1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm Pim Fox. 2 00:00:08,760 --> 00:00:11,560 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg p m L 6 00:00:20,840 --> 00:00:32,080 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot Com. I 7 00:00:32,120 --> 00:00:34,640 Speaker 1: want to bring in Mutaba Ramen, the practice head of 8 00:00:34,840 --> 00:00:37,680 Speaker 1: Europe for the Eurasia Group based in London, to give 9 00:00:37,760 --> 00:00:40,840 Speaker 1: us some details about the G twenty summit meeting taking 10 00:00:40,880 --> 00:00:45,519 Speaker 1: place in Hamburg. President Donald Trump meeting today with Russian 11 00:00:45,520 --> 00:00:49,239 Speaker 1: President Vladimir Putin. Mutaba, thank you for being with us. 12 00:00:49,440 --> 00:00:52,440 Speaker 1: Can you tell us is there a specific agenda that 13 00:00:52,560 --> 00:00:55,720 Speaker 1: these meetings follow? I mean, what actually goes on when 14 00:00:56,720 --> 00:01:00,800 Speaker 1: the heads of state meet along with their farm police experts, 15 00:01:00,840 --> 00:01:03,400 Speaker 1: What goes on in the room. It would be a 16 00:01:03,560 --> 00:01:06,760 Speaker 1: It would be fantastic to be a file on the wall, 17 00:01:06,880 --> 00:01:10,560 Speaker 1: wouldn't it. I mean, shupers have been working up the 18 00:01:10,640 --> 00:01:15,440 Speaker 1: agenda in the preceding weeks for leaders to discuss when 19 00:01:15,480 --> 00:01:21,280 Speaker 1: they get in the room. Trade, climate change, um, navigating 20 00:01:21,360 --> 00:01:24,640 Speaker 1: some of the world's hotspots, Syria. I think that this 21 00:01:24,880 --> 00:01:28,160 Speaker 1: meeting what's interesting is it's going to be the first 22 00:01:28,200 --> 00:01:32,520 Speaker 1: opportunity for the rest of the global communities leadership to 23 00:01:32,600 --> 00:01:35,880 Speaker 1: meet with Donald Trump. And that's really what I think 24 00:01:35,920 --> 00:01:38,800 Speaker 1: this meeting is is going to be about. And when 25 00:01:38,840 --> 00:01:41,280 Speaker 1: Donald Trump is not in the room, that's certainly what 26 00:01:41,400 --> 00:01:44,200 Speaker 1: I think other leaders are going to be talking about. 27 00:01:44,200 --> 00:01:47,640 Speaker 1: That there's a real focus I think on um the 28 00:01:47,920 --> 00:01:51,720 Speaker 1: US and the US commitment to the multilateral system that 29 00:01:51,880 --> 00:01:55,120 Speaker 1: has really rustled furthers, let's say, in the G twenty. 30 00:01:55,120 --> 00:01:57,760 Speaker 1: And that's really what I think this meeting will be about. 31 00:01:57,800 --> 00:02:01,080 Speaker 1: How do how do leaders perceived Trump and what's their 32 00:02:01,160 --> 00:02:03,560 Speaker 1: expectation of the direction in which he's going to take 33 00:02:03,680 --> 00:02:08,400 Speaker 1: US policy over the medium terms. Is there any difference 34 00:02:08,760 --> 00:02:13,200 Speaker 1: in terms of sort of assessing the president because he 35 00:02:13,400 --> 00:02:17,600 Speaker 1: is not a veteran political figure like many of the 36 00:02:17,600 --> 00:02:21,839 Speaker 1: other heads of state. I mean, it certainly becomes more 37 00:02:21,919 --> 00:02:25,280 Speaker 1: difficult if Trump has proven to be one thing that 38 00:02:25,440 --> 00:02:30,160 Speaker 1: is unpredictable. So it's not even clear whether the policy 39 00:02:30,200 --> 00:02:33,800 Speaker 1: position staked out by his aids will be consistent with 40 00:02:33,840 --> 00:02:36,560 Speaker 1: the line he takes when he's in the room. And 41 00:02:36,840 --> 00:02:40,080 Speaker 1: think you clearly see concerns certainly, you know, for me 42 00:02:40,200 --> 00:02:44,520 Speaker 1: covering Europe from the German perspective, a tremendous amount of 43 00:02:44,520 --> 00:02:47,799 Speaker 1: concern about what Trump is doing over trade, over what 44 00:02:47,880 --> 00:02:51,400 Speaker 1: he's doing regarding climate change, and in some ways Angela 45 00:02:51,440 --> 00:02:55,880 Speaker 1: Merkel has set herself up in opposition to Donald Trump 46 00:02:55,880 --> 00:02:57,760 Speaker 1: and some of the positions that are being taken by 47 00:02:58,160 --> 00:03:00,640 Speaker 1: this administration and not like the is going to be 48 00:03:00,680 --> 00:03:05,000 Speaker 1: one subtext of the G twenty in Hamburg over the 49 00:03:05,040 --> 00:03:08,680 Speaker 1: next few days. Indeed, the bilacteral between Trump putin that 50 00:03:08,720 --> 00:03:12,200 Speaker 1: will be another. I think also the Chinese leadership and 51 00:03:12,280 --> 00:03:14,600 Speaker 1: their interaction with the US. These are all, I think 52 00:03:14,639 --> 00:03:18,000 Speaker 1: subplots in the bigger in the bigger picture that will 53 00:03:18,040 --> 00:03:21,640 Speaker 1: that will be playing out in Hamburg Mottaba there there's 54 00:03:21,680 --> 00:03:24,800 Speaker 1: a federal German federal election that will take place as 55 00:03:24,800 --> 00:03:30,520 Speaker 1: schedule to take place on the September. How has President 56 00:03:30,600 --> 00:03:35,720 Speaker 1: Donald Trump become if not a major you know, a 57 00:03:35,760 --> 00:03:38,920 Speaker 1: feature of that campaign, but but kind of in the 58 00:03:38,920 --> 00:03:43,120 Speaker 1: shadows of that campaign that Angela Merkel is is expected 59 00:03:43,160 --> 00:03:46,880 Speaker 1: to prevail, so more than certainly more than a shadow. 60 00:03:47,040 --> 00:03:49,360 Speaker 1: I agree with this Zoo. I think Trump is front 61 00:03:49,400 --> 00:03:54,240 Speaker 1: and center that the German election, primarily because Martin Schultz, 62 00:03:54,840 --> 00:03:57,760 Speaker 1: the head of the Social Democrats, who is competing with 63 00:03:57,920 --> 00:04:02,120 Speaker 1: Merkel for the chancellery has it is running on a 64 00:04:02,200 --> 00:04:07,480 Speaker 1: very explicit anti Trump ticket. That's really the identity that 65 00:04:07,560 --> 00:04:11,520 Speaker 1: Martin Schultz has assumed in the election, tapping into, frankly, 66 00:04:11,920 --> 00:04:16,640 Speaker 1: a tremendous amount of resentment for Trump among the German population. 67 00:04:17,000 --> 00:04:20,120 Speaker 1: He is reviled in Germany. Donald Trump is and Shelters 68 00:04:20,160 --> 00:04:23,640 Speaker 1: explicitly trying to tap into that. That put Merkel in 69 00:04:23,920 --> 00:04:26,599 Speaker 1: something of a difficult situation, and I think that's why 70 00:04:26,720 --> 00:04:29,920 Speaker 1: over the last weeks and months we've also seen her 71 00:04:30,080 --> 00:04:35,279 Speaker 1: move into a slightly more critical posture via via the US. 72 00:04:35,320 --> 00:04:38,599 Speaker 1: But of course Merkel is in office, she needs to 73 00:04:38,640 --> 00:04:41,040 Speaker 1: have a working relationship with the US. I think that's 74 00:04:41,440 --> 00:04:44,680 Speaker 1: there's a recognition, of course that fundamentally European interests and 75 00:04:44,720 --> 00:04:48,480 Speaker 1: German interests of tighter a productive relationship with the US. 76 00:04:48,560 --> 00:04:52,240 Speaker 1: And as such, I think that's a you put Merkel 77 00:04:52,279 --> 00:04:55,159 Speaker 1: in something of a difficult situation. But this is front 78 00:04:55,200 --> 00:04:58,120 Speaker 1: and centers a Trump question in the context of the 79 00:04:58,160 --> 00:05:01,960 Speaker 1: German election. Wonder if you could speak about the context 80 00:05:02,080 --> 00:05:05,040 Speaker 1: of the United Kingdom and its vote to leave the 81 00:05:05,080 --> 00:05:08,680 Speaker 1: European Union. Theresa May, the Prime minister also attending the 82 00:05:08,720 --> 00:05:12,520 Speaker 1: G twenty meeting in Hamburg. What is her stature like 83 00:05:12,920 --> 00:05:17,320 Speaker 1: at at a gathering such as this? Very bad, very bad. 84 00:05:17,320 --> 00:05:21,480 Speaker 1: It's similar to what it's like in the contextus her 85 00:05:21,560 --> 00:05:24,479 Speaker 1: European Council meetings, when she turns up in Brussels and 86 00:05:24,520 --> 00:05:26,320 Speaker 1: meets with other European has to say that. I I mean, 87 00:05:26,560 --> 00:05:29,800 Speaker 1: Theresa May is essentially a prime minister who is one 88 00:05:29,839 --> 00:05:33,679 Speaker 1: mistake away from oblivion. She has no mandate, she doesn't 89 00:05:33,720 --> 00:05:36,640 Speaker 1: have the confidence of her parliamentary party, she doesn't have 90 00:05:36,960 --> 00:05:40,960 Speaker 1: the confidence of the Tory base, not the population. I mean, 91 00:05:41,000 --> 00:05:44,080 Speaker 1: it's a really difficult situation for her to be and 92 00:05:44,320 --> 00:05:49,279 Speaker 1: I don't think she has any active objective at the 93 00:05:49,320 --> 00:05:53,280 Speaker 1: G twenty. I think her mandate, if she does have one, 94 00:05:53,480 --> 00:05:56,520 Speaker 1: will probably be to deliver Brexit. Whether she can survive 95 00:05:56,600 --> 00:05:59,039 Speaker 1: to do that remains in question. I don't think she's 96 00:05:59,080 --> 00:06:01,800 Speaker 1: a player on the noble stage. I think that, you know, 97 00:06:01,839 --> 00:06:05,000 Speaker 1: the UK is is really in a very defensive, difficult 98 00:06:05,080 --> 00:06:09,080 Speaker 1: situation that is aggravated by the fact she really did 99 00:06:09,160 --> 00:06:13,400 Speaker 1: botch her election and and and and erode her majority. 100 00:06:13,680 --> 00:06:17,600 Speaker 1: It's very difficult for the reason you mentioned the anti 101 00:06:17,640 --> 00:06:22,800 Speaker 1: Trump sentiment that prevails in Germany, and I'm wondering if 102 00:06:22,839 --> 00:06:28,120 Speaker 1: that is distracting European leaders from issues such as immigration, 103 00:06:28,320 --> 00:06:34,680 Speaker 1: border controls, Greece, the potential for reunification of Cyprus. I mean, 104 00:06:34,720 --> 00:06:38,000 Speaker 1: there are a lot of very specific European issues that 105 00:06:38,200 --> 00:06:41,720 Speaker 1: have yet to be addressed. Actually not, you know, it's interesting, 106 00:06:41,760 --> 00:06:44,760 Speaker 1: actually not. I think the combination of Donald Trump and 107 00:06:44,880 --> 00:06:50,440 Speaker 1: his ambiguous commitment to multilateralism, the facts of the Brexit 108 00:06:50,560 --> 00:06:54,680 Speaker 1: votes m in combination with Emmanuel Macron in France, not 109 00:06:54,800 --> 00:06:58,520 Speaker 1: think short of a revolution in France, but Macron has 110 00:06:58,560 --> 00:07:02,000 Speaker 1: implemented and achieved in the last several months. The combination 111 00:07:02,080 --> 00:07:06,279 Speaker 1: of those three things is creating impetus in Europe to 112 00:07:06,400 --> 00:07:09,640 Speaker 1: do more in Europe, for the European Union, to become 113 00:07:09,680 --> 00:07:14,679 Speaker 1: more cohesive, to integrate and work together collectively, to address 114 00:07:14,720 --> 00:07:18,760 Speaker 1: more security and defense challenges then and of course to 115 00:07:18,840 --> 00:07:23,200 Speaker 1: put the Eurozone on a more stable footing. So thankin 116 00:07:23,320 --> 00:07:30,200 Speaker 1: Trump is actually one factor alongside Brexit um, alongside Macron 117 00:07:30,720 --> 00:07:34,200 Speaker 1: that are driving there's certainly the French and the Germans 118 00:07:34,240 --> 00:07:36,400 Speaker 1: to say, look, this is a moment that sees the 119 00:07:36,400 --> 00:07:41,000 Speaker 1: opportunity let's make let's take advantage of this difficult situation 120 00:07:41,120 --> 00:07:43,520 Speaker 1: to do more in the European context. And I think 121 00:07:43,520 --> 00:07:46,960 Speaker 1: we're going to see that over there the next few years. So, 122 00:07:47,040 --> 00:07:50,720 Speaker 1: if if anything, we're slightly more positive about the outlooks 123 00:07:50,720 --> 00:07:54,920 Speaker 1: for Europe because I do think the political conversation is 124 00:07:54,960 --> 00:07:57,600 Speaker 1: evolving in a way that's likely to be beneficial for 125 00:07:57,680 --> 00:08:00,440 Speaker 1: Europe and the EU. I want to thank very much 126 00:08:00,480 --> 00:08:04,080 Speaker 1: for spending time with us. Muttaba Ramen is the practice 127 00:08:04,120 --> 00:08:07,320 Speaker 1: head for Europe for the Eurasia Group. He was joining 128 00:08:07,400 --> 00:08:21,720 Speaker 1: us from London and here to tell us more about 129 00:08:21,720 --> 00:08:24,520 Speaker 1: steel but also medals and mining is our own Joe 130 00:08:24,560 --> 00:08:27,520 Speaker 1: Dough He is our medals and mining reporter for Bloomberg 131 00:08:27,560 --> 00:08:30,840 Speaker 1: News and he can be followed at Joe Dough d 132 00:08:31,120 --> 00:08:34,800 Speaker 1: E a u X. There you go. Um, Joe, you 133 00:08:34,840 --> 00:08:37,160 Speaker 1: were just in Pittsburgh, you were tell him you were 134 00:08:37,200 --> 00:08:41,120 Speaker 1: at a blast furnace, the original blast furnace for US Steel. Correct, 135 00:08:41,559 --> 00:08:45,280 Speaker 1: it was. Well, Um, we'll be putting out a story 136 00:08:45,600 --> 00:08:49,040 Speaker 1: sometime soon about that. To to look at the industry 137 00:08:49,040 --> 00:08:51,040 Speaker 1: on a hole and and and use steel as well. 138 00:08:51,120 --> 00:08:53,160 Speaker 1: And you know, as you just heard Andy talking about 139 00:08:53,200 --> 00:08:55,400 Speaker 1: him in the big, big focus here is in the 140 00:08:55,400 --> 00:08:58,160 Speaker 1: immediate term, what is going to come of too thirty two, 141 00:08:58,160 --> 00:09:00,320 Speaker 1: what is the Trump administration going to put out there 142 00:09:00,720 --> 00:09:03,880 Speaker 1: on steel? And again, as Andy did point out rightly, 143 00:09:04,520 --> 00:09:06,520 Speaker 1: the market is pretty much just waiting for that. I mean, 144 00:09:06,640 --> 00:09:10,600 Speaker 1: every other day there's somebody calling in asking when exactly 145 00:09:10,679 --> 00:09:12,360 Speaker 1: is this going to common. It's one of the things 146 00:09:12,360 --> 00:09:14,240 Speaker 1: that we've been chasing as well. Well. One of the things. 147 00:09:14,240 --> 00:09:16,400 Speaker 1: The reason I brought up the blast furnace actually was 148 00:09:16,440 --> 00:09:20,080 Speaker 1: because I wanted you to describe the number of people 149 00:09:20,360 --> 00:09:25,520 Speaker 1: necessary to produce steel, because of course the tariff is 150 00:09:25,600 --> 00:09:29,360 Speaker 1: linked to all right, if there are foreign imports that 151 00:09:29,400 --> 00:09:31,839 Speaker 1: are being dumped in the United States in order to 152 00:09:31,920 --> 00:09:35,199 Speaker 1: gain market share, that hurts US companies because then they 153 00:09:35,200 --> 00:09:38,640 Speaker 1: don't hire workers. And I'm wondering whether you could speak 154 00:09:38,679 --> 00:09:40,880 Speaker 1: to that. Yeah, it's it's a it's an interesting question 155 00:09:40,920 --> 00:09:43,199 Speaker 1: and one that we who cover the steel industry are 156 00:09:43,320 --> 00:09:46,559 Speaker 1: kind of bogged in, bogged down in on a daily basis. 157 00:09:46,720 --> 00:09:49,079 Speaker 1: Thomas Bauschevill, who's one of our reporters in London it 158 00:09:49,160 --> 00:09:52,439 Speaker 1: covers the European uh steel market, had a great story 159 00:09:52,480 --> 00:09:54,360 Speaker 1: in Business Week that went out about a week ago, 160 00:09:54,440 --> 00:09:57,000 Speaker 1: kind of pointing out so few people that need to 161 00:09:57,080 --> 00:10:00,319 Speaker 1: run many of the steel uh these steel mills right 162 00:10:00,400 --> 00:10:02,800 Speaker 1: and in the United States, the numbers about a hundred 163 00:10:02,840 --> 00:10:06,839 Speaker 1: and forty thousand steel workers directly related to making you 164 00:10:06,880 --> 00:10:09,720 Speaker 1: know what we know in blast furnaces or electric arc furnaces. 165 00:10:10,000 --> 00:10:11,680 Speaker 1: And we actually had a story a couple of weeks out, 166 00:10:12,240 --> 00:10:13,520 Speaker 1: a couple of weeks ago that I did. I was 167 00:10:13,559 --> 00:10:17,520 Speaker 1: talking to some industry, some downstream you know, industry associations 168 00:10:17,559 --> 00:10:21,840 Speaker 1: on steel, and they had a number what are they fabricators. Fabricators, 169 00:10:21,880 --> 00:10:24,240 Speaker 1: you know, they make the panels, or they'll make the trinkets, 170 00:10:24,280 --> 00:10:26,280 Speaker 1: that make the machines that make the metal, you know, 171 00:10:26,320 --> 00:10:28,160 Speaker 1: all the all those little things. And and in that 172 00:10:28,240 --> 00:10:30,400 Speaker 1: conversation an interesting point that they made to us that 173 00:10:30,440 --> 00:10:32,440 Speaker 1: we put out on the wire was they said, well, 174 00:10:32,440 --> 00:10:34,840 Speaker 1: there might be a hundred forty steel jobs, but we 175 00:10:35,000 --> 00:10:38,280 Speaker 1: estimate that all in jobs that are due to the 176 00:10:38,320 --> 00:10:40,640 Speaker 1: downstream that all of the steel goes into could affect 177 00:10:40,640 --> 00:10:44,280 Speaker 1: as many six point five million manufacturing jobs. So when 178 00:10:44,280 --> 00:10:46,640 Speaker 1: you start getting into the numbers of well where the 179 00:10:46,720 --> 00:10:49,720 Speaker 1: jobs at and who has more jobs you you you 180 00:10:49,760 --> 00:10:52,240 Speaker 1: start seeing some of these these these numbers that you know, 181 00:10:52,480 --> 00:10:55,160 Speaker 1: seem to maybe tell a bit of a different story 182 00:10:55,160 --> 00:10:57,280 Speaker 1: than what we might be getting in terms of a 183 00:10:57,320 --> 00:10:59,760 Speaker 1: full story from the administration, which I guess is what 184 00:10:59,880 --> 00:11:01,720 Speaker 1: I'm guessing is what you're kind of hinting. Yeah, Well, 185 00:11:01,760 --> 00:11:06,839 Speaker 1: I wanted to know, then, did these downstream companies they 186 00:11:06,920 --> 00:11:11,160 Speaker 1: for the tariffs? The downstream companies are a bit more 187 00:11:11,160 --> 00:11:14,079 Speaker 1: careful with how they do approach it, right because um, 188 00:11:14,400 --> 00:11:17,560 Speaker 1: many of them increases their input costs and they've got 189 00:11:17,559 --> 00:11:20,160 Speaker 1: to raise prices to their customers. Absolutely. And and one 190 00:11:20,200 --> 00:11:21,680 Speaker 1: of the things we had a we had a really 191 00:11:21,679 --> 00:11:24,800 Speaker 1: interesting story out of a month ago on the aluminum side. 192 00:11:24,800 --> 00:11:27,719 Speaker 1: And I was sitting at a conference and the man 193 00:11:27,760 --> 00:11:31,240 Speaker 1: who buys all of the the aluminum for Miller cores 194 00:11:31,280 --> 00:11:34,640 Speaker 1: from Molson Cores, uh says, up there, listen, if we 195 00:11:34,679 --> 00:11:37,760 Speaker 1: get tariffs on the aluminum side, it's going to cost 196 00:11:37,840 --> 00:11:40,320 Speaker 1: us and ultimately it could cost the consumers. So I 197 00:11:40,360 --> 00:11:42,720 Speaker 1: said to my editor, gosh, we gotta write this. And 198 00:11:42,800 --> 00:11:44,960 Speaker 1: sure enough, you know, it got a lot of pick 199 00:11:45,040 --> 00:11:47,560 Speaker 1: up because it's so basic, right, if you and I 200 00:11:47,720 --> 00:11:50,000 Speaker 1: are going to go out and buy our you know, 201 00:11:50,040 --> 00:11:52,400 Speaker 1: our cores light over the weekend and suddenly, you know, 202 00:11:52,440 --> 00:11:55,640 Speaker 1: the Trump administration has levied these terrorists, you know it. 203 00:11:55,760 --> 00:11:58,199 Speaker 1: I mean there's a lot, there's a lot of complexity 204 00:11:58,240 --> 00:12:00,760 Speaker 1: to it, but in some way we could incur, you know, 205 00:12:00,880 --> 00:12:03,679 Speaker 1: minor changing costs and that's really what they were getting at. 206 00:12:03,960 --> 00:12:07,320 Speaker 1: And it's very similar with what the downstream producers of 207 00:12:07,480 --> 00:12:10,080 Speaker 1: steel are trying to argue as well. Well. It seems 208 00:12:10,080 --> 00:12:12,319 Speaker 1: as though, you know, the supply chain runs both ways. 209 00:12:12,400 --> 00:12:14,760 Speaker 1: It runs up and then it runs down and ultimately 210 00:12:14,880 --> 00:12:18,520 Speaker 1: ends with the consumer. Someone ends up incurring the cost 211 00:12:18,600 --> 00:12:21,240 Speaker 1: that gets passed through, and that is the key thing 212 00:12:21,280 --> 00:12:23,679 Speaker 1: that everybody's focusing on and is why there has been 213 00:12:23,679 --> 00:12:25,920 Speaker 1: a struggle within the White House as to whether or 214 00:12:25,920 --> 00:12:28,000 Speaker 1: not they're going to levy these tariffs or a tariff 215 00:12:28,080 --> 00:12:30,520 Speaker 1: right quota, and that's something we've been chasing now for 216 00:12:30,600 --> 00:12:32,439 Speaker 1: quite a few months. All Right, Well, we look forward 217 00:12:32,480 --> 00:12:35,120 Speaker 1: to you continuing to keep us up to date as always. 218 00:12:35,120 --> 00:12:38,319 Speaker 1: In fact, I'll buy you that Coulson that Molson co. 219 00:12:39,559 --> 00:12:42,400 Speaker 1: Joe Doe is our metals and mining reporter for Bloomberg 220 00:12:42,480 --> 00:12:44,480 Speaker 1: News once again. You can follow him on Twitter at 221 00:12:44,679 --> 00:12:58,680 Speaker 1: Joe dough. Right now, I want to bring in Andrew Chamberlain. 222 00:12:58,800 --> 00:13:02,800 Speaker 1: He is the chief economist for glass Door. Uh, Andrew Chamberlain, 223 00:13:03,000 --> 00:13:04,800 Speaker 1: Great to have you with us. You know, we got 224 00:13:04,840 --> 00:13:07,400 Speaker 1: some of the details of the report, and there are 225 00:13:07,440 --> 00:13:10,440 Speaker 1: lots of numbers. But in your study, you tell us 226 00:13:10,520 --> 00:13:14,080 Speaker 1: the cities in which pay is increasing and also the 227 00:13:14,160 --> 00:13:17,240 Speaker 1: actual jobs that you want to get and the skills 228 00:13:17,280 --> 00:13:19,560 Speaker 1: that you need to get that increased pay. Tell us 229 00:13:19,559 --> 00:13:22,960 Speaker 1: about it. Yeah, Sometimes tech companies like glass Door have 230 00:13:23,040 --> 00:13:25,160 Speaker 1: better data on the labor market than the VLS and 231 00:13:25,200 --> 00:13:27,240 Speaker 1: so what we do is we look at ten big 232 00:13:27,280 --> 00:13:29,440 Speaker 1: metros around the country and we use real time shalary 233 00:13:29,480 --> 00:13:31,720 Speaker 1: data on glass or to show pay growth. So among 234 00:13:31,800 --> 00:13:35,120 Speaker 1: the cities, if you're in San Francisco, Seattle, or New York, 235 00:13:35,160 --> 00:13:39,440 Speaker 1: you're seeing fast pay growth today, well above national average. However, 236 00:13:39,480 --> 00:13:42,320 Speaker 1: if you're in Houston, Atlanta, or d C you're well 237 00:13:42,360 --> 00:13:44,120 Speaker 1: below the average. So what it shows you is that 238 00:13:44,240 --> 00:13:47,280 Speaker 1: slow overall pay number. In the VLS report, it hides 239 00:13:47,320 --> 00:13:49,120 Speaker 1: a lot of diversity depends on where you live and 240 00:13:49,120 --> 00:13:50,960 Speaker 1: what you do for a living. Well, here's some of 241 00:13:51,000 --> 00:13:54,439 Speaker 1: that diversity and the numbers right in San Francisco, median 242 00:13:54,720 --> 00:13:57,480 Speaker 1: base pay has increased more than two and a half 243 00:13:57,640 --> 00:14:01,000 Speaker 1: percent from previous year and it stands at just over 244 00:14:01,120 --> 00:14:04,880 Speaker 1: sixty eight thousand dollars a year. Yeah, that's more than 245 00:14:05,080 --> 00:14:07,320 Speaker 1: ten thousand, more than fifteen thousand dollars more than the 246 00:14:07,480 --> 00:14:11,720 Speaker 1: national average. Actually, so UM, definitely various. Pay growth varies 247 00:14:11,720 --> 00:14:14,160 Speaker 1: a lot by city today, but also varies a lot 248 00:14:14,200 --> 00:14:17,640 Speaker 1: by jobs. So if you are a recruiter today, so 249 00:14:17,679 --> 00:14:20,640 Speaker 1: in today's very tight labor market with record low unemployment, 250 00:14:20,680 --> 00:14:23,800 Speaker 1: many cities recruiters are seeing pay grow it over eight 251 00:14:23,800 --> 00:14:26,400 Speaker 1: percent year over year, so they are in demand as 252 00:14:26,400 --> 00:14:30,880 Speaker 1: companies relying on poaching candidates from the competition. UM, we're 253 00:14:30,920 --> 00:14:33,920 Speaker 1: also seeing some fast pay growth amongst some low skilled jobs, 254 00:14:33,920 --> 00:14:37,840 Speaker 1: so jobs like warehouse associate and delivery driver. These are 255 00:14:37,840 --> 00:14:40,200 Speaker 1: people that are fueling the supply chain in places like 256 00:14:40,240 --> 00:14:43,360 Speaker 1: Amazon and Walmart, and they're actually seeing a pay rise today. 257 00:14:43,560 --> 00:14:47,440 Speaker 1: I want to mention the barista and restaurant cook job 258 00:14:47,480 --> 00:14:50,720 Speaker 1: titles only because you say that those are also increasing 259 00:14:50,760 --> 00:14:54,240 Speaker 1: and pay over seven percent increase ye over a year. Yeah, 260 00:14:54,240 --> 00:14:56,800 Speaker 1: these are low skilled roles. They only pay about twenty 261 00:14:56,840 --> 00:15:00,120 Speaker 1: five to twenty nine thousand dollars per year, but we're 262 00:15:00,160 --> 00:15:04,040 Speaker 1: seeing fast rises of seven eight percent per year. This 263 00:15:04,200 --> 00:15:07,480 Speaker 1: partly reflects a huge number of job openings in leisure 264 00:15:07,480 --> 00:15:12,720 Speaker 1: and hospitality and retail today, and also um reflects the 265 00:15:12,760 --> 00:15:15,560 Speaker 1: minimum wage hikes that we've seen around the country this year. 266 00:15:15,880 --> 00:15:19,520 Speaker 1: That pay range absolutely is being affected by these you know, 267 00:15:19,640 --> 00:15:22,000 Speaker 1: twelve and fifteen dollar minimum wages we're seen in big cities. 268 00:15:22,240 --> 00:15:25,680 Speaker 1: What about some of the highest paying jobs you mentioned, 269 00:15:25,680 --> 00:15:28,000 Speaker 1: Pharmacists is at the top of the list. Tell us 270 00:15:28,040 --> 00:15:32,560 Speaker 1: about this particular trend. Yeah, well, many healthcare jobs do 271 00:15:32,600 --> 00:15:34,600 Speaker 1: show up towards the top of the list. Healthcare is 272 00:15:34,640 --> 00:15:36,880 Speaker 1: like the eight pound guerrilla of the labor market today. 273 00:15:37,000 --> 00:15:40,280 Speaker 1: Lots of jobs, like a million job openings in healthcare. UM. Yeah, 274 00:15:40,280 --> 00:15:43,440 Speaker 1: pharmacist making about a hundred and twenty four thousand dollars 275 00:15:43,480 --> 00:15:46,400 Speaker 1: per year in our data. Other high paying jobs, there's 276 00:15:46,440 --> 00:15:49,680 Speaker 1: many in tech. For example, data scientists, you're making about 277 00:15:49,760 --> 00:15:53,240 Speaker 1: nine dollars on average. Uh. And that was an increase 278 00:15:53,280 --> 00:15:55,800 Speaker 1: of nearly three percent from the previous year. Yeah, it's 279 00:15:55,840 --> 00:15:58,600 Speaker 1: high paying and it's fast growing. Uh. And you know, 280 00:15:58,640 --> 00:16:00,920 Speaker 1: and that's the national average. If you're in San Francisco, 281 00:16:01,000 --> 00:16:03,680 Speaker 1: you should expect six figures for sure for data scientists 282 00:16:03,840 --> 00:16:06,520 Speaker 1: and those other tech roles like solutions architect, which many 283 00:16:06,520 --> 00:16:08,320 Speaker 1: people may not even know what that is, but it's 284 00:16:08,320 --> 00:16:11,200 Speaker 1: a higher level software engineer who helps craft many of 285 00:16:11,240 --> 00:16:14,560 Speaker 1: the software products that we use today. UM and also 286 00:16:14,720 --> 00:16:16,800 Speaker 1: UX designers, So if you are an art student in 287 00:16:16,840 --> 00:16:19,440 Speaker 1: college and you want to get into tech, UX designer 288 00:16:19,600 --> 00:16:22,160 Speaker 1: is a someone who designs the front end that we 289 00:16:22,200 --> 00:16:24,880 Speaker 1: see on many websites. And they're seeing pay rise at 290 00:16:24,920 --> 00:16:27,400 Speaker 1: four percent per year, and it's almost almost eighty thousand 291 00:16:27,440 --> 00:16:30,760 Speaker 1: dollars a year. I'm speaking with Dr Andrew Chamberlain. He 292 00:16:30,840 --> 00:16:34,120 Speaker 1: is the chief economist for glass Door, and he's based 293 00:16:34,120 --> 00:16:39,280 Speaker 1: in Mill Valley, California. Andrew, as far as the jobs 294 00:16:39,440 --> 00:16:42,560 Speaker 1: that are showing the weakest growth, I'm wondering if you 295 00:16:42,600 --> 00:16:46,080 Speaker 1: could spend some time enlightening us about the jobs where 296 00:16:46,120 --> 00:16:48,520 Speaker 1: you don't have a lot of leverage. Well, jobs that 297 00:16:48,560 --> 00:16:51,800 Speaker 1: are being affected by automation include more than just blue 298 00:16:51,840 --> 00:16:54,760 Speaker 1: collar jobs that many people think about. There's some jobs 299 00:16:54,800 --> 00:16:59,160 Speaker 1: like a loan officer UM, for example, where you know 300 00:16:59,600 --> 00:17:02,800 Speaker 1: many people can get loans without talking to a person anymore, 301 00:17:02,880 --> 00:17:05,800 Speaker 1: just using a web based interface, so they are seeing 302 00:17:05,840 --> 00:17:08,720 Speaker 1: pay fall at about five percent year over year. Other 303 00:17:08,760 --> 00:17:12,600 Speaker 1: types of jobs that are losing ground include manufacturing related 304 00:17:12,760 --> 00:17:16,440 Speaker 1: jobs like design engineer. So a design engineer is someone 305 00:17:16,440 --> 00:17:19,280 Speaker 1: who designs many of the physical products we use. They're 306 00:17:19,280 --> 00:17:23,040 Speaker 1: seeing pay fall about four percent year over year. So, um, 307 00:17:23,080 --> 00:17:25,480 Speaker 1: what this shows is that the labor market today is 308 00:17:25,600 --> 00:17:29,000 Speaker 1: very diverse. Underneath that two and a half percent wage 309 00:17:29,000 --> 00:17:32,719 Speaker 1: growth figure from the BLS report, it's varies hugely. And 310 00:17:32,720 --> 00:17:35,960 Speaker 1: so my view is policy makers, especially at the FED, 311 00:17:36,040 --> 00:17:38,240 Speaker 1: looking at that slow growth number, they need to be 312 00:17:38,280 --> 00:17:40,639 Speaker 1: digging below the surface and looking at real jobs to 313 00:17:40,680 --> 00:17:43,800 Speaker 1: see where there's labor shortages and where there aren't today. Well, 314 00:17:43,840 --> 00:17:46,120 Speaker 1: you mentioned we were talking about remember you mentioned about 315 00:17:46,119 --> 00:17:48,240 Speaker 1: pharmacists a hundred and twenty let's say a hundred and 316 00:17:48,200 --> 00:17:50,960 Speaker 1: twenty four thousand dollars a year. But then you look 317 00:17:50,960 --> 00:17:53,920 Speaker 1: at things like certified nursing assistant making only twenty eight 318 00:17:53,920 --> 00:17:57,199 Speaker 1: thousand and a pharmacy technician making thirty thousand, so that 319 00:17:57,240 --> 00:18:00,320 Speaker 1: disparity is large. Yeah. Health care is an interesting case 320 00:18:00,320 --> 00:18:04,520 Speaker 1: where there's many very high paying roles, especially physicians, which 321 00:18:04,640 --> 00:18:07,359 Speaker 1: often earned two and three hundred thousand per year for 322 00:18:07,400 --> 00:18:10,040 Speaker 1: a median um, and then there's many lower skilled roles 323 00:18:10,080 --> 00:18:12,320 Speaker 1: like M E, M T, S, for example, and and 324 00:18:12,720 --> 00:18:17,159 Speaker 1: nursing assistant um SO. UM. On average, healthcare UM is 325 00:18:17,200 --> 00:18:20,479 Speaker 1: like a driver of middle wage job growth today, and 326 00:18:20,520 --> 00:18:25,600 Speaker 1: it is definitely the single biggest sector adding jobs today 327 00:18:25,600 --> 00:18:29,720 Speaker 1: by far. Um SO. I'm actually quite optimistic about healthcare. 328 00:18:29,720 --> 00:18:31,960 Speaker 1: As he got retiring baby boomers, he's more and more 329 00:18:31,960 --> 00:18:35,480 Speaker 1: healthcare services. UM. Let's just hope that any healthcare form 330 00:18:35,520 --> 00:18:39,560 Speaker 1: that happens and Congress does not disrupt this giant, giant 331 00:18:39,640 --> 00:18:41,879 Speaker 1: sector that's about a six of the US economy. I 332 00:18:41,880 --> 00:18:43,760 Speaker 1: want to thank you very much for spending time with us. 333 00:18:43,840 --> 00:18:47,040 Speaker 1: Dr Andrew Chamberlain. He is chief economist for glass Door 334 00:18:47,320 --> 00:19:02,920 Speaker 1: based in Mill Valley, California. Now I want to turn 335 00:19:03,000 --> 00:19:06,399 Speaker 1: to another topic that is tangential to the meeting between 336 00:19:06,480 --> 00:19:09,840 Speaker 1: President Donald Trump and President Vladimir Putin, and that is 337 00:19:09,960 --> 00:19:15,280 Speaker 1: cyber security. Michael Riley is our cybersecurity reporter for Bloomberg, 338 00:19:15,440 --> 00:19:20,920 Speaker 1: and Michael h the Russians and cyber security and cyber 339 00:19:21,000 --> 00:19:24,439 Speaker 1: attacks that's been in the news. I'm wondering if you 340 00:19:24,440 --> 00:19:28,399 Speaker 1: could talk a little bit about those potential attacks, but 341 00:19:29,160 --> 00:19:34,840 Speaker 1: add in the potential for US electrical grid disruptions because 342 00:19:34,840 --> 00:19:39,920 Speaker 1: of these kinds of technologies. Yeah. Absolutely, so, uh, the 343 00:19:40,040 --> 00:19:43,080 Speaker 1: US government sent out on alert to utilities last week. 344 00:19:43,200 --> 00:19:46,280 Speaker 1: It was pretty thin on details, but it did describe 345 00:19:46,280 --> 00:19:49,760 Speaker 1: infiltrations into the nuclear and power sector. We talked to 346 00:19:49,880 --> 00:19:53,280 Speaker 1: some senior US officials and and what we found was 347 00:19:53,320 --> 00:19:56,439 Speaker 1: that there's actually something very serious going on behind the scenes. 348 00:19:56,800 --> 00:20:00,920 Speaker 1: Even as as Vladiman Putin and Donald Trump are meeting today, 349 00:20:01,040 --> 00:20:03,600 Speaker 1: more than a dozen or at least a dozen power 350 00:20:03,600 --> 00:20:07,440 Speaker 1: plants UM have been breached since May um and these 351 00:20:07,440 --> 00:20:09,720 Speaker 1: are the the you know, there's there's not an immediate 352 00:20:09,720 --> 00:20:12,239 Speaker 1: threat to public safety, but the hackers seemed to be 353 00:20:12,280 --> 00:20:16,359 Speaker 1: looking for information and ways to get from the basic 354 00:20:16,400 --> 00:20:19,359 Speaker 1: computers of the plant into the control systems UM that 355 00:20:19,640 --> 00:20:21,440 Speaker 1: which means that they might be able to to to 356 00:20:21,600 --> 00:20:24,960 Speaker 1: impact the power going onto the grid create a cascading failure. 357 00:20:25,119 --> 00:20:27,840 Speaker 1: Why this is why this is important is that it's 358 00:20:27,920 --> 00:20:29,679 Speaker 1: you know, we've seen this happen in Ukraine twice in 359 00:20:29,680 --> 00:20:32,160 Speaker 1: the last year, where where hackers are actually taken down 360 00:20:32,160 --> 00:20:34,800 Speaker 1: the electrical grid. I wanted to ask you, excuse me, 361 00:20:34,880 --> 00:20:38,560 Speaker 1: specifically about the Wolf Creek nuclear power plant in Kansas. 362 00:20:38,600 --> 00:20:41,199 Speaker 1: What went on there? Yeah, So, when we do know 363 00:20:41,400 --> 00:20:43,080 Speaker 1: is that that among the twelve plants. One of them, 364 00:20:43,200 --> 00:20:44,639 Speaker 1: at least one of them, was a nuclear plant, and 365 00:20:44,680 --> 00:20:48,200 Speaker 1: it's a a plant in Kansas in a relatively rural 366 00:20:48,240 --> 00:20:51,080 Speaker 1: area called Wolf Creek. This is a plant that was 367 00:20:51,600 --> 00:20:54,160 Speaker 1: sort of built to be uh the model of three 368 00:20:54,160 --> 00:20:57,960 Speaker 1: Mile Island UM. It's quite an aging plant UM and 369 00:20:58,080 --> 00:21:00,000 Speaker 1: it was one of the plants hit by the hackers. 370 00:21:00,080 --> 00:21:02,360 Speaker 1: It comes to nuclear plants, both the nuclear and conventional 371 00:21:02,440 --> 00:21:05,960 Speaker 1: power plants were hit were hacked, but nuclear is of 372 00:21:05,960 --> 00:21:08,800 Speaker 1: of specific concerns because it's a it's a very particular 373 00:21:08,880 --> 00:21:12,359 Speaker 1: kind of system. And even though the nuclear core itself 374 00:21:12,440 --> 00:21:14,679 Speaker 1: is quite protected, in part because the technology is very 375 00:21:14,720 --> 00:21:17,879 Speaker 1: old and so not really vulnerable to digital attacks, it's 376 00:21:17,880 --> 00:21:19,320 Speaker 1: it's one of those things where if you can shut 377 00:21:19,320 --> 00:21:22,320 Speaker 1: down the turbin all the you still have a nuclear 378 00:21:22,359 --> 00:21:24,400 Speaker 1: core that's producing a lot of energy, and that energy 379 00:21:24,480 --> 00:21:26,520 Speaker 1: has to go somewhere. There are safety systems that are 380 00:21:26,520 --> 00:21:28,639 Speaker 1: supposed to kick in, but those safety systems themselves are 381 00:21:28,720 --> 00:21:30,960 Speaker 1: vulnerable to attack. So it gets quite scary when you 382 00:21:30,960 --> 00:21:34,720 Speaker 1: start looking at nuclear plants. You've also written that the 383 00:21:34,880 --> 00:21:37,840 Speaker 1: teams from the Homeland Security and the FBI have been 384 00:21:37,920 --> 00:21:41,840 Speaker 1: working to secure these power stations, but they're not necessarily 385 00:21:41,880 --> 00:21:46,080 Speaker 1: informing local or state officials. Why wouldn't they do that? Well, 386 00:21:46,119 --> 00:21:48,600 Speaker 1: I think the first reaction in cases like this is 387 00:21:48,640 --> 00:21:52,520 Speaker 1: that it's a national security event. These are hackers that 388 00:21:52,520 --> 00:21:54,480 Speaker 1: that are working for a different country, and any time 389 00:21:54,480 --> 00:21:57,080 Speaker 1: that they start messing around with critical infrastructure that counts 390 00:21:57,080 --> 00:21:59,120 Speaker 1: as national security. And I think that the knee jerk 391 00:21:59,119 --> 00:22:01,639 Speaker 1: reaction is keep every and quite secret. I think that 392 00:22:01,680 --> 00:22:04,199 Speaker 1: a lot of the utilities themselves would say, look, this 393 00:22:04,280 --> 00:22:06,520 Speaker 1: isn't an imminent threat to public safety. We're trying to 394 00:22:06,520 --> 00:22:09,880 Speaker 1: get these these hacks under control. That the hackers haven't 395 00:22:09,920 --> 00:22:12,879 Speaker 1: got to the control systems, so there's nothing imminent that 396 00:22:12,880 --> 00:22:14,399 Speaker 1: would cause us to sort of go to state and 397 00:22:14,440 --> 00:22:16,600 Speaker 1: local officials. But we did talk to some local officials 398 00:22:16,640 --> 00:22:19,399 Speaker 1: who said who who had gotten inkling of what was 399 00:22:19,440 --> 00:22:21,560 Speaker 1: going on? And and they were clearly a little dismay 400 00:22:21,600 --> 00:22:24,840 Speaker 1: that they hadn't been informed. You've also written that much 401 00:22:25,000 --> 00:22:28,400 Speaker 1: of the work that the hackers have done has come 402 00:22:28,480 --> 00:22:33,480 Speaker 1: from machine servers that are located in places such as Germany, Italy, Malaysia, 403 00:22:33,840 --> 00:22:38,200 Speaker 1: and Turkey. Is this new? You know? That's actually how 404 00:22:38,200 --> 00:22:41,960 Speaker 1: hackers who are trying to hide their identity work. They 405 00:22:42,040 --> 00:22:45,560 Speaker 1: don't hack directly from their home computers whatever country they're 406 00:22:45,600 --> 00:22:48,280 Speaker 1: operating from. What they do is they hack into other 407 00:22:48,320 --> 00:22:51,639 Speaker 1: computers and across the globe and use those as hot points. 408 00:22:51,880 --> 00:22:53,920 Speaker 1: And the whole idea is to try and disguise who 409 00:22:53,920 --> 00:22:58,600 Speaker 1: they are. There's a really complicated counter intelligence maneuver where 410 00:22:58,640 --> 00:23:00,879 Speaker 1: you can try and follow that track from computer computer 411 00:23:01,119 --> 00:23:02,960 Speaker 1: So it's not exactly new, it's a it's it's sort 412 00:23:03,000 --> 00:23:05,199 Speaker 1: sort of tradecraft when it comes to nation state hackers, 413 00:23:05,680 --> 00:23:07,600 Speaker 1: but it does create a sort of a veil that 414 00:23:07,640 --> 00:23:09,440 Speaker 1: has to be piers to figure out who actually is 415 00:23:09,480 --> 00:23:13,199 Speaker 1: behind these hacks. Well as far as who was behind 416 00:23:13,400 --> 00:23:17,160 Speaker 1: these hacks, the intimation is that they are state actors 417 00:23:17,240 --> 00:23:21,320 Speaker 1: right sponsored by states. That's right there, and in fact 418 00:23:21,359 --> 00:23:23,479 Speaker 1: the chief suspect now is Russia, which is why this 419 00:23:23,520 --> 00:23:27,800 Speaker 1: is such a concern um in part because the takedown 420 00:23:27,800 --> 00:23:30,199 Speaker 1: of the electrical grids in the Ukraine was done by 421 00:23:30,280 --> 00:23:32,679 Speaker 1: Russian hackers and they're clearly if you look at those 422 00:23:32,720 --> 00:23:35,640 Speaker 1: incidents in Ukraine, they're they're clearly getting more sophisticated. They're 423 00:23:35,680 --> 00:23:37,800 Speaker 1: using Ukraine it's kind of a test bed to see 424 00:23:37,840 --> 00:23:40,359 Speaker 1: how these tools are working. So you had a hit 425 00:23:41,040 --> 00:23:45,800 Speaker 1: UH in that was a little bit less sophisticated, and 426 00:23:45,840 --> 00:23:49,359 Speaker 1: then again in twenties sixteen where they had clearly automated 427 00:23:49,359 --> 00:23:51,840 Speaker 1: a lot of the processes and that the grid wasn't 428 00:23:51,880 --> 00:23:54,000 Speaker 1: down for very long. That didn't seem to be what 429 00:23:54,040 --> 00:23:55,679 Speaker 1: the attackers were trying to do. It looked like they 430 00:23:55,720 --> 00:23:58,240 Speaker 1: were testing these tools. But that means that that when 431 00:23:58,240 --> 00:24:00,640 Speaker 1: you then see that, you know how ers that link 432 00:24:00,720 --> 00:24:03,280 Speaker 1: to Russia in your own power plants here in the US, 433 00:24:03,359 --> 00:24:06,520 Speaker 1: you get really really nervous. Well, you know, you mentioned 434 00:24:06,560 --> 00:24:10,480 Speaker 1: the hack attacks and in the context of national security, 435 00:24:10,520 --> 00:24:12,680 Speaker 1: but this also involves companies. So if you think it's 436 00:24:12,720 --> 00:24:17,240 Speaker 1: just a political or a uh sort of a strategic issue, 437 00:24:17,359 --> 00:24:20,800 Speaker 1: it's also a corporate issue. Mandale International saying that a 438 00:24:20,880 --> 00:24:25,840 Speaker 1: cyber attack had crippled their corporate UH computer systems, employees 439 00:24:25,880 --> 00:24:28,879 Speaker 1: had to work from their mobile phones and that as 440 00:24:28,920 --> 00:24:31,679 Speaker 1: a result, it's going to reduce second quarter sales growth 441 00:24:31,960 --> 00:24:34,719 Speaker 1: by three percentage points. So this is an issue not 442 00:24:34,800 --> 00:24:39,040 Speaker 1: just for governments but clearly or utilities, but clearly for businesses. No, absolutely, 443 00:24:39,040 --> 00:24:41,359 Speaker 1: and if you if you put the cap of business 444 00:24:41,359 --> 00:24:44,200 Speaker 1: owners on, this is a huge and growing problem and 445 00:24:44,320 --> 00:24:47,840 Speaker 1: many companies will will talk about this in in in UH. 446 00:24:47,880 --> 00:24:50,480 Speaker 1: In general terms, they're they're basically companies and they're being 447 00:24:50,520 --> 00:24:54,320 Speaker 1: attacked increasingly by nation states. These are like the security 448 00:24:54,359 --> 00:24:57,439 Speaker 1: agencies of countries that have a huge amount of resources 449 00:24:57,840 --> 00:25:00,520 Speaker 1: many in many cases that they're they're asked in themselves 450 00:25:00,600 --> 00:25:03,439 Speaker 1: why their own governments in the US government and others 451 00:25:03,600 --> 00:25:06,280 Speaker 1: are doing more to protect them, um, and and so 452 00:25:06,320 --> 00:25:09,000 Speaker 1: it creates a very weird imbalance where if you're a 453 00:25:09,000 --> 00:25:11,840 Speaker 1: company and you're being hit by the intelligence agencies of 454 00:25:11,880 --> 00:25:14,000 Speaker 1: another state, you just don't have the same resources that 455 00:25:14,080 --> 00:25:16,840 Speaker 1: they do. Uh what I get your thoughts. I know 456 00:25:16,920 --> 00:25:21,920 Speaker 1: you're not in Hamburg, but the amount of electronics, surveillance 457 00:25:21,960 --> 00:25:25,600 Speaker 1: and countermeasures that are being deployed when you have twenty 458 00:25:25,640 --> 00:25:29,199 Speaker 1: heads of state has got to be enormous. Yeah, it 459 00:25:29,240 --> 00:25:31,280 Speaker 1: is enormous. And one of one of the things we've 460 00:25:31,359 --> 00:25:34,359 Speaker 1: learned from things like the the Snowden leaks and others 461 00:25:34,560 --> 00:25:39,320 Speaker 1: is that there's a lot of UH efforts to tap 462 00:25:39,359 --> 00:25:42,080 Speaker 1: into the communications of each of those leaders as they're 463 00:25:42,080 --> 00:25:45,080 Speaker 1: discussing with their own teams what to say and how 464 00:25:45,119 --> 00:25:48,520 Speaker 1: to say it. Um. There's some pretty UH interesting and 465 00:25:48,920 --> 00:25:52,920 Speaker 1: quite colorful examples of how spy agencies do that. Um, 466 00:25:53,440 --> 00:25:55,520 Speaker 1: but it's it's I mean, I think that that's just 467 00:25:55,560 --> 00:25:57,879 Speaker 1: part of the spy trade. They're they're basically looking for 468 00:25:58,359 --> 00:26:01,520 Speaker 1: to read the cards of the other members of the 469 00:26:01,680 --> 00:26:03,760 Speaker 1: of the of the T twenty that they're going to 470 00:26:03,800 --> 00:26:05,720 Speaker 1: be meeting with. And and this is like one of 471 00:26:05,760 --> 00:26:07,399 Speaker 1: the things that you do is you just use your 472 00:26:07,440 --> 00:26:09,760 Speaker 1: spy agencies to do that. Right. Well, we know that 473 00:26:09,760 --> 00:26:13,080 Speaker 1: that had already been a topic of conversation because of 474 00:26:13,119 --> 00:26:16,880 Speaker 1: the hack by US intelligence or the eavesdropping of US 475 00:26:16,920 --> 00:26:21,040 Speaker 1: intelligence officials on Angela Merkel in the past. I want 476 00:26:21,040 --> 00:26:23,439 Speaker 1: to thank you very much for being with us. Michael 477 00:26:23,520 --> 00:26:30,879 Speaker 1: Riley is our cybersecurity reporter for Bloomberg News. Thanks for 478 00:26:30,960 --> 00:26:33,600 Speaker 1: listening to the Bloomberg P and L podcast. You can 479 00:26:33,640 --> 00:26:37,479 Speaker 1: subscribe and listen to interviews at Apple Podcasts, SoundCloud, or 480 00:26:37,520 --> 00:26:41,000 Speaker 1: whatever podcast platform you prefer. I'm pim Fox. I'm on 481 00:26:41,040 --> 00:26:44,880 Speaker 1: Twitter at pim Fox. I'm on Twitter at Lisa Abramo 482 00:26:44,880 --> 00:26:47,600 Speaker 1: wits one. Before the podcast, you can always catch us 483 00:26:47,640 --> 00:26:49,200 Speaker 1: worldwide on Bloomberg Radio.