1 00:00:02,920 --> 00:00:07,640 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,200 --> 00:00:12,000 Speaker 2: From Mahard where innovation, money and power. 3 00:00:12,119 --> 00:00:14,480 Speaker 3: Collie in Silicon Vallet NBN. 4 00:00:14,840 --> 00:00:18,880 Speaker 4: This is Bloomberg Technology with Caroline Hyde and Ed Ludlow. 5 00:00:32,960 --> 00:00:35,760 Speaker 5: I met Lovelow in San Francisco. Caroline hides off today. 6 00:00:35,760 --> 00:00:39,680 Speaker 5: This is Bloomberg Technology coming up. Shean rethinks an IPO 7 00:00:39,760 --> 00:00:42,640 Speaker 5: in New York with its sites now set on London 8 00:00:42,920 --> 00:00:47,200 Speaker 5: as the fast fashion company faces hurdles in the US. Plus, 9 00:00:47,320 --> 00:00:52,000 Speaker 5: Nvidia's seventy million dollars supercomputer is hobbled in Florida by 10 00:00:52,080 --> 00:00:56,280 Speaker 5: laws preventing top talent from other setting foot in the state. 11 00:00:56,280 --> 00:00:59,200 Speaker 5: We'll bring you the reporting from the ground. And Instagram 12 00:00:59,240 --> 00:01:03,320 Speaker 5: competitor Lapse raises thirty million dollars from investors as it 13 00:01:03,360 --> 00:01:08,319 Speaker 5: looks to provide an alternative social platform to gen z users. 14 00:01:08,319 --> 00:01:11,520 Speaker 5: We'll discuss that and so much more throughout the hour. 15 00:01:11,640 --> 00:01:13,800 Speaker 5: Good morning, this is what your markets look like. We're 16 00:01:13,840 --> 00:01:16,399 Speaker 5: coming out of the tail end of earning season for 17 00:01:16,440 --> 00:01:20,560 Speaker 5: the technology sector and headfirst into a week where economic 18 00:01:20,640 --> 00:01:23,280 Speaker 5: data is front and center. There's been chopping trading on 19 00:01:23,280 --> 00:01:26,000 Speaker 5: a Nasdaq one hundred. We're currently up two tens percent 20 00:01:26,360 --> 00:01:29,400 Speaker 5: yields have been creeping higher. Why we've pared back our 21 00:01:29,440 --> 00:01:32,959 Speaker 5: expectations for what the FED will or won't do with 22 00:01:33,000 --> 00:01:34,960 Speaker 5: a rate cart. You look at US tenure yield four 23 00:01:34,959 --> 00:01:37,639 Speaker 5: point two eight percent, been around four point two nine percent. 24 00:01:37,920 --> 00:01:41,520 Speaker 5: One landmark moment Bitcoin just below fifty seven thousand US 25 00:01:41,600 --> 00:01:45,080 Speaker 5: dollars per token right now. Significant because that took the 26 00:01:45,120 --> 00:01:49,600 Speaker 5: global market for cryptocurrencies to two trillion dollars in market 27 00:01:49,640 --> 00:01:51,840 Speaker 5: cap total. I'm going to show you that chart later 28 00:01:51,920 --> 00:01:54,240 Speaker 5: on in the hour the crescendo of the week twenty 29 00:01:54,240 --> 00:01:57,080 Speaker 5: four hours time when we get PC data. Why because 30 00:01:57,120 --> 00:01:59,760 Speaker 5: I think will inform what the market at least thinks 31 00:02:00,240 --> 00:02:02,160 Speaker 5: is going to do. And of course higher rates we know. 32 00:02:02,200 --> 00:02:04,680 Speaker 5: The story is how that impacts the tech sector. Single 33 00:02:04,760 --> 00:02:07,320 Speaker 5: names we're watching I talked about earning season Zoom up 34 00:02:07,360 --> 00:02:10,440 Speaker 5: four point seven percent, have been much higher, a relatively 35 00:02:10,480 --> 00:02:13,280 Speaker 5: strong outlook for EPs. But they did a share buyback 36 00:02:13,320 --> 00:02:16,160 Speaker 5: one point five billion dollars, a sweetener that investors like 37 00:02:16,400 --> 00:02:19,120 Speaker 5: in Vidia has hit pause on its most recent rallies, 38 00:02:19,160 --> 00:02:20,840 Speaker 5: soft to two tens of one percent. We're about to 39 00:02:20,840 --> 00:02:23,160 Speaker 5: bring you a key story of what's going on with 40 00:02:23,320 --> 00:02:26,240 Speaker 5: Nvidia in Florida and then Google is rebounding. It's up 41 00:02:26,240 --> 00:02:29,320 Speaker 5: three tens and one percent. But there are concerns about 42 00:02:29,600 --> 00:02:32,760 Speaker 5: Gemini and the image generator side of Gemini, and we 43 00:02:32,800 --> 00:02:34,639 Speaker 5: are going to go to one analyst who has put 44 00:02:34,680 --> 00:02:38,280 Speaker 5: those concerns right at the center of their most recent note. 45 00:02:38,400 --> 00:02:40,400 Speaker 5: That is some names that are already trading. What about 46 00:02:40,400 --> 00:02:43,320 Speaker 5: one that we're all waiting on with hurdles affecting its 47 00:02:43,320 --> 00:02:46,520 Speaker 5: potential IPO in the United States, Fast fashion company she 48 00:02:46,720 --> 00:02:50,840 Speaker 5: In is considering switching its listing to overseas. The company, 49 00:02:51,080 --> 00:02:54,919 Speaker 5: which was founded in China and is now headquartered in Singapore, 50 00:02:55,320 --> 00:02:58,720 Speaker 5: is in the early stages of exploring a listing in London. 51 00:02:59,040 --> 00:03:02,600 Speaker 5: As the likelihood of the US SEC approving and IPO dim. 52 00:03:02,680 --> 00:03:05,320 Speaker 5: Sheen is still working on its application for the US, 53 00:03:05,600 --> 00:03:09,160 Speaker 5: which remains its preferred location. That's all according to Bloomberg 54 00:03:09,280 --> 00:03:12,880 Speaker 5: sources and reporting in here with more is Bloomberg's sweater 55 00:03:12,960 --> 00:03:16,080 Speaker 5: Gopinath Sweta A good morning from San Francisco. Good afternoon 56 00:03:16,120 --> 00:03:20,079 Speaker 5: to you in London. This could be big for London's market. 57 00:03:21,919 --> 00:03:24,959 Speaker 6: This could be huge for London's market. The London IPO 58 00:03:25,040 --> 00:03:28,760 Speaker 6: market has been particularly hard hit. The IPO market globally 59 00:03:28,800 --> 00:03:30,960 Speaker 6: has slowed, of course over the past eighteen months or so, 60 00:03:31,360 --> 00:03:34,360 Speaker 6: but the drop in London listings has been really stark. 61 00:03:35,200 --> 00:03:37,360 Speaker 6: Just about a billion, just under a billion in fact, 62 00:03:37,520 --> 00:03:40,240 Speaker 6: was raised all through twenty twenty three here in London. 63 00:03:40,480 --> 00:03:43,040 Speaker 6: So a deal of this size, a listing like Shean 64 00:03:43,360 --> 00:03:46,400 Speaker 6: could be the boost this market has desperately been waiting for. 65 00:03:48,680 --> 00:03:52,640 Speaker 5: Can London even support an IPO of this size? We 66 00:03:52,720 --> 00:03:54,600 Speaker 5: kind of know what the numbers are right that she 67 00:03:54,720 --> 00:03:58,200 Speaker 5: and would be looking at in terms of volume rays valuation. 68 00:03:58,720 --> 00:04:00,960 Speaker 5: I go back to what happened with our in September. 69 00:04:01,400 --> 00:04:03,680 Speaker 5: You know, you have to ask yourself, is the investor 70 00:04:03,720 --> 00:04:06,480 Speaker 5: base there to back a London IPO. 71 00:04:07,720 --> 00:04:11,160 Speaker 6: That is the billion or billions of dollars worth of 72 00:04:11,640 --> 00:04:16,240 Speaker 6: That's the billion dollar question actually, because that's that's the 73 00:04:16,279 --> 00:04:20,000 Speaker 6: obstacle that people say is holding London back. There just 74 00:04:20,120 --> 00:04:24,000 Speaker 6: isn't investor appetite here, particularly for sort of fast growing 75 00:04:24,120 --> 00:04:28,520 Speaker 6: young companies that have unprone business models. Sheen is doing 76 00:04:28,640 --> 00:04:31,000 Speaker 6: very well by most accounts, but there are problems around 77 00:04:31,000 --> 00:04:33,960 Speaker 6: it's so saying that questions about sort of the conternate 78 00:04:34,080 --> 00:04:37,040 Speaker 6: users where it comes from, sort of ethical est concerns 79 00:04:37,080 --> 00:04:39,640 Speaker 6: around that, and also just concerns about how it should 80 00:04:39,640 --> 00:04:42,400 Speaker 6: be valued. They're sort of varying estimates, estimates as for 81 00:04:42,480 --> 00:04:46,080 Speaker 6: all private companies, but more broadly, there's just questions about 82 00:04:46,120 --> 00:04:49,839 Speaker 6: how London fund managers value companies. This is the idea 83 00:04:49,880 --> 00:04:52,400 Speaker 6: that they don't value growth as highly as their US 84 00:04:52,520 --> 00:04:54,680 Speaker 6: counterparts do, which is why we've seen a lot of 85 00:04:54,680 --> 00:04:57,840 Speaker 6: British bond tech companies, including arm as you mentioned, choosing 86 00:04:57,880 --> 00:04:58,920 Speaker 6: New York over London. 87 00:05:00,680 --> 00:05:02,600 Speaker 5: All Right, it is the IPO to watch this year, 88 00:05:02,640 --> 00:05:05,120 Speaker 5: irrespective of where it ends up happening. Bloomberg, Sweat to 89 00:05:05,120 --> 00:05:06,560 Speaker 5: go Op and have great to catch up with you. 90 00:05:06,920 --> 00:05:09,080 Speaker 5: Thank you now. Staying with news out of the UK, 91 00:05:09,520 --> 00:05:13,400 Speaker 5: Sony's PlayStation London, best known for the SingStar series as 92 00:05:13,400 --> 00:05:17,479 Speaker 5: well as multiple virtual reality games, will shut down. Sony's 93 00:05:17,520 --> 00:05:21,440 Speaker 5: also laying off around nine hundred employees, accounting for about 94 00:05:21,520 --> 00:05:24,600 Speaker 5: eight percent of its video game division. The company said 95 00:05:24,600 --> 00:05:27,200 Speaker 5: that the layoffs will affect game makers across three of 96 00:05:27,240 --> 00:05:31,960 Speaker 5: its most successful subsidiaries, Insomniac, the studio behind Spider Man, 97 00:05:32,200 --> 00:05:36,839 Speaker 5: Naughty Dog behind the Last of Us, and Gorilla behind Horizon. 98 00:05:36,839 --> 00:05:39,200 Speaker 5: The news comes after the company reported earlier this month 99 00:05:39,240 --> 00:05:42,240 Speaker 5: that it would be cutting projects. Projection sorry for its 100 00:05:42,240 --> 00:05:46,400 Speaker 5: PlayStation five console. Head of PlayStation Studios Herman Holst said 101 00:05:46,400 --> 00:05:49,000 Speaker 5: in a note to staff Tuesday that the company has 102 00:05:49,040 --> 00:05:54,560 Speaker 5: also decided to cancel several games that were in development. Okay, 103 00:05:54,600 --> 00:05:56,760 Speaker 5: let's turn to none other than in Video. It is 104 00:05:56,800 --> 00:05:59,040 Speaker 5: the stock to watch most days of the week. Florida 105 00:05:59,080 --> 00:06:04,000 Speaker 5: Governor Rondasis initially predicted that a super computer bankrolled by 106 00:06:04,040 --> 00:06:07,520 Speaker 5: billionaire co founder of the chip Giant Chris Malakowski at 107 00:06:07,520 --> 00:06:11,200 Speaker 5: the University of Florida would be a magnet for AI talent. 108 00:06:11,560 --> 00:06:16,239 Speaker 5: But almost four years later, DeSantis's staunch anti China stance 109 00:06:16,600 --> 00:06:19,880 Speaker 5: is preventing some of the most highly skilled AI researchers 110 00:06:20,080 --> 00:06:22,839 Speaker 5: from ever setting foot in the state. Joining us with 111 00:06:22,880 --> 00:06:25,640 Speaker 5: the story is Bloomberg's Michael Smith, and what a story. 112 00:06:25,680 --> 00:06:27,839 Speaker 5: It is one of the most read on all Bloomberg 113 00:06:27,920 --> 00:06:30,640 Speaker 5: platforms this morning. Just explain what's happening here. There is 114 00:06:30,640 --> 00:06:35,039 Speaker 5: a supercomputer in Florida. It includes in Vidia backing and 115 00:06:35,120 --> 00:06:38,760 Speaker 5: Nvidia tech. But that computer is not getting the computer 116 00:06:38,839 --> 00:06:40,680 Speaker 5: scientists it needs to support it. 117 00:06:41,960 --> 00:06:45,160 Speaker 1: Yeah, that's basically it. Basically. 118 00:06:45,240 --> 00:06:48,480 Speaker 7: Last year, Governor DeSantis, as part of his sort of 119 00:06:49,040 --> 00:06:53,080 Speaker 7: campaign against Undo influence by China and the state of 120 00:06:53,120 --> 00:06:57,760 Speaker 7: Florida and in the country backed and heavily supported a 121 00:06:57,839 --> 00:07:03,320 Speaker 7: law that basically the legislature pass that basically prohibits state 122 00:07:03,400 --> 00:07:10,080 Speaker 7: universities in Florida from hiring graduate students PhD students from China, 123 00:07:10,520 --> 00:07:13,680 Speaker 7: Irran and five other countries of concern as they call them. 124 00:07:14,520 --> 00:07:17,480 Speaker 7: They just basically cannot bring them over to study and 125 00:07:17,800 --> 00:07:22,840 Speaker 7: perform research in any field, including AI. So that's really 126 00:07:22,880 --> 00:07:25,680 Speaker 7: put a hamper on what the University of Florida does. 127 00:07:25,920 --> 00:07:28,680 Speaker 7: And Nvidia comes in because a few years ago they 128 00:07:28,680 --> 00:07:33,840 Speaker 7: built what's called the Hypergator AI. It's the world's fastest 129 00:07:33,840 --> 00:07:39,280 Speaker 7: supercomputer on a college campus. And Chris Malakowski, who's an 130 00:07:39,320 --> 00:07:43,160 Speaker 7: alumni alumni he went to the University of Florida, basically 131 00:07:43,240 --> 00:07:46,400 Speaker 7: paid for it in conjunction with Navidia and the university 132 00:07:46,480 --> 00:07:49,400 Speaker 7: kicking some money. So they got this amazing seventy million 133 00:07:49,440 --> 00:07:53,240 Speaker 7: dollar computer, but they have a shortage of top talent 134 00:07:53,360 --> 00:07:56,760 Speaker 7: researchers to take advantage of it. So that's really putting 135 00:07:56,800 --> 00:07:58,800 Speaker 7: a hamper on what they want to do in Florida 136 00:07:59,080 --> 00:07:59,560 Speaker 7: with AI. 137 00:08:00,800 --> 00:08:02,640 Speaker 5: Mike I just point out for our audience that in 138 00:08:02,680 --> 00:08:05,600 Speaker 5: Nvidia declined to comment for your story, but I did 139 00:08:05,640 --> 00:08:09,440 Speaker 5: speak two weeks ago with Nvidia's CEO, Jensen Huang, and 140 00:08:09,440 --> 00:08:11,880 Speaker 5: on the issue of China, this is what he had 141 00:08:11,920 --> 00:08:12,240 Speaker 5: to say. 142 00:08:12,240 --> 00:08:16,520 Speaker 8: Have listen, we have to comply with American policies and 143 00:08:17,000 --> 00:08:19,640 Speaker 8: whatever the rules and regulations are, and the laws. 144 00:08:19,440 --> 00:08:21,720 Speaker 1: Are well number one, comply with that. 145 00:08:21,760 --> 00:08:24,040 Speaker 8: Our goal or in the United States would love to 146 00:08:24,040 --> 00:08:27,679 Speaker 8: see us be a successful country in one of the 147 00:08:27,720 --> 00:08:30,200 Speaker 8: pillars of national security of successful industries. 148 00:08:31,960 --> 00:08:34,280 Speaker 5: So the Nvidia side of the story is pretty clear, right, 149 00:08:34,320 --> 00:08:36,960 Speaker 5: we comply with the rules and regulations of the country 150 00:08:37,040 --> 00:08:40,400 Speaker 5: we're from, and they want America to be the most 151 00:08:40,400 --> 00:08:46,400 Speaker 5: competitive source of AI talent and operation go to run 152 00:08:46,440 --> 00:08:48,720 Speaker 5: the CENSUSUS side of this. In Florida's side of this, 153 00:08:48,800 --> 00:08:52,120 Speaker 5: what is the concern that they have in letting in 154 00:08:52,280 --> 00:08:56,400 Speaker 5: talent from overseas working in the field of artificial intelligence. 155 00:08:57,320 --> 00:09:01,000 Speaker 1: Well, Desantas has made this a big talking point. 156 00:09:01,040 --> 00:09:04,199 Speaker 7: If you will, uh he believes that or he says 157 00:09:04,320 --> 00:09:07,840 Speaker 7: that there's you know, you have to worry about Chinese 158 00:09:07,920 --> 00:09:09,359 Speaker 7: interests purchasing land. 159 00:09:09,080 --> 00:09:14,199 Speaker 1: For example, uh or or or you know, or getting. 160 00:09:13,920 --> 00:09:18,199 Speaker 7: Access to technology via universities because they might be basically 161 00:09:18,360 --> 00:09:22,400 Speaker 7: spying on behalf of the Chinese military, the government, or 162 00:09:22,440 --> 00:09:24,960 Speaker 7: even Chinese industry and stealing secrets. 163 00:09:24,520 --> 00:09:24,920 Speaker 1: Et cetera. 164 00:09:25,320 --> 00:09:27,480 Speaker 7: So in the case of and the law that he 165 00:09:27,559 --> 00:09:30,400 Speaker 7: passed covered all those areas, but a big focus is 166 00:09:30,840 --> 00:09:34,600 Speaker 7: the university side of it. So he they basically passed 167 00:09:34,600 --> 00:09:37,960 Speaker 7: this law saying, you know, major restrictions on bringing in 168 00:09:38,040 --> 00:09:44,600 Speaker 7: talent researchers from China, and he's really latching onto something 169 00:09:44,640 --> 00:09:48,600 Speaker 7: that's happened in the United States and other countries. For example, 170 00:09:49,320 --> 00:09:52,880 Speaker 7: the Trump administration issued a proclamation that allowed that ordered 171 00:09:52,920 --> 00:09:55,840 Speaker 7: the State Department to reject the visa of any aspiring 172 00:09:55,880 --> 00:09:59,520 Speaker 7: PhD student from China and several other countries who were 173 00:09:59,559 --> 00:10:02,839 Speaker 7: suspected of having any ties with the military or the 174 00:10:02,880 --> 00:10:06,000 Speaker 7: government in China. And they've been doing that, and the 175 00:10:06,000 --> 00:10:09,400 Speaker 7: Biden administration has continued that. I mean, they rejected one 176 00:10:09,480 --> 00:10:12,680 Speaker 7: year that we know almost two thousand students were not 177 00:10:12,760 --> 00:10:15,600 Speaker 7: allowed to get visas to come and be PhD researchers 178 00:10:16,120 --> 00:10:21,160 Speaker 7: in the technology fields. But the Florida law is probably 179 00:10:21,160 --> 00:10:24,120 Speaker 7: the most draconian the way because it absolutely puts an 180 00:10:24,240 --> 00:10:27,120 Speaker 7: end to it makes it impossible for professors to bring 181 00:10:27,160 --> 00:10:30,680 Speaker 7: in talent from China and Iran. That's another key market 182 00:10:30,720 --> 00:10:35,080 Speaker 7: for top AI talent in the world. Iranian PhD students. 183 00:10:36,600 --> 00:10:40,400 Speaker 7: Last year they just the University of Florida, that's the 184 00:10:40,440 --> 00:10:46,480 Speaker 7: flagship university where the hypergata is actually based, brought one 185 00:10:46,520 --> 00:10:50,880 Speaker 7: thousand students from the seven countries of concern, principally China 186 00:10:50,920 --> 00:10:52,880 Speaker 7: that are listening to this law that are now banned. 187 00:10:53,200 --> 00:10:54,920 Speaker 1: So this year. Last year they. 188 00:10:54,880 --> 00:11:00,760 Speaker 7: Brought in a thousand this year zero. If you talk 189 00:11:00,840 --> 00:11:04,200 Speaker 7: to professors, they say that this is really hampering their 190 00:11:04,240 --> 00:11:05,920 Speaker 7: ability to do cutting edge research. 191 00:11:06,240 --> 00:11:09,320 Speaker 5: Bloombers Michael Smith with a very important piece of reporting, 192 00:11:09,480 --> 00:11:22,040 Speaker 5: very well read on all bloombo platforms this morning. Thank you. Okay, 193 00:11:22,080 --> 00:11:24,280 Speaker 5: time for some news and talking tech. First off, Ali 194 00:11:24,320 --> 00:11:28,160 Speaker 5: Barber leading the largest single financing round for a Chinese 195 00:11:28,240 --> 00:11:31,160 Speaker 5: AI startup. The one billion dollar funding round is the 196 00:11:31,240 --> 00:11:34,600 Speaker 5: latest in a string of sizable investments that suggest the 197 00:11:34,960 --> 00:11:38,360 Speaker 5: e Commace firm is deploying capital in the hunt for growth. 198 00:11:38,360 --> 00:11:41,880 Speaker 5: Founded last March, moonshot Ai is among the better known 199 00:11:42,000 --> 00:11:46,439 Speaker 5: startups developing generative AI in China, hoping to eventually match 200 00:11:46,480 --> 00:11:50,480 Speaker 5: the likes of open Ai and Google Plus. Exodigo, a 201 00:11:50,559 --> 00:11:55,319 Speaker 5: startup that uses artificial intelligence and sensors to map the underground, 202 00:11:55,559 --> 00:11:58,000 Speaker 5: closed a one hundred and five million dollar funding round 203 00:11:58,120 --> 00:12:01,840 Speaker 5: led by Greenfield Partners and ZEV Ventures. Established in June 204 00:12:01,880 --> 00:12:05,120 Speaker 5: twenty twenty one. The company makes underground maps used by 205 00:12:05,120 --> 00:12:10,000 Speaker 5: some of the world's largest energy, utility, transportation, and construction companies, 206 00:12:10,440 --> 00:12:13,240 Speaker 5: and intense I has raised sixty four million dollars in 207 00:12:13,280 --> 00:12:16,440 Speaker 5: a funding round led by light Speed Venture Partners to 208 00:12:16,559 --> 00:12:20,880 Speaker 5: develop AI that can detect and help fix potential hazards 209 00:12:20,960 --> 00:12:23,520 Speaker 5: in the workplace. The company says it can help save 210 00:12:23,640 --> 00:12:27,520 Speaker 5: lives and reduce economic losses from millions of accidents every year. 211 00:12:27,760 --> 00:12:31,200 Speaker 5: The New York based startup's latest financing takes its total 212 00:12:31,240 --> 00:12:34,520 Speaker 5: funding to about ninety million dollars to date. Okay, let's 213 00:12:34,520 --> 00:12:37,640 Speaker 5: stick with AI the big story. Google plans to bring 214 00:12:37,800 --> 00:12:41,520 Speaker 5: back its AI feature that generates images of people in 215 00:12:41,640 --> 00:12:44,200 Speaker 5: quote the next couple of weeks. That, according to the 216 00:12:44,240 --> 00:12:49,319 Speaker 5: company's top AI executive, Google's Google Gemini's image generator has 217 00:12:49,360 --> 00:12:54,679 Speaker 5: been paused since last week due to criticism over inaccurate 218 00:12:54,840 --> 00:12:59,520 Speaker 5: historical depictions of race. A recent note from Mellis Research 219 00:12:59,559 --> 00:13:02,880 Speaker 5: says that quote the issue for the stock is not 220 00:13:03,000 --> 00:13:07,880 Speaker 5: the debate itself. It is the perception of truth behind 221 00:13:08,200 --> 00:13:10,959 Speaker 5: the brand. Here to explain that quote is the man 222 00:13:11,000 --> 00:13:14,200 Speaker 5: behind the research, Ben writes, his managing director and head 223 00:13:14,200 --> 00:13:18,240 Speaker 5: of technology research at Melius. I spotted this across the 224 00:13:18,240 --> 00:13:22,360 Speaker 5: Bloomberg terminal. It hit my inbox. And what you're referring to, 225 00:13:22,440 --> 00:13:25,880 Speaker 5: I think is the debate that's happening on social media, 226 00:13:26,240 --> 00:13:30,560 Speaker 5: all kinds of people weighing in about the risk of 227 00:13:30,640 --> 00:13:32,880 Speaker 5: Google getting it wrong with Gemini. Is that right? 228 00:13:34,120 --> 00:13:34,320 Speaker 3: Yeah? 229 00:13:34,400 --> 00:13:36,800 Speaker 9: I think that you want to make sure they don't 230 00:13:36,840 --> 00:13:40,559 Speaker 9: have a bud light moment, and we're not sure yet. 231 00:13:41,400 --> 00:13:44,199 Speaker 9: I don't want to weigh in on the merits of 232 00:13:44,640 --> 00:13:45,960 Speaker 9: what they did or the debate. 233 00:13:46,240 --> 00:13:47,360 Speaker 3: It's just real simple. 234 00:13:47,920 --> 00:13:49,000 Speaker 1: When you alienate a. 235 00:13:48,960 --> 00:13:51,439 Speaker 9: Part of the population and they believe that you may 236 00:13:51,480 --> 00:13:54,200 Speaker 9: not be a source of truth, that's not good for 237 00:13:54,320 --> 00:13:56,280 Speaker 9: business in their business. 238 00:13:56,600 --> 00:14:00,199 Speaker 3: And they did that, and they need to fix it, and. 239 00:14:00,160 --> 00:14:04,240 Speaker 9: They need to start getting these launches tighter too. You 240 00:14:04,280 --> 00:14:10,120 Speaker 9: can't put out a product that's not ready and shows ideology. 241 00:14:09,600 --> 00:14:11,000 Speaker 3: So it's tough. 242 00:14:11,440 --> 00:14:14,800 Speaker 9: We believe we're at a once in a lifetime crossroads 243 00:14:14,840 --> 00:14:19,360 Speaker 9: here where search is going to hand off to AI 244 00:14:19,680 --> 00:14:22,120 Speaker 9: features and if they're not a source of trust, that's 245 00:14:22,160 --> 00:14:27,080 Speaker 9: a big deal and we're surprised investors or even taking it. 246 00:14:27,360 --> 00:14:28,480 Speaker 3: You know this. 247 00:14:28,680 --> 00:14:32,160 Speaker 5: Calmly, and when you cover this stock, I'm assuming that 248 00:14:32,320 --> 00:14:36,360 Speaker 5: historically you focused on it as an advertising business. We're 249 00:14:36,360 --> 00:14:39,800 Speaker 5: going to show Google's response to what's happening on your 250 00:14:39,840 --> 00:14:42,720 Speaker 5: screen right now. But how have you had to adapt 251 00:14:43,200 --> 00:14:47,200 Speaker 5: your modeling an analysis of this company given the kind 252 00:14:47,240 --> 00:14:50,240 Speaker 5: of shift over in focus to artificial intelligence. 253 00:14:51,520 --> 00:14:55,200 Speaker 9: Well, we listen, We do the best we can. The 254 00:14:55,760 --> 00:14:59,040 Speaker 9: issue there's not any guidance, of course from the company, 255 00:14:59,040 --> 00:15:03,640 Speaker 9: which is their style, but the issue here, you know, 256 00:15:04,000 --> 00:15:06,560 Speaker 9: you've seen from Gartner that they believe twenty five percent 257 00:15:06,720 --> 00:15:09,080 Speaker 9: of traditional search is going away in a few years 258 00:15:09,400 --> 00:15:12,480 Speaker 9: to be cannibalized by AI search. Not really sure how 259 00:15:12,480 --> 00:15:16,080 Speaker 9: it got there. What we do know is search should 260 00:15:16,200 --> 00:15:20,000 Speaker 9: change due to AI. Google has already basically told you 261 00:15:20,040 --> 00:15:23,000 Speaker 9: that with their search generative experience where it's kind of 262 00:15:23,000 --> 00:15:27,240 Speaker 9: a hybrid. We think that there's upstarts such as Perplexity 263 00:15:27,280 --> 00:15:29,480 Speaker 9: AI and whatever open AI is going to do that 264 00:15:29,560 --> 00:15:31,440 Speaker 9: are going to get a good look from a lot 265 00:15:31,440 --> 00:15:34,400 Speaker 9: of younger consumers that may change their habits as we. 266 00:15:34,480 --> 00:15:35,440 Speaker 3: Enter in AI world. 267 00:15:36,240 --> 00:15:40,520 Speaker 5: One individual who is paying close attention to the Gemini 268 00:15:40,520 --> 00:15:44,200 Speaker 5: situation is Elon Musk. He's posted a lot on his 269 00:15:44,360 --> 00:15:46,840 Speaker 5: platform X in the last forty eight hours. This is 270 00:15:46,840 --> 00:15:48,880 Speaker 5: the first line of something that caught my eye. Given 271 00:15:48,880 --> 00:15:52,080 Speaker 5: that GEMINIAI will be at the heart of every Google 272 00:15:52,120 --> 00:15:55,840 Speaker 5: product and YouTube, this is extremely allarmy. It kind of 273 00:15:55,840 --> 00:15:59,040 Speaker 5: speaks to the point that you just made, Ben, how 274 00:15:59,080 --> 00:16:02,320 Speaker 5: big a risk fact is it to Alphabet, the parent 275 00:16:02,360 --> 00:16:06,120 Speaker 5: of Google, that Elon Musk clearly is focused on this 276 00:16:06,640 --> 00:16:09,640 Speaker 5: and is keeping it in the public consciousness for the 277 00:16:09,680 --> 00:16:10,200 Speaker 5: time being. 278 00:16:11,320 --> 00:16:12,080 Speaker 3: Well, it's not good. 279 00:16:12,080 --> 00:16:15,920 Speaker 9: And Elon as is well chronicled on why he could 280 00:16:15,920 --> 00:16:19,400 Speaker 9: be upset as well with what happened when the model 281 00:16:19,720 --> 00:16:24,560 Speaker 9: asked questions about him versus some unsavory You know, gentlemen, 282 00:16:25,120 --> 00:16:29,880 Speaker 9: the issue here is Elon does have a big microphone. 283 00:16:29,920 --> 00:16:32,400 Speaker 9: And like I said, I mean again, I don't want 284 00:16:32,400 --> 00:16:34,280 Speaker 9: to weigh in on the merits of the debate or 285 00:16:34,320 --> 00:16:38,240 Speaker 9: what was potentially done, but you you know, they're in 286 00:16:38,280 --> 00:16:42,520 Speaker 9: a business of trust of information and that has been 287 00:16:42,680 --> 00:16:47,640 Speaker 9: rattled and I think that Elon has a big microphone, 288 00:16:47,880 --> 00:16:51,960 Speaker 9: and obviously with his platform of X which he controls. 289 00:16:52,440 --> 00:16:54,400 Speaker 9: So you know, we've been keeping track of a lot 290 00:16:54,440 --> 00:16:57,080 Speaker 9: of the fodder on it and we'll see if that 291 00:16:57,120 --> 00:16:59,760 Speaker 9: has an impact. But the average consumer doesn't know the 292 00:16:59,760 --> 00:17:02,960 Speaker 9: differ between you know, a lot of the different products too, 293 00:17:03,080 --> 00:17:05,359 Speaker 9: so they see lack of truth in one thing, they 294 00:17:05,400 --> 00:17:07,200 Speaker 9: might think there's lack of truth in another thing. 295 00:17:08,520 --> 00:17:12,440 Speaker 5: All right, Melia's Research Managing Director and head of Technology Research, 296 00:17:12,520 --> 00:17:14,800 Speaker 5: Ben Wrights. This and Google. We was rebounding today. Now 297 00:17:14,800 --> 00:17:17,920 Speaker 5: coming up from the show, GitHub making co pilot enterprise 298 00:17:18,000 --> 00:17:22,000 Speaker 5: and AI offering that helps streamline code navigation and comprehension 299 00:17:22,040 --> 00:17:24,880 Speaker 5: and available for just thirty nine dollars per user per month. 300 00:17:24,920 --> 00:17:27,320 Speaker 5: We speak to GitHub ceo coming up next. We're going 301 00:17:27,359 --> 00:17:28,919 Speaker 5: to take a short break. We'll be right back. This 302 00:17:28,960 --> 00:17:50,119 Speaker 5: is Bloomberg Technology, all right. Today gethub and AI powered 303 00:17:50,119 --> 00:17:55,000 Speaker 5: Developer platform is making GitHub Copilot Enterprise available to every 304 00:17:55,080 --> 00:17:58,880 Speaker 5: organization for just thirty nine dollars per user per month. 305 00:17:58,960 --> 00:18:02,359 Speaker 5: The AI offering is per personalized for each organization, making 306 00:18:02,400 --> 00:18:06,159 Speaker 5: it possible to streamline code navigation and comprehension. Here with 307 00:18:06,240 --> 00:18:09,560 Speaker 5: more GitHub ceo Thomas Donkey and Thomas good morning to you. 308 00:18:09,920 --> 00:18:12,600 Speaker 5: I guess if you're a novice coder like me, or 309 00:18:12,600 --> 00:18:17,399 Speaker 5: you're a veteran. This is a useful application, and I 310 00:18:17,440 --> 00:18:20,840 Speaker 5: guess you're targeting those two very different end markets. 311 00:18:21,600 --> 00:18:22,480 Speaker 3: Yeah, absolutely No. 312 00:18:22,640 --> 00:18:24,280 Speaker 4: Ed what we have seen in the last years that 313 00:18:24,359 --> 00:18:27,639 Speaker 4: companies no longer just look at digital transformation, they're actually 314 00:18:27,640 --> 00:18:30,560 Speaker 4: looking into the AI transformation and get a Core. 315 00:18:30,600 --> 00:18:32,680 Speaker 3: Pilot started it all by giving. 316 00:18:32,480 --> 00:18:37,040 Speaker 4: Developers AI based order suggestions, not order completion in the editor, 317 00:18:37,640 --> 00:18:41,360 Speaker 4: and with Corpolate, enterprises can now be customized for all 318 00:18:41,400 --> 00:18:44,960 Speaker 4: the internal knowledge of an organization, the code based Imagine 319 00:18:45,000 --> 00:18:46,920 Speaker 4: you now you're a new developer at Bloomberg TV. 320 00:18:47,240 --> 00:18:50,439 Speaker 3: It's your first day, and you can just ask corpilot. 321 00:18:50,080 --> 00:18:52,760 Speaker 4: Holl things I don't have Bloomberg all the instant intutional 322 00:18:52,880 --> 00:18:54,399 Speaker 4: knowledge at your fingertips. 323 00:18:54,800 --> 00:18:57,320 Speaker 5: A part of this in some of the similar tools 324 00:18:57,320 --> 00:18:59,880 Speaker 5: I've seen, is kind of the predictive nature. Right, You're right, 325 00:19:00,080 --> 00:19:03,840 Speaker 5: a line of code and the rest is auto completed. 326 00:19:04,440 --> 00:19:07,840 Speaker 5: What is the performance and competence level of your technology 327 00:19:08,520 --> 00:19:12,040 Speaker 5: vis a v a veteran coder who could write it themselves. 328 00:19:12,720 --> 00:19:15,920 Speaker 3: You know, it actually depends on what you're doing yourself. 329 00:19:16,000 --> 00:19:18,400 Speaker 4: You see a lot of developers are learning to use 330 00:19:18,440 --> 00:19:21,440 Speaker 4: Core Pilot while typing code. You know, you figure out 331 00:19:22,040 --> 00:19:24,040 Speaker 4: when you write a couple of characters in. 332 00:19:24,000 --> 00:19:26,240 Speaker 3: Your editor that if you if you do it in. 333 00:19:26,240 --> 00:19:29,320 Speaker 4: A certain way, you get a better suggestion, you get 334 00:19:29,359 --> 00:19:33,080 Speaker 4: a better answer. And so developers increase their proficiency level 335 00:19:33,400 --> 00:19:35,840 Speaker 4: as they're using core Pilot. We see that they're getting 336 00:19:35,920 --> 00:19:39,119 Speaker 4: more productive, you know, up to fifty five percent in 337 00:19:39,200 --> 00:19:43,720 Speaker 4: case studies that we have blended NBSC developers actually be measurable, 338 00:19:44,119 --> 00:19:46,280 Speaker 4: more more fulfilled, more happy. 339 00:19:47,720 --> 00:19:51,240 Speaker 5: Thomas Microsoft, You your parent company, was pretty stoked about 340 00:19:51,640 --> 00:19:55,080 Speaker 5: the base version of co Pilot. Do you think thirty 341 00:19:55,160 --> 00:19:58,720 Speaker 5: nine dollars a month will will see the enterprise version 342 00:19:58,760 --> 00:19:59,359 Speaker 5: gain traction? 343 00:20:00,040 --> 00:20:00,639 Speaker 3: Absolutely? 344 00:20:00,680 --> 00:20:03,000 Speaker 4: You know, the attraction that we have seen over the 345 00:20:03,080 --> 00:20:05,760 Speaker 4: last year has been phenomenal. We have over fifty thousand 346 00:20:05,880 --> 00:20:09,359 Speaker 4: organizations already on board to copilot, more than one point 347 00:20:09,359 --> 00:20:12,720 Speaker 4: three million paid users, and we see a lot of 348 00:20:12,800 --> 00:20:16,320 Speaker 4: excitement from all kinds of industries. It's no longer just 349 00:20:16,400 --> 00:20:19,439 Speaker 4: the cool startups and the tech companies. It's it's you know, 350 00:20:19,480 --> 00:20:24,240 Speaker 4: pharmaceutical companies, automotive companies, lots of financial services institution, the 351 00:20:24,280 --> 00:20:27,520 Speaker 4: global system, and the greaters like Accentia, and so we 352 00:20:27,600 --> 00:20:30,639 Speaker 4: see tremendous excitement there. We have run the preview of 353 00:20:30,720 --> 00:20:34,520 Speaker 4: Coporate Enterprise for the last three months and receive feedback 354 00:20:34,560 --> 00:20:38,280 Speaker 4: from companies like Pigma Shopping fire Fellows telling us that 355 00:20:38,400 --> 00:20:41,240 Speaker 4: the collaboration is getting so much better if coplat knows 356 00:20:41,280 --> 00:20:43,000 Speaker 4: about what's happening within the company. 357 00:20:44,040 --> 00:20:46,680 Speaker 5: Bloomberg reported earlier this month that Apple is looking to 358 00:20:46,680 --> 00:20:50,040 Speaker 5: see something very similar with x code, you know, a 359 00:20:50,160 --> 00:20:52,560 Speaker 5: very similar tool. What do you make of that? 360 00:20:52,800 --> 00:20:53,000 Speaker 6: You know? 361 00:20:53,119 --> 00:20:56,880 Speaker 5: Clearly this is becoming a battleground the use of generative 362 00:20:56,880 --> 00:20:58,920 Speaker 5: AI within the writing of code. 363 00:20:59,200 --> 00:21:00,959 Speaker 4: You know, I think as a developer, this is the 364 00:21:01,000 --> 00:21:03,800 Speaker 4: most exciting era that I have seen over the last 365 00:21:03,840 --> 00:21:07,120 Speaker 4: thirty years, where all these companies, including Microsoft and get 366 00:21:07,200 --> 00:21:10,080 Speaker 4: up are working to make develop us more productive, to 367 00:21:10,760 --> 00:21:13,280 Speaker 4: have them focus on the things they love doing and 368 00:21:13,400 --> 00:21:15,680 Speaker 4: have AI help them with the things that they don't 369 00:21:15,720 --> 00:21:18,080 Speaker 4: love to do. Right, this is exciting for developers to 370 00:21:18,200 --> 00:21:21,200 Speaker 4: work load. The amount of code that develops have to 371 00:21:21,240 --> 00:21:24,119 Speaker 4: manage today is so big that we really need to 372 00:21:24,160 --> 00:21:25,240 Speaker 4: bring the effort. 373 00:21:24,920 --> 00:21:27,400 Speaker 3: Down of doing. 374 00:21:26,440 --> 00:21:29,600 Speaker 4: All this work, maintaining all this legacy code, code that 375 00:21:29,760 --> 00:21:32,440 Speaker 4: lasts back you know, until the nineteen sixties with Cobal 376 00:21:33,119 --> 00:21:34,920 Speaker 4: and for to one and other languages that are still 377 00:21:35,000 --> 00:21:36,240 Speaker 4: running in our production system. 378 00:21:36,280 --> 00:21:37,760 Speaker 3: So it's really exciting. 379 00:21:38,040 --> 00:21:40,600 Speaker 4: You don't see all the innovation, and we hope we 380 00:21:40,680 --> 00:21:41,439 Speaker 4: stay at the. 381 00:21:41,440 --> 00:21:42,080 Speaker 3: Forefront of this. 382 00:21:42,760 --> 00:21:45,920 Speaker 5: It's going to make hackathons interesting. Gehub CEO Thomas steine 383 00:21:45,960 --> 00:21:47,879 Speaker 5: Key get to catch up with you. Thank you for 384 00:21:47,920 --> 00:21:51,160 Speaker 5: your sign Now coming up, Canadate introduces an online safety law. 385 00:21:51,320 --> 00:21:54,800 Speaker 5: It's the whole social media. Companies responsible for harmful content 386 00:21:54,840 --> 00:22:09,280 Speaker 5: will bring you those deeds. Next, this is Bloomberg. Welcome 387 00:22:09,280 --> 00:22:12,240 Speaker 5: back to Bloomberg Technology, Ed Ludlow Here in San Francisco. 388 00:22:12,240 --> 00:22:14,720 Speaker 5: You can see behind me Microsoft down around half a 389 00:22:14,760 --> 00:22:17,199 Speaker 5: percentage point, but that does make it the biggest points 390 00:22:17,280 --> 00:22:20,879 Speaker 5: drag on the Nasdaq one hundred. A lot of reporting 391 00:22:20,920 --> 00:22:23,399 Speaker 5: in recent a lot of reporting for the last twelve 392 00:22:23,400 --> 00:22:26,600 Speaker 5: months about Microsoft's efforts in AI, but this one story 393 00:22:26,640 --> 00:22:30,920 Speaker 5: seems to be relevant. Microsoft's investment into AI startup Mistrel 394 00:22:31,280 --> 00:22:34,560 Speaker 5: is facing scrutiny in the EU, A day after the 395 00:22:34,600 --> 00:22:39,159 Speaker 5: company announced a strategic partnership that includes making the startup's 396 00:22:39,240 --> 00:22:43,800 Speaker 5: latest AI models available to customers of Microsoft's Azure Cloud. 397 00:22:43,800 --> 00:22:46,480 Speaker 5: I want to go out to London and Bloomberg's Mark Bergen. 398 00:22:47,119 --> 00:22:51,400 Speaker 5: We know very clearly the relationship between Microsoft and open Ai. 399 00:22:52,119 --> 00:22:55,800 Speaker 5: So now they're making a relationship with Mistral, and very 400 00:22:55,880 --> 00:22:59,040 Speaker 5: quickly the EU wants to look at it. What do we. 401 00:22:59,040 --> 00:23:02,600 Speaker 10: Know, Yeah, this is effectively what we know. 402 00:23:02,720 --> 00:23:04,639 Speaker 2: I mean, we know that the relations to the numbers 403 00:23:04,640 --> 00:23:05,359 Speaker 2: are very different. 404 00:23:05,440 --> 00:23:05,560 Speaker 9: Right. 405 00:23:05,600 --> 00:23:08,040 Speaker 10: So Microsoft has put it up to about thirteen billion 406 00:23:08,080 --> 00:23:12,199 Speaker 10: into open ai, largest financial backer, largest partner for a 407 00:23:12,240 --> 00:23:15,640 Speaker 10: number of years since is twenty nineteen. The Mistral partnership 408 00:23:15,680 --> 00:23:19,720 Speaker 10: includes from Microsoft's and they've told this a fifteen million 409 00:23:20,359 --> 00:23:23,639 Speaker 10: dollar investment, so I think actually fifteen million euro sorry, 410 00:23:23,640 --> 00:23:28,320 Speaker 10: so pretty small, certainly relative to their share and open Ai. 411 00:23:28,640 --> 00:23:31,119 Speaker 2: This is for both sides, you know. For Mistrel, this 412 00:23:31,680 --> 00:23:32,240 Speaker 2: makes them the. 413 00:23:32,320 --> 00:23:35,919 Speaker 10: Second company after open Ai to have their language models 414 00:23:35,960 --> 00:23:40,119 Speaker 10: available for customers on Microsoft's Azure cloud. And so this 415 00:23:40,280 --> 00:23:43,240 Speaker 10: is for them, you know, they've been there French so 416 00:23:43,320 --> 00:23:46,479 Speaker 10: far they've been well pretty relegated, I say, to Europe. 417 00:23:46,480 --> 00:23:48,879 Speaker 10: But this certainly gives them a much bigger potential to 418 00:23:48,880 --> 00:23:51,439 Speaker 10: have a global reach. And for Microsoft, they can go 419 00:23:51,480 --> 00:23:54,399 Speaker 10: out to regulators and say, look, we are not just 420 00:23:54,440 --> 00:23:57,200 Speaker 10: tied to open Ai. We have a partnership with this 421 00:23:57,680 --> 00:24:01,200 Speaker 10: French and European Championship that is using open source, which 422 00:24:01,240 --> 00:24:02,880 Speaker 10: is one of the things that the Microsoft has talked 423 00:24:02,920 --> 00:24:04,240 Speaker 10: a lot about champion recently. 424 00:24:05,440 --> 00:24:08,919 Speaker 5: We had EU Competition Commissioner Margareta invest there here in 425 00:24:09,080 --> 00:24:11,520 Speaker 5: SF a few weeks ago, and when I sat down 426 00:24:11,520 --> 00:24:13,840 Speaker 5: with her, she explained, we're basically going to look at 427 00:24:13,880 --> 00:24:18,439 Speaker 5: the entire industry for AI and understand what's happening. And 428 00:24:18,440 --> 00:24:20,359 Speaker 5: then I think back to how do things normally go 429 00:24:20,440 --> 00:24:22,960 Speaker 5: in Europe? They look at things all the time. I 430 00:24:22,960 --> 00:24:26,000 Speaker 5: think our understanding is that the commission got a copy 431 00:24:26,040 --> 00:24:30,360 Speaker 5: of the agreement between the two parties, miss Strau and Microsoft. 432 00:24:31,240 --> 00:24:33,960 Speaker 5: What happens next, you know, do they face a serious 433 00:24:34,000 --> 00:24:36,760 Speaker 5: probe or a problem? 434 00:24:37,320 --> 00:24:40,359 Speaker 10: A certainly it's probably more pressure on Microsoft's and right 435 00:24:40,400 --> 00:24:42,720 Speaker 10: they're facing a lot of they're facing a prober and 436 00:24:42,720 --> 00:24:45,679 Speaker 10: their own partnership with open Ai. They're sort of the 437 00:24:45,840 --> 00:24:48,560 Speaker 10: poster child for what has happened in the past two years. 438 00:24:48,600 --> 00:24:51,760 Speaker 10: Is this relationship between these big tech companies that have 439 00:24:51,880 --> 00:24:57,040 Speaker 10: in Microsoft, Google, Amazon, We've seeing Salesforce and Video Intel 440 00:24:57,600 --> 00:25:00,399 Speaker 10: and make these pretty large, substantial investments in the generative 441 00:25:00,400 --> 00:25:03,080 Speaker 10: AI companies. On the other side, the flip side that 442 00:25:03,160 --> 00:25:06,280 Speaker 10: comes with sort of a strings attached where they're going 443 00:25:06,320 --> 00:25:09,960 Speaker 10: to be using. In this case, Mistrel is using Microsoft's cloud. 444 00:25:10,240 --> 00:25:13,480 Speaker 10: Open AI is using Microsoft's cloud. Google's investments are going 445 00:25:13,480 --> 00:25:15,640 Speaker 10: back and using their cloud, and I think that's sort 446 00:25:15,640 --> 00:25:18,720 Speaker 10: of that's certainly what regulators have been talking about is 447 00:25:18,800 --> 00:25:20,200 Speaker 10: unpacking those relationships. 448 00:25:21,000 --> 00:25:23,720 Speaker 2: And I think, you know, going forward, Mustrell. 449 00:25:23,400 --> 00:25:26,000 Speaker 10: Is in a really interesting position because they are and 450 00:25:26,080 --> 00:25:30,280 Speaker 10: have been this champion in France making the argument that 451 00:25:30,320 --> 00:25:33,320 Speaker 10: Europe needs a strong open A player, and so I 452 00:25:33,359 --> 00:25:36,760 Speaker 10: think for a lot of in Europe, I think scrutinizing 453 00:25:36,800 --> 00:25:39,480 Speaker 10: a company like Maestrol won't be the same as going 454 00:25:39,520 --> 00:25:41,600 Speaker 10: after some of these big tech companies from the US. 455 00:25:42,480 --> 00:25:45,359 Speaker 5: Right Indice monk Bergen out of London, thank you very much. Meanwhile, 456 00:25:45,400 --> 00:25:49,520 Speaker 5: in Canada, Prime Minister Justin Trudeau's government introduced an online 457 00:25:49,560 --> 00:25:54,120 Speaker 5: safety law, joining European countries in trying to compel internet 458 00:25:54,160 --> 00:25:59,280 Speaker 5: companies to actively regulate and remove harmful content. With us 459 00:25:59,280 --> 00:26:03,280 Speaker 5: to discuss is actually Casavan managing director of the International 460 00:26:03,320 --> 00:26:08,160 Speaker 5: Association of Privacy Professionals AI Governance Center. We've been talking 461 00:26:08,160 --> 00:26:11,400 Speaker 5: about this in recent weeks. It is happening all over 462 00:26:11,440 --> 00:26:14,120 Speaker 5: the world. In the US, we're in an election cycle 463 00:26:14,760 --> 00:26:18,440 Speaker 5: where we're worried about the moderation of content online. First 464 00:26:18,440 --> 00:26:21,760 Speaker 5: of all, your reaction to what Canada has done and 465 00:26:21,840 --> 00:26:23,280 Speaker 5: how effective you think it will be. 466 00:26:25,280 --> 00:26:28,320 Speaker 11: Yeah, I think it's another in a series, as you said, 467 00:26:28,359 --> 00:26:31,359 Speaker 11: of countries that are really thinking about the implications of 468 00:26:31,480 --> 00:26:35,280 Speaker 11: online harms, especially for children. So we've seen this play 469 00:26:35,320 --> 00:26:38,720 Speaker 11: out in the US context recently with the conversations related 470 00:26:38,760 --> 00:26:43,359 Speaker 11: to Coosa and Copper, and as you mentioned, really matching 471 00:26:43,359 --> 00:26:47,040 Speaker 11: what's happening in the European context. I think it's a 472 00:26:47,080 --> 00:26:50,639 Speaker 11: positive step forward. These were commitments that were made a 473 00:26:50,680 --> 00:26:54,200 Speaker 11: long time ago by the Trudeau government and so really 474 00:26:54,240 --> 00:26:59,360 Speaker 11: happy to see that this joins other online bills both internationally, 475 00:26:59,600 --> 00:27:02,720 Speaker 11: but then also to match the Digital Charter Implementation Act 476 00:27:02,760 --> 00:27:03,280 Speaker 11: in Canada. 477 00:27:04,760 --> 00:27:10,040 Speaker 5: The Internet is the Internet, it is everywhere, it is global, 478 00:27:10,080 --> 00:27:13,240 Speaker 5: it's now in space, and every day we're talking about 479 00:27:13,240 --> 00:27:16,080 Speaker 5: a different country. At some point, do you think that 480 00:27:16,920 --> 00:27:19,320 Speaker 5: everyone should just get together and have a common set 481 00:27:19,359 --> 00:27:21,920 Speaker 5: of rules or is that a naive position to take. 482 00:27:23,200 --> 00:27:23,760 Speaker 3: I don't think so. 483 00:27:23,840 --> 00:27:26,280 Speaker 11: I think that's actually exactly the question that the play 484 00:27:26,359 --> 00:27:29,879 Speaker 11: right now by a lot of international organizations like the 485 00:27:29,920 --> 00:27:32,879 Speaker 11: g seven, like the OECD, like the World Economic Forum, 486 00:27:33,640 --> 00:27:36,760 Speaker 11: like the UN are really thinking about their position and 487 00:27:36,960 --> 00:27:41,800 Speaker 11: conveners of countries. How do we think about these digital 488 00:27:41,840 --> 00:27:47,119 Speaker 11: implications and rules for guardrails for those digital services in 489 00:27:47,160 --> 00:27:50,960 Speaker 11: a combined way. One of the challenges, I think is 490 00:27:51,320 --> 00:27:54,000 Speaker 11: the different trade offs and values that you have between 491 00:27:54,480 --> 00:27:57,640 Speaker 11: different nations, but you also have things like data transfer 492 00:27:57,720 --> 00:28:01,360 Speaker 11: issues when you're thinking about more of the national implications, 493 00:28:01,359 --> 00:28:03,320 Speaker 11: and so how all of this is actually going to 494 00:28:03,400 --> 00:28:06,960 Speaker 11: play out in an international space I think. 495 00:28:08,760 --> 00:28:09,840 Speaker 1: Remains to be seen. 496 00:28:11,840 --> 00:28:14,840 Speaker 5: Actually for the platforms of the tech companies, there is 497 00:28:14,880 --> 00:28:21,159 Speaker 5: a policy consideration and then there's a technology consideration. Is 498 00:28:21,200 --> 00:28:23,879 Speaker 5: there any one social media platform or company that you 499 00:28:23,960 --> 00:28:26,840 Speaker 5: think is doing a good job of their own volition 500 00:28:27,080 --> 00:28:28,120 Speaker 5: to moderate content. 501 00:28:30,240 --> 00:28:33,439 Speaker 11: We are a policy neutral organization, so I won't speak 502 00:28:33,480 --> 00:28:37,879 Speaker 11: to one organization over the other. But I think that 503 00:28:37,920 --> 00:28:40,080 Speaker 11: we can start to see some best practices that are 504 00:28:40,120 --> 00:28:43,720 Speaker 11: emerging in different types of use cases, where we're thinking 505 00:28:43,880 --> 00:28:49,200 Speaker 11: again about rules around children's safety, making sure that there 506 00:28:49,200 --> 00:28:51,640 Speaker 11: are certain types of parental controls that are in place, 507 00:28:52,320 --> 00:28:55,320 Speaker 11: making sure that we're even seeing where there's alerts that 508 00:28:55,360 --> 00:28:58,640 Speaker 11: are being provided with the amount of time that a 509 00:28:58,720 --> 00:29:03,760 Speaker 11: child's been online, or anybody has been online giving you 510 00:29:03,800 --> 00:29:07,560 Speaker 11: alerts to you as an individual directly. So I think 511 00:29:07,600 --> 00:29:09,920 Speaker 11: these are some of the emerging trends that we're seeing, 512 00:29:10,280 --> 00:29:13,120 Speaker 11: and they're coming out from a variety of different companies. 513 00:29:13,320 --> 00:29:18,280 Speaker 11: And it's not to say that that then makes one 514 00:29:18,400 --> 00:29:21,840 Speaker 11: company more proficient in that space over the other. I 515 00:29:21,880 --> 00:29:26,080 Speaker 11: think the whole community is really learning together, both policy 516 00:29:26,120 --> 00:29:30,760 Speaker 11: makers and industry players that have these research capacities within 517 00:29:30,760 --> 00:29:31,920 Speaker 11: their organizations. 518 00:29:32,720 --> 00:29:36,640 Speaker 5: Ashley Cassavan the iapp AI Governance Center. Great to have 519 00:29:36,720 --> 00:29:38,880 Speaker 5: your time, Thank you so much. Now coming up, we'll 520 00:29:38,880 --> 00:29:42,840 Speaker 5: speak with the new VC firm Pari Passuventure Partners, just 521 00:29:42,880 --> 00:29:48,480 Speaker 5: coming out of Stealth. Managing partner Julia Guddish Kriega joins us. Next, 522 00:29:48,560 --> 00:29:52,560 Speaker 5: we'll also let's stick with AI. Why not everyday AI 523 00:29:52,840 --> 00:29:55,000 Speaker 5: all the time? Here are two names that we touch 524 00:29:55,080 --> 00:29:59,200 Speaker 5: on every so often. SoundHound AI and Big Bear AI 525 00:29:59,640 --> 00:30:02,680 Speaker 5: both up big time twenty three percent. For Big Bear, 526 00:30:02,920 --> 00:30:04,680 Speaker 5: I'm looking on the Bloomberg terminal, I'm looking at the 527 00:30:04,680 --> 00:30:08,320 Speaker 5: news wires. Big Bear has its AGM today. I doubt 528 00:30:08,400 --> 00:30:11,120 Speaker 5: that is the causal link of what's driving the stock higher. 529 00:30:11,320 --> 00:30:14,960 Speaker 5: And SoundHound is among the most discussed on stock twits 530 00:30:15,000 --> 00:30:18,280 Speaker 5: and other Reddit forums. But these names have behave like 531 00:30:18,360 --> 00:30:20,560 Speaker 5: meme stocks as well as being caught up in the 532 00:30:20,680 --> 00:30:24,160 Speaker 5: kind of hype cycle we've seen the AI related names 533 00:30:24,160 --> 00:30:28,160 Speaker 5: and equity markets for two big movers this Tuesday. We'll 534 00:30:28,160 --> 00:30:45,920 Speaker 5: be right back. This is bloom of technology. Okay, quick update. 535 00:30:46,080 --> 00:30:49,440 Speaker 5: The global crypso total market cap is now at two 536 00:30:49,480 --> 00:30:53,240 Speaker 5: trillion dollars, driven basically by the rally in bitcoin Bitcoin 537 00:30:53,320 --> 00:30:56,040 Speaker 5: surge around twelve percent already in the last seven days 538 00:30:56,120 --> 00:30:59,440 Speaker 5: or so is trading near that high level of December 539 00:30:59,520 --> 00:31:01,760 Speaker 5: twenty twenty one. But the main point is if you 540 00:31:01,800 --> 00:31:04,320 Speaker 5: look right in the far right hand side of your screen, 541 00:31:04,800 --> 00:31:08,920 Speaker 5: two trillion dollars of total market cap for all digital tokens, 542 00:31:09,160 --> 00:31:15,280 Speaker 5: as tracked by www dot coin, marketcap dot com. What fun. 543 00:31:15,360 --> 00:31:17,800 Speaker 5: All right, Let's stick with these assets and zero in 544 00:31:18,040 --> 00:31:21,680 Speaker 5: on n ft SO. It's went from being touted as 545 00:31:21,720 --> 00:31:24,840 Speaker 5: the cutting edge of the digital frontier to frankly the 546 00:31:24,920 --> 00:31:29,800 Speaker 5: punchline for the most recent cryptobus. But suddenly the staging 547 00:31:29,840 --> 00:31:32,800 Speaker 5: and unlikely comeback joining me on set is bloombog. Hannah Miller, 548 00:31:33,240 --> 00:31:35,760 Speaker 5: you write with such fun about this, but it's true. 549 00:31:36,200 --> 00:31:38,240 Speaker 5: You know, we kind of went from all the craze 550 00:31:38,520 --> 00:31:43,320 Speaker 5: crazy dollar values to some memes and some jokes about 551 00:31:43,360 --> 00:31:46,280 Speaker 5: people that had bought in. What does the data tell 552 00:31:46,320 --> 00:31:46,760 Speaker 5: you now? 553 00:31:47,320 --> 00:31:50,280 Speaker 12: Yeah, so the big question here is can NFTs ride 554 00:31:50,280 --> 00:31:53,840 Speaker 12: the crypto comeback? You know, can they match bitcoins price rise? 555 00:31:54,200 --> 00:31:56,080 Speaker 12: And what we're seeing here is that, you know, there's 556 00:31:56,080 --> 00:31:59,080 Speaker 12: an overall upward trend in terms of total volume for 557 00:31:59,200 --> 00:32:03,040 Speaker 12: NFT sales, but it's been a little bumpy. So there 558 00:32:03,120 --> 00:32:06,760 Speaker 12: are these questions about what NFTs are good for, and 559 00:32:06,840 --> 00:32:10,000 Speaker 12: startup founders are doubling down on gaming, finance and art 560 00:32:10,400 --> 00:32:13,080 Speaker 12: as the main areas that we can use NFTs in. 561 00:32:13,680 --> 00:32:16,640 Speaker 5: We just showed that chart. Alice the director, bring back 562 00:32:16,680 --> 00:32:20,760 Speaker 5: the chart because it's astonishing what happened in December. You 563 00:32:20,880 --> 00:32:24,840 Speaker 5: kind of had like a weird twenty twenty three quiet summer, 564 00:32:25,480 --> 00:32:28,400 Speaker 5: and then December the market becomes alive again. 565 00:32:28,880 --> 00:32:29,080 Speaker 2: Yeah. 566 00:32:29,160 --> 00:32:32,360 Speaker 12: So we know that there was a lot of optimism 567 00:32:32,520 --> 00:32:36,160 Speaker 12: over the expected approval of the Bitcoin ETF which did 568 00:32:36,200 --> 00:32:40,360 Speaker 12: happen in January. And there are these NFT like objects 569 00:32:40,360 --> 00:32:43,680 Speaker 12: called ordinals that are based on the Bitcoin blockchain. We 570 00:32:43,760 --> 00:32:47,280 Speaker 12: saw huge, huge sales for ordinals during the fall as 571 00:32:47,280 --> 00:32:49,960 Speaker 12: people were getting really revved up about bitcoin. So we 572 00:32:50,080 --> 00:32:53,440 Speaker 12: have seen this optimism about bitcoin, this excitement about the 573 00:32:53,440 --> 00:32:56,800 Speaker 12: ETF bleed over into the NFT market just really quick. 574 00:32:56,880 --> 00:32:59,200 Speaker 5: Gaming is a big part of the story. Helped me 575 00:32:59,240 --> 00:33:00,320 Speaker 5: explain that one. 576 00:33:01,560 --> 00:33:04,480 Speaker 12: Yeah, so a lot of startups, including Yuga Labs, the 577 00:33:04,480 --> 00:33:07,560 Speaker 12: creator of the board Ape Yacht Club NFT collection, are 578 00:33:07,600 --> 00:33:09,960 Speaker 12: doubling down on gaming. They see this as a way 579 00:33:10,080 --> 00:33:13,960 Speaker 12: to bring mainstream audiences into crypto, into NFTs and create 580 00:33:14,000 --> 00:33:17,040 Speaker 12: games that are fun to play but also have blockchain. 581 00:33:16,640 --> 00:33:17,320 Speaker 2: In the background. 582 00:33:18,080 --> 00:33:21,840 Speaker 5: Blumbo's Hannah Miller top top Reporting. Thank you very much. Right, 583 00:33:21,960 --> 00:33:24,440 Speaker 5: let's get to today's VC spotlight and bring in a 584 00:33:24,480 --> 00:33:28,720 Speaker 5: firm that's just coming out as Stealth Parry Passu Venture Partners, 585 00:33:28,760 --> 00:33:32,920 Speaker 5: which brands itself as a founder led and backed early 586 00:33:32,960 --> 00:33:37,440 Speaker 5: stage venture firm investing at the intersection of tech and retail, 587 00:33:37,960 --> 00:33:41,840 Speaker 5: SaaS and consumer tech. It's invested in nineteen companies to date, 588 00:33:42,040 --> 00:33:45,360 Speaker 5: which check sizes ranging from one hundred thousand dollars to 589 00:33:45,480 --> 00:33:49,080 Speaker 5: over three million dollars per company. Managing partner Julia Gudish 590 00:33:49,120 --> 00:33:53,440 Speaker 5: Krieger joins me now from New York. Good morning to you, Julia, 591 00:33:53,600 --> 00:33:56,920 Speaker 5: this is an interesting concept. We all the time we 592 00:33:57,040 --> 00:34:00,440 Speaker 5: have startup founders on the show that have come out stealth. 593 00:34:00,520 --> 00:34:02,800 Speaker 5: They kind of work behind the scenes for a year, 594 00:34:03,280 --> 00:34:06,160 Speaker 5: raise money discreetly, and then say this is what we do. 595 00:34:06,520 --> 00:34:10,280 Speaker 5: You're a venture firm coming out of stealth, why so discreet. 596 00:34:10,680 --> 00:34:14,440 Speaker 13: So what we're launching today is an app called Perry Pursue, 597 00:34:14,800 --> 00:34:19,360 Speaker 13: which is a member's only investor network that allows founders 598 00:34:19,560 --> 00:34:24,200 Speaker 13: and operators and a credit tech enthusiasts to back leading 599 00:34:24,520 --> 00:34:28,959 Speaker 13: startups within SaaS you know, as you mentioned e commerce, tech, 600 00:34:29,080 --> 00:34:32,640 Speaker 13: consumer tech, but really alongside leading global venture capital farms. 601 00:34:32,880 --> 00:34:36,600 Speaker 13: And so for us as founders backing founders, we know 602 00:34:36,640 --> 00:34:38,760 Speaker 13: it takes a village, and we know that to get 603 00:34:38,840 --> 00:34:41,760 Speaker 13: a company to the scale that you need to succeed 604 00:34:41,840 --> 00:34:45,600 Speaker 13: and to exit, that village is super charged by having, 605 00:34:45,680 --> 00:34:48,200 Speaker 13: you know, operators that have been in the trenches themselves, 606 00:34:48,200 --> 00:34:50,160 Speaker 13: that have created themselves, that know how to roll up 607 00:34:50,160 --> 00:34:53,280 Speaker 13: their sleeves and support. That's the village that we want 608 00:34:53,400 --> 00:34:56,480 Speaker 13: our founders to have, and we're launching Perry Pursue to 609 00:34:56,560 --> 00:35:00,279 Speaker 13: create the world's most powerful network of founders, support and 610 00:35:00,360 --> 00:35:05,120 Speaker 13: unlock access to highly competitive venture capital deals, but for 611 00:35:05,200 --> 00:35:07,279 Speaker 13: operators that can put in as little as ten. 612 00:35:07,239 --> 00:35:09,799 Speaker 5: K, well, well, you say it takes a village, but 613 00:35:09,840 --> 00:35:13,280 Speaker 5: there's an element of exclusivity to it because it's members only. 614 00:35:13,880 --> 00:35:15,279 Speaker 5: Could you explain that part to me? 615 00:35:15,480 --> 00:35:18,320 Speaker 13: Yes, And so the members only piece is really because 616 00:35:18,320 --> 00:35:20,759 Speaker 13: we want to make sure we're supercharging the value out 617 00:35:20,800 --> 00:35:24,680 Speaker 13: on cap tables and so focusing first on letting on 618 00:35:25,560 --> 00:35:28,719 Speaker 13: founders operators that are VP level enough, people that can 619 00:35:28,760 --> 00:35:30,680 Speaker 13: really add a tremendous amount of value. Because if we're 620 00:35:30,680 --> 00:35:33,640 Speaker 13: writing a million dollar check into a company, and let's 621 00:35:33,640 --> 00:35:35,960 Speaker 13: say it's twenty people behind the scenes that are writing 622 00:35:36,480 --> 00:35:38,520 Speaker 13: checks from ten K checks of two million, you know, 623 00:35:38,600 --> 00:35:41,960 Speaker 13: sometimes historically we want those people to be able to 624 00:35:42,000 --> 00:35:45,440 Speaker 13: be highly value add we want those people to be 625 00:35:45,440 --> 00:35:47,200 Speaker 13: able to have a network for an introduction, if a 626 00:35:47,200 --> 00:35:49,120 Speaker 13: company wants to speak to a brand, right, if a 627 00:35:49,160 --> 00:35:52,320 Speaker 13: company wants to navigate the right equity package for a 628 00:35:52,360 --> 00:35:55,840 Speaker 13: certain higher right. And I think that village being operators 629 00:35:55,840 --> 00:35:59,920 Speaker 13: themselves ends up being tremendously impactful for the success of 630 00:36:00,040 --> 00:36:00,680 Speaker 13: these companies. 631 00:36:01,760 --> 00:36:04,960 Speaker 5: A kind of phenomenon I guess that we've covered on 632 00:36:05,000 --> 00:36:08,319 Speaker 5: the show of the last twelve months is the appetite 633 00:36:08,360 --> 00:36:12,160 Speaker 5: of the everyday investor, let's call them retail investor to 634 00:36:12,280 --> 00:36:16,640 Speaker 5: get particularly into growth stage companies like SpaceX and open Ai. Right, 635 00:36:16,719 --> 00:36:20,880 Speaker 5: there was suddenly a turnaround in what is normally in 636 00:36:20,920 --> 00:36:24,440 Speaker 5: a liquid market. And I wonder if through Parry Passu, 637 00:36:25,000 --> 00:36:27,359 Speaker 5: you think you'll see the same sort of engagement at 638 00:36:27,400 --> 00:36:31,080 Speaker 5: the early stage where people that traditionally just cannot or 639 00:36:31,120 --> 00:36:35,480 Speaker 5: won't invest in startups through venture funds now can. 640 00:36:36,760 --> 00:36:37,000 Speaker 3: Yeah. 641 00:36:37,080 --> 00:36:39,680 Speaker 13: I think part of the bottleneck is if you want 642 00:36:39,680 --> 00:36:42,600 Speaker 13: to invest in a venture fund, typically it's a two 643 00:36:42,680 --> 00:36:45,279 Speaker 13: fifty k minimum check, even if you do have access right, 644 00:36:45,320 --> 00:36:47,080 Speaker 13: and so a lot of the founders and operators that 645 00:36:47,120 --> 00:36:50,600 Speaker 13: are still building who'se you know, cash is more sitting 646 00:36:50,640 --> 00:36:52,719 Speaker 13: in the form of equity, you know, for that future win, 647 00:36:53,640 --> 00:36:56,160 Speaker 13: they're not able to participate in that mechanism. And so 648 00:36:56,520 --> 00:36:59,839 Speaker 13: Perry Pursue is really opening the doors to allow those 649 00:37:00,040 --> 00:37:04,000 Speaker 13: operators to be able to invest in a really accessible 650 00:37:04,480 --> 00:37:10,319 Speaker 13: access point, but building a highly curated ecosystem. 651 00:37:12,160 --> 00:37:14,160 Speaker 5: I introed you saying you're going to invest in the 652 00:37:14,160 --> 00:37:18,279 Speaker 5: intersection of basically everything. But I wonder, is there one 653 00:37:18,400 --> 00:37:20,560 Speaker 5: main area that you're really excited about. We just have 654 00:37:20,640 --> 00:37:21,440 Speaker 5: thirty seconds. 655 00:37:21,560 --> 00:37:24,640 Speaker 13: Sure, so I would say we have a pretty unfair 656 00:37:24,680 --> 00:37:28,480 Speaker 13: advantage in terms of e commerce SaaS across the partnership. 657 00:37:28,760 --> 00:37:32,359 Speaker 13: We have lived many lives in that ecosystem. My two 658 00:37:32,440 --> 00:37:34,960 Speaker 13: partners sold the largest Shopify agency in that space. So 659 00:37:35,000 --> 00:37:38,279 Speaker 13: you think about having access to hundreds of brand CEOs 660 00:37:38,440 --> 00:37:40,440 Speaker 13: and the e commerce sex selling into it, that's what 661 00:37:40,560 --> 00:37:43,360 Speaker 13: you want on your cap table. But I've spent the 662 00:37:43,400 --> 00:37:46,200 Speaker 13: better part of two decades in both the venture and 663 00:37:46,200 --> 00:37:50,160 Speaker 13: founder ecosystem alongside my partners as well, and so the 664 00:37:50,200 --> 00:37:52,720 Speaker 13: deep relationships that you can only forge over that amount 665 00:37:52,719 --> 00:37:54,600 Speaker 13: of time or what give us access to some of 666 00:37:54,640 --> 00:37:57,320 Speaker 13: the most highly competitive deals in venture. 667 00:37:57,640 --> 00:38:01,839 Speaker 5: All right, Perry passuventjupon is managing partner Julia GUDISKRUGA. Great 668 00:38:01,880 --> 00:38:03,520 Speaker 5: to have you on the program. Thank you for your 669 00:38:03,560 --> 00:38:16,320 Speaker 5: time lapse. The latest Instagram competitors raised thirty million dollars 670 00:38:16,360 --> 00:38:19,760 Speaker 5: in its series A funding round. The platform allows users 671 00:38:19,760 --> 00:38:23,640 Speaker 5: to take photos with a distinctly vintage feel that cannot 672 00:38:23,640 --> 00:38:27,040 Speaker 5: be seen for a few hours until they develop. These 673 00:38:27,040 --> 00:38:29,719 Speaker 5: new funds allow the friends focused photo sharing app to 674 00:38:29,760 --> 00:38:34,320 Speaker 5: expand engineering and technical teams and implement community led product updates. 675 00:38:34,360 --> 00:38:37,279 Speaker 5: Continuing to iterate on the user experience of lighted to 676 00:38:37,320 --> 00:38:40,600 Speaker 5: say that co founder Dan Silberton joins me. Now, so 677 00:38:40,680 --> 00:38:43,640 Speaker 5: this is like a very interesting concept. You don't just 678 00:38:43,719 --> 00:38:46,440 Speaker 5: take a photo and use basically a filter, right that 679 00:38:46,480 --> 00:38:49,480 Speaker 5: gives it a vintage feel. Your platform then makes the 680 00:38:49,600 --> 00:38:53,840 Speaker 5: user wait a number of hours to replicate the film 681 00:38:53,880 --> 00:38:55,440 Speaker 5: development process, explain it. 682 00:38:56,800 --> 00:38:57,719 Speaker 3: That's exactly right. 683 00:38:57,800 --> 00:39:00,360 Speaker 14: And one of the things that makes laps with different 684 00:39:00,400 --> 00:39:02,880 Speaker 14: from the way that you take photos on either your 685 00:39:02,960 --> 00:39:05,560 Speaker 14: native camera or any other app today is that we 686 00:39:05,680 --> 00:39:09,120 Speaker 14: really closely mimic not just an aesthetic, but also experience 687 00:39:09,600 --> 00:39:12,160 Speaker 14: the feeling of using either as spotal camera or film camera. 688 00:39:12,560 --> 00:39:15,200 Speaker 14: And we really find that not just in terms of 689 00:39:15,200 --> 00:39:18,000 Speaker 14: actually that the way the photos look, but actually the 690 00:39:18,080 --> 00:39:20,160 Speaker 14: ability to keep you in the moment because often when 691 00:39:20,200 --> 00:39:22,680 Speaker 14: you take a photo, you're doing something really exciting that 692 00:39:22,719 --> 00:39:25,839 Speaker 14: you want to live as fully as you can. Our 693 00:39:25,920 --> 00:39:29,800 Speaker 14: users really appreciate that feeling, other than other country social 694 00:39:29,800 --> 00:39:30,960 Speaker 14: media which really suck them in. 695 00:39:32,480 --> 00:39:34,320 Speaker 5: Let's think about your business. You have a good runway 696 00:39:34,320 --> 00:39:36,359 Speaker 5: now because you raise thirty million dollars, but is this 697 00:39:36,400 --> 00:39:39,239 Speaker 5: going to be an ad based platform or a subscription 698 00:39:39,360 --> 00:39:40,920 Speaker 5: based platform. 699 00:39:41,480 --> 00:39:44,040 Speaker 14: So it's quite early to say or too early to stay. 700 00:39:44,040 --> 00:39:47,440 Speaker 14: At the moment, we have early hypotheses that we will 701 00:39:47,600 --> 00:39:50,279 Speaker 14: not monetize through ads, but right now we're focused on 702 00:39:50,360 --> 00:39:53,200 Speaker 14: just building the best possible user experience and scaling that 703 00:39:53,239 --> 00:39:55,680 Speaker 14: to as many users as we can. What we've seen 704 00:39:55,680 --> 00:39:58,520 Speaker 14: with the traditional social platforms is that monetization tends to 705 00:39:58,560 --> 00:40:00,680 Speaker 14: come a lot later in their life side. And with 706 00:40:00,719 --> 00:40:03,360 Speaker 14: these new investors that we've brought on, they've all invested 707 00:40:03,400 --> 00:40:06,600 Speaker 14: in the incumbent social platforms that previously started and seen 708 00:40:06,640 --> 00:40:09,080 Speaker 14: that journey, and so they're fully behind us in terms 709 00:40:09,080 --> 00:40:12,640 Speaker 14: of understanding that monetization necessarily comes a lot later. 710 00:40:13,480 --> 00:40:16,359 Speaker 5: I'm really interested in in lapses growth. Dan, you know, 711 00:40:16,440 --> 00:40:21,200 Speaker 5: are you seeing engagement through iOS or Android? Geographically? Where 712 00:40:21,200 --> 00:40:22,520 Speaker 5: do you think you're going to be strong? 713 00:40:23,880 --> 00:40:24,440 Speaker 3: Good question. 714 00:40:24,520 --> 00:40:27,160 Speaker 14: So today the app is only on iOS. We're not 715 00:40:27,200 --> 00:40:29,160 Speaker 14: on Android yet. That will come in the future, but 716 00:40:29,440 --> 00:40:32,640 Speaker 14: today the focus is very much iOS. We're very strong 717 00:40:32,640 --> 00:40:35,040 Speaker 14: in the US, so most of our user in the US, 718 00:40:35,080 --> 00:40:37,239 Speaker 14: but we also still have some in the UK and 719 00:40:37,280 --> 00:40:39,840 Speaker 14: in Canada as well. But for us, very much to focus, 720 00:40:39,840 --> 00:40:41,480 Speaker 14: like I said, is in the US, and we see 721 00:40:41,480 --> 00:40:45,600 Speaker 14: a very strong bias towards gen z and female users, 722 00:40:45,920 --> 00:40:48,160 Speaker 14: which tend to be the early adoptor cohorts for these 723 00:40:48,200 --> 00:40:50,799 Speaker 14: new social platforms, which is really encouraging to see that 724 00:40:50,800 --> 00:40:53,719 Speaker 14: those are the users that are organically using the platform. 725 00:40:54,080 --> 00:40:57,120 Speaker 5: I want to go to who you think you're competing 726 00:40:57,160 --> 00:40:59,960 Speaker 5: with us talking with Bloomberg technology? Is Jackie Lopez about this? 727 00:41:00,200 --> 00:41:03,520 Speaker 5: You're making the user weight. I'm assuming the technology doesn't 728 00:41:03,800 --> 00:41:05,719 Speaker 5: require you to wait, You're just doing it as a 729 00:41:05,760 --> 00:41:08,600 Speaker 5: feature that puts you on a collision course with who 730 00:41:08,680 --> 00:41:10,160 Speaker 5: Instagram Snap. 731 00:41:12,239 --> 00:41:12,560 Speaker 3: Exactly. 732 00:41:12,640 --> 00:41:14,719 Speaker 14: And then also actually the users native camera as well, 733 00:41:14,719 --> 00:41:18,320 Speaker 14: we actually see because the cameras the app rather opens 734 00:41:18,360 --> 00:41:21,640 Speaker 14: to camera. We actually see ourselves very much first as 735 00:41:21,680 --> 00:41:25,120 Speaker 14: a camera app, because we believe that if we were 736 00:41:25,160 --> 00:41:27,920 Speaker 14: able to own the top of funnel content capture for users, 737 00:41:28,160 --> 00:41:29,759 Speaker 14: then all of the other things that can come off 738 00:41:29,800 --> 00:41:33,800 Speaker 14: the back of that, like the sharing, the journaling, the 739 00:41:34,200 --> 00:41:38,000 Speaker 14: kind of curating and the highlights, all of that is 740 00:41:38,040 --> 00:41:40,760 Speaker 14: downstream from the capture. So we're really focused on providing 741 00:41:40,800 --> 00:41:43,520 Speaker 14: the best possible capture experience for users and to actually 742 00:41:43,880 --> 00:41:46,040 Speaker 14: one of the best or one of the biggest competitors 743 00:41:46,080 --> 00:41:48,880 Speaker 14: to us is the users native camera on their phone. 744 00:41:49,960 --> 00:41:52,680 Speaker 5: All right, laps co founded Dan Silverton, hot off a 745 00:41:52,719 --> 00:41:55,839 Speaker 5: series a round thirty million dollars. Thank you for your time. 746 00:41:55,880 --> 00:41:58,600 Speaker 5: That does it for this edition of Bloomberg Technology. We 747 00:41:58,640 --> 00:42:01,200 Speaker 5: won't make you wait. Check out podcasts. It will be 748 00:42:01,200 --> 00:42:05,279 Speaker 5: online soon on the terminal Bloomberg platforms, Apple, iHeart and 749 00:42:05,400 --> 00:42:09,080 Speaker 5: of course on Spotify. Brace yourselves. There's a big week 750 00:42:09,120 --> 00:42:12,280 Speaker 5: of economic data and it's going to impact these markets. 751 00:42:12,320 --> 00:42:14,280 Speaker 5: Tune in. This is Bloomberg Technology.