1 00:00:10,080 --> 00:00:13,360 Speaker 1: Hello, and welcome to another episode of the Odd Thoughts Podcast. 2 00:00:13,440 --> 00:00:16,960 Speaker 1: I'm Tracy Alloway and I'm Joe. Joe. Do you remember 3 00:00:17,120 --> 00:00:20,960 Speaker 1: when we had Gordon McGill on to talk about trucking. Yeah, 4 00:00:21,040 --> 00:00:23,680 Speaker 1: that's what that's like an iconic. I mean, there's recent, 5 00:00:23,760 --> 00:00:27,000 Speaker 1: but that's like a odd lots classic already and a 6 00:00:27,080 --> 00:00:29,040 Speaker 1: bunch of people of reaching. That was a great episode. 7 00:00:29,160 --> 00:00:31,720 Speaker 1: It was he said, I mean, he said a bunch 8 00:00:31,760 --> 00:00:34,360 Speaker 1: of things that were very interesting, but he said something 9 00:00:34,440 --> 00:00:38,520 Speaker 1: in particular where he was talking about an upcoming book 10 00:00:39,200 --> 00:00:44,040 Speaker 1: by a Cornell University professor called Data Driven, and the 11 00:00:44,080 --> 00:00:48,199 Speaker 1: idea was to explore the role of technology in the 12 00:00:48,240 --> 00:00:51,280 Speaker 1: trucking industry. Yeah. Absolutely, And you know, I think this 13 00:00:51,400 --> 00:00:54,400 Speaker 1: is a really important area to explore because, of course, 14 00:00:54,440 --> 00:00:57,440 Speaker 1: as we've discussed with Gored, but also other episodes that 15 00:00:57,480 --> 00:01:02,560 Speaker 1: we've done on trucking, truckers are monitored increasingly. They're these 16 00:01:02,800 --> 00:01:05,720 Speaker 1: e l d s electronic logging devices, the track hours, 17 00:01:05,959 --> 00:01:10,640 Speaker 1: things like that, and you know, obviously this affects truckers, 18 00:01:10,680 --> 00:01:12,840 Speaker 1: but you know we may all be tracked by e 19 00:01:12,959 --> 00:01:14,800 Speaker 1: l d s as workers were. Right, So there's two 20 00:01:14,880 --> 00:01:18,160 Speaker 1: interesting things here. So one you would expect that monitor 21 00:01:18,520 --> 00:01:21,720 Speaker 1: monitoring technology UM like the e l D s that 22 00:01:21,760 --> 00:01:25,080 Speaker 1: they're using would be sort of like anatama to a 23 00:01:25,160 --> 00:01:28,240 Speaker 1: lot of the truck driving industry, Like a lot of 24 00:01:28,280 --> 00:01:30,920 Speaker 1: truckers go into it thinking they're going to be independent, 25 00:01:30,959 --> 00:01:32,840 Speaker 1: they have the freedom of the road, and then it 26 00:01:32,880 --> 00:01:36,200 Speaker 1: turns out that they're like eye movements and brain waves 27 00:01:36,240 --> 00:01:39,160 Speaker 1: are going to be tracked and monitored. So that's interesting. 28 00:01:39,200 --> 00:01:43,000 Speaker 1: And then secondly, given the shift towards work from home 29 00:01:43,160 --> 00:01:47,119 Speaker 1: for a wide variety of workers, it does seem like 30 00:01:47,520 --> 00:01:51,680 Speaker 1: surveillance technology in general could become more of a thing. Well, 31 00:01:51,720 --> 00:01:53,960 Speaker 1: and the other really important thing that I think maybe 32 00:01:54,000 --> 00:01:56,800 Speaker 1: I don't know something profound about how business works or 33 00:01:56,880 --> 00:02:00,320 Speaker 1: capitalism works, etcetera, is like there is this core problem right, 34 00:02:00,360 --> 00:02:03,640 Speaker 1: like why does e l D exist in the first place, 35 00:02:03,640 --> 00:02:06,600 Speaker 1: attempt to solve problems truckers on the road for too 36 00:02:06,640 --> 00:02:10,040 Speaker 1: long that creates fatigue, that creates accidents, like some sort 37 00:02:10,040 --> 00:02:13,880 Speaker 1: of impulse behind these rules that curtail how long someone 38 00:02:13,919 --> 00:02:16,919 Speaker 1: could be on the road, But rather than address why 39 00:02:16,919 --> 00:02:18,640 Speaker 1: are they on the road to love, why is there 40 00:02:18,639 --> 00:02:22,280 Speaker 1: truck or fatigue? Why has fatigue been part of trucking? 41 00:02:22,480 --> 00:02:25,800 Speaker 1: And you know forever, rather than figuring out different ways 42 00:02:25,800 --> 00:02:27,960 Speaker 1: to do that, let's just crow come up with a 43 00:02:28,000 --> 00:02:31,720 Speaker 1: monitor system, rather than addressing the core issue that creates 44 00:02:31,720 --> 00:02:34,239 Speaker 1: the need for these time constraints and monitoring systems in 45 00:02:34,280 --> 00:02:36,799 Speaker 1: the first right. So the question is also whether or 46 00:02:36,840 --> 00:02:40,000 Speaker 1: not technology is the right solution to the problem it's 47 00:02:40,000 --> 00:02:43,200 Speaker 1: trying to solve. Okay, well, I'm very happy to say 48 00:02:43,200 --> 00:02:45,080 Speaker 1: we have the perfect guest because we are going to 49 00:02:45,120 --> 00:02:47,359 Speaker 1: be speaking with the author of the book that was 50 00:02:47,440 --> 00:02:49,680 Speaker 1: referenced by Gord. We are going to be speaking with 51 00:02:49,760 --> 00:02:54,040 Speaker 1: Karen Leavy. She is an associate professor at Cornell University 52 00:02:54,120 --> 00:02:57,920 Speaker 1: and also the author of Data Driven Truckers Technology and 53 00:02:57,960 --> 00:03:02,040 Speaker 1: the New Workplace Surveillance, which just just came out. So, Karen, 54 00:03:02,080 --> 00:03:04,320 Speaker 1: thank you so much for coming on all thoughts, Thanks 55 00:03:04,320 --> 00:03:07,200 Speaker 1: so much for having me. This is a really interesting 56 00:03:07,240 --> 00:03:09,720 Speaker 1: topic and I'm glad we have the chance to dive 57 00:03:09,760 --> 00:03:12,120 Speaker 1: into it further. Maybe just to begin with, you know, 58 00:03:12,200 --> 00:03:14,200 Speaker 1: Joe kind of alluded to this in the intro, but 59 00:03:14,280 --> 00:03:19,640 Speaker 1: what exactly is the problem that this workplace surveillance technology 60 00:03:19,680 --> 00:03:22,560 Speaker 1: that's been mandatory in the trucking industry. I think since 61 00:03:23,080 --> 00:03:26,480 Speaker 1: seen what exactly is it trying to solve Yeah, I 62 00:03:26,480 --> 00:03:28,160 Speaker 1: mean the way you guys talked about it kind of 63 00:03:28,200 --> 00:03:30,200 Speaker 1: in the lead up, I feel like perfectly encapsulates it. 64 00:03:30,280 --> 00:03:32,520 Speaker 1: So the e l D, the electronic logging device, as 65 00:03:32,520 --> 00:03:36,360 Speaker 1: you mentioned, is federally mandated in the trucking industry. All 66 00:03:36,520 --> 00:03:39,200 Speaker 1: long haul truckers have to buy and install and use them. 67 00:03:39,720 --> 00:03:43,480 Speaker 1: And ostensibly the goal is related to safety, right, as 68 00:03:43,520 --> 00:03:45,920 Speaker 1: you alluded to, we know that truckers are really tired. 69 00:03:46,000 --> 00:03:49,600 Speaker 1: We know they're incredibly overworked and underslept, and obviously that 70 00:03:49,680 --> 00:03:52,280 Speaker 1: has you know, that's high stakes, right. There are thousands 71 00:03:52,320 --> 00:03:55,440 Speaker 1: of truck related accidents a year and fatalities from those accidents. 72 00:03:55,440 --> 00:03:58,600 Speaker 1: It's really cost billions of dollars every year. Um, So 73 00:03:58,680 --> 00:04:01,320 Speaker 1: like nobody wants that, right, and including truckers, nobody wants 74 00:04:01,640 --> 00:04:03,080 Speaker 1: the roads to be on safe. I don't want to 75 00:04:03,080 --> 00:04:05,040 Speaker 1: be next to a tired trucker on the road, nobody, Like, 76 00:04:05,120 --> 00:04:08,000 Speaker 1: we're all aligned on that. But what's interesting, as you 77 00:04:08,040 --> 00:04:10,760 Speaker 1: point out, is not like is you know the way 78 00:04:10,800 --> 00:04:12,680 Speaker 1: that we have sort of chosen to address that in 79 00:04:12,680 --> 00:04:15,920 Speaker 1: this case is via technology. So the electronic logging device 80 00:04:16,080 --> 00:04:19,400 Speaker 1: is sort of, um the centerpiece of this effort to 81 00:04:19,560 --> 00:04:23,560 Speaker 1: make truckers comply with the timekeeping regulations that they're subject to. 82 00:04:23,960 --> 00:04:25,640 Speaker 1: I think this is something Gordon talked about two in 83 00:04:25,720 --> 00:04:28,320 Speaker 1: the episode he was on right, Like, truckers for since 84 00:04:28,360 --> 00:04:30,440 Speaker 1: the thirties have been bound to, you know, work no 85 00:04:30,480 --> 00:04:32,760 Speaker 1: more than a certain number of hours every day and 86 00:04:32,800 --> 00:04:36,360 Speaker 1: every week, and that's for safety reasons. The problem, as 87 00:04:36,440 --> 00:04:38,520 Speaker 1: I know, you know you'll have discussed, you know, on 88 00:04:38,520 --> 00:04:40,400 Speaker 1: on Board show and on other shows, is that the 89 00:04:40,440 --> 00:04:43,480 Speaker 1: way truckers are paid, like does not align with those incentives. Right. 90 00:04:43,480 --> 00:04:46,400 Speaker 1: They're paid by the mile. Truckers have the saying if 91 00:04:46,400 --> 00:04:49,080 Speaker 1: the wheel ain't turn in, you ain't earnin right, Like, 92 00:04:49,080 --> 00:04:51,200 Speaker 1: they're only paid for the time that they're actually moving 93 00:04:51,240 --> 00:04:54,080 Speaker 1: down the highway, not to like sit around resting, right, 94 00:04:54,160 --> 00:04:55,400 Speaker 1: or not to do all the other things that are 95 00:04:55,400 --> 00:04:58,480 Speaker 1: an important, essential part of being a trucker but aren't compensated. 96 00:04:58,680 --> 00:05:01,240 Speaker 1: So the result is that truck ers just want to 97 00:05:01,240 --> 00:05:02,640 Speaker 1: stay on the road as much as they can because 98 00:05:02,680 --> 00:05:05,240 Speaker 1: that's how they make a living. They're in pretty dire 99 00:05:05,279 --> 00:05:07,760 Speaker 1: economic straits to begin with, Like, it's not a mystery 100 00:05:07,760 --> 00:05:09,880 Speaker 1: why they why they end up doing these things. And 101 00:05:09,920 --> 00:05:11,359 Speaker 1: so the E L D is sort of scene is 102 00:05:11,400 --> 00:05:12,960 Speaker 1: like an answer to this, right, a way to sort 103 00:05:12,960 --> 00:05:16,640 Speaker 1: of police this behavior by making it ostensibly harder, although 104 00:05:16,680 --> 00:05:19,279 Speaker 1: not impossible, for truckers to tamper with the log books 105 00:05:19,279 --> 00:05:20,560 Speaker 1: that they used to keep. So they used to do 106 00:05:20,600 --> 00:05:23,359 Speaker 1: this using paper and pencil um and that is like 107 00:05:23,360 --> 00:05:25,520 Speaker 1: a really easy system to kind of falsify or make 108 00:05:25,520 --> 00:05:27,479 Speaker 1: it look like you're running legal when you're not. And 109 00:05:27,480 --> 00:05:29,000 Speaker 1: so the e L the sort of scene as an 110 00:05:29,000 --> 00:05:32,479 Speaker 1: answer to that. You know, I I always was a 111 00:05:32,480 --> 00:05:34,680 Speaker 1: big fan of the song Convoy and there's a line 112 00:05:34,680 --> 00:05:37,240 Speaker 1: of tearing up your swindle sheets, which I didn't really 113 00:05:37,400 --> 00:05:39,640 Speaker 1: you realize the origin of that term until I read 114 00:05:39,640 --> 00:05:42,760 Speaker 1: your book, and the swindle sheets was a nickname for 115 00:05:42,880 --> 00:05:46,039 Speaker 1: the old physical pieces of paper that the truckers used 116 00:05:46,040 --> 00:05:49,039 Speaker 1: to log their hours talk about like, you know, they 117 00:05:49,040 --> 00:05:52,200 Speaker 1: had all different ways I guess of sort of swindle 118 00:05:52,480 --> 00:05:56,280 Speaker 1: lying on these cards. How long was that push to 119 00:05:57,120 --> 00:05:59,280 Speaker 1: How long had that effort been in place to get 120 00:05:59,320 --> 00:06:01,680 Speaker 1: truckers to give up the physical pieces of paper in 121 00:06:01,839 --> 00:06:04,520 Speaker 1: favor of these e L D s. So ELD is 122 00:06:04,600 --> 00:06:07,560 Speaker 1: like first start popping up in you know, discussion around 123 00:06:07,560 --> 00:06:10,440 Speaker 1: regulation in the nineteen eighties, and there's a long history 124 00:06:10,520 --> 00:06:13,800 Speaker 1: of kind of how they eventually become mandatory. At first, 125 00:06:13,920 --> 00:06:16,279 Speaker 1: the government starts to do things like, well, you know, 126 00:06:16,279 --> 00:06:18,479 Speaker 1: maybe we'll make these mandatory for people who have like 127 00:06:18,520 --> 00:06:20,680 Speaker 1: really bad safety records, right for some subset of the 128 00:06:20,680 --> 00:06:24,360 Speaker 1: industry that we kind of think of as habitual VIL violators. 129 00:06:25,000 --> 00:06:28,640 Speaker 1: Then there's a proposed mandate in the that goes nowhere 130 00:06:28,720 --> 00:06:30,599 Speaker 1: right there, legal challenges. It's kind of like a long 131 00:06:30,640 --> 00:06:32,360 Speaker 1: and rocky road to get to the e L demandate 132 00:06:32,400 --> 00:06:35,039 Speaker 1: that we eventually end up with in SEV. But we 133 00:06:35,080 --> 00:06:37,479 Speaker 1: do eventually get there, right And you know, a lot 134 00:06:37,520 --> 00:06:39,200 Speaker 1: of the kind of the research that's in for my 135 00:06:39,200 --> 00:06:42,200 Speaker 1: book is trying to understand that transition looking at the 136 00:06:42,279 --> 00:06:43,960 Speaker 1: arguments that get made on either side of the e 137 00:06:44,080 --> 00:06:46,760 Speaker 1: L DE mandate. You know, from the nineteen eighties and 138 00:06:46,800 --> 00:06:49,560 Speaker 1: even before right, there's some predecessor technologies to that that 139 00:06:49,960 --> 00:06:52,040 Speaker 1: you know, I think set the stage for this up 140 00:06:52,080 --> 00:06:54,440 Speaker 1: to you know, post mandate, looking at what the effects 141 00:06:54,440 --> 00:06:56,960 Speaker 1: have been on the industry. So we definitely want to 142 00:06:57,000 --> 00:06:59,560 Speaker 1: dive into those various arguments. But before we do, can 143 00:06:59,600 --> 00:07:02,559 Speaker 1: you talk little bit about the technology that goes into 144 00:07:02,600 --> 00:07:04,640 Speaker 1: e L d s Because my understanding is. They sort 145 00:07:04,680 --> 00:07:09,080 Speaker 1: of range from pretty basic models to more sophisticated things 146 00:07:09,160 --> 00:07:12,360 Speaker 1: that actually, you know, can measure your brain waves to 147 00:07:12,440 --> 00:07:15,640 Speaker 1: see how awake you are or how much attention you're 148 00:07:15,640 --> 00:07:17,560 Speaker 1: paying to the road. So talk to us about the 149 00:07:17,640 --> 00:07:20,880 Speaker 1: variety of e l d s that we have right now. Yeah, 150 00:07:20,920 --> 00:07:23,240 Speaker 1: this is really crucial. So the e l D itself 151 00:07:23,720 --> 00:07:27,200 Speaker 1: captures some important data, but like, as you say, sometimes 152 00:07:27,240 --> 00:07:29,560 Speaker 1: maybe not that much, like a fairly minimal amount. Basically 153 00:07:29,600 --> 00:07:32,920 Speaker 1: where the truck is and how long it's been being driven, right, Like, 154 00:07:32,960 --> 00:07:37,560 Speaker 1: those are basically the core requirements in the regulations. However, 155 00:07:38,200 --> 00:07:40,680 Speaker 1: it's almost as though the government told everybody, like you 156 00:07:40,720 --> 00:07:43,040 Speaker 1: have to buy a phone that makes phone calls or something, right, 157 00:07:43,080 --> 00:07:45,320 Speaker 1: like you can. It's very hard to find a phone 158 00:07:45,320 --> 00:07:46,800 Speaker 1: that only makes phone calls. Right. If you're going to 159 00:07:46,840 --> 00:07:48,840 Speaker 1: buy a phone that makes phone calls, you're also going 160 00:07:48,840 --> 00:07:51,040 Speaker 1: to buy a phone that takes photos and connects to 161 00:07:51,080 --> 00:07:53,320 Speaker 1: the internet and all this other stuff. Right, Because the 162 00:07:53,360 --> 00:07:56,120 Speaker 1: tech tends to like bundle these things together, and the 163 00:07:56,200 --> 00:07:57,840 Speaker 1: same thing happens in e l d s. So e 164 00:07:57,960 --> 00:08:00,800 Speaker 1: l d s are actually more commonly like a module 165 00:08:01,080 --> 00:08:03,960 Speaker 1: in what's sometimes called the fleet management system or FMS. 166 00:08:04,160 --> 00:08:06,160 Speaker 1: People sometimes call them like a qual calm because qual 167 00:08:06,200 --> 00:08:08,040 Speaker 1: Calm used to be like have a big market share 168 00:08:08,040 --> 00:08:10,840 Speaker 1: in this area. They're all kinds of names for him. 169 00:08:10,880 --> 00:08:13,200 Speaker 1: But now what people think of is the E L D. Actually, 170 00:08:13,240 --> 00:08:15,480 Speaker 1: it's kind of like, you know, it's almost like naming 171 00:08:15,480 --> 00:08:16,960 Speaker 1: the part for the whole, Like it sort of stands 172 00:08:16,960 --> 00:08:20,200 Speaker 1: in for this much broader range of data collection technologies. 173 00:08:20,600 --> 00:08:22,760 Speaker 1: And as you point out, Tracy, those can include things. 174 00:08:22,920 --> 00:08:26,200 Speaker 1: You know, it commonly includes stuff like two way messaging 175 00:08:26,520 --> 00:08:29,720 Speaker 1: or you know, alerting a back office about your fuel 176 00:08:29,840 --> 00:08:32,439 Speaker 1: use or how hard you're breaking or how fast you're going, 177 00:08:33,160 --> 00:08:35,720 Speaker 1: or you know, whether you're changing lanes without signaling, like 178 00:08:35,760 --> 00:08:39,400 Speaker 1: all kinds of like pretty fine grain driving behavior data, 179 00:08:39,480 --> 00:08:42,760 Speaker 1: which is just like very different from what trucking work 180 00:08:42,800 --> 00:08:44,560 Speaker 1: has look like for a long time. Right, Like, if 181 00:08:44,600 --> 00:08:46,440 Speaker 1: you ask folks why they get into trucking, many of 182 00:08:46,440 --> 00:08:49,280 Speaker 1: them say explicitly because they don't want that, right, they 183 00:08:49,280 --> 00:08:51,880 Speaker 1: don't want someone looking over their shoulder kind of measuring 184 00:08:51,880 --> 00:08:54,280 Speaker 1: how they do their work. And you know, some of 185 00:08:54,280 --> 00:08:56,400 Speaker 1: the stuff that's coming up on the horizon more recently 186 00:08:56,559 --> 00:08:58,600 Speaker 1: includes the types of stuff you allude to, right, things 187 00:08:58,640 --> 00:09:02,200 Speaker 1: like driver facing cameras have become much more common. Sometimes 188 00:09:02,200 --> 00:09:06,160 Speaker 1: those are augmented by artificial intelligence that will do things 189 00:09:06,160 --> 00:09:09,160 Speaker 1: like try to assess how fatigued a driver is, and 190 00:09:09,200 --> 00:09:11,520 Speaker 1: they might do that by doing things like measuring, um, 191 00:09:11,559 --> 00:09:14,800 Speaker 1: whether the dry driver's eyelids are fluttering, you know, as 192 00:09:14,880 --> 00:09:16,120 Speaker 1: you know, like when you start to fall asleep, you're 193 00:09:16,200 --> 00:09:18,720 Speaker 1: lets start to flutter your head nods. They can measure 194 00:09:18,760 --> 00:09:21,000 Speaker 1: those kinds of things, and like you know, infer that 195 00:09:21,000 --> 00:09:23,800 Speaker 1: the driver is tired. UM. Some of them, you know, 196 00:09:23,800 --> 00:09:26,440 Speaker 1: they are wearable devices that sometimes get sort of integrated 197 00:09:26,440 --> 00:09:28,840 Speaker 1: into these systems or use kind of um in a 198 00:09:28,840 --> 00:09:31,360 Speaker 1: complimentary way to these systems that do things like you 199 00:09:31,360 --> 00:09:34,120 Speaker 1: said that measure a driver's brain waves, measure a driver's 200 00:09:34,160 --> 00:09:37,240 Speaker 1: heart rate, you know, collecting these other kind of biometric 201 00:09:37,360 --> 00:09:41,000 Speaker 1: signals to alert the back office or to alert you know, 202 00:09:41,040 --> 00:09:43,679 Speaker 1: the driver's safety manager whoever kind of what state they're in. 203 00:09:44,160 --> 00:09:48,480 Speaker 1: So technically the law just require something the bare minimum 204 00:09:48,559 --> 00:09:51,480 Speaker 1: of like okay, something track the truck, the hours on 205 00:09:51,520 --> 00:09:54,520 Speaker 1: the road, etcetera. That needed an electronic version of the 206 00:09:54,520 --> 00:09:57,400 Speaker 1: log book. But it's like the trojan Trojan horse atone, 207 00:09:57,440 --> 00:10:00,880 Speaker 1: like once you get that electronic device in the truck itself, 208 00:10:00,880 --> 00:10:03,600 Speaker 1: then why not add all these other things. Where is 209 00:10:03,640 --> 00:10:07,720 Speaker 1: the impulse coming from to add these other features onto 210 00:10:07,760 --> 00:10:11,920 Speaker 1: the field? Is it from the sort of like yeah, 211 00:10:12,160 --> 00:10:15,360 Speaker 1: who who's benefiting from this and who's making the decision? Yeah, 212 00:10:15,400 --> 00:10:17,360 Speaker 1: we want to stick a camera in the face of 213 00:10:17,360 --> 00:10:20,000 Speaker 1: the trucker and see their face at all time and 214 00:10:20,080 --> 00:10:24,920 Speaker 1: start counting, uh, you know, eyelids opening and closing. Yeah, 215 00:10:25,040 --> 00:10:27,040 Speaker 1: I think that's exactly right. It's like a scaffold, or 216 00:10:27,040 --> 00:10:28,839 Speaker 1: it's like a you know, a Christmas tree, and once 217 00:10:28,840 --> 00:10:30,080 Speaker 1: you have the Christmas tree, you can just pay more 218 00:10:30,160 --> 00:10:32,280 Speaker 1: ornaments on the Christmas tree because you got the Christmas tree. Now. 219 00:10:32,679 --> 00:10:35,120 Speaker 1: So what has happened is that, yes, the government has 220 00:10:35,320 --> 00:10:37,600 Speaker 1: you know, started to say like, we need to collect 221 00:10:37,600 --> 00:10:40,000 Speaker 1: a certain sort of information digitally, and then a lot 222 00:10:40,040 --> 00:10:43,160 Speaker 1: of the impetus for more data collection comes not necessarily 223 00:10:43,200 --> 00:10:45,800 Speaker 1: from the government per se, but from trekking companies. Right 224 00:10:45,840 --> 00:10:48,760 Speaker 1: because those workplace analytics, as you alluded to right in 225 00:10:48,760 --> 00:10:52,160 Speaker 1: the setup to the conversation, workplaces like across all kinds 226 00:10:52,160 --> 00:10:55,840 Speaker 1: of industries and all kinds of professions are increasingly interested 227 00:10:55,960 --> 00:10:59,640 Speaker 1: in using software, sensors or other technologies that are cheaper 228 00:10:59,640 --> 00:11:02,120 Speaker 1: and easy to come by to measure stuff about what 229 00:11:02,160 --> 00:11:04,600 Speaker 1: their workers are doing. And so in some sense, truck 230 00:11:04,679 --> 00:11:06,800 Speaker 1: like trucking companies are doing what all kinds of employers 231 00:11:06,840 --> 00:11:09,760 Speaker 1: are doing, which is measuring more information about what their 232 00:11:09,800 --> 00:11:12,319 Speaker 1: workers are doing on It Actually doesn't end there though, 233 00:11:12,440 --> 00:11:15,240 Speaker 1: right Like on top of that, there are third parties 234 00:11:15,280 --> 00:11:17,520 Speaker 1: that become very interested in this data too, right, So 235 00:11:17,559 --> 00:11:21,160 Speaker 1: insurance companies become interested in it. Um companies that sell 236 00:11:21,280 --> 00:11:24,160 Speaker 1: like parking space reservations to truckers other stuff like that, like, 237 00:11:24,200 --> 00:11:26,720 Speaker 1: they become interested in it. So the data becomes very 238 00:11:26,760 --> 00:11:29,800 Speaker 1: valuable to many different parties. It's just not valuable to 239 00:11:29,840 --> 00:11:32,600 Speaker 1: the trucker, right The trucker is kind of isolated against 240 00:11:32,640 --> 00:11:35,720 Speaker 1: all these parties that are interested in gathering the stata 241 00:11:35,720 --> 00:11:48,880 Speaker 1: about what he's doing. Can you talk a little bit 242 00:11:48,880 --> 00:11:51,400 Speaker 1: more about the insurance angle, because this is sort of 243 00:11:51,440 --> 00:11:55,559 Speaker 1: a a recent theme for us, this idea of insurers 244 00:11:55,600 --> 00:11:59,120 Speaker 1: sort of incentivizing different types of behavior collecting more granular 245 00:11:59,200 --> 00:12:03,200 Speaker 1: data in order to do that. Does the installation of 246 00:12:03,360 --> 00:12:06,560 Speaker 1: y l d s does that actually you know, reduce 247 00:12:06,640 --> 00:12:09,720 Speaker 1: insurance rates for instance, for truck drivers or the truck 248 00:12:09,800 --> 00:12:15,320 Speaker 1: driving companies yeah. So in what I have seen, um, 249 00:12:15,360 --> 00:12:18,480 Speaker 1: it does or it can, and that some insurers insure, 250 00:12:18,480 --> 00:12:20,200 Speaker 1: as I think at this point, are mostly just interested 251 00:12:20,200 --> 00:12:23,079 Speaker 1: in getting the data rather than like really doing much 252 00:12:23,080 --> 00:12:25,240 Speaker 1: with it beyond just like getting the data and integrating 253 00:12:25,240 --> 00:12:27,720 Speaker 1: it into their modeling. So what I've seen is is 254 00:12:28,080 --> 00:12:30,679 Speaker 1: insurance companies offering what they sometimes call like a plug 255 00:12:30,720 --> 00:12:32,840 Speaker 1: in discount. This is not so different from like what 256 00:12:32,880 --> 00:12:36,600 Speaker 1: Progressive does with commercial or with passenger vehicles, right where 257 00:12:36,600 --> 00:12:39,760 Speaker 1: you get some discount on your premia for just installing 258 00:12:39,760 --> 00:12:43,440 Speaker 1: the thing or like just giving data access. I haven't 259 00:12:43,480 --> 00:12:45,920 Speaker 1: yet seen that it's actually had a measurable impact on 260 00:12:46,640 --> 00:12:49,079 Speaker 1: like actual rates within the industry, but you can imagine 261 00:12:49,160 --> 00:12:51,040 Speaker 1: right like this is this is the goal, right is 262 00:12:51,080 --> 00:12:54,880 Speaker 1: to do something like that. So, I mean, we have 263 00:12:55,040 --> 00:12:58,520 Speaker 1: had this mandate in effect for a few years now. 264 00:12:59,360 --> 00:13:02,360 Speaker 1: Is there evident stead is making the road safer? No, 265 00:13:02,760 --> 00:13:05,800 Speaker 1: there's not. There's not evidence that there's making the read safe. 266 00:13:05,800 --> 00:13:08,720 Speaker 1: Thank you for that set up. Um No. So what 267 00:13:08,760 --> 00:13:11,600 Speaker 1: we have seen is that in the few years after 268 00:13:11,720 --> 00:13:15,520 Speaker 1: the e l D mandate took effect, traffic like or 269 00:13:16,120 --> 00:13:18,839 Speaker 1: truck crash fatalities went up. So they hit a thirty 270 00:13:18,920 --> 00:13:22,640 Speaker 1: year high the year after the mandate. UM crash rates 271 00:13:22,640 --> 00:13:24,920 Speaker 1: haven't gone down in some some segments of the industry, 272 00:13:24,920 --> 00:13:26,840 Speaker 1: they've gone up. This is not based on my analysis. 273 00:13:26,840 --> 00:13:29,720 Speaker 1: This is based on like quantitative analysis by some business 274 00:13:29,720 --> 00:13:33,000 Speaker 1: school professors that I talked about in the book. UM. 275 00:13:33,040 --> 00:13:35,640 Speaker 1: But yeah, like if the goal is safety, right, like, 276 00:13:35,679 --> 00:13:38,320 Speaker 1: there is no demonstration that even on its own terms, 277 00:13:38,320 --> 00:13:40,240 Speaker 1: the e l D is succeeding in making the road safer. 278 00:13:40,280 --> 00:13:41,640 Speaker 1: And if you think about why that is, there are 279 00:13:41,640 --> 00:13:44,000 Speaker 1: a couple of reasons that maybe happening. So one of 280 00:13:44,040 --> 00:13:47,760 Speaker 1: them that's like really intuitive is if you tell people, like, hey, 281 00:13:47,840 --> 00:13:50,000 Speaker 1: you know, you need to get from point A to 282 00:13:50,080 --> 00:13:53,800 Speaker 1: point B in eleven hours, like about roughly, Like let 283 00:13:53,800 --> 00:13:55,640 Speaker 1: me try to get home for Christmas in eleven hours, 284 00:13:55,679 --> 00:13:58,240 Speaker 1: Like I will do that. But if it's eleven hours 285 00:13:58,240 --> 00:14:00,480 Speaker 1: and five minutes or if it's twelve hours, like, it's 286 00:14:00,480 --> 00:14:02,280 Speaker 1: not the end of the world. And if I need 287 00:14:02,280 --> 00:14:04,200 Speaker 1: to stop and get a cup of coffee, or if 288 00:14:04,200 --> 00:14:06,040 Speaker 1: I need to like pull over and see if my 289 00:14:06,120 --> 00:14:08,679 Speaker 1: tire looks weird or something like, I can do those things, right, 290 00:14:08,679 --> 00:14:10,800 Speaker 1: I'm not going to like drive like about at a 291 00:14:10,840 --> 00:14:13,040 Speaker 1: hell to do it because I know that that flexibility 292 00:14:13,160 --> 00:14:15,640 Speaker 1: is sort of built into the system. If I tell 293 00:14:15,679 --> 00:14:18,400 Speaker 1: somebody like you have exactly eleven hours and you have 294 00:14:18,440 --> 00:14:20,880 Speaker 1: no more because I'm like I'm tracking this, like I'm 295 00:14:20,880 --> 00:14:23,800 Speaker 1: monitoring you, people will drive very differently, right, And that's 296 00:14:23,880 --> 00:14:26,000 Speaker 1: very intuitive. So what we have seen is that like 297 00:14:26,080 --> 00:14:29,400 Speaker 1: rates of speeding and reckless driving have gone up because 298 00:14:29,520 --> 00:14:32,960 Speaker 1: people feel this extra rigidity in the rules and so, 299 00:14:33,080 --> 00:14:36,720 Speaker 1: like not shockingly, they compensate for this lost productivity by 300 00:14:36,720 --> 00:14:38,560 Speaker 1: trying to make up for that in the way that 301 00:14:38,600 --> 00:14:41,960 Speaker 1: they drive. So that's like one clear mechanism. Another thing 302 00:14:42,000 --> 00:14:44,080 Speaker 1: that like we don't have as clear up data on this, 303 00:14:44,200 --> 00:14:46,760 Speaker 1: but clearly seems to be happening based on my conversations 304 00:14:46,760 --> 00:14:48,920 Speaker 1: with drivers. You know, trucking is kind of an aging 305 00:14:48,920 --> 00:14:51,480 Speaker 1: workforce anyway, like the median trucker ages I think in 306 00:14:51,520 --> 00:14:53,640 Speaker 1: the late forties, and a lot of folks if you 307 00:14:53,680 --> 00:14:56,480 Speaker 1: talk to them, you know, like they are not interested 308 00:14:56,520 --> 00:14:59,359 Speaker 1: in this, right, Veteran drivers who have been driving professionally 309 00:14:59,440 --> 00:15:03,120 Speaker 1: for me be decades, millions of miles without accidents. You 310 00:15:03,200 --> 00:15:05,320 Speaker 1: tell them like, guess what, guess what we're putting in 311 00:15:05,360 --> 00:15:07,640 Speaker 1: the truck now, Like they're not going to stick around 312 00:15:07,640 --> 00:15:10,120 Speaker 1: for that. Trucking has this very high turnover rate anyway, 313 00:15:10,240 --> 00:15:12,160 Speaker 1: people turn in and out of the industry all the time, 314 00:15:12,360 --> 00:15:14,280 Speaker 1: And like, those are the drivers that are the safest. 315 00:15:14,320 --> 00:15:17,080 Speaker 1: You want those drivers who are the most resistant in 316 00:15:17,120 --> 00:15:19,640 Speaker 1: many ways to this kind of oversight. That's the guy 317 00:15:19,640 --> 00:15:21,000 Speaker 1: you want to be next to on the road. You 318 00:15:21,040 --> 00:15:22,680 Speaker 1: don't want to be next to the eighteen year old 319 00:15:22,720 --> 00:15:25,640 Speaker 1: who just got their CDL who like maybe it doesn't 320 00:15:25,640 --> 00:15:28,320 Speaker 1: know any different, right, like, never grew up trucking any 321 00:15:28,400 --> 00:15:31,000 Speaker 1: other way, It's hard to find a blue collar job 322 00:15:31,040 --> 00:15:34,400 Speaker 1: that doesn't involve a lot of oversight and managerial surveillance. 323 00:15:34,640 --> 00:15:36,320 Speaker 1: So those are the folks that will stomach this kind 324 00:15:36,320 --> 00:15:39,640 Speaker 1: of thing, but they're not the safest drivers. Um. Actually, 325 00:15:39,720 --> 00:15:41,720 Speaker 1: this reminds me. I want to ask you. You know 326 00:15:41,880 --> 00:15:44,600 Speaker 1: you studied this issue. I think when we spoke to 327 00:15:44,720 --> 00:15:47,520 Speaker 1: Gored he mentioned a tenure study or something like that. 328 00:15:47,560 --> 00:15:49,920 Speaker 1: I'm not sure if that time frame is entirely accurate, 329 00:15:49,960 --> 00:15:52,720 Speaker 1: but talk to us about how you actually went about 330 00:15:52,920 --> 00:15:57,600 Speaker 1: gathering data and anecdotes for your work. Yeah, so it 331 00:15:57,640 --> 00:16:00,120 Speaker 1: has been cord is right. It has taken me a 332 00:16:00,240 --> 00:16:03,080 Speaker 1: very long time to process the study but that's been good. 333 00:16:03,120 --> 00:16:05,120 Speaker 1: Like I've spent like a quarter of my life thinking 334 00:16:05,120 --> 00:16:07,840 Speaker 1: about trucking at this point, which you know, life comes 335 00:16:07,840 --> 00:16:11,120 Speaker 1: at a best. But um, I started the study inn 336 00:16:11,120 --> 00:16:14,000 Speaker 1: I was a grad student. I was studying sociology um 337 00:16:14,040 --> 00:16:15,640 Speaker 1: and I was a lawyer before that, so I was 338 00:16:15,680 --> 00:16:19,000 Speaker 1: really interested in kind of thinking about law and how 339 00:16:19,040 --> 00:16:21,040 Speaker 1: does it how does it function on the ground, and 340 00:16:21,040 --> 00:16:22,800 Speaker 1: how do people respond to it? And especially I was 341 00:16:22,840 --> 00:16:26,320 Speaker 1: really interested in kind of wine technology and what happens 342 00:16:26,320 --> 00:16:28,840 Speaker 1: when we like surveil people, what happens when we collect 343 00:16:28,840 --> 00:16:31,280 Speaker 1: a bunch of information about what people are doing to 344 00:16:31,680 --> 00:16:34,040 Speaker 1: kind of assess whether they're breaking the rules. And I 345 00:16:34,080 --> 00:16:35,880 Speaker 1: was like sort of looking around for like where's a 346 00:16:35,920 --> 00:16:38,600 Speaker 1: place where I can see this transition in action, Like 347 00:16:38,640 --> 00:16:40,520 Speaker 1: I can really see that happening on the ground, so 348 00:16:40,560 --> 00:16:43,120 Speaker 1: I can get a good sense for like how that 349 00:16:43,200 --> 00:16:44,960 Speaker 1: unfolds or how it changes the way people relate to 350 00:16:45,000 --> 00:16:47,720 Speaker 1: one another. And just by chance, it was like completely 351 00:16:47,760 --> 00:16:50,920 Speaker 1: a fluke. I heard a story on the radio about 352 00:16:51,440 --> 00:16:54,560 Speaker 1: trucking and how like truckers were upset because they were 353 00:16:54,760 --> 00:16:56,840 Speaker 1: dealing with this. But this was again twenty eleven, so 354 00:16:56,880 --> 00:16:59,000 Speaker 1: the e l Demandate was being discussed, although it wasn't 355 00:16:59,040 --> 00:17:02,240 Speaker 1: effective for several years after that, But about this mandate, 356 00:17:02,280 --> 00:17:05,080 Speaker 1: and I thought like, well, that's maybe it. And I 357 00:17:05,119 --> 00:17:07,679 Speaker 1: didn't know any truckers, like I have no truck truckers 358 00:17:07,680 --> 00:17:10,040 Speaker 1: in my family, but I went that day to a 359 00:17:10,119 --> 00:17:12,760 Speaker 1: truck stop just to see, like, what is it like 360 00:17:12,840 --> 00:17:14,919 Speaker 1: to talk to a truck driver? Like is it easy? 361 00:17:15,080 --> 00:17:18,560 Speaker 1: Will they talk to me? And I was like instantaneously hooked. 362 00:17:18,600 --> 00:17:21,520 Speaker 1: I found that truckers were like, they had such interesting stories, 363 00:17:21,560 --> 00:17:24,800 Speaker 1: they were so forthcoming, they were incredibly generous, you know, 364 00:17:24,880 --> 00:17:27,520 Speaker 1: in talking about the stuff that I admit I had 365 00:17:27,600 --> 00:17:30,439 Speaker 1: never thought about. Right, it sounds like the origins of 366 00:17:30,440 --> 00:17:34,399 Speaker 1: of odd lots interest in trucking as well. Yeah, so 367 00:17:34,440 --> 00:17:39,160 Speaker 1: we keep doing trucking episodes. Obviously it's a fascinating, fascinating, 368 00:17:40,119 --> 00:17:42,200 Speaker 1: fascinating angle. You know what you mentioned, you know, the 369 00:17:42,320 --> 00:17:45,119 Speaker 1: legal background, and this brings up something a point that 370 00:17:45,119 --> 00:17:47,600 Speaker 1: I thought was really interesting in your book, which is 371 00:17:47,640 --> 00:17:51,120 Speaker 1: that like society kind of depends on people being able 372 00:17:51,160 --> 00:17:53,400 Speaker 1: to break the law a little bit, that you can't 373 00:17:53,440 --> 00:17:58,160 Speaker 1: actually run a society on everywhere everyone always strictly hewing 374 00:17:58,240 --> 00:18:00,159 Speaker 1: to the law and being punished if they don't you 375 00:18:00,200 --> 00:18:01,960 Speaker 1: explain that a little bit more, because I think that's 376 00:18:02,040 --> 00:18:03,560 Speaker 1: key when you talk about you know what the E 377 00:18:03,680 --> 00:18:06,320 Speaker 1: L D. You can't be ten minutes later, you can't 378 00:18:06,359 --> 00:18:08,960 Speaker 1: drive an extra ten minutes. But what is this concept 379 00:18:08,960 --> 00:18:12,359 Speaker 1: of like we need a little bit of loose areas? Yeah, no, 380 00:18:12,480 --> 00:18:15,040 Speaker 1: I think this is exactly right. Right. It's very easy 381 00:18:15,160 --> 00:18:18,080 Speaker 1: and intuitive to say, like, well, if we have a law, 382 00:18:18,160 --> 00:18:21,080 Speaker 1: people should follow the law, and like therefore, you know, 383 00:18:21,240 --> 00:18:23,560 Speaker 1: if we assume that the law is legitimate or has 384 00:18:23,600 --> 00:18:25,720 Speaker 1: been reached in like a democratic way or whatever, like 385 00:18:25,720 --> 00:18:28,480 Speaker 1: people should just follow it. But like that doesn't really 386 00:18:28,520 --> 00:18:30,600 Speaker 1: hold up too much scrutiny, right, And there's tons of 387 00:18:30,640 --> 00:18:34,080 Speaker 1: places where we see that actually, like social life would 388 00:18:34,119 --> 00:18:36,679 Speaker 1: kind of fall apart if people actually followed laws to 389 00:18:36,720 --> 00:18:39,879 Speaker 1: the letter. Um, you know, like one of the examples 390 00:18:39,920 --> 00:18:41,560 Speaker 1: days in the book is just like speeding, right, Like 391 00:18:41,600 --> 00:18:44,000 Speaker 1: I think if you were told that you were if 392 00:18:44,000 --> 00:18:45,720 Speaker 1: you got a ticket for driving sixty six and a 393 00:18:45,760 --> 00:18:47,399 Speaker 1: sixty five, not as a trucker but just as like 394 00:18:47,440 --> 00:18:50,359 Speaker 1: a regular car driver, people like we would be like, what, 395 00:18:50,480 --> 00:18:53,560 Speaker 1: that's not the law, and like it is technically, but 396 00:18:53,640 --> 00:18:57,000 Speaker 1: like nobody really expects that that's the law. Another thing 397 00:18:57,000 --> 00:18:58,480 Speaker 1: I talked about in the book that kind of hits 398 00:18:58,480 --> 00:19:01,320 Speaker 1: this home is um in in like labor actions and 399 00:19:01,440 --> 00:19:05,040 Speaker 1: you know collective action, like a pretty common worker strategy 400 00:19:05,160 --> 00:19:07,080 Speaker 1: is the work to rule labor action. I don't know 401 00:19:07,080 --> 00:19:10,040 Speaker 1: if this is something it's familiar to odd lets listeners 402 00:19:10,040 --> 00:19:11,760 Speaker 1: that we're in work to rule. What you do, it's 403 00:19:11,840 --> 00:19:14,760 Speaker 1: it's like a work slowdown, and you achieve it by 404 00:19:14,800 --> 00:19:17,000 Speaker 1: like suddenly you like really pay attention to all the 405 00:19:17,080 --> 00:19:19,960 Speaker 1: rules that are in like the cool books. You start 406 00:19:19,680 --> 00:19:22,119 Speaker 1: track like spells out that you work this amount of 407 00:19:22,119 --> 00:19:24,600 Speaker 1: hours every week, and you work exactly that amount of 408 00:19:24,600 --> 00:19:27,679 Speaker 1: hours and you just do it right. And like the 409 00:19:27,720 --> 00:19:30,760 Speaker 1: whole point is that by following the rules like that 410 00:19:30,880 --> 00:19:33,920 Speaker 1: is not actually what we necessarily wanted people to do, 411 00:19:34,080 --> 00:19:36,600 Speaker 1: because if you do it like, that's the slowdown. Right. 412 00:19:36,640 --> 00:19:38,520 Speaker 1: The reason it's effective is because that's clearly not the 413 00:19:38,560 --> 00:19:41,040 Speaker 1: expectation most of the time. And there's this happens just 414 00:19:41,080 --> 00:19:44,080 Speaker 1: all over the place. UM there was like a few 415 00:19:44,160 --> 00:19:46,159 Speaker 1: years ago in New York City and in many cities 416 00:19:46,280 --> 00:19:48,840 Speaker 1: right there are these big moves like um these vision 417 00:19:48,920 --> 00:19:50,959 Speaker 1: zero kind of projects where they're like we should have 418 00:19:51,000 --> 00:19:55,840 Speaker 1: no jaywalking because jaywalking leads to traffic accidents. And then 419 00:19:55,920 --> 00:19:58,640 Speaker 1: like when police start really cracking down on jaywalking, there's 420 00:19:58,680 --> 00:20:01,399 Speaker 1: like some economics analysis where people say, like, you know, 421 00:20:01,440 --> 00:20:04,560 Speaker 1: actually jaywalking is really efficient, and if we like we 422 00:20:04,560 --> 00:20:07,040 Speaker 1: would lose like a good deal of economic efficiency if 423 00:20:07,040 --> 00:20:09,600 Speaker 1: people actually follow these rules, because like we actually want 424 00:20:09,600 --> 00:20:11,199 Speaker 1: people to bend the rules a lot of the time, right, 425 00:20:11,240 --> 00:20:14,240 Speaker 1: We've built a society in which, like, following a bunch 426 00:20:14,240 --> 00:20:17,040 Speaker 1: of rules to the letter can be really inefficient or 427 00:20:17,040 --> 00:20:19,679 Speaker 1: ineffective or can cause other harms. And that's definitely the 428 00:20:19,720 --> 00:20:22,240 Speaker 1: case in trucking, right, Like, clearly a lot of problems 429 00:20:22,240 --> 00:20:24,760 Speaker 1: with the status quo and trucking. Like I'm not suggesting 430 00:20:24,800 --> 00:20:27,080 Speaker 1: that everything is great and trucking and the labor conditions 431 00:20:27,080 --> 00:20:28,560 Speaker 1: are wonderful and if we got rid of the E 432 00:20:28,640 --> 00:20:30,720 Speaker 1: L D s everything would be fine, Like, I don't 433 00:20:30,840 --> 00:20:33,640 Speaker 1: view it that way, but it is definitely the case 434 00:20:33,720 --> 00:20:36,000 Speaker 1: that we have all sort of come to rely on 435 00:20:36,440 --> 00:20:39,320 Speaker 1: the rules being more like guidelines in this context, right, 436 00:20:39,359 --> 00:20:43,000 Speaker 1: and that people budge them in order to move stuff 437 00:20:43,040 --> 00:20:46,000 Speaker 1: at the pace at which we kind of all demand it. Um, 438 00:20:46,080 --> 00:20:47,879 Speaker 1: So when we crack down on a rule without kind 439 00:20:47,880 --> 00:20:50,080 Speaker 1: of considering that broader context, you end up with this 440 00:20:50,119 --> 00:20:53,280 Speaker 1: weird situation where you know you haven't addressed the underlying 441 00:20:53,320 --> 00:20:55,439 Speaker 1: reasons people are breaking the rule. You're just kind of 442 00:20:55,440 --> 00:20:58,280 Speaker 1: policing them harder for it. You know. Joe mentioned the 443 00:20:58,359 --> 00:21:02,439 Speaker 1: swindle books earlier, like the the analog log books that 444 00:21:02,440 --> 00:21:06,400 Speaker 1: truck drivers used to keep. And I remember I used to, Um, 445 00:21:06,440 --> 00:21:08,360 Speaker 1: I used to fly planes when I was like nine 446 00:21:08,440 --> 00:21:10,480 Speaker 1: years old. I don't know that you flew because my 447 00:21:10,520 --> 00:21:12,840 Speaker 1: dad's out. Yeah, but I didn't know you were a pilot. 448 00:21:12,960 --> 00:21:15,720 Speaker 1: Well not, but I had a pilot's log book and 449 00:21:15,920 --> 00:21:17,840 Speaker 1: I was supposed to put in like my hours in 450 00:21:17,880 --> 00:21:21,080 Speaker 1: my tiny little cessna, but like I was nine years old, 451 00:21:21,080 --> 00:21:23,159 Speaker 1: and so I just used to pretend to fill it 452 00:21:23,200 --> 00:21:27,520 Speaker 1: out all the time. I didn't know. So my question is, like, 453 00:21:27,840 --> 00:21:31,480 Speaker 1: moving from the analog log books to the e l 454 00:21:31,560 --> 00:21:35,679 Speaker 1: D S how infallible actually is that system? Can that 455 00:21:35,800 --> 00:21:39,840 Speaker 1: information be tampered with or falsified? Yeah, it is a 456 00:21:39,840 --> 00:21:42,480 Speaker 1: great question, and I love that you yourself have falsified 457 00:21:42,480 --> 00:21:47,680 Speaker 1: a log book. So yeah, right, So the whole idea writer, 458 00:21:47,840 --> 00:21:49,640 Speaker 1: like the rhetoric around the e l D is like, oh, 459 00:21:49,640 --> 00:21:51,520 Speaker 1: this is tamper This is a tamper proof version of 460 00:21:51,520 --> 00:21:53,120 Speaker 1: the thing that people have been doing for a long time, 461 00:21:53,200 --> 00:21:56,160 Speaker 1: and certainly it is more difficult writer. It presents different 462 00:21:56,200 --> 00:21:58,840 Speaker 1: kinds of challenges to falsify data and an e l 463 00:21:58,880 --> 00:22:01,080 Speaker 1: D than it would be on a you know, using 464 00:22:01,119 --> 00:22:03,280 Speaker 1: pencil and paper, where like anybody could do it with 465 00:22:03,440 --> 00:22:06,159 Speaker 1: five minutes of thought. Um, but it's not impossible, right, 466 00:22:06,200 --> 00:22:08,560 Speaker 1: as you point out, um, in one of the chapters 467 00:22:08,560 --> 00:22:10,080 Speaker 1: of the book, I go through, like all of these 468 00:22:10,080 --> 00:22:13,240 Speaker 1: different strategies that truckers and other folks used to kind 469 00:22:13,280 --> 00:22:16,800 Speaker 1: of still bend the rules sometimes, right, because like as 470 00:22:16,840 --> 00:22:18,919 Speaker 1: I mentioned, right, you still sometimes need that, or like 471 00:22:18,960 --> 00:22:21,919 Speaker 1: the economy depends on that, or they're expected to do 472 00:22:21,960 --> 00:22:24,639 Speaker 1: it by their managers or whatever it is. Um, Like 473 00:22:24,680 --> 00:22:27,679 Speaker 1: one of the things people will do is what and 474 00:22:27,720 --> 00:22:29,879 Speaker 1: they did this with paper lugs too, is what sometimes 475 00:22:29,920 --> 00:22:33,720 Speaker 1: called a ghost log. So like you sign in as Karen, right, 476 00:22:33,800 --> 00:22:36,800 Speaker 1: if you're Karen, and you do your eleven hours, and 477 00:22:36,840 --> 00:22:39,399 Speaker 1: then you sign in as Joe or as Tracy right 478 00:22:39,520 --> 00:22:41,960 Speaker 1: using some other right, which is like that's not and 479 00:22:42,000 --> 00:22:44,359 Speaker 1: then you have eleven more hours, right, And sometimes like 480 00:22:44,440 --> 00:22:48,280 Speaker 1: some companies they often get like a demo account like 481 00:22:48,280 --> 00:22:50,560 Speaker 1: a John Do kind of account when they sign up 482 00:22:50,600 --> 00:22:53,119 Speaker 1: with the E l D vendor, and so they like 483 00:22:53,240 --> 00:22:56,480 Speaker 1: have these kind of like slush you know, drivers that 484 00:22:56,520 --> 00:22:58,760 Speaker 1: they can use in some cases. I don't think like 485 00:22:58,800 --> 00:23:00,840 Speaker 1: not everybody is doing this, but the strategy that was 486 00:23:00,880 --> 00:23:04,439 Speaker 1: described to me, um other things folks will do. I 487 00:23:05,000 --> 00:23:07,080 Speaker 1: once I was watching I was sitting with a safety 488 00:23:07,280 --> 00:23:09,600 Speaker 1: or with a dispatcher at a trucking company and she 489 00:23:09,680 --> 00:23:11,600 Speaker 1: was talking to this driver and the driver was like, well, 490 00:23:11,640 --> 00:23:13,320 Speaker 1: I'm like four miles from where I need to be, 491 00:23:13,760 --> 00:23:16,239 Speaker 1: but I'm out of hours. What do I do? And 492 00:23:16,280 --> 00:23:20,159 Speaker 1: she was like, well, pull over and then roll to 493 00:23:20,280 --> 00:23:21,960 Speaker 1: the place you have to get to roll to the 494 00:23:22,000 --> 00:23:25,320 Speaker 1: drop off point at less than fifteen miles an hour. 495 00:23:25,880 --> 00:23:27,680 Speaker 1: And the reason was because the e l D s 496 00:23:27,720 --> 00:23:29,840 Speaker 1: all have like a threshold which is usually set by 497 00:23:29,840 --> 00:23:33,080 Speaker 1: the company, and that threshold is like, under fifteen miles 498 00:23:33,080 --> 00:23:34,960 Speaker 1: an hour, it won't register is driving, right, So she 499 00:23:35,040 --> 00:23:37,400 Speaker 1: knew that like, it wouldn't register is driving. What's interesting 500 00:23:37,440 --> 00:23:40,520 Speaker 1: and like both of these examples actually is that it's 501 00:23:40,560 --> 00:23:43,200 Speaker 1: not necessarily the trucker that's like pushing this, right, it's 502 00:23:43,240 --> 00:23:46,679 Speaker 1: oftentimes the company that's telling the driver like here's what 503 00:23:46,760 --> 00:23:48,200 Speaker 1: you need to do. Right, Like it's kind of like 504 00:23:48,240 --> 00:23:50,960 Speaker 1: a winky strategy because they kind of wanted both ways 505 00:23:50,960 --> 00:23:53,800 Speaker 1: in some sense, right, Like they want the control that E. 506 00:23:53,880 --> 00:23:56,119 Speaker 1: L D S afford them, but they also like just 507 00:23:56,160 --> 00:23:59,040 Speaker 1: want the stuff to move. So sometimes they kind of 508 00:23:59,160 --> 00:24:13,679 Speaker 1: coerce or compel drivers to do these things. So, you know, 509 00:24:13,760 --> 00:24:15,520 Speaker 1: one of the things, and I think this is a 510 00:24:15,600 --> 00:24:18,760 Speaker 1: thing that's gonna matter for all workers who are especially 511 00:24:18,840 --> 00:24:20,920 Speaker 1: you know, work from home. That's gonna we know that 512 00:24:20,920 --> 00:24:23,760 Speaker 1: that's going to increase the amount of surveillance on workers 513 00:24:23,760 --> 00:24:26,919 Speaker 1: like are you actually at your desk responding to customers, etcetera? 514 00:24:26,960 --> 00:24:28,840 Speaker 1: Whatever the job. But I think like one thing that 515 00:24:28,880 --> 00:24:31,760 Speaker 1: all this data collection is going to affect everyone is 516 00:24:31,760 --> 00:24:36,600 Speaker 1: like who who knows about how we're done? How who? Who? 517 00:24:36,640 --> 00:24:39,320 Speaker 1: Where is the knowledge stored about how to do a 518 00:24:39,359 --> 00:24:41,680 Speaker 1: good job? And you know, so in the case of trucking, 519 00:24:41,680 --> 00:24:44,159 Speaker 1: like at one point the idea is like, Okay, the 520 00:24:44,160 --> 00:24:46,720 Speaker 1: truck drivers they really like know their area, they know 521 00:24:46,880 --> 00:24:49,119 Speaker 1: the road, they know the warehouses that they go to, 522 00:24:49,240 --> 00:24:52,320 Speaker 1: and there's some sort of like knowledge base that exists 523 00:24:52,359 --> 00:24:55,560 Speaker 1: primarily in the head of the truck driver, that's difficult 524 00:24:55,560 --> 00:24:58,160 Speaker 1: to replicate. And then I imagine, you know, okay, now 525 00:24:58,320 --> 00:25:00,879 Speaker 1: with e l D s, maybe a lot of that 526 00:25:00,960 --> 00:25:03,240 Speaker 1: knowledge isn't at the level of the driver, but maybe 527 00:25:03,440 --> 00:25:06,119 Speaker 1: at uh, you know, whatever, the fleet owner or some 528 00:25:06,200 --> 00:25:08,560 Speaker 1: other entity. But I also have to imagine that even 529 00:25:08,600 --> 00:25:11,720 Speaker 1: for the owner of the trucking fleet, they may be 530 00:25:11,960 --> 00:25:14,639 Speaker 1: losing to some third party company that's building a black 531 00:25:14,680 --> 00:25:18,239 Speaker 1: box AI model that even the company can't access. And 532 00:25:18,320 --> 00:25:20,719 Speaker 1: so like the locusts of like all this knowledge and 533 00:25:20,840 --> 00:25:23,360 Speaker 1: you know, the power that one has with that knowledge 534 00:25:23,400 --> 00:25:26,520 Speaker 1: gets sort of distributed. Can you talk a little bit about, 535 00:25:26,560 --> 00:25:29,520 Speaker 1: like I guess I would say the shifting distribution of 536 00:25:29,600 --> 00:25:33,679 Speaker 1: power and knowledge within the industry as these manufacturers E 537 00:25:33,760 --> 00:25:38,480 Speaker 1: l D manufacturers, third party data providers, software companies that 538 00:25:38,560 --> 00:25:41,960 Speaker 1: provide uh you know, become like, oh, we have we 539 00:25:42,000 --> 00:25:44,680 Speaker 1: have the database, we have the information. Yeah, no, I 540 00:25:44,720 --> 00:25:47,960 Speaker 1: think that's exactly right, right, Like, so the information moves 541 00:25:48,040 --> 00:25:50,679 Speaker 1: right in trucking like people used to talk about it, 542 00:25:50,720 --> 00:25:52,640 Speaker 1: like you're the captain of your ship, right, you get 543 00:25:52,680 --> 00:25:54,840 Speaker 1: to make all the calls because like you're there, nobody 544 00:25:54,840 --> 00:25:57,240 Speaker 1: else is there, and you know what the conditions are, 545 00:25:57,280 --> 00:25:59,320 Speaker 1: like and nobody else does because nobody else is there. 546 00:26:00,080 --> 00:26:02,199 Speaker 1: But as you point out, right, like now more of 547 00:26:02,240 --> 00:26:05,080 Speaker 1: that data is distributed to the firm or to a 548 00:26:05,160 --> 00:26:08,679 Speaker 1: third party. And what's important to recognize about that, I 549 00:26:08,680 --> 00:26:11,159 Speaker 1: think is that like, not only does that change the 550 00:26:11,200 --> 00:26:13,320 Speaker 1: game because somebody else has the data, but the data 551 00:26:13,359 --> 00:26:14,840 Speaker 1: they have is like not quite the same as the 552 00:26:14,920 --> 00:26:16,879 Speaker 1: data you have, right, Like they have some kind of 553 00:26:16,920 --> 00:26:21,640 Speaker 1: abstracted guests about what things are like, Like they know 554 00:26:21,800 --> 00:26:24,040 Speaker 1: what you're how many hours register on your eel D 555 00:26:24,160 --> 00:26:26,000 Speaker 1: and they're going to use that to kind of assess 556 00:26:26,040 --> 00:26:28,439 Speaker 1: whether you have the right to be tired, right, or 557 00:26:28,440 --> 00:26:29,880 Speaker 1: like whether you have the right to stop. Or they 558 00:26:29,920 --> 00:26:32,800 Speaker 1: have some sense of what the weather is like because 559 00:26:32,800 --> 00:26:35,439 Speaker 1: they can look at the weather channel. But that's not 560 00:26:35,480 --> 00:26:37,119 Speaker 1: the same as being in the place and knowing what 561 00:26:37,160 --> 00:26:40,000 Speaker 1: it's like, right. So as things get abstracted, a lot 562 00:26:40,000 --> 00:26:42,720 Speaker 1: of times those you know, kind of more abstract data 563 00:26:42,760 --> 00:26:45,119 Speaker 1: sources get used to challenge the accounts of workers on 564 00:26:45,160 --> 00:26:47,439 Speaker 1: the ground because they're measurable, right, They're the kinds of 565 00:26:47,440 --> 00:26:49,679 Speaker 1: things we can measure. And this happens again not just 566 00:26:49,760 --> 00:26:52,480 Speaker 1: in trucking but all over the place. So, like I 567 00:26:52,520 --> 00:26:55,240 Speaker 1: have a study from a different contacts where I like 568 00:26:55,240 --> 00:26:57,720 Speaker 1: looked at retail workers than kind of how technology is 569 00:26:57,720 --> 00:27:00,679 Speaker 1: affecting them, and they're too, right, Like tail workers and 570 00:27:00,760 --> 00:27:03,160 Speaker 1: some set in some settings do this practice that's called 571 00:27:03,200 --> 00:27:06,080 Speaker 1: client telling, where you like keep a book of business 572 00:27:06,080 --> 00:27:08,000 Speaker 1: and you kind of like know the sizes of your 573 00:27:08,040 --> 00:27:10,240 Speaker 1: favorite customers and stuff, and you know when their birthdays 574 00:27:10,240 --> 00:27:13,960 Speaker 1: are and so like that really is empowering exactly right, 575 00:27:14,000 --> 00:27:17,480 Speaker 1: Like sex stores, those workers like have at the work, 576 00:27:17,520 --> 00:27:19,160 Speaker 1: they work on commission and they have like a really 577 00:27:19,160 --> 00:27:23,320 Speaker 1: good relationship to certain customers. Increasingly, like now they're not 578 00:27:23,400 --> 00:27:26,080 Speaker 1: allowed to keep their own books in business. They have 579 00:27:26,160 --> 00:27:29,560 Speaker 1: to like input that data into some centralized system. And 580 00:27:29,600 --> 00:27:32,000 Speaker 1: what that means is that when they it comes time 581 00:27:32,000 --> 00:27:34,159 Speaker 1: for them to argue for a raise or something like, 582 00:27:34,200 --> 00:27:36,160 Speaker 1: they don't have those bargaining chips because, as you said, 583 00:27:36,200 --> 00:27:38,440 Speaker 1: the data is not in their head. It's now owned 584 00:27:38,440 --> 00:27:40,200 Speaker 1: by the company or it's owned by the software vendor 585 00:27:40,280 --> 00:27:43,600 Speaker 1: or whatever, and so that reduces like they're more substitute 586 00:27:43,600 --> 00:27:45,920 Speaker 1: herbal right, it's it reduces their bargaining power of easy 587 00:27:46,359 --> 00:27:47,960 Speaker 1: the company. And I think you see this kind of 588 00:27:47,960 --> 00:27:50,640 Speaker 1: thing happening just across the board really and a lot 589 00:27:50,640 --> 00:27:53,359 Speaker 1: of different labor contexts. So just on this topic, and 590 00:27:53,440 --> 00:27:56,439 Speaker 1: this is kind of a Devil's Advocate question, but you know, 591 00:27:56,520 --> 00:27:58,960 Speaker 1: our last trucking episode, we were talking a lot about 592 00:27:59,000 --> 00:28:02,080 Speaker 1: how truck drivers end working more hours than they're actually 593 00:28:02,119 --> 00:28:04,960 Speaker 1: paid for because they end up going to warehouses or 594 00:28:05,000 --> 00:28:07,240 Speaker 1: depots or whatever and they have to wait hours and 595 00:28:07,280 --> 00:28:09,400 Speaker 1: hours and hours to pick up or drop off a load. 596 00:28:10,000 --> 00:28:13,119 Speaker 1: If you have this kind of surveillance technology, could it 597 00:28:13,440 --> 00:28:17,680 Speaker 1: not be used to measure, for instance, how much time 598 00:28:17,680 --> 00:28:22,800 Speaker 1: you're idling and then maybe like used to avoid inefficient 599 00:28:22,920 --> 00:28:26,200 Speaker 1: warehouses or make some of those problems, you know, try 600 00:28:26,240 --> 00:28:30,439 Speaker 1: to ameliorate some of those problems. Yeah, definitely, I definitely 601 00:28:30,440 --> 00:28:32,880 Speaker 1: think that can be. I don't think it like redeems 602 00:28:32,920 --> 00:28:34,840 Speaker 1: the whole thing, but it is a silver lining, right that. 603 00:28:34,960 --> 00:28:37,000 Speaker 1: And and exactly those kinds of tools have been built 604 00:28:37,080 --> 00:28:38,920 Speaker 1: using e l D data that kind of give you 605 00:28:39,200 --> 00:28:41,240 Speaker 1: It's almost like the thing where Google will tell you like, 606 00:28:41,360 --> 00:28:43,040 Speaker 1: is there a long wait at this restaurant? Right like 607 00:28:43,080 --> 00:28:44,760 Speaker 1: it will do the same thing for truckers, being like, 608 00:28:45,360 --> 00:28:47,880 Speaker 1: you know, is there a long detention period on average 609 00:28:47,880 --> 00:28:50,880 Speaker 1: at a particular shipper and that potentially can be useful 610 00:28:50,880 --> 00:28:52,640 Speaker 1: to you, right, like to the extent you get to 611 00:28:52,720 --> 00:28:54,840 Speaker 1: control whether you know which which loads are going to 612 00:28:54,920 --> 00:28:57,040 Speaker 1: take to help you kind of control your business. So 613 00:28:57,080 --> 00:29:00,400 Speaker 1: I agree that that kind of transparency can be useful, 614 00:29:00,480 --> 00:29:03,400 Speaker 1: right And it's one example of where you know, it's 615 00:29:03,480 --> 00:29:05,840 Speaker 1: kind of like a more labor friendly way of using 616 00:29:05,840 --> 00:29:08,840 Speaker 1: this data to actually help truckers out. I don't think 617 00:29:08,840 --> 00:29:11,520 Speaker 1: it like redeems the whole because it's like a nice 618 00:29:11,520 --> 00:29:12,840 Speaker 1: thing to kind of put on top of all of 619 00:29:12,880 --> 00:29:15,000 Speaker 1: this additional control. There would be like other ways we 620 00:29:15,040 --> 00:29:17,680 Speaker 1: could ensure that truckers don't lose a bunch of time 621 00:29:17,720 --> 00:29:20,720 Speaker 1: to detention that would be much more direct. But I'm 622 00:29:20,720 --> 00:29:22,680 Speaker 1: in agreement with you that that is like kind of 623 00:29:22,720 --> 00:29:25,520 Speaker 1: a bright spot. Well, although it also seems like, look, 624 00:29:25,560 --> 00:29:27,959 Speaker 1: we just had these two years of like terrible supply 625 00:29:28,040 --> 00:29:31,760 Speaker 1: chain disruptions, Like we're why didn't all this data help us? Then? 626 00:29:31,800 --> 00:29:33,440 Speaker 1: I mean maybe because this is like a once in 627 00:29:33,480 --> 00:29:37,560 Speaker 1: a hundred year crisis we just experienced. But you figured, like, well, 628 00:29:37,960 --> 00:29:40,920 Speaker 1: if all this data was so great at improving supply chains, 629 00:29:41,440 --> 00:29:43,360 Speaker 1: you know, it didn't exactly step up to the plate. 630 00:29:44,200 --> 00:29:47,480 Speaker 1: There's so many things I fascinating angles of this something 631 00:29:47,480 --> 00:29:50,240 Speaker 1: I I wanna again something else in your book that 632 00:29:50,280 --> 00:29:53,280 Speaker 1: really struck me, the social that you mentioned, Like, Okay, 633 00:29:53,600 --> 00:29:55,640 Speaker 1: in many of the cases, the truckers you really want 634 00:29:55,680 --> 00:29:57,840 Speaker 1: on the road are the ones who might be most 635 00:29:57,880 --> 00:30:01,240 Speaker 1: repelled by having this electronic monitor device staring them in 636 00:30:01,320 --> 00:30:03,600 Speaker 1: the face all day. Can you talk a little bit 637 00:30:03,600 --> 00:30:07,440 Speaker 1: about these efforts that UM, the E l D makers 638 00:30:07,480 --> 00:30:10,000 Speaker 1: and companies have put forth to sort of like get 639 00:30:10,000 --> 00:30:13,720 Speaker 1: people comfortable at them. Yeah. So one thing that companies 640 00:30:13,760 --> 00:30:15,920 Speaker 1: sometimes will do, and I've talked to E l D 641 00:30:16,040 --> 00:30:18,800 Speaker 1: vendors that have talked about these strategies and to trucking 642 00:30:18,800 --> 00:30:22,120 Speaker 1: companies themselves that will use them, is that, like in 643 00:30:22,320 --> 00:30:25,080 Speaker 1: rolling out an E l D system or a fleet 644 00:30:25,080 --> 00:30:28,040 Speaker 1: management system, you know, a lot does depend on kind 645 00:30:28,040 --> 00:30:31,400 Speaker 1: of the messaging around that or like what kind of 646 00:30:31,560 --> 00:30:34,040 Speaker 1: culture the company builds around that. UM, And there's a 647 00:30:34,040 --> 00:30:35,800 Speaker 1: bunch of obviously, like the tools can be used in 648 00:30:35,840 --> 00:30:37,440 Speaker 1: all kinds of different ways, and there's a bunch of 649 00:30:37,560 --> 00:30:40,320 Speaker 1: variation and how companies roll them out. One kind of 650 00:30:40,320 --> 00:30:43,440 Speaker 1: interesting thing I saw happening is that, you know, because 651 00:30:43,480 --> 00:30:46,760 Speaker 1: there's now all this data on like like their scorecards, 652 00:30:46,840 --> 00:30:50,000 Speaker 1: that can be generated for each driver based on fuel 653 00:30:50,040 --> 00:30:52,800 Speaker 1: efficiency and how many safety incidents they have, and that 654 00:30:52,880 --> 00:30:56,040 Speaker 1: kind of thing like that becomes really easy. And then 655 00:30:56,080 --> 00:30:59,440 Speaker 1: that also facilitates like not only measuring the driver, but 656 00:30:59,520 --> 00:31:03,200 Speaker 1: comparing the driver against other workers, right, or incentivizing the 657 00:31:03,280 --> 00:31:05,520 Speaker 1: driver kind of like making a like gamifying it, right, 658 00:31:05,560 --> 00:31:07,160 Speaker 1: making it a game about like, well, who can have 659 00:31:07,160 --> 00:31:09,400 Speaker 1: the lowest fuel efficiency. That's a big one, right, because 660 00:31:09,400 --> 00:31:12,440 Speaker 1: that's a big cost driver for trucking firms. And so 661 00:31:12,560 --> 00:31:14,280 Speaker 1: companies will do all kinds of stuff to sort of 662 00:31:14,280 --> 00:31:18,600 Speaker 1: incentivize drivers to sort of comply. Um, you know, like 663 00:31:18,640 --> 00:31:20,520 Speaker 1: a really basic thing is just putting up a list 664 00:31:20,920 --> 00:31:23,520 Speaker 1: of who's the most fuel efficient driver, which they of 665 00:31:23,560 --> 00:31:26,240 Speaker 1: course now have that data, like putting that up you 666 00:31:26,240 --> 00:31:28,719 Speaker 1: know in the central office or something. I've there's some 667 00:31:28,760 --> 00:31:31,680 Speaker 1: pictures in the book of um you know some of 668 00:31:31,680 --> 00:31:34,080 Speaker 1: these what these charts look like they might attach like 669 00:31:34,160 --> 00:31:37,520 Speaker 1: a small financial incentive to it. So, like I talked 670 00:31:37,520 --> 00:31:39,680 Speaker 1: to a company that it was like they drove for 671 00:31:39,760 --> 00:31:41,320 Speaker 1: restaurants and they're like, oh, well, we'll give you a 672 00:31:41,320 --> 00:31:43,840 Speaker 1: gittu divigate to the restaurant that you drive to, which 673 00:31:43,840 --> 00:31:46,200 Speaker 1: doesn't cost them very much, but I guess it's nice 674 00:31:46,200 --> 00:31:50,360 Speaker 1: for the driver. My favorite one is, um where a 675 00:31:50,360 --> 00:31:53,719 Speaker 1: company they were interested in kind of um the drivers 676 00:31:53,800 --> 00:31:56,760 Speaker 1: driving efficiently, and so what they decided to do was 677 00:31:56,880 --> 00:32:00,760 Speaker 1: issue these like small bonuses two drivers wives in the 678 00:32:00,800 --> 00:32:05,000 Speaker 1: wives names when their husbands like met whatever the fuel 679 00:32:05,040 --> 00:32:07,200 Speaker 1: efficiency goal was. And the thinking I asked like, well, 680 00:32:07,200 --> 00:32:09,040 Speaker 1: why the wives, and like, well, you know, once you 681 00:32:09,160 --> 00:32:11,480 Speaker 1: get the wife fund board. Funny, I'm so proud of 682 00:32:11,480 --> 00:32:15,960 Speaker 1: your efficient driving. I'm so proud of your e l 683 00:32:16,040 --> 00:32:20,040 Speaker 1: D compliance fuel efficient driver. Thrilled what people want to 684 00:32:20,040 --> 00:32:21,960 Speaker 1: here when they get home. I know that's what I 685 00:32:21,960 --> 00:32:25,240 Speaker 1: say to my husband every night. No, I mean like 686 00:32:25,280 --> 00:32:27,080 Speaker 1: you have to sort of admire the antinuity. But it 687 00:32:27,160 --> 00:32:30,520 Speaker 1: is an interesting strategy, right to like yeah, exactly like 688 00:32:30,560 --> 00:32:32,960 Speaker 1: you said, to sort of bring the family dynamic into 689 00:32:33,680 --> 00:32:37,960 Speaker 1: this kind of datification of the work. Incredible. Um, well, Karen, 690 00:32:38,080 --> 00:32:39,840 Speaker 1: one of the reasons we wanted to have you on 691 00:32:40,040 --> 00:32:43,640 Speaker 1: is I mean, a, we're fascinated by the trucking industry 692 00:32:43,640 --> 00:32:47,000 Speaker 1: in general. But be we mentioned this in the intro. 693 00:32:47,400 --> 00:32:50,560 Speaker 1: As the world sort of shifts to work from home, 694 00:32:50,760 --> 00:32:53,160 Speaker 1: or at least as there's a little bit more work 695 00:32:53,160 --> 00:32:55,640 Speaker 1: from home than there was before the pandemic. It does 696 00:32:55,800 --> 00:32:58,000 Speaker 1: feel like there is an opportunity for more of this 697 00:32:58,120 --> 00:33:01,400 Speaker 1: kind of workplace surveillance technology to be deployed to a 698 00:33:01,520 --> 00:33:05,440 Speaker 1: broader um workforce. Can you talk to us a little 699 00:33:05,480 --> 00:33:09,040 Speaker 1: bit about what your research tells us about the application 700 00:33:09,240 --> 00:33:13,160 Speaker 1: of this tech to the rest of the labor market. Yeah, 701 00:33:13,240 --> 00:33:17,480 Speaker 1: I definitely agree that workplace surveillance has a very long history, right, 702 00:33:17,560 --> 00:33:20,719 Speaker 1: and the motivations behind it are like not new. So 703 00:33:20,840 --> 00:33:26,600 Speaker 1: companies have always wanted more productivity, more efficiency, and watching 704 00:33:26,640 --> 00:33:28,720 Speaker 1: your workers to kind of get at that is something 705 00:33:28,760 --> 00:33:31,520 Speaker 1: that like you know, get dates back to at least 706 00:33:31,560 --> 00:33:34,360 Speaker 1: the Industrial Revolution, probably before that, right, Like, this is 707 00:33:34,360 --> 00:33:36,760 Speaker 1: not a new strategy in some ways, but it is 708 00:33:36,880 --> 00:33:40,240 Speaker 1: changing in some important respects, right, And one of those 709 00:33:40,280 --> 00:33:41,760 Speaker 1: that I talked about in the book is like, well, 710 00:33:41,840 --> 00:33:44,680 Speaker 1: first just that you can integrate these things into new workplaces. 711 00:33:44,760 --> 00:33:47,960 Speaker 1: So truck cabs, for a long time, we're pretty immune 712 00:33:48,400 --> 00:33:50,760 Speaker 1: to the kinds of workplace oversight that you saw like 713 00:33:50,880 --> 00:33:54,840 Speaker 1: in warehouses or in factories or even in offices, right, 714 00:33:54,920 --> 00:33:57,200 Speaker 1: just because of the nature of the work and like, 715 00:33:57,240 --> 00:33:59,320 Speaker 1: in some ways they're like a lagging indicator, right, Like 716 00:33:59,360 --> 00:34:02,080 Speaker 1: they're they're catching up with some other types of blue 717 00:34:02,120 --> 00:34:05,200 Speaker 1: collar work. Um, but there are other things I think 718 00:34:05,240 --> 00:34:07,480 Speaker 1: about kind of what the trucking case tells us. And 719 00:34:07,800 --> 00:34:11,080 Speaker 1: as you mentioned right, like post pandemic, there's a lot 720 00:34:11,120 --> 00:34:14,279 Speaker 1: more concern from managers about workers kind of not given 721 00:34:14,320 --> 00:34:17,000 Speaker 1: it they're roll or shirking or not paying attention or 722 00:34:17,040 --> 00:34:19,160 Speaker 1: doing those kinds of things. And so these types of 723 00:34:19,960 --> 00:34:23,680 Speaker 1: data like data tracking, whether that's like um, you know, 724 00:34:23,800 --> 00:34:27,040 Speaker 1: location tracking where your workers are, or detecting what they're 725 00:34:27,040 --> 00:34:29,000 Speaker 1: looking at on a screen right when they're supposed to 726 00:34:29,040 --> 00:34:31,680 Speaker 1: be working, or what windows they have open. Um. You 727 00:34:31,719 --> 00:34:33,919 Speaker 1: see the stuff in school sittings to like the same 728 00:34:34,000 --> 00:34:36,239 Speaker 1: kind of like procting software that you sometimes see for 729 00:34:36,320 --> 00:34:39,799 Speaker 1: taking remote exams. Functionally is not so different from some 730 00:34:39,840 --> 00:34:43,719 Speaker 1: of this workplace technology tracking like productivity monitoring. More and 731 00:34:43,760 --> 00:34:47,200 Speaker 1: more kind of back end workplace systems like Microsoft three 732 00:34:47,239 --> 00:34:51,359 Speaker 1: sixty five include these capabilities that will like report back 733 00:34:51,440 --> 00:34:54,920 Speaker 1: to a manager about whether the like how many you know, 734 00:34:55,000 --> 00:34:57,799 Speaker 1: minutes you spend in power Point or whatever stuff like that. 735 00:34:58,040 --> 00:35:00,880 Speaker 1: So this kind of like measuring work, this is just 736 00:35:00,960 --> 00:35:02,879 Speaker 1: happening all over the place, and I really don't see 737 00:35:02,920 --> 00:35:06,040 Speaker 1: that going away anytime soon, you know. One of the 738 00:35:06,120 --> 00:35:08,279 Speaker 1: tricks here, one of the problems is that when you 739 00:35:08,320 --> 00:35:10,960 Speaker 1: measure stuff, you like inherently lose a lot of contexts, 740 00:35:10,960 --> 00:35:13,120 Speaker 1: and oftentimes the things you can measure, like how many 741 00:35:13,200 --> 00:35:16,520 Speaker 1: emails somebody said or whatever, is like not an amazing 742 00:35:16,560 --> 00:35:20,439 Speaker 1: indicator of whether they're doing like meaningful work. And so 743 00:35:20,920 --> 00:35:23,000 Speaker 1: workers a lot of times, you know, still have to 744 00:35:23,000 --> 00:35:24,719 Speaker 1: do the meaningful work, but they also are sort of 745 00:35:24,800 --> 00:35:28,399 Speaker 1: burdened with making themselves legible to these systems that are 746 00:35:28,400 --> 00:35:30,719 Speaker 1: like sort of incentivizing or gamifying these things that may 747 00:35:30,760 --> 00:35:33,040 Speaker 1: or may not be all that meaningful. So I do 748 00:35:33,120 --> 00:35:35,960 Speaker 1: think that those those dynamics are just popping up, like 749 00:35:36,040 --> 00:35:38,359 Speaker 1: across a lot of blue collar work, but also like 750 00:35:38,360 --> 00:35:40,200 Speaker 1: what we think of as kind of more white color 751 00:35:40,280 --> 00:35:44,480 Speaker 1: professionalized industries like medicine and law and other contexts. Um. 752 00:35:44,520 --> 00:35:46,560 Speaker 1: There was this really great article in The New York 753 00:35:46,600 --> 00:35:50,280 Speaker 1: Times a few months ago that maybe folks saw um 754 00:35:50,320 --> 00:35:53,400 Speaker 1: by Jodie Cantor and Aria Sundaram, where they talk about 755 00:35:53,400 --> 00:35:56,560 Speaker 1: this kind of productivity monitoring that's happening, and like one 756 00:35:56,560 --> 00:35:58,680 Speaker 1: of the examples that they use is of a hospice 757 00:35:58,760 --> 00:36:02,680 Speaker 1: chaplain like it gets some certain number of points from 758 00:36:02,680 --> 00:36:05,600 Speaker 1: her employer based on how many visits she makes to 759 00:36:05,680 --> 00:36:08,759 Speaker 1: like dying people. I mean, it's like just super super dystopiated. 760 00:36:08,760 --> 00:36:11,760 Speaker 1: But it's like they talk about a range of different 761 00:36:11,760 --> 00:36:13,360 Speaker 1: industries and it's clear that this is just sort of 762 00:36:13,400 --> 00:36:16,640 Speaker 1: coming for everybody in its own Alright, Well, I look 763 00:36:16,680 --> 00:36:19,399 Speaker 1: forward to our dystopian future where Joe and I are 764 00:36:19,440 --> 00:36:24,319 Speaker 1: just sending each other to word emails, constantly productive game 765 00:36:24,360 --> 00:36:28,320 Speaker 1: the algorithm you're prepping for a podcast. Yeah, all right, Karen, 766 00:36:28,360 --> 00:36:30,319 Speaker 1: we're going to have to leave it there. Thank you 767 00:36:30,360 --> 00:36:32,839 Speaker 1: so much for coming on all thoughts and talking about 768 00:36:32,880 --> 00:36:36,520 Speaker 1: your work. Absolutely fascinating. Oh it's my pleasure. Thanks for 769 00:36:36,560 --> 00:36:52,759 Speaker 1: having me, Joe. That was interesting. That was so interesting. 770 00:36:52,880 --> 00:36:55,680 Speaker 1: There's so many I feel like we've gould have talked 771 00:36:55,719 --> 00:36:58,440 Speaker 1: to Karen for a long time, so many different threads 772 00:36:58,440 --> 00:37:02,280 Speaker 1: about how like business worked, where power is the appoint 773 00:37:02,360 --> 00:37:05,279 Speaker 1: the purpose of like you know, letter of the law, 774 00:37:05,320 --> 00:37:07,239 Speaker 1: a letter of the rules, so many interesting things. Yeah, 775 00:37:07,239 --> 00:37:10,120 Speaker 1: I do think that point about how we have rules 776 00:37:10,160 --> 00:37:13,759 Speaker 1: in place, but actually flexibility to work outside of the 777 00:37:13,800 --> 00:37:16,680 Speaker 1: rules is really important, like both because it kind of 778 00:37:16,760 --> 00:37:19,719 Speaker 1: lubricates just what we're doing, like living in New York 779 00:37:19,760 --> 00:37:24,000 Speaker 1: would be really really difficult if we actually never j walked, 780 00:37:24,040 --> 00:37:28,080 Speaker 1: for instance. Um, but also there's there's an element of 781 00:37:28,120 --> 00:37:30,560 Speaker 1: power to it. And you mentioned this, but if you 782 00:37:30,600 --> 00:37:33,560 Speaker 1: have a manager in a workplace and they say like, oh, 783 00:37:33,600 --> 00:37:36,720 Speaker 1: you know, don't worry about this deadline, or you're supposed 784 00:37:36,760 --> 00:37:39,279 Speaker 1: to work from nine to five, but actually I don't 785 00:37:39,280 --> 00:37:42,080 Speaker 1: mind if you come in slightly late. That's also a 786 00:37:42,080 --> 00:37:47,240 Speaker 1: bargaining chip for workplace relationships. Absolutely, And then you think like, Okay, 787 00:37:47,280 --> 00:37:49,839 Speaker 1: all these businesses like, oh yeah, we love the idea 788 00:37:49,840 --> 00:37:52,640 Speaker 1: of having this software that allows us to track our 789 00:37:52,680 --> 00:37:56,120 Speaker 1: workers and track our worker productivity. But then the software 790 00:37:56,400 --> 00:37:59,600 Speaker 1: maker then itself becomes a source of power because then 791 00:37:59,640 --> 00:38:02,759 Speaker 1: they see the equivalent data for fifty other companies that 792 00:38:02,800 --> 00:38:05,160 Speaker 1: they service, and then they can aggregate that data, and 793 00:38:05,160 --> 00:38:08,440 Speaker 1: then they know things about how your employees benchmark against 794 00:38:08,480 --> 00:38:12,000 Speaker 1: employees from other companies that you, as a business owner 795 00:38:12,160 --> 00:38:14,200 Speaker 1: don't have, and then they sell you things on top 796 00:38:14,239 --> 00:38:17,320 Speaker 1: of that. So I think that like, as more jobs 797 00:38:17,320 --> 00:38:20,240 Speaker 1: are sort of under this surveillance, like again, it seems 798 00:38:20,280 --> 00:38:23,160 Speaker 1: like it's gonna be this sort of like fascinating power dynamic. 799 00:38:23,200 --> 00:38:25,560 Speaker 1: Who is the information about how stuff really works. Yeah, 800 00:38:25,600 --> 00:38:30,759 Speaker 1: and also the thorny issue of measuring productivity in quality right, 801 00:38:30,800 --> 00:38:33,399 Speaker 1: and this is something not to get really naval gay 802 00:38:33,480 --> 00:38:35,759 Speaker 1: zy here, but in the media industry, this is something 803 00:38:35,800 --> 00:38:38,080 Speaker 1: that's been going on for years where you know, we 804 00:38:38,120 --> 00:38:40,719 Speaker 1: can look at pure traffic conumbers and say, like, this 805 00:38:40,880 --> 00:38:43,480 Speaker 1: article generated a lot of traffic, but you can compare 806 00:38:43,520 --> 00:38:46,360 Speaker 1: it with something that maybe didn't get as much traffic 807 00:38:46,360 --> 00:38:49,360 Speaker 1: and say, well, this is objectively a better article like 808 00:38:49,400 --> 00:38:51,479 Speaker 1: this one has more nuance and more detail, but maybe 809 00:38:51,480 --> 00:38:54,480 Speaker 1: it wasn't as popular. That's always been a sort of 810 00:38:54,520 --> 00:38:57,480 Speaker 1: issue in journalists. But Tracy, you and I we've always 811 00:38:57,480 --> 00:39:00,880 Speaker 1: been good at traffic on two both, like I always 812 00:39:00,920 --> 00:39:03,080 Speaker 1: liked those rules because I usually like to pretty good 813 00:39:03,080 --> 00:39:06,840 Speaker 1: at that. We do quality and quantity here. But the 814 00:39:06,920 --> 00:39:11,560 Speaker 1: gamification of a place absolutely is an interesting one. Tons 815 00:39:11,560 --> 00:39:13,680 Speaker 1: of stuff to talk about that was fasting. Shall we 816 00:39:13,719 --> 00:39:15,360 Speaker 1: leave it there, Let's leave it there, all right. This 817 00:39:15,440 --> 00:39:18,160 Speaker 1: has been another episode of the All Thoughts podcast. I'm 818 00:39:18,160 --> 00:39:20,640 Speaker 1: Tracy Alloway. You can follow me on Twitter at Tracy 819 00:39:20,680 --> 00:39:23,000 Speaker 1: Alloway and I'm Joe wi Isn't Though. You can follow 820 00:39:23,040 --> 00:39:26,760 Speaker 1: me on Twitter at the Stalwart Follow our guest Karen Leavy. 821 00:39:26,880 --> 00:39:32,560 Speaker 1: She's at Karen Underscore, ec Underscore Levy. Follow our producer 822 00:39:32,640 --> 00:39:36,400 Speaker 1: Carmen Rodriguez at Carmen armand and check out all of 823 00:39:36,400 --> 00:39:40,600 Speaker 1: our podcasts Bloomberg under the handle at podcasts, and for 824 00:39:40,680 --> 00:39:43,280 Speaker 1: more Odd Lots content, go to Bloomberg dot com slash 825 00:39:43,320 --> 00:39:46,040 Speaker 1: odd Lots, where we push transcripts. Tracy and I blog, 826 00:39:46,360 --> 00:39:48,080 Speaker 1: and we have a weekly newsletter that you can go 827 00:39:48,160 --> 00:40:04,239 Speaker 1: to and subscribe to. Thanks for listening to