1 00:00:01,440 --> 00:00:05,120 Speaker 1: From the heart where Innovation, money and power Collie in 2 00:00:05,240 --> 00:00:10,160 Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hide 3 00:00:10,200 --> 00:00:23,240 Speaker 1: and Ed Ludlow. 4 00:00:24,880 --> 00:00:27,000 Speaker 2: I'm Caroline Hyde at Bloomberg's Wheldo quarters in. 5 00:00:27,000 --> 00:00:29,639 Speaker 3: New York, and I'm Ed Ludlow out here in San Francisco. 6 00:00:29,760 --> 00:00:31,120 Speaker 3: This is Bloomberg Technology. 7 00:00:31,200 --> 00:00:31,600 Speaker 4: Coming up. 8 00:00:31,640 --> 00:00:34,360 Speaker 2: We'll break down the battle between Twitter and Threads. Is 9 00:00:34,400 --> 00:00:38,280 Speaker 2: the new platform hits seventeen million users like Elon Musk's 10 00:00:38,280 --> 00:00:39,919 Speaker 2: company threatens legal action. 11 00:00:39,920 --> 00:00:44,319 Speaker 3: Against Meta Plus, Ali Barber surges as China finds Ang 12 00:00:44,400 --> 00:00:47,640 Speaker 3: Group and ten Cent, signaling the end of a regulatory 13 00:00:47,640 --> 00:00:49,919 Speaker 3: crackdown could be near. We'll have more details. 14 00:00:49,560 --> 00:00:52,600 Speaker 2: Ahead, and we'll get inside into Apple's vision pro plans 15 00:00:52,840 --> 00:00:55,040 Speaker 2: as it read is a slow rollout of the three 16 00:00:55,040 --> 00:00:57,920 Speaker 2: and a half thousand dollars headset. All that so much 17 00:00:57,920 --> 00:00:59,640 Speaker 2: more to calm. Let's stick with threads and pieces, say 18 00:00:59,640 --> 00:01:02,560 Speaker 2: Bloomg opinion columnists Davely has been writing all about it. 19 00:01:02,560 --> 00:01:04,360 Speaker 2: It talks about how it was like run into a 20 00:01:04,400 --> 00:01:08,479 Speaker 2: destination wedding with familiar faces but a pretty unfamiliar setting. Eventually, 21 00:01:08,520 --> 00:01:11,560 Speaker 2: someone might be brave enough to quietly mutter, So do 22 00:01:11,560 --> 00:01:12,440 Speaker 2: you think it'll last. 23 00:01:12,680 --> 00:01:13,120 Speaker 4: That's Ring M. 24 00:01:13,120 --> 00:01:15,800 Speaker 2: Blomberg, opinion tech columnist Daily. With all the analogies, it 25 00:01:15,800 --> 00:01:18,760 Speaker 2: was a beautiful analogy of a destination wedding. Before I 26 00:01:18,800 --> 00:01:20,679 Speaker 2: get into the wedding gifts that we're giving them of 27 00:01:20,720 --> 00:01:22,480 Speaker 2: our privacy data and the money that they might be 28 00:01:22,480 --> 00:01:24,280 Speaker 2: able to reap off the back of it, I want 29 00:01:24,280 --> 00:01:26,840 Speaker 2: to ask just how this day too, of the destination 30 00:01:26,920 --> 00:01:28,760 Speaker 2: wedding is going. Is the party still there is at 31 00:01:28,760 --> 00:01:30,319 Speaker 2: hot people remaining dedicated? 32 00:01:30,440 --> 00:01:31,199 Speaker 4: Well, I think day. 33 00:01:31,080 --> 00:01:33,560 Speaker 5: Two may be the wedding itself. Right, everyone's here, The 34 00:01:33,600 --> 00:01:36,400 Speaker 5: people who perhaps slow from other places have arrived. 35 00:01:37,800 --> 00:01:38,080 Speaker 4: Soon. 36 00:01:38,200 --> 00:01:40,280 Speaker 5: I guess we're going to start having questions about what 37 00:01:40,360 --> 00:01:43,399 Speaker 5: exactly the staying power of this network is. And I 38 00:01:43,400 --> 00:01:46,240 Speaker 5: think already you're starting to see people sort of pick 39 00:01:46,280 --> 00:01:49,880 Speaker 5: apart what the future of Threads might be depending on 40 00:01:50,040 --> 00:01:52,120 Speaker 5: what they're going to be allowed to do on the platform, 41 00:01:52,120 --> 00:01:54,440 Speaker 5: allowed to say on the platform. There's been some unease 42 00:01:54,480 --> 00:01:57,200 Speaker 5: about how tightly linked it is to Instagram, for example, 43 00:01:57,480 --> 00:01:59,600 Speaker 5: and also people have been saying, well, where are all 44 00:01:59,600 --> 00:02:02,360 Speaker 5: the feat If you go on Twitter, you can do spaces, 45 00:02:02,400 --> 00:02:04,760 Speaker 5: you can do direct messages, you can do many other 46 00:02:04,800 --> 00:02:08,600 Speaker 5: things that right now threads can't do. Adam Musseeri, the 47 00:02:08,600 --> 00:02:11,679 Speaker 5: CEO of Instagram, he's there saying, don't worry, we're working 48 00:02:11,760 --> 00:02:11,920 Speaker 5: on it. 49 00:02:11,960 --> 00:02:13,280 Speaker 4: We're going to get to that. 50 00:02:14,040 --> 00:02:15,919 Speaker 5: You just have to be patient, because what, of course 51 00:02:15,960 --> 00:02:19,160 Speaker 5: they did was because Elon Musk was having such trouble 52 00:02:19,200 --> 00:02:22,480 Speaker 5: with Twitter. They moved it forward so they could capitalize 53 00:02:22,480 --> 00:02:25,000 Speaker 5: on that trouble. And so it's not perhaps quite as 54 00:02:25,040 --> 00:02:27,080 Speaker 5: ready as they might have wanted it to be at 55 00:02:27,080 --> 00:02:28,840 Speaker 5: the point that they brought it onto the market. But 56 00:02:28,880 --> 00:02:33,760 Speaker 5: that initial reaction seventy million people, even though it's Facebook 57 00:02:33,760 --> 00:02:36,160 Speaker 5: and they have this big head start with Instagram obviously, 58 00:02:36,720 --> 00:02:39,760 Speaker 5: getting seventy million people to download a new app in 59 00:02:39,800 --> 00:02:41,440 Speaker 5: the space of a couple of days, I mean, it's 60 00:02:41,440 --> 00:02:43,320 Speaker 5: a remarkable feat of onboarding. 61 00:02:43,360 --> 00:02:43,600 Speaker 4: Really. 62 00:02:43,960 --> 00:02:47,280 Speaker 2: Now you're mentioning your musk in a wedding, there's always 63 00:02:47,360 --> 00:02:51,120 Speaker 2: that moment where you can say, does anyone here perhaps speak 64 00:02:51,160 --> 00:02:54,200 Speaker 2: against this nutal? And I feel like Elil Musk's that 65 00:02:54,240 --> 00:02:56,760 Speaker 2: guy who's just walked into the church and saying, I 66 00:02:56,919 --> 00:02:58,400 Speaker 2: protest you cannot get married. 67 00:02:58,639 --> 00:02:59,360 Speaker 4: What is going on? 68 00:02:59,440 --> 00:03:02,920 Speaker 2: What is and what is the realism of this legal letter? 69 00:03:03,000 --> 00:03:06,200 Speaker 2: Because he sends a few around as the mistership prop. 70 00:03:06,160 --> 00:03:08,840 Speaker 5: He does, he does, and the particular lawyer that sent 71 00:03:08,919 --> 00:03:12,080 Speaker 5: the letter has been Elon Musk's right hand man in 72 00:03:12,120 --> 00:03:14,320 Speaker 5: all of his legal battles, and you know, I've been 73 00:03:14,320 --> 00:03:17,000 Speaker 5: in the court room for many of them. I mean, look, 74 00:03:17,080 --> 00:03:19,280 Speaker 5: until we see the substance of any complaint, I think 75 00:03:19,280 --> 00:03:20,240 Speaker 5: it's really hard to note, right. 76 00:03:20,240 --> 00:03:21,280 Speaker 4: I mean, because the. 77 00:03:21,360 --> 00:03:24,520 Speaker 5: Letter made some claims that there was people at Meta 78 00:03:24,639 --> 00:03:28,519 Speaker 5: that had been taken from Twitter purposely to build threads, even. 79 00:03:28,400 --> 00:03:30,959 Speaker 2: Though Twitter have lost so many employing white. 80 00:03:31,040 --> 00:03:34,680 Speaker 5: I mean, they let them go themselves and perhaps they've 81 00:03:34,680 --> 00:03:37,760 Speaker 5: gone on to new jobs at Meta. They've said no 82 00:03:38,040 --> 00:03:40,920 Speaker 5: form of Twitter employees were involved in the creation of threads, 83 00:03:41,080 --> 00:03:43,320 Speaker 5: and so I think it's really up to Elon Musk 84 00:03:43,360 --> 00:03:47,240 Speaker 5: and his lawyer to bring that evidence and say here's 85 00:03:47,280 --> 00:03:49,880 Speaker 5: why we think we have a complaint. There's a big 86 00:03:49,960 --> 00:03:53,000 Speaker 5: jump between threatening to take legal action and actually taking 87 00:03:53,120 --> 00:03:55,360 Speaker 5: legal action, and so I think until we see that moment, 88 00:03:55,680 --> 00:03:59,160 Speaker 5: that's when we really know how serious Elon Musk feels 89 00:03:59,160 --> 00:04:02,040 Speaker 5: about being copied by Meta. 90 00:04:04,040 --> 00:04:06,960 Speaker 3: Just a quick point, Karen Dave, I read that letter 91 00:04:07,760 --> 00:04:11,120 Speaker 3: that was sent on must behalf to Zuckerberg and Meta's 92 00:04:11,200 --> 00:04:11,960 Speaker 3: legal counsel. 93 00:04:12,360 --> 00:04:13,080 Speaker 4: So what's funny. 94 00:04:13,760 --> 00:04:16,960 Speaker 3: Linda Yakarino, the CEO of Twitter, is not one of 95 00:04:16,960 --> 00:04:19,839 Speaker 3: the co signatories, and she's not in copy either. It 96 00:04:19,920 --> 00:04:24,120 Speaker 3: is sent on Elon Musks behalf to Meta Dave. 97 00:04:24,200 --> 00:04:24,800 Speaker 4: What do you make of that? 98 00:04:25,400 --> 00:04:28,200 Speaker 5: Well, I think this is another question, isn't it of 99 00:04:28,240 --> 00:04:31,200 Speaker 5: the degree to which Linda Yakarino is in charge of 100 00:04:31,240 --> 00:04:34,280 Speaker 5: Twitter at all? I mean, when Twitter was having its 101 00:04:34,279 --> 00:04:37,800 Speaker 5: issues last weekend with the big rape limiting problems where 102 00:04:37,839 --> 00:04:40,240 Speaker 5: you could only see a certain number of tweets, the 103 00:04:40,279 --> 00:04:43,960 Speaker 5: CEO of the company didn't say anything for several days, 104 00:04:43,960 --> 00:04:46,240 Speaker 5: and her interactions were only to like the tweets that 105 00:04:46,240 --> 00:04:49,040 Speaker 5: Elon Musk had been posting. And even when she was 106 00:04:49,360 --> 00:04:52,520 Speaker 5: first put into that job, I wrote at the time, 107 00:04:52,560 --> 00:04:55,040 Speaker 5: and many others did, that there's only ever really going 108 00:04:55,080 --> 00:04:56,920 Speaker 5: to be one person in charge of Twitter, and that's 109 00:04:56,920 --> 00:04:59,400 Speaker 5: Elon Musk. And we've just seen that yet again with 110 00:04:59,560 --> 00:05:02,640 Speaker 5: like you say this letter. One of the things I 111 00:05:02,640 --> 00:05:06,400 Speaker 5: think is striking about the lawyer in particular is that 112 00:05:06,440 --> 00:05:08,800 Speaker 5: it wasn't that long ago that he was describing his 113 00:05:08,920 --> 00:05:10,719 Speaker 5: role as no longer being at Twitter, and that he 114 00:05:10,839 --> 00:05:14,839 Speaker 5: was still Elon Musk's personal lawyer, but his business with 115 00:05:14,920 --> 00:05:17,680 Speaker 5: Twitter had taken a backseat and the company was dealing 116 00:05:17,720 --> 00:05:18,279 Speaker 5: with itself. 117 00:05:18,320 --> 00:05:21,279 Speaker 4: And so this may be an instance. 118 00:05:20,960 --> 00:05:22,719 Speaker 5: And we've seen it from Elon Musk in the past, 119 00:05:23,000 --> 00:05:25,880 Speaker 5: this maybe an instance where he's just incensed personally and 120 00:05:26,360 --> 00:05:28,839 Speaker 5: wants to go after this. But again, until we see 121 00:05:28,880 --> 00:05:31,520 Speaker 5: exactly what he thinks his case is, it's very hard 122 00:05:31,920 --> 00:05:32,440 Speaker 5: to address. 123 00:05:32,520 --> 00:05:34,120 Speaker 4: You know what the merits are of that. 124 00:05:35,320 --> 00:05:38,279 Speaker 3: Let's get back to the technology and the platform, right 125 00:05:39,400 --> 00:05:42,719 Speaker 3: You point out in the Bloomberg opinion column that the 126 00:05:42,800 --> 00:05:46,080 Speaker 3: onboarding is a big part of this. You can just 127 00:05:46,240 --> 00:05:50,279 Speaker 3: simply take your Instagram following convert it over to threads. 128 00:05:50,320 --> 00:05:52,400 Speaker 3: And you know, Caroline's heard me say this a few 129 00:05:52,440 --> 00:05:54,479 Speaker 3: times in the last few days. I went back and 130 00:05:54,520 --> 00:05:57,279 Speaker 3: looked at Twitter's s one. It took them a year 131 00:05:57,520 --> 00:06:01,200 Speaker 3: to get from thirty million users to seventy million between 132 00:06:01,200 --> 00:06:03,800 Speaker 3: March or twenty ten and March twenty eleven. Met has 133 00:06:03,800 --> 00:06:05,240 Speaker 3: done it in forty eight hours. 134 00:06:06,920 --> 00:06:11,039 Speaker 5: I mean, it's clearly a huge competitive advantage to have 135 00:06:11,240 --> 00:06:14,919 Speaker 5: Instagram in your back pocket to launch a new social network. 136 00:06:15,080 --> 00:06:17,279 Speaker 5: I think one of the interesting comparisons that I've seen 137 00:06:17,320 --> 00:06:21,640 Speaker 5: recently is Facebook Meta has only managed to get two 138 00:06:21,760 --> 00:06:24,760 Speaker 5: hundred thousand people into the metaverse, right, so this has 139 00:06:24,800 --> 00:06:28,400 Speaker 5: already been vastly more successful than what their big strategy 140 00:06:29,040 --> 00:06:33,440 Speaker 5: was meant to have been. I mean, look, it's being 141 00:06:33,440 --> 00:06:36,599 Speaker 5: able to bring those Instagram followers over is obviously a 142 00:06:36,600 --> 00:06:37,360 Speaker 5: great benefit. 143 00:06:37,600 --> 00:06:39,240 Speaker 4: What I do think would be an issue. 144 00:06:38,920 --> 00:06:40,840 Speaker 5: And something that I've heard people sort of talking about, 145 00:06:40,880 --> 00:06:42,680 Speaker 5: is you know, what does that say about the nature 146 00:06:42,720 --> 00:06:45,120 Speaker 5: of what Threads is going to be Because Twitter was 147 00:06:45,120 --> 00:06:49,440 Speaker 5: a very diverse grouping of people on the Internet, whereas 148 00:06:49,480 --> 00:06:53,880 Speaker 5: Instagram was particularly popular with influencers and creators and some 149 00:06:53,880 --> 00:06:56,599 Speaker 5: of the more sort of celebrity culture online. And I 150 00:06:56,600 --> 00:07:00,480 Speaker 5: think one of the risks might be that while Threads 151 00:07:00,520 --> 00:07:03,000 Speaker 5: may be a Twitter killer, so to speak, it may 152 00:07:03,000 --> 00:07:05,599 Speaker 5: not eventually be a Twitter replacement. You know, there could 153 00:07:05,640 --> 00:07:07,719 Speaker 5: be a shift of people going from one platform to 154 00:07:07,800 --> 00:07:10,840 Speaker 5: the other, but we might still lose what made Twitter 155 00:07:11,120 --> 00:07:13,280 Speaker 5: a worthwhile place to be on the Internet, which is why, 156 00:07:13,400 --> 00:07:15,480 Speaker 5: you know, when you look at some of these new alternatives, 157 00:07:15,520 --> 00:07:18,440 Speaker 5: the smaller alternatives like blue Sky and T two and 158 00:07:18,560 --> 00:07:20,640 Speaker 5: spill and we don't have time to say them all, 159 00:07:21,120 --> 00:07:24,760 Speaker 5: I suspect they might be feeling quite downheartened by the 160 00:07:24,800 --> 00:07:26,960 Speaker 5: fact that Meta have done this, But actually I think 161 00:07:27,000 --> 00:07:29,400 Speaker 5: in time there will still be space for these, for 162 00:07:29,440 --> 00:07:30,520 Speaker 5: these smaller alternatives. 163 00:07:30,520 --> 00:07:32,880 Speaker 2: And to that point you mentioned their Marsterden. There's also 164 00:07:33,040 --> 00:07:37,800 Speaker 2: Blue Sky. These are decentralized. And what's been interesting is 165 00:07:37,800 --> 00:07:40,040 Speaker 2: this talk of a fediverse and the fact that eventually 166 00:07:40,440 --> 00:07:43,880 Speaker 2: Threads could be built upon, you know, one of these 167 00:07:44,760 --> 00:07:48,160 Speaker 2: overall protocols where you could take your people, your followers 168 00:07:48,200 --> 00:07:50,520 Speaker 2: with you and share them amongst all these sorts of 169 00:07:50,560 --> 00:07:53,920 Speaker 2: social media platforms. When do you think that might happen? 170 00:07:54,000 --> 00:07:56,360 Speaker 2: Because we got teased it and then it didn't go live, 171 00:07:56,440 --> 00:07:58,120 Speaker 2: of course, because they sort of pushed forward the outlet. 172 00:07:58,200 --> 00:08:01,760 Speaker 5: Yeah, I mean, it's certainly something they've committed to. I'd 173 00:08:01,840 --> 00:08:04,320 Speaker 5: like to see it quite soon. I think that would 174 00:08:04,360 --> 00:08:07,840 Speaker 5: be one thing that could convince me that Meta is 175 00:08:08,000 --> 00:08:10,160 Speaker 5: serious about being more open with the Internet. 176 00:08:10,400 --> 00:08:11,640 Speaker 4: This is them. 177 00:08:12,560 --> 00:08:15,120 Speaker 5: I think it depends what they see the value of 178 00:08:15,160 --> 00:08:17,720 Speaker 5: threads being right, if Threads is a way to bring 179 00:08:17,760 --> 00:08:21,760 Speaker 5: people into the Facebook family of apps as they call them, 180 00:08:22,680 --> 00:08:25,480 Speaker 5: with the view of having more engagement on Instagram, more 181 00:08:25,480 --> 00:08:27,640 Speaker 5: engagement on the Facebook Blue app, the main app, which 182 00:08:27,680 --> 00:08:30,720 Speaker 5: is really not the place where things happen anymore, then 183 00:08:30,760 --> 00:08:32,000 Speaker 5: it could potentially be useful. 184 00:08:32,000 --> 00:08:35,120 Speaker 4: But yes, you're right, they could lose the ability. 185 00:08:34,320 --> 00:08:37,720 Speaker 5: To own this entire community, put their ads in there, 186 00:08:37,920 --> 00:08:40,440 Speaker 5: monetize it as much as possible. And that's where I 187 00:08:40,440 --> 00:08:42,640 Speaker 5: think the most difficult thing for Threads is going to 188 00:08:42,640 --> 00:08:46,520 Speaker 5: be almost fighting against the instincts of Meta the company. 189 00:08:46,720 --> 00:08:49,000 Speaker 5: They want to have the control, they want to eventually 190 00:08:49,040 --> 00:08:51,880 Speaker 5: monetize it. You'd think, if history is to go by, 191 00:08:51,960 --> 00:08:56,080 Speaker 5: monetize it within an inch of its life, basically, and 192 00:08:56,200 --> 00:08:58,720 Speaker 5: yet that might be the death knel for it. So 193 00:08:59,080 --> 00:09:00,920 Speaker 5: the degree to which it will be open with these 194 00:09:00,960 --> 00:09:04,520 Speaker 5: other with the rest of the fediverse, that feels like 195 00:09:04,559 --> 00:09:08,240 Speaker 5: a very interesting experiment. Whether Metro is going to be 196 00:09:08,520 --> 00:09:11,400 Speaker 5: corporately comfortable with that, I think that's a big test. 197 00:09:12,679 --> 00:09:15,280 Speaker 3: What was it that Jack Dorsey said, Caro, we wanted 198 00:09:15,320 --> 00:09:19,800 Speaker 3: flying cars and instead we got seven Twitter clones debate 199 00:09:19,880 --> 00:09:22,719 Speaker 3: for another program, I think Dave Lee Bloomberg Opinion check 200 00:09:22,760 --> 00:09:25,600 Speaker 3: out his latest opinion column on bloombog dot com. Thank 201 00:09:25,600 --> 00:09:28,480 Speaker 3: you very much, all right, the show continues coming up. 202 00:09:28,520 --> 00:09:31,760 Speaker 3: Today's June jobs report showed relatively strong gains in the 203 00:09:31,840 --> 00:09:34,400 Speaker 3: labor market We're going to talk to Kara Brennan, Chief 204 00:09:34,440 --> 00:09:37,960 Speaker 3: People OFFICO officer over at Lattice about the trends that 205 00:09:38,080 --> 00:09:42,240 Speaker 3: she's seeing in the technology sector. This is Bloomberg Technology. 206 00:09:52,040 --> 00:09:55,400 Speaker 2: Looks some pretty mixed macro signals today from the US 207 00:09:55,559 --> 00:09:56,080 Speaker 2: labor market. 208 00:09:56,120 --> 00:09:56,720 Speaker 4: Let's go through them. 209 00:09:56,720 --> 00:10:00,880 Speaker 2: Because jobs growth melling out a little bit, they're still 210 00:10:00,920 --> 00:10:03,600 Speaker 2: increasing at a faster than expected pace. In the last month. 211 00:10:04,080 --> 00:10:06,920 Speaker 2: US added two hundred and nine thousand non farm jobs 212 00:10:06,960 --> 00:10:09,440 Speaker 2: in June, making it the smallest advance it's the end 213 00:10:09,440 --> 00:10:12,320 Speaker 2: of twenty twenty, but those wage gains still show the 214 00:10:12,320 --> 00:10:15,400 Speaker 2: inflationary pressure of this pretty resilient labor market, and of 215 00:10:15,400 --> 00:10:17,800 Speaker 2: course unemployment rate coming in at three point six percent. 216 00:10:18,040 --> 00:10:19,920 Speaker 2: We're lucky enough to be able to discuss all of this. 217 00:10:20,480 --> 00:10:24,120 Speaker 2: The Cara Brennan, San Francisco based Chief People officer over 218 00:10:24,160 --> 00:10:27,160 Speaker 2: atlettis great towsent time with your Caaren. What did you make? 219 00:10:27,200 --> 00:10:29,840 Speaker 2: Are you glass half empty? Glass off full? We somewhere 220 00:10:29,840 --> 00:10:30,240 Speaker 2: in the middle. 221 00:10:31,480 --> 00:10:33,720 Speaker 6: I'd say I'm probably somewhere in the middle. What I 222 00:10:33,720 --> 00:10:38,400 Speaker 6: can say is that among our five thousand customers who 223 00:10:38,480 --> 00:10:40,920 Speaker 6: are chief people officers, heads of people, there's a lot 224 00:10:40,960 --> 00:10:43,640 Speaker 6: of discussion about what the second half of this year 225 00:10:43,679 --> 00:10:46,720 Speaker 6: looks like in preparation for next year. I do think 226 00:10:46,840 --> 00:10:51,000 Speaker 6: that the medium term, people are optimistic from sitting in 227 00:10:51,040 --> 00:10:54,720 Speaker 6: my seat, because we're seeing really great talent in the market, 228 00:10:55,640 --> 00:11:01,280 Speaker 6: and we're seeing some softening in some of our businesses 229 00:11:02,200 --> 00:11:06,000 Speaker 6: as it relates to software sales and others. Is softening 230 00:11:06,120 --> 00:11:09,719 Speaker 6: of the of the really tough times that we've been 231 00:11:09,760 --> 00:11:12,600 Speaker 6: through over the past six to twelve months. So we're 232 00:11:12,640 --> 00:11:16,000 Speaker 6: seeing those glimmers, but I can tell you that right 233 00:11:16,040 --> 00:11:19,199 Speaker 6: now we're still in a place where we're all very conservative. 234 00:11:20,160 --> 00:11:25,199 Speaker 6: Headcount is really being thoughtful in terms of who we're 235 00:11:25,200 --> 00:11:27,560 Speaker 6: going to hire, where we're going to hire, and we're 236 00:11:27,640 --> 00:11:31,720 Speaker 6: keeping a very close eye on the compensation data because 237 00:11:31,720 --> 00:11:35,160 Speaker 6: what we're seeing is reflecting what these broader trends are 238 00:11:35,200 --> 00:11:36,760 Speaker 6: as well well. 239 00:11:36,800 --> 00:11:41,160 Speaker 3: In the technology sector. What do wages look like? How 240 00:11:41,160 --> 00:11:46,040 Speaker 3: competitive is it from it but using compensation as a proxy. 241 00:11:46,679 --> 00:11:51,080 Speaker 6: Yeah, what's interesting is our data legs behind the real market. 242 00:11:51,160 --> 00:11:54,160 Speaker 6: We know it does by about six months. So we're 243 00:11:54,200 --> 00:11:57,040 Speaker 6: still seeing data that was from the end of last 244 00:11:57,120 --> 00:12:00,200 Speaker 6: year that's still very high. And the hope is is 245 00:12:00,200 --> 00:12:04,000 Speaker 6: that we will see some of those rates come down 246 00:12:04,559 --> 00:12:08,160 Speaker 6: ultimately so that we can have better balance in terms 247 00:12:08,200 --> 00:12:11,280 Speaker 6: of our salary spin, which, as everyone knows, is around 248 00:12:11,679 --> 00:12:16,760 Speaker 6: a seventy percent of our total spin as businesses. We 249 00:12:16,800 --> 00:12:19,000 Speaker 6: are starting to see that. We're starting to see that 250 00:12:19,160 --> 00:12:24,240 Speaker 6: in some of those really spiky roles are top engineering roles, 251 00:12:25,200 --> 00:12:28,400 Speaker 6: some of our roles on the sales side. We're seeing 252 00:12:28,440 --> 00:12:31,640 Speaker 6: some of that come down, But it's not across the board. 253 00:12:32,480 --> 00:12:36,719 Speaker 2: I mean, AI, I can imagine prompt engineers, you name 254 00:12:36,760 --> 00:12:39,440 Speaker 2: it are going to be still pretty demanded for and 255 00:12:39,600 --> 00:12:43,560 Speaker 2: recompensed in light of that car up. Do you feel though, 256 00:12:43,600 --> 00:12:46,400 Speaker 2: the bracing that we had in the tech sector of 257 00:12:46,520 --> 00:12:50,640 Speaker 2: yet more job losses, yet larger ten twenty percent cuts, 258 00:12:50,800 --> 00:12:52,920 Speaker 2: is that a thing of the past Now people right 259 00:12:53,000 --> 00:12:54,400 Speaker 2: size to a certain degree. 260 00:12:56,320 --> 00:12:56,880 Speaker 4: In general. 261 00:12:57,000 --> 00:13:01,680 Speaker 6: Yes, that's what I'm hearing from our customers, the folks 262 00:13:01,679 --> 00:13:04,640 Speaker 6: that are now focused on the people that they have 263 00:13:04,800 --> 00:13:07,880 Speaker 6: in seat, knowing that the growth is slowed for the 264 00:13:07,920 --> 00:13:10,240 Speaker 6: second half of this year. But I do feel like 265 00:13:10,280 --> 00:13:12,480 Speaker 6: people feel like their hands are on the wheel now 266 00:13:12,960 --> 00:13:15,520 Speaker 6: and they're able to look forward to the next six 267 00:13:15,600 --> 00:13:18,680 Speaker 6: months with the plan that's realistic. It's very different than 268 00:13:18,760 --> 00:13:21,400 Speaker 6: this time last year when nobody knew what was happening, 269 00:13:21,400 --> 00:13:23,040 Speaker 6: and we were all trying to manage to it. 270 00:13:23,720 --> 00:13:26,240 Speaker 2: Well, said Ed. I mean when we look at the 271 00:13:26,280 --> 00:13:28,760 Speaker 2: broader labor data and on FUM payrolls, it's very hard 272 00:13:28,760 --> 00:13:31,760 Speaker 2: to really discern what's happening at a tech level, and 273 00:13:31,800 --> 00:13:35,480 Speaker 2: indeed how broad the tech issues are the asyncratic to 274 00:13:35,640 --> 00:13:38,079 Speaker 2: that particular sector or speak more broadly to for the 275 00:13:38,120 --> 00:13:38,960 Speaker 2: overall environment. 276 00:13:40,240 --> 00:13:42,480 Speaker 3: Yeah, I mean, let me jump in here Carol real quick, 277 00:13:42,520 --> 00:13:44,880 Speaker 3: because I think Caroline's raised a really good point that 278 00:13:45,000 --> 00:13:49,560 Speaker 3: what I hear from founders, public company executives and everyone 279 00:13:49,640 --> 00:13:53,200 Speaker 3: in between doesn't match the data. Think about the challenger 280 00:13:53,280 --> 00:13:55,719 Speaker 3: Christmas in Gray. We put so much emphasis on that 281 00:13:56,120 --> 00:13:59,640 Speaker 3: and all the layoffs that were announced, but those were announced, 282 00:13:59,640 --> 00:14:03,040 Speaker 3: not necessarily enacted. And there are loads of companies I 283 00:14:03,080 --> 00:14:06,480 Speaker 3: know that are scrambling to find people they're desperate to hire. 284 00:14:06,960 --> 00:14:09,160 Speaker 3: Why is there some inconsistency there? 285 00:14:10,440 --> 00:14:13,400 Speaker 6: Think consistency is that we had a period of time, 286 00:14:13,559 --> 00:14:16,280 Speaker 6: the two years during the pandemic where we were just 287 00:14:16,640 --> 00:14:20,400 Speaker 6: hiring to hire, honestly, and what we've done is gone 288 00:14:20,440 --> 00:14:24,880 Speaker 6: back to what the foundation of good businesses and knowing 289 00:14:24,920 --> 00:14:27,640 Speaker 6: that we have to build for the future state, and 290 00:14:27,680 --> 00:14:30,040 Speaker 6: we have to have the right people in the right seats. 291 00:14:30,280 --> 00:14:33,480 Speaker 6: So what has happened on the HR side is there 292 00:14:33,600 --> 00:14:37,000 Speaker 6: been lots of meetups, lots of webinars, lots of discussion 293 00:14:37,040 --> 00:14:40,400 Speaker 6: about org design and how to make sure that you're 294 00:14:40,440 --> 00:14:43,720 Speaker 6: optimizing the performance and productivity of the people who are 295 00:14:43,720 --> 00:14:47,080 Speaker 6: in the seat. We know that because that's what Lattis does. 296 00:14:47,120 --> 00:14:49,840 Speaker 6: We're a talent management platform. We've seen the usage of 297 00:14:49,880 --> 00:14:53,880 Speaker 6: our tool increase month over month and year every year 298 00:14:54,000 --> 00:14:56,720 Speaker 6: from our customers. People want to do a better job 299 00:14:56,720 --> 00:14:59,120 Speaker 6: of managing the talent they have. It's not a hiring 300 00:14:59,160 --> 00:14:59,920 Speaker 6: spree anymore. 301 00:15:01,800 --> 00:15:02,000 Speaker 4: Carr. 302 00:15:02,160 --> 00:15:05,960 Speaker 3: You know this sector. You know Silicon Valley. Elon Musk 303 00:15:06,000 --> 00:15:10,960 Speaker 3: and his lawyer have threatened to sue Meta claiming abusive 304 00:15:11,040 --> 00:15:16,800 Speaker 3: trade secrets and through the employment of Twitter employees. Can 305 00:15:16,840 --> 00:15:18,880 Speaker 3: you enforce an NDA in Silicon Valley? What do you 306 00:15:18,960 --> 00:15:19,800 Speaker 3: make of that situation? 307 00:15:21,320 --> 00:15:26,400 Speaker 6: I can't speak to Elon and Meta's particular situation. What 308 00:15:26,440 --> 00:15:30,240 Speaker 6: I can tell you is, in my experience, we're very 309 00:15:30,280 --> 00:15:34,560 Speaker 6: clear in Silicon Valley that the state of California does 310 00:15:34,600 --> 00:15:37,920 Speaker 6: not allow non competes to be enforced. For the most part, 311 00:15:38,360 --> 00:15:43,880 Speaker 6: depends on the specific situation. And I've seen lots of 312 00:15:43,920 --> 00:15:47,440 Speaker 6: CEOs and lots of nasty letters in the course of 313 00:15:47,480 --> 00:15:50,680 Speaker 6: my career. And know that sitting in the seat that 314 00:15:51,040 --> 00:15:54,160 Speaker 6: you move on, you bring your talent in and you're 315 00:15:54,200 --> 00:15:56,880 Speaker 6: not as concerned about non competees as you might be 316 00:15:56,920 --> 00:15:57,640 Speaker 6: in other states. 317 00:15:58,680 --> 00:16:01,320 Speaker 3: Car Brennan, Chief people of Sir laddis always on the 318 00:16:01,360 --> 00:16:04,120 Speaker 3: heartbeat of the tech sector. What's going on in jobs? 319 00:16:04,120 --> 00:16:14,520 Speaker 3: Thank you so much for your time time for talking tech. 320 00:16:14,600 --> 00:16:18,280 Speaker 3: First up, Samsung reported a forty six billion dollar decline 321 00:16:18,360 --> 00:16:21,280 Speaker 3: in sales, it's worst in quarterly revenue since two thousand 322 00:16:21,280 --> 00:16:25,400 Speaker 3: and nine. The tech company's operating profit plus ninety six percent, 323 00:16:25,680 --> 00:16:28,520 Speaker 3: but still ahead of market expectations. Is the demand for 324 00:16:28,560 --> 00:16:32,680 Speaker 3: electronics and memory chips has slowed, and US audit officials 325 00:16:32,680 --> 00:16:35,560 Speaker 3: have started a new round of inspections for New York 326 00:16:35,640 --> 00:16:38,840 Speaker 3: listed Chinese companies. The inspection comes there's a trade walk 327 00:16:38,960 --> 00:16:42,960 Speaker 3: on technology from AI to chip manufacturing continues to mount. 328 00:16:43,040 --> 00:16:47,000 Speaker 3: Tensions between the world's two largest economies. Plus China imposed 329 00:16:47,000 --> 00:16:49,480 Speaker 3: more than one billion dollars in fines on tech giants 330 00:16:49,520 --> 00:16:52,840 Speaker 3: and and ten cents, signaling an end to a crackdown 331 00:16:53,000 --> 00:16:55,520 Speaker 3: on a sector that wiped out billions in market value 332 00:16:55,760 --> 00:16:58,520 Speaker 3: and derailed the world's biggest IPO. 333 00:16:58,240 --> 00:17:00,600 Speaker 2: Carol, And let's dig in on that because some policy 334 00:17:00,680 --> 00:17:02,800 Speaker 2: saving mag News is Isabelle Lee is with us to 335 00:17:02,880 --> 00:17:05,840 Speaker 2: just break down why the shares have popped quite so 336 00:17:06,000 --> 00:17:08,440 Speaker 2: much for the likes of Ali Baba for ten cent, 337 00:17:09,080 --> 00:17:11,359 Speaker 2: it's a hefty fine. But is it just relief that 338 00:17:11,440 --> 00:17:12,960 Speaker 2: this over it? 339 00:17:13,040 --> 00:17:16,160 Speaker 7: Definitely is? It sounds kind of counterintuitive, But then many 340 00:17:16,200 --> 00:17:18,920 Speaker 7: of the analysts are saying that, okay, maybe this will 341 00:17:18,920 --> 00:17:20,880 Speaker 7: put a lid to the multi year crack that we've 342 00:17:20,920 --> 00:17:22,760 Speaker 7: seen in the tech sector that costs some of the 343 00:17:22,760 --> 00:17:25,840 Speaker 7: Wall Street's biggest firms to call China uninvestable, and we've 344 00:17:25,840 --> 00:17:28,840 Speaker 7: seen some of the biggest companies shed billions of dollars. 345 00:17:28,840 --> 00:17:30,800 Speaker 7: But then now if that's going to be over and 346 00:17:30,840 --> 00:17:33,480 Speaker 7: they can move on, and there are some speculations that, oh, 347 00:17:33,520 --> 00:17:36,560 Speaker 7: maybe this can pave the way for another renewed attempt 348 00:17:36,600 --> 00:17:39,480 Speaker 7: for ant to have an IPO. Possibly, but more people 349 00:17:39,520 --> 00:17:42,600 Speaker 7: are just relieved. Even the NaSTA China Golden Dragon indexes 350 00:17:42,680 --> 00:17:43,560 Speaker 7: up around three percent. 351 00:17:44,560 --> 00:17:46,000 Speaker 4: What is moving on look like? Then? 352 00:17:46,119 --> 00:17:48,040 Speaker 3: You know, we talk about the breakup of Ali Baba 353 00:17:48,080 --> 00:17:50,040 Speaker 3: as an example, and what might be spun off. 354 00:17:50,920 --> 00:17:54,240 Speaker 7: Yes, exactly. So we know that China has been really 355 00:17:54,280 --> 00:17:56,400 Speaker 7: cracking down on the tech firms and this may look 356 00:17:56,480 --> 00:17:59,440 Speaker 7: like the end of it. Like Ali Baba CEO Jack 357 00:17:59,520 --> 00:18:04,520 Speaker 7: Ma came in March, there's a renewed appetite to kind 358 00:18:04,520 --> 00:18:07,040 Speaker 7: of reinvigorate the tech sector and that's how many people 359 00:18:07,040 --> 00:18:09,960 Speaker 7: are seeing. But this thing is an ANTIPO isn't looking 360 00:18:10,040 --> 00:18:13,440 Speaker 7: like it's going to happen anytime soon because their bottom 361 00:18:13,440 --> 00:18:16,760 Speaker 7: line is really not looking good. Their December earnings quarter 362 00:18:17,080 --> 00:18:19,480 Speaker 7: was down fifty six percent. And if you go technically, 363 00:18:20,040 --> 00:18:21,879 Speaker 7: if you want a list in China so called a 364 00:18:22,000 --> 00:18:24,520 Speaker 7: share market, you shouldn't have had a change in corporate 365 00:18:24,600 --> 00:18:26,960 Speaker 7: leadership in the past three years two years for the 366 00:18:27,080 --> 00:18:29,600 Speaker 7: Shanghai Star market and one year for Hong Kong. But 367 00:18:29,640 --> 00:18:31,800 Speaker 7: still at least hopes are there and that's probably what's 368 00:18:31,880 --> 00:18:32,960 Speaker 7: buoying the market. 369 00:18:33,320 --> 00:18:35,920 Speaker 2: Just take us back a little bit as well as 370 00:18:35,920 --> 00:18:39,640 Speaker 2: to what's shifted in China's mindsot why have we sort 371 00:18:39,640 --> 00:18:41,800 Speaker 2: of come to the end of the scrutiny we have. 372 00:18:41,920 --> 00:18:45,480 Speaker 7: At least people are saying it's because when China locked 373 00:18:45,480 --> 00:18:49,040 Speaker 7: down during the pandemic it really wasn't good for their economy, 374 00:18:49,119 --> 00:18:52,199 Speaker 7: and when they were going to reopen, even strategists here 375 00:18:52,200 --> 00:18:53,840 Speaker 7: in Wall Street thought that there would be a boom 376 00:18:53,840 --> 00:18:56,159 Speaker 7: and that global growth will write on that, but that 377 00:18:56,320 --> 00:18:58,640 Speaker 7: so called boom didn't quite happen. So I think they're 378 00:18:58,680 --> 00:19:01,879 Speaker 7: just really trying to revive their sector, and tech is 379 00:19:01,880 --> 00:19:04,159 Speaker 7: the biggest so why not try to do that? And 380 00:19:04,520 --> 00:19:06,359 Speaker 7: if it will be good for China, it'll also be 381 00:19:06,400 --> 00:19:08,920 Speaker 7: good for the world, like even for here in the US. 382 00:19:08,960 --> 00:19:11,280 Speaker 7: So it's really just that shift in mentality and who 383 00:19:11,280 --> 00:19:12,720 Speaker 7: wants to be called uninvestable? 384 00:19:12,720 --> 00:19:14,480 Speaker 4: I think, so that's one. 385 00:19:14,680 --> 00:19:25,280 Speaker 3: All right, Bloombergs Isabelle Lee. Welcome back to Bloomberg Technology. 386 00:19:25,440 --> 00:19:27,000 Speaker 3: Ed Ludlow here in San Francisco, and. 387 00:19:27,000 --> 00:19:28,639 Speaker 2: I'm Caroline heid in New York. Let's just check in 388 00:19:28,680 --> 00:19:30,680 Speaker 2: on the new favorite platform for everyone to be getting 389 00:19:30,680 --> 00:19:32,240 Speaker 2: to know each other a slightly different destination. 390 00:19:32,280 --> 00:19:33,160 Speaker 4: We're all talking about. 391 00:19:33,000 --> 00:19:38,160 Speaker 3: Threads flat on a Friday talking Threads up almost four 392 00:19:38,200 --> 00:19:42,160 Speaker 3: percent over five sessions, excluding July the fourth. Let's talk 393 00:19:42,200 --> 00:19:45,080 Speaker 3: more on Threads and bring in Stephen Wolf Perera, Three 394 00:19:45,080 --> 00:19:48,480 Speaker 3: Parts Studios, Chief business officer. You are a multi platform 395 00:19:48,560 --> 00:19:52,320 Speaker 3: entertainment company. I know you you're close to Threads and 396 00:19:52,359 --> 00:19:56,639 Speaker 3: how it came about. Did what you saw on launch 397 00:19:56,680 --> 00:19:59,520 Speaker 3: with Threads match what Meta told you it was going 398 00:19:59,600 --> 00:19:59,800 Speaker 3: to be? 399 00:20:01,560 --> 00:20:02,080 Speaker 4: Absolutely? 400 00:20:02,119 --> 00:20:07,080 Speaker 8: I mean it's just been I think, exceeding everyone's expectations 401 00:20:07,240 --> 00:20:10,879 Speaker 8: for it to launch and for literally the onboarding to 402 00:20:11,000 --> 00:20:13,359 Speaker 8: be less than four seconds. Cause if you have an 403 00:20:13,359 --> 00:20:16,760 Speaker 8: Instagram account, you effectively just poured over all that information 404 00:20:16,920 --> 00:20:19,200 Speaker 8: data and now you're able to kind of bring that over. 405 00:20:20,160 --> 00:20:24,040 Speaker 8: It's just been wildly successful, I think, beyond everyone's expectations. 406 00:20:24,240 --> 00:20:26,480 Speaker 8: I read a stat today that was over fifty million 407 00:20:26,480 --> 00:20:30,080 Speaker 8: subscribers already on the platform. So when you have three 408 00:20:30,080 --> 00:20:33,000 Speaker 8: billion users across all of metas you know apps, you 409 00:20:33,000 --> 00:20:35,720 Speaker 8: know being used every single day, uh, three billion people 410 00:20:35,880 --> 00:20:38,120 Speaker 8: being able to kind of solve that cold star problem 411 00:20:38,359 --> 00:20:40,719 Speaker 8: of getting audience, you know, finding how to bring your 412 00:20:40,760 --> 00:20:42,320 Speaker 8: followers over onto a new platform. 413 00:20:42,480 --> 00:20:43,480 Speaker 4: I think they've really done well. 414 00:20:44,080 --> 00:20:47,479 Speaker 2: H You are someone who helps tell stories. How are 415 00:20:47,560 --> 00:20:50,840 Speaker 2: brands telling stories on this new platform? How playful? How 416 00:20:51,160 --> 00:20:53,480 Speaker 2: how different are we seeing things evolve over that? 417 00:20:54,720 --> 00:20:56,080 Speaker 4: I I think that's a great point, Caroline. 418 00:20:56,119 --> 00:20:58,840 Speaker 8: I mean to see some of the brands really understand 419 00:20:58,840 --> 00:21:02,320 Speaker 8: that this is a different medium and it's not images, right, 420 00:21:02,320 --> 00:21:05,159 Speaker 8: it's not video, it's text, and so being very witty 421 00:21:05,240 --> 00:21:07,800 Speaker 8: understanding how do you have your brand voice come through? 422 00:21:08,040 --> 00:21:09,439 Speaker 8: I think that something got a lot of brands are 423 00:21:09,480 --> 00:21:10,600 Speaker 8: excited about because right. 424 00:21:10,520 --> 00:21:12,679 Speaker 4: Now it's fun. It is positive. 425 00:21:13,320 --> 00:21:15,520 Speaker 8: You don't have all the trolls and all the cesspool 426 00:21:15,560 --> 00:21:18,200 Speaker 8: of so many different other platforms. So the fact that 427 00:21:18,280 --> 00:21:21,280 Speaker 8: you have brands like American Eagle, Wendy's, you know, just 428 00:21:21,320 --> 00:21:23,000 Speaker 8: you know, follow some of their threads and you know, 429 00:21:23,080 --> 00:21:25,840 Speaker 8: it's it's funny. And I feel like that's kind of 430 00:21:25,880 --> 00:21:28,840 Speaker 8: this gone by era of the early days of social 431 00:21:28,880 --> 00:21:31,119 Speaker 8: where people were really excited to kind of open up 432 00:21:31,200 --> 00:21:33,520 Speaker 8: the app and see what people were saying, what brands 433 00:21:33,520 --> 00:21:35,600 Speaker 8: were saying, and of course what creators are going to 434 00:21:35,640 --> 00:21:37,200 Speaker 8: be saying, including celebrities. 435 00:21:38,320 --> 00:21:40,840 Speaker 3: Stephen, just an update for you and our Bloomberg Technology 436 00:21:40,920 --> 00:21:43,480 Speaker 3: audience when you were racing over to our La studio, 437 00:21:44,119 --> 00:21:48,520 Speaker 3: Mark Zuckerberg updated, it's seventy seven zero million now on 438 00:21:48,640 --> 00:21:51,200 Speaker 3: board onto threads. So there you go. 439 00:21:52,720 --> 00:21:53,320 Speaker 4: But it's interesting. 440 00:21:53,680 --> 00:21:56,480 Speaker 8: I think he said something actually in the thread talking 441 00:21:56,480 --> 00:21:59,239 Speaker 8: about he wasn't really thinking about monetization until they hit 442 00:21:59,320 --> 00:22:02,560 Speaker 8: a billions describers and so obviously this is one of 443 00:22:02,640 --> 00:22:07,080 Speaker 8: the fastest app launches in history. Certainly very exciting to 444 00:22:07,080 --> 00:22:10,200 Speaker 8: see this trajectory. But let's see that billion dollar or 445 00:22:10,320 --> 00:22:12,960 Speaker 8: that billion subscriber threshold and see what's going to happen 446 00:22:12,960 --> 00:22:13,560 Speaker 8: when they hit that. 447 00:22:13,560 --> 00:22:16,399 Speaker 2: Soon that billion, I think it was like on clear 448 00:22:16,480 --> 00:22:19,760 Speaker 2: path to a billion. What do you think about the 449 00:22:19,840 --> 00:22:23,440 Speaker 2: monetization options here and how they do that while also 450 00:22:23,520 --> 00:22:28,560 Speaker 2: pretending well eventually to be within the federverse to be decentralized. 451 00:22:28,760 --> 00:22:31,639 Speaker 2: Is it that the advertising remains in the app and 452 00:22:31,680 --> 00:22:34,040 Speaker 2: then if it's moving across to different use cases and 453 00:22:34,119 --> 00:22:37,320 Speaker 2: masterden you wouldn't be advertised against like what wore the 454 00:22:37,320 --> 00:22:38,680 Speaker 2: discussions like and can. 455 00:22:39,320 --> 00:22:41,639 Speaker 8: Yeah, So I just got back from ken Lyons the 456 00:22:42,000 --> 00:22:46,280 Speaker 8: International Festival of Advertising Creativity Alvin Bowles, who's met as 457 00:22:46,400 --> 00:22:50,520 Speaker 8: new Global VP of their Global Business group. Alvin is 458 00:22:50,560 --> 00:22:52,960 Speaker 8: now kind of overseeing what brands are going to be 459 00:22:53,040 --> 00:22:55,760 Speaker 8: doing on their platform across all the different apps you know, 460 00:22:55,800 --> 00:22:58,560 Speaker 8: from obviously Instagram, WhatsApp, Facebook Blue. 461 00:22:59,200 --> 00:23:00,680 Speaker 4: But the reality is this was. 462 00:23:00,640 --> 00:23:03,199 Speaker 8: Something that was really being you know, I think on 463 00:23:03,280 --> 00:23:06,159 Speaker 8: everyone's lips trying to understand how are we going to 464 00:23:06,200 --> 00:23:08,800 Speaker 8: really integrate how we're going to use this and certainly 465 00:23:08,840 --> 00:23:12,680 Speaker 8: when can we actually buy ads across you know, the threads, 466 00:23:12,720 --> 00:23:15,880 Speaker 8: you know, kind of engagement and users and targeting advertising. 467 00:23:16,080 --> 00:23:17,440 Speaker 8: I think all that is going to be in a 468 00:23:17,480 --> 00:23:20,040 Speaker 8: wait and see because again, I think they want to 469 00:23:20,080 --> 00:23:23,320 Speaker 8: really build the engagement, build the audience. And when you 470 00:23:23,359 --> 00:23:25,919 Speaker 8: think about celebrities, you know, certainly I work with al 471 00:23:25,960 --> 00:23:27,960 Speaker 8: Hindu the best. He's the biggest Latino actor. 472 00:23:27,720 --> 00:23:28,240 Speaker 4: In the world. 473 00:23:28,520 --> 00:23:31,000 Speaker 8: You know, he is a comedian, so you know, he's 474 00:23:31,040 --> 00:23:33,440 Speaker 8: excited about getting onto threads to kind of talk about 475 00:23:33,480 --> 00:23:36,040 Speaker 8: ways to engage his audience in you know, certainly lots 476 00:23:36,040 --> 00:23:38,880 Speaker 8: of different languages. So when you see brands really trying 477 00:23:38,880 --> 00:23:40,960 Speaker 8: to figure out when can we engage, I think there's 478 00:23:40,960 --> 00:23:42,040 Speaker 8: going to be a little bit of a weight and 479 00:23:42,160 --> 00:23:44,720 Speaker 8: see from metap because they really want to make sure 480 00:23:44,760 --> 00:23:47,040 Speaker 8: that it's a safe place that they want the positivity 481 00:23:47,040 --> 00:23:47,400 Speaker 8: and the fun. 482 00:23:49,440 --> 00:23:51,480 Speaker 3: So how do you now think about Twitter and what 483 00:23:51,680 --> 00:23:55,119 Speaker 3: was the talk about Twitter can lyon? You know, is 484 00:23:55,160 --> 00:23:58,280 Speaker 3: this to kind of eat Twitter's lunch scenario to your mind? 485 00:23:59,720 --> 00:24:02,040 Speaker 8: So well, it's at a little bit of an inflection point. 486 00:24:02,080 --> 00:24:04,200 Speaker 8: I know, lots of people like to dunk on Twitter. 487 00:24:04,280 --> 00:24:07,760 Speaker 8: And again, Linda's been there for you know, eleven seconds, right, 488 00:24:07,800 --> 00:24:10,440 Speaker 8: so certainly she is going to uh have her work 489 00:24:10,520 --> 00:24:12,399 Speaker 8: out out for her. But the reality is, you know, 490 00:24:12,440 --> 00:24:15,920 Speaker 8: Twitter wasn't uh represented at camlines the way they used 491 00:24:15,920 --> 00:24:17,080 Speaker 8: to be. Uh, you know, there used to be a 492 00:24:17,119 --> 00:24:19,840 Speaker 8: big Twitter beach that was no longer there. They didn't 493 00:24:19,960 --> 00:24:23,520 Speaker 8: uh have that space. But obviously they're focused on a 494 00:24:23,560 --> 00:24:26,280 Speaker 8: really big vision and a big plan around X, and 495 00:24:26,359 --> 00:24:28,919 Speaker 8: so I would say don't count them out. They obviously 496 00:24:29,000 --> 00:24:31,280 Speaker 8: are trying to do something different. They want to take 497 00:24:31,280 --> 00:24:33,840 Speaker 8: it into a different direction. Elon obviously is thinking about 498 00:24:34,040 --> 00:24:36,000 Speaker 8: taking this into that super app of X. 499 00:24:36,840 --> 00:24:39,400 Speaker 4: So I feel that Linda is really. 500 00:24:39,119 --> 00:24:41,879 Speaker 8: Smart and obviously she is an active dialogue with so 501 00:24:41,880 --> 00:24:44,720 Speaker 8: many different brands and advertisers. And when you see you 502 00:24:44,720 --> 00:24:46,919 Speaker 8: know kind of what folks are doing, whether it's you know, 503 00:24:47,000 --> 00:24:49,280 Speaker 8: Marsa Thalberg over at Sea World, or you're looking at 504 00:24:49,400 --> 00:24:51,680 Speaker 8: Deacon Morgan Chase and you know Carla Hassen, or you know, 505 00:24:51,720 --> 00:24:53,959 Speaker 8: all these different companies that are trying to figure out 506 00:24:54,160 --> 00:24:55,919 Speaker 8: where am I going to engage, how am I going 507 00:24:56,000 --> 00:24:57,560 Speaker 8: to find audience, and how do I really get my 508 00:24:57,600 --> 00:24:59,560 Speaker 8: message out there? I feel like these are the different 509 00:24:59,600 --> 00:25:01,679 Speaker 8: ways brands are going to be looking to engage. 510 00:25:02,200 --> 00:25:06,240 Speaker 3: You know, Linda Yakarino, you have history in that relationship, 511 00:25:06,240 --> 00:25:09,159 Speaker 3: but will you put money to play it in the 512 00:25:09,200 --> 00:25:12,080 Speaker 3: Twitter platform right now? 513 00:25:12,600 --> 00:25:14,760 Speaker 8: As you know three Pass you know, certainly we will 514 00:25:14,800 --> 00:25:18,080 Speaker 8: because we actually have some films launching this fall. We 515 00:25:18,160 --> 00:25:20,800 Speaker 8: actually have a film that one Sundance called Radical that's 516 00:25:20,840 --> 00:25:23,840 Speaker 8: going to be coming out in October. So absolutely, I mean, 517 00:25:23,840 --> 00:25:25,919 Speaker 8: we want to be where the audience is. And you know, 518 00:25:25,960 --> 00:25:27,879 Speaker 8: to this day, you know, Twitter is still have a 519 00:25:27,960 --> 00:25:31,760 Speaker 8: very active, engaged audience base. Obviously there's concerns around you know, 520 00:25:31,840 --> 00:25:34,080 Speaker 8: kind of privacy or you know, the rhetoric or whatever 521 00:25:34,119 --> 00:25:36,240 Speaker 8: it is, but there's still a very active audience. And 522 00:25:36,320 --> 00:25:38,800 Speaker 8: you know, when you see Olheno with you know, one 523 00:25:38,800 --> 00:25:40,879 Speaker 8: of the largest you know, kind of social footprints, he 524 00:25:40,920 --> 00:25:43,800 Speaker 8: has over eighty million flowers across all social Twitter is 525 00:25:43,800 --> 00:25:46,000 Speaker 8: obviously a platform that we're still involved with, but we're 526 00:25:46,000 --> 00:25:47,560 Speaker 8: still going to be looking for other platforms. 527 00:25:48,840 --> 00:25:51,680 Speaker 3: Stephen Wolf Perera Three PUS Studios, thank you for joining 528 00:25:51,760 --> 00:25:53,879 Speaker 3: us out there in La c S. 529 00:25:54,040 --> 00:25:54,600 Speaker 4: Thanks so much. 530 00:25:55,440 --> 00:25:58,640 Speaker 3: Right, another top story here on Bloomberg Technology, Apple's headset 531 00:25:58,720 --> 00:26:02,960 Speaker 3: will be available in retail stores early next year, but 532 00:26:03,040 --> 00:26:05,680 Speaker 3: the rollout it's going to be a little slope. Mark 533 00:26:05,720 --> 00:26:09,119 Speaker 3: German as the details. Next, this is Bloom Big Technology. 534 00:26:18,800 --> 00:26:19,000 Speaker 7: Time. 535 00:26:19,080 --> 00:26:21,560 Speaker 2: Now for VC round up by d you. Once India's 536 00:26:21,600 --> 00:26:25,080 Speaker 2: most valuable upstart is internal after missing a deadline on 537 00:26:25,119 --> 00:26:27,440 Speaker 2: financial statements giving payments on a one point two billion 538 00:26:27,480 --> 00:26:30,200 Speaker 2: dollar alone and now that crisis is setting alarm bells 539 00:26:30,240 --> 00:26:34,479 Speaker 2: throughout India's VC world, prompted Bloom Ventures, for example, one 540 00:26:34,480 --> 00:26:37,159 Speaker 2: of India's biggest venture capital firms, to cut back on 541 00:26:37,240 --> 00:26:40,800 Speaker 2: what they call frivolous investments and to push portfolio companies 542 00:26:40,840 --> 00:26:45,840 Speaker 2: to increasingly shift focus to profitability. Meanwhile, while when Old 543 00:26:45,920 --> 00:26:48,720 Speaker 2: is New again, Albert Einstein might have died in ninety 544 00:26:48,760 --> 00:26:51,840 Speaker 2: fifty five, but the physicist is still a prolific conversationalist. 545 00:26:52,359 --> 00:26:55,199 Speaker 2: As a chatbot of course, character Ai, one of the 546 00:26:55,280 --> 00:26:58,120 Speaker 2: strangers startups on the Heyhi scene, but valued at one 547 00:26:58,119 --> 00:27:02,000 Speaker 2: billion dollars Einstein. Einstein has responded to one point six 548 00:27:02,119 --> 00:27:07,520 Speaker 2: million messages apparently, from theories of relativity to pat recommendation Zed. 549 00:27:08,760 --> 00:27:11,000 Speaker 3: That is the best story we've done on the Bloombog 550 00:27:11,080 --> 00:27:16,080 Speaker 3: technology ever. That's a big sta wow Yeah, no, it's 551 00:27:16,080 --> 00:27:20,320 Speaker 3: a big sayman, but Einstein's back baby. Apple's headset is 552 00:27:20,359 --> 00:27:23,359 Speaker 3: coming soon ish to store near you. 553 00:27:23,480 --> 00:27:24,520 Speaker 4: Bit of a hard pivot there. 554 00:27:24,560 --> 00:27:27,560 Speaker 3: The tech giant planning a retail launch of its Vision 555 00:27:27,560 --> 00:27:31,280 Speaker 3: Pro headset, but only in select US markets early next 556 00:27:31,359 --> 00:27:36,520 Speaker 3: year before expanding further. Bloomberg's Mark Gunman broke those details overnight. Mark, 557 00:27:36,600 --> 00:27:40,080 Speaker 3: there is a very clear strategy here according to your reporting. 558 00:27:41,160 --> 00:27:43,679 Speaker 9: Yeah, I wonder what Albert Einstein would think about virtual 559 00:27:43,720 --> 00:27:47,520 Speaker 9: reality and the Vision Pro. But in terms of this story, 560 00:27:47,560 --> 00:27:49,320 Speaker 9: so what we're hearing is that there's going to be 561 00:27:49,359 --> 00:27:51,520 Speaker 9: a bit of a slow goo ramp up to the 562 00:27:51,560 --> 00:27:54,320 Speaker 9: Apple Vision Pro launch. This is a thirty five hundred 563 00:27:54,320 --> 00:27:59,399 Speaker 9: dollars device. It requires an immense amount of customization, the logistics, 564 00:27:59,520 --> 00:28:01,160 Speaker 9: the way they're going to launch this thing a both 565 00:28:01,240 --> 00:28:04,119 Speaker 9: online and in stores. It's a very cumbersome process. So 566 00:28:04,160 --> 00:28:07,000 Speaker 9: here's how it's going to work. Early next year, so 567 00:28:07,040 --> 00:28:09,200 Speaker 9: early twenty twenty four in the US, you'll be able 568 00:28:09,200 --> 00:28:11,840 Speaker 9: to order the device either online or in an Apple 569 00:28:11,880 --> 00:28:14,679 Speaker 9: retail store. If you're buying it in a retail store, 570 00:28:14,800 --> 00:28:17,480 Speaker 9: if you're in one of those major markets La New York, 571 00:28:17,600 --> 00:28:22,320 Speaker 9: San Francisco, Texas. In those retail stores, those bigger stores, 572 00:28:22,359 --> 00:28:26,400 Speaker 9: you'll have a new virtual reality Vision Pro experience area 573 00:28:26,480 --> 00:28:29,639 Speaker 9: where you can buy the device, experience the device, have 574 00:28:29,760 --> 00:28:32,720 Speaker 9: it set up, and customize, get training on it. Right 575 00:28:32,800 --> 00:28:34,520 Speaker 9: If you're in a smaller market store, you'd be able 576 00:28:34,560 --> 00:28:36,119 Speaker 9: to go in there and buy it, maybe in a 577 00:28:36,160 --> 00:28:39,200 Speaker 9: small corner at a table somewhere, But over time it'll 578 00:28:39,280 --> 00:28:41,880 Speaker 9: ramp up that new experience center. But the important thing 579 00:28:41,880 --> 00:28:43,560 Speaker 9: to note is you'll be able to buy the vision 580 00:28:43,600 --> 00:28:46,960 Speaker 9: Pro in any US retail store from. 581 00:28:46,760 --> 00:28:47,200 Speaker 1: The get go. 582 00:28:47,720 --> 00:28:50,920 Speaker 9: Where things get complicated is if you have a vision prescription, 583 00:28:51,360 --> 00:28:55,120 Speaker 9: so you'll have to upload your vision prescription to a website, 584 00:28:55,160 --> 00:28:58,040 Speaker 9: either through Apple or through Zice because Apple doesn't want 585 00:28:58,080 --> 00:29:01,880 Speaker 9: you handing that medical information directly to an Apple retail employee. 586 00:29:01,960 --> 00:29:05,280 Speaker 9: You'll do that at the time of purchase, either online 587 00:29:05,720 --> 00:29:08,320 Speaker 9: or Another important factor is it's going to be sold 588 00:29:08,360 --> 00:29:11,000 Speaker 9: by appointment only in stores, so when you make that appointment, 589 00:29:11,200 --> 00:29:12,480 Speaker 9: they'll upload that information. 590 00:29:13,320 --> 00:29:17,000 Speaker 2: Mark the slow rollout. How much of that is about 591 00:29:17,160 --> 00:29:21,080 Speaker 2: market education, about trying to navigate demand, and how much 592 00:29:21,160 --> 00:29:23,200 Speaker 2: is it the supply side that also holds it up. 593 00:29:24,040 --> 00:29:27,600 Speaker 9: I think it's a combination of all three, but definitely 594 00:29:27,680 --> 00:29:31,800 Speaker 9: I would lean mostly towards supply. Right they don't have 595 00:29:31,920 --> 00:29:34,320 Speaker 9: enough supply to do a global launch at this point. 596 00:29:34,480 --> 00:29:36,600 Speaker 9: They think they'll have enough supply by the tail end 597 00:29:36,640 --> 00:29:40,000 Speaker 9: of twenty twenty four or into twenty twenty five, and 598 00:29:40,080 --> 00:29:41,880 Speaker 9: so what they're going to do is they're going to 599 00:29:41,960 --> 00:29:45,320 Speaker 9: launch in Asia in early twenty twenty five. At the 600 00:29:45,400 --> 00:29:49,040 Speaker 9: end of twenty twenty four, they'll be launching in Canada. 601 00:29:48,480 --> 00:29:49,200 Speaker 4: And the UK. 602 00:29:49,960 --> 00:29:54,000 Speaker 9: Over time, they're going to expand to France and Germany. Basically, 603 00:29:54,040 --> 00:29:56,880 Speaker 9: they're going to probably get to about ten major regions 604 00:29:57,160 --> 00:29:59,920 Speaker 9: by the end of twenty twenty five. In twenty two 605 00:30:00,480 --> 00:30:03,080 Speaker 9: they're also looking at expanding to resellers as well. 606 00:30:04,080 --> 00:30:07,080 Speaker 2: Fascinating the way in which these things, the logistics of 607 00:30:07,120 --> 00:30:09,040 Speaker 2: getting this into our hands. Mark German, with all the 608 00:30:09,040 --> 00:30:11,800 Speaker 2: news as we know, we thank you so much. Meanwhile, 609 00:30:11,960 --> 00:30:14,520 Speaker 2: well let's talk about what's going viral right now. The 610 00:30:14,560 --> 00:30:17,440 Speaker 2: Barbie movie. It has sixty four years, the Barbie doll 611 00:30:17,520 --> 00:30:21,040 Speaker 2: has been both an icon and an embarrassment. Now with 612 00:30:21,240 --> 00:30:24,720 Speaker 2: Greta Gerwig's new film, Mattel, the company that builds them, 613 00:30:24,760 --> 00:30:26,960 Speaker 2: is now trying to prove that Barbie isn't hopelessly out 614 00:30:26,960 --> 00:30:27,320 Speaker 2: of date? 615 00:30:27,920 --> 00:30:28,320 Speaker 4: Did that? 616 00:30:28,360 --> 00:30:30,360 Speaker 2: Betpan out? We're going to see. Let's bringing billiam ogs 617 00:30:30,400 --> 00:30:32,520 Speaker 2: Thomas Buckling for more on that, because there's been a 618 00:30:32,520 --> 00:30:35,440 Speaker 2: beautiful big take written by billiam Bag today all about 619 00:30:35,800 --> 00:30:38,520 Speaker 2: how the birth of this occurred, and really all goes 620 00:30:38,560 --> 00:30:41,280 Speaker 2: back to Mattel realizing that they're an IP company as 621 00:30:41,320 --> 00:30:43,800 Speaker 2: much as they're a toy company. 622 00:30:44,120 --> 00:30:46,400 Speaker 10: Absolutely, that's soritally right. And you know, before I launch 623 00:30:46,400 --> 00:30:47,520 Speaker 10: into BET, I had to say that I was just 624 00:30:47,560 --> 00:30:49,560 Speaker 10: watching Mark Gooman and Second Mons temper each others of 625 00:30:49,600 --> 00:30:54,480 Speaker 10: his Pete Walls madst the interview from there, So it's 626 00:30:54,520 --> 00:30:57,280 Speaker 10: incredibly interesting. I mean Ian Craze, the CEO of Mattel, 627 00:30:57,320 --> 00:30:59,440 Speaker 10: who came in to run the company in twenty eighteen, 628 00:31:00,200 --> 00:31:03,040 Speaker 10: background has been in entertainment for really most of his career, 629 00:31:03,520 --> 00:31:05,320 Speaker 10: so I think that when he took hold of Mattel, 630 00:31:05,400 --> 00:31:08,680 Speaker 10: he realized, Look, we've got these phenomenal brands, including Barbie, 631 00:31:08,760 --> 00:31:12,760 Speaker 10: Hot Wheels, Barney and more that would be perfectly suited 632 00:31:12,800 --> 00:31:15,320 Speaker 10: to the screen. And what's interesting is that Barbie has 633 00:31:15,360 --> 00:31:17,840 Speaker 10: had its rations on screen, whether it's video games or 634 00:31:17,840 --> 00:31:20,240 Speaker 10: whether it's straight to TV films, but it's the first 635 00:31:20,240 --> 00:31:24,200 Speaker 10: time ever that's had a comprehensive theatrical release helmed by 636 00:31:24,200 --> 00:31:26,880 Speaker 10: Greta Girl because you know, and produced by Warner Brothers. 637 00:31:26,920 --> 00:31:28,400 Speaker 10: That a cost one hundred million dollars. 638 00:31:30,160 --> 00:31:33,680 Speaker 3: Thomas producer John Harland makes a really good point, which 639 00:31:33,720 --> 00:31:38,000 Speaker 3: is that the hype around this has been astonishing. It's 640 00:31:38,120 --> 00:31:42,040 Speaker 3: been everywhere social media, on all the streaming platforms. I 641 00:31:42,080 --> 00:31:45,240 Speaker 3: see ads for it. But the origins of this product, 642 00:31:45,280 --> 00:31:48,640 Speaker 3: the history of Barbie. Some people say, actually, this is 643 00:31:48,640 --> 00:31:50,960 Speaker 3: a pretty big risk for the company. Explain that to me. 644 00:31:52,400 --> 00:31:53,120 Speaker 4: That's actually right. 645 00:31:53,160 --> 00:31:55,080 Speaker 10: So I think mention it when Barbie launched in nineteen 646 00:31:55,080 --> 00:31:57,959 Speaker 10: fifty nine, and she was immensely popular for several decades. 647 00:31:58,000 --> 00:31:58,640 Speaker 4: I mean, she was. 648 00:31:58,560 --> 00:32:02,440 Speaker 10: Modeled on the German build Lily, and you know, in 649 00:32:02,480 --> 00:32:05,480 Speaker 10: a country that had recently embraced the eranism. So I 650 00:32:05,480 --> 00:32:08,120 Speaker 10: think that, you know, the marketing of the doll at 651 00:32:08,120 --> 00:32:12,360 Speaker 10: its early stages, blue eyed blonde Head appealed to you know, 652 00:32:12,400 --> 00:32:16,120 Speaker 10: a set number of people, but wasn't necessarily that wide ranging. Now, 653 00:32:16,280 --> 00:32:19,160 Speaker 10: Barbie's brand actually suffered because it was being perceived as 654 00:32:19,240 --> 00:32:22,400 Speaker 10: very elitist in the past couple of decades and I 655 00:32:22,400 --> 00:32:23,840 Speaker 10: think that what this film is going to try to 656 00:32:23,880 --> 00:32:27,040 Speaker 10: do is really undo a lot of those preconceived notions 657 00:32:27,320 --> 00:32:30,480 Speaker 10: that Barbie is only suitable to a set demographic by 658 00:32:30,480 --> 00:32:33,120 Speaker 10: making her more accessible. And that really follows the trend 659 00:32:33,160 --> 00:32:36,440 Speaker 10: of actually making the doll itself more accessible. A number 660 00:32:36,480 --> 00:32:39,800 Speaker 10: of Barbies have been produced, you know, in current times 661 00:32:39,840 --> 00:32:43,560 Speaker 10: that have, for example, alopecia or down syndrome or different 662 00:32:43,600 --> 00:32:46,200 Speaker 10: skin tones in a bit to actually build the brand 663 00:32:46,240 --> 00:32:48,080 Speaker 10: into something a lot more accessible than it has been 664 00:32:48,120 --> 00:32:48,560 Speaker 10: in the past. 665 00:32:48,840 --> 00:32:52,720 Speaker 2: I mean, I go back to that amazing outfit by 666 00:32:52,840 --> 00:32:56,320 Speaker 2: jay Z and Beyonce when they came as Barbie dolls, 667 00:32:56,360 --> 00:32:58,760 Speaker 2: really to highlight the point it was a Halloween costume 668 00:32:59,400 --> 00:33:02,080 Speaker 2: to that end. If this does do well and there 669 00:33:02,080 --> 00:33:05,120 Speaker 2: are limitations, we understand, you know, they've rouffle feathers to 670 00:33:05,160 --> 00:33:08,840 Speaker 2: put it mildly in Vietnam, But what do they manage 671 00:33:08,880 --> 00:33:10,560 Speaker 2: to make this a sequel if it goes well, if 672 00:33:10,560 --> 00:33:12,840 Speaker 2: they managed to overcome those issues of Nam. 673 00:33:13,680 --> 00:33:16,440 Speaker 10: There are some ongoing considerations. I believe, according to a 674 00:33:16,480 --> 00:33:20,720 Speaker 10: familiar that a sequel is perhaps being put into development. 675 00:33:21,040 --> 00:33:22,920 Speaker 10: It all depends on how well the film does. I mean, 676 00:33:22,960 --> 00:33:25,320 Speaker 10: as ed rightly pointed out that the hype for this 677 00:33:25,880 --> 00:33:29,800 Speaker 10: is really unmatched in recent times in Hollywood. I think that, 678 00:33:29,880 --> 00:33:31,280 Speaker 10: you know, the only film that I can think of 679 00:33:31,320 --> 00:33:34,880 Speaker 10: that's had as much hypersthisis maybe the Avatar sequel. Certainly, 680 00:33:34,920 --> 00:33:37,840 Speaker 10: nothing this year has even come close. And we're observing 681 00:33:37,880 --> 00:33:39,680 Speaker 10: the tracking for this film that is to say, how 682 00:33:39,680 --> 00:33:42,600 Speaker 10: it's going to perform on this opening weekend, and the 683 00:33:42,680 --> 00:33:45,480 Speaker 10: tracking jumped domestically by fifty four percent just in the 684 00:33:45,560 --> 00:33:48,040 Speaker 10: last week alone. Set to open at about one hundred 685 00:33:48,040 --> 00:33:50,760 Speaker 10: and ten million dollars. Now, if the film can grows 686 00:33:50,800 --> 00:33:52,440 Speaker 10: you know, something like half a billion, which is a 687 00:33:52,520 --> 00:33:55,080 Speaker 10: fall ask but not impossible given some of the hype 688 00:33:55,120 --> 00:33:58,360 Speaker 10: around it, then I'm fairly certain that a sequel would 689 00:33:58,360 --> 00:33:59,200 Speaker 10: be put into production. 690 00:34:00,760 --> 00:34:03,800 Speaker 3: A right Bloomberg Thommas Buckley with the Barbie latest Thank You, 691 00:34:12,719 --> 00:34:16,080 Speaker 3: Time for Another Story and You. Study from Watchfinder showed 692 00:34:16,120 --> 00:34:18,920 Speaker 3: that as many as ten percent of the watches received 693 00:34:18,920 --> 00:34:22,799 Speaker 3: from sellers last year were determined to be fakes during 694 00:34:22,800 --> 00:34:27,000 Speaker 3: an authentication process, and half of those fakes were Rolexes. 695 00:34:27,040 --> 00:34:29,319 Speaker 3: And the problem is set to become worse for the 696 00:34:29,320 --> 00:34:33,040 Speaker 3: twenty seven billion dollars secondary market as fakes become more 697 00:34:33,040 --> 00:34:35,840 Speaker 3: sophisticated and hard at a spot. The question out for you, 698 00:34:35,960 --> 00:34:39,080 Speaker 3: Caroline is how does technology fix that? 699 00:34:39,400 --> 00:34:41,600 Speaker 2: And there's a will, there's a way ed, and let's 700 00:34:41,600 --> 00:34:43,879 Speaker 2: bring in someone who knows the luxury space specifically using 701 00:34:43,880 --> 00:34:47,760 Speaker 2: technology to verify goods. Entropy authentication uses like a combination 702 00:34:47,800 --> 00:34:51,839 Speaker 2: of artificial intelligence, of course it does, and microscopy. Microscopy, 703 00:34:51,840 --> 00:34:54,680 Speaker 2: of course, the objectively kind of says is the authenticity 704 00:34:54,680 --> 00:34:57,279 Speaker 2: of an item. I'm pleased to say the founders with us, 705 00:34:57,440 --> 00:35:03,400 Speaker 2: the CEO of Entropy, it's video tun Okay, how does AI? 706 00:35:03,760 --> 00:35:07,560 Speaker 2: How does micross compy help in the scenarios? What exactly 707 00:35:07,560 --> 00:35:08,759 Speaker 2: are you doing and able to tell us? 708 00:35:09,040 --> 00:35:12,879 Speaker 11: Sure? Firstly, thanks for having me. And what we do 709 00:35:13,120 --> 00:35:17,440 Speaker 11: is we use visual we use images. We run images 710 00:35:17,840 --> 00:35:21,320 Speaker 11: through AI models and help them understand the minute differences. 711 00:35:21,400 --> 00:35:23,719 Speaker 11: So my new share per se and this could be 712 00:35:23,920 --> 00:35:29,120 Speaker 11: at such a deep resolution that it's almost impossible, not impossible, 713 00:35:29,120 --> 00:35:34,120 Speaker 11: but almost impossible for counterfeiters to actually beat that. And 714 00:35:34,800 --> 00:35:37,600 Speaker 11: we train computers at scale to understand these differences. And 715 00:35:37,640 --> 00:35:41,640 Speaker 11: then when our customers, anybody needs on demand authentication, Someone 716 00:35:41,719 --> 00:35:43,200 Speaker 11: has an item in front of them and they need 717 00:35:43,239 --> 00:35:47,279 Speaker 11: to know if it's real or not. We are providing 718 00:35:47,320 --> 00:35:50,279 Speaker 11: them the ability to take images of the item, ship 719 00:35:50,320 --> 00:35:52,520 Speaker 11: it out of the cloud. AI says yes or no. 720 00:35:52,680 --> 00:35:54,920 Speaker 11: If it's a yes, then we give them a certificate 721 00:35:54,920 --> 00:35:56,080 Speaker 11: and a financial guarantee. 722 00:35:56,080 --> 00:36:00,000 Speaker 4: So it's almost like insurance videos. 723 00:36:00,080 --> 00:36:03,600 Speaker 3: Where's the technology come from? Have you developed this yourself 724 00:36:04,160 --> 00:36:07,640 Speaker 3: or you're bringing in the software component from a third party? 725 00:36:08,760 --> 00:36:14,240 Speaker 11: No, everything is built in house, including the microscopy even 726 00:36:14,360 --> 00:36:17,880 Speaker 11: any other camera information that we get is all processed 727 00:36:17,920 --> 00:36:20,920 Speaker 11: in house. The data we collect and we train computers 728 00:36:21,560 --> 00:36:24,319 Speaker 11: on is also completely in house, which makes it kind 729 00:36:24,320 --> 00:36:29,680 Speaker 11: of unique and also valuable. And yeah, I mean when 730 00:36:29,719 --> 00:36:31,719 Speaker 11: we started this there was no one else doing it. 731 00:36:31,719 --> 00:36:33,839 Speaker 4: It was also a weird thing to do. Think about it. 732 00:36:35,120 --> 00:36:38,080 Speaker 11: You're a Bloomingdale's, would you expect someone to walk around 733 00:36:38,120 --> 00:36:40,719 Speaker 11: with a device like that? But it turns out there's 734 00:36:40,719 --> 00:36:43,080 Speaker 11: a lot of value because about ten to twelve percent 735 00:36:43,120 --> 00:36:45,440 Speaker 11: of all the items that we authenticate our counterfeit. 736 00:36:45,480 --> 00:36:46,960 Speaker 4: And this is only with businesses. 737 00:36:47,239 --> 00:36:50,640 Speaker 2: Wow, what's interesting. I'm looking at a handbag and can 738 00:36:50,760 --> 00:36:53,680 Speaker 2: really understand how your technology would work for that. Are 739 00:36:53,719 --> 00:36:56,120 Speaker 2: there items we were just talking about watches that sort 740 00:36:56,120 --> 00:36:58,040 Speaker 2: of impossible because you've got to basically take the thing 741 00:36:58,080 --> 00:37:00,400 Speaker 2: apart to be able to take the photos. Where are 742 00:37:00,400 --> 00:37:02,520 Speaker 2: the limitations? Where is the scalability for you? 743 00:37:02,840 --> 00:37:07,160 Speaker 11: Sure so scalability where scalability is available in almost any 744 00:37:07,239 --> 00:37:10,600 Speaker 11: category where there are kind of fits. Because the beauty 745 00:37:10,640 --> 00:37:15,160 Speaker 11: of AI is this flywheel of learning and improving constantly. Typically, 746 00:37:15,239 --> 00:37:18,879 Speaker 11: when you do that long enough and at large scales enough, 747 00:37:19,480 --> 00:37:23,080 Speaker 11: you are disincentivizing counterfeiters from constantly going off of that 748 00:37:23,120 --> 00:37:26,080 Speaker 11: space making better quality fakes because at some point that 749 00:37:26,360 --> 00:37:29,280 Speaker 11: their margin is at a ceiling, right, so you're always 750 00:37:29,320 --> 00:37:31,719 Speaker 11: half a step ahead. So that's where the scalability and 751 00:37:31,760 --> 00:37:35,040 Speaker 11: the AI piece is useful. Limitations wise, I think you 752 00:37:35,160 --> 00:37:37,120 Speaker 11: raise a very good point. When it comes to certain 753 00:37:37,160 --> 00:37:39,400 Speaker 11: types of goods, there's form and there's function. 754 00:37:40,040 --> 00:37:40,200 Speaker 4: Right. 755 00:37:40,520 --> 00:37:42,680 Speaker 11: Forum is say a handbag or a shoe, or a 756 00:37:42,719 --> 00:37:46,000 Speaker 11: piece of a paddle or an necessity. Function would mean 757 00:37:46,120 --> 00:37:47,000 Speaker 11: does it work well? 758 00:37:47,080 --> 00:37:49,120 Speaker 4: Right? So for a whilech you need to do both. 759 00:37:49,239 --> 00:37:53,040 Speaker 11: AI is excellent at form, might not be the most 760 00:37:53,040 --> 00:37:55,640 Speaker 11: ideal solution at function, but there are other ways. So 761 00:37:55,680 --> 00:37:57,799 Speaker 11: the question is how can you put both together in 762 00:37:57,840 --> 00:37:58,680 Speaker 11: a seamless way. 763 00:37:58,880 --> 00:37:59,880 Speaker 4: That's what we're working on. 764 00:38:00,480 --> 00:38:02,680 Speaker 2: Come back when you nailed the watch market as well. 765 00:38:02,760 --> 00:38:04,319 Speaker 2: Really great to have some time with you. Thank you 766 00:38:04,640 --> 00:38:07,800 Speaker 2: for any of us on their Entropy founder and CEO. 767 00:38:08,120 --> 00:38:11,080 Speaker 2: We thank him while we all suddenly consider our goods 768 00:38:11,080 --> 00:38:13,200 Speaker 2: and whether they're real or not. And I mean that 769 00:38:13,239 --> 00:38:15,279 Speaker 2: does it for this edition of Bloomberg Technology. It's been 770 00:38:15,280 --> 00:38:17,320 Speaker 2: a very quick, short week, but a fun one. 771 00:38:17,600 --> 00:38:20,719 Speaker 3: Yeah, a short week dominated by threads, but there's a 772 00:38:20,760 --> 00:38:24,080 Speaker 3: lot more to recap. It's interesting how real focus on 773 00:38:24,120 --> 00:38:26,399 Speaker 3: social media, so much going on in China. A lot 774 00:38:26,400 --> 00:38:29,520 Speaker 3: with the hardware side, with Apple and Mark German's reporting, 775 00:38:29,520 --> 00:38:31,040 Speaker 3: so you can recap it. Don't forget. 776 00:38:31,239 --> 00:38:32,240 Speaker 4: We have our podcast. 777 00:38:32,280 --> 00:38:34,319 Speaker 3: You can find it on the Bloomberg terminal as well 778 00:38:34,320 --> 00:38:39,080 Speaker 3: as online on Apple, Spotify, and iHeart, wherever you choose 779 00:38:39,280 --> 00:38:42,279 Speaker 3: to get your podcasts. A short week, a big week 780 00:38:42,320 --> 00:38:44,399 Speaker 3: in the world of technology, coming up from New York 781 00:38:44,440 --> 00:38:48,040 Speaker 3: and San Francisco. This is Bloomberg Technology.