1 00:00:01,360 --> 00:00:04,120 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, along 2 00:00:04,120 --> 00:00:06,200 Speaker 1: with my co host of Bonnie Quinn. Every business day 3 00:00:06,240 --> 00:00:10,360 Speaker 1: we bring you interviews from CEOs, market pros, and Bloomberg experts, 4 00:00:10,400 --> 00:00:13,600 Speaker 1: along with essential market moving news. Find the Bloomberg Markets 5 00:00:13,600 --> 00:00:17,000 Speaker 1: Podcast on Apple podcast or wherever you listen to podcasts, 6 00:00:17,000 --> 00:00:20,720 Speaker 1: and on Bloomberg dot com. Our Carol Master in for 7 00:00:20,840 --> 00:00:22,759 Speaker 1: Paul and Bonnie and our most read story on the 8 00:00:22,760 --> 00:00:25,959 Speaker 1: Bloomberg in the past eight hours. It's about Wall Street 9 00:00:26,000 --> 00:00:29,080 Speaker 1: bonuses and how once again, those big Wall Street firms 10 00:00:29,080 --> 00:00:33,680 Speaker 1: are kind of reminding their employer employees, I should say, 11 00:00:33,720 --> 00:00:36,840 Speaker 1: really specifically traders to kind of temper back their expectations 12 00:00:36,840 --> 00:00:38,639 Speaker 1: when it comes to those bonus checks. So let's get 13 00:00:38,640 --> 00:00:41,400 Speaker 1: into it with her own Lenan new And she is 14 00:00:41,520 --> 00:00:43,640 Speaker 1: FINANCEI reporter at Bloomberg News. She's with us on the 15 00:00:43,680 --> 00:00:46,560 Speaker 1: phone in New York City. Uh Nan, no surprise that 16 00:00:46,680 --> 00:00:49,559 Speaker 1: this is your most read story on the Bloomberg. You know, 17 00:00:49,600 --> 00:00:52,720 Speaker 1: but I feel like these bonus stories and a tempering 18 00:00:52,720 --> 00:00:55,920 Speaker 1: of expectations have been trickling out over the last month 19 00:00:56,040 --> 00:00:58,960 Speaker 1: or so. What's up with it? Especially in a year 20 00:00:59,080 --> 00:01:04,800 Speaker 1: where Trader did really well for these firms, Thanks Carol. Yeah, 21 00:01:04,800 --> 00:01:07,720 Speaker 1: the traders really crushed it this year, and obviously that's 22 00:01:07,760 --> 00:01:10,400 Speaker 1: maybe a function also of the markets being so wild 23 00:01:10,480 --> 00:01:13,400 Speaker 1: and volatile as well. So um, you know, there's a 24 00:01:13,480 --> 00:01:16,240 Speaker 1: little bit of credit to the traders for doing well, 25 00:01:16,280 --> 00:01:18,840 Speaker 1: but also you know, the environment was just better for them. 26 00:01:18,959 --> 00:01:22,880 Speaker 1: So um. Even though some of the banks really recorded 27 00:01:22,920 --> 00:01:26,400 Speaker 1: sort of huge, huge jumps in trading revenue, it seems 28 00:01:26,480 --> 00:01:28,440 Speaker 1: like this year, you know, the banks are going to 29 00:01:28,480 --> 00:01:30,880 Speaker 1: be a little bit more prudent, a little bit more cautious, 30 00:01:30,920 --> 00:01:34,120 Speaker 1: and not not make those big payouts. We're hearing, you 31 00:01:34,120 --> 00:01:37,040 Speaker 1: know from messaging from across the street that looks some 32 00:01:37,280 --> 00:01:41,160 Speaker 1: big performers will get compensated, but others, um will not 33 00:01:41,480 --> 00:01:43,760 Speaker 1: or you know, will be disappointed. All right, I just 34 00:01:43,800 --> 00:01:46,120 Speaker 1: want to know what those discussions are, like, Lennan, Like, 35 00:01:46,240 --> 00:01:48,559 Speaker 1: is it like the big bosses like send a little 36 00:01:48,600 --> 00:01:51,240 Speaker 1: email around or the big boss tells the boss right 37 00:01:51,320 --> 00:01:52,920 Speaker 1: under it just to like tell your team it's not 38 00:01:52,960 --> 00:01:54,320 Speaker 1: going to be as rosy as you thought. Like, how 39 00:01:54,320 --> 00:01:57,240 Speaker 1: does it play out on Wall Street? Um? Well, I 40 00:01:57,520 --> 00:02:00,320 Speaker 1: don't think much of it happens by email, but probably 41 00:02:00,320 --> 00:02:02,800 Speaker 1: not a kind of it's a telegraphing, right, it's a 42 00:02:02,880 --> 00:02:06,560 Speaker 1: signaling effect. You know. We we heard about a manager 43 00:02:06,600 --> 00:02:08,840 Speaker 1: at Bank of America who had a kind of Sunday 44 00:02:09,000 --> 00:02:12,120 Speaker 1: Sunday afternoon call with his teams kind of trying to 45 00:02:12,160 --> 00:02:15,600 Speaker 1: manage expectations. And that's really important here because you don't 46 00:02:15,720 --> 00:02:18,240 Speaker 1: want people going into you know, Jan said, thinking they're 47 00:02:18,240 --> 00:02:20,799 Speaker 1: going to get at race in their bonus and then 48 00:02:20,880 --> 00:02:24,200 Speaker 1: get zero. Right. Yeah, you know, it's a it's a 49 00:02:24,280 --> 00:02:28,240 Speaker 1: really kind of important signaling mechanism that happens that towards 50 00:02:28,320 --> 00:02:30,880 Speaker 1: your end. And also there are a few test balloons 51 00:02:30,919 --> 00:02:33,200 Speaker 1: as well. You know, if they send out some messages 52 00:02:33,200 --> 00:02:36,000 Speaker 1: and um, you know people are up in arms, then 53 00:02:36,360 --> 00:02:38,760 Speaker 1: managers can go back to say the board or the 54 00:02:38,800 --> 00:02:40,960 Speaker 1: executive committee and say, look, you know I need more 55 00:02:41,280 --> 00:02:44,000 Speaker 1: and it's a bigger pot for my for my guys. So, um, 56 00:02:44,320 --> 00:02:46,400 Speaker 1: it is a negotiation process. So we want to make 57 00:02:46,440 --> 00:02:49,200 Speaker 1: that clear there there are still negotiations taking places, not 58 00:02:49,280 --> 00:02:53,160 Speaker 1: all completely baked yet they're always is wiggle room right? Hey? 59 00:02:53,240 --> 00:02:56,440 Speaker 1: Is it different though for fixed income versus equities versus 60 00:02:56,440 --> 00:02:58,359 Speaker 1: you know, different asset classes when it comes to those 61 00:02:58,400 --> 00:03:01,120 Speaker 1: bonuses this year, I'm assuming it usually is but I'm 62 00:03:01,120 --> 00:03:03,839 Speaker 1: wondering if this year is any different. Yeah, I think 63 00:03:04,000 --> 00:03:06,280 Speaker 1: most what we're hearing is that fixed income is going 64 00:03:06,320 --> 00:03:08,720 Speaker 1: to do better UM. And again this is varies depending 65 00:03:08,760 --> 00:03:11,359 Speaker 1: on the strength of the bank UM and where their 66 00:03:11,400 --> 00:03:13,440 Speaker 1: skill sets are. But it sounds to me like the 67 00:03:13,480 --> 00:03:17,400 Speaker 1: fixed income groups are going to um get more than 68 00:03:17,440 --> 00:03:21,960 Speaker 1: their equities trading counterparts. UM. So that's the general tone. 69 00:03:22,000 --> 00:03:24,119 Speaker 1: But again that is going to very bank to bank 70 00:03:24,160 --> 00:03:25,880 Speaker 1: and death to death. This is going to be an 71 00:03:25,960 --> 00:03:29,280 Speaker 1: unusual year in which the performance and the comp is 72 00:03:29,280 --> 00:03:31,720 Speaker 1: going to be very very um you know, kind of 73 00:03:32,639 --> 00:03:35,960 Speaker 1: varied among the banks because of just the strange the 74 00:03:36,000 --> 00:03:39,040 Speaker 1: strangeness is pandemic and the performance. Listen, I love in 75 00:03:39,120 --> 00:03:42,440 Speaker 1: your story. There's a quote from City Group CEO Mike 76 00:03:42,480 --> 00:03:45,960 Speaker 1: Corbett UM who's on his way out. UM. But he says, 77 00:03:46,000 --> 00:03:48,480 Speaker 1: we've got to be mindful of our returns and our shareholders. 78 00:03:48,520 --> 00:03:50,800 Speaker 1: We've got to be mindful of our environment that we're 79 00:03:50,840 --> 00:03:53,000 Speaker 1: in and the many challenges that are out there for 80 00:03:53,040 --> 00:03:55,520 Speaker 1: people in certain businesses. At the same time, he said, 81 00:03:55,600 --> 00:03:59,040 Speaker 1: you know, we've got to be competitive in our industry. Uh. 82 00:03:59,080 --> 00:04:01,000 Speaker 1: So it is interesting thing. I feel like it was 83 00:04:01,040 --> 00:04:03,640 Speaker 1: this way Lennon. After the financial crisis, there was a 84 00:04:03,680 --> 00:04:07,960 Speaker 1: real sensitivity out there, um about what was going on 85 00:04:08,000 --> 00:04:10,800 Speaker 1: across the nation, and it was a tempering back of 86 00:04:11,360 --> 00:04:14,680 Speaker 1: holiday parties, it was a tempering back of you know, 87 00:04:14,840 --> 00:04:18,880 Speaker 1: big time spending by executives at you know, financial front, 88 00:04:18,960 --> 00:04:21,479 Speaker 1: Like there was just a mood reset. And I do 89 00:04:21,640 --> 00:04:24,760 Speaker 1: feel like that is also going on here on Wall Street. 90 00:04:24,800 --> 00:04:26,599 Speaker 1: Is that fair to say? Is that also at play here? 91 00:04:27,360 --> 00:04:30,400 Speaker 1: I definitely think so, particularly for the banks that have 92 00:04:30,800 --> 00:04:33,280 Speaker 1: large consumer divisions, like City Group or a Bank of 93 00:04:33,279 --> 00:04:36,479 Speaker 1: America JP Morgan. It's not a good look when so 94 00:04:36,600 --> 00:04:39,960 Speaker 1: much of the country is suffering from pandemic and from 95 00:04:39,960 --> 00:04:43,400 Speaker 1: economic strains to then start paying you know, huge huge 96 00:04:43,480 --> 00:04:46,680 Speaker 1: bonuses to traders um. Obviously, some of these will be 97 00:04:46,920 --> 00:04:50,520 Speaker 1: publicly reported by the New York government as well in 98 00:04:50,520 --> 00:04:53,480 Speaker 1: New York City, so you know, we will have aggregate numbers. 99 00:04:53,480 --> 00:04:55,560 Speaker 1: And so I think the banks are very sensitive to 100 00:04:55,600 --> 00:04:58,480 Speaker 1: the fact that if they make huge trader payouts, um, 101 00:04:58,480 --> 00:05:02,880 Speaker 1: eventually they may face the scorn of republic during such 102 00:05:02,920 --> 00:05:05,960 Speaker 1: a difficult time. So I think, uh, you know, there's 103 00:05:05,960 --> 00:05:08,560 Speaker 1: also an excuse to be cindy as well. Right at 104 00:05:08,560 --> 00:05:11,000 Speaker 1: the same time, if I'm a trader, and I brought 105 00:05:11,000 --> 00:05:13,720 Speaker 1: in a ton of money. Five biggest US investment banks 106 00:05:13,720 --> 00:05:15,800 Speaker 1: on pace for their first a hundred billion dollar year 107 00:05:15,839 --> 00:05:18,400 Speaker 1: for trading revenue in more than decade. I gotta say 108 00:05:18,440 --> 00:05:21,160 Speaker 1: I might be like, are you kidding me? I'm just 109 00:05:21,200 --> 00:05:23,279 Speaker 1: gonna say, yeah. I mean, I think I think that's 110 00:05:23,279 --> 00:05:25,839 Speaker 1: a general trader mortality. You have a huge year, you 111 00:05:26,040 --> 00:05:28,640 Speaker 1: always want more. Um. And I think the rock stars, 112 00:05:28,720 --> 00:05:32,599 Speaker 1: to be frank, will be compensated this year. UM. But again, 113 00:05:32,800 --> 00:05:34,880 Speaker 1: maybe not all of the rock stars, and maybe they're 114 00:05:34,880 --> 00:05:37,320 Speaker 1: not going to set as much as they expect. Yeah. 115 00:05:37,320 --> 00:05:40,560 Speaker 1: That tempering of expectations. Interesting stuff and as we said, 116 00:05:40,560 --> 00:05:42,880 Speaker 1: the most read story on the Bloomberg in the past 117 00:05:42,920 --> 00:05:45,880 Speaker 1: eight hours, So Lennon good stuff. Lenan new In, uh 118 00:05:45,960 --> 00:05:48,560 Speaker 1: financi reporter at Bloomberg News on the phone from New 119 00:05:48,640 --> 00:05:50,159 Speaker 1: York City. You can check her out on Twitter at 120 00:05:50,200 --> 00:05:54,240 Speaker 1: Lennon T. New In. That's l A N A n 121 00:05:54,400 --> 00:05:57,640 Speaker 1: h t N g U y e N. Always good 122 00:05:57,640 --> 00:06:02,480 Speaker 1: stuff from her. This column among the Bloomberg opinion pieces, 123 00:06:02,480 --> 00:06:06,560 Speaker 1: definitely uh standing out. It's called the Shes Session. How 124 00:06:06,560 --> 00:06:09,640 Speaker 1: it's very real in particular for minority women and It's 125 00:06:09,680 --> 00:06:12,760 Speaker 1: written by Louisa Blanco. She is professor of public policy 126 00:06:12,760 --> 00:06:17,800 Speaker 1: at Pepperdine University. She specializes in economic development, international economics, 127 00:06:17,839 --> 00:06:21,080 Speaker 1: and the financial well being of minorities in the United States. 128 00:06:21,080 --> 00:06:23,320 Speaker 1: So my guess is she has had a busy year 129 00:06:23,320 --> 00:06:25,880 Speaker 1: and a lot to write about. Luisa joining us on 130 00:06:25,960 --> 00:06:29,919 Speaker 1: the phone from Malibu, California. Professor Blanco, nice to have 131 00:06:30,040 --> 00:06:33,240 Speaker 1: you here on Bloomberg. You know, I've had this conversation 132 00:06:33,360 --> 00:06:38,920 Speaker 1: with uh, some different CEOs, including Verizon Business CEO Tammy Irwin, 133 00:06:39,040 --> 00:06:44,200 Speaker 1: about how this pandemic is disproportionately impacting women. But as 134 00:06:44,240 --> 00:06:47,039 Speaker 1: you say, and so smartly and rightfully, so you drill 135 00:06:47,120 --> 00:06:51,280 Speaker 1: down even further, we are seeing again a distinction between 136 00:06:51,279 --> 00:06:53,960 Speaker 1: white women and minority women in terms of the impact. 137 00:06:54,000 --> 00:06:57,080 Speaker 1: Talked to us about that. If you would, thank you 138 00:06:57,160 --> 00:07:01,520 Speaker 1: so much for bloom Show today, some of my insights 139 00:07:01,600 --> 00:07:05,279 Speaker 1: and work and this very appreciate invitations. Um so yes. 140 00:07:05,440 --> 00:07:07,839 Speaker 1: So I think you know, when I hear people talking 141 00:07:07,839 --> 00:07:11,800 Speaker 1: about the sheet session, I realized that, well, you know, well, 142 00:07:11,880 --> 00:07:15,320 Speaker 1: it is true. It aggregates all women, right and at 143 00:07:15,320 --> 00:07:18,440 Speaker 1: the end of the day, you know, women, uh, minority 144 00:07:18,520 --> 00:07:21,800 Speaker 1: women have a different experience right, and especially when it 145 00:07:21,800 --> 00:07:25,720 Speaker 1: comes to the to the labor market and um to here. 146 00:07:25,800 --> 00:07:28,200 Speaker 1: You know, when we look at minority women, um, there 147 00:07:28,240 --> 00:07:31,400 Speaker 1: are several reasons why you know, they are doing worse 148 00:07:31,480 --> 00:07:34,800 Speaker 1: when it comes to employment and and being part of 149 00:07:34,800 --> 00:07:39,200 Speaker 1: the labor force. And it is because, um, we we 150 00:07:39,320 --> 00:07:41,920 Speaker 1: see that minority women are more likely to work on 151 00:07:42,000 --> 00:07:45,480 Speaker 1: sectors that have been directly affected by lockdowns and stay 152 00:07:45,480 --> 00:07:48,480 Speaker 1: out ward orders so and and less likely to tell 153 00:07:48,520 --> 00:07:50,840 Speaker 1: a work. So that's one of the first reasons. And 154 00:07:50,960 --> 00:07:54,720 Speaker 1: the other reason is that minority women are more exposed 155 00:07:54,760 --> 00:07:58,400 Speaker 1: to COVID nineteen and more likely to get thick. And 156 00:07:58,400 --> 00:08:04,480 Speaker 1: then another very important reason is that even different family circumstances, 157 00:08:04,520 --> 00:08:07,720 Speaker 1: actually being part of the labor force has been more 158 00:08:07,720 --> 00:08:11,640 Speaker 1: difficult for minority women because they need to take care 159 00:08:11,680 --> 00:08:16,360 Speaker 1: of children. With the closure of childcare and childcare censors 160 00:08:16,400 --> 00:08:20,080 Speaker 1: and the schools, right, um, taking care of children has 161 00:08:20,120 --> 00:08:24,000 Speaker 1: become very important. Yeah, you know, this is where in 162 00:08:24,040 --> 00:08:27,480 Speaker 1: a world where we have so many data points, right, 163 00:08:27,480 --> 00:08:30,320 Speaker 1: But if you look at data on the macro level, 164 00:08:30,360 --> 00:08:33,240 Speaker 1: maybe it doesn't it tells you a broad theme or 165 00:08:33,280 --> 00:08:35,760 Speaker 1: broad story. But as you start to drill down, then 166 00:08:35,760 --> 00:08:38,320 Speaker 1: you really understand what's going on, and I think drilling 167 00:08:38,360 --> 00:08:41,120 Speaker 1: down into the data of is going to be so important, 168 00:08:41,240 --> 00:08:44,880 Speaker 1: especially as we try to address some of the inequalities, 169 00:08:44,880 --> 00:08:48,080 Speaker 1: the inequities that are out there in society. So you 170 00:08:48,160 --> 00:08:52,120 Speaker 1: talk about you know, family arrangements, taking care of the family, 171 00:08:52,280 --> 00:08:54,800 Speaker 1: being you know, exposed to certain jobs where you can't 172 00:08:54,800 --> 00:08:56,800 Speaker 1: do them from home, and having to make a choice 173 00:08:56,800 --> 00:09:00,000 Speaker 1: between taking care of your family or you know, continue 174 00:09:00,000 --> 00:09:03,040 Speaker 1: doing work. So I think, here we embark on a 175 00:09:03,120 --> 00:09:06,920 Speaker 1: new year, a new administration. Um, we have a vice 176 00:09:06,960 --> 00:09:11,000 Speaker 1: presidental act who is a minority, and you know, hopefully 177 00:09:11,040 --> 00:09:13,920 Speaker 1: we'll fold in all of her experiences as well when 178 00:09:13,960 --> 00:09:16,440 Speaker 1: it comes to policy, at least it will be you know, 179 00:09:16,520 --> 00:09:18,160 Speaker 1: she'll have a seat at the table. So what do 180 00:09:18,200 --> 00:09:20,680 Speaker 1: we need to do as policymakers to address some of 181 00:09:20,679 --> 00:09:23,520 Speaker 1: these issues so that they're not forgotten once we get 182 00:09:23,520 --> 00:09:27,120 Speaker 1: on the other side of COVID. Yeah, thank you for 183 00:09:27,240 --> 00:09:29,520 Speaker 1: bringing that right up. I feel like, you know, we 184 00:09:29,800 --> 00:09:32,080 Speaker 1: hear all the time about the problem, and that's where 185 00:09:32,200 --> 00:09:34,559 Speaker 1: I'm getting tired. You know, we hear about all these 186 00:09:34,640 --> 00:09:39,800 Speaker 1: data showing these these parties, and it's overwhelming, and it's stuff. 187 00:09:39,840 --> 00:09:42,760 Speaker 1: It is painful, right, and I'm glad that you jump 188 00:09:42,880 --> 00:09:45,640 Speaker 1: right away into the solutions. I really appreciate that because 189 00:09:45,679 --> 00:09:47,240 Speaker 1: when I wrote the off it, I say, you know, 190 00:09:47,280 --> 00:09:48,839 Speaker 1: I want to talk about the problem, but i really 191 00:09:48,880 --> 00:09:51,680 Speaker 1: want to spend time on the solutions because I'm tired 192 00:09:51,720 --> 00:09:56,120 Speaker 1: of just hearing people talking about the problem, right right. So, um, 193 00:09:56,200 --> 00:09:58,920 Speaker 1: so yeah, I think you know, as the new administration comescened, 194 00:09:59,040 --> 00:10:03,480 Speaker 1: you know, what it is important is that we addressed uh, 195 00:10:03,640 --> 00:10:06,960 Speaker 1: these labor market disparities, and and we need to be 196 00:10:07,080 --> 00:10:11,239 Speaker 1: very strategic because the longer we weighed, the more problematic 197 00:10:11,280 --> 00:10:14,880 Speaker 1: it will be. So we economists always worry about long 198 00:10:15,000 --> 00:10:18,840 Speaker 1: term unemployment because we know that the longer people stay 199 00:10:18,880 --> 00:10:22,000 Speaker 1: out out of the labor market, uh, the more their 200 00:10:22,120 --> 00:10:26,160 Speaker 1: skills at trophy right, and then also self confidence. Right. So, 201 00:10:26,160 --> 00:10:28,640 Speaker 1: so I think, you know, for the new administration, it 202 00:10:28,800 --> 00:10:33,160 Speaker 1: is very important that we addressed, especially um, the child 203 00:10:33,200 --> 00:10:38,800 Speaker 1: care challenges that minority women are facing these days. UM. 204 00:10:38,960 --> 00:10:40,839 Speaker 1: So to do these, you know, we really need to 205 00:10:40,880 --> 00:10:44,600 Speaker 1: be focusing on the safe reopening of the schools and 206 00:10:44,760 --> 00:10:47,640 Speaker 1: access to child care. And to do these, you know, 207 00:10:47,760 --> 00:10:52,440 Speaker 1: first we need to establish testing and safety measures for 208 00:10:53,160 --> 00:10:55,440 Speaker 1: schools and child care centers so that they you know, 209 00:10:55,520 --> 00:10:58,840 Speaker 1: kids can go back to school safely, and and and 210 00:10:58,840 --> 00:11:03,000 Speaker 1: and parents and go back to work, especially models, and 211 00:11:03,760 --> 00:11:06,560 Speaker 1: we need Uh. I think what's very important as we're 212 00:11:06,600 --> 00:11:09,920 Speaker 1: talking about you know, vaccines and and you know, thankfully 213 00:11:09,960 --> 00:11:13,320 Speaker 1: we we have that as a hope, right, we need 214 00:11:13,360 --> 00:11:17,480 Speaker 1: to set priority for vaccines for teachers and support staff 215 00:11:17,559 --> 00:11:21,199 Speaker 1: and chald care workers. I think that's that's crucial as 216 00:11:21,200 --> 00:11:23,720 Speaker 1: we're trying to decide you know, who who gets the 217 00:11:23,840 --> 00:11:27,480 Speaker 1: vaccines as they come in UM. And then I think, 218 00:11:27,520 --> 00:11:30,080 Speaker 1: you know, another important area that we really need to 219 00:11:30,120 --> 00:11:35,319 Speaker 1: be spending resources is to invest in outreach to encourage 220 00:11:35,800 --> 00:11:40,080 Speaker 1: vaccine of septance. And how problematic is that? We said, like, 221 00:11:40,120 --> 00:11:43,560 Speaker 1: I do think about it's really remarkable. And I even 222 00:11:43,640 --> 00:11:47,920 Speaker 1: was talking with UM members, the head of a medical network, 223 00:11:47,960 --> 00:11:51,400 Speaker 1: with all of doctors. Basically you know that the skepticism 224 00:11:51,480 --> 00:11:54,120 Speaker 1: going into this that there needed to be some convincing 225 00:11:54,200 --> 00:11:57,600 Speaker 1: So UM, I do wonder how problematic this could ultimately 226 00:11:57,640 --> 00:12:00,840 Speaker 1: be for minority women and they are ability to bounce 227 00:12:00,880 --> 00:12:05,640 Speaker 1: back faster. Yes, I think, you know, the issue of 228 00:12:05,960 --> 00:12:10,160 Speaker 1: vaccine hesitancy is very important and that's something we need 229 00:12:10,200 --> 00:12:13,800 Speaker 1: to get our hands right on right now. Because otherwise 230 00:12:13,800 --> 00:12:17,040 Speaker 1: if we wait, it is just we're gonna miss the opportunity. Right. 231 00:12:17,120 --> 00:12:20,439 Speaker 1: So a thing with minorities, there is a there is 232 00:12:20,520 --> 00:12:25,480 Speaker 1: a lack of tactics of reliable information, right especially you know, 233 00:12:25,559 --> 00:12:31,360 Speaker 1: information that can be language and cultural appropriate. Um. Then 234 00:12:31,400 --> 00:12:35,880 Speaker 1: there itself so dis mistrust right so on the government, 235 00:12:36,559 --> 00:12:40,440 Speaker 1: especially in the last couple of years. Right. Um, So 236 00:12:40,520 --> 00:12:43,439 Speaker 1: we have big barriers when it comes to vaccine acceptance. 237 00:12:43,520 --> 00:12:46,320 Speaker 1: And I think that, um, you know, we and I 238 00:12:46,400 --> 00:12:48,120 Speaker 1: was going to mention, you know, all these efforts that 239 00:12:48,160 --> 00:12:52,600 Speaker 1: I'm talking about, you know, establishing testing measures, vaccines for 240 00:12:52,720 --> 00:12:55,920 Speaker 1: teachers and then also vaccine acceptance. I mean, um, what 241 00:12:56,240 --> 00:13:00,000 Speaker 1: we need to make sure right that our efforts are 242 00:13:00,040 --> 00:13:05,760 Speaker 1: focusing places proportions of lower income minority students. Right. Um. 243 00:13:05,840 --> 00:13:08,320 Speaker 1: So I've seen you know, working on and I already 244 00:13:08,360 --> 00:13:12,320 Speaker 1: be touching to some people because I worry about these issues. Yeah, listen, 245 00:13:12,360 --> 00:13:15,120 Speaker 1: it's it's it's important as we you know, really dig 246 00:13:15,160 --> 00:13:17,880 Speaker 1: into how do we come back here and come back 247 00:13:17,920 --> 00:13:20,400 Speaker 1: stronger and make sure nobody has left out. So these 248 00:13:20,480 --> 00:13:24,640 Speaker 1: these propositions and proposals that you're really putting out there, um, 249 00:13:24,679 --> 00:13:27,040 Speaker 1: make a lot of sense. And for things certainly for 250 00:13:27,080 --> 00:13:29,560 Speaker 1: the for the administration to consider. Hey, Louisa, thank you 251 00:13:29,559 --> 00:13:33,240 Speaker 1: so much. Professor Louisa Blanco, Professor of public policy at 252 00:13:33,240 --> 00:13:37,520 Speaker 1: Pepperdine University, joining us on the phone from Malibu, California. 253 00:13:39,679 --> 00:13:42,880 Speaker 1: You know, it's funny how headline shifts so dramatically because 254 00:13:42,920 --> 00:13:44,680 Speaker 1: it's just a few weeks ago that we were talking 255 00:13:44,720 --> 00:13:49,200 Speaker 1: about what will become known as the largest cybersecurity attack 256 00:13:49,360 --> 00:13:51,719 Speaker 1: in the United States. And recent memory has to do 257 00:13:51,760 --> 00:13:55,120 Speaker 1: with those suspected Russian hackers breaching the internal networks of 258 00:13:55,160 --> 00:13:58,680 Speaker 1: at least two customers, including the US government, UH, several 259 00:13:58,720 --> 00:14:02,720 Speaker 1: agencies within the US government, also private companies and a 260 00:14:02,760 --> 00:14:07,720 Speaker 1: cybersecurity firm. And we continue to find out more about this. Heck, 261 00:14:07,800 --> 00:14:10,200 Speaker 1: let's get into it though with Steve Groman. He is 262 00:14:10,240 --> 00:14:13,320 Speaker 1: senior vice president, chief Technology officer of at McAfee. He 263 00:14:13,400 --> 00:14:15,840 Speaker 1: joins us on the phone from Plano, Texas. Steve, nice 264 00:14:15,880 --> 00:14:19,280 Speaker 1: to have you here on Bloomberg Radio. It's interesting go 265 00:14:19,400 --> 00:14:21,520 Speaker 1: back even a few years, and I feel like we 266 00:14:21,600 --> 00:14:24,720 Speaker 1: had a conversation on air several times a day that 267 00:14:24,880 --> 00:14:28,560 Speaker 1: dealt with cybersecurity issues and it kind of got pushed back. Uh, 268 00:14:28,640 --> 00:14:30,760 Speaker 1: certainly in a year like when we had so many 269 00:14:30,760 --> 00:14:33,960 Speaker 1: other things on our mind and then solar winds happens 270 00:14:34,040 --> 00:14:37,160 Speaker 1: and then we're all reminded that man, we have huge 271 00:14:37,320 --> 00:14:42,480 Speaker 1: still cyber risks out there. Hey, hey, Carol, thanks for 272 00:14:42,640 --> 00:14:45,400 Speaker 1: having me. This really was a wake up call that 273 00:14:45,480 --> 00:14:49,560 Speaker 1: hit us right at the end of and it is 274 00:14:49,800 --> 00:14:53,560 Speaker 1: unlike anything we've ever seen before, you know, as as 275 00:14:53,600 --> 00:14:58,240 Speaker 1: you know, we've seen major cyber incidents throughout the last 276 00:14:58,240 --> 00:15:01,320 Speaker 1: few years, whether you go back to the Sony hack 277 00:15:02,120 --> 00:15:06,560 Speaker 1: by North Korea or we saw major worms such as 278 00:15:06,600 --> 00:15:11,720 Speaker 1: back in want to cry, impacted businesses across the globe. 279 00:15:12,680 --> 00:15:16,680 Speaker 1: But what makes this so unique, and in many ways 280 00:15:17,360 --> 00:15:20,640 Speaker 1: I almost think of this as a cyber pearl harbor, 281 00:15:21,080 --> 00:15:26,600 Speaker 1: is it gives the Russian or the suspected Russian actors 282 00:15:27,440 --> 00:15:32,480 Speaker 1: what we call hands on keyboard access to a large 283 00:15:32,560 --> 00:15:37,280 Speaker 1: number of very important US government as well as private 284 00:15:37,480 --> 00:15:42,280 Speaker 1: private sector UH organizations, really all at the same time. 285 00:15:42,800 --> 00:15:46,680 Speaker 1: And that's something that we haven't yet seen. Well, so 286 00:15:46,760 --> 00:15:49,360 Speaker 1: explain that for us a little bit more, Steve. When 287 00:15:49,360 --> 00:15:53,600 Speaker 1: you say a keyboard um attack, what does that mean? 288 00:15:53,640 --> 00:15:57,360 Speaker 1: It sounds to me like it gives them access like 289 00:15:57,400 --> 00:16:00,440 Speaker 1: if I was sitting down to any of those systems 290 00:16:00,480 --> 00:16:02,560 Speaker 1: in front of the keyboard and had the access. But 291 00:16:02,760 --> 00:16:04,840 Speaker 1: explain it and this isn't my world, that your world, 292 00:16:04,840 --> 00:16:08,960 Speaker 1: so explain what the significance of that is. That's exactly right. 293 00:16:09,080 --> 00:16:14,040 Speaker 1: So think of it as it creates a virtual connection 294 00:16:14,760 --> 00:16:21,000 Speaker 1: between a human cyber attacker sitting somewhere around the world 295 00:16:21,280 --> 00:16:25,680 Speaker 1: into one of these very sensitive U S organizations where 296 00:16:25,840 --> 00:16:31,120 Speaker 1: they can look for assets of opportunity to steal or 297 00:16:31,200 --> 00:16:36,360 Speaker 1: look for systems of opportunity to implant malware. And because 298 00:16:36,760 --> 00:16:41,440 Speaker 1: it's being executed by a human as opposed to a 299 00:16:41,920 --> 00:16:48,359 Speaker 1: preprogrammed UH playbook, it can make on the fly decisions 300 00:16:48,840 --> 00:16:52,560 Speaker 1: to ratchet up the lethality of the attack or to 301 00:16:52,720 --> 00:16:57,800 Speaker 1: identify assets that are much more valuable from an espionage 302 00:16:57,920 --> 00:17:01,840 Speaker 1: perspective UH than And something like want to Cry that 303 00:17:01,880 --> 00:17:05,840 Speaker 1: we saw in which was more analogous to it's like 304 00:17:05,880 --> 00:17:10,080 Speaker 1: a dumb bomb where it ran the same code everywhere 305 00:17:10,119 --> 00:17:13,560 Speaker 1: that it landed and we knew exactly the damage it did. 306 00:17:14,000 --> 00:17:17,520 Speaker 1: In this case Department of State. You might have a 307 00:17:17,640 --> 00:17:22,560 Speaker 1: Russian actor feeling very different information from Department of Energy, 308 00:17:22,640 --> 00:17:25,640 Speaker 1: and every organization that's going to be a little bit different. Well, 309 00:17:25,680 --> 00:17:27,760 Speaker 1: what's interesting too, is do you think this was a 310 00:17:27,800 --> 00:17:32,000 Speaker 1: Solar Winds problem, Because let's remind everybody, it was, you know, 311 00:17:32,440 --> 00:17:35,320 Speaker 1: a supply chain attack or a third party attack. That's 312 00:17:35,320 --> 00:17:38,080 Speaker 1: how it's been described. The initial target, right, wasn't the 313 00:17:38,160 --> 00:17:41,520 Speaker 1: US government or some of those other institutions, but one 314 00:17:41,560 --> 00:17:47,359 Speaker 1: of its software suppliers. So shame on UM Solar Winds, 315 00:17:47,359 --> 00:17:49,280 Speaker 1: shame on the U S Government for maybe not being 316 00:17:49,280 --> 00:17:52,359 Speaker 1: more careful with its supply chain. What is the lesson 317 00:17:52,440 --> 00:17:54,720 Speaker 1: to be learned here? And just got about a minute 318 00:17:54,800 --> 00:17:58,480 Speaker 1: or so, so, so the main lesson is that there's 319 00:17:58,560 --> 00:18:01,200 Speaker 1: no single thing that we can count on in order 320 00:18:01,240 --> 00:18:05,879 Speaker 1: to defend our environments. We need good defensive technology. We 321 00:18:05,920 --> 00:18:10,000 Speaker 1: need to scrutinize our suppliers, We need to have well 322 00:18:10,080 --> 00:18:14,879 Speaker 1: trained people operating our cybersecurity defense. It's it's a little 323 00:18:14,880 --> 00:18:18,040 Speaker 1: bit like driving a car safely. You want to have 324 00:18:18,080 --> 00:18:22,040 Speaker 1: the right technology seatbelt, air bags, any lock breaks, but 325 00:18:22,040 --> 00:18:24,880 Speaker 1: but you also have to pay attention to what you're 326 00:18:24,880 --> 00:18:27,400 Speaker 1: doing or else you can still get into an accident 327 00:18:27,600 --> 00:18:31,160 Speaker 1: and making sure that you have all of those components. 328 00:18:31,560 --> 00:18:33,880 Speaker 1: It's something that will have to look at more broadly 329 00:18:34,240 --> 00:18:37,240 Speaker 1: across the industry. Yeah, I do wonder just quickly too. 330 00:18:37,280 --> 00:18:40,280 Speaker 1: I mean, just what happened here? Right? I mean we 331 00:18:40,320 --> 00:18:42,240 Speaker 1: all know that these problems are out there. We've got 332 00:18:42,240 --> 00:18:46,080 Speaker 1: to be careful. I just is it just somebody a 333 00:18:46,160 --> 00:18:48,720 Speaker 1: major screw up or something more significant, because I know 334 00:18:48,760 --> 00:18:50,840 Speaker 1: Solar Winds, you know, has come out and said listen, 335 00:18:50,840 --> 00:18:53,399 Speaker 1: we were warning people of some lack of security. And 336 00:18:53,440 --> 00:18:57,800 Speaker 1: again sorry just about thirty seconds. Yeah, it comes down 337 00:18:57,840 --> 00:19:00,760 Speaker 1: to we all need to up our game. Aim. So 338 00:19:00,760 --> 00:19:06,320 Speaker 1: software suppliers need to scrutinize their development process, the way 339 00:19:06,359 --> 00:19:10,879 Speaker 1: that they're operating UH their own environments so they don't 340 00:19:10,920 --> 00:19:14,879 Speaker 1: become the avenue for UH Nation States or or other 341 00:19:14,960 --> 00:19:21,240 Speaker 1: cybercriminals even to get into environments through legitimate software. Every 342 00:19:21,320 --> 00:19:24,639 Speaker 1: legitimate software maker needs to up their game. All right, 343 00:19:24,680 --> 00:19:27,119 Speaker 1: good stuff, Hey, Steve, thank you so much. Steve Groban, 344 00:19:27,240 --> 00:19:30,280 Speaker 1: he's a senior vice president chief Technology officer at McAfee. 345 00:19:30,359 --> 00:19:32,560 Speaker 1: He is joining us on the phone from Plano, Texas. 346 00:19:34,840 --> 00:19:37,000 Speaker 1: I want to talk a little bit more about the 347 00:19:37,040 --> 00:19:39,280 Speaker 1: trade today with a guest who's got some specific thoughts 348 00:19:39,280 --> 00:19:42,439 Speaker 1: when it comes to the global airline industry. Frank Holmes 349 00:19:42,520 --> 00:19:44,960 Speaker 1: is with US CEO and Chief Investment Officer at US 350 00:19:45,000 --> 00:19:49,840 Speaker 1: Global Advisors investors excuse me, US Global Investors with some 351 00:19:49,920 --> 00:19:54,800 Speaker 1: five thirty four million dollars in assets under management, and 352 00:19:54,840 --> 00:19:57,800 Speaker 1: he joins us on the phone from San Antonio, Texas. Frank, 353 00:19:57,920 --> 00:20:01,920 Speaker 1: nice to have you back here on Bloomberg Video. So, um, 354 00:20:02,000 --> 00:20:05,240 Speaker 1: let's talk about the airline industry. I think they are 355 00:20:05,280 --> 00:20:07,680 Speaker 1: just counting down the days. So there are a lot 356 00:20:07,720 --> 00:20:11,640 Speaker 1: more flyers and passengers and consumers ready to get on planes, 357 00:20:11,680 --> 00:20:13,640 Speaker 1: but it's going to be at least a few more 358 00:20:13,680 --> 00:20:18,159 Speaker 1: months before we get to even something close uh to normal. 359 00:20:18,320 --> 00:20:21,040 Speaker 1: How do you see it? And where our investors kind 360 00:20:21,040 --> 00:20:22,800 Speaker 1: of positioning money right now? What are you seeing in 361 00:20:22,920 --> 00:20:25,720 Speaker 1: terms of trends. Well, one of the first things that 362 00:20:25,840 --> 00:20:28,480 Speaker 1: just to add to the data points you gave JETS 363 00:20:28,600 --> 00:20:31,320 Speaker 1: E t F is a three big in dollar E 364 00:20:31,440 --> 00:20:35,560 Speaker 1: t F now, um, and you mentioned five million. That 365 00:20:35,640 --> 00:20:38,080 Speaker 1: was just the mutual funds. The E t s are 366 00:20:38,160 --> 00:20:40,680 Speaker 1: are bigger today and that's been a phenomena for the 367 00:20:40,840 --> 00:20:43,840 Speaker 1: mutual fund industry as a shift has gone to ETS 368 00:20:44,200 --> 00:20:46,560 Speaker 1: just has been a phenomena since March when it hit 369 00:20:46,600 --> 00:20:49,520 Speaker 1: thirty five million dollars and growing to three billion dollars 370 00:20:50,880 --> 00:20:54,000 Speaker 1: in this forward looking everyone's forward looking on this of 371 00:20:54,080 --> 00:20:57,720 Speaker 1: anticipating in twelve months. So what happened after every other 372 00:20:57,800 --> 00:21:01,159 Speaker 1: crisis that we've had in the past thirty years, and 373 00:21:01,720 --> 00:21:06,399 Speaker 1: the airline's industry seems to bounce back within twelve months. 374 00:21:06,440 --> 00:21:09,280 Speaker 1: So we've seen a lot of money come into the 375 00:21:09,359 --> 00:21:12,680 Speaker 1: airlines through the jets as a product, which I think 376 00:21:12,760 --> 00:21:16,000 Speaker 1: is most fascinating betting. And then on this weekend we 377 00:21:16,080 --> 00:21:19,080 Speaker 1: had one point three million tourists one point three million, 378 00:21:19,119 --> 00:21:23,800 Speaker 1: even with record uh COVID negative numbers. So people are moving. 379 00:21:23,960 --> 00:21:27,800 Speaker 1: Business is not moving like, it's not booming yet. It's 380 00:21:27,800 --> 00:21:29,680 Speaker 1: going to take a while. Everyone's zooming when it comes 381 00:21:29,680 --> 00:21:33,720 Speaker 1: to business transactions, but tourism is up substantially. The major 382 00:21:33,760 --> 00:21:36,360 Speaker 1: airlines are rerouting their rather than going through a hub, 383 00:21:36,359 --> 00:21:40,400 Speaker 1: They're going NonStop from Pennsylvania, New York to smaller cities 384 00:21:40,680 --> 00:21:44,080 Speaker 1: rapdown to Florida. Uh same thing we're seeing Southwest go 385 00:21:44,280 --> 00:21:48,240 Speaker 1: from Phoenix rapdown to Cobble St. Lucas. So help me 386 00:21:48,280 --> 00:21:51,240 Speaker 1: out kind of where I mean. Listen, this is you 387 00:21:51,280 --> 00:21:53,600 Speaker 1: can't market time, but it is about timing the market 388 00:21:53,640 --> 00:21:56,800 Speaker 1: here when it comes to when you think you know 389 00:21:56,880 --> 00:21:59,560 Speaker 1: truly air travel gets back to normal. It's the same 390 00:21:59,560 --> 00:22:03,040 Speaker 1: thing for the hospitality industry. I mentioned Carnival before we 391 00:22:03,080 --> 00:22:05,439 Speaker 1: got going. It's you know, everybody's trying to kind of 392 00:22:05,440 --> 00:22:09,200 Speaker 1: make their best guess. We know things will as dark 393 00:22:09,240 --> 00:22:10,880 Speaker 1: as they may feel now and make it a little 394 00:22:10,880 --> 00:22:14,560 Speaker 1: bit darker as we get into January. Um that because 395 00:22:14,600 --> 00:22:17,560 Speaker 1: of the vaccine, we have started to kind of map 396 00:22:17,600 --> 00:22:20,720 Speaker 1: at our playbook for getting back to normal. But again 397 00:22:20,760 --> 00:22:23,920 Speaker 1: it's a timing issue. So at this point you don't 398 00:22:23,960 --> 00:22:26,440 Speaker 1: think it's too early to put money to play here. 399 00:22:26,480 --> 00:22:28,239 Speaker 1: When it comes to those big airline industries. I mean, 400 00:22:28,240 --> 00:22:31,800 Speaker 1: which if you look at them individually, Dealta still downcent 401 00:22:31,920 --> 00:22:34,840 Speaker 1: this year. United is down fifty percent this year. You know, 402 00:22:34,880 --> 00:22:38,560 Speaker 1: I could kind of go on, you can right across 403 00:22:38,560 --> 00:22:41,480 Speaker 1: the board and if the economy gets back to where 404 00:22:41,480 --> 00:22:45,240 Speaker 1: it was, America flying two million people a day total 405 00:22:45,320 --> 00:22:47,800 Speaker 1: was two point seven million people seven thousand coming in 406 00:22:47,880 --> 00:22:51,680 Speaker 1: for Asia, Latin America in Europe, but two million people 407 00:22:51,680 --> 00:22:54,240 Speaker 1: flying a day. As we saw that drop down to 408 00:22:54,359 --> 00:22:57,720 Speaker 1: nineties tho in April. The busiest airport in the world 409 00:22:57,720 --> 00:23:01,159 Speaker 1: at that time was an Anchorage a Lass shipping medical 410 00:23:01,160 --> 00:23:04,879 Speaker 1: equipment to North American and Europe. It's all changed and 411 00:23:04,920 --> 00:23:07,200 Speaker 1: it's all improving. And I think you have to be 412 00:23:07,400 --> 00:23:10,200 Speaker 1: optimistic and take a look at the G twenty countries. 413 00:23:10,760 --> 00:23:14,639 Speaker 1: What they're sort of MMT is called modern monetary theory 414 00:23:14,920 --> 00:23:17,840 Speaker 1: of printing money. But they're all doing it collectively. It's 415 00:23:17,880 --> 00:23:21,879 Speaker 1: not one country is the valuing the country's currency against another. 416 00:23:21,960 --> 00:23:24,960 Speaker 1: They're all doing collectively to fight this Third World war 417 00:23:25,080 --> 00:23:28,600 Speaker 1: called Corvette. And and that is basically showing up in 418 00:23:28,800 --> 00:23:32,800 Speaker 1: faith and hope in the economy. Well. And the thing is, though, listen, 419 00:23:32,880 --> 00:23:35,200 Speaker 1: this is contingent on you know, I just saw another headline. 420 00:23:35,200 --> 00:23:38,240 Speaker 1: I think it was Chili, uh coming up and finding 421 00:23:38,560 --> 00:23:41,560 Speaker 1: you know, the first case of Chili announcing its first 422 00:23:41,560 --> 00:23:43,320 Speaker 1: patient with a new strain of COVID nineteen. I mean, 423 00:23:43,359 --> 00:23:46,199 Speaker 1: this is an angle that maybe we weren't already for, 424 00:23:46,400 --> 00:23:48,040 Speaker 1: Frank and I do wonder we'll have to watch and 425 00:23:48,040 --> 00:23:50,440 Speaker 1: see where it goes. The expectations are at least we've 426 00:23:50,440 --> 00:23:54,320 Speaker 1: heard early on that the current COVID nineteen vaccine will 427 00:23:54,359 --> 00:23:56,840 Speaker 1: take care of these variants. But it's just a reminder 428 00:23:56,880 --> 00:23:58,879 Speaker 1: that it's going to take a while force to completely 429 00:23:58,960 --> 00:24:02,719 Speaker 1: feel you know, comfortable maybe about moving around in the 430 00:24:02,760 --> 00:24:06,320 Speaker 1: world and more importantly, Business travelers a smaller percentage of 431 00:24:06,359 --> 00:24:09,440 Speaker 1: airline passengers, but they are typically twice as lucrative. Right, 432 00:24:09,480 --> 00:24:13,080 Speaker 1: they contribute so much, maybe as much as of an 433 00:24:13,080 --> 00:24:15,800 Speaker 1: airline's profits. That's going to take a little bit longer, 434 00:24:15,800 --> 00:24:17,440 Speaker 1: so we're gonna have to be patient to see maybe 435 00:24:17,440 --> 00:24:20,359 Speaker 1: that impact when it comes to the airline's bottom Linees 436 00:24:20,400 --> 00:24:23,680 Speaker 1: just got about thirty seconds here. Well, the big part 437 00:24:23,720 --> 00:24:26,720 Speaker 1: we forget is that all these additional fees have been charging, 438 00:24:27,280 --> 00:24:30,320 Speaker 1: and they can ramp those up for tourist traveling as 439 00:24:30,359 --> 00:24:32,800 Speaker 1: they get back and push back to one point five 440 00:24:32,800 --> 00:24:35,880 Speaker 1: million people flying a day, getting that in the five 441 00:24:36,280 --> 00:24:39,800 Speaker 1: thousand of business travel that can come later because they 442 00:24:39,880 --> 00:24:43,439 Speaker 1: made up so much money. Ancillary fees were massive, and 443 00:24:43,480 --> 00:24:46,919 Speaker 1: they're bet on those. Yeah, Listen, airlines have gotten so 444 00:24:46,960 --> 00:24:49,880 Speaker 1: good at charging us for everything. Hey, Frank Colmes, thank 445 00:24:49,920 --> 00:24:52,120 Speaker 1: you so much. Have a happy New Year's CEO, chief 446 00:24:52,119 --> 00:24:55,320 Speaker 1: investment officer at US Global Investors, on the phone from 447 00:24:55,359 --> 00:24:59,880 Speaker 1: San Antonio, Texas. Thanks for listening to Bloomberg Markets podcast. 448 00:25:00,160 --> 00:25:03,480 Speaker 1: You can subscribe and listen to interviews at Apple Podcasts 449 00:25:03,600 --> 00:25:06,840 Speaker 1: or whatever a podcast platform you prefer. I'm Bonnie Quinn. 450 00:25:06,960 --> 00:25:09,639 Speaker 1: I'm on Twitter at Bonnie Quinn and I'm Paul Sweeney. 451 00:25:09,640 --> 00:25:12,280 Speaker 1: I'm on Twitter at pt Sweeney. Before the podcast, you 452 00:25:12,320 --> 00:25:14,720 Speaker 1: can always catch us worldwide at Bloomberg Radio