1 00:00:00,600 --> 00:00:03,120 Speaker 1: Decrypted is brought to you by red Hat, whose broad 2 00:00:03,160 --> 00:00:06,520 Speaker 1: portfolio of open source technologies for the enterprise helps you 3 00:00:06,559 --> 00:00:08,760 Speaker 1: get from where you are to where you want to be. 4 00:00:09,560 --> 00:00:12,879 Speaker 1: Red Hat the open technology to help you realize your vision. 5 00:00:13,320 --> 00:00:16,079 Speaker 1: Learn more at red hat dot com slash open Tech. 6 00:00:21,960 --> 00:00:24,720 Speaker 1: That that truck we call the red and white one, 7 00:00:24,720 --> 00:00:27,880 Speaker 1: that's the bust of our videos. Um, what's the truck 8 00:00:27,920 --> 00:00:33,120 Speaker 1: we're writing called buster Buster? Okay? A couple of weeks ago, 9 00:00:33,400 --> 00:00:35,760 Speaker 1: I went down to South Florida for a road trip 10 00:00:35,960 --> 00:00:44,640 Speaker 1: in a self driving truck. Ready. Yeah, I was sitting 11 00:00:44,640 --> 00:00:46,720 Speaker 1: in the back of a cabin on a bed, actually 12 00:00:46,800 --> 00:00:50,360 Speaker 1: in a freight Liner Cascadia. That's like the biggest, baddest 13 00:00:50,360 --> 00:00:53,240 Speaker 1: truck you can buy. We're on Florida's Highway twenty seven 14 00:00:53,479 --> 00:00:56,320 Speaker 1: with the Everglades on either side. Truckers call this part 15 00:00:56,320 --> 00:00:59,640 Speaker 1: of Florida Alligator Alley. I was riding along with a 16 00:00:59,720 --> 00:01:11,000 Speaker 1: team from star Ski Robotics, and then the driver, Jeff Runions, 17 00:01:11,040 --> 00:01:13,240 Speaker 1: took his hands off the steering wheel and slid his 18 00:01:13,280 --> 00:01:16,560 Speaker 1: feet off the petals. Oh my god, this sounds terrifying. 19 00:01:16,600 --> 00:01:18,240 Speaker 1: And you're on a bed. I mean there are there 20 00:01:18,280 --> 00:01:21,280 Speaker 1: even seatbelts no seatbelts, and Brad, I don't know if 21 00:01:21,319 --> 00:01:23,880 Speaker 1: you realize how big and heavy these trucks are, and 22 00:01:23,880 --> 00:01:26,720 Speaker 1: they go fast, and all of a sudden he takes 23 00:01:26,760 --> 00:01:28,319 Speaker 1: his feet off the petals and then a gust of 24 00:01:28,319 --> 00:01:31,119 Speaker 1: wind hit the truck and the computer sort of lurched 25 00:01:31,200 --> 00:01:37,160 Speaker 1: us towards the left wane. He's trying to fight. I 26 00:01:37,200 --> 00:01:39,280 Speaker 1: was freaking out, but everybody else in the truck was 27 00:01:39,319 --> 00:01:42,120 Speaker 1: acting pretty much normal. Maybe they knew something about the 28 00:01:42,120 --> 00:01:44,279 Speaker 1: air bags in the back of the vehicle that you didn't. 29 00:01:44,760 --> 00:01:47,640 Speaker 1: There weren't any air bags, but there were algorithms, and 30 00:01:47,680 --> 00:01:50,320 Speaker 1: those algorithms need a little bit of time to understand 31 00:01:50,360 --> 00:01:52,800 Speaker 1: the driving conditions on a given day and to figure 32 00:01:52,840 --> 00:01:54,919 Speaker 1: out just how much to sort of push the steering 33 00:01:54,920 --> 00:01:57,920 Speaker 1: wheel to fight against the wind. You are in the morning, 34 00:01:57,960 --> 00:02:08,480 Speaker 1: when before you have your bath. Star Ski is still 35 00:02:08,560 --> 00:02:11,040 Speaker 1: fine tuning its technology to make sure it works in 36 00:02:11,080 --> 00:02:14,600 Speaker 1: all scenarios, and if they succeed for truckers, which is 37 00:02:14,639 --> 00:02:17,200 Speaker 1: one of the most common jobs in the United States, 38 00:02:17,400 --> 00:02:41,040 Speaker 1: that could mean huge changes. Back. Hi, I'm Brad Stone 39 00:02:41,520 --> 00:02:44,480 Speaker 1: and I'm Max Chapkin, And this week on Decrypted, I 40 00:02:44,600 --> 00:02:48,799 Speaker 1: ride a robot truck from Fort Lauderdale to Tallahassee. From 41 00:02:48,800 --> 00:02:51,680 Speaker 1: small startups to Silicon Valley's biggest tech companies in the 42 00:02:51,680 --> 00:02:55,160 Speaker 1: world's largest automakers, there's a race to build a software 43 00:02:55,160 --> 00:02:58,320 Speaker 1: that could power a future of cards without drivers. Long 44 00:02:58,360 --> 00:03:00,920 Speaker 1: haul trucking will likely be one of the first areas 45 00:03:00,960 --> 00:03:04,680 Speaker 1: to feel this profound shake up. This technology promises to 46 00:03:04,680 --> 00:03:07,720 Speaker 1: make our road safer and our goods cheaper, but it's 47 00:03:07,720 --> 00:03:11,280 Speaker 1: a transition that will have consequences. Trucking is worth seven 48 00:03:11,360 --> 00:03:14,000 Speaker 1: hundred billion dollars in the U. S Alone right, and 49 00:03:14,040 --> 00:03:16,519 Speaker 1: it employs a lot of people. More than three million 50 00:03:16,560 --> 00:03:19,560 Speaker 1: Americans drive trucks for a living, and another four million 51 00:03:19,639 --> 00:03:23,160 Speaker 1: support the trucking economy and jobs like waitressing and roadside 52 00:03:23,200 --> 00:03:26,239 Speaker 1: cafes and being a gas station attendant. It's also a 53 00:03:26,320 --> 00:03:29,840 Speaker 1: job that lower income Americans depend on. What happens to 54 00:03:29,919 --> 00:03:32,560 Speaker 1: these workers is a question that needs to be answered. 55 00:03:33,120 --> 00:03:35,960 Speaker 1: This episode kicks off a series of pieces about technology 56 00:03:36,000 --> 00:03:38,640 Speaker 1: that threatens to replace jobs. It's one of the most 57 00:03:38,680 --> 00:03:41,840 Speaker 1: important tech stories of our time. If you have a 58 00:03:41,840 --> 00:03:43,560 Speaker 1: story to share with us about your job and the 59 00:03:43,600 --> 00:03:46,800 Speaker 1: oncoming wave of automation, record a voice message and send 60 00:03:46,840 --> 00:03:55,640 Speaker 1: it to decrypted at Bloomberg dot net. So Max, Right now, 61 00:03:55,680 --> 00:03:59,600 Speaker 1: self driving car research is absolutely dominated by big name 62 00:03:59,720 --> 00:04:04,520 Speaker 1: comp and is Google, Uber, Ford and and GM, pretty 63 00:04:04,560 --> 00:04:07,880 Speaker 1: much every Detroit automaker, the Chinese companies. So how did 64 00:04:07,920 --> 00:04:10,800 Speaker 1: you find the small start up Starsky Robotics and why 65 00:04:10,840 --> 00:04:13,520 Speaker 1: does the startup even matter? Right now? What's interesting? I 66 00:04:13,520 --> 00:04:15,680 Speaker 1: got tipped off by one of their investors. They were 67 00:04:15,680 --> 00:04:18,919 Speaker 1: backed by y Combinator, so shortly before demo day, I 68 00:04:18,920 --> 00:04:21,600 Speaker 1: got tipped off, and people suggest I talked to him. 69 00:04:21,640 --> 00:04:23,680 Speaker 1: But I think it's important to remember that a lot 70 00:04:23,680 --> 00:04:27,000 Speaker 1: of these big companies working on driver list tech were 71 00:04:27,000 --> 00:04:30,160 Speaker 1: really small, just very recently. The other thing that that 72 00:04:30,240 --> 00:04:32,320 Speaker 1: drew me this is this guy was a little different. 73 00:04:32,560 --> 00:04:35,080 Speaker 1: So I went to South Florida to see Starsky's trucks 74 00:04:35,200 --> 00:04:39,520 Speaker 1: in action. My name is Stephan Seltzox Marker, and I'm 75 00:04:39,520 --> 00:04:42,880 Speaker 1: the CEO and co founder of star Skirobotics, self driving 76 00:04:42,920 --> 00:04:47,720 Speaker 1: truck company. Stephen is Starsky CEO. I found him interesting 77 00:04:47,800 --> 00:04:52,279 Speaker 1: partly because of what he's not like. So I didn't 78 00:04:52,279 --> 00:04:55,120 Speaker 1: go to Stanford. I didn't participate with two thousand four 79 00:04:55,160 --> 00:04:59,360 Speaker 1: Dropper Grand Challenge. I'm not a Google exer. I don't 80 00:04:59,480 --> 00:05:02,080 Speaker 1: really have any of the credentials of someone who's supposed 81 00:05:02,080 --> 00:05:05,000 Speaker 1: to be good at this space. What interested me about 82 00:05:05,000 --> 00:05:07,760 Speaker 1: Starsky was the sort of difference there, and also the 83 00:05:07,800 --> 00:05:09,960 Speaker 1: fact that they had this different approach, which is that 84 00:05:10,000 --> 00:05:13,359 Speaker 1: they're planning to use remote control drivers, basically people in 85 00:05:13,440 --> 00:05:16,359 Speaker 1: call centers who are overseeing trucks. So if you're a 86 00:05:16,400 --> 00:05:19,320 Speaker 1: truck driver, you don't necessarily have to spend three weeks 87 00:05:19,320 --> 00:05:22,080 Speaker 1: in a truck, sleeping in your cab, eating truck stop food, 88 00:05:22,120 --> 00:05:24,679 Speaker 1: talking on a CB radio. You just go to an office, 89 00:05:25,040 --> 00:05:27,120 Speaker 1: you sit in a desk, you drive a truck for 90 00:05:27,120 --> 00:05:28,960 Speaker 1: eight hours, and then you go home and see your family. 91 00:05:29,200 --> 00:05:31,520 Speaker 1: I'm imagining this new new job is a little bit 92 00:05:31,600 --> 00:05:33,880 Speaker 1: like playing Mario Kart for a living. It actually sounds 93 00:05:33,960 --> 00:05:37,560 Speaker 1: kind of fun. The other thing is that Starsky, unlike 94 00:05:37,560 --> 00:05:41,200 Speaker 1: other truck companies, is actually hauling cargo. So while they're 95 00:05:41,200 --> 00:05:43,719 Speaker 1: collecting data and trying to log all these research midles, 96 00:05:43,720 --> 00:05:46,080 Speaker 1: like all the other companies, they've also got like a 97 00:05:46,120 --> 00:05:48,239 Speaker 1: load in the back of the truck that they're getting paid, 98 00:05:48,480 --> 00:05:50,960 Speaker 1: you know, a modest amount of money for right, And 99 00:05:51,000 --> 00:05:53,159 Speaker 1: we should know that many of these other companies have 100 00:05:53,279 --> 00:05:56,000 Speaker 1: deployed lots of capital to to this kind of research. 101 00:05:56,040 --> 00:05:59,680 Speaker 1: Starsky is only raised around five million dollars. Is the 102 00:05:59,720 --> 00:06:04,680 Speaker 1: strategy of using real cargo presenting a different set of challenges. Though, Yeah, 103 00:06:04,720 --> 00:06:06,960 Speaker 1: it sounds easy to run a trucking company, but it's 104 00:06:07,000 --> 00:06:10,400 Speaker 1: not necessarily their Their first truck, which they nicknamed Rosebud, 105 00:06:10,680 --> 00:06:12,640 Speaker 1: broke down even before they could get it out of 106 00:06:12,640 --> 00:06:17,200 Speaker 1: San Francisco. And what was wrong with Rosebud, like, well, 107 00:06:17,400 --> 00:06:20,400 Speaker 1: the bad alternator or something. So have you ever bought 108 00:06:20,440 --> 00:06:24,880 Speaker 1: a truck before? Neither? And I that's that's the core 109 00:06:24,920 --> 00:06:27,920 Speaker 1: problem with Rosebud. I bought. I bought a truck off 110 00:06:27,920 --> 00:06:35,760 Speaker 1: an auction site. Starsky, as we mentioned, is just one 111 00:06:35,800 --> 00:06:39,240 Speaker 1: of many companies working on this. The Silicon Valley companies 112 00:06:39,279 --> 00:06:42,160 Speaker 1: of course, Detroit and and the big companies like bay 113 00:06:42,240 --> 00:06:44,359 Speaker 1: Do in China that think they can make an impact, 114 00:06:44,480 --> 00:06:47,960 Speaker 1: and Brad, don't forget the big trucking companies Volvo, Daimler, 115 00:06:48,480 --> 00:06:50,440 Speaker 1: all the big names and trucking are also getting it 116 00:06:50,520 --> 00:06:53,760 Speaker 1: in the space. So where is Starsky relative to these 117 00:06:53,839 --> 00:06:57,000 Speaker 1: giants in terms of progress? So what stars He's going 118 00:06:57,080 --> 00:07:00,360 Speaker 1: for is what's known as level three autonomy. And let 119 00:07:00,360 --> 00:07:04,000 Speaker 1: me just explain quickly Level one autonomy is cruise control, 120 00:07:04,080 --> 00:07:06,679 Speaker 1: your basic thing that is in lots of cars. Level 121 00:07:06,720 --> 00:07:09,880 Speaker 1: two is cruise control and what's known as lane keeping, 122 00:07:09,880 --> 00:07:12,320 Speaker 1: where the car will sort of steer for you. Okay, 123 00:07:12,320 --> 00:07:14,480 Speaker 1: so take your hands off the wheel, but for god's sake, 124 00:07:14,520 --> 00:07:16,760 Speaker 1: don't take a nap, right And Level three, what we're 125 00:07:16,760 --> 00:07:20,080 Speaker 1: talking about with Starsky, means that the driver needs to 126 00:07:20,120 --> 00:07:22,680 Speaker 1: be there in case of an emergency, but the driver 127 00:07:22,760 --> 00:07:26,160 Speaker 1: can basically not pay attention during other times. Level four 128 00:07:26,200 --> 00:07:29,480 Speaker 1: means driver can be totally out of picture, could be unconscious, 129 00:07:29,520 --> 00:07:32,920 Speaker 1: could be texting whatever. And that's where the big companies, 130 00:07:32,920 --> 00:07:40,000 Speaker 1: the Googles and forwards are focused on, honestly industry, yeah, 131 00:07:40,080 --> 00:07:42,040 Speaker 1: whereas we want to be like a very real business. 132 00:07:42,400 --> 00:07:45,440 Speaker 1: Len to be real business. Now, when Stephan says he 133 00:07:45,520 --> 00:07:48,320 Speaker 1: wants to be a real business, he means that Starsky's 134 00:07:48,320 --> 00:07:50,960 Speaker 1: plan is to have the driver only intervened in an emergency. 135 00:07:51,240 --> 00:07:53,920 Speaker 1: But unlike say Tesla, which is also going for this 136 00:07:54,080 --> 00:07:57,440 Speaker 1: same idea with a driver in the driver's seat, Starsky's 137 00:07:57,480 --> 00:08:00,240 Speaker 1: idea is that the driver is going to be someplace else. 138 00:08:03,520 --> 00:08:06,080 Speaker 1: We're building hours, so it's not needed person behind the 139 00:08:06,080 --> 00:08:07,760 Speaker 1: wheel Because the point for US is to not have 140 00:08:07,800 --> 00:08:11,600 Speaker 1: a person behind the wheel. So this is kind of 141 00:08:11,640 --> 00:08:14,840 Speaker 1: like the military's use of drone pilots. Yeah, exactly. So 142 00:08:14,880 --> 00:08:18,000 Speaker 1: Starsky when they raised their five million dollar funding round, 143 00:08:18,040 --> 00:08:20,200 Speaker 1: they did so thanks to a video that they made 144 00:08:20,200 --> 00:08:22,720 Speaker 1: where they were remote control driving a truck in a 145 00:08:22,800 --> 00:08:25,960 Speaker 1: parking lot and it was making a sort of pseudo delivery. 146 00:08:26,080 --> 00:08:28,080 Speaker 1: Their plan, and they want to do this by the 147 00:08:28,160 --> 00:08:30,400 Speaker 1: end of the year, is to have a real delivery 148 00:08:30,440 --> 00:08:33,120 Speaker 1: with real cargo where they pick up at the port, 149 00:08:33,320 --> 00:08:36,760 Speaker 1: drive the whole distance, and then drop off, all using 150 00:08:36,880 --> 00:08:40,040 Speaker 1: teleoperation using a remote control driver. And would that be 151 00:08:40,080 --> 00:08:42,880 Speaker 1: the first such achievement of its kind as far as 152 00:08:42,880 --> 00:08:45,520 Speaker 1: I know, Yes, And and Starsky is actually closer to 153 00:08:45,600 --> 00:08:49,400 Speaker 1: this milestone than you might think because trucks, unlike say, cars, 154 00:08:49,600 --> 00:08:51,600 Speaker 1: spend most of their time on highways, which is of 155 00:08:51,600 --> 00:08:56,120 Speaker 1: course the easiest place to do autonomous driving, right, no pedestrians, 156 00:08:56,120 --> 00:08:59,360 Speaker 1: no bikes, no traffic lights. Yeah, and and trucks are 157 00:08:59,440 --> 00:09:02,160 Speaker 1: big and heavy, which means that it's a lot easier 158 00:09:02,160 --> 00:09:04,880 Speaker 1: to get the sensors on them. And there's also this 159 00:09:04,960 --> 00:09:08,000 Speaker 1: safety component. Trucks drive a ton of miles and they're 160 00:09:08,000 --> 00:09:10,920 Speaker 1: involved in a lot of fatal accidents. So in theory, 161 00:09:10,920 --> 00:09:12,840 Speaker 1: you could eliminate a lot of those if you'd let 162 00:09:12,880 --> 00:09:18,520 Speaker 1: a computer do most of the driving. But of course 163 00:09:18,559 --> 00:09:22,240 Speaker 1: there's a dark aligning to this potentially silver cloud. Trucking 164 00:09:22,320 --> 00:09:25,280 Speaker 1: is a huge sector of the economy, employing millions of people. 165 00:09:25,679 --> 00:09:28,559 Speaker 1: Having a computer drive the truck raises the specter of 166 00:09:28,679 --> 00:09:31,880 Speaker 1: job losses on an epic scale. So it makes me 167 00:09:31,920 --> 00:09:34,560 Speaker 1: want to run for the hills. It makes me want 168 00:09:34,559 --> 00:09:37,520 Speaker 1: to hug my kids. I hope they have a future. 169 00:09:37,840 --> 00:09:41,559 Speaker 1: That's Ed Witkin, the president of the Transportation Trades Department 170 00:09:41,679 --> 00:09:43,560 Speaker 1: for the a f l C i OH. He's basically 171 00:09:43,640 --> 00:09:46,440 Speaker 1: a big union muckety muck in the world of trucking. 172 00:09:46,760 --> 00:09:49,120 Speaker 1: Um unions including the a f l C i O 173 00:09:49,240 --> 00:09:51,959 Speaker 1: and the Teamsters, which are the big well known trucking union, 174 00:09:52,200 --> 00:09:54,880 Speaker 1: are warning that this could be really, really bad for 175 00:09:54,920 --> 00:09:58,280 Speaker 1: their members, which isn't surprising, right. It taps into an 176 00:09:58,280 --> 00:10:02,040 Speaker 1: almost primal fear. Computer come, they take our jobs, right, 177 00:10:02,080 --> 00:10:05,120 Speaker 1: But trucking is weird because it's one of those jobs 178 00:10:05,120 --> 00:10:08,839 Speaker 1: that a lot of people don't really want. This is me, 179 00:10:08,880 --> 00:10:12,760 Speaker 1: I don't take take companies because truck and burgers sixties 180 00:10:12,760 --> 00:10:15,360 Speaker 1: seventy hours. Some of the like going on players of 181 00:10:15,440 --> 00:10:17,600 Speaker 1: working eighty hours just to trying to pay that drop. 182 00:10:18,480 --> 00:10:21,520 Speaker 1: I had a big, stretched out truck, big thing. But 183 00:10:21,840 --> 00:10:23,880 Speaker 1: you know I live in this drug staying it for 184 00:10:23,920 --> 00:10:25,600 Speaker 1: a couple of days, and you'll know what I'm found about. 185 00:10:26,400 --> 00:10:28,800 Speaker 1: That's Jeff Runyons again. The truck driver was behind the 186 00:10:28,800 --> 00:10:31,600 Speaker 1: wheel on the drive in Florida. He told me what 187 00:10:31,720 --> 00:10:35,640 Speaker 1: being a long haul trucker was like, plus to eat terrible. Yeah, 188 00:10:35,679 --> 00:10:36,920 Speaker 1: you know, you get truck stout. I used to go 189 00:10:36,960 --> 00:10:38,360 Speaker 1: to memphitt Of all the time. That's why I was 190 00:10:38,400 --> 00:10:41,960 Speaker 1: two fifty. Stopped at the drug stops. You got green 191 00:10:42,000 --> 00:10:44,640 Speaker 1: fright to make in the cafes. You go to any 192 00:10:44,679 --> 00:10:47,280 Speaker 1: truck stop and you'll see everything's fatty. Yeah, yeah, yeah. 193 00:10:47,320 --> 00:10:50,880 Speaker 1: I think companies treat you like they say drivers or 194 00:10:50,920 --> 00:10:55,080 Speaker 1: dom a dozen't so. Despite the glorification of the occupation 195 00:10:55,160 --> 00:10:57,240 Speaker 1: in the seventies and movies like Smoking in the Band 196 00:10:57,280 --> 00:11:00,920 Speaker 1: at bad Food, poor treatment from your employer doesn't sound great. 197 00:11:01,160 --> 00:11:03,120 Speaker 1: And the wages are bad too. I mean, people think 198 00:11:03,160 --> 00:11:05,600 Speaker 1: of trucking as being this this kind of good paying job, 199 00:11:05,640 --> 00:11:07,920 Speaker 1: but the truth is the average trucker makes around forty 200 00:11:08,280 --> 00:11:11,840 Speaker 1: dollars a year, and that easily includes you know, sixty 201 00:11:11,960 --> 00:11:14,840 Speaker 1: seventy hours of work every week, so in the end, 202 00:11:14,880 --> 00:11:17,240 Speaker 1: you're not making that much better than minimum wage, right, 203 00:11:17,280 --> 00:11:19,800 Speaker 1: And that explains why the industry sees such high turnover. 204 00:11:20,080 --> 00:11:22,400 Speaker 1: There's a massive labor shortage. And this is what you 205 00:11:22,440 --> 00:11:24,120 Speaker 1: hear if you talk to people who are who are 206 00:11:24,160 --> 00:11:27,600 Speaker 1: involved in autonomous trucking. The American Trucking Association, the big 207 00:11:27,600 --> 00:11:30,640 Speaker 1: trade group, says that around fifty thou trucking jobs are 208 00:11:30,720 --> 00:11:35,600 Speaker 1: unfilled at the moment, so there is appetite for more 209 00:11:35,640 --> 00:11:39,040 Speaker 1: and more truckers. That's my colleague josh Idolson, who covers 210 00:11:39,120 --> 00:11:43,080 Speaker 1: labor relations for Bloomberg. The volume of stuff to move 211 00:11:43,120 --> 00:11:47,120 Speaker 1: around the country keeps getting bigger, and the number of 212 00:11:47,240 --> 00:11:51,560 Speaker 1: people coming in to the industry to drive and staying 213 00:11:51,600 --> 00:11:54,360 Speaker 1: in it is not keeping up, in large part because 214 00:11:54,400 --> 00:11:57,679 Speaker 1: so many people leave. Turnover is so high because being 215 00:11:57,720 --> 00:12:00,959 Speaker 1: a trucker is pretty much terrible, but in some of 216 00:12:01,000 --> 00:12:03,000 Speaker 1: the poorest parts of the country, it's also one of 217 00:12:03,000 --> 00:12:05,840 Speaker 1: the only jobs available. One way to look at it 218 00:12:05,880 --> 00:12:08,760 Speaker 1: is these are some of the most cherished, mediocre jobs 219 00:12:08,960 --> 00:12:12,959 Speaker 1: in the United States. What about military service? Max is 220 00:12:13,000 --> 00:12:16,679 Speaker 1: that comparable. It's yeah, Actually, it's it's very comparable. Josh 221 00:12:16,720 --> 00:12:19,679 Speaker 1: says that, um, it's the it's people who are who 222 00:12:19,760 --> 00:12:22,800 Speaker 1: might enlist in military service that the trucking companies are 223 00:12:22,840 --> 00:12:27,800 Speaker 1: competing for. Maybe if they could get the federal law 224 00:12:28,200 --> 00:12:32,440 Speaker 1: adjusted so that people who are younger than twenty one 225 00:12:32,679 --> 00:12:36,440 Speaker 1: can do interstate trucking, maybe they could get people before 226 00:12:36,480 --> 00:12:39,600 Speaker 1: they would otherwise decide to go to the military, for example. 227 00:12:40,200 --> 00:12:42,600 Speaker 1: And this is why Starsky and the other Silicon Valley 228 00:12:42,600 --> 00:12:45,079 Speaker 1: companies are so interested in the sector. This is a 229 00:12:45,200 --> 00:12:48,640 Speaker 1: rare industry where computers could really help what the technology 230 00:12:48,840 --> 00:12:51,200 Speaker 1: is and what it will mean for trucking jobs. That's 231 00:12:51,200 --> 00:12:57,400 Speaker 1: coming up right after this word from our sponsor. You 232 00:12:57,440 --> 00:12:59,920 Speaker 1: know where you want to be. Red hat has the 233 00:13:00,000 --> 00:13:03,320 Speaker 1: broad portfolio of open source technologies to get you there. 234 00:13:03,760 --> 00:13:07,600 Speaker 1: Meet your evolving business challenges head on with secure solutions 235 00:13:07,640 --> 00:13:13,120 Speaker 1: for the enterprise, including Linux platforms and containers, hybrid cloud infrastructure, 236 00:13:13,400 --> 00:13:19,000 Speaker 1: application integration and development, operations management, and beyond. Visit red 237 00:13:19,000 --> 00:13:22,240 Speaker 1: hat dot com slash open tech to learn more. Red 238 00:13:22,280 --> 00:13:25,679 Speaker 1: hat the open technology to help you realize your vision 239 00:13:34,640 --> 00:13:37,280 Speaker 1: before the break. Max, you're explaining that even though the 240 00:13:37,280 --> 00:13:40,600 Speaker 1: trucking industry employs millions of people. It's actually facing a 241 00:13:40,600 --> 00:13:43,920 Speaker 1: big shortage of workers, and automating the driving process could 242 00:13:44,000 --> 00:13:49,120 Speaker 1: directly address this. So let's explain what exactly automation involves. Okay, 243 00:13:49,120 --> 00:13:51,920 Speaker 1: so there are two basic components that the sensors that 244 00:13:51,960 --> 00:13:55,760 Speaker 1: are collecting data and the algorithms. So the sensors they 245 00:13:56,000 --> 00:13:58,920 Speaker 1: tell the computer where the vehicle is, what's around it, 246 00:13:59,080 --> 00:14:01,920 Speaker 1: and then the algorithm crunched the data to figure out 247 00:14:02,000 --> 00:14:04,840 Speaker 1: what it should tell the vehicle to do. Companies that 248 00:14:04,880 --> 00:14:08,000 Speaker 1: are aiming for the most advanced autonomy use something called LIGHTER, 249 00:14:08,120 --> 00:14:11,160 Speaker 1: which stands for Light Detection and Ranging. Of course, it's 250 00:14:11,200 --> 00:14:14,559 Speaker 1: somewhat controversial because Uber and Google are now fighting over 251 00:14:14,600 --> 00:14:18,880 Speaker 1: some intellectual property around lighter. Star Ski like Tesla uses 252 00:14:18,960 --> 00:14:22,520 Speaker 1: raiders and camera. Yeah, it's a lower tech approach and 253 00:14:22,800 --> 00:14:25,440 Speaker 1: Stephen laid it out for me during our ride. So 254 00:14:25,840 --> 00:14:28,480 Speaker 1: like what we have here in the front are like 255 00:14:28,600 --> 00:14:32,440 Speaker 1: to automotive grade really high quality cameras yea with high 256 00:14:32,480 --> 00:14:38,400 Speaker 1: body lens feed until our software got it. So while 257 00:14:38,400 --> 00:14:41,640 Speaker 1: we drove, Jeff the driver basically kept his hands off 258 00:14:41,640 --> 00:14:44,560 Speaker 1: the wheel and and Kevin Keo, a star Ski engineer, 259 00:14:44,800 --> 00:14:47,000 Speaker 1: kept an eye on the systems using a laptop and 260 00:14:47,040 --> 00:14:49,840 Speaker 1: a tablet that was built into the dashboard. So at 261 00:14:49,840 --> 00:14:53,920 Speaker 1: the moment and we have we have mop based roots, 262 00:14:54,440 --> 00:14:58,080 Speaker 1: we do, but at the moment it relies heavily on 263 00:14:58,240 --> 00:15:01,040 Speaker 1: the landmarks in front. Got it getting some information from 264 00:15:01,080 --> 00:15:04,440 Speaker 1: the GPS, but currently and it's getting most of its 265 00:15:04,480 --> 00:15:07,920 Speaker 1: information from the laemark so similar it is like teslaut. 266 00:15:08,760 --> 00:15:11,280 Speaker 1: So the car is basically watching the road through cameras 267 00:15:11,280 --> 00:15:13,400 Speaker 1: and sensors and trying to stay in the lane the 268 00:15:13,400 --> 00:15:16,320 Speaker 1: way a real driver would. So Max what happens if 269 00:15:16,320 --> 00:15:20,360 Speaker 1: the lane markings are faded or simply aren't there. In general, 270 00:15:20,440 --> 00:15:23,240 Speaker 1: interstate highways are pretty good, and Starski is making life 271 00:15:23,280 --> 00:15:25,600 Speaker 1: a little easier for itself by saying that the driver, 272 00:15:25,960 --> 00:15:29,680 Speaker 1: not the computer, should handle those edge cases. That's that's 273 00:15:29,720 --> 00:15:31,560 Speaker 1: part of the same reason why Google has done like 274 00:15:31,600 --> 00:15:34,080 Speaker 1: three million miles and is it ready to play anything. 275 00:15:34,560 --> 00:15:36,160 Speaker 1: It is because like they're trying to look for all 276 00:15:36,200 --> 00:15:38,880 Speaker 1: of those weird curves, all of like the weird times 277 00:15:38,920 --> 00:15:44,600 Speaker 1: where U where whatever weird thing happens. Now Google wants 278 00:15:44,600 --> 00:15:48,400 Speaker 1: it's a computer to basically handle everything. Yeah, Starsky's thought 279 00:15:48,400 --> 00:15:50,280 Speaker 1: is the safety driver will be sitting in a call 280 00:15:50,320 --> 00:15:54,000 Speaker 1: center somewhere monitoring maybe five or ten trucks on alarm 281 00:15:54,040 --> 00:15:56,400 Speaker 1: will go off if something weird happens, and then he'll 282 00:15:56,440 --> 00:15:59,360 Speaker 1: immediately seize the wheel and take control. Let's hope the 283 00:15:59,400 --> 00:16:03,160 Speaker 1: wireless vous is reliable. How how does that work? Starsky 284 00:16:03,200 --> 00:16:06,680 Speaker 1: doesn't want to say exactly how they're transmitting data, but 285 00:16:06,800 --> 00:16:11,080 Speaker 1: basically they're controlling the truck wirelessly by remote control. Right now, 286 00:16:11,120 --> 00:16:13,280 Speaker 1: the system looks a bit like you what you'd have 287 00:16:13,360 --> 00:16:15,760 Speaker 1: if you were really into video games. There's a steering 288 00:16:15,800 --> 00:16:18,760 Speaker 1: wheel and then three screens, and the driver just sits 289 00:16:18,760 --> 00:16:20,560 Speaker 1: there and sort of drives like in a video game. 290 00:16:20,600 --> 00:16:22,720 Speaker 1: But the idea is that most of the time the 291 00:16:22,720 --> 00:16:28,720 Speaker 1: computer is driving itself. Now, let's go back to the beginning. 292 00:16:28,840 --> 00:16:31,600 Speaker 1: You're in one of these trucks in Florida. You're on 293 00:16:31,640 --> 00:16:34,360 Speaker 1: a bed in the back, and and the wind is 294 00:16:34,400 --> 00:16:38,640 Speaker 1: blowing causing the truck to sway and the computer to overcorrect. Right, 295 00:16:38,680 --> 00:16:41,440 Speaker 1: we were hauling a lot of weight on this particular day. 296 00:16:41,520 --> 00:16:44,360 Speaker 1: It was It was a shipping container which is been 297 00:16:44,400 --> 00:16:47,080 Speaker 1: out of stainless steel. It's heavier than the standard illuminum 298 00:16:47,120 --> 00:16:50,760 Speaker 1: boxes that Starsky had been testing before, and that proved 299 00:16:50,960 --> 00:16:59,440 Speaker 1: slightly problematic. Here's gonna be something new right here. This, Yeah, 300 00:16:59,480 --> 00:17:01,360 Speaker 1: now this curve during the week we were taking a 301 00:17:01,440 --> 00:17:07,720 Speaker 1: point container, we'll see a new experiment. Basically, we're getting 302 00:17:07,720 --> 00:17:09,720 Speaker 1: pushed by the wind and the computer a bit like 303 00:17:09,760 --> 00:17:12,720 Speaker 1: a human driver was struggling to figure out exactly just 304 00:17:12,840 --> 00:17:14,920 Speaker 1: how hard how to respond, just how hard to turn 305 00:17:14,960 --> 00:17:18,360 Speaker 1: that wheel? Max. You deserve hazard pay for this? Why 306 00:17:18,359 --> 00:17:20,840 Speaker 1: why were they doing this test in Florida. So a 307 00:17:20,880 --> 00:17:23,439 Speaker 1: bunch of states are trying to regulate driverless cars, but 308 00:17:23,520 --> 00:17:28,680 Speaker 1: Florida is really really ridiculously relaxed about this industry. You 309 00:17:28,720 --> 00:17:32,040 Speaker 1: don't need a special permit or any special insurance to 310 00:17:32,040 --> 00:17:34,240 Speaker 1: to drive a driverless car in Florida, and you don't 311 00:17:34,280 --> 00:17:36,840 Speaker 1: even need a human driver behind the wheel as long 312 00:17:36,880 --> 00:17:45,120 Speaker 1: as somebody somewhere is in control of the car. Now 313 00:17:45,160 --> 00:17:47,359 Speaker 1: here's the most interesting part. It gives us a peek 314 00:17:47,359 --> 00:17:50,240 Speaker 1: at the future. Maybe robots won't take all our jobs, 315 00:17:50,359 --> 00:17:53,000 Speaker 1: but it's likely that they replace many jobs, including what 316 00:17:53,040 --> 00:17:55,560 Speaker 1: we think of as blue collar jobs that involve manage 317 00:17:55,560 --> 00:17:58,560 Speaker 1: a computer. So I think like humans and technology are 318 00:17:58,560 --> 00:18:00,480 Speaker 1: probably going to be better than either want to. Yeah, 319 00:18:01,080 --> 00:18:05,560 Speaker 1: probably forever. That's Stephan again, the stars Key CEO. Stars 320 00:18:05,600 --> 00:18:09,840 Speaker 1: Keep brings together these two unlikely groups rod En m 321 00:18:09,920 --> 00:18:14,640 Speaker 1: Gene seven dollar lotts, Yeah, mixed with truck drivers who 322 00:18:14,800 --> 00:18:17,320 Speaker 1: some of them live in trailers, many of them like 323 00:18:17,400 --> 00:18:21,760 Speaker 1: they never went to college. Self driving engineers like Kevin Kyo, 324 00:18:22,280 --> 00:18:24,960 Speaker 1: the engineer who works for Starsky, can make millions, even 325 00:18:25,000 --> 00:18:28,200 Speaker 1: tens of millions at companies like Google. Truckers are basically 326 00:18:28,200 --> 00:18:32,119 Speaker 1: treated as disposable by their employers. Were we were straddling 327 00:18:32,160 --> 00:18:34,439 Speaker 1: people who thought that Bernie didn't go far enough and 328 00:18:34,440 --> 00:18:37,480 Speaker 1: people who didn't think that Trump went far enough. Stephan 329 00:18:37,680 --> 00:18:40,320 Speaker 1: argues that this is going to be really good for truckers, 330 00:18:40,480 --> 00:18:42,400 Speaker 1: which I also heard from Jeff who was our driver 331 00:18:42,520 --> 00:18:46,000 Speaker 1: for the day. Some people who are negative, Yeah, and 332 00:18:46,000 --> 00:18:49,440 Speaker 1: then some are really interested in attack behind him. Yeah. 333 00:18:49,560 --> 00:18:51,159 Speaker 1: And then you tell them about how you're gonna make 334 00:18:51,160 --> 00:18:53,600 Speaker 1: it so they have forty hour weeks that seven the 335 00:18:53,680 --> 00:18:56,480 Speaker 1: hours the weekend have, you know, instead of me and 336 00:18:56,560 --> 00:19:00,080 Speaker 1: gone all the time. They can make same amount of money. 337 00:19:00,160 --> 00:19:03,200 Speaker 1: But Jeff, a star Sky driver, isn't exactly an objective 338 00:19:03,200 --> 00:19:05,959 Speaker 1: source here, is he? No, And it's a real threat 339 00:19:06,000 --> 00:19:09,480 Speaker 1: that many drivers will get left behind. That's what one driver, 340 00:19:09,840 --> 00:19:14,199 Speaker 1: Tom George told my colleague Josh. Even where they're not 341 00:19:14,280 --> 00:19:19,200 Speaker 1: a driver shortage, there would certainly be technology trying to 342 00:19:19,200 --> 00:19:23,439 Speaker 1: to edge into uh, supplanting a driver. I've been seeing 343 00:19:23,920 --> 00:19:32,080 Speaker 1: uh trucking, uh just truck manufacturing developing into a you 344 00:19:32,119 --> 00:19:34,960 Speaker 1: know what I always called trying to engineer the driver 345 00:19:35,080 --> 00:19:41,159 Speaker 1: out of the truck. So Max, it's hard to know 346 00:19:41,240 --> 00:19:43,880 Speaker 1: what the long term impact of artificial intelligence is going 347 00:19:43,920 --> 00:19:46,600 Speaker 1: to mean for all of us, not just truckers, right right, 348 00:19:46,720 --> 00:19:48,560 Speaker 1: and and there are there are a few different schools 349 00:19:48,600 --> 00:19:50,840 Speaker 1: of thought, which I'll break down for you quickly. The 350 00:19:51,000 --> 00:19:54,959 Speaker 1: first is basically that advances in robotics artificial intelligence are 351 00:19:55,000 --> 00:19:58,560 Speaker 1: going to make us more productive, make us superhumans, and 352 00:19:58,560 --> 00:20:00,240 Speaker 1: and those of us who lose our jobs going to 353 00:20:00,320 --> 00:20:04,040 Speaker 1: get even better, more creative, more fulfilling jobs. The second 354 00:20:04,200 --> 00:20:06,879 Speaker 1: idea school of thought is that technology is going to 355 00:20:07,000 --> 00:20:09,640 Speaker 1: kill so many jobs that's going to create this permanent 356 00:20:09,720 --> 00:20:11,679 Speaker 1: underclass where where you have a bunch of people who 357 00:20:11,680 --> 00:20:14,120 Speaker 1: can't find anything. And the third school of thought says 358 00:20:14,720 --> 00:20:17,160 Speaker 1: this is all hype, don't pay any attention to it. Yeah, 359 00:20:17,160 --> 00:20:20,160 Speaker 1: I've also noticed the strain of optimism and Silicon Valley 360 00:20:20,160 --> 00:20:23,720 Speaker 1: that says the job destruction will be real, but this 361 00:20:23,720 --> 00:20:26,080 Speaker 1: this is a natural cycle of the economy, and that 362 00:20:26,160 --> 00:20:28,520 Speaker 1: it will evolve a new work that we haven't yet 363 00:20:28,520 --> 00:20:31,680 Speaker 1: imagined will kind of take care of the disruption, right. 364 00:20:31,720 --> 00:20:34,359 Speaker 1: And I think it's important to remember that that for 365 00:20:34,400 --> 00:20:36,920 Speaker 1: most of the country, this sort of Silicon value way 366 00:20:36,920 --> 00:20:39,280 Speaker 1: of thinking is not how they see it. They see 367 00:20:39,280 --> 00:20:43,200 Speaker 1: this as basically science fiction. Just the other day, Transportation 368 00:20:43,240 --> 00:20:46,920 Speaker 1: Secretary Lane Chow told reporters that explaining self driving technology 369 00:20:46,920 --> 00:20:49,440 Speaker 1: of the public would be one of the biggest hurdles 370 00:20:49,560 --> 00:20:52,040 Speaker 1: in terms of getting people to adopt it. And even 371 00:20:52,080 --> 00:20:54,840 Speaker 1: truckers like Tom George, who we just heard from, are 372 00:20:54,880 --> 00:20:57,359 Speaker 1: convinced they'll be able to drive trucks better than a 373 00:20:57,400 --> 00:21:00,960 Speaker 1: computer for some time to come. That's gonna be really, 374 00:21:01,040 --> 00:21:06,439 Speaker 1: really hard for automation to take over because there's so 375 00:21:06,520 --> 00:21:09,520 Speaker 1: much interaction that the driver has to be a part 376 00:21:09,560 --> 00:21:14,000 Speaker 1: of that can't be automated by a computer. So who's 377 00:21:14,040 --> 00:21:16,480 Speaker 1: going to notice that a strap is loose on your 378 00:21:16,520 --> 00:21:19,000 Speaker 1: load while you're driving down the road. I don't know 379 00:21:19,440 --> 00:21:22,160 Speaker 1: that the computer is going to be able to first 380 00:21:22,240 --> 00:21:24,600 Speaker 1: bull notice it. And second of all, how are they 381 00:21:24,600 --> 00:21:26,480 Speaker 1: going to just do it? What are they gonna do? 382 00:21:27,440 --> 00:21:30,040 Speaker 1: And Tom's not wrong. It's true that there are thousands 383 00:21:30,080 --> 00:21:33,880 Speaker 1: of rare cases, those edge cases, those emergencies where computers 384 00:21:33,920 --> 00:21:36,680 Speaker 1: don't work very well and they may not work well 385 00:21:36,720 --> 00:21:39,720 Speaker 1: for a long time. Yeah, but that doesn't mean it's 386 00:21:39,760 --> 00:21:41,399 Speaker 1: going to take a long time to get to that 387 00:21:41,520 --> 00:21:43,959 Speaker 1: hybridge stage. And Starsky is a version of this with 388 00:21:44,000 --> 00:21:47,119 Speaker 1: remote control. But there are others. Auto and another driverless 389 00:21:47,119 --> 00:21:50,480 Speaker 1: trucking startup called Embark talk about having drivers in the 390 00:21:50,520 --> 00:21:53,520 Speaker 1: cabs but allowing them to sleep. Peloton, which is another 391 00:21:53,800 --> 00:21:57,000 Speaker 1: Silicon Valley trucking startup, has a convoy style approach where 392 00:21:57,040 --> 00:21:58,919 Speaker 1: you could imagine a big line of trucks with one 393 00:21:59,000 --> 00:22:02,200 Speaker 1: driver just in the refront. But the bottom line is 394 00:22:02,240 --> 00:22:04,879 Speaker 1: that all of these companies, all of them are trying 395 00:22:04,920 --> 00:22:08,320 Speaker 1: to make trucking require fewer workers. So for now, what's 396 00:22:08,359 --> 00:22:12,040 Speaker 1: next for Starsky? So what they're trying to do is 397 00:22:12,160 --> 00:22:14,480 Speaker 1: have this big test on the highway where they're going 398 00:22:14,520 --> 00:22:18,240 Speaker 1: to try to drive a whole uh delivery without having 399 00:22:18,240 --> 00:22:20,520 Speaker 1: a human in the car. And I think probably they'll 400 00:22:20,560 --> 00:22:23,040 Speaker 1: they'll try to raise some more money. Meanwhile, I guess 401 00:22:23,080 --> 00:22:25,720 Speaker 1: things are looking less optimistic for drivers who don't have 402 00:22:25,760 --> 00:22:28,479 Speaker 1: a Silicon Valley startup to get involved with. It's not 403 00:22:28,560 --> 00:22:32,040 Speaker 1: just about drivers and people that operate trains and buses. 404 00:22:32,080 --> 00:22:35,119 Speaker 1: It's also about people that do sort of ordinary jobs 405 00:22:35,119 --> 00:22:37,480 Speaker 1: that you and I encounter during the day. That was 406 00:22:37,720 --> 00:22:41,879 Speaker 1: Ed our union leader again. Um Ed's bigger idea was 407 00:22:41,920 --> 00:22:44,000 Speaker 1: that at a certain point the government needs to take 408 00:22:44,080 --> 00:22:48,000 Speaker 1: some responsibility for job losses and and making sure that 409 00:22:48,800 --> 00:22:51,960 Speaker 1: this scary underclass scenario never plays out. And the other 410 00:22:52,000 --> 00:22:55,840 Speaker 1: thing worth mentioning is that ordinary jobs quote unquote might 411 00:22:55,960 --> 00:22:58,680 Speaker 1: also include a bunch of the jobs that we think 412 00:22:58,720 --> 00:23:02,000 Speaker 1: of as being good lawyers, some kinds of doctors and 413 00:23:02,040 --> 00:23:06,119 Speaker 1: Brad I hate to say it, but maybe journalists. What 414 00:23:06,200 --> 00:23:10,600 Speaker 1: about podcast hosts? Or they say always forever? But Max, 415 00:23:10,680 --> 00:23:12,879 Speaker 1: using your experience at the back of this truck, do 416 00:23:12,920 --> 00:23:15,160 Speaker 1: you really believe this is going to happen in our lifetime? 417 00:23:15,200 --> 00:23:18,879 Speaker 1: Do you really see those engineers feeling comfortable stepping away 418 00:23:18,920 --> 00:23:21,280 Speaker 1: from the from the vehicle? And then, you know, can 419 00:23:21,320 --> 00:23:25,480 Speaker 1: the same AI techniques apply to these other occupations where 420 00:23:25,560 --> 00:23:28,199 Speaker 1: you know you do need human intuition, human input. At 421 00:23:28,280 --> 00:23:30,320 Speaker 1: least for now, I don't think it's gonna be such 422 00:23:30,320 --> 00:23:33,760 Speaker 1: a stark change. And I mean there's with trucking, there 423 00:23:33,840 --> 00:23:35,879 Speaker 1: is this labor shortage, so there's sort of a built 424 00:23:35,880 --> 00:23:38,720 Speaker 1: in cushion, and a lot of people said things like 425 00:23:38,960 --> 00:23:41,480 Speaker 1: anyone who's a truck driver in their sort of career 426 00:23:41,560 --> 00:23:43,800 Speaker 1: is not going to be seriously affected by this. I'm 427 00:23:43,840 --> 00:23:45,320 Speaker 1: not sure how the true that is. And I think 428 00:23:45,359 --> 00:23:46,960 Speaker 1: the other thing that we have to keep in mind 429 00:23:47,040 --> 00:23:49,679 Speaker 1: is a lot of time, these automations don't happen in 430 00:23:49,800 --> 00:23:53,119 Speaker 1: one big chunk. They happen in sort of small ways 431 00:23:53,160 --> 00:23:56,160 Speaker 1: that that affect people's jobs, causing them to make less money. 432 00:23:56,200 --> 00:23:59,159 Speaker 1: I mean, one type of automation that's pretty underrated is 433 00:23:59,200 --> 00:24:04,040 Speaker 1: the advent of automatic transmission, which coincides with the declining 434 00:24:04,080 --> 00:24:06,719 Speaker 1: wages of trucking. Now that also there are other causes 435 00:24:06,720 --> 00:24:08,239 Speaker 1: of that, but but one thing it did is make 436 00:24:08,320 --> 00:24:11,400 Speaker 1: driving a truck easier, which meant that trucking companies could 437 00:24:11,520 --> 00:24:15,040 Speaker 1: use lower skilled workers. It kind of goes along with 438 00:24:15,040 --> 00:24:18,280 Speaker 1: this notion that AI is this magical thing that doesn't exist, 439 00:24:18,640 --> 00:24:20,600 Speaker 1: and yet when it does exist, we don't call it 440 00:24:20,640 --> 00:24:23,720 Speaker 1: AI anymore. We just sort of assume assume that it's there. 441 00:24:23,760 --> 00:24:27,320 Speaker 1: It's it's machinery. I like the Starsky approach, this melding 442 00:24:27,400 --> 00:24:31,760 Speaker 1: of AI, telematics and and then humans because you know 443 00:24:31,800 --> 00:24:33,960 Speaker 1: that this is the model that we've seen in medicine, 444 00:24:34,200 --> 00:24:38,000 Speaker 1: in law, really in journalism technology not replacing humans but 445 00:24:38,119 --> 00:24:40,520 Speaker 1: making them more efficient. Yeah, it strikes me as as 446 00:24:40,640 --> 00:24:44,600 Speaker 1: as fundamentally like realistic, but also it has a certain 447 00:24:44,680 --> 00:24:47,280 Speaker 1: humanity to it. And what I liked about this company, 448 00:24:47,280 --> 00:24:49,560 Speaker 1: and I think what the companies that succeed in in 449 00:24:49,880 --> 00:24:53,080 Speaker 1: this world are going to have at least some empathy 450 00:24:53,200 --> 00:24:55,840 Speaker 1: for the workers who are being affected by by these changes. 451 00:24:55,880 --> 00:24:58,840 Speaker 1: And those that are just thinking about this as pure technology, 452 00:24:58,920 --> 00:25:00,960 Speaker 1: I think are going to run into trouble because because 453 00:25:00,960 --> 00:25:03,920 Speaker 1: they're not being realistic about how the world actually works. Yeah, 454 00:25:03,920 --> 00:25:06,159 Speaker 1: the star Ski approach does see him like it's something 455 00:25:06,200 --> 00:25:08,760 Speaker 1: that is a little bit more practical sooner. But of 456 00:25:08,800 --> 00:25:10,560 Speaker 1: course I didn't ask you the biggest question. The most 457 00:25:10,600 --> 00:25:13,240 Speaker 1: important question is what's what the name? Is? This a 458 00:25:13,280 --> 00:25:16,159 Speaker 1: reference to the popular seventies TV show It is so 459 00:25:16,240 --> 00:25:19,160 Speaker 1: Stephen told me that he was actually looking for CB 460 00:25:19,400 --> 00:25:22,480 Speaker 1: Radio Lingo, so like ten four good Buddy type type 461 00:25:22,520 --> 00:25:24,879 Speaker 1: of things. And I guess Starsky and Hutch was slang 462 00:25:25,000 --> 00:25:28,800 Speaker 1: CB slang in the seventies for a team of drivers, 463 00:25:28,800 --> 00:25:30,399 Speaker 1: so he was thinking that it was going to be 464 00:25:30,440 --> 00:25:32,400 Speaker 1: like you have a call center for drivers, So it's 465 00:25:32,440 --> 00:25:34,679 Speaker 1: sort of like a star Ski and Hutch, or maybe 466 00:25:34,760 --> 00:25:36,880 Speaker 1: a lot of star Skis and a lot of Hutches. 467 00:25:37,320 --> 00:25:49,640 Speaker 1: Good buddy, and that's it for this week's episode of Decrypted. 468 00:25:49,960 --> 00:25:53,280 Speaker 1: Thanks for listening. We want to hear your stories. Tell 469 00:25:53,359 --> 00:25:56,160 Speaker 1: us about your job and whether you're worried about technology 470 00:25:56,160 --> 00:25:59,240 Speaker 1: and automation coming for you. Recorded voice message and send 471 00:25:59,280 --> 00:26:02,120 Speaker 1: it to decrypt it at Bloomberg dot net. Also, I'm 472 00:26:02,160 --> 00:26:05,639 Speaker 1: on Twitter at Chapkin and I'm at brad Stone. If 473 00:26:05,680 --> 00:26:08,240 Speaker 1: you haven't already subscribed to our show wherever you get 474 00:26:08,280 --> 00:26:11,280 Speaker 1: your podcasts. While you're there, please leave us a rating 475 00:26:11,320 --> 00:26:14,359 Speaker 1: in the review. It really helps more listeners find our show. 476 00:26:14,840 --> 00:26:16,879 Speaker 1: And by the way, we're crying a new thing on 477 00:26:16,960 --> 00:26:19,200 Speaker 1: LinkedIn where we give you a behind the scenes look 478 00:26:19,200 --> 00:26:21,919 Speaker 1: at our episodes each week and the discussion on some 479 00:26:22,000 --> 00:26:25,119 Speaker 1: of the thornier problems we'll discuss on the show. LinkedIn 480 00:26:25,200 --> 00:26:28,119 Speaker 1: also has their own tech show called Work in Progress, 481 00:26:28,119 --> 00:26:30,439 Speaker 1: where they talk about the future of work. One of 482 00:26:30,480 --> 00:26:33,600 Speaker 1: their recent episodes is about universal basic income, which is 483 00:26:33,640 --> 00:26:36,359 Speaker 1: also something we've been looking into here at Bloomberg. This 484 00:26:36,400 --> 00:26:40,240 Speaker 1: episode was produced by Piagkari Aki Ito, Liz Smit, and 485 00:26:40,359 --> 00:26:44,440 Speaker 1: Magnus Hendrickson. Thanks to Nico Grant and Isabel Gottlieb for 486 00:26:44,480 --> 00:26:46,760 Speaker 1: all their work on this episode. My Business Week story 487 00:26:46,840 --> 00:26:49,520 Speaker 1: was co written by jos Idolsen and edited by Jim 488 00:26:49,520 --> 00:26:52,000 Speaker 1: Alee and Nick Summers. You can read it on bloomberg 489 00:26:52,040 --> 00:26:54,920 Speaker 1: dot com slash business Week or on the brand new 490 00:26:54,920 --> 00:26:57,440 Speaker 1: Business Week app Alec Mchabe is the head of Bloomberg 491 00:26:57,520 --> 00:27:17,119 Speaker 1: podcast We'll See You next Week over Now. Decrypted is 492 00:27:17,160 --> 00:27:19,800 Speaker 1: brought to you by red Hat, whose broad portfolio of 493 00:27:19,840 --> 00:27:22,800 Speaker 1: open source technologies for the enterprise helps you get from 494 00:27:22,800 --> 00:27:25,640 Speaker 1: where you are to where you want to be. Red 495 00:27:25,680 --> 00:27:28,720 Speaker 1: Hat the open technology to help you realize your vision. 496 00:27:29,160 --> 00:27:31,920 Speaker 1: Learn more at red hat dot com slash open tech.