WEBVTT - Episode 8: How to Keep a Robot from Stealing Your Job 0:00:00.200 --> 0:00:03.800 Hi, and welcome back to Bloomberg Benchmark. It is October 0:00:03.920 --> 0:00:07.800 twenty two, Wednesday. Oh God, why are dates so hard 0:00:07.840 --> 0:00:16.560 for me? I mean, would never make that mistake. Hi, 0:00:16.680 --> 0:00:19.439 and welcome back to Bloomberg Benchmark, a podcast about the 0:00:19.480 --> 0:00:24.079 global economy. It is Thursday, October twenty two. I'm Tori Stillwell, 0:00:24.160 --> 0:00:26.920 a US economics reporter in d C with Bloomberg News. 0:00:27.360 --> 0:00:30.280 I am with my colleagues and go hosts. Dan Moss, 0:00:30.400 --> 0:00:34.080 our executive editor for International Economics who just landed in Ottawa, 0:00:34.560 --> 0:00:38.640 and Akiedo, our editor for Benchmark in San Francisco. Hey guys, 0:00:40.080 --> 0:00:43.320 you guys are always traveling. I'm just stuck here in 0:00:43.440 --> 0:00:46.839 d C. Dan, send me to Sydney. I'm ready to 0:00:47.159 --> 0:00:49.839 beat it up there. Well, I'll have to give you 0:00:49.920 --> 0:00:54.160 some great recommendations before you go. Okay, deal. So Aki, 0:00:54.680 --> 0:00:57.640 you've been in Japan for part of the last week. 0:00:58.080 --> 0:01:01.320 When did you get back and what perspectives on the 0:01:01.320 --> 0:01:04.200 news did you bring back with you? Well, I got 0:01:04.240 --> 0:01:07.679 back late Saturday night and had a wonderful time there. 0:01:07.880 --> 0:01:10.679 I thought i'd talked about the Bank of Japan or 0:01:10.680 --> 0:01:13.800 maybe Chinese economic statistics today because I was in Asia 0:01:13.880 --> 0:01:16.319 all week. But then I just saw this piece of 0:01:16.400 --> 0:01:19.559 news roll in this morning that was really interesting. It's 0:01:19.880 --> 0:01:22.560 a little nerdy and a little niche, but there's this 0:01:22.680 --> 0:01:27.480 government committee that oversees Sweden's central bank and apparently they're 0:01:27.480 --> 0:01:32.280 getting together to potentially revisit the central bank's mandate there, 0:01:32.360 --> 0:01:35.600 which sounds like really technical jargon and really boring, but 0:01:35.680 --> 0:01:37.880 it could potentially be a really big deal if it 0:01:37.920 --> 0:01:42.160 does end up leading to some legal changes to the 0:01:42.160 --> 0:01:46.360 way Sweden's central bank operates. UM. And right now Sweden 0:01:46.400 --> 0:01:49.840 has this inflation target of two. Some people are saying 0:01:49.880 --> 0:01:53.720 maybe Sweden needs to raise the central bank inflation target 0:01:54.120 --> 0:01:56.960 to create more of a cushion between the target and 0:01:57.560 --> 0:02:00.880 zero percent UM. Some people we're saying, maybe you need 0:02:00.920 --> 0:02:04.920 a lawer the inflation target because you shouldn't really have 0:02:04.960 --> 0:02:07.120 the school that you can't achieve at the end of 0:02:07.120 --> 0:02:09.760 the day. Um. But it comes down to this question 0:02:09.840 --> 0:02:13.160 of what happens when you have the school but you're 0:02:13.200 --> 0:02:17.040 not able to deliver on it for years and years 0:02:17.040 --> 0:02:20.880 and years. So I'm gonna be watching this really closely. Yeah, 0:02:20.919 --> 0:02:24.600 I agree with you, that's really quite sexy. That country 0:02:24.680 --> 0:02:27.400 central bank, the ricks Bank was one of the first 0:02:28.720 --> 0:02:33.200 to adopt the flesh inflation targeting, and more recently, they 0:02:33.280 --> 0:02:38.680 found themselves in the crosshairs um principally but not only, 0:02:38.960 --> 0:02:43.560 the cross hairs of Paul Krookman for raising rates quickly 0:02:44.400 --> 0:02:46.680 once what appeared to be the worst of the global 0:02:46.760 --> 0:02:50.400 recession was over, only to find themselves in a situation 0:02:50.440 --> 0:02:54.640 where they had to reverse and not only cut but 0:02:54.760 --> 0:02:59.280 do que So it's quite fascinating. There's almost a parable 0:02:59.360 --> 0:03:02.600 of moderns in full banking day. Yeah, definitely, they're really 0:03:02.880 --> 0:03:06.560 ahead of the pack. Um, Tori, what's your what's your 0:03:06.680 --> 0:03:10.239 current event chatter of the week. Well, I am focused 0:03:10.240 --> 0:03:14.040 as always on the US and right now we're in 0:03:14.080 --> 0:03:19.080 the midst of this like monthly cycle of housing data 0:03:19.160 --> 0:03:21.880 that we get, and it actually turns out things are 0:03:21.919 --> 0:03:25.840 looking pretty good. Um. Home builder confidence is at a 0:03:25.919 --> 0:03:29.960 decade high, which is great. We got housing starts data 0:03:30.200 --> 0:03:36.119 on Tuesday that showed that construction of new homes rose 0:03:36.160 --> 0:03:38.840 to the second highest level in eight years, So that's 0:03:38.880 --> 0:03:41.880 great news. We're gonna get more data over the next week, 0:03:42.480 --> 0:03:45.560 so I'm definitely keeping an eye on that. Cool. So, Tori, 0:03:45.680 --> 0:03:50.280 the idea is housing will help underpin things while manufacturing 0:03:50.320 --> 0:03:54.320 and exports of suffering. Precisely, consumers have really been doing 0:03:54.360 --> 0:03:57.200 the heavy lifting for growth in the US, and this 0:03:58.160 --> 0:04:01.360 shows that housing is no exception. Well, I want to 0:04:01.360 --> 0:04:04.800 talk this week, Tory about a story that you published 0:04:04.800 --> 0:04:08.280 on Monday called Social skills are the Last Line of 0:04:08.320 --> 0:04:12.840 Defense for humans seeking work? And what better date? We're 0:04:12.840 --> 0:04:19.880 recording this on Wednesday, October, the date Marty McFly arrived 0:04:19.920 --> 0:04:23.560 in the future. Now, we've all written and read stories 0:04:23.640 --> 0:04:29.560 about robotics and their increasing news in say, vehicle assembly lines, 0:04:30.200 --> 0:04:35.600 but most of that commentary has also suggested that more 0:04:35.920 --> 0:04:41.880 people focused softer emotional intelligence skills in the workplace. They're 0:04:41.920 --> 0:04:45.159 still some years away for robots. But you had an 0:04:45.240 --> 0:04:50.560 interesting adventure with somebody or rather something called Amy Ingram 0:04:50.600 --> 0:04:52.599 when you were developing this story. Why don't you talk 0:04:52.640 --> 0:04:55.360 about Amy and how she is essentially what this story 0:04:55.440 --> 0:04:59.240 is about. Yeah, So, when I was doing research into 0:04:59.279 --> 0:05:03.600 this story, I initially saw a great paper out by 0:05:03.760 --> 0:05:07.520 David Demming over at Harvard University about social skills in 0:05:07.520 --> 0:05:11.400 the job market, and he basically found that almost all 0:05:11.480 --> 0:05:13.839 the job growth since nineteen eighty has been in work 0:05:13.920 --> 0:05:18.039 that is social skill intensive. So um, while I was 0:05:18.240 --> 0:05:21.880 doing a little more research. I wanted to reach out 0:05:22.000 --> 0:05:25.880 to this founder of a technology startup. They do virtual 0:05:25.960 --> 0:05:29.120 personal assistance. I send him an email. Can we set 0:05:29.160 --> 0:05:31.279 up a time to chat over the phone. He's like, 0:05:31.320 --> 0:05:34.080 no problem, I'm gonna have Amy set it up. Amy, 0:05:34.160 --> 0:05:37.200 can you just take it from here. Um. So she 0:05:37.279 --> 0:05:44.280 sent me preferred day and time. This is around seven PM, 0:05:44.360 --> 0:05:46.719 and I actually had an event that night, so I 0:05:46.760 --> 0:05:51.760 wasn't checking email. Then at three twenty one am, she 0:05:51.880 --> 0:05:55.600 emails me again and it's like I wanted to follow up. 0:05:55.839 --> 0:05:57.640 Of course I didn't see it because I was sound asleep. 0:05:57.880 --> 0:06:00.240 And four hours later she sent me another mel This 0:06:00.360 --> 0:06:03.159 is about seven twenty. At this point, it's like, I 0:06:03.240 --> 0:06:06.440 haven't heard back from you about this meeting. So fortunately 0:06:06.440 --> 0:06:08.560 for Amy, I'm up and getting ready for work and 0:06:08.560 --> 0:06:11.080 I'm checking my emails. I'm already stressed about getting out 0:06:11.120 --> 0:06:14.360 the door, and Amy keeps bugging me about this meeting 0:06:14.400 --> 0:06:16.240 that I could handle like as soon as I got 0:06:16.279 --> 0:06:19.000 to work, and so I think I got a little 0:06:19.040 --> 0:06:22.080 irritated with her. I thought it was there's no way 0:06:22.080 --> 0:06:23.640 that a humans going to email me at three am 0:06:23.640 --> 0:06:27.120 about a meeting. Um. So it was at that point 0:06:27.200 --> 0:06:28.760 that I was like, all right, this has got to 0:06:28.839 --> 0:06:31.680 be a machine. And when I brought up this whole 0:06:31.720 --> 0:06:37.840 experience to Dennis Mortenson, who is the founder of AMY, 0:06:37.960 --> 0:06:40.080 I guess I should say. Um. He was like, this 0:06:40.160 --> 0:06:42.600 is exactly the thing that we're looking to figure out. 0:06:42.640 --> 0:06:46.039 You know, how many social skills do we need to 0:06:46.080 --> 0:06:50.000 embed these agents, these machines with and how do we 0:06:50.120 --> 0:06:57.120 do that? Okay, So just so we're clear, Amy Ingram, Um, 0:06:57.120 --> 0:07:01.360 she's a she's a machine. She's a virtual person assistant. UM, 0:07:01.440 --> 0:07:04.560 she answers emails, she sends emails as she sets up 0:07:04.560 --> 0:07:10.960 your meetings. And for AMY, ingram stands for artificial intelligence. Yeah, 0:07:10.960 --> 0:07:14.000 they share the share of the same initials UM and 0:07:14.360 --> 0:07:18.360 ingram is actually, God, this is like so over my 0:07:18.400 --> 0:07:20.440 head he was trying to explain it TV. But it's 0:07:20.480 --> 0:07:24.880 like a model used in natural language processing that helps 0:07:25.000 --> 0:07:29.440 machines understand human speech. UM. So you know, for all 0:07:29.640 --> 0:07:33.280 like the suber Nerds out there, they're probably oh, yeah, 0:07:33.280 --> 0:07:37.440 that's so cool. But but yeah, she's she's a robot 0:07:37.520 --> 0:07:40.120 for sure. And every any listeners who are interested in this, 0:07:40.280 --> 0:07:43.720 it's uh. The website is x dot ai. If you 0:07:43.760 --> 0:07:45.520 guys want to check it out. You know, one of 0:07:45.520 --> 0:07:48.600 the things that intrigued me about this story is it 0:07:48.800 --> 0:07:53.400 suggests that because Amy is sort of groping for some 0:07:53.880 --> 0:07:58.160 emotional and empathetic characteristics, that it's not that far off 0:07:58.160 --> 0:08:02.880 in the future. Yeah, I mean, I think computer scientists 0:08:02.880 --> 0:08:06.560 will quickly tell you that they are trying to figure 0:08:06.600 --> 0:08:11.200 out how to at least get robots to mimic social skills. Well, actually, 0:08:11.240 --> 0:08:14.360 you're in San Francisco, You're in the heart of all this, 0:08:14.600 --> 0:08:17.040 and you've spent a lot of time writing about the 0:08:17.120 --> 0:08:21.320 impact of technological advances on the economy, not just the 0:08:21.360 --> 0:08:24.440 macro economy, at what's happening in the micro economy at 0:08:24.440 --> 0:08:29.440 an enterprise level. Did tory story mesh with your experience? 0:08:29.480 --> 0:08:33.080 Didn't resonate with you? Yeah, definitely. I mean, so I 0:08:33.120 --> 0:08:37.120 wrote this big story about artificial intelligence UM about a 0:08:37.200 --> 0:08:40.160 year and a half ago, and even since then, even 0:08:40.200 --> 0:08:42.800 in that year and a half, there's been a remarkable 0:08:42.880 --> 0:08:46.240 amount of progress in that field. It's amazing how quickly 0:08:46.559 --> 0:08:49.240 scientists are making progress in this field. But at the 0:08:49.280 --> 0:08:51.680 same time, you know, one thing that I learned that 0:08:51.800 --> 0:08:55.040 was really important is that there are a lot of 0:08:55.040 --> 0:08:58.200 things that computers can do, but there are many, many, 0:08:58.240 --> 0:09:02.160 many other things that computers cannot do. Um Our human 0:09:02.200 --> 0:09:06.040 brains are amazing. They're so complex in ways that you 0:09:06.200 --> 0:09:09.319 never would have thought before, and are capable of doing 0:09:09.400 --> 0:09:12.840 so many things that robots aren't able to do. Daniel said, Like, 0:09:12.920 --> 0:09:16.760 it feels like we're not that far away from computers 0:09:17.160 --> 0:09:20.440 being able to replicate the full social skills of a 0:09:20.559 --> 0:09:23.960 human being. UM. From the conversations that I had with 0:09:24.080 --> 0:09:27.920 artificial intelligence experts, most people felt that this was something 0:09:28.000 --> 0:09:31.679 that was decades and decades away. We should probably define 0:09:31.720 --> 0:09:35.120 what where, what social skills aren't, what qualifies as social skills? 0:09:35.120 --> 0:09:38.040 What you guys think, Yeah, definitely, I mean, let's let's 0:09:38.040 --> 0:09:40.400 start with this. So, like, you know, imagine this kind 0:09:40.400 --> 0:09:45.720 of spectrum between of tasks. Some's tasks are really repetitive. 0:09:45.760 --> 0:09:47.920 You're doing the same thing over and over again, and 0:09:47.960 --> 0:09:50.560 it's easy to write out a full instruction manual of 0:09:50.559 --> 0:09:53.240 what you're supposed to do. There's this, you know, on 0:09:53.280 --> 0:09:56.400 the other end of the spectrum, there are tasks that 0:09:56.520 --> 0:10:00.800 are really complex, really diverse. You're doing something different every 0:10:00.840 --> 0:10:03.719 single day. It's really creative. You're you're coming up with 0:10:03.760 --> 0:10:06.920 novel solutions all the time. The kind of tasks that 0:10:06.960 --> 0:10:10.160 are on the repetitive end of the spectrum are really 0:10:10.240 --> 0:10:14.440 easy to replicate, and those jobs are already gone. You 0:10:14.480 --> 0:10:16.880 can think of something you would do in a factory 0:10:16.880 --> 0:10:19.280 and like an auto factory, for example, where you're putting 0:10:19.280 --> 0:10:22.120 on the same part over and over again. Those kind 0:10:22.120 --> 0:10:25.319 of jobs are gone. But you think of something really complicated, 0:10:25.800 --> 0:10:29.760 like um being an executive editor like Dan, where you're 0:10:29.760 --> 0:10:33.840 making all these different decisions every single day, you're providing oversight, 0:10:33.960 --> 0:10:36.520 You're coming up with new things. That's the kind of 0:10:36.520 --> 0:10:40.000 thing a computer hasn't been able to do yet. Right, 0:10:40.040 --> 0:10:45.040 So it's a sense of collaboration, reading people like, kind 0:10:45.080 --> 0:10:49.040 of working off of social cues. All those things qualify 0:10:49.280 --> 0:10:52.320 social skills, and at least one expert that I talked 0:10:52.400 --> 0:10:57.760 to said that upward of the workforce needs at least 0:10:57.840 --> 0:11:02.319 some sort of collaboration to get their job done. So 0:11:02.360 --> 0:11:05.920 it's a lot. If we've advanced this quickly, what makes 0:11:06.000 --> 0:11:10.760 us think that that final stage is still some steps away? 0:11:10.960 --> 0:11:13.920 Because zacky, this made me recall a story you wrote 0:11:14.320 --> 0:11:19.280 in twenty fourteen about robots being deployed in lawyers offices 0:11:19.840 --> 0:11:22.920 doing legal work. You know, that's a whole other step 0:11:22.960 --> 0:11:26.160 away from the generic shot of a robot putting an 0:11:26.200 --> 0:11:30.360 engine head into an suv. Yeah, definitely. So this was 0:11:30.760 --> 0:11:34.800 you know, a very specific task in that lawyers UM 0:11:35.080 --> 0:11:38.200 currently are performing called you know, it's document reading in 0:11:38.280 --> 0:11:42.120 this initial stage of litigation where you have to decide 0:11:42.160 --> 0:11:45.400 which documents are relevant and which documents aren't relevant to 0:11:45.440 --> 0:11:48.960 your case. Before, it was impossible to have computers do 0:11:49.040 --> 0:11:51.880 that because it was just too complicated of a task. 0:11:52.400 --> 0:11:56.040 Every case of litigation was too different in order to 0:11:56.120 --> 0:11:58.720 kind of come up with this like master set of rules. 0:11:59.280 --> 0:12:01.760 But they found a way to do that do that 0:12:01.920 --> 0:12:06.120 by um giving a small group of lawyers the small 0:12:06.240 --> 0:12:10.000 subset of documents and having them say whether something is 0:12:10.040 --> 0:12:13.080 relevant or not relevant, and the computer is watching those 0:12:13.160 --> 0:12:16.640 humans make those decisions, and then the computer learns from 0:12:16.679 --> 0:12:21.040 that experience and is able to amplify that experience across 0:12:21.040 --> 0:12:25.240 a much broader set of documents for that specific case. UM. 0:12:25.240 --> 0:12:29.360 This leads to a lot of savings and dollars you know, 0:12:29.400 --> 0:12:32.360 in terms of like the labor that you have to employ, 0:12:32.400 --> 0:12:35.920 and it also makes the litigation process go a lot more, 0:12:36.200 --> 0:12:40.320 um go much faster. But I feel like to really 0:12:40.360 --> 0:12:44.520 illustrate how far away robots are in terms of just 0:12:44.679 --> 0:12:48.480 really being able to moot very well, maybe we should 0:12:48.520 --> 0:12:50.800 try to have a little bit of a conversation with 0:12:50.920 --> 0:12:54.160 Siri and see how that goes. Yeah, let's do it. 0:12:55.000 --> 0:13:01.240 Let's see, Siri, I'm feeling really sad today. I would 0:13:01.240 --> 0:13:03.840 give you a shoulder to cry on, Victoria if I 0:13:03.920 --> 0:13:08.480 had one. Thanks, that's really nice, but it didn't really 0:13:08.520 --> 0:13:14.720 make me feel better. You're welcome, okay, all right, Well, 0:13:15.080 --> 0:13:17.800 I'm not sure that has quite the same effect as 0:13:17.840 --> 0:13:21.679 if you know, Aki was asking me exactly why I 0:13:21.840 --> 0:13:24.680 was sad. I feel like if if I told you 0:13:24.760 --> 0:13:27.160 I was sad and you didn't ask me why or 0:13:27.559 --> 0:13:29.959 try to make me feel a little bit better about it, 0:13:30.280 --> 0:13:34.760 I might have to unfriend you. So that's that's a 0:13:35.120 --> 0:13:38.200 you know, a kind of social skill that Siri has 0:13:38.240 --> 0:13:43.840 been unable to learn yet. Can we follow or um 0:13:44.280 --> 0:13:47.960 framed Amy? I mean, let's talk a bit about Amy. 0:13:48.120 --> 0:13:53.560 Does she or it exists inside a computer, inside a 0:13:53.640 --> 0:13:58.800 micro ship or in this chap's office tory? Is there 0:13:58.840 --> 0:14:03.679 a humanoid look machine typing away to keyboard and her 0:14:04.040 --> 0:14:07.720 or it the name happens to be Amy. What are 0:14:07.720 --> 0:14:11.200 the dimensions that we're dealing with here? Amy is a machine. 0:14:11.200 --> 0:14:13.480 I mean she's not. She's not a robot sitting at 0:14:13.480 --> 0:14:17.280 a computer typing. You know, she she recognized. The machine 0:14:17.360 --> 0:14:21.840 recognizes patterns in the emails, you know, today, tomorrow, a time, 0:14:22.560 --> 0:14:26.360 and they use that very similar to how Google does. 0:14:26.480 --> 0:14:28.800 If if any of the listeners out there have Gmail, 0:14:28.920 --> 0:14:31.560 I'm sure they see when when someone sends an email 0:14:31.600 --> 0:14:33.520 with a time and a date, they'll see these dash 0:14:33.600 --> 0:14:37.120 lines under it and you can add it to your calendar. Um. 0:14:37.160 --> 0:14:40.520 You know, Amy is analyzing these these dates and these 0:14:40.600 --> 0:14:44.600 times and these words, um and being able to respond 0:14:44.640 --> 0:14:48.240 to that and and added to your calendar. So UM, 0:14:48.280 --> 0:14:49.680 I don't think you should think of it as like 0:14:49.880 --> 0:14:52.760 a robot sitting at a at a computer, like slaving 0:14:52.760 --> 0:15:01.520 away at a keyboard. Should we be scared of Amy? Well, 0:15:01.560 --> 0:15:04.320 I don't think we should be scared of Amy. As 0:15:04.360 --> 0:15:08.920 a Dennis Mortinson again, the creator Um said he's just 0:15:09.000 --> 0:15:13.840 trying to sort of eliminate this email back and forth 0:15:13.960 --> 0:15:17.520 that any human has to do. Whether you're lucky enough 0:15:17.520 --> 0:15:20.360 to have a personal assistant and your personal assistant does it, 0:15:20.640 --> 0:15:23.600 or like Aggie and I have to do it. Um, 0:15:23.640 --> 0:15:26.160 he just wants to eliminate that, so it frees you 0:15:26.280 --> 0:15:29.040 up to do things that are more productive and more 0:15:29.160 --> 0:15:32.040 worth a humans time. So I don't think we should 0:15:32.080 --> 0:15:34.320 be scared of amy. It's really interesting because I was 0:15:34.360 --> 0:15:38.160 doing research for this story, I was able to talk 0:15:38.200 --> 0:15:40.600 to people about what they think the future looks like 0:15:40.640 --> 0:15:44.120 in terms of how much work robots will be taking over, 0:15:44.200 --> 0:15:50.160 et cetera. And Paedro Domingos over at the University of Washington. 0:15:50.520 --> 0:15:52.480 He's he's the author of a new book on machine 0:15:52.560 --> 0:15:55.160 learning called The Master Algorithm, if any of you guys 0:15:55.200 --> 0:15:58.560 want to check it out. Um, but he sort of 0:15:58.680 --> 0:16:03.080 envisioned this world are robots will be able to do 0:16:03.160 --> 0:16:07.000 basically everything that humans currently do now in their work, 0:16:07.600 --> 0:16:09.960 but they're going to be certain things that will want 0:16:10.000 --> 0:16:13.000 a human touch for. So, you know, you don't want 0:16:13.080 --> 0:16:17.040 to go to the bar and like pour out like 0:16:17.120 --> 0:16:20.360 your feelings about how your girlfriend dumped you to a 0:16:20.520 --> 0:16:24.000 robot bartender. You want a real bartender who can take 0:16:24.040 --> 0:16:26.360 a shot with you and who you can like go 0:16:26.400 --> 0:16:28.560 back and forth on how terrible she was. Like. You 0:16:28.600 --> 0:16:31.240 want someone who really understands what you're looking for. And 0:16:31.320 --> 0:16:33.520 he says that these things are going to start to 0:16:33.680 --> 0:16:36.040 command a premium in the labor market, and there's gonna 0:16:36.040 --> 0:16:39.280 be way fewer of them, but they will still exist. 0:16:39.320 --> 0:16:42.600 They'll be a luxury sort of. But also in that 0:16:42.680 --> 0:16:46.160 new world, we're gonna totally have to rethink how people 0:16:46.320 --> 0:16:50.440 get money, how people live. Um. There's this idea floating 0:16:50.480 --> 0:16:55.200 around about basic income, and it's this this theory that 0:16:55.280 --> 0:16:58.560 you know, as robots basically take over the way that 0:16:58.640 --> 0:17:01.280 we earn a livelihood, and you're gonna have to be 0:17:01.360 --> 0:17:05.000 able to guarantee people some sort of fixed amount of 0:17:05.040 --> 0:17:07.840 income um that they can spend, whether that comes from 0:17:07.840 --> 0:17:10.560 the government, whether that comes from taxing the people who 0:17:10.600 --> 0:17:13.440 create robots that are gonna take all our jobs. We're 0:17:13.440 --> 0:17:16.880 gonna have to change sort of the distribution of capital 0:17:16.920 --> 0:17:19.679 because right now, you earn money based on you know, 0:17:19.720 --> 0:17:23.360 the scarcity of your labor um, and and once robots 0:17:23.359 --> 0:17:26.439 are taking that over, the money is going to be 0:17:26.480 --> 0:17:29.720 held by the people who control the robots, who invented them. 0:17:29.760 --> 0:17:32.080 So we're gonna have to rethink that. Going back to 0:17:32.119 --> 0:17:36.080 your bartending example, like if you're really really amazing bartender 0:17:36.200 --> 0:17:41.360 with these incredible, interpersonable skills and you're really warm and 0:17:41.440 --> 0:17:45.000 you make these really cool creative cocktails, then yeah, your 0:17:45.080 --> 0:17:48.600 future is golden. You're gonna be fine, and if anything, 0:17:48.640 --> 0:17:50.800 you're probably going to be making even more money in 0:17:50.840 --> 0:17:53.320 the future. But if you're kind of like the middle 0:17:53.440 --> 0:17:56.199 of the road bartender, like you know, you're you're kind 0:17:56.240 --> 0:17:59.560 of friendly, but not that friendly, and your cocktails aren't 0:17:59.560 --> 0:18:02.240 that created of it's pretty much, you know, the same 0:18:02.280 --> 0:18:07.160 stuff as what's on other menus around the city, then uh, 0:18:07.280 --> 0:18:10.040 your future isn't that bright. That probably means that your 0:18:10.119 --> 0:18:16.679 job is um much more vulnerable to robots and software. Right, 0:18:16.840 --> 0:18:19.959 creativity is going to be just a huge game changer. 0:18:20.600 --> 0:18:23.159 It already is now, but that's going to be really 0:18:23.160 --> 0:18:26.320 how you can leverage yourself, is what these experts said. Right, 0:18:26.359 --> 0:18:28.160 So I can't help but feel like it would lead 0:18:28.200 --> 0:18:33.000 to more inequality in terms of who wins and who loses. Yeah, 0:18:33.119 --> 0:18:34.919 you know, it reminds me of some of the issues 0:18:34.960 --> 0:18:38.879 we talked about in our inaugural episode with our friend 0:18:38.920 --> 0:18:43.280 Barry Bosworth of Brookings. He sketched for us a history 0:18:43.280 --> 0:18:49.399 of automation going back to the spinning wheel, steam engine, electricity, 0:18:49.800 --> 0:18:56.000 personal computers, where do you think Amy and machines like 0:18:56.119 --> 0:19:01.359 her fit into that historical sweepe. There's this weird dichotomy 0:19:01.400 --> 0:19:03.879 where we have people who are both afraid that robots 0:19:03.880 --> 0:19:06.480 are going to take all our jobs and also afraid 0:19:06.560 --> 0:19:11.000 that we're not innovating enough. Uh, that productivity is just 0:19:11.080 --> 0:19:16.320 gonna be sluggish forever. I don't I don't know which 0:19:16.359 --> 0:19:20.080 one is right. I don't think it's probably either extreme. 0:19:20.160 --> 0:19:23.560 I think it's going to be somewhere in the middle. Um, 0:19:23.760 --> 0:19:26.920 the so called Internet of things is really changing how 0:19:26.960 --> 0:19:31.119 we look at productivity and how we use technology to 0:19:31.280 --> 0:19:34.639 get data and to drive decision making and to to 0:19:34.800 --> 0:19:39.399 make our processes more efficient and smarter. Um. It just 0:19:39.680 --> 0:19:41.600 it may take some time for these things to show 0:19:41.680 --> 0:19:44.120 up in the data. I think that's what people are 0:19:44.160 --> 0:19:45.879 trying to figure out. But Dan, I think I have 0:19:45.920 --> 0:19:49.440 a more important question, and that is are you a robot? 0:19:50.280 --> 0:20:02.159 And Victoria we should probably wrap up, Tori. Tori, you 0:20:02.160 --> 0:20:05.399 know we started this episode with a question of whether 0:20:05.480 --> 0:20:08.880 a robot will be able to take our jobs? Should 0:20:08.920 --> 0:20:11.880 we ask Sirie? Oh? Yeah, we should definitely ask Seria. Okay, 0:20:11.960 --> 0:20:16.240 let's see what she says. Is a robot going to 0:20:16.359 --> 0:20:22.960 take all of our jobs. Interesting question, Victoria, all right, 0:20:23.080 --> 0:20:30.479 that was a super late answer, Siri. Yes, yes, old 0:20:30.520 --> 0:20:35.879 technology for the win. Sirie needs some work. Thanks again 0:20:35.960 --> 0:20:38.920 for listening to Bloomberg Benchmark. We will be back next 0:20:38.960 --> 0:20:44.120 week and you can find us on Bloomberg dot com, iTunes, Podcasts, SoundCloud, Stitcher, 0:20:44.600 --> 0:20:47.959 all the places um as well as on the Bloomberg terminal. 0:20:48.400 --> 0:20:51.520 And if you're on any of those platforms, please take 0:20:51.520 --> 0:20:53.640 a moment to rate and review the show so other 0:20:53.720 --> 0:20:56.040 listeners can find us and let us know what you 0:20:56.119 --> 0:20:58.760 thought of this show. You can reach us and follow 0:20:58.880 --> 0:21:02.280 us on Twitter at Daniel mass d C, Tori Stillwell, 0:21:02.440 --> 0:21:06.320 and Kita seven. We'll see you next week. And I 0:21:06.359 --> 0:21:07.440 am not a robot.