1 00:00:01,400 --> 00:00:05,080 Speaker 1: From Marhart. We're Innovation of Money and Power Collie in 2 00:00:05,200 --> 00:00:10,120 Speaker 1: Silicon Vallet NBN. This is Bloomberg Technology with Caroline Hyde 3 00:00:10,160 --> 00:00:11,080 Speaker 1: and Ed Ludlow. 4 00:00:25,280 --> 00:00:28,320 Speaker 2: I'm Ed Lovelow here in San Francisco. Caroline hides off today. 5 00:00:28,360 --> 00:00:31,040 Speaker 2: This is Blueberg Technology coming up on the program and 6 00:00:31,080 --> 00:00:33,879 Speaker 2: Elon must bid to make X and Everything app. The 7 00:00:33,920 --> 00:00:38,520 Speaker 2: company's changing its policies to expand data collection while offering 8 00:00:38,520 --> 00:00:40,880 Speaker 2: an array of new features. Will break down exactly what's 9 00:00:40,960 --> 00:00:44,360 Speaker 2: changed overnight for the social media company. Plus full earnings 10 00:00:44,360 --> 00:00:48,280 Speaker 2: coverage Ahead will break down results from Salesforce, CrowdStrike, and Octa, 11 00:00:48,360 --> 00:00:51,800 Speaker 2: where CEO Tom McKinnon will join us for an exclusive conversation, 12 00:00:52,320 --> 00:00:55,560 Speaker 2: and the race for the first Bitcoin etf Who's going 13 00:00:55,640 --> 00:00:58,120 Speaker 2: to win? We're going to discuss with one firm buying 14 00:00:58,160 --> 00:01:01,400 Speaker 2: for that title. Bit Wise CEO Hougan joins us to 15 00:01:01,480 --> 00:01:03,920 Speaker 2: weigh in. Right to our top story and back to 16 00:01:04,280 --> 00:01:07,200 Speaker 2: X planning to collect biometric data from users. 17 00:01:07,440 --> 00:01:09,560 Speaker 3: So how are they getting it and just how or 18 00:01:09,600 --> 00:01:09,960 Speaker 3: they use it? 19 00:01:10,040 --> 00:01:12,319 Speaker 2: Let's break it all down with Bloomberg's ashe accounts who 20 00:01:12,319 --> 00:01:13,520 Speaker 2: reported on the story over night. 21 00:01:13,600 --> 00:01:15,000 Speaker 3: Let's start with the new policy. 22 00:01:15,520 --> 00:01:18,000 Speaker 2: What is it that you noticed X is doing differently 23 00:01:18,160 --> 00:01:20,440 Speaker 2: in terms of which data is being collected. 24 00:01:20,640 --> 00:01:22,560 Speaker 4: Well, if you look at their postily added in this 25 00:01:22,640 --> 00:01:25,560 Speaker 4: section about biometric data. Now, in actual policily, they don't 26 00:01:25,600 --> 00:01:27,480 Speaker 4: go into what that means. But from what we've seen 27 00:01:27,520 --> 00:01:29,920 Speaker 4: other companies do, we know that it means things like 28 00:01:29,959 --> 00:01:33,480 Speaker 4: fingerprints or iris scans or facial images. And so they, 29 00:01:33,640 --> 00:01:37,200 Speaker 4: based on your consent, will start collecting that data if 30 00:01:37,240 --> 00:01:38,080 Speaker 4: you allow them to. 31 00:01:38,240 --> 00:01:41,040 Speaker 2: There's also i think jobs data, right, jobs data and 32 00:01:41,560 --> 00:01:43,960 Speaker 2: education as well. So you and I had the same 33 00:01:44,040 --> 00:01:46,920 Speaker 2: question straight away, which was how do they collect this 34 00:01:47,000 --> 00:01:49,360 Speaker 2: biometric data? So I asked X and this is what 35 00:01:49,400 --> 00:01:51,960 Speaker 2: they told us, mister director, if we can bring up 36 00:01:52,000 --> 00:01:55,440 Speaker 2: their statement to us on the screen, basically two ways 37 00:01:55,920 --> 00:01:58,400 Speaker 2: you have the option to issue them a government issued 38 00:01:58,440 --> 00:02:01,480 Speaker 2: ID and take a self as sort of a two 39 00:02:01,520 --> 00:02:05,200 Speaker 2: stage verification. What do you make of the process that 40 00:02:05,240 --> 00:02:06,440 Speaker 2: they outlined. 41 00:02:06,240 --> 00:02:07,880 Speaker 4: You know, it sort of fits in line with what 42 00:02:08,000 --> 00:02:09,679 Speaker 4: Musk has said, right. One of the things he talked 43 00:02:09,720 --> 00:02:12,360 Speaker 4: about when he changed the whole verification process was like, 44 00:02:12,400 --> 00:02:15,360 Speaker 4: there's too many bots and spam and fake accounts, and 45 00:02:15,400 --> 00:02:17,440 Speaker 4: so if you think about it being able to upload 46 00:02:17,440 --> 00:02:19,280 Speaker 4: your government ID and then a picture is a much 47 00:02:19,320 --> 00:02:22,880 Speaker 4: more robust verification process because up until now all they 48 00:02:22,919 --> 00:02:24,959 Speaker 4: really did was ask people to have a verified phone 49 00:02:25,000 --> 00:02:27,480 Speaker 4: number and then a display photo, which you know, you can. 50 00:02:27,320 --> 00:02:29,160 Speaker 1: Sort of game that system a bit. So it falls 51 00:02:29,160 --> 00:02:29,600 Speaker 1: in line. 52 00:02:29,480 --> 00:02:31,240 Speaker 2: With that, and what they're saying to us in this 53 00:02:31,320 --> 00:02:34,600 Speaker 2: statement is that, you know, impersonation is still an issue 54 00:02:34,600 --> 00:02:37,200 Speaker 2: on the platform. There are a couple of Ed Ludlow's 55 00:02:37,200 --> 00:02:40,400 Speaker 2: floating around that are not me. They've since been taken down, 56 00:02:40,600 --> 00:02:42,240 Speaker 2: but they think that it will deal with that. 57 00:02:42,520 --> 00:02:43,720 Speaker 3: Wasn't the only piece of news. 58 00:02:43,800 --> 00:02:47,639 Speaker 2: Musk kind of followed up afterwards to say there will 59 00:02:47,680 --> 00:02:51,040 Speaker 2: be an offer of video and audio calling, but without 60 00:02:51,280 --> 00:02:52,919 Speaker 2: having to submit a cell phone number. 61 00:02:53,160 --> 00:02:54,960 Speaker 3: What was the new stuff there? 62 00:02:55,400 --> 00:02:57,960 Speaker 4: It's part of this whole idea of the everything app right. 63 00:02:58,000 --> 00:03:00,639 Speaker 4: He's been very vocal about we want more video, more audio, 64 00:03:00,720 --> 00:03:02,640 Speaker 4: like we want people to come to X and do everything, 65 00:03:02,880 --> 00:03:04,400 Speaker 4: and so this seems like a play on that and 66 00:03:04,440 --> 00:03:06,280 Speaker 4: not having to use a phone number. You just get 67 00:03:06,320 --> 00:03:07,800 Speaker 4: on it and you can sort of chat with people. 68 00:03:07,800 --> 00:03:09,560 Speaker 4: We'll see what it looks like, but it falls in 69 00:03:09,560 --> 00:03:11,600 Speaker 4: line with sort of his broader vision to make that 70 00:03:11,639 --> 00:03:12,480 Speaker 4: everything up all right. 71 00:03:12,520 --> 00:03:14,919 Speaker 2: Bloomberg's Asha counts dealing with what was some pretty late 72 00:03:14,960 --> 00:03:18,480 Speaker 2: news last night, but it's had a big response. So 73 00:03:18,560 --> 00:03:22,120 Speaker 2: let's keep the conversation going with Jennifer greig Or, Syracuse 74 00:03:22,200 --> 00:03:27,720 Speaker 2: University Associate Professor of Communications. Jennifer in the first instance, 75 00:03:28,400 --> 00:03:33,120 Speaker 2: the specific data sets, X is asking for your reaction. 76 00:03:32,840 --> 00:03:37,400 Speaker 5: To that, well special thanks to Asia and everybody, you know, 77 00:03:37,480 --> 00:03:40,200 Speaker 5: reading the fine print and highlighting the things that are 78 00:03:40,880 --> 00:03:44,000 Speaker 5: coming and being added, because that tells you something, right. 79 00:03:44,080 --> 00:03:47,640 Speaker 5: And so sure there's the biometrics piece. I'm sure he's 80 00:03:47,720 --> 00:03:51,320 Speaker 5: clarified now that it's for you know, validating that they 81 00:03:51,360 --> 00:03:55,200 Speaker 5: are who they say they are for these authenticated accounts, right. 82 00:03:55,240 --> 00:03:58,280 Speaker 5: But you know, I think the fact that it's jobs, 83 00:03:58,600 --> 00:04:03,840 Speaker 5: that it's education, your job and your education tell you something, right, 84 00:04:04,520 --> 00:04:08,480 Speaker 5: They tell you maybe your income, how they can influence you. 85 00:04:08,840 --> 00:04:13,640 Speaker 5: But it's a demographic that provides you know, targeted marketing, 86 00:04:14,400 --> 00:04:17,000 Speaker 5: I would say, and that's one reason they're capturing that 87 00:04:17,080 --> 00:04:18,279 Speaker 5: maybe why that's a new thing. 88 00:04:20,560 --> 00:04:26,479 Speaker 2: X talks about the impact this will have encountering impersonators 89 00:04:26,480 --> 00:04:31,360 Speaker 2: on the platform. I myself have had impersonation accounts of 90 00:04:31,720 --> 00:04:35,400 Speaker 2: me pop up on the platform several times. In recent months, 91 00:04:36,080 --> 00:04:37,960 Speaker 2: do you give them the benefit of the doubt that 92 00:04:38,120 --> 00:04:40,520 Speaker 2: that will be an effective way of countering that. 93 00:04:42,920 --> 00:04:45,839 Speaker 5: I think that's what they're hoping for. But again, you know, 94 00:04:45,920 --> 00:04:48,680 Speaker 5: bad actors can always get around things, right. But the 95 00:04:48,760 --> 00:04:53,159 Speaker 5: more trust we then put into this authenticated identity, the 96 00:04:53,240 --> 00:04:57,359 Speaker 5: more risk there might be introduced too. So I'm remembering back, 97 00:04:57,440 --> 00:04:59,920 Speaker 5: you know, to like when the ap account was had 98 00:05:00,200 --> 00:05:03,039 Speaker 5: and had said that you know, something happened at the 99 00:05:03,040 --> 00:05:06,120 Speaker 5: White House and then the stock market crashed, right, you know, 100 00:05:06,200 --> 00:05:09,120 Speaker 5: So I think once you put more trust in it 101 00:05:09,120 --> 00:05:12,800 Speaker 5: too is problematic. I think what we've learned over the 102 00:05:12,839 --> 00:05:17,039 Speaker 5: past year since Musk is that centralization is inherently risky 103 00:05:17,360 --> 00:05:20,560 Speaker 5: and that maybe it's not a good idea to have, 104 00:05:20,720 --> 00:05:23,960 Speaker 5: you know, so many professionals and jarlists and everyone tied 105 00:05:24,000 --> 00:05:27,320 Speaker 5: into the space which is controlled essentially by you know, 106 00:05:27,600 --> 00:05:30,160 Speaker 5: an individual. Now, it was a corporation before, but there 107 00:05:30,160 --> 00:05:32,520 Speaker 5: were issues under Dorsey and what it was corporate too. 108 00:05:32,480 --> 00:05:33,680 Speaker 6: So I don't know. 109 00:05:33,720 --> 00:05:37,480 Speaker 5: I think that you know, he's trying to solve or something, 110 00:05:37,520 --> 00:05:40,800 Speaker 5: but the risks aren't necessarily new and they're not going away. 111 00:05:40,839 --> 00:05:43,279 Speaker 5: But he is trying to get more people to stick around, 112 00:05:43,360 --> 00:05:47,520 Speaker 5: and again, Twitter X platform whatever you want to call 113 00:05:47,560 --> 00:05:51,640 Speaker 5: it now hasn't totally failed, right, so, but it has evolved. 114 00:05:51,720 --> 00:05:54,560 Speaker 5: So I think we need to keep an eye on, 115 00:05:54,640 --> 00:05:56,520 Speaker 5: you know, how it's changing, how he's trying to retain 116 00:05:56,600 --> 00:05:59,760 Speaker 5: people and to maybe generate some revenue. 117 00:06:00,839 --> 00:06:04,000 Speaker 2: X the platform formerly known as Twitter is the line 118 00:06:04,040 --> 00:06:08,279 Speaker 2: that we repeat daily on this show, Bloomberg Technology, Jennifer. 119 00:06:08,279 --> 00:06:11,840 Speaker 2: There are certainly some jurisdictional and legal questions that arise 120 00:06:11,880 --> 00:06:15,640 Speaker 2: from this policy move. I note that if you go 121 00:06:15,720 --> 00:06:18,520 Speaker 2: to the policy page, the biometric data in particular is 122 00:06:18,640 --> 00:06:22,279 Speaker 2: quote based on your consent as a user, but it 123 00:06:22,360 --> 00:06:27,279 Speaker 2: is personally identifying information. And therefore I ask about GDPR 124 00:06:27,400 --> 00:06:30,120 Speaker 2: in Europe and what you think will happen in that region. 125 00:06:31,120 --> 00:06:34,440 Speaker 5: Yeah, and so I like how Musk is clarifying that 126 00:06:34,480 --> 00:06:36,960 Speaker 5: this is for the premium users. But there are class 127 00:06:37,000 --> 00:06:39,720 Speaker 5: actions here even in the United States about the capture 128 00:06:39,720 --> 00:06:42,320 Speaker 5: of biometrics data in like the state of Illinois. That's 129 00:06:42,360 --> 00:06:45,960 Speaker 5: where that's most relevant because they have some unique kind 130 00:06:45,960 --> 00:06:49,479 Speaker 5: of legislation there. But it wasn't really transparent that this 131 00:06:49,640 --> 00:06:52,120 Speaker 5: is what they were doing as a company. But you know, 132 00:06:52,360 --> 00:06:55,400 Speaker 5: photo scans are pretty standard. We saw issues with meta 133 00:06:55,400 --> 00:06:58,039 Speaker 5: Facebook as well in the past. And when it comes 134 00:06:58,080 --> 00:07:02,880 Speaker 5: to Europe, you know, they have definitely more privacy initiatives 135 00:07:02,880 --> 00:07:05,719 Speaker 5: over there. We're way behind here in the United States. 136 00:07:06,440 --> 00:07:10,200 Speaker 5: But gdpr essentially is about consent, you know, so it's 137 00:07:10,600 --> 00:07:13,120 Speaker 5: nice to know when something you know really personal is 138 00:07:13,160 --> 00:07:16,920 Speaker 5: being collected, but also the ability to like opt out 139 00:07:16,960 --> 00:07:20,440 Speaker 5: of things. I don't know, I feel like this free 140 00:07:20,480 --> 00:07:23,640 Speaker 5: Twitter app, if you're a free user, you're just you're 141 00:07:23,640 --> 00:07:27,320 Speaker 5: paying more and more with things like not being able 142 00:07:27,400 --> 00:07:30,280 Speaker 5: to consent to certain things being collected about you. And 143 00:07:30,320 --> 00:07:32,400 Speaker 5: maybe some people are like whatever, I don't care scan 144 00:07:32,520 --> 00:07:34,560 Speaker 5: my face, or I don't care a track, or I 145 00:07:34,600 --> 00:07:36,840 Speaker 5: go on the internet. But it's what happens to that 146 00:07:37,000 --> 00:07:41,559 Speaker 5: data in aggregate, right? And then who does Musk and 147 00:07:41,920 --> 00:07:46,360 Speaker 5: other corporate actors share this data with? And I particularly 148 00:07:46,440 --> 00:07:50,120 Speaker 5: am interested in, like how do they share data with governments? 149 00:07:50,280 --> 00:07:53,000 Speaker 5: So not just the you know, you know, the United States, 150 00:07:53,000 --> 00:07:56,160 Speaker 5: but also like Saudi Arabia, right that is also an 151 00:07:56,160 --> 00:08:00,960 Speaker 5: investor in Musk's enterprise. So you know, once that data 152 00:08:01,040 --> 00:08:04,560 Speaker 5: is collected, these tools are created, you know, the risks 153 00:08:05,640 --> 00:08:08,760 Speaker 5: essentially grow sometimes without the public knowing. 154 00:08:09,240 --> 00:08:11,080 Speaker 1: So I just want to raise some awareness around that. 155 00:08:11,000 --> 00:08:14,240 Speaker 2: Too, Jennifer, you raise the idea of the data being 156 00:08:14,320 --> 00:08:17,000 Speaker 2: used in aggregate and the other piece of news overnight 157 00:08:17,200 --> 00:08:23,120 Speaker 2: is X offering video and audio call function without having 158 00:08:23,160 --> 00:08:26,440 Speaker 2: to submit a cell phone number, you know, moving away 159 00:08:26,480 --> 00:08:29,640 Speaker 2: from social media. Your view on and everything app and 160 00:08:29,920 --> 00:08:31,480 Speaker 2: the data relationship there. 161 00:08:32,960 --> 00:08:35,120 Speaker 5: It really is proving to be an everything app. He 162 00:08:35,200 --> 00:08:38,760 Speaker 5: wants to collect everything. That's really, so. 163 00:08:38,679 --> 00:08:39,760 Speaker 1: The more data the better. 164 00:08:40,240 --> 00:08:43,040 Speaker 5: If they're making something like more squishy and loose, is 165 00:08:43,080 --> 00:08:46,600 Speaker 5: because they want you to contribute more. They're essentially lowering 166 00:08:46,640 --> 00:08:49,880 Speaker 5: the barrier to entry, right, So I would say, if 167 00:08:49,880 --> 00:08:52,760 Speaker 5: you don't have to include a phone number, which again 168 00:08:52,840 --> 00:08:55,480 Speaker 5: can be considered personal information, a lot more things are 169 00:08:55,480 --> 00:08:58,959 Speaker 5: tied and validated through our cell phone numbers. So I 170 00:08:59,000 --> 00:09:02,120 Speaker 5: would say that wants to you know, maybe generate some 171 00:09:02,200 --> 00:09:06,240 Speaker 5: more views and use of live streaming, the video apps, 172 00:09:05,880 --> 00:09:10,160 Speaker 5: the voice you know, audio and that can be collected too. 173 00:09:10,200 --> 00:09:12,200 Speaker 5: So that's another biometric piece. 174 00:09:12,240 --> 00:09:13,080 Speaker 1: Two is your your. 175 00:09:12,960 --> 00:09:16,520 Speaker 5: Voice as as a print to it as well. So 176 00:09:17,440 --> 00:09:20,200 Speaker 5: and I think we have to really just continue to 177 00:09:20,280 --> 00:09:24,200 Speaker 5: keep an eye on AI and how all of this 178 00:09:24,280 --> 00:09:27,720 Speaker 5: data is being captured to train future models. So you know, 179 00:09:27,760 --> 00:09:31,040 Speaker 5: again the more voice that submitted, the more that can 180 00:09:31,080 --> 00:09:34,320 Speaker 5: be analyzed and used in the future, potentially even without 181 00:09:34,360 --> 00:09:37,280 Speaker 5: our consent. So we just have to be careful, you know, 182 00:09:37,320 --> 00:09:39,840 Speaker 5: what we're contributing, and hopefully at some point we can 183 00:09:39,880 --> 00:09:43,959 Speaker 5: get away from such centralized, you know, spaces that are 184 00:09:43,960 --> 00:09:47,640 Speaker 5: governed by you know, wealthy folks like Musk or corporations, 185 00:09:48,120 --> 00:09:51,720 Speaker 5: because it becomes really a target of intervention of governments 186 00:09:51,760 --> 00:09:54,200 Speaker 5: and stay actors, and that that's just going to get 187 00:09:54,400 --> 00:09:55,920 Speaker 5: oppressive for the people. 188 00:09:56,240 --> 00:09:56,840 Speaker 1: Down the road. 189 00:09:58,040 --> 00:10:03,000 Speaker 2: Jennifer Greigel, Syracuse University, Associate Professor of Communications. Always great 190 00:10:03,400 --> 00:10:15,160 Speaker 2: to have this discussion on Bloomberg technology, the journey to 191 00:10:15,240 --> 00:10:18,520 Speaker 2: a potential Bitcoin ETF. There's been a bumpy one, but 192 00:10:18,600 --> 00:10:21,959 Speaker 2: some key decisions in the race are trickling in this week. 193 00:10:22,000 --> 00:10:25,520 Speaker 2: And first start with bitwise. The SEC expected to respond 194 00:10:25,520 --> 00:10:29,280 Speaker 2: to its own filings, followed by black Rock, Invesco and 195 00:10:29,320 --> 00:10:31,960 Speaker 2: others over the next forty eight hours or so. Delighted 196 00:10:31,960 --> 00:10:36,120 Speaker 2: to say we're joined by bitwise, So Matt Hogan, so 197 00:10:36,200 --> 00:10:39,120 Speaker 2: let's start there. Have you heard anything from the SEC, 198 00:10:39,160 --> 00:10:43,720 Speaker 2: any updated communications or questions, because my understanding is the 199 00:10:43,760 --> 00:10:48,240 Speaker 2: deadline to respond to your ETF application is Friday. 200 00:10:49,440 --> 00:10:52,040 Speaker 7: That's exactly right, thanks for having me on. The SEC 201 00:10:52,160 --> 00:10:56,440 Speaker 7: has forty five days to initially respond to applications for 202 00:10:56,559 --> 00:10:59,960 Speaker 7: novel ETFs like a spot bitcoin ETF, and that window 203 00:11:00,200 --> 00:11:05,720 Speaker 7: ends Friday for us. Historically, the SEC has extended their review. 204 00:11:05,800 --> 00:11:09,040 Speaker 7: They can move those deadlines out by another forty five days, 205 00:11:09,480 --> 00:11:12,959 Speaker 7: and that may happen. If I were an investor looking ahead, 206 00:11:13,000 --> 00:11:17,360 Speaker 7: I'd circle October sixteenth as the next big deadline to watch. 207 00:11:17,400 --> 00:11:20,920 Speaker 7: That's forty five more days after this weekend, and also 208 00:11:21,040 --> 00:11:24,000 Speaker 7: forty five days after the Grayscale ruling, which is the 209 00:11:24,080 --> 00:11:27,840 Speaker 7: last day the SEC could appeal. So this week is momentous. 210 00:11:28,120 --> 00:11:30,559 Speaker 7: October sixteenth may be a very good day to keep 211 00:11:30,559 --> 00:11:30,960 Speaker 7: an eye on. 212 00:11:32,520 --> 00:11:37,360 Speaker 2: There's a question on how the SEC acts. Do they 213 00:11:37,440 --> 00:11:41,720 Speaker 2: approve each application one by one, to your mind, or 214 00:11:41,720 --> 00:11:44,960 Speaker 2: do they go by a common clock whereby they let 215 00:11:45,000 --> 00:11:46,240 Speaker 2: everyone launch it once? 216 00:11:46,320 --> 00:11:47,000 Speaker 3: What do you think? 217 00:11:47,920 --> 00:11:50,000 Speaker 1: Yeah, you know, it's a great question. We don't know. 218 00:11:50,200 --> 00:11:52,400 Speaker 7: If you look back at the history of the SEC's 219 00:11:52,400 --> 00:11:55,920 Speaker 7: treatment of ETFs, you can see examples of each and 220 00:11:55,960 --> 00:11:58,880 Speaker 7: so we have no idea what their plans are. I 221 00:11:58,960 --> 00:12:02,800 Speaker 7: will say, on half of investors, the best outcome is 222 00:12:03,080 --> 00:12:05,719 Speaker 7: likely to line up multiple ETFs and allow them to 223 00:12:05,800 --> 00:12:09,880 Speaker 7: launch it once. That'll create the most competition, the lowest prices, 224 00:12:10,040 --> 00:12:13,040 Speaker 7: the best products, and be the fairest probably to be 225 00:12:13,160 --> 00:12:15,920 Speaker 7: asset managers who have worked so hard over ten years 226 00:12:16,000 --> 00:12:18,080 Speaker 7: to get a spot Bitcoin ETF approved. 227 00:12:18,360 --> 00:12:19,880 Speaker 1: So if we do get approval, that's what. 228 00:12:19,840 --> 00:12:22,960 Speaker 7: I'm hoping to see, But of course anything could happen. 229 00:12:23,360 --> 00:12:25,160 Speaker 1: We could see it in any which way. 230 00:12:26,280 --> 00:12:29,760 Speaker 2: We're showing Bitcoin's performance in the session off by one 231 00:12:29,760 --> 00:12:32,640 Speaker 2: and a half cent, but at around twenty seven thousand 232 00:12:32,720 --> 00:12:35,960 Speaker 2: US dollars per token. The point being that the sort 233 00:12:35,960 --> 00:12:38,760 Speaker 2: of euphoria of the US Appeals decision. 234 00:12:39,040 --> 00:12:39,800 Speaker 3: Was short lived. 235 00:12:40,320 --> 00:12:42,520 Speaker 2: I thought that that was supposed to be a starting 236 00:12:42,559 --> 00:12:45,560 Speaker 2: gun or signal that this is great news for crypto. 237 00:12:46,880 --> 00:12:49,319 Speaker 1: Yeah, I think it is great news for crypto. 238 00:12:49,400 --> 00:12:51,840 Speaker 7: Right Crypto is going from a niche asset to a 239 00:12:51,920 --> 00:12:55,640 Speaker 7: mainstream asset. This was an important step along this journey. 240 00:12:56,000 --> 00:12:59,520 Speaker 7: But my expectation is about what we're seeing today. I 241 00:12:59,520 --> 00:13:03,000 Speaker 7: think people likely to overestimate the short term impact of 242 00:13:03,000 --> 00:13:07,240 Speaker 7: an ETAF and underestimate the long term impact of an ETF. 243 00:13:07,559 --> 00:13:10,040 Speaker 7: That's what we saw when gold ETFs were approved in 244 00:13:10,080 --> 00:13:13,280 Speaker 7: two thousand and three that really transformed gold as an 245 00:13:13,320 --> 00:13:14,679 Speaker 7: asset class, moved. 246 00:13:14,440 --> 00:13:15,479 Speaker 1: It into the mainstream. 247 00:13:15,720 --> 00:13:17,880 Speaker 7: But it's not as if gold took off on the 248 00:13:18,000 --> 00:13:20,160 Speaker 7: day it was approved, and I'd expect the same thing 249 00:13:20,240 --> 00:13:23,160 Speaker 7: about bitcoin. People want this to be a one day story. 250 00:13:23,320 --> 00:13:25,880 Speaker 7: It's fun to follow the race, but this is actually 251 00:13:25,920 --> 00:13:28,920 Speaker 7: a ten year story. This is about bitcoin moving from 252 00:13:28,960 --> 00:13:31,920 Speaker 7: the edge of the investment community to the center of 253 00:13:31,960 --> 00:13:33,760 Speaker 7: the mainstream allocation space. 254 00:13:34,000 --> 00:13:35,199 Speaker 1: That's really exciting. 255 00:13:35,360 --> 00:13:37,200 Speaker 7: But if you're investing in that, you should be thinking 256 00:13:37,280 --> 00:13:40,079 Speaker 7: about ten years and not a few days. 257 00:13:41,160 --> 00:13:44,199 Speaker 2: When you say if you're investing, I wonder who you're 258 00:13:44,240 --> 00:13:47,480 Speaker 2: referring to there, because the kind of two mainstay concerns 259 00:13:47,480 --> 00:13:52,920 Speaker 2: of the SEC is the participation of retail investors also 260 00:13:53,040 --> 00:13:56,920 Speaker 2: the idea of market manipulation. Have you been active in it? 261 00:13:57,360 --> 00:14:00,880 Speaker 2: In sort of addressing those concerns with the I get itsa. 262 00:14:01,600 --> 00:14:02,599 Speaker 1: Yeah, absolutely. 263 00:14:02,640 --> 00:14:05,280 Speaker 7: You know, we've submitted over four hundred pages of academic 264 00:14:05,360 --> 00:14:09,200 Speaker 7: research demonstrating that the bitcoin market of today is not 265 00:14:09,360 --> 00:14:12,000 Speaker 7: the bitcoin market of five or ten years ago. 266 00:14:12,080 --> 00:14:13,360 Speaker 1: This market has matured. 267 00:14:13,679 --> 00:14:15,720 Speaker 7: I think it's matured to the space that we can 268 00:14:16,200 --> 00:14:20,040 Speaker 7: have a bitcoin etf available. And the big point is 269 00:14:20,480 --> 00:14:24,480 Speaker 7: investors are allocating to bitcoin already, they're using consumer apps, 270 00:14:24,640 --> 00:14:27,720 Speaker 7: they're using other tools. All an ETF would do is 271 00:14:27,800 --> 00:14:32,240 Speaker 7: make it safer, cheaper, and provide more regulatory protections to 272 00:14:32,320 --> 00:14:35,960 Speaker 7: help investors get better exposure to something that they're finding 273 00:14:36,000 --> 00:14:38,560 Speaker 7: their way to get exposure to as well. I will 274 00:14:38,600 --> 00:14:41,120 Speaker 7: say we started this journey on spot bitcoin ETFs ten 275 00:14:41,200 --> 00:14:45,040 Speaker 7: years ago, and the SEC was right to deny bitcoin ETFs. 276 00:14:45,040 --> 00:14:45,600 Speaker 1: At that point. 277 00:14:45,640 --> 00:14:49,680 Speaker 7: The industry wasn't ready, the institutions weren't ready, the infrastructure 278 00:14:49,760 --> 00:14:52,480 Speaker 7: wasn't ready. But I think today it is and it's 279 00:14:52,520 --> 00:14:53,760 Speaker 7: time for us to move forward. 280 00:14:55,480 --> 00:14:56,720 Speaker 3: How do you move forward? 281 00:14:56,800 --> 00:14:59,880 Speaker 2: And when you know, we just showed the calendar of 282 00:15:00,080 --> 00:15:03,480 Speaker 2: everyone that could potentially have an ETF approved, it's quite 283 00:15:03,480 --> 00:15:06,720 Speaker 2: a few of you. So how are you competitive against 284 00:15:06,720 --> 00:15:07,520 Speaker 2: the large pack? 285 00:15:08,640 --> 00:15:12,040 Speaker 7: Yeah? Bit wise, crypto is all that we do. We've 286 00:15:12,080 --> 00:15:15,360 Speaker 7: been doing this for six years. We work with thousands 287 00:15:15,400 --> 00:15:18,920 Speaker 7: of financial advisors and tens of thousands of investors. We've 288 00:15:18,960 --> 00:15:22,320 Speaker 7: helped them navigate the great parts of crypto and the challenges. 289 00:15:22,680 --> 00:15:25,840 Speaker 7: I think some investors will want to turn towards large, 290 00:15:25,840 --> 00:15:29,320 Speaker 7: familiar asset managers like Blackrock, and others will want to 291 00:15:29,360 --> 00:15:31,680 Speaker 7: turn to a crypto expert that they can call up 292 00:15:31,880 --> 00:15:34,600 Speaker 7: and get answers from that are informed about what's going 293 00:15:34,640 --> 00:15:38,560 Speaker 7: on in crypto. If you look at ETF's historically, specialist 294 00:15:38,560 --> 00:15:41,640 Speaker 7: asset managers that focus on something twenty four to seven 295 00:15:41,720 --> 00:15:45,120 Speaker 7: three sixty five win more than their fair share of assets. 296 00:15:45,120 --> 00:15:47,000 Speaker 1: And that's what Bitwise is provided. 297 00:15:47,040 --> 00:15:50,120 Speaker 7: You know, we're the largest crypto index fund provider in 298 00:15:50,160 --> 00:15:53,080 Speaker 7: America and we want to take that expertise into this 299 00:15:53,120 --> 00:15:56,560 Speaker 7: spot bitcoin ETF market and serve investors that way. 300 00:15:57,920 --> 00:16:01,600 Speaker 2: Bit Wise CIO Matt Hayugen just such a timely conversation 301 00:16:02,000 --> 00:16:04,720 Speaker 2: as it develops. Come back and join us on bloombog 302 00:16:04,760 --> 00:16:08,200 Speaker 2: Technology blow by blow. Thank you very much. They're coming 303 00:16:08,280 --> 00:16:11,000 Speaker 2: up here on the show. Activist investors called on salesforce 304 00:16:11,040 --> 00:16:14,360 Speaker 2: to boost profits and the company's making good on that bet. 305 00:16:14,400 --> 00:16:17,400 Speaker 2: We'll recap the numbers from Mark Benioffs Software Empire. 306 00:16:17,640 --> 00:16:18,200 Speaker 3: Coming up next. 307 00:16:18,240 --> 00:16:20,240 Speaker 2: We're also looking at shares by the way of CrowdStrike. 308 00:16:20,320 --> 00:16:24,520 Speaker 2: Another earning story, biggest indusday rise since May. The security 309 00:16:24,560 --> 00:16:28,200 Speaker 2: software company issued forecast feeding second quarter earnings and a 310 00:16:28,240 --> 00:16:32,200 Speaker 2: guidance upgrade, prompting Frankly, several analysts to raise their price 311 00:16:32,280 --> 00:16:35,480 Speaker 2: targets on the stock up nine percent in the session. 312 00:16:35,520 --> 00:16:53,960 Speaker 2: This is Bloomberg Technology time for talking tech. First up, 313 00:16:53,960 --> 00:16:55,960 Speaker 2: we spoke a little bit yesterday about the release of 314 00:16:56,040 --> 00:17:00,400 Speaker 2: Huawei's surprise smartphone. Now Chinese state media is ailing the 315 00:17:00,480 --> 00:17:03,680 Speaker 2: launch of the Mate sixty pro as a victory in 316 00:17:03,720 --> 00:17:06,560 Speaker 2: the ever so tense tech war against the United States. 317 00:17:06,600 --> 00:17:10,280 Speaker 2: A number of Beijing back columns have run overnight praising 318 00:17:10,359 --> 00:17:13,760 Speaker 2: Huawei since the device's released, which happened to coincide with 319 00:17:13,880 --> 00:17:18,560 Speaker 2: Common Secretary Gina Romando's visit to China. Sigma Smartphones Apple 320 00:17:18,640 --> 00:17:21,360 Speaker 2: taking a greener approach to producing some of its upcoming 321 00:17:21,400 --> 00:17:24,960 Speaker 2: smart watches, Bloomberg reporting the companies trying out three D 322 00:17:25,119 --> 00:17:27,960 Speaker 2: printers to make the gadgets in order to use less 323 00:17:28,040 --> 00:17:31,119 Speaker 2: slabs of metal. The new approach has the potential to 324 00:17:31,200 --> 00:17:34,040 Speaker 2: streamline Apple supply chain and be more eco friendly. 325 00:17:34,320 --> 00:17:35,920 Speaker 3: Apple declined to comment on the story. 326 00:17:35,960 --> 00:17:39,400 Speaker 2: Plus, in an effort to fend off further antitrust scrutiny 327 00:17:39,440 --> 00:17:42,800 Speaker 2: by the European Union, Microsoft is offering to unbundle its 328 00:17:42,840 --> 00:17:46,320 Speaker 2: Teams video conferencing unit from its business software package. 329 00:17:46,320 --> 00:17:46,960 Speaker 1: In Europe. 330 00:17:47,080 --> 00:17:51,080 Speaker 2: EU investigators are examining whether Microsoft breached competition rules by 331 00:17:51,119 --> 00:17:54,760 Speaker 2: offering teams with Office and Microsoft three sixty five in 332 00:17:54,800 --> 00:17:58,280 Speaker 2: a package. This follows a complaint from Salesforce and its 333 00:17:58,280 --> 00:18:01,520 Speaker 2: messaging platform Slack, which they may three years ago. Sticking 334 00:18:01,520 --> 00:18:05,480 Speaker 2: with Salesforce, the company boosting investor confidence after posting strong 335 00:18:05,520 --> 00:18:08,119 Speaker 2: second quarter earnings beat lest I set the numbers with 336 00:18:08,160 --> 00:18:10,680 Speaker 2: Bloomberg Intelligence senior tech analysts An A. 337 00:18:10,720 --> 00:18:11,320 Speaker 1: Rag Rana. 338 00:18:11,840 --> 00:18:14,520 Speaker 2: There was a lot that Salesforce did to their credit right, 339 00:18:14,640 --> 00:18:17,240 Speaker 2: raised prices for the first time in seven years July. 340 00:18:17,640 --> 00:18:20,560 Speaker 2: They've cut headcount. What jumped out to you from that 341 00:18:20,640 --> 00:18:21,320 Speaker 2: earnings print. 342 00:18:22,760 --> 00:18:25,000 Speaker 6: I think the bookings number or what we look at 343 00:18:25,040 --> 00:18:28,359 Speaker 6: called CRPO grew about eleven percent in constant currency, and 344 00:18:28,400 --> 00:18:31,359 Speaker 6: I think that was a surprise to a number of them, 345 00:18:31,520 --> 00:18:33,639 Speaker 6: number of people, I would say, which is partially the 346 00:18:33,720 --> 00:18:34,960 Speaker 6: reason why the star is up. 347 00:18:35,240 --> 00:18:35,399 Speaker 1: You know. 348 00:18:35,440 --> 00:18:37,679 Speaker 6: I think we were expecting that going in because we 349 00:18:37,760 --> 00:18:40,760 Speaker 6: already heard from the likes of Microsoft and Amazon that 350 00:18:40,800 --> 00:18:44,000 Speaker 6: there is some stabilization in the decline in take spending, 351 00:18:44,840 --> 00:18:47,280 Speaker 6: so we weren't looking for a bad print, but I 352 00:18:47,280 --> 00:18:50,520 Speaker 6: think the expectations were very negative going into the quarter. 353 00:18:53,119 --> 00:18:54,840 Speaker 3: AI we just got to talk about it. 354 00:18:54,840 --> 00:18:57,080 Speaker 2: You know, we lean in on this idea that on 355 00:18:57,119 --> 00:19:01,080 Speaker 2: a per user basis, Salesforce can shock seventy five hundred 356 00:19:01,119 --> 00:19:04,919 Speaker 2: dollars per user. Right, top line growth was eleven percent 357 00:19:04,960 --> 00:19:06,439 Speaker 2: in the court of gone. But this is a company 358 00:19:06,440 --> 00:19:08,879 Speaker 2: that used to grow twenty to thirty percent top line. 359 00:19:09,080 --> 00:19:11,600 Speaker 2: Does AI return them to that level of growth? 360 00:19:12,359 --> 00:19:12,520 Speaker 5: Oh? 361 00:19:12,560 --> 00:19:14,440 Speaker 6: No, No, twenty to thirty is very I mean, it's 362 00:19:14,680 --> 00:19:17,160 Speaker 6: out of the question in my view. When you look 363 00:19:17,200 --> 00:19:20,080 Speaker 6: at a company like Salesforce, given its size, given the 364 00:19:20,200 --> 00:19:22,520 Speaker 6: end markets, you know, this is a company that, in 365 00:19:22,560 --> 00:19:25,760 Speaker 6: our view should grow revenue somewhere between ten and fifteen percent. 366 00:19:26,000 --> 00:19:28,760 Speaker 6: You know, if you're really in good times, it will 367 00:19:28,800 --> 00:19:31,320 Speaker 6: be closer to the thirteen to fifteen percent mark. In 368 00:19:31,400 --> 00:19:33,440 Speaker 6: bad times it will be around the ten percent mark. 369 00:19:33,840 --> 00:19:36,960 Speaker 6: The end markets are the software products that they're selling. 370 00:19:37,240 --> 00:19:39,679 Speaker 6: I mean they are close to I would say, you know, 371 00:19:39,800 --> 00:19:42,960 Speaker 6: in line with the software industry. Not not so much 372 00:19:43,000 --> 00:19:44,520 Speaker 6: in that twenty percent range anymore. 373 00:19:45,600 --> 00:19:46,439 Speaker 3: And Rag, I know you. 374 00:19:47,000 --> 00:19:50,120 Speaker 2: Zero it in on the fundamentals. You have better command 375 00:19:50,119 --> 00:19:52,200 Speaker 2: of the numbers than anyone I know. But I want 376 00:19:52,200 --> 00:19:54,679 Speaker 2: to ask about Mark Bennioff. You know, investors made it 377 00:19:54,680 --> 00:19:57,679 Speaker 2: clear what they wanted and Mark Benioff did it focused 378 00:19:57,720 --> 00:19:58,119 Speaker 2: on profit. 379 00:20:00,680 --> 00:20:01,760 Speaker 3: You see, it's a lot. 380 00:20:01,640 --> 00:20:03,879 Speaker 6: More to goal when it comes to profit, and Mark's 381 00:20:03,920 --> 00:20:05,919 Speaker 6: done a good job about it. But I think you know, 382 00:20:05,960 --> 00:20:08,080 Speaker 6: it's going to be a lot more than where we 383 00:20:08,119 --> 00:20:10,439 Speaker 6: are right now. And the only reason is because they 384 00:20:10,440 --> 00:20:12,800 Speaker 6: spends still a lot of money on sales and marketing 385 00:20:13,040 --> 00:20:15,800 Speaker 6: compared to their rivals like Microsoft or Article, so we 386 00:20:16,119 --> 00:20:18,920 Speaker 6: expect margins to go even hired over the next few years. 387 00:20:19,760 --> 00:20:22,480 Speaker 2: Yeah, they raised their operating margin guidance for the fiscal 388 00:20:22,560 --> 00:20:25,679 Speaker 2: year by two percentage points. Anarag Rana, senior tech analyst 389 00:20:25,680 --> 00:20:27,400 Speaker 2: for Bloomberg Intelligence Goods catch up. 390 00:20:27,680 --> 00:20:28,399 Speaker 3: Thank you very much. 391 00:20:36,880 --> 00:20:39,960 Speaker 2: Welcome back to Bloomberg Technology ed Lovelow here in San Francisco. 392 00:20:40,040 --> 00:20:42,360 Speaker 2: This is what the technology sector looks like in e 393 00:20:42,359 --> 00:20:45,800 Speaker 2: equity markets andw's that one hundred slightly higher. The story 394 00:20:45,800 --> 00:20:48,000 Speaker 2: of the week's kind of in economic data and earnings. 395 00:20:48,000 --> 00:20:51,000 Speaker 2: But remember we get jobs data Friday that will inform 396 00:20:51,040 --> 00:20:54,159 Speaker 2: what the Fed does and therefore the rates narrative and 397 00:20:54,200 --> 00:20:57,000 Speaker 2: how we value many of these tech stocks, particularly on 398 00:20:57,000 --> 00:21:00,280 Speaker 2: the NATS. That one hundred elsewhere slight outperformance for chip stocks. 399 00:21:00,280 --> 00:21:02,479 Speaker 2: We're going to talk a bit later about Broadcon reporting 400 00:21:02,520 --> 00:21:05,600 Speaker 2: after the bell that dragging it higher. Ev Names pushing 401 00:21:05,600 --> 00:21:08,080 Speaker 2: it a little higher as well for General Motors up 402 00:21:08,160 --> 00:21:09,040 Speaker 2: nine tens one percent. 403 00:21:09,080 --> 00:21:09,920 Speaker 3: Tesla flat. 404 00:21:10,160 --> 00:21:12,880 Speaker 2: Why news that the United States is going to offer 405 00:21:12,920 --> 00:21:17,040 Speaker 2: twelve billion dollars of public money to retrofit plants that 406 00:21:17,119 --> 00:21:21,240 Speaker 2: currently make combustion engine vehicles and transition them to plants 407 00:21:21,240 --> 00:21:26,400 Speaker 2: that make electric vehicle offerings and platforms. Tesla not going 408 00:21:26,400 --> 00:21:28,400 Speaker 2: to benefit from that because, of course it's pure play. 409 00:21:28,440 --> 00:21:31,480 Speaker 2: It doesn't need to convert any of its existing US facilities, 410 00:21:31,480 --> 00:21:33,080 Speaker 2: but it is a story that will continue to track 411 00:21:33,119 --> 00:21:36,360 Speaker 2: those funds coming from the US Department of Energy. Finally, 412 00:21:36,560 --> 00:21:39,200 Speaker 2: in terms of what's driving US higher modestly on the 413 00:21:39,240 --> 00:21:42,680 Speaker 2: nazet one hundred, it's the megacaps really that are doing well. 414 00:21:42,720 --> 00:21:45,720 Speaker 2: Amazon is pushing higher Broadcom, as we talked about, reports 415 00:21:45,760 --> 00:21:48,359 Speaker 2: earnings after the bell up two point three percent, pushing 416 00:21:48,400 --> 00:21:50,439 Speaker 2: the Philadelphia Semiconductor. 417 00:21:49,760 --> 00:21:52,240 Speaker 3: Index higher to the upside. 418 00:21:52,280 --> 00:21:54,879 Speaker 2: Apple actually lost some of its earlier gains in the session, 419 00:21:54,920 --> 00:21:56,840 Speaker 2: and Meta is actually one of the big points drivers 420 00:21:56,880 --> 00:21:59,640 Speaker 2: to the upside on the social media side, up eight 421 00:21:59,680 --> 00:22:03,840 Speaker 2: ten of one percent. Right, our other top story, let's 422 00:22:03,880 --> 00:22:06,439 Speaker 2: turn back to the data policy change is at X, 423 00:22:06,800 --> 00:22:11,800 Speaker 2: the platform formerly known as Twitter, planning to collect biometric data. 424 00:22:11,960 --> 00:22:13,640 Speaker 3: Job and school history. 425 00:22:13,880 --> 00:22:15,560 Speaker 2: I do want to point out very quickly, though, that 426 00:22:15,600 --> 00:22:19,040 Speaker 2: the company told me this morning this only applies to 427 00:22:19,160 --> 00:22:22,560 Speaker 2: premium users who pay for an ex subscription. Joining us 428 00:22:22,600 --> 00:22:27,359 Speaker 2: now with reaction Adam Kovekovich, Chamber of Progress founder and CEO, 429 00:22:27,440 --> 00:22:31,040 Speaker 2: and let's start there. Your reaction to this policy change. 430 00:22:31,800 --> 00:22:33,679 Speaker 8: Well, I think sometimes when you see companies roll out 431 00:22:33,720 --> 00:22:35,439 Speaker 8: these terms of service, they're a little bit of a 432 00:22:35,560 --> 00:22:37,360 Speaker 8: roadmap of their plans. 433 00:22:37,400 --> 00:22:38,960 Speaker 1: They may or may not come to fruition. 434 00:22:39,560 --> 00:22:42,560 Speaker 8: They write them to give them sort of maximum flexibility. 435 00:22:42,600 --> 00:22:44,439 Speaker 8: But I do think these are probably a bit of 436 00:22:44,480 --> 00:22:47,760 Speaker 8: an X ray into the product plans inside ELI and 437 00:22:47,800 --> 00:22:51,639 Speaker 8: Musk's head. And on the one hand, it shows that Mask, 438 00:22:51,800 --> 00:22:53,719 Speaker 8: like a lot of leaders of the big companies, are 439 00:22:53,760 --> 00:22:56,080 Speaker 8: sort of racing to compete and invad each other's product 440 00:22:56,119 --> 00:22:58,760 Speaker 8: spaces and kind everybody kind of wants to become the 441 00:22:58,800 --> 00:23:01,399 Speaker 8: everything app. That's probably great for consumers and it creates 442 00:23:01,400 --> 00:23:04,920 Speaker 8: more competition. On the other hand, Musk has frankly done 443 00:23:04,920 --> 00:23:07,960 Speaker 8: allowed to destroy user trust and X over the last 444 00:23:07,960 --> 00:23:09,879 Speaker 8: few months, So it's kind of far from clear that 445 00:23:10,280 --> 00:23:14,160 Speaker 8: consumers are prepared to follow him on this expansion agenda. 446 00:23:15,600 --> 00:23:18,440 Speaker 2: I'd note that on my X timeline at least lots 447 00:23:18,440 --> 00:23:21,280 Speaker 2: of users have responded to me saying, you know that 448 00:23:21,320 --> 00:23:24,280 Speaker 2: they're not comfortable with this move or they cannot believe 449 00:23:24,320 --> 00:23:27,480 Speaker 2: that you have to pay for the privilege of handing 450 00:23:27,480 --> 00:23:32,679 Speaker 2: over a wider set of data to X and elon Musk. 451 00:23:33,000 --> 00:23:36,960 Speaker 2: The company's position is that by having dual verification a 452 00:23:37,000 --> 00:23:40,879 Speaker 2: government issued ID and then a selfie or picture as 453 00:23:40,960 --> 00:23:43,879 Speaker 2: part of biometrics, and those biometrics come from your government 454 00:23:43,880 --> 00:23:47,639 Speaker 2: issued ID, that will help them crack down on impersonators. 455 00:23:48,080 --> 00:23:50,600 Speaker 2: This is what the company told me this morning in 456 00:23:50,640 --> 00:23:53,679 Speaker 2: a statement. What do you make of that, Adam, the 457 00:23:53,760 --> 00:23:57,640 Speaker 2: ability through this data to tackle impersonation? 458 00:23:58,920 --> 00:24:01,520 Speaker 1: Well, I think it really on why they're doing it. 459 00:24:01,560 --> 00:24:05,240 Speaker 8: Do they need biometrics to become the next Venmo or 460 00:24:05,320 --> 00:24:08,200 Speaker 8: do they need it to solve a trust in verification 461 00:24:08,400 --> 00:24:11,760 Speaker 8: problem that must himself create it? Right, look for payment 462 00:24:11,800 --> 00:24:15,640 Speaker 8: services like Venmo, like Apple Pay, biometric data is very 463 00:24:15,720 --> 00:24:19,320 Speaker 8: valuable for securing phones and transactions. And if that's why 464 00:24:19,320 --> 00:24:21,359 Speaker 8: they're doing it, that's a level of security most people 465 00:24:21,400 --> 00:24:24,879 Speaker 8: would welcome. But if they're doing this because they have 466 00:24:25,000 --> 00:24:28,520 Speaker 8: a bot problem and a false impersonation problem, no one 467 00:24:28,560 --> 00:24:30,760 Speaker 8: did more to create that problem than Elon Musk himself. 468 00:24:30,800 --> 00:24:32,199 Speaker 1: Right. He completely misunderstood the. 469 00:24:32,240 --> 00:24:35,960 Speaker 8: Value of Twitter's old Blue check value verification system and 470 00:24:36,040 --> 00:24:38,520 Speaker 8: sort of auctioned it off as a source of revenue. 471 00:24:38,840 --> 00:24:41,359 Speaker 8: That in turn meant that a lot of bots controls 472 00:24:41,920 --> 00:24:44,760 Speaker 8: have bought blue checks now and sort of diminished user 473 00:24:44,840 --> 00:24:47,280 Speaker 8: trust in the platform. So after taking all those steps, 474 00:24:47,359 --> 00:24:49,440 Speaker 8: is a bit rich to say that they need biometrics 475 00:24:49,440 --> 00:24:51,439 Speaker 8: to solve the problem. It's a little like saying, you know, 476 00:24:51,480 --> 00:24:54,000 Speaker 8: we need to install a top of the line alarm 477 00:24:54,040 --> 00:24:55,959 Speaker 8: system because oops, we got rid of all of our 478 00:24:56,000 --> 00:24:56,960 Speaker 8: doors and locks. 479 00:24:56,720 --> 00:24:59,360 Speaker 1: A few months back. It's maybe best to just focus 480 00:24:59,359 --> 00:25:00,480 Speaker 1: on the basics first. 481 00:25:01,480 --> 00:25:04,119 Speaker 2: What's interesting with that position, Adam, is that the Chamber 482 00:25:04,160 --> 00:25:08,399 Speaker 2: of Progress represents the industry, right, the technology companies, And 483 00:25:08,440 --> 00:25:11,920 Speaker 2: I understand that your work is so that they could 484 00:25:11,960 --> 00:25:16,240 Speaker 2: contribute it in a better way to society. But give 485 00:25:16,280 --> 00:25:21,560 Speaker 2: me your personal your specific thoughts on a platform like 486 00:25:21,840 --> 00:25:28,760 Speaker 2: X including biometric data, jobs data, education data, albeit at 487 00:25:28,760 --> 00:25:30,680 Speaker 2: will and only to pay subscribers. 488 00:25:32,280 --> 00:25:35,480 Speaker 8: Well, I think the reality is that Again, if they're 489 00:25:35,520 --> 00:25:40,080 Speaker 8: doing this to inject more competition into the marketplace, for example, 490 00:25:40,080 --> 00:25:42,919 Speaker 8: to compete with LinkedIn in job search, or to compete 491 00:25:42,960 --> 00:25:44,480 Speaker 8: with Venmo and payments, or. 492 00:25:44,400 --> 00:25:46,679 Speaker 1: WhatsApp and messaging, all that is great. 493 00:25:47,400 --> 00:25:49,600 Speaker 8: You know, I think that it's a healthy dynamic that 494 00:25:49,640 --> 00:25:51,680 Speaker 8: we have companies and if I almost having a degree 495 00:25:51,680 --> 00:25:55,320 Speaker 8: of paranoia that leads them to compete with each other, 496 00:25:55,320 --> 00:25:57,200 Speaker 8: particularly the big companies, well resource companies. 497 00:25:57,240 --> 00:25:59,520 Speaker 1: That creates more options for consumers. All of that is 498 00:25:59,560 --> 00:25:59,960 Speaker 1: really great. 499 00:26:00,720 --> 00:26:03,479 Speaker 8: But on the other hand, the ability to do that 500 00:26:03,560 --> 00:26:06,960 Speaker 8: does depend on trust. And you know, so I think, 501 00:26:07,000 --> 00:26:10,359 Speaker 8: for example, a lot of people tried out threads because 502 00:26:10,400 --> 00:26:14,359 Speaker 8: they were dissatisfied with Twitter and all the changes going 503 00:26:14,400 --> 00:26:17,560 Speaker 8: on there. But frankly, Meta had done a lot too 504 00:26:17,720 --> 00:26:20,439 Speaker 8: over the last you know, several years to establish trust 505 00:26:20,480 --> 00:26:24,399 Speaker 8: with its users, you know, billion Instagram users, right, They 506 00:26:24,440 --> 00:26:27,800 Speaker 8: had invest in things like content moderation and so, you know, 507 00:26:27,840 --> 00:26:30,520 Speaker 8: I think it's great to see companies. 508 00:26:30,119 --> 00:26:32,240 Speaker 1: Expand and compete with their rivals. 509 00:26:32,240 --> 00:26:35,199 Speaker 8: That's beneficial for consumers, but it's only possible if you 510 00:26:35,240 --> 00:26:37,720 Speaker 8: sort of have a baseline of trust you've established with 511 00:26:37,760 --> 00:26:39,200 Speaker 8: your core users to begin with. 512 00:26:40,840 --> 00:26:43,200 Speaker 2: This all comes down to the idea of an everything app, 513 00:26:43,520 --> 00:26:46,359 Speaker 2: you know, moving towards what we see in Southeast Asia 514 00:26:46,400 --> 00:26:50,480 Speaker 2: and other parts of Asia where it's multifunction platform. The 515 00:26:50,520 --> 00:26:52,960 Speaker 2: other piece of news of Night was the addition or 516 00:26:53,000 --> 00:26:56,280 Speaker 2: the upcoming edition of video and audio calls without having 517 00:26:56,320 --> 00:26:59,600 Speaker 2: to register a cell phone number. What do you make 518 00:26:59,640 --> 00:27:01,879 Speaker 2: of that move towards the everything app? 519 00:27:03,280 --> 00:27:06,480 Speaker 8: Well, again, I think in some ways a number of 520 00:27:06,520 --> 00:27:09,040 Speaker 8: companies are trying to create the everything app. 521 00:27:09,080 --> 00:27:10,960 Speaker 1: They'd all like to create the everything app, right. 522 00:27:11,680 --> 00:27:15,159 Speaker 8: You see, you know, TikTok getting into e commerce to 523 00:27:15,200 --> 00:27:20,320 Speaker 8: compete with Amazon. You see again both TikTok and Meta 524 00:27:20,600 --> 00:27:24,440 Speaker 8: competing against Twitter in this sort of text only posts, 525 00:27:25,040 --> 00:27:27,760 Speaker 8: this audio video calling, you know, in some ways could 526 00:27:27,760 --> 00:27:32,679 Speaker 8: be competing with WhatsApp and Facebook Messenger and an I message, 527 00:27:32,720 --> 00:27:35,120 Speaker 8: and that could be beneficial as well. So I think 528 00:27:35,240 --> 00:27:37,760 Speaker 8: the quest for the everything app is a great one. 529 00:27:38,800 --> 00:27:41,119 Speaker 8: It has to be done in a way that solves 530 00:27:41,160 --> 00:27:44,440 Speaker 8: a problem for consumers, you know, I think I could 531 00:27:44,520 --> 00:27:45,960 Speaker 8: you know, I think a lot of times, for example, 532 00:27:45,960 --> 00:27:48,960 Speaker 8: when I'm doing a DM conversation with people in Twitter 533 00:27:49,080 --> 00:27:50,960 Speaker 8: or used to be knows Twitter, I could see times 534 00:27:51,000 --> 00:27:53,399 Speaker 8: with that be beneficial to turn that into a call. 535 00:27:53,720 --> 00:27:55,120 Speaker 1: On the other hand, if. 536 00:27:55,080 --> 00:27:58,399 Speaker 8: If any one follower of another person on X is 537 00:27:58,440 --> 00:27:59,840 Speaker 8: able to bring them out of the blue, that could 538 00:27:59,840 --> 00:28:00,960 Speaker 8: be a recipe for stam call. 539 00:28:01,000 --> 00:28:02,400 Speaker 1: So it depends on how it's done right. 540 00:28:02,720 --> 00:28:05,000 Speaker 8: And you know, he talked about X becoming a global 541 00:28:05,040 --> 00:28:08,160 Speaker 8: address book, but Twitter is not a global address book 542 00:28:08,160 --> 00:28:10,080 Speaker 8: for most people. Most people aren't even on Twitter. And 543 00:28:10,280 --> 00:28:11,600 Speaker 8: there's a little bit of a guest here. But I 544 00:28:11,640 --> 00:28:13,800 Speaker 8: think between how he uses it and how most people. 545 00:28:13,600 --> 00:28:18,640 Speaker 2: Use it, Adam does X, the platform formerly known as Twitter, 546 00:28:19,040 --> 00:28:22,959 Speaker 2: have an outsized side to impact relative to its size. 547 00:28:23,040 --> 00:28:27,280 Speaker 8: Still, oh, absolutely always has. I mean, and he and 548 00:28:27,280 --> 00:28:30,000 Speaker 8: trust me, I think that he you know, he's done 549 00:28:30,000 --> 00:28:32,320 Speaker 8: a lot to eroad that, but you know, there's there's 550 00:28:32,400 --> 00:28:34,440 Speaker 8: still a lot of real. 551 00:28:34,280 --> 00:28:35,720 Speaker 1: Time conversation happening there. 552 00:28:35,760 --> 00:28:39,360 Speaker 8: Of course, metas trying to challenge that with threads, and 553 00:28:39,440 --> 00:28:41,280 Speaker 8: I think, you know, has has drawn a share of 554 00:28:41,320 --> 00:28:41,920 Speaker 8: that conversation. 555 00:28:41,960 --> 00:28:45,000 Speaker 1: But there's no question that there's still a lot happening 556 00:28:45,040 --> 00:28:45,440 Speaker 1: on X. 557 00:28:45,480 --> 00:28:47,440 Speaker 8: I still enjoy using it a lot of the time, 558 00:28:47,480 --> 00:28:50,320 Speaker 8: and and and so I think that, you know, I 559 00:28:50,360 --> 00:28:52,920 Speaker 8: hope that it actually becomes a healthier place for a 560 00:28:52,960 --> 00:28:56,360 Speaker 8: conversation because that kind of trust and verification aspective is 561 00:28:56,480 --> 00:28:58,160 Speaker 8: kind of essential to being a place that people want 562 00:28:58,160 --> 00:28:58,560 Speaker 8: to hang out. 563 00:28:58,640 --> 00:29:02,320 Speaker 2: Yes, Kobaco, its Chamber of Progress founder and CEO. 564 00:29:02,400 --> 00:29:03,680 Speaker 3: Great catch up, Thank you. 565 00:29:04,200 --> 00:29:06,280 Speaker 2: Coming up here on Bloomberg Technology, We're going to talk 566 00:29:06,360 --> 00:29:11,360 Speaker 2: AI funding and defense tech with Blauserbiri, partner at Lux Capital. 567 00:29:11,600 --> 00:29:14,239 Speaker 2: Our VC Spotlight segment is coming up next. I have 568 00:29:14,280 --> 00:29:16,560 Speaker 2: to say that's a pretty old photo of Blau. 569 00:29:17,000 --> 00:29:17,440 Speaker 3: Check it out. 570 00:29:17,480 --> 00:29:19,560 Speaker 2: He see what he looks like when he's on set. Next, 571 00:29:19,760 --> 00:29:41,760 Speaker 2: this is Bloomberg Technology. AI's startup co here is working 572 00:29:41,800 --> 00:29:44,640 Speaker 2: with banks to raise a fresh round of financing, just 573 00:29:44,680 --> 00:29:47,280 Speaker 2: a few months after its last one. That according to 574 00:29:47,280 --> 00:29:50,680 Speaker 2: Bloomberg sources, the Toronto based company backed by investors like 575 00:29:50,720 --> 00:29:54,280 Speaker 2: Oracle and Nvidia's being advised by JP Morgan and Goldman Sachs. 576 00:29:54,320 --> 00:29:58,000 Speaker 2: On the potential round, the open AI competitor just raised 577 00:29:58,000 --> 00:30:01,320 Speaker 2: two hundred and seventy million dollars in all right, let's 578 00:30:01,360 --> 00:30:04,240 Speaker 2: stick with AI startups and venture money and get the 579 00:30:04,320 --> 00:30:08,000 Speaker 2: VC perspective on today's VC Spotlight and bring in Blau Zabiri, 580 00:30:08,120 --> 00:30:11,200 Speaker 2: general partner at Lux Capital, a five billion dollar firm. 581 00:30:11,640 --> 00:30:15,200 Speaker 2: The founds and funds emerging science and tech ventures pretty much. 582 00:30:15,480 --> 00:30:16,200 Speaker 3: All over the world. 583 00:30:16,240 --> 00:30:18,720 Speaker 2: And that's why I'm happy to see you, because since 584 00:30:18,800 --> 00:30:20,600 Speaker 2: last we spoke, you seem to have spent a lot 585 00:30:20,640 --> 00:30:23,920 Speaker 2: of time on an airplane and visited a lot of countries. 586 00:30:23,960 --> 00:30:25,760 Speaker 9: What have you been up to, Oh, it's been a 587 00:30:25,800 --> 00:30:28,240 Speaker 9: busy time in Ventory Capital. I've been traveling around the 588 00:30:28,240 --> 00:30:32,440 Speaker 9: world visiting portfolio companies in Europe, in Asia, Who've been 589 00:30:32,480 --> 00:30:36,920 Speaker 9: to several countries in Europe, went to Pakistan, went to Jordan, 590 00:30:37,840 --> 00:30:41,000 Speaker 9: and then obviously all over the US with companies coast 591 00:30:41,000 --> 00:30:41,400 Speaker 9: to coast. 592 00:30:42,160 --> 00:30:45,600 Speaker 2: Is there any sort of thematic divide of what you 593 00:30:46,040 --> 00:30:49,320 Speaker 2: invest in in Europe visa b what you've invested in here. 594 00:30:49,200 --> 00:30:49,880 Speaker 3: In the United States? 595 00:30:49,960 --> 00:30:52,240 Speaker 2: Is there sort of a distinct pool of talent there? 596 00:30:53,000 --> 00:30:55,760 Speaker 9: You know? Generally no, So as you said, we invest 597 00:30:55,640 --> 00:30:59,000 Speaker 9: in the intercision of technology and sciences, solving hard problems 598 00:30:59,720 --> 00:31:04,040 Speaker 9: and frankly matter that matters. You know. While a lot 599 00:31:04,040 --> 00:31:06,480 Speaker 9: of people in Slicon Valley might like to be thought 600 00:31:06,520 --> 00:31:10,320 Speaker 9: leaders and think of vision themselves as visionities, the reality 601 00:31:10,440 --> 00:31:14,120 Speaker 9: is capital follows talent, and talent follows interests, and the 602 00:31:14,160 --> 00:31:17,320 Speaker 9: interest right now is in solving important, interesting problems, and 603 00:31:17,320 --> 00:31:19,360 Speaker 9: that's where we're spending our time. So you know, a 604 00:31:19,360 --> 00:31:21,880 Speaker 9: couple of areas that emerge frankly globally for us to 605 00:31:21,920 --> 00:31:26,400 Speaker 9: invest in. It is securing life and environment, So it 606 00:31:26,520 --> 00:31:29,360 Speaker 9: is companies like sale Drome that are working on climate 607 00:31:29,480 --> 00:31:33,560 Speaker 9: change and you know, hurricane intensity and understanding whether patterns 608 00:31:33,600 --> 00:31:37,120 Speaker 9: that affect globally, and defense and national security. It is 609 00:31:37,560 --> 00:31:45,520 Speaker 9: advancing and promoting you know, our profitability and productivity. So 610 00:31:45,600 --> 00:31:49,320 Speaker 9: companies like Applied Intuition building autonomous cars and enabling transition 611 00:31:49,360 --> 00:31:53,680 Speaker 9: to electric vehicles. Globally, we're working on enabling free expression. 612 00:31:53,720 --> 00:31:56,640 Speaker 9: Democracy is very important. So companies like hugging Face that 613 00:31:56,680 --> 00:31:59,520 Speaker 9: are like sort of a global leader in the largest 614 00:31:59,520 --> 00:32:02,360 Speaker 9: community machine learning developers around the world frankly, and the 615 00:32:02,360 --> 00:32:05,160 Speaker 9: companies based in Europe as you know, headquartered in Paris. 616 00:32:05,920 --> 00:32:08,360 Speaker 2: And then we had Clemmed the CEO on the shoah. 617 00:32:08,920 --> 00:32:10,000 Speaker 2: Was it this week or last week? 618 00:32:10,000 --> 00:32:10,200 Speaker 3: Guys? 619 00:32:10,200 --> 00:32:12,080 Speaker 2: I think it was last week, But they've just raised 620 00:32:12,120 --> 00:32:14,479 Speaker 2: money as well. And what was interesting there is that 621 00:32:14,480 --> 00:32:16,720 Speaker 2: they're going to use it for the talent because it's 622 00:32:16,800 --> 00:32:19,160 Speaker 2: creving expensive. Just talk a little bit specifically about the 623 00:32:19,240 --> 00:32:20,640 Speaker 2: hugging Face investment. 624 00:32:20,840 --> 00:32:23,680 Speaker 9: I think, you know, machine learning AI revolution is happening globally, 625 00:32:24,040 --> 00:32:27,640 Speaker 9: and I think it's happening with people. So this is 626 00:32:27,640 --> 00:32:31,240 Speaker 9: not something that requires significant amounts of capital and deployment 627 00:32:31,240 --> 00:32:33,320 Speaker 9: into capex, into hardware systems. 628 00:32:34,160 --> 00:32:35,040 Speaker 1: But what's really. 629 00:32:34,800 --> 00:32:38,240 Speaker 9: Happening is people around the world are developing solutions using 630 00:32:38,280 --> 00:32:41,080 Speaker 9: AI that previously was simply not available or just too 631 00:32:41,120 --> 00:32:45,160 Speaker 9: expensive to develop. And that is happening everything from you know, 632 00:32:45,400 --> 00:32:49,000 Speaker 9: fintech and traditional enterprise software all the way to solving 633 00:32:49,040 --> 00:32:54,000 Speaker 9: healthcare problems and productivity and industrial solutions. So that's what 634 00:32:54,080 --> 00:32:56,280 Speaker 9: Hugging Face is doing is the largest community of machine 635 00:32:56,320 --> 00:33:00,600 Speaker 9: learning developers and models literally all around the world, and 636 00:33:00,720 --> 00:33:02,240 Speaker 9: the capital that they raise is to make sure that 637 00:33:02,280 --> 00:33:03,000 Speaker 9: they invest in it. 638 00:33:03,080 --> 00:33:05,080 Speaker 3: Yeah, cleansed along on the show last week. 639 00:33:05,120 --> 00:33:06,840 Speaker 2: The story for the firm this year has been that 640 00:33:06,960 --> 00:33:10,560 Speaker 2: latest one point one five billion dollar funds for science 641 00:33:10,640 --> 00:33:14,360 Speaker 2: deep tech. In the time that that was announced back 642 00:33:14,400 --> 00:33:17,959 Speaker 2: in April, I think, have you literally just been writing checks? 643 00:33:18,000 --> 00:33:22,120 Speaker 2: How does it work mechanically when a firm raises new funds, 644 00:33:22,320 --> 00:33:25,680 Speaker 2: Can you just go out there and start deploying technically. 645 00:33:25,280 --> 00:33:28,880 Speaker 9: Yes, we start deploying capital when we raised it from 646 00:33:28,880 --> 00:33:31,280 Speaker 9: the LPs. We have obviously, this is our fund aids. 647 00:33:31,320 --> 00:33:33,480 Speaker 9: We've been you know, we five billion dollars under management. 648 00:33:33,520 --> 00:33:36,640 Speaker 9: We've been deploying capital from previous funds as well. Our 649 00:33:36,680 --> 00:33:39,080 Speaker 9: focus is going to continue to be the investor the 650 00:33:39,080 --> 00:33:42,400 Speaker 9: intersectionion of technology and sciences, and this could there could 651 00:33:42,440 --> 00:33:45,800 Speaker 9: be physical sciences, everything from semiconductor chips to autonomy, automation, 652 00:33:45,920 --> 00:33:48,880 Speaker 9: industrial automation. You saw the news about Apple using three 653 00:33:48,960 --> 00:33:52,040 Speaker 9: D printers, et cetera. It could be life sciences, you know, 654 00:33:52,080 --> 00:33:56,720 Speaker 9: reducing human suffering, developing new drugs and therapies. And it 655 00:33:56,760 --> 00:34:00,000 Speaker 9: could be computer sciences, you know, machine learning, AI, cybersecurity, 656 00:34:00,120 --> 00:34:01,080 Speaker 9: and preparing for defense. 657 00:34:01,160 --> 00:34:05,400 Speaker 2: Frankly, and an umbrella area of interest to myself, my 658 00:34:05,480 --> 00:34:08,359 Speaker 2: colleague Zett Chapman, who's been writing about it, but also 659 00:34:08,560 --> 00:34:11,879 Speaker 2: LP's is defense. What do you say, why is this 660 00:34:12,040 --> 00:34:15,480 Speaker 2: now in vogue? For want of a better expression, I 661 00:34:15,480 --> 00:34:16,399 Speaker 2: don't know if it's in vogue. 662 00:34:16,440 --> 00:34:19,360 Speaker 9: I think it's more importantly a realization that, whether we 663 00:34:19,480 --> 00:34:21,080 Speaker 9: like it or not, we are at some sort of 664 00:34:21,080 --> 00:34:22,880 Speaker 9: a war. There is a war going on in Europe 665 00:34:22,960 --> 00:34:26,200 Speaker 9: between Ukraine and Russia and US technologies from the pub 666 00:34:26,360 --> 00:34:27,879 Speaker 9: private sector and the public sector have. 667 00:34:27,840 --> 00:34:29,160 Speaker 1: Been very influential there. 668 00:34:30,040 --> 00:34:32,760 Speaker 9: We are at odds with the CCP that have aggressive 669 00:34:32,800 --> 00:34:36,400 Speaker 9: designs that is increasingly hostile towards the US, and the 670 00:34:36,440 --> 00:34:39,040 Speaker 9: realization that the last twenty years of warfare were a 671 00:34:39,080 --> 00:34:43,040 Speaker 9: gorilla warfare in the Middle East, going downtown house to house, 672 00:34:43,200 --> 00:34:47,080 Speaker 9: and now we're facing an extremely sophisticated enemy with digital tools, 673 00:34:47,120 --> 00:34:50,880 Speaker 9: autonomous systems AI based solutions that are frankly pervasive in 674 00:34:50,920 --> 00:34:54,239 Speaker 9: our lives and offensive from the CCP. They see that 675 00:34:54,320 --> 00:34:56,600 Speaker 9: as Zyzego some games. So we have to prepare for that, 676 00:34:56,640 --> 00:34:58,040 Speaker 9: we have to work for that, and I think the 677 00:34:58,160 --> 00:35:01,440 Speaker 9: change that has happened is a realization that the old 678 00:35:01,520 --> 00:35:04,799 Speaker 9: school way of working through five six large primes and 679 00:35:04,880 --> 00:35:07,640 Speaker 9: developing technologies just doesn't work. You have to work with 680 00:35:07,680 --> 00:35:10,799 Speaker 9: the private sector to bring new technologies in, whether it's 681 00:35:11,040 --> 00:35:14,239 Speaker 9: drone systems or detection systems. You know, Sale drone has 682 00:35:14,320 --> 00:35:18,320 Speaker 9: hundreds of drones around the globe, connecting data and analyzing data. 683 00:35:18,800 --> 00:35:21,280 Speaker 9: This is what's needed for where the water is going, 684 00:35:21,520 --> 00:35:24,080 Speaker 9: and hopefully we can actually reduce human suffering and reduce 685 00:35:24,320 --> 00:35:27,400 Speaker 9: casualties in doing that, not putting humans in harms. 686 00:35:27,080 --> 00:35:30,600 Speaker 2: With Lawsaberry General partner Locks Capital. Great to have you 687 00:35:30,640 --> 00:35:32,680 Speaker 2: on set in San Francisco. You've been around the world 688 00:35:33,040 --> 00:35:33,480 Speaker 2: this year. 689 00:35:40,960 --> 00:35:44,920 Speaker 10: I mean you have to just engineer things to operate 690 00:35:45,640 --> 00:35:49,200 Speaker 10: within certain guardrails and boundaries. You have to seer things 691 00:35:49,239 --> 00:35:52,239 Speaker 10: in the right direction. So is the world suggesting that 692 00:35:52,920 --> 00:35:57,120 Speaker 10: the whole world replaces human connection with the chatbot? If 693 00:35:57,120 --> 00:36:00,320 Speaker 10: that is truly the case, I promise you've been bigger 694 00:36:00,360 --> 00:36:03,640 Speaker 10: issues than moble. I don't think you will ever replace 695 00:36:05,080 --> 00:36:07,040 Speaker 10: the need for real love and human connection. 696 00:36:08,320 --> 00:36:11,520 Speaker 2: That was Bumbles, CEO and founder talking with Bloomberg's Emily 697 00:36:11,600 --> 00:36:15,640 Speaker 2: Chang about how chatbots are no substitute for a real date. 698 00:36:15,680 --> 00:36:18,440 Speaker 2: To take that from me now, tune into that conversation 699 00:36:18,520 --> 00:36:21,359 Speaker 2: tonight at ten pm Easteron on Bloomberg Television and at 700 00:36:21,360 --> 00:36:22,560 Speaker 2: eight pm Easton. 701 00:36:22,560 --> 00:36:24,160 Speaker 3: On Bloomberg Originals. 702 00:36:24,280 --> 00:36:27,600 Speaker 2: Now, Identity management company Octa out with earnings that beat 703 00:36:27,680 --> 00:36:31,200 Speaker 2: expectations and announcing it's raised it's full year forecast. Joining 704 00:36:31,280 --> 00:36:34,760 Speaker 2: us now for an exclusive conversation Todd McKinnon, octor CEO. 705 00:36:34,920 --> 00:36:37,120 Speaker 3: Tod it's so interesting to me some. 706 00:36:37,040 --> 00:36:39,320 Speaker 2: Of the size of the deals in the quarter gone 707 00:36:39,560 --> 00:36:41,600 Speaker 2: and the impact that the size of those deals have 708 00:36:41,680 --> 00:36:45,200 Speaker 2: had just explain that the psychology and behavior of some 709 00:36:45,239 --> 00:36:47,520 Speaker 2: of your customers and what they're willing to commit to. 710 00:36:48,800 --> 00:36:51,080 Speaker 11: Thanks for having me on Bloomberg Technology ED. It's great 711 00:36:51,120 --> 00:36:55,360 Speaker 11: to be here. Large organizations particularly are really seeing the 712 00:36:55,440 --> 00:36:58,960 Speaker 11: value of identity. Whether it's a big company like NTT 713 00:36:59,160 --> 00:37:02,360 Speaker 11: Data globally is using Octa to secure their workforce and 714 00:37:02,400 --> 00:37:05,719 Speaker 11: allowing them to adopt different technologies and work from anywhere, 715 00:37:06,160 --> 00:37:09,120 Speaker 11: or it's one of the biggest consumer packaged goods companies 716 00:37:09,120 --> 00:37:12,000 Speaker 11: in the world that has disparate brands. They're trying to 717 00:37:12,000 --> 00:37:16,080 Speaker 11: bring a single unified login experience to Identity is at 718 00:37:16,080 --> 00:37:19,840 Speaker 11: the key to that. And these companies, despite macroeconomic uncertainty, 719 00:37:19,880 --> 00:37:23,680 Speaker 11: are pushing forward with these strategic projects, and we're lucky 720 00:37:23,760 --> 00:37:25,279 Speaker 11: enough to be able to work with them, and the 721 00:37:25,360 --> 00:37:27,680 Speaker 11: results are reflected in our business. 722 00:37:29,600 --> 00:37:30,520 Speaker 3: CRPO. 723 00:37:31,200 --> 00:37:33,839 Speaker 2: There are some analysts that kind of we're a little 724 00:37:33,880 --> 00:37:36,800 Speaker 2: disappointed your reaction to the Bears. 725 00:37:38,560 --> 00:37:41,840 Speaker 11: Well, I think that we're being cautious about the economic outlook. 726 00:37:42,400 --> 00:37:46,520 Speaker 11: There's still an economic headwind in the technology sector, and 727 00:37:46,600 --> 00:37:49,200 Speaker 11: we're being proven about our forward guidance to make sure 728 00:37:49,239 --> 00:37:51,280 Speaker 11: that we manage through that appropriately. 729 00:37:51,320 --> 00:37:53,120 Speaker 1: But at the same time, our growth. 730 00:37:52,840 --> 00:37:55,520 Speaker 11: Was very strong in the quarter, twenty three percent revenue 731 00:37:55,520 --> 00:37:58,800 Speaker 11: growth in this environment, and most importantly, we generated almost 732 00:37:58,800 --> 00:38:00,840 Speaker 11: fifty million dollars of free ash flow in the quarter. 733 00:38:01,160 --> 00:38:03,759 Speaker 11: So we've proven that the market for identity is so 734 00:38:03,800 --> 00:38:06,719 Speaker 11: attractive that in any economic environment, we can grow the 735 00:38:06,760 --> 00:38:08,560 Speaker 11: business with profitability. 736 00:38:10,280 --> 00:38:12,920 Speaker 2: Net retention rate, I know, it's something that our Bloomberg 737 00:38:12,920 --> 00:38:16,120 Speaker 2: intelligence and lists looked in. What's driving that in its 738 00:38:16,200 --> 00:38:17,600 Speaker 2: kind of downward trajectory. 739 00:38:18,520 --> 00:38:22,040 Speaker 11: Well, it did tick down from last quarter, but remember 740 00:38:22,360 --> 00:38:25,239 Speaker 11: we have one hundred and fifteen percent net retention, So 741 00:38:25,320 --> 00:38:28,640 Speaker 11: customers bought a dollar a year ago, that same customer 742 00:38:28,640 --> 00:38:30,800 Speaker 11: on average is now paying us a dollar. 743 00:38:30,560 --> 00:38:31,440 Speaker 1: Than fifteen cents. 744 00:38:31,520 --> 00:38:35,120 Speaker 11: So that's incredibly an incredible testament to our customer success 745 00:38:35,120 --> 00:38:38,120 Speaker 11: and also the product innovation we've delivered. Just in the 746 00:38:38,600 --> 00:38:41,200 Speaker 11: last few weeks, we've announced Octa for the Global two thousand, 747 00:38:41,239 --> 00:38:44,960 Speaker 11: which lets the largest organization in the world flexibly adjust 748 00:38:44,960 --> 00:38:48,200 Speaker 11: their business strategy on a solid identity foundation. Is they 749 00:38:48,200 --> 00:38:52,280 Speaker 11: adjust their strategy in these macro economic times. And another 750 00:38:52,320 --> 00:38:56,160 Speaker 11: one is Octa Device Access, which secures the actual log 751 00:38:56,200 --> 00:38:58,040 Speaker 11: in at the end computer all the way through to 752 00:38:58,400 --> 00:39:03,960 Speaker 11: the company's entire infrastructure. So the business is strong and 753 00:39:04,040 --> 00:39:07,440 Speaker 11: customers are having success, and we're excited to work with them. 754 00:39:08,239 --> 00:39:10,759 Speaker 2: Tod, I think I heard you say AI on the 755 00:39:10,800 --> 00:39:13,640 Speaker 2: call eight times and chat GPT four times. 756 00:39:14,080 --> 00:39:15,920 Speaker 3: What's the AI story for Octa? 757 00:39:16,960 --> 00:39:19,520 Speaker 11: Well, first of all, in the big picture, Ed, I 758 00:39:19,560 --> 00:39:23,200 Speaker 11: mean our industry in the world hypes a lot of things. 759 00:39:23,600 --> 00:39:27,560 Speaker 11: I think AI may be under hyped, and that's the potential. 760 00:39:27,560 --> 00:39:30,239 Speaker 11: I mean, you have all the right ingredients of a 761 00:39:30,320 --> 00:39:34,600 Speaker 11: true technology revolution. You have a breakthrough hardware with what's 762 00:39:34,640 --> 00:39:38,960 Speaker 11: happened with GPUs, you have computer science breakthroughs, which with 763 00:39:39,160 --> 00:39:41,680 Speaker 11: what happened a few years ago with the Transformers paper 764 00:39:41,719 --> 00:39:45,120 Speaker 11: and that pouring into large language models, and you have 765 00:39:45,160 --> 00:39:49,080 Speaker 11: a killer app. You had Chat GPT come out and 766 00:39:49,239 --> 00:39:52,120 Speaker 11: just take over the world. And that's the Netscape moment 767 00:39:52,120 --> 00:39:54,799 Speaker 11: of this AI revolution. And talk about the economy, I 768 00:39:54,840 --> 00:39:57,360 Speaker 11: think what really could get the economy in terms of 769 00:39:57,400 --> 00:40:01,720 Speaker 11: tech spending and tech investment really down is this focus 770 00:40:01,760 --> 00:40:05,280 Speaker 11: on AI. Every company I talked to is thinking about 771 00:40:05,280 --> 00:40:07,600 Speaker 11: how they can become an AI company. And it's just 772 00:40:07,719 --> 00:40:10,000 Speaker 11: similar like in the late nineties when every company had 773 00:40:10,000 --> 00:40:10,880 Speaker 11: an Internet strategy. 774 00:40:11,200 --> 00:40:11,759 Speaker 1: That's what we're on. 775 00:40:11,800 --> 00:40:14,719 Speaker 11: The precipice of and we benefit from that. Actor is 776 00:40:14,760 --> 00:40:18,080 Speaker 11: the login for chat gbut they use our customer Identity 777 00:40:18,080 --> 00:40:22,000 Speaker 11: cloud to connect their customers to Chatgbut and so it's 778 00:40:22,080 --> 00:40:26,319 Speaker 11: every whether it's open AI, whether it's scale AI or 779 00:40:26,560 --> 00:40:30,520 Speaker 11: recurrency SAI. We're the identity company for all these AI 780 00:40:30,600 --> 00:40:32,800 Speaker 11: startups and innovators. 781 00:40:32,560 --> 00:40:34,560 Speaker 2: And doctor shares off session high as was still up 782 00:40:34,560 --> 00:40:36,720 Speaker 2: twelve percent, on track for the best days since March 783 00:40:36,960 --> 00:40:39,360 Speaker 2: on an ch day basis. Tom mckinn and opt to CEO, 784 00:40:39,560 --> 00:40:41,960 Speaker 2: thank you so much. Sadly, that does it for this 785 00:40:42,120 --> 00:40:45,560 Speaker 2: edition of Bloomberg Technology. But don't forget recap on our podcast. 786 00:40:45,680 --> 00:40:47,920 Speaker 2: You can find it on the terminal as well as 787 00:40:47,920 --> 00:40:52,160 Speaker 2: online Apple, Spotify, an iHeart from here in San Francisco. 788 00:40:52,360 --> 00:40:54,080 Speaker 3: This is Bloomberg Technology.