> 00:37:05,520 Speaker 5: as a parent company brings in. 747 00:37:05,600 --> 00:37:07,400 Speaker 3: But okay, it's more than Twitter was making. 748 00:37:08,840 --> 00:37:11,640 Speaker 4: But how do we reconcile that it will ever make money? 749 00:37:11,680 --> 00:37:14,120 Speaker 4: Because if you think about it, just in allow producers, 750 00:37:14,120 --> 00:37:17,040 Speaker 4: just as stus, when does it become Twitter? The early 751 00:37:17,080 --> 00:37:19,200 Speaker 4: engagements I've had on Threads is everyone saying, we like 752 00:37:19,280 --> 00:37:22,240 Speaker 4: that there's no ads, we like that it's just text, 753 00:37:22,760 --> 00:37:23,799 Speaker 4: simple platform. 754 00:37:23,920 --> 00:37:26,360 Speaker 5: I'm sure, but we all said that about all of 755 00:37:26,400 --> 00:37:28,879 Speaker 5: the previous and then we slowly but surely realize that 756 00:37:28,880 --> 00:37:31,160 Speaker 5: that is the quid pro quo. You use it, you 757 00:37:31,680 --> 00:37:33,480 Speaker 5: of course have to pay for it in some way. 758 00:37:33,520 --> 00:37:34,399 Speaker 3: It can't just be free. 759 00:37:34,400 --> 00:37:36,759 Speaker 5: But I think what's more for a stumbling rot for 760 00:37:36,800 --> 00:37:39,640 Speaker 5: me is the decentralized element of it. We know that 761 00:37:39,719 --> 00:37:42,759 Speaker 5: it's not part of the feederverse quite here yet, but 762 00:37:42,800 --> 00:37:44,600 Speaker 5: eventually it will be. And what does that mean for 763 00:37:44,640 --> 00:37:49,120 Speaker 5: putting advertising within well, the use on its own servers 764 00:37:49,160 --> 00:37:50,480 Speaker 5: and the like, and what does it mean when it 765 00:37:50,480 --> 00:37:51,560 Speaker 5: becomes more distributed? 766 00:37:52,200 --> 00:37:54,480 Speaker 4: And Elon Musk has kind of leaned into that in 767 00:37:54,520 --> 00:37:56,759 Speaker 4: his jabs on Twitter over the weekend, some of which 768 00:37:56,760 --> 00:37:59,400 Speaker 4: he directed at Mark Zuckerberg. The complaint that I that 769 00:37:59,480 --> 00:38:03,879 Speaker 4: I hear people thread me about is also discoverability. How 770 00:38:03,920 --> 00:38:06,040 Speaker 4: do you find communities and groups that you want to 771 00:38:06,080 --> 00:38:09,200 Speaker 4: share a conversation with? But it's a simple platform, and 772 00:38:09,280 --> 00:38:11,600 Speaker 4: I love that We're already talking about how much money 773 00:38:11,640 --> 00:38:13,879 Speaker 4: is this going to make? Well, according to Evercore eight 774 00:38:13,920 --> 00:38:15,160 Speaker 4: billion by twenty twenty five. 775 00:38:15,280 --> 00:38:17,480 Speaker 5: Nice, I mean that is what financial journalists do. It 776 00:38:17,520 --> 00:38:19,600 Speaker 5: all comes back to the money, isn't it. But to 777 00:38:19,640 --> 00:38:22,520 Speaker 5: that point, it's also still a baby, and they kind 778 00:38:22,520 --> 00:38:26,120 Speaker 5: of probably slightly rushed it coming to the fall because 779 00:38:26,120 --> 00:38:29,240 Speaker 5: they were capitalizing on kind of difficult moment for Twitter. 780 00:38:29,600 --> 00:38:31,800 Speaker 3: They will start to have hashtags, I'm sure. 781 00:38:32,080 --> 00:38:34,319 Speaker 5: Who knows when they have a competitor to spaces and 782 00:38:34,360 --> 00:38:36,239 Speaker 5: we can do go cross platform and that one too. 783 00:38:36,960 --> 00:38:39,360 Speaker 4: It's just a baby. I won't do the voice, but 784 00:38:39,440 --> 00:38:41,759 Speaker 4: it's grown fast, one hundred million uses in a week. 785 00:38:41,960 --> 00:38:45,320 Speaker 5: What can you say helps when they're just easily porting 786 00:38:45,360 --> 00:38:47,200 Speaker 5: over from Instagram. I meanwhile, that does it for this 787 00:38:47,320 --> 00:38:49,799 Speaker 5: edition of Really Bag Technology. And look, John Felton, he's 788 00:38:49,800 --> 00:38:52,680 Speaker 5: going to be joining Amazon SVP Worldwide Operation, going to 789 00:38:52,680 --> 00:38:55,160 Speaker 5: be talking Amazon's shopping extravaganza. 790 00:38:55,280 --> 00:38:57,160 Speaker 3: It's prim Day. You don't want to miss that conversation. 791 00:38:57,960 --> 00:38:59,719 Speaker 4: Yeah, And if you missed any of today or you 792 00:38:59,719 --> 00:39:02,160 Speaker 4: want to recap, don't forget about the podcast. There's a 793 00:39:02,160 --> 00:39:05,279 Speaker 4: lot going on in every show, from AI to what's 794 00:39:05,320 --> 00:39:09,800 Speaker 4: happening in hardware and video games. So the podcast on Apple, Spotify, iHeart, 795 00:39:10,000 --> 00:39:13,040 Speaker 4: and of course on our Bloomberg platforms. We have a 796 00:39:13,239 --> 00:39:15,719 Speaker 4: massive week in the world of technology coming up from 797 00:39:15,719 --> 00:39:18,440 Speaker 4: here in San Francisco and out in New York with Caroline. 798 00:39:18,560 --> 00:39:20,440 Speaker 4: This is Bloomberg Technology