1 00:00:04,760 --> 00:00:08,080 Speaker 1: Welcome to the Bloomberg pm L podcast. I'm pim Fox. 2 00:00:08,119 --> 00:00:11,200 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,280 --> 00:00:14,480 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:14,520 --> 00:00:16,880 Speaker 1: you and your money, whether at the grocery store or 5 00:00:16,920 --> 00:00:20,680 Speaker 1: the trading floor. Find the Bloomberg p L Podcast on iTunes, 6 00:00:20,840 --> 00:00:29,680 Speaker 1: SoundCloud and at Bloomberg dot com. Well, how to greet 7 00:00:29,680 --> 00:00:33,000 Speaker 1: the year two thousand seventeen by being a pessimist. One 8 00:00:33,000 --> 00:00:34,800 Speaker 1: of the things you probably would do is you would 9 00:00:34,840 --> 00:00:38,040 Speaker 1: call up our next guest, John fra Her. He is 10 00:00:38,240 --> 00:00:41,960 Speaker 1: executive editor for the International Government division of Bloomberg News. 11 00:00:41,960 --> 00:00:45,320 Speaker 1: He's a pest Well clearly now he is a pessimist 12 00:00:45,320 --> 00:00:50,520 Speaker 1: because he is responsible for our Pessimists Guy to seventeen. John, 13 00:00:51,159 --> 00:00:53,720 Speaker 1: all right, tell us, when you know, do we take cover, 14 00:00:53,800 --> 00:00:56,360 Speaker 1: should we build a shelter? What what's going to happen? 15 00:00:56,400 --> 00:00:58,360 Speaker 1: But part of the exercise of the pessimist guy does 16 00:00:58,400 --> 00:01:00,800 Speaker 1: not necessarily a series of predict is. What what we're 17 00:01:00,840 --> 00:01:04,440 Speaker 1: trying to do is encourage readers to think outside the box. 18 00:01:04,680 --> 00:01:06,480 Speaker 1: I just can I interrupt you for one second, though, 19 00:01:07,160 --> 00:01:10,800 Speaker 1: last year's guide to the Pessimistic Guide pretty much entirely 20 00:01:10,880 --> 00:01:13,479 Speaker 1: came true, right, I mean the base case of Trump 21 00:01:13,520 --> 00:01:16,240 Speaker 1: becoming president. So this time last year, it definitely did 22 00:01:16,280 --> 00:01:18,280 Speaker 1: pay to be a presimist, yes, because we we put 23 00:01:18,360 --> 00:01:20,399 Speaker 1: in front of readers the idea that breggs that could 24 00:01:20,400 --> 00:01:23,360 Speaker 1: actually happen. And actually the most controversial I think that 25 00:01:23,400 --> 00:01:25,920 Speaker 1: we had last year, the one that caused most internal 26 00:01:25,959 --> 00:01:28,520 Speaker 1: debates and arguments as we were debating it, was should 27 00:01:28,560 --> 00:01:31,199 Speaker 1: we put in front of readers the possibility of Donald 28 00:01:31,200 --> 00:01:33,400 Speaker 1: Trump winning the winning the White House? And it was 29 00:01:33,400 --> 00:01:35,280 Speaker 1: actually the last thing that we put in because there 30 00:01:35,400 --> 00:01:37,440 Speaker 1: was so much heated debate about it. We were told, 31 00:01:37,480 --> 00:01:39,720 Speaker 1: this is a joke, it's going to be over by February. 32 00:01:39,760 --> 00:01:43,320 Speaker 1: It's going to make us look stupid. But sometimes outlandish 33 00:01:43,400 --> 00:01:45,560 Speaker 1: things do happen. As the last the last filve months 34 00:01:45,600 --> 00:01:48,480 Speaker 1: is taught, did you make the correlation between Donald Trump 35 00:01:48,600 --> 00:01:51,760 Speaker 1: being elected president and the doubt being up more than 36 00:01:51,800 --> 00:01:53,680 Speaker 1: ten percent and the S and P up more than 37 00:01:53,720 --> 00:01:55,520 Speaker 1: seven and a half percent. I think that bit we 38 00:01:55,560 --> 00:01:58,040 Speaker 1: sort of wed. We we didn't quite factor that in, 39 00:01:58,120 --> 00:02:00,040 Speaker 1: but again it goes to show you that all of 40 00:02:00,160 --> 00:02:03,440 Speaker 1: these events have have unintended consequences, and what we're encouraging 41 00:02:03,480 --> 00:02:06,320 Speaker 1: people to do is keep an open mind not only 42 00:02:06,320 --> 00:02:08,440 Speaker 1: about the events that can happen, but how markets will 43 00:02:08,480 --> 00:02:10,400 Speaker 1: react to it as well. Okay, so John, here we 44 00:02:10,480 --> 00:02:12,560 Speaker 1: have our crystal ball. You can look into it. What's 45 00:02:12,560 --> 00:02:14,800 Speaker 1: going to happen next year? If you're a pessimist, if 46 00:02:14,840 --> 00:02:17,799 Speaker 1: you're if you're a pessimist. Um Well, one of the things, 47 00:02:17,919 --> 00:02:20,080 Speaker 1: one of the sort of the threads that runs through 48 00:02:20,400 --> 00:02:22,840 Speaker 1: at the coverage, for example, is this idea of of 49 00:02:22,840 --> 00:02:26,239 Speaker 1: of America puming back from from the rest of the world. 50 00:02:26,760 --> 00:02:29,560 Speaker 1: Um And one of the things that we've thought about 51 00:02:29,600 --> 00:02:32,040 Speaker 1: is what if on the morning of Donald Trump's inauguration 52 00:02:32,120 --> 00:02:36,800 Speaker 1: he decides to label China a currency manipulator over Twitter. Now, 53 00:02:37,000 --> 00:02:39,679 Speaker 1: it actually nearly happened last Friday night about believe it 54 00:02:39,760 --> 00:02:42,440 Speaker 1: or not again, which causing another sort of range of 55 00:02:42,560 --> 00:02:45,440 Speaker 1: you know, huge debate with me and my co my 56 00:02:45,440 --> 00:02:48,519 Speaker 1: my sort of fellow pessimists. I got one email on 57 00:02:48,560 --> 00:02:50,320 Speaker 1: Friday night saying we need to get this thing published 58 00:02:50,320 --> 00:02:53,080 Speaker 1: now because this could happen. So that's one thing to 59 00:02:53,080 --> 00:02:56,079 Speaker 1: think about Trump starting a trade war with China. But 60 00:02:56,120 --> 00:02:57,720 Speaker 1: then the flip side of that, and I think it 61 00:02:57,840 --> 00:03:00,560 Speaker 1: really important to see that Donald Trump may well be 62 00:03:00,639 --> 00:03:03,120 Speaker 1: thinking about and certainly people in Washington are thinking about it. 63 00:03:03,200 --> 00:03:05,640 Speaker 1: Is this North Korea angle, right, So there are people 64 00:03:05,639 --> 00:03:08,320 Speaker 1: in South Korea who say the whole notion of North 65 00:03:08,400 --> 00:03:10,519 Speaker 1: Korea building a missile that could hit the West coast 66 00:03:10,720 --> 00:03:13,480 Speaker 1: on which you could fit and miniaturized nuclear device is 67 00:03:13,480 --> 00:03:16,280 Speaker 1: not that outlandish at all, which then raises the question 68 00:03:16,360 --> 00:03:18,760 Speaker 1: for Donald Trump. Okay, fine, you want to go hard 69 00:03:18,840 --> 00:03:21,920 Speaker 1: on China on trade, but if you're really genuinely worried 70 00:03:21,960 --> 00:03:24,960 Speaker 1: about North Korea, you need Beijing on board. You need 71 00:03:25,000 --> 00:03:27,800 Speaker 1: Beijing to help you deal with this, with this rogue regime. 72 00:03:28,040 --> 00:03:31,720 Speaker 1: So again one of the things that worked in the past. Well, 73 00:03:31,880 --> 00:03:34,800 Speaker 1: it's very difficult. I mean, China is sort of access 74 00:03:34,800 --> 00:03:38,680 Speaker 1: North Korea's protector, and certainly they are very reluctant to 75 00:03:38,720 --> 00:03:41,000 Speaker 1: be seen to be how thin America out on the 76 00:03:41,040 --> 00:03:43,120 Speaker 1: North Korea issue. But it just goes to show you 77 00:03:43,160 --> 00:03:45,120 Speaker 1: how all of these issues that are interrelated. If you 78 00:03:45,160 --> 00:03:47,640 Speaker 1: do want to go hard on China trade, you also 79 00:03:47,640 --> 00:03:49,200 Speaker 1: need to think the back of your mind that you 80 00:03:49,280 --> 00:03:52,160 Speaker 1: might need China to help you out with North Korean's well, 81 00:03:52,200 --> 00:03:55,320 Speaker 1: and talking about China if there were a trade war, 82 00:03:56,040 --> 00:03:59,200 Speaker 1: I believe in this Pessimists Guide, you talked about a 83 00:03:59,200 --> 00:04:01,880 Speaker 1: potential recession and in China that could result from this. Right, 84 00:04:02,120 --> 00:04:03,840 Speaker 1: that's right, And you have to remember all at the 85 00:04:03,880 --> 00:04:06,320 Speaker 1: same time that this is happening, there's a big leadership 86 00:04:06,720 --> 00:04:09,520 Speaker 1: reshuffle happening in China this this time next year, so 87 00:04:09,560 --> 00:04:12,080 Speaker 1: that the Standing Committee, which sort of sits on the 88 00:04:12,160 --> 00:04:15,080 Speaker 1: very apex of Chinese power, is all going to be reshuffled, 89 00:04:15,280 --> 00:04:17,559 Speaker 1: and Ping and as Prime Minister will stay, but everyone 90 00:04:17,560 --> 00:04:20,200 Speaker 1: else will probably get rotated out. And of course Chinese 91 00:04:20,240 --> 00:04:22,240 Speaker 1: politics is very opaque, but you can bet that there's 92 00:04:22,240 --> 00:04:23,800 Speaker 1: going to be a lot of people in Beijing are 93 00:04:23,839 --> 00:04:26,400 Speaker 1: going to be focusing on the succession to see when 94 00:04:26,440 --> 00:04:28,919 Speaker 1: perhaps they should be spending more time on keeping the 95 00:04:28,920 --> 00:04:31,479 Speaker 1: economy going. And they will also be dealing with the 96 00:04:31,560 --> 00:04:34,160 Speaker 1: US President, who you know is going to be on 97 00:04:34,640 --> 00:04:37,039 Speaker 1: orthodox at the very least. Well, we're going to speak 98 00:04:37,279 --> 00:04:39,560 Speaker 1: later on in the program with Arthur Kroeber. He is 99 00:04:39,600 --> 00:04:43,640 Speaker 1: a founding partner at the Gave Call Dragon Nomics, also 100 00:04:43,680 --> 00:04:47,280 Speaker 1: a fellow at the Brookings Chingao Center for Public Policy. 101 00:04:47,279 --> 00:04:49,240 Speaker 1: He'll be talking about China later on in the program. 102 00:04:49,360 --> 00:04:51,799 Speaker 1: Can we just go to Germany for a moment, what's 103 00:04:51,960 --> 00:04:55,240 Speaker 1: what's the prognosis, what's the pessimist So the pestim scenario there, 104 00:04:55,240 --> 00:04:57,279 Speaker 1: I mean Angela Merkel of course is on is on, 105 00:04:57,520 --> 00:04:59,920 Speaker 1: is on the back back foot big time in Jerem 106 00:05:00,000 --> 00:05:02,520 Speaker 1: and she's facing her own populist insurgency there. She's got 107 00:05:02,520 --> 00:05:05,720 Speaker 1: her own elections in September next year. So one of 108 00:05:05,760 --> 00:05:07,880 Speaker 1: the scenarios we put out there is that she loses. 109 00:05:07,920 --> 00:05:09,880 Speaker 1: She loses that election. Now we're not saying that the 110 00:05:09,880 --> 00:05:12,000 Speaker 1: populace will come to power, but she could be so 111 00:05:12,480 --> 00:05:15,760 Speaker 1: her authority could could be so heavily dented by this 112 00:05:15,880 --> 00:05:19,040 Speaker 1: election that she's forced to stand down. Angel America has 113 00:05:19,040 --> 00:05:21,120 Speaker 1: been the glue that's kept Europe together eatly for the 114 00:05:21,200 --> 00:05:23,960 Speaker 1: last decade, whether it's Greece or whether it's the refugee crisis, 115 00:05:24,279 --> 00:05:27,479 Speaker 1: So that in and of itself, her leaving would be 116 00:05:27,520 --> 00:05:30,640 Speaker 1: a hugely destabilizing event for event for Europe. And then 117 00:05:30,720 --> 00:05:34,400 Speaker 1: turning back to the US, how about sort of social 118 00:05:34,440 --> 00:05:38,760 Speaker 1: backdrop in the United States with Donald Trump as the president? 119 00:05:38,960 --> 00:05:41,400 Speaker 1: I mean, could there be a sort of radical movement 120 00:05:41,440 --> 00:05:43,040 Speaker 1: that evolves and what would the effect to be there? 121 00:05:43,120 --> 00:05:44,960 Speaker 1: So we are we have looked at that we could 122 00:05:45,040 --> 00:05:47,120 Speaker 1: you could see a sort of a coalition of the 123 00:05:47,200 --> 00:05:50,599 Speaker 1: left or the center left in the streets. Also, we 124 00:05:50,640 --> 00:05:55,160 Speaker 1: talked about, for example, Californian billionaire liberal Californian billionaires deciding 125 00:05:55,200 --> 00:05:58,080 Speaker 1: to group together to sort of form a resistance movement 126 00:05:58,080 --> 00:06:01,920 Speaker 1: if you like. That's then entire group is Californian billionaires, 127 00:06:02,680 --> 00:06:05,039 Speaker 1: billionaire And then what happens in Congress? Yes, so, I 128 00:06:05,080 --> 00:06:07,320 Speaker 1: mean there is I'm just curious when they found that group, 129 00:06:07,720 --> 00:06:10,880 Speaker 1: are they going to be subject to the same kinds 130 00:06:10,880 --> 00:06:14,279 Speaker 1: of conditions at a foreign state would be subject to 131 00:06:14,320 --> 00:06:16,880 Speaker 1: if they want government contracts for any of their products 132 00:06:16,960 --> 00:06:18,840 Speaker 1: or services. That's a very good question. But I mean 133 00:06:18,880 --> 00:06:22,720 Speaker 1: it's called calex it, right, calex is this idea that 134 00:06:22,760 --> 00:06:26,320 Speaker 1: sort of been brewing, brewing since the election. Um. But 135 00:06:26,440 --> 00:06:27,800 Speaker 1: one of the things to think about as well is 136 00:06:27,839 --> 00:06:30,560 Speaker 1: if you did get if you did get broad social 137 00:06:30,640 --> 00:06:35,520 Speaker 1: unrest or deep unhappiness with Trump's domestic agenda, what would 138 00:06:35,560 --> 00:06:37,480 Speaker 1: that do to unity and Congress when he tries to 139 00:06:37,480 --> 00:06:40,799 Speaker 1: get a fiscal fiscal stimulus through. So something it's worth 140 00:06:40,920 --> 00:06:44,040 Speaker 1: connecting those dots together. And I'm not saying this this 141 00:06:44,080 --> 00:06:46,679 Speaker 1: pessimist guide, will you know it is predicting the future, 142 00:06:46,680 --> 00:06:48,640 Speaker 1: although it has it has a pretty good track record 143 00:06:48,640 --> 00:06:51,360 Speaker 1: at this point. But it's encouraging leaders to think about 144 00:06:51,400 --> 00:06:54,920 Speaker 1: all of these differentencies and how they could link together. Putin, 145 00:06:56,279 --> 00:07:00,360 Speaker 1: Vladimir Putin and President elect Donald Trump new best friends forever. 146 00:07:00,520 --> 00:07:02,679 Speaker 1: What's going to happen here? I think it's pretty interesting, actually, 147 00:07:02,720 --> 00:07:05,400 Speaker 1: I mean, if you look at that's certainly the narrative 148 00:07:05,440 --> 00:07:07,680 Speaker 1: has been the narrative through the campaign. But who then 149 00:07:07,680 --> 00:07:09,480 Speaker 1: would have thought that Donald Trump would have packed his 150 00:07:09,600 --> 00:07:15,080 Speaker 1: cabinet with with former Goldman Sachs employees. So in theory, 151 00:07:15,320 --> 00:07:16,960 Speaker 1: you know, Donald Trump has had he wants a better 152 00:07:17,040 --> 00:07:21,000 Speaker 1: relationship with Putin, but you know, Putin himself is a 153 00:07:21,160 --> 00:07:24,520 Speaker 1: very hard to predict guy who will only act in 154 00:07:24,720 --> 00:07:27,440 Speaker 1: Russia's self interest, So that romance, it could be a 155 00:07:27,440 --> 00:07:30,400 Speaker 1: short lived one. But one thing to think about is 156 00:07:30,400 --> 00:07:34,160 Speaker 1: that if if Trump does carry through on his pledge 157 00:07:34,200 --> 00:07:37,360 Speaker 1: to sort of really question how much Europe spends on defense, 158 00:07:37,640 --> 00:07:40,320 Speaker 1: he could he could he could basically give Angela Merkel 159 00:07:40,480 --> 00:07:42,920 Speaker 1: and European leads a very difficult choice to make. Do 160 00:07:43,040 --> 00:07:47,000 Speaker 1: we decide to ramp up spending on defense good for 161 00:07:47,040 --> 00:07:51,560 Speaker 1: defense companies um or without America support. Do we have 162 00:07:51,640 --> 00:07:54,000 Speaker 1: to learn to do deals with the Vladimir Putin in 163 00:07:54,040 --> 00:07:56,680 Speaker 1: Eastern Europe? Do we have to accept the fact that 164 00:07:56,760 --> 00:07:59,440 Speaker 1: Vladimir Putin is serious about the fact that Ukrainis is 165 00:07:59,480 --> 00:08:02,800 Speaker 1: in his of influence? Is Vladimir? Do we have to 166 00:08:02,840 --> 00:08:05,000 Speaker 1: recognize the fact that history of influence doesn't he go 167 00:08:05,200 --> 00:08:07,360 Speaker 1: right up to name of naval's borders. Those can be 168 00:08:07,480 --> 00:08:22,840 Speaker 1: very difficult decisions fans of Americans to make. This is 169 00:08:22,880 --> 00:08:25,960 Speaker 1: the segment that I have been waiting for. This is 170 00:08:26,000 --> 00:08:28,200 Speaker 1: a question that we have been talking about a lot. 171 00:08:28,800 --> 00:08:32,760 Speaker 1: With all of the populist uprisings that we've seen around 172 00:08:32,880 --> 00:08:37,240 Speaker 1: the world, What does this potential civil unrest and in 173 00:08:37,280 --> 00:08:40,840 Speaker 1: some places actual what effect does it have on markets? 174 00:08:40,840 --> 00:08:44,560 Speaker 1: With us to answer that question is Irene Finelle Hanigman, Magic, 175 00:08:44,600 --> 00:08:48,280 Speaker 1: Professor of International Affairs at Columbia University, and Nick Kolas, 176 00:08:48,360 --> 00:08:52,439 Speaker 1: chief market strategist at converge x UM. Nick, I want 177 00:08:52,440 --> 00:08:55,640 Speaker 1: to start with you. So far, we saw a bit 178 00:08:55,640 --> 00:08:58,040 Speaker 1: of a reaction. We saw three month reaction, three week 179 00:08:58,040 --> 00:09:01,360 Speaker 1: reaction at Brexit, we saw three day reaction to Trump, 180 00:09:01,400 --> 00:09:06,280 Speaker 1: and we saw a three minute reaction to Italy's referendum vote. 181 00:09:06,679 --> 00:09:09,439 Speaker 1: Do markets does not care. You know, as you look 182 00:09:09,440 --> 00:09:11,960 Speaker 1: at the data back to the nineteen twenties and look 183 00:09:12,000 --> 00:09:14,680 Speaker 1: at the whole cadence of history from the Great Depression 184 00:09:14,679 --> 00:09:17,240 Speaker 1: to now, I think the data shows us that the 185 00:09:17,280 --> 00:09:21,160 Speaker 1: markets anticipates a lot of this change long before it 186 00:09:21,200 --> 00:09:23,760 Speaker 1: actually occurs. So if you look at the Great Depression, 187 00:09:24,120 --> 00:09:27,080 Speaker 1: the SMP five hundred and its predecessors were down seventy 188 00:09:27,640 --> 00:09:30,760 Speaker 1: from night to nineteen thirty two, and then we had 189 00:09:30,760 --> 00:09:33,920 Speaker 1: this very long wave of social unrest afterwards, culminating in 190 00:09:33,960 --> 00:09:36,400 Speaker 1: World War Two. If you look at the financial crisis, 191 00:09:36,400 --> 00:09:38,880 Speaker 1: down thirty six percent back in two thousand and eight, 192 00:09:39,160 --> 00:09:41,480 Speaker 1: and we've had this kind of rolling set of crises 193 00:09:41,559 --> 00:09:43,960 Speaker 1: ever since. So I think the bottom line is markets 194 00:09:44,240 --> 00:09:47,120 Speaker 1: to some degree create the conditions to this unrest and 195 00:09:47,200 --> 00:09:49,640 Speaker 1: to some degree anticipate what it's going to mean. Some 196 00:09:49,720 --> 00:09:51,800 Speaker 1: by the time it actually occurs, a lot of it's 197 00:09:51,840 --> 00:09:57,040 Speaker 1: baked into investor expectations. Irene, give us your thoughts. I 198 00:09:57,120 --> 00:10:01,400 Speaker 1: basically agree with with Nick. I think also the situation 199 00:10:01,480 --> 00:10:04,480 Speaker 1: in Europe is the markets did react very strongly and 200 00:10:04,520 --> 00:10:08,680 Speaker 1: negatively uh in two thousand ten that March May period 201 00:10:08,720 --> 00:10:12,640 Speaker 1: of enormous and certitude about greece potential default once it 202 00:10:12,720 --> 00:10:15,800 Speaker 1: became clear in the next year or so that this 203 00:10:15,920 --> 00:10:20,000 Speaker 1: was constantly ongoing, and that every crisis was awful in 204 00:10:20,040 --> 00:10:22,720 Speaker 1: and of itself, and yet did not necessarily mean that 205 00:10:22,760 --> 00:10:25,840 Speaker 1: the euro was failing or that Europe was failing, but 206 00:10:25,960 --> 00:10:28,920 Speaker 1: that in a way, unfortunately was constantly pushed the can 207 00:10:29,000 --> 00:10:32,000 Speaker 1: down the road or muddled through. So this seems to 208 00:10:32,000 --> 00:10:35,800 Speaker 1: be again the case Italy. There is built in cynicism, 209 00:10:35,840 --> 00:10:39,520 Speaker 1: building skepticism. We have looked at sixty different governments, of 210 00:10:39,559 --> 00:10:42,160 Speaker 1: which Tibert Lusconi was the longest in Italy since World 211 00:10:42,200 --> 00:10:46,200 Speaker 1: War two. UH. The assumption was that this probably would 212 00:10:46,200 --> 00:10:50,400 Speaker 1: go as a no against the constitutional changes that Rensey 213 00:10:50,440 --> 00:10:53,959 Speaker 1: wanted to impose, and we're really not understood. The thing 214 00:10:54,000 --> 00:10:56,480 Speaker 1: that is to me also very interesting is, for example, 215 00:10:56,559 --> 00:11:00,080 Speaker 1: in France right now, we've seen France go through all 216 00:11:00,120 --> 00:11:03,760 Speaker 1: sorts of dry rations with alone deciding he will not 217 00:11:03,920 --> 00:11:09,280 Speaker 1: present his candidacy again, with a new UH candidate, EMANUELA mccollon, 218 00:11:09,360 --> 00:11:13,400 Speaker 1: former finance minister, creating a new party with unexpectedly coming 219 00:11:13,440 --> 00:11:16,040 Speaker 1: to the forefront of his repay a center right. Yet 220 00:11:16,520 --> 00:11:19,200 Speaker 1: there doesn't really seem to be an impact from that. 221 00:11:19,320 --> 00:11:21,720 Speaker 1: Everyone is sort of a weight and see and I 222 00:11:21,760 --> 00:11:24,280 Speaker 1: think this weight and see mood is kind of everywhere. 223 00:11:24,760 --> 00:11:27,880 Speaker 1: Um we also and again I certainly agree we have 224 00:11:27,920 --> 00:11:30,800 Speaker 1: a lot of it basically baked in. Uh, there will 225 00:11:30,840 --> 00:11:34,000 Speaker 1: be volatility and as long as some of the economic 226 00:11:34,080 --> 00:11:37,920 Speaker 1: fundamentals don't look that dramatic, well just assume we don't 227 00:11:38,000 --> 00:11:40,680 Speaker 1: really know where the political situation is going right now, Okay, 228 00:11:40,679 --> 00:11:43,200 Speaker 1: so just go sticking. In Europe, one of the biggest 229 00:11:43,240 --> 00:11:45,560 Speaker 1: to wild cards that a lot of people talk about 230 00:11:45,559 --> 00:11:48,240 Speaker 1: for next year is the status of the euro and 231 00:11:48,240 --> 00:11:51,079 Speaker 1: the fact that a lot of anti European Union factions 232 00:11:51,160 --> 00:11:54,080 Speaker 1: in Europe have been gaining power. I mean, that is 233 00:11:54,120 --> 00:11:56,160 Speaker 1: not priced into the market right now. The breakup of 234 00:11:56,200 --> 00:11:59,439 Speaker 1: the Eurozone. Correct, that is correct, because I don't think 235 00:11:59,440 --> 00:12:02,480 Speaker 1: it will occur. I think again, the fascinating thing about 236 00:12:02,520 --> 00:12:06,240 Speaker 1: it is just like in Greece, they absolutely hated what 237 00:12:06,520 --> 00:12:09,760 Speaker 1: Brussels or Europe was imposing on them, but they did 238 00:12:09,800 --> 00:12:11,880 Speaker 1: not want to get out of the Eurozone. To come 239 00:12:11,920 --> 00:12:15,000 Speaker 1: back to the Drachma. In Italy it seems largely to 240 00:12:15,080 --> 00:12:18,520 Speaker 1: be the same thing there is in the Northern faction, 241 00:12:18,559 --> 00:12:21,360 Speaker 1: et cetera. In some of the very extreme factions more 242 00:12:21,360 --> 00:12:25,160 Speaker 1: of an anti euro, anti EU sentiment, But fundamentally the 243 00:12:25,200 --> 00:12:27,600 Speaker 1: Italian electorate does not want to get out of the 244 00:12:27,640 --> 00:12:31,200 Speaker 1: Euro and certainly returned to the lira and its gyrations. 245 00:12:31,240 --> 00:12:35,400 Speaker 1: So I think there's a very bizarre paradox between people, 246 00:12:35,440 --> 00:12:39,760 Speaker 1: oddly enough not really wanting to break things apart, yet 247 00:12:39,840 --> 00:12:44,640 Speaker 1: being very unhappy with the way things are. Nicholas, Yeah, 248 00:12:44,679 --> 00:12:47,040 Speaker 1: I was actually looking at the data back to say 249 00:12:47,080 --> 00:12:51,200 Speaker 1: World War two, which is an interesting, much more violent analog. 250 00:12:51,280 --> 00:12:53,760 Speaker 1: Obviously what's going on in Europe now, And for U 251 00:12:53,840 --> 00:12:56,719 Speaker 1: S stocks it wasn't early all that bad. S and 252 00:12:56,760 --> 00:13:02,680 Speaker 1: P returned down etcent and that was it. After that 253 00:13:02,800 --> 00:13:05,560 Speaker 1: defense spending and all that stimulus ended up having the 254 00:13:05,559 --> 00:13:09,200 Speaker 1: equity markets up between nineteen and twenty five percent, with 255 00:13:10,480 --> 00:13:13,480 Speaker 1: being up thirty six percent when the victor was actually one. 256 00:13:13,960 --> 00:13:16,559 Speaker 1: So looking at a much more extreme version of unrest 257 00:13:16,600 --> 00:13:19,559 Speaker 1: in Europe and you see US equities actually did pretty well. 258 00:13:20,040 --> 00:13:22,800 Speaker 1: So what's the trade, Nick Cholis? Everyone seems to be 259 00:13:22,840 --> 00:13:26,960 Speaker 1: pessimistic offering up all these dire scenarios. You're telling me 260 00:13:27,000 --> 00:13:30,960 Speaker 1: that the analysis says you can make some money here. Yeah, 261 00:13:31,040 --> 00:13:34,920 Speaker 1: the bottom line is markets climb that clichee wall of uncertainty. 262 00:13:35,120 --> 00:13:38,160 Speaker 1: And we have so any particular sector, any specific ideal 263 00:13:38,240 --> 00:13:40,560 Speaker 1: or do you just do you know an SMP index. No, 264 00:13:40,760 --> 00:13:42,640 Speaker 1: we're seeing, you know, among our clients, we're seeing a 265 00:13:42,679 --> 00:13:46,040 Speaker 1: ton of rotation. I mean, like I haven't seen five 266 00:13:46,120 --> 00:13:50,640 Speaker 1: years from tech out of tech into financials first and foremost, 267 00:13:50,679 --> 00:13:53,520 Speaker 1: and then industrials and healthcare pretty much in that order. 268 00:13:53,800 --> 00:13:55,920 Speaker 1: If you look at et F flows over the past month, 269 00:13:56,240 --> 00:14:00,360 Speaker 1: nine billion dollars, nine billion dollars into industrials and then 270 00:14:00,600 --> 00:14:04,120 Speaker 1: between two and three into short into financials and industrials 271 00:14:04,120 --> 00:14:07,040 Speaker 1: and healthcare is two to three billion dollars tech negative 272 00:14:07,120 --> 00:14:09,200 Speaker 1: draws for example. Irene, and I'd love to get your 273 00:14:09,240 --> 00:14:14,240 Speaker 1: perspective on at what point in history civil unrest has, 274 00:14:14,280 --> 00:14:18,920 Speaker 1: if it has ever bled into markets that I think 275 00:14:19,040 --> 00:14:23,320 Speaker 1: is really uh interestingly enough, often there's there's really not 276 00:14:23,480 --> 00:14:27,560 Speaker 1: a lot of correlation. I mean, yes, we we can 277 00:14:27,600 --> 00:14:31,040 Speaker 1: certainly look at at even as Nick mentioned, during the 278 00:14:31,560 --> 00:14:34,320 Speaker 1: Great Depression, there seemed to be a sort of a 279 00:14:35,160 --> 00:14:40,920 Speaker 1: slow revival. Uh. The issue is right now and what 280 00:14:40,960 --> 00:14:44,360 Speaker 1: we have seen basically in in many decades is that 281 00:14:44,440 --> 00:14:49,240 Speaker 1: the euro dollar parody has not been that we have 282 00:14:49,320 --> 00:14:53,720 Speaker 1: not gone through that many disruptive moments, and at even 283 00:14:53,720 --> 00:14:56,440 Speaker 1: when the crisis in Europe were occurring, we were not 284 00:14:56,560 --> 00:15:00,520 Speaker 1: really going through a currency crisis, and intr stanly enough 285 00:15:00,520 --> 00:15:05,920 Speaker 1: in September after there was a near no vote on 286 00:15:05,960 --> 00:15:09,400 Speaker 1: the Mastrok referendum where there actually was a currency crisis 287 00:15:09,440 --> 00:15:11,640 Speaker 1: and most of the currency had to drop out of 288 00:15:11,720 --> 00:15:15,680 Speaker 1: actually the parody bands within the European Union what would 289 00:15:15,720 --> 00:15:19,880 Speaker 1: become the Eurozone. Uh. Interestingly enough, that did not necessarily 290 00:15:19,960 --> 00:15:23,720 Speaker 1: lead to a political crisis. So the interesting issue is 291 00:15:23,720 --> 00:15:27,280 Speaker 1: that we don't really have a set correlation. The one 292 00:15:27,480 --> 00:15:30,120 Speaker 1: thing that I'm still quite concerned about because I don't 293 00:15:30,120 --> 00:15:32,840 Speaker 1: know how it plays out or how it will finally 294 00:15:32,840 --> 00:15:36,320 Speaker 1: play at long is Brexit whether that will have longer 295 00:15:36,440 --> 00:15:40,760 Speaker 1: impact on banks on the UK banks wet Irene, we 296 00:15:40,840 --> 00:15:42,800 Speaker 1: gotta leave it there, but thank you very much, Professor 297 00:15:42,840 --> 00:15:47,520 Speaker 1: Irene Finel Hanegmann, Adjunct Professor, International Affairs, Columbia University, and 298 00:15:47,520 --> 00:15:50,720 Speaker 1: our thanks also the Nick Culis, chief market strategist at 299 00:15:50,880 --> 00:16:06,680 Speaker 1: converge x. Well. Imagine that you wanted to know everything 300 00:16:06,840 --> 00:16:10,280 Speaker 1: that you could about China, you would probably call Arthur Kroeber. 301 00:16:10,400 --> 00:16:13,920 Speaker 1: He is a founding partner of Gavical dragon Omics, and 302 00:16:14,000 --> 00:16:16,160 Speaker 1: he joins US now. He's also the author of the 303 00:16:16,200 --> 00:16:20,640 Speaker 1: new book entitled China's Economy What Everyone Needs to Know. Arthur, 304 00:16:20,640 --> 00:16:22,720 Speaker 1: thank you very much for being with us. So what 305 00:16:22,760 --> 00:16:26,240 Speaker 1: do we need to know about China right now? Well? 306 00:16:26,280 --> 00:16:30,880 Speaker 1: I think two things. First of all, um economic growth 307 00:16:31,200 --> 00:16:34,000 Speaker 1: is reasonably stable. Isn't going to stay so for the 308 00:16:34,040 --> 00:16:37,640 Speaker 1: next year because the government is heading towards a major 309 00:16:37,640 --> 00:16:41,280 Speaker 1: political event Party Congress about a year from now, and 310 00:16:41,280 --> 00:16:44,840 Speaker 1: they're gonna want to have very stable, calm growth between 311 00:16:44,840 --> 00:16:47,320 Speaker 1: now and then to set the stage for that. But 312 00:16:47,360 --> 00:16:50,920 Speaker 1: then the second thing is that I think President Trump 313 00:16:50,920 --> 00:16:54,000 Speaker 1: has thrown a wild card into that by getting collected, 314 00:16:54,200 --> 00:16:57,000 Speaker 1: and he has the potential to to make things a 315 00:16:57,040 --> 00:17:00,880 Speaker 1: little bit more volatile by throwing some trade ancents at China. 316 00:17:00,960 --> 00:17:03,720 Speaker 1: And so it'll be interesting to see how the Chinese 317 00:17:03,720 --> 00:17:06,600 Speaker 1: respond to that if if in fact occurs, Arthur. You know, 318 00:17:06,640 --> 00:17:09,160 Speaker 1: there's been a lot of discussion about how a potential 319 00:17:09,200 --> 00:17:13,520 Speaker 1: trade war would negatively impact the US. What about China. 320 00:17:13,600 --> 00:17:16,600 Speaker 1: How much does China have to lose by this type 321 00:17:16,640 --> 00:17:19,439 Speaker 1: of trade argument? Well, they have a fair amount, because 322 00:17:19,520 --> 00:17:22,159 Speaker 1: it's a it's a very trade dependent economy. They have 323 00:17:22,200 --> 00:17:25,080 Speaker 1: a trade surplus, it's about five percent of GDP, and 324 00:17:25,240 --> 00:17:27,920 Speaker 1: they do rely heavily on exports. But I think when 325 00:17:27,920 --> 00:17:30,879 Speaker 1: you look at at this, it seems pretty clear that 326 00:17:30,920 --> 00:17:32,920 Speaker 1: the US has a lot more to lose from a 327 00:17:33,000 --> 00:17:36,320 Speaker 1: trade war with China than China does. Just a simple, 328 00:17:36,480 --> 00:17:39,440 Speaker 1: simple example, one of the biggest exports that China since 329 00:17:39,480 --> 00:17:42,239 Speaker 1: the United States is the iPhone, which is not a 330 00:17:42,280 --> 00:17:45,000 Speaker 1: product that ultimately is from a Chinese company, is from 331 00:17:45,000 --> 00:17:48,200 Speaker 1: an American company. So if you impose tariffs across the board, 332 00:17:48,520 --> 00:17:50,879 Speaker 1: you'll be hurting American companies at least as much as 333 00:17:50,960 --> 00:17:54,640 Speaker 1: Chinese companies, and maybe more so. At this point, I mean, 334 00:17:54,720 --> 00:17:59,280 Speaker 1: given the rhetoric that we've seen from President elect Trump, 335 00:18:00,560 --> 00:18:03,600 Speaker 1: will what will it take for China to respond with 336 00:18:04,000 --> 00:18:05,880 Speaker 1: a sort of heavy hand, because so far they've taken 337 00:18:05,880 --> 00:18:09,119 Speaker 1: a pretty light approach in response. Well, I think on 338 00:18:09,160 --> 00:18:11,560 Speaker 1: the economic stuff, they're just waiting to see what he 339 00:18:11,560 --> 00:18:13,840 Speaker 1: actually does. Is he and he said all kinds of things, 340 00:18:13,880 --> 00:18:16,560 Speaker 1: But we don't know what he will actually do in 341 00:18:16,720 --> 00:18:19,639 Speaker 1: terms of economic sanctions or tariffs or whatever until he 342 00:18:19,680 --> 00:18:22,240 Speaker 1: takes office. So I think the Chinese are playing cool 343 00:18:22,240 --> 00:18:25,200 Speaker 1: on that. What we saw over the weekend was this 344 00:18:25,720 --> 00:18:29,520 Speaker 1: conversation between Trump and the president of Taiwan. Yeah, the 345 00:18:29,600 --> 00:18:32,480 Speaker 1: Chinese are playing that very cool. Also, again, I think 346 00:18:32,520 --> 00:18:36,400 Speaker 1: they're taking the calculation that what Trump Trump does as 347 00:18:36,440 --> 00:18:39,680 Speaker 1: president elect when he's still really a private citizen, may 348 00:18:39,720 --> 00:18:41,840 Speaker 1: be somewhat different than what he does when he's actually 349 00:18:41,840 --> 00:18:43,640 Speaker 1: in office. So I think they're just going to wait 350 00:18:43,640 --> 00:18:46,639 Speaker 1: and see. Arthur, if you've got a call from the 351 00:18:46,760 --> 00:18:50,399 Speaker 1: CEO of a major U. S industrial company, or indeed 352 00:18:50,400 --> 00:18:52,800 Speaker 1: someone in the United States that has a product or 353 00:18:52,800 --> 00:18:55,080 Speaker 1: a service that they'd like to sell into China, what 354 00:18:55,160 --> 00:18:57,240 Speaker 1: would you be telling them about how to navigate the 355 00:18:57,280 --> 00:19:00,919 Speaker 1: business world right now and what kind of insights can 356 00:19:00,960 --> 00:19:04,400 Speaker 1: you offer to get a deal done well. First of all, 357 00:19:04,400 --> 00:19:07,000 Speaker 1: I think you have to understand whether you're entering into 358 00:19:07,240 --> 00:19:09,520 Speaker 1: a market that's growing in a market that's stable. So 359 00:19:09,680 --> 00:19:13,240 Speaker 1: China has a lot of different markets and they're behaving differently. 360 00:19:13,760 --> 00:19:16,520 Speaker 1: The key is to have products and services that focus 361 00:19:16,600 --> 00:19:19,639 Speaker 1: on the needs of what we would call upscale consumers, 362 00:19:19,640 --> 00:19:21,919 Speaker 1: and these are households that have at least twenty dollars 363 00:19:21,920 --> 00:19:24,960 Speaker 1: in income each year. This is the fastest growing part 364 00:19:25,040 --> 00:19:28,160 Speaker 1: of the population. So if you're selling higher end products 365 00:19:28,200 --> 00:19:31,399 Speaker 1: that appeal to these affluent consumers, you're doing well, you know. 366 00:19:31,520 --> 00:19:33,240 Speaker 1: And it's that in major cities, because you know, when 367 00:19:33,240 --> 00:19:35,119 Speaker 1: you think about a big country, it can be very 368 00:19:35,200 --> 00:19:37,680 Speaker 1: daunting to go and do business there. But if you 369 00:19:37,920 --> 00:19:40,360 Speaker 1: narrow it down to specific cities, then you can maybe 370 00:19:40,400 --> 00:19:42,480 Speaker 1: do a little bit better. Well. But I think it's 371 00:19:42,520 --> 00:19:45,960 Speaker 1: important to understand that this this population of affluent consumers 372 00:19:46,000 --> 00:19:48,080 Speaker 1: exist not just in two or three cities, but probably 373 00:19:48,080 --> 00:19:50,800 Speaker 1: in about fifty or sixty cities sprinkled up and down 374 00:19:50,800 --> 00:19:53,359 Speaker 1: the coast. So I think you you do need to 375 00:19:53,400 --> 00:19:56,000 Speaker 1: have a broader strategy. But the other thing is, if 376 00:19:56,040 --> 00:19:59,240 Speaker 1: you're just entering China, don't go in with a grand idea. 377 00:19:59,359 --> 00:20:02,880 Speaker 1: Go in with something small, something managementle that you can control, 378 00:20:02,920 --> 00:20:05,240 Speaker 1: and then scale it up gradually over time. The biggest 379 00:20:05,240 --> 00:20:07,439 Speaker 1: mistake that companies make when they go into China is 380 00:20:08,040 --> 00:20:09,720 Speaker 1: they think it's a giant market and they're going to 381 00:20:09,800 --> 00:20:12,200 Speaker 1: take it by storm and they have a grand plan, 382 00:20:12,280 --> 00:20:16,080 Speaker 1: and that usually comes to grief. Arthur, China's un has 383 00:20:16,119 --> 00:20:19,000 Speaker 1: been sort of in the backdrop this year, but there 384 00:20:19,040 --> 00:20:23,080 Speaker 1: still is the possibility for a very disorderly uh devaluation 385 00:20:23,400 --> 00:20:26,560 Speaker 1: of the currency. There still are a lot of outflows 386 00:20:27,119 --> 00:20:30,119 Speaker 1: capital outflows from China. How worried are you about this? 387 00:20:30,840 --> 00:20:33,040 Speaker 1: I'm not worried in the short term. If you look 388 00:20:33,040 --> 00:20:37,359 Speaker 1: at the recent capital outflow numbers, basically they're not real outflows. 389 00:20:37,359 --> 00:20:40,159 Speaker 1: They reflect the fact that China's holdings of things like 390 00:20:40,200 --> 00:20:43,080 Speaker 1: the euro and the yen have gone down in value 391 00:20:43,119 --> 00:20:45,760 Speaker 1: as the dollar has rallied over the last a couple 392 00:20:45,760 --> 00:20:49,040 Speaker 1: of months. So we don't really see strong capital outflows now. 393 00:20:49,040 --> 00:20:50,760 Speaker 1: And the other thing that we've seen is that when 394 00:20:50,760 --> 00:20:53,200 Speaker 1: f flows do occur, that Chinese are very effective at 395 00:20:53,520 --> 00:20:56,879 Speaker 1: imposing capital controls to keep things under wraps. So I 396 00:20:56,880 --> 00:20:59,840 Speaker 1: think for the next few months, not a big worry. 397 00:21:00,160 --> 00:21:01,919 Speaker 1: The worry I do have is, let's say we have 398 00:21:01,960 --> 00:21:06,280 Speaker 1: another six or eight months of the dollar rising very rapidly. 399 00:21:06,720 --> 00:21:09,080 Speaker 1: That will create a lot of incentives for Chinese investors 400 00:21:09,119 --> 00:21:11,680 Speaker 1: to switch from rem and b ASTs the dollar. ST's 401 00:21:11,720 --> 00:21:14,119 Speaker 1: move money out. But we're not there yet. Arthur Krober, 402 00:21:14,160 --> 00:21:16,240 Speaker 1: thank you so much for being with us. Arthur Kroeber, 403 00:21:16,320 --> 00:21:20,840 Speaker 1: founding partner of Gavacal dragon Omics, China focused economic research 404 00:21:20,920 --> 00:21:37,440 Speaker 1: consultancy in Beijing, and joining us here in studio is 405 00:21:37,480 --> 00:21:41,520 Speaker 1: Shelley Bancho, Bloomberg Gadfly columnist, to tell us all about 406 00:21:41,800 --> 00:21:44,440 Speaker 1: shopping at an Amazon store. I thought that was only 407 00:21:44,480 --> 00:21:47,080 Speaker 1: going to be online, but apparently eventually I'm going to 408 00:21:47,160 --> 00:21:48,959 Speaker 1: be walking in and out of a store and not 409 00:21:49,040 --> 00:21:52,600 Speaker 1: having to take out my wallet and somehow Amazon will 410 00:21:52,800 --> 00:21:55,399 Speaker 1: still be involved. Yeah, you just walk in, put the 411 00:21:55,440 --> 00:21:57,760 Speaker 1: things in your pocket or your card or your bag, 412 00:21:58,160 --> 00:22:01,119 Speaker 1: and walk out. And the way that it was build 413 00:22:01,240 --> 00:22:06,199 Speaker 1: was that it's all sensor technology artificial intelligence. Basically, they 414 00:22:06,200 --> 00:22:08,640 Speaker 1: have little sensors on all the on all the items, 415 00:22:08,640 --> 00:22:10,879 Speaker 1: and they can tell when you're buying them, and then 416 00:22:10,920 --> 00:22:14,959 Speaker 1: they credit it to your Amazon Prime account and charge you. 417 00:22:14,960 --> 00:22:18,840 Speaker 1: You know, it's just like Amazon already dominated retailers on 418 00:22:18,880 --> 00:22:21,560 Speaker 1: the online space, and now just to sort of take 419 00:22:21,640 --> 00:22:24,199 Speaker 1: some salt and rub it in those wounds, they're going 420 00:22:24,240 --> 00:22:26,880 Speaker 1: to have a brick and mortar place that's running better 421 00:22:26,920 --> 00:22:29,480 Speaker 1: than the ones that are in existence already. I mean, 422 00:22:30,040 --> 00:22:32,480 Speaker 1: how much do you retailers have to fear from this 423 00:22:32,600 --> 00:22:36,639 Speaker 1: sort of Amazonian invasion of their you know, bread and butter. 424 00:22:37,080 --> 00:22:40,360 Speaker 1: I think a lot, because you know, Amazon has done 425 00:22:40,359 --> 00:22:42,680 Speaker 1: so well at so many things. They've also not done 426 00:22:42,800 --> 00:22:45,159 Speaker 1: very well at at many things. But but this is 427 00:22:45,200 --> 00:22:48,320 Speaker 1: something you know, just focusing on checkout for example. Check 428 00:22:48,359 --> 00:22:51,639 Speaker 1: Out is something that all these big traditional retailers have 429 00:22:51,680 --> 00:22:54,120 Speaker 1: struggled with for decades and no one's been able to 430 00:22:54,240 --> 00:22:56,680 Speaker 1: kind of figure it out. And here comes Amazon basically 431 00:22:56,720 --> 00:22:59,160 Speaker 1: being like, ha ha, we have solved your biggest brick 432 00:22:59,200 --> 00:23:02,080 Speaker 1: and mortars big problems just like that with our first store. 433 00:23:02,200 --> 00:23:04,880 Speaker 1: But so this is my other question. How is Amazon 434 00:23:04,960 --> 00:23:06,360 Speaker 1: able to do this? I mean, if you go into 435 00:23:06,480 --> 00:23:08,360 Speaker 1: CVS or if you go into some of these other 436 00:23:08,400 --> 00:23:11,040 Speaker 1: stores they try these self checkout counters that often are 437 00:23:11,119 --> 00:23:13,840 Speaker 1: counterproductive because you end up having as many people standing 438 00:23:13,840 --> 00:23:16,640 Speaker 1: by them to fix any technical glitches as you actually 439 00:23:16,680 --> 00:23:19,120 Speaker 1: do just letting it to work, you know. But how 440 00:23:19,200 --> 00:23:21,919 Speaker 1: is this going to work? Yeah? I think that it'll 441 00:23:21,920 --> 00:23:23,840 Speaker 1: remain to be seeing what happens with Amazon. Like it 442 00:23:23,880 --> 00:23:27,320 Speaker 1: could completely fail and that would probably be fine with Amazon. 443 00:23:27,359 --> 00:23:29,920 Speaker 1: Which is the difference between some of these traditional brick 444 00:23:29,960 --> 00:23:32,879 Speaker 1: and mortar retailers. Um Walmart, for example, launched this thing 445 00:23:32,920 --> 00:23:34,720 Speaker 1: called Scan and Go a couple of years ago where 446 00:23:34,720 --> 00:23:36,760 Speaker 1: you would literally take your cell phone and scan have 447 00:23:36,840 --> 00:23:39,600 Speaker 1: to scan each item of each product and for that's 448 00:23:39,600 --> 00:23:41,359 Speaker 1: fine if you're going into a CBS, but when you 449 00:23:41,359 --> 00:23:43,760 Speaker 1: go to Walmart, you pick up a hundred items in 450 00:23:43,840 --> 00:23:47,160 Speaker 1: one big swoop. Um, it didn't work well for people, 451 00:23:47,160 --> 00:23:49,440 Speaker 1: so they scraped that. And you know, people have been 452 00:23:49,960 --> 00:23:52,840 Speaker 1: struggling with this. Why does Amazon want to do this? 453 00:23:53,400 --> 00:23:56,480 Speaker 1: Amazon wants grocery for sure, They need to learn grocery. 454 00:23:56,480 --> 00:23:59,800 Speaker 1: It's this huge Do they really like going to the 455 00:24:00,040 --> 00:24:02,000 Speaker 1: Rose three stores that I mean? They've said, okay, so 456 00:24:02,040 --> 00:24:04,040 Speaker 1: they want to be able to supply both because a 457 00:24:04,119 --> 00:24:06,639 Speaker 1: lot of grocery stores, for example, Kroger, you can go 458 00:24:06,680 --> 00:24:08,760 Speaker 1: to the store, you can pick it up, It'll all 459 00:24:08,800 --> 00:24:11,800 Speaker 1: be boxed for you. They've got an internet connection program 460 00:24:11,840 --> 00:24:14,159 Speaker 1: already in place. Yeah, I don't think it's ever going 461 00:24:14,200 --> 00:24:17,200 Speaker 1: to be Amazon's gonna replicate Walmart with these two hundred 462 00:24:17,240 --> 00:24:21,320 Speaker 1: thousand square foot you know, huge supercenters for grocery. They're 463 00:24:21,320 --> 00:24:23,120 Speaker 1: going to do it their own way. You know, they 464 00:24:23,119 --> 00:24:25,280 Speaker 1: have they might have a few hundred of these stores 465 00:24:25,680 --> 00:24:29,000 Speaker 1: with um, you know, just a certain amount of items 466 00:24:29,000 --> 00:24:31,920 Speaker 1: that they can actually learn about how people a shot 467 00:24:31,960 --> 00:24:34,439 Speaker 1: for groceries. Because right now Amazon is really good at 468 00:24:34,440 --> 00:24:36,600 Speaker 1: a lot of things, but grocery is not one of them, 469 00:24:36,600 --> 00:24:38,680 Speaker 1: and it's something they've they've been trying to do for 470 00:24:39,040 --> 00:24:42,440 Speaker 1: a decade now and have not managed to crack. So 471 00:24:42,560 --> 00:24:45,760 Speaker 1: how do you avoid people stealing things? I don't understand 472 00:24:45,800 --> 00:24:47,840 Speaker 1: this tracking sensor? Do you have like a little tracker 473 00:24:47,960 --> 00:24:50,600 Speaker 1: on you that you have to carry around that's connected 474 00:24:50,640 --> 00:24:53,440 Speaker 1: to your cell phone? That I mean, I'm just trying 475 00:24:53,480 --> 00:24:56,240 Speaker 1: to understand that. Yeah, I think every item has some 476 00:24:56,359 --> 00:24:58,959 Speaker 1: sort of tracker sensor thing in it. And so if 477 00:24:59,000 --> 00:25:02,080 Speaker 1: you walk out of your of the store without it, 478 00:25:02,359 --> 00:25:06,320 Speaker 1: you know, having you know, been linked to your smartphone, 479 00:25:06,560 --> 00:25:09,080 Speaker 1: then you know, maybe that'll set off bells or whistles 480 00:25:09,160 --> 00:25:12,000 Speaker 1: or something like that, if the whole place to start screaming, 481 00:25:12,440 --> 00:25:14,560 Speaker 1: like all the bells and whistles coming from every corner 482 00:25:14,600 --> 00:25:16,359 Speaker 1: of it. But this is supposed to be, I thought, 483 00:25:16,400 --> 00:25:19,840 Speaker 1: exclusively for Amazon Prime customers, So they're gonna know exactly 484 00:25:19,840 --> 00:25:22,600 Speaker 1: who you are almost before you get into the store. 485 00:25:22,760 --> 00:25:25,480 Speaker 1: When you walk in, you're supposed to turn your phone on, 486 00:25:25,760 --> 00:25:27,480 Speaker 1: you know, kind of the store mode that actually a 487 00:25:27,520 --> 00:25:30,399 Speaker 1: lot of retailers have already, and so that knows who 488 00:25:30,520 --> 00:25:32,919 Speaker 1: you are and then it can touch and you can 489 00:25:33,160 --> 00:25:35,720 Speaker 1: consents what you're putting into your car or your bag. 490 00:25:35,920 --> 00:25:38,440 Speaker 1: Then as you walk out, it kind of all rings up. 491 00:25:38,640 --> 00:25:40,560 Speaker 1: Where's the first store going to be? The first door 492 00:25:40,640 --> 00:25:44,720 Speaker 1: is in Seattle on Amazon's kind of downtown campus there, 493 00:25:44,800 --> 00:25:47,240 Speaker 1: and so it's already been it's already up and running 494 00:25:47,240 --> 00:25:49,480 Speaker 1: for employees, and employees have been using it. So they 495 00:25:49,480 --> 00:25:52,320 Speaker 1: made this kind of big announcement yesterday that they're going 496 00:25:52,359 --> 00:25:54,600 Speaker 1: to open it in early January to the public. So 497 00:25:54,800 --> 00:25:57,000 Speaker 1: we'll see, you know, kind of what happens, and if 498 00:25:57,040 --> 00:26:00,560 Speaker 1: it goes well, they'll, you know, they'll open. And they're 499 00:26:00,560 --> 00:26:02,760 Speaker 1: also testing out all sorts of different concepts and so 500 00:26:02,760 --> 00:26:04,720 Speaker 1: I don't think this is the end of brick and 501 00:26:04,760 --> 00:26:06,879 Speaker 1: mortar for Amazon yet. Well, when you come out of 502 00:26:06,880 --> 00:26:08,879 Speaker 1: the store that can drop the products on you that 503 00:26:08,960 --> 00:26:11,040 Speaker 1: you forgot to buy on the exactly, or send a 504 00:26:11,040 --> 00:26:13,560 Speaker 1: little drone after you to follow you, I gotta say 505 00:26:13,560 --> 00:26:16,880 Speaker 1: those stock is up. Stock up seven dollars right now, 506 00:26:17,119 --> 00:26:20,520 Speaker 1: seven hundred and sixty six dollars for a share of Amazon. 507 00:26:20,560 --> 00:26:23,359 Speaker 1: It's up more than three percent since they announced this. 508 00:26:23,560 --> 00:26:25,800 Speaker 1: Uh this new foray. Can you imagine a drone going 509 00:26:25,840 --> 00:26:29,160 Speaker 1: after you you forgot vegetables? You didn't have enough vegetables. 510 00:26:29,160 --> 00:26:33,159 Speaker 1: Sally Banjo Bloomberg gad Fly column is covering the retail sector. 511 00:26:33,200 --> 00:26:40,480 Speaker 1: Thank you so much for joining us. Thanks for listening 512 00:26:40,480 --> 00:26:43,480 Speaker 1: to the Bloomberg P and L podcast. You can subscribe 513 00:26:43,480 --> 00:26:48,080 Speaker 1: and listen to interviews at iTunes, SoundCloud, or whatever podcast 514 00:26:48,119 --> 00:26:51,159 Speaker 1: platform you prefer. I'm pim Fox. I'm out there on 515 00:26:51,200 --> 00:26:54,400 Speaker 1: Twitter at pim Fox. I'm out there on Twitter at 516 00:26:54,560 --> 00:26:57,480 Speaker 1: Lisa Abramo. It's one before the podcast. You can always 517 00:26:57,600 --> 00:27:07,320 Speaker 1: catch us worldwide on Bloomberg Radio. Y de Lord