1 00:00:01,800 --> 00:00:04,400 Speaker 1: This is Bloomberg Business Week. I'm Carol Masser and I'm 2 00:00:04,440 --> 00:00:07,280 Speaker 1: Bloomberg Quick Takes Tim Stanibek. We're here every day bringing 3 00:00:07,280 --> 00:00:09,799 Speaker 1: you the latest news from the world of business and finance, 4 00:00:09,840 --> 00:00:13,600 Speaker 1: plus technology, politics, economics, all harnessing the power of Business 5 00:00:13,640 --> 00:00:17,119 Speaker 1: Week reporters and editors, not to mention our journalists and 6 00:00:17,120 --> 00:00:19,600 Speaker 1: analyst in more than one and twenty countries. You can 7 00:00:19,640 --> 00:00:23,200 Speaker 1: download Bloomberg Business Week and iTunes, SoundCloud, or Bloomberg dot Com. 8 00:00:23,400 --> 00:00:25,120 Speaker 1: You can also listen to our radio show at two 9 00:00:25,160 --> 00:00:27,840 Speaker 1: pm Eastern Time on Bloomberg Radio, or watch us on 10 00:00:27,880 --> 00:00:34,320 Speaker 1: YouTube search Bloomberg Global News. Everyone's talking about those new 11 00:00:34,320 --> 00:00:36,720 Speaker 1: findings in the UK that how the COVID I ten 12 00:00:36,840 --> 00:00:39,600 Speaker 1: vaccines are providing a high level of protection against infection 13 00:00:39,640 --> 00:00:42,680 Speaker 1: and illness after a single dose. I mean, there's implications 14 00:00:42,680 --> 00:00:45,240 Speaker 1: of this, there are, but it's great news that you know. Look, 15 00:00:45,320 --> 00:00:47,159 Speaker 1: this is happening in real time. We've talked about this 16 00:00:47,159 --> 00:00:50,480 Speaker 1: a lot totally. These vaccines are used under emergency use authorization. 17 00:00:50,560 --> 00:00:53,240 Speaker 1: It means they haven't been tested in the same way 18 00:00:53,240 --> 00:00:55,920 Speaker 1: that all vaccines are usually tested over a year's long period. 19 00:00:56,160 --> 00:00:57,880 Speaker 1: So we are learning things now that we didn't know 20 00:00:57,960 --> 00:01:00,040 Speaker 1: yet write the data sets continue to come out, So 21 00:01:00,120 --> 00:01:03,279 Speaker 1: let's get into it with Dr Sandro Galleia. He's back 22 00:01:03,320 --> 00:01:05,920 Speaker 1: with Ustina, professor at Boston University School of Public Health, 23 00:01:05,959 --> 00:01:08,679 Speaker 1: author of the upcoming book The Contagion Next Time that 24 00:01:08,800 --> 00:01:11,120 Speaker 1: is due out in the fall. Dr Galia is so 25 00:01:11,240 --> 00:01:13,280 Speaker 1: nice to have you back with us. Um, how are you. 26 00:01:14,600 --> 00:01:16,560 Speaker 1: I'm wel Carol, thank you for having me to hear 27 00:01:16,600 --> 00:01:18,440 Speaker 1: from you again. Well, it's great to hear from you 28 00:01:18,520 --> 00:01:20,720 Speaker 1: as well. So tell us about this news out of 29 00:01:20,760 --> 00:01:23,360 Speaker 1: the UK, and I think there's you know what this 30 00:01:23,440 --> 00:01:29,640 Speaker 1: might mean in terms of asymptomatic infection and you know, 31 00:01:29,840 --> 00:01:32,880 Speaker 1: transmission of COVID. I mean, these are things that are 32 00:01:32,920 --> 00:01:35,000 Speaker 1: that are coming out. Tell us about the significance of 33 00:01:35,040 --> 00:01:38,160 Speaker 1: this or what you see maybe as a significance. Yeah, 34 00:01:38,240 --> 00:01:42,039 Speaker 1: this vaccine has been consistently remarkable, really vaccine news. I 35 00:01:42,040 --> 00:01:44,120 Speaker 1: mean we have we shouldn't forget as a world that 36 00:01:44,200 --> 00:01:46,840 Speaker 1: we have achieved something extraordinary in terms of vaccines available 37 00:01:47,040 --> 00:01:49,440 Speaker 1: in such a short period of time. The latest news 38 00:01:49,600 --> 00:01:53,279 Speaker 1: shows that the single dose of the vaccine is highly 39 00:01:53,320 --> 00:01:57,080 Speaker 1: efficacious in terms of preventing symptomatic COVID, but also I'll 40 00:01:57,080 --> 00:02:00,320 Speaker 1: get to in a second, preventing severe COVID and really 41 00:02:00,360 --> 00:02:02,720 Speaker 1: makes a difference, right because we have been we have 42 00:02:02,840 --> 00:02:06,520 Speaker 1: been accounting for vaccination rates assuming that everybody is getting 43 00:02:06,520 --> 00:02:08,679 Speaker 1: two vaccines. But if we can have a lot of 44 00:02:08,720 --> 00:02:11,000 Speaker 1: people getting one vaccine, that will allow us to do 45 00:02:11,080 --> 00:02:14,240 Speaker 1: things much faster. On the Fiser vaccine front, news also 46 00:02:14,280 --> 00:02:16,800 Speaker 1: came out that the Fiser vaccine can be kept in 47 00:02:16,800 --> 00:02:19,080 Speaker 1: a regular freezer, doesn't have to be a minus a 48 00:02:19,120 --> 00:02:22,000 Speaker 1: DE freezer, which from a supply chain point of view 49 00:02:22,000 --> 00:02:26,480 Speaker 1: makes a big difference. So this really creates opportunities for 50 00:02:26,639 --> 00:02:31,880 Speaker 1: national governments to stretch the vaccine much further then we 51 00:02:31,919 --> 00:02:33,799 Speaker 1: have done so far, because of course, as you know, 52 00:02:34,360 --> 00:02:38,080 Speaker 1: our limitation has been the manufacturing that we have not 53 00:02:38,120 --> 00:02:40,360 Speaker 1: been able to manufacture and provide as many vaccines as 54 00:02:40,400 --> 00:02:43,280 Speaker 1: we need to vaccinate everybody as quickly as possible. So 55 00:02:43,520 --> 00:02:46,320 Speaker 1: this is really all good. And you also Carrel mentioned 56 00:02:46,320 --> 00:02:51,240 Speaker 1: the variance and the data are that this vaccine appears 57 00:02:51,240 --> 00:02:54,360 Speaker 1: to be explicacious as well against the UK variant. Now 58 00:02:54,360 --> 00:02:57,320 Speaker 1: there's still some questions about the South African variant, but 59 00:02:57,560 --> 00:03:00,440 Speaker 1: this is all this is all positive know. So much 60 00:03:00,440 --> 00:03:03,200 Speaker 1: of the early data that we see come from countries 61 00:03:03,280 --> 00:03:06,760 Speaker 1: using the visor BioNTech vaccine, But we don't see many 62 00:03:06,760 --> 00:03:10,040 Speaker 1: of these early results showing what's going on with the 63 00:03:10,080 --> 00:03:12,919 Speaker 1: MODERNA vaccine, which is the vaccine that so many Americans 64 00:03:13,520 --> 00:03:17,639 Speaker 1: have been getting. Should we expect that we'd see similar 65 00:03:17,720 --> 00:03:22,040 Speaker 1: results because they're both MR and A vaccines, I think so. 66 00:03:22,160 --> 00:03:24,519 Speaker 1: I think I think you can for a variet of reasons. 67 00:03:24,520 --> 00:03:27,560 Speaker 1: One is the very similar mechanism, as you say, and 68 00:03:27,560 --> 00:03:29,959 Speaker 1: and the data that we do have actually showed that 69 00:03:30,000 --> 00:03:32,160 Speaker 1: the two words very similarly. We didn't talk in the 70 00:03:32,160 --> 00:03:36,240 Speaker 1: first round about severe COVID. So what's the most amazing 71 00:03:36,240 --> 00:03:40,240 Speaker 1: thing about these vaccines is that they have essentially reduced 72 00:03:40,280 --> 00:03:43,720 Speaker 1: severe COVID dramatically, even much more dramatic than a sinmtematic COVID. 73 00:03:43,880 --> 00:03:46,280 Speaker 1: And ultimately that's what we want, right, Ultimately we do 74 00:03:46,360 --> 00:03:50,360 Speaker 1: not want COVID to be severe. So all the vaccine 75 00:03:50,400 --> 00:03:52,760 Speaker 1: trials out there, there really has not been a single 76 00:03:53,120 --> 00:03:55,760 Speaker 1: death from COVID in anybody who receives vaccine. And that's 77 00:03:55,840 --> 00:03:59,200 Speaker 1: in all the five vaccines that have been whose trials, 78 00:03:59,240 --> 00:04:01,960 Speaker 1: factory trials, and they're so far so all these vaccines 79 00:04:02,000 --> 00:04:05,680 Speaker 1: really are highly effective against reducing what we want them 80 00:04:05,720 --> 00:04:08,880 Speaker 1: to reduce, which is severe COVID, right, And that's what 81 00:04:09,000 --> 00:04:14,520 Speaker 1: it ultimately is about. Do you have um confidence by 82 00:04:14,600 --> 00:04:21,080 Speaker 1: spring by summer that we will have her immunity. Well, 83 00:04:21,160 --> 00:04:22,960 Speaker 1: confidence is a tough thing to come by and things 84 00:04:23,000 --> 00:04:27,159 Speaker 1: like that these days, I think, I think the best 85 00:04:27,360 --> 00:04:31,120 Speaker 1: models out there, including the cdseas on models, suggests that 86 00:04:31,200 --> 00:04:36,120 Speaker 1: we are will be getting towards towards enough immunity that 87 00:04:36,240 --> 00:04:39,280 Speaker 1: essentially reduces rapid transmission. Of avoiding the term herd immunity 88 00:04:39,279 --> 00:04:42,600 Speaker 1: because it's become bit of a loaded term um by 89 00:04:42,600 --> 00:04:45,080 Speaker 1: by summer. Now, whether or not that means by the 90 00:04:45,160 --> 00:04:46,760 Speaker 1: end of spring, beginning of summer, or by the middle 91 00:04:46,760 --> 00:04:48,840 Speaker 1: of summer, late summer, I think that's very hard to say. 92 00:04:48,880 --> 00:04:51,520 Speaker 1: I think the big caveat that everybody says, when you 93 00:04:51,560 --> 00:04:54,200 Speaker 1: think about this, and all the good models say correctly 94 00:04:54,880 --> 00:04:58,800 Speaker 1: that that assumes the transmissibility of the virus as it 95 00:04:58,839 --> 00:05:01,680 Speaker 1: is now, which means it doesn't take into account if 96 00:05:01,720 --> 00:05:04,279 Speaker 1: any of the variants take over and they're much more transmissible, 97 00:05:04,279 --> 00:05:06,360 Speaker 1: because of course that changes the whole formula. You were 98 00:05:06,400 --> 00:05:10,400 Speaker 1: hesitant about using the word herd immunity. Tell me why well, 99 00:05:10,440 --> 00:05:12,800 Speaker 1: I think that term, like many other terms, as we've 100 00:05:12,839 --> 00:05:16,279 Speaker 1: discussed on the show before, has become politicized. It has somehow. 101 00:05:16,560 --> 00:05:19,120 Speaker 1: I mean, it's a term that has been used in 102 00:05:19,320 --> 00:05:22,600 Speaker 1: um infectiously epitemology for a long time simply to mean 103 00:05:23,080 --> 00:05:25,040 Speaker 1: they wouldn't have a population where there are not enough 104 00:05:25,080 --> 00:05:28,240 Speaker 1: people who are susceptible to the disease that it stops transmission. 105 00:05:28,440 --> 00:05:31,880 Speaker 1: But the terms has acquired these political undertones of well, 106 00:05:31,920 --> 00:05:35,040 Speaker 1: are we letting people get the disease intentionally, which obviously 107 00:05:35,200 --> 00:05:37,120 Speaker 1: is not what it means. And all it means is 108 00:05:37,560 --> 00:05:39,880 Speaker 1: you don't have enough people who are susceptible and the 109 00:05:40,000 --> 00:05:44,120 Speaker 1: virus stops transmitting. Hey, Dr galia Um. Look, these vaccines 110 00:05:44,160 --> 00:05:45,680 Speaker 1: have been out for for more than two months at 111 00:05:45,760 --> 00:05:47,839 Speaker 1: least here in the US. More and more people are 112 00:05:47,920 --> 00:05:50,920 Speaker 1: getting vaccinated. Don't you think that people can start to 113 00:05:50,960 --> 00:05:54,280 Speaker 1: act differently after they've been vaccinated, given how affective these 114 00:05:54,360 --> 00:05:58,160 Speaker 1: vaccines are. Yeah, that's an excellent question, and this has 115 00:05:58,200 --> 00:06:01,160 Speaker 1: been I think controversial lately. You know, I think what 116 00:06:01,279 --> 00:06:05,000 Speaker 1: we're seeing is a lag in our guidance and in 117 00:06:05,120 --> 00:06:07,640 Speaker 1: our systems that are have been put in place to 118 00:06:07,720 --> 00:06:10,960 Speaker 1: protect us with the reality of a post vaccination world. 119 00:06:11,279 --> 00:06:14,120 Speaker 1: And and we've seen some some more and more people writing, well, 120 00:06:14,320 --> 00:06:17,120 Speaker 1: you know, if people are vaccinated, is it not reasonable 121 00:06:17,240 --> 00:06:19,320 Speaker 1: that whence you're vaccine you can expect to re enter 122 00:06:19,360 --> 00:06:22,160 Speaker 1: the world much more fully? The four answer is yes absolutely. 123 00:06:22,200 --> 00:06:25,120 Speaker 1: The short answer is yes absolutely. The problem is the problem. 124 00:06:25,560 --> 00:06:28,000 Speaker 1: The problem is that we don't have enough people vaccinated 125 00:06:28,120 --> 00:06:30,920 Speaker 1: yet to be able to say defectively. All right, I'm 126 00:06:30,920 --> 00:06:33,320 Speaker 1: gonna leave it there. Thank you so much, really appreciate it. 127 00:06:33,400 --> 00:06:36,320 Speaker 1: Dr Sandra Gala doing at the Boston University School of 128 00:06:36,360 --> 00:06:38,520 Speaker 1: Public Health joining us on the phone from Boston. That 129 00:06:38,560 --> 00:06:40,279 Speaker 1: was an important point you made. It's a big question 130 00:06:40,320 --> 00:06:42,000 Speaker 1: that so many of my friends have right now. Right 131 00:06:42,160 --> 00:06:43,920 Speaker 1: it's like our parents are getting vaccinated, when are we 132 00:06:43,920 --> 00:06:45,159 Speaker 1: going to be able to see them? When can our 133 00:06:45,200 --> 00:06:47,159 Speaker 1: kids see their grandparents? Well, and this concern about can 134 00:06:47,200 --> 00:06:50,280 Speaker 1: you get the vaccine but still transmit COVID, it's a key. 135 00:06:50,480 --> 00:06:54,479 Speaker 1: This is Bloomberg Business Week with Carol Masser and Bloomberg 136 00:06:54,560 --> 00:06:58,720 Speaker 1: Quick Takes Tim Stinovic from Bloomberg Radio. This story it 137 00:06:58,880 --> 00:07:01,440 Speaker 1: is still among Burg Business Week, or I should say 138 00:07:01,440 --> 00:07:03,400 Speaker 1: this is still among the most read on the Bloomberg 139 00:07:03,560 --> 00:07:06,640 Speaker 1: terminal and it's by Bloomberg business Week future writer and 140 00:07:06,960 --> 00:07:10,160 Speaker 1: New York Times bestselling author Ashley Vance. He wrote this story. 141 00:07:10,440 --> 00:07:13,240 Speaker 1: He's also host of Hello World and author of Elon Musk, 142 00:07:13,280 --> 00:07:16,040 Speaker 1: Tesla SpaceX, and The Quest for a Fantastic Future. He's 143 00:07:16,080 --> 00:07:18,760 Speaker 1: on the phone from Palo Alto, California. He joins us 144 00:07:18,760 --> 00:07:21,720 Speaker 1: along with Bloomberg business Week nitor Joel Weber, And it's 145 00:07:21,720 --> 00:07:24,520 Speaker 1: about this twenty seven year old who actually, you know, 146 00:07:24,640 --> 00:07:26,320 Speaker 1: living at home with your parents, Joel can be a 147 00:07:26,360 --> 00:07:30,200 Speaker 1: good thing. Yeah, And and Ashley, um, I thought, did 148 00:07:30,280 --> 00:07:34,680 Speaker 1: this amazing job explaining about this twenty seven year old 149 00:07:34,680 --> 00:07:39,720 Speaker 1: who became a COVID nineteen data superstar. And actually, why 150 00:07:39,720 --> 00:07:42,119 Speaker 1: don't you just take us away here? What's his story 151 00:07:42,160 --> 00:07:46,280 Speaker 1: and what's so significant amount? What do you what he's done? Yeah, 152 00:07:46,640 --> 00:07:48,680 Speaker 1: I mean, this is an interesting story that kind of 153 00:07:48,720 --> 00:07:51,640 Speaker 1: pulled me back to the early days of the pandemic. 154 00:07:51,760 --> 00:07:54,640 Speaker 1: But there's a data scientist. His name is Yu Yangu 155 00:07:55,040 --> 00:07:58,280 Speaker 1: and like you mentioned, he was twenty six at the time. 156 00:07:58,320 --> 00:08:00,320 Speaker 1: He's twenty seven now. But you know, if you go 157 00:08:00,440 --> 00:08:05,000 Speaker 1: back to March in April, we were getting all these 158 00:08:05,080 --> 00:08:08,600 Speaker 1: these forecasts for how bad COVID might end up being. 159 00:08:08,720 --> 00:08:11,920 Speaker 1: And there was Imperial College in London that was putting 160 00:08:11,920 --> 00:08:15,120 Speaker 1: out a prominent forecast, and h M E which is 161 00:08:15,160 --> 00:08:19,040 Speaker 1: an institute based in Seattle. And these these forecasts were 162 00:08:19,040 --> 00:08:21,280 Speaker 1: all over the place. One group said, you know, the 163 00:08:21,400 --> 00:08:24,280 Speaker 1: US alone might see two million deaths by the summer. 164 00:08:24,640 --> 00:08:27,760 Speaker 1: H M said fifty thousand deaths, and the pandemic was 165 00:08:27,760 --> 00:08:30,040 Speaker 1: going to wind down quickly. And so there's this kid, 166 00:08:30,960 --> 00:08:33,160 Speaker 1: all these models and the figures all over the place, 167 00:08:33,240 --> 00:08:36,960 Speaker 1: and he decided to get into forecasting death on his own, 168 00:08:37,080 --> 00:08:39,480 Speaker 1: and he turned out to have pretty much the most 169 00:08:39,559 --> 00:08:43,960 Speaker 1: accurate forecast all the way up through about November. All right, 170 00:08:44,040 --> 00:08:46,520 Speaker 1: So Ashley, how did good do it? What? What were 171 00:08:46,760 --> 00:08:48,640 Speaker 1: his data points? I mean, this is a guy who 172 00:08:48,760 --> 00:08:53,760 Speaker 1: understands algorithms and analytic modelings. Yeah, there's kind of two 173 00:08:53,800 --> 00:08:55,400 Speaker 1: things to it. I mean one is he looked at 174 00:08:55,440 --> 00:08:58,079 Speaker 1: the models who were out there and he said, these 175 00:08:58,520 --> 00:09:01,079 Speaker 1: even though these people were sessionals and he had no 176 00:09:01,280 --> 00:09:03,599 Speaker 1: background in this field at all, he said, you know, 177 00:09:03,640 --> 00:09:05,679 Speaker 1: they're putting too many things in their models. So I'm 178 00:09:05,679 --> 00:09:08,000 Speaker 1: going to keep mind really simple. I Am just going 179 00:09:08,040 --> 00:09:12,239 Speaker 1: to look at the death figures being reported by governments. 180 00:09:12,720 --> 00:09:16,280 Speaker 1: And then predict future deaths from that. And then he 181 00:09:16,960 --> 00:09:21,520 Speaker 1: had a skill at machine learning software and basically AI algorithms, 182 00:09:21,640 --> 00:09:24,160 Speaker 1: and so he applied that to his figures, that to 183 00:09:24,320 --> 00:09:27,400 Speaker 1: constantly tune them over time. When the real death figures 184 00:09:27,400 --> 00:09:29,559 Speaker 1: would arrive, he would compare that to his poecast and 185 00:09:29,640 --> 00:09:32,199 Speaker 1: so he kept tuning his model. So he kind of 186 00:09:32,360 --> 00:09:34,719 Speaker 1: kept it simple in some ways, but then added this 187 00:09:34,920 --> 00:09:38,719 Speaker 1: new sophistication that some other people were not using. What 188 00:09:38,920 --> 00:09:43,720 Speaker 1: can we learn about modeling here? Considering that there were 189 00:09:43,800 --> 00:09:46,880 Speaker 1: so many professional organizations actually got that that didn't get 190 00:09:46,920 --> 00:09:49,800 Speaker 1: this right, but this then twenty six year old was 191 00:09:49,880 --> 00:09:53,440 Speaker 1: able to do this much more accurately. Yeah, I mean, 192 00:09:53,480 --> 00:09:54,960 Speaker 1: I guess there are a couple of things I picked 193 00:09:55,040 --> 00:09:57,880 Speaker 1: up from my interviews. You know. One is that I 194 00:09:58,080 --> 00:10:02,160 Speaker 1: was surprised that sort of this field wasn't, you know, 195 00:10:02,800 --> 00:10:06,199 Speaker 1: already dialed in, and that there was are like tried 196 00:10:06,320 --> 00:10:09,079 Speaker 1: and true things that we we knew you could go to. 197 00:10:09,320 --> 00:10:11,960 Speaker 1: And and so you know, his argument and the argument 198 00:10:12,040 --> 00:10:14,280 Speaker 1: that some other people made to me is that is 199 00:10:14,320 --> 00:10:17,600 Speaker 1: that academics, professors, people at some of these institutes, they 200 00:10:17,720 --> 00:10:21,240 Speaker 1: tend to try and make their models more sophisticated and 201 00:10:21,360 --> 00:10:23,800 Speaker 1: nuanced by adding complexity because they're kind of trying to 202 00:10:23,840 --> 00:10:27,400 Speaker 1: show off and do something new that their peers aren't doing. 203 00:10:27,520 --> 00:10:29,599 Speaker 1: And so, you know, he went the simple route. I 204 00:10:29,679 --> 00:10:32,240 Speaker 1: was like, no, this is this is actually how you 205 00:10:32,320 --> 00:10:35,000 Speaker 1: get closer to the truth. And the other thing is that, 206 00:10:35,200 --> 00:10:39,319 Speaker 1: you know, we've seen over time that the individual models 207 00:10:39,440 --> 00:10:44,199 Speaker 1: really do not work as well as taking models together, 208 00:10:44,360 --> 00:10:47,600 Speaker 1: blending them and then looking at what that blended model 209 00:10:47,679 --> 00:10:50,160 Speaker 1: tells you. And so, you know, a big takeaway was 210 00:10:50,600 --> 00:10:53,559 Speaker 1: the next time there's a health crisis, you don't want 211 00:10:53,600 --> 00:10:57,079 Speaker 1: to play too much stock. Maybe in these these individual 212 00:10:57,160 --> 00:10:59,319 Speaker 1: models that come out in the first week or two 213 00:10:59,360 --> 00:11:02,439 Speaker 1: weeks of a crisis, you hopefully we'd be in a 214 00:11:02,480 --> 00:11:07,000 Speaker 1: better situation where there's there's numerous models right from the 215 00:11:07,040 --> 00:11:08,880 Speaker 1: get go that we can kind of blend together and 216 00:11:09,440 --> 00:11:13,160 Speaker 1: get something closer to the truth. So what what's Goog 217 00:11:13,640 --> 00:11:16,480 Speaker 1: doing now? And what what does he uh? What does 218 00:11:16,520 --> 00:11:18,800 Speaker 1: he think he'll crack next? It seems like you know 219 00:11:19,120 --> 00:11:21,439 Speaker 1: this kind of modeling, Uh, you know, you go work 220 00:11:21,480 --> 00:11:24,040 Speaker 1: at a hedge fund and make billions of dollars? Is that? 221 00:11:24,120 --> 00:11:27,280 Speaker 1: What is that in the store? Well, this was the 222 00:11:27,320 --> 00:11:29,280 Speaker 1: funny I mean it's the interesting part of the story. 223 00:11:29,280 --> 00:11:31,199 Speaker 1: I mean, he was, he was in fight it, he 224 00:11:31,320 --> 00:11:35,280 Speaker 1: was doing trading forecast, and then right right when the 225 00:11:35,280 --> 00:11:37,839 Speaker 1: parademics start, right before it started, he had decided to 226 00:11:38,440 --> 00:11:40,240 Speaker 1: do a start up with the buddy. He was going 227 00:11:40,280 --> 00:11:43,240 Speaker 1: to get into sports analytics. He was pretty kg about 228 00:11:43,280 --> 00:11:45,400 Speaker 1: telling me exactly what it was. It sounded like some 229 00:11:45,600 --> 00:11:49,120 Speaker 1: sort of like maybe sports betting thing or something like that. 230 00:11:49,440 --> 00:11:53,400 Speaker 1: And then you know, he volunteered his time during these 231 00:11:53,880 --> 00:11:57,720 Speaker 1: death forecasts and and has been sucked into the public 232 00:11:57,920 --> 00:12:01,880 Speaker 1: health world. And now he's he's getting recruited by um 233 00:12:02,120 --> 00:12:04,160 Speaker 1: all kinds of folks, and I think he's kind of 234 00:12:04,240 --> 00:12:06,160 Speaker 1: like changed the course of his life. I think he 235 00:12:06,200 --> 00:12:08,760 Speaker 1: wants to get into public health now. He's he's sort 236 00:12:08,800 --> 00:12:12,280 Speaker 1: of he's kind of averse to all the politics of 237 00:12:12,320 --> 00:12:14,280 Speaker 1: the money that didn't He just wants to be a pure, 238 00:12:14,480 --> 00:12:17,959 Speaker 1: pure data driven guy. Well, but what actually has happened 239 00:12:17,960 --> 00:12:19,680 Speaker 1: to his data sets now, because from what I understand 240 00:12:19,720 --> 00:12:23,079 Speaker 1: from your story, kind of stopped right yeah, when he 241 00:12:23,160 --> 00:12:28,800 Speaker 1: stopped the deaf forecast around October November, because everybody's numbers 242 00:12:28,840 --> 00:12:31,839 Speaker 1: started to kind of coalesce around a pretty similar place. Now, 243 00:12:32,080 --> 00:12:36,320 Speaker 1: he's forecasting basically kind of herd immunity. He's looking at 244 00:12:36,720 --> 00:12:40,199 Speaker 1: the vaccination rate, the you know, how many people have 245 00:12:40,320 --> 00:12:43,240 Speaker 1: actually caught COVID. He thinks a lot more people have 246 00:12:43,400 --> 00:12:46,600 Speaker 1: caught it than than sort of the official numbers reports. 247 00:12:46,880 --> 00:12:48,920 Speaker 1: And so he's trying to model out kind of where 248 00:12:49,000 --> 00:12:51,360 Speaker 1: we might end up. And so for the US, you know, 249 00:12:51,520 --> 00:12:54,040 Speaker 1: her immunity is kind of like a sort of a 250 00:12:54,120 --> 00:12:57,640 Speaker 1: vague nobody knows exactly what the right number is on that, 251 00:12:57,800 --> 00:13:00,240 Speaker 1: but you know, he has us in the U asked 252 00:13:00,240 --> 00:13:04,320 Speaker 1: about six of the population would either have the vaccine 253 00:13:04,400 --> 00:13:08,360 Speaker 1: or have already caught um the virus and be approaching 254 00:13:08,640 --> 00:13:12,040 Speaker 1: something like her immunity by June. All right, well, time 255 00:13:12,080 --> 00:13:17,079 Speaker 1: stamp it. Yeah, it's impressive. Was he living in the 256 00:13:17,120 --> 00:13:18,880 Speaker 1: parents basement? That's what we all We just want to 257 00:13:18,920 --> 00:13:21,679 Speaker 1: know when he did this. Yeah, he was. He was 258 00:13:21,760 --> 00:13:24,160 Speaker 1: in Santa Clara's I live out here in Silicon Valley. 259 00:13:24,160 --> 00:13:26,800 Speaker 1: We don't really have basements, but it's definitely a living room, 260 00:13:28,160 --> 00:13:30,679 Speaker 1: like a side room. Um, and yeah, so he was 261 00:13:30,760 --> 00:13:33,720 Speaker 1: staying with his parents during there. In the bulk he's 262 00:13:33,760 --> 00:13:37,160 Speaker 1: moved and moved to New York. Well, we look forward 263 00:13:37,160 --> 00:13:39,679 Speaker 1: to see what more on that next chapter. Ashley, Thank 264 00:13:39,720 --> 00:13:42,400 Speaker 1: you so much. Ashley Vance features writer Bloomberg Business Week 265 00:13:42,440 --> 00:13:44,920 Speaker 1: on the phone from Palo Alto, along with Joe Webber, 266 00:13:45,160 --> 00:13:47,240 Speaker 1: editor of Bloomberg buses this week on the remote access 267 00:13:47,360 --> 00:13:55,440 Speaker 1: from Brooklyn. This is Bloomberg Business Week with Carol Masser 268 00:13:55,679 --> 00:14:00,560 Speaker 1: and Bloomberg Quick Takes. Tim Stinovic on Bloomberg Radio. All right, 269 00:14:00,600 --> 00:14:02,360 Speaker 1: you are listening to Bloomberg Business Week. It's brought to 270 00:14:02,400 --> 00:14:05,200 Speaker 1: you by SEI, with an operating platform designed to support 271 00:14:05,240 --> 00:14:08,880 Speaker 1: all major asset classes, diverse strategies, and investment vehicles. SEI 272 00:14:09,360 --> 00:14:11,599 Speaker 1: is redefining wealth management. Learn more at se i C 273 00:14:11,760 --> 00:14:14,720 Speaker 1: dot com slash I m s. Well, there are Tim 274 00:14:14,840 --> 00:14:17,040 Speaker 1: a couple of provocative columns on China to tell you 275 00:14:17,080 --> 00:14:20,320 Speaker 1: about today. One about China's plan for cashless society and 276 00:14:20,360 --> 00:14:23,440 Speaker 1: the goal and implications of such. That's something to come, 277 00:14:23,960 --> 00:14:26,600 Speaker 1: and then something that's already here, China's massive and impressive 278 00:14:26,640 --> 00:14:29,080 Speaker 1: infrastructure and how it's really time for the US to 279 00:14:29,160 --> 00:14:31,560 Speaker 1: get worked up about it. I love these columns. Thought 280 00:14:31,600 --> 00:14:34,320 Speaker 1: provoking and let's get into it with Bloomberg New Economy 281 00:14:34,440 --> 00:14:37,880 Speaker 1: Editorial director Andy Brown. He writes about both a he 282 00:14:38,080 --> 00:14:40,400 Speaker 1: is on the phone in our New York City Bureau. Andy. 283 00:14:40,440 --> 00:14:42,360 Speaker 1: I know you're just around the corner, one floor down, 284 00:14:42,880 --> 00:14:46,160 Speaker 1: but we're all socially distancing. Great columns, And I have 285 00:14:46,280 --> 00:14:49,480 Speaker 1: to say, these two stories remind me in many ways 286 00:14:49,560 --> 00:14:52,280 Speaker 1: how China is way ahead of the US and the 287 00:14:52,360 --> 00:14:56,880 Speaker 1: rest of the world on so much already exactly. I mean, 288 00:14:57,000 --> 00:15:02,880 Speaker 1: these blackouts in Texas illustrate the perless state of US infrastructure. 289 00:15:02,960 --> 00:15:06,240 Speaker 1: And it's it's not just the power grid, it's the 290 00:15:06,320 --> 00:15:14,800 Speaker 1: transportation network. It's highways, it's railways, it's airports, it's telecommunications networks, 291 00:15:14,840 --> 00:15:17,360 Speaker 1: and and and in all these areas, one can't help 292 00:15:17,440 --> 00:15:21,280 Speaker 1: but compare and contrust with China, which is which is 293 00:15:21,360 --> 00:15:23,960 Speaker 1: streets ahead? I mean, this is this is a national 294 00:15:24,320 --> 00:15:26,040 Speaker 1: This has risen to the level I think of a 295 00:15:26,200 --> 00:15:29,360 Speaker 1: national security threat for the United States. I mean it 296 00:15:29,520 --> 00:15:35,120 Speaker 1: goes to the heart of economic competitiveness, military readiness, climate change, resilience, 297 00:15:35,520 --> 00:15:40,200 Speaker 1: innovation capacity. Um, you know, you you've got a situation 298 00:15:40,400 --> 00:15:45,080 Speaker 1: now where China has rolled out a five G network 299 00:15:45,160 --> 00:15:48,360 Speaker 1: in America hasn't really got started, and it's giving China 300 00:15:48,480 --> 00:15:51,400 Speaker 1: a jump on all of the industries of the future 301 00:15:51,480 --> 00:15:57,240 Speaker 1: from smart cities, advanced manufacturing, autonomous vehicles, and so on 302 00:15:57,400 --> 00:15:59,840 Speaker 1: and so forth. This is the way to compete with 303 00:16:00,240 --> 00:16:02,920 Speaker 1: as Joe Biden is trying to say, is we need 304 00:16:03,040 --> 00:16:05,920 Speaker 1: to invest in infrastructure or China is going to come 305 00:16:06,160 --> 00:16:08,800 Speaker 1: and eat a lunch. And actually, an area after area, 306 00:16:08,920 --> 00:16:12,480 Speaker 1: China has already eaten America's lunch. So what's the way 307 00:16:12,560 --> 00:16:16,440 Speaker 1: to do this? In Washington where at this point many 308 00:16:16,520 --> 00:16:19,560 Speaker 1: Republicans will not even acknowledge that there was a free 309 00:16:19,600 --> 00:16:22,720 Speaker 1: and fair election that elected Joe Biden. What I'm saying 310 00:16:22,840 --> 00:16:26,040 Speaker 1: is there's not broad agreement across the challenges that America 311 00:16:26,120 --> 00:16:29,560 Speaker 1: faces or perhaps the solutions to those challenges. Well, I 312 00:16:29,600 --> 00:16:32,480 Speaker 1: guess what I'm saying is is the first step is 313 00:16:32,560 --> 00:16:36,760 Speaker 1: to recognize the threat um you know, and and America 314 00:16:36,840 --> 00:16:41,000 Speaker 1: has been here before. You know, back in nineteen fifty seven, 315 00:16:41,120 --> 00:16:45,240 Speaker 1: there was this spot Nik moment right where Russia launches 316 00:16:45,400 --> 00:16:49,360 Speaker 1: this satellite that you know, that that around around around 317 00:16:49,400 --> 00:16:54,600 Speaker 1: the Earth. And suddenly the Eisenhower administration freaks out and 318 00:16:54,760 --> 00:16:59,360 Speaker 1: realizes that Russia is leaving America behind when it comes 319 00:16:59,400 --> 00:17:03,200 Speaker 1: to space, and he invests, he launches NASA, he makes 320 00:17:03,360 --> 00:17:08,160 Speaker 1: massive investments in science and technology, which ultimately give rise 321 00:17:08,240 --> 00:17:12,280 Speaker 1: to Silicon Valley and spawned the likes of Facebook and Google, 322 00:17:12,440 --> 00:17:15,720 Speaker 1: and and and so on. The danger now, as Eric 323 00:17:15,800 --> 00:17:17,840 Speaker 1: Schmidt pointed out in a in a in a piece 324 00:17:17,960 --> 00:17:20,800 Speaker 1: just the other day in the Financial Times, you know, 325 00:17:21,240 --> 00:17:25,119 Speaker 1: is that the next tech giants and the products and 326 00:17:25,240 --> 00:17:28,840 Speaker 1: the services that they produce are not going to be American. 327 00:17:28,920 --> 00:17:32,119 Speaker 1: They're going to be Chinese. Right. And of course, Eric Schmidt, 328 00:17:32,160 --> 00:17:34,560 Speaker 1: the former chief of Google, andy, you know what, why 329 00:17:34,640 --> 00:17:39,200 Speaker 1: aren't we scared enough as Americans in terms of how 330 00:17:39,280 --> 00:17:42,000 Speaker 1: advanced China is getting on a lot of things. And 331 00:17:42,080 --> 00:17:44,520 Speaker 1: they are very clear about their mission, their long term 332 00:17:44,600 --> 00:17:48,040 Speaker 1: planning to be much more advanced on a lot of 333 00:17:48,080 --> 00:17:52,080 Speaker 1: sophisticated areas and industries. It's it's a really good question. 334 00:17:52,160 --> 00:17:55,400 Speaker 1: And and and maybe it's because there wasn't this one 335 00:17:55,760 --> 00:17:59,639 Speaker 1: big dramatic event, like you know, the launch of Spotnik 336 00:17:59,720 --> 00:18:03,959 Speaker 1: that this has really crept up on on America. I mean, 337 00:18:04,000 --> 00:18:06,359 Speaker 1: I lived there for for more than twenty years, and 338 00:18:06,760 --> 00:18:08,480 Speaker 1: and so this happened. I mean, the build out of 339 00:18:08,520 --> 00:18:11,719 Speaker 1: their high speed rail network was phenomenal. I mean they 340 00:18:11,800 --> 00:18:15,120 Speaker 1: now have the most extensive high speed network in the world. 341 00:18:15,119 --> 00:18:16,920 Speaker 1: In fact, it's it's longer than the rest of the 342 00:18:16,960 --> 00:18:19,680 Speaker 1: world's network combined. And by the way, it's about to 343 00:18:19,840 --> 00:18:22,399 Speaker 1: double a game by two thousand and and and and 344 00:18:22,560 --> 00:18:25,960 Speaker 1: thirty five. And all of this was accomplished in a decade. 345 00:18:26,080 --> 00:18:28,000 Speaker 1: You know, I mean, Joe Biden was was talking to 346 00:18:28,080 --> 00:18:30,040 Speaker 1: senators the other day and was saying, you know, we 347 00:18:30,200 --> 00:18:33,160 Speaker 1: we need to pay attention to to China's railway network. 348 00:18:33,240 --> 00:18:35,160 Speaker 1: You know, well, yes, you know. He's He's a guy 349 00:18:35,240 --> 00:18:38,760 Speaker 1: that used to travel every day on a train track, 350 00:18:38,880 --> 00:18:41,679 Speaker 1: Joe between Delaware and Washington, d C. It took him 351 00:18:41,760 --> 00:18:44,359 Speaker 1: ninety minutes. If it sat on a similar lengths Traine, 352 00:18:44,640 --> 00:18:46,640 Speaker 1: it was about a hundred miles hundred miles in China. 353 00:18:46,680 --> 00:18:48,240 Speaker 1: You do that in half the time. You do it 354 00:18:48,880 --> 00:18:52,080 Speaker 1: minutes and and and it works like clockwork. Hey, Andy, 355 00:18:52,119 --> 00:18:54,159 Speaker 1: I was in Changhai a couple of years ago with 356 00:18:54,480 --> 00:18:57,600 Speaker 1: graduate school classmates, and we were there on a school 357 00:18:57,640 --> 00:19:00,919 Speaker 1: trip and some classmates of mine went to buy something 358 00:19:01,040 --> 00:19:03,239 Speaker 1: in the mall that was close to our hotel. They 359 00:19:03,280 --> 00:19:06,480 Speaker 1: had cash and credit cards, they couldn't buy something because 360 00:19:06,520 --> 00:19:09,560 Speaker 1: they didn't hadn't downloaded the app that so many people 361 00:19:09,680 --> 00:19:11,879 Speaker 1: in China used to actually pay for something so they 362 00:19:12,040 --> 00:19:15,680 Speaker 1: end up coming back empty handed. What's China doing by 363 00:19:15,720 --> 00:19:20,639 Speaker 1: eliminating cash? Right? So? Well, yeah, exactly. So for a 364 00:19:20,720 --> 00:19:24,359 Speaker 1: long time, basically there was no need uh to carry 365 00:19:24,520 --> 00:19:26,119 Speaker 1: cash at all when I when I would go out 366 00:19:26,160 --> 00:19:28,080 Speaker 1: in Shanghai. I mean, it didn't tell to me to 367 00:19:28,920 --> 00:19:30,720 Speaker 1: get to take notes. I mean you, of course you 368 00:19:30,880 --> 00:19:33,800 Speaker 1: it's everything is swiping your bar code, whatever, whatever it 369 00:19:33,920 --> 00:19:37,240 Speaker 1: is you're you're you're buying. What they're now doing is 370 00:19:37,400 --> 00:19:41,800 Speaker 1: launching and in a digital currency. They're going to do 371 00:19:41,920 --> 00:19:46,000 Speaker 1: away with notes all together. And China invented the banknote, 372 00:19:46,040 --> 00:19:48,920 Speaker 1: by the way, about fifteen hundred years ago. It was 373 00:19:48,960 --> 00:19:51,399 Speaker 1: one of the things that Marco Polo marveled about when 374 00:19:51,440 --> 00:19:53,600 Speaker 1: he traveled to China in the thirteenth century. They're going 375 00:19:53,680 --> 00:19:55,239 Speaker 1: to get rid of notes, are going to get rid 376 00:19:55,240 --> 00:19:59,240 Speaker 1: of coins. It's going to be a digital currency. And not, 377 00:19:59,440 --> 00:20:02,320 Speaker 1: by the way, at the bitcoin that that we we've 378 00:20:02,320 --> 00:20:04,440 Speaker 1: been hearing about today. I mean, this is this is 379 00:20:04,520 --> 00:20:08,720 Speaker 1: not your distributed ledger, your anonymous transaction. This is a 380 00:20:09,040 --> 00:20:12,840 Speaker 1: central bank issued currency which is going to give the 381 00:20:12,920 --> 00:20:19,320 Speaker 1: central absolute visibility into every transaction in China, every donation, 382 00:20:19,960 --> 00:20:23,199 Speaker 1: every trade. The state is going to be in your wallet. 383 00:20:23,280 --> 00:20:25,679 Speaker 1: This's this big brother like you've never had it before. 384 00:20:26,080 --> 00:20:28,960 Speaker 1: It's fascinating. And what's interesting is I feel like China 385 00:20:28,960 --> 00:20:31,680 Speaker 1: always wants to be much more part of kind of 386 00:20:31,960 --> 00:20:34,960 Speaker 1: the system, but I feel like this will seclude them potentially. Andy, 387 00:20:35,000 --> 00:20:37,680 Speaker 1: and just got about twenty five seconds. Yeah, they wanted 388 00:20:37,920 --> 00:20:39,840 Speaker 1: they of course, they want to displace the dollar. They 389 00:20:39,880 --> 00:20:42,000 Speaker 1: want to have a global currency. Nobody wants to use 390 00:20:42,000 --> 00:20:45,119 Speaker 1: a digital currency overseas with with with the knowledge that 391 00:20:45,240 --> 00:20:49,480 Speaker 1: the Chinese state has absolute visibility as eyes on it, 392 00:20:49,600 --> 00:20:51,960 Speaker 1: I don't think so. I mean, so it'll work in China, 393 00:20:52,080 --> 00:20:55,680 Speaker 1: but the internationally highly doubtful. It's fascinating. I want to 394 00:20:55,680 --> 00:20:58,680 Speaker 1: do more on this. I love this story, and uh Andy, 395 00:20:58,760 --> 00:21:01,040 Speaker 1: thank you so much. Really appreciate at it. Andy Brown 396 00:21:01,119 --> 00:21:04,200 Speaker 1: he is Bloomberg New Economy Editorial Director. Check out his 397 00:21:04,400 --> 00:21:06,600 Speaker 1: columns on The Bloomberg and at Bloomberg dot Com. We 398 00:21:06,640 --> 00:21:08,880 Speaker 1: have to talk more about this one because I think, um, 399 00:21:09,480 --> 00:21:11,680 Speaker 1: that's a big deal. Yeah, it's a huge deal. That 400 00:21:11,760 --> 00:21:14,879 Speaker 1: kind of access by the government is amazing. This is 401 00:21:14,960 --> 00:21:18,880 Speaker 1: Bloomberg Business Week with Carol Messer and Bloomberg Quick Takes. 402 00:21:19,000 --> 00:21:22,320 Speaker 1: Tim Stinovich from Bloomberg Radio. Tim, I have to say 403 00:21:22,359 --> 00:21:24,080 Speaker 1: when I read this story over the weekend, the pictures, 404 00:21:24,119 --> 00:21:25,879 Speaker 1: I had to look at them several times for it 405 00:21:26,000 --> 00:21:29,160 Speaker 1: to register about what had happened. Everybody saw that video. 406 00:21:29,240 --> 00:21:31,600 Speaker 1: I feel like at this point of the engine on 407 00:21:31,760 --> 00:21:35,240 Speaker 1: the Boeing Triple seven on fire just minutes after taking 408 00:21:35,280 --> 00:21:38,760 Speaker 1: off from Denver International Airport, right and then pieces of 409 00:21:38,920 --> 00:21:42,040 Speaker 1: it just in someone's yard, like it was just crazy. 410 00:21:42,160 --> 00:21:44,440 Speaker 1: So let's get into it and find out what's the 411 00:21:44,520 --> 00:21:46,959 Speaker 1: latest and what's going on. Let's get the latest from 412 00:21:47,000 --> 00:21:50,000 Speaker 1: Julie Johnson Aerospace Report at Bloomberg News joining us from Chicago. 413 00:21:50,000 --> 00:21:51,560 Speaker 1: And I want to mention that Boeing shares I know 414 00:21:51,680 --> 00:21:54,480 Speaker 1: we're hired earlier. And I'll take a look at the 415 00:21:54,560 --> 00:21:57,720 Speaker 1: trade raytheon though was seeing some pressure raytheon of course, 416 00:21:57,760 --> 00:22:00,080 Speaker 1: the owner of Pratt and Whitney, Julie, good to have 417 00:22:00,200 --> 00:22:03,400 Speaker 1: you here. So where are we on this story. Well, 418 00:22:04,840 --> 00:22:08,920 Speaker 1: the investigators are starting, you know, to dig into the 419 00:22:09,359 --> 00:22:14,520 Speaker 1: root causes of this and the Triple seven jets around 420 00:22:14,520 --> 00:22:17,359 Speaker 1: the world, and there weren't that many of them that 421 00:22:17,520 --> 00:22:21,600 Speaker 1: had this particular engine on them are are all grounded 422 00:22:21,720 --> 00:22:25,600 Speaker 1: right now, and crews are taking a close look at 423 00:22:25,640 --> 00:22:28,960 Speaker 1: the engines to see if there are other cracking issues. 424 00:22:29,960 --> 00:22:33,920 Speaker 1: How long do you anticipate some type of investigation such 425 00:22:33,960 --> 00:22:36,320 Speaker 1: as this lasts. When will there be some sort of 426 00:22:36,359 --> 00:22:39,440 Speaker 1: answer as to the cause of this engine failure and 427 00:22:40,240 --> 00:22:44,520 Speaker 1: other scares in the past. Oh, you know, that's that's 428 00:22:44,560 --> 00:22:48,840 Speaker 1: really good question. And these um, you know, these investigations 429 00:22:49,160 --> 00:22:52,840 Speaker 1: tend to take a long time. Uh. The f A 430 00:22:53,400 --> 00:22:57,920 Speaker 1: is just super thorough about you know, examining causes and 431 00:22:58,640 --> 00:23:00,679 Speaker 1: and you know I should just add that right now, 432 00:23:00,800 --> 00:23:04,640 Speaker 1: it's very early on UM so it it looks bad 433 00:23:04,920 --> 00:23:08,959 Speaker 1: because there have been a couple of very similar incidents 434 00:23:09,920 --> 00:23:15,080 Speaker 1: with this engine and these really old, very early Triple 435 00:23:15,160 --> 00:23:20,200 Speaker 1: seven models. UM. But yeah, it's early days, early days, 436 00:23:20,280 --> 00:23:23,840 Speaker 1: So stay tuned. Basically, I mean, what are we waiting 437 00:23:23,920 --> 00:23:27,960 Speaker 1: for now, Like what happens next? Well, I think, um, 438 00:23:28,640 --> 00:23:31,560 Speaker 1: you know, we'll we'll have to see, um, you know, 439 00:23:31,640 --> 00:23:33,560 Speaker 1: we'll have to wait for more details to come out 440 00:23:33,640 --> 00:23:36,840 Speaker 1: from the f A A and from Pratt and Whitney, 441 00:23:36,920 --> 00:23:41,160 Speaker 1: which is you know, the raytheon Um subsidiary that made 442 00:23:41,200 --> 00:23:47,840 Speaker 1: the engines and um, and so I don't know if 443 00:23:47,920 --> 00:23:51,159 Speaker 1: you you know how how deep you got into the 444 00:23:51,280 --> 00:23:54,840 Speaker 1: rabbit hole with us. I. You know, I spent a 445 00:23:55,240 --> 00:23:58,560 Speaker 1: huge chunk of my weekend on Twitter looking at you know, 446 00:23:59,200 --> 00:24:02,960 Speaker 1: photos and videos, and what became apparent very early on 447 00:24:03,880 --> 00:24:08,040 Speaker 1: was that two fan blades had broken off, and Um, 448 00:24:08,480 --> 00:24:10,920 Speaker 1: so I think the FAA is going to take a 449 00:24:11,040 --> 00:24:16,360 Speaker 1: really long, hard look at the way that these blades 450 00:24:16,520 --> 00:24:21,440 Speaker 1: are inspected, and um and so Pratt and you know, 451 00:24:21,520 --> 00:24:24,359 Speaker 1: mechanics around the world need to get into, you know, 452 00:24:24,520 --> 00:24:28,040 Speaker 1: the grain of the metal to look for very early 453 00:24:28,240 --> 00:24:31,320 Speaker 1: signs of fatigue that aren't visible to the human eye. 454 00:24:31,560 --> 00:24:35,560 Speaker 1: They use ultrasonic tools for this. It has been a 455 00:24:35,960 --> 00:24:39,480 Speaker 1: very bad few years for Boeing, to to put it mildly, 456 00:24:39,720 --> 00:24:44,000 Speaker 1: with the seven thirty seven Max disaster the subsequent grounding 457 00:24:44,400 --> 00:24:47,320 Speaker 1: of those planes. Um, what does this mean for Boeing 458 00:24:47,359 --> 00:24:50,600 Speaker 1: given that it's actually a relatively small number of planes 459 00:24:50,680 --> 00:24:52,800 Speaker 1: that are affected and seems to be less of an 460 00:24:52,840 --> 00:24:55,359 Speaker 1: issue with the plane and more of an issue with 461 00:24:55,560 --> 00:24:59,200 Speaker 1: the Pratt and Whitney engine. Yes, and I think that's 462 00:24:59,240 --> 00:25:01,520 Speaker 1: one of the reason that's why we saw Boeing sell 463 00:25:01,600 --> 00:25:04,520 Speaker 1: off this morning, and then the market sort of take 464 00:25:04,720 --> 00:25:07,480 Speaker 1: a little bit of a you know, a deep breath 465 00:25:08,400 --> 00:25:11,439 Speaker 1: and so right now, I mean, this does not help Bowing. 466 00:25:11,800 --> 00:25:15,399 Speaker 1: This really this is not great for their reputations that 467 00:25:15,680 --> 00:25:21,800 Speaker 1: the optics are horrendous. But in terms of financial liability, 468 00:25:22,960 --> 00:25:25,719 Speaker 1: at this moment, it doesn't look like this is going 469 00:25:25,760 --> 00:25:28,840 Speaker 1: to be an issue for Boeing and UM we're you know, 470 00:25:28,960 --> 00:25:32,440 Speaker 1: we're talking a very small percentage of the global fleet 471 00:25:32,600 --> 00:25:37,040 Speaker 1: that's affected. And for Pratt, I've seen estimates that maybe 472 00:25:37,680 --> 00:25:41,880 Speaker 1: you know, it's a few few cents of earnings potentially clipped. 473 00:25:42,080 --> 00:25:45,280 Speaker 1: I mean, but bottom line, Julie and I should just update. 474 00:25:45,640 --> 00:25:49,639 Speaker 1: Boeing had traded up about one it's now down about 475 00:25:49,640 --> 00:25:53,159 Speaker 1: one point four percent, and shares of raytheon owner of 476 00:25:53,200 --> 00:25:56,280 Speaker 1: pret and Whitney or down just under one percent. Um 477 00:25:56,640 --> 00:25:59,879 Speaker 1: Who ultimately is responsible though? Is it United and the 478 00:26:00,000 --> 00:26:03,240 Speaker 1: Air Force for checking on the planes? I'm just trying 479 00:26:03,280 --> 00:26:05,440 Speaker 1: to figure out where the fault might lie. Yeah, and 480 00:26:05,520 --> 00:26:08,200 Speaker 1: that's going to be a great question for the you know, 481 00:26:08,359 --> 00:26:09,840 Speaker 1: for the F A A and N T S B 482 00:26:10,040 --> 00:26:14,240 Speaker 1: as well. Now, I'm I don't know if you'll remember, 483 00:26:14,320 --> 00:26:18,160 Speaker 1: but back in another United jet was on its way 484 00:26:18,200 --> 00:26:21,560 Speaker 1: to Hawaii and had just that, you know, as engine 485 00:26:21,600 --> 00:26:27,200 Speaker 1: explode on the wing, and um, and so the root 486 00:26:27,320 --> 00:26:30,080 Speaker 1: cause looks to have been quite similar to what happened 487 00:26:30,119 --> 00:26:34,399 Speaker 1: on Saturday, and Pratt at that point was was asked 488 00:26:34,400 --> 00:26:37,320 Speaker 1: by the f A to go in and inspect all 489 00:26:37,400 --> 00:26:42,000 Speaker 1: of the fan blades um on this particular type of engine. 490 00:26:42,840 --> 00:26:47,399 Speaker 1: And so in very short order, um, we had a 491 00:26:47,600 --> 00:26:51,000 Speaker 1: japan Airlines jet have an engine failure in December that 492 00:26:51,160 --> 00:26:54,560 Speaker 1: was like this in the United plane on Saturday. So 493 00:26:55,119 --> 00:26:57,240 Speaker 1: so um, I think the f A is going to 494 00:26:57,280 --> 00:27:00,639 Speaker 1: be taking a close look at how Pratt but measure 495 00:27:00,800 --> 00:27:03,720 Speaker 1: the damage. All right, We appreciate it, Julie, Thank you 496 00:27:03,800 --> 00:27:07,119 Speaker 1: so much. Julie Johnson, Aerospace report at Bloomberg News on 497 00:27:07,280 --> 00:27:09,560 Speaker 1: the phone from Chicago's I mentioned bowing down about one 498 00:27:09,560 --> 00:27:13,480 Speaker 1: point four percent, raytheon down just under one percent? Is it? Well? What? Well, 499 00:27:13,640 --> 00:27:15,560 Speaker 1: you know, it's it's so interesting. These planes are designed 500 00:27:15,600 --> 00:27:17,920 Speaker 1: to fly on one engine, but as Alan Levin told 501 00:27:18,000 --> 00:27:20,280 Speaker 1: us on Quick Take earlier today, not when it's the 502 00:27:20,520 --> 00:27:29,159 Speaker 1: aerodynamics change so so significantly. Yeah, exactly, I'm bro Mac Journal. 503 00:27:30,240 --> 00:27:32,320 Speaker 1: Yeah but you let me drive? Oh no, no, no, no, 504 00:27:32,560 --> 00:27:36,880 Speaker 1: who's going to drug home? Please? I'll do the riding gravel. 505 00:27:37,640 --> 00:27:45,600 Speaker 1: Lets me. I want to drive, Just drive baby the 506 00:27:45,800 --> 00:27:56,920 Speaker 1: question trying. This is the drive to the Globe community. Thanks, 507 00:27:56,960 --> 00:28:00,600 Speaker 1: we'll dry up. Ja Don on Bloomberg Radio. All right, folks, 508 00:28:00,680 --> 00:28:03,480 Speaker 1: just spent eleven minutes left in today's trading session. Let's 509 00:28:03,480 --> 00:28:05,440 Speaker 1: get to the drive to the close. James chock Mark 510 00:28:05,600 --> 00:28:08,240 Speaker 1: is back with US, partner and portfolio analyst. He focuses 511 00:28:08,280 --> 00:28:11,199 Speaker 1: on tech Stox. He knows this area so well at 512 00:28:11,240 --> 00:28:13,920 Speaker 1: the asset management firm Clockwise Capital, joining us on the 513 00:28:13,960 --> 00:28:18,720 Speaker 1: phone from Miami. So is it warm in Miami? It's 514 00:28:18,720 --> 00:28:22,520 Speaker 1: a bit chilly today about Come on? That really hurts. 515 00:28:23,640 --> 00:28:25,920 Speaker 1: You're a former New Yorker, you know you gotta miss 516 00:28:25,960 --> 00:28:30,320 Speaker 1: the February. Yeah, I don't know what winter is like. 517 00:28:31,320 --> 00:28:34,440 Speaker 1: We had snow earlier. I'll be back. I'll be back, 518 00:28:34,560 --> 00:28:37,720 Speaker 1: all right. So, um, how are you and how do 519 00:28:37,760 --> 00:28:40,520 Speaker 1: you think the technology areas doing? We saw a little 520 00:28:40,520 --> 00:28:44,240 Speaker 1: bit of a pullback, some concerns once again, valuation talk. 521 00:28:44,320 --> 00:28:49,400 Speaker 1: How do you see it? However? Priced? Are we uh? Slightly? Um? 522 00:28:49,640 --> 00:28:53,160 Speaker 1: You know, we anticipated you know, somewhat of a five 523 00:28:53,240 --> 00:28:56,560 Speaker 1: to ten percent pulled back. Um, you know going in 524 00:28:57,120 --> 00:29:00,560 Speaker 1: the recent kind of lows that we've seen. So we 525 00:29:00,720 --> 00:29:02,960 Speaker 1: have been raising cash, you know, in our fund to 526 00:29:03,280 --> 00:29:07,040 Speaker 1: take advantage of any pullbacks that we do see. Because 527 00:29:07,120 --> 00:29:08,920 Speaker 1: right now, if you look at the border markets trading 528 00:29:08,920 --> 00:29:13,000 Speaker 1: about twenty three times earnings fore, you know, which is 529 00:29:13,200 --> 00:29:16,200 Speaker 1: a little lofty by historical standards, but kind of the 530 00:29:16,200 --> 00:29:18,160 Speaker 1: way we look at the border tech sector, you know, 531 00:29:18,240 --> 00:29:21,640 Speaker 1: we still want to be heavily invested because these companies 532 00:29:22,320 --> 00:29:25,640 Speaker 1: have market opportunities far bigger than we ever had before. 533 00:29:26,080 --> 00:29:30,720 Speaker 1: They don't have the barriers on geography, on infrastructure and 534 00:29:31,040 --> 00:29:34,920 Speaker 1: ability to reach consumers, and and those bigger opportunities should 535 00:29:34,960 --> 00:29:37,560 Speaker 1: yield bigger returns as we look out over the next 536 00:29:37,600 --> 00:29:40,720 Speaker 1: couple of years. So we're going to be tactical and 537 00:29:41,080 --> 00:29:44,520 Speaker 1: surgical and opportunistic on pullbacks. On the names was like, 538 00:29:45,080 --> 00:29:47,040 Speaker 1: when do you think there will be a pullback, James? 539 00:29:47,080 --> 00:29:49,280 Speaker 1: And and once there is one and you can deploy 540 00:29:49,360 --> 00:29:52,880 Speaker 1: that cash, where is it going to go? Well, we're 541 00:29:52,920 --> 00:29:55,440 Speaker 1: already seeing it, right, I mean, the nastack was what 542 00:29:55,760 --> 00:29:58,040 Speaker 1: down two percent today to an after percent? You know, 543 00:29:58,320 --> 00:30:00,880 Speaker 1: that's not a little you know, So I think we're 544 00:30:00,960 --> 00:30:02,920 Speaker 1: chipping away at it. I don't. We don't think it's 545 00:30:02,960 --> 00:30:05,960 Speaker 1: going to be a prolonged one um because of the 546 00:30:06,040 --> 00:30:08,320 Speaker 1: fact that or it's going to be any degree of 547 00:30:08,440 --> 00:30:11,400 Speaker 1: severity with what we saw last year because of the 548 00:30:11,480 --> 00:30:14,200 Speaker 1: fact that you know, these are real companies with real learnings. Yes, 549 00:30:14,520 --> 00:30:17,960 Speaker 1: some of the valuations are high, but at the same time, 550 00:30:18,080 --> 00:30:20,880 Speaker 1: you know, these are the fastest growing companies that we've 551 00:30:20,920 --> 00:30:23,760 Speaker 1: seen and you can grow into their evaluation and on 552 00:30:23,840 --> 00:30:27,000 Speaker 1: the relatively quick uh period we think and as far 553 00:30:27,080 --> 00:30:29,080 Speaker 1: as where we're putting our money kind of we put 554 00:30:29,120 --> 00:30:31,719 Speaker 1: it in three buckets. One is what is the absolutely 555 00:30:31,840 --> 00:30:35,520 Speaker 1: essential infrastructure? Those are companies like Amazon and Apple. What 556 00:30:35,680 --> 00:30:39,000 Speaker 1: are the fastest growing companies that are leveraging the cloud 557 00:30:39,080 --> 00:30:41,920 Speaker 1: and that infrastructure. You know, that's company like syntech, like 558 00:30:42,080 --> 00:30:45,440 Speaker 1: Square and a firm you also like the companies more 559 00:30:45,680 --> 00:30:49,040 Speaker 1: established ones like Nvidia. And then you have third bucket, 560 00:30:49,120 --> 00:30:52,000 Speaker 1: which is which are the legacy companies that can benefit 561 00:30:52,160 --> 00:30:56,720 Speaker 1: from added productivity and they're manufacturing and and really taking 562 00:30:56,720 --> 00:30:59,880 Speaker 1: advantage of right sizing. The companies after COVID. You know, 563 00:31:00,040 --> 00:31:05,880 Speaker 1: those are companies like llpool. It's world Pool, mhm, world Pool. 564 00:31:07,080 --> 00:31:12,000 Speaker 1: So it's it's not a traditional you know, tech stock um, 565 00:31:12,080 --> 00:31:14,880 Speaker 1: but you know what we're looking for in that third 566 00:31:14,960 --> 00:31:19,040 Speaker 1: bucket is company the legacy companies that have the opportunity 567 00:31:19,600 --> 00:31:25,200 Speaker 1: to improve their productivity through improving their manufacturing crepibility and 568 00:31:25,320 --> 00:31:27,800 Speaker 1: also coming out of COVID in a much better margin 569 00:31:27,880 --> 00:31:30,280 Speaker 1: profile than Fire. Well, it's interesting because we actually talked 570 00:31:30,320 --> 00:31:32,000 Speaker 1: to the World Pool CEO a few months ago, and 571 00:31:32,160 --> 00:31:34,400 Speaker 1: you know, we talked a lot about consumer habits and 572 00:31:34,520 --> 00:31:37,640 Speaker 1: how you know, his thinking is like, listen, things are 573 00:31:37,680 --> 00:31:40,200 Speaker 1: not going to change. Consumers are not going to change 574 00:31:40,240 --> 00:31:42,560 Speaker 1: what they've been doing for the last year overnight and 575 00:31:42,640 --> 00:31:45,720 Speaker 1: maybe not forever, you know, in terms of the focus 576 00:31:45,800 --> 00:31:47,800 Speaker 1: on home and so on and so forth. So um, 577 00:31:48,400 --> 00:31:50,080 Speaker 1: it was interesting, and I wonder how much of that 578 00:31:50,720 --> 00:31:52,520 Speaker 1: goes into your thinking about some of the names that 579 00:31:52,560 --> 00:31:56,160 Speaker 1: you want to invest in right now. A lot, because 580 00:31:56,240 --> 00:31:59,000 Speaker 1: I mean, we really think that we will not go 581 00:31:59,200 --> 00:32:01,480 Speaker 1: back to the way things work. And that's the way 582 00:32:01,800 --> 00:32:04,360 Speaker 1: kind of we're looking at the world right now because 583 00:32:04,440 --> 00:32:08,560 Speaker 1: what companies have noticed is and and and employees and 584 00:32:08,920 --> 00:32:12,040 Speaker 1: people in general have noticed that life does go on 585 00:32:12,360 --> 00:32:15,040 Speaker 1: without having to go into the office every single day, 586 00:32:15,800 --> 00:32:18,600 Speaker 1: and and businesses are able to move forward and able 587 00:32:18,640 --> 00:32:22,720 Speaker 1: to do so in a much more um profitable manner. 588 00:32:22,880 --> 00:32:24,280 Speaker 1: You know, when you don't have to say, is on 589 00:32:24,440 --> 00:32:27,400 Speaker 1: office space and whatnot and being able to hire people 590 00:32:27,480 --> 00:32:30,440 Speaker 1: from around the country. Um, if you're not limited to 591 00:32:30,520 --> 00:32:34,760 Speaker 1: geography and and um kind of fixed costs, you know, 592 00:32:35,200 --> 00:32:37,600 Speaker 1: you can still continue to grow your business and potentially 593 00:32:37,640 --> 00:32:40,080 Speaker 1: even get more productivity out of your people since you 594 00:32:40,160 --> 00:32:42,720 Speaker 1: don't have to commute and things of that nature. So, 595 00:32:43,280 --> 00:32:44,920 Speaker 1: you know, we think that we will not go back 596 00:32:44,960 --> 00:32:47,720 Speaker 1: to the way things were. So which companies will allow you, 597 00:32:48,560 --> 00:32:51,920 Speaker 1: uh to help as a company as an individual to 598 00:32:52,040 --> 00:32:55,239 Speaker 1: better navigate this this new life, you know, coming out 599 00:32:55,320 --> 00:32:58,320 Speaker 1: of the pandemic, And we certainly think about businesses that 600 00:32:58,440 --> 00:33:00,760 Speaker 1: we invest in that way. Hey, James, what do you 601 00:33:00,800 --> 00:33:03,960 Speaker 1: think of of Snap the company hitting a hundred billion 602 00:33:03,960 --> 00:33:07,680 Speaker 1: dollars in market value? It's doubled in in four months. 603 00:33:07,680 --> 00:33:09,239 Speaker 1: I know this is a company that you followed very 604 00:33:09,280 --> 00:33:11,400 Speaker 1: closely for years. What do you think of how it's 605 00:33:11,440 --> 00:33:15,640 Speaker 1: doing right now? It's remarkable. I mean, they've they've come 606 00:33:15,760 --> 00:33:17,960 Speaker 1: such a long way. And then you know, initially I 607 00:33:18,080 --> 00:33:20,720 Speaker 1: came out positive on the company when I was on 608 00:33:21,000 --> 00:33:24,520 Speaker 1: the cuth side giving recommendations on these docks, but um, 609 00:33:24,680 --> 00:33:26,480 Speaker 1: you know, it took a while for them to gain 610 00:33:26,520 --> 00:33:29,280 Speaker 1: their traction and obviously you had Facebook company with them, 611 00:33:29,560 --> 00:33:31,800 Speaker 1: but at the end of the day, the whole case 612 00:33:31,960 --> 00:33:36,719 Speaker 1: behind it, and including companies like Interest and whatnot, but UM, 613 00:33:37,040 --> 00:33:40,000 Speaker 1: the case behind it was that you can there's two 614 00:33:40,040 --> 00:33:42,440 Speaker 1: ways to monetize that advertising. You know, you can either 615 00:33:42,520 --> 00:33:44,680 Speaker 1: go super broad with a lot of users and do 616 00:33:44,800 --> 00:33:47,840 Speaker 1: a lot of campaigns like that, or you can have 617 00:33:48,000 --> 00:33:52,000 Speaker 1: a niche product with a very very higher engagement rate 618 00:33:52,400 --> 00:33:56,240 Speaker 1: and monetize investments. And that was the direction that's Snap went. 619 00:33:56,840 --> 00:33:59,080 Speaker 1: And you know, they have a they have a good 620 00:33:59,160 --> 00:34:02,200 Speaker 1: user base of grown a user based and and covid 621 00:34:02,240 --> 00:34:06,680 Speaker 1: has also benefited UM then UM from that standpoint, and 622 00:34:07,320 --> 00:34:08,880 Speaker 1: because of the R O I s and are improving 623 00:34:08,920 --> 00:34:11,839 Speaker 1: on the tech platform at platforms. You know that that's 624 00:34:11,840 --> 00:34:14,719 Speaker 1: a company that has certainly sezed on the opportunity that 625 00:34:14,800 --> 00:34:17,880 Speaker 1: they originally had. Following the I P O I got 626 00:34:17,920 --> 00:34:19,600 Speaker 1: two questions and I only have time for once. I'm 627 00:34:19,640 --> 00:34:21,960 Speaker 1: just gonna go with it. We've got the twileto CFO 628 00:34:22,200 --> 00:34:24,680 Speaker 1: coming on later on. This stock has been on a 629 00:34:24,800 --> 00:34:27,840 Speaker 1: tear this year last year. Thirty seconds, what would you 630 00:34:27,880 --> 00:34:34,919 Speaker 1: ask him? UM? I would ask him on the let's 631 00:34:34,920 --> 00:34:37,680 Speaker 1: see on the kind of the analytics and then the 632 00:34:37,800 --> 00:34:40,719 Speaker 1: customer relationship management fronts kind of you know, they made 633 00:34:40,760 --> 00:34:45,520 Speaker 1: some acquisitions to boost UH in that regard beyond just 634 00:34:45,719 --> 00:34:48,960 Speaker 1: their communication capabilities, but also how do I improve the 635 00:34:49,000 --> 00:34:53,719 Speaker 1: productivity in house? UM, and for for the companies that 636 00:34:53,960 --> 00:34:56,560 Speaker 1: are their customers and being able to interact with their 637 00:34:56,560 --> 00:34:59,200 Speaker 1: customers and be smarter about how they interact with the 638 00:34:59,480 --> 00:35:03,640 Speaker 1: customers before. How are they helping their customers leverage the data? 639 00:35:03,880 --> 00:35:06,840 Speaker 1: Cool stuff. UM, I'll tell you the next question, the 640 00:35:06,920 --> 00:35:09,200 Speaker 1: second question next time. When you come back, you gotta 641 00:35:09,239 --> 00:35:12,520 Speaker 1: come back. James, stay warm, enjoy. James Chock Mock partner 642 00:35:12,560 --> 00:35:14,879 Speaker 1: and technology analyst O our Clockwise Capital, on the phone 643 00:35:14,920 --> 00:35:19,640 Speaker 1: from a seventy degrees warmy Bommy with Miami. I'm a 644 00:35:19,719 --> 00:35:25,720 Speaker 1: little bitter. Thanks for listening to Bloomberg Business Week. Download 645 00:35:25,719 --> 00:35:29,000 Speaker 1: the podcast on iTunes, SoundCloud, or Bloomberg dot com, and 646 00:35:29,080 --> 00:35:30,759 Speaker 1: you can also listen to our radio show at two 647 00:35:30,800 --> 00:35:33,680 Speaker 1: pm Eastern on Bloomberg Radio or watch us on YouTube. 648 00:35:33,840 --> 00:35:35,279 Speaker 1: Search to Bloomberg Global News