1 00:00:14,080 --> 00:00:17,840 Speaker 1: I read Lovelow in San Francisco. This is Bloomberg Technology 2 00:00:17,880 --> 00:00:20,680 Speaker 1: coming up. The US labor market looks resilient after a 3 00:00:20,720 --> 00:00:24,119 Speaker 1: strong November jobs report, but things aren't so rosy in 4 00:00:24,160 --> 00:00:26,640 Speaker 1: the tech market with a wave of layoffs. We break 5 00:00:26,680 --> 00:00:29,360 Speaker 1: down what this report means for the FED and Silicon 6 00:00:29,440 --> 00:00:32,760 Speaker 1: Valley at large. Plus Elon Musk says he is releasing 7 00:00:32,880 --> 00:00:36,960 Speaker 1: quote what really happened with the Hunter Biden's story suppression 8 00:00:37,240 --> 00:00:40,680 Speaker 1: by Twitter? Two years after Twitter changed its policies in 9 00:00:40,720 --> 00:00:42,800 Speaker 1: the wake of the New York Post report. We have 10 00:00:42,920 --> 00:00:46,320 Speaker 1: the latest and Uber's CEO says the company is ready 11 00:00:46,400 --> 00:00:50,160 Speaker 1: for any economic environment with no plans to cut the workforce, 12 00:00:50,400 --> 00:00:54,680 Speaker 1: as competitors announced layoffs and new names revealed job cuts. First, 13 00:00:55,040 --> 00:00:56,760 Speaker 1: of course, we've got to take a look at how 14 00:00:56,840 --> 00:01:02,400 Speaker 1: markets ended Friday. Major technology into companies mostly lower after 15 00:01:02,520 --> 00:01:05,320 Speaker 1: what was a pretty hot jobs report. There than has 16 00:01:05,360 --> 00:01:07,679 Speaker 1: that one actually pairing most of its losses to close 17 00:01:07,760 --> 00:01:10,480 Speaker 1: down four tenths of one percent, some under performance and 18 00:01:10,560 --> 00:01:13,920 Speaker 1: chip names really interesting out performance in the US listed 19 00:01:13,959 --> 00:01:16,800 Speaker 1: shares of Chinese tech companies. Then as that Golden Dragon 20 00:01:16,800 --> 00:01:19,800 Speaker 1: Index closing up five point four percent, it's highest level 21 00:01:19,880 --> 00:01:23,399 Speaker 1: since September. The story they're really around the idea we 22 00:01:23,480 --> 00:01:25,880 Speaker 1: might see some easing of policy in China that would 23 00:01:25,880 --> 00:01:28,280 Speaker 1: be supportive for the economy. Of course, yields were a 24 00:01:28,319 --> 00:01:31,240 Speaker 1: big story on that hot jobs print, but actually the 25 00:01:31,280 --> 00:01:33,440 Speaker 1: jump we saw on the tenure yield reverse by the 26 00:01:33,480 --> 00:01:36,160 Speaker 1: time we were finished on Friday afternoon. In terms of 27 00:01:36,200 --> 00:01:38,640 Speaker 1: individual names, it was some of the mega caps that 28 00:01:38,680 --> 00:01:41,440 Speaker 1: were hardest hit by that job's report, leading declines on 29 00:01:41,480 --> 00:01:44,480 Speaker 1: the NASA one hundred. But along with the broadest story, 30 00:01:44,840 --> 00:01:49,200 Speaker 1: they two pair those those declined the worst performance than 31 00:01:49,200 --> 00:01:52,559 Speaker 1: has that one hundred Z scaler down as much as percent? 32 00:01:52,680 --> 00:01:57,120 Speaker 1: Friday Cloud Security Company, It's forecast tepid. How often has 33 00:01:57,160 --> 00:02:00,400 Speaker 1: that been the story throughout this earning season, analysts noticing 34 00:02:00,400 --> 00:02:04,400 Speaker 1: that revenue and billings growth decelerating along with macro headwinds. 35 00:02:04,400 --> 00:02:07,600 Speaker 1: But the story of the day, of course, jobs, jobs 36 00:02:07,640 --> 00:02:09,240 Speaker 1: and more jobs. So let's get into all of this 37 00:02:09,360 --> 00:02:12,480 Speaker 1: and recap the data brutal week as well of layoffs 38 00:02:12,639 --> 00:02:16,240 Speaker 1: across the technology industry. His Bloomberg's Katie Gray felt, Katie, 39 00:02:16,280 --> 00:02:19,239 Speaker 1: what was your read on the print we got Friday morning? Well, 40 00:02:19,320 --> 00:02:22,119 Speaker 1: at eight thirty am Eastern was a long time ago. 41 00:02:22,160 --> 00:02:24,200 Speaker 1: So let's just quickly go through the numbers. Hit there. 42 00:02:24,240 --> 00:02:27,959 Speaker 1: You had two hundred sixty thousand jobs added last month, 43 00:02:28,120 --> 00:02:31,960 Speaker 1: just shattering expectations. The unemployment rates stayed put at three 44 00:02:32,000 --> 00:02:35,440 Speaker 1: point seven per cent, that is extremely low. The big 45 00:02:35,440 --> 00:02:37,720 Speaker 1: thing that stuck out to me that was average hourly 46 00:02:37,800 --> 00:02:41,040 Speaker 1: earnings month over month. Those rose by point six percent. 47 00:02:41,120 --> 00:02:44,639 Speaker 1: That was double the estimates. And that's what really spooked 48 00:02:44,880 --> 00:02:48,600 Speaker 1: markets early, particularly when it comes to wages, because snack 49 00:02:48,760 --> 00:02:52,120 Speaker 1: great news for Federal Reserve just trying it's darnedst to 50 00:02:52,480 --> 00:02:55,880 Speaker 1: cool price pressures without having to break the economy in 51 00:02:55,960 --> 00:02:57,960 Speaker 1: the process. With that in mind, I mean, you saw 52 00:02:57,960 --> 00:03:00,359 Speaker 1: a lot of movement early, a big drop the SMP 53 00:03:00,480 --> 00:03:04,280 Speaker 1: five hundred in the NASTAC one hundred. Stocks did clawback 54 00:03:04,320 --> 00:03:06,600 Speaker 1: a lot of those losses, but like you pointed out, 55 00:03:06,639 --> 00:03:08,799 Speaker 1: you look at the NASTAC one hundred still finished down 56 00:03:08,800 --> 00:03:11,880 Speaker 1: about four tenths of a percent. Because ed, I don't 57 00:03:11,919 --> 00:03:15,160 Speaker 1: need to tell you that higher rates typically aren't good 58 00:03:15,160 --> 00:03:19,520 Speaker 1: for the kind of growth tech names that populate that index. Yeah, 59 00:03:19,560 --> 00:03:21,280 Speaker 1: I'm glad you said that, because I sit here in 60 00:03:21,320 --> 00:03:23,320 Speaker 1: this chair and people say to me, this is a 61 00:03:23,360 --> 00:03:25,880 Speaker 1: technology show. Why are you so focused on the Fed? 62 00:03:25,960 --> 00:03:28,360 Speaker 1: It's always about the Fed, right, I guess you know. 63 00:03:28,480 --> 00:03:31,359 Speaker 1: The outlook for rates is unclear at the best of times. 64 00:03:31,360 --> 00:03:34,040 Speaker 1: What do we learn from that market reaction about what 65 00:03:34,080 --> 00:03:36,960 Speaker 1: we might see from the Fed this month and going forward? Well, 66 00:03:37,000 --> 00:03:38,920 Speaker 1: and you and I speak the same language. It's all 67 00:03:38,960 --> 00:03:41,400 Speaker 1: about the Fed. And in terms of what the Fed 68 00:03:41,480 --> 00:03:44,320 Speaker 1: might do at this month's meeting. I mean, we heard 69 00:03:44,320 --> 00:03:48,120 Speaker 1: from Jerome Peal on Wednesday saying that they could moderate 70 00:03:48,160 --> 00:03:51,360 Speaker 1: the pace of rate hikes this month. It seems like 71 00:03:51,400 --> 00:03:53,800 Speaker 1: that is what markets are sticking with. Because you didn't 72 00:03:53,800 --> 00:03:57,040 Speaker 1: really see bets change on a fifty basis point move 73 00:03:57,120 --> 00:04:00,360 Speaker 1: for this month versus seventy five basis points. You did 74 00:04:00,440 --> 00:04:03,400 Speaker 1: see wagers on how high the terminal rate the end 75 00:04:03,440 --> 00:04:06,480 Speaker 1: destination for the Federal Reserve. That did nudge up a 76 00:04:06,520 --> 00:04:08,360 Speaker 1: little bit, but that's also in line with what we 77 00:04:08,440 --> 00:04:11,440 Speaker 1: heard from Pal on Wednesday, that that could be higher 78 00:04:11,480 --> 00:04:15,840 Speaker 1: than their previous forecasts, which we got back in September. Alright, 79 00:04:15,920 --> 00:04:18,240 Speaker 1: Katy Gray felt happy Friday, get out of there, enjoy 80 00:04:18,279 --> 00:04:21,480 Speaker 1: your weekend, but we will continue the conversation of bringing 81 00:04:21,560 --> 00:04:25,560 Speaker 1: John le chief economist for Decision Intelligence Company morning consult 82 00:04:25,880 --> 00:04:28,599 Speaker 1: for his read on it. John, Welcome to the show 83 00:04:28,960 --> 00:04:33,120 Speaker 1: Data hot Print for Jobs Friday. What's your interpretation? Yeah, 84 00:04:33,160 --> 00:04:35,200 Speaker 1: thanks for having me. I mean, I think the headline 85 00:04:35,240 --> 00:04:38,720 Speaker 1: numbers that things didn't sort of move from the tech 86 00:04:38,800 --> 00:04:43,640 Speaker 1: sector into the broader economy. They remain relatively contained. Um, 87 00:04:43,680 --> 00:04:46,159 Speaker 1: But the twelve month trend is pretty clear at this 88 00:04:46,200 --> 00:04:49,000 Speaker 1: point we are seeing a slowing rate of jobs growth, 89 00:04:49,279 --> 00:04:51,320 Speaker 1: probably not quite as slow as the FED would like, 90 00:04:51,800 --> 00:04:55,400 Speaker 1: but you know, I think going forward there's some room 91 00:04:55,440 --> 00:04:59,400 Speaker 1: for near term optimism in particularly because these tech layoffs 92 00:04:59,400 --> 00:05:02,880 Speaker 1: have yet to trickle over and affect the broader economy. 93 00:05:03,320 --> 00:05:05,480 Speaker 1: I think it's hard to make sense of a lot 94 00:05:05,520 --> 00:05:08,600 Speaker 1: of this, right, The data is incongruous with with with 95 00:05:08,680 --> 00:05:11,720 Speaker 1: the different data points that we're getting every single day, 96 00:05:11,760 --> 00:05:16,760 Speaker 1: how is it that we're seeing very rapid announcements of layoffs. 97 00:05:16,760 --> 00:05:18,760 Speaker 1: So I'm thinking, of course, of the challenge of grade 98 00:05:18,839 --> 00:05:22,320 Speaker 1: data those announcements of layoffs in the tech industry. And 99 00:05:22,400 --> 00:05:24,600 Speaker 1: yet if you take a step back and look at 100 00:05:24,600 --> 00:05:27,039 Speaker 1: the broader picture for the US economy, we see a 101 00:05:27,080 --> 00:05:31,640 Speaker 1: resilient labor market. Where is the disconnected? Yeah, I don't 102 00:05:31,640 --> 00:05:34,880 Speaker 1: see a disconnect. I mean we We track the share 103 00:05:34,880 --> 00:05:37,640 Speaker 1: of tech workers who lost pay or income every week, 104 00:05:37,680 --> 00:05:41,200 Speaker 1: and we've seen that pretty consistently ticked down over the 105 00:05:41,240 --> 00:05:44,960 Speaker 1: course of November. In any of our high frequency unemployment data, 106 00:05:45,000 --> 00:05:48,120 Speaker 1: we don't see dramatic increases in the tech sector. I 107 00:05:48,160 --> 00:05:52,040 Speaker 1: think what's you know the difference is that these folks 108 00:05:52,080 --> 00:05:56,280 Speaker 1: who are being laid off are essentially finding jobs very quickly, 109 00:05:56,400 --> 00:05:59,800 Speaker 1: either in the tech sector or in some other sector 110 00:05:59,839 --> 00:06:03,039 Speaker 1: that requires those type of skills. Those skills are really 111 00:06:03,080 --> 00:06:06,039 Speaker 1: in demand. We still have an imbalance in the economy 112 00:06:06,160 --> 00:06:09,640 Speaker 1: between supply and demand um and that that sort of 113 00:06:09,720 --> 00:06:13,120 Speaker 1: limited supply right now is going to allow companies that 114 00:06:13,160 --> 00:06:16,880 Speaker 1: are underperforming for those workers to be reabsorbed very quickly. 115 00:06:17,800 --> 00:06:20,920 Speaker 1: There's an interesting point of personal finance. Like a lot 116 00:06:21,000 --> 00:06:24,680 Speaker 1: of people that watch this show work in the technology industry, 117 00:06:24,680 --> 00:06:27,600 Speaker 1: and over the course of the pandemic, they worked at 118 00:06:27,680 --> 00:06:31,640 Speaker 1: companies that retain staff, paid bonuses helped offset some of 119 00:06:31,680 --> 00:06:34,560 Speaker 1: the economic hardship. Do you think it's fair to say 120 00:06:34,600 --> 00:06:37,680 Speaker 1: that those workers from the technology sector that even if 121 00:06:37,680 --> 00:06:39,920 Speaker 1: they're being laid off, they have a buffer, that they 122 00:06:40,000 --> 00:06:42,400 Speaker 1: have personal finance cash in the bank that they built 123 00:06:42,720 --> 00:06:45,880 Speaker 1: from the pandemic era. Yeah, that's exactly right. That's what 124 00:06:45,920 --> 00:06:49,039 Speaker 1: all of our research shows that higher income, higher educated 125 00:06:49,080 --> 00:06:52,240 Speaker 1: workers who have been employed over the course of the 126 00:06:52,279 --> 00:06:57,320 Speaker 1: pandemic have stashed away enough savings essentially to last at 127 00:06:57,400 --> 00:07:02,320 Speaker 1: least three months without working um after losing their job. 128 00:07:02,360 --> 00:07:04,720 Speaker 1: And so I think that's really the security that I 129 00:07:04,760 --> 00:07:08,240 Speaker 1: have when I think about how might these tech layoffs 130 00:07:08,520 --> 00:07:11,320 Speaker 1: affect the broader macro economy. We're not seeing it in 131 00:07:11,400 --> 00:07:13,800 Speaker 1: terms of broader layoffs, and I don't think we'll see 132 00:07:13,800 --> 00:07:17,080 Speaker 1: it in terms of a deterioration in spending or consumer 133 00:07:17,080 --> 00:07:19,920 Speaker 1: credit conditions. Hey, John, I want to go back really 134 00:07:20,000 --> 00:07:23,000 Speaker 1: quick to that challenge of grade data. A d two 135 00:07:23,040 --> 00:07:26,080 Speaker 1: thousand or so job cuts announced by the tech sector 136 00:07:26,200 --> 00:07:28,840 Speaker 1: year to date, but fifty three thousand of them in 137 00:07:28,880 --> 00:07:32,080 Speaker 1: the month of November alone. Do you expect that trend 138 00:07:32,160 --> 00:07:34,520 Speaker 1: to continue through the end of this year and into 139 00:07:34,560 --> 00:07:36,680 Speaker 1: the next year or is the worst of it over? 140 00:07:38,240 --> 00:07:39,880 Speaker 1: You know, it's hard to say exactly. I think what 141 00:07:40,120 --> 00:07:42,240 Speaker 1: is certainly going to be the case going forward is 142 00:07:42,240 --> 00:07:45,200 Speaker 1: that you're going to hear about more layoffs in general, 143 00:07:45,280 --> 00:07:49,600 Speaker 1: because we're entering a period in economic environment where you know, 144 00:07:49,720 --> 00:07:53,000 Speaker 1: businesses operating conditions are going to be challenged, and you're 145 00:07:53,040 --> 00:07:55,440 Speaker 1: going to start to see certain companies when in certain 146 00:07:55,480 --> 00:07:59,240 Speaker 1: companies lose. When you've got a rising tide essentially driven 147 00:07:59,240 --> 00:08:04,040 Speaker 1: by very low interest rates, high corporate profits, it's easy, 148 00:08:04,160 --> 00:08:07,800 Speaker 1: essentially for companies to hide some of their underperformance. When 149 00:08:07,840 --> 00:08:10,680 Speaker 1: you move into the operating environment where are currently that's 150 00:08:10,680 --> 00:08:12,240 Speaker 1: going to be exposed and I think that's where you're 151 00:08:12,240 --> 00:08:13,800 Speaker 1: going to see some of this churn in the labor 152 00:08:13,800 --> 00:08:16,640 Speaker 1: market was certainly one to watch that you know, we 153 00:08:16,720 --> 00:08:19,600 Speaker 1: reflected on the screen just then the data that a 154 00:08:19,640 --> 00:08:21,920 Speaker 1: lot of these cuts coming out here on the West Coast, 155 00:08:22,000 --> 00:08:24,320 Speaker 1: and that's kind of correlating with the industries that are here. 156 00:08:24,400 --> 00:08:27,400 Speaker 1: John Lear, chief economist at Morning consult Thank you for 157 00:08:27,480 --> 00:08:33,720 Speaker 1: joining us. All right, stick with us because Bloomberg talked 158 00:08:33,720 --> 00:08:37,079 Speaker 1: to Brooke Jenkins, san Francisco's new d A, about her 159 00:08:37,200 --> 00:08:39,600 Speaker 1: vision for the city and the future of Teking the 160 00:08:39,640 --> 00:08:43,359 Speaker 1: Bay Era. She was appointed after her predecessor was recalled. 161 00:08:43,440 --> 00:08:46,000 Speaker 1: For context, this is the first time a serving d 162 00:08:46,120 --> 00:08:49,280 Speaker 1: A has been recalled in San Francisco's history. Here's what 163 00:08:49,440 --> 00:08:51,280 Speaker 1: she had to say about the work her office will 164 00:08:51,320 --> 00:08:53,800 Speaker 1: do in fighting crime in this city. And how it's 165 00:08:53,800 --> 00:08:58,200 Speaker 1: going to impact the economy. I've said, and I made 166 00:08:58,200 --> 00:09:01,359 Speaker 1: it very clear, all crime is illegal again in San Francisco. 167 00:09:01,400 --> 00:09:04,200 Speaker 1: There will be some level of consequence for the people 168 00:09:04,240 --> 00:09:07,040 Speaker 1: who commit those crimes so that we could protect that 169 00:09:07,160 --> 00:09:11,960 Speaker 1: economic engine that those these retailers, these businesses, they provide jobs, 170 00:09:12,160 --> 00:09:15,600 Speaker 1: jobs that are residents need in order to support their families. 171 00:09:15,840 --> 00:09:18,560 Speaker 1: We can't sit by and allow businesses to feel the 172 00:09:18,600 --> 00:09:22,120 Speaker 1: need to move to other states because crime is too 173 00:09:22,200 --> 00:09:25,800 Speaker 1: much of a problem, not only for their business bottom line, 174 00:09:25,800 --> 00:09:27,800 Speaker 1: but also for their workers who need to be able 175 00:09:27,840 --> 00:09:30,600 Speaker 1: to come safely to work. And so I'm committed to 176 00:09:30,640 --> 00:09:34,000 Speaker 1: making sure that we protect those jobs and these businesses 177 00:09:34,040 --> 00:09:36,280 Speaker 1: because that is what we need in order to remain 178 00:09:36,600 --> 00:09:40,920 Speaker 1: the beautiful city that we are. Coming out, we'll discuss 179 00:09:41,000 --> 00:09:43,680 Speaker 1: the latest news out of Twitter and the future of 180 00:09:43,720 --> 00:09:47,400 Speaker 1: content moderation on the platform. That's next This is bloom Bug. 181 00:09:57,800 --> 00:10:01,320 Speaker 1: Here's what's been going viral today. Even Elon Musk's Twitter, 182 00:10:01,720 --> 00:10:05,319 Speaker 1: there are red lines for permissible content, and Kanye West 183 00:10:05,400 --> 00:10:08,600 Speaker 1: or now known as ye who calls himself Yea, crossed 184 00:10:08,640 --> 00:10:11,240 Speaker 1: one of them with a late Thursday night post that 185 00:10:11,320 --> 00:10:14,960 Speaker 1: prompted the platform to suspend his account. And all of 186 00:10:14,960 --> 00:10:18,839 Speaker 1: this comes as we're awaiting patiently some news from Elon 187 00:10:18,920 --> 00:10:22,480 Speaker 1: Musk regarding Hunter Biden to lots of digests. Let's bring 188 00:10:22,480 --> 00:10:25,720 Speaker 1: in Bloomberg Sarah Fire, who leads our coverage of big 189 00:10:25,760 --> 00:10:28,719 Speaker 1: tech here at Bloomberg, Sarah. This is pretty confusing, but 190 00:10:28,800 --> 00:10:31,000 Speaker 1: let's start with the news of the day. I suppose 191 00:10:31,080 --> 00:10:36,000 Speaker 1: so far an action by Elon Musk that Yea formerly 192 00:10:36,000 --> 00:10:39,800 Speaker 1: known as Kana West, went too far. Give us the details. Well, 193 00:10:39,880 --> 00:10:44,720 Speaker 1: he posted a swastika, and Elon Musk, who had previously 194 00:10:44,880 --> 00:10:48,839 Speaker 1: seemed to embrace him even though he's a controversial figure, 195 00:10:48,880 --> 00:10:52,720 Speaker 1: they have had a history of, you know, semi friendship. 196 00:10:53,880 --> 00:10:57,040 Speaker 1: And then he said, well, listen, you've gone too far. 197 00:10:57,360 --> 00:11:02,520 Speaker 1: This isn't love, he said, and he suspended yea um. 198 00:11:02,520 --> 00:11:07,040 Speaker 1: And I think I think that was certainly shocking for 199 00:11:07,040 --> 00:11:09,560 Speaker 1: everyone who's been a fan of Yea, even though we've 200 00:11:09,600 --> 00:11:13,240 Speaker 1: we've heard these reports of his anti Semitic um comments 201 00:11:13,240 --> 00:11:16,280 Speaker 1: in the past. But I also think that some of 202 00:11:16,440 --> 00:11:21,559 Speaker 1: Elon Musk's supporters who thought, well, you're building this version 203 00:11:21,559 --> 00:11:25,400 Speaker 1: of Twitter where anything goes, where it's a free speech, 204 00:11:25,520 --> 00:11:30,960 Speaker 1: absolutist environment. We're a little. You're surprised that that he 205 00:11:31,040 --> 00:11:34,959 Speaker 1: did end up taking action with Gay's posts as opposed 206 00:11:34,960 --> 00:11:37,760 Speaker 1: to saying, you know, anything goes here on on this 207 00:11:37,840 --> 00:11:41,760 Speaker 1: new version of Twitter. BuzzFeed reported and shared an image 208 00:11:41,760 --> 00:11:45,160 Speaker 1: of a screenshot from his account where appeared to show 209 00:11:45,240 --> 00:11:47,760 Speaker 1: that the suspension was only for twelve hours. Bloom Bugs 210 00:11:47,800 --> 00:11:50,800 Speaker 1: not verified that. We don't know, but but I thought 211 00:11:50,800 --> 00:11:53,400 Speaker 1: that was interesting. The other thing we're waiting for is 212 00:11:53,440 --> 00:11:57,040 Speaker 1: the launch of this new verification system, Twitter Blue color 213 00:11:57,120 --> 00:12:01,040 Speaker 1: codes for different entities and individuals. We don't think it's 214 00:12:01,040 --> 00:12:04,440 Speaker 1: happened quite yet, but it could happen. You know, verification 215 00:12:04,559 --> 00:12:08,640 Speaker 1: is so complicated, right because it it is only something 216 00:12:08,679 --> 00:12:14,280 Speaker 1: that has been available to a select few public official celebrities, journalists, 217 00:12:14,320 --> 00:12:18,679 Speaker 1: sports figures on the internet who are at risk of impersonation. 218 00:12:19,360 --> 00:12:23,000 Speaker 1: And what Musk is trying to do with Twitter Blue 219 00:12:23,360 --> 00:12:27,080 Speaker 1: is let anyone pay for it, and in the process, 220 00:12:27,640 --> 00:12:30,680 Speaker 1: maybe those people will end up impersonating these brands that 221 00:12:30,679 --> 00:12:33,360 Speaker 1: we its just had. The first time they launched it, it 222 00:12:33,280 --> 00:12:35,240 Speaker 1: it was a total mess because we saw all these 223 00:12:35,280 --> 00:12:40,800 Speaker 1: major brands from Lately to Nintendo be impersonated and lose value, 224 00:12:40,880 --> 00:12:43,880 Speaker 1: and we saw advertisers pull their pull their spend. So 225 00:12:43,920 --> 00:12:46,120 Speaker 1: I'm not surprised that it's delayed. It's it's a tough 226 00:12:46,160 --> 00:12:50,439 Speaker 1: thing to get right, and um, you know a hard 227 00:12:50,640 --> 00:12:53,880 Speaker 1: problem to just come at come at this and say 228 00:12:53,880 --> 00:12:55,280 Speaker 1: anyone can have a blue check as long as they 229 00:12:55,280 --> 00:12:58,400 Speaker 1: pay dollars. Bloom bug, Sarahfra stay with us just for 230 00:12:58,400 --> 00:13:00,319 Speaker 1: a minute. Let's carry on the come to station that 231 00:13:00,360 --> 00:13:03,600 Speaker 1: I'm bringing. Claire Diaz or Ti. She was formerly Twitter's 232 00:13:03,640 --> 00:13:08,560 Speaker 1: head of Corporate Social Innovation and Philanthropy two thousand fourteen, 233 00:13:08,960 --> 00:13:13,160 Speaker 1: currently VC scout for Kleiner Perkins in Latin America. You 234 00:13:13,200 --> 00:13:16,400 Speaker 1: also wrote a book about Twitter, Twitter for Good. Um, 235 00:13:17,160 --> 00:13:21,960 Speaker 1: let's start, well, let's start. Let's start on this issue 236 00:13:22,000 --> 00:13:29,480 Speaker 1: of the day. The decision to suspend Yeah's account Musque 237 00:13:29,520 --> 00:13:32,640 Speaker 1: does appear to have redlines. You know, the Twitter platform. 238 00:13:32,720 --> 00:13:34,360 Speaker 1: You were there for many years, although it's been eight 239 00:13:34,480 --> 00:13:37,120 Speaker 1: years since you left the company. What is your read 240 00:13:37,559 --> 00:13:41,800 Speaker 1: on content moderation and policy decisions so far to this point. 241 00:13:43,000 --> 00:13:45,560 Speaker 1: So I think it's really important to reframe kind of 242 00:13:45,600 --> 00:13:47,560 Speaker 1: the language we use a little bit. We hear a 243 00:13:47,559 --> 00:13:50,440 Speaker 1: lot of people talking about content moderation, and I understand 244 00:13:50,520 --> 00:13:52,240 Speaker 1: that is sort of what we call it, but that 245 00:13:52,280 --> 00:13:56,160 Speaker 1: really sits within trust and safety. And safety is really 246 00:13:56,200 --> 00:13:59,600 Speaker 1: important in the connection between free speech and safety. Is 247 00:13:59,640 --> 00:14:02,480 Speaker 1: what On does not seem to understand. Uh, you know, 248 00:14:02,520 --> 00:14:04,880 Speaker 1: I kind of sometimes say, you know, watching Elon building 249 00:14:04,920 --> 00:14:06,760 Speaker 1: public is kind of a mess because he really doesn't 250 00:14:06,760 --> 00:14:09,400 Speaker 1: know what he's doing. Right. He's going back to what 251 00:14:09,440 --> 00:14:12,880 Speaker 1: we thought social media was fifteen years ago and using 252 00:14:13,000 --> 00:14:15,959 Speaker 1: a lot of that language and a lot of those guidelines. 253 00:14:16,280 --> 00:14:17,960 Speaker 1: But we've now seen, you know, those of us who've 254 00:14:18,000 --> 00:14:19,800 Speaker 1: worked at these companies, who've been on these trust and 255 00:14:19,800 --> 00:14:22,880 Speaker 1: safety teams. There's written books about social media, we know 256 00:14:22,960 --> 00:14:25,320 Speaker 1: that those things don't work right. In the early days 257 00:14:25,720 --> 00:14:28,440 Speaker 1: of Twitter of social media in general, we simply didn't 258 00:14:28,520 --> 00:14:32,640 Speaker 1: understand that we were creating information silos, that we were 259 00:14:32,680 --> 00:14:35,440 Speaker 1: a fertile breeding ground for misinformation, and we really didn't 260 00:14:35,520 --> 00:14:41,160 Speaker 1: understand that certain people were more persecuted than others. When 261 00:14:41,200 --> 00:14:43,040 Speaker 1: you talk about it, I mean, it is a problem 262 00:14:43,080 --> 00:14:46,080 Speaker 1: that seems easier to solve from the outside, right. Why 263 00:14:46,120 --> 00:14:48,200 Speaker 1: why won't we just let everyone say whatever they want 264 00:14:48,240 --> 00:14:51,520 Speaker 1: to say? Well, because then it creates a terrible user experience. 265 00:14:52,400 --> 00:14:56,680 Speaker 1: As you see, must sort of rediscover all the things 266 00:14:56,720 --> 00:14:58,680 Speaker 1: that you had discovered when you were on Twitter. You 267 00:14:58,680 --> 00:15:01,280 Speaker 1: know what has been surprising to so far? I'll tell 268 00:15:01,320 --> 00:15:06,239 Speaker 1: you I was surprised today by him um categorizing harassment 269 00:15:06,320 --> 00:15:08,480 Speaker 1: as spam. I thought that that was that was an 270 00:15:08,520 --> 00:15:12,720 Speaker 1: interesting new way of thinking about harassments that maybe actually 271 00:15:12,800 --> 00:15:15,240 Speaker 1: is is positive. What what what do you think is 272 00:15:15,240 --> 00:15:18,360 Speaker 1: is you know, becoming a real to him now in 273 00:15:18,400 --> 00:15:21,480 Speaker 1: a way that maybe it wasn't before. Well, I mean, 274 00:15:21,520 --> 00:15:23,480 Speaker 1: some of these things are coming real to him, right, 275 00:15:23,480 --> 00:15:26,120 Speaker 1: Like he's starting to understand Okay, harassment might actually be 276 00:15:26,160 --> 00:15:28,560 Speaker 1: a problem in a way I didn't understand it because 277 00:15:28,720 --> 00:15:30,560 Speaker 1: I am the richest man in the world, and I 278 00:15:30,600 --> 00:15:33,360 Speaker 1: am a white grow who hasn't had concerns about trust 279 00:15:33,360 --> 00:15:35,160 Speaker 1: and safety in a long time in my life. Right, 280 00:15:35,520 --> 00:15:37,960 Speaker 1: So some of these things maybe he's slowly starting to 281 00:15:38,040 --> 00:15:41,040 Speaker 1: see sort of the light or gained some empathy on 282 00:15:41,120 --> 00:15:44,600 Speaker 1: but ultimately it's way too slow and it's not happening. 283 00:15:45,320 --> 00:15:48,040 Speaker 1: So it's just very concerning for users out there who 284 00:15:48,080 --> 00:15:50,760 Speaker 1: want to actually use the platform and for advertisements you 285 00:15:50,840 --> 00:15:53,080 Speaker 1: want to people to use it. I think I'm right 286 00:15:53,080 --> 00:15:58,320 Speaker 1: in saying many years ago, then known as Kanye West, 287 00:15:58,400 --> 00:16:01,760 Speaker 1: now known as Ya walked into the building and sent 288 00:16:01,880 --> 00:16:05,040 Speaker 1: his first tweet while you were there. Can you give 289 00:16:05,080 --> 00:16:08,480 Speaker 1: us the details of around that and what happened? Yeah, 290 00:16:08,520 --> 00:16:11,760 Speaker 1: I remember in the summer of two thousand ten in California, 291 00:16:12,160 --> 00:16:14,520 Speaker 1: Kanye came in one day, lit up a blunt and 292 00:16:14,520 --> 00:16:16,480 Speaker 1: then sent his first tweet. And the first tweet, if 293 00:16:16,520 --> 00:16:18,960 Speaker 1: you go back and look at it is is him 294 00:16:19,000 --> 00:16:22,280 Speaker 1: spelling Silicon Valley wrong and then following it up with 295 00:16:22,320 --> 00:16:26,840 Speaker 1: a joke about women's breast implanoffs. Okay, we'll move on 296 00:16:26,920 --> 00:16:31,360 Speaker 1: from that subject of of YEA, Generally speaking, you have 297 00:16:32,080 --> 00:16:35,600 Speaker 1: focused your writing and your time at Twitter on on 298 00:16:35,640 --> 00:16:41,360 Speaker 1: the platform as a mechanism for social good, and Musque 299 00:16:41,640 --> 00:16:45,880 Speaker 1: talks about Twitter being the global town square, and he 300 00:16:46,320 --> 00:16:50,320 Speaker 1: has shared data in recent days about the growth of 301 00:16:50,320 --> 00:16:54,840 Speaker 1: the platform but also the decline of what what appears 302 00:16:54,880 --> 00:16:57,720 Speaker 1: to be the decline of hate speech. Are the rainy areas. 303 00:16:57,720 --> 00:17:00,320 Speaker 1: You actually agree with Musk on things that he's done, 304 00:17:00,360 --> 00:17:04,880 Speaker 1: things that he said about the potential for the platform. 305 00:17:04,920 --> 00:17:06,879 Speaker 1: So I do. I mean, if there's one thing I 306 00:17:06,920 --> 00:17:10,320 Speaker 1: won't begrudge elon Musk, it's it's it is that he 307 00:17:10,400 --> 00:17:13,520 Speaker 1: really should be redoing the verification platform and it's good 308 00:17:13,560 --> 00:17:16,159 Speaker 1: to see him try, although he's failing a lot and 309 00:17:16,200 --> 00:17:19,360 Speaker 1: doing things we did ten or fifteen years ago. Verification, 310 00:17:19,560 --> 00:17:22,320 Speaker 1: since it started in two thousand nine, when you know, 311 00:17:22,359 --> 00:17:25,760 Speaker 1: a baseball manager was impersonated on Twitter and then sued 312 00:17:25,800 --> 00:17:28,879 Speaker 1: Twitter as a result, has always been a dumpster fire, 313 00:17:29,040 --> 00:17:31,680 Speaker 1: and it's always been very complicated, and from the beginning 314 00:17:31,680 --> 00:17:33,399 Speaker 1: there were not enough staff to handle it. You know, 315 00:17:33,800 --> 00:17:35,879 Speaker 1: I was one of many people tasked with sort of 316 00:17:35,880 --> 00:17:39,000 Speaker 1: working out some of the guidelines, and it's never really worked. 317 00:17:39,119 --> 00:17:42,879 Speaker 1: So in principle, the idea that people could pay to 318 00:17:43,000 --> 00:17:46,199 Speaker 1: be verified is actually something I think could be a 319 00:17:46,200 --> 00:17:48,719 Speaker 1: good idea. It may not be something I would pay for, 320 00:17:48,800 --> 00:17:50,960 Speaker 1: it may not be something you would pay for, but 321 00:17:51,119 --> 00:17:53,320 Speaker 1: it is something that many people out there would pay for, 322 00:17:53,400 --> 00:17:57,359 Speaker 1: and it certainly will ideally reduce the demand. Right anyone 323 00:17:57,400 --> 00:18:00,000 Speaker 1: who's ever worked at Twitter, ten years later, there's still 324 00:18:00,040 --> 00:18:03,200 Speaker 1: getting d m s in their in their Twitter feed, 325 00:18:03,240 --> 00:18:06,240 Speaker 1: people asking to be verified. Right, So there's a huge 326 00:18:06,280 --> 00:18:09,960 Speaker 1: demand for it that he can ultimately hopefully capture some 327 00:18:10,080 --> 00:18:14,920 Speaker 1: of by charging for. Unfortunately, when he launched Twitter Blue 328 00:18:15,000 --> 00:18:18,600 Speaker 1: last month for the eight dollars seven ninety nine monthly fee, 329 00:18:18,800 --> 00:18:22,480 Speaker 1: he ran into his own problem of content moderation and 330 00:18:23,000 --> 00:18:26,520 Speaker 1: sam bots just going crazy with the service. What do 331 00:18:26,560 --> 00:18:29,520 Speaker 1: you think about his own content and the way that 332 00:18:29,600 --> 00:18:33,280 Speaker 1: he's been posting on Twitter in this in this really 333 00:18:33,840 --> 00:18:37,840 Speaker 1: showman showman like manner, when where every few hours there's 334 00:18:37,840 --> 00:18:41,359 Speaker 1: another bombshell even today that you know, this this teasing 335 00:18:41,600 --> 00:18:44,880 Speaker 1: of a big drop of a story that's about to come. 336 00:18:45,160 --> 00:18:46,960 Speaker 1: I mean, this is this is the way that he's 337 00:18:46,960 --> 00:18:51,040 Speaker 1: been running in and and perhaps is leading to these 338 00:18:51,119 --> 00:18:55,040 Speaker 1: higher user numbers or sign ups that that we're seeing 339 00:18:55,480 --> 00:18:58,520 Speaker 1: or that he was talking about. Is that a sustainable 340 00:18:58,560 --> 00:19:02,199 Speaker 1: way to run Twitter? Is that something that um, you know, 341 00:19:02,280 --> 00:19:05,000 Speaker 1: even if it leads to user growth, would be uh, 342 00:19:05,160 --> 00:19:09,280 Speaker 1: something you would recommend doing long term. It's absolutely not sustainable, 343 00:19:09,400 --> 00:19:11,920 Speaker 1: obviously from the side of user growth, but also from 344 00:19:11,920 --> 00:19:14,240 Speaker 1: the side of advertisers. You know, more than half his 345 00:19:14,440 --> 00:19:18,920 Speaker 1: advertising budget has drops since advertisers are all pulling out. 346 00:19:19,160 --> 00:19:21,200 Speaker 1: I mean, I think it's important to understand what he's 347 00:19:21,200 --> 00:19:24,800 Speaker 1: doing today on Twitter, with this proposed live chat thing 348 00:19:24,840 --> 00:19:26,919 Speaker 1: he's supposed to be going live with right now, is 349 00:19:26,960 --> 00:19:29,520 Speaker 1: really an example of the way he behaves. Um. You know, 350 00:19:29,560 --> 00:19:31,040 Speaker 1: the last time I was on I compared him to 351 00:19:31,080 --> 00:19:34,320 Speaker 1: some of my twin children who don't have prefrontal corps 352 00:19:34,720 --> 00:19:36,320 Speaker 1: cortex is and I feel like he's sort of the 353 00:19:36,359 --> 00:19:39,280 Speaker 1: same way. But I mean, what he's doing today is 354 00:19:39,280 --> 00:19:43,159 Speaker 1: he's trying to get back at the Twitter security and 355 00:19:43,760 --> 00:19:46,960 Speaker 1: Twitter safety and trust head who was just kicked out, 356 00:19:47,200 --> 00:19:49,240 Speaker 1: and so this is sort of a revenge saying he's doing. 357 00:19:49,680 --> 00:19:52,399 Speaker 1: Um you all. Roth went on Kara Switchers podcast and 358 00:19:52,400 --> 00:19:56,480 Speaker 1: basically discussed what had happened during the Hunter Biden laptop 359 00:19:57,000 --> 00:20:00,800 Speaker 1: tweet censoring back in the fall of twenty twenty. And so, 360 00:20:00,920 --> 00:20:03,080 Speaker 1: I mean, now he's just coming out because Rath is 361 00:20:03,119 --> 00:20:05,640 Speaker 1: now quit and now he's going to go against him. 362 00:20:05,640 --> 00:20:09,760 Speaker 1: It's it's the horrible Okay. That was the take from 363 00:20:09,960 --> 00:20:14,080 Speaker 1: Claire diez Ortiz, formerly Twitter's head of Corporate Social Innovation, 364 00:20:14,080 --> 00:20:16,640 Speaker 1: and Flamproby. I'dad you did leave Twitter eight years ago 365 00:20:16,720 --> 00:20:19,320 Speaker 1: and that was your opinion, but you know, many people 366 00:20:19,400 --> 00:20:22,200 Speaker 1: see you long musk as in the end improving the 367 00:20:22,200 --> 00:20:24,720 Speaker 1: companies that he works on. It's a wait and see 368 00:20:24,760 --> 00:20:27,399 Speaker 1: when it comes to Twitter. Also Bloomberg Sarah Fryer, who 369 00:20:27,480 --> 00:20:38,919 Speaker 1: leads big Tech coverage here, thank you both time for 370 00:20:38,960 --> 00:20:41,040 Speaker 1: talking tech and look at what's going on in the 371 00:20:41,040 --> 00:20:44,240 Speaker 1: world of venture capital and startups. Starting with the Asian 372 00:20:44,320 --> 00:20:47,560 Speaker 1: crypto exchange zipmex It's about to get acquired by a 373 00:20:47,640 --> 00:20:51,200 Speaker 1: VC fund for about one billion dollars. Thirty million will 374 00:20:51,240 --> 00:20:54,600 Speaker 1: be in cash, but the remainder will be in crypto tokens. 375 00:20:54,840 --> 00:20:57,119 Speaker 1: This is one of the first rescues in Asia since 376 00:20:57,320 --> 00:21:01,160 Speaker 1: a wave of defaults ripped through the sector and more layoffs. 377 00:21:01,200 --> 00:21:04,960 Speaker 1: Light Street Capital Management just dismissed some staff focused on 378 00:21:05,040 --> 00:21:08,160 Speaker 1: bets in private markets. This comes after a steep drop 379 00:21:08,359 --> 00:21:11,880 Speaker 1: in the hedge funds tech heavy portfolio. That's according to sources. 380 00:21:11,960 --> 00:21:15,159 Speaker 1: The firm already shut down in San Francisco office a 381 00:21:15,200 --> 00:21:18,320 Speaker 1: few months ago. Finally, the defense tech startup and A 382 00:21:18,440 --> 00:21:21,159 Speaker 1: Rill has raised about one point five billion dollars in 383 00:21:21,160 --> 00:21:24,080 Speaker 1: a new round, as reported by The Financial Times. The 384 00:21:24,119 --> 00:21:26,840 Speaker 1: investment values the firm it's seven billion dollars, up from 385 00:21:26,880 --> 00:21:29,600 Speaker 1: four point two billion dollars eighteen months ago. And A 386 00:21:29,680 --> 00:21:32,280 Speaker 1: Hills founder Parmer Lucky plans to use the new funds 387 00:21:32,440 --> 00:21:35,479 Speaker 1: to build a large defense company using new tech like 388 00:21:35,560 --> 00:21:46,439 Speaker 1: AI and drones. I think this is one of the 389 00:21:46,480 --> 00:21:48,560 Speaker 1: most uncertain environments that i've been a part of you know, 390 00:21:48,640 --> 00:21:52,280 Speaker 1: we it's very difficult to tell where things are going 391 00:21:52,320 --> 00:21:54,200 Speaker 1: to wind up. I think Europe is certainly going to 392 00:21:54,280 --> 00:21:59,320 Speaker 1: be weaker and is likely headed into a recession. We're 393 00:21:59,320 --> 00:22:02,359 Speaker 1: preparing for that. In the US, it's unclear recession might happen, 394 00:22:02,359 --> 00:22:04,800 Speaker 1: it might be a soft landing, etcetera. So I think 395 00:22:04,840 --> 00:22:08,920 Speaker 1: from our standpoint, we want to be prepared for any eventuality. 396 00:22:09,760 --> 00:22:12,679 Speaker 1: When you look at our marketplace, we are a marketplace business, 397 00:22:12,680 --> 00:22:16,280 Speaker 1: so we don't have significant fixed costs. UH. In a 398 00:22:16,320 --> 00:22:21,040 Speaker 1: weaker labor environment, our supply position will tend to get better. 399 00:22:21,560 --> 00:22:23,800 Speaker 1: We will be a place where more drivers can come 400 00:22:24,200 --> 00:22:28,600 Speaker 1: to earn real money. On this last quarter, for example, 401 00:22:29,200 --> 00:22:32,680 Speaker 1: UH earners earn more than ten billion dollars on our 402 00:22:32,680 --> 00:22:36,640 Speaker 1: platform up over, So we do think that our marketplace 403 00:22:37,000 --> 00:22:39,720 Speaker 1: gets more attractive to drivers as it gets more traffic 404 00:22:39,800 --> 00:22:44,800 Speaker 1: drivers prices come down, and that in turn attracts riders 405 00:22:44,840 --> 00:22:47,320 Speaker 1: as well. UH. So we think the business model is 406 00:22:47,320 --> 00:22:49,760 Speaker 1: a good model that can you know, do well in 407 00:22:50,320 --> 00:22:54,160 Speaker 1: UH in strong economies and can perform in weaker economies. 408 00:22:54,480 --> 00:22:57,760 Speaker 1: And I think as a company and as a technology company, 409 00:22:57,880 --> 00:23:00,679 Speaker 1: we have been relatively forward thinking in making sure that 410 00:23:00,720 --> 00:23:03,800 Speaker 1: we prepare ourselves for an uncertain world, making sure we're 411 00:23:03,800 --> 00:23:06,600 Speaker 1: conservative in terms of the investments that we're making, and 412 00:23:06,640 --> 00:23:10,320 Speaker 1: an investment isn't paying off, pullback employer money where the 413 00:23:10,359 --> 00:23:12,840 Speaker 1: growth is and I think it's showing in the redls. 414 00:23:14,760 --> 00:23:18,439 Speaker 1: Welcome back to Bloomdow Technology IDDO in San Francisco. That 415 00:23:18,560 --> 00:23:22,200 Speaker 1: was Uber CEO Dara Kostrashai last month when asked about 416 00:23:22,200 --> 00:23:25,600 Speaker 1: the impact of the potential recession and now. While Door, 417 00:23:25,680 --> 00:23:29,120 Speaker 1: Dash and Lift slash their staff to reduce costs, Uber 418 00:23:29,200 --> 00:23:31,840 Speaker 1: says it's not making any cutbacks, at least from a 419 00:23:31,880 --> 00:23:34,879 Speaker 1: head count perspective costs. He said this week that the 420 00:23:34,880 --> 00:23:37,199 Speaker 1: company is in a good place and to shift in 421 00:23:37,240 --> 00:23:41,920 Speaker 1: consumer spending from retail to services is helping bloombirds. Jackie 422 00:23:42,000 --> 00:23:44,639 Speaker 1: davl Loss covers all of the ride hailing and delivery 423 00:23:44,680 --> 00:23:47,600 Speaker 1: companies for US. She's out in d C. Run us 424 00:23:47,640 --> 00:23:51,200 Speaker 1: through what Darra had to say in the last couple 425 00:23:51,200 --> 00:23:53,920 Speaker 1: of days, because it's certainly not what we're hearing from 426 00:23:53,920 --> 00:23:57,760 Speaker 1: the rest of the sector. Absolutely, Uber seems to be 427 00:23:57,800 --> 00:24:00,919 Speaker 1: one of the rare safe zones right now in Silicon Valley, 428 00:24:00,920 --> 00:24:03,440 Speaker 1: and the reason for that is because people are still 429 00:24:03,560 --> 00:24:07,879 Speaker 1: ordering marked up burritos and taking rides to work, And 430 00:24:07,920 --> 00:24:11,320 Speaker 1: what that really shows is that their customer demand is 431 00:24:11,800 --> 00:24:14,440 Speaker 1: is keeping them afloat. They've already gone through a lot 432 00:24:14,440 --> 00:24:17,479 Speaker 1: of the difficult decisions to cost cut that a lot 433 00:24:17,520 --> 00:24:20,159 Speaker 1: of companies are having to do now more under the 434 00:24:20,200 --> 00:24:23,840 Speaker 1: gun of, you know, the specter of an economic recession looming. Um. 435 00:24:23,880 --> 00:24:27,600 Speaker 1: But Uber actually did that earlier on in the pandemic. 436 00:24:27,640 --> 00:24:30,400 Speaker 1: If you remember, they had a really tough time cut 437 00:24:30,400 --> 00:24:33,239 Speaker 1: around six thousand jobs in the course of just a 438 00:24:33,240 --> 00:24:36,760 Speaker 1: few months. So I think that cost cutting measures um, 439 00:24:36,960 --> 00:24:40,080 Speaker 1: they largely got ahead of it and now are relying 440 00:24:40,200 --> 00:24:42,560 Speaker 1: on that strong customer demand to kind of see them 441 00:24:42,560 --> 00:24:45,360 Speaker 1: through for for now at least. I want to bring 442 00:24:45,440 --> 00:24:47,440 Speaker 1: up on the screen some of the things that dark 443 00:24:47,480 --> 00:24:50,520 Speaker 1: Costra Shaw he was saying at that event in New York, 444 00:24:50,560 --> 00:24:53,800 Speaker 1: because I find it really interesting. It's it's bullish talk, 445 00:24:53,960 --> 00:24:56,960 Speaker 1: it's fighting talk. But at the same time, Jackie, I 446 00:24:56,960 --> 00:25:00,240 Speaker 1: think I'm right in saying that Uber, you know, all 447 00:25:00,280 --> 00:25:03,400 Speaker 1: being financially disciplined, there is an element of belt tightening 448 00:25:03,400 --> 00:25:08,240 Speaker 1: and being conservative when it comes to herring totally. And look, 449 00:25:08,280 --> 00:25:13,320 Speaker 1: I think when it comes to telegraphing what the potential 450 00:25:13,359 --> 00:25:16,639 Speaker 1: for layoffs might be. He is still taking somewhat of 451 00:25:16,680 --> 00:25:19,720 Speaker 1: a pragmatic approach. Just in the clip that you've played 452 00:25:20,160 --> 00:25:23,040 Speaker 1: right before this, you know he has said earlier this 453 00:25:23,119 --> 00:25:25,680 Speaker 1: year that's he's going to slow hiring, which is something 454 00:25:25,720 --> 00:25:29,320 Speaker 1: that door Dash wasn't considering. UM. You know, back when 455 00:25:29,800 --> 00:25:33,360 Speaker 1: companies really started, you know, thinking about putting a pause 456 00:25:33,520 --> 00:25:38,280 Speaker 1: on on hiring and you know, eliminating parts of their workforce. UM. 457 00:25:38,320 --> 00:25:41,960 Speaker 1: So he was a little bit more cautious even earlier 458 00:25:42,000 --> 00:25:44,120 Speaker 1: this year. Now when you take a look at door 459 00:25:44,240 --> 00:25:48,560 Speaker 1: Dash Tony Shoe, their CEO was very forthcoming when it 460 00:25:48,600 --> 00:25:51,919 Speaker 1: came to, you know, taking accountability for growing too quickly. UM. 461 00:25:51,960 --> 00:25:55,560 Speaker 1: In Southeast Asia, you know, Kassel laid off or plans 462 00:25:55,600 --> 00:25:58,360 Speaker 1: to lay off ten percent of their staff, and their 463 00:25:58,400 --> 00:26:01,000 Speaker 1: CEO as well said, you know, we too quickly. So 464 00:26:01,359 --> 00:26:05,120 Speaker 1: I think Uber's move here, UM it really shows that 465 00:26:05,160 --> 00:26:07,800 Speaker 1: you know, they have been kind of slowing down on 466 00:26:07,840 --> 00:26:10,240 Speaker 1: the hiring front, but there's other ways to cut costs. 467 00:26:10,240 --> 00:26:13,040 Speaker 1: I mean, you know, they hived off their autonomous vehicle 468 00:26:13,119 --> 00:26:16,080 Speaker 1: research UM and are now really doubling down on on 469 00:26:16,160 --> 00:26:19,159 Speaker 1: food delivery, which is shown to be pretty profitable for them. 470 00:26:19,240 --> 00:26:21,080 Speaker 1: You're right that there's some nuance in it, right. The 471 00:26:21,119 --> 00:26:24,639 Speaker 1: headlines this week have been layoffs, job cuts. You know, 472 00:26:24,680 --> 00:26:27,680 Speaker 1: in Europe, for example, Intel is movings cut more jobs 473 00:26:28,680 --> 00:26:31,520 Speaker 1: HP last week, six thousand jobs, up to six thousand 474 00:26:31,600 --> 00:26:34,000 Speaker 1: jobs cut over the next three years or so. I 475 00:26:34,000 --> 00:26:36,280 Speaker 1: thought the nuance in your story about door Dash was 476 00:26:36,359 --> 00:26:39,679 Speaker 1: important because even coming out of the pandemic, this is 477 00:26:39,680 --> 00:26:44,320 Speaker 1: a pandemic darling that's continued to grow um and you know, 478 00:26:44,480 --> 00:26:47,880 Speaker 1: the risk, it seems to me, is that door dash 479 00:26:48,040 --> 00:26:52,879 Speaker 1: sees its operating expenses just simply outpacing it's growth on 480 00:26:52,920 --> 00:26:55,000 Speaker 1: the revenue front, on the growth booking fronts. Is that 481 00:26:55,040 --> 00:26:59,040 Speaker 1: the equation that Tony's use thinking about exactly, And it's 482 00:26:59,080 --> 00:27:03,879 Speaker 1: because the market environment and investors focus has really shifted 483 00:27:04,200 --> 00:27:07,040 Speaker 1: and now it's less about growth and it's more about 484 00:27:07,080 --> 00:27:10,800 Speaker 1: the bottom line. And that's why you're seeing executives take 485 00:27:10,840 --> 00:27:13,960 Speaker 1: that tone in earnings calls. They're focused on free cash flow, 486 00:27:13,960 --> 00:27:16,840 Speaker 1: they're focused on um their net income. You know, you 487 00:27:16,880 --> 00:27:20,639 Speaker 1: see a lot of these e commerce companies pointing investors 488 00:27:20,680 --> 00:27:24,080 Speaker 1: to adjusted earnings figures, which strip out a lot of 489 00:27:24,119 --> 00:27:28,280 Speaker 1: the stock based compensation and that's what's been moving up, um, 490 00:27:28,320 --> 00:27:31,200 Speaker 1: these costs you're seeing when you saw a lift cut 491 00:27:31,680 --> 00:27:34,800 Speaker 1: of its workforce. It also mentioned um that it was 492 00:27:34,840 --> 00:27:37,919 Speaker 1: going to shift its strategy when it came to hiring, 493 00:27:38,200 --> 00:27:40,840 Speaker 1: looking more to Canada and East during Europe, which you know, 494 00:27:40,920 --> 00:27:44,000 Speaker 1: they don't have stock based comp in their in their 495 00:27:44,000 --> 00:27:46,119 Speaker 1: packages the way they do here in the US, so 496 00:27:46,480 --> 00:27:50,240 Speaker 1: different strategies there, um, But it it really does show 497 00:27:50,280 --> 00:27:54,840 Speaker 1: that the focus is much more on profitability, and doordesh 498 00:27:55,160 --> 00:27:58,480 Speaker 1: isn't a great example of that. Their customer demand also 499 00:27:58,560 --> 00:28:02,240 Speaker 1: super strong, but I think they're preparing for a much 500 00:28:02,320 --> 00:28:07,440 Speaker 1: rockier period ahead. Okay, Layoffs never a pleasant subject to 501 00:28:07,520 --> 00:28:09,800 Speaker 1: cover in this industry, whether it's here in San Francisco 502 00:28:09,960 --> 00:28:12,960 Speaker 1: or out on the East Coast. Bloomberg's Jackie Davlos, thank 503 00:28:13,000 --> 00:28:16,159 Speaker 1: you for your reporting. Meanwhile, the November job struck a 504 00:28:16,320 --> 00:28:19,000 Speaker 1: very different picture from what's playing out here in Silicon 505 00:28:19,080 --> 00:28:21,000 Speaker 1: Valley and the tech sector. This has been the story 506 00:28:21,040 --> 00:28:23,280 Speaker 1: of the week. What we've been discussing on the show. 507 00:28:23,600 --> 00:28:26,399 Speaker 1: The market now expecting the FED to push rates higher 508 00:28:26,760 --> 00:28:30,080 Speaker 1: and the possibility of a recession is now a much 509 00:28:30,119 --> 00:28:32,840 Speaker 1: real risk. Joining us to discuss what's going on with 510 00:28:32,960 --> 00:28:35,440 Speaker 1: jobs in the tech sector. What the sentiment of tech 511 00:28:35,480 --> 00:28:39,200 Speaker 1: workers is is Carab Brennan a LaMnO, Chief People Officer 512 00:28:39,320 --> 00:28:43,120 Speaker 1: at laticea software management platform for fast growing businesses in 513 00:28:43,160 --> 00:28:46,880 Speaker 1: the US and UK. So you've been crunching some numbers, Car, 514 00:28:47,480 --> 00:28:50,720 Speaker 1: I mean, there's a human side to this story and 515 00:28:50,720 --> 00:28:53,920 Speaker 1: then there's the workforce side to this story. Um let's 516 00:28:53,920 --> 00:28:56,080 Speaker 1: start with the workforce side. You know, what is your 517 00:28:56,120 --> 00:29:00,200 Speaker 1: interpretation of what we're seeing across the technology set to 518 00:29:00,560 --> 00:29:04,720 Speaker 1: from a job caught perspective. Well, what's interesting is is 519 00:29:04,760 --> 00:29:07,600 Speaker 1: what you've been talking about, um in in the past 520 00:29:07,600 --> 00:29:11,240 Speaker 1: half an hour, is that the headlines are interesting, their 521 00:29:11,280 --> 00:29:14,760 Speaker 1: eye catching, but really the devil's in the details when 522 00:29:14,760 --> 00:29:17,000 Speaker 1: you go down a few layers in terms of what's 523 00:29:17,040 --> 00:29:21,320 Speaker 1: happening with the layoffs. Fundamentally, what we know and when 524 00:29:21,320 --> 00:29:23,320 Speaker 1: I'm talking to the other c h R o s 525 00:29:23,320 --> 00:29:26,400 Speaker 1: that are actually executing these layoffs at places like Meta 526 00:29:26,520 --> 00:29:30,360 Speaker 1: and door Dash, the actual makeup of the folks and 527 00:29:30,400 --> 00:29:34,280 Speaker 1: the teams that are being affected tend to be more 528 00:29:34,320 --> 00:29:40,480 Speaker 1: of your front um, frontline support, your recruiters, your HR folks, 529 00:29:40,880 --> 00:29:44,720 Speaker 1: folks that were here during the pandemic to really help 530 00:29:44,840 --> 00:29:50,000 Speaker 1: shore up a level of morale and this this momentum 531 00:29:50,040 --> 00:29:53,280 Speaker 1: toward growth, and as we're looking toward next year, we 532 00:29:53,400 --> 00:29:57,040 Speaker 1: have a lot of people um on the VC side, 533 00:29:57,280 --> 00:29:59,840 Speaker 1: a lot of financial leadership, a lot of CEO s 534 00:30:00,000 --> 00:30:03,960 Speaker 1: saying going into the future, we need to be more conservative. 535 00:30:04,120 --> 00:30:07,120 Speaker 1: I want to send a message around efficiency and effectiveness 536 00:30:07,560 --> 00:30:11,200 Speaker 1: and our priority will be profitability in the next two years. 537 00:30:11,640 --> 00:30:15,520 Speaker 1: And this is the start of the action around those sentiments. 538 00:30:15,960 --> 00:30:17,840 Speaker 1: Is it part of this as well? People just looking 539 00:30:17,880 --> 00:30:20,600 Speaker 1: for something new, new jobs? I know that companies are 540 00:30:20,600 --> 00:30:23,280 Speaker 1: taking that decision out of hands, but there is an 541 00:30:23,360 --> 00:30:26,200 Speaker 1: element as well that within the tech sector there's opportunities, 542 00:30:26,240 --> 00:30:29,360 Speaker 1: people are hiring and they're looking to do something new. Yeah, definitely. 543 00:30:29,520 --> 00:30:33,440 Speaker 1: I think what some folks are missing is that we 544 00:30:33,840 --> 00:30:36,560 Speaker 1: let us did a survey a few months ago, and 545 00:30:36,600 --> 00:30:41,200 Speaker 1: we found that of employees weren't convinced that their company 546 00:30:41,320 --> 00:30:43,800 Speaker 1: was going to grow in the next twelve months and 547 00:30:43,960 --> 00:30:46,200 Speaker 1: half of the half of the employees that are in 548 00:30:46,280 --> 00:30:48,720 Speaker 1: seat don't know if they're going to be able to 549 00:30:48,800 --> 00:30:52,080 Speaker 1: grow in their careers. So those are the bigger questions 550 00:30:52,120 --> 00:30:55,440 Speaker 1: that people are leading with when they're making decisions, and 551 00:30:55,720 --> 00:30:58,440 Speaker 1: leaders are are thinking about that. They're thinking about I 552 00:30:58,480 --> 00:31:01,800 Speaker 1: have employees that potentially could have quiet quit in the 553 00:31:01,920 --> 00:31:04,800 Speaker 1: last year. I want to look back into my org 554 00:31:05,280 --> 00:31:08,560 Speaker 1: and understand really where people's heads are and drive forward 555 00:31:08,600 --> 00:31:11,840 Speaker 1: a level of productivity and performance as we go into 556 00:31:11,960 --> 00:31:15,440 Speaker 1: this potential bear bear environment in the next twelve months. 557 00:31:15,920 --> 00:31:19,440 Speaker 1: Do you have any read on the attitude towards at home, 558 00:31:19,640 --> 00:31:22,880 Speaker 1: hybrid working models, or just simply being in the office. Yeah. 559 00:31:22,920 --> 00:31:25,120 Speaker 1: What we also know from some of our research is 560 00:31:25,160 --> 00:31:29,000 Speaker 1: thirty of tech workers are saying that they would not 561 00:31:29,240 --> 00:31:31,480 Speaker 1: say at their job if they could not be in 562 00:31:31,520 --> 00:31:34,280 Speaker 1: a hybrid or remote first work environment. We're definitely seeing 563 00:31:34,280 --> 00:31:38,720 Speaker 1: that play out UM in technology companies. A c h 564 00:31:38,800 --> 00:31:42,320 Speaker 1: R O often owns the real estate or facilities component, 565 00:31:42,640 --> 00:31:46,080 Speaker 1: and I can tell you talking amongst my colleagues, UM, 566 00:31:46,120 --> 00:31:50,120 Speaker 1: we've already planned for not renewing leases, for reducing our 567 00:31:50,120 --> 00:31:53,440 Speaker 1: real estate footprint. So for us, the debate is over. 568 00:31:54,160 --> 00:31:57,320 Speaker 1: The plans are in place, the strategy is moving forward. 569 00:31:58,000 --> 00:32:01,000 Speaker 1: And I still see headlines where are saying folks are 570 00:32:01,040 --> 00:32:04,480 Speaker 1: battling it out. We're not, because we know that in 571 00:32:04,640 --> 00:32:06,720 Speaker 1: order to get the talent that we need to run 572 00:32:06,720 --> 00:32:10,959 Speaker 1: our businesses, we have to be providing that flexibility. Okay, Lattice, 573 00:32:11,040 --> 00:32:14,760 Speaker 1: Chief People Officer Cara Brennan, Alamanna, thank you. Now let's 574 00:32:14,800 --> 00:32:17,640 Speaker 1: head over. It's a Stanford where Bloomberg spoke Thursday with 575 00:32:17,680 --> 00:32:20,600 Speaker 1: the CEO of General Motors, Mary Barra. She talks about 576 00:32:20,600 --> 00:32:22,760 Speaker 1: whether the company thinks it will be able to sell 577 00:32:22,960 --> 00:32:26,520 Speaker 1: one million e vis in. Here's what she had to say. 578 00:32:27,480 --> 00:32:30,400 Speaker 1: We made that statement and that set that goal for 579 00:32:30,440 --> 00:32:32,840 Speaker 1: ourselves that we'll have the capacity to be able to 580 00:32:32,840 --> 00:32:36,040 Speaker 1: sell a million units in North America and frankly in China. 581 00:32:37,600 --> 00:32:40,000 Speaker 1: And we think with the strong product port Bullier, we're 582 00:32:40,000 --> 00:32:43,000 Speaker 1: going to have at different price points, you know, from 583 00:32:43,040 --> 00:32:48,320 Speaker 1: from the Cadillac Lyric to the Hummer, the Chevy Silverado, 584 00:32:48,400 --> 00:32:52,240 Speaker 1: the Gmccrara down to a Chevy Equinox as a Chevy Blazer. 585 00:32:52,360 --> 00:32:54,760 Speaker 1: We're going to have the products across the market that 586 00:32:54,800 --> 00:32:57,400 Speaker 1: are going to allow us to achieve that matter. So 587 00:32:57,840 --> 00:33:00,200 Speaker 1: we think we've got the right plan. This was all 588 00:33:00,240 --> 00:33:03,520 Speaker 1: in place before the incentive package came as a part 589 00:33:03,600 --> 00:33:05,880 Speaker 1: of I R A. But we're you know, we think 590 00:33:05,920 --> 00:33:08,320 Speaker 1: that will help. But it's doing what it was supposed to. 591 00:33:08,640 --> 00:33:11,040 Speaker 1: We think it was intended to do. Was to drive 592 00:33:11,120 --> 00:33:14,360 Speaker 1: the ev adaption. And you know, we've invested a lot 593 00:33:14,480 --> 00:33:16,680 Speaker 1: in the United States creating jobs, which I think is 594 00:33:16,680 --> 00:33:19,479 Speaker 1: going to create a stronger economy. So I think it's 595 00:33:19,520 --> 00:33:22,600 Speaker 1: gonna accomplish the objectives. And you know what happened to 596 00:33:23,200 --> 00:33:25,880 Speaker 1: be very aligned with the plan we were already execute. 597 00:33:26,240 --> 00:33:29,959 Speaker 1: That was Mary barras CEO of General Motors, coming up, 598 00:33:30,640 --> 00:33:32,720 Speaker 1: the former chair of the f d i C on 599 00:33:32,760 --> 00:33:36,480 Speaker 1: the need for crypto regulation as US or authorities ramp 600 00:33:36,560 --> 00:33:40,600 Speaker 1: up their investigation of f t X. This is bloom bug. 601 00:33:53,640 --> 00:33:58,360 Speaker 1: The reality is like Sam and his cohorts perpetuated a fraud. 602 00:33:58,800 --> 00:34:01,960 Speaker 1: They used customer money to make bets that he poorly 603 00:34:02,040 --> 00:34:05,320 Speaker 1: risk managed after he made them, Like, let's forget about 604 00:34:05,360 --> 00:34:08,360 Speaker 1: the risk management. The problem was he took our money 605 00:34:08,719 --> 00:34:11,640 Speaker 1: and so he needs to get prosecuted. The authorities need 606 00:34:11,680 --> 00:34:14,240 Speaker 1: to dig in and figure out exactly what happened. People 607 00:34:14,239 --> 00:34:16,399 Speaker 1: will go to jail and should go to jail. We've 608 00:34:16,400 --> 00:34:19,279 Speaker 1: taken our exchange balances down as a precaution. We've gone 609 00:34:19,280 --> 00:34:21,719 Speaker 1: through each exchange, talked to the guys that run them, 610 00:34:22,080 --> 00:34:25,440 Speaker 1: look at the auditing, look at proof of reserves, and 611 00:34:25,480 --> 00:34:27,920 Speaker 1: we make our best bet right. When you were putting 612 00:34:27,960 --> 00:34:30,239 Speaker 1: money on exchange, you never expected it to be all 613 00:34:30,280 --> 00:34:33,400 Speaker 1: at risk, right. Exchanges put a lot of your coins 614 00:34:33,400 --> 00:34:36,479 Speaker 1: in cold storage. Uh, and then the rest was left 615 00:34:36,520 --> 00:34:38,640 Speaker 1: on exchange. So you really thought you only had risk 616 00:34:38,719 --> 00:34:43,000 Speaker 1: of hacking. Uh. Very few people thought risk of somebody 617 00:34:43,080 --> 00:34:46,160 Speaker 1: stealing your money. And that's a new risk that people 618 00:34:46,200 --> 00:34:48,680 Speaker 1: are gonna have to look at a lot, a lot closer. 619 00:34:50,360 --> 00:34:53,920 Speaker 1: That was Galaxy Digitals. Mike novegrats his opinion. We should 620 00:34:53,960 --> 00:34:57,200 Speaker 1: know that no formal charges have been filed against Sam 621 00:34:57,280 --> 00:35:01,080 Speaker 1: Bankman Freed, but US authorities have set up their invested 622 00:35:01,239 --> 00:35:04,759 Speaker 1: investigation into his collapse Crypto Exchange f t X. They're 623 00:35:04,760 --> 00:35:07,879 Speaker 1: asking investors and trading firms that works closely with fd 624 00:35:08,160 --> 00:35:10,920 Speaker 1: X to hand over information on the company and its 625 00:35:11,000 --> 00:35:13,440 Speaker 1: key figures, Among them bank Man Freed and the former 626 00:35:13,480 --> 00:35:18,040 Speaker 1: head of his Alameda Research Investment arm, Caroline Ellison. Bloomberg 627 00:35:18,080 --> 00:35:20,759 Speaker 1: spoke with Sheila Bear, former f d i C chair 628 00:35:21,080 --> 00:35:24,680 Speaker 1: and senior fellow at the Center for Financial Stability about 629 00:35:24,760 --> 00:35:29,920 Speaker 1: how we regulate crypto. The current regulatory powers are adequate 630 00:35:29,960 --> 00:35:31,919 Speaker 1: for the best majority of this market, and I would 631 00:35:32,000 --> 00:35:33,880 Speaker 1: tend to agree with care against her that most of 632 00:35:33,880 --> 00:35:37,880 Speaker 1: these tokens are securities. There is a bit of a 633 00:35:37,960 --> 00:35:43,520 Speaker 1: gap with the CFTC's jurisdiction. They have regulatory power of derivatives, 634 00:35:44,040 --> 00:35:48,279 Speaker 1: but the cash market, the actual crypto asset itself, they 635 00:35:48,320 --> 00:35:50,680 Speaker 1: only have enforcement authority, So that is that is a 636 00:35:50,719 --> 00:35:53,120 Speaker 1: gap that could be fulfilled, but that's a very very 637 00:35:53,120 --> 00:35:56,640 Speaker 1: small part of the market. Most of these tokens, most 638 00:35:56,680 --> 00:35:59,960 Speaker 1: of these assets, especially the problematic ones, I think approple 639 00:36:00,000 --> 00:36:03,520 Speaker 1: we follow under the fallow under the SEC jurisdiction, and 640 00:36:03,560 --> 00:36:06,720 Speaker 1: the SEC frankly, just needs to go after them more aggressively. Sheila, 641 00:36:06,800 --> 00:36:09,680 Speaker 1: what about the controls that were in place for banks 642 00:36:09,719 --> 00:36:13,960 Speaker 1: carrying out fat transactions on behalf of cryptocurrent companies like 643 00:36:14,080 --> 00:36:16,680 Speaker 1: f t X. Do you think that they were sufficient? 644 00:36:16,719 --> 00:36:19,600 Speaker 1: Given your experience, of course as as a banking regulator, 645 00:36:20,120 --> 00:36:22,840 Speaker 1: so I think that the exposure to the regulated f 646 00:36:23,000 --> 00:36:26,040 Speaker 1: d i C insured banks was was very very limited. Obviously, 647 00:36:26,040 --> 00:36:28,719 Speaker 1: they had bank accounts. Everybody's a bank account to do, 648 00:36:28,960 --> 00:36:33,520 Speaker 1: you know, your your your regular transactions, payment transactions. But 649 00:36:33,600 --> 00:36:36,560 Speaker 1: it has not appeared that there was any significant exposure. 650 00:36:36,719 --> 00:36:39,360 Speaker 1: There's been a lot of press coverage about f t 651 00:36:39,640 --> 00:36:43,360 Speaker 1: X or news elamated taking a significant investment interest in 652 00:36:43,400 --> 00:36:46,400 Speaker 1: a small bank in Washington State called Moonstone. They renamed 653 00:36:46,400 --> 00:36:48,799 Speaker 1: at Moonstone. But as here as I can tell that 654 00:36:48,880 --> 00:36:51,600 Speaker 1: they hadn't really used the bank for anything, So I 655 00:36:51,719 --> 00:36:54,280 Speaker 1: don't it just looks like to except they were using banks, 656 00:36:54,280 --> 00:36:56,640 Speaker 1: it was regret and better payments. I don't think this 657 00:36:56,719 --> 00:37:02,080 Speaker 1: affects the banking system at all, um and uh so 658 00:37:02,160 --> 00:37:05,720 Speaker 1: that that's a good news that it does not really 659 00:37:05,760 --> 00:37:08,759 Speaker 1: have any kind of impact on mainstream banking or the 660 00:37:08,760 --> 00:37:11,200 Speaker 1: insured banks that we all rely on for them, you know, 661 00:37:11,280 --> 00:37:13,879 Speaker 1: checking and savings accounts. But the banks that did work 662 00:37:13,880 --> 00:37:15,640 Speaker 1: with ft X and alimated, do you think that it 663 00:37:15,719 --> 00:37:17,920 Speaker 1: was a mistake for them to bring f t X 664 00:37:18,040 --> 00:37:20,839 Speaker 1: onto you know? That is that is a really tough 665 00:37:20,960 --> 00:37:24,280 Speaker 1: question because there's been a big controversy generally about whether 666 00:37:24,400 --> 00:37:27,719 Speaker 1: regulators should, you know, tell banks you just can't do 667 00:37:27,800 --> 00:37:31,520 Speaker 1: business with certain entities, you know, certain markets are certain 668 00:37:32,239 --> 00:37:34,799 Speaker 1: you know, and that's hard to do. I think if 669 00:37:34,800 --> 00:37:37,799 Speaker 1: a business is legal, then to tell a bank not 670 00:37:37,920 --> 00:37:40,680 Speaker 1: to do, not to have dealings with them, I think 671 00:37:40,920 --> 00:37:44,480 Speaker 1: is hard. And I am unaware that any of the 672 00:37:44,560 --> 00:37:49,920 Speaker 1: entities doing business with US banks were illegal, So I 673 00:37:49,960 --> 00:37:52,320 Speaker 1: think it's hard, it's it's it's a very controversially, is 674 00:37:52,360 --> 00:37:55,120 Speaker 1: to try to use banks to shut insured banks to 675 00:37:55,160 --> 00:37:58,799 Speaker 1: shut all of this down. That was former fd I 676 00:37:58,840 --> 00:38:03,120 Speaker 1: c chat Sheila Bear. Meanwhile, the lawyer who represented Bernie 677 00:38:03,200 --> 00:38:06,719 Speaker 1: Maddof has this advice for Sam Bankman Freed. Zip It 678 00:38:07,239 --> 00:38:10,200 Speaker 1: Irish Sorkin was lead defense lawyer for Madof seen here, 679 00:38:10,440 --> 00:38:12,840 Speaker 1: who was the mastermind of one of the greatest Ponzi 680 00:38:12,920 --> 00:38:17,120 Speaker 1: schemes of all time. Saukin says SPF is digging himself 681 00:38:17,160 --> 00:38:20,479 Speaker 1: into a hole with his media apology tour. Sorkin says 682 00:38:20,680 --> 00:38:25,080 Speaker 1: Bankman Freed should listen to his lawyers and stopped talking immediately. 683 00:38:25,239 --> 00:38:28,240 Speaker 1: Representatives for bank Man Freed and f t X didn't 684 00:38:28,280 --> 00:38:42,000 Speaker 1: immediately respond to request for comment on this story. Welcome 685 00:38:42,080 --> 00:38:44,680 Speaker 1: to swift Tonomics. That's what happens that when a post 686 00:38:44,680 --> 00:38:47,360 Speaker 1: COVID demand shock causes ticket sales to go up to 687 00:38:47,600 --> 00:38:51,200 Speaker 1: forty thousands of dollars, Many Taylor Swift fans were very 688 00:38:51,239 --> 00:38:53,839 Speaker 1: disappointed when they couldn't get their hands on tickets back 689 00:38:53,840 --> 00:38:56,719 Speaker 1: in November. The demand was so high that it cost 690 00:38:56,800 --> 00:39:00,239 Speaker 1: ticket Master's system to fail, and it raised all sorts 691 00:39:00,239 --> 00:39:03,600 Speaker 1: of questions about the platform's monopoly. But there's more here. 692 00:39:03,680 --> 00:39:07,360 Speaker 1: Swift Ties, or fans of Taylor Swift, represent a particular 693 00:39:07,440 --> 00:39:11,360 Speaker 1: moment in the global economy. They are the supercharged consumer 694 00:39:11,480 --> 00:39:14,040 Speaker 1: that's willing to spend a lot of money on experiences 695 00:39:14,040 --> 00:39:16,759 Speaker 1: that they missed out on during the pandemic. Many in 696 00:39:16,800 --> 00:39:19,319 Speaker 1: this category are gen z s or millennials who come 697 00:39:19,360 --> 00:39:22,800 Speaker 1: out of the pandemic with historically high levels of savings. 698 00:39:22,840 --> 00:39:25,560 Speaker 1: They've waited for four years to see their favorite pop star, 699 00:39:25,880 --> 00:39:28,719 Speaker 1: and they don't mind splurging ten months worth of savings 700 00:39:28,840 --> 00:39:31,040 Speaker 1: to see her. It's all because of you, Thank you 701 00:39:31,239 --> 00:39:35,160 Speaker 1: so som In the early two thousand's, economist Alan Krueger 702 00:39:35,239 --> 00:39:38,400 Speaker 1: came up with a concept called rockconomics. He uses it 703 00:39:38,480 --> 00:39:40,840 Speaker 1: to explain the global economy through the lens of the 704 00:39:40,920 --> 00:39:44,240 Speaker 1: music industry. He often talks about Taylor Swift as somebody 705 00:39:44,280 --> 00:39:48,160 Speaker 1: who's very cleverly playing with strategies to boost ticket sales. Well, 706 00:39:48,200 --> 00:39:50,480 Speaker 1: I think Taylor Swift is an economic genius. But the 707 00:39:50,480 --> 00:39:53,400 Speaker 1: bigger question of how much longer consumers are going to 708 00:39:53,520 --> 00:39:56,719 Speaker 1: spend this much money in an economy of highly alarming 709 00:39:56,760 --> 00:40:00,719 Speaker 1: interest rates is something that swift aconomics can't and for now, 710 00:40:00,880 --> 00:40:08,280 Speaker 1: economists are going to have to shake it off. Thanks 711 00:40:08,280 --> 00:40:10,640 Speaker 1: to Marius Kavash for that one. Some other stories we're 712 00:40:10,640 --> 00:40:14,560 Speaker 1: following In entertainment, Amazon's top media executive is stepping down 713 00:40:14,640 --> 00:40:18,240 Speaker 1: as the company begins a restructuring. Jeff Blackburn is retiring 714 00:40:18,280 --> 00:40:21,280 Speaker 1: at the end of two after twenty four years at Amazon. 715 00:40:21,440 --> 00:40:24,360 Speaker 1: According to a Memo Center staff today, Blackburn helped the 716 00:40:24,400 --> 00:40:27,560 Speaker 1: company pivot into streaming and lead the charge on the 717 00:40:27,680 --> 00:40:30,560 Speaker 1: MGM acquisition as well as the billion dollar Lord of 718 00:40:30,560 --> 00:40:33,160 Speaker 1: the Rings series. Amazon has been in cost cutting mode 719 00:40:33,280 --> 00:40:36,360 Speaker 1: due to a sales slow down, and Meta is urging 720 00:40:36,400 --> 00:40:39,880 Speaker 1: policymakers to wait before new rules governing the metaverse. A 721 00:40:39,960 --> 00:40:43,239 Speaker 1: policy paper released by Meta argues that many of the 722 00:40:43,239 --> 00:40:46,560 Speaker 1: world's existing laws and REGs will also applies activity in 723 00:40:46,600 --> 00:40:50,440 Speaker 1: the metaverse. Regulators could stymy innovation if they act too quickly, 724 00:40:50,600 --> 00:40:52,960 Speaker 1: according to the paper, Well that does it. For this 725 00:40:53,120 --> 00:40:56,400 Speaker 1: edition of Bloomberg Technology, don't forget to check out our podcasts. 726 00:40:56,600 --> 00:40:58,239 Speaker 1: You can find it on the terminal as well as 727 00:40:58,239 --> 00:41:02,520 Speaker 1: online on Apple, Spotify, and I Heart Radio. This is 728 00:41:02,880 --> 00:41:11,160 Speaker 1: bloom Bag h