1 00:00:02,520 --> 00:00:12,920 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. Bloomberg Tech is live 2 00:00:12,960 --> 00:00:16,759 Speaker 1: from coast to coast with Caroline Hide in New York 3 00:00:17,079 --> 00:00:19,520 Speaker 1: and Vla low in sentrances. 4 00:00:18,920 --> 00:00:25,079 Speaker 2: Go, this is Bloomberg Tech coming up hot week for IPOs. 5 00:00:25,200 --> 00:00:29,120 Speaker 2: After fintech company Klana yesterday, we look today to blockchain 6 00:00:29,200 --> 00:00:32,000 Speaker 2: lender Figure will be joined by its executive chairman. 7 00:00:32,360 --> 00:00:35,960 Speaker 3: Plus, Apple sentiment sours again with more analyst downgrades, the 8 00:00:36,000 --> 00:00:37,040 Speaker 3: lowest of five years. 9 00:00:37,240 --> 00:00:41,000 Speaker 4: We talk the stock price and iPhone price, and. 10 00:00:40,920 --> 00:00:43,520 Speaker 2: We speak with box CEO Aaron Levy, is the company 11 00:00:43,560 --> 00:00:47,000 Speaker 2: of bails new AI products at its annual Boxworks conference. 12 00:00:47,320 --> 00:00:49,680 Speaker 3: Let's check in on these markets because as we anticipate 13 00:00:49,680 --> 00:00:52,280 Speaker 3: in conversation on AI with Aaron Levy, we think about 14 00:00:52,320 --> 00:00:55,040 Speaker 3: how much AI stocks are supporting this market rally. We 15 00:00:55,080 --> 00:00:57,600 Speaker 3: are up for a seventh straight day on the Nazak 16 00:00:57,640 --> 00:01:00,480 Speaker 3: one hundred. We're in a new record high five tenths 17 00:01:00,520 --> 00:01:03,320 Speaker 3: of a percent. But you get underneath the index ed 18 00:01:03,440 --> 00:01:05,119 Speaker 3: and show us the individual's stocks to look at. 19 00:01:05,760 --> 00:01:08,120 Speaker 2: Yeah, some of the technology stories today are stock stories. 20 00:01:08,120 --> 00:01:10,680 Speaker 2: So mikecron got its price target raised at City. There 21 00:01:10,680 --> 00:01:13,479 Speaker 2: are other names in the streets saying Micron has earning 22 00:01:13,520 --> 00:01:16,480 Speaker 2: September twenty third. We talk a little bit about Oracle 23 00:01:16,800 --> 00:01:19,280 Speaker 2: and the momentum in the hyperscale as well memory Chip's 24 00:01:19,360 --> 00:01:21,679 Speaker 2: key to the data center. They see upside for that 25 00:01:21,920 --> 00:01:24,759 Speaker 2: Oracle had its best day since nineteen ninety two yesterday. 26 00:01:25,080 --> 00:01:27,759 Speaker 2: It is now giving back some of those gains down 27 00:01:27,959 --> 00:01:29,880 Speaker 2: a little bit. And then there's Klaner, a company that 28 00:01:29,920 --> 00:01:32,319 Speaker 2: iPod yesterday at forty. 29 00:01:32,080 --> 00:01:32,920 Speaker 5: Opened at fifty two. 30 00:01:32,959 --> 00:01:35,400 Speaker 2: I think I'm right in saying session high fifty seven 31 00:01:35,480 --> 00:01:38,839 Speaker 2: closed around forty five. Where we at we're at forty 32 00:01:38,840 --> 00:01:41,200 Speaker 2: five there or thereabouts. There's a lot more to discuss 33 00:01:41,240 --> 00:01:42,920 Speaker 2: on that IPO momentum, though correct. 34 00:01:42,800 --> 00:01:44,360 Speaker 4: No, it isn't this a perfect person to do it with. 35 00:01:44,360 --> 00:01:47,320 Speaker 3: Bloomberg's Aishagani joins us you cover Klan I have done 36 00:01:47,319 --> 00:01:49,760 Speaker 3: for years, spoken a lot with the AI version of 37 00:01:49,760 --> 00:01:51,280 Speaker 3: the CEO and the CEO himself. 38 00:01:51,360 --> 00:01:51,720 Speaker 4: I share. 39 00:01:51,960 --> 00:01:54,560 Speaker 3: I'm just interested as to what you deem as the 40 00:01:54,600 --> 00:01:57,400 Speaker 3: success story of yesterday's listing in the performance today. 41 00:01:58,840 --> 00:02:04,400 Speaker 6: Absolutely just to set the scene. With Sequoia, who have 42 00:02:04,640 --> 00:02:07,600 Speaker 6: of course invested in Kana for a long time, they 43 00:02:07,720 --> 00:02:11,200 Speaker 6: made a two point seven billion dollar windfall and over 44 00:02:11,200 --> 00:02:14,040 Speaker 6: the years, they had invested around five hundred million, which 45 00:02:14,120 --> 00:02:18,040 Speaker 6: kind of indicates that when Karna popped yesterday, a lot 46 00:02:18,080 --> 00:02:21,639 Speaker 6: of investors were quite happy. Others had held on to 47 00:02:21,760 --> 00:02:26,040 Speaker 6: their onto their shares while others sold so it was 48 00:02:26,160 --> 00:02:30,639 Speaker 6: extremely successful day, but we yet to see how things 49 00:02:30,680 --> 00:02:31,720 Speaker 6: will pan out. 50 00:02:32,919 --> 00:02:35,200 Speaker 2: One of the things that we discussed with the company 51 00:02:35,360 --> 00:02:38,520 Speaker 2: CEO yesterday on the program was the idea that this 52 00:02:38,639 --> 00:02:41,200 Speaker 2: isn't just a buy now, pay later name. They want 53 00:02:41,200 --> 00:02:43,240 Speaker 2: to be this kind of everything app and when a 54 00:02:43,280 --> 00:02:45,840 Speaker 2: company IPOs, it's not always about raising money, like you 55 00:02:45,880 --> 00:02:48,640 Speaker 2: just pointed out, it's about getting the name out there. 56 00:02:48,840 --> 00:02:51,160 Speaker 2: Do you get a sense I show that that story 57 00:02:51,240 --> 00:02:54,440 Speaker 2: is resonating that Klana is so much more than buy now, 58 00:02:54,480 --> 00:02:54,960 Speaker 2: pay later. 59 00:02:56,840 --> 00:02:59,959 Speaker 6: I'm sure as many of your viewers would have seen 60 00:03:00,240 --> 00:03:03,360 Speaker 6: yesterday across Ford Street and of Macy's, they would have 61 00:03:03,400 --> 00:03:11,359 Speaker 6: seen the Kana banner draping many of New York's hotspots. 62 00:03:11,720 --> 00:03:14,920 Speaker 6: But I suppose what's been fascinating about their story is 63 00:03:15,040 --> 00:03:18,679 Speaker 6: this is a European fintech and it is now global. 64 00:03:18,960 --> 00:03:22,799 Speaker 6: Their largest market is in the US, and a lot 65 00:03:22,800 --> 00:03:26,200 Speaker 6: of people will be quite familiar with Klana at checkout 66 00:03:26,240 --> 00:03:31,600 Speaker 6: when they're shopping, and of course now Klana is going 67 00:03:31,600 --> 00:03:34,960 Speaker 6: into banking and that's basically the next big push in 68 00:03:35,040 --> 00:03:36,920 Speaker 6: order to keep those customers. 69 00:03:36,440 --> 00:03:37,320 Speaker 4: On the app for longer. 70 00:03:37,760 --> 00:03:41,520 Speaker 3: Look, it is a success story for European VC, for 71 00:03:41,680 --> 00:03:46,000 Speaker 3: European tech, and all eyes on Revolute now the UK 72 00:03:46,160 --> 00:03:48,120 Speaker 3: success story, it has a much bigger evaluation in the 73 00:03:48,160 --> 00:03:48,840 Speaker 3: private market. 74 00:03:50,320 --> 00:03:52,640 Speaker 6: I'm glad you raised Revolue. 75 00:03:52,840 --> 00:03:53,480 Speaker 4: Last week. 76 00:03:53,680 --> 00:03:59,480 Speaker 6: I was reporting that Revlue is now engaging its staff 77 00:03:59,520 --> 00:04:03,480 Speaker 6: in a semi twenty five billion dollar share sale and 78 00:04:03,640 --> 00:04:07,120 Speaker 6: which is absolutely staggering when you think about it. But 79 00:04:07,200 --> 00:04:10,440 Speaker 6: of course Klana has been private for twenty years now, 80 00:04:10,520 --> 00:04:15,640 Speaker 6: so it's an indication of where private firms like Revolute 81 00:04:15,680 --> 00:04:19,640 Speaker 6: and Klana are thinking in terms of next steps. Will 82 00:04:19,680 --> 00:04:20,800 Speaker 6: definitely be eyeing this. 83 00:04:21,400 --> 00:04:22,720 Speaker 7: Of course, Klana. 84 00:04:23,920 --> 00:04:26,320 Speaker 6: Has iPod on the New York Stock Exchange and a 85 00:04:26,320 --> 00:04:29,120 Speaker 6: lot of these firms are still deciding where to go. 86 00:04:29,480 --> 00:04:31,159 Speaker 6: But I'm sure a lots of them will be looking 87 00:04:31,160 --> 00:04:36,159 Speaker 6: at the Klana CEO Sebastian Simikowski and the sort of 88 00:04:36,760 --> 00:04:40,160 Speaker 6: media attention that he had received and also the investor 89 00:04:40,839 --> 00:04:43,280 Speaker 6: sentiment as well, and would be hoping that they could 90 00:04:43,360 --> 00:04:46,000 Speaker 6: capture some of that too Bloomberg. 91 00:04:46,040 --> 00:04:48,599 Speaker 2: Zai Shighani, thank you very much. So let's bring Anna 92 00:04:48,640 --> 00:04:51,560 Speaker 2: Rathburn on broader tech market. She's the CEO and founder 93 00:04:51,760 --> 00:04:54,240 Speaker 2: of wealth management firm Grenadilla Advisory. 94 00:04:54,279 --> 00:04:55,920 Speaker 5: And you heard what I had to say, right. 95 00:04:55,960 --> 00:04:59,040 Speaker 2: You know, it's been several consecutive weeks and months of 96 00:04:59,120 --> 00:05:02,599 Speaker 2: interesting ipo, many of them fintech related. Is there a 97 00:05:02,640 --> 00:05:06,800 Speaker 2: broader signal that you've taken in the investor response to 98 00:05:06,839 --> 00:05:07,599 Speaker 2: those IPOs? 99 00:05:07,600 --> 00:05:12,080 Speaker 8: Anna, Yeah, good morning. I think it's nice to see 100 00:05:12,400 --> 00:05:16,040 Speaker 8: fintech IPOs, and I think it's also nice to see 101 00:05:16,120 --> 00:05:20,040 Speaker 8: tech stories that are not necessarily AI related. These are 102 00:05:20,080 --> 00:05:24,720 Speaker 8: fintech companies that are coming in the line to actually 103 00:05:24,880 --> 00:05:29,520 Speaker 8: directly interact with the consumers. And if we pay attention 104 00:05:29,600 --> 00:05:33,240 Speaker 8: to what the CEO said yesterday, it was really about 105 00:05:33,279 --> 00:05:38,320 Speaker 8: revolutionizing the way people actually fund their purchases. And I 106 00:05:38,320 --> 00:05:40,560 Speaker 8: think some of this is very refreshing. I think we're 107 00:05:40,560 --> 00:05:43,520 Speaker 8: going to have to have a lot of creative ideas 108 00:05:43,760 --> 00:05:47,240 Speaker 8: like that one in order for investors to get excited, 109 00:05:47,279 --> 00:05:50,119 Speaker 8: because right now all the attention seems to be on AI. 110 00:05:51,480 --> 00:05:54,400 Speaker 2: And a specific to Klana is the discussion about going 111 00:05:54,440 --> 00:05:57,640 Speaker 2: public versus staying private, like some of their peers did, 112 00:05:58,120 --> 00:06:01,679 Speaker 2: and we discussed on the program the merits of staying private, 113 00:06:01,720 --> 00:06:04,400 Speaker 2: what it does for some of Clona's peers, like Revolute. 114 00:06:04,760 --> 00:06:07,200 Speaker 2: But on the other side of the table, what kind 115 00:06:07,240 --> 00:06:09,360 Speaker 2: of a signal is it that a company is choosing 116 00:06:09,440 --> 00:06:11,480 Speaker 2: to go public in this environment? What does it tell 117 00:06:11,560 --> 00:06:14,800 Speaker 2: you about the health of public markets for technology in America. 118 00:06:16,080 --> 00:06:19,400 Speaker 8: Yeah, I think it's okay for companies to stay private, 119 00:06:19,440 --> 00:06:21,719 Speaker 8: And frankly, this has been a trend for a very 120 00:06:21,760 --> 00:06:26,719 Speaker 8: long time. This isn't anything new and it's okay because, frankly, 121 00:06:26,880 --> 00:06:29,559 Speaker 8: a lot of the innovation, especially on the AI front, 122 00:06:29,760 --> 00:06:33,599 Speaker 8: that is taking place, it's so new and so experimental 123 00:06:33,960 --> 00:06:37,800 Speaker 8: that if you have those companies ipo too early, we 124 00:06:37,920 --> 00:06:41,000 Speaker 8: may have something like the nineteen nineties tech bubble, where 125 00:06:41,160 --> 00:06:43,839 Speaker 8: you know, the cash flow isn't a steady it's not 126 00:06:43,920 --> 00:06:46,600 Speaker 8: as stable, and then you have a crash, right and 127 00:06:46,680 --> 00:06:50,400 Speaker 8: everything is done on hope. So I actually don't mind companies, 128 00:06:50,520 --> 00:06:54,160 Speaker 8: especially tech companies that are innovating and doing something very new, 129 00:06:54,520 --> 00:06:57,720 Speaker 8: staying private for longer, and then after they have stabilized 130 00:06:57,760 --> 00:07:01,040 Speaker 8: and have they're big enough to enter the io market. 131 00:07:01,080 --> 00:07:04,520 Speaker 8: For the health of the retail investors, especially. 132 00:07:04,960 --> 00:07:08,760 Speaker 3: What's fascinating is going back to the nineties. Yesterday Oracle 133 00:07:08,880 --> 00:07:12,040 Speaker 3: had its biggest chev move in the night sense the nineties, 134 00:07:12,040 --> 00:07:15,320 Speaker 3: and we saw significant value accretion not just for the 135 00:07:15,320 --> 00:07:18,120 Speaker 3: shareholders but the main owner of those shares, who is 136 00:07:18,440 --> 00:07:21,720 Speaker 3: the co founder, Larry Ellison, now the world's wealthiest ana. 137 00:07:21,960 --> 00:07:24,360 Speaker 3: What do you make of the love being shown for 138 00:07:24,440 --> 00:07:27,000 Speaker 3: the companies that can add to the infrastructure build out here? 139 00:07:28,120 --> 00:07:32,000 Speaker 8: Yeah, and this has to do with the things that 140 00:07:32,120 --> 00:07:35,400 Speaker 8: everybody is concerned about, which is a concentration. 141 00:07:34,880 --> 00:07:36,119 Speaker 7: In the S and P five hundred. 142 00:07:36,720 --> 00:07:41,320 Speaker 8: It is made up of mega, mega tech stacks and frankly, 143 00:07:41,400 --> 00:07:44,679 Speaker 8: if you think about what it takes to build infrastructure, 144 00:07:44,760 --> 00:07:47,760 Speaker 8: to build the picks and shovels, it takes a very 145 00:07:47,880 --> 00:07:51,800 Speaker 8: very big check. And these aren't things that venture companies 146 00:07:51,840 --> 00:07:54,360 Speaker 8: can do. These aren't things that small companies can do 147 00:07:54,440 --> 00:07:57,120 Speaker 8: and put on their balance sheet. These are things that big, 148 00:07:57,160 --> 00:08:01,040 Speaker 8: big companies can do. So I think invest are pouring 149 00:08:01,080 --> 00:08:03,680 Speaker 8: out their love for all these big companies that are 150 00:08:03,720 --> 00:08:07,400 Speaker 8: building the infrastructure because down the line, we don't know when, 151 00:08:07,560 --> 00:08:11,160 Speaker 8: but down the line the timeline we're expecting to see 152 00:08:11,200 --> 00:08:15,200 Speaker 8: the software companies really innovate and develop and bring out 153 00:08:15,520 --> 00:08:19,680 Speaker 8: more than proof of concept and utilize these hardware and infrastructure. 154 00:08:19,800 --> 00:08:22,040 Speaker 3: I mean, we need to see that because salesfills down 155 00:08:22,040 --> 00:08:24,120 Speaker 3: on the year, Adobe has been suffering, and we've got 156 00:08:24,160 --> 00:08:25,320 Speaker 3: the earnings after the Ballana. 157 00:08:26,400 --> 00:08:28,520 Speaker 8: Yeah, we do need to see it, and I think 158 00:08:28,560 --> 00:08:32,320 Speaker 8: the investors need to sort of fine tune their expectations 159 00:08:32,360 --> 00:08:36,400 Speaker 8: and maybe start with dividing out hardware from software. Hardware 160 00:08:36,559 --> 00:08:39,600 Speaker 8: we already know, we're very, very excited software. I think 161 00:08:39,640 --> 00:08:42,040 Speaker 8: investors need to be a little bit more patient. What 162 00:08:42,080 --> 00:08:44,960 Speaker 8: I see down the line potentially is, and really down 163 00:08:45,040 --> 00:08:48,280 Speaker 8: the line, is that some of these venture companies will 164 00:08:48,320 --> 00:08:52,800 Speaker 8: be successful in innovating and bringing out software that actually 165 00:08:52,960 --> 00:08:57,520 Speaker 8: works and is reliable, and probably companies like Salesforce will 166 00:08:57,600 --> 00:09:01,120 Speaker 8: end up acquiring them to incorporate them into their infrastructure. 167 00:09:02,320 --> 00:09:04,280 Speaker 5: I know we're jumping around a bit with you, but 168 00:09:04,320 --> 00:09:05,040 Speaker 5: we're enjoying it. 169 00:09:05,160 --> 00:09:07,800 Speaker 2: You have a great command of everything across hardware and software, 170 00:09:07,800 --> 00:09:09,760 Speaker 2: and is that not the world of technology? 171 00:09:09,880 --> 00:09:11,280 Speaker 5: So I'm going to ask you about Micron. 172 00:09:11,880 --> 00:09:15,200 Speaker 2: The market has a lot of enthusiasm for Micron, and 173 00:09:15,240 --> 00:09:18,720 Speaker 2: they're just extrapolating from everything that's happened so far. Micron 174 00:09:18,760 --> 00:09:22,200 Speaker 2: reports earnings on September twenty third. It's a memory chip maker, 175 00:09:22,480 --> 00:09:24,920 Speaker 2: and the market's logic seems to be, well, if every 176 00:09:24,960 --> 00:09:27,760 Speaker 2: other part of the data center story is booming, micro 177 00:09:27,920 --> 00:09:31,400 Speaker 2: must be experiencing the same. Is that a typical behavior 178 00:09:31,440 --> 00:09:33,360 Speaker 2: in markets that you see that kind of logic? 179 00:09:35,160 --> 00:09:35,400 Speaker 7: You know? 180 00:09:35,600 --> 00:09:38,640 Speaker 8: I think it is because it's sort of the tie 181 00:09:38,640 --> 00:09:42,520 Speaker 8: that lifts all boats, type of a mentality. It's not 182 00:09:42,600 --> 00:09:45,199 Speaker 8: unlike to, oh, interest rates are falling, the FED is 183 00:09:45,240 --> 00:09:48,480 Speaker 8: going to cut rates, Let's buy everything. And so I 184 00:09:48,520 --> 00:09:51,560 Speaker 8: think in tech, as long as AI is still a 185 00:09:51,679 --> 00:09:55,080 Speaker 8: little bit nebulous in terms of what we're expecting, I 186 00:09:55,120 --> 00:09:56,320 Speaker 8: think you can expect that. 187 00:09:56,360 --> 00:09:57,160 Speaker 4: And that's why this. 188 00:09:57,120 --> 00:10:00,720 Speaker 8: AI train and tech train is a really hard thing 189 00:10:00,800 --> 00:10:04,679 Speaker 8: to stop, even going into the fall in what historically 190 00:10:04,840 --> 00:10:07,800 Speaker 8: is a difficult year, difficult time for stock market. 191 00:10:09,800 --> 00:10:12,920 Speaker 2: What happens next? This is my favorite question to end 192 00:10:12,920 --> 00:10:15,800 Speaker 2: a segment quickly. Balance of the year in tech? 193 00:10:16,760 --> 00:10:21,920 Speaker 8: Yeah, I think rates cutting, Yeah, it's great for markets, 194 00:10:21,960 --> 00:10:24,319 Speaker 8: but tech is going to go up anyway. I think 195 00:10:24,360 --> 00:10:27,000 Speaker 8: the AI story is very very strong. I do think 196 00:10:27,000 --> 00:10:29,440 Speaker 8: that there is a risk still that we're not talking 197 00:10:29,440 --> 00:10:32,440 Speaker 8: about necessarily because It's been a while since China and 198 00:10:32,480 --> 00:10:36,000 Speaker 8: the negotiations on tariffs have been on the docket for discussion. 199 00:10:36,080 --> 00:10:39,040 Speaker 8: It's been very, very quiet and in the background. Now, 200 00:10:39,080 --> 00:10:41,880 Speaker 8: what happens in November with the Supreme Court, I think 201 00:10:41,960 --> 00:10:45,160 Speaker 8: could be a risk. It's a binary risk, but I 202 00:10:45,280 --> 00:10:48,679 Speaker 8: think I don't think the tech market is without its 203 00:10:48,760 --> 00:10:50,680 Speaker 8: hurtles going into the end. 204 00:10:50,600 --> 00:10:51,000 Speaker 4: Of the year. 205 00:10:51,320 --> 00:10:53,640 Speaker 3: Anna Rathman, I've gone into the advisory It's so great 206 00:10:53,640 --> 00:10:55,319 Speaker 3: to have you back on the show. We appreciate it. 207 00:10:55,400 --> 00:10:58,160 Speaker 3: And coming up another day with an upsized typo. We're 208 00:10:58,200 --> 00:11:00,000 Speaker 3: going to be talking with Mike Cagney, co found running 209 00:11:00,000 --> 00:11:03,720 Speaker 3: exective chairman of blockchain based lender Figure as a company 210 00:11:03,920 --> 00:11:04,840 Speaker 3: goes public as a. 211 00:11:04,800 --> 00:11:05,679 Speaker 4: Bloomberg tech. 212 00:11:17,920 --> 00:11:20,559 Speaker 2: The ipo market is proving strong this year. Take a 213 00:11:20,600 --> 00:11:22,360 Speaker 2: look at some of the largest IPOs of twenty twenty 214 00:11:22,360 --> 00:11:25,680 Speaker 2: five so far. Blockchain based Figure is next up and 215 00:11:25,720 --> 00:11:28,400 Speaker 2: the company and investors raise seven hundred and eighty seven 216 00:11:28,440 --> 00:11:30,640 Speaker 2: point five million dollars it shares the set to begin 217 00:11:30,720 --> 00:11:34,960 Speaker 2: trading today. Figure co founder and executive chairman Mike Cagney 218 00:11:35,040 --> 00:11:37,360 Speaker 2: joins US from the Nasdaq. We've also got Tim Senovik 219 00:11:37,400 --> 00:11:42,240 Speaker 2: with US, host of Bloomberg Crypto as well, Mike, where 220 00:11:42,240 --> 00:11:45,040 Speaker 2: we stand shares indicated to open at thirty four dollars. 221 00:11:45,040 --> 00:11:47,960 Speaker 2: You price the IPO at twenty five. But there's intense 222 00:11:48,040 --> 00:11:51,559 Speaker 2: interest in figure for two reasons, you've elected to go public, 223 00:11:51,840 --> 00:11:54,680 Speaker 2: so we want to understand the rationale. But there's also 224 00:11:54,720 --> 00:11:57,520 Speaker 2: this mechanism where you, as a founder or co founder 225 00:11:57,920 --> 00:12:01,120 Speaker 2: retain a lot of control to explain the logic behind 226 00:12:01,120 --> 00:12:01,920 Speaker 2: those two points. 227 00:12:02,559 --> 00:12:04,840 Speaker 9: Sure, I think starting with the latter point, you know, 228 00:12:04,840 --> 00:12:07,040 Speaker 9: obviously there's a lot of empirical research that shows that 229 00:12:07,080 --> 00:12:10,560 Speaker 9: founder leg companies outperform, and it's important to keep that 230 00:12:10,600 --> 00:12:13,160 Speaker 9: founder DNA within the business. You know, I'm fortunate to 231 00:12:13,200 --> 00:12:15,199 Speaker 9: have a partner with Mike tan Obama CEO, where we've 232 00:12:15,200 --> 00:12:17,680 Speaker 9: worked together before and we have a huge amount of synergy. 233 00:12:18,280 --> 00:12:20,640 Speaker 9: But you know, obviously being able to drive product direction 234 00:12:20,720 --> 00:12:23,360 Speaker 9: and innovation for Figures is very important for me and 235 00:12:23,440 --> 00:12:25,959 Speaker 9: something I can continue to add value for I think, 236 00:12:26,080 --> 00:12:28,320 Speaker 9: you know, as it relates to the IPO. You know, 237 00:12:28,360 --> 00:12:30,760 Speaker 9: what we've done in a very different way is used 238 00:12:30,760 --> 00:12:33,800 Speaker 9: real world access or real world assets within blockchain. So 239 00:12:34,240 --> 00:12:36,640 Speaker 9: we started originating loans on blockchain in twenty eighteen. We 240 00:12:36,640 --> 00:12:38,839 Speaker 9: were on the first entities to do that. We've done 241 00:12:38,840 --> 00:12:42,160 Speaker 9: over seventeen billion dollars of loan origination on public chain, 242 00:12:42,200 --> 00:12:45,240 Speaker 9: over fifty five billion dollars of transactions, and we've been 243 00:12:45,280 --> 00:12:49,079 Speaker 9: able to build a very profitable, rapidly growing company in 244 00:12:49,120 --> 00:12:51,559 Speaker 9: the last four years, which have been incredibly difficult from 245 00:12:51,559 --> 00:12:54,959 Speaker 9: a regulatory standpoint. So going public now in a situation 246 00:12:55,000 --> 00:12:57,000 Speaker 9: where the public market is now opening up to the 247 00:12:57,040 --> 00:12:59,880 Speaker 9: opportunity at blockchain, you know, I view it and now 248 00:13:00,240 --> 00:13:02,440 Speaker 9: so there's a magnificent seven and Web two point zero, 249 00:13:02,480 --> 00:13:04,280 Speaker 9: I think there's going to be the equivalent in Web 250 00:13:04,280 --> 00:13:06,760 Speaker 9: three point zero. I think we're one of those companies, 251 00:13:06,760 --> 00:13:09,040 Speaker 9: and so we're very excited to be going in public today. 252 00:13:09,320 --> 00:13:12,480 Speaker 3: At the NASDAC and the NASDAK itself, Mike is pushing 253 00:13:12,520 --> 00:13:16,880 Speaker 3: towards those tokenized real world assets in the form of equities. 254 00:13:17,080 --> 00:13:19,000 Speaker 3: How much of a tailwind is that going to be 255 00:13:19,040 --> 00:13:20,760 Speaker 3: to the business, So how much you want to actually 256 00:13:20,840 --> 00:13:21,640 Speaker 3: own that space? 257 00:13:22,400 --> 00:13:24,599 Speaker 9: Yeah, I think equities is the next big area we 258 00:13:24,640 --> 00:13:26,800 Speaker 9: want to lean in on. So obviously we've done an 259 00:13:26,920 --> 00:13:28,960 Speaker 9: enormous amount of work in the private credit space and 260 00:13:29,000 --> 00:13:32,200 Speaker 9: brought the benefits of blockchain into that ecosystem through liquidity 261 00:13:32,400 --> 00:13:35,280 Speaker 9: and financing capabilities. But equities are the next thing we 262 00:13:35,360 --> 00:13:37,360 Speaker 9: want to lean in to do. And I think one 263 00:13:37,400 --> 00:13:39,480 Speaker 9: of the things that we're exploring is the ability for 264 00:13:39,520 --> 00:13:42,640 Speaker 9: figure to do a second fast follow issuance of stock, 265 00:13:42,720 --> 00:13:45,880 Speaker 9: but do it native to blockchain, not as a DTCC security, 266 00:13:46,400 --> 00:13:49,880 Speaker 9: and that introduces some efficiencies and introduces some liquidity benefits, 267 00:13:49,920 --> 00:13:52,400 Speaker 9: but I think most importantly for the buyside, it introduces 268 00:13:52,400 --> 00:13:55,400 Speaker 9: the ability to control your stock for stock loan, and 269 00:13:55,480 --> 00:13:57,480 Speaker 9: I think that's a huge differentiator over the way that 270 00:13:57,520 --> 00:13:58,599 Speaker 9: we do things historically. 271 00:13:58,760 --> 00:14:00,400 Speaker 3: And Mike, how do you want to be the owners 272 00:14:00,760 --> 00:14:03,080 Speaker 3: either the token ised sequities or in the equity more broadly, 273 00:14:03,160 --> 00:14:05,360 Speaker 3: have you looking to the real retail community or is 274 00:14:05,400 --> 00:14:06,959 Speaker 3: this much more about institutional holding. 275 00:14:08,040 --> 00:14:10,040 Speaker 9: Look, one of the things I really like about blockchain 276 00:14:10,080 --> 00:14:12,680 Speaker 9: is it's a great leveling force. It's a democratization of 277 00:14:12,679 --> 00:14:15,920 Speaker 9: financial services. And so, you know, we gave retail one 278 00:14:15,920 --> 00:14:18,000 Speaker 9: of the largest allocations of the IPO. I think that 279 00:14:18,120 --> 00:14:20,960 Speaker 9: maybe anyone's ever done because of how important retail is 280 00:14:21,000 --> 00:14:23,760 Speaker 9: to us, and so you know, we view that as 281 00:14:23,760 --> 00:14:26,120 Speaker 9: an integral part of the ecosystem that we're building out 282 00:14:26,400 --> 00:14:28,680 Speaker 9: That doesn't mean that the institutions aren't equally important. We 283 00:14:28,680 --> 00:14:30,600 Speaker 9: have over one hundred and seventy partners that use our 284 00:14:30,640 --> 00:14:33,120 Speaker 9: tech to originate assets on chain, and they're critical to 285 00:14:33,160 --> 00:14:36,480 Speaker 9: our growth and success. But retail is very much right 286 00:14:36,480 --> 00:14:38,920 Speaker 9: down the center of the area of focus for us. 287 00:14:39,200 --> 00:14:41,600 Speaker 10: Mike, considering the success that you've had with on chain 288 00:14:41,640 --> 00:14:47,200 Speaker 10: loan origination and using AI to quickly approve these loans digitally, 289 00:14:47,640 --> 00:14:51,520 Speaker 10: what's going to stop a traditional financial institution coming in 290 00:14:51,600 --> 00:14:53,720 Speaker 10: and doing the same thing. What's your mode here? 291 00:14:54,120 --> 00:14:56,400 Speaker 9: Yeah, I think the biggest mode is the liquity that 292 00:14:56,440 --> 00:14:59,119 Speaker 9: we've been able to build within the ecosystem, the marketplace 293 00:14:59,160 --> 00:15:01,680 Speaker 9: that we have we call it Figure Connect, and for 294 00:15:01,720 --> 00:15:04,480 Speaker 9: the first time, we're allowing originators to be able to 295 00:15:04,480 --> 00:15:08,160 Speaker 9: directly access capital outside of the GSS, outside of Fanny 296 00:15:08,160 --> 00:15:11,000 Speaker 9: and Freddie, where they can sell forward production, guarantee pricing, 297 00:15:11,040 --> 00:15:14,400 Speaker 9: guarantee liquidity. That's a huge moat. I think that's going 298 00:15:14,400 --> 00:15:17,760 Speaker 9: to become very pressing as the Stable Coin Act begins 299 00:15:17,800 --> 00:15:21,360 Speaker 9: to take effect and you start to see deposit flight 300 00:15:21,400 --> 00:15:23,720 Speaker 9: out of banks into coin. Banks are going to be 301 00:15:23,760 --> 00:15:25,600 Speaker 9: stressed in the liability side. They're not going to have 302 00:15:25,600 --> 00:15:27,960 Speaker 9: their balance sheet the way they historically have, and I 303 00:15:27,960 --> 00:15:30,840 Speaker 9: think that's where blockchain and in particular decentralized finance can 304 00:15:30,840 --> 00:15:33,520 Speaker 9: step in and fill that void. And so I don't 305 00:15:33,560 --> 00:15:35,800 Speaker 9: see so much people trying to replicate the model, but 306 00:15:35,920 --> 00:15:38,520 Speaker 9: leverage the economy of scale that's there and actually lean 307 00:15:38,560 --> 00:15:39,360 Speaker 9: in and be part of it. 308 00:15:39,600 --> 00:15:43,120 Speaker 10: No question, crypto assets have absolutely had a moment over 309 00:15:43,160 --> 00:15:45,480 Speaker 10: the last year. A big part of that has to 310 00:15:45,520 --> 00:15:48,840 Speaker 10: do with this administration. But crypto winter is not too 311 00:15:48,880 --> 00:15:52,760 Speaker 10: far into recent memory. What happens to figure during the 312 00:15:52,800 --> 00:15:55,240 Speaker 10: next crypto winter? What do investors need to know about 313 00:15:55,280 --> 00:15:56,440 Speaker 10: how you sustain. 314 00:15:56,560 --> 00:15:59,720 Speaker 9: Yeah, Look, crypto is obviously integral to the workings of 315 00:16:00,040 --> 00:16:03,080 Speaker 9: book blockchains. It allows for the decentralization of the networks. 316 00:16:03,440 --> 00:16:05,800 Speaker 9: We are very much a blockchain company, and so what 317 00:16:05,840 --> 00:16:10,080 Speaker 9: we're doing is bring the transactional efficiency, liquidity and financing 318 00:16:10,080 --> 00:16:13,680 Speaker 9: benefits of blockchain into every asset. So we're somewhat immune 319 00:16:13,680 --> 00:16:16,160 Speaker 9: from the volatility of the price of crypto and bitcoin 320 00:16:16,200 --> 00:16:19,320 Speaker 9: in particular. You know, obviously we're involved in those markets, 321 00:16:19,360 --> 00:16:21,320 Speaker 9: but we're a little more insulated and a little more 322 00:16:21,320 --> 00:16:25,080 Speaker 9: exposed to traditional finance. But on a web three construct. 323 00:16:24,840 --> 00:16:29,040 Speaker 3: Mike You've obviously created a liquidity moment for you for employees. 324 00:16:29,280 --> 00:16:32,840 Speaker 3: You're also raising funds potentially to make acquisitions where would 325 00:16:32,880 --> 00:16:33,720 Speaker 3: be a useful addition. 326 00:16:34,600 --> 00:16:38,480 Speaker 9: Certainly, the blockchain space, in the crypto space is generally subscale, 327 00:16:38,920 --> 00:16:42,040 Speaker 9: so there's an enormous amount of consolidation. I like to 328 00:16:42,120 --> 00:16:45,920 Speaker 9: view the raising of this capital analogous to a last company. 329 00:16:45,960 --> 00:16:48,240 Speaker 9: I raised a billion dollars of capital, was the largest 330 00:16:48,240 --> 00:16:51,160 Speaker 9: private raise ever done. I raised it from SoftBank, and 331 00:16:51,280 --> 00:16:53,720 Speaker 9: it ended up being an enormous competitive advantage for us 332 00:16:53,720 --> 00:16:55,360 Speaker 9: in the balance sheet in terms of our ability to 333 00:16:55,400 --> 00:16:58,200 Speaker 9: take risks that our competitors couldn't. I viewed that analogous 334 00:16:58,200 --> 00:17:00,680 Speaker 9: here as well. It's really having that balance sheet to 335 00:17:00,760 --> 00:17:03,080 Speaker 9: lean in and do some really disruptive work in blockchain 336 00:17:03,120 --> 00:17:04,560 Speaker 9: and crypto over the next several years. 337 00:17:04,840 --> 00:17:07,240 Speaker 10: You said you want to do a second fast follow. 338 00:17:07,320 --> 00:17:09,080 Speaker 10: You also said in the S one that you plan 339 00:17:09,200 --> 00:17:13,120 Speaker 10: to use some of the proceeds from this IPO for acquisitions. Specifically, 340 00:17:13,160 --> 00:17:13,720 Speaker 10: what are those. 341 00:17:14,520 --> 00:17:16,960 Speaker 9: We don't have any specific acquisition and in line right now, 342 00:17:17,000 --> 00:17:20,040 Speaker 9: we're looking at the market. We're always open to those conversations. Again, 343 00:17:20,080 --> 00:17:22,240 Speaker 9: it's a subscale space, so there's an enormous amount of 344 00:17:22,280 --> 00:17:25,000 Speaker 9: opportunity out there, but you know, we're very focused on 345 00:17:25,040 --> 00:17:26,280 Speaker 9: just core execution right now. 346 00:17:27,000 --> 00:17:30,000 Speaker 3: Mike Cagney, so good to have you co founder executive 347 00:17:30,040 --> 00:17:32,280 Speaker 3: chair for Figure along with our own Timsteentivic. 348 00:17:32,760 --> 00:17:34,480 Speaker 4: Thank you. And then you've got some breaking news. 349 00:17:35,359 --> 00:17:38,400 Speaker 2: Yeah, we do have some breaking news crossing the Bloomberg terminal. 350 00:17:38,680 --> 00:17:42,880 Speaker 2: Hyundai has decided to delay the construction of a battery 351 00:17:42,920 --> 00:17:47,000 Speaker 2: plant in Georgia following a raid by the United States, 352 00:17:47,240 --> 00:17:49,800 Speaker 2: which was part of a broader effort you'll remember, to 353 00:17:49,920 --> 00:17:54,560 Speaker 2: crack down on undocumented workers. Hyundai has communicated its decision 354 00:17:54,840 --> 00:17:57,080 Speaker 2: to delay the construction of that Georgia plant in an 355 00:17:57,119 --> 00:18:02,040 Speaker 2: interview with Bloomberg, which was with the CEO Josimonnos. He's 356 00:18:02,080 --> 00:18:04,960 Speaker 2: basically saying that there is a plan still for the 357 00:18:05,040 --> 00:18:07,879 Speaker 2: JV which is between Hyundai and LG, to resume in 358 00:18:07,920 --> 00:18:10,440 Speaker 2: two to three months, and that the plant will need 359 00:18:10,560 --> 00:18:13,720 Speaker 2: new workers to resume construction. But for now, that is 360 00:18:13,720 --> 00:18:17,000 Speaker 2: the breaking news headline from Bloomberg. The Hyundai in a 361 00:18:17,080 --> 00:18:20,760 Speaker 2: JV with LG has decided to delay construction in Georgia 362 00:18:20,800 --> 00:18:25,200 Speaker 2: following that US raid, which was on September the eighth, 363 00:18:25,200 --> 00:18:26,400 Speaker 2: you'll remember Kroen. 364 00:18:26,359 --> 00:18:28,320 Speaker 4: Such an important geopolitical story. 365 00:18:28,480 --> 00:18:38,080 Speaker 2: D Apple getting hit with a pair of downgrades today 366 00:18:38,119 --> 00:18:40,960 Speaker 2: at DA Davidson and Philip Security is the latest sign 367 00:18:41,000 --> 00:18:44,080 Speaker 2: a bit of caution toward the iPhone maker, which has 368 00:18:44,119 --> 00:18:47,480 Speaker 2: sharply underperformed it's large cap tech piers this year. It's 369 00:18:47,480 --> 00:18:49,520 Speaker 2: off the back of its new iPhone lineup launch earlier 370 00:18:49,560 --> 00:18:52,960 Speaker 2: this week. Bloomberg's consumer tech managing editor Mark German is 371 00:18:53,000 --> 00:18:56,159 Speaker 2: back and joins us for more. There's so much analysis 372 00:18:56,160 --> 00:18:58,240 Speaker 2: that needs to be done on the line up of 373 00:18:58,280 --> 00:19:02,840 Speaker 2: the iPhone seventeen. It's technological capabilities, but also it's price points. 374 00:19:03,359 --> 00:19:05,000 Speaker 5: And in your latest. 375 00:19:05,000 --> 00:19:08,359 Speaker 2: You write simply that this might be Apple getting us 376 00:19:08,480 --> 00:19:11,840 Speaker 2: ready for the idea of a two thousand dollars handset, 377 00:19:12,040 --> 00:19:14,720 Speaker 2: which you just explained the reporting and argument you're making. 378 00:19:16,240 --> 00:19:18,560 Speaker 11: Of course, so for the first time in the US, 379 00:19:18,640 --> 00:19:22,280 Speaker 11: Apple's offering a two thousand dollars tier of the iPhone. 380 00:19:22,359 --> 00:19:26,040 Speaker 11: It's a niche device, it's the iPhone seventeen promacs with 381 00:19:26,160 --> 00:19:29,919 Speaker 11: two terabytes of storage. But the point I'm really making, 382 00:19:30,040 --> 00:19:32,040 Speaker 11: beyond the fact that there is now a two thousand 383 00:19:32,080 --> 00:19:35,120 Speaker 11: dollars tier, is that we are getting towards the era 384 00:19:35,280 --> 00:19:38,560 Speaker 11: of two thousand dollars phones. Anyways, look at the pricing 385 00:19:38,600 --> 00:19:41,080 Speaker 11: for this year's models. They did not raise the prices. 386 00:19:41,160 --> 00:19:44,399 Speaker 11: I think two reasons. One Samsung and Google didn't, so 387 00:19:44,440 --> 00:19:46,239 Speaker 11: it would be really hard for Apple to do so 388 00:19:46,280 --> 00:19:49,119 Speaker 11: in a meaningful way. Two of the tariffs really haven't 389 00:19:49,200 --> 00:19:50,919 Speaker 11: kicked in for Apple yet, so it would be a 390 00:19:50,960 --> 00:19:53,679 Speaker 11: bad look for them to raise prices two to tariffs 391 00:19:53,720 --> 00:19:55,400 Speaker 11: when the tariffs haven't even started. 392 00:19:55,800 --> 00:19:58,040 Speaker 7: The reality is these phone tariffs will kick in. 393 00:19:58,000 --> 00:20:01,000 Speaker 11: At some point, and so I'm not room Apple raising 394 00:20:01,080 --> 00:20:04,520 Speaker 11: prices later when the prices actually have to be adjusted 395 00:20:04,520 --> 00:20:07,119 Speaker 11: for Apple to retain its margins, and that is probably 396 00:20:07,160 --> 00:20:09,200 Speaker 11: going to bring the phone up a couple hundred bucks, right, 397 00:20:09,680 --> 00:20:12,080 Speaker 11: And then you look at next year. Next year they're 398 00:20:12,080 --> 00:20:16,000 Speaker 11: going to release their first foldable. Samsung's foldable already costs 399 00:20:16,040 --> 00:20:19,240 Speaker 11: over two thousand dollars. The iPhone Air, which is essentially 400 00:20:19,400 --> 00:20:23,280 Speaker 11: as the foldable, maybe less, that costs one thousand dollars. 401 00:20:23,600 --> 00:20:26,640 Speaker 11: So you can put pretty good money on the idea 402 00:20:26,960 --> 00:20:30,199 Speaker 11: that Apple will be launching a base price two thousand 403 00:20:30,280 --> 00:20:33,520 Speaker 11: dollars iPhone twelve months from now. And then you look 404 00:20:33,560 --> 00:20:37,960 Speaker 11: beyond twenty twenty seven to twenty anniversary iPhone, the glass wing, 405 00:20:38,200 --> 00:20:42,040 Speaker 11: it's all glass, it has translucency, much more advanced technologies, 406 00:20:42,480 --> 00:20:46,040 Speaker 11: very similar concept to one thousand dollars iPhone ten in 407 00:20:46,240 --> 00:20:47,160 Speaker 11: twenty seventeen. 408 00:20:47,520 --> 00:20:50,960 Speaker 7: You're going to see price increases there too. So maybe the. 409 00:20:50,920 --> 00:20:54,080 Speaker 11: Two thousand dollars iPhone because it's the highest tier two 410 00:20:54,119 --> 00:20:57,399 Speaker 11: terabyte model this year, may seem like a joke, but 411 00:20:58,000 --> 00:20:59,280 Speaker 11: it will happen. 412 00:20:59,080 --> 00:21:01,119 Speaker 7: In the next twelve twenty four months regardless. 413 00:21:01,240 --> 00:21:03,439 Speaker 3: And maybe Mark that's what analysts need to see to 414 00:21:03,560 --> 00:21:05,800 Speaker 3: really start to get excited about the stock again from 415 00:21:05,800 --> 00:21:08,919 Speaker 3: a profitability perspective, because at the moment, with these two downgrades, 416 00:21:08,960 --> 00:21:12,240 Speaker 3: looking at gil Luria saying basically we were left uninspired, 417 00:21:12,560 --> 00:21:14,959 Speaker 3: it feels as though they haven't managed to garner much 418 00:21:15,000 --> 00:21:16,040 Speaker 3: excitement this time round. 419 00:21:17,359 --> 00:21:20,240 Speaker 11: Yeah, you know, I rarely agree with the Wall Street analysts, 420 00:21:20,240 --> 00:21:23,000 Speaker 11: and I will hold to that. I actually think that 421 00:21:23,040 --> 00:21:26,000 Speaker 11: this year's iPhones are going to sell incredibly well. 422 00:21:26,040 --> 00:21:28,639 Speaker 7: As I've said many times on this program and elsewhere. 423 00:21:28,800 --> 00:21:32,480 Speaker 7: What really sells new iPhones is the design. The orange color. 424 00:21:32,680 --> 00:21:33,199 Speaker 7: I like it. 425 00:21:33,280 --> 00:21:36,399 Speaker 11: Some may find it ridiculous, but don't discount it. Don't 426 00:21:36,400 --> 00:21:39,960 Speaker 11: discount Apple's marketing around dat device and how new it is, 427 00:21:40,000 --> 00:21:41,720 Speaker 11: the aluminum unibody. 428 00:21:41,400 --> 00:21:41,919 Speaker 12: You name it. 429 00:21:41,960 --> 00:21:45,439 Speaker 11: The functionality may not be much different, but it looks different, 430 00:21:45,600 --> 00:21:47,639 Speaker 11: and then that's going to be good enough to spur sales. 431 00:21:48,040 --> 00:21:49,520 Speaker 7: Don't forget there are. 432 00:21:49,440 --> 00:21:51,199 Speaker 11: A lot of people who have held on to their 433 00:21:51,200 --> 00:21:54,240 Speaker 11: iPhones the past four or five years because the design 434 00:21:54,240 --> 00:21:55,120 Speaker 11: hasn't changed much. 435 00:21:55,359 --> 00:21:57,240 Speaker 7: Now it's finally changed, there's probably going to be some 436 00:21:57,280 --> 00:21:58,000 Speaker 7: pent up demand. 437 00:21:58,240 --> 00:22:01,240 Speaker 3: I'm all in on Orange, particularly today, and we appreciate it. 438 00:22:06,640 --> 00:22:07,920 Speaker 4: Welcome back to bloombag Tech. 439 00:22:08,040 --> 00:22:10,720 Speaker 3: Let's get to Adobe reporting its earnings after the closing 440 00:22:10,720 --> 00:22:11,280 Speaker 3: bell today. 441 00:22:11,480 --> 00:22:14,439 Speaker 4: Investors, well, they have watched Adobe's. 442 00:22:13,880 --> 00:22:17,520 Speaker 3: Shares get left behind in the AI craze. Maybe they've 443 00:22:17,520 --> 00:22:19,960 Speaker 3: got a little rison to be optimistic ahead of its results. 444 00:22:20,200 --> 00:22:22,840 Speaker 4: Bloomberg's tech reporter Matt Day is here to discuss. 445 00:22:22,880 --> 00:22:24,680 Speaker 3: We are down twenty one percent year to date, with 446 00:22:24,760 --> 00:22:28,080 Speaker 3: down forty percent in terms of the last twelve months. Matt, 447 00:22:28,359 --> 00:22:31,399 Speaker 3: it's all about competition. Is there anyway it can prove 448 00:22:31,680 --> 00:22:32,840 Speaker 3: that actually it's still got it? 449 00:22:33,680 --> 00:22:35,440 Speaker 13: I think a lot's going to depend on the forecast. 450 00:22:35,480 --> 00:22:37,920 Speaker 13: You know, Adobe's been trying to put AI all over 451 00:22:37,960 --> 00:22:40,600 Speaker 13: its products. It's got a Firefly product, It's invested a 452 00:22:40,600 --> 00:22:43,440 Speaker 13: whole lot into that kind of smatters AI across its 453 00:22:43,720 --> 00:22:46,320 Speaker 13: creative suite. But investors haven't seen the results that they 454 00:22:46,359 --> 00:22:48,359 Speaker 13: would have hoped for. You know, the last few quarters 455 00:22:48,400 --> 00:22:50,840 Speaker 13: has been disappointing guidance after disappointing guidance. It's like they're 456 00:22:50,840 --> 00:22:54,800 Speaker 13: looking at flatish profitability that go around, So they'd have 457 00:22:54,840 --> 00:22:56,679 Speaker 13: to definitely do something to change the narrative. 458 00:22:58,320 --> 00:22:59,840 Speaker 5: We focus a lot on the stock performance. 459 00:23:00,119 --> 00:23:02,240 Speaker 2: It's down forty percent over the last year, twenty five 460 00:23:02,280 --> 00:23:05,919 Speaker 2: percent year to day. But if you park it one 461 00:23:06,000 --> 00:23:09,200 Speaker 2: to one moment, I feel like Adobe has not stopped 462 00:23:09,240 --> 00:23:11,600 Speaker 2: talking about all of its AI stuff, Like do we 463 00:23:11,720 --> 00:23:15,280 Speaker 2: understand in the newsroom at the story about why Adobe's 464 00:23:15,320 --> 00:23:18,280 Speaker 2: just not been able to convince anyone that all the 465 00:23:18,359 --> 00:23:20,800 Speaker 2: AI attachments you talked about are actually paying off? 466 00:23:21,920 --> 00:23:24,240 Speaker 13: Well, I think they got to convince their buyers, you know, 467 00:23:24,280 --> 00:23:27,040 Speaker 13: first off, that this is worth you know, sometimes showing 468 00:23:27,080 --> 00:23:29,600 Speaker 13: an extra four, sometimes using you know, it's it's largely 469 00:23:29,640 --> 00:23:32,440 Speaker 13: a similar story to to what we've seen with other 470 00:23:32,480 --> 00:23:34,840 Speaker 13: AI tools in the enterprise, right that like is it 471 00:23:35,000 --> 00:23:36,800 Speaker 13: is it something fuel because the're piloting is something they're 472 00:23:36,800 --> 00:23:39,800 Speaker 13: taking wholesale and for now, you know, it'll be facing 473 00:23:39,840 --> 00:23:41,680 Speaker 13: a lot of inroads from you know, not just the 474 00:23:41,720 --> 00:23:46,720 Speaker 13: sort of freemium AI native upstarts, but also just chat GBT. Right, 475 00:23:46,840 --> 00:23:50,159 Speaker 13: are the pure AI tools kind of taking taking some 476 00:23:50,200 --> 00:23:52,800 Speaker 13: of that creative demand and illustrations and all like that 477 00:23:52,960 --> 00:23:53,760 Speaker 13: Adobe depends on. 478 00:23:54,520 --> 00:23:57,439 Speaker 3: And it's a global story. We understand that, you know, 479 00:23:57,520 --> 00:23:59,320 Speaker 3: the likes of deep Sea can Now Bite Dance. They 480 00:23:59,400 --> 00:24:01,800 Speaker 3: got great in the generations that are taking the world 481 00:24:01,800 --> 00:24:05,560 Speaker 3: by storm across the field. When you're looking at the fundamentals, 482 00:24:05,840 --> 00:24:09,119 Speaker 3: revenue isn't that bad. We're likely to post nine percent growth. 483 00:24:09,119 --> 00:24:11,199 Speaker 3: But the problem is it just keeps on shrinking in 484 00:24:11,280 --> 00:24:11,960 Speaker 3: terms of growth. 485 00:24:12,800 --> 00:24:14,800 Speaker 13: Yeah, and the other problem, I mean they've they've forecasted 486 00:24:14,840 --> 00:24:16,919 Speaker 13: something like two hundred and fifty million dollars in recurring 487 00:24:17,000 --> 00:24:19,160 Speaker 13: revenue from AI. You know, that's that's not a ton 488 00:24:19,200 --> 00:24:21,400 Speaker 13: on their enormous user base. Folks would like to see 489 00:24:21,440 --> 00:24:23,960 Speaker 13: more to move the stock. So they still have a 490 00:24:23,960 --> 00:24:25,920 Speaker 13: whole lot to prove in terms of people actually form 491 00:24:25,960 --> 00:24:27,639 Speaker 13: the shell out and we'll want to use these tools. 492 00:24:27,640 --> 00:24:29,399 Speaker 13: You know, the expense of a canva or a FIGMA 493 00:24:29,720 --> 00:24:30,400 Speaker 13: or a CHGPT. 494 00:24:31,520 --> 00:24:34,639 Speaker 2: If anything, it just demonstrates us like earning season continues, 495 00:24:34,720 --> 00:24:37,280 Speaker 2: the next big tech name to go is Adobe, and 496 00:24:37,320 --> 00:24:39,879 Speaker 2: that's why we're grateful for Bloomberg's Matt Day joining us 497 00:24:39,880 --> 00:24:42,280 Speaker 2: here on Bloomberg Tech. After the Bell, we'll go back 498 00:24:42,280 --> 00:24:43,000 Speaker 2: to it tomorrow. 499 00:24:43,119 --> 00:24:43,439 Speaker 5: Okay. 500 00:24:43,560 --> 00:24:46,920 Speaker 2: For the next episode of Bloomberg Tech Europe, Tom McKenzie 501 00:24:46,960 --> 00:24:51,520 Speaker 2: speaks exclusively with Demis Hassabis, the Nobel laureate responsible for 502 00:24:51,560 --> 00:24:54,480 Speaker 2: all of Alphabet's core AI work and the co founder 503 00:24:54,480 --> 00:24:57,240 Speaker 2: and CEO of Google deep Mind. The subace is also 504 00:24:57,400 --> 00:25:00,440 Speaker 2: on a mission to harness AI to quote soul all 505 00:25:00,480 --> 00:25:03,959 Speaker 2: disease through his work at the London base Alphabet subsidiary 506 00:25:04,280 --> 00:25:07,080 Speaker 2: Isomorphic Labs. Here's a part of that conversation. 507 00:25:09,560 --> 00:25:13,280 Speaker 14: Well, that was always the kind of holy girl in 508 00:25:13,320 --> 00:25:15,640 Speaker 14: a way, is to try and tackle cancer. But we've 509 00:25:15,680 --> 00:25:18,840 Speaker 14: also done it for practical reasons as well, because you know, 510 00:25:18,880 --> 00:25:22,720 Speaker 14: the clinical aspects of that are favorable for new drugs 511 00:25:23,200 --> 00:25:26,080 Speaker 14: because obviously the disease is so serious and so there 512 00:25:26,080 --> 00:25:28,320 Speaker 14: are a number of reasons we pick those two areas. Also, 513 00:25:28,400 --> 00:25:30,639 Speaker 14: partly what we think is going to the platform is 514 00:25:30,640 --> 00:25:32,480 Speaker 14: going to be able to do early on. 515 00:25:33,320 --> 00:25:36,080 Speaker 15: There's the speed and efficiency that the platform brings to it. 516 00:25:36,080 --> 00:25:37,160 Speaker 12: There's the quality as well. 517 00:25:37,200 --> 00:25:40,399 Speaker 15: Of course, when we're thinking about pre clinical research and 518 00:25:40,440 --> 00:25:43,880 Speaker 15: AI drug discovery usually using the old methods. You were 519 00:25:43,920 --> 00:25:47,080 Speaker 15: talking what three to six years? How to what extent 520 00:25:47,119 --> 00:25:49,200 Speaker 15: can you cut that time frame down? 521 00:25:49,720 --> 00:25:51,760 Speaker 14: I think in the fullness of time, when our platform 522 00:25:51,800 --> 00:25:54,320 Speaker 14: is mature in the next couple of years, I'd like 523 00:25:54,400 --> 00:25:57,160 Speaker 14: to see that cut down into a matter of months 524 00:25:57,200 --> 00:25:59,520 Speaker 14: instead of years. That's what I think is possible, perhaps 525 00:25:59,560 --> 00:26:02,320 Speaker 14: even far than that, so kind of order of magnitude 526 00:26:02,480 --> 00:26:04,760 Speaker 14: speed up. But you know, we'll see if that's possible. 527 00:26:04,800 --> 00:26:06,520 Speaker 14: There's a lot of work we've got to do first. 528 00:26:06,600 --> 00:26:10,399 Speaker 14: But you know, initial, initial worker initial signs are very promising. 529 00:26:10,440 --> 00:26:12,320 Speaker 15: Back in March, you've raised your first extent of funding 530 00:26:12,320 --> 00:26:14,840 Speaker 15: six hundred million US dollars and you're putting that to 531 00:26:14,840 --> 00:26:17,399 Speaker 15: play in different paths to build out the platform. But 532 00:26:17,400 --> 00:26:19,760 Speaker 15: also talent seems to be part of that. When you 533 00:26:19,760 --> 00:26:21,360 Speaker 15: think about talent, when you think about people like Mark 534 00:26:21,440 --> 00:26:24,480 Speaker 15: Zuckerberg signing checks for two hundred million dollars, what does 535 00:26:24,480 --> 00:26:25,520 Speaker 15: that signal to you? 536 00:26:26,200 --> 00:26:28,320 Speaker 14: Well, Lena, that's a slightly different market, which is the 537 00:26:28,359 --> 00:26:31,639 Speaker 14: pure sort of AGI market, let's say. But it's and 538 00:26:32,480 --> 00:26:34,080 Speaker 14: you know whether or not that's rational, and I think 539 00:26:34,119 --> 00:26:36,360 Speaker 14: our organizations need to think through themselves. But I do 540 00:26:36,359 --> 00:26:39,040 Speaker 14: think AI is going to be one of the biggest technologies, 541 00:26:39,040 --> 00:26:41,680 Speaker 14: if not the biggest, that humanity will ever event so 542 00:26:41,880 --> 00:26:44,720 Speaker 14: in some sense that is rational. But on the other hand, 543 00:26:44,760 --> 00:26:47,480 Speaker 14: in it's particlarly with isomorphic. We have such a compelling 544 00:26:47,480 --> 00:26:51,280 Speaker 14: mission of applying AI to to improve human health. I mean, 545 00:26:51,359 --> 00:26:53,880 Speaker 14: what better use of AI is there than that? And 546 00:26:54,000 --> 00:26:57,960 Speaker 14: that's a really compelling and compelling proposition for both our investors, 547 00:26:58,200 --> 00:26:59,200 Speaker 14: our and our staff. 548 00:27:00,000 --> 00:27:01,879 Speaker 3: We've got to catch the full conversation and it's in 549 00:27:01,880 --> 00:27:04,440 Speaker 3: the next edition of Bloemberg Tech Europe. It's quite early 550 00:27:04,480 --> 00:27:07,359 Speaker 3: though Eastern time, one thirty am six thirty am in London, 551 00:27:07,400 --> 00:27:09,360 Speaker 3: but you can catch it online too. Now, coming up, 552 00:27:09,680 --> 00:27:12,560 Speaker 3: we speak with Repless CEO I'm Jad Massad about the 553 00:27:12,560 --> 00:27:15,639 Speaker 3: startup's latest funding round and the excitement surrounding the Vibe 554 00:27:15,640 --> 00:27:16,320 Speaker 3: coding space. 555 00:27:16,800 --> 00:27:17,760 Speaker 4: This is Bloomberg Tech. 556 00:27:33,720 --> 00:27:37,760 Speaker 2: AI coding startup Replet has a new valuation three billion dollars, 557 00:27:37,800 --> 00:27:39,960 Speaker 2: nearly tripling its previous value. 558 00:27:40,160 --> 00:27:40,800 Speaker 5: We broke the story. 559 00:27:40,880 --> 00:27:43,200 Speaker 2: Yesterday it closed the two hundred and fifty million dollar 560 00:27:43,240 --> 00:27:46,600 Speaker 2: funding round led by Prison Capital, with American Express Ventures, 561 00:27:46,600 --> 00:27:50,360 Speaker 2: Google's AI Futures Fund, and existing backers like Andreson Howitz 562 00:27:50,560 --> 00:27:54,600 Speaker 2: and why Combinator I'm Jad Masad Replet CEO and founder 563 00:27:55,000 --> 00:27:59,200 Speaker 2: joins us. Now something is happening in AI and coding 564 00:27:59,520 --> 00:28:01,879 Speaker 2: in particular. There is a lot of momentum behind you 565 00:28:01,960 --> 00:28:04,959 Speaker 2: and a number of your peers. Why, you know, like 566 00:28:05,240 --> 00:28:09,200 Speaker 2: the valuations, the headline, but what's the thing underneath that 567 00:28:09,200 --> 00:28:09,960 Speaker 2: that's driving it? 568 00:28:10,640 --> 00:28:15,639 Speaker 16: You know, Since the dawn of computing, the holy grail 569 00:28:15,840 --> 00:28:20,080 Speaker 16: the vision of using computers is the ability to program them. 570 00:28:20,560 --> 00:28:23,120 Speaker 16: And there's been many attempts across the years, in most 571 00:28:23,160 --> 00:28:26,440 Speaker 16: recently with the no code and low code movement, but it. 572 00:28:26,359 --> 00:28:28,359 Speaker 12: Really never achieved the potential. 573 00:28:29,280 --> 00:28:33,440 Speaker 16: And today with AI, you can conjure up software by 574 00:28:33,480 --> 00:28:37,119 Speaker 16: merely speaking them and that's really a magical feeling and 575 00:28:37,160 --> 00:28:40,240 Speaker 16: it's transforming all sorts of jobs, not just programming, but 576 00:28:40,360 --> 00:28:41,080 Speaker 16: others as well. 577 00:28:41,120 --> 00:28:44,200 Speaker 2: And Jack, can we do a vibe check and talk 578 00:28:44,240 --> 00:28:47,240 Speaker 2: about vibe coding? Are you in the camp of people 579 00:28:47,280 --> 00:28:51,480 Speaker 2: that accept that vibe coding was coined by Andre Kape 580 00:28:51,720 --> 00:28:54,480 Speaker 2: earlier in the year and now. 581 00:28:54,320 --> 00:28:55,080 Speaker 5: It's a big thing. 582 00:28:55,960 --> 00:28:59,120 Speaker 2: I think our audience would really appreciate understanding what is 583 00:28:59,200 --> 00:29:01,360 Speaker 2: vibe coding and what do people say things like that? 584 00:29:01,520 --> 00:29:05,160 Speaker 16: Yeah, yeah, I mean looking again at the history of computing, 585 00:29:06,160 --> 00:29:09,200 Speaker 16: Grace Hopper in the nineteen fifties, then Venor the compiler 586 00:29:09,640 --> 00:29:12,880 Speaker 16: talked about what we need to do is get to 587 00:29:13,000 --> 00:29:15,360 Speaker 16: a position where we're coding in English, and that was 588 00:29:15,400 --> 00:29:19,080 Speaker 16: always a vision, and so Carpathi last year, in his 589 00:29:19,200 --> 00:29:23,440 Speaker 16: experience coding, said, I'm starting to trust AI more and 590 00:29:23,560 --> 00:29:26,760 Speaker 16: as I'm coding THEI is presenting the code, I'm just 591 00:29:26,800 --> 00:29:29,920 Speaker 16: typing natural language and I just accepted. So that was 592 00:29:31,160 --> 00:29:33,560 Speaker 16: sort of the coinage of the term. But obviously that's 593 00:29:33,760 --> 00:29:36,200 Speaker 16: something that people have been doing since chat GBT. 594 00:29:36,560 --> 00:29:40,320 Speaker 12: We started seeing it at a Replet. We released Replet. 595 00:29:40,040 --> 00:29:44,200 Speaker 16: Agents in September twenty twenty four and it was the 596 00:29:44,320 --> 00:29:47,200 Speaker 16: first coding agent on the market where you don't even 597 00:29:47,240 --> 00:29:50,880 Speaker 16: have to look at the code, so you just type 598 00:29:51,520 --> 00:29:53,680 Speaker 16: your prompt just say I want to build like a 599 00:29:53,720 --> 00:29:59,120 Speaker 16: storefront for my pet store, and it will like in 600 00:29:59,160 --> 00:30:00,000 Speaker 16: a few minutes. 601 00:29:59,760 --> 00:30:01,360 Speaker 12: You'll to see the website. 602 00:30:01,600 --> 00:30:03,320 Speaker 16: You can say I want to add login, I want 603 00:30:03,360 --> 00:30:06,200 Speaker 16: to add my stripe integration, and boom, you have a 604 00:30:06,240 --> 00:30:11,080 Speaker 16: website that's being built. So vibe coding initially was coined 605 00:30:11,120 --> 00:30:14,479 Speaker 16: by a Carpaty for professionals, saying that you can move 606 00:30:14,520 --> 00:30:15,600 Speaker 16: a lot faster, you can. 607 00:30:15,520 --> 00:30:18,960 Speaker 12: Iterate very quickly. But our take on it is really. 608 00:30:18,720 --> 00:30:21,920 Speaker 16: Anyone can program, and that's transforming not just software engineering. 609 00:30:22,240 --> 00:30:27,520 Speaker 16: We have people designers, product managers, finance people, operations people. 610 00:30:27,960 --> 00:30:32,640 Speaker 16: Everyone is automating their jobs, building tools around their jobs. 611 00:30:33,000 --> 00:30:34,680 Speaker 12: And that's what vibecoding is doing. 612 00:30:35,280 --> 00:30:37,920 Speaker 3: And so now with the progress in your agents from 613 00:30:38,000 --> 00:30:42,360 Speaker 3: agent one of just doing two minutes of work and 614 00:30:42,360 --> 00:30:44,800 Speaker 3: now we're up to two hundred minutes with your Agent three, 615 00:30:45,160 --> 00:30:47,720 Speaker 3: are you doing engineers and software designers out of a job? 616 00:30:49,240 --> 00:30:52,280 Speaker 12: Well, I don't think so. I think we're empowering them. 617 00:30:52,600 --> 00:30:54,840 Speaker 16: So if you look at some of our customers, what 618 00:30:54,880 --> 00:30:58,320 Speaker 16: they're saying, for example, we have dual Lingo and Zilos, 619 00:30:58,400 --> 00:31:03,400 Speaker 16: some of are exciting customers in the enterprise. What they're 620 00:31:03,440 --> 00:31:08,320 Speaker 16: telling us is they're cutting product development life cycle by 621 00:31:08,440 --> 00:31:12,000 Speaker 16: up to fifty percent. So previously, when you want to 622 00:31:12,080 --> 00:31:14,800 Speaker 16: produce a new feature, new product, what you would do 623 00:31:14,880 --> 00:31:17,600 Speaker 16: a product manager would sit down write a very long 624 00:31:17,720 --> 00:31:20,360 Speaker 16: document that we call a PRD. We'll pass it to 625 00:31:20,400 --> 00:31:24,720 Speaker 16: the designer. Designer takes that. Typically they misunderstand some aspect 626 00:31:24,760 --> 00:31:27,200 Speaker 16: of it because it's a lot, and they produce a 627 00:31:27,320 --> 00:31:29,440 Speaker 16: mock and then a mock goes to the engineer. They 628 00:31:29,440 --> 00:31:31,640 Speaker 16: also understand some aspect of it, so there's a lot 629 00:31:31,680 --> 00:31:34,880 Speaker 16: of communication issues there. So what they're doing right now 630 00:31:35,000 --> 00:31:37,560 Speaker 16: is a product manager, instead of going from text to 631 00:31:37,600 --> 00:31:41,000 Speaker 16: design to program, they go straight to program. So they 632 00:31:41,080 --> 00:31:44,360 Speaker 16: generate a prototype, they pass that prototype to the designer 633 00:31:44,440 --> 00:31:47,480 Speaker 16: and the engineer and the iteration loop just goes much faster. 634 00:31:47,600 --> 00:31:50,720 Speaker 16: So companies are a lot more productive, they're shipping faster, 635 00:31:51,160 --> 00:31:54,000 Speaker 16: and that's really how we're transforming software in the enterprise. 636 00:31:54,320 --> 00:31:57,000 Speaker 2: And I think the blombag tech audience would love to 637 00:31:57,080 --> 00:31:59,160 Speaker 2: learn a bit more about Redplit as a company. Yeah, 638 00:31:59,200 --> 00:32:01,720 Speaker 2: you know again, headline is the tripling of the valuation. 639 00:32:01,920 --> 00:32:04,440 Speaker 2: But how have you grown internally? What are the main 640 00:32:04,480 --> 00:32:06,400 Speaker 2: priorities for you in terms of onboarding talent? 641 00:32:06,880 --> 00:32:09,200 Speaker 16: You know, we set up this really big vision when 642 00:32:09,240 --> 00:32:11,960 Speaker 16: we started the company in twenty sixteen. We said we're 643 00:32:11,960 --> 00:32:14,040 Speaker 16: going to empower a billion people to be able to 644 00:32:14,040 --> 00:32:17,560 Speaker 16: create software, and people were investors especially, who were laughing 645 00:32:17,720 --> 00:32:20,040 Speaker 16: us out of the room. It's like, yeah, no, no way, 646 00:32:20,080 --> 00:32:22,920 Speaker 16: A billion people would want to learn how to code. 647 00:32:23,160 --> 00:32:25,320 Speaker 16: But we kept talking about how AI is going to 648 00:32:25,360 --> 00:32:29,040 Speaker 16: change things. We built a platform that removes all the 649 00:32:29,120 --> 00:32:33,080 Speaker 16: complexity from creating software. And that's not just the code, 650 00:32:33,120 --> 00:32:36,720 Speaker 16: that's you know, creating the development environment, that's creating a 651 00:32:36,800 --> 00:32:39,680 Speaker 16: database and managing it, that's deploying it. And so we 652 00:32:39,760 --> 00:32:43,520 Speaker 16: spent ten years, eight or nine years before the company 653 00:32:43,520 --> 00:32:47,240 Speaker 16: took off commercially. You know, last year about this time, 654 00:32:47,400 --> 00:32:51,520 Speaker 16: we're about three million dollars in annual recurrent revenue release 655 00:32:51,600 --> 00:32:55,120 Speaker 16: replet Agent one that sent us immediately to nine million dollars, 656 00:32:55,160 --> 00:32:57,120 Speaker 16: but by the end of the year we were about 657 00:32:57,200 --> 00:32:59,760 Speaker 16: you know, nine or ten million dollars, and then from 658 00:32:59,800 --> 00:33:05,080 Speaker 16: there until July we fifteen x tow one hundred and 659 00:33:05,120 --> 00:33:09,120 Speaker 16: fifty million dollar arr Agent V two made it so 660 00:33:09,160 --> 00:33:11,280 Speaker 16: that you can go from the agent, you can give 661 00:33:11,280 --> 00:33:13,080 Speaker 16: it an idea or work for two minutes and come 662 00:33:13,080 --> 00:33:15,400 Speaker 16: back to you with questions or a lot of struggle. 663 00:33:15,800 --> 00:33:18,200 Speaker 16: V two could work for twenty minutes. Now V three 664 00:33:18,240 --> 00:33:21,240 Speaker 16: could work for two hundred minutes, and it's really becoming 665 00:33:21,360 --> 00:33:24,120 Speaker 16: like your own programmer. You don't have to go hire 666 00:33:24,120 --> 00:33:26,800 Speaker 16: a programmer off the market. You can just go to rapport, 667 00:33:26,840 --> 00:33:29,840 Speaker 16: put in your idea, a report will work like a teammate. 668 00:33:30,760 --> 00:33:33,240 Speaker 3: But you are hiring, and in many ways, that's why 669 00:33:33,440 --> 00:33:36,200 Speaker 3: you've raised the funds. Amjad, I'm going to make it 670 00:33:36,240 --> 00:33:38,920 Speaker 3: personal if you don't mind, but knowing your story of 671 00:33:38,960 --> 00:33:41,000 Speaker 3: coming here to the United States back in what was 672 00:33:41,000 --> 00:33:43,200 Speaker 3: it twenty twelve, the fact you came to New York, 673 00:33:43,240 --> 00:33:45,760 Speaker 3: I just spoke with the Governor Kathy Hochel of New 674 00:33:45,840 --> 00:33:48,560 Speaker 3: York who's worried about the access of talent for building 675 00:33:48,560 --> 00:33:50,520 Speaker 3: technology here in New York. And I'm sure it's something 676 00:33:50,520 --> 00:33:52,840 Speaker 3: you think about on the West Coast. Can you get 677 00:33:52,880 --> 00:33:55,560 Speaker 3: the talent you need, particularly from outside of the United States. 678 00:33:57,640 --> 00:33:59,720 Speaker 16: I think there's a lot of talent here and the 679 00:34:00,000 --> 00:34:02,880 Speaker 16: out of states. Still, I think the salaries are getting 680 00:34:02,920 --> 00:34:05,960 Speaker 16: quite absurd because of the funding that's going in AI, 681 00:34:06,400 --> 00:34:09,040 Speaker 16: but I think we should do Obviously, immigration is still 682 00:34:09,080 --> 00:34:11,680 Speaker 16: going to be very important to getting the best and 683 00:34:11,760 --> 00:34:14,560 Speaker 16: the brightest people from all over the world to America. 684 00:34:14,640 --> 00:34:16,680 Speaker 16: But also there's a lot of local talent here and 685 00:34:16,719 --> 00:34:19,560 Speaker 16: I think Replet's also mission is. 686 00:34:19,480 --> 00:34:21,200 Speaker 12: To teach people those skills. 687 00:34:21,480 --> 00:34:24,520 Speaker 16: So Replets has always been used in schools and universities, 688 00:34:24,880 --> 00:34:27,360 Speaker 16: and it's the best time ever if you're a student 689 00:34:27,760 --> 00:34:31,080 Speaker 16: to start learning how to make software is so easy. 690 00:34:31,120 --> 00:34:32,640 Speaker 12: You can make your first piece of. 691 00:34:32,600 --> 00:34:36,600 Speaker 16: Software in a matter of like fifteen to twenty minutes. 692 00:34:36,960 --> 00:34:39,160 Speaker 16: And so I think we're going to see an explosion 693 00:34:39,640 --> 00:34:42,480 Speaker 16: of talent and people coming on the market being able 694 00:34:42,800 --> 00:34:46,080 Speaker 16: to not just code, but you know, use chat, GPT, 695 00:34:46,760 --> 00:34:49,120 Speaker 16: use mid journey, be able to design, be able to 696 00:34:49,120 --> 00:34:52,040 Speaker 16: create videos. I think it's an amazing time for a 697 00:34:52,080 --> 00:34:55,600 Speaker 16: student to be building a broad set of skills, and 698 00:34:55,680 --> 00:34:56,959 Speaker 16: I think I don't think we're going. 699 00:34:56,840 --> 00:34:57,920 Speaker 12: To have a talent problem. 700 00:34:58,320 --> 00:35:02,080 Speaker 3: Really appreciate that on So thank you, Jadamasad's Replet CEO 701 00:35:02,160 --> 00:35:11,120 Speaker 3: and founder. Congratulations on the Rais file management company. Box 702 00:35:11,160 --> 00:35:13,359 Speaker 3: has unveiled a new set of agentic tools that it's 703 00:35:13,400 --> 00:35:16,560 Speaker 3: annual BlockWorks conference and includes a kind of operating system 704 00:35:16,560 --> 00:35:19,960 Speaker 3: for AI agents and an a cybersecurity tool. Let's get 705 00:35:19,960 --> 00:35:23,120 Speaker 3: into it in Box CEO Aaron Levy, who joins us. Now, Aaron, 706 00:35:23,520 --> 00:35:25,439 Speaker 3: what are you most excited about? Because there's a raft 707 00:35:25,480 --> 00:35:27,959 Speaker 3: of things you're unveiling, whether it's about extracting data, whether 708 00:35:28,000 --> 00:35:31,440 Speaker 3: it's about managing the raft of agents we're about to face. 709 00:35:31,840 --> 00:35:33,520 Speaker 4: How are your consumers going to adopt it? 710 00:35:34,680 --> 00:35:37,000 Speaker 17: Yeah, So I think the thing we're most excited about 711 00:35:37,120 --> 00:35:40,719 Speaker 17: is that box we help companies manage their unstructured data. 712 00:35:40,800 --> 00:35:42,840 Speaker 17: So if you think about ninety percent of data in 713 00:35:42,880 --> 00:35:48,759 Speaker 17: the enterprise, are things like financial documents, contracts, invoices, research materials, 714 00:35:49,040 --> 00:35:51,360 Speaker 17: all of that data. Traditionally you've never been able to 715 00:35:51,400 --> 00:35:54,399 Speaker 17: tap into its scale inside of an organization. You kind 716 00:35:54,400 --> 00:35:56,239 Speaker 17: of create it, you store it, you may look at 717 00:35:56,239 --> 00:35:59,440 Speaker 17: it again, but you never really are able to actually 718 00:35:59,480 --> 00:36:02,560 Speaker 17: put it in to a workflow or deeply understand what's 719 00:36:02,560 --> 00:36:05,680 Speaker 17: inside that data. So we're announcing a set of capabilities 720 00:36:05,719 --> 00:36:08,480 Speaker 17: with AI agents to let you actually finally tap into 721 00:36:08,480 --> 00:36:08,920 Speaker 17: that data. 722 00:36:09,360 --> 00:36:10,799 Speaker 7: The probably biggest. 723 00:36:10,440 --> 00:36:13,239 Speaker 17: Part of the announcements of are new workflow automation capability 724 00:36:13,280 --> 00:36:16,120 Speaker 17: Box automate, where you can design an end to end 725 00:36:16,120 --> 00:36:19,640 Speaker 17: business process directly in box and then drop in AI 726 00:36:19,760 --> 00:36:22,720 Speaker 17: agents at any step or multiple steps in that business 727 00:36:22,719 --> 00:36:25,719 Speaker 17: process to let you go and bring automation to your 728 00:36:25,800 --> 00:36:29,440 Speaker 17: unstructured workflows. So think about client onboarding at a bank, 729 00:36:29,719 --> 00:36:32,520 Speaker 17: reviewing a contract at a law firm, being able to 730 00:36:32,560 --> 00:36:34,600 Speaker 17: work through healthcare data. These are what you're going to 731 00:36:34,640 --> 00:36:36,960 Speaker 17: be able to automate now at scale. 732 00:36:36,480 --> 00:36:39,640 Speaker 3: And you're in like two thirds of the fortune five hundred. 733 00:36:40,320 --> 00:36:42,120 Speaker 4: I really want to get a sense check of. 734 00:36:42,200 --> 00:36:45,800 Speaker 3: How they're embracing these sorts of products, what effectiveness they're seeing, 735 00:36:45,840 --> 00:36:48,760 Speaker 3: because with that MIT report blowing up all the vibes 736 00:36:48,800 --> 00:36:52,080 Speaker 3: around whether or not this stuff is actually practically delivering 737 00:36:52,360 --> 00:36:54,160 Speaker 3: on productivity, what are you seeing? 738 00:36:55,239 --> 00:36:59,799 Speaker 17: Yeah, so we're seeing probably a different trend from what 739 00:37:00,239 --> 00:37:02,360 Speaker 17: I think showed up in that MIT report. That's obviously 740 00:37:02,360 --> 00:37:05,919 Speaker 17: a very broad based survey across lots of different types 741 00:37:05,960 --> 00:37:08,880 Speaker 17: of implementations of AI. And one thing actually in particular 742 00:37:08,960 --> 00:37:11,399 Speaker 17: that was found in the MIT report was a very 743 00:37:11,440 --> 00:37:14,799 Speaker 17: different success and failure rate based on if companies try 744 00:37:14,800 --> 00:37:18,000 Speaker 17: and build out their own technology versus work with software 745 00:37:18,080 --> 00:37:20,880 Speaker 17: vendors that have pre built capabilities to let them go 746 00:37:20,960 --> 00:37:23,799 Speaker 17: and deploy against their data and workflows. So we're seeing 747 00:37:23,840 --> 00:37:27,200 Speaker 17: a much higher success rate because within box customers already 748 00:37:27,239 --> 00:37:30,400 Speaker 17: have their data, they already have security. Now we're introducing 749 00:37:30,440 --> 00:37:33,680 Speaker 17: workflow capabilities that they can drop agents into, all of 750 00:37:33,719 --> 00:37:37,440 Speaker 17: which provide the guardrails to make agents much more successful 751 00:37:37,480 --> 00:37:40,040 Speaker 17: in their environment. So we're seeing very different results. But 752 00:37:40,080 --> 00:37:43,320 Speaker 17: I think there are important lessons within that MIT survey 753 00:37:43,320 --> 00:37:46,120 Speaker 17: that CIOs should be paying attention to as they go 754 00:37:46,160 --> 00:37:48,040 Speaker 17: and deploy AI and their enterprise. 755 00:37:48,920 --> 00:37:52,640 Speaker 2: Aaron extract tot to mat and she'ld pro this package 756 00:37:52,680 --> 00:37:56,400 Speaker 2: expansion for you. You're a one billion dollar revenue AEA company, 757 00:37:57,000 --> 00:37:59,319 Speaker 2: do you just kind of see immediate acceleration of that 758 00:37:59,440 --> 00:38:03,400 Speaker 2: because you think it's what your customers are wanting right now. 759 00:38:04,680 --> 00:38:08,200 Speaker 17: Yeah, So we introduced a new plan called Enterprise Advanced. 760 00:38:08,320 --> 00:38:11,479 Speaker 17: And what Enterprise Advance does is it has our most 761 00:38:11,520 --> 00:38:15,839 Speaker 17: advanced AI capabilities, our AI agent builder, now, our new 762 00:38:15,880 --> 00:38:19,920 Speaker 17: workflow capabilities with agents, and that's providing a really kind 763 00:38:19,960 --> 00:38:23,279 Speaker 17: of great upgrade cycle from a revenue standpoint, but it's 764 00:38:23,280 --> 00:38:25,720 Speaker 17: also making it very easy for our customers to actually 765 00:38:25,719 --> 00:38:28,879 Speaker 17: get into these advanced capabilities in a seamless way. So 766 00:38:29,000 --> 00:38:31,200 Speaker 17: we think it's a very good match for again driving 767 00:38:31,280 --> 00:38:34,360 Speaker 17: a revenue sort of cycle for us that is driving 768 00:38:34,560 --> 00:38:37,560 Speaker 17: a very healthy upgrade rate. We've seen some great results 769 00:38:37,560 --> 00:38:41,400 Speaker 17: where we beat guidance and consensus on our recent numbers, 770 00:38:41,640 --> 00:38:44,360 Speaker 17: and that's really driven by the enterprise advanced momentum that 771 00:38:44,440 --> 00:38:46,960 Speaker 17: will only continue based on the announcements we're making today 772 00:38:47,040 --> 00:38:49,879 Speaker 17: on our new set of features that we're launching aeron. 773 00:38:50,000 --> 00:38:53,480 Speaker 2: I've really enjoyed following your posts on X in recent months, 774 00:38:53,520 --> 00:38:55,880 Speaker 2: your on stage appearances. I've been trying to think, like, 775 00:38:56,320 --> 00:38:58,680 Speaker 2: what is the Aaron Levy summary of what's happening in 776 00:38:58,719 --> 00:38:59,640 Speaker 2: AI right now? 777 00:39:00,040 --> 00:39:01,000 Speaker 5: And correct me if I'm wrong. 778 00:39:01,040 --> 00:39:04,000 Speaker 2: I think your position is like, don't take your position 779 00:39:04,040 --> 00:39:05,600 Speaker 2: in the world of software for granted. 780 00:39:05,920 --> 00:39:07,320 Speaker 5: Everything is up for grabs. 781 00:39:07,760 --> 00:39:09,960 Speaker 2: Would you say that's fair and that you apply that 782 00:39:10,000 --> 00:39:11,359 Speaker 2: philosophy to Box as well. 783 00:39:12,840 --> 00:39:15,920 Speaker 17: Yeah, I mean, I'm certainly a student of history, you know, 784 00:39:15,960 --> 00:39:20,000 Speaker 17: Andy Grove. Obviously only the paranoids survive. I sort of 785 00:39:20,040 --> 00:39:22,120 Speaker 17: grew up in the tech industry at a point when 786 00:39:22,160 --> 00:39:24,680 Speaker 17: that was just locked in on everybody. You had this 787 00:39:24,880 --> 00:39:28,520 Speaker 17: new wave of disruptors go after many incumbent industries. We 788 00:39:28,520 --> 00:39:31,040 Speaker 17: were one of those, and so by living and breathing 789 00:39:31,040 --> 00:39:34,520 Speaker 17: that ourselves, I think we know how tenuous these positions 790 00:39:34,520 --> 00:39:36,200 Speaker 17: can be if you don't adapt and if you don't 791 00:39:36,200 --> 00:39:38,800 Speaker 17: move quickly. So the way we run Box today is 792 00:39:39,200 --> 00:39:41,319 Speaker 17: we have this mindset of what would our company do 793 00:39:41,719 --> 00:39:44,880 Speaker 17: if we started from scratch in twenty twenty five, how 794 00:39:44,880 --> 00:39:47,319 Speaker 17: would we work internally, how would we operate? And then, 795 00:39:47,320 --> 00:39:49,960 Speaker 17: most importantly, what value would we deliver for our customers 796 00:39:50,280 --> 00:39:53,400 Speaker 17: and for us. We're just sitting on this incredible amount 797 00:39:53,440 --> 00:39:56,000 Speaker 17: of data that our customers have entrusted us with that 798 00:39:56,080 --> 00:39:58,480 Speaker 17: we can now help them bring all new use cases 799 00:39:58,520 --> 00:40:01,040 Speaker 17: to life with the power of AI. We're incredibly excited. 800 00:40:01,040 --> 00:40:03,880 Speaker 17: This is the most exciting I've ever been, you know, 801 00:40:03,920 --> 00:40:08,200 Speaker 17: when we've been running Box, and this is more exciting 802 00:40:08,200 --> 00:40:11,080 Speaker 17: than the founding days, just given how many things we 803 00:40:11,120 --> 00:40:12,319 Speaker 17: can go and help customers with. 804 00:40:13,360 --> 00:40:15,680 Speaker 2: Aaron Levy, co founder and CEO of Box, It's great 805 00:40:15,680 --> 00:40:17,640 Speaker 2: to have you back here on Bloomberg Tech. Thank you 806 00:40:17,719 --> 00:40:21,120 Speaker 2: so much. That does it for this edition of Bloomberg Tech. 807 00:40:21,400 --> 00:40:24,120 Speaker 2: My goodness, September has been brutal, and what a week 808 00:40:24,160 --> 00:40:24,560 Speaker 2: it's been. 809 00:40:24,560 --> 00:40:27,759 Speaker 3: Karroc relentless. I feel it might be. There is so 810 00:40:27,920 --> 00:40:30,960 Speaker 3: much you've got to digest and catch up on and revisit. 811 00:40:30,600 --> 00:40:31,320 Speaker 4: With our podcast. 812 00:40:31,480 --> 00:40:33,160 Speaker 3: You can find it on the timeline and on the 813 00:40:33,280 --> 00:40:36,399 Speaker 3: terminal with Apple, Spotify and I Heart. 814 00:40:36,840 --> 00:40:37,799 Speaker 4: This is Bloomberg Tech.