1 00:00:01,280 --> 00:00:05,120 Speaker 1: We're from Mahard where innovation, money and power. Collie in 2 00:00:05,240 --> 00:00:10,160 Speaker 1: Silicon Vallet NBN. This is Bloomberg Technology with Caroline Hyde 3 00:00:10,200 --> 00:00:11,120 Speaker 1: and Ed Ludlow. 4 00:00:25,079 --> 00:00:28,200 Speaker 2: Amed Ludlow in San Francisco carries off on assignment. 5 00:00:28,320 --> 00:00:29,600 Speaker 3: This is Bloomberg Technology. 6 00:00:29,640 --> 00:00:32,280 Speaker 2: Coming up, We're going to talk earnings as Oracle posts 7 00:00:32,320 --> 00:00:35,560 Speaker 2: its biggest gain in two years amid a spiking bookings 8 00:00:35,600 --> 00:00:40,200 Speaker 2: in its cloud computing business. Details ahead, plus Bitcoin continuing 9 00:00:40,320 --> 00:00:43,760 Speaker 2: its record breaking run as two point seven billion dollars 10 00:00:43,800 --> 00:00:47,080 Speaker 2: flowed into crypto assets last week. We'll discuss with the 11 00:00:47,080 --> 00:00:52,400 Speaker 2: Delta Blockchain Fund CEO, and legislation that would force TikTok's 12 00:00:52,400 --> 00:00:54,920 Speaker 2: parent company to sell it or face a ban in 13 00:00:54,960 --> 00:00:58,240 Speaker 2: the US is picking up speed in Congress. We'll discuss 14 00:00:58,320 --> 00:01:01,120 Speaker 2: that and so much more throughout the hour. Let's get 15 00:01:01,200 --> 00:01:03,560 Speaker 2: right to market. Said it was a hotter than expected 16 00:01:03,600 --> 00:01:06,959 Speaker 2: CPI print that drove the narrative this morning. Actually in 17 00:01:07,000 --> 00:01:09,840 Speaker 2: the equity space, it was kind of a negative reaction 18 00:01:09,920 --> 00:01:12,679 Speaker 2: at first. We haven't really changed our mind about the 19 00:01:12,720 --> 00:01:15,720 Speaker 2: idea that if a rate cut is coming from the 20 00:01:15,800 --> 00:01:18,320 Speaker 2: Federal Reserve, it isn't coming till June. But actually look 21 00:01:18,319 --> 00:01:20,039 Speaker 2: at the Nazak one hundred. We're now up by more 22 00:01:20,040 --> 00:01:23,040 Speaker 2: than a percentage point. A lot of the legwork coming 23 00:01:23,040 --> 00:01:24,600 Speaker 2: from some of the earning story that we're going to 24 00:01:24,600 --> 00:01:28,840 Speaker 2: get into. Semiconductors also higher, outperforming one point four percent, even. 25 00:01:28,640 --> 00:01:30,119 Speaker 3: As you do see rates creep high. 26 00:01:30,240 --> 00:01:32,800 Speaker 2: US ten y yield was up about five basis points 27 00:01:32,800 --> 00:01:35,679 Speaker 2: four point one four percent, and now Bitcoin is down 28 00:01:35,720 --> 00:01:38,440 Speaker 2: one point three six percent at this moment in time, 29 00:01:39,000 --> 00:01:42,280 Speaker 2: just below or above sorry, seventy one thousand US dollars 30 00:01:42,319 --> 00:01:45,600 Speaker 2: per token. I talked about the kind of upside inequities 31 00:01:45,680 --> 00:01:48,960 Speaker 2: Nazak one hundred. It's probably Oracle that accounts for a 32 00:01:48,960 --> 00:01:51,160 Speaker 2: lot of that. At one point in the session, on 33 00:01:51,200 --> 00:01:54,000 Speaker 2: course for its biggest jump since the end of December 34 00:01:54,240 --> 00:01:58,559 Speaker 2: twenty twenty one, the story twenty five percent top line 35 00:01:58,600 --> 00:02:00,760 Speaker 2: growth in its cloud business. If you look at all 36 00:02:00,760 --> 00:02:04,680 Speaker 2: of the analyst notes, there is an AI tailwind here 37 00:02:04,800 --> 00:02:07,440 Speaker 2: is Oracle tries to compete with the hyperscalers. Let's get 38 00:02:07,520 --> 00:02:10,000 Speaker 2: right to that conversation and bring in Bloomberg's Brody Ford, 39 00:02:10,280 --> 00:02:11,959 Speaker 2: the Oracle whisperer in chief. 40 00:02:12,120 --> 00:02:14,120 Speaker 3: I mean that for me was the story right, you know. 41 00:02:14,200 --> 00:02:18,160 Speaker 2: Overall top line growth seven percent matched estimates, but that 42 00:02:18,280 --> 00:02:20,760 Speaker 2: performance in the cloud unit that was pretty good. 43 00:02:21,600 --> 00:02:25,520 Speaker 4: Yeah, they booked a lot more business than anybody expected. 44 00:02:25,639 --> 00:02:28,919 Speaker 4: And the executives attributed that to the cloud business, saying 45 00:02:28,919 --> 00:02:33,480 Speaker 4: that right now everybody needs more cloud computing power. Era 46 00:02:33,600 --> 00:02:37,760 Speaker 4: of AI training models plus just mass digitization means that 47 00:02:38,160 --> 00:02:40,720 Speaker 4: there's not enough computing power to go around. And a 48 00:02:40,760 --> 00:02:42,880 Speaker 4: lot of folks are signing that piece of paper with 49 00:02:43,000 --> 00:02:45,400 Speaker 4: Oracle and we should see that turned into revenue in 50 00:02:45,440 --> 00:02:46,240 Speaker 4: the coming quarters. 51 00:02:48,040 --> 00:02:50,520 Speaker 2: Brody, one of the great things about you is you 52 00:02:50,560 --> 00:02:54,680 Speaker 2: can take any technology company that on paper, I'm saying 53 00:02:54,680 --> 00:02:58,360 Speaker 2: on paper can be a bit dry, right. They used 54 00:02:58,400 --> 00:03:02,359 Speaker 2: all this jargon that we don't fully understand. In Oracles case, 55 00:03:02,840 --> 00:03:06,880 Speaker 2: remaining performance obligation. If anyone phones you up and said 56 00:03:06,880 --> 00:03:09,840 Speaker 2: that you'd hang up, you fall asleep. But actually, in 57 00:03:09,880 --> 00:03:14,079 Speaker 2: Oracles case, this is a mighty impressive backlog of business, 58 00:03:14,320 --> 00:03:15,760 Speaker 2: and the street's really paying attention. 59 00:03:16,720 --> 00:03:19,840 Speaker 4: Yeah, absolutely, it is the backlog, as you said, right, 60 00:03:19,840 --> 00:03:22,839 Speaker 4: it's business that's been signed. And what's interesting about that 61 00:03:23,280 --> 00:03:25,480 Speaker 4: is the reason it's a backlog is because they don't 62 00:03:25,520 --> 00:03:28,280 Speaker 4: have the physical space, right, I mean, the cloud isn't 63 00:03:28,320 --> 00:03:32,440 Speaker 4: this infinite thing in the Sky. It's data centers in Virginia, right, 64 00:03:32,440 --> 00:03:34,839 Speaker 4: and they need to build more of them. They said 65 00:03:34,880 --> 00:03:37,040 Speaker 4: that in the next year they're going to spend ten 66 00:03:37,080 --> 00:03:40,680 Speaker 4: billion dollars building out their data center presence around the country. 67 00:03:40,960 --> 00:03:43,320 Speaker 4: And think about that capex compared to a couple of 68 00:03:43,400 --> 00:03:45,000 Speaker 4: years ago, it was two billion. I mean, this is 69 00:03:45,000 --> 00:03:48,400 Speaker 4: a huge increase and that's what's giving investors confidence that 70 00:03:48,440 --> 00:03:51,800 Speaker 4: they truly are building out their footprint in response to 71 00:03:51,880 --> 00:03:52,800 Speaker 4: customer demand. 72 00:03:54,120 --> 00:03:57,200 Speaker 2: So I talked about the AI tailwind. That is what 73 00:03:58,080 --> 00:04:01,960 Speaker 2: the bulls see here. These Oracles AI story, Where did 74 00:04:02,000 --> 00:04:04,360 Speaker 2: they fit in and everything that's going on. 75 00:04:05,000 --> 00:04:07,720 Speaker 4: Yeah, I mean every software company wants to say that 76 00:04:07,760 --> 00:04:10,720 Speaker 4: any outperformance is due to AI Oracles no different. It's 77 00:04:10,720 --> 00:04:13,240 Speaker 4: hard to exactly parts how much is coming from it. 78 00:04:13,640 --> 00:04:16,520 Speaker 4: But there is some real third party voices here saying 79 00:04:16,520 --> 00:04:20,479 Speaker 4: that Oracle's cloud is particularly good for training and influencing 80 00:04:20,480 --> 00:04:26,240 Speaker 4: these AI models, which are notoriously resource intensive, right, And 81 00:04:26,279 --> 00:04:28,200 Speaker 4: so we've heard of a lot of folks going to 82 00:04:28,279 --> 00:04:32,920 Speaker 4: Oracle to train their models, and yeah, so that has 83 00:04:32,960 --> 00:04:37,720 Speaker 4: been fueling some of this demand, but there is some skepticism, right. 84 00:04:38,480 --> 00:04:41,400 Speaker 4: Brent phil was on here yesterday saying that the actual 85 00:04:41,600 --> 00:04:45,080 Speaker 4: current amount of revenue coming from AI workloads is not massive, 86 00:04:45,640 --> 00:04:48,400 Speaker 4: but the idea is that in the coming quarters, in 87 00:04:48,440 --> 00:04:51,160 Speaker 4: the coming year, as AI becomes a more mainstream part 88 00:04:51,200 --> 00:04:53,160 Speaker 4: of the tech stack, that that will increase. 89 00:04:54,440 --> 00:04:57,039 Speaker 2: All right, Bloomberg's Brady Ford and as we talked about earlier, 90 00:04:57,600 --> 00:05:00,599 Speaker 2: Oracle a real kind of contribute to to the upside 91 00:05:00,640 --> 00:05:02,839 Speaker 2: we see in ectees, particularly in the tech sector. 92 00:05:03,080 --> 00:05:04,080 Speaker 3: This morning. Good Seamate. 93 00:05:04,160 --> 00:05:06,599 Speaker 2: Now let's turn to another top story we've been tracking 94 00:05:06,600 --> 00:05:10,799 Speaker 2: for about a week, latest in Elon Musk's lawsuit against 95 00:05:10,839 --> 00:05:13,960 Speaker 2: open ai and Sam Outman. Open Ai saying in a 96 00:05:14,040 --> 00:05:19,200 Speaker 2: court filing that the billionaires claims quote rest on convoluted, 97 00:05:19,400 --> 00:05:24,720 Speaker 2: often incoherent, factual premises. Let's bring in Bloomberg's AI reporter 98 00:05:25,320 --> 00:05:29,320 Speaker 2: Rachel Metz. What is it that open ai is trying 99 00:05:29,360 --> 00:05:32,200 Speaker 2: to say here in normal people speak. 100 00:05:31,960 --> 00:05:36,520 Speaker 5: Rachel, Open ai is actually echoing in this corre filing 101 00:05:36,839 --> 00:05:39,600 Speaker 5: a number of the things that they said previously in 102 00:05:39,839 --> 00:05:41,880 Speaker 5: a blog post that they put up that had a 103 00:05:41,920 --> 00:05:45,240 Speaker 5: lot of juicy emails. This was last week, and also 104 00:05:45,320 --> 00:05:50,160 Speaker 5: some internal memos that we got a hold of recently. Basically, 105 00:05:50,160 --> 00:05:52,760 Speaker 5: they're saying there was no agreement for us to do 106 00:05:53,000 --> 00:05:57,120 Speaker 5: what you said, what you're saying is incorrect, and you 107 00:05:57,160 --> 00:05:59,800 Speaker 5: know a few other tidbits of just saying as you 108 00:05:59,839 --> 00:06:04,159 Speaker 5: say said, your argument is incoherent. And what's funny about 109 00:06:04,200 --> 00:06:07,280 Speaker 5: this is it is a procedural filing that they were 110 00:06:07,279 --> 00:06:09,520 Speaker 5: putting in this filing to say that we want this 111 00:06:09,600 --> 00:06:11,599 Speaker 5: court case to be classified as what's known as a 112 00:06:11,600 --> 00:06:16,920 Speaker 5: complex case in California and then I'll give it a 113 00:06:16,960 --> 00:06:19,680 Speaker 5: tiny bit of treatment in a different way by a 114 00:06:19,760 --> 00:06:23,000 Speaker 5: judge who you know can understand this stuff and also 115 00:06:23,320 --> 00:06:24,720 Speaker 5: like get things done quickly. 116 00:06:26,080 --> 00:06:27,400 Speaker 3: Well, there's also a reason why. 117 00:06:27,480 --> 00:06:29,800 Speaker 2: So Vinode Koestler, who is one of the early open 118 00:06:29,839 --> 00:06:33,680 Speaker 2: ai investors, was on the show yesterday and one of 119 00:06:33,720 --> 00:06:36,800 Speaker 2: the things that he was arguing for was that, you know, 120 00:06:36,880 --> 00:06:41,000 Speaker 2: open ai is critically important from a national security context, 121 00:06:41,040 --> 00:06:44,599 Speaker 2: right if America wants to be competitive in AI, then 122 00:06:44,640 --> 00:06:46,960 Speaker 2: we need to support open ai in doing that. And 123 00:06:47,000 --> 00:06:49,680 Speaker 2: I my understanding is that one of the things open 124 00:06:49,720 --> 00:06:53,320 Speaker 2: ai did with this filing is basically to say, hey, 125 00:06:53,760 --> 00:06:56,640 Speaker 2: if we go through with this lawsuit, the discovery process 126 00:06:56,920 --> 00:07:00,679 Speaker 2: will give away all our secrets in the context of AI. 127 00:07:02,160 --> 00:07:02,360 Speaker 3: Yeah. 128 00:07:02,640 --> 00:07:05,480 Speaker 5: See, they did argue that although to be fair. 129 00:07:05,760 --> 00:07:06,720 Speaker 3: One could argue that. 130 00:07:06,640 --> 00:07:09,760 Speaker 5: Any discovery process might have a risk of bringing up 131 00:07:09,800 --> 00:07:11,680 Speaker 5: things that you don't watch over at the public grade. 132 00:07:11,680 --> 00:07:14,600 Speaker 5: I mean, that's part of what makes it just interesting 133 00:07:14,640 --> 00:07:16,440 Speaker 5: to the rest of us. Right, What comes out in 134 00:07:16,480 --> 00:07:19,880 Speaker 5: the discovery process can be very interesting. That could be, 135 00:07:20,120 --> 00:07:24,280 Speaker 5: But hopefully we'll get some interesting information and some of 136 00:07:24,320 --> 00:07:26,480 Speaker 5: the things that they are hoping to keep a prietary 137 00:07:26,560 --> 00:07:27,600 Speaker 5: will beget proprietary. 138 00:07:28,440 --> 00:07:28,720 Speaker 1: All right. 139 00:07:28,720 --> 00:07:31,480 Speaker 2: Bloombers Rachel Metz covering a story that we've been covering 140 00:07:31,520 --> 00:07:34,280 Speaker 2: almost daily for a week now, and we will continue 141 00:07:34,520 --> 00:07:36,880 Speaker 2: to do so. Another big story in the world of 142 00:07:36,920 --> 00:07:40,760 Speaker 2: technology and video and Data Bricks are facing copyright infringement 143 00:07:40,840 --> 00:07:44,680 Speaker 2: lawsuits from a group of authors alleging the company's respective 144 00:07:44,720 --> 00:07:48,720 Speaker 2: AI models are trained on their books without permission. The 145 00:07:48,760 --> 00:07:52,720 Speaker 2: pair of proposed class actions, filed in San Francisco Federal court, 146 00:07:53,120 --> 00:07:56,040 Speaker 2: alleged Nvidia and Data Bricks built their models on a 147 00:07:56,120 --> 00:08:00,120 Speaker 2: library of pirated digital e books known as Books three. 148 00:08:00,200 --> 00:08:02,400 Speaker 2: The suits were filed in March eighth, and they joined 149 00:08:02,440 --> 00:08:06,240 Speaker 2: dozens of authors and copyright owners around the country suing 150 00:08:06,360 --> 00:08:10,120 Speaker 2: top AI companies, and those include open ai and Meta 151 00:08:10,320 --> 00:08:13,440 Speaker 2: all Right. Coming up on Bloomberg Technology Bitcoin hovers near 152 00:08:13,480 --> 00:08:17,800 Speaker 2: that all time high. Can the cryptocurrency run be sustained? 153 00:08:17,840 --> 00:08:21,400 Speaker 2: Seventy one six nine two dollars per token right now, 154 00:08:21,520 --> 00:08:24,080 Speaker 2: we're also taking a look at shares of ARM, the 155 00:08:24,200 --> 00:08:25,040 Speaker 2: chip design firm. 156 00:08:25,200 --> 00:08:27,559 Speaker 3: 'tis a story as old as time. 157 00:08:28,200 --> 00:08:31,240 Speaker 2: The lockup is ending, and the concern from shareholders is 158 00:08:31,240 --> 00:08:34,199 Speaker 2: that those insiders that are now free to sell shares 159 00:08:34,559 --> 00:08:38,640 Speaker 2: will do so. Of course, it's a story that happens 160 00:08:39,040 --> 00:08:41,599 Speaker 2: for many in the immediate post IPO and aftermath. And 161 00:08:41,640 --> 00:08:44,160 Speaker 2: think about the number of technius that have listed in 162 00:08:44,200 --> 00:08:46,560 Speaker 2: the last six months or so. Continue tracking that soft 163 00:08:46,679 --> 00:08:49,120 Speaker 2: three tens to one percent on on. This is Bloomberg 164 00:08:49,120 --> 00:09:04,760 Speaker 2: Technology Bitcoin's record breaking run, showing few signs of slowing, 165 00:09:04,800 --> 00:09:09,040 Speaker 2: with large amounts of capital inflows strong demand for the cryptocurrencies, 166 00:09:09,040 --> 00:09:11,520 Speaker 2: so it reached an all time high of seventy two, 167 00:09:11,840 --> 00:09:14,840 Speaker 2: eight hundred and eighty one dollars on Monday, with some 168 00:09:15,000 --> 00:09:19,640 Speaker 2: investors eyeing a target of eighty thousand US dollars in 169 00:09:19,720 --> 00:09:23,920 Speaker 2: the medium term. Meanwhile, Gray Scale Investments is looking to 170 00:09:24,000 --> 00:09:27,520 Speaker 2: launch a clone of the world's largest bitcoin fund after 171 00:09:27,520 --> 00:09:31,360 Speaker 2: it saw more than eleven billion dollars of outflows from 172 00:09:31,400 --> 00:09:36,520 Speaker 2: its GBTC fund, the analysis from Cavita Gupta, Delta Blockchain 173 00:09:36,600 --> 00:09:40,480 Speaker 2: fun founder and general partner. So there's something going on 174 00:09:40,520 --> 00:09:43,480 Speaker 2: in the short term, and then there's the debate about 175 00:09:43,480 --> 00:09:45,840 Speaker 2: what's going on in the long term. Let's start with 176 00:09:45,880 --> 00:09:50,439 Speaker 2: the short term. The flows data seems to imply sustained 177 00:09:50,559 --> 00:09:56,040 Speaker 2: momentum behind at least institutional interest right in bitcoin. Just 178 00:09:56,040 --> 00:09:58,080 Speaker 2: give me your assessment of the hear in the now. 179 00:10:00,120 --> 00:10:03,200 Speaker 6: So if I look at the short term, ed I 180 00:10:03,440 --> 00:10:07,559 Speaker 6: really see that the Bitcoin ETF demand has surpassed a 181 00:10:07,640 --> 00:10:11,240 Speaker 6: lot of people's expectations. I mean, traditionally we have always 182 00:10:11,280 --> 00:10:15,960 Speaker 6: expected that before bitcoin having the prices always goes up. 183 00:10:16,280 --> 00:10:19,560 Speaker 6: But even when we see the sell pressure from the market, 184 00:10:19,559 --> 00:10:23,160 Speaker 6: people who have been through the beer had actually hold it, 185 00:10:23,640 --> 00:10:25,960 Speaker 6: we see the buy is just too much. So it's 186 00:10:26,000 --> 00:10:27,880 Speaker 6: been a very interesting cycle for bitcoin. 187 00:10:29,120 --> 00:10:31,760 Speaker 2: Something that I've been bringing up on air in the 188 00:10:31,840 --> 00:10:34,840 Speaker 2: last few days, if not weeks, is that there is 189 00:10:34,880 --> 00:10:39,800 Speaker 2: a scenario where those that are bullish from cryptocurrencies, particularly bitcoin, 190 00:10:40,240 --> 00:10:43,720 Speaker 2: and those that are bearish on bitcoin, seem to agree 191 00:10:43,760 --> 00:10:46,400 Speaker 2: on one thing. That is, from where we are now, 192 00:10:46,800 --> 00:10:50,360 Speaker 2: it is likely Bitcoin goes to a higher milestone we 193 00:10:50,440 --> 00:10:53,400 Speaker 2: just talked about eighty thousand as a medium term call. 194 00:10:53,640 --> 00:10:56,600 Speaker 2: I've also seen one hundred thousand, but there are many 195 00:10:56,640 --> 00:10:59,520 Speaker 2: that agree that it will then pull back. What's your 196 00:10:59,559 --> 00:11:01,640 Speaker 2: understand the why behind that? 197 00:11:03,040 --> 00:11:07,160 Speaker 6: I think combination of things. The first thing is after 198 00:11:07,200 --> 00:11:09,720 Speaker 6: the bitcoin having we always see some sort of a 199 00:11:09,760 --> 00:11:13,160 Speaker 6: plateau or ten to twenty percent pull in squeeze. We 200 00:11:13,200 --> 00:11:15,240 Speaker 6: are also expecting a lot of people in the market 201 00:11:15,280 --> 00:11:18,280 Speaker 6: are expecting interest rate to be cut down before elections, 202 00:11:18,720 --> 00:11:21,439 Speaker 6: and I think the elections being this year in the 203 00:11:21,520 --> 00:11:25,400 Speaker 6: United States going to really define how the political roadmap, 204 00:11:25,559 --> 00:11:29,280 Speaker 6: whether pro crypto or not that much procrypto, going to define. 205 00:11:29,760 --> 00:11:33,400 Speaker 6: But beyond bitcoin, we have already started seeing the technology rally, 206 00:11:33,480 --> 00:11:36,800 Speaker 6: which is with et and Polygon and Solana, so I 207 00:11:36,840 --> 00:11:40,160 Speaker 6: think that's also going to define this roadmap of bitcoin prices. 208 00:11:42,320 --> 00:11:46,040 Speaker 2: There is an interesting news story this very morning where 209 00:11:46,080 --> 00:11:50,560 Speaker 2: the Risbank governor, a central banker, gave his opinion that 210 00:11:50,640 --> 00:11:57,520 Speaker 2: he wanted to minimize bitcoins I guess influence presence in 211 00:11:57,559 --> 00:12:01,920 Speaker 2: the financial sector. He said, I want as little bitcoin 212 00:12:02,000 --> 00:12:05,760 Speaker 2: as possible in the Swedish financial system. What do you 213 00:12:05,800 --> 00:12:08,120 Speaker 2: make of the central bank governor weighing in like that. 214 00:12:09,400 --> 00:12:12,040 Speaker 6: I mean, we have seen that again and again every 215 00:12:12,120 --> 00:12:18,200 Speaker 6: bull cycle. We see some country or some influential Web 216 00:12:18,240 --> 00:12:21,520 Speaker 6: two world institution coming and saying this could be a problem, 217 00:12:21,640 --> 00:12:24,960 Speaker 6: and then in the bear market they're like, we told 218 00:12:25,000 --> 00:12:28,480 Speaker 6: you it's a problem. And then more institutional adoption, more 219 00:12:28,480 --> 00:12:31,440 Speaker 6: retail adoption, and then more and more states and countries 220 00:12:31,480 --> 00:12:35,840 Speaker 6: started using it. I mean, so it's not new said that. 221 00:12:36,120 --> 00:12:39,760 Speaker 6: I think it's beyond any particular state, city, country at 222 00:12:39,760 --> 00:12:42,560 Speaker 6: this point of time, all the way to us where 223 00:12:42,640 --> 00:12:46,120 Speaker 6: now we do have bitcoin ETFs. We are only three 224 00:12:46,200 --> 00:12:48,880 Speaker 6: years back, the question was bitcoin will be even legal 225 00:12:49,000 --> 00:12:50,400 Speaker 6: or will it exist or not? 226 00:12:50,520 --> 00:12:51,920 Speaker 3: Back in twenty one twenty. 227 00:12:51,640 --> 00:12:55,440 Speaker 2: Two itself, the central bank governor's argument was that it's 228 00:12:55,440 --> 00:12:57,920 Speaker 2: not a threat to financial stability, but he's worried about 229 00:12:57,920 --> 00:13:01,280 Speaker 2: the impact on consumers that may or may choose to hold. 230 00:13:01,040 --> 00:13:02,880 Speaker 3: That as a risk asset. 231 00:13:03,280 --> 00:13:06,280 Speaker 2: What is the big picture that's happening here beyond bitcoin? 232 00:13:06,720 --> 00:13:09,120 Speaker 2: Where what is the state of industry and right now 233 00:13:09,120 --> 00:13:11,520 Speaker 2: for Cryptakeovita, I. 234 00:13:11,440 --> 00:13:14,160 Speaker 6: Think it's all about technology. We usually it like you 235 00:13:14,200 --> 00:13:16,720 Speaker 6: and me have talked about this before. We always talked 236 00:13:16,720 --> 00:13:19,400 Speaker 6: about just the bitcoin and the prices, Like even when 237 00:13:19,440 --> 00:13:22,000 Speaker 6: we talk about anything in this space, we always talk 238 00:13:22,040 --> 00:13:24,880 Speaker 6: about whatever has caught height, like oh, NFT is done by. 239 00:13:24,760 --> 00:13:26,160 Speaker 3: Some Hollywood actor. 240 00:13:26,559 --> 00:13:28,920 Speaker 6: But the real thing out here is the technology, the 241 00:13:28,960 --> 00:13:32,520 Speaker 6: decentralization of things, which are now used by some of 242 00:13:32,559 --> 00:13:36,000 Speaker 6: the biggest companies, some of the biggest institutions globally, and 243 00:13:36,040 --> 00:13:38,600 Speaker 6: that's why you see ethereum rally as the second biggest 244 00:13:38,640 --> 00:13:39,559 Speaker 6: asset currency. 245 00:13:39,840 --> 00:13:40,880 Speaker 3: Then you're seeing. 246 00:13:40,600 --> 00:13:43,760 Speaker 6: All coins which I want to call technology tokens. Actually, 247 00:13:44,000 --> 00:13:47,640 Speaker 6: whether it's Polygon, whether it's Solana, whether it's arbitrum. Optimism 248 00:13:47,720 --> 00:13:50,000 Speaker 6: is doing very well, which is letting the transaction per 249 00:13:50,080 --> 00:13:54,400 Speaker 6: second to be way much more scalable and much less 250 00:13:54,480 --> 00:13:57,800 Speaker 6: cost So I think it is about the technology that 251 00:13:57,920 --> 00:14:00,839 Speaker 6: which is not just Web three native, but adoption and 252 00:14:00,920 --> 00:14:06,000 Speaker 6: supply chains, adoption in international remittances, adoption and stable coin yields. 253 00:14:06,280 --> 00:14:08,440 Speaker 6: And I think we need to start talking in the 254 00:14:08,480 --> 00:14:12,560 Speaker 6: mainstream media more about the underlining tech than just the 255 00:14:12,559 --> 00:14:16,120 Speaker 6: price movement. It's really amazing when we have trillion dollars 256 00:14:16,160 --> 00:14:19,200 Speaker 6: to talk about, but I think the technology should start 257 00:14:19,240 --> 00:14:20,720 Speaker 6: making the highlights now. 258 00:14:22,160 --> 00:14:26,880 Speaker 2: On the technology and the long term there seems to 259 00:14:27,000 --> 00:14:34,560 Speaker 2: be bipartisan not support, but probably acknowledgment of what's happening 260 00:14:34,560 --> 00:14:38,600 Speaker 2: with cryptocurrencies and the underlying blockchain technologies. Do you agree 261 00:14:38,600 --> 00:14:41,360 Speaker 2: with that that that's what's happening in DC in particular. 262 00:14:42,480 --> 00:14:45,600 Speaker 6: Yeah, but I also feel like DC is much more 263 00:14:45,680 --> 00:14:48,440 Speaker 6: informed today than what it was five years or four 264 00:14:48,520 --> 00:14:51,560 Speaker 6: years back. But at the same time, we should look 265 00:14:51,560 --> 00:14:54,480 Speaker 6: at blockchain as a more of a trusted technology than 266 00:14:54,640 --> 00:14:57,080 Speaker 6: just oh, whether it's a cash currency bitcoin. So just 267 00:14:57,120 --> 00:14:58,720 Speaker 6: to give you a quick example, because you were just 268 00:14:58,760 --> 00:15:02,840 Speaker 6: talking about open AI today, you can scrap data from anywhere, 269 00:15:02,880 --> 00:15:05,680 Speaker 6: but doing the verification of that data has always been 270 00:15:05,720 --> 00:15:08,240 Speaker 6: a problem with the social media and the AI chat 271 00:15:08,280 --> 00:15:11,280 Speaker 6: GPT process, and that's what gives you on blockchain, the 272 00:15:11,360 --> 00:15:14,920 Speaker 6: verification of the data, the ownership of that content and 273 00:15:15,000 --> 00:15:16,960 Speaker 6: whom to give it back to if it's their an 274 00:15:16,960 --> 00:15:20,240 Speaker 6: ip right associated with it. Very similar to what New 275 00:15:20,320 --> 00:15:22,640 Speaker 6: York Times and a lot of news media actually tried 276 00:15:22,680 --> 00:15:25,200 Speaker 6: to blame and see open GPT. I think we are 277 00:15:25,200 --> 00:15:28,400 Speaker 6: forgetting that at the end of the day, whether it's ethereum, 278 00:15:28,400 --> 00:15:32,040 Speaker 6: where it's blockchain, any of those things. Bitcoin, we are 279 00:15:32,080 --> 00:15:38,520 Speaker 6: talking about the underlining traceability, verification, decentralization technology, and I 280 00:15:38,560 --> 00:15:41,720 Speaker 6: think going back to DC. More and more people are 281 00:15:41,720 --> 00:15:44,880 Speaker 6: getting informed about it, but just the charm of talking 282 00:15:44,880 --> 00:15:48,920 Speaker 6: about INDK Bitcoin prices are just too difficult to get 283 00:15:48,960 --> 00:15:49,720 Speaker 6: away from. 284 00:15:50,320 --> 00:15:52,240 Speaker 2: We didn't get to Ethereum, but I would note that 285 00:15:52,280 --> 00:15:55,760 Speaker 2: there are lots of headlines about investment into the Ethereum ecosystem, 286 00:15:55,840 --> 00:15:58,760 Speaker 2: which you'd point out Cavita, Gitta, the Delta blockchain. Find 287 00:15:58,760 --> 00:15:59,200 Speaker 2: great to have. 288 00:15:59,160 --> 00:15:59,680 Speaker 3: You on the show. 289 00:16:07,600 --> 00:16:09,880 Speaker 2: Okay, it's time for talking tech and first up, Jamie 290 00:16:09,920 --> 00:16:13,800 Speaker 2: Diamond has once again hailed artificial intelligence, pointing to the 291 00:16:13,840 --> 00:16:18,360 Speaker 2: technologies quote unbelievable potential for the banking industry. The JP 292 00:16:18,480 --> 00:16:23,400 Speaker 2: Morgan CEO says ais use across functions like risk, fraud, marketing, 293 00:16:23,520 --> 00:16:27,200 Speaker 2: customer relations has caused it to grow in importance within 294 00:16:27,600 --> 00:16:31,520 Speaker 2: the company. And Jaomi, a Beijing based electronics firm best 295 00:16:31,600 --> 00:16:34,720 Speaker 2: known for its smartphones, had its biggest intra day share 296 00:16:34,760 --> 00:16:37,520 Speaker 2: gain in more than a year after the company announced 297 00:16:37,560 --> 00:16:41,000 Speaker 2: it will start selling its long awaited electric. 298 00:16:40,640 --> 00:16:42,120 Speaker 3: Vehicles this month. 299 00:16:42,320 --> 00:16:45,720 Speaker 2: The company's multi billion dollar bet marks its entry into 300 00:16:45,720 --> 00:16:48,880 Speaker 2: a red hot contest in China's EV market, which is led, 301 00:16:48,920 --> 00:16:53,360 Speaker 2: of course by Tesla and BYD plus. Legislation that would 302 00:16:53,400 --> 00:16:57,280 Speaker 2: force TikTok's Chinese parent to sell itself. All face of 303 00:16:57,360 --> 00:17:00,480 Speaker 2: ban in the US is picking up speeding Congress, posing 304 00:17:00,520 --> 00:17:05,000 Speaker 2: a dilemma for Republican lawmakers after former President Donald Trump 305 00:17:05,320 --> 00:17:09,080 Speaker 2: reversed his previous stance and suggested the app should not 306 00:17:09,160 --> 00:17:12,959 Speaker 2: be banned, after saying all young TikTok users would quote 307 00:17:13,280 --> 00:17:17,359 Speaker 2: go crazy without it. Let's stick with TikTok, and let's 308 00:17:17,359 --> 00:17:20,320 Speaker 2: turn to its current place in the political landscape. Every 309 00:17:20,359 --> 00:17:24,240 Speaker 2: election cycle, one social media platform emerges ahead of the 310 00:17:24,280 --> 00:17:27,280 Speaker 2: rest as a means of connecting with young voters. In 311 00:17:27,280 --> 00:17:32,320 Speaker 2: this cycle, all signs point to TikTok, but is campaigning 312 00:17:32,359 --> 00:17:37,360 Speaker 2: on TikTok even worth it. Bloomberg's Alex Barrinka has done 313 00:17:37,400 --> 00:17:41,760 Speaker 2: the hands on and scientific reporting of examining Katie Porter's 314 00:17:41,800 --> 00:17:44,800 Speaker 2: campaign for a run for the Senate seat right here 315 00:17:44,840 --> 00:17:48,000 Speaker 2: in California, which did rely heavily on TikTok. 316 00:17:48,320 --> 00:17:50,120 Speaker 3: Explain your epidology, Alex. 317 00:17:50,560 --> 00:17:54,480 Speaker 7: Yeah, So, I looked at Katie Porter and opponent Adam Shift, 318 00:17:54,480 --> 00:17:58,160 Speaker 7: who was the front market runner, and their performance on TikTok. 319 00:17:58,400 --> 00:18:01,480 Speaker 7: Porter is known as kind of a below of TikToker. 320 00:18:01,920 --> 00:18:06,240 Speaker 7: Her engagement rates are better than even the best influencers, 321 00:18:06,440 --> 00:18:08,399 Speaker 7: so I wanted to see how that played out in 322 00:18:08,440 --> 00:18:12,040 Speaker 7: the open race for the Senate primary, which goes to 323 00:18:12,080 --> 00:18:15,320 Speaker 7: the top two vote getters regardless of party. While on TikTok, 324 00:18:15,400 --> 00:18:18,560 Speaker 7: Katie Porter well outperformed out of shift. She posted half 325 00:18:18,600 --> 00:18:21,919 Speaker 7: as many videos but had twice as many views total. 326 00:18:22,200 --> 00:18:25,439 Speaker 7: Her engagement rates were stellar, and going into the primary 327 00:18:26,600 --> 00:18:31,879 Speaker 7: in early March, she was pulling incredibly well with young voters. 328 00:18:32,040 --> 00:18:35,439 Speaker 7: The vast majority of voters under thirty preferred Porter. The 329 00:18:35,560 --> 00:18:39,760 Speaker 7: problem with campaigning on social media was laid bare though 330 00:18:39,840 --> 00:18:42,159 Speaker 7: with the results. Porter came in third, which means she 331 00:18:42,200 --> 00:18:46,199 Speaker 7: will not be on the ballot in November. Why ed, Well, 332 00:18:46,320 --> 00:18:50,000 Speaker 7: those young people on TikTok didn't show up to the polls. 333 00:18:50,200 --> 00:18:52,200 Speaker 7: Only ten percent of voters were under the age of 334 00:18:52,240 --> 00:18:55,639 Speaker 7: thirty in the primary, forty seven percent were over the 335 00:18:55,680 --> 00:18:58,119 Speaker 7: age of sixty five, And Ed, I just don't know 336 00:18:58,160 --> 00:19:00,200 Speaker 7: if those folks are spending a lot of their time 337 00:19:00,280 --> 00:19:00,840 Speaker 7: on TikTok. 338 00:19:02,040 --> 00:19:04,040 Speaker 2: I go from being the host of the technology shows 339 00:19:04,040 --> 00:19:06,480 Speaker 2: being the one time political reporter, and I was up 340 00:19:06,520 --> 00:19:09,879 Speaker 2: on Super Tuesday. Hear at the desk covering California. 341 00:19:10,040 --> 00:19:10,680 Speaker 3: Is so right? 342 00:19:11,080 --> 00:19:13,600 Speaker 2: There are the same number of voters in California over 343 00:19:13,720 --> 00:19:17,000 Speaker 2: age sixty five as under thirty five. The difference is 344 00:19:17,040 --> 00:19:19,560 Speaker 2: the under thirty fives didn't show up, and in Katie 345 00:19:19,600 --> 00:19:22,439 Speaker 2: Porter's case, TikTok didn't work. But clearly it has a 346 00:19:22,480 --> 00:19:25,320 Speaker 2: place in this election, right And I think there's about 347 00:19:25,320 --> 00:19:28,159 Speaker 2: one hundred and seventy million US users of TikTok. I 348 00:19:28,200 --> 00:19:30,800 Speaker 2: think I'm right in saying Alex So just put that 349 00:19:30,840 --> 00:19:33,200 Speaker 2: into perspective here in the context of this bill that's 350 00:19:33,240 --> 00:19:34,160 Speaker 2: playing out in DC. 351 00:19:35,160 --> 00:19:37,800 Speaker 7: There are and it really kind of this this primary 352 00:19:37,920 --> 00:19:41,200 Speaker 7: lays bear also a pain point of the Democrats. They 353 00:19:41,240 --> 00:19:43,159 Speaker 7: really need young voters to show up. They showed up 354 00:19:43,200 --> 00:19:45,560 Speaker 7: in hordes for Biden. They showed up in the midterms 355 00:19:45,600 --> 00:19:48,840 Speaker 7: and the last cycle and stopped that prophesized red wave. 356 00:19:49,160 --> 00:19:52,000 Speaker 7: So for those one hundred and seventy million Americans, forty 357 00:19:52,040 --> 00:19:54,119 Speaker 7: three percent of them say they often get their news 358 00:19:54,160 --> 00:19:58,280 Speaker 7: on TikTok. So this is certainly a place for politicians 359 00:19:58,320 --> 00:20:02,280 Speaker 7: to spend their time because those traditional TV AD dollars, 360 00:20:02,320 --> 00:20:05,040 Speaker 7: they're looking away from those to reach younger voters. I 361 00:20:05,040 --> 00:20:08,480 Speaker 7: think the important thing in November, particularly for a camp 362 00:20:08,520 --> 00:20:11,640 Speaker 7: like Biden who's now on TikTok with a campaign account 363 00:20:11,880 --> 00:20:14,840 Speaker 7: is not only reaching them, not only speaking the Internet 364 00:20:14,960 --> 00:20:17,960 Speaker 7: language like Katie Porter was so good at with memes 365 00:20:18,040 --> 00:20:21,639 Speaker 7: and references to pop culture, but also getting those folks 366 00:20:21,640 --> 00:20:24,720 Speaker 7: to not just agitate online but get them into the polls. 367 00:20:24,960 --> 00:20:28,800 Speaker 7: Bridging that gap is what political experts and digital strategists 368 00:20:28,840 --> 00:20:31,760 Speaker 7: told me is going to be the absolute hardest thing, 369 00:20:32,119 --> 00:20:35,359 Speaker 7: And for Biden in particular, as he battles some of 370 00:20:35,400 --> 00:20:38,520 Speaker 7: the narrative around his age, TikTok seems to be a 371 00:20:38,520 --> 00:20:42,320 Speaker 7: place where he can kind of be relatable, be authentic, 372 00:20:42,680 --> 00:20:44,000 Speaker 7: be a candidate. 373 00:20:43,520 --> 00:20:44,360 Speaker 3: For those voters. 374 00:20:44,480 --> 00:20:46,399 Speaker 7: Again, he's going to have to clear some of the 375 00:20:46,440 --> 00:20:49,320 Speaker 7: bars to get them to not just vote with their fingers, 376 00:20:49,400 --> 00:20:51,840 Speaker 7: with their likes and comments, but to get on their 377 00:20:51,880 --> 00:20:53,199 Speaker 7: feet and head out to the polls. 378 00:20:54,119 --> 00:20:55,480 Speaker 3: Bloomberg A xiety's for Rinkov. 379 00:20:55,600 --> 00:20:58,520 Speaker 2: I really recommend you read that reporting on Katie Porter 380 00:20:58,880 --> 00:20:59,600 Speaker 2: on Bloomberg. 381 00:21:00,160 --> 00:21:01,200 Speaker 3: Coming up on the show, we're going to. 382 00:21:01,119 --> 00:21:04,480 Speaker 2: Take the pulse of the IPO market and Reddit with 383 00:21:04,640 --> 00:21:06,720 Speaker 2: Raymaker Securities Greg Martin. 384 00:21:06,800 --> 00:21:08,560 Speaker 3: Let's check a look of the markets. 385 00:21:08,560 --> 00:21:10,960 Speaker 2: These are your major indices that we're looking at, and 386 00:21:10,960 --> 00:21:12,640 Speaker 2: I think the story is after we got that hot 387 00:21:12,640 --> 00:21:16,280 Speaker 2: CPI print markets and traders were braced for the worst 388 00:21:16,280 --> 00:21:20,320 Speaker 2: case scenario, but in reality, nothing's really changed. The market 389 00:21:20,359 --> 00:21:23,800 Speaker 2: is still betting that a first rate cut would come 390 00:21:23,840 --> 00:21:26,280 Speaker 2: in June, if at all. And you look at the 391 00:21:26,880 --> 00:21:30,560 Speaker 2: NASDAK one hundreds of example slight outperformance relative d S 392 00:21:30,600 --> 00:21:32,320 Speaker 2: and P five hundred. A big part of that, as 393 00:21:32,320 --> 00:21:35,920 Speaker 2: we know, is Oracle and it's strong earnings. This is 394 00:21:35,920 --> 00:21:50,440 Speaker 2: Bloomberg Technology. Welcome back to Bloomberg Technology, Ed Ludlow here 395 00:21:50,720 --> 00:21:54,560 Speaker 2: in San Francisco, Okay. Reddit's IPO is still the talk 396 00:21:54,600 --> 00:21:58,680 Speaker 2: of well Reddit. Last night the company posted an ama 397 00:21:58,840 --> 00:22:01,840 Speaker 2: or in this case and our ask me almost anything, 398 00:22:02,000 --> 00:22:06,280 Speaker 2: and in it, reddit admins invited redditors to ask questions 399 00:22:06,480 --> 00:22:10,080 Speaker 2: to the CEO, COO, and CFO through the end of 400 00:22:10,160 --> 00:22:13,440 Speaker 2: March twelfth. Reddit then plans to take a selection of 401 00:22:13,480 --> 00:22:16,560 Speaker 2: the highest up voted posts and respond to them on 402 00:22:16,600 --> 00:22:20,320 Speaker 2: March eighteenth in a video, because legally it can't respond 403 00:22:20,359 --> 00:22:21,600 Speaker 2: to them in the comments section. 404 00:22:21,640 --> 00:22:22,200 Speaker 3: Here's the thing. 405 00:22:22,680 --> 00:22:26,320 Speaker 2: As of this morning, the most upvoted post by redditors 406 00:22:26,400 --> 00:22:31,800 Speaker 2: posed this question quote the general consensus vibe among Reddit 407 00:22:31,880 --> 00:22:34,919 Speaker 2: users about the IPO and what it portends for the 408 00:22:34,960 --> 00:22:37,879 Speaker 2: future of the site seems to be extremely negative. 409 00:22:38,280 --> 00:22:40,960 Speaker 3: Why do you think that is? Question mark? 410 00:22:41,480 --> 00:22:45,040 Speaker 2: It speaks to the historically combative relationship redditors have on 411 00:22:45,080 --> 00:22:47,720 Speaker 2: the site. And remember, redditors are going to be able 412 00:22:47,760 --> 00:22:50,560 Speaker 2: to claim eight percent of the shares on offer in 413 00:22:50,680 --> 00:22:54,360 Speaker 2: reddits IPO. Let's talk about the listing with Greg Martin, 414 00:22:54,440 --> 00:22:58,600 Speaker 2: co founder and managing director of Rainmaker Securities. 415 00:22:58,720 --> 00:22:59,400 Speaker 3: Let's start with. 416 00:22:59,280 --> 00:23:03,440 Speaker 2: Ba greg reaction to the idea that this social media 417 00:23:03,480 --> 00:23:05,680 Speaker 2: company is trying to market eight percent of the offering 418 00:23:05,720 --> 00:23:09,719 Speaker 2: to redditors who don't seem to like the company offering them. 419 00:23:10,080 --> 00:23:11,560 Speaker 8: Well, thank you for having me ed and good to 420 00:23:11,560 --> 00:23:14,880 Speaker 8: see again. You know it's smart. Clearly, there's a risk 421 00:23:14,960 --> 00:23:18,600 Speaker 8: that Reddit sees with you know, just being a commercial 422 00:23:18,720 --> 00:23:22,000 Speaker 8: enterprise at all, and now you know, selling themselves, you know, 423 00:23:22,080 --> 00:23:24,720 Speaker 8: as a public company and you know, trying to maximize 424 00:23:24,720 --> 00:23:25,360 Speaker 8: for profits. 425 00:23:25,480 --> 00:23:26,400 Speaker 1: It's kind of. 426 00:23:26,400 --> 00:23:29,639 Speaker 8: Violates, you know, the relationship that they have with their users, 427 00:23:29,680 --> 00:23:33,920 Speaker 8: who contribute a vast amount of content for free, and 428 00:23:34,160 --> 00:23:37,440 Speaker 8: now they're potentially you know, the commercialization becomes a risk factor. 429 00:23:37,480 --> 00:23:40,920 Speaker 8: And so how do they mitigate that risk. They make 430 00:23:41,000 --> 00:23:44,280 Speaker 8: their users and their content generators part of the company. 431 00:23:44,280 --> 00:23:47,480 Speaker 1: They give them an economic incentive to say, you. 432 00:23:47,400 --> 00:23:50,880 Speaker 8: Know, positive things about Reddit, because you know, now they're 433 00:23:50,920 --> 00:23:53,520 Speaker 8: hopefully going to be i PO share owners, and they're 434 00:23:53,520 --> 00:23:56,800 Speaker 8: going to be motivated to you know, speak well of Reddit. 435 00:23:56,880 --> 00:23:58,760 Speaker 1: So I think it's smart in what they're doing. 436 00:23:58,800 --> 00:24:02,080 Speaker 8: But there's still a risk that that the Reddit backlash, 437 00:24:02,320 --> 00:24:04,240 Speaker 8: you know, from people who don't own shares, or are 438 00:24:04,320 --> 00:24:07,359 Speaker 8: people who you know, shirk the opportunity to own shares 439 00:24:07,400 --> 00:24:10,360 Speaker 8: because they want to remain on this you know, bohemian community. 440 00:24:10,440 --> 00:24:12,280 Speaker 8: There's still that risk. But I think it's smart what 441 00:24:12,320 --> 00:24:13,040 Speaker 8: Reddit is doing. 442 00:24:14,080 --> 00:24:16,640 Speaker 2: I've been reflecting in the last twenty four forty eight 443 00:24:16,680 --> 00:24:18,960 Speaker 2: hours on the timing of all of this. Now, I 444 00:24:19,040 --> 00:24:23,119 Speaker 2: remember back in twenty twenty one when they confidentially filed. 445 00:24:23,200 --> 00:24:25,359 Speaker 2: Twenty twenty one was like the banner year for the 446 00:24:25,480 --> 00:24:29,399 Speaker 2: USIPO market, and I just wonder how you frame the 447 00:24:29,440 --> 00:24:32,960 Speaker 2: timing of whether they missed the boat or actually they 448 00:24:32,960 --> 00:24:36,040 Speaker 2: were right to wait until this point. 449 00:24:37,760 --> 00:24:37,920 Speaker 1: Yeah. 450 00:24:38,000 --> 00:24:39,600 Speaker 8: Well, I mean, listen, it would have been it would 451 00:24:39,600 --> 00:24:41,280 Speaker 8: have been great if they could have gone out, you know, 452 00:24:41,359 --> 00:24:44,720 Speaker 8: maybe in twenty nineteen, twenty twenty, and you know, had 453 00:24:45,400 --> 00:24:48,000 Speaker 8: a few years of the zero interest rate environment that 454 00:24:48,000 --> 00:24:50,879 Speaker 8: we had and and sort of you know, shored things up, 455 00:24:50,920 --> 00:24:53,720 Speaker 8: short up their after market performance. But I think going 456 00:24:53,760 --> 00:24:55,399 Speaker 8: public at the end of twenty one would have been 457 00:24:55,400 --> 00:24:58,360 Speaker 8: a big mistake. Obviously twenty twenty two was a significant 458 00:24:58,359 --> 00:25:01,320 Speaker 8: down year. I think their value would have been hammered. 459 00:25:01,359 --> 00:25:04,040 Speaker 8: They would have lost all of the halo effect of 460 00:25:04,040 --> 00:25:07,520 Speaker 8: being a new public company. They were clearly, you know, 461 00:25:08,200 --> 00:25:11,439 Speaker 8: way behind in terms of revenues at that point. They 462 00:25:11,440 --> 00:25:13,840 Speaker 8: didn't have an AI story like they're trying to really 463 00:25:13,880 --> 00:25:16,840 Speaker 8: tell now to beep up their valuation. So I think 464 00:25:16,840 --> 00:25:18,479 Speaker 8: it's a good thing that they didn't go public. 465 00:25:18,520 --> 00:25:18,680 Speaker 1: Then. 466 00:25:19,080 --> 00:25:22,000 Speaker 8: I think the markets are more favorable now for more 467 00:25:22,040 --> 00:25:25,840 Speaker 8: of a long term successful story, and I think they're 468 00:25:25,880 --> 00:25:28,800 Speaker 8: going to price their IPO at a much more reasonable 469 00:25:29,320 --> 00:25:31,800 Speaker 8: valuation now than they would have in twenty twenty one. 470 00:25:32,960 --> 00:25:34,879 Speaker 2: I want to talk about the valuation, but I also 471 00:25:34,960 --> 00:25:37,560 Speaker 2: do want to talk about AI. You said that they're 472 00:25:37,600 --> 00:25:41,239 Speaker 2: now telling the story. That story is essentially what we 473 00:25:41,280 --> 00:25:47,440 Speaker 2: could do is license the content to those who want 474 00:25:47,440 --> 00:25:51,440 Speaker 2: the data to train large language models or foundation models. 475 00:25:54,000 --> 00:25:57,320 Speaker 2: How does that sell to somebody deciding whether or not 476 00:25:57,359 --> 00:25:58,439 Speaker 2: to buy into an IPO. 477 00:26:00,119 --> 00:26:01,720 Speaker 1: Well, that's the million dollar question. 478 00:26:02,440 --> 00:26:05,600 Speaker 8: There's there's no doubt that the company today is ninety 479 00:26:05,600 --> 00:26:09,160 Speaker 8: eight percent revenue advertising revenue. So it's a media company 480 00:26:09,200 --> 00:26:12,240 Speaker 8: and it should trade more like a pinterest or a Snapchat, 481 00:26:12,920 --> 00:26:16,040 Speaker 8: but they are trying to trying to push the narrative 482 00:26:16,119 --> 00:26:18,920 Speaker 8: to sell more like an AI data company. They do 483 00:26:19,000 --> 00:26:22,359 Speaker 8: have a lot of proprietary data that's this user generated 484 00:26:22,400 --> 00:26:25,800 Speaker 8: content around various interest groups, whether it's stocks or cars 485 00:26:25,880 --> 00:26:28,080 Speaker 8: or all kinds of interest groups, so that they do 486 00:26:28,119 --> 00:26:30,520 Speaker 8: have a lot of content that their users are generating 487 00:26:31,080 --> 00:26:35,719 Speaker 8: and it's proprietary. And so they've done deals already with Google. 488 00:26:35,760 --> 00:26:38,480 Speaker 8: They've talked about you know, two hundred million of sort 489 00:26:38,480 --> 00:26:41,160 Speaker 8: of this data revenue over the next three years, including 490 00:26:41,240 --> 00:26:43,639 Speaker 8: us you know, around a sixty million dollar deal with Google. 491 00:26:44,080 --> 00:26:48,040 Speaker 8: So they do have actual revenue associated with this you 492 00:26:48,040 --> 00:26:51,159 Speaker 8: know AI training, you know model, you know the AI 493 00:26:51,760 --> 00:26:55,200 Speaker 8: coming onto their site, you know, crawling through their data 494 00:26:55,240 --> 00:26:57,280 Speaker 8: and training itself. So there is a there is a 495 00:26:57,280 --> 00:27:00,760 Speaker 8: story there, but it also again runs the risk of 496 00:27:01,040 --> 00:27:04,800 Speaker 8: inflaving their users who are like, hey, you're selling my data, You're. 497 00:27:04,680 --> 00:27:06,840 Speaker 1: Having an AI train on me, and I'm not getting 498 00:27:06,880 --> 00:27:07,480 Speaker 1: paid for it. 499 00:27:07,560 --> 00:27:09,960 Speaker 8: So there's there's a risk of that strategy as well, 500 00:27:09,960 --> 00:27:11,960 Speaker 8: But I think they're trying to get away from the 501 00:27:12,160 --> 00:27:15,000 Speaker 8: just being a pure advertising revenue narrative, and I think 502 00:27:15,040 --> 00:27:17,120 Speaker 8: it's a good strategy if they want to lift their valuation. 503 00:27:17,960 --> 00:27:20,480 Speaker 2: Okay, so valuation at the upper end of the range 504 00:27:20,520 --> 00:27:23,600 Speaker 2: thirty four dollars a share on a diluted basis, taking 505 00:27:23,640 --> 00:27:28,440 Speaker 2: into account RSUs and whatnot. Six point four billion. How 506 00:27:28,560 --> 00:27:31,280 Speaker 2: marketable is that? You know, people pay a lot of 507 00:27:31,320 --> 00:27:33,800 Speaker 2: attention to a headline figure on valuation. 508 00:27:35,640 --> 00:27:35,800 Speaker 1: Yeah. 509 00:27:35,840 --> 00:27:38,240 Speaker 8: Well, the company did about eight hundred million of revenue 510 00:27:38,280 --> 00:27:41,200 Speaker 8: last year, grew about twenty percent, so you know, kind 511 00:27:41,240 --> 00:27:44,879 Speaker 8: of pedestrian growth. Ninety eight percent of that revenue is advertising. 512 00:27:44,920 --> 00:27:47,360 Speaker 8: If you just were to apply a you know, five 513 00:27:47,400 --> 00:27:49,800 Speaker 8: to seven x multiple to that, which is where their 514 00:27:49,840 --> 00:27:52,680 Speaker 8: comps Pinterest and Snapchat, would you know, sort of trade 515 00:27:52,680 --> 00:27:54,399 Speaker 8: in that range, you'd come up with a four to 516 00:27:54,920 --> 00:27:58,000 Speaker 8: six billion dollar valuation. So six at the very high end, 517 00:27:58,080 --> 00:28:02,000 Speaker 8: and frankly probably should be closer to five. We're seeing 518 00:28:02,080 --> 00:28:04,159 Speaker 8: you know, trades on our platform, you know, at the 519 00:28:04,160 --> 00:28:06,480 Speaker 8: four point eight to five billion rangeing on the private 520 00:28:06,520 --> 00:28:07,480 Speaker 8: market before. 521 00:28:07,200 --> 00:28:08,960 Speaker 1: They went hit the road this week. 522 00:28:09,320 --> 00:28:13,199 Speaker 8: So I think that it's a stretch and again, I 523 00:28:13,200 --> 00:28:15,760 Speaker 8: think it's a question of whether the market will buy 524 00:28:15,880 --> 00:28:19,720 Speaker 8: into this emerging you know, data AI story that they're 525 00:28:19,720 --> 00:28:21,760 Speaker 8: trying to tell. They do have some contracts, they do 526 00:28:21,840 --> 00:28:24,760 Speaker 8: have some revenue, and in order to you know, push 527 00:28:24,840 --> 00:28:28,919 Speaker 8: clear of a traditional social media multiple, that's what the 528 00:28:28,960 --> 00:28:31,560 Speaker 8: market's going to have to believe. And that's why you know, 529 00:28:31,640 --> 00:28:35,320 Speaker 8: they speak up of Sam Altman, you know, with his 530 00:28:35,400 --> 00:28:38,280 Speaker 8: investment and him being involved with the company, and there's 531 00:28:38,280 --> 00:28:40,520 Speaker 8: a lot around the AI story in order to push 532 00:28:40,600 --> 00:28:42,080 Speaker 8: that multiple up. But I think it's going to be 533 00:28:42,120 --> 00:28:42,800 Speaker 8: a challenge. 534 00:28:43,720 --> 00:28:45,520 Speaker 2: I did note that I don't think there's a single 535 00:28:45,600 --> 00:28:48,280 Speaker 2: mention of Alexis o'hanian, one of the other co founders 536 00:28:48,280 --> 00:28:49,840 Speaker 2: in the S one, and when he's next on the show, 537 00:28:49,840 --> 00:28:52,200 Speaker 2: I'm certainly going to ask him about that. Greg Martin, 538 00:28:52,280 --> 00:28:56,680 Speaker 2: co founder managing director Raymaker Securities, always knows the pulse 539 00:28:56,680 --> 00:29:07,920 Speaker 2: of what's going on in the ground. All right, let's 540 00:29:07,960 --> 00:29:10,840 Speaker 2: get some news. Venture capital firm Excel is backing an 541 00:29:10,840 --> 00:29:16,840 Speaker 2: AIS startup that's using the technology to kill finance paperwork, Nananetes, 542 00:29:16,840 --> 00:29:20,840 Speaker 2: which uses artificial intelligence to help businesses square accounts and 543 00:29:20,880 --> 00:29:23,960 Speaker 2: manage budgets, raised twenty nine million dollars in an early 544 00:29:24,040 --> 00:29:28,440 Speaker 2: round led by Excel India, Y Combinator and Elevation Capital, 545 00:29:28,640 --> 00:29:31,200 Speaker 2: and others took part in the Series B round, which 546 00:29:31,240 --> 00:29:35,160 Speaker 2: brings the San Francisco based startups total funding to forty 547 00:29:35,600 --> 00:29:38,440 Speaker 2: million dollars. Let's talk a little bit more about startups 548 00:29:38,480 --> 00:29:41,960 Speaker 2: and venture with Selil Deshpande on today's VC Spotlight. He 549 00:29:42,600 --> 00:29:45,680 Speaker 2: is the founder and general partner of Uncorrelated Ventures, which 550 00:29:45,760 --> 00:29:49,160 Speaker 2: raised a new three hundred and fifteen million dollar fund 551 00:29:49,440 --> 00:29:52,960 Speaker 2: just a few weeks ago to focus on software and crypto. 552 00:29:53,520 --> 00:29:56,480 Speaker 2: And we'd point out that for a solo GP, that's 553 00:29:56,520 --> 00:30:01,440 Speaker 2: a pretty big chunk of change. Two focuses software crypto, 554 00:30:02,000 --> 00:30:03,360 Speaker 2: which is the bigger focus. 555 00:30:03,960 --> 00:30:07,040 Speaker 9: Infrastructure software is a larger focus. That's eighty percent. Why 556 00:30:07,120 --> 00:30:11,440 Speaker 9: crypto is twenty percent, Well, partly it's my background. It's 557 00:30:11,440 --> 00:30:16,360 Speaker 9: all been in infrastructure software, and a lot of crypto 558 00:30:16,440 --> 00:30:20,160 Speaker 9: is infrastructure software. Not all of it is, but portions 559 00:30:20,200 --> 00:30:23,320 Speaker 9: of it are just decentralized infrastructure software. 560 00:30:24,480 --> 00:30:29,080 Speaker 2: How much I don't know how to put this feel good? 561 00:30:29,480 --> 00:30:35,480 Speaker 2: Does spaces like infrastructure software SaaS get from what's happening 562 00:30:35,480 --> 00:30:38,040 Speaker 2: in AI? As far as I can tell, the very 563 00:30:38,080 --> 00:30:42,640 Speaker 2: basic pitch for AI is that it is a value 564 00:30:42,720 --> 00:30:44,600 Speaker 2: add to existing software platforms. 565 00:30:45,360 --> 00:30:45,560 Speaker 1: Well. 566 00:30:45,600 --> 00:30:49,320 Speaker 9: AI is a subset of infrastructure software, so it gets 567 00:30:49,400 --> 00:30:52,560 Speaker 9: a lot of feel good value from AI. Infrastructure software 568 00:30:52,600 --> 00:30:59,160 Speaker 9: is just software that is not application software. So AI 569 00:30:58,080 --> 00:31:05,720 Speaker 9: is very, very mean friendly right now, it's the principally, 570 00:31:06,040 --> 00:31:07,800 Speaker 9: it's the sentiment is too positive. 571 00:31:08,240 --> 00:31:09,080 Speaker 1: It's a hot mess. 572 00:31:09,600 --> 00:31:12,080 Speaker 9: Where I'd like to be investing is in the tools 573 00:31:12,080 --> 00:31:17,080 Speaker 9: and tool chains layer, but they are just too many companies, 574 00:31:17,120 --> 00:31:21,800 Speaker 9: it's too crowded, evaluations are high. The lower layer hardware 575 00:31:21,840 --> 00:31:24,520 Speaker 9: is off limits to venture investors, right that's the realm 576 00:31:24,560 --> 00:31:28,160 Speaker 9: of larger companies. The layer above that, the foundation models. 577 00:31:29,000 --> 00:31:33,240 Speaker 9: Those are also tough to invest in because they're more 578 00:31:33,320 --> 00:31:38,520 Speaker 9: like operating systems, which are tougher to monetize, than like databases, 579 00:31:38,520 --> 00:31:41,400 Speaker 9: which are easier to monetize. So the third layer, tools 580 00:31:41,400 --> 00:31:45,160 Speaker 9: and tool chains, is the best area for venture investors 581 00:31:45,160 --> 00:31:48,360 Speaker 9: to be investing in, but it's a hot mess. The 582 00:31:48,400 --> 00:31:52,000 Speaker 9: fourth layer, which is just using AI or leveraging AI, 583 00:31:52,600 --> 00:31:57,120 Speaker 9: that's been so far a bit easier for me to invest. 584 00:31:57,400 --> 00:32:00,360 Speaker 2: He keeps saying that it's a hot mess. Be an 585 00:32:00,360 --> 00:32:03,160 Speaker 2: opportunity or a way for you to navigate the hot 586 00:32:03,200 --> 00:32:07,360 Speaker 2: mess because you've raised a sizeable fund and the LPs 587 00:32:07,440 --> 00:32:10,480 Speaker 2: must back your vision to invest in that layer. 588 00:32:11,320 --> 00:32:15,200 Speaker 9: Yeah, there are a lot of great opportunities. There are 589 00:32:15,240 --> 00:32:18,360 Speaker 9: just so many companies that it's tough to weed through 590 00:32:19,440 --> 00:32:21,800 Speaker 9: all of them and find the right ones. And when 591 00:32:21,800 --> 00:32:24,640 Speaker 9: you find the right ones, the valuations are very high, 592 00:32:24,680 --> 00:32:28,160 Speaker 9: the rounds are very hot, they close fast, so it's 593 00:32:28,200 --> 00:32:31,840 Speaker 9: just tougher to prosecute. I do have half a dozen 594 00:32:31,960 --> 00:32:34,920 Speaker 9: or so seed investments in that area, but we'll see 595 00:32:34,920 --> 00:32:35,520 Speaker 9: how well they do. 596 00:32:35,560 --> 00:32:36,240 Speaker 3: But that's a good point. 597 00:32:36,280 --> 00:32:38,080 Speaker 2: So my next question would have been, well, at what 598 00:32:38,240 --> 00:32:41,960 Speaker 2: stage and where geographically are you finding my success? 599 00:32:42,120 --> 00:32:48,320 Speaker 9: Initially seed Series A, small checks and Series B, and 600 00:32:48,640 --> 00:32:54,120 Speaker 9: geographically US or world markets with an exception for India. 601 00:32:55,240 --> 00:32:58,880 Speaker 2: The other focus of the fund is crypto, and I'm 602 00:32:58,920 --> 00:33:02,680 Speaker 2: guessing crypto j and startups that they're working more likely 603 00:33:02,720 --> 00:33:06,760 Speaker 2: on the underlying technology. Is that right, well as opposed 604 00:33:06,760 --> 00:33:08,160 Speaker 2: to digital tokens themselves. 605 00:33:09,640 --> 00:33:12,320 Speaker 9: Well, yes, although sometimes the only way to invest in 606 00:33:12,360 --> 00:33:15,240 Speaker 9: the projects is to buy the token. Sometimes you buy 607 00:33:15,280 --> 00:33:20,120 Speaker 9: equity right and eventually the equity converts to tokens. But yeah, 608 00:33:20,160 --> 00:33:23,239 Speaker 9: to the extent that it is decentralized infrastructure. It's in 609 00:33:23,480 --> 00:33:27,000 Speaker 9: scope for me. Not all of crypto is infrastructure, though. 610 00:33:27,320 --> 00:33:31,880 Speaker 2: Three hundred and fifteen million dollars is a sizeable fund. 611 00:33:32,280 --> 00:33:35,840 Speaker 2: And I'm really interested in this economic environment, in this 612 00:33:36,000 --> 00:33:39,360 Speaker 2: rates environment, what the LPs look like. You know where 613 00:33:39,400 --> 00:33:42,560 Speaker 2: you were able to raise those funds from and how 614 00:33:42,680 --> 00:33:44,000 Speaker 2: quickly you were able to do it. 615 00:33:45,560 --> 00:33:50,480 Speaker 9: They were mostly institutional, so seventy seventy five percent is institutional, 616 00:33:50,720 --> 00:33:54,920 Speaker 9: and there's a long tail of family offices and individuals. 617 00:33:54,920 --> 00:33:58,160 Speaker 9: For the rest. There are four sovereign wealth funds. There's 618 00:33:58,200 --> 00:34:08,160 Speaker 9: one university endowment. The fundraising was not too bad, it 619 00:34:07,200 --> 00:34:09,120 Speaker 9: was it was pretty smooth. 620 00:34:09,840 --> 00:34:11,520 Speaker 3: Let's really quickly go back to AI. 621 00:34:11,880 --> 00:34:16,640 Speaker 2: You expressed your concerns about AI valuations right now, or 622 00:34:16,680 --> 00:34:20,400 Speaker 2: at least the impact of the interest in AI on valuations. 623 00:34:21,440 --> 00:34:23,799 Speaker 2: Do you feel like that that is something that will 624 00:34:23,840 --> 00:34:27,560 Speaker 2: continue throughout the year or investors have kind of had 625 00:34:27,600 --> 00:34:32,279 Speaker 2: their fill of the high multiple AI names that they've 626 00:34:32,320 --> 00:34:33,120 Speaker 2: gone into. 627 00:34:34,080 --> 00:34:37,680 Speaker 9: AI is a huge long term opportunity, it's going to 628 00:34:37,760 --> 00:34:42,160 Speaker 9: change everything. Everyone's going to leverage AI, and AI is 629 00:34:42,200 --> 00:34:47,919 Speaker 9: available to everybody through an API call. But short term, 630 00:34:47,960 --> 00:34:51,000 Speaker 9: I'm a little worried the positive sentiment makes me a 631 00:34:51,040 --> 00:34:57,760 Speaker 9: little nervous and hoping for the trough of disillusionment to 632 00:34:57,800 --> 00:35:00,759 Speaker 9: invest more. But I think beyond the traph of disillusionment, 633 00:35:00,960 --> 00:35:04,920 Speaker 9: it'll it'll be you know, it'll be an upcrend again. 634 00:35:05,760 --> 00:35:09,400 Speaker 2: An AI landscape that is a hot mess and underpinned 635 00:35:09,440 --> 00:35:12,640 Speaker 2: by a trough of disillusionment. Salil Deshpande, founder and general 636 00:35:12,680 --> 00:35:16,839 Speaker 2: partner of Uncorrelated Adventures, really appreciate that conversation. 637 00:35:17,440 --> 00:35:19,000 Speaker 3: Let's get a quick update. 638 00:35:19,200 --> 00:35:21,879 Speaker 2: Earlier in the show, we reported on open AI's court 639 00:35:21,920 --> 00:35:26,480 Speaker 2: filing calling must suit against them incoherent. During the show, 640 00:35:26,719 --> 00:35:31,080 Speaker 2: Musk emailed me his response to say, quote, open ai 641 00:35:31,719 --> 00:35:32,400 Speaker 2: is a lie. 642 00:35:32,440 --> 00:35:35,120 Speaker 3: And that's all that. His response to the story. 643 00:35:34,880 --> 00:35:46,120 Speaker 2: Said, Rent the Runway, a platform where customers can subscribe 644 00:35:46,280 --> 00:35:49,280 Speaker 2: rent items a la carte and shop resale from hundreds 645 00:35:49,280 --> 00:35:53,040 Speaker 2: of designer brands, is gifting one million dollars in free 646 00:35:53,040 --> 00:35:56,719 Speaker 2: subscriptions Throughout this Women's History Month, two women who have 647 00:35:56,800 --> 00:35:59,480 Speaker 2: recently reached a new milestone in their careers, such as 648 00:35:59,600 --> 00:36:03,360 Speaker 2: a promote job, successful launch, or new campaign. 649 00:36:03,480 --> 00:36:04,440 Speaker 3: Here are the details. 650 00:36:04,880 --> 00:36:08,160 Speaker 2: CEO Jennifer Hyman, and this is also a collaboration with 651 00:36:08,239 --> 00:36:12,160 Speaker 2: LinkedIn on the platform side, just explain why you're doing it. 652 00:36:13,640 --> 00:36:13,879 Speaker 3: Well. 653 00:36:13,960 --> 00:36:17,040 Speaker 10: I recently had an experience where I had my third child, 654 00:36:17,200 --> 00:36:23,200 Speaker 10: and it was fascinating because I received more personal congratulations 655 00:36:23,520 --> 00:36:28,200 Speaker 10: and love than when I ipo'd Runt the Runway back 656 00:36:28,239 --> 00:36:31,440 Speaker 10: in kind of twenty twenty one, and I started thinking 657 00:36:31,440 --> 00:36:34,440 Speaker 10: about the fact that within the context of our culture, 658 00:36:35,200 --> 00:36:38,480 Speaker 10: it is kind of part of the game that women 659 00:36:38,520 --> 00:36:42,960 Speaker 10: feel more comfortable sharing their personal milestones and personal accomplishments 660 00:36:43,040 --> 00:36:46,600 Speaker 10: than their professional ones. And given that we live in 661 00:36:46,640 --> 00:36:49,400 Speaker 10: a world that the more senior you get in an organization, 662 00:36:50,040 --> 00:36:53,600 Speaker 10: the more your ability to rise is based on your brand, 663 00:36:54,120 --> 00:36:58,319 Speaker 10: the stories that you yourself tell about yourself, and the 664 00:36:58,360 --> 00:37:01,600 Speaker 10: stories that others tell about you. This is a campaign 665 00:37:01,680 --> 00:37:05,240 Speaker 10: to encourage more women to feel comfortable sharing their professional 666 00:37:05,280 --> 00:37:08,520 Speaker 10: accomplishments on a platform like LinkedIn where it matters. 667 00:37:09,640 --> 00:37:11,600 Speaker 2: Jen, as you know and as I've disclosed on this 668 00:37:11,600 --> 00:37:14,319 Speaker 2: program before, we are a Rent the Runway household. My 669 00:37:14,360 --> 00:37:18,360 Speaker 2: wife is a longtime subscriber, user, customer of Rent the Runway. 670 00:37:18,719 --> 00:37:20,560 Speaker 2: But I think back to a year ago when you're 671 00:37:20,560 --> 00:37:23,560 Speaker 2: on the show in particularly in the summer of twenty 672 00:37:23,600 --> 00:37:27,799 Speaker 2: twenty three, you cut back on incentives and discounts like this, 673 00:37:27,920 --> 00:37:30,600 Speaker 2: and the logic you outlined at the time was that 674 00:37:30,719 --> 00:37:34,120 Speaker 2: those new users or subscribers that sign up in the 675 00:37:34,160 --> 00:37:37,520 Speaker 2: face of lower discount are likely to stay longer. So 676 00:37:37,600 --> 00:37:41,680 Speaker 2: what's the strategy here with this incentive in the context 677 00:37:41,719 --> 00:37:42,760 Speaker 2: of subscriber growth. 678 00:37:43,880 --> 00:37:47,680 Speaker 10: Our brand has always been known as really a power 679 00:37:47,719 --> 00:37:51,880 Speaker 10: tool for working women. So one of the main reasons 680 00:37:51,920 --> 00:37:54,279 Speaker 10: why someone signs up for rent the Runway is to 681 00:37:54,360 --> 00:37:59,120 Speaker 10: feel empowered and confident every single day at work. So 682 00:37:59,239 --> 00:38:03,040 Speaker 10: the fact now that women are going to be referring 683 00:38:03,680 --> 00:38:08,200 Speaker 10: their colleagues, their peers, their friends for an accolade that 684 00:38:08,239 --> 00:38:12,880 Speaker 10: they recently deserve in their career and to receive a 685 00:38:12,960 --> 00:38:16,759 Speaker 10: Rent the Runway subscription, we feel that this is exactly 686 00:38:16,840 --> 00:38:20,040 Speaker 10: how we want to bring our brand kind of back 687 00:38:20,040 --> 00:38:24,400 Speaker 10: into the universe and create momentum around our brand being 688 00:38:24,840 --> 00:38:26,839 Speaker 10: a brand that supports professional women. 689 00:38:27,840 --> 00:38:30,400 Speaker 2: When I posted on social media you were coming on 690 00:38:30,440 --> 00:38:33,200 Speaker 2: the show, a lot of people brought up a Newly 691 00:38:33,480 --> 00:38:35,799 Speaker 2: and they point out that this is a younger platform, 692 00:38:36,239 --> 00:38:37,160 Speaker 2: but it has more. 693 00:38:37,040 --> 00:38:40,319 Speaker 3: Subscribers than Rent the Runway has, and they. 694 00:38:40,239 --> 00:38:44,120 Speaker 2: Ask if you've learned any lessons from Newly and taken 695 00:38:44,160 --> 00:38:46,840 Speaker 2: anything from their experience that you think you might now 696 00:38:46,920 --> 00:38:48,080 Speaker 2: apply to Rent the Runway. 697 00:38:49,520 --> 00:38:53,960 Speaker 10: I think that it's incredible that a market that we 698 00:38:54,239 --> 00:38:59,600 Speaker 10: created around renting clothes is now a real market in 699 00:38:59,640 --> 00:39:03,280 Speaker 10: the US, that millions of women per year are renting 700 00:39:03,320 --> 00:39:07,400 Speaker 10: fashion subscribing to fashion, and there are different platforms and 701 00:39:07,440 --> 00:39:10,680 Speaker 10: different choices that provide different kinds of designer brands and 702 00:39:10,719 --> 00:39:14,880 Speaker 10: different aesthetics. Rent the Runway is about catering to a 703 00:39:14,920 --> 00:39:20,640 Speaker 10: woman who is accelerating in her career, who also you know, 704 00:39:20,719 --> 00:39:23,960 Speaker 10: wants to socialize, who is also traveling, who's trying to 705 00:39:24,000 --> 00:39:29,480 Speaker 10: optimize her own time. So I think that really focusing 706 00:39:29,560 --> 00:39:32,960 Speaker 10: in on who our core customer is this campaign being 707 00:39:33,280 --> 00:39:37,080 Speaker 10: a exemplifier of who that customer is is a critical 708 00:39:37,120 --> 00:39:41,239 Speaker 10: part of us continuing to grow and accelerate. I think 709 00:39:41,320 --> 00:39:44,719 Speaker 10: Newly has done a really great job at targeting, you know, 710 00:39:44,920 --> 00:39:47,720 Speaker 10: a significantly younger customer than Rent the Runway. 711 00:39:49,480 --> 00:39:56,800 Speaker 3: Jim, what's the AI story. We've Rent the Runway. 712 00:39:54,520 --> 00:39:59,120 Speaker 10: So AI is really an important part of how women 713 00:39:59,480 --> 00:40:03,000 Speaker 10: search for product on our site. We launched AI enabled 714 00:40:03,120 --> 00:40:06,680 Speaker 10: kind of search and styling earlier in the year that 715 00:40:07,040 --> 00:40:11,040 Speaker 10: enable women to have a long tail use case like 716 00:40:11,360 --> 00:40:14,319 Speaker 10: what should I wear to the tailors Swift concert and 717 00:40:14,400 --> 00:40:17,840 Speaker 10: to get back a whole host of suggestions that. 718 00:40:17,840 --> 00:40:19,839 Speaker 3: Are kind of tailor made for her. 719 00:40:20,400 --> 00:40:24,600 Speaker 10: So you'll see us continuing to iterate on styling in 720 00:40:24,680 --> 00:40:29,000 Speaker 10: general and search because part of our platform is about 721 00:40:29,200 --> 00:40:33,440 Speaker 10: offering discovery and enabling women to wear things that they 722 00:40:33,440 --> 00:40:37,120 Speaker 10: wouldn't otherwise buy. So AI is really a perfect compliment 723 00:40:37,160 --> 00:40:37,480 Speaker 10: to that. 724 00:40:38,480 --> 00:40:40,839 Speaker 2: Jen the final question from our audience, it's been tough 725 00:40:40,880 --> 00:40:43,000 Speaker 2: for rent the wrong way. The shares have had a 726 00:40:43,040 --> 00:40:47,040 Speaker 2: hard run. Would you consider taking the company private to 727 00:40:47,440 --> 00:40:49,320 Speaker 2: go away and kind of go back to growth. 728 00:40:50,719 --> 00:40:53,839 Speaker 10: So our focus is on driving the business to free 729 00:40:53,840 --> 00:40:58,920 Speaker 10: cash flow, profitability, and on making the right long term 730 00:40:58,960 --> 00:41:03,279 Speaker 10: decisions for this business. I think that what has really 731 00:41:03,280 --> 00:41:05,480 Speaker 10: been proven out over the last few years is that 732 00:41:05,600 --> 00:41:09,160 Speaker 10: Rendel is a real market. It is growing far quicker 733 00:41:09,160 --> 00:41:12,200 Speaker 10: than the overall fashion industry. There's millions of women who 734 00:41:12,239 --> 00:41:15,440 Speaker 10: now are comfortable and confident with signing up for a 735 00:41:15,440 --> 00:41:18,799 Speaker 10: subscription to fashion that was not the case even five 736 00:41:18,880 --> 00:41:23,319 Speaker 10: years ago. And so it's our responsibility to ensure that 737 00:41:23,680 --> 00:41:26,719 Speaker 10: this is a profitable, sustainable business and that we take 738 00:41:26,760 --> 00:41:29,880 Speaker 10: advantage of the market that we were a part in 739 00:41:29,960 --> 00:41:31,800 Speaker 10: creating rend. 740 00:41:31,640 --> 00:41:34,319 Speaker 2: The Runway CEO and founder Jennifer Hyman, great to have 741 00:41:34,360 --> 00:41:35,120 Speaker 2: you on the program. 742 00:41:35,160 --> 00:41:35,520 Speaker 3: Thank you. 743 00:41:35,680 --> 00:41:38,879 Speaker 2: That does it for this edition of Bloomberg Technology. 744 00:41:38,880 --> 00:41:39,680 Speaker 3: What a show it's been. 745 00:41:39,840 --> 00:41:42,200 Speaker 2: Check out the pod where you get your podcasts from 746 00:41:42,239 --> 00:41:42,960 Speaker 2: San Francisco. 747 00:41:43,640 --> 00:41:45,400 Speaker 3: This is Bloomberg Technology.