1 00:00:14,600 --> 00:00:16,919 Speaker 1: I'm Caroline High and I'm ad Love though, both of 2 00:00:16,960 --> 00:00:20,200 Speaker 1: us in San Francisco. The final time this week, Busy Friday. 3 00:00:20,320 --> 00:00:23,720 Speaker 1: This is Bloomberg Technology. The markets risk on Friday, the 4 00:00:23,800 --> 00:00:26,520 Speaker 1: headlines flying left right in center, and I'll tell you 5 00:00:26,560 --> 00:00:29,080 Speaker 1: there's one asset class that is not risk on today too. 6 00:00:29,200 --> 00:00:31,600 Speaker 1: We'll dig into all of that. First coming up, Amazon 7 00:00:31,680 --> 00:00:35,479 Speaker 1: puts construction of its second headquarters on hold following its 8 00:00:35,479 --> 00:00:40,000 Speaker 1: biggest ever job cuts, and Apple's executive exodus continues. This 9 00:00:40,080 --> 00:00:42,159 Speaker 1: time is the person in charge of I Cloud, I 10 00:00:42,280 --> 00:00:47,000 Speaker 1: Message and FaceTime and Silvergate's struggle for survival. It's weighing 11 00:00:47,080 --> 00:00:51,200 Speaker 1: on overall crypto sentiment. Bloomberg breaking the story that constructions 12 00:00:51,240 --> 00:00:54,880 Speaker 1: being paused Carrow in Virginia on HQU two. That second 13 00:00:54,880 --> 00:00:58,400 Speaker 1: phase of construction the three twenty two story towers really interesting. 14 00:00:58,640 --> 00:01:01,240 Speaker 1: Let's get into the d Tell the person who broke 15 00:01:01,280 --> 00:01:03,880 Speaker 1: the story one, Matt Day is with us, and just 16 00:01:03,960 --> 00:01:07,600 Speaker 1: talk to us, Matt about the importance of this second 17 00:01:07,640 --> 00:01:09,920 Speaker 1: phase of the construction, because it's actually the bigger part 18 00:01:09,920 --> 00:01:12,640 Speaker 1: of the construction. Right, Yeah, that's right. They're just now 19 00:01:12,680 --> 00:01:15,800 Speaker 1: wrapping up the construction of two twenty two story towers. 20 00:01:15,840 --> 00:01:18,520 Speaker 1: That's the first phase. It's basically done. Amazon moves their 21 00:01:18,560 --> 00:01:21,000 Speaker 1: eight thousand workers in the area into those fieldities this 22 00:01:21,040 --> 00:01:23,679 Speaker 1: spring and summer. What's a issue here is a much 23 00:01:23,760 --> 00:01:26,679 Speaker 1: larger three twenty two story towers. It's the part of 24 00:01:26,680 --> 00:01:28,520 Speaker 1: the complex. It's going to have the helix, the sort 25 00:01:28,520 --> 00:01:31,880 Speaker 1: of spiral poopamoji shaped building that's going to have a 26 00:01:31,959 --> 00:01:34,200 Speaker 1: bunch of plants in it, big conference center, the centerpiece 27 00:01:34,280 --> 00:01:37,400 Speaker 1: architecturally of the campus that Amazon hopes to build. All 28 00:01:37,440 --> 00:01:42,080 Speaker 1: that's on pause right now, no digging for the foreseable future. Hey, Matt, 29 00:01:42,280 --> 00:01:44,840 Speaker 1: I know that last April this was approved and sources 30 00:01:44,840 --> 00:01:47,120 Speaker 1: of telling us that Amazon was ready to pull the trigger. 31 00:01:47,120 --> 00:01:49,680 Speaker 1: They held off. They then considered getting shovels in the 32 00:01:49,680 --> 00:01:52,680 Speaker 1: ground to start the year. What did Amazon tell you 33 00:01:52,960 --> 00:01:56,559 Speaker 1: about this decision? Why they decided to hit the brakes? 34 00:01:56,840 --> 00:01:58,680 Speaker 1: So they said some of the obvious things you'd think, 35 00:01:58,720 --> 00:02:00,560 Speaker 1: you know, like listen, we're not sure what kind of 36 00:02:00,640 --> 00:02:02,080 Speaker 1: hiring we're going to be doing in the next couple 37 00:02:02,120 --> 00:02:04,840 Speaker 1: of years. They've had a hiring freezon since late last year. 38 00:02:04,960 --> 00:02:07,240 Speaker 1: We don't know when that lifts. And there's also just 39 00:02:07,280 --> 00:02:09,560 Speaker 1: a different vibe around Amazon today. Than there was when 40 00:02:09,560 --> 00:02:11,519 Speaker 1: they announced this campus in the first place. You know, 41 00:02:11,520 --> 00:02:13,440 Speaker 1: back then they were growing billiaps and bounds. They'd run 42 00:02:13,440 --> 00:02:15,359 Speaker 1: out of space in Seattle. They were searching for really 43 00:02:15,400 --> 00:02:18,120 Speaker 1: anywhere to put people all across the country. So twenty 44 00:02:18,160 --> 00:02:20,640 Speaker 1: five thousand was not so ambitious a target then. It 45 00:02:20,639 --> 00:02:24,800 Speaker 1: looks a lot more ambitious today. Just looking at pictures 46 00:02:24,840 --> 00:02:28,400 Speaker 1: of that poop emojie as you called it, I thought 47 00:02:28,400 --> 00:02:30,079 Speaker 1: it was a helix. I think we hadn't heard it 48 00:02:30,160 --> 00:02:35,240 Speaker 1: called that officially yet, Matt, talk to us though about 49 00:02:35,280 --> 00:02:37,840 Speaker 1: you know, the serious nature of this is that Arlington, 50 00:02:37,880 --> 00:02:40,040 Speaker 1: Virginia is going to be worried. In particular, they think 51 00:02:40,040 --> 00:02:42,799 Speaker 1: of the tax breaks well eight hundred million dollars worth 52 00:02:42,960 --> 00:02:45,160 Speaker 1: given to Amazon to lure them to bring in what 53 00:02:45,240 --> 00:02:48,639 Speaker 1: twenty five thousand workers, that's right. But the good news 54 00:02:48,639 --> 00:02:51,720 Speaker 1: for Arlington anyways, they haven't handed anything over yet. You know, 55 00:02:51,800 --> 00:02:55,560 Speaker 1: some of the tax breakes related to hotel occupancy fees 56 00:02:55,600 --> 00:02:57,720 Speaker 1: I would get sent Amazon's way, But a lot of 57 00:02:57,720 --> 00:02:59,919 Speaker 1: them were just job related, Like once they started put 58 00:03:00,280 --> 00:03:02,200 Speaker 1: butts and seats and they hit certain targets, you know, 59 00:03:02,240 --> 00:03:05,560 Speaker 1: then they'd get tax breaks on on the payroll taxes there, 60 00:03:05,600 --> 00:03:07,799 Speaker 1: so that that money hasn't started changing hands yet. That's 61 00:03:08,000 --> 00:03:10,400 Speaker 1: the good news for Arlington is if Amazon says they're 62 00:03:10,400 --> 00:03:12,600 Speaker 1: committed to it, they still think that they're getting a 63 00:03:12,600 --> 00:03:15,320 Speaker 1: pretty good deal winning if Amazon does complete the campus 64 00:03:15,320 --> 00:03:19,400 Speaker 1: as they've planned. Bloomberg's Matt Day reporting out of Seattle. 65 00:03:19,600 --> 00:03:21,959 Speaker 1: Good stuff, Thank you so much. Let's branch out a 66 00:03:22,000 --> 00:03:24,519 Speaker 1: little bit and talk about tech more broadly. Denny Fish, 67 00:03:24,600 --> 00:03:28,800 Speaker 1: portfolio manager at Janie Henderson, managing four billion dollars across 68 00:03:28,840 --> 00:03:33,280 Speaker 1: funds in the tech space, leading technology research. It's interesting, Denny. 69 00:03:33,560 --> 00:03:35,040 Speaker 1: I won't put you on the spot when it comes 70 00:03:35,040 --> 00:03:37,120 Speaker 1: to Amazon specifically. I know you hold that stock in 71 00:03:37,200 --> 00:03:40,920 Speaker 1: your funds, But as an example, Amazon kind of hitting 72 00:03:41,000 --> 00:03:44,360 Speaker 1: the brakes on the real estate side, having already worked 73 00:03:44,400 --> 00:03:46,800 Speaker 1: on the layoff side. What is that telling you about 74 00:03:46,840 --> 00:03:50,120 Speaker 1: about the technology set right now? The prudence that it's 75 00:03:50,200 --> 00:03:53,880 Speaker 1: kind of going through, well, it's it's definitely different than 76 00:03:53,920 --> 00:03:56,240 Speaker 1: it was twelve months ago in terms of the general 77 00:03:56,280 --> 00:04:01,240 Speaker 1: philosophy for the background, both in terms of in Amazon's 78 00:04:01,280 --> 00:04:05,000 Speaker 1: case specifically, it's a company that overhired during the pandemic, 79 00:04:06,360 --> 00:04:10,040 Speaker 1: put a lot of capital to work and logistics and fulfillment. 80 00:04:10,440 --> 00:04:13,720 Speaker 1: You know, we're just talking about, you know, the headquarters 81 00:04:13,720 --> 00:04:17,680 Speaker 1: being put on pause. And the challenge for Amazon is twofold. 82 00:04:17,760 --> 00:04:21,560 Speaker 1: You have this highly profitable growth business, Amazon Web Services 83 00:04:21,600 --> 00:04:24,840 Speaker 1: that's starting to slow. But more importantly for them, they're 84 00:04:24,839 --> 00:04:27,839 Speaker 1: really a physical business with a digital front end. And 85 00:04:27,920 --> 00:04:30,560 Speaker 1: as a result, there's only one way to get out 86 00:04:30,720 --> 00:04:34,480 Speaker 1: of that situation when you overinvest, and you either have 87 00:04:34,520 --> 00:04:37,560 Speaker 1: to significantly reduce your footprint, which is really hard for 88 00:04:37,600 --> 00:04:39,800 Speaker 1: them to do because of the nature of what fulfillment 89 00:04:39,800 --> 00:04:42,680 Speaker 1: and logistics represents. So they have to grow out of it, 90 00:04:42,880 --> 00:04:46,279 Speaker 1: and they have to get efficiencies on a unit profitability basis, 91 00:04:46,520 --> 00:04:49,599 Speaker 1: and that's what the company is grinding through right now. 92 00:04:49,880 --> 00:04:54,320 Speaker 1: And then when you exacerbate that with a tough economic environment, 93 00:04:54,520 --> 00:04:56,520 Speaker 1: it just makes it that much harder and that much 94 00:04:56,560 --> 00:05:00,400 Speaker 1: longer to recoup and start to actually drive some leverage 95 00:05:00,400 --> 00:05:03,080 Speaker 1: out of those investments that they've made. The other story 96 00:05:03,120 --> 00:05:04,800 Speaker 1: of the week has been earnings, and I think about 97 00:05:04,880 --> 00:05:07,880 Speaker 1: names like Dell and then contrast that with Broad Common. 98 00:05:07,920 --> 00:05:10,960 Speaker 1: We're getting kind of really make signals about demand from 99 00:05:10,960 --> 00:05:14,560 Speaker 1: corporate America, global corporates and the consumer kind of what's 100 00:05:14,600 --> 00:05:17,120 Speaker 1: your tea leaves read of this week and what we're 101 00:05:17,160 --> 00:05:19,400 Speaker 1: seeing from tech. You know, I would say broadly the 102 00:05:19,560 --> 00:05:22,919 Speaker 1: entire earning season if we wanted to unpack that, and 103 00:05:23,000 --> 00:05:25,760 Speaker 1: this was just a continuation of that, and that is 104 00:05:25,839 --> 00:05:29,560 Speaker 1: that if we look across sectors, you know, semiconductors are 105 00:05:29,600 --> 00:05:33,520 Speaker 1: always the leading indicators for global economic growth or global 106 00:05:33,520 --> 00:05:37,760 Speaker 1: economic slowdowns, and we've seen a material slowdown there, most 107 00:05:37,760 --> 00:05:42,280 Speaker 1: companies lowering expectations pretty materially for the year. But nonetheless 108 00:05:42,320 --> 00:05:44,760 Speaker 1: the stocks have actually been responding all right, which is 109 00:05:44,760 --> 00:05:47,679 Speaker 1: really encouraging because semiconductors are the first ones to actually 110 00:05:47,720 --> 00:05:50,840 Speaker 1: start to discount the upturn as well when they're incredible 111 00:05:50,880 --> 00:05:55,600 Speaker 1: businesses with great industry structure, but nonetheless that's where we're at, 112 00:05:55,680 --> 00:05:59,520 Speaker 1: and they're highly economically sensitive. And then you have kind 113 00:05:59,520 --> 00:06:03,000 Speaker 1: of the secular growth stories and software and internet and 114 00:06:03,040 --> 00:06:08,120 Speaker 1: other sectors where things are still growing, but they're just 115 00:06:08,160 --> 00:06:11,720 Speaker 1: not growing nearly as fast as the economy starts to 116 00:06:11,760 --> 00:06:14,960 Speaker 1: take hold as well as there was just so much 117 00:06:15,000 --> 00:06:17,560 Speaker 1: capital that came into the space over you know, a 118 00:06:17,600 --> 00:06:21,159 Speaker 1: five to seven year period, and the market's really starting 119 00:06:21,200 --> 00:06:25,160 Speaker 1: to discern now within these names on the unit economics, 120 00:06:25,240 --> 00:06:28,880 Speaker 1: and you know, companies that have really good, strong, durable 121 00:06:28,920 --> 00:06:33,479 Speaker 1: competitive positions, really durable unit economics, and those are the 122 00:06:33,480 --> 00:06:35,800 Speaker 1: companies that are likely to perform as we get to 123 00:06:35,839 --> 00:06:38,839 Speaker 1: the other side of this, you know, economic situation that 124 00:06:38,839 --> 00:06:40,960 Speaker 1: we're in right now and strong balance sheets. And I 125 00:06:41,040 --> 00:06:43,760 Speaker 1: just want to bring this particular point that was really 126 00:06:43,839 --> 00:06:45,800 Speaker 1: driven home by g Sam a little bit earlier. Just 127 00:06:45,839 --> 00:06:52,120 Speaker 1: take you listen to any tech is getting grouped in 128 00:06:52,160 --> 00:06:54,320 Speaker 1: with growth quote unquote if you will. But there's a 129 00:06:54,320 --> 00:06:56,839 Speaker 1: lot of quality. There's good balance sheet, there's a lot 130 00:06:56,880 --> 00:06:59,120 Speaker 1: of cash, there's the ability to still do m and 131 00:06:59,200 --> 00:07:03,480 Speaker 1: a strong activities. And so again we're really focused on 132 00:07:03,600 --> 00:07:07,400 Speaker 1: there are some strong, solid micro stories and it's less hey, 133 00:07:07,520 --> 00:07:12,480 Speaker 1: let's buy the index. All people sort of throwing babies 134 00:07:12,480 --> 00:07:14,720 Speaker 1: out with bath waters, to use that awful phrase, but 135 00:07:14,840 --> 00:07:18,800 Speaker 1: ultimately people tying everything up. Some great big babies in there. 136 00:07:19,040 --> 00:07:22,320 Speaker 1: You're throwing those out? Yeah? Or have we now discerned 137 00:07:22,440 --> 00:07:25,160 Speaker 1: that Microsoft, which I think is your largest holding, some 138 00:07:25,240 --> 00:07:28,640 Speaker 1: other companies are of a different beast and actually perhaps 139 00:07:28,640 --> 00:07:30,240 Speaker 1: don't see them as growth stocks in the way they 140 00:07:30,320 --> 00:07:34,240 Speaker 1: used to. Well, I think it's twofold. There are a 141 00:07:34,280 --> 00:07:36,080 Speaker 1: couple of dynamics that are going on. If we just 142 00:07:36,120 --> 00:07:39,080 Speaker 1: think about what I call the gigacaps. Okay, so kind 143 00:07:39,080 --> 00:07:41,840 Speaker 1: of think about feng Ma. You know, broadly speaking, we 144 00:07:41,960 --> 00:07:44,360 Speaker 1: have started to see a lot more discernment in terms 145 00:07:44,400 --> 00:07:47,320 Speaker 1: of the multiples we've seen where you know, Google's traded 146 00:07:47,440 --> 00:07:50,200 Speaker 1: much lower in terms of its you know, expected earnings multiple. 147 00:07:50,600 --> 00:07:53,640 Speaker 1: Microsoft and Apple have continued to hold their multiples, I think, 148 00:07:53,720 --> 00:07:56,520 Speaker 1: just given the confidence in those business models over the 149 00:07:56,560 --> 00:07:59,880 Speaker 1: longer term, and all of these companies are getting a 150 00:07:59,880 --> 00:08:02,000 Speaker 1: lot more competitive with each other. You know, if we 151 00:08:02,120 --> 00:08:04,680 Speaker 1: just think about the idea of AI and what we've 152 00:08:04,720 --> 00:08:08,000 Speaker 1: seen with open AI and chat GPT and the potential 153 00:08:08,040 --> 00:08:12,960 Speaker 1: implications to Google search business. But that's all upside for Microsoft, 154 00:08:13,040 --> 00:08:17,280 Speaker 1: and so the market's trying to figure out what this 155 00:08:17,520 --> 00:08:20,680 Speaker 1: is going to mean over the longer term. And then, yeah, 156 00:08:20,680 --> 00:08:22,720 Speaker 1: I would say twenty twenty two, we just kind of 157 00:08:22,720 --> 00:08:25,960 Speaker 1: threw everything out, you know it with the bathwater, and 158 00:08:26,000 --> 00:08:28,720 Speaker 1: then early this year we've had this risk on rally, 159 00:08:28,760 --> 00:08:30,720 Speaker 1: not you know, the last week and a half or so, 160 00:08:30,800 --> 00:08:33,400 Speaker 1: which has been a little choppier, but coming into this 161 00:08:33,480 --> 00:08:36,559 Speaker 1: year now like it's it's one of those classic early 162 00:08:36,720 --> 00:08:39,280 Speaker 1: cycle potential rallies. I don't know if it's a bear 163 00:08:39,360 --> 00:08:41,520 Speaker 1: market rally. I don't know if it's an early cycle rally, 164 00:08:41,679 --> 00:08:44,040 Speaker 1: but it feels more like an early cycle rally because 165 00:08:44,280 --> 00:08:47,800 Speaker 1: everything then rises and then what usually happens is you 166 00:08:47,840 --> 00:08:49,880 Speaker 1: get six or nine months down the road. It's the 167 00:08:49,880 --> 00:08:53,120 Speaker 1: companies that have the real financial performance that really start 168 00:08:53,200 --> 00:08:56,079 Speaker 1: shining through, and that's where you start to generate your 169 00:08:56,080 --> 00:08:58,839 Speaker 1: returns over a multi year basis. And tech Okay, you 170 00:08:58,880 --> 00:09:02,680 Speaker 1: mentioned Chat Pizza, and one of the key megacaps that 171 00:09:02,760 --> 00:09:05,040 Speaker 1: has outperformed so far this year is in Nvidia. I 172 00:09:05,080 --> 00:09:08,680 Speaker 1: know it's holding of uls. All you making bets now 173 00:09:08,800 --> 00:09:11,439 Speaker 1: on who is best place in the artificial intelligence race 174 00:09:11,559 --> 00:09:14,760 Speaker 1: right now? Yeah, we are. You know, obviously, as you mentioned, 175 00:09:15,240 --> 00:09:19,240 Speaker 1: Invidia's an obvious one for the moment because the fact 176 00:09:19,240 --> 00:09:22,600 Speaker 1: of the matter is you can't deploy GPT without a 177 00:09:22,600 --> 00:09:24,960 Speaker 1: ton of GPUs, right, I mean, it's just like that's 178 00:09:25,200 --> 00:09:29,360 Speaker 1: that's what it is. And now is that going to persist? 179 00:09:29,600 --> 00:09:32,160 Speaker 1: You know, there's still the opportunity for companies to develop 180 00:09:32,600 --> 00:09:36,839 Speaker 1: application specific processors that might be more efficient for certain 181 00:09:36,840 --> 00:09:39,920 Speaker 1: types of models. So that's a risk. One of the 182 00:09:39,960 --> 00:09:42,880 Speaker 1: most amazing parts of Nvidia's growth story over many years 183 00:09:42,880 --> 00:09:45,959 Speaker 1: has been Kuda. It's software platform for developers that makes 184 00:09:46,000 --> 00:09:48,920 Speaker 1: it easier to program against GPUs and sort of has 185 00:09:48,920 --> 00:09:52,280 Speaker 1: allowed it to win the race. Is that going to 186 00:09:52,320 --> 00:09:54,680 Speaker 1: be a competitive advantage in a world of generative AI 187 00:09:54,720 --> 00:09:57,760 Speaker 1: and large, large language models. We're not quite sure on that, 188 00:09:58,000 --> 00:10:01,640 Speaker 1: but for now there's probably no cleaner play than than 189 00:10:01,720 --> 00:10:05,960 Speaker 1: in video. But as you start thinking across the ecosystem, 190 00:10:06,080 --> 00:10:09,160 Speaker 1: you know, it's interesting. We just mentioned Microsoft for example. 191 00:10:09,520 --> 00:10:12,839 Speaker 1: Anything they do with being is all upside, you know, 192 00:10:13,000 --> 00:10:16,720 Speaker 1: And could they can potentially displace Google as the default 193 00:10:16,800 --> 00:10:21,320 Speaker 1: on iOS? Could they What do you think they potentially could? 194 00:10:21,520 --> 00:10:25,679 Speaker 1: It's clearly in their ambition and all things equal, Um, 195 00:10:26,040 --> 00:10:28,800 Speaker 1: if you're Apple, you just want to provide a consistent 196 00:10:28,880 --> 00:10:32,240 Speaker 1: user experience and you want to extract as much economic 197 00:10:32,440 --> 00:10:35,480 Speaker 1: value as you can GPT GPU. I'll hit you in 198 00:10:35,559 --> 00:10:38,760 Speaker 1: another acronym that Karen and I have been hearing every day. Fomo. 199 00:10:40,160 --> 00:10:43,480 Speaker 1: That was comes you by me is what we're seeing 200 00:10:43,520 --> 00:10:46,559 Speaker 1: in the market broadly. Fomo. You know, C three AI 201 00:10:46,800 --> 00:10:51,559 Speaker 1: is a name another third percent today based on fundamentals, 202 00:10:51,600 --> 00:10:54,480 Speaker 1: I think I think not, No, it's not and and 203 00:10:54,720 --> 00:10:57,559 Speaker 1: so fomo is driving you know, so when I talked 204 00:10:57,600 --> 00:11:00,439 Speaker 1: about that classic so we have two things. We've kind 205 00:11:00,440 --> 00:11:03,000 Speaker 1: of had that classic early cycle behavior in terms of 206 00:11:03,000 --> 00:11:06,160 Speaker 1: the market, and then we also have you know, the 207 00:11:06,720 --> 00:11:12,480 Speaker 1: thematic artificial intelligent. It's kind of you know, investing environment 208 00:11:12,559 --> 00:11:15,480 Speaker 1: and institution and retail investors get behind and it's easy 209 00:11:15,520 --> 00:11:18,160 Speaker 1: to kind of throw some capital at some names like that. 210 00:11:18,240 --> 00:11:20,560 Speaker 1: But and so the reality is like that is a 211 00:11:20,600 --> 00:11:23,600 Speaker 1: lot of FOMO, right because the fundamentals haven't changed that much. 212 00:11:23,920 --> 00:11:27,840 Speaker 1: There aren't a lot of pure ways to actually play 213 00:11:27,960 --> 00:11:32,720 Speaker 1: artificial intelligence, and it's really going to be more of 214 00:11:33,320 --> 00:11:37,240 Speaker 1: what can actually enhance the value of an existing business um, 215 00:11:37,960 --> 00:11:41,239 Speaker 1: you know, where companies are well positioned on the infrastructure 216 00:11:41,280 --> 00:11:44,760 Speaker 1: side to you know benefit from you know, the massive 217 00:11:44,760 --> 00:11:46,640 Speaker 1: buildouts that we're going to actually have to see to 218 00:11:46,760 --> 00:11:50,920 Speaker 1: support this. And and but we're also going to see 219 00:11:50,920 --> 00:11:53,880 Speaker 1: a ton we always see this, a ton of speculation 220 00:11:54,360 --> 00:11:56,880 Speaker 1: around the edges and sort of maybe some of the 221 00:11:56,960 --> 00:11:59,640 Speaker 1: less proven. But you know, a company that has AI 222 00:11:59,760 --> 00:12:05,319 Speaker 1: and it's you know, yeah, exactly, well let's go from 223 00:12:05,320 --> 00:12:09,760 Speaker 1: AI to VR because ED was helping break great scoops 224 00:12:09,760 --> 00:12:13,920 Speaker 1: on Meta today and ultimately this company is having to 225 00:12:14,000 --> 00:12:18,320 Speaker 1: cut prices of its pretty expensive virtual reality headsets. That 226 00:12:18,480 --> 00:12:20,280 Speaker 1: still there's a lot of hope, a lot of hype, 227 00:12:20,360 --> 00:12:23,320 Speaker 1: perhaps not in the purchasing that many had anticipated, but 228 00:12:23,400 --> 00:12:25,920 Speaker 1: you've been adding to your meta position. What was another 229 00:12:26,000 --> 00:12:29,240 Speaker 1: back of that, Yeah, so it's it's it's interesting. It's 230 00:12:29,280 --> 00:12:33,640 Speaker 1: not because of ARVR, to be clear. Yeah, yeah, it's 231 00:12:33,640 --> 00:12:35,400 Speaker 1: not the metaver you know. The metaverse is like, it's 232 00:12:35,400 --> 00:12:37,280 Speaker 1: this thing we've been talking about for many, many years, 233 00:12:37,559 --> 00:12:40,200 Speaker 1: and it's going to evolve more slowly, right because you're 234 00:12:40,200 --> 00:12:43,440 Speaker 1: gonna have a bunch of different metaverses, consumer, enterprise, different 235 00:12:43,520 --> 00:12:46,960 Speaker 1: use cases. What's interesting about meta one is the stock 236 00:12:47,000 --> 00:12:50,520 Speaker 1: got too darn cheap. Okay, the expectation was, you know, 237 00:12:50,600 --> 00:12:53,000 Speaker 1: they had to do something to repair their stock price 238 00:12:53,040 --> 00:12:55,480 Speaker 1: and get aggressive with their cost structure, and they did 239 00:12:55,520 --> 00:12:57,199 Speaker 1: so that was part of the thesis. But there's another 240 00:12:57,200 --> 00:13:01,440 Speaker 1: part of the thesis too, and that is about eighteen 241 00:13:01,480 --> 00:13:07,080 Speaker 1: months ago, Apple effectively exercised its market power and you 242 00:13:07,160 --> 00:13:11,200 Speaker 1: had two things you hadda yeah, yeah, yeah exactly, or 243 00:13:11,400 --> 00:13:15,360 Speaker 1: reduced ad tracking right with IDFA and ATT and oh 244 00:13:18,160 --> 00:13:22,680 Speaker 1: they're they're two more and that really hurt meta. It 245 00:13:22,880 --> 00:13:26,280 Speaker 1: really inhibited their ability to drive return on AD spend 246 00:13:26,559 --> 00:13:30,679 Speaker 1: because they lost the ability to retarget and measure consumers 247 00:13:30,920 --> 00:13:35,280 Speaker 1: across the broader Internet. Now what's interesting is they are 248 00:13:35,440 --> 00:13:38,480 Speaker 1: one of the largest buyers of GPUs right now on 249 00:13:38,520 --> 00:13:41,120 Speaker 1: the planet. They're rear connecting their data centers and the 250 00:13:41,200 --> 00:13:43,200 Speaker 1: reason they're doing it is they're all in on AI 251 00:13:43,640 --> 00:13:46,960 Speaker 1: to drive better AD measurement. To the extent they're able 252 00:13:47,000 --> 00:13:50,319 Speaker 1: to do that, they might actually widen their competitive advantage 253 00:13:50,360 --> 00:13:54,720 Speaker 1: over time. And it's kind of like a maybe not 254 00:13:54,880 --> 00:13:58,120 Speaker 1: quite as obvious way to think about a company that 255 00:13:58,160 --> 00:14:01,400 Speaker 1: could benefit from AI, given how bad it's been hurt 256 00:14:01,440 --> 00:14:06,360 Speaker 1: by Apples so far. Love that thesis. Come back, Denny. 257 00:14:06,400 --> 00:14:08,160 Speaker 1: It's been a joy to actually here's that next to 258 00:14:08,160 --> 00:14:10,840 Speaker 1: you up San Francisco. But hope you come back into 259 00:14:10,840 --> 00:14:12,319 Speaker 1: the office. We thank you for making a special trip 260 00:14:12,360 --> 00:14:16,920 Speaker 1: to Sidney. Jannis Henderson, portfolio manager, Denny Quish coming up 261 00:14:17,240 --> 00:14:20,160 Speaker 1: another key Apple executive, just talking about that company. When 262 00:14:20,160 --> 00:14:22,800 Speaker 1: they're leaving the company, will discuss why this has Bloomberg, 263 00:14:33,680 --> 00:14:36,880 Speaker 1: the executive in charge of Apple's I Cloud, I message 264 00:14:36,880 --> 00:14:40,720 Speaker 1: and FaceTime infrastructure, is leaving the company, according to sources, 265 00:14:40,760 --> 00:14:43,120 Speaker 1: after five years in the role. Michael Abbott is stepping 266 00:14:43,200 --> 00:14:46,520 Speaker 1: down as the company's cloud chief next month. It adds 267 00:14:46,560 --> 00:14:50,000 Speaker 1: to the revolving door of executives at the company. Bloomberg 268 00:14:50,080 --> 00:14:53,200 Speaker 1: Senior technology editor Nigg Turner is here with us the 269 00:14:53,280 --> 00:14:56,800 Speaker 1: revolving door. I think that's part of the story, isn't it. Yes. 270 00:14:56,880 --> 00:14:59,160 Speaker 1: I mean, you know, it's not necessarily uncommon around the 271 00:14:59,240 --> 00:15:01,440 Speaker 1: end of the year for certain number of Apple executives 272 00:15:01,440 --> 00:15:04,560 Speaker 1: to leave. It feels like more than normal this year, 273 00:15:05,280 --> 00:15:07,880 Speaker 1: especially between now and like the sort of the last 274 00:15:07,880 --> 00:15:10,200 Speaker 1: few months of the year, you've had a lot of 275 00:15:10,240 --> 00:15:12,840 Speaker 1: sort of VP level people in charge of everything from 276 00:15:12,880 --> 00:15:16,120 Speaker 1: kind of industrial design, the chief privacy officer to the 277 00:15:16,200 --> 00:15:21,240 Speaker 1: head of the website, you know, depart resart. So April 278 00:15:21,480 --> 00:15:23,520 Speaker 1: is the time that he's going to be particularly leaving. 279 00:15:23,560 --> 00:15:26,280 Speaker 1: Michael about nuts in large part because of when it 280 00:15:26,480 --> 00:15:30,800 Speaker 1: shares best right. I'm interested though, on whether they're filling 281 00:15:31,120 --> 00:15:34,720 Speaker 1: these roles that swichly in some cases, and in this case, 282 00:15:34,760 --> 00:15:36,960 Speaker 1: they are having someone take over his duties, which is 283 00:15:37,000 --> 00:15:40,480 Speaker 1: Jeff Robin, who's the guy who invented iTunes. So that's 284 00:15:40,520 --> 00:15:45,640 Speaker 1: not a bad flax claim nice, you know. So in 285 00:15:45,760 --> 00:15:49,080 Speaker 1: some other cases they're basically divvying up duties and giving 286 00:15:49,080 --> 00:15:50,840 Speaker 1: them other people. There's not really going to be a 287 00:15:50,920 --> 00:15:53,480 Speaker 1: chief privacy officer the way there was before, for instance, 288 00:15:53,840 --> 00:15:57,760 Speaker 1: the industrial design person. They essentially giving those duties to 289 00:15:57,800 --> 00:15:59,800 Speaker 1: different people, which is pretty big deal because that used 290 00:15:59,840 --> 00:16:02,320 Speaker 1: to be Johnny I and right, you know the heart 291 00:16:02,360 --> 00:16:04,920 Speaker 1: of Apple more real quick, We've only got about thirty seconds. 292 00:16:04,920 --> 00:16:07,960 Speaker 1: But the serious thing is that services is important, right, well, 293 00:16:08,000 --> 00:16:10,880 Speaker 1: so everything cloud related. That's been huge for them. I 294 00:16:10,880 --> 00:16:12,640 Speaker 1: think there's been a bit attention over the years in 295 00:16:12,680 --> 00:16:14,760 Speaker 1: terms of how much they rely on like Amazon and 296 00:16:14,800 --> 00:16:18,280 Speaker 1: Google to do cloud versus doing it themselves. And so 297 00:16:18,400 --> 00:16:20,520 Speaker 1: maybe that's something the new person will have to sort 298 00:16:20,520 --> 00:16:24,000 Speaker 1: of sort out too, but they do basically a mix 299 00:16:24,040 --> 00:16:28,280 Speaker 1: at the moment. Nick, fascinating, great story coming from Mark 300 00:16:28,320 --> 00:16:30,760 Speaker 1: German and Nick turn a key team pays. We thank 301 00:16:30,760 --> 00:16:42,240 Speaker 1: you so much for coming on explaining it all time. 302 00:16:42,280 --> 00:16:46,160 Speaker 1: Now for talking tech, let's start with AI artificial intelligence 303 00:16:46,200 --> 00:16:49,600 Speaker 1: startup Inflection AI seeking to raise as much as six 304 00:16:49,720 --> 00:16:52,880 Speaker 1: hundred and seventy five million dollars that according to the 305 00:16:52,920 --> 00:16:57,000 Speaker 1: Financial Times, citing sources. That's the company Carrow, founded by 306 00:16:57,080 --> 00:17:00,480 Speaker 1: Mustapha Sulliman, one of Deep Minds founders, as well as 307 00:17:00,520 --> 00:17:05,280 Speaker 1: LinkedIn creator Reid Hoffman, speaking of which Reid Hoffman has 308 00:17:05,320 --> 00:17:08,720 Speaker 1: announced he'll step back from the Open Ai board and 309 00:17:08,880 --> 00:17:14,040 Speaker 1: citing greater transparency because Graylock his firm is increasingly investing 310 00:17:14,080 --> 00:17:18,359 Speaker 1: in companies using open ais APIs, Hoffman says this cou 311 00:17:18,480 --> 00:17:21,680 Speaker 1: cause a conflict of interest. He's been a personal investor 312 00:17:21,720 --> 00:17:24,600 Speaker 1: through his charities and on the board of open Ai 313 00:17:24,880 --> 00:17:28,320 Speaker 1: since twenty fifteen. And finally, don't miss this one, Gwyneth 314 00:17:28,400 --> 00:17:32,320 Speaker 1: Paltrow Kinship Ventures raising seventy five million dollars for its 315 00:17:32,400 --> 00:17:36,560 Speaker 1: debut fund Axios reporting without citing where it got the information, 316 00:17:36,560 --> 00:17:39,240 Speaker 1: act says the firm is looking to invest in early 317 00:17:39,359 --> 00:17:44,080 Speaker 1: stage consumer goods and technology companies. Very on brand for 318 00:17:44,080 --> 00:17:46,920 Speaker 1: one gwyn Poutrow Caroline, and interesting that she's doing it 319 00:17:46,960 --> 00:17:50,280 Speaker 1: in line with Mujera we understand as well. Now MOS 320 00:17:50,480 --> 00:17:53,720 Speaker 1: was coming from Beauty Khan and really focusing on the 321 00:17:53,760 --> 00:17:57,320 Speaker 1: beauty side of investment, but also luckily having interacted a 322 00:17:57,320 --> 00:18:00,959 Speaker 1: plenty with Mos in the previous times through women's networks, 323 00:18:01,000 --> 00:18:03,119 Speaker 1: I know how focus she is on ensuring that women 324 00:18:03,600 --> 00:18:07,040 Speaker 1: that diverse leadership and indeed people get a seat at 325 00:18:07,040 --> 00:18:10,160 Speaker 1: the cap Table are able to invest in fast growing companies. 326 00:18:10,200 --> 00:18:13,640 Speaker 1: So this is an interesting area that of course herself, 327 00:18:13,920 --> 00:18:17,479 Speaker 1: Gwyneth paltrowa business lady Goop no Lesque getting into and 328 00:18:17,480 --> 00:18:20,000 Speaker 1: also she moved on from acting to be an entrepreneur. 329 00:18:20,240 --> 00:18:22,320 Speaker 1: And I think the idea is very much like, I've 330 00:18:22,359 --> 00:18:26,000 Speaker 1: got some money to play with here to support entrepreneurs 331 00:18:26,040 --> 00:18:29,040 Speaker 1: that I see myself. I'm, you know, Avench capitalists finding 332 00:18:29,119 --> 00:18:33,480 Speaker 1: like minded people, similarly experienced people. Speaking of Avench capital 333 00:18:33,520 --> 00:18:35,200 Speaker 1: I think we've got to go back to read Hoffman. 334 00:18:35,520 --> 00:18:39,280 Speaker 1: This is really interesting. The basics are we are investing 335 00:18:39,320 --> 00:18:43,320 Speaker 1: in all sorts of AI related startups. Those AI related 336 00:18:43,400 --> 00:18:48,720 Speaker 1: startups are using GPT three point five. I can't be 337 00:18:48,760 --> 00:18:50,679 Speaker 1: on the board anymore. That is a big piece of 338 00:18:50,680 --> 00:18:53,720 Speaker 1: industry news, given everything that's happened so far this year. Yeah, 339 00:18:53,760 --> 00:18:56,920 Speaker 1: I really think that we've been interviewing the likes of Neiva, 340 00:18:57,000 --> 00:19:00,200 Speaker 1: for example, which in some ways as a competitor to 341 00:19:00,280 --> 00:19:02,760 Speaker 1: the likes of open Ai by not only having a 342 00:19:02,800 --> 00:19:06,399 Speaker 1: search engine that's built around these sort of large language models, 343 00:19:06,440 --> 00:19:08,760 Speaker 1: but also being able to cite where the information is 344 00:19:08,760 --> 00:19:11,719 Speaker 1: coming from. We've interviewed that CEO before and that's our 345 00:19:11,760 --> 00:19:13,960 Speaker 1: relationships were certainly see such a screen looks. So I 346 00:19:13,960 --> 00:19:16,840 Speaker 1: think this is an interesting area that there's gonna be 347 00:19:16,920 --> 00:19:19,840 Speaker 1: a lot of investment being thrown into the AI space, 348 00:19:19,920 --> 00:19:22,280 Speaker 1: and some will be competitors and some will be able 349 00:19:22,320 --> 00:19:24,119 Speaker 1: to grow in tandem. Well very quick. We had some 350 00:19:24,440 --> 00:19:26,679 Speaker 1: Motmedi his colleague on the show just a couple of 351 00:19:26,680 --> 00:19:28,640 Speaker 1: weeks ago, right, and he makes the point I'm looking 352 00:19:28,680 --> 00:19:32,680 Speaker 1: at enterprise companies how they can take existing tools because 353 00:19:32,720 --> 00:19:35,080 Speaker 1: AI will improve every single company I think, is what 354 00:19:35,119 --> 00:19:38,600 Speaker 1: he said. So this makes sense based on the activity 355 00:19:38,680 --> 00:19:50,640 Speaker 1: that we're seeing out there. Welcome back to being my technology. 356 00:19:50,640 --> 00:19:52,919 Speaker 1: I'm Parranghi and I made lovelow, both of us in 357 00:19:52,960 --> 00:19:55,320 Speaker 1: San Francisco for the final time this week. I'm sad 358 00:19:55,600 --> 00:19:58,440 Speaker 1: to say. Now, let's get to crypto markets. They're struggling 359 00:19:58,840 --> 00:20:02,919 Speaker 1: to power through the implosion of silver Gate Capital, the 360 00:20:02,960 --> 00:20:06,520 Speaker 1: bank one of the main banking providers for the crypto industry, 361 00:20:06,920 --> 00:20:10,000 Speaker 1: which is why we're seeing sentiment drop in other crypto 362 00:20:10,080 --> 00:20:13,720 Speaker 1: related assets, Bitcoin hitting a two week low. For more, 363 00:20:14,280 --> 00:20:17,960 Speaker 1: Let's bring in Bloomberg's Uchi Yanguchi. So many headlines on 364 00:20:18,000 --> 00:20:21,400 Speaker 1: the Bloomberg this Friday. What is the latest with silver Gate. 365 00:20:21,920 --> 00:20:25,480 Speaker 1: So we're really is still seeing the ripple effect from 366 00:20:25,480 --> 00:20:29,440 Speaker 1: the collapse of FTX last November. As we know, FTX 367 00:20:29,560 --> 00:20:32,959 Speaker 1: is a major client of Surrogate, and the Surrogate as 368 00:20:32,960 --> 00:20:35,679 Speaker 1: a results suffered from a bank round last year. But 369 00:20:35,760 --> 00:20:38,240 Speaker 1: then this week the bank said that they couldn't really 370 00:20:38,280 --> 00:20:41,280 Speaker 1: file their annual filings on time. They said that this 371 00:20:41,520 --> 00:20:45,240 Speaker 1: disclosed more losses for selling securities, and then they said 372 00:20:45,240 --> 00:20:49,000 Speaker 1: they're evaluating if they can stay aflawed internally, and they 373 00:20:49,040 --> 00:20:52,960 Speaker 1: also indicated risk of being investigated by the Department of Justice. 374 00:20:53,280 --> 00:20:57,639 Speaker 1: So all of these really prompted exodus of all the 375 00:20:57,760 --> 00:21:01,639 Speaker 1: keyp hunters of users on the silver Gate platform, which 376 00:21:01,680 --> 00:21:04,679 Speaker 1: is one of the few and key payments platform for 377 00:21:04,760 --> 00:21:08,200 Speaker 1: crypto companies to transfer US dollar between each other in 378 00:21:08,280 --> 00:21:13,000 Speaker 1: real time. Quin mascalicxy Digital, Paxos Trust, all of these 379 00:21:13,040 --> 00:21:15,360 Speaker 1: saying look card ties with silver Gate are being put 380 00:21:15,359 --> 00:21:17,679 Speaker 1: on hold for the time being. Where then do we 381 00:21:17,800 --> 00:21:21,080 Speaker 1: go for these sorts of ability to transfer, to be 382 00:21:21,160 --> 00:21:24,320 Speaker 1: able to ensure that the pipes are still working, the 383 00:21:24,320 --> 00:21:28,040 Speaker 1: infrastructure still there. That is the question that the industry 384 00:21:28,040 --> 00:21:31,840 Speaker 1: players are trying to figure out. Another bank that's in 385 00:21:31,840 --> 00:21:34,960 Speaker 1: this space is signature bank, but they already announced that 386 00:21:35,000 --> 00:21:38,919 Speaker 1: they're actually planning to reduce the deposits from crypto companies 387 00:21:39,320 --> 00:21:43,840 Speaker 1: and and so that's so there's not many options right 388 00:21:43,880 --> 00:21:46,840 Speaker 1: now for crypto players to find a bank that's willing 389 00:21:46,880 --> 00:21:50,199 Speaker 1: to take them out on and replace the role of surrogate. 390 00:21:51,280 --> 00:21:53,480 Speaker 1: You tee more of a PSA for our audience, But 391 00:21:53,640 --> 00:21:56,399 Speaker 1: some of the ratings agencies catching out ratings cut by 392 00:21:56,440 --> 00:21:59,840 Speaker 1: Moodies at silver Gate not so sure, that's a surprise. 393 00:22:00,480 --> 00:22:03,439 Speaker 1: Where do we go from here? You know, Caroline and 394 00:22:03,440 --> 00:22:05,680 Speaker 1: I were talking actually earlier on a Twitter spaces about 395 00:22:05,680 --> 00:22:08,359 Speaker 1: how we were talking at the end of last year 396 00:22:08,440 --> 00:22:10,679 Speaker 1: the beginning of this year that Silvergate was in trouble. 397 00:22:10,720 --> 00:22:15,440 Speaker 1: Why do they keep falling further and further. So we're 398 00:22:15,480 --> 00:22:19,720 Speaker 1: seeing regulatory pressure from the DC as we know that 399 00:22:19,840 --> 00:22:24,320 Speaker 1: after the bankruptcy of FTX, federal banking regulators all came 400 00:22:24,359 --> 00:22:28,320 Speaker 1: out with warnings talking about the risk of serving cryptal 401 00:22:28,359 --> 00:22:31,040 Speaker 1: companies for banks, and they're really trying to make sure 402 00:22:31,119 --> 00:22:34,800 Speaker 1: that any contagious risk from the industry will not leak 403 00:22:34,920 --> 00:22:38,760 Speaker 1: into the real world economy. So Silvergate is one part 404 00:22:38,800 --> 00:22:42,280 Speaker 1: of that. We're seeing that the bank is now trying 405 00:22:42,280 --> 00:22:45,720 Speaker 1: to figure out a solution about their pass going forward. 406 00:22:46,600 --> 00:22:50,760 Speaker 1: Deposits from cryptal companies were the majority of their depositor base, 407 00:22:50,920 --> 00:22:53,320 Speaker 1: and they're really unique in the sense that they're getting 408 00:22:53,359 --> 00:22:57,600 Speaker 1: hit pretty hard because their depositors are fleeing the bank. Essentially, 409 00:22:59,359 --> 00:23:02,600 Speaker 1: the problem a problems of centralized finance within the world 410 00:23:02,640 --> 00:23:05,840 Speaker 1: of decentralized finance. Chang, we thank you so much in 411 00:23:05,840 --> 00:23:07,720 Speaker 1: New York staying late. We appreciate it. Go have a 412 00:23:07,720 --> 00:23:10,639 Speaker 1: wonderful weekend. Let'sting more into this though. We've got a 413 00:23:10,640 --> 00:23:14,800 Speaker 1: perfect voice, Anastasia Bez, CEO of Cadaana. It's a scalable 414 00:23:14,840 --> 00:23:18,040 Speaker 1: proof of work blockchain that actually spun off from JP 415 00:23:18,160 --> 00:23:22,720 Speaker 1: Morgan's private project and associate infrastructures that your heart basically 416 00:23:22,840 --> 00:23:24,600 Speaker 1: in terms of some of what you're trying to build 417 00:23:24,800 --> 00:23:27,680 Speaker 1: a Cadana and support within the ecosystem. But just first 418 00:23:27,680 --> 00:23:33,080 Speaker 1: and foremost take us to the interplay of centralized finance 419 00:23:33,160 --> 00:23:37,040 Speaker 1: and traditional finance trying to interweave itself with decentralized finance. 420 00:23:37,480 --> 00:23:39,760 Speaker 1: How much are people worried about some of the contagion 421 00:23:39,840 --> 00:23:43,600 Speaker 1: risks within Signature bank can Indeed, I'm not Signature apologies, 422 00:23:43,640 --> 00:23:46,840 Speaker 1: but Slivegate and what it ultimately means for the growth 423 00:23:46,880 --> 00:23:52,359 Speaker 1: in the ecosystem. I thanks. I think that one of 424 00:23:52,359 --> 00:23:54,280 Speaker 1: the big things is that we still have to interact 425 00:23:54,320 --> 00:23:57,520 Speaker 1: with centralized thinking, especially you know, companies that are based 426 00:23:57,560 --> 00:23:59,159 Speaker 1: in the US. So if you want to have these 427 00:23:59,280 --> 00:24:02,560 Speaker 1: US companies, we need to have access to bank accounts 428 00:24:02,560 --> 00:24:05,240 Speaker 1: and be able to do pay roll and healthcare and 429 00:24:05,320 --> 00:24:07,959 Speaker 1: things like that. So it's it's one of those realities 430 00:24:07,960 --> 00:24:12,679 Speaker 1: that in a decentralized world, we still have to be 431 00:24:12,760 --> 00:24:16,280 Speaker 1: able to play and integrate with you know, banks. So 432 00:24:17,080 --> 00:24:19,720 Speaker 1: I don't know quite where it's going, but it needs 433 00:24:19,760 --> 00:24:22,639 Speaker 1: to be something that happens, otherwise we're not going to 434 00:24:22,680 --> 00:24:24,680 Speaker 1: be able to have companies in the way that functions. 435 00:24:25,840 --> 00:24:28,160 Speaker 1: As a case in point Anastasia, know we are talking 436 00:24:28,200 --> 00:24:32,600 Speaker 1: about silver Gate, a lender, almost traditional lender, but lending 437 00:24:32,600 --> 00:24:36,480 Speaker 1: to the crypto industry. As someone participating in that space. 438 00:24:36,520 --> 00:24:39,080 Speaker 1: This is what we asked our audience. Okay, whether this 439 00:24:39,160 --> 00:24:40,800 Speaker 1: is kind of it, the worst has happened, or if 440 00:24:40,840 --> 00:24:43,480 Speaker 1: there's more to come, how do you go about running 441 00:24:43,480 --> 00:24:47,160 Speaker 1: a business as COO? We asked, is the contagion spillover 442 00:24:47,240 --> 00:24:50,200 Speaker 1: contained seventy percent of respondents said, no, are you kind 443 00:24:50,200 --> 00:24:53,000 Speaker 1: of worried about the health of this industry right now? 444 00:24:56,560 --> 00:24:58,280 Speaker 1: It's so hard to tell because I think that there 445 00:24:58,560 --> 00:25:00,679 Speaker 1: keeps being moments where people say this is it, and 446 00:25:00,720 --> 00:25:03,240 Speaker 1: then there's something new that comes out. So I think 447 00:25:03,240 --> 00:25:05,480 Speaker 1: that there's sort of been a continuing ripple effect. But 448 00:25:05,560 --> 00:25:07,840 Speaker 1: I think also, I mean, I'm at e Fenver right now, 449 00:25:07,880 --> 00:25:10,679 Speaker 1: and there's a lot of optimism about the technology and 450 00:25:10,720 --> 00:25:14,240 Speaker 1: what people are building and sort of tech infrastructure, and 451 00:25:14,600 --> 00:25:17,399 Speaker 1: I'm seeing a lot of you know, collaboration and cooperation 452 00:25:17,520 --> 00:25:20,800 Speaker 1: between you know, interchain projects where people are seeing the 453 00:25:20,800 --> 00:25:23,000 Speaker 1: strength of one project and how that can work with another. 454 00:25:23,320 --> 00:25:25,679 Speaker 1: And so as much as there's sort of these these 455 00:25:25,920 --> 00:25:29,320 Speaker 1: very real concerns, I also think that people are still 456 00:25:29,359 --> 00:25:32,159 Speaker 1: innovating and building things that are pretty exciting. But you know, 457 00:25:32,240 --> 00:25:34,320 Speaker 1: I mean, we want to do it, Relly. We want 458 00:25:34,359 --> 00:25:36,480 Speaker 1: to build things that are resilient, and that means being 459 00:25:36,520 --> 00:25:40,080 Speaker 1: resilient in the business world, not just in the tech world. Okay, 460 00:25:40,119 --> 00:25:43,480 Speaker 1: so Anna Saysia, let's talk about what you're building. It's 461 00:25:43,520 --> 00:25:47,160 Speaker 1: you know, a proof of work blockchain some would say 462 00:25:47,200 --> 00:25:50,280 Speaker 1: equivalent to a bitcoin, but it's scalable as fast as 463 00:25:50,320 --> 00:25:53,440 Speaker 1: the idea In particular, what are you hoping that Cadena 464 00:25:53,520 --> 00:25:59,439 Speaker 1: can solve within the infrastructure needs. So one of the 465 00:25:59,520 --> 00:26:04,280 Speaker 1: things is we are fast but also secure because we 466 00:26:04,359 --> 00:26:08,080 Speaker 1: have a structure that has braided chain so that it 467 00:26:08,119 --> 00:26:11,520 Speaker 1: refers to other parts of the chain. But really what 468 00:26:11,560 --> 00:26:13,440 Speaker 1: we're trying to offer is for businesses to be able 469 00:26:13,480 --> 00:26:14,879 Speaker 1: to build on top of us and be able to 470 00:26:14,880 --> 00:26:18,199 Speaker 1: take that security and also the scalability without having to 471 00:26:18,240 --> 00:26:20,960 Speaker 1: worry about, you know, as many of the security risks. 472 00:26:21,000 --> 00:26:23,120 Speaker 1: And you know, we also have a smart contract language, 473 00:26:23,160 --> 00:26:28,000 Speaker 1: so that's the programming language that people write different blockchain 474 00:26:28,040 --> 00:26:31,119 Speaker 1: applications in called packed, and that has a lot of 475 00:26:31,160 --> 00:26:33,440 Speaker 1: safety features to it. So that's something that we see 476 00:26:33,720 --> 00:26:36,600 Speaker 1: being more accessible and easier for people to use as 477 00:26:36,640 --> 00:26:39,879 Speaker 1: developers because of the way that it's structured, which hopefully 478 00:26:39,920 --> 00:26:42,840 Speaker 1: means that people can build more viable businesses because you'll 479 00:26:42,880 --> 00:26:45,040 Speaker 1: be able to it's human readable, so I can read 480 00:26:45,040 --> 00:26:47,520 Speaker 1: one of the contracts and see what it says. You know, 481 00:26:47,560 --> 00:26:49,680 Speaker 1: as a non engineer, that's really valuable to be able 482 00:26:49,680 --> 00:26:52,880 Speaker 1: to understand what's happening, even though you know you want 483 00:26:52,880 --> 00:26:54,080 Speaker 1: to get to a world where you don't have to 484 00:26:54,119 --> 00:26:55,680 Speaker 1: look under the hood and you can trust with that, 485 00:26:56,880 --> 00:26:59,200 Speaker 1: well said, I mean Cadena, as I sort of said 486 00:26:59,240 --> 00:27:02,240 Speaker 1: at the beginning, was spun out of a private blockchain 487 00:27:03,119 --> 00:27:06,480 Speaker 1: project being built within JP Morgan. The founders helping exercise 488 00:27:06,560 --> 00:27:09,800 Speaker 1: really that first blockchain at JP Morgan. And therefore, I 489 00:27:09,880 --> 00:27:15,520 Speaker 1: ask you, what is traditional companies appetite to continue to 490 00:27:15,560 --> 00:27:22,119 Speaker 1: invest or to adopt within overall crypto space, whether it 491 00:27:22,240 --> 00:27:24,720 Speaker 1: be building their own private blockchain so that be trying 492 00:27:24,760 --> 00:27:27,600 Speaker 1: to harness the power of the technology. Were at the 493 00:27:27,640 --> 00:27:30,800 Speaker 1: same side, they've got this worry about ultimately how much 494 00:27:30,840 --> 00:27:36,159 Speaker 1: bad press the ecosystem gets. I think that companies are 495 00:27:36,200 --> 00:27:38,760 Speaker 1: trying to balance that where there is some real innovation 496 00:27:38,960 --> 00:27:42,280 Speaker 1: or people have this appetite for new things that might 497 00:27:42,320 --> 00:27:44,720 Speaker 1: solve problems, but that they are trying to balance that 498 00:27:44,800 --> 00:27:47,639 Speaker 1: with you know, being a little bit more conservative at 499 00:27:47,680 --> 00:27:50,800 Speaker 1: large companies, and that's you know, it's a balance that 500 00:27:50,840 --> 00:27:53,000 Speaker 1: they have to strike. But there is still interests. There's 501 00:27:53,040 --> 00:27:57,080 Speaker 1: definitely still companies that are doing partnerships and you know, 502 00:27:57,119 --> 00:27:59,240 Speaker 1: finding different ways to build in the space, you know, 503 00:27:59,280 --> 00:28:03,840 Speaker 1: in ways that I think very real, whether that's you know, 504 00:28:03,880 --> 00:28:05,560 Speaker 1: it's just a variety of different things. But I think 505 00:28:05,560 --> 00:28:07,480 Speaker 1: that some of it where it's going to go is 506 00:28:07,640 --> 00:28:10,199 Speaker 1: not partnerships that you understand like that are visible, but 507 00:28:10,240 --> 00:28:12,959 Speaker 1: just under the hood, there's a blockchain technology doing something. 508 00:28:13,920 --> 00:28:16,080 Speaker 1: I want to dig into blockchain technology. I know this 509 00:28:16,119 --> 00:28:18,800 Speaker 1: is an area you care deeply about in tech literacy. 510 00:28:19,160 --> 00:28:21,120 Speaker 1: So Caroline and I have been talking a lot about 511 00:28:21,160 --> 00:28:24,080 Speaker 1: artificial intelligence. We've been experimenting with it, using it. You 512 00:28:24,119 --> 00:28:26,560 Speaker 1: see everyone having a go, and I feel like with 513 00:28:26,640 --> 00:28:29,280 Speaker 1: the crypto hysteria, hype, whatever you want to call it 514 00:28:29,440 --> 00:28:31,680 Speaker 1: of the last two years, a lot of people miss 515 00:28:31,720 --> 00:28:36,120 Speaker 1: the basics. Would you say that that's fair. I think 516 00:28:36,119 --> 00:28:38,600 Speaker 1: that there's a huge need for education and a huge 517 00:28:38,600 --> 00:28:44,640 Speaker 1: opportunity for education still in the crypto space, where there's 518 00:28:44,640 --> 00:28:47,320 Speaker 1: a lot to learn and right now. I mean, I 519 00:28:47,440 --> 00:28:49,480 Speaker 1: use this example, but I've talked to my mom about 520 00:28:49,520 --> 00:28:51,520 Speaker 1: how she wants to do things in crypto and it 521 00:28:51,640 --> 00:28:58,080 Speaker 1: just becomes this huge bureaucratic onboarding experience. And so I 522 00:28:58,120 --> 00:29:02,520 Speaker 1: definitely agree that more education should happen, and it is happening. 523 00:29:02,640 --> 00:29:04,920 Speaker 1: But I also I see more and more people understanding 524 00:29:05,200 --> 00:29:07,560 Speaker 1: some of these things around, for example, self custody and 525 00:29:07,600 --> 00:29:10,440 Speaker 1: what that means, whether you have children's on in exchange 526 00:29:10,560 --> 00:29:13,000 Speaker 1: or you have custody of them yourself. I mean, that's 527 00:29:13,040 --> 00:29:18,600 Speaker 1: like a really important education point. His two moms wanting 528 00:29:18,640 --> 00:29:22,240 Speaker 1: to get into crypto and Astasia thanks so much, Anastasias 529 00:29:23,520 --> 00:29:25,960 Speaker 1: of Cadena, Thank you so much for your time. Keep 530 00:29:26,040 --> 00:29:30,000 Speaker 1: enjoying if Denver. Meanwhile, let's turn to the VC world 531 00:29:30,040 --> 00:29:33,320 Speaker 1: now and some of that hype around AI. I caught 532 00:29:33,400 --> 00:29:36,600 Speaker 1: up with Aldelue Dennis, general partner of an Initialized Capital, 533 00:29:36,920 --> 00:29:41,680 Speaker 1: and Avanta Acatit COO of a Frame Brands as part 534 00:29:41,680 --> 00:29:44,600 Speaker 1: of Bloomberg's International Women's Day event. I asked them how 535 00:29:44,680 --> 00:29:47,440 Speaker 1: they cut through some of the noise, the hype, some 536 00:29:47,480 --> 00:29:50,280 Speaker 1: of the warriors as well. Take a listen. I think 537 00:29:50,280 --> 00:29:52,080 Speaker 1: if the answer is to actually have more diversity on 538 00:29:52,120 --> 00:29:55,239 Speaker 1: your investing team, because there's a certain attraction that some 539 00:29:55,280 --> 00:29:59,200 Speaker 1: people who are more interested in certain sectors, more interested 540 00:29:59,200 --> 00:30:04,600 Speaker 1: in the heart any object will be attracted Tom. And meanwhile, 541 00:30:04,600 --> 00:30:06,840 Speaker 1: they may ignore a very large sector such as family 542 00:30:06,880 --> 00:30:10,160 Speaker 1: tech for example. Yes, that is a huge market that 543 00:30:10,240 --> 00:30:13,960 Speaker 1: impacts you know, uh, you know, billions and billions of lives. 544 00:30:14,000 --> 00:30:17,840 Speaker 1: That has as much potential and so for me personally, 545 00:30:17,920 --> 00:30:22,160 Speaker 1: spending time on an industry like crypto or CHATSBT, there's 546 00:30:22,160 --> 00:30:24,920 Speaker 1: plenty or AI. I mean, there's plenty of investors who 547 00:30:24,960 --> 00:30:27,520 Speaker 1: are going to follow that, and I think the value 548 00:30:27,600 --> 00:30:30,720 Speaker 1: is had in finding large markets that are underserved. Yeah, 549 00:30:30,760 --> 00:30:33,840 Speaker 1: which I think also Starry is also going back to 550 00:30:33,920 --> 00:30:37,360 Speaker 1: even like the old tech adages of problem solution, like 551 00:30:37,480 --> 00:30:40,800 Speaker 1: we need to actually solve problems for people as opposed 552 00:30:40,800 --> 00:30:42,560 Speaker 1: to like as opposed to kind of getting caught up 553 00:30:42,600 --> 00:30:46,080 Speaker 1: in the hype. What about some of the ethics around that? Yes, hum, 554 00:30:46,080 --> 00:30:49,200 Speaker 1: when it comes to bias and how are you thinking, Oh, 555 00:30:49,280 --> 00:30:54,120 Speaker 1: I'm frightened. Yes, because AI is only as good as 556 00:30:54,160 --> 00:30:57,240 Speaker 1: what you put into it, and it's humans have bias 557 00:30:57,280 --> 00:31:00,440 Speaker 1: and so they bias is going to be rolled into it. 558 00:31:00,600 --> 00:31:03,200 Speaker 1: And actually often when I talk about bias, when in 559 00:31:03,240 --> 00:31:06,920 Speaker 1: building structures and orgs, I think biases like gravity, it's 560 00:31:06,920 --> 00:31:08,880 Speaker 1: something that you can you can't see, It's like it 561 00:31:08,920 --> 00:31:11,280 Speaker 1: just exists, and you have to figure out how if 562 00:31:11,280 --> 00:31:12,720 Speaker 1: you want to be able to fly, you want to 563 00:31:12,760 --> 00:31:15,320 Speaker 1: defy gravity, you need to build a whole bunch of things. 564 00:31:15,400 --> 00:31:17,959 Speaker 1: You need to build a plane that is intentional towards 565 00:31:18,000 --> 00:31:20,800 Speaker 1: being able to do that. And so if you want 566 00:31:20,800 --> 00:31:22,720 Speaker 1: to be able to try and remove bias from things. 567 00:31:22,760 --> 00:31:24,560 Speaker 1: You have to build a whole bunch of stuff and 568 00:31:24,640 --> 00:31:27,560 Speaker 1: be intentional on drawing that out. And I think especially 569 00:31:27,600 --> 00:31:31,000 Speaker 1: as hype is building, people are not being as not 570 00:31:31,160 --> 00:31:35,560 Speaker 1: slowing down just to be as intentional about it, which 571 00:31:35,600 --> 00:31:41,240 Speaker 1: is worrisome. Also, lujenis general partner Initialized Capital and Avanta 572 00:31:41,400 --> 00:31:44,120 Speaker 1: Ratgi their COO of a famous brams and ed the 573 00:31:44,200 --> 00:31:47,080 Speaker 1: reason Avanta's point of view is so important when it 574 00:31:47,080 --> 00:31:49,760 Speaker 1: comes to bias when it comes to building new businesses 575 00:31:49,840 --> 00:31:53,720 Speaker 1: is she is a proud trans woman of color and 576 00:31:53,920 --> 00:31:57,080 Speaker 1: she was very happy to go there to this vulnerable 577 00:31:57,080 --> 00:31:59,000 Speaker 1: space with us of what it was like being a 578 00:31:59,000 --> 00:32:01,200 Speaker 1: man in the room to them being a woman in 579 00:32:01,240 --> 00:32:03,960 Speaker 1: the room when trying to discuss the building of businesses. 580 00:32:04,200 --> 00:32:07,400 Speaker 1: She now is all about trying to create new brands 581 00:32:07,640 --> 00:32:11,640 Speaker 1: that serve a diverse population to have that bias stripped 582 00:32:11,640 --> 00:32:14,360 Speaker 1: out from the very beginning. If anyone is going to 583 00:32:14,360 --> 00:32:17,120 Speaker 1: be attuned to what AI She's also helps build virtual worlds, 584 00:32:17,120 --> 00:32:18,840 Speaker 1: for example, in the world of fashion, she's going to 585 00:32:18,840 --> 00:32:20,440 Speaker 1: be attuned to what's some of the ethics that we 586 00:32:20,520 --> 00:32:21,960 Speaker 1: need in place ahead of it. What I took from 587 00:32:21,960 --> 00:32:24,920 Speaker 1: that is why the Bloomberg New Voices initiatives. So important 588 00:32:25,080 --> 00:32:27,480 Speaker 1: is to bring the people that are actually having the 589 00:32:27,480 --> 00:32:31,360 Speaker 1: conversation into the room together. But two really core tenets 590 00:32:31,480 --> 00:32:34,800 Speaker 1: of venture capital and founders. What is a problem that 591 00:32:34,800 --> 00:32:37,640 Speaker 1: we're trying to solve and what's the technological solution? And 592 00:32:37,680 --> 00:32:40,520 Speaker 1: then how do you build a company? And they just 593 00:32:40,560 --> 00:32:43,640 Speaker 1: cut through the noise as you said, Ai Crypto. No, 594 00:32:43,800 --> 00:32:47,320 Speaker 1: actually it's a problem solution, make a company to solve it. 595 00:32:47,320 --> 00:32:50,200 Speaker 1: I think that's terrific. And Alses I mean, background is phenomenal, 596 00:32:50,280 --> 00:32:52,680 Speaker 1: having been GC over at the Founders found but then 597 00:32:52,680 --> 00:32:56,920 Speaker 1: has gone on to work on She's written personal checks 598 00:32:57,000 --> 00:33:00,080 Speaker 1: to space sex. She has had real success in some 599 00:33:00,120 --> 00:33:03,320 Speaker 1: of the companies which is built and invested in within 600 00:33:03,560 --> 00:33:05,920 Speaker 1: We've got to remember initialized eighty percent led by women. 601 00:33:06,280 --> 00:33:08,600 Speaker 1: The New Voices scheme so loud and proud about it 602 00:33:08,600 --> 00:33:10,280 Speaker 1: here at Bloomberg. If you want to get media trained, 603 00:33:10,360 --> 00:33:12,280 Speaker 1: come in and understand a little bit more right And 604 00:33:12,320 --> 00:33:14,240 Speaker 1: if you know someone out there in the industry, come 605 00:33:14,280 --> 00:33:16,760 Speaker 1: to us, tell us about them and then we will 606 00:33:16,800 --> 00:33:19,920 Speaker 1: talk to them. Now coming up a new TikTok trend 607 00:33:20,160 --> 00:33:24,760 Speaker 1: about the rising costs of dating, racking up millions of views. 608 00:33:25,360 --> 00:33:41,080 Speaker 1: That's next. This is Bloomberg. Democratic Representative Rocana has represented 609 00:33:41,120 --> 00:33:44,720 Speaker 1: Silicon Valley since twenty seventeen. Now he's working to look 610 00:33:44,800 --> 00:33:49,240 Speaker 1: broaden the US technology focus of San Francisco. Kanna was 611 00:33:49,320 --> 00:33:51,480 Speaker 1: a key player in the passage of the Chipsack to 612 00:33:51,520 --> 00:33:54,840 Speaker 1: jump start semiconductor production in the ninety teenth States. Now 613 00:33:54,880 --> 00:33:58,360 Speaker 1: he has a two trillion dollar pitch tree vitalize US 614 00:33:58,440 --> 00:34:02,520 Speaker 1: manufacturing in what he calls economic patriotism. Is how he's 615 00:34:02,560 --> 00:34:06,959 Speaker 1: framing it, a new economic patriotism. It simply means that 616 00:34:07,600 --> 00:34:12,000 Speaker 1: the private sector can work with universities, community colleges, labor, 617 00:34:12,280 --> 00:34:16,400 Speaker 1: the government to have a common purpose of investing in 618 00:34:16,440 --> 00:34:20,719 Speaker 1: America's production capability. What I think we need to do 619 00:34:20,840 --> 00:34:24,120 Speaker 1: is look at regions that have been decimated, be investrialized, 620 00:34:24,400 --> 00:34:26,360 Speaker 1: say what are their assets? How can we have a 621 00:34:26,400 --> 00:34:29,040 Speaker 1: moon shot there? How can we have place based policy 622 00:34:29,040 --> 00:34:34,759 Speaker 1: for economic revitalization. What some of your proudest accomplishments so far? 623 00:34:35,320 --> 00:34:37,200 Speaker 1: It was early the chips in Shigne Tech that I 624 00:34:37,280 --> 00:34:43,080 Speaker 1: helped draft, led to two Intel factories coming to Ohio, 625 00:34:43,280 --> 00:34:47,480 Speaker 1: led to microd being in Upstate New York with almost 626 00:34:47,480 --> 00:34:51,080 Speaker 1: one hundred billion dollar investment. I believe for us to 627 00:34:51,160 --> 00:34:54,400 Speaker 1: have the next generation of manufacturing, we need new technology, 628 00:34:54,760 --> 00:34:59,600 Speaker 1: We need immigrants, we need a educated workforce, we need 629 00:34:59,640 --> 00:35:03,239 Speaker 1: the skill trades and vocational education, and we need government financing. 630 00:35:03,400 --> 00:35:06,080 Speaker 1: And this can be a common national purpose, the economic 631 00:35:06,400 --> 00:35:10,000 Speaker 1: revitalization of the country. I've said we need a chip 632 00:35:10,040 --> 00:35:14,440 Speaker 1: SAT every year for many different industries. This can't be 633 00:35:14,480 --> 00:35:18,440 Speaker 1: a one off. Now, both parties recognize that the gutting 634 00:35:18,719 --> 00:35:21,279 Speaker 1: of factory towns was a huge mistake. The offshoring of 635 00:35:21,320 --> 00:35:25,640 Speaker 1: production was a huge mistake. The economic disparity is polarizing America. 636 00:35:25,880 --> 00:35:29,080 Speaker 1: I've called for a two trillion dollar investment over ten years. 637 00:35:29,080 --> 00:35:31,560 Speaker 1: The chip sacked was about fifty billion. We need to 638 00:35:31,640 --> 00:35:34,600 Speaker 1: have the same urgency, the same scale to build this 639 00:35:34,680 --> 00:35:38,800 Speaker 1: production base. It is a difficult argument to convince people 640 00:35:38,920 --> 00:35:42,080 Speaker 1: on this spending. I have not gotten people to buy 641 00:35:42,120 --> 00:35:44,160 Speaker 1: into the two trillion dollar investment. I did the bill 642 00:35:44,239 --> 00:35:47,319 Speaker 1: with Marco Rubio. He's good with twenty billion investment, which 643 00:35:47,360 --> 00:35:49,879 Speaker 1: is a start, but I will continue to make it 644 00:35:50,440 --> 00:35:52,120 Speaker 1: that we have to go big and bold on this 645 00:35:54,080 --> 00:35:57,719 Speaker 1: great report coming from our colleague Kalas Shader, and it 646 00:35:57,840 --> 00:36:00,799 Speaker 1: is notable that we're trying to see this focus in California. 647 00:36:00,920 --> 00:36:06,719 Speaker 1: Of yes, technology has been the heartbeat center, particularly from 648 00:36:06,719 --> 00:36:09,319 Speaker 1: a revenue perspective of anything about taxes. But they have 649 00:36:09,360 --> 00:36:12,120 Speaker 1: to diversify. And you think about California, I also think 650 00:36:12,480 --> 00:36:15,200 Speaker 1: of the ports. I also think of infrastructure. I also 651 00:36:15,200 --> 00:36:17,440 Speaker 1: think of rebuilding, and I think of a global supply chain. 652 00:36:17,480 --> 00:36:19,480 Speaker 1: There's up in arms. It's a really good point. How 653 00:36:19,520 --> 00:36:21,160 Speaker 1: much time have I spent down at the Port of 654 00:36:21,200 --> 00:36:23,319 Speaker 1: Los Angeles in the last two years. But you know 655 00:36:23,320 --> 00:36:26,720 Speaker 1: when I talked to founders and I talked to vcs whatever, 656 00:36:26,840 --> 00:36:29,640 Speaker 1: that they kind of want to protect the brain trust 657 00:36:29,719 --> 00:36:32,879 Speaker 1: coming out of the universities here. There's also a societal 658 00:36:33,320 --> 00:36:35,840 Speaker 1: discussion around living in San Francisco, the Bay Area, how 659 00:36:35,920 --> 00:36:38,719 Speaker 1: difficult it can be. But the conclusion they will come 660 00:36:38,760 --> 00:36:40,200 Speaker 1: to is we need to invest a bit more money. 661 00:36:40,520 --> 00:36:43,239 Speaker 1: Money talks, doesn't it all right? Let's pivot to a 662 00:36:43,320 --> 00:36:48,839 Speaker 1: new trend gaining traction. Guess where TikTok dating spreeze. It's 663 00:36:48,880 --> 00:36:51,560 Speaker 1: beginning to make sense why dating apps are testing those 664 00:36:51,600 --> 00:36:54,840 Speaker 1: premium subscriptions, some of them upwards of five hundred dollars 665 00:36:54,840 --> 00:36:58,680 Speaker 1: per month. TikTokers are racking up millions of views, breaking 666 00:36:58,680 --> 00:37:01,640 Speaker 1: down the high cost of dating. Some went on twenty 667 00:37:01,640 --> 00:37:06,400 Speaker 1: eight dates in twenty eight days to show how inflation 668 00:37:06,520 --> 00:37:09,759 Speaker 1: has impacted their love life. Yes, I did say that. 669 00:37:09,800 --> 00:37:12,960 Speaker 1: One event went on twenty eight bind date dates to 670 00:37:13,080 --> 00:37:16,839 Speaker 1: raise money for charity. Now Here to explain Bloomberg's misser 671 00:37:16,920 --> 00:37:23,160 Speaker 1: Lena A Kufapulu, missy, what is this all about? Well ed? 672 00:37:23,200 --> 00:37:26,200 Speaker 1: If you're looking at for examples about how TikTok is 673 00:37:26,320 --> 00:37:29,440 Speaker 1: changing the quests for love, we have one for you. 674 00:37:29,440 --> 00:37:32,200 Speaker 1: You mentioned it. One thirty one year old girl in 675 00:37:32,200 --> 00:37:34,799 Speaker 1: New York City decided in the month of February to 676 00:37:34,920 --> 00:37:38,160 Speaker 1: go on twenty eight dates in twenty eight days, and 677 00:37:38,239 --> 00:37:41,919 Speaker 1: of course, naturally she chronicled all of those days on 678 00:37:41,960 --> 00:37:46,080 Speaker 1: her social media platforms. She gained thousands of followers in 679 00:37:46,120 --> 00:37:48,960 Speaker 1: the process, and all of them as she was doing that. 680 00:37:49,560 --> 00:37:52,440 Speaker 1: She told it in an interview at Bloomberg that they 681 00:37:52,480 --> 00:37:55,640 Speaker 1: were asking the same question how much is this costing you? 682 00:37:55,840 --> 00:37:58,719 Speaker 1: And who is doing the pain? Which I think is 683 00:37:58,719 --> 00:38:00,640 Speaker 1: a key question for a lot of people looking for 684 00:38:00,719 --> 00:38:06,480 Speaker 1: love right now. In most cases, was it shed, did 685 00:38:06,480 --> 00:38:09,640 Speaker 1: they break it down, did they joint me pay? What 686 00:38:09,760 --> 00:38:12,560 Speaker 1: was the up? And they come out out of all 687 00:38:12,560 --> 00:38:15,600 Speaker 1: of this well, Caroline, I think the answer to that 688 00:38:15,680 --> 00:38:18,160 Speaker 1: is it depends on who you ask. It's definitely a 689 00:38:18,280 --> 00:38:21,640 Speaker 1: question that a lot of people are passionate about different cultures, 690 00:38:21,640 --> 00:38:24,640 Speaker 1: and different people have different opinions about it. So we 691 00:38:24,680 --> 00:38:28,040 Speaker 1: spoke to one etiquette expert who basically said the weight 692 00:38:28,200 --> 00:38:31,759 Speaker 1: of the cost should fall on whoever is doing the asking, 693 00:38:31,880 --> 00:38:34,000 Speaker 1: And so if you're not able to afford a day 694 00:38:34,000 --> 00:38:37,880 Speaker 1: you're asking someone out on, then maybe think of other alternative, 695 00:38:37,920 --> 00:38:40,399 Speaker 1: cheaper dates that you can go to, because they really 696 00:38:40,440 --> 00:38:43,440 Speaker 1: can wrap up in the hundreds of dollars per the 697 00:38:43,440 --> 00:38:46,200 Speaker 1: TikTok's that we saw this one year thirty one year 698 00:38:46,239 --> 00:38:48,920 Speaker 1: old post on her profile. And so really, if you're 699 00:38:48,960 --> 00:38:52,880 Speaker 1: asking someone out and Caroline, make sure you're the one pay. 700 00:38:53,520 --> 00:38:56,000 Speaker 1: And when they're doing the TikTok aft, is they using 701 00:38:56,360 --> 00:38:59,279 Speaker 1: the bold glamor filter that therefore makes you look nothing 702 00:38:59,320 --> 00:39:01,719 Speaker 1: like you actually do in real life too, So that's 703 00:39:01,760 --> 00:39:04,360 Speaker 1: the key question for all of it. Bloomberg's Israeliane Egokofa, 704 00:39:04,400 --> 00:39:15,840 Speaker 1: who thank you so much for joining us now Netflix. 705 00:39:16,040 --> 00:39:19,160 Speaker 1: It's going viral on several fronts. Let's start with one 706 00:39:19,160 --> 00:39:21,600 Speaker 1: of the Murdock murders in case you missed it, This week, 707 00:39:21,640 --> 00:39:25,200 Speaker 1: a jury convicted Alec Murdock, a prominent South Carolina lawyer, 708 00:39:25,239 --> 00:39:27,160 Speaker 1: of murdering his wife and son back in June twenty 709 00:39:27,200 --> 00:39:30,440 Speaker 1: twenty one, as he was sentenced to life in prison. 710 00:39:30,800 --> 00:39:33,399 Speaker 1: Now here's the other kind of unnursing part about all 711 00:39:33,400 --> 00:39:36,480 Speaker 1: of this. Murdoch was convicted just days after a doctor 712 00:39:36,600 --> 00:39:40,600 Speaker 1: series on the matter was released on Murdflix. And this 713 00:39:40,680 --> 00:39:44,920 Speaker 1: is where sort of art starts to reflect what's happening 714 00:39:44,960 --> 00:39:46,960 Speaker 1: in the here and the now. Yes, from a long 715 00:39:47,000 --> 00:39:49,160 Speaker 1: time we've wondered when streaming companies will sort of getting 716 00:39:49,120 --> 00:39:52,759 Speaker 1: into news or real time events, and it is they are, 717 00:39:52,800 --> 00:39:54,840 Speaker 1: but it's also kind of not new. And my mind 718 00:39:54,920 --> 00:39:57,520 Speaker 1: goes to Tiger King and bear with me, but remember 719 00:39:57,560 --> 00:39:59,880 Speaker 1: the sort of media hype around it, and then the question, 720 00:40:00,080 --> 00:40:02,640 Speaker 1: So that that Doctor series raised about some of the characters. 721 00:40:03,040 --> 00:40:07,080 Speaker 1: Tiger King was a massive one for Netflix. You see 722 00:40:07,080 --> 00:40:10,399 Speaker 1: it there on the screen and it induced follow ones, 723 00:40:10,480 --> 00:40:13,440 Speaker 1: and that's not it. They're also thinking about a really 724 00:40:13,480 --> 00:40:19,040 Speaker 1: old school, old fashioned concept live television and also timing 725 00:40:19,080 --> 00:40:22,040 Speaker 1: of releases. For example, we're going to get Chris Rock. 726 00:40:22,520 --> 00:40:25,520 Speaker 1: He's going to be coming with his stand up comedy 727 00:40:25,600 --> 00:40:29,080 Speaker 1: routine to Netflix exactly the week before the Academy Awards 728 00:40:29,120 --> 00:40:32,120 Speaker 1: the Oscars, which of course was so famed for the slap, 729 00:40:32,280 --> 00:40:35,000 Speaker 1: and they're doing it live. Who would have thought it? Yeah, 730 00:40:35,040 --> 00:40:37,759 Speaker 1: it's what March fourth that it's coming? Yeah, a week 731 00:40:38,480 --> 00:40:41,439 Speaker 1: the Oscars next week. Meanwhile, that doesn't for this edition 732 00:40:41,440 --> 00:40:44,799 Speaker 1: of Bloombog Technology, don't forget a lot to recap incredible 733 00:40:44,880 --> 00:40:49,520 Speaker 1: week the two of us together. Check out the podcast Apple, Spotify, iHeart, 734 00:40:49,560 --> 00:40:50,760 Speaker 1: wherever you get your podcast