1 00:00:00,440 --> 00:00:02,960 Speaker 1: This is Bloomberg Business Week. I'm Carol Masser and I'm 2 00:00:03,040 --> 00:00:05,480 Speaker 1: Jason Kelly. We're here every day bringing you the latest 3 00:00:05,519 --> 00:00:09,800 Speaker 1: news from the world's of business and finance, plus technology, politics, economics, 4 00:00:09,840 --> 00:00:13,480 Speaker 1: all harnessing the power of Bloomberg Business Week reporters and editors, 5 00:00:13,480 --> 00:00:16,759 Speaker 1: not to mention our journalists and analysts more than a 6 00:00:16,800 --> 00:00:19,520 Speaker 1: hundred and twenty countries. You can download Bloomberg Business Week 7 00:00:19,560 --> 00:00:22,439 Speaker 1: on iTunes, SoundCloud, or Bloomberg dot Com. You can also 8 00:00:22,480 --> 00:00:24,919 Speaker 1: listen to our radio show weekdays at two pm Eastern 9 00:00:25,000 --> 00:00:30,280 Speaker 1: only on Bloomberg Radio. Carol, we are keeping an eye on, obviously, 10 00:00:30,320 --> 00:00:33,279 Speaker 1: everything related to the health side of this. No one 11 00:00:33,479 --> 00:00:36,560 Speaker 1: is following it more closely at Bloomberg than Drew Armstrong. 12 00:00:36,600 --> 00:00:38,840 Speaker 1: He leaves all of our health coverage here in the US. 13 00:00:38,920 --> 00:00:42,000 Speaker 1: He is spearheading a massive team that is coming at 14 00:00:42,040 --> 00:00:46,680 Speaker 1: this from all angles. He joins us on the phone today, Drew, 15 00:00:47,280 --> 00:00:51,479 Speaker 1: how are you hey, guys? Um, greetings from the suburbs 16 00:00:51,520 --> 00:00:55,840 Speaker 1: of New York City. I'm too and well good good um. 17 00:00:55,880 --> 00:00:58,160 Speaker 1: So tell us what's the one thing we need to 18 00:00:58,200 --> 00:01:00,240 Speaker 1: know today? I mean, you're seeing all of this so 19 00:01:00,480 --> 00:01:02,720 Speaker 1: in depth. What do we need to know, based on 20 00:01:02,840 --> 00:01:06,119 Speaker 1: what we've heard from public officials, what you're hearing from 21 00:01:06,160 --> 00:01:09,399 Speaker 1: health officials, and sort of squaring that with maybe this 22 00:01:09,480 --> 00:01:13,680 Speaker 1: market enthusiasm. Yeah, And I think the big thing that 23 00:01:13,720 --> 00:01:16,319 Speaker 1: people are watching right now, and this has really been, 24 00:01:16,640 --> 00:01:20,160 Speaker 1: you know, the thing to watch all along is what 25 00:01:20,160 --> 00:01:24,400 Speaker 1: are we seeing in new US cases in the various 26 00:01:24,400 --> 00:01:27,120 Speaker 1: outbreaks here, And that's I want to be clear, you know, 27 00:01:27,760 --> 00:01:31,960 Speaker 1: looking at the number of new cases is a leading indicator, 28 00:01:32,000 --> 00:01:34,280 Speaker 1: but it's also a very imperfect one just because of 29 00:01:34,319 --> 00:01:39,640 Speaker 1: the significant problems in the US with broad comprehensive testing 30 00:01:39,680 --> 00:01:41,559 Speaker 1: for this disease you would like to do. I'm sure 31 00:01:41,600 --> 00:01:44,640 Speaker 1: a lot of your listeners have read stories, many of 32 00:01:44,680 --> 00:01:47,080 Speaker 1: them written by us, about some of the problems with 33 00:01:47,160 --> 00:01:49,320 Speaker 1: getting enough test out they are getting everybody tested and 34 00:01:49,400 --> 00:01:51,560 Speaker 1: who needs to be tested and so on and so forth. 35 00:01:51,600 --> 00:01:54,120 Speaker 1: But you know, right now, as a leading indicator, it's 36 00:01:54,200 --> 00:01:56,960 Speaker 1: kind of the best thing that we have right now. 37 00:01:57,040 --> 00:02:00,840 Speaker 1: And we've seen a number of, you know, indications that 38 00:02:00,960 --> 00:02:03,120 Speaker 1: in New York and New Jersey, which are two of 39 00:02:03,120 --> 00:02:05,680 Speaker 1: the hottest outbreaks going on in the United States, that 40 00:02:05,800 --> 00:02:09,720 Speaker 1: new infections imperfectly counted as they may be, appear to 41 00:02:09,760 --> 00:02:14,280 Speaker 1: be slowing, and that, you know is overall good news 42 00:02:14,360 --> 00:02:17,080 Speaker 1: in terms of when can some of these restrictions on 43 00:02:17,160 --> 00:02:19,600 Speaker 1: movement and on business being open and on people having 44 00:02:19,639 --> 00:02:24,120 Speaker 1: to stay home begin to be loosened. There are a 45 00:02:24,120 --> 00:02:26,919 Speaker 1: ton of caveats attached to that, but if you were 46 00:02:26,960 --> 00:02:29,160 Speaker 1: looking for good news, this does feel to be a 47 00:02:29,200 --> 00:02:31,919 Speaker 1: little bit of that right. Global cases topping one point 48 00:02:32,000 --> 00:02:35,000 Speaker 1: thirty nine million deaths succeeding seventy nine thousand. Those are 49 00:02:35,000 --> 00:02:37,560 Speaker 1: the latest numbers from John's Hopkins. You know, you speak 50 00:02:37,720 --> 00:02:43,440 Speaker 1: of um imperfect models. Drew is China the model from 51 00:02:43,480 --> 00:02:47,760 Speaker 1: a health case basis the right model to be looking 52 00:02:47,800 --> 00:02:52,320 Speaker 1: in terms of the trajectory. You know, I think there 53 00:02:52,360 --> 00:02:54,680 Speaker 1: are some things that China tells us and some things 54 00:02:54,720 --> 00:02:57,320 Speaker 1: that don't. There's there's two ways to think about, Like 55 00:02:57,320 --> 00:03:00,280 Speaker 1: when we look at what happened in China and will Hunt, 56 00:03:00,919 --> 00:03:04,400 Speaker 1: we've seen the exact theme dynamic play out in other 57 00:03:04,480 --> 00:03:06,880 Speaker 1: locations around the world. You know, I mean and I 58 00:03:07,000 --> 00:03:09,880 Speaker 1: and I and I mean that from the standpoint that 59 00:03:10,320 --> 00:03:13,119 Speaker 1: you know, there are some significant screw ups and then 60 00:03:13,320 --> 00:03:16,640 Speaker 1: problems and then consequences that happened there that has happened 61 00:03:17,240 --> 00:03:19,600 Speaker 1: everywhere else. I mean you know, first in China, we 62 00:03:19,680 --> 00:03:22,720 Speaker 1: had basically the authorities say, hey, this isn't that big 63 00:03:22,760 --> 00:03:25,280 Speaker 1: of a deal. We have this well in hand, you know, 64 00:03:25,919 --> 00:03:27,480 Speaker 1: I think they were even saying it doesn't appear to 65 00:03:27,480 --> 00:03:30,519 Speaker 1: be human human transmission. And then we entered a period 66 00:03:30,560 --> 00:03:33,399 Speaker 1: where they didn't really have enough testing capacity, and so 67 00:03:33,880 --> 00:03:36,400 Speaker 1: it seemed like cases will only rising a little bit. 68 00:03:36,440 --> 00:03:38,680 Speaker 1: But in fact we had no idea how big the 69 00:03:38,720 --> 00:03:42,000 Speaker 1: outbreak was there because they didn't have great testing capacity. 70 00:03:42,080 --> 00:03:44,680 Speaker 1: Then all of a sudden they got it. Cases exploded, 71 00:03:44,800 --> 00:03:48,000 Speaker 1: and then by the time that happened, the healthcare system 72 00:03:48,000 --> 00:03:50,360 Speaker 1: in Wuhan got overwhelmed and they had to build immense 73 00:03:50,400 --> 00:03:54,240 Speaker 1: new capacity. They had a lot of excess death. Um. 74 00:03:54,280 --> 00:03:57,800 Speaker 1: Those that's exactly what happened in Italy. Um, that's a 75 00:03:57,960 --> 00:04:00,120 Speaker 1: that's that's very similar what happened in the United States. Mean, 76 00:04:00,200 --> 00:04:02,120 Speaker 1: we had, you know, federal leadership here that was saying, 77 00:04:02,400 --> 00:04:04,600 Speaker 1: this is well contained, it's not that big of a deal, 78 00:04:04,960 --> 00:04:07,040 Speaker 1: and then we had a massive problem with testing and 79 00:04:07,040 --> 00:04:08,320 Speaker 1: then all of a sudden this thing was out of 80 00:04:08,360 --> 00:04:11,240 Speaker 1: the list. I mean, we've seen the same exact dynamic 81 00:04:11,320 --> 00:04:13,920 Speaker 1: play out. Um. What I think the lessons we can't 82 00:04:13,920 --> 00:04:17,120 Speaker 1: take away from from China. Are you know, one, they 83 00:04:17,160 --> 00:04:21,000 Speaker 1: did a lockdown that I think would never be allowed 84 00:04:21,120 --> 00:04:25,440 Speaker 1: in a democracy period. I mean, people were physically unable 85 00:04:25,480 --> 00:04:28,760 Speaker 1: to leave their homes. In many cases, there were reports 86 00:04:28,760 --> 00:04:31,080 Speaker 1: of house to house searches to find six people and 87 00:04:31,240 --> 00:04:35,040 Speaker 1: um haul them off to quarantine centers. Um. You know, 88 00:04:35,120 --> 00:04:37,800 Speaker 1: it may look similar in some respects, but I think 89 00:04:37,800 --> 00:04:39,760 Speaker 1: that there are some aspects of that that were much 90 00:04:39,800 --> 00:04:42,839 Speaker 1: more um uh severe. And you know the other issues 91 00:04:42,880 --> 00:04:45,000 Speaker 1: that we've seen reports from you know, the US intelligence 92 00:04:45,000 --> 00:04:48,920 Speaker 1: community that they appeared to significantly undercounted, um or underrepresented 93 00:04:49,440 --> 00:04:52,360 Speaker 1: the severity of the of the outbreak there. And so 94 00:04:52,480 --> 00:04:55,480 Speaker 1: I think we still have some missing pieces of information 95 00:04:55,640 --> 00:04:59,120 Speaker 1: from China, uh that we really don't know how that 96 00:04:59,200 --> 00:05:00,920 Speaker 1: it was, and that may have influenced how the world 97 00:05:00,920 --> 00:05:02,640 Speaker 1: thought about this disease as well. I mean, you know, 98 00:05:03,440 --> 00:05:06,640 Speaker 1: academics and healthcare folks, they're they're early understanding this thing 99 00:05:06,720 --> 00:05:09,240 Speaker 1: was really relied upon by what we heard coming out 100 00:05:09,279 --> 00:05:11,520 Speaker 1: of China, and it's possible that we got a very 101 00:05:11,520 --> 00:05:14,599 Speaker 1: imperfect picture of that. So, Drew, we were just talking 102 00:05:14,600 --> 00:05:17,479 Speaker 1: with our colleague Sarah Ponzac, you know, well, uh just 103 00:05:17,480 --> 00:05:19,560 Speaker 1: a few minutes ago. She drove from New York to 104 00:05:19,600 --> 00:05:22,360 Speaker 1: Florida over the past few days, and you know, painted 105 00:05:22,360 --> 00:05:25,040 Speaker 1: a picture for US of a country, as you know 106 00:05:25,240 --> 00:05:28,800 Speaker 1: very well, that is very uneven in its response. As 107 00:05:28,880 --> 00:05:32,720 Speaker 1: you talked to bureau chiefs, your reporters across the country. 108 00:05:32,920 --> 00:05:36,640 Speaker 1: What's this sense you're getting of the US response which 109 00:05:36,680 --> 00:05:41,039 Speaker 1: feels sort of checkered at best. I think there's something 110 00:05:41,080 --> 00:05:43,960 Speaker 1: that you you all have to understand about the response here. 111 00:05:43,960 --> 00:05:45,640 Speaker 1: And I and I say that I and I agree 112 00:05:45,640 --> 00:05:48,000 Speaker 1: with you as the response is checkered. But also you know, 113 00:05:48,120 --> 00:05:52,880 Speaker 1: outbreaks are local disease commission happened locally. It is a 114 00:05:52,920 --> 00:05:56,719 Speaker 1: person to person phenomenon, and so you know it is 115 00:05:56,880 --> 00:06:00,200 Speaker 1: when we talk about a US response, UM, I don't 116 00:06:00,200 --> 00:06:02,680 Speaker 1: think there should be you know, it doesn't feel like 117 00:06:02,680 --> 00:06:05,480 Speaker 1: there should be homogenevy. And that's just because that's not 118 00:06:05,480 --> 00:06:07,320 Speaker 1: how it works. I mean, neither are These are responses 119 00:06:07,360 --> 00:06:10,159 Speaker 1: that need to be locally calibrated. And one thing that 120 00:06:10,240 --> 00:06:12,000 Speaker 1: you know, there has been quite a quite a bit 121 00:06:12,040 --> 00:06:14,760 Speaker 1: of criticism of the federal government in its response to this, 122 00:06:15,040 --> 00:06:18,080 Speaker 1: But you know, a response that's appropriate in New York, 123 00:06:18,120 --> 00:06:21,520 Speaker 1: where we have hundred forty thousand almost cases confirmed so far, 124 00:06:22,000 --> 00:06:25,400 Speaker 1: maybe very very different than a what a response looks 125 00:06:25,440 --> 00:06:29,200 Speaker 1: like in Wyoming, which you know, doesn't have a massive 126 00:06:29,200 --> 00:06:32,240 Speaker 1: subway system where everybody's holding the same pole and shared 127 00:06:32,400 --> 00:06:35,960 Speaker 1: cars and you know, close confines and things like that. Um, 128 00:06:36,000 --> 00:06:40,040 Speaker 1: you know, responses are almost inevitably going to be appropriate 129 00:06:40,400 --> 00:06:43,760 Speaker 1: to the local situation, even if they do share some 130 00:06:44,360 --> 00:06:49,080 Speaker 1: um some commonalities throughout aspects of social distancing and things 131 00:06:49,120 --> 00:06:51,440 Speaker 1: like that, in order to avoid spreading cases. But in 132 00:06:51,560 --> 00:06:54,640 Speaker 1: urban location is gonna be really different than a rural one. Um, 133 00:06:54,680 --> 00:06:56,960 Speaker 1: A dense city is going to be very you know, 134 00:06:57,040 --> 00:06:59,400 Speaker 1: dense public transportation city is going to be very different 135 00:06:59,400 --> 00:07:01,960 Speaker 1: from a car are driven city. So, you know, I 136 00:07:01,960 --> 00:07:04,200 Speaker 1: think that when we talk about a patchwork, I think 137 00:07:04,240 --> 00:07:06,680 Speaker 1: in some cases, you know, that is appropriate. I think 138 00:07:06,680 --> 00:07:08,760 Speaker 1: what you don't want to see is a patchwork of 139 00:07:08,760 --> 00:07:12,120 Speaker 1: how seriously people take it or how closely they you know, 140 00:07:12,480 --> 00:07:14,880 Speaker 1: follow the expert recommendations that are being given to them 141 00:07:14,920 --> 00:07:18,000 Speaker 1: based on those circumstances. Hey, just quickly, you know you 142 00:07:18,040 --> 00:07:21,040 Speaker 1: write about treatment versus cure? Is it the race for 143 00:07:21,080 --> 00:07:23,360 Speaker 1: a treatment versus the race for a cure for the 144 00:07:23,440 --> 00:07:26,040 Speaker 1: virus helping or complicating the US and the world's ability 145 00:07:26,080 --> 00:07:29,160 Speaker 1: to get on top of all of this. Well, you know, 146 00:07:29,240 --> 00:07:31,680 Speaker 1: that's a really important dynamic that you that you bring up, 147 00:07:31,680 --> 00:07:36,880 Speaker 1: because in reality, a cure here in almost certainly does 148 00:07:36,960 --> 00:07:40,800 Speaker 1: not mean, you know, a drug that eliminates, that just 149 00:07:40,960 --> 00:07:43,320 Speaker 1: kills off the virus. We have we have cured. We 150 00:07:43,360 --> 00:07:47,040 Speaker 1: have cured in the in the true sense, one virus ever, 151 00:07:47,080 --> 00:07:50,440 Speaker 1: and that's hepatitis c UM. So it's you know, humanity 152 00:07:50,920 --> 00:07:55,000 Speaker 1: one UH viruses a whole lot more than that. However, 153 00:07:55,160 --> 00:07:58,520 Speaker 1: we're pretty good at developing vaccines, and our own immune 154 00:07:58,520 --> 00:08:01,360 Speaker 1: systems are a relatively good at at pushing back some 155 00:08:01,440 --> 00:08:03,760 Speaker 1: of these infections, and so when we talk about cure, 156 00:08:03,800 --> 00:08:06,360 Speaker 1: we should really be talking about vaccines. UM. You know, 157 00:08:06,520 --> 00:08:09,600 Speaker 1: therapeutics are are are in all likelihood going to be 158 00:08:09,640 --> 00:08:11,160 Speaker 1: the type of things that are used for people who 159 00:08:11,200 --> 00:08:14,200 Speaker 1: are sick enough to be in the hospital and need them. Um. 160 00:08:14,400 --> 00:08:17,800 Speaker 1: You don't need to be administering therapeutics UM in all 161 00:08:17,840 --> 00:08:22,920 Speaker 1: likelihood to people who are experiencing the you know, relatively mild, 162 00:08:23,040 --> 00:08:26,080 Speaker 1: if extremely unpleasant version of this. You know, the body 163 00:08:27,440 --> 00:08:30,400 Speaker 1: patients from what we've seen so far, are recovering from 164 00:08:30,400 --> 00:08:33,720 Speaker 1: this disease more or less on their own. You know, 165 00:08:33,760 --> 00:08:35,800 Speaker 1: some of those books are getting hospitalizing and are getting 166 00:08:35,800 --> 00:08:38,160 Speaker 1: supportive care, and some of that can be quite intense. 167 00:08:38,280 --> 00:08:40,760 Speaker 1: But you know, I think that when we think about 168 00:08:40,760 --> 00:08:43,480 Speaker 1: how therapeutics, what the role therapeutics are gonna play, it's 169 00:08:43,520 --> 00:08:45,480 Speaker 1: probably going to be for some of the people who 170 00:08:45,480 --> 00:08:47,880 Speaker 1: are the most most severe folks and in most in 171 00:08:47,960 --> 00:08:50,680 Speaker 1: need of medical attention. And so when you're talking to 172 00:08:50,760 --> 00:08:54,800 Speaker 1: your medical folks, Drew Armstrong, what are they saying about 173 00:08:54,840 --> 00:08:57,480 Speaker 1: this whole push within the administration or at least in 174 00:08:57,600 --> 00:09:01,160 Speaker 1: corners of the administration around this time? Llaria drug which 175 00:09:01,200 --> 00:09:04,040 Speaker 1: I will not try and pronounce, Uh, that is certainly 176 00:09:04,040 --> 00:09:08,560 Speaker 1: catching on in at least rhetorically with the president. Yeah, 177 00:09:08,600 --> 00:09:13,640 Speaker 1: so that drug and hydroxy chloroquine. It's an old malaria drug, listen. 178 00:09:13,679 --> 00:09:15,400 Speaker 1: I mean, you know, if you look around at the evidence, 179 00:09:15,400 --> 00:09:19,640 Speaker 1: it's extremely limited. Um, there haven't really been redoubts from 180 00:09:19,640 --> 00:09:21,959 Speaker 1: any meaningful trial that tells you whether or not this 181 00:09:22,080 --> 00:09:25,640 Speaker 1: thing actually works. People say, you know, hey, we gave 182 00:09:25,640 --> 00:09:29,680 Speaker 1: this to temptatients and they all got better, Um, you know, 183 00:09:29,880 --> 00:09:31,520 Speaker 1: or a lot of them got better, or you know, 184 00:09:31,679 --> 00:09:33,560 Speaker 1: six out of ten of them got better in the 185 00:09:33,600 --> 00:09:36,600 Speaker 1: reality is that most people do get better on their own. 186 00:09:36,640 --> 00:09:39,240 Speaker 1: I think the data on this has been extremely checkery. 187 00:09:39,280 --> 00:09:41,520 Speaker 1: There's been a lot of christmatism of these studies. There 188 00:09:41,520 --> 00:09:45,400 Speaker 1: has not been a large conclusive study that tests this 189 00:09:45,520 --> 00:09:49,040 Speaker 1: drug against a placebo Um. You know, President Trump has 190 00:09:49,080 --> 00:09:52,760 Speaker 1: faced pushed back from the stage by people like um, 191 00:09:52,800 --> 00:09:55,679 Speaker 1: you know Dr Tony Faucci, who's ahead of the sub 192 00:09:55,800 --> 00:09:58,360 Speaker 1: unit of the of the NH. There's just very very 193 00:09:58,360 --> 00:10:01,079 Speaker 1: little evidence right now in support is if it works, 194 00:10:01,160 --> 00:10:03,760 Speaker 1: that's fantastic news. I think, you know, one of the 195 00:10:03,800 --> 00:10:06,040 Speaker 1: things that we hear over and over and over again 196 00:10:06,080 --> 00:10:08,240 Speaker 1: from the people though who are developing these drugs is 197 00:10:08,480 --> 00:10:11,680 Speaker 1: you need to find out what actually does work. And 198 00:10:11,960 --> 00:10:14,920 Speaker 1: you know, having a system where we let everyone try 199 00:10:14,960 --> 00:10:20,040 Speaker 1: everything without any proof whatsoever muddies the waters terribly, puts 200 00:10:20,080 --> 00:10:23,160 Speaker 1: people on therapies that may or may not work, and 201 00:10:23,160 --> 00:10:26,680 Speaker 1: excludes them from therapies that may that we that actually 202 00:10:26,880 --> 00:10:29,800 Speaker 1: may and it it it makes it much harder for 203 00:10:29,880 --> 00:10:33,080 Speaker 1: us to get an answer on what works here, what 204 00:10:33,160 --> 00:10:34,800 Speaker 1: is going to help the most number of people to 205 00:10:34,880 --> 00:10:37,400 Speaker 1: get that answer quickly and then to get people onto 206 00:10:37,400 --> 00:10:39,800 Speaker 1: those drugs. So Um, there is a There is an 207 00:10:39,880 --> 00:10:43,760 Speaker 1: urging inside the scientific community to do this the right way. Um. 208 00:10:43,800 --> 00:10:45,520 Speaker 1: You know, you want to you want to give people 209 00:10:45,600 --> 00:10:47,320 Speaker 1: hope and you want to give people care, but you 210 00:10:47,360 --> 00:10:49,280 Speaker 1: also want to give people something that works and that's 211 00:10:49,320 --> 00:10:51,800 Speaker 1: backed by evidence. So we don't waste their you know, 212 00:10:51,880 --> 00:10:55,719 Speaker 1: their medical opportunities, their time, their money, all of those things, um, 213 00:10:56,000 --> 00:10:58,839 Speaker 1: while while testing you know, while while trying therapies that 214 00:10:58,920 --> 00:11:00,960 Speaker 1: don't have an effect, and right. We want to get 215 00:11:00,960 --> 00:11:02,760 Speaker 1: it right because we also don't want to see any 216 00:11:02,840 --> 00:11:05,480 Speaker 1: kind of relapse of an outbreak. You know. One thing 217 00:11:05,480 --> 00:11:07,040 Speaker 1: I want to ask you, Drew is and this was 218 00:11:07,240 --> 00:11:10,040 Speaker 1: based on a conversation Jason I had yesterday, you know 219 00:11:10,360 --> 00:11:12,440 Speaker 1: where one of our guests said, basically, we're not gonna 220 00:11:12,440 --> 00:11:15,000 Speaker 1: be able to get back to work fully or you know, 221 00:11:15,040 --> 00:11:17,559 Speaker 1: embrace kind of our social lives like we used to 222 00:11:17,720 --> 00:11:21,560 Speaker 1: like big stadiums until we get a vaccine. Is that 223 00:11:21,720 --> 00:11:26,760 Speaker 1: the case? I think that's probably you know, there are 224 00:11:26,760 --> 00:11:28,880 Speaker 1: definitely aspects to that. I think you're you know, think 225 00:11:28,920 --> 00:11:31,560 Speaker 1: about your own lives and you know, if if someone 226 00:11:31,600 --> 00:11:34,480 Speaker 1: if Governor Cuomo or President Frum said, hey, you know 227 00:11:34,640 --> 00:11:37,440 Speaker 1: we've mostly got the all clear, there's some circulating. I 228 00:11:37,440 --> 00:11:39,800 Speaker 1: don't know if you know how many people would necessarily 229 00:11:40,200 --> 00:11:42,680 Speaker 1: hop right out to a restaurant, you know, start hugging 230 00:11:42,679 --> 00:11:44,720 Speaker 1: and you know, shaking hands with people they haven't seen 231 00:11:44,760 --> 00:11:47,319 Speaker 1: in months, you know, grab the subway pole, getting a taxicab, 232 00:11:47,400 --> 00:11:48,920 Speaker 1: go back to work. I think this is going to 233 00:11:49,000 --> 00:11:52,320 Speaker 1: take time. You know. One thing that vaccines are are 234 00:11:52,400 --> 00:11:55,000 Speaker 1: good at also is you know, is creating a level 235 00:11:55,040 --> 00:11:58,160 Speaker 1: of certainty and comfort. Um. You know, people want to 236 00:11:58,200 --> 00:12:02,400 Speaker 1: know not only have we successfully pushed back this disease 237 00:12:02,440 --> 00:12:04,560 Speaker 1: into a small or smaller and smaller and smaller number 238 00:12:04,600 --> 00:12:07,920 Speaker 1: of people through the social distancing measures and the mitigation measures, 239 00:12:08,480 --> 00:12:11,079 Speaker 1: but also that you know, even if they are struck 240 00:12:11,120 --> 00:12:13,079 Speaker 1: by it, that they are you know that they or 241 00:12:13,160 --> 00:12:15,040 Speaker 1: they are exposed to it, that they're protective. I think 242 00:12:15,080 --> 00:12:19,160 Speaker 1: a vaccine is pretty crucial in all likelihood and creating 243 00:12:19,160 --> 00:12:23,440 Speaker 1: that level of confidence just from a personal comfort standpoint. 244 00:12:24,120 --> 00:12:27,800 Speaker 1: And Drew, what about this notion of the antibody test, 245 00:12:28,160 --> 00:12:30,679 Speaker 1: How realistic is that? Because that's something that a lot 246 00:12:30,679 --> 00:12:33,040 Speaker 1: of folks that I've been talking to even casually are 247 00:12:33,080 --> 00:12:35,520 Speaker 1: saying that could be something that sort of helps spur 248 00:12:35,640 --> 00:12:40,959 Speaker 1: us back to work or back into something resembling normal life. Yeah, 249 00:12:41,040 --> 00:12:44,360 Speaker 1: it's theoretically a very very interesting idea. And if it's 250 00:12:44,440 --> 00:12:47,560 Speaker 1: perfect and if it's perfectly accurate, you know, which is 251 00:12:47,600 --> 00:12:50,439 Speaker 1: not necessarily given. Let's be really clear here. You can 252 00:12:50,520 --> 00:12:53,080 Speaker 1: have all of these tests comes when tend to come 253 00:12:53,120 --> 00:12:56,360 Speaker 1: with some level of false positives or false negatives. And 254 00:12:56,440 --> 00:12:58,240 Speaker 1: one of the things that you know, critics of this 255 00:12:58,320 --> 00:13:01,600 Speaker 1: idea say is that, well, you know, if two percent 256 00:13:01,640 --> 00:13:04,080 Speaker 1: of the population has it, you know, or ten percent 257 00:13:04,080 --> 00:13:06,839 Speaker 1: of the population has it, and the false positive rate 258 00:13:06,920 --> 00:13:09,559 Speaker 1: on these antibody tests, which you know, these are detections 259 00:13:09,559 --> 00:13:11,720 Speaker 1: of did you at some point have this virus and 260 00:13:11,800 --> 00:13:14,200 Speaker 1: might you now have some level of immunity? You know, 261 00:13:14,240 --> 00:13:18,080 Speaker 1: if your rate of the disease in society is anywhere 262 00:13:18,160 --> 00:13:21,199 Speaker 1: close to the false positive rate on your trial, you 263 00:13:21,320 --> 00:13:23,720 Speaker 1: might run, you know, you might run these antibody tests 264 00:13:23,720 --> 00:13:27,360 Speaker 1: on ten thousand people and say, you know, well, hey, 265 00:13:27,480 --> 00:13:30,239 Speaker 1: we found you know, x number of people who are positives. 266 00:13:30,360 --> 00:13:32,760 Speaker 1: These people, you know, presumably they're immune, but it might 267 00:13:32,800 --> 00:13:35,000 Speaker 1: just be that, you know, those are the people who 268 00:13:35,000 --> 00:13:37,719 Speaker 1: are who are the false positives in your test, And 269 00:13:37,800 --> 00:13:40,240 Speaker 1: what you're detecting isn't people who are immune but just 270 00:13:40,440 --> 00:13:43,880 Speaker 1: erroneous test um. So I think they're going to be 271 00:13:44,040 --> 00:13:45,240 Speaker 1: you know, I think one of the things that we 272 00:13:45,520 --> 00:13:47,360 Speaker 1: people do think that they're going to be quite useful 273 00:13:47,360 --> 00:13:51,520 Speaker 1: for is measuring the prevalence of this disease, especially in 274 00:13:51,559 --> 00:13:54,760 Speaker 1: places that have had a lot of it like New York, um, 275 00:13:54,800 --> 00:13:57,040 Speaker 1: and where I think it may be, you know where 276 00:13:57,080 --> 00:13:59,079 Speaker 1: I think it. It remains to be seen how useful 277 00:13:59,120 --> 00:14:02,040 Speaker 1: it is is going to be on a purely individual basis, 278 00:14:02,120 --> 00:14:04,240 Speaker 1: And I think to do that, you're going to need 279 00:14:04,400 --> 00:14:07,880 Speaker 1: a level of certainty about the accuracy of these and 280 00:14:07,920 --> 00:14:09,960 Speaker 1: that is something that you know that that we know 281 00:14:10,040 --> 00:14:12,439 Speaker 1: people are looking at now. I mean you obviously there 282 00:14:12,520 --> 00:14:15,600 Speaker 1: is a desire to have this be as accurate as possible, 283 00:14:15,600 --> 00:14:18,320 Speaker 1: so you can get exactly the type of answers about, Hey, 284 00:14:18,400 --> 00:14:20,440 Speaker 1: do I have some level of community here? Can I 285 00:14:20,480 --> 00:14:22,680 Speaker 1: go back to work? You know? Did I have this? 286 00:14:22,760 --> 00:14:25,560 Speaker 1: And was I perhaps mildly symptomatical? Was that you know 287 00:14:25,600 --> 00:14:27,760 Speaker 1: the thing that I thought was flew back in February 288 00:14:27,800 --> 00:14:29,800 Speaker 1: in fact this Those are the types of questions that 289 00:14:29,840 --> 00:14:33,080 Speaker 1: you can get answers to if these tests are accurate enough. Right, 290 00:14:33,760 --> 00:14:36,560 Speaker 1: you are are rock star, No doubt about it. Drew Armstrong, 291 00:14:36,640 --> 00:14:38,680 Speaker 1: thank you so much. It is a must read on 292 00:14:38,720 --> 00:14:41,040 Speaker 1: the Bloomberg terminal and also at Bloomberg dot com, so 293 00:14:41,080 --> 00:14:44,080 Speaker 1: everyone should check it out. Drew Armstrong busy, busy guy 294 00:14:44,160 --> 00:14:46,560 Speaker 1: and his team. He is the team leader for US Healthcare, 295 00:14:46,560 --> 00:14:49,080 Speaker 1: a Bloomberg News joining us once again on the phone 296 00:14:49,120 --> 00:14:50,800 Speaker 1: from New York City. I always feel like I get 297 00:14:50,840 --> 00:14:53,280 Speaker 1: clarity after I talk to Drew. Listen, I've told the 298 00:14:53,360 --> 00:14:55,320 Speaker 1: entire New York hereau of this that when we look 299 00:14:55,360 --> 00:14:57,880 Speaker 1: back on this, Drew Armstrong is going to be the 300 00:14:57,920 --> 00:14:59,520 Speaker 1: hero and all of it. I mean, the way he's 301 00:14:59,560 --> 00:15:03,560 Speaker 1: coordinated to the coverage has just been unbelievable and highly 302 00:15:03,640 --> 00:15:06,680 Speaker 1: highly recommend and we talked about this, we recommended all 303 00:15:06,680 --> 00:15:09,160 Speaker 1: the time. If you go to Bloomberg dot com slash Coronavirus, 304 00:15:09,160 --> 00:15:11,840 Speaker 1: you will get all of his team's coverage and much more. Uh, 305 00:15:11,840 --> 00:15:15,560 Speaker 1: it's it's a great destination. You're listening to Bloomberg Business 306 00:15:15,640 --> 00:15:19,680 Speaker 1: Week with Carol Messer and Jason Kelly on Bloomberg Radio. 307 00:15:20,120 --> 00:15:24,160 Speaker 1: Let's talk about a story that is in the magazine. Well, 308 00:15:24,200 --> 00:15:26,960 Speaker 1: and it's not just a story, it's a book. Carol, 309 00:15:27,200 --> 00:15:30,120 Speaker 1: I can't I've been so excited for this book to 310 00:15:30,160 --> 00:15:32,600 Speaker 1: come out. Sarah Fryer wrote it. It's called No Filter, 311 00:15:32,760 --> 00:15:36,680 Speaker 1: It's about Instagram. There's a fantastic excerpt in the magazine 312 00:15:36,680 --> 00:15:40,120 Speaker 1: this week. Can't get enough of it. Uh, Sarah joins 313 00:15:40,200 --> 00:15:43,040 Speaker 1: us on the phone from San Francisco. First of all, congratulations, 314 00:15:43,040 --> 00:15:46,320 Speaker 1: this is an amazing, amazing accomplishment. How are you feeling? 315 00:15:47,720 --> 00:15:50,160 Speaker 1: I feel good? I mean it. This is what was 316 00:15:50,160 --> 00:15:53,040 Speaker 1: crazy to me about this story is just how much 317 00:15:53,080 --> 00:15:57,520 Speaker 1: of it hadn't been hadn't been uncovered, And so I'm 318 00:15:57,560 --> 00:16:00,760 Speaker 1: really excited to share the first experts today. Also feel 319 00:16:00,800 --> 00:16:03,160 Speaker 1: like there's there's so much that people will be able 320 00:16:03,160 --> 00:16:07,479 Speaker 1: to learn about, not just the tension between Instagram and Facebook, 321 00:16:07,680 --> 00:16:12,240 Speaker 1: but also Instagram's cultural impact on our world. So I 322 00:16:12,280 --> 00:16:14,200 Speaker 1: want to also bring in Joe Webber, of course, editor 323 00:16:14,240 --> 00:16:17,000 Speaker 1: Bloomberg Business Week. He's on the phone from Brooklyn. I mean, 324 00:16:17,400 --> 00:16:19,680 Speaker 1: this is such you know, it's a great we know, 325 00:16:19,840 --> 00:16:22,320 Speaker 1: a great book already, Uh, and you've got an excerpt 326 00:16:22,400 --> 00:16:24,440 Speaker 1: in the magazine, but it is such a Business Week 327 00:16:24,480 --> 00:16:27,560 Speaker 1: story as well. We've been following Instagram and Facebook and 328 00:16:27,640 --> 00:16:30,080 Speaker 1: kind of I just want to say the strife between 329 00:16:30,120 --> 00:16:35,160 Speaker 1: the two Joe. Yeah, I am so excited for Sarah. Um. 330 00:16:35,200 --> 00:16:37,640 Speaker 1: I think this book is just going to be amazing. 331 00:16:37,840 --> 00:16:40,320 Speaker 1: And I was really honored to be able to publish 332 00:16:40,600 --> 00:16:43,520 Speaker 1: um this particular excerpt because I think it really shows 333 00:16:44,520 --> 00:16:48,000 Speaker 1: a side of Instagram and Facebook that you know, we've 334 00:16:48,040 --> 00:16:51,120 Speaker 1: just no one's really seen it like this before. And 335 00:16:51,160 --> 00:16:55,040 Speaker 1: you know, Instagram was this little darling app just barely 336 00:16:55,520 --> 00:16:58,240 Speaker 1: a decade ago, and you know, it just looks like 337 00:16:58,280 --> 00:17:02,000 Speaker 1: this bargain that Mark Zuckerberg happened to pick up for 338 00:17:02,120 --> 00:17:06,120 Speaker 1: you know, pennies, and now he's turned it into a 339 00:17:06,160 --> 00:17:09,439 Speaker 1: major cash cow that is part of this family of 340 00:17:09,560 --> 00:17:12,480 Speaker 1: apps that he's built at Facebook. But in order to 341 00:17:12,520 --> 00:17:15,399 Speaker 1: get there, along the way, there was a lot of 342 00:17:15,560 --> 00:17:18,879 Speaker 1: internal stripes and tension, and that's ultimately what Sarah was 343 00:17:18,920 --> 00:17:20,879 Speaker 1: able to bring to light in this particular part of 344 00:17:20,880 --> 00:17:24,399 Speaker 1: our excerpt. And Um, Sarah, you know, like there's this 345 00:17:24,560 --> 00:17:29,119 Speaker 1: pastry that makes an appearance and I just asked you, 346 00:17:29,880 --> 00:17:35,800 Speaker 1: this is what what is uh? What is the cruffin? Oh, 347 00:17:35,840 --> 00:17:39,359 Speaker 1: It's it's the San Francisco version of the Rainbow bagel. Basically, 348 00:17:39,440 --> 00:17:44,119 Speaker 1: it's it's a it's a crispant muffin and it plays 349 00:17:45,359 --> 00:17:49,880 Speaker 1: the launch event for Instagram TV. This this launch event 350 00:17:49,960 --> 00:17:52,920 Speaker 1: was like the most Instagram a little thing possible. They 351 00:17:52,960 --> 00:17:58,520 Speaker 1: had Assay e Bols and Macho Late's and UM at 352 00:17:58,520 --> 00:18:01,520 Speaker 1: a Tistor event in New York have like champagne filled 353 00:18:01,960 --> 00:18:07,520 Speaker 1: this cotton candy. Uh really. I use that events as 354 00:18:07,520 --> 00:18:11,760 Speaker 1: an example to show the contract between Instagram and Facebook. 355 00:18:11,840 --> 00:18:17,480 Speaker 1: Instagram is really about UM presenting your life as this 356 00:18:17,480 --> 00:18:23,320 Speaker 1: this beautiful, manicured version, and Facebook is about this building 357 00:18:23,359 --> 00:18:28,960 Speaker 1: your network and having friend connections, and the philosophies of 358 00:18:29,040 --> 00:18:32,080 Speaker 1: the two products really aligned with the founders and what 359 00:18:32,200 --> 00:18:36,399 Speaker 1: kind of people they are. Zuckerberg being this dominant force 360 00:18:36,560 --> 00:18:42,200 Speaker 1: trying to win over more and more of of humanity's attention, 361 00:18:42,720 --> 00:18:46,800 Speaker 1: and Instagram trying to create a place where you know, 362 00:18:46,880 --> 00:18:52,200 Speaker 1: culture can be appreciated and people can become uh, become 363 00:18:52,200 --> 00:18:58,120 Speaker 1: recognized for their own brands and so so eventually these 364 00:18:58,119 --> 00:19:03,320 Speaker 1: two clash, and when they're announcing I DTV in twenty team, 365 00:19:03,359 --> 00:19:07,280 Speaker 1: that was a moment for System realizing that Instagram wasn't 366 00:19:07,280 --> 00:19:12,920 Speaker 1: really going to be allowed to thrive without without Zuckerberg's 367 00:19:12,960 --> 00:19:16,639 Speaker 1: intense involvement in every step of the way, which is 368 00:19:16,720 --> 00:19:19,600 Speaker 1: very different than the independence the company rejected in the past. 369 00:19:21,600 --> 00:19:25,320 Speaker 1: And and Mark Zuckerberg, I mean you really tease out 370 00:19:25,480 --> 00:19:29,560 Speaker 1: some sort of personality conflicts too, that Ivious had huge 371 00:19:29,560 --> 00:19:32,480 Speaker 1: business applications as you're alluding to, Uh, Sarah, I would 372 00:19:32,520 --> 00:19:36,000 Speaker 1: imagine there's much more in the book about this, because 373 00:19:36,040 --> 00:19:38,399 Speaker 1: that was sort of the the key point of tension 374 00:19:38,400 --> 00:19:42,960 Speaker 1: that Carol alluded to at the top of the conversation. Yeah, 375 00:19:43,000 --> 00:19:48,000 Speaker 1: Zuckerberg is is all about winning. He He's not about 376 00:19:48,080 --> 00:19:52,040 Speaker 1: you know, being careful and and having um, you know, 377 00:19:52,080 --> 00:19:54,800 Speaker 1: a lot of attention to detail and so and so 378 00:19:54,880 --> 00:19:56,840 Speaker 1: we see that play out in their product strategy. Means 379 00:19:56,880 --> 00:20:00,520 Speaker 1: Zuckerberg tried a million different things to counter the rise 380 00:20:00,520 --> 00:20:05,320 Speaker 1: of Snapchat. Instagram tried one and it worked and and 381 00:20:05,359 --> 00:20:11,320 Speaker 1: I think that uh, eventually, when Instagram's growth actually accelerated 382 00:20:11,359 --> 00:20:16,320 Speaker 1: after copying Snapchat stories, Zuckerberg saw a threat to Facebook. 383 00:20:16,720 --> 00:20:20,119 Speaker 1: He saw that the Instagram way of doing things was 384 00:20:20,440 --> 00:20:25,120 Speaker 1: gaining popularity, maybe at the expense of Facebook's longevity. And 385 00:20:25,200 --> 00:20:30,480 Speaker 1: being the dominating force that he is and really caring 386 00:20:30,520 --> 00:20:35,159 Speaker 1: about his flagship products, he started talking about cannibalization, the 387 00:20:35,240 --> 00:20:40,040 Speaker 1: idea that Instagram success would eat into Facebook potential and 388 00:20:40,200 --> 00:20:43,440 Speaker 1: started driving the fingers away right and a pact basically 389 00:20:43,520 --> 00:20:48,720 Speaker 1: his baby Facebook. Like, it's just it's really phenomenal. Um, Sarah, congratulations, 390 00:20:48,760 --> 00:20:51,000 Speaker 1: were so excited for you. The book is No Filter, 391 00:20:51,119 --> 00:20:54,320 Speaker 1: the inside story of Instagram. Check out the book, check 392 00:20:54,320 --> 00:20:57,240 Speaker 1: out the excerpt that is in the magazine. This week, 393 00:20:57,480 --> 00:21:01,080 Speaker 1: you're listening to Bloomberg Business Week with Carol Masser and 394 00:21:01,200 --> 00:21:04,879 Speaker 1: Jason Kelly on Bloomberg Radio. Well, companies, as we know, 395 00:21:04,960 --> 00:21:08,520 Speaker 1: are retrenching. We talk about cutting back on spending, letting 396 00:21:08,520 --> 00:21:11,879 Speaker 1: go of workers, stopping buy backs and conserving cash and 397 00:21:11,920 --> 00:21:14,280 Speaker 1: cutting costs in light of the virus. Here with what 398 00:21:14,320 --> 00:21:16,720 Speaker 1: that means for I T spending, the good and the bad. 399 00:21:16,800 --> 00:21:21,040 Speaker 1: Crawford del Perette his president of I DC Research Xerox 400 00:21:21,119 --> 00:21:23,439 Speaker 1: or I d C forgive me I d C, and 401 00:21:23,480 --> 00:21:27,800 Speaker 1: he's on the phone from Framingham, Massachusetts. UM, nice to 402 00:21:27,840 --> 00:21:30,000 Speaker 1: have you back with us. Crawford talked to us a 403 00:21:30,000 --> 00:21:32,800 Speaker 1: little bit about UM I T spending because we do 404 00:21:32,880 --> 00:21:36,199 Speaker 1: know companies are retrenching. Any kind of early data that 405 00:21:36,240 --> 00:21:40,760 Speaker 1: you guys are seeing, yeah, absolutely, thanks for having me, Carol. Um, 406 00:21:40,880 --> 00:21:43,639 Speaker 1: so there is some early data. I mean, this has 407 00:21:43,680 --> 00:21:46,760 Speaker 1: been an extraordinary set of circumstances that we've seen. So 408 00:21:46,800 --> 00:21:50,400 Speaker 1: we were looking at an I T market UH last year. UM, 409 00:21:50,440 --> 00:21:53,000 Speaker 1: you know, you're looking at a market that is growing 410 00:21:53,040 --> 00:21:56,080 Speaker 1: in excess of UH two years ago, excess five percent 411 00:21:56,200 --> 00:21:59,239 Speaker 1: last year, excess in n SS of almost five per 412 00:21:59,240 --> 00:22:02,080 Speaker 1: so for point eight percent in January this year, we're 413 00:22:02,119 --> 00:22:04,959 Speaker 1: forecasting a market to grow about five point one percent. 414 00:22:05,240 --> 00:22:07,680 Speaker 1: We've taken that down to the I T market will 415 00:22:07,720 --> 00:22:11,320 Speaker 1: shrink by almost three percent this year, down about two 416 00:22:11,400 --> 00:22:14,760 Speaker 1: point seven percent. And that's based on a GDP forecast 417 00:22:14,840 --> 00:22:18,080 Speaker 1: of about a two percent decline, which is not uncommon 418 00:22:18,119 --> 00:22:20,679 Speaker 1: where I T tends to UM, you know, get stalled. 419 00:22:20,720 --> 00:22:23,040 Speaker 1: There's a lot of tangible things that you can stop buying, 420 00:22:23,359 --> 00:22:27,280 Speaker 1: and then you you tend to see a slap down. UM. 421 00:22:27,359 --> 00:22:30,480 Speaker 1: There's a lot of characteristics here that are different and 422 00:22:30,840 --> 00:22:32,879 Speaker 1: UH different from say the cycle that we saw in 423 00:22:32,920 --> 00:22:35,600 Speaker 1: two thousand and eight, UH in two thousand nine, where 424 00:22:35,640 --> 00:22:37,760 Speaker 1: we think this might be a little bit less severe. 425 00:22:38,040 --> 00:22:41,919 Speaker 1: But for sure, we've seen a huge a huge readjust 426 00:22:42,000 --> 00:22:44,560 Speaker 1: and and not surprisingly we're seeing it across the board, 427 00:22:44,560 --> 00:22:46,840 Speaker 1: but of course we're seeing it in the vertical segments. 428 00:22:46,840 --> 00:22:52,280 Speaker 1: The industry segments like hospitality, transportation, um man, parts of manufacturing. 429 00:22:52,440 --> 00:22:55,199 Speaker 1: But we're also seeing it in the category you were 430 00:22:55,200 --> 00:22:57,280 Speaker 1: just talking about, which is the small and medium business, 431 00:22:57,400 --> 00:23:00,000 Speaker 1: particularly the emerging companies, where they just need a lockdown, 432 00:23:00,000 --> 00:23:03,200 Speaker 1: own all their expenses and really try to go into 433 00:23:03,200 --> 00:23:05,720 Speaker 1: survival mode a bit until we get through this well. 434 00:23:05,760 --> 00:23:07,680 Speaker 1: And survival mode is exactly where I was going to 435 00:23:07,760 --> 00:23:10,719 Speaker 1: go next, Crawford, you and spend it so beautifully. You know, 436 00:23:10,880 --> 00:23:13,320 Speaker 1: this notion that you had a lot of companies and 437 00:23:13,560 --> 00:23:15,560 Speaker 1: I mean, I think our company would fall into this 438 00:23:15,640 --> 00:23:20,240 Speaker 1: category who probably did some I wouldn't call it panic spending, 439 00:23:20,280 --> 00:23:23,400 Speaker 1: but some unexpected spending to get everybody set up and 440 00:23:23,600 --> 00:23:26,440 Speaker 1: sort of get people in a place where they could 441 00:23:26,520 --> 00:23:31,119 Speaker 1: continue operating and and if not blowing budgets, at least 442 00:23:31,160 --> 00:23:35,119 Speaker 1: reallocating things. And I wonder how long that takes to 443 00:23:35,160 --> 00:23:37,560 Speaker 1: sort out. So take us a level down and help 444 00:23:37,640 --> 00:23:41,280 Speaker 1: us understand what what companies generally are thinking around that. Yeah. 445 00:23:41,440 --> 00:23:43,600 Speaker 1: So it's a great point, Jason. And we've seen a 446 00:23:43,600 --> 00:23:46,000 Speaker 1: lot of this, right, We've seen you know, we've been 447 00:23:46,000 --> 00:23:49,280 Speaker 1: in constant communication with the end customers as well as 448 00:23:49,320 --> 00:23:52,119 Speaker 1: the intermediary companies. The companies that provide the technology and 449 00:23:52,160 --> 00:23:55,240 Speaker 1: provide the services. And we've seen that across the board, 450 00:23:55,480 --> 00:23:57,720 Speaker 1: you know, I've I've talked to large service providers that 451 00:23:57,760 --> 00:24:02,080 Speaker 1: have had to stand up health organizations in places like um, 452 00:24:02,880 --> 00:24:06,320 Speaker 1: large major cities, large government bodies where you know, over 453 00:24:06,400 --> 00:24:09,359 Speaker 1: a weekend that they need an instant you know, a 454 00:24:09,359 --> 00:24:14,200 Speaker 1: new set of laptops for you know, multiple hundreds of customers. Um. 455 00:24:14,200 --> 00:24:16,359 Speaker 1: You know, good luck if you if you're in the 456 00:24:16,400 --> 00:24:18,920 Speaker 1: market right now for a laptop as a as a 457 00:24:18,960 --> 00:24:21,280 Speaker 1: small business or a consumer. We've seen a big surge 458 00:24:21,560 --> 00:24:24,959 Speaker 1: in demand there. But interestingly, when you get underneath that 459 00:24:25,080 --> 00:24:28,280 Speaker 1: for the whole year, we actually still think that you'll 460 00:24:28,280 --> 00:24:32,880 Speaker 1: see um contraction UM in those in those kinds of segments. 461 00:24:32,880 --> 00:24:34,760 Speaker 1: We think that you know, you look at the you 462 00:24:34,760 --> 00:24:36,760 Speaker 1: look at the PC market, which it had you know, 463 00:24:36,800 --> 00:24:40,119 Speaker 1: a relatively you know, nice run. We're looking at you know, 464 00:24:40,160 --> 00:24:42,080 Speaker 1: an I T spending, you know, we we expect that 465 00:24:42,119 --> 00:24:45,399 Speaker 1: market to drop in almost including tablet's almost ten percent. 466 00:24:45,640 --> 00:24:48,720 Speaker 1: We expect the infrastructure market, you know, servers and large 467 00:24:48,760 --> 00:24:51,600 Speaker 1: storage systems that will drop by about four percent UM. 468 00:24:51,600 --> 00:24:54,840 Speaker 1: I T services that will drop between two and four percent. 469 00:24:55,160 --> 00:24:58,480 Speaker 1: The only category that will likely show growth. To your 470 00:24:58,520 --> 00:25:02,280 Speaker 1: point is interesting. It's software business. UM, that business we 471 00:25:02,359 --> 00:25:05,200 Speaker 1: expect well and that that business will just for perspective, 472 00:25:05,240 --> 00:25:07,919 Speaker 1: that business was between about nine percent growth, that'll go 473 00:25:07,960 --> 00:25:11,400 Speaker 1: down to about two growth. But again, what and this 474 00:25:11,480 --> 00:25:13,239 Speaker 1: is an interesting trend that you know, we we've been 475 00:25:13,280 --> 00:25:15,440 Speaker 1: talking about for a while, and that is that when 476 00:25:15,520 --> 00:25:18,600 Speaker 1: you start buying these things as a service, right, you 477 00:25:18,640 --> 00:25:23,320 Speaker 1: can't shut them off. You've basically bet your business as 478 00:25:23,560 --> 00:25:25,679 Speaker 1: so that basically means that you know you're in it 479 00:25:25,720 --> 00:25:27,320 Speaker 1: for a penny, you're in for a pound, and and 480 00:25:27,359 --> 00:25:29,360 Speaker 1: you're going to continue to buy these services if you're 481 00:25:29,400 --> 00:25:32,280 Speaker 1: a thing going forward, which we expect most men, many 482 00:25:32,280 --> 00:25:35,040 Speaker 1: companies today. Crawford, you're one of those individuals that we 483 00:25:35,359 --> 00:25:38,640 Speaker 1: folks at Bloomberg have been talking to for years, and 484 00:25:38,680 --> 00:25:42,240 Speaker 1: we have talked to you through various crises, whether it's 485 00:25:42,240 --> 00:25:45,680 Speaker 1: coming out of the financial crisis, you know, whether it's 486 00:25:45,720 --> 00:25:47,439 Speaker 1: after nine eleven. I mean, we have talked to you 487 00:25:47,560 --> 00:25:50,800 Speaker 1: for a long time about UM, the industry and the 488 00:25:50,800 --> 00:25:54,040 Speaker 1: tech industry generally speaking. I want to ask you, I mean, 489 00:25:54,560 --> 00:25:56,119 Speaker 1: I hope you guys are doing okay. I hope your 490 00:25:56,119 --> 00:25:58,720 Speaker 1: team is doing okay, And I'm curious how you see 491 00:25:58,760 --> 00:26:02,679 Speaker 1: this virus um changing the world. I mean, what do 492 00:26:02,720 --> 00:26:04,840 Speaker 1: you see is the most important in a way that 493 00:26:04,880 --> 00:26:08,360 Speaker 1: the world's going to be different on the other side. Yeah, well, 494 00:26:08,359 --> 00:26:10,800 Speaker 1: thanks for thanks for that, and you know, we're all 495 00:26:10,840 --> 00:26:13,280 Speaker 1: doing it is best that we can. You know, we're 496 00:26:13,359 --> 00:26:15,880 Speaker 1: we're a company a thousand people in in fifty countries 497 00:26:15,920 --> 00:26:18,000 Speaker 1: around the world, and we're all basically working from home 498 00:26:18,119 --> 00:26:20,240 Speaker 1: right now. But as far as we can tell, most 499 00:26:20,240 --> 00:26:23,359 Speaker 1: people are safe, and that's that's that's very important. So 500 00:26:23,440 --> 00:26:25,320 Speaker 1: I think that what you're going to see on the 501 00:26:25,320 --> 00:26:27,760 Speaker 1: other side is a couple of things, and some will 502 00:26:27,800 --> 00:26:30,959 Speaker 1: be very tangible and some sorry sorry very um I 503 00:26:30,960 --> 00:26:33,160 Speaker 1: think top of mind, and some maybe a little bit less. 504 00:26:33,160 --> 00:26:35,239 Speaker 1: So I think that one of the most tangible things 505 00:26:35,280 --> 00:26:38,159 Speaker 1: you'll see, the barriers are going to come down in 506 00:26:38,280 --> 00:26:42,359 Speaker 1: some things that regulation has been stopping those barriers from 507 00:26:42,400 --> 00:26:45,679 Speaker 1: coming down. I'm talking about stuff like Hella medicine. I 508 00:26:45,720 --> 00:26:48,480 Speaker 1: think you're going to start to see a scenario where 509 00:26:48,840 --> 00:26:50,760 Speaker 1: you know what, payers are going to have to get 510 00:26:50,800 --> 00:26:52,760 Speaker 1: out of the way, and if a cursing can be 511 00:26:52,800 --> 00:26:56,360 Speaker 1: diagnosed over a webcam, if if a person can can 512 00:26:56,400 --> 00:27:00,000 Speaker 1: still be part of the system and get a quality diagnosis. Um, 513 00:27:00,000 --> 00:27:03,240 Speaker 1: whether that's crossing state lines that aren't able to be 514 00:27:03,280 --> 00:27:05,879 Speaker 1: crossed today, I think those things start to change. So 515 00:27:05,880 --> 00:27:08,119 Speaker 1: I think we're gonna see a big change in in 516 00:27:08,160 --> 00:27:11,160 Speaker 1: those kinds of services going forward as we come out 517 00:27:11,240 --> 00:27:14,000 Speaker 1: the other side. I also think we're going to see 518 00:27:14,880 --> 00:27:19,159 Speaker 1: that how um the sort of people are willing to 519 00:27:19,240 --> 00:27:22,280 Speaker 1: work and where they live. I think we might see 520 00:27:22,720 --> 00:27:24,960 Speaker 1: more of a move to you know what, it's okay 521 00:27:25,040 --> 00:27:27,919 Speaker 1: if I don't commute every single day, It's okay for 522 00:27:28,000 --> 00:27:29,880 Speaker 1: me to work at home. You're not going to see 523 00:27:29,880 --> 00:27:32,280 Speaker 1: that same stigma. So I mean, I'm just gonna put 524 00:27:32,280 --> 00:27:34,680 Speaker 1: it out there and stigma associated with you know, what's 525 00:27:34,720 --> 00:27:36,800 Speaker 1: going on with that person because they're working at home. Well, 526 00:27:36,840 --> 00:27:38,959 Speaker 1: the fact of the matter is we've proven that the 527 00:27:39,000 --> 00:27:42,000 Speaker 1: world can be very productive from home, and I think 528 00:27:42,000 --> 00:27:44,200 Speaker 1: the tools are there, and I think it was it's 529 00:27:44,240 --> 00:27:47,920 Speaker 1: really about things like social norms and social etiquette when 530 00:27:47,920 --> 00:27:50,280 Speaker 1: you're in the office, to be inclusive of the people 531 00:27:50,640 --> 00:27:53,360 Speaker 1: that are not necessarily in the office. And I think 532 00:27:53,400 --> 00:27:56,800 Speaker 1: that now with new kinds of services, we're gonna be 533 00:27:56,840 --> 00:27:59,680 Speaker 1: a lot more inclusive. On the other side. I think 534 00:27:59,680 --> 00:28:02,320 Speaker 1: the one that's debatable and one that comes up a 535 00:28:02,359 --> 00:28:05,320 Speaker 1: lot is what does this do for education? And I 536 00:28:05,359 --> 00:28:09,960 Speaker 1: think for for education, I think that UM, the underserved 537 00:28:10,000 --> 00:28:12,760 Speaker 1: and the nonserved people in emerging legions around the world, 538 00:28:13,000 --> 00:28:15,359 Speaker 1: they will take advantage of these kinds of tools. But 539 00:28:15,400 --> 00:28:17,800 Speaker 1: I'm a little more cynical when it comes to the 540 00:28:17,880 --> 00:28:21,520 Speaker 1: Western education system. I think that, unfortunately, it's a system 541 00:28:21,560 --> 00:28:24,000 Speaker 1: that's based on classrooms. It's a system that was set 542 00:28:24,080 --> 00:28:27,800 Speaker 1: up for classrooms, and I just think that, UM, there's 543 00:28:27,800 --> 00:28:30,639 Speaker 1: sort of an eliteness that comes from being in that 544 00:28:30,720 --> 00:28:33,880 Speaker 1: classroom at a university, and I think we're probably gonna 545 00:28:33,880 --> 00:28:36,520 Speaker 1: fall back into that um in the future. But I 546 00:28:36,560 --> 00:28:39,280 Speaker 1: do think that on the other side, this is going 547 00:28:39,320 --> 00:28:41,880 Speaker 1: to be a moment, and it's gonna be a moment 548 00:28:41,920 --> 00:28:44,160 Speaker 1: where things are going to change. I agree with you. 549 00:28:44,280 --> 00:28:47,080 Speaker 1: I think you make some really really good points. I 550 00:28:47,120 --> 00:28:49,520 Speaker 1: hope you're right on a lot of them. Uh, And 551 00:28:49,600 --> 00:28:51,320 Speaker 1: we'd love to keep checking in with you because we 552 00:28:51,360 --> 00:28:53,920 Speaker 1: know you really have you and your team, uh, your 553 00:28:53,960 --> 00:28:56,400 Speaker 1: finger on the pulse of how we think about technology, 554 00:28:56,400 --> 00:28:58,680 Speaker 1: because you've got the data. We love the data, all right. 555 00:28:58,960 --> 00:29:01,680 Speaker 1: Crawford del Prett thank you so much, President of I 556 00:29:01,920 --> 00:29:04,280 Speaker 1: d C International Data Corp. Joining us on the phone 557 00:29:04,680 --> 00:29:12,680 Speaker 1: from Framingham, Massachusetts. I'm bro Macro a journal. Yeah, but 558 00:29:12,840 --> 00:29:14,960 Speaker 1: you let me drive? Oh no, no, no, no, who's 559 00:29:15,000 --> 00:29:18,440 Speaker 1: going to drive home? Honey? Please, I'll do the right 560 00:29:18,600 --> 00:29:25,840 Speaker 1: drivel Let me. I want to drive, Just drive baby. 561 00:29:27,720 --> 00:29:37,680 Speaker 1: The question trying This is the drive to the globe. 562 00:29:38,480 --> 00:29:42,040 Speaker 1: Give me thanks. We'll drying us down on Bloomberg Radio. 563 00:29:42,520 --> 00:29:44,600 Speaker 1: It is time for the drive to the clothes back 564 00:29:44,600 --> 00:29:47,320 Speaker 1: with us as George Mateo, chief investment officer at Key 565 00:29:47,400 --> 00:29:51,240 Speaker 1: Private Bank, joining us on the phone from Cleveland. George, 566 00:29:51,320 --> 00:29:54,000 Speaker 1: nice to have you here with us. Um, how do 567 00:29:54,000 --> 00:29:56,240 Speaker 1: you look at this market? Bear market or could we 568 00:29:56,320 --> 00:30:00,240 Speaker 1: be at the beginning of a new bull market? Well, 569 00:30:00,320 --> 00:30:02,040 Speaker 1: great to be with you, and thanks for having me back. 570 00:30:02,120 --> 00:30:04,000 Speaker 1: You know, I think it's probably good of both. I mean, 571 00:30:04,040 --> 00:30:05,600 Speaker 1: I think we're just going to be in this sideways 572 00:30:05,640 --> 00:30:08,280 Speaker 1: shop for a while. So we'll have days like today 573 00:30:08,320 --> 00:30:10,800 Speaker 1: we'll so good about things, and days will still probably 574 00:30:10,840 --> 00:30:12,520 Speaker 1: a little less good. So I think we just have 575 00:30:12,600 --> 00:30:15,760 Speaker 1: to kind of fasten our seatbelt and hold on. And 576 00:30:15,840 --> 00:30:20,720 Speaker 1: so what are you hearing from uh, customers and clients 577 00:30:20,720 --> 00:30:22,720 Speaker 1: and and one of the reasons I'm especially interested to 578 00:30:23,200 --> 00:30:26,960 Speaker 1: I think we're both interested to hear you answer that, George, 579 00:30:27,000 --> 00:30:29,000 Speaker 1: is because you're not in New York City. You're there 580 00:30:29,040 --> 00:30:33,440 Speaker 1: in in Cleveland, And obviously I think this crisis looks, uh, 581 00:30:33,720 --> 00:30:37,960 Speaker 1: while broadly the same, maybe individually locally and regionally a 582 00:30:38,000 --> 00:30:41,760 Speaker 1: little bit different. So how are how are people reacting, 583 00:30:41,760 --> 00:30:45,200 Speaker 1: how are they interacting with you? Yeah, well, I hope 584 00:30:45,240 --> 00:30:47,320 Speaker 1: you both are keeping safe too, and all your listeners 585 00:30:47,320 --> 00:30:49,360 Speaker 1: for sure. I mean, I think Ohio is a little 586 00:30:49,360 --> 00:30:50,720 Speaker 1: bit of as a curve. It seems like we've had 587 00:30:50,760 --> 00:30:53,640 Speaker 1: a pretty progressive government get out in front of this, 588 00:30:53,760 --> 00:30:55,720 Speaker 1: but I think everybody is still in the same level 589 00:30:55,760 --> 00:30:58,800 Speaker 1: anxiety where wherever you live, uh, And that's just kinda 590 00:30:58,840 --> 00:31:01,480 Speaker 1: feels more anxiety to some that but I think people 591 00:31:01,640 --> 00:31:03,720 Speaker 1: are trying to measure about it and trying to kind 592 00:31:03,720 --> 00:31:06,440 Speaker 1: of maintain their composure, and investors need to do the 593 00:31:06,480 --> 00:31:09,160 Speaker 1: same thing. So, you know, in spite of all this uncertainty, 594 00:31:09,200 --> 00:31:11,280 Speaker 1: we really want to encourage people to maintain their long 595 00:31:11,400 --> 00:31:14,840 Speaker 1: term discipline and stay stay in the market excep they can, 596 00:31:15,000 --> 00:31:17,160 Speaker 1: and really stick to their long term plan. And that's 597 00:31:17,160 --> 00:31:19,840 Speaker 1: really the message we've been trying to emphasize how many 598 00:31:19,880 --> 00:31:23,160 Speaker 1: of those of your clients their long term plan has 599 00:31:23,200 --> 00:31:26,200 Speaker 1: really been upended by such a dramatic pullback in the 600 00:31:26,280 --> 00:31:30,280 Speaker 1: equity markets. You know, A good questions, not a lot though, 601 00:31:30,320 --> 00:31:34,480 Speaker 1: I mean I think people um not to this extent certainly, 602 00:31:34,480 --> 00:31:36,400 Speaker 1: but I think people might have been anticipating in some 603 00:31:36,440 --> 00:31:38,840 Speaker 1: degree of alatility. We try to take a long term approach. 604 00:31:38,840 --> 00:31:42,120 Speaker 1: We try to counsel clients to expect volatility will be 605 00:31:42,840 --> 00:31:47,080 Speaker 1: a feature of the investment landscape. Again, the magnitude has 606 00:31:47,080 --> 00:31:50,440 Speaker 1: probably been unprecedented for sure, and often use word but 607 00:31:50,480 --> 00:31:52,760 Speaker 1: I think it's fits. But I think people know that 608 00:31:52,840 --> 00:31:54,960 Speaker 1: volatility is something that kind of comes with the territory. 609 00:31:55,040 --> 00:31:58,120 Speaker 1: So we've got some great financial planning tools and techniques 610 00:31:58,160 --> 00:32:00,680 Speaker 1: that really kind of focus that folk on that first, 611 00:32:01,080 --> 00:32:02,800 Speaker 1: and then from there you can really hopefully build a 612 00:32:02,840 --> 00:32:05,040 Speaker 1: long term investment plan and shake to it. In market 613 00:32:05,080 --> 00:32:08,600 Speaker 1: site today. So, George, we love talking names with you. 614 00:32:08,840 --> 00:32:11,880 Speaker 1: Tell us about Microsoft. I'm especially interested because we had 615 00:32:11,880 --> 00:32:14,040 Speaker 1: a great conversation just a little while ago with Crawford 616 00:32:14,040 --> 00:32:16,560 Speaker 1: del Prett over at I d C. You know, talking 617 00:32:16,600 --> 00:32:19,720 Speaker 1: about uh I T spending. But one of the bright 618 00:32:19,840 --> 00:32:22,160 Speaker 1: spots and maybe this is why you have this pick 619 00:32:22,560 --> 00:32:26,400 Speaker 1: Uh is around software that obviously is an area where 620 00:32:26,600 --> 00:32:30,120 Speaker 1: we've seen tremendous growth, and his team is essentially predicting 621 00:32:30,160 --> 00:32:32,920 Speaker 1: that it's the one area in I T where we 622 00:32:33,000 --> 00:32:36,760 Speaker 1: might still see a little bit of optimism this year. Yeah, 623 00:32:36,800 --> 00:32:38,560 Speaker 1: I would agree with that, and I think it again 624 00:32:38,600 --> 00:32:42,000 Speaker 1: speaks to the notion of really signing high quality companies 625 00:32:42,080 --> 00:32:45,040 Speaker 1: with really strong balance sheets. You know, people are companies 626 00:32:45,040 --> 00:32:47,280 Speaker 1: that are led by strong management teams and people can 627 00:32:47,280 --> 00:32:50,479 Speaker 1: really identify with their services. Software. I think you're right 628 00:32:50,560 --> 00:32:52,120 Speaker 1: is kind of a goacher areas. It's kind of a 629 00:32:52,120 --> 00:32:55,840 Speaker 1: good core defensive name within the tech landscape, really strong 630 00:32:55,880 --> 00:32:59,120 Speaker 1: cash flow generation, strong balance sheet, good earning. The visibility 631 00:32:59,240 --> 00:33:03,240 Speaker 1: I mean Abody's tarningt pssibilities a little bit mired DSE days, 632 00:33:03,320 --> 00:33:05,680 Speaker 1: but we think that Microsoft can shine through an environment 633 00:33:05,720 --> 00:33:07,840 Speaker 1: like this, going toward about a good sect of hell 634 00:33:07,920 --> 00:33:09,880 Speaker 1: and set their back to it really can describe that 635 00:33:09,960 --> 00:33:12,280 Speaker 1: grows form, we think. So does that mean when it 636 00:33:12,400 --> 00:33:16,320 Speaker 1: dipped to below one forty, well below one forty, that 637 00:33:16,400 --> 00:33:18,720 Speaker 1: you guys were doing some buying. I think the low 638 00:33:18,800 --> 00:33:20,880 Speaker 1: I'm just looking the most recent lows around one thirty 639 00:33:20,880 --> 00:33:22,840 Speaker 1: four and change or so, did you do a bunch 640 00:33:22,840 --> 00:33:26,160 Speaker 1: of buying into Microsoft? I say, we'd be a bunch 641 00:33:26,200 --> 00:33:28,400 Speaker 1: of buying, but yeah, we've been nibbling at it. For sure. 642 00:33:28,960 --> 00:33:30,800 Speaker 1: It's come back and um, you know we still like 643 00:33:30,880 --> 00:33:32,840 Speaker 1: the name though, so on a long term basis, we'd 644 00:33:33,000 --> 00:33:35,280 Speaker 1: be buying it today. Actually, what about something like a 645 00:33:35,280 --> 00:33:38,600 Speaker 1: dollar general? Weal, Yeah, we talked about this a lot, 646 00:33:38,640 --> 00:33:41,840 Speaker 1: and we've been talking, um, Jason and I about the 647 00:33:41,880 --> 00:33:45,400 Speaker 1: overall retail sector and what's going on. UM, tell us 648 00:33:45,440 --> 00:33:47,400 Speaker 1: a bit about what's your what's your thesis is for 649 00:33:47,400 --> 00:33:50,280 Speaker 1: this one? Well? Does that much extent? They're kind of 650 00:33:50,280 --> 00:33:52,240 Speaker 1: a mass of their own destiny. I mean something the 651 00:33:52,280 --> 00:33:55,000 Speaker 1: macro environment is going to be headwind for a lot 652 00:33:55,080 --> 00:33:57,880 Speaker 1: of folks, and hopefully these things are more short term 653 00:33:57,880 --> 00:34:00,040 Speaker 1: in nature, but they really do offer the best a 654 00:34:00,120 --> 00:34:03,880 Speaker 1: breach format within kind of a smaller size box. Um, 655 00:34:04,000 --> 00:34:06,120 Speaker 1: They've got some really good levers they can pull to 656 00:34:06,160 --> 00:34:09,279 Speaker 1: try and and enhance their merchandizing. They've got some new 657 00:34:09,800 --> 00:34:12,640 Speaker 1: initiatives that are really poised for some growth going forward. 658 00:34:13,120 --> 00:34:15,880 Speaker 1: They've also got a environment where they their closest competitor 659 00:34:16,080 --> 00:34:18,840 Speaker 1: is a little bit hampered as antar term and so 660 00:34:18,920 --> 00:34:20,080 Speaker 1: I think there's a lot of things they can do 661 00:34:20,200 --> 00:34:22,160 Speaker 1: to kind of get through this better than others. So 662 00:34:22,200 --> 00:34:23,880 Speaker 1: I think they're well positioned over the long term as 663 00:34:23,880 --> 00:34:26,640 Speaker 1: well well. And it's interesting to think about that name 664 00:34:26,840 --> 00:34:29,759 Speaker 1: to George right, like knowing enough to be dangerous about 665 00:34:29,760 --> 00:34:32,440 Speaker 1: their history. You know, KKR bought them kind of at 666 00:34:32,520 --> 00:34:34,600 Speaker 1: the tail end of the last thing, right before the 667 00:34:34,640 --> 00:34:37,839 Speaker 1: financial crisis, and it was a great name to have 668 00:34:38,320 --> 00:34:42,200 Speaker 1: as we went into a period of economic uncertainty where 669 00:34:42,320 --> 00:34:46,839 Speaker 1: a discount retailer maybe more attractive, and they are servicing 670 00:34:47,480 --> 00:34:50,280 Speaker 1: a big chunk of the country that is largely under 671 00:34:50,400 --> 00:34:55,520 Speaker 1: market or undermarketed or under under retail sways underserved. Yeah, 672 00:34:55,640 --> 00:34:57,520 Speaker 1: I agree with you, Yeah, hardly. I think that's so 673 00:34:57,960 --> 00:35:00,319 Speaker 1: that's a key part the thesis for sure. But I 674 00:35:00,320 --> 00:35:02,320 Speaker 1: do wonder. I do wonder too, that's going to be 675 00:35:02,360 --> 00:35:05,080 Speaker 1: the segment of I feel like our population that's going 676 00:35:05,120 --> 00:35:08,000 Speaker 1: to be hit the hardest. So does a dollar general 677 00:35:08,040 --> 00:35:11,120 Speaker 1: benefit or get hurt because of that? You know, there 678 00:35:11,280 --> 00:35:13,319 Speaker 1: are some short term headwinds for sure, But I think 679 00:35:13,360 --> 00:35:16,440 Speaker 1: that they've plun to be seller operators. Uh, they're finding 680 00:35:16,480 --> 00:35:19,520 Speaker 1: people definding ways to get people into the store. Some 681 00:35:19,560 --> 00:35:21,360 Speaker 1: continuing options they've set up at the back of the 682 00:35:21,400 --> 00:35:23,399 Speaker 1: store that allow people to take up their goods, um, 683 00:35:23,560 --> 00:35:25,319 Speaker 1: you know, without having to go in the store, are 684 00:35:25,440 --> 00:35:28,040 Speaker 1: a big part of their growth also um. But again, 685 00:35:28,040 --> 00:35:30,279 Speaker 1: I think they're really kind of focused on the core demographic. 686 00:35:30,640 --> 00:35:32,399 Speaker 1: Really freshen of the stores a little bit too, which 687 00:35:32,400 --> 00:35:35,600 Speaker 1: will actually hope we drive traffic. And adding new categories 688 00:35:35,880 --> 00:35:38,400 Speaker 1: like food for example, will be another key driver for that. 689 00:35:38,480 --> 00:35:41,040 Speaker 1: And people still need to eat. Yeah, well, and one 690 00:35:41,239 --> 00:35:43,960 Speaker 1: exactly and one of the things that they have on 691 00:35:44,040 --> 00:35:47,000 Speaker 1: their side, I think, right, George, is this notion that 692 00:35:47,239 --> 00:35:50,560 Speaker 1: they are in these underserved markets, and it's it's sort 693 00:35:50,560 --> 00:35:52,960 Speaker 1: of the general part of the Dollar General that like 694 00:35:53,040 --> 00:35:55,040 Speaker 1: they're the only game in town or one of the 695 00:35:55,080 --> 00:35:57,160 Speaker 1: few games in town, and a lot of these more 696 00:35:57,760 --> 00:36:01,520 Speaker 1: rural parts of the South and elsewhere. I think that's right. Yeah, 697 00:36:01,520 --> 00:36:02,920 Speaker 1: that's too why I put it the general part of 698 00:36:02,920 --> 00:36:04,600 Speaker 1: the stores, It was just the Dollar General. I think 699 00:36:04,640 --> 00:36:07,160 Speaker 1: that's a good analogy. Yeah, they've definitely been hiring. I 700 00:36:07,200 --> 00:36:09,000 Speaker 1: was just looking at some of the most recent headlines 701 00:36:09,000 --> 00:36:11,840 Speaker 1: are hiring up to fifty thousand new employees to support 702 00:36:11,880 --> 00:36:14,160 Speaker 1: their operations. By the end of April they were doing that, 703 00:36:14,520 --> 00:36:16,200 Speaker 1: and then they were also giving a bunch of their 704 00:36:16,200 --> 00:36:20,439 Speaker 1: employees a bunch of bonuses, like thirty million dollars in bonuses. Yeah. 705 00:36:20,560 --> 00:36:23,799 Speaker 1: Now it's really interesting. Great, great to catch up with you. 706 00:36:23,800 --> 00:36:27,080 Speaker 1: We really appreciate it. George Mateo is chief investment officer 707 00:36:27,120 --> 00:36:30,560 Speaker 1: out of Keep Private Bank, on the phone from Cleveland. 708 00:36:30,600 --> 00:36:32,840 Speaker 1: We hope you and your team stay safe and and 709 00:36:32,920 --> 00:36:35,319 Speaker 1: hope you guys continue to be ahead of the curve. 710 00:36:35,360 --> 00:36:37,920 Speaker 1: As you say, Uh, Mike Dwyne, I believe the governor 711 00:36:38,320 --> 00:36:41,400 Speaker 1: of Ohio, as George pointed out, you know, he was 712 00:36:41,440 --> 00:36:43,520 Speaker 1: one of the first he moved the primary bat. You know, 713 00:36:43,560 --> 00:36:47,320 Speaker 1: there were a lot of big actions uh that uh 714 00:36:47,360 --> 00:36:49,520 Speaker 1: he was taking up. Believe he's a Republican to you know. 715 00:36:49,600 --> 00:36:53,400 Speaker 1: So it's an interesting, uh case study in many ways 716 00:36:53,400 --> 00:36:56,320 Speaker 1: as we continue to talk about governors and what not, 717 00:36:56,480 --> 00:36:59,840 Speaker 1: so interesting to hear his perspective. As always, thanks for 718 00:37:00,000 --> 00:37:02,400 Speaker 1: listening to Bloomberg Business Week. You can subscribe to the 719 00:37:02,440 --> 00:37:05,560 Speaker 1: podcast on iTunes, SoundCloud, or Bloomberg dot com. You can 720 00:37:05,600 --> 00:37:08,080 Speaker 1: also listen to our radio show every weekday at two 721 00:37:08,080 --> 00:37:10,200 Speaker 1: pm Eastern only on Bloomberg Radio