1 00:00:16,041 --> 00:00:19,083 ♪ ♪ 2 00:00:20,542 --> 00:00:23,583 (rattling) 3 00:00:28,125 --> 00:00:30,291 (Artificial Intelligence voice speaking) 4 00:00:33,417 --> 00:00:36,208 We're riding in the back of an autonomous truck. 5 00:00:36,291 --> 00:00:39,500 It's about to take a left turn into oncoming traffic. 6 00:00:39,583 --> 00:00:41,083 So, if their sensors-- 7 00:00:41,166 --> 00:00:43,291 (AI voice speaks) 8 00:00:46,166 --> 00:00:48,750 Andavolu: It's accelerating on its own. It's breaking on its own. 9 00:00:48,834 --> 00:00:50,458 It's steering on its own. 10 00:00:51,542 --> 00:00:53,208 Trucking in the United States is, like, 11 00:00:53,291 --> 00:00:55,375 a $700-billion-a-year industry, 12 00:00:55,458 --> 00:00:58,083 and it employs 1.8 million people. 13 00:00:58,166 --> 00:00:59,458 Thus far, it's been pretty 14 00:00:59,542 --> 00:01:02,291 immune to the changes of globalization and technology, 15 00:01:02,375 --> 00:01:05,542 but that's about to change with technology like this. 16 00:01:05,625 --> 00:01:07,750 (AI voice speaking) 17 00:01:08,917 --> 00:01:11,583 ♪ ♪ 18 00:01:11,667 --> 00:01:13,083 Andavolu: There's an operator here, 19 00:01:13,166 --> 00:01:14,667 there's a safety engineer here, 20 00:01:14,750 --> 00:01:15,875 'cause they're still experimenting. 21 00:01:15,959 --> 00:01:17,291 It's still kind of in development. 22 00:01:17,375 --> 00:01:19,166 (AI voice speaks) 23 00:01:19,792 --> 00:01:22,000 Andavolu: Have you put your hand on the wheel at any point? 24 00:01:22,083 --> 00:01:24,208 (operator speaks) 25 00:01:25,041 --> 00:01:27,125 Andavolu: This is like a pretty complex traffic situation. 26 00:01:27,208 --> 00:01:29,750 There are cars merging. There are cars passing us. 27 00:01:29,834 --> 00:01:33,625 This truck is changing lanes to go around slow traffic, 28 00:01:33,709 --> 00:01:35,917 anticipating when people are stopping. 29 00:01:36,458 --> 00:01:38,583 And honestly, I just, like, kind of can't believe it. 30 00:01:38,667 --> 00:01:40,583 It's... It's driving itself, 31 00:01:40,667 --> 00:01:42,500 and it's doing a pretty good job. 32 00:01:42,583 --> 00:01:44,083 (AI voice speaks) 33 00:01:44,166 --> 00:01:47,750 Operator: Here we go, making our right turn into our driveway. 34 00:01:47,834 --> 00:01:51,417 ♪ ♪ 35 00:01:51,500 --> 00:01:55,583 Chuck Price: We recognize that this is a highly disruptive technology, 36 00:01:55,667 --> 00:01:57,750 on the order of 10 million people, 37 00:01:57,834 --> 00:02:01,750 and displacing rapidly that many people 38 00:02:01,834 --> 00:02:04,583 -would have a dramatic societal impact. -Mm-hmm. 39 00:02:04,667 --> 00:02:07,625 We certainly don't want to see that. We're not targeting that. 40 00:02:07,709 --> 00:02:10,458 We're focused on relieving a shortage. 41 00:02:10,542 --> 00:02:14,291 But what we're hoping is that there will be a natural evolution 42 00:02:14,375 --> 00:02:16,959 into the jobs of the future, just as there has been 43 00:02:17,041 --> 00:02:19,166 in every other technological change. 44 00:02:19,250 --> 00:02:24,166 -What do you tell a trucker? -We try not to tell truckers things. We try to listen. 45 00:02:24,250 --> 00:02:27,083 ♪ ♪ 46 00:02:30,667 --> 00:02:32,458 (sizzling) 47 00:02:32,542 --> 00:02:35,542 (patrons chattering) 48 00:02:35,625 --> 00:02:37,041 This is Chuck. 49 00:02:37,125 --> 00:02:39,792 -Hello, Chuck. -Chuck runs a company that is 50 00:02:39,875 --> 00:02:43,041 developing and is gonna produce self-driving trucks, 51 00:02:43,125 --> 00:02:46,083 that are autonomous, that do it themselves. 52 00:02:46,166 --> 00:02:48,250 -You guys are truck drivers. -Don Schrader: Yes, sir. 53 00:02:48,333 --> 00:02:50,333 How do you feel about it? 54 00:02:50,417 --> 00:02:51,834 If the truck doesn't have a driver in it, 55 00:02:51,917 --> 00:02:54,667 is somebody at headquarters sitting behind a monitor? 56 00:02:54,750 --> 00:02:56,000 Schrader: No, there's a driver in there. 57 00:02:56,083 --> 00:02:58,208 No. In the future, 58 00:02:58,291 --> 00:03:01,041 when there isn't, you know, once it's fully tested... 59 00:03:01,125 --> 00:03:03,041 -Oh. -...and we don't need to have a driver in it. 60 00:03:03,125 --> 00:03:05,834 -Oh, driver assist? -No, it's not driver assist. 61 00:03:05,917 --> 00:03:08,709 It is purely self-driving. 62 00:03:08,792 --> 00:03:10,166 So, like a GPS basically? 63 00:03:10,250 --> 00:03:12,500 You're going from point A to point B? 64 00:03:12,583 --> 00:03:14,375 There's GPS in it. There's a lot of tech, 65 00:03:14,458 --> 00:03:16,625 -but GPS is a part of it. -Okay. 66 00:03:17,500 --> 00:03:19,667 Andavolu: He took me into one of his trucks today. 67 00:03:19,750 --> 00:03:22,458 We did a 90-minute run up and down on I-10. 68 00:03:22,542 --> 00:03:24,959 Traffic was merging in. It was changing lanes. 69 00:03:25,041 --> 00:03:27,291 -It was braking, it was accelerating. It was like-- -Schrader: Really? 70 00:03:27,375 --> 00:03:29,458 So, does it do really good when the cars cut you off? 71 00:03:29,542 --> 00:03:32,375 A car cut us off! And you know what? It didn't slam on the brakes. 72 00:03:32,458 --> 00:03:34,500 The truck just kind of kept rolling. 73 00:03:34,583 --> 00:03:37,166 Schrader: How is your equipment handling the high winds? 74 00:03:37,250 --> 00:03:40,041 -We're actually surprisingly good in high winds. -Really? 75 00:03:40,125 --> 00:03:41,750 How much weight in the trailer? 76 00:03:41,834 --> 00:03:43,250 We've gone empty and loaded. 77 00:03:43,333 --> 00:03:44,875 -Empty and loaded? -Yes, sir. 78 00:03:44,959 --> 00:03:47,500 We have very complex control algorithms, 79 00:03:47,583 --> 00:03:49,625 and now we hold it with such precision that 80 00:03:49,709 --> 00:03:51,375 it is perfectly straight. 81 00:03:51,458 --> 00:03:53,208 Yeah. It's tough. 82 00:03:53,291 --> 00:03:55,208 Andavolu: You seem pretty impressed. 83 00:03:55,291 --> 00:03:56,834 Actually, yeah, I am. 84 00:03:56,917 --> 00:03:59,417 I wasn't all for it, but, I mean, it's... 85 00:03:59,500 --> 00:04:01,208 I gotta, I gotta see this. 86 00:04:01,291 --> 00:04:03,917 ♪ ♪ 87 00:04:09,125 --> 00:04:11,291 Price: That's laser. That's lidar. 88 00:04:11,375 --> 00:04:14,917 It's a laser, like a laser radar. 89 00:04:15,000 --> 00:04:17,166 Andavolu: What do you love about driving a truck? 90 00:04:17,250 --> 00:04:18,583 Oh, gosh. (chuckles) 91 00:04:18,667 --> 00:04:21,458 I love to drive. For me, it's a privilege. 92 00:04:22,458 --> 00:04:24,083 It's a privilege to get out there, 93 00:04:24,166 --> 00:04:25,875 get behind the wheel of 80,000 pounds, 94 00:04:25,959 --> 00:04:29,083 drive that thing down the road, knowing, hey, you know what? 95 00:04:29,166 --> 00:04:32,083 -I can do this, you can't do it. -No, I can't. 96 00:04:32,166 --> 00:04:34,041 Schrader: And I know I'm providing the US. 97 00:04:34,125 --> 00:04:35,291 I'm providing the world with whatever 98 00:04:35,375 --> 00:04:37,041 I got in the back of my freight. 99 00:04:37,125 --> 00:04:39,792 -I deliver your clothes, your food that you're eating. -Mm-hmm. 100 00:04:39,875 --> 00:04:41,375 A lot of people don't see that. 101 00:04:41,458 --> 00:04:44,500 And it's a good feeling as a driver, as a human being. 102 00:04:45,500 --> 00:04:47,333 Schrader: You really gotta have faith 103 00:04:47,417 --> 00:04:50,417 to rely on, is this gonna kill me or... 104 00:04:50,500 --> 00:04:53,041 Yep, and we'll have to do a lot of testing 105 00:04:53,125 --> 00:04:54,458 -to prove that. -Wow. 106 00:04:54,542 --> 00:04:55,875 All right. 107 00:04:55,959 --> 00:04:58,125 That's definitely different. 108 00:04:58,208 --> 00:05:01,959 Over the next year, we're building a fleet of 200 trucks, 109 00:05:02,041 --> 00:05:04,291 and we're gonna be operating them day and night, 110 00:05:04,375 --> 00:05:06,458 just to validate it, to prove it. 111 00:05:06,542 --> 00:05:07,959 We have to prove to you. 112 00:05:08,041 --> 00:05:11,792 We have to prove to the regulators, to the states. 113 00:05:11,875 --> 00:05:14,208 We have to prove to ourselves. 114 00:05:14,291 --> 00:05:16,500 Schrader: When you asked me what would I do if I didn't drive? 115 00:05:16,583 --> 00:05:17,959 I can't honestly answer that 116 00:05:18,041 --> 00:05:20,208 'cause I really don't know what I would do. 117 00:05:20,291 --> 00:05:22,041 I'd... I'd be scared. 118 00:05:26,041 --> 00:05:27,417 ♪ ♪ 119 00:05:27,500 --> 00:05:29,291 (beeping) 120 00:05:29,375 --> 00:05:31,542 (engine rumbling) 121 00:05:32,709 --> 00:05:35,417 ♪ ♪ 122 00:05:44,375 --> 00:05:46,667 ♪ ♪ 123 00:05:46,750 --> 00:05:50,333 Andavolu: It's one of the first questions that every kid gets asked. 124 00:05:50,417 --> 00:05:52,750 What do you wanna be when you grow up? 125 00:05:52,834 --> 00:05:54,375 Previous generations grew up confident 126 00:05:54,458 --> 00:05:57,500 that no matter the answer, they'd be something. 127 00:05:57,583 --> 00:05:59,583 That's actually not so certain anymore. 128 00:05:59,667 --> 00:06:01,792 Automation already affects most jobs, 129 00:06:01,875 --> 00:06:03,166 but the pace of change 130 00:06:03,250 --> 00:06:06,667 and the sheer capabilities of artificial intelligence 131 00:06:06,750 --> 00:06:10,166 are revolutionizing our relationship to work, 132 00:06:10,250 --> 00:06:12,917 something economists are paying close attention to. 133 00:06:13,959 --> 00:06:16,709 Technologies are tools. They don't decide what we do. 134 00:06:16,792 --> 00:06:18,291 We decide what we do with those tools. 135 00:06:18,375 --> 00:06:21,583 If we have more powerful tools, by definition, 136 00:06:21,667 --> 00:06:25,125 we have more power to change the world than we ever had before. 137 00:06:25,208 --> 00:06:28,709 Andrew McAfee: It took about 600 years 138 00:06:28,792 --> 00:06:30,750 for global average incomes 139 00:06:30,834 --> 00:06:32,709 -to increase by 50%. -Wow. 140 00:06:32,792 --> 00:06:35,250 McAfee: Here we are, from 1988 to now, 141 00:06:35,333 --> 00:06:38,709 almost 50% or more increases across the board of humanity. 142 00:06:38,792 --> 00:06:41,500 This is because of economic freedom 143 00:06:41,583 --> 00:06:43,083 and because of technological progress. 144 00:06:43,166 --> 00:06:44,959 It just expands the possibilities, 145 00:06:45,041 --> 00:06:46,709 and therefore, expands our income 146 00:06:46,792 --> 00:06:49,792 so much more quickly than we've ever seen before. 147 00:06:49,875 --> 00:06:53,500 Joseph Stiglitz: The problem is that as the economic pie's gotten bigger, 148 00:06:53,583 --> 00:06:56,375 not everyone has shared. 149 00:06:56,458 --> 00:06:58,792 Wages at the bottom today are the same, 150 00:06:58,875 --> 00:07:02,417 -adjusted for inflation, as they were 60 years ago. -Mm-hmm. 151 00:07:02,500 --> 00:07:04,291 So, all that growth 152 00:07:04,375 --> 00:07:08,041 didn't go down to the people at the bottom. 153 00:07:08,125 --> 00:07:10,417 Andavolu: The future of work has even caught the attention 154 00:07:10,500 --> 00:07:11,917 of experts like Richard Haass, 155 00:07:12,000 --> 00:07:14,041 and the Council on Foreign Relations, 156 00:07:14,125 --> 00:07:15,667 who've authored a report on the threats 157 00:07:15,750 --> 00:07:18,500 it poses to geopolitical stability. 158 00:07:18,583 --> 00:07:20,834 Millions of jobs are beginning to disappear. 159 00:07:20,917 --> 00:07:22,834 You've got now a whole new generation, 160 00:07:22,917 --> 00:07:24,333 whether it's artificial intelligence, 161 00:07:24,417 --> 00:07:26,125 robotics or autonomous 162 00:07:26,208 --> 00:07:28,667 or driverless vehicles that are coming along, 163 00:07:28,750 --> 00:07:32,000 that will displace millions of workers in this country, 164 00:07:32,083 --> 00:07:34,417 in the United States, but also around the world. 165 00:07:34,500 --> 00:07:37,667 And then suddenly, these technologies come along, 166 00:07:37,750 --> 00:07:41,083 and they destroy our existing relationships. 167 00:07:41,166 --> 00:07:43,291 The stakes for us as individuals are enormous, 168 00:07:43,375 --> 00:07:46,333 and unless we can replace all that, what's gonna come of us? 169 00:07:47,208 --> 00:07:51,166 Now, you drop a few drops of blood in the shark tank... 170 00:07:51,250 --> 00:07:52,792 -Krishna; Which is AI. -...which is AI, 171 00:07:52,875 --> 00:07:54,291 -and machine learning. -Machine learning. 172 00:07:54,375 --> 00:07:56,000 Goolsbee: And we're gonna shut down factories, 173 00:07:56,083 --> 00:07:57,667 we're gonna replace truck drivers. 174 00:07:57,750 --> 00:08:00,417 What if everything in the country's owned by three people 175 00:08:00,500 --> 00:08:02,291 who are the ones who invented the robots... 176 00:08:02,375 --> 00:08:05,375 -Jeff Bezos. Mark Zukerberg. -...and we're all begging them for a crumb. 177 00:08:05,458 --> 00:08:07,959 What can I eat today? Please, will you hand me something? 178 00:08:08,041 --> 00:08:09,792 You can see why people get upset. 179 00:08:09,875 --> 00:08:12,500 Even thinking about it, your blood is gonna boil. 180 00:08:13,041 --> 00:08:19,125 The discontent that is evident in the Brexit vote in 2016, 181 00:08:19,208 --> 00:08:21,500 the vote for Trump in 2016. 182 00:08:21,583 --> 00:08:24,333 What is very clear, there's a lot of discontent. 183 00:08:24,417 --> 00:08:27,291 A lot of people have not been doing very well. 184 00:08:27,375 --> 00:08:28,917 It isn't clear how long we have 185 00:08:29,000 --> 00:08:32,917 before the political system comes under enormous stresses. 186 00:08:33,417 --> 00:08:37,000 We as a society have not even begun to have a sustained 187 00:08:37,083 --> 00:08:39,166 or comprehensive national conversation. 188 00:08:39,250 --> 00:08:41,000 And what worries me is 189 00:08:41,083 --> 00:08:44,792 by the time we really get around to dealing with this, it's gonna be too late. 190 00:08:44,875 --> 00:08:46,333 (beeps) 191 00:08:46,417 --> 00:08:48,291 (sizzling) 192 00:08:48,375 --> 00:08:49,875 ♪ ♪ 193 00:08:49,959 --> 00:08:52,583 (indistinct chatter) 194 00:08:52,667 --> 00:08:56,000 Andavolu: Over 3.5 million people work in fast food. 195 00:08:56,083 --> 00:08:57,834 It's one of the easiest jobs to get, 196 00:08:57,917 --> 00:09:00,458 and a good first step on the ladder up to a job 197 00:09:00,542 --> 00:09:02,500 with better pay and less grease. 198 00:09:02,583 --> 00:09:05,250 But at Southern California's Caliburger, 199 00:09:05,333 --> 00:09:08,625 Gianna Toboni found out even this first step is in jeopardy. 200 00:09:09,542 --> 00:09:11,875 Our vision is that we want to take the restaurant industry, 201 00:09:11,959 --> 00:09:13,875 and more broadly the retail industry, 202 00:09:13,959 --> 00:09:16,709 and make it operate more like the internet. 203 00:09:16,792 --> 00:09:19,125 We wanna automate as much as we can, 204 00:09:19,208 --> 00:09:21,375 and allow merchants that operate 205 00:09:21,458 --> 00:09:23,875 brick-and-mortar businesses to see their customers 206 00:09:23,959 --> 00:09:26,083 in the same way that Amazon sees their customers. 207 00:09:26,166 --> 00:09:29,333 -There you go. So, you're done. -That was 10 seconds, probably. 208 00:09:33,083 --> 00:09:34,208 (sizzling) 209 00:09:34,291 --> 00:09:37,417 ♪ ♪ 210 00:09:38,750 --> 00:09:41,542 Miller: This was our first robot to work on the grill. 211 00:09:41,625 --> 00:09:43,542 The entire fleet of robots that we deploy 212 00:09:43,625 --> 00:09:46,083 learns from the training that takes place here in Pasadena. 213 00:09:46,166 --> 00:09:49,500 So, unlike humans, we teach one robot and you perfect it, 214 00:09:49,583 --> 00:09:50,959 and then you can deploy that software 215 00:09:51,041 --> 00:09:53,166 to all the robots in the field. 216 00:09:54,750 --> 00:09:58,166 What are the advantages of automating a business like this? 217 00:09:58,250 --> 00:10:01,583 For a restaurant chain, consistency is critical to success. 218 00:10:01,667 --> 00:10:03,041 Right? Critical to scale. 219 00:10:03,125 --> 00:10:05,250 These restaurants will be safer when you automate them. 220 00:10:05,333 --> 00:10:07,917 Humans touching the food less. 221 00:10:08,000 --> 00:10:09,542 Labor costs is a big issue right now, right? 222 00:10:09,625 --> 00:10:11,917 It's not just rising minimum wage, but it's turnover. 223 00:10:12,000 --> 00:10:14,208 They come in, they get trained, and then they leave 224 00:10:14,291 --> 00:10:16,917 to go drive an Uber or do something else. 225 00:10:17,000 --> 00:10:19,333 I notice you still have some employees back here. 226 00:10:19,417 --> 00:10:21,417 -It's not just Flippy running the whole show. -That's right. Yeah. 227 00:10:21,500 --> 00:10:23,000 Miller: So, we currently think about this 228 00:10:23,083 --> 00:10:25,083 as a cobotic working arrangement, 229 00:10:25,166 --> 00:10:27,959 where we have the robot automating certain tasks 230 00:10:28,041 --> 00:10:29,875 that humans don't necessarily like to do. 231 00:10:29,959 --> 00:10:31,875 But we still need people 232 00:10:31,959 --> 00:10:33,667 in the kitchen managing the robotic systems 233 00:10:33,750 --> 00:10:36,291 and working side-by-side with the robot to do things 234 00:10:36,375 --> 00:10:38,333 that it's not possible to automate at this point in time. 235 00:10:38,417 --> 00:10:40,333 Toboni: Natalie, how do you like working here? 236 00:10:40,417 --> 00:10:42,834 I really like it. It's something different. 237 00:10:42,917 --> 00:10:45,208 It takes a little bit of getting used to, 238 00:10:45,291 --> 00:10:47,917 -but I really do like it. Yeah. -Yeah. Cool. 239 00:10:49,208 --> 00:10:51,750 (whirring) 240 00:10:51,834 --> 00:10:54,875 ♪ ♪ 241 00:10:56,041 --> 00:10:59,000 (whirring) 242 00:11:01,083 --> 00:11:04,417 ♪ ♪ 243 00:11:06,792 --> 00:11:08,458 Andavolu: "Cobotics" is a new word, 244 00:11:08,542 --> 00:11:10,458 but the idea has been around forever: 245 00:11:10,542 --> 00:11:14,166 Let's integrate new tools into old tasks and do them faster. 246 00:11:14,250 --> 00:11:16,583 (beeping, whirring) 247 00:11:18,250 --> 00:11:20,834 At Amazon's robotic-enabled fulfillment centers, 248 00:11:20,917 --> 00:11:23,875 thousands of newly developed AI-powered cobots 249 00:11:23,959 --> 00:11:27,333 are rebuilding how man and machine work together. 250 00:11:27,417 --> 00:11:29,083 We actually started introducing 251 00:11:29,166 --> 00:11:31,709 robotics in around 2012. 252 00:11:32,208 --> 00:11:33,792 -So just, like, seven years ago? -Yep. 253 00:11:33,875 --> 00:11:37,667 Brady: Since then, we have created almost 300,000 jobs. 254 00:11:37,750 --> 00:11:40,000 -Just in fulfillment centers like this? -In fulfillment centers 255 00:11:40,083 --> 00:11:42,125 across the Amazon workforce. 256 00:11:42,792 --> 00:11:46,792 -We have this first example of human-machine collaboration. -Uh-huh. 257 00:11:47,375 --> 00:11:49,667 These are mobile shelves that are drive units 258 00:11:49,750 --> 00:11:52,250 of the little orange robots that you see below? -Andavolu: Yeah. 259 00:11:52,333 --> 00:11:56,041 Brady: You can move those at will, any shelf, at any time. 260 00:11:56,125 --> 00:11:57,750 And at the right time, like magic, 261 00:11:57,834 --> 00:11:59,542 that universal station, it's gonna say, 262 00:11:59,625 --> 00:12:02,208 -"Hey, I think that object is right here." -Andavolu: Wow. 263 00:12:02,291 --> 00:12:04,709 Brady: Now, she is gonna do a pick operations, 264 00:12:04,792 --> 00:12:07,417 that's scanned, and if it passes that bar, it's the right object. 265 00:12:07,500 --> 00:12:10,417 -Look at them go! These shelves are crazy! -Brady: It's awesome. 266 00:12:11,041 --> 00:12:13,208 Andavolu: But it's interesting. So, who is sort of... 267 00:12:13,291 --> 00:12:15,959 Who's working for who here? 'Cause, like, the robots are coming to her. 268 00:12:16,041 --> 00:12:17,834 She's taking the stuff out and putting it in, 269 00:12:17,917 --> 00:12:20,208 but then she has to say to the robot, "Oh yeah, it's actually it." 270 00:12:20,291 --> 00:12:23,208 -Is it on her time, or is it on the robot's time? -(laughing): I love it. 271 00:12:23,291 --> 00:12:25,333 It's... I love that question, it's great. 272 00:12:25,417 --> 00:12:28,125 -It is a symphony of humans and machines working together. -Uh-huh. 273 00:12:28,208 --> 00:12:32,125 You put them both together in order to create a better system. 274 00:12:32,208 --> 00:12:34,500 Is there a day where there's gonna be a robot 275 00:12:34,583 --> 00:12:37,166 who can pick and look through things just as good as she can? 276 00:12:37,250 --> 00:12:38,959 And is that a day that you're planning for? 277 00:12:39,041 --> 00:12:40,333 Are you already planning for it? 278 00:12:40,417 --> 00:12:42,458 Brady: Humans are amazing at problem-solving. 279 00:12:42,542 --> 00:12:44,667 Humans are amazing at generalization. 280 00:12:44,750 --> 00:12:47,458 Humans have high value, high judgment, right? 281 00:12:47,542 --> 00:12:51,291 Why would we ever want to separate that away from our machines? 282 00:12:51,375 --> 00:12:53,875 We actually want to make that more cohesive. 283 00:12:53,959 --> 00:12:55,625 ♪ ♪ 284 00:12:55,709 --> 00:12:58,959 -What's cool is that Amazon is growing big time, right? -Yeah. 285 00:12:59,041 --> 00:13:00,417 And it's creating a lot of jobs. 286 00:13:00,500 --> 00:13:02,542 It's one of the biggest job creators in the world. 287 00:13:02,625 --> 00:13:06,166 So, with automation working hand in hand with people, 288 00:13:06,250 --> 00:13:07,792 is it making jobs better? 289 00:13:07,875 --> 00:13:10,917 I think it is making it better. First of all, our associates, 290 00:13:11,000 --> 00:13:14,333 -they choose to come and work in our fulfillment centers. -They choose to be here, yeah. 291 00:13:14,417 --> 00:13:17,166 And we're really proud of the wage benefit 292 00:13:17,250 --> 00:13:19,041 that we are offering our associates. 293 00:13:19,125 --> 00:13:20,959 -It's a $15 minimum... -Uh-huh. 294 00:13:21,041 --> 00:13:23,291 ...that we instituted this year. We're really proud of that. 295 00:13:23,375 --> 00:13:25,959 They are the reason that we're so successful 296 00:13:26,041 --> 00:13:27,709 inside our fulfillment centers. 297 00:13:30,583 --> 00:13:31,875 Andavolu: This is Jav. 298 00:13:31,959 --> 00:13:34,792 He's 23 and at the very beginning of his career. 299 00:13:34,875 --> 00:13:36,959 He dropped out of college for financial reasons, 300 00:13:37,041 --> 00:13:38,625 then left a job at an elementary school 301 00:13:38,709 --> 00:13:41,333 to become an Amazon associate because it paid better. 302 00:13:41,417 --> 00:13:46,250 He still works there, which is why he asked us not to use his last name. 303 00:13:46,333 --> 00:13:47,667 When I heard we was working with robots, 304 00:13:47,750 --> 00:13:49,500 -I thought the idea was cool. -Uh-huh. 305 00:13:49,583 --> 00:13:53,917 It's something I only imagined coming out of a sci-fi movie. 306 00:13:56,458 --> 00:13:59,041 But, um, I guess for the most part, 307 00:13:59,125 --> 00:14:01,375 I dislike the stowing process. 308 00:14:01,458 --> 00:14:03,291 Stowing, so what's that exactly? 309 00:14:03,375 --> 00:14:08,500 Just imagine just standing there, just... all day (laughs), 310 00:14:08,583 --> 00:14:10,041 -for those 10 hours. -Yeah. 311 00:14:10,125 --> 00:14:13,333 You know, you feel like you have to move fast. 312 00:14:13,417 --> 00:14:16,500 You have to do right by the robots, you know? 313 00:14:16,583 --> 00:14:19,792 Do the robots work for you, or do you work for the robots? 314 00:14:19,875 --> 00:14:21,500 Wow, that's a good question. 315 00:14:21,583 --> 00:14:23,750 I feel like I work for the robots. (laughs) 316 00:14:25,417 --> 00:14:29,000 At Amazon, data seems to be like this huge thing. 317 00:14:29,083 --> 00:14:32,166 Like, do they track your data as a human? 318 00:14:32,250 --> 00:14:33,625 They track everyone. (chuckles) 319 00:14:33,709 --> 00:14:36,208 How many products are you actually moving in a single day? 320 00:14:36,291 --> 00:14:39,333 I think the highest I've ever stowed was... 321 00:14:40,125 --> 00:14:42,458 -2,300 units. Yeah. -Wow. 322 00:14:43,250 --> 00:14:46,375 You feel like the robots you're working with. (laughs) 323 00:14:46,458 --> 00:14:48,709 ♪ ♪ 324 00:14:48,792 --> 00:14:49,625 Oh! 325 00:14:50,208 --> 00:14:52,041 Andavolu: Something you wanna keep doing for a while? 326 00:14:52,125 --> 00:14:54,000 You wanna stick around with it? 327 00:14:54,083 --> 00:14:55,542 -What, Amazon? No. -Yeah. 328 00:14:55,625 --> 00:14:56,959 (laughter) 329 00:14:57,041 --> 00:14:59,333 -That was quick, right? -(laughing) 330 00:15:00,750 --> 00:15:02,458 (panting) 331 00:15:02,542 --> 00:15:04,959 What agency do you have 332 00:15:05,041 --> 00:15:06,875 when you step into that building? 333 00:15:06,959 --> 00:15:10,125 What am I gonna eat for lunch? (laughs) 334 00:15:11,250 --> 00:15:12,375 But it's funny 'cause it's like, 335 00:15:12,458 --> 00:15:16,000 I'm hearing from people at Amazon that 336 00:15:16,083 --> 00:15:18,750 human creativity and problem-solving 337 00:15:18,834 --> 00:15:21,542 is still something that they value. 338 00:15:23,542 --> 00:15:26,792 -You heard that from someone? -I did. 339 00:15:28,417 --> 00:15:29,875 I don't know. 340 00:15:29,959 --> 00:15:33,750 I haven't been put in a position where I can like, you know, 341 00:15:33,834 --> 00:15:35,583 be creative. 342 00:15:35,667 --> 00:15:37,458 I'm pretty sure a lot of people haven't. 343 00:15:37,542 --> 00:15:39,917 ♪ ♪ 344 00:15:40,000 --> 00:15:42,917 I know that one day, I would like to get a career 345 00:15:43,000 --> 00:15:45,417 that I don't feel like I need a vacation from. 346 00:15:45,500 --> 00:15:48,417 It's the whole thing of being useful, you know, like, 347 00:15:48,500 --> 00:15:50,083 that's part of being a human. 348 00:15:50,166 --> 00:15:53,834 We all have to feel useful for something, you know? 349 00:15:55,166 --> 00:15:57,750 -Andavolu: Do you feel replaceable? -Jav: Yeah, of course. 350 00:15:57,834 --> 00:16:01,625 I know that if I get fired, there'll be another person in my place... 351 00:16:01,709 --> 00:16:04,458 -ASAP, so... -(indistinct bus announcement) 352 00:16:04,542 --> 00:16:07,542 Andavolu: Do you think they're gonna try to automate you out of your job? 353 00:16:09,625 --> 00:16:11,125 Jav: Yeah, 'cause... 354 00:16:11,208 --> 00:16:13,291 they wouldn't have to worry about 355 00:16:13,375 --> 00:16:16,333 people being injured on the job, you know? 356 00:16:16,417 --> 00:16:18,750 So, if they could replace us with robots, 357 00:16:18,834 --> 00:16:21,333 I think it could be done. 358 00:16:21,417 --> 00:16:23,041 (whirring) 359 00:16:23,125 --> 00:16:25,041 Andavolu: From 2015 to 2017, 360 00:16:25,125 --> 00:16:28,500 Amazon held competitions where college teams designed robots 361 00:16:28,583 --> 00:16:31,417 to do more or less what Jav does all day: 362 00:16:31,500 --> 00:16:33,542 single out objects, grab them, 363 00:16:33,625 --> 00:16:35,792 then stow them in a specific place. 364 00:16:35,875 --> 00:16:37,083 See how I can collapse the hand, 365 00:16:37,166 --> 00:16:39,458 so I can get it into where it is? 366 00:16:39,542 --> 00:16:41,792 A robotic hand, just I haven't seen anything 367 00:16:41,875 --> 00:16:43,291 with that sort of dexterity. 368 00:16:43,375 --> 00:16:44,667 Andavolu: That's Tye Brady, 369 00:16:44,750 --> 00:16:46,917 the same Amazon exec we met earlier. 370 00:16:47,000 --> 00:16:50,166 Sure, he hasn't seen a robot perform that task yet, 371 00:16:50,250 --> 00:16:53,083 but that's exactly why it's the holy grail 372 00:16:53,166 --> 00:16:56,333 for making robots as physically versatile as humans. 373 00:16:56,417 --> 00:16:59,625 ♪ ♪ 374 00:17:00,917 --> 00:17:03,083 -Can I... Will it hurt my hand? -Lillian Chin: No. 375 00:17:03,166 --> 00:17:06,500 -(whirs) -Andavolu (stammers): Hi. Yeah, that's not bad. 376 00:17:06,583 --> 00:17:08,166 Yeah, that's actually kind of gentle. 377 00:17:08,250 --> 00:17:11,375 So, we've spent, I don't know, however many hundreds of PhDs 378 00:17:11,458 --> 00:17:14,166 and decades trying to make robots smarter at grasping. 379 00:17:14,250 --> 00:17:15,667 We're starting to get there with 380 00:17:15,750 --> 00:17:18,166 -artificial intelligence and neural networks... -Uh-huh. 381 00:17:18,250 --> 00:17:19,875 ...but even still, it's still very early 382 00:17:19,959 --> 00:17:21,667 -in terms of our ability to grasp. -Right. 383 00:17:21,750 --> 00:17:24,709 Right? And the Amazon picking challenge is a perfect example. 384 00:17:24,792 --> 00:17:26,625 A bunch of minds working on it for a long time, 385 00:17:26,709 --> 00:17:28,166 and we still haven't figured out 386 00:17:28,250 --> 00:17:30,375 how to just pick things from a bin. 387 00:17:30,458 --> 00:17:32,041 And that's a multi-billion dollar, 388 00:17:32,125 --> 00:17:34,542 potentially trillion dollar value proposition. 389 00:17:34,625 --> 00:17:36,208 What's so hard about it? 390 00:17:36,291 --> 00:17:37,458 Computers are smart, right? 391 00:17:37,542 --> 00:17:39,000 What we think is hard is very different 392 00:17:39,083 --> 00:17:40,709 from what computers think is hard. 393 00:17:40,792 --> 00:17:43,208 So, we think that being a chess grandmaster is a hard challenge, 394 00:17:43,291 --> 00:17:45,625 but the computer can just go through all the possibilities. 395 00:17:45,709 --> 00:17:48,792 Whereas this, there's infinite possibilities to grab that apple. 396 00:17:48,875 --> 00:17:51,375 What happens when we crack the grasping problem? 397 00:17:51,458 --> 00:17:54,041 -A lot fewer Amazon employees. -(laughing) 398 00:17:54,125 --> 00:17:55,750 (whirring) 399 00:17:57,208 --> 00:17:59,583 Andavolu: This March, MIT and Harvard debuted 400 00:17:59,667 --> 00:18:01,542 a new concept of the grabber, 401 00:18:01,625 --> 00:18:04,875 saying Amazon's just the kind of company that could use it. 402 00:18:05,000 --> 00:18:08,166 Amazon already sucks up nearly 50% 403 00:18:08,250 --> 00:18:10,375 of America's e-commerce transactions, 404 00:18:10,458 --> 00:18:14,083 and makes a dollar for every 20 spent by American shoppers. 405 00:18:14,166 --> 00:18:17,417 There's a reason it's valued at nearly a trillion dollars. 406 00:18:17,500 --> 00:18:19,667 But Amazon and its tech peers, like Apple, 407 00:18:19,750 --> 00:18:22,458 Google, and Facebook, employ far fewer people 408 00:18:22,542 --> 00:18:25,375 than the richest companies of previous eras. 409 00:18:25,458 --> 00:18:27,709 So, even if robots aren't helping you find the right size 410 00:18:27,792 --> 00:18:29,583 at a brick-and-mortar Gap quite yet, 411 00:18:29,667 --> 00:18:32,834 that doesn't mean they aren't eroding the future of retail work. 412 00:18:32,917 --> 00:18:36,083 -Goolsbee: I spent most of my childhood in Southern California. -Mm-hmm. 413 00:18:36,166 --> 00:18:39,834 The Whitwood Mall and La Puente Mall were the... 414 00:18:39,917 --> 00:18:42,291 the center of social life. 415 00:18:42,375 --> 00:18:45,083 Andavolu: Austin Goolsbee is a professor of economics 416 00:18:45,166 --> 00:18:47,709 and was the chair of economic advisors under President Obama. 417 00:18:47,792 --> 00:18:52,250 -Retail was, kind of, often an entry-level job... -Mm-hmm. 418 00:18:52,333 --> 00:18:55,125 ...with probably 16 million, 15 million people 419 00:18:55,208 --> 00:18:57,458 -in the United States working retail. -Yeah. 420 00:18:57,542 --> 00:18:59,000 And this technology, 421 00:18:59,083 --> 00:19:01,041 -if you wanna think of it as that... -Yeah, it's interesting. 422 00:19:01,125 --> 00:19:03,125 ...replaced a different kind of retail. 423 00:19:03,208 --> 00:19:05,792 Mm-hmm. So, could you see a mall like this 424 00:19:05,875 --> 00:19:07,959 an example of kind of creative destruction? 425 00:19:08,041 --> 00:19:10,667 I mean, you can see the destruction. 426 00:19:11,667 --> 00:19:14,083 How could you make a living doing that? 427 00:19:14,166 --> 00:19:16,125 You know, the Hat World. 428 00:19:16,208 --> 00:19:17,417 Hat World. Well, there's two. 429 00:19:17,500 --> 00:19:18,959 There's Hat World and Hat Station! 430 00:19:19,041 --> 00:19:21,417 -You know, they're competing against each other. -Yeah. 431 00:19:21,500 --> 00:19:22,458 And now, 432 00:19:22,542 --> 00:19:25,000 -it almost seems quaint. -Mm-hmm. 433 00:19:25,083 --> 00:19:29,667 From the '80s and the '90s, and into the 2000s, 434 00:19:29,750 --> 00:19:33,458 if you had, say, a college degree, 435 00:19:33,542 --> 00:19:35,417 the technology has been great. 436 00:19:35,500 --> 00:19:38,208 -Mm-hmm. -And it's allowed you to increase your income a lot. 437 00:19:38,291 --> 00:19:41,166 -That's right, yeah. -If you're the financial guy, 438 00:19:41,250 --> 00:19:44,667 the number of deals you can do has expanded exponentially 439 00:19:44,750 --> 00:19:47,667 as the computing power has gone up. 440 00:19:47,750 --> 00:19:52,208 Those same technologies have come at the expense 441 00:19:52,291 --> 00:19:55,834 -of expensive physical labor. -Mm-hmm. 442 00:19:55,917 --> 00:19:58,208 That's the first thing they tried to replace. 443 00:19:58,291 --> 00:20:01,500 ♪ ♪ 444 00:20:01,583 --> 00:20:04,959 Andavolu: One virtue of technology is that it's impersonal. 445 00:20:05,041 --> 00:20:07,000 It's an equal opportunity disruptor. 446 00:20:07,083 --> 00:20:10,792 So, even as automation and AI hit lower-wage jobs, 447 00:20:10,875 --> 00:20:13,583 they're coming for higher-wage jobs too. 448 00:20:13,667 --> 00:20:17,250 And the people at this MIT conference are pretty excited about it. 449 00:20:17,333 --> 00:20:20,041 (applause) 450 00:20:20,125 --> 00:20:21,875 Eric Schmidt: The biggest, I think, focus 451 00:20:21,959 --> 00:20:24,208 for a while is gonna be AI acceleration. 452 00:20:24,291 --> 00:20:27,041 Basically, can we use machine learning and AI 453 00:20:27,125 --> 00:20:28,875 in fields that have not had it so far? 454 00:20:28,959 --> 00:20:30,250 What do we have to do? Let's fund them, 455 00:20:30,333 --> 00:20:31,834 let's hire the people, and so forth, 456 00:20:31,917 --> 00:20:34,583 to bring those tools and techniques to science. 457 00:20:36,458 --> 00:20:38,083 McAfee: Most of us walk around 458 00:20:38,166 --> 00:20:40,917 with this implicit rule of thumb in our heads 459 00:20:41,000 --> 00:20:44,041 about how we should divide up all the work that needs to get done 460 00:20:44,125 --> 00:20:45,583 between human beings and machines. 461 00:20:45,667 --> 00:20:48,625 It says, look, the machines are better than us at arithmetic. 462 00:20:48,709 --> 00:20:50,625 They're better at transaction processing. 463 00:20:50,709 --> 00:20:52,375 They're better at record keeping. 464 00:20:52,458 --> 00:20:55,417 They're better at all this low-level detail stuff than we are. 465 00:20:55,500 --> 00:20:58,166 Awesome. Give all that work to the machines. 466 00:20:58,250 --> 00:21:00,750 Let the human beings do the judgment jobs, 467 00:21:00,834 --> 00:21:03,083 the communication jobs, the pattern-matching jobs. 468 00:21:03,166 --> 00:21:05,250 When I think about the progress that we're seeing 469 00:21:05,333 --> 00:21:07,750 with AI and machine learning right now, 470 00:21:07,834 --> 00:21:10,333 that progress is calling into question 471 00:21:10,417 --> 00:21:13,250 that rule of thumb in a really profound way. 472 00:21:13,333 --> 00:21:14,834 -Right. -Because what we're seeing 473 00:21:14,917 --> 00:21:16,500 over and over is that the computers 474 00:21:16,583 --> 00:21:18,375 are better at pattern matching than we are, 475 00:21:18,458 --> 00:21:19,917 even the expert human beings, 476 00:21:20,000 --> 00:21:22,208 and actually they've got better judgment. 477 00:21:22,291 --> 00:21:24,333 ♪ ♪ 478 00:21:24,417 --> 00:21:25,750 Andavolu: Judgment calls are basically 479 00:21:25,834 --> 00:21:27,917 all we do at the office every day. 480 00:21:28,000 --> 00:21:31,458 We take the facts at hand, run them through past experiences, 481 00:21:31,542 --> 00:21:34,041 give them a gut check, and then execute. 482 00:21:34,125 --> 00:21:39,291 And these days, we're offloading judgment calls to computers all the time. 483 00:21:39,375 --> 00:21:41,000 Whether it's to take the subway, 484 00:21:41,083 --> 00:21:43,375 take the streets, or take the highway, 485 00:21:43,458 --> 00:21:46,458 or what to binge-watch next. 486 00:21:46,542 --> 00:21:48,542 And what's behind these decision-making tools 487 00:21:48,625 --> 00:21:51,208 is a technology called machine learning. 488 00:21:51,291 --> 00:21:53,041 Some machine learning relies on programmers 489 00:21:53,125 --> 00:21:55,625 presetting the rules of the game, like chess. 490 00:21:55,709 --> 00:21:57,667 Newswoman: As world champion Garry Kasparov 491 00:21:57,750 --> 00:21:59,250 walked away from the match, 492 00:21:59,333 --> 00:22:01,875 never looking back at the computer that just beat him. 493 00:22:01,959 --> 00:22:04,291 Others utilize what's called neural networks 494 00:22:04,375 --> 00:22:07,667 to figure out the rules for themselves, like a baby. 495 00:22:07,750 --> 00:22:10,166 This is called deep learning. 496 00:22:10,250 --> 00:22:13,667 However it's done, programmers use other recent innovations, 497 00:22:13,750 --> 00:22:15,875 like natural language processing, 498 00:22:15,959 --> 00:22:18,458 image recognition and speech recognition 499 00:22:18,542 --> 00:22:22,250 to take the messy world as we know it, and shove it into the machine. 500 00:22:22,333 --> 00:22:26,417 And the machine can process more data, more dimensions of data, 501 00:22:26,500 --> 00:22:29,500 more outcomes from the past, more everything, 502 00:22:29,583 --> 00:22:32,875 and could even go from helping making judgments in real time 503 00:22:32,959 --> 00:22:35,500 to making predictions about the future. 504 00:22:35,583 --> 00:22:40,834 With vast amounts of data available in the legal, medical, and financial world, 505 00:22:40,917 --> 00:22:45,458 and the tools to shove them all into the computer, the machines are coming. 506 00:22:45,542 --> 00:22:47,250 ♪ ♪ 507 00:22:51,834 --> 00:22:54,083 Gianna visited a tech company called LawGeex 508 00:22:54,166 --> 00:22:56,500 to see whether their new machine-learning software 509 00:22:56,583 --> 00:22:59,041 could put lawyers on the chopping block. 510 00:22:59,125 --> 00:23:01,041 We've built an AI engine 511 00:23:01,125 --> 00:23:04,458 that was trained after reviewing 512 00:23:04,542 --> 00:23:05,875 many, many, many different contracts. 513 00:23:05,959 --> 00:23:09,667 -How many? Wow. -Tens of thousands, even more. 514 00:23:09,750 --> 00:23:11,083 We decided to focus on 515 00:23:11,166 --> 00:23:13,709 automating the review and approval of contracts. 516 00:23:13,792 --> 00:23:17,166 So, simplifying and making that process faster. 517 00:23:17,250 --> 00:23:19,792 The actual analysis that happens on the back end 518 00:23:19,875 --> 00:23:21,000 takes a couple of seconds. 519 00:23:21,083 --> 00:23:23,291 The work is actually going through the report 520 00:23:23,375 --> 00:23:24,750 that the system generates, 521 00:23:24,834 --> 00:23:27,542 and then fixing whatever issue that is found. 522 00:23:27,625 --> 00:23:30,000 So, the system has flagged all of these items for you. 523 00:23:30,083 --> 00:23:31,250 -Bechor: Exactly. -Toboni: Okay. 524 00:23:31,333 --> 00:23:32,458 Some of them are marked red, 525 00:23:32,542 --> 00:23:34,500 meaning that they don't match my policy, 526 00:23:34,583 --> 00:23:36,125 and some of them are marked green, 527 00:23:36,208 --> 00:23:37,875 meaning that they do match my policy. 528 00:23:37,959 --> 00:23:40,917 It gives me all of the guidelines about what 529 00:23:41,000 --> 00:23:43,834 the problem means, and also what I need to do in order to fix it, 530 00:23:43,917 --> 00:23:45,917 but then I fix the problem. 531 00:23:46,000 --> 00:23:49,375 So, didn't spell-check start doing this in the '90s? 532 00:23:49,458 --> 00:23:50,875 -Okay, that's... -(both laugh) 533 00:23:50,959 --> 00:23:52,458 -Like, how is this different? -Yeah. 534 00:23:52,542 --> 00:23:54,709 The way LawGeex works is very, very different. 535 00:23:54,792 --> 00:23:56,583 It actually looks at the text, 536 00:23:56,667 --> 00:23:58,709 understands the meaning behind the text. 537 00:23:58,792 --> 00:24:00,417 -Oh, wow. -Very similar 538 00:24:00,500 --> 00:24:02,709 to how a human lawyer would review it, right? 539 00:24:02,792 --> 00:24:05,917 The only difference is that with the AI system, 540 00:24:06,000 --> 00:24:08,000 it never forgets, 541 00:24:08,083 --> 00:24:09,709 it doesn't get tired, 542 00:24:09,792 --> 00:24:11,709 and it doesn't need to drink coffee. 543 00:24:11,792 --> 00:24:13,083 -Hey. Gianna. -Tunji. 544 00:24:13,166 --> 00:24:14,500 -Nice to meet you. -How's it going? 545 00:24:15,083 --> 00:24:16,583 -You ready for this? -Yeah, I think so. 546 00:24:16,667 --> 00:24:18,917 -Let's do this. -All right. I feel like John Henry. 547 00:24:19,000 --> 00:24:20,208 (laughs) 548 00:24:20,291 --> 00:24:22,750 So, Tunji, you're going up against this AI system 549 00:24:22,834 --> 00:24:25,875 with Noory to spot legal issues in two NDAs. 550 00:24:25,959 --> 00:24:29,375 We're rating you guys on both speed and accuracy. 551 00:24:29,458 --> 00:24:31,583 And because this isn't a commercial for LawGeex, 552 00:24:31,667 --> 00:24:33,041 I need you to try your hardest 553 00:24:33,125 --> 00:24:35,125 -to beat this computer. -I'm on it. 554 00:24:35,208 --> 00:24:37,333 All righty. On your marks, 555 00:24:37,417 --> 00:24:38,792 get set, 556 00:24:38,875 --> 00:24:39,625 go! 557 00:24:39,709 --> 00:24:41,917 ♪ ♪ 558 00:24:47,083 --> 00:24:48,709 (clicking) 559 00:24:52,208 --> 00:24:54,792 (quietly): So, we snuck a little phrase into this contract. 560 00:24:54,875 --> 00:24:57,333 It says, "In case of any breach by the recipient 561 00:24:57,417 --> 00:24:58,792 "of any obligations under this agreement, 562 00:24:58,875 --> 00:25:01,458 "the recipient will pay Vice News a penalty 563 00:25:01,542 --> 00:25:03,875 of $15,000 per event." 564 00:25:03,959 --> 00:25:05,166 So, we're gonna see if Tunji 565 00:25:05,250 --> 00:25:07,625 and the AI system can find it. 566 00:25:08,250 --> 00:25:10,917 ♪ ♪ 567 00:25:15,583 --> 00:25:19,041 ♪ ♪ 568 00:25:19,834 --> 00:25:22,500 Poor Tunji's just still working away over here. 569 00:25:22,583 --> 00:25:24,250 So far, it's taken him more than 570 00:25:24,333 --> 00:25:26,333 double the time that it took the computer. 571 00:25:26,417 --> 00:25:29,542 And meanwhile, Noory and I just got a coffee. 572 00:25:29,625 --> 00:25:31,000 He's having a meeting right now. 573 00:25:31,083 --> 00:25:34,875 Pretty clear just how much time this technology saves. 574 00:25:37,458 --> 00:25:38,625 Done. 575 00:25:40,417 --> 00:25:41,750 Toboni: Okay, guys. 576 00:25:41,834 --> 00:25:44,041 -The results are in. -Tunji: Okay. 577 00:25:44,125 --> 00:25:48,834 LawGeex: 95% on the first NDA. 578 00:25:48,917 --> 00:25:51,000 Tunji, 85%. 579 00:25:51,083 --> 00:25:52,709 Second NDA, 580 00:25:52,792 --> 00:25:54,875 95% for the computer, 581 00:25:54,959 --> 00:25:56,875 83% for Tunji. 582 00:25:56,959 --> 00:25:58,375 You don't seem disappointed. 583 00:25:58,458 --> 00:26:00,709 I wasn't disappointed when the iPhone came out 584 00:26:00,792 --> 00:26:02,000 and I could do more things 585 00:26:02,083 --> 00:26:03,291 with this new piece of technology. 586 00:26:03,375 --> 00:26:05,083 So, this is exciting to me. 587 00:26:05,166 --> 00:26:07,041 So, Noory, did the computer catch 588 00:26:07,125 --> 00:26:09,375 the phrase we put in there about 589 00:26:09,458 --> 00:26:10,875 Vice News? 590 00:26:10,959 --> 00:26:12,083 Yeah, the computer caught it, 591 00:26:12,166 --> 00:26:14,041 and I was able to strike it out. 592 00:26:14,125 --> 00:26:15,542 Tunji, did you catch it? 593 00:26:15,625 --> 00:26:18,333 I straight up missed it. I didn't see it at all. 594 00:26:18,417 --> 00:26:20,041 We take cash, check, 595 00:26:20,125 --> 00:26:22,417 whatever you have. (laughs) Just kidding. 596 00:26:22,500 --> 00:26:25,750 So, McKinsey says that 22% of a lawyer's job, 597 00:26:25,834 --> 00:26:27,750 35% of a paralegal's job, 598 00:26:27,834 --> 00:26:30,417 can now, today, be automated. 599 00:26:30,500 --> 00:26:32,959 So then, what happens? These jobs don't go away? 600 00:26:33,041 --> 00:26:34,500 People just take longer lunch breaks 601 00:26:34,583 --> 00:26:36,125 or take on more clients or what? 602 00:26:36,208 --> 00:26:38,208 We're kidding ourselves if we think that 603 00:26:38,291 --> 00:26:39,709 things are not going to change. 604 00:26:39,792 --> 00:26:42,500 But similar to pilots with the autopilot, 605 00:26:42,583 --> 00:26:44,792 it's not like we don't need pilots anymore. 606 00:26:44,875 --> 00:26:46,709 Could this technology pass a bar? 607 00:26:46,792 --> 00:26:47,583 No. 608 00:26:47,667 --> 00:26:49,417 Okay, so we still need lawyers 609 00:26:49,500 --> 00:26:51,667 to be signing off on these legal documents, 610 00:26:51,750 --> 00:26:53,542 even if they're not doing the nitty-gritty of them. 611 00:26:53,625 --> 00:26:55,458 Absolutely. Defining the policy, 612 00:26:55,542 --> 00:26:58,500 serving escalation points, handling negotiation, 613 00:26:58,583 --> 00:27:01,458 and then also handling more complex contract categories. 614 00:27:01,542 --> 00:27:04,125 -Mm-hmm. -You have a new generation of lawyers 615 00:27:04,208 --> 00:27:05,750 that are much more tech-savvy. 616 00:27:05,834 --> 00:27:08,792 The ones that can actually leverage technology 617 00:27:08,875 --> 00:27:12,917 are the ones that manage to prosper. 618 00:27:13,000 --> 00:27:17,458 Andavolu: Unlike the law, medicine has always been intertwined with technology. 619 00:27:17,542 --> 00:27:20,125 But it's relationship with robotics isn't just graceful, 620 00:27:20,208 --> 00:27:22,083 it's miraculous. 621 00:27:22,166 --> 00:27:23,875 Toboni: What is the most groundbreaking thing 622 00:27:23,959 --> 00:27:26,166 about how far we've come with robotic surgery? 623 00:27:26,250 --> 00:27:28,792 So, robotic surgery allows us to really 624 00:27:28,875 --> 00:27:31,417 treat tissue in a more delicate way... 625 00:27:31,500 --> 00:27:32,792 (whirring) 626 00:27:32,875 --> 00:27:35,208 ...allow us to be much more efficient in suturing, 627 00:27:35,291 --> 00:27:36,458 decreasing bleeding. 628 00:27:36,542 --> 00:27:39,083 We are improving outcomes, 629 00:27:39,166 --> 00:27:40,792 shortening hospital stay, 630 00:27:40,875 --> 00:27:43,709 and that's why we're using more and more now. 631 00:27:43,792 --> 00:27:45,667 So, the robot is enabling surgeons, 632 00:27:45,750 --> 00:27:48,125 and by enabling surgeons is giving 633 00:27:48,208 --> 00:27:50,583 access to more patients to minimal invasive surgery. 634 00:27:51,333 --> 00:27:52,667 Horgan: Okay, we're good to go. 635 00:27:52,750 --> 00:27:54,792 ♪ ♪ 636 00:27:54,875 --> 00:27:55,875 Toboni: That's great, right? 637 00:27:55,959 --> 00:27:57,500 'Cause one of the huge problems 638 00:27:57,583 --> 00:28:00,667 in our health care system is that not enough patients are 639 00:28:00,750 --> 00:28:02,834 getting seen when they need to be seen. 640 00:28:02,917 --> 00:28:05,041 Not only that, not every patient get the same care. 641 00:28:05,125 --> 00:28:07,500 You may have great surgeons in one area, 642 00:28:07,583 --> 00:28:09,834 but not in other areas with the same experience. 643 00:28:09,917 --> 00:28:12,458 The robot is flattening that. 644 00:28:12,542 --> 00:28:13,834 -We're ready? -Man: Ready. 645 00:28:13,917 --> 00:28:17,500 ♪ ♪ 646 00:28:17,583 --> 00:28:19,625 Horgan: I think that the biggest groundbreaking part 647 00:28:19,709 --> 00:28:23,417 is the fact that I am operating on a console, 648 00:28:23,500 --> 00:28:25,667 and the system is on the patient. 649 00:28:25,750 --> 00:28:27,583 We have all the degrees of articulation 650 00:28:27,667 --> 00:28:28,875 you would have in your hand 651 00:28:28,959 --> 00:28:30,667 inside the abdomen. 652 00:28:30,750 --> 00:28:32,375 That's revolutionary. 653 00:28:32,458 --> 00:28:34,792 Man: Geez. That's close. 654 00:28:36,417 --> 00:28:37,542 Horgan: Okay! 655 00:28:37,625 --> 00:28:40,709 I'm gonna go out and have my espresso. 656 00:28:40,792 --> 00:28:44,542 Toboni: The way robotic surgery is evolving, do you see any jobs, 657 00:28:44,625 --> 00:28:48,208 like the job of a technician or a physician's assistant going away? 658 00:28:48,291 --> 00:28:50,166 No, but what we have seen is the opposite-- 659 00:28:50,250 --> 00:28:52,709 is them being much more involved. 660 00:28:52,792 --> 00:28:54,250 Okay, stapler. 661 00:28:54,333 --> 00:28:55,959 Assistant: Loading up the seam guard. 662 00:28:56,041 --> 00:28:58,208 Horgan: You need a nurse that knows how to move it around. 663 00:28:58,291 --> 00:29:01,083 You need a scrub tech that knows how to load the instruments, 664 00:29:01,166 --> 00:29:03,166 how to clean the camera, and how to move the arm, 665 00:29:03,250 --> 00:29:04,917 -how to undock. -Toboni: Right. 666 00:29:05,583 --> 00:29:07,000 Horgan: I like it a lot there. What do you think? 667 00:29:07,083 --> 00:29:09,083 -Assistant: Yeah, looks good. -Horgan: Only good? 668 00:29:09,166 --> 00:29:11,166 -Assistant: Perfect. Unbelievable. -Horgan: Eh? 669 00:29:11,250 --> 00:29:12,875 Horgan: Look at that. 670 00:29:12,959 --> 00:29:15,166 Toboni: How do you think automation will 671 00:29:15,250 --> 00:29:17,166 evolve in this area? 672 00:29:17,250 --> 00:29:20,041 The anatomy changes from patient to patient, 673 00:29:20,125 --> 00:29:21,792 but with machine learning, 674 00:29:21,875 --> 00:29:24,834 the system will be able to recognize different tissues. 675 00:29:24,917 --> 00:29:28,083 -Mm. -And I'm sure that in the next year, we'll see the system 676 00:29:28,166 --> 00:29:31,583 -telling you that's cancer, that's not cancer. -Wow. 677 00:29:31,667 --> 00:29:33,709 And that's where the benefits are gonna be. 678 00:29:34,375 --> 00:29:36,834 ♪ ♪ 679 00:29:36,917 --> 00:29:38,792 Andavolu: Finance has been cashing in on the benefits 680 00:29:38,875 --> 00:29:40,417 of machine learning for years, 681 00:29:40,500 --> 00:29:41,959 hiring so many programmers 682 00:29:42,041 --> 00:29:45,083 that it's virtually indistinguishable from tech. 683 00:29:45,166 --> 00:29:47,250 Michael Moynihan visited Goldman Sachs 684 00:29:47,333 --> 00:29:50,417 to see how they're leveraging AI's predictive capabilities 685 00:29:50,500 --> 00:29:52,250 to make more money. 686 00:29:52,333 --> 00:29:55,125 How has your job changed, 687 00:29:55,208 --> 00:29:56,959 and how has this industry changed? 688 00:29:57,041 --> 00:30:00,166 Over time, there's been a lot of automation. 689 00:30:00,250 --> 00:30:02,291 I think it lends itself naturally to trading, right? 690 00:30:02,375 --> 00:30:04,166 If I'm trading Google, 691 00:30:04,250 --> 00:30:05,625 if I have to make a decision whether 692 00:30:05,709 --> 00:30:07,500 I'm going to buy it or sell it at a certain price, 693 00:30:07,583 --> 00:30:10,792 there are hundreds of variables that go into that decision. 694 00:30:10,875 --> 00:30:13,041 And you can code an algorithm 695 00:30:13,125 --> 00:30:15,458 to assess all those variables, 696 00:30:15,542 --> 00:30:17,125 and when you swing that bat 1,000 times, 697 00:30:17,208 --> 00:30:19,500 it's going to do it more efficiently than a human would. 698 00:30:19,583 --> 00:30:23,959 -(bell ringing) -So, when you look on the floor of the New York Stock Exchange, 699 00:30:24,041 --> 00:30:26,458 there aren't a lot of traders down there anymore. 700 00:30:26,542 --> 00:30:29,458 Moynihan: If I went down there today, what would I see? 701 00:30:29,542 --> 00:30:31,291 Levine: You wouldn't see a lot of people. 702 00:30:31,375 --> 00:30:33,083 You might see them clustered. 703 00:30:33,166 --> 00:30:35,000 Moynihan: Little tiny clusters of people? 704 00:30:35,083 --> 00:30:37,083 Levine: It'll be a cluster. 705 00:30:37,166 --> 00:30:39,542 Moynihan: So, to be in this industry now, 706 00:30:39,625 --> 00:30:41,959 do you need to understand 707 00:30:42,041 --> 00:30:45,000 lines of code and what they do and how to produce it? 708 00:30:45,083 --> 00:30:46,750 Sinead Strain: I'd say more and more, yes. 709 00:30:46,834 --> 00:30:49,083 If we look at this firm, 710 00:30:49,166 --> 00:30:52,291 the number of engineers, technologists that we have here, 711 00:30:52,375 --> 00:30:54,834 we're probably the largest division within the firm. 712 00:30:54,917 --> 00:30:56,875 ♪ ♪ 713 00:30:56,959 --> 00:30:58,208 So, you have a math background? 714 00:30:58,291 --> 00:30:59,917 Yeah, math and computer science. 715 00:31:00,000 --> 00:31:02,375 -But yours is economics and computer science? -Yes. 716 00:31:02,458 --> 00:31:04,166 Moynihan: But you learned this on the fly? 717 00:31:04,250 --> 00:31:05,542 Jameson Schriber: Yeah, in school or here. 718 00:31:05,625 --> 00:31:08,166 More likely, most of the things were picked up here. 719 00:31:08,250 --> 00:31:10,333 Can I see like, you know, an algorithm, 720 00:31:10,417 --> 00:31:12,291 give me some sense of code? 721 00:31:12,375 --> 00:31:14,166 Phillips: I'll show you a really simple example. 722 00:31:14,250 --> 00:31:17,208 So, in this case, I'm gonna run a volatility function, 723 00:31:17,291 --> 00:31:20,375 essentially an algorithm, and this is showing me now, 724 00:31:20,458 --> 00:31:22,208 on a 22-day rolling day basis, 725 00:31:22,291 --> 00:31:24,709 what's the volatility level of the SandP 500. 726 00:31:24,792 --> 00:31:26,875 Essentially, we would write code to do that. 727 00:31:26,959 --> 00:31:28,375 So, in this library, 728 00:31:28,458 --> 00:31:30,875 this is the volatility function. It's actually fairly simple. 729 00:31:30,959 --> 00:31:32,709 -But, we-- -Wait, I'm sorry. 730 00:31:32,792 --> 00:31:34,291 That's fairly simple? 731 00:31:34,375 --> 00:31:35,500 This is all documentation. 732 00:31:35,583 --> 00:31:37,250 This is actually a fairly simple algorithm. 733 00:31:37,333 --> 00:31:40,125 It's essentially the code that's generating what you're seeing. 734 00:31:41,083 --> 00:31:43,083 Looks terribly complicated to me. 735 00:31:43,166 --> 00:31:45,166 People like you guys still 736 00:31:45,250 --> 00:31:47,208 need to exist to create these things, right? 737 00:31:47,291 --> 00:31:49,125 I mean, are they self-sustaining? 738 00:31:49,208 --> 00:31:51,834 Or can you write yourself out of a job? 739 00:31:51,917 --> 00:31:54,500 -I think we're... Yeah. -I think that would be hard. 740 00:31:54,583 --> 00:31:56,291 ♪ ♪ 741 00:31:56,375 --> 00:31:58,208 Moynihan: Formerly a tech CEO, 742 00:31:58,291 --> 00:32:00,458 Marty Chavez is now global co-head 743 00:32:00,542 --> 00:32:03,041 of Goldman's securities division. 744 00:32:03,125 --> 00:32:05,333 It strikes me, obviously, that this is an industry 745 00:32:05,417 --> 00:32:08,583 that has been on the forefront of using 746 00:32:08,667 --> 00:32:11,417 AI, computers, to... 747 00:32:11,500 --> 00:32:13,917 you know, make big decisions and make a lot of money. 748 00:32:14,000 --> 00:32:17,542 What has that done to the kind of, you know, 749 00:32:17,625 --> 00:32:20,917 the job market within-- even within this company. 750 00:32:21,000 --> 00:32:26,542 It's creating new jobs that couldn't have existed before. 751 00:32:26,625 --> 00:32:28,542 Whole new businesses 752 00:32:28,625 --> 00:32:31,041 now exist for us and out in the world 753 00:32:31,125 --> 00:32:32,917 that wouldn't have been possible 754 00:32:33,000 --> 00:32:34,750 without the technologies 755 00:32:34,834 --> 00:32:36,500 that have arisen over the past few years, 756 00:32:36,583 --> 00:32:38,959 whether they're machine learning, cloud services, 757 00:32:39,041 --> 00:32:40,250 open-source, right? 758 00:32:40,333 --> 00:32:43,125 All of those activities go into, 759 00:32:43,208 --> 00:32:45,709 for instance, our new consumer 760 00:32:45,792 --> 00:32:48,041 lending and deposit taking activity. 761 00:32:51,542 --> 00:32:53,500 You know the counterargument to this, don't you? 762 00:32:53,583 --> 00:32:56,083 These are jobs that are being created for smart people, 763 00:32:56,166 --> 00:32:58,667 -educated people, rich people. -Mm-hmm. 764 00:32:58,750 --> 00:33:01,625 Other people out there, who are being made redundant by robots, 765 00:33:01,709 --> 00:33:03,542 don't have the skills. 766 00:33:03,625 --> 00:33:06,500 -It's only for a rarefied few. -Mm. 767 00:33:06,583 --> 00:33:08,250 How do you respond to that? 768 00:33:08,333 --> 00:33:10,500 Technological change is disruptive, 769 00:33:10,583 --> 00:33:12,250 and there's a lot of pain, 770 00:33:12,333 --> 00:33:15,625 and it's something that we must concern ourselves with. 771 00:33:15,709 --> 00:33:18,000 What do you do during the disruption, 772 00:33:18,083 --> 00:33:23,291 which has been continuous since the agricultural and industrial Revolutions, 773 00:33:23,375 --> 00:33:25,709 and I expect it will continue 774 00:33:25,792 --> 00:33:28,667 and will accelerate in all likelihood. 775 00:33:28,750 --> 00:33:31,959 And so, sitting back and complaining about it, 776 00:33:32,041 --> 00:33:34,125 sitting back and doing nothing about it, 777 00:33:34,208 --> 00:33:35,792 don't seem to be options. 778 00:33:35,875 --> 00:33:37,667 At the same time, I don't think the answer is 779 00:33:37,750 --> 00:33:41,375 -to stop the progression of technology. -Mm. 780 00:33:41,458 --> 00:33:43,000 Haven't seen that work. 781 00:33:43,083 --> 00:33:46,125 (brakes screeching) 782 00:33:47,583 --> 00:33:51,208 (PA announcement): This is a Queens-bound M local train. 783 00:33:51,291 --> 00:33:54,041 The next stop is Myrtle Avenue. 784 00:33:55,542 --> 00:33:58,542 It's worth noting at this point that even if AI tools 785 00:33:58,625 --> 00:34:01,834 are better at making decisions, high-level judgment jobs 786 00:34:01,917 --> 00:34:04,375 aren't about to be automated away any time soon. 787 00:34:04,458 --> 00:34:06,709 And let's be clear, the people who are 788 00:34:06,792 --> 00:34:09,834 most excited about AI's creative potential 789 00:34:09,917 --> 00:34:11,709 aren't the ones getting replaced. 790 00:34:11,792 --> 00:34:15,208 So the question becomes, as this disruption accelerates, 791 00:34:15,291 --> 00:34:18,792 how do you benefit from it if you're not already rich, 792 00:34:18,875 --> 00:34:21,750 or white, or male, or have a spot at the top? 793 00:34:22,875 --> 00:34:24,709 Let's put it this way. 794 00:34:24,792 --> 00:34:27,834 It's a lot easier for all of us to get directions, 795 00:34:27,917 --> 00:34:29,625 but it's becoming increasingly hard 796 00:34:29,709 --> 00:34:33,125 for most people to find their way to a stable career. 797 00:34:33,208 --> 00:34:35,291 And there are not a lot of people whining about it. 798 00:34:35,375 --> 00:34:38,125 There are a lot of people who are racing to catch up. 799 00:34:39,834 --> 00:34:42,583 ♪ ♪ 800 00:34:42,667 --> 00:34:44,500 We visited a class at Per Scholas, 801 00:34:44,583 --> 00:34:46,750 a non-profit that skills up people 802 00:34:46,834 --> 00:34:48,083 in New York and other cities 803 00:34:48,166 --> 00:34:50,041 around the country for free. 804 00:34:50,125 --> 00:34:51,792 (indistinct chatter) 805 00:34:55,166 --> 00:34:57,500 (chatter continuing) 806 00:35:00,333 --> 00:35:03,834 Student: I have had just about every terrible job you can imagine. 807 00:35:03,917 --> 00:35:06,917 Fast food, grocery store, stock clerk. 808 00:35:07,000 --> 00:35:10,792 I was a supervisor at a retail store while I was in college. 809 00:35:10,875 --> 00:35:13,625 I was a mechanic. I worked in the industry for over 10 years. 810 00:35:13,709 --> 00:35:15,291 Then I went to substitute teaching. 811 00:35:15,375 --> 00:35:17,834 Then I went into solar, doing sales. 812 00:35:17,917 --> 00:35:21,166 And then I was a writer, and then I became an English teacher. 813 00:35:21,250 --> 00:35:22,583 Reservation sales agent. 814 00:35:22,667 --> 00:35:24,375 Customer service, marketing. 815 00:35:24,458 --> 00:35:27,083 Now I am here at Per Scholas. 816 00:35:27,166 --> 00:35:29,959 -I should do that in CodePen, right? -Yeah. 817 00:35:30,041 --> 00:35:32,917 The job market is really shifting towards a gig economy. 818 00:35:33,000 --> 00:35:35,083 What do you have that you can work for yourself, 819 00:35:35,166 --> 00:35:36,667 or work for someone else? 820 00:35:36,750 --> 00:35:38,667 A year ago, I would've told you that I was gonna go 821 00:35:38,750 --> 00:35:42,125 to grad school and get a PhD and all that good stuff. 822 00:35:42,208 --> 00:35:44,291 But the reality is when I graduated, 823 00:35:44,375 --> 00:35:45,709 and I was looking for jobs, 824 00:35:45,792 --> 00:35:47,291 one of the main things that kept popping up 825 00:35:47,375 --> 00:35:49,375 was software engineer, coding, tech. 826 00:35:49,458 --> 00:35:52,542 Do you think you would've made the move if this place wasn't free? 827 00:35:53,291 --> 00:35:54,291 No. (laughs) 828 00:35:54,375 --> 00:35:56,208 Because just graduated college, 829 00:35:56,291 --> 00:35:57,959 so, you know, Sally Mae is still knocking on my door. 830 00:35:58,041 --> 00:35:59,417 -They're knocking on my door right now. -(knocking on door) 831 00:35:59,500 --> 00:36:02,083 -Is that them? -You see? 832 00:36:02,166 --> 00:36:04,667 -(laughter) -I just said their name, and here they are. 833 00:36:04,750 --> 00:36:06,667 When I put the comma, right? 834 00:36:06,750 --> 00:36:09,667 And I put the P, does that mean parent and P? 835 00:36:09,750 --> 00:36:11,291 -I'm a mom of three. -Mm-hmm. 836 00:36:11,375 --> 00:36:14,208 So, my last job, I was working at a busy call center, 837 00:36:14,291 --> 00:36:17,166 and I was in a place where I felt you know, undervalued. 838 00:36:17,250 --> 00:36:19,166 Robots was gonna come and take my job, 839 00:36:19,250 --> 00:36:20,667 and I would've been without a job, 840 00:36:20,750 --> 00:36:24,000 and what would I've been able to pass onto my children? 841 00:36:24,083 --> 00:36:25,291 "Hi, how are you doing?" 842 00:36:25,375 --> 00:36:27,333 With this, I can give them a skill. 843 00:36:27,417 --> 00:36:30,333 They can be in a better place in the next 10, 20 years, 844 00:36:30,417 --> 00:36:33,667 opposed to how long it took for me to figure this out. 845 00:36:33,750 --> 00:36:37,125 Do you think you're at a disadvantage as far as the job market itself? 846 00:36:37,208 --> 00:36:40,125 You look at the tech industry, and it's like, 847 00:36:40,208 --> 00:36:42,333 other than south Asians, east Asians, 848 00:36:42,417 --> 00:36:44,583 it's like, there are not a lot of people of color. 849 00:36:44,667 --> 00:36:46,250 Yeah, I wanted to touch on that. 850 00:36:46,333 --> 00:36:48,375 This is actually why I decided to specifically 851 00:36:48,458 --> 00:36:50,166 go into coding because this is one where, 852 00:36:50,250 --> 00:36:52,208 at the end of the day, it's just 853 00:36:52,291 --> 00:36:55,792 how good your applications are, your codes are. 854 00:36:55,875 --> 00:36:57,834 Growing up, I used to think this was just magic, 855 00:36:57,917 --> 00:37:02,458 or like it's done by all the smart kids in California, you know? 856 00:37:02,542 --> 00:37:04,750 So, I wanna prove that it can be done 857 00:37:04,834 --> 00:37:08,375 by some kid in Queens who just wanted to do it. 858 00:37:08,458 --> 00:37:11,000 (indistinct chatter) 859 00:37:11,083 --> 00:37:13,000 Kelly Richardson: The perfect world is 860 00:37:13,083 --> 00:37:15,583 that we have enough investment 861 00:37:15,667 --> 00:37:17,709 that we could grow to meet 862 00:37:17,792 --> 00:37:20,041 -both the size of the demand and the size of the supply. -Mm-hmm. 863 00:37:20,125 --> 00:37:24,291 Richardson: We have more employer partners willing to hire than we have graduates for. 864 00:37:24,375 --> 00:37:25,917 We have more students or applicants 865 00:37:26,000 --> 00:37:28,500 -applying to Per Scholas than we have spots for. -Right. 866 00:37:28,583 --> 00:37:30,750 The constraint to our growth is resources. 867 00:37:30,834 --> 00:37:34,000 The domestic investment in workforce retraining 868 00:37:34,083 --> 00:37:36,875 -is so small, and... -Mm. 869 00:37:36,959 --> 00:37:38,834 ...the impact automation is going to have 870 00:37:38,917 --> 00:37:40,583 -is not going to be equitable. -Mm-hmm. 871 00:37:40,667 --> 00:37:42,792 That it's largely people of color, 872 00:37:42,875 --> 00:37:46,000 largely women who are in the current low-wage 873 00:37:46,083 --> 00:37:48,000 occupations that are gonna be displaced. 874 00:37:48,083 --> 00:37:50,041 There really should be some critical thinking 875 00:37:50,125 --> 00:37:52,375 and some action that legislators are taking 876 00:37:52,458 --> 00:37:54,291 to invest in programs like this. 877 00:37:54,375 --> 00:37:56,583 For decades, the federal government has repeatedly 878 00:37:56,667 --> 00:37:58,583 taken action to fund reskilling. 879 00:37:58,667 --> 00:38:01,792 I am proud today to sign into law 880 00:38:01,875 --> 00:38:03,625 the Job Training Partnership Act, 881 00:38:03,709 --> 00:38:07,125 a program that looks to the future instead of the past. 882 00:38:07,208 --> 00:38:10,417 Giving all Americans the tools they need to learn for a lifetime 883 00:38:10,500 --> 00:38:14,917 is critical to our ability to continue to grow. 884 00:38:15,000 --> 00:38:16,959 So, the bill I'm about to sign will give communities 885 00:38:17,041 --> 00:38:19,959 more certainty to invest in job training programs for the long run. 886 00:38:20,041 --> 00:38:21,291 Andavolu: But in today's dollars, 887 00:38:21,375 --> 00:38:24,417 that funding has fallen for years. 888 00:38:24,500 --> 00:38:25,667 President Trump campaigned 889 00:38:25,750 --> 00:38:27,959 on bringing jobs back to American workers. 890 00:38:28,041 --> 00:38:32,125 A Trump administration will stop the jobs from leaving America. 891 00:38:32,208 --> 00:38:35,166 Andavolu: And he signed an executive order enacting, what he calls, 892 00:38:35,250 --> 00:38:38,041 the White House's Pledge to America's Workers, 893 00:38:38,125 --> 00:38:40,417 installing his advisor and daughter, 894 00:38:40,500 --> 00:38:42,375 Ivanka Trump, to lead the charge. 895 00:38:42,458 --> 00:38:46,834 It's one of my favorite words, reskilling. Reskilling. 896 00:38:46,917 --> 00:38:49,667 We're calling upon government and the private sector 897 00:38:49,750 --> 00:38:51,709 to equip our students and workers 898 00:38:51,792 --> 00:38:55,792 with the skills they need to thrive in the modern economy. 899 00:38:55,875 --> 00:38:58,625 ♪ ♪ 900 00:39:02,750 --> 00:39:06,417 (indistinct PA announcement) 901 00:39:10,250 --> 00:39:12,083 (horn beeps) 902 00:39:12,166 --> 00:39:13,834 Andavolu: The Trump administration's strategy 903 00:39:13,917 --> 00:39:16,125 on reskilling America's workforce is a lot 904 00:39:16,208 --> 00:39:19,542 like its strategy for other big problems America is facing. 905 00:39:19,625 --> 00:39:21,500 Rather than increasing public investment, 906 00:39:21,583 --> 00:39:25,333 they prefer to see private industry fill the void. 907 00:39:25,417 --> 00:39:27,250 For the past nine months, the Trump administration 908 00:39:27,333 --> 00:39:30,542 has been twisting the arms of CEOs to promise funding 909 00:39:30,625 --> 00:39:33,917 for worker education and training. 910 00:39:34,000 --> 00:39:37,166 And so far, more than 200 companies have signed on, 911 00:39:37,250 --> 00:39:38,875 with Toyota being the latest. 912 00:39:38,959 --> 00:39:41,166 Ivanka: Toyota signed our pledge 913 00:39:41,250 --> 00:39:44,166 to America's workers for 100,000 914 00:39:44,250 --> 00:39:48,041 enhanced career opportunities, apprenticeship, um... 915 00:39:48,125 --> 00:39:49,875 -new jobs... -New development. 916 00:39:49,959 --> 00:39:51,792 New development, workforce training. 917 00:39:51,875 --> 00:39:54,834 I think James was a little bit inspired. 918 00:39:54,917 --> 00:39:57,834 He's just increased that commitment to 200,000, 919 00:39:57,917 --> 00:40:00,375 -which is unbelievably exciting. -(cheering) 920 00:40:00,458 --> 00:40:02,750 Bevin: The American worker is great, 921 00:40:02,834 --> 00:40:04,750 but the Kentucky worker 922 00:40:04,834 --> 00:40:07,333 -is greater still. -(cheering) 923 00:40:07,417 --> 00:40:09,709 (applause) 924 00:40:09,792 --> 00:40:12,041 Does government have a role? Absolutely. 925 00:40:12,125 --> 00:40:15,542 But is it to define what the workforce looks like? No. 926 00:40:15,625 --> 00:40:17,792 If the state were to develop a program 927 00:40:17,875 --> 00:40:19,542 for Toyota's workers of the future, 928 00:40:19,625 --> 00:40:21,917 it would be a failure. I'm telling you, straight up. 929 00:40:22,000 --> 00:40:23,709 Toyota knows what Toyota needs. 930 00:40:23,792 --> 00:40:27,458 But the risk, perhaps, is a reliance on a company 931 00:40:27,542 --> 00:40:30,333 that isn't owned by you and me, like the government is. 932 00:40:30,417 --> 00:40:32,709 It's owned by shareholders. Is there a problem with having 933 00:40:32,792 --> 00:40:34,625 a private response to a public problem? 934 00:40:34,709 --> 00:40:37,750 Who has more of a vested interest in getting this right? 935 00:40:37,834 --> 00:40:40,959 The government or the private company? The private company does. 936 00:40:41,041 --> 00:40:44,583 It is in the best interest of Toyota or any other company 937 00:40:44,667 --> 00:40:47,125 to train the best quality people, 938 00:40:47,208 --> 00:40:48,917 pay them as much as possible, 939 00:40:49,000 --> 00:40:51,709 give them the standard of living and the quality of life 940 00:40:51,792 --> 00:40:53,208 that makes them wanna come 941 00:40:53,291 --> 00:40:56,583 and retire 20, 30, 40 years later 942 00:40:56,667 --> 00:40:58,166 from the very same company. 943 00:40:58,250 --> 00:41:00,792 It's in the company's interest until it isn't. 944 00:41:00,875 --> 00:41:02,792 And the thing about a company is, like, we don't elect 945 00:41:02,875 --> 00:41:05,792 their executives, but we elect our government officials. 946 00:41:05,875 --> 00:41:08,458 But the idea that we're gonna rely on government 947 00:41:08,542 --> 00:41:11,000 and "elected people," to come up with rules 948 00:41:11,083 --> 00:41:13,500 for training people for jobs that you will 949 00:41:13,583 --> 00:41:16,917 volitionally want to buy the products of is crazy. 950 00:41:17,000 --> 00:41:18,291 ♪ ♪ 951 00:41:18,375 --> 00:41:19,792 Andavolu: Under the pledge, 952 00:41:19,875 --> 00:41:22,166 they're promising 200,000 new opportunities. 953 00:41:22,250 --> 00:41:23,959 They're promising to reskill 954 00:41:24,041 --> 00:41:26,625 and retrain 200,000 people. 955 00:41:26,709 --> 00:41:28,125 They have the capacity to do that. 956 00:41:28,208 --> 00:41:29,750 What happens if they break the promise? 957 00:41:29,834 --> 00:41:31,500 Again, they're gonna do their-- 958 00:41:31,583 --> 00:41:33,417 Can you mandate the government does it? 959 00:41:33,500 --> 00:41:37,166 If they don't do it, it's not like we're gonna not reelect their CEO. 960 00:41:37,250 --> 00:41:40,458 They don't do it, they might have some bad publicity-- 961 00:41:40,542 --> 00:41:42,458 You think it won't affect their CEO? 962 00:41:42,542 --> 00:41:44,291 Here's what happens. If they don't do it, 963 00:41:44,375 --> 00:41:46,500 if they're not making these investments, 964 00:41:46,583 --> 00:41:48,542 there's not a chance that they survive. 965 00:41:48,625 --> 00:41:50,333 (applause) 966 00:41:50,417 --> 00:41:53,917 ♪ ♪ 967 00:42:01,709 --> 00:42:02,834 (indistinct chatter) 968 00:42:02,917 --> 00:42:04,291 Andavolu: This is Landon and Tim, 969 00:42:04,375 --> 00:42:07,333 two buddies who work together at the Toyota factory. 970 00:42:07,417 --> 00:42:11,834 Tim's just retired, but Landon sees himself working at Toyota for decades. 971 00:42:11,917 --> 00:42:13,959 It must be kind of nice to at least know that 972 00:42:14,041 --> 00:42:16,542 at a high level, they're thinking about 973 00:42:16,625 --> 00:42:18,500 your jobs, the things that you guys do, 974 00:42:18,583 --> 00:42:19,875 the opportunities that you guys have. 975 00:42:19,959 --> 00:42:21,500 Does it strike you that way? 976 00:42:21,583 --> 00:42:23,166 Tim Smith: With Ivanka, 977 00:42:23,250 --> 00:42:25,458 I just looked and said okay, it's PR. 978 00:42:26,458 --> 00:42:28,291 Do people want to be reskilled? 979 00:42:28,375 --> 00:42:30,834 If their job depended on it, 980 00:42:30,917 --> 00:42:32,291 and they know it's coming, 981 00:42:32,375 --> 00:42:34,500 I would say sure. Yes. 982 00:42:34,583 --> 00:42:37,875 It would be depending on your personal circumstances, 983 00:42:37,959 --> 00:42:39,083 that could be very hard 984 00:42:39,166 --> 00:42:42,917 because if you work, you know, a full shift 985 00:42:43,000 --> 00:42:45,083 and you have a family, 986 00:42:45,166 --> 00:42:49,458 you may only have an hour or two of free time a day, 987 00:42:49,542 --> 00:42:52,917 are you supposed to go drive to a training facility 988 00:42:53,000 --> 00:42:57,250 and spend a few hours a day there before you go do your shift, 989 00:42:57,333 --> 00:42:59,166 -you work your shift? -Mm-hmm. 990 00:42:59,250 --> 00:43:01,625 I don't think very many people would do that. 991 00:43:02,291 --> 00:43:04,375 Do you guys like your job, or do you like working there? 992 00:43:04,458 --> 00:43:06,166 (chuckles) 993 00:43:06,834 --> 00:43:08,583 -You go, Timmy. -(laughing) 994 00:43:08,667 --> 00:43:10,834 -I actually love my job. -Yeah. 995 00:43:10,917 --> 00:43:13,125 Um, you know, because I've always 996 00:43:13,208 --> 00:43:15,250 loved working on cars, 997 00:43:15,333 --> 00:43:18,125 so my job was pretty much up my alley. 998 00:43:19,208 --> 00:43:21,291 -There are parts of my job that I like. -Mm-hmm. 999 00:43:21,375 --> 00:43:23,750 It's, you know, you're creating something. 1000 00:43:23,834 --> 00:43:25,250 There's no way it could exist 1001 00:43:25,333 --> 00:43:26,750 without someone putting it together. 1002 00:43:26,834 --> 00:43:28,792 (laughing) 1003 00:43:28,875 --> 00:43:30,792 -Exactly. -And so... 1004 00:43:30,875 --> 00:43:33,417 -Yeah, I have a wife and two daughters. -Right. 1005 00:43:33,500 --> 00:43:36,125 -I wanna spend as much time with them as I can. -Mm-hmm. 1006 00:43:36,208 --> 00:43:39,083 They're young and they're not gonna stay young for long, 1007 00:43:39,166 --> 00:43:43,250 and I hate missing the time I miss with them at work already. 1008 00:43:43,333 --> 00:43:45,875 I just wanna spend as much time as I can with them. 1009 00:43:45,959 --> 00:43:49,417 I wanna retire. That's the... that's the goal. 1010 00:43:49,500 --> 00:43:51,333 Andavolu: For people in the workforce today, 1011 00:43:51,417 --> 00:43:55,500 reskilling boils down to doing more work just to keep up. 1012 00:43:55,583 --> 00:43:58,625 To Andrew Yang, a former job creation specialist, 1013 00:43:58,709 --> 00:44:00,875 and now a long-shot presidential candidate, 1014 00:44:00,959 --> 00:44:03,834 the spotlight on reskilling hides a larger imbalance 1015 00:44:03,917 --> 00:44:05,458 between the goals of workers 1016 00:44:05,542 --> 00:44:07,458 and the goals of their employers. 1017 00:44:07,542 --> 00:44:09,542 We are so brainwashed by the market that otherwise 1018 00:44:09,625 --> 00:44:12,125 intelligent, well-meaning people will legitimately say, 1019 00:44:12,208 --> 00:44:14,125 "We should retrain the coal miners to be coders." 1020 00:44:14,208 --> 00:44:15,083 Yep. 1021 00:44:15,166 --> 00:44:16,500 Yang: We are trained to think 1022 00:44:16,583 --> 00:44:19,125 that we have no value unless 1023 00:44:19,208 --> 00:44:23,291 the market says that there's a need for what we do. 1024 00:44:23,375 --> 00:44:26,792 And so, if coal miners now have zero value, then the thought process is, 1025 00:44:26,875 --> 00:44:31,208 "Oh, we have to turn them into something that does have value. What has value? Coders!" 1026 00:44:31,291 --> 00:44:32,959 And then, 12 years from now, 1027 00:44:33,041 --> 00:44:35,500 -AI's gonna be able to do basic coding anyway. -Sure. 1028 00:44:35,583 --> 00:44:38,375 So, this is a race we will not win. 1029 00:44:38,458 --> 00:44:41,125 The goal posts are gonna move the whole time on us. 1030 00:44:41,208 --> 00:44:43,250 What the solutions? What are you proposing? 1031 00:44:43,333 --> 00:44:44,875 We start issuing a dividend 1032 00:44:44,959 --> 00:44:47,750 to all American adults, starting at age 18, 1033 00:44:47,834 --> 00:44:49,417 where everyone gets $1,000 a month. 1034 00:44:49,500 --> 00:44:51,834 So, basically a universal basic income. 1035 00:44:51,917 --> 00:44:54,458 -Yes. We've rebranded it the Freedom Dividend... -Okay. 1036 00:44:54,542 --> 00:44:57,667 ...because it tests much better with conservatives with the word freedom in it. 1037 00:44:57,750 --> 00:44:59,166 (laughing) 1038 00:44:59,250 --> 00:45:01,333 And it's not a basic income, it's a dividend. 1039 00:45:01,417 --> 00:45:03,834 Which is to say the economy is at a surplus, 1040 00:45:03,917 --> 00:45:05,834 so everyone deserves a piece of the pie. 1041 00:45:05,917 --> 00:45:09,458 Yeah, no. All of us are owners and shareholders of the richest 1042 00:45:09,542 --> 00:45:11,709 society in the history of the world that can easily afford 1043 00:45:11,792 --> 00:45:13,542 a dividend of $1,000 per adult. 1044 00:45:13,625 --> 00:45:17,041 People need meaning, structure, purpose, fulfillment. 1045 00:45:17,125 --> 00:45:20,959 And that is the generational challenge that faces us. 1046 00:45:21,041 --> 00:45:22,709 It's not like the Freedom Dividend, giving everyone 1047 00:45:22,792 --> 00:45:25,208 $1,000 a month, solves that challenge. It does not. 1048 00:45:25,291 --> 00:45:27,375 -But what it does is it-- -Buys us time? 1049 00:45:27,458 --> 00:45:29,917 It buys us time and also channels resources 1050 00:45:30,000 --> 00:45:32,542 into the pursuit of meeting that challenge. 1051 00:45:32,625 --> 00:45:35,917 It ends up supercharging our ability 1052 00:45:36,000 --> 00:45:39,750 to address what we should be doing. 1053 00:45:39,834 --> 00:45:43,500 Andavolu: Universal basic income, once a marginal political fantasy, 1054 00:45:43,583 --> 00:45:47,542 has been embraced by more than a few rabid capitalists in recent years. 1055 00:45:47,625 --> 00:45:50,208 We should explore ideas like universal basic income 1056 00:45:50,291 --> 00:45:53,250 to make sure that everyone has a cushion to try new ideas. 1057 00:45:53,333 --> 00:45:54,917 It's free money for everybody. 1058 00:45:55,000 --> 00:45:58,375 Enough to pay for your basic needs: food, shelter, education. 1059 00:45:58,458 --> 00:45:59,959 I don't think we're gonna have a choice. 1060 00:46:00,041 --> 00:46:01,166 It will come about one day. 1061 00:46:01,250 --> 00:46:02,750 -And I think-- -Out of necessity? 1062 00:46:02,834 --> 00:46:04,959 Out of necessity, and... 1063 00:46:05,041 --> 00:46:08,792 and I think that cities should experiment. 1064 00:46:08,875 --> 00:46:12,208 Andavolu: Michael Moynihan went to one city that already is. 1065 00:46:12,291 --> 00:46:13,792 -Moynihan: Mayor. -Good morning. 1066 00:46:13,875 --> 00:46:15,667 Michael Moynihan. How are you? 1067 00:46:15,750 --> 00:46:18,500 Moynihan: Stockton's mayor won election at just 26 years old, 1068 00:46:18,583 --> 00:46:21,959 taking office five years after the city declared bankruptcy. 1069 00:46:22,041 --> 00:46:23,083 So, you grew up here? 1070 00:46:23,166 --> 00:46:24,166 -Born and raised. -Born and raised? 1071 00:46:24,250 --> 00:46:25,750 -This is home. -And you left 1072 00:46:25,834 --> 00:46:27,083 for a brief period to go to Stanford? 1073 00:46:27,166 --> 00:46:29,417 Left for four years, I came right back. 1074 00:46:29,500 --> 00:46:31,083 Moynihan: With funding from Silicon Valley, 1075 00:46:31,166 --> 00:46:34,750 he's launched a pilot program that's giving 125 residents 1076 00:46:34,834 --> 00:46:37,333 $500 a month for 18 months. 1077 00:46:37,417 --> 00:46:40,417 Why do Silicon Valley guys like this so much? 1078 00:46:40,500 --> 00:46:43,041 Can't speak for all of them, but I think a lot of them see 1079 00:46:43,125 --> 00:46:45,041 how detrimental it would be to society 1080 00:46:45,125 --> 00:46:47,583 if there's a mass number of people who are automated 1081 00:46:47,667 --> 00:46:49,625 without any way to make a means for themselves, 1082 00:46:49,709 --> 00:46:51,834 without any way to provide for themselves. 1083 00:46:51,917 --> 00:46:54,834 I mean, is it people in tech kind of paying indulgences, 1084 00:46:54,917 --> 00:46:57,959 and saying, "Hey, we're kind of screwing this up. 1085 00:46:58,041 --> 00:47:00,041 We feel bad about it. Here's some money"? 1086 00:47:00,125 --> 00:47:02,125 I know some people are talking about robot taxes 1087 00:47:02,208 --> 00:47:03,917 and things of that sort, and I... 1088 00:47:04,000 --> 00:47:05,291 I'm in very much agreement with that. 1089 00:47:05,375 --> 00:47:07,333 That they have a responsibility 1090 00:47:07,417 --> 00:47:08,834 to society. 1091 00:47:08,917 --> 00:47:11,458 It's voluntary now, but it might be at the point of a gun later. 1092 00:47:11,542 --> 00:47:13,542 Yeah, voluntary to start and pilot, 1093 00:47:13,625 --> 00:47:15,583 but absolutely I think to scale it, 1094 00:47:15,667 --> 00:47:16,834 it's not gonna be about generosity. 1095 00:47:16,917 --> 00:47:18,166 It's gonna be a matter of policy. 1096 00:47:18,250 --> 00:47:19,667 ♪ ♪ 1097 00:47:19,750 --> 00:47:21,458 Moynihan: The criticism that most people get 1098 00:47:21,542 --> 00:47:23,625 for UBI type experiments is that, 1099 00:47:23,709 --> 00:47:26,750 all right, you're just giving people money, a handout, etc. 1100 00:47:26,834 --> 00:47:28,333 That's a start, 1101 00:47:28,417 --> 00:47:31,583 but what's the long-term goal for jobs in Stockton? 1102 00:47:31,667 --> 00:47:33,417 The hypothesis we're working on is 1103 00:47:33,500 --> 00:47:35,542 that folks have their basic needs met, 1104 00:47:35,625 --> 00:47:38,959 and then create the workforce of the future. 1105 00:47:39,041 --> 00:47:41,792 Primarily starting with the kids in our schools now, but also with the adults, 1106 00:47:41,875 --> 00:47:44,834 to give them opportunities for retraining, reskilling, 1107 00:47:44,917 --> 00:47:47,333 but also supporting people in their entrepreneurial pursuits. 1108 00:47:47,417 --> 00:47:49,500 Folks with a lot of potential but historically folks 1109 00:47:49,583 --> 00:47:51,667 who haven't been seen as important enough for investment 1110 00:47:51,750 --> 00:47:53,875 or important enough for government to really partner with, 1111 00:47:53,959 --> 00:47:55,458 so that's what makes people excited 1112 00:47:55,542 --> 00:47:57,250 about the work we're doing in Stockton. 1113 00:47:58,083 --> 00:48:01,667 So yeah, this is the downtown marina. Watch your step. 1114 00:48:01,750 --> 00:48:03,375 Moynihan: There was basically nothing here 1115 00:48:03,458 --> 00:48:05,875 when you were a kid, when you were five, six years old. 1116 00:48:05,959 --> 00:48:07,667 Tubbs: I never really came out here 1117 00:48:07,750 --> 00:48:09,542 till I came back for city council. 1118 00:48:09,625 --> 00:48:12,750 -Yeah. -This wasn't part of my Stockton, but I think for me, 1119 00:48:12,834 --> 00:48:15,750 this spot's so important 'cause it represents real potential. 1120 00:48:15,834 --> 00:48:19,333 -There's not many cities that have... this. -Yeah. 1121 00:48:20,458 --> 00:48:24,041 When we talk about the future of work, we're talking about 1122 00:48:24,125 --> 00:48:26,250 how do we ensure that those left behind today 1123 00:48:26,333 --> 00:48:27,875 aren't further left behind tomorrow, 1124 00:48:27,959 --> 00:48:30,083 and that's my biggest fear. 1125 00:48:30,166 --> 00:48:31,875 The folks who are making the least now 1126 00:48:31,959 --> 00:48:35,166 are the most likely to be automated out of jobs and making anything. 1127 00:48:35,250 --> 00:48:38,667 So for me, a basic income isn't even about the future of work. 1128 00:48:38,750 --> 00:48:41,041 It's about getting our foundation set in the present, 1129 00:48:41,125 --> 00:48:42,792 so that when the future work happens, 1130 00:48:42,875 --> 00:48:45,125 we have a firm foundation on which we can pivot 1131 00:48:45,208 --> 00:48:47,417 and figure out what we can do with and for people. 1132 00:48:47,500 --> 00:48:50,709 Andavolu: What's attractive about UBI is its simplicity. 1133 00:48:50,792 --> 00:48:53,875 But that's also what makes it vulnerable to critique. 1134 00:48:53,959 --> 00:48:57,375 Daron Acemoglu: I don't think these Utopian ideas 1135 00:48:57,458 --> 00:48:59,291 of universal basic income, 1136 00:48:59,375 --> 00:49:01,166 robots do all the production and then everybody 1137 00:49:01,250 --> 00:49:04,208 stays at home with a decent income and plays video games. 1138 00:49:04,291 --> 00:49:06,125 I think that's a very dystopic future, 1139 00:49:06,208 --> 00:49:09,959 and it won't work, and I think it will lead to a huge amount of discontent. 1140 00:49:10,041 --> 00:49:13,917 We really have no option but create jobs for the future. 1141 00:49:14,000 --> 00:49:16,417 Politicians have to start engaging these issues 1142 00:49:16,500 --> 00:49:19,583 to figure out what's politically feasible. 1143 00:49:19,667 --> 00:49:22,041 It's gonna trigger fundamental debates 1144 00:49:22,125 --> 00:49:23,875 about things like a universal basic income 1145 00:49:23,959 --> 00:49:26,417 that everybody ought to get money and the question is, 1146 00:49:26,500 --> 00:49:28,917 okay, where's that money gonna come from? 1147 00:49:29,000 --> 00:49:31,709 Andavolu: According to a progressive think tank's analysis, 1148 00:49:31,792 --> 00:49:35,625 a UBI program giving every American $10,000 a year 1149 00:49:35,709 --> 00:49:39,208 would cost the government more than $3 trillion annually. 1150 00:49:39,291 --> 00:49:44,291 The entire 2018 federal budget was just over $4 trillion. 1151 00:49:44,375 --> 00:49:47,375 Universal basic income isn't the only policy being floated 1152 00:49:47,458 --> 00:49:51,000 to shore up the workforce's shaky financial foundation, 1153 00:49:51,083 --> 00:49:55,125 whether it's using federal funds to guarantee jobs for anyone who wants one, 1154 00:49:55,208 --> 00:49:58,583 giving tax breaks to companies to create more jobs, 1155 00:49:58,667 --> 00:50:01,792 or strong-arming companies to keep their factories open. 1156 00:50:01,875 --> 00:50:04,959 What all these policies and strategies tell us 1157 00:50:05,041 --> 00:50:07,417 is that the broad consensus is that right now... 1158 00:50:07,500 --> 00:50:09,041 Protesters: We say fight back! 1159 00:50:09,125 --> 00:50:11,542 Andavolu: ...things aren't working. 1160 00:50:14,583 --> 00:50:16,583 ♪ ♪ 1161 00:50:18,125 --> 00:50:20,333 Brynjolfsson: We've designed a system where, 1162 00:50:20,417 --> 00:50:23,417 as the technology creates more wealth, 1163 00:50:23,500 --> 00:50:27,041 some people, bizarrely, are made worse off. 1164 00:50:27,667 --> 00:50:30,500 We have to update and reinvent our system 1165 00:50:30,583 --> 00:50:35,000 so that this explosion of wealth and productivity 1166 00:50:35,083 --> 00:50:37,375 benefits not just the few, but the many. 1167 00:50:37,458 --> 00:50:39,458 Haass: This challenge of new technology 1168 00:50:39,542 --> 00:50:42,083 is not taking place in a vacuum. 1169 00:50:42,166 --> 00:50:43,875 This country's already divided. 1170 00:50:43,959 --> 00:50:48,166 It's divided geographically. It's divided culturally, politically. 1171 00:50:48,250 --> 00:50:50,375 We've gotta be prepared for the fact that it could 1172 00:50:50,458 --> 00:50:51,875 actually take the social differences 1173 00:50:51,959 --> 00:50:54,500 we already have, and make it worse. 1174 00:50:54,583 --> 00:50:57,083 There's gotta be a sense of urgency here. 1175 00:50:58,083 --> 00:50:59,792 A higher level of disconnection, 1176 00:50:59,875 --> 00:51:02,500 alienation, more declines in social capital, 1177 00:51:02,583 --> 00:51:04,500 more groups of people left behind, 1178 00:51:04,583 --> 00:51:06,583 more geographic areas left behind. 1179 00:51:06,667 --> 00:51:08,250 -Protester: What do we want? -All: Justice! 1180 00:51:08,333 --> 00:51:10,125 -Protester: When do we want it? -All: Now! 1181 00:51:10,208 --> 00:51:13,625 McAfee: This is not a recipe for a stable, prosperous, happy society. 1182 00:51:13,709 --> 00:51:16,458 My worry is not that the robots will take all the jobs. 1183 00:51:16,542 --> 00:51:18,792 My worry is that more people will be left behind 1184 00:51:18,875 --> 00:51:21,375 and will feel left behind by what's going on. 1185 00:51:21,458 --> 00:51:25,041 Stiglitz: If we continue in the way we've run our economy 1186 00:51:25,125 --> 00:51:29,667 for the last 40 years, it will be disastrous. 1187 00:51:29,750 --> 00:51:32,542 So, when you talk about the future of work, 1188 00:51:32,625 --> 00:51:34,583 we're kind of talking about the future of the whole system? 1189 00:51:34,667 --> 00:51:37,417 That's right. I mean, you cannot have 1190 00:51:37,500 --> 00:51:40,792 a prosperous economy without prosperous workers. 1191 00:51:40,875 --> 00:51:42,667 Voltaire said that work saves us 1192 00:51:42,750 --> 00:51:44,500 from three great evils. 1193 00:51:44,583 --> 00:51:46,792 Boredom, vice, and need. 1194 00:51:46,875 --> 00:51:50,500 Haass: So much of our identities are tied up with our jobs. 1195 00:51:50,583 --> 00:51:52,750 People ask you how you doing, who you are, what you do, 1196 00:51:52,834 --> 00:51:55,667 and that's essential to our sense of self. 1197 00:51:56,375 --> 00:51:58,667 If we have millions, or tens of millions, 1198 00:51:58,750 --> 00:52:01,333 of chronically, long-term unemployed, 1199 00:52:01,417 --> 00:52:02,583 what's gonna become of those people? 1200 00:52:02,667 --> 00:52:04,583 It's not simply a question of how are they 1201 00:52:04,667 --> 00:52:07,333 going to support themselves. What are they gonna do? 1202 00:52:09,041 --> 00:52:10,375 (horn honks) 1203 00:52:12,667 --> 00:52:14,250 (engine rumbling) 1204 00:52:16,041 --> 00:52:19,250 (drill whirring) 1205 00:52:24,041 --> 00:52:26,208 This is your NOx sensor drift. 1206 00:52:27,000 --> 00:52:29,083 -One box. -Conversion efficiency. 1207 00:52:29,166 --> 00:52:31,792 Yep. One box gone. 1208 00:52:31,875 --> 00:52:33,083 (rattling) 1209 00:52:33,166 --> 00:52:35,041 You know, someone might say 1210 00:52:35,125 --> 00:52:37,959 robots are coming. Might as well hang up the keys now. 1211 00:52:38,041 --> 00:52:39,125 Does that go through your head? 1212 00:52:39,208 --> 00:52:40,875 It's gonna happen. 1213 00:52:40,959 --> 00:52:42,834 -It's gonna happen, I mean-- -What's gonna happen? 1214 00:52:42,917 --> 00:52:45,834 With these vehicles driving by themselves. 1215 00:52:45,917 --> 00:52:48,667 Change is good. Some change ain't good, you know? 1216 00:52:48,750 --> 00:52:51,667 I mean, that's gonna be a lot of people out of work, and... 1217 00:52:51,750 --> 00:52:53,333 Andavolu: What do you think you're gonna do? 1218 00:52:53,417 --> 00:52:55,625 ♪ ♪ 1219 00:52:55,709 --> 00:52:57,625 There's gonna be a crash. 1220 00:52:57,709 --> 00:53:00,500 I think there's gonna be a lot of outrage. 1221 00:53:00,583 --> 00:53:03,625 -Riots more or less, you know what I mean? -Sure. 1222 00:53:03,709 --> 00:53:06,375 'Cause they're gonna fight to try to keep their job. 1223 00:53:06,458 --> 00:53:09,125 I mean, would I do it? Yeah, I would do it. 1224 00:53:09,208 --> 00:53:11,542 It's gonna be a chain reaction. 1225 00:53:11,625 --> 00:53:13,667 In reality, you gotta look at the economy 1226 00:53:13,750 --> 00:53:15,250 and what is it gonna do to the economy. 1227 00:53:15,333 --> 00:53:17,875 What is it gonna do to the American people? 1228 00:53:18,583 --> 00:53:22,458 Not just the industry, I mean to... to the world. 1229 00:53:22,542 --> 00:53:25,458 What's gonna happen you got the whole world pissed off? 1230 00:53:25,542 --> 00:53:28,166 (hammering, clatter) 1231 00:53:28,500 --> 00:53:30,333 But me, I wanna die in a truck. 1232 00:53:30,417 --> 00:53:33,166 -Really? (laughs) -I'm gonna die in a truck, brother. 1233 00:53:33,250 --> 00:53:36,333 I've told all my friends, I've told my family. 1234 00:53:36,417 --> 00:53:39,959 -That's when I retire is when I die in a truck. -Yeah. 1235 00:53:40,041 --> 00:53:43,458 I mean, I been doing it for too long. 1236 00:53:43,542 --> 00:53:45,000 It's in the blood. 1237 00:53:45,083 --> 00:53:48,125 ♪ ♪ 1238 00:54:14,291 --> 00:54:17,333 ♪ ♪