1 00:00:02,720 --> 00:00:10,559 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:17,880 Speaker 1: Eastern on Apple Corplay and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,040 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,560 Speaker 1: or watch us live on YouTube. 6 00:00:24,400 --> 00:00:29,200 Speaker 2: Lots of IPOs. That's certainly been the score for much 7 00:00:29,200 --> 00:00:31,160 Speaker 2: of twenty twenty six. And we said the big investment 8 00:00:31,160 --> 00:00:33,639 Speaker 2: banks reporter earnings last week, and they were toouting how 9 00:00:33,760 --> 00:00:36,559 Speaker 2: much of the deals they're getting done and how good 10 00:00:36,560 --> 00:00:39,200 Speaker 2: their pipeline looks, and we got it work still coming here, 11 00:00:39,479 --> 00:00:42,120 Speaker 2: Let's keep it. Our next guest, very busy Belly Lipschualtz, 12 00:00:42,159 --> 00:00:46,680 Speaker 2: Senior equities reporter for Bloomberg News. Moonshot AI. Oh boy, 13 00:00:46,720 --> 00:00:48,480 Speaker 2: I'm afraid to ask, what is this thing? And when 14 00:00:48,520 --> 00:00:48,960 Speaker 2: is it coming? 15 00:00:49,159 --> 00:00:51,320 Speaker 3: You haven't heard of moonshot ami though I have not. 16 00:00:51,680 --> 00:00:54,360 Speaker 3: You are not what really? 17 00:00:54,800 --> 00:00:55,120 Speaker 4: Really? 18 00:00:55,240 --> 00:00:57,560 Speaker 3: Oh my gosh, I mean they moved markets on Friday. 19 00:00:57,560 --> 00:00:59,880 Speaker 3: They're the reason that Friday I was all that be 20 00:01:00,280 --> 00:01:02,400 Speaker 3: Oh you he that you got some sun Okay Moonshot 21 00:01:02,680 --> 00:01:05,320 Speaker 3: Moonshot runs. The Kimmi model Kimmy K three was the 22 00:01:05,360 --> 00:01:09,160 Speaker 3: latest kind of model Kimmy K three is a AI. 23 00:01:09,400 --> 00:01:12,280 Speaker 2: We'll have to put that next to Claude and well, they're. 24 00:01:12,080 --> 00:01:16,199 Speaker 3: A Chinese company, so I think I know your answer there. 25 00:01:16,760 --> 00:01:18,240 Speaker 3: But so this is a company that really burst onto 26 00:01:18,240 --> 00:01:20,720 Speaker 3: the scene while you were on a beach enjoying the 27 00:01:21,080 --> 00:01:24,520 Speaker 3: nice weather, that really kind of caught took the world 28 00:01:24,560 --> 00:01:26,679 Speaker 3: by storm, I guess you could say, and very quickly. 29 00:01:26,680 --> 00:01:29,399 Speaker 3: Bloomberg is reporting they are now eyeing a Hong Kong 30 00:01:29,480 --> 00:01:31,280 Speaker 3: ipo in the next six months, so that would be 31 00:01:31,280 --> 00:01:34,679 Speaker 3: a quick turnaround companies, according to our reporting, working to 32 00:01:34,720 --> 00:01:37,679 Speaker 3: close a thirty funny round at thirty billion dollar valuation. 33 00:01:37,760 --> 00:01:41,960 Speaker 3: So relatively small potatoes compared to the anthropics and open 34 00:01:42,000 --> 00:01:44,920 Speaker 3: aiyes of the world, but it is pretty interesting just 35 00:01:44,959 --> 00:01:49,960 Speaker 3: given how quickly the company again became for some household 36 00:01:50,040 --> 00:01:52,720 Speaker 3: name overnight, similar to that Deep Seek moment we had 37 00:01:52,880 --> 00:01:53,840 Speaker 3: a few years back. 38 00:01:54,160 --> 00:01:57,480 Speaker 5: Yeah, and trying to capitalize on that that excitement. It's 39 00:01:57,480 --> 00:01:59,960 Speaker 5: a Beijing based startup. It's held talks with China International 40 00:02:00,160 --> 00:02:03,320 Speaker 5: Capital Corp. And Goldman Sachs about working on an offering. 41 00:02:04,160 --> 00:02:06,400 Speaker 5: The one part of the deals market that's been kind 42 00:02:06,400 --> 00:02:12,080 Speaker 5: of sluggish is exits from private equity and you exiting 43 00:02:12,320 --> 00:02:16,240 Speaker 5: their investments and listing them on the public markets. Jersey 44 00:02:16,280 --> 00:02:19,200 Speaker 5: Mikes is kind of in defiance of that if it 45 00:02:19,280 --> 00:02:20,800 Speaker 5: actually does go through it with an IPO. 46 00:02:20,919 --> 00:02:23,360 Speaker 3: Yeah, and we've seen actually Blackstone try to live up 47 00:02:23,360 --> 00:02:25,880 Speaker 3: to their promises from Jonathan Gray that this was going 48 00:02:25,919 --> 00:02:27,440 Speaker 3: to be the year of an IPO. Of the IPO, 49 00:02:27,520 --> 00:02:30,799 Speaker 3: they did take out Blackstone Digital, which is kind of 50 00:02:30,840 --> 00:02:33,520 Speaker 3: an AI digit data center roll up firm, and they 51 00:02:33,560 --> 00:02:36,800 Speaker 3: also took out Liftoff, which is in the app space. 52 00:02:36,880 --> 00:02:39,280 Speaker 3: But Jersey Mike's more of a household name. I think 53 00:02:39,520 --> 00:02:41,440 Speaker 3: you've been to one of those, yes, Okay, so you know, 54 00:02:41,520 --> 00:02:45,839 Speaker 3: Jersey Mike's the company in their backers, including Blackstone, looking 55 00:02:45,919 --> 00:02:47,760 Speaker 3: to sell up to one point one billion dollars in 56 00:02:47,800 --> 00:02:50,240 Speaker 3: this IPO. What value the company about eight billion dollars, 57 00:02:50,240 --> 00:02:52,600 Speaker 3: So that is kind of a big valuation relative to 58 00:02:52,680 --> 00:02:57,520 Speaker 3: most kind of growing casual chains, if you will. The 59 00:02:57,520 --> 00:02:59,280 Speaker 3: big pitch, at least when I talk to investors is 60 00:02:59,280 --> 00:03:01,520 Speaker 3: they look at the success from Cava, they look at 61 00:03:01,560 --> 00:03:04,440 Speaker 3: Jersey Mike's Jersey Mike's Blackstone asset. But they brought in 62 00:03:04,480 --> 00:03:07,160 Speaker 3: the leadership team from Wing Stops, so you have season 63 00:03:07,240 --> 00:03:10,280 Speaker 3: vets running the show. You have Blackstone, which is going 64 00:03:10,320 --> 00:03:12,359 Speaker 3: to make a decent amount of money on this IPO, 65 00:03:12,440 --> 00:03:14,920 Speaker 3: and then you also have the international pitch. They partnered 66 00:03:14,960 --> 00:03:17,800 Speaker 3: with the Jersey Mike's founder to help lead an expansion 67 00:03:17,800 --> 00:03:21,040 Speaker 3: in the UK. So fast growing, pretty darn good franchise 68 00:03:21,080 --> 00:03:25,320 Speaker 3: model here in the US, international ambitions and private equity 69 00:03:25,360 --> 00:03:27,640 Speaker 3: sponsor is what kind of blackrockets own. 70 00:03:27,560 --> 00:03:28,960 Speaker 2: This for about a cup of coffee. 71 00:03:29,120 --> 00:03:29,679 Speaker 4: Let's be honest. 72 00:03:29,720 --> 00:03:30,840 Speaker 2: When they bought this just a few. 73 00:03:30,800 --> 00:03:33,480 Speaker 3: Years and Blackstone closed the deal in January of last year. Yes, 74 00:03:33,560 --> 00:03:35,520 Speaker 3: so in terms of the turn one and a half years, 75 00:03:35,520 --> 00:03:36,560 Speaker 3: this is fast. 76 00:03:36,400 --> 00:03:39,240 Speaker 2: Awesome for them. I mean, and there, IRR. I'll go 77 00:03:39,280 --> 00:03:42,040 Speaker 2: do the math on the train ride home, but I'm 78 00:03:42,080 --> 00:03:43,880 Speaker 2: going to guess, IRR, is monstrous. 79 00:03:43,920 --> 00:03:45,680 Speaker 3: Yeah, it's a big return, and it's a big again, 80 00:03:45,720 --> 00:03:49,960 Speaker 3: a quick return in a time when people, to Scarlett's point, 81 00:03:50,080 --> 00:03:52,560 Speaker 3: private equity hasn't been super active and getting these exits. 82 00:03:52,600 --> 00:03:55,080 Speaker 3: The interesting thing is you step back when that deal 83 00:03:55,240 --> 00:03:57,880 Speaker 3: was announced back in November of twenty twenty four and 84 00:03:57,920 --> 00:04:00,200 Speaker 3: consumer bankers were like, this is a company that have 85 00:04:00,200 --> 00:04:03,440 Speaker 3: gone public then, so then you bring in Blackstone. They 86 00:04:04,160 --> 00:04:06,440 Speaker 3: do a little bit of pre private equity magic. But 87 00:04:06,480 --> 00:04:08,840 Speaker 3: again they bring in an experienced management team, get the 88 00:04:08,840 --> 00:04:12,200 Speaker 3: operations where they want it in a quick turn, quick money, 89 00:04:12,480 --> 00:04:14,160 Speaker 3: and at least when I talk to folks on the 90 00:04:14,160 --> 00:04:15,320 Speaker 3: buy side, they. 91 00:04:15,280 --> 00:04:18,440 Speaker 2: Like it, and that is super important when they got 92 00:04:18,440 --> 00:04:20,680 Speaker 2: to raise the next fund, which is when I was 93 00:04:20,680 --> 00:04:22,640 Speaker 2: looking at private equity maybe twenty years ago, I was 94 00:04:22,680 --> 00:04:26,400 Speaker 2: told pretty easy to raise money, it's really easy to 95 00:04:26,440 --> 00:04:29,920 Speaker 2: invest money. It's really hard to sell to get a 96 00:04:29,960 --> 00:04:32,839 Speaker 2: good exit, and so the way you get raised money 97 00:04:32,880 --> 00:04:35,960 Speaker 2: is to say, hey, I'm generating returns, I'm getting exits 98 00:04:36,000 --> 00:04:37,480 Speaker 2: successful exits. 99 00:04:37,080 --> 00:04:40,280 Speaker 3: And they happen to be I mean, Blackstone obviously is huge, 100 00:04:40,320 --> 00:04:44,000 Speaker 3: but you're not over exposed to software, which is a 101 00:04:44,040 --> 00:04:46,159 Speaker 3: good timing because you talk to some of the folks 102 00:04:46,200 --> 00:04:48,360 Speaker 3: at the Toma Bravos of the world and it's like, okay, 103 00:04:48,400 --> 00:04:51,839 Speaker 3: well you're super bullsh on software. This is the opposite time. 104 00:04:51,720 --> 00:04:55,320 Speaker 5: Totally different narrative. Right, very quickly, Billy, is there anything 105 00:04:55,360 --> 00:04:57,880 Speaker 5: new with Anthropic or open AI going public? 106 00:04:58,360 --> 00:04:58,720 Speaker 1: This week? 107 00:04:58,800 --> 00:05:01,560 Speaker 3: Anthropic is meeting with some of the largest long only investors. 108 00:05:01,640 --> 00:05:03,000 Speaker 3: We put that out at the end of last week, 109 00:05:03,040 --> 00:05:06,279 Speaker 3: so that'll be the formal starting to my understanding of 110 00:05:06,279 --> 00:05:08,720 Speaker 3: testing the waters meetings, talking about what the size could 111 00:05:08,720 --> 00:05:10,720 Speaker 3: look like, what the company looks like. Again, a lot 112 00:05:10,720 --> 00:05:13,400 Speaker 3: of these funds own it, so they're meeting with Dario 113 00:05:13,480 --> 00:05:15,359 Speaker 3: and team that they know. But the big thing is 114 00:05:15,400 --> 00:05:18,880 Speaker 3: this seems to be indicating that that September October timeframe 115 00:05:19,080 --> 00:05:21,080 Speaker 3: is in play, whereas Open AI for all intents and 116 00:05:21,080 --> 00:05:23,599 Speaker 3: purposes is very much seen as a twenty twenty seven story. 117 00:05:23,839 --> 00:05:27,080 Speaker 2: And are we expecting activity to once a week gets 118 00:05:27,080 --> 00:05:28,680 Speaker 2: back in vacation September kickback in? 119 00:05:28,720 --> 00:05:30,040 Speaker 3: Is that kind of the Yeah, we got we get 120 00:05:30,040 --> 00:05:32,640 Speaker 3: a little burst with Jersey Mikes. People expect Cumberland Farms. 121 00:05:33,040 --> 00:05:35,600 Speaker 3: Cumberland Farms. Yeah, they grew up with cumber Yeah, you 122 00:05:35,680 --> 00:05:37,520 Speaker 3: g group. Yeah, that's that'll be another big. 123 00:05:37,400 --> 00:05:40,000 Speaker 2: One that's a competitor to Wall Wall John So, I 124 00:05:40,040 --> 00:05:43,080 Speaker 2: mean it's a person. 125 00:05:43,760 --> 00:05:47,480 Speaker 4: Remember that Farms is Now, I'm not going to characterize 126 00:05:47,520 --> 00:05:48,800 Speaker 4: it's good in trouble, but. 127 00:05:48,760 --> 00:05:50,760 Speaker 3: I've never been there. Stay with us. 128 00:05:50,800 --> 00:05:53,039 Speaker 5: More from Bloomberg Intelligence coming up after this. 129 00:05:57,200 --> 00:06:00,880 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 130 00:06:00,960 --> 00:06:03,680 Speaker 1: weekdays at ten am. He's done on Apple, Cocklay and 131 00:06:03,680 --> 00:06:06,520 Speaker 1: Android Auto with the Blue Berg business app listen on 132 00:06:06,560 --> 00:06:09,840 Speaker 1: demand wherever you get your podcasts, or watch us live 133 00:06:09,920 --> 00:06:10,560 Speaker 1: on YouTube. 134 00:06:11,240 --> 00:06:16,560 Speaker 2: We heard another company, you know, Ipo deep Seek, just 135 00:06:16,560 --> 00:06:19,680 Speaker 2: about deep Sea because that rocked the AI world I 136 00:06:19,680 --> 00:06:21,400 Speaker 2: don't know a year ago, because they came out with 137 00:06:21,440 --> 00:06:24,680 Speaker 2: a pretty good AI product at a much lower cost 138 00:06:25,080 --> 00:06:25,760 Speaker 2: by deep Seek. 139 00:06:25,960 --> 00:06:29,000 Speaker 6: Yeah, so in January of twenty twenty five, deep deep 140 00:06:29,000 --> 00:06:32,360 Speaker 6: Seat came out to the Chinese owned Chinese based AI 141 00:06:33,800 --> 00:06:37,320 Speaker 6: chatbot and basically ramped up on downloads, about one hundred 142 00:06:37,320 --> 00:06:40,520 Speaker 6: and seventy million downloads in twenty twenty five alone, is 143 00:06:40,560 --> 00:06:45,120 Speaker 6: as you mentioned, significantly cheaper for many of the tasks 144 00:06:45,120 --> 00:06:47,400 Speaker 6: that people ask it to do. So one study I 145 00:06:47,480 --> 00:06:51,159 Speaker 6: read that for a basic intelligent task it would cost 146 00:06:51,240 --> 00:06:54,359 Speaker 6: two cents versus two seventy five for some of the 147 00:06:54,360 --> 00:06:56,880 Speaker 6: other models. So just to put that into perspective. But 148 00:06:57,160 --> 00:07:00,960 Speaker 6: what has happened since then is that download have subsequently 149 00:07:01,360 --> 00:07:04,040 Speaker 6: declined this year and year to date it's only been 150 00:07:04,080 --> 00:07:07,159 Speaker 6: downloaded thirty four million times and only two million times 151 00:07:07,160 --> 00:07:09,600 Speaker 6: within the US. So there could be a number of 152 00:07:09,640 --> 00:07:12,880 Speaker 6: reasons for that. One might be the fear of you know, security. 153 00:07:12,880 --> 00:07:15,400 Speaker 6: The servers are based in China, so there might be 154 00:07:16,040 --> 00:07:19,000 Speaker 6: questions about national security and the security of the information 155 00:07:19,280 --> 00:07:22,880 Speaker 6: if people use it, So that's one of the options, 156 00:07:22,920 --> 00:07:25,880 Speaker 6: as well as other products such as chat GBT and 157 00:07:26,000 --> 00:07:29,280 Speaker 6: Claude ramping up their features and functionality. 158 00:07:29,680 --> 00:07:33,440 Speaker 2: So it seems like on these AI tools here, whether 159 00:07:33,480 --> 00:07:39,200 Speaker 2: it's a claud or a Gemini, the iterates so often right, yes, yes, 160 00:07:39,240 --> 00:07:45,160 Speaker 2: and they make material jumps in quality and capabilities. How 161 00:07:45,160 --> 00:07:47,160 Speaker 2: do you guys track that? How do you guys track like? 162 00:07:47,200 --> 00:07:48,520 Speaker 2: Who are the big ones? Who are the good ones? 163 00:07:48,520 --> 00:07:49,240 Speaker 2: How do you guys do that? 164 00:07:49,480 --> 00:07:52,679 Speaker 6: So based on our data, we have a proprietary first 165 00:07:52,680 --> 00:07:55,840 Speaker 6: party panel, so we keep track of every time an 166 00:07:55,880 --> 00:07:59,240 Speaker 6: app is downloaded, every time someone opens an app, how 167 00:07:59,280 --> 00:08:02,360 Speaker 6: long they spend on the app, the number of sessions. Similarly, 168 00:08:02,400 --> 00:08:06,400 Speaker 6: we also have website data where we also track when 169 00:08:06,400 --> 00:08:09,960 Speaker 6: they go on these sites as well as we started 170 00:08:10,040 --> 00:08:12,400 Speaker 6: keeping track of the percentage of ads that are seen 171 00:08:12,400 --> 00:08:14,880 Speaker 6: by chat GPT users, so we also have advertising data. 172 00:08:14,920 --> 00:08:17,720 Speaker 6: So we have a wide breadth of different data sets 173 00:08:17,720 --> 00:08:19,520 Speaker 6: that we look at and combined to figure out what 174 00:08:19,560 --> 00:08:22,400 Speaker 6: we think is happening in the space, and so we 175 00:08:22,440 --> 00:08:25,360 Speaker 6: don't keep as much to figure out if you know, 176 00:08:25,640 --> 00:08:27,920 Speaker 6: version one is better than version three. It's just more 177 00:08:27,920 --> 00:08:30,920 Speaker 6: about are they getting more engagement, are they getting more users, 178 00:08:30,920 --> 00:08:32,720 Speaker 6: and what's driving those changes. 179 00:08:32,800 --> 00:08:35,800 Speaker 2: I think most tech companies, in my experience, they have 180 00:08:35,960 --> 00:08:38,800 Speaker 2: this fear in the back of their minds that a 181 00:08:38,840 --> 00:08:41,319 Speaker 2: couple of kids in some garage and Palo Altough are 182 00:08:41,360 --> 00:08:43,680 Speaker 2: a day away from putting me out of business coming 183 00:08:43,720 --> 00:08:47,320 Speaker 2: up with something better mousetrap. I know we feel that 184 00:08:47,360 --> 00:08:50,160 Speaker 2: way here at Bloomberg, and that's kind of what drives 185 00:08:50,200 --> 00:08:53,440 Speaker 2: you forward to continue to innovate in this space. It 186 00:08:53,679 --> 00:08:56,480 Speaker 2: really seems like it because it feels like we're on 187 00:08:56,559 --> 00:08:58,760 Speaker 2: a wild West of this. We don't know where this 188 00:08:58,800 --> 00:09:02,480 Speaker 2: technology is coming from. I don't It's come from everywhere, yes, 189 00:09:02,600 --> 00:09:05,760 Speaker 2: and it's going it's such a rapid pace. As an 190 00:09:05,760 --> 00:09:07,800 Speaker 2: IPO investor, I be like, you want me to invest 191 00:09:07,800 --> 00:09:10,400 Speaker 2: put all my eggs in your basket, knowing that tomorrow 192 00:09:10,559 --> 00:09:12,640 Speaker 2: there could be some kid who comes out of somewhere 193 00:09:12,640 --> 00:09:15,120 Speaker 2: in the middle of the world. That's kind of how 194 00:09:15,120 --> 00:09:15,640 Speaker 2: it is out there. 195 00:09:15,679 --> 00:09:17,920 Speaker 6: It seems like it is, but there are definitely leaders 196 00:09:18,000 --> 00:09:20,040 Speaker 6: in the space. So when you look at total active 197 00:09:20,120 --> 00:09:24,320 Speaker 6: users globally, chat gibt is over fifty five percent Gemini 198 00:09:24,600 --> 00:09:27,640 Speaker 6: is also I would say forty five to fifty percent 199 00:09:27,840 --> 00:09:31,040 Speaker 6: very high. The other players, while they are growing, are 200 00:09:31,080 --> 00:09:34,599 Speaker 6: significantly smaller. And the way you know, Gemini has a 201 00:09:34,640 --> 00:09:38,120 Speaker 6: lot of advantages, it's in your Android operating system. They 202 00:09:38,120 --> 00:09:40,440 Speaker 6: can connect to Gemini without going to the app, So 203 00:09:40,480 --> 00:09:43,959 Speaker 6: it's probably even understated versus them active users that we show. 204 00:09:44,480 --> 00:09:46,760 Speaker 6: Chat GBT is sort of the first mover advantage, first 205 00:09:46,800 --> 00:09:49,680 Speaker 6: app to reach a billion and mus and the shortest 206 00:09:49,720 --> 00:09:54,240 Speaker 6: amount of time. Chat GBT sort of become the Google word, right, 207 00:09:54,720 --> 00:09:56,920 Speaker 6: And so when you think about B two C customers, 208 00:09:56,960 --> 00:09:59,360 Speaker 6: I think a lot of people first start out using 209 00:09:59,440 --> 00:10:02,040 Speaker 6: chat GP and for an average user, they probably don't 210 00:10:02,040 --> 00:10:05,480 Speaker 6: see much difference between any of them, Whereas Claude has 211 00:10:05,640 --> 00:10:08,679 Speaker 6: rapidly grown and it seems to be more focused on enterprise. 212 00:10:08,720 --> 00:10:12,040 Speaker 6: Seventy percent of their user's time spent is on the web, right, 213 00:10:12,080 --> 00:10:14,320 Speaker 6: So maybe you get a split versus B to B 214 00:10:14,440 --> 00:10:17,160 Speaker 6: and B two C. But while the other players are growing, 215 00:10:17,200 --> 00:10:18,480 Speaker 6: they are very small. 216 00:10:18,320 --> 00:10:20,840 Speaker 2: And isn't the cost of it If I want to 217 00:10:21,320 --> 00:10:23,920 Speaker 2: in an enterprise and have Claude used by my employees, 218 00:10:24,800 --> 00:10:26,239 Speaker 2: I got to pay for that, right. 219 00:10:26,200 --> 00:10:29,160 Speaker 6: Yes, you get token, so when you when you ask 220 00:10:29,200 --> 00:10:31,640 Speaker 6: a task or when you have a task, it requires 221 00:10:31,640 --> 00:10:34,040 Speaker 6: some sort of payment in the token. And then also 222 00:10:34,080 --> 00:10:35,440 Speaker 6: in the back end of this is a lot of 223 00:10:35,559 --> 00:10:39,840 Speaker 6: energy to do these calculations. So it's an expensive endeavor 224 00:10:39,880 --> 00:10:42,360 Speaker 6: and that is the benefit that deep Sea has is 225 00:10:42,400 --> 00:10:46,000 Speaker 6: that it is much cheaper. And there are US companies 226 00:10:46,040 --> 00:10:50,360 Speaker 6: testing it. Again because of national security and whatnot, I mean, 227 00:10:50,360 --> 00:10:52,920 Speaker 6: you're allowed to use it here, but we'll see how 228 00:10:52,960 --> 00:10:54,920 Speaker 6: well it's adopted in certain Western countries. 229 00:10:55,000 --> 00:10:57,559 Speaker 2: So just in terms of the timing of potential IPOs, 230 00:10:57,559 --> 00:10:59,880 Speaker 2: I guess Anthropic is the first one we think we 231 00:11:00,120 --> 00:11:00,920 Speaker 2: might see. 232 00:11:01,200 --> 00:11:02,600 Speaker 5: They have mentioned it. 233 00:11:02,640 --> 00:11:06,160 Speaker 6: The Anthropic owns Claude Open AI for chat GPT, and 234 00:11:06,240 --> 00:11:08,839 Speaker 6: now deep Seek said maybe early twenty twenty seven. I 235 00:11:08,920 --> 00:11:12,439 Speaker 6: think a lot of that depends on how the market goes, 236 00:11:12,520 --> 00:11:16,640 Speaker 6: how we see SpaceX perform, just given its exposure to 237 00:11:16,679 --> 00:11:20,480 Speaker 6: AI and GROCK, so yep, I think that will be 238 00:11:20,520 --> 00:11:24,319 Speaker 6: market dependent. But I think they certainly would all like, yeah. 239 00:11:24,440 --> 00:11:28,120 Speaker 2: So I'm looking at space exploration SpaceX kind of a 240 00:11:28,200 --> 00:11:30,520 Speaker 2: quase AI played to say the least, that's certainly what 241 00:11:30,520 --> 00:11:32,840 Speaker 2: they pitched. Yeah, I don't know when public at one 242 00:11:32,880 --> 00:11:36,000 Speaker 2: hundred and thirty five bucks went over two hundred. We're 243 00:11:36,000 --> 00:11:38,600 Speaker 2: now back down. We're one twenty three, so we're below 244 00:11:38,640 --> 00:11:40,959 Speaker 2: the IPO price and blow the first print at one fifty. 245 00:11:41,040 --> 00:11:42,880 Speaker 2: So see how that plays at Michelle Vice. 246 00:11:43,160 --> 00:11:44,959 Speaker 3: Thank you, stay with us. 247 00:11:45,040 --> 00:11:47,200 Speaker 5: More from Bloomberg Intelligence coming up after this. 248 00:11:50,880 --> 00:11:54,560 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 249 00:11:54,640 --> 00:11:57,720 Speaker 1: weekdays at ten am Eastern on Apple Coarclay and Android 250 00:11:57,760 --> 00:12:01,200 Speaker 1: Auto with the Bloomberg Business app on demand wherever you 251 00:12:01,240 --> 00:12:04,640 Speaker 1: get your podcasts, or watch us live on YouTube. 252 00:12:05,400 --> 00:12:07,280 Speaker 2: We're at the stage of this AI story in the 253 00:12:07,280 --> 00:12:10,960 Speaker 2: marketplace where the market is definitely trying to find winners 254 00:12:10,960 --> 00:12:13,320 Speaker 2: and losers. One of the areas that the market is 255 00:12:13,320 --> 00:12:17,120 Speaker 2: concerned about is software. Certain areas of software software is 256 00:12:17,160 --> 00:12:20,560 Speaker 2: a service, and that includes the next company here. The 257 00:12:20,600 --> 00:12:25,360 Speaker 2: company is called Clavio and the symbols KVYO. The stock 258 00:12:25,480 --> 00:12:27,880 Speaker 2: is down about forty five percent year to day. It's say, yep, 259 00:12:29,679 --> 00:12:34,680 Speaker 2: customer relationship management company using technology. There think something I 260 00:12:34,760 --> 00:12:37,160 Speaker 2: can to Salesforce dot com. We want to get the 261 00:12:37,200 --> 00:12:39,880 Speaker 2: latest on how AI is impacting this company this part 262 00:12:39,920 --> 00:12:43,200 Speaker 2: of the software space, and we're please to welcome Serby 263 00:12:43,240 --> 00:12:48,520 Speaker 2: Gupta Chief Technology Officer and the company's name is Clayvio. Again, 264 00:12:48,600 --> 00:12:50,880 Speaker 2: Kvyo is the tick or serby. Thanks so much for 265 00:12:50,960 --> 00:12:53,079 Speaker 2: joining us here talk to us about kind of what 266 00:12:53,120 --> 00:12:56,439 Speaker 2: you guys do at Clayvio and how you're integrating AI 267 00:12:56,800 --> 00:12:57,760 Speaker 2: into your business. 268 00:12:58,960 --> 00:13:01,720 Speaker 4: Yeah, thanks for having me. So Cleve is an autonomous 269 00:13:01,760 --> 00:13:05,800 Speaker 4: B two C CRM and we bring marketing, customer service 270 00:13:05,920 --> 00:13:11,000 Speaker 4: analytics AI all under one umbrella, and it's all powered 271 00:13:11,000 --> 00:13:15,319 Speaker 4: by a data and we are building products and features 272 00:13:16,040 --> 00:13:19,480 Speaker 4: to help our marketers be the best they can be. 273 00:13:19,720 --> 00:13:23,760 Speaker 4: And we actually just launched composer and Customer Agent to 274 00:13:23,800 --> 00:13:24,320 Speaker 4: help with this. 275 00:13:24,960 --> 00:13:27,559 Speaker 5: Okay, so let's bring it down to how a consumer 276 00:13:27,679 --> 00:13:32,600 Speaker 5: might interact interface with your technology, with your products, your 277 00:13:32,640 --> 00:13:35,480 Speaker 5: B two C platform, which means business to consumer. So 278 00:13:35,640 --> 00:13:39,280 Speaker 5: the consumer does have some exposure. How might Paul, when 279 00:13:39,320 --> 00:13:43,920 Speaker 5: he is, you know, buying something online, experience your work. 280 00:13:45,679 --> 00:13:48,920 Speaker 4: So we have two hundred thousand customers and let's say 281 00:13:48,920 --> 00:13:54,679 Speaker 4: there's Glossier, Away, Mattel, Toy, Birch. If you, as a 282 00:13:54,679 --> 00:13:57,640 Speaker 4: as a customer, go and interact with any of these brands, 283 00:13:58,000 --> 00:14:00,840 Speaker 4: you can get a very personalized experiens And the way 284 00:14:00,880 --> 00:14:03,320 Speaker 4: you do this is because these brands work with Clavio, 285 00:14:03,640 --> 00:14:07,640 Speaker 4: and we help with powering each of these relationships. 286 00:14:08,679 --> 00:14:11,280 Speaker 2: Is AI a friend or an enemy to you and 287 00:14:11,320 --> 00:14:14,800 Speaker 2: your company and your and your software. 288 00:14:15,720 --> 00:14:19,040 Speaker 4: Oh, we're all into AI. There's there's a lot that 289 00:14:19,080 --> 00:14:22,760 Speaker 4: we're using AI for. Even if we just take our 290 00:14:22,840 --> 00:14:27,400 Speaker 4: composer product, it's your marketer's right hand. The goal is 291 00:14:27,440 --> 00:14:30,760 Speaker 4: to help you be as effective as you can. You 292 00:14:30,840 --> 00:14:32,880 Speaker 4: have all these ideas of things that you want to 293 00:14:32,880 --> 00:14:37,400 Speaker 4: build and experiences that you want to create, and we 294 00:14:37,440 --> 00:14:39,440 Speaker 4: want to make that much easier for you. And AI 295 00:14:39,520 --> 00:14:42,480 Speaker 4: is at the forefront of helping make this a reality. 296 00:14:44,120 --> 00:14:46,960 Speaker 5: So talk a little bit about consumer behavior and how 297 00:14:47,040 --> 00:14:51,840 Speaker 5: that enables the use the application of a lot of 298 00:14:51,880 --> 00:14:54,160 Speaker 5: the AI tools that you are developing. 299 00:14:56,240 --> 00:14:58,920 Speaker 4: So, uh, you know, one thing that's really exciting about 300 00:14:58,920 --> 00:15:03,080 Speaker 4: this wave is that AI is not only helping with 301 00:15:03,240 --> 00:15:05,960 Speaker 4: what we build, but also how we build it. So 302 00:15:06,520 --> 00:15:09,960 Speaker 4: on when we think of what we build, we're able 303 00:15:09,960 --> 00:15:13,800 Speaker 4: to build out these experiences much faster, right, and we're 304 00:15:13,800 --> 00:15:15,960 Speaker 4: able to get out more ideas in the hands of 305 00:15:16,000 --> 00:15:20,160 Speaker 4: our customers. And I look at the entire build process 306 00:15:20,200 --> 00:15:23,720 Speaker 4: is significantly changing. We see the velocity going up a lot. 307 00:15:24,080 --> 00:15:28,080 Speaker 4: We see that our engineers are able to shift these 308 00:15:28,080 --> 00:15:32,440 Speaker 4: features much faster now. You know, you might say that, hey, 309 00:15:32,560 --> 00:15:36,600 Speaker 4: like software engineering, writing code has become so much easier 310 00:15:36,640 --> 00:15:38,520 Speaker 4: that you know, there's so many other parts of the 311 00:15:38,520 --> 00:15:42,360 Speaker 4: software development life cycle that we see that the bottlenecks 312 00:15:42,400 --> 00:15:44,520 Speaker 4: move to, and we're working through all of those and 313 00:15:45,160 --> 00:15:49,200 Speaker 4: AI is helping us with massive velocity increases. 314 00:15:49,920 --> 00:15:53,480 Speaker 2: Are your customers are they saying, hey, we need more 315 00:15:53,520 --> 00:15:57,800 Speaker 2: AI in our our more AI capabilities in our marketing 316 00:15:57,800 --> 00:16:00,840 Speaker 2: promotion campaigns. Are they pulling that from you? Are you 317 00:16:01,000 --> 00:16:03,480 Speaker 2: or are you more pushing that to them? 318 00:16:04,120 --> 00:16:08,440 Speaker 4: Our customers are driven by growing their business. They want 319 00:16:08,480 --> 00:16:10,720 Speaker 4: to help grow their brands. They want to help reach 320 00:16:10,800 --> 00:16:13,640 Speaker 4: their customers much better, and that's what we are focused on. 321 00:16:13,960 --> 00:16:16,440 Speaker 4: And when we create these products, when we show them 322 00:16:16,440 --> 00:16:19,720 Speaker 4: demos of the features that we've built, they want those features. 323 00:16:20,120 --> 00:16:23,440 Speaker 4: So the end goal for us is not that hey, 324 00:16:23,480 --> 00:16:25,880 Speaker 4: we're going to use AI in this way and convince 325 00:16:25,920 --> 00:16:27,960 Speaker 4: you why it's important. We are going to build the 326 00:16:27,960 --> 00:16:31,280 Speaker 4: best products that we can. And we believe that AI 327 00:16:31,440 --> 00:16:35,720 Speaker 4: and these these models have really unlocked the ability to 328 00:16:35,800 --> 00:16:38,680 Speaker 4: create a whole new set of features that we can 329 00:16:38,800 --> 00:16:42,480 Speaker 4: put in their hands and that's what we've seen with Composer. 330 00:16:42,720 --> 00:16:45,120 Speaker 4: We've put this in front of our customers. They've been 331 00:16:45,240 --> 00:16:48,000 Speaker 4: using this to create their campaigns and flows, and when 332 00:16:48,040 --> 00:16:51,880 Speaker 4: it works, it's magic. We've taken away so much work 333 00:16:51,920 --> 00:16:54,520 Speaker 4: from them and they can do the work that they're 334 00:16:54,520 --> 00:16:57,120 Speaker 4: best at, which is coming up with really good ideas, 335 00:16:57,680 --> 00:16:58,920 Speaker 4: and we can help make. 336 00:16:58,760 --> 00:17:02,000 Speaker 5: That a reality with us. More from Bloomberg Intelligence coming 337 00:17:02,080 --> 00:17:02,760 Speaker 5: up after this. 338 00:17:06,920 --> 00:17:10,600 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 339 00:17:10,680 --> 00:17:13,800 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 340 00:17:13,800 --> 00:17:17,120 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 341 00:17:17,160 --> 00:17:20,280 Speaker 1: you get your podcasts, or watch us live on YouTube. 342 00:17:20,840 --> 00:17:24,200 Speaker 5: We talk a lot about this younger generation of consumers, 343 00:17:24,240 --> 00:17:27,800 Speaker 5: Gen Z, and how their spending is very different than 344 00:17:27,960 --> 00:17:31,480 Speaker 5: say Gen X or Boomers. They'd like to spend on experiences. 345 00:17:31,560 --> 00:17:34,320 Speaker 5: I mean, they do buy some hardcore products. They're rediscovering 346 00:17:34,320 --> 00:17:39,440 Speaker 5: things like landlines and Polaroid cameras. What does it look 347 00:17:39,520 --> 00:17:43,600 Speaker 5: like when it comes to beauty products, What does it 348 00:17:43,640 --> 00:17:45,720 Speaker 5: look like when it comes to that part of the 349 00:17:45,800 --> 00:17:49,320 Speaker 5: consumer market. Let's bring in Lindsay Dutch. She is our 350 00:17:49,560 --> 00:17:53,000 Speaker 5: BI senior industry analysts covering this sector. And she joins 351 00:17:53,040 --> 00:17:55,720 Speaker 5: us now and lindsay, you and your team put together 352 00:17:55,920 --> 00:17:59,399 Speaker 5: some research on what gen Z likes, what they don't like. 353 00:17:59,480 --> 00:18:03,280 Speaker 7: What did you mind? Yeah, hi, Scarlett, thanks for having me. 354 00:18:04,119 --> 00:18:07,080 Speaker 7: We ran our semi annual beauty survey and we just 355 00:18:07,119 --> 00:18:09,640 Speaker 7: got the results back. And in general, when you look 356 00:18:09,680 --> 00:18:11,840 Speaker 7: at the market, it's one hundred and twenty billion dollar 357 00:18:11,920 --> 00:18:15,720 Speaker 7: market in the US. Demand is relatively soft for beauty. 358 00:18:15,800 --> 00:18:17,879 Speaker 7: It kind of weakened a little bit over the past 359 00:18:17,960 --> 00:18:22,360 Speaker 7: six to twelve months. It's sitting near lows two year 360 00:18:22,440 --> 00:18:25,440 Speaker 7: lows based on our series of surveys. But one thing 361 00:18:25,520 --> 00:18:29,760 Speaker 7: that stood out was gen Z. Gen Z tends to 362 00:18:29,880 --> 00:18:34,000 Speaker 7: have more intensive beauty routines, a lot more of them, 363 00:18:34,200 --> 00:18:37,760 Speaker 7: use a lot of products compared to average on our survey, 364 00:18:38,440 --> 00:18:41,840 Speaker 7: and their demand signals that I look at, which is, 365 00:18:41,920 --> 00:18:44,640 Speaker 7: you know, would you cut back on other things before beauty? 366 00:18:44,960 --> 00:18:48,480 Speaker 7: You know, how important is continuing your beauty routine? Are 367 00:18:48,480 --> 00:18:50,600 Speaker 7: you trying new products? When I look at those types 368 00:18:50,640 --> 00:18:54,919 Speaker 7: of indicators, gen Z not only shows steady demand, but 369 00:18:54,960 --> 00:18:57,720 Speaker 7: it actually seems to be improving, which is a pretty 370 00:18:57,760 --> 00:19:00,000 Speaker 7: big contrast to what we're seeing in the broader market. 371 00:19:00,119 --> 00:19:04,960 Speaker 2: Kit So gen Z I mean, they're very, very comfortable 372 00:19:05,040 --> 00:19:08,600 Speaker 2: with buying pretty much anything and everything online. 373 00:19:08,920 --> 00:19:13,679 Speaker 7: How about beauty, Yeah, so gen Z does show a 374 00:19:13,840 --> 00:19:17,680 Speaker 7: high interest of shopping in store. Our survey show is 375 00:19:17,680 --> 00:19:21,359 Speaker 7: about seventy five percent of that cohort prefers to shop 376 00:19:21,359 --> 00:19:23,440 Speaker 7: in store for beauty. They like to see the product, 377 00:19:23,480 --> 00:19:26,199 Speaker 7: they like to test it out on their skin, so 378 00:19:26,240 --> 00:19:29,000 Speaker 7: they are interested in that. But they do use online 379 00:19:29,040 --> 00:19:33,840 Speaker 7: for discovery. So social media is very big for beauty 380 00:19:33,880 --> 00:19:37,440 Speaker 7: and discovering new product. You know, gen Z, a lot 381 00:19:37,480 --> 00:19:43,359 Speaker 7: of them do look there for inspiration. And one interesting fact, 382 00:19:43,400 --> 00:19:45,119 Speaker 7: you know, I ask a question about, you know, what 383 00:19:45,119 --> 00:19:48,000 Speaker 7: would inspire you to buy something that you were not 384 00:19:48,200 --> 00:19:51,719 Speaker 7: shopping for, and gen Z ranked seeing it on social 385 00:19:51,760 --> 00:19:56,560 Speaker 7: media ahead of a discount on a certain product. More 386 00:19:56,680 --> 00:19:59,760 Speaker 7: broader results were that, you know, a discounted product would 387 00:19:59,760 --> 00:20:01,960 Speaker 7: spur them to make that purchase decision. 388 00:20:02,680 --> 00:20:04,800 Speaker 5: So within your research you also find that gen Z 389 00:20:05,040 --> 00:20:09,920 Speaker 5: leans into fragrance. How do you discover fragrance online? 390 00:20:10,119 --> 00:20:14,320 Speaker 7: Yeah, fragrance discovery is hard online, but gen Z is 391 00:20:14,400 --> 00:20:18,240 Speaker 7: fueling sort of a pickup in the fragrance market and 392 00:20:18,400 --> 00:20:22,119 Speaker 7: with this trend called fragrance layering, which is basically buying 393 00:20:22,440 --> 00:20:26,000 Speaker 7: a lot of different products for scent. You know, you 394 00:20:26,040 --> 00:20:28,960 Speaker 7: can do lotion, you could do a spray, and sort 395 00:20:28,960 --> 00:20:31,080 Speaker 7: of they mix them all together to kind of create 396 00:20:31,119 --> 00:20:34,600 Speaker 7: their own personal scent. And this is driving demand in 397 00:20:34,640 --> 00:20:39,320 Speaker 7: the industry, and it's demand from mass brands all the 398 00:20:39,320 --> 00:20:42,120 Speaker 7: way up to luxury. So it's something that we're seeing 399 00:20:42,200 --> 00:20:46,400 Speaker 7: and the survey results really sort of validated that ALTA 400 00:20:47,040 --> 00:20:52,120 Speaker 7: who caters to the younger consumer, you know, they're really 401 00:20:52,160 --> 00:20:55,359 Speaker 7: looking to be a leader in fragrance, really expanding that 402 00:20:55,440 --> 00:20:59,280 Speaker 7: assortment and trying to capture that demand. 403 00:21:00,880 --> 00:21:05,600 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apple, Spotify, 404 00:21:05,760 --> 00:21:09,240 Speaker 1: and anywhere else you get your podcasts. Listen live each 405 00:21:09,280 --> 00:21:13,000 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 406 00:21:13,119 --> 00:21:16,680 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 407 00:21:17,080 --> 00:21:20,040 Speaker 1: You can also watch us live every weekday on YouTube 408 00:21:20,400 --> 00:21:22,640 Speaker 1: and always on the Bloomberg terminal.