1 00:00:13,920 --> 00:00:16,439 Speaker 1: And Caroline hide up Bloomberg's world head quarters in New York, 2 00:00:16,880 --> 00:00:19,320 Speaker 1: and I made Ludlow in San Francisco. This is Bloomberg 3 00:00:19,360 --> 00:00:23,279 Speaker 1: Technology in Caroline, big tech sounding or warning for three. 4 00:00:23,320 --> 00:00:26,720 Speaker 1: The market seems really calm about what they're seeing, managing 5 00:00:26,760 --> 00:00:29,040 Speaker 1: to shake off some of the chip concern even as 6 00:00:29,080 --> 00:00:32,120 Speaker 1: that isn't just around earnings but about geopolitics as well. 7 00:00:32,200 --> 00:00:34,640 Speaker 1: And we're back here in New York. They're they're in SF. 8 00:00:34,640 --> 00:00:36,960 Speaker 1: But we're both going to be looking at the Nasdaq 9 00:00:37,000 --> 00:00:39,840 Speaker 1: one hundred best week in months. Hey, but not without 10 00:00:40,080 --> 00:00:44,199 Speaker 1: major fluctuations in the semiconductor space after Intel's dismal earnings 11 00:00:44,440 --> 00:00:48,120 Speaker 1: and there's new curbs for China made chips. Then Elon 12 00:00:48,200 --> 00:00:51,800 Speaker 1: Musk faces an SEC probe over his role in misguided 13 00:00:51,880 --> 00:00:56,400 Speaker 1: claims about Tesla's autopilot system the Bloomberg scoop and thousands 14 00:00:56,440 --> 00:00:58,840 Speaker 1: of tech workers got laid off after they came to 15 00:00:58,880 --> 00:01:02,160 Speaker 1: the US on work is what one nonprofits doing about it? 16 00:01:02,600 --> 00:01:04,800 Speaker 1: But first I check in all these markets, and Ed 17 00:01:04,920 --> 00:01:07,039 Speaker 1: was talking about it, the fact that we see resilience, 18 00:01:07,240 --> 00:01:09,840 Speaker 1: even though there's some worrying signs amid a number of 19 00:01:09,840 --> 00:01:11,399 Speaker 1: the earliers that came out this week, and we think 20 00:01:11,400 --> 00:01:14,640 Speaker 1: of the intels and Microsoft of course Texas instruments as well. 21 00:01:14,640 --> 00:01:16,160 Speaker 1: But then as that manages to push on up more 22 00:01:16,200 --> 00:01:18,560 Speaker 1: than a percentage point. In fact, we are really seeing 23 00:01:18,600 --> 00:01:21,440 Speaker 1: this particular index on a role highest in September over 24 00:01:21,440 --> 00:01:24,520 Speaker 1: the last year, four straight weeks of growth that we've 25 00:01:24,520 --> 00:01:26,920 Speaker 1: seen four straight weeks of games. We haven't seen that 26 00:01:26,959 --> 00:01:30,200 Speaker 1: back since August two. Or Country World and Next also 27 00:01:30,240 --> 00:01:32,200 Speaker 1: gets a slight bid today, up about a quarter percent, 28 00:01:32,400 --> 00:01:35,319 Speaker 1: nowhere near the enthusiasm in the US though many focused 29 00:01:35,360 --> 00:01:37,559 Speaker 1: on the Federal Reserve next sweep. Many focused, of course 30 00:01:37,720 --> 00:01:39,440 Speaker 1: on what it means for interest rate path. We're looking 31 00:01:39,480 --> 00:01:42,440 Speaker 1: at Bloomberg Commodity Index. Interesting me thinking ahead of China's 32 00:01:42,440 --> 00:01:44,640 Speaker 1: reopening after the looonar New Year next week, maybe some 33 00:01:44,640 --> 00:01:47,319 Speaker 1: money coming back out of copper for example. Let's look 34 00:01:47,360 --> 00:01:49,800 Speaker 1: it on. Let's look at what is a really for 35 00:01:50,480 --> 00:01:53,680 Speaker 1: out and out technology focus That is the socks. Because 36 00:01:53,720 --> 00:01:56,440 Speaker 1: as much as we saw that enthusiasm around the NASDAC head, 37 00:01:56,680 --> 00:01:59,080 Speaker 1: really the chip sector ruling US lower off by seven 38 00:01:59,120 --> 00:02:03,480 Speaker 1: tenths of ascent for the lived at Philadelphia Semiconductor Index. Yeah, look, 39 00:02:03,480 --> 00:02:05,320 Speaker 1: I'm going to start on the positive here and focus 40 00:02:05,400 --> 00:02:09,160 Speaker 1: straight on Tesla. Really big jump on Friday, continuing the 41 00:02:09,200 --> 00:02:11,600 Speaker 1: momentum for earnings, but the thirty three percent gain on 42 00:02:11,639 --> 00:02:13,920 Speaker 1: the week it's best week going back to May of 43 00:02:14,000 --> 00:02:18,519 Speaker 1: two thousand thirteen. Clearly investors are believing what Elon must 44 00:02:18,560 --> 00:02:21,320 Speaker 1: has to say about momentum. The negative is behind me, right? 45 00:02:21,360 --> 00:02:24,120 Speaker 1: Which is Intel having its worst week since December, It's 46 00:02:24,120 --> 00:02:28,119 Speaker 1: biggest drop on Friday for a single session since September. 47 00:02:28,840 --> 00:02:31,160 Speaker 1: Is this a question about what's happening in a cyclical 48 00:02:31,240 --> 00:02:34,360 Speaker 1: chip sector or is this a question about what is 49 00:02:34,400 --> 00:02:38,400 Speaker 1: happening with Intel specifically? That is the question that I have. 50 00:02:38,639 --> 00:02:41,520 Speaker 1: What is wrong with Intel? So great? We had the 51 00:02:41,520 --> 00:02:45,040 Speaker 1: best person talk with about this, Daniel Flax, Managing director 52 00:02:45,240 --> 00:02:48,640 Speaker 1: senior research analysts covering the technology sector broadly at New 53 00:02:48,680 --> 00:02:53,000 Speaker 1: Burger Berman seven billions of course in assets under management. 54 00:02:53,120 --> 00:02:58,840 Speaker 1: That is the question what is wrong with Intel? Dan? Intel? 55 00:02:58,919 --> 00:03:02,840 Speaker 1: He is going through a multi year transition, and if 56 00:03:02,880 --> 00:03:05,079 Speaker 1: we step back and think about the past decade, the 57 00:03:05,200 --> 00:03:10,919 Speaker 1: company uh lost competitiveness. They weren't making products that were 58 00:03:10,919 --> 00:03:13,800 Speaker 1: as cutting edge as they had previously, and so what's 59 00:03:13,840 --> 00:03:15,880 Speaker 1: changed in the last couple of years is You've had 60 00:03:16,160 --> 00:03:21,480 Speaker 1: CEO Pat Gelsinger come back to the company really reinvest aggressively, 61 00:03:21,600 --> 00:03:25,120 Speaker 1: bringing new people like CFO Dave's inster UH whom we 62 00:03:25,200 --> 00:03:27,640 Speaker 1: think highly of along with Pat and and they are 63 00:03:27,760 --> 00:03:31,960 Speaker 1: working to to UH execute better on the product roadmaps, 64 00:03:32,160 --> 00:03:34,920 Speaker 1: UH deliver value to customers and certainly when we speak 65 00:03:34,960 --> 00:03:38,839 Speaker 1: to the customers, Intel remains strategic and so looking out, 66 00:03:38,920 --> 00:03:42,120 Speaker 1: I think there's a multi year journey still ahead for Intel. 67 00:03:42,520 --> 00:03:45,520 Speaker 1: But if they can make better products, I think UH, 68 00:03:45,640 --> 00:03:48,240 Speaker 1: I think they can deliver value for customers and ultimately 69 00:03:48,280 --> 00:03:52,160 Speaker 1: for shareholders as well, then it's replating this out further. 70 00:03:52,360 --> 00:03:55,040 Speaker 1: How worried about you about the end markets? You know, 71 00:03:55,120 --> 00:03:59,560 Speaker 1: you look at PC, desktop, also servers, and what Intel 72 00:03:59,560 --> 00:04:01,280 Speaker 1: had to say about the first half of this year 73 00:04:01,280 --> 00:04:04,600 Speaker 1: in particular, what does that tell you about what's to 74 00:04:04,680 --> 00:04:09,000 Speaker 1: come for the technology sector. Certainly we're in a period 75 00:04:09,000 --> 00:04:11,880 Speaker 1: where where there's cyclical headwinds. Um If we think back 76 00:04:12,320 --> 00:04:14,760 Speaker 1: over the past couple of years, there was enormous demands 77 00:04:14,800 --> 00:04:19,080 Speaker 1: save for for PCs as one example, during lockdowns during 78 00:04:19,440 --> 00:04:21,920 Speaker 1: during during the worst of COVID, and so there's a 79 00:04:21,960 --> 00:04:25,640 Speaker 1: little bit of digestion of that. So I I expect 80 00:04:25,680 --> 00:04:28,200 Speaker 1: over the next several months the the industry will work 81 00:04:28,240 --> 00:04:31,960 Speaker 1: through UH. Inventories in PCs is one example. The economy 82 00:04:32,080 --> 00:04:36,000 Speaker 1: is slowing, its impacting enterprises as they spend. But if 83 00:04:36,040 --> 00:04:38,240 Speaker 1: we look out till later this year and really further 84 00:04:38,279 --> 00:04:42,160 Speaker 1: into four I think we'll see better growth. And so 85 00:04:42,400 --> 00:04:45,880 Speaker 1: the companies that have the products, that have the solutions UH, 86 00:04:46,160 --> 00:04:49,159 Speaker 1: those can benefit. And if we step back and look 87 00:04:49,200 --> 00:04:51,760 Speaker 1: at some of the secular trends, so for example, artificial 88 00:04:51,800 --> 00:04:56,000 Speaker 1: intelligence UH, the the ability to deliver value in areas 89 00:04:56,279 --> 00:04:58,760 Speaker 1: like drug discovery. Look at a company like in video 90 00:04:58,880 --> 00:05:02,479 Speaker 1: it two faces cyclical headwinds, but they have tremendous growth 91 00:05:02,520 --> 00:05:06,159 Speaker 1: drivers and product cycles in areas like the data center. 92 00:05:06,240 --> 00:05:09,640 Speaker 1: So we still see opportunities. Certainly their near term headwinds though. 93 00:05:10,080 --> 00:05:13,680 Speaker 1: Talk about those headwinds that aren't just perhaps cyclical in nature, Daniel, 94 00:05:13,760 --> 00:05:16,680 Speaker 1: but they're also related to geopolitics, and the video is 95 00:05:16,720 --> 00:05:18,880 Speaker 1: just one of them, which of course has China exposure. 96 00:05:18,920 --> 00:05:21,520 Speaker 1: I'm thinking today of the the scoop really coming from 97 00:05:21,520 --> 00:05:24,760 Speaker 1: Bloomberg talking about how the Biden administration is getting closer 98 00:05:24,800 --> 00:05:27,120 Speaker 1: to getting Japan the Netherlands to come on side to 99 00:05:27,279 --> 00:05:30,880 Speaker 1: limit China's access to the semiconductor technology that it so 100 00:05:30,960 --> 00:05:34,320 Speaker 1: wishes to build. How is that help or hindrance to 101 00:05:34,400 --> 00:05:39,640 Speaker 1: some of these US players. I think the geopolitical and 102 00:05:39,640 --> 00:05:41,960 Speaker 1: the trade tension click. Clearly, it's been with US for 103 00:05:42,040 --> 00:05:45,279 Speaker 1: several years and it's continuing to intensify. It is having 104 00:05:45,360 --> 00:05:49,840 Speaker 1: impact on some companies ability to ship the most advanced 105 00:05:49,880 --> 00:05:54,120 Speaker 1: technologies into markets like China. But but I think globally 106 00:05:54,320 --> 00:05:56,960 Speaker 1: demand for these products over the next several years is 107 00:05:57,000 --> 00:05:59,680 Speaker 1: likely to be healthy, and that's driven by by build 108 00:05:59,720 --> 00:06:03,080 Speaker 1: out example of the cloud in really this digital infrastructure. 109 00:06:03,160 --> 00:06:06,480 Speaker 1: And so in the near term the restrictions are going 110 00:06:06,520 --> 00:06:09,960 Speaker 1: to impact companies that that do sell in certain cases 111 00:06:10,279 --> 00:06:13,320 Speaker 1: with certain products into China. But I think ultimately that 112 00:06:13,440 --> 00:06:17,200 Speaker 1: demand UH does grow and those companies like a SM 113 00:06:17,279 --> 00:06:21,359 Speaker 1: Lithography is one example, we will continue to have attractive 114 00:06:21,440 --> 00:06:25,240 Speaker 1: multi year growth prospects. What about the R and D 115 00:06:25,320 --> 00:06:28,280 Speaker 1: that then is still needed as we see the cyclical nature, 116 00:06:28,320 --> 00:06:30,440 Speaker 1: the fall away of PC demand, the fact that we're 117 00:06:30,440 --> 00:06:33,280 Speaker 1: going to see slowing revenues that some analysts just said 118 00:06:33,360 --> 00:06:37,520 Speaker 1: we're what was phenomenal staggering was one of the words used. 119 00:06:38,200 --> 00:06:40,280 Speaker 1: Are we're still going to have to see this sort 120 00:06:40,320 --> 00:06:42,840 Speaker 1: of spend come in that means of seeing these fabs 121 00:06:42,880 --> 00:06:44,599 Speaker 1: come up in the United States and all bringing it 122 00:06:44,600 --> 00:06:49,520 Speaker 1: closer shore. I think the build out in the United States, 123 00:06:49,560 --> 00:06:53,280 Speaker 1: in Europe, in other geographies outside of Asia will continue, 124 00:06:53,440 --> 00:06:56,279 Speaker 1: but but it's a multi year process to build them 125 00:06:56,320 --> 00:06:59,360 Speaker 1: and to equip them, and and of course a modern 126 00:06:59,480 --> 00:07:02,719 Speaker 1: fab could cost around twenty billion dollars, so it's no 127 00:07:02,839 --> 00:07:07,480 Speaker 1: small financial undertaking. I think the the industry will remain cyclical. 128 00:07:07,600 --> 00:07:09,359 Speaker 1: But but if we look out to the middle and 129 00:07:09,400 --> 00:07:12,600 Speaker 1: really the latter part of the decade, demand for silicon 130 00:07:12,760 --> 00:07:15,640 Speaker 1: is likely to continue growing. And that's because it's not 131 00:07:15,800 --> 00:07:18,960 Speaker 1: just in PCs and smartphones. It's really going everywhere. And 132 00:07:19,000 --> 00:07:21,680 Speaker 1: that I think is the bigger story in many ways 133 00:07:21,720 --> 00:07:25,880 Speaker 1: in terms of this transformation of the industrial landscape putting 134 00:07:25,880 --> 00:07:29,800 Speaker 1: intelligence into all sorts of devices. You have companies like 135 00:07:29,920 --> 00:07:32,400 Speaker 1: Qualcom for example, that do that. So there's a lot 136 00:07:32,440 --> 00:07:35,280 Speaker 1: of growth even with cyclicality. Words an I want to 137 00:07:35,280 --> 00:07:37,760 Speaker 1: go to Calcom really quickly. They report next week. The 138 00:07:37,840 --> 00:07:41,400 Speaker 1: story there is about diversifying away from the smartphone. Do 139 00:07:41,400 --> 00:07:43,280 Speaker 1: you think that they'll buck the trend we've seen in 140 00:07:43,320 --> 00:07:45,760 Speaker 1: a limited way so sl far from the chip sector. 141 00:07:47,240 --> 00:07:49,640 Speaker 1: They're they're also going to be impacted by it by 142 00:07:49,680 --> 00:07:52,880 Speaker 1: some of the smartphone industry inventories that have built up. 143 00:07:53,080 --> 00:07:55,360 Speaker 1: But as we look out over the next several months, 144 00:07:55,440 --> 00:07:57,880 Speaker 1: I think what we'll see is that China is beginning 145 00:07:57,880 --> 00:08:01,280 Speaker 1: to open up. China is an enormous market for smartphones 146 00:08:01,320 --> 00:08:04,080 Speaker 1: and other mobile devices, and Quacom will benefit from that 147 00:08:04,320 --> 00:08:08,559 Speaker 1: from that growth rebound. I think the bigger story, though, 148 00:08:08,800 --> 00:08:12,400 Speaker 1: beyond that, is their success in areas like automobiles, where 149 00:08:12,400 --> 00:08:15,160 Speaker 1: they have a thirty billion dollar design wind pipeline, and 150 00:08:15,240 --> 00:08:18,920 Speaker 1: so CEO Christiano amon I think is making the right investments. 151 00:08:18,960 --> 00:08:23,160 Speaker 1: They're diversifying the company and their intellectual property in key 152 00:08:23,200 --> 00:08:27,720 Speaker 1: areas like connectivity and security and automation are powerful and 153 00:08:27,800 --> 00:08:30,600 Speaker 1: valuable to customers. Daniel Gregg's have some time with the 154 00:08:30,840 --> 00:08:33,360 Speaker 1: have a wonderful weekend. Daniel Flax, Managing director, SENI to 155 00:08:33,400 --> 00:08:35,439 Speaker 1: research analyst over a Newberger berm and really going through 156 00:08:35,559 --> 00:08:37,800 Speaker 1: whole host of names in the chip space. We've got 157 00:08:37,800 --> 00:08:40,120 Speaker 1: another one for you, because let's talk about in Vidia. 158 00:08:40,559 --> 00:08:43,000 Speaker 1: It's conserving pretty well of late and the president of 159 00:08:43,000 --> 00:08:45,840 Speaker 1: the CEO, Jensen Kuang, who's been doing particularly well to 160 00:08:45,960 --> 00:08:48,719 Speaker 1: be perhaps building his wealth, largely in part because of 161 00:08:48,720 --> 00:08:52,560 Speaker 1: the sore and popularity of chat GPT. This man has 162 00:08:52,559 --> 00:08:55,320 Speaker 1: seen his fortune climb by three percent this year of 163 00:08:55,440 --> 00:08:58,839 Speaker 1: eighteen billion dollars now is his overall fortune after videos 164 00:08:58,920 --> 00:09:03,160 Speaker 1: become basically a dominant player empowering AI applications, automous cars, robots, 165 00:09:03,160 --> 00:09:06,400 Speaker 1: to crypto, to mining. Of course, and speciality that's made 166 00:09:06,400 --> 00:09:08,800 Speaker 1: at one of Wall Street's top wages for how to 167 00:09:08,880 --> 00:09:11,480 Speaker 1: profit from the rapid business interest in AI. So I 168 00:09:11,520 --> 00:09:14,040 Speaker 1: guess in spite of Intel's fall, in spite of the 169 00:09:14,040 --> 00:09:16,600 Speaker 1: doom and the gloom ed, it feels as though in 170 00:09:16,640 --> 00:09:19,920 Speaker 1: particular there's some hope around chipmakers. Morgan Stanley actually had 171 00:09:19,920 --> 00:09:23,760 Speaker 1: this take on it. We do like some of the 172 00:09:23,760 --> 00:09:27,839 Speaker 1: semiconductors down here that that look like they're starting to 173 00:09:28,200 --> 00:09:32,400 Speaker 1: put encycliculturals in the traditional sense. Some of the software 174 00:09:32,440 --> 00:09:39,280 Speaker 1: services companies nice pivot there from semiconductors to software companies. 175 00:09:39,280 --> 00:09:41,640 Speaker 1: And we can delve into one key software company now 176 00:09:41,679 --> 00:09:45,679 Speaker 1: because Salesforce appointing three new independent directors to its board 177 00:09:45,840 --> 00:09:48,800 Speaker 1: as the pressure builds from activists and mrs. Of course, 178 00:09:49,160 --> 00:09:51,200 Speaker 1: that's talked to be mostly Anna Baker for more on this, 179 00:09:51,360 --> 00:09:53,559 Speaker 1: And really it was the scoop from d Hammond yesterday 180 00:09:53,559 --> 00:09:56,120 Speaker 1: and from your team that we would likely to see 181 00:09:56,160 --> 00:09:58,920 Speaker 1: these caster characters come onto the board, and well it 182 00:09:58,920 --> 00:10:02,240 Speaker 1: occurs to the Anna, So some of the casts of 183 00:10:02,360 --> 00:10:05,200 Speaker 1: characters have come on the board Value Act. Uh you 184 00:10:05,240 --> 00:10:08,000 Speaker 1: know that mystery activist investor. We were trying to figure 185 00:10:08,040 --> 00:10:11,800 Speaker 1: out who else was in there besides Starboard, Elliott and 186 00:10:11,880 --> 00:10:14,880 Speaker 1: Jeff of it. So this is really the start of something. 187 00:10:15,720 --> 00:10:18,920 Speaker 1: Value Acts certainly will have an influence on the board. 188 00:10:18,960 --> 00:10:22,920 Speaker 1: They're very interested in, you know, corporate governance and making changes, 189 00:10:22,960 --> 00:10:25,480 Speaker 1: but in a friendly way with the company. But there's 190 00:10:25,480 --> 00:10:28,440 Speaker 1: still the question of what happened with these other activists 191 00:10:28,480 --> 00:10:31,640 Speaker 1: who are still very much involved. And the shares were 192 00:10:31,720 --> 00:10:35,160 Speaker 1: muted today, so um, you know, the news was received 193 00:10:35,360 --> 00:10:37,199 Speaker 1: okay by the market, but I think there's going to 194 00:10:37,280 --> 00:10:41,000 Speaker 1: be some more action on this one. Leona. Let's stick 195 00:10:41,040 --> 00:10:43,000 Speaker 1: with that kind of main protagonist. Then I suppose for 196 00:10:43,080 --> 00:10:45,520 Speaker 1: one of a better description, who is Mason will fit 197 00:10:45,559 --> 00:10:48,319 Speaker 1: and talk to us about Value Act. They track record 198 00:10:48,400 --> 00:10:52,040 Speaker 1: what it is they come into companies and do well. 199 00:10:52,040 --> 00:10:54,280 Speaker 1: They're based on your part of the world. San Francisco 200 00:10:54,640 --> 00:10:58,320 Speaker 1: may have a A A thirteen billion under management, and Mason 201 00:10:58,400 --> 00:11:01,800 Speaker 1: Morpha is no stranger to tech apology. He sat on 202 00:11:01,840 --> 00:11:05,880 Speaker 1: the Microsoft board for about four years from fourteen to seventeen. 203 00:11:06,280 --> 00:11:09,400 Speaker 1: I heard the stock in that time doubled, So he 204 00:11:09,600 --> 00:11:14,679 Speaker 1: will definitely bring that experience public board experience with him. 205 00:11:14,920 --> 00:11:17,760 Speaker 1: Uh and you know they're they're known for making changes, 206 00:11:17,800 --> 00:11:20,720 Speaker 1: as I mentioned in a friendly way with the company, 207 00:11:21,200 --> 00:11:23,600 Speaker 1: So it'll be good to have some sort of uh 208 00:11:23,760 --> 00:11:28,080 Speaker 1: investor viewpoint on the board to help because the shares 209 00:11:28,120 --> 00:11:32,160 Speaker 1: are trading so great for Salesforce bloom Bosleyana Baker leading 210 00:11:32,200 --> 00:11:34,400 Speaker 1: a big week for our deal's team out of New York, 211 00:11:34,440 --> 00:11:37,520 Speaker 1: Thank you so much. Meanwhile, back here in San Francisco, 212 00:11:37,600 --> 00:11:42,760 Speaker 1: regulators investigating Elon Musk's role in shaping Tesla's self driving 213 00:11:43,040 --> 00:11:46,079 Speaker 1: car claims. The review is part of an ongoing SEC 214 00:11:46,280 --> 00:11:50,800 Speaker 1: probe of the company's statements about its autopilot driver autopilot 215 00:11:50,840 --> 00:11:54,239 Speaker 1: driver assistant system that, according to a Bloomberg surce, officials 216 00:11:54,400 --> 00:11:59,240 Speaker 1: awaghing whether Mosque specifically may have inappropriately made forward looking 217 00:11:59,320 --> 00:12:02,920 Speaker 1: statements and carry this this story, this reporting hot on 218 00:12:02,960 --> 00:12:05,000 Speaker 1: the heels of that other scoot from Danna Hole in 219 00:12:05,040 --> 00:12:08,720 Speaker 1: Sean o'caine earlier this month about the video right the 220 00:12:08,800 --> 00:12:12,680 Speaker 1: emails where Elon Musk is directing the language used behind 221 00:12:12,760 --> 00:12:16,960 Speaker 1: how they described Autopilot in those nation stages, and the 222 00:12:17,080 --> 00:12:19,280 Speaker 1: SEC is just hot in his heels, isn't it? In 223 00:12:19,320 --> 00:12:22,040 Speaker 1: every sort of direction of this. But ultimately it all 224 00:12:22,080 --> 00:12:24,560 Speaker 1: comes down to the fact, and it's almost reminiscent of 225 00:12:24,559 --> 00:12:26,559 Speaker 1: what he's just been in court trying to explain around 226 00:12:26,559 --> 00:12:29,720 Speaker 1: his tweet of funding secured. Is when you're a CEO, 227 00:12:30,160 --> 00:12:33,200 Speaker 1: the leader of a business, the visionary, when you say 228 00:12:33,240 --> 00:12:36,160 Speaker 1: certain things, people believe them and they end up being 229 00:12:36,320 --> 00:12:38,640 Speaker 1: forward looking guidance basically whether or not he wants them 230 00:12:38,679 --> 00:12:41,439 Speaker 1: to be. Yeah, and Boomberg's source pointing out this is 231 00:12:41,440 --> 00:12:45,120 Speaker 1: an extension of the existing SEC probe, which was originally 232 00:12:45,120 --> 00:12:48,400 Speaker 1: reported by a number of media outlets back in October. 233 00:12:56,880 --> 00:13:01,360 Speaker 1: It's Today's Bloomberg Big Take Daiy Albert Kurt Wagner taking 234 00:13:01,400 --> 00:13:04,679 Speaker 1: a deep dive into one of Elon Musk's most faithful supporters. 235 00:13:05,800 --> 00:13:09,000 Speaker 1: She Twitter's head of trust and safety and seems willing 236 00:13:09,080 --> 00:13:12,839 Speaker 1: to overlook protocol to appease her new boss. Those cut 237 00:13:12,880 --> 00:13:15,480 Speaker 1: Wagner joins us now on it. So, first and foremost, 238 00:13:15,960 --> 00:13:21,040 Speaker 1: who is Yeah, so Ella is the head of Trust 239 00:13:21,040 --> 00:13:23,719 Speaker 1: and Safety at Twitter. So she's essentially in charge of 240 00:13:23,760 --> 00:13:26,240 Speaker 1: a lot of the teams that work on you know, 241 00:13:26,360 --> 00:13:30,240 Speaker 1: keeping Twitter safe. Right. They used to fight misinformation, they 242 00:13:30,320 --> 00:13:33,000 Speaker 1: used to take down hate speech. Um. You know, this 243 00:13:33,080 --> 00:13:36,040 Speaker 1: is the same group that, for example, was responsible for 244 00:13:36,160 --> 00:13:38,680 Speaker 1: suspending President Donald Trump a few years ago. Right. And 245 00:13:38,760 --> 00:13:41,040 Speaker 1: so this is a pretty high profile job. A lot 246 00:13:41,080 --> 00:13:45,120 Speaker 1: of these decisions we've seen be very controversial over the years. 247 00:13:45,120 --> 00:13:47,200 Speaker 1: So the person who kind of runs trust and safety 248 00:13:47,200 --> 00:13:50,120 Speaker 1: at Twitter has always been a high profile role in 249 00:13:50,120 --> 00:13:52,679 Speaker 1: that regard because the decisions that are made are so 250 00:13:52,760 --> 00:13:55,920 Speaker 1: important to free speech on the internet. Right. And so 251 00:13:56,400 --> 00:13:59,839 Speaker 1: she stepped into a very senior position under Elon in 252 00:14:00,040 --> 00:14:02,400 Speaker 1: just the last couple of months. But that was what 253 00:14:02,440 --> 00:14:04,040 Speaker 1: I found so interesting about this. You and I have 254 00:14:04,120 --> 00:14:07,120 Speaker 1: done a lot of reportings together over recent months and 255 00:14:07,240 --> 00:14:09,679 Speaker 1: more than recent months, honestly, and that was the name 256 00:14:09,720 --> 00:14:12,840 Speaker 1: I didn't recognize. You know, we we documented on a 257 00:14:12,920 --> 00:14:16,280 Speaker 1: day by day basis the people leaving Twitter those that 258 00:14:16,400 --> 00:14:19,040 Speaker 1: chose to stay. So now my question is what is 259 00:14:19,040 --> 00:14:21,320 Speaker 1: it that she's doing? You know, what is the crux 260 00:14:21,360 --> 00:14:23,920 Speaker 1: of your big tape story that you've learned about her 261 00:14:24,040 --> 00:14:27,840 Speaker 1: role within the building. Yeah, well you probably aren't familiar 262 00:14:27,880 --> 00:14:30,600 Speaker 1: with her name because she really just joined Twitter over 263 00:14:30,640 --> 00:14:33,960 Speaker 1: the summer, just a few months before Elon Musk took over, Right, 264 00:14:34,000 --> 00:14:36,920 Speaker 1: So she was not necessarily a senior leader in the 265 00:14:36,960 --> 00:14:39,760 Speaker 1: way that a lot of employees were, someone who a 266 00:14:39,800 --> 00:14:42,240 Speaker 1: lot of employees were familiar with by the time that 267 00:14:42,320 --> 00:14:45,280 Speaker 1: the deal closed. Right. But what we you know, pointed 268 00:14:45,280 --> 00:14:46,760 Speaker 1: out in our big take and why I think this 269 00:14:46,800 --> 00:14:49,640 Speaker 1: is an interesting story is that, you know, they're kind 270 00:14:49,640 --> 00:14:52,440 Speaker 1: of bucking a lot of the trends of of of 271 00:14:52,760 --> 00:14:55,120 Speaker 1: trust and safety over at Twitter these days. Right. I mean, 272 00:14:55,160 --> 00:14:58,080 Speaker 1: this is a this is an organization that exists at 273 00:14:58,120 --> 00:15:01,440 Speaker 1: Facebook and YouTube and all the other social players, and 274 00:15:01,800 --> 00:15:03,760 Speaker 1: you know, the last couple of years, we've seen these 275 00:15:03,760 --> 00:15:06,160 Speaker 1: groups get really refined. Right. They have tons of policies, 276 00:15:06,160 --> 00:15:08,720 Speaker 1: they have tons of procedures. Um, there's a there's kind 277 00:15:08,720 --> 00:15:11,560 Speaker 1: of a standard way of doing some of this stuff, right, 278 00:15:11,560 --> 00:15:13,800 Speaker 1: and under Elon, a lot of that has gotten thrown 279 00:15:13,800 --> 00:15:16,320 Speaker 1: out the window. And so we've seen, um, you know, 280 00:15:16,720 --> 00:15:19,760 Speaker 1: for example, they're just got rid of their COVID misinformation policies. 281 00:15:19,800 --> 00:15:22,280 Speaker 1: One example, you may remember ed a few weeks ago 282 00:15:22,360 --> 00:15:25,960 Speaker 1: they just started suspending journalists, uh, sort of without a 283 00:15:26,080 --> 00:15:28,960 Speaker 1: great explanation, right, And so what we've seen is just 284 00:15:29,040 --> 00:15:30,800 Speaker 1: kind of like this playbook that I think a lot 285 00:15:30,840 --> 00:15:32,800 Speaker 1: of folks are familiar with get tossed out. And that's 286 00:15:32,800 --> 00:15:35,440 Speaker 1: why we find it interesting. Well, give us some some 287 00:15:35,560 --> 00:15:39,520 Speaker 1: sort of granularity around the decisions or actions that she 288 00:15:39,760 --> 00:15:43,760 Speaker 1: is carrying out I suppose on a month behalf. Yeah, Well, 289 00:15:43,760 --> 00:15:46,960 Speaker 1: typically these types of things would go through a strict process, right, 290 00:15:47,040 --> 00:15:49,960 Speaker 1: you know, they might go through multiple teams, multiple layers, 291 00:15:49,960 --> 00:15:52,840 Speaker 1: certainly multiple levels of review. And what we've seen, and 292 00:15:52,880 --> 00:15:54,960 Speaker 1: what we mentioned in our story was that, you know, 293 00:15:55,000 --> 00:15:57,440 Speaker 1: we've seen screenshots of things like, hey, we want this 294 00:15:57,440 --> 00:16:00,240 Speaker 1: account suspended on the order of Elon, or want to 295 00:16:00,240 --> 00:16:02,440 Speaker 1: suspend it on the order of Ella, right, and these 296 00:16:02,480 --> 00:16:05,880 Speaker 1: are just again not the way that usually these checks 297 00:16:05,880 --> 00:16:08,040 Speaker 1: and balances tend to work. And so there's a lot 298 00:16:08,080 --> 00:16:09,960 Speaker 1: of people in the world of trust and safety who 299 00:16:10,000 --> 00:16:12,760 Speaker 1: are worried that there's simply just too much power being 300 00:16:13,120 --> 00:16:15,480 Speaker 1: held by one or two people here in this instance, 301 00:16:15,760 --> 00:16:18,160 Speaker 1: and that you know, if they're not following a lot 302 00:16:18,200 --> 00:16:21,000 Speaker 1: of these checks and balances that were used to you know, 303 00:16:21,040 --> 00:16:23,320 Speaker 1: you don't know exactly where where that's going to end up, right, 304 00:16:23,360 --> 00:16:26,280 Speaker 1: And again, these journalists being sort of suspended out of 305 00:16:26,280 --> 00:16:28,720 Speaker 1: the blue is just one example, but you can imagine 306 00:16:28,720 --> 00:16:32,320 Speaker 1: that being extrapolated to a bunch of different accounts. Her 307 00:16:32,520 --> 00:16:37,240 Speaker 1: rise or indeed her remaining. How have people talked about 308 00:16:37,240 --> 00:16:39,920 Speaker 1: that and the way in which she's navigated her career 309 00:16:39,920 --> 00:16:43,000 Speaker 1: with Twitter? Sure? Well, I mean I think the issue 310 00:16:43,080 --> 00:16:45,920 Speaker 1: is not at all that Ella is is I'm incompetent 311 00:16:46,200 --> 00:16:48,400 Speaker 1: or anything like that. I mean, she's had a bunch 312 00:16:48,440 --> 00:16:52,400 Speaker 1: of really strong roles at tech companies before. She's described 313 00:16:52,400 --> 00:16:54,880 Speaker 1: to us as an incredible operator, someone who's very organized, 314 00:16:54,920 --> 00:16:57,800 Speaker 1: he's very passionate, who who's very um, you know, emotional 315 00:16:57,840 --> 00:16:59,880 Speaker 1: about the things that that she cares about. But I 316 00:17:00,040 --> 00:17:03,080 Speaker 1: don't think she had much experience doing uh, you know, 317 00:17:03,160 --> 00:17:05,360 Speaker 1: speech policy, right, Like a lot of times will see 318 00:17:05,359 --> 00:17:07,959 Speaker 1: with trust and safety, their their academics or their lawyers, 319 00:17:08,000 --> 00:17:09,680 Speaker 1: or the people who have maybe spent their whole life 320 00:17:09,680 --> 00:17:13,399 Speaker 1: thinking about things like affection two thirty. That does not 321 00:17:13,800 --> 00:17:16,080 Speaker 1: that is not Ella's background, right, And so I think 322 00:17:16,080 --> 00:17:18,640 Speaker 1: there's a little bit of concern Again, from people who 323 00:17:18,880 --> 00:17:21,239 Speaker 1: were within Twitter's trust and safety, but even just like 324 00:17:21,280 --> 00:17:24,080 Speaker 1: the broader industry that you know, she's now in a 325 00:17:24,200 --> 00:17:26,919 Speaker 1: role that requires that type of nuance, and you know, 326 00:17:27,000 --> 00:17:28,800 Speaker 1: it's something that you build over time, and I'm just 327 00:17:28,840 --> 00:17:30,680 Speaker 1: not sure that she's been in that role long enough, 328 00:17:30,920 --> 00:17:33,960 Speaker 1: especially with Ellen and how he operates that it's instilling 329 00:17:33,960 --> 00:17:36,719 Speaker 1: a lot of confidence in people right now. Bloom bes 330 00:17:36,840 --> 00:17:39,280 Speaker 1: Kurt wagging a great report saying keeping us posted on 331 00:17:39,320 --> 00:17:41,800 Speaker 1: what's happening inside the bird's nest. We'll get more from 332 00:17:41,800 --> 00:17:44,679 Speaker 1: you next week, Thank you very much. Meanwhile, the information 333 00:17:44,800 --> 00:17:48,040 Speaker 1: says Apple plans to develop software to help users of 334 00:17:48,080 --> 00:17:51,440 Speaker 1: its mixed reality headset build their own acts. The company's 335 00:17:51,480 --> 00:17:54,200 Speaker 1: working on its own are content for its three thousand 336 00:17:54,240 --> 00:17:57,160 Speaker 1: dollar headset. According to the report, Apple hopes that even 337 00:17:57,200 --> 00:18:00,480 Speaker 1: people who don't know computer code can use the software 338 00:18:00,520 --> 00:18:03,000 Speaker 1: to tell the headset via Syrie to build an a 339 00:18:03,119 --> 00:18:06,000 Speaker 1: r app. That application would then be available on its 340 00:18:06,000 --> 00:18:10,199 Speaker 1: app store for others to download. Carrol, you reckon you 341 00:18:10,240 --> 00:18:12,560 Speaker 1: can be an app developer through the headset? Is that 342 00:18:12,560 --> 00:18:17,280 Speaker 1: within your skill set? Most definitely probably through chat GPT rank. 343 00:18:17,440 --> 00:18:19,600 Speaker 1: I mean we've played around with the headsets I can't 344 00:18:19,800 --> 00:18:22,639 Speaker 1: imagine going as far as sort of coding within it 345 00:18:22,760 --> 00:18:26,240 Speaker 1: or making a command to beyond video games. It's interesting 346 00:18:26,359 --> 00:18:29,760 Speaker 1: use case. Yeah, I mean, I'm going to leave it 347 00:18:29,800 --> 00:18:32,520 Speaker 1: to the professionals, But I mean, who knows how creative 348 00:18:32,560 --> 00:18:35,000 Speaker 1: we can become when left to war own devices. I 349 00:18:35,119 --> 00:18:36,960 Speaker 1: first need the three thousand dollars you can give me 350 00:18:37,000 --> 00:18:39,600 Speaker 1: that why I can then start doing it. Yeah, I mean, 351 00:18:39,640 --> 00:18:41,800 Speaker 1: this story about the headsets just not going to go away? 352 00:18:41,880 --> 00:18:45,360 Speaker 1: Is it? Anyway? Coming up? More pain for Indian tycoon 353 00:18:45,400 --> 00:18:48,360 Speaker 1: Galton at Danney's Corporate Empire will tell you the extent 354 00:18:48,440 --> 00:18:52,000 Speaker 1: of the damages caused by that short sells report. That's next. 355 00:18:52,080 --> 00:19:16,680 Speaker 1: This is bloom Buck Who are Hindenberg Research? The u 356 00:19:16,800 --> 00:19:20,840 Speaker 1: S investment firm says it specializes in forensic financial research 357 00:19:20,960 --> 00:19:24,560 Speaker 1: and publishing short sellers reports. Hindenberg's in the news again 358 00:19:24,640 --> 00:19:29,040 Speaker 1: with wide ranging allegations against conglomerates owned by galtam Adani, 359 00:19:29,200 --> 00:19:32,200 Speaker 1: Asia's richest person. Founded by a guy called Nate Anderson, 360 00:19:32,240 --> 00:19:36,800 Speaker 1: the firm's named after the seven Hindenburg disaster. Hindenberg says 361 00:19:36,840 --> 00:19:39,399 Speaker 1: it looks for similar man made disasters that may be 362 00:19:39,480 --> 00:19:42,440 Speaker 1: floating around the market and aims to shed light on them. 363 00:19:42,640 --> 00:19:45,600 Speaker 1: Hindenberg says it seeks results around covering hard to find 364 00:19:45,640 --> 00:19:50,320 Speaker 1: information from quote atypical sources, but warns investors, opinions and 365 00:19:50,400 --> 00:19:54,080 Speaker 1: investigative commentary are his own readers advised. The use of 366 00:19:54,080 --> 00:19:56,760 Speaker 1: its material is at your own risk. The firm really 367 00:19:56,840 --> 00:20:00,240 Speaker 1: hit the headlines in targeting electric truck making the killer, 368 00:20:00,440 --> 00:20:04,040 Speaker 1: and it's founded Trevor Milton. They claimed Milton deceived investors. 369 00:20:04,320 --> 00:20:08,240 Speaker 1: Ultimately Milton resigned. There was a criminal investigation and trial 370 00:20:08,560 --> 00:20:11,840 Speaker 1: and Milton was found guilty of securities and Wye fraud. 371 00:20:14,240 --> 00:20:16,400 Speaker 1: Now that you know what Hindenberg does, here's a little 372 00:20:16,440 --> 00:20:18,720 Speaker 1: bit more about why you heard about them this week. 373 00:20:18,760 --> 00:20:22,800 Speaker 1: It issued a report on Tuesday detailing wide ranging allegations 374 00:20:22,840 --> 00:20:26,400 Speaker 1: of corporate malpractice at A Darni Enterprises following a two 375 00:20:26,440 --> 00:20:29,720 Speaker 1: year investigation. The market route that followed a raised more 376 00:20:29,760 --> 00:20:32,720 Speaker 1: than fifty one billion dollars of value this week across 377 00:20:32,760 --> 00:20:36,199 Speaker 1: the Empire, which is owned by Gautamer Danny, Asia's richest man. 378 00:20:36,280 --> 00:20:39,200 Speaker 1: The Darney Group, the main listed part of the conglomerate, 379 00:20:39,440 --> 00:20:43,600 Speaker 1: disputed Hindenberg's allegations Thursday, but it's stopped just kept plunging. 380 00:20:43,600 --> 00:20:47,320 Speaker 1: The firm lost almost on Friday, biggest drop Carrow since 381 00:20:48,080 --> 00:20:51,200 Speaker 1: seen an issue as he wants to raise funds. An 382 00:20:51,200 --> 00:20:53,760 Speaker 1: issue is well, because tell us why this is a 383 00:20:53,800 --> 00:20:57,200 Speaker 1: technology story. What is it in the conglomerate that in 384 00:20:57,280 --> 00:21:00,840 Speaker 1: our industry? Two reasons One a on he's moved into 385 00:21:00,880 --> 00:21:04,840 Speaker 1: media companies. But to the thesis from Hindenburg is that 386 00:21:04,920 --> 00:21:09,440 Speaker 1: even his cement companies trade, they have multiples like text stocks, 387 00:21:09,680 --> 00:21:12,399 Speaker 1: and that for them was a worrying side. And Hindenberg, 388 00:21:12,840 --> 00:21:16,000 Speaker 1: again a short seller, take a caution what they say, 389 00:21:16,040 --> 00:21:20,400 Speaker 1: but they see potential downside drop from where they think 390 00:21:20,400 --> 00:21:23,760 Speaker 1: these assets are really valued. Well, that's certainly made their 391 00:21:23,800 --> 00:21:27,359 Speaker 1: impact from a monetary value perspective over there in India. 392 00:21:27,480 --> 00:21:29,480 Speaker 1: So we're going to keep all over that sort of 393 00:21:29,520 --> 00:21:39,879 Speaker 1: a story. Welcome back to Bloomberg Technology. I'm Karen Hyde 394 00:21:40,000 --> 00:21:43,160 Speaker 1: back in New York, San Francisco based Ed Ludlow. You're there, 395 00:21:43,200 --> 00:21:45,840 Speaker 1: and and actually both of us took a keen eye 396 00:21:45,840 --> 00:21:48,120 Speaker 1: on what's happening in terms of earnings, particularly credit card 397 00:21:48,160 --> 00:21:51,880 Speaker 1: giant American Express today right because their shares absolutely spiraling 398 00:21:51,960 --> 00:21:54,800 Speaker 1: higher up ten and it's because the company gave a 399 00:21:54,880 --> 00:21:57,720 Speaker 1: pretty upbeat revenue forecast. They had record number of new 400 00:21:57,760 --> 00:22:00,680 Speaker 1: cardholders two and I actually got to have a quick 401 00:22:00,760 --> 00:22:03,680 Speaker 1: chat with the CEO CT Scurry earlier this morning. He's 402 00:22:03,680 --> 00:22:05,960 Speaker 1: talking about as resilient customer base, of course a pretty 403 00:22:06,000 --> 00:22:09,000 Speaker 1: premium customer ed, but he also has some really interesting 404 00:22:09,040 --> 00:22:12,200 Speaker 1: takes some technology, particularly when it comes to small businesses, 405 00:22:12,240 --> 00:22:15,639 Speaker 1: because he was saying, look, I've noticed that digital advertising 406 00:22:15,680 --> 00:22:18,840 Speaker 1: spenders down for those small businesses that I serve, And 407 00:22:18,920 --> 00:22:21,640 Speaker 1: he said, to quote him, I don't know if that's 408 00:22:21,680 --> 00:22:24,920 Speaker 1: a function of the economy or a function of confusion. 409 00:22:25,280 --> 00:22:27,119 Speaker 1: And he's talking about the fact that at the moment 410 00:22:27,400 --> 00:22:29,840 Speaker 1: TikTok Twitter, where you're looking at meta at Google, the 411 00:22:30,280 --> 00:22:34,920 Speaker 1: regulatory implications, some of the political implications of societal he's saying, 412 00:22:34,960 --> 00:22:36,800 Speaker 1: that's a lot to deal with to try and digest 413 00:22:36,840 --> 00:22:38,920 Speaker 1: as a small business as to whether you want to 414 00:22:38,920 --> 00:22:41,440 Speaker 1: be putting your money to work there. Well, he's interesting 415 00:22:41,440 --> 00:22:43,040 Speaker 1: on the other side, the consumer side as well, because 416 00:22:43,040 --> 00:22:45,000 Speaker 1: you know, I scroll my phone often, see a product 417 00:22:45,040 --> 00:22:47,600 Speaker 1: I like on social jump jump into it. That's part 418 00:22:47,600 --> 00:22:50,040 Speaker 1: of the e commerce ecosystem for me. What jumped out 419 00:22:50,160 --> 00:22:53,920 Speaker 1: a company giving guidance for the year incidents, and I 420 00:22:54,000 --> 00:22:56,360 Speaker 1: think it is a technology right because it's payment, it's 421 00:22:56,400 --> 00:22:59,679 Speaker 1: people facilitating transactions, and great job getting on the phone 422 00:22:59,720 --> 00:23:01,959 Speaker 1: because we need to know what's going on. Yeah, he 423 00:23:02,000 --> 00:23:04,520 Speaker 1: was really saying, I've got clarity out and I've got 424 00:23:04,560 --> 00:23:08,159 Speaker 1: confidence to four Who else can say that and I can. 425 00:23:08,200 --> 00:23:09,800 Speaker 1: I can tell you not many people in perhaps the 426 00:23:09,840 --> 00:23:11,960 Speaker 1: crypto world. I've got that sort of level of foresight 427 00:23:12,119 --> 00:23:13,920 Speaker 1: right now. And let's just go to that now. Because 428 00:23:14,119 --> 00:23:16,960 Speaker 1: Anthony Scaramucci Skybridge Capital, we know the kind of pain 429 00:23:17,040 --> 00:23:19,320 Speaker 1: that that's been in. It's lost in fact, and its 430 00:23:19,480 --> 00:23:22,800 Speaker 1: biggest funds after some pretty wrong way bets, whether it's 431 00:23:22,800 --> 00:23:25,120 Speaker 1: crypto that they invested with or of course they now 432 00:23:25,280 --> 00:23:28,880 Speaker 1: bankrupt FTX relationship. Let's bring in the one and only 433 00:23:28,880 --> 00:23:31,280 Speaker 1: Shanali Bassak, who's about to head out to Miami to 434 00:23:31,359 --> 00:23:34,440 Speaker 1: meet a few crypto players as well as hedge fund Glitterati. 435 00:23:35,040 --> 00:23:37,000 Speaker 1: Talk to us about scar Mucci and how much he's 436 00:23:37,000 --> 00:23:39,760 Speaker 1: been burned by the space Listen. He has been pivoting 437 00:23:39,840 --> 00:23:42,360 Speaker 1: Skybridge to focus more and more on crypto the last 438 00:23:42,359 --> 00:23:45,120 Speaker 1: couple of years. And remember this is not just buy encryptocurrencies. 439 00:23:45,160 --> 00:23:47,720 Speaker 1: He had a stake in f t X in various 440 00:23:47,840 --> 00:23:50,359 Speaker 1: rounds of the financing, so he was really hurt in 441 00:23:50,440 --> 00:23:53,960 Speaker 1: November when FTX filed for bankruptcy. We also know there's 442 00:23:53,960 --> 00:23:56,880 Speaker 1: another issue here that f t X Ventures had taken 443 00:23:56,880 --> 00:23:59,400 Speaker 1: a stake here in Skybridge, and so that had come 444 00:23:59,440 --> 00:24:01,879 Speaker 1: up for a question and when they filed for bankruptcy 445 00:24:01,920 --> 00:24:04,600 Speaker 1: on whether he'd be able to buy his steak back. Now, 446 00:24:04,760 --> 00:24:07,600 Speaker 1: the big issue here is Skybridge tumbled thirty nine percent 447 00:24:07,760 --> 00:24:11,040 Speaker 1: last year. It is significantly tumbled. A big question here 448 00:24:11,119 --> 00:24:13,960 Speaker 1: because they have limited withdrawals when it comes to Skybridge. 449 00:24:14,800 --> 00:24:17,160 Speaker 1: What I know about Skybridge is a lot of the bitcoin, 450 00:24:17,240 --> 00:24:19,840 Speaker 1: for example, that they bought was that about eighteen thousand 451 00:24:19,920 --> 00:24:23,040 Speaker 1: dollars on average. So to some extent that they could 452 00:24:23,080 --> 00:24:26,600 Speaker 1: see some rebound this year, it would be hugely helpful 453 00:24:26,920 --> 00:24:29,840 Speaker 1: because of the route that they're facing. Because remember Skybridge 454 00:24:29,840 --> 00:24:33,359 Speaker 1: is not just invested in cryptocurrencies and crypto kind of 455 00:24:33,440 --> 00:24:36,159 Speaker 1: venture here. They're also invested in a lot of hedge funds. 456 00:24:36,359 --> 00:24:38,080 Speaker 1: And while some hedge funds have had a tough year, 457 00:24:38,440 --> 00:24:40,639 Speaker 1: there are a couple that did a little better last 458 00:24:40,680 --> 00:24:42,800 Speaker 1: year too. So taking the good with the bad in 459 00:24:42,840 --> 00:24:45,720 Speaker 1: a tough year for Anthony Scaramuchi. I hope the audience 460 00:24:45,760 --> 00:24:48,520 Speaker 1: got the audible gasp from Caroline in the background because 461 00:24:48,560 --> 00:24:50,400 Speaker 1: I gave a similar one there. I guess, I guess 462 00:24:50,400 --> 00:24:52,399 Speaker 1: my question right. You're you're a fund. I think on 463 00:24:52,440 --> 00:24:54,840 Speaker 1: the crypto side, they're looking a lot at private companies. 464 00:24:54,880 --> 00:24:57,240 Speaker 1: As you said, they also invest in some hedge funds. 465 00:24:57,480 --> 00:25:00,800 Speaker 1: That was how did they get them selves out that 466 00:25:00,840 --> 00:25:04,040 Speaker 1: and back on the right track. Yeah, you know, I 467 00:25:04,119 --> 00:25:07,200 Speaker 1: can't help but talk already about this big Miami trip 468 00:25:07,240 --> 00:25:10,280 Speaker 1: that many hedge fund managers are making into this weekend 469 00:25:10,280 --> 00:25:13,639 Speaker 1: into next week, because starting Sunday night, starting Monday, you 470 00:25:13,720 --> 00:25:16,760 Speaker 1: have the Managed Funds Association, a big hedge fund community, 471 00:25:17,119 --> 00:25:21,560 Speaker 1: starting off with a series of events on restoring faith 472 00:25:21,840 --> 00:25:25,600 Speaker 1: in crypto at a reception also that's sponsored by coin Base. 473 00:25:25,680 --> 00:25:28,680 Speaker 1: So you do have the crypto community really pushing here 474 00:25:28,720 --> 00:25:31,080 Speaker 1: to restore that faith. You have Mike Novograt speaking at 475 00:25:31,119 --> 00:25:35,000 Speaker 1: a separate conference Eye Connections, also in Miami to start 476 00:25:35,000 --> 00:25:37,359 Speaker 1: restoring faith in crypto. You have a lot of fund 477 00:25:37,359 --> 00:25:41,359 Speaker 1: managers alongside traditional fund managers talking to the same big 478 00:25:41,400 --> 00:25:45,320 Speaker 1: investors to bring people back in. In my own conversations, 479 00:25:45,320 --> 00:25:47,520 Speaker 1: what I'll tell you is that there are a lot 480 00:25:47,560 --> 00:25:50,280 Speaker 1: of people out there that still say, listen, bitcoin didn't 481 00:25:50,280 --> 00:25:52,880 Speaker 1: fail last year, even though so much of the market did. 482 00:25:53,160 --> 00:25:55,919 Speaker 1: There's a lot of conversation among traditional asset managers that 483 00:25:55,960 --> 00:25:58,120 Speaker 1: will be down there next week about whether they buy 484 00:25:58,160 --> 00:26:01,280 Speaker 1: some of these claims out of bankruptcy. And there is 485 00:26:01,320 --> 00:26:03,879 Speaker 1: a lot of question around how to invest as a 486 00:26:03,920 --> 00:26:06,679 Speaker 1: traditional investor. But again there are still a lot of 487 00:26:06,760 --> 00:26:09,240 Speaker 1: people who have felt a lot of pain from last year. 488 00:26:09,280 --> 00:26:12,680 Speaker 1: Head Well, the big headline this Friday is the bloombergtion. 489 00:26:12,720 --> 00:26:15,359 Speaker 1: Ali Basso because off to Miami, So Miami watch out, 490 00:26:15,920 --> 00:26:17,840 Speaker 1: and I'm sure we'll get a lot of headlines from her, 491 00:26:18,280 --> 00:26:20,480 Speaker 1: not just have crypt over the world of fintech as well, 492 00:26:20,520 --> 00:26:25,320 Speaker 1: and staying on fintech, Fidelity again slashing its valuation. This 493 00:26:25,400 --> 00:26:28,880 Speaker 1: is interesting of ants group. According to filings, Fidelity cut 494 00:26:28,880 --> 00:26:32,200 Speaker 1: its estimate for ant by about nine percent to sixty 495 00:26:32,200 --> 00:26:34,960 Speaker 1: three point eight billion dollars as at the end of November. 496 00:26:35,000 --> 00:26:38,000 Speaker 1: That's down from two hundred and thirty five billion just 497 00:26:38,080 --> 00:26:42,240 Speaker 1: before ants i Po was halted in November, has been 498 00:26:42,280 --> 00:26:46,240 Speaker 1: overhauling its business to comply with Chinese government demands. As 499 00:26:46,280 --> 00:26:49,880 Speaker 1: we have been discussing Caroline about the situation in mainland 500 00:26:49,960 --> 00:26:52,480 Speaker 1: China for some time now, I would have broader broaden 501 00:26:52,520 --> 00:26:57,640 Speaker 1: out this discussion around valuations, particularly from the venture capital perspective, 502 00:26:57,680 --> 00:27:02,600 Speaker 1: and bring in Blouziberry general part at Lucks Capital. Interesting 503 00:27:02,600 --> 00:27:05,440 Speaker 1: what we've just heard. There are lots of threads valuations 504 00:27:05,480 --> 00:27:07,800 Speaker 1: in China. But I just actually want to go with 505 00:27:07,840 --> 00:27:09,840 Speaker 1: the week that was this. This has been a really 506 00:27:09,920 --> 00:27:13,640 Speaker 1: volatile week, not just in terms of markets but headlines 507 00:27:14,119 --> 00:27:17,720 Speaker 1: markets to the upside. As a VC that's just heard 508 00:27:17,760 --> 00:27:19,639 Speaker 1: all of that from our program and sat through a 509 00:27:19,680 --> 00:27:22,560 Speaker 1: week like this, what's your experience of the week been. 510 00:27:22,800 --> 00:27:26,440 Speaker 1: What's your takeaway from this week's news cycle? I mean, 511 00:27:26,480 --> 00:27:29,200 Speaker 1: you know, Nazdak is up about ten percent this year, right, 512 00:27:29,280 --> 00:27:32,280 Speaker 1: so in some sense this is good news. But at 513 00:27:32,280 --> 00:27:34,480 Speaker 1: the end of the day, vcs we invest in early 514 00:27:34,520 --> 00:27:38,040 Speaker 1: stage technology companies for us at Lux Capital specifically, we 515 00:27:38,119 --> 00:27:41,760 Speaker 1: invest in the intersection of technology and sciences, and this 516 00:27:41,840 --> 00:27:44,440 Speaker 1: is a great time to be building those companies. Great 517 00:27:44,480 --> 00:27:47,680 Speaker 1: talent is becoming available. They're starting companies to solve real 518 00:27:47,720 --> 00:27:50,760 Speaker 1: world problems. You know, think about physical security, think about 519 00:27:50,760 --> 00:27:54,359 Speaker 1: saving lives all the shootings and gun detection technology for example, 520 00:27:54,400 --> 00:27:58,560 Speaker 1: that We've invested in drug discovery companies, automation, autonomy, bringing 521 00:27:58,560 --> 00:28:01,399 Speaker 1: manufacturing back to the US. So regardless of what sector 522 00:28:01,480 --> 00:28:04,359 Speaker 1: you look at right now, there's interesting companies that are 523 00:28:04,359 --> 00:28:07,240 Speaker 1: getting founded and invested in. And the good thing is 524 00:28:07,280 --> 00:28:10,040 Speaker 1: that nobody's looking at exits, you know, for these companies 525 00:28:10,040 --> 00:28:12,040 Speaker 1: in the next year or two. We're just making sure 526 00:28:12,080 --> 00:28:14,480 Speaker 1: these companies are fully funded and are able to create 527 00:28:14,520 --> 00:28:18,280 Speaker 1: great products and take them to market. Butw Caroline and 528 00:28:18,320 --> 00:28:22,160 Speaker 1: I were reflecting on our weekly Twitter spaces earlier that 529 00:28:22,480 --> 00:28:25,520 Speaker 1: the headlines on layoffs came right at the beginning of 530 00:28:25,560 --> 00:28:28,959 Speaker 1: this week, and you there's been so many stories from 531 00:28:28,960 --> 00:28:32,719 Speaker 1: the world of technology that you forget. Actually throughout it 532 00:28:32,760 --> 00:28:37,000 Speaker 1: have been some some pretty negative headlines, especially those around layoffs, 533 00:28:37,080 --> 00:28:42,040 Speaker 1: IBM Spotify being examples. Does that represent an opportunity for 534 00:28:42,080 --> 00:28:44,160 Speaker 1: a venture captists like you? Are you kind of putting 535 00:28:44,200 --> 00:28:47,120 Speaker 1: pressure on your portfolio founders to say, hey, go out 536 00:28:47,120 --> 00:28:51,120 Speaker 1: there and hire these people. They're on the market. My 537 00:28:51,200 --> 00:28:53,760 Speaker 1: inbox is filled with the regimes that have come in 538 00:28:53,840 --> 00:28:56,320 Speaker 1: from people who have been laid off from these companies, 539 00:28:56,440 --> 00:28:59,880 Speaker 1: and I'm absolutely working hard to get them, you know, hired. 540 00:29:00,000 --> 00:29:02,680 Speaker 1: It's some of the best talent hired into our portfolio companies. 541 00:29:02,960 --> 00:29:04,920 Speaker 1: This is two years ago that we told our portfolio 542 00:29:04,960 --> 00:29:07,200 Speaker 1: companies to raise capital because we sort of knew in 543 00:29:07,280 --> 00:29:09,480 Speaker 1: some ways that the peak had arrived. We obviously didn't 544 00:29:09,480 --> 00:29:12,120 Speaker 1: know exactly when, but we wanted them to raise as 545 00:29:12,200 --> 00:29:14,200 Speaker 1: much capital as they're good to be able to survive 546 00:29:14,400 --> 00:29:16,680 Speaker 1: the long term. And these companies that are sitting on 547 00:29:16,760 --> 00:29:19,880 Speaker 1: that capital now is not only able to hire these candidates, 548 00:29:19,920 --> 00:29:22,160 Speaker 1: but also able to acquire some other companies along the 549 00:29:22,160 --> 00:29:24,760 Speaker 1: way to bring more technology into the mix and and 550 00:29:24,800 --> 00:29:29,080 Speaker 1: bring it to their customers. Let's talk about hype or 551 00:29:29,120 --> 00:29:31,880 Speaker 1: not therefore, because amid some of the concerns and the 552 00:29:32,240 --> 00:29:36,280 Speaker 1: and the worries about the technology pulling back valuations, putting 553 00:29:36,280 --> 00:29:39,280 Speaker 1: back people being like, go, there feels like there's another 554 00:29:39,320 --> 00:29:43,520 Speaker 1: sort of n f T crypto vibe around AI. Can 555 00:29:43,520 --> 00:29:46,280 Speaker 1: you tell us about what you're feeling there? But as 556 00:29:46,280 --> 00:29:49,239 Speaker 1: to whether you're getting that exuberance, what are we at 557 00:29:49,240 --> 00:29:53,920 Speaker 1: the dawn of a new Internet stage? Uh? I think 558 00:29:53,960 --> 00:29:56,280 Speaker 1: there's something called the Gardner's hype cycle. So every new 559 00:29:56,320 --> 00:29:59,400 Speaker 1: technology it comes with its own hype and you just 560 00:29:59,440 --> 00:30:01,480 Speaker 1: have to sort of live through it and know that 561 00:30:01,520 --> 00:30:03,680 Speaker 1: there will be ups and downs along the way, and 562 00:30:03,720 --> 00:30:07,440 Speaker 1: that's okay. Um, there is a lot of noise around AI, 563 00:30:07,520 --> 00:30:09,960 Speaker 1: but AI is also real. I think, you know, there's 564 00:30:09,960 --> 00:30:12,720 Speaker 1: something unique that happens with these you know, chat bots 565 00:30:12,720 --> 00:30:14,960 Speaker 1: and LM and other things that have happened that when 566 00:30:14,960 --> 00:30:17,840 Speaker 1: you have very large parameters we're talking about twenty billion 567 00:30:17,880 --> 00:30:21,240 Speaker 1: parameters or more, you start to see very interesting results. 568 00:30:21,280 --> 00:30:23,120 Speaker 1: And I think people are now starting to figure out 569 00:30:23,200 --> 00:30:26,360 Speaker 1: how to utilize them to solve real world problems. So 570 00:30:26,480 --> 00:30:30,120 Speaker 1: we're seeing you know, the applications of AI in new 571 00:30:30,160 --> 00:30:35,240 Speaker 1: industries like healthcare, like education, like in construction, in autonomy 572 00:30:35,320 --> 00:30:37,280 Speaker 1: and and and automation, and I think there's going to 573 00:30:37,320 --> 00:30:40,120 Speaker 1: be a real companies built. We're super excited about that. 574 00:30:40,720 --> 00:30:43,760 Speaker 1: At the same time, you know, just like any other industry, 575 00:30:43,800 --> 00:30:46,640 Speaker 1: I wouldn't just say that for f t X, for crypto. 576 00:30:46,800 --> 00:30:48,920 Speaker 1: This same thing happened in the world of drones and 577 00:30:49,000 --> 00:30:50,760 Speaker 1: b R a R. You have to be just very 578 00:30:50,760 --> 00:30:53,080 Speaker 1: careful that you build real world companies that are solving 579 00:30:53,160 --> 00:30:56,840 Speaker 1: problems and creating real value for the customers at the end. Yeah, 580 00:30:56,840 --> 00:30:59,720 Speaker 1: and real value is something that's now being questioned. For example, 581 00:30:59,720 --> 00:31:02,760 Speaker 1: for a public company like Tesla, which a lot of 582 00:31:02,800 --> 00:31:07,520 Speaker 1: it's big focus long term value is about automated driving. 583 00:31:08,840 --> 00:31:10,840 Speaker 1: I know that's a narrative expertise for you. I know 584 00:31:10,920 --> 00:31:12,800 Speaker 1: that's somewhere that you've been investing in with some of 585 00:31:12,840 --> 00:31:15,520 Speaker 1: your portfolio companies and leading that for Lux Capital. Where 586 00:31:15,560 --> 00:31:19,080 Speaker 1: are we in that overall hype cycle that we're just 587 00:31:19,120 --> 00:31:22,440 Speaker 1: talking through. I mean, look, there's some level of autonomy 588 00:31:22,440 --> 00:31:24,360 Speaker 1: available to all of us. You know, if you can 589 00:31:24,400 --> 00:31:26,400 Speaker 1: buy a car now, it will do self driving, it 590 00:31:26,440 --> 00:31:30,640 Speaker 1: will do lane keeping, you will update, uh, you know, 591 00:31:30,720 --> 00:31:34,960 Speaker 1: new software will update more frequently. But obviously Tesla is 592 00:31:34,960 --> 00:31:37,680 Speaker 1: further advanced than most of the other automotive o EM's 593 00:31:37,720 --> 00:31:41,800 Speaker 1: out there. But I think automotive and autonomous technologies here. 594 00:31:41,960 --> 00:31:44,360 Speaker 1: But I also want to add that AI m L 595 00:31:44,840 --> 00:31:49,160 Speaker 1: similar technology platforms such as um fontomous driving is being 596 00:31:49,160 --> 00:31:53,160 Speaker 1: applied to other industries as well. Right distributed sensor sensors 597 00:31:53,280 --> 00:31:56,320 Speaker 1: are being used to detect guns and weapons that entrances 598 00:31:56,320 --> 00:31:58,720 Speaker 1: to buildings. A I m L is being applied to 599 00:31:59,440 --> 00:32:03,120 Speaker 1: looking for threats in those systems. Completely different industry, you know, 600 00:32:03,120 --> 00:32:06,760 Speaker 1: a company like Evolved Technologies focused on that, completely different 601 00:32:06,800 --> 00:32:09,560 Speaker 1: than autonomous technology. But using the same paradigms and using 602 00:32:09,600 --> 00:32:11,680 Speaker 1: some of the same technologies, and in some ways we 603 00:32:11,760 --> 00:32:14,240 Speaker 1: call it the peace dividend of the autonomous wars, that 604 00:32:14,320 --> 00:32:17,360 Speaker 1: all the technology that's getting developed from autonomous cars are 605 00:32:17,360 --> 00:32:19,680 Speaker 1: going to be used in many other industries and create 606 00:32:19,760 --> 00:32:23,760 Speaker 1: value elsewhere as well. Fascinating. I've got to get you back. 607 00:32:24,080 --> 00:32:26,240 Speaker 1: Really always loved speaking with the VC community at the 608 00:32:26,280 --> 00:32:29,800 Speaker 1: moment about where there is they really focused to be 609 00:32:29,840 --> 00:32:32,280 Speaker 1: getting the value below. We thank you so much, of 610 00:32:32,320 --> 00:32:35,400 Speaker 1: course below it's so very hears of course, the Black's capital. 611 00:32:35,720 --> 00:32:37,840 Speaker 1: Let's check in though on some of the public side 612 00:32:37,840 --> 00:32:39,800 Speaker 1: of the equation. We're just talking about Tessa and some 613 00:32:39,840 --> 00:32:42,000 Speaker 1: of the other discussions. But I mean, chip stocks have 614 00:32:42,040 --> 00:32:44,160 Speaker 1: really been taking it hard, right then, Yeah, and I 615 00:32:44,160 --> 00:32:46,000 Speaker 1: want to go back to Intel because at one point 616 00:32:46,000 --> 00:32:49,120 Speaker 1: on Friday, the stocks down the most since October one. 617 00:32:49,120 --> 00:32:51,160 Speaker 1: It paired some of its declines. I think we had 618 00:32:51,160 --> 00:32:54,000 Speaker 1: our biggest drops since September, the worst week for this 619 00:32:54,120 --> 00:32:56,960 Speaker 1: stock in about five weeks. But you know, this kind 620 00:32:56,960 --> 00:32:58,880 Speaker 1: of killed the momentum, right, I know, then, has that 621 00:32:58,920 --> 00:33:02,680 Speaker 1: one d pushed higher Friday? The Philadelphia Semiconductor Index or 622 00:33:02,800 --> 00:33:06,560 Speaker 1: socks did react negatively but again paired some of its losses. 623 00:33:06,560 --> 00:33:10,320 Speaker 1: What was astonishing for the market is that expectations for 624 00:33:10,400 --> 00:33:13,560 Speaker 1: Intel were already pretty low, and this was even worse 625 00:33:13,680 --> 00:33:16,600 Speaker 1: than that the forecast for the first quarter and outlook 626 00:33:16,640 --> 00:33:18,720 Speaker 1: for the rest of the year, and with so many 627 00:33:18,760 --> 00:33:21,200 Speaker 1: chip names still to report next week and particularly get 628 00:33:21,240 --> 00:33:24,600 Speaker 1: Qualcom right, Caroline, I think now we're probably bracing a 629 00:33:24,600 --> 00:33:27,320 Speaker 1: little bit. You know, we're probably a bit concerned about 630 00:33:27,360 --> 00:33:30,160 Speaker 1: the outlook, particularly for consumer electronics for the rest of 631 00:33:31,280 --> 00:33:34,320 Speaker 1: Some of the turns of phrase being used by the analysts, 632 00:33:34,400 --> 00:33:40,640 Speaker 1: Bernstein calling it astonishingly bad and stunning. So um, I mean, 633 00:33:40,960 --> 00:33:43,440 Speaker 1: just we hope, well, I suppose for many we hope 634 00:33:43,440 --> 00:33:46,400 Speaker 1: that it's not going to replicated next week were coming 635 00:33:46,480 --> 00:33:48,840 Speaker 1: up d Well, maybe next week we'll hear more of 636 00:33:48,880 --> 00:33:52,320 Speaker 1: this as well. Tech layoffs. Look, it's impacting workers on 637 00:33:52,400 --> 00:33:55,600 Speaker 1: temporary visas too. There is no one nonprofit that's trying 638 00:33:55,640 --> 00:34:12,480 Speaker 1: to learn a hand in all of that. We talk 639 00:34:12,520 --> 00:34:15,600 Speaker 1: about it time and time again at tech companies cutting costs, 640 00:34:15,880 --> 00:34:18,040 Speaker 1: looking at the future that perhaps as a bit bleaker 641 00:34:18,480 --> 00:34:21,000 Speaker 1: and in particular it's affecting the workers, isn't it. In particular? 642 00:34:21,160 --> 00:34:24,240 Speaker 1: H one b visa holders whose status in the US 643 00:34:24,360 --> 00:34:26,560 Speaker 1: is directly tied to their employment, and so you and 644 00:34:26,600 --> 00:34:30,080 Speaker 1: I we took to our audience. We got on overall 645 00:34:30,120 --> 00:34:33,200 Speaker 1: the focus on Twitter because some companies like Spotify Day 646 00:34:33,200 --> 00:34:35,160 Speaker 1: at the beginning of the week when they announced layoffs, 647 00:34:35,239 --> 00:34:37,759 Speaker 1: said they're going to be helping out the employees who 648 00:34:37,800 --> 00:34:40,880 Speaker 1: were on visas. And interestingly, when we asked our audience, 649 00:34:40,920 --> 00:34:46,200 Speaker 1: should this be standard, said yeah, yeah, they should be. Yeah, 650 00:34:46,200 --> 00:34:48,400 Speaker 1: you know, you know, and I'm not surprised by that. Caroline. 651 00:34:48,440 --> 00:34:50,759 Speaker 1: Think about where I am in San Francisco, the Bay Area, 652 00:34:50,880 --> 00:34:52,759 Speaker 1: even where you are on the East Coast, not just 653 00:34:52,880 --> 00:34:56,440 Speaker 1: New York, with cities like Chicago, other cities. Many of 654 00:34:56,480 --> 00:34:59,640 Speaker 1: these workers are here in this sector technology, whether they're 655 00:34:59,640 --> 00:35:02,920 Speaker 1: on the s West Side or otherwise on visas. You know, 656 00:35:02,960 --> 00:35:06,080 Speaker 1: it's just an incredibly difficult situation for them. We've got 657 00:35:06,080 --> 00:35:09,960 Speaker 1: a perfect guest to talk about this, Brad Henderson, CEO 658 00:35:10,160 --> 00:35:12,960 Speaker 1: of P thirty three. This is a not for profit 659 00:35:13,480 --> 00:35:17,799 Speaker 1: in Chicago which is basically trying to directly help with this. 660 00:35:17,960 --> 00:35:21,160 Speaker 1: Right Brad, You're basically saying, let's take what is an 661 00:35:21,200 --> 00:35:26,239 Speaker 1: available portion of the workforce and find a solution here 662 00:35:26,280 --> 00:35:29,359 Speaker 1: talk us through what you're trying to achieve. Sure, we're 663 00:35:29,360 --> 00:35:31,520 Speaker 1: really trying to respond at this moment in time where 664 00:35:32,400 --> 00:35:34,520 Speaker 1: so many people are being laid off, and if you 665 00:35:34,560 --> 00:35:37,640 Speaker 1: think about our workforce in the technical field, it is 666 00:35:37,640 --> 00:35:40,640 Speaker 1: far and born. Many of these individuals have sixty days 667 00:35:40,719 --> 00:35:44,000 Speaker 1: right now to find a job or leave the country 668 00:35:44,239 --> 00:35:47,360 Speaker 1: with their families. And so what excites us in Chicago 669 00:35:47,440 --> 00:35:50,719 Speaker 1: is we're just stock full of these companies that do 670 00:35:50,800 --> 00:35:53,200 Speaker 1: real things in the real economy and read people's lives, 671 00:35:53,200 --> 00:35:55,920 Speaker 1: and we're actually have tons of open positions, a hundred 672 00:35:55,920 --> 00:36:00,000 Speaker 1: and sixty thou jobs in technical fields open in Chicago. 673 00:36:00,040 --> 00:36:02,920 Speaker 1: Go today and really just to reach out to the 674 00:36:02,960 --> 00:36:05,520 Speaker 1: thousands of people around the country who are in dire 675 00:36:05,560 --> 00:36:08,560 Speaker 1: straits but highly skilled, and say, if you've got sixty 676 00:36:08,600 --> 00:36:10,400 Speaker 1: days and you want to find a great opportunity to 677 00:36:10,400 --> 00:36:12,600 Speaker 1: have an impact on a business that matters, go to 678 00:36:12,640 --> 00:36:15,640 Speaker 1: our website, apply to a job, and contribute to our economy, 679 00:36:16,200 --> 00:36:19,120 Speaker 1: and talk to us about the fact that actually that 680 00:36:19,200 --> 00:36:21,440 Speaker 1: adds in many ways to diversity. When I think of 681 00:36:21,560 --> 00:36:23,319 Speaker 1: h one b vs is, I think of all my 682 00:36:23,400 --> 00:36:25,359 Speaker 1: husband was on one until we're lucky enough to get 683 00:36:25,400 --> 00:36:28,160 Speaker 1: our green cards. But there's also an awful lot of 684 00:36:28,440 --> 00:36:31,359 Speaker 1: people coming from India in particular, this is people of color. 685 00:36:31,480 --> 00:36:34,000 Speaker 1: That is this going to add diversity of thought as well? 686 00:36:34,040 --> 00:36:37,040 Speaker 1: Over in Chicago. Sure, if you think about the long 687 00:36:37,160 --> 00:36:40,680 Speaker 1: term trajectory for tech, we are short in our country, 688 00:36:41,120 --> 00:36:44,719 Speaker 1: hundreds and hundreds of thousands of jobs, talented people. I've 689 00:36:44,760 --> 00:36:46,759 Speaker 1: apologized to have thousands of people. How where do you 690 00:36:46,800 --> 00:36:49,520 Speaker 1: find those folks? You're gonna have to find new channels. 691 00:36:49,560 --> 00:36:52,800 Speaker 1: Those channels exist for talented people in countries like India 692 00:36:53,120 --> 00:36:55,960 Speaker 1: and countries in Africa and Europe, etcetera. And they exist 693 00:36:55,960 --> 00:36:58,480 Speaker 1: in other forms of diversity neighborhoods in Chicago that I 694 00:36:58,520 --> 00:37:01,200 Speaker 1: haven't always had access to jobs like this. In order 695 00:37:01,239 --> 00:37:03,200 Speaker 1: for us to meet the challenge of the technical age, 696 00:37:03,680 --> 00:37:05,799 Speaker 1: we need to find talent wherever we can find it, 697 00:37:05,880 --> 00:37:09,040 Speaker 1: and diversity ends up being a huge strength and meeting 698 00:37:09,040 --> 00:37:12,760 Speaker 1: those objectives. Brad was showing a graphic on the screen 699 00:37:12,800 --> 00:37:15,400 Speaker 1: which outlines how severe some of the cuts have been 700 00:37:15,440 --> 00:37:20,319 Speaker 1: at names like Meta, Sales Force, Alphabet, Microsoft, IBM. Are 701 00:37:20,400 --> 00:37:23,279 Speaker 1: these the names where these workers that are reaching out 702 00:37:23,320 --> 00:37:26,719 Speaker 1: to you coming from Are you literally getting interest in 703 00:37:26,719 --> 00:37:30,480 Speaker 1: inquiry from the technology sector. It's been two days since 704 00:37:30,520 --> 00:37:33,760 Speaker 1: their job board is up, We've had sixteen thousand unique 705 00:37:33,840 --> 00:37:36,840 Speaker 1: visitors from all across the country. From the very names 706 00:37:36,840 --> 00:37:39,879 Speaker 1: that you're describing, we're getting to engage with people who 707 00:37:40,200 --> 00:37:44,000 Speaker 1: may have been focused on engineering tweets, who now bet 708 00:37:44,000 --> 00:37:46,239 Speaker 1: an opportunity to look at a company like Walgreens, which 709 00:37:46,280 --> 00:37:49,320 Speaker 1: is a Chicago based company who's using a digital technology 710 00:37:49,400 --> 00:37:53,040 Speaker 1: to transform healthcare UH and and really access those people 711 00:37:53,080 --> 00:37:55,760 Speaker 1: who had jobs in big tech on tweets and social 712 00:37:55,760 --> 00:37:59,080 Speaker 1: media projects can put them to work companies like Walgreens 713 00:37:59,200 --> 00:38:02,400 Speaker 1: solving critical healthcare challenges. We're seeing folks from all over 714 00:38:02,440 --> 00:38:06,279 Speaker 1: the country respond to that how easy or difficult is 715 00:38:06,320 --> 00:38:09,080 Speaker 1: it to transfer an H one B visa? How much 716 00:38:09,719 --> 00:38:11,480 Speaker 1: is it going to have to be an uphill battle 717 00:38:11,560 --> 00:38:13,919 Speaker 1: for some of these companies like a Walgreen space where 718 00:38:13,960 --> 00:38:16,719 Speaker 1: you are, who want to have this talent. This is 719 00:38:16,760 --> 00:38:18,719 Speaker 1: the best part about what we're trying to do here 720 00:38:18,800 --> 00:38:21,320 Speaker 1: is if you think about the broader question of reforming 721 00:38:21,520 --> 00:38:23,920 Speaker 1: the U. S. Immigration system, that's really hard and going 722 00:38:23,960 --> 00:38:27,280 Speaker 1: to take a very long time. This program, there's actually 723 00:38:27,400 --> 00:38:31,600 Speaker 1: quite a misnomer that it's cumbersome or particularly costly. All 724 00:38:31,600 --> 00:38:34,279 Speaker 1: the companies we're working with our companies that already go 725 00:38:34,400 --> 00:38:37,200 Speaker 1: through this process, that already have budget to support these 726 00:38:37,239 --> 00:38:40,480 Speaker 1: workers transfers. So the jobs are just sitting there waiting. 727 00:38:40,480 --> 00:38:41,880 Speaker 1: And what we've done on our job board, I think 728 00:38:41,920 --> 00:38:44,160 Speaker 1: one of the things that's really made it so the 729 00:38:44,200 --> 00:38:46,879 Speaker 1: workers around the country have responded to what we're doing 730 00:38:47,360 --> 00:38:49,800 Speaker 1: is all the jobs we posted, it's thousands of jobs 731 00:38:49,800 --> 00:38:52,000 Speaker 1: available at this site are ones that are H one 732 00:38:52,040 --> 00:38:54,799 Speaker 1: B VISA eligible. And so the reality is this one 733 00:38:54,840 --> 00:38:59,120 Speaker 1: is actually pretty straightforward. They do this, it's affordable. They've 734 00:38:59,160 --> 00:39:01,600 Speaker 1: got a process of aguaring and out every time someone applies, 735 00:39:01,640 --> 00:39:04,839 Speaker 1: and so for this particular program, it's just a matter 736 00:39:04,880 --> 00:39:07,880 Speaker 1: of doing the right thing and connecting the dots. Brad Henderson, 737 00:39:08,320 --> 00:39:10,759 Speaker 1: fixing a real problem right here right now for many 738 00:39:11,000 --> 00:39:13,400 Speaker 1: we thank you and indeed their families. It's the CEO 739 00:39:13,600 --> 00:39:17,319 Speaker 1: of P thirty three. And what an interesting way to 740 00:39:17,360 --> 00:39:19,799 Speaker 1: actually be looking at what is in many ways just 741 00:39:20,120 --> 00:39:22,920 Speaker 1: a market inefficiency. There there's the people who want the work, 742 00:39:23,200 --> 00:39:25,160 Speaker 1: who need it very swiftly, and it's just trying to 743 00:39:25,160 --> 00:39:27,839 Speaker 1: find the right way to line them up. Yeah, I 744 00:39:27,880 --> 00:39:30,720 Speaker 1: cannot believe what he's just told us that their ports 745 00:39:30,840 --> 00:39:34,080 Speaker 1: or their board has been open for two days. Sixteen 746 00:39:34,200 --> 00:39:38,160 Speaker 1: thousand people have inquired and the majority of them all 747 00:39:38,400 --> 00:39:40,000 Speaker 1: you know, many of them is he put it from 748 00:39:40,320 --> 00:39:43,480 Speaker 1: the technology sector. You know. It shows what's happening in 749 00:39:43,520 --> 00:39:46,400 Speaker 1: real times in this economy and how quickly it's moving. 750 00:39:55,000 --> 00:39:57,560 Speaker 1: It's happening. We have a new stock marketing session and 751 00:39:57,600 --> 00:40:01,320 Speaker 1: it's artificial intelligence Q and umber of stocks and investors 752 00:40:01,320 --> 00:40:03,960 Speaker 1: destintly trying to get in on the AI tech trend 753 00:40:04,320 --> 00:40:06,160 Speaker 1: the way they do it, or maybe they buy into 754 00:40:06,200 --> 00:40:09,160 Speaker 1: a small cat name like C three AI and makes 755 00:40:09,320 --> 00:40:13,759 Speaker 1: enterprise AI applications. It's stock on a record month so 756 00:40:13,840 --> 00:40:16,239 Speaker 1: far and getting like a buzz feed, its shared price 757 00:40:16,280 --> 00:40:19,359 Speaker 1: spiking three on the week because it announces a deal 758 00:40:19,400 --> 00:40:22,480 Speaker 1: with open ai to be using it within its content creation. 759 00:40:22,760 --> 00:40:27,040 Speaker 1: We've also had little known voice AI makers like SoundHound 760 00:40:27,120 --> 00:40:29,600 Speaker 1: AI also on the upside. We're getting in video. The 761 00:40:29,640 --> 00:40:32,400 Speaker 1: chip maker which has its chips used by AI surfaces, 762 00:40:32,520 --> 00:40:35,239 Speaker 1: also getting bit up some on the month. This all 763 00:40:35,280 --> 00:40:40,480 Speaker 1: feels pretty reminiscent of the past. Quite recently, everyone was 764 00:40:40,480 --> 00:40:42,719 Speaker 1: trying to have an n f T kind of part 765 00:40:42,760 --> 00:40:46,160 Speaker 1: of their business. Maybe back in seen you remember when 766 00:40:46,280 --> 00:40:51,200 Speaker 1: Long Island iced tea rebranded its company name as long Blockchain, 767 00:40:51,600 --> 00:40:54,280 Speaker 1: and therefore we also go back to the n nineties 768 00:40:54,280 --> 00:40:57,600 Speaker 1: when suddenly every company had an Internet strategy or changed 769 00:40:57,640 --> 00:41:00,719 Speaker 1: its name to dot com. These sort of fads do 770 00:41:00,880 --> 00:41:03,279 Speaker 1: come and go. The question is, as a one got 771 00:41:03,280 --> 00:41:07,480 Speaker 1: staying power, what do you think in fad or here 772 00:41:07,520 --> 00:41:10,560 Speaker 1: to stay? So it's a case by case basis right 773 00:41:10,719 --> 00:41:12,919 Speaker 1: on on all of these things, what the AI does, 774 00:41:13,000 --> 00:41:18,400 Speaker 1: and the valuations, the money behind it. The buzz Feed story, Um, 775 00:41:18,440 --> 00:41:21,480 Speaker 1: I'm a journalist, you're a journalist being replaced by I. 776 00:41:21,680 --> 00:41:24,000 Speaker 1: But I get the business case. You know, buzz Feed's 777 00:41:24,000 --> 00:41:25,600 Speaker 1: got to feed the content on its site and you 778 00:41:25,640 --> 00:41:28,719 Speaker 1: look at the market response, all yeah, thankfully, I think 779 00:41:28,719 --> 00:41:30,799 Speaker 1: that's it's going to be. It's games and it's quizz as. 780 00:41:30,800 --> 00:41:33,399 Speaker 1: We hope that it's going to be really putting those 781 00:41:33,440 --> 00:41:36,400 Speaker 1: AI applications towards. But what warriores me is it suddenly 782 00:41:36,400 --> 00:41:39,120 Speaker 1: becomes a scavenger hunt for the right ticker symbol, right 783 00:41:39,200 --> 00:41:41,640 Speaker 1: the acronym that you're using when it comes when you 784 00:41:41,680 --> 00:41:45,560 Speaker 1: summarize what your business does on a Bloomberg for example, 785 00:41:45,600 --> 00:41:48,840 Speaker 1: and what C three AIY s A R go like 786 00:41:48,960 --> 00:41:52,279 Speaker 1: no wonder, it's getting a load of interest. Yeah, and 787 00:41:52,360 --> 00:41:54,320 Speaker 1: look there's real momentum behind this dog. I think the 788 00:41:54,680 --> 00:41:58,719 Speaker 1: Friday rally was looking at a jump of around over 789 00:41:59,000 --> 00:42:02,160 Speaker 1: a few months basis. But the month to date performance 790 00:42:02,360 --> 00:42:06,560 Speaker 1: the stocks up six so far in January. And I 791 00:42:06,600 --> 00:42:10,000 Speaker 1: go to analyst recommendations on the Bloomberg only one by 792 00:42:10,280 --> 00:42:13,480 Speaker 1: on this three cells only, and there's seven holds. Like 793 00:42:13,719 --> 00:42:16,480 Speaker 1: the analysts looking at the stock aren't thinking that it 794 00:42:16,480 --> 00:42:18,359 Speaker 1: should have this sort of a price level at moment. 795 00:42:18,360 --> 00:42:22,080 Speaker 1: The overall price targets the next twelve months is sixty three. 796 00:42:22,160 --> 00:42:25,080 Speaker 1: It's more the euphoria on round. It feels a little 797 00:42:25,080 --> 00:42:28,000 Speaker 1: bit mini. Yeah, no fundamentals here. And then we spoke 798 00:42:28,040 --> 00:42:31,279 Speaker 1: about Jensen and the video earlier in the show. Right, 799 00:42:31,320 --> 00:42:35,280 Speaker 1: this is driving wealth and is driving valuation, Yeah, stonishing. 800 00:42:35,560 --> 00:42:37,200 Speaker 1: Next week we're going to have a lot more on 801 00:42:37,239 --> 00:42:39,839 Speaker 1: the driving wealth and valuations when it comes to earnings. Right, 802 00:42:39,960 --> 00:42:41,560 Speaker 1: But for so far, that does it for this additional 803 00:42:41,560 --> 00:42:44,799 Speaker 1: bloom bleg technology. Don't forget lots of earnings to come 804 00:42:44,840 --> 00:42:47,799 Speaker 1: next week and catch our podcast wherever you get it. 805 00:42:48,160 --> 00:42:49,839 Speaker 1: This is Bloomberg