1 00:00:14,160 --> 00:00:17,120 Speaker 1: I'm Caroline had a Bloomberg's World headquarters in New York 2 00:00:17,600 --> 00:00:20,400 Speaker 1: and the d Ludlow in San Francisco. This is Bloomberg 3 00:00:20,400 --> 00:00:23,880 Speaker 1: Technology coming up in the next hour. Uncertainties around China's 4 00:00:23,920 --> 00:00:27,319 Speaker 1: COVID curbs, the human story and the technology story. What 5 00:00:27,480 --> 00:00:32,000 Speaker 1: unrest means for Apple and iPhone production in the months ahead. Last, 6 00:00:32,040 --> 00:00:34,920 Speaker 1: sticking with Apple, the tech giant is being called out 7 00:00:35,000 --> 00:00:37,800 Speaker 1: by none other then Elon Musk, the tip Twitter chief, 8 00:00:38,000 --> 00:00:41,720 Speaker 1: tweeting it's CEO Tim Cook asking if the company quote 9 00:00:42,000 --> 00:00:46,440 Speaker 1: hates free speech, and another one bites the dust. Crypto 10 00:00:46,520 --> 00:00:49,279 Speaker 1: lender block Fire has filed for bankruptcy in the aftermath 11 00:00:49,280 --> 00:00:52,160 Speaker 1: of f t X, and the US government is among 12 00:00:52,240 --> 00:00:55,800 Speaker 1: one of the key creditors. Will explain, but first head 13 00:00:55,880 --> 00:00:57,880 Speaker 1: let's check in on those markets, because today was a 14 00:00:57,960 --> 00:01:00,560 Speaker 1: day once again of Federal Reserve of Matt grow picture 15 00:01:00,840 --> 00:01:03,480 Speaker 1: impacting the text dogs. We're seeing the SMP from hundreds 16 00:01:03,440 --> 00:01:04,920 Speaker 1: off by one and a half percent, biggest sell off 17 00:01:04,959 --> 00:01:07,160 Speaker 1: since November the ninth. Similar moves in terms of the 18 00:01:07,240 --> 00:01:09,880 Speaker 1: NAZAC off by one point six percent. Big tech rolls 19 00:01:09,959 --> 00:01:13,240 Speaker 1: over as once again fed speaks seems to irk the market. 20 00:01:13,480 --> 00:01:16,360 Speaker 1: Federal reserve. The leaders over at New York FED, for example, 21 00:01:16,440 --> 00:01:18,440 Speaker 1: John Williams, or you're looking over at Jim Bullard over 22 00:01:18,480 --> 00:01:20,880 Speaker 1: at St. Louis FED, both talking about the need for 23 00:01:20,959 --> 00:01:23,800 Speaker 1: further rate hikes. Rates go higher, the dollar goes higher, 24 00:01:23,800 --> 00:01:26,880 Speaker 1: Crypto goes lower. Bitcoin, of course resets at this particular hour, 25 00:01:26,959 --> 00:01:29,240 Speaker 1: but it has been trading lower versus the US dollar, 26 00:01:29,280 --> 00:01:31,080 Speaker 1: and we're seeing that at sixteen thousand, two hundred and 27 00:01:31,160 --> 00:01:34,039 Speaker 1: six down about two on the day of Monday today. 28 00:01:34,040 --> 00:01:36,039 Speaker 1: It just resets and we're calling it flat. Let's move 29 00:01:36,080 --> 00:01:39,399 Speaker 1: on to see what also was irking investors was also 30 00:01:39,440 --> 00:01:42,360 Speaker 1: this dampening on sentiment about what is happening in China, 31 00:01:42,440 --> 00:01:45,680 Speaker 1: the protests. What's interesting here, though, is that the Naza 32 00:01:45,760 --> 00:01:50,000 Speaker 1: Golden Dragon China actually rallid. Why why when you see 33 00:01:50,080 --> 00:01:53,360 Speaker 1: unprecedented levels of protest versus Yjing Ping versus of course 34 00:01:53,600 --> 00:01:56,480 Speaker 1: the concerns of a COVID lockdown. Well maybe just maybe 35 00:01:56,800 --> 00:01:58,920 Speaker 1: the Chinese stocks being traded here in the US were 36 00:01:59,040 --> 00:02:01,680 Speaker 1: upside ed because something that actually that will mean a 37 00:02:01,720 --> 00:02:04,120 Speaker 1: movement towards the reopening of the economy. We're up two 38 00:02:04,120 --> 00:02:07,720 Speaker 1: point eight. Yeah. E commerce are really big driver in 39 00:02:07,760 --> 00:02:10,040 Speaker 1: the markets. On individual names. I'm looking at Amazon, one 40 00:02:10,080 --> 00:02:11,880 Speaker 1: of the through few stocks in the green on the 41 00:02:11,960 --> 00:02:15,480 Speaker 1: NaSTA one on Monday, probably the biggest points gainer as well. 42 00:02:15,520 --> 00:02:19,359 Speaker 1: There's optimism around the sales outlook from Black Friday going 43 00:02:19,400 --> 00:02:21,960 Speaker 1: into Cyber Monday, which is ongoing. Of course, that stock 44 00:02:22,080 --> 00:02:25,560 Speaker 1: higher by around six tenths of one percent. Pindo do 45 00:02:25,760 --> 00:02:29,760 Speaker 1: really interesting, really strong earnings from a Chinese e commerce 46 00:02:29,800 --> 00:02:32,520 Speaker 1: giant on the lower end, the discount end having its 47 00:02:32,520 --> 00:02:34,880 Speaker 1: best days since August, the stock trading at its highest 48 00:02:34,960 --> 00:02:37,519 Speaker 1: level in a year. Again that story as well. Caroline 49 00:02:37,520 --> 00:02:40,160 Speaker 1: probably a factor that what we're seeing play out in 50 00:02:40,240 --> 00:02:43,480 Speaker 1: China might result in a policy change which would be 51 00:02:43,520 --> 00:02:46,480 Speaker 1: supportive for business and tech. Disney down three percent according 52 00:02:46,520 --> 00:02:49,399 Speaker 1: to people listening into Bob Igner's first town hall, they're 53 00:02:49,400 --> 00:02:51,120 Speaker 1: going to keep with it when it comes to the 54 00:02:51,200 --> 00:02:54,560 Speaker 1: hiring freeze, and they're warning about a move from linear TV, 55 00:02:54,880 --> 00:02:57,600 Speaker 1: saying activisions are stop. We' getting into later very quickly. Apple, 56 00:02:57,840 --> 00:02:59,919 Speaker 1: We've got to talk about this stock, Caroline, because it's 57 00:03:00,040 --> 00:03:02,320 Speaker 1: under pressure with what we're seeing play out in China 58 00:03:02,360 --> 00:03:05,000 Speaker 1: and the impact to production as well. Yeah, I look 59 00:03:05,000 --> 00:03:06,919 Speaker 1: at that over the last couple of days, Apple down 60 00:03:06,960 --> 00:03:09,679 Speaker 1: four and a half percent. You've been driving that story home, 61 00:03:09,840 --> 00:03:12,840 Speaker 1: ed and let's dive into a little bit more, because well, 62 00:03:12,880 --> 00:03:16,359 Speaker 1: it's been gripping the entire world, China's growing process against 63 00:03:16,400 --> 00:03:19,840 Speaker 1: COVID curbs and a record number of infections complicating the 64 00:03:19,919 --> 00:03:23,560 Speaker 1: nations path to reopening. Here's what some Bloomberg TV guests 65 00:03:23,600 --> 00:03:26,400 Speaker 1: had to say about the sense of uncertainty just sweeping 66 00:03:26,440 --> 00:03:30,160 Speaker 1: through Chinese markets. As long as we're seeing the the 67 00:03:30,240 --> 00:03:33,959 Speaker 1: ongoing COVID zero policy, we're not going to see um, 68 00:03:34,000 --> 00:03:37,600 Speaker 1: you know, much of a stabilization in domestic demand unless 69 00:03:37,640 --> 00:03:41,720 Speaker 1: the government really moves away from its targeted approach. At 70 00:03:41,720 --> 00:03:46,760 Speaker 1: this point, it looks like that a rabbit or um 71 00:03:46,800 --> 00:03:51,360 Speaker 1: a reckless opening of the economy will be worse for 72 00:03:51,760 --> 00:03:55,280 Speaker 1: China's growth because the biggest problem now, of course, is 73 00:03:55,320 --> 00:03:57,680 Speaker 1: in the labor market. For the time being, it seems 74 00:03:58,080 --> 00:04:00,920 Speaker 1: that our performance that we have seen in the Greater 75 00:04:01,000 --> 00:04:04,320 Speaker 1: China region UM could be kept in the very near term. 76 00:04:04,600 --> 00:04:06,840 Speaker 1: The next few months are clearly going to be challenging, 77 00:04:07,160 --> 00:04:10,240 Speaker 1: but we still feel comfortable that in the second half, 78 00:04:11,000 --> 00:04:13,800 Speaker 1: China is going to be a much better story from 79 00:04:13,800 --> 00:04:17,920 Speaker 1: an investment perspective. Interestingly, the all that's doing well primarily 80 00:04:17,920 --> 00:04:21,440 Speaker 1: because people can't travel. So we've seen seeing you know, 81 00:04:22,080 --> 00:04:24,760 Speaker 1: a good spending and a good foot traffic in them 82 00:04:24,760 --> 00:04:28,840 Speaker 1: are a macro perspective there. Let's dig into the technology 83 00:04:28,920 --> 00:04:31,400 Speaker 1: impact as well. Blue Makes Debbie over in Washington, and 84 00:04:31,440 --> 00:04:34,200 Speaker 1: of course there be you cover in particular Fox. Can 85 00:04:34,320 --> 00:04:37,520 Speaker 1: you cover in particular Taiwanese companies as well as Chinese 86 00:04:37,520 --> 00:04:39,880 Speaker 1: and just talk to us about whether or not this 87 00:04:39,920 --> 00:04:42,360 Speaker 1: is an unprecedented in nature and what it really means 88 00:04:42,400 --> 00:04:46,760 Speaker 1: for business for the economy in China right now. So 89 00:04:46,920 --> 00:04:50,200 Speaker 1: what we have seen is u this is becoming a 90 00:04:50,400 --> 00:04:54,200 Speaker 1: challenging situation for supply chin in China over world. White 91 00:04:54,200 --> 00:04:57,440 Speaker 1: House set today that they don't see a major impact 92 00:04:57,720 --> 00:05:02,359 Speaker 1: um supply Chinda. For more recent UH part protest aura 93 00:05:02,800 --> 00:05:06,919 Speaker 1: undress in China over the weekend and in this instance, 94 00:05:07,200 --> 00:05:11,000 Speaker 1: Foxang over the weekend actually is offering its sixteen staff 95 00:05:11,360 --> 00:05:15,560 Speaker 1: a monthly bonus of as much as eighteen hundred UH 96 00:05:15,680 --> 00:05:20,280 Speaker 1: yen to UH continue working for the company in Central 97 00:05:20,360 --> 00:05:24,400 Speaker 1: China throughout December and January. And that means South Foksang 98 00:05:24,560 --> 00:05:28,080 Speaker 1: is working very hard to return workers to make sure 99 00:05:28,120 --> 00:05:32,479 Speaker 1: that the Apple will have far enough iPhones to offer 100 00:05:32,520 --> 00:05:36,400 Speaker 1: its customers to the holiday quarter, and our coholique in 101 00:05:36,720 --> 00:05:41,240 Speaker 1: Asia have reported UH earlier this week that Apple is 102 00:05:41,360 --> 00:05:44,120 Speaker 1: UH and Baksang are actually a facing illustration that they 103 00:05:44,160 --> 00:05:47,640 Speaker 1: could face a short full of as many as six 104 00:05:47,720 --> 00:05:52,599 Speaker 1: millionaire units of iPhone pros, the most sought after models 105 00:05:52,600 --> 00:05:55,960 Speaker 1: of new iPhones this year due to a reason memorial 106 00:05:56,120 --> 00:06:00,080 Speaker 1: in it at the plant in central China. Debbie, do 107 00:06:00,080 --> 00:06:01,720 Speaker 1: we have any sense of when things are going to 108 00:06:01,839 --> 00:06:04,240 Speaker 1: normalize the Apple in terms of the production line you 109 00:06:04,279 --> 00:06:06,919 Speaker 1: mentioned the six million unit short for I think the 110 00:06:06,960 --> 00:06:09,960 Speaker 1: source also told Bloomberg that there was confidence they can 111 00:06:10,040 --> 00:06:14,640 Speaker 1: make up ground in three uh. Yes, But at the 112 00:06:14,680 --> 00:06:17,680 Speaker 1: same time, the situation remains fluids because we are not 113 00:06:17,720 --> 00:06:20,479 Speaker 1: sure whether there's going to be a full of lockdowns 114 00:06:20,520 --> 00:06:23,680 Speaker 1: in the region that could have an impact on this situation. 115 00:06:24,040 --> 00:06:27,080 Speaker 1: So another person familiar with the situation has told Boomberg 116 00:06:27,160 --> 00:06:32,200 Speaker 1: that the existing workers affected by ongoing luckdowns are being 117 00:06:32,400 --> 00:06:35,440 Speaker 1: able to return to work at the Faxangsa plant in 118 00:06:35,480 --> 00:06:40,320 Speaker 1: central China is a major factor affecting a production as well. 119 00:06:40,440 --> 00:06:42,440 Speaker 1: So I guess we just have to wait and see 120 00:06:42,480 --> 00:06:47,320 Speaker 1: what happens over the next couple of weeks. Okay, Bloomberg, steady, Well, 121 00:06:47,400 --> 00:06:50,800 Speaker 1: thank you very much. Let's continue the conversation now with 122 00:06:50,920 --> 00:06:54,520 Speaker 1: Scott Muscovitz, Asia Pacific GEO political risk analyst for the 123 00:06:54,600 --> 00:06:58,279 Speaker 1: Decision Intelligence Company. Morning Console, And I guess the question 124 00:06:58,320 --> 00:07:02,400 Speaker 1: we go to now is is this a flashpoint, Scott, 125 00:07:02,600 --> 00:07:05,800 Speaker 1: or is this going to be an extended period of 126 00:07:06,320 --> 00:07:10,120 Speaker 1: political social disruption in China in response to what is 127 00:07:10,240 --> 00:07:15,080 Speaker 1: essentially policy right from the Chinese government. Yeah, thanks for 128 00:07:15,160 --> 00:07:19,000 Speaker 1: having me, Um. I think that's an excellent question. Uh, certainly, 129 00:07:19,240 --> 00:07:23,920 Speaker 1: these protests are unprecedented there, unlike anything we've seen, the 130 00:07:24,000 --> 00:07:26,640 Speaker 1: quickness with which they spiraled, in the fact that they 131 00:07:26,640 --> 00:07:31,400 Speaker 1: are taking place across multiple cities, across multiple geographies, and 132 00:07:31,680 --> 00:07:34,760 Speaker 1: that somehow people in one city seem to be aware 133 00:07:34,800 --> 00:07:38,360 Speaker 1: of people in other cities were protesting, and we're you know, 134 00:07:38,480 --> 00:07:42,720 Speaker 1: drawing inspiration from each other. That is something that rarely 135 00:07:42,720 --> 00:07:45,120 Speaker 1: see in China, where the state is normally so good 136 00:07:45,160 --> 00:07:47,800 Speaker 1: at atomizing these things and sort of swooping them under 137 00:07:47,840 --> 00:07:49,920 Speaker 1: the rug. This is going to be much harder to 138 00:07:49,960 --> 00:07:52,800 Speaker 1: put it back in the bottle, so to speak, Scott. Already, 139 00:07:52,880 --> 00:07:56,559 Speaker 1: of course, many an international company has been worried about 140 00:07:56,560 --> 00:07:59,360 Speaker 1: doing business in China, largely because of the COVID lockdowns, 141 00:07:59,400 --> 00:08:02,680 Speaker 1: and also add that healthy dose of geo political tensions 142 00:08:02,720 --> 00:08:05,360 Speaker 1: between for example, the U S and China antagonized in 143 00:08:05,400 --> 00:08:07,680 Speaker 1: some way, perhaps over the course of the weekend the 144 00:08:07,680 --> 00:08:10,880 Speaker 1: news that zt and and the likes of Huawei won't 145 00:08:10,880 --> 00:08:13,560 Speaker 1: be able to sell their products into the US. How 146 00:08:13,600 --> 00:08:18,120 Speaker 1: does this affect, for example, U S China relations going forward. Well, 147 00:08:18,160 --> 00:08:20,400 Speaker 1: I don't know how much bearing this has specifically on 148 00:08:20,560 --> 00:08:23,800 Speaker 1: US channel relations, but US officials have to tread really 149 00:08:24,000 --> 00:08:26,400 Speaker 1: carefully here because they want to show their support and 150 00:08:26,400 --> 00:08:28,200 Speaker 1: they want to express their support for any sort of 151 00:08:28,560 --> 00:08:31,840 Speaker 1: you know, free and open protest, especially one that might 152 00:08:32,360 --> 00:08:35,440 Speaker 1: signal a greater desire even for democracy some people have 153 00:08:35,520 --> 00:08:38,720 Speaker 1: been talking about, which is very rare. But the second 154 00:08:38,880 --> 00:08:41,600 Speaker 1: U S officials step in and start, you know, saying 155 00:08:41,640 --> 00:08:44,160 Speaker 1: how much they support it, China can latch onto those 156 00:08:44,200 --> 00:08:47,280 Speaker 1: sorts of narratives and start to spin it as this 157 00:08:47,760 --> 00:08:50,160 Speaker 1: is something that you know was cooked up by the 158 00:08:50,240 --> 00:08:53,880 Speaker 1: U S. Start going to this sort of conspiracy theory propaganda, 159 00:08:54,080 --> 00:08:57,400 Speaker 1: and so that's something they have to tread very lately. Um, 160 00:08:57,440 --> 00:08:59,959 Speaker 1: not just because they don't want to, you know, make 161 00:09:00,040 --> 00:09:04,480 Speaker 1: the progesters look bad, but also because that could also 162 00:09:04,840 --> 00:09:07,880 Speaker 1: you know, really harm us channelations which are just starting 163 00:09:07,920 --> 00:09:11,360 Speaker 1: to recover after the recent t did in meeting Scott 164 00:09:11,360 --> 00:09:14,720 Speaker 1: earlier today, I spoke to the CFO of in Finian, 165 00:09:14,760 --> 00:09:18,360 Speaker 1: a chip maker that has a significant operational and sales 166 00:09:18,400 --> 00:09:20,760 Speaker 1: footprint in China. This is what he had to say 167 00:09:20,760 --> 00:09:25,679 Speaker 1: about the situation. We have really improved our resilience a 168 00:09:25,720 --> 00:09:29,720 Speaker 1: lot over the over the past quarters, months and years, 169 00:09:29,760 --> 00:09:33,600 Speaker 1: and therefore we are looking at with with concern. But 170 00:09:33,880 --> 00:09:37,240 Speaker 1: up to now there is no direct implication visible on 171 00:09:37,280 --> 00:09:40,960 Speaker 1: our operations in China or the region, and we have 172 00:09:41,160 --> 00:09:45,359 Speaker 1: backup plans in case it becomes necessary to make adjustments. 173 00:09:46,400 --> 00:09:48,840 Speaker 1: When I listened to Spend, he makes it seem like 174 00:09:48,880 --> 00:09:52,040 Speaker 1: this is just the latest in a string of ongoing disruption. 175 00:09:52,480 --> 00:09:55,160 Speaker 1: What is your assessment about the reality of life on 176 00:09:55,200 --> 00:09:59,480 Speaker 1: the ground and operating in China right now? Well, I 177 00:09:59,480 --> 00:10:01,720 Speaker 1: don't get this. It's that everything has ground to the 178 00:10:01,720 --> 00:10:04,000 Speaker 1: halt to a halt, But I do get the sense 179 00:10:04,040 --> 00:10:06,080 Speaker 1: that we're in sort of a weight and see moment. 180 00:10:06,160 --> 00:10:09,120 Speaker 1: It doesn't seem like they've been able to dispurse these crowds. 181 00:10:09,559 --> 00:10:12,000 Speaker 1: They sort of missed the window on you know, censoring 182 00:10:12,080 --> 00:10:14,040 Speaker 1: and isolating it right away. And even as they start 183 00:10:14,080 --> 00:10:16,839 Speaker 1: to do that, people know that these protests are going on, 184 00:10:17,000 --> 00:10:19,520 Speaker 1: and they you know, are aware people are showing up 185 00:10:19,520 --> 00:10:21,600 Speaker 1: with their blank pieces of paper. That's been the real 186 00:10:21,720 --> 00:10:25,200 Speaker 1: prop of the protest um. Whether it's sort of grinding 187 00:10:25,240 --> 00:10:27,400 Speaker 1: to a halt. We saw you talked about earlier those 188 00:10:27,800 --> 00:10:29,800 Speaker 1: you know, protests in Jung Joe, but those were those 189 00:10:29,840 --> 00:10:31,600 Speaker 1: were more isolated. I get the sense that these are 190 00:10:31,679 --> 00:10:34,440 Speaker 1: more you know, urban middle class protests. We haven't seen 191 00:10:34,520 --> 00:10:38,160 Speaker 1: so much labor protests outside of that. So it's a question, um. 192 00:10:38,240 --> 00:10:40,360 Speaker 1: But there's a lot of uncertainty, and we know that 193 00:10:40,440 --> 00:10:44,840 Speaker 1: sometimes COVID lockdowns have been used almost politically, and so 194 00:10:44,960 --> 00:10:46,920 Speaker 1: you know, anything is sort of on the table, really 195 00:10:46,920 --> 00:10:49,400 Speaker 1: waiting to see how this will unfold. Now. I know, 196 00:10:49,520 --> 00:10:54,000 Speaker 1: in some ways what has occurred at Fox con iPhone cities, 197 00:10:54,040 --> 00:10:57,319 Speaker 1: it's known it's somewhat different to what's happening now on 198 00:10:57,360 --> 00:10:59,960 Speaker 1: the streets of Beijing or Shanghai. But they all really 199 00:11:00,000 --> 00:11:02,320 Speaker 1: it did. And a lot of this is frustration about 200 00:11:02,360 --> 00:11:06,080 Speaker 1: the way in which people survive amid these COVID lockdown Scott. 201 00:11:06,520 --> 00:11:09,000 Speaker 1: Many have been looking at Apple in this situation. Of course, 202 00:11:09,080 --> 00:11:11,439 Speaker 1: key supply of being that a fox gone, their exposure 203 00:11:11,480 --> 00:11:15,600 Speaker 1: being significant, that they just didn't build enough alternatives, They 204 00:11:15,600 --> 00:11:17,880 Speaker 1: didn't focus enough. They were too heavily dependent as a 205 00:11:17,920 --> 00:11:20,200 Speaker 1: supply chain on China and didn't do much to to 206 00:11:20,320 --> 00:11:24,760 Speaker 1: really disperse that. Do you think companies are now significantly 207 00:11:24,760 --> 00:11:27,600 Speaker 1: moving away from a Chinese supply chain or they look 208 00:11:27,720 --> 00:11:31,559 Speaker 1: to just think that this is an episode. Oh, I 209 00:11:31,600 --> 00:11:33,680 Speaker 1: think you're absolutely right, and I think it predates this. 210 00:11:33,800 --> 00:11:35,840 Speaker 1: I think it was over the summer. Uh, you know, 211 00:11:35,880 --> 00:11:38,800 Speaker 1: after how speaker Pelosity visited Taiwan and there was that 212 00:11:39,520 --> 00:11:44,280 Speaker 1: deeply jinguistic military reaction. People got really stooped and they 213 00:11:44,280 --> 00:11:46,720 Speaker 1: started for the first time to envision a future where 214 00:11:46,800 --> 00:11:49,880 Speaker 1: China might become for a lot of multinational companies geo 215 00:11:50,040 --> 00:11:53,200 Speaker 1: politically off limits. And they all started to scramble and started, 216 00:11:53,240 --> 00:11:56,720 Speaker 1: I think, wow, we need to look at serious contingency planning. 217 00:11:57,160 --> 00:11:59,839 Speaker 1: And things only seem to get worse from there. That's 218 00:11:59,840 --> 00:12:03,120 Speaker 1: what we saw on our data. Um, you know, relations 219 00:12:03,120 --> 00:12:07,839 Speaker 1: seem to get worse and worse. Bilateral negativity only got worse, 220 00:12:08,160 --> 00:12:10,400 Speaker 1: and then it just really did seem to be affecting 221 00:12:10,480 --> 00:12:12,800 Speaker 1: both China and the US. People were concerned. She and 222 00:12:12,880 --> 00:12:16,440 Speaker 1: Biden met. It seemed to calm temperatures, not change anything. 223 00:12:16,440 --> 00:12:19,559 Speaker 1: But really I could a four under relations and this happens, 224 00:12:19,640 --> 00:12:23,000 Speaker 1: you know, right, Scott. We asked our audience what's top 225 00:12:23,040 --> 00:12:25,199 Speaker 1: of mind for them and concerned about what's happening on 226 00:12:25,240 --> 00:12:29,360 Speaker 1: the ground, specifically with Apple. Their answer very clear, working conditions. 227 00:12:29,720 --> 00:12:32,800 Speaker 1: My question to you those working conditions, what we've seen 228 00:12:32,800 --> 00:12:35,880 Speaker 1: on the streets. Do we see a policy pivot from 229 00:12:35,920 --> 00:12:39,800 Speaker 1: the Chinese government. Yeah. I think one thing is that 230 00:12:39,880 --> 00:12:43,959 Speaker 1: the government is sometimes more responsive than people realizing China 231 00:12:44,080 --> 00:12:47,000 Speaker 1: because they don't have the release valve of elections like 232 00:12:47,040 --> 00:12:49,080 Speaker 1: we have, So they're going to have to move in 233 00:12:49,160 --> 00:12:52,440 Speaker 1: order to well dissent. But they have to be very 234 00:12:52,480 --> 00:12:56,400 Speaker 1: careful because you know, if they open up too quickly, 235 00:12:56,640 --> 00:13:00,079 Speaker 1: then it could really emboldened and enable these protests. So 236 00:13:00,120 --> 00:13:02,240 Speaker 1: I think they're going to tread lately. But I don't 237 00:13:02,400 --> 00:13:05,760 Speaker 1: see anything really terrible happening just yet. But we're waiting. 238 00:13:06,080 --> 00:13:09,000 Speaker 1: It could be a victory for the broadesters. You don't know, 239 00:13:09,240 --> 00:13:13,040 Speaker 1: there might be smart movements, you know, Okay, Scott Scott 240 00:13:13,120 --> 00:13:15,960 Speaker 1: Musk of b It's Asia specific GEO political risk analyst 241 00:13:16,200 --> 00:13:18,760 Speaker 1: for Morning Consult, Thank you. Let's stick with Apple, the 242 00:13:18,800 --> 00:13:22,000 Speaker 1: company and it's CEO being called out by Elon Musk, 243 00:13:22,240 --> 00:13:25,120 Speaker 1: who claims Apple has cut back its advertising on Twitter 244 00:13:25,440 --> 00:13:28,240 Speaker 1: and even threatened to withhold the social network from its 245 00:13:28,240 --> 00:13:31,600 Speaker 1: app store. Musk tweeting quote, do they hate free speech 246 00:13:31,640 --> 00:13:35,160 Speaker 1: in America? Question, then posting again, this time including the 247 00:13:35,160 --> 00:13:38,440 Speaker 1: Twitter account of Apple Chief executive Officer Tim Cook. No 248 00:13:38,600 --> 00:13:41,000 Speaker 1: response from Tim Cook on Twitter. Will have more on 249 00:13:41,080 --> 00:13:44,320 Speaker 1: that story later in the hour and coming up. Activision 250 00:13:44,400 --> 00:13:47,800 Speaker 1: Blizzard is gaining some fans while some analysts are raising 251 00:13:47,840 --> 00:13:52,120 Speaker 1: their recommendation despite concerns over Microsoft steel for a takeover 252 00:13:52,200 --> 00:14:10,160 Speaker 1: the Cisplinberg. So Activision Blizzard has gained fans on Wall 253 00:14:10,160 --> 00:14:13,120 Speaker 1: Street as a florry of analysts raised their recommendations on 254 00:14:13,120 --> 00:14:16,040 Speaker 1: the stock, even as microsoft planned acquisition looks more and more. 255 00:14:16,080 --> 00:14:18,559 Speaker 1: Dicey joining us now as being a Bloomberg's m and 256 00:14:18,600 --> 00:14:21,880 Speaker 1: a reporter eachen Sen and Echun just talk to us 257 00:14:21,880 --> 00:14:24,560 Speaker 1: about what you're reporting, showing first and foremost one of 258 00:14:24,600 --> 00:14:27,440 Speaker 1: the odds that this deal actually goes through. Sure, Yeah, 259 00:14:27,560 --> 00:14:31,040 Speaker 1: so I will say, I'm hearing from murder arch traders 260 00:14:31,120 --> 00:14:34,320 Speaker 1: as they see the deal probably is having only roughly 261 00:14:34,400 --> 00:14:37,880 Speaker 1: forty to fifty percent of going through, so it's pretty dicey. 262 00:14:38,080 --> 00:14:40,920 Speaker 1: But it's interesting that we are seeing among the Wall 263 00:14:40,960 --> 00:14:45,360 Speaker 1: Street analysts there brustings here reading on the active Exhibition 264 00:14:45,440 --> 00:14:48,720 Speaker 1: the start itself mainly for two reasons I would say, 265 00:14:48,920 --> 00:14:52,200 Speaker 1: first it's a strong fundamental value and second is it's 266 00:14:52,240 --> 00:14:56,840 Speaker 1: a very attractive root forward profile given the Microsoft situation. 267 00:14:57,200 --> 00:15:00,840 Speaker 1: Because remember Exhibition is in the process as of being 268 00:15:00,920 --> 00:15:04,960 Speaker 1: acquired by Microsoft for sixty nine billions. That's a huge deal. Right. 269 00:15:05,120 --> 00:15:08,280 Speaker 1: But there was a report last week saying that the FTC, 270 00:15:08,720 --> 00:15:11,400 Speaker 1: which as the u S regulator, is going to block 271 00:15:11,520 --> 00:15:14,960 Speaker 1: and challenge the transaction. The sort the starts tumbled, right, 272 00:15:15,240 --> 00:15:18,120 Speaker 1: but the endless points as they see, and they like 273 00:15:18,320 --> 00:15:22,520 Speaker 1: the stock whist or without Microsoft transaction because their case 274 00:15:22,600 --> 00:15:26,080 Speaker 1: has been as a stalear loan company active mission, it 275 00:15:26,200 --> 00:15:30,120 Speaker 1: has strong fundamental, very solid growth outlook and its franchise 276 00:15:30,200 --> 00:15:33,240 Speaker 1: including call of duty. Right, and they will get three 277 00:15:33,360 --> 00:15:35,960 Speaker 1: billion break on fee to add on the balance sheet, 278 00:15:36,160 --> 00:15:39,200 Speaker 1: right if the deal force apart. But on the flip side, 279 00:15:39,280 --> 00:15:42,240 Speaker 1: if you know it goes through, then look at where 280 00:15:42,840 --> 00:15:46,080 Speaker 1: the start is treating. It's like twenty percent below the 281 00:15:46,200 --> 00:15:49,960 Speaker 1: takeover offer. So that's a huge upside to capture in 282 00:15:50,160 --> 00:15:54,320 Speaker 1: that scenario. Ch And you've taught me so much about 283 00:15:54,360 --> 00:15:56,760 Speaker 1: the twists and turns of deal making, you know. You 284 00:15:56,880 --> 00:15:59,520 Speaker 1: and I followed the Twitter deal so closely for months 285 00:15:59,600 --> 00:16:01,720 Speaker 1: for examp and Pool and the thing you taught me 286 00:16:02,000 --> 00:16:04,680 Speaker 1: throughout that process not just to crunch the numbers from 287 00:16:04,720 --> 00:16:07,040 Speaker 1: the murder ofs, but it's to look out for key 288 00:16:07,120 --> 00:16:11,200 Speaker 1: inflection points, key decisions. So what's the big key decision 289 00:16:11,320 --> 00:16:15,480 Speaker 1: or key moment we're looking for between Activision and Microsoft? Right? 290 00:16:15,600 --> 00:16:18,920 Speaker 1: So I would say anti trust, anti trust progress has 291 00:16:19,040 --> 00:16:21,760 Speaker 1: always been the focus. So Right now is waiting on 292 00:16:21,880 --> 00:16:25,800 Speaker 1: the approval from FTC, but also it's facing the probe 293 00:16:26,200 --> 00:16:29,640 Speaker 1: under u k C, m A and European Commission. So 294 00:16:29,880 --> 00:16:33,000 Speaker 1: regulators have raised a concern you or if they're still 295 00:16:33,160 --> 00:16:36,120 Speaker 1: combining the numbers three and number five largest player in 296 00:16:36,160 --> 00:16:39,640 Speaker 1: the industry, will give Microsoft, you know, too much of 297 00:16:39,760 --> 00:16:43,080 Speaker 1: advantage in a space, or if they well with whole 298 00:16:43,320 --> 00:16:48,440 Speaker 1: popular titles against their competitors, and I will say another 299 00:16:48,520 --> 00:16:52,240 Speaker 1: thing to watch, it's just more broadly um FTC or 300 00:16:52,280 --> 00:16:57,000 Speaker 1: the buider administration has been helped this very aggressive narratives 301 00:16:57,120 --> 00:17:01,280 Speaker 1: around big players all years of you know when it 302 00:17:01,360 --> 00:17:04,399 Speaker 1: comes to antitrust issue. So that's particular of putting this 303 00:17:04,720 --> 00:17:08,960 Speaker 1: mega tag deal under the spotline. What's interesting though today 304 00:17:09,240 --> 00:17:11,920 Speaker 1: are great colleagues of Breenberg Intelligence put out a piece 305 00:17:12,000 --> 00:17:17,200 Speaker 1: Jennifer for example, talking about how actually Microsoft snow uh 306 00:17:17,560 --> 00:17:19,320 Speaker 1: new one trippony when it comes to M and A. 307 00:17:19,400 --> 00:17:21,840 Speaker 1: They've been on this rodeo before and quite often they 308 00:17:21,960 --> 00:17:24,800 Speaker 1: make concessions. Are we likely to see that when the 309 00:17:24,920 --> 00:17:27,800 Speaker 1: obs are talking about this right right? I think that's 310 00:17:27,840 --> 00:17:30,639 Speaker 1: definitely something people are watching, and people are watching. The 311 00:17:30,760 --> 00:17:35,399 Speaker 1: next catalyst is UM. When FTC going to job um 312 00:17:35,560 --> 00:17:39,200 Speaker 1: their final decision on a case on this deal. So 313 00:17:39,520 --> 00:17:42,000 Speaker 1: I think media reports say that could come, you know, 314 00:17:42,200 --> 00:17:47,280 Speaker 1: either later next month or earlier next year. So it 315 00:17:47,400 --> 00:17:50,040 Speaker 1: will depend on if they offer so on the mergial 316 00:17:50,080 --> 00:17:53,080 Speaker 1: party side, if they offer some behavior remedy. But if 317 00:17:53,160 --> 00:17:56,479 Speaker 1: you know, the regulator accepts that, and if there's actually 318 00:17:56,600 --> 00:18:00,320 Speaker 1: FTC decided to block the deal, then Microstofts do have 319 00:18:00,520 --> 00:18:03,040 Speaker 1: the option too. It could appeal the case in the 320 00:18:03,200 --> 00:18:07,280 Speaker 1: court and remembers there's a similar case between United House 321 00:18:07,320 --> 00:18:11,520 Speaker 1: and chance Housecare Right. They got challenged from the o J, 322 00:18:11,840 --> 00:18:15,560 Speaker 1: but eventually win the costs approval on it so that 323 00:18:15,680 --> 00:18:18,760 Speaker 1: they are eventually close out. Okay, thanks to Bloom Bang 324 00:18:18,840 --> 00:18:29,879 Speaker 1: Bloom Bags sen two and a half years ago, we 325 00:18:30,040 --> 00:18:34,120 Speaker 1: lost eighty percent of our business. We laid off employees, 326 00:18:34,600 --> 00:18:36,600 Speaker 1: and I said at that time that we are going 327 00:18:36,640 --> 00:18:39,600 Speaker 1: to be now prepared for anything to come. We don't 328 00:18:40,520 --> 00:18:42,920 Speaker 1: need to lay off half the workforce to achieve the 329 00:18:42,960 --> 00:18:45,880 Speaker 1: efficiency levels that we want to achieve. We did announce 330 00:18:45,880 --> 00:18:49,680 Speaker 1: a small restructuring. Ours is really about rebalancing the number 331 00:18:49,720 --> 00:18:51,200 Speaker 1: of head count we had at the beginning of the 332 00:18:51,280 --> 00:18:53,280 Speaker 1: year we had tonight. We're gonna have the same number 333 00:18:53,280 --> 00:18:54,960 Speaker 1: of heads at the end of the year. We are 334 00:18:55,040 --> 00:18:57,280 Speaker 1: not stepping on the brakes. In fact, we're stepping on 335 00:18:57,359 --> 00:18:59,880 Speaker 1: the gas. We are still hiring. We're not freezing, we're 336 00:18:59,880 --> 00:19:04,639 Speaker 1: not cutting, We're growing. That's what some of the biggest 337 00:19:04,680 --> 00:19:07,200 Speaker 1: tech ceo has had to say about their own strategies 338 00:19:07,480 --> 00:19:10,560 Speaker 1: surrounding layoffs, and in today's Talking Tech, we're taking a 339 00:19:10,600 --> 00:19:13,399 Speaker 1: look at those companies that are not well so optimistic. 340 00:19:13,760 --> 00:19:16,760 Speaker 1: In Mexico used cars startup Cavac is the latest global 341 00:19:16,800 --> 00:19:20,320 Speaker 1: tech name to cut jobs. Latin America's biggest startup is 342 00:19:20,440 --> 00:19:23,920 Speaker 1: reducing headcount and firing managers due to higher interest rates 343 00:19:24,119 --> 00:19:26,920 Speaker 1: and a slowing economy. That's according to a memo from 344 00:19:26,920 --> 00:19:30,920 Speaker 1: the Unicorns CEO seen by Bloomberg News Now last week, 345 00:19:31,119 --> 00:19:33,240 Speaker 1: HP said it would cut as many as six thousand 346 00:19:33,359 --> 00:19:36,520 Speaker 1: jobs over the next three years, joining names like Amazon 347 00:19:36,800 --> 00:19:41,520 Speaker 1: and Cisco who also planned layoffs right now. Employment historically 348 00:19:41,600 --> 00:19:44,560 Speaker 1: strong globally. The jobless rate was at four point four 349 00:19:44,600 --> 00:19:48,520 Speaker 1: percent in September across major developed economies, according to the 350 00:19:48,600 --> 00:19:50,920 Speaker 1: o e c D. That's the lowest level since the 351 00:19:51,000 --> 00:19:54,880 Speaker 1: nineteen eighties. But this time around, tech already has staffing 352 00:19:54,960 --> 00:19:58,639 Speaker 1: numbers way above pre pandemic levels. Layoffs have already begune, 353 00:19:58,960 --> 00:20:01,480 Speaker 1: so the sectors bray sing for more cuts to come. 354 00:20:01,680 --> 00:20:04,920 Speaker 1: That's talking to really great global perspective. Let's get a 355 00:20:04,960 --> 00:20:07,080 Speaker 1: little bit more global right now. Another story that's keeping 356 00:20:07,480 --> 00:20:10,080 Speaker 1: our attention. The main privacy watchdog for Meta in the 357 00:20:10,160 --> 00:20:13,400 Speaker 1: European Union has fined Facebook's parent company to want seventy 358 00:20:13,400 --> 00:20:16,200 Speaker 1: seven million dollars from a massive data breach. Now Matter 359 00:20:16,440 --> 00:20:18,760 Speaker 1: was penalized for failing to prevent the leak of the 360 00:20:18,840 --> 00:20:21,240 Speaker 1: personal data of more than half a billion users. The 361 00:20:21,320 --> 00:20:25,760 Speaker 1: fine was imposed by the Irish Data Protection Commission. Meanwhile, 362 00:20:25,800 --> 00:20:28,840 Speaker 1: coming up, what retail data can reveal about the state 363 00:20:28,880 --> 00:20:31,479 Speaker 1: of the supply chain in this holiday shopping season. All 364 00:20:31,560 --> 00:20:34,560 Speaker 1: these insights and more. Next some Ari Trasda. He's the 365 00:20:34,640 --> 00:20:36,760 Speaker 1: CEO and founder of the open data supply chain company. 366 00:20:36,880 --> 00:20:54,119 Speaker 1: Chris this is Bloomberg. I think this intentionality actually is 367 00:20:54,240 --> 00:20:56,919 Speaker 1: incredibly indicative of the state of the consumer. Consumers are 368 00:20:57,000 --> 00:20:59,280 Speaker 1: really looking for a value right now. They're looking for quality. 369 00:20:59,440 --> 00:21:01,240 Speaker 1: They want to buy from their favorite brands, but they 370 00:21:01,320 --> 00:21:03,200 Speaker 1: want a good deal from their favorite brands, and they're 371 00:21:03,200 --> 00:21:06,560 Speaker 1: willing to wait, whether it's Cyber Black Friday or ceremony 372 00:21:06,640 --> 00:21:08,560 Speaker 1: to buy that. But that's part of it. The second 373 00:21:08,600 --> 00:21:10,399 Speaker 1: piece of it. You mentioned this Alex in your in 374 00:21:10,480 --> 00:21:13,080 Speaker 1: your opening, you talked about omni channel. This idea that 375 00:21:13,160 --> 00:21:16,000 Speaker 1: consumers want to buy whatever in whatever means that is 376 00:21:16,000 --> 00:21:18,560 Speaker 1: most communitied for them, whether that's online or offline on 377 00:21:18,680 --> 00:21:21,440 Speaker 1: social media. That is now steady state. When you add 378 00:21:21,520 --> 00:21:23,800 Speaker 1: that to direct to consumer as a business model, which 379 00:21:23,840 --> 00:21:26,560 Speaker 1: really connects the merchant and the consumer directly. I think 380 00:21:26,640 --> 00:21:30,520 Speaker 1: that's the current state of retail and it's exciting. Welcome 381 00:21:30,520 --> 00:21:32,719 Speaker 1: back to Blue Big Technology. I'm Caroline Hide in New York. 382 00:21:32,800 --> 00:21:36,120 Speaker 1: That was Shopify president Harley Finkst on Blue Bag Television 383 00:21:36,119 --> 00:21:38,760 Speaker 1: a little earlier today. And you're taking a closer look 384 00:21:38,840 --> 00:21:42,080 Speaker 1: at Shopify and other retailers post Black Friday. Just give 385 00:21:42,160 --> 00:21:44,760 Speaker 1: us the big picture. Was it good? Yeah? Yeah, well, 386 00:21:44,880 --> 00:21:47,280 Speaker 1: I mean the market seems to think it's been okay 387 00:21:47,440 --> 00:21:50,959 Speaker 1: so far. Shopify gave some prelim numbers for Black Black Friday. 388 00:21:51,040 --> 00:21:53,520 Speaker 1: The street seems bullish on those prelim numbers. There is 389 00:21:53,600 --> 00:21:57,120 Speaker 1: evidence that the consumer is taking advantage of those deals. 390 00:21:57,200 --> 00:21:58,639 Speaker 1: Some of the stocks that we've been watching are the 391 00:21:58,760 --> 00:22:01,920 Speaker 1: names that in that econor space. Amazon had a good 392 00:22:01,960 --> 00:22:05,200 Speaker 1: start Monday's session kind of fell away, but Shopify and outperformer. 393 00:22:05,240 --> 00:22:07,440 Speaker 1: A lot of names on the cell side jumping on 394 00:22:07,920 --> 00:22:10,800 Speaker 1: the early data coming out of them. Wayfair, another one 395 00:22:10,920 --> 00:22:14,000 Speaker 1: Peloton interesting. According to Adobe Analytics, it really was a 396 00:22:14,080 --> 00:22:16,959 Speaker 1: fit at home fitness equipment that did well at one 397 00:22:17,000 --> 00:22:19,480 Speaker 1: point up eight point eight percent, but really paired those 398 00:22:19,560 --> 00:22:22,080 Speaker 1: games to close up eight tenths and one percent. Adobe 399 00:22:22,119 --> 00:22:23,920 Speaker 1: Analytics data is where I want to go. Actually, this 400 00:22:24,040 --> 00:22:26,520 Speaker 1: is a third party measure of how things are going 401 00:22:26,600 --> 00:22:29,800 Speaker 1: in this holiday shopping season. For me, the psychology about 402 00:22:29,920 --> 00:22:32,800 Speaker 1: when which they carryed you get out your laptop or 403 00:22:32,840 --> 00:22:35,440 Speaker 1: go on your smartphone and buy something. And according to 404 00:22:35,520 --> 00:22:38,600 Speaker 1: the data from Adobe Analytics, it's very much cyber Monday. 405 00:22:38,680 --> 00:22:41,199 Speaker 1: That is when across that holiday shopping period as an 406 00:22:41,240 --> 00:22:44,680 Speaker 1: individual day, they expect eleven point two billion dollars to 407 00:22:44,800 --> 00:22:49,119 Speaker 1: be spent. You look for example of Thanksgiving days comparison 408 00:22:49,240 --> 00:22:51,840 Speaker 1: Black Friday as a comparison, it seems like consumers eat 409 00:22:51,840 --> 00:22:54,080 Speaker 1: a few days to get over their Turkey trip and 410 00:22:54,200 --> 00:22:56,440 Speaker 1: get out there online to buy some deals. Now this 411 00:22:56,640 --> 00:22:59,000 Speaker 1: year related to other is let's look at two and 412 00:22:59,040 --> 00:23:03,400 Speaker 1: where we're headed. We do expect growth across that holiday 413 00:23:03,640 --> 00:23:06,480 Speaker 1: season e commerce spending in the United States. We're on 414 00:23:06,560 --> 00:23:10,399 Speaker 1: track for billion dollars. That represents two point five percent 415 00:23:10,520 --> 00:23:14,000 Speaker 1: game on the holiday season here in the United States. 416 00:23:14,200 --> 00:23:16,959 Speaker 1: But I find that fascinating carry because it's still growth 417 00:23:17,400 --> 00:23:19,920 Speaker 1: at a time where we're really worried about a slowdown 418 00:23:19,960 --> 00:23:22,440 Speaker 1: in the economy. We're worried about the strength of the consumer. 419 00:23:22,480 --> 00:23:25,360 Speaker 1: And what I'm reading in the data is actually discounting 420 00:23:25,600 --> 00:23:27,960 Speaker 1: and deals seems to be doing the trick, albeit not 421 00:23:28,080 --> 00:23:30,320 Speaker 1: in the same way it's done in previous years. Is 422 00:23:30,359 --> 00:23:33,879 Speaker 1: it inflation adjusted? That's my key question. Let's put it 423 00:23:34,000 --> 00:23:36,840 Speaker 1: to our transdell at a great setup, and we've now 424 00:23:36,880 --> 00:23:38,440 Speaker 1: got the CEO and founder of christ with Us who 425 00:23:38,440 --> 00:23:40,800 Speaker 1: can enlighten us as to the supply chain data the 426 00:23:40,880 --> 00:23:44,160 Speaker 1: platform that he brings for brands, for distributors, for retailers, 427 00:23:44,200 --> 00:23:46,840 Speaker 1: you really give them an inside track on what's happening 428 00:23:46,880 --> 00:23:48,560 Speaker 1: in terms of their own supply chain how to make 429 00:23:48,600 --> 00:23:50,960 Speaker 1: a more efficientary. But just go back to that data. 430 00:23:51,040 --> 00:23:54,399 Speaker 1: The growth are we seeing on an inflation adjusted basis 431 00:23:54,840 --> 00:23:57,680 Speaker 1: growth in terms of spending? Does it look healthy? It 432 00:23:57,800 --> 00:23:59,680 Speaker 1: looks healthier, but I think it was. At the same time, 433 00:24:00,240 --> 00:24:03,560 Speaker 1: have a lot of supply chain challenges still looming, so 434 00:24:03,960 --> 00:24:06,560 Speaker 1: you're seeing it's kind of a paradox because you're seeing 435 00:24:06,960 --> 00:24:11,000 Speaker 1: on one hand, you're seeing that retailers are reporting that 436 00:24:11,119 --> 00:24:13,880 Speaker 1: they have too much inventory and at the same time 437 00:24:13,960 --> 00:24:17,600 Speaker 1: they're reporting that they're out of stock. Um So retail 438 00:24:17,760 --> 00:24:21,800 Speaker 1: is highly seasonal, as you know, and retailers are still 439 00:24:21,880 --> 00:24:25,440 Speaker 1: struggling with getting the last season and the season before 440 00:24:25,520 --> 00:24:28,480 Speaker 1: that sold, and at the same time they have a 441 00:24:28,560 --> 00:24:32,680 Speaker 1: lot of supply chain challenges getting the seasonal products in 442 00:24:32,920 --> 00:24:37,000 Speaker 1: for for this upcoming upcoming period. Aria hear you a 443 00:24:37,119 --> 00:24:40,480 Speaker 1: lot on the supply chain challenge side, and when we 444 00:24:40,560 --> 00:24:43,119 Speaker 1: talk about supply chains for Caroline and I over the 445 00:24:43,240 --> 00:24:47,199 Speaker 1: last eighteen months, that's meant difficulty in moving goods from 446 00:24:47,240 --> 00:24:49,000 Speaker 1: A to B. But the other issue we hear about 447 00:24:49,080 --> 00:24:52,760 Speaker 1: is inventories. What roller inventories playing for some of these 448 00:24:53,040 --> 00:24:57,000 Speaker 1: online retailers this holiday season. Yeah, they it's the same, 449 00:24:57,240 --> 00:24:59,480 Speaker 1: it's the same challenge. Really, you kind of have a 450 00:24:59,680 --> 00:25:02,480 Speaker 1: very long supply chain to get get get get the 451 00:25:02,520 --> 00:25:06,919 Speaker 1: inventory to the consumer. So you have retailers, you have distributors, wholesalers, 452 00:25:06,960 --> 00:25:10,560 Speaker 1: your manufacturers, imports, exporters, etcetera. And it takes a very 453 00:25:10,680 --> 00:25:13,480 Speaker 1: long time for this inventory to actually get through this 454 00:25:13,720 --> 00:25:17,680 Speaker 1: entire supply chain. So the numbers that we've got a 455 00:25:17,760 --> 00:25:21,400 Speaker 1: few days ago here is that of the retailers say 456 00:25:21,440 --> 00:25:24,960 Speaker 1: that they have too much inventory, and at the same time, 457 00:25:25,119 --> 00:25:28,000 Speaker 1: the eight percent say that they're already starting to run 458 00:25:28,080 --> 00:25:32,719 Speaker 1: out of inventory for key items. So historically it's been 459 00:25:32,840 --> 00:25:35,480 Speaker 1: very easy to plan, a much easier to plan, but 460 00:25:35,920 --> 00:25:38,600 Speaker 1: everything that's going on now with with with the inflation, 461 00:25:38,680 --> 00:25:42,440 Speaker 1: consumer change, a huge shift to to to online for instance, 462 00:25:42,440 --> 00:25:45,199 Speaker 1: it's made it really hard to plan because of very 463 00:25:45,240 --> 00:25:48,240 Speaker 1: long lead times in the supply chains. So so that 464 00:25:48,400 --> 00:25:51,000 Speaker 1: makes it hard when everything is not real time. Sorry, 465 00:25:51,040 --> 00:25:52,560 Speaker 1: I'm going to ask you to talk your book here 466 00:25:52,600 --> 00:25:54,920 Speaker 1: so it don't go too much when you're talking about it. 467 00:25:55,040 --> 00:25:57,840 Speaker 1: But let's think about the companies that have managed them 468 00:25:57,920 --> 00:26:00,760 Speaker 1: interventories pretty well. Macy's is one of them. And actually, again, 469 00:26:00,800 --> 00:26:04,720 Speaker 1: this is a company that's invested a lot in technology, 470 00:26:05,080 --> 00:26:06,879 Speaker 1: in the ability to ensure that they've got the right 471 00:26:06,920 --> 00:26:08,119 Speaker 1: thing in the right time and be able to be 472 00:26:08,160 --> 00:26:10,439 Speaker 1: a bit swifter about getting the things that are going 473 00:26:10,520 --> 00:26:13,160 Speaker 1: to sell in the door. Talk to us about how 474 00:26:13,320 --> 00:26:16,800 Speaker 1: your technology is perhaps helping alleviate some of the supply 475 00:26:16,960 --> 00:26:20,080 Speaker 1: chain headaches that have been such an hallmark. And basically 476 00:26:20,160 --> 00:26:23,439 Speaker 1: every e commerce and what bricks and mortar store at 477 00:26:23,480 --> 00:26:26,119 Speaker 1: the moment, right yeah, before the pandemic, everybody thought we 478 00:26:26,160 --> 00:26:29,400 Speaker 1: could push button today and tomorrow the the product would 479 00:26:29,400 --> 00:26:33,280 Speaker 1: show up, and in reality you really need them to 480 00:26:33,320 --> 00:26:36,600 Speaker 1: premendous amount of data between the different trading partners. So 481 00:26:37,040 --> 00:26:39,920 Speaker 1: where we started in food, food that there are over 482 00:26:40,040 --> 00:26:43,600 Speaker 1: ten million companies involved in getting food from where it's 483 00:26:43,600 --> 00:26:47,359 Speaker 1: produced to where it's consumed. But the technology that actually 484 00:26:47,440 --> 00:26:50,480 Speaker 1: is connecting all of this is based upon something was 485 00:26:50,520 --> 00:26:53,080 Speaker 1: invented in three year I was portnant, which is called 486 00:26:53,240 --> 00:26:56,600 Speaker 1: d I and it's very reactive in terms of curtures 487 00:26:56,680 --> 00:26:59,960 Speaker 1: orders that go through the whole supply chain. So everybody's 488 00:27:00,000 --> 00:27:01,880 Speaker 1: with the supply chain need to have reused time data 489 00:27:02,000 --> 00:27:04,600 Speaker 1: to understand what consumers are doing, what the prices, how 490 00:27:04,680 --> 00:27:07,440 Speaker 1: much inventory exists. So everybody through the whole supply chain 491 00:27:07,720 --> 00:27:10,800 Speaker 1: can have used time data and then we avoid these 492 00:27:11,200 --> 00:27:16,920 Speaker 1: huge overstocks that we've seen and also the shortages. That's 493 00:27:17,000 --> 00:27:20,760 Speaker 1: interesting to me because you're talking about the company controlling 494 00:27:20,800 --> 00:27:23,920 Speaker 1: what it can control, but how do you control the consumer. 495 00:27:24,000 --> 00:27:28,240 Speaker 1: We went into this period knowing that promotions would be key. 496 00:27:28,720 --> 00:27:30,800 Speaker 1: We knew that they would be greater than last year. 497 00:27:30,840 --> 00:27:33,439 Speaker 1: I think the data shows US promotions were greater than 498 00:27:33,520 --> 00:27:37,280 Speaker 1: last year. What's your analysis of the role that discounts 499 00:27:37,320 --> 00:27:39,760 Speaker 1: played for the online retailers. I think this kind of 500 00:27:39,800 --> 00:27:44,080 Speaker 1: something incredively important, but I think very often blunt instruments 501 00:27:44,160 --> 00:27:49,399 Speaker 1: are used when you can have surgical precision instead and uh, 502 00:27:50,040 --> 00:27:52,040 Speaker 1: in order to do surgical precision, you really need to 503 00:27:52,160 --> 00:27:55,480 Speaker 1: understand what store is the product selling in, what's price point, 504 00:27:55,800 --> 00:28:00,480 Speaker 1: how inflation sensitive is that particular area, So the discounting 505 00:28:01,160 --> 00:28:04,400 Speaker 1: can be much more precise in a way. Some care 506 00:28:04,520 --> 00:28:07,960 Speaker 1: more about about price and others, but you have to 507 00:28:08,000 --> 00:28:10,600 Speaker 1: have really granular data now to the skew and to 508 00:28:10,720 --> 00:28:13,920 Speaker 1: the store level, and also understand inventory levels and pricing 509 00:28:13,960 --> 00:28:17,920 Speaker 1: and what competitors are doing. So I thinks, see, historically 510 00:28:18,000 --> 00:28:20,920 Speaker 1: we've seen a lot of kind of plant instruments being 511 00:28:21,040 --> 00:28:24,120 Speaker 1: used and you can have for a very little affordable price. 512 00:28:24,200 --> 00:28:27,240 Speaker 1: You can use surgical data instead to to optimize it. 513 00:28:27,480 --> 00:28:30,119 Speaker 1: To us about how that really works, and say you 514 00:28:30,280 --> 00:28:32,760 Speaker 1: realize that a lot more shop is wanting a particular 515 00:28:32,840 --> 00:28:36,520 Speaker 1: item in Brooklyn, Visa VI, what's happening in Minnesota? And 516 00:28:36,600 --> 00:28:38,800 Speaker 1: you can therefore have a cheaper price point. But with 517 00:28:38,920 --> 00:28:40,600 Speaker 1: the e commerce being so prevalent, the fact that I 518 00:28:40,680 --> 00:28:42,400 Speaker 1: can while I'm in the store pick up my phone 519 00:28:42,400 --> 00:28:45,360 Speaker 1: and decide whether this is the price point I want 520 00:28:45,360 --> 00:28:46,760 Speaker 1: to pay out, whether I can wait a couple of 521 00:28:46,840 --> 00:28:49,800 Speaker 1: days and get it cheaper, how do you manage that 522 00:28:49,960 --> 00:28:53,040 Speaker 1: amount of data the consumer has? Yeah, no, you have 523 00:28:53,120 --> 00:28:55,840 Speaker 1: to put together all of the consumer data. There, data 524 00:28:55,920 --> 00:28:59,040 Speaker 1: that the retailer have, the distributor have, and the whole 525 00:28:59,080 --> 00:29:02,600 Speaker 1: supply chain in one kind of north star. In terms 526 00:29:02,640 --> 00:29:07,120 Speaker 1: of data understanding, very many just look at their own data, 527 00:29:07,240 --> 00:29:10,440 Speaker 1: but they are part of a big ecosystem of of 528 00:29:10,880 --> 00:29:14,040 Speaker 1: of companies that all need need the data. So that 529 00:29:14,280 --> 00:29:17,440 Speaker 1: the sharing of data cross companies was something that didn't 530 00:29:17,480 --> 00:29:21,560 Speaker 1: happen as much before the pandemic, but kind of operated 531 00:29:21,600 --> 00:29:25,480 Speaker 1: in their own silo. But now people see that sharing 532 00:29:25,640 --> 00:29:28,760 Speaker 1: data matters a lot because then the manufacturer can be 533 00:29:28,880 --> 00:29:32,360 Speaker 1: ahead of it. They can when build their products and 534 00:29:32,440 --> 00:29:35,280 Speaker 1: they can get the products out in in time. Now 535 00:29:35,360 --> 00:29:38,840 Speaker 1: the returns about tremendous amount of products sitting that was 536 00:29:38,920 --> 00:29:43,120 Speaker 1: for the last last season, So getting there in time, 537 00:29:43,800 --> 00:29:50,080 Speaker 1: that's when that's that's incredibly important. It's novembery give us 538 00:29:50,120 --> 00:29:54,560 Speaker 1: the rich steal outlook for the rest of the holiday season, 539 00:29:54,600 --> 00:29:58,480 Speaker 1: which seems to go on indefinitely. That's always Aliday season, 540 00:29:58,480 --> 00:30:00,880 Speaker 1: the season in retail, which is which is incredible. So 541 00:30:01,400 --> 00:30:05,240 Speaker 1: I I think we're gonna Scostico see continued spend in 542 00:30:05,840 --> 00:30:10,920 Speaker 1: in two four six seven eight percent increase in spending 543 00:30:10,960 --> 00:30:13,240 Speaker 1: two four. I still think we're gonna see a lot 544 00:30:13,320 --> 00:30:16,680 Speaker 1: of price reductions UM, and we're going to see a 545 00:30:16,720 --> 00:30:19,280 Speaker 1: lot of shortage, shortage of products. I think a lot 546 00:30:19,320 --> 00:30:22,880 Speaker 1: of people are gonna realize that the supply chain crisis 547 00:30:23,000 --> 00:30:25,800 Speaker 1: are are not over UM and that is going to 548 00:30:25,840 --> 00:30:29,520 Speaker 1: continue kind of to ripple through the supply chain. All Right, 549 00:30:29,640 --> 00:30:32,840 Speaker 1: our h Hostile CEO and founder of Chris thank you. 550 00:30:44,960 --> 00:30:48,400 Speaker 1: We see the same confidence and the full confidence in 551 00:30:48,480 --> 00:30:50,400 Speaker 1: point based that we've seen previously, and we do not 552 00:30:50,520 --> 00:30:54,400 Speaker 1: see anybody leaving the space. And that said, given that 553 00:30:55,160 --> 00:30:57,600 Speaker 1: Inmia and when I mean in Meda, I not only 554 00:30:57,720 --> 00:31:01,120 Speaker 1: mean the European Union with the MIKA regular but also 555 00:31:01,240 --> 00:31:05,000 Speaker 1: the Financial Services and Markets build a passing through the 556 00:31:05,160 --> 00:31:08,240 Speaker 1: UK Parliament at the moment, as well as the creation 557 00:31:08,400 --> 00:31:12,000 Speaker 1: of the VARA, the Virtual Asset Regulatory Authority in Dubai 558 00:31:12,560 --> 00:31:16,720 Speaker 1: is really leading the charge in creating regulatory clarity and 559 00:31:16,840 --> 00:31:21,840 Speaker 1: a regulatory framework that we can work within. Daniel see 560 00:31:21,880 --> 00:31:24,080 Speaker 1: for their m e a, Vice President managing director of 561 00:31:24,120 --> 00:31:27,040 Speaker 1: coin Base. Now let's talk about confidence more broadly in 562 00:31:27,120 --> 00:31:30,560 Speaker 1: the space, particularly after today's perhaps inevitable news the block 563 00:31:30,640 --> 00:31:33,719 Speaker 1: fires finally had to file for bankruptcy joining us now 564 00:31:33,760 --> 00:31:36,160 Speaker 1: has been in motion basa, can I say finally because 565 00:31:36,600 --> 00:31:39,440 Speaker 1: it was anticipated because the White Knight had been the 566 00:31:39,520 --> 00:31:42,680 Speaker 1: now bankrupt FTX for them previously. We have been expecting 567 00:31:42,680 --> 00:31:44,880 Speaker 1: a filing and reporting now for at least a week 568 00:31:44,960 --> 00:31:46,960 Speaker 1: here that this would be expected at some point, and 569 00:31:47,040 --> 00:31:49,040 Speaker 1: now we have some details and what do we have 570 00:31:49,280 --> 00:31:52,320 Speaker 1: from them? We have not just the plan to file 571 00:31:52,360 --> 00:31:55,000 Speaker 1: for bankruptcy in New Jersey, we have some details about 572 00:31:55,040 --> 00:31:58,200 Speaker 1: the scope of how big this bankruptcy proceeding is going 573 00:31:58,280 --> 00:32:00,560 Speaker 1: to be. So one billion to ten million dollars in 574 00:32:00,640 --> 00:32:05,840 Speaker 1: assets is the general size of black quite a large size, 575 00:32:05,880 --> 00:32:08,080 Speaker 1: but more than a hundred thousand creditors. We would have 576 00:32:08,120 --> 00:32:09,760 Speaker 1: to keep an eye on future filings to see how 577 00:32:09,840 --> 00:32:13,160 Speaker 1: many that will ultimately be. Remember f t X also 578 00:32:13,240 --> 00:32:15,680 Speaker 1: had checked that last box on their filing and found 579 00:32:15,720 --> 00:32:17,800 Speaker 1: out later that was about a million people at play 580 00:32:17,880 --> 00:32:19,840 Speaker 1: here that we're talking about. We also know that the 581 00:32:19,920 --> 00:32:23,480 Speaker 1: concentration is different among the unsecured creditor group. So what 582 00:32:23,720 --> 00:32:26,160 Speaker 1: is that exactly? It's more than seven hundred million that 583 00:32:26,440 --> 00:32:30,080 Speaker 1: is really highlighted here earmark to the trustee of different 584 00:32:30,160 --> 00:32:32,800 Speaker 1: depositors f t X itself, which we'll talk a little 585 00:32:32,800 --> 00:32:35,080 Speaker 1: more about it now. And then also you have the 586 00:32:35,240 --> 00:32:40,120 Speaker 1: sec from the remainder of them before exactly. So there 587 00:32:40,120 --> 00:32:41,800 Speaker 1: are a lot of names that you don't have here 588 00:32:41,880 --> 00:32:44,360 Speaker 1: similar to f t X. But you know that those 589 00:32:44,560 --> 00:32:47,400 Speaker 1: amounts are kind of isolated here between one million and 590 00:32:47,480 --> 00:32:50,480 Speaker 1: thirty million dollars. So you might see more contagion there 591 00:32:50,560 --> 00:32:53,640 Speaker 1: among the people that they're owed money too, because you 592 00:32:53,680 --> 00:32:55,480 Speaker 1: don't know what they'll get back yet, but we know 593 00:32:55,760 --> 00:32:59,160 Speaker 1: that that is the general size of per per customer 594 00:32:59,240 --> 00:33:02,640 Speaker 1: per creditor that has owed money and how much generally 595 00:33:02,680 --> 00:33:07,280 Speaker 1: their own. There's a question of chronology and timeline because 596 00:33:07,440 --> 00:33:11,560 Speaker 1: it's clear Block five had issues before f t x 597 00:33:11,720 --> 00:33:14,480 Speaker 1: is collapse, but at the same time f t x 598 00:33:14,600 --> 00:33:17,560 Speaker 1: is collapse seems to have made things worse for Block 599 00:33:17,640 --> 00:33:20,680 Speaker 1: fi um if you follow that sonale, what is the 600 00:33:20,760 --> 00:33:24,600 Speaker 1: relationship now between the two? The claim here on the 601 00:33:24,760 --> 00:33:28,080 Speaker 1: bankruptcy filing is about two seventy five million dollars for 602 00:33:28,320 --> 00:33:31,920 Speaker 1: f t X earmarked. There's some really interesting unanswered questions here. 603 00:33:32,080 --> 00:33:35,400 Speaker 1: Ed if you look at the testimony by an advisor here, 604 00:33:35,720 --> 00:33:38,720 Speaker 1: you had seen that they didn't get money. They didn't 605 00:33:38,720 --> 00:33:40,360 Speaker 1: get all the money that they had asked for from 606 00:33:40,440 --> 00:33:42,520 Speaker 1: f t X to begin with, according to the Block 607 00:33:42,600 --> 00:33:45,640 Speaker 1: five filings. Now remember to your points, some of these 608 00:33:45,680 --> 00:33:47,720 Speaker 1: issues started before f t X. That's why f t 609 00:33:47,960 --> 00:33:49,840 Speaker 1: X got involved in the first place. They were related 610 00:33:49,880 --> 00:33:53,240 Speaker 1: to three Arrows in ft x is filing. I would 611 00:33:53,440 --> 00:33:56,480 Speaker 1: also point out that apparently the US business of f 612 00:33:56,640 --> 00:34:00,360 Speaker 1: t X lent money to block FI in part through 613 00:34:00,480 --> 00:34:04,200 Speaker 1: that ft t token. And so my question here is 614 00:34:04,480 --> 00:34:08,440 Speaker 1: this entire agreement, who owes who what and who is 615 00:34:08,480 --> 00:34:12,680 Speaker 1: responsible for actually paying each other back after this kind 616 00:34:12,719 --> 00:34:17,680 Speaker 1: of tight web that needed knitted after the three Arrows debacle, 617 00:34:17,920 --> 00:34:21,160 Speaker 1: I think is interesting. Now, remember blockedby is also said 618 00:34:21,280 --> 00:34:24,399 Speaker 1: that getting money back from f t X might take 619 00:34:24,440 --> 00:34:26,800 Speaker 1: time give an f t x is own bankruptcy. So 620 00:34:27,120 --> 00:34:31,920 Speaker 1: things are certainly complicated between the two Shinali. Over the weekend, 621 00:34:32,560 --> 00:34:35,160 Speaker 1: it looked as though much of crypto twittersho decided to 622 00:34:35,280 --> 00:34:37,680 Speaker 1: up sticks and leave to the Bahamas at some point 623 00:34:37,719 --> 00:34:39,799 Speaker 1: into their own digging in some way, shape or form. 624 00:34:40,080 --> 00:34:42,680 Speaker 1: That is, because of the different jurisdictions with which ft 625 00:34:42,880 --> 00:34:45,160 Speaker 1: X has been enveloped the same thing he will block 626 00:34:45,200 --> 00:34:47,759 Speaker 1: five bit. Obviously, Chapter eleven isn't as a protection here 627 00:34:47,800 --> 00:34:50,000 Speaker 1: in the US, but it wasn't just based in the US. 628 00:34:50,160 --> 00:34:52,839 Speaker 1: I spent an inordinate amount of time with Bankruptcy US 629 00:34:52,960 --> 00:34:55,560 Speaker 1: today for that reason because it to that end they 630 00:34:55,719 --> 00:34:58,120 Speaker 1: had many people that win shine truly in this case, 631 00:34:58,239 --> 00:34:59,839 Speaker 1: I will tell you just how much. In a moment, 632 00:35:00,120 --> 00:35:02,760 Speaker 1: for Block five, they file the petition with the Bermuda 633 00:35:02,760 --> 00:35:05,880 Speaker 1: and Supreme Court here so that they would have provisional 634 00:35:06,120 --> 00:35:10,080 Speaker 1: liquidators in both regions. Now, remember from Bahamas point of 635 00:35:10,120 --> 00:35:12,279 Speaker 1: view for f t X, there is a lot of 636 00:35:12,719 --> 00:35:16,320 Speaker 1: dispute that has occurred since the bankruptcy proceedings have begune 637 00:35:16,840 --> 00:35:18,880 Speaker 1: and to the point that you even have the Attorney 638 00:35:18,920 --> 00:35:21,759 Speaker 1: General saying that some of the words that have come 639 00:35:21,840 --> 00:35:24,360 Speaker 1: from the new f t X EO are regrettable. So 640 00:35:24,680 --> 00:35:27,520 Speaker 1: to the extent that the Bermuda government in the US 641 00:35:27,960 --> 00:35:31,160 Speaker 1: stay in line in this bankruptcy filing. Again, they want 642 00:35:31,200 --> 00:35:34,040 Speaker 1: to recoup money for their customers, for their own people. 643 00:35:34,200 --> 00:35:36,040 Speaker 1: And at the end of the day, what it seems 644 00:35:36,080 --> 00:35:38,120 Speaker 1: like from the statements we're seeing from the Bahamas is 645 00:35:38,239 --> 00:35:42,720 Speaker 1: also redefined the credibility of every single jurisdictions financial system 646 00:35:42,840 --> 00:35:47,800 Speaker 1: and ability to embrace crypto still but have tight rules 647 00:35:47,960 --> 00:35:50,320 Speaker 1: around the industry such that people don't lose their funds. 648 00:35:50,520 --> 00:35:52,400 Speaker 1: We go to go shnati, but you teased us how 649 00:35:52,480 --> 00:35:56,040 Speaker 1: much of the bankrupcy loise million dollars? So right for some, 650 00:35:57,160 --> 00:35:59,200 Speaker 1: we thank you so much. She's going to go back 651 00:35:59,200 --> 00:36:01,359 Speaker 1: to those bankrupcy to is now. Meanwhile, coming up, Elon 652 00:36:01,440 --> 00:36:04,560 Speaker 1: Musk picks a fight with Apple. I'll tell you why next. 653 00:36:04,920 --> 00:36:30,200 Speaker 1: This is black So today Elon Musk has once again 654 00:36:30,440 --> 00:36:32,880 Speaker 1: been going viral on his own platform and others. This 655 00:36:33,080 --> 00:36:35,960 Speaker 1: time he's stirring a fight with none other than Apple. Now. 656 00:36:36,080 --> 00:36:39,000 Speaker 1: Mask says Apple has halted most of its advertising on 657 00:36:39,080 --> 00:36:42,480 Speaker 1: Twitter and asked the company quote if they hate free 658 00:36:42,520 --> 00:36:45,680 Speaker 1: speech in America. He even appealed directly to Apple CEO, 659 00:36:45,960 --> 00:36:49,040 Speaker 1: asking what's going on here? Tim Cook. He also went 660 00:36:49,120 --> 00:36:52,600 Speaker 1: on to accuse the company threatening to remove the platform 661 00:36:52,680 --> 00:36:55,439 Speaker 1: from its app store. He did said not say why though, 662 00:36:55,600 --> 00:36:59,160 Speaker 1: And now this will comes as many companies have halted spending. 663 00:36:59,200 --> 00:37:01,480 Speaker 1: We know on it and make concerns about your mass 664 00:37:01,520 --> 00:37:04,440 Speaker 1: content moderation plans for the site watch dogs site and 665 00:37:04,480 --> 00:37:07,760 Speaker 1: fact medium Matters reported last week that half of Twitter's 666 00:37:07,840 --> 00:37:11,000 Speaker 1: top advertisers had pulled their advertising on Twitter after concerns 667 00:37:11,000 --> 00:37:13,200 Speaker 1: about the direction of Twitter Look Forward Jeep among them. 668 00:37:13,400 --> 00:37:16,960 Speaker 1: Elon Musk, meanwhile, has been blaming activists for pressuring advertisers 669 00:37:17,200 --> 00:37:19,680 Speaker 1: and has talked about Twitter seeing a massive drop in 670 00:37:19,760 --> 00:37:22,640 Speaker 1: revenue already. He said in the past that he wants 671 00:37:22,719 --> 00:37:24,640 Speaker 1: to make money for Twitter by turning it into a 672 00:37:24,719 --> 00:37:28,080 Speaker 1: paid subscription service, with a relaunch of its paid verified 673 00:37:28,120 --> 00:37:31,000 Speaker 1: services due Friday, of course, but for now, the vast 674 00:37:31,040 --> 00:37:35,880 Speaker 1: majority of Twitter's revenue still comes from advertising. Ed Now, 675 00:37:36,040 --> 00:37:38,440 Speaker 1: let's stick with this story, bringing Bloomberg's Kurt Wagner, a 676 00:37:38,600 --> 00:37:41,640 Speaker 1: social media reporter, for more. I mean, Kurt, there's a 677 00:37:41,680 --> 00:37:46,840 Speaker 1: lot to unpick here. What happens if Apple took Twitter 678 00:37:46,920 --> 00:37:50,719 Speaker 1: off the app store. I guess that's the place to start, sure, 679 00:37:50,800 --> 00:37:53,320 Speaker 1: I mean, that's a pretty dramatic outcome, right, Like this 680 00:37:53,480 --> 00:37:57,480 Speaker 1: would be that Twitter is routinely, uh, you know, allowing 681 00:37:58,239 --> 00:38:01,080 Speaker 1: terrible content on the plan from usually Apple reserves this 682 00:38:01,239 --> 00:38:05,600 Speaker 1: for you know, Gregis violations, and so if this worted 683 00:38:05,640 --> 00:38:07,879 Speaker 1: to be the case, of course, that's a huge blow 684 00:38:08,000 --> 00:38:11,160 Speaker 1: to Twitter. Right. The app store is is probably the 685 00:38:11,360 --> 00:38:13,600 Speaker 1: main way, or one of the main ways that most 686 00:38:13,640 --> 00:38:16,680 Speaker 1: of its users get the app get app updates, so 687 00:38:16,920 --> 00:38:18,680 Speaker 1: you know, for example, I believe the app would still 688 00:38:18,719 --> 00:38:20,360 Speaker 1: work on my phone, but in order to you know, 689 00:38:20,440 --> 00:38:23,279 Speaker 1: get an updated version I have an iPhone, I might 690 00:38:23,360 --> 00:38:25,040 Speaker 1: have to you know, figure out a way to download 691 00:38:25,080 --> 00:38:26,440 Speaker 1: that from the web. It's the kind of thing that's 692 00:38:26,440 --> 00:38:28,400 Speaker 1: going to erode over time and cause a lot of 693 00:38:28,440 --> 00:38:31,279 Speaker 1: people to either stop using Twitter or or you know, 694 00:38:31,800 --> 00:38:33,920 Speaker 1: possibly have to move to a different device. And so 695 00:38:34,480 --> 00:38:36,200 Speaker 1: that's a huge issue. And one of the reasons I 696 00:38:36,280 --> 00:38:39,200 Speaker 1: tweeted that I think Twitter needs Apple a lot more 697 00:38:39,280 --> 00:38:41,960 Speaker 1: than Apple needs Twitter here, and that's because Apple has 698 00:38:42,040 --> 00:38:44,880 Speaker 1: the distribution that it's at its fingertips. And actually I 699 00:38:45,000 --> 00:38:47,360 Speaker 1: was pretty surprised that Apple was one of the biggest 700 00:38:47,360 --> 00:38:54,040 Speaker 1: advertisers with Twitter cut. Meanwhile, cryptic sort of war almost 701 00:38:54,120 --> 00:38:57,359 Speaker 1: he's declared on Apple continues and talk to us about 702 00:38:57,400 --> 00:39:01,439 Speaker 1: what he means by a free sprint speech suppression. He says, 703 00:39:01,520 --> 00:39:04,160 Speaker 1: he said in a tweet, well, the Twitter falls on 704 00:39:04,520 --> 00:39:07,560 Speaker 1: free speech. This suppression will soon be published on the 705 00:39:07,640 --> 00:39:09,680 Speaker 1: social media platform. What does he mean was he going 706 00:39:09,760 --> 00:39:13,319 Speaker 1: to declare on that? Well, we don't know, but he's 707 00:39:13,360 --> 00:39:16,440 Speaker 1: certainly kind of piquing our interest, right He's saying, hey, 708 00:39:16,520 --> 00:39:18,759 Speaker 1: look now that I'm inside the building now that I've 709 00:39:18,800 --> 00:39:21,200 Speaker 1: had time to figure out, you know, what's been going 710 00:39:21,239 --> 00:39:24,080 Speaker 1: on in the code or behind the scenes. Uh, you know, 711 00:39:24,440 --> 00:39:27,080 Speaker 1: I'm going to kind of unveil what Twitter has been 712 00:39:27,160 --> 00:39:30,880 Speaker 1: hiding from you this whole time, right and and presumably, um, 713 00:39:30,960 --> 00:39:32,560 Speaker 1: the way I'm reading that is that means he's going 714 00:39:32,640 --> 00:39:34,440 Speaker 1: to come out and say, you know, here are the 715 00:39:34,680 --> 00:39:37,760 Speaker 1: types of tweets that maybe we're down ranked in the algorithm, 716 00:39:37,880 --> 00:39:40,080 Speaker 1: or here's the types of accounts that had been removed 717 00:39:40,200 --> 00:39:43,080 Speaker 1: that maybe you didn't know about. Again, this is speculation, right, 718 00:39:43,120 --> 00:39:47,320 Speaker 1: because he's intentionally sort of building this drama there. But again, 719 00:39:47,640 --> 00:39:49,680 Speaker 1: you know, what he has tried to say all along 720 00:39:49,840 --> 00:39:53,440 Speaker 1: is that he is this free speech uh kind of evangelist, 721 00:39:53,480 --> 00:39:55,440 Speaker 1: and he's gonna come in and he's going to make 722 00:39:55,520 --> 00:39:58,239 Speaker 1: Twitter the free speech version of the service that people 723 00:39:58,280 --> 00:40:00,160 Speaker 1: wanted to be. That it has not been be as 724 00:40:00,200 --> 00:40:02,200 Speaker 1: it has you know, rules around what you can and 725 00:40:02,280 --> 00:40:07,240 Speaker 1: cannot say. We know the new look verification systems coming Friday, 726 00:40:07,600 --> 00:40:11,719 Speaker 1: what has must said? What are the details? Yeah, well 727 00:40:11,800 --> 00:40:13,560 Speaker 1: you mentioned and I think we talked about this and 728 00:40:13,760 --> 00:40:16,000 Speaker 1: actually on spaces on Friday when we were there, that 729 00:40:16,080 --> 00:40:19,080 Speaker 1: there's going to be a series of different colored badges, right, 730 00:40:19,160 --> 00:40:21,359 Speaker 1: at least that's the plan for now, and I'm I'm 731 00:40:21,400 --> 00:40:23,080 Speaker 1: going to forget the colors off the top of my head. 732 00:40:23,120 --> 00:40:25,879 Speaker 1: But presumably you know, uh, you might have one color 733 00:40:25,920 --> 00:40:27,800 Speaker 1: if you are an elected official. You might have another 734 00:40:27,840 --> 00:40:31,000 Speaker 1: color if you are a brand, or if you're running 735 00:40:31,080 --> 00:40:33,720 Speaker 1: kind of an account that's representing you know, a company 736 00:40:34,000 --> 00:40:37,480 Speaker 1: versus an individual person. Right, And so this is actually 737 00:40:37,520 --> 00:40:40,160 Speaker 1: not a new idea. This is something that folks have 738 00:40:40,760 --> 00:40:43,160 Speaker 1: inside Twitter have kicked around for a long time before 739 00:40:43,239 --> 00:40:45,960 Speaker 1: Elon showed up, this idea that maybe there should be 740 00:40:46,040 --> 00:40:49,040 Speaker 1: different kind of labels for different accounts, including one for bots. 741 00:40:49,080 --> 00:40:51,440 Speaker 1: Believe it or not, there was an idea that, you know, 742 00:40:51,600 --> 00:40:53,640 Speaker 1: maybe bots should exist on Twitter, but they should be 743 00:40:53,719 --> 00:40:56,360 Speaker 1: labeled as bots that you know, you're interacting with a 744 00:40:56,480 --> 00:40:59,239 Speaker 1: kind of an automated account. So, as you know, Ed 745 00:40:59,280 --> 00:41:03,040 Speaker 1: and Caroline, this plan for verification has changed almost daily 746 00:41:03,160 --> 00:41:05,279 Speaker 1: from the last couple of weeks. So we'll see what 747 00:41:05,440 --> 00:41:07,480 Speaker 1: it looks like when we actually see it live. But 748 00:41:07,560 --> 00:41:09,200 Speaker 1: as of now, you know, it seems to be that 749 00:41:09,280 --> 00:41:11,560 Speaker 1: you're starting to get a little bit more strategic around 750 00:41:11,920 --> 00:41:13,839 Speaker 1: um instead of everyone gets the blue check. There's going 751 00:41:13,880 --> 00:41:17,040 Speaker 1: to be different variations of that green, gold, blue. We 752 00:41:17,160 --> 00:41:18,560 Speaker 1: wait to see what the rainbow is going to be. Like, 753 00:41:19,200 --> 00:41:21,680 Speaker 1: Kurt Wagner, so great that you join us on spaces. 754 00:41:21,760 --> 00:41:23,160 Speaker 1: That was a nice little tea. You've gotta come and 755 00:41:23,239 --> 00:41:26,959 Speaker 1: join us on Friday, Twitter Spaces and indeed then digging 756 00:41:26,960 --> 00:41:28,640 Speaker 1: a little bit more on what's happening. I'm sure there's 757 00:41:28,640 --> 00:41:31,200 Speaker 1: going to be plenty to discuss come Friday. It's only Monday, Kurt. 758 00:41:31,239 --> 00:41:32,640 Speaker 1: We're gonna let you get back to your day job, 759 00:41:32,680 --> 00:41:35,480 Speaker 1: which is tracking what the cryptic tweet doesn't even mean, 760 00:41:35,640 --> 00:41:38,720 Speaker 1: but and it really is a story that continues to unfold. 761 00:41:38,800 --> 00:41:41,360 Speaker 1: And the fact that you know, Musque, Yes, the wealthiest 762 00:41:41,360 --> 00:41:44,920 Speaker 1: individual is taking on Apple and almost retweeting out there 763 00:41:45,040 --> 00:41:48,320 Speaker 1: what some of the videos the Epic Games made about 764 00:41:48,440 --> 00:41:52,319 Speaker 1: Apple's business practices. It really does feel like he's going 765 00:41:52,400 --> 00:41:55,800 Speaker 1: for them. It's gold check for companies, great check for government, 766 00:41:55,840 --> 00:41:58,359 Speaker 1: and blue for individuals. By the way, Carrot, that does 767 00:41:58,400 --> 00:42:00,800 Speaker 1: it for this edition of Bloomberg Technology. Make sure to 768 00:42:00,920 --> 00:42:04,360 Speaker 1: join us again tomorrow. AWS c O Adams Litski joins 769 00:42:04,440 --> 00:42:07,000 Speaker 1: us as Amazon kicks off it's annual cloud conference. You 770 00:42:07,080 --> 00:42:09,759 Speaker 1: don't want to miss it. This Carrow is Bloomberg