WEBVTT - Robots Aren't Coming For Your Job. They're Already Here 0:00:00.600 --> 0:00:04.200 From executive search to talent strategy, leadership development, rewards and 0:00:04.200 --> 0:00:06.880 succession planning. Corn Fairy can help you realize the full 0:00:06.880 --> 0:00:09.879 potential of your people so you can take your business 0:00:09.920 --> 0:00:12.520 where it wants to go up. Learn more at corn 0:00:12.560 --> 0:00:16.000 Ferry dot com slash up. There's a lot of talk 0:00:16.040 --> 0:00:19.079 about how robots are coming to take our jobs, how 0:00:19.120 --> 0:00:21.959 in the future near or far, our jobs will be 0:00:22.000 --> 0:00:27.280 replaced by gleaming machines that will work tirelessly, precisely and efficiently. 0:00:27.800 --> 0:00:30.320 But let me tell you something. The robots aren't coming 0:00:30.320 --> 0:00:34.280 to take our jobs. They're already here. This is game plan. 0:00:45.960 --> 0:00:48.640 I am Sam Grobert. I'm a writer at Bloomberg Business 0:00:48.640 --> 0:00:52.000 Week magazine. I am joined here by We're back in Greenfield. 0:00:52.040 --> 0:00:54.600 I'm a reporter at Bloomberg where I cover the workplace, 0:00:55.000 --> 0:00:58.160 and today we're going to talk about automation and what 0:00:58.200 --> 0:01:01.400 it means for all of us at work today, tomorrow, 0:01:01.520 --> 0:01:05.000 and even beyond them. So, Becca, you've written a story 0:01:05.280 --> 0:01:08.280 about this topic. Do you want to tell our dear 0:01:08.319 --> 0:01:10.679 listeners a little bit about what you found. Yeah, So, 0:01:10.760 --> 0:01:15.039 automation has been in the news recently because of our 0:01:15.120 --> 0:01:18.840 President elect Donald Trump saying that he is going to 0:01:18.959 --> 0:01:21.840 keep factory work here in the US and brokering that 0:01:21.880 --> 0:01:26.720 dear deal with Carrier a plant in Indiana, that BLOCKBUSTERR deal, Yes, 0:01:26.760 --> 0:01:30.880 saving a round a thousand jobs human jobs. This brought 0:01:30.959 --> 0:01:34.160 up a conversation about automation. I know that as a 0:01:34.319 --> 0:01:36.880 reader of business news, there are so many articles about 0:01:36.920 --> 0:01:39.039 the robots are coming for your jobs, but I tend 0:01:39.040 --> 0:01:41.200 to gloss over them because a lot of them are 0:01:41.400 --> 0:01:45.000 just basically saying that and little else. Yeah, it's exactly, 0:01:45.000 --> 0:01:48.520 it's not very relatable. But now in this case, people 0:01:48.560 --> 0:01:51.720 are pointing out that often the jobs that have been 0:01:51.760 --> 0:01:55.720 lost in the manufacturing industry aren't being replaced by cheaper 0:01:55.800 --> 0:02:00.440 laborers in Mexico or China, but much cheaper robots who 0:02:00.600 --> 0:02:03.200 are much more productive and do a lot of the 0:02:03.240 --> 0:02:06.240 work that human used to do. So there are kind 0:02:06.240 --> 0:02:09.839 of some startling statistics about this. If I may, oh, 0:02:09.919 --> 0:02:13.119 you may, indeed, I want to hear them. So America 0:02:13.160 --> 0:02:16.359 has lost more than seven million factory jobs since manufacturing 0:02:16.360 --> 0:02:20.760 employment peaked in nineteen seventy nine, but we now produce 0:02:20.919 --> 0:02:23.840 far more. We've more than doubled our production in the 0:02:23.919 --> 0:02:29.440 same time span. Right, so making more stuff with fewer people. Right, 0:02:29.600 --> 0:02:33.400 So America is the second biggest manufacturing producer in the 0:02:33.440 --> 0:02:37.360 world after China. And another study that came out last 0:02:37.440 --> 0:02:40.639 year found that trade, which is the big Donald Trump 0:02:40.680 --> 0:02:44.080 talking point, accounted for about of America's lost factory jobs, 0:02:44.080 --> 0:02:46.720 which is you know, has been accelerated in the last 0:02:47.040 --> 0:02:49.639 decade and people really do feel that, and obviously that's 0:02:49.639 --> 0:02:52.240 why his message did resonate with so many people. But 0:02:53.240 --> 0:02:55.760 of those jobs were taken by robots. And if you 0:02:55.800 --> 0:02:57.639 even think about it, there was a story out in 0:02:57.680 --> 0:03:00.080 the l A Times by our former colleague Natalie at 0:03:00.160 --> 0:03:04.280 Drop who wrote about warehouse jobs. So warehouse jobs have 0:03:04.400 --> 0:03:07.920 brought a lot of human jobs to the US, but 0:03:08.480 --> 0:03:11.040 robots are quickly taking a lot of that work. I 0:03:11.080 --> 0:03:15.120 remember a few years ago Amazon had this Kiva robot, right, 0:03:15.320 --> 0:03:17.639 I've seen the Kiva robot in action. Actually, I was 0:03:17.680 --> 0:03:21.119 in Devon's, Massachusetts once at a company called Quiet Logistics, 0:03:21.520 --> 0:03:23.960 which has the Kiva robots themselves. In the way that 0:03:24.000 --> 0:03:27.000 the warehouse was organized is there was one zone for 0:03:27.040 --> 0:03:29.920 the humans and then there was another zone that you 0:03:29.919 --> 0:03:33.520 didn't go into for the robots. Because the robots are 0:03:33.520 --> 0:03:36.640 moving around rather quickly and they're carrying very heavy things, 0:03:36.760 --> 0:03:38.960 and so it wouldn't be very safe to go into 0:03:39.000 --> 0:03:40.680 the robot zone. But the idea was that there was 0:03:40.680 --> 0:03:42.880 a bit of a division of labor there. The robots 0:03:43.000 --> 0:03:46.920 would go and retrieve items from way back in the warehouse, 0:03:47.000 --> 0:03:50.320 you know, an eighth of a mile away, and bring 0:03:50.360 --> 0:03:54.600 them to a human who could then box it, packaging 0:03:54.880 --> 0:03:57.480 label something a robot is not as good at yet, 0:03:57.680 --> 0:04:00.600 not yet yet, that's the word. That's what my story 0:04:00.720 --> 0:04:03.720 is about. It's about manufacturing in jobs that we can 0:04:03.720 --> 0:04:06.720 see robots doing. But robots are coming, as you mentioned, 0:04:07.000 --> 0:04:10.040 for other jobs that we never really thought about already. 0:04:10.080 --> 0:04:15.160 Already there are robots replacing financial analysts, for example, and 0:04:15.160 --> 0:04:17.120 these are robots more in the digital sense. They're not 0:04:17.520 --> 0:04:21.760 physical robots, but they are bots in manipulating data. Yeah, 0:04:21.760 --> 0:04:23.240 and so what are we going to do when all 0:04:23.279 --> 0:04:26.000 those people are mad about their jobs being gone? Or 0:04:26.040 --> 0:04:27.880 what will their jobs look like are they're going to 0:04:27.960 --> 0:04:30.520 be gone? Those are some of the questions that I've had, 0:04:30.920 --> 0:04:32.599 and then I try to answer in the story. Well, 0:04:32.760 --> 0:04:35.279 I do suggest that everybody go and read that story. 0:04:35.320 --> 0:04:37.400 But while you're listening here, we are going to be 0:04:37.480 --> 0:04:41.000 joined by someone who can help answer those questions right 0:04:41.000 --> 0:04:55.400 here in your headphones. We are joined here by Professor 0:04:55.480 --> 0:04:59.799 David Demming from Harvard's Graduate School of Education. Hi David, 0:05:00.040 --> 0:05:02.599 Hi Sam, how are you great? Thank you. I was wondering, 0:05:02.800 --> 0:05:05.320 as we're talking here about the role of robots and 0:05:05.480 --> 0:05:07.719 labor and how we feel about them coming to take 0:05:07.720 --> 0:05:11.280 our jobs and so forth, could you possibly give us 0:05:11.320 --> 0:05:14.480 a brief description of the current state of the art 0:05:14.880 --> 0:05:18.080 of robotics in the workplace, Like where are they most prevalent, 0:05:18.600 --> 0:05:21.360 what are they doing well, what can't they do well yet, 0:05:21.480 --> 0:05:24.279 and what needs to happen for that to change? Sure? So, 0:05:24.320 --> 0:05:27.200 I mean, I think robotics are pretty prevalent in the workplace, 0:05:27.240 --> 0:05:31.520 but mostly in manufacturing. So um. One of the things 0:05:31.520 --> 0:05:33.359 that's been in the news a lot recently is the 0:05:33.440 --> 0:05:36.680 decline of manufacturing jobs. And so you might think that 0:05:36.720 --> 0:05:39.160 manufacturing itself is in decline, but actually that's not true. 0:05:39.200 --> 0:05:41.920 Manufacturing output has continued to grow. This is actually sort 0:05:41.920 --> 0:05:44.000 of a boom time for manufacturing output. It's just that 0:05:44.000 --> 0:05:47.640 it's robots doing the job instead of humans. Robots are 0:05:47.680 --> 0:05:50.320 also prevalent in other sectors of the economy. But the 0:05:50.320 --> 0:05:53.680 thing that robots and machines and computers really still can't 0:05:53.720 --> 0:05:57.400 do very well at all is socially interact with people 0:05:57.520 --> 0:06:00.600 or with each other. Alright, So so social reaction just 0:06:00.720 --> 0:06:05.640 means the non routine, hard to script out process of 0:06:05.680 --> 0:06:09.200 having a conversation with another human being, figuring out if 0:06:09.240 --> 0:06:10.719 you and I are going to work together on a team, 0:06:10.720 --> 0:06:11.839 what are you going to do? What am I going 0:06:11.880 --> 0:06:15.520 to do? Being flexible, being empathetic. That's something that at 0:06:15.560 --> 0:06:17.840 least right now, we still don't have a good way 0:06:17.880 --> 0:06:21.160 to program to automate, and I definitely want to get 0:06:21.200 --> 0:06:24.520 to that. But there's this paper that you site, and 0:06:24.560 --> 0:06:29.159 I've seen a lot about how cent of total US 0:06:29.200 --> 0:06:31.760 employment is at high risk of automation over the next 0:06:31.880 --> 0:06:35.200 one to two decades. So you're saying manufacturing work has 0:06:35.240 --> 0:06:39.839 already been gutted. Basically, what is the next frontier of automation. 0:06:40.680 --> 0:06:42.640 That's a great question. I mean, I guess it's really 0:06:42.640 --> 0:06:44.440 hard to predict the future. But I think one of 0:06:44.480 --> 0:06:46.799 the reasons why you see a lot of um newspaper 0:06:46.880 --> 0:06:49.400 articles that I would call our other people also would 0:06:49.440 --> 0:06:53.080 call automation anxiety kind of pieces, it's because you see 0:06:53.120 --> 0:06:56.440 automation increasingly UM grabbing onto what we might think of 0:06:56.520 --> 0:06:59.279 is white color or professional work. So one example is 0:06:59.600 --> 0:07:01.839 ecovery in the legal world. If you're if you're an 0:07:01.839 --> 0:07:03.920 attorney or you have friend who attorneys already know about this. 0:07:03.920 --> 0:07:06.000 This is you used to be an attorney's job to 0:07:06.200 --> 0:07:09.560 comb through reams of paper looking for bits of information 0:07:09.600 --> 0:07:12.120 that might be relevant to a case or to a deposition. 0:07:12.160 --> 0:07:14.440 Now we've got software to do that, and so that's 0:07:14.480 --> 0:07:18.160 kind of been really great for UM for legal work. 0:07:18.240 --> 0:07:20.920 It's made legal work much more productive, but it's putting 0:07:21.000 --> 0:07:22.760 lawyers out of work. And you see some of that 0:07:22.800 --> 0:07:25.800 with automation of maybe you guys know this short kind 0:07:25.800 --> 0:07:29.240 of newspaper articles. Fact based articles are being automated, are 0:07:29.240 --> 0:07:33.400 automatically produced recaps of sports games, things like that. And 0:07:33.440 --> 0:07:35.120 so when you start to see that happen, those are 0:07:35.120 --> 0:07:37.720 the types of changes that are affecting the people that 0:07:37.800 --> 0:07:39.880 we all know, people that journalists know, And so I 0:07:39.880 --> 0:07:41.760 think you see a lot more articles about it. But 0:07:41.840 --> 0:07:44.680 I think, in fact, the progress of automation has been 0:07:44.920 --> 0:07:47.840 pretty continuous over the past several decades. David. When I 0:07:47.880 --> 0:07:51.800 think of manufacturing robots, I I suppose, like many people 0:07:51.920 --> 0:07:56.040 imagine those big sort of arms in a say auto plant, right, 0:07:56.280 --> 0:07:59.560 kind of assembling a car body. Um, those have been 0:07:59.600 --> 0:08:03.200 around for some decades now in one form or another. 0:08:03.920 --> 0:08:07.320 What kinds of robots are being built or will be 0:08:07.360 --> 0:08:12.520 built that can work presumably alongside humans? What? What? What 0:08:12.560 --> 0:08:14.960 do they start to look like? Yeah? Exactly. So when 0:08:14.960 --> 0:08:16.720 you when you think of robots in your mind, you 0:08:16.720 --> 0:08:20.120 have this image, at least I do, growing up in 0:08:20.120 --> 0:08:22.400 the in the eighties. I think of like the Transformers, right, 0:08:22.520 --> 0:08:25.760 stuff like that. But actually, automation is not just robots, 0:08:25.840 --> 0:08:28.920 is any process that can be scripted out in advance. 0:08:29.240 --> 0:08:31.840 And so you know, you don't think of things like 0:08:32.080 --> 0:08:35.880 I don't know, Siri or basically um computer programs that 0:08:35.960 --> 0:08:38.720 can like maybe customer service, like if you call a 0:08:38.760 --> 0:08:40.959 company and you want to get help with your credit card, 0:08:41.000 --> 0:08:43.080 your credit card gets stolen or you want to call 0:08:43.120 --> 0:08:45.480 and complain. You don't think of the voice on the 0:08:45.480 --> 0:08:47.360 other end that's not a human as being a robot. 0:08:47.440 --> 0:08:51.800 But that's actually the same manifestation of robotics, meaning scripting 0:08:51.800 --> 0:08:54.920 out what used to be human performance in a predictable way. 0:08:55.040 --> 0:08:57.120 So I think that's been going on for a long time. 0:08:57.160 --> 0:08:59.959 Those things are also robots in some sense, and you're 0:09:00.000 --> 0:09:02.080 going to see more of that, robots being more and 0:09:02.120 --> 0:09:05.320 more human like. One of the more modern expressions of 0:09:05.320 --> 0:09:09.560 that are these messenger based bots. Yes, you can chat 0:09:09.559 --> 0:09:11.360 bots that you can like say I want to flight 0:09:11.440 --> 0:09:13.840 to Florida, and it'll be like, I see that you 0:09:13.880 --> 0:09:16.480 want to flight to Florida. These are your options. I'll 0:09:16.520 --> 0:09:18.400 take that one. And then it makes a joke about 0:09:18.400 --> 0:09:22.000 Florida or something. Right. Yeah, they're path yeah, they they're 0:09:22.000 --> 0:09:23.559 getting picked here all the time. And that's the thing 0:09:23.600 --> 0:09:26.000 that's kind of the at least of my understanding the 0:09:26.000 --> 0:09:29.319 frontier is we're getting better at natural language processing. You 0:09:29.360 --> 0:09:31.720 think about think about Sirie. Okay. I don't know how 0:09:31.760 --> 0:09:33.720 much you guys, you Siri, but you have to say 0:09:33.720 --> 0:09:35.280 things in a certain way to Siri to get the 0:09:35.320 --> 0:09:37.760 kind of response. Otherwise you get the you know, searching 0:09:37.760 --> 0:09:41.440 the web for right. Yeah, and that's Sirie being not 0:09:41.559 --> 0:09:44.160 understanding natural language. Right. So I'll give you an example. Um, 0:09:44.440 --> 0:09:46.920 my children, maybe as many children do, love to play 0:09:46.960 --> 0:09:50.120 with my phone and with Siri, and so they were 0:09:50.160 --> 0:09:53.400 asking Siri, Sirie, what color is my water bottle? Okay, 0:09:53.400 --> 0:09:56.520 which Siri could actually has the technology to figure that out. 0:09:56.520 --> 0:09:58.240 Siri could turn its camera into the water bottle and 0:09:58.240 --> 0:10:00.480 say your water bottles red. But series you stupid to 0:10:00.480 --> 0:10:02.760 figure that out? So Siri says, searching on the web 0:10:02.800 --> 0:10:04.760 for what color is my water bottle? But if you 0:10:04.760 --> 0:10:07.240 ask Siri, what's thirty seven times fifty nine, as I 0:10:07.320 --> 0:10:09.760 encourage my daughter to do, Syria can do that right away. 0:10:09.760 --> 0:10:11.120 So I said to her, why do you think, Siri? 0:10:11.200 --> 0:10:13.240 Can you know this is a cognitive process. It's really 0:10:13.360 --> 0:10:15.480 most people can't just do thirty seven times fifty nine 0:10:15.480 --> 0:10:18.240 in their head. Syria can do it instantaneously. And that's 0:10:18.280 --> 0:10:21.080 because the problem space is defined. You have a database 0:10:21.080 --> 0:10:23.000 where you say, these are all the multiplications. You know, 0:10:23.040 --> 0:10:25.120 do this, and Syrie can do that really quickly. But 0:10:25.280 --> 0:10:27.720 common sense tasks like telling you what color your water 0:10:27.720 --> 0:10:30.040 bottle is, which are very simple and natural for humans 0:10:30.120 --> 0:10:33.640 to do, is really hard. Actually, a program up happens 0:10:33.640 --> 0:10:36.320 when the power and potential of every employee and leader 0:10:36.360 --> 0:10:39.600 in your workforce is released, and corn Ferry can get 0:10:39.640 --> 0:10:44.319 you there by aligning your people to your strategy, attracting, developing, engaging, 0:10:44.360 --> 0:10:47.720 and rewarding them to reach new heights with corn Ferry. 0:10:47.880 --> 0:10:50.920 You get a partner who truly understands people, leadership and 0:10:50.920 --> 0:10:53.520 the new landscape of work, a partner who knows how 0:10:53.520 --> 0:10:56.600 to take your business up. Learn more at corn Ferry 0:10:56.640 --> 0:11:00.440 dot com slash up. So all of us talk about 0:11:00.520 --> 0:11:04.600 robots taking our jobs is scary, but I found some 0:11:04.640 --> 0:11:07.880 comfort in your working paper the growing importance of social 0:11:07.920 --> 0:11:11.280 skills in the labor market, probably as somebody who uses 0:11:11.320 --> 0:11:15.040 social skills in her job a lot, but you found 0:11:15.080 --> 0:11:17.000 that there are some skills that are going to be 0:11:17.080 --> 0:11:19.920 valued in the labor market because robots can't do them. 0:11:20.320 --> 0:11:23.120 So can you talk about that and maybe what kinds 0:11:23.160 --> 0:11:26.280 of jobs are the safest? Sure? So, the way you 0:11:26.320 --> 0:11:29.440 want to think about how technological progress affects the labor 0:11:29.480 --> 0:11:31.840 market is that there's really two things going on, and 0:11:31.880 --> 0:11:33.480 we tend to focus on one of them, which is 0:11:34.160 --> 0:11:38.719 how technology takes away jobs what economists would say substitutes 0:11:38.920 --> 0:11:42.079 away for for human labor. So you've got robots or 0:11:42.280 --> 0:11:45.680 computers substituting away for human labor, and that's the scary side. 0:11:46.160 --> 0:11:48.360 But there's a friendly side too, and that is it 0:11:48.480 --> 0:11:52.440 also makes people who aren't substituted away much more productive. Right, So, 0:11:52.480 --> 0:11:55.880 for example, you used to have to do complex mathematical 0:11:55.920 --> 0:11:58.400 calculations by hand. You might hire an accountant to do this. 0:11:58.720 --> 0:12:00.440 Let's say you own a small business. Now you've got 0:12:00.480 --> 0:12:03.480 tools like Microsoft Excel that allow that process to be 0:12:03.520 --> 0:12:06.400 automated and very quick and very customizable. Right, so we 0:12:06.440 --> 0:12:08.440 focus on the putting the account and out of business, 0:12:08.440 --> 0:12:10.080 but the other side of it, if you make business 0:12:10.080 --> 0:12:13.560 decisions for a living, you're much more productive. So the 0:12:13.679 --> 0:12:16.880 lesson that we've learned from the study of how technological 0:12:16.960 --> 0:12:19.920 change affects labor markets is that anything that is not 0:12:20.040 --> 0:12:24.480 directly replaced by technology is generally complimented by it, which 0:12:24.559 --> 0:12:26.920 is an economist way of saying makes you more productive. 0:12:27.480 --> 0:12:29.120 So you have to look at the labor market and say, 0:12:29.160 --> 0:12:32.000 what are the things that technology still can't do very well? 0:12:32.320 --> 0:12:35.240 And those things, those tasks, those job tasks are going 0:12:35.240 --> 0:12:37.040 to be made more productive and thus going to be 0:12:37.040 --> 0:12:39.800 more important. And that's kind of the thesis of the paper, 0:12:40.200 --> 0:12:42.400 which is that, look, at least right now, we don't 0:12:42.400 --> 0:12:45.280 know about the future, but right now, machines can't have 0:12:45.400 --> 0:12:47.920 an unscripted conversation within a human being or with each 0:12:47.960 --> 0:12:51.040 other in any kind of meaningful way. They can't socially interact, 0:12:51.080 --> 0:12:53.680 they can't work on a team, they're very inflexible. And 0:12:53.720 --> 0:12:56.200 so people who can do that are going to be 0:12:56.240 --> 0:12:59.120 more valuable in the workplace than they were before because 0:12:59.160 --> 0:13:01.320 we've got technology to do all the other things you 0:13:01.360 --> 0:13:03.360 want to hear a really left field example of what 0:13:03.360 --> 0:13:06.040 you're talking about. Okay, So I was done in Florida 0:13:06.160 --> 0:13:09.640 last year at Florida Atlantic University, and there's a team 0:13:09.679 --> 0:13:14.400 of engineers who are developing these um robot boats, basically 0:13:14.840 --> 0:13:20.960 water based drones that can inspect bridge pilings underwater. And 0:13:21.000 --> 0:13:22.880 this was a job that people used to have to 0:13:22.920 --> 0:13:25.880 do themselves. They had to go underwater with a diving 0:13:26.000 --> 0:13:29.160 rig and it was actually pretty dangerous and it took 0:13:29.160 --> 0:13:31.200 a really long time, and you can only send out, 0:13:31.360 --> 0:13:34.080 you know, one person at a time. And now what 0:13:34.160 --> 0:13:37.559 they're doing is the robots are going around and constantly 0:13:37.679 --> 0:13:40.600 just floating around these bridges and looking at them underwater 0:13:40.640 --> 0:13:42.920 with their cameras. The people who used to have to 0:13:42.920 --> 0:13:46.520 go into the water stay on land at a place 0:13:46.520 --> 0:13:49.160 where they're watching what the robots are looking at and 0:13:49.240 --> 0:13:52.040 making decisions about, oh, now we have to go fix that. 0:13:52.880 --> 0:13:55.880 So their job is safer, and they actually get to 0:13:56.040 --> 0:13:59.959 cover more territory because the robots don't ever stop work. 0:14:00.840 --> 0:14:02.720 You have to ask yourself. The U is a great example. 0:14:02.760 --> 0:14:05.680 You ask the question, why don't we automate the final step, 0:14:05.840 --> 0:14:08.280 which is telling the you know, telling the robots what 0:14:08.280 --> 0:14:11.360 to do once they get there. It's because it's unpredictable, right, 0:14:11.440 --> 0:14:13.920 and so people are just very easily and naturally able 0:14:13.960 --> 0:14:15.440 to say, oh, look, I see the problem, let's do 0:14:15.440 --> 0:14:17.640 this instead. But if you're going to write a kind 0:14:17.679 --> 0:14:19.560 of a script or a computer program to do that, 0:14:19.640 --> 0:14:21.680 you have to be able to anticipate anything that might 0:14:21.720 --> 0:14:25.120 possibly happen. And some situations are really uncertain, and people 0:14:25.120 --> 0:14:28.400 are still much better than machines at reacting to uncertainty. 0:14:28.800 --> 0:14:31.440 So these skills, I've seen them called a couple of 0:14:31.440 --> 0:14:36.840 things like empathy, labor, non cognitive skills, social skills, social labor. 0:14:37.440 --> 0:14:39.720 They also are happening in high skilled labor. I now 0:14:39.720 --> 0:14:43.360 we're talking a lot about manufacturing and transportation. Do you 0:14:43.440 --> 0:14:46.640 think that our education system is pushing these enough? Are 0:14:46.680 --> 0:14:49.800 we teaching this well enough? Are these are this? Is 0:14:49.840 --> 0:14:52.040 this valued enough? Because I get a sense I'm just 0:14:52.080 --> 0:14:54.720 going to say that it's not because we're hearing so 0:14:54.800 --> 0:14:58.280 much about the push for STEM, and you know how 0:14:58.320 --> 0:15:00.400 everybody needs to be a coder and learned to code, 0:15:00.840 --> 0:15:05.720 but you never hear people raising their hand for learn 0:15:05.800 --> 0:15:09.320 to be more empathetic. It's a great question, um, and 0:15:09.320 --> 0:15:10.960 I'm glad you brought up STEM because it does tie 0:15:11.000 --> 0:15:13.120 into that. You do see a lot of discussion of 0:15:13.200 --> 0:15:15.400 you know, we need more STEM jobs, and I think 0:15:15.840 --> 0:15:17.960 that is not untrue. The way I would say it 0:15:18.040 --> 0:15:21.240 is that you want people to have technical skills because 0:15:21.280 --> 0:15:23.520 they need to understand how the machines work and how 0:15:23.560 --> 0:15:26.120 things are happening under the hood, so to speak. But 0:15:26.200 --> 0:15:28.200 you also want them to have these social skills. It's 0:15:28.200 --> 0:15:30.000 just not enough to have one and not the other. 0:15:30.480 --> 0:15:33.000 I would predict, for example, that twenty years from now, 0:15:33.320 --> 0:15:35.840 you'll see many fewer jobs where you just write software 0:15:35.880 --> 0:15:37.520 code all day, but you'll see a lot more jobs 0:15:37.560 --> 0:15:39.200 where you have to do something like that as part 0:15:39.200 --> 0:15:40.760 of many other things you do on the job. So 0:15:40.760 --> 0:15:44.240 I think the workplace is just becoming more integrated in 0:15:44.320 --> 0:15:46.000 terms of which task keep to do in and less 0:15:46.040 --> 0:15:48.760 predictable in part because of this change that anything that's 0:15:48.760 --> 0:15:51.560 predictable and and and can be scripted, we can automate, 0:15:51.880 --> 0:15:54.040 and so you need people to be able to um 0:15:54.080 --> 0:15:56.600 flexibly rotate between many different things in a kind of 0:15:56.680 --> 0:16:00.240 unpredictable way. And so I talked about this in the paper. 0:16:00.240 --> 0:16:01.840 I mean there's a kind of way to formalize this, 0:16:01.880 --> 0:16:03.800 but essentially it's like, look, if you don't have any 0:16:03.800 --> 0:16:05.600 technical skills, you don't know how to do anything, then 0:16:05.600 --> 0:16:08.400 the fact that you're you have good social skills is 0:16:08.400 --> 0:16:10.520 not that useful actually, because there are a lot of 0:16:10.520 --> 0:16:11.800 people who can do that. There are a lot of 0:16:11.800 --> 0:16:13.800 people who can who can empathize with others. But what 0:16:13.840 --> 0:16:15.720 there aren't a lot of people who can do is 0:16:15.920 --> 0:16:19.120 people who have the technical skills who understand how everything 0:16:19.200 --> 0:16:22.000 is working. But also those people kind of tend to 0:16:22.040 --> 0:16:24.560 be people, tend to be one or the other. And 0:16:24.600 --> 0:16:27.160 so I think what you see very clearly in the data, 0:16:27.400 --> 0:16:28.960 and what I talked about in the paper is that 0:16:29.000 --> 0:16:31.960 you see increasing labor market returns for people who have 0:16:32.400 --> 0:16:35.000 both types of skills what kondom is often called cognitive 0:16:35.040 --> 0:16:37.680 skills or hard skills and soft skills or social skills. 0:16:37.960 --> 0:16:40.320 I wouldn't be doing my job as a business journalist 0:16:40.400 --> 0:16:43.880 if I didn't refer to Steve Jobs probably one severy 0:16:43.920 --> 0:16:46.600 week or so. But what you're talking about really reminds 0:16:46.600 --> 0:16:49.200 me of one of his keynotes where he said that 0:16:49.480 --> 0:16:52.840 the key to his success or Apple's success was the 0:16:52.880 --> 0:16:57.360 marriage of technology and the liberal arts. That it couldn't 0:16:57.400 --> 0:17:00.640 just be numbers and engineering, but it also had to 0:17:00.720 --> 0:17:04.960 have the inspiration and the artistic and cultural influences of 0:17:05.040 --> 0:17:07.439 literature and music and all these other things. And so 0:17:07.480 --> 0:17:11.880 the two brought together is what really makes for interesting products, 0:17:11.880 --> 0:17:14.720 interesting workers, and so forth. Uh, you can't just have 0:17:14.800 --> 0:17:17.199 the one. I think that's right, and I think what 0:17:17.240 --> 0:17:20.639 he's really talking about is the marrying of human creativity 0:17:20.680 --> 0:17:22.919 with all the things that are possible with technology. You know, 0:17:22.960 --> 0:17:25.320 you can't just if you're just training yourself, or we're 0:17:25.320 --> 0:17:27.520 training students in schools to just be like the machines, 0:17:27.960 --> 0:17:29.440 then we're not gonna be able to make full use 0:17:29.480 --> 0:17:32.600 of these technological advances. It's really putting the two together 0:17:32.760 --> 0:17:36.040 that's so important. David, thank you so much. This has 0:17:36.080 --> 0:17:39.520 been a really eye opening conversation. Really appreciate it, my pleasure. 0:17:39.520 --> 0:17:50.199 It's great to talk to you guys. So David scared 0:17:50.359 --> 0:17:53.359 us obviously because the robots are coming for all the jobs. 0:17:53.400 --> 0:17:56.520 He even mentioned journalism right. One thing that we didn't 0:17:56.520 --> 0:17:58.040 get to talk to him about that I thought was 0:17:58.119 --> 0:18:00.080 interesting that he and I had talked about when I 0:18:00.119 --> 0:18:02.560 talked to him for my story was about the gender 0:18:02.600 --> 0:18:06.320 component in all of this. Manufacturing obviously tends to be 0:18:06.359 --> 0:18:11.080 a very male dominated field, and a lot of empathy 0:18:11.160 --> 0:18:14.480 jobs that are really growing in this country tend to 0:18:14.480 --> 0:18:21.080 be female dominated jobs like nursing, home health AIDS, physical therapists, teaching, 0:18:21.840 --> 0:18:23.560 and these are skills that he's saying are going to 0:18:23.600 --> 0:18:27.040 be valued more and more. But I do wonder a 0:18:27.119 --> 0:18:30.159 lot of them aren't very well paid now, and you 0:18:30.160 --> 0:18:32.040 still don't see a lot of men going into nursing, 0:18:32.040 --> 0:18:34.800 even though it's a high growth field that does pay well. 0:18:35.280 --> 0:18:37.840 I'm wondering how that will change, and if more men 0:18:37.960 --> 0:18:40.480 do go into these fields, that they will become higher paying, 0:18:40.520 --> 0:18:43.760 because that has happened historically. Once men go into a field, 0:18:43.800 --> 0:18:46.040 it tends to be more like we need some more 0:18:46.040 --> 0:18:48.960 money here. This is ridiculous. Which happened with computer programming 0:18:49.040 --> 0:18:51.359 is one big example. But I remember there was a 0:18:51.359 --> 0:18:54.199 study that I cite that talks about um how janitors 0:18:54.280 --> 0:18:57.679 make a lot more than house cleaners, for example, or 0:18:57.880 --> 0:19:01.120 HR people tend to be female, men make less than 0:19:01.200 --> 0:19:03.920 someone with a similar skill set in the office, things 0:19:03.960 --> 0:19:06.399 that don't make sense at all except for gender. It 0:19:06.480 --> 0:19:08.919 is interesting when you think about sort of the progression 0:19:09.200 --> 0:19:14.680 of automation that it occurs across different categories. So we 0:19:14.840 --> 0:19:18.960 start with blue collar jobs, and then it moves to 0:19:19.680 --> 0:19:22.760 white collar jobs, and then people like us in the 0:19:22.760 --> 0:19:25.040 media start to get really scared because we go, oh, wait, 0:19:25.119 --> 0:19:28.719 it's for me, which just proves the sort of bias 0:19:28.840 --> 0:19:30.959 that's in the implicit in all of that. But then 0:19:31.000 --> 0:19:33.800 there's also the gender. So it's like blue collar jobs, 0:19:33.800 --> 0:19:38.240 which often have been male dominated jobs. So it'll be 0:19:38.560 --> 0:19:40.800 I don't know, the next fifty years will be Yeah, 0:19:40.840 --> 0:19:44.280 we'll see if quote unquote pink collar jobs stay pink 0:19:44.359 --> 0:19:48.840 color or will that become more prestige work right, and 0:19:48.920 --> 0:19:51.240 just the jobs that everybody needs and wants to have, 0:19:51.359 --> 0:19:53.920 because they're the ones that actually are left. I was 0:19:54.000 --> 0:19:55.520 talk to a lot of people who said managers are 0:19:55.520 --> 0:19:58.119 going to be more valued in the future because managers 0:19:58.400 --> 0:20:01.200 tend to have to work with people, promote cross teams, 0:20:01.280 --> 0:20:03.239 and again, those are skills that are tend to be 0:20:03.280 --> 0:20:06.160 associated with women. I guess I should go be a manager. 0:20:06.280 --> 0:20:08.680 I know it's sorry everyone, I know nobody. Nobody wants 0:20:08.720 --> 0:20:11.600 to hear that like that idea at all. Anyway, if 0:20:11.600 --> 0:20:13.080 you don't want to row about to take your job, 0:20:13.160 --> 0:20:17.919 get some social skills, I would have to say that. 0:20:17.960 --> 0:20:23.359 But I do love that we're standing up for social skills. 0:20:23.400 --> 0:20:26.000 I have relied on social skills more than intelligence or 0:20:26.040 --> 0:20:29.320