ride-share giants are in many ways more agile and cheaper for the consumer than the taxi services they’re slowly destroying, and these companies are accordingly popular with large investment funds, for one primary reason: their drivers are independent contractors who have no bargaining power, no benefits, and very few legal protections. Everything these companies do—from the rewards programs they set up for their contractors to the way the algorithms that assign rides to drivers seem to punish casual driving—is actually designed to nudge their drivers inch by inch toward a full-time employment that they aren’t allowed to call full-time employment. The moment this state of affairs is recognized, all kinds of rules will apply to these companies, making them even more unprofitable and likely putting them out of business. But until such a moment, the companies will explain to you ad nauseam how they’re different and new and how you are missing the point when you apply established categories to them.

This book is about concepts and ideas that pretend to be novel but that are actually old motifs playing dress-up in a hoodie. The rhetoric of Silicon Valley may seem unprecedented, but in truth it is steeped in some pretty long-standing American traditions—from the tent revival to the infomercial, from predestination to self-help. The point of concepts in general is to help us make distinctions that matter, but the concepts I discuss in the chapters that follow frequently serve to obscure such distinctions. The inverse can also be true: some of the concepts in this book aim to create distinctions where there are none. Again and again we’ll come across two phenomena that to the untrained eye look identical, but a whole propaganda industry exists to tell us they are not. Taxi company loses money; Uber loses money—apparently not the same. The tech industry ideas portrayed in this book are not wrong, but they allow the rich and powerful to make distinctions without difference, and elide differences that are politically important to recognize. They aren’t dangerous ideas in themselves. Their danger lies in the fact that they will probably lead to bad thinking.

In the following chapters, I will try to show not only how certain ideas permeate the world of the tech industry, but also how that industry represents itself to a press hungry for tech heroes and villains, for spectacular stories in what is ultimately a pretty unspectacular industry. A study like this one almost by necessity has to foreground the highly visible founders, funders, and thought leaders. To find out how ordinary coders or designers think, to say nothing of all the folks making up the tech industry who aren’t customarily thought of as belonging to that industry, is a very interesting project in its own right, but it isn’t the project of this book. For better and for worse, the media has a fixation on tech thought leaders. It seems to need certain figures to be able to spin its narrative. Peter Thiel, Elon Musk, Steve Jobs, and others like them knew how to manipulate that—something they learned from another California global export: 1960s counterculture.

Unfortunately, my own spotlighting of these leaders means this book risks recapitulating one of the central misperceptions of the tech industry; it’s anything but clear whether figures like Mark Zuckerberg, Elon Musk, or even Steve Jobs really embody the way the tech sector understands itself. But what is clear is that they represent the way the tech sector has communicated with the outside world. They are easy identificatory figures when one is dealing with an industry that can be disturbingly amorphous and decentralized. (This is, after all, how the pars pro toto “Silicon Valley” has functioned in general.) They are creatures of the media, inviting us to project our fears, giving shape to our hopes. Most important, they encourage us to think that someone, whether charismatic or nefarious, knows where the journey is going. Visibility in the press is not, of course, the same as representativeness. Making a Theranos movie is not cool. You know what’s cool? Making an Elizabeth Holmes movie.

Giving these ideas’ history back is central to any attempt to interrogate the claims the tech industry makes about itself. But there’s another question that we can ask once we’ve figured out where these ideas come from: Why were these ideas convenient to adapt, and why was it convenient to forget their history? The story of these ideas intersects with the great transformations that information technology has undergone in the last seventy years. Coding went from being clerical busywork done by women to a well-paid profession dominated by men. In recent years, competencies around technology went from highly specialized to broadly distributed, to the point where “learn to code” has become a panacea for any and all of the ravages of capitalism.

And the environment around tech has changed: the government went from basically owning the tech industry to struggling to regulate it; computer science went from an exotic field seeking to establish itself to one of the most popular university majors. The cultural visibility of the sector and its practices has transformed even since the film The Social Network came out in 2010. Perhaps Foer had it only half right: When the companies of Silicon Valley reconfigure your ideals, it’s not just in order to sustain their business model. It’s also to avoid cognitive dissonance in their thinking about gender, race, class, history, and capitalism.

Many of the ideas traced in this book had analogous trajectories. For one thing, they emerged from a similar era. They were new ideas when given definitive shape in the sixties, frequently by the counterculture. They attained their shape outside of the university, though they were always on the periphery of it. As the management-science scholar Stephen Adams has pointed out, a lot of the institutions of learning and research featured in this book grew out of a desire to stanch a persistent brain drain of bright young people moving from the West Coast eastward. Around these

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