1 00:00:00,520 --> 00:00:05,640 Speaker 1: When we abandoned the policy of America. First, we started 2 00:00:05,680 --> 00:00:11,479 Speaker 1: rebuilding other countries instead of our own. The skyscrapers went 3 00:00:11,560 --> 00:00:15,200 Speaker 1: up in Beijing and many other cities around the world, 4 00:00:15,720 --> 00:00:20,840 Speaker 1: while the factories and neighborhoods crumbled right here in Detroit. 5 00:00:25,079 --> 00:00:28,520 Speaker 1: In August, Donald Trump, then still accountidate, laid out his 6 00:00:28,560 --> 00:00:31,800 Speaker 1: economic agenda in a major speech. In it, he pledged 7 00:00:31,800 --> 00:00:35,520 Speaker 1: to generate more manufacturing jobs for Americans, and he couldn't 8 00:00:35,560 --> 00:00:39,800 Speaker 1: have chosen a more appropriate location. Detroit, the poster child 9 00:00:39,840 --> 00:00:47,200 Speaker 1: for post industrial decline. It did redounded sense of wanting 10 00:00:47,240 --> 00:00:51,920 Speaker 1: to bring back Detroit. In the audience was a twenty 11 00:00:51,960 --> 00:00:55,240 Speaker 1: six year old Detroit native, A. K. Bennett. We've heard it, 12 00:00:55,320 --> 00:00:59,160 Speaker 1: you know, a few times, you know, from multiple politicians. UM. 13 00:01:00,040 --> 00:01:04,600 Speaker 1: And there still hasn't been that true push to really 14 00:01:05,360 --> 00:01:10,240 Speaker 1: bring things back, um in terms of manufacturing. But as president, 15 00:01:10,280 --> 00:01:13,080 Speaker 1: Donald Trump has made a US manufacturing revival one of 16 00:01:13,120 --> 00:01:20,880 Speaker 1: his biggest priorities in terms of spurring new manufacturing and industry. Um. 17 00:01:20,920 --> 00:01:25,800 Speaker 1: There has not been a lot of outside help, not 18 00:01:25,920 --> 00:01:28,840 Speaker 1: a lot of outside help yet, but there is a 19 00:01:28,920 --> 00:01:31,679 Speaker 1: chance that could be about to change. Just one month 20 00:01:31,720 --> 00:01:35,199 Speaker 1: after Trump's November elections, came the first promise to return 21 00:01:35,280 --> 00:01:39,080 Speaker 1: significant jobs to US shores. We're gonna get things coming. 22 00:01:39,080 --> 00:01:41,959 Speaker 1: We're gonna get Apple to start building their damn computers 23 00:01:41,959 --> 00:01:44,960 Speaker 1: and things in this country instead of in other countries. 24 00:01:46,520 --> 00:01:49,120 Speaker 1: It's not Apple, but a company that works very closely 25 00:01:49,160 --> 00:01:52,520 Speaker 1: with it. Fox Con is a Taiwanese company which makes 26 00:01:52,520 --> 00:01:55,760 Speaker 1: the iPhone for Apple and the Xbox for Microsoft. It 27 00:01:55,840 --> 00:01:58,520 Speaker 1: has promised to invest seven billion dollars in the US 28 00:01:58,720 --> 00:02:11,040 Speaker 1: and generate as many as fifty new jobs in the process. Hi. 29 00:02:11,120 --> 00:02:13,919 Speaker 1: I'm Brad Stone, and I'm Alex Wet and this week 30 00:02:13,960 --> 00:02:16,520 Speaker 1: I'm Decrypted. We'll take a very close look at Fox 31 00:02:16,600 --> 00:02:19,600 Speaker 1: Comp's plan and whether it really will create those good 32 00:02:19,639 --> 00:02:23,240 Speaker 1: American factory jobs that President Trump is calling for. This 33 00:02:23,360 --> 00:02:25,760 Speaker 1: is our third episode in a series looking at the 34 00:02:25,800 --> 00:02:28,600 Speaker 1: impact that automation could have on our jobs. If you 35 00:02:28,639 --> 00:02:31,480 Speaker 1: have a story to share, send it to Decrypted at 36 00:02:31,480 --> 00:02:35,720 Speaker 1: bloomberg dot net. Advances an industrial automation are making it 37 00:02:35,800 --> 00:02:39,079 Speaker 1: cheaper than ever to bring manufacturing jobs back to the US, 38 00:02:39,639 --> 00:02:42,400 Speaker 1: But by its very nature, that's because it would require 39 00:02:42,520 --> 00:02:45,880 Speaker 1: fewer workers on the factory floor. We'll see whether those 40 00:02:45,960 --> 00:02:48,120 Speaker 1: jobs could still give a boost to communities in the 41 00:02:48,160 --> 00:02:50,560 Speaker 1: heartland and we'll find out what's in it for fox 42 00:02:50,639 --> 00:03:00,239 Speaker 1: Con stay with us. So, Alex, were you surpris eyes 43 00:03:00,280 --> 00:03:02,320 Speaker 1: by fox cons announcement that it was going to build 44 00:03:02,320 --> 00:03:04,680 Speaker 1: a factory in the US. Yeah. I've been looking really 45 00:03:04,720 --> 00:03:07,560 Speaker 1: closely at like the costs of making iPhones and any 46 00:03:07,560 --> 00:03:11,160 Speaker 1: other electronic device, and it's the labor costs which make 47 00:03:11,200 --> 00:03:14,600 Speaker 1: a huge difference between China and the US, and that's 48 00:03:14,600 --> 00:03:16,680 Speaker 1: why they do this stuff in in in the cities 49 00:03:16,720 --> 00:03:20,200 Speaker 1: around China. There's so much theater right now around companies 50 00:03:20,240 --> 00:03:22,440 Speaker 1: saying they're going to bring jobs back to the U S. 51 00:03:22,480 --> 00:03:24,480 Speaker 1: How real is this and when when do we think 52 00:03:24,480 --> 00:03:27,760 Speaker 1: we'll see a fox complant in the United States? It 53 00:03:27,840 --> 00:03:29,840 Speaker 1: clearly takes a while to build these things up, but 54 00:03:30,160 --> 00:03:31,880 Speaker 1: it could be, you know, next year that we see 55 00:03:31,919 --> 00:03:34,519 Speaker 1: something that The question is when they bring jobs back 56 00:03:34,760 --> 00:03:37,040 Speaker 1: quite how many jobs will come and what kind of 57 00:03:37,040 --> 00:03:43,680 Speaker 1: a boost does this give to potentially rest belt cities. A. K. 58 00:03:43,840 --> 00:03:47,760 Speaker 1: Bennett comes from a family of auto workers on both 59 00:03:47,800 --> 00:03:50,960 Speaker 1: sides of both my mother and father's side. I would 60 00:03:50,960 --> 00:03:56,600 Speaker 1: say about were employed in the by the Big Three. 61 00:03:57,400 --> 00:04:00,000 Speaker 1: The so called Big three means they work for General motors, 62 00:04:00,120 --> 00:04:04,400 Speaker 1: Ford and Chrysler, you know, through the fifties, sixties, seventies, 63 00:04:04,400 --> 00:04:09,240 Speaker 1: and eighties. But grandparents, uh, two of my four grandparents 64 00:04:09,240 --> 00:04:13,840 Speaker 1: were multiple and uncles, cousins. The post war Haiti I 65 00:04:13,960 --> 00:04:16,200 Speaker 1: motor city, it was kind of like a golden age 66 00:04:16,240 --> 00:04:20,800 Speaker 1: for American manufacturing. There are still some cousins and to 67 00:04:20,920 --> 00:04:23,320 Speaker 1: the day that are working for the auto industry, whether 68 00:04:23,320 --> 00:04:25,359 Speaker 1: it be for a supplier or one of the Big Three. 69 00:04:25,920 --> 00:04:28,080 Speaker 1: But of course it's not just Detroit. The whole Rust 70 00:04:28,080 --> 00:04:30,440 Speaker 1: Belt was booming with industrial activity for much of the 71 00:04:30,480 --> 00:04:34,440 Speaker 1: twentieth century. Ohio typically ranked second only to Indiana in 72 00:04:34,440 --> 00:04:38,440 Speaker 1: the US in steel production. Wisconsin meanwhile, fed that metallurgy 73 00:04:38,480 --> 00:04:41,560 Speaker 1: industry with foundry and metal working equipment, which were a 74 00:04:41,600 --> 00:04:44,599 Speaker 1: staple of its economy. Donald Trump is quick to evoke 75 00:04:44,640 --> 00:04:49,599 Speaker 1: those times when he talks about manufacturing what you have 76 00:04:49,760 --> 00:04:54,279 Speaker 1: in mind, or the jobs from the nineteen seventies of 77 00:04:54,480 --> 00:04:58,000 Speaker 1: people kind of standing on an assembly line, being able 78 00:04:58,040 --> 00:05:04,799 Speaker 1: to go from high school into uh good job that 79 00:05:05,080 --> 00:05:08,760 Speaker 1: enables you to buy a house, in a car and 80 00:05:09,480 --> 00:05:12,640 Speaker 1: have a pretty good life. That's Caroline Freud from the 81 00:05:12,680 --> 00:05:16,360 Speaker 1: Pizson Institute, a DC based economics think tank, and those 82 00:05:16,440 --> 00:05:21,200 Speaker 1: jobs frankly don't exist anymore. There's a few issues of 83 00:05:21,240 --> 00:05:23,919 Speaker 1: play here. One big problem has been the cost of 84 00:05:23,960 --> 00:05:27,719 Speaker 1: full time employment. American workers are more expensive than workers 85 00:05:27,760 --> 00:05:30,480 Speaker 1: in China or other parts of the world. A factory 86 00:05:30,480 --> 00:05:33,280 Speaker 1: worker here can cost close to forty when you take 87 00:05:33,320 --> 00:05:36,279 Speaker 1: into account health, insurance and other costs. He accounts bought 88 00:05:36,320 --> 00:05:39,120 Speaker 1: in China cost just one tenth of that. But cheaper 89 00:05:39,200 --> 00:05:42,760 Speaker 1: still are robots, which offer the tantalizing prospect of a 90 00:05:42,920 --> 00:05:47,000 Speaker 1: US manufacturing resurgence. The snag is that with robots doing 91 00:05:47,000 --> 00:05:49,400 Speaker 1: a lot of the work, they probably won't bring back 92 00:05:49,400 --> 00:05:51,800 Speaker 1: the millions of jobs that Trump is looking for. The 93 00:05:51,880 --> 00:05:54,920 Speaker 1: number of Americans and manufacturing jobs has fallen from a 94 00:05:55,040 --> 00:05:58,760 Speaker 1: ninety nine peak of almost twenty million to about twelve 95 00:05:58,800 --> 00:06:02,200 Speaker 1: point four million today, but industrial output in that time 96 00:06:02,240 --> 00:06:06,520 Speaker 1: has gone up because factories are increasingly automated. That also 97 00:06:06,560 --> 00:06:09,279 Speaker 1: helps explain why labor costs in the US are higher. 98 00:06:09,800 --> 00:06:14,440 Speaker 1: Rather than assembling components manually, these workers operate complex machinery, 99 00:06:14,520 --> 00:06:16,960 Speaker 1: doing work that would otherwise be done by several people. 100 00:06:17,720 --> 00:06:22,240 Speaker 1: If you look at manufacturing, there's a big increase in 101 00:06:23,080 --> 00:06:27,880 Speaker 1: managerial work, or work that requires computer or engineering skills 102 00:06:28,240 --> 00:06:32,680 Speaker 1: and a decline in the production worker because robots can 103 00:06:32,760 --> 00:06:41,799 Speaker 1: do those kind of routine tasks more efficiently. Back in Detroit, 104 00:06:42,120 --> 00:06:46,000 Speaker 1: Ak works as a project manager in the construction industry. Now, 105 00:06:46,120 --> 00:06:48,599 Speaker 1: a K says he's not a Trump supporter, but he 106 00:06:48,640 --> 00:06:51,800 Speaker 1: went to see Trump layout his economic plan, and Detroit 107 00:06:51,880 --> 00:06:54,400 Speaker 1: was also one of the final three locations that fox 108 00:06:54,480 --> 00:06:57,159 Speaker 1: Con is considering for a mega factory in the US. 109 00:06:57,720 --> 00:07:01,159 Speaker 1: Detroit is up against Racine, Wisconsin, and Columbus, Ohio to 110 00:07:01,200 --> 00:07:03,640 Speaker 1: win the plant, according to people familiar with the matter, 111 00:07:04,480 --> 00:07:06,640 Speaker 1: which means A K is in a pretty good place 112 00:07:06,680 --> 00:07:09,520 Speaker 1: of a huge industrial building project comes to his town. 113 00:07:10,080 --> 00:07:14,920 Speaker 1: This would be a massive project. Uh that would you know? 114 00:07:15,200 --> 00:07:19,640 Speaker 1: There's there'd be thousands of trades persons on site. But 115 00:07:19,840 --> 00:07:22,800 Speaker 1: let's break this down. When fox Con talks about creating 116 00:07:22,840 --> 00:07:25,960 Speaker 1: fifty jobs in the US, it doesn't mean the factory 117 00:07:26,040 --> 00:07:29,760 Speaker 1: will have fifty workers. That number also includes the people 118 00:07:29,800 --> 00:07:31,960 Speaker 1: who build the plant and then work in the extended 119 00:07:32,000 --> 00:07:35,880 Speaker 1: supply chain feeding it with components. The plant would be 120 00:07:35,960 --> 00:07:41,200 Speaker 1: huge in terms of construction jobs, design jobs, engineering jobs, 121 00:07:41,920 --> 00:07:46,480 Speaker 1: and then um going forward for an additional logistics and 122 00:07:46,520 --> 00:07:50,040 Speaker 1: other suppliers. Potentially, my understanding is from talking to sources 123 00:07:50,120 --> 00:07:52,760 Speaker 1: that the factory could employ close to ten thousand people 124 00:07:52,800 --> 00:07:55,560 Speaker 1: in the long term, and that fox could build other 125 00:07:55,560 --> 00:07:58,920 Speaker 1: satellite plants elsewhere in the US and alex What will 126 00:07:58,960 --> 00:08:00,880 Speaker 1: they be making there. The idea would be that this 127 00:08:00,960 --> 00:08:03,760 Speaker 1: main plant would make large l c D display panels 128 00:08:03,840 --> 00:08:06,520 Speaker 1: which would then be shipped to these other factories to 129 00:08:06,560 --> 00:08:10,400 Speaker 1: make things like computer and TV screens, And these are 130 00:08:10,400 --> 00:08:13,520 Speaker 1: the factories that Foxconn is thinking about building, So not 131 00:08:13,680 --> 00:08:16,760 Speaker 1: the famous iPhone, right. It still requires a lot of 132 00:08:16,800 --> 00:08:19,600 Speaker 1: manual labor to put an iPhone together, so the iPhone 133 00:08:19,680 --> 00:08:22,280 Speaker 1: isn't the ideal project to build in a highly automated 134 00:08:22,320 --> 00:08:24,960 Speaker 1: factory in the U S. L c D screens have 135 00:08:25,000 --> 00:08:28,200 Speaker 1: been built by robots for years, so what specifically is 136 00:08:28,240 --> 00:08:30,920 Speaker 1: holding back the iPhone from being produced or at least 137 00:08:30,960 --> 00:08:33,400 Speaker 1: assembled in the US. So a lot of the components 138 00:08:33,559 --> 00:08:36,120 Speaker 1: which go into an iPhone can be built by robots, 139 00:08:36,160 --> 00:08:38,880 Speaker 1: but actually that final process of sticking all these bits 140 00:08:38,920 --> 00:08:42,880 Speaker 1: together can require some really fiddly screws to piece it 141 00:08:42,880 --> 00:08:45,240 Speaker 1: all together, and that's not something that's very easy for 142 00:08:45,320 --> 00:08:48,800 Speaker 1: robots to do, right. Those fiddly screws are difficult for 143 00:08:48,920 --> 00:08:51,960 Speaker 1: robots and tasks like printing silicon chips that's far better 144 00:08:52,000 --> 00:08:54,520 Speaker 1: suited for automation, which is why there is still so 145 00:08:54,600 --> 00:08:58,360 Speaker 1: much semiconductor manufacturing in the US right. Intel is building plans, 146 00:08:58,400 --> 00:09:05,680 Speaker 1: particularly in Arizona, to processors. Well, there's a somewhat morbid 147 00:09:05,760 --> 00:09:07,560 Speaker 1: joke that the fact of the future will just to 148 00:09:07,600 --> 00:09:12,599 Speaker 1: have two employees, a human and a dog. That's Eric Brynjolfson. 149 00:09:12,880 --> 00:09:15,120 Speaker 1: He's a professor the m I. T. Sloan School of 150 00:09:15,120 --> 00:09:18,760 Speaker 1: Management and the co author of the influential book The 151 00:09:18,800 --> 00:09:22,320 Speaker 1: Second Machine Age, Work, Progress and Prosperity In a time 152 00:09:22,320 --> 00:09:26,160 Speaker 1: of brilliant technologies, the humans job will be to feed 153 00:09:26,200 --> 00:09:28,160 Speaker 1: the dog, and the dog's job will be to keep 154 00:09:28,160 --> 00:09:30,880 Speaker 1: the human from touching any of the equipment. Although I 155 00:09:30,880 --> 00:09:33,320 Speaker 1: should say we're probably not at the dog and human 156 00:09:33,360 --> 00:09:36,760 Speaker 1: stage yet. But one thing Eric did emphasize to me 157 00:09:36,880 --> 00:09:39,120 Speaker 1: is that for all the rhetoric we hear from politicians 158 00:09:39,160 --> 00:09:42,280 Speaker 1: about how manufacturing will bring back jobs for companies like 159 00:09:42,320 --> 00:09:45,319 Speaker 1: Fox Com, this really isn't about jobs at all. You know, 160 00:09:45,360 --> 00:09:47,400 Speaker 1: if you walk around a lot of American factories that 161 00:09:47,480 --> 00:09:50,400 Speaker 1: virtually lights out with very few humans, and the few 162 00:09:50,480 --> 00:09:53,400 Speaker 1: humans who are there, you know they're paid pretty good wages, 163 00:09:53,840 --> 00:09:56,600 Speaker 1: but it's not the thousands or millions of jobs we 164 00:09:56,720 --> 00:09:59,680 Speaker 1: used to have in that kind of work. It's really 165 00:09:59,720 --> 00:10:02,840 Speaker 1: the to establish good relations with the Trump administration that 166 00:10:02,880 --> 00:10:05,960 Speaker 1: it's driving fox Kin here and automation is what's making 167 00:10:05,960 --> 00:10:08,640 Speaker 1: it possible. Will dig into that some more later in 168 00:10:08,679 --> 00:10:17,360 Speaker 1: the show. Companies usually employ consultants who help them find 169 00:10:17,400 --> 00:10:22,000 Speaker 1: potential sites for new factories. It always starts with sitting 170 00:10:22,000 --> 00:10:25,440 Speaker 1: down with them and defining what exactly they need to 171 00:10:25,520 --> 00:10:29,280 Speaker 1: be successful. I chatted with Darren Budau. He's based in 172 00:10:29,360 --> 00:10:33,800 Speaker 1: Chicago and leads Deloitte site selection practice. We call those 173 00:10:33,800 --> 00:10:37,040 Speaker 1: critical location factors. We really want to understand what makes 174 00:10:37,080 --> 00:10:41,319 Speaker 1: them tick and what makes this new deployment of their's successful. 175 00:10:42,120 --> 00:10:45,920 Speaker 1: It's a process of elimination. We will be reviewing potential sites, 176 00:10:46,000 --> 00:10:50,560 Speaker 1: actual actual properties, whether they're existing buildings or land sites 177 00:10:50,960 --> 00:10:54,120 Speaker 1: that have what the client needs. The consultants have a 178 00:10:54,160 --> 00:10:57,800 Speaker 1: list of criteria, things like labor costs, transport links, proximity 179 00:10:57,840 --> 00:11:02,040 Speaker 1: to key suppliers and customers. From there, there's always a 180 00:11:02,120 --> 00:11:07,360 Speaker 1: due diligence process of conducting additional analysis and study on 181 00:11:07,480 --> 00:11:11,240 Speaker 1: the labor market. On the the technical aspects of the 182 00:11:11,280 --> 00:11:14,880 Speaker 1: site itself from a from a utilities and infrastructure perspective, 183 00:11:15,280 --> 00:11:24,600 Speaker 1: we want to identify any possible risks before negotiations. With 184 00:11:24,679 --> 00:11:27,480 Speaker 1: their shortlist of sites ready, the company then approaches the 185 00:11:27,520 --> 00:11:30,600 Speaker 1: local government authorities to see what sweeten is they can offer. 186 00:11:31,440 --> 00:11:35,120 Speaker 1: Those can come in three main forms. Tax incentives and rebates, 187 00:11:35,360 --> 00:11:39,959 Speaker 1: infrastructure improvements like new bus and subway stations, and labor incentives, 188 00:11:40,160 --> 00:11:44,920 Speaker 1: usually education programs to train staff. Incentive packages can sometimes 189 00:11:44,920 --> 00:11:48,160 Speaker 1: stretch into the hundreds and millions of dollars. Fox Con 190 00:11:48,280 --> 00:11:50,839 Speaker 1: is being advised by e Y, the consultant we probably 191 00:11:50,840 --> 00:11:52,880 Speaker 1: know as Ernst and Young, which is helping it get 192 00:11:52,920 --> 00:11:55,600 Speaker 1: the best possible deal. We said earlier in the show. 193 00:11:56,000 --> 00:11:58,120 Speaker 1: Our sources tell us that e Y has whittled the 194 00:11:58,120 --> 00:12:01,000 Speaker 1: shortlist down to Detroit, New Michigan, We're seeing in Wisconsin 195 00:12:01,160 --> 00:12:04,840 Speaker 1: and Columbus in Ohio. But there's still one nagging question, Alex, 196 00:12:05,200 --> 00:12:15,240 Speaker 1: what is in it for fox Com? Hi? Everyone? Every week, 197 00:12:15,440 --> 00:12:19,760 Speaker 1: our team here at Bloomberg Technology spends so much work 198 00:12:20,200 --> 00:12:23,960 Speaker 1: making sure that this show is great, and we have 199 00:12:24,040 --> 00:12:27,160 Speaker 1: a special request to ask of you. If you like 200 00:12:27,240 --> 00:12:30,280 Speaker 1: the show, um, please help us get the word out 201 00:12:30,559 --> 00:12:32,800 Speaker 1: so more people can find us. Maybe you have a 202 00:12:32,840 --> 00:12:35,920 Speaker 1: friend who likes technology a lot who's never listened to 203 00:12:35,920 --> 00:12:40,040 Speaker 1: a podcast before, or if you listen on Apple podcasts, 204 00:12:40,160 --> 00:12:43,320 Speaker 1: you can leave us a rating and a review. To 205 00:12:43,400 --> 00:12:46,080 Speaker 1: do that, just search for Decrypted then hit write a 206 00:12:46,120 --> 00:12:49,880 Speaker 1: review inside the reviews tab. Thanks so much for supporting 207 00:12:49,880 --> 00:12:57,680 Speaker 1: our show. We spend ages puzzling out of this all. 208 00:12:57,760 --> 00:13:00,280 Speaker 1: I will take your word for that. Seriously. I spoke 209 00:13:00,320 --> 00:13:02,760 Speaker 1: to like tons of people over the course of several 210 00:13:02,800 --> 00:13:05,800 Speaker 1: weeks trying to work out what exactly is the business 211 00:13:05,800 --> 00:13:09,800 Speaker 1: appeal of Wisconsin or Michigan in particular. They don't check 212 00:13:09,840 --> 00:13:13,120 Speaker 1: obvious boxes like having really good transport links which could 213 00:13:13,360 --> 00:13:16,000 Speaker 1: allow equipment to be shipped to Asia for final assembly, 214 00:13:16,200 --> 00:13:18,959 Speaker 1: or even proximity to customers. Well what about proximity to 215 00:13:19,000 --> 00:13:22,080 Speaker 1: car makers? I mean, all these automobile manufacturers now need 216 00:13:22,200 --> 00:13:25,000 Speaker 1: new technology, and the suppliers like Fox kind of help 217 00:13:25,080 --> 00:13:28,400 Speaker 1: them put screens and digital technologies into their cars. Yeah. 218 00:13:28,400 --> 00:13:31,000 Speaker 1: I did think about that, because autonomous cars will give 219 00:13:31,080 --> 00:13:33,679 Speaker 1: drivers more time to serve the web and watch films 220 00:13:33,720 --> 00:13:35,320 Speaker 1: because they don't have to pay as much attention to 221 00:13:35,360 --> 00:13:37,440 Speaker 1: the road. But if that were the case, they wanted 222 00:13:37,440 --> 00:13:39,920 Speaker 1: to be near those customers. The South would probably make 223 00:13:40,000 --> 00:13:42,520 Speaker 1: more sense. There are more luxury car makers there who 224 00:13:42,520 --> 00:13:44,320 Speaker 1: are going to be perhaps the first to have the 225 00:13:44,320 --> 00:13:47,280 Speaker 1: autonomous cars, and the labor costs they are lower. Well, 226 00:13:47,400 --> 00:13:50,480 Speaker 1: lots of declining industry in the Midwest. Is there something 227 00:13:50,520 --> 00:13:52,520 Speaker 1: to the to the kind of labor base and the 228 00:13:52,559 --> 00:13:56,040 Speaker 1: availability of possible workers in you know, former steel factories, 229 00:13:56,080 --> 00:13:59,199 Speaker 1: former chemical factories that makes us appealing to fox Camp. Well, actually, 230 00:13:59,200 --> 00:14:02,160 Speaker 1: the chemical factory idea we thought might be that you 231 00:14:02,200 --> 00:14:04,559 Speaker 1: need chemicals to make l C D and so proximity 232 00:14:04,600 --> 00:14:07,440 Speaker 1: to those guys would help. And of course Detroit has 233 00:14:07,720 --> 00:14:10,839 Speaker 1: a legacy workforce from the automotive industry, but it's still 234 00:14:10,920 --> 00:14:13,040 Speaker 1: a very different kind of skill set you need to 235 00:14:13,120 --> 00:14:17,559 Speaker 1: run an electronics factory. From all my conversations, it increasingly 236 00:14:17,600 --> 00:14:21,440 Speaker 1: seemed like there could be another motivation at play. If 237 00:14:21,440 --> 00:14:25,760 Speaker 1: you look at the hun plan and there's a plant 238 00:14:25,880 --> 00:14:29,600 Speaker 1: in the states that they're considering, Um, it didn't surprise 239 00:14:29,680 --> 00:14:32,400 Speaker 1: me that so many of them happened to be swing states. 240 00:14:33,480 --> 00:14:36,320 Speaker 1: That's Caroline the economist we heard from earlier. That's a 241 00:14:36,320 --> 00:14:38,960 Speaker 1: good point. In Wisconsin. Trump won with a marchin of 242 00:14:39,000 --> 00:14:42,120 Speaker 1: twenty three thou votes and had just eleven thousand more 243 00:14:42,200 --> 00:14:45,120 Speaker 1: votes in Hillary Clinton in the state of Michigan. In Ohio, 244 00:14:45,240 --> 00:14:47,920 Speaker 1: my home state, Trump had a more substantial victory, but 245 00:14:48,000 --> 00:14:58,000 Speaker 1: it has long been considered a swing state. The sheer 246 00:14:58,080 --> 00:15:02,320 Speaker 1: dollars are significant, I think for any state. David Welch 247 00:15:02,360 --> 00:15:05,040 Speaker 1: is the Bloomberg Bureau chief in Detroit. He's been keeping 248 00:15:05,080 --> 00:15:07,240 Speaker 1: his air close to the ground as FOTS gone whittles 249 00:15:07,280 --> 00:15:10,600 Speaker 1: down its shortness. I asked him how significant an investment 250 00:15:10,640 --> 00:15:12,640 Speaker 1: the plant would be for the region. But this would 251 00:15:12,640 --> 00:15:16,600 Speaker 1: really tower, and it's important for for southeastern Michigan because 252 00:15:17,600 --> 00:15:20,080 Speaker 1: the state has been trying to wean itself off of 253 00:15:21,040 --> 00:15:23,720 Speaker 1: its master reliance on the auto industry for a long time, 254 00:15:23,800 --> 00:15:26,720 Speaker 1: with with not a lot of success. The two other 255 00:15:26,760 --> 00:15:28,960 Speaker 1: sites in the running have a similar story to tell. 256 00:15:29,360 --> 00:15:33,040 Speaker 1: In Racine, politicians have worked hard to reduce unemployment from 257 00:15:33,040 --> 00:15:36,040 Speaker 1: a two thousand ten peak of eighteen by bringing in 258 00:15:36,120 --> 00:15:39,960 Speaker 1: new investment. It now stands at about five. Columbus is 259 00:15:40,000 --> 00:15:42,440 Speaker 1: perhaps the town that needs the investment the least, but 260 00:15:42,520 --> 00:15:44,800 Speaker 1: that also means it already has a strong labor force, 261 00:15:44,800 --> 00:15:48,600 Speaker 1: which would suit Foxcount's needs. Any governor trying to attract 262 00:15:48,640 --> 00:15:51,960 Speaker 1: a new manufacturing project has to negotiate a delicate balance 263 00:15:51,960 --> 00:15:54,760 Speaker 1: between trying to attract projects that will create jobs but 264 00:15:54,800 --> 00:15:57,520 Speaker 1: without appearing to plan it a big business. Here's what 265 00:15:57,600 --> 00:16:01,600 Speaker 1: David said about Michigan's Republican governor Rix Nida Governor Snider 266 00:16:01,680 --> 00:16:04,080 Speaker 1: has not been a big fan of just handing money 267 00:16:04,280 --> 00:16:08,480 Speaker 1: to companies. He's been more interested in building infrastructure, giving 268 00:16:08,520 --> 00:16:13,080 Speaker 1: training grants and and and sort of facilitating um as 269 00:16:13,080 --> 00:16:16,120 Speaker 1: opposed to throwing money at people. Are companies? And what 270 00:16:16,160 --> 00:16:19,560 Speaker 1: about the political calculation? Alex Trump wont all three states 271 00:16:19,560 --> 00:16:21,960 Speaker 1: in the last election, but the wins in Michigan and 272 00:16:21,960 --> 00:16:25,440 Speaker 1: Wisconsin were both unexpected and critical. If it really is 273 00:16:25,480 --> 00:16:28,520 Speaker 1: a political calculation, Michigan and Wisconsin have got to be 274 00:16:28,560 --> 00:16:31,600 Speaker 1: favorites because they had had such a narrow margin of 275 00:16:31,640 --> 00:16:34,840 Speaker 1: victory for the Republicans last time around. But whichever state 276 00:16:34,920 --> 00:16:37,680 Speaker 1: eventually wins the Megaplan, Trump will likely be able to 277 00:16:37,680 --> 00:16:40,720 Speaker 1: stay credit for it. And Trump is going to be telling, 278 00:16:41,040 --> 00:16:43,160 Speaker 1: you know, in a couple of years, he's gonna be 279 00:16:43,240 --> 00:16:45,240 Speaker 1: telling the people in this area, hey, look, you know 280 00:16:45,240 --> 00:16:47,840 Speaker 1: I got you some particularly it's Michigan, I got you 281 00:16:47,880 --> 00:16:50,640 Speaker 1: a huge plant that's that's not automotive. I brought you 282 00:16:50,760 --> 00:16:54,320 Speaker 1: something else, and we're talking about a few thousand workers, 283 00:16:54,320 --> 00:17:04,160 Speaker 1: So it's it's it's a pretty big deal, no question. Okay. 284 00:17:04,560 --> 00:17:07,000 Speaker 1: So even if fox CON's megaplant is going to be 285 00:17:07,160 --> 00:17:09,960 Speaker 1: very highly automated, it's still going to create an investment 286 00:17:09,960 --> 00:17:12,359 Speaker 1: to give a boost to a city like Detroit. So 287 00:17:12,440 --> 00:17:14,960 Speaker 1: there's a clear advantage to Trump and building a plant here. 288 00:17:15,200 --> 00:17:17,840 Speaker 1: But what's in it for fox Con? The short on 289 00:17:17,920 --> 00:17:20,160 Speaker 1: SWA is that having a factory in the US could 290 00:17:20,160 --> 00:17:22,240 Speaker 1: make life easier for false Cone when it tries to 291 00:17:22,320 --> 00:17:25,040 Speaker 1: import iPhones made in China. Okay, so I think I 292 00:17:25,080 --> 00:17:27,880 Speaker 1: get it. If fox Kind builds a plant or even 293 00:17:27,920 --> 00:17:31,639 Speaker 1: several plants, employing thousands of workers in the US, it 294 00:17:31,680 --> 00:17:35,040 Speaker 1: could convince Trump not to slap import duties on iPhones 295 00:17:35,040 --> 00:17:38,040 Speaker 1: made in China and ship to the States, exactly because 296 00:17:38,119 --> 00:17:41,200 Speaker 1: fos Cone could fairly say it's already bringing jobs here 297 00:17:41,960 --> 00:17:44,560 Speaker 1: and bringing iPhone manufacturing to the U. S would be 298 00:17:44,560 --> 00:17:48,200 Speaker 1: a far more involved prospect, Yes, because replicating the rich 299 00:17:48,240 --> 00:17:51,840 Speaker 1: ecosystem of supplies that exist around these main manufacturing hubs 300 00:17:51,880 --> 00:17:54,800 Speaker 1: in China would be incredibly complicated, right, And as we 301 00:17:54,920 --> 00:17:58,560 Speaker 1: discussed earlier. iPhone production is just much harder to automate 302 00:17:58,600 --> 00:18:00,920 Speaker 1: than making displays. In a sense, you could say that 303 00:18:01,200 --> 00:18:04,200 Speaker 1: Scone is buying a stay of execution for its Chinese 304 00:18:04,200 --> 00:18:07,159 Speaker 1: iPhone making business. Okay, So, in the sense we're seeing 305 00:18:07,200 --> 00:18:11,000 Speaker 1: three tiers of manufacturing. You have basic repetitive tasks which 306 00:18:11,000 --> 00:18:13,879 Speaker 1: can be done by robots, more complex tasks which are 307 00:18:13,960 --> 00:18:16,359 Speaker 1: present need a lot of manpower, and a high level 308 00:18:16,400 --> 00:18:19,720 Speaker 1: automation which will requires small numbers of staff and their 309 00:18:19,760 --> 00:18:23,600 Speaker 1: dogs who might even need engineering degrees. Deloitte has one 310 00:18:23,640 --> 00:18:26,480 Speaker 1: foecust showing the US could be more competitive than China 311 00:18:26,600 --> 00:18:29,360 Speaker 1: as soon as because of this. And while it's not 312 00:18:29,440 --> 00:18:31,440 Speaker 1: yet clear who will win the race when it comes 313 00:18:31,440 --> 00:18:35,560 Speaker 1: to attracting high tech manufacturing projects, the bleak reality facing 314 00:18:35,600 --> 00:18:39,760 Speaker 1: many American workers is the possibility that large scale manufacturing 315 00:18:40,080 --> 00:18:46,439 Speaker 1: is unlikely ever to return douled automation. You know, you 316 00:18:46,480 --> 00:18:49,080 Speaker 1: may have a job for five years, but then five 317 00:18:49,160 --> 00:19:01,719 Speaker 1: years a job could be gone to a robot. And 318 00:19:01,800 --> 00:19:05,199 Speaker 1: that's it for this week's Decrypted. Thanks for listening. We 319 00:19:05,280 --> 00:19:06,920 Speaker 1: always like to hear what you think of the show. 320 00:19:07,160 --> 00:19:09,399 Speaker 1: Record a voice message and send it to Decrypted at 321 00:19:09,400 --> 00:19:12,560 Speaker 1: Bloomberg dot net or I'm on Twitter at a TB 322 00:19:12,760 --> 00:19:16,240 Speaker 1: web and I'm at brad Stone. If you haven't already, 323 00:19:16,480 --> 00:19:19,240 Speaker 1: please subscribe to our show wherever you get your podcast. 324 00:19:19,600 --> 00:19:22,080 Speaker 1: While you're there, please leave us at reading and a review. 325 00:19:22,320 --> 00:19:25,840 Speaker 1: It really helps more listeners find the show. This episode 326 00:19:25,880 --> 00:19:29,160 Speaker 1: was produced by Pierre Gadkari, Liz Smith, and Magnus Hendrickson. 327 00:19:29,600 --> 00:19:32,359 Speaker 1: Thanks to Isabel Gottlieb for her help on today's show, 328 00:19:32,560 --> 00:19:35,000 Speaker 1: as well as David Welch and Detroit and John McCormick 329 00:19:35,040 --> 00:19:37,600 Speaker 1: in Chicago for their tireless hounding of the story. In 330 00:19:37,640 --> 00:19:41,760 Speaker 1: Michigan and Wisconsin. Aliceabar edited my print story about box 331 00:19:41,800 --> 00:19:44,560 Speaker 1: cons Megaplant, which you can read at Bloomberg dot com, 332 00:19:44,640 --> 00:19:48,080 Speaker 1: Forward slash Tech. Alec McCabe is head of Bloomberg Podcasts. 333 00:19:48,320 --> 00:19:49,240 Speaker 1: We'll see you next week.