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Through talking with Flynn and Mark we learned that the printers get commercial bulk discounts on sending mail that were better than the media mail rates we could get on our own. Part of that discount was because it required an address validation offered by USPS that reduced the amount of returned mail, which was a recurring problem for us. As an added bonus, Sheridan could handle international orders, which we had always found prohibitively expensive and time consuming to fulfill. So there were a lot of things in there that made it make sense to switch up how we did things.

JF: There was one particularly dark issue where you were traveling, and I had to send out the whole issue by myself, packing and addressing and stamping every book. It was like a three day affair. XW: Oh, man, I’m so glad that you guys don’t have to deal with that anymore. JF: It was fun when people said that they were a Logic subscriber to be able to say: yeah, I recognize your name, I shipped you your issue from my dining room. But I’m glad that phase is over. XW: You mentioned that we didn’t have a proper way to have subscriptions at the beginning. How did our handling of subscriptions change over time? JF: When we started out our subscriptions were just one-off items in our webstore that were tracked by one big google sheet that we managed ourselves. Not having a way to let people automatically resubscribe was a big challenge for us that first year, and we ended up moving to software called Chargebee. That ended up working well and handled a lot of the problems that I didn’t even know we’d encounter–notifying people multiple times that their subscriptions were renewing, sending out bulk emails, handling what happened if people’s cards expired, what happened if there were chargebacks or people wanted refunds.

The havoc wrought by the mortgage crisis in turn opened space for new algorithmically enabled land grabs. Invitation Homes, a private equity-backed firm, broke new ground by adopting machine learning systems to assess rental acquisitions, buying up huge swaths of property foreclosed during the subprime crisis on the wager that Americans would be willing to rent suburban houses they could no longer afford to own. Today, one-fourth of single-family rentals belong to institutional investors, which have developed streamlined, smartphone-driven systems to manage them. Digital technology mediates all interactions between tenants and the company, from viewings to lease signings to repairs, inaugurating what scholar Desiree Fields calls the age of the “automated landlord.” (In March 2020, New York Governor Andrew Cuomo tasked William Mulrow, a senior director at the private equity giant Blackstone Group, with spearheading the state’s coronavirus economic recovery; Blackstone held a major stake in Invitation Homes until November 2019, when it sold its last shares for a total of around $7 billion.) As Fields has documented, the digital dimensions of this high-tech landgrab go much deeper than a shiny interface. An assemblage of platforms and data analytics drive what the National Rental Home Council, a trade association, describes as “property management at scale.” First, Invitation Home’s proprietary underwriting algorithm determines what properties the company should purchase by considering factors including “neighborhood desirability, proximity to employment centers, transportation corridors, community amenities, construction type, and required ongoing capital needs.” Then networked technology allows investors to oversee large portfolios of far-flung units, with information quickly conveyed to capital markets so that additional money can be raised to expand the enterprise, while regular people who want to purchase a place to live are forced to compete with distant cash-rich investors working at digital speed. Hedge funds are happy to let buildings sit empty, waiting for them to appreciate, while locals pay the price.

Meanwhile, old biases persist and compound even when the platform is cutting-edge. Invitation Homes, for example, targets people of color who lack other housing options while charging them sky-high rates to meet Wall Street’s outsized expectations. In other instances, opaque systems make discrimination difficult to prove. Automated decision-making enshrines socioeconomic disparities in an invisible, technical process, locking certain populations out or including them on predatory terms. One recent study from UC Berkeley found that, among ​online​ mortgage applicants, Black and Latinx borrowers paid over five basis points more in interest than white borrowers with similar financial backgrounds.  Racism is encoded in bad datasets and reinforced by the biases of disproportionately white, male, and privileged engineers—a process scholar Ruha Benjamin calls the “New Jim Code.” Recently, the Trump Administration’s Department of Housing and Urban Development proposed new rules that would effectively permit automated discrimination in the housing market, allowing algorithms to exclude and segregate on a landlord or mortgage lender’s behalf, effectively exempting digital technology from civil rights regulations. “It’s going to drive people toward these algorithmic tools, and I think we’ll end up in a marketplace where everyone is taking advantage of this loophole,” Paul Goodman, a housing justice advocate, told ​Dissent.​ The powerful may soon be allowed to have computers mark certain populations as “risky” in order to dispossess them, and to do so without risking a lawsuit.

As far as I know, we have not seen this happening at scale in the United States yet. It might happen in 2020. But it’s a technique that Modi really perfected. So journalists and opposition leaders and NGO leaders and reformers have all suffered and continue to suffer tremendously because of these troll farms and their activities in India.

How is the experience of Facebook different in other countries? And what role does Free Basics play? Free Basics is a program that Facebook created to spread internet connectivity, especially in poorer parts of the world. It offers poor people in developing countries a data channel for something close to free. If you have a smartphone and you can’t pay for a data plan, you can use an app that Facebook created to get online. But the app doesn’t give you access to the entire internet: it only lets you use Facebook and other sites that Facebook has approved.

As coverage of the robo-debt scandal spread, calls for the government to suspend the scheme mounted. Yet it refused to halt the program until an inquiry by the Australian Senate finally ordered it to do so in May 2017. Dehumanized Debt Automation is dehumanizing in a literal sense: it removes human experience from the equation. In the case of the robo-debt scandal, automation also stripped humans of their narrative power. The algorithm that generated these debt notices presented welfare recipients with contrasting stories: the recipients claimed they’d followed the rules, but the computer said otherwise.

There were few official ways to explain one’s circumstances: twenty-nine million calls to Centrelink went unanswered in 2016, and Centrelink’s Twitter account seems explicitly designed to discourage conversational exchange. One source of narrative resistance is notmydebt.com.au, a website run entirely by volunteers that gathers false debt stories from ordinary Australians so that the “scandal can’t be plausibly minimised or denied.” Over time it was revealed that many of these debts were miscalculated or, in some cases, non-existent. One man I’d read about was on a government pension and saddled with a $4,500 bill, which was revised down months later to $65. Another recipient, who was on disability as a result of mental illness, had a debt notice of $80,000 that was later recalled. A small proportion of recipients were exclusively in contact with private debt collectors and received no official notice from Centrelink at all.

Logic came together at a particular moment in time. And, over the years, the moment changed—the broader political context, the broader context of writing about technology. Eventually, I started to feel like we had outlived our usefulness. Earlier this year, I brought that feeling to the core Logic team, and everyone broadly agreed. Our initial thought was to shut down the magazine. Then Khadijah and Xiaowei came up with a proposal to continue the magazine by reinventing it. And we all loved the idea.

XW: Honestly, a big issue is just time. A lot of Logic pieces are edited by Ben while he’s not watching his children. Not that I want to say that Ben’s a negligent father. BT: But it’s true. And we should get that on record. XW: There’s a lot of free labor involved. And that leads to burnout. Finding writers, growing writers—that stuff takes an enormous amount of time. So does building and running all of the invisible infrastructure around maintaining editorial calendars, lining up copyeditors, and so on. And everyone’s doing it on top of the job that pays the bills. So, at a certain point, you either have to find a bunch of money or come to terms with the fact that it can’t last forever.

Patients aren’t the only ones who have recognized this need. In 1976, a computer scientist at MIT named Joseph Weizenbaum declared the notion that “a computer can be programmed to become an effective psychotherapist” was nothing but “perverse.” This was a renegade position for the MIT of the era, where a powerful belief in the capabilities of artificial intelligence reigned. Weizenbaum was well aware that he was committing apostasy, but his assault on the idea of computerized therapy was all the more forceful because he had created the first virtual therapist himself.

Alien Worlds The ELIZA program that Weizenbaum created, which he released in 1966, is often called the world’s first chatbot. Cleverly, Weizenbaum designed ELIZA around the stereotype of a psychotherapist’s propensity to turn a client’s statement into a question. (People don’t like me. Why do you think people don’t like you?) This was an elegant way to create the effect of a computer holding its own in a conversation with the user.  Of course, ELIZA, which Weizenbam named after Eliza Doolittle, the character in the George Bernard Shaw play Pygmalion, possessed no real understanding of what its human partner was saying. As the information technologist Ted Nelson wrote of the program in his classic 1974 book about hacker culture, Computer Lib / Dream Machines: “Now, do not be fooled. There is no Ghost in the Machine. The program does not ‘understand’ the user. THE PROGRAM IS MERELY ACTING OUT THE BAG OF TRICKS THAT JOE WEIZENBAUM THOUGHT UP.” The explanation (and emphasis) was necessary because ELIZA’s noncommittal responses were surprisingly good at convincing users either that it did understand them, or that there was actually another human being on the other side of the teletype.  Indeed, ELIZA sometimes seemed to perform flawlessly, as in this example from Weizenbaum’s original 1966 paper describing his creation: Men are all alike.IN WHAT WAYThey’re always bugging us about something or other.CAN YOU THINK OF A SPECIFIC EXAMPLEWell, my boyfriend made me come here.YOUR BOYFRIEND MADE YOU COME HEREHe says I’m depressed much of the time.I AM SORRY TO HEAR YOU ARE DEPRESSEDIt’s true. I am unhappy.DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPYI need some help, that much seems certain.

So in that sense, it does enable movements and people to try out new stuff and succeed without going through the usual institutional gatekeepers. As far as which political orientations have used the internet most successfully, it’s hard to say. The left movements behind Bernie and Corbyn, both fairly successful, were heavily assisted by the internet. Corbyn says as much.

I don’t think the Right has been successful in the same sense, but obviously they rack up huge audiences, especially in the InfoWars and InfoWars-adjacent part of the internet. So it’s obviously not a universally positive medium. Some people worry that the internet is bad for leftist politics: that it encourages insularity, “uncomradely” conversations, and so on. What’s your response to that? I don’t think the internet is bad as much as certain subcultures on the internet are bad. I’ve seen those same subcultures operate in real life, especially when I was a student in Boston. They are just as self-destructive and stupid there as they are online.

This rhetoric of self-reliance prevailed through the mid-1990s and beyond, in which policies of “mutual obligation” were introduced by the conservative government led by John Howard. As Griffin observes, these policies ensured “eligibility for income support was reconfigured, newly coupled with requirements.” These included “obligatory participation in prescribed employment assistance activities, lower benefit payments and shorter benefit periods, means testing, and punishments for failures to attend job interviews.” And the slightest failure to follow these mandates led to a loss of income.

The bureaucratic language surrounding these benefits—“Work for the Dole”—marked welfare for the economically marginalized as a provisional rather than enshrined right for citizens. It’s support with strings attached. Yet the irony of stigmatizing welfare recipients is that better-off Australians are major beneficiaries of social spending. The Australian writer Tim Winton notes that the country’s middle class has “an increasing sense of entitlement to welfare,” which is “duly disbursed largely at the expense of the poor, the sick, and the unemployed.” These include tax concessions on contributions to “superannuation,” which are funds designed to help Australians save for their retirement. Such concessions are distortionary: they’re levied at a flat rate of 15 percent, rather than at a progressive rate according to one’s income, which means their benefits are reaped overwhelmingly by the rich.

Do you have a Facebook account? I do. Do you have any other social media accounts? LinkedIn. Instagram, but I haven’t figured it out yet. I guess you just take pictures, but I’m not sure. Yeah. That’s it. And I did a lot of writing on Quora. While we were on the inside, The Last Mile had us write responses. We’d actually write them out and then somebody on the outside uploaded all the content. We’d hear about our posts getting up-voted or down-voted. But when I got out and saw my own account—and how many people were following me on there and asking me questions—it was kind of cool. I’ve been able to do it in real time now.

Do you know anyone else who is now outside of prison who went through the program? Yeah. The Last Mile has been kind of like a family. We all keep track of each other and try to support each other. Often software companies will run background checks for potential employment even if it’s not clear why they need them. I’m curious if that’s a problem that you’ve seen, and if there any particular barriers to employment that folks who have graduated from the program are experiencing? I’ve heard about it. One friend from The Last Mile went through the Hack Reactor program on the outside and graduated just recently, about a month or two ago. Now they have him filling out the applications and going through the tech interviews and things like that. He told me that with some of the applications, once they find out about his criminal history, his application gets automatically kicked to the side. He’s saying he’s getting excluded from some opportunities because of his record.

Terrains of Choice-making People say “the personal is political” a lot, and I think almost always in a reductive way. It doesn’t just mean that our individual “personal” issues—like our sexuality, our families, our fucking—are political negotiations. Is it even useful to call these “personal” issues? Aren’t impersonal issues also political? And if so, everything is political, so why use the word to delineate anything at all? Perhaps like this: The personal is political because personhood is political. Who gets to be a person and how? How are persons formed, categorized, and organized in and through relations with each other? These are determined by operations of power. The personal is not political because personal choices are necessarily political choices, but because the very terrain of what gets to be a choice and what types of persons get to be choosers—what types of persons get to be—are shaped by political power. The sort of political power that whispers through human histories of convention formation and maintenance, of hierarchy and adherence to it, of regimes of expertise, of oppression, of struggles and paradigm shifts.

Remember how Meryl Streep’s character in The Devil Wears Prada chastises Anne Hathaway’s character, the naive assistant, for thinking she had agency when she’d chosen to buy a blue sweater? A Foucauldian point well made: capital didn’t make her choose and buy that color sweater, but it did overdetermine the conditions of possibility for any such purchase.

Ongoing monitoring The Care.com platform is split into multiple apps. On “Care.com Caregiver,” nannies, dog walkers, cleaning workers, and others can sign up, submit to background checks, fill out their profile and upload photos, and search and apply for jobs. Another app, titled simply “Care.com,” allows care seekers to create job postings and browse caregivers’ profiles. With a Premium subscription (listed at $39.99 USD monthly, $89.97 quarterly, or around $160 USD annually), users can also contact caregivers directly—a power that was abused to repeatedly send unwanted messages to one of our interviewees—and to “have access to results of all background checks,” along with other “benefits.” Workers can also sign up for Premium for the same prices, granting them access to “higher ranking in search results,” and a promise that they will be “5X more likely to get hired.” Care Premium also comes with a free annual “CareCheck” background screening, which is required to begin applying for jobs. If workers choose not to sign up for Premium, the CareCheck costs $14.99 USD per year, due when they register. Care.com’s official materials admit that background checks can be inaccurate, yet two of our interviewees discovered that the platform’s bans are decisive and uncontestable, and the platform doesn’t disclose whatever supposed violation led to the ban. In addition, the platform advises care seekers to purchase additional background checks on their applicants “at time of hire” for increased security. They note that background checks are “not a substitute for conducting thorough in-person interviews, reference checks, online and social media searches, obtaining copies of the candidate’s identification documents, and conducting ongoing monitoring of any individual you hire.” “Ongoing monitoring,” however, is not simply used to ensure the safety and well-being of one’s children. It’s also a retaliation tactic. Kendra told us that her client ramped up monitoring immediately after she attempted to renegotiate her pay and hours. “She had started asking more of me after that. So it was like, ‘Oh, well, you’re gonna need to step up your game.’ And it was like, ‘You need to write down every single thing that my kid does’… It was almost like a punishment… I had to write down every activity, I had to write down everything they ate, every time they went to the bathroom, every time. Like, everything.” This documentation took place on printed sheets of paper that her client supplied to her, which were inspected before she left the home “to make sure I did a good job.” She described this work as “exhausting, especially on top of having two children to watch. I would have to take time away from them to fill it out.”

Nannies sometimes don’t know the full scope of monitoring until they start a particular job—and the fact that their jobs are usually performed in clients’ homes puts them at a disadvantage in negotiations. As Marie put it: “People can act whatever way they want to on the phone,” she said. “Until you go into their house, you just don’t know.” She described one interview that stuck with her: “We’re just gonna watch you,” a parent told her. She was instructed to take their baby, a bottle, and a three-year-old downstairs while the parents observed her on a camera feed. “There is no privacy” Marie felt deeply uncomfortable that the family hadn’t told her before the interview that she’d be monitored. But many nannies resign themselves to surveillance, reasoning that a client has the right to do what they like in their own home. Jade told us, “I didn’t think it was all that big of a deal that they didn’t ask for consent or anything like that. Especially because I wasn’t doing anything that they would be worried about in the first place… When you step into someone else’s home, there is no privacy. That is something that you pretty much have relinquished as soon as you step into that home.” Celia adjusted her behavior for the presence of the cameras. “I used to sit down, back to the camera, and eat very fast because I’m feeling so weird,” she shared. Nevertheless, she concluded, “I know it’s not my house, you know, they can do whatever they want.” Some interviewees quit in search of a better gig after their clients crossed a boundary. Sometimes this works, they told us; “more authentic” working relationships are possible. But at the heart of acquiescence to surveillance is the unequal status of the caregivers and the employers. Turning down needed income is not an easy decision. As Cassandra said, “Sometimes I work up the nerve to ask if they have cameras… it depends on how many offers I’m getting lately… If I’m more, like, desperate to get a job, I just won’t bring it up.”

The mission needs to be drawn broader than code. Ensuring that the wisdom of the crowd can produce social change means creating pathways for offline action that can effectively challenge wrongdoing. Ensuring that the wisdom of the crowd can reach accurate results requires more inclusive, diverse bodies of participants. Both speak to a political agenda that cannot be achieved merely by designing tools and making them openly available.

Experimentation must be accelerated at the edges. Although we depend heavily on a few key platforms, the internet is still a vast space. Today’s platforms emerged from experimentation at the edges. To produce new generation of robust platforms, we need more experimentation—a proliferation and wide exploration of alternative spaces for crowds to gather online.

By willingly serving up ratings, reviews, and brunch photos to brutally efficient urban housing markets, millennials are turning prestige into price, collaborating in making their cities uninhabitable. A recent Twitter ad for Foursquare Swarm aims to appeal to users who are “constantly traveling.” To a user who responded, “I wish I could afford to travel,” the company replied, “exploring can be as close to home as walking a new way to work or checking out a neighborhood nearby!” But even this attenuated form of travel may no longer be feasible in the near future. This is the final irony of LBS: the users who sustain these platforms are working hard to evict themselves from the cities they’re mapping for free. Just as their labor helped fuel the market dynamics that pushed people out of gentrifying neighborhoods, they will themselves be pushed out as values climb higher. The volunteer crawlers may eventually crawl themselves right out of town.

Technology alone isn’t silencing our languages. But it won’t single-handedly save them either. At first it seemed like the internet would reinforce the dominance of “killer languages” like English, Spanish, and Chinese. Then came the hope, in the last few years, that the internet might make room for a multitude of smaller languages. Maybe the virtual world, as it grew ever more oral and bottom-up, would become as deeply, humanly diverse as the physical one.

It’s one thing to say no doxxing. But what’s doxxing? Let’s look at Twitter as an example. What is doxxing on Twitter? Is doxxing accidentally retweeting someone’s phone number, which led to Rose McGowan having her Twitter account suspended for twelve hours? Is doxxing the release of someone’s real name? Could you argue that doxxing is tweeting the email of one of your senators—or maybe only if it’s their personal email? You need to be able to say, “Doxxing is this event, but not this event.” You need to provide people with specific examples.

When you can provide those examples, you are providing a lot more clarity. The downside, however, is that you’ll sometimes have people arguing in a very pedantic way: “I didn’t do this exactly.” Well, no, but you did something within the scope of this. And people can always argue that something’s not harassment, that it’s more light-hearted trolling.

The Zillow American Dream On the other hand, surely Zillow’s outsized place in the firmament of internet content has something to do with a particular voyeuristic American interest in real estate. In the 2014 essay “The American Room,” Paul Ford noted that one unstated appeal of YouTube was that it let us see into other people’s bedrooms; a half-decade on, in The New York Times John Herrman saw TikTok providing a similar view of the American job, as users regularly post videos of themselves chopping vegetables, welding metal, and restocking airliners. Zillow has some of this same appeal: It gives you the ability to peek into spaces you might not normally ever see. If home ownership is the key requirement for achieving “the American dream,” Zillow is the best record we have of what the American dream might actually look like. But where the “American Room” of YouTube was surprising for how unvarying it was—white walls, one window, popcorn ceiling—the American dream as seen on Zillow is hilariously, amazingly wide-ranging: from crumbling farmhouses on dozens of acres to manufactured housing on leased trailer-park land, from urban townhouses with original detailing to suburban ranch houses with sex dungeons, and from bloated McMansions to, well, other bloated McMansions.

Sometimes the photo lighting is good, sometimes it sucks. Sometimes a house has been staged, sometimes it’s a dump full of stained carpets, overstuffed closet shelves, and rumpled beds. Even the houses with the most baffling design decisions or the creepiest doll collections are, presumably, what someone wanted to live in, or what someone believes someone else would want to live in. Whereas a generation or two ago, we might’ve gawked at the mansions on Lifestyles of the Rich and Famous or MTV Cribs, now we seem to get as much enjoyment casting a voyeuristic eye on a broad swath of ordinary middle-class homes. Because they are created to sell houses, rather than to engage or entertain bored people scrolling their phones, Zillow listings have an appealing, counterintuitive authenticity. These houses were built for desires and imperatives that lived outside the platforms on which they might now go viral. Who builds or buys an impractical house for the Vine? Then again, who exactly is building and buying houses these days? Internet natives—who are, speaking generationally, often locked out of the housing market by forces outside their control—might now see a modest two-bedroom home with a decent commute as an aspirational dream. “Older folks are much more derogatory about the homes that I share,” More, who hosts @Zillowtastrophes, observed. “Like, ‘I could never live there.’ But younger people, especially on my TikTok more than my Instagram, they find things that they like about even the worst houses.” Zillow’s appeal as a media company is partly rooted in voyeurism and partly in escapism—two of the modern internet’s orienting values. The Zestimate value may be prominent (and may be put to cruel use by school kids), but it’s the photos that make Zillow Zillow. Who needs a metaverse when you can tour millions of real homes from your browser, and see yourself in every one? When I was giving birth to my oldest son, the attending told me it would feel like an elephant was stepping on my chest. They made the C-section incision right below my belly. With the high dose of epidural, my whole body shook like I was having a seizure. Then, a few minutes later, it was over, relieved of ten months of pregnancy and the clinical intrusions that left my body feeling like it no longer belonged to me.

They are happy to offer employees small ways to live more sustainable lives, however. The company runs various recycling programs, encourages employees to “skip the straw” to reduce plastic consumption, and funds sustainability hackathons. (One hackathon project involved using AI/ML to detect trash in the ocean.) More broadly, Microsoft works hard to present an environmentally friendly public face. Its most ambitious green initiative is its promise to power its energy-hungry data centers with renewable sources. In 2016, Microsoft announced its goal to transition its data centers to 50 percent renewable energy by 2018. Hitting that target one year early, president and chief legal officer Brad Smith announced that the next goal is to surpass 70 percent renewable by 2023. “Time is too short, resources too thin and the impact too large to wait for all the answers to act,” he said.  On the surface, then, Microsoft appears to be committed to fighting climate change. Google has constructed a similar reputation. But in reality, these companies are doing just enough to keep their critics distracted while teaming up with the industry that is at the root of the climate crisis. Why go through the effort of using clean energy to power your data centers when those same data centers are being used by companies like Chevron to produce more oil? After Empire At the workshop in Atyrau, a young Kazakhstani data scientist approached me to ask about a project that he was migrating to Microsoft’s cloud platform. He didn’t speak English fluently, but I could tell he was a good engineer. I wasn’t sure if he really needed my help. It seemed like he just wanted to chat with another engineer in a room filled with businesspeople.

Afterwards, he told me a bit about how he ended up working for TCO, and how he wasn’t able to find any other opportunities in the country that could match the offer. He had attended Purdue University to get an undergraduate degree in computer science. But since the Kazakhstan government paid for his tuition, he had to return to the country to work. “It means that I have to work in oil,” he said. “It’s basically the only industry that pays.”  Speaking with him made me realize the extent of oil’s dominance in Kazakhstan. Oil is by far the biggest economic sector, accounting for 63 percent of the country’s total exports. In 2013, TCO made $15 billion in direct payments to the government — an enormous figure, considering that the country’s entire tax revenue that year came to $21 billion. TCO is also a major source of wealth for the region. For years, the venture has invested millions of dollars into building schools, community centers, and fitness centers for the local people.  Kazakhstan’s dependence on oil has only grown over the past decade. In 2016, TCO announced a $36.8 billion expansion to the Tengiz project, tying the country’s economic future even more closely to fossil fuels. To make matters worse, the country’s ability to produce oil relies heavily on multinational oil companies. At the time of its founding, TCO was a fifty-fifty partnership between Chevron and the state-owned KazMunayGas. Since then, ExxonMobil and the Russian oil company LukArco have joined the venture, but only KazMunayGas’s share has been diluted.

This practice disrupts the presumption of autonomy between the spheres of art, consumer society, and industrial production—a presumption that was also investigated in Marx’s critique of capitalism. Italian political theorist Paolo Virno, in his essay “Virtuosity and Revolution,” shows that Marx had already addressed cultural production in his notes for Capital. Marx distinguished between two kinds of intellectual labor: “commodities which exist separately from the producer,” like paintings or books, and those in which “the product is not separable from the act of producing,” like the performing arts. As Virno argues, within the context of the service work that dominates advanced economies, “activity-without-a-finished-work moves from being a special and problematic case to becoming the prototype of waged labor in general.” The artist is in the same category as the service worker, in other words: “virtuosic activity comes across as universal servile labor.” In this light, an artistic practice in itself does not escape the boundaries of the capitalist mode of production, which has included service work and the dissemination of cultural phenomena since the days of the printing press. Speaking to The Fader, Pierre pointed out that the sound of the TB-303 itself has now become subject to imitation by an arm of the culture industry. “I think since people have made all these clones of the 303,” he says, “to me that’s just like other manufacturers making bass guitars or lead guitars, or pianos.” To his ears, much of the electronic music that has followed simply repeats an old formula: “Even dubstep, as crazy as that stuff sounds, has sounds that are connected to a previous instrument—it’s still copying something in some kind of way.” The real legacy of acid house may not reside in its sound, but in its method. Acid shows us a new way to relate to the machinery that increasingly populates our everyday lives, one that shifts our experience from the passive mode of consumerism into the realm of creative activity. The knowledge we share as operators of this machinery has the potential, as Virno puts it, to “affirm itself as an autonomous public sphere,” but only “if it cuts the linkage that binds it to the production of commodities and wage labor.” Acid house teaches us that the potential for a radical practice of culture may not lie in making a certain kind of sound, but in something more fundamental: not doing what it says in the manual.

How did you first get involved with Computer People for Peace (CPP)? In the 1960s, I was working at IBM, which was a marvelous job for me because I was a single mother with a kid. Programming was the only thing that paid a woman a living wage. And they trained me, so that was wonderful. Around 1968, I joined CPP. We had a steering committee of six to eight people, which I served on. We were working for different companies, mostly doing programming. It was mainframe-based. We had a lot of demonstrations in New York because it was the 1960s. It was the war that started us out—that’s why we put peace in our name. I remember you saying something at one point about a commune. Were you all living together? It was only a small part of CPP, but yes. In 1971, seven of us who met through the collective started a commune in Brooklyn. Our commune was in an amazing old brownstone with original oak panelling and a big kitchen on the lower level. We reasoned that it would only take three people working at any one time to support the house and buy all the food and everything, and then the others could be doing movement work—anti-war work or what I got into, which was trying to organize a computer workers’ union. You tried to organize a computer workers’ union at IBM? No, this was during the year I took off. We each took six months to a year off to do movement work. CPP had the idea that if we could organize mainframe programmers and mainframe operators, then we could shut down everything. I worked on implementing that vision, but it was very difficult. The only inroads I remember making were at NYU and a city agency. It was difficult because the workers were well-paid and thought of themselves as professionals. One of the things that the mainframe era did was to enable women and working-class people to walk into a professional job and earn a decent living. It just required a college education. My division of IBM had a lot of working-class young men who got draft deferments for working there. It was a leg on the rung of the middle class.

Early Retirement In 1965, a young computer worker named Anne celebrated her retirement. Decked out in a punched paper tape train that was made to resemble a bride’s veil, Anne celebrated the end of her career with her fellow twenty-something women colleagues at the computer company. To modern eyes, a retirement party thrown for a woman in her twenties might seem incongruous—all the more so because Anne’s technical skills were much in demand. British businesses and government agencies were scrambling to hire people who had computer skills, and yet here was a computer worker retiring from the workforce at the beginning of her career.

A few years earlier, a novel called Anne in Electronics—Anne was a popular name in Britain in the mid-twentieth century—had been published. Young women might read a light, pulpy novel like this as they rode to work each day on the London Tube, or younger women might read it to see what they had to look forward to. The book reflected the experience of many working women, but made it more glamorous and exciting.

He usually just tells his clients to simply do the right thing. He tries to counsel twenty-five-year-old disruptors to be caretakers of a legacy. Beyond your contractual obligations, how do you make sure not to fuck over employees or investors? How do you make sure your company’s final chapter lives up to the vision you had for it? Failure, he points out, puts Silicon Valley’s high-flying language to the test much faster than unbridled success.

Another thing he tells his clients: the IP is gone. You can’t sell it or take it with you—but you can make it open-source and help others out, or yourself down the line. If you don’t, it may end up once again at Sherwood Partners. Because Sherwood isn’t just a repository of a kind of wisdom that Silicon Valley doesn’t have time for. It’s also a literal repository of IP that arrived too late, too early, or too close on the heels of a similar idea.

These goals reflect the structure of the consortium. The consortium is a Silicon Valley–based nonprofit, and many of the people who do its work—assessing new alphabets for inclusion, doing the codification—are volunteers. But the power to decide what gets encoded by the consortium ultimately rests with the top tier of its paying membership, a group of about eight of the world’s largest tech companies (including Apple, Facebook, Google, and Microsoft) along with a small handful of governments and a university, which buy into the consortium for roughly $10,000 to $20,000 per year, and get voting power as a result. (By paying as little as $35, other individuals and institutions can be members of the consortium, but they don’t get voting privileges.)  Since 2007, as part of its overarching aim to expand the use of digital technologies, the consortium has taken on the standardization of emojis. Emojis not only help encourage user engagement with consumer technologies, but also provide a valuable seam of minable data—for example, in the millions of emoji reactions to Facebook posts that occur every day. As of Unicode 13.1, released in September 2020, there are 3,521 emojis, as well as more than 143,000 characters in dozens of different scripts. By encoding new emojis, the consortium is deciding which characters can exist in this new visual language. This gives Unicode a powerful form of sovereignty over digital life, and adds a further political dimension to the consortium’s work, since it decides what sorts of representation—interracial couples, say—get universalized as emojis, and which do not.  The consortium itself is not always clear on or honest about the significance of its work. It tends to see emojis as “playful, colourful representations,” as one Unicode document puts it. It also likes to present the process of creating new emojis as a fairly open one. It’s true that, by submitting a formal proposal, including a provisional design, anyone can suggest a new emoji to the organization, out of which about sixty to seventy new emojis are approved every year. However, the proposals are evaluated behind closed doors by the consortium’s Emoji Subcommittee, and then voted on by the highest tier of corporate, state, and institutional members. Although there are criteria that new emojis supposedly must fulfill, the extent to which an emoji does meet those criteria is a matter of broad interpretation. What’s more, the criteria are designed to ensure that an emoji is as widely used as possible, so that tech companies can derive the maximum monetary value from it. A narrow focus on these standards can obscure the larger cultural and political significance a new emoji might bear.

To maintain its unique role in encoding the world’s language scripts, the consortium presents itself as neutral and above politics. It tries to avoid political conflicts by admonishing designers not to “justify the addition of emoji because they further a ‘cause,’ no matter how worthwhile.” All the same, “a proposal may be advanced despite a ‘cause’ argument—if other factors are compelling,” states the document detailing how to submit emoji proposals, which was written in 2009 by an Apple employee. As the scholars Luke Stark and Kate Crawford observed in a 2015 paper, “proposed solution[s] for improving emoji diversity in fact [signal] a further evolution in the business models of affective digital communications.” In other words, the political and cultural representation furnished by emojis—brown skin tones, a trans rights flag 🏳️‍⚧—may be deeply meaningful to users, but for the Unicode Consortium and its members, allowing such representation is primarily a means to get more people to use more digital technology more often.

Collective Memory During the mid-1970s, computing underwent a rapid change. The availability of inexpensive microprocessors like the Intel 8080 helped spur a hobbyist hacking community interested in building smaller, more “personal” computers. The mainframe and the time-sharing system didn’t die with the rise of personal computing, but its centrality to the imagined future of computing—along with the politics of the information utility—faded away.

In popular histories of computing, personal computers often seem like an inevitable conclusion. The model of a single-user, privately owned device became so dominant that utility computing of the time-sharing era came to seem like an aberration—a strange detour that never amounted to much. Yet more recent scholarship reveals the importance of that detour to our digital present. The historian Joy Lisi Rankin has shown how people organized around time-shared devices in schools and universities inspired the first conceptions of computing as a socially minded and community-oriented activity. The scholar Tung-Hui Hu sees the return of the old utility model in modern cloud computing.

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.

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.

At the beginning, the idea was to transmit real-time figures from the factories to CORFO through the network of Telex machines, which we called “Cybernet.” Then the figures would be sent to a computer that ran a software program called "Cyberstride" that analyzed the data. The outcome of this analysis was displayed in the Cybersyn “operations room.” We worked with industrial designers at CORFO’s Committee for Technological Research (INTEC) to create a room conducive to non-hierarchical management. The chairs, which each had slide-control panels, were placed in a circular arrangement. The room did not have tables; data and graphics were displayed on a panel in the main wall. Everything was designed to facilitate a relaxed environment to produce ideas for the future of Chilean socialism. The operations room epitomized the Cybersyn project. It was an extraordinary design produced by a transnational team. Many people thought it was too flashy, too technological, and not something that was connected to the workers. I think it was the opposite—the room was designed to facilitate creativity and imagination. It was offering something that couldn't be obtained by doing something trivial like sitting in front of a computer. Today, after all these years, I am amazed. I really think Beer was an imaginative man. In hindsight, what do you think were some of the limitations of your team? We had more to learn about the cybernetics of organizations. Stafford Beer knew that subject well, but we were only learning. And when you’re only learning, your bias is towards the technology. So our approach tended to be technocratic. We wanted to design good indices of economic performance. And those indices would be designed by specialists, experts in operational research who could do all the technical aspects of economic modeling in mathematical terms.

However, while that work is necessary and useful, it is not sufficient in the social sense. We needed to get workers much more involved in the meaning of these indices. We needed to give them far more opportunities to influence the design of Cybersyn. To do that, we had to build up rapport with workers—but this wasn’t always possible.

The Politics of Poop Emboldened by the false sense of security offered by the tunnels and other hydraulic engineering works, government leaders over the decades since 1975 have had no qualms pushing for further growth of the metropolitan region, even as the aquifer dwindles. The growth of the metropolis has not only generated more humans dumping waste. It has also led to more buildings and roads, shrinking the green areas that once allowed water to infiltrate into the groundwater aquifer, rather than run off into the drainage system.

Today, the capacity of the Deep Drainage System is no longer sufficient during the rainy season. During heavy storms, engineers find themselves in an uncomfortable predicament: they have to start closing certain floodgate connections to the surface sewers, or else the tunnels will overflow in spectacular ways they cannot control. With these gates closed, water from the surface sewers has nowhere to go except the streets.

It made sense in an industry based on scalable software, an industry that had long valued companies in terms of their number of users. If value resides in number of users, why not use them for something else? It was everything we never wanted at Logic. 3/ In October 2016, a group of friends in San Francisco put up a website. The website said we would soon start putting out a print magazine. What were we thinking? We were thinking, we wanted to make a place for people to publish the kinds of pieces we ourselves wanted to read about technology. We were thinking, we would give ourselves one issue. If, after that issue, we were not losing money, we would keep going. But above all, we were thinking, we needed an occasion to throw parties. In the era of pivot-to-video, it was the only reasonable reason to start a paper magazine.

The party thing was a joke, but like all jokes, was partly serious. We wanted to bring people together, not only within the pages of the magazine but also in little rooms you had to cram into. Our timing turned out to be fortuitous, in one sense. The election of Donald Trump, with his open support of various manosphere creeps and white nationalists, and his stated desire to use digital technology to build a “Muslim registry,” caused a lot of people in the tech industry to think differently about what they were building. The magazine ended up developing alongside a new tech worker movement, whose participants and fellow travelers shaped our thinking and often contributed to our pages.

Dressmaking is the kind of thing that’s easy to industrialize. The pieces of the process can be categorized, standardized, and delegated. The language we use to refer to the parts of the dress, and the tasks associated with the job, are clear. Reducing the qualifications for participation in dressmaking renders individuals interchangeable and disposable.

Industrialization has been applied to almost every field in which something is produced and sold. Now, EMRs are applying it to medicine. In the industrialized conception of medicine, as in the industrialized conception of all professions, more tasks become routine, and routine tasks are delegated downward. It’s no surprise that in the health policy world the introduction of EMRs often accompanies a discussion about hiring less educated professionals, like nurses and pharmacists. Meanwhile, fewer and fewer spaces are designated as safe for creativity and intuition, because these are considered unpredictable and unreliable.

Early synthesizers were as complicated—and as large—as electronic manufacturing technology. But Roland’s instruments were appearing in a new context. Before the widespread extension of electrical power to residential areas in the 1920s, machinery under capitalism had been an instrument of labor. In Marx’s “Fragment on Machines,” he remarked on the place of technology in the capitalist mode of production: Nature builds no machines, no locomotives, railways, electric telegraphs, self-acting mules etc. These are products of human industry: natural material transformed into organs of the human will over nature, or of human participation in nature. They are organs of the human brain, created by the human hand: the power of knowledge, objectified. Over the course of the twentieth century, consumer electronics dispersed scientific knowledge further still. Beyond being a tool that workers used in the factory, electrical machinery became present in the home. Automation took on a presence in everyday life as well as within the means of production. The amplification of sound was inextricably linked to this process, with the development and distribution of electricity tied from its origin to telephone communication and radio broadcasting. By the mid-twentieth century, most homes had radios, making their operation familiar to a far broader population than expert telegraphers.

In the mold of consumer devices like the portable stereo, the TR-808 was built for home use. Its target audience was musicians who needed accompaniment for practice sessions, and its interface more closely resembled a stereo than a circuit board. In the late 1970s, however, the TR-808 began to migrate from the home to the recording studio. It started to appear on pop records—Marvin Gaye’s “Sexual Healing” is a famous example—many of which may have been played by Knuckles or Hardy during their expansive DJ sets. Its rigid, aggressive drum sound may have driven professional musicians crazy, but its mathematically precise rhythmic divisions were perfect for DJ mixing, a process of matching up beats by adjusting the speed of rotation on turntables.

The Cyborg Manifesto had such a tremendous impact, and continues to. What did you make of its reception? People read it as they do. Sometimes I find it interesting. But sometimes I just want to jump into a foxhole and pull the cover over me.  In the manifesto, you distinguish yourself from two other socialist feminist positions. The first is the techno-optimist position that embraces aggressive technological interventions in order to modify human biology. This is often associated with Shulamith Firestone’s book The Dialectic of Sex (1970), and in particular her proposal for “artificial wombs” that could reproduce humans outside of a woman’s body.

Yes, although Firestone gets slotted into a quite narrow, blissed-out techno-bunny role, as if all her work was about reproduction without wombs. She is remembered for one technological proposal, but her critique of the historical materialist conditions of mothering and reproduction was very deep and broad.

At that time, from New York, I wrote in an article: Many people may not understand why feminism is a "sensitive" topic, and I have always felt the same way. Regardless of the personal views of its participants, China’s feminist movement does not oppose the government agenda, and it has always paid more attention to economic, social, and cultural rights than civil and political rights. The policies and reforms advocated by the feminist movement do not touch the core of political power. However, we do not make the rules. I have gradually come to understand that there are three other factors that had to be considered. The first is that feminism is ultimately critical and serves to ask, “Who is responsible?” Second, any force that shows social organization and mobilization will be taboo, no matter what its claims are. Third, when the public space collapses, feminism cannot escape that kind of disaster. When dissenting thoughts and opinions are removed, feminist thought is also removed. In the future, we can go underground, but we will become isolated. Feminists cannot publicly preach and advocate for our cause… At that time, I said, "We have no choice but to resist." But how were we supposed to resist? Even though I was free, in the United States, I felt like a person who was being held captive.

In the most painful period, I was grateful for the companionship and dedication of my friends.  I had never met many of them. They were our readers, and they created pictures, articles, and comics for Feminist Voices. Their contributions that were now deleted by the online platforms, their accounts that were canceled and no longer existed—all that they had sacrificed became part of a precious friendship.

It was nearly 4 a.m. in China when I hit play on the video entitled “Zhao Benshan: King of Poetry!”, but the ghosts of a thousand past viewers were still distracting me with their chatter. The video is a remix collage of CCTV Spring Festival Gala skits starring iconic comedian Zhao Benshan—source material familiar to anyone who grew up near a TV in China—edited and AutoTune’d into a military-grade earworm. But I could barely piece that together through the fog of text left behind by previous viewers: laments about the catchiness of the song, compliments to creators, jokes, and echoes of favorite lyrics. In the end, I had to watch the video twice, as I often do on the social video site Bilibili: once with the bullet comments turned off so that I could follow the source material, and once more for the real experience, the chitchat obscuring the content.

Bullet comments, or 弹幕 (“danmu”), are text-based user reactions superimposed onto online videos: a visual commentary track to which anyone can contribute. When a beloved character dies in a web series, a river of grieving kaomoji (╥﹏╥)—a kind of emoticon first popularized in Japan—washes over whatever happens next. A child’s overly honest response to a TV anchor’s question triggers a blizzard of different ways to signify laughter (2333, 哈哈哈哈). When the (Chinese) good guy punches out the (American) bad guy in 2017’s blockbuster Wolf Warrior 2, jingoistic cries of “Long live China!” erupt across the screen. Each comment is synchronized to an exact moment in the video, and will fly across the screen on cue on every subsequent replay. On particularly popular videos, they pile up so thick that they can cover the original entirely. The result is a viscerally social experience, like an opening night crowd at a movie theater that you can summon anytime.

The NIYC was founded in 1961 as a nationwide coalition focused on environmental justice work and an intergenerational fight against US colonialism and economic extraction. In the subsequent decades, the NIYC argued countless environmental court cases, wrote numerous policy reports, and utilized every political and scientific tool to fight against the settler policy and technologies of the US empire.  One of the tools they used was the Environmental Impact Statement (EIS). These statements were created in 1969 by the National Environmental Protection Act to help guide government decisions on new development projects. In the Colorado River Basin region, developers manipulated the EIS format to justify the impacts of their projects in light of those projects’ beneficial use—in other words, to show that the profits outweighed the environmental costs.  In 1976, student youth members of the NIYC of Albuquerque, New Mexico, wrote their own anti-colonial EIS for the Bureau of Indian Affairs. They recognized that the EIS was a powerful policy medium that could be repurposed for environmental justice work. They were attempting to intervene against a recent data-driven decision about the allocation of water from the San Juan River—a major tributary of the Colorado River—as part of the Navajo Indian Irrigation Project. The project was supposed to take water from the Navajo Dam and Reservoir, created in early ’60s, and use it to irrigate land in San Juan County in northwestern New Mexico.  The students supported the irrigation project in principle—as a founding member of the NIYC, John Redhouse, said in another context, “we’re not anti-development, we’re just anti-exploitation”—but they were concerned that Native oversight was lacking, and that the project would thereby undermine Navajo self-governance. They focused their EIS on the New Mexico state government’s opaque decision to divert 330,000 acre-feet of water from the Navajo Dam, which they pointed out was just one among countless decisions made without robust structures of Native oversight.

In their statement, the students recounted how Native American water rights had been diminished since at least the early twentieth century by the doctrine of beneficial use. They pointed to the ways that dominant data-driven decision-making processes had been used to disavow Native American claims to water, and questioned the specific hydrological data employed to make the decision in this case. They decried the influence of corporate agriculture in the decision, as well as recent changes in the Navajo Council that weakened possibilities for total self-sufficiency from US resource governance. They also provided a clear vision for Navajo self-sufficiency, which included breaking away from the domination of US agribusiness to create a system of food production and distribution organized into small family farming and local Navajo food-producing cooperatives.  Perhaps most importantly, they demanded transparency and power in the decision-making process, so that they could assert their voice within the Native council, and break from US water management and the technological regime of optimization. This has become a central tenet of contemporary Indigenous Environmental Justice and Indigenous Data Sovereignty resistance movements: the right to collection, ownership, and application of all data about Indigenous peoples, their lifeways, and territories.  Crisis Epistemology Water justice requires acknowledging historical pasts as much as imagining new futures, but optimization frameworks flatten past, present, and future into calculations of profit-driven time.

I know there are differences among these differences. But I think about it when I think about the shocked men. How hard do we work not to know what we know? When do we decide we have to? (“We” who?) Some of the specific details that have come out of #MeToo are shocking. The executive throwing the young reporter down the stairs. The button under the desk that locked the office door, locking you in. But the gist—that men in an unequal world use women to feel powerful, that they abuse their power over women, and often weaponize sex to do so… Who can remember the time before they knew that? The fact that we can be raped is the subtext of so many warnings we receive as girls. Do not wear this. Do not go there. Try your best not to exist, in public.

Clickbait Deathmatch To get to my provocation: I suspect that what #MeToo is revealing about the behavior of men within the media industry is less new than what it reveals about the industry itself. #MeToo is registering a change that has less to do with what we know than how. Whatever else it is, #MeToo is great digital content. Nobody does not have a stake. Nobody does not have an opinion. Stories of workplace abuse come with a titillating hint of pornography—particularly in the privileged, mostly white settings that have received the most media attention. We are supposed to be watching some kind of battle of the sexes, and we may be. But more often, these are battles between young women and older women, between Cool Girls and “cry-bully” victims, staged as a clickbait deathmatch.

The goal of Japanese telerobotics isn’t to provide better access to remote expertise in the form of higher-skilled workers, in other words, but to technologically recuperate lower-skilled workers who might otherwise be excluded from the workforce entirely. What is envisioned is nothing less than a digital platform for physical work, one that could utilize previously untapped labor reserves to power global networks of on-demand robot avatars.  This may open up potentially meaningful opportunities for those unable to physically travel to a work site. At the same time, it risks further isolating the already immobilized, enabling remote access to their physical labor while fixing barriers to their social mobility ever more firmly in place. While the stated promise of teleworker robots is one of freedom for those employed, in practice it is employers who are most liberated by the arrangement.  Working from Home Japan has been at the forefront of experiments with embodied telework since the 1980s, but it wasn’t the first in the field. Japanese researchers were building on initial teleoperator experiments in the United States that took place in the 1940s in the aftermath of the Manhattan Project, as military scientists tried to find a way to allow workers to physically manipulate radioactive materials while maintaining a safe physical distance. Subsequent American teleoperator research has continued to have a strong focus on operation in hostile environments, such as systems for military drone pilots to carry out missions remotely.  By contrast, teleoperator research in Japan took shape against the background of the country’s pacifist postwar constitution — itself a product of the American occupation —  which for a long time amounted to both a formal and informal taboo around research with direct military applications. As a result, Japanese researchers of the 1980s and 1990s worked on more everyday telerobotics uses, from caring for the elderly to housekeeping to helping the visually impaired navigate city streets.

In recent years, this emphasis on the everyday has come to center on a more pressing social challenge: the intensifying labor shortage triggered by the country’s aging population. As of December 2018, there were an average of 168 jobs for every 100 job seekers in Japan, with a shortage of 6.44 million workers expected by 2030. Some of the most acute shortages are in physical labor and service sector work. Convenience stores, for example, are increasingly struggling to find staff, even as much of the country has come to rely on them for access to basic goods and services.

Reich saw this orgasmic or “orgone” energy as a potentially revolutionary force. He consequently devised the “orgone energy accumulator” box, which was supposed to increase biopower, “potency,” and cure physical and psychological illness. The design of the orgone accumulator was crude: Reich’s blueprints call for the construction of a large pine box—a “collapsible cabinet”—lined with layers of steel and glass wool. While William S. Burroughs claimed to have experienced a spontaneous orgasm while in his orgone box, Reich expected its effects to be more mundane. He instructed users to do “daily, regular sittings” in the box for limited periods of time as sensations of warmth flowed through them—a bit like a charging station for an electric car.

The orgone accumulator is laughable from an engineering point of view, but it was immense in its ambitions. If sextech ventures like OMGYes take a reformist approach, hoping to educate people about sexuality to produce better sexual outcomes, Reich called for full-scale revolution, using sexual energy to destroy capitalism. His endgame wasn’t merely the hedonistic pursuit of individual pleasure, but the dismantling of the entire traditional Western family structure, the patriarchal social order, and the conditions of capitalist production.

Technology helps set the parameters of possibility. It frames our range of potential futures, but it doesn’t select one for us. The potential futures framed by big data have a particularly wide range: they run from the somewhat annoying to the very miserable, from the reasonably humane to the delightfully utopian. Where we land in this grid will come down to who owns the machines, and how they’re used—a matter for power, and politics, to decide.

Where does innovation come from? Startups. Where do startups come from? Venture capital. For nearly half a century, this has been the conventional wisdom of Silicon Valley. Venture capital is the lifeblood of the Bay Area tech industry, ebbing and flowing with every business cycle. More broadly, venture has provided the initial form of financing for most of the world’s fifty most valuable publicly traded global corporations that were founded after the late 1960s. And it has become a standard part of how institutional investors allocate capital, from public employee pension funds to university endowments to wealthy families.

I/O models are based on a table that looks like an Excel spreadsheet, in which columns for materials (such as oil, steel, or coal) intersect with rows for industries (such as agriculture or manufacturing). The basic idea is that an economic system can be measured as the overall ratio of resources used (input) to goods produced (output). In the Cold War period, scientists at Harvard, the Pentagon, and elsewhere developed mathematical techniques known as linear programming algorithms to determine the optimal input and output numbers for a desired objective, such as optimizing resources for military development at the lowest possible cost.  After being successfully implemented in imperial bombing campaigns and New England manufacturing, I/O economics and linear programming algorithms began expanding westward in the 1950s into water management on the Colorado River, becoming the dominant mode of modeling water within a decade. Researchers and students at the land-grant universities were charged with optimizing water distribution across the region. As the basis of their analysis, they carved the diverse land into relatively homogenous virtual quadrants known as “problem settings.” For example, in proposals for the Central Utah Project, initially formalized in the 1960s, the state of Utah was represented as a square divided into a grid of eight to ten smaller squares that obliterated all distinctions about whose land, histories, and water rights the grid overlaid. Researchers then used computer programs to figure out how to distribute X acre-feet of water for Y farm plots across each problem setting, so as to maximize profits and minimize costs for the region’s large agribusinesses and other industries.

Youth Against the Empire The optimization regime is so entrenched in water policy and technology that, even in the midst of catastrophic climate change, it is difficult to imagine other futures for the Colorado River Basin and proximate regions. But in the late 1970s, a group of Native American students did just that, fighting back against a water diversion program in New Mexico, and providing a model for activism and water management that could guide us today.

Intro I don’t know how I ended up         here       yeah actually I know. I called it              I made myself a dumb prophet & cuffed my own wrists like a God who creates & creates         & creates         too many worlds to wave his hand                     or whatever he believes he’s doing              over & grant the prayers of his reckless children. He gets mad because he gets shown up.           He fails at the feet of his creations. I know how I got here.         When I first came down    they tested my criminal- ity by sitting me down in a small room                        an office            giving me a battery of statements like If my fam- ily gets hurt I feel the urge to retaliate & some people deserve to be pun- ished (that one I laughed at). I was to answer with agreement                      or strong denial. I must have passed                             my report read Low Prob- ability of Reoffense but a sentence is a sentence             & now it’s almost a decade with more to go & all my files in a drawer full of other men’s histories                      so many histories. Do you know the stories       Do you know who I am       Do you understand what I am           Can I tell you I’ll try to sing this broken song    & summon my tribe      ones who will one day carry me home    & damn            damn       I know it’s a moonshot but        maybe you’ll come find me before I lose myself in this jungle.

1/ What’s in a cloud?  Writers have long used clouds as metaphors for metaphor-making. In Hamlet, Hamlet messes with his girlfriend’s dad, the courtier Polonius, by pointing out the different shapes he sees:  H: Do you see yonder cloud that’s almost in the shape of a camel?P: By th’ mass, and ’tis like a camel indeed.H: Methinks it is like a weasel.P: It is backed like a weasel.H: Or like a whale?P: Very like a whale.

A similar initiative is taking place in the Yuhua district of Shijiazhuang, the capital city of Hebei Province. There, local courts have opted to offer a WeChat “mini-program”—a limited-purpose feature nested within the popular chat app WeChat—known as a “Laolai Map.” This map displays blacklisted people, companies, and other organizations within a given area, alongside slogans such as “Recognize laolai, avoid risks.” It’s not clear if real-time location data or individuals’ home addresses are used to populate the map. The mini-program also enables users to look up blacklisted entities with a search function, and to see the offenses that landed them on the blacklist in the first place. While some descriptions are straightforward, such as “failure to report property ownership,” most are simply listed as defaulting on court orders without going into further detail.

Other state-tech collaborations are more ambitious in scope. For example, the budding “credit cities” concept is a spin on “smart cities” that involves tech companies building out digital scoring platforms that use a mix of government data and private sector data. With such initiatives, people who are deemed more trustworthy can rent bicycles and even apartments without providing a deposit, or delay immediate payment for cab rides and hospital visits. The participation of large tech companies in these ventures tends to be downplayed, as the credit city platforms are associated with their respective municipal governments and generally rely on smaller local firms.Still, details on the specifics of these partnerships are scarce. While major Chinese tech companies are not serving the social credit system the way foreign media has thus far portrayed—surveillance cameras are not using facial recognition to link misbehavior to a centralized scoring database, for instance—the ways in which they do partner with the state to coproduce the system are generally kept from the public’s eye. What little we know comes from news coverage of the signing ceremonies held when tech companies conclude agreements with the NDRC. At each of these, representatives of the companies’ senior leadership refer to their joint efforts as a form of social responsibility. The CEO of Meituan-Dianping, an online group-buying and food delivery service, notably said that co-constructing a social credit system is “every industry's—especially platform-based internet companies'—duty-bound responsibility.” But how effective is the social credit system at improving “trustworthiness”? Thus far, state media has portrayed blacklists strengthened by the social credit system as having succeeded at encouraging people to be more honest and to break the law less often. The official news agency Xinhua, for instance, praised Ant Financial’s use of blacklist data to restrict certain purchases via mobile wallet app Alipay and to lower scores in its Sesame Credit credit scoring product, arguing that the company’s punishment of 1.2 million debt defaulters encouraged over 100,000 of them to repay their debts.

It sounds like what you’re both saying is that profit-driven platforms produce algorithmic racism, algorithmic sexism, and misinformation. Broadly, they are producing a democratic deficit in the digital sphere. So what are some possible solutions? Should we think about trying to reform these companies from within? Should we think about regulation? Nationalization? Building alternatives? Sarah: The answer is yes.

Safiya: All of the above. Sarah: I always go back to Jennifer Light’s 1995 essay “The Digital Landscape: New Space For Women?” about online feminism. She was imagining the internet as having all this potential. It would provide new ways of being, interacting, communicating. Since then, so much of that potential has been foreclosed by a model of rampant profiteering.

Reinforcing such concerns, prison leaders are acculturated to view innovation with suspicion, cued to the worst case and inclined in most matters to default to security considerations. “When you work in corrections, you tend to be risk averse,” says Pennsylvania Secretary of Corrections John Wetzel. For good measure, technology has characteristically been deemed a luxury of which prisoners are undeserving—certainly nothing the public would pay for.

Accordingly, there’s a perfunctory hair-shirt aspect to prison technology. It stands in relation to free-world technology as prison chow does to a restaurant meal or prison-issue fatigues do to a tailored suit. Proprietary prison email systems allow inmates to correspond with approved contacts. But in- and outbound messages are typically subject to fees—between twenty-five and sixty cents, according to Alex Friedmann, associate director of the Human Rights Defense Center and managing editor of Prison Legal News. And stingy length limits apply. These range from 1500 to 6000 characters, at the lower end of which Martin Luther King Jr. would have had to piece out his “Letter from a Birmingham Jail” on an installment plan, attorney Stephen Raher notes.

Patterson wasted no time in using this cultural capital, immediately positioning his service as a kind of sex ex machina. While Com-Pat ran advertisements that promised people would meet their “true match,” Dateline ran ads that positioned their service as an “adventure.” Patterson’s questionnaires had more to do with sexual preferences and sexual compatibility than Compat’s personality-based approach.

When he got flack, Patterson doubled down, creating another service that was even more sexually explicit in its questions. That service asked users how sexually experienced they required their partners to be, along with which specific sex acts customers had engaged in previously, and which ones they wished to perform in the future.

We have been thinking about environmental engineering wrong. It does not “solve problems” as is popularly believed. It transforms problems, creating new and different challenges that burden other people—and future generations. The challenge we face as a society is to build the structures of popular power to decide collectively which burdens are worth their weight, and how to distribute them justly. These are not choices we should leave to politicians, or even engineers.

Draining a Sinking City The official reason for Mexico City’s 1951 flood was clogged drains. But engineers knew something else was to blame: the city was sinking, rendering its drainage system a mess. By the 1940s, scientists and engineers like Nabor Carillo had concrete evidence that this sinking was not natural, but anthropogenic. From the turn of the twentieth century, the rapidly growing city had turned to extracting groundwater using mechanical pumps, depleting the water trapped in the soil below. As the clay soils of the former lakebed upon which the city was built dried out, they shrunk irreversibly, leading to a phenomenon known as “land subsidence”—sinking.

It is so rare to find a role-playing game like this. There is no plot, no mystery, no dragons, no romance, no treasure. I still don’t know who I am or where I came from: my amnesia is never resolved. But I know why I am here, and that is enough. Finally, around hour thirty of playtime, the dialogue starts to repeat: my villager-friends have run out of things to say. I feel that I have spent as much of my vacation harvesting bell-peppers as I’d like.

An Orb in the Woods It’s around this time that everything collapses. I’m walking in the woods south of my village when I stumble across something atypical—a mysterious floating orb in a section of the woods I’d never seen. The screen goes white and I'm in a prerecorded cutscene where my amnesiac character regains a fraction of their memory.

Facebook’s famous former motto, “Move fast and break things,” captured the spirit of the new era well. It was an era that rewarded audacity, in software development as much as in CEOs. Venture capital firms, on the hunt for “unicorns,” poured record amounts into the technology sector during the 2010s, and they wanted to see results quickly. Competing with startups required the ability to change on a dime, to release constantly, and to develop at breakneck speed. The risk calculus shifted: it now seemed dangerous to stick with waterfall, when Agile promised so much speed.

Equally, it seems, what it meant to be a software developer had changed. In the 1970s and 1980s, experts held up the systems-minded, logic-loving scientist as the ideal software worker. But over the years, this ideal had failed to take root. The programmers of the 1990s read Wired, not Datamation. If their characteristics can be intuited from the Agile Manifesto, they were intently committed to the highest standards, working quickly and confidently because managers “trust them to get the job done.” They refused to do things just because they’ve always been done that way, turning their minds to “continuous attention to technical excellence.” They weren’t thrown by fluid, fast-moving requirements; instead, they embraced them as an opportunity to “harness change for the customer’s competitive advantage.”  The image of the free-thinking nonconformist fits the philosophy of Agile. The manifesto’s authors may have looked like textbook engineers, in button-downs with cell-phone holsters, but “a bigger group of organizational anarchists would be hard to find,” according to Jim Highsmith, one of their number. Particularly in the early days, there was a lot of talk about the challenge Agile posed to the traditional management paradigm. Agile’s proponents were proud of this nonconformity: the framework “scares the bejeebers out of traditionalists,” wrote Highsmith in 2001. “Agile was openly, militantly, anti-management in the beginning,” writes the software developer and consultant Al Tenhundfeld. “For example, Ken Schwaber [a manifesto author] was vocal and explicit about his goal to get rid of all project managers.”  Anti-management, maybe, but not anti-corporate, not really. It’s tempting to see the archetypal Agile developer as a revival of the long-haired countercultural weirdo who lurked around the punch card machines of the late 1960s. But the two personas differ in important respects. The eccentrics of computing’s early years wanted to program for the sheer thrill of putting this new technology to work. The coder of Agile’s imagination is committed, above all, to the project. He hates administrative intrusion because it gets in the way of his greatest aspiration, which is to do his job at the highest level of professional performance. Like the developers in Aaron Sorkin’s The Social Network, he wants most of all to be in “the zone”: headphones on, distractions eliminated, in a state of pure communion with his labor.

It goes back to asking questions and following them, whether it’s operating systems or legacy software or what the police are doing in Portland. I always wanted the deep dives to be what distinguished us. Really good, easy-to-understand explanations of technical topics that everybody bullshits about.  You’re interested in demystifying.  Yeah. We need to understand how things work for ourselves. And, if you’re asking questions about how technology impacts people’s lives, policing and surveillance keep coming up. Palantir, facial recognition, who had access to what databases after the 2016 election—there was no way to avoid those topics. So, that's what brought the Portland protest blog into The Recompiler’s wheelhouse.

Can you talk about the organizational structures of The Recompiler and how you find contributors and collaborators? I usually give my title as either publisher or editor-in-chief, depending on who I'm talking to. Basically, I pay the contributors. The only person who does not generally get paid is me. I would love to be doing this full-time, but this is why I've come back to having a day job. I did do The Recompiler full-time for a couple of years, but funding-wise it's better for me to have a software engineer job.  That is also part of Logic's funding model.

Shifting gears, I want to ask you about the role of tech workers. Amazon recently fired two tech workers in Seattle for organizing. Shortly after that, a VP “quit in dismay” after watching the event that you spoke at, partly because he was so moved by hearing from warehouse workers. How do you see the role of tech workers in your struggle against Amazon? In Poland, we don’t really have... Well, we do have a tech arm of Amazon in Poland. We know there are a few hundred tech workers in Gdańsk working on Alexa, but we’ve never been in touch with them. Amazon doesn’t have many of these upper-level workers in our country. For the most part, Amazon in Poland is just warehousing for the West. So we don’t have an organic connection.  We do appreciate our discussions over the last few months with tech workers from Seattle. I think what they did was brave, and we need their support. We don’t have the symbolic position they have, so it’s powerful when they can give us access to the space they get. But the challenge for our work together will be whether tech workers are able to see themselves as workers who are dependent on their wages. If they are able to organize on those grounds, then we’ll have a foundation to build on together. What we would rather avoid is a situation where they only see us as pitiful, helpless people. If the only thing they do is talk about how scandalous our conditions are, that’s not useful. We need to recognize our power, and increase it together so we can make real change. The balance of power is so unequal now. We’re past the point of calling on Amazon management to make a little change here and there.

Another challenge is that as warehouse workers we build our movement on our own anger; we know exactly why we’re angry with Amazon. But if you are a tech worker and you design all these tools to discipline us, your experience is very different. You have to be aware of what you’re doing. The tools they’re creating are not neutral. They’re designed to spy on us every second of our ten-hour shift, constantly increase our productivity, and literally work us to death. Last week, a worker in our warehouse died on the shop floor. The tech workers don’t see that.

In the introduction to your new book you write, “the problem with Facebook is Facebook.” What do you mean? When we look at the various crises that Facebook has faced in the past couple of years, people talk about them as failures or breakdowns or meltdowns. In fact, it’s just the opposite. None of these are mistakes—they’re fulfillments of a vision.

Facebook intended to connect billions of people. It intended to create an algorithm that would favor engagement around highly emotional content. It intended to create an advertising platform that was more efficient, more accurate, and more specialized than any that had ever been created. These features were all intentional. And they have produced almost every negative externality that we’ve seen come out of Facebook. Those externalities are a fulfillment of Facebook’s design.

We’re on our way to look at a rustic cabin in a nearby state park to see if it might be a suitable place for Carmen to live temporarily and host her son and parents. She has been coming to Green Bank (even the town’s name sounds better with her rolled R) for two years, just on weekends or a day or two when she is feeling extra sick. But no one in her family has ever come with her.

Diane directs Carmen to drive west on the two-lane road that runs through Green Bank, and she obeys, driving slowly. Carmen emigrated from Spain to marry her husband, who she’d met as a high school exchange student. She is slim and tan with long dark hair and a soft-looking denim shirt. Diane is used to tour-guiding and does it well, pointing out Green Bank Middle School, which abuts the observatory property. Unlike most schools these days, this one doesn’t have any Wi-Fi, only hardwired computers.

The second is that cellular agriculture at scale could help restructure agricultural land use by reducing demand for animal feed, thereby opening up the space for more progressive food politics. If a government-financed land bank purchased even a small fraction of the 800 million acres currently dedicated to feeding animals in the US, it could resell millions of acres of land at favorable terms for bold new uses: establishing agro-ecological and regenerative farms that strengthen local foodways; supporting community and worker-owned farms; providing land to people from communities that have been historically dispossessed and excluded from owning land; returning lands to tribal nations; rewilding and conservation initiatives. Many of these ideas are championed by critics of cultured meat, who often suggest it is incompatible with the holistic, ecological sensibilities of slow, small and local. But all of these ideas become more feasible in a world with commercially viable labriculture.

Finally, there’s nothing inherent to cellular agriculture technology that favors venture capital or restrictive intellectual property regimes. Those who want cellular agriculture to live up to its lofty potential shouldn’t just be worried about the malignant influence of capital, they should be finding practical ways to limit it. What’s needed is the political vision and energy to liberate this technology from the grips of corporate stakeholders, and to use it for the radical project of improving the human and animal condition around the world.

I came to think of the group as the student lounge of online schools. Online schools can replicate the classroom space to a certain extent. But what they do a really bad job doing is the other important part of school: the social part. We don’t know how to do social online when it comes to education. Which is weird, right, given all of the social media people have? But we have not figured out how to give that social experience to students online—and some students need it more than others.

The whole point of going to an elite school when you’re not elite is so you can make friends that will change your life—they even put it in the pamphlet. The students in the Swaggers group were trying to do the same thing. They were hacking the inequalities of education using social media. They were hacking their educational spaces to make up for the deficiencies of their programs.

The Perversity Thesis When we consider the social effects of computers in political and social life, we usually think in terms of expanded power and new possibilities. This perspective on computation permeates even our critical visions of technology. But we should also be attentive to the power that computers and the accompanying language of “systems” and “complexity” have to narrow our conception of the politically possible.

Forrester thought that the basic problem of urban planning—and making social policy in general—was that “the human mind is not adapted to interpreting how social systems behave.” In a paper serialized in two early issues of Reason, the libertarian magazine founded in 1968, Forrester argued that for most of human history, people have only needed to understand basic cause-and-effect relationships, but that our social systems are governed by complex processes that unfold over long periods of time. He claimed that our “mental models,” the cognitive maps we have of the world, are ill-suited to help us navigate the web of  interrelationships that make up the structure of our society. For him, this complexity meant that policy interventions could, and usually would, have very different social effects than those imagined by policymakers. This led him to make the stark assertion that “the intuitive solutions to the problems of complex social systems” are “wrong most of the time.” In essence, anything we do to try to improve society will backfire and make things even worse.

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.

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.

We must recognize that the pearl-clutching anti-sex work moralists who fear that porn is warping kids’ minds have a point. Online porn plays a powerful formative role in our lives, especially the millennials among us, informing notions of what sex gets to be. Given this fact, the need for political and ethical work towards a world of porn with better taxonomies and worker protections is obvious. My ex saw political heroes in his favorite porn stars, which would be fine, if he had thought of them as workers first. There is no escaping economics.

It’s perhaps unsurprising, given the picture I’ve painted, that my relationship with this man ended in violent catastrophe. I grew to hate him for many reasons, but not before I had spent months, which bled into years, rethinking my approach to sexual desire. It was a revaluation of values and assumptions about what I want, for which I’ll always be grateful and in which I continue to engage to this day. In the years since we parted ways, I’ve had far more of the sex he would have deemed “radical” than I ever did with him. Some of it was transformative, some hot, some of it love, some boring and irritating—none of it revolutionary.

Zagat’s crowdsourcing model represented a major innovation for urban information systems—but it would take digital technology to realize its full potential. The Location Layer In 2009, serial entrepreneur Dennis Crowley founded one of the most popular contemporary LBS: Foursquare. Early in his career, Crowley had worked as a software developer for a company that licensed Zagat’s reviews for PalmPilot mobile devices in the first dot-com boom, and he sold his first company, an early LBS prototype based on text messages called Dodgeball, to Google in 2005.

Working with data from Zagat and other publishers gave Crowley an idea. He saw the potential for dynamically updated reviews from mobile devices, contributed by users distributed throughout the city. While Zagat crowdsourced data from diners once a year, Crowley would let them weigh in on their phones in real time.

“Thank God,” says Carmen. The question of Wi-Fi in schools has received some mainstream attention. A 2014 article publishing the results of a study conducted by researchers at the University of California San Diego made the case for limiting children’s exposure to EMR when possible, including removing Wi-Fi from American public schools. In two Canadian cities, parents petitioned unsuccessfully to have the Wi-Fi removed from their children’s schools.

Over and over, many scientists and laypeople have pointed out that there is no hard evidence that links EMR exposure to the kinds of health effects that electrosensitives describe. But after a documentary about the health effects of Wi-Fi on children was shown widely in Israel, the mayor of Haifa announced that it was immediately removing Wi-Fi from its public schools, saying, “When there is a doubt, when it comes to our children, there is no doubt. We must take excessive precaution.” It’s this kind of precaution that Martin Blank, a retired professor in the Department of Physiology and Cellular Biophysics at Columbia University, argues for in his book on the subject, Overpowered. “Just as the United States became the first nation in the world to regulate the production of chlorofluorocarbons (CFCs) when science indicated the threat to earth’s ozone layer—long before there was definitive proof of such a link—our governments should respond to the significant public health threat of EMF exposure,” he writes.

One common response from AI researchers to the oppressive aspects of ImageNet, and to the crisis of algorithmic injustice more generally, is that the problem lies with the data: if we get more or different data, then all these problems will inevitably go away. This was the response that Yann LeCun, one of the “godfathers” of deep learning and chief AI scientist at Facebook, provided when a machine learning model designed to depixelate faces ended up whitening them as well. Timnit Gebru, co-lead of Google’s Ethical AI team, struck back, underscoring how AI systems cause real harm and exacerbate racial inequality, and arguing that improving them must mean more than just focusing on better data collection. (Disclosure: two of us, Hanna and Denton, are members of Gebru’s team.) Furthermore, data collection efforts aimed at increasing the representation of marginalized groups within training data are often executed through exploitative or extractive mechanisms such as, for example, IBM’s attempt to “diversify” faces by scraping millions of images from Flickr without the consent of people in them. As Gebru explained during a tutorial at the Computer Vision and Pattern Recognition conference in June 2020, “Fairness is not just about datasets, and it’s not just about math. Fairness is about society as well, and as engineers, as scientists, we can’t really shy away from that fact.” A particularly pernicious consequence of focusing solely on data is that discussions of the “fairness” of AI systems become merely about having sufficient data. When failures are attributed to the underrepresentation of a marginalized population within a dataset, solutions are subsumed to a logic of accumulation; the underlying presumption being that larger and more diverse datasets will eventually morph into (mythical) unbiased datasets. According to this view, firms that already sit on massive caches of data and computing power—large tech companies and AI-centric startups—are the only ones that can make models more "fair."

A Genealogy for the Many Exploring the history of ImageNet has implications not only for how we discuss the problems and failures of AI, but also for how we make critiques and formulate solutions to those issues. We need to develop genealogies of data to show that datasets are the product of myriad contingent assumptions, choices and decisions, and that could, in fact, be otherwise. Genealogy is an interpretive method of analysis, which we can apply to the historical conditions of dataset creation. Understanding these conditions illuminates the origins of certain problems, but it also opens up new paths of contestation by enabling us to imagine new standards, new methods for evaluating AI progress, and new approaches for developing ethical data practices in AI.  Instead of the narrow focus on "bias," we can start to ask deeper questions such as: How did particular datasets emerge? What agendas, values, decisions, and choices governed their production? Who collected the data and with what purpose? Are the people represented in the datasets aware that they are participants in them? Can they meaningfully opt out? How about the workers, like the Amazon MTurkers, who annotated them? Were they fully recognized for their labor and fairly remunerated? And, most importantly, does the creation of the datasets serve the interests of the many or only those of the few? In March 2019, the Unicode Consortium, which controls the publication of emojis worldwide, released an emoji of a Hindu temple. Until then, a Hindu temple had been conspicuously missing from the set of emojis representing religious places, which included a Christian church ⛪ and Shinto shrine ⛩️ (approved as part of Unicode 5.2, in 2009) and a kaaba 🕋, mosque 🕌, and synagogue 🕍 (Unicode 8.0, 2015). The most popular emojis are the ones that convey emotion, such as the crying-while-laughing face 😂, but many emojis are also powerful ways to represent cultures and identities (a woman in a headscarf 🧕🏽, same-sex couples 👬, different skin tones 👋🏻👋🏼👋🏽👋🏾👋🏿). On the face of it, then, the publication of an emoji evoking the religion of 1.2 billion people seemed like an important act of inclusion.

Management’s Revenge Back at my library job, I kept an eye on the developers, admiring their teamwork and pragmatism. As time went by, though, I couldn’t help but notice some cracks in the team’s veneer. Despite the velocity chart and the disciplined feature-tracking, the developers didn’t seem to be making all that much progress. They were all working hard, that was clear, but there was a fatal flaw: no one really knew what the project was ultimately supposed to look like, or exactly what purpose it was supposed to serve. The team members could develop features, but it wasn’t clear what all these features were being tacked on to. Maybe that problem came from my workplace’s own dysfunction, which was considerable. Still, I began to wonder whether the Agile methodology had some limitations.

And, in fact, anyone with any proximity to software development has likely heard rumblings about Agile. For all the promise of the manifesto, one starts to get the sense when talking to people who work in technology that laboring under Agile may not be the liberatory experience it’s billed as. Indeed, software development is in crisis again—but, this time, it’s an Agile crisis. On the web, everyone from regular developers to some of the original manifesto authors is raising concerns about Agile practices. They talk about the “Agile-industrial complex,” the network of consultants, speakers, and coaches who charge large fees to fine-tune Agile processes. And almost everyone complains that Agile has taken a wrong turn: somewhere in the last two decades, Agile has veered from the original manifesto’s vision, becoming something more restrictive, taxing, and stressful than it was meant to be.  Part of the issue is Agile’s flexibility. Jan Wischweh, a freelance developer, calls this the “no true Scotsman” problem. Any Agile practice someone doesn’t like is not Agile at all, it inevitably turns out. The construction of the manifesto makes this almost inescapable: because the manifesto doesn’t prescribe any specific activities, one must gauge the spirit of the methods in place, which all depends on the person experiencing them. Because it insists on its status as a “mindset,” not a methodology, Agile seems destined to take on some of the characteristics of any organization that adopts it. And it is remarkably immune to criticism, since it can’t be reduced to a specific set of methods. “If you do one thing wrong and it’s not working for you, people will assume it’s because you’re doing it wrong,” one product manager told me. “Not because there’s anything wrong with the framework.” Despite this flexibility in its definition, many developers have lost faith in the idea of Agile. Wischweh himself encountered a turning point while describing a standup meeting to an aunt, a lawyer. She was incredulous. The notion that a competent professional would need to justify his work every day, in tiny units, was absurd to her. Wischweh began to think about the ways in which Agile encourages developers to see themselves as cogs in a machine. They may not be buried under layers of managers, as they were in the waterfall model, but they nevertheless have internalized the business’s priorities as their own. “As developers, IT professionals, we like to think of ourselves as knowledge workers, whose work can’t be rationalized or commodified. But I think Agile tries to accomplish the exact opposite approach,” said Wischweh.

On the other hand, small acts — especially those that involve some sort of coordinated deception — may awaken a willingness to defy that eventually enables larger, more decisive acts. Whether any of the acts of sabotage, subterfuge, or evasion committed by Amazon workers are accreting a hazardous reef remains to be seen. What is certain is that, one way or the other, we need to sink the ship.

The Rule of the Rate Amazon’s global distribution infrastructure — from fulfillment and sort centers, to cross-docks, delivery stations, and Prime Now Hubs — now covers almost 222 million square feet, an area roughly a third the size of Manhattan. In every province of this fiefdom, as one Amazon warehouse worker quipped on Reddit, “the almighty rate rules” — that is, the speed at which each worker does her job.  Amazon has risen to monopolistic dominance by shortening the critical time between the production and realization (i.e. sale) of commodities. Incorporating the lessons of “just-in-time” production innovated by Japanese car manufacturers, Amazon’s vast logistics network is designed to minimize the amount of time that products sit still. “The longer something sits and isn’t in motion, the less money Amazon makes,” said Charlie, the Pennsylvania fulfillment center worker. Whether stowing, picking, sorting, or delivering Amazon products, workers are expected to perform at a breakneck pace — while maintaining accuracy.  Since the advent of wage labor, time has been a key disciplinary tool for bosses. But Amazon has taken this technique to a new level, building a massive and intricate system of surveillance and control to accelerate the rate of productivity. “There is no privacy,” Charlie said. Many sort and fulfillment centers are vast, brightly lit affairs, with unforgiving concrete floors and high ceilings. Cameras are ubiquitous. “You should start thinking of it like a prison structure… We just presume we’re always being watched.”  When Amazon was granted a patent last year for a haptic wristband with a motion sensor designed to guide workers’ hands toward inventory items — or, as some darkly speculated, to buzz when they fall behind and need some haptic motivation to speed up — privacy advocates gasped. But such innovations, current and former workers tell me, would only augment existing tactics.

I think things will only spiral out of control, and we will increasingly see automated decision-making systems and other forms of artificial intelligence emerge as a civil and human rights issue that we cannot ignore. Sarah: For the past twenty years, the public has been told that the internet is a place of increased democracy and participation. And the public still largely labors under this image of the internet as a great democratizing force—an image actively encouraged by the tech industry.

In reality, the internet is a series of privatized islands with their own private governance. It’s not a democratic place. But the public doesn’t fully realize that—and this confusion greatly impedes people’s ability to have a critical lens on what’s going on. The undemocratic quality of the internet—its domination by the profit motive—is obfuscated by the tech industry on purpose.

JF: I think the final thing I’ll say on the “why print” topic is–a number of us starting the magazine just had an affinity for the world of small magazines and literary publishing. Many of us had worked with and were attracted to the feel of something like the Paris Review, or Granta, or early McSweeneys–that kind of softcover, perfect bound, largely black and white book. We based a lot of the physical specs of Logic on Animal Shelter by Semiotex(t)e, which we really liked from a design perspective. It was intentionally a different approach from the big, bright, design-forward, in-your-face aesthetic of something like Wired.

Luckily for us, it was also massively less expensive to print a black and white book versus a full color glossy magazine format. Bootstrapping Everything XW: Can you talk a bit about our process of choosing a printer, and laying out the book? JF: We use InDesign to lay out the magazine. I can’t remember where the original layouts and the template pages came from. I eventually got more involved in operationalizing things but wasn’t that involved with the original design.

Sometimes, however, such encounters have led to controversy. In 2016, just as short-video platforms were starting to appear, a public account on WeChat named “Doctor X” wrote a post criticizing rural users for the “vulgarity” of their videos. The author argued that rural users were seeking attention by showing particularly impoverished households or by performing dangerous stunts like eating glass or raw meat. If their content became popular, the users could then cash in by promoting ads or receiving gifts from fans. The videos reflected poorly on Chinese rural life, the author warned, and underscored the unbridgeable gap between the countryside and the cities.

Similarly, the official news has criticized short-video platforms for not designing “socially accountable algorithms,” and has called for them to take responsibility for feeding higher-quality content to users. In January 2019, the China Netcasting Services Association, a national industry body, published a set of guidelines on short-video platforms, which forbid the publication of 100 topics that are politically sensitive or considered “immoral” or “unhealthy.” It also required platforms to review content before it goes live, which would require hiring more moderators. Companies like Bytedance already employ thousands of moderators to review the content on their platforms, ensuring it remains within acceptable parameters. But the China Netcasting Services Association wants them to do more: “In theory, the number of reviewers should be above one a thousandth of the number of videos published on the platform per day.” Yet for all the controversy around short-video platforms, there is ample evidence that they also create opportunities for mutual understanding and exchange across the urban-rural divide. For example, the Huanong Brothers are two young men from the countryside who have become some of the most popular social-media influencers in China in 2018. After working in urban areas for a few years, they returned to their village and started a small business raising and selling bamboo rats, a type of rodent that’s used in local dishes in Jiangxi province. They document their everyday life of raising, cleaning, and cooking bamboo rats on Xigua Video, a platform also owned by Bytedance that allows longer videos than TikTok. The Huanong Brothers’ videos have gone viral on the Chinese internet, earning over 500 million views and 2 million followers. Their online success has also translated into offline income: in addition to earning advertising revenue on their videos, the Huanong Brothers have also sold an increasing number of pre-orders for their bamboo rats.

The platform the family works for pays them a few cents per task, in cryptocurrency. They are only allowed to transfer the money to their online wallet once they have made at least the equivalent of ten dollars. After working every day of the week, they usually earn around twice that much, but recently they have barely made the minimum. “Last week, we couldn’t cash in,” Maria told me. “We couldn’t even make five dollars in total.” Her family dreads the day when the tasks will stop coming, the computer breaks, or they will lose access to the internet and electricity. Ofelia, another data annotation worker, who has diabetes, depends entirely on the platform to purchase insulin. “I would die without this income,” she told me. “I would literally die.” Income from data annotation is essential to Ophelia, María’s family, and the other Venezuelans who do this work because hyperinflation has made the official monthly minimum wage in the country worth only a few dollars, which is not enough to afford staple foods to survive even a week. That has rendered most jobs paid in bolivars, the national currency, unsustainable. After years of economic mismanagement due to government corruption and its economic dependency on oil, Venezuela has a goods and services shortage and has inflation levels that are consistently among the highest in the world. This situation, combined with its existing internet infrastructure, has made the country an appealing target of crowdsourcing platforms. In the absence of a robust social safety net, workers often see these platforms as their most reliable source of income in US dollars.  Before the pandemic, María and her family were migrants in neighboring Colombia for a year. María worked at a beauty salon while her husband Rodrigo worked selling coffees in the streets. The children all studied in the public education system. These were difficult but more stable times for the family. When the pandemic hit, María lost her job and, with deserted streets, Rodrigo couldn’t find many clients. With no other choice, they decided to return to Venezuela. “Here we had to look for options, and a friend recommended the platform to us,” Maria said. When the pandemic stopped in-person teaching, it meant that her three eldest children were stuck at home too, and could also perform data annotation work. In dozens of interviews with platform workers in Latin America, many of whom are or were migrants, I have heard similar stories: they were collectivizing platform labor across their household members, with teenage children doing more and more work after the onset of the pandemic.

In these ways, the political and economic crisis in Venezuela, as well as the pandemic and remote schooling, have turned out to be productive for data annotation platforms, their clients, and the venture capitalists that back them. (These crises have also generated profits for companies selling information to carceral states: Onfido, the identity verification company used by electronic wallets, shares the identity and facial recognition data it collects with the United Kingdom police.) The thousands of companies and research institutions that develop artificial intelligence are using platforms to find cheap outsourced labor, especially from low-income economies, for global markets in which data and labor are sold as commodities. One of the results is a race to the bottom in which wages get lower and lower as competition between platforms—and their ability to find pools of ready labor even among people living in refugee camps—goes up.

Tachi imagines using a global team of teleworkers to keep factories productive around the clock. He imagines three daily shifts split between workers in Nigeria, Japan, and Mexico, enabling a Japanese plant to stay in operation twenty-four hours a day. Along with reducing labor costs, telerobotics will also keep workers in their home countries. Tachi believes this would prevent the “problems” that an increase in physical immigration might cause.

This vague reference to “problems” echoes a recurring theme in Japanese robotics, as identified by the anthropologist Jennifer Robertson: a preference for technological solutions to the labor shortage as a less culturally threatening (and more politically palatable) alternative to increasing the number of foreign workers by relaxing Japan’s immigration laws. While the ruling Liberal Democratic Party recently pushed a controversial immigration reform bill through the Japanese legislature — set to increase the number of lower and medium-skilled work visas by 345,000 over the next five years — the changes still fall far short of fully addressing the labor crunch. Existing programs to provide foreign labor on a more short-term basis, such as the Technical Intern Training Program, have been notoriously vulnerable to employee abuse and exploitation.  Whereas autonomous robots would seek to replace these human workers entirely, telerobotics instead seeks to import only those aspects of human embodiment deemed economically useful. Somewhat ironically, Hirose, Tachi, and Yoshifuji each present their telerobot systems as a more human alternative to full-scale automation, even as they play up the technology’s advantage over on-site human labor and increased immigration.

With Indymedia, thousands of people were publishing stories and sharing photos and videos across movements and across the world. Indymedia’s open source codebase, of which multiple versions emerged over the years, had been created specifically for this purpose. As Mansur Jacobi and Matthew Arnison, software programmers who were core in developing the open-publishing framework for Indymedia, put it in the very first post published to the Seattle site: The web dramatically alters the balance between multinational and activist media. With just a bit of coding and some cheap equipment, we can set up a live automated website that rivals the corporates. Prepare to be swamped by the tide of activist media makers on the ground in Seattle and around the world, telling the real story behind the World Trade [Organization].

The site’s open-publishing architecture presaged the social media networks that would begin to emerge years later and eventually subsume how we communicate online.  Tech Taking a Backseat Although open publishing was key to the success of Indymedia, the technical aspects alone weren’t what attracted its user base. Just as important were the anti-capitalist and justice-centered values. I came to the Tennessee Indymedia Center’s website, tnimc.org, to write and read stories about how people in Nashville, my hometown, were dying because of cuts to state health care, about how coal extraction had decimated whole mountains and polluted local water supplies, about how police were increasing their presence in public schools.  Local corporate media at the time were either ignoring these issues or, if they were covering them, failed to consistently center the voices of the people and communities affected. Our thinking was that it would be awfully hard to change local policy if our neighbors didn’t know what was happening, and we couldn’t count on the mainstream media to make people understand enough to care. In this way, as grassroots journalists on Indymedia, our work was tactical. We were reporting with an agenda.  Other Indymedia organizers and activists I spoke to felt similarly. “Self-publishing is great. I’m into it,” an early organizer of Indybay told me, who asked to remain anonymous. “But I feel like the main strength of Indymedia was this idea about tactical media. There’s like a purpose to what you’re doing that’s not just about publishing your story.” If you hung around Indymedia types during the early 2000s, there’s a good chance you heard the term “tactical media” batted around. What differentiates tactical media from some imaginary idea of pure journalism is that tactical media is made in support of a political project.

Early 2019 — The U.S. government, concerned about China-based companies such as Huawei and ZTE growing their presence in global IT infrastructure, charges Huawei with sanctions and intellectual property violations while urging friendly governments to block the company's products from next-generation 5G wireless networks.

Early 2020s and Beyond: What’s Next? Back in the 1990s, both Chinese officials concerned about retaining control and American observers hoping for liberalization believed that the internet would change China. Now that the fourth and fifth eras of China’s internet development have cultivated a largely separate online ecosystem, the question becomes whether China will change the rest of the world.

Things get even trickier if you want to get ahold of information about what ships are carrying. That data exists, but it tends to be stored within the software systems of shipping companies and amongst the paperwork of customs agencies—there’s no central clearinghouse for information about cargo. AIS data can tell you where ships are, but it can’t tell you what they’re carrying. In the absence of real-time cargo data, companies like CargoMetrics use historical data and proxy information (about which types of vessels are at sea and where they’re headed) in an attempt to derive information about which cargo is in motion. Figuring out who’s selling to whom is even more complicated. There’s no one organization that keeps track of this kind of transaction information, and supply chains are rife with small outfits that spring up and then vanish from sight at a confounding pace. So the data exists, but the paper persists. The various stores of information are in separate, locked-down databases, kept apart by competing business interests, incompatible data models, and differing legal frameworks. To make things more complicated, when we talk about the “supply chain,” we’re not really talking about one industry; instead, we’re talking about a stunning variety of disparate players, all engaged in moving stuff: freight forwarders, charterers, drayage companies, container lines, truckers, terminal operators, and chassis providers, to name just a few. Each has its own data, but as often as not, information moves between operators on paper. “It is not uncommon for a shipping document package to contain fifty sheets of paper that must, in some cases, be exchanged between thirty different stakeholders,” reports the Digital Container Shipping Association (DCSA). In the absence of a unified mode of communication, paper patches over the gaps between systems. The bill of lading, for example, a critical document that describes a shipment and acts as a bill of title, is almost always paper: the DCSA reports that only 1.2 percent of bills of lading were digitally transmitted in 2021. At each stop on the cargo’s journey, the physical document must pass from hand to hand until it reaches its destination. In the UK and some other countries, there was until recently no legal provision for the possession of an electronic title; anyone seeking to prove ownership had to have that ownership on paper. Different countries have different laws, different industries need different information, and different carriers each have their own requirements. During the height of the Covid-19 pandemic, when many flights were grounded, some containers were stuck at ports because the paper documents necessary to release them couldn’t be couriered via airfreight. The global supply chain is a stunning feat of communication and technology, but surprisingly little of that communication is accomplished digitally at the moment.

So the issue is not that movement isn’t tracked and computerized; it’s that it is tracked too many different ways, in scores of different proprietary systems, most of them locked down. Individual carriers might have granular detail about their vessels’ condition and cargo, but in the absence of a network that stitches all of that information together, the big picture remains incomplete. “You have to connect data from multiple data sources, and to be able to do that, you have to be able to access data that resides within the systems of different carriers, different freight forwarders, different customs brokers,” logistics expert Inna Kuznetsova told the Journal of Commerce in 2019. Paper documents like bills of lading may be unwieldy, but they’re still more convenient to exchange than unintelligible or inadmissible digital records. Data standards would make a difference: if information could be exchanged in a predictable format, companies could share it electronically. These standards do exist, but there are also significant barriers to their adoption. In 2015, the United Nations Centre for Trade Facilitation and Electronic Business (UN/CEFACT) published a set of data models that lay out definitions and specifications for trade documents, designed to be encoded in data exchange formats such as JSON or XML. If a trading partner knows how information will be formatted, it can design a computer system to interpret that data without human intervention. Data standards aren’t unlike the shipping container itself: once everyone agreed on the box’s dimensions, containers could be swapped in and out without any need for discussion. Likewise, if everyone agrees to use the same data standards, information can be handed over as easily as a shipping container moves from cargo hold to truck chassis.

The dream of a digitally run economy is an old one, then. But it’s rapidly becoming more workable, as vast new quantities of information become available. The problem of planning is primarily a problem of information. Friedrich Hayek famously said that planning couldn’t work because markets have more information than the planners. Markets give us prices, and prices determine what to produce, how to allocate assets, and so on. Without markets, you don’t have the price mechanism, and thus you lose a critical source of information. In Hayek’s view, this explained the inefficiencies of Soviet-style command economies, and their failure to meet people’s material demands.

As more of our economy is encoded as data, however, Hayek’s critique no longer holds. The Soviet planner couldn’t possibly see the entire economy. But the planner of the near future might. Data is like the dye that doctors inject into a patient’s veins for an MRI—it illuminates the entire organism. The information delivered by prices looks crude by comparison. Who needs prices when you know everything? Greater transparency enables greater coordination. Imagine a continuous stream of data that describes all economic activity in granular detail. This data could be analyzed to obtain a clearer picture of people’s needs, and to figure out how to fulfill those needs in the most efficient and sustainable way.

3. It will always be in the interest of the men who own the machines to say their machines will make the world a better place. But they have a point. The internet has been a godsend for countless people who were poorly served by more standardized forms of sexual culture—from queer teens to divorcees to professional dominatrices to people with disabilities.

On the other hand, capitalism is pretty adept at cooptation. It is, as they say, complicated. An app lets you source whatever strain of sex you want—or at least play a video game about people within a ten mile radius who might have sex with you. But it only lets you make some choices. Most choices it makes for you. It sorts you by a set of rules, because all algorithms are sets of rules. Above all, it converts your sex life into a subject of surveillance, and a stream of profit. Each intimate instant is making someone else money, from the first swipe right to the first relationship status to the first post-breakup revenge selfie.

I always had an interest in applying what I learned to the realm of technology. The platform cooperativism movement was emerging at the same time, and the first DWeb Summit happened in 2016. I attended that summit and came away with this feeling that there was an opening happening: this was a blossoming community of people who wanted to question the ownership of technologies, to question how infrastructure was being built and who was controlling it. And they weren’t just talking about it, they were actively building alternatives.

So after Shareable I worked on projects that took me in that direction of supporting and building alternatives. I worked at the Oakland Public Library, which completely changed my view on all this. I now focus on projects where I feel like there’s potential to experiment and shape how people think about decentralized technologies—but more than that, build networks of solidarity based on organizational decentralization and interdependence.  Decentralization and distributed systems have had a long history on the internet, but recently it feels like there’s been a focused energy and community forming around particular decentralized technologies. What’s your perspective on the historical context that informs these current movements? Decentralization is a core tenet of the internet and the development of the web, but it’s had its peaks and valleys. Email, which was invented in 1971, is the most solid decentralized protocol that has enabled interoperability in a way that many other protocols haven’t.  There was a long valley of consolidation after that. But then decentralization hit a new renaissance with the filesharing era. As a millennial, the trend that’s most memorable to me was the peer-to-peer networks in the late ’90s and early 2000s like Pirate Bay and Napster. These emerged in response to the way the internet provided great technical affordance to access and remix content, while legal apparatuses like the DMCA (Digital Millennium Copyright Act) got in the way of doing that. Laws like the DMCA led to a backlash against big entrenched content publishers like Hollywood—an industry that had not properly contended with the ways the internet undermined their copyright monopolies. There was a big boom in filesharing, using new decentralized tech like torrents, and this helped people to change their thinking around the structure and purpose of the internet.

When I look at his arguments about how the forms of media that emerged in the 1980s contributed to the trivialization of public discourse and the fracturing of the public sphere, I think he wasn’t wrong. It was only going to get worse. Neil did not live long enough to see Facebook. But had we taken him more seriously, maybe we could have done better as we rolled out new communicative models. Maybe we could have built some forums for fostering deep deliberation and examination, or preserved and subsidized existing ones, knowing that there was going to be tremendous commercial pressure.

Instead, we did the opposite. We rolled back funding for libraries and universities and public media. We rolled back funding for the arts and humanities. We erased any argument about market failure. Market failure was the argument for public broadcasting. It was the argument for public schools. But by the 1980s and 1990s, hardly anyone was talking about market failure.

This piece includes material from Chapter Five (“The Cultural Construction of Technologized Touch”) of the author’s forthcoming book, entitled Archaeologies of Touch: Interfacing with Haptics from Electricity to Computing *(University of Minnesota Press, 2018). Scrolling through pictures, swiping right or left on a touchscreen, effortless and nearly instant contact in the event of a match… these are the experiences that define contemporary computer dating. But computer dating has been around for far longer than Tinder, Grindr, or even the personal computer.

The first computer dating systems looked something like this: Your preferences were written down, usually in questionnaire form. They were punched onto cards. You received a printout with addresses, so you could write to your matches. Or perhaps, if you were lucky, a phone number. No pictures, and no information about their preferences, were included.

In 1979, they succeeded. The Department of Labor ruled that diversification across an entire portfolio could be a factor in weighing the prudence of an individual investment. Risky investments were allowed, so long as they were balanced out with safer ones. This was also the year after the capital gains tax rate was nearly slashed in half, from 49 percent to 28 percent, under the Carter Administration. As a result, the venture capital industry exploded in the 1980s, growing from between $100 to $200 million raised a year to $4 billion by the end of the decade.

“And all of a sudden University of California was willing to give us money, Yale University was willing to give us money, Alcoa was willing,” recalled Sequoia’s Don Valentine. “So a whole bunch of people who had been sitting on the edge of their seats to make more aggressive investments with a very small part of their funds finally got a small go ahead, and people like us were able to start in business.” The floodgates had opened. The influx of money into venture capital in the coming decades would help propel Silicon Valley’s growth, and transform a handful of startups into some of the world’s biggest companies.

Think Different What would today’s octopus look like? In recent years, the alt-right has been particularly effective at minting new symbols that capture big ideas that are difficult to articulate. Chief among them, perhaps, is the “red pill,” which dragged the perception-shifting plot device from The Matrix through the fetid and paranoid misogyny of “men’s rights activist” forums into a politically actionable concept.

The red pill is a toxic idea, but it is also a powerful one. It provides a new way to talk about how ideology shapes the world and extends an invitation to consider how the world could be radically different. The left, unfortunately, has been lacking in concepts of similar reach. To develop them, we will need a way of talking about Big Tech that is viscerally affecting, that intuitively communicates what these technologies do, and that wrenches open a way to imagine a better future.

Tinker and Spy Karl Marx once quipped that under communism man would be a fisherman in the morning and an artist in the afternoon. By the 1960s, the Bulgarian Communist Party believed that automation had brought Marx’s vision to the cusp of realization. Thousands of engineers were trained in the country’s universities, and cybernetics became a watchword for the party’s economic programs. Computers would streamline information flows, provide objective information on the economy, and allow planners in Sofia to accurately predict the future. The Politburo trumpeted that “science would be a productive force.” Just a generation before, that idea of Bulgaria’s future had seemed unthinkable. Back in 1944, when the party assumed power as Stalin’s Red Army rolled over the Danube, this small Balkan state was largely an agricultural producer. In the nascent field of international development, Bulgaria was considered an example of the region’s “trap,” which could only be escaped through large-scale investment. That is precisely what the Soviets brought, in the guise of breakneck Stalinist industrialization. Throughout the 1950s, the country assumed the Soviet economic model: central planning, smokestack industries, and a growing industrial proletariat squeezed into the cities.  By the 1960s, however, Bulgaria’s extensive growth period was tapering off, and the country was suffering a debt crisis. On the advice of Bulgarian engineers trained in western Europe, the party turned to electronics as the good of the future. As the party’s leader, Todor Zhivkov, put it, “We couldn’t industrialize with tomatoes and eggs.” Heavy state investment was combined with Japanese licensing and a prodigious espionage program to create the Bulgarian computer industry. By the 1970s, dozens of Bulgarian factories and institutes were churning out CPUs, mini-computers, and peripherals, such as ES-1020 mainframes and IZOT hard drives. Most of this technology was licensed or reverse-engineered after the Bulgarian intelligence services had procured it in the West; from its inception, the nation’s hardware industry was based on spying and tinkering, rather than true domestic innovation. The golden goose was the hard disk, which Bulgaria almost monopolized in the Eastern Bloc.

The 1980s were marked by continual heavy investment in robotics and personal computing, bringing the automation age into the office and onto the factory floor. Imperfect as it was, Bulgarian automation did take hold to some extent—around 200,000 workers in a country of eight million worked in the electronics sector, the second largest industrial workforce in the country. Its IBM 360/370-copies, Winchester hard drives, and an Apple II clone known as the Pravetz, found their way into socialist enterprises throughout the Global South. Bulgarian computers flew on the Soviet space station Mir, were used for nuclear research in India, and equipped Mozambique’s nascent statistical authority. Though often slowly, and in a piecemeal fashion, automation entered car production processes and cement factories, monitored milk levels in collective farms, and increasingly suffused social and government services.  Unfortunately, Bulgarian products often fared badly on the global capitalist market due to their sometimes shoddy quality. Rather than looking at its economic principles such as central planning as a potential source of the problem, the party focused on the “subjective factor.” In their view, it was the Bulgarian worker rather than the system that was to blame—shirking responsibilities, pilfering the petty change, and sleeping on the job. Only the computer and robot would solve this problem, by eliminating the human strategies of survival in the shortage economy of socialism, where personal links and the grey market were key to procuring scarce goods. These were the peculiar conditions within which the next, truly computerized generation, would have to grow up.

Compounding this neglect was no doubt the paternalism of Fort Rodman’s mission, and the belief in Black cultural inferiority that the project embodied. In the 1966 promotional film, for example, as the camera fixates on the face of a young Black man working through a math lesson, the narrator intones: No one has ever given a damn about him until now. He’s failed in school. He’s failed with his family. He’s failed within society. And so he is turned inwards and in a very bad way. We have to convert this history of serious failure into a present history of success.

This viewpoint—that it was the young men at Fort Rodman who were broken and needed fixing, not the systems of racist and sexist capitalism that Fort Rodman was, in theory, training them for—reflected the “culture of poverty” idea underlying many of President Johnson’s Great Society programs. This idea held that Blacks were poor because they had an inferior culture that didn’t prioritize work and individual responsibility, among other things; in order to change, Black people had to experience and adopt the “right”—supposedly white—cultural values. At the same time, Fort Rodman isolated young men from the communities that provided acceptance, care, safety, and pride for who they were as people.  For its part, the local community in New Bedford made it clear that the young men weren’t wanted there; in May 1966, worried about “unruly elements” at the camp, as a Washington Post report put it, the city council asked President Johnson to move the Job Corps center out of Fort Rodman. Though Fort Rodman had enrolled more than 870 young men by then, the Johnson administration pressured IBM to close it. “The experience caused us some real soul-searching, because there were more problems than we anticipated,” IBM President Tom Watson admitted in his memoirs. “IBM ended up hiring very few Camp Rodman ‘graduates,’ and I doubt any other company did either.” Racism as a Business Model Fort Rodman may have been a failure, but IBM invented a number of other diversity programs that continued, with limited success, into the late 1970s. Several of its initiatives were aimed at luring Black people to IBM through job fairs and targeted advertising in Black media outlets, as well as by loaning equipment and funding faculty positions at historically Black colleges and universities. The company’s primary focus, though, was on developing the “supply side” of the labor market by training the folks it hoped would fill its demand for technical workers. These efforts were smaller in scale than Fort Rodman, but similar in spirit.

In the panopticon, the central tower is feared. The Leviathan, however, is a trusted figure. A god-like figure that is trusted to act in our individual and collective interest.  But I don’t want to trust. And I don’t want the rest of us to trust. I don’t trust systems. Systems are built by designers who work for corporations that have very specific ideas about how such systems should work.  Is there any hope then of taming the Leviathan? One could imagine a community of algorithmic systems. A plethora. These systems would be designed to have a socially agreed-upon wisdom. I’m not ready to grant a single Leviathan that wisdom. I believe in the multiple. I believe in the differences among us. We can make systems that embody those differences: not quite Leviathans, but committed resources that stand in for the differences among us. That’s about as close as I can get to envisioning a better future at the moment. I’m not ready to write about it yet. I’m thinking about it. I’m working on it. If you look at my resume, you can see that I write books ten years at a time. But I don’t think I have ten more years. I really don’t. So if I’m able to make the Leviathan my next thing, or my last thing, that’s good enough for me.

Why did you make EvictorBook? Erin: Particularly since 2008, we’ve seen the rise of corporate landlordism. We’ve seen huge investment companies—Blackstone and Invitation Homes are two of the biggest in the US, but there are many others—that will buy up swaths of property with unique limited liability company (LLC) or limited partnership names.  Take 55 Dolores Street LLC and 49 Guerrero Street LLC. These two LLCs are subsidiaries of Urban Green Investments, which is a big investment company that evicted many tenants in San Francisco in 2013. In the process of buying those properties, the company established a separate LLC for each one, which helped them with a number of things in terms of finance and liability, and also afforded them anonymity. It’s often very hard for tenants to know which other buildings in the city are owned by their landlord if each property has a unique-sounding ownership name.  When that was happening in 2013, the Anti-Eviction Mapping Project was just getting started, and we didn’t have a tool like EvictorBook, so we were doing property research manually. We’d create static websites where we’d list all the LLCs and all the evictions that we were able to connect to that investment company, with the idea that this information should be public and that tenant organizers should be able to use it for campaigns—ideally, multibuilding campaigns against large-scale landlords. They were essentially profile pages on different landlords. That’s useful because you have a much stronger chance of winning a fight against your landlord if you’re working together with other tenants across that landlord’s other buildings.

Yeah, so far The Last Mile has a perfect record in terms of recidivism. Nobody that’s gone through the program has gone back to prison. I think the support network and going to work and having marketable skills makes a huge difference to a guy getting out of prison. They need a safe place to live. They need a job. They need to be able to support themselves. They need resources because limited resources lead to crime, in my opinion.

If we want to address recidivism, we need to give people a chance. I think so too. They spend so much on keeping people in prison, it just seems like some of that money could be diverted into helping guys stay out of prison. You know, guys inside prison aren’t how they’re portrayed on TV and in movies. You should see what’s going on in San Quentin in terms of education and rehabilitation. It’s not being offered by the state—it’s being done by outside volunteers. People from the community are coming into San Quentin and sharing knowledge and the guys in there are soaking it up like sponges.

This is one of the most transformative features of the big data landscape: the creep of criminal justice surveillance into other, non–criminal justice institutions. I encountered many examples of law enforcement using external data originally collected for non–criminal justice purposes, including LexisNexis, but also TransUnion’s TLOxp (which contains one hundred billion public and proprietary data points, including social security numbers, employment records, and address records); databases for repossession and collection agencies; social media, foreclosure, and electronic toll pass data; and address and usage information from utility bills.  Respondents added that they were working on integrating hospital, pay-parking lot, and university camera feeds, as well as rebate data, pizza chain customer lists, and so on. One interviewee in the LAPD’s Information Technology Division said they had their eye on consumer data: “Other stuff, shopping data. You can buy it, you know, certainly other vendors are. So why not?” In some instances, it is simply easier for law enforcement to purchase privately collected data than to rely on in-house data, partly because there are fewer protections and less oversight over private sector surveillance and data collection.

Another of the most substantively important shifts that have accompanied the rise of big data policing is the shift from query-based systems to alert-based systems. By “query-based systems,” I mean those databases that operate in response to a user query, such as when an officer runs your license plate during a traffic stop. In alert-based systems, by contrast, users receive real-time notifications when certain variables or configurations of variables become present in the data. High-frequency data collection makes alert-based systems possible, and that carries enormous implications for the relational structure of surveillance.

People in power love to tell us that there is no alternative. But there are, in fact, many alternatives. The obstacles to human flourishing aren’t inevitable. They’re not eternal facts of life—they’re produced by the specific ways we organize our society. And we can organize our society differently.

With the fires burning and flood tides rising and nuclear war one tweet away, more and more people seem to realize that we need to—and fast. But to reorganize our world the right way will require a new moral vision. We have inherited a particular set of metrics that guide how we build and implement technologies: clicks, downloads, conversion—which all, in the end, roll up to profit. But what if we optimized for different outcomes: sustaining the earth, empowering all who live on it, enlarging the horizon of human possibility? Close your eyes. What does Justice see? Let’s start with the idea that technology is always a force for good. This strain of thought is pervasive in Silicon Valley. Where does it come from? What are its origins? It owes its origins to 1960s communalism. A brief primer on the counterculture: there were actually two countercultures. One, the New Left, did politics to change politics. It was very much focused on institutions, and not really afraid of hierarchy.

RF devices generally don’t pinpoint a person’s location. Instead, they only monitor whether or not the wearer is at home, and are used for “house arrest.” (Usually, a person on house arrest may only leave their home for a set of prescribed activities, like going to work or attending religious services.) They connect to a base station that is plugged in at home, and link to the internet via a landline or a dedicated cellular unit.

By contrast, GPS monitors track location based on satellite information. As a result, they can alert parole officers and other officials if the wearer enters a restricted area, such as a school zone. GPS systems tend to lead to less restrictive rules, but also share more information with the police. Some GPS ankle monitors have transmission built-in; others require that a dedicated cell phone or other transmitter be carried at all times. For all types of devices, police are alerted if the device strap is cut or if the sensors detect other types of tampering.

Another employee at Palantir demonstrated how the platform can be used for retroactive investigative purposes: Law enforcement had a name of someone they thought was involved in trafficking. They ran a property search, which yielded the person of interest’s address and date of birth. Then they ran a search for common addresses (whether there are any other people in the system associated with the same address). One turned out to be a sibling of the initial person of interest, which sent investigators searching again, this time coming up with a police report for operating a vehicle without a license. They also searched the address of a third sibling, who lived at a different address. A radius search revealed several tips concerning this same house: one neighbor had called in to report a loud argument, and another reported that a suspicious number of cars was stopping at the house.  With this information, the police were able to set up in-person surveillance and subpoena phone records, which were run through Palantir’s “time wheel” function to identify temporal patterns. Modeling revealed phone calls to one or two phone numbers at the same time each week; using those phone numbers, police got a new database hit. They found a name and a police report and identified their suspect.

In another instance, I saw a user search for a car using just a partial license plate. They entered “67” and accessed all of the crime reports, traffic citations, field interview cards, automatic license plate readings, names, addresses, and border crossings associated with cars whose license plate contained these numbers in this order.  Advanced analytic tools on the platform include geo-temporal and topical analysis, each of which can be visualized differently. For example, users can plot (geo-analysis) all the types of crime they are interested in (topical analysis) during a given period of time (temporal analysis). Users can visualize the data on a map or along a chronological axis, as well as conduct secondary and tertiary analyses in which they analyze the results by, for example, modus operandi (e.g., using a bolt cutter) or proximity of robberies to a parolee’s residence.

Machine-learning recognition tools typically measure their success in terms of their detection rate. The best nudity detection algorithm claims to detect nudity with 94 percent accuracy and a false positive rate of 5 percent—that is, it spots nearly all the nude images while misidentifying as nude only one of every twenty non-nude images. In 2015 Twitter purchased Madbits, which promised an algorithm that could identify NSFW (not safe for work) images, including porn, violence, and gore, with 99 percent accuracy and a 7 percent false positive rate.

Is 94 percent good? What about 99 percent? It depends on what the platforms hope to do with these tools. These are, from one vantage point, phenomenal achievements. But when it comes to culture and expression, even a few false positives can be a cause for real concern, depending on whether those errors are idiosyncratic or systemic.

Yet Amazon’s approach to discipline also points to its greatest vulnerability: its need for speed. “In the idealised world-picture of logistics,” writes Jasper Bernes, “manufacture is merely one moment in a continuous, Heraclitean flux; the factory dissolves into planetary flows, chopped up into modular, component processes which, separated by thousands of miles, combine and recombine according to the changing whims of capital.” If this dream of frictionless flow has produced the dismal conditions within the Amazon warehouse, it also offers those same workers a potential source of leverage.  Whereas workers in a factory have the power to slow or halt production, workers in the logistics industry have the power to block circulation, to clot the channels through which capital flows and learns about itself. Longshoremen at major ports have wielded this power to great effect for a century, their strikes functioning as de facto blockades. And just-in-time logistics is potentially even more vulnerable to worker disruption, since it eschews the redundancies and backups that might have compensated for circulatory blockages in the past.  Logistics is both the circulatory and nervous system of contemporary capitalism. Amazon prefigures the worker as a seamless conduit — a neuron and a blood cell — in the free movement of information and commodities. Her behavior is minutely calibrated, at every moment, to serve the ever-fluctuating demands of a dynamic and hydraulic world system. If she and her coworkers refuse to play their role in this meticulously choreographed operation, however, the whole system seizes up.

Of course, such a project will rely on Amazon workers developing the sort of solidarity that is disincentivized by the disciplinary apparatus erected around them. It means reaching beyond forms of micro-resistance that may mitigate the most dehumanizing aspects of the work, but which are ultimately comfortably accommodated (if not actively encouraged) by the company structure.  Community Engagement To date, the only group of Amazon workers who have managed to collectively force a negotiation with management are those at the Shakopee, Minnesota fulfillment center outside Minneapolis. With the help of organizers from the Awood Center, a worker center funded by the Service Employees International Union, the predominantly Somali workforce has staged a series of protests against an ever-increasing pace of work which punishes devout Muslim employees for using break time to pray. On December 14, 2018, at the peak of the holiday rush, forty Shakopee warehouse workers walked off the job.  These actions have forced Amazon to come to the table. They’ve agreed to have Somali-speaking managers present for firings related to productivity and to hold quarterly meetings with the workers. An Amazon spokesperson told the New York Times that “the company did not see its work with the East African workers as a negotiation but rather as a form of community engagement similar to its outreach efforts with veterans and lesbian, gay, bisexual, and transgender employees.”  But the Shakopee workers aren’t looking for “community engagement.” They’re fighting for changes in, and more control over, the conditions of their work. “Workers are using every avenue possible to try and win jobs that are safe and that invest in our families and our communities,” said Abdirahman Muse, director of the Awood Center.  During the night shift on March 7, 2019, around thirty stowers at Shakopee staged another walkout, returning to the warehouse after three hours with a list of demands. “In addition to calling for an ‘end [to] the unfair rates that force errors and end careers,’” Labor Notes reported, “they called on Amazon to stop the use of temporary employees, to ‘stop counting prayer and bathroom breaks against rate,’ and to better maintain the equipment that most often leads to injury.” [Eds.: On July 16, 2019, during the first day of the two-day Prime Day sale, organizers at Shakopee engaged in another work stoppage to protest working conditions.]

I love that. I do think of libraries as one of the most socialist institutions that we have in the US in 2020: they are funded with public money. They are not means-tested. They're everywhere. You talk to the average librarian about why they got into the profession and there is a real love for the public. You can go into a library and you don't even have to use it for its explicit purpose of looking at books or magazines or computers. You can just go there and be, even if you’re the kind of person that late capitalism has decided is not worthwhile. To the library, you are. And this is a radical idea. Libraries’ doors are still open, even as their budgets get cut. They’re special spaces and I want to help them expand what they're capable of doing.

At the same time, right now, seven of the top ten companies by market capitalization are tech companies. Seven out of ten are using data that they take from us, without our consent, to create their products. That is part of our labor power: those products are made with our emotional labor, our mental labor. Privacy is a way to reclaim our labor power. I want people to think about those relationships.  And, yeah, I also want people to not get their identities stolen. All of the more concrete problems are still important to me, especially when you think about who is subject to them—it's poor people and elderly people and people who don't have power. But with all of this work, I'm really trying to force a conversation about who controls the internet and what that means for our lives.

Fact Sets Once wardens and guards leave Guantánamo, they tend to go quietly into the night of civilian life. The uproar over Bogdan’s appointment at UNC Charlotte was an anomaly. Members of the Coalition to Remove John Bogdan met in the GroupMe chat and in the library late at night to imagine a campus without him. They amplified their cause by tweeting and chalking the streets. In their written statements, coalition members drew heavily on reports written by Amnesty International and other organizations that had made a concerted effort to track and trace the wrongs that Bogdan’s Guantánamo had wrought. There was plenty of documentation of what was done to detainees. Reports from Human Rights Watch noted that a federal district judge had ordered Bogdan to explain a standing order that called for the use of restraint chairs during the force-feeding of detainees. But there was little information about who apart from Bogdan had been involved in doing it.  It is fair to say that the administrators of UNC Charlotte did not imagine their campus would become the site of a battle over the legacy, meaning, and future of detention facilities that are over a thousand miles away. After the coalition began drawing attention to Bogdan’s alleged crimes, the university’s chancellor issued what he called a “fact set” to defend Bogdan’s reputation and employment history. But the document also went many steps further, legitimizing Guantánamo as just one of the US military’s hundreds of bases.  Bogdan fought back as well. In an interview with a local reporter, he pushed the thesis that “the mission here is not far off from the military.” He declared, “The mission of the Army is to fight and win the nation’s wars. And you do that by developing a team, and teaching and growing and building the future of the nation. That’s exactly what universities do, right?” Around the same time, university administrators instructed the social media team associated with the admissions department to block the coalition on Twitter, so that prospective students were less likely to come across their arguments against the colonel.

Ultimately, the coalition couldn’t translate their understanding of Guantánamo into a campaign that resonated with most of their fellow students. In part, the Zoomers had faced the challenge of teaching themselves and their peers what the military prison was and why it mattered. More importantly, perhaps, in setting out to learn about Guantánamo, they were never going to encounter examples of other struggles like theirs. In fact, there has only been one analogous case relating directly to Gitmo: since 2009, Berkeley law students have repeatedly called for the dismissal of professor John Yoo, who gained the nickname “architect of torture” for his role in justifying harsh CIA interrogation techniques deployed at Guantánamo. The students’ Google searching had led them back in time, to a period during their childhoods when Bogdan was running Guantánamo, but it brought them no closer to a blueprint for how to hold people like Bogdan accountable in the present.

Clouds are part of the weather, and another sense of weather is the Romance language one: le temps, el tiempo, il tempo. Time itself. This issue explores other temporalities, and the bodies they are tied to. One writer celebrates “crip time” as an alternative to “flow,” which is not about fulfilling work discipline, but rather about learning to be stuck. Another writer imagines other times altogether. This issue contains the first speculative fiction we have published.

3/ Throughout human history, clouds have acted as omens. Aeromancy is the art of divining the future from the sky. This issue considers possible futures, without being too predictive; the sky is a complex canvas, and its patterns shift quickly with the wind. I first encountered Agile when I got a job in a library. I’d been hired to help get a new digital scholarship center off the ground and sometimes worked with the library’s software development team to build tools to support our projects. There were about six members of this team, and I noticed right away that they did things differently from the non-technical staff. At meetings, they didn’t talk about product features, but “user stories”—tiny narratives that described features—to which they assigned “story points” that measured the effort involved in completing the associated tasks. They met every morning for “standup,” a meeting literally conducted standing up, the better to enforce brevity. A whiteboard had pride of place in their workspace, and I watched the developers move Post-it notes across the board to signify their state of completion. They worked in “sprints,” two-week stretches devoted to particular tasks.

All Watched Over by Machines at the Workplace New technologies aren’t just augmenting capitalism’s insecurity-generating tendencies in the spaces that we call home. They are also intensifying those tendencies in the other domain that defines most of our lives: the workplace.  The other night a friend regaled me with stories of working at a Brooklyn café. The place has a vintage and vaguely Parisian aesthetic—decidedly retro and low-tech. There are, of course, regulars, including a medievalist who likes to chat. A few months ago, on a slow day, another barista on duty was exchanging pleasantries with the medievalist when her phone rang: the owner was watching the security camera live-feed from his laptop, and told her to stop being so nice. When I asked my friend how many cameras are installed in the small space, she could identify at least eight, and said there might be more. The charming café is, in fact, a panopticon—the boss can tune in at any time from anywhere, and see from nearly every angle. The workers are always on edge, even when all they want to do is show a bit of kindness to a local eccentric.

As the scholar George Rigakos reminds us in his book Security/Capital, employers have been deploying cameras toward similar ends for decades. In the early 1990s, Rigakos worked at a bakery where the staff regularly took home broken and unsaleable loaves. Management had always looked the other way. But weeks before the business was scheduled to be closed, the owners installed security cameras to catch workers in the act. Lifelong employees were summarily fired, losing their retirement benefits. “The security cameras must have saved the company thousands upon thousands in severance and pension dollars,” Rigakos recounts.  Today, employees no longer need to labor in the same physical space to be surveilled, nor is a human being required to do the surveilling. Instead, isolated and geographically dispersed, workers can be tracked and controlled remotely, whether they are driving for UPS or making deliveries for DoorDash or transcribing material for Rev. By harnessing digital technology, companies are able to offload more risk onto individuals, whom they categorize as independent contractors to bypass minimum wage laws and other protections. A dwindling number of people are entitled to severance or pension dollars in the first place.

To observe that Facebook has relatively few workers is not to suggest that the work they perform is not important. Without content moderators, data center technicians, site reliability engineers, and others, Facebook’s product would become unusable and its business would collapse. But their collective labor, like that of the workers within Engels’ father’s factory, depends on many concentric circles of collective labor outside of it. And, for Facebook and the other firms that fall under the umbrella of tech, the share of value supplied by these external layers is especially vast.

One source is the workers who invented the software, hardware, protocols, and programming languages that laid the basis for today’s tech industry. These were developed over the course of several decades, starting with the creation of the first modern electronic computers in the 1940s, and relied heavily, often exclusively, on US military funding. Another source is the workers who, in the present day, continue to make and maintain the stuff on which tech profits depend. While this work takes many forms, most of it is menial or dangerous. It includes manufacturing smartphones, mining rare earth elements, and labeling training data for machine learning models.

Exactly. You think about complexity and you think about efficiency and you measure it, but Big O analysis as an interview tool is only useful to measure how much attention you paid in your second year of college. It’s one of my pet peeves. It’s a symptom of a disease. Nobody Puts COBOL in the Corner As I understand it, you’re not currently a permanent employee with the federal government. How would you describe your position? My position right now is a termed position. Technically, in eight months, I am supposed to go back to the private sector and move on with my life. I probably won’t because this is what I want to do with my life. So I am going to fight to find a way to stay beyond my term. For the government, every change requires years.

As an example, take my current effort to replace a specific mainframe overseeing the benefits and livelihoods of two and a half million people. Getting away from that system will take more than five years, because of the amount of technical debt that has to be paid. We have led a successful acquisition of a mainframe that took three months and we’re very proud of that. Usually it takes more like seven months to finish an acquisition process in government. We bought a new mainframe, which is a horrible thing to say, but that was the right thing to do because the old system was really about six, seven years past the end of life, and we needed five years to pay the technical debt to fix the system. So, we had to buy a new one and we did it in three months.

Erin: You do so much, Azad. He’s working on both EvictorBook and the COVID-19 Global Housing Protection Legislation and Housing Justice Action Map, which tracks tenant protections, housing justice actions, landlord retaliation, and soon will include tenants’ oral histories since Covid. It’s an interesting moment because, for a while, we had these three chapters, and each chapter was working on its own projects, so I don’t even know a lot of what’s happening in places where I’m not there, going to those meetings. I know in the Bay, there’s a thirty-minute film that’s an homage to tenant organizing that weaves together three struggles: the anti-Google fight in San Jose, the fight for rent control in Santa Rosa, and the fight for Aunti Frances’s home—she’s a former Black Panther who was evicted from her home in Oakland a few years ago. Our Counterpoints atlas will come out later this year with PM Press. That’s a project that many, many people have been working on for almost three years. Probably well over a hundred people contributed to the atlas, some affiliated with AEMP and others not. There’s also the Black Exodus zine that came out last year that tracks Black experiences of gentrification and resistance in San Francisco. I’m sure Azad knows about a lot of projects in LA that I don’t know about.  With EvictorBook, we started this interchapter collaboration model that more of our work is taking on, since Covid means that we’re all meeting online and not in person anyway. We’re having interchapter meetings every month whereas we used to have those every few months. But, yeah, it’s all volunteers, and we’re always trying to find ways to create more sustainability and be better organized. People are dedicated, and it somehow keeps going.

Azad: I think it’s partly that these are decentralized projects, and we’re just providing a framework. The framework is: How does this relate to housing justice? You’re not just making this for your own curiosity or to scratch your own itch; you can do that by yourself. So what is the nexus between how we see our role in the housing movement and the kinds of group projects that people are willing to take on?  When the LA chapter started, we would pair with another organization in the movement and they would say, “Help us look into this.” More recently, it’s become less of that model. Instead, we have a north star—it might be a little vague—and within that project, people are decentralized and empowered to work toward the ends they define. That’s the post-and-beam structure that AEMP provides.

To be sure, the workers at Shakopee have benefited from a tighter labor market. In Minnesota, Amazon can’t rely exclusively on washout and turnover to fix its labor problems. They’ve also benefited from preexisting cultural and communal ties which have provided fertile ground for building workplace solidarity. “One thing to know about our community — we talk a lot on the phone and chat over coffee,” Muse told the New York Times. “That makes organizing easier.” Most fundamentally, however, the workers have been successful because they’ve done large-scale actions together — actions that a pose a genuine threat to Amazon’s productivity goals, to the frictionless flow of goods, and therefore, to its bottom line.  The fact is that organizing beyond daily resistance is hard. So is overcoming fear inside a system designed to inspire it, and developing close bonds when the work demands callousness. Amazon has already begun to retaliate against workers who participate in small-scale protests. They will no doubt intensify their efforts if larger-scale unrest begins to stir. The experience in Shakopee suggests that mobilizing workers’ networks outside the warehouse is a necessary part of the strategy. Warehouses packed with thousands of workers can amplify impersonality and isolation; the neighborhood instead of the shopfloor may offer a more promising site for organizing.

And online forums, like those I consulted for this piece, may also be a place where solidarity and strategy is cultivated. “We’re not getting a raise unless we could organize something drastic,” wrote one worker in February 2018 on the Amazon warehouse subreddit, “like striking during Prime Week across the network. I’m talking representation in all shifts (days and nights), all departments.”  In response, another worker posted, “Funny enough, Someone wrote ‘Amazon needs a union!’ on the ‘voice of the associates board.’ Next day it was erased with no response.” The American criminal justice system has never been great for minorities. But in 2011, it got a lot worse. This was the year that the tech industry innovated its way into policing. It began with a group of researchers at the University of California, Los Angeles, who developed a system for predicting which areas of a city crimes were most likely to occur. Police could then flood these areas with officers in order to prevent offenses from being committed, or so the thinking went. In 2011, the Santa Cruz police department became the first law enforcement agency in the country to pilot the software. Time magazine promptly named “pre-emptive policing” one of the fifty best inventions of the year.  PredPol, as it would come to be called, became widely popular: more than sixty police departments across the country now use the software. Moreover, it would soon be accompanied by many other technologies that are currently transforming different aspects of the criminal justice system. These include everything from facial recognition software to algorithmic sentencing, which calculates “risk assessment” scores to inform criminal sentencing decisions.

There is now an urgent need to abandon that framework and imagine better water futures. Luckily, the resources for this work already exist. In the period when settler-colonial water policy was being entrenched in digital algorithms, a group of young people from the National Indian Youth Council (NIYC) were developing critiques of the optimization regime, and articulating a different vision for the Colorado River Basin. That vision can help us support sustainability and justice-centered water policy for generations to come.  Computing Landscapes The story of how the optimization regime took over the Colorado River Basin is a complicated one. But at its core are innovations in policy, law, and technology that enthroned profit as the guiding principle of resource distribution in the region.

In the nineteenth century, two doctrines guided the US empire’s allocation of Colorado River water. The first, known as the doctrine of prior appropriation, was basically a “first come, first served” rule that privileged the first interests to lay claim to the use of a given amount of water. The second, known as the doctrine of equitable apportionment, split water between US territories. In theory, these water doctrines might have favored Native American water rights, as they did after the 1908 Winters v. United States Supreme Court decision, which upheld the water rights of the Fort Belknap Indian Reservation over encroaching settlers. But since then, white settlers have managed to subordinate water rights under the principle of “beneficial use,” which holds that in the case of disputes, water should be allocated to the parties that intend to use it for vaguely defined beneficial purposes. In practice, this has usually meant profits for technological developers and extractive energy industries.

The Rise of the Code School In Lower Ed, you discuss the current landscape of technology education. In my experience, people typically don’t think of coding bootcamps as for-profit colleges, even if they recognize them as for-profit institutions. How do these camps fit into the world of for-profit education, and into the economy more broadly? In my book, I talk about the “Wall Street era” of for-profit college expansion that began in the mid-1990s. Well, that’s also when the current coding bootcamp moment began, because of Y2K.

The Y2K crisis started heating up around 1997. There was this widespread coding error related to calendar data that people feared would break computer systems in 2000—so you needed a mass army of workers to go out and fix it. The question was: who was going to reprogram all of these systems? So you saw an explosion of short-term training schools for A+ certificates, an entry-level IT certification, or the C++ programming language. In fact, many of those schools were the precursors for the growth of for-profit colleges.

Drill Baby Drill The multi-million-dollar partnership between Microsoft and Chevron was the reason I went to Kazakhstan. Microsoft sent me to Atyrau for a week-long workshop to help the Tengiz oil field adopt our technology. I was there to talk about computer vision, a field of AI/ML that gives computers the ability to understand digital images, but the workshop covered a range of topics in both AI/ML and cloud computing. We held it for a team at TCO tasked with boosting daily oil production from 600,000 barrels to 1 million. They wanted to learn about how Microsoft technology could help them modernize their oil field and increase efficiency.

The workshop took place in a large conference room in one of the TCO office buildings. The building itself wasn’t particularly fancy. The exterior was run-down: it looked like it was last renovated in the 1980s. Aside from the security guards dressed in dark clothing, the interior was mostly white, with bright marble floors. The only bits of color came from the biscuits and pastries that were laid out on tables in front of the conference rooms.  At the workshop, I gave a short technical demonstration about running computer vision at scale on Microsoft’s cloud computing platform. There were about forty people in the audience, predominantly businesspeople. My presentation felt like a marketing technique: the point was to flex Microsoft’s engineering prowess to a technically illiterate business crowd. I made sure to include a lot of engineering jargon: “distributed training,” “offline scoring,” “Docker-compatible.” On the third day of the workshop, a small group of us convened at TCO headquarters in Atyrau to discuss specific AI/ML scenarios they wanted to implement. The meeting room was much nicer than where the workshop was held. It featured new videoconferencing equipment and plush ergonomic chairs. A half-dozen TCO managers were present. Yet, strangely, none of their technical staff attended. The TCO managers were mostly Americans and, with one exception, all white men. They wore monochrome suits and polished leather shoes. I felt out of place wearing sneakers and an oversized button-down. There was not a single Kazakhstani in the room.  To kick off the meeting, a Microsoft account manager gave a PowerPoint presentation that discussed common problems in the oil and gas industry that could be solved using AI/ML. One of the most complex use-cases involved using AI/ML to improve oil exploration. The traditional way to find a new oil or gas deposit is to perform a seismic survey. This is a technique that sends sound waves into the earth and then analyzes the time it takes for those waves to reflect off of different geological features. Because the data is volumetric and spans hundreds of kilometers at a minute granularity, the data collected from a single seismic survey can run over a petabyte. (A petabyte is a million gigabytes.) The output of this data is a 3D geological map, which geophysicists can study in order to recommend promising locations to build wells.

Leasing as Liberation Typically, mainstream observers see two defining features of the business model of Uber and Lyft. The first is that the people who drive “on” their platforms are treated not as employees of the companies, but rather as independent contractors using those platforms to run their own personal taxicab businesses. In the US, direct employment increases corporate costs by roughly one-third, so classifying workers as independent contractors significantly increases profitability. (This is essentially the labor arrangement underlying the entire gig economy, from people delivering food and groceries to those performing rote tasks for Amazon’s Mechanical Turk.) The second defining feature is the technology—the apps and the various pricing and dispatching algorithms behind them—that the corporations use to exert enough control over drivers in order to provide a more or less on-demand service. In both cases—the non-employment model and the technologies—Uber and Lyft share more in common with the previous generation of taxicab companies than many people understand.

The independent contractor model that underlies today’s gig economy first developed in the taxicab industry in the late 1970s, as the United States shifted towards a neoliberal conception of society in which almost everything was to be subjected to the forces of competition. Workers and households were reimagined as entrepreneurial concerns, sole proprietorships that should fend for themselves in the tumult of the great American marketplace. Embodying this logic, the taxicab industry was one of the first among US businesses to slough off the costs and liabilities—minimum wages, healthcare benefits, disability insurance, among other things—associated with direct employment.  Taking advantage of rank-and-file discontent with traditional unions, as well as an existing carve-out for independent contractors in labor laws, cab companies all over the US reorganized their business models in this period, claiming to “free” their drivers from the imagined restraints of employment. In San Francisco, taxi companies approached drivers in 1976 and asked if they would like to “lease” their taxis on a shift-by-shift basis, so that the companies could rid themselves of the expenses and risks associated with employing workers, including unemployment insurance, workers’ compensation, and a guaranteed commission that taxi drivers had previously been paid.

As far as what they’re buying—yes, they’re avoiding paying more for a potential competitor later. But the inherent value in a talent acquisition comes from acknowledging that most projects in software fail. Finding a team that can actually ship something that gets out the door is rare. Even at big companies, most projects will not see the light of day. So to find a group of people that have managed to build something—even if it’s small, even if it’s humble—means they’re probably a team that works well together. So they’re worth a premium. That’s the theory behind it, at least.

Also, they could make us sign a contract that locked us in for a long time. The deal to acquire our startup was a lump of cash and a job offer. We had to take both together. About half of the payment came up front, in the form of the cash. And the rest would come to us through salary and stock-based compensation on a vesting schedule over the course of four years.

We soon began creating a number of tools to communicate with each other. Cameras became our main translators. My parents took pictures of everything: activities, places, people, foods. They used the photos to make sure I understood them and to teach me how to make choices by picking photos of what I wanted. I could escape a plate of cauliflower by bringing them a logo for KFC. Each photograph was labelled with the printed word, so I could learn sight words and begin to understand what they were saying. Still, not everything could be captured in a photo. I needed other means of communication to say “stop” when my dad tickled me and I needed to catch my breath, and a way to say “bathroom,” when I needed to pee NOW. And so we developed a basic sign language to convey essential messages. Instead of insisting I join their speaking world, my parents learned these new languages with me.

When it was time to start regular kindergarten at my neighborhood school, I brought my languages with me. Before long, my classmates and I were all using photos and learning to spell with our fingers. But participating in school also required new technologies. I started using a simple voice-output device like the single-switch BIGmack and Cheap Talk 8 that allowed me to play pre-recorded messages in either my mom’s or dad’s voice to answer questions during class. Because I had learned to communicate in these ways, I was taught to read and write, first with laminated sight words and later with a seventeen dollar label maker from Staples.

I actually interviewed two years ago for a job with Twitter. Anil Dash recommended that the head of product talk to me. I had just wanted to consult with them about problems inside of Twitter. And they were like, “Well, we need a design researcher. You should interview.” And I was like, “I’m not going to make it through your interview process, because I’ve read on the internet about how you hire people, and it’s this really weird formulaic space where it’s clearly designed for people coming out of Stanford’s d.school.” [1] Those are not the skills that I have. And that’s not the way I conduct research. And I didn’t make it through the interview, because they asked me how I would study the effectiveness of their harassment reporting system if they’d made changes. It’s a standard question.

I was like, “What if you send out a survey?” And the guy said, “Only 2 percent of people respond to surveys.” So I proposed sending out a survey to people who have filed reports more than five or six times over the course of a month, and asking them to chat individually. I wanted to actually speak to victims of harassment.

Labor Shortage and New Entrants But if home-based work never disappears, that doesn’t mean it’s a static process, or that the dynamics Chuan and Yuan have encountered in their lives and work will persist. Almost all the women weavers in the handicraft ecommerce villages in Shandong are forty or older, and most of the younger women show contempt for weaving, dismissing it as physically demanding and culturally inferior to digital labor and urban service work. Not all types of home-based labor on the periphery are the same. Some are more desirable than others. Weaving is monotonous and physically arduous; weavers often suffer from chronic back pain and arthritis after a lifetime spent weaving. Handicraft making was also poorly paid. At a piece rate, a skilled female weaver had to work from dawn to dusk seven days a week to earn as much as an average full-time ecommerce customer service girl or supermarket cashier could earn—about $600 USD per month. This shortage of women weavers already threatens the long-term sustainability of the ecommerce industry. Some village entrepreneurs hope that the rising per-unit price of woven goods would encourage younger women to take up weaving again, especially as they age and find it harder to work in the urban service sector. Others seek solutions in new handicraft styles that require less human weaving labor, or by outsourcing production to less economically developed villages nearby, where labor costs are lower. Ultimately, like many of the challenges Chuan’s Taobao county faces in maintaining the competitiveness of its handicraft ecommerce industry, solving the weaving labor shortage will require a collective effort. Decades of entrepreneurialism in family-based labor, and the recent rise of ecommerce, has drawn some migrant birds back home—but the growing atomization of village culture and intra-village inequality does not bode well for collective politics. As China’s living standard rose, the labor pool in Prato also started to drain. As a result, Prato’s home factories received new guests. Since the 2010s, the number of Chinese immigrants registered in Prato has stagnated. Just as young people in Shandong despised weaving, young Wenzhounese no longer saw garment work in Italy as an attractive option. Many older workers and shop owners either moved to the service sector (such as cafés and bars) or went back to China. “If you can easily make six to seven thousand yuan [about $1,000 USD a month] by delivering food in Wenzhou, why sacrifice so much to make garments in Prato?” Yuan chuckled in 2015.

For many, Covid-19 was the last straw. During the pandemic, lockdowns and the consequent economic downturn pushed many garment workshops out of business. Yuan’s company was one of them. She closed her workshop in 2021 and returned to China with her husband. This year, they started a new business in Wenzhou selling “authentic” Italian gelato to a growing number of middle-class consumers.

A supply-side theory of Zillow meme accounts The collation of publicly available data, as discussed above, is useful for nosy neighbors who want to know how their home’s value compares to others around them. More important than the data, however, is that Zillow features pictures. People interested in a house on Zillow can pull a listing up and see it not just from the outside, but also take a virtual tour of the bedrooms, living rooms, kitchens, and basements. If a house has been sold in the past decade, the odds are pretty good that you can scope it out on Zillow. The surfeit of images is useful to prospective home buyers. But it is equally, if not more useful to social-media platforms. Modern platforms, typified by centralized, algorithmically personalized feeds, and measuring their success in engagement and time-on-site, need content to fill the feeds and draw in users. Yet not everyone is a celebrity or a natural comedian or can undergo a viral-worthy experience every day. Something else has to fill the gaps.

That’s where Zillow comes in. Zillow is an enormous store of data that users can extract fodder from as needed. In this sense, it is similar to Wikipedia (a consistent source of “did you know?” trivia), YouTube (full of archival footage one can tie to current events or timeless viral videos), or any number of streaming services whose endless conveyor belt of shows and movies can be mined for no-context “reaction” clips and GIFs. It may be that Zillow is a popular source of memes and viral posts not because of some particular voyeuristic American interest in real estate, but simply because it is there, and it has a lot of pictures.