“We’re building their data”: Labor,alienation,and idiocy in the smart city

Abstract

In 2017, Uber Technologies Inc. launched a new service called Uber Movement. Designed by a team of 10 engineers, the new service provided a select number of cities access to Uber’s vast trove of transportation data. One of the first cities to partner with Uber on this initiative was Washington, DC. Playing directly to the city’s longstanding “smart city” aspirations, the initiative was greeted warmly by city officials eager to market the region as a symbol of data-driven urban growth and smart technology. Largely missing from this response, however, was any mention of Uber drivers themselves. Over the course of the paper, and drawing on 40 interviews conducted with Washington, DC-based Uber drivers, we examine the labor conditions that we argue are central to the production of Uber’s smart data. Beyond placing labor more centrally in critiques of the smart city, the paper suggests that the experience of Uber drivers offers us a window into the type of smart city on offer. As we argue, the city that emerges from our interviews is less a city defined by data-driven growth, than it is a city defined by alienation and isolation.

Keywords

Smart cities labor data Uber gig-economy alienation idiocy

Smart technology and intelligent use of data are critical to the success of the nation’s cities and the District of Columbia is committed to using these tools to keep pace with the rapid growth of our neighborhoods. We’re excited to be one of the early partners with Uber on this new platform. We want to employ as many data sources as possible to mitigate traffic congestion, improve infrastructure, and make our streets safer for every visitor and resident in the nation’s capital. (Muriel Bowser, Mayor of Washington, DC, 8 January 2017)

I joke, people keep talking about driverless cars, which I still think [are] way far off, but honestly if you look at it, we’re building their data, […] Uber has monstrous data now to see what the trends are, what times of day are people driving, what roads are they using, what roads does the map recommend versus what way would a driver go. […] They’re going to have that data to make it happen. And I joke with people, we’re their guinea pigs now. We’re building that data for them, and they have it. (Joe, Uber driver, interview with authors, 19 May 2016)

Introduction

In 2017, the global “ridesharing” company Uber Technologies Inc. began the year by launching a service called Uber Movement. Designed by a team of 10 engineers, Uber Movement was created as a free web-based service allowing users to access a vast trove of city-specific transportation data—all of which had been collected through Uber’s “ridesharing” app (Dwoskin and Siddiqui, 2017; Newcomb, 2017). Marketed to both urban transportation planners and municipal policy makers, Uber Movement’s primary selling point was clear. In short, and to paraphrase company representatives Joan Golberston and Andrew Salzberg, Uber Movement promised to help “urban planners and civic communities” use data to “crack their city’s commute” (Newcomb, 2017). One of the first cities in the US to partner with Uber on this new initiative was Washington, DC. Within months of its launch, DC residents were able to access a select cache of historic data on congestion patterns and road usage. Of course, for local city officials the appeal of Uber Movement went beyond transportation. Uber Movement not only marked an opportunity to build on “smart city” initiatives like SmartDC—an early smart city initiative—but to remake the city as a national symbol of data-driven urban growth, “smart technology” and the intelligent use of data (Office of Chief of Technology, 2018).¹ Indeed, for the city’s mayor, Muriel Bowser, this was precisely the goal (Shueh, 2017).

While there was a great deal of excitement leading up to the launch of Uber Movement, questions remained. What type of data were to be released? How many data would Uber be willing to share?² What exactly was meant by “smart technology”? In mitigating the city’s traffic congestion or improving the city’s infrastructure, what type of information would Uber really be willing to divulge? Of course, for yet other observers, the lead up to Uber Movement’s launch was notable for another reason—namely the wholesale absence of any mention of Uber drivers themselves. This absence was especially notable in light of the comments made by Joe, the Uber driver quoted in the above epigraph. For Joe and any number of other drivers in Washington, DC, Uber’s vast dataset was not simply a product of 10 engineers, it was also a partial product of their labor. To quote Joe directly: “we’re building that data for them, and they have it” (Interview with authors, 19 May 2016). This paper starts from the presumption that Joe is, indeed, correct. When Uber drivers take the wheel they are invariably producing more than a service called “ridesharing.” They are also helping to produce a thing called “data.”³ For those engaged in debates on smart cities, this paper argues that there is a desperate need to pay attention to the laboring experience of workers like Joe. For one, it allows us to see “data” not as a thing but, instead, as the product of a definite set of social relations. These are relations between drivers and Uber, between drivers themselves, and between drivers and the wider public. More importantly, it allows us to see another side of the smart city—one defined less by “data-driven growth” than by alienation and privatized isolation.

The paper proceeds as follows. In the first section we focus on the idea of the smart city. We suggest that while there are many important critiques of the smart city, such critiques have rarely touched on questions of labor. In building a critique that takes labor seriously, we argue that one place to start is with the work of scholars like Christian Fuchs (2010) as well as the recent work of Jim Thatcher et al. (2016). Such scholars have placed labor at the center of their critiques of both platform capitalism and “big data.” Building on such work, we argue that a labor critique of the smart city must also wrestle with the experience of workers like Joe. After a brief comment on methods, we then move to our case study. Here we draw from interviews conducted with 40 Uber drivers in the Washington, DC area. Drawing on these interviews, we suggest, that to start with the labor process under Uber is to get a rather dark picture of both Uber Movement and the smart city idea to which it was designed to appeal. In our conclusion we draw on the Marxist inflected notion of idiocy and alienation to both sketch out that picture, and to name the potential costs of the smart city on offer—especially when built upon the shoulders of the workers that we profile below.

Where is labor in the smart city?

For city officials and policy makers eager to use data to improve the delivery of public services—and often within an environment of increasing competition and budget constrains—initiatives like Uber Movement are invariably appealing. In many ways, their appeal is that of the smart city more generally. As is commonly understood, a smart city is simply a city capable of using the “data from connected devices […] to keep traffic flowing […] and improve the delivery of public services” (Kitchin, 2014; Why HPC Matters: Smart Cities, 2017). Given the increasing proliferation of sensors, smart phones, and information communication technologies, proponents of smart cities are generally those who believe that cities can put such technologies to good use. As they argue, to the degree that cities can capture and analyze the data generated by ICTs or smart sensors, addressing the problems that so often plague urban residents will be that much easier—be these challenges associated with transportation, the delivery of public services, or public participation (Why HPC Matters: Smart Cities, 2017).

Of course, as is always the case, there are those who take a more critical stance (see, inter alia, Coletta and Kitchin, 2017; Greenfield, 2013; Hollands, 2008, 2015; Humphries, 2013; Iveson and Maalsen, 2018; Kitchin, 2015; Leszczynski, 2016; Sennett, 2012; Soderstrom et al., 2014; Townsend, 2013; Vanolo, 2014; Van Zoonen, 2016; Wiig, 2016; Wiig and Wyly, 2016; Zook, 2013). Perhaps the most notable critiques of the smart city have come from the scholars Robert Hollands and Rob Kitchin. As Hollands (2008) argues, the experience of “self-designated smart cities” has often been less than ideal. Despite their promise to be more “livable, secure, functional, competitive and sustainable,” the reality can be quite different (Kitchin, 2014: 2). Self-designated smart cities, Hollands asserts, are often merely cities where the needs of global capital trump those of more “stationary ordinary citizens” (Hollands, 2008: 311) Rob Kitchin (2014) has argued much the same thing. As Kitchin asserts, the rhetoric of the smart city too often masks the production of cities defined by technocratic governance, corporate capture, and increased surveillance. Rather than producing the smart city, we invariably find the production of the panoptic city, the post-political city, the corporate city, or the technocratic one (Bartoli et al., 2011; Greenfield, 2006; Kitchin, 2014; Wiig, 2016).

Where the works of Hollands and Kitchin have echoed other critiques of the smart city they have also revealed what is too often missing in those critiques—namely any discussion of labor, particularly the labor that goes into producing data. While questions of privacy, corporate capture, and governance remain important, this paper presumes that any critical appraisal of smart cities also ought to take up the question implicit in comments like those from Joe—namely, what type of labor goes into producing the smart city? And, just as importantly, what are the conditions of that labor?

One potential way of addressing this question is to look to the work of scholars like Christian Fuchs. Focused on the rise of what he has deemed “platform capitalism” or “informational capitalism,” in his essay with Sebastian Sevignani, Fuchs argues that much of the data so central to the accumulation strategies of companies like Facebook, YouTube, and Google rest on both “the exploitation of ‘users’ unpaid labor,” as well as on the ability of such companies to blur the distinction between leisure time and labor time (Fuchs and Sevignani, 2013: 237; Fumagalli et al., 2018). According to Fuchs (2010, 2014), when we browse YouTube videos, or connect with friends via Facebook, not only are we producing value for these various companies but we are invariably doing so as unpaid digital laborers (a category that he distinguishes from digital workers). As Fuchs argues, and using the example of platforms like Facebook, “Capitalist Internet produsage is an extreme form of exploitation, in which the produsers work completely for free and are therefore infinitely exploited [sic]” (2010: 191).⁴ Scholars like Abigail de Kosnik (2012), Mark Andrejevic (2012), and Trebor Sholz (2012), yet more recently, Shoshana Zuboff (2019) have all offered similar analyses.

In their article “Data Colonialism through Accumulation by Dispossession” authors Jim Thatcher et al. (2016) provide yet another possible approach to the question of labor in the smart city. Their piece focuses on the process by which “individual data” generated by “an individual user of technology becomes” the exchangeable market commodity called “big data.” As Thatcher et al. point out, this process is not only defined by “asymmetrical power relations” between technology providers and users, but also “the commodification and extraction of personal information as data.” These data are ultimately alienated from the very people who produce them—often through tacit data license agreements (2016: 991,995).⁵ According to Thatcher et al. this practice—whereby labor relations are extended yet further into “previously private times and spaces”—is best described, as a process of “data colonialism through accumulation by dispossession.” By appealing to Harvey’s (2004) notion of “accumulation by dispossession,” in many ways, Thatcher et al. advance an argument parallel to that Fuchs—namely, that “big data,” like platform capitalism, more broadly, rests on “an extreme form of exploitation.”

In making sense of the relationship between smart city initiatives like Uber Movement and the experience of Uber drivers like Joe, the ideas of both Fuchs (2010) and Thatcher et al. (2016) are useful. In short, and as the case study will show, where Uber has accumulated a vast trove of data on cities, it has done so through processes that are defined by “asymmetrical power relations,” and by the “extension of labor relations into previously private times and spaces” (Thatcher et al., 2016: 991). Of course, the work of Fuchs and Thatcher et al. can only get us so far. Where such work may help us see the exploitation of Uber drivers as central to initiatives like Uber Movement, such work also tells us very little about the nature of that exploitation, or what that exploitation looks like from the perspective of driver themselves. Such a perspective, we argue, is important. It is important because it reveals, in visceral terms, what is required to produce Uber’s data, and what types of social relations may be obscured in the demand for the smart city.⁶

Of course beyond placing labor and the experience of labor more centrally within scholarship on smart cities, the following case study also builds on the work of scholars like Alex Rosenblatt (2018), Rosenblatt and Stark (2016), Rosaria Berliner and Gil Tal (2018), Ryan Calo (2017), and Sarah Mason (2018)—all of whom have offered an inside look into the lives of Uber drivers. While our case study uncovers many of the same labor conditions explored in these studies, we suggest that these conditions also ought to matter for how we think about data, the smart city, and the costs of data-driven urban growth.

A brief word on methods…

Before proceeding further, we might offer a brief comment on the case study itself. Our initial research project aimed at exploring the work lives of Uber drivers based in Washington, DC. We based our study in Washington, DC for several reasons. Not only had the city emerged as one of Uber’s top markets, but it had also emerged as something of a model city with respect to Uber-friendly legislation (Hall and Krueger, 2015; Interview with authors, Uber Lobbyist, 28 July 2016). When we began our study in 2016, Uber had already been operating in the city for five years. As in many municipalities, Uber first appeared as a modified black car service—using a smart phone app to provide on-demand trips to passengers via luxury sedans. In 2013, Uber expanded on this operation with the launch of UberX. Where Uber’s black car service worked with drivers already licensed to provide livery services, UberX opened the door to any individual with a private automobile, a regular driver’s license, and a willingness to pick up strangers (Hendrix and Aratani, 2013). As a low cost alternative to local taxis, Uber enjoyed almost immediate popularity. Of course, Uber’s entry into the city raised any number of questions—the most pressing for us being on the working conditions of the drivers.

Over the course of 2016, we conducted 40 interviews with Uber drivers in the DC area. We also conducted an additional 22 interviews with local officials and stakeholders engaged in debates on Uber’s role in the city. While many local officials and civic leaders were eager to talk to us, enlisting drivers in our study was far more challenging. Ultimately we recruited drivers through UberPeople.net—an online chat room frequented by DC-based Uber drivers. These interviews were conducted in person and were roughly an hour in length. Our questions ranged from the broad to the specific. In addition to exploring drivers’ motivations, their work history and their own feelings about Uber as a company, the bulk of our questions explored the more quotidian elements of the job—from their daily routine to their strategies for making money. Almost (34) all of our 40 informants also completed an online survey giving us both basic demographic and financial data. When Uber Movement launched in 2017 we had already finished our data collection. As a result, our interviews rarely broached issues explicitly related to smart cities or smart data. With that said, it quickly became apparent that our findings were relevant to debates on those very issues. Indeed, our findings seemed to address a fundamental gap in the smart city literature—namely, the absence of any discussion of the people we profile below—the workers, who we argue, remain central to the production of Uber’s data.⁷

Into the “data mill” we go!

[W]e’re their guinea pigs now. We’re building that data for them, and they have it. (Joe, interview with authors, 19 May 2016)

When we met Joe in mid-May of 2016, he had already been driving for Uber for a year.⁸ Like many Uber drivers we interviewed, Joe had taken up Uber as a way to supplement his income. As the first in his family to go to college, let alone graduate school, Joe was used to picking up odd-jobs. Even so, as he explained, this last year had been especially full. On top of spending roughly 20 hours a week behind the wheel for Uber, Joe reported working an additional 55 hours a week as a security guard and as an outreach coordinator for a local university. As Joe explained, the differences between the jobs could not be starker. As a security guard, he observed “I’m paid the same rate all the time, whereas [with] Uber it can fluctuate.” As Joe explained: “Uber is a strategy game, especially if you do it part time, because you want to pick the hours that it’s going to be busy.” As Joe implied, picking those hours could be difficult. On the day we met Joe, he had just worked a morning shift for Uber. He was already regretting it.

This morning, I woke up randomly. It was 4:30 and I decided, I haven’t done a morning shift in a while, I want to see how it is. Well, I got gas, [and] was on the road from my house [by] approximately […] 5:15. [I] turned the app on, and there was absolutely no business. And Uber right now, Uber offers different incentives to get drivers out. And right now they’re offering, your fares will be 1.4 for every fare more if you are out during this time, and that time was 5–10 am. Well, I made $8.78 for the hour that I was online, so I called it a day, that was about 7:15. […] So there’s times like that where it reinforced that I’m not going to do morning shifts ever again, because it completely wasn’t worth it. (Interview with authors, 19 May 2016)

For Joe, the idea of doing another morning shift was out of the question. At $8.78 an hour, the pay rate was simply too low. Of course, from Uber’s perspective, Joe’s morning had been a raging success. In his 2016 article, “How Uber profits even while its drivers aren’t earning any money” tech writer Jay Cassano (2016) explains why. As Cassano argues: “even when drivers don’t have passengers” they are still generating data for Uber. More importantly, these data are central to how Uber sets prices, directs drivers, and manages incentives.⁹ In the case of Joe, the same argument applies. Over the course of 2 hours, Uber had not only secured a 25% commission on Joe’s fare revenue, but it had gained valuable information about city roads, about traffic patterns and about passengers themselves, specifically their pickup and drop off locations—be these locations restaurants, nightclubs, hotels, or job sites. Over those 2 hours, Joe had produced more than a service called “ridesharing.” He had also produced data. These data were then mobilized by Uber to manage workflow, alter incentives, or redirect drivers. Indeed, as some observers have argued, such data, may be central to Uber’s stratospheric valuation—especially given its potential value to advertisers, governments, and private retailers (Calvey, 2015; Hirson, 2015). Where services like Uber Movement play directly to the hopes of smart city proponents—many of whom hope to put such data to good use—the following section offers a caveat. Namely it starts from the presumption that Uber’s data cannot be separated from the laboring conditions described below. These conditions are not only defined by unpaid labor or asymmetrical power relations—following Fuchs and Thatcher et al. and the work of writers like Cassano—but by a labor process that encourages isolation and alienation.

Chasing the surge

In early May 2016, we interviewed Diana. At the time, Diana was working as both an Uber driver and as a server at the Fuddruckers fast food chain. As Diana explained, after only four weeks of driving for Uber, she had already figured it out. The best way to make money, she said, was to work weekday mornings from roughly 5 to 10 a.m., and weekends from roughly 9 p.m. to 5 a.m. Weekend evenings, she added, were especially lucrative because they invariably involved reaping the benefits of what Uber called “surge pricing.” As she recounted, surges occurred at distinct times and in distinct areas of the city. During a so-called “surge,” a driver might potentially double, triple, or even quadruple what they might make under normal circumstances. For Diana, and many others, one of the central strategies for making money as an Uber driver involved “chasing in the surge” (Interview with authors, 11 May 2016). Another part-time driver named Jack noted the same thing. Jack was an information technology specialist who had taken up driving for Uber to make some extra money. For him, like Diana, driving during a surge was the only thing that made the job worth it.

I just do it mainly when there’s huge demand with surge, or when I feel like driving. Because I do enjoy driving sometimes. But since I work in downtown Arlington, off of a major road, there’s always some sort of demand every now and then. So basically it’s like, I work my daytime job, and then I turn [the app] on after I’m leaving my office to see if there’s a surge. And if there’s a reasonable surge, then I’ll drive, because then it’s worth my time. (Interview with authors, Jack, 27 June 2016)

Jack and Diana were hardly alone in their reliance on the surge. In our survey, a driver named Harry wrote: “Driving for base rates = financial suicide.” In 2016, Uber modified its surge pricing model by introducing a new system of “Boosts.” Boosts were automatic surges granted to drivers who had successfully completed a predetermined number of trips during the previous week. Uber’s “Boost system” consisted of four levels: Bronze, Silver, Gold, and Platinum. As one driver explained, all drivers begin at Bronze. If a driver completes 35 trips in a seven-day period, they are boosted to Silver for the following week. For drivers who reach Silver, every fare within a designated area of the city is automatically “surge priced” at 1.4 or 1.6 times the base fare. The surge coefficients at the Gold and Platinum level are even higher. For Ben, another driver we interviewed, Uber’s system of boosts reoriented his entire strategy. Rather than simply “chasing the surge,” Ben aimed to “chase Platinum.”¹⁰ As he explained:

So my first week driving, I literally hit the road. I think it was like July 4th weekend, and I had some days off from work. So I was on the road. I mean, I just had coffee upon coffee, and I’m just on the road. So I end up doing […] 94 rides that week, and it was actually a short week. It was 94 rides in essentially 3 days. So […] I didn’t know this, but after you do 90 rides, I think its 90 or 95, they bump you to platinum. So at the time, I got lucky, when it came to timing, because in the DC/Maryland/Virginia region, they have like an overlay on your Uber map. So they literally gave me 2.2x every ride I picked up […] that whole week. And then, basically, you’re always chasing Platinum. So the minute you get it, then you have it for that whole week. So now you’re motivated to go do another 100 plus or more, just keep going. So at the end of every week, they’ll send you a text and tell you, “congratulations, you made Platinum.” (Interview with authors, 9 August, 2016)

For drivers unmoved by Uber’s “boosts” the company sought other methods of encouragement. One driver we interview named Mark had been driving for Uber for just under a year. Over the course of that time he made note of what he called “Uber’s McMessages.” To quote Mark directly:

They send me weekly emails on my earnings: how many trips I did, what the top drivers are doing. I can benchmark myself in terms of revenue per hour, number of trips per hour, hours online. They send a lot of what I call McMessages out to drivers, like “It’s raining, get ready for big demand, have fun!” Or “There’s a [baseball] game tonight!” (Interview with authors, 11 May 2016)

Another driver named Anthony noted the same practice. As Anthony recalled, however, Uber’s “McMessages” often carried with them a subtle reproach—especially when drivers, for whatever reason, declined to pick up a passenger.

It’s interesting, they kind of do a little nudge. I don’t think they’re going to quit sending me fares, but they say, “Oh you could have made more money today!” It doesn’t say how much, but it does say, “You didn’t accept a fare! You could have more money!” Right, and it’s interesting because it’s a psychological nudge. It’s saying, hey buddy… (Interview with authors, 25 July 2016)

While such messages might be annoying, they hardly elicited the fears so often associated with yet another one of Uber’s practices—namely the company’s practice of suspending or deactivating drivers for what they deemed a substandard performance. According to the drivers we interviewed, one of the most common reasons that Uber might suspend a driver is in response to their “star rating.” Ranked on a scale of 5 stars, a driver’s star rating is based on reviews issued by passengers on everything from a driver’s navigation skills to the cleanliness of the car. As many drivers recalled, a star rating below 4.2 put that them at risk of deactivation. Uber had a similar policy related to a driver’s acceptance rate. Where drivers are given the option of either accepting or rejecting a ride-request, too many ride rejections over a given period might also prompt Uber to issue a suspension. In many ways Uber’s deactivation policies—although far more punitive—served the same purpose as its use of “surge pricing” or “boosts.” That purpose: to keep as many drivers working, and on the platform as possible.

In making sense of Uber’s use of what one driver deemed “carrots and sticks and incentives and prods and slaps on the hand” (Interview with authors, 11 May 2016) the work of Sarah Mason (2018) is useful. In her essay “High score, low pay: why the gig economy loves gamification,” Mason defines gamification as the “use of game elements […] to increase worker’s psychological investment in completing otherwise uninspiring tasks.” These elements include: “point scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play” (2018: 1). Borrowing from the work of Michael Burroway, Mason argues that the gamification of work not only has the effect of tapping into a “worker’s desire for self-determination,” but of directing that “desire towards the production of profit for the employer” (2018: 1). Almost all of the game elements that Mason identifies as part of the gamification process are a part of how Uber manages its drivers. In the case of Uber, however, these elements not only direct drivers to work longer hours, thus producing more data for Uber, but they direct drivers to work in ways that are inherently isolating and that function to undermine anything approaching collective action (whether that action involves a collective appeal for higher wages, or safer working conditions).

Working alone

Of the 40 Uber drivers we interviewed, only a small handful had ever met another driver. Of these same drivers, an even smaller number recalled having anything approaching a meaningful interaction. Of course, given the incentives described above, this is hardly surprising. There is little about the Uber platform that seems designed to foster collaboration or to bring drivers together. Indeed, all evidence suggests the opposite. Where drivers are required to engage in a zero-sum competition for passengers, and where they are rewarded for being the quickest to respond to a surge bloom, Uber drivers are less likely to see each other as co-workers than they are too see each other as competitors or, in some instances, as dupes who haven’t figured the game out yet. A driver we interviewed named Noam argued that the resulting isolation of drivers was precisely the goal. When we interviewed Noam he had just moved to Washington, DC from Penang, Malaysia. According to Noam, DC-based Uber drivers were especially isolated. This, he argued, was intentional:

Well, in the United States, here’s my cynical theory about this. In the United States, because to be an Uber drivers is to be kind of exploited by Uber, to have them all connected like in Penang would probably cause them to be easier to unionize, create lawsuits, and put more pressure on Uber to create employees […]. And so I think that’s probably something they foresaw when they were planning, when they brought Uber to D.C. or to Baltimore. They said, yeah, we gotta make sure they don’t know each other, that we just keep funneling them through the farm, so to speak. (Interview with authors, Noam, 12 May 2016)

While it is hard to verify Noam’s account of Penang, his larger point is an important one. To the extent that Uber can make sure that drivers “don’t know each other” they, invariably, limit the possibilities for collective action.¹¹ Mark, whom we quoted earlier, expanded on this point. Although virulently anti-union, Mark was quick to admit that the very structure of Uber’s work made such efforts difficult. It was not simply America’s cultural of “everyman for himself,” as Mark put it, but the platform itself:

… there have been attempts at informal strikes, even in the D.C. Market. There was one organized, and they promoted the heck out of it everywhere, trying to get a strike. But no one, no other driver knows if you go online or not. It’s not enforceable, you know what I’m saying? In a steel plant, […] you have to scab, you have to cross a picket line. (Interview with authors, 11 May 2016)

As Mark argued, the platform nature of the work, and the ability of drivers to log in and log off anonymously, militates against the very types of collective actions that have long defined unions and that have long been central to winning improved working conditions. Unlike picketing a factory, fighting Uber requires picketing an algorithm. More centrally still, the ability of Uber to surge price, or boost those drivers willing to “scab” poses yet another barrier to the collective strategies that unions and other groups often rely upon.

Of course, to the degree that Uber has organized work in ways that seem to discourage collective action, the alienation and antagonisms that define the labor process extend in yet other directions. Mark’s own story shows precisely this pattern. When we interviewed Mark, he was 59 years old. A father of four, he lived with his wife in McLean, Virginia. As Mark admitted, his career had been defined by reinvention. Before working as an Uber driver, he had been a patent lawyer, a founder of a tech firm, and a radio technician. At the time we interviewed him, he was balancing his work as an Uber driver with a full-time job as a mid-level manager at a local research firm. When we asked Mark why he drove for Uber, his response was an interesting one:

I think it was the interplay of several things … One was my commute from McLean to Alexandria is long: 16, 17 miles. So I looked at Uber initially as a way to monetize my commute … And then the second aspect was, my family is tightly budgeted. I mean, I have, as I said, 2 kids at college, 2 in high school. We live in McLean. My wife only works part time. Money is hard to come by. We rent. We don’t own a house. My tax deductions are limited. And we’re not exactly paycheck to paycheck, but we have no safety cushion. So I was thinking that this would be a way to add to my income without really affecting my regular job. (Interview with authors, 11 May 2016)

As with the majority of the drivers we interviewed, Mark had never met another Uber driver “face to face.” Indeed, Mark seemed rather uninterested in the prospect. As he freely admitted, his own class sensibilities got in the way.

I see them on the streets, and I see them in airport lots, but I don’t have any real interest in socializing with other Uber drivers. Partly, I think that—and I don’t know this—but partly, I think that I’m, hmm, how to say this? Overqualified for the job. And my wife’s not completely comfortable with it, because when I first started doing it, she kept saying, “You’re now a taxi driver,” and I think she sees that maybe it’s a stereotype that everybody’s an immigrant, without speaking English, and that kind of thing. (Interview with authors, 11 May 2016)

As Mark tried to make clear, given his background and status, socializing with other drivers made little sense. Rather than building relations with other drivers, Mark seemed more interested in shoring up his status with paying passengers. Indeed, one of his few complaints about Uber was that the company did too little to reward drivers like himself—drivers who performed at his level.

I think I’m one of the better drivers out there. I really do. I’m experienced, I’ve never been in an accident in my whole life, I’m a very careful driver. And I’d like to be rewarded for that. I’d like to have some upward mobility. Not just in, ok [Mark], you’re a VIP driver. But something substantive that allows for some kind of a status with passengers. (Interview with authors, 11 May 2016)

As much as he enjoyed aspects of the job—especially meeting new people—as the interview went on, it also became clear that Mark himself was beginning to have misgivings. Not only did it seem “below” his class position, and not only did his wife disapprove because it took him away from his family responsibilities, but whatever work-life balance he had struck before taking up with Uber had largely disappeared. On a normal day, Mark explained, he worked a 13-hour day—8 hours at his research job and then 5 additional hours as an Uber driver. After leaving the house at 6 a.m., Mark returned home at 7 p.m. This schedule was beginning to take a toll.

It’s adversely affected my work life balance, and I don’t do things that I used to do. For example, I used to study a lot more in my field. I’m not really keeping up as much with my field. Secondly, I used to work longer hours at my current job. I used to work, say 7:30–6, and now I don’t. Part of that is because I don’t like my current job and the people, I don’t feel like they like me either. So I’m giving them everything they want but no more than that … Before I started with Uber I used to ride my bike once a week, all the way from McLean to Alexandria. It’s a long ride. And I really love it. Sometimes I would ride twice a week. But now since I started Uber-ing, it now costs money if I ride my bike. So I found myself not riding as often … Although I am basically healthy, knock on wood, I think I’m probably not as good aerobic shape as I was … (Interview with authors, 11 May 2016)

While Uber’s impact on Mark’s quality of life was not wholly positive, the prospects of making money were simply too great. Indeed, when we interviewed Mark in the spring of 2016, those prospects seemed even brighter. Washington, DC was on the cusp of what would later be labeled the “Metropocalypse.” Following a series of track fires, officials in DC were mandating extensive upgrades to the entire metro system. The impact on service was projected to be dire—namely, countless delays and slower commute times. For drivers like Mark, of course, this was hardly a problem at all. As he noted: “it’s going to be huge for Uber. There’s nothing like a Metro glitch to send fares spiking.” Mark was not alone in this belief. Diana, whom we quote earlier in the paper, expressed the same excitement: “I’m ready … I’m so ready … I’m going to make so much money. So many customers. It’s going to be a lot of riders out there, a lot of customers out there. I like when Metro don’t work, that’s how I make my money” (Interview with authors, 11 May 2016).

Where Uber has organized work in such a way as to encourage a certain degree of isolation, Mark’s story suggests that some drivers—whether as a result of their own class sensibilities, or their desire for anonymity—may choose isolation on their own accord. Mark’s story, however, also points to the ways in which the alienation that seems to characterize the labor process also extends into other realms. Where Mark and other drivers find themselves gleefully celebrating the potential of a “Metro glitch” because it might send “fares spiking,” the alienation and isolation evidenced here is not the alienation of an isolated driver from other drivers but the alienation of a driver from the public itself and the collective good. Of course, perhaps the most significant takeaway from Mark’s story is how Uber has reshaped his own sense of leisure time—which now appears to him with a price tag. As Mark admitted: “since I started Uber-ing, it now costs money if I ride my bike.” Such comments, of course, speak to an alienation directed inward—namely one’s alienation from one’s own free time, one’s health and from one’s life outside of work. In this feeling, Mark was hardly alone. Over the course of 40 interviews, stories of drivers working 13 hours a day, missing meals, and putting themselves in danger were common. With such drivers, as had been the case with Mark, free time and leisure no longer appeared as escapes from the drudgery of work, they now appeared as sunk costs, lost wages, and a missed opportunity to ride the surge or to reach the boost.

Uber has amassed an incredible amount of data on cities, data that cities like Washington, DC want to put to use. This paper suggests that such smart data cannot be separated from the laboring conditions described above. These are conditions that, to quote Noam, keep drivers “funneling through the farm” (Interview with authors, Noam, 12 May 2016). Of course, to talk to any number of drivers, the experience of working for Uber is not entirely negative. As we learned from our interviews, not only can working for Uber provide supplemental income, but it can also be quite enjoyable. Even for those who have found joy in working for Uber, the suspicions of drivers like Noam seem defensible and worth exploring. For Noam, the reality of working for Uber in Washington, DC was a reality defined by isolation and a lack of connection. For Noam, this was purposeful. By making sure that drivers “don’t know each other” Uber might inoculate itself from attempts by drivers to unionize, “create lawsuits,” or demand that they be recognized as employees—as opposed to independent contractors. If the precise degree to which Uber drivers have been atomized is difficult to measure or verify—our own small sample of 40 drivers notwithstanding—the design of the Uber platform itself does little to assuage the suspicion that isolation is a feature rather than a bug. Citing Mason (2018: 1), the design of the Uber platform—characterized by point scoring, levels, a zero-sum competition with others, and ratings—is one that remains focused on individualizing work and discouraging anything approaching collective action. Of course, the isolation that drivers face is certainly never total—see the recent strike of Uber drivers in Los Angeles (Bhuiyan, 2019). Still, where drivers do establish connections with other drivers, they often do so in spite of the platform itself. Recalling the experience of drivers like Mark, we find a platform that not only seems designed to promote alienation between individual drivers, or between drivers and the collective good, but between drivers and their own “free time.”¹²

Returning to Uber Movement, the question remains: what do the above findings mean for how we think about smart cities, or, how we think about labor in the smart city? In some ways, the work of Fuchs (2010) and Thatcher et al. (2016) remain central. From their reading, we might conclude that the smart city—which relies greatly on data collected from sensors and smart phones—is necessarily a city built on asymmetrical power relations, data colonialism, and the unpaid labor of people like Mark, Joe and Diana. The experiences outlined above suggest that such a reading may not be too far off. Where drivers have little say over the types of data that Uber collects from them, and where they produce such data even when they are not collecting fare revenue, it is easy to see them, and the smart city to which their data may serve, in precisely the terms that Fuchs (2010) and Thatcher et al. (2016) describe. Indeed, to the extent that drivers begin to see their free time as a sunk cost, Thacher et al.’s (2016) comment on the extension of labor relations into “previously private times and spaces” seems apt. Of course, the above findings are also useful in making a far narrower argument. That argument is about the social relations that emerge from the labor process described above. Moreover, it is an argument about what those social relations reveal about the type of smart city on offer. As the concluding section suggests, the nature of that city is best described by turning to Marx and Engels.

Conclusion: The idiocy of the smart city

Social relations are closely bound up with productive forces. In acquiring new productive forces men change their mode of production and in changing their mode of production, in changing the way of earning, their living, they change all their social relations. The hand-mill gives you society with the feudal lord; the steam-mill, society with the industrial capitalist. (Karl Marx, The Poverty of Philosophy 1966[1847])

Where initiatives like Uber Movement have played directly to those taken by the promise of the smart city, this paper has suggested that both proponents and critics of the smart city idea ought to pay attention to the experience of Uber drivers themselves—many of whom are well aware of their role in producing Uber’s data. The benefits of focusing on the labor of such drivers are multiple. Not only do observers get a sense of the exploitation and asymmetrical power relations central to the production of smart data, but they also get a sense of the social relations that emerge from the production process itself. To look at the experience of drivers like Joe is, we argue, to get a darker view of the smart city—especially where Uber is involved. Drawing on Marx’s (1966: 95) pithy anecdote from The Poverty of Philosophy, this last section begins with a question. To the extent that the “hand-mill gives you society with the feudal lord and the steam-mill, society with the industrial capitalist,” the question for proponents of the smart city is clear: what type of smart city does the Uber app give you?¹³

Based on the above case study, and once again drawing on Marx, one possible answer is the idiotic city. Writing, as they were, in the midst of the industrial revolution, Marx and Engels ([1848] 1998) penned their most famous work the Communist Manifesto while witness to the emergence of enormous cities. Whether it was Manchester or Glasgow, the industrial city of the 19th century was necessarily one of contradiction. Not only were such cities defined by the unprecedented accumulation of wealth but also by the creation of unprecedented poverty and sprawling slums. For Marx and Engels, the contradictions at the heart of the industrial city went deeper. If cities were central to the growth of industrial capitalism, they also held out the promise of its negation through new forms of collective struggle. Part of that promise rested on what Marx and Engels saw as the inventible role of cities in rescuing workers from what they termed “the idiocy of rural life.” What Marx and Engels meant by idiocy was, of course, not that rural life was stupid or dumb, but rather that rural life was isolating, alienating, and fundamentally apolitical. As Hal Draper (2004: 220) argues in his re-translation of the Manifesto, Marx and Engels were drawing on the classical meaning of idiocy to denote “privatized isolation.” As Marx and Engels implied, to the extent that rural life was inherently isolating and apolitical, the city promised just the opposite. It was in cities, as Engels (1936 [1844]: 122) wrote in The Condition of the English Working Class, that “workers begin to feel as a class, as a whole” and where their “consciousness of oppression awakens.” It was in these same cities that workers not only came face to face with their shared immiseration but with their foreordained role as historic actors in the fight against economic exploitation. As has been argued elsewhere (Attoh, 2017a, 2017b), the reality of the contemporary city suggests that Marx and Engels may have been a bit optimistic. Rather than cities that provide a basis for collective class struggle, we find cities defined by the same privatized isolation that Marx and Engels ascribed to the country side. From the perspective of drivers like Joe or Mark—drivers who work in relative isolation and who are incentivized to think of work as a game played against other drivers—it is this latter city, the idiotic city that seems most resonant.

While Uber Movement has garnered little public attention since its launch, Uber continues to figure prominently in Washington, DC’s efforts to market itself as a smart city. In late 2017, Mayor Muriel Bowser chose to highlight Uber as part of #ObviouslyDC week—a seven-day campaign aimed at highlighting why Washington, DC should be selected as the site of the new Amazon headquarters (Executive Office of the Mayor, 2017). As that campaign was clear to note, the city had become “a leader in mobility and smart city innovation” (ObviouslyDC, 2018). In 2018, Mayor Muriel Bowser joined Uber CEO Dara Khosrowshahi for a panel discussion on the future of mobility. The panel was held at Uber’s recently opened Uber Greenlight hub—a brick and mortar resource center for local Uber drivers. At the event, and once again appealing to local smart city proponents, Uber announced a “first of its kind partnership.” This partnership would be between Uber, the city, and National Association of City Transportation Officials. Through the partnership Uber would yield yet additional data to both the District Department of Transportation and the Department of For Hire Vehicles (DFHV; Executive Office of the Mayor, 2018). For smart city proponents in Washington, DC, once again, it appeared that Uber’s data would lead the way.

Whether in response to initiatives like Uber Movement, or to Uber’s new partnership with the DFHV, this paper has emphasized the importance of returning to the comments of Joe, the Uber driver we interviewed in 2016. As Joe claimed: “we’re building that data for them, and they have it” (Interview with authors, 19 May 2016). Based on such comments, we have argued for placing labor more centrally in how we think about smart cities. Where the work of scholars on platform capitalism (Fuchs, 2010; Sholz, 2012), big data (Leszczynski, 2016; Thatcher et al., 2016; Tufekci, 2014), and now surveillance capitalism (Zuboff, 2019) offers one way of thinking about the relationship between labor and data, we presume that there is equal value in examining the lives of workers like Joe—workers central to the production of Uber’s smart data. For both critics and proponents of smart cities, the above case study ought to be illuminating. Not only do we get a sense of the labor that goes into producing Uber’s “smart data,” but we invariably get a sense of the type of smart city that emerges. Despite the fears of critics like Hollands (2008) and Kitchin (2014) the city that emerges from our interviews is as much the panoptic city, the post-political city, and the corporate city, as it is the idiotic city—one defined by competition, alienation, and privatized isolation. Where the above case study certainly lays the groundwork for further critiques of the smart city, it also arguably invites us to think about alternatives. Whether those alternatives are modeled on the smart city experience of cities like Barcelona or Madrid—cities that have attempted to use the proliferation of sensors and ICTs to expand civic participation—our paper suggests that labor questions still ought to remain central (Bakici et al., 2013; Capdevila and Zarlenga, 2015; Gutierrez, 2016). Indeed, part of imagining a smart city that is less isolating, more democratic, and where workers like Joe remain unalienated from the data they produce requires that we both take seriously the experience of the workers described above as well the labor process itself.

Footnotes

Acknowledgements

In addition to the helpful guidance of the three reviewers we are also grateful to Don Mitchell, Joaquin Villanueva, Penny Lewis, and Joe Nevins whose comments and support helped us clarify our argument and contribution.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received funding from the Kauffman Foundation under Grant# 2015642.

Notes

Kafui Attoh is an Assistant Professor at the CUNY School for Labor and Urban Studies.

Katie Wells is an Urban Studies Foundation Postdoctoral Research Fellow at Georgetown University's Kalmanovitz Initiative for Labor and the Working Poor.

Declan Cullen is a Visiting Professor in the Department of Geography at The George Washington University.

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