Prompt: But the history of the information utility shows that there are alternatives. It reminds us that there is nothing natural about computing’s current form. It suggests that the problems of digital capitalism are inseparable from the problems of capitalism itself. Technologies can’t make social choices for us. The hope that computers could fix the market, make a just society, and democratize politics or information are nearly as old as the machines themselves. To define the technology we want means first defining the society we want. It means asking old questions: How can democracy coexist with capitalism? Are certain things too valuable to be bought and sold? Who will own the machines? There’s a distinctive rhetoric around technology for disabled bodies, one that emphasizes the power of technology to make people whole. To become disabled is to be exposed to a barrage of techno-optimistic rhetoric about “overcoming” the state of your body. Recovery means not only reaching a certain level of “old” abilities but becoming even better than you once were by consuming the latest technologies. From TED talks to news articles to commercials that feature paralympians, the technologized disabled body is offered as a site of redemption for “broken” people.

Response: These “techno-ableist” ideas dominate how we imagine disabled bodies—and how we design for them. Techno-ableism suggests that using technologies to restore physical abilities is the key to addressing disability—and that disabled bodies are inferior when they are not properly equipped with those technologies.


Prompt: But this rhetoric reflects a severely limited imagination about disabled bodies. And the consequences of these limitations couldn’t be more concrete for disabled people. Context Matters One failure of imagination is structural. The technologies that are imagined for disabled people often neglect the larger structures that shape how disabled people access those technologies. These include health insurance systems, prescriptions for devices, vocational rehabilitation programs, warranties, systems of maintenance, and service contracts. All of these factors determine who gets which technologies when—and if they get them in the first place.

Response: The iBOT power wheelchair clearly illustrates this failure of imagination. In the early 2000s, Dean Kamen, the creator of the Segway, invented the iBOT. The wheelchair magazine New Mobility praised it as “a revolutionary four-wheel-drive wheelchair that can climb up and down stairs and curbs, roll across terrain, and raise a seated user to eye-level-standing height by rising up and balancing on two wheels.” Many disabled folks liked this invention. But the iBOT was expensive—it cost around $25,000, and was never covered by insurance. The FDA classified the chair as a Class III medical device—a higher-risk category that includes pacemakers—instead of as a Class II device, which is the rating for most power wheelchairs. The difference is significant: Medicare and Medicaid might be willing to cover the cost of the iBOT as a Class II device—and most insurance programs take the lead of Medicare and Medicaid when it comes to their coverage.


Prompt: Technology has to be understood in context. And in the context of disability, problems are rarely just technological: designers also need to consider the structures in which disabled people have to operate. Otherwise they are just creating fancy one-off machines that only a few people will ever be able to try out.

Response: Fortunately, more people may soon be able to use the iBOT. Discontinued in 2009 due to slow sales, it’s now being rolled out again—this time with a Class II rating and the backing of Toyota. Bodies Together If one failure of technological imagination ignores how disabled people actually access technology, another only sees that technology in purely personal terms. Often, we imagine technologies as individual solutions for individual disabled people. Literally, this device will help you—we can rebuild you. This is the impetus for building more impressive wheelchairs, exoskeletons, and prostheses. But the technological world doesn’t just consist of individual tools or devices; rather, it’s a social world shaped by common infrastructure.


Prompt: Yesterday, I had to meet with another faculty member. That wouldn’t usually be a problem. I have a fancy leg as an amputee, and the coffee shop where we were meeting on campus was not terribly far from parking. However, it was raining. Here’s a thing about disability parking: it fills up when it rains. Many of us don’t use the disabled parking except when we need it. And, for people with mobility disabilities, we are much more likely to need it in adverse weather conditions that make traversing longer distances slippery, icy, or otherwise challenging. I was lucky to find parking, but it was further away than planned, and I had to make a dicey choice: either to take a ramp with no handrail in the pouring rain or to climb some stairs with a slippery handrail.

Response: I took the stairs, and made it to the building. Then I walked inside, and I stood on the small, already soaked, floor mat. I was stuck. Moving off the mat would mean slipping and probably falling, which has happened too many times before for me to be bold about it. The person I was meeting for the first time recognized me from my faculty picture and came over, perhaps confused as to why I was just standing in a double-doored hallway. I explained that I was going to have to wait until my shoes were a little drier to move.


Prompt: Was I stuck standing on a mat due to my disability, or was I stuck due to a slippery surface? I was lucky that my colleague was very chill about the whole thing, so the social weirdness of the situation wasn’t a concern. But I could also provide a long list of places in my community that I dare not go when it rains.

Response: Disability inclusion and empowerment can’t be achieved with individual fixes alone. Rather, it has to involve designing physical environments where disabled people are not stigmatized. Some environments literally keep people out: steps, out-of-order elevators, ill-conceived bathroom spaces, disability access around the back of buildings or in other inconvenient and hard-to-find locations, slippery or bumpy surfaces, poor acoustics, flashing lights for no good reason, lack of ramps, lack of handrails or grab bars. People are forced away, or put into discomfort, pain, or danger in order to enter or exist in a given place.


Prompt: To see technology for disability as merely about solving individual problems for individual people is to fail to see that bodies are never bodies alone. Every body has a context in which it sits—and our technological imagination must take that context into account. Room to Play My friend Mallory Kay Nelson has an idea that she calls “transmobility.” Transmobility says that many disabled bodies actually have more ways of being and moving in the world than nondisabled ones do. For instance, Nelson herself has spent time with a prosthetic leg, but now chooses between a wheelchair and a set of forearm crutches for navigating her daily life. She has at least three modes of mobility—unlike the nondisabled, most of whom have probably never been forced to think creatively about the movement of their bodies.

Response: Transmobility insists that there aren’t bad ways of moving through the world. Dominant ideas about disabled bodies point toward two-legged non-limping ambulation as the ideal. This ideal plays out in exoskeletons and prosthetic legs and orthotics, as well as in physical therapy environments and most medical contexts. We imagine solutions as correcting a person’s body, or restoring them to “normal” functioning. By contrast, transmobility gives us a space to think about disabled bodies as adaptable and adapting.


Prompt: To think about disabled bodies as good, whole, and valuable as they are is to reject many of the medicalized notions that dominate the technological design spaces that concern disabled bodies. This doesn’t mean we can’t also have fun with technology. Rather, it means we shouldn’t have to conform or perform as normal. There should be room to play.

Response: In fact, successful design for disability comes not from a place of stigma and loathing, but from playing with the material world and our contextualized embodiments within it. This takes us back to the individual, but opens up space for community as well. The notion that technology has the power to make people whole is seductive. But it also reinforces ableist tropes that work strongly against disability inclusion and flourishing. The stakes are real: this rhetoric informs the design of the world all around us, and the self-perceptions of disabled people themselves, who live with bodies that become stigmatized through our failures of technological imagination.


Prompt: Disabled bodies are often used as ammunition for techno-optimist arguments: that we should want to fix them is a goal for many technological thinkers. But this view of human progress forecloses new possibilities for all sorts of bodies. It kills creativity, caps adaptability, and prevents disabled people from being seen as already complete as they are.

Response: 1. Life must be lived as play, Plato wrote, but don’t take his word for it. Just go to the park and look for an animal. Better yet: at least two animals. A dog runs for a ball. Two dogs chase and tumble with each other. The way they bite looks different from a real bite. The difference reflects a power of abstraction. The difference is the style that communicates: This “bite” is a game.


Prompt: As long as it lasts, playfulness extends an invitation to another reality. We are here, but there is also somewhere else we can go. Come with me? As soon as play ends you can tell. When two dogs stop “biting” and start biting, you separate them, fast. They might not know why they switched. Then again, a philosopher might not know whether he is playing with his cat, or whether his cat is playing with a philosopher. The instinct to play may be a deeper instinct for abstraction than language—an older way to make new realities. Making new realities may be the first way that humans, and other animals, sort ourselves into teams.

Response: In 1938, a few years before the Nazis locked him up, the Dutch scholar Johan Huizinga wrote a book about this. He called it Homo Ludens, “the playing man,” a joke that is also an argument. Huizinga argued that all of culture comes from the human instinct to make up rules and follow them, together, and against one another. Enter: athletes, music, courtship, war.


Prompt: We tend to think of play as frivolous. In fact, nothing could be more serious. It is no accident that so many traditions depict the gods as playing to make the universe. No accident, the dead seriousness with which small children take on imaginary tasks. We hate the spoilsport more than the cheat, Huizinga said. The cheat at least honors the rules that sort us into our lives, while the spoilsport destroys the order that holds our team together.

Response: In other words, In the beginning was the game. 2. Games aren’t just at the origins of our social order. They also lie at the origins of our digital order. Modern computing is largely a creation of the US military. And one of the things that the US military has always liked to do with computers is play games. During the Cold War, the Pentagon’s favorite game was simulating war with the Soviet Union—something that computers were good at.


Prompt: This is memorably illustrated in the 1983 cult classic WarGames. A teenager played by Matthew Broderick hacks into a NORAD supercomputer and sets off a simulation that almost triggers World War III. The problem is that the computer can’t tell the difference between playing a thermonuclear war and actually fighting one, between a “bite” and a bite.

Response: But nuclear annihilation wasn’t the only game that computers could play. In 1962, a programmer at MIT came up with Spacewar!, a multiplayer space combat game that became a huge hit in computing centers around the country. Spacewar! turned the unwieldy, intimidating machines of mid-century computing into instruments of play and pleasure. It made computers fun.


Prompt: It also helped inspire the personal computing revolution. In 1972, the famed cultural entrepreneur Stewart Brand watched a group of people play Spacewar! at the Stanford Artificial Intelligence Laboratory. What he saw was a revelation—“good news,” he called it, “maybe the best since psychedelics.” In Spacewar!, he saw the possibility for a new kind of computing—one that “bonded human and machine through a responsive broadband interface of live graphics display” and “served primarily as a communication device between humans.” Spacewar! offered a vision of the digital age as interactive, social, personal. In the coming years and decades, engineers and entrepreneurs would implement this vision, and create the building blocks of our digital age.

Response: 3. Kids these days still fall in love with computers by playing with them. The writers in this issue describe finding their way into computers through games. Some gamers are just in it for fun, while others get into managing gamer communities, and even go pro. Some gamers discover that the game isn’t what they expected—or that play is something different than what the game’s designers intended.


Prompt: Sometimes, though, gaming gets serious. Games can embody a set of assumptions, even an ideology. Playing a game about cities, for example, you can absorb assumptions about how cities are supposed to be run. A model for gaming an auction can become the basis for an entire platform economy. New rules can restructure global financial markets. Does it work? Sometimes it seems that the game where humans are self-transparent rational actors, working with complete information, is the biggest make-believe of all.

Response: Managers have promoted “teamwork” at work for decades. More recently, gamification has become a buzzword. In every job that must be done there is an element of fun, the song goes. You find the fun and—snap!—the job’s a game. But in reality, gamification may serve less as a technology to speed chores than to destroy solidarity, coaching people to compete constantly against their natural allies—and themselves.


Prompt: In the attention economy, successful platforms find a way to turn more and more of our fun into an opportunity to extract profit. Ironically, the people who make computer games have themselves become highly exploited. Platforms that claim to be public squares or even playing fields can in fact encourage actors to game them in bad faith. Trolls attacking the serious bite bite, then back off saying it was “just a joke.” Transgression repeated, in feedback loops, can transform into boredom. As data-driven porn converges on predictable categories, we search for our safety word: What do you say when your kink isn’t naughty anymore? 4.

Response: If the end game of gamification is playing yourself, what would it take to reclaim play? Writers in this issue explore how homo ludens has put and might still put our instinct to better ends. How to build a better “bite”? As we gain new computational powers, we can use them to build and explore virtual alternatives to vanishing public space. We can tinker and jigger, attempting to build networks on entirely new principles.


Prompt: A defining feature of play—a form of creativity that depends on restrictions, or rules—may be that it teaches us to adapt what we have to make what we want. This it shares with code, which encodes but can also be decoded, recoded. It is an education for an era of democratic deficits and dwindling resources.

Response: Ours are times of anger and grief—but no time for despair. After the game is before the game, as legendary soccer player and coach Sepp Herberger once said. The task, now, may be to approach the limitations of the present with the intense seriousness of a child whom nothing makes happier than to start the same game—Again! “The game represents a need that is strictly the product of a society whose dominant interests demand repression.”    — Michael Burawoy, Manufacturing Consent In May 2016, after months of failing to find a traditional job, I began driving for the ride-hailing company Lyft. I was enticed by an online advertisement that promised new drivers in the Los Angeles area a $500 “sign-up bonus” after completing their first 75 rides. The calculation was simple: I had a car and I needed the money. So, I clicked the link, filled out the application, and, when prompted, drove to the nearest Pep Boys for a vehicle inspection. I received my flamingo-pink Lyft emblems almost immediately and, within a few days, I was on the road.  Initially, I told myself that this sort of gig work was preferable to the nine-to-five grind. It would be temporary, I thought. Plus, I needed to enroll in a statistics class and finish my graduate school applications—tasks that felt impossible while working a full-time desk job with an hour-long commute. But within months of taking on this readily-available yet strangely precarious form of work, I was weirdly drawn in.


Prompt: Every week, Lyft sends its drivers a personalized “Weekly Feedback Summary.” The summary includes passenger comments from the previous week’s rides and a freshly calculated driver rating. It also contains a bar graph showing how a driver’s current rating “stacks up” against previous weeks, and tells them whether they’ve been “flagged” for cleanliness, friendliness, navigation, or safety. At first, I looked forward to my summaries; for the most part, they were a welcome boost to my self-esteem. My rating consistently fluctuated between 4.89 stars and 4.96 stars, and the comments said things like,“Good driver, positive attitude,” and “Thanks for getting me to the airport on time!!” There was the occasional critique, like: “She weird” or just “Attitude,” but overall, the comments served as a kind of positive reinforcement mechanism. I felt good knowing that I was helping people and that people liked me.

Response: But one week, after completing what felt like a million rides, I opened my feedback summary to discover that my rating had plummeted from a 4.91 (“Awesome”) to a 4.79 (“Okay”), without comment. Stunned, I combed through my ride history trying to recall any unusual interactions or disgruntled passengers. Nothing. What happened? What did I do? I felt sick to my stomach.  Because driver ratings are calculated using your last one hundred passenger reviews, one logical solution is to crowd out the old, bad ratings with new, presumably better ratings, as fast as humanly possible. And that’s exactly what I did.


Prompt: For the next several weeks, I deliberately avoided opening my feedback summaries. I stocked my vehicle with water bottles, breakfast bars and miscellaneous mini candies to inspire riders to smash that fifth star. I developed a borderline-obsessive vacuuming habit and upped my car-wash game from twice a week to every other day. I experimented with different air-fresheners and radio stations. I drove, and I drove, and I drove.

Response: Aggressively Managing Freedom The language of choice, freedom, and autonomy saturate discussions of ride hailing. “On-demand companies are pointing the way to a more promising future, where people have more freedom to choose when and where they work,” Travis Kalanick, the founder and former CEO of Uber, wrote in October 2015. “Put simply,” he continued, “the future of work is about independence and flexibility.”  In a certain sense, Kalanick is right. Unlike employees in a spatially-fixed worksite (the factory, the office, the distribution center), ride-hailing drivers are technically free to choose when they work, where they work, and for how long. They are liberated from the constraining rhythms of 9-to-5 employment or shift work. But that apparent freedom poses a unique challenge to the platforms’ need to provide reliable, “on-demand” service to its riders—and so a driver’s freedom has to be aggressively, if subtly, managed. One of the main ways  these companies have sought to do this is through the use of gamification.  Gamification is “the Silicon Valley buzzword du jour,” the tech researcher PJ Rey recently observed. Simply defined, it is the use of game elements—point scoring, levels, competition with others, measurable evidence of accomplishment, ratings, and rules of play— in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge,” workers’ behavior.  This is what my weekly feedback summary, my starred ratings, and other gamified features of the Lyft app did. Gamification also serves the useful function of redirecting conflict away from capital, as workers become consumed with the more urgent task of beating the game.


Prompt: There is a growing body of evidence to suggest that gamifying business operations has real, quantifiable effects. Target, the U.S.- based retail giant, reports that gamifying its in-store checkout process has resulted in lower customer wait times and shorter lines. During checkout, a cashier’s screen flashes green if items are scanned at an “optimum rate.” If the cashier goes too slowly, the screen flashes red. Scores are logged and cashiers are expected to maintain an 88% green rating. In online communities for Target employees, cashiers compare scores, share techniques, and bemoan the game’s most challenging obstacles.

Response: But color-coding checkout screens is like an early-gen Nintendo of gamification. In the digitally mediated world of ride-hailing work, where almost the entirety of one’s work is prompted and guided by screen—and where everything can be measured, logged, and analyzed—there are few limitations on what can be gamified.


Prompt: Making Out In 1974, Michael Burawoy, a doctoral student in Sociology at the University of Chicago and self-described Marxist, began working as a miscellaneous machine operator in the engine division of Allied Corporation, a large manufacturer of agricultural equipment. He was attempting to answer the following question: why do workers work as hard as they do? In Marx’s time, the answer to this question was simple: coercion. Workers had no protections and could be fired at will for failing to fulfill their quotas. One’s ability to obtain a subsistence wage was directly tied to the amount of effort one applied to the work process. However, in the early 20th century, with the emergence of federal and state labor protections, the elimination of the piece-rate pay system, the rise of strong industrial unions, and a more robust social safety net, the coercive power of employers waned.  Yet workers continued to work hard, Burawoy observed. They cooperated with speed-ups and exceeded production targets. They took on extra tasks and sought out productive ways to use their down time. They worked overtime and off the clock. They kissed ass. After ten months at Allied, Burawoy concluded that workers were willingly and even enthusiastically consenting to their own exploitation. What could explain this? One answer, Burawoy suggested, was “the game.” The history of gamification is unclear. Microsoft’s Ross Smith traces it back to ancient Egypt, when pyramid builders organized workers into gangs who would compete over beer and bread by seeing who could carve and haul the most stone. In the mid twentieth century, the industrial sociologist Donald Roy observed that factory workers played a series of games on the job as a way to “keep from going nuts” in the face of monotony. Some workers would generate their own incentive systems: when they assembled one thousand steel dies they could take a drink of water; if they assembled two thousand, they rewarded themselves with a bathroom break.

Response: For Burawoy, “the game” described the way in which workers manipulated the production process in order to reap various material and immaterial rewards. When workers were successful at this manipulation, they were said to be “making out.” Like the levels of a video game, operators needed to overcome a series of consecutive challenges in order to make out and beat the game.


Prompt: At the beginning of every shift, operators encountered their first challenge: securing the most lucrative assignment from the “scheduling man,” the person responsible for doling out workers’ daily tasks. Their next challenge was a trip to “the crib” to find the blueprint and tooling needed to perform the operation. If the crib attendant was slow to dispense the necessary blueprints, tools, towels, and fixtures, operators could lose a considerable amount of time that would otherwise go towards making or beating their quota. (Burawoy won the cooperation of the crib attendant by gifting him a Christmas ham.) After facing off against the truckers, who were responsible for bringing stock to the machine, and the inspectors, who were responsible for enforcing the specifications of the blueprint, the operator was finally left alone to battle it out against the clock with his machine.

Response: According to Burawoy, production at Allied was deliberately organized by management to encourage workers to play the game and thus “make out.” Quotas were established and incentive-pay was given to those who exceeded production targets. But workers also tried to game the game, making sure not to consistently exceed the targets set by management, lest management then raise the targets.


Prompt: When work took the form of a game, Burawoy observed, something interesting happened: workers’ primary source of conflict was no longer with the boss. Instead, tensions were dispersed laterally between workers (the scheduling man, the truckers, the inspectors), between operators and their machines, and between operators and their own physical limitations (their stamina, precision of movement, and focus).

Response: The battle to beat the quota also transformed a monotonous, soul-crushing job into an exciting outlet for workers to exercise their creativity, speed, and skill. Workers attached notions of status and prestige to their output, and the game presented them with a series of choices throughout the day, affording them a sense of relative autonomy and control. It tapped into workers’ desire for self-determination and self-expression. Then, it directed that desire towards the production of surplus value.


Prompt: As Marx remarked in his Grundrisse, “It is not the individuals who are set free by free competition; it is, rather, capital which is set free.” Challenge Accepted Every Sunday morning, I receive an algorithmically generated “challenge” from Lyft that goes something like this: “Complete 34 rides between the hours of 5 AM on Monday and 5 AM on Sunday to receive a $63 bonus.” I scroll down, concerned about the declining value of my bonuses which once hovered around $100-$220 per week, but have now dropped to less than half that. “Click here to accept this challenge.” I tap the screen to accept.  Now, whenever I log into driver mode, a stat meter will appear showing my progress: only 21 more rides before I hit my first bonus. Lyft does not disclose how its weekly ride challenges are generated, but the value seems to vary according to anticipated demand and driver behavior. The higher the anticipated demand, the higher the value of my bonus. The more I hit my bonus targets, or ride quotas, the higher subsequent targets will be. Sometimes, if it’s been awhile since I’ve logged on, I’ll be offered an uncharacteristically lucrative bonus, north of $100, though it’s been happening less and less of late.  Behavioral scientists and video game designers are well aware that tasks are likely to be completed faster and with greater enthusiasm if one can visualize them as part of a progression towards a larger, pre-established goal. Like the HUD, or head-up display in a first-person shooter game, the Lyft stat meter is always present, always showing you what your acceptance rating is, how many rides you’ve completed, how far you have to go to reach your goal.

Response: In addition to enticing drivers to show up when and where demand hits, one of the main goals of this gamification is worker retention. According to Uber, 50 percent of drivers stop using the application within their first two months, and a recent report from the University of California Davis’ Institute of Transportation Studies suggests that just 4 percent of ride-hailing drivers make it past their first year.


Prompt: Retention is a problem in large part because the economics of driving are so bad. Researchers have struggled to establish exactly how much money drivers make, but with the release of two recent reports, one from the Economic and Policy Institute and one from MIT, a consensus on driver-pay seems to be emerging: drivers make, on average, between $9.21 and $10.87 per hour. What these findings confirm is what many of us in the game already know: in most major U.S cities, drivers are pulling in wages that fall below local minimum wage requirements. According to an internal slide deck obtained by the New York Times, Uber actually identifies McDonalds as its biggest competition in attracting new drivers. When I began driving for Lyft, I made the same calculation most drivers make: it’s better to make $9.00 per hour than to make nothing.  Before Lyft rolled out weekly ride challenges, there was the “Power Driver Bonus,” a weekly challenge that required drivers to complete a set number of regular rides. I sometimes worked more than 50 hours per week trying to secure my PDB, which often meant driving in unsafe conditions, at irregular hours, and accepting nearly every ride request including those that felt potentially dangerous (I’m thinking specifically of an extremely drunk and visibly agitated late-night passenger).  Of course, this was largely motivated by a real need for a boost in my weekly earnings. But, in addition to a hope that I would somehow transcend Lyft’s shitty economics, the intensity with which I pursued my PDBs was also the result of what Burawoy observed four decades ago: a bizarre desire to beat the game.  Slot Machines on Wheels Like the wages at Allied Steel, drivers’ per-mile earnings are supplemented by a number of rewards, both material and immaterial. Uber drivers can earn “Achievement Badges” for completing a certain number of five-star rides and “Excellent Service Badges” for leaving customers satisfied. Lyft’s “Accelerate Rewards” program encourages drivers to “level up” by completing a certain number of rides per month in order to unlock special rewards like fuel discounts from Shell (Gold Level) and free roadside assistance (Platinum Level).

Response: In addition to offering meaningless badges and meager savings at the pump, ride-hailing companies have also adopted some of the same design elements used by gambling firms to promote addictive behavior among slot machine users. One of things the anthropologist and NYU Media Studies professor Natasha Dow Schüll found during a decade-long study of machine gamblers in Las Vegas is that casinos use networked slot machines that allow them to surveille, track, and analyze the behavior of individual gamblers in real time—just as ride-hailing apps do. This means that casinos can “triangulate any given gambler’s player data with her demographic data, piecing together a profile that can be used to customize game offerings and marketing appeals specifically for her.” Like these customized game offerings, Lyft tells me that my weekly ride challenge has been “personalized just for you!”   Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification—a dozen new likes or retweets— but they know that gratification will eventually come. This unpredictability is addictive: behavioral psychologists have long understood that gambling uses variable reinforcement schedules—unpredictable intervals of uncertainty, anticipation and feedback—to condition players into playing just one more round.  We are only beginning to uncover the extent to which these reinforcement schedules are hardwired into ride-hailing apps. But one example is primetime or surge pricing. The phrase “chasing the pink” is used in online forums by Lyft drivers to refer to the tendency to drive towards “primetime” areas, denoted by pink-tinted heat maps in the app that signify increased fares at precise locations. This is irrational because the likelihood of catching a good primetime fare is slim, and primetime is extremely unpredictable. The pink appears and disappears, moving from one location to the next, sometimes in a matter of minutes. Lyft and Uber have to dole out just enough of these higher-paid periods to keep people driving to the areas where they predict drivers will be needed. And occasionally—cherry, cherry, cherry—it works: after  the Rose Bowl Parade last year, I made in 40 minutes over half of what I usually make in a day of nonstop shuttling.


Prompt: It is not uncommon to hear ride-hailing drivers compare even the mundane act of operating their vehicles to the immersive and addictive experience of playing a video game or a slot machine. In an article published by the Financial Times, longtime driver Herb Croakley put it perfectly: “It gets to a point where the app sort of takes over your motor functions in a way. It becomes almost like a hypnotic experience. You can talk to drivers and you’ll hear them say things like, I just drove a bunch of Uber pools for two hours, I probably picked up 30-40 people and I have no idea where I went. In that state, they are literally just listening to the sounds [of the driver’s apps]. Stopping when they said stop, pick up when they say pick up, turn when they say turn. You get into a rhythm of that, and you begin to feel almost like an android.” A Good Old-Fashioned Work Stoppage So who sets the rules for all these games?  “Ok so let’s organize this. At 1:50 everyone that wants to join in, turn off your app so that primetime goes up…”  It’s half past midnight on a Friday night and the Lyft Drivers Lounge, a closed Facebook group for active drivers, is divided. The debate began, as many do, with an assertion about the algorithm. “The algorithm” refers to the opaque and often unpredictable system of automated, “data-driven” management employed by ride-hailing companies to dispatch drivers, match riders into Pools (Uber) or Lines (Lyft), and generate “surge” or “primetime” fares, also known as “dynamic pricing”.	  The algorithm is at the heart of the ride-hailing game, and of the coercion that the game conceals. In their foundational text Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers, Alex Rosenblat and Luke Stark write, “Uber’s self-proclaimed role as a connective intermediary belies the important employment structures and hierarchies that emerge through its software and interface design.” “Algorithmic management” is the term Rosenblat and Stark use to describe the mechanisms through which Uber and Lyft drivers are directed. To be clear, there is no singular algorithm. Rather, there are a number of algorithms operating and interacting with one another at any given moment. Taken together, they produce a seamless system of automatic decision making that requires very little human intervention.

Response: For many on-demand platforms, algorithmic management has completely replaced the decision-making roles previously occupied by shift supervisors, foremen, and middle to upper-level management. Uber actually refers to its algorithms as “decision engines.” These “decision engines” track, log, and crunch millions of metrics each day, from ride frequency to the harshness with which individual drivers break. It then uses these analytics to deliver gamified prompts perfectly matched to drivers’ data profiles.  Because the logic of the algorithm is largely unknown and constantly changing, drivers are left to speculate about what it is doing and why. Such speculation is a regular topic of conversation in online forums, where drivers post screen grabs of nonsensical ride requests and compare increasingly lackluster, algorithmically generated bonus opportunities. It is not uncommon for drivers to accuse ride-hailing companies of programming their algorithms to favor the interests of the corporation. To resolve this alleged favoritism, drivers routinely hypothesize and experiment with ways to manipulate or “game” the system back.


Prompt: When the bars let out after last call at 2 AM, demand spikes. Drivers have a greater likelihood of scoring “surge” or “primetime” fares. There are no guarantees, but it’s why we’re all out there. To increase the prospect of surge pricing, drivers in online forums regularly propose deliberate, coordinated, mass “log-offs” with the expectation that a sudden drop in available drivers will “trick” the algorithm into generating higher surges. I’ve never seen one work, but the authors of a recently published paper say that mass log-offs are occasionally successful.  Viewed from another angle, though, mass log-offs can be understood as good, old-fashioned work stoppages. The temporary and purposeful cessation of work as a form of protest is the core of strike action and remains the sharpest weapon workers have to fight exploitation. But the ability to log-off enmasse has not assumed a particularly emancipatory function. Burawoy’s insights might tell us why.

Response: Gaming the game, Burawoy observed, allowed workers to assert some limited control over the labor process and to “make out” as a result. In turn, that win had the effect of reproducing the players’ commitment to playing and their consent to the rules of the game. When players were unsuccessful, their dissatisfaction was directed at the game’s obstacles, not at the capitalist class, which sets the rules. The inbuilt antagonism between the player and the game replaces, in the mind of the worker, the deeper antagonism between  boss and worker. Learning how to operate cleverly within the game’s parameters becomes the only imaginable option. And now there is another layer interposed between labor and capital: the algorithm.


Prompt: Rules of the Game After weeks of driving like a maniac in order to restore my higher-than-average driver rating, I managed to raise it back up to a 4.93. Though it felt great, it’s almost shameful and astonishing to admit that one’s rating, so long as it stays above 4.6, has no actual bearing on anything other than your sense of self-worth. You do not receive a weekly bonus for being a highly-rated driver. Your rate of pay does not increase for being a highly-rated driver. (In fact, I was losing money trying to flatter customers with candy and keep my car scrupulously clean. ). And yet, I wanted to be a highly-rated driver.  And this is the thing that is so brilliant and awful about Lyft and Uber’s gamification: it preys on our desire to be of service, to be liked, to be good. On weeks that I am rated highly, I am more motivated to drive. On weeks that I am rated poorly, I am more motivated to drive. It works on me, even though I know better.  “It is by constituting our lives as a series of games, a set of limited choices,” Burawoy writes, “that capitalist relations not only become objects of consent but are taken as given and immutable. We do not collectively decide what the rules of making out will be; rather, we are compelled to play the game, and we proceed to defend the rules.” To date, I have completed more than 2,200 rides.

Response: I was in a rented home being used as a set somewhere in the hills north of Los Angeles. “You have to say ‘stepmom’ three times before we get to the sex, then you can just call her ‘mom,’” the director told the younger of the two actresses. “Also, remember to say, ‘This is wrong!’ or ‘This is fucked up!’ once or twice, but never the word ‘No!’ because that messes up the consent.” The director then told the other actor there, who was playing the stepson, to refer explicitly to “being home from college” in order to establish that his character was over eighteen.


Prompt: For three years, I covered the adult industry for LA Weekly as a writer and photographer. As porn professionals became comfortable with my presence on sets, photoshoots, and at award shows, I was allowed a rare level of access into a production process that, for obvious reasons, tends to be secretive. Producers, directors, and performers would often tell me things, share gossip, and answer questions—both on- and off-the-record—about the many aspects of the business that often baffle outsiders.

Response: Over the course of these experiences, I learned about a major new force reshaping the industry: data. That day on set, the director’s instructions came directly from the production company, which decided on the topic and vetted the script. And the company based its creative direction on specific fantasies proposed by paying customers on an online forum that it owned. (“The stepsister should catch her stepbrother masturbating and she should humiliate him for being a dork,” one commenter suggested. And that was cut, pasted, and embellished into the script emailed to the director.) In the pre-internet days, a producer might notice that a particular kind of porn movie sold well, and then try to make more like it. Today, a corporate porn conglomerate can analyze a continuous stream of information from online viewers, who supply feedback in the form of comments, and leave behind a data trail as they travel around porn sites.


Prompt: The internet isn’t just revolutionizing how porn is distributed and consumed. It’s also revolutionizing how porn is made, by enabling companies to cater more closely to the perceived tastes of their audience. Rise of the Tube Sites The epicenter of porn production in the US—and, due to the size of the US porn industry, the world—is the San Fernando Valley north of the city of Los Angeles. Before 2006, the Valley was also the heart of the business side of porn. Trade publication AVN’s headquarters is still in Chatsworth and the sign for Vivid Entertainment, a key brand during the DVD years, can still be seen across the street from Universal Studios, in Studio City.

Response: But between 2006 and 2007, the adult video business changed dramatically. Internet porn swallowed up the entire industry through the “tube sites”—porn-specific YouTube knockoffs like YouPorn, RedTube, and the unquestioned winner of the competition among them, Pornhub. These sites are owned by larger entities that are typically not based in the Valley.


Prompt: The aggregator sites, much like YouTube, were first pitched as a way for people to upload content that they owned. But they quickly grew, Pornhub in particular, by not particularly enforcing the removal of copyrighted material that was being uploaded. Initially porn producers decried the practice as outright theft, but a few years back they were forced to make peace with the unstoppable hegemonic forces of what everyone in the industry calls “the tube era.” MindGeek is a Montreal-based juggernaut that owns most of the top-ten tube sites, including Pornhub. It also owns key LA-based studios like Brazzers, Reality Kings, Digital Playground, Mofos, and the gay-focused Sean Cody. Its major competitors include Gamma Entertainment, another Montreal company that provides affiliate services (the standard marketing monetization scheme that allows people to collect money for online customer referrals) and owns several popular studios; and WGCZ Holdings, which owns XVideos.com, Porn.com, the BangBros studio, and recently acquired the Penthouse brand.

Response: Big Porn’s new business model is online advertising. MindGeek boasts that it employs over 1,000 tech workers worldwide, with its main center of operations in Montreal. Many of these employees are tasked with compiling, analyzing, and interpreting the data generated by users, which is then used to sell targeted ads.


Prompt: In other words, the tube sites make money the same way that Facebook does. And the fact that the same companies also own many of the big studios means that they can use the data they collect not only to sell targeted ads but to make their videos even more engaging so that users spend even more time watching them, thus generating even more data. They are creating a vertically integrated data porn empire.

Response: Don’t Fight the Data While a lot of people (most likely you and everyone you know) are consumers of internet porn (i.e., they watch it but don’t pay for it), a tiny fraction of those people are customers. Customers pay for porn, typically by clicking an ad on a tube site, going to a specific content site (often owned by MindGeek), and entering their credit card information.


Prompt: This “consumer” vs. “customer” division is key to understanding the use of data to perpetuate categories that seem peculiar to many people both inside and outside the industry. “We started partitioning this idea of consumers and customers a few years ago,” Adam Grayson, CFO of the legacy studio Evil Angel, told AVN. “It used to be a perfect one-to-one in our business, right? If somebody consumed your stuff, they paid for it. But now it’s probably 10,000 to one, or something.” There’s an analogy to be made with US politics: political analysts refer to “what the people want,” when in fact a fraction of “the people” are registered voters, and of those, only a percentage show up and vote. Candidates often try to cater to that subset of “likely voters”— regardless of what the majority of the people want. In porn, it’s similar. You have the people (the consumers), the registered voters (the customers), and the actual people who vote (the customers who result in a conversion—a specific payment for a website subscription, a movie, or a scene). Porn companies, when trying to figure out what people want, focus on the customers who convert. It’s their tastes that set the tone for professionally produced content and the industry as a whole.

Response: By 2018, we are now over a decade into the tube era. That means that most LA-area studios are getting their marching orders from out-of-town business people armed with up-to-the-minute customer data. Porn performers tend to roll their eyes at  some of these orders, but they don’t have much choice. I have been on sets where performers crack up at some of the messages that are coming “from above,” particularly concerning a repetitive obsession with scenes of “family roleplay” (incest-themed material that uses words like “stepmother,” “stepfather,” and “stepdaughter”) or what the industry calls “IR” (which stands for “interracial” and invariably means a larger, dark-skinned black man and a smaller light-skinned white woman, playing up supposed taboos via dialogue and scenarios).


Prompt: These particular “taboo” genres have existed since the early days of commercial American porn. For instance, see the stellar performance by black actor Johnnie Keyes as Marilyn Chambers’ orgy partner in 1972’s cinematic Behind the Green Door, or the VHS-era incest-focused sensation Taboo from 1980. But backed by online data of paid customers seemingly obsessed with these topics, the twenty-first-century porn industry—which this year, to much fanfare, was for the first time legally allowed to film performers born in this millennium—has seen a spike in titles devoted to these (frankly old-fashioned) fantasies.

Response: Most performers take any jobs their agents send them out for. The competition is fierce—the ever-replenishing supply of wannabe performers far outweighs the demand for roles—and they don’t want to be seen as “difficult” (particularly the women). Most of the time, the actors don’t see the scripts or know any specific details until they get to set. To the actors rolling their eyes at yet another prompt to declaim, “But you’re my stepdad!” or, “Show me your big black dick,” the directors shrug, point at the emailed instructions and say, “That’s what they want…” We Know What You Like The data collected by porn companies doesn’t just shape what happens on set, however. It is also starting to shape how the media understands the state of sexuality today.


Prompt: In mid-2013, Pornhub launched a sister website called Pornhub Insights. Unlike their main product, the high-traffic aggregator of sex videos, Pornhub Insights is designed to be safe for work. Its mission is to provide “research and analysis directly from the Pornhub team… to explore the intricacies of online porn viewership.” Pornhub Insights posts regularly, but its greatest hit is its “Year in Review” article, which offers crunched numbers and spiffy graphics about its viewers’ habits over the previous twelve months. “Welcome to Pornhub’s 5th annual Year in Review,” Insights posted in January 2018, “the best place to discover and reflect on what we’ve collectively been searching for and how we’ve been viewing porn in 2017.” These discoveries and reflections included self-reported traffic numbers (“28.5 billion visitors, which turns out to be an average of 81 million people per day!”), a ranking of “the searches that defined 2017” (#1 Porn for Women, #2 Rick and Morty, #3 Fidget Spinner), most searched terms (lesbian, hentai, MILF, stepmom), most searched porn stars (female and male), countries by traffic (#1 US, #2 UK, #3 India), top searches by country (e.g., Netherland’s #1 search is for “dutch”), and many other easily shareable graphics and numbers. The Year in Review also offers in-house analysis: “2017 seems to have been the year where women have come forward to express their desires more openly,” wrote Dr. Laurie Betito, who is credited as “sex therapist and director of the Pornhub Sexual Wellness Center.” Noticeably absent in the Pornhub Insights reports are links to any of the actual data about porn consumption gathered by the company, however. This is unsurprising: the actual user, traffic, and search data is one of the company’s most valuable assets. MindGeek’s dominance over the industry is based on its ability to monetize content via targeted advertising and an algorithm that delivers certain videos to certain customers, not to mention its treasure trove of unique consumer data.

Response: But the Year in Review’s lack of substantiating information doesn’t stop reporters from repeating its claims as facts. Nor does it stop them from extrapolating those claims to stand in for all of online porn, or to speak definitively about “our” sexual predilections. Over the past few years, a new genre of story about sex has become widespread in the mainstream press. These stories purport to tell the general public about what “people,” “women,” “gay men,” “people in Southern states,” “Russians,” “Mormons,” “millennials,” “Americans,” or any other broad category of humans—including sometimes an absurdly universal “we”—are really into sexually. The italics on really are important, because the assumption of most of these articles is that “people” have a public self that is either asexual, ashamed of discussing sexuality, or vanilla—but that there is another self, a more “true” or “real” self, that comes out at 2 a.m. when these same people search for porn on the internet.


Prompt: This Victorian Jekyll-and-Hyde model of desire and sexuality informs the recent bestseller Everybody Lies by Seth Stephens-Davidowitz. Stephens-Davidowitz, who holds a PhD in economics and worked briefly at Google as a data scientist, claims that for his sections on pornography he was given access to “the Pornhub data,” though his method and data sets are unclear from the endnotes or accompanying website. His argument is simple: people’s public personas are deceptions that conceal a secret, more “truthful” self obsessed with taboos, kinks, and unconventional sexual desires.

Response: The many pieces of clickbait that claim to illuminate these desires by drawing on Pornhub Insights are often ridiculous. “While interest may be hot and heavy,” Mashable wrote in 2017 about the supposed rise in searches for fidget spinner porn, “what you’ll actually find on Pornhub is pretty hilarious. It’s just a lot of video of fidget spinners…spinning. […] For instance, a video called ‘1000MPH Fidget Spinner Bisexual Threesome’ literally features three spinners ramming into each other with excellent narration.” More often than not, Pornhub Insights press releases get spun into stories about broad trends in human sexuality. On August 2017, for example, Insights published a post called “Boobs: Sizing Up the Searches” looking at data about “the most popular breast related searches.” The post revealed that “Pornhub visitors between the ages of 18 to 24 are 19% less likely to search for breasts when compared to all other age groups.” A Maxim writer then turned that single line in the report into the headline “Millennials Aren’t All That Interested in Breasts, According to Pretty Depressing New Study.” Playboy’s popular Twitter account received almost 10K likes, 2.8K retweets, and 2.4K comments for an 100% on-brand tweet that read “Millennials aren’t as interested in breasts as older generations. Why?” In fact, if you search “millennial” and “breasts,” Google will return countless hits about this supposedly data-based “fact,” all clustered around August 2017 and all sourced from a couple of quotes from that one Pornhub Insights report.


Prompt: Ever since Pornhub Insights launched, a large number of articles in the mainstream press about sexual proclivities in the US and around the world have drawn their sourcing from it, or from press releases sent to journalists by Pornhub. This is the other side of data-driven porn. Not only is it changing how the industry makes porn—it’s also increasingly changing the popular narrative about sexuality, by supplying the fodder for sensational stories about people’s “true” kinks.

Response: The resulting dynamic benefits both Pornhub and the media. Pornhub strategically releases “reports” they know will make good clickbait, based on proprietary data that is not independently verifiable. The clickbait, amplified by Twitter and other social media (because sex always sells), then drives traffic both to the news sites that produce it and back to Pornhub itself. In the era of the attention economy, media and porn are in the same business, and have forged a symbiotic relationship to their mutual benefit.


Prompt: After the Fall It would be easy to see the rise of data-driven porn as a familiar internet narrative: the corporate betrayal of a digital utopia. Online porn was supposed to give everyone unfettered access to their own particular kinks, with tech-hippies sharing a new world of hyper-specific fetishes with a like-minded crowd of early Burning Man adopters and subscribers to Mondo2000 magazine via newsgroups with names like alt.sex.aluminum.baseball.bat (an actual newsgroup).

Response: In this account, the prelapsarian idyll of internet porn seems to have suffered the same fate as the internet as a whole: corporate consolidation and the monetization of our attention. A few massive companies control the online porn industry, and use sophisticated techniques to grab more of our attention and sell it to advertisers, or to coax us into becoming paying customers.


Prompt: The story, of course, is more complicated. Internet porn has become a big business, but amateur communities are thriving. Just as millions of little-known SoundCloud musicians didn’t prevent the rise of a megastar like Taylor Swift, the ready availability of free amateur erotic content on spawn-of-newsgroups megasites like Reddit coexists with the unstoppable rise of Pornhub. The spirit of the older internet endures there, albeit in a different form.

Response: As for the performers, many are torn between endorsing the “success” narrative being pushed by Big Porn outfits like Pornhub and voicing very real grievances—some of them brought about, in their opinion, by the rise of the tube sites. In September 2018, Pornhub produced their first award show at a historic theater in Downtown Los Angeles, enlisting a large number of actresses and actors with the promise that the winners would be “decided by the viewing patterns of the site’s millions of visitors and loyal members.” Shortly before that, Kanye West had mentioned the site on Jimmy Kimmel’s late night talk show. West referred to “Pornhub” as a generic term for online porn, much like older people in the 1990s would call the internet “AOL.” Pornhub promptly scrapped their original plans for their event, and made West “artistic director” of the evening.


Prompt: The Pornhub Awards afterparty, a swanky affair held on the rooftop of affluent hipster mecca the Ace Hotel, was organized by Greg Lansky. The 2018 porn industry includes larger-than-life characters like Lansky—creator of big international brands like Vixen, Tushy, and the biggest interracial brand of all time, Blacked—who are attempting to be to today’s digital porn content what Playboy was to an earlier era of paper-based erotica.

Response: Obviously inspired by Hugh Hefner in terms of exposure, projected lifestyle, and the desire to be embraced by mainstream publications, the French-born Lansky is known to pay top rate to his performers and spends lavishly on promotional stunts and PR. By crowning girls “Vixen Angel” of the month or the year, Lansky deliberately fosters an old-fashioned, glossy star system of sorts within the industry. His graphic design and aesthetics owe a lot to early 2000s French Vogue and American Apparel billboards.


Prompt: The partnership between Big Porn outfits like Pornhub and ambitious entrepreneurs like Lansky is lucrative. As of September 2018, Lansky’s Pornhub channels Blacked (mentioned by Kanye West as his favorite “category” of Pornhub), Vixen, and Tushy, which drive content to his sites, are ranked #4, #6 and #9 on the tube site. The videos on the Blacked channel, founded four years ago, have 892,613,843 views.

Response: These are rarified success stories in an industry that, at the production level, has a habit of complaining about its perceived “collapse” from the halcyon days before the internet changed the game—especially when talent or crew wants better pay or better working conditions. By all accounts, the rates usually paid to performers (even those featured on the trendy sweatshirts that Kanye West designed for the Pornhub awards) have been stagnant for a decade. If the new tech overlords in Montreal are doing as well as their PR blitzes imply, it might be about time to spread the wealth.


Prompt: Steve Jobs wanted customers to understand the Apple Store “with one sweep of the eye,” as if they were gods standing on Mount Olympus. Indeed, the iconic outlets seem to speak for themselves. Bright, uncluttered, and clad in glass, they couldn’t contrast more sharply with the big-box labyrinths they were designed to replace.  Neither could their profit margins. Since launching in 2001, the instantly recognizable Stores have raked in more profits—in total and per square foot—than any other retailer on the planet, transforming Apple into the world’s richest company in the process. Yet the very transparency of the Apple Store, paradoxically, conceals how those profits are made.

Response: When we think of “tech,” we rarely think of retail stores, and when we think of “tech workers” we rarely think of the low-waged “geniuses” who staff them. Almost all media coverage of tech companies encourages us to forget that the vast majority of their employees are not in fact coders in Silicon Valley: they’re the suicidal assemblers of your phone, the call-center support staff, the delivery drivers, and the smiling shop floor staff who make up the majority of Apple’s workforce.  The first open secret of the Apple Store, in other words, is people. The place is far less focused on fancy gadgets than might be assumed. In fact, the Apple Store was explicitly designed as a brand embassy rather than a dedicated source of technical knowledge. As Ron Johnson, the former Target executive who came up with the concept, told the Harvard Business Review, “People come to the Apple Store for the experience—and they’re willing to pay a premium for that . . . Apple is in the relationship business as much as the computer business.”  Johnson and Jobs wanted ambassadors whose ostensible role was not to sell products—uniquely, Apple Store employees do not receive a commission—but to create positive customer sentiment and repair trust in the brand when it broke. That was hard to do if your stuff was lumped in with everyone else’s in a big electronics store, overseen by third-party staff lacking any special expertise or interest in what you wanted to sell.  The goal was to take full control of the brand image while humanizing it. The problem, however, was that humans can be rather unruly.  No Sticks, No Carrots Fortunately for Apple, someone had been hard at work fixing that bug. In 1984 a group of professors at Harvard Business School published a book, Managing Human Assets, aimed at updating workplace organization for a new era. Previously, the book argued, labor discipline could be achieved in a relatively straightforward top-down manner, but now it required something else. “The limitations of hierarchy have forced a search for other mechanisms of social control,” the authors said. The mechanisms they proposed consisted, at root, of treating employees as nominal stakeholders in business success, within narrow limits that would increase rather than challenge shareholder profitability.


Prompt: Managing Human Assets was based on the first new compulsory course at the Harvard Business School in a generation, launched in 1981. Johnson started his MBA at Harvard the next year, graduating as the book itself was released. Reading Johnson’s Apple Store as the mechanical translation of Managing Human Assets would of course be reductive—yet key features of the Apple Store’s practice bear an unmistakable similarity to it.  Johnson found the first cohort of Apple Store employees by personally interviewing every manager and offering jobs to upbeat staff working for competitors. Then he developed a training program for the in-house production of “geniuses.” (Jobs reportedly hated the term at first, finding it ridiculous. True to form, he asked his lawyers to apply for a patent the following day). How do you create an engaged, happy, knowledgeable workforce that can pass, however implausibly, as an entire battalion of geniuses in towns across the country? More importantly, how do you do all of that without the stick of the authoritarian boss or the carrot of a juicy commission? Apple’s solution was to foster a sense of commitment to a higher calling while flattering employees that they were the chosen few to represent it. The Genius Training Student Workbook is the vaguely comical title of the manual from which Apple Store employees learn their art. Prospective geniuses are taught to use empathetic communication to control customer experience and defuse tension, aiming to make them happy and relax their purse strings. It might be expected that Apple Store employees are, as their name implies, tech gurus with incredible intellects. But their true role has always been to use emotional guile to sell products. Before they get to do that, though, Apple uses similar techniques to foster their own relationship with the brand.

Response: By counterintuitively raising the bar of admission, crafting a long series of interviews to weed out the mercenary or misanthropic, Johnson soon attracted more applicants than there were posts. Those keen enough to go through the onerous hiring process were almost by definition a better “fit” for the devotional ethos of the brand, far more receptive to the fiction that they weren’t selling things but, in an oft-repeated phrase, “enriching people’s lives,” as if they’d landed a job at a charity. “When people are hired,” Johnson explained, “they feel honored to be on the team, and the team respects them from day one because they’ve made it through the gauntlet. That’s very different from trying to find somebody at the lowest cost who’s available on Saturdays from 8 to 12.”  While not the lowest, the cost of these eager staff was still low—both relative to industry averages, to the amount they made for the company, and to the $400 million that Johnson earned in his seven years at Apple. Lower wages also had another, less obvious effect. As Apple Store managers explained to the New York Times, the lack of commissions meant that the job didn’t pay well enough to support those with dependents: older workers were functionally excluded from representing the brand without the need for a formal policy—or the attendant specter of discrimination lawsuits that it would raise. Deploying psychology, not the maximizing calculus of economic rationality (money), allowed Apple to turn hiring and wages into managerial props.


Prompt: The sense of higher calling and flattery doesn’t stop with the hiring process, of course. Make it through the gauntlet and you are “clapped in” by existing employees: given a standing ovation as if receiving a prize. The clapping, according to employees, continues until new hires, perhaps after a confused delay, begin clapping too, graduating from outside spectator to part of the performance—part of the team. Leave the company and you’re “clapped out.”  Products are clapped, customers waiting overnight to buy them are clapped, their purchases are clapped, claps are clapped. Clap, clap, clap. “My hands would sting from all the clapping,” said one manager. Claps, cheers, performances of rapturous engagement provided, by design, a ready-mixed social glue to bind teams together, reaffirming both the character of the brand and employees’ cultish devotion to it.  Playing Smart The Genius Training Student Workbook schools prospective geniuses in a similar set of methods to manage customers. In effect, the Workbook trains employees to train customers to love Apple. It teaches emotionally savvy, algorithmic solutions to customer gripes, such as the “three Fs”: Feel, Felt, Found—then offers a series of role-playing scenarios to rehearse them.  Here’s an example from the book, meant to be role-played by trainees: Customer: This Mac is just too expensive.Genius: I can see how you’d feel this way. I felt the price was a little high, but I found it’s a real value because of all the built-in software and capabilities.  When customers run into trouble with their products, geniuses are encouraged to sympathize, but only by apologizing that customers feel bad, lest they implicate Apple’s products as the source of the trouble. In this gas-lit performance of a “problem free” brand philosophy, the very language with which to articulate discord is erased: many words are actually verboten for staff.  Do not use words like crash, hang, bug, or problem, employees are told. Instead say does not respond, stops responding, condition, issue, or situation. Avoid saying incompatible; instead use does not work with. Staff have reported the absurdist dialogues that can result, like when they are not allowed to tell customers that they cannot help even in the most hopeless cases, leading customers into circular conversations with employees able neither to help nor to refuse to do so.

Response: Staging the Genius All these performances—genius, surreal, or tragicomic as they may be—take place on a platform managed just as carefully as the staff. This stage, the second key ingredient to Apple’s lucrative relationship business, is so obvious that it’s easy to overlook. Jobs and Johnson wanted to control every detail, like the specific color of the bathroom signs, or ensuring that every store had just one entrance, to manage the customer experience. Apple has trademarked almost every aspect of its stores, from stairs to display tables to storage racks. Even the supposedly “intuitive” layout, so obvious that it can be understood by all, is considered unique enough to warrant a suite of intellectual property protections.


Prompt: Like a true artist, the company has also turned faults into features through its control of space, as demonstrated when Apple decided to launch their first store in New York City. The only available location on Fifth Avenue was a spot near Central Park owned by mogul Harry Macklowe, empty for seven years. No other retailer had wanted to rent it for a rather basic reason: the space was a dark concrete cave. Undeterred, Apple built a hardened glass staircase and a cube to let sunlight (and customers) stream down through it. The store has become the third most popular tourist attraction in the city and the most profitable store in the world, a monument to Apple’s apparent power to defy retail gravity. The glass cherry on top is, predictably, trademarked.  Yet even as this austere cube has levitated into history, the Apple Store itself has recently started to evolve. In part to counter the falling sales volume of a saturated market, Apple has spent the past two years overhauling its stores to work even harder. Potted trees have been added to give a green splash to the signature gray and, in a move so ridiculous it’s almost certain to be a hit, the Genius Bar has been rebranded the “Genius Grove.” Windows are opened to blur the distinction between inside and outside, and the stores are promoted as quasi-public spaces. As the new head of retail at Apple, former Burberry executive Angela Ahrendts (2017 salary: $24,216,072), told the press, “We actually don’t call them stores anymore. We call them town squares.” The town square. It’s an almost quaint symbol of participatory civic life—a world away from the big-box sprawl that characterized the retail imaginary of the late 20th century, or even the digital isolation of the 21st. Apple’s goal has been to create spaces for people to just hang out in, extending the original insight that focusing on everything other than cold hard cash will paradoxically be the best way to rake it in. Apple has continued to buy former public buildings or prominent places within them—think of the Apple Stores in Grand Central or the former Prince Street Post Office in New York, or the Carnegie Library in Washington, D.C.—while also repurposing actual town squares like Piazza Liberty in Milan, all to give a sense of grandeur, permanence, and public provision.

Response: In Ahrendts’s vision, "the store becomes one with the community." But the real hope seems to be closer to the opposite, that the community will become one with the store. Like the gentrification pushing the poor out of their neighborhoods, Apple’s role-playing of civic virtue redefines the public, and does so by excluding most people from it. Yet Apple’s ability to creatively reinterpret the line between public and private runs far deeper than this, into Apple’s products themselves.


Prompt: Ex Nihilo The word “genius” in Latin originally meant “deity of generation and birth,” gaining the idea of intellectual ability in the 16th century. Like “genesis,” it suggests a theological account of novelty: divine creative power. To be a genius, in this sense, is to be someone who in-spires, who breathes life into a void. Witness the hagiographies of Saint Steve Jobs by people like Walter Isaacson—innovators are modern mystics possessed with powers of the divine.  This is the creation myth of Silicon Valley, whose prime pulpit is the Genius Bar, and it is tightly connected to questions of justice. If Apple is the origin of all that it creates, then it might seem fair that they keep the proceeds, particularly when they’re nice enough to let us use their free Wi-Fi. After the company recently won the race to surpass a one trillion dollar valuation, CEO Tim Cook emailed staff to explain, “Financial returns are simply the result of Apple's innovation, putting our products and customers first, and always staying true to our values.”  While seductive, this story is, like the Apple Store itself, a managed, theological fiction. Jobs wasn’t a saint—he was a control freak. Similarly, Apple’s system of operation, as we have seen, is less the result of genius than of capture and control. Semiconductors, microprocessors, hard drives, touch screens, the internet and its protocols, GPS: all of these ingredients to Apple’s immense profitability were funded through public dollars channeled into research through the Keynesian institution called the US military. They are the basis of Apple’s products, as the economist Mariana Mazzucato has shown.  Even the most distinctive parts of Apple’s lineup turn out to owe their existence to public funding. In 2000, for example, the US military’s in-house R&D arm, DARPA, sought to build a “virtual office assistant” for military use. The agency funded the Stanford Research Institute (SRI) to develop the assistant, which SRI then commercialized using Reagan-era legislation allowing public research to be converted into private property. To that end, SRI formed a spin-off company called SIRI. Thanks to a voice artist and the undisclosed sum Apple paid for the company in 2010, you can now talk to SIRI yourself on your state-funded phone.

Response: Apple’s extraordinary wealth is not simply a reward for innovation, or the legacy of “innovators” like Steve Jobs. Rather, it flows from the privatization of publicly funded research, mixed with the ability to command the low-wage labor of our Chinese peers, sold by empathetic retailers forbidden from saying “crash.” The profits have been stashed offshore, tax free, repatriated only to enrich those with enough spare cash to invest in a belief in future profitability.  But, as the public well from which it has drawn past innovations runs dry, the company’s ability to repeat the success of the iPhone is evaporating. Federal funding for scientific research is in deep decline, and Apple isn’t likely to make up the gap. To keep profitability high, Apple is moving to ever-more-luxury price tags (like the iPhone XS Max) and expanding its ability to extract rent by controlling the creativity of others (through Apple Music or the App Store), all while its embassies sell a different story with a smile.


Prompt: Command and Control All of this raises interesting questions about how Apple—and with it, capitalist accumulation more broadly—should be understood. Marx, on whose edifice most contemporary critiques of capitalism are built, assumed a cutthroat market that pushed prices ever downward. “The battle of competition is fought,” he wrote in the third volume of Capital, “by cheapening of commodities. The cheapness of commodities depends, ceteris paribus, on the productiveness of labor, and this again on the scale of production.” Yet this analysis is far from adequate to understand how Apple is now able to charge over a thousand dollars for a phone—and not just because it’s hard to convincingly explain how the same labor of Apple’s geniuses, combined with that of Foxconn’s manufacturers, is transformed into climbing prices, or why similar labor performed in stores like J.C. Penney is not.

Response: Instead, we could do worse than redeploy a concept recently resuscitated to think about Big Tech companies like Amazon: monopoly. As Lina Khan has argued in an influential article, Amazon has avoided regulatory intervention, despite its growing control over digital commerce and infrastructure, because price rather than market structure has become the optic for viewing corporate power. Amazon’s prices are low, so where’s the problem?  This definition of monopoly is quite new, developed by Chicago School acolytes like Richard Posner, a judge and economist, and Robert Bork, a former Attorney General and failed Supreme Court nominee. Part of the so-called “Law and Economics” movement (essentially the view of law as economics), they dissolved problems of monopoly into neoclassical price theory. Monopoly became the ability to raise prices above market norms, an exception to an otherwise equitable rule. Despite the obvious differences, political and theoretical, between this and Marx’s critique, both positions find ironic unity in their belief that markets are essentially competitive, and governed by the icy calculation of maximizing motives.


Prompt: As we have seen, however, Apple’s success relies on something different. Apple neither hires nor sells to the homo economicus of economic theory—it produces emotive, devoted disciples. Apple neither cheapens commodities to battle the competition, nor does it raise prices above market norms for the simple reason that it has created its own. Instead, Apple suggests that monopoly is less an exception to the rule of competitive capitalism than something that speaks to its core. Here the connection between management and monopoly becomes clearer, because one is simply the scaled version of the other: Apple’s profits, at root, are a product of its power to control.

Response: Apple’s ability to govern its employees, supply chains, and image allow it to restrict behavior and creativity in its interests—try getting a genius to say “crash,” the company to pay tax, or your music out of your iPhone. Apple’s ability to assert proprietary control over public goods, from the town square to government research, allow it to generate income far in excess of anything it could hope to wring from its staff. Apple’s performance of friendliness and innovation allow it to soothe customers while convincing both them and investors that it is the source of a happier, richer destiny. Apple’s profit does not come from packaging the labor of the past, in other words, but from the power to organize the present in a way that makes others believe that it is inventing the future.   Rather than clap the company’s ability to sell us that narrative, we should question it. We should use Apple’s history to help imagine something different—perhaps a future in which Apple is made public again.


Prompt: The Kenyan tech scene was born in pain.  On December 30, 2007, Ory Okolloh was blogging as quickly as she could. A Nairobi-based lawyer and investment adviser, Okolloh was writing about the recent presidential election—which the incumbent, Mwai Kibaki, had just won amid allegations of fraud. The election had been violent. The post-election was going to be worse.

Response: “I can barely breathe,” Okolloh wrote, “I’m so upset at the circumvention of democracy.” A few days later, as clashes continued between the ethnic group aligned with Kibaki and those that opposed him, Okolloh blogged about the deteriorating situation in Nairobi. “I hang out with people on both sides yesterday evening at different times and you cannot have a civil conversation if you’re not on the same side,” she wrote. “It is really very scary... I felt like I was on the set of some bad movie about ethnic cleansing.”  Soon after, Okolloh decided to leave the country and wait out the violence in Johannesburg. The number of murders would rise above a thousand: hacked, shot, burned alive. It was a lasting shock to a generation used to being shocked. Okolloh agonized over leaving Kenya, but felt she had to put her child’s safety first. Still, she wanted to find a way to help. From Johannesburg, she wrote: Google Earth supposedly shows in great detail where the damage is being done on the ground. It occurs to me that it will be useful to keep a record of this, if one is thinking long-term. For the reconciliation process to occur at the local level the truth of what happened will first have to come out. Guys looking to do something—any techies out there willing to do a mashup of where the violence and destruction is occurring using Google Maps?  Over the weekend of January 5-6, 2008, two software developers teamed up to answer Okolloh’s call. One, Erik Hersman, had been raised in Kenya but was then based in Florida; the other was a Kenyan based in Alabama named David Kobia. Together they built a website called Ushahidi, which means “testimony” in Swahili. Gathering data from Kenyans and NGO workers and anyone else who seemed credible, Hersman and Kobia developed a crowdsourced map of the post-election violence. On January 9, Okolloh introduced it to the world: Last week, in between nightmares about where my country was going, I was dreaming of a Google Mashup to document incidents of violence, looting etc. that have occurred during the post-election crisis. Today, Ushahidi is born.


Prompt: Ushahidi went on to become a great success story for Kenyan tech and an inspiration to entrepreneurs across the continent. Its tools for incident reporting have been deployed around the world, often for the purposes of election monitoring, crisis response, and human rights reporting.  Soon after launching the site, the Ushahidi crew started a tech hub in Nairobi called iHub. Within a few years, every major African city seemed to have a tech hub of its own, if not several. These ventures in turn built on the success of M-Pesa, a mobile payments system launched in 2007 by the Kenyan telecom Safaricom. M-Pesa revolutionized African banking and, like Ushahidi, quickly went global.  The Kenyan tech scene, soon dubbed the “Silicon Savannah,” had arrived.

Response: Leapfrogging Politics The story of Ushahidi illustrates an important point about tech in Kenya, and in much of Africa. Tech is a metaphor as well as a business. It’s a metaphor for a future that is not only prosperous, but also free from politics. African tech promoters often talk about “leapfrogging” to accelerate development: for example, by skipping fixed phone lines and going straight to mobile. The biggest leapfrog of all would be to skip over corrupt states and parasitical elites to a post-political internet where Africa’s large and underemployed youth population might have a shot at escaping poverty.   But politics has proved hard to avoid. Even as a desire to transcend politics fuels the fascination with tech, politics finds ways to reassert itself. “Technology, especially digital technology, and politics are inseparable,” Nanjala Nyabola tells me. A leading Kenyan public intellectual and author of the Digital Democracy, Analogue Politics: How the Internet Era Is Transforming Kenya, Nyabola says that tech is often seen as something that “can cure the problems of Kenyan politics.” It’s a promise that tech can’t possibly keep, however: “Unless there is a political change, tech won’t fix it. You’ll just end up chasing your tail.” This dynamic is far older than the Silicon Savannah. Indeed, Kenyan tech has long been shaped by its complex relationship with Kenyan politics.  In early 1994, three young Kenyans—two at MIT, one at Harvard—started Africa Online, which would become the leading internet service provider (ISP) on the continent. Initially, the Kenyan government opposed the internet altogether. The state-owned telecom monopoly took out a full-page advertisement in 1995 warning entrepreneurs that offering internet access amounted to resale of services and was therefore illegal. It targeted the internet partly to protect its turf, partly to control political speech in an unstable country, and partly because it could hardly keep up with the demand for fixed voice lines, let alone internet service.


Prompt: Yet the Kenyan state’s hostility to the internet didn’t kill Kenyan tech. In fact, it may have served as a catalyst. As the Kenyan consultant Muriuki Mureithi wrote in 2016, Kenya’s digital success was shaped by “government-imposed barriers that spawned innovations.”  For example, in 2000 several Kenyan ISPs formed an internet exchange point (IXP) in order to connect their networks. The state telecom considered this move illegal. A week later, the state regulatory agency sent officers to literally unplug the IXP. Yet over the following year, the ISPs built relationships with the regulators. The state telecom was struggling to maintain its fixed lines: if it couldn’t provide adequate service, the ISPs argued, then it shouldn’t have the power to prevent other companies from offering alternatives.  This point of view started to make sense to the regulators, who began to explore undermining the state telecom’s monopoly status. By 2002, the ISPs had won. They got their IXP licensed, just in time for the arrival of General Packet Radio Service (GPRS) technology, the first system that enabled mobile internet access. Because access to mobile internet ran through the ISPs, they gained serious leverage over the state telecom. The internet came to Kenya through cell phones: today, Kenyans get online mostly through mobile devices.  Kenya’s leapfrog to mobile internet was driven not just by technology, but politics. It wouldn’t have happened if Kenyan ISPs hadn’t skillfully exploited a power struggle within the state brought on by the hopelessness of existing telecom services.  Today, the Kenyan state has officially embraced its tech sector. The Silicon Savannah is good for the government brand: it makes the state seem youthful and forward-looking. Even Ushahidi and iHub, born in revulsion at election-related violence, now receive government funding. The government also fuels the hope that tech will somehow provide, through the magic of internet entrepreneurship, some significant portion of the jobs that Kenyan young people need.

Response: Uber for Ambulances If an earlier generation of Kenyan technologists was forced to circumvent the state, the new generation is being asked to do the state’s job. I saw this dynamic at Nairobi Innovation Week, an annual tech conference. It took place in the gleaming new Chandaria Centre for Performing Arts—financed by the industrialist Chandaria family, of Gujarati descent—in the equally gleaming, equally new University of Nairobi Towers. A crowd of young entrepreneurs made their pitches to a room of investors, as the government’s cabinet secretary for information and communications technologies, Joseph Mucheru, looked on.


Prompt: One pitch was for a company that would help girls understand menstruation and have access to pads. (The cofounder talked about a 25-year-old woman “who didn’t know menstruation was normal. She thought it was just her family.”) Another was for an SMS system to circulate study aids by phone, given that schoolkids can remain illiterate even after several years of attending public school. The founder hoped that telecoms might subsidize the rates so the service cost could be held at $1 per month. Another startup would provide help with “soft skills” like mastering email in order to boost employment. Then there was an “Uber for ambulances” so people might avoid having to take taxis to the emergency room. The seed money being sought was usually in the $10,000 range.  I began to hope the minister might simply get up and apologize for a government that was looking to penniless young entrepreneurs to provide, some day, basic social services. It wasn’t clear that even these tiny projects would be getting any funding—or that the government would take anything more than a spectator’s interest.

Response: Much of the funding for Kenyan tech comes from non-Kenyan sources. Kenyan tech draws heavily from philanthropists and so-called “impact investing” funds. Ushahidi and iHub, for instance, were financed mainly by American philanthropists. And the design for M-Pesa came from a project financed by the UK’s Department for International Development, as the mobile payments system was seen as a way to provide the advantages of banking to the unbanked and to enable microfinance.   The founders who attract investment are often not African themselves. A 2017 study by Village Capital found that 72% of startup investment in 2015-16 in East Africa went to three companies: M-Kopa (a solar power company cofounded by the same Nick Hughes who pioneered M-Pesa), Off-Grid Electric (founded by three Westerners, two with Oxford MBAs, who had an interest in social entrepreneurship), and Angaza (a solar company based in San Francisco with an office in Nairobi). The report found that “investors are only investing in founders from the US or Europe or who attended a prestigious university.”   Anywhere in the world, investors usually invest in founders who look like them, which tends to perpetuate inequalities. But it isn’t as though that 72% of startup investment is crowding out other capital. There are African venture capitalists, but African investors have more lucrative and safer options in a capital-poor region — investing in urban real estate is the main one, a sector with high profits and relatively weak foreign competition. For African tech to grow, African capital might need to rearrange its priorities a bit.   The state can help, whether with tax incentives, educational initiatives, infrastructure investment, or simply by staying out of the way. Kenya, like Nigeria and other major African states, is now officially pro-tech and pro-internet, not least because tech provides hope for the young and the prospect of outsourcing some (already missing) public services to the private sector. But state commitments to tech are very thin, and states’ willingness to keep from interfering in internet speech is getting weaker.


Prompt: Welcome to Sheba Valley Driving between downtown Nairobi and the airport, you see occasional flashes of white light. You are being photographed. Terrorism is a very real danger in Kenya and the state and its partners—Safaricom again—have been happy to work with both China and the United States to use advanced technology to improve surveillance in the city. Counterterrorism, for now, is that rare area where the two tech superpowers can agree that spending large amounts of money in the poor world on up-to-date technology is a good thing. For once, worries about technology transfer don’t arise.  Upon arrival in Addis Ababa, the capital of Ethiopia, the pace slows a bit. A visa costs $50 in cash. Ethiopian birr are not convertible, and the country faces a grave shortage of hard currency. Once I had paid, the customs official looked awkwardly away. “I can give you a receipt,” she said, “but I don’t have a pen.”  Ethiopia and Kenya are the giants of East Africa and the Horn but in many ways are opposites: an authoritarian, vestigially Marxist state isolated from global markets versus a multiparty democracy with a relatively light-touch state and strong foreign investment. Yet both have robust tech scenes and ambitions. If tech is going to transform East Africa it will have to do so in both places, as dissimilar as they may be.  In some ways traveling from Kenya to Ethiopia is like going from the West to China circa 1995. With 100 million people, Ethiopia has more than twice the population of Kenya. The government recently embarked on reform after decades of stifling political speech, jailing opponents, and making its biggest decisions in secret. Blue Soviet-era Lada taxis still cruise the streets. With a development policy based on cheap labor and low-value-added manufacturing for export, the economy, fueled by government spending based on debt, has been among the fastest-growing emerging markets, if from a very low starting point. (Kenya’s GDP per capita is about twice that of Ethiopia.) Addis’s tin-roofed center is ringed by acres of new, quickly built apartment blocks.

Response: Addis is bustling but it’s a hollow, rattling bustle. Many of Ethiopia’s universities barely function. The sole, state-run telecom struggles to address its backlog of phone-line requests. The internet cable is poorly maintained, and only businesses or the wealthy can afford to bring a line in and pay the high rates. Internet access for everyone else is through cell phones, but that too is iffy. When the government worried about political unrest in 2016-17, it cut off mobile access to the internet—and even, for a while, the fixed lines. When it worried about kids using the internet to cheat on exams, it cut it off for a week. When I was there, the prime minister had resigned so another state of emergency was declared and the internet got cut off.  This can make tech entrepreneurship difficult. Still, a tech startup scene has taken root in Addis, known as “Sheba Valley.” An Ethiopian expat coined the phrase, as a reference to the Queen of Sheba mentioned in ancient sources from the Torah to the Koran. The Ethiopian royal family claims descent from her, and her name functions as a kind of mega-brand: for example, Ethiopian Airlines calls its frequent-flyer program ShebaMiles.  Keeping the Power On To reach the offices of Apposit, one of Sheba Valley’s leading startups, I made my way along Addis’s main thoroughfares to a cafe which was said to be next to the building that housed the company’s offices. The cafe reference was important because, to the limited degree that Addis has street numbers at all, no one seems to know them. After dozens of conversations and an exploration of two other buildings, I found the air-conditioned, buzzing offices of Apposit.  “We started just less than ten years ago,” Eric Chijioke, Apposit’s head of technology services, told me in his glass-walled office. “The three founding partners, myself, Adam [Abate] and Simon [Solomon], we had all worked in Ethiopia on a project the Kennedy School was running, building out the government’s financial systems.” Chijioke and Abate had gone to school together at Brown; Chijioke got a bachelor’s in mechanical and electrical engineering, while Abate’s degree was in economics and development studies. Harvard’s Kennedy School was executing the project for Ethiopia’s government on behalf of Western government funders.


Prompt: More than half of the enterprise-level work in Ethiopia, according to Chijioke and three other Ethiopian tech people I interviewed, is governmental. The government finds it hard to run its own IT systems, so it farms the work out to contractors. But there are neither enough vendors nor enough work to sustain a competitive market, so the government in effect sets the prices. One of the many challenges of building tech for the government, says Chijioke, is the extreme variability of state infrastructure: The systems that we built, and that still run, they needed to take account of the fact that it’s a distributed platform that’s supposed to run right down to the local government area level. But at the local government area, the actual functioning of the infrastructure is at a highly variable level, from nothing—you know, paper and pen—to computers that might run a few times a day to a few times a week, to some offices that might be highly automated and have satellite connectivity. You have to build for this: everything from zero to high-spec, everywhere in between.  Apposit’s leading project isn’t government-related, however. It’s in mobile finance. Apposit’s main client is Pagatech, a company that aims to build an M-Pesa-style mobile payments system for Nigeria. Pagatech is Chijioke’s responsibility. He got involved with the company early on: “I knew the founder — a gentleman named Tayo Oviosu. He’s an impressive guy — he went to business school with my younger brother, at Stanford, and he was thinking of starting up the business and he needed some technical advice.”  Apposit started out serving Pagatech when the company was still small. They did such a good job that Pagatech was willing to grow its developer team in Ethiopia rather than start a new one in Nigeria. “All of the developers and database engineers are based here,” Chijioke said. “On that particular team we have close to 30 people, and this is high-skilled jobs. Everyone on the team is Ethiopian except myself and one junior architectural engineer. The biggest problem is sometimes, for political reasons, the internet might be disconnected.”

Response: Feleg Tsegaye, who runs Deliver Addis, Ethiopia’s largest e-commerce startup, also works at the mercy of an insecure, sclerotic, and capricious state. When the first internet shutdown came, Deliver Addis had to reconfigure its entire business to work offline, via text messaging. During another emergency, it had to configure its own email service. “Most people here don’t use the internet on a regular basis, besides Facebook and social media,” Tsegaye said. “There is not an impression that actual commerce is done via the internet, so the government doesn’t expect blocking it to have a great impact on businesses.”  To press their case with the government, Ethiopian tech companies have formed lobby groups—one for hardware, another for software—and startups have organized themselves around a handful of small institutions, all of which receive some funding from governments abroad, including RENEW, an American impact investing firm that backs Deliver Addis. The last time the government shut down the internet, beginning in October 2016, some 200 tech businesspeople met with the communications ministry to argue that technology, and the internet in particular, might actually have something to offer for the Ethiopian economy.  It promises to be a long battle. For now the government has placed its bets on manufacturing parks, complete with tax breaks and special immigration offices. Foreign companies, mainly Asian-owned, take advantage of low wages and the tariff-free access that manufacturers from Ethiopia, as a less-developed country, have to Western markets. But the inputs, such as those needed for making textiles, are often not from Ethiopia, and neither is most of the intellectual, manufacturing, or financial capital. Not even all of the labor is Ethiopian; managers and supervisors are usually Asian.   Still, even though the Ethiopian government has prioritized manufacturing, it hasn’t entirely neglected tech. Around 2002, the government announced it would be building an industrial park for tech companies. Sixteen years later, it is just opening—and there hasn’t been much take-up. Kenya built something similar, with roughly similar results.